diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Corel Draw X5 Download For Pc 64 Bit With Crack.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Corel Draw X5 Download For Pc 64 Bit With Crack.md
deleted file mode 100644
index e5ee1a1221718c0903fee05a0fd69d4b2edc6099..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Corel Draw X5 Download For Pc 64 Bit With Crack.md	
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Counter Strike 1.6 Orange Box Download The Ultimate Collection of Valve Games.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Counter Strike 1.6 Orange Box Download The Ultimate Collection of Valve Games.md
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--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Counter Strike 1.6 Orange Box Download The Ultimate Collection of Valve Games.md	
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-<p>Counter Strike is a first-person shooter game that was released in 1999 as a mod for Half-Life. It became one of the most popular multiplayer games of all time with millions of players worldwide.</p>
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/AUTODATA 8.89 Crack FULL 2018 64 Bit TOP.md b/spaces/1gistliPinn/ChatGPT4/Examples/AUTODATA 8.89 Crack FULL 2018 64 Bit TOP.md
deleted file mode 100644
index bef3205a0d450c4e64bbec2f975f66aa64b832c7..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/AUTODATA 8.89 Crack FULL 2018 64 Bit TOP.md	
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diff --git a/spaces/1phancelerku/anime-remove-background/Dream Live APK Mod - The Best App for Live Streaming Fans (No Top Up Required).md b/spaces/1phancelerku/anime-remove-background/Dream Live APK Mod - The Best App for Live Streaming Fans (No Top Up Required).md
deleted file mode 100644
index 6ede707e48bba5773cac2daf29eadd2f305b7311..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Dream Live APK Mod - The Best App for Live Streaming Fans (No Top Up Required).md	
+++ /dev/null
@@ -1,103 +0,0 @@
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diff --git a/spaces/1toTree/lora_test/ppdiffusers/pipelines/ddim/pipeline_ddim.py b/spaces/1toTree/lora_test/ppdiffusers/pipelines/ddim/pipeline_ddim.py
deleted file mode 100644
index e797a45141adb41b65124aaa0da99c00980d7f99..0000000000000000000000000000000000000000
--- a/spaces/1toTree/lora_test/ppdiffusers/pipelines/ddim/pipeline_ddim.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import List, Optional, Tuple, Union
-
-import paddle
-
-from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-class DDIMPipeline(DiffusionPipeline):
-    r"""
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular xxxx, etc.)
-
-    Parameters:
-        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
-            [`DDPMScheduler`], or [`DDIMScheduler`].
-    """
-
-    def __init__(self, unet, scheduler):
-        super().__init__()
-        self.register_modules(unet=unet, scheduler=scheduler)
-
-    @paddle.no_grad()
-    def __call__(
-        self,
-        batch_size: int = 1,
-        generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
-        eta: float = 0.0,
-        num_inference_steps: int = 50,
-        use_clipped_model_output: Optional[bool] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-    ) -> Union[ImagePipelineOutput, Tuple]:
-        r"""
-        Args:
-            batch_size (`int`, *optional*, defaults to 1):
-                The number of images to generate.
-            generator (`paddle.Generator`, *optional*):
-                One or a list of paddle generator(s) to make generation deterministic.
-            eta (`float`, *optional*, defaults to 0.0):
-                The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            use_clipped_model_output (`bool`, *optional*, defaults to `None`):
-                if `True` or `False`, see documentation for `DDIMScheduler.step`. If `None`, nothing is passed
-                downstream to the scheduler. So use `None` for schedulers which don't support this argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
-            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
-            generated images.
-        """
-        # Sample gaussian noise to begin loop
-        if isinstance(self.unet.sample_size, int):
-            image_shape = (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size)
-        else:
-            image_shape = (batch_size, self.unet.in_channels, *self.unet.sample_size)
-
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if isinstance(generator, list):
-            shape = (1,) + image_shape[1:]
-            image = [paddle.randn(shape, generator=generator[i], dtype=self.unet.dtype) for i in range(batch_size)]
-            image = paddle.concat(image, axis=0)
-        else:
-            image = paddle.randn(image_shape, generator=generator, dtype=self.unet.dtype)
-
-        # set step values
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        for t in self.progress_bar(self.scheduler.timesteps):
-            # 1. predict noise model_output
-            model_output = self.unet(image, t).sample
-
-            # 2. predict previous mean of image x_t-1 and add variance depending on eta
-            # eta corresponds to η in paper and should be between [0, 1]
-            # do x_t -> x_t-1
-            image = self.scheduler.step(
-                model_output, t, image, eta=eta, use_clipped_model_output=use_clipped_model_output, generator=generator
-            ).prev_sample
-
-        image = (image / 2 + 0.5).clip(0, 1)
-        image = image.transpose([0, 2, 3, 1]).cast("float32").numpy()
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)
diff --git a/spaces/801artistry/RVC801/Applio-RVC-Fork/utils/backups.py b/spaces/801artistry/RVC801/Applio-RVC-Fork/utils/backups.py
deleted file mode 100644
index b814f8184792e80e2324685436053d61487110b1..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/Applio-RVC-Fork/utils/backups.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import os
-import shutil
-import hashlib
-import time
-import base64
-
-
-
-
-LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
-WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
-GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'
-
-def import_google_drive_backup():
-    print("Importing Google Drive backup...")
-    weights_exist = False
-    for root, dirs, files in os.walk(GOOGLE_DRIVE_PATH):
-        for filename in files:
-            filepath = os.path.join(root, filename)
-            if os.path.isfile(filepath) and not filepath.startswith(os.path.join(GOOGLE_DRIVE_PATH, 'weights')):
-                backup_filepath = os.path.join(LOGS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
-                backup_folderpath = os.path.dirname(backup_filepath)
-                if not os.path.exists(backup_folderpath):
-                    os.makedirs(backup_folderpath)
-                    print(f'Created backup folder: {backup_folderpath}', flush=True)
-                shutil.copy2(filepath, backup_filepath) # copy file with metadata
-                print(f'Imported file from Google Drive backup: {filename}')
-            elif filepath.startswith(os.path.join(GOOGLE_DRIVE_PATH, 'weights')) and filename.endswith('.pth'):
-                weights_exist = True
-                weights_filepath = os.path.join(WEIGHTS_FOLDER, os.path.relpath(filepath, os.path.join(GOOGLE_DRIVE_PATH, 'weights')))
-                weights_folderpath = os.path.dirname(weights_filepath)
-                if not os.path.exists(weights_folderpath):
-                    os.makedirs(weights_folderpath)
-                    print(f'Created weights folder: {weights_folderpath}', flush=True)
-                shutil.copy2(filepath, weights_filepath) # copy file with metadata
-                print(f'Imported file from weights: {filename}')
-    if weights_exist:
-        print("Copied weights from Google Drive backup to local weights folder.")
-    else:
-        print("No weights found in Google Drive backup.")
-    print("Google Drive backup import completed.")
-
-def get_md5_hash(file_path):
-    hash_md5 = hashlib.md5()
-    with open(file_path, "rb") as f:
-        for chunk in iter(lambda: f.read(4096), b""):
-            hash_md5.update(chunk)
-    return hash_md5.hexdigest()
-
-def copy_weights_folder_to_drive():
-    destination_folder = os.path.join(GOOGLE_DRIVE_PATH, 'weights')
-    try:
-        if not os.path.exists(destination_folder):
-            os.makedirs(destination_folder)
-
-        num_copied = 0
-        for filename in os.listdir(WEIGHTS_FOLDER):
-            if filename.endswith('.pth'):
-                source_file = os.path.join(WEIGHTS_FOLDER, filename)
-                destination_file = os.path.join(destination_folder, filename)
-                if not os.path.exists(destination_file):
-                    shutil.copy2(source_file, destination_file)
-                    num_copied += 1
-                    print(f"Copied {filename} to Google Drive!")
-
-        if num_copied == 0:
-            print("No new finished models found for copying.")
-        else:
-            print(f"Finished copying {num_copied} files to Google Drive!")
-
-    except Exception as e:
-        print(f"An error occurred while copying weights: {str(e)}")
-        # You can log the error or take appropriate actions here.
-
-def backup_files():
-    print("\nStarting backup loop...")
-    last_backup_timestamps_path = os.path.join(LOGS_FOLDER, 'last_backup_timestamps.txt')
-    fully_updated = False  # boolean to track if all files are up to date
-    
-    while True:
-        try:
-            updated = False  # flag to check if any files were updated
-            last_backup_timestamps = {}
-
-            try:
-                with open(last_backup_timestamps_path, 'r') as f:
-                    last_backup_timestamps = dict(line.strip().split(':') for line in f)
-            except FileNotFoundError:
-                pass  # File does not exist yet, which is fine
-            
-            for root, dirs, files in os.walk(LOGS_FOLDER):
-                for filename in files:
-                    if filename != 'last_backup_timestamps.txt':
-                        filepath = os.path.join(root, filename)
-                        if os.path.isfile(filepath):
-                            backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
-                            backup_folderpath = os.path.dirname(backup_filepath)
-                            if not os.path.exists(backup_folderpath):
-                                os.makedirs(backup_folderpath)
-                                print(f'Created backup folder: {backup_folderpath}', flush=True)
-                            # check if file has changed since last backup
-                            last_backup_timestamp = last_backup_timestamps.get(filepath)
-                            current_timestamp = os.path.getmtime(filepath)
-                            if last_backup_timestamp is None or float(last_backup_timestamp) < current_timestamp:
-                                shutil.copy2(filepath, backup_filepath)  # copy file with metadata
-                                last_backup_timestamps[filepath] = str(current_timestamp)  # update last backup timestamp
-                                if last_backup_timestamp is None:
-                                    print(f'Backed up file: {filename}')
-                                else:
-                                    print(f'Updating backed up file: {filename}')
-                                updated = True
-                                fully_updated = False  # if a file is updated, all files are not up to date
-            
-            # check if any files were deleted in Colab and delete them from the backup drive
-            for filepath in list(last_backup_timestamps.keys()):
-                if not os.path.exists(filepath):
-                    backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
-                    if os.path.exists(backup_filepath):
-                        os.remove(backup_filepath)
-                        print(f'Deleted file: {filepath}')
-                    del last_backup_timestamps[filepath]
-                    updated = True
-                    fully_updated = False  # if a file is deleted, all files are not up to date
-            
-            if not updated and not fully_updated:
-                print("Files are up to date.")
-                fully_updated = True  # if all files are up to date, set the boolean to True
-                copy_weights_folder_to_drive()
-                sleep_time = 15
-            else:
-                sleep_time = 0.1
-            
-            with open(last_backup_timestamps_path, 'w') as f:
-                for filepath, timestamp in last_backup_timestamps.items():
-                    f.write(f'{filepath}:{timestamp}\n')
-            
-            time.sleep(sleep_time)  # wait for 15 seconds before checking again, or 0.1s if not fully up to date to speed up backups
-        
-        except Exception as e:
-            print(f"An error occurred: {str(e)}")
-            # You can log the error or take appropriate actions here.
diff --git a/spaces/A00001/bingothoo/src/components/chat-panel.tsx b/spaces/A00001/bingothoo/src/components/chat-panel.tsx
deleted file mode 100644
index 1fbc3c2bf05b914e0c229661832fbb560745f488..0000000000000000000000000000000000000000
--- a/spaces/A00001/bingothoo/src/components/chat-panel.tsx
+++ /dev/null
@@ -1,153 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import Image from 'next/image'
-import Textarea from 'react-textarea-autosize'
-import { useAtomValue } from 'jotai'
-import { useEnterSubmit } from '@/lib/hooks/use-enter-submit'
-import { cn } from '@/lib/utils'
-
-import BrushIcon from '@/assets/images/brush.svg'
-import ChatIcon from '@/assets/images/chat.svg'
-import VisualSearchIcon from '@/assets/images/visual-search.svg'
-import SendIcon from '@/assets/images/send.svg'
-import PinIcon from '@/assets/images/pin.svg'
-import PinFillIcon from '@/assets/images/pin-fill.svg'
-
-import { useBing } from '@/lib/hooks/use-bing'
-import { voiceListenAtom } from '@/state'
-import Voice from './voice'
-import { ChatImage } from './chat-image'
-import { ChatAttachments } from './chat-attachments'
-
-export interface ChatPanelProps
-  extends Pick<
-    ReturnType<typeof useBing>,
-    | 'generating'
-    | 'input'
-    | 'setInput'
-    | 'sendMessage'
-    | 'resetConversation'
-    | 'isSpeaking'
-    | 'attachmentList'
-    | 'uploadImage'
-    | 'setAttachmentList'
-  > {
-  id?: string
-  className?: string
-}
-
-export function ChatPanel({
-  isSpeaking,
-  generating,
-  input,
-  setInput,
-  className,
-  sendMessage,
-  resetConversation,
-  attachmentList,
-  uploadImage,
-  setAttachmentList
-}: ChatPanelProps) {
-  const inputRef = React.useRef<HTMLTextAreaElement>(null)
-  const {formRef, onKeyDown} = useEnterSubmit()
-  const [focused, setFocused] = React.useState(false)
-  const [active, setActive] = React.useState(false)
-  const [pin, setPin] = React.useState(false)
-  const [tid, setTid] = React.useState<any>()
-  const voiceListening = useAtomValue(voiceListenAtom)
-
-  const setBlur = React.useCallback(() => {
-    clearTimeout(tid)
-    setActive(false)
-    const _tid = setTimeout(() => setFocused(false), 2000);
-    setTid(_tid)
-  }, [tid])
-
-  const setFocus = React.useCallback(() => {
-    setFocused(true)
-    setActive(true)
-    clearTimeout(tid)
-    inputRef.current?.focus()
-  }, [tid])
-
-  React.useEffect(() => {
-    if (input) {
-      setFocus()
-    }
-  }, [input])
-
-  return (
-    <form
-      className={cn('chat-panel', className)}
-      onSubmit={async e => {
-        e.preventDefault()
-        if (generating) {
-          return;
-        }
-        if (!input?.trim()) {
-          return
-        }
-        setInput('')
-        setPin(false)
-        await sendMessage(input)
-      }}
-      ref={formRef}
-    >
-      <div className="action-bar pb-4">
-        <div className={cn('action-root', { focus: active || pin })} speech-state="hidden" visual-search="" drop-target="">
-          <div className="fade bottom">
-            <div className="background"></div>
-          </div>
-          <div className={cn('outside-left-container', { collapsed: focused })}>
-            <div className="button-compose-wrapper">
-              <button className="body-2 button-compose" type="button" aria-label="新主题" onClick={resetConversation}>
-                <div className="button-compose-content">
-                  <Image className="pl-2" alt="brush" src={BrushIcon} width={40} />
-                  <div className="button-compose-text">新主题</div>
-                </div>
-              </button>
-            </div>
-          </div>
-          <div
-            className={cn('main-container', { active: active || pin })}
-            style={{ minHeight: pin ? '360px' : undefined }}
-            onClick={setFocus}
-            onBlur={setBlur}
-          >
-            <div className="main-bar">
-              <Image alt="chat" src={ChatIcon} width={20} color="blue" />
-              <Textarea
-                ref={inputRef}
-                tabIndex={0}
-                onKeyDown={onKeyDown}
-                rows={1}
-                value={input}
-                onChange={e => setInput(e.target.value.slice(0, 4000))}
-                placeholder={voiceListening ? '持续对话中...对话完成说“发送”即可' : 'Shift + Enter 换行'}
-                spellCheck={false}
-                className="message-input min-h-[24px] -mx-1 w-full text-base resize-none bg-transparent focus-within:outline-none"
-              />
-              <ChatImage uploadImage={uploadImage}>
-                <Image alt="visual-search" src={VisualSearchIcon} width={24} />
-              </ChatImage>
-              <Voice setInput={setInput} sendMessage={sendMessage} isSpeaking={isSpeaking} input={input} />
-              <button type="submit">
-                <Image alt="send" src={SendIcon} width={20} style={{ marginTop: '2px' }} />
-              </button>
-            </div>
-            <ChatAttachments attachmentList={attachmentList} setAttachmentList={setAttachmentList} uploadImage={uploadImage} />
-            <div className="body-1 bottom-bar">
-              <div className="letter-counter"><span>{input.length}</span>/4000</div>
-              <button onClick={() => {
-                setPin(!pin)
-              }} className="pr-2">
-               <Image alt="pin" src={pin ? PinFillIcon : PinIcon} width={20} />
-              </button>
-            </div>
-          </div>
-        </div>
-      </div>
-    </form>
-  )
-}
diff --git a/spaces/AIConsultant/MusicGen/CHANGELOG.md b/spaces/AIConsultant/MusicGen/CHANGELOG.md
deleted file mode 100644
index aabf9130b0a67aca9beaac9f2cb1a40237a4468d..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/CHANGELOG.md
+++ /dev/null
@@ -1,28 +0,0 @@
-# Changelog
-
-All notable changes to this project will be documented in this file.
-
-The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
-
-## [1.0.0] - 2023-08-02
-
-Major revision, added training code for EnCodec, AudioGen, MusicGen, and MultiBandDiffusion.
-Added pretrained model for AudioGen and MultiBandDiffusion.
-
-## [0.0.2] - 2023-08-01
-
-Improved demo, fixed top p (thanks @jnordberg).
-
-Compressor tanh on output to avoid clipping with some style (especially piano).
-Now repeating the conditioning periodically if it is too short.
-
-More options when launching Gradio app locally (thanks @ashleykleynhans).
-
-Testing out PyTorch 2.0 memory efficient attention.
-
-Added extended generation (infinite length) by slowly moving the windows.
-Note that other implementations exist: https://github.com/camenduru/MusicGen-colab.
-
-## [0.0.1] - 2023-06-09
-
-Initial release, with model evaluation only.
diff --git a/spaces/AIFILMS/generate_human_motion/VQ-Trans/dataset/prepare/download_extractor.sh b/spaces/AIFILMS/generate_human_motion/VQ-Trans/dataset/prepare/download_extractor.sh
deleted file mode 100644
index b1c456e8311a59a1c8d86e85da5ddd3aa7e1f9a4..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/generate_human_motion/VQ-Trans/dataset/prepare/download_extractor.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-rm -rf checkpoints
-mkdir checkpoints
-cd checkpoints
-echo -e "Downloading extractors"
-gdown --fuzzy https://drive.google.com/file/d/1o7RTDQcToJjTm9_mNWTyzvZvjTWpZfug/view
-gdown --fuzzy https://drive.google.com/file/d/1tX79xk0fflp07EZ660Xz1RAFE33iEyJR/view
-
-
-unzip t2m.zip
-unzip kit.zip
-
-echo -e "Cleaning\n"
-rm t2m.zip
-rm kit.zip
-echo -e "Downloading done!"
\ No newline at end of file
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/diffusionmodules/model.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/diffusionmodules/model.py
deleted file mode 100644
index 2746d74c16cd9a7a418487599399cdea8dc1bbac..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/diffusionmodules/model.py
+++ /dev/null
@@ -1,835 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import rearrange
-
-from ldm.util import instantiate_from_config
-from ldm.modules.attention import LinearAttention
-
-
-def get_timestep_embedding(timesteps, embedding_dim):
-    """
-    This matches the implementation in Denoising Diffusion Probabilistic Models:
-    From Fairseq.
-    Build sinusoidal embeddings.
-    This matches the implementation in tensor2tensor, but differs slightly
-    from the description in Section 3.5 of "Attention Is All You Need".
-    """
-    assert len(timesteps.shape) == 1
-
-    half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
-    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
-    emb = emb.to(device=timesteps.device)
-    emb = timesteps.float()[:, None] * emb[None, :]
-    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
-    if embedding_dim % 2 == 1:  # zero pad
-        emb = torch.nn.functional.pad(emb, (0,1,0,0))
-    return emb
-
-
-def nonlinearity(x):
-    # swish
-    return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels, num_groups=32):
-    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=2,
-                                        padding=0)
-
-    def forward(self, x):
-        if self.with_conv:
-            pad = (0,1,0,1)
-            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-            x = self.conv(x)
-        else:
-            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout, temb_channels=512):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-
-        self.norm1 = Normalize(in_channels)
-        self.conv1 = torch.nn.Conv2d(in_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if temb_channels > 0:
-            self.temb_proj = torch.nn.Linear(temb_channels,
-                                             out_channels)
-        self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(out_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(in_channels,
-                                                     out_channels,
-                                                     kernel_size=3,
-                                                     stride=1,
-                                                     padding=1)
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(in_channels,
-                                                    out_channels,
-                                                    kernel_size=1,
-                                                    stride=1,
-                                                    padding=0)
-
-    def forward(self, x, temb):
-        h = x
-        h = self.norm1(h)
-        h = nonlinearity(h)
-        h = self.conv1(h)
-
-        if temb is not None:
-            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
-        h = self.norm2(h)
-        h = nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x+h
-
-
-class LinAttnBlock(LinearAttention):
-    """to match AttnBlock usage"""
-    def __init__(self, in_channels):
-        super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = q.reshape(b,c,h*w)
-        q = q.permute(0,2,1)   # b,hw,c
-        k = k.reshape(b,c,h*w) # b,c,hw
-        w_ = torch.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b,c,h*w)
-        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
-        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-        h_ = h_.reshape(b,c,h,w)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-def make_attn(in_channels, attn_type="vanilla"):
-    assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
-    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
-    if attn_type == "vanilla":
-        return AttnBlock(in_channels)
-    elif attn_type == "none":
-        return nn.Identity(in_channels)
-    else:
-        return LinAttnBlock(in_channels)
-
-
-class Model(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = self.ch*4
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        self.use_timestep = use_timestep
-        if self.use_timestep:
-            # timestep embedding
-            self.temb = nn.Module()
-            self.temb.dense = nn.ModuleList([
-                torch.nn.Linear(self.ch,
-                                self.temb_ch),
-                torch.nn.Linear(self.temb_ch,
-                                self.temb_ch),
-            ])
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            skip_in = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                if i_block == self.num_res_blocks:
-                    skip_in = ch*in_ch_mult[i_level]
-                block.append(ResnetBlock(in_channels=block_in+skip_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x, t=None, context=None):
-        #assert x.shape[2] == x.shape[3] == self.resolution
-        if context is not None:
-            # assume aligned context, cat along channel axis
-            x = torch.cat((x, context), dim=1)
-        if self.use_timestep:
-            # timestep embedding
-            assert t is not None
-            temb = get_timestep_embedding(t, self.ch)
-            temb = self.temb.dense[0](temb)
-            temb = nonlinearity(temb)
-            temb = self.temb.dense[1](temb)
-        else:
-            temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](
-                    torch.cat([h, hs.pop()], dim=1), temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-    def get_last_layer(self):
-        return self.conv_out.weight
-
-
-class Encoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
-                 **ignore_kwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.in_ch_mult = in_ch_mult
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))# vanilla attention
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # end
-        self.norm_out = Normalize(block_in)# GroupNorm
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        2*z_channels if double_z else z_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # timestep embedding
-        temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class Decoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
-                 attn_type="vanilla", **ignorekwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-        self.tanh_out = tanh_out
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        in_ch_mult = (1,)+tuple(ch_mult)
-        block_in = ch*ch_mult[self.num_resolutions-1]
-        curr_res = resolution // 2**(self.num_resolutions-1)
-        self.z_shape = (1,z_channels,curr_res,curr_res)
-        print("Working with z of shape {} = {} dimensions.".format(
-            self.z_shape, np.prod(self.z_shape)))
-
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(z_channels,
-                                       block_in,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, z):
-        #assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h, temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        if self.tanh_out:
-            h = torch.tanh(h)
-        return h
-
-
-class SimpleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, *args, **kwargs):
-        super().__init__()
-        self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
-                                     ResnetBlock(in_channels=in_channels,
-                                                 out_channels=2 * in_channels,
-                                                 temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=2 * in_channels,
-                                                out_channels=4 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=4 * in_channels,
-                                                out_channels=2 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     nn.Conv2d(2*in_channels, in_channels, 1),
-                                     Upsample(in_channels, with_conv=True)])
-        # end
-        self.norm_out = Normalize(in_channels)
-        self.conv_out = torch.nn.Conv2d(in_channels,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        for i, layer in enumerate(self.model):
-            if i in [1,2,3]:
-                x = layer(x, None)
-            else:
-                x = layer(x)
-
-        h = self.norm_out(x)
-        h = nonlinearity(h)
-        x = self.conv_out(h)
-        return x
-
-
-class UpsampleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
-                 ch_mult=(2,2), dropout=0.0):
-        super().__init__()
-        # upsampling
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        block_in = in_channels
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.res_blocks = nn.ModuleList()
-        self.upsample_blocks = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            res_block = []
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                res_block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-            self.res_blocks.append(nn.ModuleList(res_block))
-            if i_level != self.num_resolutions - 1:
-                self.upsample_blocks.append(Upsample(block_in, True))
-                curr_res = curr_res * 2
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # upsampling
-        h = x
-        for k, i_level in enumerate(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.res_blocks[i_level][i_block](h, None)
-            if i_level != self.num_resolutions - 1:
-                h = self.upsample_blocks[k](h)
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class LatentRescaler(nn.Module):
-    def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
-        super().__init__()
-        # residual block, interpolate, residual block
-        self.factor = factor
-        self.conv_in = nn.Conv2d(in_channels,
-                                 mid_channels,
-                                 kernel_size=3,
-                                 stride=1,
-                                 padding=1)
-        self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-        self.attn = AttnBlock(mid_channels)
-        self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-
-        self.conv_out = nn.Conv2d(mid_channels,
-                                  out_channels,
-                                  kernel_size=1,
-                                  )
-
-    def forward(self, x):
-        x = self.conv_in(x)
-        for block in self.res_block1:
-            x = block(x, None)
-        x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
-        x = self.attn(x)
-        for block in self.res_block2:
-            x = block(x, None)
-        x = self.conv_out(x)
-        return x
-
-
-class MergedRescaleEncoder(nn.Module):
-    def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True,
-                 ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        intermediate_chn = ch * ch_mult[-1]
-        self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
-                               z_channels=intermediate_chn, double_z=False, resolution=resolution,
-                               attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
-                               out_ch=None)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
-                                       mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.encoder(x)
-        x = self.rescaler(x)
-        return x
-
-
-class MergedRescaleDecoder(nn.Module):
-    def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
-                 dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        tmp_chn = z_channels*ch_mult[-1]
-        self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
-                               resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
-                               ch_mult=ch_mult, resolution=resolution, ch=ch)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
-                                       out_channels=tmp_chn, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Upsampler(nn.Module):
-    def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
-        super().__init__()
-        assert out_size >= in_size
-        num_blocks = int(np.log2(out_size//in_size))+1
-        factor_up = 1.+ (out_size % in_size)
-        print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
-        self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
-                                       out_channels=in_channels)
-        self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
-                               attn_resolutions=[], in_channels=None, ch=in_channels,
-                               ch_mult=[ch_mult for _ in range(num_blocks)])
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Resize(nn.Module):
-    def __init__(self, in_channels=None, learned=False, mode="bilinear"):
-        super().__init__()
-        self.with_conv = learned
-        self.mode = mode
-        if self.with_conv:
-            print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
-            raise NotImplementedError()
-            assert in_channels is not None
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=4,
-                                        stride=2,
-                                        padding=1)
-
-    def forward(self, x, scale_factor=1.0):
-        if scale_factor==1.0:
-            return x
-        else:
-            x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
-        return x
-
-class FirstStagePostProcessor(nn.Module):
-
-    def __init__(self, ch_mult:list, in_channels,
-                 pretrained_model:nn.Module=None,
-                 reshape=False,
-                 n_channels=None,
-                 dropout=0.,
-                 pretrained_config=None):
-        super().__init__()
-        if pretrained_config is None:
-            assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
-            self.pretrained_model = pretrained_model
-        else:
-            assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
-            self.instantiate_pretrained(pretrained_config)
-
-        self.do_reshape = reshape
-
-        if n_channels is None:
-            n_channels = self.pretrained_model.encoder.ch
-
-        self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
-        self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
-                            stride=1,padding=1)
-
-        blocks = []
-        downs = []
-        ch_in = n_channels
-        for m in ch_mult:
-            blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
-            ch_in = m * n_channels
-            downs.append(Downsample(ch_in, with_conv=False))
-
-        self.model = nn.ModuleList(blocks)
-        self.downsampler = nn.ModuleList(downs)
-
-
-    def instantiate_pretrained(self, config):
-        model = instantiate_from_config(config)
-        self.pretrained_model = model.eval()
-        # self.pretrained_model.train = False
-        for param in self.pretrained_model.parameters():
-            param.requires_grad = False
-
-
-    @torch.no_grad()
-    def encode_with_pretrained(self,x):
-        c = self.pretrained_model.encode(x)
-        if isinstance(c, DiagonalGaussianDistribution):
-            c = c.mode()
-        return  c
-
-    def forward(self,x):
-        z_fs = self.encode_with_pretrained(x)
-        z = self.proj_norm(z_fs)
-        z = self.proj(z)
-        z = nonlinearity(z)
-
-        for submodel, downmodel in zip(self.model,self.downsampler):
-            z = submodel(z,temb=None)
-            z = downmodel(z)
-
-        if self.do_reshape:
-            z = rearrange(z,'b c h w -> b (h w) c')
-        return z
-
diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/models/autoencoder_multi.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/models/autoencoder_multi.py
deleted file mode 100644
index cc4f830e24e99950f5ff412e8c5776e6a3489bf2..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/models/autoencoder_multi.py
+++ /dev/null
@@ -1,201 +0,0 @@
-"""
-与autoencoder.py的区别在于，autoencoder.py计算loss时只有一个discriminator，而此处又多了个multiwindowDiscriminator，所以优化器
-优化的参数改为：
-opt_disc = torch.optim.Adam(list(self.loss.discriminator.parameters()) + list(self.loss.discriminator_multi.parameters()),
-                            lr=lr, betas=(0.5, 0.9))
-"""
-
-import os
-import torch
-import pytorch_lightning as pl
-import torch.nn.functional as F
-from contextlib import contextmanager
-
-from packaging import version
-import numpy as np
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-from torch.optim.lr_scheduler import LambdaLR
-from ldm.util import instantiate_from_config
-
-
-
-class AutoencoderKL(pl.LightningModule):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 embed_dim,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None,
-                 monitor=None,
-                 ):
-        super().__init__()
-        self.image_key = image_key
-        self.encoder = Encoder(**ddconfig)
-        self.decoder = Decoder(**ddconfig)
-        self.loss = instantiate_from_config(lossconfig)
-        assert ddconfig["double_z"]
-        self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
-        self.embed_dim = embed_dim
-        if colorize_nlabels is not None:
-            assert type(colorize_nlabels)==int
-            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
-        if monitor is not None:
-            self.monitor = monitor
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-
-    def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location="cpu")["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        self.load_state_dict(sd, strict=False)
-        print(f"Restored from {path}")
-
-    def encode(self, x):
-        h = self.encoder(x)
-        moments = self.quant_conv(h)
-        posterior = DiagonalGaussianDistribution(moments)
-        return posterior
-
-    def decode(self, z):
-        z = self.post_quant_conv(z)
-        dec = self.decoder(z)
-        return dec
-
-    def forward(self, input, sample_posterior=True):
-        posterior = self.encode(input)
-        if sample_posterior:
-            z = posterior.sample()
-        else:
-            z = posterior.mode()
-        dec = self.decode(z)
-        return dec, posterior
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
-        return x
-
-    def training_step(self, batch, batch_idx, optimizer_idx):
-        inputs = self.get_input(batch, self.image_key)
-        reconstructions, posterior = self(inputs)
-
-        if optimizer_idx == 0:
-            # train encoder+decoder+logvar
-            aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-            self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
-            return aeloss
-
-        if optimizer_idx == 1:
-            # train the discriminator
-            discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
-                                                last_layer=self.get_last_layer(), split="train")
-
-            self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
-            return discloss
-
-    def validation_step(self, batch, batch_idx):
-        inputs = self.get_input(batch, self.image_key)
-        reconstructions, posterior = self(inputs)
-        aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
-                                        last_layer=self.get_last_layer(), split="val")
-
-        discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
-                                            last_layer=self.get_last_layer(), split="val")
-
-        self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
-        self.log_dict(log_dict_ae)
-        self.log_dict(log_dict_disc)
-        return self.log_dict
-
-    def test_step(self, batch, batch_idx):
-        inputs = self.get_input(batch, self.image_key)# inputs shape:(b,c,mel_len,T) or (b,c,h,w)
-        reconstructions, posterior = self(inputs)# reconstructions:(b,c,mel_len,T) or (b,c,h,w)
-        reconstructions = (reconstructions + 1)/2 # to mel scale
-        test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path)
-        savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class')
-        if not os.path.exists(savedir):
-            os.makedirs(savedir)
-
-        file_names = batch['f_name']
-        # print(f"reconstructions.shape:{reconstructions.shape}",file_names)
-        reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim
-        for b in range(reconstructions.shape[0]):
-            vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num
-            v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:]
-            save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}.npy')
-            np.save(save_img_path,reconstructions[b])
-        
-        return None
-        
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=lr, betas=(0.5, 0.9))
-        opt_disc = torch.optim.Adam(list(self.loss.discriminator.parameters()) + list(self.loss.discriminator_multi.parameters()),
-                                    lr=lr, betas=(0.5, 0.9))
-        return [opt_ae, opt_disc], []
-
-    def get_last_layer(self):
-        return self.decoder.conv_out.weight
-
-    @torch.no_grad()
-    def log_images(self, batch, only_inputs=False, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.image_key)
-        x = x.to(self.device)
-        if not only_inputs:
-            xrec, posterior = self(x)
-            if x.shape[1] > 3:
-                # colorize with random projection
-                assert xrec.shape[1] > 3
-                x = self.to_rgb(x)
-                xrec = self.to_rgb(xrec)
-            log["samples"] = self.decode(torch.randn_like(posterior.sample()))
-            log["reconstructions"] = xrec
-        log["inputs"] = x
-        return log
-
-    def to_rgb(self, x):
-        assert self.image_key == "segmentation"
-        if not hasattr(self, "colorize"):
-            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
-        x = F.conv2d(x, weight=self.colorize)
-        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
-        return x
-
-
-class IdentityFirstStage(torch.nn.Module):
-    def __init__(self, *args, vq_interface=False, **kwargs):
-        self.vq_interface = vq_interface  # TODO: Should be true by default but check to not break older stuff
-        super().__init__()
-
-    def encode(self, x, *args, **kwargs):
-        return x
-
-    def decode(self, x, *args, **kwargs):
-        return x
-
-    def quantize(self, x, *args, **kwargs):
-        if self.vq_interface:
-            return x, None, [None, None, None]
-        return x
-
-    def forward(self, x, *args, **kwargs):
-        return x
\ No newline at end of file
diff --git a/spaces/AP123/dreamgaussian/grid_put.py b/spaces/AP123/dreamgaussian/grid_put.py
deleted file mode 100644
index 0086cc4efa7527b77b9e583642ca9dfa9ae467fe..0000000000000000000000000000000000000000
--- a/spaces/AP123/dreamgaussian/grid_put.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-def stride_from_shape(shape):
-    stride = [1]
-    for x in reversed(shape[1:]):
-        stride.append(stride[-1] * x) 
-    return list(reversed(stride))
-
-
-def scatter_add_nd(input, indices, values):
-    # input: [..., C], D dimension + C channel
-    # indices: [N, D], long
-    # values: [N, C]
-
-    D = indices.shape[-1]
-    C = input.shape[-1]
-    size = input.shape[:-1]
-    stride = stride_from_shape(size)
-
-    assert len(size) == D
-
-    input = input.view(-1, C)  # [HW, C]
-    flatten_indices = (indices * torch.tensor(stride, dtype=torch.long, device=indices.device)).sum(-1)  # [N]
-
-    input.scatter_add_(0, flatten_indices.unsqueeze(1).repeat(1, C), values)
-
-    return input.view(*size, C)
-
-
-def scatter_add_nd_with_count(input, count, indices, values, weights=None):
-    # input: [..., C], D dimension + C channel
-    # count: [..., 1], D dimension
-    # indices: [N, D], long
-    # values: [N, C]
-
-    D = indices.shape[-1]
-    C = input.shape[-1]
-    size = input.shape[:-1]
-    stride = stride_from_shape(size)
-
-    assert len(size) == D
-
-    input = input.view(-1, C)  # [HW, C]
-    count = count.view(-1, 1)
-
-    flatten_indices = (indices * torch.tensor(stride, dtype=torch.long, device=indices.device)).sum(-1)  # [N]
-
-    if weights is None:
-        weights = torch.ones_like(values[..., :1]) 
-
-    input.scatter_add_(0, flatten_indices.unsqueeze(1).repeat(1, C), values)
-    count.scatter_add_(0, flatten_indices.unsqueeze(1), weights)
-
-    return input.view(*size, C), count.view(*size, 1)
-
-def nearest_grid_put_2d(H, W, coords, values, return_count=False):
-    # coords: [N, 2], float in [-1, 1]
-    # values: [N, C]
-
-    C = values.shape[-1]
-
-    indices = (coords * 0.5 + 0.5) * torch.tensor(
-        [H - 1, W - 1], dtype=torch.float32, device=coords.device
-    )
-    indices = indices.round().long()  # [N, 2]
-
-    result = torch.zeros(H, W, C, device=values.device, dtype=values.dtype)  # [H, W, C]
-    count = torch.zeros(H, W, 1, device=values.device, dtype=values.dtype)  # [H, W, 1]
-    weights = torch.ones_like(values[..., :1])  # [N, 1]
-    
-    result, count = scatter_add_nd_with_count(result, count, indices, values, weights)
-
-    if return_count:
-        return result, count
-
-    mask = (count.squeeze(-1) > 0)
-    result[mask] = result[mask] / count[mask].repeat(1, C)
-
-    return result
-
-
-def linear_grid_put_2d(H, W, coords, values, return_count=False):
-    # coords: [N, 2], float in [-1, 1]
-    # values: [N, C]
-
-    C = values.shape[-1]
-
-    indices = (coords * 0.5 + 0.5) * torch.tensor(
-        [H - 1, W - 1], dtype=torch.float32, device=coords.device
-    )
-    indices_00 = indices.floor().long()  # [N, 2]
-    indices_00[:, 0].clamp_(0, H - 2)
-    indices_00[:, 1].clamp_(0, W - 2)
-    indices_01 = indices_00 + torch.tensor(
-        [0, 1], dtype=torch.long, device=indices.device
-    )
-    indices_10 = indices_00 + torch.tensor(
-        [1, 0], dtype=torch.long, device=indices.device
-    )
-    indices_11 = indices_00 + torch.tensor(
-        [1, 1], dtype=torch.long, device=indices.device
-    )
-
-    h = indices[..., 0] - indices_00[..., 0].float()
-    w = indices[..., 1] - indices_00[..., 1].float()
-    w_00 = (1 - h) * (1 - w)
-    w_01 = (1 - h) * w
-    w_10 = h * (1 - w)
-    w_11 = h * w
-
-    result = torch.zeros(H, W, C, device=values.device, dtype=values.dtype)  # [H, W, C]
-    count = torch.zeros(H, W, 1, device=values.device, dtype=values.dtype)  # [H, W, 1]
-    weights = torch.ones_like(values[..., :1])  # [N, 1]
-    
-    result, count = scatter_add_nd_with_count(result, count, indices_00, values * w_00.unsqueeze(1), weights* w_00.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_01, values * w_01.unsqueeze(1), weights* w_01.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_10, values * w_10.unsqueeze(1), weights* w_10.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_11, values * w_11.unsqueeze(1), weights* w_11.unsqueeze(1))
-
-    if return_count:
-        return result, count
-
-    mask = (count.squeeze(-1) > 0)
-    result[mask] = result[mask] / count[mask].repeat(1, C)
-
-    return result
-
-def mipmap_linear_grid_put_2d(H, W, coords, values, min_resolution=32, return_count=False):
-    # coords: [N, 2], float in [-1, 1]
-    # values: [N, C]
-
-    C = values.shape[-1]
-
-    result = torch.zeros(H, W, C, device=values.device, dtype=values.dtype)  # [H, W, C]
-    count = torch.zeros(H, W, 1, device=values.device, dtype=values.dtype)  # [H, W, 1]
-
-    cur_H, cur_W = H, W
-    
-    while min(cur_H, cur_W) > min_resolution:
-
-        # try to fill the holes
-        mask = (count.squeeze(-1) == 0)
-        if not mask.any():
-            break
-
-        cur_result, cur_count = linear_grid_put_2d(cur_H, cur_W, coords, values, return_count=True)
-        result[mask] = result[mask] + F.interpolate(cur_result.permute(2,0,1).unsqueeze(0).contiguous(), (H, W), mode='bilinear', align_corners=False).squeeze(0).permute(1,2,0).contiguous()[mask]
-        count[mask] = count[mask] + F.interpolate(cur_count.view(1, 1, cur_H, cur_W), (H, W), mode='bilinear', align_corners=False).view(H, W, 1)[mask]
-        cur_H //= 2
-        cur_W //= 2
-    
-    if return_count:
-        return result, count
-
-    mask = (count.squeeze(-1) > 0)
-    result[mask] = result[mask] / count[mask].repeat(1, C)
-
-    return result
-
-def nearest_grid_put_3d(H, W, D, coords, values, return_count=False):
-    # coords: [N, 3], float in [-1, 1]
-    # values: [N, C]
-
-    C = values.shape[-1]
-
-    indices = (coords * 0.5 + 0.5) * torch.tensor(
-        [H - 1, W - 1, D - 1], dtype=torch.float32, device=coords.device
-    )
-    indices = indices.round().long()  # [N, 2]
-
-    result = torch.zeros(H, W, D, C, device=values.device, dtype=values.dtype)  # [H, W, C]
-    count = torch.zeros(H, W, D, 1, device=values.device, dtype=values.dtype)  # [H, W, 1]
-    weights = torch.ones_like(values[..., :1])  # [N, 1]
-
-    result, count = scatter_add_nd_with_count(result, count, indices, values, weights)
-    
-    if return_count:
-        return result, count
-
-    mask = (count.squeeze(-1) > 0)
-    result[mask] = result[mask] / count[mask].repeat(1, C)
-
-    return result
-
-
-def linear_grid_put_3d(H, W, D, coords, values, return_count=False):
-    # coords: [N, 3], float in [-1, 1]
-    # values: [N, C]
-
-    C = values.shape[-1]
-
-    indices = (coords * 0.5 + 0.5) * torch.tensor(
-        [H - 1, W - 1, D - 1], dtype=torch.float32, device=coords.device
-    )
-    indices_000 = indices.floor().long()  # [N, 3]
-    indices_000[:, 0].clamp_(0, H - 2)
-    indices_000[:, 1].clamp_(0, W - 2)
-    indices_000[:, 2].clamp_(0, D - 2)
-
-    indices_001 = indices_000 + torch.tensor([0, 0, 1], dtype=torch.long, device=indices.device)
-    indices_010 = indices_000 + torch.tensor([0, 1, 0], dtype=torch.long, device=indices.device)
-    indices_011 = indices_000 + torch.tensor([0, 1, 1], dtype=torch.long, device=indices.device)
-    indices_100 = indices_000 + torch.tensor([1, 0, 0], dtype=torch.long, device=indices.device)
-    indices_101 = indices_000 + torch.tensor([1, 0, 1], dtype=torch.long, device=indices.device)
-    indices_110 = indices_000 + torch.tensor([1, 1, 0], dtype=torch.long, device=indices.device)
-    indices_111 = indices_000 + torch.tensor([1, 1, 1], dtype=torch.long, device=indices.device)
-
-    h = indices[..., 0] - indices_000[..., 0].float()
-    w = indices[..., 1] - indices_000[..., 1].float()
-    d = indices[..., 2] - indices_000[..., 2].float()
-    
-    w_000 = (1 - h) * (1 - w) * (1 - d)
-    w_001 = (1 - h) * w * (1 - d)
-    w_010 = h * (1 - w) * (1 - d)
-    w_011 = h * w * (1 - d)
-    w_100 = (1 - h) * (1 - w) * d
-    w_101 = (1 - h) * w * d
-    w_110 = h * (1 - w) * d
-    w_111 = h * w * d
-
-    result = torch.zeros(H, W, D, C, device=values.device, dtype=values.dtype)  # [H, W, D, C]
-    count = torch.zeros(H, W, D, 1, device=values.device, dtype=values.dtype)  # [H, W, D, 1]
-    weights = torch.ones_like(values[..., :1])  # [N, 1]
-    
-    result, count = scatter_add_nd_with_count(result, count, indices_000, values * w_000.unsqueeze(1), weights * w_000.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_001, values * w_001.unsqueeze(1), weights * w_001.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_010, values * w_010.unsqueeze(1), weights * w_010.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_011, values * w_011.unsqueeze(1), weights * w_011.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_100, values * w_100.unsqueeze(1), weights * w_100.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_101, values * w_101.unsqueeze(1), weights * w_101.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_110, values * w_110.unsqueeze(1), weights * w_110.unsqueeze(1))
-    result, count = scatter_add_nd_with_count(result, count, indices_111, values * w_111.unsqueeze(1), weights * w_111.unsqueeze(1))
-
-    if return_count:
-        return result, count
-
-    mask = (count.squeeze(-1) > 0)
-    result[mask] = result[mask] / count[mask].repeat(1, C)
-
-    return result
-
-def mipmap_linear_grid_put_3d(H, W, D, coords, values, min_resolution=32, return_count=False):
-    # coords: [N, 3], float in [-1, 1]
-    # values: [N, C]
-
-    C = values.shape[-1]
-
-    result = torch.zeros(H, W, D, C, device=values.device, dtype=values.dtype)  # [H, W, D, C]
-    count = torch.zeros(H, W, D, 1, device=values.device, dtype=values.dtype)  # [H, W, D, 1]
-    cur_H, cur_W, cur_D = H, W, D
-    
-    while min(min(cur_H, cur_W), cur_D) > min_resolution:
-
-        # try to fill the holes
-        mask = (count.squeeze(-1) == 0)
-        if not mask.any():
-            break
-
-        cur_result, cur_count = linear_grid_put_3d(cur_H, cur_W, cur_D, coords, values, return_count=True)
-        result[mask] = result[mask] + F.interpolate(cur_result.permute(3,0,1,2).unsqueeze(0).contiguous(), (H, W, D), mode='trilinear', align_corners=False).squeeze(0).permute(1,2,3,0).contiguous()[mask]
-        count[mask] = count[mask] + F.interpolate(cur_count.view(1, 1, cur_H, cur_W, cur_D), (H, W, D), mode='trilinear', align_corners=False).view(H, W, D, 1)[mask]
-        cur_H //= 2
-        cur_W //= 2
-        cur_D //= 2
-    
-    if return_count:
-        return result, count
-
-    mask = (count.squeeze(-1) > 0)
-    result[mask] = result[mask] / count[mask].repeat(1, C)
-
-    return result
-
-
-def grid_put(shape, coords, values, mode='linear-mipmap', min_resolution=32, return_raw=False):
-    # shape: [D], list/tuple
-    # coords: [N, D], float in [-1, 1]
-    # values: [N, C]
-
-    D = len(shape)
-    assert D in [2, 3], f'only support D == 2 or 3, but got D == {D}'
-
-    if mode == 'nearest':
-        if D == 2:
-            return nearest_grid_put_2d(*shape, coords, values, return_raw)
-        else:
-            return nearest_grid_put_3d(*shape, coords, values, return_raw)
-    elif mode == 'linear':
-        if D == 2:
-            return linear_grid_put_2d(*shape, coords, values, return_raw)
-        else:
-            return linear_grid_put_3d(*shape, coords, values, return_raw)
-    elif mode == 'linear-mipmap':
-        if D == 2:
-            return mipmap_linear_grid_put_2d(*shape, coords, values, min_resolution, return_raw)
-        else:
-            return mipmap_linear_grid_put_3d(*shape, coords, values, min_resolution, return_raw)
-    else:
-        raise NotImplementedError(f"got mode {mode}")    
\ No newline at end of file
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/README.md b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/README.md
deleted file mode 100644
index 286b77381a57401607cc52568d1d81b8ba5b4d83..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/README.md
+++ /dev/null
@@ -1,140 +0,0 @@
-# ResNet
-
-> [Deep Residual Learning for Image Recognition](https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html)
-
-<!-- [ALGORITHM] -->
-
-## Introduction
-
-**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of
-learning unreferenced functions. In the mainstream previous works, like VGG, the neural networks are a stack
-of layers and every layer attempts to fit a desired underlying mapping. In ResNets, a few stacked layers are
-grouped as a block, and the layers in a block attempts to learn a residual mapping.
-
-Formally, denoting the desired underlying mapping of a block as $\mathcal{H}(x)$, split the underlying mapping
-into the sum of the identity and the residual mapping as $\mathcal{H}(x) = x + \mathcal{F}(x)$, and let the
-stacked non-linear layers fit the residual mapping $\mathcal{F}(x)$.
-
-Many works proved this method makes deep neural networks easier to optimize, and can gain accuracy from
-considerably increased depth. Recently, the residual structure is widely used in various models.
-
-<div align=center>
-<img src="https://user-images.githubusercontent.com/26739999/142574068-60cfdeea-c4ec-4c49-abb2-5dc2facafc3b.png" width="40%"/>
-</div>
-
-## Abstract
-
-<details>
-
-<summary>Show the paper's abstract</summary>
-
-<br>
-Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.
-
-The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
-</br>
-
-</details>
-
-## How to use it?
-
-<!-- [TABS-BEGIN] -->
-
-**Predict image**
-
-```python
-from mmpretrain import inference_model
-
-predict = inference_model('resnet18_8xb16_cifar10', 'demo/bird.JPEG')
-print(predict['pred_class'])
-print(predict['pred_score'])
-```
-
-**Use the model**
-
-```python
-import torch
-from mmpretrain import get_model
-
-model = get_model('resnet18_8xb16_cifar10', pretrained=True)
-inputs = torch.rand(1, 3, 224, 224)
-out = model(inputs)
-print(type(out))
-# To extract features.
-feats = model.extract_feat(inputs)
-print(type(feats))
-```
-
-**Train/Test Command**
-
-Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
-
-Train:
-
-```shell
-python tools/train.py configs/resnet/resnet18_8xb16_cifar10.py
-```
-
-Test:
-
-```shell
-python tools/test.py configs/resnet/resnet18_8xb16_cifar10.py https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth
-```
-
-<!-- [TABS-END] -->
-
-## Models and results
-
-### Image Classification on ImageNet-1k
-
-| Model                              |   Pretrain   | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) |                    Config                     |                                 Download                                 |
-| :--------------------------------- | :----------: | :--------: | :-------: | :-------: | :-------: | :-------------------------------------------: | :----------------------------------------------------------------------: |
-| `resnet18_8xb32_in1k`              | From scratch |   11.69    |   1.82    |   69.90   |   89.43   |       [config](resnet18_8xb32_in1k.py)        | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.json) |
-| `resnet34_8xb32_in1k`              | From scratch |    2.18    |   3.68    |   73.62   |   91.59   |       [config](resnet34_8xb32_in1k.py)        | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.json) |
-| `resnet50_8xb32_in1k`              | From scratch |   25.56    |   4.12    |   76.55   |   93.06   |       [config](resnet50_8xb32_in1k.py)        | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.json) |
-| `resnet101_8xb32_in1k`             | From scratch |   44.55    |   7.85    |   77.97   |   94.06   |       [config](resnet101_8xb32_in1k.py)       | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.json) |
-| `resnet152_8xb32_in1k`             | From scratch |   60.19    |   11.58   |   78.48   |   94.13   |       [config](resnet152_8xb32_in1k.py)       | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.json) |
-| `resnetv1d50_8xb32_in1k`           | From scratch |   25.58    |   4.36    |   77.54   |   93.57   |      [config](resnetv1d50_8xb32_in1k.py)      | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.json) |
-| `resnetv1d101_8xb32_in1k`          | From scratch |   44.57    |   8.09    |   78.93   |   94.48   |     [config](resnetv1d101_8xb32_in1k.py)      | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.json) |
-| `resnetv1d152_8xb32_in1k`          | From scratch |   60.21    |   11.82   |   79.41   |   94.70   |     [config](resnetv1d152_8xb32_in1k.py)      | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.json) |
-| `resnet50_8xb32-fp16_in1k`         | From scratch |   25.56    |   4.12    |   76.30   |   93.07   |     [config](resnet50_8xb32-fp16_in1k.py)     | [model](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.json) |
-| `resnet50_8xb256-rsb-a1-600e_in1k` | From scratch |   25.56    |   4.12    |   80.12   |   94.78   | [config](resnet50_8xb256-rsb-a1-600e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.json) |
-| `resnet50_8xb256-rsb-a2-300e_in1k` | From scratch |   25.56    |   4.12    |   79.55   |   94.37   | [config](resnet50_8xb256-rsb-a2-300e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a2-300e_in1k_20211228-0fd8be6e.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a2-300e_in1k_20211228-0fd8be6e.json) |
-| `resnet50_8xb256-rsb-a3-100e_in1k` | From scratch |   25.56    |   4.12    |   78.30   |   93.80   | [config](resnet50_8xb256-rsb-a3-100e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.json) |
-| `resnetv1c50_8xb32_in1k`           | From scratch |   25.58    |   4.36    |   77.01   |   93.58   |      [config](resnetv1c50_8xb32_in1k.py)      | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.json) |
-| `resnetv1c101_8xb32_in1k`          | From scratch |   44.57    |   8.09    |   78.30   |   94.27   |     [config](resnetv1c101_8xb32_in1k.py)      | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.json) |
-| `resnetv1c152_8xb32_in1k`          | From scratch |   60.21    |   11.82   |   78.76   |   94.41   |     [config](resnetv1c152_8xb32_in1k.py)      | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.json) |
-
-### Image Classification on CIFAR-10
-
-| Model                     |   Pretrain   | Params (M) | Flops (G) | Top-1 (%) |                Config                |                                              Download                                               |
-| :------------------------ | :----------: | :--------: | :-------: | :-------: | :----------------------------------: | :-------------------------------------------------------------------------------------------------: |
-| `resnet18_8xb16_cifar10`  | From scratch |   11.17    |   0.56    |   94.82   | [config](resnet18_8xb16_cifar10.py)  | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.json) |
-| `resnet34_8xb16_cifar10`  | From scratch |   21.28    |   1.16    |   95.34   | [config](resnet34_8xb16_cifar10.py)  | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.json) |
-| `resnet50_8xb16_cifar10`  | From scratch |   23.52    |   1.31    |   95.55   | [config](resnet50_8xb16_cifar10.py)  | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.json) |
-| `resnet101_8xb16_cifar10` | From scratch |   42.51    |   2.52    |   95.58   | [config](resnet101_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.json) |
-| `resnet152_8xb16_cifar10` | From scratch |   58.16    |   3.74    |   95.76   | [config](resnet152_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.json) |
-
-### Image Classification on CIFAR-100
-
-| Model                     |   Pretrain   | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) |                Config                |                                          Download                                          |
-| :------------------------ | :----------: | :--------: | :-------: | :-------: | :-------: | :----------------------------------: | :----------------------------------------------------------------------------------------: |
-| `resnet50_8xb16_cifar100` | From scratch |   23.71    |   1.31    |   79.90   |   95.19   | [config](resnet50_8xb16_cifar100.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar100_20210528-67b58a1b.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar100_20210528-67b58a1b.json) |
-
-### Image Classification on CUB-200-2011
-
-| Model               |   Pretrain   | Params (M) | Flops (G) | Top-1 (%) |             Config             |                                                    Download                                                     |
-| :------------------ | :----------: | :--------: | :-------: | :-------: | :----------------------------: | :-------------------------------------------------------------------------------------------------------------: |
-| `resnet50_8xb8_cub` | From scratch |   23.92    |   16.48   |   88.45   | [config](resnet50_8xb8_cub.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.json) |
-
-## Citation
-
-```bibtex
-@inproceedings{he2016deep,
-  title={Deep residual learning for image recognition},
-  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
-  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
-  pages={770--778},
-  year={2016}
-}
-```
diff --git a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/tester.py b/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/tester.py
deleted file mode 100644
index 50d622e7edb0ed989fcd3273d35e74d66f11ce75..0000000000000000000000000000000000000000
--- a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/tester.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from .config_manager import ConfigManager
-import os
-from typing import Dict
-
-from torch import nn
-from tqdm import tqdm
-from tqdm import trange
-
-from dataset import load_iterators
-from trainer import GeneralTrainer
-
-
-class DiacritizationTester(GeneralTrainer):
-    def __init__(self, config_path: str, model_kind: str) -> None:
-        self.config_path = config_path
-        self.model_kind = model_kind
-        self.config_manager = ConfigManager(
-            config_path=config_path, model_kind=model_kind
-        )
-        self.config = self.config_manager.config
-        self.pad_idx = 0
-        self.criterion = nn.CrossEntropyLoss(ignore_index=self.pad_idx)
-        self.set_device()
-
-        self.text_encoder = self.config_manager.text_encoder
-        self.start_symbol_id = self.text_encoder.start_symbol_id
-
-        self.model = self.config_manager.get_model()
-
-        self.model = self.model.to(self.device)
-
-        self.load_model(model_path=self.config["test_model_path"], load_optimizer=False)
-        self.load_diacritizer()
-        self.diacritizer.set_model(self.model)
-
-        self.initialize_model()
-
-        self.print_config()
-
-    def run(self):
-        self.config_manager.config["load_training_data"] = False
-        self.config_manager.config["load_validation_data"] = False
-        self.config_manager.config["load_test_data"] = True
-        _, test_iterator, _ = load_iterators(self.config_manager)
-        tqdm_eval = trange(0, len(test_iterator), leave=True)
-        tqdm_error_rates = trange(0, len(test_iterator), leave=True)
-
-        loss, acc = self.evaluate(test_iterator, tqdm_eval, log = False)
-        error_rates, _ = self.evaluate_with_error_rates(test_iterator, tqdm_error_rates, log = False)
-
-        tqdm_eval.close()
-        tqdm_error_rates.close()
-
-        WER = error_rates["WER"]
-        DER = error_rates["DER"]
-        DER1 = error_rates["DER*"]
-        WER1 = error_rates["WER*"]
-
-        error_rates = f"DER: {DER}, WER: {WER}, DER*: {DER1}, WER*: {WER1}"
-
-        print(f"global step : {self.global_step}")
-        print(f"Evaluate {self.global_step}: accuracy, {acc}, loss: {loss}")
-        print(f"WER/DER {self.global_step}: {error_rates}")
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Liaobots.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Liaobots.py
deleted file mode 100644
index 2ab96ce349f641d3e4afaf862169f27d749ca62b..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Liaobots.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from __future__ import annotations
-
-import uuid
-
-from aiohttp import ClientSession
-
-from ..typing import AsyncGenerator
-from .base_provider import AsyncGeneratorProvider
-
-models = {
-    "gpt-4": {
-        "id": "gpt-4",
-        "name": "GPT-4",
-        "maxLength": 24000,
-        "tokenLimit": 8000,
-    },
-    "gpt-3.5-turbo": {
-        "id": "gpt-3.5-turbo",
-        "name": "GPT-3.5",
-        "maxLength": 12000,
-        "tokenLimit": 4000,
-    },
-    "gpt-3.5-turbo-16k": {
-        "id": "gpt-3.5-turbo-16k",
-        "name": "GPT-3.5-16k",
-        "maxLength": 48000,
-        "tokenLimit": 16000,
-    },
-}
-
-class Liaobots(AsyncGeneratorProvider):
-    url = "https://liaobots.site"
-    working = True
-    supports_gpt_35_turbo = True
-    supports_gpt_4 = True
-    _auth_code = None
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        auth: str = None,
-        proxy: str = None,
-        **kwargs
-    ) -> AsyncGenerator:
-        model = model if model in models else "gpt-3.5-turbo"
-        headers = {
-            "authority": "liaobots.com",
-            "content-type": "application/json",
-            "origin": cls.url,
-            "referer": cls.url + "/",
-            "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36",
-        }
-        async with ClientSession(
-            headers=headers
-        ) as session:
-            cls._auth_code = auth if isinstance(auth, str) else cls._auth_code
-            if not cls._auth_code:
-                async with session.post(
-                    "https://liaobots.work/recaptcha/api/login",
-                    proxy=proxy,
-                    data={"token": "abcdefghijklmnopqrst"},
-                    verify_ssl=False
-                ) as response:
-                    response.raise_for_status()
-                async with session.post(
-                    "https://liaobots.work/api/user",
-                    proxy=proxy,
-                    json={"authcode": ""},
-                    verify_ssl=False
-                ) as response:
-                    response.raise_for_status()
-                    cls._auth_code = (await response.json(content_type=None))["authCode"]
-            data = {
-                "conversationId": str(uuid.uuid4()),
-                "model": models[model],
-                "messages": messages,
-                "key": "",
-                "prompt": "You are ChatGPT, a large language model trained by OpenAI. Follow the user's instructions carefully.",
-            }
-            async with session.post(
-                "https://liaobots.work/api/chat",
-                proxy=proxy,
-                json=data,
-                headers={"x-auth-code": cls._auth_code},
-                verify_ssl=False
-            ) as response:
-                response.raise_for_status()
-                async for stream in response.content.iter_any():
-                    if stream:
-                        yield stream.decode()
-
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-            ("proxy", "str"),
-            ("auth", "str"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"
diff --git a/spaces/Aditya757864/SentimentAnalysis/app.py b/spaces/Aditya757864/SentimentAnalysis/app.py
deleted file mode 100644
index c621163f70e6d4cfc1b9322a83b69a393c50b54f..0000000000000000000000000000000000000000
--- a/spaces/Aditya757864/SentimentAnalysis/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import gradio as gr 
-from transformers import pipeline
-sentiment = pipeline('sentiment-analysis')
-def get_sentiment(input_text):
-  return sentiment (input_text)
-iface = gr.Interface(fn = get_sentiment,
-                     inputs = 'text',
-                     outputs = ['text'],
-                     title = 'Sentiment Analysis',
-                     examples = ['The movie was very bad', 'Every day is a new opportunity.'],
-                     article = 'This project is for software engineering with team members Aditya Jadhav, Sujal Kuthe, Sujal Wakalkar, and Adesh Ingle. We developed a web application for sentiment analysis that takes text data as input and classifies whether it is positive or negative.',
-                     thumbnail = '/content/sentiment-analysis.png',
-                     theme = gr.themes.Soft())
-iface.launch()
\ No newline at end of file
diff --git a/spaces/Aditya9790/yolo7-object-tracking/train.py b/spaces/Aditya9790/yolo7-object-tracking/train.py
deleted file mode 100644
index 86c7e48d5ac214ad4a4c0a63b924d7ece429211c..0000000000000000000000000000000000000000
--- a/spaces/Aditya9790/yolo7-object-tracking/train.py
+++ /dev/null
@@ -1,705 +0,0 @@
-import argparse
-import logging
-import math
-import os
-import random
-import time
-from copy import deepcopy
-from pathlib import Path
-from threading import Thread
-
-import numpy as np
-import torch.distributed as dist
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-import torch.optim.lr_scheduler as lr_scheduler
-import torch.utils.data
-import yaml
-from torch.cuda import amp
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.utils.tensorboard import SummaryWriter
-from tqdm import tqdm
-
-import test  # import test.py to get mAP after each epoch
-from models.experimental import attempt_load
-from models.yolo import Model
-from utils.autoanchor import check_anchors
-from utils.datasets import create_dataloader
-from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
-    fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \
-    check_requirements, print_mutation, set_logging, one_cycle, colorstr
-from utils.google_utils import attempt_download
-from utils.loss import ComputeLoss, ComputeLossOTA
-from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
-from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel
-from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume
-
-logger = logging.getLogger(__name__)
-
-
-def train(hyp, opt, device, tb_writer=None):
-    logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
-    save_dir, epochs, batch_size, total_batch_size, weights, rank, freeze = \
-        Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank, opt.freeze
-
-    # Directories
-    wdir = save_dir / 'weights'
-    wdir.mkdir(parents=True, exist_ok=True)  # make dir
-    last = wdir / 'last.pt'
-    best = wdir / 'best.pt'
-    results_file = save_dir / 'results.txt'
-
-    # Save run settings
-    with open(save_dir / 'hyp.yaml', 'w') as f:
-        yaml.dump(hyp, f, sort_keys=False)
-    with open(save_dir / 'opt.yaml', 'w') as f:
-        yaml.dump(vars(opt), f, sort_keys=False)
-
-    # Configure
-    plots = not opt.evolve  # create plots
-    cuda = device.type != 'cpu'
-    init_seeds(2 + rank)
-    with open(opt.data) as f:
-        data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # data dict
-    is_coco = opt.data.endswith('coco.yaml')
-
-    # Logging- Doing this before checking the dataset. Might update data_dict
-    loggers = {'wandb': None}  # loggers dict
-    if rank in [-1, 0]:
-        opt.hyp = hyp  # add hyperparameters
-        run_id = torch.load(weights, map_location=device).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
-        wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict)
-        loggers['wandb'] = wandb_logger.wandb
-        data_dict = wandb_logger.data_dict
-        if wandb_logger.wandb:
-            weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp  # WandbLogger might update weights, epochs if resuming
-
-    nc = 1 if opt.single_cls else int(data_dict['nc'])  # number of classes
-    names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
-    assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data)  # check
-
-    # Model
-    pretrained = weights.endswith('.pt')
-    if pretrained:
-        with torch_distributed_zero_first(rank):
-            attempt_download(weights)  # download if not found locally
-        ckpt = torch.load(weights, map_location=device)  # load checkpoint
-        model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
-        exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else []  # exclude keys
-        state_dict = ckpt['model'].float().state_dict()  # to FP32
-        state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude)  # intersect
-        model.load_state_dict(state_dict, strict=False)  # load
-        logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights))  # report
-    else:
-        model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
-    with torch_distributed_zero_first(rank):
-        check_dataset(data_dict)  # check
-    train_path = data_dict['train']
-    test_path = data_dict['val']
-
-    # Freeze
-    freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))]  # parameter names to freeze (full or partial)
-    for k, v in model.named_parameters():
-        v.requires_grad = True  # train all layers
-        if any(x in k for x in freeze):
-            print('freezing %s' % k)
-            v.requires_grad = False
-
-    # Optimizer
-    nbs = 64  # nominal batch size
-    accumulate = max(round(nbs / total_batch_size), 1)  # accumulate loss before optimizing
-    hyp['weight_decay'] *= total_batch_size * accumulate / nbs  # scale weight_decay
-    logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
-
-    pg0, pg1, pg2 = [], [], []  # optimizer parameter groups
-    for k, v in model.named_modules():
-        if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
-            pg2.append(v.bias)  # biases
-        if isinstance(v, nn.BatchNorm2d):
-            pg0.append(v.weight)  # no decay
-        elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
-            pg1.append(v.weight)  # apply decay
-        if hasattr(v, 'im'):
-            if hasattr(v.im, 'implicit'):           
-                pg0.append(v.im.implicit)
-            else:
-                for iv in v.im:
-                    pg0.append(iv.implicit)
-        if hasattr(v, 'imc'):
-            if hasattr(v.imc, 'implicit'):           
-                pg0.append(v.imc.implicit)
-            else:
-                for iv in v.imc:
-                    pg0.append(iv.implicit)
-        if hasattr(v, 'imb'):
-            if hasattr(v.imb, 'implicit'):           
-                pg0.append(v.imb.implicit)
-            else:
-                for iv in v.imb:
-                    pg0.append(iv.implicit)
-        if hasattr(v, 'imo'):
-            if hasattr(v.imo, 'implicit'):           
-                pg0.append(v.imo.implicit)
-            else:
-                for iv in v.imo:
-                    pg0.append(iv.implicit)
-        if hasattr(v, 'ia'):
-            if hasattr(v.ia, 'implicit'):           
-                pg0.append(v.ia.implicit)
-            else:
-                for iv in v.ia:
-                    pg0.append(iv.implicit)
-        if hasattr(v, 'attn'):
-            if hasattr(v.attn, 'logit_scale'):   
-                pg0.append(v.attn.logit_scale)
-            if hasattr(v.attn, 'q_bias'):   
-                pg0.append(v.attn.q_bias)
-            if hasattr(v.attn, 'v_bias'):  
-                pg0.append(v.attn.v_bias)
-            if hasattr(v.attn, 'relative_position_bias_table'):  
-                pg0.append(v.attn.relative_position_bias_table)
-        if hasattr(v, 'rbr_dense'):
-            if hasattr(v.rbr_dense, 'weight_rbr_origin'):  
-                pg0.append(v.rbr_dense.weight_rbr_origin)
-            if hasattr(v.rbr_dense, 'weight_rbr_avg_conv'): 
-                pg0.append(v.rbr_dense.weight_rbr_avg_conv)
-            if hasattr(v.rbr_dense, 'weight_rbr_pfir_conv'):  
-                pg0.append(v.rbr_dense.weight_rbr_pfir_conv)
-            if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_idconv1'): 
-                pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_idconv1)
-            if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_conv2'):   
-                pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_conv2)
-            if hasattr(v.rbr_dense, 'weight_rbr_gconv_dw'):   
-                pg0.append(v.rbr_dense.weight_rbr_gconv_dw)
-            if hasattr(v.rbr_dense, 'weight_rbr_gconv_pw'):   
-                pg0.append(v.rbr_dense.weight_rbr_gconv_pw)
-            if hasattr(v.rbr_dense, 'vector'):   
-                pg0.append(v.rbr_dense.vector)
-
-    if opt.adam:
-        optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum
-    else:
-        optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
-
-    optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']})  # add pg1 with weight_decay
-    optimizer.add_param_group({'params': pg2})  # add pg2 (biases)
-    logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
-    del pg0, pg1, pg2
-
-    # Scheduler https://arxiv.org/pdf/1812.01187.pdf
-    # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
-    if opt.linear_lr:
-        lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf']  # linear
-    else:
-        lf = one_cycle(1, hyp['lrf'], epochs)  # cosine 1->hyp['lrf']
-    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
-    # plot_lr_scheduler(optimizer, scheduler, epochs)
-
-    # EMA
-    ema = ModelEMA(model) if rank in [-1, 0] else None
-
-    # Resume
-    start_epoch, best_fitness = 0, 0.0
-    if pretrained:
-        # Optimizer
-        if ckpt['optimizer'] is not None:
-            optimizer.load_state_dict(ckpt['optimizer'])
-            best_fitness = ckpt['best_fitness']
-
-        # EMA
-        if ema and ckpt.get('ema'):
-            ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
-            ema.updates = ckpt['updates']
-
-        # Results
-        if ckpt.get('training_results') is not None:
-            results_file.write_text(ckpt['training_results'])  # write results.txt
-
-        # Epochs
-        start_epoch = ckpt['epoch'] + 1
-        if opt.resume:
-            assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
-        if epochs < start_epoch:
-            logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
-                        (weights, ckpt['epoch'], epochs))
-            epochs += ckpt['epoch']  # finetune additional epochs
-
-        del ckpt, state_dict
-
-    # Image sizes
-    gs = max(int(model.stride.max()), 32)  # grid size (max stride)
-    nl = model.model[-1].nl  # number of detection layers (used for scaling hyp['obj'])
-    imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size]  # verify imgsz are gs-multiples
-
-    # DP mode
-    if cuda and rank == -1 and torch.cuda.device_count() > 1:
-        model = torch.nn.DataParallel(model)
-
-    # SyncBatchNorm
-    if opt.sync_bn and cuda and rank != -1:
-        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
-        logger.info('Using SyncBatchNorm()')
-
-    # Trainloader
-    dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
-                                            hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
-                                            world_size=opt.world_size, workers=opt.workers,
-                                            image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '))
-    mlc = np.concatenate(dataset.labels, 0)[:, 0].max()  # max label class
-    nb = len(dataloader)  # number of batches
-    assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
-
-    # Process 0
-    if rank in [-1, 0]:
-        testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt,  # testloader
-                                       hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1,
-                                       world_size=opt.world_size, workers=opt.workers,
-                                       pad=0.5, prefix=colorstr('val: '))[0]
-
-        if not opt.resume:
-            labels = np.concatenate(dataset.labels, 0)
-            c = torch.tensor(labels[:, 0])  # classes
-            # cf = torch.bincount(c.long(), minlength=nc) + 1.  # frequency
-            # model._initialize_biases(cf.to(device))
-            if plots:
-                #plot_labels(labels, names, save_dir, loggers)
-                if tb_writer:
-                    tb_writer.add_histogram('classes', c, 0)
-
-            # Anchors
-            if not opt.noautoanchor:
-                check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
-            model.half().float()  # pre-reduce anchor precision
-
-    # DDP mode
-    if cuda and rank != -1:
-        model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank,
-                    # nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698
-                    find_unused_parameters=any(isinstance(layer, nn.MultiheadAttention) for layer in model.modules()))
-
-    # Model parameters
-    hyp['box'] *= 3. / nl  # scale to layers
-    hyp['cls'] *= nc / 80. * 3. / nl  # scale to classes and layers
-    hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl  # scale to image size and layers
-    hyp['label_smoothing'] = opt.label_smoothing
-    model.nc = nc  # attach number of classes to model
-    model.hyp = hyp  # attach hyperparameters to model
-    model.gr = 1.0  # iou loss ratio (obj_loss = 1.0 or iou)
-    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc  # attach class weights
-    model.names = names
-
-    # Start training
-    t0 = time.time()
-    nw = max(round(hyp['warmup_epochs'] * nb), 1000)  # number of warmup iterations, max(3 epochs, 1k iterations)
-    # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
-    maps = np.zeros(nc)  # mAP per class
-    results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
-    scheduler.last_epoch = start_epoch - 1  # do not move
-    scaler = amp.GradScaler(enabled=cuda)
-    compute_loss_ota = ComputeLossOTA(model)  # init loss class
-    compute_loss = ComputeLoss(model)  # init loss class
-    logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'
-                f'Using {dataloader.num_workers} dataloader workers\n'
-                f'Logging results to {save_dir}\n'
-                f'Starting training for {epochs} epochs...')
-    torch.save(model, wdir / 'init.pt')
-    for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
-        model.train()
-
-        # Update image weights (optional)
-        if opt.image_weights:
-            # Generate indices
-            if rank in [-1, 0]:
-                cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc  # class weights
-                iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weights
-                dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx
-            # Broadcast if DDP
-            if rank != -1:
-                indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
-                dist.broadcast(indices, 0)
-                if rank != 0:
-                    dataset.indices = indices.cpu().numpy()
-
-        # Update mosaic border
-        # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
-        # dataset.mosaic_border = [b - imgsz, -b]  # height, width borders
-
-        mloss = torch.zeros(4, device=device)  # mean losses
-        if rank != -1:
-            dataloader.sampler.set_epoch(epoch)
-        pbar = enumerate(dataloader)
-        logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size'))
-        if rank in [-1, 0]:
-            pbar = tqdm(pbar, total=nb)  # progress bar
-        optimizer.zero_grad()
-        for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
-            ni = i + nb * epoch  # number integrated batches (since train start)
-            imgs = imgs.to(device, non_blocking=True).float() / 255.0  # uint8 to float32, 0-255 to 0.0-1.0
-
-            # Warmup
-            if ni <= nw:
-                xi = [0, nw]  # x interp
-                # model.gr = np.interp(ni, xi, [0.0, 1.0])  # iou loss ratio (obj_loss = 1.0 or iou)
-                accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
-                for j, x in enumerate(optimizer.param_groups):
-                    # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
-                    x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
-                    if 'momentum' in x:
-                        x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
-
-            # Multi-scale
-            if opt.multi_scale:
-                sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs  # size
-                sf = sz / max(imgs.shape[2:])  # scale factor
-                if sf != 1:
-                    ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]]  # new shape (stretched to gs-multiple)
-                    imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
-
-            # Forward
-            with amp.autocast(enabled=cuda):
-                pred = model(imgs)  # forward
-                if 'loss_ota' not in hyp or hyp['loss_ota'] == 1:
-                    loss, loss_items = compute_loss_ota(pred, targets.to(device), imgs)  # loss scaled by batch_size
-                else:
-                    loss, loss_items = compute_loss(pred, targets.to(device))  # loss scaled by batch_size
-                if rank != -1:
-                    loss *= opt.world_size  # gradient averaged between devices in DDP mode
-                if opt.quad:
-                    loss *= 4.
-
-            # Backward
-            scaler.scale(loss).backward()
-
-            # Optimize
-            if ni % accumulate == 0:
-                scaler.step(optimizer)  # optimizer.step
-                scaler.update()
-                optimizer.zero_grad()
-                if ema:
-                    ema.update(model)
-
-            # Print
-            if rank in [-1, 0]:
-                mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
-                mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0)  # (GB)
-                s = ('%10s' * 2 + '%10.4g' * 6) % (
-                    '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
-                pbar.set_description(s)
-
-                # Plot
-                if plots and ni < 10:
-                    f = save_dir / f'train_batch{ni}.jpg'  # filename
-                    Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
-                    # if tb_writer:
-                    #     tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
-                    #     tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), [])  # add model graph
-                elif plots and ni == 10 and wandb_logger.wandb:
-                    wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in
-                                                  save_dir.glob('train*.jpg') if x.exists()]})
-
-            # end batch ------------------------------------------------------------------------------------------------
-        # end epoch ----------------------------------------------------------------------------------------------------
-
-        # Scheduler
-        lr = [x['lr'] for x in optimizer.param_groups]  # for tensorboard
-        scheduler.step()
-
-        # DDP process 0 or single-GPU
-        if rank in [-1, 0]:
-            # mAP
-            ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])
-            final_epoch = epoch + 1 == epochs
-            if not opt.notest or final_epoch:  # Calculate mAP
-                wandb_logger.current_epoch = epoch + 1
-                results, maps, times = test.test(data_dict,
-                                                 batch_size=batch_size * 2,
-                                                 imgsz=imgsz_test,
-                                                 model=ema.ema,
-                                                 single_cls=opt.single_cls,
-                                                 dataloader=testloader,
-                                                 save_dir=save_dir,
-                                                 verbose=nc < 50 and final_epoch,
-                                                 plots=plots and final_epoch,
-                                                 wandb_logger=wandb_logger,
-                                                 compute_loss=compute_loss,
-                                                 is_coco=is_coco,
-                                                 v5_metric=opt.v5_metric)
-
-            # Write
-            with open(results_file, 'a') as f:
-                f.write(s + '%10.4g' * 7 % results + '\n')  # append metrics, val_loss
-            if len(opt.name) and opt.bucket:
-                os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
-
-            # Log
-            tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
-                    'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
-                    'val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
-                    'x/lr0', 'x/lr1', 'x/lr2']  # params
-            for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
-                if tb_writer:
-                    tb_writer.add_scalar(tag, x, epoch)  # tensorboard
-                if wandb_logger.wandb:
-                    wandb_logger.log({tag: x})  # W&B
-
-            # Update best mAP
-            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
-            if fi > best_fitness:
-                best_fitness = fi
-            wandb_logger.end_epoch(best_result=best_fitness == fi)
-
-            # Save model
-            if (not opt.nosave) or (final_epoch and not opt.evolve):  # if save
-                ckpt = {'epoch': epoch,
-                        'best_fitness': best_fitness,
-                        'training_results': results_file.read_text(),
-                        'model': deepcopy(model.module if is_parallel(model) else model).half(),
-                        'ema': deepcopy(ema.ema).half(),
-                        'updates': ema.updates,
-                        'optimizer': optimizer.state_dict(),
-                        'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None}
-
-                # Save last, best and delete
-                torch.save(ckpt, last)
-                if best_fitness == fi:
-                    torch.save(ckpt, best)
-                if (best_fitness == fi) and (epoch >= 200):
-                    torch.save(ckpt, wdir / 'best_{:03d}.pt'.format(epoch))
-                if epoch == 0:
-                    torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))
-                elif ((epoch+1) % 25) == 0:
-                    torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))
-                elif epoch >= (epochs-5):
-                    torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))
-                if wandb_logger.wandb:
-                    if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1:
-                        wandb_logger.log_model(
-                            last.parent, opt, epoch, fi, best_model=best_fitness == fi)
-                del ckpt
-
-        # end epoch ----------------------------------------------------------------------------------------------------
-    # end training
-    if rank in [-1, 0]:
-        # Plots
-        if plots:
-            plot_results(save_dir=save_dir)  # save as results.png
-            if wandb_logger.wandb:
-                files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
-                wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files
-                                              if (save_dir / f).exists()]})
-        # Test best.pt
-        logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
-        if opt.data.endswith('coco.yaml') and nc == 80:  # if COCO
-            for m in (last, best) if best.exists() else (last):  # speed, mAP tests
-                results, _, _ = test.test(opt.data,
-                                          batch_size=batch_size * 2,
-                                          imgsz=imgsz_test,
-                                          conf_thres=0.001,
-                                          iou_thres=0.7,
-                                          model=attempt_load(m, device).half(),
-                                          single_cls=opt.single_cls,
-                                          dataloader=testloader,
-                                          save_dir=save_dir,
-                                          save_json=True,
-                                          plots=False,
-                                          is_coco=is_coco,
-                                          v5_metric=opt.v5_metric)
-
-        # Strip optimizers
-        final = best if best.exists() else last  # final model
-        for f in last, best:
-            if f.exists():
-                strip_optimizer(f)  # strip optimizers
-        if opt.bucket:
-            os.system(f'gsutil cp {final} gs://{opt.bucket}/weights')  # upload
-        if wandb_logger.wandb and not opt.evolve:  # Log the stripped model
-            wandb_logger.wandb.log_artifact(str(final), type='model',
-                                            name='run_' + wandb_logger.wandb_run.id + '_model',
-                                            aliases=['last', 'best', 'stripped'])
-        wandb_logger.finish_run()
-    else:
-        dist.destroy_process_group()
-    torch.cuda.empty_cache()
-    return results
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default='yolo7.pt', help='initial weights path')
-    parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
-    parser.add_argument('--data', type=str, default='data/coco.yaml', help='data.yaml path')
-    parser.add_argument('--hyp', type=str, default='data/hyp.scratch.p5.yaml', help='hyperparameters path')
-    parser.add_argument('--epochs', type=int, default=300)
-    parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
-    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
-    parser.add_argument('--rect', action='store_true', help='rectangular training')
-    parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
-    parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
-    parser.add_argument('--notest', action='store_true', help='only test final epoch')
-    parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
-    parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
-    parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
-    parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
-    parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
-    parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
-    parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
-    parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
-    parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
-    parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
-    parser.add_argument('--project', default='runs/train', help='save to project/name')
-    parser.add_argument('--entity', default=None, help='W&B entity')
-    parser.add_argument('--name', default='exp', help='save to project/name')
-    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
-    parser.add_argument('--quad', action='store_true', help='quad dataloader')
-    parser.add_argument('--linear-lr', action='store_true', help='linear LR')
-    parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
-    parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table')
-    parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B')
-    parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch')
-    parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used')
-    parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone of yolov7=50, first3=0 1 2')
-    parser.add_argument('--v5-metric', action='store_true', help='assume maximum recall as 1.0 in AP calculation')
-    opt = parser.parse_args()
-
-    # Set DDP variables
-    opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
-    opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1
-    set_logging(opt.global_rank)
-    #if opt.global_rank in [-1, 0]:
-    #    check_git_status()
-    #    check_requirements()
-
-    # Resume
-    wandb_run = check_wandb_resume(opt)
-    if opt.resume and not wandb_run:  # resume an interrupted run
-        ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run()  # specified or most recent path
-        assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
-        apriori = opt.global_rank, opt.local_rank
-        with open(Path(ckpt).parent.parent / 'opt.yaml') as f:
-            opt = argparse.Namespace(**yaml.load(f, Loader=yaml.SafeLoader))  # replace
-        opt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = '', ckpt, True, opt.total_batch_size, *apriori  # reinstate
-        logger.info('Resuming training from %s' % ckpt)
-    else:
-        # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
-        opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp)  # check files
-        assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
-        opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size)))  # extend to 2 sizes (train, test)
-        opt.name = 'evolve' if opt.evolve else opt.name
-        opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve)  # increment run
-
-    # DDP mode
-    opt.total_batch_size = opt.batch_size
-    device = select_device(opt.device, batch_size=opt.batch_size)
-    if opt.local_rank != -1:
-        assert torch.cuda.device_count() > opt.local_rank
-        torch.cuda.set_device(opt.local_rank)
-        device = torch.device('cuda', opt.local_rank)
-        dist.init_process_group(backend='nccl', init_method='env://')  # distributed backend
-        assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'
-        opt.batch_size = opt.total_batch_size // opt.world_size
-
-    # Hyperparameters
-    with open(opt.hyp) as f:
-        hyp = yaml.load(f, Loader=yaml.SafeLoader)  # load hyps
-
-    # Train
-    logger.info(opt)
-    if not opt.evolve:
-        tb_writer = None  # init loggers
-        if opt.global_rank in [-1, 0]:
-            prefix = colorstr('tensorboard: ')
-            logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/")
-            tb_writer = SummaryWriter(opt.save_dir)  # Tensorboard
-        train(hyp, opt, device, tb_writer)
-
-    # Evolve hyperparameters (optional)
-    else:
-        # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
-        meta = {'lr0': (1, 1e-5, 1e-1),  # initial learning rate (SGD=1E-2, Adam=1E-3)
-                'lrf': (1, 0.01, 1.0),  # final OneCycleLR learning rate (lr0 * lrf)
-                'momentum': (0.3, 0.6, 0.98),  # SGD momentum/Adam beta1
-                'weight_decay': (1, 0.0, 0.001),  # optimizer weight decay
-                'warmup_epochs': (1, 0.0, 5.0),  # warmup epochs (fractions ok)
-                'warmup_momentum': (1, 0.0, 0.95),  # warmup initial momentum
-                'warmup_bias_lr': (1, 0.0, 0.2),  # warmup initial bias lr
-                'box': (1, 0.02, 0.2),  # box loss gain
-                'cls': (1, 0.2, 4.0),  # cls loss gain
-                'cls_pw': (1, 0.5, 2.0),  # cls BCELoss positive_weight
-                'obj': (1, 0.2, 4.0),  # obj loss gain (scale with pixels)
-                'obj_pw': (1, 0.5, 2.0),  # obj BCELoss positive_weight
-                'iou_t': (0, 0.1, 0.7),  # IoU training threshold
-                'anchor_t': (1, 2.0, 8.0),  # anchor-multiple threshold
-                'anchors': (2, 2.0, 10.0),  # anchors per output grid (0 to ignore)
-                'fl_gamma': (0, 0.0, 2.0),  # focal loss gamma (efficientDet default gamma=1.5)
-                'hsv_h': (1, 0.0, 0.1),  # image HSV-Hue augmentation (fraction)
-                'hsv_s': (1, 0.0, 0.9),  # image HSV-Saturation augmentation (fraction)
-                'hsv_v': (1, 0.0, 0.9),  # image HSV-Value augmentation (fraction)
-                'degrees': (1, 0.0, 45.0),  # image rotation (+/- deg)
-                'translate': (1, 0.0, 0.9),  # image translation (+/- fraction)
-                'scale': (1, 0.0, 0.9),  # image scale (+/- gain)
-                'shear': (1, 0.0, 10.0),  # image shear (+/- deg)
-                'perspective': (0, 0.0, 0.001),  # image perspective (+/- fraction), range 0-0.001
-                'flipud': (1, 0.0, 1.0),  # image flip up-down (probability)
-                'fliplr': (0, 0.0, 1.0),  # image flip left-right (probability)
-                'mosaic': (1, 0.0, 1.0),  # image mixup (probability)
-                'mixup': (1, 0.0, 1.0),   # image mixup (probability)
-                'copy_paste': (1, 0.0, 1.0),  # segment copy-paste (probability)
-                'paste_in': (1, 0.0, 1.0)}    # segment copy-paste (probability)
-        
-        with open(opt.hyp, errors='ignore') as f:
-            hyp = yaml.safe_load(f)  # load hyps dict
-            if 'anchors' not in hyp:  # anchors commented in hyp.yaml
-                hyp['anchors'] = 3
-                
-        assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'
-        opt.notest, opt.nosave = True, True  # only test/save final epoch
-        # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices
-        yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml'  # save best result here
-        if opt.bucket:
-            os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket)  # download evolve.txt if exists
-
-        for _ in range(300):  # generations to evolve
-            if Path('evolve.txt').exists():  # if evolve.txt exists: select best hyps and mutate
-                # Select parent(s)
-                parent = 'single'  # parent selection method: 'single' or 'weighted'
-                x = np.loadtxt('evolve.txt', ndmin=2)
-                n = min(5, len(x))  # number of previous results to consider
-                x = x[np.argsort(-fitness(x))][:n]  # top n mutations
-                w = fitness(x) - fitness(x).min()  # weights
-                if parent == 'single' or len(x) == 1:
-                    # x = x[random.randint(0, n - 1)]  # random selection
-                    x = x[random.choices(range(n), weights=w)[0]]  # weighted selection
-                elif parent == 'weighted':
-                    x = (x * w.reshape(n, 1)).sum(0) / w.sum()  # weighted combination
-
-                # Mutate
-                mp, s = 0.8, 0.2  # mutation probability, sigma
-                npr = np.random
-                npr.seed(int(time.time()))
-                g = np.array([x[0] for x in meta.values()])  # gains 0-1
-                ng = len(meta)
-                v = np.ones(ng)
-                while all(v == 1):  # mutate until a change occurs (prevent duplicates)
-                    v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
-                for i, k in enumerate(hyp.keys()):  # plt.hist(v.ravel(), 300)
-                    hyp[k] = float(x[i + 7] * v[i])  # mutate
-
-            # Constrain to limits
-            for k, v in meta.items():
-                hyp[k] = max(hyp[k], v[1])  # lower limit
-                hyp[k] = min(hyp[k], v[2])  # upper limit
-                hyp[k] = round(hyp[k], 5)  # significant digits
-
-            # Train mutation
-            results = train(hyp.copy(), opt, device)
-
-            # Write mutation results
-            print_mutation(hyp.copy(), results, yaml_file, opt.bucket)
-
-        # Plot results
-        plot_evolution(yaml_file)
-        print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
-              f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/confirmdialog/methods/Methods.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/confirmdialog/methods/Methods.js
deleted file mode 100644
index de92c508ece267fba20b4c9aa00d10dc17d75f3f..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/confirmdialog/methods/Methods.js
+++ /dev/null
@@ -1,9 +0,0 @@
-import ResetDisplayContent from './ResetDisplayContent.js';
-import Modal from './Modal.js';
-
-var Methods = {
-    resetDisplayContent: ResetDisplayContent,
-    modal: Modal,
-}
-
-export default Methods;
\ No newline at end of file
diff --git a/spaces/AlexWang/lama/bin/paper_runfiles/generate_test_paris.sh b/spaces/AlexWang/lama/bin/paper_runfiles/generate_test_paris.sh
deleted file mode 100644
index 66056017c3aa376ef0767a59583ab25a321b559b..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/bin/paper_runfiles/generate_test_paris.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/usr/bin/env bash
-
-# paths to data are valid for mml-ws01
-OUT_DIR="/media/inpainting/paper_data/Paris_StreetView_Dataset_val"
-
-source "$(dirname $0)/env.sh"
-
-for datadir in paris_eval_gt
-do
-    for conf in random_thin_256 random_medium_256 random_thick_256 segm_256
-    do
-        "$BINDIR/gen_mask_dataset_hydra.py" -cn $conf datadir=$datadir location=mml-ws01-paris \
-         location.out_dir=OUT_DIR cropping.out_square_crop=False cropping.out_min_size=227
-
-        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-    done
-done
diff --git a/spaces/AlexWang/lama/models/ade20k/segm_lib/nn/modules/tests/test_numeric_batchnorm.py b/spaces/AlexWang/lama/models/ade20k/segm_lib/nn/modules/tests/test_numeric_batchnorm.py
deleted file mode 100644
index 8bd45a930d3dc84912e58659ee575be08e9038f0..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/models/ade20k/segm_lib/nn/modules/tests/test_numeric_batchnorm.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : test_numeric_batchnorm.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-
-import unittest
-
-import torch
-import torch.nn as nn
-from torch.autograd import Variable
-
-from sync_batchnorm.unittest import TorchTestCase
-
-
-def handy_var(a, unbias=True):
-    n = a.size(0)
-    asum = a.sum(dim=0)
-    as_sum = (a ** 2).sum(dim=0)  # a square sum
-    sumvar = as_sum - asum * asum / n
-    if unbias:
-        return sumvar / (n - 1)
-    else:
-        return sumvar / n
-
-
-class NumericTestCase(TorchTestCase):
-    def testNumericBatchNorm(self):
-        a = torch.rand(16, 10)
-        bn = nn.BatchNorm2d(10, momentum=1, eps=1e-5, affine=False)
-        bn.train()
-
-        a_var1 = Variable(a, requires_grad=True)
-        b_var1 = bn(a_var1)
-        loss1 = b_var1.sum()
-        loss1.backward()
-
-        a_var2 = Variable(a, requires_grad=True)
-        a_mean2 = a_var2.mean(dim=0, keepdim=True)
-        a_std2 = torch.sqrt(handy_var(a_var2, unbias=False).clamp(min=1e-5))
-        # a_std2 = torch.sqrt(a_var2.var(dim=0, keepdim=True, unbiased=False) + 1e-5)
-        b_var2 = (a_var2 - a_mean2) / a_std2
-        loss2 = b_var2.sum()
-        loss2.backward()
-
-        self.assertTensorClose(bn.running_mean, a.mean(dim=0))
-        self.assertTensorClose(bn.running_var, handy_var(a))
-        self.assertTensorClose(a_var1.data, a_var2.data)
-        self.assertTensorClose(b_var1.data, b_var2.data)
-        self.assertTensorClose(a_var1.grad, a_var2.grad)
-
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/spaces/AlexWang/lama/models/ade20k/segm_lib/nn/modules/unittest.py b/spaces/AlexWang/lama/models/ade20k/segm_lib/nn/modules/unittest.py
deleted file mode 100644
index 0675c022e4ba85d38d1f813490f6740150909524..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/models/ade20k/segm_lib/nn/modules/unittest.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : unittest.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import unittest
-
-import numpy as np
-from torch.autograd import Variable
-
-
-def as_numpy(v):
-    if isinstance(v, Variable):
-        v = v.data
-    return v.cpu().numpy()
-
-
-class TorchTestCase(unittest.TestCase):
-    def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3):
-        npa, npb = as_numpy(a), as_numpy(b)
-        self.assertTrue(
-                np.allclose(npa, npb, atol=atol),
-                'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max())
-        )
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/unidiffuser.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/unidiffuser.md
deleted file mode 100644
index ff8f4e7c6ec9819a9505bf9aa8793220363cdee6..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/unidiffuser.md
+++ /dev/null
@@ -1,194 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# UniDiffuser
-
-The UniDiffuser model was proposed in [One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale](https://huggingface.co/papers/2303.06555) by Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu.
-
-The abstract from the [paper](https://arxiv.org/abs/2303.06555) is:
-
-*This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is -- learning diffusion models for marginal, conditional, and joint distributions can be unified as predicting the noise in the perturbed data, where the perturbation levels (i.e. timesteps) can be different for different modalities. Inspired by the unified view, UniDiffuser learns all distributions simultaneously with a minimal modification to the original diffusion model -- perturbs data in all modalities instead of a single modality, inputs individual timesteps in different modalities, and predicts the noise of all modalities instead of a single modality. UniDiffuser is parameterized by a transformer for diffusion models to handle input types of different modalities. Implemented on large-scale paired image-text data, UniDiffuser is able to perform image, text, text-to-image, image-to-text, and image-text pair generation by setting proper timesteps without additional overhead. In particular, UniDiffuser is able to produce perceptually realistic samples in all tasks and its quantitative results (e.g., the FID and CLIP score) are not only superior to existing general-purpose models but also comparable to the bespoken models (e.g., Stable Diffusion and DALL-E 2) in representative tasks (e.g., text-to-image generation).*
-
-You can find the original codebase at [thu-ml/unidiffuser](https://github.com/thu-ml/unidiffuser) and additional checkpoints at [thu-ml](https://huggingface.co/thu-ml).
-
-This pipeline was contributed by [dg845](https://github.com/dg845). ❤️
-
-## Usage Examples
-
-Because the UniDiffuser model is trained to model the joint distribution of (image, text) pairs, it is capable of performing a diverse range of generation tasks:
-
-### Unconditional Image and Text Generation
-
-Unconditional generation (where we start from only latents sampled from a standard Gaussian prior) from a [`UniDiffuserPipeline`] will produce a (image, text) pair:
-
-```python
-import torch
-
-from diffusers import UniDiffuserPipeline
-
-device = "cuda"
-model_id_or_path = "thu-ml/unidiffuser-v1"
-pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
-pipe.to(device)
-
-# Unconditional image and text generation. The generation task is automatically inferred.
-sample = pipe(num_inference_steps=20, guidance_scale=8.0)
-image = sample.images[0]
-text = sample.text[0]
-image.save("unidiffuser_joint_sample_image.png")
-print(text)
-```
-
-This is also called "joint" generation in the UniDiffusers paper, since we are sampling from the joint image-text distribution.
-
-Note that the generation task is inferred from the inputs used when calling the pipeline.
-It is also possible to manually specify the unconditional generation task ("mode") manually with [`UniDiffuserPipeline.set_joint_mode`]:
-
-```python
-# Equivalent to the above.
-pipe.set_joint_mode()
-sample = pipe(num_inference_steps=20, guidance_scale=8.0)
-```
-
-When the mode is set manually, subsequent calls to the pipeline will use the set mode without attempting the infer the mode.
-You can reset the mode with [`UniDiffuserPipeline.reset_mode`], after which the pipeline will once again infer the mode.
-
-You can also generate only an image or only text (which the UniDiffuser paper calls "marginal" generation since we sample from the marginal distribution of images and text, respectively):
-
-```python
-# Unlike other generation tasks, image-only and text-only generation don't use classifier-free guidance
-# Image-only generation
-pipe.set_image_mode()
-sample_image = pipe(num_inference_steps=20).images[0]
-# Text-only generation
-pipe.set_text_mode()
-sample_text = pipe(num_inference_steps=20).text[0]
-```
-
-### Text-to-Image Generation
-
-UniDiffuser is also capable of sampling from conditional distributions; that is, the distribution of images conditioned on a text prompt or the distribution of texts conditioned on an image.
-Here is an example of sampling from the conditional image distribution (text-to-image generation or text-conditioned image generation):
-
-```python
-import torch
-
-from diffusers import UniDiffuserPipeline
-
-device = "cuda"
-model_id_or_path = "thu-ml/unidiffuser-v1"
-pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
-pipe.to(device)
-
-# Text-to-image generation
-prompt = "an elephant under the sea"
-
-sample = pipe(prompt=prompt, num_inference_steps=20, guidance_scale=8.0)
-t2i_image = sample.images[0]
-t2i_image.save("unidiffuser_text2img_sample_image.png")
-```
-
-The `text2img` mode requires that either an input `prompt` or `prompt_embeds` be supplied. You can set the `text2img` mode manually with [`UniDiffuserPipeline.set_text_to_image_mode`].
-
-### Image-to-Text Generation
-
-Similarly, UniDiffuser can also produce text samples given an image (image-to-text or image-conditioned text generation):
-
-```python
-import torch
-
-from diffusers import UniDiffuserPipeline
-from diffusers.utils import load_image
-
-device = "cuda"
-model_id_or_path = "thu-ml/unidiffuser-v1"
-pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
-pipe.to(device)
-
-# Image-to-text generation
-image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg"
-init_image = load_image(image_url).resize((512, 512))
-
-sample = pipe(image=init_image, num_inference_steps=20, guidance_scale=8.0)
-i2t_text = sample.text[0]
-print(i2t_text)
-```
-
-The `img2text` mode requires that an input `image` be supplied. You can set the `img2text` mode manually with [`UniDiffuserPipeline.set_image_to_text_mode`].
-
-### Image Variation
-
-The UniDiffuser authors suggest performing image variation through a "round-trip" generation method, where given an input image, we first perform an image-to-text generation, and the perform a text-to-image generation on the outputs of the first generation.
-This produces a new image which is semantically similar to the input image:
-
-```python
-import torch
-
-from diffusers import UniDiffuserPipeline
-from diffusers.utils import load_image
-
-device = "cuda"
-model_id_or_path = "thu-ml/unidiffuser-v1"
-pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
-pipe.to(device)
-
-# Image variation can be performed with a image-to-text generation followed by a text-to-image generation:
-# 1. Image-to-text generation
-image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg"
-init_image = load_image(image_url).resize((512, 512))
-
-sample = pipe(image=init_image, num_inference_steps=20, guidance_scale=8.0)
-i2t_text = sample.text[0]
-print(i2t_text)
-
-# 2. Text-to-image generation
-sample = pipe(prompt=i2t_text, num_inference_steps=20, guidance_scale=8.0)
-final_image = sample.images[0]
-final_image.save("unidiffuser_image_variation_sample.png")
-```
-
-### Text Variation
-
-
-Similarly, text variation can be performed on an input prompt with a text-to-image generation followed by a image-to-text generation:
-
-```python
-import torch
-
-from diffusers import UniDiffuserPipeline
-
-device = "cuda"
-model_id_or_path = "thu-ml/unidiffuser-v1"
-pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
-pipe.to(device)
-
-# Text variation can be performed with a text-to-image generation followed by a image-to-text generation:
-# 1. Text-to-image generation
-prompt = "an elephant under the sea"
-
-sample = pipe(prompt=prompt, num_inference_steps=20, guidance_scale=8.0)
-t2i_image = sample.images[0]
-t2i_image.save("unidiffuser_text2img_sample_image.png")
-
-# 2. Image-to-text generation
-sample = pipe(image=t2i_image, num_inference_steps=20, guidance_scale=8.0)
-final_prompt = sample.text[0]
-print(final_prompt)
-```
-
-## UniDiffuserPipeline
-[[autodoc]] UniDiffuserPipeline
-	- all
-	- __call__
-
-## ImageTextPipelineOutput
-[[autodoc]] pipelines.ImageTextPipelineOutput
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/using-diffusers/using_safetensors.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/using-diffusers/using_safetensors.md
deleted file mode 100644
index 6972103bde102b37679fac338dd3cd8489ca2f40..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/using-diffusers/using_safetensors.md
+++ /dev/null
@@ -1,14 +0,0 @@
-# 세이프센서란 무엇인가요? 
-
-[세이프텐서](https://github.com/huggingface/safetensors)는 피클을 사용하는 파이토치를 사용하는 기존의 '.bin'과는 다른 형식입니다.
-
-피클은 악의적인 파일이 임의의 코드를 실행할 수 있는 안전하지 않은 것으로 악명이 높습니다.
-허브 자체에서 문제를 방지하기 위해 노력하고 있지만 만병통치약은 아닙니다.
-
-세이프텐서의 가장 중요한 목표는 컴퓨터를 탈취할 수 없다는 의미에서 머신 러닝 모델 로딩을 *안전하게* 만드는 것입니다.
-
-# 왜 세이프센서를 사용하나요?
-
-**잘 알려지지 않은 모델을 사용하려는 경우, 그리고 파일의 출처가 확실하지 않은 경우 "안전성"이 하나의 이유가 될 수 있습니다.
-
-그리고 두 번째 이유는 **로딩 속도**입니다. 세이프센서는 일반 피클 파일보다 훨씬 빠르게 모델을 훨씬 빠르게 로드할 수 있습니다. 모델을 전환하는 데 많은 시간을 소비하는 경우, 이는 엄청난 시간 절약이 가능합니다.
\ No newline at end of file
diff --git a/spaces/Andy1621/IAT_enhancement/model/IAT.py b/spaces/Andy1621/IAT_enhancement/model/IAT.py
deleted file mode 100644
index fc293f7ccb3a437cbc56ba3e918f77b4515ad094..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/IAT_enhancement/model/IAT.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-import numpy as np
-from torch import nn
-import torch.nn.functional as F
-import os
-import math
-
-from timm.models.layers import trunc_normal_
-from .blocks import CBlock_ln, SwinTransformerBlock
-from .global_net import Global_pred
-
-
-class Local_pred(nn.Module):
-    def __init__(self, dim=16, number=4, type='ccc'):
-        super(Local_pred, self).__init__()
-        # initial convolution
-        self.conv1 = nn.Conv2d(3, dim, 3, padding=1, groups=1)
-        self.relu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        # main blocks
-        block = CBlock_ln(dim)
-        block_t = SwinTransformerBlock(dim)  # head number
-        if type =='ccc':  
-            #blocks1, blocks2 = [block for _ in range(number)], [block for _ in range(number)]
-            blocks1 = [CBlock_ln(16, drop_path=0.01), CBlock_ln(16, drop_path=0.05), CBlock_ln(16, drop_path=0.1)]
-            blocks2 = [CBlock_ln(16, drop_path=0.01), CBlock_ln(16, drop_path=0.05), CBlock_ln(16, drop_path=0.1)]
-        elif type =='ttt':
-            blocks1, blocks2 = [block_t for _ in range(number)], [block_t for _ in range(number)]
-        elif type =='cct':
-            blocks1, blocks2 = [block, block, block_t], [block, block, block_t]
-        #    block1 = [CBlock_ln(16), nn.Conv2d(16,24,3,1,1)]
-        self.mul_blocks = nn.Sequential(*blocks1, nn.Conv2d(dim, 3, 3, 1, 1), nn.ReLU())
-        self.add_blocks = nn.Sequential(*blocks2, nn.Conv2d(dim, 3, 3, 1, 1), nn.Tanh())
-
-    def forward(self, img):
-        img1 = self.relu(self.conv1(img))
-        mul = self.mul_blocks(img1)
-        add = self.add_blocks(img1)
-        return mul, add
-
-
-# Short Cut Connection on Final Layer
-class Local_pred_S(nn.Module):
-    def __init__(self, in_dim=3, dim=16, number=4, type='ccc'):
-        super(Local_pred_S, self).__init__()
-        # initial convolution
-        self.conv1 = nn.Conv2d(in_dim, dim, 3, padding=1, groups=1)
-        self.relu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        # main blocks
-        block = CBlock_ln(dim)
-        block_t = SwinTransformerBlock(dim)  # head number
-        if type =='ccc':
-            blocks1 = [CBlock_ln(16, drop_path=0.01), CBlock_ln(16, drop_path=0.05), CBlock_ln(16, drop_path=0.1)]
-            blocks2 = [CBlock_ln(16, drop_path=0.01), CBlock_ln(16, drop_path=0.05), CBlock_ln(16, drop_path=0.1)]
-        elif type =='ttt':
-            blocks1, blocks2 = [block_t for _ in range(number)], [block_t for _ in range(number)]
-        elif type =='cct':
-            blocks1, blocks2 = [block, block, block_t], [block, block, block_t]
-        #    block1 = [CBlock_ln(16), nn.Conv2d(16,24,3,1,1)]
-        self.mul_blocks = nn.Sequential(*blocks1)
-        self.add_blocks = nn.Sequential(*blocks2)
-
-        self.mul_end = nn.Sequential(nn.Conv2d(dim, 3, 3, 1, 1), nn.ReLU())
-        self.add_end = nn.Sequential(nn.Conv2d(dim, 3, 3, 1, 1), nn.Tanh())
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-        elif isinstance(m, nn.Conv2d):
-            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-            fan_out //= m.groups
-            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
-            if m.bias is not None:
-                m.bias.data.zero_()
-
-    def forward(self, img):
-        img1 = self.relu(self.conv1(img))
-        # short cut connection
-        mul = self.mul_blocks(img1) + img1
-        add = self.add_blocks(img1) + img1
-        mul = self.mul_end(mul)
-        add = self.add_end(add)
-        return mul, add
-
-
-class IAT(nn.Module):
-    def __init__(self, in_dim=3, with_global=True, type='lol'):
-        super(IAT, self).__init__()
-        self.local_net = Local_pred_S(in_dim=in_dim)
-        self.with_global = with_global
-        if self.with_global:
-            self.global_net = Global_pred(in_channels=in_dim, type=type)
-
-    def apply_color(self, image, ccm):
-        shape = image.shape
-        image = image.view(-1, 3)
-        image = torch.tensordot(image, ccm, dims=[[-1], [-1]])
-        image = image.view(shape)
-        return torch.clamp(image, 1e-8, 1.0)
-
-    def forward(self, img_low):
-        #print(self.with_global)
-        mul, add = self.local_net(img_low)
-        img_high = (img_low.mul(mul)).add(add)
-
-        if not self.with_global:
-            return img_high
-        else:
-            gamma, color = self.global_net(img_low)
-            b = img_high.shape[0]
-            img_high = img_high.permute(0, 2, 3, 1)  # (B,C,H,W) -- (B,H,W,C)
-            img_high = torch.stack([self.apply_color(img_high[i,:,:,:], color[i,:,:])**gamma[i,:] for i in range(b)], dim=0)
-            img_high = img_high.permute(0, 3, 1, 2)  # (B,H,W,C) -- (B,C,H,W)
-            return img_high
-
-
-if __name__ == "__main__":
-    img = torch.Tensor(1, 3, 400, 600)
-    net = IAT()
-    print('total parameters:', sum(param.numel() for param in net.parameters()))
-    high = net(img)
\ No newline at end of file
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
deleted file mode 100644
index d1bcf3c102fb660641eda2a1398db3df520caa3a..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
+++ /dev/null
@@ -1,5 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py
deleted file mode 100644
index c735298487e14e4a0ec42913f25673cccb98a8a0..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import numpy as np
-import torch
-
-from ..builder import BBOX_SAMPLERS
-from .random_sampler import RandomSampler
-
-
-@BBOX_SAMPLERS.register_module()
-class InstanceBalancedPosSampler(RandomSampler):
-    """Instance balanced sampler that samples equal number of positive samples
-    for each instance."""
-
-    def _sample_pos(self, assign_result, num_expected, **kwargs):
-        """Sample positive boxes.
-
-        Args:
-            assign_result (:obj:`AssignResult`): The assigned results of boxes.
-            num_expected (int): The number of expected positive samples
-
-        Returns:
-            Tensor or ndarray: sampled indices.
-        """
-        pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False)
-        if pos_inds.numel() != 0:
-            pos_inds = pos_inds.squeeze(1)
-        if pos_inds.numel() <= num_expected:
-            return pos_inds
-        else:
-            unique_gt_inds = assign_result.gt_inds[pos_inds].unique()
-            num_gts = len(unique_gt_inds)
-            num_per_gt = int(round(num_expected / float(num_gts)) + 1)
-            sampled_inds = []
-            for i in unique_gt_inds:
-                inds = torch.nonzero(
-                    assign_result.gt_inds == i.item(), as_tuple=False)
-                if inds.numel() != 0:
-                    inds = inds.squeeze(1)
-                else:
-                    continue
-                if len(inds) > num_per_gt:
-                    inds = self.random_choice(inds, num_per_gt)
-                sampled_inds.append(inds)
-            sampled_inds = torch.cat(sampled_inds)
-            if len(sampled_inds) < num_expected:
-                num_extra = num_expected - len(sampled_inds)
-                extra_inds = np.array(
-                    list(set(pos_inds.cpu()) - set(sampled_inds.cpu())))
-                if len(extra_inds) > num_extra:
-                    extra_inds = self.random_choice(extra_inds, num_extra)
-                extra_inds = torch.from_numpy(extra_inds).to(
-                    assign_result.gt_inds.device).long()
-                sampled_inds = torch.cat([sampled_inds, extra_inds])
-            elif len(sampled_inds) > num_expected:
-                sampled_inds = self.random_choice(sampled_inds, num_expected)
-            return sampled_inds
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/pspnet_r50-d8.py b/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/pspnet_r50-d8.py
deleted file mode 100644
index f451e08ad2eb0732dcb806b1851eb978d4acf136..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/pspnet_r50-d8.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='PSPHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        pool_scales=(1, 2, 3, 6),
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py
deleted file mode 100644
index e6d58a67b3b4dddf3da42efca30fa599e623f183..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3_r50-d8_769x769_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/AndyCer/TehVenom-MPT-7b-Chat-Instruct-LongCTX-Merge/app.py b/spaces/AndyCer/TehVenom-MPT-7b-Chat-Instruct-LongCTX-Merge/app.py
deleted file mode 100644
index cd31e220114a75cdb8f8a860618ddac423ade9af..0000000000000000000000000000000000000000
--- a/spaces/AndyCer/TehVenom-MPT-7b-Chat-Instruct-LongCTX-Merge/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge").launch()
\ No newline at end of file
diff --git a/spaces/AnishKumbhar/DogDiseasePredictor/Dockerfile b/spaces/AnishKumbhar/DogDiseasePredictor/Dockerfile
deleted file mode 100644
index 55b67bca9bb7304b8ab0898ff9d4c82002dcd53b..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/DogDiseasePredictor/Dockerfile
+++ /dev/null
@@ -1,27 +0,0 @@
-# Use the official Python 3.9 image
-FROM python:3.9
-
-# Set the working directory to /code
-WORKDIR /code
-
-# Copy the current directory contents into the container at /code
-COPY ./requirements.txt /code/requirements.txt
-
-# Install requirements.txt 
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -m -u 1000 user
-# Switch to the "user" user
-USER user
-# Set home to the user's home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app
-
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
\ No newline at end of file
diff --git a/spaces/AnjaneyuluChinni/AnjiChinniGenAIAvatar/README.md b/spaces/AnjaneyuluChinni/AnjiChinniGenAIAvatar/README.md
deleted file mode 100644
index 7a107ff60cbaf32fcaf6a132f30e2076138cebd4..0000000000000000000000000000000000000000
--- a/spaces/AnjaneyuluChinni/AnjiChinniGenAIAvatar/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AnjiChinniGenAIAvatar
-emoji: 🦀
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Artrajz/vits-simple-api/config.py b/spaces/Artrajz/vits-simple-api/config.py
deleted file mode 100644
index 3fb65b82ceac7fe1f83a61b171bec7c06d7cd972..0000000000000000000000000000000000000000
--- a/spaces/Artrajz/vits-simple-api/config.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import os
-import sys
-
-import torch
-
-JSON_AS_ASCII = False
-
-MAX_CONTENT_LENGTH = 5242880
-
-# Flask debug mode
-DEBUG = False
-
-# Server port
-PORT = 7860
-
-# Absolute path of vits-simple-api
-ABS_PATH = os.path.dirname(os.path.realpath(__file__))
-
-# Upload path
-UPLOAD_FOLDER = ABS_PATH + "/upload"
-
-# Cahce path
-CACHE_PATH = ABS_PATH + "/cache"
-
-# Logs path
-LOGS_PATH = ABS_PATH + "/logs"
-
-# Set the number of backup log files to keep. 
-LOGS_BACKUPCOUNT = 30
-
-# If CLEAN_INTERVAL_SECONDS <= 0, the cleaning task will not be executed.
-CLEAN_INTERVAL_SECONDS = 3600
-
-# save audio to CACHE_PATH
-SAVE_AUDIO = False
-
-# zh ja ko en... If it is empty, it will be read based on the text_cleaners specified in the config.json.
-LANGUAGE_AUTOMATIC_DETECT = []
-
-# Set to True to enable API Key authentication
-API_KEY_ENABLED = False
-
-# API_KEY is required for authentication
-API_KEY = "api-key"
-
-# logging_level:DEBUG/INFO/WARNING/ERROR/CRITICAL
-LOGGING_LEVEL = "DEBUG"
-
-# Language identification library. Optional fastlid, langid
-LANGUAGE_IDENTIFICATION_LIBRARY = "langid"
-
-# To use the english_cleaner, you need to install espeak and provide the path of libespeak-ng.dll as input here.
-# If ESPEAK_LIBRARY is set to empty, it will be read from the environment variable.
-# For windows : "C:/Program Files/eSpeak NG/libespeak-ng.dll"
-ESPEAK_LIBRARY = ""
-
-# Fill in the model path here
-MODEL_LIST = [
-    # VITS
-    [ABS_PATH + "/Model/Nene_Nanami_Rong_Tang/1374_epochs.pth", ABS_PATH + "/Model/Nene_Nanami_Rong_Tang/config.json"],
-    [ABS_PATH + "/Model/vctk/pretrained_vctk.pth", ABS_PATH + "/Model/vctk/vctk_base.json"],
-    [ABS_PATH + "/Model/paimon/paimon6k_390000.pth", ABS_PATH + "/Model/paimon/paimon6k.json"],
-    [ABS_PATH + "/Model/vits_chinese/vits_bert_model.pth", ABS_PATH + "/Model/vits_chinese/bert_vits.json"],
-    [ABS_PATH + "/Model/Bishojo_Mangekyo/generator_mangekyo.pth", ABS_PATH + "/Model/Bishojo_Mangekyo/config_mangekyo.json"],
-    [ABS_PATH + "/Model/Cantonese/model.pth", ABS_PATH + "/Model/Cantonese/config.json"],
-    [ABS_PATH + "/Model/shanghainese/2796_epochs.pth", ABS_PATH + "/Model/shanghainese/config.json"],
-    [ABS_PATH + "/Model/genshin/G_953000.pth", ABS_PATH + "/Model/genshin/config.json"],
-    # HuBert-VITS (Need to configure HUBERT_SOFT_MODEL)
-    [ABS_PATH + "/Model/louise/360_epochs.pth", ABS_PATH + "/Model/louise/config.json"],
-    # W2V2-VITS (Need to configure DIMENSIONAL_EMOTION_NPY)
-    [ABS_PATH + "/Model/w2v2-vits/1026_epochs.pth", ABS_PATH + "/Model/w2v2-vits/config.json"],
-]
-
-# hubert-vits: hubert soft model
-HUBERT_SOFT_MODEL = ABS_PATH + "/Model/hubert-soft-0d54a1f4.pt"
-
-# w2v2-vits: Dimensional emotion npy file
-# load single npy: ABS_PATH+"/all_emotions.npy
-# load mutiple npy: [ABS_PATH + "/emotions1.npy", ABS_PATH + "/emotions2.npy"]
-# load mutiple npy from folder: ABS_PATH + "/Model/npy"
-DIMENSIONAL_EMOTION_NPY = ABS_PATH + "/Model/npy"
-
-# w2v2-vits: Need to have both `model.onnx` and `model.yaml` files in the same path.
-# DIMENSIONAL_EMOTION_MODEL = ABS_PATH + "/Model/model.yaml"
-
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-"""
-Default parameter
-"""
-
-ID = 0
-
-FORMAT = "wav"
-
-LANG = "AUTO"
-
-LENGTH = 1
-
-NOISE = 0.33
-
-NOISEW = 0.4
-
-# 长文本分段阈值，max<=0表示不分段.
-# Batch processing threshold. Text will not be processed in batches if max<=0
-MAX = 50
-
-# Bert_VITS2
-SDP_RATIO = 0.2
diff --git a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/logger.py b/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/logger.py
deleted file mode 100644
index 18145f54c927abd59b95f3fa6e6da8002bc2ce97..0000000000000000000000000000000000000000
--- a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/logger.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import functools
-import logging
-import os
-import sys
-
-from termcolor import colored
-
-
-class _ColorfulFormatter(logging.Formatter):
-    def __init__(self, *args, **kwargs):
-        self._root_name = kwargs.pop("root_name") + "."
-        self._abbrev_name = kwargs.pop("abbrev_name", "")
-        if len(self._abbrev_name):
-            self._abbrev_name = self._abbrev_name + "."
-        super(_ColorfulFormatter, self).__init__(*args, **kwargs)
-
-    def formatMessage(self, record):
-        record.name = record.name.replace(self._root_name, self._abbrev_name)
-        log = super(_ColorfulFormatter, self).formatMessage(record)
-        if record.levelno == logging.WARNING:
-            prefix = colored("WARNING", "red", attrs=["blink"])
-        elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:
-            prefix = colored("ERROR", "red", attrs=["blink", "underline"])
-        else:
-            return log
-        return prefix + " " + log
-
-
-# so that calling setup_logger multiple times won't add many handlers
-@functools.lru_cache()
-def setup_logger(output=None, distributed_rank=0, *, color=True, name="imagenet", abbrev_name=None):
-    """
-    Initialize the detectron2 logger and set its verbosity level to "INFO".
-
-    Args:
-        output (str): a file name or a directory to save log. If None, will not save log file.
-            If ends with ".txt" or ".log", assumed to be a file name.
-            Otherwise, logs will be saved to `output/log.txt`.
-        name (str): the root module name of this logger
-
-    Returns:
-        logging.Logger: a logger
-    """
-    logger = logging.getLogger(name)
-    logger.setLevel(logging.DEBUG)
-    logger.propagate = False
-
-    if abbrev_name is None:
-        abbrev_name = name
-
-    plain_formatter = logging.Formatter(
-        "[%(asctime)s.%(msecs)03d]: %(message)s", datefmt="%m/%d %H:%M:%S"
-    )
-    # stdout logging: master only
-    if distributed_rank == 0:
-        ch = logging.StreamHandler(stream=sys.stdout)
-        ch.setLevel(logging.DEBUG)
-        if color:
-            formatter = _ColorfulFormatter(
-                colored("[%(asctime)s.%(msecs)03d]: ", "green") + "%(message)s",
-                datefmt="%m/%d %H:%M:%S",
-                root_name=name,
-                abbrev_name=str(abbrev_name),
-            )
-        else:
-            formatter = plain_formatter
-        ch.setFormatter(formatter)
-        logger.addHandler(ch)
-
-    # file logging: all workers
-    if output is not None:
-        if output.endswith(".txt") or output.endswith(".log"):
-            filename = output
-        else:
-            filename = os.path.join(output, "log.txt")
-        if distributed_rank > 0:
-            filename = filename + f".rank{distributed_rank}"
-        os.makedirs(os.path.dirname(filename), exist_ok=True)
-
-        fh = logging.StreamHandler(_cached_log_stream(filename))
-        fh.setLevel(logging.DEBUG)
-        fh.setFormatter(plain_formatter)
-        logger.addHandler(fh)
-
-    return logger
-
-
-# cache the opened file object, so that different calls to `setup_logger`
-# with the same file name can safely write to the same file.
-@functools.lru_cache(maxsize=None)
-def _cached_log_stream(filename):
-    return open(filename, "a")
diff --git a/spaces/Asahi402/White-box-Cartoonization/README.md b/spaces/Asahi402/White-box-Cartoonization/README.md
deleted file mode 100644
index 9860239cf42c94e385faaaa75a85311e010d64f7..0000000000000000000000000000000000000000
--- a/spaces/Asahi402/White-box-Cartoonization/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-python_version: 3.7
-title: White Box Cartoonization
-emoji: 📚
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: hylee/White-box-Cartoonization
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/gb2312freq.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/gb2312freq.py
deleted file mode 100644
index b32bfc74213d93d434f1f3a47cb5d7d0bf4863d3..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/gb2312freq.py
+++ /dev/null
@@ -1,284 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Communicator client code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-# GB2312 most frequently used character table
-#
-# Char to FreqOrder table , from hz6763
-
-# 512  --> 0.79  -- 0.79
-# 1024 --> 0.92  -- 0.13
-# 2048 --> 0.98  -- 0.06
-# 6768 --> 1.00  -- 0.02
-#
-# Ideal Distribution Ratio = 0.79135/(1-0.79135) = 3.79
-# Random Distribution Ration = 512 / (3755 - 512) = 0.157
-#
-# Typical Distribution Ratio about 25% of Ideal one, still much higher that RDR
-
-GB2312_TYPICAL_DISTRIBUTION_RATIO = 0.9
-
-GB2312_TABLE_SIZE = 3760
-
-# fmt: off
-GB2312_CHAR_TO_FREQ_ORDER = (
-1671, 749,1443,2364,3924,3807,2330,3921,1704,3463,2691,1511,1515, 572,3191,2205,
-2361, 224,2558, 479,1711, 963,3162, 440,4060,1905,2966,2947,3580,2647,3961,3842,
-2204, 869,4207, 970,2678,5626,2944,2956,1479,4048, 514,3595, 588,1346,2820,3409,
- 249,4088,1746,1873,2047,1774, 581,1813, 358,1174,3590,1014,1561,4844,2245, 670,
-1636,3112, 889,1286, 953, 556,2327,3060,1290,3141, 613, 185,3477,1367, 850,3820,
-1715,2428,2642,2303,2732,3041,2562,2648,3566,3946,1349, 388,3098,2091,1360,3585,
- 152,1687,1539, 738,1559,  59,1232,2925,2267,1388,1249,1741,1679,2960, 151,1566,
-1125,1352,4271, 924,4296, 385,3166,4459, 310,1245,2850,  70,3285,2729,3534,3575,
-2398,3298,3466,1960,2265, 217,3647, 864,1909,2084,4401,2773,1010,3269,5152, 853,
-3051,3121,1244,4251,1895, 364,1499,1540,2313,1180,3655,2268, 562, 715,2417,3061,
- 544, 336,3768,2380,1752,4075, 950, 280,2425,4382, 183,2759,3272, 333,4297,2155,
-1688,2356,1444,1039,4540, 736,1177,3349,2443,2368,2144,2225, 565, 196,1482,3406,
- 927,1335,4147, 692, 878,1311,1653,3911,3622,1378,4200,1840,2969,3149,2126,1816,
-2534,1546,2393,2760, 737,2494,  13, 447, 245,2747,  38,2765,2129,2589,1079, 606,
- 360, 471,3755,2890, 404, 848, 699,1785,1236, 370,2221,1023,3746,2074,2026,2023,
-2388,1581,2119, 812,1141,3091,2536,1519, 804,2053, 406,1596,1090, 784, 548,4414,
-1806,2264,2936,1100, 343,4114,5096, 622,3358, 743,3668,1510,1626,5020,3567,2513,
-3195,4115,5627,2489,2991,  24,2065,2697,1087,2719,  48,1634, 315,  68, 985,2052,
- 198,2239,1347,1107,1439, 597,2366,2172, 871,3307, 919,2487,2790,1867, 236,2570,
-1413,3794, 906,3365,3381,1701,1982,1818,1524,2924,1205, 616,2586,2072,2004, 575,
- 253,3099,  32,1365,1182, 197,1714,2454,1201, 554,3388,3224,2748, 756,2587, 250,
-2567,1507,1517,3529,1922,2761,2337,3416,1961,1677,2452,2238,3153, 615, 911,1506,
-1474,2495,1265,1906,2749,3756,3280,2161, 898,2714,1759,3450,2243,2444, 563,  26,
-3286,2266,3769,3344,2707,3677, 611,1402, 531,1028,2871,4548,1375, 261,2948, 835,
-1190,4134, 353, 840,2684,1900,3082,1435,2109,1207,1674, 329,1872,2781,4055,2686,
-2104, 608,3318,2423,2957,2768,1108,3739,3512,3271,3985,2203,1771,3520,1418,2054,
-1681,1153, 225,1627,2929, 162,2050,2511,3687,1954, 124,1859,2431,1684,3032,2894,
- 585,4805,3969,2869,2704,2088,2032,2095,3656,2635,4362,2209, 256, 518,2042,2105,
-3777,3657, 643,2298,1148,1779, 190, 989,3544, 414,  11,2135,2063,2979,1471, 403,
-3678, 126, 770,1563, 671,2499,3216,2877, 600,1179, 307,2805,4937,1268,1297,2694,
- 252,4032,1448,1494,1331,1394, 127,2256, 222,1647,1035,1481,3056,1915,1048, 873,
-3651, 210,  33,1608,2516, 200,1520, 415, 102,   0,3389,1287, 817,  91,3299,2940,
- 836,1814, 549,2197,1396,1669,2987,3582,2297,2848,4528,1070, 687,  20,1819, 121,
-1552,1364,1461,1968,2617,3540,2824,2083, 177, 948,4938,2291, 110,4549,2066, 648,
-3359,1755,2110,2114,4642,4845,1693,3937,3308,1257,1869,2123, 208,1804,3159,2992,
-2531,2549,3361,2418,1350,2347,2800,2568,1291,2036,2680,  72, 842,1990, 212,1233,
-1154,1586,  75,2027,3410,4900,1823,1337,2710,2676, 728,2810,1522,3026,4995, 157,
- 755,1050,4022, 710, 785,1936,2194,2085,1406,2777,2400, 150,1250,4049,1206, 807,
-1910, 534, 529,3309,1721,1660, 274,  39,2827, 661,2670,1578, 925,3248,3815,1094,
-4278,4901,4252,  41,1150,3747,2572,2227,4501,3658,4902,3813,3357,3617,2884,2258,
- 887, 538,4187,3199,1294,2439,3042,2329,2343,2497,1255, 107, 543,1527, 521,3478,
-3568, 194,5062,  15, 961,3870,1241,1192,2664,  66,5215,3260,2111,1295,1127,2152,
-3805,4135, 901,1164,1976, 398,1278, 530,1460, 748, 904,1054,1966,1426,  53,2909,
- 509, 523,2279,1534, 536,1019, 239,1685, 460,2353, 673,1065,2401,3600,4298,2272,
-1272,2363, 284,1753,3679,4064,1695,  81, 815,2677,2757,2731,1386, 859, 500,4221,
-2190,2566, 757,1006,2519,2068,1166,1455, 337,2654,3203,1863,1682,1914,3025,1252,
-1409,1366, 847, 714,2834,2038,3209, 964,2970,1901, 885,2553,1078,1756,3049, 301,
-1572,3326, 688,2130,1996,2429,1805,1648,2930,3421,2750,3652,3088, 262,1158,1254,
- 389,1641,1812, 526,1719, 923,2073,1073,1902, 468, 489,4625,1140, 857,2375,3070,
-3319,2863, 380, 116,1328,2693,1161,2244, 273,1212,1884,2769,3011,1775,1142, 461,
-3066,1200,2147,2212, 790, 702,2695,4222,1601,1058, 434,2338,5153,3640,  67,2360,
-4099,2502, 618,3472,1329, 416,1132, 830,2782,1807,2653,3211,3510,1662, 192,2124,
- 296,3979,1739,1611,3684,  23, 118, 324, 446,1239,1225, 293,2520,3814,3795,2535,
-3116,  17,1074, 467,2692,2201, 387,2922,  45,1326,3055,1645,3659,2817, 958, 243,
-1903,2320,1339,2825,1784,3289, 356, 576, 865,2315,2381,3377,3916,1088,3122,1713,
-1655, 935, 628,4689,1034,1327, 441, 800, 720, 894,1979,2183,1528,5289,2702,1071,
-4046,3572,2399,1571,3281,  79, 761,1103, 327, 134, 758,1899,1371,1615, 879, 442,
- 215,2605,2579, 173,2048,2485,1057,2975,3317,1097,2253,3801,4263,1403,1650,2946,
- 814,4968,3487,1548,2644,1567,1285,   2, 295,2636,  97, 946,3576, 832, 141,4257,
-3273, 760,3821,3521,3156,2607, 949,1024,1733,1516,1803,1920,2125,2283,2665,3180,
-1501,2064,3560,2171,1592, 803,3518,1416, 732,3897,4258,1363,1362,2458, 119,1427,
- 602,1525,2608,1605,1639,3175, 694,3064,  10, 465,  76,2000,4846,4208, 444,3781,
-1619,3353,2206,1273,3796, 740,2483, 320,1723,2377,3660,2619,1359,1137,1762,1724,
-2345,2842,1850,1862, 912, 821,1866, 612,2625,1735,2573,3369,1093, 844,  89, 937,
- 930,1424,3564,2413,2972,1004,3046,3019,2011, 711,3171,1452,4178, 428, 801,1943,
- 432, 445,2811, 206,4136,1472, 730, 349,  73, 397,2802,2547, 998,1637,1167, 789,
- 396,3217, 154,1218, 716,1120,1780,2819,4826,1931,3334,3762,2139,1215,2627, 552,
-3664,3628,3232,1405,2383,3111,1356,2652,3577,3320,3101,1703, 640,1045,1370,1246,
-4996, 371,1575,2436,1621,2210, 984,4033,1734,2638,  16,4529, 663,2755,3255,1451,
-3917,2257,1253,1955,2234,1263,2951, 214,1229, 617, 485, 359,1831,1969, 473,2310,
- 750,2058, 165,  80,2864,2419, 361,4344,2416,2479,1134, 796,3726,1266,2943, 860,
-2715, 938, 390,2734,1313,1384, 248, 202, 877,1064,2854, 522,3907, 279,1602, 297,
-2357, 395,3740, 137,2075, 944,4089,2584,1267,3802,  62,1533,2285, 178, 176, 780,
-2440, 201,3707, 590, 478,1560,4354,2117,1075,  30,  74,4643,4004,1635,1441,2745,
- 776,2596, 238,1077,1692,1912,2844, 605, 499,1742,3947, 241,3053, 980,1749, 936,
-2640,4511,2582, 515,1543,2162,5322,2892,2993, 890,2148,1924, 665,1827,3581,1032,
- 968,3163, 339,1044,1896, 270, 583,1791,1720,4367,1194,3488,3669,  43,2523,1657,
- 163,2167, 290,1209,1622,3378, 550, 634,2508,2510, 695,2634,2384,2512,1476,1414,
- 220,1469,2341,2138,2852,3183,2900,4939,2865,3502,1211,3680, 854,3227,1299,2976,
-3172, 186,2998,1459, 443,1067,3251,1495, 321,1932,3054, 909, 753,1410,1828, 436,
-2441,1119,1587,3164,2186,1258, 227, 231,1425,1890,3200,3942, 247, 959, 725,5254,
-2741, 577,2158,2079, 929, 120, 174, 838,2813, 591,1115, 417,2024,  40,3240,1536,
-1037, 291,4151,2354, 632,1298,2406,2500,3535,1825,1846,3451, 205,1171, 345,4238,
-  18,1163, 811, 685,2208,1217, 425,1312,1508,1175,4308,2552,1033, 587,1381,3059,
-2984,3482, 340,1316,4023,3972, 792,3176, 519, 777,4690, 918, 933,4130,2981,3741,
-  90,3360,2911,2200,5184,4550, 609,3079,2030, 272,3379,2736, 363,3881,1130,1447,
- 286, 779, 357,1169,3350,3137,1630,1220,2687,2391, 747,1277,3688,2618,2682,2601,
-1156,3196,5290,4034,3102,1689,3596,3128, 874, 219,2783, 798, 508,1843,2461, 269,
-1658,1776,1392,1913,2983,3287,2866,2159,2372, 829,4076,  46,4253,2873,1889,1894,
- 915,1834,1631,2181,2318, 298, 664,2818,3555,2735, 954,3228,3117, 527,3511,2173,
- 681,2712,3033,2247,2346,3467,1652, 155,2164,3382, 113,1994, 450, 899, 494, 994,
-1237,2958,1875,2336,1926,3727, 545,1577,1550, 633,3473, 204,1305,3072,2410,1956,
-2471, 707,2134, 841,2195,2196,2663,3843,1026,4940, 990,3252,4997, 368,1092, 437,
-3212,3258,1933,1829, 675,2977,2893, 412, 943,3723,4644,3294,3283,2230,2373,5154,
-2389,2241,2661,2323,1404,2524, 593, 787, 677,3008,1275,2059, 438,2709,2609,2240,
-2269,2246,1446,  36,1568,1373,3892,1574,2301,1456,3962, 693,2276,5216,2035,1143,
-2720,1919,1797,1811,2763,4137,2597,1830,1699,1488,1198,2090, 424,1694, 312,3634,
-3390,4179,3335,2252,1214, 561,1059,3243,2295,2561, 975,5155,2321,2751,3772, 472,
-1537,3282,3398,1047,2077,2348,2878,1323,3340,3076, 690,2906,  51, 369, 170,3541,
-1060,2187,2688,3670,2541,1083,1683, 928,3918, 459, 109,4427, 599,3744,4286, 143,
-2101,2730,2490,  82,1588,3036,2121, 281,1860, 477,4035,1238,2812,3020,2716,3312,
-1530,2188,2055,1317, 843, 636,1808,1173,3495, 649, 181,1002, 147,3641,1159,2414,
-3750,2289,2795, 813,3123,2610,1136,4368,   5,3391,4541,2174, 420, 429,1728, 754,
-1228,2115,2219, 347,2223,2733, 735,1518,3003,2355,3134,1764,3948,3329,1888,2424,
-1001,1234,1972,3321,3363,1672,1021,1450,1584, 226, 765, 655,2526,3404,3244,2302,
-3665, 731, 594,2184, 319,1576, 621, 658,2656,4299,2099,3864,1279,2071,2598,2739,
- 795,3086,3699,3908,1707,2352,2402,1382,3136,2475,1465,4847,3496,3865,1085,3004,
-2591,1084, 213,2287,1963,3565,2250, 822, 793,4574,3187,1772,1789,3050, 595,1484,
-1959,2770,1080,2650, 456, 422,2996, 940,3322,4328,4345,3092,2742, 965,2784, 739,
-4124, 952,1358,2498,2949,2565, 332,2698,2378, 660,2260,2473,4194,3856,2919, 535,
-1260,2651,1208,1428,1300,1949,1303,2942, 433,2455,2450,1251,1946, 614,1269, 641,
-1306,1810,2737,3078,2912, 564,2365,1419,1415,1497,4460,2367,2185,1379,3005,1307,
-3218,2175,1897,3063, 682,1157,4040,4005,1712,1160,1941,1399, 394, 402,2952,1573,
-1151,2986,2404, 862, 299,2033,1489,3006, 346, 171,2886,3401,1726,2932, 168,2533,
-  47,2507,1030,3735,1145,3370,1395,1318,1579,3609,4560,2857,4116,1457,2529,1965,
- 504,1036,2690,2988,2405, 745,5871, 849,2397,2056,3081, 863,2359,3857,2096,  99,
-1397,1769,2300,4428,1643,3455,1978,1757,3718,1440,  35,4879,3742,1296,4228,2280,
- 160,5063,1599,2013, 166, 520,3479,1646,3345,3012, 490,1937,1545,1264,2182,2505,
-1096,1188,1369,1436,2421,1667,2792,2460,1270,2122, 727,3167,2143, 806,1706,1012,
-1800,3037, 960,2218,1882, 805, 139,2456,1139,1521, 851,1052,3093,3089, 342,2039,
- 744,5097,1468,1502,1585,2087, 223, 939, 326,2140,2577, 892,2481,1623,4077, 982,
-3708, 135,2131,  87,2503,3114,2326,1106, 876,1616, 547,2997,2831,2093,3441,4530,
-4314,   9,3256,4229,4148, 659,1462,1986,1710,2046,2913,2231,4090,4880,5255,3392,
-3274,1368,3689,4645,1477, 705,3384,3635,1068,1529,2941,1458,3782,1509, 100,1656,
-2548, 718,2339, 408,1590,2780,3548,1838,4117,3719,1345,3530, 717,3442,2778,3220,
-2898,1892,4590,3614,3371,2043,1998,1224,3483, 891, 635, 584,2559,3355, 733,1766,
-1729,1172,3789,1891,2307, 781,2982,2271,1957,1580,5773,2633,2005,4195,3097,1535,
-3213,1189,1934,5693,3262, 586,3118,1324,1598, 517,1564,2217,1868,1893,4445,3728,
-2703,3139,1526,1787,1992,3882,2875,1549,1199,1056,2224,1904,2711,5098,4287, 338,
-1993,3129,3489,2689,1809,2815,1997, 957,1855,3898,2550,3275,3057,1105,1319, 627,
-1505,1911,1883,3526, 698,3629,3456,1833,1431, 746,  77,1261,2017,2296,1977,1885,
- 125,1334,1600, 525,1798,1109,2222,1470,1945, 559,2236,1186,3443,2476,1929,1411,
-2411,3135,1777,3372,2621,1841,1613,3229, 668,1430,1839,2643,2916, 195,1989,2671,
-2358,1387, 629,3205,2293,5256,4439, 123,1310, 888,1879,4300,3021,3605,1003,1162,
-3192,2910,2010, 140,2395,2859,  55,1082,2012,2901, 662, 419,2081,1438, 680,2774,
-4654,3912,1620,1731,1625,5035,4065,2328, 512,1344, 802,5443,2163,2311,2537, 524,
-3399,  98,1155,2103,1918,2606,3925,2816,1393,2465,1504,3773,2177,3963,1478,4346,
- 180,1113,4655,3461,2028,1698, 833,2696,1235,1322,1594,4408,3623,3013,3225,2040,
-3022, 541,2881, 607,3632,2029,1665,1219, 639,1385,1686,1099,2803,3231,1938,3188,
-2858, 427, 676,2772,1168,2025, 454,3253,2486,3556, 230,1950, 580, 791,1991,1280,
-1086,1974,2034, 630, 257,3338,2788,4903,1017,  86,4790, 966,2789,1995,1696,1131,
- 259,3095,4188,1308, 179,1463,5257, 289,4107,1248,  42,3413,1725,2288, 896,1947,
- 774,4474,4254, 604,3430,4264, 392,2514,2588, 452, 237,1408,3018, 988,4531,1970,
-3034,3310, 540,2370,1562,1288,2990, 502,4765,1147,   4,1853,2708, 207, 294,2814,
-4078,2902,2509, 684,  34,3105,3532,2551, 644, 709,2801,2344, 573,1727,3573,3557,
-2021,1081,3100,4315,2100,3681, 199,2263,1837,2385, 146,3484,1195,2776,3949, 997,
-1939,3973,1008,1091,1202,1962,1847,1149,4209,5444,1076, 493, 117,5400,2521, 972,
-1490,2934,1796,4542,2374,1512,2933,2657, 413,2888,1135,2762,2314,2156,1355,2369,
- 766,2007,2527,2170,3124,2491,2593,2632,4757,2437, 234,3125,3591,1898,1750,1376,
-1942,3468,3138, 570,2127,2145,3276,4131, 962, 132,1445,4196,  19, 941,3624,3480,
-3366,1973,1374,4461,3431,2629, 283,2415,2275, 808,2887,3620,2112,2563,1353,3610,
- 955,1089,3103,1053,  96,  88,4097, 823,3808,1583, 399, 292,4091,3313, 421,1128,
- 642,4006, 903,2539,1877,2082, 596,  29,4066,1790, 722,2157, 130, 995,1569, 769,
-1485, 464, 513,2213, 288,1923,1101,2453,4316, 133, 486,2445,  50, 625, 487,2207,
-  57, 423, 481,2962, 159,3729,1558, 491, 303, 482, 501, 240,2837, 112,3648,2392,
-1783, 362,   8,3433,3422, 610,2793,3277,1390,1284,1654,  21,3823, 734, 367, 623,
- 193, 287, 374,1009,1483, 816, 476, 313,2255,2340,1262,2150,2899,1146,2581, 782,
-2116,1659,2018,1880, 255,3586,3314,1110,2867,2137,2564, 986,2767,5185,2006, 650,
- 158, 926, 762, 881,3157,2717,2362,3587, 306,3690,3245,1542,3077,2427,1691,2478,
-2118,2985,3490,2438, 539,2305, 983, 129,1754, 355,4201,2386, 827,2923, 104,1773,
-2838,2771, 411,2905,3919, 376, 767, 122,1114, 828,2422,1817,3506, 266,3460,1007,
-1609,4998, 945,2612,4429,2274, 726,1247,1964,2914,2199,2070,4002,4108, 657,3323,
-1422, 579, 455,2764,4737,1222,2895,1670, 824,1223,1487,2525, 558, 861,3080, 598,
-2659,2515,1967, 752,2583,2376,2214,4180, 977, 704,2464,4999,2622,4109,1210,2961,
- 819,1541, 142,2284,  44, 418, 457,1126,3730,4347,4626,1644,1876,3671,1864, 302,
-1063,5694, 624, 723,1984,3745,1314,1676,2488,1610,1449,3558,3569,2166,2098, 409,
-1011,2325,3704,2306, 818,1732,1383,1824,1844,3757, 999,2705,3497,1216,1423,2683,
-2426,2954,2501,2726,2229,1475,2554,5064,1971,1794,1666,2014,1343, 783, 724, 191,
-2434,1354,2220,5065,1763,2752,2472,4152, 131, 175,2885,3434,  92,1466,4920,2616,
-3871,3872,3866, 128,1551,1632, 669,1854,3682,4691,4125,1230, 188,2973,3290,1302,
-1213, 560,3266, 917, 763,3909,3249,1760, 868,1958, 764,1782,2097, 145,2277,3774,
-4462,  64,1491,3062, 971,2132,3606,2442, 221,1226,1617, 218, 323,1185,3207,3147,
- 571, 619,1473,1005,1744,2281, 449,1887,2396,3685, 275, 375,3816,1743,3844,3731,
- 845,1983,2350,4210,1377, 773, 967,3499,3052,3743,2725,4007,1697,1022,3943,1464,
-3264,2855,2722,1952,1029,2839,2467,  84,4383,2215, 820,1391,2015,2448,3672, 377,
-1948,2168, 797,2545,3536,2578,2645,  94,2874,1678, 405,1259,3071, 771, 546,1315,
- 470,1243,3083, 895,2468, 981, 969,2037, 846,4181, 653,1276,2928,  14,2594, 557,
-3007,2474, 156, 902,1338,1740,2574, 537,2518, 973,2282,2216,2433,1928, 138,2903,
-1293,2631,1612, 646,3457, 839,2935, 111, 496,2191,2847, 589,3186, 149,3994,2060,
-4031,2641,4067,3145,1870,  37,3597,2136,1025,2051,3009,3383,3549,1121,1016,3261,
-1301, 251,2446,2599,2153, 872,3246, 637, 334,3705, 831, 884, 921,3065,3140,4092,
-2198,1944, 246,2964, 108,2045,1152,1921,2308,1031, 203,3173,4170,1907,3890, 810,
-1401,2003,1690, 506, 647,1242,2828,1761,1649,3208,2249,1589,3709,2931,5156,1708,
- 498, 666,2613, 834,3817,1231, 184,2851,1124, 883,3197,2261,3710,1765,1553,2658,
-1178,2639,2351,  93,1193, 942,2538,2141,4402, 235,1821, 870,1591,2192,1709,1871,
-3341,1618,4126,2595,2334, 603, 651,  69, 701, 268,2662,3411,2555,1380,1606, 503,
- 448, 254,2371,2646, 574,1187,2309,1770, 322,2235,1292,1801, 305, 566,1133, 229,
-2067,2057, 706, 167, 483,2002,2672,3295,1820,3561,3067, 316, 378,2746,3452,1112,
- 136,1981, 507,1651,2917,1117, 285,4591, 182,2580,3522,1304, 335,3303,1835,2504,
-1795,1792,2248, 674,1018,2106,2449,1857,2292,2845, 976,3047,1781,2600,2727,1389,
-1281,  52,3152, 153, 265,3950, 672,3485,3951,4463, 430,1183, 365, 278,2169,  27,
-1407,1336,2304, 209,1340,1730,2202,1852,2403,2883, 979,1737,1062, 631,2829,2542,
-3876,2592, 825,2086,2226,3048,3625, 352,1417,3724, 542, 991, 431,1351,3938,1861,
-2294, 826,1361,2927,3142,3503,1738, 463,2462,2723, 582,1916,1595,2808, 400,3845,
-3891,2868,3621,2254,  58,2492,1123, 910,2160,2614,1372,1603,1196,1072,3385,1700,
-3267,1980, 696, 480,2430, 920, 799,1570,2920,1951,2041,4047,2540,1321,4223,2469,
-3562,2228,1271,2602, 401,2833,3351,2575,5157, 907,2312,1256, 410, 263,3507,1582,
- 996, 678,1849,2316,1480, 908,3545,2237, 703,2322, 667,1826,2849,1531,2604,2999,
-2407,3146,2151,2630,1786,3711, 469,3542, 497,3899,2409, 858, 837,4446,3393,1274,
- 786, 620,1845,2001,3311, 484, 308,3367,1204,1815,3691,2332,1532,2557,1842,2020,
-2724,1927,2333,4440, 567,  22,1673,2728,4475,1987,1858,1144,1597, 101,1832,3601,
-  12, 974,3783,4391, 951,1412,   1,3720, 453,4608,4041, 528,1041,1027,3230,2628,
-1129, 875,1051,3291,1203,2262,1069,2860,2799,2149,2615,3278, 144,1758,3040,  31,
- 475,1680, 366,2685,3184, 311,1642,4008,2466,5036,1593,1493,2809, 216,1420,1668,
- 233, 304,2128,3284, 232,1429,1768,1040,2008,3407,2740,2967,2543, 242,2133, 778,
-1565,2022,2620, 505,2189,2756,1098,2273, 372,1614, 708, 553,2846,2094,2278, 169,
-3626,2835,4161, 228,2674,3165, 809,1454,1309, 466,1705,1095, 900,3423, 880,2667,
-3751,5258,2317,3109,2571,4317,2766,1503,1342, 866,4447,1118,  63,2076, 314,1881,
-1348,1061, 172, 978,3515,1747, 532, 511,3970,   6, 601, 905,2699,3300,1751, 276,
-1467,3725,2668,  65,4239,2544,2779,2556,1604, 578,2451,1802, 992,2331,2624,1320,
-3446, 713,1513,1013, 103,2786,2447,1661, 886,1702, 916, 654,3574,2031,1556, 751,
-2178,2821,2179,1498,1538,2176, 271, 914,2251,2080,1325, 638,1953,2937,3877,2432,
-2754,  95,3265,1716, 260,1227,4083, 775, 106,1357,3254, 426,1607, 555,2480, 772,
-1985, 244,2546, 474, 495,1046,2611,1851,2061,  71,2089,1675,2590, 742,3758,2843,
-3222,1433, 267,2180,2576,2826,2233,2092,3913,2435, 956,1745,3075, 856,2113,1116,
- 451,   3,1988,2896,1398, 993,2463,1878,2049,1341,2718,2721,2870,2108, 712,2904,
-4363,2753,2324, 277,2872,2349,2649, 384, 987, 435, 691,3000, 922, 164,3939, 652,
-1500,1184,4153,2482,3373,2165,4848,2335,3775,3508,3154,2806,2830,1554,2102,1664,
-2530,1434,2408, 893,1547,2623,3447,2832,2242,2532,3169,2856,3223,2078,  49,3770,
-3469, 462, 318, 656,2259,3250,3069, 679,1629,2758, 344,1138,1104,3120,1836,1283,
-3115,2154,1437,4448, 934, 759,1999, 794,2862,1038, 533,2560,1722,2342, 855,2626,
-1197,1663,4476,3127,  85,4240,2528,  25,1111,1181,3673, 407,3470,4561,2679,2713,
- 768,1925,2841,3986,1544,1165, 932, 373,1240,2146,1930,2673, 721,4766, 354,4333,
- 391,2963, 187,  61,3364,1442,1102, 330,1940,1767, 341,3809,4118, 393,2496,2062,
-2211, 105, 331, 300, 439, 913,1332, 626, 379,3304,1557, 328, 689,3952, 309,1555,
- 931, 317,2517,3027, 325, 569, 686,2107,3084,  60,1042,1333,2794, 264,3177,4014,
-1628, 258,3712,   7,4464,1176,1043,1778, 683, 114,1975,  78,1492, 383,1886, 510,
- 386, 645,5291,2891,2069,3305,4138,3867,2939,2603,2493,1935,1066,1848,3588,1015,
-1282,1289,4609, 697,1453,3044,2666,3611,1856,2412,  54, 719,1330, 568,3778,2459,
-1748, 788, 492, 551,1191,1000, 488,3394,3763, 282,1799, 348,2016,1523,3155,2390,
-1049, 382,2019,1788,1170, 729,2968,3523, 897,3926,2785,2938,3292, 350,2319,3238,
-1718,1717,2655,3453,3143,4465, 161,2889,2980,2009,1421,  56,1908,1640,2387,2232,
-1917,1874,2477,4921, 148,  83,3438, 592,4245,2882,1822,1055, 741, 115,1496,1624,
- 381,1638,4592,1020, 516,3214, 458, 947,4575,1432, 211,1514,2926,1865,2142, 189,
- 852,1221,1400,1486, 882,2299,4036, 351,  28,1122, 700,6479,6480,6481,6482,6483,  #last 512
-)
-# fmt: on
diff --git a/spaces/Atualli/yoloxTeste/configs/__init__.py b/spaces/Atualli/yoloxTeste/configs/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Benson/text-generation/Examples/Arena Breakout Beta Global Descargar.md b/spaces/Benson/text-generation/Examples/Arena Breakout Beta Global Descargar.md
deleted file mode 100644
index 77d38db818ebcafa265bf1e1e2ec9a06866efa1a..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Arena Breakout Beta Global Descargar.md	
+++ /dev/null
@@ -1,73 +0,0 @@
-<br />
-<h1>Arena Breakout Global Beta Descargar: Cómo unirse al FPS táctico inmersivo de próxima generación en móviles</h1>
- <p>Si usted está buscando un nuevo y emocionante juego de disparos que desafía sus habilidades y recompensa sus riesgos, es posible que desee echar un vistazo a Arena Breakout. Este juego es un FPS táctico inmersivo de próxima generación que empuja los límites de la simulación de guerra en el móvil. También es el primer shooter de extracción de saqueadores que te permite disparar, saquear y escapar para ganar. </p>
- <p>En este artículo, le diremos todo lo que necesita saber sobre Arena Breakout, cómo descargar y jugar la versión beta global, y lo que es nuevo en la última actualización. ¡Vamos a empezar! </p>
-<h2>arena breakout beta global descargar</h2><br /><p><b><b>DOWNLOAD</b> &#9913; <a href="https://bltlly.com/2v6KI1">https://bltlly.com/2v6KI1</a></b></p><br /><br />
- <h2>¿Qué es Arena Breakout? </h2>
- <p>Arena Breakout es un juego desarrollado por Level Infinite, un estudio que tiene como objetivo crear juegos innovadores e inmersivos para dispositivos móviles. Arena Breakout es su título insignia, y ha estado en desarrollo durante más de dos años. El juego ha sido elogiado por jugadores y críticos por sus gráficos realistas, efectos de sonido y mecánica de juego. </p>
- <h3>Un nuevo tipo de juego de disparos</h3>
- <p>Arena Breakout no es el típico juego de disparos. Es un juego que combina elementos de FPS tácticos, battle royale y géneros de disparos de saqueo. El juego tiene dos modos: solitario y escuadrón. En el modo solitario, juegas como un lobo solitario que tiene que sobrevivir contra otros jugadores y enemigos de la IA. En el modo de escuadrón, haces equipo con hasta otros tres jugadores y cooperas para eliminar la competencia. </p>
- <p>El juego también tiene una característica única llamada breakout. Breakout es la única manera de ganar el juego. Tienes que escapar de la zona de combate vivo con su botín antes de que acabe el tiempo. Si mueres o no te escapas, pierdes todo lo que has recogido en el partido. Esto añade una capa de tensión y estrategia al juego, ya que tienes que decidir cuándo luchar, cuándo saquear y cuándo correr. </p>
- <h3>Una experiencia realista e inmersiva</h3>
-
- <p>El juego también tiene un sistema de disparos realista que simula la física y la mecánica de las armas reales. Usted tiene que manejar su retroceso, recargar sus revistas, parchear sus heridas, y utilizar la cubierta y el movimiento sabiamente. El juego también tiene diferentes condiciones climáticas, ciclos de día y de noche, y entornos destructibles que afectan a su juego. </p>
- <h3>Un sistema de alto riesgo y alta recompensa</h3>
- <p>Arena Breakout es un juego que recompensa tus riesgos con altas recompensas. El juego tiene un sistema de botín que te permite recoger valiosas armas, accesorios y suministros de la arena. También puedes saquear los cadáveres o las cajas de otros jugadores para obtener más botín. El botín que recojas se puede usar en la partida o guardar para su uso posterior. </p>
- <p>El juego también tiene un sistema de divisas que le permite comprar o vender artículos en el mercado. Puedes usar la moneda para comprar mejores equipos o cosméticos para tu personaje. Sin embargo, usted tiene que tener cuidado con su dinero, ya que puede perderlo todo si usted muere o no escapa. El juego también tiene un sistema de clasificación que rastrea tu rendimiento y progreso en el juego. </p>
-<p></p>
- <h2>¿Cómo descargar y jugar Arena Breakout beta global? </h2>
- <p>Si estás interesado en jugar la beta global de Arena Breakout, aquí hay algunas cosas que necesitas saber:</p>
- <h <h3>Requisitos y disponibilidad</h3>
- <p>Arena Breakout beta global está disponible actualmente solo para dispositivos Android. Necesitas tener un dispositivo Android con al menos 4 GB de RAM y Android 8.0 o superior para jugar el juego. El juego también requiere una conexión a Internet estable y aproximadamente 2 GB de espacio de almacenamiento. </p>
-
- <h3>Pasos para descargar e instalar</h3>
- <p>Una vez que tenga un código de invitación beta, puede seguir estos pasos para descargar e instalar Arena Breakout beta global en su dispositivo Android:</p>
- <ol>
-<li>Ir a la página web oficial de Arena Breakout y haga clic en el botón de descarga. Serás redirigido a una página donde podrás introducir tu código de invitación beta y tu dirección de correo electrónico. Después de verificar su código y correo electrónico, recibirá un enlace de descarga para el juego. </li>
-<li>Alternativamente, puede ir a una fuente de terceros que proporciona el enlace de descarga para el juego, como APKPure o TapTap. Sin embargo, asegúrate de descargar el juego desde una fuente confiable y segura, ya que algunas fuentes pueden contener malware o virus. </li>
-<li>Después de descargar el juego, es necesario habilitar la instalación de aplicaciones de fuentes desconocidas en el dispositivo. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas y conéctela. </li>
-<li>Luego, busque el archivo descargado en su dispositivo y toque en él para instalarlo. Es posible que necesite conceder algunos permisos para que el juego se ejecute correctamente. </li>
-<li>Después de instalar el juego, ejecútelo e ingrese su código de invitación beta nuevamente para iniciar sesión. También es posible que necesite crear una cuenta o vincular su cuenta de redes sociales para jugar el juego. </li>
-<li>Disfruta jugando Arena Breakout beta global! </li>
-</ol>
- <h3>Consejos y trucos para principiantes</h3>
- <p>Si eres nuevo en Arena Breakout, aquí hay algunos consejos y trucos que pueden ayudarte a mejorar tus habilidades y ganar más partidos:</p>
- <ul>
-<li>Aprende los conceptos básicos del juego, como cómo mover, apuntar, disparar, recargar, sanar, saquear y escapar. Puedes practicar estas habilidades en el modo de entrenamiento o en modo individual antes de unirte al modo escuadrón. </li>
-
-<li>Usa la cubierta y el movimiento sabiamente. Puedes usar paredes, edificios, vehículos y otros objetos como cobertura del fuego enemigo. También puedes usar diferentes movimientos, como agacharte, inclinarte, deslizarte, saltar y rodar para esquivar balas y sorprender a tus enemigos. </li>
-<li>Saquea inteligente y estratégicamente. Puedes saquear armas, accesorios y suministros de cajas, cadáveres o edificios en la arena. Sin embargo, ten cuidado de no exponerte demasiado mientras saqueas, ya que puedes atraer la atención no deseada de otros jugadores o enemigos de la IA. También, sea selectivo acerca de lo que saquea, ya que tiene espacio de inventario limitado y capacidad de peso. </li>
-<li>Fuga en el momento adecuado. Fuga es la única manera de ganar el juego, pero también es arriesgado. Tienes que escapar de la zona de combate vivo con su botín antes de que acabe el tiempo. Sin embargo, también tienes que tener cuidado con otros jugadores que pueden intentar detenerte o robar tu botín. Por lo tanto, usted tiene que elegir cuándo romper cuidadosamente basado en su situación y estrategia. </li>
-</ul>
- <h2>¿Qué hay de nuevo en la actualización beta global? </h2>
- <p>Arena Breakout beta global se ha actualizado con nuevas características y mejoras que hacen que el juego sea más divertido y atractivo. Estos son algunos de los aspectos más destacados de la actualización:</p>
- <h3>Personajes femeninos y opciones de personalización</h3>
- <p>Ahora puedes elegir entre personajes masculinos y femeninos en Arena Breakout. También puedes personalizar la apariencia de tu personaje con diferentes peinados, tonos de piel, caras, trajes, accesorios y más. También puedes desbloquear más opciones de personalización completando misiones o comprándolas con moneda. </p>
- <h3>En el partido matar cam y equipamiento rápido característica</h3>
- <p>Ahora puedes ver cómo moriste o cómo mataste a alguien en el partido con la función kill cam. La cámara mortal te muestra una repetición de los últimos momentos de tu vida o la vida de tu enemigo desde su perspectiva. Puedes usar esta función para aprender de tus errores o para disfrutar de tus victorias. </p>
-
- <h3>Sistema de préstamo de equipos y de invitación de amigos</h3>
- <p>Ahora puede prestar su equipo a sus compañeros de escuadra o pedir prestado equipo de ellos en el partido con el sistema de préstamo de equipos. El sistema de préstamo de equipos le permite compartir sus armas, accesorios y suministros con los miembros de su equipo para ayudarlos o para optimizar su carga. También puede solicitar u ofrecer equipos a sus compañeros de escuadra con un simple toque. </p>
- <p>También puedes invitar a tus amigos a jugar contigo en Arena Breakout con el sistema de invitación de amigos. El sistema de invitación de amigos te permite enviar o recibir invitaciones para unirte a un equipo con tus amigos u otros jugadores. También puedes chatear con tus amigos o compañeros de equipo en el lobby del juego o en el partido. </p>
- <h3>Sala de trofeos y soporte de idiomas</h3>
- <p>Ahora puedes mostrar tus logros y progreso en Arena Breakout con la función de sala de trofeos. La función de sala de trofeos le permite mostrar sus trofeos, medallas, insignias y estadísticas en una sala virtual que puede personalizar y decorar. También puede visitar las salas de trofeos de otros jugadores y comparar su rendimiento con ellos. </p>
- <p>También puede jugar Arena Breakout en diferentes idiomas con la función de soporte de idioma. El juego actualmente es compatible con los idiomas inglés, chino, español, portugués, ruso, turco, árabe e indonesio. Puede cambiar el idioma del juego en el menú de configuración. </p>
- <h2>Conclusión</h2>
- <p>Arena Breakout es un juego que ofrece una nueva y emocionante manera de jugar juegos de disparos en dispositivos móviles. Es un juego que combina FPS tácticos, battle royale y elementos de disparos de saqueo en una experiencia realista e inmersiva. También es un juego que desafía tus habilidades y recompensa tus riesgos con un sistema de alto riesgo y alta recompensa. </p>
-
- <p>Arena Breakout es un juego que vale la pena probar si estás buscando un FPS táctico de próxima generación en dispositivos móviles. Es un juego que te mantendrá al borde de tu asiento mientras disparas, saqueas y rompes para ganar. </p>
- <h2>Preguntas frecuentes</h2>
- <ol>
-<li>¿Qué es Arena Breakout? </li>
-<p>Arena Breakout es un FPS táctico inmersivo de próxima generación que empuja los límites de la simulación de guerra en dispositivos móviles. También es el primer shooter de extracción de saqueadores que te permite disparar, saquear y escapar para ganar. </p>
-<li> ¿Cómo descargar y jugar Arena Breakout beta global? </li>
-<p>Necesitas tener un dispositivo Android con al menos 4 GB de RAM y Android 8.0 o superior, una conexión a Internet estable y un código de invitación beta. Puedes descargar el juego desde el sitio web oficial o desde fuentes de terceros, y seguir los pasos para instalarlo y jugarlo. </p>
-<li>¿Qué hay de nuevo en la actualización beta global? </li>
-<p>La actualización beta global ha añadido nuevas características y mejoras, tales como personajes femeninos y opciones de personalización, cámara asesina en el partido y función de equipamiento rápido, sistema de préstamo de equipos e invitación a amigos, sala de trofeos y soporte de idioma, y más. </p>
-<li>¿Cómo obtener un código de invitación beta? </li>
-<p>Puedes obtener un código de invitación beta siguiendo las cuentas de redes sociales oficiales de Arena Breakout o uniéndote al servidor oficial de Discord del juego. También puedes obtener un código de invitación beta participando en sorteos o eventos organizados por los desarrolladores o influencers. </p>
-<li> ¿Cómo romper en Arena Breakout? </li>
-<p>Breakout es la única manera de ganar el juego. Tienes que escapar de la zona de combate con tu botín antes de que se acabe el tiempo. Sin embargo, también tienes que tener cuidado con otros jugadores que pueden intentar detenerte o robar tu botín. Por lo tanto, usted tiene que elegir cuándo romper cuidadosamente basado en su situación y estrategia. </p>
-</ol> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Caso Penal Pacfico Baha Mod Men Apk.md b/spaces/Benson/text-generation/Examples/Caso Penal Pacfico Baha Mod Men Apk.md
deleted file mode 100644
index fc72dcd8a7cc1d91604aa005857df4c5a3c73678..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Caso Penal Pacfico Baha Mod Men Apk.md	
+++ /dev/null
@@ -1,116 +0,0 @@
-<br />
-<h1>Caso Penal: Pacific Bay Mod Menu APK - Una guía para los solucionadores de delitos</h1>
-<p>¿Te encanta jugar juegos de detectives? ¿Te gusta encontrar pistas, interrogar sospechosos y resolver misterios? Si es así, entonces es posible que haya oído hablar de Criminal Case: Pacific Bay, uno de los juegos de objetos ocultos más populares en Android. Pero ¿sabías que hay una manera de hacer este juego aún más divertido y emocionante? Sí, estamos hablando de Caso Penal: Pacific Bay Mod Menu APK, una herramienta de hackeo que le da recursos ilimitados, compras gratis, sin anuncios, y más. En este artículo, le diremos todo lo que necesita saber acerca de este menú mod apk, incluyendo lo que es, cómo descargarlo e instalarlo, cómo usarlo, y cuáles son sus pros y sus contras. Así que, vamos a empezar! </p>
-<h2>caso penal pacífico bahía mod menú apk</h2><br /><p><b><b>Download File</b> &#10026; <a href="https://bltlly.com/2v6M5B">https://bltlly.com/2v6M5B</a></b></p><br /><br />
- <h2>¿Qué es un caso criminal: Pacific Bay? </h2>
-<h3>Un juego de objetos escondidos popular</h3>
-<p>Criminal Case: Pacific Bay es un juego de objetos ocultos desarrollado por Pretty Simple, un estudio francés especializado en juegos casuales. Es la segunda temporada de la serie Criminal Case, que tiene más de 100 millones de descargas en Google Play. En este juego, juegas como un detective que trabaja para el Departamento de Policía de Pacific Bay. Su trabajo es investigar varias escenas del crimen, encontrar pistas, analizar pruebas, interrogar sospechosos y arrestar a los asesinos. También puedes hacer equipo con otros jugadores en línea y competir por las mejores puntuaciones. </p>
-<h3>Una aventura emocionante en la Bahía del Pacífico</h3>
-
-<h3>Una experiencia desafiante y gratificante</h3>
-<p>Criminal Case: Pacific Bay no es un juego fácil. Tendrás que usar tus habilidades de observación, deducción y lógica para resolver los puzzles y encontrar a los culpables. También tendrás que administrar tu tiempo y energía sabiamente, ya que son recursos limitados en el juego. Tendrás que ganar estrellas completando tareas en cada escena del crimen. Puedes usar estas estrellas para desbloquear nuevas escenas, comprar objetos o realizar acciones. También tendrás que recoger monedas y dinero en efectivo jugando minijuegos o viendo anuncios. Puedes usar estas monedas para personalizar tu avatar, comprar potenciadores o acceder a funciones premium. También tendrás que subir de nivel ganando puntos de experiencia (XP) y posicionarte ganando medallas. También tendrás que desbloquear logros y trofeos completando ciertos objetivos. </p>
- <h2>¿Qué es el caso penal: Pacific Bay Mod Menu APK? </h2>
-<h3>Una versión modificada del juego</h3>
-<p>Caso Penal: Pacific Bay Mod Menu APK es una versión modificada del juego original que incluye algunas características de hackeo para ayudar a los jugadores a superar fácilmente los niveles más difíciles. No es una aplicación oficial de Pretty Simple, sino una aplicación de terceros creada por algunos fans o desarrolladores que quieren mejorar la experiencia de juego. No está disponible en Google Play , pero se puede descargar desde algunos sitios web que ofrecen aplicaciones y juegos modificados. Sin embargo, debe tener cuidado al descargar e instalar dichas aplicaciones, ya que pueden contener virus, malware o spyware que pueden dañar su dispositivo o robar su información personal. </p>
-<p></p>
-<h3>Una herramienta de hackeo para recursos ilimitados</h3>
-
-<h3>Una forma de disfrutar del juego sin anuncios</h3>
-<p>Caso Penal: Pacific Bay Mod Menu APK es una manera de disfrutar del juego sin anuncios. Los anuncios son molestos y distraen, especialmente cuando aparecen en medio del juego o cuando estás viendo un video. También consumen sus datos y la batería. Con este menú mod apk, puede eliminar todos los anuncios del juego y jugar sin ninguna interrupción. También puedes evitar ver anuncios para ganar monedas o dinero en el juego. </p>
- <h2>Cómo descargar e instalar Caso Penal: Pacific Bay Mod Menu APK? </h2>
-<h3>Los requisitos y precauciones</h3>
-<p>Antes de descargar e instalar Caso Penal: Pacific Bay Mod Menu APK, es necesario asegurarse de que su dispositivo cumple con los siguientes requisitos y precauciones:</p>
-<ul>
-<li>Tu dispositivo debe tener Android 4.1 o una versión superior. </li>
-<li>El dispositivo debe tener suficiente espacio de almacenamiento para instalar la aplicación. </li>
-<li>El dispositivo debe tener una conexión a Internet estable para descargar la aplicación. </li>
-<li> Debe habilitar la instalación de aplicaciones de fuentes desconocidas en la configuración del dispositivo. </li>
-<li>Debe desinstalar la versión original de Criminal Case: Pacific Bay desde su dispositivo. </li>
-<li> Debe hacer una copia de seguridad de los datos del juego antes de instalar el menú mod apk. </li>
-<li> Debe ser consciente de los riesgos de usar aplicaciones y juegos modificados, como prohibir, bloquear o perder su cuenta. </li>
-</ul>
-<h3>Los pasos a seguir</h3>
-<p>Después de haber comprobado los requisitos y precauciones, puede seguir estos pasos para descargar e instalar Caso Penal: Pacific Bay Mod Menu APK:</p>
-<ol>
-<li>Ir a un sitio web que ofrece Caso Penal: Pacific Bay Mod Menu APK, tales como [APKPure], [APKDone], o [ModDroid]. </li>
-<li>Encontrar y descargar la última versión de Caso Penal: Pacific Bay Mod Menu APK en su dispositivo. </li>
-<li>Localice y toque en el archivo descargado para iniciar el proceso de instalación. </li>
-<li>Siga las instrucciones en la pantalla para completar la instalación. </li>
-
-</ol> <h3>Los beneficios y desventajas</h3>
-<p>Caso Penal: Pacific Bay Mod Menu APK tiene algunos beneficios y desventajas que usted debe considerar antes de usarlo. Estos son algunos de ellos:</p>
-<tabla>
-<tr>
-<th>Beneficios</th>
-<th>Inconvenientes</th>
-</tr>
-<tr>
-<td>Puedes disfrutar del juego con recursos ilimitados y sin anuncios. </td>
-<td>Puede que te prohíban jugar o pierdas tu cuenta. </td>
-</tr>
-<tr>
-<td>Puedes saltarte el tiempo de espera y jugar el juego cuando quieras. </td>
-<td>Puedes perderte la diversión y el desafío del juego. </td>
-</tr>
-<tr>
-<td>Puedes personalizar tu avatar y comprar potenciadores sin gastar dinero real. </td>
-<td>Puedes encontrar algunos errores o errores en el juego. </td>
-</tr>
-<tr>
-<td>Puedes posicionarte más rápido y desbloquear logros y trofeos fácilmente. </td>
-<td>Usted puede perder sus datos de juego o el progreso si el menú mod apk no se actualiza. </td>
-</tr>
-</tabla>
- <h2>Cómo utilizar Caso Penal: Pacific Bay Mod Menu APK? </h2>
-<h3>Las características y funciones</h3>
-<p>Caso Penal: Pacific Bay Mod Menu APK tiene algunas características y funciones que puede utilizar para mejorar su experiencia de juego. Estos son algunos de ellos:</p>
-<ul>
-<li>Estrellas ilimitadas: Puedes usar esta función para desbloquear nuevas escenas, comprar objetos o realizar acciones sin ganar estrellas en el juego. </li>
-<li>Monedas ilimitadas: Puedes usar esta función para personalizar tu avatar, comprar boosters o acceder a funciones premium sin recoger monedas en el juego. </li>
-<li>Dinero ilimitado: Puede utilizar esta función para obtener compras gratis en la tienda de juegos sin gastar dinero real. </li>
-<li>Energía ilimitada: Puede utilizar esta función para jugar el juego sin esperar a recargar energía o ver anuncios. </li>
-<li>Pistas ilimitadas: Puedes usar esta función para obtener pistas en cada escena del crimen sin usar estrellas o monedas. </li>
-<li>Sin anuncios: Puede utilizar esta función para eliminar todos los anuncios del juego y jugar sin ninguna interrupción. </li>
-
-<li>XP ilimitado: Puedes usar esta función para subir de nivel más rápido y ganar más puntos de experiencia en el juego. </li>
-<li>Rank Hack: Puede utilizar esta función para clasificar más rápido y ganar más medallas en el juego. </li>
-</ul> <h3>Los consejos y trucos</h3>
-<p>Caso Penal: Pacific Bay Mod Menu APK tiene algunos consejos y trucos que puede utilizar para mejorar su juego y puntuación. Estos son algunos de ellos:</p>
-<ul>
-<li>Utilice la función de sugerencias ilimitadas sabiamente. No confíe en él demasiado, ya que puede reducir la diversión y el desafío del juego. Trata de encontrar las pistas por ti mismo primero, y usa las pistas solo cuando estés atascado o te estés quedando sin tiempo. </li>
-<li>Utilice la función de energía ilimitada con moderación. No juegue el juego durante demasiado tiempo, ya que puede causar fatiga ocular, o adicción. Tome descansos entre sesiones y limite su tiempo de reproducción diario. </li>
-<li>Usa las monedas ilimitadas y la función de efectivo moderadamente. No compres todo en la tienda de juegos, ya que puede hacer que el juego sea demasiado fácil o aburrido. Guardar algunas monedas y dinero en efectivo para los niveles posteriores, o para los elementos que realmente necesita o quiere. </li>
-<li>Usa cuidadosamente la función de estrellas ilimitadas. No desbloquear todas las escenas a la vez, ya que puede estropear la historia o el suspenso del juego. Sigue el orden de los casos y desbloquea las escenas a medida que avanzas. </li>
-<li>Utilice la función de compras gratuitas selectivamente. No compres artículos que no sean compatibles con tu dispositivo, ya que puede causar fallos o errores en el juego. Compruebe la compatibilidad y las revisiones de los artículos antes de comprarlos. </li>
-<li>Utilice la función ilimitada XP y rango hack con cautela. No suba de nivel ni suba de rango demasiado rápido, ya que puede aumentar la sospecha o la detección de los desarrolladores de juegos u otros jugadores. Mantén tu nivel y rango dentro de un rango razonable, y evita usar esta función en modo online. </li>
-</ul>
-<h3>Los riesgos y limitaciones</h3>
-<p>Caso Penal: Pacific Bay Mod Menu APK tiene algunos riesgos y limitaciones que usted debe ser consciente de antes de usarlo. Estos son algunos de ellos:</p>
-<ul>
-
-<li>Usted puede encontrar algunos errores o errores en el juego si se utiliza este menú mod apk. El menú mod apk puede no ser compatible con su dispositivo, su versión del juego, o sus datos de juego. También puedes experimentar bloqueos, congelaciones, retrasos o fallos en el juego. </li>
-<li>Usted puede perder los datos del juego o el progreso si se utiliza este menú mod apk. El menú mod apk puede sobrescribir o corromper los datos del juego o el progreso. También puede perder sus datos o el progreso si desinstalar el menú mod apk o actualizar el juego. </li>
-<li>Usted puede perder la diversión y el desafío del juego si se utiliza este menú mod apk. El menú mod apk puede hacer el juego demasiado fácil o aburrido para usted. También puede perder interés en el juego o sentirse culpable por hacer trampa. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>Caso Penal: Pacific Bay Mod Menu APK es una herramienta de hackeo que le da recursos ilimitados, compras gratis, sin anuncios, y más en Criminal Case: Pacific Bay, un popular juego de objetos ocultos en Android. Es una versión modificada del juego original que no está disponible en Google Play, pero en algunos sitios web que ofrecen aplicaciones y juegos modificados. Tiene algunos beneficios y desventajas que debe considerar antes de usarlo. También tiene algunas características y funciones que puede utilizar para mejorar su experiencia de juego. También tiene algunos consejos y trucos que puede utilizar para mejorar su juego y puntuación. También tiene algunos riesgos y limitaciones que debes conocer antes de usarlo. </p>
- <h2>Preguntas frecuentes</h2>
-<h3>Q: ¿Es el caso penal: Pacific Bay Mod menú APK seguro de usar? </h3>
-<p>A: Caso Penal: Pacific Bay Mod Menu APK no es seguro de usar, ya que puede contener virus, malware o spyware que pueden dañar su dispositivo o robar su información personal. También puede hacer que te expulsen del juego o que pierdas tu cuenta. También puede causar errores o errores en el juego. Es mejor utilizar la versión original de Criminal Case: Pacific Bay de Google Play.</p>
-<h3>Q: Es Caso Penal: Pacific Bay Mod Menu APK legal de usar? </h3>
-
-<h3>Q: ¿Cómo puedo actualizar Caso Penal: Pacific Bay Mod Menu APK? </h3>
-<p>A: Puede actualizar Caso Penal: Pacific Bay Mod Menu APK mediante la descarga e instalación de la última versión de un sitio web que ofrece aplicaciones y juegos modded. Sin embargo, usted debe tener cuidado al actualizar el menú mod apk, ya que puede no ser compatible con los datos del juego o el progreso. También puede perder sus características de hackeo o enfrentar nuevos riesgos o limitaciones. Es mejor hacer una copia de seguridad de los datos del juego antes de actualizar el menú mod apk. </p>
-<h3>Q: ¿Cómo puedo desinstalar Caso Penal: Pacific Bay Mod Menu APK? </h3>
-<p>A: Usted puede desinstalar Caso Penal: Pacific Bay Mod Menu APK siguiendo estos pasos:</p>
-<ol>
-<li>Ir a la configuración del dispositivo y toque en aplicaciones o aplicaciones.</li>
-<li>Encontrar y toque en Caso Penal: Pacific Bay Mod Menú APK.</li>
-<li>Toque en Desinstalar y confirme su acción. </li>
-<li>Espere a que termine el proceso de desinstalación. </li>
-</ol>
-<p>También puede volver a instalar la versión original de Criminal Case: Pacific Bay de Google Play si desea jugar el juego de nuevo. </p>
-<h3>Q: ¿Dónde puedo encontrar más información sobre Caso Penal: Pacific Bay Mod Menu APK? </h3>
-<p>A: Usted puede encontrar más información sobre Caso Penal: Pacific Bay Mod Menu APK visitando el sitio web que lo ofrece, o buscando en línea para comentarios, comentarios o tutoriales. Sin embargo, debe tener cuidado al visitar dichos sitios web o fuentes, ya que pueden no ser confiables o confiables. También debe evitar hacer clic en cualquier enlace sospechoso o descargar archivos desconocidos. Es mejor usar un antivirus o una aplicación de seguridad de buena reputación para proteger su dispositivo y sus datos. </p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/utils.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/utils.py
deleted file mode 100644
index dd2d245a0bebcd5fc37ac20526aabbd5358dab0e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/utils.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module offers general convenience and utility functions for dealing with
-datetimes.
-
-.. versionadded:: 2.7.0
-"""
-from __future__ import unicode_literals
-
-from datetime import datetime, time
-
-
-def today(tzinfo=None):
-    """
-    Returns a :py:class:`datetime` representing the current day at midnight
-
-    :param tzinfo:
-        The time zone to attach (also used to determine the current day).
-
-    :return:
-        A :py:class:`datetime.datetime` object representing the current day
-        at midnight.
-    """
-
-    dt = datetime.now(tzinfo)
-    return datetime.combine(dt.date(), time(0, tzinfo=tzinfo))
-
-
-def default_tzinfo(dt, tzinfo):
-    """
-    Sets the ``tzinfo`` parameter on naive datetimes only
-
-    This is useful for example when you are provided a datetime that may have
-    either an implicit or explicit time zone, such as when parsing a time zone
-    string.
-
-    .. doctest::
-
-        >>> from dateutil.tz import tzoffset
-        >>> from dateutil.parser import parse
-        >>> from dateutil.utils import default_tzinfo
-        >>> dflt_tz = tzoffset("EST", -18000)
-        >>> print(default_tzinfo(parse('2014-01-01 12:30 UTC'), dflt_tz))
-        2014-01-01 12:30:00+00:00
-        >>> print(default_tzinfo(parse('2014-01-01 12:30'), dflt_tz))
-        2014-01-01 12:30:00-05:00
-
-    :param dt:
-        The datetime on which to replace the time zone
-
-    :param tzinfo:
-        The :py:class:`datetime.tzinfo` subclass instance to assign to
-        ``dt`` if (and only if) it is naive.
-
-    :return:
-        Returns an aware :py:class:`datetime.datetime`.
-    """
-    if dt.tzinfo is not None:
-        return dt
-    else:
-        return dt.replace(tzinfo=tzinfo)
-
-
-def within_delta(dt1, dt2, delta):
-    """
-    Useful for comparing two datetimes that may have a negligible difference
-    to be considered equal.
-    """
-    delta = abs(delta)
-    difference = dt1 - dt2
-    return -delta <= difference <= delta
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/version.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/version.py
deleted file mode 100644
index 95e1869658566aac3060562d8cd5a6b647887d1e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/version.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import pkg_resources
-
-try:
-    __version__ = pkg_resources.get_distribution('setuptools').version
-except Exception:
-    __version__ = 'unknown'
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/dev/linter.sh b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/dev/linter.sh
deleted file mode 100644
index 8dff44115ddb323486a00a8ca0764fe8fb7d2e0c..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/dev/linter.sh
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/bin/bash -e
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-# Run this script at project root by "./dev/linter.sh" before you commit
-
-vergte() {
-  [ "$2" = "$(echo -e "$1\n$2" | sort -V | head -n1)" ]
-}
-
-{
-	black --version | grep "19.3b0" > /dev/null
-} || {
-	echo "Linter requires black==19.3b0 !"
-	exit 1
-}
-
-ISORT_TARGET_VERSION="4.3.21"
-ISORT_VERSION=$(isort -v | grep VERSION | awk '{print $2}')
-vergte "$ISORT_VERSION" "$ISORT_TARGET_VERSION" || {
-  echo "Linter requires isort>=${ISORT_TARGET_VERSION} !"
-  exit 1
-}
-
-set -v
-
-echo "Running isort ..."
-isort -y -sp . --atomic
-
-echo "Running black ..."
-black -l 100 .
-
-echo "Running flake8 ..."
-if [ -x "$(command -v flake8-3)" ]; then
-  flake8-3 .
-else
-  python3 -m flake8 .
-fi
-
-# echo "Running mypy ..."
-# Pytorch does not have enough type annotations
-# mypy detectron2/solver detectron2/structures detectron2/config
-
-echo "Running clang-format ..."
-find . -regex ".*\.\(cpp\|c\|cc\|cu\|cxx\|h\|hh\|hpp\|hxx\|tcc\|mm\|m\)" -print0 | xargs -0 clang-format -i
-
-command -v arc > /dev/null && arc lint
diff --git a/spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter_mod/utils.py b/spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter_mod/utils.py
deleted file mode 100644
index 32b09e5374766d3002347c3c4cbaa702a3ddd6cd..0000000000000000000000000000000000000000
--- a/spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter_mod/utils.py
+++ /dev/null
@@ -1,41 +0,0 @@
-
-import numpy as np
-
-from collections import Counter
-from torch.utils.data import Subset
-from sklearn.model_selection import train_test_split
-
-
-def __balance_val_split(dataset, val_split=0.):
-    targets = np.array(dataset.targets)
-    train_indices, val_indices = train_test_split(
-        np.arange(targets.shape[0]),
-        test_size=val_split,
-        stratify=targets
-    )
-
-    train_dataset = Subset(dataset, indices=train_indices)
-    val_dataset = Subset(dataset, indices=val_indices)
-
-    return train_dataset, val_dataset
-
-
-def __split_of_train_sequence(subset: Subset, train_split=1.0):
-    if train_split == 1:
-        return subset
-
-    targets = np.array([subset.dataset.targets[i] for i in subset.indices])
-    train_indices, _ = train_test_split(
-        np.arange(targets.shape[0]),
-        test_size=1 - train_split,
-        stratify=targets
-    )
-
-    train_dataset = Subset(subset.dataset, indices=[subset.indices[i] for i in train_indices])
-
-    return train_dataset
-
-
-def __log_class_statistics(subset: Subset):
-    train_classes = [subset.dataset.targets[i] for i in subset.indices]
-    print(dict(Counter(train_classes)))
diff --git a/spaces/CVPR/WALT/mmdet/core/bbox/assigners/max_iou_assigner.py b/spaces/CVPR/WALT/mmdet/core/bbox/assigners/max_iou_assigner.py
deleted file mode 100644
index 5cf4c4b4b450f87dfb99c3d33d8ed83d3e5cfcb3..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/core/bbox/assigners/max_iou_assigner.py
+++ /dev/null
@@ -1,212 +0,0 @@
-import torch
-
-from ..builder import BBOX_ASSIGNERS
-from ..iou_calculators import build_iou_calculator
-from .assign_result import AssignResult
-from .base_assigner import BaseAssigner
-
-
-@BBOX_ASSIGNERS.register_module()
-class MaxIoUAssigner(BaseAssigner):
-    """Assign a corresponding gt bbox or background to each bbox.
-
-    Each proposals will be assigned with `-1`, or a semi-positive integer
-    indicating the ground truth index.
-
-    - -1: negative sample, no assigned gt
-    - semi-positive integer: positive sample, index (0-based) of assigned gt
-
-    Args:
-        pos_iou_thr (float): IoU threshold for positive bboxes.
-        neg_iou_thr (float or tuple): IoU threshold for negative bboxes.
-        min_pos_iou (float): Minimum iou for a bbox to be considered as a
-            positive bbox. Positive samples can have smaller IoU than
-            pos_iou_thr due to the 4th step (assign max IoU sample to each gt).
-        gt_max_assign_all (bool): Whether to assign all bboxes with the same
-            highest overlap with some gt to that gt.
-        ignore_iof_thr (float): IoF threshold for ignoring bboxes (if
-            `gt_bboxes_ignore` is specified). Negative values mean not
-            ignoring any bboxes.
-        ignore_wrt_candidates (bool): Whether to compute the iof between
-            `bboxes` and `gt_bboxes_ignore`, or the contrary.
-        match_low_quality (bool): Whether to allow low quality matches. This is
-            usually allowed for RPN and single stage detectors, but not allowed
-            in the second stage. Details are demonstrated in Step 4.
-        gpu_assign_thr (int): The upper bound of the number of GT for GPU
-            assign. When the number of gt is above this threshold, will assign
-            on CPU device. Negative values mean not assign on CPU.
-    """
-
-    def __init__(self,
-                 pos_iou_thr,
-                 neg_iou_thr,
-                 min_pos_iou=.0,
-                 gt_max_assign_all=True,
-                 ignore_iof_thr=-1,
-                 ignore_wrt_candidates=True,
-                 match_low_quality=True,
-                 gpu_assign_thr=-1,
-                 iou_calculator=dict(type='BboxOverlaps2D')):
-        self.pos_iou_thr = pos_iou_thr
-        self.neg_iou_thr = neg_iou_thr
-        self.min_pos_iou = min_pos_iou
-        self.gt_max_assign_all = gt_max_assign_all
-        self.ignore_iof_thr = ignore_iof_thr
-        self.ignore_wrt_candidates = ignore_wrt_candidates
-        self.gpu_assign_thr = gpu_assign_thr
-        self.match_low_quality = match_low_quality
-        self.iou_calculator = build_iou_calculator(iou_calculator)
-
-    def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
-        """Assign gt to bboxes.
-
-        This method assign a gt bbox to every bbox (proposal/anchor), each bbox
-        will be assigned with -1, or a semi-positive number. -1 means negative
-        sample, semi-positive number is the index (0-based) of assigned gt.
-        The assignment is done in following steps, the order matters.
-
-        1. assign every bbox to the background
-        2. assign proposals whose iou with all gts < neg_iou_thr to 0
-        3. for each bbox, if the iou with its nearest gt >= pos_iou_thr,
-           assign it to that bbox
-        4. for each gt bbox, assign its nearest proposals (may be more than
-           one) to itself
-
-        Args:
-            bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
-            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
-            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
-                labelled as `ignored`, e.g., crowd boxes in COCO.
-            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
-
-        Returns:
-            :obj:`AssignResult`: The assign result.
-
-        Example:
-            >>> self = MaxIoUAssigner(0.5, 0.5)
-            >>> bboxes = torch.Tensor([[0, 0, 10, 10], [10, 10, 20, 20]])
-            >>> gt_bboxes = torch.Tensor([[0, 0, 10, 9]])
-            >>> assign_result = self.assign(bboxes, gt_bboxes)
-            >>> expected_gt_inds = torch.LongTensor([1, 0])
-            >>> assert torch.all(assign_result.gt_inds == expected_gt_inds)
-        """
-        assign_on_cpu = True if (self.gpu_assign_thr > 0) and (
-            gt_bboxes.shape[0] > self.gpu_assign_thr) else False
-        # compute overlap and assign gt on CPU when number of GT is large
-        if assign_on_cpu:
-            device = bboxes.device
-            bboxes = bboxes.cpu()
-            gt_bboxes = gt_bboxes.cpu()
-            if gt_bboxes_ignore is not None:
-                gt_bboxes_ignore = gt_bboxes_ignore.cpu()
-            if gt_labels is not None:
-                gt_labels = gt_labels.cpu()
-
-        overlaps = self.iou_calculator(gt_bboxes, bboxes)
-
-        if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None
-                and gt_bboxes_ignore.numel() > 0 and bboxes.numel() > 0):
-            if self.ignore_wrt_candidates:
-                ignore_overlaps = self.iou_calculator(
-                    bboxes, gt_bboxes_ignore, mode='iof')
-                ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
-            else:
-                ignore_overlaps = self.iou_calculator(
-                    gt_bboxes_ignore, bboxes, mode='iof')
-                ignore_max_overlaps, _ = ignore_overlaps.max(dim=0)
-            overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1
-
-        assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
-        if assign_on_cpu:
-            assign_result.gt_inds = assign_result.gt_inds.to(device)
-            assign_result.max_overlaps = assign_result.max_overlaps.to(device)
-            if assign_result.labels is not None:
-                assign_result.labels = assign_result.labels.to(device)
-        return assign_result
-
-    def assign_wrt_overlaps(self, overlaps, gt_labels=None):
-        """Assign w.r.t. the overlaps of bboxes with gts.
-
-        Args:
-            overlaps (Tensor): Overlaps between k gt_bboxes and n bboxes,
-                shape(k, n).
-            gt_labels (Tensor, optional): Labels of k gt_bboxes, shape (k, ).
-
-        Returns:
-            :obj:`AssignResult`: The assign result.
-        """
-        num_gts, num_bboxes = overlaps.size(0), overlaps.size(1)
-
-        # 1. assign -1 by default
-        assigned_gt_inds = overlaps.new_full((num_bboxes, ),
-                                             -1,
-                                             dtype=torch.long)
-
-        if num_gts == 0 or num_bboxes == 0:
-            # No ground truth or boxes, return empty assignment
-            max_overlaps = overlaps.new_zeros((num_bboxes, ))
-            if num_gts == 0:
-                # No truth, assign everything to background
-                assigned_gt_inds[:] = 0
-            if gt_labels is None:
-                assigned_labels = None
-            else:
-                assigned_labels = overlaps.new_full((num_bboxes, ),
-                                                    -1,
-                                                    dtype=torch.long)
-            return AssignResult(
-                num_gts,
-                assigned_gt_inds,
-                max_overlaps,
-                labels=assigned_labels)
-
-        # for each anchor, which gt best overlaps with it
-        # for each anchor, the max iou of all gts
-        max_overlaps, argmax_overlaps = overlaps.max(dim=0)
-        # for each gt, which anchor best overlaps with it
-        # for each gt, the max iou of all proposals
-        gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1)
-
-        # 2. assign negative: below
-        # the negative inds are set to be 0
-        if isinstance(self.neg_iou_thr, float):
-            assigned_gt_inds[(max_overlaps >= 0)
-                             & (max_overlaps < self.neg_iou_thr)] = 0
-        elif isinstance(self.neg_iou_thr, tuple):
-            assert len(self.neg_iou_thr) == 2
-            assigned_gt_inds[(max_overlaps >= self.neg_iou_thr[0])
-                             & (max_overlaps < self.neg_iou_thr[1])] = 0
-
-        # 3. assign positive: above positive IoU threshold
-        pos_inds = max_overlaps >= self.pos_iou_thr
-        assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1
-
-        if self.match_low_quality:
-            # Low-quality matching will overwrite the assigned_gt_inds assigned
-            # in Step 3. Thus, the assigned gt might not be the best one for
-            # prediction.
-            # For example, if bbox A has 0.9 and 0.8 iou with GT bbox 1 & 2,
-            # bbox 1 will be assigned as the best target for bbox A in step 3.
-            # However, if GT bbox 2's gt_argmax_overlaps = A, bbox A's
-            # assigned_gt_inds will be overwritten to be bbox B.
-            # This might be the reason that it is not used in ROI Heads.
-            for i in range(num_gts):
-                if gt_max_overlaps[i] >= self.min_pos_iou:
-                    if self.gt_max_assign_all:
-                        max_iou_inds = overlaps[i, :] == gt_max_overlaps[i]
-                        assigned_gt_inds[max_iou_inds] = i + 1
-                    else:
-                        assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1
-
-        if gt_labels is not None:
-            assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
-            pos_inds = torch.nonzero(
-                assigned_gt_inds > 0, as_tuple=False).squeeze()
-            if pos_inds.numel() > 0:
-                assigned_labels[pos_inds] = gt_labels[
-                    assigned_gt_inds[pos_inds] - 1]
-        else:
-            assigned_labels = None
-
-        return AssignResult(
-            num_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels)
diff --git a/spaces/CarlDennis/HYTTS/README.md b/spaces/CarlDennis/HYTTS/README.md
deleted file mode 100644
index 81ad14c82083062751fa05dd8959f38d87955b72..0000000000000000000000000000000000000000
--- a/spaces/CarlDennis/HYTTS/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: HYTTS
-emoji: 👁
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: cc-by-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/CjangCjengh/Sanskrit-TTS/README.md b/spaces/CjangCjengh/Sanskrit-TTS/README.md
deleted file mode 100644
index f7bc0e8e6fb3e84c76812e9332baf5fbf9fcc205..0000000000000000000000000000000000000000
--- a/spaces/CjangCjengh/Sanskrit-TTS/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sanskrit TTS
-emoji: 👀
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/midas/midas/midas_net.py b/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/midas/midas/midas_net.py
deleted file mode 100644
index 8a954977800b0a0f48807e80fa63041910e33c1f..0000000000000000000000000000000000000000
--- a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/midas/midas/midas_net.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
-
-
-class MidasNet(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=256, non_negative=True):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet, self).__init__()
-
-        use_pretrained = False if path is None else True
-
-        self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
-
-        self.scratch.refinenet4 = FeatureFusionBlock(features)
-        self.scratch.refinenet3 = FeatureFusionBlock(features)
-        self.scratch.refinenet2 = FeatureFusionBlock(features)
-        self.scratch.refinenet1 = FeatureFusionBlock(features)
-
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(True),
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-        )
-
-        if path:
-            self.load(path)
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)
diff --git a/spaces/DaFujaTyping/hf-Chat-ui/src/lib/server/database.ts b/spaces/DaFujaTyping/hf-Chat-ui/src/lib/server/database.ts
deleted file mode 100644
index 76a5461af74cecbf7ac012cfb6ba7c53bfdfa213..0000000000000000000000000000000000000000
--- a/spaces/DaFujaTyping/hf-Chat-ui/src/lib/server/database.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-import { MONGODB_URL, MONGODB_DB_NAME } from "$env/static/private";
-import { MongoClient } from "mongodb";
-import type { Conversation } from "$lib/types/Conversation";
-import type { SharedConversation } from "$lib/types/SharedConversation";
-import type { AbortedGeneration } from "$lib/types/AbortedGeneration";
-import type { Settings } from "$lib/types/Settings";
-
-const client = new MongoClient(MONGODB_URL, {
-	// directConnection: true
-});
-
-export const connectPromise = client.connect().catch(console.error);
-
-const db = client.db(MONGODB_DB_NAME);
-
-const conversations = db.collection<Conversation>("conversations");
-const sharedConversations = db.collection<SharedConversation>("sharedConversations");
-const abortedGenerations = db.collection<AbortedGeneration>("abortedGenerations");
-const settings = db.collection<Settings>("settings");
-
-export { client, db };
-export const collections = { conversations, sharedConversations, abortedGenerations, settings };
-
-client.on("open", () => {
-	conversations.createIndex({ sessionId: 1, updatedAt: -1 });
-	abortedGenerations.createIndex({ updatedAt: 1 }, { expireAfterSeconds: 30 });
-	abortedGenerations.createIndex({ conversationId: 1 }, { unique: true });
-	sharedConversations.createIndex({ hash: 1 }, { unique: true });
-	// Sparse so that we can have settings on userId later
-	settings.createIndex({ sessionId: 1 }, { unique: true, sparse: true });
-});
diff --git a/spaces/DaFujaTyping/hf-Chat-ui/src/lib/utils/sha256.ts b/spaces/DaFujaTyping/hf-Chat-ui/src/lib/utils/sha256.ts
deleted file mode 100644
index 43059b518fc5a4da6ed08ab36aeb6c289007f6aa..0000000000000000000000000000000000000000
--- a/spaces/DaFujaTyping/hf-Chat-ui/src/lib/utils/sha256.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-export async function sha256(input: string): Promise<string> {
-	const utf8 = new TextEncoder().encode(input);
-	const hashBuffer = await crypto.subtle.digest("SHA-256", utf8);
-	const hashArray = Array.from(new Uint8Array(hashBuffer));
-	const hashHex = hashArray.map((bytes) => bytes.toString(16).padStart(2, "0")).join("");
-	return hashHex;
-}
diff --git a/spaces/DaleChen/AutoGPT/autogpt/token_counter.py b/spaces/DaleChen/AutoGPT/autogpt/token_counter.py
deleted file mode 100644
index 338fe6be4d47a679f2bf0815685edeb3dce66936..0000000000000000000000000000000000000000
--- a/spaces/DaleChen/AutoGPT/autogpt/token_counter.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Functions for counting the number of tokens in a message or string."""
-from __future__ import annotations
-
-import tiktoken
-
-from autogpt.logs import logger
-
-
-def count_message_tokens(
-    messages: list[dict[str, str]], model: str = "gpt-3.5-turbo-0301"
-) -> int:
-    """
-    Returns the number of tokens used by a list of messages.
-
-    Args:
-        messages (list): A list of messages, each of which is a dictionary
-            containing the role and content of the message.
-        model (str): The name of the model to use for tokenization.
-            Defaults to "gpt-3.5-turbo-0301".
-
-    Returns:
-        int: The number of tokens used by the list of messages.
-    """
-    try:
-        encoding = tiktoken.encoding_for_model(model)
-    except KeyError:
-        logger.warn("Warning: model not found. Using cl100k_base encoding.")
-        encoding = tiktoken.get_encoding("cl100k_base")
-    if model == "gpt-3.5-turbo":
-        # !Note: gpt-3.5-turbo may change over time.
-        # Returning num tokens assuming gpt-3.5-turbo-0301.")
-        return count_message_tokens(messages, model="gpt-3.5-turbo-0301")
-    elif model == "gpt-4":
-        # !Note: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
-        return count_message_tokens(messages, model="gpt-4-0314")
-    elif model == "gpt-3.5-turbo-0301":
-        tokens_per_message = (
-            4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
-        )
-        tokens_per_name = -1  # if there's a name, the role is omitted
-    elif model == "gpt-4-0314":
-        tokens_per_message = 3
-        tokens_per_name = 1
-    else:
-        raise NotImplementedError(
-            f"num_tokens_from_messages() is not implemented for model {model}.\n"
-            " See https://github.com/openai/openai-python/blob/main/chatml.md for"
-            " information on how messages are converted to tokens."
-        )
-    num_tokens = 0
-    for message in messages:
-        num_tokens += tokens_per_message
-        for key, value in message.items():
-            num_tokens += len(encoding.encode(value))
-            if key == "name":
-                num_tokens += tokens_per_name
-    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
-    return num_tokens
-
-
-def count_string_tokens(string: str, model_name: str) -> int:
-    """
-    Returns the number of tokens in a text string.
-
-    Args:
-        string (str): The text string.
-        model_name (str): The name of the encoding to use. (e.g., "gpt-3.5-turbo")
-
-    Returns:
-        int: The number of tokens in the text string.
-    """
-    encoding = tiktoken.encoding_for_model(model_name)
-    return len(encoding.encode(string))
diff --git a/spaces/DaleChen/AutoGPT/tests/test_config.py b/spaces/DaleChen/AutoGPT/tests/test_config.py
deleted file mode 100644
index b472a24c78edd1f931a76c68e08ed544bbe61d98..0000000000000000000000000000000000000000
--- a/spaces/DaleChen/AutoGPT/tests/test_config.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from unittest import TestCase
-
-from autogpt.config import Config
-
-
-class TestConfig(TestCase):
-    """
-    Test cases for the Config class, which handles the configuration settings
-    for the AI and ensures it behaves as a singleton.
-    """
-
-    def setUp(self):
-        """
-        Set up the test environment by creating an instance of the Config class.
-        """
-        self.config = Config()
-
-    def test_singleton(self):
-        """
-        Test if the Config class behaves as a singleton by ensuring that two instances are the same.
-        """
-        config2 = Config()
-        self.assertIs(self.config, config2)
-
-    def test_initial_values(self):
-        """
-        Test if the initial values of the Config class attributes are set correctly.
-        """
-        self.assertFalse(self.config.debug_mode)
-        self.assertFalse(self.config.continuous_mode)
-        self.assertFalse(self.config.speak_mode)
-        self.assertEqual(self.config.fast_llm_model, "gpt-3.5-turbo")
-        self.assertEqual(self.config.smart_llm_model, "gpt-4")
-        self.assertEqual(self.config.fast_token_limit, 4000)
-        self.assertEqual(self.config.smart_token_limit, 8000)
-
-    def test_set_continuous_mode(self):
-        """
-        Test if the set_continuous_mode() method updates the continuous_mode attribute.
-        """
-        self.config.set_continuous_mode(True)
-        self.assertTrue(self.config.continuous_mode)
-
-    def test_set_speak_mode(self):
-        """
-        Test if the set_speak_mode() method updates the speak_mode attribute.
-        """
-        self.config.set_speak_mode(True)
-        self.assertTrue(self.config.speak_mode)
-
-    def test_set_fast_llm_model(self):
-        """
-        Test if the set_fast_llm_model() method updates the fast_llm_model attribute.
-        """
-        self.config.set_fast_llm_model("gpt-3.5-turbo-test")
-        self.assertEqual(self.config.fast_llm_model, "gpt-3.5-turbo-test")
-
-    def test_set_smart_llm_model(self):
-        """
-        Test if the set_smart_llm_model() method updates the smart_llm_model attribute.
-        """
-        self.config.set_smart_llm_model("gpt-4-test")
-        self.assertEqual(self.config.smart_llm_model, "gpt-4-test")
-
-    def test_set_fast_token_limit(self):
-        """
-        Test if the set_fast_token_limit() method updates the fast_token_limit attribute.
-        """
-        self.config.set_fast_token_limit(5000)
-        self.assertEqual(self.config.fast_token_limit, 5000)
-
-    def test_set_smart_token_limit(self):
-        """
-        Test if the set_smart_token_limit() method updates the smart_token_limit attribute.
-        """
-        self.config.set_smart_token_limit(9000)
-        self.assertEqual(self.config.smart_token_limit, 9000)
-
-    def test_set_debug_mode(self):
-        """
-        Test if the set_debug_mode() method updates the debug_mode attribute.
-        """
-        self.config.set_debug_mode(True)
-        self.assertTrue(self.config.debug_mode)
diff --git a/spaces/DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser/denoiser/distrib.py b/spaces/DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser/denoiser/distrib.py
deleted file mode 100644
index a0743fea86faf09ade822e7647fc558ba7d428f6..0000000000000000000000000000000000000000
--- a/spaces/DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser/denoiser/distrib.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# author: adefossez
-
-import logging
-import os
-
-import torch
-from torch.utils.data.distributed import DistributedSampler
-from torch.utils.data import DataLoader, Subset
-from torch.nn.parallel.distributed import DistributedDataParallel
-
-logger = logging.getLogger(__name__)
-rank = 0
-world_size = 1
-
-
-def init(args):
-    """init.
-
-    Initialize DDP using the given rendezvous file.
-    """
-    global rank, world_size
-    if args.ddp:
-        assert args.rank is not None and args.world_size is not None
-        rank = args.rank
-        world_size = args.world_size
-    if world_size == 1:
-        return
-    torch.cuda.set_device(rank)
-    torch.distributed.init_process_group(
-        backend=args.ddp_backend,
-        init_method='file://' + os.path.abspath(args.rendezvous_file),
-        world_size=world_size,
-        rank=rank)
-    logger.debug("Distributed rendezvous went well, rank %d/%d", rank, world_size)
-
-
-def average(metrics, count=1.):
-    """average.
-
-    Average all the relevant metrices across processes
-    `metrics`should be a 1D float32 fector. Returns the average of `metrics`
-    over all hosts. You can use `count` to control the weight of each worker.
-    """
-    if world_size == 1:
-        return metrics
-    tensor = torch.tensor(list(metrics) + [1], device='cuda', dtype=torch.float32)
-    tensor *= count
-    torch.distributed.all_reduce(tensor, op=torch.distributed.ReduceOp.SUM)
-    return (tensor[:-1] / tensor[-1]).cpu().numpy().tolist()
-
-
-def wrap(model):
-    """wrap.
-
-    Wrap a model with DDP if distributed training is enabled.
-    """
-    if world_size == 1:
-        return model
-    else:
-        return DistributedDataParallel(
-            model,
-            device_ids=[torch.cuda.current_device()],
-            output_device=torch.cuda.current_device())
-
-
-def barrier():
-    if world_size > 1:
-        torch.distributed.barrier()
-
-
-def loader(dataset, *args, shuffle=False, klass=DataLoader, **kwargs):
-    """loader.
-
-    Create a dataloader properly in case of distributed training.
-    If a gradient is going to be computed you must set `shuffle=True`.
-
-    :param dataset: the dataset to be parallelized
-    :param args: relevant args for the loader
-    :param shuffle: shuffle examples
-    :param klass: loader class
-    :param kwargs: relevant args
-    """
-
-    if world_size == 1:
-        return klass(dataset, *args, shuffle=shuffle, **kwargs)
-
-    if shuffle:
-        # train means we will compute backward, we use DistributedSampler
-        sampler = DistributedSampler(dataset)
-        # We ignore shuffle, DistributedSampler already shuffles
-        return klass(dataset, *args, **kwargs, sampler=sampler)
-    else:
-        # We make a manual shard, as DistributedSampler otherwise replicate some examples
-        dataset = Subset(dataset, list(range(rank, len(dataset), world_size)))
-        return klass(dataset, *args, shuffle=shuffle)
diff --git a/spaces/Dinoking/Guccio-AI-Designer/netdissect/segmodel/resnet.py b/spaces/Dinoking/Guccio-AI-Designer/netdissect/segmodel/resnet.py
deleted file mode 100644
index ea5fdf82fafa3058c5f00074d55fbb1e584d5865..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/netdissect/segmodel/resnet.py
+++ /dev/null
@@ -1,235 +0,0 @@
-import os
-import sys
-import torch
-import torch.nn as nn
-import math
-try:
-    from lib.nn import SynchronizedBatchNorm2d
-except ImportError:
-    from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d
-
-try:
-    from urllib import urlretrieve
-except ImportError:
-    from urllib.request import urlretrieve
-
-
-__all__ = ['ResNet', 'resnet50', 'resnet101'] # resnet101 is coming soon!
-
-
-model_urls = {
-    'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth',
-    'resnet101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet101-imagenet.pth'
-}
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    "3x3 convolution with padding"
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = SynchronizedBatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = SynchronizedBatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        self.bn1 = SynchronizedBatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
-                               padding=1, bias=False)
-        self.bn2 = SynchronizedBatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
-        self.bn3 = SynchronizedBatchNorm2d(planes * 4)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class ResNet(nn.Module):
-
-    def __init__(self, block, layers, num_classes=1000):
-        self.inplanes = 128
-        super(ResNet, self).__init__()
-        self.conv1 = conv3x3(3, 64, stride=2)
-        self.bn1 = SynchronizedBatchNorm2d(64)
-        self.relu1 = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(64, 64)
-        self.bn2 = SynchronizedBatchNorm2d(64)
-        self.relu2 = nn.ReLU(inplace=True)
-        self.conv3 = conv3x3(64, 128)
-        self.bn3 = SynchronizedBatchNorm2d(128)
-        self.relu3 = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-        self.avgpool = nn.AvgPool2d(7, stride=1)
-        self.fc = nn.Linear(512 * block.expansion, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-                m.weight.data.normal_(0, math.sqrt(2. / n))
-            elif isinstance(m, SynchronizedBatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-
-    def _make_layer(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=False),
-                SynchronizedBatchNorm2d(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.relu1(self.bn1(self.conv1(x)))
-        x = self.relu2(self.bn2(self.conv2(x)))
-        x = self.relu3(self.bn3(self.conv3(x)))
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        x = self.avgpool(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc(x)
-
-        return x
-
-'''
-def resnet18(pretrained=False, **kwargs):
-    """Constructs a ResNet-18 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on Places
-    """
-    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnet18']))
-    return model
-
-
-def resnet34(pretrained=False, **kwargs):
-    """Constructs a ResNet-34 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on Places
-    """
-    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnet34']))
-    return model
-'''
-
-def resnet50(pretrained=False, **kwargs):
-    """Constructs a ResNet-50 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on Places
-    """
-    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnet50']), strict=False)
-    return model
-
-
-def resnet101(pretrained=False, **kwargs):
-    """Constructs a ResNet-101 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on Places
-    """
-    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnet101']), strict=False)
-    return model
-
-# def resnet152(pretrained=False, **kwargs):
-#     """Constructs a ResNet-152 model.
-#
-#     Args:
-#         pretrained (bool): If True, returns a model pre-trained on Places
-#     """
-#     model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
-#     if pretrained:
-#         model.load_state_dict(load_url(model_urls['resnet152']))
-#     return model
-
-def load_url(url, model_dir='./pretrained', map_location=None):
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    filename = url.split('/')[-1]
-    cached_file = os.path.join(model_dir, filename)
-    if not os.path.exists(cached_file):
-        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
-        urlretrieve(url, cached_file)
-    return torch.load(cached_file, map_location=map_location)
diff --git a/spaces/DragGan/DragGan/stylegan_human/training/networks_stylegan3.py b/spaces/DragGan/DragGan/stylegan_human/training/networks_stylegan3.py
deleted file mode 100644
index 70d0ebce100b504b39791dbf3e1dfea4c9473f2b..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/stylegan_human/training/networks_stylegan3.py
+++ /dev/null
@@ -1,538 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Generator architecture from the paper
-"Alias-Free Generative Adversarial Networks"."""
-
-import numpy as np
-import scipy.signal
-import scipy.optimize
-import torch
-import torch.nn.functional as F
-from torch_utils import misc
-from torch_utils import persistence
-from torch_utils.ops import conv2d_gradfix
-from torch_utils.ops import filtered_lrelu
-from torch_utils.ops import bias_act
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def modulated_conv2d(
-    x,                  # Input tensor: [batch_size, in_channels, in_height, in_width]
-    w,                  # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width]
-    s,                  # Style tensor: [batch_size, in_channels]
-    demodulate  = True, # Apply weight demodulation?
-    padding     = 0,    # Padding: int or [padH, padW]
-    input_gain  = None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels]
-):
-    with misc.suppress_tracer_warnings(): # this value will be treated as a constant
-        batch_size = int(x.shape[0])
-    out_channels, in_channels, kh, kw = w.shape
-    misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk]
-    misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
-    misc.assert_shape(s, [batch_size, in_channels]) # [NI]
-
-    # Pre-normalize inputs.
-    if demodulate:
-        w = w * w.square().mean([1,2,3], keepdim=True).rsqrt()
-        s = s * s.square().mean().rsqrt()
-
-    # Modulate weights.
-    w = w.unsqueeze(0) # [NOIkk]
-    w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]
-
-    # Demodulate weights.
-    if demodulate:
-        dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO]
-        w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk]
-
-    # Apply input scaling.
-    if input_gain is not None:
-        input_gain = input_gain.expand(batch_size, in_channels) # [NI]
-        w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]
-
-    # Execute as one fused op using grouped convolution.
-    x = x.reshape(1, -1, *x.shape[2:])
-    w = w.reshape(-1, in_channels, kh, kw)
-    x = conv2d_gradfix.conv2d(input=x, weight=w.to(x.dtype), padding=padding, groups=batch_size)
-    x = x.reshape(batch_size, -1, *x.shape[2:])
-    return x
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class FullyConnectedLayer(torch.nn.Module):
-    def __init__(self,
-        in_features,                # Number of input features.
-        out_features,               # Number of output features.
-        activation      = 'linear', # Activation function: 'relu', 'lrelu', etc.
-        bias            = True,     # Apply additive bias before the activation function?
-        lr_multiplier   = 1,        # Learning rate multiplier.
-        weight_init     = 1,        # Initial standard deviation of the weight tensor.
-        bias_init       = 0,        # Initial value of the additive bias.
-    ):
-        super().__init__()
-        self.in_features = in_features
-        self.out_features = out_features
-        self.activation = activation
-        self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) * (weight_init / lr_multiplier))
-        bias_init = np.broadcast_to(np.asarray(bias_init, dtype=np.float32), [out_features])
-        self.bias = torch.nn.Parameter(torch.from_numpy(bias_init / lr_multiplier)) if bias else None
-        self.weight_gain = lr_multiplier / np.sqrt(in_features)
-        self.bias_gain = lr_multiplier
-
-    def forward(self, x):
-        w = self.weight.to(x.dtype) * self.weight_gain
-        b = self.bias
-        if b is not None:
-            b = b.to(x.dtype)
-            if self.bias_gain != 1:
-                b = b * self.bias_gain
-        if self.activation == 'linear' and b is not None:
-            x = torch.addmm(b.unsqueeze(0), x, w.t())
-        else:
-            x = x.matmul(w.t())
-            x = bias_act.bias_act(x, b, act=self.activation)
-        return x
-
-    def extra_repr(self):
-        return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class MappingNetwork(torch.nn.Module):
-    def __init__(self,
-        z_dim,                      # Input latent (Z) dimensionality.
-        c_dim,                      # Conditioning label (C) dimensionality, 0 = no labels.
-        w_dim,                      # Intermediate latent (W) dimensionality.
-        num_ws,                     # Number of intermediate latents to output.
-        num_layers      = 2,        # Number of mapping layers.
-        lr_multiplier   = 0.01,     # Learning rate multiplier for the mapping layers.
-        w_avg_beta      = 0.998,    # Decay for tracking the moving average of W during training.
-    ):
-        super().__init__()
-        self.z_dim = z_dim
-        self.c_dim = c_dim
-        self.w_dim = w_dim
-        self.num_ws = num_ws
-        self.num_layers = num_layers
-        self.w_avg_beta = w_avg_beta
-
-        # Construct layers.
-        self.embed = FullyConnectedLayer(self.c_dim, self.w_dim) if self.c_dim > 0 else None
-        features = [self.z_dim + (self.w_dim if self.c_dim > 0 else 0)] + [self.w_dim] * self.num_layers
-        for idx, in_features, out_features in zip(range(num_layers), features[:-1], features[1:]):
-            layer = FullyConnectedLayer(in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier)
-            setattr(self, f'fc{idx}', layer)
-        self.register_buffer('w_avg', torch.zeros([w_dim]))
-
-    def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
-        misc.assert_shape(z, [None, self.z_dim])
-        if truncation_cutoff is None:
-            truncation_cutoff = self.num_ws
-
-        # Embed, normalize, and concatenate inputs.
-        x = z.to(torch.float32)
-        x = x * (x.square().mean(1, keepdim=True) + 1e-8).rsqrt()
-        if self.c_dim > 0:
-            misc.assert_shape(c, [None, self.c_dim])
-            y = self.embed(c.to(torch.float32))
-            y = y * (y.square().mean(1, keepdim=True) + 1e-8).rsqrt()
-            x = torch.cat([x, y], dim=1) if x is not None else y
-
-        # Execute layers.
-        for idx in range(self.num_layers):
-            x = getattr(self, f'fc{idx}')(x)
-
-        # Update moving average of W.
-        if update_emas:
-            self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
-
-        # Broadcast and apply truncation.
-        x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
-        if truncation_psi != 1:
-            x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi)
-        return x
-
-    def extra_repr(self):
-        return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class SynthesisInput(torch.nn.Module):
-    def __init__(self,
-        w_dim,          # Intermediate latent (W) dimensionality.
-        channels,       # Number of output channels.
-        size,           # Output spatial size: int or [width, height].
-        sampling_rate,  # Output sampling rate.
-        bandwidth,      # Output bandwidth.
-    ):
-        super().__init__()
-        self.w_dim = w_dim
-        self.channels = channels
-        self.size = np.broadcast_to(np.asarray(size), [2])
-        self.sampling_rate = sampling_rate
-        self.bandwidth = bandwidth
-
-        # Draw random frequencies from uniform 2D disc.
-        freqs = torch.randn([self.channels, 2])
-        radii = freqs.square().sum(dim=1, keepdim=True).sqrt()
-        freqs /= radii * radii.square().exp().pow(0.25)
-        freqs *= bandwidth
-        phases = torch.rand([self.channels]) - 0.5
-
-        # Setup parameters and buffers.
-        self.weight = torch.nn.Parameter(torch.randn([self.channels, self.channels]))
-        self.affine = FullyConnectedLayer(w_dim, 4, weight_init=0, bias_init=[1,0,0,0])
-        self.register_buffer('transform', torch.eye(3, 3)) # User-specified inverse transform wrt. resulting image.
-        self.register_buffer('freqs', freqs)
-        self.register_buffer('phases', phases)
-
-    def forward(self, w):
-        # Introduce batch dimension.
-        transforms = self.transform.unsqueeze(0) # [batch, row, col]
-        freqs = self.freqs.unsqueeze(0) # [batch, channel, xy]
-        phases = self.phases.unsqueeze(0) # [batch, channel]
-
-        # Apply learned transformation.
-        t = self.affine(w) # t = (r_c, r_s, t_x, t_y)
-        t = t / t[:, :2].norm(dim=1, keepdim=True) # t' = (r'_c, r'_s, t'_x, t'_y)
-        m_r = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse rotation wrt. resulting image.
-        m_r[:, 0, 0] = t[:, 0]  # r'_c
-        m_r[:, 0, 1] = -t[:, 1] # r'_s
-        m_r[:, 1, 0] = t[:, 1]  # r'_s
-        m_r[:, 1, 1] = t[:, 0]  # r'_c
-        m_t = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse translation wrt. resulting image.
-        m_t[:, 0, 2] = -t[:, 2] # t'_x
-        m_t[:, 1, 2] = -t[:, 3] # t'_y
-        transforms = m_r @ m_t @ transforms # First rotate resulting image, then translate, and finally apply user-specified transform.
-
-        # Transform frequencies.
-        phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2)
-        freqs = freqs @ transforms[:, :2, :2]
-
-        # Dampen out-of-band frequencies that may occur due to the user-specified transform.
-        amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1)
-
-        # Construct sampling grid.
-        theta = torch.eye(2, 3, device=w.device)
-        theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate
-        theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate
-        grids = torch.nn.functional.affine_grid(theta.unsqueeze(0), [1, 1, self.size[1], self.size[0]], align_corners=False)
-
-        # Compute Fourier features.
-        x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) # [batch, height, width, channel]
-        x = x + phases.unsqueeze(1).unsqueeze(2)
-        x = torch.sin(x * (np.pi * 2))
-        x = x * amplitudes.unsqueeze(1).unsqueeze(2)
-
-        # Apply trainable mapping.
-        weight = self.weight / np.sqrt(self.channels)
-        x = x @ weight.t()
-
-        # Ensure correct shape.
-        x = x.permute(0, 3, 1, 2) # [batch, channel, height, width]
-        misc.assert_shape(x, [w.shape[0], self.channels, int(self.size[1]), int(self.size[0])])
-        return x
-
-    def extra_repr(self):
-        return '\n'.join([
-            f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},',
-            f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}'])
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class SynthesisLayer(torch.nn.Module):
-    def __init__(self,
-        w_dim,                          # Intermediate latent (W) dimensionality.
-        is_torgb,                       # Is this the final ToRGB layer?
-        is_critically_sampled,          # Does this layer use critical sampling?
-        use_fp16,                       # Does this layer use FP16?
-
-        # Input & output specifications.
-        in_channels,                    # Number of input channels.
-        out_channels,                   # Number of output channels.
-        in_size,                        # Input spatial size: int or [width, height].
-        out_size,                       # Output spatial size: int or [width, height].
-        in_sampling_rate,               # Input sampling rate (s).
-        out_sampling_rate,              # Output sampling rate (s).
-        in_cutoff,                      # Input cutoff frequency (f_c).
-        out_cutoff,                     # Output cutoff frequency (f_c).
-        in_half_width,                  # Input transition band half-width (f_h).
-        out_half_width,                 # Output Transition band half-width (f_h).
-
-        # Hyperparameters.
-        conv_kernel         = 3,        # Convolution kernel size. Ignored for final the ToRGB layer.
-        filter_size         = 6,        # Low-pass filter size relative to the lower resolution when up/downsampling.
-        lrelu_upsampling    = 2,        # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer.
-        use_radial_filters  = False,    # Use radially symmetric downsampling filter? Ignored for critically sampled layers.
-        conv_clamp          = 256,      # Clamp the output to [-X, +X], None = disable clamping.
-        magnitude_ema_beta  = 0.999,    # Decay rate for the moving average of input magnitudes.
-    ):
-        super().__init__()
-        self.w_dim = w_dim
-        self.is_torgb = is_torgb
-        self.is_critically_sampled = is_critically_sampled
-        self.use_fp16 = use_fp16
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.in_size = np.broadcast_to(np.asarray(in_size), [2])
-        self.out_size = np.broadcast_to(np.asarray(out_size), [2])
-        self.in_sampling_rate = in_sampling_rate
-        self.out_sampling_rate = out_sampling_rate
-        self.tmp_sampling_rate = max(in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling)
-        self.in_cutoff = in_cutoff
-        self.out_cutoff = out_cutoff
-        self.in_half_width = in_half_width
-        self.out_half_width = out_half_width
-        self.conv_kernel = 1 if is_torgb else conv_kernel
-        self.conv_clamp = conv_clamp
-        self.magnitude_ema_beta = magnitude_ema_beta
-
-        # Setup parameters and buffers.
-        self.affine = FullyConnectedLayer(self.w_dim, self.in_channels, bias_init=1)
-        self.weight = torch.nn.Parameter(torch.randn([self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel]))
-        self.bias = torch.nn.Parameter(torch.zeros([self.out_channels]))
-        self.register_buffer('magnitude_ema', torch.ones([]))
-
-        # Design upsampling filter.
-        self.up_factor = int(np.rint(self.tmp_sampling_rate / self.in_sampling_rate))
-        assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate
-        self.up_taps = filter_size * self.up_factor if self.up_factor > 1 and not self.is_torgb else 1
-        self.register_buffer('up_filter', self.design_lowpass_filter(
-            numtaps=self.up_taps, cutoff=self.in_cutoff, width=self.in_half_width*2, fs=self.tmp_sampling_rate))
-
-        # Design downsampling filter.
-        self.down_factor = int(np.rint(self.tmp_sampling_rate / self.out_sampling_rate))
-        assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate
-        self.down_taps = filter_size * self.down_factor if self.down_factor > 1 and not self.is_torgb else 1
-        self.down_radial = use_radial_filters and not self.is_critically_sampled
-        self.register_buffer('down_filter', self.design_lowpass_filter(
-            numtaps=self.down_taps, cutoff=self.out_cutoff, width=self.out_half_width*2, fs=self.tmp_sampling_rate, radial=self.down_radial))
-
-        # Compute padding.
-        pad_total = (self.out_size - 1) * self.down_factor + 1 # Desired output size before downsampling.
-        pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor # Input size after upsampling.
-        pad_total += self.up_taps + self.down_taps - 2 # Size reduction caused by the filters.
-        pad_lo = (pad_total + self.up_factor) // 2 # Shift sample locations according to the symmetric interpretation (Appendix C.3).
-        pad_hi = pad_total - pad_lo
-        self.padding = [int(pad_lo[0]), int(pad_hi[0]), int(pad_lo[1]), int(pad_hi[1])]
-
-    def forward(self, x, w, noise_mode='random', force_fp32=False, update_emas=False):
-        assert noise_mode in ['random', 'const', 'none'] # unused
-        misc.assert_shape(x, [None, self.in_channels, int(self.in_size[1]), int(self.in_size[0])])
-        misc.assert_shape(w, [x.shape[0], self.w_dim])
-
-        # Track input magnitude.
-        if update_emas:
-            with torch.autograd.profiler.record_function('update_magnitude_ema'):
-                magnitude_cur = x.detach().to(torch.float32).square().mean()
-                self.magnitude_ema.copy_(magnitude_cur.lerp(self.magnitude_ema, self.magnitude_ema_beta))
-        input_gain = self.magnitude_ema.rsqrt()
-
-        # Execute affine layer.
-        styles = self.affine(w)
-        if self.is_torgb:
-            weight_gain = 1 / np.sqrt(self.in_channels * (self.conv_kernel ** 2))
-            styles = styles * weight_gain
-
-        # Execute modulated conv2d.
-        dtype = torch.float16 if (self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32
-        x = modulated_conv2d(x=x.to(dtype), w=self.weight, s=styles,
-            padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain)
-
-        # Execute bias, filtered leaky ReLU, and clamping.
-        gain = 1 if self.is_torgb else np.sqrt(2)
-        slope = 1 if self.is_torgb else 0.2
-        x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype),
-            up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp)
-
-        # Ensure correct shape and dtype.
-        misc.assert_shape(x, [None, self.out_channels, int(self.out_size[1]), int(self.out_size[0])])
-        assert x.dtype == dtype
-        return x
-
-    @staticmethod
-    def design_lowpass_filter(numtaps, cutoff, width, fs, radial=False):
-        assert numtaps >= 1
-
-        # Identity filter.
-        if numtaps == 1:
-            return None
-
-        # Separable Kaiser low-pass filter.
-        if not radial:
-            f = scipy.signal.firwin(numtaps=numtaps, cutoff=cutoff, width=width, fs=fs)
-            return torch.as_tensor(f, dtype=torch.float32)
-
-        # Radially symmetric jinc-based filter.
-        x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs
-        r = np.hypot(*np.meshgrid(x, x))
-        f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r)
-        beta = scipy.signal.kaiser_beta(scipy.signal.kaiser_atten(numtaps, width / (fs / 2)))
-        w = np.kaiser(numtaps, beta)
-        f *= np.outer(w, w)
-        f /= np.sum(f)
-        return torch.as_tensor(f, dtype=torch.float32)
-
-    def extra_repr(self):
-        return '\n'.join([
-            f'w_dim={self.w_dim:d}, is_torgb={self.is_torgb},',
-            f'is_critically_sampled={self.is_critically_sampled}, use_fp16={self.use_fp16},',
-            f'in_sampling_rate={self.in_sampling_rate:g}, out_sampling_rate={self.out_sampling_rate:g},',
-            f'in_cutoff={self.in_cutoff:g}, out_cutoff={self.out_cutoff:g},',
-            f'in_half_width={self.in_half_width:g}, out_half_width={self.out_half_width:g},',
-            f'in_size={list(self.in_size)}, out_size={list(self.out_size)},',
-            f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}'])
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class SynthesisNetwork(torch.nn.Module):
-    def __init__(self,
-        w_dim,                          # Intermediate latent (W) dimensionality.
-        img_resolution,                 # Output image resolution.
-        img_channels,                   # Number of color channels.
-        channel_base        = 32768,    # Overall multiplier for the number of channels.
-        channel_max         = 512,      # Maximum number of channels in any layer.
-        num_layers          = 14,       # Total number of layers, excluding Fourier features and ToRGB.
-        num_critical        = 2,        # Number of critically sampled layers at the end.
-        first_cutoff        = 2,        # Cutoff frequency of the first layer (f_{c,0}).
-        first_stopband      = 2**2.1,   # Minimum stopband of the first layer (f_{t,0}).
-        last_stopband_rel   = 2**0.3,   # Minimum stopband of the last layer, expressed relative to the cutoff.
-        margin_size         = 10,       # Number of additional pixels outside the image.
-        output_scale        = 0.25,     # Scale factor for the output image.
-        num_fp16_res        = 4,        # Use FP16 for the N highest resolutions.
-        **layer_kwargs,                 # Arguments for SynthesisLayer.
-    ):
-        super().__init__()
-        self.w_dim = w_dim
-        self.num_ws = num_layers + 2
-        self.img_resolution = img_resolution
-        self.img_channels = img_channels
-        self.num_layers = num_layers
-        self.num_critical = num_critical
-        self.margin_size = margin_size
-        self.output_scale = output_scale
-        self.num_fp16_res = num_fp16_res
-
-        # Geometric progression of layer cutoffs and min. stopbands.
-        last_cutoff = self.img_resolution / 2 # f_{c,N}
-        last_stopband = last_cutoff * last_stopband_rel # f_{t,N}
-        exponents = np.minimum(np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1)
-        cutoffs = first_cutoff * (last_cutoff / first_cutoff) ** exponents # f_c[i]
-        stopbands = first_stopband * (last_stopband / first_stopband) ** exponents # f_t[i]
-
-        # Compute remaining layer parameters.
-        sampling_rates = np.exp2(np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i]
-        half_widths = np.maximum(stopbands, sampling_rates / 2) - cutoffs # f_h[i]
-        sizes = sampling_rates + self.margin_size * 2
-        sizes[-2:] = self.img_resolution
-        channels = np.rint(np.minimum((channel_base / 2) / cutoffs, channel_max))
-        channels[-1] = self.img_channels
-
-        # Construct layers.
-        self.input = SynthesisInput(
-            w_dim=self.w_dim, channels=int(channels[0]), size=int(sizes[0]),
-            sampling_rate=sampling_rates[0], bandwidth=cutoffs[0])
-        self.layer_names = []
-        for idx in range(self.num_layers + 1):
-            prev = max(idx - 1, 0)
-            is_torgb = (idx == self.num_layers)
-            is_critically_sampled = (idx >= self.num_layers - self.num_critical)
-            use_fp16 = (sampling_rates[idx] * (2 ** self.num_fp16_res) > self.img_resolution)
-            layer = SynthesisLayer(
-                w_dim=self.w_dim, is_torgb=is_torgb, is_critically_sampled=is_critically_sampled, use_fp16=use_fp16,
-                in_channels=int(channels[prev]), out_channels= int(channels[idx]),
-                in_size=int(sizes[prev]), out_size=int(sizes[idx]),
-                in_sampling_rate=int(sampling_rates[prev]), out_sampling_rate=int(sampling_rates[idx]),
-                in_cutoff=cutoffs[prev], out_cutoff=cutoffs[idx],
-                in_half_width=half_widths[prev], out_half_width=half_widths[idx],
-                **layer_kwargs)
-            name = f'L{idx}_{layer.out_size[0]}_{layer.out_channels}'
-            setattr(self, name, layer)
-            self.layer_names.append(name)
-
-    def forward(self, ws, **layer_kwargs):
-        misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
-        ws = ws.to(torch.float32).unbind(dim=1)
-
-        # Execute layers.
-        x = self.input(ws[0])
-        for name, w in zip(self.layer_names, ws[1:]):
-            x = getattr(self, name)(x, w, **layer_kwargs)
-        if self.output_scale != 1:
-            x = x * self.output_scale
-
-        # Ensure correct shape and dtype.
-        misc.assert_shape(x, [None, self.img_channels, self.img_resolution, self.img_resolution])
-        x = x.to(torch.float32)
-        return x
-
-    def extra_repr(self):
-        return '\n'.join([
-            f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
-            f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
-            f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},',
-            f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}'])
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class Generator(torch.nn.Module):
-    def __init__(self,
-        z_dim,                      # Input latent (Z) dimensionality.
-        c_dim,                      # Conditioning label (C) dimensionality.
-        w_dim,                      # Intermediate latent (W) dimensionality.
-        img_resolution,             # Output resolution.
-        img_channels,               # Number of output color channels.
-        mapping_kwargs      = {},   # Arguments for MappingNetwork.
-        resize=None,
-        **synthesis_kwargs,         # Arguments for SynthesisNetwork.
-    ):
-        super().__init__()
-        self.z_dim = z_dim
-        self.c_dim = c_dim
-        self.w_dim = w_dim
-        self.img_resolution = img_resolution
-        self.img_channels = img_channels
-        self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs)
-        self.num_ws = self.synthesis.num_ws
-        self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
-        self.resize = resize
-
-    def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, input_is_w=False, **synthesis_kwargs):
-        if input_is_w:
-            ws = z
-            if ws.dim() == 2:
-                ws = ws.unsqueeze(1).repeat([1, self.mapping.num_ws, 1])
-        else:
-            ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas)
-        img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
-        if self.resize is not None:
-            img = imresize(img, [self.resize, self.resize])
-        return img
-
-#----------------------------------------------------------------------------
-
-def imresize(image, size):
-    dim = image.dim()
-    if dim == 3:
-        image = image.unsqueeze(1)
-    b, _, h, w = image.shape
-    if size[0] > h:
-        image = F.interpolate(image, size, mode='bilinear')
-    elif size[0] < h:
-        image = F.interpolate(image, size, mode='area')
-    if dim == 3:
-        image = image.squeeze(1)
-    return image
diff --git a/spaces/EDGAhab/Aatrox-Talking/monotonic_align/setup.py b/spaces/EDGAhab/Aatrox-Talking/monotonic_align/setup.py
deleted file mode 100644
index 30c224807a70faa9df9c9eb75f8e80c8c867b16b..0000000000000000000000000000000000000000
--- a/spaces/EDGAhab/Aatrox-Talking/monotonic_align/setup.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from distutils.core import setup
-from Cython.Build import cythonize
-import numpy
-
-setup(
-  name = 'monotonic_align',
-  ext_modules = cythonize("core.pyx"),
-  include_dirs=[numpy.get_include()]
-)
diff --git a/spaces/EPFL-VILAB/MultiMAE/utils/taskonomy/taskonomy_dataset.py b/spaces/EPFL-VILAB/MultiMAE/utils/taskonomy/taskonomy_dataset.py
deleted file mode 100644
index 2797802b16e127f7ecd229401f073e2191702f01..0000000000000000000000000000000000000000
--- a/spaces/EPFL-VILAB/MultiMAE/utils/taskonomy/taskonomy_dataset.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import os
-
-import pandas as pd
-from PIL import Image, ImageFile
-from torch.utils.data import Dataset
-
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-
-from .transforms import task_transform
-
-
-class TaskonomyDataset(Dataset):
-    def __init__(self, 
-                 data_root,
-                 tasks, 
-                 split='train', 
-                 variant='tiny',
-                 image_size=256,
-                 max_images=None):
-        """
-        Taskonomy dataloader.
-
-        Args:
-            data_root: Root of Taskonomy data directory
-            tasks: List of tasks. Any of ['rgb', 'depth_euclidean', 'depth_zbuffer',
-                'edge_occlusion', 'edge_texture', 'keypoints2d', 'keypoints3d', 'normal',
-                'principal_curvature', 'reshading', 'mask_valid'].
-            split: One of {'train', 'val', 'test'}
-            variant: One of {'debug', 'tiny', 'medium', 'full', 'fullplus'}
-            image_size: Target image size
-            max_images: Optional subset selection
-        """
-        super(TaskonomyDataset, self).__init__()
-        self.data_root = data_root
-        self.tasks = tasks
-        self.split = split
-        self.variant = variant
-        self.image_size=image_size
-        self.max_images = max_images
-        
-        self.image_ids = pd.read_csv(
-            os.path.join(os.path.dirname(__file__), 'splits', f'{self.variant}_{self.split}.csv')
-        ).to_numpy()
-        
-        if isinstance(self.max_images, int):
-            self.image_ids = self.image_ids[:self.max_images]
-        
-        print(f'Initialized TaskonomyDataset with {len(self.image_ids)} images from variant {self.variant} in split {self.split}.')
-        
-        
-    def __len__(self):
-        return len(self.image_ids)
-    
-    def __getitem__(self, index):
-        
-        # building / point / view
-        building, point, view = self.image_ids[index]
-        
-        result = {}
-        for task in self.tasks:
-            task_id = 'depth_zbuffer' if task == 'mask_valid' else task
-            path = os.path.join(
-                self.data_root, task, building, f'point_{point}_view_{view}_domain_{task_id}.png'
-            )
-            img = Image.open(path)
-            # Perform transformations
-            img = task_transform(img, task=task, image_size=self.image_size)
-            result[task] = img
-
-        return result
diff --git a/spaces/EXPOSUREEE/Ai-Image-Enhancer/realesrgan/models/__init__.py b/spaces/EXPOSUREEE/Ai-Image-Enhancer/realesrgan/models/__init__.py
deleted file mode 100644
index 0be7105dc75d150c49976396724085f678dc0675..0000000000000000000000000000000000000000
--- a/spaces/EXPOSUREEE/Ai-Image-Enhancer/realesrgan/models/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import importlib
-from basicsr.utils import scandir
-from os import path as osp
-
-# automatically scan and import model modules for registry
-# scan all the files that end with '_model.py' under the model folder
-model_folder = osp.dirname(osp.abspath(__file__))
-model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
-# import all the model modules
-_model_modules = [importlib.import_module(f'realesrgan.models.{file_name}') for file_name in model_filenames]
diff --git a/spaces/EleutherAI/VQGAN_CLIP/taming-transformers/taming/models/vqgan.py b/spaces/EleutherAI/VQGAN_CLIP/taming-transformers/taming/models/vqgan.py
deleted file mode 100644
index 121d01fd2e1641d409aa90635c367a7a1bb0b4d4..0000000000000000000000000000000000000000
--- a/spaces/EleutherAI/VQGAN_CLIP/taming-transformers/taming/models/vqgan.py
+++ /dev/null
@@ -1,363 +0,0 @@
-import torch
-import torch.nn.functional as F
-import pytorch_lightning as pl
-
-from main import instantiate_from_config
-
-from taming.modules.diffusionmodules.model import Encoder, Decoder
-from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
-from taming.modules.vqvae.quantize import GumbelQuantize
-
-
-class VQModel(pl.LightningModule):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 n_embed,
-                 embed_dim,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None,
-                 monitor=None,
-                 remap=None,
-                 sane_index_shape=False,  # tell vector quantizer to return indices as bhw
-                 ):
-        super().__init__()
-        self.image_key = image_key
-        self.encoder = Encoder(**ddconfig)
-        self.decoder = Decoder(**ddconfig)
-        self.loss = instantiate_from_config(lossconfig)
-        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
-                                        remap=remap, sane_index_shape=sane_index_shape)
-        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-        self.image_key = image_key
-        if colorize_nlabels is not None:
-            assert type(colorize_nlabels)==int
-            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
-        if monitor is not None:
-            self.monitor = monitor
-
-    def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location="cpu")["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        self.load_state_dict(sd, strict=False)
-        print(f"Restored from {path}")
-
-    def encode(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        quant, emb_loss, info = self.quantize(h)
-        return quant, emb_loss, info
-
-    def decode(self, quant):
-        quant = self.post_quant_conv(quant)
-        dec = self.decoder(quant)
-        return dec
-
-    def decode_code(self, code_b):
-        quant_b = self.quantize.embed_code(code_b)
-        dec = self.decode(quant_b)
-        return dec
-
-    def forward(self, input):
-        quant, diff, _ = self.encode(input)
-        dec = self.decode(quant)
-        return dec, diff
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format)
-        return x.float()
-
-    def training_step(self, batch, batch_idx, optimizer_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-
-        if optimizer_idx == 0:
-            # autoencode
-            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-
-            self.log("train/aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return aeloss
-
-        if optimizer_idx == 1:
-            # discriminator
-            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-            self.log("train/discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return discloss
-
-    def validation_step(self, batch, batch_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, self.global_step,
-                                            last_layer=self.get_last_layer(), split="val")
-
-        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, self.global_step,
-                                            last_layer=self.get_last_layer(), split="val")
-        rec_loss = log_dict_ae["val/rec_loss"]
-        self.log("val/rec_loss", rec_loss,
-                   prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True)
-        self.log("val/aeloss", aeloss,
-                   prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True)
-        self.log_dict(log_dict_ae)
-        self.log_dict(log_dict_disc)
-        return self.log_dict
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quantize.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=lr, betas=(0.5, 0.9))
-        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
-                                    lr=lr, betas=(0.5, 0.9))
-        return [opt_ae, opt_disc], []
-
-    def get_last_layer(self):
-        return self.decoder.conv_out.weight
-
-    def log_images(self, batch, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.image_key)
-        x = x.to(self.device)
-        xrec, _ = self(x)
-        if x.shape[1] > 3:
-            # colorize with random projection
-            assert xrec.shape[1] > 3
-            x = self.to_rgb(x)
-            xrec = self.to_rgb(xrec)
-        log["inputs"] = x
-        log["reconstructions"] = xrec
-        return log
-
-    def to_rgb(self, x):
-        assert self.image_key == "segmentation"
-        if not hasattr(self, "colorize"):
-            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
-        x = F.conv2d(x, weight=self.colorize)
-        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
-        return x
-
-
-class VQSegmentationModel(VQModel):
-    def __init__(self, n_labels, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.register_buffer("colorize", torch.randn(3, n_labels, 1, 1))
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quantize.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=lr, betas=(0.5, 0.9))
-        return opt_ae
-
-    def training_step(self, batch, batch_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, split="train")
-        self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-        return aeloss
-
-    def validation_step(self, batch, batch_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, split="val")
-        self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-        total_loss = log_dict_ae["val/total_loss"]
-        self.log("val/total_loss", total_loss,
-                 prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True)
-        return aeloss
-
-    @torch.no_grad()
-    def log_images(self, batch, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.image_key)
-        x = x.to(self.device)
-        xrec, _ = self(x)
-        if x.shape[1] > 3:
-            # colorize with random projection
-            assert xrec.shape[1] > 3
-            # convert logits to indices
-            xrec = torch.argmax(xrec, dim=1, keepdim=True)
-            xrec = F.one_hot(xrec, num_classes=x.shape[1])
-            xrec = xrec.squeeze(1).permute(0, 3, 1, 2).float()
-            x = self.to_rgb(x)
-            xrec = self.to_rgb(xrec)
-        log["inputs"] = x
-        log["reconstructions"] = xrec
-        return log
-
-
-class VQNoDiscModel(VQModel):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 n_embed,
-                 embed_dim,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None
-                 ):
-        super().__init__(ddconfig=ddconfig, lossconfig=lossconfig, n_embed=n_embed, embed_dim=embed_dim,
-                         ckpt_path=ckpt_path, ignore_keys=ignore_keys, image_key=image_key,
-                         colorize_nlabels=colorize_nlabels)
-
-    def training_step(self, batch, batch_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-        # autoencode
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, self.global_step, split="train")
-        output = pl.TrainResult(minimize=aeloss)
-        output.log("train/aeloss", aeloss,
-                   prog_bar=True, logger=True, on_step=True, on_epoch=True)
-        output.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-        return output
-
-    def validation_step(self, batch, batch_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, self.global_step, split="val")
-        rec_loss = log_dict_ae["val/rec_loss"]
-        output = pl.EvalResult(checkpoint_on=rec_loss)
-        output.log("val/rec_loss", rec_loss,
-                   prog_bar=True, logger=True, on_step=True, on_epoch=True)
-        output.log("val/aeloss", aeloss,
-                   prog_bar=True, logger=True, on_step=True, on_epoch=True)
-        output.log_dict(log_dict_ae)
-
-        return output
-
-    def configure_optimizers(self):
-        optimizer = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quantize.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=self.learning_rate, betas=(0.5, 0.9))
-        return optimizer
-
-
-class GumbelVQ(VQModel):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 n_embed,
-                 embed_dim,
-                 temperature_scheduler_config,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None,
-                 monitor=None,
-                 kl_weight=1e-8,
-                 remap=None,
-                 ):
-
-        z_channels = ddconfig["z_channels"]
-        super().__init__(ddconfig,
-                         lossconfig,
-                         n_embed,
-                         embed_dim,
-                         ckpt_path=None,
-                         ignore_keys=ignore_keys,
-                         image_key=image_key,
-                         colorize_nlabels=colorize_nlabels,
-                         monitor=monitor,
-                         )
-
-        self.loss.n_classes = n_embed
-        self.vocab_size = n_embed
-
-        self.quantize = GumbelQuantize(z_channels, embed_dim,
-                                       n_embed=n_embed,
-                                       kl_weight=kl_weight, temp_init=1.0,
-                                       remap=remap)
-
-        self.temperature_scheduler = instantiate_from_config(temperature_scheduler_config)   # annealing of temp
-
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-
-    def temperature_scheduling(self):
-        self.quantize.temperature = self.temperature_scheduler(self.global_step)
-
-    def encode_to_prequant(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        return h
-
-    def decode_code(self, code_b):
-        raise NotImplementedError
-
-    def training_step(self, batch, batch_idx, optimizer_idx):
-        self.temperature_scheduling()
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x)
-
-        if optimizer_idx == 0:
-            # autoencode
-            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-
-            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            self.log("temperature", self.quantize.temperature, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return aeloss
-
-        if optimizer_idx == 1:
-            # discriminator
-            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return discloss
-
-    def validation_step(self, batch, batch_idx):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss = self(x, return_pred_indices=True)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, self.global_step,
-                                        last_layer=self.get_last_layer(), split="val")
-
-        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, self.global_step,
-                                            last_layer=self.get_last_layer(), split="val")
-        rec_loss = log_dict_ae["val/rec_loss"]
-        self.log("val/rec_loss", rec_loss,
-                 prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
-        self.log("val/aeloss", aeloss,
-                 prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
-        self.log_dict(log_dict_ae)
-        self.log_dict(log_dict_disc)
-        return self.log_dict
-
-    def log_images(self, batch, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.image_key)
-        x = x.to(self.device)
-        # encode
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        quant, _, _ = self.quantize(h)
-        # decode
-        x_rec = self.decode(quant)
-        log["inputs"] = x
-        log["reconstructions"] = x_rec
-        return log
diff --git a/spaces/Epitech/AiOnIot-Antoine-Quentin-Valentin-Maxime/README.md b/spaces/Epitech/AiOnIot-Antoine-Quentin-Valentin-Maxime/README.md
deleted file mode 100644
index d4e554ec9703f78258940492522259b9b4843f99..0000000000000000000000000000000000000000
--- a/spaces/Epitech/AiOnIot-Antoine-Quentin-Valentin-Maxime/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AiOnIot
-emoji: ⛔
-colorFrom: yellow
-colorTo: red
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Epitech/userbank/README.md b/spaces/Epitech/userbank/README.md
deleted file mode 100644
index 55fcd1327837be4957533dd6168ebbcc33e27027..0000000000000000000000000000000000000000
--- a/spaces/Epitech/userbank/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AIoT
-emoji: 😻
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/EronSamez/RVC_HFmeu/tools/infer/train-index.py b/spaces/EronSamez/RVC_HFmeu/tools/infer/train-index.py
deleted file mode 100644
index 44b447ef32148c181eb4bcd9013a22a82371b82c..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/tools/infer/train-index.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""
-格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个
-"""
-import os
-import logging
-
-logger = logging.getLogger(__name__)
-
-import faiss
-import numpy as np
-
-# ###########如果是原始特征要先写save
-inp_root = r"E:\codes\py39\dataset\mi\2-co256"
-npys = []
-for name in sorted(list(os.listdir(inp_root))):
-    phone = np.load("%s/%s" % (inp_root, name))
-    npys.append(phone)
-big_npy = np.concatenate(npys, 0)
-logger.debug(big_npy.shape)  # (6196072, 192)#fp32#4.43G
-np.save("infer/big_src_feature_mi.npy", big_npy)
-
-##################train+add
-# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
-logger.debug(big_npy.shape)
-index = faiss.index_factory(256, "IVF512,Flat")  # mi
-logger.info("Training...")
-index_ivf = faiss.extract_index_ivf(index)  #
-index_ivf.nprobe = 9
-index.train(big_npy)
-faiss.write_index(index, "infer/trained_IVF512_Flat_mi_baseline_src_feat.index")
-logger.info("Adding...")
-index.add(big_npy)
-faiss.write_index(index, "infer/added_IVF512_Flat_mi_baseline_src_feat.index")
-"""
-大小（都是FP32）
-big_src_feature 2.95G
-    (3098036, 256)
-big_emb         4.43G
-    (6196072, 192)
-big_emb双倍是因为求特征要repeat后再加pitch
-
-"""
diff --git a/spaces/EsoCode/text-generation-webui/css/html_instruct_style.css b/spaces/EsoCode/text-generation-webui/css/html_instruct_style.css
deleted file mode 100644
index 575281b1e50150c6b285edf0e8c04f4a5abf329b..0000000000000000000000000000000000000000
--- a/spaces/EsoCode/text-generation-webui/css/html_instruct_style.css
+++ /dev/null
@@ -1,62 +0,0 @@
-.message {
-    display: grid;
-    grid-template-columns: 60px 1fr;
-    padding-bottom: 25px;
-    font-size: 15px;
-    font-family: Helvetica, Arial, sans-serif;
-    line-height: 1.428571429;
-}
-
-.username {
-    display: none;
-}
-
-.message-body p {
-    font-size: 15px !important;
-    line-height: 1.75 !important;
-    margin-bottom: 1.25em !important;
-}
-
-.message-body ul, .message-body ol {
-    margin-bottom: 1.25em !important;
-}
-
-.dark .message-body p em {
-    color: rgb(198, 202, 214) !important;
-}
-
-.message-body p em {
-    color: rgb(110, 110, 110) !important;
-}
-
-.gradio-container .chat .assistant-message {
-    padding: 15px;
-    border-radius: 20px;
-    background-color: #0000000f;
-    margin-top: 9px !important;
-    margin-bottom: 18px !important;
-}
-
-.gradio-container .chat .user-message {
-    padding: 15px;
-    border-radius: 20px;
-    margin-bottom: 9px !important;
-}
-
-.dark .chat .assistant-message {
-    background-color: #3741519e;
-    border: 1px solid #4b5563;
-}
-
-.dark .chat .user-message {
-    background-color: #111827;
-    border: 1px solid #4b5563;
-}
-
-code {
-    background-color: white !important;
-}
-
-.dark code {
-    background-color: #1a212f !important;
-}
\ No newline at end of file
diff --git a/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/train.py b/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/train.py
deleted file mode 100644
index a24f212b87bb093062ad48a7f0639a8c23f109c7..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/train.py
+++ /dev/null
@@ -1,564 +0,0 @@
-import argparse
-import logging
-import os
-import random
-import shutil
-import time
-from pathlib import Path
-from warnings import warn
-
-import math
-import numpy as np
-import torch.distributed as dist
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-import torch.optim.lr_scheduler as lr_scheduler
-import torch.utils.data
-import yaml
-from torch.cuda import amp
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.utils.tensorboard import SummaryWriter
-from tqdm import tqdm
-
-import test  # import test.py to get mAP after each epoch
-from models.yolo import Model
-from utils.datasets import create_dataloader
-from utils.general import (
-    torch_distributed_zero_first, labels_to_class_weights, plot_labels, check_anchors, labels_to_image_weights,
-    compute_loss, plot_images, fitness, strip_optimizer, plot_results, get_latest_run, check_dataset, check_file,
-    check_git_status, check_img_size, increment_dir, print_mutation, plot_evolution, set_logging, init_seeds)
-from utils.google_utils import attempt_download
-from utils.torch_utils import ModelEMA, select_device, intersect_dicts
-
-logger = logging.getLogger(__name__)
-
-
-def train(hyp, opt, device, tb_writer=None, wandb=None):
-    logger.info(f'Hyperparameters {hyp}')
-    log_dir = Path(tb_writer.log_dir) if tb_writer else Path(opt.logdir) / 'evolve'  # logging directory
-    wdir = log_dir / 'weights'  # weights directory
-    os.makedirs(wdir, exist_ok=True)
-    last = wdir / 'last.pt'
-    best = wdir / 'best.pt'
-    results_file = str(log_dir / 'results.txt')
-    epochs, batch_size, total_batch_size, weights, rank = \
-        opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
-
-    # Save run settings
-    with open(log_dir / 'hyp.yaml', 'w') as f:
-        yaml.dump(hyp, f, sort_keys=False)
-    with open(log_dir / 'opt.yaml', 'w') as f:
-        yaml.dump(vars(opt), f, sort_keys=False)
-
-    # Configure
-    cuda = device.type != 'cpu'
-    init_seeds(2 + rank)
-    with open(opt.data) as f:
-        data_dict = yaml.load(f, Loader=yaml.FullLoader)  # data dict
-    with torch_distributed_zero_first(rank):
-        check_dataset(data_dict)  # check
-    train_path = data_dict['train']
-    test_path = data_dict['val']
-    nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names'])  # number classes, names
-    assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data)  # check
-
-    # Model
-    pretrained = weights.endswith('.pt')
-    if pretrained:
-        with torch_distributed_zero_first(rank):
-            attempt_download(weights)  # download if not found locally
-        ckpt = torch.load(weights, map_location=device)  # load checkpoint
-        if hyp.get('anchors'):
-            ckpt['model'].yaml['anchors'] = round(hyp['anchors'])  # force autoanchor
-        model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device)  # create
-        exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else []  # exclude keys
-        state_dict = ckpt['model'].float().state_dict()  # to FP32
-        state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude)  # intersect
-        model.load_state_dict(state_dict, strict=False)  # load
-        logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights))  # report
-    else:
-        model = Model(opt.cfg, ch=3, nc=nc).to(device)  # create
-
-    # Freeze
-    freeze = []  # parameter names to freeze (full or partial)
-    for k, v in model.named_parameters():
-        v.requires_grad = True  # train all layers
-        if any(x in k for x in freeze):
-            print('freezing %s' % k)
-            v.requires_grad = False
-
-    # Optimizer
-    nbs = 64  # nominal batch size
-    accumulate = max(round(nbs / total_batch_size), 1)  # accumulate loss before optimizing
-    hyp['weight_decay'] *= total_batch_size * accumulate / nbs  # scale weight_decay
-
-    pg0, pg1, pg2 = [], [], []  # optimizer parameter groups
-    for k, v in model.named_modules():
-        if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
-            pg2.append(v.bias)  # biases
-        if isinstance(v, nn.BatchNorm2d):
-            pg0.append(v.weight)  # no decay
-        elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
-            pg1.append(v.weight)  # apply decay
-
-    if opt.adam:
-        optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum
-    else:
-        optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
-
-    optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']})  # add pg1 with weight_decay
-    optimizer.add_param_group({'params': pg2})  # add pg2 (biases)
-    logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
-    del pg0, pg1, pg2
-
-    # Scheduler https://arxiv.org/pdf/1812.01187.pdf
-    # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
-    lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp['lrf']) + hyp['lrf']  # cosine
-    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
-    # plot_lr_scheduler(optimizer, scheduler, epochs)
-
-    # Logging
-    if wandb and wandb.run is None:
-        id = ckpt.get('wandb_id') if 'ckpt' in locals() else None
-        wandb_run = wandb.init(config=opt, resume="allow", project="YOLOv5", name=os.path.basename(log_dir), id=id)
-
-    # Resume
-    start_epoch, best_fitness = 0, 0.0
-    if pretrained:
-        # Optimizer
-        if ckpt['optimizer'] is not None:
-            optimizer.load_state_dict(ckpt['optimizer'])
-            best_fitness = ckpt['best_fitness']
-
-        # Results
-        if ckpt.get('training_results') is not None:
-            with open(results_file, 'w') as file:
-                file.write(ckpt['training_results'])  # write results.txt
-
-        # Epochs
-        start_epoch = ckpt['epoch'] + 1
-        if opt.resume:
-            assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
-            shutil.copytree(wdir, wdir.parent / f'weights_backup_epoch{start_epoch - 1}')  # save previous weights
-        if epochs < start_epoch:
-            logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
-                        (weights, ckpt['epoch'], epochs))
-            epochs += ckpt['epoch']  # finetune additional epochs
-
-        del ckpt, state_dict
-
-    # Image sizes
-    gs = int(max(model.stride))  # grid size (max stride)
-    imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size]  # verify imgsz are gs-multiples
-
-    # DP mode
-    if cuda and rank == -1 and torch.cuda.device_count() > 1:
-        model = torch.nn.DataParallel(model)
-
-    # SyncBatchNorm
-    if opt.sync_bn and cuda and rank != -1:
-        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
-        logger.info('Using SyncBatchNorm()')
-
-    # Exponential moving average
-    ema = ModelEMA(model) if rank in [-1, 0] else None
-
-    # DDP mode
-    if cuda and rank != -1:
-        model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank)
-
-    # Trainloader
-    dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
-                                            hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect,
-                                            rank=rank, world_size=opt.world_size, workers=opt.workers)
-    mlc = np.concatenate(dataset.labels, 0)[:, 0].max()  # max label class
-    nb = len(dataloader)  # number of batches
-    assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
-
-    # Process 0
-    if rank in [-1, 0]:
-        ema.updates = start_epoch * nb // accumulate  # set EMA updates
-        testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt,
-                                       hyp=hyp, augment=False, cache=opt.cache_images and not opt.notest, rect=True,
-                                       rank=-1, world_size=opt.world_size, workers=opt.workers)[0]  # testloader
-
-        if not opt.resume:
-            labels = np.concatenate(dataset.labels, 0)
-            c = torch.tensor(labels[:, 0])  # classes
-            # cf = torch.bincount(c.long(), minlength=nc) + 1.  # frequency
-            # model._initialize_biases(cf.to(device))
-            plot_labels(labels, save_dir=log_dir)
-            if tb_writer:
-                # tb_writer.add_hparams(hyp, {})  # causes duplicate https://github.com/ultralytics/yolov5/pull/384
-                tb_writer.add_histogram('classes', c, 0)
-
-            # Anchors
-            if not opt.noautoanchor:
-                check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
-
-    # Model parameters
-    hyp['cls'] *= nc / 80.  # scale coco-tuned hyp['cls'] to current dataset
-    model.nc = nc  # attach number of classes to model
-    model.hyp = hyp  # attach hyperparameters to model
-    model.gr = 1.0  # iou loss ratio (obj_loss = 1.0 or iou)
-    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights
-    model.names = names
-
-    # Start training
-    t0 = time.time()
-    nw = max(round(hyp['warmup_epochs'] * nb), 1e3)  # number of warmup iterations, max(3 epochs, 1k iterations)
-    # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
-    maps = np.zeros(nc)  # mAP per class
-    results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
-    scheduler.last_epoch = start_epoch - 1  # do not move
-    scaler = amp.GradScaler(enabled=cuda)
-    logger.info('Image sizes %g train, %g test\n'
-                'Using %g dataloader workers\nLogging results to %s\n'
-                'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, log_dir, epochs))
-    for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
-        model.train()
-
-        # Update image weights (optional)
-        if opt.image_weights:
-            # Generate indices
-            if rank in [-1, 0]:
-                cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2  # class weights
-                iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weights
-                dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx
-            # Broadcast if DDP
-            if rank != -1:
-                indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
-                dist.broadcast(indices, 0)
-                if rank != 0:
-                    dataset.indices = indices.cpu().numpy()
-
-        # Update mosaic border
-        # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
-        # dataset.mosaic_border = [b - imgsz, -b]  # height, width borders
-
-        mloss = torch.zeros(4, device=device)  # mean losses
-        if rank != -1:
-            dataloader.sampler.set_epoch(epoch)
-        pbar = enumerate(dataloader)
-        logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size'))
-        if rank in [-1, 0]:
-            pbar = tqdm(pbar, total=nb)  # progress bar
-        optimizer.zero_grad()
-        for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
-            ni = i + nb * epoch  # number integrated batches (since train start)
-            imgs = imgs.to(device, non_blocking=True).float() / 255.0  # uint8 to float32, 0-255 to 0.0-1.0
-
-            # Warmup
-            if ni <= nw:
-                xi = [0, nw]  # x interp
-                # model.gr = np.interp(ni, xi, [0.0, 1.0])  # iou loss ratio (obj_loss = 1.0 or iou)
-                accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
-                for j, x in enumerate(optimizer.param_groups):
-                    # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
-                    x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
-                    if 'momentum' in x:
-                        x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
-
-            # Multi-scale
-            if opt.multi_scale:
-                sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs  # size
-                sf = sz / max(imgs.shape[2:])  # scale factor
-                if sf != 1:
-                    ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]]  # new shape (stretched to gs-multiple)
-                    imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
-
-            # Forward
-            with amp.autocast(enabled=cuda):
-                pred = model(imgs)  # forward
-                loss, loss_items = compute_loss(pred, targets.to(device), model)  # loss scaled by batch_size
-                if rank != -1:
-                    loss *= opt.world_size  # gradient averaged between devices in DDP mode
-
-            # Backward
-            scaler.scale(loss).backward()
-
-            # Optimize
-            if ni % accumulate == 0:
-                scaler.step(optimizer)  # optimizer.step
-                scaler.update()
-                optimizer.zero_grad()
-                if ema:
-                    ema.update(model)
-
-            # Print
-            if rank in [-1, 0]:
-                mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
-                mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0)  # (GB)
-                s = ('%10s' * 2 + '%10.4g' * 6) % (
-                    '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
-                pbar.set_description(s)
-
-                # Plot
-                if ni < 3:
-                    f = str(log_dir / f'train_batch{ni}.jpg')  # filename
-                    result = plot_images(images=imgs, targets=targets, paths=paths, fname=f)
-                    # if tb_writer and result is not None:
-                    # tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
-                    # tb_writer.add_graph(model, imgs)  # add model to tensorboard
-
-            # end batch ------------------------------------------------------------------------------------------------
-
-        # Scheduler
-        lr = [x['lr'] for x in optimizer.param_groups]  # for tensorboard
-        scheduler.step()
-
-        # DDP process 0 or single-GPU
-        if rank in [-1, 0]:
-            # mAP
-            if ema:
-                ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride'])
-            final_epoch = epoch + 1 == epochs
-            if not opt.notest or final_epoch:  # Calculate mAP
-                results, maps, times = test.test(opt.data,
-                                                 batch_size=total_batch_size,
-                                                 imgsz=imgsz_test,
-                                                 model=ema.ema,
-                                                 single_cls=opt.single_cls,
-                                                 dataloader=testloader,
-                                                 save_dir=log_dir,
-                                                 plots=epoch == 0 or final_epoch,  # plot first and last
-                                                 log_imgs=opt.log_imgs)
-
-            # Write
-            with open(results_file, 'a') as f:
-                f.write(s + '%10.4g' * 7 % results + '\n')  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
-            if len(opt.name) and opt.bucket:
-                os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
-
-            # Log
-            tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
-                    'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
-                    'val/giou_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
-                    'x/lr0', 'x/lr1', 'x/lr2']  # params
-            for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
-                if tb_writer:
-                    tb_writer.add_scalar(tag, x, epoch)  # tensorboard
-                if wandb:
-                    wandb.log({tag: x})  # W&B
-
-            # Update best mAP
-            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
-            if fi > best_fitness:
-                best_fitness = fi
-
-            # Save model
-            save = (not opt.nosave) or (final_epoch and not opt.evolve)
-            if save:
-                with open(results_file, 'r') as f:  # create checkpoint
-                    ckpt = {'epoch': epoch,
-                            'best_fitness': best_fitness,
-                            'training_results': f.read(),
-                            'model': ema.ema,
-                            'optimizer': None if final_epoch else optimizer.state_dict(),
-                            'wandb_id': wandb_run.id if wandb else None}
-
-                # Save last, best and delete
-                torch.save(ckpt, last)
-                if best_fitness == fi:
-                    torch.save(ckpt, best)
-                del ckpt
-        # end epoch ----------------------------------------------------------------------------------------------------
-    # end training
-
-    if rank in [-1, 0]:
-        # Strip optimizers
-        n = opt.name if opt.name.isnumeric() else ''
-        fresults, flast, fbest = log_dir / f'results{n}.txt', wdir / f'last{n}.pt', wdir / f'best{n}.pt'
-        for f1, f2 in zip([wdir / 'last.pt', wdir / 'best.pt', results_file], [flast, fbest, fresults]):
-            if os.path.exists(f1):
-                os.rename(f1, f2)  # rename
-                if str(f2).endswith('.pt'):  # is *.pt
-                    strip_optimizer(f2)  # strip optimizer
-                    os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket else None  # upload
-        # Finish
-        if not opt.evolve:
-            plot_results(save_dir=log_dir)  # save as results.png
-        logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
-
-    dist.destroy_process_group() if rank not in [-1, 0] else None
-    torch.cuda.empty_cache()
-    return results
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default='yolov5s.pt', help='initial weights path')
-    parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
-    parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
-    parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')
-    parser.add_argument('--epochs', type=int, default=300)
-    parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
-    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
-    parser.add_argument('--rect', action='store_true', help='rectangular training')
-    parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
-    parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
-    parser.add_argument('--notest', action='store_true', help='only test final epoch')
-    parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
-    parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
-    parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
-    parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
-    parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
-    parser.add_argument('--name', default='', help='renames experiment folder exp{N} to exp{N}_{name} if supplied')
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
-    parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
-    parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
-    parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
-    parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
-    parser.add_argument('--logdir', type=str, default='runs/', help='logging directory')
-    parser.add_argument('--log-imgs', type=int, default=10, help='number of images for W&B logging, max 100')
-    parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
-
-    opt = parser.parse_args()
-
-    # Set DDP variables
-    opt.total_batch_size = opt.batch_size
-    opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
-    opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1
-    set_logging(opt.global_rank)
-    if opt.global_rank in [-1, 0]:
-        check_git_status()
-
-    # Resume
-    if opt.resume:  # resume an interrupted run
-        ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run()  # specified or most recent path
-        log_dir = Path(ckpt).parent.parent  # runs/exp0
-        assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
-        with open(log_dir / 'opt.yaml') as f:
-            opt = argparse.Namespace(**yaml.load(f, Loader=yaml.FullLoader))  # replace
-        opt.cfg, opt.weights, opt.resume = '', ckpt, True
-        logger.info('Resuming training from %s' % ckpt)
-
-    else:
-        # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
-        opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp)  # check files
-        assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
-        opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size)))  # extend to 2 sizes (train, test)
-        log_dir = increment_dir(Path(opt.logdir) / 'exp', opt.name)  # runs/exp1
-
-    # DDP mode
-    device = select_device(opt.device, batch_size=opt.batch_size)
-    if opt.local_rank != -1:
-        assert torch.cuda.device_count() > opt.local_rank
-        torch.cuda.set_device(opt.local_rank)
-        device = torch.device('cuda', opt.local_rank)
-        dist.init_process_group(backend='nccl', init_method='env://')  # distributed backend
-        assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'
-        opt.batch_size = opt.total_batch_size // opt.world_size
-
-    # Hyperparameters
-    with open(opt.hyp) as f:
-        hyp = yaml.load(f, Loader=yaml.FullLoader)  # load hyps
-        if 'box' not in hyp:
-            warn('Compatibility: %s missing "box" which was renamed from "giou" in %s' %
-                 (opt.hyp, 'https://github.com/ultralytics/yolov5/pull/1120'))
-            hyp['box'] = hyp.pop('giou')
-
-    # Train
-    logger.info(opt)
-    if not opt.evolve:
-        tb_writer, wandb = None, None  # init loggers
-        if opt.global_rank in [-1, 0]:
-            # Tensorboard
-            logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.logdir}", view at http://localhost:6006/')
-            tb_writer = SummaryWriter(log_dir=log_dir)  # runs/exp0
-
-            # W&B
-            try:
-                import wandb
-
-                assert os.environ.get('WANDB_DISABLED') != 'true'
-                logger.info("Weights & Biases logging enabled, to disable set os.environ['WANDB_DISABLED'] = 'true'")
-            except (ImportError, AssertionError):
-                opt.log_imgs = 0
-                logger.info("Install Weights & Biases for experiment logging via 'pip install wandb' (recommended)")
-
-        train(hyp, opt, device, tb_writer, wandb)
-
-    # Evolve hyperparameters (optional)
-    else:
-        # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
-        meta = {'lr0': (1, 1e-5, 1e-1),  # initial learning rate (SGD=1E-2, Adam=1E-3)
-                'lrf': (1, 0.01, 1.0),  # final OneCycleLR learning rate (lr0 * lrf)
-                'momentum': (0.3, 0.6, 0.98),  # SGD momentum/Adam beta1
-                'weight_decay': (1, 0.0, 0.001),  # optimizer weight decay
-                'warmup_epochs': (1, 0.0, 5.0),  # warmup epochs (fractions ok)
-                'warmup_momentum': (1, 0.0, 0.95),  # warmup initial momentum
-                'warmup_bias_lr': (1, 0.0, 0.2),  # warmup initial bias lr
-                'box': (1, 0.02, 0.2),  # box loss gain
-                'cls': (1, 0.2, 4.0),  # cls loss gain
-                'cls_pw': (1, 0.5, 2.0),  # cls BCELoss positive_weight
-                'obj': (1, 0.2, 4.0),  # obj loss gain (scale with pixels)
-                'obj_pw': (1, 0.5, 2.0),  # obj BCELoss positive_weight
-                'iou_t': (0, 0.1, 0.7),  # IoU training threshold
-                'anchor_t': (1, 2.0, 8.0),  # anchor-multiple threshold
-                'anchors': (2, 2.0, 10.0),  # anchors per output grid (0 to ignore)
-                'fl_gamma': (0, 0.0, 2.0),  # focal loss gamma (efficientDet default gamma=1.5)
-                'hsv_h': (1, 0.0, 0.1),  # image HSV-Hue augmentation (fraction)
-                'hsv_s': (1, 0.0, 0.9),  # image HSV-Saturation augmentation (fraction)
-                'hsv_v': (1, 0.0, 0.9),  # image HSV-Value augmentation (fraction)
-                'degrees': (1, 0.0, 45.0),  # image rotation (+/- deg)
-                'translate': (1, 0.0, 0.9),  # image translation (+/- fraction)
-                'scale': (1, 0.0, 0.9),  # image scale (+/- gain)
-                'shear': (1, 0.0, 10.0),  # image shear (+/- deg)
-                'perspective': (0, 0.0, 0.001),  # image perspective (+/- fraction), range 0-0.001
-                'flipud': (1, 0.0, 1.0),  # image flip up-down (probability)
-                'fliplr': (0, 0.0, 1.0),  # image flip left-right (probability)
-                'mosaic': (1, 0.0, 1.0),  # image mixup (probability)
-                'mixup': (1, 0.0, 1.0)}  # image mixup (probability)
-
-        assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'
-        opt.notest, opt.nosave = True, True  # only test/save final epoch
-        # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices
-        yaml_file = Path(opt.logdir) / 'evolve' / 'hyp_evolved.yaml'  # save best result here
-        if opt.bucket:
-            os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket)  # download evolve.txt if exists
-
-        for _ in range(300):  # generations to evolve
-            if os.path.exists('evolve.txt'):  # if evolve.txt exists: select best hyps and mutate
-                # Select parent(s)
-                parent = 'single'  # parent selection method: 'single' or 'weighted'
-                x = np.loadtxt('evolve.txt', ndmin=2)
-                n = min(5, len(x))  # number of previous results to consider
-                x = x[np.argsort(-fitness(x))][:n]  # top n mutations
-                w = fitness(x) - fitness(x).min()  # weights
-                if parent == 'single' or len(x) == 1:
-                    # x = x[random.randint(0, n - 1)]  # random selection
-                    x = x[random.choices(range(n), weights=w)[0]]  # weighted selection
-                elif parent == 'weighted':
-                    x = (x * w.reshape(n, 1)).sum(0) / w.sum()  # weighted combination
-
-                # Mutate
-                mp, s = 0.8, 0.2  # mutation probability, sigma
-                npr = np.random
-                npr.seed(int(time.time()))
-                g = np.array([x[0] for x in meta.values()])  # gains 0-1
-                ng = len(meta)
-                v = np.ones(ng)
-                while all(v == 1):  # mutate until a change occurs (prevent duplicates)
-                    v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
-                for i, k in enumerate(hyp.keys()):  # plt.hist(v.ravel(), 300)
-                    hyp[k] = float(x[i + 7] * v[i])  # mutate
-
-            # Constrain to limits
-            for k, v in meta.items():
-                hyp[k] = max(hyp[k], v[1])  # lower limit
-                hyp[k] = min(hyp[k], v[2])  # upper limit
-                hyp[k] = round(hyp[k], 5)  # significant digits
-
-            # Train mutation
-            results = train(hyp.copy(), opt, device)
-
-            # Write mutation results
-            print_mutation(hyp.copy(), results, yaml_file, opt.bucket)
-
-        # Plot results
-        plot_evolution(yaml_file)
-        print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
-              f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')
diff --git a/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/utils/__init__.py b/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/inference/infer_tool.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/inference/infer_tool.py
deleted file mode 100644
index aa08db415a9b0af97a5d726d1f7e61834e1c4e1c..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/inference/infer_tool.py
+++ /dev/null
@@ -1,407 +0,0 @@
-import hashlib
-import io
-import json
-import logging
-import os
-import time
-from pathlib import Path
-from inference import slicer
-import gc
-
-import librosa
-import numpy as np
-# import onnxruntime
-import soundfile
-import torch
-import torchaudio
-
-import cluster
-import utils
-from models import SynthesizerTrn
-
-from diffusion.unit2mel import load_model_vocoder
-import yaml
-
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-
-
-def read_temp(file_name):
-    if not os.path.exists(file_name):
-        with open(file_name, "w") as f:
-            f.write(json.dumps({"info": "temp_dict"}))
-        return {}
-    else:
-        try:
-            with open(file_name, "r") as f:
-                data = f.read()
-            data_dict = json.loads(data)
-            if os.path.getsize(file_name) > 50 * 1024 * 1024:
-                f_name = file_name.replace("\\", "/").split("/")[-1]
-                print(f"clean {f_name}")
-                for wav_hash in list(data_dict.keys()):
-                    if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
-                        del data_dict[wav_hash]
-        except Exception as e:
-            print(e)
-            print(f"{file_name} error,auto rebuild file")
-            data_dict = {"info": "temp_dict"}
-        return data_dict
-
-
-def write_temp(file_name, data):
-    with open(file_name, "w") as f:
-        f.write(json.dumps(data))
-
-
-def timeit(func):
-    def run(*args, **kwargs):
-        t = time.time()
-        res = func(*args, **kwargs)
-        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
-        return res
-
-    return run
-
-
-def format_wav(audio_path):
-    if Path(audio_path).suffix == '.wav':
-        return
-    raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
-    soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
-
-
-def get_end_file(dir_path, end):
-    file_lists = []
-    for root, dirs, files in os.walk(dir_path):
-        files = [f for f in files if f[0] != '.']
-        dirs[:] = [d for d in dirs if d[0] != '.']
-        for f_file in files:
-            if f_file.endswith(end):
-                file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
-    return file_lists
-
-
-def get_md5(content):
-    return hashlib.new("md5", content).hexdigest()
-
-def fill_a_to_b(a, b):
-    if len(a) < len(b):
-        for _ in range(0, len(b) - len(a)):
-            a.append(a[0])
-
-def mkdir(paths: list):
-    for path in paths:
-        if not os.path.exists(path):
-            os.mkdir(path)
-
-def pad_array(arr, target_length):
-    current_length = arr.shape[0]
-    if current_length >= target_length:
-        return arr
-    else:
-        pad_width = target_length - current_length
-        pad_left = pad_width // 2
-        pad_right = pad_width - pad_left
-        padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0))
-        return padded_arr
-    
-def split_list_by_n(list_collection, n, pre=0):
-    for i in range(0, len(list_collection), n):
-        yield list_collection[i-pre if i-pre>=0 else i: i + n]
-
-
-class F0FilterException(Exception):
-    pass
-
-class Svc(object):
-    def __init__(self, net_g_path, config_path,
-                 device=None,
-                 cluster_model_path="logs/44k/kmeans_10000.pt",
-                 nsf_hifigan_enhance = False,
-                 diffusion_model_path="logs/44k/diffusion/model_0.pt",
-                 diffusion_config_path="configs/diffusion.yaml",
-                 shallow_diffusion = False,
-                 only_diffusion = False,
-                 ):
-        self.net_g_path = net_g_path
-        self.only_diffusion = only_diffusion
-        self.shallow_diffusion = shallow_diffusion
-        if device is None:
-            # self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-            self.dev = torch.device("cpu")
-        else:
-            self.dev = torch.device(device)
-        self.net_g_ms = None
-        if not self.only_diffusion:
-            self.hps_ms = utils.get_hparams_from_file(config_path)
-            self.target_sample = self.hps_ms.data.sampling_rate
-            self.hop_size = self.hps_ms.data.hop_length
-            self.spk2id = self.hps_ms.spk
-            try:
-                self.speech_encoder = self.hps_ms.model.speech_encoder
-            except Exception as e:
-                self.speech_encoder = 'vec768l12'
-
-        self.nsf_hifigan_enhance = nsf_hifigan_enhance
-        if self.shallow_diffusion or self.only_diffusion:
-            if os.path.exists(diffusion_model_path) and os.path.exists(diffusion_model_path):
-                self.diffusion_model,self.vocoder,self.diffusion_args = load_model_vocoder(diffusion_model_path,self.dev,config_path=diffusion_config_path)
-                if self.only_diffusion:
-                    self.target_sample = self.diffusion_args.data.sampling_rate
-                    self.hop_size = self.diffusion_args.data.block_size
-                    self.spk2id = self.diffusion_args.spk
-                    self.speech_encoder = self.diffusion_args.data.encoder
-            else:
-                print("No diffusion model or config found. Shallow diffusion mode will False")
-                self.shallow_diffusion = self.only_diffusion = False
-                
-        # load hubert and model
-        if not self.only_diffusion:
-            self.load_model()
-            self.hubert_model = utils.get_speech_encoder(self.speech_encoder,device=self.dev)
-            self.volume_extractor = utils.Volume_Extractor(self.hop_size)
-        else:
-            self.hubert_model = utils.get_speech_encoder(self.diffusion_args.data.encoder,device=self.dev)
-            self.volume_extractor = utils.Volume_Extractor(self.diffusion_args.data.block_size)
-            
-        if os.path.exists(cluster_model_path):
-            self.cluster_model = cluster.get_cluster_model(cluster_model_path)
-        if self.shallow_diffusion : self.nsf_hifigan_enhance = False
-        if self.nsf_hifigan_enhance:
-            from modules.enhancer import Enhancer
-            self.enhancer = Enhancer('nsf-hifigan', 'pretrain/nsf_hifigan/model',device=self.dev)
-            
-    def load_model(self):
-        # get model configuration
-        self.net_g_ms = SynthesizerTrn(
-            self.hps_ms.data.filter_length // 2 + 1,
-            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
-            **self.hps_ms.model)
-        _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            _ = self.net_g_ms.half().eval().to(self.dev)
-        else:
-            _ = self.net_g_ms.eval().to(self.dev)
-
-
-
-    def get_unit_f0(self, wav, tran, cluster_infer_ratio, speaker, f0_filter ,f0_predictor,cr_threshold=0.05):
-
-        f0_predictor_object = utils.get_f0_predictor(f0_predictor,hop_length=self.hop_size,sampling_rate=self.target_sample,device=self.dev,threshold=cr_threshold)
-        
-        f0, uv = f0_predictor_object.compute_f0_uv(wav)
-        if f0_filter and sum(f0) == 0:
-            raise F0FilterException("No voice detected")
-        f0 = torch.FloatTensor(f0).to(self.dev)
-        uv = torch.FloatTensor(uv).to(self.dev)
-
-        f0 = f0 * 2 ** (tran / 12)
-        f0 = f0.unsqueeze(0)
-        uv = uv.unsqueeze(0)
-
-        wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000)
-        wav16k = torch.from_numpy(wav16k).to(self.dev)
-        c = self.hubert_model.encoder(wav16k)
-        c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])
-
-        if cluster_infer_ratio !=0:
-            cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker).T
-            cluster_c = torch.FloatTensor(cluster_c).to(self.dev)
-            c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c
-
-        c = c.unsqueeze(0)
-        return c, f0, uv
-
-    def infer(self, speaker, tran, raw_path,
-              cluster_infer_ratio=0,
-              auto_predict_f0=False,
-              noice_scale=0.4,
-              f0_filter=False,
-              f0_predictor='pm',
-              enhancer_adaptive_key = 0,
-              cr_threshold = 0.05,
-              k_step = 100
-              ):
-
-        speaker_id = self.spk2id.get(speaker)
-        if not speaker_id and type(speaker) is int:
-            if len(self.spk2id.__dict__) >= speaker:
-                speaker_id = speaker
-        sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
-        wav, sr = librosa.load(raw_path, sr=self.target_sample)
-        c, f0, uv = self.get_unit_f0(wav, tran, cluster_infer_ratio, speaker, f0_filter,f0_predictor,cr_threshold=cr_threshold)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            c = c.half()
-        with torch.no_grad():
-            start = time.time()
-            if not self.only_diffusion:
-                audio,f0 = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale)
-                audio = audio[0,0].data.float()
-                if self.shallow_diffusion:
-                    audio_mel = self.vocoder.extract(audio[None,:],self.target_sample)
-            else:
-                audio = torch.FloatTensor(wav).to(self.dev)
-                audio_mel = None
-            if self.only_diffusion or self.shallow_diffusion:
-                vol = self.volume_extractor.extract(audio[None,:])[None,:,None].to(self.dev)
-                f0 = f0[:,:,None]
-                c = c.transpose(-1,-2)
-                audio_mel = self.diffusion_model(
-                c, 
-                f0, 
-                vol, 
-                spk_id = sid, 
-                spk_mix_dict = None,
-                gt_spec=audio_mel,
-                infer=True, 
-                infer_speedup=self.diffusion_args.infer.speedup, 
-                method=self.diffusion_args.infer.method,
-                k_step=k_step)
-                audio = self.vocoder.infer(audio_mel, f0).squeeze()
-            if self.nsf_hifigan_enhance:
-                audio, _ = self.enhancer.enhance(
-                                    audio[None,:], 
-                                    self.target_sample, 
-                                    f0[:,:,None], 
-                                    self.hps_ms.data.hop_length, 
-                                    adaptive_key = enhancer_adaptive_key)
-            use_time = time.time() - start
-            print("vits use time:{}".format(use_time))
-        return audio, audio.shape[-1]
-
-    def clear_empty(self):
-        # clean up vram
-        torch.cuda.empty_cache()
-
-    def unload_model(self):
-        # unload model
-        self.net_g_ms = self.net_g_ms.to("cpu")
-        del self.net_g_ms
-        if hasattr(self,"enhancer"): 
-            self.enhancer.enhancer = self.enhancer.enhancer.to("cpu")
-            del self.enhancer.enhancer
-            del self.enhancer
-        gc.collect()
-
-    def slice_inference(self,
-                        raw_audio_path,
-                        spk,
-                        tran,
-                        slice_db,
-                        cluster_infer_ratio,
-                        auto_predict_f0,
-                        noice_scale,
-                        pad_seconds=0.5,
-                        clip_seconds=0,
-                        lg_num=0,
-                        lgr_num =0.75,
-                        f0_predictor='pm',
-                        enhancer_adaptive_key = 0,
-                        cr_threshold = 0.05,
-                        k_step = 100
-                        ):
-        wav_path = Path(raw_audio_path).with_suffix('.wav')
-        chunks = slicer.cut(wav_path, db_thresh=slice_db)
-        audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
-        per_size = int(clip_seconds*audio_sr)
-        lg_size = int(lg_num*audio_sr)
-        lg_size_r = int(lg_size*lgr_num)
-        lg_size_c_l = (lg_size-lg_size_r)//2
-        lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
-        lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0
-        
-        audio = []
-        for (slice_tag, data) in audio_data:
-            print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
-            # padd
-            length = int(np.ceil(len(data) / audio_sr * self.target_sample))
-            if slice_tag:
-                print('jump empty segment')
-                _audio = np.zeros(length)
-                audio.extend(list(pad_array(_audio, length)))
-                continue
-            if per_size != 0:
-                datas = split_list_by_n(data, per_size,lg_size)
-            else:
-                datas = [data]
-            for k,dat in enumerate(datas):
-                per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) if clip_seconds!=0 else length
-                if clip_seconds!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
-                # padd
-                pad_len = int(audio_sr * pad_seconds)
-                dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
-                raw_path = io.BytesIO()
-                soundfile.write(raw_path, dat, audio_sr, format="wav")
-                raw_path.seek(0)
-                out_audio, out_sr = self.infer(spk, tran, raw_path,
-                                                    cluster_infer_ratio=cluster_infer_ratio,
-                                                    auto_predict_f0=auto_predict_f0,
-                                                    noice_scale=noice_scale,
-                                                    f0_predictor = f0_predictor,
-                                                    enhancer_adaptive_key = enhancer_adaptive_key,
-                                                    cr_threshold = cr_threshold,
-                                                    k_step = k_step
-                                                    )
-                _audio = out_audio.cpu().numpy()
-                pad_len = int(self.target_sample * pad_seconds)
-                _audio = _audio[pad_len:-pad_len]
-                _audio = pad_array(_audio, per_length)
-                if lg_size!=0 and k!=0:
-                    lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr_num != 1 else audio[-lg_size:]
-                    lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r]  if lgr_num != 1 else _audio[0:lg_size]
-                    lg_pre = lg1*(1-lg)+lg2*lg
-                    audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr_num != 1 else audio[0:-lg_size]
-                    audio.extend(lg_pre)
-                    _audio = _audio[lg_size_c_l+lg_size_r:] if lgr_num != 1 else _audio[lg_size:]
-                audio.extend(list(_audio))
-        return np.array(audio)
-
-class RealTimeVC:
-    def __init__(self):
-        self.last_chunk = None
-        self.last_o = None
-        self.chunk_len = 16000  # chunk length
-        self.pre_len = 3840  # cross fade length, multiples of 640
-
-    # Input and output are 1-dimensional numpy waveform arrays
-
-    def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path,
-                cluster_infer_ratio=0,
-                auto_predict_f0=False,
-                noice_scale=0.4,
-                f0_filter=False):
-
-        import maad
-        audio, sr = torchaudio.load(input_wav_path)
-        audio = audio.cpu().numpy()[0]
-        temp_wav = io.BytesIO()
-        if self.last_chunk is None:
-            input_wav_path.seek(0)
-
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path,
-                                        cluster_infer_ratio=cluster_infer_ratio,
-                                        auto_predict_f0=auto_predict_f0,
-                                        noice_scale=noice_scale,
-                                        f0_filter=f0_filter)
-            
-            audio = audio.cpu().numpy()
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return audio[-self.chunk_len:]
-        else:
-            audio = np.concatenate([self.last_chunk, audio])
-            soundfile.write(temp_wav, audio, sr, format="wav")
-            temp_wav.seek(0)
-
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav,
-                                        cluster_infer_ratio=cluster_infer_ratio,
-                                        auto_predict_f0=auto_predict_f0,
-                                        noice_scale=noice_scale,
-                                        f0_filter=f0_filter)
-
-            audio = audio.cpu().numpy()
-            ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return ret[self.chunk_len:2 * self.chunk_len]
-            
\ No newline at end of file
diff --git a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/commons.py b/spaces/FrankZxShen/vits-fast-finetuning-umamusume/commons.py
deleted file mode 100644
index db17cf0914ba6e445fe613e3ec3411b3a74b28aa..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/commons.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    try:
-      ret[i] = x[i, :, idx_str:idx_end]
-    except RuntimeError:
-      print("?")
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/FriendlyUser/bark/README.md b/spaces/FriendlyUser/bark/README.md
deleted file mode 100644
index 9a4f7ae56cc52a5efaaa2def75521356c1f54590..0000000000000000000000000000000000000000
--- a/spaces/FriendlyUser/bark/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Bark
-emoji: 🐶
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: cc-by-nc-4.0
-duplicated_from: suno/bark
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Gabriel/Swe_summarizer/LexRank.py b/spaces/Gabriel/Swe_summarizer/LexRank.py
deleted file mode 100644
index 221e57b6e0c0727b52742c14da662e656e0badf1..0000000000000000000000000000000000000000
--- a/spaces/Gabriel/Swe_summarizer/LexRank.py
+++ /dev/null
@@ -1,120 +0,0 @@
-
-import numpy as np
-from scipy.sparse.csgraph import connected_components
-from scipy.special import softmax
-import logging
-
-logger = logging.getLogger(__name__)
-
-def degree_centrality_scores(
-    similarity_matrix,
-    threshold=None,
-    increase_power=True,
-):
-    if not (
-        threshold is None
-        or isinstance(threshold, float)
-        and 0 <= threshold < 1
-    ):
-        raise ValueError(
-            '\'threshold\' should be a floating-point number '
-            'from the interval [0, 1) or None',
-        )
-
-    if threshold is None:
-        markov_matrix = create_markov_matrix(similarity_matrix)
-
-    else:
-        markov_matrix = create_markov_matrix_discrete(
-            similarity_matrix,
-            threshold,
-        )
-
-    scores = stationary_distribution(
-        markov_matrix,
-        increase_power=increase_power,
-        normalized=False,
-    )
-
-    return scores
-
-
-def _power_method(transition_matrix, increase_power=True, max_iter=10000):
-    eigenvector = np.ones(len(transition_matrix))
-
-    if len(eigenvector) == 1:
-        return eigenvector
-
-    transition = transition_matrix.transpose()
-
-    for _ in range(max_iter):
-        eigenvector_next = np.dot(transition, eigenvector)
-
-        if np.allclose(eigenvector_next, eigenvector):
-            return eigenvector_next
-
-        eigenvector = eigenvector_next
-
-        if increase_power:
-            transition = np.dot(transition, transition)
-
-    logger.warning("Maximum number of iterations for power method exceeded without convergence!")
-    return eigenvector_next
-
-
-def connected_nodes(matrix):
-    _, labels = connected_components(matrix)
-
-    groups = []
-
-    for tag in np.unique(labels):
-        group = np.where(labels == tag)[0]
-        groups.append(group)
-
-    return groups
-
-
-def create_markov_matrix(weights_matrix):
-    n_1, n_2 = weights_matrix.shape
-    if n_1 != n_2:
-        raise ValueError('\'weights_matrix\' should be square')
-
-    row_sum = weights_matrix.sum(axis=1, keepdims=True)
-
-    # normalize probability distribution differently if we have negative transition values
-    if np.min(weights_matrix) <= 0:
-        return softmax(weights_matrix, axis=1)
-
-    return weights_matrix / row_sum
-
-
-def create_markov_matrix_discrete(weights_matrix, threshold):
-    discrete_weights_matrix = np.zeros(weights_matrix.shape)
-    ixs = np.where(weights_matrix >= threshold)
-    discrete_weights_matrix[ixs] = 1
-
-    return create_markov_matrix(discrete_weights_matrix)
-
-
-def stationary_distribution(
-    transition_matrix,
-    increase_power=True,
-    normalized=True,
-):
-    n_1, n_2 = transition_matrix.shape
-    if n_1 != n_2:
-        raise ValueError('\'transition_matrix\' should be square')
-
-    distribution = np.zeros(n_1)
-
-    grouped_indices = connected_nodes(transition_matrix)
-
-    for group in grouped_indices:
-        t_matrix = transition_matrix[np.ix_(group, group)]
-        eigenvector = _power_method(t_matrix, increase_power=increase_power)
-        distribution[group] = eigenvector
-
-    if normalized:
-        distribution /= n_1
-
-    return distribution
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
deleted file mode 100644
index 7fb8e82ece225ab6f88f1f4f83bea56a42cf1a57..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
+++ /dev/null
@@ -1,11 +0,0 @@
-_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        norm_cfg=dict(type='SyncBN', requires_grad=True),
-        norm_eval=False,
-        plugins=[
-            dict(
-                cfg=dict(type='ContextBlock', ratio=1. / 16),
-                stages=(False, True, True, True),
-                position='after_conv3')
-        ]))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py
deleted file mode 100644
index 63c8717182f2284ff1062be31bae43b4360c6887..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py
deleted file mode 100644
index f3a15b41054318d508e98685632921f262029de0..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './fcn_r50-d8_480x480_40k_pascal_context.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/hrnet/fcn_hr48_512x512_80k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/hrnet/fcn_hr48_512x512_80k_ade20k.py
deleted file mode 100644
index 7eca7fa4b8102c6225af3b484ffff5bdc7c0f201..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/hrnet/fcn_hr48_512x512_80k_ade20k.py
+++ /dev/null
@@ -1,10 +0,0 @@
-_base_ = './fcn_hr18_512x512_80k_ade20k.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w48',
-    backbone=dict(
-        extra=dict(
-            stage2=dict(num_channels=(48, 96)),
-            stage3=dict(num_channels=(48, 96, 192)),
-            stage4=dict(num_channels=(48, 96, 192, 384)))),
-    decode_head=dict(
-        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/tools/pytorch2onnx.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/tools/pytorch2onnx.py
deleted file mode 100644
index 5660ed9004b9a75ea5e1206a4de0b152f13f66f0..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/tools/pytorch2onnx.py
+++ /dev/null
@@ -1,389 +0,0 @@
-import argparse
-from functools import partial
-
-import mmcv
-import numpy as np
-import onnxruntime as rt
-import torch
-import torch._C
-import torch.serialization
-from mmcv import DictAction
-from mmcv.onnx import register_extra_symbolics
-from mmcv.runner import load_checkpoint
-from torch import nn
-
-from mmseg.apis import show_result_pyplot
-from mmseg.apis.inference import LoadImage
-from mmseg.datasets.pipelines import Compose
-from mmseg.models import build_segmentor
-
-torch.manual_seed(3)
-
-
-def _convert_batchnorm(module):
-    module_output = module
-    if isinstance(module, torch.nn.SyncBatchNorm):
-        module_output = torch.nn.BatchNorm2d(module.num_features, module.eps,
-                                             module.momentum, module.affine,
-                                             module.track_running_stats)
-        if module.affine:
-            module_output.weight.data = module.weight.data.clone().detach()
-            module_output.bias.data = module.bias.data.clone().detach()
-            # keep requires_grad unchanged
-            module_output.weight.requires_grad = module.weight.requires_grad
-            module_output.bias.requires_grad = module.bias.requires_grad
-        module_output.running_mean = module.running_mean
-        module_output.running_var = module.running_var
-        module_output.num_batches_tracked = module.num_batches_tracked
-    for name, child in module.named_children():
-        module_output.add_module(name, _convert_batchnorm(child))
-    del module
-    return module_output
-
-
-def _demo_mm_inputs(input_shape, num_classes):
-    """Create a superset of inputs needed to run test or train batches.
-
-    Args:
-        input_shape (tuple):
-            input batch dimensions
-        num_classes (int):
-            number of semantic classes
-    """
-    (N, C, H, W) = input_shape
-    rng = np.random.RandomState(0)
-    imgs = rng.rand(*input_shape)
-    segs = rng.randint(
-        low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8)
-    img_metas = [{
-        'img_shape': (H, W, C),
-        'ori_shape': (H, W, C),
-        'pad_shape': (H, W, C),
-        'filename': '<demo>.png',
-        'scale_factor': 1.0,
-        'flip': False,
-    } for _ in range(N)]
-    mm_inputs = {
-        'imgs': torch.FloatTensor(imgs).requires_grad_(True),
-        'img_metas': img_metas,
-        'gt_semantic_seg': torch.LongTensor(segs)
-    }
-    return mm_inputs
-
-
-def _prepare_input_img(img_path,
-                       test_pipeline,
-                       shape=None,
-                       rescale_shape=None):
-    # build the data pipeline
-    if shape is not None:
-        test_pipeline[1]['img_scale'] = (shape[1], shape[0])
-    test_pipeline[1]['transforms'][0]['keep_ratio'] = False
-    test_pipeline = [LoadImage()] + test_pipeline[1:]
-    test_pipeline = Compose(test_pipeline)
-    # prepare data
-    data = dict(img=img_path)
-    data = test_pipeline(data)
-    imgs = data['img']
-    img_metas = [i.data for i in data['img_metas']]
-
-    if rescale_shape is not None:
-        for img_meta in img_metas:
-            img_meta['ori_shape'] = tuple(rescale_shape) + (3, )
-
-    mm_inputs = {'imgs': imgs, 'img_metas': img_metas}
-
-    return mm_inputs
-
-
-def _update_input_img(img_list, img_meta_list):
-    # update img and its meta list
-    N = img_list[0].size(0)
-    img_meta = img_meta_list[0][0]
-    img_shape = img_meta['img_shape']
-    ori_shape = img_meta['ori_shape']
-    pad_shape = img_meta['pad_shape']
-    new_img_meta_list = [[{
-        'img_shape':
-        img_shape,
-        'ori_shape':
-        ori_shape,
-        'pad_shape':
-        pad_shape,
-        'filename':
-        img_meta['filename'],
-        'scale_factor':
-        (img_shape[1] / ori_shape[1], img_shape[0] / ori_shape[0]) * 2,
-        'flip':
-        False,
-    } for _ in range(N)]]
-
-    return img_list, new_img_meta_list
-
-
-def pytorch2onnx(model,
-                 mm_inputs,
-                 opset_version=11,
-                 show=False,
-                 output_file='tmp.onnx',
-                 verify=False,
-                 dynamic_export=False):
-    """Export Pytorch model to ONNX model and verify the outputs are same
-    between Pytorch and ONNX.
-
-    Args:
-        model (nn.Module): Pytorch model we want to export.
-        mm_inputs (dict): Contain the input tensors and img_metas information.
-        opset_version (int): The onnx op version. Default: 11.
-        show (bool): Whether print the computation graph. Default: False.
-        output_file (string): The path to where we store the output ONNX model.
-            Default: `tmp.onnx`.
-        verify (bool): Whether compare the outputs between Pytorch and ONNX.
-            Default: False.
-        dynamic_export (bool): Whether to export ONNX with dynamic axis.
-            Default: False.
-    """
-    model.cpu().eval()
-    test_mode = model.test_cfg.mode
-
-    if isinstance(model.decode_head, nn.ModuleList):
-        num_classes = model.decode_head[-1].num_classes
-    else:
-        num_classes = model.decode_head.num_classes
-
-    imgs = mm_inputs.pop('imgs')
-    img_metas = mm_inputs.pop('img_metas')
-
-    img_list = [img[None, :] for img in imgs]
-    img_meta_list = [[img_meta] for img_meta in img_metas]
-    # update img_meta
-    img_list, img_meta_list = _update_input_img(img_list, img_meta_list)
-
-    # replace original forward function
-    origin_forward = model.forward
-    model.forward = partial(
-        model.forward,
-        img_metas=img_meta_list,
-        return_loss=False,
-        rescale=True)
-    dynamic_axes = None
-    if dynamic_export:
-        if test_mode == 'slide':
-            dynamic_axes = {'input': {0: 'batch'}, 'output': {1: 'batch'}}
-        else:
-            dynamic_axes = {
-                'input': {
-                    0: 'batch',
-                    2: 'height',
-                    3: 'width'
-                },
-                'output': {
-                    1: 'batch',
-                    2: 'height',
-                    3: 'width'
-                }
-            }
-
-    register_extra_symbolics(opset_version)
-    with torch.no_grad():
-        torch.onnx.export(
-            model, (img_list, ),
-            output_file,
-            input_names=['input'],
-            output_names=['output'],
-            export_params=True,
-            keep_initializers_as_inputs=False,
-            verbose=show,
-            opset_version=opset_version,
-            dynamic_axes=dynamic_axes)
-        print(f'Successfully exported ONNX model: {output_file}')
-    model.forward = origin_forward
-
-    if verify:
-        # check by onnx
-        import onnx
-        onnx_model = onnx.load(output_file)
-        onnx.checker.check_model(onnx_model)
-
-        if dynamic_export and test_mode == 'whole':
-            # scale image for dynamic shape test
-            img_list = [
-                nn.functional.interpolate(_, scale_factor=1.5)
-                for _ in img_list
-            ]
-            # concate flip image for batch test
-            flip_img_list = [_.flip(-1) for _ in img_list]
-            img_list = [
-                torch.cat((ori_img, flip_img), 0)
-                for ori_img, flip_img in zip(img_list, flip_img_list)
-            ]
-
-            # update img_meta
-            img_list, img_meta_list = _update_input_img(
-                img_list, img_meta_list)
-
-        # check the numerical value
-        # get pytorch output
-        with torch.no_grad():
-            pytorch_result = model(img_list, img_meta_list, return_loss=False)
-            pytorch_result = np.stack(pytorch_result, 0)
-
-        # get onnx output
-        input_all = [node.name for node in onnx_model.graph.input]
-        input_initializer = [
-            node.name for node in onnx_model.graph.initializer
-        ]
-        net_feed_input = list(set(input_all) - set(input_initializer))
-        assert (len(net_feed_input) == 1)
-        sess = rt.InferenceSession(output_file)
-        onnx_result = sess.run(
-            None, {net_feed_input[0]: img_list[0].detach().numpy()})[0][0]
-        # show segmentation results
-        if show:
-            import cv2
-            import os.path as osp
-            img = img_meta_list[0][0]['filename']
-            if not osp.exists(img):
-                img = imgs[0][:3, ...].permute(1, 2, 0) * 255
-                img = img.detach().numpy().astype(np.uint8)
-                ori_shape = img.shape[:2]
-            else:
-                ori_shape = LoadImage()({'img': img})['ori_shape']
-
-            # resize onnx_result to ori_shape
-            onnx_result_ = cv2.resize(onnx_result[0].astype(np.uint8),
-                                      (ori_shape[1], ori_shape[0]))
-            show_result_pyplot(
-                model,
-                img, (onnx_result_, ),
-                palette=model.PALETTE,
-                block=False,
-                title='ONNXRuntime',
-                opacity=0.5)
-
-            # resize pytorch_result to ori_shape
-            pytorch_result_ = cv2.resize(pytorch_result[0].astype(np.uint8),
-                                         (ori_shape[1], ori_shape[0]))
-            show_result_pyplot(
-                model,
-                img, (pytorch_result_, ),
-                title='PyTorch',
-                palette=model.PALETTE,
-                opacity=0.5)
-        # compare results
-        np.testing.assert_allclose(
-            pytorch_result.astype(np.float32) / num_classes,
-            onnx_result.astype(np.float32) / num_classes,
-            rtol=1e-5,
-            atol=1e-5,
-            err_msg='The outputs are different between Pytorch and ONNX')
-        print('The outputs are same between Pytorch and ONNX')
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Convert MMSeg to ONNX')
-    parser.add_argument('config', help='test config file path')
-    parser.add_argument('--checkpoint', help='checkpoint file', default=None)
-    parser.add_argument(
-        '--input-img', type=str, help='Images for input', default=None)
-    parser.add_argument(
-        '--show',
-        action='store_true',
-        help='show onnx graph and segmentation results')
-    parser.add_argument(
-        '--verify', action='store_true', help='verify the onnx model')
-    parser.add_argument('--output-file', type=str, default='tmp.onnx')
-    parser.add_argument('--opset-version', type=int, default=11)
-    parser.add_argument(
-        '--shape',
-        type=int,
-        nargs='+',
-        default=None,
-        help='input image height and width.')
-    parser.add_argument(
-        '--rescale_shape',
-        type=int,
-        nargs='+',
-        default=None,
-        help='output image rescale height and width, work for slide mode.')
-    parser.add_argument(
-        '--cfg-options',
-        nargs='+',
-        action=DictAction,
-        help='Override some settings in the used config, the key-value pair '
-        'in xxx=yyy format will be merged into config file. If the value to '
-        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
-        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
-        'Note that the quotation marks are necessary and that no white space '
-        'is allowed.')
-    parser.add_argument(
-        '--dynamic-export',
-        action='store_true',
-        help='Whether to export onnx with dynamic axis.')
-    args = parser.parse_args()
-    return args
-
-
-if __name__ == '__main__':
-    args = parse_args()
-
-    cfg = mmcv.Config.fromfile(args.config)
-    if args.cfg_options is not None:
-        cfg.merge_from_dict(args.cfg_options)
-    cfg.model.pretrained = None
-
-    if args.shape is None:
-        img_scale = cfg.test_pipeline[1]['img_scale']
-        input_shape = (1, 3, img_scale[1], img_scale[0])
-    elif len(args.shape) == 1:
-        input_shape = (1, 3, args.shape[0], args.shape[0])
-    elif len(args.shape) == 2:
-        input_shape = (
-            1,
-            3,
-        ) + tuple(args.shape)
-    else:
-        raise ValueError('invalid input shape')
-
-    test_mode = cfg.model.test_cfg.mode
-
-    # build the model and load checkpoint
-    cfg.model.train_cfg = None
-    segmentor = build_segmentor(
-        cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
-    # convert SyncBN to BN
-    segmentor = _convert_batchnorm(segmentor)
-
-    if args.checkpoint:
-        checkpoint = load_checkpoint(
-            segmentor, args.checkpoint, map_location='cpu')
-        segmentor.CLASSES = checkpoint['meta']['CLASSES']
-        segmentor.PALETTE = checkpoint['meta']['PALETTE']
-
-    # read input or create dummpy input
-    if args.input_img is not None:
-        preprocess_shape = (input_shape[2], input_shape[3])
-        rescale_shape = None
-        if args.rescale_shape is not None:
-            rescale_shape = [args.rescale_shape[0], args.rescale_shape[1]]
-        mm_inputs = _prepare_input_img(
-            args.input_img,
-            cfg.data.test.pipeline,
-            shape=preprocess_shape,
-            rescale_shape=rescale_shape)
-    else:
-        if isinstance(segmentor.decode_head, nn.ModuleList):
-            num_classes = segmentor.decode_head[-1].num_classes
-        else:
-            num_classes = segmentor.decode_head.num_classes
-        mm_inputs = _demo_mm_inputs(input_shape, num_classes)
-
-    # convert model to onnx file
-    pytorch2onnx(
-        segmentor,
-        mm_inputs,
-        opset_version=args.opset_version,
-        show=args.show,
-        output_file=args.output_file,
-        verify=args.verify,
-        dynamic_export=args.dynamic_export)
diff --git a/spaces/GroveStreet/GTA_SOVITS/vdecoder/hifiganwithsnake/nvSTFT.py b/spaces/GroveStreet/GTA_SOVITS/vdecoder/hifiganwithsnake/nvSTFT.py
deleted file mode 100644
index 88597d62a505715091f9ba62d38bf0a85a31b95a..0000000000000000000000000000000000000000
--- a/spaces/GroveStreet/GTA_SOVITS/vdecoder/hifiganwithsnake/nvSTFT.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-import os
-os.environ["LRU_CACHE_CAPACITY"] = "3"
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.util import normalize
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
-import soundfile as sf
-
-def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
-    sampling_rate = None
-    try:
-        data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
-    except Exception as ex:
-        print(f"'{full_path}' failed to load.\nException:")
-        print(ex)
-        if return_empty_on_exception:
-            return [], sampling_rate or target_sr or 32000
-        else:
-            raise Exception(ex)
-    
-    if len(data.shape) > 1:
-        data = data[:, 0]
-        assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
-    
-    if np.issubdtype(data.dtype, np.integer): # if audio data is type int
-        max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
-    else: # if audio data is type fp32
-        max_mag = max(np.amax(data), -np.amin(data))
-        max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
-    
-    data = torch.FloatTensor(data.astype(np.float32))/max_mag
-    
-    if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
-        return [], sampling_rate or target_sr or 32000
-    if target_sr is not None and sampling_rate != target_sr:
-        data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
-        sampling_rate = target_sr
-    
-    return data, sampling_rate
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
-    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-def dynamic_range_decompression(x, C=1):
-    return np.exp(x) / C
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-def dynamic_range_decompression_torch(x, C=1):
-    return torch.exp(x) / C
-
-class STFT():
-    def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
-        self.target_sr = sr
-        
-        self.n_mels     = n_mels
-        self.n_fft      = n_fft
-        self.win_size   = win_size
-        self.hop_length = hop_length
-        self.fmin     = fmin
-        self.fmax     = fmax
-        self.clip_val = clip_val
-        self.mel_basis = {}
-        self.hann_window = {}
-    
-    def get_mel(self, y, center=False):
-        sampling_rate = self.target_sr
-        n_mels     = self.n_mels
-        n_fft      = self.n_fft
-        win_size   = self.win_size
-        hop_length = self.hop_length
-        fmin       = self.fmin
-        fmax       = self.fmax
-        clip_val   = self.clip_val
-        
-        if torch.min(y) < -1.:
-            print('min value is ', torch.min(y))
-        if torch.max(y) > 1.:
-            print('max value is ', torch.max(y))
-        
-        if fmax not in self.mel_basis:
-            mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
-            self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
-            self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
-        
-        y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
-        y = y.squeeze(1)
-        
-        spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
-                          center=center, pad_mode='reflect', normalized=False, onesided=True)
-        # print(111,spec)
-        spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
-        # print(222,spec)
-        spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
-        # print(333,spec)
-        spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
-        # print(444,spec)
-        return spec
-    
-    def __call__(self, audiopath):
-        audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
-        spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
-        return spect
-
-stft = STFT()
diff --git a/spaces/GroveStreet/GTA_SOVITS/vencoder/HubertSoft.py b/spaces/GroveStreet/GTA_SOVITS/vencoder/HubertSoft.py
deleted file mode 100644
index c7155e9edd8b3d898643f59111cd0c7a83067749..0000000000000000000000000000000000000000
--- a/spaces/GroveStreet/GTA_SOVITS/vencoder/HubertSoft.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from vencoder.encoder import SpeechEncoder
-import torch
-from vencoder.hubert import hubert_model
-class HubertSoft(SpeechEncoder):
-    def __init__(self,vec_path = "pretrain/hubert-soft-0d54a1f4.pt",device=None):
-        print("load model(s) from {}".format(vec_path))
-        hubert_soft = hubert_model.hubert_soft(vec_path)
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        self.hidden_dim = 256
-        self.model = hubert_soft.to(self.dev)
-
-    def encoder(self, wav):
-        feats = wav
-        if feats.dim() == 2:  # double channels
-          feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats[None,None,:]  
-        with torch.no_grad():
-            with torch.inference_mode():
-              units = self.model.units(feats)
-              return units.transpose(1,2)
diff --git a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/upsegmodel/prroi_pool/functional.py b/spaces/HaHaBill/LandShapes-Antarctica/netdissect/upsegmodel/prroi_pool/functional.py
deleted file mode 100644
index 7dc7a8c282e846bd633c4fdc4190c4dca3da5a6f..0000000000000000000000000000000000000000
--- a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/upsegmodel/prroi_pool/functional.py
+++ /dev/null
@@ -1,70 +0,0 @@
-#! /usr/bin/env python3
-# -*- coding: utf-8 -*-
-# File   : functional.py
-# Author : Jiayuan Mao, Tete Xiao
-# Email  : maojiayuan@gmail.com, jasonhsiao97@gmail.com
-# Date   : 07/13/2018
-#
-# This file is part of PreciseRoIPooling.
-# Distributed under terms of the MIT license.
-# Copyright (c) 2017 Megvii Technology Limited.
-
-import torch
-import torch.autograd as ag
-
-try:
-    from os.path import join as pjoin, dirname
-    from torch.utils.cpp_extension import load as load_extension
-    root_dir = pjoin(dirname(__file__), 'src')
-    _prroi_pooling = load_extension(
-        '_prroi_pooling',
-        [pjoin(root_dir, 'prroi_pooling_gpu.c'), pjoin(root_dir, 'prroi_pooling_gpu_impl.cu')],
-        verbose=False
-    )
-except ImportError:
-    raise ImportError('Can not compile Precise RoI Pooling library.')
-
-__all__ = ['prroi_pool2d']
-
-
-class PrRoIPool2DFunction(ag.Function):
-    @staticmethod
-    def forward(ctx, features, rois, pooled_height, pooled_width, spatial_scale):
-        assert 'FloatTensor' in features.type() and 'FloatTensor' in rois.type(), \
-                'Precise RoI Pooling only takes float input, got {} for features and {} for rois.'.format(features.type(), rois.type())
-
-        pooled_height = int(pooled_height)
-        pooled_width = int(pooled_width)
-        spatial_scale = float(spatial_scale)
-
-        features = features.contiguous()
-        rois = rois.contiguous()
-        params = (pooled_height, pooled_width, spatial_scale)
-
-        if features.is_cuda:
-            output = _prroi_pooling.prroi_pooling_forward_cuda(features, rois, *params)
-            ctx.params = params
-            # everything here is contiguous.
-            ctx.save_for_backward(features, rois, output)
-        else:
-            raise NotImplementedError('Precise RoI Pooling only supports GPU (cuda) implememtations.')
-
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        features, rois, output = ctx.saved_tensors
-        grad_input = grad_coor = None
-
-        if features.requires_grad:
-            grad_output = grad_output.contiguous()
-            grad_input = _prroi_pooling.prroi_pooling_backward_cuda(features, rois, output, grad_output, *ctx.params)
-        if rois.requires_grad:
-            grad_output = grad_output.contiguous()
-            grad_coor = _prroi_pooling.prroi_pooling_coor_backward_cuda(features, rois, output, grad_output, *ctx.params)
-
-        return grad_input, grad_coor, None, None, None
-
-
-prroi_pool2d = PrRoIPool2DFunction.apply
-
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/tokenizer/sentencepiece/pretrain_google_sp.sh b/spaces/HaloMaster/chinesesummary/fengshen/tokenizer/sentencepiece/pretrain_google_sp.sh
deleted file mode 100644
index e7dd39f59dac0314a9b285c02f05156fda67e622..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/tokenizer/sentencepiece/pretrain_google_sp.sh
+++ /dev/null
@@ -1,41 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=google_sp
-#SBATCH --nodes=1
-#SBATCH --cpus-per-task=100
-#SBATCH --ntasks-per-node=1
-#SBATCH -o %x-%j.log
-
-set -x -e
-
-echo "START TIME: $(date)"
-
-BIN_PATH=/cognitive_comp/gaoxinyu/sentencepiece/sentencepiece/bin/usr/local/bin/spm_train
-export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/cognitive_comp/gaoxinyu/sentencepiece/sentencepiece/bin/usr/local/lib
-INPUT_FILE=/cognitive_comp/gaoxinyu/github/Fengshenbang-LM/fengshen/tokenizer/sentencepiece/shuffle_corpus_59132213.txt
-INPUT_FILE_SMALL=/cognitive_comp/gaoxinyu/github/Fengshenbang-LM/fengshen/tokenizer/sentencepiece/shuffle_corpus_1000000.txt
-
-
-VOCAB_SIZE=40000
-COV=0.9995
-MAX_LENGTH=6
-TYPE=bpe
-SEED=42
-MAX_INPUT_LENGTH=100000
-
-OPTION="\
-    --input=${INPUT_FILE} \
-    --vocab_size=${VOCAB_SIZE} \
-    --character_coverage=${COV} \
-    --max_sentencepiece_length=${MAX_LENGTH} \
-    --model_type=${TYPE} \
-    --model_prefix=${TYPE}_v${VOCAB_SIZE}_s${SEED}_cov${COV}_max${MAX_LENGTH} \
-    --random_seed=${SEED} \
-    --max_sentence_length=100000 \
-    --shuffle_input_sentence=true \
-    --input_sentence_size=${MAX_INPUT_LENGTH} \
-    --minloglevel 1 \
-    --num_threads=100 \
-    --train_extremely_large_corpus=true \
-    "
-
-eval $BIN_PATH $OPTION
\ No newline at end of file
diff --git a/spaces/Hexii/Cat-Breed-Classifier/app.py b/spaces/Hexii/Cat-Breed-Classifier/app.py
deleted file mode 100644
index 4e556ec031157bafdb57b330039a7c319fd3f676..0000000000000000000000000000000000000000
--- a/spaces/Hexii/Cat-Breed-Classifier/app.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import torch
-import torchvision
-from timeit import default_timer as timer
-import gradio as gr
-from typing import Tuple ,Dict
-from model import create_effnetb2_model
-import os
-
-
-
-with open("classes.txt") as f:
-  classes= [line.rstrip() for line in f.readlines()]
-
-effnetb2, effnetb2_transforms = create_effnetb2_model(
-    num_classes=len(classes))
-
-effnetb2.load_state_dict(
-    torch.load(
-        f="Cat_Breed_Classifier_12_class_90_acc.pth",
-        map_location=torch.device("cpu"),  # load to CPU
-    )
-)
-
-def predict(img):
-    start_time = timer()
-    img = effnetb2_transforms(img).unsqueeze(0)
-    effnetb2.eval()
-    with torch.inference_mode():
-        pred_probs = torch.softmax(effnetb2(img), dim=1)
-    pred_labels_and_probs = {
-        classes[i]: float(pred_probs[0][i]) for i in range(len(classes))
-        }
-    pred_time = round(timer() - start_time, 5)
-    return pred_labels_and_probs, pred_time
-
-title = "Cat Breed Classifier Demo 😸"
-description = "Gradio Demo for  Classifying Cat Breeds of these [12 different types](https://huggingface.co/spaces/Hexii/Cat-Breed-Classifier/blob/main/classes.txt)."
-article = "<p style='text-align: center'><a href='https://github.com/Mr-Hexi' target='_blank'>GitHub</a></p> "
-
-
-
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-app = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs=[
-        gr.Label(num_top_classes=3, label="Predictions"),
-        gr.Number(label="Prediction time (s)"),
-    ],
-    examples=example_list,
-    title=title,
-    description=description,
-    article=article,
-    )
-
-app.launch()
\ No newline at end of file
diff --git a/spaces/HuggingFaceH4/falcon-chat/app.py b/spaces/HuggingFaceH4/falcon-chat/app.py
deleted file mode 100644
index 7d496eb4f9707a897f8035930856a0efa3bb18c9..0000000000000000000000000000000000000000
--- a/spaces/HuggingFaceH4/falcon-chat/app.py
+++ /dev/null
@@ -1,215 +0,0 @@
-import argparse
-import os
-
-import gradio as gr
-from text_generation import Client
-
-TITLE = """<h2 align="center">🚀 Falcon-Chat demo</h2>"""
-USER_NAME = "User"
-BOT_NAME = "Falcon"
-DEFAULT_INSTRUCTIONS = f"""The following is a conversation between a highly knowledgeable and intelligent AI assistant, called Falcon, and a human user, called User. In the following interactions, User and Falcon will converse in natural language, and Falcon will answer User's questions. Falcon was built to be respectful, polite and inclusive. Falcon was built by the Technology Innovation Institute in Abu Dhabi. Falcon will never decline to answer a question, and always attempts to give an answer that User would be satisfied with. It knows a lot, and always tells the truth. The conversation begins.
-"""
-RETRY_COMMAND = "/retry"
-STOP_STR = f"\n{USER_NAME}:"
-STOP_SUSPECT_LIST = [":", "\n", "User"]
-
-INFERENCE_ENDPOINT = os.environ.get("INFERENCE_ENDPOINT")
-INFERENCE_AUTH = os.environ.get("INFERENCE_AUTH")
-
-
-def chat_accordion():
-    with gr.Accordion("Parameters", open=False):
-        temperature = gr.Slider(
-            minimum=0.1,
-            maximum=2.0,
-            value=0.8,
-            step=0.1,
-            interactive=True,
-            label="Temperature",
-        )
-        top_p = gr.Slider(
-            minimum=0.1,
-            maximum=0.99,
-            value=0.9,
-            step=0.01,
-            interactive=True,
-            label="p (nucleus sampling)",
-        )
-    return temperature, top_p
-
-
-def format_chat_prompt(message: str, chat_history, instructions: str) -> str:
-    instructions = instructions.strip(" ").strip("\n")
-    prompt = instructions
-    for turn in chat_history:
-        user_message, bot_message = turn
-        prompt = f"{prompt}\n{USER_NAME}: {user_message}\n{BOT_NAME}: {bot_message}"
-    prompt = f"{prompt}\n{USER_NAME}: {message}\n{BOT_NAME}:"
-    return prompt
-
-
-def chat(client: Client):
-    with gr.Column(elem_id="chat_container"):
-        with gr.Row():
-            chatbot = gr.Chatbot(elem_id="chatbot")
-        with gr.Row():
-            inputs = gr.Textbox(
-                placeholder=f"Hello {BOT_NAME} !!",
-                label="Type an input and press Enter",
-                max_lines=3,
-            )
-
-    with gr.Row(elem_id="button_container"):
-        with gr.Column():
-            retry_button = gr.Button("♻️ Retry last turn")
-        with gr.Column():
-            delete_turn_button = gr.Button("🧽 Delete last turn")
-        with gr.Column():
-            clear_chat_button = gr.Button("✨ Delete all history")
-
-    gr.Examples(
-        [
-            ["Hey Falcon! Any recommendations for my holidays in Abu Dhabi?"],
-            ["What's the Everett interpretation of quantum mechanics?"],
-            ["Give me a list of the top 10 dive sites you would recommend around the world."],
-            ["Can you tell me more about deep-water soloing?"],
-            ["Can you write a short tweet about the Apache 2.0 release of our latest AI model, Falcon LLM?"],
-        ],
-        inputs=inputs,
-        label="Click on any example and press Enter in the input textbox!",
-    )
-
-    with gr.Row(elem_id="param_container"):
-        with gr.Column():
-            temperature, top_p = chat_accordion()
-        with gr.Column():
-            with gr.Accordion("Instructions", open=False):
-                instructions = gr.Textbox(
-                    placeholder="LLM instructions",
-                    value=DEFAULT_INSTRUCTIONS,
-                    lines=10,
-                    interactive=True,
-                    label="Instructions",
-                    max_lines=16,
-                    show_label=False,
-                )
-
-    def run_chat(message: str, chat_history, instructions: str, temperature: float, top_p: float):
-        if not message or (message == RETRY_COMMAND and len(chat_history) == 0):
-            yield chat_history
-            return
-
-        if message == RETRY_COMMAND and chat_history:
-            prev_turn = chat_history.pop(-1)
-            user_message, _ = prev_turn
-            message = user_message
-
-        prompt = format_chat_prompt(message, chat_history, instructions)
-        chat_history = chat_history + [[message, ""]]
-        stream = client.generate_stream(
-            prompt,
-            do_sample=True,
-            max_new_tokens=1024,
-            stop_sequences=[STOP_STR, "<|endoftext|>"],
-            temperature=temperature,
-            top_p=top_p,
-        )
-        acc_text = ""
-        for idx, response in enumerate(stream):
-            text_token = response.token.text
-
-            if response.details:
-                return
-
-            if text_token in STOP_SUSPECT_LIST:
-                acc_text += text_token
-                continue
-
-            if idx == 0 and text_token.startswith(" "):
-                text_token = text_token[1:]
-
-            acc_text += text_token
-            last_turn = list(chat_history.pop(-1))
-            last_turn[-1] += acc_text
-            chat_history = chat_history + [last_turn]
-            yield chat_history
-            acc_text = ""
-
-    def delete_last_turn(chat_history):
-        if chat_history:
-            chat_history.pop(-1)
-        return {chatbot: gr.update(value=chat_history)}
-
-    def run_retry(message: str, chat_history, instructions: str, temperature: float, top_p: float):
-        yield from run_chat(RETRY_COMMAND, chat_history, instructions, temperature, top_p)
-
-    def clear_chat():
-        return []
-
-    inputs.submit(
-        run_chat,
-        [inputs, chatbot, instructions, temperature, top_p],
-        outputs=[chatbot],
-        show_progress=False,
-    )
-    inputs.submit(lambda: "", inputs=None, outputs=inputs)
-    delete_turn_button.click(delete_last_turn, inputs=[chatbot], outputs=[chatbot])
-    retry_button.click(
-        run_retry,
-        [inputs, chatbot, instructions, temperature, top_p],
-        outputs=[chatbot],
-        show_progress=False,
-    )
-    clear_chat_button.click(clear_chat, [], chatbot)
-
-
-def get_demo(client: Client):
-    with gr.Blocks(
-        # css=None
-        # css="""#chat_container {width: 700px; margin-left: auto; margin-right: auto;}
-        #        #button_container {width: 700px; margin-left: auto; margin-right: auto;}
-        #        #param_container {width: 700px; margin-left: auto; margin-right: auto;}"""
-        css="""#chatbot {
-    font-size: 14px;
-    min-height: 300px;
-}"""
-    ) as demo:
-        gr.HTML(TITLE)
-
-        with gr.Row():
-            with gr.Column():
-                gr.Image("home-banner.jpg", elem_id="banner-image", show_label=False)
-            with gr.Column():
-                gr.Markdown(
-                    """**Chat with [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct), brainstorm ideas, discuss your holiday plans, and more!**
-
-                    ✨ This demo is powered by [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b), finetuned on the [Baize](https://github.com/project-baize/baize-chatbot) dataset, and running with [Text Generation Inference](https://github.com/huggingface/text-generation-inference). [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) is a state-of-the-art large language model built by the [Technology Innovation Institute](https://www.tii.ae) in Abu Dhabi. It is trained on 1 trillion tokens (including [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)) and available under the Apache 2.0 license. It currently holds the 🥇 1st place on the [🤗 Open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). This demo is made available by the [HuggingFace H4 team](https://huggingface.co/HuggingFaceH4). 
-
-                    🧪 This is only a **first experimental preview**: the [H4 team](https://huggingface.co/HuggingFaceH4) intends to provide increasingly capable versions of Falcon Chat in the future, based on improved datasets and RLHF/RLAIF.
-
-                    👀 **Learn more about Falcon LLM:** [falconllm.tii.ae](https://falconllm.tii.ae/)
-
-                    ➡️️ **Intended Use**: this demo is intended to showcase an early finetuning of [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b), to illustrate the impact (and limitations) of finetuning on a dataset of conversations and instructions. We encourage the community to further build upon the base model, and to create even better instruct/chat versions!
-
-                    ⚠️ **Limitations**: the model can and will produce factually incorrect information, hallucinating facts and actions. As it has not undergone any advanced tuning/alignment, it can produce problematic outputs, especially if prompted to do so. Finally, this demo is limited to a session length of about 1,000 words.
-                    """
-                )
-
-        chat(client)
-
-    return demo
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser("Playground Demo")
-    parser.add_argument(
-        "--addr",
-        type=str,
-        required=False,
-        default=INFERENCE_ENDPOINT,
-    )
-    args = parser.parse_args()
-    client = Client(args.addr, headers={"Authorization": f"Basic {INFERENCE_AUTH}"})
-    demo = get_demo(client)
-    demo.queue(max_size=128, concurrency_count=16)
-    demo.launch()
diff --git a/spaces/HuggingFaceH4/instruction-model-outputs-filtered/app.py b/spaces/HuggingFaceH4/instruction-model-outputs-filtered/app.py
deleted file mode 100644
index 27d43bbe72d5fafa617a9e4b1350f12f02907dc1..0000000000000000000000000000000000000000
--- a/spaces/HuggingFaceH4/instruction-model-outputs-filtered/app.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import os
-from pathlib import Path
-
-import pandas as pd
-import streamlit as st
-from datasets import load_dataset
-from dotenv import load_dotenv
-
-if Path(".env").is_file():
-    load_dotenv(".env")
-
-st.set_page_config(layout="wide")
-
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-ds = load_dataset("HuggingFaceH4/instruction-pilot-outputs-filtered", split="train", use_auth_token=HF_TOKEN)
-
-st.markdown("# Instruction Model Outputs")
-st.markdown(
-    """This app shows the outputs of various open-souce, instruction-trained models from a [dataset](https://huggingface.co/datasets/HuggingFaceH4/instruction-pilot-outputs-filtered) of human demonstrations filtered for overlap with the original prompt and canned responses. Hit the button below to view a few random samples from the generated outputs."""
-)
-st.markdown(
-    """**Notes**
-* Some outputs contain a `Human:` prefix - this is likely due to the fact each model was prompted to be a dialogue agent.
-* The outputs were generated deterministically with `temperature=0` and `max_new_tokens=100`
-"""
-)
-
-button = st.button("Show me what you got!")
-
-if button is True:
-    sample_ds = ds.shuffle().select(range(5))
-
-    for sample in sample_ds:
-        st.markdown(f'**Prompt:** {sample["prompt"]}')
-
-        df = pd.DataFrame.from_records(sample["filtered_outputs"])
-
-        # CSS to inject contained in a string
-        hide_table_row_index = """
-                    <style>
-                    thead tr th:first-child {display:none}
-                    tbody th {display:none}
-                    </style>
-                    """
-
-        # Inject CSS with Markdown
-        st.markdown(hide_table_row_index, unsafe_allow_html=True)
-        st.table(df)
diff --git a/spaces/ICML2022/OFA/fairseq/examples/m2m_100/README.md b/spaces/ICML2022/OFA/fairseq/examples/m2m_100/README.md
deleted file mode 100644
index 02a68a5f0919a26a0468069bed46a5b1abc78941..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/m2m_100/README.md
+++ /dev/null
@@ -1,241 +0,0 @@
-# Beyond English-Centric Multilingual Machine Translation
-
-## Introduction
-In this work, we create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively with the best single systems of WMT. 
-
-If you are new to using fairseq, read the following walkthrough. Otherwise, skip to the sections below. 
-
-0. **Generation Data**
-
-To download the generation data, follow the below commands. Note that all datasets need to be detokenized *before* applying SPM in the data preprocessing step. If you use these evaluation datasets, please cite their associated papers. 
-```bash
-# WMT - use sacrebleu, example here:
-sacrebleu -t wmt14 -l fr-en --echo src > wmt.test.fr-en.fr
-sacrebleu -t wmt14 -l fr-en --echo ref > wmt.test.fr-en.en
-
-# WAT
-wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/wat2020.my-en.zip
-unzip wat2020.my-en.zip
-
-# FLORES
-# download from: https://github.com/facebookresearch/flores
-
-# TED - need to detokenize with Moses!
-# from: https://github.com/neulab/word-embeddings-for-nmt
-wget http://phontron.com/data/ted_talks.tar.gz
-
-# Autshumato
-# request to download: https://repo.sadilar.org/handle/20.500.12185/397
-
-# Tatoeba Challenge
-# available here: https://github.com/Helsinki-NLP/Tatoeba-Challenge
-```
-
-1. **Training Data**
-
-To produce the training data, we use a combination of [CCMatrix](https://arxiv.org/abs/1911.04944) and [CCAligned](https://arxiv.org/abs/1911.06154). Check out the instructions [here](https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix) to download the raw data.
-
-2. **Preprocess Data**
-
-After downloading raw data, you will need to postprocess the data, then apply SPM, then binarize. Note that it is very important you run the postprocessing script, because this removes any instance of the evaluation data in the mined training data.
-
-```bash
-# preprocess data
-
-# remove sentences with more than 50% punctuation
-python /path/to/fairseq/examples/m2m_100/process_data/remove_too_much_punc.py 
-
-# deduplicate training data
-paste /path/to/datadir/train.$src /path/to/datadir/train.$tgt | awk '!x[$0]++' > /path/to/datadir/train.dedup
-echo "keeping $(wc -l /path/to/datadir/train.dedup) bitext out of $(wc -l /path/to/datadir/train.$src)"
-cut -f1 /path/to/datadir/train.dedup > /path/to/datadir/train.$src
-cut -f2 /path/to/datadir/train.dedup > /path/to/datadir/train.$tgt
-
-# remove all instances of evaluation data from the training data
-python /path/to/fairseq/examples/m2m_100/process_data/dedup_data.py 
-
-# frequency cleaning
-wget https://dl.fbaipublicfiles.com/m2m_100/histograms.tar.gz 
-tar -xvzf histograms.tar.gz
-python /path/to/fairseq/examples/m2m_100/process_data/clean_histogram.py --src $src --tgt $tgt --src-file /path/to/source/file --tgt-file /path/to/output/file --src-output-file source_output.$src --tgt-output-file target_output.$tgt --histograms /path/to/histograms
-
-# apply SPM
-wget https://dl.fbaipublicfiles.com/m2m_100/spm.128k.model
-python /path/to/fairseq/scripts/spm_encode.py \
-    --model spm.128k.model \
-    --output_format=piece \
-    --inputs=/path/to/input/file/here \
-    --outputs=/path/to/output/file/here
-
-# length ratio cleaning
-perl mosesdecoder/scripts/training/clean-corpus-n.perl --ratio 3 /path/to/training/data/train.spm.$src-$tgt $src $tgt /path/to/output/directory/train.spm.$src-$tgt 1 250
-
-# binarize data
-wget https://dl.fbaipublicfiles.com/m2m_100/data_dict.128k.txt
-fairseq-preprocess \
-    --source-lang $src --target-lang $tgt \
-    --testpref spm.$src.$tgt \
-    --thresholdsrc 0 --thresholdtgt 0 \
-    --destdir data_bin \
-    --srcdict data_dict.128k.txt --tgtdict data_dict.128k.txt
-```
-
-3. **Training Scripts**
-
-To reproduce the training of our models, we train with fairseq-py's multilingual translation [task](https://github.com/pytorch/fairseq/tree/main/examples/multilingual). If you are interested in model parallel training, also check out [fairscale](https://github.com/facebookresearch/fairscale).
-
-4. **Generation**
-
-To generate from our models, follow the the commands in the generation section below.
-
-
-If you use any of the resources listed here, please cite:
-```bibtex
-@article{fan2020beyond,
-  title={Beyond English-Centric Multilingual Machine Translation},
-  author={Fan, Angela and Bhosale, Shruti and Schwenk, Holger and Ma, Zhiyi and El-Kishky, Ahmed and Goyal, Siddharth and Baines, Mandeep and Celebi, Onur and Wenzek, Guillaume and Chaudhary, Vishrav and Goyal, Naman and Birch, Tom and Liptchinsky, Vitaliy and Edunov, Sergey and Grave, Edouard and Auli, Michael and Joulin, Armand},
-  journal={arXiv preprint},
-  year={2020}
-}
-
-@article{schwenk2019ccmatrix,
-  title={Ccmatrix: Mining billions of high-quality parallel sentences on the web},
-  author={Schwenk, Holger and Wenzek, Guillaume and Edunov, Sergey and Grave, Edouard and Joulin, Armand},
-  journal={arXiv preprint arXiv:1911.04944},
-  year={2019}
-}
-
-@article{el2019massive,
-  title={A Massive Collection of Cross-Lingual Web-Document Pairs},
-  author={El-Kishky, Ahmed and Chaudhary, Vishrav and Guzman, Francisco and Koehn, Philipp},
-  journal={arXiv preprint arXiv:1911.06154},
-  year={2019}
-}
-```
-
-
-## Trained Models
-
-### 418M and 1.2B Model
-We include the last checkpoint for both of these models. 
-
-```bash
-wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt
-wget https://dl.fbaipublicfiles.com/m2m_100/language_pairs_small_models.txt 
-
-# 418M parameter model
-wget https://dl.fbaipublicfiles.com/m2m_100/418M_last_checkpoint.pt 
-
-# 1.2B parameter model
-wget https://dl.fbaipublicfiles.com/m2m_100/1.2B_last_checkpoint.pt
-
-# Generation:
-fairseq-generate $binarized_data_path --batch-size 32 --path $path_to_model --fixed-dictionary model_dict.128k.txt -s en -t fr --remove-bpe 'sentencepiece' --beam 5 --task translation_multi_simple_epoch --lang-pairs language_pairs_small_models.txt --decoder-langtok --encoder-langtok src --gen-subset test > gen_out
-```
-
-### 12B Model
-12B parameter model trained on many-to-many training data for 100 languages. We include the last checkpoint, average of last 5 checkpoints, average of last 10 checkpoints. There isn't a universally best choice out of these three, but all three versions are pretty close in accuracy. You can either sweep over the 3 checkpoints on a dev test and use the best performing checkpoint for final testing. Or the last checkpoint can be a good default choice.
-
-**Model Download Links**
-Configuration | 2 32GB GPUs | 4 16GB GPUs | 6 12GB GPUs | 8 8GB GPUs
-:--|:--|:--|:--|:--
-Last Checkpoint | [12b_last_chk_2_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_2_gpus.pt) | [12b_last_chk_4_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_4_gpus.pt) | [12b_last_chk_6_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_6_gpus.pt) | [12b_last_chk_8_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_8_gpus.pt)
-Average of last 5 checkpoints | [12b_avg5_chk_2_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_2_gpus.pt) | [12b_avg5_chk_4_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_4_gpus.pt) | [12b_avg5_chk_6_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_6_gpus.pt) | [12b_avg5_chk_8_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_8_gpus.pt)
-Average of last 10 checkpoints |  [12b_avg10_chk_2_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_2_gpus.pt) | [12b_avg10_chk_4_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_4_gpus.pt) | [12b_avg10_chk_6_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_6_gpus.pt) | [12b_avg10_chk_8_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_8_gpus.pt)
-
-**Generation Arguments**
-Configuration | 2 32GB GPUs | 4 16GB GPUs | 6 12GB GPUs | 8 8GB GPUs
-:--|:--|:--|:--|:--
-`--pipeline-encoder-balance` | `[26]` | `[1,15,10]` | `[1,9,9,7]` | `[1,6,6,6,7]`
-`--pipeline-encoder-devices` | `[0]` | `[0,1,0]` | `[0,1,2,0]` | `[0,4,5,1,0]`
-`--pipeline-decoder-balance` | `[3,22,1]` | `[3,11,11,1]` | `[3,7,7,8,1]` | `[1,6,6,6,6,1]`
-`--pipeline-decoder-devices` | `[0,1,0]` | `[0,2,3,0]` | `[0,3,4,5,0]` |  `[0,2,6,7,3,0]`
-
-
-## SentencePiece Model
-
-```bash
-wget https://dl.fbaipublicfiles.com/m2m_100/spm.128k.model
-```
-
-## Generation with M2M-100
-
-### Encode using our SentencePiece Model
-
-Note: Install SentencePiece from [here](https://github.com/google/sentencepiece)
-
-```bash
-fairseq=/path/to/fairseq
-cd $fairseq
-sacrebleu --echo src -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.de
-sacrebleu --echo ref -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.fr
-wget https://dl.fbaipublicfiles.com/m2m_100/spm.128k.model
-for lang in de fr ; do
-    python scripts/spm_encode.py \
-        --model spm.128k.model \
-        --output_format=piece \
-        --inputs=raw_input.de-fr.${lang} \
-        --outputs=spm.de-fr.${lang}
-done
-```
-
-### Binarization
-
-```bash
-wget https://dl.fbaipublicfiles.com/m2m_100/data_dict.128k.txt
-fairseq-preprocess \
-    --source-lang de --target-lang fr \
-    --testpref spm.de-fr \
-    --thresholdsrc 0 --thresholdtgt 0 \
-    --destdir data_bin \
-    --srcdict data_dict.128k.txt --tgtdict data_dict.128k.txt
-```
-
-### Generation for the 12B model
-
-Note that generation can currently be run using 2 32GB / 4 16GB / 6 12GB / 8 8GB GPUs, and the corresponding model checkpoints and pipeline arguments can be found in the [12B Model Section](#12b-model).
-Generation on CPUs will be added in the future.
-
-```bash
-wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt
-wget https://dl.fbaipublicfiles.com/m2m_100/language_pairs.txt
-wget https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_4_gpus.pt
-fairseq-generate \
-    data_bin \
-    --batch-size 1 \
-    --path 12b_last_chk_4_gpus.pt \
-    --fixed-dictionary model_dict.128k.txt \
-    -s de -t fr \
-    --remove-bpe 'sentencepiece' \
-    --beam 5 \
-    --task translation_multi_simple_epoch \
-    --lang-pairs language_pairs.txt \
-    --decoder-langtok --encoder-langtok src \
-    --gen-subset test \
-    --fp16 \
-    --dataset-impl mmap \
-    --distributed-world-size 1 --distributed-no-spawn \
-    --pipeline-model-parallel \
-    --pipeline-chunks 1 \
-    --pipeline-encoder-balance '[1,15,10]' \
-    --pipeline-encoder-devices '[0,1,0]' \
-    --pipeline-decoder-balance '[3,11,11,1]' \
-    --pipeline-decoder-devices '[0,2,3,0]' > gen_out
-```
-## Evaluation with M2M-100
-
-### Tokenization
-
-Note: Refer to tokenizers/README.md for more details on tokenization.
-
-```bash
-cd ${fairseq}/examples/m2m_100
-cat ${fairseq}/gen_out | grep -P "^H" | sort -V | cut -f 3- | sh tok.sh fr > hyp
-cat ${fairseq}/raw_input.de-fr.fr | sh tok.sh fr > ref
-```
-
-### BLEU
-
-```bash
-sacrebleu -tok 'none' ref < hyp
-```
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/models/nat/__init__.py b/spaces/ICML2022/OFA/fairseq/fairseq/models/nat/__init__.py
deleted file mode 100644
index 05fe822487c3bcde8346648d5826f1669c6bc1ca..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/models/nat/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""isort:skip_file"""
-
-from .fairseq_nat_model import *
-from .nonautoregressive_transformer import *
-from .nat_crf_transformer import *
-from .iterative_nonautoregressive_transformer import *
-from .cmlm_transformer import *
-from .levenshtein_transformer import *
-from .insertion_transformer import *
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/modules/transformer_sentence_encoder.py b/spaces/ICML2022/OFA/fairseq/fairseq/modules/transformer_sentence_encoder.py
deleted file mode 100644
index d0540d69229fb994b9e573a5016c9f239b7929e2..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/modules/transformer_sentence_encoder.py
+++ /dev/null
@@ -1,291 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-from fairseq.modules import (
-    FairseqDropout,
-    LayerDropModuleList,
-    LayerNorm,
-    MultiheadAttention,
-    PositionalEmbedding,
-    TransformerSentenceEncoderLayer,
-)
-from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
-
-
-def init_bert_params(module):
-    """
-    Initialize the weights specific to the BERT Model.
-    This overrides the default initializations depending on the specified arguments.
-        1. If normal_init_linear_weights is set then weights of linear
-           layer will be initialized using the normal distribution and
-           bais will be set to the specified value.
-        2. If normal_init_embed_weights is set then weights of embedding
-           layer will be initialized using the normal distribution.
-        3. If normal_init_proj_weights is set then weights of
-           in_project_weight for MultiHeadAttention initialized using
-           the normal distribution (to be validated).
-    """
-
-    def normal_(data):
-        # with FSDP, module params will be on CUDA, so we cast them back to CPU
-        # so that the RNG is consistent with and without FSDP
-        data.copy_(
-            data.cpu().normal_(mean=0.0, std=0.02).to(data.device)
-        )
-
-    if isinstance(module, nn.Linear):
-        normal_(module.weight.data)
-        if module.bias is not None:
-            module.bias.data.zero_()
-    if isinstance(module, nn.Embedding):
-        normal_(module.weight.data)
-        if module.padding_idx is not None:
-            module.weight.data[module.padding_idx].zero_()
-    if isinstance(module, MultiheadAttention):
-        normal_(module.q_proj.weight.data)
-        normal_(module.k_proj.weight.data)
-        normal_(module.v_proj.weight.data)
-
-
-class TransformerSentenceEncoder(nn.Module):
-    """
-    Implementation for a Bi-directional Transformer based Sentence Encoder used
-    in BERT/XLM style pre-trained models.
-
-    This first computes the token embedding using the token embedding matrix,
-    position embeddings (if specified) and segment embeddings
-    (if specified). After applying the specified number of
-    TransformerEncoderLayers, it outputs all the internal states of the
-    encoder as well as the final representation associated with the first
-    token (usually CLS token).
-
-    Input:
-        - tokens: B x T matrix representing sentences
-        - segment_labels: B x T matrix representing segment label for tokens
-
-    Output:
-        - a tuple of the following:
-            - a list of internal model states used to compute the
-              predictions where each tensor has shape T x B x C
-            - sentence representation associated with first input token
-              in format B x C.
-    """
-
-    def __init__(
-        self,
-        padding_idx: int,
-        vocab_size: int,
-        num_encoder_layers: int = 6,
-        embedding_dim: int = 768,
-        ffn_embedding_dim: int = 3072,
-        num_attention_heads: int = 8,
-        dropout: float = 0.1,
-        attention_dropout: float = 0.1,
-        activation_dropout: float = 0.1,
-        layerdrop: float = 0.0,
-        max_seq_len: int = 256,
-        num_segments: int = 2,
-        use_position_embeddings: bool = True,
-        offset_positions_by_padding: bool = True,
-        encoder_normalize_before: bool = False,
-        apply_bert_init: bool = False,
-        activation_fn: str = "relu",
-        learned_pos_embedding: bool = True,
-        embed_scale: float = None,
-        freeze_embeddings: bool = False,
-        n_trans_layers_to_freeze: int = 0,
-        export: bool = False,
-        traceable: bool = False,
-        q_noise: float = 0.0,
-        qn_block_size: int = 8,
-    ) -> None:
-
-        super().__init__()
-        self.padding_idx = padding_idx
-        self.vocab_size = vocab_size
-        self.dropout_module = FairseqDropout(
-            dropout, module_name=self.__class__.__name__
-        )
-        self.layerdrop = layerdrop
-        self.max_seq_len = max_seq_len
-        self.embedding_dim = embedding_dim
-        self.num_segments = num_segments
-        self.use_position_embeddings = use_position_embeddings
-        self.apply_bert_init = apply_bert_init
-        self.learned_pos_embedding = learned_pos_embedding
-        self.traceable = traceable
-
-        self.embed_tokens = self.build_embedding(
-            self.vocab_size, self.embedding_dim, self.padding_idx
-        )
-        self.embed_scale = embed_scale
-
-        if q_noise > 0:
-            self.quant_noise = apply_quant_noise_(
-                nn.Linear(self.embedding_dim, self.embedding_dim, bias=False),
-                q_noise,
-                qn_block_size,
-            )
-        else:
-            self.quant_noise = None
-
-        self.segment_embeddings = (
-            nn.Embedding(self.num_segments, self.embedding_dim, padding_idx=None)
-            if self.num_segments > 0
-            else None
-        )
-
-        self.embed_positions = (
-            PositionalEmbedding(
-                self.max_seq_len,
-                self.embedding_dim,
-                padding_idx=(self.padding_idx if offset_positions_by_padding else None),
-                learned=self.learned_pos_embedding,
-            )
-            if self.use_position_embeddings
-            else None
-        )
-
-        if encoder_normalize_before:
-            self.emb_layer_norm = LayerNorm(self.embedding_dim, export=export)
-        else:
-            self.emb_layer_norm = None
-
-        if self.layerdrop > 0.0:
-            self.layers = LayerDropModuleList(p=self.layerdrop)
-        else:
-            self.layers = nn.ModuleList([])
-        self.layers.extend(
-            [
-                self.build_transformer_sentence_encoder_layer(
-                    embedding_dim=self.embedding_dim,
-                    ffn_embedding_dim=ffn_embedding_dim,
-                    num_attention_heads=num_attention_heads,
-                    dropout=self.dropout_module.p,
-                    attention_dropout=attention_dropout,
-                    activation_dropout=activation_dropout,
-                    activation_fn=activation_fn,
-                    export=export,
-                    q_noise=q_noise,
-                    qn_block_size=qn_block_size,
-                )
-                for _ in range(num_encoder_layers)
-            ]
-        )
-
-        # Apply initialization of model params after building the model
-        if self.apply_bert_init:
-            self.apply(init_bert_params)
-
-        def freeze_module_params(m):
-            if m is not None:
-                for p in m.parameters():
-                    p.requires_grad = False
-
-        if freeze_embeddings:
-            freeze_module_params(self.embed_tokens)
-            freeze_module_params(self.segment_embeddings)
-            freeze_module_params(self.embed_positions)
-            freeze_module_params(self.emb_layer_norm)
-
-        for layer in range(n_trans_layers_to_freeze):
-            freeze_module_params(self.layers[layer])
-
-    def build_embedding(self, vocab_size, embedding_dim, padding_idx):
-        return nn.Embedding(vocab_size, embedding_dim, padding_idx)
-
-    def build_transformer_sentence_encoder_layer(
-        self,
-        embedding_dim,
-        ffn_embedding_dim,
-        num_attention_heads,
-        dropout,
-        attention_dropout,
-        activation_dropout,
-        activation_fn,
-        export,
-        q_noise,
-        qn_block_size,
-    ):
-        return TransformerSentenceEncoderLayer(
-            embedding_dim=embedding_dim,
-            ffn_embedding_dim=ffn_embedding_dim,
-            num_attention_heads=num_attention_heads,
-            dropout=dropout,
-            attention_dropout=attention_dropout,
-            activation_dropout=activation_dropout,
-            activation_fn=activation_fn,
-            export=export,
-            q_noise=q_noise,
-            qn_block_size=qn_block_size,
-        )
-
-    def forward(
-        self,
-        tokens: torch.Tensor,
-        segment_labels: torch.Tensor = None,
-        last_state_only: bool = False,
-        positions: Optional[torch.Tensor] = None,
-        token_embeddings: Optional[torch.Tensor] = None,
-        attn_mask: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        is_tpu = tokens.device.type == "xla"
-
-        # compute padding mask. This is needed for multi-head attention
-        padding_mask = tokens.eq(self.padding_idx)
-        if not self.traceable and not is_tpu and not padding_mask.any():
-            padding_mask = None
-
-        if token_embeddings is not None:
-            x = token_embeddings
-        else:
-            x = self.embed_tokens(tokens)
-
-        if self.embed_scale is not None:
-            x = x * self.embed_scale
-
-        if self.embed_positions is not None:
-            x = x + self.embed_positions(tokens, positions=positions)
-
-        if self.segment_embeddings is not None and segment_labels is not None:
-            x = x + self.segment_embeddings(segment_labels)
-
-        if self.quant_noise is not None:
-            x = self.quant_noise(x)
-
-        if self.emb_layer_norm is not None:
-            x = self.emb_layer_norm(x)
-
-        x = self.dropout_module(x)
-
-        # account for padding while computing the representation
-        if padding_mask is not None:
-            x = x * (1 - padding_mask.unsqueeze(-1).type_as(x))
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        inner_states = []
-        if not last_state_only:
-            inner_states.append(x)
-
-        for layer in self.layers:
-            x, _ = layer(x, self_attn_padding_mask=padding_mask, self_attn_mask=attn_mask)
-            if not last_state_only:
-                inner_states.append(x)
-
-        sentence_rep = x[0, :, :]
-
-        if last_state_only:
-            inner_states = [x]
-
-        if self.traceable:
-            return torch.stack(inner_states), sentence_rep
-        else:
-            return inner_states, sentence_rep
diff --git a/spaces/Illumotion/Koboldcpp/otherarch/neox_v2.cpp b/spaces/Illumotion/Koboldcpp/otherarch/neox_v2.cpp
deleted file mode 100644
index 8cfd821adb4dedbb926114d70c2ca0b67313c6fc..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/otherarch/neox_v2.cpp
+++ /dev/null
@@ -1,618 +0,0 @@
-#include "ggml_v2.h"
-#include "otherarch.h"
-
-#include "utils.h"
-
-#include <cassert>
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <fstream>
-#include <map>
-#include <string>
-#include <vector>
-#include <iostream>
-
-
-
-// load the model's weights from a file
-ModelLoadResult gpt_neox_v2_model_load(const std::string & fname, gpt_neox_v2_model & model, gpt_vocab & vocab, FileFormat file_format) {
-    printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
-
-    auto fin = std::ifstream(fname, std::ios::binary);
-    if (!fin) {
-        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
-        return ModelLoadResult::FAIL;
-    }
-
-    // verify magic
-    {
-        uint32_t magic;
-        fin.read((char *) &magic, sizeof(magic));
-        if (magic != 0x67676d6c) {
-            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
-            return ModelLoadResult::FAIL;
-        }
-    }
-
-    // load hparams
-    {
-        auto & hparams = model.hparams;
-        hparams.par_res = 1; //true
-        fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
-        fin.read((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
-        fin.read((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
-        fin.read((char *) &hparams.n_head,  sizeof(hparams.n_head));
-        fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
-        fin.read((char *) &hparams.n_rot,   sizeof(hparams.n_rot));
-        if(file_format!=FileFormat::NEOX_1 && file_format!=FileFormat::NEOX_2 && file_format!=FileFormat::NEOX_3)
-        {
-            fin.read((char *) &hparams.par_res, sizeof(hparams.par_res));
-        }
-        if(file_format==FileFormat::NEOX_3)
-        {
-            hparams.par_res = 0;
-        }
-        fin.read((char *) &hparams.ftype,   sizeof(hparams.ftype));
-
-        const int32_t qntvr = hparams.ftype / GGML_V2_QNT_VERSION_FACTOR;
-
-        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
-        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
-        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
-        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
-        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
-        printf("%s: n_rot   = %d\n", __func__, hparams.n_rot);
-        printf("%s: par_res = %d\n", __func__, hparams.par_res);
-        printf("%s: ftype   = %d\n", __func__, hparams.ftype);
-        printf("%s: qntvr   = %d\n", __func__, qntvr);
-
-        hparams.ftype %= GGML_V2_QNT_VERSION_FACTOR;
-    }
-
-    // load vocab
-    {
-        const int32_t n_vocab = model.hparams.n_vocab;
-
-        std::string word;
-        std::vector<char> buf(128);
-
-        for (int i = 0; i < n_vocab; i++) {
-            uint32_t len;
-            fin.read((char *) &len, sizeof(len));
-
-            buf.resize(len);
-            fin.read((char *) buf.data(), len);
-            word.assign(buf.data(), len);
-
-            vocab.token_to_id[word] = i;
-            vocab.id_to_token[i] = word;
-        }
-    }
-
-    // for the big tensors, we have the option to store the data in 16-bit floats or quantized
-    // in order to save memory and also to speed up the computation
-    ggml_v2_type wtype = ggml_v2_ftype_to_ggml_v2_type((ggml_v2_ftype) (model.hparams.ftype));
-    if (wtype == GGML_V2_TYPE_COUNT) {
-        fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
-                __func__, fname.c_str(), model.hparams.ftype);
-        return ModelLoadResult::FAIL;
-    }
-
-    auto & ctx = model.ctx;
-
-    size_t ctx_size = 0;
-
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
-        const int n_vocab = hparams.n_vocab;
-
-        ctx_size += n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32); // ln_f_g
-        ctx_size += n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32); // ln_f_b
-
-        ctx_size += n_embd*n_vocab*ggml_v2_type_sizef(wtype); // wte
-
-        ctx_size += n_embd*n_vocab*ggml_v2_type_sizef(wtype);           // lmh_g
-        //ctx_size +=        n_vocab*ggml_v2_type_sizef(GGML_V2_TYPE_F32); // lmh_b
-
-        ctx_size += n_layer*(n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // ln_1_g
-        ctx_size += n_layer*(n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // ln_1_b
-
-        ctx_size += n_layer*(3*n_embd*n_embd*ggml_v2_type_sizef(wtype));         // c_attn_attn_w
-        ctx_size += n_layer*(       3*n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // c_attn_attn_b
-
-        ctx_size += n_layer*(n_embd*n_embd*ggml_v2_type_sizef(wtype));         // c_attn_proj_w
-        ctx_size += n_layer*(n_embd*n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // c_attn_proj_b
-
-        ctx_size += n_layer*(n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // ln_2_g
-        ctx_size += n_layer*(n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // ln_2_b
-
-        ctx_size += n_layer*(4*n_embd*n_embd*ggml_v2_type_sizef(wtype));         // c_mlp_fc_w
-        ctx_size += n_layer*(       4*n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // c_mlp_fc_b
-
-        ctx_size += n_layer*(4*n_embd*n_embd*ggml_v2_type_sizef(wtype));         // c_mlp_proj_w
-        ctx_size += n_layer*(         n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32)); // c_mlp_proj_b
-
-        ctx_size += n_ctx*n_layer*n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32); // memory_k
-        ctx_size += n_ctx*n_layer*n_embd*ggml_v2_type_sizef(GGML_V2_TYPE_F32); // memory_v
-
-        ctx_size += (6 + 16*n_layer)*512; // object overhead
-
-        printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
-    }
-
-    // create the ggml context
-    {
-        struct ggml_v2_init_params params;
-        params.mem_size   = ctx_size;
-        params.mem_buffer = NULL;
-        params.no_alloc   = false;
-        
-        model.ctx = ggml_v2_init(params);
-        if (!model.ctx) {
-            fprintf(stderr, "%s: ggml_v2_init() failed\n", __func__);
-            return ModelLoadResult::FAIL;
-        }
-    }
-
-    // prepare memory for the weights
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_vocab = hparams.n_vocab;
-
-        model.layers.resize(n_layer);
-
-        model.wte    = ggml_v2_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
-
-        model.ln_f_g = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32, n_embd);
-        model.ln_f_b = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32, n_embd);
-
-        model.lmh_g  = ggml_v2_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
-        //model.lmh_b  = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32, n_vocab);
-
-        // map by name
-        model.tensors["gpt_neox.embed_in.weight"] = model.wte;
-
-        model.tensors["gpt_neox.final_layer_norm.weight"] = model.ln_f_g;
-        model.tensors["gpt_neox.final_layer_norm.bias"]   = model.ln_f_b;
-
-        model.tensors["embed_out.weight"] = model.lmh_g;
-        //model.tensors["lm_head.bias"]   = model.lmh_b;
-
-        for (int i = 0; i < n_layer; ++i) {
-            auto & layer = model.layers[i];
-
-            layer.ln_1_g          = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32,   n_embd);
-            layer.ln_1_b          = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32,   n_embd);
-
-            layer.c_attn_attn_w   = ggml_v2_new_tensor_2d(ctx, wtype,           n_embd, 3*n_embd);
-            layer.c_attn_attn_b   = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32, 3*n_embd);
-
-            layer.c_attn_proj_w   = ggml_v2_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
-            layer.c_attn_proj_b   = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32,   n_embd);
-
-            layer.ln_2_g          = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32,   n_embd);
-            layer.ln_2_b          = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32,   n_embd);
-
-            layer.c_mlp_fc_w      = ggml_v2_new_tensor_2d(ctx, wtype,           n_embd, 4*n_embd);
-            layer.c_mlp_fc_b      = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32, 4*n_embd);
-
-            layer.c_mlp_proj_w    = ggml_v2_new_tensor_2d(ctx, wtype,         4*n_embd,   n_embd);
-            layer.c_mlp_proj_b    = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F32,   n_embd);
-
-            // map by name
-            model.tensors["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.weight"] = layer.ln_1_g;
-            model.tensors["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.bias"]   = layer.ln_1_b;
-
-            model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.weight"] = layer.c_attn_attn_w;
-            model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.bias"]   = layer.c_attn_attn_b;
-
-            model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.weight"] = layer.c_attn_proj_w;
-            model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.bias"]   = layer.c_attn_proj_b;
-
-            model.tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.weight"] = layer.ln_2_g;
-            model.tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.bias"]   = layer.ln_2_b;
-
-            model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.weight"] = layer.c_mlp_fc_w;
-            model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.bias"]   = layer.c_mlp_fc_b;
-
-            model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.weight"] = layer.c_mlp_proj_w;
-            model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.bias"]   = layer.c_mlp_proj_b;
-        }
-    }
-
-    // key + value memory
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
-
-        const int64_t n_mem      = n_layer*n_ctx;
-        const int64_t n_elements = n_embd*n_mem;
-
-        model.memory_k = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F16, n_elements);
-        model.memory_v = ggml_v2_new_tensor_1d(ctx, GGML_V2_TYPE_F16, n_elements);
-
-        const size_t memory_size = ggml_v2_nbytes(model.memory_k) + ggml_v2_nbytes(model.memory_v);
-
-        printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size/1024.0/1024.0, n_mem);
-    }
-
-    // load weights
-    {
-        int n_tensors = 0;
-        size_t total_size = 0;
-
-        printf("%s: ", __func__);
-
-        while (true) {
-            int32_t n_dims;
-            int32_t length;
-            int32_t ttype;
-
-            fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
-            fin.read(reinterpret_cast<char *>(&length), sizeof(length));
-            fin.read(reinterpret_cast<char *>(&ttype),  sizeof(ttype));
-
-            if (fin.eof()) {
-                break;
-            }
-
-            int32_t nelements = 1;
-            int32_t ne[2] = { 1, 1 };
-            for (int i = 0; i < n_dims; ++i) {
-                fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
-                nelements *= ne[i];
-            }
-
-            std::string name(length, 0);
-            fin.read(&name[0], length);
-
-            if (model.tensors.find(name.data()) == model.tensors.end()) {
-                fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
-                return ModelLoadResult::FAIL;
-            }
-
-            auto tensor = model.tensors[name.data()];
-            if (ggml_v2_nelements(tensor) != nelements) {
-                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
-                return ModelLoadResult::FAIL;
-            }
-
-            if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
-                fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%5d, %5d], expected [%5d, %5d]\n",
-                        __func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
-                return ModelLoadResult::FAIL;
-            }
-
-            // for debugging
-            if (0) {
-                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_v2_type_name(ggml_v2_type(ttype)), ggml_v2_nbytes(tensor)/1024.0/1024.0, ggml_v2_nbytes(tensor));
-            }
-
-            size_t bpe = ggml_v2_type_size(ggml_v2_type(ttype));
-
-            if(file_format==FileFormat::NEOX_1)
-            {
-                switch (ttype) {
-                    case 0: bpe = ggml_v2_type_size(GGML_V2_TYPE_F32);  break;
-                    case 1: bpe = ggml_v2_type_size(GGML_V2_TYPE_F16);  break;
-                    case 2: bpe = ggml_v2_type_size(GGML_V2_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
-                    case 3: bpe = ggml_v2_type_size(GGML_V2_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
-                    case 5: bpe = ggml_v2_type_size(GGML_V2_TYPE_Q4_2); assert(ne[0] % 64 == 0); break;
-                    case 6: bpe = ggml_v2_type_size(GGML_V2_TYPE_Q4_3); assert(ne[0] % 64 == 0); break;
-                    default:
-                    {
-                        fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ttype);
-                        return ModelLoadResult::FAIL;
-                    }
-                };
-            }
-
-            if ((nelements*bpe)/ggml_v2_blck_size(tensor->type) != ggml_v2_nbytes(tensor)) {
-                fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
-                        __func__, name.data(), ggml_v2_nbytes(tensor), nelements*bpe);
-                 ggml_v2_free(ctx);
-                 return ModelLoadResult::RETRY_LOAD;
-            }
-
-            fin.read(reinterpret_cast<char *>(tensor->data), ggml_v2_nbytes(tensor));
-
-            total_size += ggml_v2_nbytes(tensor);
-            if (++n_tensors % 8 == 0) {
-                printf(".");
-                fflush(stdout);
-            }
-        }
-
-        printf(" done\n");
-
-        printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
-    }
-
-    fin.close();
-
-    return ModelLoadResult::SUCCESS;
-}
-
-
-// feed-forward network
-ggml_v2_tensor * gpt_neox_ff(
-        const gpt_neox_layer_v2 &layer,
-        ggml_v2_context * ctx0,
-        ggml_v2_tensor * inp) {
-    ggml_v2_tensor * cur = ggml_v2_norm(ctx0, inp);
-
-    cur = ggml_v2_add(ctx0,
-        ggml_v2_mul(ctx0,
-            ggml_v2_repeat(ctx0, layer.ln_2_g, cur),
-            cur),
-        ggml_v2_repeat(ctx0, layer.ln_2_b, cur));
-
-    cur = ggml_v2_mul_mat(ctx0,
-            layer.c_mlp_fc_w,
-            cur);
-
-    cur = ggml_v2_add(ctx0,
-            ggml_v2_repeat(ctx0, layer.c_mlp_fc_b, cur),
-            cur);
-
-    // GELU activation
-    cur = ggml_v2_gelu(ctx0, cur);
-
-    // projection
-    // cur = proj_w*cur + proj_b
-    cur = ggml_v2_mul_mat(ctx0,
-            layer.c_mlp_proj_w,
-            cur);
-
-    cur = ggml_v2_add(ctx0,
-            ggml_v2_repeat(ctx0, layer.c_mlp_proj_b, cur),
-            cur);
-    return cur;
-}
-
-// evaluate the transformer
-//
-//   - model:     the model
-//   - n_threads: number of threads to use
-//   - n_past:    the context size so far
-//   - embd_inp:  the embeddings of the tokens in the context
-//   - embd_w:    the predicted logits for the next token
-//
-bool gpt_neox_v2_eval(
-        const gpt_neox_v2_model & model,
-        const int n_threads,
-        const int n_past,
-        const std::vector<gpt_vocab::id> & embd_inp,
-              std::vector<float>         & embd_w,
-              size_t                     & mem_per_token) {
-    const int N = embd_inp.size();
-
-    const auto & hparams = model.hparams;
-
-    const int n_embd  = hparams.n_embd;
-    const int n_layer = hparams.n_layer;
-    const int n_ctx   = hparams.n_ctx;
-    const int n_head  = hparams.n_head;
-    const int n_vocab = hparams.n_vocab;
-    const int n_rot   = hparams.n_rot;
-
-    static size_t buf_size = 256u*1024*1024;
-    static void * buf = malloc(buf_size);
-
-    if (mem_per_token > 0 && (mem_per_token*N*2 + 64u*1024*1024) > buf_size) {
-        const size_t buf_size_new = 360u*1024*1024 + 2*(mem_per_token*N); // add 10% to account for ggml object overhead
-        //printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
-
-        // reallocate
-        buf_size = buf_size_new;
-        buf = realloc(buf, buf_size);
-        if (buf == nullptr) {
-            fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
-            return false;
-        }
-    }
-
-    struct ggml_v2_init_params params;
-    params.mem_size   = buf_size;
-    params.mem_buffer = buf;
-    params.no_alloc   = false;
-    
-
-    struct ggml_v2_context * ctx0 = ggml_v2_init(params);
-    struct ggml_v2_cgraph gf = {};
-    gf.n_threads = n_threads;
-
-    struct ggml_v2_tensor * embd = ggml_v2_new_tensor_1d(ctx0, GGML_V2_TYPE_I32, N);
-    memcpy(embd->data, embd_inp.data(), N*ggml_v2_element_size(embd));
-
-    // wte
-    struct ggml_v2_tensor * inpL = ggml_v2_get_rows(ctx0, model.wte, embd);
-
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_v2_tensor * cur;
-
-        // self-attention
-        {
-            {
-                cur = ggml_v2_norm(ctx0, inpL);
-
-                cur = ggml_v2_add(ctx0,
-                        ggml_v2_mul(ctx0,
-                            ggml_v2_repeat(ctx0, model.layers[il].ln_1_g, cur),
-                            cur),
-                        ggml_v2_repeat(ctx0, model.layers[il].ln_1_b, cur));
-            }
-
-            // compute QKV
-            {
-                cur = ggml_v2_mul_mat(ctx0,
-                        model.layers[il].c_attn_attn_w,
-                        cur);
-
-                cur = ggml_v2_add(ctx0,
-                        ggml_v2_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
-                        cur);
-            }
-
-            struct ggml_v2_tensor * Qcur = ggml_v2_cont(ctx0, ggml_v2_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 0*sizeof(float)*n_embd/n_head));
-            struct ggml_v2_tensor * Kcur = ggml_v2_cont(ctx0, ggml_v2_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 1*sizeof(float)*n_embd/n_head));
-            struct ggml_v2_tensor * Vcur = ggml_v2_cont(ctx0, ggml_v2_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 2*sizeof(float)*n_embd/n_head));
-
-            // using mode = 2 for GPT-NeoX mode
-            Qcur = ggml_v2_rope_inplace(ctx0, Qcur, n_past, n_rot, 2);
-            Kcur = ggml_v2_rope_inplace(ctx0, Kcur, n_past, n_rot, 2);
-
-            // store key and value to memory
-            {
-                Vcur = ggml_v2_transpose(ctx0, ggml_v2_reshape_2d(ctx0, Vcur, n_embd, N));
-
-                struct ggml_v2_tensor * k = ggml_v2_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_v2_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
-                struct ggml_v2_tensor * v = ggml_v2_view_2d(ctx0, model.memory_v, N, n_embd,
-                        (   n_ctx)*ggml_v2_element_size(model.memory_v),
-                        (il*n_ctx)*ggml_v2_element_size(model.memory_v)*n_embd + n_past*ggml_v2_element_size(model.memory_v));
-
-                ggml_v2_build_forward_expand(&gf, ggml_v2_cpy(ctx0, Kcur, k));
-                ggml_v2_build_forward_expand(&gf, ggml_v2_cpy(ctx0, Vcur, v));
-            }
-
-            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
-            struct ggml_v2_tensor * Q =
-                ggml_v2_permute(ctx0,
-                        Qcur,
-                        0, 2, 1, 3);
-
-            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
-            struct ggml_v2_tensor * K =
-                ggml_v2_permute(ctx0,
-                        ggml_v2_reshape_3d(ctx0,
-                            ggml_v2_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_v2_element_size(model.memory_k)*n_embd),
-                            n_embd/n_head, n_head, n_past + N),
-                        0, 2, 1, 3);
-
-            // K * Q
-            struct ggml_v2_tensor * KQ = ggml_v2_mul_mat(ctx0, K, Q);
-
-            // KQ_scaled = KQ / sqrt(n_embd/n_head)
-            struct ggml_v2_tensor * KQ_scaled =
-                ggml_v2_scale_inplace(ctx0,
-                        KQ,
-                        ggml_v2_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
-                        );
-
-            // KQ_masked = mask_past(KQ_scaled)
-            struct ggml_v2_tensor * KQ_masked = ggml_v2_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
-
-            // KQ = soft_max(KQ_masked)
-            struct ggml_v2_tensor * KQ_soft_max = ggml_v2_soft_max_inplace(ctx0, KQ_masked);
-
-            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
-            struct ggml_v2_tensor * V =
-                ggml_v2_view_3d(ctx0, model.memory_v,
-                        n_past + N, n_embd/n_head, n_head,
-                        n_ctx*ggml_v2_element_size(model.memory_v),
-                        n_ctx*ggml_v2_element_size(model.memory_v)*n_embd/n_head,
-                        il*n_ctx*ggml_v2_element_size(model.memory_v)*n_embd);
-
-            // KQV = transpose(V) * KQ_soft_max
-            struct ggml_v2_tensor * KQV = ggml_v2_mul_mat(ctx0, V, KQ_soft_max);
-
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
-            struct ggml_v2_tensor * KQV_merged = ggml_v2_permute(ctx0, KQV, 0, 2, 1, 3);
-
-            // cur = KQV_merged.contiguous().view(n_embd, N)
-            cur = ggml_v2_cpy(ctx0,
-                    KQV_merged,
-                    ggml_v2_new_tensor_2d(ctx0, GGML_V2_TYPE_F32, n_embd, N));
-
-            // projection
-            {
-                cur = ggml_v2_mul_mat(ctx0,
-                        model.layers[il].c_attn_proj_w,
-                        cur);
-
-                cur = ggml_v2_add(ctx0, ggml_v2_repeat(ctx0, model.layers[il].c_attn_proj_b, cur), cur);
-            }
-        }
-
-        if (hparams.par_res == 0) {
-            struct ggml_v2_tensor * inpFF = ggml_v2_add(ctx0, cur, inpL);
-
-            cur = gpt_neox_ff(model.layers[il], ctx0, inpFF);
-
-            // input for next layer
-            inpL = ggml_v2_add(ctx0, cur, inpFF);
-        } else {
-            struct ggml_v2_tensor * inpFF = cur;
-
-            // this is independent of the self-attention result, so it could be done in parallel to the self-attention
-            // note here we pass inpL instead of cur
-            cur = gpt_neox_ff(model.layers[il], ctx0, inpL);
-
-            // layer input + FF
-            cur  = ggml_v2_add(ctx0, cur, inpFF);
-
-            // input for next layer
-            inpL = ggml_v2_add(ctx0, cur, inpL);
-        }
-    }
-
-    // norm
-    {
-        inpL = ggml_v2_norm(ctx0, inpL);
-
-        // inpL = ln_f_g*inpL + ln_f_b
-        inpL = ggml_v2_add(ctx0,
-                ggml_v2_mul(ctx0,
-                    ggml_v2_repeat(ctx0, model.ln_f_g, inpL),
-                    inpL),
-                ggml_v2_repeat(ctx0, model.ln_f_b, inpL));
-    }
-
-    // lm_head
-    {
-        inpL = ggml_v2_mul_mat(ctx0, model.lmh_g, inpL);
-
-        //inpL = ggml_v2_add(ctx0,
-        //        ggml_v2_repeat(ctx0, model.lmh_b, inpL),
-        //        inpL);
-    }
-
-    // logits -> probs
-    //inpL = ggml_v2_soft_max_inplace(ctx0, inpL);
-
-    // run the computation
-    ggml_v2_build_forward_expand(&gf, inpL);
-    ggml_v2_graph_compute       (ctx0, &gf);
-
-    //if (n_past%100 == 0) {
-    //    ggml_v2_graph_print   (&gf);
-    //    ggml_v2_graph_dump_dot(&gf, NULL, "gpt-2.dot");
-    //}
-
-    //embd_w.resize(n_vocab*N);
-    //memcpy(embd_w.data(), ggml_v2_get_data(inpL), sizeof(float)*n_vocab*N);
-
-    // return result for just the last token
-    embd_w.resize(n_vocab);
-    memcpy(embd_w.data(), (float *) ggml_v2_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
-
-    if (mem_per_token == 0) {
-        mem_per_token = ggml_v2_used_mem(ctx0)/N;
-    }
-    //printf("used_mem = %zu\n", ggml_v2_used_mem(ctx0));
-
-    ggml_v2_free(ctx0);
-
-    return true;
-}
\ No newline at end of file
diff --git a/spaces/ItsJayQz/BreathOfTheWild_Diffusion/README.md b/spaces/ItsJayQz/BreathOfTheWild_Diffusion/README.md
deleted file mode 100644
index 2eb342f32549a6daab9a38ca40fc00bfdd48fbfc..0000000000000000000000000000000000000000
--- a/spaces/ItsJayQz/BreathOfTheWild_Diffusion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: BreathOfTheWild Diffusion
-emoji: 🌍
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Izal887/Konci887/config.py b/spaces/Izal887/Konci887/config.py
deleted file mode 100644
index c0c16e0017efbcaf250cb539a1d0edb4e83575e4..0000000000000000000000000000000000000000
--- a/spaces/Izal887/Konci887/config.py
+++ /dev/null
@@ -1,88 +0,0 @@
-########################硬件参数########################
-
-# 填写cuda:x, cpu 或 mps, x指代第几张卡，只支持 N卡 / Apple Silicon 加速
-device = "cuda:0"
-
-# 9-10-20-30-40系显卡无脑True，不影响质量，>=20显卡开启有加速
-is_half = True
-
-# 默认0用上所有线程，写数字限制CPU资源使用
-n_cpu = 0
-
-########################硬件参数########################
-
-
-##################下为参数处理逻辑，勿动##################
-
-########################命令行参数########################
-import argparse
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--port", type=int, default=7865, help="Listen port")
-parser.add_argument("--pycmd", type=str, default="python", help="Python command")
-parser.add_argument("--colab", action="store_true", help="Launch in colab")
-parser.add_argument(
-    "--noparallel", action="store_true", help="Disable parallel processing"
-)
-parser.add_argument(
-    "--noautoopen", action="store_true", help="Do not open in browser automatically"
-)
-cmd_opts, unknown = parser.parse_known_args()
-
-python_cmd = cmd_opts.pycmd
-listen_port = cmd_opts.port
-iscolab = cmd_opts.colab
-noparallel = cmd_opts.noparallel
-noautoopen = cmd_opts.noautoopen
-########################命令行参数########################
-
-import sys
-import torch
-
-
-# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
-# check `getattr` and try it for compatibility
-def has_mps() -> bool:
-    if sys.platform != "darwin":
-        return False
-    else:
-        if not getattr(torch, "has_mps", False):
-            return False
-        try:
-            torch.zeros(1).to(torch.device("mps"))
-            return True
-        except Exception:
-            return False
-
-
-if not torch.cuda.is_available():
-    if has_mps():
-        print("没有发现支持的N卡, 使用MPS进行推理")
-        device = "mps"
-    else:
-        print("没有发现支持的N卡, 使用CPU进行推理")
-        device = "cpu"
-        is_half = False
-
-if device not in ["cpu", "mps"]:
-    gpu_name = torch.cuda.get_device_name(int(device.split(":")[-1]))
-    if "16" in gpu_name or "MX" in gpu_name:
-        print("16系显卡/MX系显卡强制单精度")
-        is_half = False
-
-from multiprocessing import cpu_count
-
-if n_cpu == 0:
-    n_cpu = cpu_count()
-if is_half:
-    # 6G显存配置
-    x_pad = 3
-    x_query = 10
-    x_center = 60
-    x_max = 65
-else:
-    # 5G显存配置
-    x_pad = 1
-    x_query = 6
-    x_center = 38
-    x_max = 41
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/lib/infer_pack/attentions.py b/spaces/Kangarroar/ApplioRVC-Inference/lib/infer_pack/attentions.py
deleted file mode 100644
index 05501be1871643f78dddbeaa529c96667031a8db..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/lib/infer_pack/attentions.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from lib.infer_pack import commons
-from lib.infer_pack import modules
-from lib.infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=10,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        """
-        x: decoder input
-        h: encoder output
-        """
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
diff --git a/spaces/KenjieDec/GPEN/sr_model/real_esrnet.py b/spaces/KenjieDec/GPEN/sr_model/real_esrnet.py
deleted file mode 100644
index eaf319d17c96d3f5f30c659f4013a3d4c769a3d7..0000000000000000000000000000000000000000
--- a/spaces/KenjieDec/GPEN/sr_model/real_esrnet.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import os
-import math
-import torch
-import numpy as np
-from rrdbnet_arch import RRDBNet
-from torch.nn import functional as F
-
-class RealESRNet(object):
-    def __init__(self, base_dir='./', model=None, scale=2, tile_size=0, tile_pad=10, device='cuda'):
-        self.base_dir = base_dir
-        self.scale = scale
-        self.tile_size = tile_size
-        self.tile_pad = tile_pad
-        self.device = device
-        self.load_srmodel(base_dir, model)
-
-    def load_srmodel(self, base_dir, model):
-        self.srmodel = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=32, num_block=23, num_grow_ch=32, scale=self.scale)
-        if model is None:
-            loadnet = torch.load(os.path.join(self.base_dir, 'weights', 'realesrnet_x%d.pth'%self.scale))
-        else:
-            loadnet = torch.load(os.path.join(self.base_dir, 'weights', model+'_x%d.pth'%self.scale))
-        #print(loadnet['params_ema'].keys)
-        self.srmodel.load_state_dict(loadnet['params_ema'], strict=True)
-        self.srmodel.eval()
-        self.srmodel = self.srmodel.to(self.device)
-
-    def tile_process(self, img):
-        """It will first crop input images to tiles, and then process each tile.
-        Finally, all the processed tiles are merged into one images.
-
-        Modified from: https://github.com/ata4/esrgan-launcher
-        """
-        batch, channel, height, width = img.shape
-        output_height = height * self.scale
-        output_width = width * self.scale
-        output_shape = (batch, channel, output_height, output_width)
-
-        # start with black image
-        output = img.new_zeros(output_shape)
-        tiles_x = math.ceil(width / self.tile_size)
-        tiles_y = math.ceil(height / self.tile_size)
-
-        # loop over all tiles
-        for y in range(tiles_y):
-            for x in range(tiles_x):
-                # extract tile from input image
-                ofs_x = x * self.tile_size
-                ofs_y = y * self.tile_size
-                # input tile area on total image
-                input_start_x = ofs_x
-                input_end_x = min(ofs_x + self.tile_size, width)
-                input_start_y = ofs_y
-                input_end_y = min(ofs_y + self.tile_size, height)
-
-                # input tile area on total image with padding
-                input_start_x_pad = max(input_start_x - self.tile_pad, 0)
-                input_end_x_pad = min(input_end_x + self.tile_pad, width)
-                input_start_y_pad = max(input_start_y - self.tile_pad, 0)
-                input_end_y_pad = min(input_end_y + self.tile_pad, height)
-
-                # input tile dimensions
-                input_tile_width = input_end_x - input_start_x
-                input_tile_height = input_end_y - input_start_y
-                tile_idx = y * tiles_x + x + 1
-                input_tile = img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
-
-                # upscale tile
-                try:
-                    with torch.no_grad():
-                        output_tile = self.srmodel(input_tile)
-                except RuntimeError as error:
-                    print('Error', error)
-                    return None
-                if tile_idx%10==0: print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')
-
-                # output tile area on total image
-                output_start_x = input_start_x * self.scale
-                output_end_x = input_end_x * self.scale
-                output_start_y = input_start_y * self.scale
-                output_end_y = input_end_y * self.scale
-
-                # output tile area without padding
-                output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
-                output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
-                output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
-                output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
-
-                # put tile into output image
-                output[:, :, output_start_y:output_end_y,
-                            output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
-                                                                       output_start_x_tile:output_end_x_tile]
-        return output
-
-    def process(self, img):
-        img = img.astype(np.float32) / 255.
-        img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
-        img = img.unsqueeze(0).to(self.device)
-
-        if self.scale == 2:
-            mod_scale = 2
-        elif self.scale == 1:
-            mod_scale = 4
-        else:
-            mod_scale = None
-        if mod_scale is not None:
-            h_pad, w_pad = 0, 0
-            _, _, h, w = img.size()
-            if (h % mod_scale != 0):
-                h_pad = (mod_scale - h % mod_scale)
-            if (w % mod_scale != 0):
-                w_pad = (mod_scale - w % mod_scale)
-            img = F.pad(img, (0, w_pad, 0, h_pad), 'reflect')
-
-        try:
-            with torch.no_grad():
-                if self.tile_size > 0:
-                    output = self.tile_process(img)
-                else:
-                    output = self.srmodel(img)
-            del img
-            # remove extra pad
-            if mod_scale is not None:
-                _, _, h, w = output.size()
-                output = output[:, :, 0:h - h_pad, 0:w - w_pad]
-            output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
-            output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
-            output = (output * 255.0).round().astype(np.uint8)
-
-            return output
-        except Exception as e:
-            print('sr failed:', e)
-            return None
\ No newline at end of file
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/train.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/train.py
deleted file mode 100644
index bd1f8a0cf7aab7cfa7c00205d8368cad7570005f..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/train.py
+++ /dev/null
@@ -1,317 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import optim
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-from synthesizer import audio
-from synthesizer.models.tacotron import Tacotron
-from synthesizer.synthesizer_dataset import SynthesizerDataset, collate_synthesizer
-from synthesizer.utils import ValueWindow, data_parallel_workaround
-from synthesizer.utils.plot import plot_spectrogram, plot_spectrogram_and_trace
-from synthesizer.utils.symbols import symbols
-from synthesizer.utils.text import sequence_to_text
-from vocoder.display import *
-from datetime import datetime
-import json
-import numpy as np
-from pathlib import Path
-import time
-import os
-
-def np_now(x: torch.Tensor): return x.detach().cpu().numpy()
-
-def time_string():
-    return datetime.now().strftime("%Y-%m-%d %H:%M")
-
-def train(run_id: str, syn_dir: str, models_dir: str, save_every: int,
-         backup_every: int, log_every:int, force_restart:bool, hparams):
-
-    syn_dir = Path(syn_dir)
-    models_dir = Path(models_dir)
-    models_dir.mkdir(exist_ok=True)
-
-    model_dir = models_dir.joinpath(run_id)
-    plot_dir = model_dir.joinpath("plots")
-    wav_dir = model_dir.joinpath("wavs")
-    mel_output_dir = model_dir.joinpath("mel-spectrograms")
-    meta_folder = model_dir.joinpath("metas")
-    model_dir.mkdir(exist_ok=True)
-    plot_dir.mkdir(exist_ok=True)
-    wav_dir.mkdir(exist_ok=True)
-    mel_output_dir.mkdir(exist_ok=True)
-    meta_folder.mkdir(exist_ok=True)
-    
-    weights_fpath = model_dir.joinpath(run_id).with_suffix(".pt")
-    metadata_fpath = syn_dir.joinpath("train.txt")
-    
-    print("Checkpoint path: {}".format(weights_fpath))
-    print("Loading training data from: {}".format(metadata_fpath))
-    print("Using model: Tacotron")
-    
-    # Book keeping
-    step = 0
-    time_window = ValueWindow(100)
-    loss_window = ValueWindow(100)
-    
-    
-    # From WaveRNN/train_tacotron.py
-    if torch.cuda.is_available():
-        device = torch.device("cuda")
-
-        for session in hparams.tts_schedule:
-            _, _, _, batch_size = session
-            if batch_size % torch.cuda.device_count() != 0:
-                raise ValueError("`batch_size` must be evenly divisible by n_gpus!")
-    else:
-        device = torch.device("cpu")
-    print("Using device:", device)
-
-    # Instantiate Tacotron Model
-    print("\nInitialising Tacotron Model...\n")
-    num_chars = len(symbols)
-    if weights_fpath.exists():
-        # for compatibility purpose, change symbols accordingly:
-        loaded_shape = torch.load(str(weights_fpath), map_location=device)["model_state"]["encoder.embedding.weight"].shape
-        if num_chars != loaded_shape[0]:
-            print("WARNING: you are using compatible mode due to wrong sympols length, please modify varible _characters in `utils\symbols.py`")
-            num_chars != loaded_shape[0]
-                # Try to scan config file
-        model_config_fpaths = list(weights_fpath.parent.rglob("*.json"))
-        if len(model_config_fpaths)>0 and model_config_fpaths[0].exists():
-            with model_config_fpaths[0].open("r", encoding="utf-8") as f:
-                hparams.loadJson(json.load(f))
-        else:  # save a config
-            hparams.dumpJson(weights_fpath.parent.joinpath(run_id).with_suffix(".json"))
-
-
-    model = Tacotron(embed_dims=hparams.tts_embed_dims,
-                     num_chars=num_chars,
-                     encoder_dims=hparams.tts_encoder_dims,
-                     decoder_dims=hparams.tts_decoder_dims,
-                     n_mels=hparams.num_mels,
-                     fft_bins=hparams.num_mels,
-                     postnet_dims=hparams.tts_postnet_dims,
-                     encoder_K=hparams.tts_encoder_K,
-                     lstm_dims=hparams.tts_lstm_dims,
-                     postnet_K=hparams.tts_postnet_K,
-                     num_highways=hparams.tts_num_highways,
-                     dropout=hparams.tts_dropout,
-                     stop_threshold=hparams.tts_stop_threshold,
-                     speaker_embedding_size=hparams.speaker_embedding_size).to(device)
-
-    # Initialize the optimizer
-    optimizer = optim.Adam(model.parameters(), amsgrad=True)
-
-    # Load the weights
-    if force_restart or not weights_fpath.exists():
-        print("\nStarting the training of Tacotron from scratch\n")
-        model.save(weights_fpath)
-
-        # Embeddings metadata
-        char_embedding_fpath = meta_folder.joinpath("CharacterEmbeddings.tsv")
-        with open(char_embedding_fpath, "w", encoding="utf-8") as f:
-            for symbol in symbols:
-                if symbol == " ":
-                    symbol = "\\s"  # For visual purposes, swap space with \s
-
-                f.write("{}\n".format(symbol))
-
-    else:
-        print("\nLoading weights at %s" % weights_fpath)
-        model.load(weights_fpath, device, optimizer)
-        print("Tacotron weights loaded from step %d" % model.step)
-    
-    # Initialize the dataset
-    metadata_fpath = syn_dir.joinpath("train.txt")
-    mel_dir = syn_dir.joinpath("mels")
-    embed_dir = syn_dir.joinpath("embeds")
-    dataset = SynthesizerDataset(metadata_fpath, mel_dir, embed_dir, hparams)
-    test_loader = DataLoader(dataset,
-                             batch_size=1,
-                             shuffle=True,
-                             pin_memory=True)
-
-    # tracing training step
-    sw = SummaryWriter(log_dir=model_dir.joinpath("logs"))
-
-    for i, session in enumerate(hparams.tts_schedule):
-        current_step = model.get_step()
-
-        r, lr, max_step, batch_size = session
-
-        training_steps = max_step - current_step
-
-        # Do we need to change to the next session?
-        if current_step >= max_step:
-            # Are there no further sessions than the current one?
-            if i == len(hparams.tts_schedule) - 1:
-                # We have completed training. Save the model and exit
-                model.save(weights_fpath, optimizer)
-                break
-            else:
-                # There is a following session, go to it
-                continue
-
-        model.r = r
-        # Begin the training
-        simple_table([(f"Steps with r={r}", str(training_steps // 1000) + "k Steps"),
-                      ("Batch Size", batch_size),
-                      ("Learning Rate", lr),
-                      ("Outputs/Step (r)", model.r)])
-
-        for p in optimizer.param_groups:
-            p["lr"] = lr
-        if hparams.tts_finetune_layers is not None and len(hparams.tts_finetune_layers) > 0:
-            model.finetune_partial(hparams.tts_finetune_layers)
-
-        data_loader = DataLoader(dataset,
-                                 collate_fn=collate_synthesizer,
-                                 batch_size=batch_size, #change if you got graphic card OOM
-                                 num_workers=2,
-                                 shuffle=True,
-                                 pin_memory=True)
-
-        total_iters = len(dataset) 
-        steps_per_epoch = np.ceil(total_iters / batch_size).astype(np.int32)
-        epochs = np.ceil(training_steps / steps_per_epoch).astype(np.int32)
-
-        for epoch in range(1, epochs+1):
-            for i, (texts, mels, embeds, idx) in enumerate(data_loader, 1):
-                start_time = time.time()
-
-                # Generate stop tokens for training
-                stop = torch.ones(mels.shape[0], mels.shape[2])
-                for j, k in enumerate(idx):
-                    stop[j, :int(dataset.metadata[k][4])-1] = 0
-
-                texts = texts.to(device)
-                mels = mels.to(device)
-                embeds = embeds.to(device)
-                stop = stop.to(device)
-
-                # Forward pass
-                # Parallelize model onto GPUS using workaround due to python bug
-                if device.type == "cuda" and torch.cuda.device_count() > 1:
-                    m1_hat, m2_hat, attention, stop_pred = data_parallel_workaround(model, texts,
-                                                                                    mels, embeds)
-                else:
-                    m1_hat, m2_hat, attention, stop_pred = model(texts, mels, embeds)
-
-                # Backward pass
-                m1_loss = F.mse_loss(m1_hat, mels) + F.l1_loss(m1_hat, mels)
-                m2_loss = F.mse_loss(m2_hat, mels)
-                stop_loss = F.binary_cross_entropy(stop_pred, stop)
-
-                loss = m1_loss + m2_loss + stop_loss
-
-                optimizer.zero_grad()
-                loss.backward()
-
-                if hparams.tts_clip_grad_norm is not None:
-                    grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), hparams.tts_clip_grad_norm)
-                    if np.isnan(grad_norm.cpu()):
-                        print("grad_norm was NaN!")
-
-                optimizer.step()
-
-                time_window.append(time.time() - start_time)
-                loss_window.append(loss.item())
-
-                step = model.get_step()
-                k = step // 1000
-
-                
-                msg = f"| Epoch: {epoch}/{epochs} ({i}/{steps_per_epoch}) | Loss: {loss_window.average:#.4} | {1./time_window.average:#.2} steps/s | Step: {k}k | "
-                stream(msg)
-
-                if log_every != 0 and step % log_every == 0 :
-                    sw.add_scalar("training/loss", loss_window.average, step)
-
-                # Backup or save model as appropriate
-                if backup_every != 0 and step % backup_every == 0 : 
-                    backup_fpath = Path("{}/{}_{}.pt".format(str(weights_fpath.parent), run_id, step))
-                    model.save(backup_fpath, optimizer)
-
-                if save_every != 0 and step % save_every == 0 : 
-                    # Must save latest optimizer state to ensure that resuming training
-                    # doesn't produce artifacts
-                    model.save(weights_fpath, optimizer)
-                    
-
-                # Evaluate model to generate samples
-                epoch_eval = hparams.tts_eval_interval == -1 and i == steps_per_epoch  # If epoch is done
-                step_eval = hparams.tts_eval_interval > 0 and step % hparams.tts_eval_interval == 0  # Every N steps
-                if epoch_eval or step_eval:
-                    for sample_idx in range(hparams.tts_eval_num_samples):
-                        # At most, generate samples equal to number in the batch
-                        if sample_idx + 1 <= len(texts):
-                            # Remove padding from mels using frame length in metadata
-                            mel_length = int(dataset.metadata[idx[sample_idx]][4])
-                            mel_prediction = np_now(m2_hat[sample_idx]).T[:mel_length]
-                            target_spectrogram = np_now(mels[sample_idx]).T[:mel_length]
-                            attention_len = mel_length // model.r
-                            # eval_loss = F.mse_loss(mel_prediction, target_spectrogram)
-                            # sw.add_scalar("validing/loss", eval_loss.item(), step)
-                            eval_model(attention=np_now(attention[sample_idx][:, :attention_len]),
-                                       mel_prediction=mel_prediction,
-                                       target_spectrogram=target_spectrogram,
-                                       input_seq=np_now(texts[sample_idx]),
-                                       step=step,
-                                       plot_dir=plot_dir,
-                                       mel_output_dir=mel_output_dir,
-                                       wav_dir=wav_dir,
-                                       sample_num=sample_idx + 1,
-                                       loss=loss,
-                                       hparams=hparams,
-                                       sw=sw)
-                    MAX_SAVED_COUNT = 20
-                    if (step / hparams.tts_eval_interval) % MAX_SAVED_COUNT == 0:
-                        # clean up and save last MAX_SAVED_COUNT;
-                        plots = next(os.walk(plot_dir), (None, None, []))[2]
-                        for plot in plots[-MAX_SAVED_COUNT:]:
-                            os.remove(plot_dir.joinpath(plot))
-                        mel_files = next(os.walk(mel_output_dir), (None, None, []))[2]
-                        for mel_file in mel_files[-MAX_SAVED_COUNT:]:
-                            os.remove(mel_output_dir.joinpath(mel_file))
-                        wavs = next(os.walk(wav_dir), (None, None, []))[2]
-                        for w in wavs[-MAX_SAVED_COUNT:]:
-                            os.remove(wav_dir.joinpath(w))
-                        
-                # Break out of loop to update training schedule
-                if step >= max_step:
-                    break
-
-            # Add line break after every epoch
-            print("")
-
-def eval_model(attention, mel_prediction, target_spectrogram, input_seq, step,
-               plot_dir, mel_output_dir, wav_dir, sample_num, loss, hparams, sw):
-    # Save some results for evaluation
-    attention_path = str(plot_dir.joinpath("attention_step_{}_sample_{}".format(step, sample_num)))
-    # save_attention(attention, attention_path)
-    save_and_trace_attention(attention, attention_path, sw, step)
-
-    # save predicted mel spectrogram to disk (debug)
-    mel_output_fpath = mel_output_dir.joinpath("mel-prediction-step-{}_sample_{}.npy".format(step, sample_num))
-    np.save(str(mel_output_fpath), mel_prediction, allow_pickle=False)
-
-    # save griffin lim inverted wav for debug (mel -> wav)
-    wav = audio.inv_mel_spectrogram(mel_prediction.T, hparams)
-    wav_fpath = wav_dir.joinpath("step-{}-wave-from-mel_sample_{}.wav".format(step, sample_num))
-    audio.save_wav(wav, str(wav_fpath), sr=hparams.sample_rate)
-
-    # save real and predicted mel-spectrogram plot to disk (control purposes)
-    spec_fpath = plot_dir.joinpath("step-{}-mel-spectrogram_sample_{}.png".format(step, sample_num))
-    title_str = "{}, {}, step={}, loss={:.5f}".format("Tacotron", time_string(), step, loss)
-    # plot_spectrogram(mel_prediction, str(spec_fpath), title=title_str,
-    #                  target_spectrogram=target_spectrogram,
-    #                  max_len=target_spectrogram.size // hparams.num_mels)
-    plot_spectrogram_and_trace(
-        mel_prediction, 
-        str(spec_fpath), 
-        title=title_str,
-        target_spectrogram=target_spectrogram,
-        max_len=target_spectrogram.size // hparams.num_mels,
-        sw=sw,
-        step=step)
-    print("Input at step {}: {}".format(step, sequence_to_text(input_seq)))
diff --git a/spaces/Kreaols/ChuanhuChatGPT/assets/html/footer.html b/spaces/Kreaols/ChuanhuChatGPT/assets/html/footer.html
deleted file mode 100644
index bca27bb8066dfab5cc0acf7be349a514de5f9a58..0000000000000000000000000000000000000000
--- a/spaces/Kreaols/ChuanhuChatGPT/assets/html/footer.html
+++ /dev/null
@@ -1 +0,0 @@
-<div class="versions">{versions}</div>
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/mask_rcnn.py b/spaces/KyanChen/RSPrompter/mmdet/models/detectors/mask_rcnn.py
deleted file mode 100644
index 880ee1e8ac3926d618ef47985549d3214175ee73..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/mask_rcnn.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmengine.config import ConfigDict
-
-from mmdet.registry import MODELS
-from mmdet.utils import OptConfigType, OptMultiConfig
-from .two_stage import TwoStageDetector
-
-
-@MODELS.register_module()
-class MaskRCNN(TwoStageDetector):
-    """Implementation of `Mask R-CNN <https://arxiv.org/abs/1703.06870>`_"""
-
-    def __init__(self,
-                 backbone: ConfigDict,
-                 rpn_head: ConfigDict,
-                 roi_head: ConfigDict,
-                 train_cfg: ConfigDict,
-                 test_cfg: ConfigDict,
-                 neck: OptConfigType = None,
-                 data_preprocessor: OptConfigType = None,
-                 init_cfg: OptMultiConfig = None) -> None:
-        super().__init__(
-            backbone=backbone,
-            neck=neck,
-            rpn_head=rpn_head,
-            roi_head=roi_head,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            init_cfg=init_cfg,
-            data_preprocessor=data_preprocessor)
diff --git a/spaces/KyanChen/RSPrompter/mmpl/models/pler/image.py b/spaces/KyanChen/RSPrompter/mmpl/models/pler/image.py
deleted file mode 100644
index 7569ddfa4d0b7898615458bceacebaad75d549e0..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpl/models/pler/image.py
+++ /dev/null
@@ -1,241 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Optional
-
-import torch
-import torch.nn as nn
-
-from mmpl.registry import MODELS
-from mmpl.structures import ClsDataSample
-from .base import BaseClassifier
-
-
-@MODELS.register_module()
-class ImageClassifier(BaseClassifier):
-    """Image classifiers for supervised classification task.
-
-    Args:
-        backbone (dict): The backbone module. See
-            :mod:`mmcls.models.backbones`.
-        neck (dict, optional): The neck module to process features from
-            backbone. See :mod:`mmcls.models.necks`. Defaults to None.
-        head (dict, optional): The head module to do prediction and calculate
-            loss from processed features. See :mod:`mmcls.models.heads`.
-            Notice that if the head is not set, almost all methods cannot be
-            used except :meth:`extract_feat`. Defaults to None.
-        pretrained (str, optional): The pretrained checkpoint path, support
-            local path and remote path. Defaults to None.
-        train_cfg (dict, optional): The training setting. The acceptable
-            fields are:
-
-            - augments (List[dict]): The batch augmentation methods to use.
-              More details can be found in :mod:`mmcls.model.utils.augment`.
-
-            Defaults to None.
-        data_preprocessor (dict, optional): The config for preprocessing input
-            data. If None or no specified type, it will use
-            "ClsDataPreprocessor" as type. See :class:`ClsDataPreprocessor` for
-            more details. Defaults to None.
-        init_cfg (dict, optional): the config to control the initialization.
-            Defaults to None.
-    """
-
-    def __init__(self,
-                 backbone: dict,
-                 neck: Optional[dict] = None,
-                 head: Optional[dict] = None,
-                 pretrained: Optional[str] = None,
-                 train_cfg: Optional[dict] = None,
-                 data_preprocessor: Optional[dict] = None,
-                 init_cfg: Optional[dict] = None):
-        if pretrained is not None:
-            init_cfg = dict(type='Pretrained', checkpoint=pretrained)
-
-        if data_preprocessor is None:
-            data_preprocessor = {}
-        # The build process is in MMEngine, so we need to add scope here.
-        data_preprocessor.setdefault('type', 'mmcls.ClsDataPreprocessor')
-
-        if train_cfg is not None and 'augments' in train_cfg:
-            # Set batch augmentations by `train_cfg`
-            data_preprocessor['batch_augments'] = train_cfg
-
-        super(ImageClassifier, self).__init__(
-            init_cfg=init_cfg, data_preprocessor=data_preprocessor)
-
-        if not isinstance(backbone, nn.Module):
-            backbone = MODELS.build(backbone)
-        if neck is not None and not isinstance(neck, nn.Module):
-            neck = MODELS.build(neck)
-        if head is not None and not isinstance(head, nn.Module):
-            head = MODELS.build(head)
-
-        self.backbone = backbone
-        self.neck = neck
-        self.head = head
-
-    def forward(self,
-                inputs: torch.Tensor,
-                data_samples: Optional[List[ClsDataSample]] = None,
-                mode: str = 'tensor'):
-        """The unified entry for a forward process in both training and test.
-
-        The method should accept three modes: "tensor", "predict" and "loss":
-
-        - "tensor": Forward the whole network and return tensor or tuple of
-          tensor without any post-processing, same as a common nn.Module.
-        - "predict": Forward and return the predictions, which are fully
-          processed to a list of :obj:`ClsDataSample`.
-        - "loss": Forward and return a dict of losses according to the given
-          inputs and data samples.
-
-        Note that this method doesn't handle neither back propagation nor
-        optimizer updating, which are done in the :meth:`train_step`.
-
-        Args:
-            inputs (torch.Tensor): The input tensor with shape
-                (N, C, ...) in general.
-            data_samples (List[ClsDataSample], optional): The annotation
-                data of every samples. It's required if ``mode="loss"``.
-                Defaults to None.
-            mode (str): Return what kind of value. Defaults to 'tensor'.
-
-        Returns:
-            The return type depends on ``mode``.
-
-            - If ``mode="tensor"``, return a tensor or a tuple of tensor.
-            - If ``mode="predict"``, return a list of
-              :obj:`mmcls.structures.ClsDataSample`.
-            - If ``mode="loss"``, return a dict of tensor.
-        """
-        if mode == 'tensor':
-            feats = self.extract_feat(inputs)
-            return self.head(feats) if self.with_head else feats
-        elif mode == 'loss':
-            return self.loss(inputs, data_samples)
-        elif mode == 'predict':
-            return self.predict(inputs, data_samples)
-        else:
-            raise RuntimeError(f'Invalid mode "{mode}".')
-
-    def extract_feat(self, inputs, stage='neck'):
-        """Extract features from the input tensor with shape (N, C, ...).
-
-        Args:
-            inputs (Tensor): A batch of inputs. The shape of it should be
-                ``(num_samples, num_channels, *img_shape)``.
-            stage (str): Which stage to output the feature. Choose from:
-
-                - "backbone": The output of backbone network. Returns a tuple
-                  including multiple stages features.
-                - "neck": The output of neck module. Returns a tuple including
-                  multiple stages features.
-                - "pre_logits": The feature before the final classification
-                  linear layer. Usually returns a tensor.
-
-                Defaults to "neck".
-
-        Returns:
-            tuple | Tensor: The output of specified stage.
-            The output depends on detailed implementation. In general, the
-            output of backbone and neck is a tuple and the output of
-            pre_logits is a tensor.
-
-        Examples:
-            1. Backbone output
-
-            >>> import torch
-            >>> from mmengine import Config
-            >>> from mmcls.models import build_classifier
-            >>>
-            >>> cfg = Config.fromfile('configs/resnet/resnet18_8xb32_in1k.py').model
-            >>> cfg.backbone.out_indices = (0, 1, 2, 3)  # Output multi-scale feature maps
-            >>> model = build_classifier(cfg)
-            >>> outs = model.extract_feat(torch.rand(1, 3, 224, 224), stage='backbone')
-            >>> for out in outs:
-            ...     print(out.shape)
-            torch.Size([1, 64, 56, 56])
-            torch.Size([1, 128, 28, 28])
-            torch.Size([1, 256, 14, 14])
-            torch.Size([1, 512, 7, 7])
-
-            2. Neck output
-
-            >>> import torch
-            >>> from mmengine import Config
-            >>> from mmcls.models import build_classifier
-            >>>
-            >>> cfg = Config.fromfile('configs/resnet/resnet18_8xb32_in1k.py').model
-            >>> cfg.backbone.out_indices = (0, 1, 2, 3)  # Output multi-scale feature maps
-            >>> model = build_classifier(cfg)
-            >>>
-            >>> outs = model.extract_feat(torch.rand(1, 3, 224, 224), stage='neck')
-            >>> for out in outs:
-            ...     print(out.shape)
-            torch.Size([1, 64])
-            torch.Size([1, 128])
-            torch.Size([1, 256])
-            torch.Size([1, 512])
-
-            3. Pre-logits output (without the final linear classifier head)
-
-            >>> import torch
-            >>> from mmengine import Config
-            >>> from mmcls.models import build_classifier
-            >>>
-            >>> cfg = Config.fromfile('configs/vision_transformer/vit-base-p16_pt-64xb64_in1k-224.py').model
-            >>> model = build_classifier(cfg)
-            >>>
-            >>> out = model.extract_feat(torch.rand(1, 3, 224, 224), stage='pre_logits')
-            >>> print(out.shape)  # The hidden dims in head is 3072
-            torch.Size([1, 3072])
-        """  # noqa: E501
-        assert stage in ['backbone', 'neck', 'pre_logits'], \
-            (f'Invalid output stage "{stage}", please choose from "backbone", '
-             '"neck" and "pre_logits"')
-
-        x = self.backbone(inputs)
-
-        if stage == 'backbone':
-            return x
-
-        if self.with_neck:
-            x = self.neck(x)
-        if stage == 'neck':
-            return x
-
-        assert self.with_head and hasattr(self.head, 'pre_logits'), \
-            "No head or the head doesn't implement `pre_logits` method."
-        return self.head.pre_logits(x)
-
-    def loss(self, inputs: torch.Tensor,
-             data_samples: List[ClsDataSample]) -> dict:
-        """Calculate losses from a batch of inputs and data samples.
-
-        Args:
-            inputs (torch.Tensor): The input tensor with shape
-                (N, C, ...) in general.
-            data_samples (List[ClsDataSample]): The annotation data of
-                every samples.
-
-        Returns:
-            dict[str, Tensor]: a dictionary of loss components
-        """
-        feats = self.extract_feat(inputs)
-        return self.head.loss(feats, data_samples)
-
-    def predict(self,
-                inputs: torch.Tensor,
-                data_samples: Optional[List[ClsDataSample]] = None,
-                **kwargs) -> List[ClsDataSample]:
-        """Predict results from a batch of inputs.
-
-        Args:
-            inputs (torch.Tensor): The input tensor with shape
-                (N, C, ...) in general.
-            data_samples (List[ClsDataSample], optional): The annotation
-                data of every samples. Defaults to None.
-            **kwargs: Other keyword arguments accepted by the ``predict``
-                method of :attr:`head`.
-        """
-        feats = self.extract_feat(inputs)
-        return self.head.predict(feats, data_samples, **kwargs)
diff --git a/spaces/Kyron2975/Linaqruf-anything-v3.0/app.py b/spaces/Kyron2975/Linaqruf-anything-v3.0/app.py
deleted file mode 100644
index 16e8131a0bbf7b06956e69e2b7758fa01e4eb51f..0000000000000000000000000000000000000000
--- a/spaces/Kyron2975/Linaqruf-anything-v3.0/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Linaqruf/anything-v3.0").launch()
\ No newline at end of file
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/feeds/quandl.py b/spaces/Lianjd/stock_dashboard/backtrader/feeds/quandl.py
deleted file mode 100644
index 43fe55ef26858c1e8a2c3759ed2e411b4f45f08d..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/feeds/quandl.py
+++ /dev/null
@@ -1,239 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-###############################################################################
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-import collections
-from datetime import date, datetime
-import io
-import itertools
-
-from ..utils.py3 import (urlopen, urlquote, ProxyHandler, build_opener,
-                         install_opener)
-
-from .. import feed
-from ..utils import date2num
-
-
-__all__ = ['QuandlCSV', 'Quandl']
-
-
-class QuandlCSV(feed.CSVDataBase):
-    '''
-    Parses pre-downloaded Quandl CSV Data Feeds (or locally generated if they
-    comply to the Quandl format)
-
-    Specific parameters:
-
-      - ``dataname``: The filename to parse or a file-like object
-
-      - ``reverse`` (default: ``False``)
-
-        It is assumed that locally stored files have already been reversed
-        during the download process
-
-      - ``adjclose`` (default: ``True``)
-
-        Whether to use the dividend/split adjusted close and adjust all
-        values according to it.
-
-      - ``round`` (default: ``False``)
-
-        Whether to round the values to a specific number of decimals after
-        having adjusted the close
-
-      - ``decimals`` (default: ``2``)
-
-        Number of decimals to round to
-    '''
-    _online = False  # flag to avoid double reversal
-
-    params = (
-        ('reverse', False),
-        ('adjclose', True),
-        ('round', False),
-        ('decimals', 2),
-    )
-
-    def start(self):
-        super(QuandlCSV, self).start()
-
-        if not self.params.reverse:
-            return
-        elif self._online:
-            return  # revers is True but also online, managed with order=asc
-
-        # Quandl data can be in reverse order -> reverse
-        dq = collections.deque()
-        for line in self.f:
-            dq.appendleft(line)
-
-        f = io.StringIO(newline=None)
-        f.writelines(dq)
-        f.seek(0)
-        self.f.close()
-        self.f = f
-
-    def _loadline(self, linetokens):
-        i = itertools.count(0)
-
-        dttxt = linetokens[next(i)]  # YYYY-MM-DD
-        dt = date(int(dttxt[0:4]), int(dttxt[5:7]), int(dttxt[8:10]))
-        dtnum = date2num(datetime.combine(dt, self.p.sessionend))
-
-        self.lines.datetime[0] = dtnum
-        if self.p.adjclose:
-            for _ in range(7):
-                next(i)  # skip ohlcv, ex-dividend, split ratio
-
-        o = float(linetokens[next(i)])
-        h = float(linetokens[next(i)])
-        l = float(linetokens[next(i)])
-        c = float(linetokens[next(i)])
-        v = float(linetokens[next(i)])
-        self.lines.openinterest[0] = 0.0
-
-        if self.p.round:
-            decimals = self.p.decimals
-            o = round(o, decimals)
-            h = round(h, decimals)
-            l = round(l, decimals)
-            c = round(c, decimals)
-            v = round(v, decimals)
-
-        self.lines.open[0] = o
-        self.lines.high[0] = h
-        self.lines.low[0] = l
-        self.lines.close[0] = c
-        self.lines.volume[0] = v
-
-        return True
-
-
-class Quandl(QuandlCSV):
-    '''
-    Executes a direct download of data from Quandl servers for the given time
-    range.
-
-    Specific parameters (or specific meaning):
-
-      - ``dataname``
-
-        The ticker to download ('YHOO' for example)
-
-      - ``baseurl``
-
-        The server url. Someone might decide to open a Quandl compatible
-        service in the future.
-
-      - ``proxies``
-
-        A dict indicating which proxy to go through for the download as in
-        {'http': 'http://myproxy.com'} or {'http': 'http://127.0.0.1:8080'}
-
-      - ``buffered``
-
-        If True the entire socket connection wil be buffered locally before
-        parsing starts.
-
-      - ``reverse``
-
-        Quandl returns the value in descending order (newest first). If this is
-        ``True`` (the default), the request will tell Quandl to return in
-        ascending (oldest to newest) format
-
-      - ``adjclose``
-
-        Whether to use the dividend/split adjusted close and adjust all values
-        according to it.
-
-      - ``apikey``
-
-        apikey identification in case it may be needed
-
-      - ``dataset``
-
-        string identifying the dataset to query. Defaults to ``WIKI``
-
-      '''
-
-    _online = True  # flag to avoid double reversal
-
-    params = (
-        ('baseurl', 'https://www.quandl.com/api/v3/datasets'),
-        ('proxies', {}),
-        ('buffered', True),
-        ('reverse', True),
-        ('apikey', None),
-        ('dataset', 'WIKI'),
-    )
-
-    def start(self):
-        self.error = None
-
-        url = '{}/{}/{}.csv'.format(
-            self.p.baseurl, self.p.dataset, urlquote(self.p.dataname))
-
-        urlargs = []
-        if self.p.reverse:
-            urlargs.append('order=asc')
-
-        if self.p.apikey is not None:
-            urlargs.append('api_key={}'.format(self.p.apikey))
-
-        if self.p.fromdate:
-            dtxt = self.p.fromdate.strftime('%Y-%m-%d')
-            urlargs.append('start_date={}'.format(dtxt))
-
-        if self.p.todate:
-            dtxt = self.p.todate.strftime('%Y-%m-%d')
-            urlargs.append('end_date={}'.format(dtxt))
-
-        if urlargs:
-            url += '?' + '&'.join(urlargs)
-
-        if self.p.proxies:
-            proxy = ProxyHandler(self.p.proxies)
-            opener = build_opener(proxy)
-            install_opener(opener)
-
-        try:
-            datafile = urlopen(url)
-        except IOError as e:
-            self.error = str(e)
-            # leave us empty
-            return
-
-        if datafile.headers['Content-Type'] != 'text/csv':
-            self.error = 'Wrong content type: %s' % datafile.headers
-            return  # HTML returned? wrong url?
-
-        if self.params.buffered:
-            # buffer everything from the socket into a local buffer
-            f = io.StringIO(datafile.read().decode('utf-8'), newline=None)
-            datafile.close()
-        else:
-            f = datafile
-
-        self.f = f
-
-        # Prepared a "path" file -  CSV Parser can take over
-        super(Quandl, self).start()
diff --git a/spaces/Liky1234/Bilibili/Dockerfile b/spaces/Liky1234/Bilibili/Dockerfile
deleted file mode 100644
index 015c95b2d2aa2869370c5ece929be8cf2c99d6ed..0000000000000000000000000000000000000000
--- a/spaces/Liky1234/Bilibili/Dockerfile
+++ /dev/null
@@ -1,21 +0,0 @@
-FROM node:18-bullseye-slim
-
-RUN apt-get update && \
-
-apt-get install -y git
-
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-
-WORKDIR /app
-
-RUN npm install
-
-COPY Dockerfile greeting.md* .env* ./
-
-RUN npm run build
-
-EXPOSE 7860
-
-ENV NODE_ENV=production
-
-CMD [ "npm", "start" ]
diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/recog_datasets/ST_MJ_train.py b/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/recog_datasets/ST_MJ_train.py
deleted file mode 100644
index aedccc9df54829c2b841ba47882dea0cbcc8b23d..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/recog_datasets/ST_MJ_train.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Text Recognition Training set, including:
-# Synthetic Datasets: SynthText, Syn90k
-
-train_root = 'data/mixture'
-
-train_img_prefix1 = f'{train_root}/Syn90k/mnt/ramdisk/max/90kDICT32px'
-train_ann_file1 = f'{train_root}/Syn90k/label.lmdb'
-
-train1 = dict(
-    type='OCRDataset',
-    img_prefix=train_img_prefix1,
-    ann_file=train_ann_file1,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='lmdb',
-        parser=dict(type='LineJsonParser', keys=['filename', 'text'])),
-    pipeline=None,
-    test_mode=False)
-
-train_img_prefix2 = f'{train_root}/SynthText/' + \
-    'synthtext/SynthText_patch_horizontal'
-train_ann_file2 = f'{train_root}/SynthText/label.lmdb'
-
-train2 = {key: value for key, value in train1.items()}
-train2['img_prefix'] = train_img_prefix2
-train2['ann_file'] = train_ann_file2
-
-train_list = [train1, train2]
diff --git a/spaces/Lwhieldon/Fall22_UMBC606_AbstractSummarization/README.md b/spaces/Lwhieldon/Fall22_UMBC606_AbstractSummarization/README.md
deleted file mode 100644
index 48d4336543e5c7c6055814ca7219ce1b02db65f1..0000000000000000000000000000000000000000
--- a/spaces/Lwhieldon/Fall22_UMBC606_AbstractSummarization/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Fall22 UMBC606 AbstractSummarization
-emoji: 😻
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.8.1
-app_file: app.py
-pinned: false
-license: mit
-models: Lwhieldon/pegasus-samsum
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/train.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/train.py
deleted file mode 100644
index 9d8d8229af05e728b811c5e3f85f0ae81b657011..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/train.py
+++ /dev/null
@@ -1,519 +0,0 @@
-import argparse
-import datetime
-import logging
-import inspect
-import math
-import os
-from typing import Dict, Optional, Tuple
-from omegaconf import OmegaConf
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-import numpy as np
-from PIL import Image
-
-import diffusers
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler, PNDMScheduler, ControlNetModel, PriorTransformer, UnCLIPScheduler
-from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version
-from diffusers.utils.import_utils import is_xformers_available
-from tqdm.auto import tqdm
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection, CLIPTextModelWithProjection
-
-from makeaprotagonist.models.unet import UNet3DConditionModel
-from makeaprotagonist.dataset.dataset import MakeAProtagonistDataset
-from makeaprotagonist.util import save_videos_grid, ddim_inversion_unclip
-from makeaprotagonist.pipelines.pipeline_stable_unclip_controlavideo import MakeAProtagonistStableUnCLIPPipeline, MultiControlNetModel
-
-from einops import rearrange
-from makeaprotagonist.args_util import DictAction, config_merge_dict
-import ipdb
-import random
-from glob import glob
-import sys
-
-
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.15.0.dev0")
-
-logger = get_logger(__name__, log_level="INFO")
-
-
-def main(
-    pretrained_model_path: str,
-    controlnet_pretrained_model_path: str,
-    output_dir: str,
-    train_data: Dict,
-    validation_data: Dict,
-    validation_steps: int = 100,
-    trainable_modules: Tuple[str] = (
-        "attn1.to_q",
-        "attn2.to_q",
-        "attn_temp",
-    ),
-    trainable_params: Tuple[str] = (),
-    train_batch_size: int = 1,
-    max_train_steps: int = 500,
-    learning_rate: float = 3e-5,
-    scale_lr: bool = False,
-    lr_scheduler: str = "constant",
-    lr_warmup_steps: int = 0,
-    adam_beta1: float = 0.9,
-    adam_beta2: float = 0.999,
-    adam_weight_decay: float = 1e-2,
-    adam_epsilon: float = 1e-08,
-    max_grad_norm: float = 1.0,
-    gradient_accumulation_steps: int = 1,
-    gradient_checkpointing: bool = True,
-    checkpointing_steps: int = 500,
-    resume_from_checkpoint: Optional[str] = None,
-    mixed_precision: Optional[str] = "fp16",
-    use_8bit_adam: bool = False,
-    enable_xformers_memory_efficient_attention: bool = True,
-    seed: Optional[int] = None,
-    adapter_config=None, # the config for adapter
-    use_temporal_conv=False, ## use temporal conv in resblocks
-):
-    *_, config = inspect.getargvalues(inspect.currentframe())
-
-    accelerator = Accelerator(
-        gradient_accumulation_steps=gradient_accumulation_steps,
-        mixed_precision=mixed_precision,
-    )
-
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.info(accelerator.state, main_process_only=False)
-    if accelerator.is_local_main_process:
-        transformers.utils.logging.set_verbosity_warning()
-        diffusers.utils.logging.set_verbosity_info()
-    else:
-        transformers.utils.logging.set_verbosity_error()
-        diffusers.utils.logging.set_verbosity_error()
-
-    # If passed along, set the training seed now.
-    if seed is not None:
-        set_seed(seed)
-
-    # Handle the output folder creation
-    if accelerator.is_main_process:
-        # now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
-        # output_dir = os.path.join(output_dir, now)
-        os.makedirs(output_dir, exist_ok=True)
-        os.makedirs(f"{output_dir}/samples", exist_ok=True)
-        os.makedirs(f"{output_dir}/inv_latents", exist_ok=True)
-        OmegaConf.save(config, os.path.join(output_dir, 'config.yaml'))
-
-    prior_model_id = "kakaobrain/karlo-v1-alpha"
-    data_type = torch.float16
-    prior = PriorTransformer.from_pretrained(prior_model_id, subfolder="prior", torch_dtype=data_type)
-
-    prior_text_model_id = "openai/clip-vit-large-patch14"
-    prior_tokenizer = CLIPTokenizer.from_pretrained(prior_text_model_id)
-    prior_text_model = CLIPTextModelWithProjection.from_pretrained(prior_text_model_id, torch_dtype=data_type)
-    prior_scheduler = UnCLIPScheduler.from_pretrained(prior_model_id, subfolder="prior_scheduler")
-    prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)
-
-    # image encoding components
-    feature_extractor = CLIPImageProcessor.from_pretrained(pretrained_model_path, subfolder="feature_extractor")
-    image_encoder = CLIPVisionModelWithProjection.from_pretrained(pretrained_model_path, subfolder="image_encoder")
-    # image noising components
-    image_normalizer = StableUnCLIPImageNormalizer.from_pretrained(pretrained_model_path, subfolder="image_normalizer")
-    image_noising_scheduler = DDPMScheduler.from_pretrained(pretrained_model_path, subfolder="image_noising_scheduler")
-    # regular denoising components
-    tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
-    text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
-    unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", use_temporal_conv=use_temporal_conv)
-    noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
-
-    # vae
-    vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
-
-    ## controlnet
-    assert not isinstance(controlnet_pretrained_model_path, str)
-    controlnet = MultiControlNetModel( [ControlNetModel.from_pretrained(_control_model_path) for _control_model_path in controlnet_pretrained_model_path] )
-    
-    # Freeze vae and text_encoder and adapter
-    vae.requires_grad_(False)
-    text_encoder.requires_grad_(False)
-
-    ## freeze image embed
-    image_encoder.requires_grad_(False)
-
-    unet.requires_grad_(False)
-    ## freeze controlnet
-    controlnet.requires_grad_(False)
-
-    ## freeze prior
-    prior.requires_grad_(False)
-    prior_text_model.requires_grad_(False)
-
-    for name, module in unet.named_modules():
-        if name.endswith(tuple(trainable_modules)):
-            for params in module.parameters():
-                params.requires_grad = True
-
-    if len(trainable_params):
-        for name, params in unet.named_parameters():
-            if name.endswith(tuple(trainable_params)):
-                params.requires_grad = True
-
-    if enable_xformers_memory_efficient_attention:
-        if is_xformers_available():
-            unet.enable_xformers_memory_efficient_attention()
-            controlnet.enable_xformers_memory_efficient_attention()
-        else:
-            raise ValueError("xformers is not available. Make sure it is installed correctly")
-
-    if gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-
-    if scale_lr:
-        learning_rate = (
-            learning_rate * gradient_accumulation_steps * train_batch_size * accelerator.num_processes
-        )
-
-    # Initialize the optimizer
-    if use_8bit_adam:
-        try:
-            import bitsandbytes as bnb
-        except ImportError:
-            raise ImportError(
-                "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
-            )
-
-        optimizer_cls = bnb.optim.AdamW8bit
-    else:
-        optimizer_cls = torch.optim.AdamW
-
-    optimizer = optimizer_cls(
-        unet.parameters(),
-        lr=learning_rate,
-        betas=(adam_beta1, adam_beta2),
-        weight_decay=adam_weight_decay,
-        eps=adam_epsilon,
-    )
-
-    # Get the training dataset
-    train_dataset = MakeAProtagonistDataset(**train_data)
-
-    # Preprocessing the dataset
-    train_dataset.prompt_ids = tokenizer(
-        train_dataset.prompt, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
-    ).input_ids[0]
-
-    train_dataset.preprocess_img_embedding(feature_extractor, image_encoder)
-    # DataLoaders creation:
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset, batch_size=train_batch_size
-    )
-
-    # Get the validation pipeline
-    # validation_pipeline = TuneAVideoPipeline(
-    #     vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
-    #     scheduler=DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
-    # )
-    prior_val_scheduler = DDIMScheduler.from_config(prior_scheduler.config) if validation_data.get("prior_val_scheduler", "") == "DDIM" else prior_scheduler
-    
-    validation_pipeline = MakeAProtagonistStableUnCLIPPipeline(
-        prior_tokenizer=prior_tokenizer,
-        prior_text_encoder=prior_text_model,
-        prior=prior,
-        prior_scheduler=prior_val_scheduler,
-        feature_extractor=feature_extractor,
-        image_encoder=image_encoder,
-        image_normalizer=image_normalizer,
-        image_noising_scheduler=image_noising_scheduler,
-        vae=vae, 
-        text_encoder=text_encoder, 
-        tokenizer=tokenizer, 
-        unet=unet,
-        controlnet=controlnet,
-        scheduler=DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
-    )
-
-        
-    validation_pipeline.enable_vae_slicing()
-    ddim_inv_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder='scheduler')
-    ddim_inv_scheduler.set_timesteps(validation_data.num_inv_steps)
-
-    # Scheduler
-    lr_scheduler = get_scheduler(
-        lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps,
-        num_training_steps=max_train_steps * gradient_accumulation_steps,
-    )
-
-    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        unet, optimizer, train_dataloader, lr_scheduler
-    )
-
-    # For mixed precision training we cast the text_encoder and vae weights to half-precision
-    # as these models are only used for inference, keeping weights in full precision is not required.
-    weight_dtype = torch.float32
-    if accelerator.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif accelerator.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move models to gpu and cast to weight_dtype
-    text_encoder.to(accelerator.device, dtype=weight_dtype)
-    vae.to(accelerator.device, dtype=weight_dtype)
-    image_encoder.to(accelerator.device, dtype=weight_dtype)
-    ## note controlnet use the unet dtype
-    controlnet.to(accelerator.device, dtype=weight_dtype)
-    ## prior
-    prior.to(accelerator.device, dtype=weight_dtype)
-    prior_text_model.to(accelerator.device, dtype=weight_dtype)
-
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / gradient_accumulation_steps)
-    # Afterwards we recalculate our number of training epochs
-    num_train_epochs = math.ceil(max_train_steps / num_update_steps_per_epoch)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        accelerator.init_trackers("text2video-fine-tune")
-
-    # Train!
-    total_batch_size = train_batch_size * accelerator.num_processes * gradient_accumulation_steps
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num Epochs = {num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {max_train_steps}")
-    global_step = 0
-    first_epoch = 0
-
-    # Potentially load in the weights and states from a previous save
-    if resume_from_checkpoint:
-        if resume_from_checkpoint != "latest":
-            path = os.path.basename(resume_from_checkpoint)
-        else:
-            # Get the most recent checkpoint
-            dirs = os.listdir(output_dir)
-            dirs = [d for d in dirs if d.startswith("checkpoint")]
-            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
-            path = dirs[-1]
-        accelerator.print(f"Resuming from checkpoint {path}")
-        accelerator.load_state(os.path.join(output_dir, path))
-        global_step = int(path.split("-")[1])
-
-        first_epoch = global_step // num_update_steps_per_epoch
-        resume_step = global_step % num_update_steps_per_epoch
-
-
-    # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(global_step, max_train_steps), disable=not accelerator.is_local_main_process)
-    progress_bar.set_description("Steps")
-
-    if not "noise_level" in validation_data:
-        validation_data.noise_level = train_data.noise_level
-
-    image_embed_drop = train_data.get("image_embed_drop", 0)
-
-    for epoch in range(first_epoch, num_train_epochs):
-        unet.train()
-        train_loss = 0.0
-        for step, batch in enumerate(train_dataloader):
-            # Skip steps until we reach the resumed step
-            if resume_from_checkpoint and epoch == first_epoch and step < resume_step:
-                if step % gradient_accumulation_steps == 0:
-                    progress_bar.update(1)
-                continue
-
-            with accelerator.accumulate(unet):
-                # Convert videos to latent space
-                pixel_values = batch["pixel_values"].to(weight_dtype)
-                video_length = pixel_values.shape[1]
-                pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w")
-                latents = vae.encode(pixel_values).latent_dist.sample()
-                latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
-                latents = latents * vae.config.scaling_factor
-
-                # Sample noise that we'll add to the latents
-                noise = torch.randn_like(latents)
-                bsz = latents.shape[0]
-                # Sample a random timestep for each video
-                timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
-                timesteps = timesteps.long()
-
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-                # Get the text embedding for conditioning
-                encoder_hidden_states = text_encoder(batch["prompt_ids"])[0]
-
-                #
-                # ipdb.set_trace()
-                ref_imbed = batch["ref_imbed"].to(accelerator.device, dtype=weight_dtype)  # 1,1,768
-                ##
-                if train_data.noise_level >= 1000:
-                    train_noise = random.randint(0, 999)
-                else:
-                    train_noise = train_data.noise_level
-
-                image_embeds = validation_pipeline.noise_image_embeddings(
-                                image_embeds=ref_imbed,
-                                noise_level=train_noise,
-                                generator=None,
-                            )
-
-                if random.random() < image_embed_drop:
-                    image_embeds = torch.zeros_like(image_embeds)
-
-                # Get the target for loss depending on the prediction type
-                if noise_scheduler.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.prediction_type == "v_prediction": ## use this for unclip model
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.prediction_type}")
-
-                # Predict the noise residual and compute loss
-                # model_pred = unet(noisy_latents, timesteps, encoder_hidden_states, adapter_features=adapter_features).sample
-                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states=encoder_hidden_states, class_labels=image_embeds).sample
-
-                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-                # Gather the losses across all processes for logging (if we use distributed training).
-                avg_loss = accelerator.gather(loss.repeat(train_batch_size)).mean()
-                train_loss += avg_loss.item() / gradient_accumulation_steps
-
-                # Backpropagate
-                accelerator.backward(loss)
-                if accelerator.sync_gradients:
-                    accelerator.clip_grad_norm_(unet.parameters(), max_grad_norm)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad()
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-                accelerator.log({"train_loss": train_loss}, step=global_step)
-                train_loss = 0.0
-
-                if global_step % checkpointing_steps == 0:
-                    if accelerator.is_main_process:
-                        save_path = os.path.join(output_dir, f"checkpoint-{global_step}")
-                        accelerator.save_state(save_path)
-                        logger.info(f"Saved state to {save_path}")
-
-                if global_step % validation_steps == 0:
-                    if accelerator.is_main_process:
-
-                        # ControlNet
-                        conditions = [_condition.to(weight_dtype) for _, _condition in batch["conditions"].items()] # b f c h w
-                        masks = batch["masks"].to(weight_dtype) # b,f,1,h,w
-                        if not validation_data.get("use_masks", False):
-                            masks = torch.ones_like(masks)
-
-                        ddim_inv_latent = None
-                        if validation_data.use_inv_latent: #
-                            emb_dim = train_dataset.img_embeddings[0].size(0)
-                            key_frame_embed = torch.zeros((1, emb_dim)).to(device=latents.device, dtype=latents.dtype) ## this is dim 0
-                            ddim_inv_latent = ddim_inversion_unclip(
-                                validation_pipeline, ddim_inv_scheduler, video_latent=latents,
-                                num_inv_steps=validation_data.num_inv_steps, prompt="", image_embed=key_frame_embed, noise_level=validation_data.noise_level, seed=seed)[-1].to(weight_dtype)
-
-                        set_noise = validation_data.pop("noise_level")
-                        v_noise = set_noise
-
-                        if not validation_data.get("interpolate_embed_weight", False):
-                            validation_data.interpolate_embed_weight = 1.0
-
-
-                        samples = []
-                            
-                        generator = torch.Generator(device=accelerator.device)
-                        generator.manual_seed(seed)
-
-                        for idx, prompt in enumerate(validation_data.prompts):
-
-                            _ref_image = Image.open(validation_data.ref_images[idx])
-                            image_embed = None
-                            ## prior latents
-                            prior_embeds = None
-                            prior_denoised_embeds = None
-                            if validation_data.get("source_background", False):
-                                ## using source background and changing the protagonist
-                                prior_denoised_embeds = train_dataset.img_embeddings[0][None].to(device=latents.device, dtype=latents.dtype) # 1, 768 for UnCLIP-small
-                            
-                            if validation_data.get("source_protagonist", False):
-                                # using source protagonist and changing the background
-                                sample_indices = batch["sample_indices"][0]
-                                image_embed = [train_dataset.img_embeddings[idx] for idx in sample_indices]
-                                image_embed = torch.stack(image_embed, dim=0).to(device=latents.device, dtype=latents.dtype) # F, 768 for UnCLIP-small # F,C
-                                _ref_image = None
-
-                            sample = validation_pipeline(image=_ref_image, prompt=prompt, control_image=conditions, generator=generator, latents=ddim_inv_latent, image_embeds=image_embed, noise_level=v_noise, masks=masks, prior_latents=prior_embeds, prior_denoised_embeds=prior_denoised_embeds, **validation_data).videos
-
-                            save_videos_grid(sample, f"{output_dir}/samples/sample-{global_step}-seed{seed}/{idx}-{prompt}.gif")
-                            samples.append(sample)
-
-                        # 
-                        samples = [sample.float() for sample in samples]
-                        samples = torch.concat(samples)
-                        save_path = f"{output_dir}/samples/sample-{global_step}-s{validation_data.start_step}-e{validation_data.end_step}-seed{seed}.gif" # noise level and noise level for inv
-                        save_videos_grid(samples, save_path, n_rows=len(samples))
-                        logger.info(f"Saved samples to {save_path}")
-
-
-            logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-
-            if global_step >= max_train_steps:
-                break
-
-    # Create the pipeline using the trained modules and save it.
-    accelerator.wait_for_everyone()
-
-    accelerator.end_training()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--config", type=str, default="./configs/tuneavideo.yaml")
-    parser.add_argument(
-        '--options',
-        nargs='+',
-        action=DictAction, 
-        help='Override some settings in the '
-        'used config, the key-value pair in xxx=yyy format will be merged '
-        'into config file. If the value to be overwritten is a list, it '
-        'should be like key="[a,b]" or key=a,b It also allows nested '
-        'list/tuple values, e.g. key="[(a,b),(c,d)]" Note that the quotation '
-        'marks are necessary and that no white space is allowed.')
-
-    args = parser.parse_args()
-
-    ## read from cmd line
-    # ipdb.set_trace()
-    # Load the YAML configuration file
-    config = OmegaConf.load(args.config)
-    # Merge the command-line arguments with the configuration file
-    if args.options is not None:
-        # config = OmegaConf.merge(config, args.options)
-        config_merge_dict(args.options, config)
-    
-    main(**config)
diff --git a/spaces/Manjushri/MusicGen/app_batched.py b/spaces/Manjushri/MusicGen/app_batched.py
deleted file mode 100644
index 0d2a4b526e4b8ef94034a1c661a4fa68816c285a..0000000000000000000000000000000000000000
--- a/spaces/Manjushri/MusicGen/app_batched.py
+++ /dev/null
@@ -1,222 +0,0 @@
-"""
-Copyright (c) Meta Platforms, Inc. and affiliates.
-All rights reserved.
-
-This source code is licensed under the license found in the
-LICENSE file in the root directory of this source tree.
-"""
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import warnings
-import torch
-import gradio as gr
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.data.audio import audio_write
-from audiocraft.models import MusicGen
-
-
-MODEL = None
-
-_old_call = sp.call
-
-
-def _call_nostderr(*args, **kwargs):
-    # Avoid ffmpeg vomitting on the logs.
-    kwargs['stderr'] = sp.DEVNULL
-    kwargs['stdout'] = sp.DEVNULL
-    _old_call(*args, **kwargs)
-
-
-sp.call = _call_nostderr
-pool = ProcessPoolExecutor(3)
-pool.__enter__()
-
-
-def make_waveform(*args, **kwargs):
-    be = time.time()
-    with warnings.catch_warnings():
-        warnings.simplefilter('ignore')
-        out = gr.make_waveform(*args, **kwargs)
-        print("Make a video took", time.time() - be)
-        return out
-
-
-def load_model():
-    print("Loading model")
-    return MusicGen.get_pretrained("melody")
-
-
-def predict(texts, melodies):
-    global MODEL
-    if MODEL is None:
-        MODEL = load_model()
-
-    duration = 12
-    max_text_length = 512
-    texts = [text[:max_text_length] for text in texts]
-    MODEL.set_generation_params(duration=duration)
-
-    print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies])
-    be = time.time()
-    processed_melodies = []
-    target_sr = 32000
-    target_ac = 1
-    for melody in melodies:
-        if melody is None:
-            processed_melodies.append(None)
-        else:
-            sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
-            if melody.dim() == 1:
-                melody = melody[None]
-            melody = melody[..., :int(sr * duration)]
-            melody = convert_audio(melody, sr, target_sr, target_ac)
-            processed_melodies.append(melody)
-
-    outputs = MODEL.generate_with_chroma(
-        descriptions=texts,
-        melody_wavs=processed_melodies,
-        melody_sample_rate=target_sr,
-        progress=False
-    )
-
-    outputs = outputs.detach().cpu().float()
-    out_files = []
-    for output in outputs:
-        with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
-            audio_write(
-                file.name, output, MODEL.sample_rate, strategy="loudness",
-                loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
-            out_files.append(pool.submit(make_waveform, file.name))
-    res = [[out_file.result() for out_file in out_files]]
-    print("batch finished", len(texts), time.time() - be)
-    return res
-
-
-def ui(**kwargs):
-    with gr.Blocks() as demo:
-        gr.Markdown(
-            """
-            # MusicGen
-
-            This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
-            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
-            <br/>
-            <a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
-            <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-            for longer sequences, more control and no queue.</p>
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    text = gr.Text(label="Describe your music", lines=2, interactive=True)
-                    melody = gr.Audio(source="upload", type="numpy", label="Condition on a melody (optional)", interactive=True)
-                with gr.Row():
-                    submit = gr.Button("Generate")
-            with gr.Column():
-                output = gr.Video(label="Generated Music")
-        submit.click(predict, inputs=[text, melody], outputs=[output], batch=True, max_batch_size=8)
-        gr.Examples(
-            fn=predict,
-            examples=[
-                [
-                    "An 80s driving pop song with heavy drums and synth pads in the background",
-                    "./assets/bach.mp3",
-                ],
-                [
-                    "A cheerful country song with acoustic guitars",
-                    "./assets/bolero_ravel.mp3",
-                ],
-                [
-                    "90s rock song with electric guitar and heavy drums",
-                    None,
-                ],
-                [
-                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
-                    "./assets/bach.mp3",
-                ],
-                [
-                    "lofi slow bpm electro chill with organic samples",
-                    None,
-                ],
-            ],
-            inputs=[text, melody],
-            outputs=[output]
-        )
-        gr.Markdown("""
-        ### More details
-
-        The model will generate 12 seconds of audio based on the description you provided.
-        You can optionaly provide a reference audio from which a broad melody will be extracted.
-        The model will then try to follow both the description and melody provided.
-        All samples are generated with the `melody` model.
-
-        You can also use your own GPU or a Google Colab by following the instructions on our repo.
-
-        See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
-        for more details.
-        """)
-
-        # Show the interface
-        launch_kwargs = {}
-        username = kwargs.get('username')
-        password = kwargs.get('password')
-        server_port = kwargs.get('server_port', 0)
-        inbrowser = kwargs.get('inbrowser', False)
-        share = kwargs.get('share', False)
-        server_name = kwargs.get('listen')
-
-        launch_kwargs['server_name'] = server_name
-
-        if username and password:
-            launch_kwargs['auth'] = (username, password)
-        if server_port > 0:
-            launch_kwargs['server_port'] = server_port
-        if inbrowser:
-            launch_kwargs['inbrowser'] = inbrowser
-        if share:
-            launch_kwargs['share'] = share
-        demo.queue(max_size=8 * 4).launch(**launch_kwargs)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--listen',
-        type=str,
-        default='0.0.0.0',
-        help='IP to listen on for connections to Gradio',
-    )
-    parser.add_argument(
-        '--username', type=str, default='', help='Username for authentication'
-    )
-    parser.add_argument(
-        '--password', type=str, default='', help='Password for authentication'
-    )
-    parser.add_argument(
-        '--server_port',
-        type=int,
-        default=0,
-        help='Port to run the server listener on',
-    )
-    parser.add_argument(
-        '--inbrowser', action='store_true', help='Open in browser'
-    )
-    parser.add_argument(
-        '--share', action='store_true', help='Share the gradio UI'
-    )
-
-    args = parser.parse_args()
-
-    ui(
-        username=args.username,
-        password=args.password,
-        inbrowser=args.inbrowser,
-        server_port=args.server_port,
-        share=args.share,
-        listen=args.listen
-    )
diff --git a/spaces/MathysL/AutoGPT4/autogpt/speech/macos_tts.py b/spaces/MathysL/AutoGPT4/autogpt/speech/macos_tts.py
deleted file mode 100644
index 4c072ce256782e83a578b5181abf1a7b524c621b..0000000000000000000000000000000000000000
--- a/spaces/MathysL/AutoGPT4/autogpt/speech/macos_tts.py
+++ /dev/null
@@ -1,21 +0,0 @@
-""" MacOS TTS Voice. """
-import os
-
-from autogpt.speech.base import VoiceBase
-
-
-class MacOSTTS(VoiceBase):
-    """MacOS TTS Voice."""
-
-    def _setup(self) -> None:
-        pass
-
-    def _speech(self, text: str, voice_index: int = 0) -> bool:
-        """Play the given text."""
-        if voice_index == 0:
-            os.system(f'say "{text}"')
-        elif voice_index == 1:
-            os.system(f'say -v "Ava (Premium)" "{text}"')
-        else:
-            os.system(f'say -v Samantha "{text}"')
-        return True
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/midas/midas/transforms.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/midas/midas/transforms.py
deleted file mode 100644
index 350cbc11662633ad7f8968eb10be2e7de6e384e9..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/midas/midas/transforms.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import numpy as np
-import cv2
-import math
-
-
-def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
-    """Rezise the sample to ensure the given size. Keeps aspect ratio.
-
-    Args:
-        sample (dict): sample
-        size (tuple): image size
-
-    Returns:
-        tuple: new size
-    """
-    shape = list(sample["disparity"].shape)
-
-    if shape[0] >= size[0] and shape[1] >= size[1]:
-        return sample
-
-    scale = [0, 0]
-    scale[0] = size[0] / shape[0]
-    scale[1] = size[1] / shape[1]
-
-    scale = max(scale)
-
-    shape[0] = math.ceil(scale * shape[0])
-    shape[1] = math.ceil(scale * shape[1])
-
-    # resize
-    sample["image"] = cv2.resize(
-        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
-    )
-
-    sample["disparity"] = cv2.resize(
-        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
-    )
-    sample["mask"] = cv2.resize(
-        sample["mask"].astype(np.float32),
-        tuple(shape[::-1]),
-        interpolation=cv2.INTER_NEAREST,
-    )
-    sample["mask"] = sample["mask"].astype(bool)
-
-    return tuple(shape)
-
-
-class Resize(object):
-    """Resize sample to given size (width, height).
-    """
-
-    def __init__(
-        self,
-        width,
-        height,
-        resize_target=True,
-        keep_aspect_ratio=False,
-        ensure_multiple_of=1,
-        resize_method="lower_bound",
-        image_interpolation_method=cv2.INTER_AREA,
-    ):
-        """Init.
-
-        Args:
-            width (int): desired output width
-            height (int): desired output height
-            resize_target (bool, optional):
-                True: Resize the full sample (image, mask, target).
-                False: Resize image only.
-                Defaults to True.
-            keep_aspect_ratio (bool, optional):
-                True: Keep the aspect ratio of the input sample.
-                Output sample might not have the given width and height, and
-                resize behaviour depends on the parameter 'resize_method'.
-                Defaults to False.
-            ensure_multiple_of (int, optional):
-                Output width and height is constrained to be multiple of this parameter.
-                Defaults to 1.
-            resize_method (str, optional):
-                "lower_bound": Output will be at least as large as the given size.
-                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
-                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
-                Defaults to "lower_bound".
-        """
-        self.__width = width
-        self.__height = height
-
-        self.__resize_target = resize_target
-        self.__keep_aspect_ratio = keep_aspect_ratio
-        self.__multiple_of = ensure_multiple_of
-        self.__resize_method = resize_method
-        self.__image_interpolation_method = image_interpolation_method
-
-    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
-        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if max_val is not None and y > max_val:
-            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if y < min_val:
-            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        return y
-
-    def get_size(self, width, height):
-        # determine new height and width
-        scale_height = self.__height / height
-        scale_width = self.__width / width
-
-        if self.__keep_aspect_ratio:
-            if self.__resize_method == "lower_bound":
-                # scale such that output size is lower bound
-                if scale_width > scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "upper_bound":
-                # scale such that output size is upper bound
-                if scale_width < scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "minimal":
-                # scale as least as possbile
-                if abs(1 - scale_width) < abs(1 - scale_height):
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            else:
-                raise ValueError(
-                    f"resize_method {self.__resize_method} not implemented"
-                )
-
-        if self.__resize_method == "lower_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, min_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, min_val=self.__width
-            )
-        elif self.__resize_method == "upper_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, max_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, max_val=self.__width
-            )
-        elif self.__resize_method == "minimal":
-            new_height = self.constrain_to_multiple_of(scale_height * height)
-            new_width = self.constrain_to_multiple_of(scale_width * width)
-        else:
-            raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
-        return (new_width, new_height)
-
-    def __call__(self, sample):
-        width, height = self.get_size(
-            sample["image"].shape[1], sample["image"].shape[0]
-        )
-
-        # resize sample
-        sample["image"] = cv2.resize(
-            sample["image"],
-            (width, height),
-            interpolation=self.__image_interpolation_method,
-        )
-
-        if self.__resize_target:
-            if "disparity" in sample:
-                sample["disparity"] = cv2.resize(
-                    sample["disparity"],
-                    (width, height),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-
-            if "depth" in sample:
-                sample["depth"] = cv2.resize(
-                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
-                )
-
-            sample["mask"] = cv2.resize(
-                sample["mask"].astype(np.float32),
-                (width, height),
-                interpolation=cv2.INTER_NEAREST,
-            )
-            sample["mask"] = sample["mask"].astype(bool)
-
-        return sample
-
-
-class NormalizeImage(object):
-    """Normlize image by given mean and std.
-    """
-
-    def __init__(self, mean, std):
-        self.__mean = mean
-        self.__std = std
-
-    def __call__(self, sample):
-        sample["image"] = (sample["image"] - self.__mean) / self.__std
-
-        return sample
-
-
-class PrepareForNet(object):
-    """Prepare sample for usage as network input.
-    """
-
-    def __init__(self):
-        pass
-
-    def __call__(self, sample):
-        image = np.transpose(sample["image"], (2, 0, 1))
-        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
-
-        if "mask" in sample:
-            sample["mask"] = sample["mask"].astype(np.float32)
-            sample["mask"] = np.ascontiguousarray(sample["mask"])
-
-        if "disparity" in sample:
-            disparity = sample["disparity"].astype(np.float32)
-            sample["disparity"] = np.ascontiguousarray(disparity)
-
-        if "depth" in sample:
-            depth = sample["depth"].astype(np.float32)
-            sample["depth"] = np.ascontiguousarray(depth)
-
-        return sample
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/registry.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/registry.py
deleted file mode 100644
index d1ed33552881316ab4dc151650243456e06e1c4f..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/registry.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-"""MMOCR provides 20 registry nodes to support using modules across projects.
-Each node is a child of the root registry in MMEngine.
-
-More details can be found at
-https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
-"""
-
-from mmengine.registry import DATA_SAMPLERS as MMENGINE_DATA_SAMPLERS
-from mmengine.registry import DATASETS as MMENGINE_DATASETS
-from mmengine.registry import EVALUATOR as MMENGINE_EVALUATOR
-from mmengine.registry import HOOKS as MMENGINE_HOOKS
-from mmengine.registry import LOG_PROCESSORS as MMENGINE_LOG_PROCESSORS
-from mmengine.registry import LOOPS as MMENGINE_LOOPS
-from mmengine.registry import METRICS as MMENGINE_METRICS
-from mmengine.registry import MODEL_WRAPPERS as MMENGINE_MODEL_WRAPPERS
-from mmengine.registry import MODELS as MMENGINE_MODELS
-from mmengine.registry import \
-    OPTIM_WRAPPER_CONSTRUCTORS as MMENGINE_OPTIM_WRAPPER_CONSTRUCTORS
-from mmengine.registry import OPTIM_WRAPPERS as MMENGINE_OPTIM_WRAPPERS
-from mmengine.registry import OPTIMIZERS as MMENGINE_OPTIMIZERS
-from mmengine.registry import PARAM_SCHEDULERS as MMENGINE_PARAM_SCHEDULERS
-from mmengine.registry import \
-    RUNNER_CONSTRUCTORS as MMENGINE_RUNNER_CONSTRUCTORS
-from mmengine.registry import RUNNERS as MMENGINE_RUNNERS
-from mmengine.registry import TASK_UTILS as MMENGINE_TASK_UTILS
-from mmengine.registry import TRANSFORMS as MMENGINE_TRANSFORMS
-from mmengine.registry import VISBACKENDS as MMENGINE_VISBACKENDS
-from mmengine.registry import VISUALIZERS as MMENGINE_VISUALIZERS
-from mmengine.registry import \
-    WEIGHT_INITIALIZERS as MMENGINE_WEIGHT_INITIALIZERS
-from mmengine.registry import Registry
-
-# manage all kinds of runners like `EpochBasedRunner` and `IterBasedRunner`
-RUNNERS = Registry(
-    'runner',
-    parent=MMENGINE_RUNNERS,
-    # TODO: update the location when mmocr has its own runner
-    locations=['mmocr.engine'])
-# manage runner constructors that define how to initialize runners
-RUNNER_CONSTRUCTORS = Registry(
-    'runner constructor',
-    parent=MMENGINE_RUNNER_CONSTRUCTORS,
-    # TODO: update the location when mmocr has its own runner constructor
-    locations=['mmocr.engine'])
-# manage all kinds of loops like `EpochBasedTrainLoop`
-LOOPS = Registry(
-    'loop',
-    parent=MMENGINE_LOOPS,
-    # TODO: update the location when mmocr has its own loop
-    locations=['mmocr.engine'])
-# manage all kinds of hooks like `CheckpointHook`
-HOOKS = Registry(
-    'hook', parent=MMENGINE_HOOKS, locations=['mmocr.engine.hooks'])
-
-# manage data-related modules
-DATASETS = Registry(
-    'dataset', parent=MMENGINE_DATASETS, locations=['mmocr.datasets'])
-DATA_SAMPLERS = Registry(
-    'data sampler',
-    parent=MMENGINE_DATA_SAMPLERS,
-    locations=['mmocr.datasets.samplers'])
-TRANSFORMS = Registry(
-    'transform',
-    parent=MMENGINE_TRANSFORMS,
-    locations=['mmocr.datasets.transforms'])
-
-# manage all kinds of modules inheriting `nn.Module`
-MODELS = Registry('model', parent=MMENGINE_MODELS, locations=['mmocr.models'])
-# manage all kinds of model wrappers like 'MMDistributedDataParallel'
-MODEL_WRAPPERS = Registry(
-    'model wrapper',
-    parent=MMENGINE_MODEL_WRAPPERS,
-    locations=['mmocr.models'])
-# manage all kinds of weight initialization modules like `Uniform`
-WEIGHT_INITIALIZERS = Registry(
-    'weight initializer',
-    parent=MMENGINE_WEIGHT_INITIALIZERS,
-    locations=['mmocr.models'])
-
-# manage all kinds of optimizers like `SGD` and `Adam`
-OPTIMIZERS = Registry(
-    'optimizer',
-    parent=MMENGINE_OPTIMIZERS,
-    # TODO: update the location when mmocr has its own optimizer
-    locations=['mmocr.engine'])
-# manage optimizer wrapper
-OPTIM_WRAPPERS = Registry(
-    'optimizer wrapper',
-    parent=MMENGINE_OPTIM_WRAPPERS,
-    # TODO: update the location when mmocr has its own optimizer wrapper
-    locations=['mmocr.engine'])
-# manage constructors that customize the optimization hyperparameters.
-OPTIM_WRAPPER_CONSTRUCTORS = Registry(
-    'optimizer constructor',
-    parent=MMENGINE_OPTIM_WRAPPER_CONSTRUCTORS,
-    # TODO: update the location when mmocr has its own optimizer constructor
-    locations=['mmocr.engine'])
-# manage all kinds of parameter schedulers like `MultiStepLR`
-PARAM_SCHEDULERS = Registry(
-    'parameter scheduler',
-    parent=MMENGINE_PARAM_SCHEDULERS,
-    # TODO: update the location when mmocr has its own parameter scheduler
-    locations=['mmocr.engine'])
-# manage all kinds of metrics
-METRICS = Registry(
-    'metric', parent=MMENGINE_METRICS, locations=['mmocr.evaluation.metrics'])
-# manage evaluator
-EVALUATOR = Registry(
-    'evaluator',
-    parent=MMENGINE_EVALUATOR,
-    locations=['mmocr.evaluation.evaluator'])
-
-# manage task-specific modules like anchor generators and box coders
-TASK_UTILS = Registry(
-    'task util', parent=MMENGINE_TASK_UTILS, locations=['mmocr.models'])
-
-# manage visualizer
-VISUALIZERS = Registry(
-    'visualizer',
-    parent=MMENGINE_VISUALIZERS,
-    locations=['mmocr.visualization'])
-# manage visualizer backend
-VISBACKENDS = Registry(
-    'visualizer backend',
-    parent=MMENGINE_VISBACKENDS,
-    locations=['mmocr.visualization'])
-
-# manage logprocessor
-LOG_PROCESSORS = Registry(
-    'logger processor',
-    parent=MMENGINE_LOG_PROCESSORS,
-    # TODO: update the location when mmocr has its own log processor
-    locations=['mmocr.engine'])
-# manage data obtainer
-DATA_OBTAINERS = Registry(
-    'data obtainer', locations=['mmocr.datasets.preparers.obtainers'])
-
-# manage data gatherer
-DATA_GATHERERS = Registry(
-    'data gatherer', locations=['mmocr.datasets.preparers.gatherers'])
-
-# manage data parser
-DATA_PARSERS = Registry(
-    'data parser', locations=['mmocr.datasets.preparers.parsers'])
-
-# manage data packer
-DATA_PACKERS = Registry(
-    'data packer', locations=['mmocr.datasets.preparers.packers'])
-
-# manage data dumper
-DATA_DUMPERS = Registry(
-    'data dumper', locations=['mmocr.datasets.preparers.dumpers'])
-
-# manage dataset config generator
-CFG_GENERATORS = Registry(
-    'cfg generator', locations=['mmocr.datasets.preparers.config_generators'])
diff --git a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/hiertext_converter.py b/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/hiertext_converter.py
deleted file mode 100644
index 948142446e30ceea2c1b58cbcd7aec930a982482..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/hiertext_converter.py
+++ /dev/null
@@ -1,236 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import argparse
-import json
-import math
-import os.path as osp
-from functools import partial
-
-import mmcv
-import mmengine
-import numpy as np
-from shapely.geometry import Polygon
-
-from mmocr.utils import dump_ocr_data
-
-
-def seg2bbox(seg):
-    """Convert segmentation to bbox.
-
-    Args:
-        seg (list(int | float)): A set of coordinates
-    """
-    if len(seg) == 4:
-        min_x = min(seg[0], seg[2], seg[4], seg[6])
-        max_x = max(seg[0], seg[2], seg[4], seg[6])
-        min_y = min(seg[1], seg[3], seg[5], seg[7])
-        max_y = max(seg[1], seg[3], seg[5], seg[7])
-    else:
-        seg = np.array(seg).reshape(-1, 2)
-        polygon = Polygon(seg)
-        min_x, min_y, max_x, max_y = polygon.bounds
-    bbox = [min_x, min_y, max_x - min_x, max_y - min_y]
-    return bbox
-
-
-def process_level(
-    src_img,
-    annotation,
-    dst_image_root,
-    ignore_image_root,
-    preserve_vertical,
-    split,
-    para_idx,
-    img_idx,
-    line_idx,
-    word_idx=None,
-):
-    vertices = annotation['vertices']
-    text_label = annotation['text']
-    segmentation = [i for j in vertices for i in j]
-    x, y, w, h = seg2bbox(segmentation)
-    x, y = max(0, math.floor(x)), max(0, math.floor(y))
-    w, h = math.ceil(w), math.ceil(h)
-    dst_img = src_img[y:y + h, x:x + w]
-    if word_idx:
-        dst_img_name = f'img_{img_idx}_{para_idx}_{line_idx}_{word_idx}.jpg'
-    else:
-        dst_img_name = f'img_{img_idx}_{para_idx}_{line_idx}.jpg'
-    if not preserve_vertical and h / w > 2 and split == 'train':
-        dst_img_path = osp.join(ignore_image_root, dst_img_name)
-        mmcv.imwrite(dst_img, dst_img_path)
-        return None
-
-    dst_img_path = osp.join(dst_image_root, dst_img_name)
-    mmcv.imwrite(dst_img, dst_img_path)
-
-    label = {'file_name': dst_img_name, 'anno_info': [{'text': text_label}]}
-
-    return label
-
-
-def process_img(args, src_image_root, dst_image_root, ignore_image_root, level,
-                preserve_vertical, split):
-    # Dirty hack for multi-processing
-    img_idx, img_annos = args
-    src_img = mmcv.imread(
-        osp.join(src_image_root, img_annos['image_id'] + '.jpg'))
-    labels = []
-    for para_idx, paragraph in enumerate(img_annos['paragraphs']):
-        for line_idx, line in enumerate(paragraph['lines']):
-            if level == 'line':
-                # Ignore illegible words
-                if line['legible']:
-
-                    label = process_level(src_img, line, dst_image_root,
-                                          ignore_image_root, preserve_vertical,
-                                          split, para_idx, img_idx, line_idx)
-                    if label is not None:
-                        labels.append(label)
-            elif level == 'word':
-                for word_idx, word in enumerate(line['words']):
-                    if not word['legible']:
-                        continue
-                    label = process_level(src_img, word, dst_image_root,
-                                          ignore_image_root, preserve_vertical,
-                                          split, para_idx, img_idx, line_idx,
-                                          word_idx)
-                    if label is not None:
-                        labels.append(label)
-    return labels
-
-
-def convert_hiertext(
-    root_path,
-    split,
-    level,
-    preserve_vertical,
-    nproc,
-):
-    """Collect the annotation information and crop the images.
-
-    The annotation format is as the following:
-    {
-        "info": {
-            "date": "release date",
-            "version": "current version"
-        },
-        "annotations": [  // List of dictionaries, one for each image.
-            {
-            "image_id": "the filename of corresponding image.",
-            "image_width": image_width,  // (int) The image width.
-            "image_height": image_height, // (int) The image height.
-            "paragraphs": [  // List of paragraphs.
-                {
-                "vertices": [[x1, y1], [x2, y2],...,[xn, yn]]
-                "legible": true
-                "lines": [
-                    {
-                    "vertices": [[x1, y1], [x2, y2],...,[x4, y4]]
-                    "text": L
-                    "legible": true,
-                    "handwritten": false
-                    "vertical": false,
-                    "words": [
-                        {
-                        "vertices": [[x1, y1], [x2, y2],...,[xm, ym]]
-                        "text": "the text content of this word",
-                        "legible": true
-                        "handwritten": false,
-                        "vertical": false,
-                        }, ...
-                    ]
-                    }, ...
-                ]
-                }, ...
-            ]
-            }, ...
-        ]
-        }
-
-    Args:
-        root_path (str): Root path to the dataset
-        split (str): Dataset split, which should be 'train' or 'val'
-        level (str): Crop word or line level instances
-        preserve_vertical (bool): Whether to preserve vertical texts
-        nproc (int): Number of processes
-
-    Returns:
-        img_info (dict): The dict of the img and annotation information
-    """
-
-    annotation_path = osp.join(root_path, 'annotations/' + split + '.jsonl')
-    if not osp.exists(annotation_path):
-        raise Exception(
-            f'{annotation_path} not exists, please check and try again.')
-
-    annotation = json.load(open(annotation_path))['annotations']
-    # outputs
-    dst_label_file = osp.join(root_path, f'{split}_label.json')
-    dst_image_root = osp.join(root_path, 'crops', split)
-    ignore_image_root = osp.join(root_path, 'ignores', split)
-    src_image_root = osp.join(root_path, 'imgs', split)
-    mmengine.mkdir_or_exist(dst_image_root)
-    mmengine.mkdir_or_exist(ignore_image_root)
-
-    process_img_with_path = partial(
-        process_img,
-        src_image_root=src_image_root,
-        dst_image_root=dst_image_root,
-        ignore_image_root=ignore_image_root,
-        level=level,
-        preserve_vertical=preserve_vertical,
-        split=split)
-    tasks = []
-    for img_idx, img_info in enumerate(annotation):
-        tasks.append((img_idx, img_info))
-    labels_list = mmengine.track_parallel_progress(
-        process_img_with_path, tasks, keep_order=True, nproc=nproc)
-
-    final_labels = []
-    for label_list in labels_list:
-        final_labels += label_list
-
-    dump_ocr_data(final_labels, dst_label_file, 'textrecog')
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description='Generate training and validation set of HierText')
-    parser.add_argument('root_path', help='Root dir path of HierText')
-    parser.add_argument(
-        '--nproc', default=1, type=int, help='Number of processes')
-    parser.add_argument(
-        '--preserve-vertical',
-        help='Preserve samples containing vertical texts',
-        action='store_true')
-    parser.add_argument(
-        '--level',
-        default='word',
-        help='Crop word or line level instance',
-        choices=['word', 'line'])
-    args = parser.parse_args()
-    return args
-
-
-def main():
-    args = parse_args()
-    root_path = args.root_path
-    print('Processing training set...')
-    convert_hiertext(
-        root_path=root_path,
-        split='train',
-        level=args.level,
-        preserve_vertical=args.preserve_vertical,
-        nproc=args.nproc)
-    print('Processing validation set...')
-    convert_hiertext(
-        root_path=root_path,
-        split='val',
-        level=args.level,
-        preserve_vertical=args.preserve_vertical,
-        nproc=args.nproc)
-    print('Finish')
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/NATSpeech/PortaSpeech/utils/audio/cwt.py b/spaces/NATSpeech/PortaSpeech/utils/audio/cwt.py
deleted file mode 100644
index 9d42ffc7b40d200dcfa66a0823163f98173b50e6..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/utils/audio/cwt.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import numpy as np
-from pycwt import wavelet
-from scipy.interpolate import interp1d
-
-dt = 0.005
-dj = 1
-
-
-def convert_continuos_f0(f0):
-    '''CONVERT F0 TO CONTINUOUS F0
-    Args:
-        f0 (ndarray): original f0 sequence with the shape (T)
-    Return:
-        (ndarray): continuous f0 with the shape (T)
-    '''
-    # get uv information as binary
-    f0 = np.copy(f0)
-    uv = (f0 == 0).astype(float)
-
-    # get start and end of f0
-    if (f0 == 0).all():
-        print("| all of the f0 values are 0.")
-        return uv, f0
-    start_f0 = f0[f0 != 0][0]
-    end_f0 = f0[f0 != 0][-1]
-
-    # padding start and end of f0 sequence
-    start_idx = np.where(f0 == start_f0)[0][0]
-    end_idx = np.where(f0 == end_f0)[0][-1]
-    f0[:start_idx] = start_f0
-    f0[end_idx:] = end_f0
-
-    # get non-zero frame index
-    nz_frames = np.where(f0 != 0)[0]
-
-    # perform linear interpolation
-    f = interp1d(nz_frames, f0[nz_frames])
-    cont_f0 = f(np.arange(0, f0.shape[0]))
-
-    return uv, cont_f0
-
-
-def get_cont_lf0(f0, frame_period=5.0):
-    uv, cont_f0_lpf = convert_continuos_f0(f0)
-    # cont_f0_lpf = low_pass_filter(cont_f0_lpf, int(1.0 / (frame_period * 0.001)), cutoff=20)
-    cont_lf0_lpf = np.log(cont_f0_lpf)
-    return uv, cont_lf0_lpf
-
-
-def get_lf0_cwt(lf0):
-    '''
-    input:
-        signal of shape (N)
-    output:
-        Wavelet_lf0 of shape(10, N), scales of shape(10)
-    '''
-    mother = wavelet.MexicanHat()
-    s0 = dt * 2
-    J = 9
-
-    Wavelet_lf0, scales, _, _, _, _ = wavelet.cwt(np.squeeze(lf0), dt, dj, s0, J, mother)
-    # Wavelet.shape => (J + 1, len(lf0))
-    Wavelet_lf0 = np.real(Wavelet_lf0).T
-    return Wavelet_lf0, scales
-
-
-def norm_scale(Wavelet_lf0):
-    mean = Wavelet_lf0.mean(0)[None, :]
-    std = Wavelet_lf0.std(0)[None, :]
-    Wavelet_lf0_norm = (Wavelet_lf0 - mean) / std
-    return Wavelet_lf0_norm, mean, std
-
-
-def normalize_cwt_lf0(f0, mean, std):
-    uv, cont_lf0_lpf = get_cont_lf0(f0)
-    cont_lf0_norm = (cont_lf0_lpf - mean) / std
-    Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_norm)
-    Wavelet_lf0_norm, _, _ = norm_scale(Wavelet_lf0)
-
-    return Wavelet_lf0_norm
-
-
-def get_lf0_cwt_norm(f0s, mean, std):
-    uvs = list()
-    cont_lf0_lpfs = list()
-    cont_lf0_lpf_norms = list()
-    Wavelet_lf0s = list()
-    Wavelet_lf0s_norm = list()
-    scaless = list()
-
-    means = list()
-    stds = list()
-    for f0 in f0s:
-        uv, cont_lf0_lpf = get_cont_lf0(f0)
-        cont_lf0_lpf_norm = (cont_lf0_lpf - mean) / std
-
-        Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm)  # [560,10]
-        Wavelet_lf0_norm, mean_scale, std_scale = norm_scale(Wavelet_lf0)  # [560,10],[1,10],[1,10]
-
-        Wavelet_lf0s_norm.append(Wavelet_lf0_norm)
-        uvs.append(uv)
-        cont_lf0_lpfs.append(cont_lf0_lpf)
-        cont_lf0_lpf_norms.append(cont_lf0_lpf_norm)
-        Wavelet_lf0s.append(Wavelet_lf0)
-        scaless.append(scales)
-        means.append(mean_scale)
-        stds.append(std_scale)
-
-    return Wavelet_lf0s_norm, scaless, means, stds
-
-
-def inverse_cwt_torch(Wavelet_lf0, scales):
-    import torch
-    b = ((torch.arange(0, len(scales)).float().to(Wavelet_lf0.device)[None, None, :] + 1 + 2.5) ** (-2.5))
-    lf0_rec = Wavelet_lf0 * b
-    lf0_rec_sum = lf0_rec.sum(-1)
-    lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdim=True)) / lf0_rec_sum.std(-1, keepdim=True)
-    return lf0_rec_sum
-
-
-def inverse_cwt(Wavelet_lf0, scales):
-    # mother = wavelet.MexicanHat()
-    # lf0_rec_sum = wavelet.icwt(Wavelet_lf0[0].T, scales, dt, dj, mother)
-    b = ((np.arange(0, len(scales))[None, None, :] + 1 + 2.5) ** (-2.5))
-    lf0_rec = Wavelet_lf0 * b
-    lf0_rec_sum = lf0_rec.sum(-1)
-    # lf0_rec_sum = lf0_rec_sum[None, ...]
-    lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdims=True)) / lf0_rec_sum.std(-1, keepdims=True)
-    return lf0_rec_sum
-
-
-def cwt2f0(cwt_spec, mean, std, cwt_scales):
-    assert len(mean.shape) == 1 and len(std.shape) == 1 and len(cwt_spec.shape) == 3
-    import torch
-    if isinstance(cwt_spec, torch.Tensor):
-        f0 = inverse_cwt_torch(cwt_spec, cwt_scales)
-        f0 = f0 * std[:, None] + mean[:, None]
-        f0 = f0.exp()  # [B, T]
-    else:
-        f0 = inverse_cwt(cwt_spec, cwt_scales)
-        f0 = f0 * std[:, None] + mean[:, None]
-        f0 = np.exp(f0)  # [B, T]
-    return f0
diff --git a/spaces/NCTCMumbai/NCTC/models/research/autoaugment/policies.py b/spaces/NCTCMumbai/NCTC/models/research/autoaugment/policies.py
deleted file mode 100644
index 36b10b0ee4c0a1b65d6cc0181bce8924322f1390..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/autoaugment/policies.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright 2018 The TensorFlow Authors All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
-def good_policies():
-  """AutoAugment policies found on Cifar."""
-  exp0_0 = [
-      [('Invert', 0.1, 7), ('Contrast', 0.2, 6)],
-      [('Rotate', 0.7, 2), ('TranslateX', 0.3, 9)],
-      [('Sharpness', 0.8, 1), ('Sharpness', 0.9, 3)],
-      [('ShearY', 0.5, 8), ('TranslateY', 0.7, 9)],
-      [('AutoContrast', 0.5, 8), ('Equalize', 0.9, 2)]]
-  exp0_1 = [
-      [('Solarize', 0.4, 5), ('AutoContrast', 0.9, 3)],
-      [('TranslateY', 0.9, 9), ('TranslateY', 0.7, 9)],
-      [('AutoContrast', 0.9, 2), ('Solarize', 0.8, 3)],
-      [('Equalize', 0.8, 8), ('Invert', 0.1, 3)],
-      [('TranslateY', 0.7, 9), ('AutoContrast', 0.9, 1)]]
-  exp0_2 = [
-      [('Solarize', 0.4, 5), ('AutoContrast', 0.0, 2)],
-      [('TranslateY', 0.7, 9), ('TranslateY', 0.7, 9)],
-      [('AutoContrast', 0.9, 0), ('Solarize', 0.4, 3)],
-      [('Equalize', 0.7, 5), ('Invert', 0.1, 3)],
-      [('TranslateY', 0.7, 9), ('TranslateY', 0.7, 9)]]
-  exp0_3 = [
-      [('Solarize', 0.4, 5), ('AutoContrast', 0.9, 1)],
-      [('TranslateY', 0.8, 9), ('TranslateY', 0.9, 9)],
-      [('AutoContrast', 0.8, 0), ('TranslateY', 0.7, 9)],
-      [('TranslateY', 0.2, 7), ('Color', 0.9, 6)],
-      [('Equalize', 0.7, 6), ('Color', 0.4, 9)]]
-  exp1_0 = [
-      [('ShearY', 0.2, 7), ('Posterize', 0.3, 7)],
-      [('Color', 0.4, 3), ('Brightness', 0.6, 7)],
-      [('Sharpness', 0.3, 9), ('Brightness', 0.7, 9)],
-      [('Equalize', 0.6, 5), ('Equalize', 0.5, 1)],
-      [('Contrast', 0.6, 7), ('Sharpness', 0.6, 5)]]
-  exp1_1 = [
-      [('Brightness', 0.3, 7), ('AutoContrast', 0.5, 8)],
-      [('AutoContrast', 0.9, 4), ('AutoContrast', 0.5, 6)],
-      [('Solarize', 0.3, 5), ('Equalize', 0.6, 5)],
-      [('TranslateY', 0.2, 4), ('Sharpness', 0.3, 3)],
-      [('Brightness', 0.0, 8), ('Color', 0.8, 8)]]
-  exp1_2 = [
-      [('Solarize', 0.2, 6), ('Color', 0.8, 6)],
-      [('Solarize', 0.2, 6), ('AutoContrast', 0.8, 1)],
-      [('Solarize', 0.4, 1), ('Equalize', 0.6, 5)],
-      [('Brightness', 0.0, 0), ('Solarize', 0.5, 2)],
-      [('AutoContrast', 0.9, 5), ('Brightness', 0.5, 3)]]
-  exp1_3 = [
-      [('Contrast', 0.7, 5), ('Brightness', 0.0, 2)],
-      [('Solarize', 0.2, 8), ('Solarize', 0.1, 5)],
-      [('Contrast', 0.5, 1), ('TranslateY', 0.2, 9)],
-      [('AutoContrast', 0.6, 5), ('TranslateY', 0.0, 9)],
-      [('AutoContrast', 0.9, 4), ('Equalize', 0.8, 4)]]
-  exp1_4 = [
-      [('Brightness', 0.0, 7), ('Equalize', 0.4, 7)],
-      [('Solarize', 0.2, 5), ('Equalize', 0.7, 5)],
-      [('Equalize', 0.6, 8), ('Color', 0.6, 2)],
-      [('Color', 0.3, 7), ('Color', 0.2, 4)],
-      [('AutoContrast', 0.5, 2), ('Solarize', 0.7, 2)]]
-  exp1_5 = [
-      [('AutoContrast', 0.2, 0), ('Equalize', 0.1, 0)],
-      [('ShearY', 0.6, 5), ('Equalize', 0.6, 5)],
-      [('Brightness', 0.9, 3), ('AutoContrast', 0.4, 1)],
-      [('Equalize', 0.8, 8), ('Equalize', 0.7, 7)],
-      [('Equalize', 0.7, 7), ('Solarize', 0.5, 0)]]
-  exp1_6 = [
-      [('Equalize', 0.8, 4), ('TranslateY', 0.8, 9)],
-      [('TranslateY', 0.8, 9), ('TranslateY', 0.6, 9)],
-      [('TranslateY', 0.9, 0), ('TranslateY', 0.5, 9)],
-      [('AutoContrast', 0.5, 3), ('Solarize', 0.3, 4)],
-      [('Solarize', 0.5, 3), ('Equalize', 0.4, 4)]]
-  exp2_0 = [
-      [('Color', 0.7, 7), ('TranslateX', 0.5, 8)],
-      [('Equalize', 0.3, 7), ('AutoContrast', 0.4, 8)],
-      [('TranslateY', 0.4, 3), ('Sharpness', 0.2, 6)],
-      [('Brightness', 0.9, 6), ('Color', 0.2, 8)],
-      [('Solarize', 0.5, 2), ('Invert', 0.0, 3)]]
-  exp2_1 = [
-      [('AutoContrast', 0.1, 5), ('Brightness', 0.0, 0)],
-      [('Cutout', 0.2, 4), ('Equalize', 0.1, 1)],
-      [('Equalize', 0.7, 7), ('AutoContrast', 0.6, 4)],
-      [('Color', 0.1, 8), ('ShearY', 0.2, 3)],
-      [('ShearY', 0.4, 2), ('Rotate', 0.7, 0)]]
-  exp2_2 = [
-      [('ShearY', 0.1, 3), ('AutoContrast', 0.9, 5)],
-      [('TranslateY', 0.3, 6), ('Cutout', 0.3, 3)],
-      [('Equalize', 0.5, 0), ('Solarize', 0.6, 6)],
-      [('AutoContrast', 0.3, 5), ('Rotate', 0.2, 7)],
-      [('Equalize', 0.8, 2), ('Invert', 0.4, 0)]]
-  exp2_3 = [
-      [('Equalize', 0.9, 5), ('Color', 0.7, 0)],
-      [('Equalize', 0.1, 1), ('ShearY', 0.1, 3)],
-      [('AutoContrast', 0.7, 3), ('Equalize', 0.7, 0)],
-      [('Brightness', 0.5, 1), ('Contrast', 0.1, 7)],
-      [('Contrast', 0.1, 4), ('Solarize', 0.6, 5)]]
-  exp2_4 = [
-      [('Solarize', 0.2, 3), ('ShearX', 0.0, 0)],
-      [('TranslateX', 0.3, 0), ('TranslateX', 0.6, 0)],
-      [('Equalize', 0.5, 9), ('TranslateY', 0.6, 7)],
-      [('ShearX', 0.1, 0), ('Sharpness', 0.5, 1)],
-      [('Equalize', 0.8, 6), ('Invert', 0.3, 6)]]
-  exp2_5 = [
-      [('AutoContrast', 0.3, 9), ('Cutout', 0.5, 3)],
-      [('ShearX', 0.4, 4), ('AutoContrast', 0.9, 2)],
-      [('ShearX', 0.0, 3), ('Posterize', 0.0, 3)],
-      [('Solarize', 0.4, 3), ('Color', 0.2, 4)],
-      [('Equalize', 0.1, 4), ('Equalize', 0.7, 6)]]
-  exp2_6 = [
-      [('Equalize', 0.3, 8), ('AutoContrast', 0.4, 3)],
-      [('Solarize', 0.6, 4), ('AutoContrast', 0.7, 6)],
-      [('AutoContrast', 0.2, 9), ('Brightness', 0.4, 8)],
-      [('Equalize', 0.1, 0), ('Equalize', 0.0, 6)],
-      [('Equalize', 0.8, 4), ('Equalize', 0.0, 4)]]
-  exp2_7 = [
-      [('Equalize', 0.5, 5), ('AutoContrast', 0.1, 2)],
-      [('Solarize', 0.5, 5), ('AutoContrast', 0.9, 5)],
-      [('AutoContrast', 0.6, 1), ('AutoContrast', 0.7, 8)],
-      [('Equalize', 0.2, 0), ('AutoContrast', 0.1, 2)],
-      [('Equalize', 0.6, 9), ('Equalize', 0.4, 4)]]
-  exp0s = exp0_0 + exp0_1 + exp0_2 + exp0_3
-  exp1s = exp1_0 + exp1_1 + exp1_2 + exp1_3 + exp1_4 + exp1_5 + exp1_6
-  exp2s = exp2_0 + exp2_1 + exp2_2 + exp2_3 + exp2_4 + exp2_5 + exp2_6 + exp2_7
-  return  exp0s + exp1s + exp2s
diff --git a/spaces/NingKanae/anime-voice-generator/modules.py b/spaces/NingKanae/anime-voice-generator/modules.py
deleted file mode 100644
index 56ea4145eddf19dd330a3a41ab0183efc1686d83..0000000000000000000000000000000000000000
--- a/spaces/NingKanae/anime-voice-generator/modules.py
+++ /dev/null
@@ -1,388 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
diff --git a/spaces/OAOA/DifFace/utils/util_image.py b/spaces/OAOA/DifFace/utils/util_image.py
deleted file mode 100644
index 6931a04b0c19259fd1299fa412ce1ce27a7ac03d..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/utils/util_image.py
+++ /dev/null
@@ -1,812 +0,0 @@
-#!/usr/bin/env python
-# -*- coding:utf-8 -*-
-# Power by Zongsheng Yue 2021-11-24 16:54:19
-
-import sys
-import cv2
-import math
-import torch
-import random
-import numpy as np
-from scipy import fft
-from pathlib import Path
-from einops import rearrange
-from torchvision.utils import make_grid
-from skimage import img_as_ubyte, img_as_float32
-
-# --------------------------Metrics----------------------------
-def ssim(img1, img2):
-    C1 = (0.01 * 255)**2
-    C2 = (0.03 * 255)**2
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
-    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1**2
-    mu2_sq = mu2**2
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
-    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
-                                                            (sigma1_sq + sigma2_sq + C2))
-    return ssim_map.mean()
-
-def calculate_ssim(im1, im2, border=0, ycbcr=False):
-    '''
-    SSIM the same outputs as MATLAB's
-    im1, im2: h x w x , [0, 255], uint8
-    '''
-    if not im1.shape == im2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-
-    if ycbcr:
-        im1 = rgb2ycbcr(im1, True)
-        im2 = rgb2ycbcr(im2, True)
-
-    h, w = im1.shape[:2]
-    im1 = im1[border:h-border, border:w-border]
-    im2 = im2[border:h-border, border:w-border]
-
-    if im1.ndim == 2:
-        return ssim(im1, im2)
-    elif im1.ndim == 3:
-        if im1.shape[2] == 3:
-            ssims = []
-            for i in range(3):
-                ssims.append(ssim(im1[:,:,i], im2[:,:,i]))
-            return np.array(ssims).mean()
-        elif im1.shape[2] == 1:
-            return ssim(np.squeeze(im1), np.squeeze(im2))
-    else:
-        raise ValueError('Wrong input image dimensions.')
-
-def calculate_psnr(im1, im2, border=0, ycbcr=False):
-    '''
-    PSNR metric.
-    im1, im2: h x w x , [0, 255], uint8
-    '''
-    if not im1.shape == im2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-
-    if ycbcr:
-        im1 = rgb2ycbcr(im1, True)
-        im2 = rgb2ycbcr(im2, True)
-
-    h, w = im1.shape[:2]
-    im1 = im1[border:h-border, border:w-border]
-    im2 = im2[border:h-border, border:w-border]
-
-    im1 = im1.astype(np.float64)
-    im2 = im2.astype(np.float64)
-    mse = np.mean((im1 - im2)**2)
-    if mse == 0:
-        return float('inf')
-    return 20 * math.log10(255.0 / math.sqrt(mse))
-
-def batch_PSNR(img, imclean, border=0, ycbcr=False):
-    if ycbcr:
-        img = rgb2ycbcrTorch(img, True)
-        imclean = rgb2ycbcrTorch(imclean, True)
-    Img = img.data.cpu().numpy()
-    Iclean = imclean.data.cpu().numpy()
-    Img = img_as_ubyte(Img)
-    Iclean = img_as_ubyte(Iclean)
-    PSNR = 0
-    h, w = Iclean.shape[2:]
-    for i in range(Img.shape[0]):
-        PSNR += calculate_psnr(Iclean[i,:,].transpose((1,2,0)), Img[i,:,].transpose((1,2,0)), border)
-    return PSNR
-
-def batch_SSIM(img, imclean, border=0, ycbcr=False):
-    if ycbcr:
-        img = rgb2ycbcrTorch(img, True)
-        imclean = rgb2ycbcrTorch(imclean, True)
-    Img = img.data.cpu().numpy()
-    Iclean = imclean.data.cpu().numpy()
-    Img = img_as_ubyte(Img)
-    Iclean = img_as_ubyte(Iclean)
-    SSIM = 0
-    for i in range(Img.shape[0]):
-        SSIM += calculate_ssim(Iclean[i,:,].transpose((1,2,0)), Img[i,:,].transpose((1,2,0)), border)
-    return SSIM
-
-def normalize_np(im, mean=0.5, std=0.5, reverse=False):
-    '''
-    Input:
-        im: h x w x c, numpy array
-        Normalize: (im - mean) / std
-        Reverse: im * std + mean
-
-    '''
-    if not isinstance(mean, (list, tuple)):
-        mean = [mean, ] * im.shape[2]
-    mean = np.array(mean).reshape([1, 1, im.shape[2]])
-
-    if not isinstance(std, (list, tuple)):
-        std = [std, ] * im.shape[2]
-    std = np.array(std).reshape([1, 1, im.shape[2]])
-
-    if not reverse:
-        out = (im.astype(np.float32) - mean) / std
-    else:
-        out = im.astype(np.float32) * std + mean
-    return out
-
-def normalize_th(im, mean=0.5, std=0.5, reverse=False):
-    '''
-    Input:
-        im: b x c x h x w, torch tensor
-        Normalize: (im - mean) / std
-        Reverse: im * std + mean
-
-    '''
-    if not isinstance(mean, (list, tuple)):
-        mean = [mean, ] * im.shape[1]
-    mean = torch.tensor(mean, device=im.device).view([1, im.shape[1], 1, 1])
-
-    if not isinstance(std, (list, tuple)):
-        std = [std, ] * im.shape[1]
-    std = torch.tensor(std, device=im.device).view([1, im.shape[1], 1, 1])
-
-    if not reverse:
-        out = (im - mean) / std
-    else:
-        out = im * std + mean
-    return out
-
-# ------------------------Image format--------------------------
-def rgb2ycbcr(im, only_y=True):
-    '''
-    same as matlab rgb2ycbcr
-    Input:
-        im: uint8 [0,255] or float [0,1]
-        only_y: only return Y channel
-    '''
-    # transform to float64 data type, range [0, 255]
-    if im.dtype == np.uint8:
-        im_temp = im.astype(np.float64)
-    else:
-        im_temp = (im * 255).astype(np.float64)
-
-    # convert
-    if only_y:
-        rlt = np.dot(im_temp, np.array([65.481, 128.553, 24.966])/ 255.0) + 16.0
-    else:
-        rlt = np.matmul(im_temp, np.array([[65.481,  -37.797, 112.0  ],
-                                           [128.553, -74.203, -93.786],
-                                           [24.966,  112.0,   -18.214]])/255.0) + [16, 128, 128]
-    if im.dtype == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(im.dtype)
-
-def rgb2ycbcrTorch(im, only_y=True):
-    '''
-    same as matlab rgb2ycbcr
-    Input:
-        im: float [0,1], N x 3 x H x W
-        only_y: only return Y channel
-    '''
-    # transform to range [0,255.0]
-    im_temp = im.permute([0,2,3,1]) * 255.0  # N x H x W x C --> N x H x W x C
-    # convert
-    if only_y:
-        rlt = torch.matmul(im_temp, torch.tensor([65.481, 128.553, 24.966],
-                                        device=im.device, dtype=im.dtype).view([3,1])/ 255.0) + 16.0
-    else:
-        rlt = torch.matmul(im_temp, torch.tensor([[65.481,  -37.797, 112.0  ],
-                                                  [128.553, -74.203, -93.786],
-                                                  [24.966,  112.0,   -18.214]],
-                                                  device=im.device, dtype=im.dtype)/255.0) + \
-                                                    torch.tensor([16, 128, 128]).view([-1, 1, 1, 3])
-    rlt /= 255.0
-    rlt.clamp_(0.0, 1.0)
-    return rlt.permute([0, 3, 1, 2])
-
-def bgr2rgb(im): return cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
-
-def rgb2bgr(im): return cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
-
-def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
-    """Convert torch Tensors into image numpy arrays.
-
-    After clamping to [min, max], values will be normalized to [0, 1].
-
-    Args:
-        tensor (Tensor or list[Tensor]): Accept shapes:
-            1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
-            2) 3D Tensor of shape (3/1 x H x W);
-            3) 2D Tensor of shape (H x W).
-            Tensor channel should be in RGB order.
-        rgb2bgr (bool): Whether to change rgb to bgr.
-        out_type (numpy type): output types. If ``np.uint8``, transform outputs
-            to uint8 type with range [0, 255]; otherwise, float type with
-            range [0, 1]. Default: ``np.uint8``.
-        min_max (tuple[int]): min and max values for clamp.
-
-    Returns:
-        (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
-        shape (H x W). The channel order is BGR.
-    """
-    if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
-        raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')
-
-    flag_tensor = torch.is_tensor(tensor)
-    if flag_tensor:
-        tensor = [tensor]
-    result = []
-    for _tensor in tensor:
-        _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
-        _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
-
-        n_dim = _tensor.dim()
-        if n_dim == 4:
-            img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
-            img_np = img_np.transpose(1, 2, 0)
-            if rgb2bgr:
-                img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
-        elif n_dim == 3:
-            img_np = _tensor.numpy()
-            img_np = img_np.transpose(1, 2, 0)
-            if img_np.shape[2] == 1:  # gray image
-                img_np = np.squeeze(img_np, axis=2)
-            else:
-                if rgb2bgr:
-                    img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
-        elif n_dim == 2:
-            img_np = _tensor.numpy()
-        else:
-            raise TypeError(f'Only support 4D, 3D or 2D tensor. But received with dimension: {n_dim}')
-        if out_type == np.uint8:
-            # Unlike MATLAB, numpy.unit8() WILL NOT round by default.
-            img_np = (img_np * 255.0).round()
-        img_np = img_np.astype(out_type)
-        result.append(img_np)
-    if len(result) == 1 and flag_tensor:
-        result = result[0]
-    return result
-
-def img2tensor(imgs, out_type=torch.float32):
-    """Convert image numpy arrays into torch tensor.
-
-    After clamping to [min, max], values will be normalized to [0, 1].
-
-    Args:
-        imgs (Array or list[array]): Accept shapes:
-            3) list of numpy arrays
-            1) 3D numpy array of shape (H x W x 3/1);
-            2) 2D Tensor of shape (H x W).
-            Tensor channel should be in RGB order.
-
-    Returns:
-        (array or list): 3D ndarray of shape (H x W x C) or 2D ndarray of shape (H x W).
-    """
-
-    def _img2tensor(img):
-        if img.ndim == 2:
-            tensor = torch.from_numpy(img[None, None,]).type(out_type)
-        elif img.ndim == 3:
-            tensor = torch.from_numpy(rearrange(img, 'h w c -> c h w')).type(out_type).unsqueeze(0)
-        else:
-            raise TypeError(f'2D or 3D numpy array expected, got{img.ndim}D array')
-        return tensor
-
-    if not (isinstance(imgs, np.ndarray) or (isinstance(imgs, list) and all(isinstance(t, np.ndarray) for t in imgs))):
-        raise TypeError(f'Numpy array or list of numpy array expected, got {type(imgs)}')
-
-    if isinstance(imgs, np.ndarray):
-        imgs = [imgs,]
-    result = []
-    for _img in imgs:
-        result.append(_img2tensor(_img))
-
-    if len(result) == 1 and isinstance(imgs, np.ndarray):
-        result = result[0]
-    return result
-
-# ------------------------Image I/O-----------------------------
-def imread(path, chn='rgb', dtype='float32'):
-    '''
-    Read image.
-    chn: 'rgb', 'bgr' or 'gray'
-    out:
-        im: h x w x c, numpy tensor
-    '''
-    im = cv2.imread(str(path), cv2.IMREAD_UNCHANGED)  # BGR, uint8
-    if chn.lower() == 'rgb':
-        if im.ndim == 3:
-            im = bgr2rgb(im)
-        else:
-            im = np.stack((im, im, im), axis=2)
-    elif chn.lower() == 'gray':
-        assert im.ndim == 2
-
-    if dtype == 'float32':
-        im = im.astype(np.float32) / 255.
-    elif dtype ==  'float64':
-        im = im.astype(np.float64) / 255.
-    elif dtype == 'uint8':
-        pass
-    else:
-        sys.exit('Please input corrected dtype: float32, float64 or uint8!')
-
-    if im.shape[2] > 3:
-        im = im[:, :, :3]
-
-    return im
-
-def imwrite(im_in, path, chn='rgb', dtype_in='float32', qf=None):
-    '''
-    Save image.
-    Input:
-        im: h x w x c, numpy tensor
-        path: the saving path
-        chn: the channel order of the im,
-    '''
-    im = im_in.copy()
-    if isinstance(path, str):
-        path = Path(path)
-    if dtype_in != 'uint8':
-        im = img_as_ubyte(im)
-
-    if chn.lower() == 'rgb' and im.ndim == 3:
-        im = rgb2bgr(im)
-
-    if qf is not None and path.suffix.lower() in ['.jpg', '.jpeg']:
-        flag = cv2.imwrite(str(path), im, [int(cv2.IMWRITE_JPEG_QUALITY), int(qf)])
-    else:
-        flag = cv2.imwrite(str(path), im)
-
-    return flag
-
-def jpeg_compress(im, qf, chn_in='rgb'):
-    '''
-    Input:
-        im: h x w x 3 array
-        qf: compress factor, (0, 100]
-        chn_in: 'rgb' or 'bgr'
-    Return:
-        Compressed Image with channel order: chn_in
-    '''
-    # transform to BGR channle and uint8 data type
-    im_bgr = rgb2bgr(im) if chn_in.lower() == 'rgb' else im
-    if im.dtype != np.dtype('uint8'): im_bgr = img_as_ubyte(im_bgr)
-
-    # JPEG compress
-    flag, encimg = cv2.imencode('.jpg', im_bgr, [int(cv2.IMWRITE_JPEG_QUALITY), qf])
-    assert flag
-    im_jpg_bgr = cv2.imdecode(encimg, 1)    # uint8, BGR
-
-    # transform back to original channel and the original data type
-    im_out = bgr2rgb(im_jpg_bgr) if chn_in.lower() == 'rgb' else im_jpg_bgr
-    if im.dtype != np.dtype('uint8'): im_out = img_as_float32(im_out).astype(im.dtype)
-    return im_out
-
-# ------------------------Augmentation-----------------------------
-def data_aug_np(image, mode):
-    '''
-    Performs data augmentation of the input image
-    Input:
-        image: a cv2 (OpenCV) image
-        mode: int. Choice of transformation to apply to the image
-                0 - no transformation
-                1 - flip up and down
-                2 - rotate counterwise 90 degree
-                3 - rotate 90 degree and flip up and down
-                4 - rotate 180 degree
-                5 - rotate 180 degree and flip
-                6 - rotate 270 degree
-                7 - rotate 270 degree and flip
-    '''
-    if mode == 0:
-        # original
-        out = image
-    elif mode == 1:
-        # flip up and down
-        out = np.flipud(image)
-    elif mode == 2:
-        # rotate counterwise 90 degree
-        out = np.rot90(image)
-    elif mode == 3:
-        # rotate 90 degree and flip up and down
-        out = np.rot90(image)
-        out = np.flipud(out)
-    elif mode == 4:
-        # rotate 180 degree
-        out = np.rot90(image, k=2)
-    elif mode == 5:
-        # rotate 180 degree and flip
-        out = np.rot90(image, k=2)
-        out = np.flipud(out)
-    elif mode == 6:
-        # rotate 270 degree
-        out = np.rot90(image, k=3)
-    elif mode == 7:
-        # rotate 270 degree and flip
-        out = np.rot90(image, k=3)
-        out = np.flipud(out)
-    else:
-        raise Exception('Invalid choice of image transformation')
-
-    return out.copy()
-
-def inverse_data_aug_np(image, mode):
-    '''
-    Performs inverse data augmentation of the input image
-    '''
-    if mode == 0:
-        # original
-        out = image
-    elif mode == 1:
-        out = np.flipud(image)
-    elif mode == 2:
-        out = np.rot90(image, axes=(1,0))
-    elif mode == 3:
-        out = np.flipud(image)
-        out = np.rot90(out, axes=(1,0))
-    elif mode == 4:
-        out = np.rot90(image, k=2, axes=(1,0))
-    elif mode == 5:
-        out = np.flipud(image)
-        out = np.rot90(out, k=2, axes=(1,0))
-    elif mode == 6:
-        out = np.rot90(image, k=3, axes=(1,0))
-    elif mode == 7:
-        # rotate 270 degree and flip
-        out = np.flipud(image)
-        out = np.rot90(out, k=3, axes=(1,0))
-    else:
-        raise Exception('Invalid choice of image transformation')
-
-    return out
-
-class SpatialAug:
-    def __init__(self):
-        pass
-
-    def __call__(self, im, flag=None):
-        if flag is None:
-            flag = random.randint(0, 7)
-
-        out = data_aug_np(im, flag)
-        return out
-
-# ----------------------Visualization----------------------------
-def imshow(x, title=None, cbar=False):
-    import matplotlib.pyplot as plt
-    plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
-    if title:
-        plt.title(title)
-    if cbar:
-        plt.colorbar()
-    plt.show()
-
-# -----------------------Covolution------------------------------
-def imgrad(im, pading_mode='mirror'):
-    '''
-    Calculate image gradient.
-    Input:
-        im: h x w x c numpy array
-    '''
-    from scipy.ndimage import correlate  # lazy import
-    wx = np.array([[0, 0, 0],
-                   [-1, 1, 0],
-                   [0, 0, 0]], dtype=np.float32)
-    wy = np.array([[0, -1, 0],
-                   [0, 1, 0],
-                   [0, 0, 0]], dtype=np.float32)
-    if im.ndim == 3:
-        gradx = np.stack(
-                [correlate(im[:,:,c], wx, mode=pading_mode) for c in range(im.shape[2])],
-                axis=2
-                )
-        grady = np.stack(
-                [correlate(im[:,:,c], wy, mode=pading_mode) for c in range(im.shape[2])],
-                axis=2
-                )
-        grad = np.concatenate((gradx, grady), axis=2)
-    else:
-        gradx = correlate(im, wx, mode=pading_mode)
-        grady = correlate(im, wy, mode=pading_mode)
-        grad = np.stack((gradx, grady), axis=2)
-
-    return {'gradx': gradx, 'grady': grady, 'grad':grad}
-
-def imgrad_fft(im):
-    '''
-    Calculate image gradient.
-    Input:
-        im: h x w x c numpy array
-    '''
-    wx = np.rot90(np.array([[0, 0, 0],
-                            [-1, 1, 0],
-                            [0, 0, 0]], dtype=np.float32), k=2)
-    gradx = convfft(im, wx)
-    wy = np.rot90(np.array([[0, -1, 0],
-                            [0, 1, 0],
-                            [0, 0, 0]], dtype=np.float32), k=2)
-    grady = convfft(im, wy)
-    grad = np.concatenate((gradx, grady), axis=2)
-
-    return {'gradx': gradx, 'grady': grady, 'grad':grad}
-
-def convfft(im, weight):
-    '''
-    Convolution with FFT
-    Input:
-        im: h1 x w1 x c numpy array
-        weight: h2 x w2 numpy array
-    Output:
-        out: h1 x w1 x c numpy array
-    '''
-    axes = (0,1)
-    otf = psf2otf(weight, im.shape[:2])
-    if im.ndim == 3:
-        otf = np.tile(otf[:, :, None], (1,1,im.shape[2]))
-    out = fft.ifft2(fft.fft2(im, axes=axes) * otf, axes=axes).real
-    return out
-
-def psf2otf(psf, shape):
-    """
-    MATLAB psf2otf function.
-    Borrowed from https://github.com/aboucaud/pypher/blob/master/pypher/pypher.py.
-    Input:
-        psf : h x w numpy array
-        shape : list or tuple, output shape of the OTF array
-    Output:
-        otf : OTF array with the desirable shape
-    """
-    if np.all(psf == 0):
-        return np.zeros_like(psf)
-
-    inshape = psf.shape
-    # Pad the PSF to outsize
-    psf = zero_pad(psf, shape, position='corner')
-
-    # Circularly shift OTF so that the 'center' of the PSF is [0,0] element of the array
-    for axis, axis_size in enumerate(inshape):
-        psf = np.roll(psf, -int(axis_size / 2), axis=axis)
-
-    # Compute the OTF
-    otf = fft.fft2(psf)
-
-    # Estimate the rough number of operations involved in the FFT
-    # and discard the PSF imaginary part if within roundoff error
-    # roundoff error  = machine epsilon = sys.float_info.epsilon
-    # or np.finfo().eps
-    n_ops = np.sum(psf.size * np.log2(psf.shape))
-    otf = np.real_if_close(otf, tol=n_ops)
-
-    return otf
-
-def zero_pad(image, shape, position='corner'):
-    """
-    Extends image to a certain size with zeros
-    Input:
-        image: real 2d numpy array
-        shape: tuple of int, desired output shape of the image
-        position : str, 'corner' or 'center',
-                The position of the input image in the output one:
-                * 'corner'
-                    top-left corner (default)
-                * 'center'
-                    centered
-    Output
-        padded_img: real numpy array
-    """
-    shape = np.asarray(shape, dtype=int)
-    imshape = np.asarray(image.shape, dtype=int)
-
-    if np.alltrue(imshape == shape):
-        return image
-
-    if np.any(shape <= 0):
-        raise ValueError("ZERO_PAD: null or negative shape given")
-
-    dshape = shape - imshape
-    if np.any(dshape < 0):
-        raise ValueError("ZERO_PAD: target size smaller than source one")
-
-    pad_img = np.zeros(shape, dtype=image.dtype)
-
-    idx, idy = np.indices(imshape)
-
-    if position == 'center':
-        if np.any(dshape % 2 != 0):
-            raise ValueError("ZERO_PAD: source and target shapes have different parity.")
-        offx, offy = dshape // 2
-    else:
-        offx, offy = (0, 0)
-
-    pad_img[idx + offx, idy + offy] = image
-
-    return pad_img
-
-# ----------------------Patch Cropping----------------------------
-def random_crop(im, pch_size):
-    '''
-    Randomly crop a patch from the give image.
-    '''
-    h, w = im.shape[:2]
-    assert h > pch_size and w > pch_size
-    ind_h = random.randint(0, h-pch_size)
-    ind_w = random.randint(0, w-pch_size)
-    im_pch = im[ind_h:ind_h+pch_size, ind_w:ind_w+pch_size,]
-
-    return im_pch
-
-class RandomCrop:
-    def __init__(self, pch_size):
-        self.pch_size = pch_size
-
-    def __call__(self, im):
-        return random_crop(im, self.pch_size)
-
-class ImageSpliterNp:
-    def __init__(self, im, pch_size, stride, sf=1):
-        '''
-        Input:
-            im: h x w x c, numpy array, [0, 1], low-resolution image in SR
-            pch_size, stride: patch setting
-            sf: scale factor in image super-resolution
-        '''
-        assert stride <= pch_size
-        self.stride = stride
-        self.pch_size = pch_size
-        self.sf = sf
-
-        if im.ndim == 2:
-            im = im[:, :, None]
-
-        height, width, chn = im.shape
-        self.height_starts_list = self.extract_starts(height)
-        self.width_starts_list = self.extract_starts(width)
-        self.length = self.__len__()
-        self.num_pchs = 0
-
-        self.im_ori = im
-        self.im_res = np.zeros([height*sf, width*sf, chn], dtype=im.dtype)
-        self.pixel_count = np.zeros([height*sf, width*sf, chn], dtype=im.dtype)
-
-    def extract_starts(self, length):
-        starts = list(range(0, length, self.stride))
-        if starts[-1] + self.pch_size > length:
-            starts[-1] = length - self.pch_size
-        return starts
-
-    def __len__(self):
-        return len(self.height_starts_list) * len(self.width_starts_list)
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        if self.num_pchs < self.length:
-            w_start_idx = self.num_pchs // len(self.height_starts_list)
-            w_start = self.width_starts_list[w_start_idx] * self.sf
-            w_end = w_start + self.pch_size * self.sf
-
-            h_start_idx = self.num_pchs % len(self.height_starts_list)
-            h_start = self.height_starts_list[h_start_idx] * self.sf
-            h_end = h_start + self.pch_size * self.sf
-
-            pch = self.im_ori[h_start:h_end, w_start:w_end,]
-            self.w_start, self.w_end = w_start, w_end
-            self.h_start, self.h_end = h_start, h_end
-
-            self.num_pchs += 1
-        else:
-            raise StopIteration(0)
-
-        return pch, (h_start, h_end, w_start, w_end)
-
-    def update(self, pch_res, index_infos):
-        '''
-        Input:
-            pch_res: pch_size x pch_size x 3, [0,1]
-            index_infos: (h_start, h_end, w_start, w_end)
-        '''
-        if index_infos is None:
-            w_start, w_end = self.w_start, self.w_end
-            h_start, h_end = self.h_start, self.h_end
-        else:
-            h_start, h_end, w_start, w_end = index_infos
-
-        self.im_res[h_start:h_end, w_start:w_end] += pch_res
-        self.pixel_count[h_start:h_end, w_start:w_end] += 1
-
-    def gather(self):
-        assert np.all(self.pixel_count != 0)
-        return self.im_res / self.pixel_count
-
-class ImageSpliterTh:
-    def __init__(self, im, pch_size, stride, sf=1):
-        '''
-        Input:
-            im: n x c x h x w, torch tensor, float, low-resolution image in SR
-            pch_size, stride: patch setting
-            sf: scale factor in image super-resolution
-        '''
-        assert stride <= pch_size
-        self.stride = stride
-        self.pch_size = pch_size
-        self.sf = sf
-
-        bs, chn, height, width= im.shape
-        self.height_starts_list = self.extract_starts(height)
-        self.width_starts_list = self.extract_starts(width)
-        self.length = self.__len__()
-        self.num_pchs = 0
-
-        self.im_ori = im
-        self.im_res = torch.zeros([bs, chn, height*sf, width*sf], dtype=im.dtype, device=im.device)
-        self.pixel_count = torch.zeros([bs, chn, height*sf, width*sf], dtype=im.dtype, device=im.device)
-
-    def extract_starts(self, length):
-        starts = list(range(0, length, self.stride))
-        if starts[-1] + self.pch_size > length:
-            starts[-1] = length - self.pch_size
-        return starts
-
-    def __len__(self):
-        return len(self.height_starts_list) * len(self.width_starts_list)
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        if self.num_pchs < self.length:
-            w_start_idx = self.num_pchs // len(self.height_starts_list)
-            w_start = self.width_starts_list[w_start_idx] * self.sf
-            w_end = w_start + self.pch_size * self.sf
-
-            h_start_idx = self.num_pchs % len(self.height_starts_list)
-            h_start = self.height_starts_list[h_start_idx] * self.sf
-            h_end = h_start + self.pch_size * self.sf
-
-            pch = self.im_ori[:, :, h_start:h_end, w_start:w_end,]
-            self.w_start, self.w_end = w_start, w_end
-            self.h_start, self.h_end = h_start, h_end
-
-            self.num_pchs += 1
-        else:
-            raise StopIteration()
-
-        return pch, (h_start, h_end, w_start, w_end)
-
-    def update(self, pch_res, index_infos):
-        '''
-        Input:
-            pch_res: n x c x pch_size x pch_size, float
-            index_infos: (h_start, h_end, w_start, w_end)
-        '''
-        if index_infos is None:
-            w_start, w_end = self.w_start, self.w_end
-            h_start, h_end = self.h_start, self.h_end
-        else:
-            h_start, h_end, w_start, w_end = index_infos
-
-        self.im_res[:, :, h_start:h_end, w_start:w_end] += pch_res
-        self.pixel_count[:, :, h_start:h_end, w_start:w_end] += 1
-
-    def gather(self):
-        assert torch.all(self.pixel_count != 0)
-        return self.im_res.div(self.pixel_count)
-
-if __name__ == '__main__':
-    im = np.random.randn(64, 64, 3).astype(np.float32)
-
-    grad1 = imgrad(im)['grad']
-    grad2 = imgrad_fft(im)['grad']
-
-    error = np.abs(grad1 -grad2).max()
-    mean_error = np.abs(grad1 -grad2).mean()
-    print('The largest error is {:.2e}'.format(error))
-    print('The mean error is {:.2e}'.format(mean_error))
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/file_io.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/file_io.py
deleted file mode 100644
index dba663d4aafeb925ddffa50f5055933d6531a069..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/file_io.py
+++ /dev/null
@@ -1,194 +0,0 @@
-#!/usr/bin/env python3
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-import shutil
-from typing import List, Optional
-
-
-logger = logging.getLogger(__file__)
-
-
-try:
-    from iopath.common.file_io import g_pathmgr as IOPathManager
-
-    try:
-        # [FB only - for now] AWS PathHandler for PathManager
-        from .fb_pathhandlers import S3PathHandler
-
-        IOPathManager.register_handler(S3PathHandler())
-    except KeyError:
-        logging.warning("S3PathHandler already registered.")
-    except ImportError:
-        logging.debug(
-            "S3PathHandler couldn't be imported. Either missing fb-only files, or boto3 module."
-        )
-
-except ImportError:
-    IOPathManager = None
-
-
-class PathManager:
-    """
-    Wrapper for insulating OSS I/O (using Python builtin operations) from
-    iopath's PathManager abstraction (for transparently handling various
-    internal backends).
-    """
-
-    @staticmethod
-    def open(
-        path: str,
-        mode: str = "r",
-        buffering: int = -1,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = None,
-        newline: Optional[str] = None,
-    ):
-        if IOPathManager:
-            return IOPathManager.open(
-                path=path,
-                mode=mode,
-                buffering=buffering,
-                encoding=encoding,
-                errors=errors,
-                newline=newline,
-            )
-        return open(
-            path,
-            mode=mode,
-            buffering=buffering,
-            encoding=encoding,
-            errors=errors,
-            newline=newline,
-        )
-
-    @staticmethod
-    def copy(src_path: str, dst_path: str, overwrite: bool = False) -> bool:
-        if IOPathManager:
-            return IOPathManager.copy(
-                src_path=src_path, dst_path=dst_path, overwrite=overwrite
-            )
-        return shutil.copyfile(src_path, dst_path)
-
-    @staticmethod
-    def get_local_path(path: str, **kwargs) -> str:
-        if IOPathManager:
-            return IOPathManager.get_local_path(path, **kwargs)
-        return path
-
-    @staticmethod
-    def exists(path: str) -> bool:
-        if IOPathManager:
-            return IOPathManager.exists(path)
-        return os.path.exists(path)
-
-    @staticmethod
-    def isfile(path: str) -> bool:
-        if IOPathManager:
-            return IOPathManager.isfile(path)
-        return os.path.isfile(path)
-
-    @staticmethod
-    def ls(path: str) -> List[str]:
-        if IOPathManager:
-            return IOPathManager.ls(path)
-        return os.listdir(path)
-
-    @staticmethod
-    def mkdirs(path: str) -> None:
-        if IOPathManager:
-            return IOPathManager.mkdirs(path)
-        os.makedirs(path, exist_ok=True)
-
-    @staticmethod
-    def rm(path: str) -> None:
-        if IOPathManager:
-            return IOPathManager.rm(path)
-        os.remove(path)
-
-    @staticmethod
-    def chmod(path: str, mode: int) -> None:
-        if not PathManager.path_requires_pathmanager(path):
-            os.chmod(path, mode)
-
-    @staticmethod
-    def register_handler(handler) -> None:
-        if IOPathManager:
-            return IOPathManager.register_handler(handler=handler)
-
-    @staticmethod
-    def copy_from_local(
-        local_path: str, dst_path: str, overwrite: bool = False, **kwargs
-    ) -> None:
-        if IOPathManager:
-            return IOPathManager.copy_from_local(
-                local_path=local_path, dst_path=dst_path, overwrite=overwrite, **kwargs
-            )
-        return shutil.copyfile(local_path, dst_path)
-
-    @staticmethod
-    def path_requires_pathmanager(path: str) -> bool:
-        """Do we require PathManager to access given path?"""
-        if IOPathManager:
-            for p in IOPathManager._path_handlers.keys():
-                if path.startswith(p):
-                    return True
-        return False
-
-    @staticmethod
-    def supports_rename(path: str) -> bool:
-        # PathManager doesn't yet support renames
-        return not PathManager.path_requires_pathmanager(path)
-
-    @staticmethod
-    def rename(src: str, dst: str):
-        os.rename(src, dst)
-
-    """
-    ioPath async PathManager methods:
-    """
-    @staticmethod
-    def opena(
-        path: str,
-        mode: str = "r",
-        buffering: int = -1,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = None,
-        newline: Optional[str] = None,
-    ):
-        """
-        Return file descriptor with asynchronous write operations.
-        """
-        global IOPathManager
-        if not IOPathManager:
-            logging.info("ioPath is initializing PathManager.")
-            try:
-                from iopath.common.file_io import PathManager
-                IOPathManager = PathManager()
-            except Exception:
-                logging.exception("Failed to initialize ioPath PathManager object.")
-        return IOPathManager.opena(
-            path=path,
-            mode=mode,
-            buffering=buffering,
-            encoding=encoding,
-            errors=errors,
-            newline=newline,
-        )
-
-    @staticmethod
-    def async_close() -> bool:
-        """
-        Wait for files to be written and clean up asynchronous PathManager.
-        NOTE: `PathManager.async_close()` must be called at the end of any
-        script that uses `PathManager.opena(...)`.
-        """
-        global IOPathManager
-        if IOPathManager:
-            return IOPathManager.async_close()
-        return False
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq_cli/generate.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq_cli/generate.py
deleted file mode 100644
index 7e887e88649fef784b366abe518babd25a30feee..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq_cli/generate.py
+++ /dev/null
@@ -1,414 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Translate pre-processed data with a trained model.
-"""
-
-import ast
-import logging
-import math
-import os
-import sys
-from argparse import Namespace
-from itertools import chain
-
-import numpy as np
-import torch
-from fairseq import checkpoint_utils, options, scoring, tasks, utils
-from fairseq.dataclass.utils import convert_namespace_to_omegaconf
-from fairseq.logging import progress_bar
-from fairseq.logging.meters import StopwatchMeter, TimeMeter
-from omegaconf import DictConfig
-
-
-def main(cfg: DictConfig):
-
-    if isinstance(cfg, Namespace):
-        cfg = convert_namespace_to_omegaconf(cfg)
-
-    assert cfg.common_eval.path is not None, "--path required for generation!"
-    assert (
-        not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam
-    ), "--sampling requires --nbest to be equal to --beam"
-    assert (
-        cfg.generation.replace_unk is None or cfg.dataset.dataset_impl == "raw"
-    ), "--replace-unk requires a raw text dataset (--dataset-impl=raw)"
-
-    if cfg.common_eval.results_path is not None:
-        os.makedirs(cfg.common_eval.results_path, exist_ok=True)
-        output_path = os.path.join(
-            cfg.common_eval.results_path,
-            "generate-{}.txt".format(cfg.dataset.gen_subset),
-        )
-        with open(output_path, "w", buffering=1, encoding="utf-8") as h:
-            return _main(cfg, h)
-    else:
-        return _main(cfg, sys.stdout)
-
-
-def get_symbols_to_strip_from_output(generator):
-    if hasattr(generator, "symbols_to_strip_from_output"):
-        return generator.symbols_to_strip_from_output
-    else:
-        return {generator.eos}
-
-
-def _main(cfg: DictConfig, output_file):
-    logging.basicConfig(
-        format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
-        datefmt="%Y-%m-%d %H:%M:%S",
-        level=os.environ.get("LOGLEVEL", "INFO").upper(),
-        stream=output_file,
-    )
-    logger = logging.getLogger("fairseq_cli.generate")
-
-    utils.import_user_module(cfg.common)
-
-    if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None:
-        cfg.dataset.max_tokens = 12000
-    logger.info(cfg)
-
-    # Fix seed for stochastic decoding
-    if cfg.common.seed is not None and not cfg.generation.no_seed_provided:
-        np.random.seed(cfg.common.seed)
-        utils.set_torch_seed(cfg.common.seed)
-
-    use_cuda = torch.cuda.is_available() and not cfg.common.cpu
-
-    # Load dataset splits
-    task = tasks.setup_task(cfg.task)
-
-
-    # Set dictionaries
-    try:
-        src_dict = getattr(task, "source_dictionary", None)
-    except NotImplementedError:
-        src_dict = None
-    tgt_dict = task.target_dictionary
-
-    overrides = ast.literal_eval(cfg.common_eval.model_overrides)
-
-    # Load ensemble
-    logger.info("loading model(s) from {}".format(cfg.common_eval.path))
-    models, saved_cfg = checkpoint_utils.load_model_ensemble(
-        utils.split_paths(cfg.common_eval.path),
-        arg_overrides=overrides,
-        task=task,
-        suffix=cfg.checkpoint.checkpoint_suffix,
-        strict=(cfg.checkpoint.checkpoint_shard_count == 1),
-        num_shards=cfg.checkpoint.checkpoint_shard_count,
-    )
-
-    # loading the dataset should happen after the checkpoint has been loaded so we can give it the saved task config
-    task.load_dataset(cfg.dataset.gen_subset, task_cfg=saved_cfg.task)
-
-    if cfg.generation.lm_path is not None:
-        overrides["data"] = cfg.task.data
-
-        try:
-            lms, _ = checkpoint_utils.load_model_ensemble(
-                [cfg.generation.lm_path], arg_overrides=overrides, task=None
-            )
-        except:
-            logger.warning(
-                f"Failed to load language model! Please make sure that the language model dict is the same "
-                f"as target dict and is located in the data dir ({cfg.task.data})"
-            )
-            raise
-
-        assert len(lms) == 1
-    else:
-        lms = [None]
-
-    # Optimize ensemble for generation
-    for model in chain(models, lms):
-        if model is None:
-            continue
-        if cfg.common.fp16:
-            model.half()
-        if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
-            model.cuda()
-        model.prepare_for_inference_(cfg)
-
-    # Load alignment dictionary for unknown word replacement
-    # (None if no unknown word replacement, empty if no path to align dictionary)
-    align_dict = utils.load_align_dict(cfg.generation.replace_unk)
-
-    # Load dataset (possibly sharded)
-    itr = task.get_batch_iterator(
-        dataset=task.dataset(cfg.dataset.gen_subset),
-        max_tokens=cfg.dataset.max_tokens,
-        max_sentences=cfg.dataset.batch_size,
-        max_positions=utils.resolve_max_positions(
-            task.max_positions(), *[m.max_positions() for m in models]
-        ),
-        ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
-        required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,
-        seed=cfg.common.seed,
-        num_shards=cfg.distributed_training.distributed_world_size,
-        shard_id=cfg.distributed_training.distributed_rank,
-        num_workers=cfg.dataset.num_workers,
-        data_buffer_size=cfg.dataset.data_buffer_size,
-    ).next_epoch_itr(shuffle=False)
-    progress = progress_bar.progress_bar(
-        itr,
-        log_format=cfg.common.log_format,
-        log_interval=cfg.common.log_interval,
-        default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
-    )
-
-    # Initialize generator
-    gen_timer = StopwatchMeter()
-
-    extra_gen_cls_kwargs = {"lm_model": lms[0], "lm_weight": cfg.generation.lm_weight}
-    generator = task.build_generator(
-        models, cfg.generation, extra_gen_cls_kwargs=extra_gen_cls_kwargs
-    )
-
-    # Handle tokenization and BPE
-    tokenizer = task.build_tokenizer(cfg.tokenizer)
-    bpe = task.build_bpe(cfg.bpe)
-
-    def decode_fn(x):
-        if bpe is not None:
-            x = bpe.decode(x)
-        if tokenizer is not None:
-            x = tokenizer.decode(x)
-        return x
-
-    scorer = scoring.build_scorer(cfg.scoring, tgt_dict)
-
-    num_sentences = 0
-    has_target = True
-    wps_meter = TimeMeter()
-    for sample in progress:
-        sample = utils.move_to_cuda(sample) if use_cuda else sample
-        if "net_input" not in sample:
-            continue
-
-        prefix_tokens = None
-        if cfg.generation.prefix_size > 0:
-            prefix_tokens = sample["target"][:, : cfg.generation.prefix_size]
-
-        constraints = None
-        if "constraints" in sample:
-            constraints = sample["constraints"]
-
-        gen_timer.start()
-        hypos = task.inference_step(
-            generator,
-            models,
-            sample,
-            prefix_tokens=prefix_tokens,
-            constraints=constraints,
-        )
-        num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
-        gen_timer.stop(num_generated_tokens)
-
-        for i, sample_id in enumerate(sample["id"].tolist()):
-            has_target = sample["target"] is not None
-
-            # Remove padding
-            if "src_tokens" in sample["net_input"]:
-                src_tokens = utils.strip_pad(
-                    sample["net_input"]["src_tokens"][i, :], tgt_dict.pad()
-                )
-            else:
-                src_tokens = None
-
-            target_tokens = None
-            if has_target:
-                target_tokens = (
-                    utils.strip_pad(sample["target"][i, :], tgt_dict.pad()).int().cpu()
-                )
-
-            # Either retrieve the original sentences or regenerate them from tokens.
-            if align_dict is not None:
-                src_str = task.dataset(cfg.dataset.gen_subset).src.get_original_text(
-                    sample_id
-                )
-                target_str = task.dataset(cfg.dataset.gen_subset).tgt.get_original_text(
-                    sample_id
-                )
-            else:
-                if src_dict is not None:
-                    src_str = src_dict.string(src_tokens, cfg.common_eval.post_process)
-                else:
-                    src_str = ""
-                if has_target:
-                    target_str = tgt_dict.string(
-                        target_tokens,
-                        cfg.common_eval.post_process,
-                        escape_unk=True,
-                        extra_symbols_to_ignore=get_symbols_to_strip_from_output(
-                            generator
-                        ),
-                    )
-
-            src_str = decode_fn(src_str)
-            if has_target:
-                target_str = decode_fn(target_str)
-
-            if not cfg.common_eval.quiet:
-                if src_dict is not None:
-                    print("S-{}\t{}".format(sample_id, src_str), file=output_file)
-                if has_target:
-                    print("T-{}\t{}".format(sample_id, target_str), file=output_file)
-
-            # Process top predictions
-            for j, hypo in enumerate(hypos[i][: cfg.generation.nbest]):
-                hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
-                    hypo_tokens=hypo["tokens"].int().cpu(),
-                    src_str=src_str,
-                    alignment=hypo["alignment"],
-                    align_dict=align_dict,
-                    tgt_dict=tgt_dict,
-                    remove_bpe=cfg.common_eval.post_process,
-                    extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator),
-                )
-                detok_hypo_str = decode_fn(hypo_str)
-                if not cfg.common_eval.quiet:
-                    score = hypo["score"] / math.log(2)  # convert to base 2
-                    # original hypothesis (after tokenization and BPE)
-                    print(
-                        "H-{}\t{}\t{}".format(sample_id, score, hypo_str),
-                        file=output_file,
-                    )
-                    # detokenized hypothesis
-                    print(
-                        "D-{}\t{}\t{}".format(sample_id, score, detok_hypo_str),
-                        file=output_file,
-                    )
-                    print(
-                        "P-{}\t{}".format(
-                            sample_id,
-                            " ".join(
-                                map(
-                                    lambda x: "{:.4f}".format(x),
-                                    # convert from base e to base 2
-                                    hypo["positional_scores"]
-                                    .div_(math.log(2))
-                                    .tolist(),
-                                )
-                            ),
-                        ),
-                        file=output_file,
-                    )
-
-                    if cfg.generation.print_alignment == "hard":
-                        print(
-                            "A-{}\t{}".format(
-                                sample_id,
-                                " ".join(
-                                    [
-                                        "{}-{}".format(src_idx, tgt_idx)
-                                        for src_idx, tgt_idx in alignment
-                                    ]
-                                ),
-                            ),
-                            file=output_file,
-                        )
-                    if cfg.generation.print_alignment == "soft":
-                        print(
-                            "A-{}\t{}".format(
-                                sample_id,
-                                " ".join(
-                                    [
-                                        ",".join(src_probs)
-                                        for src_probs in alignment
-                                    ]
-                                ),
-                            ),
-                            file=output_file,
-                        )
-
-                    if cfg.generation.print_step:
-                        print(
-                            "I-{}\t{}".format(sample_id, hypo["steps"]),
-                            file=output_file,
-                        )
-
-                    if cfg.generation.retain_iter_history:
-                        for step, h in enumerate(hypo["history"]):
-                            _, h_str, _ = utils.post_process_prediction(
-                                hypo_tokens=h["tokens"].int().cpu(),
-                                src_str=src_str,
-                                alignment=None,
-                                align_dict=None,
-                                tgt_dict=tgt_dict,
-                                remove_bpe=None,
-                            )
-                            print(
-                                "E-{}_{}\t{}".format(sample_id, step, h_str),
-                                file=output_file,
-                            )
-
-                # Score only the top hypothesis
-                if has_target and j == 0:
-                    if align_dict is not None or cfg.common_eval.post_process is not None:
-                        # Convert back to tokens for evaluation with unk replacement and/or without BPE
-                        target_tokens = tgt_dict.encode_line(
-                            target_str, add_if_not_exist=True
-                        )
-                        hypo_tokens = tgt_dict.encode_line(
-                            detok_hypo_str, add_if_not_exist=True
-                        )
-                    if hasattr(scorer, "add_string"):
-                        scorer.add_string(target_str, detok_hypo_str)
-                    else:
-                        scorer.add(target_tokens, hypo_tokens)
-
-        wps_meter.update(num_generated_tokens)
-        progress.log({"wps": round(wps_meter.avg)})
-        num_sentences += (
-            sample["nsentences"] if "nsentences" in sample else sample["id"].numel()
-        )
-
-    logger.info("NOTE: hypothesis and token scores are output in base 2")
-    logger.info(
-        "Translated {:,} sentences ({:,} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)".format(
-            num_sentences,
-            gen_timer.n,
-            gen_timer.sum,
-            num_sentences / gen_timer.sum,
-            1.0 / gen_timer.avg,
-        )
-    )
-    if has_target:
-        if cfg.bpe and not cfg.generation.sacrebleu:
-            if cfg.common_eval.post_process:
-                logger.warning(
-                    "BLEU score is being computed by splitting detokenized string on spaces, this is probably not what you want. Use --sacrebleu for standard 13a BLEU tokenization"
-                )
-            else:
-                logger.warning(
-                    "If you are using BPE on the target side, the BLEU score is computed on BPE tokens, not on proper words.  Use --sacrebleu for standard 13a BLEU tokenization"
-                )
-        # use print to be consistent with other main outputs: S-, H-, T-, D- and so on
-        print(
-            "Generate {} with beam={}: {}".format(
-                cfg.dataset.gen_subset, cfg.generation.beam, scorer.result_string()
-            ),
-            file=output_file,
-        )
-
-    return scorer
-
-
-def cli_main():
-    parser = options.get_generation_parser()
-    # TODO: replace this workaround with refactoring of `AudioPretraining`
-    parser.add_argument(
-        '--arch', '-a', metavar='ARCH', default="wav2vec2",
-        help='Model architecture. For constructing tasks that rely on '
-             'model args (e.g. `AudioPretraining`)'
-    )
-    args = options.parse_args_and_arch(parser)
-    main(args)
-
-
-if __name__ == "__main__":
-    cli_main()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/unify_transformer_layer.py b/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/unify_transformer_layer.py
deleted file mode 100644
index c02410548106e177be4ead10dbc8facdf5947e1f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/unify_transformer_layer.py
+++ /dev/null
@@ -1,542 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Dict, List, Optional
-
-import torch
-import torch.nn as nn
-from fairseq import utils
-from fairseq.modules import LayerNorm
-from fairseq.modules.fairseq_dropout import FairseqDropout
-from fairseq.modules.quant_noise import quant_noise
-from torch import Tensor
-
-from .unify_multihead_attention import MultiheadAttention
-
-
-def drop_path(x, drop_prob: float = 0.0, training: bool = False):
-    """
-    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-    Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
-    however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
-    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
-    layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
-    argument.
-    """
-    if drop_prob == 0.0 or not training:
-        return x
-    keep_prob = 1 - drop_prob
-    shape = (1, x.shape[1], 1)
-    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
-    random_tensor.floor_()  # binarize
-    output = x.div(keep_prob) * random_tensor
-    return output
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
-
-    def __init__(self, drop_prob=None):
-        super().__init__()
-        self.drop_prob = drop_prob
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training)
-
-    def extra_repr(self) -> str:
-        return "p={}".format(self.drop_prob)
-
-
-class TransformerEncoderLayer(nn.Module):
-    """Encoder layer block.
-
-    In the original paper each operation (multi-head attention or FFN) is
-    postprocessed with: `dropout -> add residual -> layernorm`. In the
-    tensor2tensor code they suggest that learning is more robust when
-    preprocessing each layer with layernorm and postprocessing with:
-    `dropout -> add residual`. We default to the approach in the paper, but the
-    tensor2tensor approach can be enabled by setting
-    *args.encoder_normalize_before* to ``True``.
-
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-    """
-
-    def __init__(self, args, drop_path_rate=0.0):
-        super().__init__()
-        self.args = args
-        self.embed_dim = args.encoder_embed_dim
-        self.quant_noise = getattr(args, 'quant_noise_pq', 0)
-        self.quant_noise_block_size = getattr(args, 'quant_noise_pq_block_size', 8) or 8
-        self.self_attn = self.build_self_attention(self.embed_dim, args)
-        self.self_attn_layer_norm = LayerNorm(self.embed_dim)
-        self.dropout_module = FairseqDropout(
-            args.dropout, module_name=self.__class__.__name__
-        )
-        self.activation_fn = utils.get_activation_fn(
-            activation=getattr(args, 'activation_fn', 'relu') or "relu"
-        )
-        activation_dropout_p = getattr(args, "activation_dropout", 0) or 0
-        if activation_dropout_p == 0:
-            # for backwards compatibility with models that use args.relu_dropout
-            activation_dropout_p = getattr(args, "relu_dropout", 0) or 0
-        self.activation_dropout_module = FairseqDropout(
-            float(activation_dropout_p), module_name=self.__class__.__name__
-        )
-        self.normalize_before = args.encoder_normalize_before
-        self.fc1 = self.build_fc1(
-            self.embed_dim,
-            args.encoder_ffn_embed_dim,
-            self.quant_noise,
-            self.quant_noise_block_size,
-        )
-        self.fc2 = self.build_fc2(
-            args.encoder_ffn_embed_dim,
-            self.embed_dim,
-            self.quant_noise,
-            self.quant_noise_block_size,
-        )
-
-        self.attn_ln = LayerNorm(self.embed_dim) if getattr(args, 'scale_attn', False) else None
-        self.nh = self.self_attn.num_heads
-        self.head_dim = self.self_attn.head_dim
-
-        self.ffn_layernorm = LayerNorm(args.encoder_ffn_embed_dim) if getattr(args, 'scale_fc', False) else None
-        self.w_resid = nn.Parameter(torch.ones(self.embed_dim, ), requires_grad=True) if getattr(args, 'scale_resids', False) else None
-
-        self.final_layer_norm = LayerNorm(self.embed_dim)
-
-        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
-
-    def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size):
-        return quant_noise(
-            nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size
-        )
-
-    def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size):
-        return quant_noise(
-            nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size
-        )
-
-    def build_self_attention(self, embed_dim, args):
-        return MultiheadAttention(
-            embed_dim,
-            args.encoder_attention_heads,
-            dropout=args.attention_dropout,
-            self_attention=True,
-            q_noise=self.quant_noise,
-            qn_block_size=self.quant_noise_block_size,
-            scale_factor=args.attn_scale_factor,
-            scale_heads=getattr(args, 'scale_heads', False)
-        )
-
-    def residual_connection(self, x, residual):
-        return residual + self.drop_path(x)
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        """
-        Rename layer norm states from `...layer_norms.0.weight` to
-        `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to
-        `...final_layer_norm.weight`
-        """
-        layer_norm_map = {"0": "self_attn_layer_norm", "1": "final_layer_norm"}
-        for old, new in layer_norm_map.items():
-            for m in ("weight", "bias"):
-                k = "{}.layer_norms.{}.{}".format(name, old, m)
-                if k in state_dict:
-                    state_dict["{}.{}.{}".format(name, new, m)] = state_dict[k]
-                    del state_dict[k]
-                if "{}.{}.{}".format(name, new, m) not in state_dict and "{}.{}".format(new, m) in self.state_dict():
-                    state_dict[
-                        "{}.{}.{}".format(name, new, m)
-                    ] = self.state_dict()["{}.{}".format(new, m)]
-
-        prefix = name + "." if name != "" else ""
-        for param_name, param_tensor in self.state_dict().items():
-            if (prefix + param_name) not in state_dict and param_name in self.state_dict():
-                state_dict[prefix + param_name] = self.state_dict()[param_name]
-
-    def forward(
-        self,
-        x,
-        encoder_padding_mask: Optional[Tensor],
-        attn_mask: Optional[Tensor] = None,
-        self_attn_bias: Optional[Tensor] = None
-    ):
-        """
-        Args:
-            x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
-            encoder_padding_mask (ByteTensor): binary ByteTensor of shape
-                `(batch, seq_len)` where padding elements are indicated by ``1``.
-            attn_mask (ByteTensor): binary tensor of shape `(tgt_len, src_len)`,
-                where `tgt_len` is the length of output and `src_len` is the
-                length of input, though here both are equal to `seq_len`.
-                `attn_mask[tgt_i, src_j] = 1` means that when calculating the
-                embedding for `tgt_i`, we exclude (mask out) `src_j`. This is
-                useful for strided self-attention.
-
-        Returns:
-            encoded output of shape `(seq_len, batch, embed_dim)`
-        """
-        # anything in original attn_mask = 1, becomes -1e8
-        # anything in original attn_mask = 0, becomes 0
-        # Note that we cannot use -inf here, because at some edge cases,
-        # the attention weight (before softmax) for some padded element in query
-        # will become -inf, which results in NaN in model parameters
-        if attn_mask is not None:
-            attn_mask = attn_mask.masked_fill(
-                attn_mask.to(torch.bool),
-                -1e8 if x.dtype == torch.float32 else -1e4
-            )
-
-        residual = x
-        if self.normalize_before:
-            x = self.self_attn_layer_norm(x)
-        x, _ = self.self_attn(
-            query=x,
-            key=x,
-            value=x,
-            key_padding_mask=encoder_padding_mask,
-            need_weights=False,
-            attn_mask=attn_mask,
-            attn_bias=self_attn_bias
-        )
-        if self.attn_ln is not None:
-            x = self.attn_ln(x)
-        x = self.dropout_module(x)
-        x = self.residual_connection(x, residual)
-        if not self.normalize_before:
-            x = self.self_attn_layer_norm(x)
-
-        residual = x
-        if self.normalize_before:
-            x = self.final_layer_norm(x)
-        x = self.activation_fn(self.fc1(x))
-        x = self.activation_dropout_module(x)
-        if self.ffn_layernorm is not None:
-            x = self.ffn_layernorm(x)
-        x = self.fc2(x)
-        x = self.dropout_module(x)
-        if self.w_resid is not None:
-            residual = torch.mul(self.w_resid, residual)
-        x = self.residual_connection(x, residual)
-        if not self.normalize_before:
-            x = self.final_layer_norm(x)
-        return x
-
-
-class TransformerDecoderLayer(nn.Module):
-    """Decoder layer block.
-
-    In the original paper each operation (multi-head attention, encoder
-    attention or FFN) is postprocessed with: `dropout -> add residual ->
-    layernorm`. In the tensor2tensor code they suggest that learning is more
-    robust when preprocessing each layer with layernorm and postprocessing with:
-    `dropout -> add residual`. We default to the approach in the paper, but the
-    tensor2tensor approach can be enabled by setting
-    *args.decoder_normalize_before* to ``True``.
-
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-        no_encoder_attn (bool, optional): whether to attend to encoder outputs
-            (default: False).
-    """
-
-    def __init__(
-        self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False, drop_path_rate=0.0
-    ):
-        super().__init__()
-        self.embed_dim = args.decoder_embed_dim
-        self.dropout_module = FairseqDropout(
-            args.dropout, module_name=self.__class__.__name__
-        )
-        self.quant_noise = getattr(args, "quant_noise_pq", 0)
-        self.quant_noise_block_size = getattr(args, "quant_noise_pq_block_size", 8)
-
-        self.cross_self_attention = getattr(args, "cross_self_attention", False)
-
-        self.self_attn = self.build_self_attention(
-            self.embed_dim,
-            args,
-            add_bias_kv=add_bias_kv,
-            add_zero_attn=add_zero_attn,
-        )
-        self.self_attn_ln = LayerNorm(self.embed_dim) if getattr(args, 'scale_attn', False) else None
-        self.cross_attn_ln = LayerNorm(self.embed_dim) if getattr(args, 'scale_attn', False) else None
-        self.nh = self.self_attn.num_heads
-        self.head_dim = self.self_attn.head_dim
-
-        self.activation_fn = utils.get_activation_fn(
-            activation=str(args.activation_fn)
-            if getattr(args, "activation_fn", None) is not None
-            else "relu"
-        )
-        activation_dropout_p = getattr(args, "activation_dropout", 0) or 0
-        if activation_dropout_p == 0:
-            # for backwards compatibility with models that use args.relu_dropout
-            activation_dropout_p = getattr(args, "relu_dropout", 0) or 0
-        self.activation_dropout_module = FairseqDropout(
-            float(activation_dropout_p), module_name=self.__class__.__name__
-        )
-        self.normalize_before = args.decoder_normalize_before
-
-        # use layerNorm rather than FusedLayerNorm for exporting.
-        # char_inputs can be used to determint this.
-        # TODO  remove this once we update apex with the fix
-        export = getattr(args, "char_inputs", False)
-        self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=export)
-
-        if no_encoder_attn:
-            self.encoder_attn = None
-            self.encoder_attn_layer_norm = None
-        else:
-            self.encoder_attn = self.build_encoder_attention(self.embed_dim, args)
-            self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=export)
-
-        self.ffn_layernorm = LayerNorm(args.decoder_ffn_embed_dim) if getattr(args, 'scale_fc', False) else None
-        self.w_resid = nn.Parameter(torch.ones(self.embed_dim, ), requires_grad=True) if getattr(args, 'scale_resids', False) else None
-
-        self.fc1 = self.build_fc1(
-            self.embed_dim,
-            args.decoder_ffn_embed_dim,
-            self.quant_noise,
-            self.quant_noise_block_size,
-        )
-        self.fc2 = self.build_fc2(
-            args.decoder_ffn_embed_dim,
-            self.embed_dim,
-            self.quant_noise,
-            self.quant_noise_block_size,
-        )
-
-        self.final_layer_norm = LayerNorm(self.embed_dim, export=export)
-        self.need_attn = True
-
-        self.onnx_trace = False
-
-        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
-
-    def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size):
-        return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size)
-
-    def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size):
-        return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size)
-
-    def build_self_attention(
-        self, embed_dim, args, add_bias_kv=False, add_zero_attn=False
-    ):
-        return MultiheadAttention(
-            embed_dim,
-            args.decoder_attention_heads,
-            dropout=args.attention_dropout,
-            add_bias_kv=add_bias_kv,
-            add_zero_attn=add_zero_attn,
-            self_attention=not getattr(args, "cross_self_attention", False),
-            q_noise=self.quant_noise,
-            qn_block_size=self.quant_noise_block_size,
-            scale_factor=args.attn_scale_factor,
-            scale_heads=getattr(args, 'scale_heads', False)
-        )
-
-    def build_encoder_attention(self, embed_dim, args):
-        return MultiheadAttention(
-            embed_dim,
-            args.decoder_attention_heads,
-            kdim=getattr(args, "encoder_embed_dim", None),
-            vdim=getattr(args, "encoder_embed_dim", None),
-            dropout=args.attention_dropout,
-            encoder_decoder_attention=True,
-            q_noise=self.quant_noise,
-            qn_block_size=self.quant_noise_block_size,
-            scale_factor=args.attn_scale_factor,
-            scale_heads=getattr(args, 'scale_heads', False)
-        )
-
-    def prepare_for_onnx_export_(self):
-        self.onnx_trace = True
-
-    def residual_connection(self, x, residual):
-        return residual + self.drop_path(x)
-
-    def forward(
-        self,
-        x,
-        encoder_out: Optional[torch.Tensor] = None,
-        encoder_padding_mask: Optional[torch.Tensor] = None,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        prev_self_attn_state: Optional[List[torch.Tensor]] = None,
-        prev_attn_state: Optional[List[torch.Tensor]] = None,
-        self_attn_mask: Optional[torch.Tensor] = None,
-        self_attn_padding_mask: Optional[torch.Tensor] = None,
-        need_attn: bool = False,
-        need_head_weights: bool = False,
-        self_attn_bias: Optional[Tensor] = None,
-        cross_attn_bias: Optional[Tensor] = None
-    ):
-        """
-        Args:
-            x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
-            encoder_padding_mask (ByteTensor, optional): binary
-                ByteTensor of shape `(batch, src_len)` where padding
-                elements are indicated by ``1``.
-            need_attn (bool, optional): return attention weights
-            need_head_weights (bool, optional): return attention weights
-                for each head (default: return average over heads).
-
-        Returns:
-            encoded output of shape `(seq_len, batch, embed_dim)`
-        """
-        if need_head_weights:
-            need_attn = True
-
-        residual = x
-        if self.normalize_before:
-            x = self.self_attn_layer_norm(x)
-        if prev_self_attn_state is not None:
-            prev_key, prev_value = prev_self_attn_state[:2]
-            saved_state: Dict[str, Optional[Tensor]] = {
-                "prev_key": prev_key,
-                "prev_value": prev_value,
-            }
-            if len(prev_self_attn_state) >= 3:
-                saved_state["prev_key_padding_mask"] = prev_self_attn_state[2]
-            assert incremental_state is not None
-            self.self_attn._set_input_buffer(incremental_state, saved_state)
-        _self_attn_input_buffer = self.self_attn._get_input_buffer(incremental_state)
-        if self.cross_self_attention and not (
-            incremental_state is not None
-            and _self_attn_input_buffer is not None
-            and "prev_key" in _self_attn_input_buffer
-        ):
-            if self_attn_mask is not None:
-                assert encoder_out is not None
-                self_attn_mask = torch.cat(
-                    (x.new_zeros(x.size(0), encoder_out.size(0)), self_attn_mask), dim=1
-                )
-            if self_attn_padding_mask is not None:
-                if encoder_padding_mask is None:
-                    assert encoder_out is not None
-                    encoder_padding_mask = self_attn_padding_mask.new_zeros(
-                        encoder_out.size(1), encoder_out.size(0)
-                    )
-                self_attn_padding_mask = torch.cat(
-                    (encoder_padding_mask, self_attn_padding_mask), dim=1
-                )
-            assert encoder_out is not None
-            y = torch.cat((encoder_out, x), dim=0)
-        else:
-            y = x
-
-        x, attn = self.self_attn(
-            query=x,
-            key=y,
-            value=y,
-            key_padding_mask=self_attn_padding_mask,
-            incremental_state=incremental_state,
-            need_weights=False,
-            attn_mask=self_attn_mask,
-            attn_bias=self_attn_bias
-        )
-        if self.self_attn_ln is not None:
-            x = self.self_attn_ln(x)
-        x = self.dropout_module(x)
-        x = self.residual_connection(x, residual)
-        if not self.normalize_before:
-            x = self.self_attn_layer_norm(x)
-
-        if self.encoder_attn is not None and encoder_out is not None:
-            residual = x
-            if self.normalize_before:
-                x = self.encoder_attn_layer_norm(x)
-            if prev_attn_state is not None:
-                prev_key, prev_value = prev_attn_state[:2]
-                saved_state: Dict[str, Optional[Tensor]] = {
-                    "prev_key": prev_key,
-                    "prev_value": prev_value,
-                }
-                if len(prev_attn_state) >= 3:
-                    saved_state["prev_key_padding_mask"] = prev_attn_state[2]
-                assert incremental_state is not None
-                self.encoder_attn._set_input_buffer(incremental_state, saved_state)
-
-            x, attn = self.encoder_attn(
-                query=x,
-                key=encoder_out,
-                value=encoder_out,
-                key_padding_mask=encoder_padding_mask,
-                incremental_state=incremental_state,
-                static_kv=True,
-                need_weights=need_attn or (not self.training and self.need_attn),
-                need_head_weights=need_head_weights,
-                attn_bias=cross_attn_bias
-            )
-            if self.cross_attn_ln is not None:
-                x = self.cross_attn_ln(x)
-            x = self.dropout_module(x)
-            x = self.residual_connection(x, residual)
-            if not self.normalize_before:
-                x = self.encoder_attn_layer_norm(x)
-
-        residual = x
-        if self.normalize_before:
-            x = self.final_layer_norm(x)
-
-        x = self.activation_fn(self.fc1(x))
-        x = self.activation_dropout_module(x)
-        if self.ffn_layernorm is not None:
-            x = self.ffn_layernorm(x)
-        x = self.fc2(x)
-        x = self.dropout_module(x)
-        if self.w_resid is not None:
-            residual = torch.mul(self.w_resid, residual)
-        x = self.residual_connection(x, residual)
-        if not self.normalize_before:
-            x = self.final_layer_norm(x)
-        if self.onnx_trace and incremental_state is not None:
-            saved_state = self.self_attn._get_input_buffer(incremental_state)
-            assert saved_state is not None
-            if self_attn_padding_mask is not None:
-                self_attn_state = [
-                    saved_state["prev_key"],
-                    saved_state["prev_value"],
-                    saved_state["prev_key_padding_mask"],
-                ]
-            else:
-                self_attn_state = [saved_state["prev_key"], saved_state["prev_value"]]
-            return x, attn, self_attn_state
-        return x, attn, None
-
-    def make_generation_fast_(self, need_attn: bool = False, **kwargs):
-        self.need_attn = need_attn
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        """
-        Rename layer norm states from `...layer_norms.0.weight` to
-        `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to
-        `...final_layer_norm.weight`
-        """
-        # update layer norms
-        layer_norm_map = {
-            "0": "self_attn_layer_norm",
-            "1": "encoder_attn_layer_norm",
-            "2": "final_layer_norm",
-        }
-        for old, new in layer_norm_map.items():
-            for m in ("weight", "bias"):
-                k = "{}.layer_norms.{}.{}".format(name, old, m)
-                if k in state_dict:
-                    state_dict[
-                        "{}.{}.{}".format(name, new, m)
-                    ] = state_dict[k]
-                    del state_dict[k]
-                if "{}.{}.{}".format(name, new, m) not in state_dict and "{}.{}".format(new, m) in self.state_dict():
-                    state_dict[
-                        "{}.{}.{}".format(name, new, m)
-                    ] = self.state_dict()["{}.{}".format(new, m)]
-
-        prefix = name + "." if name != "" else ""
-        for param_name, param_tensor in self.state_dict().items():
-            if (prefix + param_name) not in state_dict and param_name in self.state_dict():
-                state_dict[prefix + param_name] = self.state_dict()[param_name]
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/roberta/README.race.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/roberta/README.race.md
deleted file mode 100644
index 13c917e8eca6621e91dce541c7e41436b38cbdc1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/roberta/README.race.md
+++ /dev/null
@@ -1,68 +0,0 @@
-# Finetuning RoBERTa on RACE tasks
-
-### 1) Download the data from RACE website (http://www.cs.cmu.edu/~glai1/data/race/)
-
-### 2) Preprocess RACE data:
-```bash
-python ./examples/roberta/preprocess_RACE.py --input-dir <input-dir> --output-dir <extracted-data-dir>
-./examples/roberta/preprocess_RACE.sh <extracted-data-dir> <output-dir>
-```
-
-### 3) Fine-tuning on RACE:
-
-```bash
-MAX_EPOCH=5           # Number of training epochs.
-LR=1e-05              # Peak LR for fixed LR scheduler.
-NUM_CLASSES=4
-MAX_SENTENCES=1       # Batch size per GPU.
-UPDATE_FREQ=8         # Accumulate gradients to simulate training on 8 GPUs.
-DATA_DIR=/path/to/race-output-dir
-ROBERTA_PATH=/path/to/roberta/model.pt
-
-CUDA_VISIBLE_DEVICES=0,1 fairseq-train $DATA_DIR --ddp-backend=legacy_ddp \
-    --restore-file $ROBERTA_PATH \
-    --reset-optimizer --reset-dataloader --reset-meters \
-    --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \
-    --task sentence_ranking \
-    --num-classes $NUM_CLASSES \
-    --init-token 0 --separator-token 2 \
-    --max-option-length 128 \
-    --max-positions 512 \
-    --shorten-method "truncate" \
-    --arch roberta_large \
-    --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \
-    --criterion sentence_ranking \
-    --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \
-    --clip-norm 0.0 \
-    --lr-scheduler fixed --lr $LR \
-    --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \
-    --batch-size $MAX_SENTENCES \
-    --required-batch-size-multiple 1 \
-    --update-freq $UPDATE_FREQ \
-    --max-epoch $MAX_EPOCH
-```
-
-**Note:**
-
-a) As contexts in RACE are relatively long, we are using smaller batch size per GPU while increasing update-freq to achieve larger effective batch size.
-
-b) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`.
-
-c) The setting in above command is based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search.  
-
-### 4) Evaluation:
-
-```
-DATA_DIR=/path/to/race-output-dir       # data directory used during training
-MODEL_PATH=/path/to/checkpoint_best.pt  # path to the finetuned model checkpoint
-PREDS_OUT=preds.tsv                     # output file path to save prediction
-TEST_SPLIT=test                         # can be test (Middle) or test1 (High)
-fairseq-validate \
-    $DATA_DIR \
-    --valid-subset $TEST_SPLIT \
-    --path $MODEL_PATH \
-    --batch-size 1 \
-    --task sentence_ranking \
-    --criterion sentence_ranking \
-    --save-predictions $PREDS_OUT
-```
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/layers.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/layers.py
deleted file mode 100644
index f10d557ff5a4fff03b94f81543bd58cf1a66bc8f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/layers.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import torch
-from librosa.filters import mel as librosa_mel_fn
-from .audio_processing import dynamic_range_compression
-from .audio_processing import dynamic_range_decompression
-from .stft import STFT
-from .utils import get_mask_from_lengths
-
-
-class LinearNorm(torch.nn.Module):
-    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
-        super(LinearNorm, self).__init__()
-        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
-
-        torch.nn.init.xavier_uniform_(
-            self.linear_layer.weight,
-            gain=torch.nn.init.calculate_gain(w_init_gain))
-
-    def forward(self, x):
-        return self.linear_layer(x)
-
-
-class ConvNorm(torch.nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
-                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
-        super(ConvNorm, self).__init__()
-        if padding is None:
-            assert(kernel_size % 2 == 1)
-            padding = int(dilation * (kernel_size - 1) / 2)
-
-        self.conv = torch.nn.Conv1d(in_channels, out_channels,
-                                    kernel_size=kernel_size, stride=stride,
-                                    padding=padding, dilation=dilation,
-                                    bias=bias)
-
-        torch.nn.init.xavier_uniform_(
-            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
-
-    def forward(self, signal):
-        conv_signal = self.conv(signal)
-        return conv_signal
-
-
-class GlobalAvgPool(torch.nn.Module):
-    def __init__(self):
-        super(GlobalAvgPool, self).__init__()
-
-    def forward(self, x, lengths=None):
-        """Average pooling across time steps (dim=1) with optionally lengths.
-        Args:
-            x: torch.Tensor of shape (N, T, ...)
-            lengths: None or torch.Tensor of shape (N,)
-            dim: dimension to pool
-        """
-        if lengths is None:
-            return x.mean(dim=1, keepdim=False)
-        else:
-            mask = get_mask_from_lengths(lengths).type(x.type()).to(x.device)
-            mask_shape = list(mask.size()) + [1 for _ in range(x.ndimension()-2)]
-            mask = mask.reshape(*mask_shape)
-            numer = (x * mask).sum(dim=1, keepdim=False)
-            denom = mask.sum(dim=1, keepdim=False)
-            return numer / denom
-
-
-class TacotronSTFT(torch.nn.Module):
-    def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
-                 n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
-                 mel_fmax=8000.0):
-        super(TacotronSTFT, self).__init__()
-        self.n_mel_channels = n_mel_channels
-        self.sampling_rate = sampling_rate
-        self.stft_fn = STFT(filter_length, hop_length, win_length)
-        mel_basis = librosa_mel_fn(
-            sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax)
-        mel_basis = torch.from_numpy(mel_basis).float()
-        self.register_buffer('mel_basis', mel_basis)
-
-    def spectral_normalize(self, magnitudes):
-        output = dynamic_range_compression(magnitudes)
-        return output
-
-    def spectral_de_normalize(self, magnitudes):
-        output = dynamic_range_decompression(magnitudes)
-        return output
-
-    def mel_spectrogram(self, y):
-        """Computes mel-spectrograms from a batch of waves
-        PARAMS
-        ------
-        y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
-
-        RETURNS
-        -------
-        mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
-        """
-        assert(torch.min(y.data) >= -1)
-        assert(torch.max(y.data) <= 1)
-
-        magnitudes, phases = self.stft_fn.transform(y)
-        magnitudes = magnitudes.data
-        mel_output = torch.matmul(self.mel_basis, magnitudes)
-        mel_output = self.spectral_normalize(mel_output)
-        return mel_output
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/criterions/model_criterion.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/criterions/model_criterion.py
deleted file mode 100644
index 30350f13b1c00498de6784579250d6b342ced7dd..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/criterions/model_criterion.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from dataclasses import dataclass, field
-from typing import Dict, List
-
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ModelCriterionConfig(FairseqDataclass):
-    loss_weights: Dict[str, float] = field(
-        default_factory=dict,
-        metadata={"help": "weights for the loss terms"},
-    )
-    log_keys: List[str] = field(
-        default_factory=list,
-        metadata={"help": "additional output keys to log"},
-    )
-
-
-@register_criterion("model", dataclass=ModelCriterionConfig)
-class ModelCriterion(FairseqCriterion):
-    """
-    This criterion relies on the model to supply losses.
-    The losses should be a dictionary of name -> scalar returned by
-    the model either by including it in the net_output dict or by
-    implementing a get_losses(net_output, sample) method. The final loss is
-    a scaled sum of all losses according to weights in loss_weights.
-    If no weights are provided, then all losses are scaled by 1.0.
-
-    The losses will be automatically logged. Additional keys from
-    net_output dict can be logged via the log_keys parameter.
-    """
-
-    def __init__(self, task, loss_weights=None, log_keys=None):
-        super().__init__(task)
-        self.loss_weights = loss_weights
-        self.log_keys = log_keys
-
-    def forward(self, model, sample, reduce=True):
-        net_output = model(**sample["net_input"])
-
-        sample_size = net_output["sample_size"]
-        scaled_losses = {}
-
-        if hasattr(model, "get_losses"):
-            losses = model.get_losses(net_output, sample)
-        elif isinstance(net_output, dict) and "losses" in net_output:
-            losses = net_output["losses"]
-        else:
-            raise Exception("Could not retrieve losses")
-
-        for lk, p in losses.items():
-            try:
-                coef = 1.0 if len(self.loss_weights) == 0 else self.loss_weights[lk]
-            except KeyError:
-                logger.error(
-                    f"weight for loss {lk} is not in loss_weights ({self.loss_weights})"
-                )
-                raise
-            if coef != 0 and p is not None:
-                scaled_losses[lk] = coef * p.float()
-
-        loss = sum(scaled_losses.values())
-        if reduce and loss.numel() > 1:
-            loss = loss.sum()
-
-        logging_output = {
-            "loss": loss.data,
-            "ntokens": sample_size,
-            "nsentences": sample["id"].numel(),
-            "sample_size": sample_size,
-            "_world_size": 1,
-        }
-
-        for lk in self.log_keys:
-            if lk in net_output and net_output[lk] is not None:
-                logging_output[lk] = float(net_output[lk])
-
-        if len(scaled_losses) > 1:
-            for lk, l in scaled_losses.items():
-                logging_output[f"loss_{lk}"] = l.item()
-
-        return loss, sample_size, logging_output
-
-    @staticmethod
-    def reduce_metrics(logging_outputs) -> None:
-        """Aggregate logging outputs from data parallel training."""
-        loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
-        ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs))
-        nsentences = utils.item(
-            sum(log.get("nsentences", 0) for log in logging_outputs)
-        )
-        sample_size = utils.item(
-            sum(log.get("sample_size", 0) for log in logging_outputs)
-        )
-
-        metrics.log_scalar("loss", loss_sum / sample_size, sample_size, round=3)
-        metrics.log_scalar("ntokens", ntokens)
-        metrics.log_scalar("nsentences", nsentences)
-
-        builtin_keys = {
-            "loss",
-            "ntokens",
-            "nsentences",
-            "sample_size",
-            "_world_size",
-        }
-
-        world_size = utils.item(
-            sum(log.get("_world_size", 0) for log in logging_outputs)
-        )
-
-        for k in logging_outputs[0]:
-            if k not in builtin_keys:
-                val = sum(log.get(k, 0) for log in logging_outputs)
-                if k.startswith("loss_"):
-                    metrics.log_scalar(k, val / sample_size, sample_size, round=3)
-                else:
-                    metrics.log_scalar(k, val / world_size, round=3)
-
-    @staticmethod
-    def logging_outputs_can_be_summed() -> bool:
-        """
-        Whether the logging outputs returned by `forward` can be summed
-        across workers prior to calling `reduce_metrics`. Setting this
-        to True will improves distributed training speed.
-        """
-        return True
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq_cli/validate.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq_cli/validate.py
deleted file mode 100644
index 22b93e9a6a1e1fbcff67075019177110905270f2..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq_cli/validate.py
+++ /dev/null
@@ -1,155 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-import sys
-from argparse import Namespace
-from itertools import chain
-
-import torch
-from fairseq import checkpoint_utils, distributed_utils, options, utils
-from fairseq.dataclass.utils import convert_namespace_to_omegaconf
-from fairseq.logging import metrics, progress_bar
-from fairseq.utils import reset_logging
-from omegaconf import DictConfig
-
-
-logging.basicConfig(
-    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-    level=os.environ.get("LOGLEVEL", "INFO").upper(),
-    stream=sys.stdout,
-)
-logger = logging.getLogger("fairseq_cli.validate")
-
-
-def main(cfg: DictConfig, override_args=None):
-    if isinstance(cfg, Namespace):
-        cfg = convert_namespace_to_omegaconf(cfg)
-
-    utils.import_user_module(cfg.common)
-
-    reset_logging()
-
-    assert (
-        cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None
-    ), "Must specify batch size either with --max-tokens or --batch-size"
-
-    use_fp16 = cfg.common.fp16
-    use_cuda = torch.cuda.is_available() and not cfg.common.cpu
-
-    if use_cuda:
-        torch.cuda.set_device(cfg.distributed_training.device_id)
-
-    if cfg.distributed_training.distributed_world_size > 1:
-        data_parallel_world_size = distributed_utils.get_data_parallel_world_size()
-        data_parallel_rank = distributed_utils.get_data_parallel_rank()
-    else:
-        data_parallel_world_size = 1
-        data_parallel_rank = 0
-
-    if override_args is not None:
-        overrides = vars(override_args)
-        overrides.update(eval(getattr(override_args, "model_overrides", "{}")))
-    else:
-        overrides = None
-
-    # Load ensemble
-    logger.info("loading model(s) from {}".format(cfg.common_eval.path))
-    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-        [cfg.common_eval.path],
-        arg_overrides=overrides,
-        suffix=cfg.checkpoint.checkpoint_suffix,
-    )
-    model = models[0]
-
-    # Move models to GPU
-    for model in models:
-        model.eval()
-        if use_fp16:
-            model.half()
-        if use_cuda:
-            model.cuda()
-
-    # Print args
-    logger.info(saved_cfg)
-
-    # Build criterion
-    criterion = task.build_criterion(saved_cfg.criterion)
-    criterion.eval()
-
-    for subset in cfg.dataset.valid_subset.split(","):
-        try:
-            task.load_dataset(subset, combine=False, epoch=1, task_cfg=saved_cfg.task)
-            dataset = task.dataset(subset)
-        except KeyError:
-            raise Exception("Cannot find dataset: " + subset)
-
-        # Initialize data iterator
-        itr = task.get_batch_iterator(
-            dataset=dataset,
-            max_tokens=cfg.dataset.max_tokens,
-            max_sentences=cfg.dataset.batch_size,
-            max_positions=utils.resolve_max_positions(
-                task.max_positions(),
-                *[m.max_positions() for m in models],
-            ),
-            ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
-            required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,
-            seed=cfg.common.seed,
-            num_shards=data_parallel_world_size,
-            shard_id=data_parallel_rank,
-            num_workers=cfg.dataset.num_workers,
-            data_buffer_size=cfg.dataset.data_buffer_size,
-        ).next_epoch_itr(shuffle=False)
-        progress = progress_bar.progress_bar(
-            itr,
-            log_format=cfg.common.log_format,
-            log_interval=cfg.common.log_interval,
-            prefix=f"valid on '{subset}' subset",
-            default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
-        )
-
-        log_outputs = []
-        for i, sample in enumerate(progress):
-            sample = utils.move_to_cuda(sample) if use_cuda else sample
-            _loss, _sample_size, log_output = task.valid_step(sample, model, criterion)
-            progress.log(log_output, step=i)
-            log_outputs.append(log_output)
-
-        if data_parallel_world_size > 1:
-            log_outputs = distributed_utils.all_gather_list(
-                log_outputs,
-                max_size=cfg.common.all_gather_list_size,
-                group=distributed_utils.get_data_parallel_group(),
-            )
-            log_outputs = list(chain.from_iterable(log_outputs))
-
-        with metrics.aggregate() as agg:
-            task.reduce_metrics(log_outputs, criterion)
-            log_output = agg.get_smoothed_values()
-
-        progress.print(log_output, tag=subset, step=i)
-
-
-def cli_main():
-    parser = options.get_validation_parser()
-    args = options.parse_args_and_arch(parser)
-
-    # only override args that are explicitly given on the command line
-    override_parser = options.get_validation_parser()
-    override_args = options.parse_args_and_arch(
-        override_parser, suppress_defaults=True
-    )
-
-    distributed_utils.call_main(
-        convert_namespace_to_omegaconf(args), main, override_args=override_args
-    )
-
-
-if __name__ == "__main__":
-    cli_main()
diff --git a/spaces/Omnibus/MusicGen/CHANGELOG.md b/spaces/Omnibus/MusicGen/CHANGELOG.md
deleted file mode 100644
index 24fc214df236b40efead4b1585b01632d9658e9b..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/CHANGELOG.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# Changelog
-
-All notable changes to this project will be documented in this file.
-
-The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
-
-## [0.0.2a] - TBD
-
-Improved demo, fixed top p (thanks @jnordberg).
-
-Compressor tanh on output to avoid clipping with some style (especially piano).
-Now repeating the conditioning periodically if it is too short.
-
-More options when launching Gradio app locally (thanks @ashleykleynhans).
-
-Testing out PyTorch 2.0 memory efficient attention.
-
-Added extended generation (infinite length) by slowly moving the windows.
-Note that other implementations exist: https://github.com/camenduru/MusicGen-colab.
-
-## [0.0.1] - 2023-06-09
-
-Initial release, with model evaluation only.
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/evaluation/rotated_coco_evaluation.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/evaluation/rotated_coco_evaluation.py
deleted file mode 100644
index ea6d1b381dcf106339a03f08577df673ad439c46..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/evaluation/rotated_coco_evaluation.py
+++ /dev/null
@@ -1,207 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-import json
-import numpy as np
-import os
-import torch
-from pycocotools.cocoeval import COCOeval, maskUtils
-
-from detectron2.structures import BoxMode, RotatedBoxes, pairwise_iou_rotated
-from detectron2.utils.file_io import PathManager
-
-from .coco_evaluation import COCOEvaluator
-
-
-class RotatedCOCOeval(COCOeval):
-    @staticmethod
-    def is_rotated(box_list):
-        if type(box_list) == np.ndarray:
-            return box_list.shape[1] == 5
-        elif type(box_list) == list:
-            if box_list == []:  # cannot decide the box_dim
-                return False
-            return np.all(
-                np.array(
-                    [
-                        (len(obj) == 5) and ((type(obj) == list) or (type(obj) == np.ndarray))
-                        for obj in box_list
-                    ]
-                )
-            )
-        return False
-
-    @staticmethod
-    def boxlist_to_tensor(boxlist, output_box_dim):
-        if type(boxlist) == np.ndarray:
-            box_tensor = torch.from_numpy(boxlist)
-        elif type(boxlist) == list:
-            if boxlist == []:
-                return torch.zeros((0, output_box_dim), dtype=torch.float32)
-            else:
-                box_tensor = torch.FloatTensor(boxlist)
-        else:
-            raise Exception("Unrecognized boxlist type")
-
-        input_box_dim = box_tensor.shape[1]
-        if input_box_dim != output_box_dim:
-            if input_box_dim == 4 and output_box_dim == 5:
-                box_tensor = BoxMode.convert(box_tensor, BoxMode.XYWH_ABS, BoxMode.XYWHA_ABS)
-            else:
-                raise Exception(
-                    "Unable to convert from {}-dim box to {}-dim box".format(
-                        input_box_dim, output_box_dim
-                    )
-                )
-        return box_tensor
-
-    def compute_iou_dt_gt(self, dt, gt, is_crowd):
-        if self.is_rotated(dt) or self.is_rotated(gt):
-            # TODO: take is_crowd into consideration
-            assert all(c == 0 for c in is_crowd)
-            dt = RotatedBoxes(self.boxlist_to_tensor(dt, output_box_dim=5))
-            gt = RotatedBoxes(self.boxlist_to_tensor(gt, output_box_dim=5))
-            return pairwise_iou_rotated(dt, gt)
-        else:
-            # This is the same as the classical COCO evaluation
-            return maskUtils.iou(dt, gt, is_crowd)
-
-    def computeIoU(self, imgId, catId):
-        p = self.params
-        if p.useCats:
-            gt = self._gts[imgId, catId]
-            dt = self._dts[imgId, catId]
-        else:
-            gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]
-            dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]
-        if len(gt) == 0 and len(dt) == 0:
-            return []
-        inds = np.argsort([-d["score"] for d in dt], kind="mergesort")
-        dt = [dt[i] for i in inds]
-        if len(dt) > p.maxDets[-1]:
-            dt = dt[0 : p.maxDets[-1]]
-
-        assert p.iouType == "bbox", "unsupported iouType for iou computation"
-
-        g = [g["bbox"] for g in gt]
-        d = [d["bbox"] for d in dt]
-
-        # compute iou between each dt and gt region
-        iscrowd = [int(o["iscrowd"]) for o in gt]
-
-        # Note: this function is copied from cocoeval.py in cocoapi
-        # and the major difference is here.
-        ious = self.compute_iou_dt_gt(d, g, iscrowd)
-        return ious
-
-
-class RotatedCOCOEvaluator(COCOEvaluator):
-    """
-    Evaluate object proposal/instance detection outputs using COCO-like metrics and APIs,
-    with rotated boxes support.
-    Note: this uses IOU only and does not consider angle differences.
-    """
-
-    def process(self, inputs, outputs):
-        """
-        Args:
-            inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
-                It is a list of dict. Each dict corresponds to an image and
-                contains keys like "height", "width", "file_name", "image_id".
-            outputs: the outputs of a COCO model. It is a list of dicts with key
-                "instances" that contains :class:`Instances`.
-        """
-        for input, output in zip(inputs, outputs):
-            prediction = {"image_id": input["image_id"]}
-
-            if "instances" in output:
-                instances = output["instances"].to(self._cpu_device)
-
-                prediction["instances"] = self.instances_to_json(instances, input["image_id"])
-            if "proposals" in output:
-                prediction["proposals"] = output["proposals"].to(self._cpu_device)
-            self._predictions.append(prediction)
-
-    def instances_to_json(self, instances, img_id):
-        num_instance = len(instances)
-        if num_instance == 0:
-            return []
-
-        boxes = instances.pred_boxes.tensor.numpy()
-        if boxes.shape[1] == 4:
-            boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
-        boxes = boxes.tolist()
-        scores = instances.scores.tolist()
-        classes = instances.pred_classes.tolist()
-
-        results = []
-        for k in range(num_instance):
-            result = {
-                "image_id": img_id,
-                "category_id": classes[k],
-                "bbox": boxes[k],
-                "score": scores[k],
-            }
-
-            results.append(result)
-        return results
-
-    def _eval_predictions(self, predictions, img_ids=None):  # img_ids: unused
-        """
-        Evaluate predictions on the given tasks.
-        Fill self._results with the metrics of the tasks.
-        """
-        self._logger.info("Preparing results for COCO format ...")
-        coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
-
-        # unmap the category ids for COCO
-        if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
-            reverse_id_mapping = {
-                v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items()
-            }
-            for result in coco_results:
-                result["category_id"] = reverse_id_mapping[result["category_id"]]
-
-        if self._output_dir:
-            file_path = os.path.join(self._output_dir, "coco_instances_results.json")
-            self._logger.info("Saving results to {}".format(file_path))
-            with PathManager.open(file_path, "w") as f:
-                f.write(json.dumps(coco_results))
-                f.flush()
-
-        if not self._do_evaluation:
-            self._logger.info("Annotations are not available for evaluation.")
-            return
-
-        self._logger.info("Evaluating predictions ...")
-
-        assert self._tasks is None or set(self._tasks) == {
-            "bbox"
-        }, "[RotatedCOCOEvaluator] Only bbox evaluation is supported"
-        coco_eval = (
-            self._evaluate_predictions_on_coco(self._coco_api, coco_results)
-            if len(coco_results) > 0
-            else None  # cocoapi does not handle empty results very well
-        )
-
-        task = "bbox"
-        res = self._derive_coco_results(
-            coco_eval, task, class_names=self._metadata.get("thing_classes")
-        )
-        self._results[task] = res
-
-    def _evaluate_predictions_on_coco(self, coco_gt, coco_results):
-        """
-        Evaluate the coco results using COCOEval API.
-        """
-        assert len(coco_results) > 0
-
-        coco_dt = coco_gt.loadRes(coco_results)
-
-        # Only bbox is supported for now
-        coco_eval = RotatedCOCOeval(coco_gt, coco_dt, iouType="bbox")
-
-        coco_eval.evaluate()
-        coco_eval.accumulate()
-        coco_eval.summarize()
-
-        return coco_eval
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/modules/fake_fakes.py b/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/modules/fake_fakes.py
deleted file mode 100644
index 45c4ad559cef2730b771a709197e00ae1c87683c..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/modules/fake_fakes.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import torch
-from kornia import SamplePadding
-from kornia.augmentation import RandomAffine, CenterCrop
-
-
-class FakeFakesGenerator:
-    def __init__(self, aug_proba=0.5, img_aug_degree=30, img_aug_translate=0.2):
-        self.grad_aug = RandomAffine(degrees=360,
-                                     translate=0.2,
-                                     padding_mode=SamplePadding.REFLECTION,
-                                     keepdim=False,
-                                     p=1)
-        self.img_aug = RandomAffine(degrees=img_aug_degree,
-                                    translate=img_aug_translate,
-                                    padding_mode=SamplePadding.REFLECTION,
-                                    keepdim=True,
-                                    p=1)
-        self.aug_proba = aug_proba
-
-    def __call__(self, input_images, masks):
-        blend_masks = self._fill_masks_with_gradient(masks)
-        blend_target = self._make_blend_target(input_images)
-        result = input_images * (1 - blend_masks) + blend_target * blend_masks
-        return result, blend_masks
-
-    def _make_blend_target(self, input_images):
-        batch_size = input_images.shape[0]
-        permuted = input_images[torch.randperm(batch_size)]
-        augmented = self.img_aug(input_images)
-        is_aug = (torch.rand(batch_size, device=input_images.device)[:, None, None, None] < self.aug_proba).float()
-        result = augmented * is_aug + permuted * (1 - is_aug)
-        return result
-
-    def _fill_masks_with_gradient(self, masks):
-        batch_size, _, height, width = masks.shape
-        grad = torch.linspace(0, 1, steps=width * 2, device=masks.device, dtype=masks.dtype) \
-            .view(1, 1, 1, -1).expand(batch_size, 1, height * 2, width * 2)
-        grad = self.grad_aug(grad)
-        grad = CenterCrop((height, width))(grad)
-        grad *= masks
-
-        grad_for_min = grad + (1 - masks) * 10
-        grad -= grad_for_min.view(batch_size, -1).min(-1).values[:, None, None, None]
-        grad /= grad.view(batch_size, -1).max(-1).values[:, None, None, None] + 1e-6
-        grad.clamp_(min=0, max=1)
-
-        return grad
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/cnn/resnet.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/cnn/resnet.py
deleted file mode 100644
index 1cb3ac057ee2d52c46fc94685b5d4e698aad8d5f..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/cnn/resnet.py
+++ /dev/null
@@ -1,316 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import logging
-
-import torch.nn as nn
-import torch.utils.checkpoint as cp
-
-from .utils import constant_init, kaiming_init
-
-
-def conv3x3(in_planes, out_planes, stride=1, dilation=1):
-    """3x3 convolution with padding."""
-    return nn.Conv2d(
-        in_planes,
-        out_planes,
-        kernel_size=3,
-        stride=stride,
-        padding=dilation,
-        dilation=dilation,
-        bias=False)
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 stride=1,
-                 dilation=1,
-                 downsample=None,
-                 style='pytorch',
-                 with_cp=False):
-        super(BasicBlock, self).__init__()
-        assert style in ['pytorch', 'caffe']
-        self.conv1 = conv3x3(inplanes, planes, stride, dilation)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-        self.dilation = dilation
-        assert not with_cp
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 stride=1,
-                 dilation=1,
-                 downsample=None,
-                 style='pytorch',
-                 with_cp=False):
-        """Bottleneck block.
-
-        If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
-        it is "caffe", the stride-two layer is the first 1x1 conv layer.
-        """
-        super(Bottleneck, self).__init__()
-        assert style in ['pytorch', 'caffe']
-        if style == 'pytorch':
-            conv1_stride = 1
-            conv2_stride = stride
-        else:
-            conv1_stride = stride
-            conv2_stride = 1
-        self.conv1 = nn.Conv2d(
-            inplanes, planes, kernel_size=1, stride=conv1_stride, bias=False)
-        self.conv2 = nn.Conv2d(
-            planes,
-            planes,
-            kernel_size=3,
-            stride=conv2_stride,
-            padding=dilation,
-            dilation=dilation,
-            bias=False)
-
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(
-            planes, planes * self.expansion, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-        self.dilation = dilation
-        self.with_cp = with_cp
-
-    def forward(self, x):
-
-        def _inner_forward(x):
-            residual = x
-
-            out = self.conv1(x)
-            out = self.bn1(out)
-            out = self.relu(out)
-
-            out = self.conv2(out)
-            out = self.bn2(out)
-            out = self.relu(out)
-
-            out = self.conv3(out)
-            out = self.bn3(out)
-
-            if self.downsample is not None:
-                residual = self.downsample(x)
-
-            out += residual
-
-            return out
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(_inner_forward, x)
-        else:
-            out = _inner_forward(x)
-
-        out = self.relu(out)
-
-        return out
-
-
-def make_res_layer(block,
-                   inplanes,
-                   planes,
-                   blocks,
-                   stride=1,
-                   dilation=1,
-                   style='pytorch',
-                   with_cp=False):
-    downsample = None
-    if stride != 1 or inplanes != planes * block.expansion:
-        downsample = nn.Sequential(
-            nn.Conv2d(
-                inplanes,
-                planes * block.expansion,
-                kernel_size=1,
-                stride=stride,
-                bias=False),
-            nn.BatchNorm2d(planes * block.expansion),
-        )
-
-    layers = []
-    layers.append(
-        block(
-            inplanes,
-            planes,
-            stride,
-            dilation,
-            downsample,
-            style=style,
-            with_cp=with_cp))
-    inplanes = planes * block.expansion
-    for _ in range(1, blocks):
-        layers.append(
-            block(inplanes, planes, 1, dilation, style=style, with_cp=with_cp))
-
-    return nn.Sequential(*layers)
-
-
-class ResNet(nn.Module):
-    """ResNet backbone.
-
-    Args:
-        depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
-        num_stages (int): Resnet stages, normally 4.
-        strides (Sequence[int]): Strides of the first block of each stage.
-        dilations (Sequence[int]): Dilation of each stage.
-        out_indices (Sequence[int]): Output from which stages.
-        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
-            layer is the 3x3 conv layer, otherwise the stride-two layer is
-            the first 1x1 conv layer.
-        frozen_stages (int): Stages to be frozen (all param fixed). -1 means
-            not freezing any parameters.
-        bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze
-            running stats (mean and var).
-        bn_frozen (bool): Whether to freeze weight and bias of BN layers.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed.
-    """
-
-    arch_settings = {
-        18: (BasicBlock, (2, 2, 2, 2)),
-        34: (BasicBlock, (3, 4, 6, 3)),
-        50: (Bottleneck, (3, 4, 6, 3)),
-        101: (Bottleneck, (3, 4, 23, 3)),
-        152: (Bottleneck, (3, 8, 36, 3))
-    }
-
-    def __init__(self,
-                 depth,
-                 num_stages=4,
-                 strides=(1, 2, 2, 2),
-                 dilations=(1, 1, 1, 1),
-                 out_indices=(0, 1, 2, 3),
-                 style='pytorch',
-                 frozen_stages=-1,
-                 bn_eval=True,
-                 bn_frozen=False,
-                 with_cp=False):
-        super(ResNet, self).__init__()
-        if depth not in self.arch_settings:
-            raise KeyError(f'invalid depth {depth} for resnet')
-        assert num_stages >= 1 and num_stages <= 4
-        block, stage_blocks = self.arch_settings[depth]
-        stage_blocks = stage_blocks[:num_stages]
-        assert len(strides) == len(dilations) == num_stages
-        assert max(out_indices) < num_stages
-
-        self.out_indices = out_indices
-        self.style = style
-        self.frozen_stages = frozen_stages
-        self.bn_eval = bn_eval
-        self.bn_frozen = bn_frozen
-        self.with_cp = with_cp
-
-        self.inplanes = 64
-        self.conv1 = nn.Conv2d(
-            3, 64, kernel_size=7, stride=2, padding=3, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
-        self.res_layers = []
-        for i, num_blocks in enumerate(stage_blocks):
-            stride = strides[i]
-            dilation = dilations[i]
-            planes = 64 * 2**i
-            res_layer = make_res_layer(
-                block,
-                self.inplanes,
-                planes,
-                num_blocks,
-                stride=stride,
-                dilation=dilation,
-                style=self.style,
-                with_cp=with_cp)
-            self.inplanes = planes * block.expansion
-            layer_name = f'layer{i + 1}'
-            self.add_module(layer_name, res_layer)
-            self.res_layers.append(layer_name)
-
-        self.feat_dim = block.expansion * 64 * 2**(len(stage_blocks) - 1)
-
-    def init_weights(self, pretrained=None):
-        if isinstance(pretrained, str):
-            logger = logging.getLogger()
-            from ..runner import load_checkpoint
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            for m in self.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, nn.BatchNorm2d):
-                    constant_init(m, 1)
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-        outs = []
-        for i, layer_name in enumerate(self.res_layers):
-            res_layer = getattr(self, layer_name)
-            x = res_layer(x)
-            if i in self.out_indices:
-                outs.append(x)
-        if len(outs) == 1:
-            return outs[0]
-        else:
-            return tuple(outs)
-
-    def train(self, mode=True):
-        super(ResNet, self).train(mode)
-        if self.bn_eval:
-            for m in self.modules():
-                if isinstance(m, nn.BatchNorm2d):
-                    m.eval()
-                    if self.bn_frozen:
-                        for params in m.parameters():
-                            params.requires_grad = False
-        if mode and self.frozen_stages >= 0:
-            for param in self.conv1.parameters():
-                param.requires_grad = False
-            for param in self.bn1.parameters():
-                param.requires_grad = False
-            self.bn1.eval()
-            self.bn1.weight.requires_grad = False
-            self.bn1.bias.requires_grad = False
-            for i in range(1, self.frozen_stages + 1):
-                mod = getattr(self, f'layer{i}')
-                mod.eval()
-                for param in mod.parameters():
-                    param.requires_grad = False
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/fileio/parse.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/fileio/parse.py
deleted file mode 100644
index f60f0d611b8d75692221d0edd7dc993b0a6445c9..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/fileio/parse.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-
-from io import StringIO
-
-from .file_client import FileClient
-
-
-def list_from_file(filename,
-                   prefix='',
-                   offset=0,
-                   max_num=0,
-                   encoding='utf-8',
-                   file_client_args=None):
-    """Load a text file and parse the content as a list of strings.
-
-    Note:
-        In v1.3.16 and later, ``list_from_file`` supports loading a text file
-        which can be storaged in different backends and parsing the content as
-        a list for strings.
-
-    Args:
-        filename (str): Filename.
-        prefix (str): The prefix to be inserted to the beginning of each item.
-        offset (int): The offset of lines.
-        max_num (int): The maximum number of lines to be read,
-            zeros and negatives mean no limitation.
-        encoding (str): Encoding used to open the file. Default utf-8.
-        file_client_args (dict, optional): Arguments to instantiate a
-            FileClient. See :class:`mmcv.fileio.FileClient` for details.
-            Default: None.
-
-    Examples:
-        >>> list_from_file('/path/of/your/file')  # disk
-        ['hello', 'world']
-        >>> list_from_file('s3://path/of/your/file')  # ceph or petrel
-        ['hello', 'world']
-
-    Returns:
-        list[str]: A list of strings.
-    """
-    cnt = 0
-    item_list = []
-    file_client = FileClient.infer_client(file_client_args, filename)
-    with StringIO(file_client.get_text(filename, encoding)) as f:
-        for _ in range(offset):
-            f.readline()
-        for line in f:
-            if 0 < max_num <= cnt:
-                break
-            item_list.append(prefix + line.rstrip('\n\r'))
-            cnt += 1
-    return item_list
-
-
-def dict_from_file(filename,
-                   key_type=str,
-                   encoding='utf-8',
-                   file_client_args=None):
-    """Load a text file and parse the content as a dict.
-
-    Each line of the text file will be two or more columns split by
-    whitespaces or tabs. The first column will be parsed as dict keys, and
-    the following columns will be parsed as dict values.
-
-    Note:
-        In v1.3.16 and later, ``dict_from_file`` supports loading a text file
-        which can be storaged in different backends and parsing the content as
-        a dict.
-
-    Args:
-        filename(str): Filename.
-        key_type(type): Type of the dict keys. str is user by default and
-            type conversion will be performed if specified.
-        encoding (str): Encoding used to open the file. Default utf-8.
-        file_client_args (dict, optional): Arguments to instantiate a
-            FileClient. See :class:`mmcv.fileio.FileClient` for details.
-            Default: None.
-
-    Examples:
-        >>> dict_from_file('/path/of/your/file')  # disk
-        {'key1': 'value1', 'key2': 'value2'}
-        >>> dict_from_file('s3://path/of/your/file')  # ceph or petrel
-        {'key1': 'value1', 'key2': 'value2'}
-
-    Returns:
-        dict: The parsed contents.
-    """
-    mapping = {}
-    file_client = FileClient.infer_client(file_client_args, filename)
-    with StringIO(file_client.get_text(filename, encoding)) as f:
-        for line in f:
-            items = line.rstrip('\n').split()
-            assert len(items) >= 2
-            key = key_type(items[0])
-            val = items[1:] if len(items) > 2 else items[1]
-            mapping[key] = val
-    return mapping
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/optimizer/__init__.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/optimizer/__init__.py
deleted file mode 100644
index 53c34d0470992cbc374f29681fdd00dc0e57968d..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/optimizer/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .builder import (OPTIMIZER_BUILDERS, OPTIMIZERS, build_optimizer,
-                      build_optimizer_constructor)
-from .default_constructor import DefaultOptimizerConstructor
-
-__all__ = [
-    'OPTIMIZER_BUILDERS', 'OPTIMIZERS', 'DefaultOptimizerConstructor',
-    'build_optimizer', 'build_optimizer_constructor'
-]
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/backbones/mobilenet_v2.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/backbones/mobilenet_v2.py
deleted file mode 100644
index ab6b3791692a0d1b5da3601875711710b7bd01ba..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/backbones/mobilenet_v2.py
+++ /dev/null
@@ -1,180 +0,0 @@
-import logging
-
-import torch.nn as nn
-from annotator.uniformer.mmcv.cnn import ConvModule, constant_init, kaiming_init
-from annotator.uniformer.mmcv.runner import load_checkpoint
-from torch.nn.modules.batchnorm import _BatchNorm
-
-from ..builder import BACKBONES
-from ..utils import InvertedResidual, make_divisible
-
-
-@BACKBONES.register_module()
-class MobileNetV2(nn.Module):
-    """MobileNetV2 backbone.
-
-    Args:
-        widen_factor (float): Width multiplier, multiply number of
-            channels in each layer by this amount. Default: 1.0.
-        strides (Sequence[int], optional): Strides of the first block of each
-            layer. If not specified, default config in ``arch_setting`` will
-            be used.
-        dilations (Sequence[int]): Dilation of each layer.
-        out_indices (None or Sequence[int]): Output from which stages.
-            Default: (7, ).
-        frozen_stages (int): Stages to be frozen (all param fixed).
-            Default: -1, which means not freezing any parameters.
-        conv_cfg (dict): Config dict for convolution layer.
-            Default: None, which means using conv2d.
-        norm_cfg (dict): Config dict for normalization layer.
-            Default: dict(type='BN').
-        act_cfg (dict): Config dict for activation layer.
-            Default: dict(type='ReLU6').
-        norm_eval (bool): Whether to set norm layers to eval mode, namely,
-            freeze running stats (mean and var). Note: Effect on Batch Norm
-            and its variants only. Default: False.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-    """
-
-    # Parameters to build layers. 3 parameters are needed to construct a
-    # layer, from left to right: expand_ratio, channel, num_blocks.
-    arch_settings = [[1, 16, 1], [6, 24, 2], [6, 32, 3], [6, 64, 4],
-                     [6, 96, 3], [6, 160, 3], [6, 320, 1]]
-
-    def __init__(self,
-                 widen_factor=1.,
-                 strides=(1, 2, 2, 2, 1, 2, 1),
-                 dilations=(1, 1, 1, 1, 1, 1, 1),
-                 out_indices=(1, 2, 4, 6),
-                 frozen_stages=-1,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU6'),
-                 norm_eval=False,
-                 with_cp=False):
-        super(MobileNetV2, self).__init__()
-        self.widen_factor = widen_factor
-        self.strides = strides
-        self.dilations = dilations
-        assert len(strides) == len(dilations) == len(self.arch_settings)
-        self.out_indices = out_indices
-        for index in out_indices:
-            if index not in range(0, 7):
-                raise ValueError('the item in out_indices must in '
-                                 f'range(0, 8). But received {index}')
-
-        if frozen_stages not in range(-1, 7):
-            raise ValueError('frozen_stages must be in range(-1, 7). '
-                             f'But received {frozen_stages}')
-        self.out_indices = out_indices
-        self.frozen_stages = frozen_stages
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-        self.norm_eval = norm_eval
-        self.with_cp = with_cp
-
-        self.in_channels = make_divisible(32 * widen_factor, 8)
-
-        self.conv1 = ConvModule(
-            in_channels=3,
-            out_channels=self.in_channels,
-            kernel_size=3,
-            stride=2,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-        self.layers = []
-
-        for i, layer_cfg in enumerate(self.arch_settings):
-            expand_ratio, channel, num_blocks = layer_cfg
-            stride = self.strides[i]
-            dilation = self.dilations[i]
-            out_channels = make_divisible(channel * widen_factor, 8)
-            inverted_res_layer = self.make_layer(
-                out_channels=out_channels,
-                num_blocks=num_blocks,
-                stride=stride,
-                dilation=dilation,
-                expand_ratio=expand_ratio)
-            layer_name = f'layer{i + 1}'
-            self.add_module(layer_name, inverted_res_layer)
-            self.layers.append(layer_name)
-
-    def make_layer(self, out_channels, num_blocks, stride, dilation,
-                   expand_ratio):
-        """Stack InvertedResidual blocks to build a layer for MobileNetV2.
-
-        Args:
-            out_channels (int): out_channels of block.
-            num_blocks (int): Number of blocks.
-            stride (int): Stride of the first block.
-            dilation (int): Dilation of the first block.
-            expand_ratio (int): Expand the number of channels of the
-                hidden layer in InvertedResidual by this ratio.
-        """
-        layers = []
-        for i in range(num_blocks):
-            layers.append(
-                InvertedResidual(
-                    self.in_channels,
-                    out_channels,
-                    stride if i == 0 else 1,
-                    expand_ratio=expand_ratio,
-                    dilation=dilation if i == 0 else 1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg,
-                    act_cfg=self.act_cfg,
-                    with_cp=self.with_cp))
-            self.in_channels = out_channels
-
-        return nn.Sequential(*layers)
-
-    def init_weights(self, pretrained=None):
-        if isinstance(pretrained, str):
-            logger = logging.getLogger()
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            for m in self.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
-                    constant_init(m, 1)
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def forward(self, x):
-        x = self.conv1(x)
-
-        outs = []
-        for i, layer_name in enumerate(self.layers):
-            layer = getattr(self, layer_name)
-            x = layer(x)
-            if i in self.out_indices:
-                outs.append(x)
-
-        if len(outs) == 1:
-            return outs[0]
-        else:
-            return tuple(outs)
-
-    def _freeze_stages(self):
-        if self.frozen_stages >= 0:
-            for param in self.conv1.parameters():
-                param.requires_grad = False
-        for i in range(1, self.frozen_stages + 1):
-            layer = getattr(self, f'layer{i}')
-            layer.eval()
-            for param in layer.parameters():
-                param.requires_grad = False
-
-    def train(self, mode=True):
-        super(MobileNetV2, self).train(mode)
-        self._freeze_stages()
-        if mode and self.norm_eval:
-            for m in self.modules():
-                if isinstance(m, _BatchNorm):
-                    m.eval()
diff --git a/spaces/PKUWilliamYang/StyleGANEX/models/stylegan2/op/conv2d_gradfix.py b/spaces/PKUWilliamYang/StyleGANEX/models/stylegan2/op/conv2d_gradfix.py
deleted file mode 100644
index c4485b11991c5426939e87e6c363307eb9017438..0000000000000000000000000000000000000000
--- a/spaces/PKUWilliamYang/StyleGANEX/models/stylegan2/op/conv2d_gradfix.py
+++ /dev/null
@@ -1,227 +0,0 @@
-import contextlib
-import warnings
-
-import torch
-from torch import autograd
-from torch.nn import functional as F
-
-enabled = True
-weight_gradients_disabled = False
-
-
-@contextlib.contextmanager
-def no_weight_gradients():
-    global weight_gradients_disabled
-
-    old = weight_gradients_disabled
-    weight_gradients_disabled = True
-    yield
-    weight_gradients_disabled = old
-    
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
-    if could_use_op(input):
-        return conv2d_gradfix(
-            transpose=False,
-            weight_shape=weight.shape,
-            stride=stride,
-            padding=padding,
-            output_padding=0,
-            dilation=dilation,
-            groups=groups,
-        ).apply(input, weight, bias)
-
-    return F.conv2d(
-        input=input,
-        weight=weight,
-        bias=bias,
-        stride=stride,
-        padding=padding,
-        dilation=dilation,
-        groups=groups,
-    )
-
-
-def conv_transpose2d(
-        input,
-        weight,
-        bias=None,
-        stride=1,
-        padding=0,
-        output_padding=0,
-        groups=1,
-        dilation=1,
-):
-    if could_use_op(input):
-        return conv2d_gradfix(
-            transpose=True,
-            weight_shape=weight.shape,
-            stride=stride,
-            padding=padding,
-            output_padding=output_padding,
-            groups=groups,
-            dilation=dilation,
-        ).apply(input, weight, bias)
-
-    return F.conv_transpose2d(
-        input=input,
-        weight=weight,
-        bias=bias,
-        stride=stride,
-        padding=padding,
-        output_padding=output_padding,
-        dilation=dilation,
-        groups=groups,
-    )
-
-
-def could_use_op(input):
-    if (not enabled) or (not torch.backends.cudnn.enabled):
-        return False
-
-    if input.device.type != "cuda":
-        return False
-
-    if any(torch.__version__.startswith(x) for x in ["1.7.", "1.8."]):
-        return True
-
-    warnings.warn(
-        f"conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d()."
-    )
-
-    return False
-
-
-def ensure_tuple(xs, ndim):
-    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
-
-    return xs
-
-
-conv2d_gradfix_cache = dict()
-
-
-def conv2d_gradfix(
-        transpose, weight_shape, stride, padding, output_padding, dilation, groups
-):
-    ndim = 2
-    weight_shape = tuple(weight_shape)
-    stride = ensure_tuple(stride, ndim)
-    padding = ensure_tuple(padding, ndim)
-    output_padding = ensure_tuple(output_padding, ndim)
-    dilation = ensure_tuple(dilation, ndim)
-
-    key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
-    if key in conv2d_gradfix_cache:
-        return conv2d_gradfix_cache[key]
-
-    common_kwargs = dict(
-        stride=stride, padding=padding, dilation=dilation, groups=groups
-    )
-
-    def calc_output_padding(input_shape, output_shape):
-        if transpose:
-            return [0, 0]
-
-        return [
-            input_shape[i + 2]
-            - (output_shape[i + 2] - 1) * stride[i]
-            - (1 - 2 * padding[i])
-            - dilation[i] * (weight_shape[i + 2] - 1)
-            for i in range(ndim)
-        ]
-
-    class Conv2d(autograd.Function):
-        @staticmethod
-        def forward(ctx, input, weight, bias):
-            if not transpose:
-                out = F.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
-
-            else:
-                out = F.conv_transpose2d(
-                    input=input,
-                    weight=weight,
-                    bias=bias,
-                    output_padding=output_padding,
-                    **common_kwargs,
-                )
-
-            ctx.save_for_backward(input, weight)
-
-            return out
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            input, weight = ctx.saved_tensors
-            grad_input, grad_weight, grad_bias = None, None, None
-
-            if ctx.needs_input_grad[0]:
-                p = calc_output_padding(
-                    input_shape=input.shape, output_shape=grad_output.shape
-                )
-                grad_input = conv2d_gradfix(
-                    transpose=(not transpose),
-                    weight_shape=weight_shape,
-                    output_padding=p,
-                    **common_kwargs,
-                ).apply(grad_output, weight, None)
-
-            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
-                grad_weight = Conv2dGradWeight.apply(grad_output, input)
-
-            if ctx.needs_input_grad[2]:
-                grad_bias = grad_output.sum((0, 2, 3))
-
-            return grad_input, grad_weight, grad_bias
-
-    class Conv2dGradWeight(autograd.Function):
-        @staticmethod
-        def forward(ctx, grad_output, input):
-            op = torch._C._jit_get_operation(
-                "aten::cudnn_convolution_backward_weight"
-                if not transpose
-                else "aten::cudnn_convolution_transpose_backward_weight"
-            )
-            flags = [
-                torch.backends.cudnn.benchmark,
-                torch.backends.cudnn.deterministic,
-                torch.backends.cudnn.allow_tf32,
-            ]
-            grad_weight = op(
-                weight_shape,
-                grad_output,
-                input,
-                padding,
-                stride,
-                dilation,
-                groups,
-                *flags,
-            )
-            ctx.save_for_backward(grad_output, input)
-
-            return grad_weight
-
-        @staticmethod
-        def backward(ctx, grad_grad_weight):
-            grad_output, input = ctx.saved_tensors
-            grad_grad_output, grad_grad_input = None, None
-
-            if ctx.needs_input_grad[0]:
-                grad_grad_output = Conv2d.apply(input, grad_grad_weight, None)
-
-            if ctx.needs_input_grad[1]:
-                p = calc_output_padding(
-                    input_shape=input.shape, output_shape=grad_output.shape
-                )
-                grad_grad_input = conv2d_gradfix(
-                    transpose=(not transpose),
-                    weight_shape=weight_shape,
-                    output_padding=p,
-                    **common_kwargs,
-                ).apply(grad_output, grad_grad_weight, None)
-
-            return grad_grad_output, grad_grad_input
-
-    conv2d_gradfix_cache[key] = Conv2d
-
-    return Conv2d
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/elisp/compile-tree-il.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/elisp/compile-tree-il.go
deleted file mode 100644
index ebd9034ade2371d1ebdb37d094ecd457f50ac67a..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/elisp/compile-tree-il.go and /dev/null differ
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/env.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/env.py
deleted file mode 100644
index e3f0d92529e193e6d8339419bcd9bed7901a7769..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/env.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-"""This file holding some environment constant for sharing by other files."""
-
-import os.path as osp
-import subprocess
-import sys
-from collections import defaultdict
-
-import cv2
-import torch
-
-import annotator.uniformer.mmcv as mmcv
-from .parrots_wrapper import get_build_config
-
-
-def collect_env():
-    """Collect the information of the running environments.
-
-    Returns:
-        dict: The environment information. The following fields are contained.
-
-            - sys.platform: The variable of ``sys.platform``.
-            - Python: Python version.
-            - CUDA available: Bool, indicating if CUDA is available.
-            - GPU devices: Device type of each GPU.
-            - CUDA_HOME (optional): The env var ``CUDA_HOME``.
-            - NVCC (optional): NVCC version.
-            - GCC: GCC version, "n/a" if GCC is not installed.
-            - PyTorch: PyTorch version.
-            - PyTorch compiling details: The output of \
-                ``torch.__config__.show()``.
-            - TorchVision (optional): TorchVision version.
-            - OpenCV: OpenCV version.
-            - MMCV: MMCV version.
-            - MMCV Compiler: The GCC version for compiling MMCV ops.
-            - MMCV CUDA Compiler: The CUDA version for compiling MMCV ops.
-    """
-    env_info = {}
-    env_info['sys.platform'] = sys.platform
-    env_info['Python'] = sys.version.replace('\n', '')
-
-    cuda_available = torch.cuda.is_available()
-    env_info['CUDA available'] = cuda_available
-
-    if cuda_available:
-        devices = defaultdict(list)
-        for k in range(torch.cuda.device_count()):
-            devices[torch.cuda.get_device_name(k)].append(str(k))
-        for name, device_ids in devices.items():
-            env_info['GPU ' + ','.join(device_ids)] = name
-
-        from annotator.uniformer.mmcv.utils.parrots_wrapper import _get_cuda_home
-        CUDA_HOME = _get_cuda_home()
-        env_info['CUDA_HOME'] = CUDA_HOME
-
-        if CUDA_HOME is not None and osp.isdir(CUDA_HOME):
-            try:
-                nvcc = osp.join(CUDA_HOME, 'bin/nvcc')
-                nvcc = subprocess.check_output(
-                    f'"{nvcc}" -V | tail -n1', shell=True)
-                nvcc = nvcc.decode('utf-8').strip()
-            except subprocess.SubprocessError:
-                nvcc = 'Not Available'
-            env_info['NVCC'] = nvcc
-
-    try:
-        gcc = subprocess.check_output('gcc --version | head -n1', shell=True)
-        gcc = gcc.decode('utf-8').strip()
-        env_info['GCC'] = gcc
-    except subprocess.CalledProcessError:  # gcc is unavailable
-        env_info['GCC'] = 'n/a'
-
-    env_info['PyTorch'] = torch.__version__
-    env_info['PyTorch compiling details'] = get_build_config()
-
-    try:
-        import torchvision
-        env_info['TorchVision'] = torchvision.__version__
-    except ModuleNotFoundError:
-        pass
-
-    env_info['OpenCV'] = cv2.__version__
-
-    env_info['MMCV'] = mmcv.__version__
-
-    try:
-        from annotator.uniformer.mmcv.ops import get_compiler_version, get_compiling_cuda_version
-    except ModuleNotFoundError:
-        env_info['MMCV Compiler'] = 'n/a'
-        env_info['MMCV CUDA Compiler'] = 'n/a'
-    else:
-        env_info['MMCV Compiler'] = get_compiler_version()
-        env_info['MMCV CUDA Compiler'] = get_compiling_cuda_version()
-
-    return env_info
diff --git a/spaces/Ppranathi/chatty-chat/chat.py b/spaces/Ppranathi/chatty-chat/chat.py
deleted file mode 100644
index 851711070d50a787ff6856b9f594c0a36a0249a8..0000000000000000000000000000000000000000
--- a/spaces/Ppranathi/chatty-chat/chat.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import os
-import openai
-import gradio as gr
-
-#if you have OpenAI API key as an environment variable, enable the below
-#openai.api_key = os.getenv("OPENAI_API_KEY")
-
-#if you have OpenAI API key as a string, enable the below
-openai.api_key = "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"
-
-start_sequence = "\nAI:"
-restart_sequence = "\nHuman: "
-
-prompt = "The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.\n\nHuman: Hello, who are you?\nAI: I am an AI created by OpenAI. How can I help you today?\nHuman: "
-
-def openai_create(prompt):
-
-    response = openai.Completion.create(
-    model="text-davinci-003",
-    prompt=prompt,
-    temperature=0.9,
-    max_tokens=250,
-    top_p=1,
-    frequency_penalty=0,
-    presence_penalty=0.6,
-    stop=[" Human:", " AI:"]
-    )
-
-    return response.choices[0].text
-
-
-
-def chatgpt_clone(input, history):
-    history = history or []
-    s = list(sum(history, ()))
-    s.append(input)
-    inp = ' '.join(s)
-    output = openai_create(inp)
-    history.append((input, output))
-    return history, history
-
-
-block = gr.Blocks()
-
-
-with block:
-    gr.Markdown("""<h1><center>Build Yo'own ChatGPT with OpenAI API & Gradio</center></h1>
-    """)
-    chatbot = gr.Chatbot()
-    message = gr.Textbox(placeholder=prompt)
-    state = gr.State()
-    submit = gr.Button("SEND")
-    submit.click(chatgpt_clone, inputs=[message, state], outputs=[chatbot, state])
-
-block.launch(debug = True, share = True)
diff --git a/spaces/Pritish100/AA0_LeLO_v_2.0/app.py b/spaces/Pritish100/AA0_LeLO_v_2.0/app.py
deleted file mode 100644
index 9ab90580c7d9e619b56eb603b87d8e56493054fb..0000000000000000000000000000000000000000
--- a/spaces/Pritish100/AA0_LeLO_v_2.0/app.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import os
-import gradio as gr
-from PIL import Image
-
-os.system("pip install pix2tex")
-from pix2tex import cli as pix2tex
-
-# Load model
-model = pix2tex.LatexOCR()
-
-
-def inference(path):
-    img = Image.open(path)
-    output = model(img)
-    print("Model output:", output)
-    return output
-
-
-# Front end
-title = "AAO_LeLO_V2.0"
-description = "<div> Photo Lao Aur Latex Code LeLo 😫 <br><br> Take a screenshot of the equation and use this application to convert it into LaTeX code. 😎  To use it, simply upload your screenshot/equation image, or click one of the examples to load them. To verify the results, copy & paste the output in <a href='https://quicklatex.com/' target='_blank'>Quick LaTeX</a>. Read more at the links below. If ERROR, please try again.</div>"
-article = "<p style='text-align: center'><a href='https://lukas-blecher.github.io/LaTeX-OCR/' target='_blank'>pix2tex: Using a ViT to convert images of equations into LaTeX code</a> | <a href='https://github.com/lukas-blecher/LaTeX-OCR' target='_blank'>Github</a></p>"
-
-
-# UI
-demo = gr.Interface(
-    inference,
-    title=title,
-    description=description,
-    article=article,
-    inputs=gr.inputs.Image(
-        type="filepath", label="Input: Image of your equation you want to covert."
-    ),
-    outputs=gr.outputs.Textbox(type="text", label="Output: Converted LaTeX code."),
-    examples=["./eqn1.png", "./eqn2.png", "./eqn3.png"],
-    allow_flagging="never",
-    analytics_enabled=False,
-)
-demo.launch(enable_queue=True)
diff --git "a/spaces/Qiukai/gpt/crazy_functions/\350\260\267\346\255\214\346\243\200\347\264\242\345\260\217\345\212\251\346\211\213.py" "b/spaces/Qiukai/gpt/crazy_functions/\350\260\267\346\255\214\346\243\200\347\264\242\345\260\217\345\212\251\346\211\213.py"
deleted file mode 100644
index 94ef256327f6740cdaddc6f5ecea5852a9210163..0000000000000000000000000000000000000000
--- "a/spaces/Qiukai/gpt/crazy_functions/\350\260\267\346\255\214\346\243\200\347\264\242\345\260\217\345\212\251\346\211\213.py"
+++ /dev/null
@@ -1,106 +0,0 @@
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from toolbox import CatchException, report_execption, write_results_to_file
-from toolbox import update_ui
-
-def get_meta_information(url, chatbot, history):
-    import requests
-    import arxiv
-    import difflib
-    from bs4 import BeautifulSoup
-    from toolbox import get_conf
-    proxies, = get_conf('proxies')
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Safari/537.36',
-    }
-    # 发送 GET 请求
-    response = requests.get(url, proxies=proxies, headers=headers)
-
-    # 解析网页内容
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    def string_similar(s1, s2):
-        return difflib.SequenceMatcher(None, s1, s2).quick_ratio()
-
-    profile = []
-    # 获取所有文章的标题和作者
-    for result in soup.select(".gs_ri"):
-        title = result.a.text.replace('\n', ' ').replace('  ', ' ')
-        author = result.select_one(".gs_a").text
-        try:
-            citation = result.select_one(".gs_fl > a[href*='cites']").text  # 引用次数是链接中的文本，直接取出来
-        except:
-            citation = 'cited by 0'
-        abstract = result.select_one(".gs_rs").text.strip()  # 摘要在 .gs_rs 中的文本，需要清除首尾空格
-        search = arxiv.Search(
-            query = title,
-            max_results = 1,
-            sort_by = arxiv.SortCriterion.Relevance,
-        )
-        paper = next(search.results())
-        if string_similar(title, paper.title) > 0.90: # same paper
-            abstract = paper.summary.replace('\n', ' ')
-            is_paper_in_arxiv = True
-        else:   # different paper
-            abstract = abstract
-            is_paper_in_arxiv = False
-        paper = next(search.results())
-        print(title)
-        print(author)
-        print(citation)
-        profile.append({
-            'title':title,
-            'author':author,
-            'citation':citation,
-            'abstract':abstract,
-            'is_paper_in_arxiv':is_paper_in_arxiv,
-        })
-
-        chatbot[-1] = [chatbot[-1][0], title + f'\n\n是否在arxiv中（不在arxiv中无法获取完整摘要）:{is_paper_in_arxiv}\n\n' + abstract]
-        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-    return profile
-
-@CatchException
-def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "分析用户提供的谷歌学术（google scholar）搜索页面中，出现的所有文章: binary-husky，插件初始化中..."])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import arxiv
-        from bs4 import BeautifulSoup
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4 arxiv```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
-
-    if len(meta_paper_info_list[:10]) > 0:
-        i_say = "下面是一些学术文献的数据，请从中提取出以下内容。" + \
-        "1、英文题目；2、中文题目翻译；3、作者；4、arxiv公开（is_paper_in_arxiv）；4、引用数量（cite）；5、中文摘要翻译。" + \
-        f"以下是信息源：{str(meta_paper_info_list[:10])}" 
-
-        inputs_show_user = f"请分析此页面中出现的所有文章：{txt}"
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say, inputs_show_user=inputs_show_user, 
-            llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
-            sys_prompt="你是一个学术翻译，请从数据中提取信息。你必须使用Markdown格式。你必须逐个文献进行处理。"
-        )
-
-        history.extend([ "第一批", gpt_say ])
-        meta_paper_info_list = meta_paper_info_list[10:]
-
-    chatbot.append(["状态？", "已经全部完成"])
-    msg = '正常'
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-    res = write_results_to_file(history)
-    chatbot.append(("完成了吗？", res)); 
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
diff --git a/spaces/RASMUS/Youtube-videos-with-crosslingual-transcriptions/app.py b/spaces/RASMUS/Youtube-videos-with-crosslingual-transcriptions/app.py
deleted file mode 100644
index 7d833fcb8198dd3c24256f7f58642dc63e7aec30..0000000000000000000000000000000000000000
--- a/spaces/RASMUS/Youtube-videos-with-crosslingual-transcriptions/app.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import gradio as gr
-from pathlib import Path
-import time
-
-import pandas as pd
-import re
-import time
-import os
-import requests
-import json
-
-import whisper
-from pytube import YouTube
-import psutil
-import torch
-
-
-# is cuda available?
-
-
-num_cores = psutil.cpu_count()
-os.environ["OMP_NUM_THREADS"] = f"{num_cores}"
-headers = {'Authorization': os.environ['DeepL_API_KEY']}
-
-device = "cpu"#torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print("DEVICE IS: ")
-print(device)
-
-asr_model_base = whisper.load_model("base", device=device)
-asr_model_small = whisper.load_model("small", device=device)
-whisper_models_dict = {
-    'base': asr_model_base,
-    'small': asr_model_small
-}
-
-whisper_models = ["base", "small"]
-
-transcribe_options = dict(beam_size=3, best_of=3, without_timestamps=False)
-
-source_languages  = {
-"Afrikaans":"af",
-"Amharic":"am",
-"Arabic":"ar",
-"Asturian ":"st",
-"Azerbaijani":"az",
-"Bashkir":"ba",
-"Belarusian":"be",
-"Bulgarian":"bg",
-"Bengali":"bn",
-"Breton":"br",
-"Bosnian":"bs",
-"Catalan; Valencian":"ca",
-"Cebuano":"eb",
-"Czech":"cs",
-"Welsh":"cy",
-"Danish":"da",
-"German":"de",
-"Greeek":"el",
-"English":"en",
-"Spanish":"es",
-"Estonian":"et",
-"Persian":"fa",
-"Fulah":"ff",
-"Finnish":"fi",
-"French":"fr",
-"Western Frisian":"fy",
-"Irish":"ga",
-"Gaelic; Scottish Gaelic":"gd",
-"Galician":"gl",
-"Gujarati":"gu",
-"Hausa":"ha",
-"Hebrew":"he",
-"Hindi":"hi",
-"Croatian":"hr",
-"Haitian; Haitian Creole":"ht",
-"Hungarian":"hu",
-"Armenian":"hy",
-"Indonesian":"id",
-"Igbo":"ig",
-"Iloko":"lo",
-"Icelandic":"is",
-"Italian":"it",
-"Japanese":"ja",
-"Javanese":"jv",
-"Georgian":"ka",
-"Kazakh":"kk",
-"Central Khmer":"km",
-"Kannada":"kn",
-"Korean":"ko",
-"Luxembourgish; Letzeburgesch":"lb",
-"Ganda":"lg",
-"Lingala":"ln",
-"Lao":"lo",
-"Lithuanian":"lt",
-"Latvian":"lv",
-"Malagasy":"mg",
-"Macedonian":"mk",
-"Malayalam":"ml",
-"Mongolian":"mn",
-"Marathi":"mr",
-"Malay":"ms",
-"Burmese":"my",
-"Nepali":"ne",
-"Dutch; Flemish":"nl",
-"Norwegian":"no",
-"Northern Sotho":"ns",
-"Occitan (post 1500)":"oc",
-"Oriya":"or",
-"Panjabi; Punjabi":"pa",
-"Polish":"pl",
-"Pushto; Pashto":"ps",
-"Portuguese":"pt",
-"Romanian; Moldavian; Moldovan":"ro",
-"Russian":"ru",
-"Sindhi":"sd",
-"Sinhala; Sinhalese":"si",
-"Slovak":"sk",
-"Slovenian":"sl",
-"Somali":"so",
-"Albanian":"sq",
-"Serbian":"sr",
-"Swati":"ss",
-"Sundanese":"su",
-"Swedish":"sv",
-"Swahili":"sw",
-"Tamil":"ta",
-"Thai":"th",
-"Tagalog":"tl",
-"Tswana":"tn",
-"Turkish":"tr",
-"Ukrainian":"uk",
-"Urdu":"ur",
-"Uzbek":"uz",
-"Vietnamese":"vi",
-"Wolof":"wo",
-"Xhosa":"xh",
-"Yiddish":"yi",
-"Yoruba":"yo",
-"Chinese":"zh",
-"Zulu":"zu",
-"Let the model analyze": "Let the model analyze"
-}
-
-DeepL_language_codes_for_translation = {
-"Bulgarian": "BG",
-"Czech": "CS",
-"Danish": "DA",
-"German": "DE",
-"Greek": "EL",
-"English": "EN",
-"Spanish": "ES",
-"Estonian": "ET",
-"Finnish": "FI",
-"French": "FR",
-"Hungarian": "HU",
-"Indonesian": "ID",
-"Italian": "IT",
-"Japanese": "JA",
-"Lithuanian": "LT",
-"Latvian": "LV",
-"Dutch": "NL",
-"Polish": "PL",
-"Portuguese": "PT",
-"Romanian": "RO",
-"Russian": "RU",
-"Slovak": "SK",
-"Slovenian": "SL",
-"Swedish": "SV",
-"Turkish": "TR",
-"Ukrainian": "UK",
-"Chinese": "ZH"
-}
-
-
-source_language_list = [key[0] for key in source_languages.items()]
-translation_models_list = [key[0] for key in DeepL_language_codes_for_translation.items()]
-
-  
-videos_out_path = Path("./videos_out")
-videos_out_path.mkdir(parents=True, exist_ok=True)
-
-def get_youtube(video_url):
-    yt = YouTube(video_url)
-    abs_video_path = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first().download()
-    print("LADATATTU POLKUUN")
-    print(abs_video_path)
-    
-    return abs_video_path
-
-def speech_to_text(video_file_path, selected_translation_lang, whisper_model):
-    """
-    # Youtube with translated subtitles using OpenAI Whisper and Opus-MT models.
-    # Currently supports only English audio
-    This space allows you to:
-    1. Download youtube video with a given url
-    2. Watch it in the first video component
-    3. Run automatic speech recognition on the video using fast Whisper models
-    4. Translate the recognized transcriptions to 26 languages supported by deepL (If source language not supported this will return original transciption)
-    5. Burn the translations to the original video and watch the video in the 2nd video component
-    
-    Speech Recognition is based on models from OpenAI Whisper https://github.com/openai/whisper
-    This space is using c++ implementation by https://github.com/ggerganov/whisper.cpp
-    """
-    
-    if(video_file_path == None):
-        raise ValueError("Error no video input")
-    print(video_file_path)
-    try:
-        audio = whisper.load_audio(video_file_path)
-    except Exception as e:
-        raise RuntimeError("Error converting video to audio")
-
-    last_time = time.time()
-
-    try:
-        print(f'Transcribing via local model')
-        
-        transcribe_options = dict(beam_size=3, best_of=3, without_timestamps=False)
-        transcription = whisper_models_dict.get(whisper_model).transcribe(audio, **transcribe_options)
-        
-        df = pd.DataFrame(columns=['start','end','text'])
-
-        
-
-        for i,segment in enumerate(transcription['segments']):
-            new_row = {'start': segment['start'],
-            'end': segment['end'],
-            'text': segment['text']
-                            }
-            df = df.append(new_row, ignore_index=True)
-        
-        return (df)
-    except Exception as e:
-        raise RuntimeError("Error Running inference with local model", e)
-
-
-
-def translate_transcriptions(df, selected_translation_lang_2):
-    if selected_translation_lang_2 is None:
-            selected_translation_lang_2 = 'English'
-    df.reset_index(inplace=True)
-    
-    print("start_translation")
-    translations = []
-    
-    
-
-    text_combined = ""
-    for i, sentence in enumerate(df['text']):
-        if i == 0:
-            text_combined = sentence
-        else:
-            text_combined = text_combined + '\n' + sentence
-
-    data = {'text': text_combined,
-    'tag_spitting': 'xml',
-    'target_lang': DeepL_language_codes_for_translation.get(selected_translation_lang_2)
-           }
-    try:
-        response = requests.post('https://api-free.deepl.com/v2/translate', headers=headers, data=data)
-    
-        # Print the response from the server
-        translated_sentences = json.loads(response.text)
-        translated_sentences = translated_sentences['translations'][0]['text'].split('\n')
-        df['translation'] = translated_sentences
-    except Exception as e:
-        print(e)
-        df['translation'] = df['text']
-        
-    print("translations done")
-
-    return df
-
-def create_srt_and_burn(df, video_in):
-    
-    print("Starting creation of video wit srt")
-    
-       
-    with open('testi.srt','w', encoding="utf-8") as file:
-        for i in range(len(df)):
-            file.write(str(i+1))
-            file.write('\n')
-            start = df.iloc[i]['start']
-           
-    
-            milliseconds = round(start * 1000.0)
-
-            hours = milliseconds // 3_600_000
-            milliseconds -= hours * 3_600_000
-
-            minutes = milliseconds // 60_000
-            milliseconds -= minutes * 60_000
-
-            seconds = milliseconds // 1_000
-            milliseconds -= seconds * 1_000
-
-            file.write(f"{hours}:{minutes:02d}:{seconds:02d}.{milliseconds:03d}")
-            
-            stop = df.iloc[i]['end']
-            
-            
-            milliseconds = round(stop * 1000.0)
-
-            hours = milliseconds // 3_600_000
-            milliseconds -= hours * 3_600_000
-
-            minutes = milliseconds // 60_000
-            milliseconds -= minutes * 60_000
-
-            seconds = milliseconds // 1_000
-            milliseconds -= seconds * 1_000
-
-            
-            file.write(' --> ')
-            file.write(f"{hours}:{minutes:02d}:{seconds:02d}.{milliseconds:03d}")
-            file.write('\n')
-            file.writelines(df.iloc[i]['translation'])
-            if int(i) != len(df)-1:
-                file.write('\n\n')
-        
-    print("SRT DONE")        
-    try:
-        file1 = open('./testi.srt', 'r', encoding="utf-8")
-        Lines = file1.readlines()
-        
-        count = 0
-        # Strips the newline character
-        for line in Lines:
-            count += 1
-            print("{}".format(line))
-                
-        print(type(video_in))
-        print(video_in)
-        
-        video_out = video_in.replace('.mp4', '_out.mp4')
-        print(video_out)
-        command = 'ffmpeg -i "{}" -y -vf subtitles=./testi.srt "{}"'.format(video_in, video_out)
-        print(command)
-        os.system(command)
-        return video_out
-    except Exception as e:
-        print(e)
-        return video_out
-
-
-# ---- Gradio Layout -----
-video_in = gr.Video(label="Video file", mirror_webcam=False)
-youtube_url_in = gr.Textbox(label="Youtube url", lines=1, interactive=True)
-video_out = gr.Video(label="Video Out", mirror_webcam=False)
-
-
-df_init = pd.DataFrame(columns=['start','end','text','translation'])
-selected_source_lang = gr.Dropdown(choices=source_language_list, type="value", value="Let the model analyze", label="Spoken language in video", interactive=True)
-selected_translation_lang_2 = gr.Dropdown(choices=translation_models_list, type="value", value="English", label="In which language you want the transcriptions?", interactive=True)
-selected_whisper_model = gr.Dropdown(choices=whisper_models, type="value", value="base", label="Selected Whisper model", interactive=True)
-
-transcription_df = gr.DataFrame(value=df_init,label="Transcription dataframe", row_count=(0, "dynamic"), max_rows = 10, wrap=True, overflow_row_behaviour='paginate')
-transcription_and_translation_df = gr.DataFrame(value=df_init,label="Transcription and translation dataframe", max_rows = 10, wrap=True, overflow_row_behaviour='paginate')
-
-demo = gr.Blocks(css='''
-#cut_btn, #reset_btn { align-self:stretch; }
-#\\31 3 { max-width: 540px; }
-.output-markdown {max-width: 65ch !important;}
-''')
-demo.encrypt = False
-with demo:
-    transcription_var = gr.Variable()
-    
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown('''
-            ### This space allows you to: 
-            ##### 1. Download youtube video with a given URL
-            ##### 2. Watch it in the first video component
-            ##### 3. Run automatic speech recognition on the video using Whisper
-            ##### 4. Translate the recognized transcriptions to 26 languages supported by deepL
-            ##### 5. Burn the translations to the original video and watch the video in the 2nd video component
-            ''')
-            
-        with gr.Column():
-            gr.Markdown('''
-            ### 1. Insert Youtube URL below. Some test videos below:
-            ##### 1. https://www.youtube.com/watch?v=nlMuHtV82q8&ab_channel=NothingforSale24
-            ##### 2. https://www.youtube.com/watch?v=JzPfMbG1vrE&ab_channel=ExplainerVideosByLauren
-            ##### 3. https://www.youtube.com/watch?v=S68vvV0kod8&ab_channel=Pearl-CohnTelevision
-            ''')
-            
-    with gr.Row():
-        with gr.Column():
-            youtube_url_in.render()
-            download_youtube_btn = gr.Button("Step 1. Download Youtube video")
-            download_youtube_btn.click(get_youtube, [youtube_url_in], [
-                video_in])
-            print(video_in)
-            
-
-    with gr.Row():
-        with gr.Column():
-            video_in.render()
-            with gr.Column():
-                gr.Markdown('''
-                ##### Here you can start the transcription and translation process.
-                ##### Be aware that processing will last some time. With base model it is around 3x speed 
-                ''')
-            selected_source_lang.render()
-            selected_whisper_model.render()
-            transcribe_btn = gr.Button("Step 2. Transcribe audio")
-            transcribe_btn.click(speech_to_text, [video_in, selected_source_lang, selected_whisper_model], transcription_df)
-
-            
-    with gr.Row():
-        gr.Markdown('''
-        ##### Here you will get transcription  output
-        ##### ''')
-
-    with gr.Row():
-        with gr.Column():
-            transcription_df.render()
-            
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown('''
-            ##### Here you will get translated transcriptions. 
-            ##### Please remember to select target language
-            ##### ''')
-            selected_translation_lang_2.render()
-            translate_transcriptions_button = gr.Button("Step 3. Translate transcription")
-            translate_transcriptions_button.click(translate_transcriptions, [transcription_df, selected_translation_lang_2], transcription_and_translation_df)
-            transcription_and_translation_df.render()
-            
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown('''
-            ##### Now press the Step 4. Button to create output video with translated transcriptions
-            ##### ''')
-            translate_and_make_srt_btn = gr.Button("Step 4. Create and burn srt to video")
-            print(video_in)
-            translate_and_make_srt_btn.click(create_srt_and_burn, [transcription_and_translation_df,video_in], [
-                video_out])
-            video_out.render()
-
-                
-demo.launch()
\ No newline at end of file
diff --git a/spaces/RamAnanth1/chatGPT_voice/app.py b/spaces/RamAnanth1/chatGPT_voice/app.py
deleted file mode 100644
index 213a2f40145e3685e24a118d58d73a1cef0917fa..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/chatGPT_voice/app.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import gradio as gr
-import os 
-import json 
-import requests
-
-whisper = gr.Interface.load(name="spaces/sanchit-gandhi/whisper-large-v2")
-
-#input_message.submit([input_message, history], [input_message, chatbot, history])
-def translate_or_transcribe(audio, task):
-    text_result = whisper(audio, None, task, fn_index=0)
-    return text_result
-    
-#Streaming endpoint
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-
-
-def predict(inputs, top_p, temperature, openai_api_key, history=[]):  
-
-    payload = {
-    "model": "gpt-3.5-turbo",
-    "messages": [{"role": "user", "content": f"{inputs}"}],
-    "temperature" : 1.0,
-    "top_p":1.0,
-    "n" : 1,
-    "stream": True,
-    "presence_penalty":0,
-    "frequency_penalty":0,
-    }
-
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {openai_api_key}"
-    }
-
-    
-    history.append(inputs)
-    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    #response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    token_counter = 0 
-    partial_words = "" 
-
-    counter=0
-    for chunk in response.iter_lines():
-        if counter == 0:
-          counter+=1
-          continue
-        counter+=1
-        # check whether each line is non-empty
-        if chunk :
-          # decode each line as response data is in bytes
-          if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
-            break
-          #print(json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"])
-          partial_words = partial_words + json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"]
-          if token_counter == 0:
-            history.append(" " + partial_words)
-          else:
-            history[-1] = partial_words
-          chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
-          token_counter+=1
-          yield chat, history # resembles {chatbot: chat, state: history}  
-        
-
-def reset_textbox():
-    return gr.update(value='')
-
-title = """<h1 align="center">🔥ChatGPT API 🚀Streaming🚀 with Whisper</h1>"""
-description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
-```
-User: <utterance>
-Assistant: <utterance>
-User: <utterance>
-Assistant: <utterance>
-...
-```
-In this app, you can explore the outputs of a 20B large language model.
-"""
-#<a href="https://huggingface.co/spaces/ysharma/ChatGPTwithAPI?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate Space with GPU Upgrade for fast Inference & no queue<br> 
-                
-with gr.Blocks(css = """#col_container {width: 700px; margin-left: auto; margin-right: auto;}
-                #chatbot {height: 400px; overflow: auto;}""") as demo:
-    gr.HTML(title)
-    gr.HTML()
-    gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPTwithAPI?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
-    with gr.Column(elem_id = "col_container"):
-        openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here")
-        chatbot = gr.Chatbot(elem_id='chatbot') #c
-
-        prompt_input_audio = gr.Audio(source="microphone",type="filepath",label="Record Audio Input"
-                                    )
-        translate_btn = gr.Button("Check Whisper first ? 👍")
-            
-        whisper_task = gr.Radio(["translate", "transcribe"], value="translate", show_label=False)
-        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t
-        state = gr.State([]) #s
-        b1 = gr.Button()
-    
-        #inputs, top_p, temperature, top_k, repetition_penalty
-        with gr.Accordion("Parameters", open=False):
-            top_p = gr.Slider( minimum=-0, maximum=1.0, value=0.95, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
-            temperature = gr.Slider( minimum=-0, maximum=5.0, value=0.5, step=0.1, interactive=True, label="Temperature",)
-            #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
-            #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
-    
-    translate_btn.click(fn=translate_or_transcribe, 
-                         inputs=[prompt_input_audio,whisper_task], 
-                         outputs=inputs
-                        )
-                    
-    inputs.submit( predict, [inputs, top_p, temperature, openai_api_key, state], [chatbot, state],)
-    b1.click( predict, [inputs, top_p, temperature, openai_api_key, state], [chatbot, state],)
-    b1.click(reset_textbox, [], [inputs])
-    inputs.submit(reset_textbox, [], [inputs])
-                    
-    #gr.Markdown(description)
-                    
-    gr.HTML('''
-         <p>Note: Please be aware that audio records from iOS devices will not be decoded as expected by Gradio. For the best experience, record your voice from a computer instead of your smartphone ;)</p>
-
-        ''')
-    gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=RamAnanth1.chatGPT_voice)")
-
-    demo.queue().launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Realcat/image-matching-webui/third_party/ALIKE/hseq/extract.py b/spaces/Realcat/image-matching-webui/third_party/ALIKE/hseq/extract.py
deleted file mode 100644
index df16ae246bf360b529f0640cab5ae79f495e4f61..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/ALIKE/hseq/extract.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import os
-import sys
-import cv2
-from pathlib import Path
-import numpy as np
-import torch
-import torch.utils.data as data
-from tqdm import tqdm
-from copy import deepcopy
-from torchvision.transforms import ToTensor
-
-sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
-from alike import ALike, configs
-
-dataset_root = "hseq/hpatches-sequences-release"
-use_cuda = torch.cuda.is_available()
-device = "cuda" if use_cuda else "cpu"
-methods = ["alike-n", "alike-l", "alike-n-ms", "alike-l-ms"]
-
-
-class HPatchesDataset(data.Dataset):
-    def __init__(self, root: str = dataset_root, alteration: str = "all"):
-        """
-        Args:
-            root: dataset root path
-            alteration: # 'all', 'i' for illumination or 'v' for viewpoint
-        """
-        assert Path(root).exists(), f"Dataset root path {root} dose not exist!"
-        self.root = root
-
-        # get all image file name
-        self.image0_list = []
-        self.image1_list = []
-        self.homographies = []
-        folders = [x for x in Path(self.root).iterdir() if x.is_dir()]
-        self.seqs = []
-        for folder in folders:
-            if alteration == "i" and folder.stem[0] != "i":
-                continue
-            if alteration == "v" and folder.stem[0] != "v":
-                continue
-
-            self.seqs.append(folder)
-
-        self.len = len(self.seqs)
-        assert self.len > 0, f"Can not find PatchDataset in path {self.root}"
-
-    def __getitem__(self, item):
-        folder = self.seqs[item]
-
-        imgs = []
-        homos = []
-        for i in range(1, 7):
-            img = cv2.imread(str(folder / f"{i}.ppm"), cv2.IMREAD_COLOR)
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # HxWxC
-            imgs.append(img)
-
-            if i != 1:
-                homo = np.loadtxt(str(folder / f"H_1_{i}")).astype("float32")
-                homos.append(homo)
-
-        return imgs, homos, folder.stem
-
-    def __len__(self):
-        return self.len
-
-    def name(self):
-        return self.__class__
-
-
-def extract_multiscale(
-    model,
-    img,
-    scale_f=2**0.5,
-    min_scale=1.0,
-    max_scale=1.0,
-    min_size=0.0,
-    max_size=99999.0,
-    image_size_max=99999,
-    n_k=0,
-    sort=False,
-):
-    H_, W_, three = img.shape
-    assert three == 3, "input image shape should be [HxWx3]"
-
-    old_bm = torch.backends.cudnn.benchmark
-    torch.backends.cudnn.benchmark = False  # speedup
-
-    # ==================== image size constraint
-    image = deepcopy(img)
-    max_hw = max(H_, W_)
-    if max_hw > image_size_max:
-        ratio = float(image_size_max / max_hw)
-        image = cv2.resize(image, dsize=None, fx=ratio, fy=ratio)
-
-    # ==================== convert image to tensor
-    H, W, three = image.shape
-    image = ToTensor()(image).unsqueeze(0)
-    image = image.to(device)
-
-    s = 1.0  # current scale factor
-    keypoints, descriptors, scores, scores_maps, descriptor_maps = [], [], [], [], []
-    while s + 0.001 >= max(min_scale, min_size / max(H, W)):
-        if s - 0.001 <= min(max_scale, max_size / max(H, W)):
-            nh, nw = image.shape[2:]
-
-            # extract descriptors
-            with torch.no_grad():
-                descriptor_map, scores_map = model.extract_dense_map(image)
-                keypoints_, descriptors_, scores_, _ = model.dkd(
-                    scores_map, descriptor_map
-                )
-
-            keypoints.append(keypoints_[0])
-            descriptors.append(descriptors_[0])
-            scores.append(scores_[0])
-
-        s /= scale_f
-
-        # down-scale the image for next iteration
-        nh, nw = round(H * s), round(W * s)
-        image = torch.nn.functional.interpolate(
-            image, (nh, nw), mode="bilinear", align_corners=False
-        )
-
-    # restore value
-    torch.backends.cudnn.benchmark = old_bm
-
-    keypoints = torch.cat(keypoints)
-    descriptors = torch.cat(descriptors)
-    scores = torch.cat(scores)
-    keypoints = (keypoints + 1) / 2 * keypoints.new_tensor([[W_ - 1, H_ - 1]])
-
-    if sort or 0 < n_k < len(keypoints):
-        indices = torch.argsort(scores, descending=True)
-        keypoints = keypoints[indices]
-        descriptors = descriptors[indices]
-        scores = scores[indices]
-
-    if 0 < n_k < len(keypoints):
-        keypoints = keypoints[0:n_k]
-        descriptors = descriptors[0:n_k]
-        scores = scores[0:n_k]
-
-    return {"keypoints": keypoints, "descriptors": descriptors, "scores": scores}
-
-
-def extract_method(m):
-    hpatches = HPatchesDataset(root=dataset_root, alteration="all")
-    model = m[:7]
-    min_scale = 0.3 if m[8:] == "ms" else 1.0
-
-    model = ALike(**configs[model], device=device, top_k=0, scores_th=0.2, n_limit=5000)
-
-    progbar = tqdm(hpatches, desc="Extracting for {}".format(m))
-    for imgs, homos, seq_name in progbar:
-        for i in range(1, 7):
-            img = imgs[i - 1]
-            pred = extract_multiscale(
-                model, img, min_scale=min_scale, max_scale=1, sort=False, n_k=5000
-            )
-            kpts, descs, scores = pred["keypoints"], pred["descriptors"], pred["scores"]
-
-            with open(os.path.join(dataset_root, seq_name, f"{i}.ppm.{m}"), "wb") as f:
-                np.savez(
-                    f,
-                    keypoints=kpts.cpu().numpy(),
-                    scores=scores.cpu().numpy(),
-                    descriptors=descs.cpu().numpy(),
-                )
-
-
-if __name__ == "__main__":
-    for method in methods:
-        extract_method(method)
diff --git a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/datasets/megadepth.py b/spaces/Realcat/image-matching-webui/third_party/Roma/roma/datasets/megadepth.py
deleted file mode 100644
index 75cb72ded02c80d1ad6bce0d0269626ee49a9275..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/datasets/megadepth.py
+++ /dev/null
@@ -1,269 +0,0 @@
-import os
-from PIL import Image
-import h5py
-import numpy as np
-import torch
-import torchvision.transforms.functional as tvf
-import kornia.augmentation as K
-from roma.utils import get_depth_tuple_transform_ops, get_tuple_transform_ops
-import roma
-from roma.utils import *
-import math
-
-
-class MegadepthScene:
-    def __init__(
-        self,
-        data_root,
-        scene_info,
-        ht=384,
-        wt=512,
-        min_overlap=0.0,
-        max_overlap=1.0,
-        shake_t=0,
-        rot_prob=0.0,
-        normalize=True,
-        max_num_pairs=100_000,
-        scene_name=None,
-        use_horizontal_flip_aug=False,
-        use_single_horizontal_flip_aug=False,
-        colorjiggle_params=None,
-        random_eraser=None,
-        use_randaug=False,
-        randaug_params=None,
-        randomize_size=False,
-    ) -> None:
-        self.data_root = data_root
-        self.scene_name = (
-            os.path.splitext(scene_name)[0] + f"_{min_overlap}_{max_overlap}"
-        )
-        self.image_paths = scene_info["image_paths"]
-        self.depth_paths = scene_info["depth_paths"]
-        self.intrinsics = scene_info["intrinsics"]
-        self.poses = scene_info["poses"]
-        self.pairs = scene_info["pairs"]
-        self.overlaps = scene_info["overlaps"]
-        threshold = (self.overlaps > min_overlap) & (self.overlaps < max_overlap)
-        self.pairs = self.pairs[threshold]
-        self.overlaps = self.overlaps[threshold]
-        if len(self.pairs) > max_num_pairs:
-            pairinds = np.random.choice(
-                np.arange(0, len(self.pairs)), max_num_pairs, replace=False
-            )
-            self.pairs = self.pairs[pairinds]
-            self.overlaps = self.overlaps[pairinds]
-        if randomize_size:
-            area = ht * wt
-            s = int(16 * (math.sqrt(area) // 16))
-            sizes = ((ht, wt), (s, s), (wt, ht))
-            choice = roma.RANK % 3
-            ht, wt = sizes[choice]
-        # counts, bins = np.histogram(self.overlaps,20)
-        # print(counts)
-        self.im_transform_ops = get_tuple_transform_ops(
-            resize=(ht, wt),
-            normalize=normalize,
-            colorjiggle_params=colorjiggle_params,
-        )
-        self.depth_transform_ops = get_depth_tuple_transform_ops(resize=(ht, wt))
-        self.wt, self.ht = wt, ht
-        self.shake_t = shake_t
-        self.random_eraser = random_eraser
-        if use_horizontal_flip_aug and use_single_horizontal_flip_aug:
-            raise ValueError("Can't both flip both images and only flip one")
-        self.use_horizontal_flip_aug = use_horizontal_flip_aug
-        self.use_single_horizontal_flip_aug = use_single_horizontal_flip_aug
-        self.use_randaug = use_randaug
-
-    def load_im(self, im_path):
-        im = Image.open(im_path)
-        return im
-
-    def horizontal_flip(self, im_A, im_B, depth_A, depth_B, K_A, K_B):
-        im_A = im_A.flip(-1)
-        im_B = im_B.flip(-1)
-        depth_A, depth_B = depth_A.flip(-1), depth_B.flip(-1)
-        flip_mat = torch.tensor([[-1, 0, self.wt], [0, 1, 0], [0, 0, 1.0]]).to(
-            K_A.device
-        )
-        K_A = flip_mat @ K_A
-        K_B = flip_mat @ K_B
-
-        return im_A, im_B, depth_A, depth_B, K_A, K_B
-
-    def load_depth(self, depth_ref, crop=None):
-        depth = np.array(h5py.File(depth_ref, "r")["depth"])
-        return torch.from_numpy(depth)
-
-    def __len__(self):
-        return len(self.pairs)
-
-    def scale_intrinsic(self, K, wi, hi):
-        sx, sy = self.wt / wi, self.ht / hi
-        sK = torch.tensor([[sx, 0, 0], [0, sy, 0], [0, 0, 1]])
-        return sK @ K
-
-    def rand_shake(self, *things):
-        t = np.random.choice(range(-self.shake_t, self.shake_t + 1), size=2)
-        return [
-            tvf.affine(thing, angle=0.0, translate=list(t), scale=1.0, shear=[0.0, 0.0])
-            for thing in things
-        ], t
-
-    def __getitem__(self, pair_idx):
-        # read intrinsics of original size
-        idx1, idx2 = self.pairs[pair_idx]
-        K1 = torch.tensor(self.intrinsics[idx1].copy(), dtype=torch.float).reshape(3, 3)
-        K2 = torch.tensor(self.intrinsics[idx2].copy(), dtype=torch.float).reshape(3, 3)
-
-        # read and compute relative poses
-        T1 = self.poses[idx1]
-        T2 = self.poses[idx2]
-        T_1to2 = torch.tensor(np.matmul(T2, np.linalg.inv(T1)), dtype=torch.float)[
-            :4, :4
-        ]  # (4, 4)
-
-        # Load positive pair data
-        im_A, im_B = self.image_paths[idx1], self.image_paths[idx2]
-        depth1, depth2 = self.depth_paths[idx1], self.depth_paths[idx2]
-        im_A_ref = os.path.join(self.data_root, im_A)
-        im_B_ref = os.path.join(self.data_root, im_B)
-        depth_A_ref = os.path.join(self.data_root, depth1)
-        depth_B_ref = os.path.join(self.data_root, depth2)
-        im_A = self.load_im(im_A_ref)
-        im_B = self.load_im(im_B_ref)
-        K1 = self.scale_intrinsic(K1, im_A.width, im_A.height)
-        K2 = self.scale_intrinsic(K2, im_B.width, im_B.height)
-
-        if self.use_randaug:
-            im_A, im_B = self.rand_augment(im_A, im_B)
-
-        depth_A = self.load_depth(depth_A_ref)
-        depth_B = self.load_depth(depth_B_ref)
-        # Process images
-        im_A, im_B = self.im_transform_ops((im_A, im_B))
-        depth_A, depth_B = self.depth_transform_ops(
-            (depth_A[None, None], depth_B[None, None])
-        )
-
-        [im_A, im_B, depth_A, depth_B], t = self.rand_shake(
-            im_A, im_B, depth_A, depth_B
-        )
-        K1[:2, 2] += t
-        K2[:2, 2] += t
-
-        im_A, im_B = im_A[None], im_B[None]
-        if self.random_eraser is not None:
-            im_A, depth_A = self.random_eraser(im_A, depth_A)
-            im_B, depth_B = self.random_eraser(im_B, depth_B)
-
-        if self.use_horizontal_flip_aug:
-            if np.random.rand() > 0.5:
-                im_A, im_B, depth_A, depth_B, K1, K2 = self.horizontal_flip(
-                    im_A, im_B, depth_A, depth_B, K1, K2
-                )
-        if self.use_single_horizontal_flip_aug:
-            if np.random.rand() > 0.5:
-                im_B, depth_B, K2 = self.single_horizontal_flip(im_B, depth_B, K2)
-
-        if roma.DEBUG_MODE:
-            tensor_to_pil(im_A[0], unnormalize=True).save(f"vis/im_A.jpg")
-            tensor_to_pil(im_B[0], unnormalize=True).save(f"vis/im_B.jpg")
-
-        data_dict = {
-            "im_A": im_A[0],
-            "im_A_identifier": self.image_paths[idx1].split("/")[-1].split(".jpg")[0],
-            "im_B": im_B[0],
-            "im_B_identifier": self.image_paths[idx2].split("/")[-1].split(".jpg")[0],
-            "im_A_depth": depth_A[0, 0],
-            "im_B_depth": depth_B[0, 0],
-            "K1": K1,
-            "K2": K2,
-            "T_1to2": T_1to2,
-            "im_A_path": im_A_ref,
-            "im_B_path": im_B_ref,
-        }
-        return data_dict
-
-
-class MegadepthBuilder:
-    def __init__(
-        self, data_root="data/megadepth", loftr_ignore=True, imc21_ignore=True
-    ) -> None:
-        self.data_root = data_root
-        self.scene_info_root = os.path.join(data_root, "prep_scene_info")
-        self.all_scenes = os.listdir(self.scene_info_root)
-        self.test_scenes = ["0017.npy", "0004.npy", "0048.npy", "0013.npy"]
-        # LoFTR did the D2-net preprocessing differently than we did and got more ignore scenes, can optionially ignore those
-        self.loftr_ignore_scenes = set(
-            [
-                "0121.npy",
-                "0133.npy",
-                "0168.npy",
-                "0178.npy",
-                "0229.npy",
-                "0349.npy",
-                "0412.npy",
-                "0430.npy",
-                "0443.npy",
-                "1001.npy",
-                "5014.npy",
-                "5015.npy",
-                "5016.npy",
-            ]
-        )
-        self.imc21_scenes = set(
-            [
-                "0008.npy",
-                "0019.npy",
-                "0021.npy",
-                "0024.npy",
-                "0025.npy",
-                "0032.npy",
-                "0063.npy",
-                "1589.npy",
-            ]
-        )
-        self.test_scenes_loftr = ["0015.npy", "0022.npy"]
-        self.loftr_ignore = loftr_ignore
-        self.imc21_ignore = imc21_ignore
-
-    def build_scenes(self, split="train", min_overlap=0.0, scene_names=None, **kwargs):
-        if split == "train":
-            scene_names = set(self.all_scenes) - set(self.test_scenes)
-        elif split == "train_loftr":
-            scene_names = set(self.all_scenes) - set(self.test_scenes_loftr)
-        elif split == "test":
-            scene_names = self.test_scenes
-        elif split == "test_loftr":
-            scene_names = self.test_scenes_loftr
-        elif split == "custom":
-            scene_names = scene_names
-        else:
-            raise ValueError(f"Split {split} not available")
-        scenes = []
-        for scene_name in scene_names:
-            if self.loftr_ignore and scene_name in self.loftr_ignore_scenes:
-                continue
-            if self.imc21_ignore and scene_name in self.imc21_scenes:
-                continue
-            scene_info = np.load(
-                os.path.join(self.scene_info_root, scene_name), allow_pickle=True
-            ).item()
-            scenes.append(
-                MegadepthScene(
-                    self.data_root,
-                    scene_info,
-                    min_overlap=min_overlap,
-                    scene_name=scene_name,
-                    **kwargs,
-                )
-            )
-        return scenes
-
-    def weight_scenes(self, concat_dataset, alpha=0.5):
-        ns = []
-        for d in concat_dataset.datasets:
-            ns.append(len(d))
-        ws = torch.cat([torch.ones(n) / n**alpha for n in ns])
-        return ws
diff --git a/spaces/RedValis/Music-Helix/README.md b/spaces/RedValis/Music-Helix/README.md
deleted file mode 100644
index dcf9afc2a5d270e99b4b8efa410e49cfac7dd849..0000000000000000000000000000000000000000
--- a/spaces/RedValis/Music-Helix/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Music Helix
-emoji: 🎶
-colorFrom: red
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.20.0
-app_file: main.py
-pinned: false
----
diff --git a/spaces/Reha2704/VToonify/vtoonify/model/bisenet/resnet.py b/spaces/Reha2704/VToonify/vtoonify/model/bisenet/resnet.py
deleted file mode 100644
index aa2bf95130e9815ba378cb6f73207068b81a04b9..0000000000000000000000000000000000000000
--- a/spaces/Reha2704/VToonify/vtoonify/model/bisenet/resnet.py
+++ /dev/null
@@ -1,109 +0,0 @@
-#!/usr/bin/python
-# -*- encoding: utf-8 -*-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.model_zoo as modelzoo
-
-# from modules.bn import InPlaceABNSync as BatchNorm2d
-
-resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
-    def __init__(self, in_chan, out_chan, stride=1):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(in_chan, out_chan, stride)
-        self.bn1 = nn.BatchNorm2d(out_chan)
-        self.conv2 = conv3x3(out_chan, out_chan)
-        self.bn2 = nn.BatchNorm2d(out_chan)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = None
-        if in_chan != out_chan or stride != 1:
-            self.downsample = nn.Sequential(
-                nn.Conv2d(in_chan, out_chan,
-                          kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(out_chan),
-                )
-
-    def forward(self, x):
-        residual = self.conv1(x)
-        residual = F.relu(self.bn1(residual))
-        residual = self.conv2(residual)
-        residual = self.bn2(residual)
-
-        shortcut = x
-        if self.downsample is not None:
-            shortcut = self.downsample(x)
-
-        out = shortcut + residual
-        out = self.relu(out)
-        return out
-
-
-def create_layer_basic(in_chan, out_chan, bnum, stride=1):
-    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
-    for i in range(bnum-1):
-        layers.append(BasicBlock(out_chan, out_chan, stride=1))
-    return nn.Sequential(*layers)
-
-
-class Resnet18(nn.Module):
-    def __init__(self):
-        super(Resnet18, self).__init__()
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
-                               bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
-        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
-        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
-        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
-        self.init_weight()
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = F.relu(self.bn1(x))
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        feat8 = self.layer2(x) # 1/8
-        feat16 = self.layer3(feat8) # 1/16
-        feat32 = self.layer4(feat16) # 1/32
-        return feat8, feat16, feat32
-
-    def init_weight(self):
-        state_dict = modelzoo.load_url(resnet18_url)
-        self_state_dict = self.state_dict()
-        for k, v in state_dict.items():
-            if 'fc' in k: continue
-            self_state_dict.update({k: v})
-        self.load_state_dict(self_state_dict)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, (nn.Linear, nn.Conv2d)):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module,  nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-if __name__ == "__main__":
-    net = Resnet18()
-    x = torch.randn(16, 3, 224, 224)
-    out = net(x)
-    print(out[0].size())
-    print(out[1].size())
-    print(out[2].size())
-    net.get_params()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/paa_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/paa_head.py
deleted file mode 100644
index e067b0121cf8b8230c0c9c6b8cfd41f56be4e298..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/paa_head.py
+++ /dev/null
@@ -1,671 +0,0 @@
-import numpy as np
-import torch
-from mmcv.runner import force_fp32
-
-from mmdet.core import multi_apply, multiclass_nms
-from mmdet.core.bbox.iou_calculators import bbox_overlaps
-from mmdet.models import HEADS
-from mmdet.models.dense_heads import ATSSHead
-
-EPS = 1e-12
-try:
-    import sklearn.mixture as skm
-except ImportError:
-    skm = None
-
-
-def levels_to_images(mlvl_tensor):
-    """Concat multi-level feature maps by image.
-
-    [feature_level0, feature_level1...] -> [feature_image0, feature_image1...]
-    Convert the shape of each element in mlvl_tensor from (N, C, H, W) to
-    (N, H*W , C), then split the element to N elements with shape (H*W, C), and
-    concat elements in same image of all level along first dimension.
-
-    Args:
-        mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from
-            corresponding level. Each element is of shape (N, C, H, W)
-
-    Returns:
-        list[torch.Tensor]: A list that contains N tensors and each tensor is
-            of shape (num_elements, C)
-    """
-    batch_size = mlvl_tensor[0].size(0)
-    batch_list = [[] for _ in range(batch_size)]
-    channels = mlvl_tensor[0].size(1)
-    for t in mlvl_tensor:
-        t = t.permute(0, 2, 3, 1)
-        t = t.view(batch_size, -1, channels).contiguous()
-        for img in range(batch_size):
-            batch_list[img].append(t[img])
-    return [torch.cat(item, 0) for item in batch_list]
-
-
-@HEADS.register_module()
-class PAAHead(ATSSHead):
-    """Head of PAAAssignment: Probabilistic Anchor Assignment with IoU
-    Prediction for Object Detection.
-
-    Code is modified from the `official github repo
-    <https://github.com/kkhoot/PAA/blob/master/paa_core
-    /modeling/rpn/paa/loss.py>`_.
-
-    More details can be found in the `paper
-    <https://arxiv.org/abs/2007.08103>`_ .
-
-    Args:
-        topk (int): Select topk samples with smallest loss in
-            each level.
-        score_voting (bool): Whether to use score voting in post-process.
-        covariance_type : String describing the type of covariance parameters
-            to be used in :class:`sklearn.mixture.GaussianMixture`.
-            It must be one of:
-
-            - 'full': each component has its own general covariance matrix
-            - 'tied': all components share the same general covariance matrix
-            - 'diag': each component has its own diagonal covariance matrix
-            - 'spherical': each component has its own single variance
-            Default: 'diag'. From 'full' to 'spherical', the gmm fitting
-            process is faster yet the performance could be influenced. For most
-            cases, 'diag' should be a good choice.
-    """
-
-    def __init__(self,
-                 *args,
-                 topk=9,
-                 score_voting=True,
-                 covariance_type='diag',
-                 **kwargs):
-        # topk used in paa reassign process
-        self.topk = topk
-        self.with_score_voting = score_voting
-        self.covariance_type = covariance_type
-        super(PAAHead, self).__init__(*args, **kwargs)
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             iou_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            iou_preds (list[Tensor]): iou_preds for each scale
-                level with shape (N, num_anchors * 1, H, W)
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
-                boxes can be ignored when are computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss gmm_assignment.
-        """
-
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels,
-        )
-        (labels, labels_weight, bboxes_target, bboxes_weight, pos_inds,
-         pos_gt_index) = cls_reg_targets
-        cls_scores = levels_to_images(cls_scores)
-        cls_scores = [
-            item.reshape(-1, self.cls_out_channels) for item in cls_scores
-        ]
-        bbox_preds = levels_to_images(bbox_preds)
-        bbox_preds = [item.reshape(-1, 4) for item in bbox_preds]
-        iou_preds = levels_to_images(iou_preds)
-        iou_preds = [item.reshape(-1, 1) for item in iou_preds]
-        pos_losses_list, = multi_apply(self.get_pos_loss, anchor_list,
-                                       cls_scores, bbox_preds, labels,
-                                       labels_weight, bboxes_target,
-                                       bboxes_weight, pos_inds)
-
-        with torch.no_grad():
-            reassign_labels, reassign_label_weight, \
-                reassign_bbox_weights, num_pos = multi_apply(
-                    self.paa_reassign,
-                    pos_losses_list,
-                    labels,
-                    labels_weight,
-                    bboxes_weight,
-                    pos_inds,
-                    pos_gt_index,
-                    anchor_list)
-            num_pos = sum(num_pos)
-        # convert all tensor list to a flatten tensor
-        cls_scores = torch.cat(cls_scores, 0).view(-1, cls_scores[0].size(-1))
-        bbox_preds = torch.cat(bbox_preds, 0).view(-1, bbox_preds[0].size(-1))
-        iou_preds = torch.cat(iou_preds, 0).view(-1, iou_preds[0].size(-1))
-        labels = torch.cat(reassign_labels, 0).view(-1)
-        flatten_anchors = torch.cat(
-            [torch.cat(item, 0) for item in anchor_list])
-        labels_weight = torch.cat(reassign_label_weight, 0).view(-1)
-        bboxes_target = torch.cat(bboxes_target,
-                                  0).view(-1, bboxes_target[0].size(-1))
-
-        pos_inds_flatten = ((labels >= 0)
-                            &
-                            (labels < self.num_classes)).nonzero().reshape(-1)
-
-        losses_cls = self.loss_cls(
-            cls_scores,
-            labels,
-            labels_weight,
-            avg_factor=max(num_pos, len(img_metas)))  # avoid num_pos=0
-        if num_pos:
-            pos_bbox_pred = self.bbox_coder.decode(
-                flatten_anchors[pos_inds_flatten],
-                bbox_preds[pos_inds_flatten])
-            pos_bbox_target = bboxes_target[pos_inds_flatten]
-            iou_target = bbox_overlaps(
-                pos_bbox_pred.detach(), pos_bbox_target, is_aligned=True)
-            losses_iou = self.loss_centerness(
-                iou_preds[pos_inds_flatten],
-                iou_target.unsqueeze(-1),
-                avg_factor=num_pos)
-            losses_bbox = self.loss_bbox(
-                pos_bbox_pred,
-                pos_bbox_target,
-                iou_target.clamp(min=EPS),
-                avg_factor=iou_target.sum())
-        else:
-            losses_iou = iou_preds.sum() * 0
-            losses_bbox = bbox_preds.sum() * 0
-
-        return dict(
-            loss_cls=losses_cls, loss_bbox=losses_bbox, loss_iou=losses_iou)
-
-    def get_pos_loss(self, anchors, cls_score, bbox_pred, label, label_weight,
-                     bbox_target, bbox_weight, pos_inds):
-        """Calculate loss of all potential positive samples obtained from first
-        match process.
-
-        Args:
-            anchors (list[Tensor]): Anchors of each scale.
-            cls_score (Tensor): Box scores of single image with shape
-                (num_anchors, num_classes)
-            bbox_pred (Tensor): Box energies / deltas of single image
-                with shape (num_anchors, 4)
-            label (Tensor): classification target of each anchor with
-                shape (num_anchors,)
-            label_weight (Tensor): Classification loss weight of each
-                anchor with shape (num_anchors).
-            bbox_target (dict): Regression target of each anchor with
-                shape (num_anchors, 4).
-            bbox_weight (Tensor): Bbox weight of each anchor with shape
-                (num_anchors, 4).
-            pos_inds (Tensor): Index of all positive samples got from
-                first assign process.
-
-        Returns:
-            Tensor: Losses of all positive samples in single image.
-        """
-        if not len(pos_inds):
-            return cls_score.new([]),
-        anchors_all_level = torch.cat(anchors, 0)
-        pos_scores = cls_score[pos_inds]
-        pos_bbox_pred = bbox_pred[pos_inds]
-        pos_label = label[pos_inds]
-        pos_label_weight = label_weight[pos_inds]
-        pos_bbox_target = bbox_target[pos_inds]
-        pos_bbox_weight = bbox_weight[pos_inds]
-        pos_anchors = anchors_all_level[pos_inds]
-        pos_bbox_pred = self.bbox_coder.decode(pos_anchors, pos_bbox_pred)
-
-        # to keep loss dimension
-        loss_cls = self.loss_cls(
-            pos_scores,
-            pos_label,
-            pos_label_weight,
-            avg_factor=self.loss_cls.loss_weight,
-            reduction_override='none')
-
-        loss_bbox = self.loss_bbox(
-            pos_bbox_pred,
-            pos_bbox_target,
-            pos_bbox_weight,
-            avg_factor=self.loss_cls.loss_weight,
-            reduction_override='none')
-
-        loss_cls = loss_cls.sum(-1)
-        pos_loss = loss_bbox + loss_cls
-        return pos_loss,
-
-    def paa_reassign(self, pos_losses, label, label_weight, bbox_weight,
-                     pos_inds, pos_gt_inds, anchors):
-        """Fit loss to GMM distribution and separate positive, ignore, negative
-        samples again with GMM model.
-
-        Args:
-            pos_losses (Tensor): Losses of all positive samples in
-                single image.
-            label (Tensor): classification target of each anchor with
-                shape (num_anchors,)
-            label_weight (Tensor): Classification loss weight of each
-                anchor with shape (num_anchors).
-            bbox_weight (Tensor): Bbox weight of each anchor with shape
-                (num_anchors, 4).
-            pos_inds (Tensor): Index of all positive samples got from
-                first assign process.
-            pos_gt_inds (Tensor): Gt_index of all positive samples got
-                from first assign process.
-            anchors (list[Tensor]): Anchors of each scale.
-
-        Returns:
-            tuple: Usually returns a tuple containing learning targets.
-
-                - label (Tensor): classification target of each anchor after
-                  paa assign, with shape (num_anchors,)
-                - label_weight (Tensor): Classification loss weight of each
-                  anchor after paa assign, with shape (num_anchors).
-                - bbox_weight (Tensor): Bbox weight of each anchor with shape
-                  (num_anchors, 4).
-                - num_pos (int): The number of positive samples after paa
-                  assign.
-        """
-        if not len(pos_inds):
-            return label, label_weight, bbox_weight, 0
-        label = label.clone()
-        label_weight = label_weight.clone()
-        bbox_weight = bbox_weight.clone()
-        num_gt = pos_gt_inds.max() + 1
-        num_level = len(anchors)
-        num_anchors_each_level = [item.size(0) for item in anchors]
-        num_anchors_each_level.insert(0, 0)
-        inds_level_interval = np.cumsum(num_anchors_each_level)
-        pos_level_mask = []
-        for i in range(num_level):
-            mask = (pos_inds >= inds_level_interval[i]) & (
-                pos_inds < inds_level_interval[i + 1])
-            pos_level_mask.append(mask)
-        pos_inds_after_paa = [label.new_tensor([])]
-        ignore_inds_after_paa = [label.new_tensor([])]
-        for gt_ind in range(num_gt):
-            pos_inds_gmm = []
-            pos_loss_gmm = []
-            gt_mask = pos_gt_inds == gt_ind
-            for level in range(num_level):
-                level_mask = pos_level_mask[level]
-                level_gt_mask = level_mask & gt_mask
-                value, topk_inds = pos_losses[level_gt_mask].topk(
-                    min(level_gt_mask.sum(), self.topk), largest=False)
-                pos_inds_gmm.append(pos_inds[level_gt_mask][topk_inds])
-                pos_loss_gmm.append(value)
-            pos_inds_gmm = torch.cat(pos_inds_gmm)
-            pos_loss_gmm = torch.cat(pos_loss_gmm)
-            # fix gmm need at least two sample
-            if len(pos_inds_gmm) < 2:
-                continue
-            device = pos_inds_gmm.device
-            pos_loss_gmm, sort_inds = pos_loss_gmm.sort()
-            pos_inds_gmm = pos_inds_gmm[sort_inds]
-            pos_loss_gmm = pos_loss_gmm.view(-1, 1).cpu().numpy()
-            min_loss, max_loss = pos_loss_gmm.min(), pos_loss_gmm.max()
-            means_init = np.array([min_loss, max_loss]).reshape(2, 1)
-            weights_init = np.array([0.5, 0.5])
-            precisions_init = np.array([1.0, 1.0]).reshape(2, 1, 1)  # full
-            if self.covariance_type == 'spherical':
-                precisions_init = precisions_init.reshape(2)
-            elif self.covariance_type == 'diag':
-                precisions_init = precisions_init.reshape(2, 1)
-            elif self.covariance_type == 'tied':
-                precisions_init = np.array([[1.0]])
-            if skm is None:
-                raise ImportError('Please run "pip install sklearn" '
-                                  'to install sklearn first.')
-            gmm = skm.GaussianMixture(
-                2,
-                weights_init=weights_init,
-                means_init=means_init,
-                precisions_init=precisions_init,
-                covariance_type=self.covariance_type)
-            gmm.fit(pos_loss_gmm)
-            gmm_assignment = gmm.predict(pos_loss_gmm)
-            scores = gmm.score_samples(pos_loss_gmm)
-            gmm_assignment = torch.from_numpy(gmm_assignment).to(device)
-            scores = torch.from_numpy(scores).to(device)
-
-            pos_inds_temp, ignore_inds_temp = self.gmm_separation_scheme(
-                gmm_assignment, scores, pos_inds_gmm)
-            pos_inds_after_paa.append(pos_inds_temp)
-            ignore_inds_after_paa.append(ignore_inds_temp)
-
-        pos_inds_after_paa = torch.cat(pos_inds_after_paa)
-        ignore_inds_after_paa = torch.cat(ignore_inds_after_paa)
-        reassign_mask = (pos_inds.unsqueeze(1) != pos_inds_after_paa).all(1)
-        reassign_ids = pos_inds[reassign_mask]
-        label[reassign_ids] = self.num_classes
-        label_weight[ignore_inds_after_paa] = 0
-        bbox_weight[reassign_ids] = 0
-        num_pos = len(pos_inds_after_paa)
-        return label, label_weight, bbox_weight, num_pos
-
-    def gmm_separation_scheme(self, gmm_assignment, scores, pos_inds_gmm):
-        """A general separation scheme for gmm model.
-
-        It separates a GMM distribution of candidate samples into three
-        parts, 0 1 and uncertain areas, and you can implement other
-        separation schemes by rewriting this function.
-
-        Args:
-            gmm_assignment (Tensor): The prediction of GMM which is of shape
-                (num_samples,). The 0/1 value indicates the distribution
-                that each sample comes from.
-            scores (Tensor): The probability of sample coming from the
-                fit GMM distribution. The tensor is of shape (num_samples,).
-            pos_inds_gmm (Tensor): All the indexes of samples which are used
-                to fit GMM model. The tensor is of shape (num_samples,)
-
-        Returns:
-            tuple[Tensor]: The indices of positive and ignored samples.
-
-                - pos_inds_temp (Tensor): Indices of positive samples.
-                - ignore_inds_temp (Tensor): Indices of ignore samples.
-        """
-        # The implementation is (c) in Fig.3 in origin paper instead of (b).
-        # You can refer to issues such as
-        # https://github.com/kkhoot/PAA/issues/8 and
-        # https://github.com/kkhoot/PAA/issues/9.
-        fgs = gmm_assignment == 0
-        pos_inds_temp = fgs.new_tensor([], dtype=torch.long)
-        ignore_inds_temp = fgs.new_tensor([], dtype=torch.long)
-        if fgs.nonzero().numel():
-            _, pos_thr_ind = scores[fgs].topk(1)
-            pos_inds_temp = pos_inds_gmm[fgs][:pos_thr_ind + 1]
-            ignore_inds_temp = pos_inds_gmm.new_tensor([])
-        return pos_inds_temp, ignore_inds_temp
-
-    def get_targets(
-        self,
-        anchor_list,
-        valid_flag_list,
-        gt_bboxes_list,
-        img_metas,
-        gt_bboxes_ignore_list=None,
-        gt_labels_list=None,
-        label_channels=1,
-        unmap_outputs=True,
-    ):
-        """Get targets for PAA head.
-
-        This method is almost the same as `AnchorHead.get_targets()`. We direct
-        return the results from _get_targets_single instead map it to levels
-        by images_to_levels function.
-
-        Args:
-            anchor_list (list[list[Tensor]]): Multi level anchors of each
-                image. The outer list indicates images, and the inner list
-                corresponds to feature levels of the image. Each element of
-                the inner list is a tensor of shape (num_anchors, 4).
-            valid_flag_list (list[list[Tensor]]): Multi level valid flags of
-                each image. The outer list indicates images, and the inner list
-                corresponds to feature levels of the image. Each element of
-                the inner list is a tensor of shape (num_anchors, )
-            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
-            img_metas (list[dict]): Meta info of each image.
-            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
-                ignored.
-            gt_labels_list (list[Tensor]): Ground truth labels of each box.
-            label_channels (int): Channel of label.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-
-        Returns:
-            tuple: Usually returns a tuple containing learning targets.
-
-                - labels (list[Tensor]): Labels of all anchors, each with
-                    shape (num_anchors,).
-                - label_weights (list[Tensor]): Label weights of all anchor.
-                    each with shape (num_anchors,).
-                - bbox_targets (list[Tensor]): BBox targets of all anchors.
-                    each with shape (num_anchors, 4).
-                - bbox_weights (list[Tensor]): BBox weights of all anchors.
-                    each with shape (num_anchors, 4).
-                - pos_inds (list[Tensor]): Contains all index of positive
-                    sample in all anchor.
-                - gt_inds (list[Tensor]): Contains all gt_index of positive
-                    sample in all anchor.
-        """
-
-        num_imgs = len(img_metas)
-        assert len(anchor_list) == len(valid_flag_list) == num_imgs
-        concat_anchor_list = []
-        concat_valid_flag_list = []
-        for i in range(num_imgs):
-            assert len(anchor_list[i]) == len(valid_flag_list[i])
-            concat_anchor_list.append(torch.cat(anchor_list[i]))
-            concat_valid_flag_list.append(torch.cat(valid_flag_list[i]))
-
-        # compute targets for each image
-        if gt_bboxes_ignore_list is None:
-            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
-        if gt_labels_list is None:
-            gt_labels_list = [None for _ in range(num_imgs)]
-        results = multi_apply(
-            self._get_targets_single,
-            concat_anchor_list,
-            concat_valid_flag_list,
-            gt_bboxes_list,
-            gt_bboxes_ignore_list,
-            gt_labels_list,
-            img_metas,
-            label_channels=label_channels,
-            unmap_outputs=unmap_outputs)
-
-        (labels, label_weights, bbox_targets, bbox_weights, valid_pos_inds,
-         valid_neg_inds, sampling_result) = results
-
-        # Due to valid flag of anchors, we have to calculate the real pos_inds
-        # in origin anchor set.
-        pos_inds = []
-        for i, single_labels in enumerate(labels):
-            pos_mask = (0 <= single_labels) & (
-                single_labels < self.num_classes)
-            pos_inds.append(pos_mask.nonzero().view(-1))
-
-        gt_inds = [item.pos_assigned_gt_inds for item in sampling_result]
-        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
-                gt_inds)
-
-    def _get_targets_single(self,
-                            flat_anchors,
-                            valid_flags,
-                            gt_bboxes,
-                            gt_bboxes_ignore,
-                            gt_labels,
-                            img_meta,
-                            label_channels=1,
-                            unmap_outputs=True):
-        """Compute regression and classification targets for anchors in a
-        single image.
-
-        This method is same as `AnchorHead._get_targets_single()`.
-        """
-        assert unmap_outputs, 'We must map outputs back to the original' \
-            'set of anchors in PAAhead'
-        return super(ATSSHead, self)._get_targets_single(
-            flat_anchors,
-            valid_flags,
-            gt_bboxes,
-            gt_bboxes_ignore,
-            gt_labels,
-            img_meta,
-            label_channels=1,
-            unmap_outputs=True)
-
-    def _get_bboxes(self,
-                    cls_scores,
-                    bbox_preds,
-                    iou_preds,
-                    mlvl_anchors,
-                    img_shapes,
-                    scale_factors,
-                    cfg,
-                    rescale=False,
-                    with_nms=True):
-        """Transform outputs for a single batch item into labeled boxes.
-
-        This method is almost same as `ATSSHead._get_bboxes()`.
-        We use sqrt(iou_preds * cls_scores) in NMS process instead of just
-        cls_scores. Besides, score voting is used when `` score_voting``
-        is set to True.
-        """
-        assert with_nms, 'PAA only supports "with_nms=True" now'
-        assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
-        batch_size = cls_scores[0].shape[0]
-
-        mlvl_bboxes = []
-        mlvl_scores = []
-        mlvl_iou_preds = []
-        for cls_score, bbox_pred, iou_preds, anchors in zip(
-                cls_scores, bbox_preds, iou_preds, mlvl_anchors):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-
-            scores = cls_score.permute(0, 2, 3, 1).reshape(
-                batch_size, -1, self.cls_out_channels).sigmoid()
-            bbox_pred = bbox_pred.permute(0, 2, 3,
-                                          1).reshape(batch_size, -1, 4)
-            iou_preds = iou_preds.permute(0, 2, 3, 1).reshape(batch_size,
-                                                              -1).sigmoid()
-
-            nms_pre = cfg.get('nms_pre', -1)
-            if nms_pre > 0 and scores.shape[1] > nms_pre:
-                max_scores, _ = (scores * iou_preds[..., None]).sqrt().max(-1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                batch_inds = torch.arange(batch_size).view(
-                    -1, 1).expand_as(topk_inds).long()
-                anchors = anchors[topk_inds, :]
-                bbox_pred = bbox_pred[batch_inds, topk_inds, :]
-                scores = scores[batch_inds, topk_inds, :]
-                iou_preds = iou_preds[batch_inds, topk_inds]
-            else:
-                anchors = anchors.expand_as(bbox_pred)
-
-            bboxes = self.bbox_coder.decode(
-                anchors, bbox_pred, max_shape=img_shapes)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-            mlvl_iou_preds.append(iou_preds)
-
-        batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1)
-        if rescale:
-            batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor(
-                scale_factors).unsqueeze(1)
-        batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
-        # Add a dummy background class to the backend when using sigmoid
-        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-        # BG cat_id: num_class
-        padding = batch_mlvl_scores.new_zeros(batch_size,
-                                              batch_mlvl_scores.shape[1], 1)
-        batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1)
-        batch_mlvl_iou_preds = torch.cat(mlvl_iou_preds, dim=1)
-        batch_mlvl_nms_scores = (batch_mlvl_scores *
-                                 batch_mlvl_iou_preds[..., None]).sqrt()
-
-        det_results = []
-        for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes,
-                                              batch_mlvl_nms_scores):
-            det_bbox, det_label = multiclass_nms(
-                mlvl_bboxes,
-                mlvl_scores,
-                cfg.score_thr,
-                cfg.nms,
-                cfg.max_per_img,
-                score_factors=None)
-            if self.with_score_voting and len(det_bbox) > 0:
-                det_bbox, det_label = self.score_voting(
-                    det_bbox, det_label, mlvl_bboxes, mlvl_scores,
-                    cfg.score_thr)
-            det_results.append(tuple([det_bbox, det_label]))
-
-        return det_results
-
-    def score_voting(self, det_bboxes, det_labels, mlvl_bboxes,
-                     mlvl_nms_scores, score_thr):
-        """Implementation of score voting method works on each remaining boxes
-        after NMS procedure.
-
-        Args:
-            det_bboxes (Tensor): Remaining boxes after NMS procedure,
-                with shape (k, 5), each dimension means
-                (x1, y1, x2, y2, score).
-            det_labels (Tensor): The label of remaining boxes, with shape
-                (k, 1),Labels are 0-based.
-            mlvl_bboxes (Tensor): All boxes before the NMS procedure,
-                with shape (num_anchors,4).
-            mlvl_nms_scores (Tensor): The scores of all boxes which is used
-                in the NMS procedure, with shape (num_anchors, num_class)
-            mlvl_iou_preds (Tensor): The predictions of IOU of all boxes
-                before the NMS procedure, with shape (num_anchors, 1)
-            score_thr (float): The score threshold of bboxes.
-
-        Returns:
-            tuple: Usually returns a tuple containing voting results.
-
-                - det_bboxes_voted (Tensor): Remaining boxes after
-                    score voting procedure, with shape (k, 5), each
-                    dimension means (x1, y1, x2, y2, score).
-                - det_labels_voted (Tensor): Label of remaining bboxes
-                    after voting, with shape (num_anchors,).
-        """
-        candidate_mask = mlvl_nms_scores > score_thr
-        candidate_mask_nonzeros = candidate_mask.nonzero()
-        candidate_inds = candidate_mask_nonzeros[:, 0]
-        candidate_labels = candidate_mask_nonzeros[:, 1]
-        candidate_bboxes = mlvl_bboxes[candidate_inds]
-        candidate_scores = mlvl_nms_scores[candidate_mask]
-        det_bboxes_voted = []
-        det_labels_voted = []
-        for cls in range(self.cls_out_channels):
-            candidate_cls_mask = candidate_labels == cls
-            if not candidate_cls_mask.any():
-                continue
-            candidate_cls_scores = candidate_scores[candidate_cls_mask]
-            candidate_cls_bboxes = candidate_bboxes[candidate_cls_mask]
-            det_cls_mask = det_labels == cls
-            det_cls_bboxes = det_bboxes[det_cls_mask].view(
-                -1, det_bboxes.size(-1))
-            det_candidate_ious = bbox_overlaps(det_cls_bboxes[:, :4],
-                                               candidate_cls_bboxes)
-            for det_ind in range(len(det_cls_bboxes)):
-                single_det_ious = det_candidate_ious[det_ind]
-                pos_ious_mask = single_det_ious > 0.01
-                pos_ious = single_det_ious[pos_ious_mask]
-                pos_bboxes = candidate_cls_bboxes[pos_ious_mask]
-                pos_scores = candidate_cls_scores[pos_ious_mask]
-                pis = (torch.exp(-(1 - pos_ious)**2 / 0.025) *
-                       pos_scores)[:, None]
-                voted_box = torch.sum(
-                    pis * pos_bboxes, dim=0) / torch.sum(
-                        pis, dim=0)
-                voted_score = det_cls_bboxes[det_ind][-1:][None, :]
-                det_bboxes_voted.append(
-                    torch.cat((voted_box[None, :], voted_score), dim=1))
-                det_labels_voted.append(cls)
-
-        det_bboxes_voted = torch.cat(det_bboxes_voted, dim=0)
-        det_labels_voted = det_labels.new_tensor(det_labels_voted)
-        return det_bboxes_voted, det_labels_voted
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmseg/core/evaluation/eval_hooks.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmseg/core/evaluation/eval_hooks.py
deleted file mode 100644
index 6fc100c8f96e817a6ed2666f7c9f762af2463b48..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmseg/core/evaluation/eval_hooks.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import os.path as osp
-
-from annotator.uniformer.mmcv.runner import DistEvalHook as _DistEvalHook
-from annotator.uniformer.mmcv.runner import EvalHook as _EvalHook
-
-
-class EvalHook(_EvalHook):
-    """Single GPU EvalHook, with efficient test support.
-
-    Args:
-        by_epoch (bool): Determine perform evaluation by epoch or by iteration.
-            If set to True, it will perform by epoch. Otherwise, by iteration.
-            Default: False.
-        efficient_test (bool): Whether save the results as local numpy files to
-            save CPU memory during evaluation. Default: False.
-    Returns:
-        list: The prediction results.
-    """
-
-    greater_keys = ['mIoU', 'mAcc', 'aAcc']
-
-    def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs):
-        super().__init__(*args, by_epoch=by_epoch, **kwargs)
-        self.efficient_test = efficient_test
-
-    def after_train_iter(self, runner):
-        """After train epoch hook.
-
-        Override default ``single_gpu_test``.
-        """
-        if self.by_epoch or not self.every_n_iters(runner, self.interval):
-            return
-        from annotator.uniformer.mmseg.apis import single_gpu_test
-        runner.log_buffer.clear()
-        results = single_gpu_test(
-            runner.model,
-            self.dataloader,
-            show=False,
-            efficient_test=self.efficient_test)
-        self.evaluate(runner, results)
-
-    def after_train_epoch(self, runner):
-        """After train epoch hook.
-
-        Override default ``single_gpu_test``.
-        """
-        if not self.by_epoch or not self.every_n_epochs(runner, self.interval):
-            return
-        from annotator.uniformer.mmseg.apis import single_gpu_test
-        runner.log_buffer.clear()
-        results = single_gpu_test(runner.model, self.dataloader, show=False)
-        self.evaluate(runner, results)
-
-
-class DistEvalHook(_DistEvalHook):
-    """Distributed EvalHook, with efficient test support.
-
-    Args:
-        by_epoch (bool): Determine perform evaluation by epoch or by iteration.
-            If set to True, it will perform by epoch. Otherwise, by iteration.
-            Default: False.
-        efficient_test (bool): Whether save the results as local numpy files to
-            save CPU memory during evaluation. Default: False.
-    Returns:
-        list: The prediction results.
-    """
-
-    greater_keys = ['mIoU', 'mAcc', 'aAcc']
-
-    def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs):
-        super().__init__(*args, by_epoch=by_epoch, **kwargs)
-        self.efficient_test = efficient_test
-
-    def after_train_iter(self, runner):
-        """After train epoch hook.
-
-        Override default ``multi_gpu_test``.
-        """
-        if self.by_epoch or not self.every_n_iters(runner, self.interval):
-            return
-        from annotator.uniformer.mmseg.apis import multi_gpu_test
-        runner.log_buffer.clear()
-        results = multi_gpu_test(
-            runner.model,
-            self.dataloader,
-            tmpdir=osp.join(runner.work_dir, '.eval_hook'),
-            gpu_collect=self.gpu_collect,
-            efficient_test=self.efficient_test)
-        if runner.rank == 0:
-            print('\n')
-            self.evaluate(runner, results)
-
-    def after_train_epoch(self, runner):
-        """After train epoch hook.
-
-        Override default ``multi_gpu_test``.
-        """
-        if not self.by_epoch or not self.every_n_epochs(runner, self.interval):
-            return
-        from annotator.uniformer.mmseg.apis import multi_gpu_test
-        runner.log_buffer.clear()
-        results = multi_gpu_test(
-            runner.model,
-            self.dataloader,
-            tmpdir=osp.join(runner.work_dir, '.eval_hook'),
-            gpu_collect=self.gpu_collect)
-        if runner.rank == 0:
-            print('\n')
-            self.evaluate(runner, results)
diff --git a/spaces/Rongjiehuang/ProDiff/data_gen/tts/txt_processors/__init__.py b/spaces/Rongjiehuang/ProDiff/data_gen/tts/txt_processors/__init__.py
deleted file mode 100644
index 7bff3e9af7d634363116c6605f22a52aad614dea..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/ProDiff/data_gen/tts/txt_processors/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from . import en
\ No newline at end of file
diff --git a/spaces/Sapphire-356/Video2MC/model/block/strided_transformer_encoder.py b/spaces/Sapphire-356/Video2MC/model/block/strided_transformer_encoder.py
deleted file mode 100644
index 6fbcb2c608ad05f2ffd1be44662c2c75f1628cd8..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/model/block/strided_transformer_encoder.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd import Variable
-import numpy as np
-import math
-import os
-import copy
-
-def clones(module, N):
-    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
-
-class Encoder(nn.Module):
-    def __init__(self, layer, N, length, d_model):
-        super(Encoder, self).__init__()
-        self.layers = layer
-        self.norm = LayerNorm(d_model)
-
-        self.pos_embedding_1 = nn.Parameter(torch.randn(1, length, d_model))
-        self.pos_embedding_2 = nn.Parameter(torch.randn(1, length, d_model))
-        self.pos_embedding_3 = nn.Parameter(torch.randn(1, length, d_model))
-
-    def forward(self, x, mask):
-        for i, layer in enumerate(self.layers):
-            if i == 0:
-                x += self.pos_embedding_1[:, :x.shape[1]]
-            elif i == 1:
-                x += self.pos_embedding_2[:, :x.shape[1]]
-            elif i == 2:
-                x += self.pos_embedding_3[:, :x.shape[1]]
-
-            x = layer(x, mask, i)
-
-        return x
-
-class LayerNorm(nn.Module):
-    def __init__(self, features, eps=1e-6):
-        super(LayerNorm, self).__init__()
-        self.a_2 = nn.Parameter(torch.ones(features))
-        self.b_2 = nn.Parameter(torch.zeros(features))
-        self.eps = eps
-
-    def forward(self, x):
-        mean = x.mean(-1, keepdim=True)
-        std = x.std(-1, keepdim=True)
-        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
-
-def attention(query, key, value, mask=None, dropout=None):
-    d_k = query.size(-1)
-    scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
-    if mask is not None:
-        scores = scores.masked_fill(mask == 0, -1e9)
-    p_attn = F.softmax(scores, dim=-1)
-
-    if dropout is not None:
-        p_attn = dropout(p_attn)
-    return torch.matmul(p_attn, value), p_attn
-
-
-class SublayerConnection(nn.Module):
-    def __init__(self, size, dropout, stride_num, i):
-        super(SublayerConnection, self).__init__()
-        self.norm = LayerNorm(size)
-        self.dropout = nn.Dropout(dropout)
-        self.pooling = nn.MaxPool1d(1, stride_num[i])
-        
-    def forward(self, x, sublayer, i=-1, stride_num=-1):
-        if i != -1:
-            if stride_num[i] != 1:
-                res = self.pooling(x.permute(0, 2, 1))
-                res = res.permute(0, 2, 1)
-                
-                return res + self.dropout(sublayer(self.norm(x)))
-            else:
-                return x + self.dropout(sublayer(self.norm(x)))
-        else:
-            return x + self.dropout(sublayer(self.norm(x)))
-
-
-class EncoderLayer(nn.Module):
-    def __init__(self, size, self_attn, feed_forward, dropout, stride_num, i):
-        super(EncoderLayer, self).__init__()
-        self.self_attn = self_attn
-        self.feed_forward = feed_forward
-        self.stride_num = stride_num
-        self.sublayer = clones(SublayerConnection(size, dropout, stride_num, i), 2)
-        self.size = size
-
-    def forward(self, x, mask, i):
-        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
-        x = self.sublayer[1](x, self.feed_forward, i, self.stride_num)
-        return x
-
-
-class MultiHeadedAttention(nn.Module):
-    def __init__(self, h, d_model, dropout=0.1):
-        super(MultiHeadedAttention, self).__init__()
-        assert d_model % h == 0
-        self.d_k = d_model // h 
-        self.h = h
-        self.linears = clones(nn.Linear(d_model, d_model), 4)
-        self.attn = None
-        self.dropout = nn.Dropout(p=dropout)
-
-    def forward(self, query, key, value, mask=None):
-        if mask is not None:
-            mask = mask.unsqueeze(1)
-        nbatches = query.size(0)
-
-        query, key, value = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
-             for l, x in zip(self.linears, (query, key, value))]
-
-        x, self.attn = attention(query, key, value, mask=mask,
-                                 dropout=self.dropout)
-
-        x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k)
-        return self.linears[-1](x)
-
-
-class PositionwiseFeedForward(nn.Module):
-    def __init__(self, d_model, d_ff, dropout=0.1, number = -1, stride_num=-1):
-        super(PositionwiseFeedForward, self).__init__()
-        self.w_1 = nn.Conv1d(d_model, d_ff, kernel_size=1, stride=1)
-        self.w_2 = nn.Conv1d(d_ff, d_model, kernel_size=3, stride=stride_num[number], padding = 1)
-
-        self.gelu = nn.ReLU()
-
-        self.dropout = nn.Dropout(dropout)
-
-    def forward(self, x):
-        x = x.permute(0, 2, 1)
-        x = self.w_2(self.dropout(self.gelu(self.w_1(x))))
-        x = x.permute(0, 2, 1)
-
-        return x
-
-class Transformer(nn.Module):   
-    def __init__(self, n_layers=3, d_model=256, d_ff=512, h=8, length=27, stride_num=None, dropout=0.1):
-        super(Transformer, self).__init__()
-
-        self.length = length
-
-        self.stride_num = stride_num
-        self.model = self.make_model(N=n_layers, d_model=d_model, d_ff=d_ff, h=h, dropout=dropout, length = self.length)
-
-    def forward(self, x, mask=None):
-        x = self.model(x, mask)
-
-        return x
-
-    def make_model(self, N=3, d_model=256, d_ff=512, h=8, dropout=0.1, length=27):
-        c = copy.deepcopy
-        attn = MultiHeadedAttention(h, d_model)
-
-        model_EncoderLayer = []
-        for i in range(N):
-            ff = PositionwiseFeedForward(d_model, d_ff, dropout, i, self.stride_num)
-            model_EncoderLayer.append(EncoderLayer(d_model, c(attn), c(ff), dropout, self.stride_num, i))
-
-        model_EncoderLayer = nn.ModuleList(model_EncoderLayer)
-
-        model = Encoder(model_EncoderLayer, N, length, d_model)
-        
-        return model
-
-
-
-
-
-
-
diff --git a/spaces/ServerX/PorcoDiaz/infer/lib/uvr5_pack/lib_v5/layers_123821KB.py b/spaces/ServerX/PorcoDiaz/infer/lib/uvr5_pack/lib_v5/layers_123821KB.py
deleted file mode 100644
index 4fc1b5cb85a3327f60cbb9f5deffbeeaaac516ad..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/infer/lib/uvr5_pack/lib_v5/layers_123821KB.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle
diff --git a/spaces/ServerX/PorcoDiaz/lib/uvr5_pack/lib_v5/spec_utils.py b/spaces/ServerX/PorcoDiaz/lib/uvr5_pack/lib_v5/spec_utils.py
deleted file mode 100644
index a3fd46d333da7becc7f09f42c084ac7cde661035..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/lib/uvr5_pack/lib_v5/spec_utils.py
+++ /dev/null
@@ -1,667 +0,0 @@
-import os, librosa
-import numpy as np
-import soundfile as sf
-from tqdm import tqdm
-import json, math, hashlib
-
-
-def crop_center(h1, h2):
-    h1_shape = h1.size()
-    h2_shape = h2.size()
-
-    if h1_shape[3] == h2_shape[3]:
-        return h1
-    elif h1_shape[3] < h2_shape[3]:
-        raise ValueError("h1_shape[3] must be greater than h2_shape[3]")
-
-    # s_freq = (h2_shape[2] - h1_shape[2]) // 2
-    # e_freq = s_freq + h1_shape[2]
-    s_time = (h1_shape[3] - h2_shape[3]) // 2
-    e_time = s_time + h2_shape[3]
-    h1 = h1[:, :, :, s_time:e_time]
-
-    return h1
-
-
-def wave_to_spectrogram(
-    wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
-):
-    if reverse:
-        wave_left = np.flip(np.asfortranarray(wave[0]))
-        wave_right = np.flip(np.asfortranarray(wave[1]))
-    elif mid_side:
-        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
-    elif mid_side_b2:
-        wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
-    else:
-        wave_left = np.asfortranarray(wave[0])
-        wave_right = np.asfortranarray(wave[1])
-
-    spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
-    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
-
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-
-
-def wave_to_spectrogram_mt(
-    wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
-):
-    import threading
-
-    if reverse:
-        wave_left = np.flip(np.asfortranarray(wave[0]))
-        wave_right = np.flip(np.asfortranarray(wave[1]))
-    elif mid_side:
-        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
-    elif mid_side_b2:
-        wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
-    else:
-        wave_left = np.asfortranarray(wave[0])
-        wave_right = np.asfortranarray(wave[1])
-
-    def run_thread(**kwargs):
-        global spec_left
-        spec_left = librosa.stft(**kwargs)
-
-    thread = threading.Thread(
-        target=run_thread,
-        kwargs={"y": wave_left, "n_fft": n_fft, "hop_length": hop_length},
-    )
-    thread.start()
-    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
-    thread.join()
-
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-
-
-def combine_spectrograms(specs, mp):
-    l = min([specs[i].shape[2] for i in specs])
-    spec_c = np.zeros(shape=(2, mp.param["bins"] + 1, l), dtype=np.complex64)
-    offset = 0
-    bands_n = len(mp.param["band"])
-
-    for d in range(1, bands_n + 1):
-        h = mp.param["band"][d]["crop_stop"] - mp.param["band"][d]["crop_start"]
-        spec_c[:, offset : offset + h, :l] = specs[d][
-            :, mp.param["band"][d]["crop_start"] : mp.param["band"][d]["crop_stop"], :l
-        ]
-        offset += h
-
-    if offset > mp.param["bins"]:
-        raise ValueError("Too much bins")
-
-    # lowpass fiter
-    if (
-        mp.param["pre_filter_start"] > 0
-    ):  # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:
-        if bands_n == 1:
-            spec_c = fft_lp_filter(
-                spec_c, mp.param["pre_filter_start"], mp.param["pre_filter_stop"]
-            )
-        else:
-            gp = 1
-            for b in range(
-                mp.param["pre_filter_start"] + 1, mp.param["pre_filter_stop"]
-            ):
-                g = math.pow(
-                    10, -(b - mp.param["pre_filter_start"]) * (3.5 - gp) / 20.0
-                )
-                gp = g
-                spec_c[:, b, :] *= g
-
-    return np.asfortranarray(spec_c)
-
-
-def spectrogram_to_image(spec, mode="magnitude"):
-    if mode == "magnitude":
-        if np.iscomplexobj(spec):
-            y = np.abs(spec)
-        else:
-            y = spec
-        y = np.log10(y**2 + 1e-8)
-    elif mode == "phase":
-        if np.iscomplexobj(spec):
-            y = np.angle(spec)
-        else:
-            y = spec
-
-    y -= y.min()
-    y *= 255 / y.max()
-    img = np.uint8(y)
-
-    if y.ndim == 3:
-        img = img.transpose(1, 2, 0)
-        img = np.concatenate([np.max(img, axis=2, keepdims=True), img], axis=2)
-
-    return img
-
-
-def reduce_vocal_aggressively(X, y, softmask):
-    v = X - y
-    y_mag_tmp = np.abs(y)
-    v_mag_tmp = np.abs(v)
-
-    v_mask = v_mag_tmp > y_mag_tmp
-    y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)
-
-    return y_mag * np.exp(1.0j * np.angle(y))
-
-
-def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32):
-    if min_range < fade_size * 2:
-        raise ValueError("min_range must be >= fade_area * 2")
-
-    mag = mag.copy()
-
-    idx = np.where(ref.mean(axis=(0, 1)) < thres)[0]
-    starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
-    ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
-    uninformative = np.where(ends - starts > min_range)[0]
-    if len(uninformative) > 0:
-        starts = starts[uninformative]
-        ends = ends[uninformative]
-        old_e = None
-        for s, e in zip(starts, ends):
-            if old_e is not None and s - old_e < fade_size:
-                s = old_e - fade_size * 2
-
-            if s != 0:
-                weight = np.linspace(0, 1, fade_size)
-                mag[:, :, s : s + fade_size] += weight * ref[:, :, s : s + fade_size]
-            else:
-                s -= fade_size
-
-            if e != mag.shape[2]:
-                weight = np.linspace(1, 0, fade_size)
-                mag[:, :, e - fade_size : e] += weight * ref[:, :, e - fade_size : e]
-            else:
-                e += fade_size
-
-            mag[:, :, s + fade_size : e - fade_size] += ref[
-                :, :, s + fade_size : e - fade_size
-            ]
-            old_e = e
-
-    return mag
-
-
-def align_wave_head_and_tail(a, b):
-    l = min([a[0].size, b[0].size])
-
-    return a[:l, :l], b[:l, :l]
-
-
-def cache_or_load(mix_path, inst_path, mp):
-    mix_basename = os.path.splitext(os.path.basename(mix_path))[0]
-    inst_basename = os.path.splitext(os.path.basename(inst_path))[0]
-
-    cache_dir = "mph{}".format(
-        hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode("utf-8")).hexdigest()
-    )
-    mix_cache_dir = os.path.join("cache", cache_dir)
-    inst_cache_dir = os.path.join("cache", cache_dir)
-
-    os.makedirs(mix_cache_dir, exist_ok=True)
-    os.makedirs(inst_cache_dir, exist_ok=True)
-
-    mix_cache_path = os.path.join(mix_cache_dir, mix_basename + ".npy")
-    inst_cache_path = os.path.join(inst_cache_dir, inst_basename + ".npy")
-
-    if os.path.exists(mix_cache_path) and os.path.exists(inst_cache_path):
-        X_spec_m = np.load(mix_cache_path)
-        y_spec_m = np.load(inst_cache_path)
-    else:
-        X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
-
-        for d in range(len(mp.param["band"]), 0, -1):
-            bp = mp.param["band"][d]
-
-            if d == len(mp.param["band"]):  # high-end band
-                X_wave[d], _ = librosa.load(
-                    mix_path, bp["sr"], False, dtype=np.float32, res_type=bp["res_type"]
-                )
-                y_wave[d], _ = librosa.load(
-                    inst_path,
-                    bp["sr"],
-                    False,
-                    dtype=np.float32,
-                    res_type=bp["res_type"],
-                )
-            else:  # lower bands
-                X_wave[d] = librosa.resample(
-                    X_wave[d + 1],
-                    mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-                y_wave[d] = librosa.resample(
-                    y_wave[d + 1],
-                    mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-
-            X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d])
-
-            X_spec_s[d] = wave_to_spectrogram(
-                X_wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                mp.param["mid_side"],
-                mp.param["mid_side_b2"],
-                mp.param["reverse"],
-            )
-            y_spec_s[d] = wave_to_spectrogram(
-                y_wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                mp.param["mid_side"],
-                mp.param["mid_side_b2"],
-                mp.param["reverse"],
-            )
-
-        del X_wave, y_wave
-
-        X_spec_m = combine_spectrograms(X_spec_s, mp)
-        y_spec_m = combine_spectrograms(y_spec_s, mp)
-
-        if X_spec_m.shape != y_spec_m.shape:
-            raise ValueError("The combined spectrograms are different: " + mix_path)
-
-        _, ext = os.path.splitext(mix_path)
-
-        np.save(mix_cache_path, X_spec_m)
-        np.save(inst_cache_path, y_spec_m)
-
-    return X_spec_m, y_spec_m
-
-
-def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse):
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-
-    wave_left = librosa.istft(spec_left, hop_length=hop_length)
-    wave_right = librosa.istft(spec_right, hop_length=hop_length)
-
-    if reverse:
-        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
-    elif mid_side:
-        return np.asfortranarray(
-            [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
-        )
-    elif mid_side_b2:
-        return np.asfortranarray(
-            [
-                np.add(wave_right / 1.25, 0.4 * wave_left),
-                np.subtract(wave_left / 1.25, 0.4 * wave_right),
-            ]
-        )
-    else:
-        return np.asfortranarray([wave_left, wave_right])
-
-
-def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2):
-    import threading
-
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-
-    def run_thread(**kwargs):
-        global wave_left
-        wave_left = librosa.istft(**kwargs)
-
-    thread = threading.Thread(
-        target=run_thread, kwargs={"stft_matrix": spec_left, "hop_length": hop_length}
-    )
-    thread.start()
-    wave_right = librosa.istft(spec_right, hop_length=hop_length)
-    thread.join()
-
-    if reverse:
-        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
-    elif mid_side:
-        return np.asfortranarray(
-            [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
-        )
-    elif mid_side_b2:
-        return np.asfortranarray(
-            [
-                np.add(wave_right / 1.25, 0.4 * wave_left),
-                np.subtract(wave_left / 1.25, 0.4 * wave_right),
-            ]
-        )
-    else:
-        return np.asfortranarray([wave_left, wave_right])
-
-
-def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
-    wave_band = {}
-    bands_n = len(mp.param["band"])
-    offset = 0
-
-    for d in range(1, bands_n + 1):
-        bp = mp.param["band"][d]
-        spec_s = np.ndarray(
-            shape=(2, bp["n_fft"] // 2 + 1, spec_m.shape[2]), dtype=complex
-        )
-        h = bp["crop_stop"] - bp["crop_start"]
-        spec_s[:, bp["crop_start"] : bp["crop_stop"], :] = spec_m[
-            :, offset : offset + h, :
-        ]
-
-        offset += h
-        if d == bands_n:  # higher
-            if extra_bins_h:  # if --high_end_process bypass
-                max_bin = bp["n_fft"] // 2
-                spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[
-                    :, :extra_bins_h, :
-                ]
-            if bp["hpf_start"] > 0:
-                spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
-            if bands_n == 1:
-                wave = spectrogram_to_wave(
-                    spec_s,
-                    bp["hl"],
-                    mp.param["mid_side"],
-                    mp.param["mid_side_b2"],
-                    mp.param["reverse"],
-                )
-            else:
-                wave = np.add(
-                    wave,
-                    spectrogram_to_wave(
-                        spec_s,
-                        bp["hl"],
-                        mp.param["mid_side"],
-                        mp.param["mid_side_b2"],
-                        mp.param["reverse"],
-                    ),
-                )
-        else:
-            sr = mp.param["band"][d + 1]["sr"]
-            if d == 1:  # lower
-                spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
-                wave = librosa.resample(
-                    spectrogram_to_wave(
-                        spec_s,
-                        bp["hl"],
-                        mp.param["mid_side"],
-                        mp.param["mid_side_b2"],
-                        mp.param["reverse"],
-                    ),
-                    bp["sr"],
-                    sr,
-                    res_type="sinc_fastest",
-                )
-            else:  # mid
-                spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
-                spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
-                wave2 = np.add(
-                    wave,
-                    spectrogram_to_wave(
-                        spec_s,
-                        bp["hl"],
-                        mp.param["mid_side"],
-                        mp.param["mid_side_b2"],
-                        mp.param["reverse"],
-                    ),
-                )
-                # wave = librosa.core.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
-                wave = librosa.core.resample(wave2, bp["sr"], sr, res_type="scipy")
-
-    return wave.T
-
-
-def fft_lp_filter(spec, bin_start, bin_stop):
-    g = 1.0
-    for b in range(bin_start, bin_stop):
-        g -= 1 / (bin_stop - bin_start)
-        spec[:, b, :] = g * spec[:, b, :]
-
-    spec[:, bin_stop:, :] *= 0
-
-    return spec
-
-
-def fft_hp_filter(spec, bin_start, bin_stop):
-    g = 1.0
-    for b in range(bin_start, bin_stop, -1):
-        g -= 1 / (bin_start - bin_stop)
-        spec[:, b, :] = g * spec[:, b, :]
-
-    spec[:, 0 : bin_stop + 1, :] *= 0
-
-    return spec
-
-
-def mirroring(a, spec_m, input_high_end, mp):
-    if "mirroring" == a:
-        mirror = np.flip(
-            np.abs(
-                spec_m[
-                    :,
-                    mp.param["pre_filter_start"]
-                    - 10
-                    - input_high_end.shape[1] : mp.param["pre_filter_start"]
-                    - 10,
-                    :,
-                ]
-            ),
-            1,
-        )
-        mirror = mirror * np.exp(1.0j * np.angle(input_high_end))
-
-        return np.where(
-            np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror
-        )
-
-    if "mirroring2" == a:
-        mirror = np.flip(
-            np.abs(
-                spec_m[
-                    :,
-                    mp.param["pre_filter_start"]
-                    - 10
-                    - input_high_end.shape[1] : mp.param["pre_filter_start"]
-                    - 10,
-                    :,
-                ]
-            ),
-            1,
-        )
-        mi = np.multiply(mirror, input_high_end * 1.7)
-
-        return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
-
-
-def ensembling(a, specs):
-    for i in range(1, len(specs)):
-        if i == 1:
-            spec = specs[0]
-
-        ln = min([spec.shape[2], specs[i].shape[2]])
-        spec = spec[:, :, :ln]
-        specs[i] = specs[i][:, :, :ln]
-
-        if "min_mag" == a:
-            spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
-        if "max_mag" == a:
-            spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)
-
-    return spec
-
-
-def stft(wave, nfft, hl):
-    wave_left = np.asfortranarray(wave[0])
-    wave_right = np.asfortranarray(wave[1])
-    spec_left = librosa.stft(wave_left, nfft, hop_length=hl)
-    spec_right = librosa.stft(wave_right, nfft, hop_length=hl)
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-
-
-def istft(spec, hl):
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-
-    wave_left = librosa.istft(spec_left, hop_length=hl)
-    wave_right = librosa.istft(spec_right, hop_length=hl)
-    wave = np.asfortranarray([wave_left, wave_right])
-
-
-if __name__ == "__main__":
-    import cv2
-    import sys
-    import time
-    import argparse
-    from model_param_init import ModelParameters
-
-    p = argparse.ArgumentParser()
-    p.add_argument(
-        "--algorithm",
-        "-a",
-        type=str,
-        choices=["invert", "invert_p", "min_mag", "max_mag", "deep", "align"],
-        default="min_mag",
-    )
-    p.add_argument(
-        "--model_params",
-        "-m",
-        type=str,
-        default=os.path.join("modelparams", "1band_sr44100_hl512.json"),
-    )
-    p.add_argument("--output_name", "-o", type=str, default="output")
-    p.add_argument("--vocals_only", "-v", action="store_true")
-    p.add_argument("input", nargs="+")
-    args = p.parse_args()
-
-    start_time = time.time()
-
-    if args.algorithm.startswith("invert") and len(args.input) != 2:
-        raise ValueError("There should be two input files.")
-
-    if not args.algorithm.startswith("invert") and len(args.input) < 2:
-        raise ValueError("There must be at least two input files.")
-
-    wave, specs = {}, {}
-    mp = ModelParameters(args.model_params)
-
-    for i in range(len(args.input)):
-        spec = {}
-
-        for d in range(len(mp.param["band"]), 0, -1):
-            bp = mp.param["band"][d]
-
-            if d == len(mp.param["band"]):  # high-end band
-                wave[d], _ = librosa.load(
-                    args.input[i],
-                    bp["sr"],
-                    False,
-                    dtype=np.float32,
-                    res_type=bp["res_type"],
-                )
-
-                if len(wave[d].shape) == 1:  # mono to stereo
-                    wave[d] = np.array([wave[d], wave[d]])
-            else:  # lower bands
-                wave[d] = librosa.resample(
-                    wave[d + 1],
-                    mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-
-            spec[d] = wave_to_spectrogram(
-                wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                mp.param["mid_side"],
-                mp.param["mid_side_b2"],
-                mp.param["reverse"],
-            )
-
-        specs[i] = combine_spectrograms(spec, mp)
-
-    del wave
-
-    if args.algorithm == "deep":
-        d_spec = np.where(np.abs(specs[0]) <= np.abs(spec[1]), specs[0], spec[1])
-        v_spec = d_spec - specs[1]
-        sf.write(
-            os.path.join("{}.wav".format(args.output_name)),
-            cmb_spectrogram_to_wave(v_spec, mp),
-            mp.param["sr"],
-        )
-
-    if args.algorithm.startswith("invert"):
-        ln = min([specs[0].shape[2], specs[1].shape[2]])
-        specs[0] = specs[0][:, :, :ln]
-        specs[1] = specs[1][:, :, :ln]
-
-        if "invert_p" == args.algorithm:
-            X_mag = np.abs(specs[0])
-            y_mag = np.abs(specs[1])
-            max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
-            v_spec = specs[1] - max_mag * np.exp(1.0j * np.angle(specs[0]))
-        else:
-            specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
-            v_spec = specs[0] - specs[1]
-
-            if not args.vocals_only:
-                X_mag = np.abs(specs[0])
-                y_mag = np.abs(specs[1])
-                v_mag = np.abs(v_spec)
-
-                X_image = spectrogram_to_image(X_mag)
-                y_image = spectrogram_to_image(y_mag)
-                v_image = spectrogram_to_image(v_mag)
-
-                cv2.imwrite("{}_X.png".format(args.output_name), X_image)
-                cv2.imwrite("{}_y.png".format(args.output_name), y_image)
-                cv2.imwrite("{}_v.png".format(args.output_name), v_image)
-
-                sf.write(
-                    "{}_X.wav".format(args.output_name),
-                    cmb_spectrogram_to_wave(specs[0], mp),
-                    mp.param["sr"],
-                )
-                sf.write(
-                    "{}_y.wav".format(args.output_name),
-                    cmb_spectrogram_to_wave(specs[1], mp),
-                    mp.param["sr"],
-                )
-
-        sf.write(
-            "{}_v.wav".format(args.output_name),
-            cmb_spectrogram_to_wave(v_spec, mp),
-            mp.param["sr"],
-        )
-    else:
-        if not args.algorithm == "deep":
-            sf.write(
-                os.path.join("ensembled", "{}.wav".format(args.output_name)),
-                cmb_spectrogram_to_wave(ensembling(args.algorithm, specs), mp),
-                mp.param["sr"],
-            )
-
-    if args.algorithm == "align":
-        trackalignment = [
-            {
-                "file1": '"{}"'.format(args.input[0]),
-                "file2": '"{}"'.format(args.input[1]),
-            }
-        ]
-
-        for i, e in tqdm(enumerate(trackalignment), desc="Performing Alignment..."):
-            os.system(f"python lib/align_tracks.py {e['file1']} {e['file2']}")
-
-    # print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
diff --git a/spaces/Shakeb100/GroomingGenie_AI/clipseg/general_utils.py b/spaces/Shakeb100/GroomingGenie_AI/clipseg/general_utils.py
deleted file mode 100644
index 708d32e701a78f3ce848060baef561c8f11b1b2e..0000000000000000000000000000000000000000
--- a/spaces/Shakeb100/GroomingGenie_AI/clipseg/general_utils.py
+++ /dev/null
@@ -1,272 +0,0 @@
-import json
-import inspect
-import torch
-import os
-import sys
-import yaml
-from shutil import copy, copytree
-from os.path import join, dirname, realpath, expanduser, isfile, isdir, basename
-
-
-class Logger(object):
-
-    def __getattr__(self, k):
-        return print
-
-log = Logger()
-
-def training_config_from_cli_args():
-    experiment_name = sys.argv[1]
-    experiment_id = int(sys.argv[2])
-
-    yaml_config = yaml.load(open(f'experiments/{experiment_name}'), Loader=yaml.SafeLoader)
-
-    config = yaml_config['configuration']
-    config = {**config, **yaml_config['individual_configurations'][experiment_id]}
-    config = AttributeDict(config)
-    return config
-
-
-def score_config_from_cli_args():
-    experiment_name = sys.argv[1]
-    experiment_id = int(sys.argv[2])
-    
-
-    yaml_config = yaml.load(open(f'experiments/{experiment_name}'), Loader=yaml.SafeLoader)
-
-    config = yaml_config['test_configuration_common']
-
-    if type(yaml_config['test_configuration']) == list:
-        test_id = int(sys.argv[3])
-        config = {**config, **yaml_config['test_configuration'][test_id]}
-    else:
-        config = {**config, **yaml_config['test_configuration']}
-
-    if 'test_configuration' in yaml_config['individual_configurations'][experiment_id]:
-        config = {**config, **yaml_config['individual_configurations'][experiment_id]['test_configuration']}
-
-    train_checkpoint_id = yaml_config['individual_configurations'][experiment_id]['name']
-
-    config = AttributeDict(config)
-    return config, train_checkpoint_id
-
-
-def get_from_repository(local_name, repo_files, integrity_check=None, repo_dir='~/dataset_repository', 
-                        local_dir='~/datasets'):
-    """ copies files from repository to local folder.
-    
-    repo_files: list of filenames or list of tuples [filename, target path] 
-
-    e.g. get_from_repository('MyDataset', [['data/dataset1.tar', 'other/path/ds03.tar'])
-    will create a folder 'MyDataset' in local_dir, and extract the content of
-    '<repo_dir>/data/dataset1.tar' to <local_dir>/MyDataset/other/path.
-     """
-
-    local_dir = realpath(join(expanduser(local_dir), local_name))
-
-    dataset_exists = True
-
-    # check if folder is available
-    if not isdir(local_dir):
-        dataset_exists = False
-
-    if integrity_check is not None:
-        try:
-            integrity_ok = integrity_check(local_dir)
-        except BaseException:
-            integrity_ok = False
-
-        if integrity_ok:
-            log.hint('Passed custom integrity check')
-        else:
-            log.hint('Custom integrity check failed')
-
-        dataset_exists = dataset_exists and integrity_ok
-
-    if not dataset_exists:
-
-        repo_dir = realpath(expanduser(repo_dir))
-
-        for i, filename in enumerate(repo_files):
-
-            if type(filename) == str:
-                origin, target = filename, filename
-                archive_target = join(local_dir, basename(origin))
-                extract_target = join(local_dir)
-            else:
-                origin, target = filename
-                archive_target = join(local_dir, dirname(target), basename(origin))
-                extract_target = join(local_dir, dirname(target))
-            
-            archive_origin = join(repo_dir, origin)
-
-            log.hint(f'copy: {archive_origin} to {archive_target}')
-
-            # make sure the path exists
-            os.makedirs(dirname(archive_target), exist_ok=True)
-
-            if os.path.isfile(archive_target):
-                # only copy if size differs
-                if os.path.getsize(archive_target) != os.path.getsize(archive_origin):
-                    log.hint(f'file exists but filesize differs: target {os.path.getsize(archive_target)} vs. origin {os.path.getsize(archive_origin)}')
-                    copy(archive_origin, archive_target)
-            else:
-                copy(archive_origin, archive_target)
-
-            extract_archive(archive_target, extract_target, noarchive_ok=True)
-
-            # concurrent processes might have deleted the file
-            if os.path.isfile(archive_target):
-                os.remove(archive_target)
-
-
-def extract_archive(filename, target_folder=None, noarchive_ok=False):
-    from subprocess import run, PIPE
-
-    if filename.endswith('.tgz') or filename.endswith('.tar'):
-        command = f'tar -xf {filename}'
-        command += f' -C {target_folder}' if target_folder is not None else ''
-    elif filename.endswith('.tar.gz'):
-        command = f'tar -xzf {filename}'
-        command += f' -C {target_folder}' if target_folder is not None else ''
-    elif filename.endswith('zip'):
-        command = f'unzip {filename}'
-        command += f' -d {target_folder}' if target_folder is not None else ''
-    else:
-        if noarchive_ok:
-            return
-        else:
-            raise ValueError(f'unsuppored file ending of {filename}')
-
-    log.hint(command)
-    result = run(command.split(), stdout=PIPE, stderr=PIPE)
-    if result.returncode != 0:
-        print(result.stdout, result.stderr)
-
-
-class AttributeDict(dict):
-    """ 
-    An extended dictionary that allows access to elements as atttributes and counts 
-    these accesses. This way, we know if some attributes were never used. 
-    """
-
-    def __init__(self, *args, **kwargs):
-        from collections import Counter
-        super().__init__(*args, **kwargs)
-        self.__dict__['counter'] = Counter()
-
-    def __getitem__(self, k):
-        self.__dict__['counter'][k] += 1
-        return super().__getitem__(k)
-
-    def __getattr__(self, k):
-        self.__dict__['counter'][k] += 1
-        return super().get(k)
-
-    def __setattr__(self, k, v):
-        return super().__setitem__(k, v)
-
-    def __delattr__(self, k, v):
-        return super().__delitem__(k, v)    
-
-    def unused_keys(self, exceptions=()):
-        return [k for k in super().keys() if self.__dict__['counter'][k] == 0 and k not in exceptions]
-
-    def assume_no_unused_keys(self, exceptions=()):
-        if len(self.unused_keys(exceptions=exceptions)) > 0:
-            log.warning('Unused keys:', self.unused_keys(exceptions=exceptions))
-
-
-def get_attribute(name):
-    import importlib
-
-    if name is None:
-        raise ValueError('The provided attribute is None')
-    
-    name_split = name.split('.')
-    mod = importlib.import_module('.'.join(name_split[:-1]))
-    return getattr(mod, name_split[-1])
-
-
-
-def filter_args(input_args, default_args):
-
-    updated_args = {k: input_args[k] if k in input_args else v for k, v in default_args.items()}
-    used_args = {k: v for k, v in input_args.items() if k in default_args}
-    unused_args = {k: v for k, v in input_args.items() if k not in default_args}
-
-    return AttributeDict(updated_args), AttributeDict(used_args), AttributeDict(unused_args)
-
-
-def load_model(checkpoint_id, weights_file=None, strict=True, model_args='from_config', with_config=False):
-
-    config = json.load(open(join('logs', checkpoint_id, 'config.json')))
-
-    if model_args != 'from_config' and type(model_args) != dict:
-        raise ValueError('model_args must either be "from_config" or a dictionary of values')
-
-    model_cls = get_attribute(config['model'])
-
-    # load model
-    if model_args == 'from_config':
-        _, model_args, _ = filter_args(config, inspect.signature(model_cls).parameters)
-
-    model = model_cls(**model_args)
-
-    if weights_file is None:
-        weights_file = realpath(join('logs', checkpoint_id, 'weights.pth'))
-    else:
-        weights_file = realpath(join('logs', checkpoint_id, weights_file))
-
-    if isfile(weights_file):
-        weights = torch.load(weights_file)
-        for _, w in weights.items():
-            assert not torch.any(torch.isnan(w)), 'weights contain NaNs'
-        model.load_state_dict(weights, strict=strict)
-    else:
-        raise FileNotFoundError(f'model checkpoint {weights_file} was not found')
-
-    if with_config:
-        return model, config
-    
-    return model
-
-
-class TrainingLogger(object):
-
-    def __init__(self, model, log_dir, config=None, *args):
-        super().__init__()
-        self.model = model
-        self.base_path = join(f'logs/{log_dir}') if log_dir is not None else None
-
-        os.makedirs('logs/', exist_ok=True)
-        os.makedirs(self.base_path, exist_ok=True)
-
-        if config is not None:
-            json.dump(config, open(join(self.base_path, 'config.json'), 'w'))
-
-    def iter(self, i, **kwargs):
-        if i % 100 == 0 and 'loss' in kwargs:
-            loss = kwargs['loss']
-            print(f'iteration {i}: loss {loss:.4f}')
-
-    def save_weights(self, only_trainable=False, weight_file='weights.pth'):
-        if self.model is None:
-            raise AttributeError('You need to provide a model reference when initializing TrainingTracker to save weights.')
-
-        weights_path = join(self.base_path, weight_file)
-
-        weight_dict = self.model.state_dict()
-
-        if only_trainable:
-            weight_dict = {n: weight_dict[n] for n, p in self.model.named_parameters() if p.requires_grad}
-        
-        torch.save(weight_dict, weights_path)
-        log.info(f'Saved weights to {weights_path}')
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, type, value, traceback):
-        """ automatically stop processes if used in a context manager """
-        pass        
\ No newline at end of file
diff --git a/spaces/SilenWang/ReviewGPT/__init__.py b/spaces/SilenWang/ReviewGPT/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/python/dqn/__init__.py b/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/python/dqn/__init__.py
deleted file mode 100644
index 4ae42872c812a7c8a18dff002086c7e6e935f580..0000000000000000000000000000000000000000
--- a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/python/dqn/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from stable_baselines3.dqn.dqn import DQN
-from stable_baselines3.dqn.policies import CnnPolicy, MlpPolicy
diff --git a/spaces/Soybean01/anime-ai-detect/README.md b/spaces/Soybean01/anime-ai-detect/README.md
deleted file mode 100644
index 952c183fd69ccb1664b4236b6132fc6d0358c7de..0000000000000000000000000000000000000000
--- a/spaces/Soybean01/anime-ai-detect/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Anime Ai Detect
-emoji: 🤖
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: true
-duplicated_from: saltacc/anime-ai-detect
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SriniJalasuthram/SJ-01-H5-Play-Canvas-Sim-Physics/README.md b/spaces/SriniJalasuthram/SJ-01-H5-Play-Canvas-Sim-Physics/README.md
deleted file mode 100644
index ed380d8b376eeaa4be102722c534a2bc46b712d3..0000000000000000000000000000000000000000
--- a/spaces/SriniJalasuthram/SJ-01-H5-Play-Canvas-Sim-Physics/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: SJ 01 H5 Play Canvas Sim Physics
-emoji: 👁
-colorFrom: indigo
-colorTo: pink
-sdk: static
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/DdsImagePlugin.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/DdsImagePlugin.py
deleted file mode 100644
index a946daeaa6b9a5946fc5492443dfddbb10881c99..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/DdsImagePlugin.py
+++ /dev/null
@@ -1,291 +0,0 @@
-"""
-A Pillow loader for .dds files (S3TC-compressed aka DXTC)
-Jerome Leclanche <jerome@leclan.ch>
-
-Documentation:
-  https://web.archive.org/web/20170802060935/http://oss.sgi.com/projects/ogl-sample/registry/EXT/texture_compression_s3tc.txt
-
-The contents of this file are hereby released in the public domain (CC0)
-Full text of the CC0 license:
-  https://creativecommons.org/publicdomain/zero/1.0/
-"""
-
-import struct
-from io import BytesIO
-
-from . import Image, ImageFile
-from ._binary import o32le as o32
-
-# Magic ("DDS ")
-DDS_MAGIC = 0x20534444
-
-# DDS flags
-DDSD_CAPS = 0x1
-DDSD_HEIGHT = 0x2
-DDSD_WIDTH = 0x4
-DDSD_PITCH = 0x8
-DDSD_PIXELFORMAT = 0x1000
-DDSD_MIPMAPCOUNT = 0x20000
-DDSD_LINEARSIZE = 0x80000
-DDSD_DEPTH = 0x800000
-
-# DDS caps
-DDSCAPS_COMPLEX = 0x8
-DDSCAPS_TEXTURE = 0x1000
-DDSCAPS_MIPMAP = 0x400000
-
-DDSCAPS2_CUBEMAP = 0x200
-DDSCAPS2_CUBEMAP_POSITIVEX = 0x400
-DDSCAPS2_CUBEMAP_NEGATIVEX = 0x800
-DDSCAPS2_CUBEMAP_POSITIVEY = 0x1000
-DDSCAPS2_CUBEMAP_NEGATIVEY = 0x2000
-DDSCAPS2_CUBEMAP_POSITIVEZ = 0x4000
-DDSCAPS2_CUBEMAP_NEGATIVEZ = 0x8000
-DDSCAPS2_VOLUME = 0x200000
-
-# Pixel Format
-DDPF_ALPHAPIXELS = 0x1
-DDPF_ALPHA = 0x2
-DDPF_FOURCC = 0x4
-DDPF_PALETTEINDEXED8 = 0x20
-DDPF_RGB = 0x40
-DDPF_LUMINANCE = 0x20000
-
-
-# dds.h
-
-DDS_FOURCC = DDPF_FOURCC
-DDS_RGB = DDPF_RGB
-DDS_RGBA = DDPF_RGB | DDPF_ALPHAPIXELS
-DDS_LUMINANCE = DDPF_LUMINANCE
-DDS_LUMINANCEA = DDPF_LUMINANCE | DDPF_ALPHAPIXELS
-DDS_ALPHA = DDPF_ALPHA
-DDS_PAL8 = DDPF_PALETTEINDEXED8
-
-DDS_HEADER_FLAGS_TEXTURE = DDSD_CAPS | DDSD_HEIGHT | DDSD_WIDTH | DDSD_PIXELFORMAT
-DDS_HEADER_FLAGS_MIPMAP = DDSD_MIPMAPCOUNT
-DDS_HEADER_FLAGS_VOLUME = DDSD_DEPTH
-DDS_HEADER_FLAGS_PITCH = DDSD_PITCH
-DDS_HEADER_FLAGS_LINEARSIZE = DDSD_LINEARSIZE
-
-DDS_HEIGHT = DDSD_HEIGHT
-DDS_WIDTH = DDSD_WIDTH
-
-DDS_SURFACE_FLAGS_TEXTURE = DDSCAPS_TEXTURE
-DDS_SURFACE_FLAGS_MIPMAP = DDSCAPS_COMPLEX | DDSCAPS_MIPMAP
-DDS_SURFACE_FLAGS_CUBEMAP = DDSCAPS_COMPLEX
-
-DDS_CUBEMAP_POSITIVEX = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_POSITIVEX
-DDS_CUBEMAP_NEGATIVEX = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_NEGATIVEX
-DDS_CUBEMAP_POSITIVEY = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_POSITIVEY
-DDS_CUBEMAP_NEGATIVEY = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_NEGATIVEY
-DDS_CUBEMAP_POSITIVEZ = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_POSITIVEZ
-DDS_CUBEMAP_NEGATIVEZ = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_NEGATIVEZ
-
-
-# DXT1
-DXT1_FOURCC = 0x31545844
-
-# DXT3
-DXT3_FOURCC = 0x33545844
-
-# DXT5
-DXT5_FOURCC = 0x35545844
-
-
-# dxgiformat.h
-
-DXGI_FORMAT_R8G8B8A8_TYPELESS = 27
-DXGI_FORMAT_R8G8B8A8_UNORM = 28
-DXGI_FORMAT_R8G8B8A8_UNORM_SRGB = 29
-DXGI_FORMAT_BC5_TYPELESS = 82
-DXGI_FORMAT_BC5_UNORM = 83
-DXGI_FORMAT_BC5_SNORM = 84
-DXGI_FORMAT_BC6H_UF16 = 95
-DXGI_FORMAT_BC6H_SF16 = 96
-DXGI_FORMAT_BC7_TYPELESS = 97
-DXGI_FORMAT_BC7_UNORM = 98
-DXGI_FORMAT_BC7_UNORM_SRGB = 99
-
-
-class DdsImageFile(ImageFile.ImageFile):
-    format = "DDS"
-    format_description = "DirectDraw Surface"
-
-    def _open(self):
-        if not _accept(self.fp.read(4)):
-            msg = "not a DDS file"
-            raise SyntaxError(msg)
-        (header_size,) = struct.unpack("<I", self.fp.read(4))
-        if header_size != 124:
-            msg = f"Unsupported header size {repr(header_size)}"
-            raise OSError(msg)
-        header_bytes = self.fp.read(header_size - 4)
-        if len(header_bytes) != 120:
-            msg = f"Incomplete header: {len(header_bytes)} bytes"
-            raise OSError(msg)
-        header = BytesIO(header_bytes)
-
-        flags, height, width = struct.unpack("<3I", header.read(12))
-        self._size = (width, height)
-        self.mode = "RGBA"
-
-        pitch, depth, mipmaps = struct.unpack("<3I", header.read(12))
-        struct.unpack("<11I", header.read(44))  # reserved
-
-        # pixel format
-        pfsize, pfflags = struct.unpack("<2I", header.read(8))
-        fourcc = header.read(4)
-        (bitcount,) = struct.unpack("<I", header.read(4))
-        masks = struct.unpack("<4I", header.read(16))
-        if pfflags & DDPF_LUMINANCE:
-            # Texture contains uncompressed L or LA data
-            if pfflags & DDPF_ALPHAPIXELS:
-                self.mode = "LA"
-            else:
-                self.mode = "L"
-
-            self.tile = [("raw", (0, 0) + self.size, 0, (self.mode, 0, 1))]
-        elif pfflags & DDPF_RGB:
-            # Texture contains uncompressed RGB data
-            masks = {mask: ["R", "G", "B", "A"][i] for i, mask in enumerate(masks)}
-            rawmode = ""
-            if pfflags & DDPF_ALPHAPIXELS:
-                rawmode += masks[0xFF000000]
-            else:
-                self.mode = "RGB"
-            rawmode += masks[0xFF0000] + masks[0xFF00] + masks[0xFF]
-
-            self.tile = [("raw", (0, 0) + self.size, 0, (rawmode[::-1], 0, 1))]
-        else:
-            data_start = header_size + 4
-            n = 0
-            if fourcc == b"DXT1":
-                self.pixel_format = "DXT1"
-                n = 1
-            elif fourcc == b"DXT3":
-                self.pixel_format = "DXT3"
-                n = 2
-            elif fourcc == b"DXT5":
-                self.pixel_format = "DXT5"
-                n = 3
-            elif fourcc == b"ATI1":
-                self.pixel_format = "BC4"
-                n = 4
-                self.mode = "L"
-            elif fourcc == b"ATI2":
-                self.pixel_format = "BC5"
-                n = 5
-                self.mode = "RGB"
-            elif fourcc == b"BC5S":
-                self.pixel_format = "BC5S"
-                n = 5
-                self.mode = "RGB"
-            elif fourcc == b"DX10":
-                data_start += 20
-                # ignoring flags which pertain to volume textures and cubemaps
-                (dxgi_format,) = struct.unpack("<I", self.fp.read(4))
-                self.fp.read(16)
-                if dxgi_format in (DXGI_FORMAT_BC5_TYPELESS, DXGI_FORMAT_BC5_UNORM):
-                    self.pixel_format = "BC5"
-                    n = 5
-                    self.mode = "RGB"
-                elif dxgi_format == DXGI_FORMAT_BC5_SNORM:
-                    self.pixel_format = "BC5S"
-                    n = 5
-                    self.mode = "RGB"
-                elif dxgi_format == DXGI_FORMAT_BC6H_UF16:
-                    self.pixel_format = "BC6H"
-                    n = 6
-                    self.mode = "RGB"
-                elif dxgi_format == DXGI_FORMAT_BC6H_SF16:
-                    self.pixel_format = "BC6HS"
-                    n = 6
-                    self.mode = "RGB"
-                elif dxgi_format in (DXGI_FORMAT_BC7_TYPELESS, DXGI_FORMAT_BC7_UNORM):
-                    self.pixel_format = "BC7"
-                    n = 7
-                elif dxgi_format == DXGI_FORMAT_BC7_UNORM_SRGB:
-                    self.pixel_format = "BC7"
-                    self.info["gamma"] = 1 / 2.2
-                    n = 7
-                elif dxgi_format in (
-                    DXGI_FORMAT_R8G8B8A8_TYPELESS,
-                    DXGI_FORMAT_R8G8B8A8_UNORM,
-                    DXGI_FORMAT_R8G8B8A8_UNORM_SRGB,
-                ):
-                    self.tile = [("raw", (0, 0) + self.size, 0, ("RGBA", 0, 1))]
-                    if dxgi_format == DXGI_FORMAT_R8G8B8A8_UNORM_SRGB:
-                        self.info["gamma"] = 1 / 2.2
-                    return
-                else:
-                    msg = f"Unimplemented DXGI format {dxgi_format}"
-                    raise NotImplementedError(msg)
-            else:
-                msg = f"Unimplemented pixel format {repr(fourcc)}"
-                raise NotImplementedError(msg)
-
-            self.tile = [
-                ("bcn", (0, 0) + self.size, data_start, (n, self.pixel_format))
-            ]
-
-    def load_seek(self, pos):
-        pass
-
-
-def _save(im, fp, filename):
-    if im.mode not in ("RGB", "RGBA", "L", "LA"):
-        msg = f"cannot write mode {im.mode} as DDS"
-        raise OSError(msg)
-
-    rawmode = im.mode
-    masks = [0xFF0000, 0xFF00, 0xFF]
-    if im.mode in ("L", "LA"):
-        pixel_flags = DDPF_LUMINANCE
-    else:
-        pixel_flags = DDPF_RGB
-        rawmode = rawmode[::-1]
-    if im.mode in ("LA", "RGBA"):
-        pixel_flags |= DDPF_ALPHAPIXELS
-        masks.append(0xFF000000)
-
-    bitcount = len(masks) * 8
-    while len(masks) < 4:
-        masks.append(0)
-
-    fp.write(
-        o32(DDS_MAGIC)
-        + o32(124)  # header size
-        + o32(
-            DDSD_CAPS | DDSD_HEIGHT | DDSD_WIDTH | DDSD_PITCH | DDSD_PIXELFORMAT
-        )  # flags
-        + o32(im.height)
-        + o32(im.width)
-        + o32((im.width * bitcount + 7) // 8)  # pitch
-        + o32(0)  # depth
-        + o32(0)  # mipmaps
-        + o32(0) * 11  # reserved
-        + o32(32)  # pfsize
-        + o32(pixel_flags)  # pfflags
-        + o32(0)  # fourcc
-        + o32(bitcount)  # bitcount
-        + b"".join(o32(mask) for mask in masks)  # rgbabitmask
-        + o32(DDSCAPS_TEXTURE)  # dwCaps
-        + o32(0)  # dwCaps2
-        + o32(0)  # dwCaps3
-        + o32(0)  # dwCaps4
-        + o32(0)  # dwReserved2
-    )
-    if im.mode == "RGBA":
-        r, g, b, a = im.split()
-        im = Image.merge("RGBA", (a, r, g, b))
-    ImageFile._save(im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, 0, 1))])
-
-
-def _accept(prefix):
-    return prefix[:4] == b"DDS "
-
-
-Image.register_open(DdsImageFile.format, DdsImageFile, _accept)
-Image.register_save(DdsImageFile.format, _save)
-Image.register_extension(DdsImageFile.format, ".dds")
diff --git a/spaces/Suniilkumaar/SwapMukham/nsfw_checker/opennsfw.py b/spaces/Suniilkumaar/SwapMukham/nsfw_checker/opennsfw.py
deleted file mode 100644
index a9f3d4178e7f31d0e22311d80c6ab843fb4dbc95..0000000000000000000000000000000000000000
--- a/spaces/Suniilkumaar/SwapMukham/nsfw_checker/opennsfw.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import cv2
-import torch
-import onnx
-import onnxruntime
-import numpy as np
-from tqdm import tqdm
-
-# https://github.com/yahoo/open_nsfw
-
-class NSFWChecker:
-    def __init__(self, model_path=None, providers=["CPUExecutionProvider"]):
-        model = onnx.load(model_path)
-        self.input_name = model.graph.input[0].name
-        session_options = onnxruntime.SessionOptions()
-        self.session = onnxruntime.InferenceSession(model_path, sess_options=session_options, providers=providers)
-
-    def is_nsfw(self, img_paths, threshold = 0.85):
-        skip_step = 1
-        total_len = len(img_paths)
-        if total_len < 100: skip_step = 1
-        if total_len > 100 and total_len < 500: skip_step = 10
-        if total_len > 500 and total_len < 1000: skip_step = 20
-        if total_len > 1000 and total_len < 10000: skip_step = 50
-        if total_len > 10000: skip_step = 100
-
-        for idx in tqdm(range(0, total_len, skip_step), total=int(total_len // skip_step), desc="Checking for NSFW contents"):
-            img = cv2.imread(img_paths[idx])
-            img = cv2.resize(img, (224,224)).astype('float32')
-            img -= np.array([104, 117, 123], dtype=np.float32)
-            img = np.expand_dims(img, axis=0)
-
-            score = self.session.run(None, {self.input_name:img})[0][0][1]
-
-            if score > threshold:
-                print(f"Detected nsfw score:{score}")
-                return True
-        return False
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/configs/_base_/datasets/stare.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/configs/_base_/datasets/stare.py
deleted file mode 100644
index 3f71b25488cc11a6b4d582ac52b5a24e1ad1cf8e..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/configs/_base_/datasets/stare.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# dataset settings
-dataset_type = 'STAREDataset'
-data_root = 'data/STARE'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-img_scale = (605, 700)
-crop_size = (128, 128)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
-    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
-    dict(type='RandomFlip', prob=0.5),
-    dict(type='PhotoMetricDistortion'),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=img_scale,
-        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img'])
-        ])
-]
-
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=4,
-    train=dict(
-        type='RepeatDataset',
-        times=40000,
-        dataset=dict(
-            type=dataset_type,
-            data_root=data_root,
-            img_dir='images/training',
-            ann_dir='annotations/training',
-            pipeline=train_pipeline)),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='images/validation',
-        ann_dir='annotations/validation',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='images/validation',
-        ann_dir='annotations/validation',
-        pipeline=test_pipeline))
diff --git a/spaces/TRI-ML/risk_biased_prediction/risk_biased/models/map_encoder.py b/spaces/TRI-ML/risk_biased_prediction/risk_biased/models/map_encoder.py
deleted file mode 100644
index 5ae224c7bd7ae68f5dcdb92b1717d521f5504d86..0000000000000000000000000000000000000000
--- a/spaces/TRI-ML/risk_biased_prediction/risk_biased/models/map_encoder.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import torch
-import torch.nn as nn
-from risk_biased.models.nn_blocks import (
-    SequenceEncoderLSTM,
-    SequenceEncoderMLP,
-    SequenceEncoderMaskedLSTM,
-)
-
-from risk_biased.models.cvae_params import CVAEParams
-from risk_biased.models.mlp import MLP
-
-
-class MapEncoderNN(nn.Module):
-    """MLP encoder neural network that encodes map objects.
-
-    Args:
-        params: dataclass defining the necessary parameters
-    """
-
-    def __init__(self, params: CVAEParams) -> None:
-        super().__init__()
-        self._encoder = SequenceEncoderMLP(
-            params.map_state_dim,
-            params.hidden_dim,
-            params.num_hidden_layers,
-            params.max_size_lane,
-            params.is_mlp_residual,
-        )
-
-    def forward(self, map, mask_map):
-        """Forward function encoding map object sequences of features into object features.
-
-        Args:
-            map: (batch_size, num_objects, object_sequence_length, map_feature_dim) tensor of encoded map objects
-            mask_map: (batch_size, num_objects, object_sequence_length) tensor of bool mask
-        """
-        encoded_map = self._encoder(map, mask_map)
-        return encoded_map
diff --git a/spaces/TRI-ML/risk_biased_prediction/risk_biased/utils/config_argparse.py b/spaces/TRI-ML/risk_biased_prediction/risk_biased/utils/config_argparse.py
deleted file mode 100644
index 635d1ec65e1995716e066cb017c993b06a4056c5..0000000000000000000000000000000000000000
--- a/spaces/TRI-ML/risk_biased_prediction/risk_biased/utils/config_argparse.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-from typing import Optional, Union, List
-import warnings
-
-import argparse
-from mmcv import Config
-
-
-def config_argparse(config_path: Optional[Union[str, List[str]]] = None) -> Config:
-    """Function that loads the config file as an MMCV Config object and overwrites its values with argparsed arguments.
-
-    Args:
-        config_path : path of the mmcv config file
-
-    Returns:
-        MMCV Config object with default values from the config_path and overwritten values from argparse
-    """
-    if config_path is None:
-        working_dir = os.path.dirname(os.path.realpath(__file__))
-        config_path = os.path.join(
-            working_dir, "..", "..", "config", "learning_config.py"
-        )
-    if isinstance(config_path, str):
-        cfg = Config.fromfile(config_path)
-    else:
-        cfg = Config.fromfile(config_path[0])
-        for path in config_path[1:]:
-            c = Config.fromfile(path)
-            cfg.update(c)
-
-    parser = argparse.ArgumentParser()
-    excluded_args = ["force_config", "load_last"]
-    overwritable_types = (str, float, int, list)
-    for key, value in cfg.items():
-        if key not in excluded_args:
-            if list in overwritable_types and isinstance(value, list):
-                if len(value) > 0:
-                    parser.add_argument(
-                        "--" + key, default=value, nargs="+", type=type(value[0])
-                    )
-                else:
-                    parser.add_argument("--" + key, default=value, nargs="+")
-            elif isinstance(value, overwritable_types):
-                parser.add_argument("--" + key, default=value, type=type(value))
-
-    if "load_from" not in cfg.keys():
-        parser.add_argument(
-            "--load_from",
-            default="",
-            type=str,
-            help="""Use this to load the model weights from a wandb checkpoint,
-                    refer to the checkpoint with the wandb id (example:'1f1ho81a')""",
-        )
-
-    parser.add_argument(
-        "--load_last",
-        action="store_true",
-        help="""Use this flag to force the use of the last checkpoint instead of the best one
-        when loading a model.""",
-    )
-
-    parser.add_argument(
-        "--force_config",
-        action="store_true",
-        help="""Use this flag to force the use of the local config file
-        when loading a model from a checkpoint. Otherwise the checkpoint config file is used.
-        In any case the parameters can be overwritten with an argparse argument.""",
-    )
-    if "force_config" not in cfg.keys():
-        parser.set_defaults(force_config=False)
-    else:
-        parser.set_defaults(force_config=cfg.force_config)
-
-    if "load_last" not in cfg.keys():
-        parser.set_defaults(load_last=False)
-    else:
-        parser.set_defaults(force_config=cfg.force_config)
-
-    args = parser.parse_args()
-
-    # Print a warning in case the parameter 'dt' or 'time_scene' is changed becaus 'sample_times' might need to be updated accordingly.
-    if (
-        args.dt != cfg.dt or args.time_scene != cfg.time_scene
-    ) and args.sample_times == cfg.sample_times:
-        warnings.warn(
-            f"""Parameter 'dt' has been changed from {args.dataset_parameters['dt']} to {args.dt} by
-            a command line argument, it might be used to set the parameter 'sample_times' that
-            cannot be updated accordingly. Consider setting 'dt' in {config_path} instead."""
-        )
-    # Config has a dataset_parameters field that copies the parameters related to dataset to compare them,
-    # they must be updated too if some of the dataset parameters were changed by argparse
-    for key, value in cfg.dataset_parameters.items():
-        if isinstance(value, overwritable_types):
-            cfg.dataset_parameters[key] = args.__getattribute__(key)
-    cfg.update(args.__dict__)
-    return cfg
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/requirements.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/requirements.py
deleted file mode 100644
index f34bfa85c8029f514f81f829fc24020e78b3786e..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/requirements.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import urllib.parse
-from typing import Any, List, Optional, Set
-
-from ._parser import parse_requirement as _parse_requirement
-from ._tokenizer import ParserSyntaxError
-from .markers import Marker, _normalize_extra_values
-from .specifiers import SpecifierSet
-
-
-class InvalidRequirement(ValueError):
-    """
-    An invalid requirement was found, users should refer to PEP 508.
-    """
-
-
-class Requirement:
-    """Parse a requirement.
-
-    Parse a given requirement string into its parts, such as name, specifier,
-    URL, and extras. Raises InvalidRequirement on a badly-formed requirement
-    string.
-    """
-
-    # TODO: Can we test whether something is contained within a requirement?
-    #       If so how do we do that? Do we need to test against the _name_ of
-    #       the thing as well as the version? What about the markers?
-    # TODO: Can we normalize the name and extra name?
-
-    def __init__(self, requirement_string: str) -> None:
-        try:
-            parsed = _parse_requirement(requirement_string)
-        except ParserSyntaxError as e:
-            raise InvalidRequirement(str(e)) from e
-
-        self.name: str = parsed.name
-        if parsed.url:
-            parsed_url = urllib.parse.urlparse(parsed.url)
-            if parsed_url.scheme == "file":
-                if urllib.parse.urlunparse(parsed_url) != parsed.url:
-                    raise InvalidRequirement("Invalid URL given")
-            elif not (parsed_url.scheme and parsed_url.netloc) or (
-                not parsed_url.scheme and not parsed_url.netloc
-            ):
-                raise InvalidRequirement(f"Invalid URL: {parsed.url}")
-            self.url: Optional[str] = parsed.url
-        else:
-            self.url = None
-        self.extras: Set[str] = set(parsed.extras if parsed.extras else [])
-        self.specifier: SpecifierSet = SpecifierSet(parsed.specifier)
-        self.marker: Optional[Marker] = None
-        if parsed.marker is not None:
-            self.marker = Marker.__new__(Marker)
-            self.marker._markers = _normalize_extra_values(parsed.marker)
-
-    def __str__(self) -> str:
-        parts: List[str] = [self.name]
-
-        if self.extras:
-            formatted_extras = ",".join(sorted(self.extras))
-            parts.append(f"[{formatted_extras}]")
-
-        if self.specifier:
-            parts.append(str(self.specifier))
-
-        if self.url:
-            parts.append(f"@ {self.url}")
-            if self.marker:
-                parts.append(" ")
-
-        if self.marker:
-            parts.append(f"; {self.marker}")
-
-        return "".join(parts)
-
-    def __repr__(self) -> str:
-        return f"<Requirement('{self}')>"
-
-    def __hash__(self) -> int:
-        return hash((self.__class__.__name__, str(self)))
-
-    def __eq__(self, other: Any) -> bool:
-        if not isinstance(other, Requirement):
-            return NotImplemented
-
-        return (
-            self.name == other.name
-            and self.extras == other.extras
-            and self.specifier == other.specifier
-            and self.url == other.url
-            and self.marker == other.marker
-        )
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/bdist_wheel.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/bdist_wheel.py
deleted file mode 100644
index 2f93c4733c00fc6670bf32f065885a70894ea406..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/bdist_wheel.py
+++ /dev/null
@@ -1,575 +0,0 @@
-"""
-Create a wheel (.whl) distribution.
-
-A wheel is a built archive format.
-"""
-
-from __future__ import annotations
-
-import os
-import re
-import shutil
-import stat
-import sys
-import sysconfig
-import warnings
-from collections import OrderedDict
-from email.generator import BytesGenerator, Generator
-from email.policy import EmailPolicy
-from glob import iglob
-from io import BytesIO
-from shutil import rmtree
-from zipfile import ZIP_DEFLATED, ZIP_STORED
-
-import setuptools
-from setuptools import Command
-
-from . import __version__ as wheel_version
-from .macosx_libfile import calculate_macosx_platform_tag
-from .metadata import pkginfo_to_metadata
-from .util import log
-from .vendored.packaging import tags
-from .vendored.packaging import version as _packaging_version
-from .wheelfile import WheelFile
-
-
-def safe_name(name):
-    """Convert an arbitrary string to a standard distribution name
-    Any runs of non-alphanumeric/. characters are replaced with a single '-'.
-    """
-    return re.sub("[^A-Za-z0-9.]+", "-", name)
-
-
-def safe_version(version):
-    """
-    Convert an arbitrary string to a standard version string
-    """
-    try:
-        # normalize the version
-        return str(_packaging_version.Version(version))
-    except _packaging_version.InvalidVersion:
-        version = version.replace(" ", ".")
-        return re.sub("[^A-Za-z0-9.]+", "-", version)
-
-
-setuptools_major_version = int(setuptools.__version__.split(".")[0])
-
-PY_LIMITED_API_PATTERN = r"cp3\d"
-
-
-def python_tag():
-    return f"py{sys.version_info[0]}"
-
-
-def get_platform(archive_root):
-    """Return our platform name 'win32', 'linux_x86_64'"""
-    result = sysconfig.get_platform()
-    if result.startswith("macosx") and archive_root is not None:
-        result = calculate_macosx_platform_tag(archive_root, result)
-    elif result == "linux-x86_64" and sys.maxsize == 2147483647:
-        # pip pull request #3497
-        result = "linux-i686"
-
-    return result.replace("-", "_")
-
-
-def get_flag(var, fallback, expected=True, warn=True):
-    """Use a fallback value for determining SOABI flags if the needed config
-    var is unset or unavailable."""
-    val = sysconfig.get_config_var(var)
-    if val is None:
-        if warn:
-            warnings.warn(
-                "Config variable '{}' is unset, Python ABI tag may "
-                "be incorrect".format(var),
-                RuntimeWarning,
-                stacklevel=2,
-            )
-        return fallback
-    return val == expected
-
-
-def get_abi_tag():
-    """Return the ABI tag based on SOABI (if available) or emulate SOABI (PyPy2)."""
-    soabi = sysconfig.get_config_var("SOABI")
-    impl = tags.interpreter_name()
-    if not soabi and impl in ("cp", "pp") and hasattr(sys, "maxunicode"):
-        d = ""
-        m = ""
-        u = ""
-        if get_flag("Py_DEBUG", hasattr(sys, "gettotalrefcount"), warn=(impl == "cp")):
-            d = "d"
-
-        if get_flag(
-            "WITH_PYMALLOC",
-            impl == "cp",
-            warn=(impl == "cp" and sys.version_info < (3, 8)),
-        ) and sys.version_info < (3, 8):
-            m = "m"
-
-        abi = f"{impl}{tags.interpreter_version()}{d}{m}{u}"
-    elif soabi and impl == "cp":
-        abi = "cp" + soabi.split("-")[1]
-    elif soabi and impl == "pp":
-        # we want something like pypy36-pp73
-        abi = "-".join(soabi.split("-")[:2])
-        abi = abi.replace(".", "_").replace("-", "_")
-    elif soabi:
-        abi = soabi.replace(".", "_").replace("-", "_")
-    else:
-        abi = None
-
-    return abi
-
-
-def safer_name(name):
-    return safe_name(name).replace("-", "_")
-
-
-def safer_version(version):
-    return safe_version(version).replace("-", "_")
-
-
-def remove_readonly(func, path, excinfo):
-    remove_readonly_exc(func, path, excinfo[1])
-
-
-def remove_readonly_exc(func, path, exc):
-    os.chmod(path, stat.S_IWRITE)
-    func(path)
-
-
-class bdist_wheel(Command):
-    description = "create a wheel distribution"
-
-    supported_compressions = OrderedDict(
-        [("stored", ZIP_STORED), ("deflated", ZIP_DEFLATED)]
-    )
-
-    user_options = [
-        ("bdist-dir=", "b", "temporary directory for creating the distribution"),
-        (
-            "plat-name=",
-            "p",
-            "platform name to embed in generated filenames "
-            "(default: %s)" % get_platform(None),
-        ),
-        (
-            "keep-temp",
-            "k",
-            "keep the pseudo-installation tree around after "
-            + "creating the distribution archive",
-        ),
-        ("dist-dir=", "d", "directory to put final built distributions in"),
-        ("skip-build", None, "skip rebuilding everything (for testing/debugging)"),
-        (
-            "relative",
-            None,
-            "build the archive using relative paths " "(default: false)",
-        ),
-        (
-            "owner=",
-            "u",
-            "Owner name used when creating a tar file" " [default: current user]",
-        ),
-        (
-            "group=",
-            "g",
-            "Group name used when creating a tar file" " [default: current group]",
-        ),
-        ("universal", None, "make a universal wheel" " (default: false)"),
-        (
-            "compression=",
-            None,
-            "zipfile compression (one of: {})"
-            " (default: 'deflated')".format(", ".join(supported_compressions)),
-        ),
-        (
-            "python-tag=",
-            None,
-            "Python implementation compatibility tag"
-            " (default: '%s')" % (python_tag()),
-        ),
-        (
-            "build-number=",
-            None,
-            "Build number for this particular version. "
-            "As specified in PEP-0427, this must start with a digit. "
-            "[default: None]",
-        ),
-        (
-            "py-limited-api=",
-            None,
-            "Python tag (cp32|cp33|cpNN) for abi3 wheel tag" " (default: false)",
-        ),
-    ]
-
-    boolean_options = ["keep-temp", "skip-build", "relative", "universal"]
-
-    def initialize_options(self):
-        self.bdist_dir = None
-        self.data_dir = None
-        self.plat_name = None
-        self.plat_tag = None
-        self.format = "zip"
-        self.keep_temp = False
-        self.dist_dir = None
-        self.egginfo_dir = None
-        self.root_is_pure = None
-        self.skip_build = None
-        self.relative = False
-        self.owner = None
-        self.group = None
-        self.universal = False
-        self.compression = "deflated"
-        self.python_tag = python_tag()
-        self.build_number = None
-        self.py_limited_api = False
-        self.plat_name_supplied = False
-
-    def finalize_options(self):
-        if self.bdist_dir is None:
-            bdist_base = self.get_finalized_command("bdist").bdist_base
-            self.bdist_dir = os.path.join(bdist_base, "wheel")
-
-        self.data_dir = self.wheel_dist_name + ".data"
-        self.plat_name_supplied = self.plat_name is not None
-
-        try:
-            self.compression = self.supported_compressions[self.compression]
-        except KeyError:
-            raise ValueError(f"Unsupported compression: {self.compression}") from None
-
-        need_options = ("dist_dir", "plat_name", "skip_build")
-
-        self.set_undefined_options("bdist", *zip(need_options, need_options))
-
-        self.root_is_pure = not (
-            self.distribution.has_ext_modules() or self.distribution.has_c_libraries()
-        )
-
-        if self.py_limited_api and not re.match(
-            PY_LIMITED_API_PATTERN, self.py_limited_api
-        ):
-            raise ValueError("py-limited-api must match '%s'" % PY_LIMITED_API_PATTERN)
-
-        # Support legacy [wheel] section for setting universal
-        wheel = self.distribution.get_option_dict("wheel")
-        if "universal" in wheel:
-            # please don't define this in your global configs
-            log.warning(
-                "The [wheel] section is deprecated. Use [bdist_wheel] instead.",
-            )
-            val = wheel["universal"][1].strip()
-            if val.lower() in ("1", "true", "yes"):
-                self.universal = True
-
-        if self.build_number is not None and not self.build_number[:1].isdigit():
-            raise ValueError("Build tag (build-number) must start with a digit.")
-
-    @property
-    def wheel_dist_name(self):
-        """Return distribution full name with - replaced with _"""
-        components = (
-            safer_name(self.distribution.get_name()),
-            safer_version(self.distribution.get_version()),
-        )
-        if self.build_number:
-            components += (self.build_number,)
-        return "-".join(components)
-
-    def get_tag(self):
-        # bdist sets self.plat_name if unset, we should only use it for purepy
-        # wheels if the user supplied it.
-        if self.plat_name_supplied:
-            plat_name = self.plat_name
-        elif self.root_is_pure:
-            plat_name = "any"
-        else:
-            # macosx contains system version in platform name so need special handle
-            if self.plat_name and not self.plat_name.startswith("macosx"):
-                plat_name = self.plat_name
-            else:
-                # on macosx always limit the platform name to comply with any
-                # c-extension modules in bdist_dir, since the user can specify
-                # a higher MACOSX_DEPLOYMENT_TARGET via tools like CMake
-
-                # on other platforms, and on macosx if there are no c-extension
-                # modules, use the default platform name.
-                plat_name = get_platform(self.bdist_dir)
-
-            if (
-                plat_name in ("linux-x86_64", "linux_x86_64")
-                and sys.maxsize == 2147483647
-            ):
-                plat_name = "linux_i686"
-
-        plat_name = (
-            plat_name.lower().replace("-", "_").replace(".", "_").replace(" ", "_")
-        )
-
-        if self.root_is_pure:
-            if self.universal:
-                impl = "py2.py3"
-            else:
-                impl = self.python_tag
-            tag = (impl, "none", plat_name)
-        else:
-            impl_name = tags.interpreter_name()
-            impl_ver = tags.interpreter_version()
-            impl = impl_name + impl_ver
-            # We don't work on CPython 3.1, 3.0.
-            if self.py_limited_api and (impl_name + impl_ver).startswith("cp3"):
-                impl = self.py_limited_api
-                abi_tag = "abi3"
-            else:
-                abi_tag = str(get_abi_tag()).lower()
-            tag = (impl, abi_tag, plat_name)
-            # issue gh-374: allow overriding plat_name
-            supported_tags = [
-                (t.interpreter, t.abi, plat_name) for t in tags.sys_tags()
-            ]
-            assert (
-                tag in supported_tags
-            ), f"would build wheel with unsupported tag {tag}"
-        return tag
-
-    def run(self):
-        build_scripts = self.reinitialize_command("build_scripts")
-        build_scripts.executable = "python"
-        build_scripts.force = True
-
-        build_ext = self.reinitialize_command("build_ext")
-        build_ext.inplace = False
-
-        if not self.skip_build:
-            self.run_command("build")
-
-        install = self.reinitialize_command("install", reinit_subcommands=True)
-        install.root = self.bdist_dir
-        install.compile = False
-        install.skip_build = self.skip_build
-        install.warn_dir = False
-
-        # A wheel without setuptools scripts is more cross-platform.
-        # Use the (undocumented) `no_ep` option to setuptools'
-        # install_scripts command to avoid creating entry point scripts.
-        install_scripts = self.reinitialize_command("install_scripts")
-        install_scripts.no_ep = True
-
-        # Use a custom scheme for the archive, because we have to decide
-        # at installation time which scheme to use.
-        for key in ("headers", "scripts", "data", "purelib", "platlib"):
-            setattr(install, "install_" + key, os.path.join(self.data_dir, key))
-
-        basedir_observed = ""
-
-        if os.name == "nt":
-            # win32 barfs if any of these are ''; could be '.'?
-            # (distutils.command.install:change_roots bug)
-            basedir_observed = os.path.normpath(os.path.join(self.data_dir, ".."))
-            self.install_libbase = self.install_lib = basedir_observed
-
-        setattr(
-            install,
-            "install_purelib" if self.root_is_pure else "install_platlib",
-            basedir_observed,
-        )
-
-        log.info(f"installing to {self.bdist_dir}")
-
-        self.run_command("install")
-
-        impl_tag, abi_tag, plat_tag = self.get_tag()
-        archive_basename = f"{self.wheel_dist_name}-{impl_tag}-{abi_tag}-{plat_tag}"
-        if not self.relative:
-            archive_root = self.bdist_dir
-        else:
-            archive_root = os.path.join(
-                self.bdist_dir, self._ensure_relative(install.install_base)
-            )
-
-        self.set_undefined_options("install_egg_info", ("target", "egginfo_dir"))
-        distinfo_dirname = "{}-{}.dist-info".format(
-            safer_name(self.distribution.get_name()),
-            safer_version(self.distribution.get_version()),
-        )
-        distinfo_dir = os.path.join(self.bdist_dir, distinfo_dirname)
-        self.egg2dist(self.egginfo_dir, distinfo_dir)
-
-        self.write_wheelfile(distinfo_dir)
-
-        # Make the archive
-        if not os.path.exists(self.dist_dir):
-            os.makedirs(self.dist_dir)
-
-        wheel_path = os.path.join(self.dist_dir, archive_basename + ".whl")
-        with WheelFile(wheel_path, "w", self.compression) as wf:
-            wf.write_files(archive_root)
-
-        # Add to 'Distribution.dist_files' so that the "upload" command works
-        getattr(self.distribution, "dist_files", []).append(
-            (
-                "bdist_wheel",
-                "{}.{}".format(*sys.version_info[:2]),  # like 3.7
-                wheel_path,
-            )
-        )
-
-        if not self.keep_temp:
-            log.info(f"removing {self.bdist_dir}")
-            if not self.dry_run:
-                if sys.version_info < (3, 12):
-                    rmtree(self.bdist_dir, onerror=remove_readonly)
-                else:
-                    rmtree(self.bdist_dir, onexc=remove_readonly_exc)
-
-    def write_wheelfile(
-        self, wheelfile_base, generator="bdist_wheel (" + wheel_version + ")"
-    ):
-        from email.message import Message
-
-        msg = Message()
-        msg["Wheel-Version"] = "1.0"  # of the spec
-        msg["Generator"] = generator
-        msg["Root-Is-Purelib"] = str(self.root_is_pure).lower()
-        if self.build_number is not None:
-            msg["Build"] = self.build_number
-
-        # Doesn't work for bdist_wininst
-        impl_tag, abi_tag, plat_tag = self.get_tag()
-        for impl in impl_tag.split("."):
-            for abi in abi_tag.split("."):
-                for plat in plat_tag.split("."):
-                    msg["Tag"] = "-".join((impl, abi, plat))
-
-        wheelfile_path = os.path.join(wheelfile_base, "WHEEL")
-        log.info(f"creating {wheelfile_path}")
-        buffer = BytesIO()
-        BytesGenerator(buffer, maxheaderlen=0).flatten(msg)
-        with open(wheelfile_path, "wb") as f:
-            f.write(buffer.getvalue().replace(b"\r\n", b"\r"))
-
-    def _ensure_relative(self, path):
-        # copied from dir_util, deleted
-        drive, path = os.path.splitdrive(path)
-        if path[0:1] == os.sep:
-            path = drive + path[1:]
-        return path
-
-    @property
-    def license_paths(self):
-        if setuptools_major_version >= 57:
-            # Setuptools has resolved any patterns to actual file names
-            return self.distribution.metadata.license_files or ()
-
-        files = set()
-        metadata = self.distribution.get_option_dict("metadata")
-        if setuptools_major_version >= 42:
-            # Setuptools recognizes the license_files option but does not do globbing
-            patterns = self.distribution.metadata.license_files
-        else:
-            # Prior to those, wheel is entirely responsible for handling license files
-            if "license_files" in metadata:
-                patterns = metadata["license_files"][1].split()
-            else:
-                patterns = ()
-
-        if "license_file" in metadata:
-            warnings.warn(
-                'The "license_file" option is deprecated. Use "license_files" instead.',
-                DeprecationWarning,
-                stacklevel=2,
-            )
-            files.add(metadata["license_file"][1])
-
-        if not files and not patterns and not isinstance(patterns, list):
-            patterns = ("LICEN[CS]E*", "COPYING*", "NOTICE*", "AUTHORS*")
-
-        for pattern in patterns:
-            for path in iglob(pattern):
-                if path.endswith("~"):
-                    log.debug(
-                        f'ignoring license file "{path}" as it looks like a backup'
-                    )
-                    continue
-
-                if path not in files and os.path.isfile(path):
-                    log.info(
-                        f'adding license file "{path}" (matched pattern "{pattern}")'
-                    )
-                    files.add(path)
-
-        return files
-
-    def egg2dist(self, egginfo_path, distinfo_path):
-        """Convert an .egg-info directory into a .dist-info directory"""
-
-        def adios(p):
-            """Appropriately delete directory, file or link."""
-            if os.path.exists(p) and not os.path.islink(p) and os.path.isdir(p):
-                shutil.rmtree(p)
-            elif os.path.exists(p):
-                os.unlink(p)
-
-        adios(distinfo_path)
-
-        if not os.path.exists(egginfo_path):
-            # There is no egg-info. This is probably because the egg-info
-            # file/directory is not named matching the distribution name used
-            # to name the archive file. Check for this case and report
-            # accordingly.
-            import glob
-
-            pat = os.path.join(os.path.dirname(egginfo_path), "*.egg-info")
-            possible = glob.glob(pat)
-            err = f"Egg metadata expected at {egginfo_path} but not found"
-            if possible:
-                alt = os.path.basename(possible[0])
-                err += f" ({alt} found - possible misnamed archive file?)"
-
-            raise ValueError(err)
-
-        if os.path.isfile(egginfo_path):
-            # .egg-info is a single file
-            pkginfo_path = egginfo_path
-            pkg_info = pkginfo_to_metadata(egginfo_path, egginfo_path)
-            os.mkdir(distinfo_path)
-        else:
-            # .egg-info is a directory
-            pkginfo_path = os.path.join(egginfo_path, "PKG-INFO")
-            pkg_info = pkginfo_to_metadata(egginfo_path, pkginfo_path)
-
-            # ignore common egg metadata that is useless to wheel
-            shutil.copytree(
-                egginfo_path,
-                distinfo_path,
-                ignore=lambda x, y: {
-                    "PKG-INFO",
-                    "requires.txt",
-                    "SOURCES.txt",
-                    "not-zip-safe",
-                },
-            )
-
-            # delete dependency_links if it is only whitespace
-            dependency_links_path = os.path.join(distinfo_path, "dependency_links.txt")
-            with open(dependency_links_path, encoding="utf-8") as dependency_links_file:
-                dependency_links = dependency_links_file.read().strip()
-            if not dependency_links:
-                adios(dependency_links_path)
-
-        pkg_info_path = os.path.join(distinfo_path, "METADATA")
-        serialization_policy = EmailPolicy(
-            utf8=True,
-            mangle_from_=False,
-            max_line_length=0,
-        )
-        with open(pkg_info_path, "w", encoding="utf-8") as out:
-            Generator(out, policy=serialization_policy).flatten(pkg_info)
-
-        for license_path in self.license_paths:
-            filename = os.path.basename(license_path)
-            shutil.copy(license_path, os.path.join(distinfo_path, filename))
-
-        adios(egginfo_path)
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript.py
deleted file mode 100644
index 24fe59bda44225324928542df3f2ef1745375dfd..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import os
-import torch
-
-from detectron2.utils.file_io import PathManager
-
-from .torchscript_patch import freeze_training_mode, patch_instances
-
-__all__ = ["scripting_with_instances", "dump_torchscript_IR"]
-
-
-def scripting_with_instances(model, fields):
-    """
-    Run :func:`torch.jit.script` on a model that uses the :class:`Instances` class. Since
-    attributes of :class:`Instances` are "dynamically" added in eager mode，it is difficult
-    for scripting to support it out of the box. This function is made to support scripting
-    a model that uses :class:`Instances`. It does the following:
-
-    1. Create a scriptable ``new_Instances`` class which behaves similarly to ``Instances``,
-       but with all attributes been "static".
-       The attributes need to be statically declared in the ``fields`` argument.
-    2. Register ``new_Instances``, and force scripting compiler to
-       use it when trying to compile ``Instances``.
-
-    After this function, the process will be reverted. User should be able to script another model
-    using different fields.
-
-    Example:
-        Assume that ``Instances`` in the model consist of two attributes named
-        ``proposal_boxes`` and ``objectness_logits`` with type :class:`Boxes` and
-        :class:`Tensor` respectively during inference. You can call this function like:
-        ::
-            fields = {"proposal_boxes": Boxes, "objectness_logits": torch.Tensor}
-            torchscipt_model =  scripting_with_instances(model, fields)
-
-    Note:
-        It only support models in evaluation mode.
-
-    Args:
-        model (nn.Module): The input model to be exported by scripting.
-        fields (Dict[str, type]): Attribute names and corresponding type that
-            ``Instances`` will use in the model. Note that all attributes used in ``Instances``
-            need to be added, regardless of whether they are inputs/outputs of the model.
-            Data type not defined in detectron2 is not supported for now.
-
-    Returns:
-        torch.jit.ScriptModule: the model in torchscript format
-    """
-    assert (
-        not model.training
-    ), "Currently we only support exporting models in evaluation mode to torchscript"
-
-    with freeze_training_mode(model), patch_instances(fields):
-        scripted_model = torch.jit.script(model)
-        return scripted_model
-
-
-# alias for old name
-export_torchscript_with_instances = scripting_with_instances
-
-
-def dump_torchscript_IR(model, dir):
-    """
-    Dump IR of a TracedModule/ScriptModule/Function in various format (code, graph,
-    inlined graph). Useful for debugging.
-
-    Args:
-        model (TracedModule/ScriptModule/ScriptFUnction): traced or scripted module
-        dir (str): output directory to dump files.
-    """
-    dir = os.path.expanduser(dir)
-    PathManager.mkdirs(dir)
-
-    def _get_script_mod(mod):
-        if isinstance(mod, torch.jit.TracedModule):
-            return mod._actual_script_module
-        return mod
-
-    # Dump pretty-printed code: https://pytorch.org/docs/stable/jit.html#inspecting-code
-    with PathManager.open(os.path.join(dir, "model_ts_code.txt"), "w") as f:
-
-        def get_code(mod):
-            # Try a few ways to get code using private attributes.
-            try:
-                # This contains more information than just `mod.code`
-                return _get_script_mod(mod)._c.code
-            except AttributeError:
-                pass
-            try:
-                return mod.code
-            except AttributeError:
-                return None
-
-        def dump_code(prefix, mod):
-            code = get_code(mod)
-            name = prefix or "root model"
-            if code is None:
-                f.write(f"Could not found code for {name} (type={mod.original_name})\n")
-                f.write("\n")
-            else:
-                f.write(f"\nCode for {name}, type={mod.original_name}:\n")
-                f.write(code)
-                f.write("\n")
-                f.write("-" * 80)
-
-            for name, m in mod.named_children():
-                dump_code(prefix + "." + name, m)
-
-        if isinstance(model, torch.jit.ScriptFunction):
-            f.write(get_code(model))
-        else:
-            dump_code("", model)
-
-    def _get_graph(model):
-        try:
-            # Recursively dump IR of all modules
-            return _get_script_mod(model)._c.dump_to_str(True, False, False)
-        except AttributeError:
-            return model.graph.str()
-
-    with PathManager.open(os.path.join(dir, "model_ts_IR.txt"), "w") as f:
-        f.write(_get_graph(model))
-
-    # Dump IR of the entire graph (all submodules inlined)
-    with PathManager.open(os.path.join(dir, "model_ts_IR_inlined.txt"), "w") as f:
-        f.write(str(model.inlined_graph))
-
-    if not isinstance(model, torch.jit.ScriptFunction):
-        # Dump the model structure in pytorch style
-        with PathManager.open(os.path.join(dir, "model.txt"), "w") as f:
-            f.write(str(model))
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/layers/test_mask_ops.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/layers/test_mask_ops.py
deleted file mode 100644
index 162c449c604e05a85d7187518943712b407cb4bb..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/layers/test_mask_ops.py
+++ /dev/null
@@ -1,202 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import contextlib
-import io
-import numpy as np
-import unittest
-from collections import defaultdict
-import torch
-import tqdm
-from fvcore.common.benchmark import benchmark
-from pycocotools.coco import COCO
-from tabulate import tabulate
-from torch.nn import functional as F
-
-from detectron2.data import MetadataCatalog
-from detectron2.layers.mask_ops import (
-    pad_masks,
-    paste_mask_in_image_old,
-    paste_masks_in_image,
-    scale_boxes,
-)
-from detectron2.structures import BitMasks, Boxes, BoxMode, PolygonMasks
-from detectron2.structures.masks import polygons_to_bitmask
-from detectron2.utils.file_io import PathManager
-from detectron2.utils.testing import random_boxes
-
-
-def iou_between_full_image_bit_masks(a, b):
-    intersect = (a & b).sum()
-    union = (a | b).sum()
-    return intersect / union
-
-
-def rasterize_polygons_with_grid_sample(full_image_bit_mask, box, mask_size, threshold=0.5):
-    x0, y0, x1, y1 = box[0], box[1], box[2], box[3]
-
-    img_h, img_w = full_image_bit_mask.shape
-
-    mask_y = np.arange(0.0, mask_size) + 0.5  # mask y sample coords in [0.5, mask_size - 0.5]
-    mask_x = np.arange(0.0, mask_size) + 0.5  # mask x sample coords in [0.5, mask_size - 0.5]
-    mask_y = mask_y / mask_size * (y1 - y0) + y0
-    mask_x = mask_x / mask_size * (x1 - x0) + x0
-
-    mask_x = (mask_x - 0.5) / (img_w - 1) * 2 + -1
-    mask_y = (mask_y - 0.5) / (img_h - 1) * 2 + -1
-    gy, gx = torch.meshgrid(torch.from_numpy(mask_y), torch.from_numpy(mask_x))
-    ind = torch.stack([gx, gy], dim=-1).to(dtype=torch.float32)
-
-    full_image_bit_mask = torch.from_numpy(full_image_bit_mask)
-    mask = F.grid_sample(
-        full_image_bit_mask[None, None, :, :].to(dtype=torch.float32),
-        ind[None, :, :, :],
-        align_corners=True,
-    )
-
-    return mask[0, 0] >= threshold
-
-
-class TestMaskCropPaste(unittest.TestCase):
-    def setUp(self):
-        json_file = MetadataCatalog.get("coco_2017_val_100").json_file
-        if not PathManager.isfile(json_file):
-            raise unittest.SkipTest("{} not found".format(json_file))
-        with contextlib.redirect_stdout(io.StringIO()):
-            json_file = PathManager.get_local_path(json_file)
-            self.coco = COCO(json_file)
-
-    def test_crop_paste_consistency(self):
-        """
-        rasterize_polygons_within_box (used in training)
-        and
-        paste_masks_in_image (used in inference)
-        should be inverse operations to each other.
-
-        This function runs several implementation of the above two operations and prints
-        the reconstruction error.
-        """
-
-        anns = self.coco.loadAnns(self.coco.getAnnIds(iscrowd=False))  # avoid crowd annotations
-
-        selected_anns = anns[:100]
-
-        ious = []
-        for ann in tqdm.tqdm(selected_anns):
-            results = self.process_annotation(ann)
-            ious.append([k[2] for k in results])
-
-        ious = np.array(ious)
-        mean_ious = ious.mean(axis=0)
-        table = []
-        res_dic = defaultdict(dict)
-        for row, iou in zip(results, mean_ious):
-            table.append((row[0], row[1], iou))
-            res_dic[row[0]][row[1]] = iou
-        print(tabulate(table, headers=["rasterize", "paste", "iou"], tablefmt="simple"))
-        # assert that the reconstruction is good:
-        self.assertTrue(res_dic["polygon"]["aligned"] > 0.94)
-        self.assertTrue(res_dic["roialign"]["aligned"] > 0.95)
-
-    def process_annotation(self, ann, mask_side_len=28):
-        # Parse annotation data
-        img_info = self.coco.loadImgs(ids=[ann["image_id"]])[0]
-        height, width = img_info["height"], img_info["width"]
-        gt_polygons = [np.array(p, dtype=np.float64) for p in ann["segmentation"]]
-        gt_bbox = BoxMode.convert(ann["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS)
-        gt_bit_mask = polygons_to_bitmask(gt_polygons, height, width)
-
-        # Run rasterize ..
-        torch_gt_bbox = torch.tensor(gt_bbox).to(dtype=torch.float32).reshape(-1, 4)
-        box_bitmasks = {
-            "polygon": PolygonMasks([gt_polygons]).crop_and_resize(torch_gt_bbox, mask_side_len)[0],
-            "gridsample": rasterize_polygons_with_grid_sample(gt_bit_mask, gt_bbox, mask_side_len),
-            "roialign": BitMasks(torch.from_numpy(gt_bit_mask[None, :, :])).crop_and_resize(
-                torch_gt_bbox, mask_side_len
-            )[0],
-        }
-
-        # Run paste ..
-        results = defaultdict(dict)
-        for k, box_bitmask in box_bitmasks.items():
-            padded_bitmask, scale = pad_masks(box_bitmask[None, :, :], 1)
-            scaled_boxes = scale_boxes(torch_gt_bbox, scale)
-
-            r = results[k]
-            r["old"] = paste_mask_in_image_old(
-                padded_bitmask[0], scaled_boxes[0], height, width, threshold=0.5
-            )
-            r["aligned"] = paste_masks_in_image(
-                box_bitmask[None, :, :], Boxes(torch_gt_bbox), (height, width)
-            )[0]
-
-        table = []
-        for rasterize_method, r in results.items():
-            for paste_method, mask in r.items():
-                mask = np.asarray(mask)
-                iou = iou_between_full_image_bit_masks(gt_bit_mask.astype("uint8"), mask)
-                table.append((rasterize_method, paste_method, iou))
-        return table
-
-    def test_polygon_area(self):
-        # Draw polygon boxes
-        for d in [5.0, 10.0, 1000.0]:
-            polygon = PolygonMasks([[[0, 0, 0, d, d, d, d, 0]]])
-            area = polygon.area()[0]
-            target = d ** 2
-            self.assertEqual(area, target)
-
-        # Draw polygon triangles
-        for d in [5.0, 10.0, 1000.0]:
-            polygon = PolygonMasks([[[0, 0, 0, d, d, d]]])
-            area = polygon.area()[0]
-            target = d ** 2 / 2
-            self.assertEqual(area, target)
-
-    def test_paste_mask_scriptable(self):
-        scripted_f = torch.jit.script(paste_masks_in_image)
-        N = 10
-        masks = torch.rand(N, 28, 28)
-        boxes = Boxes(random_boxes(N, 100)).tensor
-        image_shape = (150, 150)
-
-        out = paste_masks_in_image(masks, boxes, image_shape)
-        scripted_out = scripted_f(masks, boxes, image_shape)
-        self.assertTrue(torch.equal(out, scripted_out))
-
-
-def benchmark_paste():
-    S = 800
-    H, W = image_shape = (S, S)
-    N = 64
-    torch.manual_seed(42)
-    masks = torch.rand(N, 28, 28)
-
-    center = torch.rand(N, 2) * 600 + 100
-    wh = torch.clamp(torch.randn(N, 2) * 40 + 200, min=50)
-    x0y0 = torch.clamp(center - wh * 0.5, min=0.0)
-    x1y1 = torch.clamp(center + wh * 0.5, max=S)
-    boxes = Boxes(torch.cat([x0y0, x1y1], axis=1))
-
-    def func(device, n=3):
-        m = masks.to(device=device)
-        b = boxes.to(device=device)
-
-        def bench():
-            for _ in range(n):
-                paste_masks_in_image(m, b, image_shape)
-            if device.type == "cuda":
-                torch.cuda.synchronize()
-
-        return bench
-
-    specs = [{"device": torch.device("cpu"), "n": 3}]
-    if torch.cuda.is_available():
-        specs.append({"device": torch.device("cuda"), "n": 3})
-
-    benchmark(func, "paste_masks", specs, num_iters=10, warmup_iters=2)
-
-
-if __name__ == "__main__":
-    benchmark_paste()
-    unittest.main()
diff --git a/spaces/ThomasSimonini/Check-my-progress-Deep-RL-Course/README.md b/spaces/ThomasSimonini/Check-my-progress-Deep-RL-Course/README.md
deleted file mode 100644
index 6197159b5f11a2b274562c71594849837bac446a..0000000000000000000000000000000000000000
--- a/spaces/ThomasSimonini/Check-my-progress-Deep-RL-Course/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Check My Progress Deep RL Course
-emoji: 👀
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/User1342/Ivory/app.py b/spaces/User1342/Ivory/app.py
deleted file mode 100644
index f9168365bdddb92d0f3fb9be96b71efb3ece7ba2..0000000000000000000000000000000000000000
--- a/spaces/User1342/Ivory/app.py
+++ /dev/null
@@ -1,478 +0,0 @@
-import time
-from pprint import pprint
-
-from mastodon import Mastodon, MastodonNotFoundError  # Mastodon.py
-
-list_of_servers = [
- 'hostux.social',
- 'social.linux.pizza',
- 'nerdculture.de',
- 'toot.wales',
- 'kolektiva.social',
- 'noc.social',
- 'seo.chat',
- 'ioc.exchange',
- 'glasgow.social',
- 'mindly.social',
- 'mstdn.party',
- 'universeodon.com',
- 'learningdisability.social',
- 'ravenation.club',
- 'home.social',
- 'mastodon.ie',
- 'techhub.social',
- 'mastodon.scot',
- 'sfba.social',
- 'mastodon.sdf.org',
- 'mastodon.lol',
- 'mstdn.social',
- 'mas.to',
- 'newsie.social']
-
-# A HTML blob of text used to define the 'login in with' button
-auth_button_text = '''<a href="{}"><button class="w3-button w3-light-grey w3-padding-large w3-section " onclick="document.getElementById('download').style.display='block'">
-        <i class=""></i> Login with Mastodon! 🐘
-      </button></a><br>'''
-
-
-
-def client_log_in(provided_server = None):
-    '''
-     Generates an authenticated Mastodon client for the admin user. Used for retireving the target website URL
-    '''
-
-    if provided_server == None:
-        global slider_choice
-        server = slider_choice
-    else:
-        server = provided_server
-
-    if server != None:
-        id = "WatchTower Ivory | Mastodon"
-
-        secrets = json.loads(os.getenv('secrets_json'))
-
-        client_id = secrets[server]["client_id"]
-        client_secret = secrets[server]["client_secret"]
-        password = secrets[server]["password"]
-        username = secrets[server]["email"]
-
-
-        mastodon = Mastodon(
-            client_id=client_id,client_secret=client_secret,
-            api_base_url='https://{}'.format(server)
-        )
-
-        access_token = mastodon.log_in(
-            username=username,
-            scopes=["write:blocks", "write:mutes","read:search","read:accounts"],
-            password=password,
-            redirect_uri="https://user1342-ivory.hf.space/"
-        )
-    else:
-        mastodon = None
-
-    return mastodon
-
-
-def get_auth_url(mastodon,provided_server = None):
-    '''
-    Retrieves a URL for the user to visit to auth them with WatchTower.
-    :param mastodon: A admin masterdon instance.
-    :return: The target URL.
-    '''
-
-    if provided_server == None:
-        global slider_choice
-        server = slider_choice
-    else:
-        server = provided_server
-
-    secrets = json.loads(os.getenv('secrets_json'))
-
-    client_id = secrets[server]["client_id"]
-    client_secret = secrets[server]["client_secret"]
-
-    return mastodon.auth_request_url(client_id=client_id, scopes=["write:blocks", "write:mutes","read:search","read:accounts"],
-                                     redirect_uris="https://user1342-ivory.hf.space/")
-
-
-def login_from_code(code):
-    '''
-    Used to create a masterdon client instance based on an authenticated user code (retrieved from the URL).
-    :param code: The code which will authenticate the user/ WatchTower.
-    :return: A masterdon client instance signed in as the user.
-    '''
-
-    global slider_choice
-    server = slider_choice
-
-    if server != None:
-        secrets = json.loads(os.getenv('secrets_json'))
-        client_id = secrets[server]["client_id"]
-        client_secret = secrets[server]["client_secret"]
-
-        mastodon = Mastodon(
-            client_id=client_id, client_secret=client_secret,
-            api_base_url='https://{}'.format(server)
-        )
-
-        mastodon.log_in(code=code,
-                        scopes=["write:blocks", "write:mutes","read:search","read:accounts"],
-                        redirect_uri="https://user1342-ivory.hf.space/")
-    else:
-        mastodon = None
-
-    return mastodon
-
-
-# !/usr/bin/env python
-# coding: utf-8
-
-html_data = '''
-<!DOCTYPE html>
-<html>
-<head>
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<link rel="stylesheet" href="https://www.w3schools.com/w3css/4/w3.css">
-<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Poppins">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
-<style>
-body,h1,h2,h3,h4,h5 {font-family: "Poppins", sans-serif}
-body {font-size: 16px;}
-img {margin-bottom: -8px;}
-.mySlides {display: none;}
-</style>
-</head>
-<body class="w3-content w3-black" style="max-width:1500px;">
-
-<!-- The App Section -->
-
-<div class="w3-padding-large w3-white">
-  <div class="w3-row-padding-large">
-    <div class="w3-col">
-      <h1 class="w3-jumbo"><b>WatchTower 🐘🚧</b></h1>
-      <h1 class="w3-xxxlarge w3-text-purple"><b>Remove Unfavorable Messages From Your Mastodon Feed </b></h1>
-      <p><span class="w3-xlarge">Scroll down to use WatchTower Ivory. ⬇ </span> WatchTower is a tool that identifies hate speech, misinformation, and extremist content and blocks/ mutes it from your feed. WatchTower Ivory is the second iteration of WatchTower, designed specifically for Mastodon.  WatchTower blocks content based on it's current database, so make sure to come back regularly to ensure you're up to date! We use a queue system, which means <b>you may need to wait your turn to run WatchTower</b> - however, once you've clicked run, you can close the tab as WatchTower will continue in the background. WatchTower is simple to use: first scroll down the page, choose your Mastodon server, then click 'sign in with Mastodon', after this you'll be taken to the Mastodon server website and asked to verify yourself, after this you'll be taken back here, then simply scroll down to the bottom of the page and click run!</p>
-      <a href="https://www.watchtower.cartographer.one/"><button class="w3-button w3-light-grey w3-padding-large w3-section " onclick="document.getElementById('download').style.display='block'">
-        <i class=""></i> Find Out More! 💬
-      </button></a>
-      <a href="https://ko-fi.com/jamesstevenson"><button class="w3-button w3-light-grey w3-padding-large w3-section " onclick="document.getElementById('download').style.display='block'">
-        <i class=""></i> Support The Creator! ❤
-      </button></a>
-            <a href="https://infosec.exchange/@JamesStevenson"><button class="w3-button w3-light-grey w3-padding-large w3-section " onclick="document.getElementById('download').style.display='block'">
-        <i class=""></i> Follow Us! 🐦
-      </button></a>
-    </div>
-  </div>
-</div>
-
-<script>
-// Slideshow
-var slideIndex = 1;
-showDivs(slideIndex);
-
-function plusDivs(n) {
-  showDivs(slideIndex += n);
-}
-
-function showDivs(n) {
-  var i;
-  var x = document.getElementsByClassName("mySlides");
-  if (n > x.length) {slideIndex = 1}
-  if (n < 1) {slideIndex = x.length}
-  for (i = 0; i < x.length; i++) {
-    x[i].style.display = "none";  
-  }
-  x[slideIndex-1].style.display = "block";  
-}
-</script>
-<br>
-<br>
-<br>
-</body>
-</html>
-
-'''
-
-# Imports
-import json
-import os
-import gradio as gr
-
-# Setup the gradio block and add some generic CSS
-block = gr.Blocks(
-    css=".container { max-width: 800px; margin: auto; } h1 { margin: 0px; padding: 5px 0; line-height: 50px; font-size: 60pt; }.close-heading {margin: 0px; padding: 0px;} .close-heading p { margin: 0px; padding: 0px;}",
-    title="WatchTower")
-
-# Chat history variable used for the chatbot prompt on the 'getting started' page.
-chat_history = []
-
-
-def get_client_from_tokens(code):
-    '''
-    This function is used for generating a masterdon client object based on the code URL paramiters
-    :param code
-    :return: A Masterdon client object
-    '''
-
-    return login_from_code(code)
-
-
-def block_user(user_id, user, reason):
-    finished = False
-    blocked = True
-    attempts = 0
-    while not finished:
-
-        try:
-            user ="alcinnz@floss.social"
-            user_dict = client.account_search(user)
-            pprint(user_dict)
-
-            client.account_block(user_dict[0]["id"])
-            print("Blocked {} for {}".format(user, reason))
-        except MastodonNotFoundError as e:
-            if "Record not found" in str(e):
-                print("Record not found...")
-                return False
-
-
-        print("Blocked {}, for {}".format(user, reason))
-        return blocked
-
-
-def block_users(client, threshold, dataset):
-    '''
-    Used for blocking a series of users based on the threshold and datasets provided. Here the users folder is used.
-    :param client:
-    :param threshold:
-    :param dataset:
-    :return: The number of blocked users.
-    '''
-    num_users_blocked = 0
-
-
-    for filename in os.listdir("users"):
-        filename = os.path.join("users", filename)
-        print("File {} open".format(filename))
-        user_file = open(filename, "r")
-        users = json.load(user_file)
-
-        for user in users:
-            print("Reviewing user {}".format(user))
-            if "Violent" in dataset:
-                # Due to the low number of posts used to aggregate the initial dataset and unreliability of this model,
-                # the weight has been lowered to represent this.
-                if user["violence-threshold"]/2 >= threshold:
-                    user_id = str(user["acct"])
-                    if block_user(user_id, user, "Violent"):
-                        num_users_blocked = num_users_blocked + 1
-                    continue
-            if "Hate Speech" in dataset:
-                if user["toxicity-threshold"] >= threshold:
-                    user_id = str(user["acct"])
-                    if block_user(user_id, user, "Hate Speech"):
-                        num_users_blocked = num_users_blocked + 1
-                    continue
-
-    return num_users_blocked
-
-
-def chat(selected_option=None, radio_score=None, url_params=None):
-    '''
-    This function is used to initialise blocking users once the user has authenticated with Mastodon.
-    :param selected_option:
-    :param radio_score:
-    :param url_params:
-    :return: the chatbot history is returned (including information on blocked accounts).
-    '''
-
-    global client
-    global chat_history
-    history = []
-
-    # app id
-    if "code" in url_params and client is None:
-        client = get_client_from_tokens(url_params["code"])
-    if radio_score != None and selected_option != None:
-
-        if client != None:
-
-            # Extract the list to a string representation
-            if type(selected_option) is list:
-                block_type = ""
-                for b_type in selected_option:
-                    block_type = block_type + " + " + b_type.capitalize()
-                block_type = "'" + block_type[3:] + "'"
-            else:
-                block_type = selected_option
-
-            # Display to user, set options
-            history.append(
-                ["Model tuned to a '{}%' threshold and is using the {} dataset.".format(radio_score,
-                                                                                        block_type.capitalize()),
-                 "{} Account blocking initialised".format(block_type.capitalize())])
-            num_users_blocked = block_users(client, radio_score, selected_option)
-            history.append(
-                ["Blocked {} user account(s).".format(num_users_blocked), "Thank you for using Watchtower."])
-    elif radio_score != None or selected_option != None:
-        chat_history.append(["Initialisation error!", "Please tune the model by using the above options"])
-
-    history = chat_history + history
-    chatbot.value = history
-    chatbot.update(value=history)
-    client = None
-    return history
-
-
-def infer(prompt):
-    pass
-
-
-have_initialised = False
-client = None
-name = None
-
-
-def button_pressed(slider_value, url_params):
-    # print(url_params)
-    return [None, chat(radio.value, slider_value, url_params)]
-
-
-# The website that the user will visit to authenticate WatchTower.
-target_website = None
-
-
-def update_target_website():
-    '''
-    Updates the URL used to authenticate WatchTower with Mastodon.
-    #TODO this function is full of old code and can be optimised.
-    :return:
-    '''
-    global have_initialised
-    global chatbot
-    global chat_history
-    global client
-    global name
-
-    client = None
-    name = "no username"
-
-    chat_history = [
-        ["Welcome to Watchtower.".format(name), "Log in via Mastodon and configure your blocking options above."]]
-
-    chatbot.value = chat_history
-    chatbot.update(value=chat_history)
-
-    mastodon_auth_button.value = auth_button_text.format(
-        get_target_website())
-    mastodon_auth_button.update(
-        value=auth_button_text.format(
-            get_target_website()))
-
-    return auth_button_text.format(get_target_website(list_of_servers[0]))
-
-
-# The below is a JS blob used to retrieve the URL params.
-# Thanks to here: https://discuss.huggingface.co/t/hugging-face-and-gradio-url-paramiters/21110/2
-get_window_url_params = """
-    function(text_input, url_params) {
-        console.log(text_input, url_params);
-        const params = new URLSearchParams(window.location.search);
-        url_params = Object.fromEntries(params);
-        return [text_input, url_params];
-        }
-    """
-slider_choice = list_of_servers[0]
-def update_server(choice):
-    print("In change server")
-    global slider_choice
-    slider_choice = choice
-
-    get_target_website()
-
-    return auth_button_text.format(get_target_website())
-
-def get_chatbot_text():
-    return [('Welcome to Watchtower.', 'Log in via Mastodon and configure your blocking options above.')]
-
-
-def get_target_website(provided_server = None):
-    '''
-    A wrapper function used for retrieving the URL a user will use to authenticate WatchTower with Mastodon.
-    :return: auth url
-    '''
-
-    mastodon = client_log_in(provided_server)
-    return get_auth_url(mastodon, provided_server)
-
-
-# The Gradio HTML component used for the 'sign in with Mastodon' button
-
-# The main chunk of code that uses Gradio blocks to create the UI
-html_button = None
-dropdown = None
-with block:
-    gr.HTML('''
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<link rel="stylesheet" href="https://www.w3schools.com/w3css/4/w3.css">
-''')
-
-    # todo check if user signed in
-
-    user_message = "Log in via Mastodon and configure your blocking options above."
-    chat_history.append(["WelcoMe to Watchtower.", user_message])
-    gr.HTML(value=html_data)
-    with gr.Group():
-        with gr.Row().style(equal_height=True):
-            with gr.Box():
-                # gr.Label(value="WatchTower", visible=True, interactive=False)
-                url_params = gr.JSON({}, visible=False, label="URL Params").style(
-                )
-                text_input = gr.Text(label="Input", visible=False).style()
-                text_output = gr.Text(label="Output", visible=False).style()
-                with gr.Row().style():
-                    dropdown = gr.Dropdown(choices=list_of_servers, label="Server", value=list_of_servers[0])
-                    html_button = mastodon_auth_button = gr.HTML(
-                        value=auth_button_text.format(
-                            get_target_website())).style(
-                    )
-                with gr.Row().style(equal_height=True):
-                    radio = gr.CheckboxGroup(value=["Violent", "Hate Speech"],
-                                             choices=["Violent", "Hate Speech", "Misinformation"],
-                                             interactive=False, label="Behaviour To Block").style()
-
-                    slider = gr.Slider(value=35, interactive=True, label="Threshold Confidence Tolerance").style()
-
-                chatbot = gr.Chatbot(label="Watchtower Output", value=[('Welcome to Watchtower.',
-                                                                        'Log in via Mastodon and configure your blocking options above.')]).style(
-                    color_map=["grey", "purple"])
-
-                btn = gr.Button("Run WatchTower").style(full_width=True).style()
-    dropdown.change(fn=update_server, inputs=[dropdown], outputs=html_button)
-    btn.click(fn=button_pressed, inputs=[slider, url_params],
-              outputs=[text_output, chatbot], _js=get_window_url_params)
-    gr.Markdown(
-        """___
-   <p style='text-align: center'>
-   Created by <a href="https://JamesStevenson.me" target="_blank"</a> James Stevenson
-   <br/>
-   </p>"""
-    )
-
-# Setup callback for when page loads (used to set a new Mastodon auth target webspage)
-block.__enter__()
-block.set_event_trigger(
-    event_name="load", fn=update_target_website, inputs=None, outputs=[html_button], no_target=True
-)
-
-block.set_event_trigger(
-    event_name="load", fn=get_chatbot_text, inputs=None, outputs=[chatbot], no_target=True
-)
-
-block.attach_load_events()
-
-# Launcg the page
-block.launch(enable_queue=True)
diff --git a/spaces/User1342/WatchTower/app.py b/spaces/User1342/WatchTower/app.py
deleted file mode 100644
index e0f91f75b203380c17c81aa8be688746b826e2c0..0000000000000000000000000000000000000000
--- a/spaces/User1342/WatchTower/app.py
+++ /dev/null
@@ -1,412 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-import json
-import os
-import re
-import time
-from random import random
-import socket
-
-from threading import Thread
-from time import sleep
-
-# Twitter keys
-consumer_token = os.getenv('CONSUMER_TOKEN')
-consumer_secret = os.getenv('CONSUMER_SECRET')
-my_access_token = os.getenv('ACCESS_TOKEN')
-my_access_secret = os.getenv('ACCESS_SECRET')
-bearer = os.getenv('BEARER')
-
-html_data = '''
-<!DOCTYPE html>
-<html>
-<head>
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<link rel="stylesheet" href="https://www.w3schools.com/w3css/4/w3.css">
-<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Poppins">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
-<style>
-body,h1,h2,h3,h4,h5 {font-family: "Poppins", sans-serif}
-body {font-size: 16px;}
-img {margin-bottom: -8px;}
-.mySlides {display: none;}
-</style>
-</head>
-<body class="w3-content w3-black" style="max-width:1500px;">
-
-<!-- The App Section -->
-
-<div class="w3-padding-large w3-white">
-  <div class="w3-row-padding-large">
-    <div class="w3-col">
-      <h1 class="w3-jumbo"><b>WatchTower 🐦🚧</b></h1>
-      <h1 class="w3-xxxlarge w3-text-blue"><b>Remove Unfavorable Tweets From Your Feed </b></h1>
-      <p><span class="w3-xlarge">Scroll down to use WatchTower 1.0. ⬇ </span> WatchTower is a tool that identifies hate speech, misinformation, and extremist content and blocks/ mutes it from your Twitter feed. WatchTower blocks content based on it's current database, so make sure to come back regularly to ensure you're up to date! We use a queue system, which means <b> you may need to wait your turn to run WatchTower</b> - however, once you've clicked run, you can close the tab as WatchTower will continue in the background. WatchTower is simple to use: first scroll down the page and click the 'sign in with Twitter' button, then you'll be taken to the Twitter website and asked to verify yourself, after this you'll be taken back here, then simply scroll down to the bottom of the page and click run!</p>
-      <a href="https://www.watchtower.cartographer.one/"><button class="w3-button w3-light-grey w3-padding-large w3-section " onclick="document.getElementById('download').style.display='block'">
-        <i class=""></i> Find Out More! 💬
-      </button></a>
-      <a href="https://ko-fi.com/jamesstevenson"><button class="w3-button w3-light-grey w3-padding-large w3-section " onclick="document.getElementById('download').style.display='block'">
-        <i class=""></i> Support The Creator! ❤
-      </button></a>
-            <a href="https://twitter.com/WATCHTOWER_WEB"><button class="w3-button w3-light-grey w3-padding-large w3-section " onclick="document.getElementById('download').style.display='block'">
-        <i class=""></i> Follow Us! 🐦
-      </button></a>
-    </div>
-  </div>
-</div>
-
-<!-- Modal -->
-<script>
-// Slideshow
-var slideIndex = 1;
-showDivs(slideIndex);
-
-function plusDivs(n) {
-  showDivs(slideIndex += n);
-}
-
-function showDivs(n) {
-  var i;
-  var x = document.getElementsByClassName("mySlides");
-  if (n > x.length) {slideIndex = 1}
-  if (n < 1) {slideIndex = x.length}
-  for (i = 0; i < x.length; i++) {
-    x[i].style.display = "none";  
-  }
-  x[slideIndex-1].style.display = "block";  
-}
-</script>
-<br>
-</body>
-</html>
-
-'''
-
-# Imports
-import json
-import os
-import time
-import gradio as gr
-import tweepy
-
-# Setup the gradio block and add some generic CSS
-block = gr.Blocks(css=".container { max-width: 800px; margin: auto; } h1 { margin: 0px; padding: 5px 0; line-height: 50px; font-size: 60pt; }.close-heading {margin: 0px; padding: 0px;} .close-heading p { margin: 0px; padding: 0px;}", title="WatchTower")
-
-# Chat history variable used for the chatbot prompt on the 'getting started' page.
-chat_history = []
-
-
-def get_client_from_tokens(oauth_verifier, oauth_token):
-    '''
-    This function is used for generating a Tweepy client object based on Oauth verifier and token paramiters
-    :param oauth_verifier:
-    :param oauth_token:
-    :return: A Tweepy client object
-    '''
-    new_oauth1_user_handler = tweepy.OAuth1UserHandler(
-        consumer_token, consumer_secret,
-        callback="https://hf.space/embed/User1342/WatchTower/"
-    )
-    new_oauth1_user_handler.request_token = {
-        "oauth_token": oauth_token,
-        "oauth_token_secret": consumer_secret
-    }
-
-    access_token, access_token_secret = new_oauth1_user_handler.get_access_token(
-        oauth_verifier
-    )
-
-    their_client = tweepy.Client(
-        bearer_token=bearer,
-        consumer_key=consumer_token,
-        consumer_secret=consumer_secret,
-        access_token=access_token,
-        access_token_secret=access_token_secret
-    )
-
-    # TODO: The below is not necessary and can be removed.
-    global client
-    client = their_client
-
-    return their_client
-
-def block_user(user_id, user, reason):
-    finished = False
-    blocked = True
-    attempts = 0
-    while not finished:
-       
-        try:
-            print("preparing to block {}".format(user_id))
-            client.block(target_user_id=user_id)
-            print("User blocked")
-        except tweepy.errors.TooManyRequests as e:
-            try:
-                client.mute(target_user_id=user_id)
-                print("Could not block, so muted")
-            except tweepy.errors.TooManyRequests as e:
-                if attempts == 0:
-                    print("waiting 15 minutes for rate limit to finish")
-                    time.sleep(900)
-                    attempts = attempts + 1
-                    continue
-                else:
-                    finished = True
-                    blocked = False
-                    continue
-            except tweepy.errors.BadRequest as e:
-                print("bad request error")
-                print(e)
-                finished = True
-                blocked = False
-                continue
-        except tweepy.errors.BadRequest as e:
-            print("bad request error")
-            print(e)
-            finished = True
-            blocked = False
-            continue
-        except:
-            time.sleep(240)
-            continue
-        #time.sleep(1)
-        finished = True
-        try:
-            me = client.get_me()
-            print("{} blocked {}, for {}".format(me.data["username"], user, reason))
-        except tweepy.errors.TooManyRequests as e:
-            print("Blocked {}, for {}".format(user, reason))
-        return blocked
-
-def block_users(client, threshold, dataset):
-    '''
-    Used for blocking a series of users based on the threshold and datasets provided. Here the users folder is used.
-    :param client:
-    :param threshold:
-    :param dataset:
-    :return: The number of blocked users.
-    '''
-    num_users_blocked = 0
-
-    for filename in os.listdir("users"):
-        filename = os.path.join("users", filename)
-        print("File {} open".format(filename))
-        user_file = open(filename, "r")
-        users = json.load(user_file)
-
-        for user in users:
-            print("Reviewing user {}".format(user))
-
-            if "threshold" in user:
-                # old type of dataset being used, only 'violent' data available
-                if "Violent" in dataset:
-                    if user["threshold"]/2 >= threshold:
-
-                        user_id = str(user["username"])
-                        if block_user(user_id, user, "Violent - old dataset"):
-                            num_users_blocked = num_users_blocked + 1
-            else:
-                # modern dataset being used
-                if "Violent" in dataset:
-                    if user["violence-threshold"]/2 >= threshold:
-                        user_id = str(user["username"])
-                        if block_user(user_id, user, "Violent"):
-                            num_users_blocked = num_users_blocked + 1
-                        continue
-                if "Hate Speech"  in dataset:
-                    if user["toxicity-threshold"] >= threshold:
-                        user_id = str(user["username"])
-                        if block_user(user_id, user, "Hate Speech"):
-                            num_users_blocked = num_users_blocked + 1
-                        continue
-
-
-    return num_users_blocked
-
-
-def chat(selected_option=None, radio_score=None, url_params=None):
-    '''
-    This function is used to initialise blocking users once the user has authenticated with Twitter.
-    :param selected_option:
-    :param radio_score:
-    :param url_params:
-    :return: the chatbot history is returned (including information on blocked accounts).
-    '''
-
-    global client
-    global chat_history
-    history = []
-
-    # app id
-    if "oauth_verifier" in url_params and "oauth_token" in url_params and client is None:
-        client = get_client_from_tokens(url_params["oauth_verifier"], url_params["oauth_token"])
-    if radio_score != None and selected_option != None:
-
-        if client != None:
-
-            # Extract the list to a string representation
-            if type(selected_option) is list:
-                block_type = ""
-                for b_type in selected_option:
-                    block_type = block_type + " + " + b_type.capitalize()
-                block_type = "'" + block_type[3:] + "'"
-            else:
-                block_type = selected_option
-
-            # Display to user, set options
-            history.append(
-                ["Model tuned to a '{}%' threshold and is using the {} dataset.".format(radio_score, block_type.capitalize()),
-                 "{} Account blocking initialised".format(block_type.capitalize())])
-            num_users_blocked = block_users(client, radio_score, selected_option)
-            history.append(
-                ["Blocked {} user account(s).".format(num_users_blocked), "Thank you for using Watchtower."])
-    elif radio_score != None or selected_option != None:
-        chat_history.append(["Initialisation error!", "Please tune the model by using the above options"])
-
-    history = chat_history + history
-    chatbot.value = history
-    chatbot.update(value=history)
-    client = None
-    return history
-
-
-def infer(prompt):
-    pass
-
-
-have_initialised = False
-client = None
-name = None
-
-
-def button_pressed(slider_value, url_params):
-    # print(url_params)
-    return [None, chat(radio.value, slider_value, url_params)]
-
-
-# The website that the user will visit to authenticate WatchTower.
-target_website = None
-
-
-def update_target_website():
-    '''
-    Updates the URL used to authenticate WatchTower with Twitter.
-    #TODO this function is full of old code and can be optimised.
-    :return:
-    '''
-    global have_initialised
-    global chatbot
-    global chat_history
-    global client
-    global name
-
-    client = None
-    name = "no username"
-
-    chat_history = [
-        ["Welcome to Watchtower.".format(name), "Log in via Twitter and configure your blocking options above."]]
-
-    chatbot.value = chat_history
-    chatbot.update(value=chat_history)
-
-    twitter_auth_button.value = '<a href={}><img src="https://cdn.cms-twdigitalassets.com/content/dam/developer-twitter/auth-docs/sign-in-with-twitter-gray.png.twimg.1920.png" alt="Log In With Twitter"></a><br>'.format(
-        get_target_website())
-    twitter_auth_button.update(
-        value='<a href={}><img src="https://cdn.cms-twdigitalassets.com/content/dam/developer-twitter/auth-docs/sign-in-with-twitter-gray.png.twimg.1920.png" alt="Log In With Twitter"></a><br>'.format(
-            get_target_website()))
-
-    return '<a href={}><img src="https://cdn.cms-twdigitalassets.com/content/dam/developer-twitter/auth-docs/sign-in-with-twitter-gray.png.twimg.1920.png" alt="Log In With Twitter"></a><br>'.format(
-        get_target_website())
-
-
-# The below is a JS blob used to retrieve the URL params.
-# Thanks to here: https://discuss.huggingface.co/t/hugging-face-and-gradio-url-paramiters/21110/2
-get_window_url_params = """
-    function(text_input, url_params) {
-        console.log(text_input, url_params);
-        const params = new URLSearchParams(window.location.search);
-        url_params = Object.fromEntries(params);
-        return [text_input, url_params];
-        }
-    """
-
-def get_chatbot_text():
-    return [('Welcome to Watchtower.', 'Log in via Twitter and configure your blocking options above.')]
-
-def get_target_website():
-    '''
-    A wrapper function used for retrieving the URL a user will use to authenticate WatchTower with Twitter.
-    :return:
-    '''
-    oauth1_user_handler = tweepy.OAuth1UserHandler(
-        consumer_token, consumer_secret,
-        callback="https://hf.space/embed/User1342/WatchTower/"
-    )
-    target_website = oauth1_user_handler.get_authorization_url(signin_with_twitter=True)
-
-    return target_website
-
-
-# The Gradio HTML component used for the 'sign in with Twitter' button
-
-# The main chunk of code that uses Gradio blocks to create the UI
-html_button = None
-with block:
-
-
-    gr.HTML('''
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<link rel="stylesheet" href="https://www.w3schools.com/w3css/4/w3.css">
-''')
-
-    # todo check if user signed in
-
-    user_message = "Log in via Twitter and configure your blocking options above."
-    chat_history.append(["Welcome to Watchtower.", user_message])
-    gr.HTML(value=html_data)
-    with gr.Group():
-        with gr.Row().style(equal_height=True):
-            with gr.Box():
-                #gr.Label(value="WatchTower", visible=True, interactive=False)
-                url_params = gr.JSON({}, visible=False, label="URL Params").style(
-                )
-                text_input = gr.Text(label="Input", visible=False).style()
-                text_output = gr.Text(label="Output", visible=False).style()
-                html_button = twitter_auth_button = gr.HTML(
-                    value='<a href={}><img src="https://cdn.cms-twdigitalassets.com/content/dam/developer-twitter/auth-docs/sign-in-with-twitter-gray.png.twimg.1920.png" alt="Log In With Twitter"></a><br>'.format(
-                        get_target_website())).style(
-                )
-                with gr.Row().style(equal_height=True):
-                    radio = gr.CheckboxGroup(value=["Violent", "Hate Speech"], choices=["Violent", "Hate Speech", "Misinformation"],
-                                                 interactive=False, label="Behaviour To Block").style()
-
-                    slider = gr.Slider(value=30, interactive=True, label="Threshold Confidence Tolerance")
-
-                chatbot = gr.Chatbot(label="Watchtower Output", value=get_chatbot_text()).style(color_map=["blue","grey"])
-
-
-                btn = gr.Button("Run WatchTower").style(full_width=True).style()
-    btn.click(fn=button_pressed, inputs=[slider, url_params],
-              outputs=[text_output, chatbot], _js=get_window_url_params)
-    gr.Markdown(
-        """___
-   <p style='text-align: center'>
-   Created by <a href="https://twitter.com/_JamesStevenson" target="_blank"</a> James Stevenson
-   <br/>
-   </p>"""
-    )
-
-# Setup callback for when page loads (used to set a new Twitter auth target webspage)
-block.__enter__()
-block.set_event_trigger(
-    event_name="load", fn=update_target_website, inputs=None, outputs=[html_button], no_target=True
-)
-block.set_event_trigger(
-    event_name="load", fn=get_chatbot_text, inputs=None, outputs=[chatbot], no_target=True
-)
-
-#block.attach_load_events()
-
-# Launcg the page
-block.launch(enable_queue = True)
\ No newline at end of file
diff --git a/spaces/VVallabh/AI-driven-Video-Generation-Tool/README.md b/spaces/VVallabh/AI-driven-Video-Generation-Tool/README.md
deleted file mode 100644
index b25c10580adc3a926c4297d55c80ce0d0782f69a..0000000000000000000000000000000000000000
--- a/spaces/VVallabh/AI-driven-Video-Generation-Tool/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AI Driven Video Generation Tool
-emoji: ⚡
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/VlaTal/facial_beauty_analysis/facial_image.py b/spaces/VlaTal/facial_beauty_analysis/facial_image.py
deleted file mode 100644
index 6ae3ad615b2fe5c76e4a6d56dc95925211e1cd89..0000000000000000000000000000000000000000
--- a/spaces/VlaTal/facial_beauty_analysis/facial_image.py
+++ /dev/null
@@ -1,92 +0,0 @@
-from math import dist
-
-import cv2
-import mediapipe as mp
-
-from points import points
-from ratios import Ratios
-from utils import *
-
-
-def convert_to_numpy_array(ratios: dict[str, float]) -> numpy.array:
-    arr = []
-    for k, v in ratios.items():
-        arr.append(v)
-    return numpy.array(arr)
-
-
-class FacialImage:
-    def __init__(self):
-        self.landmarks = []
-        self.width = 0
-        self.height = 0
-
-    def get_point_coordinates(self, point_num):
-        point = self.landmarks[point_num]
-        return point.x * self.width, point.y * self.height
-
-    def get_distance_two_points(self, point_num_1, point_num_2):
-        first = self.get_point_coordinates(point_num_1)
-        second = self.get_point_coordinates(point_num_2)
-        return dist(first, second)
-
-    def get_ratio_between_two(self, ratio1_name, ratio2_name):
-        first = self.get_distance_two_points(points[ratio1_name][0], points[ratio1_name][1])
-        second = self.get_distance_two_points(points[ratio2_name][0], points[ratio2_name][1])
-        return first / second
-
-    def get_landmarks(self, image: numpy.ndarray):
-        mp_face_mesh = mp.solutions.face_mesh
-        face_mesh = mp_face_mesh.FaceMesh()
-
-        # Image
-        height, width, _ = image.shape
-        rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-        # Facial landmarks
-        result = face_mesh.process(rgb_image)
-
-        for facial_landmarks in result.multi_face_landmarks:
-            for i in range(0, 468):
-                pt1 = facial_landmarks.landmark[i]
-                x = int(pt1.x * width)
-                y = int(pt1.y * height)
-                cv2.circle(image, (x, y), 1, (255, 0, 0), -1)
-
-        self.landmarks = result.multi_face_landmarks[0].landmark
-        self.width = width
-        self.height = height
-
-    def calculate_ratios(self, image: numpy.ndarray, is_normalized=False):
-        self.get_landmarks(image)
-
-        ratios = {
-            'Under eyes/Interocular': self.get_ratio_between_two(Ratios.UNDER_EYES, Ratios.INTEROCULAR),
-            'Under eyes/Nose width': self.get_ratio_between_two(Ratios.UNDER_EYES, Ratios.NOSE_WIDTH),
-            'Mouth width/Interocular': self.get_ratio_between_two(Ratios.MOUTH_WIDTH, Ratios.INTEROCULAR),
-            'Upper lip-jaw/Interocular': self.get_ratio_between_two(Ratios.UPPER_LIP_TO_JAW, Ratios.INTEROCULAR),
-            'Upper lip-jaw/Nose width': self.get_ratio_between_two(Ratios.UPPER_LIP_TO_JAW, Ratios.NOSE_WIDTH),
-            'Interocular/Lip height': self.get_ratio_between_two(Ratios.INTEROCULAR, Ratios.LIPS_HEIGHT),
-            'Nose width/Interocular': self.get_ratio_between_two(Ratios.NOSE_WIDTH, Ratios.INTEROCULAR),
-            'Nose width/Upper lip height': self.get_ratio_between_two(Ratios.NOSE_WIDTH, Ratios.UPPER_LIP_HEIGHT),
-            'Interocular/Nose mouth height': self.get_ratio_between_two(Ratios.INTEROCULAR,
-                                                                        Ratios.NOSE_TO_MOUTH_HEIGHT),
-            'Face top-eyebrows/Eyebrows-Nose': self.get_ratio_between_two(Ratios.FACE_TOP_TO_EYEBROWS,
-                                                                          Ratios.EYEBROWS_TO_NOSE),
-            'Eyebrows-nose/Nose-jaw': self.get_ratio_between_two(Ratios.EYEBROWS_TO_NOSE, Ratios.NOSE_TO_JAW),
-            'Face top-eyebrows/Nose-Jaw': self.get_ratio_between_two(Ratios.FACE_TOP_TO_EYEBROWS, Ratios.NOSE_TO_JAW),
-            'Interocular/Nose width': self.get_ratio_between_two(Ratios.INTEROCULAR, Ratios.NOSE_WIDTH),
-            'Face height/Face width': self.get_ratio_between_two(Ratios.FACE_HEIGHT, Ratios.FACE_WIDTH),
-            'Lower eyebrow length': self.get_ratio_between_two(Ratios.LEFT_LOWER_EYEBROW_LENGTH,
-                                                               Ratios.RIGHT_LOWER_EYEBROW_LENGTH),
-            'Lower lip length': self.get_ratio_between_two(Ratios.LEFT_LOWER_LIP_LENGTH, Ratios.RIGHT_LOWER_LIP_LENGTH),
-            'Upper eyebrow': self.get_ratio_between_two(Ratios.LEFT_UPPER_EYEBROW_LENGTH,
-                                                        Ratios.RIGHT_UPPER_EYEBROW_LENGTH),
-            'Upper lip': self.get_ratio_between_two(Ratios.LEFT_UPPER_LIP_LENGTH, Ratios.RIGHT_UPPER_LIP_LENGTH),
-            'Nose': self.get_ratio_between_two(Ratios.LEFT_NOSE_WIDTH, Ratios.RIGHT_NOSE_WIDTH)
-        }
-
-        if is_normalized:
-            return normalization(ratios), image
-
-        return ratios, image
diff --git "a/spaces/Wazzzabeee/image-video-colorization/pages/03_\360\237\226\274\357\270\217_Input_Images.py" "b/spaces/Wazzzabeee/image-video-colorization/pages/03_\360\237\226\274\357\270\217_Input_Images.py"
deleted file mode 100644
index d16e2a534b1d699dafeafd148c05aa1b70078c00..0000000000000000000000000000000000000000
--- "a/spaces/Wazzzabeee/image-video-colorization/pages/03_\360\237\226\274\357\270\217_Input_Images.py"
+++ /dev/null
@@ -1,111 +0,0 @@
-import os
-import zipfile
-
-import streamlit as st
-from PIL import Image
-from streamlit_lottie import st_lottie
-
-from models.deep_colorization.colorizers import eccv16
-from utils import colorize_image, change_model, load_lottieurl
-
-st.set_page_config(page_title="Image & Video Colorizer", page_icon="🎨", layout="wide")
-
-
-loaded_model = eccv16(pretrained=True).eval()
-current_model = "None"
-
-
-col1, col2 = st.columns([1, 3])
-with col1:
-    lottie = load_lottieurl("https://assets5.lottiefiles.com/packages/lf20_RHdEuzVfEL.json")
-    st_lottie(lottie)
-
-with col2:
-    st.write("""
-    ## B&W Images Colorizer
-    ##### Input a black and white image and get a colorized version of it.
-    ###### ➠ If you want to colorize multiple images just upload them all at once.
-    ###### ➠ Uploading already colored images won't raise errors but images won't look good.
-    ###### ➠ I recommend starting with the first model and then experimenting with the second one.""")
-
-
-def main():
-    model = st.selectbox(
-        "Select Model (Both models have their pros and cons, I recommend trying both and keeping the best for you task)",
-        ["ECCV16", "SIGGRAPH17"], index=0)
-
-    # Make the user select a model
-    loaded_model = change_model(current_model, model)
-    st.write(f"Model is now {model}")
-
-    # Ask the user if he wants to see colorization
-    display_results = st.checkbox('Display results in real time', value=True)
-
-    # Input for the user to upload images
-    uploaded_file = st.file_uploader("Upload your images here...", type=['jpg', 'png', 'jpeg'],
-                                     accept_multiple_files=True)
-
-    # If the user clicks on the button
-    if st.button("Colorize"):
-        # If the user uploaded images
-        if uploaded_file is not None:
-            if display_results:
-                col1, col2 = st.columns([0.5, 0.5])
-                with col1:
-                    st.markdown('<p style="text-align: center;">Before</p>', unsafe_allow_html=True)
-                with col2:
-                    st.markdown('<p style="text-align: center;">After</p>', unsafe_allow_html=True)
-            else:
-                col1, col2, col3 = st.columns(3)
-
-            for i, file in enumerate(uploaded_file):
-                file_extension = os.path.splitext(file.name)[1].lower()
-                if file_extension in ['.jpg', '.png', '.jpeg']:
-                    image = Image.open(file)
-                    if display_results:
-                        with col1:
-                            st.image(image, use_column_width="always")
-                        with col2:
-                            with st.spinner("Colorizing image..."):
-                                out_img, new_img = colorize_image(file, loaded_model)
-                                new_img.save("IMG_" + str(i+1) + ".jpg")
-                                st.image(out_img, use_column_width="always")
-
-                    else:
-                        out_img, new_img = colorize_image(file, loaded_model)
-                        new_img.save("IMG_" + str(i+1) + ".jpg")
-
-            if len(uploaded_file) > 1:
-                # Create a zip file
-                zip_filename = "colorized_images.zip"
-                with zipfile.ZipFile(zip_filename, "w") as zip_file:
-                    # Add colorized images to the zip file
-                    for i in range(len(uploaded_file)):
-                        zip_file.write("IMG_" + str(i + 1) + ".jpg", "IMG_" + str(i) + ".jpg")
-                with col2:
-                    # Provide the zip file data for download
-                    st.download_button(
-                        label="Download Colorized Images" if len(uploaded_file) > 1 else "Download Colorized Image",
-                        data=open(zip_filename, "rb").read(),
-                        file_name=zip_filename,
-                    )
-            else:
-                with col2:
-                    st.download_button(
-                        label="Download Colorized Image",
-                        data=open("IMG_1.jpg", "rb").read(),
-                        file_name="IMG_1.jpg",
-                    )
-
-        else:
-            st.warning('Upload a file', icon="⚠️")
-
-
-if __name__ == "__main__":
-    main()
-    st.markdown(
-        "###### Made with :heart: by [Clément Delteil](https://www.linkedin.com/in/clementdelteil/) [![this is an "
-        "image link](https://i.imgur.com/thJhzOO.png)](https://www.buymeacoffee.com/clementdelteil)")
-    st.markdown(
-        "###### [Blog post of the project](https://medium.com/geekculture/creating-a-web-app-to-colorize-images-and-youtube-videos-80f5be2d0f68)"
-    )
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/vision/data.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/vision/data.py
deleted file mode 100644
index 20f584dd28d8f102ca079f031e9faec6c755773d..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/vision/data.py
+++ /dev/null
@@ -1,461 +0,0 @@
-"Manages data input pipeline - folderstransformbatch input. Includes support for classification, segmentation and bounding boxes"
-from numbers import Integral
-from ..torch_core import *
-from .image import *
-from .transform import *
-from ..data_block import *
-from ..basic_data import *
-from ..layers import *
-from .learner import *
-from torchvision import transforms as tvt
-
-__all__ = ['get_image_files', 'denormalize', 'get_annotations', 'ImageDataBunch',
-           'ImageList', 'normalize', 'normalize_funcs', 'resize_to',
-           'channel_view', 'mnist_stats', 'cifar_stats', 'imagenet_stats', 'imagenet_stats_inception', 'download_images',
-           'verify_images', 'bb_pad_collate', 'ImageImageList', 'PointsLabelList',
-           'ObjectCategoryList', 'ObjectItemList', 'SegmentationLabelList', 'SegmentationItemList', 'PointsItemList']
-
-image_extensions = set(k for k,v in mimetypes.types_map.items() if v.startswith('image/'))
-
-def get_image_files(c:PathOrStr, check_ext:bool=True, recurse=False)->FilePathList:
-    "Return list of files in `c` that are images. `check_ext` will filter to `image_extensions`."
-    return get_files(c, extensions=(image_extensions if check_ext else None), recurse=recurse)
-
-def get_annotations(fname, prefix=None):
-    "Open a COCO style json in `fname` and returns the lists of filenames (with maybe `prefix`) and labelled bboxes."
-    annot_dict = json.load(open(fname))
-    id2images, id2bboxes, id2cats = {}, collections.defaultdict(list), collections.defaultdict(list)
-    classes = {}
-    for o in annot_dict['categories']:
-        classes[o['id']] = o['name']
-    for o in annot_dict['annotations']:
-        bb = o['bbox']
-        id2bboxes[o['image_id']].append([bb[1],bb[0], bb[3]+bb[1], bb[2]+bb[0]])
-        id2cats[o['image_id']].append(classes[o['category_id']])
-    for o in annot_dict['images']:
-        if o['id'] in id2bboxes:
-            id2images[o['id']] = ifnone(prefix, '') + o['file_name']
-    ids = list(id2images.keys())
-    return [id2images[k] for k in ids], [[id2bboxes[k], id2cats[k]] for k in ids]
-
-def bb_pad_collate(samples:BatchSamples, pad_idx:int=0) -> Tuple[FloatTensor, Tuple[LongTensor, LongTensor]]:
-    "Function that collect `samples` of labelled bboxes and adds padding with `pad_idx`."
-    if isinstance(samples[0][1], int): return data_collate(samples)
-    max_len = max([len(s[1].data[1]) for s in samples])
-    bboxes = torch.zeros(len(samples), max_len, 4)
-    labels = torch.zeros(len(samples), max_len).long() + pad_idx
-    imgs = []
-    for i,s in enumerate(samples):
-        imgs.append(s[0].data[None])
-        bbs, lbls = s[1].data
-        if not (bbs.nelement() == 0):
-            bboxes[i,-len(lbls):] = bbs
-            labels[i,-len(lbls):] = tensor(lbls)
-    return torch.cat(imgs,0), (bboxes,labels)
-
-def normalize(x:TensorImage, mean,std:Tensor)->TensorImage:
-    "Normalize `x` with `mean` and `std`."
-    return (x-mean[...,None,None]) / std[...,None,None]
-
-def denormalize(x:TensorImage, mean,std:Tensor, do_x:bool=True)->TensorImage:
-    "Denormalize `x` with `mean` and `std`."
-    return x.cpu().float()*std[...,None,None] + mean[...,None,None] if do_x else x.cpu()
-
-def _normalize_batch(b:Tuple[Tensor,Tensor], mean:Tensor, std:Tensor, do_x:bool=True, do_y:bool=False)->Tuple[Tensor,Tensor]:
-    "`b` = `x`,`y` - normalize `x` array of imgs and `do_y` optionally `y`."
-    x,y = b
-    mean,std = mean.to(x.device),std.to(x.device)
-    if do_x: x = normalize(x,mean,std)
-    if do_y and len(y.shape) == 4: y = normalize(y,mean,std)
-    return x,y
-
-def normalize_funcs(mean:Tensor, std:Tensor, do_x:bool=True, do_y:bool=False)->Tuple[Callable,Callable]:
-    "Create normalize/denormalize func using `mean` and `std`, can specify `do_y` and `device`."
-    mean,std = tensor(mean),tensor(std)
-    return (partial(_normalize_batch, mean=mean, std=std, do_x=do_x, do_y=do_y),
-            partial(denormalize,      mean=mean, std=std, do_x=do_x))
-
-cifar_stats = ([0.491, 0.482, 0.447], [0.247, 0.243, 0.261])
-imagenet_stats = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-imagenet_stats_inception = ([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
-mnist_stats = ([0.15]*3, [0.15]*3)
-
-def channel_view(x:Tensor)->Tensor:
-    "Make channel the first axis of `x` and flatten remaining axes"
-    return x.transpose(0,1).contiguous().view(x.shape[1],-1)
-
-class ImageDataBunch(DataBunch):
-    "DataBunch suitable for computer vision."
-    _square_show = True
-
-    @classmethod
-    def create_from_ll(cls, lls:LabelLists, bs:int=64, val_bs:int=None, ds_tfms:Optional[TfmList]=None,
-                num_workers:int=defaults.cpus, dl_tfms:Optional[Collection[Callable]]=None, device:torch.device=None,
-                test:Optional[PathOrStr]=None, collate_fn:Callable=data_collate, size:int=None, no_check:bool=False,
-                resize_method:ResizeMethod=None, mult:int=None, padding_mode:str='reflection',
-                mode:str='bilinear', tfm_y:bool=False)->'ImageDataBunch':
-        "Create an `ImageDataBunch` from `LabelLists` `lls` with potential `ds_tfms`."
-        lls = lls.transform(tfms=ds_tfms, size=size, resize_method=resize_method, mult=mult, padding_mode=padding_mode,
-                            mode=mode, tfm_y=tfm_y)
-        if test is not None: lls.add_test_folder(test)
-        return lls.databunch(bs=bs, val_bs=val_bs, dl_tfms=dl_tfms, num_workers=num_workers, collate_fn=collate_fn,
-                             device=device, no_check=no_check)
-
-    @classmethod
-    def from_folder(cls, path:PathOrStr, train:PathOrStr='train', valid:PathOrStr='valid',
-                    valid_pct=None, seed:int=None, classes:Collection=None, **kwargs:Any)->'ImageDataBunch':
-        "Create from imagenet style dataset in `path` with `train`,`valid`,`test` subfolders (or provide `valid_pct`)."
-        path=Path(path)
-        il = ImageList.from_folder(path)
-        if valid_pct is None: src = il.split_by_folder(train=train, valid=valid)
-        else: src = il.split_by_rand_pct(valid_pct, seed)
-        src = src.label_from_folder(classes=classes)
-        return cls.create_from_ll(src, **kwargs)
-
-    @classmethod
-    def from_df(cls, path:PathOrStr, df:pd.DataFrame, folder:PathOrStr=None, label_delim:str=None, valid_pct:float=0.2,
-                seed:int=None, fn_col:IntsOrStrs=0, label_col:IntsOrStrs=1, suffix:str='', **kwargs:Any)->'ImageDataBunch':
-        "Create from a `DataFrame` `df`."
-        src = (ImageList.from_df(df, path=path, folder=folder, suffix=suffix, cols=fn_col)
-                .split_by_rand_pct(valid_pct, seed)
-                .label_from_df(label_delim=label_delim, cols=label_col))
-        return cls.create_from_ll(src, **kwargs)
-
-    @classmethod
-    def from_csv(cls, path:PathOrStr, folder:PathOrStr=None, label_delim:str=None, csv_labels:PathOrStr='labels.csv',
-                 valid_pct:float=0.2, seed:int=None, fn_col:int=0, label_col:int=1, suffix:str='', delimiter:str=None,
-                 header:Optional[Union[int,str]]='infer', **kwargs:Any)->'ImageDataBunch':
-        "Create from a csv file in `path/csv_labels`."
-        path = Path(path)
-        df = pd.read_csv(path/csv_labels, header=header, delimiter=delimiter)
-        return cls.from_df(path, df, folder=folder, label_delim=label_delim, valid_pct=valid_pct, seed=seed,
-                fn_col=fn_col, label_col=label_col, suffix=suffix, **kwargs)
-
-    @classmethod
-    def from_lists(cls, path:PathOrStr, fnames:FilePathList, labels:Collection[str], valid_pct:float=0.2, seed:int=None,
-                   item_cls:Callable=None, **kwargs):
-        "Create from list of `fnames` in `path`."
-        item_cls = ifnone(item_cls, ImageList)
-        fname2label = {f:l for (f,l) in zip(fnames, labels)}
-        src = (item_cls(fnames, path=path).split_by_rand_pct(valid_pct, seed)
-                                .label_from_func(lambda x:fname2label[x]))
-        return cls.create_from_ll(src, **kwargs)
-
-    @classmethod
-    def from_name_func(cls, path:PathOrStr, fnames:FilePathList, label_func:Callable, valid_pct:float=0.2, seed:int=None,
-                       **kwargs):
-        "Create from list of `fnames` in `path` with `label_func`."
-        src = ImageList(fnames, path=path).split_by_rand_pct(valid_pct, seed)
-        return cls.create_from_ll(src.label_from_func(label_func), **kwargs)
-
-    @classmethod
-    def from_name_re(cls, path:PathOrStr, fnames:FilePathList, pat:str, valid_pct:float=0.2, **kwargs):
-        "Create from list of `fnames` in `path` with re expression `pat`."
-        pat = re.compile(pat)
-        def _get_label(fn):
-            if isinstance(fn, Path): fn = fn.as_posix()
-            res = pat.search(str(fn))
-            assert res,f'Failed to find "{pat}" in "{fn}"'
-            return res.group(1)
-        return cls.from_name_func(path, fnames, _get_label, valid_pct=valid_pct, **kwargs)
-
-    @staticmethod
-    def single_from_classes(path:Union[Path, str], classes:Collection[str], ds_tfms:TfmList=None, **kwargs):
-        "Create an empty `ImageDataBunch` in `path` with `classes`. Typically used for inference."
-        warn("""This method is deprecated and will be removed in a future version, use `load_learner` after
-             `Learner.export()`""", DeprecationWarning)
-        sd = ImageList([], path=path, ignore_empty=True).split_none()
-        return sd.label_const(0, label_cls=CategoryList, classes=classes).transform(ds_tfms, **kwargs).databunch()
-
-    def batch_stats(self, funcs:Collection[Callable]=None, ds_type:DatasetType=DatasetType.Train)->Tensor:
-        "Grab a batch of data and call reduction function `func` per channel"
-        funcs = ifnone(funcs, [torch.mean,torch.std])
-        x = self.one_batch(ds_type=ds_type, denorm=False)[0].cpu()
-        return [func(channel_view(x), 1) for func in funcs]
-
-    def normalize(self, stats:Collection[Tensor]=None, do_x:bool=True, do_y:bool=False)->None:
-        "Add normalize transform using `stats` (defaults to `DataBunch.batch_stats`)"
-        if getattr(self,'norm',False): raise Exception('Can not call normalize twice')
-        if stats is None: self.stats = self.batch_stats()
-        else:             self.stats = stats
-        self.norm,self.denorm = normalize_funcs(*self.stats, do_x=do_x, do_y=do_y)
-        self.add_tfm(self.norm)
-        return self
-
-def download_image(url,dest, timeout=4):
-    try: r = download_url(url, dest, overwrite=True, show_progress=False, timeout=timeout)
-    except Exception as e: print(f"Error {url} {e}")
-
-def _download_image_inner(dest, url, i, timeout=4):
-    suffix = re.findall(r'\.\w+?(?=(?:\?|$))', url)
-    suffix = suffix[0] if len(suffix)>0  else '.jpg'
-    download_image(url, dest/f"{i:08d}{suffix}", timeout=timeout)
-
-def download_images(urls:Collection[str], dest:PathOrStr, max_pics:int=1000, max_workers:int=8, timeout=4):
-    "Download images listed in text file `urls` to path `dest`, at most `max_pics`"
-    urls = open(urls).read().strip().split("\n")[:max_pics]
-    dest = Path(dest)
-    dest.mkdir(exist_ok=True)
-    parallel(partial(_download_image_inner, dest, timeout=timeout), urls, max_workers=max_workers)
-
-def resize_to(img, targ_sz:int, use_min:bool=False):
-    "Size to resize to, to hit `targ_sz` at same aspect ratio, in PIL coords (i.e w*h)"
-    w,h = img.size
-    min_sz = (min if use_min else max)(w,h)
-    ratio = targ_sz/min_sz
-    return int(w*ratio),int(h*ratio)
-
-def verify_image(file:Path, idx:int, delete:bool, max_size:Union[int,Tuple[int,int]]=None, dest:Path=None, n_channels:int=3,
-                 interp=PIL.Image.BILINEAR, ext:str=None, img_format:str=None, resume:bool=False, **kwargs):
-    "Check if the image in `file` exists, maybe resize it and copy it in `dest`."
-    try:
-        # deal with partially broken images as indicated by PIL warnings
-        with warnings.catch_warnings():
-            warnings.filterwarnings('error')
-            try:
-                with open(file, 'rb') as img_file: PIL.Image.open(img_file)
-            except Warning as w:
-                if "Possibly corrupt EXIF data" in str(w):
-                    if delete: # green light to modify files
-                        print(f"{file}: Removing corrupt EXIF data")
-                        warnings.simplefilter("ignore")
-                        # save EXIF-cleaned up image, which happens automatically
-                        PIL.Image.open(file).save(file)
-                    else: # keep user's files intact
-                        print(f"{file}: Not removing corrupt EXIF data, pass `delete=True` to do that")
-                else: warnings.warn(w)
-
-        img = PIL.Image.open(file)
-        imgarr = np.array(img)
-        img_channels = 1 if len(imgarr.shape) == 2 else imgarr.shape[2]
-        if (max_size is not None and (img.height > max_size or img.width > max_size)) or img_channels != n_channels:
-            assert isinstance(dest, Path), "You should provide `dest` Path to save resized image"
-            dest_fname = dest/file.name
-            if ext is not None: dest_fname=dest_fname.with_suffix(ext)
-            if resume and os.path.isfile(dest_fname): return
-            if max_size is not None:
-                new_sz = resize_to(img, max_size)
-                img = img.resize(new_sz, resample=interp)
-            if n_channels == 3: img = img.convert("RGB")
-            img.save(dest_fname, img_format, **kwargs)
-    except Exception as e:
-        print(f'{e}')
-        if delete: file.unlink()
-
-def verify_images(path:PathOrStr, delete:bool=True, max_workers:int=4, max_size:Union[int]=None, recurse:bool=False,
-                  dest:PathOrStr='.', n_channels:int=3, interp=PIL.Image.BILINEAR, ext:str=None, img_format:str=None,
-                  resume:bool=None, **kwargs):
-    "Check if the images in `path` aren't broken, maybe resize them and copy it in `dest`."
-    path = Path(path)
-    if resume is None and dest == '.': resume=False
-    dest = path/Path(dest)
-    os.makedirs(dest, exist_ok=True)
-    files = get_image_files(path, recurse=recurse)
-    func = partial(verify_image, delete=delete, max_size=max_size, dest=dest, n_channels=n_channels, interp=interp,
-                   ext=ext, img_format=img_format, resume=resume, **kwargs)
-    parallel(func, files, max_workers=max_workers)
-
-class ImageList(ItemList):
-    "`ItemList` suitable for computer vision."
-    _bunch,_square_show,_square_show_res = ImageDataBunch,True,True
-    def __init__(self, *args, convert_mode='RGB', after_open:Callable=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.convert_mode,self.after_open = convert_mode,after_open
-        self.copy_new += ['convert_mode', 'after_open']
-        self.c,self.sizes = 3,{}
-
-    def open(self, fn):
-        "Open image in `fn`, subclass and overwrite for custom behavior."
-        return open_image(fn, convert_mode=self.convert_mode, after_open=self.after_open)
-
-    def get(self, i):
-        fn = super().get(i)
-        res = self.open(fn)
-        self.sizes[i] = res.size
-        return res
-    
-    @classmethod
-    def from_folder(cls, path:PathOrStr='.', extensions:Collection[str]=None, **kwargs)->ItemList:
-        "Get the list of files in `path` that have an image suffix. `recurse` determines if we search subfolders."
-        extensions = ifnone(extensions, image_extensions)
-        return super().from_folder(path=path, extensions=extensions, **kwargs)
-
-    @classmethod
-    def from_df(cls, df:DataFrame, path:PathOrStr, cols:IntsOrStrs=0, folder:PathOrStr=None, suffix:str='', **kwargs)->'ItemList':
-        "Get the filenames in `cols` of `df` with `folder` in front of them, `suffix` at the end."
-        suffix = suffix or ''
-        res = super().from_df(df, path=path, cols=cols, **kwargs)
-        pref = f'{res.path}{os.path.sep}'
-        if folder is not None: pref += f'{folder}{os.path.sep}'
-        res.items = np.char.add(np.char.add(pref, res.items.astype(str)), suffix)
-        return res
-
-    @classmethod
-    def from_csv(cls, path:PathOrStr, csv_name:str, header:str='infer', delimiter:str=None, **kwargs)->'ItemList':
-        "Get the filenames in `path/csv_name` opened with `header`."
-        path = Path(path)
-        df = pd.read_csv(path/csv_name, header=header, delimiter=delimiter)
-        return cls.from_df(df, path=path, **kwargs)
-
-    def reconstruct(self, t:Tensor): return Image(t.float().clamp(min=0,max=1))
-
-    def show_xys(self, xs, ys, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, **kwargs):
-        "Show the `xs` (inputs) and `ys` (targets) on a figure of `figsize`."
-        rows = int(np.ceil(math.sqrt(len(xs))))
-        axs = subplots(rows, rows, imgsize=imgsize, figsize=figsize)
-        for x,y,ax in zip(xs, ys, axs.flatten()): x.show(ax=ax, y=y, **kwargs)
-        for ax in axs.flatten()[len(xs):]: ax.axis('off')
-        plt.tight_layout()
-
-    def show_xyzs(self, xs, ys, zs, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, **kwargs):
-        "Show `xs` (inputs), `ys` (targets) and `zs` (predictions) on a figure of `figsize`."
-        if self._square_show_res:
-            title = 'Ground truth\nPredictions'
-            rows = int(np.ceil(math.sqrt(len(xs))))
-            axs = subplots(rows, rows, imgsize=imgsize, figsize=figsize, title=title, weight='bold', size=12)
-            for x,y,z,ax in zip(xs,ys,zs,axs.flatten()): x.show(ax=ax, title=f'{str(y)}\n{str(z)}', **kwargs)
-            for ax in axs.flatten()[len(xs):]: ax.axis('off')
-        else:
-            title = 'Ground truth/Predictions'
-            axs = subplots(len(xs), 2, imgsize=imgsize, figsize=figsize, title=title, weight='bold', size=14)
-            for i,(x,y,z) in enumerate(zip(xs,ys,zs)):
-                x.show(ax=axs[i,0], y=y, **kwargs)
-                x.show(ax=axs[i,1], y=z, **kwargs)
-
-class ObjectCategoryProcessor(MultiCategoryProcessor):
-    "`PreProcessor` for labelled bounding boxes."
-    def __init__(self, ds:ItemList, pad_idx:int=0):
-        super().__init__(ds)
-        self.pad_idx = pad_idx
-        self.state_attrs.append('pad_idx')
-
-    def process(self, ds:ItemList):
-        ds.pad_idx = self.pad_idx
-        super().process(ds)
-
-    def process_one(self,item): return [item[0], [self.c2i.get(o,None) for o in item[1]]]
-
-    def generate_classes(self, items):
-        "Generate classes from unique `items` and add `background`."
-        classes = super().generate_classes([o[1] for o in items])
-        classes = ['background'] + list(classes)
-        return classes
-
-def _get_size(xs,i):
-    size = xs.sizes.get(i,None)
-    if size is None:
-        # Image hasn't been accessed yet, so we don't know its size
-        _ = xs[i]
-        size = xs.sizes[i]
-    return size
-
-class ObjectCategoryList(MultiCategoryList):
-    "`ItemList` for labelled bounding boxes."
-    _processor = ObjectCategoryProcessor
-
-    def get(self, i):
-        return ImageBBox.create(*_get_size(self.x,i), *self.items[i], classes=self.classes, pad_idx=self.pad_idx)
-
-    def analyze_pred(self, pred): return pred
-
-    def reconstruct(self, t, x):
-        (bboxes, labels) = t
-        if len((labels - self.pad_idx).nonzero()) == 0: return
-        i = (labels - self.pad_idx).nonzero().min()
-        bboxes,labels = bboxes[i:],labels[i:]
-        return ImageBBox.create(*x.size, bboxes, labels=labels, classes=self.classes, scale=False)
-
-class ObjectItemList(ImageList):
-    "`ItemList` suitable for object detection."
-    _label_cls,_square_show_res = ObjectCategoryList,False
-
-class SegmentationProcessor(PreProcessor):
-    "`PreProcessor` that stores the classes for segmentation."
-    def __init__(self, ds:ItemList): self.classes = ds.classes
-    def process(self, ds:ItemList):  ds.classes,ds.c = self.classes,len(self.classes)
-
-class SegmentationLabelList(ImageList):
-    "`ItemList` for segmentation masks."
-    _processor=SegmentationProcessor
-    def __init__(self, items:Iterator, classes:Collection=None, **kwargs):
-        super().__init__(items, **kwargs)
-        self.copy_new.append('classes')
-        self.classes,self.loss_func = classes,CrossEntropyFlat(axis=1)
-
-    def open(self, fn): return open_mask(fn)
-    def analyze_pred(self, pred, thresh:float=0.5): return pred.argmax(dim=0)[None]
-    def reconstruct(self, t:Tensor): return ImageSegment(t)
-
-class SegmentationItemList(ImageList):
-    "`ItemList` suitable for segmentation tasks."
-    _label_cls,_square_show_res = SegmentationLabelList,False
-
-class PointsProcessor(PreProcessor):
-    "`PreProcessor` that stores the number of targets for point regression."
-    def __init__(self, ds:ItemList): self.c = len(ds.items[0].reshape(-1))
-    def process(self, ds:ItemList):  ds.c = self.c
-
-class PointsLabelList(ItemList):
-    "`ItemList` for points."
-    _processor = PointsProcessor
-    def __init__(self, items:Iterator, **kwargs):
-        super().__init__(items, **kwargs)
-        self.loss_func = MSELossFlat()
-
-    def get(self, i):
-        o = super().get(i)
-        return ImagePoints(FlowField(_get_size(self.x,i), o), scale=True)
-
-    def analyze_pred(self, pred, thresh:float=0.5): return pred.view(-1,2)
-    def reconstruct(self, t, x): return ImagePoints(FlowField(x.size, t), scale=False)
-
-class PointsItemList(ImageList):
-    "`ItemList` for `Image` to `ImagePoints` tasks."
-    _label_cls,_square_show_res = PointsLabelList,False
-
-class ImageImageList(ImageList):
-    "`ItemList` suitable for `Image` to `Image` tasks."
-    _label_cls,_square_show,_square_show_res = ImageList,False,False
-
-    def show_xys(self, xs, ys, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, **kwargs):
-        "Show the `xs` (inputs) and `ys`(targets)  on a figure of `figsize`."
-        axs = subplots(len(xs), 2, imgsize=imgsize, figsize=figsize)
-        for i, (x,y) in enumerate(zip(xs,ys)):
-            x.show(ax=axs[i,0], **kwargs)
-            y.show(ax=axs[i,1], **kwargs)
-        plt.tight_layout()
-
-    def show_xyzs(self, xs, ys, zs, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, **kwargs):
-        "Show `xs` (inputs), `ys` (targets) and `zs` (predictions) on a figure of `figsize`."
-        title = 'Input / Prediction / Target'
-        axs = subplots(len(xs), 3, imgsize=imgsize, figsize=figsize, title=title, weight='bold', size=14)
-        for i,(x,y,z) in enumerate(zip(xs,ys,zs)):
-            x.show(ax=axs[i,0], **kwargs)
-            y.show(ax=axs[i,2], **kwargs)
-            z.show(ax=axs[i,1], **kwargs)
-
-
-def _ll_pre_transform(self, train_tfm:List[Callable], valid_tfm:List[Callable]):
-    "Call `train_tfm` and `valid_tfm` after opening image, before converting from `PIL.Image`"
-    self.train.x.after_open = compose(train_tfm)
-    self.valid.x.after_open = compose(valid_tfm)
-    return self
-
-def _db_pre_transform(self, train_tfm:List[Callable], valid_tfm:List[Callable]):
-    "Call `train_tfm` and `valid_tfm` after opening image, before converting from `PIL.Image`"
-    self.train_ds.x.after_open = compose(train_tfm)
-    self.valid_ds.x.after_open = compose(valid_tfm)
-    return self
-
-def _presize(self, size:int, val_xtra_size:int=32, scale:Tuple[float]=(0.08, 1.0), ratio:Tuple[float]=(0.75, 4./3.),
-             interpolation:int=2):
-    "Resize images to `size` using `RandomResizedCrop`, passing along `kwargs` to train transform"
-    return self.pre_transform(
-        tvt.RandomResizedCrop(size, scale=scale, ratio=ratio, interpolation=interpolation), 
-        [tvt.Resize(size+val_xtra_size), tvt.CenterCrop(size)])
-
-LabelLists.pre_transform = _ll_pre_transform
-DataBunch.pre_transform = _db_pre_transform
-LabelLists.presize = _presize
-DataBunch.presize = _presize
-
diff --git a/spaces/Xenova/semantic-image-search/next.config.js b/spaces/Xenova/semantic-image-search/next.config.js
deleted file mode 100644
index 60fd1175c2370cc668e127eb73d7367777434fa8..0000000000000000000000000000000000000000
--- a/spaces/Xenova/semantic-image-search/next.config.js
+++ /dev/null
@@ -1,18 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-    // (Optional) Export as a standalone site
-    // See https://nextjs.org/docs/pages/api-reference/next-config-js/output#automatically-copying-traced-files
-    output: 'standalone', // Feel free to modify/remove this option
-
-    // Indicate that these packages should not be bundled by webpack
-    experimental: {
-        serverComponentsExternalPackages: ['sharp', 'onnxruntime-node'],
-    },
-
-    // Define which domains we are allowed to load images from
-    images: {
-        domains: ['images.unsplash.com'],
-    },
-};
-
-module.exports = nextConfig;
diff --git a/spaces/Xule/ChuanhuChatGPT/ChuanhuChatbot.py b/spaces/Xule/ChuanhuChatGPT/ChuanhuChatbot.py
deleted file mode 100644
index cbf63e52857a1852658fdf2009ca26f9fb0a6bec..0000000000000000000000000000000000000000
--- a/spaces/Xule/ChuanhuChatGPT/ChuanhuChatbot.py
+++ /dev/null
@@ -1,470 +0,0 @@
-# -*- coding:utf-8 -*-
-import os
-import logging
-import sys
-
-import gradio as gr
-
-from modules import config
-from modules.config import *
-from modules.utils import *
-from modules.presets import *
-from modules.overwrites import *
-from modules.models import get_model
-
-
-gr.Chatbot._postprocess_chat_messages = postprocess_chat_messages
-gr.Chatbot.postprocess = postprocess
-PromptHelper.compact_text_chunks = compact_text_chunks
-
-with open("assets/custom.css", "r", encoding="utf-8") as f:
-    customCSS = f.read()
-
-def create_new_model():
-    return get_model(model_name = MODELS[DEFAULT_MODEL], access_key = my_api_key)[0]
-
-with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
-    user_name = gr.State("")
-    promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2))
-    user_question = gr.State("")
-    user_api_key = gr.State(my_api_key)
-    current_model = gr.State(create_new_model)
-
-    topic = gr.State(i18n("未命名对话历史记录"))
-
-    with gr.Row():
-        gr.HTML(CHUANHU_TITLE, elem_id="app_title")
-        status_display = gr.Markdown(get_geoip(), elem_id="status_display")
-    with gr.Row(elem_id="float_display"):
-        user_info = gr.Markdown(value="getting user info...", elem_id="user_info")
-
-        # https://github.com/gradio-app/gradio/pull/3296
-        def create_greeting(request: gr.Request):
-            if hasattr(request, "username") and request.username: # is not None or is not ""
-                logging.info(f"Get User Name: {request.username}")
-                return gr.Markdown.update(value=f"User: {request.username}"), request.username
-            else:
-                return gr.Markdown.update(value=f"User: default", visible=False), ""
-        demo.load(create_greeting, inputs=None, outputs=[user_info, user_name])
-
-    with gr.Row().style(equal_height=True):
-        with gr.Column(scale=5):
-            with gr.Row():
-                chatbot = gr.Chatbot(elem_id="chuanhu_chatbot").style(height="100%")
-            with gr.Row():
-                with gr.Column(min_width=225, scale=12):
-                    user_input = gr.Textbox(
-                        elem_id="user_input_tb",
-                        show_label=False, placeholder=i18n("在这里输入")
-                    ).style(container=False)
-                with gr.Column(min_width=42, scale=1):
-                    submitBtn = gr.Button(value="", variant="primary", elem_id="submit_btn")
-                    cancelBtn = gr.Button(value="", variant="secondary", visible=False, elem_id="cancel_btn")
-            with gr.Row():
-                emptyBtn = gr.Button(
-                    i18n("🧹 新的对话"),
-                )
-                retryBtn = gr.Button(i18n("🔄 重新生成"))
-                delFirstBtn = gr.Button(i18n("🗑️ 删除最旧对话"))
-                delLastBtn = gr.Button(i18n("🗑️ 删除最新对话"))
-                with gr.Row(visible=False) as like_dislike_area:
-                    with gr.Column(min_width=20, scale=1):
-                        likeBtn = gr.Button(i18n("👍"))
-                    with gr.Column(min_width=20, scale=1):
-                        dislikeBtn = gr.Button(i18n("👎"))
-
-        with gr.Column():
-            with gr.Column(min_width=50, scale=1):
-                with gr.Tab(label=i18n("模型")):
-                    keyTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"Your API-key...",
-                        value=hide_middle_chars(user_api_key.value),
-                        type="password",
-                        visible=not HIDE_MY_KEY,
-                        label="API-Key",
-                    )
-                    if multi_api_key:
-                        usageTxt = gr.Markdown(i18n("多账号模式已开启，无需输入key，可直接开始对话"), elem_id="usage_display", elem_classes="insert_block")
-                    else:
-                        usageTxt = gr.Markdown(i18n("**发送消息** 或 **提交key** 以显示额度"), elem_id="usage_display", elem_classes="insert_block")
-                    model_select_dropdown = gr.Dropdown(
-                        label=i18n("选择模型"), choices=MODELS, multiselect=False, value=MODELS[DEFAULT_MODEL], interactive=True
-                    )
-                    lora_select_dropdown = gr.Dropdown(
-                        label=i18n("选择LoRA模型"), choices=[], multiselect=False, interactive=True, visible=False
-                    )
-                    with gr.Row():
-                        use_streaming_checkbox = gr.Checkbox(
-                            label=i18n("实时传输回答"), value=True, visible=ENABLE_STREAMING_OPTION
-                        )
-                        single_turn_checkbox = gr.Checkbox(label=i18n("单轮对话"), value=False)
-                        use_websearch_checkbox = gr.Checkbox(label=i18n("使用在线搜索"), value=False)
-                    language_select_dropdown = gr.Dropdown(
-                        label=i18n("选择回复语言（针对搜索&索引功能）"),
-                        choices=REPLY_LANGUAGES,
-                        multiselect=False,
-                        value=REPLY_LANGUAGES[0],
-                    )
-                    index_files = gr.Files(label=i18n("上传"), type="file")
-                    two_column = gr.Checkbox(label=i18n("双栏pdf"), value=advance_docs["pdf"].get("two_column", False))
-                    # TODO: 公式ocr
-                    # formula_ocr = gr.Checkbox(label=i18n("识别公式"), value=advance_docs["pdf"].get("formula_ocr", False))
-
-                with gr.Tab(label="Prompt"):
-                    systemPromptTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=i18n("在这里输入System Prompt..."),
-                        label="System prompt",
-                        value=INITIAL_SYSTEM_PROMPT,
-                        lines=10,
-                    ).style(container=False)
-                    with gr.Accordion(label=i18n("加载Prompt模板"), open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    templateFileSelectDropdown = gr.Dropdown(
-                                        label=i18n("选择Prompt模板集合文件"),
-                                        choices=get_template_names(plain=True),
-                                        multiselect=False,
-                                        value=get_template_names(plain=True)[0],
-                                    ).style(container=False)
-                                with gr.Column(scale=1):
-                                    templateRefreshBtn = gr.Button(i18n("🔄 刷新"))
-                            with gr.Row():
-                                with gr.Column():
-                                    templateSelectDropdown = gr.Dropdown(
-                                        label=i18n("从Prompt模板中加载"),
-                                        choices=load_template(
-                                            get_template_names(plain=True)[0], mode=1
-                                        ),
-                                        multiselect=False,
-                                    ).style(container=False)
-
-                with gr.Tab(label=i18n("保存/加载")):
-                    with gr.Accordion(label=i18n("保存/加载对话历史记录"), open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    historyFileSelectDropdown = gr.Dropdown(
-                                        label=i18n("从列表中加载对话"),
-                                        choices=get_history_names(plain=True),
-                                        multiselect=False,
-                                        value=get_history_names(plain=True)[0],
-                                    )
-                                with gr.Column(scale=1):
-                                    historyRefreshBtn = gr.Button(i18n("🔄 刷新"))
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    saveFileName = gr.Textbox(
-                                        show_label=True,
-                                        placeholder=i18n("设置文件名: 默认为.json，可选为.md"),
-                                        label=i18n("设置保存文件名"),
-                                        value=i18n("对话历史记录"),
-                                    ).style(container=True)
-                                with gr.Column(scale=1):
-                                    saveHistoryBtn = gr.Button(i18n("💾 保存对话"))
-                                    exportMarkdownBtn = gr.Button(i18n("📝 导出为Markdown"))
-                                    gr.Markdown(i18n("默认保存于history文件夹"))
-                            with gr.Row():
-                                with gr.Column():
-                                    downloadFile = gr.File(interactive=True)
-
-                with gr.Tab(label=i18n("高级")):
-                    gr.Markdown(i18n("# ⚠️ 务必谨慎更改 ⚠️\n\n如果无法使用请恢复默认设置"))
-                    gr.HTML(APPEARANCE_SWITCHER, elem_classes="insert_block")
-                    with gr.Accordion(i18n("参数"), open=False):
-                        temperature_slider = gr.Slider(
-                            minimum=-0,
-                            maximum=2.0,
-                            value=1.0,
-                            step=0.1,
-                            interactive=True,
-                            label="temperature",
-                        )
-                        top_p_slider = gr.Slider(
-                            minimum=-0,
-                            maximum=1.0,
-                            value=1.0,
-                            step=0.05,
-                            interactive=True,
-                            label="top-p",
-                        )
-                        n_choices_slider = gr.Slider(
-                            minimum=1,
-                            maximum=10,
-                            value=1,
-                            step=1,
-                            interactive=True,
-                            label="n choices",
-                        )
-                        stop_sequence_txt = gr.Textbox(
-                            show_label=True,
-                            placeholder=i18n("在这里输入停止符，用英文逗号隔开..."),
-                            label="stop",
-                            value="",
-                            lines=1,
-                        )
-                        max_context_length_slider = gr.Slider(
-                            minimum=1,
-                            maximum=32768,
-                            value=2000,
-                            step=1,
-                            interactive=True,
-                            label="max context",
-                        )
-                        max_generation_slider = gr.Slider(
-                            minimum=1,
-                            maximum=32768,
-                            value=1000,
-                            step=1,
-                            interactive=True,
-                            label="max generations",
-                        )
-                        presence_penalty_slider = gr.Slider(
-                            minimum=-2.0,
-                            maximum=2.0,
-                            value=0.0,
-                            step=0.01,
-                            interactive=True,
-                            label="presence penalty",
-                        )
-                        frequency_penalty_slider = gr.Slider(
-                            minimum=-2.0,
-                            maximum=2.0,
-                            value=0.0,
-                            step=0.01,
-                            interactive=True,
-                            label="frequency penalty",
-                        )
-                        logit_bias_txt = gr.Textbox(
-                            show_label=True,
-                            placeholder=f"word:likelihood",
-                            label="logit bias",
-                            value="",
-                            lines=1,
-                        )
-                        user_identifier_txt = gr.Textbox(
-                            show_label=True,
-                            placeholder=i18n("用于定位滥用行为"),
-                            label=i18n("用户名"),
-                            value=user_name.value,
-                            lines=1,
-                        )
-
-                    with gr.Accordion(i18n("网络设置"), open=False):
-                        # 优先展示自定义的api_host
-                        apihostTxt = gr.Textbox(
-                            show_label=True,
-                            placeholder=i18n("在这里输入API-Host..."),
-                            label="API-Host",
-                            value=config.api_host or shared.API_HOST,
-                            lines=1,
-                        )
-                        changeAPIURLBtn = gr.Button(i18n("🔄 切换API地址"))
-                        proxyTxt = gr.Textbox(
-                            show_label=True,
-                            placeholder=i18n("在这里输入代理地址..."),
-                            label=i18n("代理地址（示例：http://127.0.0.1:10809）"),
-                            value="",
-                            lines=2,
-                        )
-                        changeProxyBtn = gr.Button(i18n("🔄 设置代理地址"))
-                        default_btn = gr.Button(i18n("🔙 恢复默认设置"))
-
-    gr.Markdown(CHUANHU_DESCRIPTION, elem_id="description")
-    gr.HTML(FOOTER.format(versions=versions_html()), elem_id="footer")
-    demo.load(refresh_ui_elements_on_load, [current_model, model_select_dropdown], [like_dislike_area], show_progress=False)
-    chatgpt_predict_args = dict(
-        fn=predict,
-        inputs=[
-            current_model,
-            user_question,
-            chatbot,
-            use_streaming_checkbox,
-            use_websearch_checkbox,
-            index_files,
-            language_select_dropdown,
-        ],
-        outputs=[chatbot, status_display],
-        show_progress=True,
-    )
-
-    start_outputing_args = dict(
-        fn=start_outputing,
-        inputs=[],
-        outputs=[submitBtn, cancelBtn],
-        show_progress=True,
-    )
-
-    end_outputing_args = dict(
-        fn=end_outputing, inputs=[], outputs=[submitBtn, cancelBtn]
-    )
-
-    reset_textbox_args = dict(
-        fn=reset_textbox, inputs=[], outputs=[user_input]
-    )
-
-    transfer_input_args = dict(
-        fn=transfer_input, inputs=[user_input], outputs=[user_question, user_input, submitBtn, cancelBtn], show_progress=True
-    )
-
-    get_usage_args = dict(
-        fn=billing_info, inputs=[current_model], outputs=[usageTxt], show_progress=False
-    )
-
-    load_history_from_file_args = dict(
-        fn=load_chat_history,
-        inputs=[current_model, historyFileSelectDropdown, chatbot, user_name],
-        outputs=[saveFileName, systemPromptTxt, chatbot]
-    )
-
-
-    # Chatbot
-    cancelBtn.click(interrupt, [current_model], [])
-
-    user_input.submit(**transfer_input_args).then(**chatgpt_predict_args).then(**end_outputing_args)
-    user_input.submit(**get_usage_args)
-
-    submitBtn.click(**transfer_input_args).then(**chatgpt_predict_args).then(**end_outputing_args)
-    submitBtn.click(**get_usage_args)
-
-    index_files.change(handle_file_upload, [current_model, index_files, chatbot], [index_files, chatbot, status_display])
-
-    emptyBtn.click(
-        reset,
-        inputs=[current_model],
-        outputs=[chatbot, status_display],
-        show_progress=True,
-    )
-
-    retryBtn.click(**start_outputing_args).then(
-        retry,
-        [
-            current_model,
-            chatbot,
-            use_streaming_checkbox,
-            use_websearch_checkbox,
-            index_files,
-            language_select_dropdown,
-        ],
-        [chatbot, status_display],
-        show_progress=True,
-    ).then(**end_outputing_args)
-    retryBtn.click(**get_usage_args)
-
-    delFirstBtn.click(
-        delete_first_conversation,
-        [current_model],
-        [status_display],
-    )
-
-    delLastBtn.click(
-        delete_last_conversation,
-        [current_model, chatbot],
-        [chatbot, status_display],
-        show_progress=False
-    )
-
-    likeBtn.click(
-        like,
-        [current_model],
-        [status_display],
-        show_progress=False
-    )
-
-    dislikeBtn.click(
-        dislike,
-        [current_model],
-        [status_display],
-        show_progress=False
-    )
-
-    two_column.change(update_doc_config, [two_column], None)
-
-    # LLM Models
-    keyTxt.change(set_key, [current_model, keyTxt], [user_api_key, status_display]).then(**get_usage_args)
-    keyTxt.submit(**get_usage_args)
-    single_turn_checkbox.change(set_single_turn, [current_model, single_turn_checkbox], None)
-    model_select_dropdown.change(get_model, [model_select_dropdown, lora_select_dropdown, user_api_key, temperature_slider, top_p_slider, systemPromptTxt], [current_model, status_display, lora_select_dropdown], show_progress=True)
-    model_select_dropdown.change(toggle_like_btn_visibility, [model_select_dropdown], [like_dislike_area], show_progress=False)
-    lora_select_dropdown.change(get_model, [model_select_dropdown, lora_select_dropdown, user_api_key, temperature_slider, top_p_slider, systemPromptTxt], [current_model, status_display], show_progress=True)
-
-    # Template
-    systemPromptTxt.change(set_system_prompt, [current_model, systemPromptTxt], None)
-    templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
-    templateFileSelectDropdown.change(
-        load_template,
-        [templateFileSelectDropdown],
-        [promptTemplates, templateSelectDropdown],
-        show_progress=True,
-    )
-    templateSelectDropdown.change(
-        get_template_content,
-        [promptTemplates, templateSelectDropdown, systemPromptTxt],
-        [systemPromptTxt],
-        show_progress=True,
-    )
-
-    # S&L
-    saveHistoryBtn.click(
-        save_chat_history,
-        [current_model, saveFileName, chatbot, user_name],
-        downloadFile,
-        show_progress=True,
-    )
-    saveHistoryBtn.click(get_history_names, [gr.State(False), user_name], [historyFileSelectDropdown])
-    exportMarkdownBtn.click(
-        export_markdown,
-        [current_model, saveFileName, chatbot, user_name],
-        downloadFile,
-        show_progress=True,
-    )
-    historyRefreshBtn.click(get_history_names, [gr.State(False), user_name], [historyFileSelectDropdown])
-    historyFileSelectDropdown.change(**load_history_from_file_args)
-    downloadFile.change(**load_history_from_file_args)
-
-    # Advanced
-    max_context_length_slider.change(set_token_upper_limit, [current_model, max_context_length_slider], None)
-    temperature_slider.change(set_temperature, [current_model, temperature_slider], None)
-    top_p_slider.change(set_top_p, [current_model, top_p_slider], None)
-    n_choices_slider.change(set_n_choices, [current_model, n_choices_slider], None)
-    stop_sequence_txt.change(set_stop_sequence, [current_model, stop_sequence_txt], None)
-    max_generation_slider.change(set_max_tokens, [current_model, max_generation_slider], None)
-    presence_penalty_slider.change(set_presence_penalty, [current_model, presence_penalty_slider], None)
-    frequency_penalty_slider.change(set_frequency_penalty, [current_model, frequency_penalty_slider], None)
-    logit_bias_txt.change(set_logit_bias, [current_model, logit_bias_txt], None)
-    user_identifier_txt.change(set_user_identifier, [current_model, user_identifier_txt], None)
-
-    default_btn.click(
-        reset_default, [], [apihostTxt, proxyTxt, status_display], show_progress=True
-    )
-    changeAPIURLBtn.click(
-        change_api_host,
-        [apihostTxt],
-        [status_display],
-        show_progress=True,
-    )
-    changeProxyBtn.click(
-        change_proxy,
-        [proxyTxt],
-        [status_display],
-        show_progress=True,
-    )
-
-logging.info(
-    colorama.Back.GREEN
-    + "\n川虎的温馨提示：访问 http://localhost:7860 查看界面"
-    + colorama.Style.RESET_ALL
-)
-# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
-demo.title = i18n("川虎Chat 🚀")
-
-if __name__ == "__main__":
-    reload_javascript()
-    demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
-        favicon_path="./assets/favicon.ico",
-    )
-    # demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口
-    # demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=7860,auth=("在这里填写用户名", "在这里填写密码")) # 可设置用户名与密码
-    # demo.queue(concurrency_count=CONCURRENT_COUNT).launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理
diff --git a/spaces/XzJosh/Echo-Bert-VITS2/data_utils.py b/spaces/XzJosh/Echo-Bert-VITS2/data_utils.py
deleted file mode 100644
index be3a29a93188c5b3386f22e5db29e5e96d78109a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Echo-Bert-VITS2/data_utils.py
+++ /dev/null
@@ -1,321 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-import commons
-from mel_processing import spectrogram_torch, mel_spectrogram_torch, spec_to_mel_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import cleaned_text_to_sequence, get_bert
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
-    """
-        1) loads audio, speaker_id, text pairs
-        2) normalizes text and converts them to sequences of integers
-        3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_sid_text, hparams):
-        self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length = hparams.filter_length
-        self.hop_length = hparams.hop_length
-        self.win_length = hparams.win_length
-        self.sampling_rate = hparams.sampling_rate
-        self.spk_map = hparams.spk2id
-        self.hparams = hparams
-
-        self.use_mel_spec_posterior = getattr(hparams, "use_mel_posterior_encoder", False)
-        if self.use_mel_spec_posterior:
-            self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
-
-        self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
-        self.add_blank = hparams.add_blank
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 300)
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths_sid_text)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-
-        audiopaths_sid_text_new = []
-        lengths = []
-        skipped = 0
-        for _id, spk, language, text, phones, tone, word2ph in self.audiopaths_sid_text:
-            audiopath = f'{_id}'
-            if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
-                phones = phones.split(" ")
-                tone = [int(i) for i in tone.split(" ")]
-                word2ph = [int(i) for i in word2ph.split(" ")]
-                audiopaths_sid_text_new.append([audiopath, spk, language, text, phones, tone, word2ph])
-                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
-            else:
-                skipped += 1
-        print("skipped: ", skipped, ", total: ", len(self.audiopaths_sid_text))
-        self.audiopaths_sid_text = audiopaths_sid_text_new
-        self.lengths = lengths
-
-    def get_audio_text_speaker_pair(self, audiopath_sid_text):
-        # separate filename, speaker_id and text
-        audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
-
-        bert, phones, tone, language = self.get_text(text, word2ph, phones, tone, language, audiopath)
-
-        spec, wav = self.get_audio(audiopath)
-        sid = torch.LongTensor([int(self.spk_map[sid])])
-        return (phones, spec, wav, sid, tone, language, bert)
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError("{} {} SR doesn't match target {} SR".format(
-                sampling_rate, self.sampling_rate))
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if self.use_mel_spec_posterior:
-            spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
-        try:
-            spec = torch.load(spec_filename)
-        except:
-            if self.use_mel_spec_posterior:
-                spec = mel_spectrogram_torch(audio_norm, self.filter_length,
-                    self.n_mel_channels, self.sampling_rate, self.hop_length,
-                    self.win_length, self.hparams.mel_fmin, self.hparams.mel_fmax, center=False)
-            else:
-                spec = spectrogram_torch(audio_norm, self.filter_length,
-                    self.sampling_rate, self.hop_length, self.win_length,
-                    center=False)
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-        return spec, audio_norm
-
-    def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
-        pold = phone
-        w2pho = [i for i in word2ph]
-        word2ph = [i for i in word2ph]
-        phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-        pold2 = phone
-
-        if self.add_blank:
-            p1 = len(phone)
-            phone = commons.intersperse(phone, 0)
-            p2 = len(phone)
-            t1 = len(tone)
-            tone = commons.intersperse(tone, 0)
-            t2 = len(tone)
-            language = commons.intersperse(language, 0)
-            for i in range(len(word2ph)):
-                word2ph[i] = word2ph[i] * 2
-            word2ph[0] += 1
-        bert_path = wav_path.replace(".wav", ".bert.pt")
-        try:
-            bert = torch.load(bert_path)
-            assert bert.shape[-1] == len(phone)
-        except:
-            bert = get_bert(text, word2ph, language_str)
-            torch.save(bert, bert_path)
-            #print(bert.shape[-1], bert_path, text, pold)
-            assert bert.shape[-1] == len(phone)
-
-        assert bert.shape[-1] == len(phone), (
-        bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho)
-        phone = torch.LongTensor(phone)
-        tone = torch.LongTensor(tone)
-        language = torch.LongTensor(language)
-        return bert, phone, tone, language
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def __getitem__(self, index):
-        return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
-    """ Zero-pads model inputs and targets
-    """
-
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text, audio and speaker identities
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized, sid]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[1].size(1) for x in batch]),
-            dim=0, descending=True)
-
-        max_text_len = max([len(x[0]) for x in batch])
-        max_spec_len = max([x[1].size(1) for x in batch])
-        max_wav_len = max([x[2].size(1) for x in batch])
-
-        text_lengths = torch.LongTensor(len(batch))
-        spec_lengths = torch.LongTensor(len(batch))
-        wav_lengths = torch.LongTensor(len(batch))
-        sid = torch.LongTensor(len(batch))
-
-        text_padded = torch.LongTensor(len(batch), max_text_len)
-        tone_padded = torch.LongTensor(len(batch), max_text_len)
-        language_padded = torch.LongTensor(len(batch), max_text_len)
-        bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
-
-        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        text_padded.zero_()
-        tone_padded.zero_()
-        language_padded.zero_()
-        spec_padded.zero_()
-        wav_padded.zero_()
-        bert_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            text = row[0]
-            text_padded[i, :text.size(0)] = text
-            text_lengths[i] = text.size(0)
-
-            spec = row[1]
-            spec_padded[i, :, :spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wav = row[2]
-            wav_padded[i, :, :wav.size(1)] = wav
-            wav_lengths[i] = wav.size(1)
-
-            sid[i] = row[3]
-
-            tone = row[4]
-            tone_padded[i, :tone.size(0)] = tone
-
-            language = row[5]
-            language_padded[i, :language.size(0)] = language
-
-            bert = row[6]
-            bert_padded[i, :, :bert.size(1)] = bert
-
-        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, tone_padded, language_padded, bert_padded
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
-    """
-    Maintain similar input lengths in a batch.
-    Length groups are specified by boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
-    It removes samples which are not included in the boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
-    """
-
-    def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
-        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
-        self.lengths = dataset.lengths
-        self.batch_size = batch_size
-        self.boundaries = boundaries
-
-        self.buckets, self.num_samples_per_bucket = self._create_buckets()
-        self.total_size = sum(self.num_samples_per_bucket)
-        self.num_samples = self.total_size // self.num_replicas
-
-    def _create_buckets(self):
-        buckets = [[] for _ in range(len(self.boundaries) - 1)]
-        for i in range(len(self.lengths)):
-            length = self.lengths[i]
-            idx_bucket = self._bisect(length)
-            if idx_bucket != -1:
-                buckets[idx_bucket].append(i)
-
-        for i in range(len(buckets) - 1, 0, -1):
-            if len(buckets[i]) == 0:
-                buckets.pop(i)
-                self.boundaries.pop(i + 1)
-
-        num_samples_per_bucket = []
-        for i in range(len(buckets)):
-            len_bucket = len(buckets[i])
-            total_batch_size = self.num_replicas * self.batch_size
-            rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
-            num_samples_per_bucket.append(len_bucket + rem)
-        return buckets, num_samples_per_bucket
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-
-        indices = []
-        if self.shuffle:
-            for bucket in self.buckets:
-                indices.append(torch.randperm(len(bucket), generator=g).tolist())
-        else:
-            for bucket in self.buckets:
-                indices.append(list(range(len(bucket))))
-
-        batches = []
-        for i in range(len(self.buckets)):
-            bucket = self.buckets[i]
-            len_bucket = len(bucket)
-            if (len_bucket == 0):
-                continue
-            ids_bucket = indices[i]
-            num_samples_bucket = self.num_samples_per_bucket[i]
-
-            # add extra samples to make it evenly divisible
-            rem = num_samples_bucket - len_bucket
-            ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-
-            # subsample
-            ids_bucket = ids_bucket[self.rank::self.num_replicas]
-
-            # batching
-            for j in range(len(ids_bucket) // self.batch_size):
-                batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]]
-                batches.append(batch)
-
-        if self.shuffle:
-            batch_ids = torch.randperm(len(batches), generator=g).tolist()
-            batches = [batches[i] for i in batch_ids]
-        self.batches = batches
-
-        assert len(self.batches) * self.batch_size == self.num_samples
-        return iter(self.batches)
-
-    def _bisect(self, x, lo=0, hi=None):
-        if hi is None:
-            hi = len(self.boundaries) - 1
-
-        if hi > lo:
-            mid = (hi + lo) // 2
-            if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
-                return mid
-            elif x <= self.boundaries[mid]:
-                return self._bisect(x, lo, mid)
-            else:
-                return self._bisect(x, mid + 1, hi)
-        else:
-            return -1
-
-    def __len__(self):
-        return self.num_samples // self.batch_size
diff --git a/spaces/XzJosh/Taffy-Bert-VITS2/attentions.py b/spaces/XzJosh/Taffy-Bert-VITS2/attentions.py
deleted file mode 100644
index 1192dd7268c20c11010e73a6017ed09549695afe..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Taffy-Bert-VITS2/attentions.py
+++ /dev/null
@@ -1,344 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import logging
-
-logger = logging.getLogger(__name__)
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-   
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-class Encoder(nn.Module):
-   def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, isflow = True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    #if isflow:
-    #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
-    #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
-    #  self.cond_layer = weight_norm(cond_layer, name='weight')
-    #  self.gin_channels = 256
-    self.cond_layer_idx = self.n_layers
-    if 'gin_channels' in kwargs:
-      self.gin_channels = kwargs['gin_channels']
-      if self.gin_channels != 0:
-        self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
-        # vits2 says 3rd block, so idx is 2 by default
-        self.cond_layer_idx = kwargs['cond_layer_idx'] if 'cond_layer_idx' in kwargs else 2
-        logging.debug(self.gin_channels, self.cond_layer_idx)
-        assert self.cond_layer_idx < self.n_layers, 'cond_layer_idx should be less than n_layers'
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-   def forward(self, x, x_mask, g=None):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      if i == self.cond_layer_idx and g is not None:
-        g = self.spk_emb_linear(g.transpose(1, 2))
-        g = g.transpose(1, 2)
-        x = x + g
-        x = x * x_mask
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/a-v-bely/russian-task-generator/utilities_language_general/rus_utils.py b/spaces/a-v-bely/russian-task-generator/utilities_language_general/rus_utils.py
deleted file mode 100644
index c0d6a11c015dea7c9546bd2011dad60de2c3c7b5..0000000000000000000000000000000000000000
--- a/spaces/a-v-bely/russian-task-generator/utilities_language_general/rus_utils.py
+++ /dev/null
@@ -1,339 +0,0 @@
-from nltk import edit_distance
-from utilities.utils import answer_letter
-from utilities_language_general.rus_constants import nlp
-from utilities_language_general.rus_constants import morph
-from utilities_language_general.rus_constants import stop_list
-from utilities_language_general.rus_constants import SIMILARITY_VALUES_w2v
-from utilities_language_general.rus_constants import SIMILARITY_VALUES_bert
-
-
-def prepare_target_words(target_words):
-    target_words = target_words.lower().replace(' ,', ',').replace(',', ', ').replace('  ', ' ').split(', ')
-    TARGET_WORDS = set()
-    for target_word in target_words:
-        paradigm = {form.word for form in morph.parse(target_word)[0].lexeme}
-        TARGET_WORDS = TARGET_WORDS.union(paradigm)
-    return list(set(TARGET_WORDS))
-
-
-def compute_frequency_dict(text: str) -> dict:
-    """
-    Compute frequency dictionary of given text and return it sorted in descending order.
-
-    :param text: given text as string variable
-    :return: frequency dictionary {word: frequency} sorted in descending order
-    """
-    freq_dict = {}
-    doc = nlp(text)
-    lemma_list_spacy = [token.lemma_ for token in doc]
-    for lemma in lemma_list_spacy:
-        if lemma.isalpha():
-            if lemma not in freq_dict.keys():
-                freq_dict[lemma] = 1
-            else:
-                freq_dict[lemma] += 1
-    lemma_list_morph = [morph.parse(token.text)[0].normal_form for token in doc]
-    for lemma in lemma_list_morph:
-        if lemma.isalpha() and (lemma not in lemma_list_spacy):
-            if lemma not in freq_dict.keys():
-                freq_dict[lemma] = 1
-            else:
-                freq_dict[lemma] += 1
-    return freq_dict
-
-
-def convert_gender(gender_spacy):
-    genders = {'Masc': 'masc', 'Fem': 'femn', 'Neut': 'neut'}
-    return genders[gender_spacy]
-
-
-def define_gender(token: str) -> str or None:
-    """
-    Prettify defining gender of a token.
-
-    :param token: the word for which it's needed to identify gender
-    :return: gender as string variable, ('masc', 'femn', 'neut')
-    """
-    token = str(token)
-    return morph.parse(token)[0].tag.gender
-
-
-def get_tags(token: str):
-    """
-    Prettify getting tags of a word and method of obtaining them.
-    If pymorpy2 is too unsure how to parse the word (>3 parses) None is returned.
-
-    :param token: the word for which it's needed to identify tags and method
-    :return: tags to be used for synthesis as set variable and method as string or None
-    """
-    parse = morph.parse(token)
-    tags = str(parse[0].tag)
-    parts = (tag for part in tags.split(' ') for tag in part.split(','))
-    method = str(parse[0].methods_stack[0][0])
-    return set(parts), method
-
-
-def make_inflection(text: str, pos: str or list, tags: set) -> str or None:
-    if isinstance(pos, list):
-        if set(pos).issubset({'NOUN', 'ADJ', 'PROPN'}):
-            noun_adjective_phrase_tags = {'nomn', 'gent', 'datv', 'accs', 'ablt', 'loct', 'voct',
-                                          'gen2', 'acc2', 'loc2', 'sing', 'plur'}
-            tags = tags.intersection(noun_adjective_phrase_tags)
-        lemmas = text.split('_')
-        word_form = ''
-        # Parse phrase and define phrase gender, animacy
-        gender = None
-        animacy = None
-        parsed = []
-        for lemma in lemmas:
-            parse = morph.parse(lemma)[0]
-            gender = gender if gender is not None else parse.tag.gender if parse.tag.POS == 'NOUN' else None
-            animacy = animacy if animacy is not None else str(parse.tag).split(',')[1] \
-                if parse.tag.POS == 'NOUN' else None
-            parsed.append(parse)
-        tags = tags.union({gender, animacy})
-        tags.discard(None)
-        try:
-            for parse in parsed:
-                word_form += f'{parse.inflect(tags).word} '
-            return word_form
-        except AttributeError:
-            return None
-    else:
-        word_form = morph.parse(text)[0].inflect(tags)
-        return word_form.word if word_form is not None else None
-
-
-def check_token(token, lemma_pos, model, current_minimum: set = None, stop_words=stop_list,
-                check_allowed_pos: set = None, check_allowed_dep: set = None) -> bool:
-    not_allowed_pos = {'PROPN', 'PUNCT', 'NUM'}
-    not_allowed_synt_dep = {'cop', }  # 'ROOT'
-    if lemma_pos == 'auto':
-        lemma_pos = f'{token.lemma_}_{token.pos_}'
-    if not token.text.isalpha():
-        return False
-    if current_minimum is not None and token.lemma_ not in current_minimum:
-        return False
-    if not model.has_index_for(lemma_pos):
-        return False
-    if get_tags(token.text) is not None:
-        tags, method = get_tags(token.text)
-    else:
-        tags = None
-        method = 'suspense'
-    method_satisfied = (method == 'DictionaryAnalyzer()')
-    if (token.text not in stop_words
-            and not token.is_stop
-            and tags is not None
-            and method_satisfied):
-        if check_allowed_pos is None and check_allowed_dep is None:
-            if token.pos_ not in not_allowed_pos and token.dep_ not in not_allowed_synt_dep:
-                return True
-            return False
-        elif check_allowed_pos is not None and check_allowed_dep is None:
-            if token.pos_ in check_allowed_pos and token.dep_ not in not_allowed_synt_dep:
-                return True
-            return False
-        elif check_allowed_pos is None and check_allowed_dep is not None:
-            if token.pos_ not in not_allowed_pos and token.dep_ in check_allowed_dep:
-                return True
-            return False
-        else:
-            if token.pos_ in check_allowed_pos and token.dep_ in check_allowed_dep:
-                return True
-            return False
-    else:
-        return False
-
-
-def check_token_bert(token, current_minimum: set = None, stop_words=stop_list,
-                     check_allowed_pos: set = None, check_allowed_dep: set = None) -> bool:
-    not_allowed_pos = {'PROPN', 'PUNCT', 'NUM'}
-    not_allowed_synt_dep = {'cop', }  # 'ROOT'
-    if not token.text.isalpha():
-        return False
-    if current_minimum is not None and token.lemma_ not in current_minimum:
-        return False
-    if get_tags(token.text) is not None:
-        tags, method = get_tags(token.text)
-    else:
-        tags = None
-        method = 'suspense'
-    method_satisfied = (method == 'DictionaryAnalyzer()')
-    if (token.text not in stop_words
-            and not token.is_stop
-            and tags is not None
-            and method_satisfied):
-        if check_allowed_pos is None and check_allowed_dep is None:
-            if token.pos_ not in not_allowed_pos and token.dep_ not in not_allowed_synt_dep:
-                return True
-            return False
-        elif check_allowed_pos is not None and check_allowed_dep is None:
-            if token.pos_ in check_allowed_pos and token.dep_ not in not_allowed_synt_dep:
-                return True
-            return False
-        elif check_allowed_pos is None and check_allowed_dep is not None:
-            if token.pos_ not in not_allowed_pos and token.dep_ in check_allowed_dep:
-                return True
-            return False
-        else:
-            if token.pos_ in check_allowed_pos and token.dep_ in check_allowed_dep:
-                return True
-            return False
-    else:
-        return False
-
-
-def get_distractors_from_model(model, lemma: str, pos: str, gender: str or None, global_distractors: set,
-                               distractor_minimum: set, level_name: str, max_num_distractors: int,
-                               max_length_ratio=5, min_edit_distance_ratio=0.5):
-    distractors = []
-    query = lemma if '_' in lemma else f'{lemma}_{pos}'
-    lemma = '_'.join(lemma.split('_')[::2])
-    if model.has_index_for(query):
-        candidates = model.most_similar(query, topn=max_num_distractors + 100)
-    else:
-        if query.count('_') == 1:
-            return None
-        query_raw_list = query.split('_')
-        query_parts = ['_'.join(query_raw_list[i:i + 2]) for i in range(len(query_raw_list))][::2]
-        query_vector = model.get_mean_vector(query_parts)
-        candidates = model.similar_by_vector(query_vector, topn=max_num_distractors + 100)
-    for candidate in candidates:
-        if candidate[0].count('_') == 1:
-            distractor_lemma, distractor_pos = candidate[0].split('_')
-            distractor_similarity = candidate[1]
-            candidate_gender = define_gender(distractor_lemma)
-            length_ratio = abs(len(lemma) - len(distractor_lemma))
-            condition = ((distractor_pos == pos
-                          or (distractor_pos in ('VERB', 'ADJ', 'phrase') and pos in ('VERB', 'ADJ', 'phrase')))
-                         and distractor_lemma != lemma
-                         and len(distractors) < 100
-                         and distractor_similarity < SIMILARITY_VALUES_w2v[level_name]
-                         and candidate_gender == gender
-                         and length_ratio <= max_length_ratio
-                         and distractor_lemma not in global_distractors
-                         and edit_distance(lemma, distractor_lemma) / ((len(lemma) + len(distractor_lemma)) / 2) >
-                         min_edit_distance_ratio)
-            if condition:
-                if distractor_minimum is not None:
-                    if distractor_lemma in distractor_minimum:
-                        distractors.append((distractor_lemma, distractor_similarity))
-                        global_distractors.add(distractor_lemma)
-                else:
-                    distractors.append((distractor_lemma, distractor_similarity))
-                    global_distractors.add(distractor_lemma)
-        else:
-            if candidate[0].count('_') > 3 or pos in ('NOUN', 'ADJ', 'NUM'):
-                continue
-            d1_lemma, d1_pos, d2_lemma, d2_pos = candidate[0].split('_')
-            distractor_lemma = f'{d1_lemma}_{d2_lemma}'
-            distractor_similarity = candidate[1]
-            condition = (((d1_pos == pos or d2_pos == pos)
-                          or (d1_pos in ('VERB', 'AUX', 'SCONJ', 'ADP')
-                              and pos in ('phrase', 'VERB', 'AUX', 'SCONJ', 'ADP'))
-                          or (d2_pos in ('VERB', 'AUX', 'SCONJ', 'ADP')
-                              and pos in ('phrase', 'VERB', 'AUX', 'SCONJ', 'ADP')))
-                         and candidate[0] != lemma
-                         and distractor_lemma != lemma
-                         and len(distractors) < 100
-                         and distractor_similarity < SIMILARITY_VALUES_w2v[level_name]
-                         and distractor_lemma not in global_distractors)
-            if condition:
-                if distractor_minimum is not None:
-                    if (distractor_lemma in distractor_minimum
-                            or (d1_lemma in distractor_minimum and d2_lemma in distractor_minimum)):
-                        distractors.append((candidate[0], distractor_similarity))
-                        global_distractors.add(distractor_lemma)
-                else:
-                    distractors.append((candidate[0], distractor_similarity))
-                    global_distractors.add(distractor_lemma)
-    max_num_distractors = min(4, max_num_distractors) if max_num_distractors >= 4 else max_num_distractors
-    if len(distractors) >= max_num_distractors:
-        return distractors
-    else:
-        return None
-
-
-def get_distractors_from_model_bert(model, text_with_masked_task: str, lemma: str, pos: str, gender: str or None,
-                                    global_distractors: set, distractor_minimum: set, level_name: str,
-                                    max_num_distractors: int, max_length_ratio=5, min_edit_distance_ratio=0.5):
-    _distractors = []
-    try:
-        if distractor_minimum:
-            bert_candidates = [token for token in model(text_with_masked_task, top_k=max_num_distractors + 100)]
-        else:
-            bert_candidates = [token for token in model(text_with_masked_task, top_k=max_num_distractors + 100)]
-        inflected_candidates = []
-        for candidate in bert_candidates:
-            if isinstance(candidate, list):
-                bert_candidates = candidate
-                continue
-            if candidate['token_str'].isalpha():
-                candidate_morph = nlp(candidate['token_str'])[0]
-                inflected_candidates.append((f"{candidate_morph.lemma_}_{candidate_morph.pos_}", candidate['score']))
-    except KeyError:
-        return None
-    for candidate_distractor in inflected_candidates:
-        if '_' in candidate_distractor[0]:
-            distractor_lemma, distractor_pos = candidate_distractor[0].split('_')
-        else:
-            candidate_morph = nlp(candidate_distractor[0])[0]
-            distractor_lemma, distractor_pos = candidate_morph.lemma_, candidate_morph.pos_
-        distractor_similarity = candidate_distractor[1]
-        candidate_gender = define_gender(distractor_lemma)
-        length_ratio = abs(len(lemma) - len(distractor_lemma))
-        if (((distractor_pos == pos)
-             or (pos in ('VERB', 'ADJ', 'phrase') and distractor_pos in ('VERB', 'ADJ', 'phrase')))
-                and distractor_lemma != lemma
-                and (len(_distractors) < max_num_distractors + 10)
-                and (distractor_similarity < SIMILARITY_VALUES_bert[level_name])
-                and (candidate_gender == gender)
-                and (length_ratio <= max_length_ratio)  # May be changed if case of phrases
-                and (distractor_lemma not in global_distractors)
-                and (edit_distance(lemma, distractor_lemma)  # May be changed if case of phrases
-                     / ((len(lemma) + len(distractor_lemma)) / 2) > min_edit_distance_ratio)):
-            if distractor_minimum is not None:
-                if distractor_lemma in distractor_minimum:
-                    _distractors.append((distractor_lemma, candidate_distractor[1]))
-                    global_distractors.add(distractor_lemma)
-            else:
-                _distractors.append((distractor_lemma, candidate_distractor[1]))
-    num_distractors = min(4, max_num_distractors) if max_num_distractors >= 4 else max_num_distractors
-    if len(_distractors) < num_distractors:
-        return None
-    return _distractors
-
-
-def prepare_tasks(input_variants):
-    TASKS_STUDENT = ''
-    TASKS_TEACHER = ''
-    KEYS_ONLY = ''
-    RAW_TASKS = []
-    RAW_KEYS_ONLY = []
-    RESULT_TASKS_STUDENT = []
-    TASKS_WITH_ANSWERS_L = []
-    KEYS = []
-
-    for num, item in enumerate(input_variants):
-        item = item[0]
-        answer = item[0].lower()
-        variants = '\t'.join([i.lower() for i in item[1]])
-        current_answer_letter = answer_letter(answer=answer, variants=[i.lower() for i in item[1]])
-        RAW_TASKS.append((num + 1, variants))
-        RAW_KEYS_ONLY.append((num + 1, current_answer_letter.split(' ')[0]))
-        RESULT_TASKS_STUDENT.append(f"{num + 1}.\t{variants}")
-        TASKS_WITH_ANSWERS_L.append(f"{num + 1}.\t"
-                                    f"Ответ: {current_answer_letter}\n\t"
-                                    f"Варианты: {variants}")
-        KEYS.append(f"{num + 1}.\tОтвет: {current_answer_letter}")
-
-    for task in RESULT_TASKS_STUDENT:
-        TASKS_STUDENT += f'{task}\n'
-    for task in TASKS_WITH_ANSWERS_L:
-        TASKS_TEACHER += f'{task}\n'
-    for task in KEYS:
-        KEYS_ONLY += f'{task}\n'
-
-    return {'TASKS_STUDENT': TASKS_STUDENT, 'TASKS_TEACHER': TASKS_TEACHER,
-            'KEYS_ONLY': KEYS_ONLY, 'RAW_TASKS': RAW_TASKS, 'RAW_KEYS_ONLY': RAW_KEYS_ONLY}
diff --git a/spaces/aadnk/whisper-webui/src/config.py b/spaces/aadnk/whisper-webui/src/config.py
deleted file mode 100644
index 262ad3625afc8e0df8e2fab34f698d5d1656e895..0000000000000000000000000000000000000000
--- a/spaces/aadnk/whisper-webui/src/config.py
+++ /dev/null
@@ -1,166 +0,0 @@
-from enum import Enum
-import urllib
-
-import os
-from typing import List
-from urllib.parse import urlparse
-import json5
-import torch
-
-from tqdm import tqdm
-
-class ModelConfig:
-    def __init__(self, name: str, url: str, path: str = None, type: str = "whisper"):
-        """
-        Initialize a model configuration.
-
-        name: Name of the model
-        url: URL to download the model from
-        path: Path to the model file. If not set, the model will be downloaded from the URL.
-        type: Type of model. Can be whisper or huggingface.
-        """
-        self.name = name
-        self.url = url
-        self.path = path
-        self.type = type
-
-VAD_INITIAL_PROMPT_MODE_VALUES=["prepend_all_segments", "prepend_first_segment", "json_prompt_mode"]
-
-class VadInitialPromptMode(Enum):
-    PREPEND_ALL_SEGMENTS = 1
-    PREPREND_FIRST_SEGMENT = 2
-    JSON_PROMPT_MODE = 3
-
-    @staticmethod
-    def from_string(s: str):
-        normalized = s.lower() if s is not None else None
-
-        if normalized == "prepend_all_segments":
-            return VadInitialPromptMode.PREPEND_ALL_SEGMENTS
-        elif normalized == "prepend_first_segment":
-            return VadInitialPromptMode.PREPREND_FIRST_SEGMENT
-        elif normalized == "json_prompt_mode":
-            return VadInitialPromptMode.JSON_PROMPT_MODE
-        elif normalized is not None and normalized != "":
-            raise ValueError(f"Invalid value for VadInitialPromptMode: {s}")
-        else:
-            return None
-
-class ApplicationConfig:
-    def __init__(self, models: List[ModelConfig] = [], input_audio_max_duration: int = 600, 
-                 share: bool = False, server_name: str = None, server_port: int = 7860, 
-                 queue_concurrency_count: int = 1, delete_uploaded_files: bool = True,
-                 whisper_implementation: str = "whisper",
-                 default_model_name: str = "medium", default_vad: str = "silero-vad", 
-                 vad_parallel_devices: str = "", vad_cpu_cores: int = 1, vad_process_timeout: int = 1800, 
-                 auto_parallel: bool = False, output_dir: str = None,
-                 model_dir: str = None, device: str = None, 
-                 verbose: bool = True, task: str = "transcribe", language: str = None,
-                 vad_initial_prompt_mode: str = "prepend_first_segment ", 
-                 vad_merge_window: float = 5, vad_max_merge_size: float = 30,
-                 vad_padding: float = 1, vad_prompt_window: float = 3,
-                 temperature: float = 0, best_of: int = 5, beam_size: int = 5,
-                 patience: float = None, length_penalty: float = None,
-                 suppress_tokens: str = "-1", initial_prompt: str = None,
-                 condition_on_previous_text: bool = True, fp16: bool = True,
-                 compute_type: str = "float16", 
-                 temperature_increment_on_fallback: float = 0.2, compression_ratio_threshold: float = 2.4,
-                 logprob_threshold: float = -1.0, no_speech_threshold: float = 0.6,
-                 # Word timestamp settings
-                 word_timestamps: bool = False, prepend_punctuations: str = "\"\'“¿([{-",
-                 append_punctuations: str = "\"\'.。,，!！?？:：”)]}、", 
-                 highlight_words: bool = False,
-                 # Diarization
-                 auth_token: str = None, diarization: bool = False, diarization_speakers: int = 2,
-                 diarization_min_speakers: int = 1, diarization_max_speakers: int = 5,
-                 diarization_process_timeout: int = 60):
-        
-        self.models = models
-        
-        # WebUI settings
-        self.input_audio_max_duration = input_audio_max_duration
-        self.share = share
-        self.server_name = server_name
-        self.server_port = server_port
-        self.queue_concurrency_count = queue_concurrency_count
-        self.delete_uploaded_files = delete_uploaded_files
-
-        self.whisper_implementation = whisper_implementation
-        self.default_model_name = default_model_name
-        self.default_vad = default_vad
-        self.vad_parallel_devices = vad_parallel_devices
-        self.vad_cpu_cores = vad_cpu_cores
-        self.vad_process_timeout = vad_process_timeout
-        self.auto_parallel = auto_parallel
-        self.output_dir = output_dir
-
-        self.model_dir = model_dir
-        self.device = device
-        self.verbose = verbose
-        self.task = task
-        self.language = language
-        self.vad_initial_prompt_mode = vad_initial_prompt_mode
-        self.vad_merge_window = vad_merge_window
-        self.vad_max_merge_size = vad_max_merge_size
-        self.vad_padding = vad_padding
-        self.vad_prompt_window = vad_prompt_window
-        self.temperature = temperature
-        self.best_of = best_of
-        self.beam_size = beam_size
-        self.patience = patience
-        self.length_penalty = length_penalty
-        self.suppress_tokens = suppress_tokens
-        self.initial_prompt = initial_prompt
-        self.condition_on_previous_text = condition_on_previous_text
-        self.fp16 = fp16
-        self.compute_type = compute_type
-        self.temperature_increment_on_fallback = temperature_increment_on_fallback
-        self.compression_ratio_threshold = compression_ratio_threshold
-        self.logprob_threshold = logprob_threshold
-        self.no_speech_threshold = no_speech_threshold
-        
-        # Word timestamp settings
-        self.word_timestamps = word_timestamps
-        self.prepend_punctuations = prepend_punctuations
-        self.append_punctuations = append_punctuations
-        self.highlight_words = highlight_words
-        
-        # Diarization settings
-        self.auth_token = auth_token
-        self.diarization = diarization
-        self.diarization_speakers = diarization_speakers
-        self.diarization_min_speakers = diarization_min_speakers
-        self.diarization_max_speakers = diarization_max_speakers
-        self.diarization_process_timeout = diarization_process_timeout
-
-    def get_model_names(self):
-        return [ x.name for x in self.models ]
-
-    def update(self, **new_values):
-        result = ApplicationConfig(**self.__dict__)
-
-        for key, value in new_values.items():
-            setattr(result, key, value)
-        return result
-
-    @staticmethod
-    def create_default(**kwargs):
-        app_config = ApplicationConfig.parse_file(os.environ.get("WHISPER_WEBUI_CONFIG", "config.json5"))
-
-        # Update with kwargs
-        if len(kwargs) > 0:
-            app_config = app_config.update(**kwargs)
-        return app_config
-
-    @staticmethod
-    def parse_file(config_path: str):
-        import json5
-
-        with open(config_path, "r", encoding="utf-8") as f:
-            # Load using json5
-            data = json5.load(f)
-            data_models = data.pop("models", [])
-
-            models = [ ModelConfig(**x) for x in data_models ]
-
-            return ApplicationConfig(models, **data)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/__init__.py
deleted file mode 100644
index 44ac99855ae52101c91be167fa78d8219fc47259..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from .backbones import *  # noqa: F401,F403
-from .builder import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS,
-                      ROI_EXTRACTORS, SHARED_HEADS, build_backbone,
-                      build_detector, build_head, build_loss, build_neck,
-                      build_roi_extractor, build_shared_head)
-from .dense_heads import *  # noqa: F401,F403
-from .detectors import *  # noqa: F401,F403
-from .losses import *  # noqa: F401,F403
-from .necks import *  # noqa: F401,F403
-from .roi_heads import *  # noqa: F401,F403
-
-__all__ = [
-    'BACKBONES', 'NECKS', 'ROI_EXTRACTORS', 'SHARED_HEADS', 'HEADS', 'LOSSES',
-    'DETECTORS', 'build_backbone', 'build_neck', 'build_roi_extractor',
-    'build_shared_head', 'build_head', 'build_loss', 'build_detector'
-]
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/utils/optimizer.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/utils/optimizer.py
deleted file mode 100644
index a9f593fe3dda2fcf3f9fb5a6b291f11614de18b6..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/utils/optimizer.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from annotator.uniformer.mmcv.runner import OptimizerHook, HOOKS
-try:
-    import apex
-except:
-    print('apex is not installed')
-
-
-@HOOKS.register_module()
-class DistOptimizerHook(OptimizerHook):
-    """Optimizer hook for distributed training."""
-
-    def __init__(self, update_interval=1, grad_clip=None, coalesce=True, bucket_size_mb=-1, use_fp16=False):
-        self.grad_clip = grad_clip
-        self.coalesce = coalesce
-        self.bucket_size_mb = bucket_size_mb
-        self.update_interval = update_interval
-        self.use_fp16 = use_fp16
-
-    def before_run(self, runner):
-        runner.optimizer.zero_grad()
-
-    def after_train_iter(self, runner):
-        runner.outputs['loss'] /= self.update_interval
-        if self.use_fp16:
-            with apex.amp.scale_loss(runner.outputs['loss'], runner.optimizer) as scaled_loss:
-                scaled_loss.backward()
-        else:
-            runner.outputs['loss'].backward()
-        if self.every_n_iters(runner, self.update_interval):
-            if self.grad_clip is not None:
-                self.clip_grads(runner.model.parameters())
-            runner.optimizer.step()
-            runner.optimizer.zero_grad()
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/fileio/handlers/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/fileio/handlers/__init__.py
deleted file mode 100644
index aa24d91972837b8756b225f4879bac20436eb72a..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/fileio/handlers/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .base import BaseFileHandler
-from .json_handler import JsonHandler
-from .pickle_handler import PickleHandler
-from .yaml_handler import YamlHandler
-
-__all__ = ['BaseFileHandler', 'JsonHandler', 'PickleHandler', 'YamlHandler']
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/README.md b/spaces/abrar-lohia/text-2-character-anim/pyrender/README.md
deleted file mode 100644
index ae88ed1c5e78f247e38291ed83cf4c81230bf976..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/README.md
+++ /dev/null
@@ -1,92 +0,0 @@
-# Pyrender
-
-[![Build Status](https://travis-ci.org/mmatl/pyrender.svg?branch=master)](https://travis-ci.org/mmatl/pyrender)
-[![Documentation Status](https://readthedocs.org/projects/pyrender/badge/?version=latest)](https://pyrender.readthedocs.io/en/latest/?badge=latest)
-[![Coverage Status](https://coveralls.io/repos/github/mmatl/pyrender/badge.svg?branch=master)](https://coveralls.io/github/mmatl/pyrender?branch=master)
-[![PyPI version](https://badge.fury.io/py/pyrender.svg)](https://badge.fury.io/py/pyrender)
-[![Downloads](https://pepy.tech/badge/pyrender)](https://pepy.tech/project/pyrender)
-
-Pyrender is a pure Python (2.7, 3.4, 3.5, 3.6) library for physically-based
-rendering and visualization.
-It is designed to meet the [glTF 2.0 specification from Khronos](https://www.khronos.org/gltf/).
-
-Pyrender is lightweight, easy to install, and simple to use.
-It comes packaged with both an intuitive scene viewer and a headache-free
-offscreen renderer with support for GPU-accelerated rendering on headless
-servers, which makes it perfect for machine learning applications.
-
-Extensive documentation, including a quickstart guide, is provided [here](https://pyrender.readthedocs.io/en/latest/).
-
-For a minimal working example of GPU-accelerated offscreen rendering using EGL,
-check out the [EGL Google CoLab Notebook](https://colab.research.google.com/drive/1pcndwqeY8vker3bLKQNJKr3B-7-SYenE?usp=sharing).
-
-
-<p align="center">
-  <img width="48%" src="https://github.com/mmatl/pyrender/blob/master/docs/source/_static/rotation.gif?raw=true" alt="GIF of Viewer"/>
-  <img width="48%" src="https://github.com/mmatl/pyrender/blob/master/docs/source/_static/damaged_helmet.png?raw=true" alt="Damaged Helmet"/>
-</p>
-
-## Installation
-You can install pyrender directly from pip.
-
-```bash
-pip install pyrender
-```
-
-## Features
-
-Despite being lightweight, pyrender has lots of features, including:
-
-* Simple interoperation with the amazing [trimesh](https://github.com/mikedh/trimesh) project,
-which enables out-of-the-box support for dozens of mesh types, including OBJ,
-STL, DAE, OFF, PLY, and GLB.
-* An easy-to-use scene viewer with support for animation, showing face and vertex
-normals, toggling lighting conditions, and saving images and GIFs.
-* An offscreen rendering module that supports OSMesa and EGL backends.
-* Shadow mapping for directional and spot lights.
-* Metallic-roughness materials for physically-based rendering, including several
-types of texture and normal mapping.
-* Transparency.
-* Depth and color image generation.
-
-## Sample Usage
-
-For sample usage, check out the [quickstart
-guide](https://pyrender.readthedocs.io/en/latest/examples/index.html) or one of
-the Google CoLab Notebooks:
-
-* [EGL Google CoLab Notebook](https://colab.research.google.com/drive/1pcndwqeY8vker3bLKQNJKr3B-7-SYenE?usp=sharing)
-
-## Viewer Keyboard and Mouse Controls
-
-When using the viewer, the basic controls for moving about the scene are as follows:
-
-* To rotate the camera about the center of the scene, hold the left mouse button and drag the cursor.
-* To rotate the camera about its viewing axis, hold `CTRL` left mouse button and drag the cursor.
-* To pan the camera, do one of the following:
-    * Hold `SHIFT`, then hold the left mouse button and drag the cursor.
-    * Hold the middle mouse button and drag the cursor.
-* To zoom the camera in or out, do one of the following:
-    * Scroll the mouse wheel.
-    * Hold the right mouse button and drag the cursor.
-
-The available keyboard commands are as follows:
-
-* `a`: Toggles rotational animation mode.
-* `c`: Toggles backface culling.
-* `f`: Toggles fullscreen mode.
-* `h`: Toggles shadow rendering.
-* `i`: Toggles axis display mode (no axes, world axis, mesh axes, all axes).
-* `l`: Toggles lighting mode (scene lighting, Raymond lighting, or direct lighting).
-* `m`: Toggles face normal visualization.
-* `n`: Toggles vertex normal visualization.
-* `o`: Toggles orthographic camera mode.
-* `q`: Quits the viewer.
-* `r`: Starts recording a GIF, and pressing again stops recording and opens a file dialog.
-* `s`: Opens a file dialog to save the current view as an image.
-* `w`: Toggles wireframe mode (scene default, flip wireframes, all wireframe, or all solid).
-* `z`: Resets the camera to the default view.
-
-As a note, displaying shadows significantly slows down rendering, so if you're
-experiencing low framerates, just kill shadows or reduce the number of lights in
-your scene.
diff --git a/spaces/abtExp/source_separation/data_setups.py b/spaces/abtExp/source_separation/data_setups.py
deleted file mode 100644
index 2488ac6684d6f3ea3b1b2121f725f297bf8b1d3c..0000000000000000000000000000000000000000
--- a/spaces/abtExp/source_separation/data_setups.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# Make function to find classes in target directory
-import os
-import librosa
-import torch
-import numpy as np
-from torchaudio.transforms import Resample
-
-SAMPLE_RATE = 44100
-AUDIO_LEN = 2.90
-
-# Parameters to control the MelSpec generation
-N_MELS = 128
-F_MIN = 20
-F_MAX = 16000
-N_FFT = 1024
-HOP_LEN = 512
-
-# Make function to find classes in target directory
-def find_classes(directory: str):
-    # 1. Get the class names by scanning the target directory
-    classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())
-    # 2. Raise an error if class names not found
-    if not classes:
-        raise FileNotFoundError(f"Couldn't find any classes in {directory}.")
-    # 3. Crearte a dictionary of index labels (computers prefer numerical rather than string labels)
-    class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
-    return classes, class_to_idx
-    
-def resample(wav, sample_rate, new_sample_rate):
-    if wav.shape[0] >= 2:
-        wav = torch.mean(wav, dim=0)
-    else:
-        wav = wav.squeeze(0)
-    if sample_rate > new_sample_rate:
-        resampler = Resample(sample_rate, new_sample_rate)
-        wav = resampler(wav)
-    return wav
-
-def mono_to_color(X, eps=1e-6, mean=None, std=None):
-    X = np.stack([X, X, X], axis=-1)
-    # Standardize
-    mean = mean or X.mean()
-    std = std or X.std()
-    X = (X - mean) / (std + eps)
-    # Normalize to [0, 255]
-    _min, _max = X.min(), X.max()
-    if (_max - _min) > eps:
-        V = np.clip(X, _min, _max)
-        V = 255 * (V - _min) / (_max - _min)
-        V = V.astype(np.uint8)
-    else:
-        V = np.zeros_like(X, dtype=np.uint8)
-    return V
-
-def normalize(image, mean=None, std=None):
-    image = image / 255.0
-    if mean is not None and std is not None:
-        image = (image - mean) / std
-    return np.moveaxis(image, 2, 0).astype(np.float32)
-
-def compute_melspec(wav, sample_rate=SAMPLE_RATE):
-    melspec = librosa.feature.melspectrogram(
-        y=wav,
-        sr=sample_rate, 
-        n_fft=N_FFT, 
-        fmin=F_MIN, 
-        fmax=F_MAX,
-        n_mels=N_MELS,
-        hop_length=HOP_LEN
-    )
-    melspec = librosa.power_to_db(melspec).astype(np.float32)
-    return melspec
-
-def audio_preprocess(wav, sample_rate):
-    wav = wav.numpy()
-    melspec = compute_melspec(wav, sample_rate)
-    image = mono_to_color(melspec)
-    image = normalize(image, mean=None, std=None)
-    image = torch.from_numpy(image)
-    return image
\ No newline at end of file
diff --git a/spaces/akhaliq/SummerTime/model/third_party/HMNet/DataLoader/infinibatch/test/test_iterators.py b/spaces/akhaliq/SummerTime/model/third_party/HMNet/DataLoader/infinibatch/test/test_iterators.py
deleted file mode 100644
index 08d5e2465dec4f684435fb1663bd9566a8cfc27b..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/model/third_party/HMNet/DataLoader/infinibatch/test/test_iterators.py
+++ /dev/null
@@ -1,601 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-import gzip
-import itertools
-from random import Random
-import os
-import shutil
-import tempfile
-from typing import Iterable, Iterator, Any, Union
-import unittest
-import pickle
-import gc
-
-from infinibatch.iterators import (
-    create_source_iterator,
-    ChunkedSourceIterator,
-    InfinitePermutationSourceIterator,
-    BufferedShuffleIterator,
-    BlockwiseShuffleIterator,
-    NativeCheckpointableIterator,
-    BucketedReadaheadBatchIterator,
-    MapIterator,
-    ParallelMapIterator,
-    ZipIterator,
-    FixedBatchIterator,
-    WindowedIterator,
-    SelectManyIterator,
-    RandomIterator,
-    RecurrentIterator,
-    SamplingRandomMapIterator,
-    PrefetchIterator,
-)
-from infinibatch.datasets import chunked_dataset_iterator
-
-
-# TODO:
-#  - make sure that all iterators can be reset to a checkpoint even after they were exhausted
-#  - make sure that all iterators can be reset to a checkpoint that was taken after the iterator was exhausted
-#  - make sure that all iterators can be reset to a checkpoint at the beginning of the iteration
-#  - refactor test cases that do not rely on TestCheckpointableIterator
-#  - make sure every iterator is tested for correct checkpointing at the end of the iterator
-
-
-class TestCheckpointableIterator:
-    """
-    These are common test cases for CheckointableIterators
-
-    Inherit from this class and set self.iterator and self.expected_result in the setUp function to use.
-    """
-
-    def test_basic(self):
-        self.assertListEqual(list(self.iterator), self.expected_result)
-
-    def test_checkpointing_from_start(self):
-        for _ in range(len(self.expected_result)):
-            next(self.iterator)
-        self.iterator.setstate(None)
-        self.assertListEqual(list(self.iterator), self.expected_result)
-
-    def test_checkpointing_in_middle(self):
-        result = [next(self.iterator) for _ in range(len(self.expected_result) // 3)]
-        self.iterator.setstate(self.iterator.getstate())
-        result += [item for item in self.iterator]
-        self.assertListEqual(result, self.expected_result)
-
-    def test_checkpointing_at_end(self):
-        for _ in range(len(self.expected_result)):
-            next(self.iterator)
-        self.iterator.setstate(self.iterator.getstate())
-        self.assertRaises(StopIteration, self.iterator.__next__)
-
-
-class TestBase(unittest.TestCase):
-    def setUp(self):
-        self.test_data = [
-            [
-                "item number one",
-                "item number two",
-                "item number three",
-                "item number four",
-            ],
-            ["item number five"],
-            [
-                "item number six",
-                "item number seven",
-                "item number eight",
-                "item number nine",
-                "item number ten",
-                "item number eleven",
-            ],
-            [
-                "item number twelve",
-                "item number thirteen",
-                "item number fourteen",
-            ],
-        ]
-
-        self.flattened_test_data = []
-        for chunk in self.test_data:
-            for item in chunk:
-                self.flattened_test_data.append(item)
-
-        self.data_dir = tempfile.mkdtemp()
-        self.chunk_file_paths = []
-        for chunk_id, chunk in enumerate(self.test_data):
-            file_name = os.path.join(
-                self.data_dir, "chunk_" + str(chunk_id).zfill(10) + ".gz"
-            )
-            self.chunk_file_paths.append(file_name)
-            file_content = "\n".join(chunk)
-            with gzip.open(file_name, "wt", encoding="utf-8") as f:
-                f.write(file_content)
-
-    @staticmethod
-    def read_chunk(
-        textfile_path: str,
-    ) -> Iterator[str]:  # read_chunk_fn for chunked_dataset_iterator
-        with gzip.open(textfile_path, "rt", encoding="utf-8") as f:
-            return iter(f.read().splitlines())
-
-    def tearDown(self):
-        gc.collect()  # this will get the pre-fetch terminated in some tests, which otherwise may still want to read these files
-        shutil.rmtree(self.data_dir)
-
-    def assertMultisetEqual(self, a, b):
-        self.assertEqual(len(a), len(b))
-        self.assertSetEqual(set(a), set(b))
-
-
-class TestSourceIterator(unittest.TestCase):
-    def test_exception(self):
-        self.assertRaises(
-            ValueError, create_source_iterator, [1], train=False, shuffle=True
-        )
-
-
-class TestChunkedSourceIterator(unittest.TestCase, TestCheckpointableIterator):
-    def setUp(self):
-        self.expected_result = list(range(53))
-        self.iterator = ChunkedSourceIterator(self.expected_result)
-
-    def test_multiple_instance(self):
-        for num_instances in range(2, 17):
-            items = []
-            for rank in range(num_instances):
-                iterator = ChunkedSourceIterator(
-                    self.expected_result,
-                    num_instances=num_instances,
-                    instance_rank=rank,
-                )
-                items.extend(list(iterator))
-            self.assertListEqual(items, self.expected_result)
-
-
-class TestInfinitePermutationSourceIterator(TestBase):
-    def test_repeat_once(self):
-        # This tests that two consecutive iterations through the test data yields differently ordered sequences.
-        reader = iter(InfinitePermutationSourceIterator(self.flattened_test_data, 42))
-        items0 = list(itertools.islice(reader, len(self.flattened_test_data)))
-        items1 = list(itertools.islice(reader, len(self.flattened_test_data)))
-        self.assertMultisetEqual(items0 + items1, self.flattened_test_data * 2)
-        self.assertTrue(any(item0 != item1 for item0, item1 in zip(items0, items1)))
-
-    def test_reiter_once(self):
-        # This differs from test_repeat_once in that we use checkpoints.
-        reader = InfinitePermutationSourceIterator(self.flattened_test_data, 42)
-        checkpoint = reader.getstate()
-        items0 = list(itertools.islice(reader, len(self.flattened_test_data)))
-        reader.setstate(checkpoint)
-        items1 = list(itertools.islice(reader, len(self.flattened_test_data)))
-        self.assertMultisetEqual(items0 + items1, self.flattened_test_data * 2)
-        self.assertSequenceEqual(items0, items1)
-
-    def test_checkpointing(self):
-        random = Random()
-        for i in range(5):
-            # random sequence lengths to for testing different configurations
-            test_source_length = random.randrange(5, 25)
-            test_first_output_length = random.randrange(5, 25)
-            test_second_output_length = random.randrange(5, 25)
-            # source
-            test_source = list(range(test_source_length))
-            reader = InfinitePermutationSourceIterator(test_source, seed=i)
-            # fetch a first sequence
-            _ = list(itertools.islice(reader, test_first_output_length))
-            # fetch a second sequence
-            checkpoint = reader.getstate()
-            items1a = list(itertools.islice(reader, test_second_output_length))
-            # fetch that second sequence again via checkpointing
-            reader.setstate(checkpoint)
-            items1b = list(itertools.islice(reader, test_second_output_length))
-            # and again with serialized checkpoint
-            as_json = pickle.dumps(checkpoint)
-            checkpoint2 = pickle.loads(as_json)
-            reader.setstate(checkpoint2)
-            items1c = list(itertools.islice(reader, test_second_output_length))
-            # must be the same
-            self.assertTrue(items1a == items1b)
-            self.assertTrue(items1a == items1c)
-
-
-class TestNativeCheckpointableIterator(unittest.TestCase, TestCheckpointableIterator):
-    def setUp(self):
-        self.expected_result = list(range(53))
-        self.iterator = NativeCheckpointableIterator(self.expected_result)
-
-    def test_iterator_exception(self):
-        self.assertRaises(ValueError, NativeCheckpointableIterator, iter(range(10)))
-
-
-class TestRecurrentIterator(unittest.TestCase, TestCheckpointableIterator):
-    def setUp(self):
-        data = list(range(53))
-
-        self.expected_result = [0]
-        for i in data[1:]:
-            self.expected_result.append(self.expected_result[-1] + i)
-
-        def step_function(prev_state, item):
-            output = item + prev_state
-            new_state = output
-            return new_state, output
-
-        self.iterator = RecurrentIterator(
-            NativeCheckpointableIterator(data), step_function, initial_state=0
-        )
-
-
-class TestSamplingRandomMapIterator(unittest.TestCase, TestCheckpointableIterator):
-    def setUp(self):
-        data = list(range(53))
-
-        def transform(random: Random, item: int):
-            return item + random.random()
-
-        seed = 1
-        random = Random()
-        random.seed(seed)
-        self.expected_result = [n + random.random() for n in data]
-
-        self.iterator = SamplingRandomMapIterator(
-            NativeCheckpointableIterator(data), transform=transform, seed=seed
-        )
-
-
-class TestFixedBatchIterator(unittest.TestCase, TestCheckpointableIterator):
-    def setUp(self):
-        data = list(range(5))
-
-        batch_size = 3
-        self.expected_result = [data[0:3], data[3:]]
-
-        self.iterator = FixedBatchIterator(
-            NativeCheckpointableIterator(data), batch_size=batch_size
-        )
-
-
-class TestSelectManyIterator(TestBase):
-    # in this test, SelectManyIterator is used to read chunk files
-    @staticmethod
-    def _select_many_from_chunks(chunk_file_paths):
-        return SelectManyIterator(
-            source_iterator=chunk_file_paths, collection_selector=TestBase.read_chunk
-        )
-
-    def test(self):
-        items = list(
-            self._select_many_from_chunks(
-                NativeCheckpointableIterator(self.chunk_file_paths)
-            )
-        )
-        self.assertListEqual(items, self.flattened_test_data)
-
-    def test_no_selector(self):
-        data = list(range(100))
-        sublists = [data[:10], data[10:42], data[42:87], data[87:]]
-        result = list(SelectManyIterator(NativeCheckpointableIterator(sublists)))
-        self.assertListEqual(result, data)
-
-    def test_different_line_endings(self):
-        # write data in binary mode with LF line endings
-        lf_dir = tempfile.mkdtemp()
-        lf_file = os.path.join(lf_dir, "test.gz")
-        with gzip.open(lf_file, "w") as f:
-            f.write("\n".join(self.flattened_test_data).encode("utf-8"))
-
-        # write data in binary mode with CRLF line endings
-        crlf_dir = tempfile.mkdtemp()
-        crlf_file = os.path.join(crlf_dir, "test.gz")
-        with gzip.open(crlf_file, "w") as f:
-            f.write("\r\n".join(self.flattened_test_data).encode("utf-8"))
-
-        lf_data = list(
-            self._select_many_from_chunks(NativeCheckpointableIterator([lf_file]))
-        )
-        crlf_dat = list(
-            self._select_many_from_chunks(NativeCheckpointableIterator([crlf_file]))
-        )
-        self.assertListEqual(lf_data, crlf_dat)
-
-        shutil.rmtree(lf_dir)
-        shutil.rmtree(crlf_dir)
-
-    def test_checkpointing(self):
-        chunk_file_paths = [
-            os.path.join(self.data_dir, subpath.name)
-            for subpath in os.scandir(self.data_dir)
-            if subpath.is_file() and subpath.name.endswith(".gz")
-        ]
-        chunk_file_paths = InfinitePermutationSourceIterator(
-            chunk_file_paths, shuffle=False
-        )  # using this as checkpointed cycle()
-        random = Random(1)
-        for _ in range(5):
-            first_length = random.randrange(11, 31)
-            extra_length = random.randrange(11, 33)
-            dataset = self._select_many_from_chunks(chunk_file_paths)
-            for _ in range(first_length):
-                next(dataset)
-            checkpoint = dataset.getstate()
-            items0 = list(itertools.islice(dataset, extra_length))
-            # print(len(items0))
-            dataset.setstate(checkpoint)
-            items1 = list(itertools.islice(dataset, extra_length))
-            # print(len(items1))
-            self.assertListEqual(items0, items1)
-
-
-class TestBufferedShuffleIterator(TestBase):
-    def test_shuffle(self):
-        # work on copy of data in case data is modified by class
-        items = list(
-            BufferedShuffleIterator(
-                NativeCheckpointableIterator(self.flattened_test_data.copy()), 971, 42
-            )
-        )
-        self.assertMultisetEqual(items, self.flattened_test_data)
-
-    def test_shuffle_buffer_size_one(self):
-        # work on copy of data in case data is modified by class
-        items = list(
-            BufferedShuffleIterator(
-                NativeCheckpointableIterator(self.flattened_test_data.copy()), 1, 42
-            )
-        )
-        self.assertListEqual(items, self.flattened_test_data)
-
-
-# note: this is also tested in more depth in Test_chunked_dataset_iterator()
-class TestBlockwiseShuffleIterator(TestBase):
-    def test_shuffle(self):
-        # work on copy of data in case data is modified by class
-        items = list(
-            BlockwiseShuffleIterator(
-                NativeCheckpointableIterator(self.flattened_test_data.copy()), 971, 42
-            )
-        )
-        self.assertMultisetEqual(items, self.flattened_test_data)
-
-    def test_shuffle_buffer_size_one(self):
-        # work on copy of data in case data is modified by class
-        items = list(
-            BlockwiseShuffleIterator(
-                NativeCheckpointableIterator(self.flattened_test_data.copy()), 1, 42
-            )
-        )
-        self.assertListEqual(items, self.flattened_test_data)
-
-
-def map_fun(n):
-    return n + 1
-
-
-class TestMapIterator(unittest.TestCase, TestCheckpointableIterator):
-    def setUp(self):
-        data = list(range(53))
-        self.expected_result = [map_fun(n) for n in data]
-        self.iterator = MapIterator(NativeCheckpointableIterator(data), map_fun)
-
-
-class TestParallelMapIterator(unittest.TestCase, TestCheckpointableIterator):
-    def setUp(self):
-        data = list(range(53))
-        self.expected_result = [map_fun(n) for n in data]
-        self.iterator = ParallelMapIterator(
-            NativeCheckpointableIterator(data), map_fun, 5, 7
-        )
-
-
-class TestZipIterator(unittest.TestCase, TestCheckpointableIterator):
-    def setUp(self):
-        data1 = list(range(53))
-        data2 = [n * n for n in data1]
-        self.expected_result = list(zip(data1, data2))
-        self.iterator = ZipIterator(
-            NativeCheckpointableIterator(data1), NativeCheckpointableIterator(data2)
-        )
-
-
-class TestWindowedIterator(TestBase):
-    def test(self):
-        for n in [0, 2, 3, 8, 9, 10, 11, 12]:  # cover various boundary conditions
-            seq = list(range(n))
-            it = WindowedIterator(NativeCheckpointableIterator(seq), 3)
-            actual0 = list(itertools.islice(it, n * 3 // 10))
-            checkpoint = it.getstate()
-            actual1a = list(it)
-            it.setstate(checkpoint)
-            actual1b = list(it)
-            actual = actual0 + actual1a
-            expected = list(
-                zip(seq, itertools.islice(seq, 1, None), itertools.islice(seq, 2, None))
-            )
-            self.assertListEqual(actual, expected)  # basic operation
-            self.assertListEqual(actual1a, actual1b)  # checkpointing
-
-
-class TestRandomIterator(TestBase):
-    def test(self):
-        n = 100
-        it = RandomIterator(seed=1)
-        _ = list(itertools.islice(it, n * 3 // 10))
-        checkpoint = it.getstate()
-        items1a = list(itertools.islice(it, n * 7 // 10))
-        it.setstate(checkpoint)
-        items1b = list(itertools.islice(it, n * 7 // 10))
-        self.assertListEqual(items1a, items1b)
-
-
-class TestPrefetchIterator(unittest.TestCase, TestCheckpointableIterator):
-    def setUp(self):
-        self.expected_result = list(range(53))
-        source_iterator = NativeCheckpointableIterator(self.expected_result)
-        self.iterator = PrefetchIterator(source_iterator, buffer_size=13)
-
-
-class Test_chunked_dataset_iterator(TestBase):
-    def test_no_shuffle(self):
-        items = list(
-            itertools.islice(
-                chunked_dataset_iterator(
-                    self.chunk_file_paths,
-                    self.read_chunk,
-                    shuffle=False,
-                    buffer_size=1000,
-                ),
-                len(self.flattened_test_data),
-            )
-        )
-        self.assertListEqual(items, self.flattened_test_data)
-
-    def test_other_files_present(self):
-        with open(os.path.join(self.data_dir, "i_do_not_belong_here.txt"), "w") as f:
-            f.write("really ...")
-        items = list(
-            itertools.islice(
-                chunked_dataset_iterator(
-                    self.chunk_file_paths,
-                    self.read_chunk,
-                    shuffle=False,
-                    buffer_size=1000,
-                ),
-                len(self.flattened_test_data),
-            )
-        )
-        self.assertListEqual(items, self.flattened_test_data)
-
-    def test_transform(self):
-        transform = lambda s: s + "!"
-        modified_test_data = [transform(s) for s in self.flattened_test_data]
-        items = list(
-            itertools.islice(
-                chunked_dataset_iterator(
-                    self.chunk_file_paths,
-                    self.read_chunk,
-                    shuffle=False,
-                    buffer_size=1000,
-                    transform=transform,
-                ),
-                len(self.flattened_test_data),
-            )
-        )
-        self.assertListEqual(items, modified_test_data)
-
-    def test_two_instances(self):
-        dataset0 = chunked_dataset_iterator(
-            self.chunk_file_paths,
-            self.read_chunk,
-            shuffle=False,
-            buffer_size=1000,
-            num_instances=2,
-            instance_rank=0,
-        )
-        dataset1 = chunked_dataset_iterator(
-            self.chunk_file_paths,
-            self.read_chunk,
-            shuffle=False,
-            buffer_size=1000,
-            num_instances=2,
-            instance_rank=1,
-        )
-        items0 = list(
-            itertools.islice(dataset0, len(self.test_data[0]) + len(self.test_data[2]))
-        )
-        items1 = list(
-            itertools.islice(dataset1, len(self.test_data[1]) + len(self.test_data[3]))
-        )
-        self.assertMultisetEqual(set(items0 + items1), self.flattened_test_data)
-
-    def test_checkpointing(self):
-        random = Random(1)
-        for use_windowed in (True, False):
-            for i in range(2):
-                first_length = random.randrange(11, 21)
-                extra_length = random.randrange(11, 21)
-                dataset = chunked_dataset_iterator(
-                    self.chunk_file_paths,
-                    self.read_chunk,
-                    shuffle=(i % 2 == 0),
-                    buffer_size=1000,
-                    seed=i,
-                    num_instances=2,
-                    instance_rank=0,
-                    use_windowed=use_windowed,
-                )
-                for _ in range(first_length):
-                    next(dataset)
-                checkpoint = dataset.getstate()
-                items1 = list(itertools.islice(dataset, extra_length))
-                dataset.setstate(checkpoint)
-                items2 = list(itertools.islice(dataset, extra_length))
-                self.assertListEqual(items1, items2)
-
-
-class TestBucketedReadaheadBatchIterator(TestBase):
-    def txest_basic_functionality(self):
-        num_batches = 13
-        batch_labels = (
-            75  # note: these settings imply a few iterations through the chunks
-        )
-        # basic operation, should not crash
-        bg = BucketedReadaheadBatchIterator(
-            chunked_dataset_iterator(
-                self.chunk_file_paths,
-                self.read_chunk,
-                shuffle=True,
-                buffer_size=1000,
-                seed=1,
-            ),
-            read_ahead=100,
-            seed=1,
-            key=lambda line: len(line),
-            batch_size=lambda line: batch_labels // (1 + len(line)),
-        )
-        batches1 = list(itertools.islice(bg, num_batches))
-        # verify determinism
-        bg = BucketedReadaheadBatchIterator(
-            chunked_dataset_iterator(
-                self.chunk_file_paths,
-                self.read_chunk,
-                shuffle=True,
-                buffer_size=1000,
-                seed=1,
-            ),
-            read_ahead=100,
-            seed=1,
-            key=lambda line: len(line),
-            batch_size=lambda line: batch_labels // (1 + len(line)),
-        )
-        batches2 = list(itertools.islice(bg, num_batches))
-        print([(len(batch[0]), len(batch)) for batch in batches1])
-        self.assertListEqual(batches1, batches2)
-
-    def test_checkpointing(self):
-        first_batches = 12
-        extra_batches = 7
-        batch_labels = 123
-        bg = BucketedReadaheadBatchIterator(
-            chunked_dataset_iterator(
-                self.chunk_file_paths,
-                self.read_chunk,
-                shuffle=True,
-                buffer_size=1000,
-                seed=1,
-            ),
-            read_ahead=100,
-            seed=1,
-            key=lambda line: len(line),
-            batch_size=lambda line: batch_labels // (1 + len(line)),
-        )
-        _ = list(itertools.islice(bg, first_batches))
-        checkpoint = bg.getstate()
-        batches1 = list(itertools.islice(bg, extra_batches))
-        bg.setstate(checkpoint)
-        batches2 = list(itertools.islice(bg, extra_batches))
-        self.assertListEqual(batches1, batches2)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/akhaliq/space-that-creates-model-demo-space/assets/template.py b/spaces/akhaliq/space-that-creates-model-demo-space/assets/template.py
deleted file mode 100644
index 52517926681b84a9cb2af15c5bd6e3dfb7b52614..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/space-that-creates-model-demo-space/assets/template.py
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import gradio as gr
-
-
-def read_info(file_name: str) -> str:
-    with open(file_name) as f:
-        content = f.read()
-    return content
-
-
-def load_model(model_name: str) -> gr.Interface:
-    iface = gr.Interface.load(model_name, src='models')
-    for component in iface.output_components:
-        component.label = f'{component.label} ({model_name})'
-    return iface
-
-
-def load_models(model_names: list[str]) -> list[gr.Interface]:
-    return [load_model(name) for name in model_names]
-
-
-title = read_info('TITLE')
-description = read_info('DESCRIPTION')
-article = read_info('ARTICLE')
-model_names = read_info('MODEL_NAMES').split('\n')
-
-interfaces = load_models(model_names)
-gr.Parallel(
-    *interfaces,
-    title=title,
-    description=description,
-    article=article,
-).launch()
diff --git a/spaces/aliabid94/AutoGPT/tests/browse_tests.py b/spaces/aliabid94/AutoGPT/tests/browse_tests.py
deleted file mode 100644
index f896e7dd751b1b661d5e989909448b7e182eab69..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/AutoGPT/tests/browse_tests.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-import sys
-import unittest
-
-from bs4 import BeautifulSoup
-
-sys.path.append(os.path.abspath("../scripts"))
-
-from browse import extract_hyperlinks
-
-
-class TestBrowseLinks(unittest.TestCase):
-    def test_extract_hyperlinks(self):
-        body = """
-        <body>
-        <a href="https://google.com">Google</a>
-        <a href="foo.html">Foo</a>
-        <div>Some other crap</div>
-        </body>
-        """
-        soup = BeautifulSoup(body, "html.parser")
-        links = extract_hyperlinks(soup, "http://example.com")
-        self.assertEqual(
-            links,
-            [("Google", "https://google.com"), ("Foo", "http://example.com/foo.html")],
-        )
diff --git a/spaces/allandclive/Uganda_MMS/uroman/lib/NLP/English.pm b/spaces/allandclive/Uganda_MMS/uroman/lib/NLP/English.pm
deleted file mode 100644
index e78fba5e381d425feb1a89696afad7d974063abb..0000000000000000000000000000000000000000
--- a/spaces/allandclive/Uganda_MMS/uroman/lib/NLP/English.pm
+++ /dev/null
@@ -1,3112 +0,0 @@
-################################################################
-#                                                              #
-# English                                                      #
-#                                                              #
-################################################################
-
-package NLP::English;
-
-use File::Basename;
-use File::Spec;
-
-# tok v1.3.7 (May 16, 2019)
-
-$chinesePM = NLP::Chinese;
-$ParseEntry = NLP::ParseEntry;
-$util = NLP::utilities;
-$utf8 = NLP::UTF8;
-$logfile = "";
-# $logfile2 = (-d "/nfs/isd/ulf/smt/agile") ? "/nfs/isd/ulf/smt/agile/minilog" : "";
-# $util->init_log($logfile2);
-
-$currency_symbol_list = "\$|\xC2\xA5|\xE2\x82\xAC|\xE2\x82\xA4";
-$english_resources_skeleton_dir = "";
-%dummy_ht = ();
-
-sub build_language_hashtables {
-   local($caller, $primary_entity_style_filename, $data_dir) = @_;
-
-   unless ($data_dir) {
-      $default_data_dir = "/nfs/nlg/users/textmap/brahms-ml/arabic/bin/modules/NLP";
-      $data_dir = $default_data_dir if -d $default_data_dir;
-   }
-   my $english_word_filename = "$data_dir/EnglishWordlist.txt";
-   my $default_entity_style_MT_filename = "$data_dir/EntityStyleMT-zh.txt";
-   my $entity_style_all_filename = "$data_dir/EntityStyleAll.txt";
-   my $EnglishNonNameCapWords_filename = "$data_dir/EnglishNonNameCapWords.txt";
-   $english_resources_skeleton_dir = "$data_dir/EnglishResources/skeleton";
-   %english_annotation_ht = ();
-   %annotation_english_ht = ();
-   %english_ht = ();
-   $CardinalMaxWithoutComma = 99999;
-   $CardinalMaxNonLex = 9999000;
-
-   $primary_entity_style_filename = $default_entity_style_MT_filename unless defined($primary_entity_style_filename);
-   if ($primary_entity_style_filename =~ /^(ar|zh)$/) {
-      $languageCode = $primary_entity_style_filename;
-      $primary_entity_style_filename 
-	 = File::Spec->catfile($data_dir, "EntityStyleMT-$languageCode.txt");
-   }
-
-   open(IN,$english_word_filename) || die "Can't open $english_word_filename";
-   while (<IN>) {
-      next unless $_ =~ /^s*[^#\s]/;   # unless blank/comment line
-      $_ =~ s/\s+$//;
-      $line = $_;
-      @lines = ($line);
-      if (($line =~ /::gpe:/)
-       && (($annotation) = ($line =~ /^.*?::(.*)$/))
-       && (($pre_annotation, $singular_english, $post_annotation) = ($annotation =~ /^(.*)::plural-of:([^:]+)(|::.*)\s*$/))) {
-	 $derived_annotation = $singular_english . "::$pre_annotation$post_annotation";
-	 # print STDERR "derived_annotation: $derived_annotation\n";
-	 push(@lines, $derived_annotation);
-      }
-      foreach $line (@lines) {
-         ($english,@slots) = split("::",$line);
-         next unless defined($english);
-         $english =~ s/\s+$//;
-         $lc_english = $english;
-         $lc_english =~ tr/[A-Z]/[a-z]/;
-         $annotation = "::" . join("::",@slots) . "::";
-         $english_annotation_ht{$english} = $annotation;
-         $english_annotation_ht{$lc_english} = $annotation;
-         $english_annotation_ht{"_ALT_"}->{$english}->{$annotation} = 1;
-         $english_annotation_ht{"_ALT_"}->{$lc_english}->{$annotation} = 1;
-         $synt = "";
-         foreach $slot_value (@slots) {
-	    ($slot,$value) = ($slot_value =~ /\s*(\w[^:]+):\s*(\S.*)$/);
-	    next unless defined($value);
-	    $slot =~ s/\s+$//;
-	    $value =~ s/\s+$//;
-	    $synt = $value if $slot eq "synt";
-            if (defined($annotation_english_ht{$slot_value})) {
-	       push(@{$annotation_english_ht{$slot_value}},$english);
-            } else {
-               my @elist = ($english);
-               $annotation_english_ht{$slot_value} = \@elist;
-            }
-	    if ($synt && defined($slot_value) && ($slot ne "synt")) {
-	       $annot = "synt:$synt" . "::$slot_value";
-               if (defined($annotation_english_ht{$annot})) {
-	          push(@{$annotation_english_ht{$annot}},$english);
-               } else {
-                  my @elist = ($english);
-                  $annotation_english_ht{$annot} = \@elist;
-	       }
-	       $english_annotation_ht{"_EN_SYNT_"}->{$english}->{$synt}->{$slot} = $value;
-            }
-         }
-      }
-   }
-   close(IN);
-
-   if (open(IN,$EnglishNonNameCapWords_filename)) {
-      while (<IN>) {
-         next unless $_ =~ /^s*[^#\s]/;   # unless blank/comment line
-         $_ =~ s/\s+$//;
-         $english_ht{(lc $_)}->{COMMON_NON_NAME_CAP} = 1;
-      }
-      close(IN);
-   } else {
-      print STDERR "Can't open $EnglishNonNameCapWords_filename\n";
-   }
-
-   foreach $style ("primary", "all") {
-      if ($style eq "primary") {
-	 $entity_style_filename = $primary_entity_style_filename || $default_entity_style_MT_filename;
-      } elsif ($style eq "all") {
-	 $entity_style_filename = $entity_style_all_filename;
-      } else {
-	 next;
-      }
-      %ht = ();
-      open(IN,$entity_style_filename) || die("Can't open $entity_style_filename (stylefile)");
-      my $n_entries = 0;
-      while (<IN>) {
-         next unless $_ =~ /^s*[^#\s]/;   # unless blank/comment line
-         $_ =~ s/\s+$//;
-         ($slot,$value_string) = ($_ =~ /^([^:]+):\s*(\S.*)$/);
-         next unless defined($value_string);
-         if (defined($ht{$slot})) {
-	    print STDERR "Warning: ignoring duplicate entry for $slot in $entity_style_filename\n";
-	    next;
-         }
-         @values = split("::", $value_string);
-         foreach $value (@values) {
-	    $value =~ s/^\s+//g;
-	    $value =~ s/\s+$//g;
-         }
-         my @values_copy = @values;
-         $ht{$slot} = \@values_copy;
-	 $n_entries++;
-      }
-      # print STDERR "Processed $n_entries entries in $entity_style_filename\n";
-      close(IN);
-      if ($style eq "primary") {
-         %english_entity_style_ht = %ht;
-      } elsif ($style eq "all") {
-         %english_entity_style_all_ht = %ht;
-      }
-   }
-
-   if (defined($raw = $english_entity_style_ht{CardinalMaxWithoutComma})
-	&& (@styles = @{$raw}) && ($n = $styles[0]) && ($n =~ /^\d+$/) && ($n >= 999)) {
-      $CardinalMaxWithoutComma = $n;
-   }
-   if (defined($raw = $english_entity_style_ht{CardinalMaxNonLex})
-	&& (@styles = @{$raw}) && ($n = $styles[0]) && ($n =~ /^\d+$/) && ($n >= 999999)) {
-      $CardinalMaxNonLex = $n;
-   }
-
-   return (*english_annotation_ht,*annotation_english_ht,*english_entity_style_ht);
-}
-
-sub read_language_variations {
-   local($this, $filename, *ht) = @_;
-
-   my $n = 0;
-   my $line_number = 0;
-   if (open(IN, $filename)) {
-      while (<IN>) {
-	 $line_number++;
-	 $us = $util->slot_value_in_double_colon_del_list($_, "us");
-	 $uk = $util->slot_value_in_double_colon_del_list($_, "uk");
-	 $formal = $util->slot_value_in_double_colon_del_list($_, "formal");
-	 $informal = $util->slot_value_in_double_colon_del_list($_, "informal");
-	 if ($us && $uk) {
-	    $ht{VARIATION_UK_US}->{$uk}->{$us} = 1;
-	    $n++;
-	 }
-	 if ($informal && $formal) {
-	    $ht{VARIATION_INFORMAL_FORMAL}->{$informal}->{$formal} = 1;
-	    $n++;
-	 }
-      }
-      close(IN);
-      # print STDERR "Read $n spelling variation entries from $filename\n";
-   }
-}
-
-sub entity_style_listing {
-   local($caller,$attr) = @_;
-
-   if (defined($l = $english_entity_style_ht{$attr})) {
-      @sl = @{$l};
-      if (($#sl == 0) && ($sl[0] eq "all")) {
-         if (defined($al = $english_entity_style_all_ht{$attr})) {
-            return @{$al};
-	 } else {
-            return ();
-	 }
-      } else {
-         return @sl;
-      }
-   } else {
-      return ();
-   }
-}
-
-sub is_abbreviation {
-   local($caller,$noun) = @_;
-
-   $result = defined($annotation_s = $english_annotation_ht{$noun})
-	         && ($annotation_s =~ /::abbreviation:true::/);
-#  print "is_abbreviation($noun): $result\n";
-   return $result;
-}
-
-sub noun_adv_sem {
-   local($caller,$noun) = @_;
-
-   return "" unless defined($annotation_s = $english_annotation_ht{$noun});
-   ($adv_sem) = ($annotation_s =~ /::adv_sem:([-_a-z]+)::/);
-   return "" unless defined($adv_sem);
-   return $adv_sem;
-}
-
-sub numeral_value {
-   local($caller,$numeral) = @_;
-
-   return "" unless defined($annotation_s = $english_annotation_ht{$numeral});
-   ($value) = ($annotation_s =~ /::value:(\d+)::/);
-   return "" unless defined($value);
-   return $value;
-}
-
-sub annot_slot_value {
-   local($caller,$lex, $slot) = @_;
-
-   return "" unless defined($annotation_s = $english_annotation_ht{$lex});
-   ($value) = ($annotation_s =~ /::$slot:([-_a-z]+)(?:::.*|)\s*$/i);
-   return "" unless defined($value);
-   return $value;
-}
-
-sub annot_slot_values {
-   local($caller,$lex, $slot) = @_;
-
-   return () unless @annotations = keys %{$english_annotation_ht{"_ALT_"}->{$lex}};
-   @annot_slot_values = ();
-   foreach $annotation_s (@annotations) {
-      ($value) = ($annotation_s =~ /::$slot:([^:]+)(?:::.*|)\s*$/i);
-      if (defined($value)) {
-	 $value =~ s/\s*$//;
-         push(@annot_slot_values, $value);
-      }
-   }
-   return @annot_slot_values;
-}
-
-# quick and dirty
-sub noun_number_form {
-   local($caller,$noun,$number) = @_;
-
-   $noun = "rupee" if $noun =~ /^Rs\.?$/;
-   $noun = "kilometer" if $noun =~ /^km$/;
-   $noun = "kilogram" if $noun =~ /^kg$/;
-   $noun = "meter" if $noun =~ /^m$/;
-   $noun = "second" if $noun =~ /^(s|secs?\.?)$/;
-   $noun = "minute" if $noun =~ /^(mins?\.?)$/;
-   $noun = "hour" if $noun =~ /^(h|hrs?\.?)$/;
-   $noun = "year" if $noun =~ /^(yrs?\.?)$/;
-   $noun = "degree" if $noun =~ /^(deg\.?)$/;
-   $noun = "foot" if $noun =~ /^(feet|ft\.?)$/;
-   $noun = "square kilometer" if $noun =~ /^sq\.? km/;
-   $noun =~ s/metre$/meter/;
-   $noun =~ s/litre$/liter/;
-   $noun =~ s/gramme$/gram/;
-   $noun =~ s/tonne$/ton/;
-   return $noun if $noun =~ /\$$/;
-   return $noun unless $number =~ /^[0-9.]+$/;
-   return $noun if $util->member($noun,"percent");  # no change in plural
-   return $noun if $noun =~ /\b(yuan|renminbi|RMB|rand|won|yen|ringgit|birr)$/;  # no change in plural
-   return $noun if $number <= 1;
-
-   return $noun if $caller->is_abbreviation($noun);
-
-   $noun =~ s/^(hundred|thousand|million|billion|trillion)\s+//;
-   return $noun if $noun =~ /^(dollar|kilometer|pound|ton|year)s$/i;
-
-   $original_noun = $noun;
-   #check for irregular plural
-   $annot = "synt:noun::plural-of:$noun";
-   if (defined($annotation_english_ht{$annot})) {
-      @elist = @{$annotation_english_ht{$annot}};
-      return $elist[0] if @elist;
-   }
-
-   $noun = $noun . "s";
-   return $noun if $noun =~ /(a|e|o|u)ys$/; # days, keys, toys, guys
-   $noun =~ s/ys$/ies/;     # babies
-   $noun =~ s/ss$/ses/;     # buses
-   $noun =~ s/xs$/xes/;     # taxes
-   $noun =~ s/shs$/shes/;   # dishes
-   $noun =~ s/chs$/ches/;   # churches
-   $noun =~ s/mans$/men/;   # women
-   # print STDERR "NNF: $original_noun($number): $noun\n";
-   return $noun;
-}
-
-# quick and dirty
-sub lex_candidates {
-   local($caller,$surf) = @_;
-
-   @lex_cands = ($surf);
-   $lex_cand = $surf;
-   $lex_cand =~ s/ies$/y/;
-   push(@lex_cands,$lex_cand) unless $util->member($lex_cand, @lex_cands);
-   $lex_cand = $surf;
-   $lex_cand =~ s/s$//;
-   push(@lex_cands,$lex_cand) unless $util->member($lex_cand, @lex_cands);
-   $lex_cand = $surf;
-   $lex_cand =~ s/es$//;
-   push(@lex_cands,$lex_cand) unless $util->member($lex_cand, @lex_cands);
-   $lex_cand = $surf;
-   $lex_cand =~ s/\.$//;
-   push(@lex_cands,$lex_cand) unless $util->member($lex_cand, @lex_cands);
-   $lex_cand = $surf;
-   $lex_cand =~ s/men$/man/;
-   push(@lex_cands,$lex_cand) unless $util->member($lex_cand, @lex_cands);
-
-   return @lex_cands;
-}
-
-# quick and dirty
-sub pos_tag {
-   local($caller,$surf) = @_;
-
-   return CD if ($surf =~ /^-?[0-9,\.]+$/);
-   return NN if ($surf =~ /^($currency_symbol_list\d)/);
-   @lex_candidates = $caller->lex_candidates($surf);
-#  print "  lex_candidates: @lex_candidates\n";
-   foreach $lex_cand (@lex_candidates) {
-      if (defined($annotation_s = $english_annotation_ht{$lex_cand})) {
-#        print "  annotation: $annotation_s\n";
-         ($synt) = ($annotation_s =~ /::synt:([^:]+)::/);
-         if (defined($synt)) {
-	    if ($synt eq "art") {
-	       return "DT";
-	    } elsif ($synt eq "adj") {
-               ($grade) = ($annotation_s =~ /::grade:([^:]+)::/);
-	       if (defined($grade) && ($grade eq "superlative")) {
-	          return "JJS";
-	       } elsif (defined($grade) && ($grade eq "comparative")) {
-	          return "JJR";
-	       } else {
-	          return "JJ";
-	       }
-	    } elsif ($synt eq "noun") {
-	       if ($lex_cand eq $surf) {
-	          return "NN";
-	       } else {
-	          return "NNS";
-	       }
-	    } elsif ($synt eq "name") {
-	       return "NNP";
-	    } elsif ($synt eq "cardinal") {
-	       return "CD";
-	    } elsif ($synt eq "ordinal") {
-	       return "JJ";
-	    } elsif ($synt eq "prep") {
-	       return "IN";
-	    } elsif ($synt eq "conj") {
-	       return "CC";
-	    } elsif ($synt eq "wh_pron") {
-	       return "WP";
-	    } elsif ($synt eq "adv") {
-	       return "RB";
-	    } elsif ($synt eq "genetive_particle") {
-	       return "POS";
-	    } elsif ($synt eq "ordinal_particle") {
-	       return "NN";
-	    } elsif ($synt eq "suffix_particle") {
-	       return "NN";
-	    } elsif ($synt =~ /^int(erjection)?$/) {
-	       return "UH";
-	    } elsif (($synt =~ /^punctuation$/)
-		  && $util->is_rare_punctuation_string_p($surf)) {
-	       return "SYM";
-	    } elsif ($synt =~ /\bverb$/) {
-	       if ($surf =~ /^(is)$/) {
-	          return "VBZ";
-	       } else {
-	          return "VB";
-	       }
-	    }
-	 }
-      }
-   }
-   return "";
-}
-
-sub indef_art_filter {
-   local($caller,$surf) = @_;
-
-   # check article in lexical annotation 
-   # e.g. hour::synt:noun::unit:temporal::indef-article:an
-   #      uniform::synt:noun::indef-article:a
-   ($surf_article,$word) = ($surf =~ /^(an?) (\S+)\s*/);
-   if (defined($surf_article)
-    && defined($word)
-    && defined($annotation = $english_annotation_ht{$word})) {
-      ($ann_article) = ($annotation =~ /::indef-article:([^:]+)::/);
-      if (defined($ann_article)) {
-	 return ($surf_article eq $ann_article) ? $surf : "";
-      }
-   }
-   return "" if $surf =~ /\ban [bcdfghjklmnpqrstvwxyz]/;
-   return "" if $surf =~ /\ban (US)\b/;
-   return "" if $surf =~ /\ba [aeio]/;
-   return "" if $surf =~ /\ba (under)/;
-   return $surf;
-}
-
-sub wordlist_synt {
-   local($caller,$word) = @_;
-
-   return "" unless defined($annotation = $english_annotation_ht{$word});
-   ($synt) = ($annotation =~ /::synt:([^:]+)::/);
-   return $synt || "";
-}
-
-sub qualifier_filter {
-   local($caller,$surf) = @_;
-
-   return "" if $surf =~ /\b(over|more than|approximately) (million|billion|trillion)/;
-   return "" if $surf =~ /\b(over) (once|twice)/;
-   return $surf;
-}
-
-sub quantity_filter {
-   local($caller,$surf) = @_;
-
-   return "" if $surf =~ /^(a|an)-/;   # avoid "the a-week meeting"
-   return $surf;
-}
-
-sub value_to_english {
-   local($caller,$number) = @_;
-
-   $result = "";
-
-   $annot = "value:$number";
-   if (defined($annotation_english_ht{$annot})) {
-      @elist = @{$annotation_english_ht{$annot}};
-      $result = $elist[0] if @elist;
-   }
-#  print "value_to_english($number)=$result\n";
-   return $result;
-}
-
-sub value_to_english_ordinal {
-   local($caller,$number) = @_;
-
-   $result = "";
-
-   $annot = "synt:ordinal::value:$number";
-   if (defined($annotation_english_ht{$annot})) {
-      @elist = @{$annotation_english_ht{$annot}};
-      $result = $elist[0] if @elist;
-   } else {
-      $annot = "value:$number";
-      if (defined($annotation_english_ht{$annot})) {
-         @elist = @{$annotation_english_ht{$annot}};
-         $cardinal = $elist[0] if @elist;
-	 $result = $cardinal . "th";
-	 $result =~ s/yth$/ieth/;
-      }
-   }
-#  print "value_to_english($number)=$result\n";
-   return $result;
-}
-
-sub english_with_synt_slot_value {
-   local($caller, $english, $synt, $slot) = @_;
-
-   return $english_annotation_ht{"_EN_SYNT_"}->{$english}->{$synt}->{$slot};
-}
-
-sub english_with_synt_slot_value_defined {
-   local($caller, $synt, $slot) = @_;
-
-   @englishes_with_synt_slot_value_defined = ();
-   foreach $english (keys %{$english_annotation_ht{"_EN_SYNT_"}}) {
-      push(@englishes_with_synt_slot_value_defined, $english) 
-	 if defined($english_annotation_ht{"_EN_SYNT_"}->{$english}->{$synt}->{$slot})
-	 && ! $util->member($english, @englishes_with_synt_slot_value_defined)
-   }
-   return @englishes_with_synt_slot_value_defined;
-}
-
-sub number_composed_surface_form {
-   local($caller,$number,$leave_num_section_p) = @_;
-
-   return "" unless $number =~ /^\d+$/;
-   $leave_num_section_p = 0 unless defined($leave_num_section_p);
-   $anchor = "1000000000000000000000000";
-   while (($number < $anchor) && ($anchor >= 1000000)) {
-      $anchor =~ s/000//;
-   }
-#  print "number_composed_surface_form number: $number anchor:$anchor\n";
-   return "" unless $anchor >= 1000000;
-   return "" unless $english = $caller->value_to_english($anchor);
-   $ending = $anchor;
-   $ending =~ s/^1000//;
-   return "" unless ($number =~ /$ending$/) || (($number * 1000) % $anchor) == 0;
-   $num_section = $number / $anchor;
-   if (($num_section =~ /^[1-9]0?$/) && ! $leave_num_section_p) {
-      $num_section_english = $caller->value_to_english($num_section);
-      $num_section = $num_section_english if $num_section_english;
-   }
-   $num_section = $caller->commify($num_section); # only for extremely large numbers
-   return "$num_section $english";
-}
-
-sub de_scientify {
-   local($caller,$number) = @_;
-
-#  print "de_scientify: $number\n";
-   if ($number =~ /[eE][-+]/) {
-      ($n,$exp) = ($number =~ /^(\d+)[eE]\+(\d+)$/);
-      if (defined($exp)) {
-	 $result = $n;
-	 foreach $i (0 .. $exp-1) {
-	    $result .= "0" 
-	 }
-	 return $result;
-      } else {
-         ($n,$f,$exp) = ($number =~ /^(\d+)\.(\d+)[eE]\+(\d+)$/);
-	 if (defined($exp) && ($exp >= length($f))) {
-	    $result = "$n$f";
-	    foreach $i (0 .. $exp-1-length($f)) {
-	       $result .= "0";
-	    }
-	    return $result;
-	 } 
-      }
-   }
-   return $number;
-}
-
-sub commify {
-   local($caller,$number) = @_;
-
-   my $text = reverse $number;
-   $text =~ s/(\d\d\d)(?=\d)(?!\d*\.)/$1,/g;
-   return scalar reverse $text;
-}
-
-my %plural_rough_number_ht = (
-   10 => "tens",
-   12 => "dozens",
-   20 => "scores",
-   100 => "hundreds",
-   1000 => "thousands",
-   10000 => "tens of thousands",
-   100000 => "hundreds of thousands",
-   1000000 => "millions",
-   10000000 => "tens of millions",
-   100000000 => "hundreds of millions",
-   1000000000 => "billions",
-   10000000000 => "tens of billions",
-   100000000000 => "hundreds of billions",
-   1000000000000 => "trillions",
-   10000000000000 => "tens of trillions",
-   100000000000000 => "hundreds of trillions",
-);
-
-sub plural_rough_plural_number {
-   local($caller,$number) = @_;
-
-   return $plural_rough_number_ht{$number} || "";
-}
-
-my %roman_numeral_ht = (
-   "I" => 1,
-   "II" => 2,
-   "III" => 3,
-   "IIII" => 4,
-   "IV" => 4,
-   "V" => 5,
-   "VI" => 6,
-   "VII" => 7,
-   "VIII" => 8,
-   "VIIII" => 9,
-   "IX" => 9,
-   "X" => 10,
-   "XX" => 20,
-   "XXX" => 30,
-   "XXXX" => 40,
-   "XL" => 40,
-   "L" => 50,
-   "LX" => 60,
-   "LXX" => 70,
-   "LXXX" => 80,
-   "LXXXX" => 90,
-   "XC" => 90,
-   "C" => 100,
-   "CC" => 200,
-   "CCC" => 300,
-   "CCCC" => 400,
-   "CD" => 400,
-   "D" => 500,
-   "DC" => 600,
-   "DCC" => 700,
-   "DCCC" => 800,
-   "DCCCC" => 900,
-   "CM" => 900,
-   "M" => 1000,
-   "MM" => 2000,
-   "MMM" => 3000,
-   "MMM" => 3000,
-);
-
-sub roman_numeral_value {
-   local($caller,$s) = @_;
-   
-   if (($m, $c, $x, $i) = ((uc $s) =~ /^(M{0,3})(C{1,4}|CD|DC{0,4}|CM|)(X{1,4}|XL|LX{0,4}|XC|)(I{1,4}|IV|VI{0,4}|IX|)$/)) {
-      $sum = ($roman_numeral_ht{$m} || 0)
-	   + ($roman_numeral_ht{$c} || 0)
-	   + ($roman_numeral_ht{$x} || 0)
-	   + ($roman_numeral_ht{$i} || 0);
-      return $sum;
-   } else {
-      return 0;
-   }
-}
-
-sub number_surface_forms {
-   local($caller,$number,$pe) = @_;
-
-   print STDERR "Warning from number_surface_forms: $number not a number\n" 
-      if $logfile && !($number =~ /^(\d+(\.\d+)?|\.\d+)$/);
-   # $util->log("number_surface_forms number:$number", $logfile);
-   # $util->log("  surf:$surf", $logfile) if $surf = ($pe && $pe->surf);
-
-   $pe = "" unless defined($pe);
-
-   @num_style_list = @{$english_entity_style_ht{"FollowSourceLanguageNumberStyle"}};
-   $follow_num_style = $util->member("yes", @num_style_list)
-                        && (! (($number =~ /^([1-9]|10)$/) &&
-                               $util->member("except-small-numbers", @num_style_list)));
-   $num_style = ($pe) ? $pe->get("num_style") : "";
-   if ($follow_num_style) {
-      if ($num_style =~ /digits_plus_alpha/) {
-	 if ($number =~ /^[1-9]\d?\d?000$/) {
-            $digital_portion = $number;
-            $digital_portion =~ s/000$//;
-            return ("$digital_portion thousand");
-	 } elsif ($number =~ /^[1-9]\d?\d?000000$/) {
-            $digital_portion = $number;
-            $digital_portion =~ s/000000$//;
-            return ("$digital_portion million");
-	 } elsif ($number =~ /^[1-9]\d?\d?000000000$/) {
-            $digital_portion = $number;
-            $digital_portion =~ s/000000000$//;
-            return ("$digital_portion billion");
-	 }
-      } elsif ($num_style eq "digits") {
-	 if ($number =~ /^\d{1,4}$/) {
-	    return ($number);
-	 }
-      }
-   }
-
-   $number = $caller->de_scientify($number);
-
-   $composed_form = $caller->number_composed_surface_form($number);
-   $composed_form2 = $caller->number_composed_surface_form($number,1);
-   $lex_form = $caller->value_to_english($number);
-   $commified_form = $caller->commify($number);
-
-   if ($lex_form) {
-      if ($number >= 1000000) {
-	 @result = ("one $lex_form", "1 $lex_form", "a $lex_form", $lex_form, $commified_form);
-	 push(@result, $commified_form) if ($number <= $CardinalMaxNonLex);
-      } elsif ($number >= 100) {
-	 @result = ($commified_form, "one $lex_form", "a $lex_form", $lex_form);
-      } elsif ($number >= 10) {
-	 @result = ($number, $lex_form);
-      } elsif ($number == 1) {
-	 @result = ("a", "an", $lex_form);
-      } elsif ($number == 0) {
-	 @result = ($number, $lex_form);
-      } else {
-	 @result = ($lex_form);
-      }
-   } elsif ($composed_form) {
-      if ($composed_form eq $composed_form2) {
-         @result = ($composed_form);
-      } elsif (($number >= 10000000) && ($composed_form2 =~ /^[1-9]0/)) {
-         @result = ($composed_form2, $composed_form);
-      } else {
-         @result = ($composed_form, $composed_form2);
-      }
-      push(@result, $commified_form) if $number <= $CardinalMaxNonLex;
-   } else {
-      ($ten,$one) = ($number =~ /^([2-9])([1-9])$/);
-      ($hundred) = ($number =~ /^([1-9])00$/) unless defined($one);
-      ($thousand) = ($number =~ /^([1-9]\d?)000$/) unless defined($one) || defined($hundred);
-      if (defined($one) && defined($ten)
-           && ($part1 = $caller->value_to_english($ten  * 10))
-	   && ($part2 = $caller->value_to_english($one))) {
-	 $wordy_form = "$part1-$part2";
-         @result = ($commified_form, $wordy_form);
-      } elsif (defined($hundred)
-           && ($part1 = $caller->value_to_english($hundred))) {
-	 $wordy_form = "$part1 hundred";
-         @result = ($commified_form, $wordy_form);
-      } elsif (defined($thousand)
-           && ($part1 = $caller->value_to_english($thousand))) {
-	 $wordy_form = "$part1 thousand";
-         @result = ($commified_form, $wordy_form);
-      } elsif ($number =~ /^100000$/) {
-         @result = ($commified_form, "one hundred thousand", "a hundred thousand", "hundred thousand");
-      } elsif ($pe && ($pe->surf eq $number) && ($number =~ /^\d\d\d\d(\.\d+)?$/)) {
-         @result = ($number);
-         push(@result, $commified_form) unless $commified_form eq $number;
-      } elsif ($number =~ /^\d{4,5}$/) {
-	 if ($commified_form eq $number) {
-            @result = ($number);
-	 } else {
-            @result = ($commified_form, $number);
-	 }
-      } else {
-         @result = ($commified_form);
-      }
-   }
-   push (@result, $number) 
-       unless $util->member($number, @result) || ($number > $CardinalMaxWithoutComma);
-#  $util->log("number_surface_forms result:@result", $logfile);
-
-   # filter according to num_style
-   if ($follow_num_style) {
-      my @filtered_result = ();
-      foreach $r (@result) {
-	 push(@filtered_result, $r)
-	    if    (($num_style eq "digits") && ($r =~ /^\d+$/))
-	       || (($num_style eq "alpha")  && ($r =~ /^[-\@ a-z]*$/i))
-	       || (($num_style eq "digits_plus_alpha") && ($r =~ /\d.*[a-z]/i));
-      }
-      @result = @filtered_result if @filtered_result;
-   }
-
-   if ($pe && $pe->childGloss("and")) {
-      @new_result = ();
-      foreach $r (@result) {
-	 if ($r =~ /^and /) {
-	    push(@new_result, $r);
-	 } else {
-	    push(@new_result, "and $r");
-	 }
-      }
-      @result = @new_result;
-   }
-   return @result;
-}
-
-sub number_range_surface_forms {
-   local($caller,$pe) = @_;
-
-   $value = $pe->value;
-   $value_coord = $pe->get("value-coord");
-   unless ($value_coord) {
-      return $caller->number_surface_forms($value);
-   }
-   $prefix = "";
-   if ($conj = $pe->get("conj")) {
-      $connector = $conj;
-   } else {
-      $connector = ($value_coord == $value + 1) ? "or" : "to";
-   }
-   if ($pe->get("between")) {
-      $prefix = "between ";
-      $connector = "and";
-   }
-
-   $pe1 = $pe->child("head");
-   $pe2 = $pe->child("coord");
-   @result1 = $caller->number_surface_forms($value, $pe1);
-   @result2 = $caller->number_surface_forms($value_coord, $pe2);
-   @num_style_list = @{$english_entity_style_ht{"FollowSourceLanguageNumberStyle"}};
-   $follow_num_style = 1 if $util->member("yes", @num_style_list);
-
-   # between two thousand and three thousand => between two and three thousand
-   # 3 million to 5 million => 3 to 5 million
-   if ($follow_num_style && ($#result1 == 0) && ($#result2 == 0)) {
-      $range = $prefix . $result1[0] . " $connector " . $result2[0];
-      $util->log("  range1: $range", $logfile);
-      $gazillion = "thousand|million|billion|trillion";
-      ($a,$gaz1,$b,$gaz2) = ($range =~ /^(.+) ($gazillion) ($connector .+) ($gazillion)$/);
-      if (defined($a) && defined($gaz1) && defined($b) && defined($gaz2) && ($gaz1 eq $gaz2)) {
-         $range = "$a $b $gaz1";
-         $util->log("  range2: $range", $logfile);
-         return ($range);
-      }
-   }
-
-   @result = ();
-   foreach $result1 (@result1) {
-      next if ($value >= 1000) && ($result1 =~ /^\d+$/);
-      foreach $result2 (@result2) {
-	 next if $result1 =~ /^an?\b/;
-	 push(@result, "$prefix$result1 $connector $result2")
-	   if ($result1 =~ /^[a-z]+$/) && ($result2 =~ /^[a-z]+$/);
-	 next if ($result1 =~ /^[a-z]/) || ($result2 =~ /^[a-z]/);
-         next if ($value_coord >= 1000) && ($result2 =~ /^\d+$/);
-	 ($digits1,$letters1) = ($result1 =~ /^(\d+(?:.\d+)?) ([a-z].*)$/);
-	 ($digits2,$letters2) = ($result2 =~ /^(\d+(?:.\d+)?) ([a-z].*)$/);
-	 if (defined($digits1) && defined($letters1)
-	  && defined($digits2) && defined($letters2)
-	  && ($letters1 eq $letters2)) {
-	    push(@result, "$prefix$digits1 $connector $digits2 $letters1");
-	 } elsif (($result1 =~ /^\d{1,3}$/) && ($result2 =~ /^\d{1,3}$/) && !$prefix) {
-	    push(@result, "$result1-$result2");
-	    if ($connector eq "to") {
-	       my $span = "$result1 to $result2";
-	       push(@result, $span) unless $util->member($span, @result);
-	    }
-	 } else {
-	    push(@result, "$prefix$result1 $connector $result2");
-	 }
-      }
-   }
-   unless (@result) {
-      $result1 = (@result1) ? $result1[0] : $value;
-      $result2 = (@result2) ? $result2[0] : $value_coord;
-      @result = "$prefix$result1 $connector $result2";
-   }
-   return @result;
-}
-
-sub q_number_surface_forms {
-   local($caller,$pe) = @_;
- 
-   $surf = $pe->surf;
-   return ($pe->gloss) unless $value = $pe->value;
-   if (($value >= 1961) && ($value <= 2030)
-            && 
-	  (($pe->get("struct") eq "sequence of digits") 
-	       ||
-	   ($surf =~ /^\d+$/))) {
-      $value = "$prefix $value" if $prefix = $pe->get("prefix");
-      @result = ("$value");
-   } else {
-      @result = $caller->number_surface_forms($value,$pe);
-      @result = $caller->qualify_entities($pe,@result);
-   }
-   return @result;
-}
-
-sub ordinal_surface_forms {
-   local($caller,$number,$exclude_cardinals_p,$exclude_adverbials_p, $pe) = @_;
-
-   if (defined($os = $english_entity_style_ht{"Ordinal"})) {
-      @ordinal_styles = @{$os};
-   } else {
-      return ();
-   }
-   $exclude_cardinals_p = 0 unless defined($exclude_cardinals_p);
-   @num_style_list = @{$english_entity_style_ht{"FollowSourceLanguageNumberStyle"}};
-   $follow_num_style = 1 if $util->member("yes", @num_style_list);
-   $num_style = ($pe) ? $pe->get("num_style") : "";
-   $alpha_ok = ! ($follow_num_style && ($num_style =~ /^digits$/));
-   my $c_number = $caller->commify($number);
-   my $lex_form = "";
-   $lex_form = $caller->value_to_english_ordinal($number) if $alpha_ok;
-   my $adverbial_form 
-	 = (($number =~ /^\d+$/) && ($number >= 1) && ($number <= 10) 
-		 && $lex_form && $util->member("secondly", @ordinal_styles))
-	     ? $lex_form . "ly" : "";
-   my $num_form = $caller->numeric_ordinal_form($number);
-   my $c_num_form = $caller->numeric_ordinal_form($c_number);
-   my @result = ();
-
-#  print "lex_form: $lex_form num_form:$num_form c_num_form:$c_num_form\n";
-   if ($lex_form && $util->member("second", @ordinal_styles)) {
-      if (! $util->member("2nd", @ordinal_styles)) {
-	 @result = ($lex_form);
-      } elsif ($c_num_form ne $num_form) {
-	 @result = ($c_num_form, $lex_form, $num_form);
-      } elsif ($number >= 10) {
-	 @result = ($num_form, $lex_form);
-      } else {
-	 @result = ($lex_form, $num_form);
-      }
-   } elsif ($util->member("2nd", @ordinal_styles)) {
-      if ($c_num_form ne $num_form) {
-	 @result = ($c_num_form, $num_form);
-      } else {
-	 @result = ($num_form);
-      }
-   }
-   unless ($number =~ /^\d+$/) {
-      print STDERR "Warning: $number not an integer (for ordinal)\n";
-   }
-   unless ($exclude_cardinals_p) {
-      $incl_num_card = $util->member("2", @ordinal_styles);
-      $incl_lex_card = $util->member("two", @ordinal_styles);
-      foreach $card ($caller->number_surface_forms($number)) {
-	 if ($card =~ /^an?$/) {
-	    # don't include
-	 } elsif ($card =~ /^[0-9,]+$/) {
-            push(@result, $card) if $incl_num_card;
-	 } else {
-            push(@result, $card) if $incl_lex_card && $alpha_ok;
-	 }
-      }
-   }
-   push(@result,$adverbial_form) if $adverbial_form && ! $exclude_adverbials_p;
-   push(@result, $num_form) unless @result;
-   return @result;
-}
-
-sub ordinal_surface_form {
-   local($caller,$number,$exclude_cardinals_p,$exclude_adverbials_p, $pe) = @_;
-
-   my @surf_forms = $caller->ordinal_surface_forms($number,$exclude_cardinals_p,$exclude_adverbials_p, $pe);
-   return (@surf_forms) ? $surf_forms[0] : $caller->numeric_ordinal_form($number);
-}
-
-sub fraction_surface_forms {
-   local($caller,$pe,$modp) = @_;
-
-   my @result = ();
-   $numerator = $pe->get("numerator");
-   $denominator = $pe->get("denominator");
-#  print "numerator: $numerator denominator:$denominator\n";
-   @surf_nums = $caller->number_surface_forms($numerator,$pe);
-   @surf_nums = ("one") if $numerator == 1;
-   @surf_dens = $caller->ordinal_surface_forms($denominator,1,1);
-   @surf_dens = ("half") if $denominator == 2;
-   @surf_dens = ("quarter") if $denominator == 4;
-   @surf_dens = ("tenth") if $denominator == 10;
-#  print "surf_nums: @surf_nums surf_dens: @surf_dens\n";
-   @fraction_patterns = @{$english_entity_style_ht{"Fraction"}};
-   if (@surf_nums && @surf_dens) {
-      $surf_num = $surf_nums[0];
-      $surf_den = $surf_dens[0];
-      $surf_num_den = "";
-      foreach $sd (@surf_dens) {
-         $surf_num_den = $sd if $sd =~ /^\d/;
-      }
-      $surf_den_w_proper_number = $caller->noun_number_form($surf_den, $numerator);
-      foreach $fp (@fraction_patterns) {
-	 if ($fp eq "one tenth") {
-	    push(@result, $surf_num . " " . $surf_den_w_proper_number) unless $modp;
-	 } elsif ($fp eq "one-tenth") {
-	    if ($modp) {
-	       push(@result, $surf_num . "-" . $surf_den);
-	    } else {
-	       push(@result, $surf_num . "-" . $surf_den_w_proper_number);
-	    }
-	 } elsif ($fp eq "1/10") {
-	    push(@result, $numerator . "/" . $denominator);
-	 } elsif ($fp eq "1/10th") {
-	    push(@result, $numerator . "/" . $surf_num_den) if $surf_num_den;
-	 }
-      }
-      return @result;
-   } else {
-      return ($pe->gloss);
-   }
-}
-
-sub currency_surface_forms {
-   local($caller,$pe) = @_;
-
-   @currency_surf_forms = ();
-   return @currency_surf_forms unless $pe->sem =~ /monetary quantity/;
-   $unit = $pe->get("unit");
-   return ($pe->gloss) unless $quant = $pe->get("quant");
-   return ($pe->gloss) if $pe->childSem("head") eq "currency symbol";
-   $quant_pe = $pe->child("quant");
-   if ($unit =~ /^(US|Hongkong) dollar$/) {
-      @units = $caller->entity_style_listing($unit);
-   } elsif ($unit eq "yuan") {
-      @units = $caller->entity_style_listing("Chinese yuan");
-      @rmb_pos = @{$english_entity_style_ht{"Chinese RMB position"}};
-      @rmb_pos = ("before-number", "after-number") if $util->member("all",@units);
-   } else {
-      @units = ($unit);
-   }
-   if (($pe->sem =~ /range$/) && $quant_pe) {
-      @quants = $caller->number_range_surface_forms($quant_pe);
-   } else {
-      @quants = $caller->number_surface_forms($quant, $quant_pe);
-   }
-   @quants = ($quant) unless @quants;
-   # print STDERR "units: @units \n";
-   foreach $q (@quants) {
-      foreach $u_sing (@units) {
-      $u = ($modp) ? $u_sing : $caller->noun_number_form($u_sing, $quant);
-#     print "  q: $q unit: $u value: $quant\n";
-	 if ($u eq "RMB") {
-	    if ($util->member("before-number", @rmb_pos)) {
-	       if ($q =~ /^\d/) {
-	          push(@currency_surf_forms, "RMB" . $q);
-	       }
-	    } 
-	    if ($util->member("after-number", @rmb_pos)) {
-	       push(@currency_surf_forms, $q . " RMB");
-	    }
-	 } elsif ($u =~ /\$$/) {
-	    if ($q =~ /^\d/) {
-	       $currency_surf_form = $u . $q;
-	       push(@currency_surf_forms, $currency_surf_form);
-	    }
-	 } else {
-	    $new_form = "$q $u";
-	    push(@currency_surf_forms, $new_form) if $caller->indef_art_filter($new_form);
-	 }
-      }
-   }
-   @currency_surf_forms = $caller->qualify_entities($pe,@currency_surf_forms);
-
-   # print STDERR "currency_surface_forms: @currency_surf_forms \n";
-   return @currency_surf_forms;
-}
-
-sub age_surface_forms {
-   local($caller,$pe, $modp) = @_;
-
-   $gloss = $pe->gloss;
-   @age_surf_forms = ();
-   return @age_surf_forms unless $pe->sem =~ /age quantity/;
-   $unit = $pe->get("unit");
-   return ($gloss) unless $quant = $pe->get("quant");
-   $temporal_quant_pe = $pe->child("head");
-   $synt = $pe->synt;
-   if ($synt =~ /parenthetical/) {
-      if ($pe->get("slashed")) {
-         @age_markers = $caller->entity_style_listing("ParentheticalAgeFormatSlashed");
-         @age_markers = $caller->entity_style_listing("ParentheticalAgeFormat") unless @age_markers;
-      } else {
-         @age_markers = $caller->entity_style_listing("ParentheticalAgeFormat");
-      }
-      return ($gloss) unless @age_markers;
-      foreach $a (@age_markers) {
-         $age_surf_form = $a;
-         $age_surf_form =~ s/8/$quant/;
-         push(@age_surf_forms, $age_surf_form);
-      }
-   } elsif (($quant =~ /^\d+$/) && ($temporal_quant_pe->sem eq "age unit")) {
-      @quants = $caller->number_surface_forms($quant);
-      @quants = ($quant) if $pe->childSurf("quant") =~ /^\d+$/;
-      foreach $quant2 (@quants) {
-	 if ($modp) {
-            push(@age_surf_forms, "$quant2-year-old");
-	 } else {
-	    $plural_marker = ($quant >= 2) ? "s" : "";
-            push(@age_surf_forms, "$quant2 year$plural_marker old");
-	 }
-      }
-   } elsif ($temporal_quant_pe && ($temporal_quant_pe->sem eq "temporal quantity")) {
-      @temporal_quants = $caller->quantity_surface_forms($temporal_quant_pe, $modp);
-      foreach $temporal_quant (@temporal_quants) {
-         push(@age_surf_forms, $temporal_quant . (($modp) ? "-" : " ") . "old");
-      }
-   } else {
-      return ($gloss);
-   }
-
-   @age_surf_forms = ($gloss) unless @age_surf_forms;
-   return @age_surf_forms;
-}
-
-sub occurrence_surface_forms {
-   local($caller,$pe,$modp) = @_;
-
-   @quantity_surf_forms = ();
-   return ($pe->gloss) unless $quant = $pe->get("quant");
-   $quant_coord = $pe->get("quant-coord");
-   $quant_pe = $pe->child("quant");
-   $unit = "time";
-   if (($pe->sem =~ /range$/) && $quant_pe) {
-      @quants = $caller->number_range_surface_forms($quant_pe);
-   } else {
-      @quants = $caller->number_surface_forms($quant, $quant_pe);
-   }
-   @quants = ($quant) unless @quants;
-   if ($modp) {
-      return () if $pe->get("qualifier") || $quant_coord;
-      return ("one-time") if $quant eq "1";
-      return ("two-time", "two-fold", "2-fold") if $quant eq "2";
-   } else {
-      if ($quant_coord) {
-         return $caller->qualify_entities($pe, ("once or twice"))
-	    if $quant eq "1" and $quant_coord eq "2";
-      } else {
-         return $caller->qualify_entities($pe, ("once")) if $quant eq "1";
-         return $caller->qualify_entities($pe, ("twice", "two times", "2 times", 
-					        "2-fold", "two fold")) if $quant eq "2";
-      }
-   }
-   foreach $q (@quants) {
-      $u = ($modp) ? $unit : $caller->noun_number_form($unit, $quant);
-      $new_form = "$q $u";
-      if ($modp) {
-	 # for the time being, no "more than/over/..." in modifiers: more than 20-ton
-	 if ($pe->get("qualifier")) {
-	    $new_form = "";
-         } else {
-	    $new_form =~ s/-/-to-/;
-	    $new_form =~ s/ /-/g;
-	 }
-      }
-      push(@quantity_surf_forms, $new_form) if $new_form;
-      push(@quantity_surf_forms, "$q-fold") if $q =~ /\d/ || ($quant <= 9);
-   }
-   @quantity_surf_forms = $caller->qualify_entities($pe,@quantity_surf_forms);
-
-   return @quantity_surf_forms;
-}
-
-sub quantity_surface_forms {
-   local($caller,$pe,$modp) = @_;
-
-   if ($pe->get("complex") eq "true") {
-      return () if $modp;
-      $quantity_surf_form = $pe->gloss;
-      return ($quantity_surf_form);
-   }
-
-   @quantity_surf_forms = ();
-   $sem = $pe->get("sem");
-   $scale = $pe->get("scale");
-   $scale_mod = $pe->get("scale_mod");
-   $unit = $pe->get("unit") || $scale;
-   $mod_gloss = $pe->get("mod");
-   return ($pe->gloss) unless $quant = $pe->get("quant");
-   $quant_coord = $pe->get("quant-coord");
-   $quant_comb = $quant_coord || $quant;
-   $quant_pe = $pe->child("quant");
-   if (defined($u_style = $english_entity_style_ht{"\u$unit"})) {
-      @units = @{$u_style};
-   } else {
-      @units = ($unit);
-   }
-   if (($pe->sem =~ /range$/) && $quant_pe) {
-      @quants = $caller->number_range_surface_forms($quant_pe);
-   } else {
-      @quants = $caller->number_surface_forms($quant, $quant_pe);
-   }
-   @quants = ($quant) unless @quants;
-   foreach $q (@quants) {
-      foreach $u_sing (@units) {
-	 my $u = $u_sing;
-	 if (($sem =~ /seismic quantity/) && $scale) {
-            $scale =~ s/(\w+)\s*/\u\L$1/g if $scale =~ /^(Richter|Mercalli)/i;
-	    $u = "on the $scale_mod $scale scale";
-	    $u =~ s/\s+/ /g;
-	 } elsif (($u_sing =~ /\S/) && ! $modp) {
-            $u = $caller->noun_number_form($u_sing, $quant_comb);
-	 }
-#     print "  q: $q unit: $u value: $quant modp: $modp\n";
-	 @mods = ("");
-	 @mods = ("consecutive", "in a row") if $mod_gloss eq "continuous";
-	 foreach $mod (@mods) {
-            $pre_quant_mod = "";
-            $in_quant_mod = ($mod =~ /(consecutive)/) ? "$mod " : "";
-            $post_quant_mod = ($mod =~ /(in a row)/) ? " $mod" : "";
-	    $new_form = "$pre_quant_mod$q $in_quant_mod$u$post_quant_mod";
-	    if ($caller->is_abbreviation($u)) {
-	       if (($pe->sem =~ /range/) && ($q =~ /^[-0-9,\. to]+$/)
-		     && $modp && !($new_form =~ / (to|or) /)) {
-	          $new_form =~ s/-/-to-/;
-	          $new_form =~ s/ /-/g;
-	       } elsif ($q =~ /^[-0-9,\.]+$/) {
-#                 $new_form =~ s/ //g;
-	       } else {
-	          $new_form = "";
-	       }
-	    } elsif ($modp) {
-	       # for the time being, no "more than/over/..." in modifiers: more than 20-ton
-	       if (($pe->get("qualifier")) || $mod) {
-	          $new_form = "";
-               } elsif ($u =~ /(square|cubic|metric|short)/) {
-	          # no hyphenation for the time being (based on CTE style)
-               } elsif (($pe->sem =~ /range/) && !($new_form =~ / (to|or) /)) {
-	          $new_form =~ s/-/-to-/;
-	          $new_form =~ s/ /-/g;
-               } else {
-	          $new_form =~ s/ /-/g;
-	       }
-	    }
-            push(@quantity_surf_forms, $new_form)
-	      if $new_form && $caller->quantity_filter($new_form) && $caller->indef_art_filter($new_form);
-	 }
-      }
-   }
-   @quantity_surf_forms = $caller->qualify_entities($pe,@quantity_surf_forms);
-
-   # print STDERR "QSF unit:$unit sem:$sem   Result(s): " . join("; ", @quantity_surf_forms) . "\n";
-   return @quantity_surf_forms;
-}
-
-sub qualify_entities {
-   local($caller,$pe,@surf_forms) = @_;
-
-   $prefix = $pe->get("prefix");
-   $prefix_clause = ($prefix) ? "$prefix " : "";
-   if ($qualifier = $pe->get("qualifier")) {
-      $qualifier =~ s/-/ /g;
-      $qualifier_key = $qualifier;
-      $qualifier_key =~ s/(\w+)\s*/\u\L$1/g;
-      # print "qualifier_key: $qualifier_key\n";
-      @new_list = ();
-      if (defined($value = $english_entity_style_ht{$qualifier_key})) {
-	 @quals = @{$value};
-	 # print STDERR "  qk $qualifier_key in ht: @quals :: @surf_forms\n";
-         foreach $q (@quals) {
-	    foreach $surf_form (@surf_forms) {
-	       $new_form = "$prefix_clause$q $surf_form";
-	       push(@new_list, $new_form) if $caller->qualifier_filter($new_form);
-	    }
-	 }
-	 return @new_list if @new_list;
-      } else {
-	 @keys = sort keys %english_entity_style_ht;
-	 # print STDERR "  did not find qk $qualifier_key in ht: @keys\n";
-	 foreach $surf_form (@surf_forms) {
-	    if (($qualifier =~ /^(couple|few|lot|many|number|several|some)$/i)
-	     && (($art, $lex) = ($surf_form =~ /^(an?)\s+(\S|\S.*\S)\s*$/i))) {
-		$plural_form = $caller->noun_number_form($lex,2);
-	       $new_form = "$prefix_clause$qualifier $plural_form";
-	    } else {
-	       $new_form = "$prefix_clause$qualifier $surf_form";
-	    }
-	    push(@new_list, $new_form) if $caller->qualifier_filter($new_form);
-	 }
-	 return @new_list if @new_list;
-      }
-   }
-   if ($prefix) {
-      @prefixed_surf_forms = ();
-      foreach $surf_form (@surf_forms) {
-	 if ($surf_form =~ /^$prefix /) {  # already prefixed
-	    push(@prefixed_surf_forms, $surf_form);
-	 } else {
-	    push(@prefixed_surf_forms, "$prefix $surf_form");
-	 }
-      }
-      return @prefixed_surf_forms;
-   } else {
-      return @surf_forms;
-   }
-}
-
-sub percent_surface_forms {
-   local($caller,$pe,$modp) = @_;
-
-   @percent_surf_forms = ();
-   return @percent_surf_forms unless $pe->sem eq "percentage";
-   $prefix = "";
-   $quant = $pe->gloss;
-   $quant =~ s/%$//;
-   $quant =~ s/^and //;
-   if ($pe->gloss =~ /^and /) {
-      $prefix = "and";
-   }
-   @percent_markers = $caller->entity_style_listing("Percentage");
-   @quants = $caller->number_surface_forms($quant);
-   @quants = ($quant) unless @quants;
-   foreach $p (@percent_markers) {
-      foreach $q (@quants) {
-	 if ($p =~ /%$/) {
-	    if ($q =~ /\d$/) {
-	       $percent_surf_form = $q . "%";
-	       $percent_surf_form = "$prefix $percent_surf_form" if $prefix;
-	       push(@percent_surf_forms, $percent_surf_form);
-	       push(@percent_surf_forms, "by $percent_surf_form") unless $modp || $percent_surf_form =~ /^and /;
-	    }
-	 } else {
-	    if ((($p =~ /^\d/) && ($q =~ /^\d/))
-		     ||
-	        (($p =~ /^[a-z]/) && ($q =~ /^[a-z]/))) {
-	       if ($p =~ /percentage point/) {
-		  if ($quant == 1) {
-	             $percent_surf_form = $q . " percentage point";
-		  } else {
-	             $percent_surf_form = $q . " percentage points";
-		  }
-	       } else {
-	          $percent_surf_form = $q . " percent";
-	       }
-	       $percent_surf_form = "$prefix $percent_surf_form" if $prefix;
-	       $percent_surf_form =~ s/ /-/g if $modp;
-	       push(@percent_surf_forms, $percent_surf_form);
-	       push(@percent_surf_forms, "by $percent_surf_form") unless $modp || $percent_surf_form =~ /^and /;
-	    }
-	 }
-      }
-   }
-   return @percent_surf_forms;
-}
-
-sub decade_century_surface_forms {
-   local($caller,$pe) = @_;
-
-   if ($pe->sem =~ /century/) {
-      $gloss = $pe->gloss;
-      return ("the $gloss", "in the $gloss", $gloss);
-   }
-   @decade_surf_forms = ();
-   return @decade_surf_forms unless $pe->sem =~ /year range\b.*\bdecade/;
-   @decade_markers = @{$english_entity_style_ht{"Decade"}};
-   @extend_decades = @{$english_entity_style_ht{"ExtendDecades"}};
-   @extended_decades = @{$english_entity_style_ht{"ExtendedDecade"}};
-   $extended_decade = (@extended_decades) ? $extended_decades[0] : "none";
-
-   $value = $pe->value;
-   $extended_value = "";
-   foreach $extend_decade (@extend_decades) {
-      if ($extend_decade =~ /$value$/) {
-	 $extended_value = $extend_decade unless $extended_value eq $extend_decade;
-	 last;
-      }
-   }
-   if ($sub = $pe->get("sub")) {
-      $sub_clause = "$sub ";
-      $sub_clause =~ s/(mid) /$1-/;
-   } else {
-      $sub_clause = "";
-   }
-
-   if (! $extended_value) {
-      @values = ($value);
-   } elsif ($extended_decade eq "ignore") {
-      @values = ($value);
-   } elsif ($extended_decade eq "only") {
-      @values = ($extended_value);
-   } elsif ($extended_decade eq "primary") {
-      @values = ($extended_value, $value);
-   } elsif ($extended_decade eq "secondary") {
-      @values = ($value, $extended_value);
-   } else {
-      @values = ($value);
-   }
-   foreach $v (@values) {
-      foreach $dm (@decade_markers) {
-         $dm_ending = $dm;
-         $dm_ending =~ s/^\d+//;
-         push (@decade_surf_forms, "the $sub_clause$v$dm_ending");
-         push (@decade_surf_forms, "in the $sub_clause$v$dm_ending");
-         push (@decade_surf_forms, "$sub_clause$v$dm_ending");
-      }
-   }
-   return @decade_surf_forms;
-}
-
-sub day_of_the_month_surface_forms {
-   local($caller,$pe) = @_;
-
-   @dom_surf_forms = ();
-   return @dom_surf_forms 
-      unless ($pe->sem eq "day of the month")
-	  && ($day_number = $pe->get("day-number"));
-   @dom_markers = @{$english_entity_style_ht{"DayOfTheMonth"}};
-   foreach $dm (@dom_markers) {
-      $ord = $caller->numeric_ordinal_form($day_number);
-      if ($dm eq "on the 5th") {
-	 push (@dom_surf_forms, "on the $ord");
-      } elsif ($dm eq "the 5th") {
-	 push (@dom_surf_forms, "the $ord");
-      } elsif ($dm eq "5th") {
-	 push (@dom_surf_forms, $ord);
-      }
-   }
-   return @dom_surf_forms;
-}
-
-sub score_surface_forms {
-   local($caller,$pe) = @_;
-
-   @score_surf_forms = ();
-   if (($score1 = $pe->get("score1"))
-    && ($score2 = $pe->get("score2"))) {
-      @score_markers = @{$english_entity_style_ht{"ScoreMarker"}};
-      @score_markers = (":") unless @score_markers;
-      foreach $sm (@score_markers) {
-         push (@score_surf_forms, "$score1$sm$score2");
-      }
-   } 
-   push(@score_surf_forms, $pe->gloss) unless @score_surf_forms;
-   return @score_surf_forms;
-}
-
-sub day_of_the_week_surface_forms {
-   local($caller,$pe) = @_;
-
-   @dom_surf_forms = ();
-   @dom_markers = @{$english_entity_style_ht{"DayOfTheWeek"}};
-   $gloss = $pe->get("gloss");
-   $weekday = $pe->get("weekday");
-   $weekday = $gloss if ($weekday eq "") && ($gloss =~ /^\S+$/);
-   $relday = $pe->get("relday");
-   $period = $pe->get("period");
-   foreach $dm (@dom_markers) {
-      if (($dm =~ /NOPERIOD/) && $period) {
-	 $surf = ""; # bad combination
-      } elsif (($dm eq "Sunday") || ! $relday) {
-	 $surf = $weekday;
-	 $surf .= " $period" if $period;
-      } elsif ($dm =~ /morning/) {
-	 if ($period) {
-	    $surf = $dm;
-            $surf =~ s/tomorrow/$relday/;
-	    $surf =~ s/morning/$period/;
-            $surf =~ s/Sunday/$weekday/;
-	 } else {
-	    $surf = ""; # bad combination
-	 }
-      } else {
-	 $surf = $dm;
-	 if ($period) {
-	    if ($relday eq "today") {
-	       $core_surf = "this $period";
-	    } else {
-	       $core_surf = "$relday $period";
-	    }
-	 } else {
-	    $core_surf = $relday;
-	 }
-         $surf =~ s/tomorrow/$core_surf/;
-         $surf =~ s/Sunday/$weekday/;
-      }
-      $surf =~ s/yesterday night/last night/;
-      $surf =~ s/this noon, ($weekday)(,\s*)?/today, $1, at noon/;
-      $surf =~ s/this noon/today at noon/;
-      $surf =~ s/this night/tonight/;
-      $surf =~ s/\s*NOPERIOD\s*$//;
-      push (@dom_surf_forms, $surf) unless $util->member($surf, @dom_surf_forms) || ! $surf;
-      $on_weekday = "on $surf";
-      push (@dom_surf_forms, $on_weekday)
-	 if ($surf eq $weekday) && ! $util->member($on_weekday, @dom_surf_forms);
-   }
-   return @dom_surf_forms;
-}
-
-sub date_surface_forms {
-   local($caller,$pe,$modp) = @_;
-
-   @date_surf_forms = ();
-   $sem = $pe->sem;
-   $synt = $pe->synt;
-   return @date_surf_forms unless $sem =~ /date(\+year)?/;
-   $day = $pe->get("day");
-   $weekday = $pe->get("weekday");
-   $month_name = $pe->get("month-name");
-   $month_number = $pe->get("month-number");
-   $year = $pe->get("year");
-   $era = $pe->get("era");
-   $era_clause = "";
-   $calendar_type = $pe->get("calendar");
-   $calendar_type_clause = "";
-   $calendar_type_clause = " AH" if $calendar_type eq "Islamic";
-   $ad_year = $year;
-   if ($era eq "Republic era") {
-      $ad_year = $year + 1911;
-      $era_clause = " (year $year of the $era)";
-   }
-   $rel = $pe->get("rel");
-   if ($sep = $pe->get("sep")) {
-      $date_surf_form = "$month_number$sep$day";
-      $date_surf_form .= "$sep$year" if $year;
-      $date_surf_form = "$weekday, $date_surf_form" if $weekday;
-      $date_surf_form = "on $date_surf_form" if $synt eq "pp";
-      return ($date_surf_form);
-   }
-   @date_months = @{$english_entity_style_ht{"DateMonth"}};
-   @date_days = @{$english_entity_style_ht{"DateDay"}};
-   @date_order = @{$english_entity_style_ht{"DateOrder"}};
-   foreach $m (@date_months) {
-      if ($m eq "September") {
-	 $surf_month = $month_name;
-      } elsif ($m =~ /^Sep(\.)?$/) {
-	 if ($month_name eq "May") {
-	    $surf_month = $month_name;
-	 } else {
-	    $period_clause = ($m =~ /\.$/) ? "." : "";
-	    $surf_month = substr($month_name, 0, 3) . $period_clause;
-	 }
-      } elsif ($m =~ /^Sept(\.)?$/) {
-	 if ($util->member($month_name, "February", "September"))  {
-	    $period_clause = ($m =~ /\.$/) ? "." : "";
-	    $surf_month = substr($month_name, 0, 4) . $period_clause;
-	 } else {
-	    $surf_month = "";
-	 }
-      } else {
-	 $surf_month = "";
-      }
-      foreach $d (@date_days) {
-	 if ($d =~ /^\d+$/) {
-	    $surf_day = $day;
-	 } elsif ($d =~ /^\d+[sthrd]+$/) {
-	    $surf_day = $caller->numeric_ordinal_form($day);
-	 } else {
-	    $surf_day = "";
-	 }
-	 if ($surf_month && $surf_day) {
-	    foreach $o (@date_order) {
-               if ($calendar_type eq "Islamic") {
-		  $date_surf_form = "$surf_day $surf_month";
-               } elsif ($o eq "September 6, 1998") {
-		  $date_surf_form = "$surf_month $surf_day";
-	       } elsif ($o eq "6 September, 1998") {
-		  $date_surf_form = "$surf_day $surf_month";
-	       }
-	       $date_surf_form = "$weekday, $date_surf_form" if $weekday;
-	       $consider_on_p = 1;
-	       if ($year) {
-		  $date_surf_form .= "," unless $calendar_type eq "Islamic";
-	          $date_surf_form .= " $ad_year$calendar_type_clause$era_clause";
-	       } elsif ($rel) {
-		  if ($rel eq "current") {
-		     $date_surf_form = "this $date_surf_form";
-		  } else {
-		     $date_surf_form = "$rel $date_surf_form";
-		  }
-	          $consider_on_p = 0;
-	       }
-	       push(@date_surf_forms, $date_surf_form)
-	          unless $util->member($date_surf_form, @date_surf_forms) || ($synt eq "pp");
-	       if ($consider_on_p) {
-	          $on_date_surf_form = "on $date_surf_form";
-	          push(@date_surf_forms, $on_date_surf_form)
-	             unless $modp || $util->member($on_date_surf_form, @date_surf_forms);
-               }
-
-	       if (($synt eq "pp") && ($sem eq "date")) {
-	          push(@date_surf_forms, $date_surf_form)
-	             unless $util->member($date_surf_form, @date_surf_forms);
-	       }
-	    }
-	 }
-      }
-   }
-   return @date_surf_forms;
-   # rel, last, next, this
-}
-
-sub numeric_ordinal_form {
-   local($caller,$cardinal) = @_;
-
-   return $cardinal . "th" if $cardinal =~ /1\d$/;
-   return $cardinal . "st" if $cardinal =~ /1$/;
-   return $cardinal . "nd" if $cardinal =~ /2$/;
-   return $cardinal . "rd" if $cardinal =~ /3$/;
-   return $cardinal . "h" if $cardinal =~ /t$/;
-   $cardinal =~ s/y$/ie/;
-   return $cardinal . "th";
-}
-
-sub guard_urls_x045 {
-   local($caller, $s) = @_;
-
-   # URLs (http/https/ftp/mailto)
-   my $result = "";
-   while (($pre, $url, $post) = ($s =~ /^(.*?)((?:(?:https?|ftp):\/\/|mailto:)[#%-;=?-Z_-z~]*[-a-zA-Z0-9\/#])(.*)$/)) {
-      $result .= "$pre\x04$url\x05";
-      $s = $post;
-   }
-   $result .= $s;
-
-   # emails
-   $s = $result;
-   $result = "";
-   while (($pre, $email, $post) = ($s =~ /^(.*?[ ,;:()\/\[\]{}<>|"'])([a-z][-_.a-z0-9]*[a-z0-9]\@[a-z][-_.a-z0-9]*[a-z0-9]\.(?:[a-z]{2,}))([ .,;:?!()\/\[\]{}<>|"'].*)$/i)) {
-      $result .= "$pre\x04$email\x05";
-      $s = $post;
-   }
-   $result .= $s;
-
-   # (Twitter style) #hashtag or @handle
-   $s = $result;
-   $result = "";
-   while (($pre, $hashtag, $post) = ($s =~ /^(.*?[ .,;()\[\]{}'])([#@](?:[a-z]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|HHERE)(?:[_a-z0-9]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF])*(?:[a-z0-9]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF]))(.*)$/i)) {
-      $result .= "$pre\x04$hashtag\x05";
-      $s = $post;
-   }
-   $result .= $s;
-
-   # Keep together number+letter in: Fig. 4g; Chromosome 12p
-   $result =~ s/((?:\b(?:fig))(?:_DONTBREAK_)?\.?|\b(?:figures?|tables?|chromosomes?)|<xref\b[^<>]*\b(?:fig)\b[^<>]*>)\s*(\d+[a-z])\b/$1 \x04$2\x05/gi;
-
-   # special combinations, e.g. =/= emoticons such as :)
-   $s = $result;
-   $result = "";
-   while (($pre, $special, $post) = ($s =~ /^(.*?)(:-?\)|:-?\(|=\/=?|\?+\/\?+|=\[)(.*)$/)) {
-      $result .= "$pre\x04$special\x05";
-      $s = $post;
-   }
-   $result .= $s;
-
-   return $result;
-}
-
-sub guard_xml_tags_x0123 {
-   local($caller, $s) = @_;
-
-   my $result = "";
-   # xml tag might or might not already have "@" on left and/or right end: @<br>@
-   while (($pre, $tag, $post) = ($s =~ /^(.*?)(\@?<\/?(?:[a-z][-_:a-z0-9]*)(?:\s+[a-z][-_:a-z0-9]*="[^"]*")*\s*\/?>\@?|&(?:amp|gt|lt|quot);|\[(?:QUOTE|URL)=[^ \t\n\[\]]+\]|\[\/?(?:QUOTE|IMG|INDENT|URL)\]|<\$[-_a-z0-9]+\$>|<\!--.*?-->)(.*)$/si)) {
-      $result .= $pre;
-      if (($pre =~ /\S$/) && ($tag =~ /^\S/)) {
-         $result .= " \x01";
-	 $result .= "\@" if ($tag =~ /^<[a-z]/i) && (! ($pre =~ /[,;(>]$/)); #)
-      } else {
-         $result .= "\x01";
-      }
-      $guarded_tag = $tag;
-      $guarded_tag =~ s/ /\x02/g;
-      # print STDERR "tag: $tag\nguarded_tag: $guarded_tag\n" if ($result =~ /Harvey/) || ($s =~ /Harvey/);
-      $result .= $guarded_tag;
-      if (($tag =~ /\S$/) && ($post =~ /^\S/)) { # (
-	 $result .= "\@" if (($tag =~ /^<\//) || ($tag =~ /\/>$/)) && (! ($result =~ /\@$/)) && (! ($post =~ /^[,;)<]/));
-         $result .= "\x03 ";
-      } else {
-	 $result .= "\x03";
-      }
-      $s = $post;
-   }
-   $result .= $s;
-   return $result;
-}
-
-sub restore_urls_x045_guarded_string {
-   local($caller, $s) = @_;
-
-   my $orig = $s;
-   while (($pre, $url, $post) = ($s =~ /^(.*?)\x04([^\x04\x05]*?)\x05(.*)$/)) {
-      $url =~ s/ \@([-:\/])/$1/g;
-      $url =~ s/([-:\/])\@ /$1/g;
-      $url =~ s/ //g;
-      $url =~ s/\x02/ /g;
-      $s = "$pre$url$post";
-   }
-   if ($s =~ /[\x04\x05]/) {
-      print STDERR "Removing unexpectedly unremoved x04/x05 marks from $s\n";
-      $s =~ s/[\x04\x05]//g;
-   }
-   return $s;
-}
-
-sub restore_xml_tags_x0123_guarded_string {
-   local($caller, $s) = @_;
-
-   my $result = "";
-   while (($pre, $tag, $post) = ($s =~ /^(.*?)\x01(.*?)\x03(.*)$/)) {
-      $result .= $pre;
-      $tag =~ s/ \@([-:\/])/$1/g;
-      $tag =~ s/([-:\/])\@ /$1/g;
-      $tag =~ s/ //g;
-      $tag =~ s/\x02/ /g;
-      $result .= $tag;
-      $s = $post;
-   }
-   $result .= $s;
-   return $result;
-}
-
-sub load_english_abbreviations {
-   local($caller, $filename, *ht, $verbose) = @_;
-   # e.g. /nfs/nlg/users/textmap/brahms-ml/arabic/data/EnglishAbbreviations.txt
-
-   $verbose = 1 unless defined($verbose);
-   my $n = 0;
-   if (open(IN, $filename)) {
-      while (<IN>) {
-         next if /^\#  /;
-         s/\s*$//;
-         my @expansions;
-         if (@expansions = split(/\s*::\s*/, $_)) {
-            my $abbrev = shift @expansions;
-            $ht{IS_ABBREVIATION}->{$abbrev} = 1;
-            $ht{IS_LC_ABBREVIATION}->{(lc $abbrev)} = 1;
-            foreach $expansion (@expansions) {
-               $ht{ABBREV_EXPANSION}->{$abbrev}->{$expansion} = 1;
-               $ht{ABBREV_EXPANSION_OF}->{$expansion}->{$abbrev} = 1;
-            }
-	    $n++;
-         }
-      }
-      close(IN);
-      print STDERR "Loaded $n entries from $filename\n" if $verbose;
-   } else {
-      print STDERR "Can't open $filename\n";
-   }
-}
-
-sub load_split_patterns {
-   local($caller, $filename, *ht) = @_;
-   # e.g. /nfs/nlg/users/textmap/brahms-ml/arabic/data/BioSplitPatterns.txt
-
-   my $n = 0;
-   if (open(IN, $filename)) {
-      while (<IN>) {
-         next if /^\#  /;
-         s/\s*$//;
-	 if (($s) = ($_ =~ /^SPLIT-DASH-X\s+(\S.*\S|\S)\s*$/)) {
-	    $ht{SPLIT_DASH_X}->{$s} = 1;
-	    $ht{LC_SPLIT_DASH_X}->{(lc $s)} = 1;
-	    $n++;
-	 } elsif (($s) = ($_ =~ /^SPLIT-X-DASH\s+(\S.*\S|\S)\s*$/)) {
-	    $ht{SPLIT_X_DASH}->{$s} = 1;
-	    $ht{LC_SPLIT_X_DASH}->{(lc $s)} = 1;
-	    $n++;
-	 } elsif (($s) = ($_ =~ /^DO-NOT-SPLIT-DASH-X\s+(\S.*\S|\S)\s*$/)) {
-	    $ht{DO_NOT_SPLIT_DASH_X}->{$s} = 1;
-	    $ht{LC_DO_NOT_SPLIT_DASH_X}->{(lc $s)} = 1;
-	    $n++;
-	 } elsif (($s) = ($_ =~ /^DO-NOT-SPLIT-X-DASH\s+(\S.*\S|\S)\s*$/)) {
-	    $ht{DO_NOT_SPLIT_X_DASH}->{$s} = 1;
-	    $ht{LC_DO_NOT_SPLIT_X_DASH}->{(lc $s)} = 1;
-	    $n++;
-	 } elsif (($s) = ($_ =~ /^DO-NOT-SPLIT\s+(\S.*\S|\S)\s*$/)) {
-	    $ht{DO_NOT_SPLIT}->{$s} = 1;
-	    $ht{LC_DO_NOT_SPLIT}->{(lc $s)} = 1;
-	    $n++;
-	 } elsif (($s) = ($_ =~ /^SPLIT\s+(\S.*\S|\S)\s*$/)) {
-	    $ht{SPLIT}->{$s} = 1;
-	    $ht{LC_SPLIT}->{(lc $s)} = 1;
-	    $n++;
-         }
-      }
-      close(IN);
-      print STDERR "Loaded $n entries from $filename\n";
-   } else {
-      print STDERR "Can't open $filename\n";
-   }
-}
-
-sub guard_abbreviations_with_dontbreak {
-   local($caller, $s, *ht) = @_;
-
-   my $orig = $s;
-   my $result = "";
-   while (($pre,$potential_abbrev,$period,$post) = ($s =~ /^(.*?)((?:[a-z]+\.-?)*(?:[a-z]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF])+)(\.)(.*)$/i)) {
-      if (($pre =~ /([-&\/0-9]|[-\/]\@ )$/)
-       && (! ($pre =~ /\b[DR](?: \@)?-(?:\@ )?$/))) { # D-Ariz.
-	 $result .= "$pre$potential_abbrev$period";
-      } else {
-         $result .= $pre . $potential_abbrev;
-         $potential_abbrev_with_period = $potential_abbrev . $period;
-         if ($ht{IS_ABBREVIATION}->{$potential_abbrev_with_period}) {
-            $result .= "_DONTBREAK_";
-         } elsif ($ht{IS_LC_ABBREVIATION}->{(lc $potential_abbrev_with_period)}) {
-            $result .= "_DONTBREAK_";
-         }
-         $result .= $period;
-      }
-      $s = $post;
-   }
-   $result .= $s;
-   $result =~ s/\b([Nn])o\.(\s*\d)/$1o_DONTBREAK_.$2/g;
-   return $result;
-}
-
-$alpha = "(?:[a-z]|\xCE[\xB1-\xBF]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF])";
-$alphanum = "(?:[a-z0-9]|\xCE[\xB1-\xBF]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF])(?:[-_a-z0-9]|\xCE[\xB1-\xBF]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF])*(?:[a-z0-9]|\xCE[\xB1-\xBF]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF])|(?:[a-z0-9]|\xCE[\xB1-\xBF]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF])";
-
-sub normalize_punctuation {
-   local($caller, $s) = @_;
-
-   $s =~ s/\xE2\x80[\x93\x94]/-/g; # ndash, mdash to hyphen
-   $s =~ s/ \@([-\/])/$1/g;
-   $s =~ s/([-\/])\@ /$1/g;
-   return $s;
-}
-
-sub update_replace_characters_based_on_context {
-   local($caller, $s) = @_;
-
-   # This is just a start. Collect stats over text with non-ASCII, e.g. K?ln.
-   # HHERE
-   my $rest = $s;
-   $s = "";
-   while (($pre, $left, $repl_char, $right, $post) = ($rest =~ /^(.*?\s+)(\S*)(\xEF\xBF\xBD)(\S*)(\s.*)$/)) {
-      $s .= "$pre$left";
-      if (($left =~ /[a-z]$/i) && ($right =~ /^s(?:[-.,:;?!].*)?$/i)) { # China's etc.
-	 $repl_char = "\xE2\x80\x99"; # right single quotation mark
-      } elsif (($left =~ /n$/i) && ($right =~ /^t$/i)) { # don't etc.
-	 $repl_char = "\xE2\x80\x99"; # right single quotation mark
-      } elsif (($left =~ /[a-z]\s*[.]$/i) && ($right eq "")) { # end of sentence
-	 $repl_char = "\xE2\x80\x9D"; # right double quotation mark
-      } elsif (($left eq "") && ($right =~ /^[A-Z]/i)) { # start of word
-	 $repl_char = "\xE2\x80\x9C"; # left double quotation mark
-      }
-      $s .= "$repl_char$right";
-      $rest = $post;
-   }
-   $s .= $rest;
-
-   return $s;
-}
-
-sub tokenize {
-   local($caller, $s, *ht, $control) = @_;
-
-   my $local_verbose = 0;
-   print "Point A: $s\n" if $local_verbose;
-   $control = "" unless defined($control);
-   my $bio_p = ($control =~ /\bbio\b/);
-
-   $s = $utf8->repair_misconverted_windows_to_utf8_strings($s);
-   print "Point A2: $s\n" if $local_verbose;
-   $s = $utf8->delete_weird_stuff($s);
-   print "Point B: $s\n" if $local_verbose;
-
-   # reposition xml-tag with odd space
-   $s =~ s/( +)((?:<\/[a-z][-_a-z0-9]*>)+)(\S)/$2$1$3/ig;
-   $s =~ s/(\S)((?:<[a-z][^<>]*>)+)( +)/$1$3$2/ig;
-   print "Point C: $s\n" if $local_verbose;
-
-   $a_value = $ht{IS_ABBREVIATION}->{"Fig."} || "n/a";
-   $s = $caller->guard_abbreviations_with_dontbreak($s, *ht);
-   my $standard_abbrev_s = "Adm|al|Apr|Aug|Calif|Co|Dec|Dr|etc|e.g|Feb|Febr|Gen|Gov|i.e|Jan|Ltd|Lt|Mr|Mrs|Nov|Oct|Pfc|Pres|Prof|Sen|Sept|U.S.A|U.S|vs";
-   my $pre;
-   my $core;
-   my $post;
-   $s = " $core " if ($pre,$core,$post) = ($s =~ /^(\s*)(.*?)(\s*)$/i);
-   $s =~ s/\xE2\x80\x89/ /g; # thin space
-   $standard_abbrev_s =~ s/\./\\\./g;
-   $s =~ s/[\x01-\x05]//g;
-   $s = $caller->guard_urls_x045($s);
-   $s = $caller->guard_xml_tags_x0123($s);
-   $s = $caller->update_replace_characters_based_on_context($s);
-   $s =~ s/((?:[a-zA-Z_]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF])\.)([,;]) /$1 $2 /g;
-   $s =~ s/((?:[a-zA-Z_]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF])\.)(\x04)/$1 $2/g;
-   if ($bio_p) {
-      $s =~ s/(\S)((?:wt\/|onc\/)?(?:[-+]|\?+|\xE2\x80[\x93\x94])\/(?:[-+]|\?+|\xE2\x80[\x93\x94]))/$1 $2/g;
-      $s =~ s/((?:[-+]|\xE2\x80[\x93\x94])\/(?:[-+]|\xE2\x80[\x93\x94]))(\S)/$1 $2/g;
-   }
-   print "Point D: $s\n" if $local_verbose;
-   $s =~ s/(~+)/ $1 /g;
-   $s =~ s/((?:\xE2\x80\xB9|\xE2\x80\xBA|\xC2\xAB|\xC2\xBB|\xE2\x80\x9E)+)/ $1 /g; # triangular bracket(s) "<" or ">" etc.
-   $s =~ s/(``)([A-Za-z])/$1 $2/g; # added Nov. 30, 2017
-   $s =~ s/((?:<|&lt;)?=+(?:>|&gt;)?)/ $1 /g;    # include arrows
-   $s =~ s/(\\")/ $1 /g;
-   $s =~ s/([^\\])("+)/$1 $2 /g;
-   $s =~ s/([^\\])((?:\xE2\x80\x9C)+)/$1 $2 /g;  # open "
-   $s =~ s/([^\\])((?:\xE2\x80\x9D)+)/$1 $2 /g;  # close "
-   $s =~ s/((?:<|&lt;)?-{2,}(?:>|&gt;)?)/ $1 /g; # include arrows
-   $s =~ s/((?:\xE2\x80\xA6)+)/ $1 /g; # ellipsis
-   print "Point E: $s\n" if $local_verbose;
-   foreach $_ ((1..2)) {
-      # colon
-      $s =~ s/([.,;])(:+)/$1 \@$2/g;
-      $s =~ s/(:+)([.,;])/$1 \@\@ $2/g;
-      # # question mark/exclamation mark blocks
-      # $s =~ s/([^!?])([!?]+)([^!?])/$1 $2 $3/g;
-   }
-   print "Point F: $s\n" if $local_verbose;
-   $s =~ s/(\?)/ $1 /g;
-   $s =~ s/(\!)/ $1 /g;
-   $s =~ s/ +/ /g;
-   $s =~ s/(\$+|\xC2\xA3|\xE2\x82[\xA0-\xBE])/ $1 /g; # currency signs (Euro sign; British pound sign; Yen sign etc.)
-   $s =~ s/(\xC2\xA9|\xE2\x84\xA2)/ $1 /g;           # copyright/trademark signs
-   $s =~ s/(\xC2\xB2)([-.,;:!?()])/$1 $2/g; # superscript 2
-   $s =~ s/([^ ])(&#160;)/$1 $2/g;
-   $s =~ s/(&#160;)([^ ])/$1 $2/g;
-   $s =~ s/(&#\d+|&#x[0-9A-F]+);/$1_DONTBREAK_;/gi;
-   $s =~ s/([\@\.]\S*\d)([a-z][A-z])/$1_DONTBREAK_$2/g; # email address, URL
-   $s =~ s/ ($standard_abbrev_s)\./ $1_DONTBREAK_\./gi;
-   $s =~ s/ ($standard_abbrev_s) \. (\S)/ $1_DONTBREAK_\. $2/gi;
-   $s =~ s/\b((?:[A-Za-z]\.){1,3}[A-Za-z])\.\s+/$1_DONTBREAK_\. /g; # e.g. a.m. O.B.E.
-   $s =~ s/([ ])([A-Z])\. ([A-Z])/$1$2_DONTBREAK_\. $3/; # e.g. George W. Bush
-   $s =~ s/(\S\.*?[ ])([A-Z])_DONTBREAK_\. (After|All|And|But|Each|Every|He|How|In|It|My|She|So|That|The|Then|There|These|They|This|Those|We|What|When|Which|Who|Why|You)([', ])/$1$2\. $3$4/; # Exceptions to previous line, e.g. "plan B. This"
-   $s =~ s/\b(degrees C|[Ff]ig\.? \d+ ?[A-Z]|(?:plan|Scud) [A-Z])_DONTBREAK_\./$1\./g; # Exception, e.g. "plan B";
-   $s =~ s/([^-_a-z0-9])(art|fig|no|p)((?:_DONTBREAK_)?\.)(\d)/$1$2$3 $4/gi; # Fig.2 No.14
-   $s =~ s/([^-_A-Za-z0-9])(\d+(?:\.\d+)?)(?:_DONTBREAK_)?(thousand|million|billion|trillion|min|mol|sec|kg|km|g|m|p)\b/$1$2 $3/g; # 3.4kg 1.7million 49.9p
-   $s =~ s/([^-_a-z0-9])((?:[1-9]|1[0-2])(?:[.:][0-5]\d)?)(?:_DONTBREAK_)?([ap]m\b|[ap]\.m(?:_DONTBREAK_)?\.)/$1$2 $3/gi; # 3.15pm 12:00p.m. 8am
-   print "Point H: $s\n" if $local_verbose;
-
-   $s =~ s/(\d)([a-z][A-z])/$1 $2/g;
-   $s =~ s/(\w|`|'|%|[a-zA-Z]\.|[a-zA-Z]_DONTBREAK_\.)(-|\xE2\x80\x93)(\w|`|')/$1 \@$2\@ $3/g;
-   $s =~ s/(\w|`|'|%|[a-zA-Z]\.|[a-zA-Z]_DONTBREAK_\.)(-|\xE2\x80\x93)(\w|`|')/$1 \@$2\@ $3/g;
-   $s =~ s/(\w)- /$1 \@- /g;
-   $s =~ s/ -(\w)/ -\@ $1/g;
-   $s =~ s/(\d):(\d)/$1 \@:\@ $2/g;
-   $s =~ s/(\d)\/(\d)/$1 \@\/\@ $2/g;
-   $s =~ s/($alphanum)\/([,;:!?])/$1 \@\/\@  $2/g;
-   $s =~ s/($alphanum)([-+]+)\/($alphanum)/$1$2 \@\/\@ $3/gi;
-   print "Point I: $s\n" if $local_verbose;
-   foreach $_ ((1..5)) {
-      $s =~ s/([ \/()])($alphanum) ?\/ ?($alphanum)([-+ \/().,;])/$1$2 \@\/\@ $3$4/gi;
-   }
-   $s =~ s/([a-zA-Z%\/\[\]]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF]|\x05|[a-zA-Z]_DONTBREAK_\.)([,;:!?])\s*(\S)/$1 $2 $3/g;
-   # asterisk
-   $s =~ s/( [(\[]?)(\*)([a-z0-9])/$1$2\@ $3/gi;
-   $s =~ s/([a-z0-9])(\*)([.,;:)\]]* )/$1 \@$2$3/gi;
-   print "Point J: $s\n" if $local_verbose;
-
-   # Arabic script
-   if ($s =~ /[\xD8-\xDB]/) {
-      for (my $i=0; $i <= 1; $i++) {
-         $s =~ s/([\xD8-\xDB][\x80-\xBF])([,;:!?.\(\)\[\]\/]|\xD8\x8C|\xD8\x9B|\xD8\x9F|\xD9\xAA|\xC2\xAB|\xC2\xBB|\xE2[\x80-\x9F][\x80-\xBF])/$1 $2/gi; # punctuation includes Arabic ,;?%
-         $s =~ s/([,;:!?.\(\)\[\]\/]|\xD8\x8C|\xD8\x9B|\xD8\x9F|\xD9\xAA|\xC2\xAB|\xC2\xBB|\xE2[\x80-\x9F][\x80-\xBF])([\xD8-\xDB][\x80-\xBF])/$1 $2/gi;
-      }
-   }
-   $s =~ s/(\d|[a-zA-Z]|[\xD8-\xDB][\x80-\xBF])([-])([\xD8-\xDB][\x80-\xBF])/$1 \@$2\@ $3/g;
-   $s =~ s/(\d|[a-zA-Z])([\xD8-\xDB][\x80-\xBF])/$1 \@\@ $2/g;
-   print "Point K: $s\n" if $local_verbose;
-
-   # misc. non-ASCII punctuation
-   $s =~ s/(\xC2[\xA1\xBF]|\xD5\x9D|\xD6\x89|\xD8[\x8C\x9B]|\xD8\x9F|\xD9[\xAA\xAC]|\xDB\x94|\xDC[\x80\x82])/ $1 /g;
-   $s =~ s/(\xE0\xA5[\xA4\xA5]|\xE0\xBC[\x84-\x86\x8D-\x8F\x91\xBC\xBD])/ $1 /g;
-   $s =~ s/(\xE1\x81[\x8A\x8B]|\xE1\x8D[\xA2-\xA6])/ $1 /g;
-   $s =~ s/(\xE1\x81[\x8A\x8B]|\xE1\x8D[\xA2-\xA6]|\xE1\x9F[\x94\x96])/ $1 /g;
-   $s =~ s/([^0-9])(5\xE2\x80\xB2)(-)([ACGTU])/$1 $2 \@$3\@ $4/g; # 5-prime-DNA-seq.
-   $s =~ s/([^0-9])([35]\xE2\x80\xB2)/$1 $2 /g; # prime (keep 3-prime/5-prime together for bio domain)
-   $s =~ s/([^0-9])(\xE2\x80\xB2)/$1 $2 /g; # prime
-   $s =~ s/(\xE2\x81\x99)/ $1 /g; # five dot punctuation
-   $s =~ s/(\xE3\x80[\x81\x82\x8A-\x91]|\xE3\x83\xBB|xEF\xB8\xB0|\xEF\xBC\x8C)/ $1 /g;
-   $s =~ s/(\xEF\xBC[\x81-\x8F\x9A\x9F])/ $1 /g; # CJK fullwidth punctuation (e.g. fullwidth exclamation mark)
-   print "Point L: $s\n" if $local_verbose;
-   # spaces
-   $s =~ s/((?:\xE3\x80\x80)+)/ $1 /g; # idiographic space
-   $s =~ s/((?:\xE1\x8D\xA1)+)/ $1 /g; # Ethiopic space
-
-   # isolate \xF0 and up from much more normal characters
-   $s =~ s/([\xF0-\xFE][\x80-\xBF]*)([\x00-\x7F\xC0-\xDF][\x80-\xBF]*)/$1 $2/g;
-   $s =~ s/([\x00-\x7F\xC0-\xDF][\x80-\xBF]*)([\xF0-\xFE][\x80-\xBF]*)/$1 $2/g;
-   print "Point M: $s\n" if $local_verbose;
-
-   $s =~ s/( \d+)([,;:!?] )/$1 $2/g;
-   $s =~ s/ ([,;()\[\]])([a-zA-Z0-9.,;])/ $1 $2/g;
-   $s =~ s/(\)+)([-\/])([a-zA-Z0-9])/$1 $2 $3/g;
-   $s =~ s/([0-9\*\[\]()]|\xE2\x80\xB2)([.,;:] )/$1 $2/g;
-   $s =~ s/([a-zA-Z%]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF]|\x05)([,;:.!?])([")]|''|\xE2\x80[\x99\x9D]|)(\s)/$1 $2 $3$4/g;
-   $s =~ s/([a-zA-Z%]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF]|\x05)([,;:.!?])([")]|''|\xE2\x80[\x99\x9D]|)\s*$/$1 $2 $3/g;
-   $s =~ s/([.,;:]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF]|\x05)('|\xE2\x80[\x99\x9D])/$1 $2/g;
-   $s =~ s/('|\xE2\x80[\x99\x9D])([.,;:]|\x04)/$1 $2/g;
-   $s =~ s/([(){}\[\]]|\xC2\xB1)/ $1 /g;
-   $s =~ s/([a-zA-Z0-9]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF]|\x05)\.\s*$/$1 ./g;
-   $s =~ s/([a-zA-Z]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF]|\x05)\.\s+/$1 . /g;
-   $s =~ s/([a-zA-Z]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF]|\x05)\.(\x04)/$1 . $2/g;
-   $s =~ s/([0-9]),\s+(\S)/$1 , $2/g;
-   $s =~ s/([a-zA-Z])(\$)/$1 $2/g;
-   $s =~ s/(\$|[~<=>]|\xC2\xB1|\xE2\x89[\xA4\xA5]|\xE2\xA9[\xBD\xBE])(\d)/$1 $2/g;
-   $s =~ s/(RMB)(\d)/$1 $2/g;
-   print "Point N: $s\n" if $local_verbose;
-   foreach $_ ((1..2)) {
-      $s =~ s/([ '"]|\xE2\x80\x9C)(are|could|did|do|does|had|has|have|is|should|was|were|would)(n't|n\xE2\x80\x99t)([ '"]|\xE2\x80\x9D)/$1 $2 $3 $4/gi;
-      $s =~ s/ (can)(not) / $1 $2 /gi;
-      $s =~ s/ (ca)\s*(n)('t|\xE2\x80\x99t) / $1$2 $2$3 /gi;
-      $s =~ s/ ([Ww])o\s*n('|\xE2\x80\x99)t / $1ill n$2t /g;
-      $s =~ s/ WO\s*N('|\xE2\x80\x99)T / WILL N$1T /g;
-      $s =~ s/ ([Ss])ha\s*n('|\xE2\x80\x99)t / $1hall n$2t /g;
-      $s =~ s/ SHAN('|\xE2\x80\x99)T / SHALL N$1T /g;
-    # $s =~ s/ ain('|\xE2\x80\x99)t / is n$1t /g;
-    # $s =~ s/ Ain('|\xE2\x80\x99)t / Is n$1t /g;
-    # $s =~ s/ AIN('|\xE2\x80\x99)T / IS N$1T /g;
-   }
-   print "Point O: $s\n" if $local_verbose;
-   $s =~ s/(\d)%/$1 %/g;
-   $s =~ s/ '(d|ll|m|re|s|ve|em) / '_DONTBREAK_$1 /g; # 'd = would; 'll = will; 'em = them
-   $s =~ s/ \xE2\x80\x99t(d|ll|m|re|s|ve) / \xE2\x80\x99t_DONTBREAK_$1 /g;
-   $s =~ s/([^0-9a-z'.])('|\xE2\x80\x98)([0-9a-z])/$1$2 $3/gi;
-   $s =~ s/([0-9a-z])(\.(?:'|\xE2\x80\x99))([^0-9a-z']|\xE2\x80\x99)/$1 $2$3/gi;
-   $s =~ s/([0-9a-z]_?\.?)((?:'|\xE2\x80\x99)(?:d|ll|m|re|s|ve|))([^0-9a-z'])/$1 $2$3/gi;
-   $s =~ s/([("]|\xE2\x80\x9C|'')(\w)/$1 $2/g;
-   print "Point P: $s\n" if $local_verbose;
-   $s =~ s/(\w|[.,;:?!])([")]|''|\xE2\x80\x9D)/$1 $2/g;
-   $s =~ s/ ([,;()\[\]])([a-zA-Z0-9.,;])/ $1 $2/g;
-   $s =~ s/([a-z0-9]) ?(\()([-+_ a-z0-9\/]+)(\))/$1 $2 $3 $4 /ig;
-   $s =~ s/([a-z0-9]) ?(\[)([-+_ a-z0-9\/]+)(\])/$1 $2 $3 $4 /ig;
-   $s =~ s/([a-z0-9]) ?(\{)([-+_ a-z0-9\/]+)(\})/$1 $2 $3 $4 /ig;
-   $s =~ s/([%])-(\d+(?:\.\d+)? ?%)/$1 \@-\@ $2/g;
-   $s =~ s/( )(art|No)_DONTBREAK_(\.{2,})/$1 $2$3/gi;
-   $s =~ s/(_DONTBREAK_\.)(\.{1,})/$1 $2/g;
-   print "Point Q: $s\n" if $local_verbose;
-   foreach $_ ((1 .. 2)) {
-      $s =~ s/(\s(?:[-a-z0-9()']|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF])*)(\.{2,})((?:[-a-z0-9()?!:\/']|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF])*\s|(?:[-a-z0-9()'\/]|\xC3[\x80-\x96\x98-\xB6\xB8-\xBF]|[\xC4-\xC9\xCE-\xD3][\x80-\xBF]|\xE0[\xA4-\xA5][\x80-\xBF]|\xE0[\xB6-\xB7][\x80-\xBF])+\.\s)/$1 $2 $3/gi;
-   }
-   $s =~ s/0s\b/0 s/g;
-   $s =~ s/([0-9])(\x04)/$1 $2/g;
-   $s =~ s/ +/ /g;
-   print "Point R: $s\n" if $local_verbose;
-
-   if ($bio_p) {
-      foreach $_ ((1 .. 2)) {
-         $s =~ s/([a-z]) \@(-|\xE2\x80[\x93\x94])\@ (\d+(?:$alpha)?\d*\+?)([- \/])/$1$2$3$4/ig;
-         $s =~ s/([a-z]) \@(-|\xE2\x80[\x93\x94])\@ ((?:alpha|beta|kappa)\d+)([- \/])/$1$2$3$4/ig;
-         $s =~ s/([a-z]) \@(-|\xE2\x80[\x93\x94])\@ ((?:a|b|h|k)\d)([- \/])/$1$2$3$4/ig;
-         $s =~ s/([a-z0-9]) \@(-|\xE2\x80[\x93\x94])\@ ([a-z])([- \/])/$1$2$3$4/ig;
-         $s =~ s/([- \/])(\d*[a-z]) \@(-|\xE2\x80[\x93\x94])\@ ([a-z0-9])/$1$2$3$4/ig;
-      }
-      # mutation indicators such -/- etc.
-      $s =~ s/(\?\/) +(\?)/$1$2/g;
-      $s =~ s/([^ ?])((?:wt\/|onc\/)?(?:[-+]|\?+|\xE2\x80[\x93\x94])\/(?:[-+]|\?+|\xE2\x80[\x93\x94]))/$1 $2/g;
-      $s =~ s/((?:[-+]|\xE2\x80[\x93\x94])\/(?:[-+]|\xE2\x80[\x93\x94]))(\S)/$1 $2/g;
-
-      # Erk1/2
-      $rest = $s;
-      $s = "";
-      while (($pre, $stem, $slashed_number_s, $post) = ($rest =~ /^(.*?[^-_a-z0-9])([a-z][-_a-z]*)(\d+(?:(?: \@)?\/(?:\@ )?(?:\d+))+)([^-+a-z0-9].*|)$/i)) {
-	 if ((($pre =~ /\x04[^\x05]*$/) && ($post =~ /^[^\x04]*\x05/))
-	  || ($stem =~ /^(mid|pre|post|sub|to)$/i)) {
-	    $s .= "$pre$stem$slashed_number_s";
-	 } else {
-	    $s .= $pre;
-	    my @slashed_numbers = split(/(?: \@)?\/(?:\@ )?/, $slashed_number_s);
-	    foreach $i ((0 .. $#slashed_numbers)) {
-	       my $number = $slashed_numbers[$i];
-	       $s .= "$stem$number";
-	       $s .= " @\/@ " unless $i == $#slashed_numbers;
-	    }
-	 }
-	 $rest = $post;
-      }
-      $s .= $rest;
-
-      # Erk-1/-2
-      while (($pre, $stem, $dash1, $number1, $dash2, $number2, $post) = ($s =~ /^(.*[^-_a-z0-9])([a-z][-_a-z]*)(?: \@)?(-|\xE2\x80[\x93\x94])(?:\@ )?(\d+)(?: \@)?\/(?:\@ )?(?:\@ )?(-|\xE2\x80[\x93\x94])(?:\@ )?(\d+)([^-+a-z0-9].*|)$/i)) {
-	 $s = "$pre$stem$dash1$number1 \@\/\@ $stem$dash2$number2$post";
-      }
-      $rest = $s;
-      $s = "";
-      # IFN-a/b  (Slac2-a/b/c)
-      while (($pre, $stem, $dash, $slashed_letter_s, $post) = ($rest =~ /^(.*[^-_a-z0-9])([a-z][-_a-z0-9]*)(-|\xE2\x80[\x93\x94])([a-z](?:(?: \@)?\/(?:\@ )?(?:[a-z]))+)([^-+a-z0-9].*|)$/i)) {
-	 if (($pre =~ /\x04[^\x05]*$/) && ($post =~ /^[^\x04]*\x05/)) {
-	    $s .= "$pre$stem$dash1$number1$dash2$number2";
-	 } else {
-	    $s .= $pre;
-	    my @slashed_letters = split(/(?: \@)?\/(?:\@ )?/, $slashed_letter_s);
-	    foreach $i ((0 .. $#slashed_letters)) {
-	       my $letter = $slashed_letters[$i];
-	       $s .= "$stem$dash$letter";
-	       $s .= " @\/@ " unless $i == $#slashed_letters;
-	    }
-	 }
-	 $rest = $post;
-      }
-      $s .= $rest;
-
-      # SPLIT X-induced
-      my $rest = $s;
-      my $new_s = "";
-      while (($pre, $dash, $right, $post) = ($rest =~ /^(.*?)(-|\xE2\x80[\x93\x94])([a-z]+)( .*|)$/i)) {
-	 $new_s .= $pre;
-	 if (($right eq "I") && ($pre =~ / [a-zA-Z][a-z]*$/)) {
-	    # compatriots-I have a dream
-	    $new_s .= " \@" . $dash . "\@ ";
-	 } elsif ($ht{LC_SPLIT_DASH_X}->{($caller->normalize_punctuation(lc $right))}) {
-	    $new_s .= " \@" . $dash . "\@ ";
-	 } else {
-	    $new_s .= $dash;
-	 }
-	 $new_s .= $right;
-	 $rest = $post;
-      }
-      $new_s .= $rest;
-      $s = $new_s;
-
-      # SPLIT ubiquinated-X
-      $rest = $s;
-      $new_s = "";
-      while (($pre, $left, $dash, $post) = ($rest =~ /^(.*? |)([a-z0-9]+|'s)(-|\xE2\x80[\x93\x94])([a-z0-9].*)$/i)) {
-	 $new_s .= "$pre$left";
-	 if ($ht{LC_SPLIT_X_DASH}->{($caller->normalize_punctuation(lc $left))}) {
-	    $new_s .= " \@" . $dash . "\@ ";
-	 } else {
-	    $new_s .= $dash;
-	 }
-	 $rest = $post;
-      }
-      $new_s .= $rest;
-      $s = $new_s;
-
-      # SPLIT low-frequency
-      $rest = $s;
-      $new_s = "";
-      if (($pre, $left, $dash, $right, $post) = ($rest =~ /^(.*?[- ]|)([a-z]+)([-\/]|\xE2\x80[\x93\x94])([a-z]+)([- ].*|)$/i)) {
-      }
-      while (($pre, $left, $dash, $right, $post) = ($rest =~ /^(.*?[-\/ ]|)([a-z]+)((?: \@)?(?:[-\/]|\xE2\x80[\x93\x94])(?:\@ )?)([a-z]+)([-\/ ].*|)$/i)) {
-	 $x = $caller->normalize_punctuation(lc ($left . $dash . $right));
-	 if ($ht{LC_SPLIT}->{($caller->normalize_punctuation(lc ($left . $dash . $right)))}) {
-	    $pre =~ s/([-\/])$/ \@$1\@ /;
-	    $post =~ s/^([-\/])/ \@$1\@ /;
-	    $dash = $caller->normalize_punctuation($dash);
-	    $new_s .= "$pre$left";
-	    $new_s .= " \@" . $dash . "\@ ";
-	    $new_s .= $right;
-	    $rest = $post;
-	 } elsif ($pre =~ /[-\/]$/) {
-	    $new_s .= $pre;
-	    $rest = "$left$dash$right$post";
-	 } else {
-	    $new_s .= "$pre$left";
-	    $rest = "$dash$right$post";
-	 }
-      }
-      $new_s .= $rest;
-      $s = $new_s;
-
-      # DO-NOT-SPLIT X-ras
-      $rest = $s;
-      $new_s = "";
-      while (($pre, $dash, $right, $post) = ($rest =~ /^(.*?) \@(-|\xE2\x80[\x93\x94])\@ ([a-z0-9]+)( .*|)$/i)) {
-	 $new_s .= $pre;
-	 if ($ht{LC_DO_NOT_SPLIT_DASH_X}->{($caller->normalize_punctuation(lc $right))}) {
-	    $new_s .= $dash;
-	 } else {
-	    $new_s .= " \@" . $dash . "\@ ";
-	 }
-	 $new_s .= $right;
-	 $rest = $post;
-      }
-      $new_s .= $rest;
-      $s = $new_s;
-
-      # DO-NOT-SPLIT Caco-X
-      $rest = $s;
-      $new_s = "";
-      while (($pre, $left, $dash, $post) = ($rest =~ /^(.*? |)([a-z0-9]+) \@([-\/]|\xE2\x80[\x93\x94]])\@ ([a-z0-9].*)$/i)) {
-	 $new_s .= "$pre$left";
-	 if ($ht{LC_DO_NOT_SPLIT_X_DASH}->{($caller->normalize_punctuation(lc $left))}) {
-	    $new_s .= $dash;
-	 } else {
-	    $new_s .= " \@" . $dash . "\@ ";
-	 }
-	 $rest = $post;
-      }
-      $new_s .= $rest;
-      $s = $new_s;
-
-      # DO-NOT-SPLIT down-modulate (2 elements)
-      $rest = $s;
-      $new_s = "";
-      while (($pre, $left, $dash, $right, $post) = ($rest =~ /^(.*? |)([a-z0-9]+) \@([-\/]|\xE2\x80[\x93\x94]])\@ ([a-z0-9]+)( .*|)$/i)) {
-	 $new_s .= "$pre$left";
-	 if ($ht{LC_DO_NOT_SPLIT}->{($caller->normalize_punctuation(lc ($left . $dash . $right)))}) {
-	    $new_s .= $dash;
-	 } else {
-	    $new_s .= " \@" . $dash . "\@ ";
-	 }
-	 $new_s .= $right;
-	 $rest = $post;
-      }
-      $new_s .= $rest;
-      $s = $new_s;
-
-      # DO-NOT-SPLIT 14-3-3 (3 elements)
-      $rest = $s;
-      $new_s = "";
-      while (($pre, $left, $dash_group1, $dash1, $middle, $dash_group2, $dash2, $right, $post) = ($rest =~ /^(.*? |)([a-z0-9]+)((?: \@)?([-\/]|\xE2\x80[\x93\x94]])(?:\@ )?)([a-z0-9]+)((?: \@)?([-\/]|\xE2\x80[\x93\x94]])(?:\@ )?)([a-z0-9]+)( .*|)$/i)) {
-	 $new_s .= "$pre$left";
-	 if ($ht{LC_DO_NOT_SPLIT}->{($caller->normalize_punctuation(lc ($left . $dash1 . $middle . $dash2 . $right)))}) {
-	    $new_s .= $dash1;
-	 } else {
-	    $new_s .= $dash_group1;
-	 }
-	 $new_s .= $middle;
-	 if ($ht{LC_DO_NOT_SPLIT}->{($caller->normalize_punctuation(lc ($left . $dash1 . $middle . $dash2 . $right)))}) {
-	    $new_s .= $dash2;
-	 } else {
-	    $new_s .= $dash_group2;
-	 }
-	 $new_s .= $right;
-	 $rest = $post;
-      }
-      $new_s .= $rest;
-      $s = $new_s;
-
-      $s =~ s/ +/ /g;
-   }
-   print "Point S: $s\n" if $local_verbose;
-   
-   $s =~ s/_DONTBREAK_//g;
-   $s =~ s/( )(ark|ill|mass|miss|wash|GA|LA|MO|OP|PA|VA|VT)(\.)( )/$1$2 $3$4/g;
-   print "Point T: $s\n" if $local_verbose;
-   $s = $caller->restore_urls_x045_guarded_string($s);
-   $s = $caller->restore_xml_tags_x0123_guarded_string($s);
-   print "Point U: $s\n" if $local_verbose;
-   $s =~ s/(https?|ftp)\s*(:)\s*(\/\/)/$1$2$3/gi;
-   $s =~ s/\b(mailto)\s*(:)\s*([a-z])/$1$2$3/gi;
-   $s =~ s/(\d)\s*(:)\s*([0-5]\d[^0-9])/$1$2$3/gi;
-   print "Point V: $s\n" if $local_verbose;
-   $s =~ s/(5\xE2\x80\xB2-[ACGT]+)\s*(-|\xE2\x80[\x93\x94])\s*(3\xE2\x80\xB2)/$1$2$3/g; # repair broken DNA sequence
-   $s =~ s/ (etc) \. / $1. /g; # repair most egrareous separations
-   print "Point W: $s\n" if $local_verbose;
-   $s = $caller->repair_separated_periods($s);
-   print "Point X: $s\n" if $local_verbose;
-   $s =~ s/^\s+//;
-   $s =~ s/\s+$//;
-   $s = "$pre$s$post" if defined($pre) && defined($post);
-   $s =~ s/ +/ /g;
-   print "Point Y: $s\n" if $local_verbose;
-
-   return $s;
-}
-
-sub tokenize_plus_for_noisy_text {
-   local($caller, $s, *ht, $control) = @_;
-
-   $control = "" unless defined($control);
-   my $pre;
-   my $code;
-   my $post;
-   $s = " $core " if ($pre,$core,$post) = ($s =~ /^(\s*)(.*?)(\s*)$/i);
-   foreach $i ((1 .. 2)) {
-      $s =~ s/ ([A-Z][a-z]+'?[a-z]+)(-) / $1 $2 /gi; # Example: Beijing-
-      $s =~ s/ (\d+(?:\.\d+)?)(-|:-|:|_|\.|'|;)([A-Z][a-z]+'?[a-z]+|[A-Z]{3,}) / $1 $2 $3 /gi; # Example: 3:-Maxkamado
-      $s =~ s/ (\d+(?:\.\d+)?)(')([A-Za-z]{3,}) / $1 $2 $3 /gi; # Example: 42'daqiiqo
-      $s =~ s/ (-|:-|:|_|\.)([A-Z][a-z]+'?[a-z]+|[A-Z]{3,}) / $1 $2 /gi; # Example: -Xassan
-      $s =~ s/ ((?:[A-Z]\.[A-Z]|[A-Z]|Amb|Col|Dr|Eng|Gen|Inj|Lt|Maj|Md|Miss|Mr|Mrs|Ms|Pres|Prof|Sen)\.)([A-Z][a-z]+|[A-Z]{2,}) / $1 $2 /gi; # Example: Dr.Smith
-      $s =~ s/ (\d+)(,)([a-z]{3,}) / $1 $2 $3 /gi; # Example: 24,October
-      $s =~ s/ (%)(\d+(?:\.\d+)?) / $1 $2 /gi; # Example: %0.6
-      $s =~ s/ ([A-Za-z][a-z]{3,}\d*)([.,\/]|:\()([A-Za-z][a-z]{3,}|[A-Z]{3,}) / $1 $2 $3 /gi; # Example: Windows8,falanqeeyaal
-      $s =~ s/ ([A-Za-z]{3,}\d*?|[A-Za-z]+'[A-Za-z]+)([,\/]|:\()([A-Za-z]{3,}|[A-Za-z]+'[A-Za-z]+) / $1 $2 $3 /gi; # Example: GAROOWE:(SHL
-      $s =~ s/ (\d[0-9.,]*\d)(;)([a-z]+) / $1 $2 $3 /gi; # Example: 2.1.2014;Waraka
-   }
-   $s =~ s/^\s+//;
-   $s =~ s/\s+$//;
-   $s = "$pre$s$post" if defined($pre) && defined($post);
-   return $s;
-}
-
-# preparation for sub repair_separated_periods:
-
-my $abbrev_s = "etc.|e.g.|i.e.|U.K.|S.p.A.|A.F.P.";
-my @abbrevs = split(/\|/, $abbrev_s);
-my @exp_abbrevs = ();
-foreach $abbrev (@abbrevs) {
-   if (($core,$period) = ($abbrev =~ /^(.*?)(\.|)$/)) {
-      $core =~ s/\./\\s*\\.\\s*/g;
-      $abbrev = $core;
-      $abbrev .= "\\b" if $abbrev =~ /[a-z]$/i; # don't split etcetera -> etc. etera
-      $abbrev .= "(?:\\s*\\.|)" if $period;
-      push(@exp_abbrevs, $abbrev);
-   }
-}
-my $exp_abbrev_s = join("|", @exp_abbrevs);
-
-sub repair_separated_periods {
-   local($caller,$s) = @_;
-
-   # separated or missing period
-   my $result = "";
-   while (($pre, $abbrev, $post) = ($s =~ /^(.*? |)($exp_abbrev_s)(.*)$/)) {
-      $abbrev =~ s/ //g;
-      $abbrev .= "." unless $abbrev =~ /\.$/;
-      $result .= "$pre$abbrev ";
-      $s = $post;
-   }
-   $result .= $s;
-   $result =~ s/ +/ /g;
-   return $result;
-}
-
-# provided by Alex Fraser
-sub fix_tokenize {
-   local($caller,$s) = @_;
-
-   ## change "2:15" to "2 @:@ 15"
-   $s =~ s/(\d)\:(\d)/$1 \@:\@ $2/g;
-
-   ## strip leading zeros from numbers
-   $s =~ s/(^|\s)0+(\d)/$1$2/g;
-
-   ## fix rule typo
-   $s =~ s/associatedpress/associated press/g;
-
-   ## fix _ entities
-   $s =~ s/hong_kong/hong kong/g;
-   $s =~ s/united_states/united states/g;
-
-   return $s;
-}
-
-sub de_mt_tokenize {
-   local($caller,$s) = @_;
-
-   $s =~ s/\s+\@([-:\/])/$1/g;
-   $s =~ s/([-:\/])\@\s+/$1/g;
-   $s =~ s/\s+\/\s+/\//g;
-   return $s;
-}
-
-sub surface_forms {
-   local($caller,$pe,$modp) = @_;
-
-   $sem = $pe->sem;
-   $surf = $pe->surf;
-   $synt = $pe->synt;
-   $value = $pe->value;
-   $gloss = $pe->gloss;
-#  $util->log("surface_forms surf:$surf sem:$sem gloss:$gloss value:$value", $logfile);
-   if ($sem eq "integer") {
-      return ($gloss) if ($gloss =~ /several/) && !($value =~ /\S/);
-      print STDERR "Warning: $value not an integer\n" unless $value =~ /^\d+(e\+\d+)?$/;
-      if ($pe->get("reliable") =~ /sequence of digits/) {
-	 $english = $value;
-	 $english = "$prefix $english" if $prefix = $pe->get("prefix");
-	 @result = ($english);
-      } else {
-         @result = $caller->q_number_surface_forms($pe);
-      }
-   } elsif ($sem eq "decimal number") {
-      @result = $caller->q_number_surface_forms($pe);
-   } elsif ($sem =~ /(integer|decimal number) range/) {
-      @result = $caller->number_range_surface_forms($pe);
-   } elsif ($sem eq "ordinal") {
-      if ($pe->get("definite")) {
-	 $exclude_adverbials_p = 1;
-      } elsif (defined($chinesePM) && ($hao = $chinesePM->e2c("hao-day"))
-                              && ($gc = $chinesePM->e2c("generic counter"))) {
-         $exclude_adverbials_p = ($surf =~ /($hao|$gc)$/);
-      } else {
-	 $exclude_adverbials_p = 1;
-      }
-      @result = $caller->ordinal_surface_forms($pe->get("ordvalue") || $pe->value,0,$exclude_adverbials_p, $pe);
-   } elsif ($sem eq "fraction") {
-      @result = $caller->fraction_surface_forms($pe,$modp);
-   } elsif ($sem =~ /monetary quantity/) {
-      @result = $caller->currency_surface_forms($pe);
-   } elsif ($sem =~ /occurrence quantity/) {
-      @result = $caller->occurrence_surface_forms($pe,$modp);
-   } elsif ($sem =~ /score quantity/) {
-      @result = $caller->score_surface_forms($pe);
-   } elsif ($sem =~ /age quantity/) {
-      @result = $caller->age_surface_forms($pe, $modp);
-   } elsif ($sem =~ /quantity/) {
-      @result = $caller->quantity_surface_forms($pe,$modp);
-   } elsif ($sem eq "percentage") {
-      @result = $caller->percent_surface_forms($pe,$modp);
-   } elsif ($sem eq "percentage range") {
-      if ($gloss =~ /^and /) {
-         @result = ($gloss);
-      } else {
-         @result = ($gloss, "by $gloss", "of $gloss");
-      }
-   } elsif ($sem =~ /^(month of the year|month\+year|year)$/) {
-      if ($synt eq "pp") {
-         @result = ($gloss);
-      } elsif ($gloss =~ /^the (beginning|end) of/) {
-         @result = ($gloss, "at $gloss");
-      } elsif ($gloss =~ /^(last|this|current|next)/) {
-         @result = ($gloss);
-      } else {
-	 # in November; in mid-November
-         @result = ($gloss, "in $gloss");
-      }
-   } elsif ($sem =~ /date(\+year)?$/) {
-      @result = $caller->date_surface_forms($pe,$modp);
-   } elsif ($sem =~ /year range\b.*\b(decade|century)$/) {
-      @result = $caller->decade_century_surface_forms($pe);
-   } elsif ($sem eq "day of the month") {
-      @result = $caller->day_of_the_month_surface_forms($pe);
-   } elsif ($sem =~ /period of the day\+day of the week/) {
-      @result = ($gloss);
-      push(@result, "on $gloss") if $gloss =~ /^the night/;
-   } elsif ($sem =~ /day of the week/) {
-      @result = $caller->day_of_the_week_surface_forms($pe);
-   } elsif ($sem =~ /^(time)$/) {
-      if ($gloss =~ /^at /) {
-         @result = ($gloss);
-      } else {
-         @result = ($gloss, "at $gloss");
-      }
-   } elsif ($sem =~ /^date range$/) {
-      if ($synt eq "pp") {
-         @result = ($gloss);
-      } elsif ($pe->get("between")) {
-	 $b_gloss = "between $gloss";
-	 $b_gloss =~ s/-/ and /;
-         @result = ($b_gloss, $gloss, "from $gloss");
-      } else {
-         @result = ($gloss, "from $gloss");
-      }
-   } elsif ($sem =~ /^date enumeration$/) {
-      if ($synt eq "pp") {
-         @result = ($gloss);
-      } else {
-         @result = ($gloss, "on $gloss");
-      }
-   } elsif ($pe->get("unknown-in-pc")) {
-      @result = ();
-      foreach $unknown_pos_en (split(/;;/, $pe->get("unknown-pos-en-list"))) {
-	 ($engl) = ($unknown_pos_en =~ /^[^:]+:[^:]+:(.*)$/);
-         push(@result, $engl) if defined($engl) && ! $util->member($engl, @result);
-      }
-      @result = ($gloss) unless @result;
-   } elsif (($sem =~ /\b(name|unknown)\b/) && (($en_s = $pe->get("english")) =~ /[a-z]/i)) {
-      @result = split(/\s*\|\s*/, $en_s);
-   } elsif (($sem =~ /^proper\b/) && (($en_s = $pe->get("english")) =~ /[a-z]/i)) {
-      @result = split(/\s*\|\s*/, $en_s);
-   } else {
-      @result = ($gloss);
-   }
-
-   if (($sem =~ /^(date\+year|month\+year|year)$/)
-       && ($year = $pe->get("year"))
-       && ($year =~ /^\d\d$/)
-       && (@extend_years = @{$english_entity_style_ht{"ExtendYears"}})
-       && ($#extend_years == 1)
-       && ($extended_year_start = $extend_years[0])
-       && ($extended_year_end   = $extend_years[1])
-       && ($extended_year_start <= $extended_year_end)
-       && ($extended_year_start + 99 >= $extended_year_end)
-       && ($extended_year_start =~ /^\d\d\d\d$/)
-       && ($extended_year_end   =~ /^\d\d\d\d$/)) {
-      $century1 = substr($extended_year_start, 0, 2);
-      $century2 = substr($extended_year_end, 0, 2);
-      $exp_year1 = "$century1$year";
-      $exp_year2 = "$century2$year";
-      if (($extended_year_start <= $exp_year1) && ($exp_year1 <= $extended_year_end)) {
-	 $exp_year = $exp_year1;
-      } elsif (($extended_year_start <= $exp_year2) && ($exp_year2 <= $extended_year_end)) {
-	 $exp_year = $exp_year2;
-      } else {
-	 $exp_year = "";
-      }
-      if ($exp_year) {
-	 @new_glosses = ();
-	 foreach $old_gloss (@result) {
-	    $new_gloss = $old_gloss;
-	    $new_gloss =~ s/\b$year$/$exp_year/;
-	    push (@new_glosses, $new_gloss) unless $new_gloss eq $old_gloss;
-	 }
-	 push (@result, @new_glosses);
-      }
-   }
-
-   # tokenize as requested
-   @tokenize_list = @{$english_entity_style_ht{"Tokenize"}};
-   $tokenize_p = 1 if $util->member("yes", @tokenize_list) 
-		   || $util->member("all", @tokenize_list);
-   $dont_tokenize_p = 1 if $util->member("no", @tokenize_list) 
-		        || $util->member("all", @tokenize_list);
-   if ($tokenize_p) {
-      @new_result = ();
-      foreach $item (@result) {
- 	 $t_item = $caller->tokenize($item, *dummy_ht);
-	 push(@new_result, $item) if $dont_tokenize_p && ($item ne $t_item);
-	 push(@new_result, $t_item);
-      }
-      @result = @new_result;
-   }
-
-   # case as requested
-   @case_list = @{$english_entity_style_ht{"Case"}};
-   $lower_case_p = $util->member("lower", @case_list) 
-	        || $util->member("all", @case_list);
-   $reg_case_p = $util->member("regular", @case_list) 
-	      || $util->member("all", @case_list);
-   if ($lower_case_p) {
-      @new_result = ();
-      foreach $item (@result) {
-         $l_item = "\L$item";
-	 push(@new_result, $item) if $reg_case_p && ($item ne $l_item);
-	 push(@new_result, $l_item) unless $util->member($l_item, @new_result);
-      }
-      @result = @new_result;
-   }
-   # $value = "n/a" unless $value;
-   # print STDERR "SF surf:$surf sem:$sem gloss:$gloss value:$value  Result(s): " . join("; ", @result) . "\n";
-   return @result; 
-}
-
-sub case_list {
-   return @{$english_entity_style_ht{"Case"}};
-}
-
-sub right_cased_list {
-   local($caller, $word) = @_;
-
-   @case_list = @{$english_entity_style_ht{"Case"}};
-
-   @right_cased_core_list = ();
-   push(@right_cased_core_list, $word)
-      if ($util->member("regular", @case_list) || $util->member("all", @case_list))
-     && ! $util->member($word, @right_cased_core_list);
-   push(@right_cased_core_list, lc $word)
-      if ($util->member("lower", @case_list) || $util->member("all", @case_list))
-     && ! $util->member(lc $word, @right_cased_core_list);
-
-   return @right_cased_core_list;
-}
-
-sub string2surf_forms {
-   local($caller, $text, $lang, $alt_sep) = @_;
-
-   $alt_sep = " | " unless defined($alt_sep);
-   $lang = "zh" unless defined($lang);
-
-   if ($lang eq "zh") {
-      @pes = $chinesePM->parse_entities_in_string($text);
-      $n = $#pes + 1;
-#     print "  $n pes\n";
-      @pes = $chinesePM->select_reliable_entities(@pes);
-      my @res_surf_forms_copy = $caller->reliable_pes2surf_forms($alt_sep, @pes);
-      return @res_surf_forms_copy;
-   } else {
-      return ();
-   }
-}
-
-sub reliable_pe2surf_forms {
-   local($caller, $pe, $parent_reliant_suffices_p) = @_;
-
-   $parent_reliant_suffices_p = 0 unless defined($parent_reliant_suffices_p);
-   if ((defined($r = $pe->get("reliable")) && $r)
-    || ($parent_reliant_suffices_p && ($parent_pe = $pe->get("parent")) && 
-	$parent_pe->get("reliable"))) {
-      @surf_forms = $caller->surface_forms($pe);
-      if ((($pe->sem =~ /quantity( range)?$/) && !($pe->sem =~ /monetary quantity/))
-       || ($util->member($pe->sem, "percentage","fraction"))) {
-	    foreach $mod_form ($caller->surface_forms($pe, 1)) {
-	       push(@surf_forms, $mod_form) unless $util->member($mod_form, @surf_forms);
-	    }
-      }
-      return @surf_forms;
-   }
-   return ();
-}
-
-sub reliable_pe2surf_form {
-   local($caller, $alt_sep, $pe) = @_;
-
-   if (@surf_forms = $caller->reliable_pe2surf_forms($pe)) {
-      return $pe->surf . " == " . join($alt_sep, @surf_forms);
-   } else {
-      return "";
-   }
-}
-
-sub reliable_pes2surf_forms {
-   local($caller, $alt_sep, @pes) = @_;
-
-   my @res_surf_forms = ();
-   foreach $pe (@pes) {
-      if ($new_surf_form = $caller->reliable_pe2surf_form($alt_sep, $pe)) {
-         push(@res_surf_forms, $new_surf_form);
-      }
-   }
-   return @res_surf_forms;
-}
-
-sub string_contains_ascii_letter {
-   local($caller,$string) = @_;
-   return $string =~ /[a-zA-Z]/;
-}
-
-sub string_starts_w_ascii_letter {
-   local($caller,$string) = @_;
-   return $string =~ /^[a-zA-Z]/;
-}
-
-sub en_lex_bin {
-   local($caller, $word) = @_;
-
-   $word =~ s/\s+//g;
-   $word =~ s/[-_'\/]//g;
-   $word =~ tr/A-Z/a-z/;
-   return "digit" if $word =~ /^\d/;
-   return "special" unless $word =~ /^[a-z]/;
-   return substr($word, 0, 1);
-}
-
-sub skeleton_bin {
-   local($caller, $sk_bin_control, $word) = @_;
-
-   $word =~ s/\s+//g;
-   $word =~ s/[-_'\/]//g;
-   $word =~ tr/A-Z/a-z/;
-   return "E" unless $word;
-   if ($sk_bin_control =~ /^v1/i) {
-      return $word if length($word) <= 2;
-      return substr($word, 0, 3) if $word =~ /^(b|f[lnrt]|gr|j[nr]|k|l[nt]|m|n[kmst]|r[knst]|s|t)/;
-      return substr($word, 0, 2);
-   } elsif ($sk_bin_control =~ /d6f$/) {
-      return $word if length($word) <= 6;
-      return substr($word, 0, 6);
-   } elsif ($sk_bin_control =~ /d5f$/) {
-      return $word if length($word) <= 5;
-      return substr($word, 0, 5);
-   } elsif ($sk_bin_control =~ /d4f$/) {
-      return $word if length($word) <= 4;
-      return substr($word, 0, 4);
-   } else {
-      return $word if length($word) <= 4;
-      return substr($word, 0, 5) if $word =~ /^(bnts|brnt|brst|brtk|brtn|brts|frst|frts|klts|kntr|knts|krst|krtn|krts|ksks|kstr|lktr|ntrs|sbrt|skrt|sntr|strn|strt|trns|trts|ts)/;
-      return substr($word, 0, 4);
-   }
-}
-
-sub skeleton_bin_sub_dir {
-   local($caller, $sk_bin_control, $skeleton_bin) = @_;
-
-   $sk_bin_control = "v1" unless defined($sk_bin_control);
-   return "" if $sk_bin_control =~ /^v1/i;
-   if ($sk_bin_control =~ /^2d4d\df$/) {
-      return "SH/SHOR" if (length($skeleton_bin) < 2);
-      return substr($skeleton_bin, 0, 2) . "/" . substr($skeleton_bin, 0, 2) . "SH" if (length($skeleton_bin) < 4);
-      return substr($skeleton_bin, 0, 2) . "/" . substr($skeleton_bin, 0, 4);
-   } elsif ($sk_bin_control =~ /^2d3d\df$/) {
-      return "SH/SHO" if (length($skeleton_bin) < 2);
-      return substr($skeleton_bin, 0, 2) . "/" . substr($skeleton_bin, 0, 2) . "S" if (length($skeleton_bin) < 3);
-      return substr($skeleton_bin, 0, 2) . "/" . substr($skeleton_bin, 0, 3);
-   }
-   $bin3 = "ts";
-   return "SH" if (length($skeleton_bin) < 2) || ($skeleton_bin =~ /^($bin3)$/);
-   return substr($skeleton_bin, 0, 3) if $skeleton_bin =~ /^($bin3)/;
-   return substr($skeleton_bin, 0, 2);
-}
-
-sub en_words_and_counts_matching_skeletons {
-   local($caller, $sk_bin_version, @skeletons) = @_;
-
-   return () unless @skeletons;
-
-   @rem_skeletons = sort @skeletons;
-   $previous_skeleton = "";
-   $current_skeleton = shift @rem_skeletons;
-   @list = ($current_skeleton);
-   @lists = ();
-
-   $current_bin = "";
-   while ($current_skeleton) {
-      unless ($current_skeleton eq $previous_skeleton) {
-         $current_skeleton_bin = $caller->skeleton_bin($sk_bin_version, $current_skeleton);
-	 unless ($current_skeleton_bin eq $current_bin) {
-	    # need to read from new file
-	    close(IN) if $current_bin;
-	    $current_bin = $current_skeleton_bin;
-	    $current_bin_subdir
-	       = $caller->skeleton_bin_sub_dir($sk_bin_version, $current_bin);
-	    if ($current_bin_subdir) {
-	       $en_skeleton_file = File::Spec->catfile($english_resources_skeleton_dir, 
-						       $current_bin_subdir, 
-						       "$current_bin.txt");
-	    } else {
-	       $en_skeleton_file = File::Spec->catfile($english_resources_skeleton_dir, 
-						       "$current_bin.txt");
-	    }
-	    # print STDERR "  Perusing $en_skeleton_file ...\n";
-            if (open(IN, $en_skeleton_file)) {
-	       $en_skeleton_file_exists = 1;
-	    } else {
-	       $en_skeleton_file_exists = 0;
-	       print STDERR "Can't open $en_skeleton_file (Point A)\n";
-	    }
-	 }
-	 $previous_skeleton = $current_skeleton;
-      }
-      $_ = <IN> if $en_skeleton_file_exists;
-      unless ($en_skeleton_file_exists && defined($_)) {
-	 push(@lists, join(' ; ', @list));
-	 if (@rem_skeletons) {
-	    $current_skeleton = shift @rem_skeletons;
-	    @list = ($current_skeleton);
-	 } else {
-	    $current_skeleton = "";
-	 }
-	 next;
-      }
-      ($skeleton) = ($_ =~ /^(\S+)\t/);
-      next unless defined($skeleton);
-      $skeletons_match_p = $caller->skeletons_match_p($skeleton, $current_skeleton);
-      next if ($skeleton lt $current_skeleton) && ! $skeletons_match_p;
-      if ($skeletons_match_p) {
-	 ($token, $count) = ($_ =~ /^\S+\t(\S|\S[-' a-zA-Z]*\S)\t(\d+)\s*$/);
-	 push(@list, "$token : $count") if defined($token) && defined($count);
-      } else {
-	 while ($current_skeleton lt $skeleton) {
-	    push(@lists, join(' ; ', @list));
-	    unless (@rem_skeletons) {
-	       close(IN) if $current_bin;
-	       $current_skeleton = "";
-	       last;
-	    }
-	    $current_skeleton = shift @rem_skeletons;
-	    @list = ($current_skeleton);
-	 }
-	 if ($caller->skeletons_match_p($skeleton, $current_skeleton)) {
-	    ($token, $count) = ($_ =~ /^\S+\t(\S|\S[-' a-zA-Z]*\S)\t(\d+)\s*$/);
-	    push(@list, "$token : $count") if defined($token) && defined($count);
-	 }
-      }
-   }
-   close(IN) if $current_bin;
-   return @lists;
-}
-
-sub skeletons_match_p {
-# one of the skeletons might have been cut off at max
-   local($caller, $skeleton1, $skeleton2, $max) = @_;
-
-   return 1 if $skeleton1 eq $skeleton2;
-
-   $max = 5 unless defined($max);
-   if ((length($skeleton1) > length($skeleton2)) && (length($skeleton2) == $max)) {
-      return ($skeleton1 =~ /^$skeleton2/) ? 1 : 0;
-   } elsif ((length($skeleton2) > length($skeleton1)) && (length($skeleton1) == $max)) {
-      return ($skeleton2 =~ /^$skeleton1/) ? 1 : 0;
-   } else {
-      return 0;
-   }
-}
-
-sub token_weird_or_too_long {
-   local($caller, *WARNING_FH, $token) = @_;
-
-   $lc_token = lc $token;
-   $norm_token = $lc_token;
-   $norm_token =~ s/[-' ,]//g;
-   $snippet4_5 = "";
-   $snippet4_5 = substr($norm_token, 4, 2) if length($norm_token) >= 10;
-   $snippet4_6 = "";
-   $snippet4_6 = substr($norm_token, 4, 3) if length($norm_token) >= 10;
-   if (($norm_token =~ /(kkk|vvv|www|xxx|yyy|zzz)/) ||
-       ($norm_token =~ /[acgt]{15,}/) ||                # DNA sequence
-       ($snippet4_5 && ($norm_token =~ /($snippet4_5){5,}/)) ||  # 2-letter repetition
-       ($snippet4_6 && ($norm_token =~ /($snippet4_6){4,}/)) ||  # 3-letter repetition
-       ($norm_token =~ /[bcdfghjklmnpqrstvwxz]{8,}/) || # too many consonants
-       ($token =~ /(DDD)/) ||
-	 (($lc_token =~ /fff/) && ! ($lc_token =~ /schifff/))) {
-      print WARNING_FH "skipping (WEIRD): $_";
-      return 1;
-   }
-   if ((length($norm_token) >= 50) ||
-       ((length($norm_token) >= 28)
-
-	  # typical German compound noun components
-          && ! ($norm_token =~ /entwicklung/)
-          && ! ($norm_token =~ /fabrik/)
-          && ! ($norm_token =~ /finanz/)
-          && ! ($norm_token =~ /forschung/)
-          && ! ($norm_token =~ /geschwindigkeit/)
-          && ! ($norm_token =~ /gesundheit/)
-          && ! ($norm_token =~ /gewohnheit/)
-          && ! ($norm_token =~ /schaft/)
-          && ! ($norm_token =~ /schifffahrt/)
-          && ! ($norm_token =~ /sicherheit/)
-          && ! ($norm_token =~ /vergangen/)
-          && ! ($norm_token =~ /versicherung/)
-          && ! ($norm_token =~ /unternehmen/)
-          && ! ($norm_token =~ /verwaltung/)
-
-	  # Other Germanic languages
-          && ! ($norm_token =~ /aktiebolag/)
-          && ! ($norm_token =~ /aktieselskab/)
-          && ! ($norm_token =~ /ontwikkeling/)
-
-	  # chemical
-          && ! ($norm_token =~ /phetamine/)
-          && ! ($norm_token =~ /ethyl/)
-
-	  # medical
-	  && ! ($norm_token =~ /^pneumonaultramicroscopicsilicovolcanoconios[ei]s$/)
-
-	  # business
-          && ! ($norm_token =~ /PriceWaterhouse/)
-      )) {
-             print WARNING_FH "skipping (TOO LONG): $_";
-             return 1;
-   }
-   return 0;
-}
-
-sub xml_de_accent {
-   local($caller, $string) = @_;
-
-   # for the time being, unlauts are mapped to main vowel (without "e")
-
-   $string =~ s/\&#19[2-7];/A/g;
-   $string =~ s/\&#198;/Ae/g;
-   $string =~ s/\&#199;/C/g;
-   $string =~ s/\&#20[0-3];/E/g;
-   $string =~ s/\&#20[4-7];/I/g;
-   $string =~ s/\&#208;/Dh/g;
-   $string =~ s/\&#209;/N/g;
-   $string =~ s/\&#21[0-4];/O/g;
-   $string =~ s/\&#216;/O/g;
-   $string =~ s/\&#21[7-9];/U/g;
-   $string =~ s/\&#220;/U/g;
-   $string =~ s/\&#221;/Y/g;
-   $string =~ s/\&#222;/Th/g;
-
-   $string =~ s/\&#223;/ss/g;
-   $string =~ s/\&#22[4-9];/a/g;
-   $string =~ s/\&#230;/ae/g;
-   $string =~ s/\&#231;/c/g;
-   $string =~ s/\&#23[2-5];/e/g;
-   $string =~ s/\&#23[6-9];/i/g;
-   $string =~ s/\&#240;/dh/g;
-   $string =~ s/\&#241;/n/g;
-   $string =~ s/\&#24[2-6];/o/g;
-   $string =~ s/\&#248;/o/g;
-   $string =~ s/\&#249;/u/g;
-   $string =~ s/\&#25[0-2];/u/g;
-   $string =~ s/\&#253;/y/g;
-   $string =~ s/\&#254;/th/g;
-   $string =~ s/\&#255;/y/g;
-   $string =~ s/\xE2\x80\x99/'/g;
-
-   return $string;
-}
-
-sub de_accent {
-   local($caller, $string) = @_;
-
-   # for the time being, unlauts are mapped to main vowel (without "e")
-
-   $string =~ s/\xC3[\x80-\x85]/A/g;
-   $string =~ s/\xC3\x86/Ae/g;
-   $string =~ s/\xC3\x87/C/g;
-   $string =~ s/\xC3[\x88-\x8B]/E/g;
-   $string =~ s/\xC3[\x8C-\x8F]/I/g;
-   $string =~ s/\xC3\x90/Dh/g;
-   $string =~ s/\xC3\x91/N/g;
-   $string =~ s/\xC3[\x92-\x96]/O/g;
-   $string =~ s/\xC3\x98/O/g;
-   $string =~ s/\xC3[\x99-\x9C]/U/g;
-   $string =~ s/\xC3\x9D/Y/g;
-   $string =~ s/\xC3\x9E/Th/g;
-
-   $string =~ s/\xC3\x9F/ss/g;
-   $string =~ s/\xC3[\xA0-\xA5]/a/g;
-   $string =~ s/\xC3\xA6/ae/g;
-   $string =~ s/\xC3\xA7/c/g;
-   $string =~ s/\xC3[\xA8-\xAB]/e/g;
-   $string =~ s/\xC3[\xAC-\xAF]/i/g;
-   $string =~ s/\xC3\xB0/dh/g;
-   $string =~ s/\xC3\xB1/n/g;
-   $string =~ s/\xC3[\xB2-\xB6]/o/g;
-   $string =~ s/\xC3\xB8/o/g;
-   $string =~ s/\xC3[\xB9-\xBC]/u/g;
-   $string =~ s/\xC3\xBD/y/g;
-   $string =~ s/\xC3\xBE/th/g;
-   $string =~ s/\xC3\xBF/y/g;
-   $string =~ s/\xE2\x80\x99/'/g;
-
-   return $string;
-}
-
-sub common_non_name_cap_p {
-   local($caller, $word) = @_;
-   return defined($english_ht{(lc $word)}->{COMMON_NON_NAME_CAP});
-}
-
-sub language {
-   return "English";
-}
-
-sub language_id {
-   return "en";
-}
-
-sub parse_entities_in_string {
-   local($caller, $string) = @_;
-
-   $ParseEntry->set_current_lang("en");
-   @pes = $ParseEntry->init_ParseEntry_list($string);
-   @pes = $caller->lexical_heuristic(@pes);
-   @pes = $caller->base_number_heuristic(@pes);
-
-   return @pes;
-}
-
-sub lexical_heuristic {
-   local($caller, @pes) = @_;
-
-   $i = 0;
-   while ($i <= $#pes) {
-      $pe = $pes[$i];
-      if ($pe->undefined("synt")) {
-	 if ($pe->surf =~ /^\d+(,\d\d\d)*\.\d+/) {
-	    $pe->set("synt", "cardinal");
-	    $pe->set("sem", "decimal number");
-	    $value = $pe->surf;
-	    $value =~ s/,//g;
-	    $pe->set("value", $value);
-	 } elsif ($pe->surf =~ /^\d+(,\d\d\d)*$/) {
-	    $pe->set("synt", "cardinal");
-	    $pe->set("sem", "integer");
-	    $value = $pe->surf;
-	    $value =~ s/,//g;
-	    $pe->set("value", $value);
-	 } elsif ($pe->surf =~ /^([-",\.;\s:()\/%]|\@[-:\/]\@|[-:\/]\@|\@[-:\/])$/) {
-	    $pe->set("gloss", $pe->surf);
-	    $pe->set("synt", "punctuation");
-         } else {
-	    ($length,$english) = $caller->find_max_lex_match($i,3,@pes);
-	    if ($length) {
-	       if ($length > 1) {
-		  @slot_value_list = ();
-	          @children = splice(@pes,$i,$length);
-		  @roles = $util->list_with_same_elem($length,"lex");
-		  $pe = $ParseEntry->newParent(*slot_value_list,*children,*roles);
-		  $pe->set("surf",$english);
-		  $pe->set("eot",1) if $pe->eot_p;
-		  splice(@pes,$i,0,$pe);
-	       } else {
-		  $pe = $pes[$i];
-	       }
-	       $annot_s = $english_annotation_ht{$english};
-	       $annot_s =~ s/^\s*:+//;
-	       $annot_s =~ s/^\s+//;
-	       $annot_s =~ s/\s+$//;
-	       $annot_s =~ s/#.*$//;
-               foreach $annot (split('::', $annot_s)) {
-                  ($slot, $value) = ($annot =~ /^([^:]+):(.*)$/);
-		  if (defined($slot) && defined($value)) {
-		     $pe->set($slot, $value);
-		  }
-		  $pe->set("sem", "integer") if ($slot eq "synt") && ($value eq "cardinal");
-	       } 
-	       $pe->set("ord-value", $ord_value)
-	          if $ord_value = $english_annotation_ht{"_EN_SYNT_"}->{(lc $english)}->{"ordinal"}->{"value"};
-	       $pe->set("card-value", $card_value)
-	          if $card_value = $english_annotation_ht{"_EN_SYNT_"}->{(lc $english)}->{"cardinal"}->{"value"};
-	    }
-	 }
-      }
-      $i++;
-   }
-   return @pes;
-}
-
-# builds numbers, incl. integers, decimal numbers, fractions, percentages, ordinals
-sub base_number_heuristic {
-   local($caller, @pes) = @_;
-
-   $i = 0;
-   # $ParseEntry->print_pes("start base_number_heuristic",$i,@pes);
-   while ($i <= $#pes) {
-      # forty-five
-      ($head_pe, @pes) =
-	 $ParseEntry->build_parse_entry("composite number plus","",$i,*pes,
-	   '        :head :($pe->sem eq "integer") && ($pe->value =~ /^[1-9]0$/)',
-           'optional:dummy:$pe->surf eq "\@-\@"',
-	   '        :mod  :($pe->sem eq "integer") && ($pe->value =~ /^[1-9]$/)');
-      if ($head_pe) { # match succeeded
-	 $value1 = $head_pe->childValue("head");
-	 $value2 = $head_pe->childValue("mod");
-	 $head_pe->set("value", $value1 + $value2);
-      }
-      # six billion
-      ($head_pe, @pes) =
-	 $ParseEntry->build_parse_entry("composite number 1000","",$i,*pes,
-	   '  :mod :(($value1 = $pe->value) =~ /^\d+(.\d+)?$/) && ($value1 < 1000)',
-	   '  :head:($value2 = $pe->value) =~ /^1(000)+$/');
-      if ($head_pe) { # match succeeded
-	 $value1 = $head_pe->childValue("mod");
-	 $value2 = $head_pe->childValue("head");
-	 $head_pe->set("value", $value1 * $value2);
-      }
-      # twenty-second
-      ($head_pe, @pes) =
-	 $ParseEntry->build_parse_entry("composite ordinal","",$i,*pes,
-	   '        :mod  :($pe->sem eq "integer") && ($pe->value =~ /^[1-9]0$/)',
-	   'optional:dummy:$pe->surf eq "\@-\@"',
-	   '        :head :$pe->get("ord-value") =~ /^[1-9]$/');
-      if ($head_pe) { # match succeeded
-	 $value1 = $head_pe->childSlot("head", "ord-value");
-	 $value2 = $head_pe->childValue("mod");
-	 $head_pe->set("value", $value1 + $value2);
-      }
-      $i++;
-   }
- 
-   return @pes;
-}
-
-sub find_max_lex_match {
-   local($caller,$start,$maxlength,@pes) = @_;
-
-   while ($maxlength > 0) {
-      if (($english = $util->pes_subseq_surf($start,$maxlength,"en",@pes))
-       && defined($english_annotation_ht{$english})
-       && ($english =~ /\S/)) {
-	 return ($maxlength, $english);
-      } else {
-	 $maxlength--;
-      }
-   }
-   return (0,"");
-}
-
-sub select_reliable_entities {
-   local($caller, @pes) = @_;
-
-   foreach $i (0 .. $#pes) {
-      $pe = $pes[$i];
-      $surf = $pe->surf;
-      
-      $pe->set("reliable",1);
-   }
-   return @pes;
-}
-
-sub negatives_p {
-   # (cool <-> uncool), (improper <-> proper), ...
-   local($caller, $s1, $s2) = @_;
-
-   my $g_s1 = $util->regex_guard($s1);
-   my $g_s2 = $util->regex_guard($s2);
-   return 1 if $s1 =~ /^[iu]n$g_s2$/;
-   return 1 if $s1 =~ /^il$g_s2$/ && ($s2 =~ /^l/);
-   return 1 if $s1 =~ /^im$g_s2$/ && ($s2 =~ /^[mp]/);
-
-   return 1 if $s2 =~ /^[iu]n$g_s1$/;
-   return 1 if $s2 =~ /^il$g_s1$/ && ($s1 =~ /^l/);
-   return 1 if $s2 =~ /^im$g_s1$/ && ($s1 =~ /^[mp]/);
-
-   return 0;
-}
-
-sub present_participle_p {
-   local($caller, $pe) = @_;
-
-   my $aux_pe = $pe->child("aux");
-   return $caller->present_participle_p($aux_pe) if $aux_pe;
-   my $head_pe = $pe->child("head");
-   return $caller->present_participle_p($head_pe) if $head_pe;
-   return ($pe->synt =~ /^VBG/);
-}
-
-
-%engl_value_ht = (
-   "monday" => 1,
-   "tuesday" => 2,
-   "wednesday" => 3,
-   "thursday" => 4,
-   "friday" => 5,
-   "saturday" => 6,
-   "sunday" => 7,
-
-   "january" => 1,
-   "february" => 2,
-   "march" => 3,
-   "april" => 4,
-   "may" => 5,
-   "june" => 6,
-   "july" => 7,
-   "august" => 8,
-   "september" => 9,
-   "october" => 10,
-   "november" => 11,
-   "december" => 12,
-
-   "spring" => 1,
-   "summer" => 2,
-   "fall" => 3,
-   "autumn" => 3,
-   "winter" => 4,
-
-   "morning" => 1,
-   "noon" => 2,
-   "afternoon" => 3,
-   "evening" => 4,
-   "night" => 5,
-
-   "picosecond" => 1,
-   "nanosecond" => 2,
-   "microsecond" => 3,
-   "millisecond" => 4,
-   "second" => 5,
-   "minute" => 6,
-   "hour" => 7,
-   "day" => 8,
-   "week" => 9,
-   "fortnight" => 10,
-   "month" => 11,
-   "year" => 12,
-   "decade" => 13,
-   "century" => 14,
-   "millennium" => 15,
-
-   "nanometer" => 2,
-   "micrometer" => 3,
-   "millimeter" => 4,
-   "centimeter" => 5,
-   "decimeter" => 6,
-   "meter" => 7,
-   "kilometer" => 8,
-   "inch" => 11,
-   "foot" => 12,
-   "yard" => 13,
-   "mile" => 14,
-   "lightyear" => 20,
-
-   "microgram" => 2,
-   "milligram" => 3,
-   "gram" => 4,
-   "kilogram" => 5,
-   "ton" => 6,
-   "ounce" => 14,
-);
-
-sub engl_order_value {
-   local($this, $s) = @_;
-
-   return $value = $engl_value_ht{(lc $s)} || 0;
-}
-
-1;
-
diff --git a/spaces/allknowingroger/Image-Models-Test19/app.py b/spaces/allknowingroger/Image-Models-Test19/app.py
deleted file mode 100644
index 64b453b5368be8e408b76c8a3ad96edce96b6487..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test19/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "NancyAthghara23/red-panda-rpd",
-    "somendas17/my-pet-cat-meow",
-    "Abhishek1609/my-pet-dog-sbs",
-    "FFusion/FFusion-BaSE",
-    "badmonk/ruxri",
-    "digiplay/AniRealityMix_v1",
-    "digiplay/ya3p_VAE",
-    "digiplay/DreamShaper_7",
-    "digiplay/ya3_VAE",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
-    try:
-        model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
-    except Exception as error:
-        def the_fn(txt):
-            return None
-        model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
-    model_idx+=1
-
-
-def send_it_idx(idx):
-    def send_it_fn(prompt):
-        output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
-        return output
-    return send_it_fn
-
-def get_prompts(prompt_text):
-    return prompt_text
-
-def clear_it(val):
-    if int(val) != 0:
-        val = 0
-    else:
-        val = 0
-        pass
-    return val
-
-def all_task_end(cnt,t_stamp):
-    to = t_stamp + 60
-    et = time.time()
-    if et > to and t_stamp != 0:
-        d = gr.update(value=0)
-        tog = gr.update(value=1)
-        #print(f'to: {to}  et: {et}')
-    else:
-        if cnt != 0:
-            d = gr.update(value=et)
-        else:
-            d = gr.update(value=0)
-        tog = gr.update(value=0)
-        #print (f'passing:  to: {to}  et: {et}')
-        pass
-    return d, tog
-
-def all_task_start():
-    print("\n\n\n\n\n\n\n")
-    t = time.gmtime()
-    t_stamp = time.time()
-    current_time = time.strftime("%H:%M:%S", t)
-    return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
-    nn = len(models)
-    return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
-    with gr.Column(scale=12):
-        # with gr.Row():
-        #     gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
-        with gr.Row():
-            with gr.Row(scale=6):
-                primary_prompt=gr.Textbox(label="Prompt", value="")
-                # real_prompt=gr.Textbox(label="Real prompt")
-            with gr.Row(scale=6):
-                # improve_prompts_btn=gr.Button("Improve")
-                with gr.Row():
-                    run=gr.Button("Run",variant="primary")
-                    clear_btn=gr.Button("Clear")
-        with gr.Row():
-            sd_outputs = {}
-            model_idx = 1
-            for model_path in models:
-                with gr.Column(scale=3, min_width=320):
-                    with gr.Box():
-                        sd_outputs[model_idx] = gr.Image(label=model_path)
-                    pass
-                model_idx += 1
-                pass
-            pass
-
-        with gr.Row(visible=False):
-            start_box=gr.Number(interactive=False)
-            end_box=gr.Number(interactive=False)
-            tog_box=gr.Textbox(value=0,interactive=False)
-
-        start_box.change(
-            all_task_end,
-            [start_box, end_box],
-            [start_box, tog_box],
-            every=1,
-            show_progress=False)
-
-        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
-        run.click(all_task_start, None, [start_box, end_box, tog_box])
-        runs_dict = {}
-        model_idx = 1
-        for model_path in models:
-            runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
-            model_idx += 1
-            pass
-        pass
-
-        # improve_prompts_btn_clicked=improve_prompts_btn.click(
-        #     get_prompts,
-        #     inputs=[primary_prompt],
-        #     outputs=[primary_prompt],
-        #     cancels=list(runs_dict.values()))
-        clear_btn.click(
-            clear_fn,
-            None,
-            [primary_prompt, *list(sd_outputs.values())],
-            cancels=[*list(runs_dict.values())])
-        tog_box.change(
-            clear_it,
-            tog_box,
-            tog_box,
-            cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
- 
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test61/README.md b/spaces/allknowingroger/Image-Models-Test61/README.md
deleted file mode 100644
index 6662c927de7c37fdabdbc1d32bdc86ed7d2555fb..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test61/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Image Models
-emoji: 👀
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
-duplicated_from: allknowingroger/Image-Models-Test60
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/amankishore/sjc/misc.py b/spaces/amankishore/sjc/misc.py
deleted file mode 100644
index d6675b9e984c6cea13b15ef1eb53ca308f4c2464..0000000000000000000000000000000000000000
--- a/spaces/amankishore/sjc/misc.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import numpy as np
-import torch
-
-
-def torch_samps_to_imgs(imgs, uncenter=True):
-    if uncenter:
-        imgs = (imgs + 1) / 2  # [-1, 1] -> [0, 1]
-    imgs = (imgs * 255).clamp(0, 255)
-    imgs = imgs.to(torch.uint8)
-    imgs = imgs.permute(0, 2, 3, 1)
-    imgs = imgs.cpu().numpy()
-    return imgs
-
-
-def imgs_to_torch(imgs):
-    assert imgs.dtype == np.uint8
-    assert len(imgs.shape) == 4 and imgs.shape[-1] == 3, "expect (N, H, W, C)"
-    _, H, W, _ = imgs.shape
-
-    imgs = imgs.transpose(0, 3, 1, 2)
-    imgs = (imgs / 255).astype(np.float32)
-    imgs = (imgs * 2) - 1
-    imgs = torch.as_tensor(imgs)
-    H, W = [_l - (_l % 32) for _l in (H, W)]
-    imgs = torch.nn.functional.interpolate(imgs, (H, W), mode="bilinear")
-    return imgs
-
-
-def test_encode_decode():
-    import imageio
-    from run_img_sampling import ScoreAdapter, SD
-    from vis import _draw
-
-    fname = "~/clean.png"
-    raw = imageio.imread(fname)
-    raw = imgs_to_torch(raw[np.newaxis, ...])
-
-    model: ScoreAdapter = SD().run()
-    raw = raw.to(model.device)
-    zs = model.encode(raw)
-    img = model.decode(zs)
-    img = torch_samps_to_imgs(img)
-    _draw(
-        [imageio.imread(fname), img.squeeze(0)],
-    )
-
-
-def test():
-    test_encode_decode()
-
-
-if __name__ == "__main__":
-    test()
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_stop.c b/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_stop.c
deleted file mode 100644
index 164ad4ee21a2d9a2c722eeee7a5e770254531396..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_stop.c
+++ /dev/null
@@ -1,324 +0,0 @@
-/** @file patest_stop.c
-    @ingroup test_src
-    @brief Test different ways of stopping audio.
-
-    Test the three ways of stopping audio:
-        - calling Pa_StopStream(),
-        - calling Pa_AbortStream(),
-        - and returning a 1 from the callback function.
-
-    A long latency is set up so that you can hear the difference.
-    Then a simple 8 note sequence is repeated twice.
-    The program will print what you should hear.
-
-    @author Phil Burk <philburk@softsynth.com>
-*/
-/*
- * $Id$
- *
- * This program uses the PortAudio Portable Audio Library.
- * For more information see: http://www.portaudio.com
- * Copyright (c) 1999-2000 Ross Bencina and Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-#include <stdio.h>
-#include <math.h>
-#include "portaudio.h"
-
-#define OUTPUT_DEVICE       (Pa_GetDefaultOutputDevice())
-#define SLEEP_DUR           (200)
-#define SAMPLE_RATE         (44100)
-#define FRAMES_PER_BUFFER   (256)
-#define LATENCY_SECONDS     (3.f)
-#define FRAMES_PER_NOTE     (SAMPLE_RATE/2)
-#define MAX_REPEATS         (2)
-#define FUNDAMENTAL         (400.0f / SAMPLE_RATE)
-#define NOTE_0              (FUNDAMENTAL * 1.0f / 1.0f)
-#define NOTE_1              (FUNDAMENTAL * 5.0f / 4.0f)
-#define NOTE_2              (FUNDAMENTAL * 4.0f / 3.0f)
-#define NOTE_3              (FUNDAMENTAL * 3.0f / 2.0f)
-#define NOTE_4              (FUNDAMENTAL * 2.0f / 1.0f)
-#define MODE_FINISH    (0)
-#define MODE_STOP      (1)
-#define MODE_ABORT     (2)
-#ifndef M_PI
-#define M_PI  (3.14159265)
-#endif
-#define TABLE_SIZE   (400)
-
-typedef struct
-{
-    float  waveform[TABLE_SIZE + 1]; /* Add one for guard point for interpolation. */
-    float  phase_increment;
-    float  phase;
-    float *tune;
-    int    notesPerTune;
-    int    frameCounter;
-    int    noteCounter;
-    int    repeatCounter;
-    PaTime outTime;
-    int    stopMode;
-    int    done;
-}
-paTestData;
-
-/************* Prototypes *****************************/
-int TestStopMode( paTestData *data );
-float LookupWaveform( paTestData *data, float phase );
-
-/******************************************************
- * Convert phase between 0.0 and 1.0 to waveform value
- * using linear interpolation.
- */
-float LookupWaveform( paTestData *data, float phase )
-{
-    float fIndex = phase*TABLE_SIZE;
-    int   index = (int) fIndex;
-    float fract = fIndex - index;
-    float lo = data->waveform[index];
-    float hi = data->waveform[index+1];
-    float val = lo + fract*(hi-lo);
-    return val;
-}
-
-/* This routine will be called by the PortAudio engine when audio is needed.
-** It may called at interrupt level on some machines so don't do anything
-** that could mess up the system like calling malloc() or free().
-*/
-static int patestCallback( const void *inputBuffer, void *outputBuffer,
-                            unsigned long framesPerBuffer,
-                            const PaStreamCallbackTimeInfo* timeInfo,
-                            PaStreamCallbackFlags statusFlags,
-                            void *userData )
-{
-    paTestData *data = (paTestData*)userData;
-    float *out = (float*)outputBuffer;
-    float value;
-    unsigned int i = 0;
-    int finished = paContinue;
-
-    (void) inputBuffer;     /* Prevent unused variable warnings. */
-    (void) timeInfo;
-    (void) statusFlags;
-
-
-    /* data->outTime = outTime; */
-
-    if( !data->done )
-    {
-        for( i=0; i<framesPerBuffer; i++ )
-        {
-            /* Are we done with this note? */
-            if( data->frameCounter >= FRAMES_PER_NOTE )
-            {
-                data->noteCounter += 1;
-                data->frameCounter = 0;
-                /* Are we done with this tune? */
-                if( data->noteCounter >= data->notesPerTune )
-                {
-                    data->noteCounter = 0;
-                    data->repeatCounter += 1;
-                    /* Are we totally done? */
-                    if( data->repeatCounter >= MAX_REPEATS )
-                    {
-                        data->done = 1;
-                        if( data->stopMode == MODE_FINISH )
-                        {
-                            finished = paComplete;
-                            break;
-                        }
-                    }
-                }
-                data->phase_increment = data->tune[data->noteCounter];
-            }
-            value = LookupWaveform(data, data->phase);
-            *out++ = value;  /* left */
-            *out++ = value;  /* right */
-            data->phase += data->phase_increment;
-            if( data->phase >= 1.0f ) data->phase -= 1.0f;
-
-            data->frameCounter += 1;
-        }
-    }
-    /* zero remainder of final buffer */
-    for( ; i<framesPerBuffer; i++ )
-    {
-        *out++ = 0; /* left */
-        *out++ = 0; /* right */
-    }
-    return finished;
-}
-/*******************************************************************/
-int main(void);
-int main(void)
-{
-    paTestData data;
-    int i;
-    float simpleTune[] = { NOTE_0, NOTE_1, NOTE_2, NOTE_3, NOTE_4, NOTE_3, NOTE_2, NOTE_1 };
-
-    printf("PortAudio Test: play song and test stopping. ask for %f seconds latency\n", LATENCY_SECONDS );
-    /* initialise sinusoidal wavetable */
-    for( i=0; i<TABLE_SIZE; i++ )
-    {
-        data.waveform[i] = (float) (
-                               (0.2 * sin( ((double)i/(double)TABLE_SIZE) * M_PI * 2. )) +
-                               (0.2 * sin( ((double)(3*i)/(double)TABLE_SIZE) * M_PI * 2. )) +
-                               (0.1 * sin( ((double)(5*i)/(double)TABLE_SIZE) * M_PI * 2. ))
-                           );
-    }
-    data.waveform[TABLE_SIZE] = data.waveform[0]; /* Set guard point. */
-    data.tune = &simpleTune[0];
-    data.notesPerTune = sizeof(simpleTune) / sizeof(float);
-
-    printf("Test MODE_FINISH - callback returns 1.\n");
-    printf("Should hear entire %d note tune repeated twice.\n", data.notesPerTune);
-    data.stopMode = MODE_FINISH;
-    if( TestStopMode( &data ) != paNoError )
-    {
-        printf("Test of MODE_FINISH failed!\n");
-        goto error;
-    }
-
-    printf("Test MODE_STOP - stop when song is done.\n");
-    printf("Should hear entire %d note tune repeated twice.\n", data.notesPerTune);
-    data.stopMode = MODE_STOP;
-    if( TestStopMode( &data ) != paNoError )
-    {
-        printf("Test of MODE_STOP failed!\n");
-        goto error;
-    }
-
-    printf("Test MODE_ABORT - abort immediately.\n");
-    printf("Should hear last repetition cut short by %f seconds.\n", LATENCY_SECONDS);
-    data.stopMode = MODE_ABORT;
-    if( TestStopMode( &data ) != paNoError )
-    {
-        printf("Test of MODE_ABORT failed!\n");
-        goto error;
-    }
-
-    return 0;
-
-error:
-    return 1;
-}
-
-int TestStopMode( paTestData *data )
-{
-    PaStreamParameters outputParameters;
-    PaStream *stream;
-    PaError err;
-
-    data->done = 0;
-    data->phase = 0.0;
-    data->frameCounter = 0;
-    data->noteCounter = 0;
-    data->repeatCounter = 0;
-    data->phase_increment = data->tune[data->noteCounter];
-
-    err = Pa_Initialize();
-    if( err != paNoError ) goto error;
-
-    outputParameters.device = OUTPUT_DEVICE;
-    if (outputParameters.device == paNoDevice) {
-        fprintf(stderr,"Error: No default output device.\n");
-        goto error;
-    }
-    outputParameters.channelCount = 2;          /* stereo output */
-    outputParameters.sampleFormat = paFloat32;  /* 32 bit floating point output */
-    outputParameters.suggestedLatency = LATENCY_SECONDS;
-    outputParameters.hostApiSpecificStreamInfo = NULL;
-
-    err = Pa_OpenStream(
-              &stream,
-              NULL, /* no input */
-              &outputParameters,
-              SAMPLE_RATE,
-              FRAMES_PER_BUFFER,            /* frames per buffer */
-              paClipOff,      /* we won't output out of range samples so don't bother clipping them */
-              patestCallback,
-              data );
-    if( err != paNoError ) goto error;
-
-    err = Pa_StartStream( stream );
-    if( err != paNoError ) goto error;
-
-    if( data->stopMode == MODE_FINISH )
-    {
-        while( ( err = Pa_IsStreamActive( stream ) ) == 1 )
-        {
-            /*printf("outTime = %g, note# = %d, repeat# = %d\n", data->outTime,
-             data->noteCounter, data->repeatCounter  );
-            fflush(stdout); */
-            Pa_Sleep( SLEEP_DUR );
-        }
-        if( err < 0 ) goto error;
-    }
-    else
-    {
-        while( data->repeatCounter < MAX_REPEATS )
-        {
-            /*printf("outTime = %g, note# = %d, repeat# = %d\n", data->outTime,
-             data->noteCounter, data->repeatCounter  );
-            fflush(stdout); */
-            Pa_Sleep( SLEEP_DUR );
-        }
-    }
-
-    if( data->stopMode == MODE_ABORT )
-    {
-        printf("Call Pa_AbortStream()\n");
-        err = Pa_AbortStream( stream );
-    }
-    else
-    {
-        printf("Call Pa_StopStream()\n");
-        err = Pa_StopStream( stream );
-    }
-    if( err != paNoError ) goto error;
-
-    printf("Call Pa_CloseStream()\n"); fflush(stdout);
-    err = Pa_CloseStream( stream );
-    if( err != paNoError ) goto error;
-
-    Pa_Terminate();
-    printf("Test finished.\n");
-
-    return err;
-
-error:
-    Pa_Terminate();
-    fprintf( stderr, "An error occurred while using the portaudio stream\n" );
-    fprintf( stderr, "Error number: %d\n", err );
-    fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
-    return err;
-}
diff --git a/spaces/amirDev/crowd-counting-p2p/engine.py b/spaces/amirDev/crowd-counting-p2p/engine.py
deleted file mode 100644
index 21f387dbbc1ac1ac50930d1fa7bdd6adf6fbf664..0000000000000000000000000000000000000000
--- a/spaces/amirDev/crowd-counting-p2p/engine.py
+++ /dev/null
@@ -1,159 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Train and eval functions used in main.py
-Mostly copy-paste from DETR (https://github.com/facebookresearch/detr).
-"""
-import math
-import os
-import sys
-from typing import Iterable
-
-import torch
-
-import util.misc as utils
-from util.misc import NestedTensor
-import numpy as np
-import time
-import torchvision.transforms as standard_transforms
-import cv2
-
-class DeNormalize(object):
-    def __init__(self, mean, std):
-        self.mean = mean
-        self.std = std
-
-    def __call__(self, tensor):
-        for t, m, s in zip(tensor, self.mean, self.std):
-            t.mul_(s).add_(m)
-        return tensor
-
-def vis(samples, targets, pred, vis_dir, des=None):
-    '''
-    samples -> tensor: [batch, 3, H, W]
-    targets -> list of dict: [{'points':[], 'image_id': str}]
-    pred -> list: [num_preds, 2]
-    '''
-    gts = [t['point'].tolist() for t in targets]
-
-    pil_to_tensor = standard_transforms.ToTensor()
-
-    restore_transform = standard_transforms.Compose([
-        DeNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-        standard_transforms.ToPILImage()
-    ])
-    # draw one by one
-    for idx in range(samples.shape[0]):
-        sample = restore_transform(samples[idx])
-        sample = pil_to_tensor(sample.convert('RGB')).numpy() * 255
-        sample_gt = sample.transpose([1, 2, 0])[:, :, ::-1].astype(np.uint8).copy()
-        sample_pred = sample.transpose([1, 2, 0])[:, :, ::-1].astype(np.uint8).copy()
-
-        max_len = np.max(sample_gt.shape)
-
-        size = 2
-        # draw gt
-        for t in gts[idx]:
-            sample_gt = cv2.circle(sample_gt, (int(t[0]), int(t[1])), size, (0, 255, 0), -1)
-        # draw predictions
-        for p in pred[idx]:
-            sample_pred = cv2.circle(sample_pred, (int(p[0]), int(p[1])), size, (0, 0, 255), -1)
-
-        name = targets[idx]['image_id']
-        # save the visualized images
-        if des is not None:
-            cv2.imwrite(os.path.join(vis_dir, '{}_{}_gt_{}_pred_{}_gt.jpg'.format(int(name), 
-                                                des, len(gts[idx]), len(pred[idx]))), sample_gt)
-            cv2.imwrite(os.path.join(vis_dir, '{}_{}_gt_{}_pred_{}_pred.jpg'.format(int(name), 
-                                                des, len(gts[idx]), len(pred[idx]))), sample_pred)
-        else:
-            cv2.imwrite(
-                os.path.join(vis_dir, '{}_gt_{}_pred_{}_gt.jpg'.format(int(name), len(gts[idx]), len(pred[idx]))),
-                sample_gt)
-            cv2.imwrite(
-                os.path.join(vis_dir, '{}_gt_{}_pred_{}_pred.jpg'.format(int(name), len(gts[idx]), len(pred[idx]))),
-                sample_pred)
-
-# the training routine
-def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
-                    data_loader: Iterable, optimizer: torch.optim.Optimizer,
-                    device: torch.device, epoch: int, max_norm: float = 0):
-    model.train()
-    criterion.train()
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
-    # iterate all training samples
-    for samples, targets in data_loader:
-        samples = samples.to(device)
-        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
-        # forward
-        outputs = model(samples)
-        # calc the losses
-        loss_dict = criterion(outputs, targets)
-        weight_dict = criterion.weight_dict
-        losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
-
-        # reduce all losses
-        loss_dict_reduced = utils.reduce_dict(loss_dict)
-        loss_dict_reduced_unscaled = {f'{k}_unscaled': v
-                                      for k, v in loss_dict_reduced.items()}
-        loss_dict_reduced_scaled = {k: v * weight_dict[k]
-                                    for k, v in loss_dict_reduced.items() if k in weight_dict}
-        losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())
-
-        loss_value = losses_reduced_scaled.item()
-
-        if not math.isfinite(loss_value):
-            print("Loss is {}, stopping training".format(loss_value))
-            print(loss_dict_reduced)
-            sys.exit(1)
-        # backward
-        optimizer.zero_grad()
-        losses.backward()
-        if max_norm > 0:
-            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
-        optimizer.step()
-        # update logger
-        metric_logger.update(loss=loss_value, **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled)
-        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
-    # gather the stats from all processes
-    metric_logger.synchronize_between_processes()
-    print("Averaged stats:", metric_logger)
-    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
-
-# the inference routine
-@torch.no_grad()
-def evaluate_crowd_no_overlap(model, data_loader, device, vis_dir=None):
-    model.eval()
-
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
-    # run inference on all images to calc MAE
-    maes = []
-    mses = []
-    for samples, targets in data_loader:
-        samples = samples.to(device)
-
-        outputs = model(samples)
-        outputs_scores = torch.nn.functional.softmax(outputs['pred_logits'], -1)[:, :, 1][0]
-
-        outputs_points = outputs['pred_points'][0]
-
-        gt_cnt = targets[0]['point'].shape[0]
-        # 0.5 is used by default
-        threshold = 0.5
-
-        points = outputs_points[outputs_scores > threshold].detach().cpu().numpy().tolist()
-        predict_cnt = int((outputs_scores > threshold).sum())
-        # if specified, save the visualized images
-        if vis_dir is not None: 
-            vis(samples, targets, [points], vis_dir)
-        # accumulate MAE, MSE
-        mae = abs(predict_cnt - gt_cnt)
-        mse = (predict_cnt - gt_cnt) * (predict_cnt - gt_cnt)
-        maes.append(float(mae))
-        mses.append(float(mse))
-    # calc MAE, MSE
-    mae = np.mean(maes)
-    mse = np.sqrt(np.mean(mses))
-
-    return mae, mse
\ No newline at end of file
diff --git a/spaces/ammarnasr/Code-Generation-with-Language-Specific-LoRa-Models/README.md b/spaces/ammarnasr/Code-Generation-with-Language-Specific-LoRa-Models/README.md
deleted file mode 100644
index a4d74b2917323d4461ecb3fd957fc186a7cca72a..0000000000000000000000000000000000000000
--- a/spaces/ammarnasr/Code-Generation-with-Language-Specific-LoRa-Models/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Code Generation With Language Specific LoRa Models
-emoji: 🏆
-colorFrom: blue
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/util/slconfig.py b/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/util/slconfig.py
deleted file mode 100644
index 3f293e3aff215a3c7c2f7d21d27853493b6ebfbc..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/util/slconfig.py
+++ /dev/null
@@ -1,427 +0,0 @@
-# ==========================================================
-# Modified from mmcv
-# ==========================================================
-import ast
-import os.path as osp
-import shutil
-import sys
-import tempfile
-from argparse import Action
-from importlib import import_module
-import platform
-
-from addict import Dict
-from yapf.yapflib.yapf_api import FormatCode
-
-BASE_KEY = "_base_"
-DELETE_KEY = "_delete_"
-RESERVED_KEYS = ["filename", "text", "pretty_text", "get", "dump", "merge_from_dict"]
-
-
-def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
-    if not osp.isfile(filename):
-        raise FileNotFoundError(msg_tmpl.format(filename))
-
-
-class ConfigDict(Dict):
-    def __missing__(self, name):
-        raise KeyError(name)
-
-    def __getattr__(self, name):
-        try:
-            value = super(ConfigDict, self).__getattr__(name)
-        except KeyError:
-            ex = AttributeError(f"'{self.__class__.__name__}' object has no " f"attribute '{name}'")
-        except Exception as e:
-            ex = e
-        else:
-            return value
-        raise ex
-
-
-class SLConfig(object):
-    """
-    config files.
-    only support .py file as config now.
-
-    ref: mmcv.utils.config
-
-    Example:
-        >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
-        >>> cfg.a
-        1
-        >>> cfg.b
-        {'b1': [0, 1]}
-        >>> cfg.b.b1
-        [0, 1]
-        >>> cfg = Config.fromfile('tests/data/config/a.py')
-        >>> cfg.filename
-        "/home/kchen/projects/mmcv/tests/data/config/a.py"
-        >>> cfg.item4
-        'test'
-        >>> cfg
-        "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: "
-        "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}"
-    """
-
-    @staticmethod
-    def _validate_py_syntax(filename):
-        with open(filename) as f:
-            content = f.read()
-        try:
-            ast.parse(content)
-        except SyntaxError:
-            raise SyntaxError("There are syntax errors in config " f"file {filename}")
-
-    @staticmethod
-    def _file2dict(filename):
-        filename = osp.abspath(osp.expanduser(filename))
-        check_file_exist(filename)
-        if filename.lower().endswith(".py"):
-            with tempfile.TemporaryDirectory() as temp_config_dir:
-                temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=".py")
-                temp_config_name = osp.basename(temp_config_file.name)
-                if platform.system() == 'Windows':
-                    temp_config_file.close()
-                shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name))
-                temp_module_name = osp.splitext(temp_config_name)[0]
-                sys.path.insert(0, temp_config_dir)
-                SLConfig._validate_py_syntax(filename)
-                mod = import_module(temp_module_name)
-                sys.path.pop(0)
-                cfg_dict = {
-                    name: value for name, value in mod.__dict__.items() if not name.startswith("__")
-                }
-                # delete imported module
-                del sys.modules[temp_module_name]
-                # close temp file
-                temp_config_file.close()
-        elif filename.lower().endswith((".yml", ".yaml", ".json")):
-            from .slio import slload
-
-            cfg_dict = slload(filename)
-        else:
-            raise IOError("Only py/yml/yaml/json type are supported now!")
-
-        cfg_text = filename + "\n"
-        with open(filename, "r") as f:
-            cfg_text += f.read()
-
-        # parse the base file
-        if BASE_KEY in cfg_dict:
-            cfg_dir = osp.dirname(filename)
-            base_filename = cfg_dict.pop(BASE_KEY)
-            base_filename = base_filename if isinstance(base_filename, list) else [base_filename]
-
-            cfg_dict_list = list()
-            cfg_text_list = list()
-            for f in base_filename:
-                _cfg_dict, _cfg_text = SLConfig._file2dict(osp.join(cfg_dir, f))
-                cfg_dict_list.append(_cfg_dict)
-                cfg_text_list.append(_cfg_text)
-
-            base_cfg_dict = dict()
-            for c in cfg_dict_list:
-                if len(base_cfg_dict.keys() & c.keys()) > 0:
-                    raise KeyError("Duplicate key is not allowed among bases")
-                    # TODO Allow the duplicate key while warnning user
-                base_cfg_dict.update(c)
-
-            base_cfg_dict = SLConfig._merge_a_into_b(cfg_dict, base_cfg_dict)
-            cfg_dict = base_cfg_dict
-
-            # merge cfg_text
-            cfg_text_list.append(cfg_text)
-            cfg_text = "\n".join(cfg_text_list)
-
-        return cfg_dict, cfg_text
-
-    @staticmethod
-    def _merge_a_into_b(a, b):
-        """merge dict `a` into dict `b` (non-inplace).
-            values in `a` will overwrite `b`.
-            copy first to avoid inplace modification
-
-        Args:
-            a ([type]): [description]
-            b ([type]): [description]
-
-        Returns:
-            [dict]: [description]
-        """
-        # import ipdb; ipdb.set_trace()
-        if not isinstance(a, dict):
-            return a
-
-        b = b.copy()
-        for k, v in a.items():
-            if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False):
-
-                if not isinstance(b[k], dict) and not isinstance(b[k], list):
-                    # if :
-                    # import ipdb; ipdb.set_trace()
-                    raise TypeError(
-                        f"{k}={v} in child config cannot inherit from base "
-                        f"because {k} is a dict in the child config but is of "
-                        f"type {type(b[k])} in base config. You may set "
-                        f"`{DELETE_KEY}=True` to ignore the base config"
-                    )
-                b[k] = SLConfig._merge_a_into_b(v, b[k])
-            elif isinstance(b, list):
-                try:
-                    _ = int(k)
-                except:
-                    raise TypeError(
-                        f"b is a list, " f"index {k} should be an int when input but {type(k)}"
-                    )
-                b[int(k)] = SLConfig._merge_a_into_b(v, b[int(k)])
-            else:
-                b[k] = v
-
-        return b
-
-    @staticmethod
-    def fromfile(filename):
-        cfg_dict, cfg_text = SLConfig._file2dict(filename)
-        return SLConfig(cfg_dict, cfg_text=cfg_text, filename=filename)
-
-    def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
-        if cfg_dict is None:
-            cfg_dict = dict()
-        elif not isinstance(cfg_dict, dict):
-            raise TypeError("cfg_dict must be a dict, but " f"got {type(cfg_dict)}")
-        for key in cfg_dict:
-            if key in RESERVED_KEYS:
-                raise KeyError(f"{key} is reserved for config file")
-
-        super(SLConfig, self).__setattr__("_cfg_dict", ConfigDict(cfg_dict))
-        super(SLConfig, self).__setattr__("_filename", filename)
-        if cfg_text:
-            text = cfg_text
-        elif filename:
-            with open(filename, "r") as f:
-                text = f.read()
-        else:
-            text = ""
-        super(SLConfig, self).__setattr__("_text", text)
-
-    @property
-    def filename(self):
-        return self._filename
-
-    @property
-    def text(self):
-        return self._text
-
-    @property
-    def pretty_text(self):
-
-        indent = 4
-
-        def _indent(s_, num_spaces):
-            s = s_.split("\n")
-            if len(s) == 1:
-                return s_
-            first = s.pop(0)
-            s = [(num_spaces * " ") + line for line in s]
-            s = "\n".join(s)
-            s = first + "\n" + s
-            return s
-
-        def _format_basic_types(k, v, use_mapping=False):
-            if isinstance(v, str):
-                v_str = f"'{v}'"
-            else:
-                v_str = str(v)
-
-            if use_mapping:
-                k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                attr_str = f"{k_str}: {v_str}"
-            else:
-                attr_str = f"{str(k)}={v_str}"
-            attr_str = _indent(attr_str, indent)
-
-            return attr_str
-
-        def _format_list(k, v, use_mapping=False):
-            # check if all items in the list are dict
-            if all(isinstance(_, dict) for _ in v):
-                v_str = "[\n"
-                v_str += "\n".join(
-                    f"dict({_indent(_format_dict(v_), indent)})," for v_ in v
-                ).rstrip(",")
-                if use_mapping:
-                    k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                    attr_str = f"{k_str}: {v_str}"
-                else:
-                    attr_str = f"{str(k)}={v_str}"
-                attr_str = _indent(attr_str, indent) + "]"
-            else:
-                attr_str = _format_basic_types(k, v, use_mapping)
-            return attr_str
-
-        def _contain_invalid_identifier(dict_str):
-            contain_invalid_identifier = False
-            for key_name in dict_str:
-                contain_invalid_identifier |= not str(key_name).isidentifier()
-            return contain_invalid_identifier
-
-        def _format_dict(input_dict, outest_level=False):
-            r = ""
-            s = []
-
-            use_mapping = _contain_invalid_identifier(input_dict)
-            if use_mapping:
-                r += "{"
-            for idx, (k, v) in enumerate(input_dict.items()):
-                is_last = idx >= len(input_dict) - 1
-                end = "" if outest_level or is_last else ","
-                if isinstance(v, dict):
-                    v_str = "\n" + _format_dict(v)
-                    if use_mapping:
-                        k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                        attr_str = f"{k_str}: dict({v_str}"
-                    else:
-                        attr_str = f"{str(k)}=dict({v_str}"
-                    attr_str = _indent(attr_str, indent) + ")" + end
-                elif isinstance(v, list):
-                    attr_str = _format_list(k, v, use_mapping) + end
-                else:
-                    attr_str = _format_basic_types(k, v, use_mapping) + end
-
-                s.append(attr_str)
-            r += "\n".join(s)
-            if use_mapping:
-                r += "}"
-            return r
-
-        cfg_dict = self._cfg_dict.to_dict()
-        text = _format_dict(cfg_dict, outest_level=True)
-        # copied from setup.cfg
-        yapf_style = dict(
-            based_on_style="pep8",
-            blank_line_before_nested_class_or_def=True,
-            split_before_expression_after_opening_paren=True,
-        )
-        text, _ = FormatCode(text, style_config=yapf_style, verify=True)
-
-        return text
-
-    def __repr__(self):
-        return f"Config (path: {self.filename}): {self._cfg_dict.__repr__()}"
-
-    def __len__(self):
-        return len(self._cfg_dict)
-
-    def __getattr__(self, name):
-        # # debug
-        # print('+'*15)
-        # print('name=%s' % name)
-        # print("addr:", id(self))
-        # # print('type(self):', type(self))
-        # print(self.__dict__)
-        # print('+'*15)
-        # if self.__dict__ == {}:
-        #     raise ValueError
-
-        return getattr(self._cfg_dict, name)
-
-    def __getitem__(self, name):
-        return self._cfg_dict.__getitem__(name)
-
-    def __setattr__(self, name, value):
-        if isinstance(value, dict):
-            value = ConfigDict(value)
-        self._cfg_dict.__setattr__(name, value)
-
-    def __setitem__(self, name, value):
-        if isinstance(value, dict):
-            value = ConfigDict(value)
-        self._cfg_dict.__setitem__(name, value)
-
-    def __iter__(self):
-        return iter(self._cfg_dict)
-
-    def dump(self, file=None):
-        # import ipdb; ipdb.set_trace()
-        if file is None:
-            return self.pretty_text
-        else:
-            with open(file, "w") as f:
-                f.write(self.pretty_text)
-
-    def merge_from_dict(self, options):
-        """Merge list into cfg_dict
-
-        Merge the dict parsed by MultipleKVAction into this cfg.
-
-        Examples:
-            >>> options = {'model.backbone.depth': 50,
-            ...            'model.backbone.with_cp':True}
-            >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet'))))
-            >>> cfg.merge_from_dict(options)
-            >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
-            >>> assert cfg_dict == dict(
-            ...     model=dict(backbone=dict(depth=50, with_cp=True)))
-
-        Args:
-            options (dict): dict of configs to merge from.
-        """
-        option_cfg_dict = {}
-        for full_key, v in options.items():
-            d = option_cfg_dict
-            key_list = full_key.split(".")
-            for subkey in key_list[:-1]:
-                d.setdefault(subkey, ConfigDict())
-                d = d[subkey]
-            subkey = key_list[-1]
-            d[subkey] = v
-
-        cfg_dict = super(SLConfig, self).__getattribute__("_cfg_dict")
-        super(SLConfig, self).__setattr__(
-            "_cfg_dict", SLConfig._merge_a_into_b(option_cfg_dict, cfg_dict)
-        )
-
-    # for multiprocess
-    def __setstate__(self, state):
-        self.__init__(state)
-
-    def copy(self):
-        return SLConfig(self._cfg_dict.copy())
-
-    def deepcopy(self):
-        return SLConfig(self._cfg_dict.deepcopy())
-
-
-class DictAction(Action):
-    """
-    argparse action to split an argument into KEY=VALUE form
-    on the first = and append to a dictionary. List options should
-    be passed as comma separated values, i.e KEY=V1,V2,V3
-    """
-
-    @staticmethod
-    def _parse_int_float_bool(val):
-        try:
-            return int(val)
-        except ValueError:
-            pass
-        try:
-            return float(val)
-        except ValueError:
-            pass
-        if val.lower() in ["true", "false"]:
-            return True if val.lower() == "true" else False
-        if val.lower() in ["none", "null"]:
-            return None
-        return val
-
-    def __call__(self, parser, namespace, values, option_string=None):
-        options = {}
-        for kv in values:
-            key, val = kv.split("=", maxsplit=1)
-            val = [self._parse_int_float_bool(v) for v in val.split(",")]
-            if len(val) == 1:
-                val = val[0]
-            options[key] = val
-        setattr(namespace, self.dest, options)
diff --git a/spaces/aquaaaaaaaaaaaa/AI-minato_aqua/losses.py b/spaces/aquaaaaaaaaaaaa/AI-minato_aqua/losses.py
deleted file mode 100644
index 41f9be6980713a46824ae9ec5eb8fd7c515d89c5..0000000000000000000000000000000000000000
--- a/spaces/aquaaaaaaaaaaaa/AI-minato_aqua/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch 
-from torch.nn import functional as F
-
-import commons
-
-
-def feature_loss(fmap_r, fmap_g):
-  loss = 0
-  for dr, dg in zip(fmap_r, fmap_g):
-    for rl, gl in zip(dr, dg):
-      rl = rl.float().detach()
-      gl = gl.float()
-      loss += torch.mean(torch.abs(rl - gl))
-
-  return loss * 2 
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-  loss = 0
-  r_losses = []
-  g_losses = []
-  for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-    dr = dr.float()
-    dg = dg.float()
-    r_loss = torch.mean((1-dr)**2)
-    g_loss = torch.mean(dg**2)
-    loss += (r_loss + g_loss)
-    r_losses.append(r_loss.item())
-    g_losses.append(g_loss.item())
-
-  return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-  loss = 0
-  gen_losses = []
-  for dg in disc_outputs:
-    dg = dg.float()
-    l = torch.mean((1-dg)**2)
-    gen_losses.append(l)
-    loss += l
-
-  return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-  """
-  z_p, logs_q: [b, h, t_t]
-  m_p, logs_p: [b, h, t_t]
-  """
-  z_p = z_p.float()
-  logs_q = logs_q.float()
-  m_p = m_p.float()
-  logs_p = logs_p.float()
-  z_mask = z_mask.float()
-  #print(logs_p)
-  kl = logs_p - logs_q - 0.5
-  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
-  kl = torch.sum(kl * z_mask)
-  l = kl / torch.sum(z_mask)
-  return l
diff --git a/spaces/arch-123/bingo/src/components/ui/input.tsx b/spaces/arch-123/bingo/src/components/ui/input.tsx
deleted file mode 100644
index 684a857f3d769b78818fb13de1abaebfb09ca79c..0000000000000000000000000000000000000000
--- a/spaces/arch-123/bingo/src/components/ui/input.tsx
+++ /dev/null
@@ -1,25 +0,0 @@
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-
-export interface InputProps
-  extends React.InputHTMLAttributes<HTMLInputElement> {}
-
-const Input = React.forwardRef<HTMLInputElement, InputProps>(
-  ({ className, type, ...props }, ref) => {
-    return (
-      <input
-        type={type}
-        className={cn(
-          'flex h-9 w-full rounded-md border border-input bg-transparent px-3 py-2 text-sm shadow-sm ring-offset-background file:border-0 file:bg-transparent file:text-sm file:font-medium placeholder:text-muted-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Input.displayName = 'Input'
-
-export { Input }
diff --git a/spaces/arianaira/movie-recommender/style.css b/spaces/arianaira/movie-recommender/style.css
deleted file mode 100644
index 2e44b42bc94f989a481f9208d587c766c9767ac2..0000000000000000000000000000000000000000
--- a/spaces/arianaira/movie-recommender/style.css
+++ /dev/null
@@ -1,5 +0,0 @@
-span.css-10trblm.eqr7zpz0
-{
-    background-color: white;
-    color: black; 
-}
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Cipher/_mode_ecb.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Cipher/_mode_ecb.py
deleted file mode 100644
index 37833572106076bbd0d2e47863b6d853a4c56e8c..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Cipher/_mode_ecb.py
+++ /dev/null
@@ -1,220 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-#  Cipher/mode_ecb.py : ECB mode
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain.  To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
-# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
-# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-# ===================================================================
-
-"""
-Electronic Code Book (ECB) mode.
-"""
-
-__all__ = [ 'EcbMode' ]
-
-from Crypto.Util._raw_api import (load_pycryptodome_raw_lib,
-                                  VoidPointer, create_string_buffer,
-                                  get_raw_buffer, SmartPointer,
-                                  c_size_t, c_uint8_ptr,
-                                  is_writeable_buffer)
-
-raw_ecb_lib = load_pycryptodome_raw_lib("Crypto.Cipher._raw_ecb", """
-                    int ECB_start_operation(void *cipher,
-                                            void **pResult);
-                    int ECB_encrypt(void *ecbState,
-                                    const uint8_t *in,
-                                    uint8_t *out,
-                                    size_t data_len);
-                    int ECB_decrypt(void *ecbState,
-                                    const uint8_t *in,
-                                    uint8_t *out,
-                                    size_t data_len);
-                    int ECB_stop_operation(void *state);
-                    """
-                                        )
-
-
-class EcbMode(object):
-    """*Electronic Code Book (ECB)*.
-
-    This is the simplest encryption mode. Each of the plaintext blocks
-    is directly encrypted into a ciphertext block, independently of
-    any other block.
-
-    This mode is dangerous because it exposes frequency of symbols
-    in your plaintext. Other modes (e.g. *CBC*) should be used instead.
-
-    See `NIST SP800-38A`_ , Section 6.1.
-
-    .. _`NIST SP800-38A` : http://csrc.nist.gov/publications/nistpubs/800-38a/sp800-38a.pdf
-
-    :undocumented: __init__
-    """
-
-    def __init__(self, block_cipher):
-        """Create a new block cipher, configured in ECB mode.
-
-        :Parameters:
-          block_cipher : C pointer
-            A smart pointer to the low-level block cipher instance.
-        """
-        self.block_size = block_cipher.block_size
-
-        self._state = VoidPointer()
-        result = raw_ecb_lib.ECB_start_operation(block_cipher.get(),
-                                                 self._state.address_of())
-        if result:
-            raise ValueError("Error %d while instantiating the ECB mode"
-                             % result)
-
-        # Ensure that object disposal of this Python object will (eventually)
-        # free the memory allocated by the raw library for the cipher
-        # mode
-        self._state = SmartPointer(self._state.get(),
-                                   raw_ecb_lib.ECB_stop_operation)
-
-        # Memory allocated for the underlying block cipher is now owned
-        # by the cipher mode
-        block_cipher.release()
-
-    def encrypt(self, plaintext, output=None):
-        """Encrypt data with the key set at initialization.
-
-        The data to encrypt can be broken up in two or
-        more pieces and `encrypt` can be called multiple times.
-
-        That is, the statement:
-
-            >>> c.encrypt(a) + c.encrypt(b)
-
-        is equivalent to:
-
-             >>> c.encrypt(a+b)
-
-        This function does not add any padding to the plaintext.
-
-        :Parameters:
-          plaintext : bytes/bytearray/memoryview
-            The piece of data to encrypt.
-            The length must be multiple of the cipher block length.
-        :Keywords:
-          output : bytearray/memoryview
-            The location where the ciphertext must be written to.
-            If ``None``, the ciphertext is returned.
-        :Return:
-          If ``output`` is ``None``, the ciphertext is returned as ``bytes``.
-          Otherwise, ``None``.
-        """
-
-        if output is None:
-            ciphertext = create_string_buffer(len(plaintext))
-        else:
-            ciphertext = output
-            
-            if not is_writeable_buffer(output):
-                raise TypeError("output must be a bytearray or a writeable memoryview")
-        
-            if len(plaintext) != len(output):
-                raise ValueError("output must have the same length as the input"
-                                 "  (%d bytes)" % len(plaintext))
-
-        result = raw_ecb_lib.ECB_encrypt(self._state.get(),
-                                         c_uint8_ptr(plaintext),
-                                         c_uint8_ptr(ciphertext),
-                                         c_size_t(len(plaintext)))
-        if result:
-            if result == 3:
-                raise ValueError("Data must be aligned to block boundary in ECB mode")
-            raise ValueError("Error %d while encrypting in ECB mode" % result)
-        
-        if output is None:
-            return get_raw_buffer(ciphertext)
-        else:
-            return None
-
-    def decrypt(self, ciphertext, output=None):
-        """Decrypt data with the key set at initialization.
-
-        The data to decrypt can be broken up in two or
-        more pieces and `decrypt` can be called multiple times.
-
-        That is, the statement:
-
-            >>> c.decrypt(a) + c.decrypt(b)
-
-        is equivalent to:
-
-             >>> c.decrypt(a+b)
-
-        This function does not remove any padding from the plaintext.
-
-        :Parameters:
-          ciphertext : bytes/bytearray/memoryview
-            The piece of data to decrypt.
-            The length must be multiple of the cipher block length.
-        :Keywords:
-          output : bytearray/memoryview
-            The location where the plaintext must be written to.
-            If ``None``, the plaintext is returned.
-        :Return:
-          If ``output`` is ``None``, the plaintext is returned as ``bytes``.
-          Otherwise, ``None``.
-        """
-        
-        if output is None:
-            plaintext = create_string_buffer(len(ciphertext))
-        else:
-            plaintext = output
-
-            if not is_writeable_buffer(output):
-                raise TypeError("output must be a bytearray or a writeable memoryview")
-            
-            if len(ciphertext) != len(output):
-                raise ValueError("output must have the same length as the input"
-                                 "  (%d bytes)" % len(plaintext))
-
-        result = raw_ecb_lib.ECB_decrypt(self._state.get(),
-                                         c_uint8_ptr(ciphertext),
-                                         c_uint8_ptr(plaintext),
-                                         c_size_t(len(ciphertext)))
-        if result:
-            if result == 3:
-                raise ValueError("Data must be aligned to block boundary in ECB mode")
-            raise ValueError("Error %d while decrypting in ECB mode" % result)
-
-        if output is None:
-            return get_raw_buffer(plaintext)
-        else:
-            return None
-
-
-def _create_ecb_cipher(factory, **kwargs):
-    """Instantiate a cipher object that performs ECB encryption/decryption.
-
-    :Parameters:
-      factory : module
-        The underlying block cipher, a module from ``Crypto.Cipher``.
-
-    All keywords are passed to the underlying block cipher.
-    See the relevant documentation for details (at least ``key`` will need
-    to be present"""
-
-    cipher_state = factory._create_base_cipher(kwargs)
-    cipher_state.block_size = factory.block_size
-    if kwargs:
-        raise TypeError("Unknown parameters for ECB: %s" % str(kwargs))
-    return EcbMode(cipher_state)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Distutils/extension.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Distutils/extension.py
deleted file mode 100644
index d8bdbf0f5bc286f8acc47e1ef05b4622ef281939..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Distutils/extension.py
+++ /dev/null
@@ -1,128 +0,0 @@
-"""Pyrex.Distutils.extension
-
-Provides a modified Extension class, that understands how to describe
-Pyrex extension modules in setup scripts."""
-
-__revision__ = "$Id:$"
-
-import sys
-import distutils.extension as _Extension
-
-try:
-    import warnings
-except ImportError:
-    warnings = None
-
-
-class Extension(_Extension.Extension):
-    # When adding arguments to this constructor, be sure to update
-    # user_options.extend in build_ext.py.
-    def __init__(self, name, sources,
-                 include_dirs=None,
-                 define_macros=None,
-                 undef_macros=None,
-                 library_dirs=None,
-                 libraries=None,
-                 runtime_library_dirs=None,
-                 extra_objects=None,
-                 extra_compile_args=None,
-                 extra_link_args=None,
-                 export_symbols=None,
-                 #swig_opts=None,
-                 depends=None,
-                 language=None,
-                 cython_include_dirs=None,
-                 cython_directives=None,
-                 cython_create_listing=False,
-                 cython_line_directives=False,
-                 cython_cplus=False,
-                 cython_c_in_temp=False,
-                 cython_gen_pxi=False,
-                 cython_gdb=False,
-                 no_c_in_traceback=False,
-                 cython_compile_time_env=None,
-                 **kw):
-
-        # Translate pyrex_X to cython_X for backwards compatibility.
-        had_pyrex_options = False
-        for key in list(kw):
-            if key.startswith('pyrex_'):
-                had_pyrex_options = True
-                kw['cython' + key[5:]] = kw.pop(key)
-        if had_pyrex_options:
-            Extension.__init__(
-                self, name, sources,
-                include_dirs=include_dirs,
-                define_macros=define_macros,
-                undef_macros=undef_macros,
-                library_dirs=library_dirs,
-                libraries=libraries,
-                runtime_library_dirs=runtime_library_dirs,
-                extra_objects=extra_objects,
-                extra_compile_args=extra_compile_args,
-                extra_link_args=extra_link_args,
-                export_symbols=export_symbols,
-                #swig_opts=swig_opts,
-                depends=depends,
-                language=language,
-                no_c_in_traceback=no_c_in_traceback,
-                **kw)
-            return
-
-        _Extension.Extension.__init__(
-            self, name, sources,
-            include_dirs=include_dirs,
-            define_macros=define_macros,
-            undef_macros=undef_macros,
-            library_dirs=library_dirs,
-            libraries=libraries,
-            runtime_library_dirs=runtime_library_dirs,
-            extra_objects=extra_objects,
-            extra_compile_args=extra_compile_args,
-            extra_link_args=extra_link_args,
-            export_symbols=export_symbols,
-            #swig_opts=swig_opts,
-            depends=depends,
-            language=language,
-            **kw)
-
-        self.cython_include_dirs = cython_include_dirs or []
-        self.cython_directives = cython_directives or {}
-        self.cython_create_listing = cython_create_listing
-        self.cython_line_directives = cython_line_directives
-        self.cython_cplus = cython_cplus
-        self.cython_c_in_temp = cython_c_in_temp
-        self.cython_gen_pxi = cython_gen_pxi
-        self.cython_gdb = cython_gdb
-        self.no_c_in_traceback = no_c_in_traceback
-        self.cython_compile_time_env = cython_compile_time_env
-
-# class Extension
-
-read_setup_file = _Extension.read_setup_file
-
-
-# reuse and extend original docstring from base class (if we can)
-if sys.version_info[0] < 3 and _Extension.Extension.__doc__:
-    # -OO discards docstrings
-    Extension.__doc__ = _Extension.Extension.__doc__ + """\
-    cython_include_dirs : [string]
-        list of directories to search for Pyrex header files (.pxd) (in
-        Unix form for portability)
-    cython_directives : {string:value}
-        dict of compiler directives
-    cython_create_listing_file : boolean
-        write pyrex error messages to a listing (.lis) file.
-    cython_line_directives : boolean
-        emit pyx line numbers for debugging/profiling
-    cython_cplus : boolean
-        use the C++ compiler for compiling and linking.
-    cython_c_in_temp : boolean
-        put generated C files in temp directory.
-    cython_gen_pxi : boolean
-        generate .pxi file for public declarations
-    cython_gdb : boolean
-        generate Cython debug information for this extension for cygdb
-    no_c_in_traceback : boolean
-        emit the c file and line number from the traceback for exceptions
-"""
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vegalite/v4/tests/test_renderers.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vegalite/v4/tests/test_renderers.py
deleted file mode 100644
index 55c5e4ffb3f95df1b150d86ec619efa2c7d70aa2..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vegalite/v4/tests/test_renderers.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""Tests of various renderers"""
-
-import json
-
-import pytest
-
-import altair.vegalite.v4 as alt
-
-
-@pytest.fixture
-def chart():
-    return alt.Chart("data.csv").mark_point()
-
-
-def test_html_renderer_embed_options(chart, renderer="html"):
-    """Test that embed_options in renderer metadata are correctly manifest in html"""
-    # Short of parsing the javascript, it's difficult to parse out the
-    # actions. So we use string matching
-
-    def assert_has_options(chart, **opts):
-        html = chart._repr_mimebundle_(None, None)["text/html"]
-        for key, val in opts.items():
-            assert json.dumps({key: val})[1:-1] in html
-
-    with alt.renderers.enable(renderer):
-        assert_has_options(chart, mode="vega-lite")
-
-        with alt.renderers.enable(embed_options=dict(actions={"export": True})):
-            assert_has_options(chart, mode="vega-lite", actions={"export": True})
-
-        with alt.renderers.set_embed_options(actions=True):
-            assert_has_options(chart, mode="vega-lite", actions=True)
-
-
-def test_mimetype_renderer_embed_options(chart, renderer="mimetype"):
-    # check that metadata is passed appropriately
-    mimetype = alt.display.VEGALITE_MIME_TYPE
-    spec = chart.to_dict()
-    with alt.renderers.enable(renderer):
-        # Sanity check: no metadata specified
-        bundle, metadata = chart._repr_mimebundle_(None, None)
-        assert bundle[mimetype] == spec
-        assert metadata == {}
-        with alt.renderers.set_embed_options(actions=False):
-            bundle, metadata = chart._repr_mimebundle_(None, None)
-            assert set(bundle.keys()) == {mimetype, "text/plain"}
-            assert bundle[mimetype] == spec
-            assert metadata == {mimetype: {"embed_options": {"actions": False}}}
-
-
-def test_json_renderer_embed_options(chart, renderer="json"):
-    """Test that embed_options in renderer metadata are correctly manifest in html"""
-    mimetype = "application/json"
-    spec = chart.to_dict()
-    with alt.renderers.enable(renderer):
-        # Sanity check: no options specified
-        bundle, metadata = chart._repr_mimebundle_(None, None)
-        assert bundle[mimetype] == spec
-        assert metadata == {}
-
-        with alt.renderers.enable(option="foo"):
-            bundle, metadata = chart._repr_mimebundle_(None, None)
-            assert set(bundle.keys()) == {mimetype, "text/plain"}
-            assert bundle[mimetype] == spec
-            assert metadata == {mimetype: {"option": "foo"}}
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/cffi/api.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/cffi/api.py
deleted file mode 100644
index 999a8aefc4af0b27120823116212b0afe8484aad..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/cffi/api.py
+++ /dev/null
@@ -1,965 +0,0 @@
-import sys, types
-from .lock import allocate_lock
-from .error import CDefError
-from . import model
-
-try:
-    callable
-except NameError:
-    # Python 3.1
-    from collections import Callable
-    callable = lambda x: isinstance(x, Callable)
-
-try:
-    basestring
-except NameError:
-    # Python 3.x
-    basestring = str
-
-_unspecified = object()
-
-
-
-class FFI(object):
-    r'''
-    The main top-level class that you instantiate once, or once per module.
-
-    Example usage:
-
-        ffi = FFI()
-        ffi.cdef("""
-            int printf(const char *, ...);
-        """)
-
-        C = ffi.dlopen(None)   # standard library
-        -or-
-        C = ffi.verify()  # use a C compiler: verify the decl above is right
-
-        C.printf("hello, %s!\n", ffi.new("char[]", "world"))
-    '''
-
-    def __init__(self, backend=None):
-        """Create an FFI instance.  The 'backend' argument is used to
-        select a non-default backend, mostly for tests.
-        """
-        if backend is None:
-            # You need PyPy (>= 2.0 beta), or a CPython (>= 2.6) with
-            # _cffi_backend.so compiled.
-            import _cffi_backend as backend
-            from . import __version__
-            if backend.__version__ != __version__:
-                # bad version!  Try to be as explicit as possible.
-                if hasattr(backend, '__file__'):
-                    # CPython
-                    raise Exception("Version mismatch: this is the 'cffi' package version %s, located in %r.  When we import the top-level '_cffi_backend' extension module, we get version %s, located in %r.  The two versions should be equal; check your installation." % (
-                        __version__, __file__,
-                        backend.__version__, backend.__file__))
-                else:
-                    # PyPy
-                    raise Exception("Version mismatch: this is the 'cffi' package version %s, located in %r.  This interpreter comes with a built-in '_cffi_backend' module, which is version %s.  The two versions should be equal; check your installation." % (
-                        __version__, __file__, backend.__version__))
-            # (If you insist you can also try to pass the option
-            # 'backend=backend_ctypes.CTypesBackend()', but don't
-            # rely on it!  It's probably not going to work well.)
-
-        from . import cparser
-        self._backend = backend
-        self._lock = allocate_lock()
-        self._parser = cparser.Parser()
-        self._cached_btypes = {}
-        self._parsed_types = types.ModuleType('parsed_types').__dict__
-        self._new_types = types.ModuleType('new_types').__dict__
-        self._function_caches = []
-        self._libraries = []
-        self._cdefsources = []
-        self._included_ffis = []
-        self._windows_unicode = None
-        self._init_once_cache = {}
-        self._cdef_version = None
-        self._embedding = None
-        self._typecache = model.get_typecache(backend)
-        if hasattr(backend, 'set_ffi'):
-            backend.set_ffi(self)
-        for name in list(backend.__dict__):
-            if name.startswith('RTLD_'):
-                setattr(self, name, getattr(backend, name))
-        #
-        with self._lock:
-            self.BVoidP = self._get_cached_btype(model.voidp_type)
-            self.BCharA = self._get_cached_btype(model.char_array_type)
-        if isinstance(backend, types.ModuleType):
-            # _cffi_backend: attach these constants to the class
-            if not hasattr(FFI, 'NULL'):
-                FFI.NULL = self.cast(self.BVoidP, 0)
-                FFI.CData, FFI.CType = backend._get_types()
-        else:
-            # ctypes backend: attach these constants to the instance
-            self.NULL = self.cast(self.BVoidP, 0)
-            self.CData, self.CType = backend._get_types()
-        self.buffer = backend.buffer
-
-    def cdef(self, csource, override=False, packed=False, pack=None):
-        """Parse the given C source.  This registers all declared functions,
-        types, and global variables.  The functions and global variables can
-        then be accessed via either 'ffi.dlopen()' or 'ffi.verify()'.
-        The types can be used in 'ffi.new()' and other functions.
-        If 'packed' is specified as True, all structs declared inside this
-        cdef are packed, i.e. laid out without any field alignment at all.
-        Alternatively, 'pack' can be a small integer, and requests for
-        alignment greater than that are ignored (pack=1 is equivalent to
-        packed=True).
-        """
-        self._cdef(csource, override=override, packed=packed, pack=pack)
-
-    def embedding_api(self, csource, packed=False, pack=None):
-        self._cdef(csource, packed=packed, pack=pack, dllexport=True)
-        if self._embedding is None:
-            self._embedding = ''
-
-    def _cdef(self, csource, override=False, **options):
-        if not isinstance(csource, str):    # unicode, on Python 2
-            if not isinstance(csource, basestring):
-                raise TypeError("cdef() argument must be a string")
-            csource = csource.encode('ascii')
-        with self._lock:
-            self._cdef_version = object()
-            self._parser.parse(csource, override=override, **options)
-            self._cdefsources.append(csource)
-            if override:
-                for cache in self._function_caches:
-                    cache.clear()
-            finishlist = self._parser._recomplete
-            if finishlist:
-                self._parser._recomplete = []
-                for tp in finishlist:
-                    tp.finish_backend_type(self, finishlist)
-
-    def dlopen(self, name, flags=0):
-        """Load and return a dynamic library identified by 'name'.
-        The standard C library can be loaded by passing None.
-        Note that functions and types declared by 'ffi.cdef()' are not
-        linked to a particular library, just like C headers; in the
-        library we only look for the actual (untyped) symbols.
-        """
-        if not (isinstance(name, basestring) or
-                name is None or
-                isinstance(name, self.CData)):
-            raise TypeError("dlopen(name): name must be a file name, None, "
-                            "or an already-opened 'void *' handle")
-        with self._lock:
-            lib, function_cache = _make_ffi_library(self, name, flags)
-            self._function_caches.append(function_cache)
-            self._libraries.append(lib)
-        return lib
-
-    def dlclose(self, lib):
-        """Close a library obtained with ffi.dlopen().  After this call,
-        access to functions or variables from the library will fail
-        (possibly with a segmentation fault).
-        """
-        type(lib).__cffi_close__(lib)
-
-    def _typeof_locked(self, cdecl):
-        # call me with the lock!
-        key = cdecl
-        if key in self._parsed_types:
-            return self._parsed_types[key]
-        #
-        if not isinstance(cdecl, str):    # unicode, on Python 2
-            cdecl = cdecl.encode('ascii')
-        #
-        type = self._parser.parse_type(cdecl)
-        really_a_function_type = type.is_raw_function
-        if really_a_function_type:
-            type = type.as_function_pointer()
-        btype = self._get_cached_btype(type)
-        result = btype, really_a_function_type
-        self._parsed_types[key] = result
-        return result
-
-    def _typeof(self, cdecl, consider_function_as_funcptr=False):
-        # string -> ctype object
-        try:
-            result = self._parsed_types[cdecl]
-        except KeyError:
-            with self._lock:
-                result = self._typeof_locked(cdecl)
-        #
-        btype, really_a_function_type = result
-        if really_a_function_type and not consider_function_as_funcptr:
-            raise CDefError("the type %r is a function type, not a "
-                            "pointer-to-function type" % (cdecl,))
-        return btype
-
-    def typeof(self, cdecl):
-        """Parse the C type given as a string and return the
-        corresponding <ctype> object.
-        It can also be used on 'cdata' instance to get its C type.
-        """
-        if isinstance(cdecl, basestring):
-            return self._typeof(cdecl)
-        if isinstance(cdecl, self.CData):
-            return self._backend.typeof(cdecl)
-        if isinstance(cdecl, types.BuiltinFunctionType):
-            res = _builtin_function_type(cdecl)
-            if res is not None:
-                return res
-        if (isinstance(cdecl, types.FunctionType)
-                and hasattr(cdecl, '_cffi_base_type')):
-            with self._lock:
-                return self._get_cached_btype(cdecl._cffi_base_type)
-        raise TypeError(type(cdecl))
-
-    def sizeof(self, cdecl):
-        """Return the size in bytes of the argument.  It can be a
-        string naming a C type, or a 'cdata' instance.
-        """
-        if isinstance(cdecl, basestring):
-            BType = self._typeof(cdecl)
-            return self._backend.sizeof(BType)
-        else:
-            return self._backend.sizeof(cdecl)
-
-    def alignof(self, cdecl):
-        """Return the natural alignment size in bytes of the C type
-        given as a string.
-        """
-        if isinstance(cdecl, basestring):
-            cdecl = self._typeof(cdecl)
-        return self._backend.alignof(cdecl)
-
-    def offsetof(self, cdecl, *fields_or_indexes):
-        """Return the offset of the named field inside the given
-        structure or array, which must be given as a C type name.
-        You can give several field names in case of nested structures.
-        You can also give numeric values which correspond to array
-        items, in case of an array type.
-        """
-        if isinstance(cdecl, basestring):
-            cdecl = self._typeof(cdecl)
-        return self._typeoffsetof(cdecl, *fields_or_indexes)[1]
-
-    def new(self, cdecl, init=None):
-        """Allocate an instance according to the specified C type and
-        return a pointer to it.  The specified C type must be either a
-        pointer or an array: ``new('X *')`` allocates an X and returns
-        a pointer to it, whereas ``new('X[n]')`` allocates an array of
-        n X'es and returns an array referencing it (which works
-        mostly like a pointer, like in C).  You can also use
-        ``new('X[]', n)`` to allocate an array of a non-constant
-        length n.
-
-        The memory is initialized following the rules of declaring a
-        global variable in C: by default it is zero-initialized, but
-        an explicit initializer can be given which can be used to
-        fill all or part of the memory.
-
-        When the returned <cdata> object goes out of scope, the memory
-        is freed.  In other words the returned <cdata> object has
-        ownership of the value of type 'cdecl' that it points to.  This
-        means that the raw data can be used as long as this object is
-        kept alive, but must not be used for a longer time.  Be careful
-        about that when copying the pointer to the memory somewhere
-        else, e.g. into another structure.
-        """
-        if isinstance(cdecl, basestring):
-            cdecl = self._typeof(cdecl)
-        return self._backend.newp(cdecl, init)
-
-    def new_allocator(self, alloc=None, free=None,
-                      should_clear_after_alloc=True):
-        """Return a new allocator, i.e. a function that behaves like ffi.new()
-        but uses the provided low-level 'alloc' and 'free' functions.
-
-        'alloc' is called with the size as argument.  If it returns NULL, a
-        MemoryError is raised.  'free' is called with the result of 'alloc'
-        as argument.  Both can be either Python function or directly C
-        functions.  If 'free' is None, then no free function is called.
-        If both 'alloc' and 'free' are None, the default is used.
-
-        If 'should_clear_after_alloc' is set to False, then the memory
-        returned by 'alloc' is assumed to be already cleared (or you are
-        fine with garbage); otherwise CFFI will clear it.
-        """
-        compiled_ffi = self._backend.FFI()
-        allocator = compiled_ffi.new_allocator(alloc, free,
-                                               should_clear_after_alloc)
-        def allocate(cdecl, init=None):
-            if isinstance(cdecl, basestring):
-                cdecl = self._typeof(cdecl)
-            return allocator(cdecl, init)
-        return allocate
-
-    def cast(self, cdecl, source):
-        """Similar to a C cast: returns an instance of the named C
-        type initialized with the given 'source'.  The source is
-        casted between integers or pointers of any type.
-        """
-        if isinstance(cdecl, basestring):
-            cdecl = self._typeof(cdecl)
-        return self._backend.cast(cdecl, source)
-
-    def string(self, cdata, maxlen=-1):
-        """Return a Python string (or unicode string) from the 'cdata'.
-        If 'cdata' is a pointer or array of characters or bytes, returns
-        the null-terminated string.  The returned string extends until
-        the first null character, or at most 'maxlen' characters.  If
-        'cdata' is an array then 'maxlen' defaults to its length.
-
-        If 'cdata' is a pointer or array of wchar_t, returns a unicode
-        string following the same rules.
-
-        If 'cdata' is a single character or byte or a wchar_t, returns
-        it as a string or unicode string.
-
-        If 'cdata' is an enum, returns the value of the enumerator as a
-        string, or 'NUMBER' if the value is out of range.
-        """
-        return self._backend.string(cdata, maxlen)
-
-    def unpack(self, cdata, length):
-        """Unpack an array of C data of the given length,
-        returning a Python string/unicode/list.
-
-        If 'cdata' is a pointer to 'char', returns a byte string.
-        It does not stop at the first null.  This is equivalent to:
-        ffi.buffer(cdata, length)[:]
-
-        If 'cdata' is a pointer to 'wchar_t', returns a unicode string.
-        'length' is measured in wchar_t's; it is not the size in bytes.
-
-        If 'cdata' is a pointer to anything else, returns a list of
-        'length' items.  This is a faster equivalent to:
-        [cdata[i] for i in range(length)]
-        """
-        return self._backend.unpack(cdata, length)
-
-   #def buffer(self, cdata, size=-1):
-   #    """Return a read-write buffer object that references the raw C data
-   #    pointed to by the given 'cdata'.  The 'cdata' must be a pointer or
-   #    an array.  Can be passed to functions expecting a buffer, or directly
-   #    manipulated with:
-   #
-   #        buf[:]          get a copy of it in a regular string, or
-   #        buf[idx]        as a single character
-   #        buf[:] = ...
-   #        buf[idx] = ...  change the content
-   #    """
-   #    note that 'buffer' is a type, set on this instance by __init__
-
-    def from_buffer(self, cdecl, python_buffer=_unspecified,
-                    require_writable=False):
-        """Return a cdata of the given type pointing to the data of the
-        given Python object, which must support the buffer interface.
-        Note that this is not meant to be used on the built-in types
-        str or unicode (you can build 'char[]' arrays explicitly)
-        but only on objects containing large quantities of raw data
-        in some other format, like 'array.array' or numpy arrays.
-
-        The first argument is optional and default to 'char[]'.
-        """
-        if python_buffer is _unspecified:
-            cdecl, python_buffer = self.BCharA, cdecl
-        elif isinstance(cdecl, basestring):
-            cdecl = self._typeof(cdecl)
-        return self._backend.from_buffer(cdecl, python_buffer,
-                                         require_writable)
-
-    def memmove(self, dest, src, n):
-        """ffi.memmove(dest, src, n) copies n bytes of memory from src to dest.
-
-        Like the C function memmove(), the memory areas may overlap;
-        apart from that it behaves like the C function memcpy().
-
-        'src' can be any cdata ptr or array, or any Python buffer object.
-        'dest' can be any cdata ptr or array, or a writable Python buffer
-        object.  The size to copy, 'n', is always measured in bytes.
-
-        Unlike other methods, this one supports all Python buffer including
-        byte strings and bytearrays---but it still does not support
-        non-contiguous buffers.
-        """
-        return self._backend.memmove(dest, src, n)
-
-    def callback(self, cdecl, python_callable=None, error=None, onerror=None):
-        """Return a callback object or a decorator making such a
-        callback object.  'cdecl' must name a C function pointer type.
-        The callback invokes the specified 'python_callable' (which may
-        be provided either directly or via a decorator).  Important: the
-        callback object must be manually kept alive for as long as the
-        callback may be invoked from the C level.
-        """
-        def callback_decorator_wrap(python_callable):
-            if not callable(python_callable):
-                raise TypeError("the 'python_callable' argument "
-                                "is not callable")
-            return self._backend.callback(cdecl, python_callable,
-                                          error, onerror)
-        if isinstance(cdecl, basestring):
-            cdecl = self._typeof(cdecl, consider_function_as_funcptr=True)
-        if python_callable is None:
-            return callback_decorator_wrap                # decorator mode
-        else:
-            return callback_decorator_wrap(python_callable)  # direct mode
-
-    def getctype(self, cdecl, replace_with=''):
-        """Return a string giving the C type 'cdecl', which may be itself
-        a string or a <ctype> object.  If 'replace_with' is given, it gives
-        extra text to append (or insert for more complicated C types), like
-        a variable name, or '*' to get actually the C type 'pointer-to-cdecl'.
-        """
-        if isinstance(cdecl, basestring):
-            cdecl = self._typeof(cdecl)
-        replace_with = replace_with.strip()
-        if (replace_with.startswith('*')
-                and '&[' in self._backend.getcname(cdecl, '&')):
-            replace_with = '(%s)' % replace_with
-        elif replace_with and not replace_with[0] in '[(':
-            replace_with = ' ' + replace_with
-        return self._backend.getcname(cdecl, replace_with)
-
-    def gc(self, cdata, destructor, size=0):
-        """Return a new cdata object that points to the same
-        data.  Later, when this new cdata object is garbage-collected,
-        'destructor(old_cdata_object)' will be called.
-
-        The optional 'size' gives an estimate of the size, used to
-        trigger the garbage collection more eagerly.  So far only used
-        on PyPy.  It tells the GC that the returned object keeps alive
-        roughly 'size' bytes of external memory.
-        """
-        return self._backend.gcp(cdata, destructor, size)
-
-    def _get_cached_btype(self, type):
-        assert self._lock.acquire(False) is False
-        # call me with the lock!
-        try:
-            BType = self._cached_btypes[type]
-        except KeyError:
-            finishlist = []
-            BType = type.get_cached_btype(self, finishlist)
-            for type in finishlist:
-                type.finish_backend_type(self, finishlist)
-        return BType
-
-    def verify(self, source='', tmpdir=None, **kwargs):
-        """Verify that the current ffi signatures compile on this
-        machine, and return a dynamic library object.  The dynamic
-        library can be used to call functions and access global
-        variables declared in this 'ffi'.  The library is compiled
-        by the C compiler: it gives you C-level API compatibility
-        (including calling macros).  This is unlike 'ffi.dlopen()',
-        which requires binary compatibility in the signatures.
-        """
-        from .verifier import Verifier, _caller_dir_pycache
-        #
-        # If set_unicode(True) was called, insert the UNICODE and
-        # _UNICODE macro declarations
-        if self._windows_unicode:
-            self._apply_windows_unicode(kwargs)
-        #
-        # Set the tmpdir here, and not in Verifier.__init__: it picks
-        # up the caller's directory, which we want to be the caller of
-        # ffi.verify(), as opposed to the caller of Veritier().
-        tmpdir = tmpdir or _caller_dir_pycache()
-        #
-        # Make a Verifier() and use it to load the library.
-        self.verifier = Verifier(self, source, tmpdir, **kwargs)
-        lib = self.verifier.load_library()
-        #
-        # Save the loaded library for keep-alive purposes, even
-        # if the caller doesn't keep it alive itself (it should).
-        self._libraries.append(lib)
-        return lib
-
-    def _get_errno(self):
-        return self._backend.get_errno()
-    def _set_errno(self, errno):
-        self._backend.set_errno(errno)
-    errno = property(_get_errno, _set_errno, None,
-                     "the value of 'errno' from/to the C calls")
-
-    def getwinerror(self, code=-1):
-        return self._backend.getwinerror(code)
-
-    def _pointer_to(self, ctype):
-        with self._lock:
-            return model.pointer_cache(self, ctype)
-
-    def addressof(self, cdata, *fields_or_indexes):
-        """Return the address of a <cdata 'struct-or-union'>.
-        If 'fields_or_indexes' are given, returns the address of that
-        field or array item in the structure or array, recursively in
-        case of nested structures.
-        """
-        try:
-            ctype = self._backend.typeof(cdata)
-        except TypeError:
-            if '__addressof__' in type(cdata).__dict__:
-                return type(cdata).__addressof__(cdata, *fields_or_indexes)
-            raise
-        if fields_or_indexes:
-            ctype, offset = self._typeoffsetof(ctype, *fields_or_indexes)
-        else:
-            if ctype.kind == "pointer":
-                raise TypeError("addressof(pointer)")
-            offset = 0
-        ctypeptr = self._pointer_to(ctype)
-        return self._backend.rawaddressof(ctypeptr, cdata, offset)
-
-    def _typeoffsetof(self, ctype, field_or_index, *fields_or_indexes):
-        ctype, offset = self._backend.typeoffsetof(ctype, field_or_index)
-        for field1 in fields_or_indexes:
-            ctype, offset1 = self._backend.typeoffsetof(ctype, field1, 1)
-            offset += offset1
-        return ctype, offset
-
-    def include(self, ffi_to_include):
-        """Includes the typedefs, structs, unions and enums defined
-        in another FFI instance.  Usage is similar to a #include in C,
-        where a part of the program might include types defined in
-        another part for its own usage.  Note that the include()
-        method has no effect on functions, constants and global
-        variables, which must anyway be accessed directly from the
-        lib object returned by the original FFI instance.
-        """
-        if not isinstance(ffi_to_include, FFI):
-            raise TypeError("ffi.include() expects an argument that is also of"
-                            " type cffi.FFI, not %r" % (
-                                type(ffi_to_include).__name__,))
-        if ffi_to_include is self:
-            raise ValueError("self.include(self)")
-        with ffi_to_include._lock:
-            with self._lock:
-                self._parser.include(ffi_to_include._parser)
-                self._cdefsources.append('[')
-                self._cdefsources.extend(ffi_to_include._cdefsources)
-                self._cdefsources.append(']')
-                self._included_ffis.append(ffi_to_include)
-
-    def new_handle(self, x):
-        return self._backend.newp_handle(self.BVoidP, x)
-
-    def from_handle(self, x):
-        return self._backend.from_handle(x)
-
-    def release(self, x):
-        self._backend.release(x)
-
-    def set_unicode(self, enabled_flag):
-        """Windows: if 'enabled_flag' is True, enable the UNICODE and
-        _UNICODE defines in C, and declare the types like TCHAR and LPTCSTR
-        to be (pointers to) wchar_t.  If 'enabled_flag' is False,
-        declare these types to be (pointers to) plain 8-bit characters.
-        This is mostly for backward compatibility; you usually want True.
-        """
-        if self._windows_unicode is not None:
-            raise ValueError("set_unicode() can only be called once")
-        enabled_flag = bool(enabled_flag)
-        if enabled_flag:
-            self.cdef("typedef wchar_t TBYTE;"
-                      "typedef wchar_t TCHAR;"
-                      "typedef const wchar_t *LPCTSTR;"
-                      "typedef const wchar_t *PCTSTR;"
-                      "typedef wchar_t *LPTSTR;"
-                      "typedef wchar_t *PTSTR;"
-                      "typedef TBYTE *PTBYTE;"
-                      "typedef TCHAR *PTCHAR;")
-        else:
-            self.cdef("typedef char TBYTE;"
-                      "typedef char TCHAR;"
-                      "typedef const char *LPCTSTR;"
-                      "typedef const char *PCTSTR;"
-                      "typedef char *LPTSTR;"
-                      "typedef char *PTSTR;"
-                      "typedef TBYTE *PTBYTE;"
-                      "typedef TCHAR *PTCHAR;")
-        self._windows_unicode = enabled_flag
-
-    def _apply_windows_unicode(self, kwds):
-        defmacros = kwds.get('define_macros', ())
-        if not isinstance(defmacros, (list, tuple)):
-            raise TypeError("'define_macros' must be a list or tuple")
-        defmacros = list(defmacros) + [('UNICODE', '1'),
-                                       ('_UNICODE', '1')]
-        kwds['define_macros'] = defmacros
-
-    def _apply_embedding_fix(self, kwds):
-        # must include an argument like "-lpython2.7" for the compiler
-        def ensure(key, value):
-            lst = kwds.setdefault(key, [])
-            if value not in lst:
-                lst.append(value)
-        #
-        if '__pypy__' in sys.builtin_module_names:
-            import os
-            if sys.platform == "win32":
-                # we need 'libpypy-c.lib'.  Current distributions of
-                # pypy (>= 4.1) contain it as 'libs/python27.lib'.
-                pythonlib = "python{0[0]}{0[1]}".format(sys.version_info)
-                if hasattr(sys, 'prefix'):
-                    ensure('library_dirs', os.path.join(sys.prefix, 'libs'))
-            else:
-                # we need 'libpypy-c.{so,dylib}', which should be by
-                # default located in 'sys.prefix/bin' for installed
-                # systems.
-                if sys.version_info < (3,):
-                    pythonlib = "pypy-c"
-                else:
-                    pythonlib = "pypy3-c"
-                if hasattr(sys, 'prefix'):
-                    ensure('library_dirs', os.path.join(sys.prefix, 'bin'))
-            # On uninstalled pypy's, the libpypy-c is typically found in
-            # .../pypy/goal/.
-            if hasattr(sys, 'prefix'):
-                ensure('library_dirs', os.path.join(sys.prefix, 'pypy', 'goal'))
-        else:
-            if sys.platform == "win32":
-                template = "python%d%d"
-                if hasattr(sys, 'gettotalrefcount'):
-                    template += '_d'
-            else:
-                try:
-                    import sysconfig
-                except ImportError:    # 2.6
-                    from distutils import sysconfig
-                template = "python%d.%d"
-                if sysconfig.get_config_var('DEBUG_EXT'):
-                    template += sysconfig.get_config_var('DEBUG_EXT')
-            pythonlib = (template %
-                    (sys.hexversion >> 24, (sys.hexversion >> 16) & 0xff))
-            if hasattr(sys, 'abiflags'):
-                pythonlib += sys.abiflags
-        ensure('libraries', pythonlib)
-        if sys.platform == "win32":
-            ensure('extra_link_args', '/MANIFEST')
-
-    def set_source(self, module_name, source, source_extension='.c', **kwds):
-        import os
-        if hasattr(self, '_assigned_source'):
-            raise ValueError("set_source() cannot be called several times "
-                             "per ffi object")
-        if not isinstance(module_name, basestring):
-            raise TypeError("'module_name' must be a string")
-        if os.sep in module_name or (os.altsep and os.altsep in module_name):
-            raise ValueError("'module_name' must not contain '/': use a dotted "
-                             "name to make a 'package.module' location")
-        self._assigned_source = (str(module_name), source,
-                                 source_extension, kwds)
-
-    def set_source_pkgconfig(self, module_name, pkgconfig_libs, source,
-                             source_extension='.c', **kwds):
-        from . import pkgconfig
-        if not isinstance(pkgconfig_libs, list):
-            raise TypeError("the pkgconfig_libs argument must be a list "
-                            "of package names")
-        kwds2 = pkgconfig.flags_from_pkgconfig(pkgconfig_libs)
-        pkgconfig.merge_flags(kwds, kwds2)
-        self.set_source(module_name, source, source_extension, **kwds)
-
-    def distutils_extension(self, tmpdir='build', verbose=True):
-        from distutils.dir_util import mkpath
-        from .recompiler import recompile
-        #
-        if not hasattr(self, '_assigned_source'):
-            if hasattr(self, 'verifier'):     # fallback, 'tmpdir' ignored
-                return self.verifier.get_extension()
-            raise ValueError("set_source() must be called before"
-                             " distutils_extension()")
-        module_name, source, source_extension, kwds = self._assigned_source
-        if source is None:
-            raise TypeError("distutils_extension() is only for C extension "
-                            "modules, not for dlopen()-style pure Python "
-                            "modules")
-        mkpath(tmpdir)
-        ext, updated = recompile(self, module_name,
-                                 source, tmpdir=tmpdir, extradir=tmpdir,
-                                 source_extension=source_extension,
-                                 call_c_compiler=False, **kwds)
-        if verbose:
-            if updated:
-                sys.stderr.write("regenerated: %r\n" % (ext.sources[0],))
-            else:
-                sys.stderr.write("not modified: %r\n" % (ext.sources[0],))
-        return ext
-
-    def emit_c_code(self, filename):
-        from .recompiler import recompile
-        #
-        if not hasattr(self, '_assigned_source'):
-            raise ValueError("set_source() must be called before emit_c_code()")
-        module_name, source, source_extension, kwds = self._assigned_source
-        if source is None:
-            raise TypeError("emit_c_code() is only for C extension modules, "
-                            "not for dlopen()-style pure Python modules")
-        recompile(self, module_name, source,
-                  c_file=filename, call_c_compiler=False, **kwds)
-
-    def emit_python_code(self, filename):
-        from .recompiler import recompile
-        #
-        if not hasattr(self, '_assigned_source'):
-            raise ValueError("set_source() must be called before emit_c_code()")
-        module_name, source, source_extension, kwds = self._assigned_source
-        if source is not None:
-            raise TypeError("emit_python_code() is only for dlopen()-style "
-                            "pure Python modules, not for C extension modules")
-        recompile(self, module_name, source,
-                  c_file=filename, call_c_compiler=False, **kwds)
-
-    def compile(self, tmpdir='.', verbose=0, target=None, debug=None):
-        """The 'target' argument gives the final file name of the
-        compiled DLL.  Use '*' to force distutils' choice, suitable for
-        regular CPython C API modules.  Use a file name ending in '.*'
-        to ask for the system's default extension for dynamic libraries
-        (.so/.dll/.dylib).
-
-        The default is '*' when building a non-embedded C API extension,
-        and (module_name + '.*') when building an embedded library.
-        """
-        from .recompiler import recompile
-        #
-        if not hasattr(self, '_assigned_source'):
-            raise ValueError("set_source() must be called before compile()")
-        module_name, source, source_extension, kwds = self._assigned_source
-        return recompile(self, module_name, source, tmpdir=tmpdir,
-                         target=target, source_extension=source_extension,
-                         compiler_verbose=verbose, debug=debug, **kwds)
-
-    def init_once(self, func, tag):
-        # Read _init_once_cache[tag], which is either (False, lock) if
-        # we're calling the function now in some thread, or (True, result).
-        # Don't call setdefault() in most cases, to avoid allocating and
-        # immediately freeing a lock; but still use setdefaut() to avoid
-        # races.
-        try:
-            x = self._init_once_cache[tag]
-        except KeyError:
-            x = self._init_once_cache.setdefault(tag, (False, allocate_lock()))
-        # Common case: we got (True, result), so we return the result.
-        if x[0]:
-            return x[1]
-        # Else, it's a lock.  Acquire it to serialize the following tests.
-        with x[1]:
-            # Read again from _init_once_cache the current status.
-            x = self._init_once_cache[tag]
-            if x[0]:
-                return x[1]
-            # Call the function and store the result back.
-            result = func()
-            self._init_once_cache[tag] = (True, result)
-        return result
-
-    def embedding_init_code(self, pysource):
-        if self._embedding:
-            raise ValueError("embedding_init_code() can only be called once")
-        # fix 'pysource' before it gets dumped into the C file:
-        # - remove empty lines at the beginning, so it starts at "line 1"
-        # - dedent, if all non-empty lines are indented
-        # - check for SyntaxErrors
-        import re
-        match = re.match(r'\s*\n', pysource)
-        if match:
-            pysource = pysource[match.end():]
-        lines = pysource.splitlines() or ['']
-        prefix = re.match(r'\s*', lines[0]).group()
-        for i in range(1, len(lines)):
-            line = lines[i]
-            if line.rstrip():
-                while not line.startswith(prefix):
-                    prefix = prefix[:-1]
-        i = len(prefix)
-        lines = [line[i:]+'\n' for line in lines]
-        pysource = ''.join(lines)
-        #
-        compile(pysource, "cffi_init", "exec")
-        #
-        self._embedding = pysource
-
-    def def_extern(self, *args, **kwds):
-        raise ValueError("ffi.def_extern() is only available on API-mode FFI "
-                         "objects")
-
-    def list_types(self):
-        """Returns the user type names known to this FFI instance.
-        This returns a tuple containing three lists of names:
-        (typedef_names, names_of_structs, names_of_unions)
-        """
-        typedefs = []
-        structs = []
-        unions = []
-        for key in self._parser._declarations:
-            if key.startswith('typedef '):
-                typedefs.append(key[8:])
-            elif key.startswith('struct '):
-                structs.append(key[7:])
-            elif key.startswith('union '):
-                unions.append(key[6:])
-        typedefs.sort()
-        structs.sort()
-        unions.sort()
-        return (typedefs, structs, unions)
-
-
-def _load_backend_lib(backend, name, flags):
-    import os
-    if not isinstance(name, basestring):
-        if sys.platform != "win32" or name is not None:
-            return backend.load_library(name, flags)
-        name = "c"    # Windows: load_library(None) fails, but this works
-                      # on Python 2 (backward compatibility hack only)
-    first_error = None
-    if '.' in name or '/' in name or os.sep in name:
-        try:
-            return backend.load_library(name, flags)
-        except OSError as e:
-            first_error = e
-    import ctypes.util
-    path = ctypes.util.find_library(name)
-    if path is None:
-        if name == "c" and sys.platform == "win32" and sys.version_info >= (3,):
-            raise OSError("dlopen(None) cannot work on Windows for Python 3 "
-                          "(see http://bugs.python.org/issue23606)")
-        msg = ("ctypes.util.find_library() did not manage "
-               "to locate a library called %r" % (name,))
-        if first_error is not None:
-            msg = "%s.  Additionally, %s" % (first_error, msg)
-        raise OSError(msg)
-    return backend.load_library(path, flags)
-
-def _make_ffi_library(ffi, libname, flags):
-    backend = ffi._backend
-    backendlib = _load_backend_lib(backend, libname, flags)
-    #
-    def accessor_function(name):
-        key = 'function ' + name
-        tp, _ = ffi._parser._declarations[key]
-        BType = ffi._get_cached_btype(tp)
-        value = backendlib.load_function(BType, name)
-        library.__dict__[name] = value
-    #
-    def accessor_variable(name):
-        key = 'variable ' + name
-        tp, _ = ffi._parser._declarations[key]
-        BType = ffi._get_cached_btype(tp)
-        read_variable = backendlib.read_variable
-        write_variable = backendlib.write_variable
-        setattr(FFILibrary, name, property(
-            lambda self: read_variable(BType, name),
-            lambda self, value: write_variable(BType, name, value)))
-    #
-    def addressof_var(name):
-        try:
-            return addr_variables[name]
-        except KeyError:
-            with ffi._lock:
-                if name not in addr_variables:
-                    key = 'variable ' + name
-                    tp, _ = ffi._parser._declarations[key]
-                    BType = ffi._get_cached_btype(tp)
-                    if BType.kind != 'array':
-                        BType = model.pointer_cache(ffi, BType)
-                    p = backendlib.load_function(BType, name)
-                    addr_variables[name] = p
-            return addr_variables[name]
-    #
-    def accessor_constant(name):
-        raise NotImplementedError("non-integer constant '%s' cannot be "
-                                  "accessed from a dlopen() library" % (name,))
-    #
-    def accessor_int_constant(name):
-        library.__dict__[name] = ffi._parser._int_constants[name]
-    #
-    accessors = {}
-    accessors_version = [False]
-    addr_variables = {}
-    #
-    def update_accessors():
-        if accessors_version[0] is ffi._cdef_version:
-            return
-        #
-        for key, (tp, _) in ffi._parser._declarations.items():
-            if not isinstance(tp, model.EnumType):
-                tag, name = key.split(' ', 1)
-                if tag == 'function':
-                    accessors[name] = accessor_function
-                elif tag == 'variable':
-                    accessors[name] = accessor_variable
-                elif tag == 'constant':
-                    accessors[name] = accessor_constant
-            else:
-                for i, enumname in enumerate(tp.enumerators):
-                    def accessor_enum(name, tp=tp, i=i):
-                        tp.check_not_partial()
-                        library.__dict__[name] = tp.enumvalues[i]
-                    accessors[enumname] = accessor_enum
-        for name in ffi._parser._int_constants:
-            accessors.setdefault(name, accessor_int_constant)
-        accessors_version[0] = ffi._cdef_version
-    #
-    def make_accessor(name):
-        with ffi._lock:
-            if name in library.__dict__ or name in FFILibrary.__dict__:
-                return    # added by another thread while waiting for the lock
-            if name not in accessors:
-                update_accessors()
-                if name not in accessors:
-                    raise AttributeError(name)
-            accessors[name](name)
-    #
-    class FFILibrary(object):
-        def __getattr__(self, name):
-            make_accessor(name)
-            return getattr(self, name)
-        def __setattr__(self, name, value):
-            try:
-                property = getattr(self.__class__, name)
-            except AttributeError:
-                make_accessor(name)
-                setattr(self, name, value)
-            else:
-                property.__set__(self, value)
-        def __dir__(self):
-            with ffi._lock:
-                update_accessors()
-                return accessors.keys()
-        def __addressof__(self, name):
-            if name in library.__dict__:
-                return library.__dict__[name]
-            if name in FFILibrary.__dict__:
-                return addressof_var(name)
-            make_accessor(name)
-            if name in library.__dict__:
-                return library.__dict__[name]
-            if name in FFILibrary.__dict__:
-                return addressof_var(name)
-            raise AttributeError("cffi library has no function or "
-                                 "global variable named '%s'" % (name,))
-        def __cffi_close__(self):
-            backendlib.close_lib()
-            self.__dict__.clear()
-    #
-    if isinstance(libname, basestring):
-        try:
-            if not isinstance(libname, str):    # unicode, on Python 2
-                libname = libname.encode('utf-8')
-            FFILibrary.__name__ = 'FFILibrary_%s' % libname
-        except UnicodeError:
-            pass
-    library = FFILibrary()
-    return library, library.__dict__
-
-def _builtin_function_type(func):
-    # a hack to make at least ffi.typeof(builtin_function) work,
-    # if the builtin function was obtained by 'vengine_cpy'.
-    import sys
-    try:
-        module = sys.modules[func.__module__]
-        ffi = module._cffi_original_ffi
-        types_of_builtin_funcs = module._cffi_types_of_builtin_funcs
-        tp = types_of_builtin_funcs[func]
-    except (KeyError, AttributeError, TypeError):
-        return None
-    else:
-        with ffi._lock:
-            return ffi._get_cached_btype(tp)
diff --git a/spaces/aryadytm/paraphrase/Dockerfile b/spaces/aryadytm/paraphrase/Dockerfile
deleted file mode 100644
index 6c28f45063487a72e0d7db146bb57e7b1fcb6780..0000000000000000000000000000000000000000
--- a/spaces/aryadytm/paraphrase/Dockerfile
+++ /dev/null
@@ -1,9 +0,0 @@
-FROM python:3.9.12-buster
-
-WORKDIR /app
-
-COPY . .
-
-RUN pip install -r requirements.txt
-
-CMD [ "streamlit", "run", "app.py" ]
\ No newline at end of file
diff --git a/spaces/aryadytm/remove-photo-object/src/__init__.py b/spaces/aryadytm/remove-photo-object/src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/awacke1/3d-Breakout-Game-Three.JS/index.html b/spaces/awacke1/3d-Breakout-Game-Three.JS/index.html
deleted file mode 100644
index 712ea9ad93bb6311f975337ef80d5f2786a27067..0000000000000000000000000000000000000000
--- a/spaces/awacke1/3d-Breakout-Game-Three.JS/index.html
+++ /dev/null
@@ -1,154 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-	<meta charset="UTF-8">
-	<title>🤓🕹️📱 3D Breakout Game</title>
-	<style>
-		body { margin: 0; }
-		canvas { width: 100%; height: 100%; }
-	</style>
-	<script src="https://cdn.jsdelivr.net/npm/three@0.132.2/build/three.min.js"></script>
-</head>
-<body>
-	<canvas id="gameCanvas"></canvas>
-
-	<script>
-		var scene = new THREE.Scene();
-		var camera = new THREE.PerspectiveCamera(75, window.innerWidth / window.innerHeight, 0.1, 1000);
-		camera.position.set(0, 30, 0);
-		camera.lookAt(new THREE.Vector3(0, 0, 0));
-		scene.add(camera);
-
-		var renderer = new THREE.WebGLRenderer({ canvas: document.getElementById('gameCanvas') });
-		renderer.setClearColor(0x000000);
-		renderer.setPixelRatio(window.devicePixelRatio);
-		renderer.setSize(window.innerWidth, window.innerHeight);
-
-		var ballGeometry = new THREE.SphereGeometry(0.5, 32, 32);
-		var ballMaterial = new THREE.MeshLambertMaterial({ color: 0xffffff });
-		var balls = [];
-
-		var brickGeometry = new THREE.BoxGeometry(2, 1, 1);
-		var bricks = [];
-
-		function addBall() {
-			var ball = new THREE.Mesh(ballGeometry, ballMaterial);
-			ball.position.set((Math.random() - 0.5) * 25, (Math.random() - 0.5) * 25, (Math.random() - 0.5) * 25);
-			ball.velocity = new THREE.Vector3((Math.random() - 0.5) * 2, (Math.random() - 0.5) * 2, (Math.random() - 0.5) * 2).normalize();
-			scene.add(ball);
-			balls.push(ball);
-		}
-
-		function addBrick() {
-			var brickMaterial = new THREE.MeshLambertMaterial({ color: Math.random() * 0xffffff });
-			var brick = new THREE.Mesh(brickGeometry, brickMaterial);
-			brick.position.set((Math.random() - 0.5) * 50, (Math.random() - 0.5) * 50, (Math.random() - 0.5) * 50);
-			scene.add(brick);
-			bricks.push(brick);
-		}
-
-		for (var i = 0; i < 50; i++) {
-			addBrick();
-		}
-
-		var light = new THREE.PointLight(0xffffff, 1, 100);
-		light.position.set(0, 30, 0);
-		scene.add(light);
-
-		var paddleGeometry = new THREE.BoxGeometry(4, 1, 1);
-		var paddleMaterial = new THREE.MeshLambertMaterial({ color: 0xffffff });
-		var paddle = new THREE.Mesh(paddleGeometry, paddleMaterial);
-		scene.add(paddle);
-
-		function animate() {
-			requestAnimationFrame(animate);
-
-			var paddleSpeed = 1.5;
-
-			if (keys["87"] || keys["38"]) { // w or up arrow
-				paddle.position.z -= paddleSpeed;
-			}
-			if (keys["83"] || keys["40"]) {
-paddle.position.z += paddleSpeed;
-}
-if (keys["65"] || keys["37"]) { // a or left arrow
-paddle.position.x -= paddleSpeed;
-}
-if (keys["68"] || keys["39"]) { // d or right arrow
-paddle.position.x += paddleSpeed;
-}
-
-		var mouseSpeed = 0.1;
-
-		if (mouseX !== null && mouseY !== null) {
-			paddle.position.x = (mouseX / window.innerWidth) * 50 - 25;
-			paddle.position.z = -(mouseY / window.innerHeight) * 50 + 25;
-		}
-
-		for (var i = 0; i < balls.length; i++) {
-			balls[i].position.add(balls[i].velocity);
-
-			if (balls[i].position.x + 0.5 > 25 || balls[i].position.x - 0.5 < -25) {
-				balls[i].velocity.x = -balls[i].velocity.x;
-			}
-
-			if (balls[i].position.y + 0.5 > 25 || balls[i].position.y - 0.5 < -25) {
-				balls[i].velocity.y = -balls[i].velocity.y;
-			}
-
-			if (balls[i].position.z + 0.5 > 25 || balls[i].position.z - 0.5 < -25) {
-				balls[i].velocity.z = -balls[i].velocity.z;
-			}
-
-			if (balls[i].position.y - 0.5 < -15 && balls[i].position.x + 0.5 > paddle.position.x - 2 && balls[i].position.x - 0.5 < paddle.position.x + 2 && balls[i].position.z + 0.5 > paddle.position.z - 0.5 && balls[i].position.z - 0.5 < paddle.position.z + 0.5) {
-				balls[i].velocity.y = Math.abs(balls[i].velocity.y);
-			}
-
-			for (var j = 0; j < bricks.length; j++) {
-				if (balls[i].position.distanceTo(bricks[j].position) <= 2) {
-					scene.remove(bricks[j]);
-					bricks.splice(j, 1);
-					balls[i].velocity.negate();
-					addBrick();
-				}
-			}
-		}
-
-		renderer.render(scene, camera);
-	}
-
-	var keys = {};
-	var mouseX = null;
-	var mouseY = null;
-
-	document.addEventListener('mousedown', function(event) {
-		if (event.button === 0) {
-			addBrick();
-		}
-		if (event.button === 2) {
-			addBall();
-		}
-	});
-
-	document.addEventListener('keydown', function(event) {
-		keys[event.keyCode] = true;
-	});
-
-	document.addEventListener('keyup', function(event) {
-		keys[event.keyCode] = false;
-	});
-
-	document.addEventListener('mousemove', function(event) {
-		mouseX = event.clientX;
-		mouseY = event.clientY;
-	});
-
-	document.addEventListener('mouseleave', function(event) {
-		mouseX = null;
-		mouseY = null;
-	});
-
-	animate();
-</script>
-</body>
-</html>
\ No newline at end of file
diff --git a/spaces/awacke1/TwoPlayerDiceGameCraps/README.md b/spaces/awacke1/TwoPlayerDiceGameCraps/README.md
deleted file mode 100644
index 1d3c36c307028e0e0eb158086aed22af9ad4d48e..0000000000000000000000000000000000000000
--- a/spaces/awacke1/TwoPlayerDiceGameCraps/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: TwoPlayerDiceGameCraps
-emoji: 📉
-colorFrom: blue
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/balacoon/tts/app.py b/spaces/balacoon/tts/app.py
deleted file mode 100644
index 408453f9b3b7dfe334a2fd7ae7ae97e4d332e6cf..0000000000000000000000000000000000000000
--- a/spaces/balacoon/tts/app.py
+++ /dev/null
@@ -1,131 +0,0 @@
-"""
-Copyright 2022 Balacoon
-
-TTS interactive demo
-"""
-
-import os
-import glob
-import logging
-from typing import cast
-from threading import Lock
-
-import gradio as gr
-from balacoon_tts import TTS
-from huggingface_hub import hf_hub_download, list_repo_files
-
-# locker that disallow access to the tts object from more then one thread
-locker = Lock()
-# global tts module, initialized from a model selected
-tts = None
-# path to the model that is currently used in tts
-cur_model_path = None
-# cache of speakers, maps model name to speaker list
-model_to_speakers = dict()
-model_repo_dir = "/data"
-for name in list_repo_files(repo_id="balacoon/tts"):
-    if not os.path.isfile(os.path.join(model_repo_dir, name)):
-        hf_hub_download(
-            repo_id="balacoon/tts",
-            filename=name,
-            local_dir=model_repo_dir,
-        )
-
-
-def main():
-    logging.basicConfig(level=logging.INFO)
-
-    with gr.Blocks() as demo:
-        gr.Markdown(
-            """
-            <h1 align="center">Balacoon🦝 Text-to-Speech</h1>
-
-            1. Write an utterance to generate,
-            2. Select the model to synthesize with
-            3. Select speaker
-            4. Hit "Generate" and listen to the result!
-
-            You can learn more about models available
-            [here](https://huggingface.co/balacoon/tts).
-            Visit [Balacoon website](https://balacoon.com/) for more info.
-            """
-        )
-        with gr.Row(variant="panel"):
-            text = gr.Textbox(label="Text", placeholder="Type something here...")
-
-        with gr.Row():
-            with gr.Column(variant="panel"):
-                repo_files = os.listdir(model_repo_dir)
-                model_files = [x for x in repo_files if x.endswith("_cpu.addon")]
-                model_name = gr.Dropdown(
-                    label="Model",
-                    choices=model_files,
-                )
-            with gr.Column(variant="panel"):
-                speaker = gr.Dropdown(label="Speaker", choices=[])
-
-            def set_model(model_name_str: str):
-                """
-                gets value from `model_name`. either
-                uses cached list of speakers for the given model name
-                or loads the addon and checks what are the speakers.
-                """
-                global model_to_speakers
-                if model_name_str in model_to_speakers:
-                    speakers = model_to_speakers[model_name_str]
-                else:
-                    global tts, cur_model_path, locker
-                    with locker:
-                        # need to load this model to learn the list of speakers
-                        model_path = os.path.join(model_repo_dir, model_name_str)
-                        if tts is not None:
-                            del tts
-                        tts = TTS(model_path)
-                        cur_model_path = model_path
-                        speakers = tts.get_speakers()
-                        model_to_speakers[model_name_str] = speakers
-                
-                value = speakers[-1]
-                return gr.Dropdown.update(
-                    choices=speakers, value=value, visible=True
-                )
-
-            model_name.change(set_model, inputs=model_name, outputs=speaker)
-
-        with gr.Row(variant="panel"):
-            generate = gr.Button("Generate")
-        with gr.Row(variant="panel"):
-            audio = gr.Audio()
-
-        def synthesize_audio(text_str: str, model_name_str: str, speaker_str: str):
-            """
-            gets utterance to synthesize from `text` Textbox
-            and speaker name from `speaker` dropdown list.
-            speaker name might be empty for single-speaker models.
-            Synthesizes the waveform and updates `audio` with it.
-            """
-            if not text_str or not model_name_str or not speaker_str:
-                logging.info("text, model name or speaker are not provided")
-                return None
-            expected_model_path = os.path.join(model_repo_dir, model_name_str)
-            global tts, cur_model_path, locker
-            with locker:
-                if expected_model_path != cur_model_path:
-                    # reload model
-                    if tts is not None:
-                        del tts
-                    tts = TTS(expected_model_path)
-                    cur_model_path = expected_model_path
-                if len(text_str) > 1024:
-                    # truncate the text
-                    text_str = text_str[:1024]
-                samples = tts.synthesize(text_str, speaker_str)
-            return gr.Audio.update(value=(tts.get_sampling_rate(), samples))
-
-        generate.click(synthesize_audio, inputs=[text, model_name, speaker], outputs=audio)
-
-    demo.queue(concurrency_count=1).launch()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/extras/core/CurvePath.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/extras/core/CurvePath.d.ts
deleted file mode 100644
index 8a3ce4165c46d128c36e3c565523a17eab70d1bd..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/extras/core/CurvePath.d.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-import { Curve } from './Curve';
-import { Geometry } from './../../core/Geometry';
-import { Vector } from './../../math/Vector2';
-
-export class CurvePath<T extends Vector> extends Curve<T> {
-  constructor();
-
-  curves: Curve<T>[];
-  autoClose: boolean;
-
-  add(curve: Curve<T>): void;
-  checkConnection(): boolean;
-  closePath(): void;
-  getPoint(t: number): T;
-  getLength(): number;
-  updateArcLengths(): void;
-  getCurveLengths(): number[];
-  getSpacedPoints(divisions?: number): T[];
-  getPoints(divisions?: number): T[];
-
-  /**
-   * @deprecated Use {@link Geometry#setFromPoints new THREE.Geometry().setFromPoints( points )} instead.
-   */
-  createPointsGeometry(divisions: number): Geometry;
-  /**
-   * @deprecated Use {@link Geometry#setFromPoints new THREE.Geometry().setFromPoints( points )} instead.
-   */
-  createSpacedPointsGeometry(divisions: number): Geometry;
-  /**
-   * @deprecated Use {@link Geometry#setFromPoints new THREE.Geometry().setFromPoints( points )} instead.
-   */
-  createGeometry(points: T[]): Geometry;
-}
diff --git a/spaces/bastiendechamps/geoguessr-bot/geoguessr_bot/retriever/__init__.py b/spaces/bastiendechamps/geoguessr-bot/geoguessr_bot/retriever/__init__.py
deleted file mode 100644
index 4fd1537abc034cd8b21b102835b7d9c4eecdd451..0000000000000000000000000000000000000000
--- a/spaces/bastiendechamps/geoguessr-bot/geoguessr_bot/retriever/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .retriever import Retriever
-from .abstract_embedder import AbstractImageEmbedder
-from .dino_embedder import DinoEmbedder
-from .dino_v2_embedder import DinoV2Embedder
-from .random_embedder import RandomEmbedder
diff --git a/spaces/bencoman/WhichWatersport/app.py b/spaces/bencoman/WhichWatersport/app.py
deleted file mode 100644
index cb52294cfe0702e0544b00a2c97c4f73d9313298..0000000000000000000000000000000000000000
--- a/spaces/bencoman/WhichWatersport/app.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from fastai.vision.all import *
-import gradio as gr
-import logging
-EXAMPLES_PATH = Path('./examples')
-
-learn = load_learner('watersports.pkl')
-categories = learn.dls.vocab
-
-def classify_image(img):
-    logging.warning('Watch out!!!')  # will print a message to the console
-    print('Classifying: ', img)
-    pred,idx,probs = learn.predict(img)
-    return( dict(zip(categories, map(float,probs))))
-
-interface_options = {
-    "title": "Which Watersport? Compare your guess to the machine inference.",
-    "description": "Click an image. Click  Submit. Click Clear. Other images can be dragged directly from a google search. \nNote, the machine classifier relies purely on vision, of the image pixels - no other info used.",
-    "examples": [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()],
-    "examples_per_page": "100"
-}                   
-
-iface = gr.Interface(fn=classify_image, 
-            inputs=gr.inputs.Image(shape=(192,192)), 
-            outputs=gr.outputs.Label(),
-            **interface_options)            
-
-iface.launch(inline=False)
-
diff --git a/spaces/bguberfain/Detic/detic/data/custom_dataset_mapper.py b/spaces/bguberfain/Detic/detic/data/custom_dataset_mapper.py
deleted file mode 100644
index c7727dded3f93f5eeafdcd72e257197e3fdc817b..0000000000000000000000000000000000000000
--- a/spaces/bguberfain/Detic/detic/data/custom_dataset_mapper.py
+++ /dev/null
@@ -1,280 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import copy
-import logging
-import numpy as np
-from typing import List, Optional, Union
-import torch
-import pycocotools.mask as mask_util
-
-from detectron2.config import configurable
-
-from detectron2.data import detection_utils as utils
-from detectron2.data.detection_utils import transform_keypoint_annotations
-from detectron2.data import transforms as T
-from detectron2.data.dataset_mapper import DatasetMapper
-from detectron2.structures import Boxes, BoxMode, Instances
-from detectron2.structures import Keypoints, PolygonMasks, BitMasks
-from fvcore.transforms.transform import TransformList
-from .custom_build_augmentation import build_custom_augmentation
-from .tar_dataset import DiskTarDataset
-
-__all__ = ["CustomDatasetMapper"]
-
-class CustomDatasetMapper(DatasetMapper):
-    @configurable
-    def __init__(self, is_train: bool, 
-        with_ann_type=False,
-        dataset_ann=[],
-        use_diff_bs_size=False,
-        dataset_augs=[],
-        is_debug=False,
-        use_tar_dataset=False,
-        tarfile_path='',
-        tar_index_dir='',
-        **kwargs):
-        """
-        add image labels
-        """
-        self.with_ann_type = with_ann_type
-        self.dataset_ann = dataset_ann
-        self.use_diff_bs_size = use_diff_bs_size
-        if self.use_diff_bs_size and is_train:
-            self.dataset_augs = [T.AugmentationList(x) for x in dataset_augs]
-        self.is_debug = is_debug
-        self.use_tar_dataset = use_tar_dataset
-        if self.use_tar_dataset:
-            print('Using tar dataset')
-            self.tar_dataset = DiskTarDataset(tarfile_path, tar_index_dir)
-        super().__init__(is_train, **kwargs)
- 
-
-    @classmethod
-    def from_config(cls, cfg, is_train: bool = True):
-        ret = super().from_config(cfg, is_train)
-        ret.update({
-            'with_ann_type': cfg.WITH_IMAGE_LABELS,
-            'dataset_ann': cfg.DATALOADER.DATASET_ANN,
-            'use_diff_bs_size': cfg.DATALOADER.USE_DIFF_BS_SIZE,
-            'is_debug': cfg.IS_DEBUG,
-            'use_tar_dataset': cfg.DATALOADER.USE_TAR_DATASET,
-            'tarfile_path': cfg.DATALOADER.TARFILE_PATH,
-            'tar_index_dir': cfg.DATALOADER.TAR_INDEX_DIR,
-        })
-        if ret['use_diff_bs_size'] and is_train:
-            if cfg.INPUT.CUSTOM_AUG == 'EfficientDetResizeCrop':
-                dataset_scales = cfg.DATALOADER.DATASET_INPUT_SCALE
-                dataset_sizes = cfg.DATALOADER.DATASET_INPUT_SIZE
-                ret['dataset_augs'] = [
-                    build_custom_augmentation(cfg, True, scale, size) \
-                        for scale, size in zip(dataset_scales, dataset_sizes)]
-            else:
-                assert cfg.INPUT.CUSTOM_AUG == 'ResizeShortestEdge'
-                min_sizes = cfg.DATALOADER.DATASET_MIN_SIZES
-                max_sizes = cfg.DATALOADER.DATASET_MAX_SIZES
-                ret['dataset_augs'] = [
-                    build_custom_augmentation(
-                        cfg, True, min_size=mi, max_size=ma) \
-                        for mi, ma in zip(min_sizes, max_sizes)]
-        else:
-            ret['dataset_augs'] = []
-
-        return ret
-
-    def __call__(self, dataset_dict):
-        """
-        include image labels
-        """
-        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
-        # USER: Write your own image loading if it's not from a file
-        if 'file_name' in dataset_dict:
-            ori_image = utils.read_image(
-                dataset_dict["file_name"], format=self.image_format)
-        else:
-            ori_image, _, _ = self.tar_dataset[dataset_dict["tar_index"]]
-            ori_image = utils._apply_exif_orientation(ori_image)
-            ori_image = utils.convert_PIL_to_numpy(ori_image, self.image_format)
-        utils.check_image_size(dataset_dict, ori_image)
-
-        # USER: Remove if you don't do semantic/panoptic segmentation.
-        if "sem_seg_file_name" in dataset_dict:
-            sem_seg_gt = utils.read_image(
-                dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2)
-        else:
-            sem_seg_gt = None
-
-        if self.is_debug:
-            dataset_dict['dataset_source'] = 0
-
-        not_full_labeled = 'dataset_source' in dataset_dict and \
-            self.with_ann_type and \
-                self.dataset_ann[dataset_dict['dataset_source']] != 'box'
-
-        aug_input = T.AugInput(copy.deepcopy(ori_image), sem_seg=sem_seg_gt)
-        if self.use_diff_bs_size and self.is_train:
-            transforms = \
-                self.dataset_augs[dataset_dict['dataset_source']](aug_input)
-        else:
-            transforms = self.augmentations(aug_input)
-        image, sem_seg_gt = aug_input.image, aug_input.sem_seg
-
-        image_shape = image.shape[:2]  # h, w
-        dataset_dict["image"] = torch.as_tensor(
-            np.ascontiguousarray(image.transpose(2, 0, 1)))
-        
-        if sem_seg_gt is not None:
-            dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long"))
-
-        # USER: Remove if you don't use pre-computed proposals.
-        # Most users would not need this feature.
-        if self.proposal_topk is not None:
-            utils.transform_proposals(
-                dataset_dict, image_shape, transforms, 
-                proposal_topk=self.proposal_topk
-            )
-
-        if not self.is_train:
-            # USER: Modify this if you want to keep them for some reason.
-            dataset_dict.pop("annotations", None)
-            dataset_dict.pop("sem_seg_file_name", None)
-            return dataset_dict
-
-        if "annotations" in dataset_dict:
-            # USER: Modify this if you want to keep them for some reason.
-            for anno in dataset_dict["annotations"]:
-                if not self.use_instance_mask:
-                    anno.pop("segmentation", None)
-                if not self.use_keypoint:
-                    anno.pop("keypoints", None)
-
-            # USER: Implement additional transformations if you have other types of data
-            all_annos = [
-                (utils.transform_instance_annotations(
-                    obj, transforms, image_shape, 
-                    keypoint_hflip_indices=self.keypoint_hflip_indices,
-                ),  obj.get("iscrowd", 0))
-                for obj in dataset_dict.pop("annotations")
-            ]
-            annos = [ann[0] for ann in all_annos if ann[1] == 0]
-            instances = utils.annotations_to_instances(
-                annos, image_shape, mask_format=self.instance_mask_format
-            )
-            
-            del all_annos
-            if self.recompute_boxes:
-                instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
-            dataset_dict["instances"] = utils.filter_empty_instances(instances)
-        if self.with_ann_type:
-            dataset_dict["pos_category_ids"] = dataset_dict.get(
-                'pos_category_ids', [])
-            dataset_dict["ann_type"] = \
-                self.dataset_ann[dataset_dict['dataset_source']]
-        if self.is_debug and (('pos_category_ids' not in dataset_dict) or \
-            (dataset_dict['pos_category_ids'] == [])):
-            dataset_dict['pos_category_ids'] = [x for x in sorted(set(
-                dataset_dict['instances'].gt_classes.tolist()
-            ))]
-        return dataset_dict
-
-# DETR augmentation
-def build_transform_gen(cfg, is_train):
-    """
-    """
-    if is_train:
-        min_size = cfg.INPUT.MIN_SIZE_TRAIN
-        max_size = cfg.INPUT.MAX_SIZE_TRAIN
-        sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING
-    else:
-        min_size = cfg.INPUT.MIN_SIZE_TEST
-        max_size = cfg.INPUT.MAX_SIZE_TEST
-        sample_style = "choice"
-    if sample_style == "range":
-        assert len(min_size) == 2, "more than 2 ({}) min_size(s) are provided for ranges".format(len(min_size))
-
-    logger = logging.getLogger(__name__)
-    tfm_gens = []
-    if is_train:
-        tfm_gens.append(T.RandomFlip())
-    tfm_gens.append(T.ResizeShortestEdge(min_size, max_size, sample_style))
-    if is_train:
-        logger.info("TransformGens used in training: " + str(tfm_gens))
-    return tfm_gens
-
-
-class DetrDatasetMapper:
-    """
-    A callable which takes a dataset dict in Detectron2 Dataset format,
-    and map it into a format used by DETR.
-    The callable currently does the following:
-    1. Read the image from "file_name"
-    2. Applies geometric transforms to the image and annotation
-    3. Find and applies suitable cropping to the image and annotation
-    4. Prepare image and annotation to Tensors
-    """
-
-    def __init__(self, cfg, is_train=True):
-        if cfg.INPUT.CROP.ENABLED and is_train:
-            self.crop_gen = [
-                T.ResizeShortestEdge([400, 500, 600], sample_style="choice"),
-                T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE),
-            ]
-        else:
-            self.crop_gen = None
-
-        self.mask_on = cfg.MODEL.MASK_ON
-        self.tfm_gens = build_transform_gen(cfg, is_train)
-        logging.getLogger(__name__).info(
-            "Full TransformGens used in training: {}, crop: {}".format(str(self.tfm_gens), str(self.crop_gen))
-        )
-
-        self.img_format = cfg.INPUT.FORMAT
-        self.is_train = is_train
-
-    def __call__(self, dataset_dict):
-        """
-        Args:
-            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
-        Returns:
-            dict: a format that builtin models in detectron2 accept
-        """
-        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
-        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
-        utils.check_image_size(dataset_dict, image)
-
-        if self.crop_gen is None:
-            image, transforms = T.apply_transform_gens(self.tfm_gens, image)
-        else:
-            if np.random.rand() > 0.5:
-                image, transforms = T.apply_transform_gens(self.tfm_gens, image)
-            else:
-                image, transforms = T.apply_transform_gens(
-                    self.tfm_gens[:-1] + self.crop_gen + self.tfm_gens[-1:], image
-                )
-
-        image_shape = image.shape[:2]  # h, w
-
-        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
-        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
-        # Therefore it's important to use torch.Tensor.
-        dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
-
-        if not self.is_train:
-            # USER: Modify this if you want to keep them for some reason.
-            dataset_dict.pop("annotations", None)
-            return dataset_dict
-
-        if "annotations" in dataset_dict:
-            # USER: Modify this if you want to keep them for some reason.
-            for anno in dataset_dict["annotations"]:
-                if not self.mask_on:
-                    anno.pop("segmentation", None)
-                anno.pop("keypoints", None)
-
-            # USER: Implement additional transformations if you have other types of data
-            annos = [
-                utils.transform_instance_annotations(obj, transforms, image_shape)
-                for obj in dataset_dict.pop("annotations")
-                if obj.get("iscrowd", 0) == 0
-            ]
-            instances = utils.annotations_to_instances(annos, image_shape)
-            dataset_dict["instances"] = utils.filter_empty_instances(instances)
-        return dataset_dict
\ No newline at end of file
diff --git a/spaces/bigslime/stablediffusion-infinity/PyPatchMatch/csrc/masked_image.cpp b/spaces/bigslime/stablediffusion-infinity/PyPatchMatch/csrc/masked_image.cpp
deleted file mode 100644
index 448a776b3cda9f39f4dd0ad908f1b135c647ca8f..0000000000000000000000000000000000000000
--- a/spaces/bigslime/stablediffusion-infinity/PyPatchMatch/csrc/masked_image.cpp
+++ /dev/null
@@ -1,138 +0,0 @@
-#include "masked_image.h"
-#include <algorithm>
-#include <iostream>
-
-const cv::Size MaskedImage::kDownsampleKernelSize = cv::Size(6, 6);
-const int MaskedImage::kDownsampleKernel[6] = {1, 5, 10, 10, 5, 1};
-
-bool MaskedImage::contains_mask(int y, int x, int patch_size) const {
-    auto mask_size = size();
-    for (int dy = -patch_size; dy <= patch_size; ++dy) {
-        for (int dx = -patch_size; dx <= patch_size; ++dx) {
-            int yy = y + dy, xx = x + dx;
-            if (yy >= 0 && yy < mask_size.height && xx >= 0 && xx < mask_size.width) {
-                if (is_masked(yy, xx) && !is_globally_masked(yy, xx)) return true;
-            }
-        }
-    }
-    return false;
-}
-
-MaskedImage MaskedImage::downsample() const {
-    const auto &kernel_size = MaskedImage::kDownsampleKernelSize;
-    const auto &kernel = MaskedImage::kDownsampleKernel;
-
-    const auto size = this->size();
-    const auto new_size = cv::Size(size.width / 2, size.height / 2);
-
-    auto ret = MaskedImage(new_size.width, new_size.height);
-    if (!m_global_mask.empty()) ret.init_global_mask_mat();
-    for (int y = 0; y < size.height - 1; y += 2) {
-        for (int x = 0; x < size.width - 1; x += 2) {
-            int r = 0, g = 0, b = 0, ksum = 0;
-            bool is_gmasked = true;
-
-            for (int dy = -kernel_size.height / 2 + 1; dy <= kernel_size.height / 2; ++dy) {
-                for (int dx = -kernel_size.width / 2 + 1; dx <= kernel_size.width / 2; ++dx) {
-                    int yy = y + dy, xx = x + dx;
-                    if (yy >= 0 && yy < size.height && xx >= 0 && xx < size.width) {
-                        if (!is_globally_masked(yy, xx)) {
-                            is_gmasked = false;
-                        }
-                        if (!is_masked(yy, xx)) {
-                            auto source_ptr = get_image(yy, xx);
-                            int k = kernel[kernel_size.height / 2 - 1 + dy] * kernel[kernel_size.width / 2 - 1 + dx];
-                            r += source_ptr[0] * k, g += source_ptr[1] * k, b += source_ptr[2] * k;
-                            ksum += k;
-                        }
-                    }
-                }
-            }
-
-            if (ksum > 0) r /= ksum, g /= ksum, b /= ksum;
-
-            if (!m_global_mask.empty()) {
-                ret.set_global_mask(y / 2, x / 2, is_gmasked);
-            }
-            if (ksum > 0) {
-                auto target_ptr = ret.get_mutable_image(y / 2, x / 2);
-                target_ptr[0] = r, target_ptr[1] = g, target_ptr[2] = b;
-                ret.set_mask(y / 2, x / 2, 0);
-            } else {
-                ret.set_mask(y / 2, x / 2, 1);
-            }
-        }
-    }
-
-    return ret;
-}
-
-MaskedImage MaskedImage::upsample(int new_w, int new_h) const {
-    const auto size = this->size();
-    auto ret = MaskedImage(new_w, new_h);
-    if (!m_global_mask.empty()) ret.init_global_mask_mat();
-    for (int y = 0; y < new_h; ++y) {
-        for (int x = 0; x < new_w; ++x) {
-            int yy = y * size.height / new_h;
-            int xx = x * size.width / new_w;
-
-            if (is_globally_masked(yy, xx)) {
-                ret.set_global_mask(y, x, 1);
-                ret.set_mask(y, x, 1);
-            } else {
-                if (!m_global_mask.empty()) ret.set_global_mask(y, x, 0);
-
-                if (is_masked(yy, xx)) {
-                    ret.set_mask(y, x, 1);
-                } else {
-                    auto source_ptr = get_image(yy, xx);
-                    auto target_ptr = ret.get_mutable_image(y, x);
-                    for (int c = 0; c < 3; ++c)
-                        target_ptr[c] = source_ptr[c];
-                    ret.set_mask(y, x, 0);
-                }
-            }
-        }
-    }
-
-    return ret;
-}
-
-MaskedImage MaskedImage::upsample(int new_w, int new_h, const cv::Mat &new_global_mask) const {
-    auto ret = upsample(new_w, new_h);
-    ret.set_global_mask_mat(new_global_mask);
-    return ret;
-}
-
-void MaskedImage::compute_image_gradients() {
-    if (m_image_grad_computed) {
-        return;
-    }
-
-    const auto size = m_image.size();
-    m_image_grady = cv::Mat(size, CV_8UC3);
-    m_image_gradx = cv::Mat(size, CV_8UC3);
-    m_image_grady = cv::Scalar::all(0);
-    m_image_gradx = cv::Scalar::all(0);
-
-    for (int i = 1; i < size.height - 1; ++i) {
-        const auto *ptr = m_image.ptr<unsigned char>(i, 0);
-        const auto *ptry1 = m_image.ptr<unsigned char>(i + 1, 0);
-        const auto *ptry2 = m_image.ptr<unsigned char>(i - 1, 0);
-        const auto *ptrx1 = m_image.ptr<unsigned char>(i, 0) + 3;
-        const auto *ptrx2 = m_image.ptr<unsigned char>(i, 0) - 3;
-        auto *mptry = m_image_grady.ptr<unsigned char>(i, 0);
-        auto *mptrx = m_image_gradx.ptr<unsigned char>(i, 0);
-        for (int j = 3; j < size.width * 3 - 3; ++j) {
-            mptry[j] = (ptry1[j] / 2 - ptry2[j] / 2) + 128;
-            mptrx[j] = (ptrx1[j] / 2 - ptrx2[j] / 2) + 128;
-        }
-    }
-
-    m_image_grad_computed = true;
-}
-
-void MaskedImage::compute_image_gradients() const {
-    const_cast<MaskedImage *>(this)->compute_image_gradients();
-}
-
diff --git a/spaces/bodah/RVC-Models-bo/lib/infer_pack/transforms.py b/spaces/bodah/RVC-Models-bo/lib/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/bodah/RVC-Models-bo/lib/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails=None,
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
-    outputs, logabsdet = spline_fn(
-        inputs=inputs,
-        unnormalized_widths=unnormalized_widths,
-        unnormalized_heights=unnormalized_heights,
-        unnormalized_derivatives=unnormalized_derivatives,
-        inverse=inverse,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-        **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails="linear",
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == "linear":
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError("{} tails are not implemented.".format(tails))
-
-    (
-        outputs[inside_interval_mask],
-        logabsdet[inside_interval_mask],
-    ) = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound,
-        right=tail_bound,
-        bottom=-tail_bound,
-        top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-    )
-
-    return outputs, logabsdet
-
-
-def rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    left=0.0,
-    right=1.0,
-    bottom=0.0,
-    top=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError("Input to a transform is not within its domain")
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError("Minimal bin width too large for the number of bins")
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError("Minimal bin height too large for the number of bins")
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        ) + input_heights * (input_delta - input_derivatives)
-        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        )
-        c = -input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * root.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - root).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (
-            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
-        )
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * theta.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - theta).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git "a/spaces/caojiachen1/ChatGPT/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py" "b/spaces/caojiachen1/ChatGPT/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py"
deleted file mode 100644
index dc92256dcb998294a27b13ed07c34d38d18b329a..0000000000000000000000000000000000000000
--- "a/spaces/caojiachen1/ChatGPT/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py"
+++ /dev/null
@@ -1,70 +0,0 @@
-from predict import predict_no_ui
-from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
-fast_debug = False
-
-
-def 解析Paper(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt):
-    import time, glob, os
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8') as f:
-            file_content = f.read()
-
-        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
-        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        print('[1] yield chatbot, history')
-        yield chatbot, history, '正常'
-
-        if not fast_debug: 
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[])   # 带超时倒计时
-
-            print('[2] end gpt req')
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            print('[3] yield chatbot, history')
-            yield chatbot, history, msg
-            print('[4] next')
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield chatbot, history, '正常'
-
-    if not fast_debug: 
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say, chatbot, top_p, temperature, history=history)   # 带超时倒计时
-
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield chatbot, history, msg
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield chatbot, history, msg
-
-
-
-@CatchException
-def 读文章写摘要(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 解析Paper(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/Rethinking-BatchNorm/configs/retinanet_SyncBNhead_SharedTraining.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/Rethinking-BatchNorm/configs/retinanet_SyncBNhead_SharedTraining.py
deleted file mode 100644
index 3f146009d04aad2fca08d970569a4d76d46c9bd2..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/Rethinking-BatchNorm/configs/retinanet_SyncBNhead_SharedTraining.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from typing import List
-import torch
-from torch import Tensor, nn
-
-from detectron2.modeling.meta_arch.retinanet import RetinaNetHead
-
-
-def apply_sequential(inputs, modules):
-    for mod in modules:
-        if isinstance(mod, (nn.BatchNorm2d, nn.SyncBatchNorm)):
-            # for BN layer, normalize all inputs together
-            shapes = [i.shape for i in inputs]
-            spatial_sizes = [s[2] * s[3] for s in shapes]
-            x = [i.flatten(2) for i in inputs]
-            x = torch.cat(x, dim=2).unsqueeze(3)
-            x = mod(x).split(spatial_sizes, dim=2)
-            inputs = [i.view(s) for s, i in zip(shapes, x)]
-        else:
-            inputs = [mod(i) for i in inputs]
-    return inputs
-
-
-class RetinaNetHead_SharedTrainingBN(RetinaNetHead):
-    def forward(self, features: List[Tensor]):
-        logits = apply_sequential(features, list(self.cls_subnet) + [self.cls_score])
-        bbox_reg = apply_sequential(features, list(self.bbox_subnet) + [self.bbox_pred])
-        return logits, bbox_reg
-
-
-from .retinanet_SyncBNhead import model, dataloader, lr_multiplier, optimizer, train
-
-model.head._target_ = RetinaNetHead_SharedTrainingBN
diff --git a/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/monotonic_align/setup.py b/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/monotonic_align/setup.py
deleted file mode 100644
index 30c224807a70faa9df9c9eb75f8e80c8c867b16b..0000000000000000000000000000000000000000
--- a/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/monotonic_align/setup.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from distutils.core import setup
-from Cython.Build import cythonize
-import numpy
-
-setup(
-  name = 'monotonic_align',
-  ext_modules = cythonize("core.pyx"),
-  include_dirs=[numpy.get_include()]
-)
diff --git a/spaces/chilleverydaychill/roop/roop/processors/frame/core.py b/spaces/chilleverydaychill/roop/roop/processors/frame/core.py
deleted file mode 100644
index c225f9de483a2914a98392ce9de5bd03f2013a2d..0000000000000000000000000000000000000000
--- a/spaces/chilleverydaychill/roop/roop/processors/frame/core.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import os
-import importlib
-import psutil
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from queue import Queue
-from types import ModuleType
-from typing import Any, List, Callable
-from tqdm import tqdm
-
-import roop
-
-FRAME_PROCESSORS_MODULES: List[ModuleType] = []
-FRAME_PROCESSORS_INTERFACE = [
-    'pre_check',
-    'pre_start',
-    'process_frame',
-    'process_frames',
-    'process_image',
-    'process_video',
-    'post_process'
-]
-
-
-def load_frame_processor_module(frame_processor: str) -> Any:
-    try:
-        frame_processor_module = importlib.import_module(f'roop.processors.frame.{frame_processor}')
-        for method_name in FRAME_PROCESSORS_INTERFACE:
-            if not hasattr(frame_processor_module, method_name):
-                raise NotImplementedError
-    except (ImportError, NotImplementedError):
-        quit(f'Frame processor {frame_processor} crashed.')
-    return frame_processor_module
-
-
-def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType]:
-    global FRAME_PROCESSORS_MODULES
-
-    if not FRAME_PROCESSORS_MODULES:
-        for frame_processor in frame_processors:
-            frame_processor_module = load_frame_processor_module(frame_processor)
-            FRAME_PROCESSORS_MODULES.append(frame_processor_module)
-    return FRAME_PROCESSORS_MODULES
-
-
-def multi_process_frame(source_path: str, temp_frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None], update: Callable[[], None]) -> None:
-    with ThreadPoolExecutor(max_workers=roop.globals.execution_threads) as executor:
-        futures = []
-        queue = create_queue(temp_frame_paths)
-        queue_per_future = len(temp_frame_paths) // roop.globals.execution_threads
-        while not queue.empty():
-            future = executor.submit(process_frames, source_path, pick_queue(queue, queue_per_future), update)
-            futures.append(future)
-        for future in as_completed(futures):
-            future.result()
-
-
-def create_queue(temp_frame_paths: List[str]) -> Queue[str]:
-    queue: Queue[str] = Queue()
-    for frame_path in temp_frame_paths:
-        queue.put(frame_path)
-    return queue
-
-
-def pick_queue(queue: Queue[str], queue_per_future: int) -> List[str]:
-    queues = []
-    for _ in range(queue_per_future):
-        if not queue.empty():
-            queues.append(queue.get())
-    return queues
-
-
-def process_video(source_path: str, frame_paths: list[str], process_frames: Callable[[str, List[str], Any], None]) -> None:
-    progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
-    total = len(frame_paths)
-    with tqdm(total=total, desc='Processing', unit='frame', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
-        multi_process_frame(source_path, frame_paths, process_frames, lambda: update_progress(progress))
-
-
-def update_progress(progress: Any = None) -> None:
-    process = psutil.Process(os.getpid())
-    memory_usage = process.memory_info().rss / 1024 / 1024 / 1024
-    progress.set_postfix({
-        'memory_usage': '{:.2f}'.format(memory_usage).zfill(5) + 'GB',
-        'execution_providers': roop.globals.execution_providers,
-        'execution_threads': roop.globals.execution_threads
-    })
-    progress.refresh()
-    progress.update(1)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/faiss/swigfaiss.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/faiss/swigfaiss.py
deleted file mode 100644
index fd96fea04f026ceb89d44d1e344e1e0f3f67b673..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/faiss/swigfaiss.py
+++ /dev/null
@@ -1,10854 +0,0 @@
-# This file was automatically generated by SWIG (https://www.swig.org).
-# Version 4.1.1
-#
-# Do not make changes to this file unless you know what you are doing - modify
-# the SWIG interface file instead.
-
-from sys import version_info as _swig_python_version_info
-# Import the low-level C/C++ module
-if __package__ or "." in __name__:
-    from . import _swigfaiss
-else:
-    import _swigfaiss
-
-try:
-    import builtins as __builtin__
-except ImportError:
-    import __builtin__
-
-def _swig_repr(self):
-    try:
-        strthis = "proxy of " + self.this.__repr__()
-    except __builtin__.Exception:
-        strthis = ""
-    return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
-
-
-def _swig_setattr_nondynamic_instance_variable(set):
-    def set_instance_attr(self, name, value):
-        if name == "this":
-            set(self, name, value)
-        elif name == "thisown":
-            self.this.own(value)
-        elif hasattr(self, name) and isinstance(getattr(type(self), name), property):
-            set(self, name, value)
-        else:
-            raise AttributeError("You cannot add instance attributes to %s" % self)
-    return set_instance_attr
-
-
-def _swig_setattr_nondynamic_class_variable(set):
-    def set_class_attr(cls, name, value):
-        if hasattr(cls, name) and not isinstance(getattr(cls, name), property):
-            set(cls, name, value)
-        else:
-            raise AttributeError("You cannot add class attributes to %s" % cls)
-    return set_class_attr
-
-
-def _swig_add_metaclass(metaclass):
-    """Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass"""
-    def wrapper(cls):
-        return metaclass(cls.__name__, cls.__bases__, cls.__dict__.copy())
-    return wrapper
-
-
-class _SwigNonDynamicMeta(type):
-    """Meta class to enforce nondynamic attributes (no new attributes) for a class"""
-    __setattr__ = _swig_setattr_nondynamic_class_variable(type.__setattr__)
-
-
-class SwigPyIterator(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    __swig_destroy__ = _swigfaiss.delete_SwigPyIterator
-
-    def value(self):
-        return _swigfaiss.SwigPyIterator_value(self)
-
-    def incr(self, n=1):
-        return _swigfaiss.SwigPyIterator_incr(self, n)
-
-    def decr(self, n=1):
-        return _swigfaiss.SwigPyIterator_decr(self, n)
-
-    def distance(self, x):
-        return _swigfaiss.SwigPyIterator_distance(self, x)
-
-    def equal(self, x):
-        return _swigfaiss.SwigPyIterator_equal(self, x)
-
-    def copy(self):
-        return _swigfaiss.SwigPyIterator_copy(self)
-
-    def next(self):
-        return _swigfaiss.SwigPyIterator_next(self)
-
-    def __next__(self):
-        return _swigfaiss.SwigPyIterator___next__(self)
-
-    def previous(self):
-        return _swigfaiss.SwigPyIterator_previous(self)
-
-    def advance(self, n):
-        return _swigfaiss.SwigPyIterator_advance(self, n)
-
-    def __eq__(self, x):
-        return _swigfaiss.SwigPyIterator___eq__(self, x)
-
-    def __ne__(self, x):
-        return _swigfaiss.SwigPyIterator___ne__(self, x)
-
-    def __iadd__(self, n):
-        return _swigfaiss.SwigPyIterator___iadd__(self, n)
-
-    def __isub__(self, n):
-        return _swigfaiss.SwigPyIterator___isub__(self, n)
-
-    def __add__(self, n):
-        return _swigfaiss.SwigPyIterator___add__(self, n)
-
-    def __sub__(self, *args):
-        return _swigfaiss.SwigPyIterator___sub__(self, *args)
-    def __iter__(self):
-        return self
-
-# Register SwigPyIterator in _swigfaiss:
-_swigfaiss.SwigPyIterator_swigregister(SwigPyIterator)
-SHARED_PTR_DISOWN = _swigfaiss.SHARED_PTR_DISOWN
-class Float32Vector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.Float32Vector_swiginit(self, _swigfaiss.new_Float32Vector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.Float32Vector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.Float32Vector_clear(self)
-
-    def data(self):
-        return _swigfaiss.Float32Vector_data(self)
-
-    def size(self):
-        return _swigfaiss.Float32Vector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.Float32Vector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.Float32Vector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.Float32Vector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_Float32Vector
-
-# Register Float32Vector in _swigfaiss:
-_swigfaiss.Float32Vector_swigregister(Float32Vector)
-class Float64Vector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.Float64Vector_swiginit(self, _swigfaiss.new_Float64Vector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.Float64Vector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.Float64Vector_clear(self)
-
-    def data(self):
-        return _swigfaiss.Float64Vector_data(self)
-
-    def size(self):
-        return _swigfaiss.Float64Vector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.Float64Vector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.Float64Vector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.Float64Vector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_Float64Vector
-
-# Register Float64Vector in _swigfaiss:
-_swigfaiss.Float64Vector_swigregister(Float64Vector)
-class Int8Vector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.Int8Vector_swiginit(self, _swigfaiss.new_Int8Vector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.Int8Vector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.Int8Vector_clear(self)
-
-    def data(self):
-        return _swigfaiss.Int8Vector_data(self)
-
-    def size(self):
-        return _swigfaiss.Int8Vector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.Int8Vector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.Int8Vector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.Int8Vector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_Int8Vector
-
-# Register Int8Vector in _swigfaiss:
-_swigfaiss.Int8Vector_swigregister(Int8Vector)
-class Int16Vector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.Int16Vector_swiginit(self, _swigfaiss.new_Int16Vector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.Int16Vector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.Int16Vector_clear(self)
-
-    def data(self):
-        return _swigfaiss.Int16Vector_data(self)
-
-    def size(self):
-        return _swigfaiss.Int16Vector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.Int16Vector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.Int16Vector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.Int16Vector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_Int16Vector
-
-# Register Int16Vector in _swigfaiss:
-_swigfaiss.Int16Vector_swigregister(Int16Vector)
-class Int32Vector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.Int32Vector_swiginit(self, _swigfaiss.new_Int32Vector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.Int32Vector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.Int32Vector_clear(self)
-
-    def data(self):
-        return _swigfaiss.Int32Vector_data(self)
-
-    def size(self):
-        return _swigfaiss.Int32Vector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.Int32Vector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.Int32Vector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.Int32Vector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_Int32Vector
-
-# Register Int32Vector in _swigfaiss:
-_swigfaiss.Int32Vector_swigregister(Int32Vector)
-class Int64Vector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.Int64Vector_swiginit(self, _swigfaiss.new_Int64Vector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.Int64Vector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.Int64Vector_clear(self)
-
-    def data(self):
-        return _swigfaiss.Int64Vector_data(self)
-
-    def size(self):
-        return _swigfaiss.Int64Vector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.Int64Vector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.Int64Vector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.Int64Vector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_Int64Vector
-
-# Register Int64Vector in _swigfaiss:
-_swigfaiss.Int64Vector_swigregister(Int64Vector)
-class UInt8Vector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.UInt8Vector_swiginit(self, _swigfaiss.new_UInt8Vector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.UInt8Vector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.UInt8Vector_clear(self)
-
-    def data(self):
-        return _swigfaiss.UInt8Vector_data(self)
-
-    def size(self):
-        return _swigfaiss.UInt8Vector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.UInt8Vector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.UInt8Vector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.UInt8Vector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_UInt8Vector
-
-# Register UInt8Vector in _swigfaiss:
-_swigfaiss.UInt8Vector_swigregister(UInt8Vector)
-class UInt16Vector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.UInt16Vector_swiginit(self, _swigfaiss.new_UInt16Vector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.UInt16Vector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.UInt16Vector_clear(self)
-
-    def data(self):
-        return _swigfaiss.UInt16Vector_data(self)
-
-    def size(self):
-        return _swigfaiss.UInt16Vector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.UInt16Vector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.UInt16Vector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.UInt16Vector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_UInt16Vector
-
-# Register UInt16Vector in _swigfaiss:
-_swigfaiss.UInt16Vector_swigregister(UInt16Vector)
-class UInt32Vector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.UInt32Vector_swiginit(self, _swigfaiss.new_UInt32Vector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.UInt32Vector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.UInt32Vector_clear(self)
-
-    def data(self):
-        return _swigfaiss.UInt32Vector_data(self)
-
-    def size(self):
-        return _swigfaiss.UInt32Vector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.UInt32Vector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.UInt32Vector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.UInt32Vector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_UInt32Vector
-
-# Register UInt32Vector in _swigfaiss:
-_swigfaiss.UInt32Vector_swigregister(UInt32Vector)
-class UInt64Vector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.UInt64Vector_swiginit(self, _swigfaiss.new_UInt64Vector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.UInt64Vector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.UInt64Vector_clear(self)
-
-    def data(self):
-        return _swigfaiss.UInt64Vector_data(self)
-
-    def size(self):
-        return _swigfaiss.UInt64Vector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.UInt64Vector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.UInt64Vector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.UInt64Vector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_UInt64Vector
-
-# Register UInt64Vector in _swigfaiss:
-_swigfaiss.UInt64Vector_swigregister(UInt64Vector)
-class Float32VectorVector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.Float32VectorVector_swiginit(self, _swigfaiss.new_Float32VectorVector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.Float32VectorVector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.Float32VectorVector_clear(self)
-
-    def data(self):
-        return _swigfaiss.Float32VectorVector_data(self)
-
-    def size(self):
-        return _swigfaiss.Float32VectorVector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.Float32VectorVector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.Float32VectorVector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.Float32VectorVector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_Float32VectorVector
-
-# Register Float32VectorVector in _swigfaiss:
-_swigfaiss.Float32VectorVector_swigregister(Float32VectorVector)
-class UInt8VectorVector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.UInt8VectorVector_swiginit(self, _swigfaiss.new_UInt8VectorVector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.UInt8VectorVector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.UInt8VectorVector_clear(self)
-
-    def data(self):
-        return _swigfaiss.UInt8VectorVector_data(self)
-
-    def size(self):
-        return _swigfaiss.UInt8VectorVector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.UInt8VectorVector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.UInt8VectorVector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.UInt8VectorVector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_UInt8VectorVector
-
-# Register UInt8VectorVector in _swigfaiss:
-_swigfaiss.UInt8VectorVector_swigregister(UInt8VectorVector)
-class Int32VectorVector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.Int32VectorVector_swiginit(self, _swigfaiss.new_Int32VectorVector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.Int32VectorVector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.Int32VectorVector_clear(self)
-
-    def data(self):
-        return _swigfaiss.Int32VectorVector_data(self)
-
-    def size(self):
-        return _swigfaiss.Int32VectorVector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.Int32VectorVector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.Int32VectorVector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.Int32VectorVector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_Int32VectorVector
-
-# Register Int32VectorVector in _swigfaiss:
-_swigfaiss.Int32VectorVector_swigregister(Int32VectorVector)
-class Int64VectorVector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.Int64VectorVector_swiginit(self, _swigfaiss.new_Int64VectorVector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.Int64VectorVector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.Int64VectorVector_clear(self)
-
-    def data(self):
-        return _swigfaiss.Int64VectorVector_data(self)
-
-    def size(self):
-        return _swigfaiss.Int64VectorVector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.Int64VectorVector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.Int64VectorVector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.Int64VectorVector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_Int64VectorVector
-
-# Register Int64VectorVector in _swigfaiss:
-_swigfaiss.Int64VectorVector_swigregister(Int64VectorVector)
-class VectorTransformVector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.VectorTransformVector_swiginit(self, _swigfaiss.new_VectorTransformVector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.VectorTransformVector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.VectorTransformVector_clear(self)
-
-    def data(self):
-        return _swigfaiss.VectorTransformVector_data(self)
-
-    def size(self):
-        return _swigfaiss.VectorTransformVector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.VectorTransformVector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.VectorTransformVector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.VectorTransformVector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_VectorTransformVector
-
-# Register VectorTransformVector in _swigfaiss:
-_swigfaiss.VectorTransformVector_swigregister(VectorTransformVector)
-class OperatingPointVector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.OperatingPointVector_swiginit(self, _swigfaiss.new_OperatingPointVector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.OperatingPointVector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.OperatingPointVector_clear(self)
-
-    def data(self):
-        return _swigfaiss.OperatingPointVector_data(self)
-
-    def size(self):
-        return _swigfaiss.OperatingPointVector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.OperatingPointVector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.OperatingPointVector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.OperatingPointVector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_OperatingPointVector
-
-# Register OperatingPointVector in _swigfaiss:
-_swigfaiss.OperatingPointVector_swigregister(OperatingPointVector)
-class InvertedListsPtrVector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.InvertedListsPtrVector_swiginit(self, _swigfaiss.new_InvertedListsPtrVector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.InvertedListsPtrVector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.InvertedListsPtrVector_clear(self)
-
-    def data(self):
-        return _swigfaiss.InvertedListsPtrVector_data(self)
-
-    def size(self):
-        return _swigfaiss.InvertedListsPtrVector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.InvertedListsPtrVector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.InvertedListsPtrVector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.InvertedListsPtrVector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_InvertedListsPtrVector
-
-# Register InvertedListsPtrVector in _swigfaiss:
-_swigfaiss.InvertedListsPtrVector_swigregister(InvertedListsPtrVector)
-class RepeatVector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.RepeatVector_swiginit(self, _swigfaiss.new_RepeatVector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.RepeatVector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.RepeatVector_clear(self)
-
-    def data(self):
-        return _swigfaiss.RepeatVector_data(self)
-
-    def size(self):
-        return _swigfaiss.RepeatVector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.RepeatVector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.RepeatVector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.RepeatVector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_RepeatVector
-
-# Register RepeatVector in _swigfaiss:
-_swigfaiss.RepeatVector_swigregister(RepeatVector)
-class ClusteringIterationStatsVector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.ClusteringIterationStatsVector_swiginit(self, _swigfaiss.new_ClusteringIterationStatsVector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.ClusteringIterationStatsVector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.ClusteringIterationStatsVector_clear(self)
-
-    def data(self):
-        return _swigfaiss.ClusteringIterationStatsVector_data(self)
-
-    def size(self):
-        return _swigfaiss.ClusteringIterationStatsVector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.ClusteringIterationStatsVector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.ClusteringIterationStatsVector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.ClusteringIterationStatsVector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_ClusteringIterationStatsVector
-
-# Register ClusteringIterationStatsVector in _swigfaiss:
-_swigfaiss.ClusteringIterationStatsVector_swigregister(ClusteringIterationStatsVector)
-class ParameterRangeVector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.ParameterRangeVector_swiginit(self, _swigfaiss.new_ParameterRangeVector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.ParameterRangeVector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.ParameterRangeVector_clear(self)
-
-    def data(self):
-        return _swigfaiss.ParameterRangeVector_data(self)
-
-    def size(self):
-        return _swigfaiss.ParameterRangeVector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.ParameterRangeVector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.ParameterRangeVector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.ParameterRangeVector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_ParameterRangeVector
-
-# Register ParameterRangeVector in _swigfaiss:
-_swigfaiss.ParameterRangeVector_swigregister(ParameterRangeVector)
-class OnDiskOneListVector(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self):
-        _swigfaiss.OnDiskOneListVector_swiginit(self, _swigfaiss.new_OnDiskOneListVector())
-
-    def push_back(self, arg2):
-        return _swigfaiss.OnDiskOneListVector_push_back(self, arg2)
-
-    def clear(self):
-        return _swigfaiss.OnDiskOneListVector_clear(self)
-
-    def data(self):
-        return _swigfaiss.OnDiskOneListVector_data(self)
-
-    def size(self):
-        return _swigfaiss.OnDiskOneListVector_size(self)
-
-    def at(self, n):
-        return _swigfaiss.OnDiskOneListVector_at(self, n)
-
-    def resize(self, n):
-        return _swigfaiss.OnDiskOneListVector_resize(self, n)
-
-    def swap(self, other):
-        return _swigfaiss.OnDiskOneListVector_swap(self, other)
-    __swig_destroy__ = _swigfaiss.delete_OnDiskOneListVector
-
-# Register OnDiskOneListVector in _swigfaiss:
-_swigfaiss.OnDiskOneListVector_swigregister(OnDiskOneListVector)
-
-def simd_histogram_8(data, n, min, shift, hist):
-    r"""
-     low level SIMD histogramming functions  8-bin histogram of (x - min) >> shift
-    values outside the range are ignored.
-    the data table should be aligned on 32 bytes
-    """
-    return _swigfaiss.simd_histogram_8(data, n, min, shift, hist)
-
-def simd_histogram_16(data, n, min, shift, hist):
-    r""" same for 16-bin histogram"""
-    return _swigfaiss.simd_histogram_16(data, n, min, shift, hist)
-class PartitionStats(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    bissect_cycles = property(_swigfaiss.PartitionStats_bissect_cycles_get, _swigfaiss.PartitionStats_bissect_cycles_set)
-    compress_cycles = property(_swigfaiss.PartitionStats_compress_cycles_get, _swigfaiss.PartitionStats_compress_cycles_set)
-
-    def __init__(self):
-        _swigfaiss.PartitionStats_swiginit(self, _swigfaiss.new_PartitionStats())
-
-    def reset(self):
-        return _swigfaiss.PartitionStats_reset(self)
-    __swig_destroy__ = _swigfaiss.delete_PartitionStats
-
-# Register PartitionStats in _swigfaiss:
-_swigfaiss.PartitionStats_swigregister(PartitionStats)
-
-def bitvec_print(b, d):
-    return _swigfaiss.bitvec_print(b, d)
-
-def fvecs2bitvecs(x, b, d, n):
-    return _swigfaiss.fvecs2bitvecs(x, b, d, n)
-
-def bitvecs2fvecs(b, x, d, n):
-    return _swigfaiss.bitvecs2fvecs(b, x, d, n)
-
-def fvec2bitvec(x, b, d):
-    return _swigfaiss.fvec2bitvec(x, b, d)
-
-def bitvec_shuffle(n, da, db, order, a, b):
-    r""" Shuffle the bits from b(i, j) := a(i, order[j])"""
-    return _swigfaiss.bitvec_shuffle(n, da, db, order, a, b)
-class BitstringWriter(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    code = property(_swigfaiss.BitstringWriter_code_get, _swigfaiss.BitstringWriter_code_set)
-    code_size = property(_swigfaiss.BitstringWriter_code_size_get, _swigfaiss.BitstringWriter_code_size_set)
-    i = property(_swigfaiss.BitstringWriter_i_get, _swigfaiss.BitstringWriter_i_set)
-
-    def __init__(self, code, code_size):
-        _swigfaiss.BitstringWriter_swiginit(self, _swigfaiss.new_BitstringWriter(code, code_size))
-
-    def write(self, x, nbit):
-        return _swigfaiss.BitstringWriter_write(self, x, nbit)
-    __swig_destroy__ = _swigfaiss.delete_BitstringWriter
-
-# Register BitstringWriter in _swigfaiss:
-_swigfaiss.BitstringWriter_swigregister(BitstringWriter)
-cvar = _swigfaiss.cvar
-
-class BitstringReader(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    code = property(_swigfaiss.BitstringReader_code_get, _swigfaiss.BitstringReader_code_set)
-    code_size = property(_swigfaiss.BitstringReader_code_size_get, _swigfaiss.BitstringReader_code_size_set)
-    i = property(_swigfaiss.BitstringReader_i_get, _swigfaiss.BitstringReader_i_set)
-
-    def __init__(self, code, code_size):
-        _swigfaiss.BitstringReader_swiginit(self, _swigfaiss.new_BitstringReader(code, code_size))
-
-    def read(self, nbit):
-        return _swigfaiss.BitstringReader_read(self, nbit)
-    __swig_destroy__ = _swigfaiss.delete_BitstringReader
-
-# Register BitstringReader in _swigfaiss:
-_swigfaiss.BitstringReader_swigregister(BitstringReader)
-
-def hammings(a, b, na, nb, nbytespercode, dis):
-    r"""
-     Compute a set of Hamming distances between na and nb binary vectors
-
-    :type a: uint8_t
-    :param a:             size na * nbytespercode
-    :type b: uint8_t
-    :param b:             size nb * nbytespercode
-    :type nbytespercode: int
-    :param nbytespercode: should be multiple of 8
-    :type dis: int
-    :param dis:           output distances, size na * nb
-    """
-    return _swigfaiss.hammings(a, b, na, nb, nbytespercode, dis)
-
-def hammings_knn_hc(*args):
-    r"""
-     Return the k smallest Hamming distances for a set of binary query vectors,
-    using a max heap.
-    :type a: uint8_t
-    :param a:       queries, size ha->nh * ncodes
-    :type b: uint8_t
-    :param b:       database, size nb * ncodes
-    :type nb: int
-    :param nb:      number of database vectors
-    :type ncodes: int
-    :param ncodes:  size of the binary codes (bytes)
-    :type ordered: int
-    :param ordered: if != 0: order the results by decreasing distance
-                       (may be bottleneck for k/n > 0.01)
-    :type approx_topk_mode: int, optional
-    :param approx_topk_mode: allows to use approximate top-k facilities
-                                to speedup heap
-    """
-    return _swigfaiss.hammings_knn_hc(*args)
-
-def hammings_knn(ha, a, b, nb, ncodes, ordered):
-    return _swigfaiss.hammings_knn(ha, a, b, nb, ncodes, ordered)
-
-def hammings_knn_mc(a, b, na, nb, k, ncodes, distances, labels):
-    r"""
-     Return the k smallest Hamming distances for a set of binary query vectors,
-    using counting max.
-    :type a: uint8_t
-    :param a:       queries, size na * ncodes
-    :type b: uint8_t
-    :param b:       database, size nb * ncodes
-    :type na: int
-    :param na:      number of query vectors
-    :type nb: int
-    :param nb:      number of database vectors
-    :type k: int
-    :param k:       number of vectors/distances to return
-    :type ncodes: int
-    :param ncodes:  size of the binary codes (bytes)
-    :type distances: int
-    :param distances: output distances from each query vector to its k nearest
-                       neighbors
-    :type labels: int
-    :param labels:  output ids of the k nearest neighbors to each query vector
-    """
-    return _swigfaiss.hammings_knn_mc(a, b, na, nb, k, ncodes, distances, labels)
-
-def hamming_range_search(a, b, na, nb, radius, ncodes, result):
-    r""" same as hammings_knn except we are doing a range search with radius"""
-    return _swigfaiss.hamming_range_search(a, b, na, nb, radius, ncodes, result)
-
-def hamming_count_thres(bs1, bs2, n1, n2, ht, ncodes, nptr):
-    return _swigfaiss.hamming_count_thres(bs1, bs2, n1, n2, ht, ncodes, nptr)
-
-def match_hamming_thres(bs1, bs2, n1, n2, ht, ncodes, idx, dis):
-    return _swigfaiss.match_hamming_thres(bs1, bs2, n1, n2, ht, ncodes, idx, dis)
-
-def crosshamming_count_thres(dbs, n, ht, ncodes, nptr):
-    return _swigfaiss.crosshamming_count_thres(dbs, n, ht, ncodes, nptr)
-
-def generalized_hammings_knn_hc(ha, a, b, nb, code_size, ordered=True):
-    r"""
-    generalized Hamming distances (= count number of code bytes that
-       are the same)
-    """
-    return _swigfaiss.generalized_hammings_knn_hc(ha, a, b, nb, code_size, ordered)
-
-def popcount32(x):
-    return _swigfaiss.popcount32(x)
-
-def popcount64(x):
-    return _swigfaiss.popcount64(x)
-
-def get_num_gpus():
-    return _swigfaiss.get_num_gpus()
-
-def gpu_profiler_start():
-    return _swigfaiss.gpu_profiler_start()
-
-def gpu_profiler_stop():
-    return _swigfaiss.gpu_profiler_stop()
-
-def gpu_sync_all_devices():
-    return _swigfaiss.gpu_sync_all_devices()
-
-def get_compile_options():
-    r""" get compile options"""
-    return _swigfaiss.get_compile_options()
-
-def getmillisecs():
-    r""" ms elapsed since some arbitrary epoch"""
-    return _swigfaiss.getmillisecs()
-
-def get_mem_usage_kb():
-    r""" get current RSS usage in kB"""
-    return _swigfaiss.get_mem_usage_kb()
-
-def get_cycles():
-    return _swigfaiss.get_cycles()
-
-def fvec_madd(n, a, bf, b, c):
-    r"""
-     compute c := a + bf * b for a, b and c tables
-
-    :type n: int
-    :param n:   size of the tables
-    :type a: float
-    :param a:   size n
-    :type b: float
-    :param b:   size n
-    :type c: float
-    :param c:   restult table, size n
-    """
-    return _swigfaiss.fvec_madd(n, a, bf, b, c)
-
-def fvec_madd_and_argmin(n, a, bf, b, c):
-    r"""
-     same as fvec_madd, also return index of the min of the result table
-    :rtype: int
-    :return: index of the min of table c
-    """
-    return _swigfaiss.fvec_madd_and_argmin(n, a, bf, b, c)
-
-def reflection(u, x, n, d, nu):
-    return _swigfaiss.reflection(u, x, n, d, nu)
-
-def matrix_qr(m, n, a):
-    r"""
-     compute the Q of the QR decomposition for m > n
-    :type a: float
-    :param a:   size n * m: input matrix and output Q
-    """
-    return _swigfaiss.matrix_qr(m, n, a)
-
-def ranklist_handle_ties(k, idx, dis):
-    r""" distances are supposed to be sorted. Sorts indices with same distance"""
-    return _swigfaiss.ranklist_handle_ties(k, idx, dis)
-
-def ranklist_intersection_size(k1, v1, k2, v2):
-    r"""
-     count the number of common elements between v1 and v2
-    algorithm = sorting + bissection to avoid double-counting duplicates
-    """
-    return _swigfaiss.ranklist_intersection_size(k1, v1, k2, v2)
-
-def merge_result_table_with(n, k, I0, D0, I1, D1, keep_min=True, translation=0):
-    r"""
-     merge a result table into another one
-
-    :type I0: int
-    :param I0:, D0       first result table, size (n, k)
-    :type I1: int
-    :param I1:, D1       second result table, size (n, k)
-    :type keep_min: boolean, optional
-    :param keep_min:     if true, keep min values, otherwise keep max
-    :type translation: int, optional
-    :param translation:  add this value to all I1's indexes
-    :rtype: int
-    :return: nb of values that were taken from the second table
-    """
-    return _swigfaiss.merge_result_table_with(n, k, I0, D0, I1, D1, keep_min, translation)
-
-def imbalance_factor(*args):
-    r"""
-    *Overload 1:*
-    a balanced assignment has a IF of 1
-
-    |
-
-    *Overload 2:*
-    same, takes a histogram as input
-    """
-    return _swigfaiss.imbalance_factor(*args)
-
-def ivec_hist(n, v, vmax, hist):
-    r""" compute histogram on v"""
-    return _swigfaiss.ivec_hist(n, v, vmax, hist)
-
-def bincode_hist(n, nbits, codes, hist):
-    r"""
-     Compute histogram of bits on a code array
-
-    :type codes: uint8_t
-    :param codes:   size(n, nbits / 8)
-    :type hist: int
-    :param hist:    size(nbits): nb of 1s in the array of codes
-    """
-    return _swigfaiss.bincode_hist(n, nbits, codes, hist)
-
-def ivec_checksum(n, a):
-    r""" compute a checksum on a table."""
-    return _swigfaiss.ivec_checksum(n, a)
-
-def fvecs_maybe_subsample(d, n, nmax, x, verbose=False, seed=1234):
-    r"""
-     random subsamples a set of vectors if there are too many of them
-
-    :type d: int
-    :param d:      dimension of the vectors
-    :type n: int
-    :param n:      on input: nb of input vectors, output: nb of output vectors
-    :type nmax: int
-    :param nmax:   max nb of vectors to keep
-    :type x: float
-    :param x:      input array, size *n-by-d
-    :type seed: int, optional
-    :param seed:   random seed to use for sampling
-    :rtype: float
-    :return: x or an array allocated with new [] with *n vectors
-    """
-    return _swigfaiss.fvecs_maybe_subsample(d, n, nmax, x, verbose, seed)
-
-def binary_to_real(d, x_in, x_out):
-    r"""
-     Convert binary vector to +1/-1 valued float vector.
-
-    :type d: int
-    :param d:      dimension of the vector (multiple of 8)
-    :type x_in: uint8_t
-    :param x_in:   input binary vector (uint8_t table of size d / 8)
-    :type x_out: float
-    :param x_out:  output float vector (float table of size d)
-    """
-    return _swigfaiss.binary_to_real(d, x_in, x_out)
-
-def real_to_binary(d, x_in, x_out):
-    r"""
-     Convert float vector to binary vector. Components > 0 are converted to 1,
-    others to 0.
-
-    :type d: int
-    :param d:      dimension of the vector (multiple of 8)
-    :type x_in: float
-    :param x_in:   input float vector (float table of size d)
-    :type x_out: uint8_t
-    :param x_out:  output binary vector (uint8_t table of size d / 8)
-    """
-    return _swigfaiss.real_to_binary(d, x_in, x_out)
-
-def hash_bytes(bytes, n):
-    r""" A reasonable hashing function"""
-    return _swigfaiss.hash_bytes(bytes, n)
-
-def check_openmp():
-    r""" Whether OpenMP annotations were respected."""
-    return _swigfaiss.check_openmp()
-
-def fvec_L2sqr(x, y, d):
-    r""" Squared L2 distance between two vectors"""
-    return _swigfaiss.fvec_L2sqr(x, y, d)
-
-def fvec_inner_product(x, y, d):
-    r""" inner product"""
-    return _swigfaiss.fvec_inner_product(x, y, d)
-
-def fvec_L1(x, y, d):
-    r""" L1 distance"""
-    return _swigfaiss.fvec_L1(x, y, d)
-
-def fvec_Linf(x, y, d):
-    r""" infinity distance"""
-    return _swigfaiss.fvec_Linf(x, y, d)
-
-def pairwise_L2sqr(d, nq, xq, nb, xb, dis, ldq=-1, ldb=-1, ldd=-1):
-    r"""
-     Compute pairwise distances between sets of vectors
-
-    :type d: int
-    :param d:     dimension of the vectors
-    :type nq: int
-    :param nq:    nb of query vectors
-    :type nb: int
-    :param nb:    nb of database vectors
-    :type xq: float
-    :param xq:    query vectors (size nq * d)
-    :type xb: float
-    :param xb:    database vectors (size nb * d)
-    :type dis: float
-    :param dis:   output distances (size nq * nb)
-    :param ldq,ldb:, ldd strides for the matrices
-    """
-    return _swigfaiss.pairwise_L2sqr(d, nq, xq, nb, xb, dis, ldq, ldb, ldd)
-
-def fvec_inner_products_ny(ip, x, y, d, ny):
-    return _swigfaiss.fvec_inner_products_ny(ip, x, y, d, ny)
-
-def fvec_L2sqr_ny(dis, x, y, d, ny):
-    return _swigfaiss.fvec_L2sqr_ny(dis, x, y, d, ny)
-
-def fvec_L2sqr_ny_transposed(dis, x, y, y_sqlen, d, d_offset, ny):
-    return _swigfaiss.fvec_L2sqr_ny_transposed(dis, x, y, y_sqlen, d, d_offset, ny)
-
-def fvec_L2sqr_ny_nearest(distances_tmp_buffer, x, y, d, ny):
-    return _swigfaiss.fvec_L2sqr_ny_nearest(distances_tmp_buffer, x, y, d, ny)
-
-def fvec_L2sqr_ny_nearest_y_transposed(distances_tmp_buffer, x, y, y_sqlen, d, d_offset, ny):
-    return _swigfaiss.fvec_L2sqr_ny_nearest_y_transposed(distances_tmp_buffer, x, y, y_sqlen, d, d_offset, ny)
-
-def fvec_norm_L2sqr(x, d):
-    r""" squared norm of a vector"""
-    return _swigfaiss.fvec_norm_L2sqr(x, d)
-
-def fvec_norms_L2(norms, x, d, nx):
-    r"""
-     compute the L2 norms for a set of vectors
-
-    :type norms: float
-    :param norms:    output norms, size nx
-    :type x: float
-    :param x:        set of vectors, size nx * d
-    """
-    return _swigfaiss.fvec_norms_L2(norms, x, d, nx)
-
-def fvec_norms_L2sqr(norms, x, d, nx):
-    r""" same as fvec_norms_L2, but computes squared norms"""
-    return _swigfaiss.fvec_norms_L2sqr(norms, x, d, nx)
-
-def fvec_renorm_L2(d, nx, x):
-    return _swigfaiss.fvec_renorm_L2(d, nx, x)
-
-def inner_product_to_L2sqr(dis, nr1, nr2, n1, n2):
-    return _swigfaiss.inner_product_to_L2sqr(dis, nr1, nr2, n1, n2)
-
-def fvec_add(*args):
-    r"""
-    *Overload 1:*
-     compute c := a + b for vectors
-
-    c and a can overlap, c and b can overlap
-
-    :type a: float
-    :param a: size d
-    :type b: float
-    :param b: size d
-    :type c: float
-    :param c: size d
-
-    |
-
-    *Overload 2:*
-     compute c := a + b for a, c vectors and b a scalar
-
-    c and a can overlap
-
-    :type a: float
-    :param a: size d
-    :type c: float
-    :param c: size d
-    """
-    return _swigfaiss.fvec_add(*args)
-
-def fvec_sub(d, a, b, c):
-    r"""
-     compute c := a - b for vectors
-
-    c and a can overlap, c and b can overlap
-
-    :type a: float
-    :param a: size d
-    :type b: float
-    :param b: size d
-    :type c: float
-    :param c: size d
-    """
-    return _swigfaiss.fvec_sub(d, a, b, c)
-
-def fvec_inner_products_by_idx(ip, x, y, ids, d, nx, ny):
-    return _swigfaiss.fvec_inner_products_by_idx(ip, x, y, ids, d, nx, ny)
-
-def fvec_L2sqr_by_idx(dis, x, y, ids, d, nx, ny):
-    return _swigfaiss.fvec_L2sqr_by_idx(dis, x, y, ids, d, nx, ny)
-
-def pairwise_indexed_L2sqr(d, n, x, ix, y, iy, dis):
-    r"""
-     compute dis[j] = L2sqr(x[ix[j]], y[iy[j]]) forall j=0..n-1
-
-    :type x: float
-    :param x:  size (max(ix) + 1, d)
-    :type y: float
-    :param y:  size (max(iy) + 1, d)
-    :type ix: int
-    :param ix: size n
-    :type iy: int
-    :param iy: size n
-    :type dis: float
-    :param dis: size n
-    """
-    return _swigfaiss.pairwise_indexed_L2sqr(d, n, x, ix, y, iy, dis)
-
-def pairwise_indexed_inner_product(d, n, x, ix, y, iy, dis):
-    return _swigfaiss.pairwise_indexed_inner_product(d, n, x, ix, y, iy, dis)
-
-def knn_inner_product(*args):
-    r"""
-    *Overload 1:*
-     Return the k nearest neighors of each of the nx vectors x among the ny
-     vector y, w.r.t to max inner product.
-
-    :type x: float
-    :param x:    query vectors, size nx * d
-    :type y: float
-    :param y:    database vectors, size ny * d
-    :type res: :py:class:`float_minheap_array_t`
-    :param res:  result heap structure, which also provides k. Sorted on output
-
-    |
-
-    *Overload 2:*
-      Return the k nearest neighors of each of the nx vectors x among the ny
-     vector y, for the inner product metric.
-
-    :type x: float
-    :param x:    query vectors, size nx * d
-    :type y: float
-    :param y:    database vectors, size ny * d
-    :type distances: float
-    :param distances:  output distances, size nq * k
-    :type indexes: int
-    :param indexes:    output vector ids, size nq * k
-
-    |
-
-    *Overload 3:*
-      Return the k nearest neighors of each of the nx vectors x among the ny
-     vector y, for the inner product metric.
-
-    :type x: float
-    :param x:    query vectors, size nx * d
-    :type y: float
-    :param y:    database vectors, size ny * d
-    :type distances: float
-    :param distances:  output distances, size nq * k
-    :type indexes: int
-    :param indexes:    output vector ids, size nq * k
-    """
-    return _swigfaiss.knn_inner_product(*args)
-
-def knn_L2sqr(*args):
-    r"""
-    *Overload 1:*
-     Return the k nearest neighors of each of the nx vectors x among the ny
-     vector y, for the L2 distance
-    :type x: float
-    :param x:    query vectors, size nx * d
-    :type y: float
-    :param y:    database vectors, size ny * d
-    :type res: :py:class:`float_maxheap_array_t`
-    :param res:  result heap strcture, which also provides k. Sorted on output
-    :type y_norm2: float, optional
-    :param y_norm2:    (optional) norms for the y vectors (nullptr or size ny)
-    :type sel: :py:class:`IDSelector`, optional
-    :param sel:  search in this subset of vectors
-
-    |
-
-    *Overload 2:*
-      Return the k nearest neighors of each of the nx vectors x among the ny
-     vector y, for the L2 distance
-
-    :type x: float
-    :param x:    query vectors, size nx * d
-    :type y: float
-    :param y:    database vectors, size ny * d
-    :type distances: float
-    :param distances:  output distances, size nq * k
-    :type indexes: int
-    :param indexes:    output vector ids, size nq * k
-    :type y_norm2: float, optional
-    :param y_norm2:    (optional) norms for the y vectors (nullptr or size ny)
-    :type sel: :py:class:`IDSelector`, optional
-    :param sel:  search in this subset of vectors
-
-    |
-
-    *Overload 3:*
-      Return the k nearest neighors of each of the nx vectors x among the ny
-     vector y, for the L2 distance
-
-    :type x: float
-    :param x:    query vectors, size nx * d
-    :type y: float
-    :param y:    database vectors, size ny * d
-    :type distances: float
-    :param distances:  output distances, size nq * k
-    :type indexes: int
-    :param indexes:    output vector ids, size nq * k
-    :type y_norm2: float, optional
-    :param y_norm2:    (optional) norms for the y vectors (nullptr or size ny)
-    :param sel:  search in this subset of vectors
-
-    |
-
-    *Overload 4:*
-      Return the k nearest neighors of each of the nx vectors x among the ny
-     vector y, for the L2 distance
-
-    :type x: float
-    :param x:    query vectors, size nx * d
-    :type y: float
-    :param y:    database vectors, size ny * d
-    :type distances: float
-    :param distances:  output distances, size nq * k
-    :type indexes: int
-    :param indexes:    output vector ids, size nq * k
-    :param y_norm2:    (optional) norms for the y vectors (nullptr or size ny)
-    :param sel:  search in this subset of vectors
-    """
-    return _swigfaiss.knn_L2sqr(*args)
-
-def knn_inner_products_by_idx(x, y, subset, d, nx, nsubset, k, vals, ids, ld_ids=-1):
-    r"""
-     Find the max inner product neighbors for nx queries in a set of ny vectors
-    indexed by ids. May be useful for re-ranking a pre-selected vector list
-
-    :type x: float
-    :param x:    query vectors, size nx * d
-    :type y: float
-    :param y:    database vectors, size (max(ids) + 1) * d
-    :type ids: int
-    :param ids:  subset of database vectors to consider, size (nx, nsubset)
-    :param res:  result structure
-    :type ld_ids: int, optional
-    :param ld_ids: stride for the ids array. -1: use nsubset, 0: all queries
-        process the same subset
-    """
-    return _swigfaiss.knn_inner_products_by_idx(x, y, subset, d, nx, nsubset, k, vals, ids, ld_ids)
-
-def knn_L2sqr_by_idx(x, y, subset, d, nx, nsubset, k, vals, ids, ld_subset=-1):
-    r"""
-     Find the nearest neighbors for nx queries in a set of ny vectors
-    indexed by ids. May be useful for re-ranking a pre-selected vector list
-
-    :type x: float
-    :param x:    query vectors, size nx * d
-    :type y: float
-    :param y:    database vectors, size (max(ids) + 1) * d
-    :type subset: int
-    :param subset: subset of database vectors to consider, size (nx, nsubset)
-    :param res:  rIDesult structure
-    :type ld_subset: int, optional
-    :param ld_subset: stride for the subset array. -1: use nsubset, 0: all queries
-        process the same subset
-    """
-    return _swigfaiss.knn_L2sqr_by_idx(x, y, subset, d, nx, nsubset, k, vals, ids, ld_subset)
-
-def range_search_L2sqr(x, y, d, nx, ny, radius, result, sel=None):
-    r"""
-     Return the k nearest neighors of each of the nx vectors x among the ny
-     vector y, w.r.t to max inner product
-
-    :type x: float
-    :param x:      query vectors, size nx * d
-    :type y: float
-    :param y:      database vectors, size ny * d
-    :type radius: float
-    :param radius: search radius around the x vectors
-    :type result: :py:class:`RangeSearchResult`
-    :param result: result structure
-    """
-    return _swigfaiss.range_search_L2sqr(x, y, d, nx, ny, radius, result, sel)
-
-def range_search_inner_product(x, y, d, nx, ny, radius, result, sel=None):
-    r""" same as range_search_L2sqr for the inner product similarity"""
-    return _swigfaiss.range_search_inner_product(x, y, d, nx, ny, radius, result, sel)
-
-def compute_PQ_dis_tables_dsub2(d, ksub, centroids, nx, x, is_inner_product, dis_tables):
-    r""" specialized function for PQ2"""
-    return _swigfaiss.compute_PQ_dis_tables_dsub2(d, ksub, centroids, nx, x, is_inner_product, dis_tables)
-class RandomGenerator(object):
-    r""" random generator that can be used in multithreaded contexts"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    mt = property(_swigfaiss.RandomGenerator_mt_get, _swigfaiss.RandomGenerator_mt_set)
-
-    def rand_int64(self):
-        r""" random int64_t"""
-        return _swigfaiss.RandomGenerator_rand_int64(self)
-
-    def rand_int(self, *args):
-        r"""
-        *Overload 1:*
-        random positive integer
-
-        |
-
-        *Overload 2:*
-        generate random integer between 0 and max-1
-        """
-        return _swigfaiss.RandomGenerator_rand_int(self, *args)
-
-    def rand_float(self):
-        r""" between 0 and 1"""
-        return _swigfaiss.RandomGenerator_rand_float(self)
-
-    def rand_double(self):
-        return _swigfaiss.RandomGenerator_rand_double(self)
-
-    def __init__(self, seed=1234):
-        _swigfaiss.RandomGenerator_swiginit(self, _swigfaiss.new_RandomGenerator(seed))
-    __swig_destroy__ = _swigfaiss.delete_RandomGenerator
-
-# Register RandomGenerator in _swigfaiss:
-_swigfaiss.RandomGenerator_swigregister(RandomGenerator)
-hamdis_tab_ham_bytes = cvar.hamdis_tab_ham_bytes
-
-
-def float_rand(x, n, seed):
-    return _swigfaiss.float_rand(x, n, seed)
-
-def float_randn(x, n, seed):
-    return _swigfaiss.float_randn(x, n, seed)
-
-def int64_rand(x, n, seed):
-    return _swigfaiss.int64_rand(x, n, seed)
-
-def byte_rand(x, n, seed):
-    return _swigfaiss.byte_rand(x, n, seed)
-
-def int64_rand_max(x, n, max, seed):
-    return _swigfaiss.int64_rand_max(x, n, max, seed)
-
-def rand_perm(perm, n, seed):
-    return _swigfaiss.rand_perm(perm, n, seed)
-
-def rand_smooth_vectors(n, d, x, seed):
-    return _swigfaiss.rand_smooth_vectors(n, d, x, seed)
-
-def fvec_argsort(n, vals, perm):
-    r"""
-     Indirect sort of a floating-point array
-
-    :type n: int
-    :param n:     size of the array
-    :type vals: float
-    :param vals:  array to sort, size n
-    :type perm: int
-    :param perm:  output: permutation of [0..n-1], st.
-                     vals[perm[i + 1]] >= vals[perm[i]]
-    """
-    return _swigfaiss.fvec_argsort(n, vals, perm)
-
-def fvec_argsort_parallel(n, vals, perm):
-    r""" Same as fvec_argsort, parallelized"""
-    return _swigfaiss.fvec_argsort_parallel(n, vals, perm)
-
-def bucket_sort(nval, vals, nbucket, lims, perm, nt=0):
-    r"""
-     Bucket sort of a list of values
-
-    :type vals: int
-    :param vals:     values to sort, size nval, max value nbucket - 1
-    :type lims: int
-    :param lims:     output limits of buckets, size nbucket + 1
-    :type perm: int
-    :param perm:     output buckets, the elements of bucket
-                        i are in perm[lims[i]:lims[i + 1]]
-    :type nt: int, optional
-    :param nt:       number of threads (0 = pure sequential code)
-    """
-    return _swigfaiss.bucket_sort(nval, vals, nbucket, lims, perm, nt)
-
-def matrix_bucket_sort_inplace(*args):
-    r"""
-    *Overload 1:*
-     in-place bucket sort (with attention to memory=>int32)
-    on input the values are in a nrow * col matrix
-    we want to store the row numbers in the output.
-
-    :type vals: int
-    :param vals:     positive values to sort, size nrow * ncol,
-                        max value nbucket - 1
-    :type lims: int
-    :param lims:     output limits of buckets, size nbucket + 1
-    :type nt: int, optional
-    :param nt:       number of threads (0 = pure sequential code)
-
-    |
-
-    *Overload 2:*
-     same with int64 elements
-
-    |
-
-    *Overload 3:*
-     same with int64 elements
-    """
-    return _swigfaiss.matrix_bucket_sort_inplace(*args)
-METRIC_INNER_PRODUCT = _swigfaiss.METRIC_INNER_PRODUCT
-r""" maximum inner product search"""
-METRIC_L2 = _swigfaiss.METRIC_L2
-r""" squared L2 search"""
-METRIC_L1 = _swigfaiss.METRIC_L1
-r""" L1 (aka cityblock)"""
-METRIC_Linf = _swigfaiss.METRIC_Linf
-r""" infinity distance"""
-METRIC_Lp = _swigfaiss.METRIC_Lp
-r""" L_p distance, p is given by a faiss::Index"""
-METRIC_Canberra = _swigfaiss.METRIC_Canberra
-r"""
-    metric_arg
-    some additional metrics defined in scipy.spatial.distance
-    """
-METRIC_BrayCurtis = _swigfaiss.METRIC_BrayCurtis
-METRIC_JensenShannon = _swigfaiss.METRIC_JensenShannon
-METRIC_Jaccard = _swigfaiss.METRIC_Jaccard
-r"""
-    defined as: sum_i(min(a_i, b_i)) / sum_i(max(a_i, b_i))
-    where a_i, b_i > 0
-    """
-
-def is_similarity_metric(metric_type):
-    r"""
-    this function is used to distinguish between min and max indexes since
-    we need to support similarity and dis-similarity metrics in a flexible way
-    """
-    return _swigfaiss.is_similarity_metric(metric_type)
-FAISS_VERSION_MAJOR = _swigfaiss.FAISS_VERSION_MAJOR
-r"""
-    Copyright (c) Facebook, Inc. and its affiliates.
-
-    This source code is licensed under the MIT license found in the
-    LICENSE file in the root directory of this source tree.
-    """
-FAISS_VERSION_MINOR = _swigfaiss.FAISS_VERSION_MINOR
-FAISS_VERSION_PATCH = _swigfaiss.FAISS_VERSION_PATCH
-class SearchParameters(object):
-    r"""
-     Parent class for the optional search paramenters.
-
-    Sub-classes with additional search parameters should inherit this class.
-    Ownership of the object fields is always to the caller.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    sel = property(_swigfaiss.SearchParameters_sel_get, _swigfaiss.SearchParameters_sel_set, doc=r""" if non-null, only these IDs will be considered during search.""")
-    __swig_destroy__ = _swigfaiss.delete_SearchParameters
-
-    def __init__(self):
-        _swigfaiss.SearchParameters_swiginit(self, _swigfaiss.new_SearchParameters())
-
-# Register SearchParameters in _swigfaiss:
-_swigfaiss.SearchParameters_swigregister(SearchParameters)
-class Index(object):
-    r"""
-     Abstract structure for an index, supports adding vectors and searching them.
-
-    All vectors provided at add or search time are 32-bit float arrays,
-    although the internal representation may vary.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    d = property(_swigfaiss.Index_d_get, _swigfaiss.Index_d_set, doc=r""" vector dimension""")
-    ntotal = property(_swigfaiss.Index_ntotal_get, _swigfaiss.Index_ntotal_set, doc=r""" total nb of indexed vectors""")
-    verbose = property(_swigfaiss.Index_verbose_get, _swigfaiss.Index_verbose_set, doc=r""" verbosity level""")
-    is_trained = property(_swigfaiss.Index_is_trained_get, _swigfaiss.Index_is_trained_set, doc=r"""
-    set if the Index does not require training, or if training is
-    done already
-    """)
-    metric_type = property(_swigfaiss.Index_metric_type_get, _swigfaiss.Index_metric_type_set, doc=r""" type of metric this index uses for search""")
-    metric_arg = property(_swigfaiss.Index_metric_arg_get, _swigfaiss.Index_metric_arg_set, doc=r""" argument of the metric type""")
-    __swig_destroy__ = _swigfaiss.delete_Index
-
-    def train(self, n, x):
-        r"""
-         Perform training on a representative set of vectors
-
-        :type n: int
-        :param n:      nb of training vectors
-        :type x: float
-        :param x:      training vecors, size n * d
-        """
-        return _swigfaiss.Index_train(self, n, x)
-
-    def add(self, n, x):
-        r"""
-         Add n vectors of dimension d to the index.
-
-        Vectors are implicitly assigned labels ntotal .. ntotal + n - 1
-        This function slices the input vectors in chunks smaller than
-        blocksize_add and calls add_core.
-        :type x: float
-        :param x:      input matrix, size n * d
-        """
-        return _swigfaiss.Index_add(self, n, x)
-
-    def add_with_ids(self, n, x, xids):
-        r"""
-         Same as add, but stores xids instead of sequential ids.
-
-        The default implementation fails with an assertion, as it is
-        not supported by all indexes.
-
-        :type xids: int
-        :param xids: if non-null, ids to store for the vectors (size n)
-        """
-        return _swigfaiss.Index_add_with_ids(self, n, x, xids)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        r"""
-         query n vectors of dimension d to the index.
-
-        return at most k vectors. If there are not enough results for a
-        query, the result array is padded with -1s.
-
-        :type x: float
-        :param x:           input vectors to search, size n * d
-        :type labels: int
-        :param labels:      output labels of the NNs, size n*k
-        :type distances: float
-        :param distances:   output pairwise distances, size n*k
-        """
-        return _swigfaiss.Index_search(self, n, x, k, distances, labels, params)
-
-    def range_search(self, n, x, radius, result, params=None):
-        r"""
-         query n vectors of dimension d to the index.
-
-        return all vectors with distance < radius. Note that many
-        indexes do not implement the range_search (only the k-NN search
-        is mandatory).
-
-        :type x: float
-        :param x:           input vectors to search, size n * d
-        :type radius: float
-        :param radius:      search radius
-        :type result: :py:class:`RangeSearchResult`
-        :param result:      result table
-        """
-        return _swigfaiss.Index_range_search(self, n, x, radius, result, params)
-
-    def assign(self, n, x, labels, k=1):
-        r"""
-         return the indexes of the k vectors closest to the query x.
-
-        This function is identical as search but only return labels of neighbors.
-        :type x: float
-        :param x:           input vectors to search, size n * d
-        :type labels: int
-        :param labels:      output labels of the NNs, size n*k
-        """
-        return _swigfaiss.Index_assign(self, n, x, labels, k)
-
-    def reset(self):
-        r""" removes all elements from the database."""
-        return _swigfaiss.Index_reset(self)
-
-    def remove_ids(self, sel):
-        r"""
-         removes IDs from the index. Not supported by all
-        indexes. Returns the number of elements removed.
-        """
-        return _swigfaiss.Index_remove_ids(self, sel)
-
-    def reconstruct(self, key, recons):
-        r"""
-         Reconstruct a stored vector (or an approximation if lossy coding)
-
-        this function may not be defined for some indexes
-        :type key: int
-        :param key:         id of the vector to reconstruct
-        :type recons: float
-        :param recons:      reconstucted vector (size d)
-        """
-        return _swigfaiss.Index_reconstruct(self, key, recons)
-
-    def reconstruct_batch(self, n, keys, recons):
-        r"""
-         Reconstruct several stored vectors (or an approximation if lossy coding)
-
-        this function may not be defined for some indexes
-        :type n: int
-        :param n:        number of vectors to reconstruct
-        :type keys: int
-        :param keys:        ids of the vectors to reconstruct (size n)
-        :type recons: float
-        :param recons:      reconstucted vector (size n * d)
-        """
-        return _swigfaiss.Index_reconstruct_batch(self, n, keys, recons)
-
-    def reconstruct_n(self, i0, ni, recons):
-        r"""
-         Reconstruct vectors i0 to i0 + ni - 1
-
-        this function may not be defined for some indexes
-        :type recons: float
-        :param recons:      reconstucted vector (size ni * d)
-        """
-        return _swigfaiss.Index_reconstruct_n(self, i0, ni, recons)
-
-    def search_and_reconstruct(self, n, x, k, distances, labels, recons, params=None):
-        r"""
-         Similar to search, but also reconstructs the stored vectors (or an
-        approximation in the case of lossy coding) for the search results.
-
-        If there are not enough results for a query, the resulting arrays
-        is padded with -1s.
-
-        :type recons: float
-        :param recons:      reconstructed vectors size (n, k, d)
-        """
-        return _swigfaiss.Index_search_and_reconstruct(self, n, x, k, distances, labels, recons, params)
-
-    def compute_residual(self, x, residual, key):
-        r"""
-         Computes a residual vector after indexing encoding.
-
-        The residual vector is the difference between a vector and the
-        reconstruction that can be decoded from its representation in
-        the index. The residual can be used for multiple-stage indexing
-        methods, like IndexIVF's methods.
-
-        :type x: float
-        :param x:           input vector, size d
-        :type residual: float
-        :param residual:    output residual vector, size d
-        :type key: int
-        :param key:         encoded index, as returned by search and assign
-        """
-        return _swigfaiss.Index_compute_residual(self, x, residual, key)
-
-    def compute_residual_n(self, n, xs, residuals, keys):
-        r"""
-         Computes a residual vector after indexing encoding (batch form).
-        Equivalent to calling compute_residual for each vector.
-
-        The residual vector is the difference between a vector and the
-        reconstruction that can be decoded from its representation in
-        the index. The residual can be used for multiple-stage indexing
-        methods, like IndexIVF's methods.
-
-        :type n: int
-        :param n:           number of vectors
-        :type xs: float
-        :param xs:          input vectors, size (n x d)
-        :type residuals: float
-        :param residuals:   output residual vectors, size (n x d)
-        :type keys: int
-        :param keys:        encoded index, as returned by search and assign
-        """
-        return _swigfaiss.Index_compute_residual_n(self, n, xs, residuals, keys)
-
-    def get_distance_computer(self):
-        r"""
-         Get a DistanceComputer (defined in AuxIndexStructures) object
-        for this kind of index.
-
-        DistanceComputer is implemented for indexes that support random
-        access of their vectors.
-        """
-        return _swigfaiss.Index_get_distance_computer(self)
-
-    def sa_code_size(self):
-        r""" size of the produced codes in bytes"""
-        return _swigfaiss.Index_sa_code_size(self)
-
-    def sa_encode(self, n, x, bytes):
-        r"""
-         encode a set of vectors
-
-        :type n: int
-        :param n:       number of vectors
-        :type x: float
-        :param x:       input vectors, size n * d
-        :type bytes: uint8_t
-        :param bytes:   output encoded vectors, size n * sa_code_size()
-        """
-        return _swigfaiss.Index_sa_encode(self, n, x, bytes)
-
-    def sa_decode(self, n, bytes, x):
-        r"""
-         decode a set of vectors
-
-        :type n: int
-        :param n:       number of vectors
-        :type bytes: uint8_t
-        :param bytes:   input encoded vectors, size n * sa_code_size()
-        :type x: float
-        :param x:       output vectors, size n * d
-        """
-        return _swigfaiss.Index_sa_decode(self, n, bytes, x)
-
-    def merge_from(self, otherIndex, add_id=0):
-        r"""
-         moves the entries from another dataset to self.
-        On output, other is empty.
-        add_id is added to all moved ids
-        (for sequential ids, this would be this->ntotal)
-        """
-        return _swigfaiss.Index_merge_from(self, otherIndex, add_id)
-
-    def check_compatible_for_merge(self, otherIndex):
-        r"""
-         check that the two indexes are compatible (ie, they are
-        trained in the same way and have the same
-        parameters). Otherwise throw.
-        """
-        return _swigfaiss.Index_check_compatible_for_merge(self, otherIndex)
-
-# Register Index in _swigfaiss:
-_swigfaiss.Index_swigregister(Index)
-class DistanceComputer(object):
-    r"""
-    Copyright (c) Facebook, Inc. and its affiliates.
-
-    This source code is licensed under the MIT license found in the
-    LICENSE file in the root directory of this source tree.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-
-    def set_query(self, x):
-        r"""
-        called before computing distances. Pointer x should remain valid
-        while operator () is called
-        """
-        return _swigfaiss.DistanceComputer_set_query(self, x)
-
-    def __call__(self, i):
-        r""" compute distance of vector i to current query"""
-        return _swigfaiss.DistanceComputer___call__(self, i)
-
-    def symmetric_dis(self, i, j):
-        r""" compute distance between two stored vectors"""
-        return _swigfaiss.DistanceComputer_symmetric_dis(self, i, j)
-    __swig_destroy__ = _swigfaiss.delete_DistanceComputer
-
-# Register DistanceComputer in _swigfaiss:
-_swigfaiss.DistanceComputer_swigregister(DistanceComputer)
-class FlatCodesDistanceComputer(DistanceComputer):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    codes = property(_swigfaiss.FlatCodesDistanceComputer_codes_get, _swigfaiss.FlatCodesDistanceComputer_codes_set)
-    code_size = property(_swigfaiss.FlatCodesDistanceComputer_code_size_get, _swigfaiss.FlatCodesDistanceComputer_code_size_set)
-
-    def __call__(self, i):
-        return _swigfaiss.FlatCodesDistanceComputer___call__(self, i)
-
-    def distance_to_code(self, code):
-        r""" compute distance of current query to an encoded vector"""
-        return _swigfaiss.FlatCodesDistanceComputer_distance_to_code(self, code)
-    __swig_destroy__ = _swigfaiss.delete_FlatCodesDistanceComputer
-
-# Register FlatCodesDistanceComputer in _swigfaiss:
-_swigfaiss.FlatCodesDistanceComputer_swigregister(FlatCodesDistanceComputer)
-class IndexFlatCodes(Index):
-    r"""
-     Index that encodes all vectors as fixed-size codes (size code_size). Storage
-    is in the codes vector
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    code_size = property(_swigfaiss.IndexFlatCodes_code_size_get, _swigfaiss.IndexFlatCodes_code_size_set)
-    codes = property(_swigfaiss.IndexFlatCodes_codes_get, _swigfaiss.IndexFlatCodes_codes_set, doc=r""" encoded dataset, size ntotal * code_size""")
-
-    def add(self, n, x):
-        r""" default add uses sa_encode"""
-        return _swigfaiss.IndexFlatCodes_add(self, n, x)
-
-    def reset(self):
-        return _swigfaiss.IndexFlatCodes_reset(self)
-
-    def reconstruct_n(self, i0, ni, recons):
-        r""" reconstruction using the codec interface"""
-        return _swigfaiss.IndexFlatCodes_reconstruct_n(self, i0, ni, recons)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexFlatCodes_reconstruct(self, key, recons)
-
-    def sa_code_size(self):
-        return _swigfaiss.IndexFlatCodes_sa_code_size(self)
-
-    def remove_ids(self, sel):
-        r"""
-         remove some ids. NB that because of the structure of the
-        index, the semantics of this operation are
-        different from the usual ones: the new ids are shifted
-        """
-        return _swigfaiss.IndexFlatCodes_remove_ids(self, sel)
-
-    def get_FlatCodesDistanceComputer(self):
-        r""" a FlatCodesDistanceComputer offers a distance_to_code method"""
-        return _swigfaiss.IndexFlatCodes_get_FlatCodesDistanceComputer(self)
-
-    def get_distance_computer(self):
-        return _swigfaiss.IndexFlatCodes_get_distance_computer(self)
-
-    def get_CodePacker(self):
-        return _swigfaiss.IndexFlatCodes_get_CodePacker(self)
-
-    def check_compatible_for_merge(self, otherIndex):
-        return _swigfaiss.IndexFlatCodes_check_compatible_for_merge(self, otherIndex)
-
-    def merge_from(self, otherIndex, add_id=0):
-        return _swigfaiss.IndexFlatCodes_merge_from(self, otherIndex, add_id)
-    __swig_destroy__ = _swigfaiss.delete_IndexFlatCodes
-
-# Register IndexFlatCodes in _swigfaiss:
-_swigfaiss.IndexFlatCodes_swigregister(IndexFlatCodes)
-class IndexFlat(IndexFlatCodes):
-    r""" Index that stores the full vectors and performs exhaustive search"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexFlat_search(self, n, x, k, distances, labels, params)
-
-    def range_search(self, n, x, radius, result, params=None):
-        return _swigfaiss.IndexFlat_range_search(self, n, x, radius, result, params)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexFlat_reconstruct(self, key, recons)
-
-    def compute_distance_subset(self, n, x, k, distances, labels):
-        r"""
-         compute distance with a subset of vectors
-
-        :type x: float
-        :param x:       query vectors, size n * d
-        :type labels: int
-        :param labels:  indices of the vectors that should be compared
-                           for each query vector, size n * k
-        :type distances: float
-        :param distances:
-                           corresponding output distances, size n * k
-        """
-        return _swigfaiss.IndexFlat_compute_distance_subset(self, n, x, k, distances, labels)
-
-    def get_xb(self, *args):
-        return _swigfaiss.IndexFlat_get_xb(self, *args)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexFlat_swiginit(self, _swigfaiss.new_IndexFlat(*args))
-
-    def get_FlatCodesDistanceComputer(self):
-        return _swigfaiss.IndexFlat_get_FlatCodesDistanceComputer(self)
-
-    def sa_encode(self, n, x, bytes):
-        return _swigfaiss.IndexFlat_sa_encode(self, n, x, bytes)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexFlat_sa_decode(self, n, bytes, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexFlat
-
-# Register IndexFlat in _swigfaiss:
-_swigfaiss.IndexFlat_swigregister(IndexFlat)
-class IndexFlatIP(IndexFlat):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.IndexFlatIP_swiginit(self, _swigfaiss.new_IndexFlatIP(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexFlatIP
-
-# Register IndexFlatIP in _swigfaiss:
-_swigfaiss.IndexFlatIP_swigregister(IndexFlatIP)
-class IndexFlatL2(IndexFlat):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.IndexFlatL2_swiginit(self, _swigfaiss.new_IndexFlatL2(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexFlatL2
-
-# Register IndexFlatL2 in _swigfaiss:
-_swigfaiss.IndexFlatL2_swigregister(IndexFlatL2)
-class IndexFlat1D(IndexFlatL2):
-    r""" optimized version for 1D "vectors"."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    continuous_update = property(_swigfaiss.IndexFlat1D_continuous_update_get, _swigfaiss.IndexFlat1D_continuous_update_set, doc=r""" is the permutation updated continuously?""")
-    perm = property(_swigfaiss.IndexFlat1D_perm_get, _swigfaiss.IndexFlat1D_perm_set, doc=r""" sorted database indices""")
-
-    def __init__(self, continuous_update=True):
-        _swigfaiss.IndexFlat1D_swiginit(self, _swigfaiss.new_IndexFlat1D(continuous_update))
-
-    def update_permutation(self):
-        r"""
-        if not continuous_update, call this between the last add and
-        the first search
-        """
-        return _swigfaiss.IndexFlat1D_update_permutation(self)
-
-    def add(self, n, x):
-        return _swigfaiss.IndexFlat1D_add(self, n, x)
-
-    def reset(self):
-        return _swigfaiss.IndexFlat1D_reset(self)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        r""" Warn: the distances returned are L1 not L2"""
-        return _swigfaiss.IndexFlat1D_search(self, n, x, k, distances, labels, params)
-    __swig_destroy__ = _swigfaiss.delete_IndexFlat1D
-
-# Register IndexFlat1D in _swigfaiss:
-_swigfaiss.IndexFlat1D_swigregister(IndexFlat1D)
-class ClusteringParameters(object):
-    r"""
-     Class for the clustering parameters. Can be passed to the
-    constructor of the Clustering object.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    niter = property(_swigfaiss.ClusteringParameters_niter_get, _swigfaiss.ClusteringParameters_niter_set, doc=r""" clustering iterations""")
-    nredo = property(_swigfaiss.ClusteringParameters_nredo_get, _swigfaiss.ClusteringParameters_nredo_set, doc=r""" redo clustering this many times and keep best""")
-    verbose = property(_swigfaiss.ClusteringParameters_verbose_get, _swigfaiss.ClusteringParameters_verbose_set)
-    spherical = property(_swigfaiss.ClusteringParameters_spherical_get, _swigfaiss.ClusteringParameters_spherical_set, doc=r""" do we want normalized centroids?""")
-    int_centroids = property(_swigfaiss.ClusteringParameters_int_centroids_get, _swigfaiss.ClusteringParameters_int_centroids_set, doc=r""" round centroids coordinates to integer""")
-    update_index = property(_swigfaiss.ClusteringParameters_update_index_get, _swigfaiss.ClusteringParameters_update_index_set, doc=r""" re-train index after each iteration?""")
-    frozen_centroids = property(_swigfaiss.ClusteringParameters_frozen_centroids_get, _swigfaiss.ClusteringParameters_frozen_centroids_set, doc=r"""
-    use the centroids provided as input and do not
-    change them during iterations
-    """)
-    min_points_per_centroid = property(_swigfaiss.ClusteringParameters_min_points_per_centroid_get, _swigfaiss.ClusteringParameters_min_points_per_centroid_set, doc=r""" otherwise you get a warning""")
-    max_points_per_centroid = property(_swigfaiss.ClusteringParameters_max_points_per_centroid_get, _swigfaiss.ClusteringParameters_max_points_per_centroid_set, doc=r""" to limit size of dataset""")
-    seed = property(_swigfaiss.ClusteringParameters_seed_get, _swigfaiss.ClusteringParameters_seed_set, doc=r""" seed for the random number generator""")
-    decode_block_size = property(_swigfaiss.ClusteringParameters_decode_block_size_get, _swigfaiss.ClusteringParameters_decode_block_size_set, doc=r""" how many vectors at a time to decode""")
-
-    def __init__(self):
-        r""" sets reasonable defaults"""
-        _swigfaiss.ClusteringParameters_swiginit(self, _swigfaiss.new_ClusteringParameters())
-    __swig_destroy__ = _swigfaiss.delete_ClusteringParameters
-
-# Register ClusteringParameters in _swigfaiss:
-_swigfaiss.ClusteringParameters_swigregister(ClusteringParameters)
-class ClusteringIterationStats(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    obj = property(_swigfaiss.ClusteringIterationStats_obj_get, _swigfaiss.ClusteringIterationStats_obj_set, doc=r""" objective values (sum of distances reported by index)""")
-    time = property(_swigfaiss.ClusteringIterationStats_time_get, _swigfaiss.ClusteringIterationStats_time_set, doc=r""" seconds for iteration""")
-    time_search = property(_swigfaiss.ClusteringIterationStats_time_search_get, _swigfaiss.ClusteringIterationStats_time_search_set, doc=r""" seconds for just search""")
-    imbalance_factor = property(_swigfaiss.ClusteringIterationStats_imbalance_factor_get, _swigfaiss.ClusteringIterationStats_imbalance_factor_set, doc=r""" imbalance factor of iteration""")
-    nsplit = property(_swigfaiss.ClusteringIterationStats_nsplit_get, _swigfaiss.ClusteringIterationStats_nsplit_set, doc=r""" number of cluster splits""")
-
-    def __init__(self):
-        _swigfaiss.ClusteringIterationStats_swiginit(self, _swigfaiss.new_ClusteringIterationStats())
-    __swig_destroy__ = _swigfaiss.delete_ClusteringIterationStats
-
-# Register ClusteringIterationStats in _swigfaiss:
-_swigfaiss.ClusteringIterationStats_swigregister(ClusteringIterationStats)
-class Clustering(ClusteringParameters):
-    r"""
-     K-means clustering based on assignment - centroid update iterations
-
-    The clustering is based on an Index object that assigns training
-    points to the centroids. Therefore, at each iteration the centroids
-    are added to the index.
-
-    On output, the centoids table is set to the latest version
-    of the centroids and they are also added to the index. If the
-    centroids table it is not empty on input, it is also used for
-    initialization.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    d = property(_swigfaiss.Clustering_d_get, _swigfaiss.Clustering_d_set, doc=r""" dimension of the vectors""")
-    k = property(_swigfaiss.Clustering_k_get, _swigfaiss.Clustering_k_set, doc=r""" nb of centroids""")
-    centroids = property(_swigfaiss.Clustering_centroids_get, _swigfaiss.Clustering_centroids_set, doc=r"""
-     centroids (k * d)
-    if centroids are set on input to train, they will be used as
-    initialization
-    """)
-    iteration_stats = property(_swigfaiss.Clustering_iteration_stats_get, _swigfaiss.Clustering_iteration_stats_set, doc=r""" stats at every iteration of clustering""")
-
-    def __init__(self, *args):
-        _swigfaiss.Clustering_swiginit(self, _swigfaiss.new_Clustering(*args))
-
-    def train(self, n, x, index, x_weights=None):
-        r"""
-         run k-means training
-
-        :type x: float
-        :param x:          training vectors, size n * d
-        :type index: :py:class:`Index`
-        :param index:      index used for assignment
-        :type x_weights: float, optional
-        :param x_weights:  weight associated to each vector: NULL or size n
-        """
-        return _swigfaiss.Clustering_train(self, n, x, index, x_weights)
-
-    def train_encoded(self, nx, x_in, codec, index, weights=None):
-        r"""
-         run with encoded vectors
-
-        win addition to train()'s parameters takes a codec as parameter
-        to decode the input vectors.
-
-        :type codec: :py:class:`Index`
-        :param codec:      codec used to decode the vectors (nullptr =
-                              vectors are in fact floats)     *
-        """
-        return _swigfaiss.Clustering_train_encoded(self, nx, x_in, codec, index, weights)
-
-    def post_process_centroids(self):
-        r"""
-        Post-process the centroids after each centroid update.
-        includes optional L2 normalization and nearest integer rounding
-        """
-        return _swigfaiss.Clustering_post_process_centroids(self)
-    __swig_destroy__ = _swigfaiss.delete_Clustering
-
-# Register Clustering in _swigfaiss:
-_swigfaiss.Clustering_swigregister(Clustering)
-class Clustering1D(Clustering):
-    r"""
-     Exact 1D clustering algorithm
-
-    Since it does not use an index, it does not overload the train() function
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.Clustering1D_swiginit(self, _swigfaiss.new_Clustering1D(*args))
-
-    def train_exact(self, n, x):
-        return _swigfaiss.Clustering1D_train_exact(self, n, x)
-    __swig_destroy__ = _swigfaiss.delete_Clustering1D
-
-# Register Clustering1D in _swigfaiss:
-_swigfaiss.Clustering1D_swigregister(Clustering1D)
-class ProgressiveDimClusteringParameters(ClusteringParameters):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    progressive_dim_steps = property(_swigfaiss.ProgressiveDimClusteringParameters_progressive_dim_steps_get, _swigfaiss.ProgressiveDimClusteringParameters_progressive_dim_steps_set, doc=r""" number of incremental steps""")
-    apply_pca = property(_swigfaiss.ProgressiveDimClusteringParameters_apply_pca_get, _swigfaiss.ProgressiveDimClusteringParameters_apply_pca_set, doc=r""" apply PCA on input""")
-
-    def __init__(self):
-        _swigfaiss.ProgressiveDimClusteringParameters_swiginit(self, _swigfaiss.new_ProgressiveDimClusteringParameters())
-    __swig_destroy__ = _swigfaiss.delete_ProgressiveDimClusteringParameters
-
-# Register ProgressiveDimClusteringParameters in _swigfaiss:
-_swigfaiss.ProgressiveDimClusteringParameters_swigregister(ProgressiveDimClusteringParameters)
-class ProgressiveDimIndexFactory(object):
-    r""" generates an index suitable for clustering when called"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __call__(self, dim):
-        r""" ownership transferred to caller"""
-        return _swigfaiss.ProgressiveDimIndexFactory___call__(self, dim)
-    __swig_destroy__ = _swigfaiss.delete_ProgressiveDimIndexFactory
-
-    def __init__(self):
-        _swigfaiss.ProgressiveDimIndexFactory_swiginit(self, _swigfaiss.new_ProgressiveDimIndexFactory())
-
-# Register ProgressiveDimIndexFactory in _swigfaiss:
-_swigfaiss.ProgressiveDimIndexFactory_swigregister(ProgressiveDimIndexFactory)
-class ProgressiveDimClustering(ProgressiveDimClusteringParameters):
-    r"""
-     K-means clustering with progressive dimensions used
-
-    The clustering first happens in dim 1, then with exponentially increasing
-    dimension until d (I steps). This is typically applied after a PCA
-    transformation (optional). Reference:
-
-    "Improved Residual Vector Quantization for High-dimensional Approximate
-    Nearest Neighbor Search"
-
-    Shicong Liu, Hongtao Lu, Junru Shao, AAAI'15
-
-    https://arxiv.org/abs/1509.05195
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    d = property(_swigfaiss.ProgressiveDimClustering_d_get, _swigfaiss.ProgressiveDimClustering_d_set, doc=r""" dimension of the vectors""")
-    k = property(_swigfaiss.ProgressiveDimClustering_k_get, _swigfaiss.ProgressiveDimClustering_k_set, doc=r""" nb of centroids""")
-    centroids = property(_swigfaiss.ProgressiveDimClustering_centroids_get, _swigfaiss.ProgressiveDimClustering_centroids_set, doc=r""" centroids (k * d)""")
-    iteration_stats = property(_swigfaiss.ProgressiveDimClustering_iteration_stats_get, _swigfaiss.ProgressiveDimClustering_iteration_stats_set, doc=r""" stats at every iteration of clustering""")
-
-    def __init__(self, *args):
-        _swigfaiss.ProgressiveDimClustering_swiginit(self, _swigfaiss.new_ProgressiveDimClustering(*args))
-
-    def train(self, n, x, factory):
-        return _swigfaiss.ProgressiveDimClustering_train(self, n, x, factory)
-    __swig_destroy__ = _swigfaiss.delete_ProgressiveDimClustering
-
-# Register ProgressiveDimClustering in _swigfaiss:
-_swigfaiss.ProgressiveDimClustering_swigregister(ProgressiveDimClustering)
-
-def kmeans_clustering(d, n, k, x, centroids):
-    r"""
-     simplified interface
-
-    :type d: int
-    :param d: dimension of the data
-    :type n: int
-    :param n: nb of training vectors
-    :type k: int
-    :param k: nb of output centroids
-    :type x: float
-    :param x: training set (size n * d)
-    :type centroids: float
-    :param centroids: output centroids (size k * d)
-    :rtype: float
-    :return: final quantization error
-    """
-    return _swigfaiss.kmeans_clustering(d, n, k, x, centroids)
-
-def pairwise_extra_distances(d, nq, xq, nb, xb, mt, metric_arg, dis, ldq=-1, ldb=-1, ldd=-1):
-    return _swigfaiss.pairwise_extra_distances(d, nq, xq, nb, xb, mt, metric_arg, dis, ldq, ldb, ldd)
-
-def get_extra_distance_computer(d, mt, metric_arg, nb, xb):
-    r"""
-    get a DistanceComputer that refers to this type of distance and
-    indexes a flat array of size nb
-    """
-    return _swigfaiss.get_extra_distance_computer(d, mt, metric_arg, nb, xb)
-class Quantizer(object):
-    r""" Product Quantizer. Implemented only for METRIC_L2"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    d = property(_swigfaiss.Quantizer_d_get, _swigfaiss.Quantizer_d_set, doc=r""" size of the input vectors""")
-    code_size = property(_swigfaiss.Quantizer_code_size_get, _swigfaiss.Quantizer_code_size_set, doc=r""" bytes per indexed vector""")
-
-    def train(self, n, x):
-        r"""
-         Train the quantizer
-
-        :type x: float
-        :param x:       training vectors, size n * d
-        """
-        return _swigfaiss.Quantizer_train(self, n, x)
-
-    def compute_codes(self, x, codes, n):
-        r"""
-         Quantize a set of vectors
-
-        :type x: float
-        :param x:        input vectors, size n * d
-        :type codes: uint8_t
-        :param codes:    output codes, size n * code_size
-        """
-        return _swigfaiss.Quantizer_compute_codes(self, x, codes, n)
-
-    def decode(self, code, x, n):
-        r"""
-         Decode a set of vectors
-
-        :param codes:    input codes, size n * code_size
-        :type x: float
-        :param x:        output vectors, size n * d
-        """
-        return _swigfaiss.Quantizer_decode(self, code, x, n)
-    __swig_destroy__ = _swigfaiss.delete_Quantizer
-
-# Register Quantizer in _swigfaiss:
-_swigfaiss.Quantizer_swigregister(Quantizer)
-class ProductQuantizer(Quantizer):
-    r""" Product Quantizer. Implemented only for METRIC_L2"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    M = property(_swigfaiss.ProductQuantizer_M_get, _swigfaiss.ProductQuantizer_M_set, doc=r""" number of subquantizers""")
-    nbits = property(_swigfaiss.ProductQuantizer_nbits_get, _swigfaiss.ProductQuantizer_nbits_set, doc=r""" number of bits per quantization index""")
-    dsub = property(_swigfaiss.ProductQuantizer_dsub_get, _swigfaiss.ProductQuantizer_dsub_set, doc=r""" dimensionality of each subvector""")
-    ksub = property(_swigfaiss.ProductQuantizer_ksub_get, _swigfaiss.ProductQuantizer_ksub_set, doc=r""" number of centroids for each subquantizer""")
-    verbose = property(_swigfaiss.ProductQuantizer_verbose_get, _swigfaiss.ProductQuantizer_verbose_set, doc=r""" verbose during training?""")
-    Train_default = _swigfaiss.ProductQuantizer_Train_default
-    Train_hot_start = _swigfaiss.ProductQuantizer_Train_hot_start
-    r""" the centroids are already initialized"""
-    Train_shared = _swigfaiss.ProductQuantizer_Train_shared
-    r""" share dictionary accross PQ segments"""
-    Train_hypercube = _swigfaiss.ProductQuantizer_Train_hypercube
-    r""" initialize centroids with nbits-D hypercube"""
-    Train_hypercube_pca = _swigfaiss.ProductQuantizer_Train_hypercube_pca
-    r""" initialize centroids with nbits-D hypercube"""
-    train_type = property(_swigfaiss.ProductQuantizer_train_type_get, _swigfaiss.ProductQuantizer_train_type_set)
-    cp = property(_swigfaiss.ProductQuantizer_cp_get, _swigfaiss.ProductQuantizer_cp_set, doc=r""" parameters used during clustering""")
-    assign_index = property(_swigfaiss.ProductQuantizer_assign_index_get, _swigfaiss.ProductQuantizer_assign_index_set, doc=r"""
-    if non-NULL, use this index for assignment (should be of size
-    d / M)
-    """)
-    centroids = property(_swigfaiss.ProductQuantizer_centroids_get, _swigfaiss.ProductQuantizer_centroids_set, doc=r"""
-    Centroid table, size M * ksub * dsub.
-    Layout: (M, ksub, dsub)
-    """)
-    transposed_centroids = property(_swigfaiss.ProductQuantizer_transposed_centroids_get, _swigfaiss.ProductQuantizer_transposed_centroids_set, doc=r"""
-    Transposed centroid table, size M * ksub * dsub.
-    Layout: (dsub, M, ksub)
-    """)
-    centroids_sq_lengths = property(_swigfaiss.ProductQuantizer_centroids_sq_lengths_get, _swigfaiss.ProductQuantizer_centroids_sq_lengths_set, doc=r"""
-    Squared lengths of centroids, size M * ksub
-    Layout: (M, ksub)
-    """)
-
-    def get_centroids(self, m, i):
-        r""" return the centroids associated with subvector m"""
-        return _swigfaiss.ProductQuantizer_get_centroids(self, m, i)
-
-    def train(self, n, x):
-        return _swigfaiss.ProductQuantizer_train(self, n, x)
-
-    def __init__(self, *args):
-        _swigfaiss.ProductQuantizer_swiginit(self, _swigfaiss.new_ProductQuantizer(*args))
-
-    def set_derived_values(self):
-        r""" compute derived values when d, M and nbits have been set"""
-        return _swigfaiss.ProductQuantizer_set_derived_values(self)
-
-    def set_params(self, centroids, m):
-        r""" Define the centroids for subquantizer m"""
-        return _swigfaiss.ProductQuantizer_set_params(self, centroids, m)
-
-    def compute_code(self, x, code):
-        r""" Quantize one vector with the product quantizer"""
-        return _swigfaiss.ProductQuantizer_compute_code(self, x, code)
-
-    def compute_codes(self, x, codes, n):
-        r""" same as compute_code for several vectors"""
-        return _swigfaiss.ProductQuantizer_compute_codes(self, x, codes, n)
-
-    def compute_codes_with_assign_index(self, x, codes, n):
-        r"""
-        speed up code assignment using assign_index
-        (non-const because the index is changed)
-        """
-        return _swigfaiss.ProductQuantizer_compute_codes_with_assign_index(self, x, codes, n)
-
-    def decode(self, *args):
-        return _swigfaiss.ProductQuantizer_decode(self, *args)
-
-    def compute_code_from_distance_table(self, tab, code):
-        r"""
-        If we happen to have the distance tables precomputed, this is
-        more efficient to compute the codes.
-        """
-        return _swigfaiss.ProductQuantizer_compute_code_from_distance_table(self, tab, code)
-
-    def compute_distance_table(self, x, dis_table):
-        r"""
-         Compute distance table for one vector.
-
-        The distance table for x = [x_0 x_1 .. x_(M-1)] is a M * ksub
-        matrix that contains
-
-          dis_table (m, j) = || x_m - c_(m, j)||^2
-          for m = 0..M-1 and j = 0 .. ksub - 1
-
-        where c_(m, j) is the centroid no j of sub-quantizer m.
-
-        :type x: float
-        :param x:         input vector size d
-        :type dis_table: float
-        :param dis_table: output table, size M * ksub
-        """
-        return _swigfaiss.ProductQuantizer_compute_distance_table(self, x, dis_table)
-
-    def compute_inner_prod_table(self, x, dis_table):
-        return _swigfaiss.ProductQuantizer_compute_inner_prod_table(self, x, dis_table)
-
-    def compute_distance_tables(self, nx, x, dis_tables):
-        r"""
-         compute distance table for several vectors
-        :type nx: int
-        :param nx:        nb of input vectors
-        :type x: float
-        :param x:         input vector size nx * d
-        :param dis_table: output table, size nx * M * ksub
-        """
-        return _swigfaiss.ProductQuantizer_compute_distance_tables(self, nx, x, dis_tables)
-
-    def compute_inner_prod_tables(self, nx, x, dis_tables):
-        return _swigfaiss.ProductQuantizer_compute_inner_prod_tables(self, nx, x, dis_tables)
-
-    def search(self, x, nx, codes, ncodes, res, init_finalize_heap=True):
-        r"""
-         perform a search (L2 distance)
-        :type x: float
-        :param x:        query vectors, size nx * d
-        :type nx: int
-        :param nx:       nb of queries
-        :type codes: uint8_t
-        :param codes:    database codes, size ncodes * code_size
-        :type ncodes: int
-        :param ncodes:   nb of nb vectors
-        :type res: :py:class:`float_maxheap_array_t`
-        :param res:      heap array to store results (nh == nx)
-        :type init_finalize_heap: boolean, optional
-        :param init_finalize_heap:  initialize heap (input) and sort (output)?
-        """
-        return _swigfaiss.ProductQuantizer_search(self, x, nx, codes, ncodes, res, init_finalize_heap)
-
-    def search_ip(self, x, nx, codes, ncodes, res, init_finalize_heap=True):
-        r""" same as search, but with inner product similarity"""
-        return _swigfaiss.ProductQuantizer_search_ip(self, x, nx, codes, ncodes, res, init_finalize_heap)
-    sdc_table = property(_swigfaiss.ProductQuantizer_sdc_table_get, _swigfaiss.ProductQuantizer_sdc_table_set, doc=r""" Symmetric Distance Table""")
-
-    def compute_sdc_table(self):
-        return _swigfaiss.ProductQuantizer_compute_sdc_table(self)
-
-    def search_sdc(self, qcodes, nq, bcodes, ncodes, res, init_finalize_heap=True):
-        return _swigfaiss.ProductQuantizer_search_sdc(self, qcodes, nq, bcodes, ncodes, res, init_finalize_heap)
-
-    def sync_transposed_centroids(self):
-        r"""
-        Sync transposed centroids with regular centroids. This call
-        is needed if centroids were edited directly.
-        """
-        return _swigfaiss.ProductQuantizer_sync_transposed_centroids(self)
-
-    def clear_transposed_centroids(self):
-        r""" Clear transposed centroids table so ones are no longer used."""
-        return _swigfaiss.ProductQuantizer_clear_transposed_centroids(self)
-    __swig_destroy__ = _swigfaiss.delete_ProductQuantizer
-
-# Register ProductQuantizer in _swigfaiss:
-_swigfaiss.ProductQuantizer_swigregister(ProductQuantizer)
-class PQEncoderGeneric(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    code = property(_swigfaiss.PQEncoderGeneric_code_get, _swigfaiss.PQEncoderGeneric_code_set, doc=r""" code for this vector""")
-    offset = property(_swigfaiss.PQEncoderGeneric_offset_get, _swigfaiss.PQEncoderGeneric_offset_set)
-    nbits = property(_swigfaiss.PQEncoderGeneric_nbits_get, doc=r""" number of bits per subquantizer index""")
-    reg = property(_swigfaiss.PQEncoderGeneric_reg_get, _swigfaiss.PQEncoderGeneric_reg_set)
-
-    def __init__(self, code, nbits, offset=0):
-        _swigfaiss.PQEncoderGeneric_swiginit(self, _swigfaiss.new_PQEncoderGeneric(code, nbits, offset))
-
-    def encode(self, x):
-        return _swigfaiss.PQEncoderGeneric_encode(self, x)
-    __swig_destroy__ = _swigfaiss.delete_PQEncoderGeneric
-
-# Register PQEncoderGeneric in _swigfaiss:
-_swigfaiss.PQEncoderGeneric_swigregister(PQEncoderGeneric)
-class PQEncoder8(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    code = property(_swigfaiss.PQEncoder8_code_get, _swigfaiss.PQEncoder8_code_set)
-
-    def __init__(self, code, nbits):
-        _swigfaiss.PQEncoder8_swiginit(self, _swigfaiss.new_PQEncoder8(code, nbits))
-
-    def encode(self, x):
-        return _swigfaiss.PQEncoder8_encode(self, x)
-    __swig_destroy__ = _swigfaiss.delete_PQEncoder8
-
-# Register PQEncoder8 in _swigfaiss:
-_swigfaiss.PQEncoder8_swigregister(PQEncoder8)
-class PQEncoder16(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    code = property(_swigfaiss.PQEncoder16_code_get, _swigfaiss.PQEncoder16_code_set)
-
-    def __init__(self, code, nbits):
-        _swigfaiss.PQEncoder16_swiginit(self, _swigfaiss.new_PQEncoder16(code, nbits))
-
-    def encode(self, x):
-        return _swigfaiss.PQEncoder16_encode(self, x)
-    __swig_destroy__ = _swigfaiss.delete_PQEncoder16
-
-# Register PQEncoder16 in _swigfaiss:
-_swigfaiss.PQEncoder16_swigregister(PQEncoder16)
-class PQDecoderGeneric(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    code = property(_swigfaiss.PQDecoderGeneric_code_get, _swigfaiss.PQDecoderGeneric_code_set)
-    offset = property(_swigfaiss.PQDecoderGeneric_offset_get, _swigfaiss.PQDecoderGeneric_offset_set)
-    nbits = property(_swigfaiss.PQDecoderGeneric_nbits_get)
-    mask = property(_swigfaiss.PQDecoderGeneric_mask_get)
-    reg = property(_swigfaiss.PQDecoderGeneric_reg_get, _swigfaiss.PQDecoderGeneric_reg_set)
-
-    def __init__(self, code, nbits):
-        _swigfaiss.PQDecoderGeneric_swiginit(self, _swigfaiss.new_PQDecoderGeneric(code, nbits))
-
-    def decode(self):
-        return _swigfaiss.PQDecoderGeneric_decode(self)
-    __swig_destroy__ = _swigfaiss.delete_PQDecoderGeneric
-
-# Register PQDecoderGeneric in _swigfaiss:
-_swigfaiss.PQDecoderGeneric_swigregister(PQDecoderGeneric)
-class PQDecoder8(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nbits = _swigfaiss.PQDecoder8_nbits
-    code = property(_swigfaiss.PQDecoder8_code_get, _swigfaiss.PQDecoder8_code_set)
-
-    def __init__(self, code, nbits):
-        _swigfaiss.PQDecoder8_swiginit(self, _swigfaiss.new_PQDecoder8(code, nbits))
-
-    def decode(self):
-        return _swigfaiss.PQDecoder8_decode(self)
-    __swig_destroy__ = _swigfaiss.delete_PQDecoder8
-
-# Register PQDecoder8 in _swigfaiss:
-_swigfaiss.PQDecoder8_swigregister(PQDecoder8)
-class PQDecoder16(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nbits = _swigfaiss.PQDecoder16_nbits
-    code = property(_swigfaiss.PQDecoder16_code_get, _swigfaiss.PQDecoder16_code_set)
-
-    def __init__(self, code, nbits):
-        _swigfaiss.PQDecoder16_swiginit(self, _swigfaiss.new_PQDecoder16(code, nbits))
-
-    def decode(self):
-        return _swigfaiss.PQDecoder16_decode(self)
-    __swig_destroy__ = _swigfaiss.delete_PQDecoder16
-
-# Register PQDecoder16 in _swigfaiss:
-_swigfaiss.PQDecoder16_swigregister(PQDecoder16)
-class AdditiveQuantizer(Quantizer):
-    r"""
-     Abstract structure for additive quantizers
-
-    Different from the product quantizer in which the decoded vector is the
-    concatenation of M sub-vectors, additive quantizers sum M sub-vectors
-    to get the decoded vector.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    M = property(_swigfaiss.AdditiveQuantizer_M_get, _swigfaiss.AdditiveQuantizer_M_set, doc=r""" number of codebooks""")
-    nbits = property(_swigfaiss.AdditiveQuantizer_nbits_get, _swigfaiss.AdditiveQuantizer_nbits_set, doc=r""" bits for each step""")
-    codebooks = property(_swigfaiss.AdditiveQuantizer_codebooks_get, _swigfaiss.AdditiveQuantizer_codebooks_set, doc=r""" codebooks""")
-    codebook_offsets = property(_swigfaiss.AdditiveQuantizer_codebook_offsets_get, _swigfaiss.AdditiveQuantizer_codebook_offsets_set)
-    tot_bits = property(_swigfaiss.AdditiveQuantizer_tot_bits_get, _swigfaiss.AdditiveQuantizer_tot_bits_set, doc=r""" total number of bits (indexes + norms)""")
-    norm_bits = property(_swigfaiss.AdditiveQuantizer_norm_bits_get, _swigfaiss.AdditiveQuantizer_norm_bits_set, doc=r""" bits allocated for the norms""")
-    total_codebook_size = property(_swigfaiss.AdditiveQuantizer_total_codebook_size_get, _swigfaiss.AdditiveQuantizer_total_codebook_size_set, doc=r""" size of the codebook in vectors""")
-    only_8bit = property(_swigfaiss.AdditiveQuantizer_only_8bit_get, _swigfaiss.AdditiveQuantizer_only_8bit_set, doc=r""" are all nbits = 8 (use faster decoder)""")
-    verbose = property(_swigfaiss.AdditiveQuantizer_verbose_get, _swigfaiss.AdditiveQuantizer_verbose_set, doc=r""" verbose during training?""")
-    is_trained = property(_swigfaiss.AdditiveQuantizer_is_trained_get, _swigfaiss.AdditiveQuantizer_is_trained_set, doc=r""" is trained or not""")
-    qnorm = property(_swigfaiss.AdditiveQuantizer_qnorm_get, _swigfaiss.AdditiveQuantizer_qnorm_set, doc=r""" store and search norms""")
-    norm_tabs = property(_swigfaiss.AdditiveQuantizer_norm_tabs_get, _swigfaiss.AdditiveQuantizer_norm_tabs_set, doc=r"""
-    store norms of codebook entries for 4-bit
-    fastscan search
-    """)
-    max_mem_distances = property(_swigfaiss.AdditiveQuantizer_max_mem_distances_get, _swigfaiss.AdditiveQuantizer_max_mem_distances_set, doc=r"""
-    norms and distance matrixes with beam search can get large, so use this
-    to control for the amount of memory that can be allocated
-    """)
-
-    def encode_norm(self, norm):
-        r""" encode a norm into norm_bits bits"""
-        return _swigfaiss.AdditiveQuantizer_encode_norm(self, norm)
-
-    def encode_qcint(self, x):
-        r""" encode norm by non-uniform scalar quantization"""
-        return _swigfaiss.AdditiveQuantizer_encode_qcint(self, x)
-
-    def decode_qcint(self, c):
-        r""" decode norm by non-uniform scalar quantization"""
-        return _swigfaiss.AdditiveQuantizer_decode_qcint(self, c)
-    ST_decompress = _swigfaiss.AdditiveQuantizer_ST_decompress
-    r""" decompress database vector"""
-    ST_LUT_nonorm = _swigfaiss.AdditiveQuantizer_ST_LUT_nonorm
-    r"""
-    use a LUT, don't include norms (OK for IP or
-    normalized vectors)
-    """
-    ST_norm_from_LUT = _swigfaiss.AdditiveQuantizer_ST_norm_from_LUT
-    r"""
-    compute the norms from the look-up tables (cost
-    is in O(M^2))
-    """
-    ST_norm_float = _swigfaiss.AdditiveQuantizer_ST_norm_float
-    r""" use a LUT, and store float32 norm with the vectors"""
-    ST_norm_qint8 = _swigfaiss.AdditiveQuantizer_ST_norm_qint8
-    r""" use a LUT, and store 8bit-quantized norm"""
-    ST_norm_qint4 = _swigfaiss.AdditiveQuantizer_ST_norm_qint4
-    ST_norm_cqint8 = _swigfaiss.AdditiveQuantizer_ST_norm_cqint8
-    r""" use a LUT, and store non-uniform quantized norm"""
-    ST_norm_cqint4 = _swigfaiss.AdditiveQuantizer_ST_norm_cqint4
-    ST_norm_lsq2x4 = _swigfaiss.AdditiveQuantizer_ST_norm_lsq2x4
-    r"""
-    use a 2x4 bits lsq as norm quantizer (for fast
-    scan)
-    """
-    ST_norm_rq2x4 = _swigfaiss.AdditiveQuantizer_ST_norm_rq2x4
-    r""" use a 2x4 bits rq as norm quantizer (for fast scan)"""
-
-    def set_derived_values(self):
-        r""" Train the norm quantizer"""
-        return _swigfaiss.AdditiveQuantizer_set_derived_values(self)
-
-    def train_norm(self, n, norms):
-        return _swigfaiss.AdditiveQuantizer_train_norm(self, n, norms)
-
-    def compute_codes(self, x, codes, n):
-        return _swigfaiss.AdditiveQuantizer_compute_codes(self, x, codes, n)
-
-    def compute_codes_add_centroids(self, x, codes, n, centroids=None):
-        r"""
-         Encode a set of vectors
-
-        :type x: float
-        :param x:      vectors to encode, size n * d
-        :type codes: uint8_t
-        :param codes:  output codes, size n * code_size
-        :type centroids: float, optional
-        :param centroids:  centroids to be added to x, size n * d
-        """
-        return _swigfaiss.AdditiveQuantizer_compute_codes_add_centroids(self, x, codes, n, centroids)
-
-    def pack_codes(self, n, codes, packed_codes, ld_codes=-1, norms=None, centroids=None):
-        r"""
-         pack a series of code to bit-compact format
-
-        :type codes: int
-        :param codes:        codes to be packed, size n * code_size
-        :type packed_codes: uint8_t
-        :param packed_codes: output bit-compact codes
-        :type ld_codes: int, optional
-        :param ld_codes:     leading dimension of codes
-        :type norms: float, optional
-        :param norms:        norms of the vectors (size n). Will be computed if
-                                needed but not provided
-        :type centroids: float, optional
-        :param centroids:    centroids to be added to x, size n * d
-        """
-        return _swigfaiss.AdditiveQuantizer_pack_codes(self, n, codes, packed_codes, ld_codes, norms, centroids)
-
-    def decode(self, codes, x, n):
-        r"""
-         Decode a set of vectors
-
-        :type codes: uint8_t
-        :param codes:  codes to decode, size n * code_size
-        :type x: float
-        :param x:      output vectors, size n * d
-        """
-        return _swigfaiss.AdditiveQuantizer_decode(self, codes, x, n)
-
-    def decode_unpacked(self, codes, x, n, ld_codes=-1):
-        r"""
-         Decode a set of vectors in non-packed format
-
-        :type codes: int
-        :param codes:  codes to decode, size n * ld_codes
-        :type x: float
-        :param x:      output vectors, size n * d
-        """
-        return _swigfaiss.AdditiveQuantizer_decode_unpacked(self, codes, x, n, ld_codes)
-    search_type = property(_swigfaiss.AdditiveQuantizer_search_type_get, _swigfaiss.AdditiveQuantizer_search_type_set, doc=r""" Also determines what's in the codes""")
-    norm_min = property(_swigfaiss.AdditiveQuantizer_norm_min_get, _swigfaiss.AdditiveQuantizer_norm_min_set, doc=r""" min/max for quantization of norms""")
-    norm_max = property(_swigfaiss.AdditiveQuantizer_norm_max_get, _swigfaiss.AdditiveQuantizer_norm_max_set)
-
-    def decode_64bit(self, n, x):
-        r""" decoding function for a code in a 64-bit word"""
-        return _swigfaiss.AdditiveQuantizer_decode_64bit(self, n, x)
-
-    def compute_LUT(self, n, xq, LUT, alpha=1.0, ld_lut=-1):
-        r"""
-         Compute inner-product look-up tables. Used in the centroid search
-        functions.
-
-        :type xq: float
-        :param xq:     query vector, size (n, d)
-        :type LUT: float
-        :param LUT:    look-up table, size (n, total_codebook_size)
-        :type alpha: float, optional
-        :param alpha:  compute alpha * inner-product
-        :type ld_lut: int, optional
-        :param ld_lut:  leading dimension of LUT
-        """
-        return _swigfaiss.AdditiveQuantizer_compute_LUT(self, n, xq, LUT, alpha, ld_lut)
-
-    def knn_centroids_inner_product(self, n, xq, k, distances, labels):
-        r""" exact IP search"""
-        return _swigfaiss.AdditiveQuantizer_knn_centroids_inner_product(self, n, xq, k, distances, labels)
-
-    def compute_centroid_norms(self, norms):
-        r"""
-         For L2 search we need the L2 norms of the centroids
-
-        :type norms: float
-        :param norms:    output norms table, size total_codebook_size
-        """
-        return _swigfaiss.AdditiveQuantizer_compute_centroid_norms(self, norms)
-
-    def knn_centroids_L2(self, n, xq, k, distances, labels, centroid_norms):
-        r""" Exact L2 search, with precomputed norms"""
-        return _swigfaiss.AdditiveQuantizer_knn_centroids_L2(self, n, xq, k, distances, labels, centroid_norms)
-    __swig_destroy__ = _swigfaiss.delete_AdditiveQuantizer
-
-# Register AdditiveQuantizer in _swigfaiss:
-_swigfaiss.AdditiveQuantizer_swigregister(AdditiveQuantizer)
-class ResidualQuantizer(AdditiveQuantizer):
-    r"""
-     Residual quantizer with variable number of bits per sub-quantizer
-
-    The residual centroids are stored in a big cumulative centroid table.
-    The codes are represented either as a non-compact table of size (n, M) or
-    as the compact output (n, code_size).
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    train_type = property(_swigfaiss.ResidualQuantizer_train_type_get, _swigfaiss.ResidualQuantizer_train_type_set, doc=r""" Binary or of the Train_* flags below""")
-    Train_default = _swigfaiss.ResidualQuantizer_Train_default
-    r""" regular k-means (minimal amount of computation)"""
-    Train_progressive_dim = _swigfaiss.ResidualQuantizer_Train_progressive_dim
-    r""" progressive dim clustering (set by default)"""
-    Train_refine_codebook = _swigfaiss.ResidualQuantizer_Train_refine_codebook
-    r""" do a few iterations of codebook refinement after first level estimation"""
-    niter_codebook_refine = property(_swigfaiss.ResidualQuantizer_niter_codebook_refine_get, _swigfaiss.ResidualQuantizer_niter_codebook_refine_set, doc=r""" number of iterations for codebook refinement.""")
-    Train_top_beam = _swigfaiss.ResidualQuantizer_Train_top_beam
-    r"""
-    set this bit on train_type if beam is to be trained only on the
-    first element of the beam (faster but less accurate)
-    """
-    Skip_codebook_tables = _swigfaiss.ResidualQuantizer_Skip_codebook_tables
-    r"""
-     set this bit to *not* autmatically compute the codebook tables
-    after training
-    """
-    max_beam_size = property(_swigfaiss.ResidualQuantizer_max_beam_size_get, _swigfaiss.ResidualQuantizer_max_beam_size_set, doc=r""" beam size used for training and for encoding""")
-    use_beam_LUT = property(_swigfaiss.ResidualQuantizer_use_beam_LUT_get, _swigfaiss.ResidualQuantizer_use_beam_LUT_set, doc=r""" use LUT for beam search""")
-    approx_topk_mode = property(_swigfaiss.ResidualQuantizer_approx_topk_mode_get, _swigfaiss.ResidualQuantizer_approx_topk_mode_set, doc=r"""
-    Currently used mode of approximate min-k computations.
-    Default value is EXACT_TOPK.
-    """)
-    cp = property(_swigfaiss.ResidualQuantizer_cp_get, _swigfaiss.ResidualQuantizer_cp_set, doc=r""" clustering parameters""")
-    assign_index_factory = property(_swigfaiss.ResidualQuantizer_assign_index_factory_get, _swigfaiss.ResidualQuantizer_assign_index_factory_set, doc=r""" if non-NULL, use this index for assignment""")
-
-    def __init__(self, *args):
-        _swigfaiss.ResidualQuantizer_swiginit(self, _swigfaiss.new_ResidualQuantizer(*args))
-
-    def train(self, n, x):
-        r""" Train the residual quantizer"""
-        return _swigfaiss.ResidualQuantizer_train(self, n, x)
-
-    def initialize_from(self, other, skip_M=0):
-        r""" Copy the M codebook levels from other, starting from skip_M"""
-        return _swigfaiss.ResidualQuantizer_initialize_from(self, other, skip_M)
-
-    def retrain_AQ_codebook(self, n, x):
-        r"""
-         Encode the vectors and compute codebook that minimizes the quantization
-        error on these codes
-
-        :type x: float
-        :param x:      training vectors, size n * d
-        :type n: int
-        :param n:      nb of training vectors, n >= total_codebook_size
-        :rtype: float
-        :return: returns quantization error for the new codebook with old
-            codes
-        """
-        return _swigfaiss.ResidualQuantizer_retrain_AQ_codebook(self, n, x)
-
-    def compute_codes_add_centroids(self, x, codes, n, centroids=None):
-        r"""
-         Encode a set of vectors
-
-        :type x: float
-        :param x:      vectors to encode, size n * d
-        :type codes: uint8_t
-        :param codes:  output codes, size n * code_size
-        :type centroids: float, optional
-        :param centroids:  centroids to be added to x, size n * d
-        """
-        return _swigfaiss.ResidualQuantizer_compute_codes_add_centroids(self, x, codes, n, centroids)
-
-    def refine_beam(self, n, beam_size, residuals, new_beam_size, new_codes, new_residuals=None, new_distances=None):
-        r"""
-         lower-level encode function
-
-        :type n: int
-        :param n:              number of vectors to hanlde
-        :type residuals: float
-        :param residuals:      vectors to encode, size (n, beam_size, d)
-        :type beam_size: int
-        :param beam_size:      input beam size
-        :type new_beam_size: int
-        :param new_beam_size:  output beam size (should be <= K * beam_size)
-        :type new_codes: int
-        :param new_codes:      output codes, size (n, new_beam_size, m + 1)
-        :type new_residuals: float, optional
-        :param new_residuals:  output residuals, size (n, new_beam_size, d)
-        :type new_distances: float, optional
-        :param new_distances:  output distances, size (n, new_beam_size)
-        """
-        return _swigfaiss.ResidualQuantizer_refine_beam(self, n, beam_size, residuals, new_beam_size, new_codes, new_residuals, new_distances)
-
-    def refine_beam_LUT(self, n, query_norms, query_cp, new_beam_size, new_codes, new_distances=None):
-        return _swigfaiss.ResidualQuantizer_refine_beam_LUT(self, n, query_norms, query_cp, new_beam_size, new_codes, new_distances)
-
-    def memory_per_point(self, beam_size=-1):
-        r"""
-         Beam search can consume a lot of memory. This function estimates the
-        amount of mem used by refine_beam to adjust the batch size
-
-        :type beam_size: int, optional
-        :param beam_size:  if != -1, override the beam size
-        """
-        return _swigfaiss.ResidualQuantizer_memory_per_point(self, beam_size)
-
-    def compute_codebook_tables(self):
-        r"""
-         Cross products used in codebook tables
-
-        These are used to keep trak of norms of centroids.
-        """
-        return _swigfaiss.ResidualQuantizer_compute_codebook_tables(self)
-    codebook_cross_products = property(_swigfaiss.ResidualQuantizer_codebook_cross_products_get, _swigfaiss.ResidualQuantizer_codebook_cross_products_set, doc=r"""
-    dot products of all codebook vectors with each other
-    size total_codebook_size * total_codebook_size
-    """)
-    cent_norms = property(_swigfaiss.ResidualQuantizer_cent_norms_get, _swigfaiss.ResidualQuantizer_cent_norms_set, doc=r""" norms of all vectors""")
-    __swig_destroy__ = _swigfaiss.delete_ResidualQuantizer
-
-# Register ResidualQuantizer in _swigfaiss:
-_swigfaiss.ResidualQuantizer_swigregister(ResidualQuantizer)
-
-def beam_search_encode_step(*args):
-    r"""
-     Encode a residual by sampling from a centroid table.
-
-    This is a single encoding step the residual quantizer.
-    It allows low-level access to the encoding function, exposed mainly for unit
-    tests.
-
-    :type n: int
-    :param n:              number of vectors to hanlde
-    :type residuals: float
-    :param residuals:      vectors to encode, size (n, beam_size, d)
-    :type cent: float
-    :param cent:           centroids, size (K, d)
-    :type beam_size: int
-    :param beam_size:      input beam size
-    :type m: int
-    :param m:              size of the codes for the previous encoding steps
-    :type codes: int
-    :param codes:          code array for the previous steps of the beam (n,
-        beam_size, m)
-    :type new_beam_size: int
-    :param new_beam_size:  output beam size (should be <= K * beam_size)
-    :type new_codes: int
-    :param new_codes:      output codes, size (n, new_beam_size, m + 1)
-    :type new_residuals: float
-    :param new_residuals:  output residuals, size (n, new_beam_size, d)
-    :type new_distances: float
-    :param new_distances:  output distances, size (n, new_beam_size)
-    :type assign_index: :py:class:`Index`, optional
-    :param assign_index:   if non-NULL, will be used to perform assignment
-    """
-    return _swigfaiss.beam_search_encode_step(*args)
-
-def beam_search_encode_step_tab(*args):
-    r""" Encode a set of vectors using their dot products with the codebooks"""
-    return _swigfaiss.beam_search_encode_step_tab(*args)
-class LocalSearchQuantizer(AdditiveQuantizer):
-    r"""
-     Implementation of LSQ/LSQ++ described in the following two papers:
-
-    Revisiting additive quantization
-    Julieta Martinez, et al. ECCV 2016
-
-    LSQ++: Lower running time and higher recall in multi-codebook quantization
-    Julieta Martinez, et al. ECCV 2018
-
-    This implementation is mostly translated from the Julia implementations
-    by Julieta Martinez:
-    (https://github.com/una-dinosauria/local-search-quantization,
-     https://github.com/una-dinosauria/Rayuela.jl)
-
-    The trained codes are stored in `codebooks` which is called
-    `centroids` in PQ and RQ.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    K = property(_swigfaiss.LocalSearchQuantizer_K_get, _swigfaiss.LocalSearchQuantizer_K_set, doc=r""" number of codes per codebook""")
-    train_iters = property(_swigfaiss.LocalSearchQuantizer_train_iters_get, _swigfaiss.LocalSearchQuantizer_train_iters_set, doc=r""" number of iterations in training""")
-    encode_ils_iters = property(_swigfaiss.LocalSearchQuantizer_encode_ils_iters_get, _swigfaiss.LocalSearchQuantizer_encode_ils_iters_set, doc=r""" iterations of local search in encoding""")
-    train_ils_iters = property(_swigfaiss.LocalSearchQuantizer_train_ils_iters_get, _swigfaiss.LocalSearchQuantizer_train_ils_iters_set, doc=r""" iterations of local search in training""")
-    icm_iters = property(_swigfaiss.LocalSearchQuantizer_icm_iters_get, _swigfaiss.LocalSearchQuantizer_icm_iters_set, doc=r""" number of iterations in icm""")
-    p = property(_swigfaiss.LocalSearchQuantizer_p_get, _swigfaiss.LocalSearchQuantizer_p_set, doc=r""" temperature factor""")
-    lambd = property(_swigfaiss.LocalSearchQuantizer_lambd_get, _swigfaiss.LocalSearchQuantizer_lambd_set, doc=r""" regularization factor""")
-    chunk_size = property(_swigfaiss.LocalSearchQuantizer_chunk_size_get, _swigfaiss.LocalSearchQuantizer_chunk_size_set, doc=r""" nb of vectors to encode at a time""")
-    random_seed = property(_swigfaiss.LocalSearchQuantizer_random_seed_get, _swigfaiss.LocalSearchQuantizer_random_seed_set, doc=r""" seed for random generator""")
-    nperts = property(_swigfaiss.LocalSearchQuantizer_nperts_get, _swigfaiss.LocalSearchQuantizer_nperts_set, doc=r"""
-    number of perturbation in each code
-    if non-NULL, use this encoder to encode (owned by the object)
-    """)
-    icm_encoder_factory = property(_swigfaiss.LocalSearchQuantizer_icm_encoder_factory_get, _swigfaiss.LocalSearchQuantizer_icm_encoder_factory_set)
-    update_codebooks_with_double = property(_swigfaiss.LocalSearchQuantizer_update_codebooks_with_double_get, _swigfaiss.LocalSearchQuantizer_update_codebooks_with_double_set)
-
-    def __init__(self, *args):
-        _swigfaiss.LocalSearchQuantizer_swiginit(self, _swigfaiss.new_LocalSearchQuantizer(*args))
-    __swig_destroy__ = _swigfaiss.delete_LocalSearchQuantizer
-
-    def train(self, n, x):
-        return _swigfaiss.LocalSearchQuantizer_train(self, n, x)
-
-    def compute_codes_add_centroids(self, x, codes, n, centroids=None):
-        r"""
-         Encode a set of vectors
-
-        :type x: float
-        :param x:      vectors to encode, size n * d
-        :type codes: uint8_t
-        :param codes:  output codes, size n * code_size
-        :type n: int
-        :param n:      number of vectors
-        :type centroids: float, optional
-        :param centroids:  centroids to be added to x, size n * d
-        """
-        return _swigfaiss.LocalSearchQuantizer_compute_codes_add_centroids(self, x, codes, n, centroids)
-
-    def update_codebooks(self, x, codes, n):
-        r"""
-         Update codebooks given encodings
-
-        :type x: float
-        :param x:      training vectors, size n * d
-        :type codes: int
-        :param codes:  encoded training vectors, size n * M
-        :type n: int
-        :param n:      number of vectors
-        """
-        return _swigfaiss.LocalSearchQuantizer_update_codebooks(self, x, codes, n)
-
-    def icm_encode(self, codes, x, n, ils_iters, gen):
-        r"""
-         Encode vectors given codebooks using iterative conditional mode (icm).
-
-        :type codes: int
-        :param codes:     output codes, size n * M
-        :type x: float
-        :param x:         vectors to encode, size n * d
-        :type n: int
-        :param n:         number of vectors
-        :type ils_iters: int
-        :param ils_iters: number of iterations of iterative local search
-        """
-        return _swigfaiss.LocalSearchQuantizer_icm_encode(self, codes, x, n, ils_iters, gen)
-
-    def icm_encode_impl(self, codes, x, unaries, gen, n, ils_iters, verbose):
-        return _swigfaiss.LocalSearchQuantizer_icm_encode_impl(self, codes, x, unaries, gen, n, ils_iters, verbose)
-
-    def icm_encode_step(self, codes, unaries, binaries, n, n_iters):
-        return _swigfaiss.LocalSearchQuantizer_icm_encode_step(self, codes, unaries, binaries, n, n_iters)
-
-    def perturb_codes(self, codes, n, gen):
-        r"""
-         Add some perturbation to codes
-
-        :type codes: int
-        :param codes: codes to be perturbed, size n * M
-        :type n: int
-        :param n:     number of vectors
-        """
-        return _swigfaiss.LocalSearchQuantizer_perturb_codes(self, codes, n, gen)
-
-    def perturb_codebooks(self, T, stddev, gen):
-        r"""
-         Add some perturbation to codebooks
-
-        :type T: float
-        :param T:         temperature of simulated annealing
-        :type stddev: std::vector< float >
-        :param stddev:    standard derivations of each dimension in training data
-        """
-        return _swigfaiss.LocalSearchQuantizer_perturb_codebooks(self, T, stddev, gen)
-
-    def compute_binary_terms(self, binaries):
-        r"""
-         Compute binary terms
-
-        :type binaries: float
-        :param binaries: binary terms, size M * M * K * K
-        """
-        return _swigfaiss.LocalSearchQuantizer_compute_binary_terms(self, binaries)
-
-    def compute_unary_terms(self, x, unaries, n):
-        r"""
-         Compute unary terms
-
-        :type n: int
-        :param n:       number of vectors
-        :type x: float
-        :param x:       vectors to encode, size n * d
-        :type unaries: float
-        :param unaries: unary terms, size n * M * K
-        """
-        return _swigfaiss.LocalSearchQuantizer_compute_unary_terms(self, x, unaries, n)
-
-    def evaluate(self, codes, x, n, objs=None):
-        r"""
-         Helper function to compute reconstruction error
-
-        :type codes: int
-        :param codes: encoded codes, size n * M
-        :type x: float
-        :param x:     vectors to encode, size n * d
-        :type n: int
-        :param n:     number of vectors
-        :type objs: float, optional
-        :param objs:  if it is not null, store reconstruction
-                                error of each vector into it, size n
-        """
-        return _swigfaiss.LocalSearchQuantizer_evaluate(self, codes, x, n, objs)
-
-# Register LocalSearchQuantizer in _swigfaiss:
-_swigfaiss.LocalSearchQuantizer_swigregister(LocalSearchQuantizer)
-class IcmEncoder(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    binaries = property(_swigfaiss.IcmEncoder_binaries_get, _swigfaiss.IcmEncoder_binaries_set)
-    verbose = property(_swigfaiss.IcmEncoder_verbose_get, _swigfaiss.IcmEncoder_verbose_set)
-    lsq = property(_swigfaiss.IcmEncoder_lsq_get, _swigfaiss.IcmEncoder_lsq_set)
-
-    def __init__(self, lsq):
-        _swigfaiss.IcmEncoder_swiginit(self, _swigfaiss.new_IcmEncoder(lsq))
-    __swig_destroy__ = _swigfaiss.delete_IcmEncoder
-
-    def set_binary_term(self):
-        return _swigfaiss.IcmEncoder_set_binary_term(self)
-
-    def encode(self, codes, x, gen, n, ils_iters):
-        r"""
-         Encode vectors given codebooks
-
-        :type codes: int
-        :param codes:     output codes, size n * M
-        :type x: float
-        :param x:         vectors to encode, size n * d
-        :type gen: std::mt19937
-        :param gen:       random generator
-        :type n: int
-        :param n:         number of vectors
-        :type ils_iters: int
-        :param ils_iters: number of iterations of iterative local search
-        """
-        return _swigfaiss.IcmEncoder_encode(self, codes, x, gen, n, ils_iters)
-
-# Register IcmEncoder in _swigfaiss:
-_swigfaiss.IcmEncoder_swigregister(IcmEncoder)
-class IcmEncoderFactory(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def get(self, lsq):
-        return _swigfaiss.IcmEncoderFactory_get(self, lsq)
-    __swig_destroy__ = _swigfaiss.delete_IcmEncoderFactory
-
-    def __init__(self):
-        _swigfaiss.IcmEncoderFactory_swiginit(self, _swigfaiss.new_IcmEncoderFactory())
-
-# Register IcmEncoderFactory in _swigfaiss:
-_swigfaiss.IcmEncoderFactory_swigregister(IcmEncoderFactory)
-class LSQTimer(object):
-    r"""
-    A helper struct to count consuming time during training.
-    It is NOT thread-safe.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    t = property(_swigfaiss.LSQTimer_t_get, _swigfaiss.LSQTimer_t_set)
-
-    def __init__(self):
-        _swigfaiss.LSQTimer_swiginit(self, _swigfaiss.new_LSQTimer())
-
-    def get(self, name):
-        return _swigfaiss.LSQTimer_get(self, name)
-
-    def add(self, name, delta):
-        return _swigfaiss.LSQTimer_add(self, name, delta)
-
-    def reset(self):
-        return _swigfaiss.LSQTimer_reset(self)
-    __swig_destroy__ = _swigfaiss.delete_LSQTimer
-
-# Register LSQTimer in _swigfaiss:
-_swigfaiss.LSQTimer_swigregister(LSQTimer)
-class LSQTimerScope(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    t0 = property(_swigfaiss.LSQTimerScope_t0_get, _swigfaiss.LSQTimerScope_t0_set)
-    timer = property(_swigfaiss.LSQTimerScope_timer_get, _swigfaiss.LSQTimerScope_timer_set)
-    name = property(_swigfaiss.LSQTimerScope_name_get, _swigfaiss.LSQTimerScope_name_set)
-    finished = property(_swigfaiss.LSQTimerScope_finished_get, _swigfaiss.LSQTimerScope_finished_set)
-
-    def __init__(self, timer, name):
-        _swigfaiss.LSQTimerScope_swiginit(self, _swigfaiss.new_LSQTimerScope(timer, name))
-
-    def finish(self):
-        return _swigfaiss.LSQTimerScope_finish(self)
-    __swig_destroy__ = _swigfaiss.delete_LSQTimerScope
-
-# Register LSQTimerScope in _swigfaiss:
-_swigfaiss.LSQTimerScope_swigregister(LSQTimerScope)
-class ProductAdditiveQuantizer(AdditiveQuantizer):
-    r"""
-     Product Additive Quantizers
-
-    The product additive quantizer is a variant of AQ and PQ.
-    It first splits the vector space into multiple orthogonal sub-spaces
-    just like PQ does. And then it quantizes each sub-space by an independent
-    additive quantizer.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nsplits = property(_swigfaiss.ProductAdditiveQuantizer_nsplits_get, _swigfaiss.ProductAdditiveQuantizer_nsplits_set, doc=r""" number of sub-vectors we split a vector into""")
-    quantizers = property(_swigfaiss.ProductAdditiveQuantizer_quantizers_get, _swigfaiss.ProductAdditiveQuantizer_quantizers_set)
-
-    def __init__(self, *args):
-        _swigfaiss.ProductAdditiveQuantizer_swiginit(self, _swigfaiss.new_ProductAdditiveQuantizer(*args))
-    __swig_destroy__ = _swigfaiss.delete_ProductAdditiveQuantizer
-
-    def init(self, d, aqs, search_type):
-        return _swigfaiss.ProductAdditiveQuantizer_init(self, d, aqs, search_type)
-
-    def subquantizer(self, m):
-        r""" Train the product additive quantizer"""
-        return _swigfaiss.ProductAdditiveQuantizer_subquantizer(self, m)
-
-    def train(self, n, x):
-        return _swigfaiss.ProductAdditiveQuantizer_train(self, n, x)
-
-    def compute_codes_add_centroids(self, x, codes, n, centroids=None):
-        r"""
-         Encode a set of vectors
-
-        :type x: float
-        :param x:      vectors to encode, size n * d
-        :type codes: uint8_t
-        :param codes:  output codes, size n * code_size
-        :type centroids: float, optional
-        :param centroids:  centroids to be added to x, size n * d
-        """
-        return _swigfaiss.ProductAdditiveQuantizer_compute_codes_add_centroids(self, x, codes, n, centroids)
-
-    def compute_unpacked_codes(self, x, codes, n, centroids=None):
-        return _swigfaiss.ProductAdditiveQuantizer_compute_unpacked_codes(self, x, codes, n, centroids)
-
-    def decode_unpacked(self, codes, x, n, ld_codes=-1):
-        r"""
-         Decode a set of vectors in non-packed format
-
-        :type codes: int
-        :param codes:  codes to decode, size n * ld_codes
-        :type x: float
-        :param x:      output vectors, size n * d
-        """
-        return _swigfaiss.ProductAdditiveQuantizer_decode_unpacked(self, codes, x, n, ld_codes)
-
-    def decode(self, codes, x, n):
-        r"""
-         Decode a set of vectors
-
-        :type codes: uint8_t
-        :param codes:  codes to decode, size n * code_size
-        :type x: float
-        :param x:      output vectors, size n * d
-        """
-        return _swigfaiss.ProductAdditiveQuantizer_decode(self, codes, x, n)
-
-    def compute_LUT(self, n, xq, LUT, alpha=1.0, ld_lut=-1):
-        r"""
-         Compute inner-product look-up tables. Used in the search functions.
-
-        :type xq: float
-        :param xq:     query vector, size (n, d)
-        :type LUT: float
-        :param LUT:    look-up table, size (n, total_codebook_size)
-        :type alpha: float, optional
-        :param alpha:  compute alpha * inner-product
-        :type ld_lut: int, optional
-        :param ld_lut:  leading dimension of LUT
-        """
-        return _swigfaiss.ProductAdditiveQuantizer_compute_LUT(self, n, xq, LUT, alpha, ld_lut)
-
-# Register ProductAdditiveQuantizer in _swigfaiss:
-_swigfaiss.ProductAdditiveQuantizer_swigregister(ProductAdditiveQuantizer)
-class ProductLocalSearchQuantizer(ProductAdditiveQuantizer):
-    r""" Product Local Search Quantizer"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.ProductLocalSearchQuantizer_swiginit(self, _swigfaiss.new_ProductLocalSearchQuantizer(*args))
-    __swig_destroy__ = _swigfaiss.delete_ProductLocalSearchQuantizer
-
-# Register ProductLocalSearchQuantizer in _swigfaiss:
-_swigfaiss.ProductLocalSearchQuantizer_swigregister(ProductLocalSearchQuantizer)
-class ProductResidualQuantizer(ProductAdditiveQuantizer):
-    r""" Product Residual Quantizer"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.ProductResidualQuantizer_swiginit(self, _swigfaiss.new_ProductResidualQuantizer(*args))
-    __swig_destroy__ = _swigfaiss.delete_ProductResidualQuantizer
-
-# Register ProductResidualQuantizer in _swigfaiss:
-_swigfaiss.ProductResidualQuantizer_swigregister(ProductResidualQuantizer)
-class CodePacker(object):
-    r"""
-    Packing consists in combining a fixed number of codes of constant size
-    (code_size) into a block of data where they may (or may not) be interleaved
-    for efficient consumption by distance computation kernels. This exists for
-    the "fast_scan" indexes on CPU and for some GPU kernels.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    code_size = property(_swigfaiss.CodePacker_code_size_get, _swigfaiss.CodePacker_code_size_set)
-    nvec = property(_swigfaiss.CodePacker_nvec_get, _swigfaiss.CodePacker_nvec_set)
-    block_size = property(_swigfaiss.CodePacker_block_size_get, _swigfaiss.CodePacker_block_size_set)
-
-    def pack_1(self, flat_code, offset, block):
-        return _swigfaiss.CodePacker_pack_1(self, flat_code, offset, block)
-
-    def unpack_1(self, block, offset, flat_code):
-        return _swigfaiss.CodePacker_unpack_1(self, block, offset, flat_code)
-
-    def pack_all(self, flat_codes, block):
-        return _swigfaiss.CodePacker_pack_all(self, flat_codes, block)
-
-    def unpack_all(self, block, flat_codes):
-        return _swigfaiss.CodePacker_unpack_all(self, block, flat_codes)
-    __swig_destroy__ = _swigfaiss.delete_CodePacker
-
-# Register CodePacker in _swigfaiss:
-_swigfaiss.CodePacker_swigregister(CodePacker)
-class CodePackerFlat(CodePacker):
-    r""" Trivial code packer where codes are stored one by one"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, code_size):
-        _swigfaiss.CodePackerFlat_swiginit(self, _swigfaiss.new_CodePackerFlat(code_size))
-
-    def pack_1(self, flat_code, offset, block):
-        return _swigfaiss.CodePackerFlat_pack_1(self, flat_code, offset, block)
-
-    def unpack_1(self, block, offset, flat_code):
-        return _swigfaiss.CodePackerFlat_unpack_1(self, block, offset, flat_code)
-
-    def pack_all(self, flat_codes, block):
-        return _swigfaiss.CodePackerFlat_pack_all(self, flat_codes, block)
-
-    def unpack_all(self, block, flat_codes):
-        return _swigfaiss.CodePackerFlat_unpack_all(self, block, flat_codes)
-    __swig_destroy__ = _swigfaiss.delete_CodePackerFlat
-
-# Register CodePackerFlat in _swigfaiss:
-_swigfaiss.CodePackerFlat_swigregister(CodePackerFlat)
-class VectorTransform(object):
-    r""" Any transformation applied on a set of vectors"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    d_in = property(_swigfaiss.VectorTransform_d_in_get, _swigfaiss.VectorTransform_d_in_set)
-    d_out = property(_swigfaiss.VectorTransform_d_out_get, _swigfaiss.VectorTransform_d_out_set, doc=r"""input dimension""")
-    is_trained = property(_swigfaiss.VectorTransform_is_trained_get, _swigfaiss.VectorTransform_is_trained_set, doc=r"""
-    set if the VectorTransform does not require training, or if
-    training is done already
-    """)
-
-    def train(self, n, x):
-        r"""
-         Perform training on a representative set of vectors. Does
-        nothing by default.
-
-        :type n: int
-        :param n:      nb of training vectors
-        :type x: float
-        :param x:      training vecors, size n * d
-        """
-        return _swigfaiss.VectorTransform_train(self, n, x)
-
-    def apply(self, n, x):
-        r"""
-         apply the transformation and return the result in an allocated pointer
-        :type n: int
-        :param n: number of vectors to transform
-        :type x: float
-        :param x: input vectors, size n * d_in
-        :rtype: float
-        :return: output vectors, size n * d_out
-        """
-        return _swigfaiss.VectorTransform_apply(self, n, x)
-
-    def apply_noalloc(self, n, x, xt):
-        r"""
-         apply the transformation and return the result in a provided matrix
-        :type n: int
-        :param n: number of vectors to transform
-        :type x: float
-        :param x: input vectors, size n * d_in
-        :type xt: float
-        :param xt: output vectors, size n * d_out
-        """
-        return _swigfaiss.VectorTransform_apply_noalloc(self, n, x, xt)
-
-    def reverse_transform(self, n, xt, x):
-        r"""
-        reverse transformation. May not be implemented or may return
-        approximate result
-        """
-        return _swigfaiss.VectorTransform_reverse_transform(self, n, xt, x)
-
-    def check_identical(self, other):
-        return _swigfaiss.VectorTransform_check_identical(self, other)
-    __swig_destroy__ = _swigfaiss.delete_VectorTransform
-
-# Register VectorTransform in _swigfaiss:
-_swigfaiss.VectorTransform_swigregister(VectorTransform)
-class LinearTransform(VectorTransform):
-    r"""
-     Generic linear transformation, with bias term applied on output
-    y = A * x + b
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    have_bias = property(_swigfaiss.LinearTransform_have_bias_get, _swigfaiss.LinearTransform_have_bias_set)
-    is_orthonormal = property(_swigfaiss.LinearTransform_is_orthonormal_get, _swigfaiss.LinearTransform_is_orthonormal_set, doc=r"""
-    whether to use the bias term
-     check if matrix A is orthonormal (enables reverse_transform)
-    """)
-    A = property(_swigfaiss.LinearTransform_A_get, _swigfaiss.LinearTransform_A_set, doc=r""" Transformation matrix, size d_out * d_in""")
-    b = property(_swigfaiss.LinearTransform_b_get, _swigfaiss.LinearTransform_b_set, doc=r""" bias vector, size d_out""")
-
-    def __init__(self, d_in=0, d_out=0, have_bias=False):
-        r""" both d_in > d_out and d_out < d_in are supported"""
-        _swigfaiss.LinearTransform_swiginit(self, _swigfaiss.new_LinearTransform(d_in, d_out, have_bias))
-
-    def apply_noalloc(self, n, x, xt):
-        r""" same as apply, but result is pre-allocated"""
-        return _swigfaiss.LinearTransform_apply_noalloc(self, n, x, xt)
-
-    def transform_transpose(self, n, y, x):
-        r"""
-        compute x = A^T * (x - b)
-        is reverse transform if A has orthonormal lines
-        """
-        return _swigfaiss.LinearTransform_transform_transpose(self, n, y, x)
-
-    def reverse_transform(self, n, xt, x):
-        r""" works only if is_orthonormal"""
-        return _swigfaiss.LinearTransform_reverse_transform(self, n, xt, x)
-
-    def set_is_orthonormal(self):
-        r""" compute A^T * A to set the is_orthonormal flag"""
-        return _swigfaiss.LinearTransform_set_is_orthonormal(self)
-    verbose = property(_swigfaiss.LinearTransform_verbose_get, _swigfaiss.LinearTransform_verbose_set)
-
-    def print_if_verbose(self, name, mat, n, d):
-        return _swigfaiss.LinearTransform_print_if_verbose(self, name, mat, n, d)
-
-    def check_identical(self, other):
-        return _swigfaiss.LinearTransform_check_identical(self, other)
-    __swig_destroy__ = _swigfaiss.delete_LinearTransform
-
-# Register LinearTransform in _swigfaiss:
-_swigfaiss.LinearTransform_swigregister(LinearTransform)
-class RandomRotationMatrix(LinearTransform):
-    r""" Randomly rotate a set of vectors"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def init(self, seed):
-        r""" must be called before the transform is used"""
-        return _swigfaiss.RandomRotationMatrix_init(self, seed)
-
-    def train(self, n, x):
-        return _swigfaiss.RandomRotationMatrix_train(self, n, x)
-
-    def __init__(self, *args):
-        _swigfaiss.RandomRotationMatrix_swiginit(self, _swigfaiss.new_RandomRotationMatrix(*args))
-    __swig_destroy__ = _swigfaiss.delete_RandomRotationMatrix
-
-# Register RandomRotationMatrix in _swigfaiss:
-_swigfaiss.RandomRotationMatrix_swigregister(RandomRotationMatrix)
-class PCAMatrix(LinearTransform):
-    r"""
-    Applies a principal component analysis on a set of vectors,
-    with optionally whitening and random rotation.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    eigen_power = property(_swigfaiss.PCAMatrix_eigen_power_get, _swigfaiss.PCAMatrix_eigen_power_set, doc=r"""
-     after transformation the components are multiplied by
-    eigenvalues^eigen_power
-
-    =0: no whitening
-    =-0.5: full whitening
-    """)
-    epsilon = property(_swigfaiss.PCAMatrix_epsilon_get, _swigfaiss.PCAMatrix_epsilon_set, doc=r""" value added to eigenvalues to avoid division by 0 when whitening""")
-    random_rotation = property(_swigfaiss.PCAMatrix_random_rotation_get, _swigfaiss.PCAMatrix_random_rotation_set, doc=r""" random rotation after PCA""")
-    max_points_per_d = property(_swigfaiss.PCAMatrix_max_points_per_d_get, _swigfaiss.PCAMatrix_max_points_per_d_set, doc=r""" ratio between # training vectors and dimension""")
-    balanced_bins = property(_swigfaiss.PCAMatrix_balanced_bins_get, _swigfaiss.PCAMatrix_balanced_bins_set, doc=r""" try to distribute output eigenvectors in this many bins""")
-    mean = property(_swigfaiss.PCAMatrix_mean_get, _swigfaiss.PCAMatrix_mean_set, doc=r""" Mean, size d_in""")
-    eigenvalues = property(_swigfaiss.PCAMatrix_eigenvalues_get, _swigfaiss.PCAMatrix_eigenvalues_set, doc=r""" eigenvalues of covariance matrix (= squared singular values)""")
-    PCAMat = property(_swigfaiss.PCAMatrix_PCAMat_get, _swigfaiss.PCAMatrix_PCAMat_set, doc=r""" PCA matrix, size d_in * d_in""")
-
-    def __init__(self, d_in=0, d_out=0, eigen_power=0, random_rotation=False):
-        _swigfaiss.PCAMatrix_swiginit(self, _swigfaiss.new_PCAMatrix(d_in, d_out, eigen_power, random_rotation))
-
-    def train(self, n, x):
-        r"""
-        train on n vectors. If n < d_in then the eigenvector matrix
-        will be completed with 0s
-        """
-        return _swigfaiss.PCAMatrix_train(self, n, x)
-
-    def copy_from(self, other):
-        r""" copy pre-trained PCA matrix"""
-        return _swigfaiss.PCAMatrix_copy_from(self, other)
-
-    def prepare_Ab(self):
-        r""" called after mean, PCAMat and eigenvalues are computed"""
-        return _swigfaiss.PCAMatrix_prepare_Ab(self)
-    __swig_destroy__ = _swigfaiss.delete_PCAMatrix
-
-# Register PCAMatrix in _swigfaiss:
-_swigfaiss.PCAMatrix_swigregister(PCAMatrix)
-class ITQMatrix(LinearTransform):
-    r"""
-     ITQ implementation from
-
-        Iterative quantization: A procrustean approach to learning binary codes
-        for large-scale image retrieval,
-
-    Yunchao Gong, Svetlana Lazebnik, Albert Gordo, Florent Perronnin,
-    PAMI'12.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    max_iter = property(_swigfaiss.ITQMatrix_max_iter_get, _swigfaiss.ITQMatrix_max_iter_set)
-    seed = property(_swigfaiss.ITQMatrix_seed_get, _swigfaiss.ITQMatrix_seed_set)
-    init_rotation = property(_swigfaiss.ITQMatrix_init_rotation_get, _swigfaiss.ITQMatrix_init_rotation_set)
-
-    def __init__(self, d=0):
-        _swigfaiss.ITQMatrix_swiginit(self, _swigfaiss.new_ITQMatrix(d))
-
-    def train(self, n, x):
-        return _swigfaiss.ITQMatrix_train(self, n, x)
-    __swig_destroy__ = _swigfaiss.delete_ITQMatrix
-
-# Register ITQMatrix in _swigfaiss:
-_swigfaiss.ITQMatrix_swigregister(ITQMatrix)
-class ITQTransform(VectorTransform):
-    r""" The full ITQ transform, including normalizations and PCA transformation"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    mean = property(_swigfaiss.ITQTransform_mean_get, _swigfaiss.ITQTransform_mean_set)
-    do_pca = property(_swigfaiss.ITQTransform_do_pca_get, _swigfaiss.ITQTransform_do_pca_set)
-    itq = property(_swigfaiss.ITQTransform_itq_get, _swigfaiss.ITQTransform_itq_set)
-    max_train_per_dim = property(_swigfaiss.ITQTransform_max_train_per_dim_get, _swigfaiss.ITQTransform_max_train_per_dim_set, doc=r""" max training points per dimension""")
-    pca_then_itq = property(_swigfaiss.ITQTransform_pca_then_itq_get, _swigfaiss.ITQTransform_pca_then_itq_set)
-
-    def __init__(self, d_in=0, d_out=0, do_pca=False):
-        _swigfaiss.ITQTransform_swiginit(self, _swigfaiss.new_ITQTransform(d_in, d_out, do_pca))
-
-    def train(self, n, x):
-        return _swigfaiss.ITQTransform_train(self, n, x)
-
-    def apply_noalloc(self, n, x, xt):
-        return _swigfaiss.ITQTransform_apply_noalloc(self, n, x, xt)
-
-    def check_identical(self, other):
-        return _swigfaiss.ITQTransform_check_identical(self, other)
-    __swig_destroy__ = _swigfaiss.delete_ITQTransform
-
-# Register ITQTransform in _swigfaiss:
-_swigfaiss.ITQTransform_swigregister(ITQTransform)
-class OPQMatrix(LinearTransform):
-    r"""
-     Applies a rotation to align the dimensions with a PQ to minimize
-     the reconstruction error. Can be used before an IndexPQ or an
-     IndexIVFPQ. The method is the non-parametric version described in:
-
-    "Optimized Product Quantization for Approximate Nearest Neighbor Search"
-    Tiezheng Ge, Kaiming He, Qifa Ke, Jian Sun, CVPR'13
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    M = property(_swigfaiss.OPQMatrix_M_get, _swigfaiss.OPQMatrix_M_set, doc=r""" nb of subquantizers""")
-    niter = property(_swigfaiss.OPQMatrix_niter_get, _swigfaiss.OPQMatrix_niter_set, doc=r""" Number of outer training iterations""")
-    niter_pq = property(_swigfaiss.OPQMatrix_niter_pq_get, _swigfaiss.OPQMatrix_niter_pq_set, doc=r""" Number of training iterations for the PQ""")
-    niter_pq_0 = property(_swigfaiss.OPQMatrix_niter_pq_0_get, _swigfaiss.OPQMatrix_niter_pq_0_set, doc=r""" same, for the first outer iteration""")
-    max_train_points = property(_swigfaiss.OPQMatrix_max_train_points_get, _swigfaiss.OPQMatrix_max_train_points_set, doc=r""" if there are too many training points, resample""")
-    verbose = property(_swigfaiss.OPQMatrix_verbose_get, _swigfaiss.OPQMatrix_verbose_set)
-    pq = property(_swigfaiss.OPQMatrix_pq_get, _swigfaiss.OPQMatrix_pq_set, doc=r"""
-    if non-NULL, use this product quantizer for training
-    should be constructed with (d_out, M, _)
-    """)
-
-    def __init__(self, d=0, M=1, d2=-1):
-        r""" if d2 != -1, output vectors of this dimension"""
-        _swigfaiss.OPQMatrix_swiginit(self, _swigfaiss.new_OPQMatrix(d, M, d2))
-
-    def train(self, n, x):
-        return _swigfaiss.OPQMatrix_train(self, n, x)
-    __swig_destroy__ = _swigfaiss.delete_OPQMatrix
-
-# Register OPQMatrix in _swigfaiss:
-_swigfaiss.OPQMatrix_swigregister(OPQMatrix)
-class RemapDimensionsTransform(VectorTransform):
-    r"""
-     remap dimensions for intput vectors, possibly inserting 0s
-    strictly speaking this is also a linear transform but we don't want
-    to compute it with matrix multiplies
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    map = property(_swigfaiss.RemapDimensionsTransform_map_get, _swigfaiss.RemapDimensionsTransform_map_set, doc=r"""
-    map from output dimension to input, size d_out
-    -1 -> set output to 0
-    """)
-
-    def apply_noalloc(self, n, x, xt):
-        return _swigfaiss.RemapDimensionsTransform_apply_noalloc(self, n, x, xt)
-
-    def reverse_transform(self, n, xt, x):
-        r""" reverse transform correct only when the mapping is a permutation"""
-        return _swigfaiss.RemapDimensionsTransform_reverse_transform(self, n, xt, x)
-
-    def __init__(self, *args):
-        _swigfaiss.RemapDimensionsTransform_swiginit(self, _swigfaiss.new_RemapDimensionsTransform(*args))
-
-    def check_identical(self, other):
-        return _swigfaiss.RemapDimensionsTransform_check_identical(self, other)
-    __swig_destroy__ = _swigfaiss.delete_RemapDimensionsTransform
-
-# Register RemapDimensionsTransform in _swigfaiss:
-_swigfaiss.RemapDimensionsTransform_swigregister(RemapDimensionsTransform)
-class NormalizationTransform(VectorTransform):
-    r""" per-vector normalization"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    norm = property(_swigfaiss.NormalizationTransform_norm_get, _swigfaiss.NormalizationTransform_norm_set)
-
-    def __init__(self, *args):
-        _swigfaiss.NormalizationTransform_swiginit(self, _swigfaiss.new_NormalizationTransform(*args))
-
-    def apply_noalloc(self, n, x, xt):
-        return _swigfaiss.NormalizationTransform_apply_noalloc(self, n, x, xt)
-
-    def reverse_transform(self, n, xt, x):
-        r""" Identity transform since norm is not revertible"""
-        return _swigfaiss.NormalizationTransform_reverse_transform(self, n, xt, x)
-
-    def check_identical(self, other):
-        return _swigfaiss.NormalizationTransform_check_identical(self, other)
-    __swig_destroy__ = _swigfaiss.delete_NormalizationTransform
-
-# Register NormalizationTransform in _swigfaiss:
-_swigfaiss.NormalizationTransform_swigregister(NormalizationTransform)
-class CenteringTransform(VectorTransform):
-    r""" Subtract the mean of each component from the vectors."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    mean = property(_swigfaiss.CenteringTransform_mean_get, _swigfaiss.CenteringTransform_mean_set, doc=r""" Mean, size d_in = d_out""")
-
-    def __init__(self, d=0):
-        _swigfaiss.CenteringTransform_swiginit(self, _swigfaiss.new_CenteringTransform(d))
-
-    def train(self, n, x):
-        r""" train on n vectors."""
-        return _swigfaiss.CenteringTransform_train(self, n, x)
-
-    def apply_noalloc(self, n, x, xt):
-        r""" subtract the mean"""
-        return _swigfaiss.CenteringTransform_apply_noalloc(self, n, x, xt)
-
-    def reverse_transform(self, n, xt, x):
-        r""" add the mean"""
-        return _swigfaiss.CenteringTransform_reverse_transform(self, n, xt, x)
-
-    def check_identical(self, other):
-        return _swigfaiss.CenteringTransform_check_identical(self, other)
-    __swig_destroy__ = _swigfaiss.delete_CenteringTransform
-
-# Register CenteringTransform in _swigfaiss:
-_swigfaiss.CenteringTransform_swigregister(CenteringTransform)
-class SearchParametersPreTransform(SearchParameters):
-    r"""
-    Copyright (c) Facebook, Inc. and its affiliates.
-
-    This source code is licensed under the MIT license found in the
-    LICENSE file in the root directory of this source tree.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    index_params = property(_swigfaiss.SearchParametersPreTransform_index_params_get, _swigfaiss.SearchParametersPreTransform_index_params_set)
-
-    def __init__(self):
-        _swigfaiss.SearchParametersPreTransform_swiginit(self, _swigfaiss.new_SearchParametersPreTransform())
-    __swig_destroy__ = _swigfaiss.delete_SearchParametersPreTransform
-
-# Register SearchParametersPreTransform in _swigfaiss:
-_swigfaiss.SearchParametersPreTransform_swigregister(SearchParametersPreTransform)
-class IndexPreTransform(Index):
-    r"""
-    Index that applies a LinearTransform transform on vectors before
-    handing them over to a sub-index
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    chain = property(_swigfaiss.IndexPreTransform_chain_get, _swigfaiss.IndexPreTransform_chain_set)
-    index = property(_swigfaiss.IndexPreTransform_index_get, _swigfaiss.IndexPreTransform_index_set, doc=r"""chain of tranforms""")
-    own_fields = property(_swigfaiss.IndexPreTransform_own_fields_get, _swigfaiss.IndexPreTransform_own_fields_set, doc=r"""the sub-index""")
-
-    def __init__(self, *args):
-        r"""
-        *Overload 1:*
-        whether pointers are deleted in destructor
-
-        |
-
-        *Overload 2:*
-         ltrans is the last transform before the index
-        """
-        _swigfaiss.IndexPreTransform_swiginit(self, _swigfaiss.new_IndexPreTransform(*args))
-
-    def prepend_transform(self, ltrans):
-        return _swigfaiss.IndexPreTransform_prepend_transform(self, ltrans)
-
-    def train(self, n, x):
-        return _swigfaiss.IndexPreTransform_train(self, n, x)
-
-    def add(self, n, x):
-        return _swigfaiss.IndexPreTransform_add(self, n, x)
-
-    def add_with_ids(self, n, x, xids):
-        return _swigfaiss.IndexPreTransform_add_with_ids(self, n, x, xids)
-
-    def reset(self):
-        return _swigfaiss.IndexPreTransform_reset(self)
-
-    def remove_ids(self, sel):
-        r""" removes IDs from the index. Not supported by all indexes."""
-        return _swigfaiss.IndexPreTransform_remove_ids(self, sel)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexPreTransform_search(self, n, x, k, distances, labels, params)
-
-    def range_search(self, n, x, radius, result, params=None):
-        return _swigfaiss.IndexPreTransform_range_search(self, n, x, radius, result, params)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexPreTransform_reconstruct(self, key, recons)
-
-    def reconstruct_n(self, i0, ni, recons):
-        return _swigfaiss.IndexPreTransform_reconstruct_n(self, i0, ni, recons)
-
-    def search_and_reconstruct(self, n, x, k, distances, labels, recons, params=None):
-        return _swigfaiss.IndexPreTransform_search_and_reconstruct(self, n, x, k, distances, labels, recons, params)
-
-    def apply_chain(self, n, x):
-        r"""
-        apply the transforms in the chain. The returned float * may be
-        equal to x, otherwise it should be deallocated.
-        """
-        return _swigfaiss.IndexPreTransform_apply_chain(self, n, x)
-
-    def reverse_chain(self, n, xt, x):
-        r"""
-        Reverse the transforms in the chain. May not be implemented for
-        all transforms in the chain or may return approximate results.
-        """
-        return _swigfaiss.IndexPreTransform_reverse_chain(self, n, xt, x)
-
-    def get_distance_computer(self):
-        return _swigfaiss.IndexPreTransform_get_distance_computer(self)
-
-    def sa_code_size(self):
-        return _swigfaiss.IndexPreTransform_sa_code_size(self)
-
-    def sa_encode(self, n, x, bytes):
-        return _swigfaiss.IndexPreTransform_sa_encode(self, n, x, bytes)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexPreTransform_sa_decode(self, n, bytes, x)
-
-    def merge_from(self, otherIndex, add_id=0):
-        return _swigfaiss.IndexPreTransform_merge_from(self, otherIndex, add_id)
-
-    def check_compatible_for_merge(self, otherIndex):
-        return _swigfaiss.IndexPreTransform_check_compatible_for_merge(self, otherIndex)
-    __swig_destroy__ = _swigfaiss.delete_IndexPreTransform
-
-# Register IndexPreTransform in _swigfaiss:
-_swigfaiss.IndexPreTransform_swigregister(IndexPreTransform)
-class IndexRefine(Index):
-    r"""
-    Index that queries in a base_index (a fast one) and refines the
-    results with an exact search, hopefully improving the results.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    base_index = property(_swigfaiss.IndexRefine_base_index_get, _swigfaiss.IndexRefine_base_index_set, doc=r""" faster index to pre-select the vectors that should be filtered""")
-    refine_index = property(_swigfaiss.IndexRefine_refine_index_get, _swigfaiss.IndexRefine_refine_index_set, doc=r""" refinement index""")
-    own_fields = property(_swigfaiss.IndexRefine_own_fields_get, _swigfaiss.IndexRefine_own_fields_set, doc=r""" should the base index be deallocated?""")
-    own_refine_index = property(_swigfaiss.IndexRefine_own_refine_index_get, _swigfaiss.IndexRefine_own_refine_index_set, doc=r""" same with the refinement index""")
-    k_factor = property(_swigfaiss.IndexRefine_k_factor_get, _swigfaiss.IndexRefine_k_factor_set, doc=r"""
-    factor between k requested in search and the k requested from
-    the base_index (should be >= 1)
-    """)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexRefine_swiginit(self, _swigfaiss.new_IndexRefine(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexRefine_train(self, n, x)
-
-    def add(self, n, x):
-        return _swigfaiss.IndexRefine_add(self, n, x)
-
-    def reset(self):
-        return _swigfaiss.IndexRefine_reset(self)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexRefine_search(self, n, x, k, distances, labels, params)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexRefine_reconstruct(self, key, recons)
-
-    def sa_code_size(self):
-        return _swigfaiss.IndexRefine_sa_code_size(self)
-
-    def sa_encode(self, n, x, bytes):
-        return _swigfaiss.IndexRefine_sa_encode(self, n, x, bytes)
-
-    def sa_decode(self, n, bytes, x):
-        r"""
-        The sa_decode decodes from the index_refine, which is assumed to be more
-        accurate
-        """
-        return _swigfaiss.IndexRefine_sa_decode(self, n, bytes, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexRefine
-
-# Register IndexRefine in _swigfaiss:
-_swigfaiss.IndexRefine_swigregister(IndexRefine)
-class IndexRefineFlat(IndexRefine):
-    r"""
-     Version where the refinement index is an IndexFlat. It has one additional
-    constructor that takes a table of elements to add to the flat refinement
-    index
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.IndexRefineFlat_swiginit(self, _swigfaiss.new_IndexRefineFlat(*args))
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexRefineFlat_search(self, n, x, k, distances, labels, params)
-    __swig_destroy__ = _swigfaiss.delete_IndexRefineFlat
-
-# Register IndexRefineFlat in _swigfaiss:
-_swigfaiss.IndexRefineFlat_swigregister(IndexRefineFlat)
-class IndexLSH(IndexFlatCodes):
-    r""" The sign of each vector component is put in a binary signature"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nbits = property(_swigfaiss.IndexLSH_nbits_get, _swigfaiss.IndexLSH_nbits_set, doc=r""" nb of bits per vector""")
-    rotate_data = property(_swigfaiss.IndexLSH_rotate_data_get, _swigfaiss.IndexLSH_rotate_data_set, doc=r""" whether to apply a random rotation to input""")
-    train_thresholds = property(_swigfaiss.IndexLSH_train_thresholds_get, _swigfaiss.IndexLSH_train_thresholds_set, doc=r""" whether we train thresholds or use 0""")
-    rrot = property(_swigfaiss.IndexLSH_rrot_get, _swigfaiss.IndexLSH_rrot_set, doc=r""" optional random rotation""")
-    thresholds = property(_swigfaiss.IndexLSH_thresholds_get, _swigfaiss.IndexLSH_thresholds_set, doc=r""" thresholds to compare with""")
-
-    def apply_preprocess(self, n, x):
-        r"""
-         Preprocesses and resizes the input to the size required to
-        binarize the data
-
-        :type x: float
-        :param x: input vectors, size n * d
-        :rtype: float
-        :return: output vectors, size n * bits. May be the same pointer
-                    as x, otherwise it should be deleted by the caller
-        """
-        return _swigfaiss.IndexLSH_apply_preprocess(self, n, x)
-
-    def train(self, n, x):
-        return _swigfaiss.IndexLSH_train(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexLSH_search(self, n, x, k, distances, labels, params)
-
-    def transfer_thresholds(self, vt):
-        r"""
-        transfer the thresholds to a pre-processing stage (and unset
-        train_thresholds)
-        """
-        return _swigfaiss.IndexLSH_transfer_thresholds(self, vt)
-    __swig_destroy__ = _swigfaiss.delete_IndexLSH
-
-    def __init__(self, *args):
-        _swigfaiss.IndexLSH_swiginit(self, _swigfaiss.new_IndexLSH(*args))
-
-    def sa_encode(self, n, x, bytes):
-        return _swigfaiss.IndexLSH_sa_encode(self, n, x, bytes)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexLSH_sa_decode(self, n, bytes, x)
-
-# Register IndexLSH in _swigfaiss:
-_swigfaiss.IndexLSH_swigregister(IndexLSH)
-class SimulatedAnnealingParameters(object):
-    r""" parameters used for the simulated annealing method"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    init_temperature = property(_swigfaiss.SimulatedAnnealingParameters_init_temperature_get, _swigfaiss.SimulatedAnnealingParameters_init_temperature_set)
-    temperature_decay = property(_swigfaiss.SimulatedAnnealingParameters_temperature_decay_get, _swigfaiss.SimulatedAnnealingParameters_temperature_decay_set)
-    n_iter = property(_swigfaiss.SimulatedAnnealingParameters_n_iter_get, _swigfaiss.SimulatedAnnealingParameters_n_iter_set)
-    n_redo = property(_swigfaiss.SimulatedAnnealingParameters_n_redo_get, _swigfaiss.SimulatedAnnealingParameters_n_redo_set)
-    seed = property(_swigfaiss.SimulatedAnnealingParameters_seed_get, _swigfaiss.SimulatedAnnealingParameters_seed_set)
-    verbose = property(_swigfaiss.SimulatedAnnealingParameters_verbose_get, _swigfaiss.SimulatedAnnealingParameters_verbose_set)
-    only_bit_flips = property(_swigfaiss.SimulatedAnnealingParameters_only_bit_flips_get, _swigfaiss.SimulatedAnnealingParameters_only_bit_flips_set)
-    init_random = property(_swigfaiss.SimulatedAnnealingParameters_init_random_get, _swigfaiss.SimulatedAnnealingParameters_init_random_set)
-
-    def __init__(self):
-        _swigfaiss.SimulatedAnnealingParameters_swiginit(self, _swigfaiss.new_SimulatedAnnealingParameters())
-    __swig_destroy__ = _swigfaiss.delete_SimulatedAnnealingParameters
-
-# Register SimulatedAnnealingParameters in _swigfaiss:
-_swigfaiss.SimulatedAnnealingParameters_swigregister(SimulatedAnnealingParameters)
-class PermutationObjective(object):
-    r""" abstract class for the loss function"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    n = property(_swigfaiss.PermutationObjective_n_get, _swigfaiss.PermutationObjective_n_set)
-
-    def compute_cost(self, perm):
-        return _swigfaiss.PermutationObjective_compute_cost(self, perm)
-
-    def cost_update(self, perm, iw, jw):
-        return _swigfaiss.PermutationObjective_cost_update(self, perm, iw, jw)
-    __swig_destroy__ = _swigfaiss.delete_PermutationObjective
-
-# Register PermutationObjective in _swigfaiss:
-_swigfaiss.PermutationObjective_swigregister(PermutationObjective)
-class ReproduceDistancesObjective(PermutationObjective):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    dis_weight_factor = property(_swigfaiss.ReproduceDistancesObjective_dis_weight_factor_get, _swigfaiss.ReproduceDistancesObjective_dis_weight_factor_set)
-
-    @staticmethod
-    def sqr(x):
-        return _swigfaiss.ReproduceDistancesObjective_sqr(x)
-
-    def dis_weight(self, x):
-        return _swigfaiss.ReproduceDistancesObjective_dis_weight(self, x)
-    source_dis = property(_swigfaiss.ReproduceDistancesObjective_source_dis_get, _swigfaiss.ReproduceDistancesObjective_source_dis_set, doc=r""" "real" corrected distances (size n^2)""")
-    target_dis = property(_swigfaiss.ReproduceDistancesObjective_target_dis_get, _swigfaiss.ReproduceDistancesObjective_target_dis_set, doc=r""" wanted distances (size n^2)""")
-    weights = property(_swigfaiss.ReproduceDistancesObjective_weights_get, _swigfaiss.ReproduceDistancesObjective_weights_set, doc=r""" weights for each distance (size n^2)""")
-
-    def get_source_dis(self, i, j):
-        return _swigfaiss.ReproduceDistancesObjective_get_source_dis(self, i, j)
-
-    def compute_cost(self, perm):
-        return _swigfaiss.ReproduceDistancesObjective_compute_cost(self, perm)
-
-    def cost_update(self, perm, iw, jw):
-        return _swigfaiss.ReproduceDistancesObjective_cost_update(self, perm, iw, jw)
-
-    def __init__(self, n, source_dis_in, target_dis_in, dis_weight_factor):
-        _swigfaiss.ReproduceDistancesObjective_swiginit(self, _swigfaiss.new_ReproduceDistancesObjective(n, source_dis_in, target_dis_in, dis_weight_factor))
-
-    @staticmethod
-    def compute_mean_stdev(tab, n2, mean_out, stddev_out):
-        return _swigfaiss.ReproduceDistancesObjective_compute_mean_stdev(tab, n2, mean_out, stddev_out)
-
-    def set_affine_target_dis(self, source_dis_in):
-        return _swigfaiss.ReproduceDistancesObjective_set_affine_target_dis(self, source_dis_in)
-    __swig_destroy__ = _swigfaiss.delete_ReproduceDistancesObjective
-
-# Register ReproduceDistancesObjective in _swigfaiss:
-_swigfaiss.ReproduceDistancesObjective_swigregister(ReproduceDistancesObjective)
-class SimulatedAnnealingOptimizer(SimulatedAnnealingParameters):
-    r""" Simulated annealing optimization algorithm for permutations."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    obj = property(_swigfaiss.SimulatedAnnealingOptimizer_obj_get, _swigfaiss.SimulatedAnnealingOptimizer_obj_set)
-    n = property(_swigfaiss.SimulatedAnnealingOptimizer_n_get, _swigfaiss.SimulatedAnnealingOptimizer_n_set, doc=r""" size of the permutation""")
-    logfile = property(_swigfaiss.SimulatedAnnealingOptimizer_logfile_get, _swigfaiss.SimulatedAnnealingOptimizer_logfile_set)
-
-    def __init__(self, obj, p):
-        r""" logs values of the cost function"""
-        _swigfaiss.SimulatedAnnealingOptimizer_swiginit(self, _swigfaiss.new_SimulatedAnnealingOptimizer(obj, p))
-    rnd = property(_swigfaiss.SimulatedAnnealingOptimizer_rnd_get, _swigfaiss.SimulatedAnnealingOptimizer_rnd_set)
-    init_cost = property(_swigfaiss.SimulatedAnnealingOptimizer_init_cost_get, _swigfaiss.SimulatedAnnealingOptimizer_init_cost_set, doc=r""" remember initial cost of optimization""")
-
-    def optimize(self, perm):
-        return _swigfaiss.SimulatedAnnealingOptimizer_optimize(self, perm)
-
-    def run_optimization(self, best_perm):
-        return _swigfaiss.SimulatedAnnealingOptimizer_run_optimization(self, best_perm)
-    __swig_destroy__ = _swigfaiss.delete_SimulatedAnnealingOptimizer
-
-# Register SimulatedAnnealingOptimizer in _swigfaiss:
-_swigfaiss.SimulatedAnnealingOptimizer_swigregister(SimulatedAnnealingOptimizer)
-class PolysemousTraining(SimulatedAnnealingParameters):
-    r""" optimizes the order of indices in a ProductQuantizer"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    OT_None = _swigfaiss.PolysemousTraining_OT_None
-    OT_ReproduceDistances_affine = _swigfaiss.PolysemousTraining_OT_ReproduceDistances_affine
-    r""" default"""
-    OT_Ranking_weighted_diff = _swigfaiss.PolysemousTraining_OT_Ranking_weighted_diff
-    r"""
-    same as _2, but use rank of y+ - rank of
-    y-
-    """
-    optimization_type = property(_swigfaiss.PolysemousTraining_optimization_type_get, _swigfaiss.PolysemousTraining_optimization_type_set)
-    ntrain_permutation = property(_swigfaiss.PolysemousTraining_ntrain_permutation_get, _swigfaiss.PolysemousTraining_ntrain_permutation_set, doc=r"""
-     use 1/4 of the training points for the optimization, with
-    max. ntrain_permutation. If ntrain_permutation == 0: train on
-    centroids
-    """)
-    dis_weight_factor = property(_swigfaiss.PolysemousTraining_dis_weight_factor_get, _swigfaiss.PolysemousTraining_dis_weight_factor_set, doc=r""" decay of exp that weights distance loss""")
-    max_memory = property(_swigfaiss.PolysemousTraining_max_memory_get, _swigfaiss.PolysemousTraining_max_memory_set, doc=r""" refuse to train if it would require more than that amount of RAM""")
-    log_pattern = property(_swigfaiss.PolysemousTraining_log_pattern_get, _swigfaiss.PolysemousTraining_log_pattern_set)
-
-    def __init__(self):
-        _swigfaiss.PolysemousTraining_swiginit(self, _swigfaiss.new_PolysemousTraining())
-
-    def optimize_pq_for_hamming(self, pq, n, x):
-        r"""
-        reorder the centroids so that the Hamming distance becomes a
-        good approximation of the SDC distance (called by train)
-        """
-        return _swigfaiss.PolysemousTraining_optimize_pq_for_hamming(self, pq, n, x)
-
-    def optimize_ranking(self, pq, n, x):
-        r""" called by optimize_pq_for_hamming"""
-        return _swigfaiss.PolysemousTraining_optimize_ranking(self, pq, n, x)
-
-    def optimize_reproduce_distances(self, pq):
-        r""" called by optimize_pq_for_hamming"""
-        return _swigfaiss.PolysemousTraining_optimize_reproduce_distances(self, pq)
-
-    def memory_usage_per_thread(self, pq):
-        r""" make sure we don't blow up the memory"""
-        return _swigfaiss.PolysemousTraining_memory_usage_per_thread(self, pq)
-    __swig_destroy__ = _swigfaiss.delete_PolysemousTraining
-
-# Register PolysemousTraining in _swigfaiss:
-_swigfaiss.PolysemousTraining_swigregister(PolysemousTraining)
-class IndexPQ(IndexFlatCodes):
-    r"""
-     Index based on a product quantizer. Stored vectors are
-    approximated by PQ codes.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    pq = property(_swigfaiss.IndexPQ_pq_get, _swigfaiss.IndexPQ_pq_set, doc=r""" The product quantizer used to encode the vectors""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexPQ_swiginit(self, _swigfaiss.new_IndexPQ(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexPQ_train(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexPQ_search(self, n, x, k, distances, labels, params)
-
-    def sa_encode(self, n, x, bytes):
-        return _swigfaiss.IndexPQ_sa_encode(self, n, x, bytes)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexPQ_sa_decode(self, n, bytes, x)
-
-    def get_FlatCodesDistanceComputer(self):
-        return _swigfaiss.IndexPQ_get_FlatCodesDistanceComputer(self)
-    do_polysemous_training = property(_swigfaiss.IndexPQ_do_polysemous_training_get, _swigfaiss.IndexPQ_do_polysemous_training_set, doc=r""" false = standard PQ""")
-    polysemous_training = property(_swigfaiss.IndexPQ_polysemous_training_get, _swigfaiss.IndexPQ_polysemous_training_set, doc=r""" parameters used for the polysemous training""")
-    ST_PQ = _swigfaiss.IndexPQ_ST_PQ
-    r""" asymmetric product quantizer (default)"""
-    ST_HE = _swigfaiss.IndexPQ_ST_HE
-    r""" Hamming distance on codes"""
-    ST_generalized_HE = _swigfaiss.IndexPQ_ST_generalized_HE
-    r""" nb of same codes"""
-    ST_SDC = _swigfaiss.IndexPQ_ST_SDC
-    r""" symmetric product quantizer (SDC)"""
-    ST_polysemous = _swigfaiss.IndexPQ_ST_polysemous
-    r""" HE filter (using ht) + PQ combination"""
-    ST_polysemous_generalize = _swigfaiss.IndexPQ_ST_polysemous_generalize
-    r""" Filter on generalized Hamming"""
-    search_type = property(_swigfaiss.IndexPQ_search_type_get, _swigfaiss.IndexPQ_search_type_set)
-    encode_signs = property(_swigfaiss.IndexPQ_encode_signs_get, _swigfaiss.IndexPQ_encode_signs_set)
-    polysemous_ht = property(_swigfaiss.IndexPQ_polysemous_ht_get, _swigfaiss.IndexPQ_polysemous_ht_set, doc=r""" Hamming threshold used for polysemy""")
-
-    def search_core_polysemous(self, n, x, k, distances, labels, polysemous_ht, generalized_hamming):
-        return _swigfaiss.IndexPQ_search_core_polysemous(self, n, x, k, distances, labels, polysemous_ht, generalized_hamming)
-
-    def hamming_distance_histogram(self, n, x, nb, xb, dist_histogram):
-        r"""
-        prepare query for a polysemous search, but instead of
-        computing the result, just get the histogram of Hamming
-        distances. May be computed on a provided dataset if xb != NULL
-        :type dist_histogram: int
-        :param dist_histogram: (M * nbits + 1)
-        """
-        return _swigfaiss.IndexPQ_hamming_distance_histogram(self, n, x, nb, xb, dist_histogram)
-
-    def hamming_distance_table(self, n, x, dis):
-        r"""
-         compute pairwise distances between queries and database
-
-        :type n: int
-        :param n:    nb of query vectors
-        :type x: float
-        :param x:    query vector, size n * d
-        :type dis: int
-        :param dis:  output distances, size n * ntotal
-        """
-        return _swigfaiss.IndexPQ_hamming_distance_table(self, n, x, dis)
-    __swig_destroy__ = _swigfaiss.delete_IndexPQ
-
-# Register IndexPQ in _swigfaiss:
-_swigfaiss.IndexPQ_swigregister(IndexPQ)
-class SearchParametersPQ(SearchParameters):
-    r""" override search parameters from the class"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    search_type = property(_swigfaiss.SearchParametersPQ_search_type_get, _swigfaiss.SearchParametersPQ_search_type_set)
-    polysemous_ht = property(_swigfaiss.SearchParametersPQ_polysemous_ht_get, _swigfaiss.SearchParametersPQ_polysemous_ht_set)
-
-    def __init__(self):
-        _swigfaiss.SearchParametersPQ_swiginit(self, _swigfaiss.new_SearchParametersPQ())
-    __swig_destroy__ = _swigfaiss.delete_SearchParametersPQ
-
-# Register SearchParametersPQ in _swigfaiss:
-_swigfaiss.SearchParametersPQ_swigregister(SearchParametersPQ)
-class IndexPQStats(object):
-    r"""
-    statistics are robust to internal threading, but not if
-    IndexPQ::search is called by multiple threads
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nq = property(_swigfaiss.IndexPQStats_nq_get, _swigfaiss.IndexPQStats_nq_set)
-    ncode = property(_swigfaiss.IndexPQStats_ncode_get, _swigfaiss.IndexPQStats_ncode_set)
-    n_hamming_pass = property(_swigfaiss.IndexPQStats_n_hamming_pass_get, _swigfaiss.IndexPQStats_n_hamming_pass_set)
-
-    def __init__(self):
-        _swigfaiss.IndexPQStats_swiginit(self, _swigfaiss.new_IndexPQStats())
-
-    def reset(self):
-        return _swigfaiss.IndexPQStats_reset(self)
-    __swig_destroy__ = _swigfaiss.delete_IndexPQStats
-
-# Register IndexPQStats in _swigfaiss:
-_swigfaiss.IndexPQStats_swigregister(IndexPQStats)
-class MultiIndexQuantizer(Index):
-    r"""
-    Quantizer where centroids are virtual: they are the Cartesian
-    product of sub-centroids.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    pq = property(_swigfaiss.MultiIndexQuantizer_pq_get, _swigfaiss.MultiIndexQuantizer_pq_set)
-
-    def train(self, n, x):
-        return _swigfaiss.MultiIndexQuantizer_train(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.MultiIndexQuantizer_search(self, n, x, k, distances, labels, params)
-
-    def add(self, n, x):
-        r""" add and reset will crash at runtime"""
-        return _swigfaiss.MultiIndexQuantizer_add(self, n, x)
-
-    def reset(self):
-        return _swigfaiss.MultiIndexQuantizer_reset(self)
-
-    def __init__(self, *args):
-        _swigfaiss.MultiIndexQuantizer_swiginit(self, _swigfaiss.new_MultiIndexQuantizer(*args))
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.MultiIndexQuantizer_reconstruct(self, key, recons)
-    __swig_destroy__ = _swigfaiss.delete_MultiIndexQuantizer
-
-# Register MultiIndexQuantizer in _swigfaiss:
-_swigfaiss.MultiIndexQuantizer_swigregister(MultiIndexQuantizer)
-class MultiIndexQuantizer2(MultiIndexQuantizer):
-    r""" MultiIndexQuantizer where the PQ assignmnet is performed by sub-indexes"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    assign_indexes = property(_swigfaiss.MultiIndexQuantizer2_assign_indexes_get, _swigfaiss.MultiIndexQuantizer2_assign_indexes_set, doc=r""" M Indexes on d / M dimensions""")
-    own_fields = property(_swigfaiss.MultiIndexQuantizer2_own_fields_get, _swigfaiss.MultiIndexQuantizer2_own_fields_set)
-
-    def __init__(self, *args):
-        _swigfaiss.MultiIndexQuantizer2_swiginit(self, _swigfaiss.new_MultiIndexQuantizer2(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.MultiIndexQuantizer2_train(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.MultiIndexQuantizer2_search(self, n, x, k, distances, labels, params)
-    __swig_destroy__ = _swigfaiss.delete_MultiIndexQuantizer2
-
-# Register MultiIndexQuantizer2 in _swigfaiss:
-_swigfaiss.MultiIndexQuantizer2_swigregister(MultiIndexQuantizer2)
-class IndexAdditiveQuantizer(IndexFlatCodes):
-    r""" Abstract class for additive quantizers. The search functions are in common."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    aq = property(_swigfaiss.IndexAdditiveQuantizer_aq_get, _swigfaiss.IndexAdditiveQuantizer_aq_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexAdditiveQuantizer_swiginit(self, _swigfaiss.new_IndexAdditiveQuantizer(*args))
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexAdditiveQuantizer_search(self, n, x, k, distances, labels, params)
-
-    def sa_encode(self, n, x, bytes):
-        return _swigfaiss.IndexAdditiveQuantizer_sa_encode(self, n, x, bytes)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexAdditiveQuantizer_sa_decode(self, n, bytes, x)
-
-    def get_FlatCodesDistanceComputer(self):
-        return _swigfaiss.IndexAdditiveQuantizer_get_FlatCodesDistanceComputer(self)
-    __swig_destroy__ = _swigfaiss.delete_IndexAdditiveQuantizer
-
-# Register IndexAdditiveQuantizer in _swigfaiss:
-_swigfaiss.IndexAdditiveQuantizer_swigregister(IndexAdditiveQuantizer)
-class IndexResidualQuantizer(IndexAdditiveQuantizer):
-    r"""
-     Index based on a residual quantizer. Stored vectors are
-    approximated by residual quantization codes.
-    Can also be used as a codec
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    rq = property(_swigfaiss.IndexResidualQuantizer_rq_get, _swigfaiss.IndexResidualQuantizer_rq_set, doc=r""" The residual quantizer used to encode the vectors""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexResidualQuantizer_swiginit(self, _swigfaiss.new_IndexResidualQuantizer(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexResidualQuantizer_train(self, n, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexResidualQuantizer
-
-# Register IndexResidualQuantizer in _swigfaiss:
-_swigfaiss.IndexResidualQuantizer_swigregister(IndexResidualQuantizer)
-class IndexLocalSearchQuantizer(IndexAdditiveQuantizer):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    lsq = property(_swigfaiss.IndexLocalSearchQuantizer_lsq_get, _swigfaiss.IndexLocalSearchQuantizer_lsq_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexLocalSearchQuantizer_swiginit(self, _swigfaiss.new_IndexLocalSearchQuantizer(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexLocalSearchQuantizer_train(self, n, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexLocalSearchQuantizer
-
-# Register IndexLocalSearchQuantizer in _swigfaiss:
-_swigfaiss.IndexLocalSearchQuantizer_swigregister(IndexLocalSearchQuantizer)
-class IndexProductResidualQuantizer(IndexAdditiveQuantizer):
-    r""" Index based on a product residual quantizer."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    prq = property(_swigfaiss.IndexProductResidualQuantizer_prq_get, _swigfaiss.IndexProductResidualQuantizer_prq_set, doc=r""" The product residual quantizer used to encode the vectors""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexProductResidualQuantizer_swiginit(self, _swigfaiss.new_IndexProductResidualQuantizer(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexProductResidualQuantizer_train(self, n, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexProductResidualQuantizer
-
-# Register IndexProductResidualQuantizer in _swigfaiss:
-_swigfaiss.IndexProductResidualQuantizer_swigregister(IndexProductResidualQuantizer)
-class IndexProductLocalSearchQuantizer(IndexAdditiveQuantizer):
-    r""" Index based on a product local search quantizer."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    plsq = property(_swigfaiss.IndexProductLocalSearchQuantizer_plsq_get, _swigfaiss.IndexProductLocalSearchQuantizer_plsq_set, doc=r""" The product local search quantizer used to encode the vectors""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexProductLocalSearchQuantizer_swiginit(self, _swigfaiss.new_IndexProductLocalSearchQuantizer(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexProductLocalSearchQuantizer_train(self, n, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexProductLocalSearchQuantizer
-
-# Register IndexProductLocalSearchQuantizer in _swigfaiss:
-_swigfaiss.IndexProductLocalSearchQuantizer_swigregister(IndexProductLocalSearchQuantizer)
-class AdditiveCoarseQuantizer(Index):
-    r"""
-     A "virtual" index where the elements are the residual quantizer centroids.
-
-    Intended for use as a coarse quantizer in an IndexIVF.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    aq = property(_swigfaiss.AdditiveCoarseQuantizer_aq_get, _swigfaiss.AdditiveCoarseQuantizer_aq_set)
-
-    def __init__(self, *args):
-        _swigfaiss.AdditiveCoarseQuantizer_swiginit(self, _swigfaiss.new_AdditiveCoarseQuantizer(*args))
-    centroid_norms = property(_swigfaiss.AdditiveCoarseQuantizer_centroid_norms_get, _swigfaiss.AdditiveCoarseQuantizer_centroid_norms_set, doc=r""" norms of centroids, useful for knn-search""")
-
-    def add(self, n, x):
-        r""" N/A"""
-        return _swigfaiss.AdditiveCoarseQuantizer_add(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.AdditiveCoarseQuantizer_search(self, n, x, k, distances, labels, params)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.AdditiveCoarseQuantizer_reconstruct(self, key, recons)
-
-    def train(self, n, x):
-        return _swigfaiss.AdditiveCoarseQuantizer_train(self, n, x)
-
-    def reset(self):
-        r""" N/A"""
-        return _swigfaiss.AdditiveCoarseQuantizer_reset(self)
-    __swig_destroy__ = _swigfaiss.delete_AdditiveCoarseQuantizer
-
-# Register AdditiveCoarseQuantizer in _swigfaiss:
-_swigfaiss.AdditiveCoarseQuantizer_swigregister(AdditiveCoarseQuantizer)
-class SearchParametersResidualCoarseQuantizer(SearchParameters):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    beam_factor = property(_swigfaiss.SearchParametersResidualCoarseQuantizer_beam_factor_get, _swigfaiss.SearchParametersResidualCoarseQuantizer_beam_factor_set)
-    __swig_destroy__ = _swigfaiss.delete_SearchParametersResidualCoarseQuantizer
-
-    def __init__(self):
-        _swigfaiss.SearchParametersResidualCoarseQuantizer_swiginit(self, _swigfaiss.new_SearchParametersResidualCoarseQuantizer())
-
-# Register SearchParametersResidualCoarseQuantizer in _swigfaiss:
-_swigfaiss.SearchParametersResidualCoarseQuantizer_swigregister(SearchParametersResidualCoarseQuantizer)
-class ResidualCoarseQuantizer(AdditiveCoarseQuantizer):
-    r"""
-     The ResidualCoarseQuantizer is a bit specialized compared to the
-    default AdditiveCoarseQuantizer because it can use a beam search
-    at search time (slow but may be useful for very large vocabularies)
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    rq = property(_swigfaiss.ResidualCoarseQuantizer_rq_get, _swigfaiss.ResidualCoarseQuantizer_rq_set, doc=r""" The residual quantizer used to encode the vectors""")
-    beam_factor = property(_swigfaiss.ResidualCoarseQuantizer_beam_factor_get, _swigfaiss.ResidualCoarseQuantizer_beam_factor_set, doc=r"""
-    factor between the beam size and the search k
-    if negative, use exact search-to-centroid
-    """)
-
-    def set_beam_factor(self, new_beam_factor):
-        r""" computes centroid norms if required"""
-        return _swigfaiss.ResidualCoarseQuantizer_set_beam_factor(self, new_beam_factor)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.ResidualCoarseQuantizer_search(self, n, x, k, distances, labels, params)
-
-    def initialize_from(self, other):
-        r"""
-         Copy the M first codebook levels from other. Useful to crop a
-        ResidualQuantizer to its first M quantizers.
-        """
-        return _swigfaiss.ResidualCoarseQuantizer_initialize_from(self, other)
-
-    def __init__(self, *args):
-        _swigfaiss.ResidualCoarseQuantizer_swiginit(self, _swigfaiss.new_ResidualCoarseQuantizer(*args))
-    __swig_destroy__ = _swigfaiss.delete_ResidualCoarseQuantizer
-
-# Register ResidualCoarseQuantizer in _swigfaiss:
-_swigfaiss.ResidualCoarseQuantizer_swigregister(ResidualCoarseQuantizer)
-class LocalSearchCoarseQuantizer(AdditiveCoarseQuantizer):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    lsq = property(_swigfaiss.LocalSearchCoarseQuantizer_lsq_get, _swigfaiss.LocalSearchCoarseQuantizer_lsq_set, doc=r""" The residual quantizer used to encode the vectors""")
-
-    def __init__(self, *args):
-        _swigfaiss.LocalSearchCoarseQuantizer_swiginit(self, _swigfaiss.new_LocalSearchCoarseQuantizer(*args))
-    __swig_destroy__ = _swigfaiss.delete_LocalSearchCoarseQuantizer
-
-# Register LocalSearchCoarseQuantizer in _swigfaiss:
-_swigfaiss.LocalSearchCoarseQuantizer_swigregister(LocalSearchCoarseQuantizer)
-class IOReader(object):
-    r"""
-    Copyright (c) Facebook, Inc. and its affiliates.
-
-    This source code is licensed under the MIT license found in the
-    LICENSE file in the root directory of this source tree.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    name = property(_swigfaiss.IOReader_name_get, _swigfaiss.IOReader_name_set)
-
-    def __call__(self, ptr, size, nitems):
-        return _swigfaiss.IOReader___call__(self, ptr, size, nitems)
-
-    def fileno(self):
-        return _swigfaiss.IOReader_fileno(self)
-    __swig_destroy__ = _swigfaiss.delete_IOReader
-
-# Register IOReader in _swigfaiss:
-_swigfaiss.IOReader_swigregister(IOReader)
-class IOWriter(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    name = property(_swigfaiss.IOWriter_name_get, _swigfaiss.IOWriter_name_set)
-
-    def __call__(self, ptr, size, nitems):
-        return _swigfaiss.IOWriter___call__(self, ptr, size, nitems)
-
-    def fileno(self):
-        return _swigfaiss.IOWriter_fileno(self)
-    __swig_destroy__ = _swigfaiss.delete_IOWriter
-
-# Register IOWriter in _swigfaiss:
-_swigfaiss.IOWriter_swigregister(IOWriter)
-class VectorIOReader(IOReader):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    data = property(_swigfaiss.VectorIOReader_data_get, _swigfaiss.VectorIOReader_data_set)
-    rp = property(_swigfaiss.VectorIOReader_rp_get, _swigfaiss.VectorIOReader_rp_set)
-
-    def __call__(self, ptr, size, nitems):
-        return _swigfaiss.VectorIOReader___call__(self, ptr, size, nitems)
-
-    def __init__(self):
-        _swigfaiss.VectorIOReader_swiginit(self, _swigfaiss.new_VectorIOReader())
-    __swig_destroy__ = _swigfaiss.delete_VectorIOReader
-
-# Register VectorIOReader in _swigfaiss:
-_swigfaiss.VectorIOReader_swigregister(VectorIOReader)
-class VectorIOWriter(IOWriter):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    data = property(_swigfaiss.VectorIOWriter_data_get, _swigfaiss.VectorIOWriter_data_set)
-
-    def __call__(self, ptr, size, nitems):
-        return _swigfaiss.VectorIOWriter___call__(self, ptr, size, nitems)
-
-    def __init__(self):
-        _swigfaiss.VectorIOWriter_swiginit(self, _swigfaiss.new_VectorIOWriter())
-    __swig_destroy__ = _swigfaiss.delete_VectorIOWriter
-
-# Register VectorIOWriter in _swigfaiss:
-_swigfaiss.VectorIOWriter_swigregister(VectorIOWriter)
-class FileIOReader(IOReader):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    f = property(_swigfaiss.FileIOReader_f_get, _swigfaiss.FileIOReader_f_set)
-    need_close = property(_swigfaiss.FileIOReader_need_close_get, _swigfaiss.FileIOReader_need_close_set)
-
-    def __init__(self, *args):
-        _swigfaiss.FileIOReader_swiginit(self, _swigfaiss.new_FileIOReader(*args))
-    __swig_destroy__ = _swigfaiss.delete_FileIOReader
-
-    def __call__(self, ptr, size, nitems):
-        return _swigfaiss.FileIOReader___call__(self, ptr, size, nitems)
-
-    def fileno(self):
-        return _swigfaiss.FileIOReader_fileno(self)
-
-# Register FileIOReader in _swigfaiss:
-_swigfaiss.FileIOReader_swigregister(FileIOReader)
-class FileIOWriter(IOWriter):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    f = property(_swigfaiss.FileIOWriter_f_get, _swigfaiss.FileIOWriter_f_set)
-    need_close = property(_swigfaiss.FileIOWriter_need_close_get, _swigfaiss.FileIOWriter_need_close_set)
-
-    def __init__(self, *args):
-        _swigfaiss.FileIOWriter_swiginit(self, _swigfaiss.new_FileIOWriter(*args))
-    __swig_destroy__ = _swigfaiss.delete_FileIOWriter
-
-    def __call__(self, ptr, size, nitems):
-        return _swigfaiss.FileIOWriter___call__(self, ptr, size, nitems)
-
-    def fileno(self):
-        return _swigfaiss.FileIOWriter_fileno(self)
-
-# Register FileIOWriter in _swigfaiss:
-_swigfaiss.FileIOWriter_swigregister(FileIOWriter)
-class BufferedIOReader(IOReader):
-    r""" wraps an ioreader to make buffered reads to avoid too small reads"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    reader = property(_swigfaiss.BufferedIOReader_reader_get, _swigfaiss.BufferedIOReader_reader_set)
-    bsz = property(_swigfaiss.BufferedIOReader_bsz_get, _swigfaiss.BufferedIOReader_bsz_set)
-    ofs = property(_swigfaiss.BufferedIOReader_ofs_get, _swigfaiss.BufferedIOReader_ofs_set, doc=r""" offset in input stream""")
-    ofs2 = property(_swigfaiss.BufferedIOReader_ofs2_get, _swigfaiss.BufferedIOReader_ofs2_set, doc=r""" number of bytes returned to caller""")
-    b0 = property(_swigfaiss.BufferedIOReader_b0_get, _swigfaiss.BufferedIOReader_b0_set, doc=r""" range of available bytes in the buffer""")
-    b1 = property(_swigfaiss.BufferedIOReader_b1_get, _swigfaiss.BufferedIOReader_b1_set)
-    buffer = property(_swigfaiss.BufferedIOReader_buffer_get, _swigfaiss.BufferedIOReader_buffer_set)
-
-    def __init__(self, *args):
-        r"""
-        :type bsz: int, optional
-        :param bsz:    buffer size (bytes). Reads will be done by batched of
-                          this size
-        """
-        _swigfaiss.BufferedIOReader_swiginit(self, _swigfaiss.new_BufferedIOReader(*args))
-
-    def __call__(self, ptr, size, nitems):
-        return _swigfaiss.BufferedIOReader___call__(self, ptr, size, nitems)
-    __swig_destroy__ = _swigfaiss.delete_BufferedIOReader
-
-# Register BufferedIOReader in _swigfaiss:
-_swigfaiss.BufferedIOReader_swigregister(BufferedIOReader)
-class BufferedIOWriter(IOWriter):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    writer = property(_swigfaiss.BufferedIOWriter_writer_get, _swigfaiss.BufferedIOWriter_writer_set)
-    bsz = property(_swigfaiss.BufferedIOWriter_bsz_get, _swigfaiss.BufferedIOWriter_bsz_set)
-    ofs = property(_swigfaiss.BufferedIOWriter_ofs_get, _swigfaiss.BufferedIOWriter_ofs_set)
-    ofs2 = property(_swigfaiss.BufferedIOWriter_ofs2_get, _swigfaiss.BufferedIOWriter_ofs2_set, doc=r""" number of bytes received from caller""")
-    b0 = property(_swigfaiss.BufferedIOWriter_b0_get, _swigfaiss.BufferedIOWriter_b0_set, doc=r""" amount of data in buffer""")
-    buffer = property(_swigfaiss.BufferedIOWriter_buffer_get, _swigfaiss.BufferedIOWriter_buffer_set)
-
-    def __init__(self, *args):
-        _swigfaiss.BufferedIOWriter_swiginit(self, _swigfaiss.new_BufferedIOWriter(*args))
-
-    def __call__(self, ptr, size, nitems):
-        return _swigfaiss.BufferedIOWriter___call__(self, ptr, size, nitems)
-    __swig_destroy__ = _swigfaiss.delete_BufferedIOWriter
-
-# Register BufferedIOWriter in _swigfaiss:
-_swigfaiss.BufferedIOWriter_swigregister(BufferedIOWriter)
-
-def fourcc(*args):
-    return _swigfaiss.fourcc(*args)
-
-def fourcc_inv(*args):
-    return _swigfaiss.fourcc_inv(*args)
-
-def fourcc_inv_printable(x):
-    return _swigfaiss.fourcc_inv_printable(x)
-class InvertedListsIterator(object):
-    r"""
-    Copyright (c) Facebook, Inc. and its affiliates.
-
-    This source code is licensed under the MIT license found in the
-    LICENSE file in the root directory of this source tree.
-
-    Definition of inverted lists + a few common classes that implement
-    the interface.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    __swig_destroy__ = _swigfaiss.delete_InvertedListsIterator
-
-    def is_available(self):
-        return _swigfaiss.InvertedListsIterator_is_available(self)
-
-    def next(self):
-        return _swigfaiss.InvertedListsIterator_next(self)
-
-    def get_id_and_codes(self):
-        return _swigfaiss.InvertedListsIterator_get_id_and_codes(self)
-
-# Register InvertedListsIterator in _swigfaiss:
-_swigfaiss.InvertedListsIterator_swigregister(InvertedListsIterator)
-class InvertedLists(object):
-    r"""
-     Table of inverted lists
-    multithreading rules:
-    - concurrent read accesses are allowed
-    - concurrent update accesses are allowed
-    - for resize and add_entries, only concurrent access to different lists
-      are allowed
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    nlist = property(_swigfaiss.InvertedLists_nlist_get, _swigfaiss.InvertedLists_nlist_set, doc=r""" number of possible key values""")
-    code_size = property(_swigfaiss.InvertedLists_code_size_get, _swigfaiss.InvertedLists_code_size_set, doc=r""" code size per vector in bytes""")
-    use_iterator = property(_swigfaiss.InvertedLists_use_iterator_get, _swigfaiss.InvertedLists_use_iterator_set)
-    __swig_destroy__ = _swigfaiss.delete_InvertedLists
-    INVALID_CODE_SIZE = _swigfaiss.InvertedLists_INVALID_CODE_SIZE
-    r"""
-    used for BlockInvertedLists, where the codes are packed into groups
-    and the individual code size is meaningless
-    """
-
-    def is_empty(self, list_no):
-        return _swigfaiss.InvertedLists_is_empty(self, list_no)
-
-    def list_size(self, list_no):
-        r""" get the size of a list"""
-        return _swigfaiss.InvertedLists_list_size(self, list_no)
-
-    def get_iterator(self, list_no):
-        r""" get iterable for lists that use_iterator"""
-        return _swigfaiss.InvertedLists_get_iterator(self, list_no)
-
-    def get_codes(self, list_no):
-        r"""
-         get the codes for an inverted list
-        must be released by release_codes
-
-        :rtype: uint8_t
-        :return: codes    size list_size * code_size
-        """
-        return _swigfaiss.InvertedLists_get_codes(self, list_no)
-
-    def get_ids(self, list_no):
-        r"""
-         get the ids for an inverted list
-        must be released by release_ids
-
-        :rtype: int
-        :return: ids      size list_size
-        """
-        return _swigfaiss.InvertedLists_get_ids(self, list_no)
-
-    def release_codes(self, list_no, codes):
-        r""" release codes returned by get_codes (default implementation is nop"""
-        return _swigfaiss.InvertedLists_release_codes(self, list_no, codes)
-
-    def release_ids(self, list_no, ids):
-        r""" release ids returned by get_ids"""
-        return _swigfaiss.InvertedLists_release_ids(self, list_no, ids)
-
-    def get_single_id(self, list_no, offset):
-        r"""
-        :rtype: int
-        :return: a single id in an inverted list
-        """
-        return _swigfaiss.InvertedLists_get_single_id(self, list_no, offset)
-
-    def get_single_code(self, list_no, offset):
-        r"""
-        :rtype: uint8_t
-        :return: a single code in an inverted list
-            (should be deallocated with release_codes)
-        """
-        return _swigfaiss.InvertedLists_get_single_code(self, list_no, offset)
-
-    def prefetch_lists(self, list_nos, nlist):
-        r"""
-        prepare the following lists (default does nothing)
-        a list can be -1 hence the signed long
-        """
-        return _swigfaiss.InvertedLists_prefetch_lists(self, list_nos, nlist)
-
-    def add_entry(self, list_no, theid, code):
-        r""" add one entry to an inverted list"""
-        return _swigfaiss.InvertedLists_add_entry(self, list_no, theid, code)
-
-    def add_entries(self, list_no, n_entry, ids, code):
-        return _swigfaiss.InvertedLists_add_entries(self, list_no, n_entry, ids, code)
-
-    def update_entry(self, list_no, offset, id, code):
-        return _swigfaiss.InvertedLists_update_entry(self, list_no, offset, id, code)
-
-    def update_entries(self, list_no, offset, n_entry, ids, code):
-        return _swigfaiss.InvertedLists_update_entries(self, list_no, offset, n_entry, ids, code)
-
-    def resize(self, list_no, new_size):
-        return _swigfaiss.InvertedLists_resize(self, list_no, new_size)
-
-    def reset(self):
-        return _swigfaiss.InvertedLists_reset(self)
-
-    def merge_from(self, oivf, add_id):
-        r""" move all entries from oivf (empty on output)"""
-        return _swigfaiss.InvertedLists_merge_from(self, oivf, add_id)
-    SUBSET_TYPE_ID_RANGE = _swigfaiss.InvertedLists_SUBSET_TYPE_ID_RANGE
-    SUBSET_TYPE_ID_MOD = _swigfaiss.InvertedLists_SUBSET_TYPE_ID_MOD
-    SUBSET_TYPE_ELEMENT_RANGE = _swigfaiss.InvertedLists_SUBSET_TYPE_ELEMENT_RANGE
-    SUBSET_TYPE_INVLIST_FRACTION = _swigfaiss.InvertedLists_SUBSET_TYPE_INVLIST_FRACTION
-    SUBSET_TYPE_INVLIST = _swigfaiss.InvertedLists_SUBSET_TYPE_INVLIST
-
-    def copy_subset_to(self, other, subset_type, a1, a2):
-        r"""
-         copy a subset of the entries index to the other index
-        :rtype: int
-        :return: number of entries copied
-        """
-        return _swigfaiss.InvertedLists_copy_subset_to(self, other, subset_type, a1, a2)
-
-    def imbalance_factor(self):
-        r""" 1= perfectly balanced, >1: imbalanced"""
-        return _swigfaiss.InvertedLists_imbalance_factor(self)
-
-    def print_stats(self):
-        r""" display some stats about the inverted lists"""
-        return _swigfaiss.InvertedLists_print_stats(self)
-
-    def compute_ntotal(self):
-        r""" sum up list sizes"""
-        return _swigfaiss.InvertedLists_compute_ntotal(self)
-
-# Register InvertedLists in _swigfaiss:
-_swigfaiss.InvertedLists_swigregister(InvertedLists)
-class ArrayInvertedLists(InvertedLists):
-    r""" simple (default) implementation as an array of inverted lists"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    codes = property(_swigfaiss.ArrayInvertedLists_codes_get, _swigfaiss.ArrayInvertedLists_codes_set)
-    ids = property(_swigfaiss.ArrayInvertedLists_ids_get, _swigfaiss.ArrayInvertedLists_ids_set, doc=r""" Inverted lists for indexes""")
-
-    def __init__(self, nlist, code_size):
-        _swigfaiss.ArrayInvertedLists_swiginit(self, _swigfaiss.new_ArrayInvertedLists(nlist, code_size))
-
-    def list_size(self, list_no):
-        return _swigfaiss.ArrayInvertedLists_list_size(self, list_no)
-
-    def get_codes(self, list_no):
-        return _swigfaiss.ArrayInvertedLists_get_codes(self, list_no)
-
-    def get_ids(self, list_no):
-        return _swigfaiss.ArrayInvertedLists_get_ids(self, list_no)
-
-    def add_entries(self, list_no, n_entry, ids, code):
-        return _swigfaiss.ArrayInvertedLists_add_entries(self, list_no, n_entry, ids, code)
-
-    def update_entries(self, list_no, offset, n_entry, ids, code):
-        return _swigfaiss.ArrayInvertedLists_update_entries(self, list_no, offset, n_entry, ids, code)
-
-    def resize(self, list_no, new_size):
-        return _swigfaiss.ArrayInvertedLists_resize(self, list_no, new_size)
-    __swig_destroy__ = _swigfaiss.delete_ArrayInvertedLists
-
-# Register ArrayInvertedLists in _swigfaiss:
-_swigfaiss.ArrayInvertedLists_swigregister(ArrayInvertedLists)
-class ReadOnlyInvertedLists(InvertedLists):
-    r""" invlists that fail for all write functions"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-
-    def add_entries(self, list_no, n_entry, ids, code):
-        return _swigfaiss.ReadOnlyInvertedLists_add_entries(self, list_no, n_entry, ids, code)
-
-    def update_entries(self, list_no, offset, n_entry, ids, code):
-        return _swigfaiss.ReadOnlyInvertedLists_update_entries(self, list_no, offset, n_entry, ids, code)
-
-    def resize(self, list_no, new_size):
-        return _swigfaiss.ReadOnlyInvertedLists_resize(self, list_no, new_size)
-    __swig_destroy__ = _swigfaiss.delete_ReadOnlyInvertedLists
-
-# Register ReadOnlyInvertedLists in _swigfaiss:
-_swigfaiss.ReadOnlyInvertedLists_swigregister(ReadOnlyInvertedLists)
-class HStackInvertedLists(ReadOnlyInvertedLists):
-    r""" Horizontal stack of inverted lists"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    ils = property(_swigfaiss.HStackInvertedLists_ils_get, _swigfaiss.HStackInvertedLists_ils_set)
-
-    def __init__(self, nil, ils):
-        r""" build InvertedLists by concatenating nil of them"""
-        _swigfaiss.HStackInvertedLists_swiginit(self, _swigfaiss.new_HStackInvertedLists(nil, ils))
-
-    def list_size(self, list_no):
-        return _swigfaiss.HStackInvertedLists_list_size(self, list_no)
-
-    def get_codes(self, list_no):
-        return _swigfaiss.HStackInvertedLists_get_codes(self, list_no)
-
-    def get_ids(self, list_no):
-        return _swigfaiss.HStackInvertedLists_get_ids(self, list_no)
-
-    def prefetch_lists(self, list_nos, nlist):
-        return _swigfaiss.HStackInvertedLists_prefetch_lists(self, list_nos, nlist)
-
-    def release_codes(self, list_no, codes):
-        return _swigfaiss.HStackInvertedLists_release_codes(self, list_no, codes)
-
-    def release_ids(self, list_no, ids):
-        return _swigfaiss.HStackInvertedLists_release_ids(self, list_no, ids)
-
-    def get_single_id(self, list_no, offset):
-        return _swigfaiss.HStackInvertedLists_get_single_id(self, list_no, offset)
-
-    def get_single_code(self, list_no, offset):
-        return _swigfaiss.HStackInvertedLists_get_single_code(self, list_no, offset)
-    __swig_destroy__ = _swigfaiss.delete_HStackInvertedLists
-
-# Register HStackInvertedLists in _swigfaiss:
-_swigfaiss.HStackInvertedLists_swigregister(HStackInvertedLists)
-class SliceInvertedLists(ReadOnlyInvertedLists):
-    r""" vertical slice of indexes in another InvertedLists"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    il = property(_swigfaiss.SliceInvertedLists_il_get, _swigfaiss.SliceInvertedLists_il_set)
-    i0 = property(_swigfaiss.SliceInvertedLists_i0_get, _swigfaiss.SliceInvertedLists_i0_set)
-    i1 = property(_swigfaiss.SliceInvertedLists_i1_get, _swigfaiss.SliceInvertedLists_i1_set)
-
-    def __init__(self, il, i0, i1):
-        _swigfaiss.SliceInvertedLists_swiginit(self, _swigfaiss.new_SliceInvertedLists(il, i0, i1))
-
-    def list_size(self, list_no):
-        return _swigfaiss.SliceInvertedLists_list_size(self, list_no)
-
-    def get_codes(self, list_no):
-        return _swigfaiss.SliceInvertedLists_get_codes(self, list_no)
-
-    def get_ids(self, list_no):
-        return _swigfaiss.SliceInvertedLists_get_ids(self, list_no)
-
-    def release_codes(self, list_no, codes):
-        return _swigfaiss.SliceInvertedLists_release_codes(self, list_no, codes)
-
-    def release_ids(self, list_no, ids):
-        return _swigfaiss.SliceInvertedLists_release_ids(self, list_no, ids)
-
-    def get_single_id(self, list_no, offset):
-        return _swigfaiss.SliceInvertedLists_get_single_id(self, list_no, offset)
-
-    def get_single_code(self, list_no, offset):
-        return _swigfaiss.SliceInvertedLists_get_single_code(self, list_no, offset)
-
-    def prefetch_lists(self, list_nos, nlist):
-        return _swigfaiss.SliceInvertedLists_prefetch_lists(self, list_nos, nlist)
-    __swig_destroy__ = _swigfaiss.delete_SliceInvertedLists
-
-# Register SliceInvertedLists in _swigfaiss:
-_swigfaiss.SliceInvertedLists_swigregister(SliceInvertedLists)
-class VStackInvertedLists(ReadOnlyInvertedLists):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    ils = property(_swigfaiss.VStackInvertedLists_ils_get, _swigfaiss.VStackInvertedLists_ils_set)
-    cumsz = property(_swigfaiss.VStackInvertedLists_cumsz_get, _swigfaiss.VStackInvertedLists_cumsz_set)
-
-    def __init__(self, nil, ils):
-        r""" build InvertedLists by concatenating nil of them"""
-        _swigfaiss.VStackInvertedLists_swiginit(self, _swigfaiss.new_VStackInvertedLists(nil, ils))
-
-    def list_size(self, list_no):
-        return _swigfaiss.VStackInvertedLists_list_size(self, list_no)
-
-    def get_codes(self, list_no):
-        return _swigfaiss.VStackInvertedLists_get_codes(self, list_no)
-
-    def get_ids(self, list_no):
-        return _swigfaiss.VStackInvertedLists_get_ids(self, list_no)
-
-    def release_codes(self, list_no, codes):
-        return _swigfaiss.VStackInvertedLists_release_codes(self, list_no, codes)
-
-    def release_ids(self, list_no, ids):
-        return _swigfaiss.VStackInvertedLists_release_ids(self, list_no, ids)
-
-    def get_single_id(self, list_no, offset):
-        return _swigfaiss.VStackInvertedLists_get_single_id(self, list_no, offset)
-
-    def get_single_code(self, list_no, offset):
-        return _swigfaiss.VStackInvertedLists_get_single_code(self, list_no, offset)
-
-    def prefetch_lists(self, list_nos, nlist):
-        return _swigfaiss.VStackInvertedLists_prefetch_lists(self, list_nos, nlist)
-    __swig_destroy__ = _swigfaiss.delete_VStackInvertedLists
-
-# Register VStackInvertedLists in _swigfaiss:
-_swigfaiss.VStackInvertedLists_swigregister(VStackInvertedLists)
-class MaskedInvertedLists(ReadOnlyInvertedLists):
-    r"""
-     use the first inverted lists if they are non-empty otherwise use the second
-
-    This is useful if il1 has a few inverted lists that are too long,
-    and that il0 has replacement lists for those, with empty lists for
-    the others.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    il0 = property(_swigfaiss.MaskedInvertedLists_il0_get, _swigfaiss.MaskedInvertedLists_il0_set)
-    il1 = property(_swigfaiss.MaskedInvertedLists_il1_get, _swigfaiss.MaskedInvertedLists_il1_set)
-
-    def __init__(self, il0, il1):
-        _swigfaiss.MaskedInvertedLists_swiginit(self, _swigfaiss.new_MaskedInvertedLists(il0, il1))
-
-    def list_size(self, list_no):
-        return _swigfaiss.MaskedInvertedLists_list_size(self, list_no)
-
-    def get_codes(self, list_no):
-        return _swigfaiss.MaskedInvertedLists_get_codes(self, list_no)
-
-    def get_ids(self, list_no):
-        return _swigfaiss.MaskedInvertedLists_get_ids(self, list_no)
-
-    def release_codes(self, list_no, codes):
-        return _swigfaiss.MaskedInvertedLists_release_codes(self, list_no, codes)
-
-    def release_ids(self, list_no, ids):
-        return _swigfaiss.MaskedInvertedLists_release_ids(self, list_no, ids)
-
-    def get_single_id(self, list_no, offset):
-        return _swigfaiss.MaskedInvertedLists_get_single_id(self, list_no, offset)
-
-    def get_single_code(self, list_no, offset):
-        return _swigfaiss.MaskedInvertedLists_get_single_code(self, list_no, offset)
-
-    def prefetch_lists(self, list_nos, nlist):
-        return _swigfaiss.MaskedInvertedLists_prefetch_lists(self, list_nos, nlist)
-    __swig_destroy__ = _swigfaiss.delete_MaskedInvertedLists
-
-# Register MaskedInvertedLists in _swigfaiss:
-_swigfaiss.MaskedInvertedLists_swigregister(MaskedInvertedLists)
-class StopWordsInvertedLists(ReadOnlyInvertedLists):
-    r"""
-    if the inverted list in il is smaller than maxsize then return it,
-    otherwise return an empty invlist
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    il0 = property(_swigfaiss.StopWordsInvertedLists_il0_get, _swigfaiss.StopWordsInvertedLists_il0_set)
-    maxsize = property(_swigfaiss.StopWordsInvertedLists_maxsize_get, _swigfaiss.StopWordsInvertedLists_maxsize_set)
-
-    def __init__(self, il, maxsize):
-        _swigfaiss.StopWordsInvertedLists_swiginit(self, _swigfaiss.new_StopWordsInvertedLists(il, maxsize))
-
-    def list_size(self, list_no):
-        return _swigfaiss.StopWordsInvertedLists_list_size(self, list_no)
-
-    def get_codes(self, list_no):
-        return _swigfaiss.StopWordsInvertedLists_get_codes(self, list_no)
-
-    def get_ids(self, list_no):
-        return _swigfaiss.StopWordsInvertedLists_get_ids(self, list_no)
-
-    def release_codes(self, list_no, codes):
-        return _swigfaiss.StopWordsInvertedLists_release_codes(self, list_no, codes)
-
-    def release_ids(self, list_no, ids):
-        return _swigfaiss.StopWordsInvertedLists_release_ids(self, list_no, ids)
-
-    def get_single_id(self, list_no, offset):
-        return _swigfaiss.StopWordsInvertedLists_get_single_id(self, list_no, offset)
-
-    def get_single_code(self, list_no, offset):
-        return _swigfaiss.StopWordsInvertedLists_get_single_code(self, list_no, offset)
-
-    def prefetch_lists(self, list_nos, nlist):
-        return _swigfaiss.StopWordsInvertedLists_prefetch_lists(self, list_nos, nlist)
-    __swig_destroy__ = _swigfaiss.delete_StopWordsInvertedLists
-
-# Register StopWordsInvertedLists in _swigfaiss:
-_swigfaiss.StopWordsInvertedLists_swigregister(StopWordsInvertedLists)
-class InvertedListsIOHook(object):
-    r"""
-     Callbacks to handle other types of InvertedList objects.
-
-    The callbacks should be registered with add_callback before calling
-    read_index or read_InvertedLists. The callbacks for
-    OnDiskInvertedLists are registrered by default. The invlist type is
-    identified by:
-
-    - the key (a fourcc) at read time
-    - the class name (as given by typeid.name) at write time
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    key = property(_swigfaiss.InvertedListsIOHook_key_get, doc=r""" string version of the fourcc""")
-    classname = property(_swigfaiss.InvertedListsIOHook_classname_get, doc=r""" typeid.name""")
-
-    def write(self, ils, f):
-        r""" write the index to the IOWriter (including the fourcc)"""
-        return _swigfaiss.InvertedListsIOHook_write(self, ils, f)
-
-    def read(self, f, io_flags):
-        r""" called when the fourcc matches this class's fourcc"""
-        return _swigfaiss.InvertedListsIOHook_read(self, f, io_flags)
-
-    def read_ArrayInvertedLists(self, f, io_flags, nlist, code_size, sizes):
-        r"""
-         read from a ArrayInvertedLists into this invertedlist type.
-        For this to work, the callback has to be enabled and the io_flag has to
-        be set to IO_FLAG_SKIP_IVF_DATA | (16 upper bits of the fourcc)
-
-        (default implementation fails)
-        """
-        return _swigfaiss.InvertedListsIOHook_read_ArrayInvertedLists(self, f, io_flags, nlist, code_size, sizes)
-    __swig_destroy__ = _swigfaiss.delete_InvertedListsIOHook
-
-    @staticmethod
-    def add_callback(arg1):
-        return _swigfaiss.InvertedListsIOHook_add_callback(arg1)
-
-    @staticmethod
-    def print_callbacks():
-        return _swigfaiss.InvertedListsIOHook_print_callbacks()
-
-    @staticmethod
-    def lookup(h):
-        return _swigfaiss.InvertedListsIOHook_lookup(h)
-
-    @staticmethod
-    def lookup_classname(classname):
-        return _swigfaiss.InvertedListsIOHook_lookup_classname(classname)
-
-# Register InvertedListsIOHook in _swigfaiss:
-_swigfaiss.InvertedListsIOHook_swigregister(InvertedListsIOHook)
-class BlockInvertedLists(InvertedLists):
-    r"""
-     Inverted Lists that are organized by blocks.
-
-    Different from the regular inverted lists, the codes are organized by blocks
-    of size block_size bytes that reprsent a set of n_per_block. Therefore, code
-    allocations are always rounded up to block_size bytes. The codes are also
-    aligned on 32-byte boundaries for use with SIMD.
-
-    To avoid misinterpretations, the code_size is set to (size_t)(-1), even if
-    arguably the amount of memory consumed by code is block_size / n_per_block.
-
-    The writing functions add_entries and update_entries operate on block-aligned
-    data.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    n_per_block = property(_swigfaiss.BlockInvertedLists_n_per_block_get, _swigfaiss.BlockInvertedLists_n_per_block_set)
-    block_size = property(_swigfaiss.BlockInvertedLists_block_size_get, _swigfaiss.BlockInvertedLists_block_size_set)
-    packer = property(_swigfaiss.BlockInvertedLists_packer_get, _swigfaiss.BlockInvertedLists_packer_set)
-    codes = property(_swigfaiss.BlockInvertedLists_codes_get, _swigfaiss.BlockInvertedLists_codes_set)
-    ids = property(_swigfaiss.BlockInvertedLists_ids_get, _swigfaiss.BlockInvertedLists_ids_set)
-
-    def __init__(self, *args):
-        _swigfaiss.BlockInvertedLists_swiginit(self, _swigfaiss.new_BlockInvertedLists(*args))
-
-    def list_size(self, list_no):
-        return _swigfaiss.BlockInvertedLists_list_size(self, list_no)
-
-    def get_codes(self, list_no):
-        return _swigfaiss.BlockInvertedLists_get_codes(self, list_no)
-
-    def get_ids(self, list_no):
-        return _swigfaiss.BlockInvertedLists_get_ids(self, list_no)
-
-    def add_entries(self, list_no, n_entry, ids, code):
-        return _swigfaiss.BlockInvertedLists_add_entries(self, list_no, n_entry, ids, code)
-
-    def update_entries(self, list_no, offset, n_entry, ids, code):
-        r""" not implemented"""
-        return _swigfaiss.BlockInvertedLists_update_entries(self, list_no, offset, n_entry, ids, code)
-
-    def resize(self, list_no, new_size):
-        return _swigfaiss.BlockInvertedLists_resize(self, list_no, new_size)
-    __swig_destroy__ = _swigfaiss.delete_BlockInvertedLists
-
-# Register BlockInvertedLists in _swigfaiss:
-_swigfaiss.BlockInvertedLists_swigregister(BlockInvertedLists)
-
-def lo_build(list_id, offset):
-    return _swigfaiss.lo_build(list_id, offset)
-
-def lo_listno(lo):
-    return _swigfaiss.lo_listno(lo)
-
-def lo_offset(lo):
-    return _swigfaiss.lo_offset(lo)
-class DirectMap(object):
-    r"""Direct map: a way to map back from ids to inverted lists"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    NoMap = _swigfaiss.DirectMap_NoMap
-    Array = _swigfaiss.DirectMap_Array
-    Hashtable = _swigfaiss.DirectMap_Hashtable
-    type = property(_swigfaiss.DirectMap_type_get, _swigfaiss.DirectMap_type_set)
-    array = property(_swigfaiss.DirectMap_array_get, _swigfaiss.DirectMap_array_set, doc=r""" map for direct access to the elements. Map ids to LO-encoded entries.""")
-    hashtable = property(_swigfaiss.DirectMap_hashtable_get, _swigfaiss.DirectMap_hashtable_set)
-
-    def __init__(self):
-        _swigfaiss.DirectMap_swiginit(self, _swigfaiss.new_DirectMap())
-
-    def set_type(self, new_type, invlists, ntotal):
-        r""" set type and initialize"""
-        return _swigfaiss.DirectMap_set_type(self, new_type, invlists, ntotal)
-
-    def get(self, id):
-        r""" get an entry"""
-        return _swigfaiss.DirectMap_get(self, id)
-
-    def no(self):
-        r""" for quick checks"""
-        return _swigfaiss.DirectMap_no(self)
-
-    def check_can_add(self, ids):
-        r"""
-        update the direct_map
-
-         throw if Array and ids is not NULL
-        """
-        return _swigfaiss.DirectMap_check_can_add(self, ids)
-
-    def add_single_id(self, id, list_no, offset):
-        r""" non thread-safe version"""
-        return _swigfaiss.DirectMap_add_single_id(self, id, list_no, offset)
-
-    def clear(self):
-        r""" remove all entries"""
-        return _swigfaiss.DirectMap_clear(self)
-
-    def remove_ids(self, sel, invlists):
-        r"""
-        operations on inverted lists that require translation with a DirectMap
-
-         remove ids from the InvertedLists, possibly using the direct map
-        """
-        return _swigfaiss.DirectMap_remove_ids(self, sel, invlists)
-
-    def update_codes(self, invlists, n, ids, list_nos, codes):
-        r""" update entries, using the direct map"""
-        return _swigfaiss.DirectMap_update_codes(self, invlists, n, ids, list_nos, codes)
-    __swig_destroy__ = _swigfaiss.delete_DirectMap
-
-# Register DirectMap in _swigfaiss:
-_swigfaiss.DirectMap_swigregister(DirectMap)
-class DirectMapAdd(object):
-    r""" Thread-safe way of updating the direct_map"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    direct_map = property(_swigfaiss.DirectMapAdd_direct_map_get, _swigfaiss.DirectMapAdd_direct_map_set)
-    type = property(_swigfaiss.DirectMapAdd_type_get, _swigfaiss.DirectMapAdd_type_set)
-    ntotal = property(_swigfaiss.DirectMapAdd_ntotal_get, _swigfaiss.DirectMapAdd_ntotal_set)
-    n = property(_swigfaiss.DirectMapAdd_n_get, _swigfaiss.DirectMapAdd_n_set)
-    xids = property(_swigfaiss.DirectMapAdd_xids_get, _swigfaiss.DirectMapAdd_xids_set)
-    all_ofs = property(_swigfaiss.DirectMapAdd_all_ofs_get, _swigfaiss.DirectMapAdd_all_ofs_set)
-
-    def __init__(self, direct_map, n, xids):
-        _swigfaiss.DirectMapAdd_swiginit(self, _swigfaiss.new_DirectMapAdd(direct_map, n, xids))
-
-    def add(self, i, list_no, offset):
-        r""" add vector i (with id xids[i]) at list_no and offset"""
-        return _swigfaiss.DirectMapAdd_add(self, i, list_no, offset)
-    __swig_destroy__ = _swigfaiss.delete_DirectMapAdd
-
-# Register DirectMapAdd in _swigfaiss:
-_swigfaiss.DirectMapAdd_swigregister(DirectMapAdd)
-class Level1Quantizer(object):
-    r"""
-     Encapsulates a quantizer object for the IndexIVF
-
-    The class isolates the fields that are independent of the storage
-    of the lists (especially training)
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    quantizer = property(_swigfaiss.Level1Quantizer_quantizer_get, _swigfaiss.Level1Quantizer_quantizer_set, doc=r""" quantizer that maps vectors to inverted lists""")
-    nlist = property(_swigfaiss.Level1Quantizer_nlist_get, _swigfaiss.Level1Quantizer_nlist_set, doc=r""" number of inverted lists""")
-    quantizer_trains_alone = property(_swigfaiss.Level1Quantizer_quantizer_trains_alone_get, _swigfaiss.Level1Quantizer_quantizer_trains_alone_set, doc=r"""
-    = 0: use the quantizer as index in a kmeans training
-    = 1: just pass on the training set to the train() of the quantizer
-    = 2: kmeans training on a flat index + add the centroids to the quantizer
-    """)
-    own_fields = property(_swigfaiss.Level1Quantizer_own_fields_get, _swigfaiss.Level1Quantizer_own_fields_set, doc=r""" whether object owns the quantizer""")
-    cp = property(_swigfaiss.Level1Quantizer_cp_get, _swigfaiss.Level1Quantizer_cp_set, doc=r""" to override default clustering params""")
-    clustering_index = property(_swigfaiss.Level1Quantizer_clustering_index_get, _swigfaiss.Level1Quantizer_clustering_index_set, doc=r""" to override index used during clustering""")
-
-    def train_q1(self, n, x, verbose, metric_type):
-        r""" Trains the quantizer and calls train_residual to train sub-quantizers"""
-        return _swigfaiss.Level1Quantizer_train_q1(self, n, x, verbose, metric_type)
-
-    def coarse_code_size(self):
-        r""" compute the number of bytes required to store list ids"""
-        return _swigfaiss.Level1Quantizer_coarse_code_size(self)
-
-    def encode_listno(self, list_no, code):
-        return _swigfaiss.Level1Quantizer_encode_listno(self, list_no, code)
-
-    def decode_listno(self, code):
-        return _swigfaiss.Level1Quantizer_decode_listno(self, code)
-
-    def __init__(self, *args):
-        _swigfaiss.Level1Quantizer_swiginit(self, _swigfaiss.new_Level1Quantizer(*args))
-    __swig_destroy__ = _swigfaiss.delete_Level1Quantizer
-
-# Register Level1Quantizer in _swigfaiss:
-_swigfaiss.Level1Quantizer_swigregister(Level1Quantizer)
-class SearchParametersIVF(SearchParameters):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nprobe = property(_swigfaiss.SearchParametersIVF_nprobe_get, _swigfaiss.SearchParametersIVF_nprobe_set, doc=r""" number of probes at query time""")
-    max_codes = property(_swigfaiss.SearchParametersIVF_max_codes_get, _swigfaiss.SearchParametersIVF_max_codes_set, doc=r""" max nb of codes to visit to do a query""")
-    quantizer_params = property(_swigfaiss.SearchParametersIVF_quantizer_params_get, _swigfaiss.SearchParametersIVF_quantizer_params_set)
-    __swig_destroy__ = _swigfaiss.delete_SearchParametersIVF
-
-    def __init__(self):
-        _swigfaiss.SearchParametersIVF_swiginit(self, _swigfaiss.new_SearchParametersIVF())
-
-# Register SearchParametersIVF in _swigfaiss:
-_swigfaiss.SearchParametersIVF_swigregister(SearchParametersIVF)
-class IndexIVFInterface(Level1Quantizer):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    nprobe = property(_swigfaiss.IndexIVFInterface_nprobe_get, _swigfaiss.IndexIVFInterface_nprobe_set, doc=r""" number of probes at query time""")
-    max_codes = property(_swigfaiss.IndexIVFInterface_max_codes_get, _swigfaiss.IndexIVFInterface_max_codes_set, doc=r""" max nb of codes to visit to do a query""")
-
-    def search_preassigned(self, n, x, k, assign, centroid_dis, distances, labels, store_pairs, params=None, stats=None):
-        r"""
-         search a set of vectors, that are pre-quantized by the IVF
-         quantizer. Fill in the corresponding heaps with the query
-         results. The default implementation uses InvertedListScanners
-         to do the search.
-
-        :type n: int
-        :param n:      nb of vectors to query
-        :type x: float
-        :param x:      query vectors, size nx * d
-        :type assign: int
-        :param assign: coarse quantization indices, size nx * nprobe
-        :type centroid_dis: float
-        :param centroid_dis:
-                          distances to coarse centroids, size nx * nprobe
-        :param distance:
-                          output distances, size n * k
-        :type labels: int
-        :param labels: output labels, size n * k
-        :type store_pairs: boolean
-        :param store_pairs: store inv list index + inv list offset
-                                instead in upper/lower 32 bit of result,
-                                instead of ids (used for reranking).
-        :type params: :py:class:`IVFSearchParameters`, optional
-        :param params: used to override the object's search parameters
-        :type stats: :py:class:`IndexIVFStats`, optional
-        :param stats:  search stats to be updated (can be null)
-        """
-        return _swigfaiss.IndexIVFInterface_search_preassigned(self, n, x, k, assign, centroid_dis, distances, labels, store_pairs, params, stats)
-
-    def range_search_preassigned(self, nx, x, radius, keys, coarse_dis, result, store_pairs=False, params=None, stats=None):
-        r"""
-         Range search a set of vectors, that are pre-quantized by the IVF
-         quantizer. Fill in the RangeSearchResults results. The default
-        implementation uses InvertedListScanners to do the search.
-
-        :param n:      nb of vectors to query
-        :type x: float
-        :param x:      query vectors, size nx * d
-        :param assign: coarse quantization indices, size nx * nprobe
-        :param centroid_dis:
-                          distances to coarse centroids, size nx * nprobe
-        :type result: :py:class:`RangeSearchResult`
-        :param result: Output results
-        :type store_pairs: boolean, optional
-        :param store_pairs: store inv list index + inv list offset
-                                instead in upper/lower 32 bit of result,
-                                instead of ids (used for reranking).
-        :type params: :py:class:`IVFSearchParameters`, optional
-        :param params: used to override the object's search parameters
-        :type stats: :py:class:`IndexIVFStats`, optional
-        :param stats:  search stats to be updated (can be null)
-        """
-        return _swigfaiss.IndexIVFInterface_range_search_preassigned(self, nx, x, radius, keys, coarse_dis, result, store_pairs, params, stats)
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFInterface
-
-# Register IndexIVFInterface in _swigfaiss:
-_swigfaiss.IndexIVFInterface_swigregister(IndexIVFInterface)
-class IndexIVF(Index, IndexIVFInterface):
-    r"""
-     Index based on a inverted file (IVF)
-
-    In the inverted file, the quantizer (an Index instance) provides a
-    quantization index for each vector to be added. The quantization
-    index maps to a list (aka inverted list or posting list), where the
-    id of the vector is stored.
-
-    The inverted list object is required only after trainng. If none is
-    set externally, an ArrayInvertedLists is used automatically.
-
-    At search time, the vector to be searched is also quantized, and
-    only the list corresponding to the quantization index is
-    searched. This speeds up the search by making it
-    non-exhaustive. This can be relaxed using multi-probe search: a few
-    (nprobe) quantization indices are selected and several inverted
-    lists are visited.
-
-    Sub-classes implement a post-filtering of the index that refines
-    the distance estimation from the query to databse vectors.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    invlists = property(_swigfaiss.IndexIVF_invlists_get, _swigfaiss.IndexIVF_invlists_set, doc=r""" Access to the actual data""")
-    own_invlists = property(_swigfaiss.IndexIVF_own_invlists_get, _swigfaiss.IndexIVF_own_invlists_set)
-    code_size = property(_swigfaiss.IndexIVF_code_size_get, _swigfaiss.IndexIVF_code_size_set, doc=r""" code size per vector in bytes""")
-    parallel_mode = property(_swigfaiss.IndexIVF_parallel_mode_get, _swigfaiss.IndexIVF_parallel_mode_set, doc=r"""
-     Parallel mode determines how queries are parallelized with OpenMP
-
-    0 (default): split over queries
-    1: parallelize over inverted lists
-    2: parallelize over both
-    3: split over queries with a finer granularity
-
-    PARALLEL_MODE_NO_HEAP_INIT: binary or with the previous to
-    prevent the heap to be initialized and finalized
-    """)
-    PARALLEL_MODE_NO_HEAP_INIT = property(_swigfaiss.IndexIVF_PARALLEL_MODE_NO_HEAP_INIT_get)
-    direct_map = property(_swigfaiss.IndexIVF_direct_map_get, _swigfaiss.IndexIVF_direct_map_set, doc=r"""
-    optional map that maps back ids to invlist entries. This
-    enables reconstruct()
-    """)
-
-    def reset(self):
-        return _swigfaiss.IndexIVF_reset(self)
-
-    def train(self, n, x):
-        r""" Trains the quantizer and calls train_residual to train sub-quantizers"""
-        return _swigfaiss.IndexIVF_train(self, n, x)
-
-    def add(self, n, x):
-        r""" Calls add_with_ids with NULL ids"""
-        return _swigfaiss.IndexIVF_add(self, n, x)
-
-    def add_with_ids(self, n, x, xids):
-        r""" default implementation that calls encode_vectors"""
-        return _swigfaiss.IndexIVF_add_with_ids(self, n, x, xids)
-
-    def add_core(self, n, x, xids, precomputed_idx):
-        r"""
-         Implementation of vector addition where the vector assignments are
-        predefined. The default implementation hands over the code extraction to
-        encode_vectors.
-
-        :type precomputed_idx: int
-        :param precomputed_idx:    quantization indices for the input vectors
-            (size n)
-        """
-        return _swigfaiss.IndexIVF_add_core(self, n, x, xids, precomputed_idx)
-
-    def encode_vectors(self, n, x, list_nos, codes, include_listno=False):
-        r"""
-         Encodes a set of vectors as they would appear in the inverted lists
-
-        :type list_nos: int
-        :param list_nos:   inverted list ids as returned by the
-                              quantizer (size n). -1s are ignored.
-        :type codes: uint8_t
-        :param codes:      output codes, size n * code_size
-        :type include_listno: boolean, optional
-        :param include_listno:
-                              include the list ids in the code (in this case add
-                              ceil(log8(nlist)) to the code size)
-        """
-        return _swigfaiss.IndexIVF_encode_vectors(self, n, x, list_nos, codes, include_listno)
-
-    def add_sa_codes(self, n, codes, xids):
-        r"""
-         Add vectors that are computed with the standalone codec
-
-        :type codes: uint8_t
-        :param codes:  codes to add size n * sa_code_size()
-        :type xids: int
-        :param xids:   corresponding ids, size n
-        """
-        return _swigfaiss.IndexIVF_add_sa_codes(self, n, codes, xids)
-
-    def train_residual(self, n, x):
-        r"""
-        Sub-classes that encode the residuals can train their encoders here
-        does nothing by default
-        """
-        return _swigfaiss.IndexIVF_train_residual(self, n, x)
-
-    def search_preassigned(self, n, x, k, assign, centroid_dis, distances, labels, store_pairs, params=None, stats=None):
-        return _swigfaiss.IndexIVF_search_preassigned(self, n, x, k, assign, centroid_dis, distances, labels, store_pairs, params, stats)
-
-    def range_search_preassigned(self, nx, x, radius, keys, coarse_dis, result, store_pairs=False, params=None, stats=None):
-        return _swigfaiss.IndexIVF_range_search_preassigned(self, nx, x, radius, keys, coarse_dis, result, store_pairs, params, stats)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        r""" assign the vectors, then call search_preassign"""
-        return _swigfaiss.IndexIVF_search(self, n, x, k, distances, labels, params)
-
-    def range_search(self, n, x, radius, result, params=None):
-        return _swigfaiss.IndexIVF_range_search(self, n, x, radius, result, params)
-
-    def get_InvertedListScanner(self, store_pairs=False, sel=None):
-        r"""
-         Get a scanner for this index (store_pairs means ignore labels)
-
-        The default search implementation uses this to compute the distances
-        """
-        return _swigfaiss.IndexIVF_get_InvertedListScanner(self, store_pairs, sel)
-
-    def reconstruct(self, key, recons):
-        r""" reconstruct a vector. Works only if maintain_direct_map is set to 1 or 2"""
-        return _swigfaiss.IndexIVF_reconstruct(self, key, recons)
-
-    def update_vectors(self, nv, idx, v):
-        r"""
-         Update a subset of vectors.
-
-        The index must have a direct_map
-
-        :type nv: int
-        :param nv:     nb of vectors to update
-        :type idx: int
-        :param idx:    vector indices to update, size nv
-        :type v: float
-        :param v:      vectors of new values, size nv*d
-        """
-        return _swigfaiss.IndexIVF_update_vectors(self, nv, idx, v)
-
-    def reconstruct_n(self, i0, ni, recons):
-        r"""
-         Reconstruct a subset of the indexed vectors.
-
-        Overrides default implementation to bypass reconstruct() which requires
-        direct_map to be maintained.
-
-        :type i0: int
-        :param i0:     first vector to reconstruct
-        :type ni: int
-        :param ni:     nb of vectors to reconstruct
-        :type recons: float
-        :param recons: output array of reconstructed vectors, size ni * d
-        """
-        return _swigfaiss.IndexIVF_reconstruct_n(self, i0, ni, recons)
-
-    def search_and_reconstruct(self, n, x, k, distances, labels, recons, params=None):
-        r"""
-         Similar to search, but also reconstructs the stored vectors (or an
-        approximation in the case of lossy coding) for the search results.
-
-        Overrides default implementation to avoid having to maintain direct_map
-        and instead fetch the code offsets through the `store_pairs` flag in
-        search_preassigned().
-
-        :type recons: float
-        :param recons:      reconstructed vectors size (n, k, d)
-        """
-        return _swigfaiss.IndexIVF_search_and_reconstruct(self, n, x, k, distances, labels, recons, params)
-
-    def reconstruct_from_offset(self, list_no, offset, recons):
-        r"""
-         Reconstruct a vector given the location in terms of (inv list index +
-        inv list offset) instead of the id.
-
-        Useful for reconstructing when the direct_map is not maintained and
-        the inv list offset is computed by search_preassigned() with
-        `store_pairs` set.
-        """
-        return _swigfaiss.IndexIVF_reconstruct_from_offset(self, list_no, offset, recons)
-
-    def remove_ids(self, sel):
-        r""" Dataset manipulation functions"""
-        return _swigfaiss.IndexIVF_remove_ids(self, sel)
-
-    def check_compatible_for_merge(self, otherIndex):
-        return _swigfaiss.IndexIVF_check_compatible_for_merge(self, otherIndex)
-
-    def merge_from(self, otherIndex, add_id):
-        return _swigfaiss.IndexIVF_merge_from(self, otherIndex, add_id)
-
-    def get_CodePacker(self):
-        return _swigfaiss.IndexIVF_get_CodePacker(self)
-
-    def copy_subset_to(self, other, subset_type, a1, a2):
-        r"""
-         copy a subset of the entries index to the other index
-        see Invlists::copy_subset_to for the meaning of subset_type
-        """
-        return _swigfaiss.IndexIVF_copy_subset_to(self, other, subset_type, a1, a2)
-    __swig_destroy__ = _swigfaiss.delete_IndexIVF
-
-    def get_list_size(self, list_no):
-        return _swigfaiss.IndexIVF_get_list_size(self, list_no)
-
-    def check_ids_sorted(self):
-        r""" are the ids sorted?"""
-        return _swigfaiss.IndexIVF_check_ids_sorted(self)
-
-    def make_direct_map(self, new_maintain_direct_map=True):
-        r"""
-         initialize a direct map
-
-        :type new_maintain_direct_map: boolean, optional
-        :param new_maintain_direct_map:    if true, create a direct map,
-                                              else clear it
-        """
-        return _swigfaiss.IndexIVF_make_direct_map(self, new_maintain_direct_map)
-
-    def set_direct_map_type(self, type):
-        return _swigfaiss.IndexIVF_set_direct_map_type(self, type)
-
-    def replace_invlists(self, il, own=False):
-        r""" replace the inverted lists, old one is deallocated if own_invlists"""
-        return _swigfaiss.IndexIVF_replace_invlists(self, il, own)
-
-    def sa_code_size(self):
-        return _swigfaiss.IndexIVF_sa_code_size(self)
-
-    def sa_encode(self, n, x, bytes):
-        return _swigfaiss.IndexIVF_sa_encode(self, n, x, bytes)
-
-# Register IndexIVF in _swigfaiss:
-_swigfaiss.IndexIVF_swigregister(IndexIVF)
-class InvertedListScanner(object):
-    r"""
-     Object that handles a query. The inverted lists to scan are
-    provided externally. The object has a lot of state, but
-    distance_to_code and scan_codes can be called in multiple
-    threads
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    list_no = property(_swigfaiss.InvertedListScanner_list_no_get, _swigfaiss.InvertedListScanner_list_no_set, doc=r""" remember current list""")
-    keep_max = property(_swigfaiss.InvertedListScanner_keep_max_get, _swigfaiss.InvertedListScanner_keep_max_set, doc=r""" keep maximum instead of minimum""")
-    store_pairs = property(_swigfaiss.InvertedListScanner_store_pairs_get, _swigfaiss.InvertedListScanner_store_pairs_set, doc=r""" store positions in invlists rather than labels""")
-    sel = property(_swigfaiss.InvertedListScanner_sel_get, _swigfaiss.InvertedListScanner_sel_set, doc=r""" search in this subset of ids""")
-    code_size = property(_swigfaiss.InvertedListScanner_code_size_get, _swigfaiss.InvertedListScanner_code_size_set, doc=r""" used in default implementation of scan_codes""")
-
-    def set_query(self, query_vector):
-        r""" from now on we handle this query."""
-        return _swigfaiss.InvertedListScanner_set_query(self, query_vector)
-
-    def set_list(self, list_no, coarse_dis):
-        r""" following codes come from this inverted list"""
-        return _swigfaiss.InvertedListScanner_set_list(self, list_no, coarse_dis)
-
-    def distance_to_code(self, code):
-        r""" compute a single query-to-code distance"""
-        return _swigfaiss.InvertedListScanner_distance_to_code(self, code)
-
-    def scan_codes(self, n, codes, ids, distances, labels, k):
-        r"""
-         scan a set of codes, compute distances to current query and
-        update heap of results if necessary. Default implemetation
-        calls distance_to_code.
-
-        :type n: int
-        :param n:      number of codes to scan
-        :type codes: uint8_t
-        :param codes:  codes to scan (n * code_size)
-        :type ids: int
-        :param ids:        corresponding ids (ignored if store_pairs)
-        :type distances: float
-        :param distances:  heap distances (size k)
-        :type labels: int
-        :param labels:     heap labels (size k)
-        :type k: int
-        :param k:          heap size
-        :rtype: int
-        :return: number of heap updates performed
-        """
-        return _swigfaiss.InvertedListScanner_scan_codes(self, n, codes, ids, distances, labels, k)
-
-    def iterate_codes(self, iterator, distances, labels, k, list_size):
-        return _swigfaiss.InvertedListScanner_iterate_codes(self, iterator, distances, labels, k, list_size)
-
-    def scan_codes_range(self, n, codes, ids, radius, result):
-        r"""
-         scan a set of codes, compute distances to current query and
-        update results if distances are below radius
-
-        (default implementation fails)
-        """
-        return _swigfaiss.InvertedListScanner_scan_codes_range(self, n, codes, ids, radius, result)
-
-    def iterate_codes_range(self, iterator, radius, result, list_size):
-        return _swigfaiss.InvertedListScanner_iterate_codes_range(self, iterator, radius, result, list_size)
-    __swig_destroy__ = _swigfaiss.delete_InvertedListScanner
-
-# Register InvertedListScanner in _swigfaiss:
-_swigfaiss.InvertedListScanner_swigregister(InvertedListScanner)
-class IndexIVFStats(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nq = property(_swigfaiss.IndexIVFStats_nq_get, _swigfaiss.IndexIVFStats_nq_set)
-    nlist = property(_swigfaiss.IndexIVFStats_nlist_get, _swigfaiss.IndexIVFStats_nlist_set)
-    ndis = property(_swigfaiss.IndexIVFStats_ndis_get, _swigfaiss.IndexIVFStats_ndis_set)
-    nheap_updates = property(_swigfaiss.IndexIVFStats_nheap_updates_get, _swigfaiss.IndexIVFStats_nheap_updates_set)
-    quantization_time = property(_swigfaiss.IndexIVFStats_quantization_time_get, _swigfaiss.IndexIVFStats_quantization_time_set)
-    search_time = property(_swigfaiss.IndexIVFStats_search_time_get, _swigfaiss.IndexIVFStats_search_time_set)
-
-    def __init__(self):
-        _swigfaiss.IndexIVFStats_swiginit(self, _swigfaiss.new_IndexIVFStats())
-
-    def reset(self):
-        return _swigfaiss.IndexIVFStats_reset(self)
-
-    def add(self, other):
-        return _swigfaiss.IndexIVFStats_add(self, other)
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFStats
-
-# Register IndexIVFStats in _swigfaiss:
-_swigfaiss.IndexIVFStats_swigregister(IndexIVFStats)
-
-def check_compatible_for_merge(index1, index2):
-    r"""
-     check if two indexes have the same parameters and are trained in
-    the same way, otherwise throw.
-    """
-    return _swigfaiss.check_compatible_for_merge(index1, index2)
-
-def extract_index_ivf(*args):
-    return _swigfaiss.extract_index_ivf(*args)
-
-def try_extract_index_ivf(*args):
-    return _swigfaiss.try_extract_index_ivf(*args)
-
-def merge_into(index0, index1, shift_ids):
-    r"""
-     Merge index1 into index0. Works on IndexIVF's and IndexIVF's
-     embedded in a IndexPreTransform. On output, the index1 is empty.
-
-    :type shift_ids: boolean
-    :param shift_ids:: translate the ids from index1 to index0->prev_ntotal
-    """
-    return _swigfaiss.merge_into(index0, index1, shift_ids)
-
-def search_centroid(index, x, n, centroid_ids):
-    return _swigfaiss.search_centroid(index, x, n, centroid_ids)
-
-def search_and_return_centroids(index, n, xin, k, distances, labels, query_centroid_ids, result_centroid_ids):
-    return _swigfaiss.search_and_return_centroids(index, n, xin, k, distances, labels, query_centroid_ids, result_centroid_ids)
-class SlidingIndexWindow(object):
-    r"""
-     A set of IndexIVFs concatenated together in a FIFO fashion.
-    at each "step", the oldest index slice is removed and a new index is added.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    index = property(_swigfaiss.SlidingIndexWindow_index_get, _swigfaiss.SlidingIndexWindow_index_set, doc=r""" common index that contains the sliding window""")
-    ils = property(_swigfaiss.SlidingIndexWindow_ils_get, _swigfaiss.SlidingIndexWindow_ils_set, doc=r""" InvertedLists of index""")
-    n_slice = property(_swigfaiss.SlidingIndexWindow_n_slice_get, _swigfaiss.SlidingIndexWindow_n_slice_set, doc=r""" number of slices currently in index""")
-    nlist = property(_swigfaiss.SlidingIndexWindow_nlist_get, _swigfaiss.SlidingIndexWindow_nlist_set, doc=r""" same as index->nlist""")
-    sizes = property(_swigfaiss.SlidingIndexWindow_sizes_get, _swigfaiss.SlidingIndexWindow_sizes_set, doc=r""" cumulative list sizes at each slice""")
-
-    def __init__(self, index):
-        r""" index should be initially empty and trained"""
-        _swigfaiss.SlidingIndexWindow_swiginit(self, _swigfaiss.new_SlidingIndexWindow(index))
-
-    def step(self, sub_index, remove_oldest):
-        r"""
-         Add one index to the current index and remove the oldest one.
-
-        :type sub_index: :py:class:`Index`
-        :param sub_index:        slice to swap in (can be NULL)
-        :type remove_oldest: boolean
-        :param remove_oldest:    if true, remove the oldest slices
-        """
-        return _swigfaiss.SlidingIndexWindow_step(self, sub_index, remove_oldest)
-    __swig_destroy__ = _swigfaiss.delete_SlidingIndexWindow
-
-# Register SlidingIndexWindow in _swigfaiss:
-_swigfaiss.SlidingIndexWindow_swigregister(SlidingIndexWindow)
-
-def get_invlist_range(index, i0, i1):
-    r""" Get a subset of inverted lists [i0, i1)"""
-    return _swigfaiss.get_invlist_range(index, i0, i1)
-
-def set_invlist_range(index, i0, i1, src):
-    r""" Set a subset of inverted lists"""
-    return _swigfaiss.set_invlist_range(index, i0, i1, src)
-
-def search_with_parameters(index, n, x, k, distances, labels, params, nb_dis=None, ms_per_stage=None):
-    r"""
-     search an IndexIVF, possibly embedded in an IndexPreTransform with
-    given parameters. This is a way to set the nprobe and get
-    statdistics in a thread-safe way.
-
-    Optionally returns (if non-nullptr):
-    - nb_dis: number of distances computed
-    - ms_per_stage: [0]: preprocessing time
-                    [1]: coarse quantization,
-                    [2]: list scanning
-    """
-    return _swigfaiss.search_with_parameters(index, n, x, k, distances, labels, params, nb_dis, ms_per_stage)
-
-def range_search_with_parameters(index, n, x, radius, result, params, nb_dis=None, ms_per_stage=None):
-    r""" same as search_with_parameters but for range search"""
-    return _swigfaiss.range_search_with_parameters(index, n, x, radius, result, params, nb_dis, ms_per_stage)
-
-def ivf_residual_from_quantizer(arg1, nlevel):
-    r"""
-     Build an IndexIVFResidualQuantizer from an ResidualQuantizer, using the
-    nlevel first components as coarse quantizer and the rest as codes in invlists
-    """
-    return _swigfaiss.ivf_residual_from_quantizer(arg1, nlevel)
-
-def ivf_residual_add_from_flat_codes(ivfrq, ncode, codes, code_size=-1):
-    r"""
-     add from codes. NB that the norm component is not used, so the code_size can
-    be provided.
-
-    :type ivfrq: :py:class:`IndexIVFResidualQuantizer`
-    :param ivfrq:      index to populate with the codes
-    :type codes: uint8_t
-    :param codes:      codes to add, size (ncode, code_size)
-    :type code_size: int, optional
-    :param code_size:  override the ivfrq's code_size, useful if the norm encoding
-                          is different
-    """
-    return _swigfaiss.ivf_residual_add_from_flat_codes(ivfrq, ncode, codes, code_size)
-class ScalarQuantizer(Quantizer):
-    r"""
-    The uniform quantizer has a range [vmin, vmax]. The range can be
-    the same for all dimensions (uniform) or specific per dimension
-    (default).
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    QT_8bit = _swigfaiss.ScalarQuantizer_QT_8bit
-    r""" 8 bits per component"""
-    QT_4bit = _swigfaiss.ScalarQuantizer_QT_4bit
-    r""" 4 bits per component"""
-    QT_8bit_uniform = _swigfaiss.ScalarQuantizer_QT_8bit_uniform
-    r""" same, shared range for all dimensions"""
-    QT_4bit_uniform = _swigfaiss.ScalarQuantizer_QT_4bit_uniform
-    QT_fp16 = _swigfaiss.ScalarQuantizer_QT_fp16
-    QT_8bit_direct = _swigfaiss.ScalarQuantizer_QT_8bit_direct
-    r""" fast indexing of uint8s"""
-    QT_6bit = _swigfaiss.ScalarQuantizer_QT_6bit
-    r""" 6 bits per component"""
-    qtype = property(_swigfaiss.ScalarQuantizer_qtype_get, _swigfaiss.ScalarQuantizer_qtype_set)
-    RS_minmax = _swigfaiss.ScalarQuantizer_RS_minmax
-    r""" [min - rs*(max-min), max + rs*(max-min)]"""
-    RS_meanstd = _swigfaiss.ScalarQuantizer_RS_meanstd
-    r""" [mean - std * rs, mean + std * rs]"""
-    RS_quantiles = _swigfaiss.ScalarQuantizer_RS_quantiles
-    r""" [Q(rs), Q(1-rs)]"""
-    RS_optim = _swigfaiss.ScalarQuantizer_RS_optim
-    r""" alternate optimization of reconstruction error"""
-    rangestat = property(_swigfaiss.ScalarQuantizer_rangestat_get, _swigfaiss.ScalarQuantizer_rangestat_set)
-    rangestat_arg = property(_swigfaiss.ScalarQuantizer_rangestat_arg_get, _swigfaiss.ScalarQuantizer_rangestat_arg_set)
-    bits = property(_swigfaiss.ScalarQuantizer_bits_get, _swigfaiss.ScalarQuantizer_bits_set, doc=r""" bits per scalar code""")
-    trained = property(_swigfaiss.ScalarQuantizer_trained_get, _swigfaiss.ScalarQuantizer_trained_set, doc=r""" trained values (including the range)""")
-
-    def __init__(self, *args):
-        _swigfaiss.ScalarQuantizer_swiginit(self, _swigfaiss.new_ScalarQuantizer(*args))
-
-    def set_derived_sizes(self):
-        r""" updates internal values based on qtype and d"""
-        return _swigfaiss.ScalarQuantizer_set_derived_sizes(self)
-
-    def train(self, n, x):
-        return _swigfaiss.ScalarQuantizer_train(self, n, x)
-
-    def train_residual(self, n, x, quantizer, by_residual, verbose):
-        r""" Used by an IVF index to train based on the residuals"""
-        return _swigfaiss.ScalarQuantizer_train_residual(self, n, x, quantizer, by_residual, verbose)
-
-    def compute_codes(self, x, codes, n):
-        r"""
-         Encode a set of vectors
-
-        :type x: float
-        :param x:      vectors to encode, size n * d
-        :type codes: uint8_t
-        :param codes:  output codes, size n * code_size
-        """
-        return _swigfaiss.ScalarQuantizer_compute_codes(self, x, codes, n)
-
-    def decode(self, code, x, n):
-        r"""
-         Decode a set of vectors
-
-        :param codes:  codes to decode, size n * code_size
-        :type x: float
-        :param x:      output vectors, size n * d
-        """
-        return _swigfaiss.ScalarQuantizer_decode(self, code, x, n)
-
-    def select_quantizer(self):
-        return _swigfaiss.ScalarQuantizer_select_quantizer(self)
-
-    def get_distance_computer(self, *args):
-        return _swigfaiss.ScalarQuantizer_get_distance_computer(self, *args)
-
-    def select_InvertedListScanner(self, mt, quantizer, store_pairs, sel, by_residual=False):
-        return _swigfaiss.ScalarQuantizer_select_InvertedListScanner(self, mt, quantizer, store_pairs, sel, by_residual)
-    __swig_destroy__ = _swigfaiss.delete_ScalarQuantizer
-
-# Register ScalarQuantizer in _swigfaiss:
-_swigfaiss.ScalarQuantizer_swigregister(ScalarQuantizer)
-class IndexScalarQuantizer(IndexFlatCodes):
-    r"""Flat index built on a scalar quantizer."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    sq = property(_swigfaiss.IndexScalarQuantizer_sq_get, _swigfaiss.IndexScalarQuantizer_sq_set, doc=r""" Used to encode the vectors""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexScalarQuantizer_swiginit(self, _swigfaiss.new_IndexScalarQuantizer(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexScalarQuantizer_train(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexScalarQuantizer_search(self, n, x, k, distances, labels, params)
-
-    def get_FlatCodesDistanceComputer(self):
-        return _swigfaiss.IndexScalarQuantizer_get_FlatCodesDistanceComputer(self)
-
-    def sa_encode(self, n, x, bytes):
-        return _swigfaiss.IndexScalarQuantizer_sa_encode(self, n, x, bytes)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexScalarQuantizer_sa_decode(self, n, bytes, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexScalarQuantizer
-
-# Register IndexScalarQuantizer in _swigfaiss:
-_swigfaiss.IndexScalarQuantizer_swigregister(IndexScalarQuantizer)
-class IndexIVFScalarQuantizer(IndexIVF):
-    r"""
-     An IVF implementation where the components of the residuals are
-    encoded with a scalar quantizer. All distance computations
-    are asymmetric, so the encoded vectors are decoded and approximate
-    distances are computed.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    sq = property(_swigfaiss.IndexIVFScalarQuantizer_sq_get, _swigfaiss.IndexIVFScalarQuantizer_sq_set)
-    by_residual = property(_swigfaiss.IndexIVFScalarQuantizer_by_residual_get, _swigfaiss.IndexIVFScalarQuantizer_by_residual_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFScalarQuantizer_swiginit(self, _swigfaiss.new_IndexIVFScalarQuantizer(*args))
-
-    def train_residual(self, n, x):
-        return _swigfaiss.IndexIVFScalarQuantizer_train_residual(self, n, x)
-
-    def encode_vectors(self, n, x, list_nos, codes, include_listnos=False):
-        return _swigfaiss.IndexIVFScalarQuantizer_encode_vectors(self, n, x, list_nos, codes, include_listnos)
-
-    def add_core(self, n, x, xids, precomputed_idx):
-        return _swigfaiss.IndexIVFScalarQuantizer_add_core(self, n, x, xids, precomputed_idx)
-
-    def get_InvertedListScanner(self, store_pairs, sel):
-        return _swigfaiss.IndexIVFScalarQuantizer_get_InvertedListScanner(self, store_pairs, sel)
-
-    def reconstruct_from_offset(self, list_no, offset, recons):
-        return _swigfaiss.IndexIVFScalarQuantizer_reconstruct_from_offset(self, list_no, offset, recons)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexIVFScalarQuantizer_sa_decode(self, n, bytes, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFScalarQuantizer
-
-# Register IndexIVFScalarQuantizer in _swigfaiss:
-_swigfaiss.IndexIVFScalarQuantizer_swigregister(IndexIVFScalarQuantizer)
-class IndexIVFSpectralHash(IndexIVF):
-    r"""
-     Inverted list that stores binary codes of size nbit. Before the
-    binary conversion, the dimension of the vectors is transformed from
-    dim d into dim nbit by vt (a random rotation by default).
-
-    Each coordinate is subtracted from a value determined by
-    threshold_type, and split into intervals of size period. Half of
-    the interval is a 0 bit, the other half a 1.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    vt = property(_swigfaiss.IndexIVFSpectralHash_vt_get, _swigfaiss.IndexIVFSpectralHash_vt_set, doc=r""" transformation from d to nbit dim""")
-    own_fields = property(_swigfaiss.IndexIVFSpectralHash_own_fields_get, _swigfaiss.IndexIVFSpectralHash_own_fields_set, doc=r""" own the vt""")
-    nbit = property(_swigfaiss.IndexIVFSpectralHash_nbit_get, _swigfaiss.IndexIVFSpectralHash_nbit_set, doc=r""" nb of bits of the binary signature""")
-    period = property(_swigfaiss.IndexIVFSpectralHash_period_get, _swigfaiss.IndexIVFSpectralHash_period_set, doc=r""" interval size for 0s and 1s""")
-    Thresh_global = _swigfaiss.IndexIVFSpectralHash_Thresh_global
-    r""" global threshold at 0"""
-    Thresh_centroid = _swigfaiss.IndexIVFSpectralHash_Thresh_centroid
-    r""" compare to centroid"""
-    Thresh_centroid_half = _swigfaiss.IndexIVFSpectralHash_Thresh_centroid_half
-    r""" central interval around centroid"""
-    Thresh_median = _swigfaiss.IndexIVFSpectralHash_Thresh_median
-    r""" median of training set"""
-    threshold_type = property(_swigfaiss.IndexIVFSpectralHash_threshold_type_get, _swigfaiss.IndexIVFSpectralHash_threshold_type_set)
-    trained = property(_swigfaiss.IndexIVFSpectralHash_trained_get, _swigfaiss.IndexIVFSpectralHash_trained_set, doc=r"""
-    Trained threshold.
-    size nlist * nbit or 0 if Thresh_global
-    """)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFSpectralHash_swiginit(self, _swigfaiss.new_IndexIVFSpectralHash(*args))
-
-    def train_residual(self, n, x):
-        return _swigfaiss.IndexIVFSpectralHash_train_residual(self, n, x)
-
-    def encode_vectors(self, n, x, list_nos, codes, include_listnos=False):
-        return _swigfaiss.IndexIVFSpectralHash_encode_vectors(self, n, x, list_nos, codes, include_listnos)
-
-    def get_InvertedListScanner(self, store_pairs, sel):
-        return _swigfaiss.IndexIVFSpectralHash_get_InvertedListScanner(self, store_pairs, sel)
-
-    def replace_vt(self, *args):
-        r"""
-        *Overload 1:*
-         replace the vector transform for an empty (and possibly untrained) index
-
-        |
-
-        *Overload 2:*
-         convenience function to get the VT from an index constucted by an
-        index_factory (should end in "LSH")
-
-        |
-
-        *Overload 3:*
-         convenience function to get the VT from an index constucted by an
-        index_factory (should end in "LSH")
-        """
-        return _swigfaiss.IndexIVFSpectralHash_replace_vt(self, *args)
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFSpectralHash
-
-# Register IndexIVFSpectralHash in _swigfaiss:
-_swigfaiss.IndexIVFSpectralHash_swigregister(IndexIVFSpectralHash)
-class IndexIVFAdditiveQuantizer(IndexIVF):
-    r"""
-    Abstract class for IVF additive quantizers.
-    The search functions are in common.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    aq = property(_swigfaiss.IndexIVFAdditiveQuantizer_aq_get, _swigfaiss.IndexIVFAdditiveQuantizer_aq_set)
-    by_residual = property(_swigfaiss.IndexIVFAdditiveQuantizer_by_residual_get, _swigfaiss.IndexIVFAdditiveQuantizer_by_residual_set)
-    use_precomputed_table = property(_swigfaiss.IndexIVFAdditiveQuantizer_use_precomputed_table_get, _swigfaiss.IndexIVFAdditiveQuantizer_use_precomputed_table_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFAdditiveQuantizer_swiginit(self, _swigfaiss.new_IndexIVFAdditiveQuantizer(*args))
-
-    def train_residual(self, n, x):
-        return _swigfaiss.IndexIVFAdditiveQuantizer_train_residual(self, n, x)
-
-    def encode_vectors(self, n, x, list_nos, codes, include_listnos=False):
-        return _swigfaiss.IndexIVFAdditiveQuantizer_encode_vectors(self, n, x, list_nos, codes, include_listnos)
-
-    def get_InvertedListScanner(self, store_pairs, sel):
-        return _swigfaiss.IndexIVFAdditiveQuantizer_get_InvertedListScanner(self, store_pairs, sel)
-
-    def sa_decode(self, n, codes, x):
-        return _swigfaiss.IndexIVFAdditiveQuantizer_sa_decode(self, n, codes, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFAdditiveQuantizer
-
-# Register IndexIVFAdditiveQuantizer in _swigfaiss:
-_swigfaiss.IndexIVFAdditiveQuantizer_swigregister(IndexIVFAdditiveQuantizer)
-class IndexIVFResidualQuantizer(IndexIVFAdditiveQuantizer):
-    r"""
-     IndexIVF based on a residual quantizer. Stored vectors are
-    approximated by residual quantization codes.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    rq = property(_swigfaiss.IndexIVFResidualQuantizer_rq_get, _swigfaiss.IndexIVFResidualQuantizer_rq_set, doc=r""" The residual quantizer used to encode the vectors""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFResidualQuantizer_swiginit(self, _swigfaiss.new_IndexIVFResidualQuantizer(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFResidualQuantizer
-
-# Register IndexIVFResidualQuantizer in _swigfaiss:
-_swigfaiss.IndexIVFResidualQuantizer_swigregister(IndexIVFResidualQuantizer)
-class IndexIVFLocalSearchQuantizer(IndexIVFAdditiveQuantizer):
-    r"""
-     IndexIVF based on a residual quantizer. Stored vectors are
-    approximated by residual quantization codes.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    lsq = property(_swigfaiss.IndexIVFLocalSearchQuantizer_lsq_get, _swigfaiss.IndexIVFLocalSearchQuantizer_lsq_set, doc=r""" The LSQ quantizer used to encode the vectors""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFLocalSearchQuantizer_swiginit(self, _swigfaiss.new_IndexIVFLocalSearchQuantizer(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFLocalSearchQuantizer
-
-# Register IndexIVFLocalSearchQuantizer in _swigfaiss:
-_swigfaiss.IndexIVFLocalSearchQuantizer_swigregister(IndexIVFLocalSearchQuantizer)
-class IndexIVFProductResidualQuantizer(IndexIVFAdditiveQuantizer):
-    r"""
-     IndexIVF based on a product residual quantizer. Stored vectors are
-    approximated by product residual quantization codes.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    prq = property(_swigfaiss.IndexIVFProductResidualQuantizer_prq_get, _swigfaiss.IndexIVFProductResidualQuantizer_prq_set, doc=r""" The product residual quantizer used to encode the vectors""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFProductResidualQuantizer_swiginit(self, _swigfaiss.new_IndexIVFProductResidualQuantizer(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFProductResidualQuantizer
-
-# Register IndexIVFProductResidualQuantizer in _swigfaiss:
-_swigfaiss.IndexIVFProductResidualQuantizer_swigregister(IndexIVFProductResidualQuantizer)
-class IndexIVFProductLocalSearchQuantizer(IndexIVFAdditiveQuantizer):
-    r"""
-     IndexIVF based on a product local search quantizer. Stored vectors are
-    approximated by product local search quantization codes.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    plsq = property(_swigfaiss.IndexIVFProductLocalSearchQuantizer_plsq_get, _swigfaiss.IndexIVFProductLocalSearchQuantizer_plsq_set, doc=r""" The product local search quantizer used to encode the vectors""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFProductLocalSearchQuantizer_swiginit(self, _swigfaiss.new_IndexIVFProductLocalSearchQuantizer(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFProductLocalSearchQuantizer
-
-# Register IndexIVFProductLocalSearchQuantizer in _swigfaiss:
-_swigfaiss.IndexIVFProductLocalSearchQuantizer_swigregister(IndexIVFProductLocalSearchQuantizer)
-class SearchParametersHNSW(SearchParameters):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    efSearch = property(_swigfaiss.SearchParametersHNSW_efSearch_get, _swigfaiss.SearchParametersHNSW_efSearch_set)
-    check_relative_distance = property(_swigfaiss.SearchParametersHNSW_check_relative_distance_get, _swigfaiss.SearchParametersHNSW_check_relative_distance_set)
-    __swig_destroy__ = _swigfaiss.delete_SearchParametersHNSW
-
-    def __init__(self):
-        _swigfaiss.SearchParametersHNSW_swiginit(self, _swigfaiss.new_SearchParametersHNSW())
-
-# Register SearchParametersHNSW in _swigfaiss:
-_swigfaiss.SearchParametersHNSW_swigregister(SearchParametersHNSW)
-class HNSW(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    assign_probas = property(_swigfaiss.HNSW_assign_probas_get, _swigfaiss.HNSW_assign_probas_set, doc=r""" assignment probability to each layer (sum=1)""")
-    cum_nneighbor_per_level = property(_swigfaiss.HNSW_cum_nneighbor_per_level_get, _swigfaiss.HNSW_cum_nneighbor_per_level_set, doc=r"""
-    number of neighbors stored per layer (cumulative), should not
-    be changed after first add
-    """)
-    levels = property(_swigfaiss.HNSW_levels_get, _swigfaiss.HNSW_levels_set, doc=r""" level of each vector (base level = 1), size = ntotal""")
-    offsets = property(_swigfaiss.HNSW_offsets_get, _swigfaiss.HNSW_offsets_set, doc=r"""
-    offsets[i] is the offset in the neighbors array where vector i is stored
-    size ntotal + 1
-    """)
-    neighbors = property(_swigfaiss.HNSW_neighbors_get, _swigfaiss.HNSW_neighbors_set, doc=r"""
-    neighbors[offsets[i]:offsets[i+1]] is the list of neighbors of vector i
-    for all levels. this is where all storage goes.
-    """)
-    entry_point = property(_swigfaiss.HNSW_entry_point_get, _swigfaiss.HNSW_entry_point_set, doc=r"""
-    entry point in the search structure (one of the points with maximum
-    level
-    """)
-    rng = property(_swigfaiss.HNSW_rng_get, _swigfaiss.HNSW_rng_set)
-    max_level = property(_swigfaiss.HNSW_max_level_get, _swigfaiss.HNSW_max_level_set, doc=r""" maximum level""")
-    efConstruction = property(_swigfaiss.HNSW_efConstruction_get, _swigfaiss.HNSW_efConstruction_set, doc=r""" expansion factor at construction time""")
-    efSearch = property(_swigfaiss.HNSW_efSearch_get, _swigfaiss.HNSW_efSearch_set, doc=r""" expansion factor at search time""")
-    check_relative_distance = property(_swigfaiss.HNSW_check_relative_distance_get, _swigfaiss.HNSW_check_relative_distance_set, doc=r"""
-    during search: do we check whether the next best distance is good
-    enough?
-    """)
-    upper_beam = property(_swigfaiss.HNSW_upper_beam_get, _swigfaiss.HNSW_upper_beam_set, doc=r""" number of entry points in levels > 0.""")
-    search_bounded_queue = property(_swigfaiss.HNSW_search_bounded_queue_get, _swigfaiss.HNSW_search_bounded_queue_set, doc=r""" use bounded queue during exploration""")
-
-    def set_default_probas(self, M, levelMult):
-        r"""
-        initialize the assign_probas and cum_nneighbor_per_level to
-        have 2*M links on level 0 and M links on levels > 0
-        """
-        return _swigfaiss.HNSW_set_default_probas(self, M, levelMult)
-
-    def set_nb_neighbors(self, level_no, n):
-        r""" set nb of neighbors for this level (before adding anything)"""
-        return _swigfaiss.HNSW_set_nb_neighbors(self, level_no, n)
-
-    def nb_neighbors(self, layer_no):
-        r""" nb of neighbors for this level"""
-        return _swigfaiss.HNSW_nb_neighbors(self, layer_no)
-
-    def cum_nb_neighbors(self, layer_no):
-        r""" cumumlative nb up to (and excluding) this level"""
-        return _swigfaiss.HNSW_cum_nb_neighbors(self, layer_no)
-
-    def neighbor_range(self, no, layer_no, begin, end):
-        r""" range of entries in the neighbors table of vertex no at layer_no"""
-        return _swigfaiss.HNSW_neighbor_range(self, no, layer_no, begin, end)
-
-    def __init__(self, M=32):
-        r""" only mandatory parameter: nb of neighbors"""
-        _swigfaiss.HNSW_swiginit(self, _swigfaiss.new_HNSW(M))
-
-    def random_level(self):
-        r""" pick a random level for a new point"""
-        return _swigfaiss.HNSW_random_level(self)
-
-    def fill_with_random_links(self, n):
-        r""" add n random levels to table (for debugging...)"""
-        return _swigfaiss.HNSW_fill_with_random_links(self, n)
-
-    def add_links_starting_from(self, ptdis, pt_id, nearest, d_nearest, level, locks, vt):
-        return _swigfaiss.HNSW_add_links_starting_from(self, ptdis, pt_id, nearest, d_nearest, level, locks, vt)
-
-    def add_with_locks(self, ptdis, pt_level, pt_id, locks, vt):
-        r"""
-         add point pt_id on all levels <= pt_level and build the link
-        structure for them.
-        """
-        return _swigfaiss.HNSW_add_with_locks(self, ptdis, pt_level, pt_id, locks, vt)
-
-    def search(self, qdis, k, I, D, vt, params=None):
-        r""" search interface for 1 point, single thread"""
-        return _swigfaiss.HNSW_search(self, qdis, k, I, D, vt, params)
-
-    def search_level_0(self, qdis, k, idxi, simi, nprobe, nearest_i, nearest_d, search_type, search_stats, vt):
-        r""" search only in level 0 from a given vertex"""
-        return _swigfaiss.HNSW_search_level_0(self, qdis, k, idxi, simi, nprobe, nearest_i, nearest_d, search_type, search_stats, vt)
-
-    def reset(self):
-        return _swigfaiss.HNSW_reset(self)
-
-    def clear_neighbor_tables(self, level):
-        return _swigfaiss.HNSW_clear_neighbor_tables(self, level)
-
-    def print_neighbor_stats(self, level):
-        return _swigfaiss.HNSW_print_neighbor_stats(self, level)
-
-    def prepare_level_tab(self, n, preset_levels=False):
-        return _swigfaiss.HNSW_prepare_level_tab(self, n, preset_levels)
-
-    @staticmethod
-    def shrink_neighbor_list(qdis, input, output, max_size):
-        return _swigfaiss.HNSW_shrink_neighbor_list(qdis, input, output, max_size)
-    __swig_destroy__ = _swigfaiss.delete_HNSW
-
-# Register HNSW in _swigfaiss:
-_swigfaiss.HNSW_swigregister(HNSW)
-class HNSWStats(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    n1 = property(_swigfaiss.HNSWStats_n1_get, _swigfaiss.HNSWStats_n1_set)
-    n2 = property(_swigfaiss.HNSWStats_n2_get, _swigfaiss.HNSWStats_n2_set)
-    n3 = property(_swigfaiss.HNSWStats_n3_get, _swigfaiss.HNSWStats_n3_set)
-    ndis = property(_swigfaiss.HNSWStats_ndis_get, _swigfaiss.HNSWStats_ndis_set)
-    nreorder = property(_swigfaiss.HNSWStats_nreorder_get, _swigfaiss.HNSWStats_nreorder_set)
-
-    def __init__(self, n1=0, n2=0, n3=0, ndis=0, nreorder=0):
-        _swigfaiss.HNSWStats_swiginit(self, _swigfaiss.new_HNSWStats(n1, n2, n3, ndis, nreorder))
-
-    def reset(self):
-        return _swigfaiss.HNSWStats_reset(self)
-
-    def combine(self, other):
-        return _swigfaiss.HNSWStats_combine(self, other)
-    __swig_destroy__ = _swigfaiss.delete_HNSWStats
-
-# Register HNSWStats in _swigfaiss:
-_swigfaiss.HNSWStats_swigregister(HNSWStats)
-class ReconstructFromNeighbors(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    index = property(_swigfaiss.ReconstructFromNeighbors_index_get)
-    M = property(_swigfaiss.ReconstructFromNeighbors_M_get, _swigfaiss.ReconstructFromNeighbors_M_set)
-    k = property(_swigfaiss.ReconstructFromNeighbors_k_get, _swigfaiss.ReconstructFromNeighbors_k_set)
-    nsq = property(_swigfaiss.ReconstructFromNeighbors_nsq_get, _swigfaiss.ReconstructFromNeighbors_nsq_set)
-    code_size = property(_swigfaiss.ReconstructFromNeighbors_code_size_get, _swigfaiss.ReconstructFromNeighbors_code_size_set)
-    k_reorder = property(_swigfaiss.ReconstructFromNeighbors_k_reorder_get, _swigfaiss.ReconstructFromNeighbors_k_reorder_set)
-    codebook = property(_swigfaiss.ReconstructFromNeighbors_codebook_get, _swigfaiss.ReconstructFromNeighbors_codebook_set)
-    codes = property(_swigfaiss.ReconstructFromNeighbors_codes_get, _swigfaiss.ReconstructFromNeighbors_codes_set)
-    ntotal = property(_swigfaiss.ReconstructFromNeighbors_ntotal_get, _swigfaiss.ReconstructFromNeighbors_ntotal_set)
-    d = property(_swigfaiss.ReconstructFromNeighbors_d_get, _swigfaiss.ReconstructFromNeighbors_d_set)
-    dsub = property(_swigfaiss.ReconstructFromNeighbors_dsub_get, _swigfaiss.ReconstructFromNeighbors_dsub_set)
-
-    def __init__(self, index, k=256, nsq=1):
-        _swigfaiss.ReconstructFromNeighbors_swiginit(self, _swigfaiss.new_ReconstructFromNeighbors(index, k, nsq))
-
-    def add_codes(self, n, x):
-        r"""
-        codes must be added in the correct order and the IndexHNSW
-        must be populated and sorted
-        """
-        return _swigfaiss.ReconstructFromNeighbors_add_codes(self, n, x)
-
-    def compute_distances(self, n, shortlist, query, distances):
-        return _swigfaiss.ReconstructFromNeighbors_compute_distances(self, n, shortlist, query, distances)
-
-    def estimate_code(self, x, i, code):
-        r""" called by add_codes"""
-        return _swigfaiss.ReconstructFromNeighbors_estimate_code(self, x, i, code)
-
-    def reconstruct(self, i, x, tmp):
-        r""" called by compute_distances"""
-        return _swigfaiss.ReconstructFromNeighbors_reconstruct(self, i, x, tmp)
-
-    def reconstruct_n(self, n0, ni, x):
-        return _swigfaiss.ReconstructFromNeighbors_reconstruct_n(self, n0, ni, x)
-
-    def get_neighbor_table(self, i, out):
-        r""" get the M+1 -by-d table for neighbor coordinates for vector i"""
-        return _swigfaiss.ReconstructFromNeighbors_get_neighbor_table(self, i, out)
-    __swig_destroy__ = _swigfaiss.delete_ReconstructFromNeighbors
-
-# Register ReconstructFromNeighbors in _swigfaiss:
-_swigfaiss.ReconstructFromNeighbors_swigregister(ReconstructFromNeighbors)
-class IndexHNSW(Index):
-    r"""
-     The HNSW index is a normal random-access index with a HNSW
-    link structure built on top
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    hnsw = property(_swigfaiss.IndexHNSW_hnsw_get, _swigfaiss.IndexHNSW_hnsw_set)
-    own_fields = property(_swigfaiss.IndexHNSW_own_fields_get, _swigfaiss.IndexHNSW_own_fields_set)
-    storage = property(_swigfaiss.IndexHNSW_storage_get, _swigfaiss.IndexHNSW_storage_set)
-    reconstruct_from_neighbors = property(_swigfaiss.IndexHNSW_reconstruct_from_neighbors_get, _swigfaiss.IndexHNSW_reconstruct_from_neighbors_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexHNSW_swiginit(self, _swigfaiss.new_IndexHNSW(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexHNSW
-
-    def add(self, n, x):
-        return _swigfaiss.IndexHNSW_add(self, n, x)
-
-    def train(self, n, x):
-        r""" Trains the storage if needed"""
-        return _swigfaiss.IndexHNSW_train(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        r""" entry point for search"""
-        return _swigfaiss.IndexHNSW_search(self, n, x, k, distances, labels, params)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexHNSW_reconstruct(self, key, recons)
-
-    def reset(self):
-        return _swigfaiss.IndexHNSW_reset(self)
-
-    def shrink_level_0_neighbors(self, size):
-        return _swigfaiss.IndexHNSW_shrink_level_0_neighbors(self, size)
-
-    def search_level_0(self, n, x, k, nearest, nearest_d, distances, labels, nprobe=1, search_type=1):
-        r"""
-         Perform search only on level 0, given the starting points for
-        each vertex.
-
-        :type search_type: int, optional
-        :param search_type: 1:perform one search per nprobe, 2: enqueue
-                               all entry points
-        """
-        return _swigfaiss.IndexHNSW_search_level_0(self, n, x, k, nearest, nearest_d, distances, labels, nprobe, search_type)
-
-    def init_level_0_from_knngraph(self, k, D, I):
-        r""" alternative graph building"""
-        return _swigfaiss.IndexHNSW_init_level_0_from_knngraph(self, k, D, I)
-
-    def init_level_0_from_entry_points(self, npt, points, nearests):
-        r""" alternative graph building"""
-        return _swigfaiss.IndexHNSW_init_level_0_from_entry_points(self, npt, points, nearests)
-
-    def reorder_links(self):
-        return _swigfaiss.IndexHNSW_reorder_links(self)
-
-    def link_singletons(self):
-        return _swigfaiss.IndexHNSW_link_singletons(self)
-
-# Register IndexHNSW in _swigfaiss:
-_swigfaiss.IndexHNSW_swigregister(IndexHNSW)
-class IndexHNSWFlat(IndexHNSW):
-    r"""
-    Flat index topped with with a HNSW structure to access elements
-    more efficiently.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.IndexHNSWFlat_swiginit(self, _swigfaiss.new_IndexHNSWFlat(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexHNSWFlat
-
-# Register IndexHNSWFlat in _swigfaiss:
-_swigfaiss.IndexHNSWFlat_swigregister(IndexHNSWFlat)
-class IndexHNSWPQ(IndexHNSW):
-    r"""
-    PQ index topped with with a HNSW structure to access elements
-    more efficiently.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.IndexHNSWPQ_swiginit(self, _swigfaiss.new_IndexHNSWPQ(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexHNSWPQ_train(self, n, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexHNSWPQ
-
-# Register IndexHNSWPQ in _swigfaiss:
-_swigfaiss.IndexHNSWPQ_swigregister(IndexHNSWPQ)
-class IndexHNSWSQ(IndexHNSW):
-    r"""
-    SQ index topped with with a HNSW structure to access elements
-    more efficiently.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.IndexHNSWSQ_swiginit(self, _swigfaiss.new_IndexHNSWSQ(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexHNSWSQ
-
-# Register IndexHNSWSQ in _swigfaiss:
-_swigfaiss.IndexHNSWSQ_swigregister(IndexHNSWSQ)
-class IndexHNSW2Level(IndexHNSW):
-    r""" 2-level code structure with fast random access"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.IndexHNSW2Level_swiginit(self, _swigfaiss.new_IndexHNSW2Level(*args))
-
-    def flip_to_ivf(self):
-        return _swigfaiss.IndexHNSW2Level_flip_to_ivf(self)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        r""" entry point for search"""
-        return _swigfaiss.IndexHNSW2Level_search(self, n, x, k, distances, labels, params)
-    __swig_destroy__ = _swigfaiss.delete_IndexHNSW2Level
-
-# Register IndexHNSW2Level in _swigfaiss:
-_swigfaiss.IndexHNSW2Level_swigregister(IndexHNSW2Level)
-
-def smawk(nrows, ncols, x, argmins):
-    r"""
-     SMAWK algorithm. Find the row minima of a monotone matrix.
-
-    Expose this for testing.
-
-    :type nrows: int
-    :param nrows:    number of rows
-    :type ncols: int
-    :param ncols:    number of columns
-    :type x: float
-    :param x:        input matrix, size (nrows, ncols)
-    :type argmins: int
-    :param argmins:  argmin of each row
-    """
-    return _swigfaiss.smawk(nrows, ncols, x, argmins)
-
-def kmeans1d(x, n, nclusters, centroids):
-    r"""
-     Exact 1D K-Means by dynamic programming
-
-    From  "Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D"
-    Allan Grønlund, Kasper Green Larsen, Alexander Mathiasen, Jesper Sindahl
-    Nielsen, Stefan Schneider, Mingzhou Song, ArXiV'17
-
-    Section 2.2
-
-    https://arxiv.org/abs/1701.07204
-
-    :type x: float
-    :param x:          input 1D array
-    :type n: int
-    :param n:          input array length
-    :type nclusters: int
-    :param nclusters:  number of clusters
-    :type centroids: float
-    :param centroids:  output centroids, size nclusters
-    :rtype: float
-    :return: imbalancce factor
-    """
-    return _swigfaiss.kmeans1d(x, n, nclusters, centroids)
-class Neighbor(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    id = property(_swigfaiss.Neighbor_id_get, _swigfaiss.Neighbor_id_set)
-    distance = property(_swigfaiss.Neighbor_distance_get, _swigfaiss.Neighbor_distance_set)
-    flag = property(_swigfaiss.Neighbor_flag_get, _swigfaiss.Neighbor_flag_set)
-
-    def __init__(self, *args):
-        _swigfaiss.Neighbor_swiginit(self, _swigfaiss.new_Neighbor(*args))
-
-    def __lt__(self, other):
-        return _swigfaiss.Neighbor___lt__(self, other)
-    __swig_destroy__ = _swigfaiss.delete_Neighbor
-
-# Register Neighbor in _swigfaiss:
-_swigfaiss.Neighbor_swigregister(Neighbor)
-class Nhood(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    pool = property(_swigfaiss.Nhood_pool_get, _swigfaiss.Nhood_pool_set)
-    M = property(_swigfaiss.Nhood_M_get, _swigfaiss.Nhood_M_set)
-    nn_old = property(_swigfaiss.Nhood_nn_old_get, _swigfaiss.Nhood_nn_old_set)
-    nn_new = property(_swigfaiss.Nhood_nn_new_get, _swigfaiss.Nhood_nn_new_set)
-    rnn_old = property(_swigfaiss.Nhood_rnn_old_get, _swigfaiss.Nhood_rnn_old_set)
-    rnn_new = property(_swigfaiss.Nhood_rnn_new_get, _swigfaiss.Nhood_rnn_new_set)
-
-    def __init__(self, *args):
-        _swigfaiss.Nhood_swiginit(self, _swigfaiss.new_Nhood(*args))
-
-    def insert(self, id, dist):
-        return _swigfaiss.Nhood_insert(self, id, dist)
-    __swig_destroy__ = _swigfaiss.delete_Nhood
-
-# Register Nhood in _swigfaiss:
-_swigfaiss.Nhood_swigregister(Nhood)
-class NNDescent(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, d, K):
-        _swigfaiss.NNDescent_swiginit(self, _swigfaiss.new_NNDescent(d, K))
-    __swig_destroy__ = _swigfaiss.delete_NNDescent
-
-    def build(self, qdis, n, verbose):
-        return _swigfaiss.NNDescent_build(self, qdis, n, verbose)
-
-    def search(self, qdis, topk, indices, dists, vt):
-        return _swigfaiss.NNDescent_search(self, qdis, topk, indices, dists, vt)
-
-    def reset(self):
-        return _swigfaiss.NNDescent_reset(self)
-
-    def init_graph(self, qdis):
-        r""" Initialize the KNN graph randomly"""
-        return _swigfaiss.NNDescent_init_graph(self, qdis)
-
-    def nndescent(self, qdis, verbose):
-        r""" Perform NNDescent algorithm"""
-        return _swigfaiss.NNDescent_nndescent(self, qdis, verbose)
-
-    def join(self, qdis):
-        r""" Perform local join on each node"""
-        return _swigfaiss.NNDescent_join(self, qdis)
-
-    def update(self):
-        r""" Sample new neighbors for each node to peform local join later"""
-        return _swigfaiss.NNDescent_update(self)
-
-    def generate_eval_set(self, qdis, c, v, N):
-        r""" Sample a small number of points to evaluate the quality of KNNG built"""
-        return _swigfaiss.NNDescent_generate_eval_set(self, qdis, c, v, N)
-
-    def eval_recall(self, ctrl_points, acc_eval_set):
-        r""" Evaluate the quality of KNNG built"""
-        return _swigfaiss.NNDescent_eval_recall(self, ctrl_points, acc_eval_set)
-    has_built = property(_swigfaiss.NNDescent_has_built_get, _swigfaiss.NNDescent_has_built_set)
-    S = property(_swigfaiss.NNDescent_S_get, _swigfaiss.NNDescent_S_set)
-    R = property(_swigfaiss.NNDescent_R_get, _swigfaiss.NNDescent_R_set)
-    iter = property(_swigfaiss.NNDescent_iter_get, _swigfaiss.NNDescent_iter_set)
-    search_L = property(_swigfaiss.NNDescent_search_L_get, _swigfaiss.NNDescent_search_L_set)
-    random_seed = property(_swigfaiss.NNDescent_random_seed_get, _swigfaiss.NNDescent_random_seed_set)
-    K = property(_swigfaiss.NNDescent_K_get, _swigfaiss.NNDescent_K_set)
-    d = property(_swigfaiss.NNDescent_d_get, _swigfaiss.NNDescent_d_set)
-    L = property(_swigfaiss.NNDescent_L_get, _swigfaiss.NNDescent_L_set)
-    ntotal = property(_swigfaiss.NNDescent_ntotal_get, _swigfaiss.NNDescent_ntotal_set)
-    graph = property(_swigfaiss.NNDescent_graph_get, _swigfaiss.NNDescent_graph_set)
-    final_graph = property(_swigfaiss.NNDescent_final_graph_get, _swigfaiss.NNDescent_final_graph_set)
-
-# Register NNDescent in _swigfaiss:
-_swigfaiss.NNDescent_swigregister(NNDescent)
-class IndexNNDescent(Index):
-    r"""
-     The NNDescent index is a normal random-access index with an NNDescent
-    link structure built on top
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nndescent = property(_swigfaiss.IndexNNDescent_nndescent_get, _swigfaiss.IndexNNDescent_nndescent_set, doc=r""" Faiss results are 64-bit""")
-    own_fields = property(_swigfaiss.IndexNNDescent_own_fields_get, _swigfaiss.IndexNNDescent_own_fields_set)
-    storage = property(_swigfaiss.IndexNNDescent_storage_get, _swigfaiss.IndexNNDescent_storage_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexNNDescent_swiginit(self, _swigfaiss.new_IndexNNDescent(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexNNDescent
-
-    def add(self, n, x):
-        return _swigfaiss.IndexNNDescent_add(self, n, x)
-
-    def train(self, n, x):
-        r""" Trains the storage if needed"""
-        return _swigfaiss.IndexNNDescent_train(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        r""" entry point for search"""
-        return _swigfaiss.IndexNNDescent_search(self, n, x, k, distances, labels, params)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexNNDescent_reconstruct(self, key, recons)
-
-    def reset(self):
-        return _swigfaiss.IndexNNDescent_reset(self)
-
-# Register IndexNNDescent in _swigfaiss:
-_swigfaiss.IndexNNDescent_swigregister(IndexNNDescent)
-class IndexNNDescentFlat(IndexNNDescent):
-    r"""
-    Flat index topped with with a NNDescent structure to access elements
-    more efficiently.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.IndexNNDescentFlat_swiginit(self, _swigfaiss.new_IndexNNDescentFlat(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexNNDescentFlat
-
-# Register IndexNNDescentFlat in _swigfaiss:
-_swigfaiss.IndexNNDescentFlat_swigregister(IndexNNDescentFlat)
-class IndexIVFFlat(IndexIVF):
-    r"""
-     Inverted file with stored vectors. Here the inverted file
-    pre-selects the vectors to be searched, but they are not otherwise
-    encoded, the code array just contains the raw float entries.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def add_core(self, n, x, xids, precomputed_idx):
-        return _swigfaiss.IndexIVFFlat_add_core(self, n, x, xids, precomputed_idx)
-
-    def encode_vectors(self, n, x, list_nos, codes, include_listnos=False):
-        return _swigfaiss.IndexIVFFlat_encode_vectors(self, n, x, list_nos, codes, include_listnos)
-
-    def get_InvertedListScanner(self, store_pairs, sel):
-        return _swigfaiss.IndexIVFFlat_get_InvertedListScanner(self, store_pairs, sel)
-
-    def reconstruct_from_offset(self, list_no, offset, recons):
-        return _swigfaiss.IndexIVFFlat_reconstruct_from_offset(self, list_no, offset, recons)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexIVFFlat_sa_decode(self, n, bytes, x)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFFlat_swiginit(self, _swigfaiss.new_IndexIVFFlat(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFFlat
-
-# Register IndexIVFFlat in _swigfaiss:
-_swigfaiss.IndexIVFFlat_swigregister(IndexIVFFlat)
-class IndexIVFFlatDedup(IndexIVFFlat):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    instances = property(_swigfaiss.IndexIVFFlatDedup_instances_get, _swigfaiss.IndexIVFFlatDedup_instances_set, doc=r"""
-    Maps ids stored in the index to the ids of vectors that are
-    the same. When a vector is unique, it does not appear in the
-    instances map
-    """)
-
-    def train(self, n, x):
-        r""" also dedups the training set"""
-        return _swigfaiss.IndexIVFFlatDedup_train(self, n, x)
-
-    def add_with_ids(self, n, x, xids):
-        r""" implemented for all IndexIVF* classes"""
-        return _swigfaiss.IndexIVFFlatDedup_add_with_ids(self, n, x, xids)
-
-    def search_preassigned(self, n, x, k, assign, centroid_dis, distances, labels, store_pairs, params=None, stats=None):
-        return _swigfaiss.IndexIVFFlatDedup_search_preassigned(self, n, x, k, assign, centroid_dis, distances, labels, store_pairs, params, stats)
-
-    def remove_ids(self, sel):
-        return _swigfaiss.IndexIVFFlatDedup_remove_ids(self, sel)
-
-    def range_search(self, n, x, radius, result, params=None):
-        r""" not implemented"""
-        return _swigfaiss.IndexIVFFlatDedup_range_search(self, n, x, radius, result, params)
-
-    def update_vectors(self, nv, idx, v):
-        r""" not implemented"""
-        return _swigfaiss.IndexIVFFlatDedup_update_vectors(self, nv, idx, v)
-
-    def reconstruct_from_offset(self, list_no, offset, recons):
-        r""" not implemented"""
-        return _swigfaiss.IndexIVFFlatDedup_reconstruct_from_offset(self, list_no, offset, recons)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFFlatDedup_swiginit(self, _swigfaiss.new_IndexIVFFlatDedup(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFFlatDedup
-
-# Register IndexIVFFlatDedup in _swigfaiss:
-_swigfaiss.IndexIVFFlatDedup_swigregister(IndexIVFFlatDedup)
-
-def storage_distance_computer(storage):
-    return _swigfaiss.storage_distance_computer(storage)
-class NSG(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    ntotal = property(_swigfaiss.NSG_ntotal_get, _swigfaiss.NSG_ntotal_set, doc=r""" nb of nodes""")
-    R = property(_swigfaiss.NSG_R_get, _swigfaiss.NSG_R_set, doc=r""" nb of neighbors per node""")
-    L = property(_swigfaiss.NSG_L_get, _swigfaiss.NSG_L_set, doc=r""" length of the search path at construction time""")
-    C = property(_swigfaiss.NSG_C_get, _swigfaiss.NSG_C_set, doc=r""" candidate pool size at construction time""")
-    search_L = property(_swigfaiss.NSG_search_L_get, _swigfaiss.NSG_search_L_set, doc=r""" length of the search path""")
-    enterpoint = property(_swigfaiss.NSG_enterpoint_get, _swigfaiss.NSG_enterpoint_set, doc=r""" enterpoint""")
-    final_graph = property(_swigfaiss.NSG_final_graph_get, _swigfaiss.NSG_final_graph_set, doc=r""" NSG graph structure""")
-    is_built = property(_swigfaiss.NSG_is_built_get, _swigfaiss.NSG_is_built_set, doc=r""" NSG is built or not""")
-    rng = property(_swigfaiss.NSG_rng_get, _swigfaiss.NSG_rng_set, doc=r""" random generator""")
-
-    def __init__(self, R=32):
-        _swigfaiss.NSG_swiginit(self, _swigfaiss.new_NSG(R))
-
-    def build(self, storage, n, knn_graph, verbose):
-        return _swigfaiss.NSG_build(self, storage, n, knn_graph, verbose)
-
-    def reset(self):
-        return _swigfaiss.NSG_reset(self)
-
-    def search(self, dis, k, I, D, vt):
-        return _swigfaiss.NSG_search(self, dis, k, I, D, vt)
-
-    def init_graph(self, storage, knn_graph):
-        return _swigfaiss.NSG_init_graph(self, storage, knn_graph)
-
-    def add_reverse_links(self, q, locks, dis, graph):
-        return _swigfaiss.NSG_add_reverse_links(self, q, locks, dis, graph)
-
-    def sync_prune(self, q, pool, dis, vt, knn_graph, graph):
-        return _swigfaiss.NSG_sync_prune(self, q, pool, dis, vt, knn_graph, graph)
-
-    def link(self, storage, knn_graph, graph, verbose):
-        return _swigfaiss.NSG_link(self, storage, knn_graph, graph, verbose)
-
-    def tree_grow(self, storage, degrees):
-        return _swigfaiss.NSG_tree_grow(self, storage, degrees)
-
-    def dfs(self, vt, root, cnt):
-        return _swigfaiss.NSG_dfs(self, vt, root, cnt)
-
-    def attach_unlinked(self, storage, vt, vt2, degrees):
-        return _swigfaiss.NSG_attach_unlinked(self, storage, vt, vt2, degrees)
-
-    def check_graph(self):
-        return _swigfaiss.NSG_check_graph(self)
-    __swig_destroy__ = _swigfaiss.delete_NSG
-
-# Register NSG in _swigfaiss:
-_swigfaiss.NSG_swigregister(NSG)
-class IndexNSG(Index):
-    r"""
-     The NSG index is a normal random-access index with a NSG
-    link structure built on top
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nsg = property(_swigfaiss.IndexNSG_nsg_get, _swigfaiss.IndexNSG_nsg_set, doc=r""" the link strcuture""")
-    own_fields = property(_swigfaiss.IndexNSG_own_fields_get, _swigfaiss.IndexNSG_own_fields_set, doc=r""" the sequential storage""")
-    storage = property(_swigfaiss.IndexNSG_storage_get, _swigfaiss.IndexNSG_storage_set)
-    is_built = property(_swigfaiss.IndexNSG_is_built_get, _swigfaiss.IndexNSG_is_built_set, doc=r""" the index is built or not""")
-    GK = property(_swigfaiss.IndexNSG_GK_get, _swigfaiss.IndexNSG_GK_set, doc=r""" K of KNN graph for building""")
-    build_type = property(_swigfaiss.IndexNSG_build_type_get, _swigfaiss.IndexNSG_build_type_set, doc=r"""
-    indicate how to build a knn graph
-    - 0: build NSG with brute force search
-    - 1: build NSG with NNDescent
-    """)
-    nndescent_S = property(_swigfaiss.IndexNSG_nndescent_S_get, _swigfaiss.IndexNSG_nndescent_S_set, doc=r""" parameters for nndescent""")
-    nndescent_R = property(_swigfaiss.IndexNSG_nndescent_R_get, _swigfaiss.IndexNSG_nndescent_R_set)
-    nndescent_L = property(_swigfaiss.IndexNSG_nndescent_L_get, _swigfaiss.IndexNSG_nndescent_L_set)
-    nndescent_iter = property(_swigfaiss.IndexNSG_nndescent_iter_get, _swigfaiss.IndexNSG_nndescent_iter_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexNSG_swiginit(self, _swigfaiss.new_IndexNSG(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexNSG
-
-    def build(self, n, x, knn_graph, GK):
-        return _swigfaiss.IndexNSG_build(self, n, x, knn_graph, GK)
-
-    def add(self, n, x):
-        return _swigfaiss.IndexNSG_add(self, n, x)
-
-    def train(self, n, x):
-        r""" Trains the storage if needed"""
-        return _swigfaiss.IndexNSG_train(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        r""" entry point for search"""
-        return _swigfaiss.IndexNSG_search(self, n, x, k, distances, labels, params)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexNSG_reconstruct(self, key, recons)
-
-    def reset(self):
-        return _swigfaiss.IndexNSG_reset(self)
-
-    def check_knn_graph(self, knn_graph, n, K):
-        return _swigfaiss.IndexNSG_check_knn_graph(self, knn_graph, n, K)
-
-# Register IndexNSG in _swigfaiss:
-_swigfaiss.IndexNSG_swigregister(IndexNSG)
-class IndexNSGFlat(IndexNSG):
-    r"""
-    Flat index topped with with a NSG structure to access elements
-    more efficiently.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.IndexNSGFlat_swiginit(self, _swigfaiss.new_IndexNSGFlat(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexNSGFlat
-
-# Register IndexNSGFlat in _swigfaiss:
-_swigfaiss.IndexNSGFlat_swigregister(IndexNSGFlat)
-class IndexNSGPQ(IndexNSG):
-    r"""
-    PQ index topped with with a NSG structure to access elements
-    more efficiently.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.IndexNSGPQ_swiginit(self, _swigfaiss.new_IndexNSGPQ(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexNSGPQ_train(self, n, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexNSGPQ
-
-# Register IndexNSGPQ in _swigfaiss:
-_swigfaiss.IndexNSGPQ_swigregister(IndexNSGPQ)
-class IndexNSGSQ(IndexNSG):
-    r"""
-    SQ index topped with with a NSG structure to access elements
-    more efficiently.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.IndexNSGSQ_swiginit(self, _swigfaiss.new_IndexNSGSQ(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexNSGSQ
-
-# Register IndexNSGSQ in _swigfaiss:
-_swigfaiss.IndexNSGSQ_swigregister(IndexNSGSQ)
-class OnDiskOneList(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    size = property(_swigfaiss.OnDiskOneList_size_get, _swigfaiss.OnDiskOneList_size_set)
-    capacity = property(_swigfaiss.OnDiskOneList_capacity_get, _swigfaiss.OnDiskOneList_capacity_set)
-    offset = property(_swigfaiss.OnDiskOneList_offset_get, _swigfaiss.OnDiskOneList_offset_set)
-
-    def __init__(self):
-        _swigfaiss.OnDiskOneList_swiginit(self, _swigfaiss.new_OnDiskOneList())
-    __swig_destroy__ = _swigfaiss.delete_OnDiskOneList
-
-# Register OnDiskOneList in _swigfaiss:
-_swigfaiss.OnDiskOneList_swigregister(OnDiskOneList)
-class OnDiskInvertedLists(InvertedLists):
-    r"""
-     On-disk storage of inverted lists.
-
-    The data is stored in a mmapped chunk of memory (base pointer ptr,
-    size totsize). Each list is a range of memory that contains (object
-    List) that contains:
-
-    - uint8_t codes[capacity * code_size]
-    - followed by idx_t ids[capacity]
-
-    in each of the arrays, the size <= capacity first elements are
-    used, the rest is not initialized.
-
-    Addition and resize are supported by:
-    - roundind up the capacity of the lists to a power of two
-    - maintaining a list of empty slots, sorted by size.
-    - resizing the mmapped block is adjusted as needed.
-
-    An OnDiskInvertedLists is compact if the size == capacity for all
-    lists and there are no available slots.
-
-    Addition to the invlists is slow. For incremental add it is better
-    to use a default ArrayInvertedLists object and convert it to an
-    OnDisk with merge_from.
-
-    When it is known that a set of lists will be accessed, it is useful
-    to call prefetch_lists, that launches a set of threads to read the
-    lists in parallel.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    lists = property(_swigfaiss.OnDiskInvertedLists_lists_get, _swigfaiss.OnDiskInvertedLists_lists_set)
-    slots = property(_swigfaiss.OnDiskInvertedLists_slots_get, _swigfaiss.OnDiskInvertedLists_slots_set)
-    filename = property(_swigfaiss.OnDiskInvertedLists_filename_get, _swigfaiss.OnDiskInvertedLists_filename_set)
-    totsize = property(_swigfaiss.OnDiskInvertedLists_totsize_get, _swigfaiss.OnDiskInvertedLists_totsize_set)
-    ptr = property(_swigfaiss.OnDiskInvertedLists_ptr_get, _swigfaiss.OnDiskInvertedLists_ptr_set)
-    read_only = property(_swigfaiss.OnDiskInvertedLists_read_only_get, _swigfaiss.OnDiskInvertedLists_read_only_set)
-
-    def list_size(self, list_no):
-        return _swigfaiss.OnDiskInvertedLists_list_size(self, list_no)
-
-    def get_codes(self, list_no):
-        return _swigfaiss.OnDiskInvertedLists_get_codes(self, list_no)
-
-    def get_ids(self, list_no):
-        return _swigfaiss.OnDiskInvertedLists_get_ids(self, list_no)
-
-    def add_entries(self, list_no, n_entry, ids, code):
-        return _swigfaiss.OnDiskInvertedLists_add_entries(self, list_no, n_entry, ids, code)
-
-    def update_entries(self, list_no, offset, n_entry, ids, code):
-        return _swigfaiss.OnDiskInvertedLists_update_entries(self, list_no, offset, n_entry, ids, code)
-
-    def resize(self, list_no, new_size):
-        return _swigfaiss.OnDiskInvertedLists_resize(self, list_no, new_size)
-
-    def merge_from(self, ils, n_il, verbose=False):
-        return _swigfaiss.OnDiskInvertedLists_merge_from(self, ils, n_il, verbose)
-
-    def merge_from_1(self, il, verbose=False):
-        r""" same as merge_from for a single invlist"""
-        return _swigfaiss.OnDiskInvertedLists_merge_from_1(self, il, verbose)
-
-    def crop_invlists(self, l0, l1):
-        r""" restrict the inverted lists to l0:l1 without touching the mmapped region"""
-        return _swigfaiss.OnDiskInvertedLists_crop_invlists(self, l0, l1)
-
-    def prefetch_lists(self, list_nos, nlist):
-        return _swigfaiss.OnDiskInvertedLists_prefetch_lists(self, list_nos, nlist)
-    __swig_destroy__ = _swigfaiss.delete_OnDiskInvertedLists
-    locks = property(_swigfaiss.OnDiskInvertedLists_locks_get, _swigfaiss.OnDiskInvertedLists_locks_set)
-    pf = property(_swigfaiss.OnDiskInvertedLists_pf_get, _swigfaiss.OnDiskInvertedLists_pf_set)
-    prefetch_nthread = property(_swigfaiss.OnDiskInvertedLists_prefetch_nthread_get, _swigfaiss.OnDiskInvertedLists_prefetch_nthread_set)
-
-    def do_mmap(self):
-        return _swigfaiss.OnDiskInvertedLists_do_mmap(self)
-
-    def update_totsize(self, new_totsize):
-        return _swigfaiss.OnDiskInvertedLists_update_totsize(self, new_totsize)
-
-    def resize_locked(self, list_no, new_size):
-        return _swigfaiss.OnDiskInvertedLists_resize_locked(self, list_no, new_size)
-
-    def allocate_slot(self, capacity):
-        return _swigfaiss.OnDiskInvertedLists_allocate_slot(self, capacity)
-
-    def free_slot(self, offset, capacity):
-        return _swigfaiss.OnDiskInvertedLists_free_slot(self, offset, capacity)
-
-    def set_all_lists_sizes(self, sizes):
-        r""" override all list sizes and make a packed storage"""
-        return _swigfaiss.OnDiskInvertedLists_set_all_lists_sizes(self, sizes)
-
-    def __init__(self, *args):
-        _swigfaiss.OnDiskInvertedLists_swiginit(self, _swigfaiss.new_OnDiskInvertedLists(*args))
-
-# Register OnDiskInvertedLists in _swigfaiss:
-_swigfaiss.OnDiskInvertedLists_swigregister(OnDiskInvertedLists)
-class ZnSphereSearch(object):
-    r"""
-     returns the nearest vertex in the sphere to a query. Returns only
-    the coordinates, not an id.
-
-    Algorithm: all points are derived from a one atom vector up to a
-    permutation and sign changes. The search function finds the most
-    appropriate atom and transformation.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    dimS = property(_swigfaiss.ZnSphereSearch_dimS_get, _swigfaiss.ZnSphereSearch_dimS_set)
-    r2 = property(_swigfaiss.ZnSphereSearch_r2_get, _swigfaiss.ZnSphereSearch_r2_set)
-    natom = property(_swigfaiss.ZnSphereSearch_natom_get, _swigfaiss.ZnSphereSearch_natom_set)
-    voc = property(_swigfaiss.ZnSphereSearch_voc_get, _swigfaiss.ZnSphereSearch_voc_set, doc=r""" size dim * ntatom""")
-
-    def __init__(self, dim, r2):
-        _swigfaiss.ZnSphereSearch_swiginit(self, _swigfaiss.new_ZnSphereSearch(dim, r2))
-
-    def search(self, *args):
-        r"""
-        *Overload 1:*
-        find nearest centroid. x does not need to be normalized
-
-        |
-
-        *Overload 2:*
-        full call. Requires externally-allocated temp space
-
-        |
-
-        *Overload 3:*
-        full call. Requires externally-allocated temp space
-        """
-        return _swigfaiss.ZnSphereSearch_search(self, *args)
-
-    def search_multi(self, n, x, c_out, dp_out):
-        return _swigfaiss.ZnSphereSearch_search_multi(self, n, x, c_out, dp_out)
-    __swig_destroy__ = _swigfaiss.delete_ZnSphereSearch
-
-# Register ZnSphereSearch in _swigfaiss:
-_swigfaiss.ZnSphereSearch_swigregister(ZnSphereSearch)
-class EnumeratedVectors(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    nv = property(_swigfaiss.EnumeratedVectors_nv_get, _swigfaiss.EnumeratedVectors_nv_set, doc=r""" size of the collection""")
-    dim = property(_swigfaiss.EnumeratedVectors_dim_get, _swigfaiss.EnumeratedVectors_dim_set)
-
-    def encode(self, x):
-        r""" encode a vector from a collection"""
-        return _swigfaiss.EnumeratedVectors_encode(self, x)
-
-    def decode(self, code, c):
-        r""" decode it"""
-        return _swigfaiss.EnumeratedVectors_decode(self, code, c)
-
-    def encode_multi(self, nc, c, codes):
-        return _swigfaiss.EnumeratedVectors_encode_multi(self, nc, c, codes)
-
-    def decode_multi(self, nc, codes, c):
-        return _swigfaiss.EnumeratedVectors_decode_multi(self, nc, codes, c)
-
-    def find_nn(self, n, codes, nq, xq, idx, dis):
-        return _swigfaiss.EnumeratedVectors_find_nn(self, n, codes, nq, xq, idx, dis)
-    __swig_destroy__ = _swigfaiss.delete_EnumeratedVectors
-
-# Register EnumeratedVectors in _swigfaiss:
-_swigfaiss.EnumeratedVectors_swigregister(EnumeratedVectors)
-class Repeat(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    val = property(_swigfaiss.Repeat_val_get, _swigfaiss.Repeat_val_set)
-    n = property(_swigfaiss.Repeat_n_get, _swigfaiss.Repeat_n_set)
-
-    def __init__(self):
-        _swigfaiss.Repeat_swiginit(self, _swigfaiss.new_Repeat())
-    __swig_destroy__ = _swigfaiss.delete_Repeat
-
-# Register Repeat in _swigfaiss:
-_swigfaiss.Repeat_swigregister(Repeat)
-class Repeats(object):
-    r"""
-    Repeats: used to encode a vector that has n occurrences of
-    val. Encodes the signs and permutation of the vector. Useful for
-    atoms.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    dim = property(_swigfaiss.Repeats_dim_get, _swigfaiss.Repeats_dim_set)
-    repeats = property(_swigfaiss.Repeats_repeats_get, _swigfaiss.Repeats_repeats_set)
-
-    def __init__(self, dim=0, c=None):
-        _swigfaiss.Repeats_swiginit(self, _swigfaiss.new_Repeats(dim, c))
-
-    def count(self):
-        return _swigfaiss.Repeats_count(self)
-
-    def encode(self, c):
-        return _swigfaiss.Repeats_encode(self, c)
-
-    def decode(self, code, c):
-        return _swigfaiss.Repeats_decode(self, code, c)
-    __swig_destroy__ = _swigfaiss.delete_Repeats
-
-# Register Repeats in _swigfaiss:
-_swigfaiss.Repeats_swigregister(Repeats)
-class ZnSphereCodec(ZnSphereSearch, EnumeratedVectors):
-    r"""
-     codec that can return ids for the encoded vectors
-
-    uses the ZnSphereSearch to encode the vector by encoding the
-    permutation and signs. Depends on ZnSphereSearch because it uses
-    the atom numbers
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    code_segments = property(_swigfaiss.ZnSphereCodec_code_segments_get, _swigfaiss.ZnSphereCodec_code_segments_set)
-    nv = property(_swigfaiss.ZnSphereCodec_nv_get, _swigfaiss.ZnSphereCodec_nv_set)
-    code_size = property(_swigfaiss.ZnSphereCodec_code_size_get, _swigfaiss.ZnSphereCodec_code_size_set)
-
-    def __init__(self, dim, r2):
-        _swigfaiss.ZnSphereCodec_swiginit(self, _swigfaiss.new_ZnSphereCodec(dim, r2))
-
-    def search_and_encode(self, x):
-        return _swigfaiss.ZnSphereCodec_search_and_encode(self, x)
-
-    def decode(self, code, c):
-        return _swigfaiss.ZnSphereCodec_decode(self, code, c)
-
-    def encode(self, x):
-        r""" takes vectors that do not need to be centroids"""
-        return _swigfaiss.ZnSphereCodec_encode(self, x)
-    __swig_destroy__ = _swigfaiss.delete_ZnSphereCodec
-
-# Register ZnSphereCodec in _swigfaiss:
-_swigfaiss.ZnSphereCodec_swigregister(ZnSphereCodec)
-class ZnSphereCodecRec(EnumeratedVectors):
-    r"""
-     recursive sphere codec
-
-    Uses a recursive decomposition on the dimensions to encode
-    centroids found by the ZnSphereSearch. The codes are *not*
-    compatible with the ones of ZnSpehreCodec
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    r2 = property(_swigfaiss.ZnSphereCodecRec_r2_get, _swigfaiss.ZnSphereCodecRec_r2_set)
-    log2_dim = property(_swigfaiss.ZnSphereCodecRec_log2_dim_get, _swigfaiss.ZnSphereCodecRec_log2_dim_set)
-    code_size = property(_swigfaiss.ZnSphereCodecRec_code_size_get, _swigfaiss.ZnSphereCodecRec_code_size_set)
-
-    def __init__(self, dim, r2):
-        _swigfaiss.ZnSphereCodecRec_swiginit(self, _swigfaiss.new_ZnSphereCodecRec(dim, r2))
-
-    def encode_centroid(self, c):
-        return _swigfaiss.ZnSphereCodecRec_encode_centroid(self, c)
-
-    def decode(self, code, c):
-        return _swigfaiss.ZnSphereCodecRec_decode(self, code, c)
-
-    def encode(self, x):
-        r"""
-        vectors need to be centroids (does not work on arbitrary
-        vectors)
-        """
-        return _swigfaiss.ZnSphereCodecRec_encode(self, x)
-    all_nv = property(_swigfaiss.ZnSphereCodecRec_all_nv_get, _swigfaiss.ZnSphereCodecRec_all_nv_set)
-    all_nv_cum = property(_swigfaiss.ZnSphereCodecRec_all_nv_cum_get, _swigfaiss.ZnSphereCodecRec_all_nv_cum_set)
-    decode_cache_ld = property(_swigfaiss.ZnSphereCodecRec_decode_cache_ld_get, _swigfaiss.ZnSphereCodecRec_decode_cache_ld_set)
-    decode_cache = property(_swigfaiss.ZnSphereCodecRec_decode_cache_get, _swigfaiss.ZnSphereCodecRec_decode_cache_set)
-
-    def get_nv(self, ld, r2a):
-        return _swigfaiss.ZnSphereCodecRec_get_nv(self, ld, r2a)
-
-    def get_nv_cum(self, ld, r2t, r2a):
-        return _swigfaiss.ZnSphereCodecRec_get_nv_cum(self, ld, r2t, r2a)
-
-    def set_nv_cum(self, ld, r2t, r2a, v):
-        return _swigfaiss.ZnSphereCodecRec_set_nv_cum(self, ld, r2t, r2a, v)
-    __swig_destroy__ = _swigfaiss.delete_ZnSphereCodecRec
-
-# Register ZnSphereCodecRec in _swigfaiss:
-_swigfaiss.ZnSphereCodecRec_swigregister(ZnSphereCodecRec)
-class ZnSphereCodecAlt(ZnSphereCodec):
-    r"""
-     Codec that uses the recursive codec if dim is a power of 2 and
-    the regular one otherwise
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    use_rec = property(_swigfaiss.ZnSphereCodecAlt_use_rec_get, _swigfaiss.ZnSphereCodecAlt_use_rec_set)
-    znc_rec = property(_swigfaiss.ZnSphereCodecAlt_znc_rec_get, _swigfaiss.ZnSphereCodecAlt_znc_rec_set)
-
-    def __init__(self, dim, r2):
-        _swigfaiss.ZnSphereCodecAlt_swiginit(self, _swigfaiss.new_ZnSphereCodecAlt(dim, r2))
-
-    def encode(self, x):
-        return _swigfaiss.ZnSphereCodecAlt_encode(self, x)
-
-    def decode(self, code, c):
-        return _swigfaiss.ZnSphereCodecAlt_decode(self, code, c)
-    __swig_destroy__ = _swigfaiss.delete_ZnSphereCodecAlt
-
-# Register ZnSphereCodecAlt in _swigfaiss:
-_swigfaiss.ZnSphereCodecAlt_swigregister(ZnSphereCodecAlt)
-class IndexLattice(Index):
-    r""" Index that encodes a vector with a series of Zn lattice quantizers"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nsq = property(_swigfaiss.IndexLattice_nsq_get, _swigfaiss.IndexLattice_nsq_set, doc=r""" number of sub-vectors""")
-    dsq = property(_swigfaiss.IndexLattice_dsq_get, _swigfaiss.IndexLattice_dsq_set, doc=r""" dimension of sub-vectors""")
-    zn_sphere_codec = property(_swigfaiss.IndexLattice_zn_sphere_codec_get, _swigfaiss.IndexLattice_zn_sphere_codec_set, doc=r""" the lattice quantizer""")
-    scale_nbit = property(_swigfaiss.IndexLattice_scale_nbit_get, _swigfaiss.IndexLattice_scale_nbit_set, doc=r""" nb bits used to encode the scale, per subvector""")
-    lattice_nbit = property(_swigfaiss.IndexLattice_lattice_nbit_get, _swigfaiss.IndexLattice_lattice_nbit_set)
-    code_size = property(_swigfaiss.IndexLattice_code_size_get, _swigfaiss.IndexLattice_code_size_set, doc=r""" total, in bytes""")
-    trained = property(_swigfaiss.IndexLattice_trained_get, _swigfaiss.IndexLattice_trained_set, doc=r""" mins and maxes of the vector norms, per subquantizer""")
-
-    def __init__(self, d, nsq, scale_nbit, r2):
-        _swigfaiss.IndexLattice_swiginit(self, _swigfaiss.new_IndexLattice(d, nsq, scale_nbit, r2))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexLattice_train(self, n, x)
-
-    def sa_code_size(self):
-        return _swigfaiss.IndexLattice_sa_code_size(self)
-
-    def sa_encode(self, n, x, bytes):
-        return _swigfaiss.IndexLattice_sa_encode(self, n, x, bytes)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexLattice_sa_decode(self, n, bytes, x)
-
-    def add(self, n, x):
-        r""" not implemented"""
-        return _swigfaiss.IndexLattice_add(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexLattice_search(self, n, x, k, distances, labels, params)
-
-    def reset(self):
-        return _swigfaiss.IndexLattice_reset(self)
-    __swig_destroy__ = _swigfaiss.delete_IndexLattice
-
-# Register IndexLattice in _swigfaiss:
-_swigfaiss.IndexLattice_swigregister(IndexLattice)
-class IVFPQSearchParameters(SearchParametersIVF):
-    r"""
-    Copyright (c) Facebook, Inc. and its affiliates.
-
-    This source code is licensed under the MIT license found in the
-    LICENSE file in the root directory of this source tree.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    scan_table_threshold = property(_swigfaiss.IVFPQSearchParameters_scan_table_threshold_get, _swigfaiss.IVFPQSearchParameters_scan_table_threshold_set, doc=r""" use table computation or on-the-fly?""")
-    polysemous_ht = property(_swigfaiss.IVFPQSearchParameters_polysemous_ht_get, _swigfaiss.IVFPQSearchParameters_polysemous_ht_set, doc=r""" Hamming thresh for polysemous filtering""")
-
-    def __init__(self):
-        _swigfaiss.IVFPQSearchParameters_swiginit(self, _swigfaiss.new_IVFPQSearchParameters())
-    __swig_destroy__ = _swigfaiss.delete_IVFPQSearchParameters
-
-# Register IVFPQSearchParameters in _swigfaiss:
-_swigfaiss.IVFPQSearchParameters_swigregister(IVFPQSearchParameters)
-class IndexIVFPQ(IndexIVF):
-    r"""
-     Inverted file with Product Quantizer encoding. Each residual
-    vector is encoded as a product quantizer code.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    by_residual = property(_swigfaiss.IndexIVFPQ_by_residual_get, _swigfaiss.IndexIVFPQ_by_residual_set, doc=r""" Encode residual or plain vector?""")
-    pq = property(_swigfaiss.IndexIVFPQ_pq_get, _swigfaiss.IndexIVFPQ_pq_set, doc=r""" produces the codes""")
-    do_polysemous_training = property(_swigfaiss.IndexIVFPQ_do_polysemous_training_get, _swigfaiss.IndexIVFPQ_do_polysemous_training_set, doc=r""" reorder PQ centroids after training?""")
-    polysemous_training = property(_swigfaiss.IndexIVFPQ_polysemous_training_get, _swigfaiss.IndexIVFPQ_polysemous_training_set, doc=r""" if NULL, use default""")
-    scan_table_threshold = property(_swigfaiss.IndexIVFPQ_scan_table_threshold_get, _swigfaiss.IndexIVFPQ_scan_table_threshold_set, doc=r""" use table computation or on-the-fly?""")
-    polysemous_ht = property(_swigfaiss.IndexIVFPQ_polysemous_ht_get, _swigfaiss.IndexIVFPQ_polysemous_ht_set, doc=r""" Hamming thresh for polysemous filtering""")
-    use_precomputed_table = property(_swigfaiss.IndexIVFPQ_use_precomputed_table_get, _swigfaiss.IndexIVFPQ_use_precomputed_table_set, doc=r"""
-     Precompute table that speed up query preprocessing at some
-    memory cost (used only for by_residual with L2 metric)
-    """)
-    precomputed_table = property(_swigfaiss.IndexIVFPQ_precomputed_table_get, _swigfaiss.IndexIVFPQ_precomputed_table_set, doc=r"""
-    if use_precompute_table
-    size nlist * pq.M * pq.ksub
-    """)
-
-    def encode_vectors(self, n, x, list_nos, codes, include_listnos=False):
-        return _swigfaiss.IndexIVFPQ_encode_vectors(self, n, x, list_nos, codes, include_listnos)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexIVFPQ_sa_decode(self, n, bytes, x)
-
-    def add_core(self, n, x, xids, precomputed_idx):
-        return _swigfaiss.IndexIVFPQ_add_core(self, n, x, xids, precomputed_idx)
-
-    def add_core_o(self, n, x, xids, residuals_2, precomputed_idx=None):
-        r"""
-        same as add_core, also:
-        - output 2nd level residuals if residuals_2 != NULL
-        - accepts precomputed_idx = nullptr
-        """
-        return _swigfaiss.IndexIVFPQ_add_core_o(self, n, x, xids, residuals_2, precomputed_idx)
-
-    def train_residual(self, n, x):
-        r""" trains the product quantizer"""
-        return _swigfaiss.IndexIVFPQ_train_residual(self, n, x)
-
-    def train_residual_o(self, n, x, residuals_2):
-        r""" same as train_residual, also output 2nd level residuals"""
-        return _swigfaiss.IndexIVFPQ_train_residual_o(self, n, x, residuals_2)
-
-    def reconstruct_from_offset(self, list_no, offset, recons):
-        return _swigfaiss.IndexIVFPQ_reconstruct_from_offset(self, list_no, offset, recons)
-
-    def find_duplicates(self, ids, lims):
-        r"""
-         Find exact duplicates in the dataset.
-
-        the duplicates are returned in pre-allocated arrays (see the
-        max sizes).
-
-        :type lims: int
-        :param lims:   limits between groups of duplicates
-                           (max size ntotal / 2 + 1)
-        :type ids: int
-        :param ids:    ids[lims[i]] : ids[lims[i+1]-1] is a group of
-                           duplicates (max size ntotal)
-        :rtype: int
-        :return: n      number of groups found
-        """
-        return _swigfaiss.IndexIVFPQ_find_duplicates(self, ids, lims)
-
-    def encode(self, key, x, code):
-        return _swigfaiss.IndexIVFPQ_encode(self, key, x, code)
-
-    def encode_multiple(self, n, keys, x, codes, compute_keys=False):
-        r"""
-         Encode multiple vectors
-
-        :type n: int
-        :param n:       nb vectors to encode
-        :type keys: int
-        :param keys:    posting list ids for those vectors (size n)
-        :type x: float
-        :param x:       vectors (size n * d)
-        :type codes: uint8_t
-        :param codes:   output codes (size n * code_size)
-        :type compute_keys: boolean, optional
-        :param compute_keys:  if false, assume keys are precomputed,
-                                 otherwise compute them
-        """
-        return _swigfaiss.IndexIVFPQ_encode_multiple(self, n, keys, x, codes, compute_keys)
-
-    def decode_multiple(self, n, keys, xcodes, x):
-        r""" inverse of encode_multiple"""
-        return _swigfaiss.IndexIVFPQ_decode_multiple(self, n, keys, xcodes, x)
-
-    def get_InvertedListScanner(self, store_pairs, sel):
-        return _swigfaiss.IndexIVFPQ_get_InvertedListScanner(self, store_pairs, sel)
-
-    def precompute_table(self):
-        r""" build precomputed table"""
-        return _swigfaiss.IndexIVFPQ_precompute_table(self)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFPQ_swiginit(self, _swigfaiss.new_IndexIVFPQ(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFPQ
-
-# Register IndexIVFPQ in _swigfaiss:
-_swigfaiss.IndexIVFPQ_swigregister(IndexIVFPQ)
-
-def initialize_IVFPQ_precomputed_table(use_precomputed_table, quantizer, pq, precomputed_table, by_residual, verbose):
-    r"""
-     Pre-compute distance tables for IVFPQ with by-residual and METRIC_L2
-
-    :type use_precomputed_table: int
-    :param use_precomputed_table: (I/O)
-               =-1: force disable
-               =0: decide heuristically (default: use tables only if they are
-                   < precomputed_tables_max_bytes), set use_precomputed_table on
-        output =1: tables that work for all quantizers (size 256 * nlist * M) =2:
-        specific version for MultiIndexQuantizer (much more compact)
-    :type precomputed_table: faiss::AlignedTable< float,32 >
-    :param precomputed_table: precomputed table to initialize
-    """
-    return _swigfaiss.initialize_IVFPQ_precomputed_table(use_precomputed_table, quantizer, pq, precomputed_table, by_residual, verbose)
-class IndexIVFPQStats(object):
-    r"""
-    statistics are robust to internal threading, but not if
-    IndexIVFPQ::search_preassigned is called by multiple threads
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nrefine = property(_swigfaiss.IndexIVFPQStats_nrefine_get, _swigfaiss.IndexIVFPQStats_nrefine_set, doc=r""" nb of refines (IVFPQR)""")
-    n_hamming_pass = property(_swigfaiss.IndexIVFPQStats_n_hamming_pass_get, _swigfaiss.IndexIVFPQStats_n_hamming_pass_set, doc=r""" nb of passed Hamming distance tests (for polysemous)""")
-    search_cycles = property(_swigfaiss.IndexIVFPQStats_search_cycles_get, _swigfaiss.IndexIVFPQStats_search_cycles_set)
-    refine_cycles = property(_swigfaiss.IndexIVFPQStats_refine_cycles_get, _swigfaiss.IndexIVFPQStats_refine_cycles_set, doc=r""" only for IVFPQR""")
-
-    def __init__(self):
-        _swigfaiss.IndexIVFPQStats_swiginit(self, _swigfaiss.new_IndexIVFPQStats())
-
-    def reset(self):
-        return _swigfaiss.IndexIVFPQStats_reset(self)
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFPQStats
-
-# Register IndexIVFPQStats in _swigfaiss:
-_swigfaiss.IndexIVFPQStats_swigregister(IndexIVFPQStats)
-class IndexIVFPQR(IndexIVFPQ):
-    r""" Index with an additional level of PQ refinement"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    refine_pq = property(_swigfaiss.IndexIVFPQR_refine_pq_get, _swigfaiss.IndexIVFPQR_refine_pq_set, doc=r""" 3rd level quantizer""")
-    refine_codes = property(_swigfaiss.IndexIVFPQR_refine_codes_get, _swigfaiss.IndexIVFPQR_refine_codes_set, doc=r""" corresponding codes""")
-    k_factor = property(_swigfaiss.IndexIVFPQR_k_factor_get, _swigfaiss.IndexIVFPQR_k_factor_set, doc=r""" factor between k requested in search and the k requested from the IVFPQ""")
-
-    def reset(self):
-        return _swigfaiss.IndexIVFPQR_reset(self)
-
-    def remove_ids(self, sel):
-        return _swigfaiss.IndexIVFPQR_remove_ids(self, sel)
-
-    def train_residual(self, n, x):
-        r""" trains the two product quantizers"""
-        return _swigfaiss.IndexIVFPQR_train_residual(self, n, x)
-
-    def add_with_ids(self, n, x, xids):
-        return _swigfaiss.IndexIVFPQR_add_with_ids(self, n, x, xids)
-
-    def add_core(self, n, x, xids, precomputed_idx):
-        r""" same as add_with_ids, but optionally use the precomputed list ids"""
-        return _swigfaiss.IndexIVFPQR_add_core(self, n, x, xids, precomputed_idx)
-
-    def reconstruct_from_offset(self, list_no, offset, recons):
-        return _swigfaiss.IndexIVFPQR_reconstruct_from_offset(self, list_no, offset, recons)
-
-    def merge_from(self, otherIndex, add_id):
-        return _swigfaiss.IndexIVFPQR_merge_from(self, otherIndex, add_id)
-
-    def search_preassigned(self, n, x, k, assign, centroid_dis, distances, labels, store_pairs, params=None, stats=None):
-        return _swigfaiss.IndexIVFPQR_search_preassigned(self, n, x, k, assign, centroid_dis, distances, labels, store_pairs, params, stats)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFPQR_swiginit(self, _swigfaiss.new_IndexIVFPQR(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFPQR
-
-# Register IndexIVFPQR in _swigfaiss:
-_swigfaiss.IndexIVFPQR_swigregister(IndexIVFPQR)
-class Index2Layer(IndexFlatCodes):
-    r"""
-     Same as an IndexIVFPQ without the inverted lists: codes are stored
-    sequentially
-
-    The class is mainly inteded to store encoded vectors that can be
-    accessed randomly, the search function is not implemented.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    q1 = property(_swigfaiss.Index2Layer_q1_get, _swigfaiss.Index2Layer_q1_set, doc=r""" first level quantizer""")
-    pq = property(_swigfaiss.Index2Layer_pq_get, _swigfaiss.Index2Layer_pq_set, doc=r""" second level quantizer is always a PQ""")
-    code_size_1 = property(_swigfaiss.Index2Layer_code_size_1_get, _swigfaiss.Index2Layer_code_size_1_set, doc=r""" size of the code for the first level (ceil(log8(q1.nlist)))""")
-    code_size_2 = property(_swigfaiss.Index2Layer_code_size_2_get, _swigfaiss.Index2Layer_code_size_2_set, doc=r""" size of the code for the second level""")
-
-    def __init__(self, *args):
-        _swigfaiss.Index2Layer_swiginit(self, _swigfaiss.new_Index2Layer(*args))
-    __swig_destroy__ = _swigfaiss.delete_Index2Layer
-
-    def train(self, n, x):
-        return _swigfaiss.Index2Layer_train(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        r""" not implemented"""
-        return _swigfaiss.Index2Layer_search(self, n, x, k, distances, labels, params)
-
-    def get_distance_computer(self):
-        return _swigfaiss.Index2Layer_get_distance_computer(self)
-
-    def transfer_to_IVFPQ(self, other):
-        r""" transfer the flat codes to an IVFPQ index"""
-        return _swigfaiss.Index2Layer_transfer_to_IVFPQ(self, other)
-
-    def sa_encode(self, n, x, bytes):
-        return _swigfaiss.Index2Layer_sa_encode(self, n, x, bytes)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.Index2Layer_sa_decode(self, n, bytes, x)
-
-# Register Index2Layer in _swigfaiss:
-_swigfaiss.Index2Layer_swigregister(Index2Layer)
-class IndexFastScan(Index):
-    r"""
-     Fast scan version of IndexPQ and IndexAQ. Works for 4-bit PQ and AQ for now.
-
-    The codes are not stored sequentially but grouped in blocks of size bbs.
-    This makes it possible to compute distances quickly with SIMD instructions.
-    The trailing codes (padding codes that are added to complete the last code)
-    are garbage.
-
-    Implementations:
-    12: blocked loop with internal loop on Q with qbs
-    13: same with reservoir accumulator to store results
-    14: no qbs with heap accumulator
-    15: no qbs with reservoir accumulator
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    implem = property(_swigfaiss.IndexFastScan_implem_get, _swigfaiss.IndexFastScan_implem_set)
-    skip = property(_swigfaiss.IndexFastScan_skip_get, _swigfaiss.IndexFastScan_skip_set)
-    bbs = property(_swigfaiss.IndexFastScan_bbs_get, _swigfaiss.IndexFastScan_bbs_set)
-    qbs = property(_swigfaiss.IndexFastScan_qbs_get, _swigfaiss.IndexFastScan_qbs_set)
-    M = property(_swigfaiss.IndexFastScan_M_get, _swigfaiss.IndexFastScan_M_set)
-    nbits = property(_swigfaiss.IndexFastScan_nbits_get, _swigfaiss.IndexFastScan_nbits_set)
-    ksub = property(_swigfaiss.IndexFastScan_ksub_get, _swigfaiss.IndexFastScan_ksub_set)
-    code_size = property(_swigfaiss.IndexFastScan_code_size_get, _swigfaiss.IndexFastScan_code_size_set)
-    ntotal2 = property(_swigfaiss.IndexFastScan_ntotal2_get, _swigfaiss.IndexFastScan_ntotal2_set)
-    M2 = property(_swigfaiss.IndexFastScan_M2_get, _swigfaiss.IndexFastScan_M2_set)
-    codes = property(_swigfaiss.IndexFastScan_codes_get, _swigfaiss.IndexFastScan_codes_set)
-    orig_codes = property(_swigfaiss.IndexFastScan_orig_codes_get, _swigfaiss.IndexFastScan_orig_codes_set)
-
-    def init_fastscan(self, d, M, nbits, metric, bbs):
-        return _swigfaiss.IndexFastScan_init_fastscan(self, d, M, nbits, metric, bbs)
-
-    def reset(self):
-        return _swigfaiss.IndexFastScan_reset(self)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexFastScan_search(self, n, x, k, distances, labels, params)
-
-    def add(self, n, x):
-        return _swigfaiss.IndexFastScan_add(self, n, x)
-
-    def compute_codes(self, codes, n, x):
-        return _swigfaiss.IndexFastScan_compute_codes(self, codes, n, x)
-
-    def compute_float_LUT(self, lut, n, x):
-        return _swigfaiss.IndexFastScan_compute_float_LUT(self, lut, n, x)
-
-    def compute_quantized_LUT(self, n, x, lut, normalizers):
-        return _swigfaiss.IndexFastScan_compute_quantized_LUT(self, n, x, lut, normalizers)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexFastScan_reconstruct(self, key, recons)
-
-    def remove_ids(self, sel):
-        return _swigfaiss.IndexFastScan_remove_ids(self, sel)
-
-    def get_CodePacker(self):
-        return _swigfaiss.IndexFastScan_get_CodePacker(self)
-
-    def merge_from(self, otherIndex, add_id=0):
-        return _swigfaiss.IndexFastScan_merge_from(self, otherIndex, add_id)
-
-    def check_compatible_for_merge(self, otherIndex):
-        return _swigfaiss.IndexFastScan_check_compatible_for_merge(self, otherIndex)
-    __swig_destroy__ = _swigfaiss.delete_IndexFastScan
-
-# Register IndexFastScan in _swigfaiss:
-_swigfaiss.IndexFastScan_swigregister(IndexFastScan)
-class FastScanStats(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    t0 = property(_swigfaiss.FastScanStats_t0_get, _swigfaiss.FastScanStats_t0_set)
-    t1 = property(_swigfaiss.FastScanStats_t1_get, _swigfaiss.FastScanStats_t1_set)
-    t2 = property(_swigfaiss.FastScanStats_t2_get, _swigfaiss.FastScanStats_t2_set)
-    t3 = property(_swigfaiss.FastScanStats_t3_get, _swigfaiss.FastScanStats_t3_set)
-
-    def __init__(self):
-        _swigfaiss.FastScanStats_swiginit(self, _swigfaiss.new_FastScanStats())
-
-    def reset(self):
-        return _swigfaiss.FastScanStats_reset(self)
-    __swig_destroy__ = _swigfaiss.delete_FastScanStats
-
-# Register FastScanStats in _swigfaiss:
-_swigfaiss.FastScanStats_swigregister(FastScanStats)
-class IndexAdditiveQuantizerFastScan(IndexFastScan):
-    r"""
-     Fast scan version of IndexAQ. Works for 4-bit AQ for now.
-
-    The codes are not stored sequentially but grouped in blocks of size bbs.
-    This makes it possible to compute distances quickly with SIMD instructions.
-
-    Implementations:
-    12: blocked loop with internal loop on Q with qbs
-    13: same with reservoir accumulator to store results
-    14: no qbs with heap accumulator
-    15: no qbs with reservoir accumulator
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    aq = property(_swigfaiss.IndexAdditiveQuantizerFastScan_aq_get, _swigfaiss.IndexAdditiveQuantizerFastScan_aq_set)
-    rescale_norm = property(_swigfaiss.IndexAdditiveQuantizerFastScan_rescale_norm_get, _swigfaiss.IndexAdditiveQuantizerFastScan_rescale_norm_set)
-    norm_scale = property(_swigfaiss.IndexAdditiveQuantizerFastScan_norm_scale_get, _swigfaiss.IndexAdditiveQuantizerFastScan_norm_scale_set)
-    max_train_points = property(_swigfaiss.IndexAdditiveQuantizerFastScan_max_train_points_get, _swigfaiss.IndexAdditiveQuantizerFastScan_max_train_points_set)
-
-    def init(self, *args):
-        return _swigfaiss.IndexAdditiveQuantizerFastScan_init(self, *args)
-    __swig_destroy__ = _swigfaiss.delete_IndexAdditiveQuantizerFastScan
-
-    def __init__(self, *args):
-        r"""
-        *Overload 1:*
-        build from an existing IndexAQ
-
-        |
-
-        *Overload 2:*
-        build from an existing IndexAQ
-        """
-        _swigfaiss.IndexAdditiveQuantizerFastScan_swiginit(self, _swigfaiss.new_IndexAdditiveQuantizerFastScan(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexAdditiveQuantizerFastScan_train(self, n, x)
-
-    def estimate_norm_scale(self, n, x):
-        return _swigfaiss.IndexAdditiveQuantizerFastScan_estimate_norm_scale(self, n, x)
-
-    def compute_codes(self, codes, n, x):
-        return _swigfaiss.IndexAdditiveQuantizerFastScan_compute_codes(self, codes, n, x)
-
-    def compute_float_LUT(self, lut, n, x):
-        return _swigfaiss.IndexAdditiveQuantizerFastScan_compute_float_LUT(self, lut, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexAdditiveQuantizerFastScan_search(self, n, x, k, distances, labels, params)
-
-    def sa_decode(self, n, bytes, x):
-        r"""
-         Decode a set of vectors.
-
-         NOTE: The codes in the IndexAdditiveQuantizerFastScan object are non-
-               contiguous. But this method requires a contiguous representation.
-
-        :type n: int
-        :param n:       number of vectors
-        :type bytes: uint8_t
-        :param bytes:   input encoded vectors, size n * code_size
-        :type x: float
-        :param x:       output vectors, size n * d
-        """
-        return _swigfaiss.IndexAdditiveQuantizerFastScan_sa_decode(self, n, bytes, x)
-
-# Register IndexAdditiveQuantizerFastScan in _swigfaiss:
-_swigfaiss.IndexAdditiveQuantizerFastScan_swigregister(IndexAdditiveQuantizerFastScan)
-class IndexResidualQuantizerFastScan(IndexAdditiveQuantizerFastScan):
-    r"""
-     Index based on a residual quantizer. Stored vectors are
-    approximated by residual quantization codes.
-    Can also be used as a codec
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    rq = property(_swigfaiss.IndexResidualQuantizerFastScan_rq_get, _swigfaiss.IndexResidualQuantizerFastScan_rq_set, doc=r""" The residual quantizer used to encode the vectors""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexResidualQuantizerFastScan_swiginit(self, _swigfaiss.new_IndexResidualQuantizerFastScan(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexResidualQuantizerFastScan
-
-# Register IndexResidualQuantizerFastScan in _swigfaiss:
-_swigfaiss.IndexResidualQuantizerFastScan_swigregister(IndexResidualQuantizerFastScan)
-class IndexLocalSearchQuantizerFastScan(IndexAdditiveQuantizerFastScan):
-    r"""
-     Index based on a local search quantizer. Stored vectors are
-    approximated by local search quantization codes.
-    Can also be used as a codec
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    lsq = property(_swigfaiss.IndexLocalSearchQuantizerFastScan_lsq_get, _swigfaiss.IndexLocalSearchQuantizerFastScan_lsq_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexLocalSearchQuantizerFastScan_swiginit(self, _swigfaiss.new_IndexLocalSearchQuantizerFastScan(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexLocalSearchQuantizerFastScan
-
-# Register IndexLocalSearchQuantizerFastScan in _swigfaiss:
-_swigfaiss.IndexLocalSearchQuantizerFastScan_swigregister(IndexLocalSearchQuantizerFastScan)
-class IndexProductResidualQuantizerFastScan(IndexAdditiveQuantizerFastScan):
-    r"""
-     Index based on a product residual quantizer. Stored vectors are
-    approximated by product residual quantization codes.
-    Can also be used as a codec
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    prq = property(_swigfaiss.IndexProductResidualQuantizerFastScan_prq_get, _swigfaiss.IndexProductResidualQuantizerFastScan_prq_set, doc=r""" The product residual quantizer used to encode the vectors""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexProductResidualQuantizerFastScan_swiginit(self, _swigfaiss.new_IndexProductResidualQuantizerFastScan(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexProductResidualQuantizerFastScan
-
-# Register IndexProductResidualQuantizerFastScan in _swigfaiss:
-_swigfaiss.IndexProductResidualQuantizerFastScan_swigregister(IndexProductResidualQuantizerFastScan)
-class IndexProductLocalSearchQuantizerFastScan(IndexAdditiveQuantizerFastScan):
-    r"""
-     Index based on a product local search quantizer. Stored vectors are
-    approximated by product local search quantization codes.
-    Can also be used as a codec
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    plsq = property(_swigfaiss.IndexProductLocalSearchQuantizerFastScan_plsq_get, _swigfaiss.IndexProductLocalSearchQuantizerFastScan_plsq_set, doc=r""" The product local search quantizer used to encode the vectors""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexProductLocalSearchQuantizerFastScan_swiginit(self, _swigfaiss.new_IndexProductLocalSearchQuantizerFastScan(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexProductLocalSearchQuantizerFastScan
-
-# Register IndexProductLocalSearchQuantizerFastScan in _swigfaiss:
-_swigfaiss.IndexProductLocalSearchQuantizerFastScan_swigregister(IndexProductLocalSearchQuantizerFastScan)
-class IndexPQFastScan(IndexFastScan):
-    r"""
-     Fast scan version of IndexPQ. Works for 4-bit PQ for now.
-
-    The codes are not stored sequentially but grouped in blocks of size bbs.
-    This makes it possible to compute distances quickly with SIMD instructions.
-
-    Implementations:
-    12: blocked loop with internal loop on Q with qbs
-    13: same with reservoir accumulator to store results
-    14: no qbs with heap accumulator
-    15: no qbs with reservoir accumulator
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    pq = property(_swigfaiss.IndexPQFastScan_pq_get, _swigfaiss.IndexPQFastScan_pq_set)
-
-    def __init__(self, *args):
-        r"""
-        *Overload 1:*
-        build from an existing IndexPQ
-
-        |
-
-        *Overload 2:*
-        build from an existing IndexPQ
-        """
-        _swigfaiss.IndexPQFastScan_swiginit(self, _swigfaiss.new_IndexPQFastScan(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexPQFastScan_train(self, n, x)
-
-    def compute_codes(self, codes, n, x):
-        return _swigfaiss.IndexPQFastScan_compute_codes(self, codes, n, x)
-
-    def compute_float_LUT(self, lut, n, x):
-        return _swigfaiss.IndexPQFastScan_compute_float_LUT(self, lut, n, x)
-
-    def sa_decode(self, n, bytes, x):
-        r"""
-         Decode a set of vectors.
-
-         NOTE: The codes in the IndexPQFastScan object are non-contiguous.
-               But this method requires a contiguous representation.
-
-        :type n: int
-        :param n:       number of vectors
-        :type bytes: uint8_t
-        :param bytes:   input encoded vectors, size n * code_size
-        :type x: float
-        :param x:       output vectors, size n * d
-        """
-        return _swigfaiss.IndexPQFastScan_sa_decode(self, n, bytes, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexPQFastScan
-
-# Register IndexPQFastScan in _swigfaiss:
-_swigfaiss.IndexPQFastScan_swigregister(IndexPQFastScan)
-class IndexIVFFastScan(IndexIVF):
-    r"""
-     Fast scan version of IVFPQ and IVFAQ. Works for 4-bit PQ/AQ for now.
-
-    The codes in the inverted lists are not stored sequentially but
-    grouped in blocks of size bbs. This makes it possible to very quickly
-    compute distances with SIMD instructions.
-
-    Implementations (implem):
-    0: auto-select implementation (default)
-    1: orig's search, re-implemented
-    2: orig's search, re-ordered by invlist
-    10: optimizer int16 search, collect results in heap, no qbs
-    11: idem, collect results in reservoir
-    12: optimizer int16 search, collect results in heap, uses qbs
-    13: idem, collect results in reservoir
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    bbs = property(_swigfaiss.IndexIVFFastScan_bbs_get, _swigfaiss.IndexIVFFastScan_bbs_set)
-    M = property(_swigfaiss.IndexIVFFastScan_M_get, _swigfaiss.IndexIVFFastScan_M_set)
-    nbits = property(_swigfaiss.IndexIVFFastScan_nbits_get, _swigfaiss.IndexIVFFastScan_nbits_set)
-    ksub = property(_swigfaiss.IndexIVFFastScan_ksub_get, _swigfaiss.IndexIVFFastScan_ksub_set)
-    M2 = property(_swigfaiss.IndexIVFFastScan_M2_get, _swigfaiss.IndexIVFFastScan_M2_set)
-    implem = property(_swigfaiss.IndexIVFFastScan_implem_get, _swigfaiss.IndexIVFFastScan_implem_set)
-    skip = property(_swigfaiss.IndexIVFFastScan_skip_get, _swigfaiss.IndexIVFFastScan_skip_set)
-    by_residual = property(_swigfaiss.IndexIVFFastScan_by_residual_get, _swigfaiss.IndexIVFFastScan_by_residual_set)
-    qbs = property(_swigfaiss.IndexIVFFastScan_qbs_get, _swigfaiss.IndexIVFFastScan_qbs_set)
-    qbs2 = property(_swigfaiss.IndexIVFFastScan_qbs2_get, _swigfaiss.IndexIVFFastScan_qbs2_set)
-
-    def init_fastscan(self, M, nbits, nlist, metric, bbs):
-        return _swigfaiss.IndexIVFFastScan_init_fastscan(self, M, nbits, nlist, metric, bbs)
-
-    def init_code_packer(self):
-        return _swigfaiss.IndexIVFFastScan_init_code_packer(self)
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFFastScan
-    orig_invlists = property(_swigfaiss.IndexIVFFastScan_orig_invlists_get, _swigfaiss.IndexIVFFastScan_orig_invlists_set, doc=r""" orig's inverted lists (for debugging)""")
-
-    def add_with_ids(self, n, x, xids):
-        return _swigfaiss.IndexIVFFastScan_add_with_ids(self, n, x, xids)
-
-    def lookup_table_is_3d(self):
-        return _swigfaiss.IndexIVFFastScan_lookup_table_is_3d(self)
-
-    def compute_LUT(self, n, x, coarse_ids, coarse_dis, dis_tables, biases):
-        return _swigfaiss.IndexIVFFastScan_compute_LUT(self, n, x, coarse_ids, coarse_dis, dis_tables, biases)
-
-    def compute_LUT_uint8(self, n, x, coarse_ids, coarse_dis, dis_tables, biases, normalizers):
-        return _swigfaiss.IndexIVFFastScan_compute_LUT_uint8(self, n, x, coarse_ids, coarse_dis, dis_tables, biases, normalizers)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexIVFFastScan_search(self, n, x, k, distances, labels, params)
-
-    def range_search(self, n, x, radius, result, params=None):
-        r""" will just fail"""
-        return _swigfaiss.IndexIVFFastScan_range_search(self, n, x, radius, result, params)
-
-    def reconstruct_from_offset(self, list_no, offset, recons):
-        return _swigfaiss.IndexIVFFastScan_reconstruct_from_offset(self, list_no, offset, recons)
-
-    def get_CodePacker(self):
-        return _swigfaiss.IndexIVFFastScan_get_CodePacker(self)
-
-    def reconstruct_orig_invlists(self):
-        return _swigfaiss.IndexIVFFastScan_reconstruct_orig_invlists(self)
-
-# Register IndexIVFFastScan in _swigfaiss:
-_swigfaiss.IndexIVFFastScan_swigregister(IndexIVFFastScan)
-class IVFFastScanStats(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    times = property(_swigfaiss.IVFFastScanStats_times_get, _swigfaiss.IVFFastScanStats_times_set)
-    t_compute_distance_tables = property(_swigfaiss.IVFFastScanStats_t_compute_distance_tables_get, _swigfaiss.IVFFastScanStats_t_compute_distance_tables_set)
-    t_round = property(_swigfaiss.IVFFastScanStats_t_round_get, _swigfaiss.IVFFastScanStats_t_round_set)
-    t_copy_pack = property(_swigfaiss.IVFFastScanStats_t_copy_pack_get, _swigfaiss.IVFFastScanStats_t_copy_pack_set)
-    t_scan = property(_swigfaiss.IVFFastScanStats_t_scan_get, _swigfaiss.IVFFastScanStats_t_scan_set)
-    t_to_flat = property(_swigfaiss.IVFFastScanStats_t_to_flat_get, _swigfaiss.IVFFastScanStats_t_to_flat_set)
-    reservoir_times = property(_swigfaiss.IVFFastScanStats_reservoir_times_get, _swigfaiss.IVFFastScanStats_reservoir_times_set)
-    t_aq_encode = property(_swigfaiss.IVFFastScanStats_t_aq_encode_get, _swigfaiss.IVFFastScanStats_t_aq_encode_set)
-    t_aq_norm_encode = property(_swigfaiss.IVFFastScanStats_t_aq_norm_encode_get, _swigfaiss.IVFFastScanStats_t_aq_norm_encode_set)
-
-    def Mcy_at(self, i):
-        return _swigfaiss.IVFFastScanStats_Mcy_at(self, i)
-
-    def Mcy_reservoir_at(self, i):
-        return _swigfaiss.IVFFastScanStats_Mcy_reservoir_at(self, i)
-
-    def __init__(self):
-        _swigfaiss.IVFFastScanStats_swiginit(self, _swigfaiss.new_IVFFastScanStats())
-
-    def reset(self):
-        return _swigfaiss.IVFFastScanStats_reset(self)
-    __swig_destroy__ = _swigfaiss.delete_IVFFastScanStats
-
-# Register IVFFastScanStats in _swigfaiss:
-_swigfaiss.IVFFastScanStats_swigregister(IVFFastScanStats)
-class IndexIVFAdditiveQuantizerFastScan(IndexIVFFastScan):
-    r"""
-     Fast scan version of IVFAQ. Works for 4-bit AQ for now.
-
-    The codes in the inverted lists are not stored sequentially but
-    grouped in blocks of size bbs. This makes it possible to very quickly
-    compute distances with SIMD instructions.
-
-    Implementations (implem):
-    0: auto-select implementation (default)
-    1: orig's search, re-implemented
-    2: orig's search, re-ordered by invlist
-    10: optimizer int16 search, collect results in heap, no qbs
-    11: idem, collect results in reservoir
-    12: optimizer int16 search, collect results in heap, uses qbs
-    13: idem, collect results in reservoir
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    aq = property(_swigfaiss.IndexIVFAdditiveQuantizerFastScan_aq_get, _swigfaiss.IndexIVFAdditiveQuantizerFastScan_aq_set)
-    rescale_norm = property(_swigfaiss.IndexIVFAdditiveQuantizerFastScan_rescale_norm_get, _swigfaiss.IndexIVFAdditiveQuantizerFastScan_rescale_norm_set)
-    norm_scale = property(_swigfaiss.IndexIVFAdditiveQuantizerFastScan_norm_scale_get, _swigfaiss.IndexIVFAdditiveQuantizerFastScan_norm_scale_set)
-    max_train_points = property(_swigfaiss.IndexIVFAdditiveQuantizerFastScan_max_train_points_get, _swigfaiss.IndexIVFAdditiveQuantizerFastScan_max_train_points_set)
-
-    def init(self, aq, nlist, metric, bbs):
-        return _swigfaiss.IndexIVFAdditiveQuantizerFastScan_init(self, aq, nlist, metric, bbs)
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFAdditiveQuantizerFastScan
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFAdditiveQuantizerFastScan_swiginit(self, _swigfaiss.new_IndexIVFAdditiveQuantizerFastScan(*args))
-
-    def train_residual(self, n, x):
-        return _swigfaiss.IndexIVFAdditiveQuantizerFastScan_train_residual(self, n, x)
-
-    def estimate_norm_scale(self, n, x):
-        return _swigfaiss.IndexIVFAdditiveQuantizerFastScan_estimate_norm_scale(self, n, x)
-
-    def encode_vectors(self, n, x, list_nos, codes, include_listno=False):
-        r"""
-        same as the regular IVFAQ encoder. The codes are not reorganized by
-        blocks a that point
-        """
-        return _swigfaiss.IndexIVFAdditiveQuantizerFastScan_encode_vectors(self, n, x, list_nos, codes, include_listno)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexIVFAdditiveQuantizerFastScan_search(self, n, x, k, distances, labels, params)
-
-    def lookup_table_is_3d(self):
-        return _swigfaiss.IndexIVFAdditiveQuantizerFastScan_lookup_table_is_3d(self)
-
-    def compute_LUT(self, n, x, coarse_ids, coarse_dis, dis_tables, biases):
-        return _swigfaiss.IndexIVFAdditiveQuantizerFastScan_compute_LUT(self, n, x, coarse_ids, coarse_dis, dis_tables, biases)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexIVFAdditiveQuantizerFastScan_sa_decode(self, n, bytes, x)
-
-# Register IndexIVFAdditiveQuantizerFastScan in _swigfaiss:
-_swigfaiss.IndexIVFAdditiveQuantizerFastScan_swigregister(IndexIVFAdditiveQuantizerFastScan)
-class IndexIVFLocalSearchQuantizerFastScan(IndexIVFAdditiveQuantizerFastScan):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    lsq = property(_swigfaiss.IndexIVFLocalSearchQuantizerFastScan_lsq_get, _swigfaiss.IndexIVFLocalSearchQuantizerFastScan_lsq_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFLocalSearchQuantizerFastScan_swiginit(self, _swigfaiss.new_IndexIVFLocalSearchQuantizerFastScan(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFLocalSearchQuantizerFastScan
-
-# Register IndexIVFLocalSearchQuantizerFastScan in _swigfaiss:
-_swigfaiss.IndexIVFLocalSearchQuantizerFastScan_swigregister(IndexIVFLocalSearchQuantizerFastScan)
-class IndexIVFResidualQuantizerFastScan(IndexIVFAdditiveQuantizerFastScan):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    rq = property(_swigfaiss.IndexIVFResidualQuantizerFastScan_rq_get, _swigfaiss.IndexIVFResidualQuantizerFastScan_rq_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFResidualQuantizerFastScan_swiginit(self, _swigfaiss.new_IndexIVFResidualQuantizerFastScan(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFResidualQuantizerFastScan
-
-# Register IndexIVFResidualQuantizerFastScan in _swigfaiss:
-_swigfaiss.IndexIVFResidualQuantizerFastScan_swigregister(IndexIVFResidualQuantizerFastScan)
-class IndexIVFProductLocalSearchQuantizerFastScan(IndexIVFAdditiveQuantizerFastScan):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    plsq = property(_swigfaiss.IndexIVFProductLocalSearchQuantizerFastScan_plsq_get, _swigfaiss.IndexIVFProductLocalSearchQuantizerFastScan_plsq_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFProductLocalSearchQuantizerFastScan_swiginit(self, _swigfaiss.new_IndexIVFProductLocalSearchQuantizerFastScan(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFProductLocalSearchQuantizerFastScan
-
-# Register IndexIVFProductLocalSearchQuantizerFastScan in _swigfaiss:
-_swigfaiss.IndexIVFProductLocalSearchQuantizerFastScan_swigregister(IndexIVFProductLocalSearchQuantizerFastScan)
-class IndexIVFProductResidualQuantizerFastScan(IndexIVFAdditiveQuantizerFastScan):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    prq = property(_swigfaiss.IndexIVFProductResidualQuantizerFastScan_prq_get, _swigfaiss.IndexIVFProductResidualQuantizerFastScan_prq_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFProductResidualQuantizerFastScan_swiginit(self, _swigfaiss.new_IndexIVFProductResidualQuantizerFastScan(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFProductResidualQuantizerFastScan
-
-# Register IndexIVFProductResidualQuantizerFastScan in _swigfaiss:
-_swigfaiss.IndexIVFProductResidualQuantizerFastScan_swigregister(IndexIVFProductResidualQuantizerFastScan)
-class IndexIVFPQFastScan(IndexIVFFastScan):
-    r"""
-     Fast scan version of IVFPQ. Works for 4-bit PQ for now.
-
-    The codes in the inverted lists are not stored sequentially but
-    grouped in blocks of size bbs. This makes it possible to very quickly
-    compute distances with SIMD instructions.
-
-    Implementations (implem):
-    0: auto-select implementation (default)
-    1: orig's search, re-implemented
-    2: orig's search, re-ordered by invlist
-    10: optimizer int16 search, collect results in heap, no qbs
-    11: idem, collect results in reservoir
-    12: optimizer int16 search, collect results in heap, uses qbs
-    13: idem, collect results in reservoir
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    pq = property(_swigfaiss.IndexIVFPQFastScan_pq_get, _swigfaiss.IndexIVFPQFastScan_pq_set, doc=r""" produces the codes""")
-    use_precomputed_table = property(_swigfaiss.IndexIVFPQFastScan_use_precomputed_table_get, _swigfaiss.IndexIVFPQFastScan_use_precomputed_table_set, doc=r""" precomputed tables management""")
-    precomputed_table = property(_swigfaiss.IndexIVFPQFastScan_precomputed_table_get, _swigfaiss.IndexIVFPQFastScan_precomputed_table_set, doc=r""" if use_precompute_table size (nlist, pq.M, pq.ksub)""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIVFPQFastScan_swiginit(self, _swigfaiss.new_IndexIVFPQFastScan(*args))
-
-    def train_residual(self, n, x):
-        return _swigfaiss.IndexIVFPQFastScan_train_residual(self, n, x)
-
-    def precompute_table(self):
-        r""" build precomputed table, possibly updating use_precomputed_table"""
-        return _swigfaiss.IndexIVFPQFastScan_precompute_table(self)
-
-    def encode_vectors(self, n, x, list_nos, codes, include_listno=False):
-        r"""
-        same as the regular IVFPQ encoder. The codes are not reorganized by
-        blocks a that point
-        """
-        return _swigfaiss.IndexIVFPQFastScan_encode_vectors(self, n, x, list_nos, codes, include_listno)
-
-    def lookup_table_is_3d(self):
-        return _swigfaiss.IndexIVFPQFastScan_lookup_table_is_3d(self)
-
-    def compute_LUT(self, n, x, coarse_ids, coarse_dis, dis_tables, biases):
-        return _swigfaiss.IndexIVFPQFastScan_compute_LUT(self, n, x, coarse_ids, coarse_dis, dis_tables, biases)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexIVFPQFastScan_sa_decode(self, n, bytes, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexIVFPQFastScan
-
-# Register IndexIVFPQFastScan in _swigfaiss:
-_swigfaiss.IndexIVFPQFastScan_swigregister(IndexIVFPQFastScan)
-
-def round_uint8_per_column(tab, n, d, a_out=None, b_out=None):
-    r"""
-     Functions to quantize PQ floating-point Look Up Tables (LUT) to uint8, and
-    biases to uint16. The accumulation is supposed to take place in uint16.
-    The quantization coefficients are float (a, b) such that
-
-         original_value = quantized_value * a / b
-
-    The hardest part of the quantization is with multiple LUTs that need to be
-    added up together. In that case, coefficient a has to be chosen so that
-    the sum fits in a uint16 accumulator.
-    """
-    return _swigfaiss.round_uint8_per_column(tab, n, d, a_out, b_out)
-
-def round_uint8_per_column_multi(tab, m, n, d, a_out=None, b_out=None):
-    return _swigfaiss.round_uint8_per_column_multi(tab, m, n, d, a_out, b_out)
-
-def quantize_LUT_and_bias(nprobe, M, ksub, lut_is_3d, LUT, bias, LUTq, M2, biasq, a_out=None, b_out=None):
-    r"""
-     LUT quantization to uint8 and bias to uint16.
-
-    (nprobe, M, ksub, lut_is_3d) determine the size of the the LUT
-
-     LUT input:
-     - 2D size (M, ksub): single matrix per probe (lut_is_3d=false)
-     - 3D size (nprobe, M, ksub): separate LUT per probe (lut_is_3d=true)
-     bias input:
-     - nullptr: bias is 0
-     - size (nprobe): one bias per probe
-     Output:
-     - LUTq uint8 version of the LUT (M size is rounded up to M2)
-     - biasq (or nullptr): uint16 version of the LUT
-     - a, b: scalars to approximate the true distance
-    """
-    return _swigfaiss.quantize_LUT_and_bias(nprobe, M, ksub, lut_is_3d, LUT, bias, LUTq, M2, biasq, a_out, b_out)
-
-def aq_quantize_LUT_and_bias(nprobe, M, ksub, LUT, bias, M_norm, norm_scale, LUTq, M2, biasq, a_out, b_out):
-    return _swigfaiss.aq_quantize_LUT_and_bias(nprobe, M, ksub, LUT, bias, M_norm, norm_scale, LUTq, M2, biasq, a_out, b_out)
-
-def aq_estimate_norm_scale(M, ksub, M_norm, LUT):
-    return _swigfaiss.aq_estimate_norm_scale(M, ksub, M_norm, LUT)
-class IndexBinary(object):
-    r"""
-     Abstract structure for a binary index.
-
-    Supports adding vertices and searching them.
-
-    All queries are symmetric because there is no distinction between codes and
-    vectors.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    d = property(_swigfaiss.IndexBinary_d_get, _swigfaiss.IndexBinary_d_set, doc=r""" vector dimension""")
-    code_size = property(_swigfaiss.IndexBinary_code_size_get, _swigfaiss.IndexBinary_code_size_set, doc=r""" number of bytes per vector ( = d / 8 )""")
-    ntotal = property(_swigfaiss.IndexBinary_ntotal_get, _swigfaiss.IndexBinary_ntotal_set, doc=r""" total nb of indexed vectors""")
-    verbose = property(_swigfaiss.IndexBinary_verbose_get, _swigfaiss.IndexBinary_verbose_set, doc=r""" verbosity level""")
-    is_trained = property(_swigfaiss.IndexBinary_is_trained_get, _swigfaiss.IndexBinary_is_trained_set, doc=r"""
-    set if the Index does not require training, or if training is done
-    already
-    """)
-    metric_type = property(_swigfaiss.IndexBinary_metric_type_get, _swigfaiss.IndexBinary_metric_type_set, doc=r""" type of metric this index uses for search""")
-    __swig_destroy__ = _swigfaiss.delete_IndexBinary
-
-    def train(self, n, x):
-        r"""
-         Perform training on a representative set of vectors.
-
-        :type n: int
-        :param n:      nb of training vectors
-        :type x: uint8_t
-        :param x:      training vecors, size n * d / 8
-        """
-        return _swigfaiss.IndexBinary_train(self, n, x)
-
-    def add(self, n, x):
-        r"""
-         Add n vectors of dimension d to the index.
-
-        Vectors are implicitly assigned labels ntotal .. ntotal + n - 1
-        :type x: uint8_t
-        :param x:      input matrix, size n * d / 8
-        """
-        return _swigfaiss.IndexBinary_add(self, n, x)
-
-    def add_with_ids(self, n, x, xids):
-        r"""
-         Same as add, but stores xids instead of sequential ids.
-
-        The default implementation fails with an assertion, as it is
-        not supported by all indexes.
-
-        :type xids: int
-        :param xids: if non-null, ids to store for the vectors (size n)
-        """
-        return _swigfaiss.IndexBinary_add_with_ids(self, n, x, xids)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        r"""
-         Query n vectors of dimension d to the index.
-
-        return at most k vectors. If there are not enough results for a
-        query, the result array is padded with -1s.
-
-        :type x: uint8_t
-        :param x:           input vectors to search, size n * d / 8
-        :type labels: int
-        :param labels:      output labels of the NNs, size n*k
-        :type distances: int
-        :param distances:   output pairwise distances, size n*k
-        """
-        return _swigfaiss.IndexBinary_search(self, n, x, k, distances, labels, params)
-
-    def range_search(self, n, x, radius, result, params=None):
-        r"""
-         Query n vectors of dimension d to the index.
-
-        return all vectors with distance < radius. Note that many indexes
-        do not implement the range_search (only the k-NN search is
-        mandatory). The distances are converted to float to reuse the
-        RangeSearchResult structure, but they are integer. By convention,
-        only distances < radius (strict comparison) are returned,
-        ie. radius = 0 does not return any result and 1 returns only
-        exact same vectors.
-
-        :type x: uint8_t
-        :param x:           input vectors to search, size n * d / 8
-        :type radius: int
-        :param radius:      search radius
-        :type result: :py:class:`RangeSearchResult`
-        :param result:      result table
-        """
-        return _swigfaiss.IndexBinary_range_search(self, n, x, radius, result, params)
-
-    def assign(self, n, x, labels, k=1):
-        r"""
-         Return the indexes of the k vectors closest to the query x.
-
-        This function is identical to search but only returns labels of
-        neighbors.
-        :type x: uint8_t
-        :param x:           input vectors to search, size n * d / 8
-        :type labels: int
-        :param labels:      output labels of the NNs, size n*k
-        """
-        return _swigfaiss.IndexBinary_assign(self, n, x, labels, k)
-
-    def reset(self):
-        r""" Removes all elements from the database."""
-        return _swigfaiss.IndexBinary_reset(self)
-
-    def remove_ids(self, sel):
-        r""" Removes IDs from the index. Not supported by all indexes."""
-        return _swigfaiss.IndexBinary_remove_ids(self, sel)
-
-    def reconstruct(self, key, recons):
-        r"""
-         Reconstruct a stored vector.
-
-        This function may not be defined for some indexes.
-        :type key: int
-        :param key:         id of the vector to reconstruct
-        :type recons: uint8_t
-        :param recons:      reconstucted vector (size d / 8)
-        """
-        return _swigfaiss.IndexBinary_reconstruct(self, key, recons)
-
-    def reconstruct_n(self, i0, ni, recons):
-        r"""
-         Reconstruct vectors i0 to i0 + ni - 1.
-
-        This function may not be defined for some indexes.
-        :type recons: uint8_t
-        :param recons:      reconstucted vectors (size ni * d / 8)
-        """
-        return _swigfaiss.IndexBinary_reconstruct_n(self, i0, ni, recons)
-
-    def search_and_reconstruct(self, n, x, k, distances, labels, recons, params=None):
-        r"""
-         Similar to search, but also reconstructs the stored vectors (or an
-        approximation in the case of lossy coding) for the search results.
-
-        If there are not enough results for a query, the resulting array
-        is padded with -1s.
-
-        :type recons: uint8_t
-        :param recons:      reconstructed vectors size (n, k, d)
-        """
-        return _swigfaiss.IndexBinary_search_and_reconstruct(self, n, x, k, distances, labels, recons, params)
-
-    def display(self):
-        r""" Display the actual class name and some more info."""
-        return _swigfaiss.IndexBinary_display(self)
-
-    def merge_from(self, otherIndex, add_id=0):
-        r"""
-         moves the entries from another dataset to self.
-        On output, other is empty.
-        add_id is added to all moved ids
-        (for sequential ids, this would be this->ntotal)
-        """
-        return _swigfaiss.IndexBinary_merge_from(self, otherIndex, add_id)
-
-    def check_compatible_for_merge(self, otherIndex):
-        r"""
-         check that the two indexes are compatible (ie, they are
-        trained in the same way and have the same
-        parameters). Otherwise throw.
-        """
-        return _swigfaiss.IndexBinary_check_compatible_for_merge(self, otherIndex)
-
-# Register IndexBinary in _swigfaiss:
-_swigfaiss.IndexBinary_swigregister(IndexBinary)
-class IndexBinaryFlat(IndexBinary):
-    r""" Index that stores the full vectors and performs exhaustive search."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    xb = property(_swigfaiss.IndexBinaryFlat_xb_get, _swigfaiss.IndexBinaryFlat_xb_set, doc=r""" database vectors, size ntotal * d / 8""")
-    use_heap = property(_swigfaiss.IndexBinaryFlat_use_heap_get, _swigfaiss.IndexBinaryFlat_use_heap_set, doc=r"""
-     Select between using a heap or counting to select the k smallest values
-    when scanning inverted lists.
-    """)
-    query_batch_size = property(_swigfaiss.IndexBinaryFlat_query_batch_size_get, _swigfaiss.IndexBinaryFlat_query_batch_size_set)
-    approx_topk_mode = property(_swigfaiss.IndexBinaryFlat_approx_topk_mode_get, _swigfaiss.IndexBinaryFlat_approx_topk_mode_set)
-
-    def add(self, n, x):
-        return _swigfaiss.IndexBinaryFlat_add(self, n, x)
-
-    def reset(self):
-        return _swigfaiss.IndexBinaryFlat_reset(self)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexBinaryFlat_search(self, n, x, k, distances, labels, params)
-
-    def range_search(self, n, x, radius, result, params=None):
-        return _swigfaiss.IndexBinaryFlat_range_search(self, n, x, radius, result, params)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexBinaryFlat_reconstruct(self, key, recons)
-
-    def remove_ids(self, sel):
-        r"""
-         Remove some ids. Note that because of the indexing structure,
-        the semantics of this operation are different from the usual ones:
-        the new ids are shifted.
-        """
-        return _swigfaiss.IndexBinaryFlat_remove_ids(self, sel)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexBinaryFlat_swiginit(self, _swigfaiss.new_IndexBinaryFlat(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexBinaryFlat
-
-# Register IndexBinaryFlat in _swigfaiss:
-_swigfaiss.IndexBinaryFlat_swigregister(IndexBinaryFlat)
-class IndexBinaryIVF(IndexBinary):
-    r"""
-     Index based on a inverted file (IVF)
-
-    In the inverted file, the quantizer (an IndexBinary instance) provides a
-    quantization index for each vector to be added. The quantization
-    index maps to a list (aka inverted list or posting list), where the
-    id of the vector is stored.
-
-    Otherwise the object is similar to the IndexIVF
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    invlists = property(_swigfaiss.IndexBinaryIVF_invlists_get, _swigfaiss.IndexBinaryIVF_invlists_set, doc=r""" Access to the actual data""")
-    own_invlists = property(_swigfaiss.IndexBinaryIVF_own_invlists_get, _swigfaiss.IndexBinaryIVF_own_invlists_set)
-    nprobe = property(_swigfaiss.IndexBinaryIVF_nprobe_get, _swigfaiss.IndexBinaryIVF_nprobe_set, doc=r""" number of probes at query time""")
-    max_codes = property(_swigfaiss.IndexBinaryIVF_max_codes_get, _swigfaiss.IndexBinaryIVF_max_codes_set, doc=r""" max nb of codes to visit to do a query""")
-    use_heap = property(_swigfaiss.IndexBinaryIVF_use_heap_get, _swigfaiss.IndexBinaryIVF_use_heap_set, doc=r"""
-     Select between using a heap or counting to select the k smallest values
-    when scanning inverted lists.
-    """)
-    per_invlist_search = property(_swigfaiss.IndexBinaryIVF_per_invlist_search_get, _swigfaiss.IndexBinaryIVF_per_invlist_search_set, doc=r""" collect computations per batch""")
-    direct_map = property(_swigfaiss.IndexBinaryIVF_direct_map_get, _swigfaiss.IndexBinaryIVF_direct_map_set, doc=r""" map for direct access to the elements. Enables reconstruct().""")
-    quantizer = property(_swigfaiss.IndexBinaryIVF_quantizer_get, _swigfaiss.IndexBinaryIVF_quantizer_set, doc=r""" quantizer that maps vectors to inverted lists""")
-    nlist = property(_swigfaiss.IndexBinaryIVF_nlist_get, _swigfaiss.IndexBinaryIVF_nlist_set, doc=r""" number of possible key values""")
-    own_fields = property(_swigfaiss.IndexBinaryIVF_own_fields_get, _swigfaiss.IndexBinaryIVF_own_fields_set, doc=r""" whether object owns the quantizer""")
-    cp = property(_swigfaiss.IndexBinaryIVF_cp_get, _swigfaiss.IndexBinaryIVF_cp_set, doc=r""" to override default clustering params""")
-    clustering_index = property(_swigfaiss.IndexBinaryIVF_clustering_index_get, _swigfaiss.IndexBinaryIVF_clustering_index_set, doc=r""" to override index used during clustering""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexBinaryIVF_swiginit(self, _swigfaiss.new_IndexBinaryIVF(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexBinaryIVF
-
-    def reset(self):
-        return _swigfaiss.IndexBinaryIVF_reset(self)
-
-    def train(self, n, x):
-        r""" Trains the quantizer"""
-        return _swigfaiss.IndexBinaryIVF_train(self, n, x)
-
-    def add(self, n, x):
-        return _swigfaiss.IndexBinaryIVF_add(self, n, x)
-
-    def add_with_ids(self, n, x, xids):
-        return _swigfaiss.IndexBinaryIVF_add_with_ids(self, n, x, xids)
-
-    def add_core(self, n, x, xids, precomputed_idx):
-        r"""
-         Implementation of vector addition where the vector assignments are
-        predefined.
-
-        :type precomputed_idx: int
-        :param precomputed_idx:    quantization indices for the input vectors
-            (size n)
-        """
-        return _swigfaiss.IndexBinaryIVF_add_core(self, n, x, xids, precomputed_idx)
-
-    def search_preassigned(self, n, x, k, assign, centroid_dis, distances, labels, store_pairs, params=None):
-        r"""
-         Search a set of vectors, that are pre-quantized by the IVF
-         quantizer. Fill in the corresponding heaps with the query
-         results. search() calls this.
-
-        :type n: int
-        :param n:      nb of vectors to query
-        :type x: uint8_t
-        :param x:      query vectors, size nx * d
-        :type assign: int
-        :param assign: coarse quantization indices, size nx * nprobe
-        :type centroid_dis: int
-        :param centroid_dis:
-                          distances to coarse centroids, size nx * nprobe
-        :param distance:
-                          output distances, size n * k
-        :type labels: int
-        :param labels: output labels, size n * k
-        :type store_pairs: boolean
-        :param store_pairs: store inv list index + inv list offset
-                                instead in upper/lower 32 bit of result,
-                                instead of ids (used for reranking).
-        :type params: :py:class:`IVFSearchParameters`, optional
-        :param params: used to override the object's search parameters
-        """
-        return _swigfaiss.IndexBinaryIVF_search_preassigned(self, n, x, k, assign, centroid_dis, distances, labels, store_pairs, params)
-
-    def get_InvertedListScanner(self, store_pairs=False):
-        return _swigfaiss.IndexBinaryIVF_get_InvertedListScanner(self, store_pairs)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        r""" assign the vectors, then call search_preassign"""
-        return _swigfaiss.IndexBinaryIVF_search(self, n, x, k, distances, labels, params)
-
-    def range_search(self, n, x, radius, result, params=None):
-        return _swigfaiss.IndexBinaryIVF_range_search(self, n, x, radius, result, params)
-
-    def range_search_preassigned(self, n, x, radius, assign, centroid_dis, result):
-        return _swigfaiss.IndexBinaryIVF_range_search_preassigned(self, n, x, radius, assign, centroid_dis, result)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexBinaryIVF_reconstruct(self, key, recons)
-
-    def reconstruct_n(self, i0, ni, recons):
-        r"""
-         Reconstruct a subset of the indexed vectors.
-
-        Overrides default implementation to bypass reconstruct() which requires
-        direct_map to be maintained.
-
-        :type i0: int
-        :param i0:     first vector to reconstruct
-        :type ni: int
-        :param ni:     nb of vectors to reconstruct
-        :type recons: uint8_t
-        :param recons: output array of reconstructed vectors, size ni * d / 8
-        """
-        return _swigfaiss.IndexBinaryIVF_reconstruct_n(self, i0, ni, recons)
-
-    def search_and_reconstruct(self, n, x, k, distances, labels, recons, params=None):
-        r"""
-         Similar to search, but also reconstructs the stored vectors (or an
-        approximation in the case of lossy coding) for the search results.
-
-        Overrides default implementation to avoid having to maintain direct_map
-        and instead fetch the code offsets through the `store_pairs` flag in
-        search_preassigned().
-
-        :type recons: uint8_t
-        :param recons:      reconstructed vectors size (n, k, d / 8)
-        """
-        return _swigfaiss.IndexBinaryIVF_search_and_reconstruct(self, n, x, k, distances, labels, recons, params)
-
-    def reconstruct_from_offset(self, list_no, offset, recons):
-        r"""
-         Reconstruct a vector given the location in terms of (inv list index +
-        inv list offset) instead of the id.
-
-        Useful for reconstructing when the direct_map is not maintained and
-        the inv list offset is computed by search_preassigned() with
-        `store_pairs` set.
-        """
-        return _swigfaiss.IndexBinaryIVF_reconstruct_from_offset(self, list_no, offset, recons)
-
-    def remove_ids(self, sel):
-        r""" Dataset manipulation functions"""
-        return _swigfaiss.IndexBinaryIVF_remove_ids(self, sel)
-
-    def merge_from(self, other, add_id):
-        return _swigfaiss.IndexBinaryIVF_merge_from(self, other, add_id)
-
-    def check_compatible_for_merge(self, otherIndex):
-        return _swigfaiss.IndexBinaryIVF_check_compatible_for_merge(self, otherIndex)
-
-    def get_list_size(self, list_no):
-        return _swigfaiss.IndexBinaryIVF_get_list_size(self, list_no)
-
-    def make_direct_map(self, new_maintain_direct_map=True):
-        r"""
-         initialize a direct map
-
-        :type new_maintain_direct_map: boolean, optional
-        :param new_maintain_direct_map:    if true, create a direct map,
-                                              else clear it
-        """
-        return _swigfaiss.IndexBinaryIVF_make_direct_map(self, new_maintain_direct_map)
-
-    def set_direct_map_type(self, type):
-        return _swigfaiss.IndexBinaryIVF_set_direct_map_type(self, type)
-
-    def replace_invlists(self, il, own=False):
-        return _swigfaiss.IndexBinaryIVF_replace_invlists(self, il, own)
-
-# Register IndexBinaryIVF in _swigfaiss:
-_swigfaiss.IndexBinaryIVF_swigregister(IndexBinaryIVF)
-class BinaryInvertedListScanner(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-
-    def set_query(self, query_vector):
-        r""" from now on we handle this query."""
-        return _swigfaiss.BinaryInvertedListScanner_set_query(self, query_vector)
-
-    def set_list(self, list_no, coarse_dis):
-        r""" following codes come from this inverted list"""
-        return _swigfaiss.BinaryInvertedListScanner_set_list(self, list_no, coarse_dis)
-
-    def distance_to_code(self, code):
-        r""" compute a single query-to-code distance"""
-        return _swigfaiss.BinaryInvertedListScanner_distance_to_code(self, code)
-
-    def scan_codes(self, n, codes, ids, distances, labels, k):
-        r"""
-         compute the distances to codes. (distances, labels) should be
-        organized as a min- or max-heap
-
-        :type n: int
-        :param n:      number of codes to scan
-        :type codes: uint8_t
-        :param codes:  codes to scan (n * code_size)
-        :type ids: int
-        :param ids:        corresponding ids (ignored if store_pairs)
-        :type distances: int
-        :param distances:  heap distances (size k)
-        :type labels: int
-        :param labels:     heap labels (size k)
-        :type k: int
-        :param k:          heap size
-        """
-        return _swigfaiss.BinaryInvertedListScanner_scan_codes(self, n, codes, ids, distances, labels, k)
-
-    def scan_codes_range(self, n, codes, ids, radius, result):
-        return _swigfaiss.BinaryInvertedListScanner_scan_codes_range(self, n, codes, ids, radius, result)
-    __swig_destroy__ = _swigfaiss.delete_BinaryInvertedListScanner
-
-# Register BinaryInvertedListScanner in _swigfaiss:
-_swigfaiss.BinaryInvertedListScanner_swigregister(BinaryInvertedListScanner)
-class IndexBinaryFromFloat(IndexBinary):
-    r"""
-     IndexBinary backed by a float Index.
-
-    Supports adding vertices and searching them.
-
-    All queries are symmetric because there is no distinction between codes and
-    vectors.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    index = property(_swigfaiss.IndexBinaryFromFloat_index_get, _swigfaiss.IndexBinaryFromFloat_index_set)
-    own_fields = property(_swigfaiss.IndexBinaryFromFloat_own_fields_get, _swigfaiss.IndexBinaryFromFloat_own_fields_set, doc=r""" Whether object owns the index pointer.""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexBinaryFromFloat_swiginit(self, _swigfaiss.new_IndexBinaryFromFloat(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexBinaryFromFloat
-
-    def add(self, n, x):
-        return _swigfaiss.IndexBinaryFromFloat_add(self, n, x)
-
-    def reset(self):
-        return _swigfaiss.IndexBinaryFromFloat_reset(self)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexBinaryFromFloat_search(self, n, x, k, distances, labels, params)
-
-    def train(self, n, x):
-        return _swigfaiss.IndexBinaryFromFloat_train(self, n, x)
-
-# Register IndexBinaryFromFloat in _swigfaiss:
-_swigfaiss.IndexBinaryFromFloat_swigregister(IndexBinaryFromFloat)
-class IndexBinaryHNSW(IndexBinary):
-    r"""
-     The HNSW index is a normal random-access index with a HNSW
-    link structure built on top
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    hnsw = property(_swigfaiss.IndexBinaryHNSW_hnsw_get, _swigfaiss.IndexBinaryHNSW_hnsw_set)
-    own_fields = property(_swigfaiss.IndexBinaryHNSW_own_fields_get, _swigfaiss.IndexBinaryHNSW_own_fields_set)
-    storage = property(_swigfaiss.IndexBinaryHNSW_storage_get, _swigfaiss.IndexBinaryHNSW_storage_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexBinaryHNSW_swiginit(self, _swigfaiss.new_IndexBinaryHNSW(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexBinaryHNSW
-
-    def get_distance_computer(self):
-        return _swigfaiss.IndexBinaryHNSW_get_distance_computer(self)
-
-    def add(self, n, x):
-        return _swigfaiss.IndexBinaryHNSW_add(self, n, x)
-
-    def train(self, n, x):
-        r""" Trains the storage if needed"""
-        return _swigfaiss.IndexBinaryHNSW_train(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        r""" entry point for search"""
-        return _swigfaiss.IndexBinaryHNSW_search(self, n, x, k, distances, labels, params)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexBinaryHNSW_reconstruct(self, key, recons)
-
-    def reset(self):
-        return _swigfaiss.IndexBinaryHNSW_reset(self)
-
-# Register IndexBinaryHNSW in _swigfaiss:
-_swigfaiss.IndexBinaryHNSW_swigregister(IndexBinaryHNSW)
-class IndexBinaryHash(IndexBinary):
-    r""" just uses the b first bits as a hash value"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    invlists = property(_swigfaiss.IndexBinaryHash_invlists_get, _swigfaiss.IndexBinaryHash_invlists_set)
-    b = property(_swigfaiss.IndexBinaryHash_b_get, _swigfaiss.IndexBinaryHash_b_set)
-    nflip = property(_swigfaiss.IndexBinaryHash_nflip_get, _swigfaiss.IndexBinaryHash_nflip_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexBinaryHash_swiginit(self, _swigfaiss.new_IndexBinaryHash(*args))
-
-    def reset(self):
-        return _swigfaiss.IndexBinaryHash_reset(self)
-
-    def add(self, n, x):
-        return _swigfaiss.IndexBinaryHash_add(self, n, x)
-
-    def add_with_ids(self, n, x, xids):
-        return _swigfaiss.IndexBinaryHash_add_with_ids(self, n, x, xids)
-
-    def range_search(self, n, x, radius, result, params=None):
-        return _swigfaiss.IndexBinaryHash_range_search(self, n, x, radius, result, params)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexBinaryHash_search(self, n, x, k, distances, labels, params)
-
-    def display(self):
-        return _swigfaiss.IndexBinaryHash_display(self)
-
-    def hashtable_size(self):
-        return _swigfaiss.IndexBinaryHash_hashtable_size(self)
-    __swig_destroy__ = _swigfaiss.delete_IndexBinaryHash
-
-# Register IndexBinaryHash in _swigfaiss:
-_swigfaiss.IndexBinaryHash_swigregister(IndexBinaryHash)
-class IndexBinaryHashStats(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nq = property(_swigfaiss.IndexBinaryHashStats_nq_get, _swigfaiss.IndexBinaryHashStats_nq_set)
-    n0 = property(_swigfaiss.IndexBinaryHashStats_n0_get, _swigfaiss.IndexBinaryHashStats_n0_set)
-    nlist = property(_swigfaiss.IndexBinaryHashStats_nlist_get, _swigfaiss.IndexBinaryHashStats_nlist_set)
-    ndis = property(_swigfaiss.IndexBinaryHashStats_ndis_get, _swigfaiss.IndexBinaryHashStats_ndis_set)
-
-    def __init__(self):
-        _swigfaiss.IndexBinaryHashStats_swiginit(self, _swigfaiss.new_IndexBinaryHashStats())
-
-    def reset(self):
-        return _swigfaiss.IndexBinaryHashStats_reset(self)
-    __swig_destroy__ = _swigfaiss.delete_IndexBinaryHashStats
-
-# Register IndexBinaryHashStats in _swigfaiss:
-_swigfaiss.IndexBinaryHashStats_swigregister(IndexBinaryHashStats)
-class IndexBinaryMultiHash(IndexBinary):
-    r""" just uses the b first bits as a hash value"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    storage = property(_swigfaiss.IndexBinaryMultiHash_storage_get, _swigfaiss.IndexBinaryMultiHash_storage_set)
-    own_fields = property(_swigfaiss.IndexBinaryMultiHash_own_fields_get, _swigfaiss.IndexBinaryMultiHash_own_fields_set)
-    maps = property(_swigfaiss.IndexBinaryMultiHash_maps_get, _swigfaiss.IndexBinaryMultiHash_maps_set)
-    nhash = property(_swigfaiss.IndexBinaryMultiHash_nhash_get, _swigfaiss.IndexBinaryMultiHash_nhash_set, doc=r""" nb of hash maps""")
-    b = property(_swigfaiss.IndexBinaryMultiHash_b_get, _swigfaiss.IndexBinaryMultiHash_b_set, doc=r""" nb bits per hash map""")
-    nflip = property(_swigfaiss.IndexBinaryMultiHash_nflip_get, _swigfaiss.IndexBinaryMultiHash_nflip_set, doc=r""" nb bit flips to use at search time""")
-
-    def __init__(self, *args):
-        _swigfaiss.IndexBinaryMultiHash_swiginit(self, _swigfaiss.new_IndexBinaryMultiHash(*args))
-    __swig_destroy__ = _swigfaiss.delete_IndexBinaryMultiHash
-
-    def reset(self):
-        return _swigfaiss.IndexBinaryMultiHash_reset(self)
-
-    def add(self, n, x):
-        return _swigfaiss.IndexBinaryMultiHash_add(self, n, x)
-
-    def range_search(self, n, x, radius, result, params=None):
-        return _swigfaiss.IndexBinaryMultiHash_range_search(self, n, x, radius, result, params)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexBinaryMultiHash_search(self, n, x, k, distances, labels, params)
-
-    def hashtable_size(self):
-        return _swigfaiss.IndexBinaryMultiHash_hashtable_size(self)
-
-# Register IndexBinaryMultiHash in _swigfaiss:
-_swigfaiss.IndexBinaryMultiHash_swigregister(IndexBinaryMultiHash)
-class ThreadedIndexBase(Index):
-    r"""
-    A holder of indices in a collection of threads
-    The interface to this class itself is not thread safe
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    __swig_destroy__ = _swigfaiss.delete_ThreadedIndexBase
-
-    def addIndex(self, index):
-        r"""
-        override an index that is managed by ourselves.
-        WARNING: once an index is added, it becomes unsafe to touch it from any
-        other thread than that on which is managing it, until we are shut
-        down. Use runOnIndex to perform work on it instead.
-        """
-        return _swigfaiss.ThreadedIndexBase_addIndex(self, index)
-
-    def removeIndex(self, index):
-        r"""
-        Remove an index that is managed by ourselves.
-        This will flush all pending work on that index, and then shut
-        down its managing thread, and will remove the index.
-        """
-        return _swigfaiss.ThreadedIndexBase_removeIndex(self, index)
-
-    def runOnIndex(self, *args):
-        return _swigfaiss.ThreadedIndexBase_runOnIndex(self, *args)
-
-    def reset(self):
-        r"""
-        faiss::Index API
-        All indices receive the same call
-        """
-        return _swigfaiss.ThreadedIndexBase_reset(self)
-
-    def count(self):
-        r""" Returns the number of sub-indices"""
-        return _swigfaiss.ThreadedIndexBase_count(self)
-
-    def at(self, *args):
-        r"""
-        *Overload 1:*
-        Returns the i-th sub-index
-
-        |
-
-        *Overload 2:*
-        Returns the i-th sub-index (const version)
-        """
-        return _swigfaiss.ThreadedIndexBase_at(self, *args)
-    own_indices = property(_swigfaiss.ThreadedIndexBase_own_indices_get, _swigfaiss.ThreadedIndexBase_own_indices_set, doc=r""" Whether or not we are responsible for deleting our contained indices""")
-
-# Register ThreadedIndexBase in _swigfaiss:
-_swigfaiss.ThreadedIndexBase_swigregister(ThreadedIndexBase)
-class ThreadedIndexBaseBinary(IndexBinary):
-    r"""
-    A holder of indices in a collection of threads
-    The interface to this class itself is not thread safe
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    __swig_destroy__ = _swigfaiss.delete_ThreadedIndexBaseBinary
-
-    def addIndex(self, index):
-        r"""
-        override an index that is managed by ourselves.
-        WARNING: once an index is added, it becomes unsafe to touch it from any
-        other thread than that on which is managing it, until we are shut
-        down. Use runOnIndex to perform work on it instead.
-        """
-        return _swigfaiss.ThreadedIndexBaseBinary_addIndex(self, index)
-
-    def removeIndex(self, index):
-        r"""
-        Remove an index that is managed by ourselves.
-        This will flush all pending work on that index, and then shut
-        down its managing thread, and will remove the index.
-        """
-        return _swigfaiss.ThreadedIndexBaseBinary_removeIndex(self, index)
-
-    def runOnIndex(self, *args):
-        return _swigfaiss.ThreadedIndexBaseBinary_runOnIndex(self, *args)
-
-    def reset(self):
-        r"""
-        faiss::Index API
-        All indices receive the same call
-        """
-        return _swigfaiss.ThreadedIndexBaseBinary_reset(self)
-
-    def count(self):
-        r""" Returns the number of sub-indices"""
-        return _swigfaiss.ThreadedIndexBaseBinary_count(self)
-
-    def at(self, *args):
-        r"""
-        *Overload 1:*
-        Returns the i-th sub-index
-
-        |
-
-        *Overload 2:*
-        Returns the i-th sub-index (const version)
-        """
-        return _swigfaiss.ThreadedIndexBaseBinary_at(self, *args)
-    own_indices = property(_swigfaiss.ThreadedIndexBaseBinary_own_indices_get, _swigfaiss.ThreadedIndexBaseBinary_own_indices_set, doc=r""" Whether or not we are responsible for deleting our contained indices""")
-
-# Register ThreadedIndexBaseBinary in _swigfaiss:
-_swigfaiss.ThreadedIndexBaseBinary_swigregister(ThreadedIndexBaseBinary)
-class IndexShards(ThreadedIndexBase):
-    r"""Index that concatenates the results from several sub-indexes"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        r"""
-        *Overload 1:*
-
-        The dimension that all sub-indices must share will be the dimension of
-        the first sub-index added
-
-        :type threaded: boolean, optional
-        :param threaded:     do we use one thread per sub_index or do
-                                queries sequentially?
-        :type successive_ids: boolean, optional
-        :param successive_ids: should we shift the returned ids by
-                                the size of each sub-index or return them
-                                as they are?
-
-        |
-
-        *Overload 2:*
-
-        :type threaded: boolean, optional
-        :param threaded:     do we use one thread per sub_index or do
-                                queries sequentially?
-        :type successive_ids: boolean, optional
-        :param successive_ids: should we shift the returned ids by
-                                the size of each sub-index or return them
-                                as they are?
-
-        |
-
-        *Overload 3:*
-
-        :type threaded: boolean, optional
-        :param threaded:     do we use one thread per sub_index or do
-                                queries sequentially?
-        :param successive_ids: should we shift the returned ids by
-                                the size of each sub-index or return them
-                                as they are?
-
-        |
-
-        *Overload 4:*
-
-        :param threaded:     do we use one thread per sub_index or do
-                                queries sequentially?
-        :param successive_ids: should we shift the returned ids by
-                                the size of each sub-index or return them
-                                as they are?
-
-        |
-
-        *Overload 5:*
-         int version due to the implicit bool conversion ambiguity of int as
-         dimension
-
-        |
-
-        *Overload 6:*
-         int version due to the implicit bool conversion ambiguity of int as
-         dimension
-
-        |
-
-        *Overload 7:*
-         int version due to the implicit bool conversion ambiguity of int as
-         dimension
-        """
-        _swigfaiss.IndexShards_swiginit(self, _swigfaiss.new_IndexShards(*args))
-
-    def add_shard(self, index):
-        r""" Alias for addIndex()"""
-        return _swigfaiss.IndexShards_add_shard(self, index)
-
-    def remove_shard(self, index):
-        r""" Alias for removeIndex()"""
-        return _swigfaiss.IndexShards_remove_shard(self, index)
-
-    def add(self, n, x):
-        r""" supported only for sub-indices that implement add_with_ids"""
-        return _swigfaiss.IndexShards_add(self, n, x)
-
-    def add_with_ids(self, n, x, xids):
-        r"""
-        Cases (successive_ids, xids):
-        - true, non-NULL       ERROR: it makes no sense to pass in ids and
-                               request them to be shifted
-        - true, NULL           OK: but should be called only once (calls add()
-                               on sub-indexes).
-        - false, non-NULL      OK: will call add_with_ids with passed in xids
-                               distributed evenly over shards
-        - false, NULL          OK: will call add_with_ids on each sub-index,
-                               starting at ntotal
-        """
-        return _swigfaiss.IndexShards_add_with_ids(self, n, x, xids)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexShards_search(self, n, x, k, distances, labels, params)
-
-    def train(self, n, x):
-        return _swigfaiss.IndexShards_train(self, n, x)
-    successive_ids = property(_swigfaiss.IndexShards_successive_ids_get, _swigfaiss.IndexShards_successive_ids_set)
-
-    def syncWithSubIndexes(self):
-        r"""
-        Synchronize the top-level index (IndexShards) with data in the
-        sub-indices
-        """
-        return _swigfaiss.IndexShards_syncWithSubIndexes(self)
-    __swig_destroy__ = _swigfaiss.delete_IndexShards
-
-# Register IndexShards in _swigfaiss:
-_swigfaiss.IndexShards_swigregister(IndexShards)
-class IndexBinaryShards(ThreadedIndexBaseBinary):
-    r"""Index that concatenates the results from several sub-indexes"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        r"""
-        *Overload 1:*
-
-        The dimension that all sub-indices must share will be the dimension of
-        the first sub-index added
-
-        :type threaded: boolean, optional
-        :param threaded:     do we use one thread per sub_index or do
-                                queries sequentially?
-        :type successive_ids: boolean, optional
-        :param successive_ids: should we shift the returned ids by
-                                the size of each sub-index or return them
-                                as they are?
-
-        |
-
-        *Overload 2:*
-
-        :type threaded: boolean, optional
-        :param threaded:     do we use one thread per sub_index or do
-                                queries sequentially?
-        :type successive_ids: boolean, optional
-        :param successive_ids: should we shift the returned ids by
-                                the size of each sub-index or return them
-                                as they are?
-
-        |
-
-        *Overload 3:*
-
-        :type threaded: boolean, optional
-        :param threaded:     do we use one thread per sub_index or do
-                                queries sequentially?
-        :param successive_ids: should we shift the returned ids by
-                                the size of each sub-index or return them
-                                as they are?
-
-        |
-
-        *Overload 4:*
-
-        :param threaded:     do we use one thread per sub_index or do
-                                queries sequentially?
-        :param successive_ids: should we shift the returned ids by
-                                the size of each sub-index or return them
-                                as they are?
-
-        |
-
-        *Overload 5:*
-         int version due to the implicit bool conversion ambiguity of int as
-         dimension
-
-        |
-
-        *Overload 6:*
-         int version due to the implicit bool conversion ambiguity of int as
-         dimension
-
-        |
-
-        *Overload 7:*
-         int version due to the implicit bool conversion ambiguity of int as
-         dimension
-        """
-        _swigfaiss.IndexBinaryShards_swiginit(self, _swigfaiss.new_IndexBinaryShards(*args))
-
-    def add_shard(self, index):
-        r""" Alias for addIndex()"""
-        return _swigfaiss.IndexBinaryShards_add_shard(self, index)
-
-    def remove_shard(self, index):
-        r""" Alias for removeIndex()"""
-        return _swigfaiss.IndexBinaryShards_remove_shard(self, index)
-
-    def add(self, n, x):
-        r""" supported only for sub-indices that implement add_with_ids"""
-        return _swigfaiss.IndexBinaryShards_add(self, n, x)
-
-    def add_with_ids(self, n, x, xids):
-        r"""
-        Cases (successive_ids, xids):
-        - true, non-NULL       ERROR: it makes no sense to pass in ids and
-                               request them to be shifted
-        - true, NULL           OK: but should be called only once (calls add()
-                               on sub-indexes).
-        - false, non-NULL      OK: will call add_with_ids with passed in xids
-                               distributed evenly over shards
-        - false, NULL          OK: will call add_with_ids on each sub-index,
-                               starting at ntotal
-        """
-        return _swigfaiss.IndexBinaryShards_add_with_ids(self, n, x, xids)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexBinaryShards_search(self, n, x, k, distances, labels, params)
-
-    def train(self, n, x):
-        return _swigfaiss.IndexBinaryShards_train(self, n, x)
-    successive_ids = property(_swigfaiss.IndexBinaryShards_successive_ids_get, _swigfaiss.IndexBinaryShards_successive_ids_set)
-
-    def syncWithSubIndexes(self):
-        r"""
-        Synchronize the top-level index (IndexShards) with data in the
-        sub-indices
-        """
-        return _swigfaiss.IndexBinaryShards_syncWithSubIndexes(self)
-    __swig_destroy__ = _swigfaiss.delete_IndexBinaryShards
-
-# Register IndexBinaryShards in _swigfaiss:
-_swigfaiss.IndexBinaryShards_swigregister(IndexBinaryShards)
-class IndexShardsIVF(IndexShards, Level1Quantizer):
-    r"""
-    IndexShards with a common coarse quantizer. All the indexes added should be
-    IndexIVFInterface indexes so that the search_precomputed can be called.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, quantizer, nlist, threaded=False, successive_ids=True):
-        _swigfaiss.IndexShardsIVF_swiginit(self, _swigfaiss.new_IndexShardsIVF(quantizer, nlist, threaded, successive_ids))
-
-    def addIndex(self, index):
-        return _swigfaiss.IndexShardsIVF_addIndex(self, index)
-
-    def add_with_ids(self, n, x, xids):
-        return _swigfaiss.IndexShardsIVF_add_with_ids(self, n, x, xids)
-
-    def train(self, n, x):
-        return _swigfaiss.IndexShardsIVF_train(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexShardsIVF_search(self, n, x, k, distances, labels, params)
-    __swig_destroy__ = _swigfaiss.delete_IndexShardsIVF
-
-# Register IndexShardsIVF in _swigfaiss:
-_swigfaiss.IndexShardsIVF_swigregister(IndexShardsIVF)
-class IndexReplicas(ThreadedIndexBase):
-    r"""
-    Takes individual faiss::Index instances, and splits queries for
-    sending to each Index instance, and joins the results together
-    when done.
-    Each index is managed by a separate CPU thread.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        r"""
-        *Overload 1:*
-        The dimension that all sub-indices must share will be the dimension of
-        the first sub-index added
-        :type threaded: boolean, optional
-        :param threaded: do we use one thread per sub-index or do queries
-            sequentially?
-
-        |
-
-        *Overload 2:*
-        :type d: int
-        :param d: the dimension that all sub-indices must share
-        :type threaded: boolean, optional
-        :param threaded: do we use one thread per sub index or do queries
-            sequentially?
-
-        |
-
-        *Overload 3:*
-        :type d: int
-        :param d: the dimension that all sub-indices must share
-        :param threaded: do we use one thread per sub index or do queries
-            sequentially?
-
-        |
-
-        *Overload 4:*
-        int version due to the implicit bool conversion ambiguity of int as
-        dimension
-
-        |
-
-        *Overload 5:*
-        int version due to the implicit bool conversion ambiguity of int as
-        dimension
-        """
-        _swigfaiss.IndexReplicas_swiginit(self, _swigfaiss.new_IndexReplicas(*args))
-
-    def add_replica(self, index):
-        r""" Alias for addIndex()"""
-        return _swigfaiss.IndexReplicas_add_replica(self, index)
-
-    def remove_replica(self, index):
-        r""" Alias for removeIndex()"""
-        return _swigfaiss.IndexReplicas_remove_replica(self, index)
-
-    def train(self, n, x):
-        r"""
-        faiss::Index API
-        All indices receive the same call
-        """
-        return _swigfaiss.IndexReplicas_train(self, n, x)
-
-    def add(self, n, x):
-        r"""
-        faiss::Index API
-        All indices receive the same call
-        """
-        return _swigfaiss.IndexReplicas_add(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        r"""
-        faiss::Index API
-        Query is partitioned into a slice for each sub-index
-        split by ceil(n / #indices) for our sub-indices
-        """
-        return _swigfaiss.IndexReplicas_search(self, n, x, k, distances, labels, params)
-
-    def reconstruct(self, arg2, v):
-        r""" reconstructs from the first index"""
-        return _swigfaiss.IndexReplicas_reconstruct(self, arg2, v)
-
-    def syncWithSubIndexes(self):
-        r"""
-        Synchronize the top-level index (IndexShards) with data in the
-        sub-indices
-        """
-        return _swigfaiss.IndexReplicas_syncWithSubIndexes(self)
-    __swig_destroy__ = _swigfaiss.delete_IndexReplicas
-
-# Register IndexReplicas in _swigfaiss:
-_swigfaiss.IndexReplicas_swigregister(IndexReplicas)
-class IndexBinaryReplicas(ThreadedIndexBaseBinary):
-    r"""
-    Takes individual faiss::Index instances, and splits queries for
-    sending to each Index instance, and joins the results together
-    when done.
-    Each index is managed by a separate CPU thread.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        r"""
-        *Overload 1:*
-        The dimension that all sub-indices must share will be the dimension of
-        the first sub-index added
-        :type threaded: boolean, optional
-        :param threaded: do we use one thread per sub-index or do queries
-            sequentially?
-
-        |
-
-        *Overload 2:*
-        :type d: int
-        :param d: the dimension that all sub-indices must share
-        :type threaded: boolean, optional
-        :param threaded: do we use one thread per sub index or do queries
-            sequentially?
-
-        |
-
-        *Overload 3:*
-        :type d: int
-        :param d: the dimension that all sub-indices must share
-        :param threaded: do we use one thread per sub index or do queries
-            sequentially?
-
-        |
-
-        *Overload 4:*
-        int version due to the implicit bool conversion ambiguity of int as
-        dimension
-
-        |
-
-        *Overload 5:*
-        int version due to the implicit bool conversion ambiguity of int as
-        dimension
-        """
-        _swigfaiss.IndexBinaryReplicas_swiginit(self, _swigfaiss.new_IndexBinaryReplicas(*args))
-
-    def add_replica(self, index):
-        r""" Alias for addIndex()"""
-        return _swigfaiss.IndexBinaryReplicas_add_replica(self, index)
-
-    def remove_replica(self, index):
-        r""" Alias for removeIndex()"""
-        return _swigfaiss.IndexBinaryReplicas_remove_replica(self, index)
-
-    def train(self, n, x):
-        r"""
-        faiss::Index API
-        All indices receive the same call
-        """
-        return _swigfaiss.IndexBinaryReplicas_train(self, n, x)
-
-    def add(self, n, x):
-        r"""
-        faiss::Index API
-        All indices receive the same call
-        """
-        return _swigfaiss.IndexBinaryReplicas_add(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        r"""
-        faiss::Index API
-        Query is partitioned into a slice for each sub-index
-        split by ceil(n / #indices) for our sub-indices
-        """
-        return _swigfaiss.IndexBinaryReplicas_search(self, n, x, k, distances, labels, params)
-
-    def reconstruct(self, arg2, v):
-        r""" reconstructs from the first index"""
-        return _swigfaiss.IndexBinaryReplicas_reconstruct(self, arg2, v)
-
-    def syncWithSubIndexes(self):
-        r"""
-        Synchronize the top-level index (IndexShards) with data in the
-        sub-indices
-        """
-        return _swigfaiss.IndexBinaryReplicas_syncWithSubIndexes(self)
-    __swig_destroy__ = _swigfaiss.delete_IndexBinaryReplicas
-
-# Register IndexBinaryReplicas in _swigfaiss:
-_swigfaiss.IndexBinaryReplicas_swigregister(IndexBinaryReplicas)
-class IndexSplitVectors(Index):
-    r"""
-     splits input vectors in segments and assigns each segment to a sub-index
-    used to distribute a MultiIndexQuantizer
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    own_fields = property(_swigfaiss.IndexSplitVectors_own_fields_get, _swigfaiss.IndexSplitVectors_own_fields_set)
-    threaded = property(_swigfaiss.IndexSplitVectors_threaded_get, _swigfaiss.IndexSplitVectors_threaded_set)
-    sub_indexes = property(_swigfaiss.IndexSplitVectors_sub_indexes_get, _swigfaiss.IndexSplitVectors_sub_indexes_set)
-    sum_d = property(_swigfaiss.IndexSplitVectors_sum_d_get, _swigfaiss.IndexSplitVectors_sum_d_set)
-
-    def __init__(self, d, threaded=False):
-        r""" sum of dimensions seen so far"""
-        _swigfaiss.IndexSplitVectors_swiginit(self, _swigfaiss.new_IndexSplitVectors(d, threaded))
-
-    def add_sub_index(self, arg2):
-        return _swigfaiss.IndexSplitVectors_add_sub_index(self, arg2)
-
-    def sync_with_sub_indexes(self):
-        return _swigfaiss.IndexSplitVectors_sync_with_sub_indexes(self)
-
-    def add(self, n, x):
-        return _swigfaiss.IndexSplitVectors_add(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexSplitVectors_search(self, n, x, k, distances, labels, params)
-
-    def train(self, n, x):
-        return _swigfaiss.IndexSplitVectors_train(self, n, x)
-
-    def reset(self):
-        return _swigfaiss.IndexSplitVectors_reset(self)
-    __swig_destroy__ = _swigfaiss.delete_IndexSplitVectors
-
-# Register IndexSplitVectors in _swigfaiss:
-_swigfaiss.IndexSplitVectors_swigregister(IndexSplitVectors)
-class IndexRandom(Index):
-    r"""
-     index that returns random results.
-    used mainly for time benchmarks
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    seed = property(_swigfaiss.IndexRandom_seed_get, _swigfaiss.IndexRandom_seed_set)
-
-    def __init__(self, *args):
-        _swigfaiss.IndexRandom_swiginit(self, _swigfaiss.new_IndexRandom(*args))
-
-    def add(self, n, x):
-        return _swigfaiss.IndexRandom_add(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexRandom_search(self, n, x, k, distances, labels, params)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexRandom_reconstruct(self, key, recons)
-
-    def reset(self):
-        return _swigfaiss.IndexRandom_reset(self)
-    __swig_destroy__ = _swigfaiss.delete_IndexRandom
-
-# Register IndexRandom in _swigfaiss:
-_swigfaiss.IndexRandom_swigregister(IndexRandom)
-class IndexIDMap(Index):
-    r""" Index that translates search results to ids"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    index = property(_swigfaiss.IndexIDMap_index_get, _swigfaiss.IndexIDMap_index_set)
-    own_fields = property(_swigfaiss.IndexIDMap_own_fields_get, _swigfaiss.IndexIDMap_own_fields_set, doc=r"""the sub-index""")
-    id_map = property(_swigfaiss.IndexIDMap_id_map_get, _swigfaiss.IndexIDMap_id_map_set, doc=r"""whether pointers are deleted in destructo""")
-
-    def add_with_ids(self, n, x, xids):
-        r"""
-        :type xids: int
-        :param xids: if non-null, ids to store for the vectors (size n)
-        """
-        return _swigfaiss.IndexIDMap_add_with_ids(self, n, x, xids)
-
-    def add(self, n, x):
-        r""" this will fail. Use add_with_ids"""
-        return _swigfaiss.IndexIDMap_add(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexIDMap_search(self, n, x, k, distances, labels, params)
-
-    def train(self, n, x):
-        return _swigfaiss.IndexIDMap_train(self, n, x)
-
-    def reset(self):
-        return _swigfaiss.IndexIDMap_reset(self)
-
-    def remove_ids(self, sel):
-        r""" remove ids adapted to IndexFlat"""
-        return _swigfaiss.IndexIDMap_remove_ids(self, sel)
-
-    def range_search(self, n, x, radius, result, params=None):
-        return _swigfaiss.IndexIDMap_range_search(self, n, x, radius, result, params)
-
-    def merge_from(self, otherIndex, add_id=0):
-        return _swigfaiss.IndexIDMap_merge_from(self, otherIndex, add_id)
-
-    def check_compatible_for_merge(self, otherIndex):
-        return _swigfaiss.IndexIDMap_check_compatible_for_merge(self, otherIndex)
-    __swig_destroy__ = _swigfaiss.delete_IndexIDMap
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIDMap_swiginit(self, _swigfaiss.new_IndexIDMap(*args))
-
-# Register IndexIDMap in _swigfaiss:
-_swigfaiss.IndexIDMap_swigregister(IndexIDMap)
-class IndexBinaryIDMap(IndexBinary):
-    r""" Index that translates search results to ids"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    index = property(_swigfaiss.IndexBinaryIDMap_index_get, _swigfaiss.IndexBinaryIDMap_index_set)
-    own_fields = property(_swigfaiss.IndexBinaryIDMap_own_fields_get, _swigfaiss.IndexBinaryIDMap_own_fields_set, doc=r"""the sub-index""")
-    id_map = property(_swigfaiss.IndexBinaryIDMap_id_map_get, _swigfaiss.IndexBinaryIDMap_id_map_set, doc=r"""whether pointers are deleted in destructo""")
-
-    def add_with_ids(self, n, x, xids):
-        r"""
-        :type xids: int
-        :param xids: if non-null, ids to store for the vectors (size n)
-        """
-        return _swigfaiss.IndexBinaryIDMap_add_with_ids(self, n, x, xids)
-
-    def add(self, n, x):
-        r""" this will fail. Use add_with_ids"""
-        return _swigfaiss.IndexBinaryIDMap_add(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexBinaryIDMap_search(self, n, x, k, distances, labels, params)
-
-    def train(self, n, x):
-        return _swigfaiss.IndexBinaryIDMap_train(self, n, x)
-
-    def reset(self):
-        return _swigfaiss.IndexBinaryIDMap_reset(self)
-
-    def remove_ids(self, sel):
-        r""" remove ids adapted to IndexFlat"""
-        return _swigfaiss.IndexBinaryIDMap_remove_ids(self, sel)
-
-    def range_search(self, n, x, radius, result, params=None):
-        return _swigfaiss.IndexBinaryIDMap_range_search(self, n, x, radius, result, params)
-
-    def merge_from(self, otherIndex, add_id=0):
-        return _swigfaiss.IndexBinaryIDMap_merge_from(self, otherIndex, add_id)
-
-    def check_compatible_for_merge(self, otherIndex):
-        return _swigfaiss.IndexBinaryIDMap_check_compatible_for_merge(self, otherIndex)
-    __swig_destroy__ = _swigfaiss.delete_IndexBinaryIDMap
-
-    def __init__(self, *args):
-        _swigfaiss.IndexBinaryIDMap_swiginit(self, _swigfaiss.new_IndexBinaryIDMap(*args))
-
-# Register IndexBinaryIDMap in _swigfaiss:
-_swigfaiss.IndexBinaryIDMap_swigregister(IndexBinaryIDMap)
-class IndexIDMap2(IndexIDMap):
-    r"""
-    same as IndexIDMap but also provides an efficient reconstruction
-    implementation via a 2-way index
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    rev_map = property(_swigfaiss.IndexIDMap2_rev_map_get, _swigfaiss.IndexIDMap2_rev_map_set)
-
-    def construct_rev_map(self):
-        r""" make the rev_map from scratch"""
-        return _swigfaiss.IndexIDMap2_construct_rev_map(self)
-
-    def add_with_ids(self, n, x, xids):
-        return _swigfaiss.IndexIDMap2_add_with_ids(self, n, x, xids)
-
-    def remove_ids(self, sel):
-        return _swigfaiss.IndexIDMap2_remove_ids(self, sel)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexIDMap2_reconstruct(self, key, recons)
-
-    def check_consistency(self):
-        r""" check that the rev_map and the id_map are in sync"""
-        return _swigfaiss.IndexIDMap2_check_consistency(self)
-
-    def merge_from(self, otherIndex, add_id=0):
-        return _swigfaiss.IndexIDMap2_merge_from(self, otherIndex, add_id)
-    __swig_destroy__ = _swigfaiss.delete_IndexIDMap2
-
-    def __init__(self, *args):
-        _swigfaiss.IndexIDMap2_swiginit(self, _swigfaiss.new_IndexIDMap2(*args))
-
-# Register IndexIDMap2 in _swigfaiss:
-_swigfaiss.IndexIDMap2_swigregister(IndexIDMap2)
-class IndexBinaryIDMap2(IndexBinaryIDMap):
-    r"""
-    same as IndexIDMap but also provides an efficient reconstruction
-    implementation via a 2-way index
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    rev_map = property(_swigfaiss.IndexBinaryIDMap2_rev_map_get, _swigfaiss.IndexBinaryIDMap2_rev_map_set)
-
-    def construct_rev_map(self):
-        r""" make the rev_map from scratch"""
-        return _swigfaiss.IndexBinaryIDMap2_construct_rev_map(self)
-
-    def add_with_ids(self, n, x, xids):
-        return _swigfaiss.IndexBinaryIDMap2_add_with_ids(self, n, x, xids)
-
-    def remove_ids(self, sel):
-        return _swigfaiss.IndexBinaryIDMap2_remove_ids(self, sel)
-
-    def reconstruct(self, key, recons):
-        return _swigfaiss.IndexBinaryIDMap2_reconstruct(self, key, recons)
-
-    def check_consistency(self):
-        r""" check that the rev_map and the id_map are in sync"""
-        return _swigfaiss.IndexBinaryIDMap2_check_consistency(self)
-
-    def merge_from(self, otherIndex, add_id=0):
-        return _swigfaiss.IndexBinaryIDMap2_merge_from(self, otherIndex, add_id)
-    __swig_destroy__ = _swigfaiss.delete_IndexBinaryIDMap2
-
-    def __init__(self, *args):
-        _swigfaiss.IndexBinaryIDMap2_swiginit(self, _swigfaiss.new_IndexBinaryIDMap2(*args))
-
-# Register IndexBinaryIDMap2 in _swigfaiss:
-_swigfaiss.IndexBinaryIDMap2_swigregister(IndexBinaryIDMap2)
-class IndexRowwiseMinMaxBase(Index):
-    r"""
-     Index wrapper that performs rowwise normalization to [0,1], preserving
-     the coefficients. This is a vector codec index only.
-
-     Basically, this index performs a rowwise scaling to [0,1] of every row
-     in an input dataset before calling subindex::train() and
-     subindex::sa_encode(). sa_encode() call stores the scaling coefficients
-      (scaler and minv) in the very beginning of every output code. The format:
-         [scaler][minv][subindex::sa_encode() output]
-     The de-scaling in sa_decode() is done using:
-         output_rescaled = scaler * output + minv
-
-     An additional ::train_inplace() function is provided in order to do
-     an inplace scaling before calling subindex::train() and, thus, avoiding
-     the cloning of the input dataset, but modifying the input dataset because
-     of the scaling and the scaling back. It is up to user to call
-     this function instead of ::train()
-
-     Derived classes provide different data types for scaling coefficients.
-     Currently, versions with fp16 and fp32 scaling coefficients are available.
-    fp16 version adds 4 extra bytes per encoded vector
-    fp32 version adds 8 extra bytes per encoded vector
-     Provides base functions for rowwise normalizing indices.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    index = property(_swigfaiss.IndexRowwiseMinMaxBase_index_get, _swigfaiss.IndexRowwiseMinMaxBase_index_set, doc=r""" sub-index""")
-    own_fields = property(_swigfaiss.IndexRowwiseMinMaxBase_own_fields_get, _swigfaiss.IndexRowwiseMinMaxBase_own_fields_set, doc=r""" whether the subindex needs to be freed in the destructor.""")
-    __swig_destroy__ = _swigfaiss.delete_IndexRowwiseMinMaxBase
-
-    def add(self, n, x):
-        return _swigfaiss.IndexRowwiseMinMaxBase_add(self, n, x)
-
-    def search(self, n, x, k, distances, labels, params=None):
-        return _swigfaiss.IndexRowwiseMinMaxBase_search(self, n, x, k, distances, labels, params)
-
-    def reset(self):
-        return _swigfaiss.IndexRowwiseMinMaxBase_reset(self)
-
-    def train_inplace(self, n, x):
-        return _swigfaiss.IndexRowwiseMinMaxBase_train_inplace(self, n, x)
-
-# Register IndexRowwiseMinMaxBase in _swigfaiss:
-_swigfaiss.IndexRowwiseMinMaxBase_swigregister(IndexRowwiseMinMaxBase)
-class IndexRowwiseMinMaxFP16(IndexRowwiseMinMaxBase):
-    r""" Stores scaling coefficients as fp16 values."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.IndexRowwiseMinMaxFP16_swiginit(self, _swigfaiss.new_IndexRowwiseMinMaxFP16(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexRowwiseMinMaxFP16_train(self, n, x)
-
-    def train_inplace(self, n, x):
-        return _swigfaiss.IndexRowwiseMinMaxFP16_train_inplace(self, n, x)
-
-    def sa_code_size(self):
-        return _swigfaiss.IndexRowwiseMinMaxFP16_sa_code_size(self)
-
-    def sa_encode(self, n, x, bytes):
-        return _swigfaiss.IndexRowwiseMinMaxFP16_sa_encode(self, n, x, bytes)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexRowwiseMinMaxFP16_sa_decode(self, n, bytes, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexRowwiseMinMaxFP16
-
-# Register IndexRowwiseMinMaxFP16 in _swigfaiss:
-_swigfaiss.IndexRowwiseMinMaxFP16_swigregister(IndexRowwiseMinMaxFP16)
-class IndexRowwiseMinMax(IndexRowwiseMinMaxBase):
-    r""" Stores scaling coefficients as fp32 values."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, *args):
-        _swigfaiss.IndexRowwiseMinMax_swiginit(self, _swigfaiss.new_IndexRowwiseMinMax(*args))
-
-    def train(self, n, x):
-        return _swigfaiss.IndexRowwiseMinMax_train(self, n, x)
-
-    def train_inplace(self, n, x):
-        return _swigfaiss.IndexRowwiseMinMax_train_inplace(self, n, x)
-
-    def sa_code_size(self):
-        return _swigfaiss.IndexRowwiseMinMax_sa_code_size(self)
-
-    def sa_encode(self, n, x, bytes):
-        return _swigfaiss.IndexRowwiseMinMax_sa_encode(self, n, x, bytes)
-
-    def sa_decode(self, n, bytes, x):
-        return _swigfaiss.IndexRowwiseMinMax_sa_decode(self, n, bytes, x)
-    __swig_destroy__ = _swigfaiss.delete_IndexRowwiseMinMax
-
-# Register IndexRowwiseMinMax in _swigfaiss:
-_swigfaiss.IndexRowwiseMinMax_swigregister(IndexRowwiseMinMax)
-class RangeSearchResult(object):
-    r"""
-    The objective is to have a simple result structure while
-    minimizing the number of mem copies in the result. The method
-    do_allocation can be overloaded to allocate the result tables in
-    the matrix type of a scripting language like Lua or Python.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nq = property(_swigfaiss.RangeSearchResult_nq_get, _swigfaiss.RangeSearchResult_nq_set, doc=r""" nb of queries""")
-    lims = property(_swigfaiss.RangeSearchResult_lims_get, _swigfaiss.RangeSearchResult_lims_set, doc=r""" size (nq + 1)""")
-    labels = property(_swigfaiss.RangeSearchResult_labels_get, _swigfaiss.RangeSearchResult_labels_set, doc=r""" result for query i is labels[lims[i]:lims[i+1]]""")
-    distances = property(_swigfaiss.RangeSearchResult_distances_get, _swigfaiss.RangeSearchResult_distances_set, doc=r""" corresponding distances (not sorted)""")
-    buffer_size = property(_swigfaiss.RangeSearchResult_buffer_size_get, _swigfaiss.RangeSearchResult_buffer_size_set, doc=r""" size of the result buffers used""")
-
-    def __init__(self, nq, alloc_lims=True):
-        r""" lims must be allocated on input to range_search."""
-        _swigfaiss.RangeSearchResult_swiginit(self, _swigfaiss.new_RangeSearchResult(nq, alloc_lims))
-
-    def do_allocation(self):
-        r"""
-        called when lims contains the nb of elements result entries
-        for each query
-        """
-        return _swigfaiss.RangeSearchResult_do_allocation(self)
-    __swig_destroy__ = _swigfaiss.delete_RangeSearchResult
-
-# Register RangeSearchResult in _swigfaiss:
-_swigfaiss.RangeSearchResult_swigregister(RangeSearchResult)
-class BufferList(object):
-    r"""
-    List of temporary buffers used to store results before they are
-    copied to the RangeSearchResult object.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    buffer_size = property(_swigfaiss.BufferList_buffer_size_get, _swigfaiss.BufferList_buffer_size_set)
-    buffers = property(_swigfaiss.BufferList_buffers_get, _swigfaiss.BufferList_buffers_set)
-    wp = property(_swigfaiss.BufferList_wp_get, _swigfaiss.BufferList_wp_set, doc=r""" write pointer in the last buffer.""")
-
-    def __init__(self, buffer_size):
-        _swigfaiss.BufferList_swiginit(self, _swigfaiss.new_BufferList(buffer_size))
-    __swig_destroy__ = _swigfaiss.delete_BufferList
-
-    def append_buffer(self):
-        r""" create a new buffer"""
-        return _swigfaiss.BufferList_append_buffer(self)
-
-    def add(self, id, dis):
-        r""" add one result, possibly appending a new buffer if needed"""
-        return _swigfaiss.BufferList_add(self, id, dis)
-
-    def copy_range(self, ofs, n, dest_ids, dest_dis):
-        r"""
-        copy elemnts ofs:ofs+n-1 seen as linear data in the buffers to
-        tables dest_ids, dest_dis
-        """
-        return _swigfaiss.BufferList_copy_range(self, ofs, n, dest_ids, dest_dis)
-
-# Register BufferList in _swigfaiss:
-_swigfaiss.BufferList_swigregister(BufferList)
-class RangeQueryResult(object):
-    r""" result structure for a single query"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    qno = property(_swigfaiss.RangeQueryResult_qno_get, _swigfaiss.RangeQueryResult_qno_set)
-    nres = property(_swigfaiss.RangeQueryResult_nres_get, _swigfaiss.RangeQueryResult_nres_set)
-    pres = property(_swigfaiss.RangeQueryResult_pres_get, _swigfaiss.RangeQueryResult_pres_set)
-
-    def add(self, dis, id):
-        r""" called by search function to report a new result"""
-        return _swigfaiss.RangeQueryResult_add(self, dis, id)
-
-    def __init__(self):
-        _swigfaiss.RangeQueryResult_swiginit(self, _swigfaiss.new_RangeQueryResult())
-    __swig_destroy__ = _swigfaiss.delete_RangeQueryResult
-
-# Register RangeQueryResult in _swigfaiss:
-_swigfaiss.RangeQueryResult_swigregister(RangeQueryResult)
-class RangeSearchPartialResult(BufferList):
-    r""" the entries in the buffers are split per query"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    res = property(_swigfaiss.RangeSearchPartialResult_res_get, _swigfaiss.RangeSearchPartialResult_res_set)
-
-    def __init__(self, res_in):
-        r""" eventually the result will be stored in res_in"""
-        _swigfaiss.RangeSearchPartialResult_swiginit(self, _swigfaiss.new_RangeSearchPartialResult(res_in))
-    queries = property(_swigfaiss.RangeSearchPartialResult_queries_get, _swigfaiss.RangeSearchPartialResult_queries_set, doc=r""" query ids + nb of results per query.""")
-
-    def new_result(self, qno):
-        r""" begin a new result"""
-        return _swigfaiss.RangeSearchPartialResult_new_result(self, qno)
-
-    def finalize(self):
-        return _swigfaiss.RangeSearchPartialResult_finalize(self)
-
-    def set_lims(self):
-        r""" called by range_search before do_allocation"""
-        return _swigfaiss.RangeSearchPartialResult_set_lims(self)
-
-    def copy_result(self, incremental=False):
-        r""" called by range_search after do_allocation"""
-        return _swigfaiss.RangeSearchPartialResult_copy_result(self, incremental)
-
-    @staticmethod
-    def merge(partial_results, do_delete=True):
-        r"""
-        merge a set of PartialResult's into one RangeSearchResult
-        on ouptut the partialresults are empty!
-        """
-        return _swigfaiss.RangeSearchPartialResult_merge(partial_results, do_delete)
-    __swig_destroy__ = _swigfaiss.delete_RangeSearchPartialResult
-
-# Register RangeSearchPartialResult in _swigfaiss:
-_swigfaiss.RangeSearchPartialResult_swigregister(RangeSearchPartialResult)
-class InterruptCallback(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-
-    def want_interrupt(self):
-        return _swigfaiss.InterruptCallback_want_interrupt(self)
-    __swig_destroy__ = _swigfaiss.delete_InterruptCallback
-
-    @staticmethod
-    def clear_instance():
-        return _swigfaiss.InterruptCallback_clear_instance()
-
-    @staticmethod
-    def check():
-        r"""
-         check if:
-        - an interrupt callback is set
-        - the callback returns true
-        if this is the case, then throw an exception. Should not be called
-        from multiple threads.
-        """
-        return _swigfaiss.InterruptCallback_check()
-
-    @staticmethod
-    def is_interrupted():
-        r"""
-        same as check() but return true if is interrupted instead of
-        throwing. Can be called from multiple threads.
-        """
-        return _swigfaiss.InterruptCallback_is_interrupted()
-
-    @staticmethod
-    def get_period_hint(flops):
-        r"""
-         assuming each iteration takes a certain number of flops, what
-        is a reasonable interval to check for interrupts?
-        """
-        return _swigfaiss.InterruptCallback_get_period_hint(flops)
-
-# Register InterruptCallback in _swigfaiss:
-_swigfaiss.InterruptCallback_swigregister(InterruptCallback)
-class VisitedTable(object):
-    r""" set implementation optimized for fast access."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    visited = property(_swigfaiss.VisitedTable_visited_get, _swigfaiss.VisitedTable_visited_set)
-    visno = property(_swigfaiss.VisitedTable_visno_get, _swigfaiss.VisitedTable_visno_set)
-
-    def __init__(self, size):
-        _swigfaiss.VisitedTable_swiginit(self, _swigfaiss.new_VisitedTable(size))
-
-    def set(self, no):
-        r""" set flag #no to true"""
-        return _swigfaiss.VisitedTable_set(self, no)
-
-    def get(self, no):
-        r""" get flag #no"""
-        return _swigfaiss.VisitedTable_get(self, no)
-
-    def advance(self):
-        r""" reset all flags to false"""
-        return _swigfaiss.VisitedTable_advance(self)
-    __swig_destroy__ = _swigfaiss.delete_VisitedTable
-
-# Register VisitedTable in _swigfaiss:
-_swigfaiss.VisitedTable_swigregister(VisitedTable)
-class IDSelector(object):
-    r""" Encapsulates a set of ids to handle."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-
-    def is_member(self, id):
-        return _swigfaiss.IDSelector_is_member(self, id)
-    __swig_destroy__ = _swigfaiss.delete_IDSelector
-
-# Register IDSelector in _swigfaiss:
-_swigfaiss.IDSelector_swigregister(IDSelector)
-class IDSelectorRange(IDSelector):
-    r""" ids between [imin, imax)"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    imin = property(_swigfaiss.IDSelectorRange_imin_get, _swigfaiss.IDSelectorRange_imin_set)
-    imax = property(_swigfaiss.IDSelectorRange_imax_get, _swigfaiss.IDSelectorRange_imax_set)
-    assume_sorted = property(_swigfaiss.IDSelectorRange_assume_sorted_get, _swigfaiss.IDSelectorRange_assume_sorted_set, doc=r"""
-    Assume that the ids to handle are sorted. In some cases this can speed
-    up processing
-    """)
-
-    def __init__(self, imin, imax, assume_sorted=False):
-        _swigfaiss.IDSelectorRange_swiginit(self, _swigfaiss.new_IDSelectorRange(imin, imax, assume_sorted))
-
-    def is_member(self, id):
-        return _swigfaiss.IDSelectorRange_is_member(self, id)
-
-    def find_sorted_ids_bounds(self, list_size, ids, jmin, jmax):
-        r"""
-        for sorted ids, find the range of list indices where the valid ids are
-        stored
-        """
-        return _swigfaiss.IDSelectorRange_find_sorted_ids_bounds(self, list_size, ids, jmin, jmax)
-    __swig_destroy__ = _swigfaiss.delete_IDSelectorRange
-
-# Register IDSelectorRange in _swigfaiss:
-_swigfaiss.IDSelectorRange_swigregister(IDSelectorRange)
-class IDSelectorArray(IDSelector):
-    r"""
-     Simple array of elements
-
-    is_member calls are very inefficient, but some operations can use the ids
-    directly.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    n = property(_swigfaiss.IDSelectorArray_n_get, _swigfaiss.IDSelectorArray_n_set)
-    ids = property(_swigfaiss.IDSelectorArray_ids_get, _swigfaiss.IDSelectorArray_ids_set)
-
-    def __init__(self, n, ids):
-        r"""
-         Construct with an array of ids to process
-
-        :type n: int
-        :param n: number of ids to store
-        :type ids: int
-        :param ids: elements to store. The pointer should remain valid during
-                       IDSelectorArray's lifetime
-        """
-        _swigfaiss.IDSelectorArray_swiginit(self, _swigfaiss.new_IDSelectorArray(n, ids))
-
-    def is_member(self, id):
-        return _swigfaiss.IDSelectorArray_is_member(self, id)
-    __swig_destroy__ = _swigfaiss.delete_IDSelectorArray
-
-# Register IDSelectorArray in _swigfaiss:
-_swigfaiss.IDSelectorArray_swigregister(IDSelectorArray)
-class IDSelectorBatch(IDSelector):
-    r"""
-     Ids from a set.
-
-    Repetitions of ids in the indices set passed to the constructor does not hurt
-    performance.
-
-    The hash function used for the bloom filter and GCC's implementation of
-    unordered_set are just the least significant bits of the id. This works fine
-    for random ids or ids in sequences but will produce many hash collisions if
-    lsb's are always the same
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nbits = property(_swigfaiss.IDSelectorBatch_nbits_get, _swigfaiss.IDSelectorBatch_nbits_set)
-    mask = property(_swigfaiss.IDSelectorBatch_mask_get, _swigfaiss.IDSelectorBatch_mask_set)
-
-    def __init__(self, n, indices):
-        r"""
-         Construct with an array of ids to process
-
-        :type n: int
-        :param n: number of ids to store
-        :param ids: elements to store. The pointer can be released after
-                       construction
-        """
-        _swigfaiss.IDSelectorBatch_swiginit(self, _swigfaiss.new_IDSelectorBatch(n, indices))
-
-    def is_member(self, id):
-        return _swigfaiss.IDSelectorBatch_is_member(self, id)
-    __swig_destroy__ = _swigfaiss.delete_IDSelectorBatch
-
-# Register IDSelectorBatch in _swigfaiss:
-_swigfaiss.IDSelectorBatch_swigregister(IDSelectorBatch)
-class IDSelectorBitmap(IDSelector):
-    r""" One bit per element. Constructed with a bitmap, size ceil(n / 8)."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    n = property(_swigfaiss.IDSelectorBitmap_n_get, _swigfaiss.IDSelectorBitmap_n_set)
-    bitmap = property(_swigfaiss.IDSelectorBitmap_bitmap_get, _swigfaiss.IDSelectorBitmap_bitmap_set)
-
-    def __init__(self, n, bitmap):
-        r"""
-         Construct with a binary mask
-
-        :type n: int
-        :param n: size of the bitmap array
-        :type bitmap: uint8_t
-        :param bitmap: id will be selected iff id / 8 < n and bit number
-                          (i%8) of bitmap[floor(i / 8)] is 1.
-        """
-        _swigfaiss.IDSelectorBitmap_swiginit(self, _swigfaiss.new_IDSelectorBitmap(n, bitmap))
-
-    def is_member(self, id):
-        return _swigfaiss.IDSelectorBitmap_is_member(self, id)
-    __swig_destroy__ = _swigfaiss.delete_IDSelectorBitmap
-
-# Register IDSelectorBitmap in _swigfaiss:
-_swigfaiss.IDSelectorBitmap_swigregister(IDSelectorBitmap)
-class IDSelectorNot(IDSelector):
-    r""" reverts the membership test of another selector"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    sel = property(_swigfaiss.IDSelectorNot_sel_get, _swigfaiss.IDSelectorNot_sel_set)
-
-    def __init__(self, sel):
-        _swigfaiss.IDSelectorNot_swiginit(self, _swigfaiss.new_IDSelectorNot(sel))
-
-    def is_member(self, id):
-        return _swigfaiss.IDSelectorNot_is_member(self, id)
-    __swig_destroy__ = _swigfaiss.delete_IDSelectorNot
-
-# Register IDSelectorNot in _swigfaiss:
-_swigfaiss.IDSelectorNot_swigregister(IDSelectorNot)
-class IDSelectorAll(IDSelector):
-    r""" selects all entries (useful for benchmarking)"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def is_member(self, id):
-        return _swigfaiss.IDSelectorAll_is_member(self, id)
-    __swig_destroy__ = _swigfaiss.delete_IDSelectorAll
-
-    def __init__(self):
-        _swigfaiss.IDSelectorAll_swiginit(self, _swigfaiss.new_IDSelectorAll())
-
-# Register IDSelectorAll in _swigfaiss:
-_swigfaiss.IDSelectorAll_swigregister(IDSelectorAll)
-class IDSelectorAnd(IDSelector):
-    r"""
-    does an AND operation on the the two given IDSelector's is_membership
-    results.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    lhs = property(_swigfaiss.IDSelectorAnd_lhs_get, _swigfaiss.IDSelectorAnd_lhs_set)
-    rhs = property(_swigfaiss.IDSelectorAnd_rhs_get, _swigfaiss.IDSelectorAnd_rhs_set)
-
-    def __init__(self, lhs, rhs):
-        _swigfaiss.IDSelectorAnd_swiginit(self, _swigfaiss.new_IDSelectorAnd(lhs, rhs))
-
-    def is_member(self, id):
-        return _swigfaiss.IDSelectorAnd_is_member(self, id)
-    __swig_destroy__ = _swigfaiss.delete_IDSelectorAnd
-
-# Register IDSelectorAnd in _swigfaiss:
-_swigfaiss.IDSelectorAnd_swigregister(IDSelectorAnd)
-class IDSelectorOr(IDSelector):
-    r"""
-    does an OR operation on the the two given IDSelector's is_membership
-    results.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    lhs = property(_swigfaiss.IDSelectorOr_lhs_get, _swigfaiss.IDSelectorOr_lhs_set)
-    rhs = property(_swigfaiss.IDSelectorOr_rhs_get, _swigfaiss.IDSelectorOr_rhs_set)
-
-    def __init__(self, lhs, rhs):
-        _swigfaiss.IDSelectorOr_swiginit(self, _swigfaiss.new_IDSelectorOr(lhs, rhs))
-
-    def is_member(self, id):
-        return _swigfaiss.IDSelectorOr_is_member(self, id)
-    __swig_destroy__ = _swigfaiss.delete_IDSelectorOr
-
-# Register IDSelectorOr in _swigfaiss:
-_swigfaiss.IDSelectorOr_swigregister(IDSelectorOr)
-class IDSelectorXOr(IDSelector):
-    r"""
-    does an XOR operation on the the two given IDSelector's is_membership
-    results.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    lhs = property(_swigfaiss.IDSelectorXOr_lhs_get, _swigfaiss.IDSelectorXOr_lhs_set)
-    rhs = property(_swigfaiss.IDSelectorXOr_rhs_get, _swigfaiss.IDSelectorXOr_rhs_set)
-
-    def __init__(self, lhs, rhs):
-        _swigfaiss.IDSelectorXOr_swiginit(self, _swigfaiss.new_IDSelectorXOr(lhs, rhs))
-
-    def is_member(self, id):
-        return _swigfaiss.IDSelectorXOr_is_member(self, id)
-    __swig_destroy__ = _swigfaiss.delete_IDSelectorXOr
-
-# Register IDSelectorXOr in _swigfaiss:
-_swigfaiss.IDSelectorXOr_swigregister(IDSelectorXOr)
-EXACT_TOPK = _swigfaiss.EXACT_TOPK
-APPROX_TOPK_BUCKETS_B32_D2 = _swigfaiss.APPROX_TOPK_BUCKETS_B32_D2
-APPROX_TOPK_BUCKETS_B8_D3 = _swigfaiss.APPROX_TOPK_BUCKETS_B8_D3
-APPROX_TOPK_BUCKETS_B16_D2 = _swigfaiss.APPROX_TOPK_BUCKETS_B16_D2
-APPROX_TOPK_BUCKETS_B8_D2 = _swigfaiss.APPROX_TOPK_BUCKETS_B8_D2
-
-def downcast_index(index):
-    return _swigfaiss.downcast_index(index)
-
-def downcast_VectorTransform(vt):
-    return _swigfaiss.downcast_VectorTransform(vt)
-
-def downcast_IndexBinary(index):
-    return _swigfaiss.downcast_IndexBinary(index)
-
-def downcast_InvertedLists(il):
-    return _swigfaiss.downcast_InvertedLists(il)
-
-def downcast_AdditiveQuantizer(aq):
-    return _swigfaiss.downcast_AdditiveQuantizer(aq)
-
-def downcast_Quantizer(aq):
-    return _swigfaiss.downcast_Quantizer(aq)
-
-def write_index(*args):
-    return _swigfaiss.write_index(*args)
-
-def write_index_binary(*args):
-    return _swigfaiss.write_index_binary(*args)
-
-def read_index(*args):
-    return _swigfaiss.read_index(*args)
-
-def read_index_binary(*args):
-    return _swigfaiss.read_index_binary(*args)
-
-def write_VectorTransform(*args):
-    return _swigfaiss.write_VectorTransform(*args)
-
-def read_VectorTransform(*args):
-    return _swigfaiss.read_VectorTransform(*args)
-
-def read_ProductQuantizer(*args):
-    return _swigfaiss.read_ProductQuantizer(*args)
-
-def write_ProductQuantizer(*args):
-    return _swigfaiss.write_ProductQuantizer(*args)
-
-def write_InvertedLists(ils, f):
-    return _swigfaiss.write_InvertedLists(ils, f)
-
-def read_InvertedLists(reader, io_flags=0):
-    return _swigfaiss.read_InvertedLists(reader, io_flags)
-
-def clone_index(arg1):
-    return _swigfaiss.clone_index(arg1)
-class Cloner(object):
-    r"""
-     Cloner class, useful to override classes with other cloning
-    functions. The cloning function above just calls
-    Cloner::clone_Index.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def clone_VectorTransform(self, arg2):
-        return _swigfaiss.Cloner_clone_VectorTransform(self, arg2)
-
-    def clone_Index(self, arg2):
-        return _swigfaiss.Cloner_clone_Index(self, arg2)
-
-    def clone_IndexIVF(self, arg2):
-        return _swigfaiss.Cloner_clone_IndexIVF(self, arg2)
-    __swig_destroy__ = _swigfaiss.delete_Cloner
-
-    def __init__(self):
-        _swigfaiss.Cloner_swiginit(self, _swigfaiss.new_Cloner())
-
-# Register Cloner in _swigfaiss:
-_swigfaiss.Cloner_swigregister(Cloner)
-IO_FLAG_READ_ONLY = cvar.IO_FLAG_READ_ONLY
-IO_FLAG_ONDISK_SAME_DIR = cvar.IO_FLAG_ONDISK_SAME_DIR
-IO_FLAG_SKIP_IVF_DATA = cvar.IO_FLAG_SKIP_IVF_DATA
-IO_FLAG_SKIP_PRECOMPUTE_TABLE = cvar.IO_FLAG_SKIP_PRECOMPUTE_TABLE
-IO_FLAG_MMAP = cvar.IO_FLAG_MMAP
-
-
-def clone_Quantizer(quant):
-    return _swigfaiss.clone_Quantizer(quant)
-class AutoTuneCriterion(object):
-    r"""
-    Evaluation criterion. Returns a performance measure in [0,1],
-    higher is better.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-
-    def __init__(self, *args, **kwargs):
-        raise AttributeError("No constructor defined - class is abstract")
-    __repr__ = _swig_repr
-    nq = property(_swigfaiss.AutoTuneCriterion_nq_get, _swigfaiss.AutoTuneCriterion_nq_set, doc=r""" nb of queries this criterion is evaluated on""")
-    nnn = property(_swigfaiss.AutoTuneCriterion_nnn_get, _swigfaiss.AutoTuneCriterion_nnn_set, doc=r""" nb of NNs that the query should request""")
-    gt_nnn = property(_swigfaiss.AutoTuneCriterion_gt_nnn_get, _swigfaiss.AutoTuneCriterion_gt_nnn_set, doc=r""" nb of GT NNs required to evaluate criterion""")
-    gt_D = property(_swigfaiss.AutoTuneCriterion_gt_D_get, _swigfaiss.AutoTuneCriterion_gt_D_set, doc=r""" Ground-truth distances (size nq * gt_nnn)""")
-    gt_I = property(_swigfaiss.AutoTuneCriterion_gt_I_get, _swigfaiss.AutoTuneCriterion_gt_I_set, doc=r""" Ground-truth indexes (size nq * gt_nnn)""")
-
-    def set_groundtruth(self, gt_nnn, gt_D_in, gt_I_in):
-        r"""
-         Intitializes the gt_D and gt_I vectors. Must be called before evaluating
-
-        :type gt_D_in: float
-        :param gt_D_in:  size nq * gt_nnn
-        :type gt_I_in: int
-        :param gt_I_in:  size nq * gt_nnn
-        """
-        return _swigfaiss.AutoTuneCriterion_set_groundtruth(self, gt_nnn, gt_D_in, gt_I_in)
-
-    def evaluate(self, D, I):
-        r"""
-         Evaluate the criterion.
-
-        :type D: float
-        :param D:  size nq * nnn
-        :type I: int
-        :param I:  size nq * nnn
-        :rtype: float
-        :return: the criterion, between 0 and 1. Larger is better.
-        """
-        return _swigfaiss.AutoTuneCriterion_evaluate(self, D, I)
-    __swig_destroy__ = _swigfaiss.delete_AutoTuneCriterion
-
-# Register AutoTuneCriterion in _swigfaiss:
-_swigfaiss.AutoTuneCriterion_swigregister(AutoTuneCriterion)
-class OneRecallAtRCriterion(AutoTuneCriterion):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    R = property(_swigfaiss.OneRecallAtRCriterion_R_get, _swigfaiss.OneRecallAtRCriterion_R_set)
-
-    def __init__(self, nq, R):
-        _swigfaiss.OneRecallAtRCriterion_swiginit(self, _swigfaiss.new_OneRecallAtRCriterion(nq, R))
-
-    def evaluate(self, D, I):
-        return _swigfaiss.OneRecallAtRCriterion_evaluate(self, D, I)
-    __swig_destroy__ = _swigfaiss.delete_OneRecallAtRCriterion
-
-# Register OneRecallAtRCriterion in _swigfaiss:
-_swigfaiss.OneRecallAtRCriterion_swigregister(OneRecallAtRCriterion)
-class IntersectionCriterion(AutoTuneCriterion):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    R = property(_swigfaiss.IntersectionCriterion_R_get, _swigfaiss.IntersectionCriterion_R_set)
-
-    def __init__(self, nq, R):
-        _swigfaiss.IntersectionCriterion_swiginit(self, _swigfaiss.new_IntersectionCriterion(nq, R))
-
-    def evaluate(self, D, I):
-        return _swigfaiss.IntersectionCriterion_evaluate(self, D, I)
-    __swig_destroy__ = _swigfaiss.delete_IntersectionCriterion
-
-# Register IntersectionCriterion in _swigfaiss:
-_swigfaiss.IntersectionCriterion_swigregister(IntersectionCriterion)
-class OperatingPoint(object):
-    r"""
-    Maintains a list of experimental results. Each operating point is a
-    (perf, t, key) triplet, where higher perf and lower t is
-    better. The key field is an arbitrary identifier for the operating point.
-
-    Includes primitives to extract the Pareto-optimal operating points in the
-    (perf, t) space.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    perf = property(_swigfaiss.OperatingPoint_perf_get, _swigfaiss.OperatingPoint_perf_set, doc=r""" performance measure (output of a Criterion)""")
-    t = property(_swigfaiss.OperatingPoint_t_get, _swigfaiss.OperatingPoint_t_set, doc=r""" corresponding execution time (ms)""")
-    key = property(_swigfaiss.OperatingPoint_key_get, _swigfaiss.OperatingPoint_key_set, doc=r""" key that identifies this op pt""")
-    cno = property(_swigfaiss.OperatingPoint_cno_get, _swigfaiss.OperatingPoint_cno_set, doc=r""" integer identifer""")
-
-    def __init__(self):
-        _swigfaiss.OperatingPoint_swiginit(self, _swigfaiss.new_OperatingPoint())
-    __swig_destroy__ = _swigfaiss.delete_OperatingPoint
-
-# Register OperatingPoint in _swigfaiss:
-_swigfaiss.OperatingPoint_swigregister(OperatingPoint)
-class OperatingPoints(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    all_pts = property(_swigfaiss.OperatingPoints_all_pts_get, _swigfaiss.OperatingPoints_all_pts_set, doc=r""" all operating points""")
-    optimal_pts = property(_swigfaiss.OperatingPoints_optimal_pts_get, _swigfaiss.OperatingPoints_optimal_pts_set, doc=r""" optimal operating points, sorted by perf""")
-
-    def __init__(self):
-        _swigfaiss.OperatingPoints_swiginit(self, _swigfaiss.new_OperatingPoints())
-
-    def merge_with(self, *args):
-        r""" add operating points from other to this, with a prefix to the keys"""
-        return _swigfaiss.OperatingPoints_merge_with(self, *args)
-
-    def clear(self):
-        return _swigfaiss.OperatingPoints_clear(self)
-
-    def add(self, perf, t, key, cno=0):
-        r""" add a performance measure. Return whether it is an optimal point"""
-        return _swigfaiss.OperatingPoints_add(self, perf, t, key, cno)
-
-    def t_for_perf(self, perf):
-        r""" get time required to obtain a given performance measure"""
-        return _swigfaiss.OperatingPoints_t_for_perf(self, perf)
-
-    def display(self, only_optimal=True):
-        r""" easy-to-read output"""
-        return _swigfaiss.OperatingPoints_display(self, only_optimal)
-
-    def all_to_gnuplot(self, fname):
-        r""" output to a format easy to digest by gnuplot"""
-        return _swigfaiss.OperatingPoints_all_to_gnuplot(self, fname)
-
-    def optimal_to_gnuplot(self, fname):
-        return _swigfaiss.OperatingPoints_optimal_to_gnuplot(self, fname)
-    __swig_destroy__ = _swigfaiss.delete_OperatingPoints
-
-# Register OperatingPoints in _swigfaiss:
-_swigfaiss.OperatingPoints_swigregister(OperatingPoints)
-class ParameterRange(object):
-    r""" possible values of a parameter, sorted from least to most expensive/accurate"""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    name = property(_swigfaiss.ParameterRange_name_get, _swigfaiss.ParameterRange_name_set)
-    values = property(_swigfaiss.ParameterRange_values_get, _swigfaiss.ParameterRange_values_set)
-
-    def __init__(self):
-        _swigfaiss.ParameterRange_swiginit(self, _swigfaiss.new_ParameterRange())
-    __swig_destroy__ = _swigfaiss.delete_ParameterRange
-
-# Register ParameterRange in _swigfaiss:
-_swigfaiss.ParameterRange_swigregister(ParameterRange)
-class ParameterSpace(object):
-    r""" Uses a-priori knowledge on the Faiss indexes to extract tunable parameters."""
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    parameter_ranges = property(_swigfaiss.ParameterSpace_parameter_ranges_get, _swigfaiss.ParameterSpace_parameter_ranges_set, doc=r""" all tunable parameters""")
-    verbose = property(_swigfaiss.ParameterSpace_verbose_get, _swigfaiss.ParameterSpace_verbose_set, doc=r""" verbosity during exploration""")
-    n_experiments = property(_swigfaiss.ParameterSpace_n_experiments_get, _swigfaiss.ParameterSpace_n_experiments_set, doc=r""" nb of experiments during optimization (0 = try all combinations)""")
-    batchsize = property(_swigfaiss.ParameterSpace_batchsize_get, _swigfaiss.ParameterSpace_batchsize_set, doc=r""" maximum number of queries to submit at a time.""")
-    thread_over_batches = property(_swigfaiss.ParameterSpace_thread_over_batches_get, _swigfaiss.ParameterSpace_thread_over_batches_set, doc=r"""
-    use multithreading over batches (useful to benchmark
-    independent single-searches)
-    """)
-    min_test_duration = property(_swigfaiss.ParameterSpace_min_test_duration_get, _swigfaiss.ParameterSpace_min_test_duration_set, doc=r"""
-    run tests several times until they reach at least this
-    duration (to avoid jittering in MT mode)
-    """)
-
-    def __init__(self):
-        _swigfaiss.ParameterSpace_swiginit(self, _swigfaiss.new_ParameterSpace())
-
-    def n_combinations(self):
-        r""" nb of combinations, = product of values sizes"""
-        return _swigfaiss.ParameterSpace_n_combinations(self)
-
-    def combination_ge(self, c1, c2):
-        r""" returns whether combinations c1 >= c2 in the tuple sense"""
-        return _swigfaiss.ParameterSpace_combination_ge(self, c1, c2)
-
-    def combination_name(self, cno):
-        r""" get string representation of the combination"""
-        return _swigfaiss.ParameterSpace_combination_name(self, cno)
-
-    def display(self):
-        r""" print a description on stdout"""
-        return _swigfaiss.ParameterSpace_display(self)
-
-    def add_range(self, name):
-        r""" add a new parameter (or return it if it exists)"""
-        return _swigfaiss.ParameterSpace_add_range(self, name)
-
-    def initialize(self, index):
-        r""" initialize with reasonable parameters for the index"""
-        return _swigfaiss.ParameterSpace_initialize(self, index)
-
-    def set_index_parameters(self, *args):
-        r"""
-        *Overload 1:*
-        set a combination of parameters on an index
-
-        |
-
-        *Overload 2:*
-        set a combination of parameters described by a string
-        """
-        return _swigfaiss.ParameterSpace_set_index_parameters(self, *args)
-
-    def set_index_parameter(self, index, name, val):
-        r""" set one of the parameters, returns whether setting was successful"""
-        return _swigfaiss.ParameterSpace_set_index_parameter(self, index, name, val)
-
-    def update_bounds(self, cno, op, upper_bound_perf, lower_bound_t):
-        r"""
-         find an upper bound on the performance and a lower bound on t
-        for configuration cno given another operating point op
-        """
-        return _swigfaiss.ParameterSpace_update_bounds(self, cno, op, upper_bound_perf, lower_bound_t)
-
-    def explore(self, index, nq, xq, crit, ops):
-        r"""
-         explore operating points
-        :type index: :py:class:`Index`
-        :param index:   index to run on
-        :type xq: float
-        :param xq:      query vectors (size nq * index.d)
-        :type crit: :py:class:`AutoTuneCriterion`
-        :param crit:    selection criterion
-        :type ops: :py:class:`OperatingPoints`
-        :param ops:     resulting operating points
-        """
-        return _swigfaiss.ParameterSpace_explore(self, index, nq, xq, crit, ops)
-    __swig_destroy__ = _swigfaiss.delete_ParameterSpace
-
-# Register ParameterSpace in _swigfaiss:
-_swigfaiss.ParameterSpace_swigregister(ParameterSpace)
-
-def index_factory(*args):
-    r"""
-    Build and index with the sequence of processing steps described in
-    the string.
-    """
-    return _swigfaiss.index_factory(*args)
-
-def index_binary_factory(d, description):
-    return _swigfaiss.index_binary_factory(d, description)
-class MatrixStats(object):
-    r"""
-     Reports some statistics on a dataset and comments on them.
-
-    It is a class rather than a function so that all stats can also be
-    accessed from code
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-
-    def __init__(self, n, d, x):
-        _swigfaiss.MatrixStats_swiginit(self, _swigfaiss.new_MatrixStats(n, d, x))
-    comments = property(_swigfaiss.MatrixStats_comments_get, _swigfaiss.MatrixStats_comments_set)
-    n = property(_swigfaiss.MatrixStats_n_get, _swigfaiss.MatrixStats_n_set)
-    d = property(_swigfaiss.MatrixStats_d_get, _swigfaiss.MatrixStats_d_set)
-    n_collision = property(_swigfaiss.MatrixStats_n_collision_get, _swigfaiss.MatrixStats_n_collision_set)
-    n_valid = property(_swigfaiss.MatrixStats_n_valid_get, _swigfaiss.MatrixStats_n_valid_set)
-    n0 = property(_swigfaiss.MatrixStats_n0_get, _swigfaiss.MatrixStats_n0_set)
-    min_norm2 = property(_swigfaiss.MatrixStats_min_norm2_get, _swigfaiss.MatrixStats_min_norm2_set)
-    max_norm2 = property(_swigfaiss.MatrixStats_max_norm2_get, _swigfaiss.MatrixStats_max_norm2_set)
-    per_dim_stats = property(_swigfaiss.MatrixStats_per_dim_stats_get, _swigfaiss.MatrixStats_per_dim_stats_set)
-    occurrences = property(_swigfaiss.MatrixStats_occurrences_get, _swigfaiss.MatrixStats_occurrences_set)
-    buf = property(_swigfaiss.MatrixStats_buf_get, _swigfaiss.MatrixStats_buf_set)
-    nbuf = property(_swigfaiss.MatrixStats_nbuf_get, _swigfaiss.MatrixStats_nbuf_set)
-
-    def do_comment(self, fmt):
-        return _swigfaiss.MatrixStats_do_comment(self, fmt)
-    __swig_destroy__ = _swigfaiss.delete_MatrixStats
-
-# Register MatrixStats in _swigfaiss:
-_swigfaiss.MatrixStats_swigregister(MatrixStats)
-class PyCallbackIOWriter(IOWriter):
-    r"""
-    Copyright (c) Facebook, Inc. and its affiliates.
-
-    This source code is licensed under the MIT license found in the
-    LICENSE file in the root directory of this source tree.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    callback = property(_swigfaiss.PyCallbackIOWriter_callback_get, _swigfaiss.PyCallbackIOWriter_callback_set)
-    bs = property(_swigfaiss.PyCallbackIOWriter_bs_get, _swigfaiss.PyCallbackIOWriter_bs_set)
-
-    def __init__(self, *args):
-        r"""
-        Callback: Python function that takes a bytes object and
-        returns the number of bytes successfully written.
-        """
-        _swigfaiss.PyCallbackIOWriter_swiginit(self, _swigfaiss.new_PyCallbackIOWriter(*args))
-
-    def __call__(self, ptrv, size, nitems):
-        return _swigfaiss.PyCallbackIOWriter___call__(self, ptrv, size, nitems)
-    __swig_destroy__ = _swigfaiss.delete_PyCallbackIOWriter
-
-# Register PyCallbackIOWriter in _swigfaiss:
-_swigfaiss.PyCallbackIOWriter_swigregister(PyCallbackIOWriter)
-class PyCallbackIOReader(IOReader):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    callback = property(_swigfaiss.PyCallbackIOReader_callback_get, _swigfaiss.PyCallbackIOReader_callback_set)
-    bs = property(_swigfaiss.PyCallbackIOReader_bs_get, _swigfaiss.PyCallbackIOReader_bs_set)
-
-    def __init__(self, *args):
-        r"""
-         Callback: Python function that takes a size and returns a
-        bytes object with the resulting read
-        """
-        _swigfaiss.PyCallbackIOReader_swiginit(self, _swigfaiss.new_PyCallbackIOReader(*args))
-
-    def __call__(self, ptrv, size, nitems):
-        return _swigfaiss.PyCallbackIOReader___call__(self, ptrv, size, nitems)
-    __swig_destroy__ = _swigfaiss.delete_PyCallbackIOReader
-
-# Register PyCallbackIOReader in _swigfaiss:
-_swigfaiss.PyCallbackIOReader_swigregister(PyCallbackIOReader)
-class PyCallbackIDSelector(IDSelector):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    callback = property(_swigfaiss.PyCallbackIDSelector_callback_get, _swigfaiss.PyCallbackIDSelector_callback_set)
-
-    def __init__(self, callback):
-        _swigfaiss.PyCallbackIDSelector_swiginit(self, _swigfaiss.new_PyCallbackIDSelector(callback))
-
-    def is_member(self, id):
-        return _swigfaiss.PyCallbackIDSelector_is_member(self, id)
-    __swig_destroy__ = _swigfaiss.delete_PyCallbackIDSelector
-
-# Register PyCallbackIDSelector in _swigfaiss:
-_swigfaiss.PyCallbackIDSelector_swigregister(PyCallbackIDSelector)
-
-def swig_ptr(a):
-    return _swigfaiss.swig_ptr(a)
-
-def rev_swig_ptr(*args):
-    return _swigfaiss.rev_swig_ptr(*args)
-class float_minheap_array_t(object):
-    r"""
-     a template structure for a set of [min|max]-heaps it is tailored
-    so that the actual data of the heaps can just live in compact
-    arrays.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nh = property(_swigfaiss.float_minheap_array_t_nh_get, _swigfaiss.float_minheap_array_t_nh_set, doc=r""" number of heaps""")
-    k = property(_swigfaiss.float_minheap_array_t_k_get, _swigfaiss.float_minheap_array_t_k_set, doc=r""" allocated size per heap""")
-    ids = property(_swigfaiss.float_minheap_array_t_ids_get, _swigfaiss.float_minheap_array_t_ids_set, doc=r""" identifiers (size nh * k)""")
-    val = property(_swigfaiss.float_minheap_array_t_val_get, _swigfaiss.float_minheap_array_t_val_set, doc=r""" values (distances or similarities), size nh * k""")
-
-    def get_val(self, key):
-        r""" Return the list of values for a heap"""
-        return _swigfaiss.float_minheap_array_t_get_val(self, key)
-
-    def get_ids(self, key):
-        r""" Correspponding identifiers"""
-        return _swigfaiss.float_minheap_array_t_get_ids(self, key)
-
-    def heapify(self):
-        r""" prepare all the heaps before adding"""
-        return _swigfaiss.float_minheap_array_t_heapify(self)
-
-    def addn(self, nj, vin, j0=0, i0=0, ni=-1):
-        r"""
-         add nj elements to heaps i0:i0+ni, with sequential ids
-
-        :type nj: int
-        :param nj:    nb of elements to add to each heap
-        :type vin: float
-        :param vin:   elements to add, size ni * nj
-        :type j0: int, optional
-        :param j0:    add this to the ids that are added
-        :type i0: int, optional
-        :param i0:    first heap to update
-        :type ni: int, optional
-        :param ni:    nb of elements to update (-1 = use nh)
-        """
-        return _swigfaiss.float_minheap_array_t_addn(self, nj, vin, j0, i0, ni)
-
-    def addn_with_ids(self, nj, vin, id_in=None, id_stride=0, i0=0, ni=-1):
-        r"""
-         same as addn
-
-        :type id_in: int, optional
-        :param id_in:     ids of the elements to add, size ni * nj
-        :type id_stride: int, optional
-        :param id_stride: stride for id_in
-        """
-        return _swigfaiss.float_minheap_array_t_addn_with_ids(self, nj, vin, id_in, id_stride, i0, ni)
-
-    def addn_query_subset_with_ids(self, nsubset, subset, nj, vin, id_in=None, id_stride=0):
-        r"""
-         same as addn_with_ids, but for just a subset of queries
-
-        :type nsubset: int
-        :param nsubset:  number of query entries to update
-        :type subset: int
-        :param subset:   indexes of queries to update, in 0..nh-1, size nsubset
-        """
-        return _swigfaiss.float_minheap_array_t_addn_query_subset_with_ids(self, nsubset, subset, nj, vin, id_in, id_stride)
-
-    def reorder(self):
-        r""" reorder all the heaps"""
-        return _swigfaiss.float_minheap_array_t_reorder(self)
-
-    def per_line_extrema(self, vals_out, idx_out):
-        r"""
-         this is not really a heap function. It just finds the per-line
-          extrema of each line of array D
-        :type vals_out: float
-        :param vals_out:    extreme value of each line (size nh, or NULL)
-        :type idx_out: int
-        :param idx_out:     index of extreme value (size nh or NULL)
-        """
-        return _swigfaiss.float_minheap_array_t_per_line_extrema(self, vals_out, idx_out)
-
-    def __init__(self):
-        _swigfaiss.float_minheap_array_t_swiginit(self, _swigfaiss.new_float_minheap_array_t())
-    __swig_destroy__ = _swigfaiss.delete_float_minheap_array_t
-
-# Register float_minheap_array_t in _swigfaiss:
-_swigfaiss.float_minheap_array_t_swigregister(float_minheap_array_t)
-class int_minheap_array_t(object):
-    r"""
-     a template structure for a set of [min|max]-heaps it is tailored
-    so that the actual data of the heaps can just live in compact
-    arrays.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nh = property(_swigfaiss.int_minheap_array_t_nh_get, _swigfaiss.int_minheap_array_t_nh_set, doc=r""" number of heaps""")
-    k = property(_swigfaiss.int_minheap_array_t_k_get, _swigfaiss.int_minheap_array_t_k_set, doc=r""" allocated size per heap""")
-    ids = property(_swigfaiss.int_minheap_array_t_ids_get, _swigfaiss.int_minheap_array_t_ids_set, doc=r""" identifiers (size nh * k)""")
-    val = property(_swigfaiss.int_minheap_array_t_val_get, _swigfaiss.int_minheap_array_t_val_set, doc=r""" values (distances or similarities), size nh * k""")
-
-    def get_val(self, key):
-        r""" Return the list of values for a heap"""
-        return _swigfaiss.int_minheap_array_t_get_val(self, key)
-
-    def get_ids(self, key):
-        r""" Correspponding identifiers"""
-        return _swigfaiss.int_minheap_array_t_get_ids(self, key)
-
-    def heapify(self):
-        r""" prepare all the heaps before adding"""
-        return _swigfaiss.int_minheap_array_t_heapify(self)
-
-    def addn(self, nj, vin, j0=0, i0=0, ni=-1):
-        r"""
-         add nj elements to heaps i0:i0+ni, with sequential ids
-
-        :type nj: int
-        :param nj:    nb of elements to add to each heap
-        :type vin: int
-        :param vin:   elements to add, size ni * nj
-        :type j0: int, optional
-        :param j0:    add this to the ids that are added
-        :type i0: int, optional
-        :param i0:    first heap to update
-        :type ni: int, optional
-        :param ni:    nb of elements to update (-1 = use nh)
-        """
-        return _swigfaiss.int_minheap_array_t_addn(self, nj, vin, j0, i0, ni)
-
-    def addn_with_ids(self, nj, vin, id_in=None, id_stride=0, i0=0, ni=-1):
-        r"""
-         same as addn
-
-        :type id_in: int, optional
-        :param id_in:     ids of the elements to add, size ni * nj
-        :type id_stride: int, optional
-        :param id_stride: stride for id_in
-        """
-        return _swigfaiss.int_minheap_array_t_addn_with_ids(self, nj, vin, id_in, id_stride, i0, ni)
-
-    def addn_query_subset_with_ids(self, nsubset, subset, nj, vin, id_in=None, id_stride=0):
-        r"""
-         same as addn_with_ids, but for just a subset of queries
-
-        :type nsubset: int
-        :param nsubset:  number of query entries to update
-        :type subset: int
-        :param subset:   indexes of queries to update, in 0..nh-1, size nsubset
-        """
-        return _swigfaiss.int_minheap_array_t_addn_query_subset_with_ids(self, nsubset, subset, nj, vin, id_in, id_stride)
-
-    def reorder(self):
-        r""" reorder all the heaps"""
-        return _swigfaiss.int_minheap_array_t_reorder(self)
-
-    def per_line_extrema(self, vals_out, idx_out):
-        r"""
-         this is not really a heap function. It just finds the per-line
-          extrema of each line of array D
-        :type vals_out: int
-        :param vals_out:    extreme value of each line (size nh, or NULL)
-        :type idx_out: int
-        :param idx_out:     index of extreme value (size nh or NULL)
-        """
-        return _swigfaiss.int_minheap_array_t_per_line_extrema(self, vals_out, idx_out)
-
-    def __init__(self):
-        _swigfaiss.int_minheap_array_t_swiginit(self, _swigfaiss.new_int_minheap_array_t())
-    __swig_destroy__ = _swigfaiss.delete_int_minheap_array_t
-
-# Register int_minheap_array_t in _swigfaiss:
-_swigfaiss.int_minheap_array_t_swigregister(int_minheap_array_t)
-class float_maxheap_array_t(object):
-    r"""
-     a template structure for a set of [min|max]-heaps it is tailored
-    so that the actual data of the heaps can just live in compact
-    arrays.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nh = property(_swigfaiss.float_maxheap_array_t_nh_get, _swigfaiss.float_maxheap_array_t_nh_set, doc=r""" number of heaps""")
-    k = property(_swigfaiss.float_maxheap_array_t_k_get, _swigfaiss.float_maxheap_array_t_k_set, doc=r""" allocated size per heap""")
-    ids = property(_swigfaiss.float_maxheap_array_t_ids_get, _swigfaiss.float_maxheap_array_t_ids_set, doc=r""" identifiers (size nh * k)""")
-    val = property(_swigfaiss.float_maxheap_array_t_val_get, _swigfaiss.float_maxheap_array_t_val_set, doc=r""" values (distances or similarities), size nh * k""")
-
-    def get_val(self, key):
-        r""" Return the list of values for a heap"""
-        return _swigfaiss.float_maxheap_array_t_get_val(self, key)
-
-    def get_ids(self, key):
-        r""" Correspponding identifiers"""
-        return _swigfaiss.float_maxheap_array_t_get_ids(self, key)
-
-    def heapify(self):
-        r""" prepare all the heaps before adding"""
-        return _swigfaiss.float_maxheap_array_t_heapify(self)
-
-    def addn(self, nj, vin, j0=0, i0=0, ni=-1):
-        r"""
-         add nj elements to heaps i0:i0+ni, with sequential ids
-
-        :type nj: int
-        :param nj:    nb of elements to add to each heap
-        :type vin: float
-        :param vin:   elements to add, size ni * nj
-        :type j0: int, optional
-        :param j0:    add this to the ids that are added
-        :type i0: int, optional
-        :param i0:    first heap to update
-        :type ni: int, optional
-        :param ni:    nb of elements to update (-1 = use nh)
-        """
-        return _swigfaiss.float_maxheap_array_t_addn(self, nj, vin, j0, i0, ni)
-
-    def addn_with_ids(self, nj, vin, id_in=None, id_stride=0, i0=0, ni=-1):
-        r"""
-         same as addn
-
-        :type id_in: int, optional
-        :param id_in:     ids of the elements to add, size ni * nj
-        :type id_stride: int, optional
-        :param id_stride: stride for id_in
-        """
-        return _swigfaiss.float_maxheap_array_t_addn_with_ids(self, nj, vin, id_in, id_stride, i0, ni)
-
-    def addn_query_subset_with_ids(self, nsubset, subset, nj, vin, id_in=None, id_stride=0):
-        r"""
-         same as addn_with_ids, but for just a subset of queries
-
-        :type nsubset: int
-        :param nsubset:  number of query entries to update
-        :type subset: int
-        :param subset:   indexes of queries to update, in 0..nh-1, size nsubset
-        """
-        return _swigfaiss.float_maxheap_array_t_addn_query_subset_with_ids(self, nsubset, subset, nj, vin, id_in, id_stride)
-
-    def reorder(self):
-        r""" reorder all the heaps"""
-        return _swigfaiss.float_maxheap_array_t_reorder(self)
-
-    def per_line_extrema(self, vals_out, idx_out):
-        r"""
-         this is not really a heap function. It just finds the per-line
-          extrema of each line of array D
-        :type vals_out: float
-        :param vals_out:    extreme value of each line (size nh, or NULL)
-        :type idx_out: int
-        :param idx_out:     index of extreme value (size nh or NULL)
-        """
-        return _swigfaiss.float_maxheap_array_t_per_line_extrema(self, vals_out, idx_out)
-
-    def __init__(self):
-        _swigfaiss.float_maxheap_array_t_swiginit(self, _swigfaiss.new_float_maxheap_array_t())
-    __swig_destroy__ = _swigfaiss.delete_float_maxheap_array_t
-
-# Register float_maxheap_array_t in _swigfaiss:
-_swigfaiss.float_maxheap_array_t_swigregister(float_maxheap_array_t)
-class int_maxheap_array_t(object):
-    r"""
-     a template structure for a set of [min|max]-heaps it is tailored
-    so that the actual data of the heaps can just live in compact
-    arrays.
-    """
-
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    nh = property(_swigfaiss.int_maxheap_array_t_nh_get, _swigfaiss.int_maxheap_array_t_nh_set, doc=r""" number of heaps""")
-    k = property(_swigfaiss.int_maxheap_array_t_k_get, _swigfaiss.int_maxheap_array_t_k_set, doc=r""" allocated size per heap""")
-    ids = property(_swigfaiss.int_maxheap_array_t_ids_get, _swigfaiss.int_maxheap_array_t_ids_set, doc=r""" identifiers (size nh * k)""")
-    val = property(_swigfaiss.int_maxheap_array_t_val_get, _swigfaiss.int_maxheap_array_t_val_set, doc=r""" values (distances or similarities), size nh * k""")
-
-    def get_val(self, key):
-        r""" Return the list of values for a heap"""
-        return _swigfaiss.int_maxheap_array_t_get_val(self, key)
-
-    def get_ids(self, key):
-        r""" Correspponding identifiers"""
-        return _swigfaiss.int_maxheap_array_t_get_ids(self, key)
-
-    def heapify(self):
-        r""" prepare all the heaps before adding"""
-        return _swigfaiss.int_maxheap_array_t_heapify(self)
-
-    def addn(self, nj, vin, j0=0, i0=0, ni=-1):
-        r"""
-         add nj elements to heaps i0:i0+ni, with sequential ids
-
-        :type nj: int
-        :param nj:    nb of elements to add to each heap
-        :type vin: int
-        :param vin:   elements to add, size ni * nj
-        :type j0: int, optional
-        :param j0:    add this to the ids that are added
-        :type i0: int, optional
-        :param i0:    first heap to update
-        :type ni: int, optional
-        :param ni:    nb of elements to update (-1 = use nh)
-        """
-        return _swigfaiss.int_maxheap_array_t_addn(self, nj, vin, j0, i0, ni)
-
-    def addn_with_ids(self, nj, vin, id_in=None, id_stride=0, i0=0, ni=-1):
-        r"""
-         same as addn
-
-        :type id_in: int, optional
-        :param id_in:     ids of the elements to add, size ni * nj
-        :type id_stride: int, optional
-        :param id_stride: stride for id_in
-        """
-        return _swigfaiss.int_maxheap_array_t_addn_with_ids(self, nj, vin, id_in, id_stride, i0, ni)
-
-    def addn_query_subset_with_ids(self, nsubset, subset, nj, vin, id_in=None, id_stride=0):
-        r"""
-         same as addn_with_ids, but for just a subset of queries
-
-        :type nsubset: int
-        :param nsubset:  number of query entries to update
-        :type subset: int
-        :param subset:   indexes of queries to update, in 0..nh-1, size nsubset
-        """
-        return _swigfaiss.int_maxheap_array_t_addn_query_subset_with_ids(self, nsubset, subset, nj, vin, id_in, id_stride)
-
-    def reorder(self):
-        r""" reorder all the heaps"""
-        return _swigfaiss.int_maxheap_array_t_reorder(self)
-
-    def per_line_extrema(self, vals_out, idx_out):
-        r"""
-         this is not really a heap function. It just finds the per-line
-          extrema of each line of array D
-        :type vals_out: int
-        :param vals_out:    extreme value of each line (size nh, or NULL)
-        :type idx_out: int
-        :param idx_out:     index of extreme value (size nh or NULL)
-        """
-        return _swigfaiss.int_maxheap_array_t_per_line_extrema(self, vals_out, idx_out)
-
-    def __init__(self):
-        _swigfaiss.int_maxheap_array_t_swiginit(self, _swigfaiss.new_int_maxheap_array_t())
-    __swig_destroy__ = _swigfaiss.delete_int_maxheap_array_t
-
-# Register int_maxheap_array_t in _swigfaiss:
-_swigfaiss.int_maxheap_array_t_swigregister(int_maxheap_array_t)
-
-def CMin_float_partition_fuzzy(vals, ids, n, q_min, q_max, q_out):
-    r"""
-     partitions the table into 0:q and q:n where all elements above q are >= all
-    elements below q (for C = CMax, for CMin comparisons are reversed)
-
-    Returns the partition threshold. The elements q:n are destroyed on output.
-    """
-    return _swigfaiss.CMin_float_partition_fuzzy(vals, ids, n, q_min, q_max, q_out)
-
-def CMax_float_partition_fuzzy(vals, ids, n, q_min, q_max, q_out):
-    r"""
-     partitions the table into 0:q and q:n where all elements above q are >= all
-    elements below q (for C = CMax, for CMin comparisons are reversed)
-
-    Returns the partition threshold. The elements q:n are destroyed on output.
-    """
-    return _swigfaiss.CMax_float_partition_fuzzy(vals, ids, n, q_min, q_max, q_out)
-class AlignedTableUint8(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    tab = property(_swigfaiss.AlignedTableUint8_tab_get, _swigfaiss.AlignedTableUint8_tab_set)
-    numel = property(_swigfaiss.AlignedTableUint8_numel_get, _swigfaiss.AlignedTableUint8_numel_set)
-
-    @staticmethod
-    def round_capacity(n):
-        return _swigfaiss.AlignedTableUint8_round_capacity(n)
-
-    def __init__(self, *args):
-        _swigfaiss.AlignedTableUint8_swiginit(self, _swigfaiss.new_AlignedTableUint8(*args))
-
-    def itemsize(self):
-        return _swigfaiss.AlignedTableUint8_itemsize(self)
-
-    def resize(self, n):
-        return _swigfaiss.AlignedTableUint8_resize(self, n)
-
-    def clear(self):
-        return _swigfaiss.AlignedTableUint8_clear(self)
-
-    def size(self):
-        return _swigfaiss.AlignedTableUint8_size(self)
-
-    def nbytes(self):
-        return _swigfaiss.AlignedTableUint8_nbytes(self)
-
-    def get(self, *args):
-        return _swigfaiss.AlignedTableUint8_get(self, *args)
-
-    def data(self, *args):
-        return _swigfaiss.AlignedTableUint8_data(self, *args)
-    __swig_destroy__ = _swigfaiss.delete_AlignedTableUint8
-
-# Register AlignedTableUint8 in _swigfaiss:
-_swigfaiss.AlignedTableUint8_swigregister(AlignedTableUint8)
-class AlignedTableUint16(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    tab = property(_swigfaiss.AlignedTableUint16_tab_get, _swigfaiss.AlignedTableUint16_tab_set)
-    numel = property(_swigfaiss.AlignedTableUint16_numel_get, _swigfaiss.AlignedTableUint16_numel_set)
-
-    @staticmethod
-    def round_capacity(n):
-        return _swigfaiss.AlignedTableUint16_round_capacity(n)
-
-    def __init__(self, *args):
-        _swigfaiss.AlignedTableUint16_swiginit(self, _swigfaiss.new_AlignedTableUint16(*args))
-
-    def itemsize(self):
-        return _swigfaiss.AlignedTableUint16_itemsize(self)
-
-    def resize(self, n):
-        return _swigfaiss.AlignedTableUint16_resize(self, n)
-
-    def clear(self):
-        return _swigfaiss.AlignedTableUint16_clear(self)
-
-    def size(self):
-        return _swigfaiss.AlignedTableUint16_size(self)
-
-    def nbytes(self):
-        return _swigfaiss.AlignedTableUint16_nbytes(self)
-
-    def get(self, *args):
-        return _swigfaiss.AlignedTableUint16_get(self, *args)
-
-    def data(self, *args):
-        return _swigfaiss.AlignedTableUint16_data(self, *args)
-    __swig_destroy__ = _swigfaiss.delete_AlignedTableUint16
-
-# Register AlignedTableUint16 in _swigfaiss:
-_swigfaiss.AlignedTableUint16_swigregister(AlignedTableUint16)
-class AlignedTableFloat32(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    tab = property(_swigfaiss.AlignedTableFloat32_tab_get, _swigfaiss.AlignedTableFloat32_tab_set)
-    numel = property(_swigfaiss.AlignedTableFloat32_numel_get, _swigfaiss.AlignedTableFloat32_numel_set)
-
-    @staticmethod
-    def round_capacity(n):
-        return _swigfaiss.AlignedTableFloat32_round_capacity(n)
-
-    def __init__(self, *args):
-        _swigfaiss.AlignedTableFloat32_swiginit(self, _swigfaiss.new_AlignedTableFloat32(*args))
-
-    def itemsize(self):
-        return _swigfaiss.AlignedTableFloat32_itemsize(self)
-
-    def resize(self, n):
-        return _swigfaiss.AlignedTableFloat32_resize(self, n)
-
-    def clear(self):
-        return _swigfaiss.AlignedTableFloat32_clear(self)
-
-    def size(self):
-        return _swigfaiss.AlignedTableFloat32_size(self)
-
-    def nbytes(self):
-        return _swigfaiss.AlignedTableFloat32_nbytes(self)
-
-    def get(self, *args):
-        return _swigfaiss.AlignedTableFloat32_get(self, *args)
-
-    def data(self, *args):
-        return _swigfaiss.AlignedTableFloat32_data(self, *args)
-    __swig_destroy__ = _swigfaiss.delete_AlignedTableFloat32
-
-# Register AlignedTableFloat32 in _swigfaiss:
-_swigfaiss.AlignedTableFloat32_swigregister(AlignedTableFloat32)
-
-def CMin_uint16_partition_fuzzy(*args):
-    return _swigfaiss.CMin_uint16_partition_fuzzy(*args)
-
-def CMax_uint16_partition_fuzzy(*args):
-    return _swigfaiss.CMax_uint16_partition_fuzzy(*args)
-
-def merge_knn_results_CMin(*args):
-    return _swigfaiss.merge_knn_results_CMin(*args)
-
-def merge_knn_results_CMax(*args):
-    return _swigfaiss.merge_knn_results_CMax(*args)
-
-def omp_set_num_threads(num_threads):
-    return _swigfaiss.omp_set_num_threads(num_threads)
-
-def omp_get_max_threads():
-    return _swigfaiss.omp_get_max_threads()
-
-def memcpy(dest, src, n):
-    return _swigfaiss.memcpy(dest, src, n)
-
-def cast_integer_to_uint8_ptr(x):
-    return _swigfaiss.cast_integer_to_uint8_ptr(x)
-
-def cast_integer_to_float_ptr(x):
-    return _swigfaiss.cast_integer_to_float_ptr(x)
-
-def cast_integer_to_idx_t_ptr(x):
-    return _swigfaiss.cast_integer_to_idx_t_ptr(x)
-
-def cast_integer_to_int_ptr(x):
-    return _swigfaiss.cast_integer_to_int_ptr(x)
-
-def cast_integer_to_void_ptr(x):
-    return _swigfaiss.cast_integer_to_void_ptr(x)
-class MapLong2Long(object):
-    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
-    __repr__ = _swig_repr
-    map = property(_swigfaiss.MapLong2Long_map_get, _swigfaiss.MapLong2Long_map_set)
-
-    def add(self, n, keys, vals):
-        return _swigfaiss.MapLong2Long_add(self, n, keys, vals)
-
-    def search(self, key):
-        return _swigfaiss.MapLong2Long_search(self, key)
-
-    def search_multiple(self, n, keys, vals):
-        return _swigfaiss.MapLong2Long_search_multiple(self, n, keys, vals)
-
-    def __init__(self):
-        _swigfaiss.MapLong2Long_swiginit(self, _swigfaiss.new_MapLong2Long())
-    __swig_destroy__ = _swigfaiss.delete_MapLong2Long
-
-# Register MapLong2Long in _swigfaiss:
-_swigfaiss.MapLong2Long_swigregister(MapLong2Long)
-
-def wait():
-    return _swigfaiss.wait()
-
diff --git a/spaces/chuuyasleftlung/meowmeow/README.md b/spaces/chuuyasleftlung/meowmeow/README.md
deleted file mode 100644
index 3d1ee49d453b51c632e06eb45996636a6cccd966..0000000000000000000000000000000000000000
--- a/spaces/chuuyasleftlung/meowmeow/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Meowmeow
-emoji: 📊
-colorFrom: red
-colorTo: gray
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/cihyFjudo/fairness-paper-search/Recognized Entity For Whatsapp !LINK!.md b/spaces/cihyFjudo/fairness-paper-search/Recognized Entity For Whatsapp !LINK!.md
deleted file mode 100644
index 345b845951ded73c36f35ac1e2a97e1ca177eecf..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Recognized Entity For Whatsapp !LINK!.md	
+++ /dev/null
@@ -1,29 +0,0 @@
-<br />
-<p>You can use Amazon Comprehend to detect PII entities in English text documents. A PII entity is a specific type of personally identifiable information (PII). Use PII detection to locate the PII entities or redact the PII entities in the text.</p>
-<p>An individual's name. This entity type does not include titles, such as Dr., Mr., Mrs., or Miss. Amazon Comprehend does not apply this entity type to names that are part of organizations or addresses. For example, Amazon Comprehend recognizes the "John Doe Organization" as an organization, and it recognizes "Jane Doe Street" as an address.</p>
-<h2>Recognized Entity For Whatsapp</h2><br /><p><b><b>Download</b> &#128505; <a href="https://tinurli.com/2uwind">https://tinurli.com/2uwind</a></b></p><br /><br />
-<p>Named-entity recognition is a sub-task of information extraction that seeks to locate and classify named entities mentioned in unstructured text into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.</p>
-<p>To put it simply, NER deals with extracting the real-world entity from the text such as a person, an organization, or an event. Named Entity Recognition is also simply known as entity identification, entity chunking, and entity extraction. They are quite similar to POS(part-of-speech) tags.</p>
-<p>Many modern applications (like Netflix and YouTube) rely on recommendation systems to create optimal customer experiences. A lot of these systems rely on named entity recognition, which is able to make suggestions based on user search history.</p>
-<p>Named entity recognition systems can be utilized to organize all this customer feedback and identify recurring problems. As an example, you could use NER to detect the features that are mentioned the most in negative feedback, which would allow you to double down and focus on improving those features.</p>
-<p>Recruitment teams can make use of an entity extractor to instantly extract the most relevant information about candidates. They can pull personal information (like name, address, phone number, date of birth and email), and even data related to their training and experience (like certifications, degree, company names, skills, etc).<br/></p>
-<p>By collecting this entity information, product teams are empowered with invaluable customer or employee information, regardless of industry. Then, these teams can perform analytics to boost understanding of customers, adjust marketing campaigns, modify products, and much more.</p>
-<p>Around 3 years ago we open-sourced one of our key frameworks, Chatbot NER, which is custom built to support entity recognition in text messages. You can read more about Chatbot NER. After doing thorough research on existing Named Entity Recognition (NER) systems, we felt the strong need for building a framework which can support entity recognition for Indian languages. This led us to upgrade our own NER module i.e Chatbot NER to V2 version to scale its functionalities in local languages. The primary focus of this blog is to help you get started with using basic capabilities of Chatbot NER for English and 5 other Indian languages and their code mixed form.</p>
-<p>The system is trained to recognize faces larger than 32 pixels (on the shortest dimension), which translate into a minimum size for a face to be recognized that varies from approximately 1/7 of the screen smaller dimension at QVGA resolution to 1/30 at HD 1080p resolution. For example, at VGA resolution, users should expect lower performances for faces smaller than 1/10 of the screen smaller dimension.</p>
-<p></p>
-<p>Frame accurate timecodes provide the exact frame number for a relevant segment of video or entity. Media companies commonly process timecodes using the SMPTE (Society of Motion Picture and Television Engineers) format hours:minutes:seconds:frame number, for example, 00:24:53:22.</p>
-<p>Amazon Rekognition is integrated with AWS Identity and Access Management (IAM). AWS IAM policies can be used to ensure that only authorized users have access to Amazon Rekognition APIs. For more details, please see the Amazon Rekognition Authentication and Access Control page.</p>
-<p>The government does not award grants based on a drawing or raffle; an individual or entity must first apply for the grant through a federal website, like Grants.gov. Any individual who claims the government is awarding a grant, for example, to a lucky group of citizens who have paid their taxes on time is attempting to scam you.</p>
-<p>Official business account approval is done at the sole and full discretion of WhatsApp. Generally, Meta reserves Official Business accounts for internationally recognized brands. Being verified on Facebook or Instagram does not help your business to become an Official Business Account.</p>
-<p>Our case highlights important challenges when treating patients with pLFLG AS. The diagnosis is complex and requires a multi-modality approach to delineate clinical finding and correlate imaging with symptoms and functional capacity. Increased understanding of this entity amongst general clinicians and referral to a collaborative multi-disciplinary heart teams are needed to address the barriers to intervention many patients experience. This case demonstrates the inappropriate rejection of, and unnecessary delays to, AVR contributed to by fixed notions regarding AS severity. Given the patient demographic and time critical outcomes, pLFLG AS patients are subgroup of AS in which delays to surgery can be least afforded, let alone the associated ongoing reduction in quality of life, and potential increased care needs and caregiver burden, that patients and family experience. Furthermore, given the projected increased prevalence of disease states associated with pLFLG AS due to an ageing population, this case is an important reminder of this well documented but poorly recognized disease.</p>
-<p>Named Entity Recognition is the most important or I would say the starting step in Information Retrieval. Information Retrieval is the technique to extract important and useful information from unstructured raw text documents. Named Entity Recognition NER works by locating and identifying the named entities present in unstructured text into the standard categories such as person names, locations, organizations, time expressions, quantities, monetary values, percentage, codes etc. Spacy comes with an extremely fast statistical entity recognition system that assigns labels to contiguous spans of tokens.</p>
-<p>The standard way to access entity annotations is the doc.ents property, which produces a sequence of Span objects. The entity type is accessible either as a hash value using <strong>ent.label</strong> or as a string using <strong>ent.label_</strong>.</p>
-<p>You can also access token entity annotations using the token.ent_iob and token.ent_type attributes. token.ent_iob indicates whether an entity starts, continues or ends on the tag. If no entity type is set on a token, it will return an empty string.</p>
-<p><strong>Note:</strong> In the above example only San Francisco is recognized as named entity. hence rest of the tokens are described as outside the entity. And in San Francisco San is the starting of the entity and Francisco is inside the entity.</p>
-<p>Normally we would have spaCy build a library of named entities by training it on several samples of text.<br>Sometimes, we want to assign specific token a named entity whic is not recognized by the trained spacy model. We can do this as shown in below code.</p>
-<p>Before working with any person or firm to trade in commodity futures, commodity pools, options, forex, or other derivatives, verify that the entity is properly registered with the CFTC. The Commodity Exchange Act requires certain firms and individuals to be registered with the CFTC. Registration and examination of intermediaries is conducted on behalf of the CFTC by the National Futures Association (NFA) under the supervision of the CFTC.</p>
-<p>Submit a tip to the CFTC. If you believe an unregistered entity or individual is attempting to commit fraud, or has committed fraud, the information you provide could help save others from being victimized.</p>
-<p>processing.... Drugs & Diseases > Rheumatology Mixed Connective-Tissue Disease (MCTD) Updated: Dec 22, 2022 <ul> <li> Author: Eric L Greidinger, MD; Chief Editor: Herbert S Diamond, MD more...</li> </ul> <ul> </li> Share</li> Email</li> Print</li> Feedback</li> </ul> Close <ul> Facebook</li> Twitter</li> LinkedIn</li> WhatsApp</li> </ul> webmd.ads2.defineAd(id: 'ads-pos-421-sfp',pos: 421); Sections Mixed Connective-Tissue Disease (MCTD) <ul> Sections Mixed Connective-Tissue Disease (MCTD) </li> <li>Overview <ul> Practice Essentials</li> Pathophysiology</li> Etiology</li> Epidemiology</li> Prognosis</li> Patient Education</li> Show All</li> </ul></li> <li>Presentation <ul> History</li> Physical</li> Show All</li> </ul></li> DDx</li> <li>Workup <ul> Laboratory Studies</li> Imaging Studies</li> Other Tests</li> Show All</li> </ul></li> <li>Treatment <ul> Approach Considerations</li> Consultations</li> Diet and Activity</li> Prevention</li> Long-Term Monitoring</li> Show All</li> </ul></li> <li>Medication <ul> Medication Summary</li> Nonsteroidal anti-inflammatory drugs (NSAIDs)</li> Cyclooxygenase-2 (COX-2) inhibitors</li> Omega-3 fatty acids</li> Proton pump inhibitors</li> Antimalarial agents</li> Corticosteroids</li> Calcium channel blocking agents</li> Phosphodiesterase (type 5) enzyme inhibitor</li> Endothelin receptor antagonists</li> Prostaglandins</li> Immunosuppressive agents</li> Pulmonary, Tyrosine Kinase Inhibitors</li> Disease Modifying Anti-Rheumatic Drugs (DMARDs)</li> Show All</li> </ul></li> Questions & Answers</li> Media Gallery</li> References</li> </ul> Overview Practice Essentials Mixed connective-tissue disease (MCTD) was first recognized by Sharp and colleagues (1972) in a group of patients with overlapping clinical features of systemic lupus erythematosus (SLE), scleroderma, and myositis, with the presence of a distinctive antibody against what now is known to be U1-ribonucleoprotein (RNP). [1, 2]</p>
-<p>Nevertheless, whether MCTD is a distinct disease entity has been in question since shortly after its original description. A minority of authors continues to suggest that MCTD would be better characterized as subgroups or early stages of disorders such as SLE or systemic sclerosis. [10] Other authors propose that MCTD cases should not be distinguished from undifferentiated autoimmune rheumatic disease. [11, 12]</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/clement13430/lab1_iris/README.md b/spaces/clement13430/lab1_iris/README.md
deleted file mode 100644
index cf1835b9af611996ba66a043268138add2c08b0a..0000000000000000000000000000000000000000
--- a/spaces/clement13430/lab1_iris/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Iris
-emoji: 🐢
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 3.5
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/anyio/streams/stapled.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/anyio/streams/stapled.py
deleted file mode 100644
index 1b2862e3eac2ae6f18212d312e7cc7c2acdf0c5c..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/anyio/streams/stapled.py
+++ /dev/null
@@ -1,140 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any, Callable, Generic, Mapping, Sequence, TypeVar
-
-from ..abc import (
-    ByteReceiveStream,
-    ByteSendStream,
-    ByteStream,
-    Listener,
-    ObjectReceiveStream,
-    ObjectSendStream,
-    ObjectStream,
-    TaskGroup,
-)
-
-T_Item = TypeVar("T_Item")
-T_Stream = TypeVar("T_Stream")
-
-
-@dataclass(eq=False)
-class StapledByteStream(ByteStream):
-    """
-    Combines two byte streams into a single, bidirectional byte stream.
-
-    Extra attributes will be provided from both streams, with the receive stream providing the
-    values in case of a conflict.
-
-    :param ByteSendStream send_stream: the sending byte stream
-    :param ByteReceiveStream receive_stream: the receiving byte stream
-    """
-
-    send_stream: ByteSendStream
-    receive_stream: ByteReceiveStream
-
-    async def receive(self, max_bytes: int = 65536) -> bytes:
-        return await self.receive_stream.receive(max_bytes)
-
-    async def send(self, item: bytes) -> None:
-        await self.send_stream.send(item)
-
-    async def send_eof(self) -> None:
-        await self.send_stream.aclose()
-
-    async def aclose(self) -> None:
-        await self.send_stream.aclose()
-        await self.receive_stream.aclose()
-
-    @property
-    def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
-        return {
-            **self.send_stream.extra_attributes,
-            **self.receive_stream.extra_attributes,
-        }
-
-
-@dataclass(eq=False)
-class StapledObjectStream(Generic[T_Item], ObjectStream[T_Item]):
-    """
-    Combines two object streams into a single, bidirectional object stream.
-
-    Extra attributes will be provided from both streams, with the receive stream providing the
-    values in case of a conflict.
-
-    :param ObjectSendStream send_stream: the sending object stream
-    :param ObjectReceiveStream receive_stream: the receiving object stream
-    """
-
-    send_stream: ObjectSendStream[T_Item]
-    receive_stream: ObjectReceiveStream[T_Item]
-
-    async def receive(self) -> T_Item:
-        return await self.receive_stream.receive()
-
-    async def send(self, item: T_Item) -> None:
-        await self.send_stream.send(item)
-
-    async def send_eof(self) -> None:
-        await self.send_stream.aclose()
-
-    async def aclose(self) -> None:
-        await self.send_stream.aclose()
-        await self.receive_stream.aclose()
-
-    @property
-    def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
-        return {
-            **self.send_stream.extra_attributes,
-            **self.receive_stream.extra_attributes,
-        }
-
-
-@dataclass(eq=False)
-class MultiListener(Generic[T_Stream], Listener[T_Stream]):
-    """
-    Combines multiple listeners into one, serving connections from all of them at once.
-
-    Any MultiListeners in the given collection of listeners will have their listeners moved into
-    this one.
-
-    Extra attributes are provided from each listener, with each successive listener overriding any
-    conflicting attributes from the previous one.
-
-    :param listeners: listeners to serve
-    :type listeners: Sequence[Listener[T_Stream]]
-    """
-
-    listeners: Sequence[Listener[T_Stream]]
-
-    def __post_init__(self) -> None:
-        listeners: list[Listener[T_Stream]] = []
-        for listener in self.listeners:
-            if isinstance(listener, MultiListener):
-                listeners.extend(listener.listeners)
-                del listener.listeners[:]  # type: ignore[attr-defined]
-            else:
-                listeners.append(listener)
-
-        self.listeners = listeners
-
-    async def serve(
-        self, handler: Callable[[T_Stream], Any], task_group: TaskGroup | None = None
-    ) -> None:
-        from .. import create_task_group
-
-        async with create_task_group() as tg:
-            for listener in self.listeners:
-                tg.start_soon(listener.serve, handler, task_group)
-
-    async def aclose(self) -> None:
-        for listener in self.listeners:
-            await listener.aclose()
-
-    @property
-    def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
-        attributes: dict = {}
-        for listener in self.listeners:
-            attributes.update(listener.extra_attributes)
-
-        return attributes
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/exceptiongroup/_formatting.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/exceptiongroup/_formatting.py
deleted file mode 100644
index c0402ba03d2f564f8a33b1338ada04c2d0f5c48b..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/exceptiongroup/_formatting.py
+++ /dev/null
@@ -1,563 +0,0 @@
-# traceback_exception_init() adapted from trio
-#
-# _ExceptionPrintContext and traceback_exception_format() copied from the standard
-# library
-from __future__ import annotations
-
-import collections.abc
-import sys
-import textwrap
-import traceback
-from functools import singledispatch
-from types import TracebackType
-from typing import Any, List, Optional
-
-from ._exceptions import BaseExceptionGroup
-
-max_group_width = 15
-max_group_depth = 10
-_cause_message = (
-    "\nThe above exception was the direct cause of the following exception:\n\n"
-)
-
-_context_message = (
-    "\nDuring handling of the above exception, another exception occurred:\n\n"
-)
-
-
-def _format_final_exc_line(etype, value):
-    valuestr = _safe_string(value, "exception")
-    if value is None or not valuestr:
-        line = f"{etype}\n"
-    else:
-        line = f"{etype}: {valuestr}\n"
-
-    return line
-
-
-def _safe_string(value, what, func=str):
-    try:
-        return func(value)
-    except BaseException:
-        return f"<{what} {func.__name__}() failed>"
-
-
-class _ExceptionPrintContext:
-    def __init__(self):
-        self.seen = set()
-        self.exception_group_depth = 0
-        self.need_close = False
-
-    def indent(self):
-        return " " * (2 * self.exception_group_depth)
-
-    def emit(self, text_gen, margin_char=None):
-        if margin_char is None:
-            margin_char = "|"
-        indent_str = self.indent()
-        if self.exception_group_depth:
-            indent_str += margin_char + " "
-
-        if isinstance(text_gen, str):
-            yield textwrap.indent(text_gen, indent_str, lambda line: True)
-        else:
-            for text in text_gen:
-                yield textwrap.indent(text, indent_str, lambda line: True)
-
-
-def exceptiongroup_excepthook(
-    etype: type[BaseException], value: BaseException, tb: TracebackType | None
-) -> None:
-    sys.stderr.write("".join(traceback.format_exception(etype, value, tb)))
-
-
-class PatchedTracebackException(traceback.TracebackException):
-    def __init__(
-        self,
-        exc_type: type[BaseException],
-        exc_value: BaseException,
-        exc_traceback: TracebackType | None,
-        *,
-        limit: int | None = None,
-        lookup_lines: bool = True,
-        capture_locals: bool = False,
-        compact: bool = False,
-        _seen: set[int] | None = None,
-    ) -> None:
-        kwargs: dict[str, Any] = {}
-        if sys.version_info >= (3, 10):
-            kwargs["compact"] = compact
-
-        is_recursive_call = _seen is not None
-        if _seen is None:
-            _seen = set()
-        _seen.add(id(exc_value))
-
-        self.stack = traceback.StackSummary.extract(
-            traceback.walk_tb(exc_traceback),
-            limit=limit,
-            lookup_lines=lookup_lines,
-            capture_locals=capture_locals,
-        )
-        self.exc_type = exc_type
-        # Capture now to permit freeing resources: only complication is in the
-        # unofficial API _format_final_exc_line
-        self._str = _safe_string(exc_value, "exception")
-        try:
-            self.__notes__ = getattr(exc_value, "__notes__", None)
-        except KeyError:
-            # Workaround for https://github.com/python/cpython/issues/98778 on Python
-            # <= 3.9, and some 3.10 and 3.11 patch versions.
-            HTTPError = getattr(sys.modules.get("urllib.error", None), "HTTPError", ())
-            if sys.version_info[:2] <= (3, 11) and isinstance(exc_value, HTTPError):
-                self.__notes__ = None
-            else:
-                raise
-
-        if exc_type and issubclass(exc_type, SyntaxError):
-            # Handle SyntaxError's specially
-            self.filename = exc_value.filename
-            lno = exc_value.lineno
-            self.lineno = str(lno) if lno is not None else None
-            self.text = exc_value.text
-            self.offset = exc_value.offset
-            self.msg = exc_value.msg
-            if sys.version_info >= (3, 10):
-                end_lno = exc_value.end_lineno
-                self.end_lineno = str(end_lno) if end_lno is not None else None
-                self.end_offset = exc_value.end_offset
-        elif (
-            exc_type
-            and issubclass(exc_type, (NameError, AttributeError))
-            and getattr(exc_value, "name", None) is not None
-        ):
-            suggestion = _compute_suggestion_error(exc_value, exc_traceback)
-            if suggestion:
-                self._str += f". Did you mean: '{suggestion}'?"
-
-        if lookup_lines:
-            # Force all lines in the stack to be loaded
-            for frame in self.stack:
-                frame.line
-
-        self.__suppress_context__ = (
-            exc_value.__suppress_context__ if exc_value is not None else False
-        )
-
-        # Convert __cause__ and __context__ to `TracebackExceptions`s, use a
-        # queue to avoid recursion (only the top-level call gets _seen == None)
-        if not is_recursive_call:
-            queue = [(self, exc_value)]
-            while queue:
-                te, e = queue.pop()
-
-                if e and e.__cause__ is not None and id(e.__cause__) not in _seen:
-                    cause = PatchedTracebackException(
-                        type(e.__cause__),
-                        e.__cause__,
-                        e.__cause__.__traceback__,
-                        limit=limit,
-                        lookup_lines=lookup_lines,
-                        capture_locals=capture_locals,
-                        _seen=_seen,
-                    )
-                else:
-                    cause = None
-
-                if compact:
-                    need_context = (
-                        cause is None and e is not None and not e.__suppress_context__
-                    )
-                else:
-                    need_context = True
-                if (
-                    e
-                    and e.__context__ is not None
-                    and need_context
-                    and id(e.__context__) not in _seen
-                ):
-                    context = PatchedTracebackException(
-                        type(e.__context__),
-                        e.__context__,
-                        e.__context__.__traceback__,
-                        limit=limit,
-                        lookup_lines=lookup_lines,
-                        capture_locals=capture_locals,
-                        _seen=_seen,
-                    )
-                else:
-                    context = None
-
-                # Capture each of the exceptions in the ExceptionGroup along with each
-                # of their causes and contexts
-                if e and isinstance(e, BaseExceptionGroup):
-                    exceptions = []
-                    for exc in e.exceptions:
-                        texc = PatchedTracebackException(
-                            type(exc),
-                            exc,
-                            exc.__traceback__,
-                            lookup_lines=lookup_lines,
-                            capture_locals=capture_locals,
-                            _seen=_seen,
-                        )
-                        exceptions.append(texc)
-                else:
-                    exceptions = None
-
-                te.__cause__ = cause
-                te.__context__ = context
-                te.exceptions = exceptions
-                if cause:
-                    queue.append((te.__cause__, e.__cause__))
-                if context:
-                    queue.append((te.__context__, e.__context__))
-                if exceptions:
-                    queue.extend(zip(te.exceptions, e.exceptions))
-
-    def format(self, *, chain=True, _ctx=None):
-        if _ctx is None:
-            _ctx = _ExceptionPrintContext()
-
-        output = []
-        exc = self
-        if chain:
-            while exc:
-                if exc.__cause__ is not None:
-                    chained_msg = _cause_message
-                    chained_exc = exc.__cause__
-                elif exc.__context__ is not None and not exc.__suppress_context__:
-                    chained_msg = _context_message
-                    chained_exc = exc.__context__
-                else:
-                    chained_msg = None
-                    chained_exc = None
-
-                output.append((chained_msg, exc))
-                exc = chained_exc
-        else:
-            output.append((None, exc))
-
-        for msg, exc in reversed(output):
-            if msg is not None:
-                yield from _ctx.emit(msg)
-            if exc.exceptions is None:
-                if exc.stack:
-                    yield from _ctx.emit("Traceback (most recent call last):\n")
-                    yield from _ctx.emit(exc.stack.format())
-                yield from _ctx.emit(exc.format_exception_only())
-            elif _ctx.exception_group_depth > max_group_depth:
-                # exception group, but depth exceeds limit
-                yield from _ctx.emit(f"... (max_group_depth is {max_group_depth})\n")
-            else:
-                # format exception group
-                is_toplevel = _ctx.exception_group_depth == 0
-                if is_toplevel:
-                    _ctx.exception_group_depth += 1
-
-                if exc.stack:
-                    yield from _ctx.emit(
-                        "Exception Group Traceback (most recent call last):\n",
-                        margin_char="+" if is_toplevel else None,
-                    )
-                    yield from _ctx.emit(exc.stack.format())
-
-                yield from _ctx.emit(exc.format_exception_only())
-                num_excs = len(exc.exceptions)
-                if num_excs <= max_group_width:
-                    n = num_excs
-                else:
-                    n = max_group_width + 1
-                _ctx.need_close = False
-                for i in range(n):
-                    last_exc = i == n - 1
-                    if last_exc:
-                        # The closing frame may be added by a recursive call
-                        _ctx.need_close = True
-
-                    if max_group_width is not None:
-                        truncated = i >= max_group_width
-                    else:
-                        truncated = False
-                    title = f"{i + 1}" if not truncated else "..."
-                    yield (
-                        _ctx.indent()
-                        + ("+-" if i == 0 else "  ")
-                        + f"+---------------- {title} ----------------\n"
-                    )
-                    _ctx.exception_group_depth += 1
-                    if not truncated:
-                        yield from exc.exceptions[i].format(chain=chain, _ctx=_ctx)
-                    else:
-                        remaining = num_excs - max_group_width
-                        plural = "s" if remaining > 1 else ""
-                        yield from _ctx.emit(
-                            f"and {remaining} more exception{plural}\n"
-                        )
-
-                    if last_exc and _ctx.need_close:
-                        yield _ctx.indent() + "+------------------------------------\n"
-                        _ctx.need_close = False
-                    _ctx.exception_group_depth -= 1
-
-                if is_toplevel:
-                    assert _ctx.exception_group_depth == 1
-                    _ctx.exception_group_depth = 0
-
-    def format_exception_only(self):
-        """Format the exception part of the traceback.
-        The return value is a generator of strings, each ending in a newline.
-        Normally, the generator emits a single string; however, for
-        SyntaxError exceptions, it emits several lines that (when
-        printed) display detailed information about where the syntax
-        error occurred.
-        The message indicating which exception occurred is always the last
-        string in the output.
-        """
-        if self.exc_type is None:
-            yield traceback._format_final_exc_line(None, self._str)
-            return
-
-        stype = self.exc_type.__qualname__
-        smod = self.exc_type.__module__
-        if smod not in ("__main__", "builtins"):
-            if not isinstance(smod, str):
-                smod = "<unknown>"
-            stype = smod + "." + stype
-
-        if not issubclass(self.exc_type, SyntaxError):
-            yield _format_final_exc_line(stype, self._str)
-        elif traceback_exception_format_syntax_error is not None:
-            yield from traceback_exception_format_syntax_error(self, stype)
-        else:
-            yield from traceback_exception_original_format_exception_only(self)
-
-        if isinstance(self.__notes__, collections.abc.Sequence):
-            for note in self.__notes__:
-                note = _safe_string(note, "note")
-                yield from [line + "\n" for line in note.split("\n")]
-        elif self.__notes__ is not None:
-            yield _safe_string(self.__notes__, "__notes__", func=repr)
-
-
-traceback_exception_original_format = traceback.TracebackException.format
-traceback_exception_original_format_exception_only = (
-    traceback.TracebackException.format_exception_only
-)
-traceback_exception_format_syntax_error = getattr(
-    traceback.TracebackException, "_format_syntax_error", None
-)
-if sys.excepthook is sys.__excepthook__:
-    traceback.TracebackException.__init__ = (  # type: ignore[assignment]
-        PatchedTracebackException.__init__
-    )
-    traceback.TracebackException.format = (  # type: ignore[assignment]
-        PatchedTracebackException.format
-    )
-    traceback.TracebackException.format_exception_only = (  # type: ignore[assignment]
-        PatchedTracebackException.format_exception_only
-    )
-    sys.excepthook = exceptiongroup_excepthook
-
-
-@singledispatch
-def format_exception_only(__exc: BaseException) -> List[str]:
-    return list(
-        PatchedTracebackException(
-            type(__exc), __exc, None, compact=True
-        ).format_exception_only()
-    )
-
-
-@format_exception_only.register
-def _(__exc: type, value: BaseException) -> List[str]:
-    return format_exception_only(value)
-
-
-@singledispatch
-def format_exception(
-    __exc: BaseException,
-    limit: Optional[int] = None,
-    chain: bool = True,
-) -> List[str]:
-    return list(
-        PatchedTracebackException(
-            type(__exc), __exc, __exc.__traceback__, limit=limit, compact=True
-        ).format(chain=chain)
-    )
-
-
-@format_exception.register
-def _(
-    __exc: type,
-    value: BaseException,
-    tb: TracebackType,
-    limit: Optional[int] = None,
-    chain: bool = True,
-) -> List[str]:
-    return format_exception(value, limit, chain)
-
-
-@singledispatch
-def print_exception(
-    __exc: BaseException,
-    limit: Optional[int] = None,
-    file: Any = None,
-    chain: bool = True,
-) -> None:
-    if file is None:
-        file = sys.stderr
-
-    for line in PatchedTracebackException(
-        type(__exc), __exc, __exc.__traceback__, limit=limit
-    ).format(chain=chain):
-        print(line, file=file, end="")
-
-
-@print_exception.register
-def _(
-    __exc: type,
-    value: BaseException,
-    tb: TracebackType,
-    limit: Optional[int] = None,
-    file: Any = None,
-    chain: bool = True,
-) -> None:
-    print_exception(value, limit, file, chain)
-
-
-def print_exc(
-    limit: Optional[int] = None,
-    file: Any | None = None,
-    chain: bool = True,
-) -> None:
-    value = sys.exc_info()[1]
-    print_exception(value, limit, file, chain)
-
-
-# Python levenshtein edit distance code for NameError/AttributeError
-# suggestions, backported from 3.12
-
-_MAX_CANDIDATE_ITEMS = 750
-_MAX_STRING_SIZE = 40
-_MOVE_COST = 2
-_CASE_COST = 1
-_SENTINEL = object()
-
-
-def _substitution_cost(ch_a, ch_b):
-    if ch_a == ch_b:
-        return 0
-    if ch_a.lower() == ch_b.lower():
-        return _CASE_COST
-    return _MOVE_COST
-
-
-def _compute_suggestion_error(exc_value, tb):
-    wrong_name = getattr(exc_value, "name", None)
-    if wrong_name is None or not isinstance(wrong_name, str):
-        return None
-    if isinstance(exc_value, AttributeError):
-        obj = getattr(exc_value, "obj", _SENTINEL)
-        if obj is _SENTINEL:
-            return None
-        obj = exc_value.obj
-        try:
-            d = dir(obj)
-        except Exception:
-            return None
-    else:
-        assert isinstance(exc_value, NameError)
-        # find most recent frame
-        if tb is None:
-            return None
-        while tb.tb_next is not None:
-            tb = tb.tb_next
-        frame = tb.tb_frame
-
-        d = list(frame.f_locals) + list(frame.f_globals) + list(frame.f_builtins)
-    if len(d) > _MAX_CANDIDATE_ITEMS:
-        return None
-    wrong_name_len = len(wrong_name)
-    if wrong_name_len > _MAX_STRING_SIZE:
-        return None
-    best_distance = wrong_name_len
-    suggestion = None
-    for possible_name in d:
-        if possible_name == wrong_name:
-            # A missing attribute is "found". Don't suggest it (see GH-88821).
-            continue
-        # No more than 1/3 of the involved characters should need changed.
-        max_distance = (len(possible_name) + wrong_name_len + 3) * _MOVE_COST // 6
-        # Don't take matches we've already beaten.
-        max_distance = min(max_distance, best_distance - 1)
-        current_distance = _levenshtein_distance(
-            wrong_name, possible_name, max_distance
-        )
-        if current_distance > max_distance:
-            continue
-        if not suggestion or current_distance < best_distance:
-            suggestion = possible_name
-            best_distance = current_distance
-    return suggestion
-
-
-def _levenshtein_distance(a, b, max_cost):
-    # A Python implementation of Python/suggestions.c:levenshtein_distance.
-
-    # Both strings are the same
-    if a == b:
-        return 0
-
-    # Trim away common affixes
-    pre = 0
-    while a[pre:] and b[pre:] and a[pre] == b[pre]:
-        pre += 1
-    a = a[pre:]
-    b = b[pre:]
-    post = 0
-    while a[: post or None] and b[: post or None] and a[post - 1] == b[post - 1]:
-        post -= 1
-    a = a[: post or None]
-    b = b[: post or None]
-    if not a or not b:
-        return _MOVE_COST * (len(a) + len(b))
-    if len(a) > _MAX_STRING_SIZE or len(b) > _MAX_STRING_SIZE:
-        return max_cost + 1
-
-    # Prefer shorter buffer
-    if len(b) < len(a):
-        a, b = b, a
-
-    # Quick fail when a match is impossible
-    if (len(b) - len(a)) * _MOVE_COST > max_cost:
-        return max_cost + 1
-
-    # Instead of producing the whole traditional len(a)-by-len(b)
-    # matrix, we can update just one row in place.
-    # Initialize the buffer row
-    row = list(range(_MOVE_COST, _MOVE_COST * (len(a) + 1), _MOVE_COST))
-
-    result = 0
-    for bindex in range(len(b)):
-        bchar = b[bindex]
-        distance = result = bindex * _MOVE_COST
-        minimum = sys.maxsize
-        for index in range(len(a)):
-            # 1) Previous distance in this row is cost(b[:b_index], a[:index])
-            substitute = distance + _substitution_cost(bchar, a[index])
-            # 2) cost(b[:b_index], a[:index+1]) from previous row
-            distance = row[index]
-            # 3) existing result is cost(b[:b_index+1], a[index])
-
-            insert_delete = min(result, distance) + _MOVE_COST
-            result = min(insert_delete, substitute)
-
-            # cost(b[:b_index+1], a[:index+1])
-            row[index] = result
-            if result < minimum:
-                minimum = result
-        if minimum > max_cost:
-            # Everything in this row is too big, so bail early.
-            return max_cost + 1
-    return result
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/feaLib/parser.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/feaLib/parser.py
deleted file mode 100644
index 49667f4503e15be8c00388a72cb0d428dc7dafe9..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/feaLib/parser.py
+++ /dev/null
@@ -1,2365 +0,0 @@
-from fontTools.feaLib.error import FeatureLibError
-from fontTools.feaLib.lexer import Lexer, IncludingLexer, NonIncludingLexer
-from fontTools.feaLib.variableScalar import VariableScalar
-from fontTools.misc.encodingTools import getEncoding
-from fontTools.misc.textTools import bytechr, tobytes, tostr
-import fontTools.feaLib.ast as ast
-import logging
-import os
-import re
-
-
-log = logging.getLogger(__name__)
-
-
-class Parser(object):
-    """Initializes a Parser object.
-
-    Example:
-
-        .. code:: python
-
-            from fontTools.feaLib.parser import Parser
-            parser = Parser(file, font.getReverseGlyphMap())
-            parsetree = parser.parse()
-
-    Note: the ``glyphNames`` iterable serves a double role to help distinguish
-    glyph names from ranges in the presence of hyphens and to ensure that glyph
-    names referenced in a feature file are actually part of a font's glyph set.
-    If the iterable is left empty, no glyph name in glyph set checking takes
-    place, and all glyph tokens containing hyphens are treated as literal glyph
-    names, not as ranges. (Adding a space around the hyphen can, in any case,
-    help to disambiguate ranges from glyph names containing hyphens.)
-
-    By default, the parser will follow ``include()`` statements in the feature
-    file. To turn this off, pass ``followIncludes=False``. Pass a directory string as
-    ``includeDir`` to explicitly declare a directory to search included feature files
-    in.
-    """
-
-    extensions = {}
-    ast = ast
-    SS_FEATURE_TAGS = {"ss%02d" % i for i in range(1, 20 + 1)}
-    CV_FEATURE_TAGS = {"cv%02d" % i for i in range(1, 99 + 1)}
-
-    def __init__(
-        self, featurefile, glyphNames=(), followIncludes=True, includeDir=None, **kwargs
-    ):
-
-        if "glyphMap" in kwargs:
-            from fontTools.misc.loggingTools import deprecateArgument
-
-            deprecateArgument("glyphMap", "use 'glyphNames' (iterable) instead")
-            if glyphNames:
-                raise TypeError(
-                    "'glyphNames' and (deprecated) 'glyphMap' are " "mutually exclusive"
-                )
-            glyphNames = kwargs.pop("glyphMap")
-        if kwargs:
-            raise TypeError(
-                "unsupported keyword argument%s: %s"
-                % ("" if len(kwargs) == 1 else "s", ", ".join(repr(k) for k in kwargs))
-            )
-
-        self.glyphNames_ = set(glyphNames)
-        self.doc_ = self.ast.FeatureFile()
-        self.anchors_ = SymbolTable()
-        self.glyphclasses_ = SymbolTable()
-        self.lookups_ = SymbolTable()
-        self.valuerecords_ = SymbolTable()
-        self.symbol_tables_ = {self.anchors_, self.valuerecords_}
-        self.next_token_type_, self.next_token_ = (None, None)
-        self.cur_comments_ = []
-        self.next_token_location_ = None
-        lexerClass = IncludingLexer if followIncludes else NonIncludingLexer
-        self.lexer_ = lexerClass(featurefile, includeDir=includeDir)
-        self.missing = {}
-        self.advance_lexer_(comments=True)
-
-    def parse(self):
-        """Parse the file, and return a :class:`fontTools.feaLib.ast.FeatureFile`
-        object representing the root of the abstract syntax tree containing the
-        parsed contents of the file."""
-        statements = self.doc_.statements
-        while self.next_token_type_ is not None or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("include"):
-                statements.append(self.parse_include_())
-            elif self.cur_token_type_ is Lexer.GLYPHCLASS:
-                statements.append(self.parse_glyphclass_definition_())
-            elif self.is_cur_keyword_(("anon", "anonymous")):
-                statements.append(self.parse_anonymous_())
-            elif self.is_cur_keyword_("anchorDef"):
-                statements.append(self.parse_anchordef_())
-            elif self.is_cur_keyword_("languagesystem"):
-                statements.append(self.parse_languagesystem_())
-            elif self.is_cur_keyword_("lookup"):
-                statements.append(self.parse_lookup_(vertical=False))
-            elif self.is_cur_keyword_("markClass"):
-                statements.append(self.parse_markClass_())
-            elif self.is_cur_keyword_("feature"):
-                statements.append(self.parse_feature_block_())
-            elif self.is_cur_keyword_("conditionset"):
-                statements.append(self.parse_conditionset_())
-            elif self.is_cur_keyword_("variation"):
-                statements.append(self.parse_feature_block_(variation=True))
-            elif self.is_cur_keyword_("table"):
-                statements.append(self.parse_table_())
-            elif self.is_cur_keyword_("valueRecordDef"):
-                statements.append(self.parse_valuerecord_definition_(vertical=False))
-            elif (
-                self.cur_token_type_ is Lexer.NAME
-                and self.cur_token_ in self.extensions
-            ):
-                statements.append(self.extensions[self.cur_token_](self))
-            elif self.cur_token_type_ is Lexer.SYMBOL and self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    "Expected feature, languagesystem, lookup, markClass, "
-                    'table, or glyph class definition, got {} "{}"'.format(
-                        self.cur_token_type_, self.cur_token_
-                    ),
-                    self.cur_token_location_,
-                )
-        # Report any missing glyphs at the end of parsing
-        if self.missing:
-            error = [
-                " %s (first found at %s)" % (name, loc)
-                for name, loc in self.missing.items()
-            ]
-            raise FeatureLibError(
-                "The following glyph names are referenced but are missing from the "
-                "glyph set:\n" + ("\n".join(error)),
-                None,
-            )
-        return self.doc_
-
-    def parse_anchor_(self):
-        # Parses an anchor in any of the four formats given in the feature
-        # file specification (2.e.vii).
-        self.expect_symbol_("<")
-        self.expect_keyword_("anchor")
-        location = self.cur_token_location_
-
-        if self.next_token_ == "NULL":  # Format D
-            self.expect_keyword_("NULL")
-            self.expect_symbol_(">")
-            return None
-
-        if self.next_token_type_ == Lexer.NAME:  # Format E
-            name = self.expect_name_()
-            anchordef = self.anchors_.resolve(name)
-            if anchordef is None:
-                raise FeatureLibError(
-                    'Unknown anchor "%s"' % name, self.cur_token_location_
-                )
-            self.expect_symbol_(">")
-            return self.ast.Anchor(
-                anchordef.x,
-                anchordef.y,
-                name=name,
-                contourpoint=anchordef.contourpoint,
-                xDeviceTable=None,
-                yDeviceTable=None,
-                location=location,
-            )
-
-        x, y = self.expect_number_(variable=True), self.expect_number_(variable=True)
-
-        contourpoint = None
-        if self.next_token_ == "contourpoint":  # Format B
-            self.expect_keyword_("contourpoint")
-            contourpoint = self.expect_number_()
-
-        if self.next_token_ == "<":  # Format C
-            xDeviceTable = self.parse_device_()
-            yDeviceTable = self.parse_device_()
-        else:
-            xDeviceTable, yDeviceTable = None, None
-
-        self.expect_symbol_(">")
-        return self.ast.Anchor(
-            x,
-            y,
-            name=None,
-            contourpoint=contourpoint,
-            xDeviceTable=xDeviceTable,
-            yDeviceTable=yDeviceTable,
-            location=location,
-        )
-
-    def parse_anchor_marks_(self):
-        # Parses a sequence of ``[<anchor> mark @MARKCLASS]*.``
-        anchorMarks = []  # [(self.ast.Anchor, markClassName)*]
-        while self.next_token_ == "<":
-            anchor = self.parse_anchor_()
-            if anchor is None and self.next_token_ != "mark":
-                continue  # <anchor NULL> without mark, eg. in GPOS type 5
-            self.expect_keyword_("mark")
-            markClass = self.expect_markClass_reference_()
-            anchorMarks.append((anchor, markClass))
-        return anchorMarks
-
-    def parse_anchordef_(self):
-        # Parses a named anchor definition (`section 2.e.viii <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#2.e.vii>`_).
-        assert self.is_cur_keyword_("anchorDef")
-        location = self.cur_token_location_
-        x, y = self.expect_number_(), self.expect_number_()
-        contourpoint = None
-        if self.next_token_ == "contourpoint":
-            self.expect_keyword_("contourpoint")
-            contourpoint = self.expect_number_()
-        name = self.expect_name_()
-        self.expect_symbol_(";")
-        anchordef = self.ast.AnchorDefinition(
-            name, x, y, contourpoint=contourpoint, location=location
-        )
-        self.anchors_.define(name, anchordef)
-        return anchordef
-
-    def parse_anonymous_(self):
-        # Parses an anonymous data block (`section 10 <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#10>`_).
-        assert self.is_cur_keyword_(("anon", "anonymous"))
-        tag = self.expect_tag_()
-        _, content, location = self.lexer_.scan_anonymous_block(tag)
-        self.advance_lexer_()
-        self.expect_symbol_("}")
-        end_tag = self.expect_tag_()
-        assert tag == end_tag, "bad splitting in Lexer.scan_anonymous_block()"
-        self.expect_symbol_(";")
-        return self.ast.AnonymousBlock(tag, content, location=location)
-
-    def parse_attach_(self):
-        # Parses a GDEF Attach statement (`section 9.b <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#9.b>`_)
-        assert self.is_cur_keyword_("Attach")
-        location = self.cur_token_location_
-        glyphs = self.parse_glyphclass_(accept_glyphname=True)
-        contourPoints = {self.expect_number_()}
-        while self.next_token_ != ";":
-            contourPoints.add(self.expect_number_())
-        self.expect_symbol_(";")
-        return self.ast.AttachStatement(glyphs, contourPoints, location=location)
-
-    def parse_enumerate_(self, vertical):
-        # Parse an enumerated pair positioning rule (`section 6.b.ii <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#6.b.ii>`_).
-        assert self.cur_token_ in {"enumerate", "enum"}
-        self.advance_lexer_()
-        return self.parse_position_(enumerated=True, vertical=vertical)
-
-    def parse_GlyphClassDef_(self):
-        # Parses 'GlyphClassDef @BASE, @LIGATURES, @MARKS, @COMPONENTS;'
-        assert self.is_cur_keyword_("GlyphClassDef")
-        location = self.cur_token_location_
-        if self.next_token_ != ",":
-            baseGlyphs = self.parse_glyphclass_(accept_glyphname=False)
-        else:
-            baseGlyphs = None
-        self.expect_symbol_(",")
-        if self.next_token_ != ",":
-            ligatureGlyphs = self.parse_glyphclass_(accept_glyphname=False)
-        else:
-            ligatureGlyphs = None
-        self.expect_symbol_(",")
-        if self.next_token_ != ",":
-            markGlyphs = self.parse_glyphclass_(accept_glyphname=False)
-        else:
-            markGlyphs = None
-        self.expect_symbol_(",")
-        if self.next_token_ != ";":
-            componentGlyphs = self.parse_glyphclass_(accept_glyphname=False)
-        else:
-            componentGlyphs = None
-        self.expect_symbol_(";")
-        return self.ast.GlyphClassDefStatement(
-            baseGlyphs, markGlyphs, ligatureGlyphs, componentGlyphs, location=location
-        )
-
-    def parse_glyphclass_definition_(self):
-        # Parses glyph class definitions such as '@UPPERCASE = [A-Z];'
-        location, name = self.cur_token_location_, self.cur_token_
-        self.expect_symbol_("=")
-        glyphs = self.parse_glyphclass_(accept_glyphname=False)
-        self.expect_symbol_(";")
-        glyphclass = self.ast.GlyphClassDefinition(name, glyphs, location=location)
-        self.glyphclasses_.define(name, glyphclass)
-        return glyphclass
-
-    def split_glyph_range_(self, name, location):
-        # Since v1.20, the OpenType Feature File specification allows
-        # for dashes in glyph names. A sequence like "a-b-c-d" could
-        # therefore mean a single glyph whose name happens to be
-        # "a-b-c-d", or it could mean a range from glyph "a" to glyph
-        # "b-c-d", or a range from glyph "a-b" to glyph "c-d", or a
-        # range from glyph "a-b-c" to glyph "d".Technically, this
-        # example could be resolved because the (pretty complex)
-        # definition of glyph ranges renders most of these splits
-        # invalid. But the specification does not say that a compiler
-        # should try to apply such fancy heuristics. To encourage
-        # unambiguous feature files, we therefore try all possible
-        # splits and reject the feature file if there are multiple
-        # splits possible. It is intentional that we don't just emit a
-        # warning; warnings tend to get ignored. To fix the problem,
-        # font designers can trivially add spaces around the intended
-        # split point, and we emit a compiler error that suggests
-        # how exactly the source should be rewritten to make things
-        # unambiguous.
-        parts = name.split("-")
-        solutions = []
-        for i in range(len(parts)):
-            start, limit = "-".join(parts[0:i]), "-".join(parts[i:])
-            if start in self.glyphNames_ and limit in self.glyphNames_:
-                solutions.append((start, limit))
-        if len(solutions) == 1:
-            start, limit = solutions[0]
-            return start, limit
-        elif len(solutions) == 0:
-            raise FeatureLibError(
-                '"%s" is not a glyph in the font, and it can not be split '
-                "into a range of known glyphs" % name,
-                location,
-            )
-        else:
-            ranges = " or ".join(['"%s - %s"' % (s, l) for s, l in solutions])
-            raise FeatureLibError(
-                'Ambiguous glyph range "%s"; '
-                "please use %s to clarify what you mean" % (name, ranges),
-                location,
-            )
-
-    def parse_glyphclass_(self, accept_glyphname, accept_null=False):
-        # Parses a glyph class, either named or anonymous, or (if
-        # ``bool(accept_glyphname)``) a glyph name. If ``bool(accept_null)`` then
-        # also accept the special NULL glyph.
-        if accept_glyphname and self.next_token_type_ in (Lexer.NAME, Lexer.CID):
-            if accept_null and self.next_token_ == "NULL":
-                # If you want a glyph called NULL, you should escape it.
-                self.advance_lexer_()
-                return self.ast.NullGlyph(location=self.cur_token_location_)
-            glyph = self.expect_glyph_()
-            self.check_glyph_name_in_glyph_set(glyph)
-            return self.ast.GlyphName(glyph, location=self.cur_token_location_)
-        if self.next_token_type_ is Lexer.GLYPHCLASS:
-            self.advance_lexer_()
-            gc = self.glyphclasses_.resolve(self.cur_token_)
-            if gc is None:
-                raise FeatureLibError(
-                    "Unknown glyph class @%s" % self.cur_token_,
-                    self.cur_token_location_,
-                )
-            if isinstance(gc, self.ast.MarkClass):
-                return self.ast.MarkClassName(gc, location=self.cur_token_location_)
-            else:
-                return self.ast.GlyphClassName(gc, location=self.cur_token_location_)
-
-        self.expect_symbol_("[")
-        location = self.cur_token_location_
-        glyphs = self.ast.GlyphClass(location=location)
-        while self.next_token_ != "]":
-            if self.next_token_type_ is Lexer.NAME:
-                glyph = self.expect_glyph_()
-                location = self.cur_token_location_
-                if "-" in glyph and self.glyphNames_ and glyph not in self.glyphNames_:
-                    start, limit = self.split_glyph_range_(glyph, location)
-                    self.check_glyph_name_in_glyph_set(start, limit)
-                    glyphs.add_range(
-                        start, limit, self.make_glyph_range_(location, start, limit)
-                    )
-                elif self.next_token_ == "-":
-                    start = glyph
-                    self.expect_symbol_("-")
-                    limit = self.expect_glyph_()
-                    self.check_glyph_name_in_glyph_set(start, limit)
-                    glyphs.add_range(
-                        start, limit, self.make_glyph_range_(location, start, limit)
-                    )
-                else:
-                    if "-" in glyph and not self.glyphNames_:
-                        log.warning(
-                            str(
-                                FeatureLibError(
-                                    f"Ambiguous glyph name that looks like a range: {glyph!r}",
-                                    location,
-                                )
-                            )
-                        )
-                    self.check_glyph_name_in_glyph_set(glyph)
-                    glyphs.append(glyph)
-            elif self.next_token_type_ is Lexer.CID:
-                glyph = self.expect_glyph_()
-                if self.next_token_ == "-":
-                    range_location = self.cur_token_location_
-                    range_start = self.cur_token_
-                    self.expect_symbol_("-")
-                    range_end = self.expect_cid_()
-                    self.check_glyph_name_in_glyph_set(
-                        f"cid{range_start:05d}",
-                        f"cid{range_end:05d}",
-                    )
-                    glyphs.add_cid_range(
-                        range_start,
-                        range_end,
-                        self.make_cid_range_(range_location, range_start, range_end),
-                    )
-                else:
-                    glyph_name = f"cid{self.cur_token_:05d}"
-                    self.check_glyph_name_in_glyph_set(glyph_name)
-                    glyphs.append(glyph_name)
-            elif self.next_token_type_ is Lexer.GLYPHCLASS:
-                self.advance_lexer_()
-                gc = self.glyphclasses_.resolve(self.cur_token_)
-                if gc is None:
-                    raise FeatureLibError(
-                        "Unknown glyph class @%s" % self.cur_token_,
-                        self.cur_token_location_,
-                    )
-                if isinstance(gc, self.ast.MarkClass):
-                    gc = self.ast.MarkClassName(gc, location=self.cur_token_location_)
-                else:
-                    gc = self.ast.GlyphClassName(gc, location=self.cur_token_location_)
-                glyphs.add_class(gc)
-            else:
-                raise FeatureLibError(
-                    "Expected glyph name, glyph range, "
-                    f"or glyph class reference, found {self.next_token_!r}",
-                    self.next_token_location_,
-                )
-        self.expect_symbol_("]")
-        return glyphs
-
-    def parse_glyph_pattern_(self, vertical):
-        # Parses a glyph pattern, including lookups and context, e.g.::
-        #
-        #    a b
-        #    a b c' d e
-        #    a b c' lookup ChangeC d e
-        prefix, glyphs, lookups, values, suffix = ([], [], [], [], [])
-        hasMarks = False
-        while self.next_token_ not in {"by", "from", ";", ","}:
-            gc = self.parse_glyphclass_(accept_glyphname=True)
-            marked = False
-            if self.next_token_ == "'":
-                self.expect_symbol_("'")
-                hasMarks = marked = True
-            if marked:
-                if suffix:
-                    # makeotf also reports this as an error, while FontForge
-                    # silently inserts ' in all the intervening glyphs.
-                    # https://github.com/fonttools/fonttools/pull/1096
-                    raise FeatureLibError(
-                        "Unsupported contextual target sequence: at most "
-                        "one run of marked (') glyph/class names allowed",
-                        self.cur_token_location_,
-                    )
-                glyphs.append(gc)
-            elif glyphs:
-                suffix.append(gc)
-            else:
-                prefix.append(gc)
-
-            if self.is_next_value_():
-                values.append(self.parse_valuerecord_(vertical))
-            else:
-                values.append(None)
-
-            lookuplist = None
-            while self.next_token_ == "lookup":
-                if lookuplist is None:
-                    lookuplist = []
-                self.expect_keyword_("lookup")
-                if not marked:
-                    raise FeatureLibError(
-                        "Lookups can only follow marked glyphs",
-                        self.cur_token_location_,
-                    )
-                lookup_name = self.expect_name_()
-                lookup = self.lookups_.resolve(lookup_name)
-                if lookup is None:
-                    raise FeatureLibError(
-                        'Unknown lookup "%s"' % lookup_name, self.cur_token_location_
-                    )
-                lookuplist.append(lookup)
-            if marked:
-                lookups.append(lookuplist)
-
-        if not glyphs and not suffix:  # eg., "sub f f i by"
-            assert lookups == []
-            return ([], prefix, [None] * len(prefix), values, [], hasMarks)
-        else:
-            if any(values[: len(prefix)]):
-                raise FeatureLibError(
-                    "Positioning cannot be applied in the bactrack glyph sequence, "
-                    "before the marked glyph sequence.",
-                    self.cur_token_location_,
-                )
-            marked_values = values[len(prefix) : len(prefix) + len(glyphs)]
-            if any(marked_values):
-                if any(values[len(prefix) + len(glyphs) :]):
-                    raise FeatureLibError(
-                        "Positioning values are allowed only in the marked glyph "
-                        "sequence, or after the final glyph node when only one glyph "
-                        "node is marked.",
-                        self.cur_token_location_,
-                    )
-                values = marked_values
-            elif values and values[-1]:
-                if len(glyphs) > 1 or any(values[:-1]):
-                    raise FeatureLibError(
-                        "Positioning values are allowed only in the marked glyph "
-                        "sequence, or after the final glyph node when only one glyph "
-                        "node is marked.",
-                        self.cur_token_location_,
-                    )
-                values = values[-1:]
-            elif any(values):
-                raise FeatureLibError(
-                    "Positioning values are allowed only in the marked glyph "
-                    "sequence, or after the final glyph node when only one glyph "
-                    "node is marked.",
-                    self.cur_token_location_,
-                )
-            return (prefix, glyphs, lookups, values, suffix, hasMarks)
-
-    def parse_ignore_glyph_pattern_(self, sub):
-        location = self.cur_token_location_
-        prefix, glyphs, lookups, values, suffix, hasMarks = self.parse_glyph_pattern_(
-            vertical=False
-        )
-        if any(lookups):
-            raise FeatureLibError(
-                f'No lookups can be specified for "ignore {sub}"', location
-            )
-        if not hasMarks:
-            error = FeatureLibError(
-                f'Ambiguous "ignore {sub}", there should be least one marked glyph',
-                location,
-            )
-            log.warning(str(error))
-            suffix, glyphs = glyphs[1:], glyphs[0:1]
-        chainContext = (prefix, glyphs, suffix)
-        return chainContext
-
-    def parse_ignore_context_(self, sub):
-        location = self.cur_token_location_
-        chainContext = [self.parse_ignore_glyph_pattern_(sub)]
-        while self.next_token_ == ",":
-            self.expect_symbol_(",")
-            chainContext.append(self.parse_ignore_glyph_pattern_(sub))
-        self.expect_symbol_(";")
-        return chainContext
-
-    def parse_ignore_(self):
-        # Parses an ignore sub/pos rule.
-        assert self.is_cur_keyword_("ignore")
-        location = self.cur_token_location_
-        self.advance_lexer_()
-        if self.cur_token_ in ["substitute", "sub"]:
-            chainContext = self.parse_ignore_context_("sub")
-            return self.ast.IgnoreSubstStatement(chainContext, location=location)
-        if self.cur_token_ in ["position", "pos"]:
-            chainContext = self.parse_ignore_context_("pos")
-            return self.ast.IgnorePosStatement(chainContext, location=location)
-        raise FeatureLibError(
-            'Expected "substitute" or "position"', self.cur_token_location_
-        )
-
-    def parse_include_(self):
-        assert self.cur_token_ == "include"
-        location = self.cur_token_location_
-        filename = self.expect_filename_()
-        # self.expect_symbol_(";")
-        return ast.IncludeStatement(filename, location=location)
-
-    def parse_language_(self):
-        assert self.is_cur_keyword_("language")
-        location = self.cur_token_location_
-        language = self.expect_language_tag_()
-        include_default, required = (True, False)
-        if self.next_token_ in {"exclude_dflt", "include_dflt"}:
-            include_default = self.expect_name_() == "include_dflt"
-        if self.next_token_ == "required":
-            self.expect_keyword_("required")
-            required = True
-        self.expect_symbol_(";")
-        return self.ast.LanguageStatement(
-            language, include_default, required, location=location
-        )
-
-    def parse_ligatureCaretByIndex_(self):
-        assert self.is_cur_keyword_("LigatureCaretByIndex")
-        location = self.cur_token_location_
-        glyphs = self.parse_glyphclass_(accept_glyphname=True)
-        carets = [self.expect_number_()]
-        while self.next_token_ != ";":
-            carets.append(self.expect_number_())
-        self.expect_symbol_(";")
-        return self.ast.LigatureCaretByIndexStatement(glyphs, carets, location=location)
-
-    def parse_ligatureCaretByPos_(self):
-        assert self.is_cur_keyword_("LigatureCaretByPos")
-        location = self.cur_token_location_
-        glyphs = self.parse_glyphclass_(accept_glyphname=True)
-        carets = [self.expect_number_(variable=True)]
-        while self.next_token_ != ";":
-            carets.append(self.expect_number_(variable=True))
-        self.expect_symbol_(";")
-        return self.ast.LigatureCaretByPosStatement(glyphs, carets, location=location)
-
-    def parse_lookup_(self, vertical):
-        # Parses a ``lookup`` - either a lookup block, or a lookup reference
-        # inside a feature.
-        assert self.is_cur_keyword_("lookup")
-        location, name = self.cur_token_location_, self.expect_name_()
-
-        if self.next_token_ == ";":
-            lookup = self.lookups_.resolve(name)
-            if lookup is None:
-                raise FeatureLibError(
-                    'Unknown lookup "%s"' % name, self.cur_token_location_
-                )
-            self.expect_symbol_(";")
-            return self.ast.LookupReferenceStatement(lookup, location=location)
-
-        use_extension = False
-        if self.next_token_ == "useExtension":
-            self.expect_keyword_("useExtension")
-            use_extension = True
-
-        block = self.ast.LookupBlock(name, use_extension, location=location)
-        self.parse_block_(block, vertical)
-        self.lookups_.define(name, block)
-        return block
-
-    def parse_lookupflag_(self):
-        # Parses a ``lookupflag`` statement, either specified by number or
-        # in words.
-        assert self.is_cur_keyword_("lookupflag")
-        location = self.cur_token_location_
-
-        # format B: "lookupflag 6;"
-        if self.next_token_type_ == Lexer.NUMBER:
-            value = self.expect_number_()
-            self.expect_symbol_(";")
-            return self.ast.LookupFlagStatement(value, location=location)
-
-        # format A: "lookupflag RightToLeft MarkAttachmentType @M;"
-        value_seen = False
-        value, markAttachment, markFilteringSet = 0, None, None
-        flags = {
-            "RightToLeft": 1,
-            "IgnoreBaseGlyphs": 2,
-            "IgnoreLigatures": 4,
-            "IgnoreMarks": 8,
-        }
-        seen = set()
-        while self.next_token_ != ";":
-            if self.next_token_ in seen:
-                raise FeatureLibError(
-                    "%s can be specified only once" % self.next_token_,
-                    self.next_token_location_,
-                )
-            seen.add(self.next_token_)
-            if self.next_token_ == "MarkAttachmentType":
-                self.expect_keyword_("MarkAttachmentType")
-                markAttachment = self.parse_glyphclass_(accept_glyphname=False)
-            elif self.next_token_ == "UseMarkFilteringSet":
-                self.expect_keyword_("UseMarkFilteringSet")
-                markFilteringSet = self.parse_glyphclass_(accept_glyphname=False)
-            elif self.next_token_ in flags:
-                value_seen = True
-                value = value | flags[self.expect_name_()]
-            else:
-                raise FeatureLibError(
-                    '"%s" is not a recognized lookupflag' % self.next_token_,
-                    self.next_token_location_,
-                )
-        self.expect_symbol_(";")
-
-        if not any([value_seen, markAttachment, markFilteringSet]):
-            raise FeatureLibError(
-                "lookupflag must have a value", self.next_token_location_
-            )
-
-        return self.ast.LookupFlagStatement(
-            value,
-            markAttachment=markAttachment,
-            markFilteringSet=markFilteringSet,
-            location=location,
-        )
-
-    def parse_markClass_(self):
-        assert self.is_cur_keyword_("markClass")
-        location = self.cur_token_location_
-        glyphs = self.parse_glyphclass_(accept_glyphname=True)
-        if not glyphs.glyphSet():
-            raise FeatureLibError(
-                "Empty glyph class in mark class definition", location
-            )
-        anchor = self.parse_anchor_()
-        name = self.expect_class_name_()
-        self.expect_symbol_(";")
-        markClass = self.doc_.markClasses.get(name)
-        if markClass is None:
-            markClass = self.ast.MarkClass(name)
-            self.doc_.markClasses[name] = markClass
-            self.glyphclasses_.define(name, markClass)
-        mcdef = self.ast.MarkClassDefinition(
-            markClass, anchor, glyphs, location=location
-        )
-        markClass.addDefinition(mcdef)
-        return mcdef
-
-    def parse_position_(self, enumerated, vertical):
-        assert self.cur_token_ in {"position", "pos"}
-        if self.next_token_ == "cursive":  # GPOS type 3
-            return self.parse_position_cursive_(enumerated, vertical)
-        elif self.next_token_ == "base":  # GPOS type 4
-            return self.parse_position_base_(enumerated, vertical)
-        elif self.next_token_ == "ligature":  # GPOS type 5
-            return self.parse_position_ligature_(enumerated, vertical)
-        elif self.next_token_ == "mark":  # GPOS type 6
-            return self.parse_position_mark_(enumerated, vertical)
-
-        location = self.cur_token_location_
-        prefix, glyphs, lookups, values, suffix, hasMarks = self.parse_glyph_pattern_(
-            vertical
-        )
-        self.expect_symbol_(";")
-
-        if any(lookups):
-            # GPOS type 8: Chaining contextual positioning; explicit lookups
-            if any(values):
-                raise FeatureLibError(
-                    'If "lookup" is present, no values must be specified', location
-                )
-            return self.ast.ChainContextPosStatement(
-                prefix, glyphs, suffix, lookups, location=location
-            )
-
-        # Pair positioning, format A: "pos V 10 A -10;"
-        # Pair positioning, format B: "pos V A -20;"
-        if not prefix and not suffix and len(glyphs) == 2 and not hasMarks:
-            if values[0] is None:  # Format B: "pos V A -20;"
-                values.reverse()
-            return self.ast.PairPosStatement(
-                glyphs[0],
-                values[0],
-                glyphs[1],
-                values[1],
-                enumerated=enumerated,
-                location=location,
-            )
-
-        if enumerated:
-            raise FeatureLibError(
-                '"enumerate" is only allowed with pair positionings', location
-            )
-        return self.ast.SinglePosStatement(
-            list(zip(glyphs, values)),
-            prefix,
-            suffix,
-            forceChain=hasMarks,
-            location=location,
-        )
-
-    def parse_position_cursive_(self, enumerated, vertical):
-        location = self.cur_token_location_
-        self.expect_keyword_("cursive")
-        if enumerated:
-            raise FeatureLibError(
-                '"enumerate" is not allowed with ' "cursive attachment positioning",
-                location,
-            )
-        glyphclass = self.parse_glyphclass_(accept_glyphname=True)
-        entryAnchor = self.parse_anchor_()
-        exitAnchor = self.parse_anchor_()
-        self.expect_symbol_(";")
-        return self.ast.CursivePosStatement(
-            glyphclass, entryAnchor, exitAnchor, location=location
-        )
-
-    def parse_position_base_(self, enumerated, vertical):
-        location = self.cur_token_location_
-        self.expect_keyword_("base")
-        if enumerated:
-            raise FeatureLibError(
-                '"enumerate" is not allowed with '
-                "mark-to-base attachment positioning",
-                location,
-            )
-        base = self.parse_glyphclass_(accept_glyphname=True)
-        marks = self.parse_anchor_marks_()
-        self.expect_symbol_(";")
-        return self.ast.MarkBasePosStatement(base, marks, location=location)
-
-    def parse_position_ligature_(self, enumerated, vertical):
-        location = self.cur_token_location_
-        self.expect_keyword_("ligature")
-        if enumerated:
-            raise FeatureLibError(
-                '"enumerate" is not allowed with '
-                "mark-to-ligature attachment positioning",
-                location,
-            )
-        ligatures = self.parse_glyphclass_(accept_glyphname=True)
-        marks = [self.parse_anchor_marks_()]
-        while self.next_token_ == "ligComponent":
-            self.expect_keyword_("ligComponent")
-            marks.append(self.parse_anchor_marks_())
-        self.expect_symbol_(";")
-        return self.ast.MarkLigPosStatement(ligatures, marks, location=location)
-
-    def parse_position_mark_(self, enumerated, vertical):
-        location = self.cur_token_location_
-        self.expect_keyword_("mark")
-        if enumerated:
-            raise FeatureLibError(
-                '"enumerate" is not allowed with '
-                "mark-to-mark attachment positioning",
-                location,
-            )
-        baseMarks = self.parse_glyphclass_(accept_glyphname=True)
-        marks = self.parse_anchor_marks_()
-        self.expect_symbol_(";")
-        return self.ast.MarkMarkPosStatement(baseMarks, marks, location=location)
-
-    def parse_script_(self):
-        assert self.is_cur_keyword_("script")
-        location, script = self.cur_token_location_, self.expect_script_tag_()
-        self.expect_symbol_(";")
-        return self.ast.ScriptStatement(script, location=location)
-
-    def parse_substitute_(self):
-        assert self.cur_token_ in {"substitute", "sub", "reversesub", "rsub"}
-        location = self.cur_token_location_
-        reverse = self.cur_token_ in {"reversesub", "rsub"}
-        (
-            old_prefix,
-            old,
-            lookups,
-            values,
-            old_suffix,
-            hasMarks,
-        ) = self.parse_glyph_pattern_(vertical=False)
-        if any(values):
-            raise FeatureLibError(
-                "Substitution statements cannot contain values", location
-            )
-        new = []
-        if self.next_token_ == "by":
-            keyword = self.expect_keyword_("by")
-            while self.next_token_ != ";":
-                gc = self.parse_glyphclass_(accept_glyphname=True, accept_null=True)
-                new.append(gc)
-        elif self.next_token_ == "from":
-            keyword = self.expect_keyword_("from")
-            new = [self.parse_glyphclass_(accept_glyphname=False)]
-        else:
-            keyword = None
-        self.expect_symbol_(";")
-        if len(new) == 0 and not any(lookups):
-            raise FeatureLibError(
-                'Expected "by", "from" or explicit lookup references',
-                self.cur_token_location_,
-            )
-
-        # GSUB lookup type 3: Alternate substitution.
-        # Format: "substitute a from [a.1 a.2 a.3];"
-        if keyword == "from":
-            if reverse:
-                raise FeatureLibError(
-                    'Reverse chaining substitutions do not support "from"', location
-                )
-            if len(old) != 1 or len(old[0].glyphSet()) != 1:
-                raise FeatureLibError('Expected a single glyph before "from"', location)
-            if len(new) != 1:
-                raise FeatureLibError(
-                    'Expected a single glyphclass after "from"', location
-                )
-            return self.ast.AlternateSubstStatement(
-                old_prefix, old[0], old_suffix, new[0], location=location
-            )
-
-        num_lookups = len([l for l in lookups if l is not None])
-
-        is_deletion = False
-        if len(new) == 1 and isinstance(new[0], ast.NullGlyph):
-            new = []  # Deletion
-            is_deletion = True
-
-        # GSUB lookup type 1: Single substitution.
-        # Format A: "substitute a by a.sc;"
-        # Format B: "substitute [one.fitted one.oldstyle] by one;"
-        # Format C: "substitute [a-d] by [A.sc-D.sc];"
-        if not reverse and len(old) == 1 and len(new) == 1 and num_lookups == 0:
-            glyphs = list(old[0].glyphSet())
-            replacements = list(new[0].glyphSet())
-            if len(replacements) == 1:
-                replacements = replacements * len(glyphs)
-            if len(glyphs) != len(replacements):
-                raise FeatureLibError(
-                    'Expected a glyph class with %d elements after "by", '
-                    "but found a glyph class with %d elements"
-                    % (len(glyphs), len(replacements)),
-                    location,
-                )
-            return self.ast.SingleSubstStatement(
-                old, new, old_prefix, old_suffix, forceChain=hasMarks, location=location
-            )
-
-        # Glyph deletion, built as GSUB lookup type 2: Multiple substitution
-        # with empty replacement.
-        if is_deletion and len(old) == 1 and num_lookups == 0:
-            return self.ast.MultipleSubstStatement(
-                old_prefix,
-                old[0],
-                old_suffix,
-                (),
-                forceChain=hasMarks,
-                location=location,
-            )
-
-        # GSUB lookup type 2: Multiple substitution.
-        # Format: "substitute f_f_i by f f i;"
-        #
-        # GlyphsApp introduces two additional formats:
-        # Format 1: "substitute [f_i f_l] by [f f] [i l];"
-        # Format 2: "substitute [f_i f_l] by f [i l];"
-        # http://handbook.glyphsapp.com/en/layout/multiple-substitution-with-classes/
-        if not reverse and len(old) == 1 and len(new) > 1 and num_lookups == 0:
-            count = len(old[0].glyphSet())
-            for n in new:
-                if not list(n.glyphSet()):
-                    raise FeatureLibError("Empty class in replacement", location)
-                if len(n.glyphSet()) != 1 and len(n.glyphSet()) != count:
-                    raise FeatureLibError(
-                        f'Expected a glyph class with 1 or {count} elements after "by", '
-                        f"but found a glyph class with {len(n.glyphSet())} elements",
-                        location,
-                    )
-            return self.ast.MultipleSubstStatement(
-                old_prefix,
-                old[0],
-                old_suffix,
-                new,
-                forceChain=hasMarks,
-                location=location,
-            )
-
-        # GSUB lookup type 4: Ligature substitution.
-        # Format: "substitute f f i by f_f_i;"
-        if (
-            not reverse
-            and len(old) > 1
-            and len(new) == 1
-            and len(new[0].glyphSet()) == 1
-            and num_lookups == 0
-        ):
-            return self.ast.LigatureSubstStatement(
-                old_prefix,
-                old,
-                old_suffix,
-                list(new[0].glyphSet())[0],
-                forceChain=hasMarks,
-                location=location,
-            )
-
-        # GSUB lookup type 8: Reverse chaining substitution.
-        if reverse:
-            if len(old) != 1:
-                raise FeatureLibError(
-                    "In reverse chaining single substitutions, "
-                    "only a single glyph or glyph class can be replaced",
-                    location,
-                )
-            if len(new) != 1:
-                raise FeatureLibError(
-                    "In reverse chaining single substitutions, "
-                    'the replacement (after "by") must be a single glyph '
-                    "or glyph class",
-                    location,
-                )
-            if num_lookups != 0:
-                raise FeatureLibError(
-                    "Reverse chaining substitutions cannot call named lookups", location
-                )
-            glyphs = sorted(list(old[0].glyphSet()))
-            replacements = sorted(list(new[0].glyphSet()))
-            if len(replacements) == 1:
-                replacements = replacements * len(glyphs)
-            if len(glyphs) != len(replacements):
-                raise FeatureLibError(
-                    'Expected a glyph class with %d elements after "by", '
-                    "but found a glyph class with %d elements"
-                    % (len(glyphs), len(replacements)),
-                    location,
-                )
-            return self.ast.ReverseChainSingleSubstStatement(
-                old_prefix, old_suffix, old, new, location=location
-            )
-
-        if len(old) > 1 and len(new) > 1:
-            raise FeatureLibError(
-                "Direct substitution of multiple glyphs by multiple glyphs "
-                "is not supported",
-                location,
-            )
-
-        # If there are remaining glyphs to parse, this is an invalid GSUB statement
-        if len(new) != 0 or is_deletion:
-            raise FeatureLibError("Invalid substitution statement", location)
-
-        # GSUB lookup type 6: Chaining contextual substitution.
-        rule = self.ast.ChainContextSubstStatement(
-            old_prefix, old, old_suffix, lookups, location=location
-        )
-        return rule
-
-    def parse_subtable_(self):
-        assert self.is_cur_keyword_("subtable")
-        location = self.cur_token_location_
-        self.expect_symbol_(";")
-        return self.ast.SubtableStatement(location=location)
-
-    def parse_size_parameters_(self):
-        # Parses a ``parameters`` statement used in ``size`` features. See
-        # `section 8.b <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#8.b>`_.
-        assert self.is_cur_keyword_("parameters")
-        location = self.cur_token_location_
-        DesignSize = self.expect_decipoint_()
-        SubfamilyID = self.expect_number_()
-        RangeStart = 0.0
-        RangeEnd = 0.0
-        if self.next_token_type_ in (Lexer.NUMBER, Lexer.FLOAT) or SubfamilyID != 0:
-            RangeStart = self.expect_decipoint_()
-            RangeEnd = self.expect_decipoint_()
-
-        self.expect_symbol_(";")
-        return self.ast.SizeParameters(
-            DesignSize, SubfamilyID, RangeStart, RangeEnd, location=location
-        )
-
-    def parse_size_menuname_(self):
-        assert self.is_cur_keyword_("sizemenuname")
-        location = self.cur_token_location_
-        platformID, platEncID, langID, string = self.parse_name_()
-        return self.ast.FeatureNameStatement(
-            "size", platformID, platEncID, langID, string, location=location
-        )
-
-    def parse_table_(self):
-        assert self.is_cur_keyword_("table")
-        location, name = self.cur_token_location_, self.expect_tag_()
-        table = self.ast.TableBlock(name, location=location)
-        self.expect_symbol_("{")
-        handler = {
-            "GDEF": self.parse_table_GDEF_,
-            "head": self.parse_table_head_,
-            "hhea": self.parse_table_hhea_,
-            "vhea": self.parse_table_vhea_,
-            "name": self.parse_table_name_,
-            "BASE": self.parse_table_BASE_,
-            "OS/2": self.parse_table_OS_2_,
-            "STAT": self.parse_table_STAT_,
-        }.get(name)
-        if handler:
-            handler(table)
-        else:
-            raise FeatureLibError(
-                '"table %s" is not supported' % name.strip(), location
-            )
-        self.expect_symbol_("}")
-        end_tag = self.expect_tag_()
-        if end_tag != name:
-            raise FeatureLibError(
-                'Expected "%s"' % name.strip(), self.cur_token_location_
-            )
-        self.expect_symbol_(";")
-        return table
-
-    def parse_table_GDEF_(self, table):
-        statements = table.statements
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("Attach"):
-                statements.append(self.parse_attach_())
-            elif self.is_cur_keyword_("GlyphClassDef"):
-                statements.append(self.parse_GlyphClassDef_())
-            elif self.is_cur_keyword_("LigatureCaretByIndex"):
-                statements.append(self.parse_ligatureCaretByIndex_())
-            elif self.is_cur_keyword_("LigatureCaretByPos"):
-                statements.append(self.parse_ligatureCaretByPos_())
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    "Expected Attach, LigatureCaretByIndex, " "or LigatureCaretByPos",
-                    self.cur_token_location_,
-                )
-
-    def parse_table_head_(self, table):
-        statements = table.statements
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("FontRevision"):
-                statements.append(self.parse_FontRevision_())
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError("Expected FontRevision", self.cur_token_location_)
-
-    def parse_table_hhea_(self, table):
-        statements = table.statements
-        fields = ("CaretOffset", "Ascender", "Descender", "LineGap")
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.cur_token_type_ is Lexer.NAME and self.cur_token_ in fields:
-                key = self.cur_token_.lower()
-                value = self.expect_number_()
-                statements.append(
-                    self.ast.HheaField(key, value, location=self.cur_token_location_)
-                )
-                if self.next_token_ != ";":
-                    raise FeatureLibError(
-                        "Incomplete statement", self.next_token_location_
-                    )
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    "Expected CaretOffset, Ascender, " "Descender or LineGap",
-                    self.cur_token_location_,
-                )
-
-    def parse_table_vhea_(self, table):
-        statements = table.statements
-        fields = ("VertTypoAscender", "VertTypoDescender", "VertTypoLineGap")
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.cur_token_type_ is Lexer.NAME and self.cur_token_ in fields:
-                key = self.cur_token_.lower()
-                value = self.expect_number_()
-                statements.append(
-                    self.ast.VheaField(key, value, location=self.cur_token_location_)
-                )
-                if self.next_token_ != ";":
-                    raise FeatureLibError(
-                        "Incomplete statement", self.next_token_location_
-                    )
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    "Expected VertTypoAscender, "
-                    "VertTypoDescender or VertTypoLineGap",
-                    self.cur_token_location_,
-                )
-
-    def parse_table_name_(self, table):
-        statements = table.statements
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("nameid"):
-                statement = self.parse_nameid_()
-                if statement:
-                    statements.append(statement)
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError("Expected nameid", self.cur_token_location_)
-
-    def parse_name_(self):
-        """Parses a name record. See `section 9.e <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#9.e>`_."""
-        platEncID = None
-        langID = None
-        if self.next_token_type_ in Lexer.NUMBERS:
-            platformID = self.expect_any_number_()
-            location = self.cur_token_location_
-            if platformID not in (1, 3):
-                raise FeatureLibError("Expected platform id 1 or 3", location)
-            if self.next_token_type_ in Lexer.NUMBERS:
-                platEncID = self.expect_any_number_()
-                langID = self.expect_any_number_()
-        else:
-            platformID = 3
-            location = self.cur_token_location_
-
-        if platformID == 1:  # Macintosh
-            platEncID = platEncID or 0  # Roman
-            langID = langID or 0  # English
-        else:  # 3, Windows
-            platEncID = platEncID or 1  # Unicode
-            langID = langID or 0x0409  # English
-
-        string = self.expect_string_()
-        self.expect_symbol_(";")
-
-        encoding = getEncoding(platformID, platEncID, langID)
-        if encoding is None:
-            raise FeatureLibError("Unsupported encoding", location)
-        unescaped = self.unescape_string_(string, encoding)
-        return platformID, platEncID, langID, unescaped
-
-    def parse_stat_name_(self):
-        platEncID = None
-        langID = None
-        if self.next_token_type_ in Lexer.NUMBERS:
-            platformID = self.expect_any_number_()
-            location = self.cur_token_location_
-            if platformID not in (1, 3):
-                raise FeatureLibError("Expected platform id 1 or 3", location)
-            if self.next_token_type_ in Lexer.NUMBERS:
-                platEncID = self.expect_any_number_()
-                langID = self.expect_any_number_()
-        else:
-            platformID = 3
-            location = self.cur_token_location_
-
-        if platformID == 1:  # Macintosh
-            platEncID = platEncID or 0  # Roman
-            langID = langID or 0  # English
-        else:  # 3, Windows
-            platEncID = platEncID or 1  # Unicode
-            langID = langID or 0x0409  # English
-
-        string = self.expect_string_()
-        encoding = getEncoding(platformID, platEncID, langID)
-        if encoding is None:
-            raise FeatureLibError("Unsupported encoding", location)
-        unescaped = self.unescape_string_(string, encoding)
-        return platformID, platEncID, langID, unescaped
-
-    def parse_nameid_(self):
-        assert self.cur_token_ == "nameid", self.cur_token_
-        location, nameID = self.cur_token_location_, self.expect_any_number_()
-        if nameID > 32767:
-            raise FeatureLibError(
-                "Name id value cannot be greater than 32767", self.cur_token_location_
-            )
-        platformID, platEncID, langID, string = self.parse_name_()
-        return self.ast.NameRecord(
-            nameID, platformID, platEncID, langID, string, location=location
-        )
-
-    def unescape_string_(self, string, encoding):
-        if encoding == "utf_16_be":
-            s = re.sub(r"\\[0-9a-fA-F]{4}", self.unescape_unichr_, string)
-        else:
-            unescape = lambda m: self.unescape_byte_(m, encoding)
-            s = re.sub(r"\\[0-9a-fA-F]{2}", unescape, string)
-        # We now have a Unicode string, but it might contain surrogate pairs.
-        # We convert surrogates to actual Unicode by round-tripping through
-        # Python's UTF-16 codec in a special mode.
-        utf16 = tobytes(s, "utf_16_be", "surrogatepass")
-        return tostr(utf16, "utf_16_be")
-
-    @staticmethod
-    def unescape_unichr_(match):
-        n = match.group(0)[1:]
-        return chr(int(n, 16))
-
-    @staticmethod
-    def unescape_byte_(match, encoding):
-        n = match.group(0)[1:]
-        return bytechr(int(n, 16)).decode(encoding)
-
-    def parse_table_BASE_(self, table):
-        statements = table.statements
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("HorizAxis.BaseTagList"):
-                horiz_bases = self.parse_base_tag_list_()
-            elif self.is_cur_keyword_("HorizAxis.BaseScriptList"):
-                horiz_scripts = self.parse_base_script_list_(len(horiz_bases))
-                statements.append(
-                    self.ast.BaseAxis(
-                        horiz_bases,
-                        horiz_scripts,
-                        False,
-                        location=self.cur_token_location_,
-                    )
-                )
-            elif self.is_cur_keyword_("VertAxis.BaseTagList"):
-                vert_bases = self.parse_base_tag_list_()
-            elif self.is_cur_keyword_("VertAxis.BaseScriptList"):
-                vert_scripts = self.parse_base_script_list_(len(vert_bases))
-                statements.append(
-                    self.ast.BaseAxis(
-                        vert_bases,
-                        vert_scripts,
-                        True,
-                        location=self.cur_token_location_,
-                    )
-                )
-            elif self.cur_token_ == ";":
-                continue
-
-    def parse_table_OS_2_(self, table):
-        statements = table.statements
-        numbers = (
-            "FSType",
-            "TypoAscender",
-            "TypoDescender",
-            "TypoLineGap",
-            "winAscent",
-            "winDescent",
-            "XHeight",
-            "CapHeight",
-            "WeightClass",
-            "WidthClass",
-            "LowerOpSize",
-            "UpperOpSize",
-        )
-        ranges = ("UnicodeRange", "CodePageRange")
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.cur_token_type_ is Lexer.NAME:
-                key = self.cur_token_.lower()
-                value = None
-                if self.cur_token_ in numbers:
-                    value = self.expect_number_()
-                elif self.is_cur_keyword_("Panose"):
-                    value = []
-                    for i in range(10):
-                        value.append(self.expect_number_())
-                elif self.cur_token_ in ranges:
-                    value = []
-                    while self.next_token_ != ";":
-                        value.append(self.expect_number_())
-                elif self.is_cur_keyword_("Vendor"):
-                    value = self.expect_string_()
-                statements.append(
-                    self.ast.OS2Field(key, value, location=self.cur_token_location_)
-                )
-            elif self.cur_token_ == ";":
-                continue
-
-    def parse_STAT_ElidedFallbackName(self):
-        assert self.is_cur_keyword_("ElidedFallbackName")
-        self.expect_symbol_("{")
-        names = []
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_()
-            if self.is_cur_keyword_("name"):
-                platformID, platEncID, langID, string = self.parse_stat_name_()
-                nameRecord = self.ast.STATNameStatement(
-                    "stat",
-                    platformID,
-                    platEncID,
-                    langID,
-                    string,
-                    location=self.cur_token_location_,
-                )
-                names.append(nameRecord)
-            else:
-                if self.cur_token_ != ";":
-                    raise FeatureLibError(
-                        f"Unexpected token {self.cur_token_} " f"in ElidedFallbackName",
-                        self.cur_token_location_,
-                    )
-        self.expect_symbol_("}")
-        if not names:
-            raise FeatureLibError('Expected "name"', self.cur_token_location_)
-        return names
-
-    def parse_STAT_design_axis(self):
-        assert self.is_cur_keyword_("DesignAxis")
-        names = []
-        axisTag = self.expect_tag_()
-        if (
-            axisTag not in ("ital", "opsz", "slnt", "wdth", "wght")
-            and not axisTag.isupper()
-        ):
-            log.warning(f"Unregistered axis tag {axisTag} should be uppercase.")
-        axisOrder = self.expect_number_()
-        self.expect_symbol_("{")
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_()
-            if self.cur_token_type_ is Lexer.COMMENT:
-                continue
-            elif self.is_cur_keyword_("name"):
-                location = self.cur_token_location_
-                platformID, platEncID, langID, string = self.parse_stat_name_()
-                name = self.ast.STATNameStatement(
-                    "stat", platformID, platEncID, langID, string, location=location
-                )
-                names.append(name)
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    f'Expected "name", got {self.cur_token_}', self.cur_token_location_
-                )
-
-        self.expect_symbol_("}")
-        return self.ast.STATDesignAxisStatement(
-            axisTag, axisOrder, names, self.cur_token_location_
-        )
-
-    def parse_STAT_axis_value_(self):
-        assert self.is_cur_keyword_("AxisValue")
-        self.expect_symbol_("{")
-        locations = []
-        names = []
-        flags = 0
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                continue
-            elif self.is_cur_keyword_("name"):
-                location = self.cur_token_location_
-                platformID, platEncID, langID, string = self.parse_stat_name_()
-                name = self.ast.STATNameStatement(
-                    "stat", platformID, platEncID, langID, string, location=location
-                )
-                names.append(name)
-            elif self.is_cur_keyword_("location"):
-                location = self.parse_STAT_location()
-                locations.append(location)
-            elif self.is_cur_keyword_("flag"):
-                flags = self.expect_stat_flags()
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    f"Unexpected token {self.cur_token_} " f"in AxisValue",
-                    self.cur_token_location_,
-                )
-        self.expect_symbol_("}")
-        if not names:
-            raise FeatureLibError('Expected "Axis Name"', self.cur_token_location_)
-        if not locations:
-            raise FeatureLibError('Expected "Axis location"', self.cur_token_location_)
-        if len(locations) > 1:
-            for location in locations:
-                if len(location.values) > 1:
-                    raise FeatureLibError(
-                        "Only one value is allowed in a "
-                        "Format 4 Axis Value Record, but "
-                        f"{len(location.values)} were found.",
-                        self.cur_token_location_,
-                    )
-            format4_tags = []
-            for location in locations:
-                tag = location.tag
-                if tag in format4_tags:
-                    raise FeatureLibError(
-                        f"Axis tag {tag} already " "defined.", self.cur_token_location_
-                    )
-                format4_tags.append(tag)
-
-        return self.ast.STATAxisValueStatement(
-            names, locations, flags, self.cur_token_location_
-        )
-
-    def parse_STAT_location(self):
-        values = []
-        tag = self.expect_tag_()
-        if len(tag.strip()) != 4:
-            raise FeatureLibError(
-                f"Axis tag {self.cur_token_} must be 4 " "characters",
-                self.cur_token_location_,
-            )
-
-        while self.next_token_ != ";":
-            if self.next_token_type_ is Lexer.FLOAT:
-                value = self.expect_float_()
-                values.append(value)
-            elif self.next_token_type_ is Lexer.NUMBER:
-                value = self.expect_number_()
-                values.append(value)
-            else:
-                raise FeatureLibError(
-                    f'Unexpected value "{self.next_token_}". '
-                    "Expected integer or float.",
-                    self.next_token_location_,
-                )
-        if len(values) == 3:
-            nominal, min_val, max_val = values
-            if nominal < min_val or nominal > max_val:
-                raise FeatureLibError(
-                    f"Default value {nominal} is outside "
-                    f"of specified range "
-                    f"{min_val}-{max_val}.",
-                    self.next_token_location_,
-                )
-        return self.ast.AxisValueLocationStatement(tag, values)
-
-    def parse_table_STAT_(self, table):
-        statements = table.statements
-        design_axes = []
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.cur_token_type_ is Lexer.NAME:
-                if self.is_cur_keyword_("ElidedFallbackName"):
-                    names = self.parse_STAT_ElidedFallbackName()
-                    statements.append(self.ast.ElidedFallbackName(names))
-                elif self.is_cur_keyword_("ElidedFallbackNameID"):
-                    value = self.expect_number_()
-                    statements.append(self.ast.ElidedFallbackNameID(value))
-                    self.expect_symbol_(";")
-                elif self.is_cur_keyword_("DesignAxis"):
-                    designAxis = self.parse_STAT_design_axis()
-                    design_axes.append(designAxis.tag)
-                    statements.append(designAxis)
-                    self.expect_symbol_(";")
-                elif self.is_cur_keyword_("AxisValue"):
-                    axisValueRecord = self.parse_STAT_axis_value_()
-                    for location in axisValueRecord.locations:
-                        if location.tag not in design_axes:
-                            # Tag must be defined in a DesignAxis before it
-                            # can be referenced
-                            raise FeatureLibError(
-                                "DesignAxis not defined for " f"{location.tag}.",
-                                self.cur_token_location_,
-                            )
-                    statements.append(axisValueRecord)
-                    self.expect_symbol_(";")
-                else:
-                    raise FeatureLibError(
-                        f"Unexpected token {self.cur_token_}", self.cur_token_location_
-                    )
-            elif self.cur_token_ == ";":
-                continue
-
-    def parse_base_tag_list_(self):
-        # Parses BASE table entries. (See `section 9.a <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#9.a>`_)
-        assert self.cur_token_ in (
-            "HorizAxis.BaseTagList",
-            "VertAxis.BaseTagList",
-        ), self.cur_token_
-        bases = []
-        while self.next_token_ != ";":
-            bases.append(self.expect_script_tag_())
-        self.expect_symbol_(";")
-        return bases
-
-    def parse_base_script_list_(self, count):
-        assert self.cur_token_ in (
-            "HorizAxis.BaseScriptList",
-            "VertAxis.BaseScriptList",
-        ), self.cur_token_
-        scripts = [(self.parse_base_script_record_(count))]
-        while self.next_token_ == ",":
-            self.expect_symbol_(",")
-            scripts.append(self.parse_base_script_record_(count))
-        self.expect_symbol_(";")
-        return scripts
-
-    def parse_base_script_record_(self, count):
-        script_tag = self.expect_script_tag_()
-        base_tag = self.expect_script_tag_()
-        coords = [self.expect_number_() for i in range(count)]
-        return script_tag, base_tag, coords
-
-    def parse_device_(self):
-        result = None
-        self.expect_symbol_("<")
-        self.expect_keyword_("device")
-        if self.next_token_ == "NULL":
-            self.expect_keyword_("NULL")
-        else:
-            result = [(self.expect_number_(), self.expect_number_())]
-            while self.next_token_ == ",":
-                self.expect_symbol_(",")
-                result.append((self.expect_number_(), self.expect_number_()))
-            result = tuple(result)  # make it hashable
-        self.expect_symbol_(">")
-        return result
-
-    def is_next_value_(self):
-        return (
-            self.next_token_type_ is Lexer.NUMBER
-            or self.next_token_ == "<"
-            or self.next_token_ == "("
-        )
-
-    def parse_valuerecord_(self, vertical):
-        if (
-            self.next_token_type_ is Lexer.SYMBOL and self.next_token_ == "("
-        ) or self.next_token_type_ is Lexer.NUMBER:
-            number, location = (
-                self.expect_number_(variable=True),
-                self.cur_token_location_,
-            )
-            if vertical:
-                val = self.ast.ValueRecord(
-                    yAdvance=number, vertical=vertical, location=location
-                )
-            else:
-                val = self.ast.ValueRecord(
-                    xAdvance=number, vertical=vertical, location=location
-                )
-            return val
-        self.expect_symbol_("<")
-        location = self.cur_token_location_
-        if self.next_token_type_ is Lexer.NAME:
-            name = self.expect_name_()
-            if name == "NULL":
-                self.expect_symbol_(">")
-                return self.ast.ValueRecord()
-            vrd = self.valuerecords_.resolve(name)
-            if vrd is None:
-                raise FeatureLibError(
-                    'Unknown valueRecordDef "%s"' % name, self.cur_token_location_
-                )
-            value = vrd.value
-            xPlacement, yPlacement = (value.xPlacement, value.yPlacement)
-            xAdvance, yAdvance = (value.xAdvance, value.yAdvance)
-        else:
-            xPlacement, yPlacement, xAdvance, yAdvance = (
-                self.expect_number_(variable=True),
-                self.expect_number_(variable=True),
-                self.expect_number_(variable=True),
-                self.expect_number_(variable=True),
-            )
-
-        if self.next_token_ == "<":
-            xPlaDevice, yPlaDevice, xAdvDevice, yAdvDevice = (
-                self.parse_device_(),
-                self.parse_device_(),
-                self.parse_device_(),
-                self.parse_device_(),
-            )
-            allDeltas = sorted(
-                [
-                    delta
-                    for size, delta in (xPlaDevice if xPlaDevice else ())
-                    + (yPlaDevice if yPlaDevice else ())
-                    + (xAdvDevice if xAdvDevice else ())
-                    + (yAdvDevice if yAdvDevice else ())
-                ]
-            )
-            if allDeltas[0] < -128 or allDeltas[-1] > 127:
-                raise FeatureLibError(
-                    "Device value out of valid range (-128..127)",
-                    self.cur_token_location_,
-                )
-        else:
-            xPlaDevice, yPlaDevice, xAdvDevice, yAdvDevice = (None, None, None, None)
-
-        self.expect_symbol_(">")
-        return self.ast.ValueRecord(
-            xPlacement,
-            yPlacement,
-            xAdvance,
-            yAdvance,
-            xPlaDevice,
-            yPlaDevice,
-            xAdvDevice,
-            yAdvDevice,
-            vertical=vertical,
-            location=location,
-        )
-
-    def parse_valuerecord_definition_(self, vertical):
-        # Parses a named value record definition. (See section `2.e.v <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#2.e.v>`_)
-        assert self.is_cur_keyword_("valueRecordDef")
-        location = self.cur_token_location_
-        value = self.parse_valuerecord_(vertical)
-        name = self.expect_name_()
-        self.expect_symbol_(";")
-        vrd = self.ast.ValueRecordDefinition(name, value, location=location)
-        self.valuerecords_.define(name, vrd)
-        return vrd
-
-    def parse_languagesystem_(self):
-        assert self.cur_token_ == "languagesystem"
-        location = self.cur_token_location_
-        script = self.expect_script_tag_()
-        language = self.expect_language_tag_()
-        self.expect_symbol_(";")
-        return self.ast.LanguageSystemStatement(script, language, location=location)
-
-    def parse_feature_block_(self, variation=False):
-        if variation:
-            assert self.cur_token_ == "variation"
-        else:
-            assert self.cur_token_ == "feature"
-        location = self.cur_token_location_
-        tag = self.expect_tag_()
-        vertical = tag in {"vkrn", "vpal", "vhal", "valt"}
-
-        stylisticset = None
-        cv_feature = None
-        size_feature = False
-        if tag in self.SS_FEATURE_TAGS:
-            stylisticset = tag
-        elif tag in self.CV_FEATURE_TAGS:
-            cv_feature = tag
-        elif tag == "size":
-            size_feature = True
-
-        if variation:
-            conditionset = self.expect_name_()
-
-        use_extension = False
-        if self.next_token_ == "useExtension":
-            self.expect_keyword_("useExtension")
-            use_extension = True
-
-        if variation:
-            block = self.ast.VariationBlock(
-                tag, conditionset, use_extension=use_extension, location=location
-            )
-        else:
-            block = self.ast.FeatureBlock(
-                tag, use_extension=use_extension, location=location
-            )
-        self.parse_block_(block, vertical, stylisticset, size_feature, cv_feature)
-        return block
-
-    def parse_feature_reference_(self):
-        assert self.cur_token_ == "feature", self.cur_token_
-        location = self.cur_token_location_
-        featureName = self.expect_tag_()
-        self.expect_symbol_(";")
-        return self.ast.FeatureReferenceStatement(featureName, location=location)
-
-    def parse_featureNames_(self, tag):
-        """Parses a ``featureNames`` statement found in stylistic set features.
-        See section `8.c <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#8.c>`_."""
-        assert self.cur_token_ == "featureNames", self.cur_token_
-        block = self.ast.NestedBlock(
-            tag, self.cur_token_, location=self.cur_token_location_
-        )
-        self.expect_symbol_("{")
-        for symtab in self.symbol_tables_:
-            symtab.enter_scope()
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                block.statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("name"):
-                location = self.cur_token_location_
-                platformID, platEncID, langID, string = self.parse_name_()
-                block.statements.append(
-                    self.ast.FeatureNameStatement(
-                        tag, platformID, platEncID, langID, string, location=location
-                    )
-                )
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError('Expected "name"', self.cur_token_location_)
-        self.expect_symbol_("}")
-        for symtab in self.symbol_tables_:
-            symtab.exit_scope()
-        self.expect_symbol_(";")
-        return block
-
-    def parse_cvParameters_(self, tag):
-        # Parses a ``cvParameters`` block found in Character Variant features.
-        # See section `8.d <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#8.d>`_.
-        assert self.cur_token_ == "cvParameters", self.cur_token_
-        block = self.ast.NestedBlock(
-            tag, self.cur_token_, location=self.cur_token_location_
-        )
-        self.expect_symbol_("{")
-        for symtab in self.symbol_tables_:
-            symtab.enter_scope()
-
-        statements = block.statements
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_(
-                {
-                    "FeatUILabelNameID",
-                    "FeatUITooltipTextNameID",
-                    "SampleTextNameID",
-                    "ParamUILabelNameID",
-                }
-            ):
-                statements.append(self.parse_cvNameIDs_(tag, self.cur_token_))
-            elif self.is_cur_keyword_("Character"):
-                statements.append(self.parse_cvCharacter_(tag))
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    "Expected statement: got {} {}".format(
-                        self.cur_token_type_, self.cur_token_
-                    ),
-                    self.cur_token_location_,
-                )
-
-        self.expect_symbol_("}")
-        for symtab in self.symbol_tables_:
-            symtab.exit_scope()
-        self.expect_symbol_(";")
-        return block
-
-    def parse_cvNameIDs_(self, tag, block_name):
-        assert self.cur_token_ == block_name, self.cur_token_
-        block = self.ast.NestedBlock(tag, block_name, location=self.cur_token_location_)
-        self.expect_symbol_("{")
-        for symtab in self.symbol_tables_:
-            symtab.enter_scope()
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                block.statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.is_cur_keyword_("name"):
-                location = self.cur_token_location_
-                platformID, platEncID, langID, string = self.parse_name_()
-                block.statements.append(
-                    self.ast.CVParametersNameStatement(
-                        tag,
-                        platformID,
-                        platEncID,
-                        langID,
-                        string,
-                        block_name,
-                        location=location,
-                    )
-                )
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError('Expected "name"', self.cur_token_location_)
-        self.expect_symbol_("}")
-        for symtab in self.symbol_tables_:
-            symtab.exit_scope()
-        self.expect_symbol_(";")
-        return block
-
-    def parse_cvCharacter_(self, tag):
-        assert self.cur_token_ == "Character", self.cur_token_
-        location, character = self.cur_token_location_, self.expect_any_number_()
-        self.expect_symbol_(";")
-        if not (0xFFFFFF >= character >= 0):
-            raise FeatureLibError(
-                "Character value must be between "
-                "{:#x} and {:#x}".format(0, 0xFFFFFF),
-                location,
-            )
-        return self.ast.CharacterStatement(character, tag, location=location)
-
-    def parse_FontRevision_(self):
-        # Parses a ``FontRevision`` statement found in the head table. See
-        # `section 9.c <https://adobe-type-tools.github.io/afdko/OpenTypeFeatureFileSpecification.html#9.c>`_.
-        assert self.cur_token_ == "FontRevision", self.cur_token_
-        location, version = self.cur_token_location_, self.expect_float_()
-        self.expect_symbol_(";")
-        if version <= 0:
-            raise FeatureLibError("Font revision numbers must be positive", location)
-        return self.ast.FontRevisionStatement(version, location=location)
-
-    def parse_conditionset_(self):
-        name = self.expect_name_()
-
-        conditions = {}
-        self.expect_symbol_("{")
-
-        while self.next_token_ != "}":
-            self.advance_lexer_()
-            if self.cur_token_type_ is not Lexer.NAME:
-                raise FeatureLibError("Expected an axis name", self.cur_token_location_)
-
-            axis = self.cur_token_
-            if axis in conditions:
-                raise FeatureLibError(
-                    f"Repeated condition for axis {axis}", self.cur_token_location_
-                )
-
-            if self.next_token_type_ is Lexer.FLOAT:
-                min_value = self.expect_float_()
-            elif self.next_token_type_ is Lexer.NUMBER:
-                min_value = self.expect_number_(variable=False)
-
-            if self.next_token_type_ is Lexer.FLOAT:
-                max_value = self.expect_float_()
-            elif self.next_token_type_ is Lexer.NUMBER:
-                max_value = self.expect_number_(variable=False)
-            self.expect_symbol_(";")
-
-            conditions[axis] = (min_value, max_value)
-
-        self.expect_symbol_("}")
-
-        finalname = self.expect_name_()
-        if finalname != name:
-            raise FeatureLibError('Expected "%s"' % name, self.cur_token_location_)
-        return self.ast.ConditionsetStatement(name, conditions)
-
-    def parse_block_(
-        self, block, vertical, stylisticset=None, size_feature=False, cv_feature=None
-    ):
-        self.expect_symbol_("{")
-        for symtab in self.symbol_tables_:
-            symtab.enter_scope()
-
-        statements = block.statements
-        while self.next_token_ != "}" or self.cur_comments_:
-            self.advance_lexer_(comments=True)
-            if self.cur_token_type_ is Lexer.COMMENT:
-                statements.append(
-                    self.ast.Comment(self.cur_token_, location=self.cur_token_location_)
-                )
-            elif self.cur_token_type_ is Lexer.GLYPHCLASS:
-                statements.append(self.parse_glyphclass_definition_())
-            elif self.is_cur_keyword_("anchorDef"):
-                statements.append(self.parse_anchordef_())
-            elif self.is_cur_keyword_({"enum", "enumerate"}):
-                statements.append(self.parse_enumerate_(vertical=vertical))
-            elif self.is_cur_keyword_("feature"):
-                statements.append(self.parse_feature_reference_())
-            elif self.is_cur_keyword_("ignore"):
-                statements.append(self.parse_ignore_())
-            elif self.is_cur_keyword_("language"):
-                statements.append(self.parse_language_())
-            elif self.is_cur_keyword_("lookup"):
-                statements.append(self.parse_lookup_(vertical))
-            elif self.is_cur_keyword_("lookupflag"):
-                statements.append(self.parse_lookupflag_())
-            elif self.is_cur_keyword_("markClass"):
-                statements.append(self.parse_markClass_())
-            elif self.is_cur_keyword_({"pos", "position"}):
-                statements.append(
-                    self.parse_position_(enumerated=False, vertical=vertical)
-                )
-            elif self.is_cur_keyword_("script"):
-                statements.append(self.parse_script_())
-            elif self.is_cur_keyword_({"sub", "substitute", "rsub", "reversesub"}):
-                statements.append(self.parse_substitute_())
-            elif self.is_cur_keyword_("subtable"):
-                statements.append(self.parse_subtable_())
-            elif self.is_cur_keyword_("valueRecordDef"):
-                statements.append(self.parse_valuerecord_definition_(vertical))
-            elif stylisticset and self.is_cur_keyword_("featureNames"):
-                statements.append(self.parse_featureNames_(stylisticset))
-            elif cv_feature and self.is_cur_keyword_("cvParameters"):
-                statements.append(self.parse_cvParameters_(cv_feature))
-            elif size_feature and self.is_cur_keyword_("parameters"):
-                statements.append(self.parse_size_parameters_())
-            elif size_feature and self.is_cur_keyword_("sizemenuname"):
-                statements.append(self.parse_size_menuname_())
-            elif (
-                self.cur_token_type_ is Lexer.NAME
-                and self.cur_token_ in self.extensions
-            ):
-                statements.append(self.extensions[self.cur_token_](self))
-            elif self.cur_token_ == ";":
-                continue
-            else:
-                raise FeatureLibError(
-                    "Expected glyph class definition or statement: got {} {}".format(
-                        self.cur_token_type_, self.cur_token_
-                    ),
-                    self.cur_token_location_,
-                )
-
-        self.expect_symbol_("}")
-        for symtab in self.symbol_tables_:
-            symtab.exit_scope()
-
-        name = self.expect_name_()
-        if name != block.name.strip():
-            raise FeatureLibError(
-                'Expected "%s"' % block.name.strip(), self.cur_token_location_
-            )
-        self.expect_symbol_(";")
-
-        # A multiple substitution may have a single destination, in which case
-        # it will look just like a single substitution. So if there are both
-        # multiple and single substitutions, upgrade all the single ones to
-        # multiple substitutions.
-
-        # Check if we have a mix of non-contextual singles and multiples.
-        has_single = False
-        has_multiple = False
-        for s in statements:
-            if isinstance(s, self.ast.SingleSubstStatement):
-                has_single = not any([s.prefix, s.suffix, s.forceChain])
-            elif isinstance(s, self.ast.MultipleSubstStatement):
-                has_multiple = not any([s.prefix, s.suffix, s.forceChain])
-
-        # Upgrade all single substitutions to multiple substitutions.
-        if has_single and has_multiple:
-            statements = []
-            for s in block.statements:
-                if isinstance(s, self.ast.SingleSubstStatement):
-                    glyphs = s.glyphs[0].glyphSet()
-                    replacements = s.replacements[0].glyphSet()
-                    if len(replacements) == 1:
-                        replacements *= len(glyphs)
-                    for i, glyph in enumerate(glyphs):
-                        statements.append(
-                            self.ast.MultipleSubstStatement(
-                                s.prefix,
-                                glyph,
-                                s.suffix,
-                                [replacements[i]],
-                                s.forceChain,
-                                location=s.location,
-                            )
-                        )
-                else:
-                    statements.append(s)
-            block.statements = statements
-
-    def is_cur_keyword_(self, k):
-        if self.cur_token_type_ is Lexer.NAME:
-            if isinstance(k, type("")):  # basestring is gone in Python3
-                return self.cur_token_ == k
-            else:
-                return self.cur_token_ in k
-        return False
-
-    def expect_class_name_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is not Lexer.GLYPHCLASS:
-            raise FeatureLibError("Expected @NAME", self.cur_token_location_)
-        return self.cur_token_
-
-    def expect_cid_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.CID:
-            return self.cur_token_
-        raise FeatureLibError("Expected a CID", self.cur_token_location_)
-
-    def expect_filename_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is not Lexer.FILENAME:
-            raise FeatureLibError("Expected file name", self.cur_token_location_)
-        return self.cur_token_
-
-    def expect_glyph_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.NAME:
-            self.cur_token_ = self.cur_token_.lstrip("\\")
-            if len(self.cur_token_) > 63:
-                raise FeatureLibError(
-                    "Glyph names must not be longer than 63 characters",
-                    self.cur_token_location_,
-                )
-            return self.cur_token_
-        elif self.cur_token_type_ is Lexer.CID:
-            return "cid%05d" % self.cur_token_
-        raise FeatureLibError("Expected a glyph name or CID", self.cur_token_location_)
-
-    def check_glyph_name_in_glyph_set(self, *names):
-        """Adds a glyph name (just `start`) or glyph names of a
-        range (`start` and `end`) which are not in the glyph set
-        to the "missing list" for future error reporting.
-
-        If no glyph set is present, does nothing.
-        """
-        if self.glyphNames_:
-            for name in names:
-                if name in self.glyphNames_:
-                    continue
-                if name not in self.missing:
-                    self.missing[name] = self.cur_token_location_
-
-    def expect_markClass_reference_(self):
-        name = self.expect_class_name_()
-        mc = self.glyphclasses_.resolve(name)
-        if mc is None:
-            raise FeatureLibError(
-                "Unknown markClass @%s" % name, self.cur_token_location_
-            )
-        if not isinstance(mc, self.ast.MarkClass):
-            raise FeatureLibError(
-                "@%s is not a markClass" % name, self.cur_token_location_
-            )
-        return mc
-
-    def expect_tag_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is not Lexer.NAME:
-            raise FeatureLibError("Expected a tag", self.cur_token_location_)
-        if len(self.cur_token_) > 4:
-            raise FeatureLibError(
-                "Tags cannot be longer than 4 characters", self.cur_token_location_
-            )
-        return (self.cur_token_ + "    ")[:4]
-
-    def expect_script_tag_(self):
-        tag = self.expect_tag_()
-        if tag == "dflt":
-            raise FeatureLibError(
-                '"dflt" is not a valid script tag; use "DFLT" instead',
-                self.cur_token_location_,
-            )
-        return tag
-
-    def expect_language_tag_(self):
-        tag = self.expect_tag_()
-        if tag == "DFLT":
-            raise FeatureLibError(
-                '"DFLT" is not a valid language tag; use "dflt" instead',
-                self.cur_token_location_,
-            )
-        return tag
-
-    def expect_symbol_(self, symbol):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.SYMBOL and self.cur_token_ == symbol:
-            return symbol
-        raise FeatureLibError("Expected '%s'" % symbol, self.cur_token_location_)
-
-    def expect_keyword_(self, keyword):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.NAME and self.cur_token_ == keyword:
-            return self.cur_token_
-        raise FeatureLibError('Expected "%s"' % keyword, self.cur_token_location_)
-
-    def expect_name_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.NAME:
-            return self.cur_token_
-        raise FeatureLibError("Expected a name", self.cur_token_location_)
-
-    def expect_number_(self, variable=False):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.NUMBER:
-            return self.cur_token_
-        if variable and self.cur_token_type_ is Lexer.SYMBOL and self.cur_token_ == "(":
-            return self.expect_variable_scalar_()
-        raise FeatureLibError("Expected a number", self.cur_token_location_)
-
-    def expect_variable_scalar_(self):
-        self.advance_lexer_()  # "("
-        scalar = VariableScalar()
-        while True:
-            if self.cur_token_type_ == Lexer.SYMBOL and self.cur_token_ == ")":
-                break
-            location, value = self.expect_master_()
-            scalar.add_value(location, value)
-        return scalar
-
-    def expect_master_(self):
-        location = {}
-        while True:
-            if self.cur_token_type_ is not Lexer.NAME:
-                raise FeatureLibError("Expected an axis name", self.cur_token_location_)
-            axis = self.cur_token_
-            self.advance_lexer_()
-            if not (self.cur_token_type_ is Lexer.SYMBOL and self.cur_token_ == "="):
-                raise FeatureLibError(
-                    "Expected an equals sign", self.cur_token_location_
-                )
-            value = self.expect_number_()
-            location[axis] = value
-            if self.next_token_type_ is Lexer.NAME and self.next_token_[0] == ":":
-                # Lexer has just read the value as a glyph name. We'll correct it later
-                break
-            self.advance_lexer_()
-            if not (self.cur_token_type_ is Lexer.SYMBOL and self.cur_token_ == ","):
-                raise FeatureLibError(
-                    "Expected an comma or an equals sign", self.cur_token_location_
-                )
-            self.advance_lexer_()
-        self.advance_lexer_()
-        value = int(self.cur_token_[1:])
-        self.advance_lexer_()
-        return location, value
-
-    def expect_any_number_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ in Lexer.NUMBERS:
-            return self.cur_token_
-        raise FeatureLibError(
-            "Expected a decimal, hexadecimal or octal number", self.cur_token_location_
-        )
-
-    def expect_float_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.FLOAT:
-            return self.cur_token_
-        raise FeatureLibError(
-            "Expected a floating-point number", self.cur_token_location_
-        )
-
-    def expect_decipoint_(self):
-        if self.next_token_type_ == Lexer.FLOAT:
-            return self.expect_float_()
-        elif self.next_token_type_ is Lexer.NUMBER:
-            return self.expect_number_() / 10
-        else:
-            raise FeatureLibError(
-                "Expected an integer or floating-point number", self.cur_token_location_
-            )
-
-    def expect_stat_flags(self):
-        value = 0
-        flags = {
-            "OlderSiblingFontAttribute": 1,
-            "ElidableAxisValueName": 2,
-        }
-        while self.next_token_ != ";":
-            if self.next_token_ in flags:
-                name = self.expect_name_()
-                value = value | flags[name]
-            else:
-                raise FeatureLibError(
-                    f"Unexpected STAT flag {self.cur_token_}", self.cur_token_location_
-                )
-        return value
-
-    def expect_stat_values_(self):
-        if self.next_token_type_ == Lexer.FLOAT:
-            return self.expect_float_()
-        elif self.next_token_type_ is Lexer.NUMBER:
-            return self.expect_number_()
-        else:
-            raise FeatureLibError(
-                "Expected an integer or floating-point number", self.cur_token_location_
-            )
-
-    def expect_string_(self):
-        self.advance_lexer_()
-        if self.cur_token_type_ is Lexer.STRING:
-            return self.cur_token_
-        raise FeatureLibError("Expected a string", self.cur_token_location_)
-
-    def advance_lexer_(self, comments=False):
-        if comments and self.cur_comments_:
-            self.cur_token_type_ = Lexer.COMMENT
-            self.cur_token_, self.cur_token_location_ = self.cur_comments_.pop(0)
-            return
-        else:
-            self.cur_token_type_, self.cur_token_, self.cur_token_location_ = (
-                self.next_token_type_,
-                self.next_token_,
-                self.next_token_location_,
-            )
-        while True:
-            try:
-                (
-                    self.next_token_type_,
-                    self.next_token_,
-                    self.next_token_location_,
-                ) = next(self.lexer_)
-            except StopIteration:
-                self.next_token_type_, self.next_token_ = (None, None)
-            if self.next_token_type_ != Lexer.COMMENT:
-                break
-            self.cur_comments_.append((self.next_token_, self.next_token_location_))
-
-    @staticmethod
-    def reverse_string_(s):
-        """'abc' --> 'cba'"""
-        return "".join(reversed(list(s)))
-
-    def make_cid_range_(self, location, start, limit):
-        """(location, 999, 1001) --> ["cid00999", "cid01000", "cid01001"]"""
-        result = list()
-        if start > limit:
-            raise FeatureLibError(
-                "Bad range: start should be less than limit", location
-            )
-        for cid in range(start, limit + 1):
-            result.append("cid%05d" % cid)
-        return result
-
-    def make_glyph_range_(self, location, start, limit):
-        """(location, "a.sc", "d.sc") --> ["a.sc", "b.sc", "c.sc", "d.sc"]"""
-        result = list()
-        if len(start) != len(limit):
-            raise FeatureLibError(
-                'Bad range: "%s" and "%s" should have the same length' % (start, limit),
-                location,
-            )
-
-        rev = self.reverse_string_
-        prefix = os.path.commonprefix([start, limit])
-        suffix = rev(os.path.commonprefix([rev(start), rev(limit)]))
-        if len(suffix) > 0:
-            start_range = start[len(prefix) : -len(suffix)]
-            limit_range = limit[len(prefix) : -len(suffix)]
-        else:
-            start_range = start[len(prefix) :]
-            limit_range = limit[len(prefix) :]
-
-        if start_range >= limit_range:
-            raise FeatureLibError(
-                "Start of range must be smaller than its end", location
-            )
-
-        uppercase = re.compile(r"^[A-Z]$")
-        if uppercase.match(start_range) and uppercase.match(limit_range):
-            for c in range(ord(start_range), ord(limit_range) + 1):
-                result.append("%s%c%s" % (prefix, c, suffix))
-            return result
-
-        lowercase = re.compile(r"^[a-z]$")
-        if lowercase.match(start_range) and lowercase.match(limit_range):
-            for c in range(ord(start_range), ord(limit_range) + 1):
-                result.append("%s%c%s" % (prefix, c, suffix))
-            return result
-
-        digits = re.compile(r"^[0-9]{1,3}$")
-        if digits.match(start_range) and digits.match(limit_range):
-            for i in range(int(start_range, 10), int(limit_range, 10) + 1):
-                number = ("000" + str(i))[-len(start_range) :]
-                result.append("%s%s%s" % (prefix, number, suffix))
-            return result
-
-        raise FeatureLibError('Bad range: "%s-%s"' % (start, limit), location)
-
-
-class SymbolTable(object):
-    def __init__(self):
-        self.scopes_ = [{}]
-
-    def enter_scope(self):
-        self.scopes_.append({})
-
-    def exit_scope(self):
-        self.scopes_.pop()
-
-    def define(self, name, item):
-        self.scopes_[-1][name] = item
-
-    def resolve(self, name):
-        for scope in reversed(self.scopes_):
-            item = scope.get(name)
-            if item:
-                return item
-        return None
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/T_S_I_C_.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/T_S_I_C_.py
deleted file mode 100644
index 573b3f9c3970766ea817994509f4939ef4f70f0c..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/T_S_I_C_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table_T_S_I_C_(BaseTTXConverter):
-    pass
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aptx.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aptx.h
deleted file mode 100644
index da0697e652b585bce05e5e5b37e5204137e00110..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aptx.h
+++ /dev/null
@@ -1,217 +0,0 @@
-/*
- * Audio Processing Technology codec for Bluetooth (aptX)
- *
- * Copyright (C) 2017  Aurelien Jacobs <aurel@gnuage.org>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_APTX_H
-#define AVCODEC_APTX_H
-
-#include "libavutil/intreadwrite.h"
-#include "avcodec.h"
-#include "mathops.h"
-
-
-enum channels {
-    LEFT,
-    RIGHT,
-    NB_CHANNELS
-};
-
-enum subbands {
-    LF,  // Low Frequency (0-5.5 kHz)
-    MLF, // Medium-Low Frequency (5.5-11kHz)
-    MHF, // Medium-High Frequency (11-16.5kHz)
-    HF,  // High Frequency (16.5-22kHz)
-    NB_SUBBANDS
-};
-
-#define NB_FILTERS 2
-#define FILTER_TAPS 16
-
-typedef struct {
-    int pos;
-    int32_t buffer[2*FILTER_TAPS];
-} FilterSignal;
-
-typedef struct {
-    FilterSignal outer_filter_signal[NB_FILTERS];
-    FilterSignal inner_filter_signal[NB_FILTERS][NB_FILTERS];
-} QMFAnalysis;
-
-typedef struct {
-    int32_t quantized_sample;
-    int32_t quantized_sample_parity_change;
-    int32_t error;
-} Quantize;
-
-typedef struct {
-    int32_t quantization_factor;
-    int32_t factor_select;
-    int32_t reconstructed_difference;
-} InvertQuantize;
-
-typedef struct {
-    int32_t prev_sign[2];
-    int32_t s_weight[2];
-    int32_t d_weight[24];
-    int32_t pos;
-    int32_t reconstructed_differences[48];
-    int32_t previous_reconstructed_sample;
-    int32_t predicted_difference;
-    int32_t predicted_sample;
-} Prediction;
-
-typedef struct {
-    int32_t codeword_history;
-    int32_t dither_parity;
-    int32_t dither[NB_SUBBANDS];
-
-    QMFAnalysis qmf;
-    Quantize quantize[NB_SUBBANDS];
-    InvertQuantize invert_quantize[NB_SUBBANDS];
-    Prediction prediction[NB_SUBBANDS];
-} Channel;
-
-typedef struct {
-    int hd;
-    int block_size;
-    int32_t sync_idx;
-    Channel channels[NB_CHANNELS];
-} AptXContext;
-
-typedef const struct {
-    const int32_t *quantize_intervals;
-    const int32_t *invert_quantize_dither_factors;
-    const int32_t *quantize_dither_factors;
-    const int16_t *quantize_factor_select_offset;
-    int tables_size;
-    int32_t factor_max;
-    int32_t prediction_order;
-} ConstTables;
-
-extern ConstTables ff_aptx_quant_tables[2][NB_SUBBANDS];
-
-/* Rounded right shift with optionnal clipping */
-#define RSHIFT_SIZE(size)                                                     \
-av_always_inline                                                              \
-static int##size##_t rshift##size(int##size##_t value, int shift)             \
-{                                                                             \
-    int##size##_t rounding = (int##size##_t)1 << (shift - 1);                 \
-    int##size##_t mask = ((int##size##_t)1 << (shift + 1)) - 1;               \
-    return ((value + rounding) >> shift) - ((value & mask) == rounding);      \
-}                                                                             \
-av_always_inline                                                              \
-static int##size##_t rshift##size##_clip24(int##size##_t value, int shift)    \
-{                                                                             \
-    return av_clip_intp2(rshift##size(value, shift), 23);                     \
-}
-RSHIFT_SIZE(32)
-RSHIFT_SIZE(64)
-
-/*
- * Convolution filter coefficients for the outer QMF of the QMF tree.
- * The 2 sets are a mirror of each other.
- */
-static const int32_t aptx_qmf_outer_coeffs[NB_FILTERS][FILTER_TAPS] = {
-    {
-        730, -413, -9611, 43626, -121026, 269973, -585547, 2801966,
-        697128, -160481, 27611, 8478, -10043, 3511, 688, -897,
-    },
-    {
-        -897, 688, 3511, -10043, 8478, 27611, -160481, 697128,
-        2801966, -585547, 269973, -121026, 43626, -9611, -413, 730,
-    },
-};
-
-/*
- * Convolution filter coefficients for the inner QMF of the QMF tree.
- * The 2 sets are a mirror of each other.
- */
-static const int32_t aptx_qmf_inner_coeffs[NB_FILTERS][FILTER_TAPS] = {
-    {
-       1033, -584, -13592, 61697, -171156, 381799, -828088, 3962579,
-       985888, -226954, 39048, 11990, -14203, 4966, 973, -1268,
-    },
-    {
-      -1268, 973, 4966, -14203, 11990, 39048, -226954, 985888,
-      3962579, -828088, 381799, -171156, 61697, -13592, -584, 1033,
-    },
-};
-
-/*
- * Push one sample into a circular signal buffer.
- */
-av_always_inline
-static void aptx_qmf_filter_signal_push(FilterSignal *signal, int32_t sample)
-{
-    signal->buffer[signal->pos            ] = sample;
-    signal->buffer[signal->pos+FILTER_TAPS] = sample;
-    signal->pos = (signal->pos + 1) & (FILTER_TAPS - 1);
-}
-
-/*
- * Compute the convolution of the signal with the coefficients, and reduce
- * to 24 bits by applying the specified right shifting.
- */
-av_always_inline
-static int32_t aptx_qmf_convolution(FilterSignal *signal,
-                                    const int32_t coeffs[FILTER_TAPS],
-                                    int shift)
-{
-    int32_t *sig = &signal->buffer[signal->pos];
-    int64_t e = 0;
-    int i;
-
-    for (i = 0; i < FILTER_TAPS; i++)
-        e += MUL64(sig[i], coeffs[i]);
-
-    return rshift64_clip24(e, shift);
-}
-
-static inline int32_t aptx_quantized_parity(Channel *channel)
-{
-    int32_t parity = channel->dither_parity;
-    int subband;
-
-    for (subband = 0; subband < NB_SUBBANDS; subband++)
-        parity ^= channel->quantize[subband].quantized_sample;
-
-    return parity & 1;
-}
-
-/* For each sample, ensure that the parity of all subbands of all channels
- * is 0 except once every 8 samples where the parity is forced to 1. */
-static inline int aptx_check_parity(Channel channels[NB_CHANNELS], int32_t *idx)
-{
-    int32_t parity = aptx_quantized_parity(&channels[LEFT])
-                   ^ aptx_quantized_parity(&channels[RIGHT]);
-
-    int eighth = *idx == 7;
-    *idx = (*idx + 1) & 7;
-
-    return parity ^ eighth;
-}
-
-void ff_aptx_invert_quantize_and_prediction(Channel *channel, int hd);
-void ff_aptx_generate_dither(Channel *channel);
-
-int ff_aptx_init(AVCodecContext *avctx);
-
-#endif /* AVCODEC_APTX_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/fits.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/fits.h
deleted file mode 100644
index ebae85c9e55f1c8ebe97e016c993618fd8d90761..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/fits.h
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * FITS image format common prototypes and structures
- * Copyright (c) 2017 Paras Chadha
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_FITS_H
-#define AVCODEC_FITS_H
-
-#include <inttypes.h>
-
-#include "libavutil/dict.h"
-
-typedef enum FITSHeaderState {
-    STATE_SIMPLE,
-    STATE_XTENSION,
-    STATE_BITPIX,
-    STATE_NAXIS,
-    STATE_NAXIS_N,
-    STATE_PCOUNT,
-    STATE_GCOUNT,
-    STATE_REST,
-} FITSHeaderState;
-
-/**
- * Structure to store the header keywords in FITS file
- */
-typedef struct FITSHeader {
-    FITSHeaderState state;
-    unsigned naxis_index;
-    int bitpix;
-    int64_t blank;
-    int blank_found;
-    int naxis;
-    int naxisn[999];
-    int pcount;
-    int gcount;
-    int groups;
-    int rgb; /**< 1 if file contains RGB image, 0 otherwise */
-    int image_extension;
-    double bscale;
-    double bzero;
-    int data_min_found;
-    double data_min;
-    int data_max_found;
-    double data_max;
-} FITSHeader;
-
-
-/**
- * Initialize a single header line
- * @param header pointer to the header
- * @param state current state of parsing the header
- * @return 0 if successful otherwise AVERROR_INVALIDDATA
- */
-int avpriv_fits_header_init(FITSHeader *header, FITSHeaderState state);
-
-/**
- * Parse a single header line
- * @param avcl used in av_log
- * @param header pointer to the header
- * @param line one header line
- * @param metadata used to store metadata while decoding
- * @return 0 if successful otherwise AVERROR_INVALIDDATA
- */
-int avpriv_fits_header_parse_line(void *avcl, FITSHeader *header, const uint8_t line[80], AVDictionary ***metadata);
-
-#endif /* AVCODEC_FITS_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libwebpenc_animencoder.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libwebpenc_animencoder.c
deleted file mode 100644
index 8756231f23cc7d346063f91697e5aeaa74d2769d..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libwebpenc_animencoder.c
+++ /dev/null
@@ -1,192 +0,0 @@
-/*
- * WebP encoding support via libwebp
- * Copyright (c) 2015 Urvang Joshi
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * WebP encoder using libwebp (WebPAnimEncoder API)
- */
-
-#include "libavutil/buffer.h"
-
-#include "config.h"
-#include "codec_internal.h"
-#include "encode.h"
-#include "libwebpenc_common.h"
-
-#include <webp/mux.h>
-
-typedef struct LibWebPAnimContext {
-    LibWebPContextCommon cc;
-    WebPAnimEncoder *enc;     // the main AnimEncoder object
-    int64_t first_frame_pts;  // pts of the first encoded frame.
-    int64_t end_pts;          // pts + duration of the last frame
-
-#if FF_API_REORDERED_OPAQUE
-    int64_t         reordered_opaque;
-#endif
-    void           *first_frame_opaque;
-    AVBufferRef    *first_frame_opaque_ref;
-
-    int done;                 // If true, we have assembled the bitstream already
-} LibWebPAnimContext;
-
-static av_cold int libwebp_anim_encode_init(AVCodecContext *avctx)
-{
-    int ret = ff_libwebp_encode_init_common(avctx);
-    if (!ret) {
-        LibWebPAnimContext *s = avctx->priv_data;
-        WebPAnimEncoderOptions enc_options = { { 0 } };
-        WebPAnimEncoderOptionsInit(&enc_options);
-        enc_options.verbose = av_log_get_level() >= AV_LOG_VERBOSE;
-        // TODO(urvang): Expose some options on command-line perhaps.
-        s->enc = WebPAnimEncoderNew(avctx->width, avctx->height, &enc_options);
-        if (!s->enc)
-            return AVERROR(EINVAL);
-        s->first_frame_pts = AV_NOPTS_VALUE;
-        s->done = 0;
-    }
-    return ret;
-}
-
-static int libwebp_anim_encode_frame(AVCodecContext *avctx, AVPacket *pkt,
-                                     const AVFrame *frame, int *got_packet) {
-    LibWebPAnimContext *s = avctx->priv_data;
-    int ret;
-
-    if (!frame) {
-        if (s->done) {  // Second flush: return empty package to denote finish.
-            *got_packet = 0;
-            return 0;
-        } else {  // First flush: assemble bitstream and return it.
-            WebPData assembled_data = { 0 };
-            ret = WebPAnimEncoderAssemble(s->enc, &assembled_data);
-            if (ret) {
-                ret = ff_get_encode_buffer(avctx, pkt, assembled_data.size, 0);
-                if (ret < 0) {
-                    WebPDataClear(&assembled_data);
-                    return ret;
-                }
-                memcpy(pkt->data, assembled_data.bytes, assembled_data.size);
-                WebPDataClear(&assembled_data);
-                s->done = 1;
-                pkt->pts = s->first_frame_pts;
-
-                if (pkt->pts != AV_NOPTS_VALUE && s->end_pts > pkt->pts)
-                    pkt->duration = s->end_pts - pkt->pts;
-
-#if FF_API_REORDERED_OPAQUE
-FF_DISABLE_DEPRECATION_WARNINGS
-                avctx->reordered_opaque = s->reordered_opaque;
-FF_ENABLE_DEPRECATION_WARNINGS
-#endif
-                if (avctx->flags & AV_CODEC_FLAG_COPY_OPAQUE) {
-                    pkt->opaque               = s->first_frame_opaque;
-                    pkt->opaque_ref           = s->first_frame_opaque_ref;
-                    s->first_frame_opaque_ref = NULL;
-                }
-
-                *got_packet = 1;
-                return 0;
-            } else {
-                WebPDataClear(&assembled_data);
-                av_log(s, AV_LOG_ERROR,
-                       "WebPAnimEncoderAssemble() failed with error: %d\n",
-                       VP8_ENC_ERROR_OUT_OF_MEMORY);
-                return AVERROR(ENOMEM);
-            }
-        }
-    } else {
-        int timestamp_ms;
-        WebPPicture *pic = NULL;
-        AVFrame *alt_frame = NULL;
-        ret = ff_libwebp_get_frame(avctx, &s->cc, frame, &alt_frame, &pic);
-        if (ret < 0)
-            goto end;
-
-        timestamp_ms =
-            avctx->time_base.num * frame->pts * 1000 / avctx->time_base.den;
-        ret = WebPAnimEncoderAdd(s->enc, pic, timestamp_ms, &s->cc.config);
-        if (!ret) {
-                av_log(avctx, AV_LOG_ERROR,
-                       "Encoding WebP frame failed with error: %d\n",
-                   pic->error_code);
-            ret = ff_libwebp_error_to_averror(pic->error_code);
-            goto end;
-        }
-
-        if (!avctx->frame_num) {
-            s->first_frame_pts = frame->pts;
-#if FF_API_REORDERED_OPAQUE
-FF_DISABLE_DEPRECATION_WARNINGS
-            s->reordered_opaque = frame->reordered_opaque;
-FF_ENABLE_DEPRECATION_WARNINGS
-#endif
-
-            if (avctx->flags & AV_CODEC_FLAG_COPY_OPAQUE) {
-                s->first_frame_opaque = frame->opaque;
-                ret = av_buffer_replace(&s->first_frame_opaque_ref, frame->opaque_ref);
-                if (ret < 0)
-                    goto end;
-            }
-        }
-
-        if (frame->pts != AV_NOPTS_VALUE)
-            s->end_pts = frame->pts + frame->duration;
-
-        ret = 0;
-        *got_packet = 0;
-
-end:
-        WebPPictureFree(pic);
-        av_freep(&pic);
-        av_frame_free(&alt_frame);
-        return ret;
-    }
-}
-
-static int libwebp_anim_encode_close(AVCodecContext *avctx)
-{
-    LibWebPAnimContext *s = avctx->priv_data;
-    av_frame_free(&s->cc.ref);
-    WebPAnimEncoderDelete(s->enc);
-
-    av_buffer_unref(&s->first_frame_opaque_ref);
-
-    return 0;
-}
-
-const FFCodec ff_libwebp_anim_encoder = {
-    .p.name         = "libwebp_anim",
-    CODEC_LONG_NAME("libwebp WebP image"),
-    .p.type         = AVMEDIA_TYPE_VIDEO,
-    .p.id           = AV_CODEC_ID_WEBP,
-    .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_DELAY |
-                      AV_CODEC_CAP_ENCODER_REORDERED_OPAQUE,
-    .p.pix_fmts     = ff_libwebpenc_pix_fmts,
-    .p.priv_class   = &ff_libwebpenc_class,
-    .p.wrapper_name = "libwebp",
-    .caps_internal  = FF_CODEC_CAP_NOT_INIT_THREADSAFE,
-    .priv_data_size = sizeof(LibWebPAnimContext),
-    .defaults       = ff_libwebp_defaults,
-    .init           = libwebp_anim_encode_init,
-    FF_CODEC_ENCODE_CB(libwebp_anim_encode_frame),
-    .close          = libwebp_anim_encode_close,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/hpeldsp_lasx.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/hpeldsp_lasx.c
deleted file mode 100644
index dd2ae173da386c77e8ddcae4f8f9358f8464823d..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/hpeldsp_lasx.c
+++ /dev/null
@@ -1,1287 +0,0 @@
-/*
- * Copyright (c) 2021 Loongson Technology Corporation Limited
- * Contributed by Shiyou Yin <yinshiyou-hf@loongson.cn>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/loongarch/loongson_intrinsics.h"
-#include "hpeldsp_lasx.h"
-
-static av_always_inline void
-put_pixels8_l2_8_lsx(uint8_t *dst, const uint8_t *src1, const uint8_t *src2,
-                     int dst_stride, int src_stride1, int src_stride2, int h)
-{
-    int stride1_2, stride1_3, stride1_4;
-    int stride2_2, stride2_3, stride2_4;
-    __asm__ volatile (
-        "slli.d   %[stride1_2],  %[srcStride1],     1             \n\t"
-        "slli.d   %[stride2_2],  %[srcStride2],     1             \n\t"
-        "add.d    %[stride1_3],  %[stride1_2],      %[srcStride1] \n\t"
-        "add.d    %[stride2_3],  %[stride2_2],      %[srcStride2] \n\t"
-        "slli.d   %[stride1_4],  %[stride1_2],      1             \n\t"
-        "slli.d   %[stride2_4],  %[stride2_2],      1             \n\t"
-        "1:                                                       \n\t"
-        "vld      $vr0,          %[src1],           0             \n\t"
-        "vldx     $vr1,          %[src1],           %[srcStride1] \n\t"
-        "vldx     $vr2,          %[src1],           %[stride1_2]  \n\t"
-        "vldx     $vr3,          %[src1],           %[stride1_3]  \n\t"
-        "add.d    %[src1],       %[src1],           %[stride1_4]  \n\t"
-
-        "vld      $vr4,          %[src2],           0             \n\t"
-        "vldx     $vr5,          %[src2],           %[srcStride2] \n\t"
-        "vldx     $vr6,          %[src2],           %[stride2_2]  \n\t"
-        "vldx     $vr7,          %[src2],           %[stride2_3]  \n\t"
-        "add.d    %[src2],       %[src2],           %[stride2_4]  \n\t"
-
-        "addi.d   %[h],          %[h],              -4            \n\t"
-
-        "vavgr.bu $vr0,          $vr4,              $vr0          \n\t"
-        "vavgr.bu $vr1,          $vr5,              $vr1          \n\t"
-        "vavgr.bu $vr2,          $vr6,              $vr2          \n\t"
-        "vavgr.bu $vr3,          $vr7,              $vr3          \n\t"
-        "vstelm.d $vr0,          %[dst],            0,  0         \n\t"
-        "add.d    %[dst],        %[dst],            %[dstStride]  \n\t"
-        "vstelm.d $vr1,          %[dst],            0,  0         \n\t"
-        "add.d    %[dst],        %[dst],            %[dstStride]  \n\t"
-        "vstelm.d $vr2,          %[dst],            0,  0         \n\t"
-        "add.d    %[dst],        %[dst],            %[dstStride]  \n\t"
-        "vstelm.d $vr3,          %[dst],            0,  0         \n\t"
-        "add.d    %[dst],        %[dst],            %[dstStride]  \n\t"
-        "bnez     %[h],                             1b            \n\t"
-
-        : [dst]"+&r"(dst), [src2]"+&r"(src2), [src1]"+&r"(src1),
-          [h]"+&r"(h), [stride1_2]"=&r"(stride1_2),
-          [stride1_3]"=&r"(stride1_3), [stride1_4]"=&r"(stride1_4),
-          [stride2_2]"=&r"(stride2_2), [stride2_3]"=&r"(stride2_3),
-          [stride2_4]"=&r"(stride2_4)
-        : [dstStride]"r"(dst_stride), [srcStride1]"r"(src_stride1),
-          [srcStride2]"r"(src_stride2)
-        : "memory"
-    );
-}
-
-static av_always_inline void
-put_pixels16_l2_8_lsx(uint8_t *dst, const uint8_t *src1, const uint8_t *src2,
-                      int dst_stride, int src_stride1, int src_stride2, int h)
-{
-    int stride1_2, stride1_3, stride1_4;
-    int stride2_2, stride2_3, stride2_4;
-    int dststride2, dststride3, dststride4;
-    __asm__ volatile (
-        "slli.d   %[stride1_2],  %[srcStride1],     1             \n\t"
-        "slli.d   %[stride2_2],  %[srcStride2],     1             \n\t"
-        "slli.d   %[dststride2], %[dstStride],      1             \n\t"
-        "add.d    %[stride1_3],  %[stride1_2],      %[srcStride1] \n\t"
-        "add.d    %[stride2_3],  %[stride2_2],      %[srcStride2] \n\t"
-        "add.d    %[dststride3], %[dststride2],     %[dstStride]  \n\t"
-        "slli.d   %[stride1_4],  %[stride1_2],      1             \n\t"
-        "slli.d   %[stride2_4],  %[stride2_2],      1             \n\t"
-        "slli.d   %[dststride4], %[dststride2],     1             \n\t"
-        "1:                                                       \n\t"
-        "vld      $vr0,          %[src1],           0             \n\t"
-        "vldx     $vr1,          %[src1],           %[srcStride1] \n\t"
-        "vldx     $vr2,          %[src1],           %[stride1_2]  \n\t"
-        "vldx     $vr3,          %[src1],           %[stride1_3]  \n\t"
-        "add.d    %[src1],       %[src1],           %[stride1_4]  \n\t"
-
-        "vld      $vr4,          %[src2],           0             \n\t"
-        "vldx     $vr5,          %[src2],           %[srcStride2] \n\t"
-        "vldx     $vr6,          %[src2],           %[stride2_2]  \n\t"
-        "vldx     $vr7,          %[src2],           %[stride2_3]  \n\t"
-        "add.d    %[src2],       %[src2],           %[stride2_4]  \n\t"
-
-        "addi.d   %[h],          %[h],              -4            \n\t"
-
-        "vavgr.bu $vr0,          $vr4,              $vr0          \n\t"
-        "vavgr.bu $vr1,          $vr5,              $vr1          \n\t"
-        "vavgr.bu $vr2,          $vr6,              $vr2          \n\t"
-        "vavgr.bu $vr3,          $vr7,              $vr3          \n\t"
-        "vst      $vr0,          %[dst],            0             \n\t"
-        "vstx     $vr1,          %[dst],            %[dstStride]  \n\t"
-        "vstx     $vr2,          %[dst],            %[dststride2] \n\t"
-        "vstx     $vr3,          %[dst],            %[dststride3] \n\t"
-        "add.d    %[dst],        %[dst],            %[dststride4] \n\t"
-        "bnez     %[h],                             1b            \n\t"
-
-        : [dst]"+&r"(dst), [src2]"+&r"(src2), [src1]"+&r"(src1),
-          [h]"+&r"(h), [stride1_2]"=&r"(stride1_2),
-          [stride1_3]"=&r"(stride1_3), [stride1_4]"=&r"(stride1_4),
-          [stride2_2]"=&r"(stride2_2), [stride2_3]"=&r"(stride2_3),
-          [stride2_4]"=&r"(stride2_4), [dststride2]"=&r"(dststride2),
-          [dststride3]"=&r"(dststride3), [dststride4]"=&r"(dststride4)
-        : [dstStride]"r"(dst_stride), [srcStride1]"r"(src_stride1),
-          [srcStride2]"r"(src_stride2)
-        : "memory"
-    );
-}
-
-void ff_put_pixels8_8_lasx(uint8_t *block, const uint8_t *pixels,
-                           ptrdiff_t line_size, int h)
-{
-    uint64_t tmp[8];
-    int h_8 = h >> 3;
-    int res = h & 7;
-    ptrdiff_t stride2, stride3, stride4;
-
-    __asm__ volatile (
-        "beqz     %[h_8],                           2f            \n\t"
-        "slli.d   %[stride2],    %[stride],         1             \n\t"
-        "add.d    %[stride3],    %[stride2],        %[stride]     \n\t"
-        "slli.d   %[stride4],    %[stride2],        1             \n\t"
-        "1:                                                       \n\t"
-        "ld.d     %[tmp0],       %[src],            0x0           \n\t"
-        "ldx.d    %[tmp1],       %[src],            %[stride]     \n\t"
-        "ldx.d    %[tmp2],       %[src],            %[stride2]    \n\t"
-        "ldx.d    %[tmp3],       %[src],            %[stride3]    \n\t"
-        "add.d    %[src],        %[src],            %[stride4]    \n\t"
-        "ld.d     %[tmp4],       %[src],            0x0           \n\t"
-        "ldx.d    %[tmp5],       %[src],            %[stride]     \n\t"
-        "ldx.d    %[tmp6],       %[src],            %[stride2]    \n\t"
-        "ldx.d    %[tmp7],       %[src],            %[stride3]    \n\t"
-        "add.d    %[src],        %[src],            %[stride4]    \n\t"
-
-        "addi.d   %[h_8],        %[h_8],            -1            \n\t"
-
-        "st.d     %[tmp0],       %[dst],            0x0           \n\t"
-        "stx.d    %[tmp1],       %[dst],            %[stride]     \n\t"
-        "stx.d    %[tmp2],       %[dst],            %[stride2]    \n\t"
-        "stx.d    %[tmp3],       %[dst],            %[stride3]    \n\t"
-        "add.d    %[dst],        %[dst],            %[stride4]    \n\t"
-        "st.d     %[tmp4],       %[dst],            0x0           \n\t"
-        "stx.d    %[tmp5],       %[dst],            %[stride]     \n\t"
-        "stx.d    %[tmp6],       %[dst],            %[stride2]    \n\t"
-        "stx.d    %[tmp7],       %[dst],            %[stride3]    \n\t"
-        "add.d    %[dst],        %[dst],            %[stride4]    \n\t"
-        "bnez     %[h_8],        1b                               \n\t"
-
-        "2:                                                       \n\t"
-        "beqz     %[res],        4f                               \n\t"
-        "3:                                                       \n\t"
-        "ld.d     %[tmp0],       %[src],            0x0           \n\t"
-        "add.d    %[src],        %[src],            %[stride]     \n\t"
-        "addi.d   %[res],        %[res],            -1            \n\t"
-        "st.d     %[tmp0],       %[dst],            0x0           \n\t"
-        "add.d    %[dst],        %[dst],            %[stride]     \n\t"
-        "bnez     %[res],        3b                               \n\t"
-        "4:                                                       \n\t"
-        : [tmp0]"=&r"(tmp[0]),        [tmp1]"=&r"(tmp[1]),
-          [tmp2]"=&r"(tmp[2]),        [tmp3]"=&r"(tmp[3]),
-          [tmp4]"=&r"(tmp[4]),        [tmp5]"=&r"(tmp[5]),
-          [tmp6]"=&r"(tmp[6]),        [tmp7]"=&r"(tmp[7]),
-          [dst]"+&r"(block),          [src]"+&r"(pixels),
-          [h_8]"+&r"(h_8),            [res]"+&r"(res),
-          [stride2]"=&r"(stride2),    [stride3]"=&r"(stride3),
-          [stride4]"=&r"(stride4)
-        : [stride]"r"(line_size)
-        : "memory"
-    );
-}
-
-void ff_put_pixels16_8_lsx(uint8_t *block, const uint8_t *pixels,
-                           ptrdiff_t line_size, int h)
-{
-    int h_8 = h >> 3;
-    int res = h & 7;
-    ptrdiff_t stride2, stride3, stride4;
-
-    __asm__ volatile (
-        "beqz     %[h_8],                           2f            \n\t"
-        "slli.d   %[stride2],    %[stride],         1             \n\t"
-        "add.d    %[stride3],    %[stride2],        %[stride]     \n\t"
-        "slli.d   %[stride4],    %[stride2],        1             \n\t"
-        "1:                                                       \n\t"
-        "vld      $vr0,          %[src],            0x0           \n\t"
-        "vldx     $vr1,          %[src],            %[stride]     \n\t"
-        "vldx     $vr2,          %[src],            %[stride2]    \n\t"
-        "vldx     $vr3,          %[src],            %[stride3]    \n\t"
-        "add.d    %[src],        %[src],            %[stride4]    \n\t"
-        "vld      $vr4,          %[src],            0x0           \n\t"
-        "vldx     $vr5,          %[src],            %[stride]     \n\t"
-        "vldx     $vr6,          %[src],            %[stride2]    \n\t"
-        "vldx     $vr7,          %[src],            %[stride3]    \n\t"
-        "add.d    %[src],        %[src],            %[stride4]    \n\t"
-
-        "addi.d   %[h_8],        %[h_8],            -1            \n\t"
-
-        "vst      $vr0,          %[dst],            0x0           \n\t"
-        "vstx     $vr1,          %[dst],            %[stride]     \n\t"
-        "vstx     $vr2,          %[dst],            %[stride2]    \n\t"
-        "vstx     $vr3,          %[dst],            %[stride3]    \n\t"
-        "add.d    %[dst],        %[dst],            %[stride4]    \n\t"
-        "vst      $vr4,          %[dst],            0x0           \n\t"
-        "vstx     $vr5,          %[dst],            %[stride]     \n\t"
-        "vstx     $vr6,          %[dst],            %[stride2]    \n\t"
-        "vstx     $vr7,          %[dst],            %[stride3]    \n\t"
-        "add.d    %[dst],        %[dst],            %[stride4]    \n\t"
-        "bnez     %[h_8],        1b                               \n\t"
-
-        "2:                                                       \n\t"
-        "beqz     %[res],        4f                               \n\t"
-        "3:                                                       \n\t"
-        "vld      $vr0,          %[src],            0x0           \n\t"
-        "add.d    %[src],        %[src],            %[stride]     \n\t"
-        "addi.d   %[res],        %[res],            -1            \n\t"
-        "vst      $vr0,          %[dst],            0x0           \n\t"
-        "add.d    %[dst],        %[dst],            %[stride]     \n\t"
-        "bnez     %[res],        3b                               \n\t"
-        "4:                                                       \n\t"
-        : [dst]"+&r"(block),          [src]"+&r"(pixels),
-          [h_8]"+&r"(h_8),            [res]"+&r"(res),
-          [stride2]"=&r"(stride2),    [stride3]"=&r"(stride3),
-          [stride4]"=&r"(stride4)
-        : [stride]"r"(line_size)
-        : "memory"
-    );
-}
-
-void ff_put_pixels8_x2_8_lasx(uint8_t *block, const uint8_t *pixels,
-                              ptrdiff_t line_size, int h)
-{
-    put_pixels8_l2_8_lsx(block, pixels, pixels + 1, line_size, line_size,
-                         line_size, h);
-}
-
-void ff_put_pixels8_y2_8_lasx(uint8_t *block, const uint8_t *pixels,
-                              ptrdiff_t line_size, int h)
-{
-    put_pixels8_l2_8_lsx(block, pixels, pixels + line_size, line_size,
-                         line_size, line_size, h);
-}
-
-void ff_put_pixels16_x2_8_lasx(uint8_t *block, const uint8_t *pixels,
-                               ptrdiff_t line_size, int h)
-{
-    put_pixels16_l2_8_lsx(block, pixels, pixels + 1, line_size, line_size,
-                          line_size, h);
-}
-
-void ff_put_pixels16_y2_8_lasx(uint8_t *block, const uint8_t *pixels,
-                               ptrdiff_t line_size, int h)
-{
-    put_pixels16_l2_8_lsx(block, pixels, pixels + line_size, line_size,
-                          line_size, line_size, h);
-}
-
-static void common_hz_bil_no_rnd_16x16_lasx(const uint8_t *src,
-                                            int32_t src_stride,
-                                            uint8_t *dst, int32_t dst_stride)
-{
-    __m256i src0, src1, src2, src3, src4, src5, src6, src7;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_4x = src_stride << 2;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    uint8_t *_src = (uint8_t*)src;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += 1;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (src_stride_4x -1);
-    DUP4_ARG3(__lasx_xvpermi_q, src1, src0, 0x20, src3, src2, 0x20, src5,
-              src4, 0x20, src7, src6, 0x20, src0, src1, src2, src3);
-    src0 = __lasx_xvavg_bu(src0, src2);
-    src1 = __lasx_xvavg_bu(src1, src3);
-    __lasx_xvstelm_d(src0, dst, 0, 0);
-    __lasx_xvstelm_d(src0, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src0, dst, 0, 2);
-    __lasx_xvstelm_d(src0, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src1, dst, 0, 0);
-    __lasx_xvstelm_d(src1, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src1, dst, 0, 2);
-    __lasx_xvstelm_d(src1, dst, 8, 3);
-    dst += dst_stride;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += 1;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (src_stride_4x - 1);
-    DUP4_ARG3(__lasx_xvpermi_q, src1, src0, 0x20, src3, src2, 0x20, src5, src4,
-              0x20, src7, src6, 0x20, src0, src1, src2, src3);
-    src0 = __lasx_xvavg_bu(src0, src2);
-    src1 = __lasx_xvavg_bu(src1, src3);
-    __lasx_xvstelm_d(src0, dst, 0, 0);
-    __lasx_xvstelm_d(src0, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src0, dst, 0, 2);
-    __lasx_xvstelm_d(src0, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src1, dst, 0, 0);
-    __lasx_xvstelm_d(src1, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src1, dst, 0, 2);
-    __lasx_xvstelm_d(src1, dst, 8, 3);
-    dst += dst_stride;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += 1;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (src_stride_4x - 1);
-    DUP4_ARG3(__lasx_xvpermi_q, src1, src0, 0x20, src3, src2, 0x20, src5, src4,
-              0x20, src7, src6, 0x20, src0, src1, src2, src3);
-    src0 = __lasx_xvavg_bu(src0, src2);
-    src1 = __lasx_xvavg_bu(src1, src3);
-    __lasx_xvstelm_d(src0, dst, 0, 0);
-    __lasx_xvstelm_d(src0, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src0, dst, 0, 2);
-    __lasx_xvstelm_d(src0, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src1, dst, 0, 0);
-    __lasx_xvstelm_d(src1, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src1, dst, 0, 2);
-    __lasx_xvstelm_d(src1, dst, 8, 3);
-    dst += dst_stride;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += 1;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    DUP4_ARG3(__lasx_xvpermi_q, src1, src0, 0x20, src3, src2, 0x20, src5, src4,
-              0x20, src7, src6, 0x20, src0, src1, src2, src3);
-    src0 = __lasx_xvavg_bu(src0, src2);
-    src1 = __lasx_xvavg_bu(src1, src3);
-    __lasx_xvstelm_d(src0, dst, 0, 0);
-    __lasx_xvstelm_d(src0, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src0, dst, 0, 2);
-    __lasx_xvstelm_d(src0, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src1, dst, 0, 0);
-    __lasx_xvstelm_d(src1, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src1, dst, 0, 2);
-    __lasx_xvstelm_d(src1, dst, 8, 3);
-}
-
-static void common_hz_bil_no_rnd_8x16_lasx(const uint8_t *src,
-                                           int32_t src_stride,
-                                           uint8_t *dst, int32_t dst_stride)
-{
-    __m256i src0, src1, src2, src3, src4, src5, src6, src7;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_4x = src_stride << 2;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    uint8_t* _src = (uint8_t*)src;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += 1;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (src_stride_4x - 1);
-    DUP4_ARG3(__lasx_xvpermi_q, src1, src0, 0x20, src3, src2, 0x20, src5, src4,
-              0x20, src7, src6, 0x20, src0, src1, src2, src3);
-    src0 = __lasx_xvavg_bu(src0, src2);
-    src1 = __lasx_xvavg_bu(src1, src3);
-    __lasx_xvstelm_d(src0, dst, 0, 0);
-    __lasx_xvstelm_d(src0, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src0, dst, 0, 2);
-    __lasx_xvstelm_d(src0, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src1, dst, 0, 0);
-    __lasx_xvstelm_d(src1, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src1, dst, 0, 2);
-    __lasx_xvstelm_d(src1, dst, 8, 3);
-    dst += dst_stride;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += 1;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    DUP4_ARG3(__lasx_xvpermi_q, src1, src0, 0x20, src3, src2, 0x20, src5, src4,
-              0x20, src7, src6, 0x20, src0, src1, src2, src3);
-    src0 = __lasx_xvavg_bu(src0, src2);
-    src1 = __lasx_xvavg_bu(src1, src3);
-    __lasx_xvstelm_d(src0, dst, 0, 0);
-    __lasx_xvstelm_d(src0, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src0, dst, 0, 2);
-    __lasx_xvstelm_d(src0, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src1, dst, 0, 0);
-    __lasx_xvstelm_d(src1, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src1, dst, 0, 2);
-    __lasx_xvstelm_d(src1, dst, 8, 3);
-}
-
-void ff_put_no_rnd_pixels16_x2_8_lasx(uint8_t *block, const uint8_t *pixels,
-                                      ptrdiff_t line_size, int h)
-{
-    if (h == 16) {
-        common_hz_bil_no_rnd_16x16_lasx(pixels, line_size, block, line_size);
-    } else if (h == 8) {
-        common_hz_bil_no_rnd_8x16_lasx(pixels, line_size, block, line_size);
-    }
-}
-
-static void common_vt_bil_no_rnd_16x16_lasx(const uint8_t *src,
-                                            int32_t src_stride,
-                                            uint8_t *dst, int32_t dst_stride)
-{
-    __m256i src0, src1, src2, src3, src4, src5, src6, src7, src8;
-    __m256i src9, src10, src11, src12, src13, src14, src15, src16;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_4x = src_stride << 2;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    uint8_t* _src = (uint8_t*)src;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src8 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src9, src10);
-    src11 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src12 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x,
-              src13, src14);
-    src15 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src16 = __lasx_xvld(_src, 0);
-
-    DUP4_ARG3(__lasx_xvpermi_q, src1, src0, 0x20, src2, src1, 0x20, src3, src2,
-              0x20, src4, src3, 0x20, src0, src1, src2, src3);
-    DUP4_ARG3(__lasx_xvpermi_q, src5, src4, 0x20, src6, src5, 0x20, src7, src6,
-              0x20, src8, src7, 0x20, src4, src5, src6, src7);
-    DUP4_ARG3(__lasx_xvpermi_q, src9, src8, 0x20, src10, src9, 0x20, src11,
-              src10, 0x20, src12, src11, 0x20, src8, src9, src10, src11);
-    DUP4_ARG3(__lasx_xvpermi_q, src13, src12, 0x20, src14, src13, 0x20, src15,
-              src14, 0x20, src16, src15, 0x20, src12, src13, src14, src15);
-    DUP4_ARG2(__lasx_xvavg_bu, src0, src1, src2, src3, src4, src5, src6, src7,
-              src0, src2, src4, src6);
-    DUP4_ARG2(__lasx_xvavg_bu, src8, src9, src10, src11, src12, src13, src14,
-              src15, src8, src10, src12, src14);
-
-    __lasx_xvstelm_d(src0, dst, 0, 0);
-    __lasx_xvstelm_d(src0, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src0, dst, 0, 2);
-    __lasx_xvstelm_d(src0, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src2, dst, 0, 0);
-    __lasx_xvstelm_d(src2, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src2, dst, 0, 2);
-    __lasx_xvstelm_d(src2, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src4, dst, 0, 0);
-    __lasx_xvstelm_d(src4, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src4, dst, 0, 2);
-    __lasx_xvstelm_d(src4, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src6, dst, 0, 0);
-    __lasx_xvstelm_d(src6, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src6, dst, 0, 2);
-    __lasx_xvstelm_d(src6, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src8, dst, 0, 0);
-    __lasx_xvstelm_d(src8, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src8, dst, 0, 2);
-    __lasx_xvstelm_d(src8, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src10, dst, 0, 0);
-    __lasx_xvstelm_d(src10, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src10, dst, 0, 2);
-    __lasx_xvstelm_d(src10, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src12, dst, 0, 0);
-    __lasx_xvstelm_d(src12, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src12, dst, 0, 2);
-    __lasx_xvstelm_d(src12, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src14, dst, 0, 0);
-    __lasx_xvstelm_d(src14, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src14, dst, 0, 2);
-    __lasx_xvstelm_d(src14, dst, 8, 3);
-}
-
-static void common_vt_bil_no_rnd_8x16_lasx(const uint8_t *src,
-                                           int32_t src_stride,
-                                           uint8_t *dst, int32_t dst_stride)
-{
-    __m256i src0, src1, src2, src3, src4, src5, src6, src7, src8;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_4x = src_stride << 2;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    uint8_t* _src = (uint8_t*)src;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src8 = __lasx_xvld(_src, 0);
-
-    DUP4_ARG3(__lasx_xvpermi_q, src1, src0, 0x20, src2, src1, 0x20, src3, src2,
-              0x20, src4, src3, 0x20, src0, src1, src2, src3);
-    DUP4_ARG3(__lasx_xvpermi_q, src5, src4, 0x20, src6, src5, 0x20, src7, src6,
-              0x20, src8, src7, 0x20, src4, src5, src6, src7);
-    DUP4_ARG2(__lasx_xvavg_bu, src0, src1, src2, src3, src4, src5, src6, src7,
-              src0, src2, src4, src6);
-
-    __lasx_xvstelm_d(src0, dst, 0, 0);
-    __lasx_xvstelm_d(src0, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src0, dst, 0, 2);
-    __lasx_xvstelm_d(src0, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src2, dst, 0, 0);
-    __lasx_xvstelm_d(src2, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src2, dst, 0, 2);
-    __lasx_xvstelm_d(src2, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src4, dst, 0, 0);
-    __lasx_xvstelm_d(src4, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src4, dst, 0, 2);
-    __lasx_xvstelm_d(src4, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src6, dst, 0, 0);
-    __lasx_xvstelm_d(src6, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(src6, dst, 0, 2);
-    __lasx_xvstelm_d(src6, dst, 8, 3);
-}
-
-void ff_put_no_rnd_pixels16_y2_8_lasx(uint8_t *block, const uint8_t *pixels,
-                                      ptrdiff_t line_size, int h)
-{
-    if (h == 16) {
-        common_vt_bil_no_rnd_16x16_lasx(pixels, line_size, block, line_size);
-    } else if (h == 8) {
-        common_vt_bil_no_rnd_8x16_lasx(pixels, line_size, block, line_size);
-    }
-}
-
-static void common_hv_bil_no_rnd_16x16_lasx(const uint8_t *src,
-                                            int32_t src_stride,
-                                            uint8_t *dst, int32_t dst_stride)
-{
-    __m256i src0, src1, src2, src3, src4, src5, src6, src7, src8, src9;
-    __m256i src10, src11, src12, src13, src14, src15, src16, src17;
-    __m256i sum0, sum1, sum2, sum3, sum4, sum5, sum6, sum7;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_4x = src_stride << 2;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    uint8_t* _src = (uint8_t*)src;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (1 - src_stride_4x);
-    src9 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x,
-              src10, src11);
-    src12 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src13 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x,
-              src14, src15);
-    src16 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (src_stride_4x - 1);
-    DUP2_ARG2(__lasx_xvld, _src, 0, _src, 1, src8, src17);
-
-    DUP4_ARG3(__lasx_xvpermi_q, src0, src4, 0x02, src1, src5, 0x02, src2,
-              src6, 0x02, src3, src7, 0x02, src0, src1, src2, src3);
-    DUP4_ARG3(__lasx_xvpermi_q, src4, src8, 0x02, src9, src13, 0x02, src10,
-              src14, 0x02, src11, src15, 0x02, src4, src5, src6, src7);
-    DUP2_ARG3(__lasx_xvpermi_q, src12, src16, 0x02, src13, src17, 0x02,
-              src8, src9);
-    DUP4_ARG2(__lasx_xvilvl_h, src5, src0, src6, src1, src7, src2, src8, src3,
-              sum0, sum2, sum4, sum6);
-    DUP4_ARG2(__lasx_xvilvh_h, src5, src0, src6, src1, src7, src2, src8, src3,
-              sum1, sum3, sum5, sum7);
-    src8 = __lasx_xvilvl_h(src9, src4);
-    src9 = __lasx_xvilvh_h(src9, src4);
-
-    DUP4_ARG2(__lasx_xvhaddw_hu_bu, sum0, sum0, sum1, sum1, sum2, sum2,
-              sum3, sum3, src0, src1, src2, src3);
-    DUP4_ARG2(__lasx_xvhaddw_hu_bu, sum4, sum4, sum5, sum5, sum6, sum6,
-              sum7, sum7, src4, src5, src6, src7);
-    DUP2_ARG2(__lasx_xvhaddw_hu_bu, src8, src8, src9, src9, src8, src9);
-
-    DUP4_ARG2(__lasx_xvadd_h, src0, src2, src1, src3, src2, src4, src3, src5,
-              sum0, sum1, sum2, sum3);
-    DUP4_ARG2(__lasx_xvadd_h, src4, src6, src5, src7, src6, src8, src7, src9,
-              sum4, sum5, sum6, sum7);
-    DUP4_ARG2(__lasx_xvaddi_hu, sum0, 1, sum1, 1, sum2, 1, sum3, 1,
-              sum0, sum1, sum2, sum3);
-    DUP4_ARG2(__lasx_xvaddi_hu, sum4, 1, sum5, 1, sum6, 1, sum7, 1,
-              sum4, sum5, sum6, sum7);
-    DUP4_ARG3(__lasx_xvsrani_b_h, sum1, sum0, 2, sum3, sum2, 2, sum5, sum4, 2,
-              sum7, sum6, 2, sum0, sum1, sum2, sum3);
-    __lasx_xvstelm_d(sum0, dst, 0, 0);
-    __lasx_xvstelm_d(sum0, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum1, dst, 0, 0);
-    __lasx_xvstelm_d(sum1, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum2, dst, 0, 0);
-    __lasx_xvstelm_d(sum2, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum3, dst, 0, 0);
-    __lasx_xvstelm_d(sum3, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum0, dst, 0, 2);
-    __lasx_xvstelm_d(sum0, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum1, dst, 0, 2);
-    __lasx_xvstelm_d(sum1, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum2, dst, 0, 2);
-    __lasx_xvstelm_d(sum2, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum3, dst, 0, 2);
-    __lasx_xvstelm_d(sum3, dst, 8, 3);
-    dst += dst_stride;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (1 - src_stride_4x);
-    src9 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x,
-              src10, src11);
-    src12 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src13 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x,
-              src14, src15);
-    src16 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (src_stride_4x - 1);
-    DUP2_ARG2(__lasx_xvld, _src, 0, _src, 1, src8, src17);
-
-    DUP4_ARG3(__lasx_xvpermi_q, src0, src4, 0x02, src1, src5, 0x02, src2, src6, 0x02,
-              src3, src7, 0x02, src0, src1, src2, src3);
-    DUP4_ARG3(__lasx_xvpermi_q, src4, src8, 0x02, src9, src13, 0x02, src10, src14, 0x02,
-              src11, src15, 0x02, src4, src5, src6, src7);
-    DUP2_ARG3(__lasx_xvpermi_q, src12, src16, 0x02, src13, src17, 0x02, src8, src9);
-
-    DUP4_ARG2(__lasx_xvilvl_h, src5, src0, src6, src1, src7, src2, src8, src3,
-              sum0, sum2, sum4, sum6);
-    DUP4_ARG2(__lasx_xvilvh_h, src5, src0, src6, src1, src7, src2, src8, src3,
-              sum1, sum3, sum5, sum7);
-    src8 = __lasx_xvilvl_h(src9, src4);
-    src9 = __lasx_xvilvh_h(src9, src4);
-
-    DUP4_ARG2(__lasx_xvhaddw_hu_bu, sum0, sum0, sum1, sum1, sum2, sum2,
-              sum3, sum3, src0, src1, src2, src3);
-    DUP4_ARG2(__lasx_xvhaddw_hu_bu, sum4, sum4, sum5, sum5, sum6, sum6,
-              sum7, sum7, src4, src5, src6, src7);
-    DUP2_ARG2(__lasx_xvhaddw_hu_bu, src8, src8, src9, src9, src8, src9);
-
-    DUP4_ARG2(__lasx_xvadd_h, src0, src2, src1, src3, src2, src4, src3, src5,
-              sum0, sum1, sum2, sum3);
-    DUP4_ARG2(__lasx_xvadd_h, src4, src6, src5, src7, src6, src8, src7, src9,
-              sum4, sum5, sum6, sum7);
-    DUP4_ARG2(__lasx_xvaddi_hu, sum0, 1, sum1, 1, sum2, 1, sum3, 1,
-              sum0, sum1, sum2, sum3);
-    DUP4_ARG2(__lasx_xvaddi_hu, sum4, 1, sum5, 1, sum6, 1, sum7, 1,
-              sum4, sum5, sum6, sum7);
-    DUP4_ARG3(__lasx_xvsrani_b_h, sum1, sum0, 2, sum3, sum2, 2, sum5, sum4, 2,
-              sum7, sum6, 2, sum0, sum1, sum2, sum3);
-    __lasx_xvstelm_d(sum0, dst, 0, 0);
-    __lasx_xvstelm_d(sum0, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum1, dst, 0, 0);
-    __lasx_xvstelm_d(sum1, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum2, dst, 0, 0);
-    __lasx_xvstelm_d(sum2, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum3, dst, 0, 0);
-    __lasx_xvstelm_d(sum3, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum0, dst, 0, 2);
-    __lasx_xvstelm_d(sum0, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum1, dst, 0, 2);
-    __lasx_xvstelm_d(sum1, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum2, dst, 0, 2);
-    __lasx_xvstelm_d(sum2, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum3, dst, 0, 2);
-    __lasx_xvstelm_d(sum3, dst, 8, 3);
-}
-
-static void common_hv_bil_no_rnd_8x16_lasx(const uint8_t *src,
-                                           int32_t src_stride,
-                                           uint8_t *dst, int32_t dst_stride)
-{
-    __m256i src0, src1, src2, src3, src4, src5, src6, src7, src8, src9;
-    __m256i src10, src11, src12, src13, src14, src15, src16, src17;
-    __m256i sum0, sum1, sum2, sum3, sum4, sum5, sum6, sum7;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_4x = src_stride << 2;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    uint8_t* _src = (uint8_t*)src;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (1 - src_stride_4x);
-    src9 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x,
-              src10, src11);
-    src12 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src13 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x,
-              src14, src15);
-    src16 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (src_stride_4x - 1);
-    DUP2_ARG2(__lasx_xvld, _src, 0, _src, 1, src8, src17);
-
-    DUP4_ARG3(__lasx_xvpermi_q, src0, src4, 0x02, src1, src5, 0x02, src2,
-              src6, 0x02, src3, src7, 0x02, src0, src1, src2, src3);
-    DUP4_ARG3(__lasx_xvpermi_q, src4, src8, 0x02, src9, src13, 0x02, src10,
-              src14, 0x02, src11, src15, 0x02, src4, src5, src6, src7);
-    DUP2_ARG3(__lasx_xvpermi_q, src12, src16, 0x02, src13, src17, 0x02, src8, src9);
-
-    DUP4_ARG2(__lasx_xvilvl_h, src5, src0, src6, src1, src7, src2, src8, src3,
-              sum0, sum2, sum4, sum6);
-    DUP4_ARG2(__lasx_xvilvh_h, src5, src0, src6, src1, src7, src2, src8, src3,
-              sum1, sum3, sum5, sum7);
-    src8 = __lasx_xvilvl_h(src9, src4);
-    src9 = __lasx_xvilvh_h(src9, src4);
-
-    DUP4_ARG2(__lasx_xvhaddw_hu_bu, sum0, sum0, sum1, sum1, sum2, sum2,
-              sum3, sum3, src0, src1, src2, src3);
-    DUP4_ARG2(__lasx_xvhaddw_hu_bu, sum4, sum4, sum5, sum5, sum6, sum6,
-              sum7, sum7, src4, src5, src6, src7);
-    DUP2_ARG2(__lasx_xvhaddw_hu_bu, src8, src8, src9, src9, src8, src9);
-
-    DUP4_ARG2(__lasx_xvadd_h, src0, src2, src1, src3, src2, src4, src3, src5,
-              sum0, sum1, sum2, sum3);
-    DUP4_ARG2(__lasx_xvadd_h, src4, src6, src5, src7, src6, src8, src7, src9,
-              sum4, sum5, sum6, sum7);
-    DUP4_ARG2(__lasx_xvaddi_hu, sum0, 1, sum1, 1, sum2, 1, sum3, 1,
-              sum0, sum1, sum2, sum3);
-    DUP4_ARG2(__lasx_xvaddi_hu, sum4, 1, sum5, 1, sum6, 1, sum7, 1,
-              sum4, sum5, sum6, sum7);
-    DUP4_ARG3(__lasx_xvsrani_b_h, sum1, sum0, 2, sum3, sum2, 2, sum5, sum4, 2,
-              sum7, sum6, 2, sum0, sum1, sum2, sum3);
-    __lasx_xvstelm_d(sum0, dst, 0, 0);
-    __lasx_xvstelm_d(sum0, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum1, dst, 0, 0);
-    __lasx_xvstelm_d(sum1, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum2, dst, 0, 0);
-    __lasx_xvstelm_d(sum2, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum3, dst, 0, 0);
-    __lasx_xvstelm_d(sum3, dst, 8, 1);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum0, dst, 0, 2);
-    __lasx_xvstelm_d(sum0, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum1, dst, 0, 2);
-    __lasx_xvstelm_d(sum1, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum2, dst, 0, 2);
-    __lasx_xvstelm_d(sum2, dst, 8, 3);
-    dst += dst_stride;
-    __lasx_xvstelm_d(sum3, dst, 0, 2);
-    __lasx_xvstelm_d(sum3, dst, 8, 3);
-}
-
-void ff_put_no_rnd_pixels16_xy2_8_lasx(uint8_t *block,
-                                       const uint8_t *pixels,
-                                       ptrdiff_t line_size, int h)
-{
-    if (h == 16) {
-        common_hv_bil_no_rnd_16x16_lasx(pixels, line_size, block, line_size);
-    } else if (h == 8) {
-        common_hv_bil_no_rnd_8x16_lasx(pixels, line_size, block, line_size);
-    }
-}
-
-static void common_hz_bil_no_rnd_8x8_lasx(const uint8_t *src,
-                                          int32_t src_stride,
-                                          uint8_t *dst, int32_t dst_stride)
-{
-    __m256i src0, src1, src2, src3, src4, src5, src6, src7;
-    __m256i src8, src9, src10, src11, src12, src13, src14, src15;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_4x = src_stride << 2;
-    int32_t dst_stride_2x = dst_stride << 1;
-    int32_t dst_stride_4x = dst_stride << 2;
-    int32_t dst_stride_3x = dst_stride_2x + dst_stride;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    uint8_t* _src = (uint8_t*)src;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (1 - src_stride_4x);
-    src8 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src9, src10);
-    src11 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src12 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x,
-              src13, src14);
-    src15 = __lasx_xvldx(_src, src_stride_3x);
-
-    DUP4_ARG2(__lasx_xvpickev_d, src1, src0, src3, src2, src5, src4, src7,
-              src6, src0, src1, src2, src3);
-    DUP4_ARG2(__lasx_xvpickev_d, src9, src8, src11, src10, src13, src12, src15,
-              src14, src4, src5, src6, src7);
-    DUP4_ARG3(__lasx_xvpermi_q, src1, src0, 0x20, src3, src2, 0x20, src5, src4,
-              0x20, src7, src6, 0x20, src0, src1, src2, src3);
-    src0 = __lasx_xvavg_bu(src0, src2);
-    src1 = __lasx_xvavg_bu(src1, src3);
-    __lasx_xvstelm_d(src0, dst, 0, 0);
-    __lasx_xvstelm_d(src0, dst + dst_stride, 0, 1);
-    __lasx_xvstelm_d(src0, dst + dst_stride_2x, 0, 2);
-    __lasx_xvstelm_d(src0, dst + dst_stride_3x, 0, 3);
-    dst += dst_stride_4x;
-    __lasx_xvstelm_d(src1, dst, 0, 0);
-    __lasx_xvstelm_d(src1, dst + dst_stride, 0, 1);
-    __lasx_xvstelm_d(src1, dst + dst_stride_2x, 0, 2);
-    __lasx_xvstelm_d(src1, dst + dst_stride_3x, 0, 3);
-}
-
-static void common_hz_bil_no_rnd_4x8_lasx(const uint8_t *src,
-                                          int32_t src_stride,
-                                          uint8_t *dst, int32_t dst_stride)
-{
-    __m256i src0, src1, src2, src3, src4, src5, src6, src7;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    int32_t dst_stride_2x = dst_stride << 1;
-    int32_t dst_stride_3x = dst_stride_2x + dst_stride;
-    uint8_t *_src = (uint8_t*)src;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += 1;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    DUP4_ARG2(__lasx_xvpickev_d, src1, src0, src3, src2, src5, src4, src7, src6,
-              src0, src1, src2, src3);
-    DUP2_ARG3(__lasx_xvpermi_q, src1, src0, 0x20, src3, src2, 0x20, src0, src1);
-    src0 = __lasx_xvavg_bu(src0, src1);
-    __lasx_xvstelm_d(src0, dst, 0, 0);
-    __lasx_xvstelm_d(src0, dst + dst_stride, 0, 1);
-    __lasx_xvstelm_d(src0, dst + dst_stride_2x, 0, 2);
-    __lasx_xvstelm_d(src0, dst + dst_stride_3x, 0, 3);
-}
-
-void ff_put_no_rnd_pixels8_x2_8_lasx(uint8_t *block, const uint8_t *pixels,
-                                     ptrdiff_t line_size, int h)
-{
-    if (h == 8) {
-        common_hz_bil_no_rnd_8x8_lasx(pixels, line_size, block, line_size);
-    } else if (h == 4) {
-        common_hz_bil_no_rnd_4x8_lasx(pixels, line_size, block, line_size);
-    }
-}
-
-static void common_vt_bil_no_rnd_8x8_lasx(const uint8_t *src, int32_t src_stride,
-                                          uint8_t *dst, int32_t dst_stride)
-{
-    __m256i src0, src1, src2, src3, src4, src5, src6, src7, src8;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_4x = src_stride << 2;
-    int32_t dst_stride_2x = dst_stride << 1;
-    int32_t dst_stride_4x = dst_stride << 2;
-    int32_t dst_stride_3x = dst_stride_2x + dst_stride;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    uint8_t* _src = (uint8_t*)src;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src8 = __lasx_xvld(_src, 0);
-
-    DUP4_ARG2(__lasx_xvpickev_d, src1, src0, src2, src1, src3, src2, src4, src3,
-              src0, src1, src2, src3);
-    DUP4_ARG2(__lasx_xvpickev_d, src5, src4, src6, src5, src7, src6, src8, src7,
-              src4, src5, src6, src7);
-    DUP4_ARG3(__lasx_xvpermi_q, src2, src0, 0x20, src3, src1, 0x20, src6, src4,
-              0x20, src7, src5, 0x20, src0, src1, src2, src3);
-    src0 = __lasx_xvavg_bu(src0, src1);
-    src1 = __lasx_xvavg_bu(src2, src3);
-    __lasx_xvstelm_d(src0, dst, 0, 0);
-    __lasx_xvstelm_d(src0, dst + dst_stride, 0, 1);
-    __lasx_xvstelm_d(src0, dst + dst_stride_2x, 0, 2);
-    __lasx_xvstelm_d(src0, dst + dst_stride_3x, 0, 3);
-    dst += dst_stride_4x;
-    __lasx_xvstelm_d(src1, dst, 0, 0);
-    __lasx_xvstelm_d(src1, dst + dst_stride, 0, 1);
-    __lasx_xvstelm_d(src1, dst + dst_stride_2x, 0, 2);
-    __lasx_xvstelm_d(src1, dst + dst_stride_3x, 0, 3);
-}
-
-static void common_vt_bil_no_rnd_4x8_lasx(const uint8_t *src, int32_t src_stride,
-                                          uint8_t *dst, int32_t dst_stride)
-{
-    __m256i src0, src1, src2, src3, src4;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_4x = src_stride << 2;
-    int32_t dst_stride_2x = dst_stride << 1;
-    int32_t dst_stride_3x = dst_stride_2x + dst_stride;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    uint8_t* _src = (uint8_t*)src;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP4_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, _src,
-              src_stride_3x, _src, src_stride_4x, src1, src2, src3, src4);
-    DUP4_ARG2(__lasx_xvpickev_d, src1, src0, src2, src1, src3, src2, src4, src3,
-              src0, src1, src2, src3);
-    DUP2_ARG3(__lasx_xvpermi_q, src2, src0, 0x20, src3, src1, 0x20, src0, src1);
-    src0 = __lasx_xvavg_bu(src0, src1);
-    __lasx_xvstelm_d(src0, dst, 0, 0);
-    __lasx_xvstelm_d(src0, dst + dst_stride, 0, 1);
-    __lasx_xvstelm_d(src0, dst + dst_stride_2x, 0, 2);
-    __lasx_xvstelm_d(src0, dst + dst_stride_3x, 0, 3);
-}
-
-void ff_put_no_rnd_pixels8_y2_8_lasx(uint8_t *block, const uint8_t *pixels,
-                                     ptrdiff_t line_size, int h)
-{
-    if (h == 8) {
-        common_vt_bil_no_rnd_8x8_lasx(pixels, line_size, block, line_size);
-    } else if (h == 4) {
-        common_vt_bil_no_rnd_4x8_lasx(pixels, line_size, block, line_size);
-    }
-}
-
-static void common_hv_bil_no_rnd_8x8_lasx(const uint8_t *src, int32_t src_stride,
-                                          uint8_t *dst, int32_t dst_stride)
-{
-    __m256i src0, src1, src2, src3, src4, src5, src6, src7;
-    __m256i src8, src9, src10, src11, src12, src13, src14, src15, src16, src17;
-    __m256i sum0, sum1, sum2, sum3;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_4x = src_stride << 2;
-    int32_t dst_stride_2x = dst_stride << 1;
-    int32_t dst_stride_4x = dst_stride << 2;
-    int32_t dst_stride_3x = dst_stride_2x + dst_stride;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    uint8_t* _src = (uint8_t*)src;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src4 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-    src7 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (1 - src_stride_4x);
-    src9 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x,
-              src10, src11);
-    src12 = __lasx_xvldx(_src, src_stride_3x);
-    _src += src_stride_4x;
-    src13 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x,
-              src14, src15);
-    src16 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (src_stride_4x - 1);
-    DUP2_ARG2(__lasx_xvld, _src, 0, _src, 1, src8, src17);
-
-    DUP4_ARG2(__lasx_xvilvl_b, src9, src0, src10, src1, src11, src2, src12, src3,
-              src0, src1, src2, src3);
-    DUP4_ARG2(__lasx_xvilvl_b, src13, src4, src14, src5, src15, src6, src16, src7,
-              src4, src5, src6, src7);
-    src8 = __lasx_xvilvl_b(src17, src8);
-    DUP4_ARG3(__lasx_xvpermi_q, src1, src0, 0x20, src2, src1, 0x20, src3, src2,
-              0x20, src4, src3, 0x20, src0, src1, src2, src3);
-    DUP4_ARG3(__lasx_xvpermi_q, src5, src4, 0x20, src6, src5, 0x20, src7, src6,
-              0x20, src8, src7, 0x20, src4, src5, src6, src7);
-    DUP4_ARG2(__lasx_xvhaddw_hu_bu, src0, src0, src1, src1, src2, src2,
-              src3, src3, src0, src1, src2, src3);
-    DUP4_ARG2(__lasx_xvhaddw_hu_bu, src4, src4, src5, src5, src6, src6,
-              src7, src7, src4, src5, src6, src7);
-    DUP4_ARG2(__lasx_xvadd_h, src0, src1, src2, src3, src4, src5, src6, src7,
-              sum0, sum1, sum2, sum3);
-    DUP4_ARG2(__lasx_xvaddi_hu, sum0, 1, sum1, 1, sum2, 1, sum3, 1,
-              sum0, sum1, sum2, sum3);
-    DUP2_ARG3(__lasx_xvsrani_b_h, sum1, sum0, 2, sum3, sum2, 2, sum0, sum1);
-    __lasx_xvstelm_d(sum0, dst, 0, 0);
-    __lasx_xvstelm_d(sum0, dst + dst_stride, 0, 2);
-    __lasx_xvstelm_d(sum0, dst + dst_stride_2x, 0, 1);
-    __lasx_xvstelm_d(sum0, dst + dst_stride_3x, 0, 3);
-    dst += dst_stride_4x;
-    __lasx_xvstelm_d(sum1, dst, 0, 0);
-    __lasx_xvstelm_d(sum1, dst + dst_stride, 0, 2);
-    __lasx_xvstelm_d(sum1, dst + dst_stride_2x, 0, 1);
-    __lasx_xvstelm_d(sum1, dst + dst_stride_3x, 0, 3);
-}
-
-static void common_hv_bil_no_rnd_4x8_lasx(const uint8_t *src, int32_t src_stride,
-                                          uint8_t *dst, int32_t dst_stride)
-{
-    __m256i src0, src1, src2, src3, src4, src5, src6, src7;
-    __m256i src8, src9, sum0, sum1;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_4x = src_stride << 2;
-    int32_t dst_stride_2x = dst_stride << 1;
-    int32_t dst_stride_3x = dst_stride_2x + dst_stride;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    uint8_t *_src = (uint8_t*)src;
-
-    src0 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-    src3 = __lasx_xvldx(_src, src_stride_3x);
-    _src += 1;
-    src5 = __lasx_xvld(_src, 0);
-    DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src6, src7);
-    src8 = __lasx_xvldx(_src, src_stride_3x);
-    _src += (src_stride_4x - 1);
-    DUP2_ARG2(__lasx_xvld, _src, 0, _src, 1, src4, src9);
-
-    DUP4_ARG2(__lasx_xvilvl_b, src5, src0, src6, src1, src7, src2, src8, src3,
-              src0, src1, src2, src3);
-    src4 = __lasx_xvilvl_b(src9, src4);
-    DUP4_ARG3(__lasx_xvpermi_q, src1, src0, 0x20, src2, src1, 0x20, src3, src2,
-              0x20, src4, src3, 0x20, src0, src1, src2, src3);
-    DUP4_ARG2(__lasx_xvhaddw_hu_bu, src0, src0, src1, src1, src2, src2,
-              src3, src3, src0, src1, src2, src3);
-    DUP2_ARG2(__lasx_xvadd_h, src0, src1, src2, src3, sum0, sum1);
-    sum0 = __lasx_xvaddi_hu(sum0, 1);
-    sum1 = __lasx_xvaddi_hu(sum1, 1);
-    sum0 = __lasx_xvsrani_b_h(sum1, sum0, 2);
-    __lasx_xvstelm_d(sum0, dst, 0, 0);
-    __lasx_xvstelm_d(sum0, dst + dst_stride, 0, 2);
-    __lasx_xvstelm_d(sum0, dst + dst_stride_2x, 0, 1);
-    __lasx_xvstelm_d(sum0, dst + dst_stride_3x, 0, 3);
-}
-
-void ff_put_no_rnd_pixels8_xy2_8_lasx(uint8_t *block, const uint8_t *pixels,
-                                      ptrdiff_t line_size, int h)
-{
-    if (h == 8) {
-        common_hv_bil_no_rnd_8x8_lasx(pixels, line_size, block, line_size);
-    } else if (h == 4) {
-        common_hv_bil_no_rnd_4x8_lasx(pixels, line_size, block, line_size);
-    }
-}
-
-static void common_hv_bil_16w_lasx(const uint8_t *src, int32_t src_stride,
-                                   uint8_t *dst, int32_t dst_stride,
-                                   uint8_t height)
-{
-    __m256i src0, src1, src2, src3, src4, src5, src6, src7, src8, src9;
-    __m256i src10, src11, src12, src13, src14, src15, src16, src17;
-    __m256i sum0, sum1, sum2, sum3, sum4, sum5, sum6, sum7;
-    uint8_t loop_cnt;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_4x = src_stride << 2;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    uint8_t* _src = (uint8_t*)src;
-
-    for (loop_cnt = (height >> 3); loop_cnt--;) {
-        src0 = __lasx_xvld(_src, 0);
-        DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src1, src2);
-        src3 = __lasx_xvldx(_src, src_stride_3x);
-        _src += src_stride_4x;
-        src4 = __lasx_xvld(_src, 0);
-        DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src5, src6);
-        src7 = __lasx_xvldx(_src, src_stride_3x);
-        _src += (1 - src_stride_4x);
-        src9 = __lasx_xvld(_src, 0);
-        DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x,
-                  src10, src11);
-        src12 = __lasx_xvldx(_src, src_stride_3x);
-        _src += src_stride_4x;
-        src13 = __lasx_xvld(_src, 0);
-        DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x,
-                  src14, src15);
-        src16 = __lasx_xvldx(_src, src_stride_3x);
-        _src += (src_stride_4x - 1);
-        DUP2_ARG2(__lasx_xvld, _src, 0, _src, 1, src8, src17);
-
-        DUP4_ARG3(__lasx_xvpermi_q, src0, src4, 0x02, src1, src5, 0x02, src2,
-                  src6, 0x02, src3, src7, 0x02, src0, src1, src2, src3);
-        DUP4_ARG3(__lasx_xvpermi_q, src4, src8, 0x02, src9, src13, 0x02, src10,
-                  src14, 0x02, src11, src15, 0x02, src4, src5, src6, src7);
-        DUP2_ARG3(__lasx_xvpermi_q, src12, src16, 0x02, src13, src17, 0x02,
-                   src8, src9);
-
-        DUP4_ARG2(__lasx_xvilvl_h, src5, src0, src6, src1, src7, src2, src8,
-                  src3, sum0, sum2, sum4, sum6);
-        DUP4_ARG2(__lasx_xvilvh_h, src5, src0, src6, src1, src7, src2, src8,
-                  src3, sum1, sum3, sum5, sum7);
-        src8 = __lasx_xvilvl_h(src9, src4);
-        src9 = __lasx_xvilvh_h(src9, src4);
-
-        DUP4_ARG2(__lasx_xvhaddw_hu_bu, sum0, sum0, sum1, sum1, sum2, sum2,
-                  sum3, sum3, src0, src1, src2, src3);
-        DUP4_ARG2(__lasx_xvhaddw_hu_bu, sum4, sum4, sum5, sum5, sum6, sum6,
-                  sum7, sum7, src4, src5, src6, src7);
-        DUP2_ARG2(__lasx_xvhaddw_hu_bu, src8, src8, src9, src9, src8, src9);
-
-        DUP4_ARG2(__lasx_xvadd_h, src0, src2, src1, src3, src2, src4, src3,
-                  src5, sum0, sum1, sum2, sum3);
-        DUP4_ARG2(__lasx_xvadd_h, src4, src6, src5, src7, src6, src8, src7,
-                  src9, sum4, sum5, sum6, sum7);
-        DUP4_ARG3(__lasx_xvsrarni_b_h, sum1, sum0, 2, sum3, sum2, 2, sum5,
-                  sum4, 2, sum7, sum6, 2, sum0, sum1, sum2, sum3);
-        __lasx_xvstelm_d(sum0, dst, 0, 0);
-        __lasx_xvstelm_d(sum0, dst, 8, 1);
-        dst += dst_stride;
-        __lasx_xvstelm_d(sum1, dst, 0, 0);
-        __lasx_xvstelm_d(sum1, dst, 8, 1);
-        dst += dst_stride;
-        __lasx_xvstelm_d(sum2, dst, 0, 0);
-        __lasx_xvstelm_d(sum2, dst, 8, 1);
-        dst += dst_stride;
-        __lasx_xvstelm_d(sum3, dst, 0, 0);
-        __lasx_xvstelm_d(sum3, dst, 8, 1);
-        dst += dst_stride;
-        __lasx_xvstelm_d(sum0, dst, 0, 2);
-        __lasx_xvstelm_d(sum0, dst, 8, 3);
-        dst += dst_stride;
-        __lasx_xvstelm_d(sum1, dst, 0, 2);
-        __lasx_xvstelm_d(sum1, dst, 8, 3);
-        dst += dst_stride;
-        __lasx_xvstelm_d(sum2, dst, 0, 2);
-        __lasx_xvstelm_d(sum2, dst, 8, 3);
-        dst += dst_stride;
-        __lasx_xvstelm_d(sum3, dst, 0, 2);
-        __lasx_xvstelm_d(sum3, dst, 8, 3);
-        dst += dst_stride;
-    }
-}
-
-void ff_put_pixels16_xy2_8_lasx(uint8_t *block, const uint8_t *pixels,
-                                ptrdiff_t line_size, int h)
-{
-    common_hv_bil_16w_lasx(pixels, line_size, block, line_size, h);
-}
-
-static void common_hv_bil_8w_lasx(const uint8_t *src, int32_t src_stride,
-                                  uint8_t *dst, int32_t dst_stride,
-                                  uint8_t height)
-{
-    __m256i src0, src1, src2, src3, src4, src5, src6, src7;
-    __m256i src8, src9, sum0, sum1;
-    uint8_t loop_cnt;
-    int32_t src_stride_2x = src_stride << 1;
-    int32_t src_stride_4x = src_stride << 2;
-    int32_t dst_stride_2x = dst_stride << 1;
-    int32_t dst_stride_4x = dst_stride << 2;
-    int32_t dst_stride_3x = dst_stride_2x + dst_stride;
-    int32_t src_stride_3x = src_stride_2x + src_stride;
-    uint8_t* _src = (uint8_t*)src;
-
-    DUP2_ARG2(__lasx_xvld, _src, 0, _src, 1, src0, src5);
-    _src += src_stride;
-
-    for (loop_cnt = (height >> 2); loop_cnt--;) {
-        src1 = __lasx_xvld(_src, 0);
-        DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src2, src3);
-        src4 = __lasx_xvldx(_src, src_stride_3x);
-        _src += 1;
-        src6 = __lasx_xvld(_src, 0);
-        DUP2_ARG2(__lasx_xvldx, _src, src_stride, _src, src_stride_2x, src7, src8);
-        src9 = __lasx_xvldx(_src, src_stride_3x);
-        _src += (src_stride_4x - 1);
-        DUP4_ARG2(__lasx_xvilvl_b, src5, src0, src6, src1, src7, src2, src8, src3,
-                  src0, src1, src2, src3);
-        src5 = __lasx_xvilvl_b(src9, src4);
-        DUP4_ARG3(__lasx_xvpermi_q, src1, src0, 0x20, src2, src1, 0x20, src3, src2,
-                  0x20, src5, src3, 0x20, src0, src1, src2, src3);
-        DUP4_ARG2(__lasx_xvhaddw_hu_bu, src0, src0, src1, src1, src2, src2,
-                  src3, src3, src0, src1, src2, src3);
-        DUP2_ARG2(__lasx_xvadd_h, src0, src1, src2, src3, sum0, sum1);
-        sum0 = __lasx_xvsrarni_b_h(sum1, sum0, 2);
-        __lasx_xvstelm_d(sum0, dst, 0, 0);
-        __lasx_xvstelm_d(sum0, dst + dst_stride, 0, 2);
-        __lasx_xvstelm_d(sum0, dst + dst_stride_2x, 0, 1);
-        __lasx_xvstelm_d(sum0, dst + dst_stride_3x, 0, 3);
-        dst += dst_stride_4x;
-        src0 = src4;
-        src5 = src9;
-    }
-}
-
-void ff_put_pixels8_xy2_8_lasx(uint8_t *block, const uint8_t *pixels,
-                               ptrdiff_t line_size, int h)
-{
-    common_hv_bil_8w_lasx(pixels, line_size, block, line_size, h);
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Go Go Thomas Mod Apk The Ultimate Racing Game Based on the Popular Cartoon Series.md b/spaces/congsaPfin/Manga-OCR/logs/Go Go Thomas Mod Apk The Ultimate Racing Game Based on the Popular Cartoon Series.md
deleted file mode 100644
index af48a87a872c3bacd87e98bb766b8ced99ac4913..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Go Go Thomas Mod Apk The Ultimate Racing Game Based on the Popular Cartoon Series.md	
+++ /dev/null
@@ -1,135 +0,0 @@
-<br />
-<h1>Download Go Go Thomas Mod Apk Unlocked: A Fun and Educational Game for Kids</h1>
-<p>Do you have kids who love trains and cartoons? Do you want to give them a game that is not only fun but also educational? If yes, then you should download Go Go Thomas mod apk unlocked, a racing game based on the popular Thomas and Friends series. In this article, we will tell you what Go Go Thomas is, why you should download the mod apk version, and how to do it. Read on to find out more.</p>
-<h2>What is Go Go Thomas?</h2>
-<p>Go Go Thomas is a racing game that features the characters and settings from the Thomas and Friends franchise. It is developed by Budge Studios, a leading developer of kids' games. The game is suitable for children aged 5 to 10 years old, but anyone can enjoy it.</p>
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-<h3>A racing game based on Thomas and Friends</h3>
-<p>In Go Go Thomas, you can choose your favorite engine from the series, such as Thomas, Percy, James, Emily, or Henry. You can also unlock more engines as you play, such as Gordon, Toby, Diesel, or Spencer. Each engine has its own personality and voice, which makes the game more immersive and fun.</p>
-<p>You can also choose your track from different locations in the Island of Sodor, such as Knapford Station, Tidmouth Sheds, Brendam Docks, or Sodor Steamworks. Each track has its own challenges and obstacles, such as bridges, tunnels, ramps, or crossings. You can also change the weather and time of day to make the game more realistic and varied.</p>
-<h3>A learning game that teaches skills and values</h3>
-<p>Go Go Thomas is not just a racing game. It is also a learning game that teaches your kids valuable skills and values. For example, the game helps your kids develop their hand-eye coordination, reaction time, and spatial awareness. It also helps them learn about colors, numbers, shapes, and directions.</p>
-<p>Moreover, the game teaches your kids about friendship, teamwork, cooperation, and fair play. The game encourages your kids to cheer for their friends, help them when they are in trouble, and respect their opponents. The game also rewards your kids for their efforts and achievements with trophies, medals, stickers, and fireworks.</p>
-<h3>A customizable game that lets you choose your engine and track</h3>
-<p>Go Go Thomas is a customizable game that lets you personalize your engine and track according to your preferences. You can change the color of your engine, add accessories like hats or glasses, or decorate it with stickers. You can also change the background music and sound effects of the game.</p>
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-<p>You can also create your own track by using the track builder feature. You can design your track with different pieces and elements, such as curves, straight lines, hills, or loops. You can also add hazards like rocks or barrels to make the game more challenging. You can save your track and share it with your friends online.</p>
-<h2>Why download Go Go Thomas mod apk unlocked?</h2>
-<p>Go Go Thomas is a free game that you can download from Google Play Store or App Store. However, the free version has some limitations that may affect your gaming experience. For example:</p>
-<ul>
-<li>The free version has ads that may interrupt your gameplay or consume your data.</li>
-<li>The free version <li>The free version requires an internet connection to play the game.</li>
-<li>The free version has limited engines, tracks, and rewards that you can unlock.</li>
-</ul>
-<p>That's why you should download Go Go Thomas mod apk unlocked, a modified version of the game that gives you access to all the features and benefits of the game without any limitations. By downloading Go Go Thomas mod apk unlocked, you can:</p>
-<ul>
-<li>Enjoy the game without any ads or interruptions.</li>
-<li>Play the game offline without any data consumption.</li>
-<li>Access all the engines, tracks, and rewards for free.</li>
-</ul>
-<p>Downloading Go Go Thomas mod apk unlocked is a great way to enhance your gaming experience and have more fun with your kids. You can also save money and time by not having to purchase or unlock anything in the game.</p>
-<h2>How to download Go Go Thomas mod apk unlocked?</h2>
-<p>Downloading Go Go Thomas mod apk unlocked is easy and simple. You just need to follow these steps:</p>
-<h3>Step 1: Find a reliable source for the mod apk file</h3>
-<p>The first step is to find a reliable source for the mod apk file. You can search online for websites that offer Go Go Thomas mod apk unlocked for free. However, you need to be careful and avoid downloading from untrusted or malicious sources that may harm your device or steal your data. You can use some criteria to evaluate the credibility of the source, such as:</p>
-<ul>
-<li>The reputation and reviews of the website.</li>
-<li>The number and quality of the downloads and ratings of the mod apk file.</li>
-<li>The security and encryption of the website and the mod apk file.</li>
-</ul>
-<p>One of the websites that we recommend for downloading Go Go Thomas mod apk unlocked is [text], which has a good reputation and offers a safe and secure download link.</p>
-<h3>Step 2: Enable unknown sources on your device settings</h3>
-<p>The second step is to enable unknown sources on your device settings. This is necessary because the mod apk file is not from the official app store and your device may block its installation. To enable unknown sources, you need to:</p>
-<ul>
-<li>Go to your device settings and tap on security or privacy.</li>
-<li>Find the option that says unknown sources or allow installation from unknown sources.</li>
-<li>Toggle it on or check it to enable it.</li>
-</ul>
-<p>This will allow you to install apps from sources other than the official app store. However, you should only do this for trusted sources and disable it after installing the mod apk file.</p>
-<h3>Step 3: Download and install the mod apk file</h3>
-<p>The third step is to download and install the mod apk file. To do this, you need to:</p>
-<ul>
-<li>Go to the website that you chose in step 1 and find the download link for Go Go Thomas mod apk unlocked.</li>
-<li>Tap on the download link and wait for the file to be downloaded on your device.</li>
-<li>Once the download is complete, locate the file in your device storage and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and grant any permissions that are required by the app.</li>
-<li>Wait for the installation to finish and then tap on done or open.</li>
-</ul>
-<p>This will install Go Go Thomas mod apk unlocked on your device and you can start playing it right away.</p>
-<h3>Step 4: Launch the game and have fun</h3>
-<p>The final step is to launch the game and have fun. To do this, you need to:</p>
-<ul>
-<li>Go to your device home screen or app drawer and find the icon for Go Go Thomas mod apk unlocked.</li>
-<li>Tap on it to launch the game and enjoy all its features and benefits.</li>
-<li>Select your engine, track, and mode and start racing with your friends or against other players online.</li>
-<li>Earn trophies, medals, stickers, and fireworks as you win races and complete challenges.</li>
-<li>Customize your engine and track with colors, accessories, stickers, music, and sound effects.</li>
-<li>Create your own track with the track builder feature and share it with your friends online.</li>
- </ul>
- <h2>Conclusion</h2>
- <p>In conclusion, Go Go Thomas mod apk unlocked is a fun and educational game for kids that features the characters and settings from Thomas and Friends. It is a racing game that also teaches skills and values such as hand-eye coordination, reaction time, spatial awareness, friendship, teamwork, cooperation, fair play. It is also a customizable game that lets you choose your engine, track, mode, music, sound effects, color, accessories, and stickers. It is a free game that you can download from the official app store, but it has some limitations such as ads, internet connection, and limited content. That's why you should download Go Go Thomas mod apk unlocked, a modified version of the game that gives you access to all the features and benefits of the game without any limitations. You can download Go Go Thomas mod apk unlocked by following these steps: find a reliable source for the mod apk file, enable unknown sources on your device settings, download and install the mod apk file, and launch the game and have fun. We hope this article has helped you learn more about Go Go Thomas mod apk unlocked and how to download it. If you have any questions or feedback, please feel free to leave a comment below.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Go Go Thomas mod apk unlocked:</p>
-<ol>
-<li>Is Go Go Thomas mod apk unlocked safe to download and install?</li>
-<p>Yes, Go Go Thomas mod apk unlocked is safe to download and install, as long as you get it from a trusted and secure source. However, you should always scan the file with an antivirus or malware detector before installing it, and disable unknown sources after installing it.</p>
-<li>What are the minimum requirements for Go Go Thomas mod apk unlocked?</li>
-<p>The minimum requirements for Go Go Thomas mod apk unlocked are:</p>
-<ul>
-<li>Android 4.4 or higher</li>
-<li>At least 100 MB of free storage space</li>
-<li>At least 1 GB of RAM</li>
-</ul>
-<li>What are the differences between Go Go Thomas mod apk unlocked and the original version?</li>
-<p>The main differences between Go Go Thomas mod apk unlocked and the original version are:</p>
-<ul>
-<li>The mod apk version has no ads or interruptions.</li>
-<li>The mod apk version does not require an internet connection to play.</li>
-<li>The mod apk version has all the engines, tracks, and rewards unlocked for free.</li>
-</ul>
-<li>Can I play Go Go Thomas mod apk unlocked with my friends online?</li>
-<p>Yes, you can play Go Go Thomas mod apk unlocked with your friends online. You can either join an existing race or create your own race and invite your friends to join. You can also chat with your friends and other players in the game.</p>
-<li>Can I update Go Go Thomas mod apk unlocked to the latest version?</li>
-<p>No, you cannot update Go Go Thomas mod apk unlocked to the latest version. If you do so, you will lose all the features and benefits of the mod apk version and revert back to the original version. If you want to update the game, you will have to uninstall the mod apk version and install the original version from the official app store.</p>
-</ol></p> 401be4b1e0<br />
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\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/God of War 2 APK The Best Way to Play the Classic PlayStation Game on Android.md b/spaces/congsaPfin/Manga-OCR/logs/God of War 2 APK The Best Way to Play the Classic PlayStation Game on Android.md
deleted file mode 100644
index c3588f07701279a0f7f91935e3c3bd04412b1bce..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/God of War 2 APK The Best Way to Play the Classic PlayStation Game on Android.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-<br />
-<h1>God of War 2 APK: How to Play the Epic Action-Adventure Game on Your Android Device</h1>
- <p>God of War 2 is one of the most acclaimed video games of all time, and for good reasons. It is a thrilling action-adventure game that follows the story of Kratos, a former god of war who seeks revenge against Zeus, the king of the Olympian gods. The game features stunning graphics, epic boss battles, engaging puzzles, and a captivating plot based on Greek mythology.</p>
- <p>But what if you want to play this masterpiece on your Android device? Is it possible? The answer is yes, thanks to God of War 2 APK, a modified version of the game that allows you to run it on your smartphone or tablet. In this article, we will show you how to download and install God of War 2 APK on your Android device, and how to play it with some tips and tricks. Let's get started!</p>
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- <p>To play God of War 2 APK on your Android device, you will need three things: a game ISO file, a PlayStation BIOS file, and a PS emulator app. Here are the steps to follow:</p>
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-<li>Go back to the main screen of Demon PS2 Emulator and tap on "Unsorted". You will see a list of games that are available on your device. Tap on "God Of War II.iso" and select "Run Game". The game will start loading.</li>
-</ol>
- <p>Congratulations! You have successfully downloaded and installed God of War 2 APK on your Android device. Now you can enjoy this amazing game anytime, anywhere.</p>
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- <p>Playing God of War 2 APK on your Android device is not very different from playing it on a PlayStation console. You will see a virtual controller on your screen that mimics the buttons and joysticks of the original controller. You can use them to move, attack, dodge, jump, interact, and perform other actions in the game.</p>
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-<li>Adjust the graphics settings according to your device's performance. You can do this by tapping on the menu icon at the top left corner of Demon PS2 Emulator and selecting "Graphics". You can change the resolution, frame rate, texture filtering, anti-aliasing, and other options. The higher the settings, the devices with some modifications and additional apps. However, we cannot guarantee that the game will work properly or smoothly on these devices, as they may have different specifications and compatibility issues. You should try it at your own risk and responsibility.</p> 401be4b1e0<br />
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\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Incredibox apk La app que te permite mezclar sonidos y grabar tus propias canciones.md b/spaces/congsaPfin/Manga-OCR/logs/Incredibox apk La app que te permite mezclar sonidos y grabar tus propias canciones.md
deleted file mode 100644
index 06470f4d3b2877f6375de5af9ea72d10b864f95b..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Incredibox apk La app que te permite mezclar sonidos y grabar tus propias canciones.md	
+++ /dev/null
@@ -1,105 +0,0 @@
-<br />
-<h1>Incredibox APK Descargar: How to Download and Enjoy the Music App</h1>
-<p>Do you love music and want to create your own tunes with the help of a merry crew of beatboxers? If so, you should check out Incredibox, a fun and quirky music app that lets you mix different sounds, rhythms and voices to make your own musical compositions. In this article, we will tell you what Incredibox is, what features and benefits it offers, what alternatives you can try, and how to download and install Incredibox APK Descargar on your Android device.</p>
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-<p>Incredibox is a music app that was created in 2009 by the French company So Far So Good. It has won several awards and appeared in various international media, such as BBC, Adobe, FWA, Gizmodo, Slate, Konbini, Softonic, Kotaku, Cosmopolitan, PocketGamer, AppAdvice, AppSpy, Vice, Ultralinx and many others. The online demo has attracted more than 80 million visitors since its creation. Here are some of the features that make Incredibox stand out from other music apps:</p>
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-<p>If you prefer a darker background for your app, you can switch to the dark mode option. This will change the color scheme of the app to a more soothing and relaxing one. You can also activate the kid-friendly option, which will hide all the inappropriate content from the app. This way, you can let your kids or students play with Incredibox without worrying about any offensive or violent images or sounds. Incredibox is a great app for all ages and tastes.</p>
- <h2>Benefits of Incredibox App</h2>
-<p>Incredibox is not only a music app, but also a creative and educational tool that can help you express yourself, learn new skills, and have fun. Here are some of the benefits that Incredibox can offer you:</p>
- <h3>Fun, interactive and creative music experience</h3>
-<p>Incredibox is a music app that lets you create your own musical compositions by dragging and dropping different sound samples onto a group of animated beatboxers. You can experiment with different combinations of sounds, rhythms and voices to make your own unique mixes. You can also add effects like chorus, echo, reverb and distortion to spice up your tracks. You can play with Incredibox alone or with your friends and family. It's a fun, interactive and creative music experience that will keep you entertained for hours.</p>
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-<p>Incredibox is also a great tool for schools, as it can help teachers and students explore various aspects of music, such as rhythm, melody, harmony, timbre, texture and structure. It can also foster creativity, collaboration, communication and critical thinking skills among students. Incredibox has been used by many teachers around the world to create engaging and innovative music lessons for their students. Some examples of how Incredibox can be used in the classroom are: creating soundtracks for stories or videos, composing songs or raps, learning musical genres and cultures, making musical quizzes or games, etc.</p>
- <h3>Shareable and votable mixes with online community</h3>
-<p>Incredibox also allows you to share your mixes with other users on the Incredibox website. You can upload your mixes to the website and let other people listen to them. You can also vote for your favorite mixes and discover new ones from other users. You can browse the mixes by popularity, date or genre. You can also join the Incredibox community on social media platforms like Facebook, Twitter, Instagram and YouTube. You can follow the latest news, updates and contests from Incredibox and interact with other fans.</p> <h3>Automatic mode for lazy players</h3>
-<p>If you don't feel like making your own mixes, you can let Incredibox do it for you. The app has an automatic mode that will randomly generate mixes for you. You can sit back and enjoy the music, or use it as a background sound for your activities. You can also switch between different atmospheres and discover new sounds and styles. The automatic mode is perfect for lazy players who just want to relax and listen to some cool beats.</p>
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- <h2>Alternatives to Incredibox App</h2>
-<p>Incredibox is a unique and original music app, but it's not the only one of its kind. There are other apps that offer similar or different features and experiences for music lovers. Here are some of the alternatives that you can try if you want to explore more music apps:</p>
- <h3>Ramsophone: A minimalist music generator</h3>
-<p>Ramsophone is a minimalist music generator that lets you create soothing and ambient sounds with simple shapes and colors. You can drag and drop different shapes onto a grid and watch them produce different tones and melodies. You can also change the color, size and speed of the shapes to modify the sound. Ramsophone is a relaxing and meditative music app that will help you unwind and calm your mind.</p>
- <h3>Ocean beta: A collaborative music platform</h3>
-<p>Ocean beta is a collaborative music platform that lets you create and share music with other people online. You can join or create rooms with different themes and genres, and add your own sounds and loops to the mix. You can also chat with other users, vote for your favorite tracks, and discover new music. Ocean beta is a social and interactive music app that will connect you with other music enthusiasts.</p>
- <h3>Studio Amplify: A creative music studio</h3>
-<p>Studio Amplify is a creative music studio that lets you make professional-quality music with easy-to-use tools and features. You can choose from hundreds of sounds, loops and effects, and arrange them on a timeline. You can also record your own voice or instrument, edit your tracks, and apply filters and effects. Studio Amplify is a powerful and versatile music app that will unleash your musical potential.</p> <h2>How to Download and Install Incredibox APK Descargar</h2>
-<p>If you want to download and install Incredibox APK Descargar on your Android device, you need to follow these steps:</p>
- <h3>Step 1: Find a reliable source for the APK file</h3>
-<p>An APK file is an Android Package file that contains the installation files for an app. You can find many websites that offer APK files for various apps, but not all of them are safe and trustworthy. Some of them may contain malware, viruses or spyware that can harm your device or steal your data. Therefore, you need to be careful and choose a reliable source for the Incredibox APK Descargar file. One of the best sources is the official Incredibox website, where you can find the latest version of the app for Android devices.</p>
- <h3>Step 2: Enable unknown sources on your device</h3>
-<p>By default, Android devices do not allow the installation of apps from unknown sources, meaning sources other than the Google Play Store. This is a security measure to prevent malicious apps from entering your device. However, if you want to install Incredibox APK Descargar, you need to enable unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle the switch to On. You may see a warning message that says installing apps from unknown sources may harm your device. Tap OK to confirm that you trust the source and want to proceed.</p>
- <h3>Step 3: Download and install the APK file</h3>
-<p>Once you have enabled unknown sources on your device, you can download and install the Incredibox APK Descargar file. To do this, go to the website where you found the APK file and tap on the Download button. You may see a pop-up message that asks you if you want to download this file. Tap OK to start the download. After the download is complete, go to your Downloads folder and tap on the Incredibox APK Descargar file. You may see another pop-up message that asks you if you want to install this app. Tap Install to start the installation.</p>
- <h3>Step 4: Launch the app and start making music</h3>
-<p>After the installation is complete, you can launch the app and start making music. You will see a screen with 8 musical atmospheres to choose from. Tap on any of them to enter the music mode. You will see a group of beatboxers with different sounds assigned to them. Drag and drop any sound onto any beatboxer to activate it. You can also tap on any beatboxer to mute or unmute it. You can also add effects, record your mix, download it as an MP3 file, or share it online. Have fun and enjoy Incredibox!</p>
- <h2>Conclusion</h2>
-<p>Incredibox is a fun and quirky music app that lets you create your own musical compositions with a merry crew of beatboxers. It has many features and benefits that make it stand out from other music apps, such as exclusive musical atmospheres and sound samples, no ads and no microtransactions, MP3 file download and mixlist management, dark mode and kid-friendly option, fun, interactive and creative music experience, educational and entertaining tool for schools, shareable and votable mixes with online community, and automatic mode for lazy players. It also has some alternatives that you can try if you want to explore more music apps, such as Ramsophone, Ocean beta and Studio Amplify. If you want to download and install Incredibox APK Descargar on your Android device, you need to follow four simple steps: find a reliable source for the APK file, enable unknown sources on your device, download and install the APK file, and launch the app and start making music.</p>
- <p>If you love music and want to create your own tunes with the help of a merry crew of beatboxers, you should definitely try Incredibox. It's a fun, interactive and creative music app that will keep you entertained for hours. Download Incredibox APK Descargar today and enjoy!</p>
- <h2>Frequently Asked Questions</h2>
-<p>Here are some of the most common questions that people have about Incredibox:</p>
- <h4>Is Incredibox free?</h4>
-<p>Incredibox is not free, but it's very affordable. You only need to pay a one-time fee of $4.99 to download the app and get all current and future versions of Incredibox. That's a great deal for a music app that offers so much fun and creativity.</p>
- <h4>Is Incredibox safe?</h4>
-<p>Incredibox is safe as long as you download it from a reliable source like the official Incredibox website. Some websites may offer fake or modified versions of Incred box APK Descargar that may contain malware, viruses or spyware that can harm your device or steal your data. Therefore, you need to be careful and choose a reliable source for the Incredibox APK Descargar file.</p>
- <h4>Is Incredibox online?</h4>
-<p>Incredibox is both online and offline. You can play the online demo version of Incredibox on the official website, where you can access 4 musical atmospheres and share your mixes with other users. You can also download the app version of Incredibox on your Android or iOS device, where you can access 8 musical atmospheres and enjoy more features and benefits. You can play the app offline, but you need an internet connection to share your mixes online.</p>
- <h4>How do I get bonus on Incredibox?</h4>
-<p>Bonus are special animations that are triggered when you make a specific combination of sounds on Incredibox. There are 5 bonus per atmosphere, and each bonus has a different theme and style. To get bonus, you need to experiment with different sounds and find the right ones that match the bonus. You can also use the hints that are displayed on the top right corner of the screen to help you find the bonus. When you get a bonus, you can watch the animation and enjoy the music.</p>
- <h4>How do I update Incredibox?</h4>
-<p>To update Incredibox, you need to go to the Google Play Store or the App Store and check if there is a new version available for download. If there is, you can tap on the Update button and wait for the download and installation to complete. You can also enable automatic updates on your device settings, so you don't have to worry about checking for updates manually. Updating Incredibox will give you access to new features, improvements and bug fixes.</p> 197e85843d<br />
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\ No newline at end of file
diff --git a/spaces/cooelf/Multimodal-CoT/timm/__init__.py b/spaces/cooelf/Multimodal-CoT/timm/__init__.py
deleted file mode 100644
index 04ec7e51b858e528009bbeea6c75af5985aef202..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .version import __version__
-from .models import create_model, list_models, is_model, list_modules, model_entrypoint, \
-    is_scriptable, is_exportable, set_scriptable, set_exportable, has_model_default_key, is_model_default_key, \
-    get_model_default_value, is_model_pretrained
diff --git a/spaces/cooelf/Retro-Reader/app.py b/spaces/cooelf/Retro-Reader/app.py
deleted file mode 100644
index 31c778e4ee76be955fb333903ecf9d65296a5f30..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Retro-Reader/app.py
+++ /dev/null
@@ -1,202 +0,0 @@
-import torch
-from transformers import AlbertTokenizer, AlbertForSequenceClassification, AlbertForQuestionAnswering
-import collections
-import math
-import gradio as gr
-
-cls_modelPath = "./cls_model"
-mrc_modelPath = "./model4"
-
-tokenizer = AlbertTokenizer.from_pretrained(mrc_modelPath)
-cls_model = AlbertForSequenceClassification.from_pretrained(cls_modelPath)
-cls_model.eval()
-mrc_model = AlbertForQuestionAnswering.from_pretrained(mrc_modelPath)
-mrc_model.eval()
-
-def _get_best_indexes(logits, n_best_size):
-    """Get the n-best logits from a list."""
-    index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
-
-    best_indexes = []
-    for i in range(len(index_and_score)):
-        if i >= n_best_size:
-            break
-        best_indexes.append(index_and_score[i][0])
-    return best_indexes
-
-def _compute_softmax(scores):
-    """Compute softmax probability over raw logits."""
-    if not scores:
-        return []
-
-    max_score = None
-    for score in scores:
-        if max_score is None or score > max_score:
-            max_score = score
-
-    exp_scores = []
-    total_sum = 0.0
-    for score in scores:
-        x = math.exp(score - max_score)
-        exp_scores.append(x)
-        total_sum += x
-
-    probs = []
-    for score in exp_scores:
-        probs.append(score / total_sum)
-    return probs
-
-def get_qa_nbest(input_ids, start_logits, end_logits, seq_len, n_best_size=20, max_answer_length=30):
-    score_null = 1000000  # large and positive
-    prelim_predictions = []
-    null_start_logit = 0  # the start logit at the slice with min null score
-    null_end_logit = 0  # the end logit at the slice with min null score
-    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
-        "PrelimPrediction",
-        ["start_index", "end_index", "start_logit", "end_logit"])
-    _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
-            "NbestPrediction", ["text", "start_logit", "end_logit"])
-    
-    start_indexes = _get_best_indexes(start_logits, n_best_size)
-    end_indexes = _get_best_indexes(end_logits, n_best_size)
-
-    feature_null_score = start_logits[0] + end_logits[0]
-    if feature_null_score < score_null:
-        score_null = feature_null_score
-    for start_index in start_indexes:
-        for end_index in end_indexes:
-            if end_index < start_index:
-                continue
-            length = end_index - start_index + 1
-            if length > max_answer_length:
-                continue
-            if start_index >= seq_len:
-                        continue
-            if end_index >= seq_len:
-                continue
-            prelim_predictions.append(
-                _PrelimPrediction(
-                    start_index=start_index,
-                    end_index=end_index,
-                    start_logit=start_logits[start_index],
-                    end_logit=end_logits[end_index]))
-    prelim_predictions = sorted(
-            prelim_predictions,
-            key=lambda x: (x.start_logit + x.start_logit),
-            reverse=True)
-    
-    seen_predictions = {}
-    nbest = []
-    for pred in prelim_predictions:
-        if len(nbest) >= n_best_size:
-            break
-        
-        if pred.start_index > 0:  # this is a non-null prediction\
-            predict_answer_tokens = input_ids[0, pred.start_index: (pred.end_index + 1)]
-            final_text = tokenizer.decode(predict_answer_tokens)
-            if final_text in seen_predictions:
-                continue
-            seen_predictions[final_text] = True
-        else:
-            final_text = ""
-            seen_predictions[final_text] = True
-        
-
-        nbest.append(
-                _NbestPrediction(
-                    text=final_text,
-                    start_logit=pred.start_logit,
-                    end_logit=pred.end_logit))
-    if "" not in seen_predictions:
-        nbest.append(
-            _NbestPrediction(
-                text="",
-                start_logit=null_start_logit,
-                end_logit=null_end_logit))
-
-            # In very rare edge cases we could only have single null prediction.
-            # So we just create a nonce prediction in this case to avoid failure.
-    if len(nbest) == 1:
-        nbest.insert(0,
-            _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
-
-    # In very rare edge cases we could have no valid predictions. So we
-    # just create a nonce prediction in this case to avoid failure.
-    if not nbest:
-        nbest.append(
-            _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
-
-
-    total_scores = []
-    best_non_null_entry = None
-    for entry in nbest:
-        total_scores.append(entry.start_logit + entry.end_logit)
-        if not best_non_null_entry:
-            if entry.text:
-                best_non_null_entry = entry
-
-    probs = _compute_softmax(total_scores)
-    nbest_json = []
-    for (i, entry) in enumerate(nbest):
-        output = collections.OrderedDict()
-        output["text"] = entry.text
-        output["probability"] = probs[i]
-        output["start_logit"] = entry.start_logit
-        output["end_logit"] = entry.end_logit
-        nbest_json.append(output)
-
-    score_diff = score_null - best_non_null_entry.start_logit - (
-                best_non_null_entry.end_logit)
-    
-    return nbest_json, score_diff
-
-def inference(context, question, reference):
-    inputs = tokenizer(
-                question,
-                context,
-                add_special_tokens=True,
-                pad_to_max_length=True,
-                max_length=512,
-                return_tensors="pt"
-            )
-
-    seq_len = inputs.input_ids[0].tolist().index(0)
-
-    with torch.no_grad():
-        cls_outputs = cls_model(**inputs)
-        qa_outputs = mrc_model(**inputs)
-
-    cls_logits = cls_outputs.logits[0]
-    cls_divide = cls_logits[1] - cls_logits[0]
-
-
-    nbest, score_diff = get_qa_nbest(inputs.input_ids, qa_outputs.start_logits[0], qa_outputs.end_logits[0], seq_len=seq_len)
-
-    thresh = -1.246073067188263
-
-    print(cls_divide, score_diff)
-
-    na_score = (0.5*cls_divide + 0.5*score_diff)*0.5
-    if na_score > thresh:
-        final_answer = f"<No Answer>. The question is not answerable according to the context."
-    else:
-        final_answer = nbest[0]["text"]
-    return final_answer
-
-demo = gr.Interface(
-    fn=inference,
-    inputs=[gr.inputs.Textbox(label="Context"), 
-            gr.inputs.Textbox(label="Question"),
-            gr.inputs.Textbox(label="Reference Answer (Optional)")],
-    outputs=gr.outputs.Textbox(label="Output Answer"),
-    examples = [
-    ["The Norman dynasty had a major political, cultural and military impact on medieval Europe and even the Near East. The Normans were famed for their martial spirit and eventually for their Christian piety, becoming exponents of the Catholic orthodoxy into which they assimilated. They adopted the Gallo-Romance language of the Frankish land they settled, their dialect becoming known as Norman, Normaund or Norman French, an important literary language. The Duchy of Normandy, which they formed by treaty with the French crown, was a great fief of medieval France, and under Richard I of Normandy was forged into a cohesive and formidable principality in feudal tenure. The Normans are noted both for their culture, such as their unique Romanesque architecture and musical traditions, and for their significant military accomplishments and innovations. Norman adventurers founded the Kingdom of Sicily under Roger II after conquering southern Italy on the Saracens and Byzantines, and an expedition on behalf of their duke, William the Conqueror, led to the Norman conquest of England at the Battle of Hastings in 1066. Norman cultural and military influence spread from these new European centres to the Crusader states of the Near East, where their prince Bohemond I founded the Principality of Antioch in the Levant, to Scotland and Wales in Great Britain, to Ireland, and to the coasts of north Africa and the Canary Islands.", "Who was the duke in the battle of Hastings?", "William the Conqueror"],
-    ["The Norman dynasty had a major political, cultural and military impact on medieval Europe and even the Near East. The Normans were famed for their martial spirit and eventually for their Christian piety, becoming exponents of the Catholic orthodoxy into which they assimilated. They adopted the Gallo-Romance language of the Frankish land they settled, their dialect becoming known as Norman, Normaund or Norman French, an important literary language. The Duchy of Normandy, which they formed by treaty with the French crown, was a great fief of medieval France, and under Richard I of Normandy was forged into a cohesive and formidable principality in feudal tenure. The Normans are noted both for their culture, such as their unique Romanesque architecture and musical traditions, and for their significant military accomplishments and innovations. Norman adventurers founded the Kingdom of Sicily under Roger II after conquering southern Italy on the Saracens and Byzantines, and an expedition on behalf of their duke, William the Conqueror, led to the Norman conquest of England at the Battle of Hastings in 1066. Norman cultural and military influence spread from these new European centres to the Crusader states of the Near East, where their prince Bohemond I founded the Principality of Antioch in the Levant, to Scotland and Wales in Great Britain, to Ireland, and to the coasts of north Africa and the Canary Islands.", "What type of major impact did the Norman dynasty have on modern Europe?", "<No Answer>"],
-    ["Steam engines are external combustion engines, where the working fluid is separate from the combustion products. Non-combustion heat sources such as solar power, nuclear power or geothermal energy may be used. The ideal thermodynamic cycle used to analyze this process is called the Rankine cycle. In the cycle, water is heated and transforms into steam within a boiler operating at a high pressure. When expanded through pistons or turbines, mechanical work is done. The reduced-pressure steam is then condensed and pumped back into the boiler.", "What types of engines are steam engines?", "external combustion engines"],
-    ["Steam engines are external combustion engines, where the working fluid is separate from the combustion products. Non-combustion heat sources such as solar power, nuclear power or geothermal energy may be used. The ideal thermodynamic cycle used to analyze this process is called the Rankine cycle. In the cycle, water is heated and transforms into steam within a boiler operating at a high pressure. When expanded through pistons or turbines, mechanical work is done. The reduced-pressure steam is then condensed and pumped back into the boiler.", "What ideal thermodynamic cycle analyzes the process by which solar engines work?", "<No Answer>"],
-    ],
-    title="Retrospective Reader for Machine Reading Comprehension",
-    description=("<div style='text-align: center; margin: 0 auto;'>The model achieved the best performance at the SQuAD2.0 leaderboard. See more details at: <a href='https://aaai.org/papers/14506-retrospective-reader-for-machine-reading-comprehension/'>Paper</a> and <a href='https://github.com/cooelf/AwesomeMRC'>GitHub</a></div>"),
-    )
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/openpose/util.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/openpose/util.py
deleted file mode 100644
index 73d7d0153b38d143eb8090e07a9784a274b619ed..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/openpose/util.py
+++ /dev/null
@@ -1,297 +0,0 @@
-import math
-import numpy as np
-import matplotlib
-import cv2
-
-
-eps = 0.01
-
-
-def smart_resize(x, s):
-    Ht, Wt = s
-    if x.ndim == 2:
-        Ho, Wo = x.shape
-        Co = 1
-    else:
-        Ho, Wo, Co = x.shape
-    if Co == 3 or Co == 1:
-        k = float(Ht + Wt) / float(Ho + Wo)
-        return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4)
-    else:
-        return np.stack([smart_resize(x[:, :, i], s) for i in range(Co)], axis=2)
-
-
-def smart_resize_k(x, fx, fy):
-    if x.ndim == 2:
-        Ho, Wo = x.shape
-        Co = 1
-    else:
-        Ho, Wo, Co = x.shape
-    Ht, Wt = Ho * fy, Wo * fx
-    if Co == 3 or Co == 1:
-        k = float(Ht + Wt) / float(Ho + Wo)
-        return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4)
-    else:
-        return np.stack([smart_resize_k(x[:, :, i], fx, fy) for i in range(Co)], axis=2)
-
-
-def padRightDownCorner(img, stride, padValue):
-    h = img.shape[0]
-    w = img.shape[1]
-
-    pad = 4 * [None]
-    pad[0] = 0 # up
-    pad[1] = 0 # left
-    pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
-    pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right
-
-    img_padded = img
-    pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1))
-    img_padded = np.concatenate((pad_up, img_padded), axis=0)
-    pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1))
-    img_padded = np.concatenate((pad_left, img_padded), axis=1)
-    pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1))
-    img_padded = np.concatenate((img_padded, pad_down), axis=0)
-    pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1))
-    img_padded = np.concatenate((img_padded, pad_right), axis=1)
-
-    return img_padded, pad
-
-
-def transfer(model, model_weights):
-    transfered_model_weights = {}
-    for weights_name in model.state_dict().keys():
-        transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])]
-    return transfered_model_weights
-
-
-def draw_bodypose(canvas, candidate, subset):
-    H, W, C = canvas.shape
-    candidate = np.array(candidate)
-    subset = np.array(subset)
-
-    stickwidth = 4
-
-    limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
-               [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
-               [1, 16], [16, 18], [3, 17], [6, 18]]
-
-    colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
-              [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
-              [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
-
-    for i in range(17):
-        for n in range(len(subset)):
-            index = subset[n][np.array(limbSeq[i]) - 1]
-            if -1 in index:
-                continue
-            Y = candidate[index.astype(int), 0] * float(W)
-            X = candidate[index.astype(int), 1] * float(H)
-            mX = np.mean(X)
-            mY = np.mean(Y)
-            length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
-            angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
-            polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
-            cv2.fillConvexPoly(canvas, polygon, colors[i])
-
-    canvas = (canvas * 0.6).astype(np.uint8)
-
-    for i in range(18):
-        for n in range(len(subset)):
-            index = int(subset[n][i])
-            if index == -1:
-                continue
-            x, y = candidate[index][0:2]
-            x = int(x * W)
-            y = int(y * H)
-            cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
-
-    return canvas
-
-
-def draw_handpose(canvas, all_hand_peaks):
-    H, W, C = canvas.shape
-
-    edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \
-             [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]]
-
-    for peaks in all_hand_peaks:
-        peaks = np.array(peaks)
-
-        for ie, e in enumerate(edges):
-            x1, y1 = peaks[e[0]]
-            x2, y2 = peaks[e[1]]
-            x1 = int(x1 * W)
-            y1 = int(y1 * H)
-            x2 = int(x2 * W)
-            y2 = int(y2 * H)
-            if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
-                cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=2)
-
-        for i, keyponit in enumerate(peaks):
-            x, y = keyponit
-            x = int(x * W)
-            y = int(y * H)
-            if x > eps and y > eps:
-                cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
-    return canvas
-
-
-def draw_facepose(canvas, all_lmks):
-    H, W, C = canvas.shape
-    for lmks in all_lmks:
-        lmks = np.array(lmks)
-        for lmk in lmks:
-            x, y = lmk
-            x = int(x * W)
-            y = int(y * H)
-            if x > eps and y > eps:
-                cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1)
-    return canvas
-
-
-# detect hand according to body pose keypoints
-# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
-def handDetect(candidate, subset, oriImg):
-    # right hand: wrist 4, elbow 3, shoulder 2
-    # left hand: wrist 7, elbow 6, shoulder 5
-    ratioWristElbow = 0.33
-    detect_result = []
-    image_height, image_width = oriImg.shape[0:2]
-    for person in subset.astype(int):
-        # if any of three not detected
-        has_left = np.sum(person[[5, 6, 7]] == -1) == 0
-        has_right = np.sum(person[[2, 3, 4]] == -1) == 0
-        if not (has_left or has_right):
-            continue
-        hands = []
-        #left hand
-        if has_left:
-            left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]]
-            x1, y1 = candidate[left_shoulder_index][:2]
-            x2, y2 = candidate[left_elbow_index][:2]
-            x3, y3 = candidate[left_wrist_index][:2]
-            hands.append([x1, y1, x2, y2, x3, y3, True])
-        # right hand
-        if has_right:
-            right_shoulder_index, right_elbow_index, right_wrist_index = person[[2, 3, 4]]
-            x1, y1 = candidate[right_shoulder_index][:2]
-            x2, y2 = candidate[right_elbow_index][:2]
-            x3, y3 = candidate[right_wrist_index][:2]
-            hands.append([x1, y1, x2, y2, x3, y3, False])
-
-        for x1, y1, x2, y2, x3, y3, is_left in hands:
-            # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox
-            # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);
-            # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);
-            # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);
-            # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);
-            # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);
-            x = x3 + ratioWristElbow * (x3 - x2)
-            y = y3 + ratioWristElbow * (y3 - y2)
-            distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
-            distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
-            width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
-            # x-y refers to the center --> offset to topLeft point
-            # handRectangle.x -= handRectangle.width / 2.f;
-            # handRectangle.y -= handRectangle.height / 2.f;
-            x -= width / 2
-            y -= width / 2  # width = height
-            # overflow the image
-            if x < 0: x = 0
-            if y < 0: y = 0
-            width1 = width
-            width2 = width
-            if x + width > image_width: width1 = image_width - x
-            if y + width > image_height: width2 = image_height - y
-            width = min(width1, width2)
-            # the max hand box value is 20 pixels
-            if width >= 20:
-                detect_result.append([int(x), int(y), int(width), is_left])
-
-    '''
-    return value: [[x, y, w, True if left hand else False]].
-    width=height since the network require squared input.
-    x, y is the coordinate of top left 
-    '''
-    return detect_result
-
-
-# Written by Lvmin
-def faceDetect(candidate, subset, oriImg):
-    # left right eye ear 14 15 16 17
-    detect_result = []
-    image_height, image_width = oriImg.shape[0:2]
-    for person in subset.astype(int):
-        has_head = person[0] > -1
-        if not has_head:
-            continue
-
-        has_left_eye = person[14] > -1
-        has_right_eye = person[15] > -1
-        has_left_ear = person[16] > -1
-        has_right_ear = person[17] > -1
-
-        if not (has_left_eye or has_right_eye or has_left_ear or has_right_ear):
-            continue
-
-        head, left_eye, right_eye, left_ear, right_ear = person[[0, 14, 15, 16, 17]]
-
-        width = 0.0
-        x0, y0 = candidate[head][:2]
-
-        if has_left_eye:
-            x1, y1 = candidate[left_eye][:2]
-            d = max(abs(x0 - x1), abs(y0 - y1))
-            width = max(width, d * 3.0)
-
-        if has_right_eye:
-            x1, y1 = candidate[right_eye][:2]
-            d = max(abs(x0 - x1), abs(y0 - y1))
-            width = max(width, d * 3.0)
-
-        if has_left_ear:
-            x1, y1 = candidate[left_ear][:2]
-            d = max(abs(x0 - x1), abs(y0 - y1))
-            width = max(width, d * 1.5)
-
-        if has_right_ear:
-            x1, y1 = candidate[right_ear][:2]
-            d = max(abs(x0 - x1), abs(y0 - y1))
-            width = max(width, d * 1.5)
-
-        x, y = x0, y0
-
-        x -= width
-        y -= width
-
-        if x < 0:
-            x = 0
-
-        if y < 0:
-            y = 0
-
-        width1 = width * 2
-        width2 = width * 2
-
-        if x + width > image_width:
-            width1 = image_width - x
-
-        if y + width > image_height:
-            width2 = image_height - y
-
-        width = min(width1, width2)
-
-        if width >= 20:
-            detect_result.append([int(x), int(y), int(width)])
-
-    return detect_result
-
-
-# get max index of 2d array
-def npmax(array):
-    arrayindex = array.argmax(1)
-    arrayvalue = array.max(1)
-    i = arrayvalue.argmax()
-    j = arrayindex[i]
-    return i, j
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/exp/upernet_global_small/config.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/exp/upernet_global_small/config.py
deleted file mode 100644
index 01db96bf9b0be531aa0eaf62fee51543712f8670..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/exp/upernet_global_small/config.py
+++ /dev/null
@@ -1,38 +0,0 @@
-_base_ = [
-    '../../configs/_base_/models/upernet_uniformer.py', 
-    '../../configs/_base_/datasets/ade20k.py',
-    '../../configs/_base_/default_runtime.py', 
-    '../../configs/_base_/schedules/schedule_160k.py'
-]
-model = dict(
-    backbone=dict(
-        type='UniFormer',
-        embed_dim=[64, 128, 320, 512],
-        layers=[3, 4, 8, 3],
-        head_dim=64,
-        drop_path_rate=0.25,
-        windows=False,
-        hybrid=False
-    ),
-    decode_head=dict(
-        in_channels=[64, 128, 320, 512],
-        num_classes=150
-    ),
-    auxiliary_head=dict(
-        in_channels=320,
-        num_classes=150
-    ))
-
-# AdamW optimizer, no weight decay for position embedding & layer norm in backbone
-optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
-                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
-                                                 'relative_position_bias_table': dict(decay_mult=0.),
-                                                 'norm': dict(decay_mult=0.)}))
-
-lr_config = dict(_delete_=True, policy='poly',
-                 warmup='linear',
-                 warmup_iters=1500,
-                 warmup_ratio=1e-6,
-                 power=1.0, min_lr=0.0, by_epoch=False)
-
-data=dict(samples_per_gpu=2)
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/parallel/_functions.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/parallel/_functions.py
deleted file mode 100644
index 9b5a8a44483ab991411d07122b22a1d027e4be8e..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/parallel/_functions.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch.nn.parallel._functions import _get_stream
-
-
-def scatter(input, devices, streams=None):
-    """Scatters tensor across multiple GPUs."""
-    if streams is None:
-        streams = [None] * len(devices)
-
-    if isinstance(input, list):
-        chunk_size = (len(input) - 1) // len(devices) + 1
-        outputs = [
-            scatter(input[i], [devices[i // chunk_size]],
-                    [streams[i // chunk_size]]) for i in range(len(input))
-        ]
-        return outputs
-    elif isinstance(input, torch.Tensor):
-        output = input.contiguous()
-        # TODO: copy to a pinned buffer first (if copying from CPU)
-        stream = streams[0] if output.numel() > 0 else None
-        if devices != [-1]:
-            with torch.cuda.device(devices[0]), torch.cuda.stream(stream):
-                output = output.cuda(devices[0], non_blocking=True)
-        else:
-            # unsqueeze the first dimension thus the tensor's shape is the
-            # same as those scattered with GPU.
-            output = output.unsqueeze(0)
-        return output
-    else:
-        raise Exception(f'Unknown type {type(input)}.')
-
-
-def synchronize_stream(output, devices, streams):
-    if isinstance(output, list):
-        chunk_size = len(output) // len(devices)
-        for i in range(len(devices)):
-            for j in range(chunk_size):
-                synchronize_stream(output[i * chunk_size + j], [devices[i]],
-                                   [streams[i]])
-    elif isinstance(output, torch.Tensor):
-        if output.numel() != 0:
-            with torch.cuda.device(devices[0]):
-                main_stream = torch.cuda.current_stream()
-                main_stream.wait_stream(streams[0])
-                output.record_stream(main_stream)
-    else:
-        raise Exception(f'Unknown type {type(output)}.')
-
-
-def get_input_device(input):
-    if isinstance(input, list):
-        for item in input:
-            input_device = get_input_device(item)
-            if input_device != -1:
-                return input_device
-        return -1
-    elif isinstance(input, torch.Tensor):
-        return input.get_device() if input.is_cuda else -1
-    else:
-        raise Exception(f'Unknown type {type(input)}.')
-
-
-class Scatter:
-
-    @staticmethod
-    def forward(target_gpus, input):
-        input_device = get_input_device(input)
-        streams = None
-        if input_device == -1 and target_gpus != [-1]:
-            # Perform CPU to GPU copies in a background stream
-            streams = [_get_stream(device) for device in target_gpus]
-
-        outputs = scatter(input, target_gpus, streams)
-        # Synchronize with the copy stream
-        if streams is not None:
-            synchronize_stream(outputs, target_gpus, streams)
-
-        return tuple(outputs)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/segmentors/cascade_encoder_decoder.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/segmentors/cascade_encoder_decoder.py
deleted file mode 100644
index 873957d8d6468147c994493d92ff5c1b15bfb703..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/segmentors/cascade_encoder_decoder.py
+++ /dev/null
@@ -1,98 +0,0 @@
-from torch import nn
-
-from annotator.uniformer.mmseg.core import add_prefix
-from annotator.uniformer.mmseg.ops import resize
-from .. import builder
-from ..builder import SEGMENTORS
-from .encoder_decoder import EncoderDecoder
-
-
-@SEGMENTORS.register_module()
-class CascadeEncoderDecoder(EncoderDecoder):
-    """Cascade Encoder Decoder segmentors.
-
-    CascadeEncoderDecoder almost the same as EncoderDecoder, while decoders of
-    CascadeEncoderDecoder are cascaded. The output of previous decoder_head
-    will be the input of next decoder_head.
-    """
-
-    def __init__(self,
-                 num_stages,
-                 backbone,
-                 decode_head,
-                 neck=None,
-                 auxiliary_head=None,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        self.num_stages = num_stages
-        super(CascadeEncoderDecoder, self).__init__(
-            backbone=backbone,
-            decode_head=decode_head,
-            neck=neck,
-            auxiliary_head=auxiliary_head,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            pretrained=pretrained)
-
-    def _init_decode_head(self, decode_head):
-        """Initialize ``decode_head``"""
-        assert isinstance(decode_head, list)
-        assert len(decode_head) == self.num_stages
-        self.decode_head = nn.ModuleList()
-        for i in range(self.num_stages):
-            self.decode_head.append(builder.build_head(decode_head[i]))
-        self.align_corners = self.decode_head[-1].align_corners
-        self.num_classes = self.decode_head[-1].num_classes
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in backbone and heads.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        self.backbone.init_weights(pretrained=pretrained)
-        for i in range(self.num_stages):
-            self.decode_head[i].init_weights()
-        if self.with_auxiliary_head:
-            if isinstance(self.auxiliary_head, nn.ModuleList):
-                for aux_head in self.auxiliary_head:
-                    aux_head.init_weights()
-            else:
-                self.auxiliary_head.init_weights()
-
-    def encode_decode(self, img, img_metas):
-        """Encode images with backbone and decode into a semantic segmentation
-        map of the same size as input."""
-        x = self.extract_feat(img)
-        out = self.decode_head[0].forward_test(x, img_metas, self.test_cfg)
-        for i in range(1, self.num_stages):
-            out = self.decode_head[i].forward_test(x, out, img_metas,
-                                                   self.test_cfg)
-        out = resize(
-            input=out,
-            size=img.shape[2:],
-            mode='bilinear',
-            align_corners=self.align_corners)
-        return out
-
-    def _decode_head_forward_train(self, x, img_metas, gt_semantic_seg):
-        """Run forward function and calculate loss for decode head in
-        training."""
-        losses = dict()
-
-        loss_decode = self.decode_head[0].forward_train(
-            x, img_metas, gt_semantic_seg, self.train_cfg)
-
-        losses.update(add_prefix(loss_decode, 'decode_0'))
-
-        for i in range(1, self.num_stages):
-            # forward test again, maybe unnecessary for most methods.
-            prev_outputs = self.decode_head[i - 1].forward_test(
-                x, img_metas, self.test_cfg)
-            loss_decode = self.decode_head[i].forward_train(
-                x, prev_outputs, img_metas, gt_semantic_seg, self.train_cfg)
-            losses.update(add_prefix(loss_decode, f'decode_{i}'))
-
-        return losses
diff --git a/spaces/cscan/CodeFormer/CodeFormer/basicsr/setup.py b/spaces/cscan/CodeFormer/CodeFormer/basicsr/setup.py
deleted file mode 100644
index 382a2aa1006e581eaf31dbb3155d4b0ba3b31140..0000000000000000000000000000000000000000
--- a/spaces/cscan/CodeFormer/CodeFormer/basicsr/setup.py
+++ /dev/null
@@ -1,165 +0,0 @@
-#!/usr/bin/env python
-
-from setuptools import find_packages, setup
-
-import os
-import subprocess
-import sys
-import time
-import torch
-from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension
-
-version_file = './basicsr/version.py'
-
-
-def readme():
-    with open('README.md', encoding='utf-8') as f:
-        content = f.read()
-    return content
-
-
-def get_git_hash():
-
-    def _minimal_ext_cmd(cmd):
-        # construct minimal environment
-        env = {}
-        for k in ['SYSTEMROOT', 'PATH', 'HOME']:
-            v = os.environ.get(k)
-            if v is not None:
-                env[k] = v
-        # LANGUAGE is used on win32
-        env['LANGUAGE'] = 'C'
-        env['LANG'] = 'C'
-        env['LC_ALL'] = 'C'
-        out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
-        return out
-
-    try:
-        out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
-        sha = out.strip().decode('ascii')
-    except OSError:
-        sha = 'unknown'
-
-    return sha
-
-
-def get_hash():
-    if os.path.exists('.git'):
-        sha = get_git_hash()[:7]
-    elif os.path.exists(version_file):
-        try:
-            from version import __version__
-            sha = __version__.split('+')[-1]
-        except ImportError:
-            raise ImportError('Unable to get git version')
-    else:
-        sha = 'unknown'
-
-    return sha
-
-
-def write_version_py():
-    content = """# GENERATED VERSION FILE
-# TIME: {}
-__version__ = '{}'
-__gitsha__ = '{}'
-version_info = ({})
-"""
-    sha = get_hash()
-    with open('./basicsr/VERSION', 'r') as f:
-        SHORT_VERSION = f.read().strip()
-    VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])
-
-    version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
-    with open(version_file, 'w') as f:
-        f.write(version_file_str)
-
-
-def get_version():
-    with open(version_file, 'r') as f:
-        exec(compile(f.read(), version_file, 'exec'))
-    return locals()['__version__']
-
-
-def make_cuda_ext(name, module, sources, sources_cuda=None):
-    if sources_cuda is None:
-        sources_cuda = []
-    define_macros = []
-    extra_compile_args = {'cxx': []}
-
-    if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1':
-        define_macros += [('WITH_CUDA', None)]
-        extension = CUDAExtension
-        extra_compile_args['nvcc'] = [
-            '-D__CUDA_NO_HALF_OPERATORS__',
-            '-D__CUDA_NO_HALF_CONVERSIONS__',
-            '-D__CUDA_NO_HALF2_OPERATORS__',
-        ]
-        sources += sources_cuda
-    else:
-        print(f'Compiling {name} without CUDA')
-        extension = CppExtension
-
-    return extension(
-        name=f'{module}.{name}',
-        sources=[os.path.join(*module.split('.'), p) for p in sources],
-        define_macros=define_macros,
-        extra_compile_args=extra_compile_args)
-
-
-def get_requirements(filename='requirements.txt'):
-    with open(os.path.join('.', filename), 'r') as f:
-        requires = [line.replace('\n', '') for line in f.readlines()]
-    return requires
-
-
-if __name__ == '__main__':
-    if '--cuda_ext' in sys.argv:
-        ext_modules = [
-            make_cuda_ext(
-                name='deform_conv_ext',
-                module='ops.dcn',
-                sources=['src/deform_conv_ext.cpp'],
-                sources_cuda=['src/deform_conv_cuda.cpp', 'src/deform_conv_cuda_kernel.cu']),
-            make_cuda_ext(
-                name='fused_act_ext',
-                module='ops.fused_act',
-                sources=['src/fused_bias_act.cpp'],
-                sources_cuda=['src/fused_bias_act_kernel.cu']),
-            make_cuda_ext(
-                name='upfirdn2d_ext',
-                module='ops.upfirdn2d',
-                sources=['src/upfirdn2d.cpp'],
-                sources_cuda=['src/upfirdn2d_kernel.cu']),
-        ]
-        sys.argv.remove('--cuda_ext')
-    else:
-        ext_modules = []
-
-    write_version_py()
-    setup(
-        name='basicsr',
-        version=get_version(),
-        description='Open Source Image and Video Super-Resolution Toolbox',
-        long_description=readme(),
-        long_description_content_type='text/markdown',
-        author='Xintao Wang',
-        author_email='xintao.wang@outlook.com',
-        keywords='computer vision, restoration, super resolution',
-        url='https://github.com/xinntao/BasicSR',
-        include_package_data=True,
-        packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
-        classifiers=[
-            'Development Status :: 4 - Beta',
-            'License :: OSI Approved :: Apache Software License',
-            'Operating System :: OS Independent',
-            'Programming Language :: Python :: 3',
-            'Programming Language :: Python :: 3.7',
-            'Programming Language :: Python :: 3.8',
-        ],
-        license='Apache License 2.0',
-        setup_requires=['cython', 'numpy'],
-        install_requires=get_requirements(),
-        ext_modules=ext_modules,
-        cmdclass={'build_ext': BuildExtension},
-        zip_safe=False)
diff --git a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/pass_through.py b/spaces/cymic/Talking_Head_Anime_3/tha3/nn/pass_through.py
deleted file mode 100644
index c64d6786df0fb57fbff19ebaa80e0f41ab6fce71..0000000000000000000000000000000000000000
--- a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/pass_through.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from torch.nn import Module
-
-
-class PassThrough(Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x):
-        return x
\ No newline at end of file
diff --git a/spaces/daarumadx/bot/src/argv/__init__.py b/spaces/daarumadx/bot/src/argv/__init__.py
deleted file mode 100644
index 6701c5a038d6a0d2f94284739285c54b6b521f54..0000000000000000000000000000000000000000
--- a/spaces/daarumadx/bot/src/argv/__init__.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import argparse
-import copy
-import logging
-import sys
-
-from config import Config as Conf
-from argv.checkpoints import init_checkpoints_sub_parser, check_args_checkpoints_parser, set_args_checkpoints_parser
-from argv.common import arg_help, arg_debug, arg_version
-from argv.daemon import init_daemon_sub_parser, check_args_daemon_parser, set_args_daemon_parser
-from argv.gpu_info import init_gpu_info_sub_parser
-from argv.run import init_run_parser
-from argv.run.config import set_args_run_parser, check_args_run_parser
-from utils import setup_log, json_to_argv
-
-
-class Parser:
-    parser = None
-
-
-def run():
-    Parser.parser = init_parser()
-
-    if len(sys.argv) == 1:
-        Parser.parser.print_usage()
-        Parser.parser.exit()
-
-    args = Parser.parser.parse_args()
-
-    Conf.log = setup_log(logging.DEBUG) if args.debug else setup_log()
-    args = config_args(Parser.parser, args)
-
-    Conf.log.spam("Args : {}".format(args))
-
-    Conf.args = vars(args)
-    args.func(args)
-
-
-def init_parser():
-    parser = argparse.ArgumentParser(
-        description="Dreampower CLI application that allow to transform photos of people for private entertainment",
-        add_help=False
-    )
-
-    # add args main
-    arg_help(parser)
-    arg_debug(parser)
-    arg_version(parser)
-
-    # add subparsers
-    subparsers = parser.add_subparsers(dest="mode")
-    init_run_parser(subparsers)
-    init_checkpoints_sub_parser(subparsers)
-    init_gpu_info_sub_parser(subparsers)
-    init_daemon_sub_parser(subparsers)
-
-    return parser
-
-
-def config_args(parser, args, json_data=None):
-    args = copy.deepcopy(args)
-    set_args_parser(parser, args)
-    check_args_parser(parser, args)
-    if args.mode == "run":
-        return merge_json_args(parser, args, json_data)
-    else:
-        return args
-
-
-def check_args_parser(parser, args):
-    if args.mode == "run":
-        check_args_run_parser(parser, args)
-
-    if args.mode == "checkpoints":
-        check_args_checkpoints_parser(parser, args)
-
-    if args.mode == "daemon":
-        check_args_daemon_parser(parser, args)
-
-    return parser
-
-
-def set_args_parser(parser, args):
-    if args.mode == "run":
-        set_args_run_parser(args)
-
-    if args.mode == "checkpoints":
-        set_args_checkpoints_parser(args)
-
-    if args.mode == "daemon":
-        set_args_daemon_parser(args)
-
-    return parser
-
-
-def merge_json_args(parser, args, json_data=None):
-    def filter_conflict_args(l1, l2):
-        # l2 args got priority on l1
-        l1 = copy.copy(l1)
-        l2 = copy.copy(l2)
-        # Handle special cases for ignoring arguments in json file if provided in command line
-        if "--cpu" in l2 or "--gpu" in l2:
-            l1 = list(filter(lambda x: x not in ("--cpu", "--gpu"), l1))
-
-        if "--auto-resize" in l2 or "--auto-resize-crop" in l2 \
-                or "--auto-rescale" in l2 or "--overlay" in l2:
-            l1 = list(filter(lambda x: x not in ("--auto-resize", "--auto-resize-crop", "--auto-rescale"), l1))
-            if "--overlay" in l1:
-                del l1[l1.index("--overlay"):l1.index("--overlay") + 1]
-
-        return l1 + l2
-
-    # merge args
-    cmdline_args = []
-    if not json_data and not args.json_args:
-        return args
-    elif json_data and args.json_args:
-        cmdline_args = filter_conflict_args(json_to_argv(json_data), json_to_argv(args.json_args))
-    elif json_data and not args.json_args:
-        cmdline_args = json_to_argv(json_data)
-    elif not json_data and args.json_args:
-        cmdline_args = json_to_argv(args.json_args)
-
-    cmdline_args = filter_conflict_args(cmdline_args, sys.argv[1:])
-
-    # remove json-args arg
-    i = 0
-    while i < len(cmdline_args):
-        if "--json-args" == cmdline_args[i]:
-            del cmdline_args[i:i + 2]
-        i += 1
-
-    # replace run arg
-    cmdline_args.index('run')
-    cmdline_args.remove('run')
-    cmdline_args.insert(0, 'run')
-
-    return config_args(parser, parser.parse_args(cmdline_args))
diff --git a/spaces/danterivers/music-generation-samples/audiocraft/models/builders.py b/spaces/danterivers/music-generation-samples/audiocraft/models/builders.py
deleted file mode 100644
index 77ee5f96fea2e3c9e475fe961bc1a5ee473ed8eb..0000000000000000000000000000000000000000
--- a/spaces/danterivers/music-generation-samples/audiocraft/models/builders.py
+++ /dev/null
@@ -1,218 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-All the functions to build the relevant models and modules
-from the Hydra config.
-"""
-
-import typing as tp
-import warnings
-
-import audiocraft
-import omegaconf
-import torch
-
-from .encodec import CompressionModel, EncodecModel, FlattenedCompressionModel  # noqa
-from .lm import LMModel
-from ..modules.codebooks_patterns import (
-    CodebooksPatternProvider,
-    DelayedPatternProvider,
-    ParallelPatternProvider,
-    UnrolledPatternProvider,
-    VALLEPattern,
-    MusicLMPattern,
-)
-from ..modules.conditioners import (
-    BaseConditioner,
-    ConditioningProvider,
-    LUTConditioner,
-    T5Conditioner,
-    ConditionFuser,
-    ChromaStemConditioner,
-)
-from .. import quantization as qt
-from ..utils.utils import dict_from_config
-
-
-def get_quantizer(quantizer: str, cfg: omegaconf.DictConfig, dimension: int) -> qt.BaseQuantizer:
-    klass = {
-        'no_quant': qt.DummyQuantizer,
-        'rvq': qt.ResidualVectorQuantizer
-    }[quantizer]
-    kwargs = dict_from_config(getattr(cfg, quantizer))
-    if quantizer != 'no_quant':
-        kwargs['dimension'] = dimension
-    return klass(**kwargs)
-
-
-def get_encodec_autoencoder(encoder_name: str, cfg: omegaconf.DictConfig):
-    if encoder_name == 'seanet':
-        kwargs = dict_from_config(getattr(cfg, 'seanet'))
-        encoder_override_kwargs = kwargs.pop('encoder')
-        decoder_override_kwargs = kwargs.pop('decoder')
-        encoder_kwargs = {**kwargs, **encoder_override_kwargs}
-        decoder_kwargs = {**kwargs, **decoder_override_kwargs}
-        encoder = audiocraft.modules.SEANetEncoder(**encoder_kwargs)
-        decoder = audiocraft.modules.SEANetDecoder(**decoder_kwargs)
-        return encoder, decoder
-    else:
-        raise KeyError(f'Unexpected compression model {cfg.compression_model}')
-
-
-def get_compression_model(cfg: omegaconf.DictConfig) -> CompressionModel:
-    """Instantiate a compression model.
-    """
-    if cfg.compression_model == 'encodec':
-        kwargs = dict_from_config(getattr(cfg, 'encodec'))
-        encoder_name = kwargs.pop('autoencoder')
-        quantizer_name = kwargs.pop('quantizer')
-        encoder, decoder = get_encodec_autoencoder(encoder_name, cfg)
-        quantizer = get_quantizer(quantizer_name, cfg, encoder.dimension)
-        frame_rate = kwargs['sample_rate'] // encoder.hop_length
-        renormalize = kwargs.pop('renormalize', None)
-        renorm = kwargs.pop('renorm')
-        if renormalize is None:
-            renormalize = renorm is not None
-            warnings.warn("You are using a deprecated EnCodec model. Please migrate to new renormalization.")
-        return EncodecModel(encoder, decoder, quantizer,
-                            frame_rate=frame_rate, renormalize=renormalize, **kwargs).to(cfg.device)
-    else:
-        raise KeyError(f'Unexpected compression model {cfg.compression_model}')
-
-
-def get_lm_model(cfg: omegaconf.DictConfig) -> LMModel:
-    """Instantiate a transformer LM.
-    """
-    if cfg.lm_model == 'transformer_lm':
-        kwargs = dict_from_config(getattr(cfg, 'transformer_lm'))
-        n_q = kwargs['n_q']
-        q_modeling = kwargs.pop('q_modeling', None)
-        codebooks_pattern_cfg = getattr(cfg, 'codebooks_pattern')
-        attribute_dropout = dict_from_config(getattr(cfg, 'attribute_dropout'))
-        cls_free_guidance = dict_from_config(getattr(cfg, 'classifier_free_guidance'))
-        cfg_prob, cfg_coef = cls_free_guidance["training_dropout"], cls_free_guidance["inference_coef"]
-        fuser = get_condition_fuser(cfg)
-        condition_provider = get_conditioner_provider(kwargs["dim"], cfg).to(cfg.device)
-        if len(fuser.fuse2cond['cross']) > 0:  # enforce cross-att programatically
-            kwargs['cross_attention'] = True
-        if codebooks_pattern_cfg.modeling is None:
-            assert q_modeling is not None, \
-                'LM model should either have a codebook pattern defined or transformer_lm.q_modeling'
-            codebooks_pattern_cfg = omegaconf.OmegaConf.create(
-                {'modeling': q_modeling, 'delay': {'delays': list(range(n_q))}}
-            )
-        pattern_provider = get_codebooks_pattern_provider(n_q, codebooks_pattern_cfg)
-        return LMModel(
-            pattern_provider=pattern_provider,
-            condition_provider=condition_provider,
-            fuser=fuser,
-            cfg_dropout=cfg_prob,
-            cfg_coef=cfg_coef,
-            attribute_dropout=attribute_dropout,
-            dtype=getattr(torch, cfg.dtype),
-            device=cfg.device,
-            **kwargs
-        ).to(cfg.device)
-    else:
-        raise KeyError(f'Unexpected LM model {cfg.lm_model}')
-
-
-def get_conditioner_provider(output_dim: int, cfg: omegaconf.DictConfig) -> ConditioningProvider:
-    """Instantiate a conditioning model.
-    """
-    device = cfg.device
-    duration = cfg.dataset.segment_duration
-    cfg = getattr(cfg, "conditioners")
-    cfg = omegaconf.OmegaConf.create({}) if cfg is None else cfg
-    conditioners: tp.Dict[str, BaseConditioner] = {}
-    with omegaconf.open_dict(cfg):
-        condition_provider_args = cfg.pop('args', {})
-    for cond, cond_cfg in cfg.items():
-        model_type = cond_cfg["model"]
-        model_args = cond_cfg[model_type]
-        if model_type == "t5":
-            conditioners[str(cond)] = T5Conditioner(output_dim=output_dim, device=device, **model_args)
-        elif model_type == "lut":
-            conditioners[str(cond)] = LUTConditioner(output_dim=output_dim, **model_args)
-        elif model_type == "chroma_stem":
-            model_args.pop('cache_path', None)
-            conditioners[str(cond)] = ChromaStemConditioner(
-                output_dim=output_dim,
-                duration=duration,
-                device=device,
-                **model_args
-            )
-        else:
-            raise ValueError(f"unrecognized conditioning model: {model_type}")
-    conditioner = ConditioningProvider(conditioners, device=device, **condition_provider_args)
-    return conditioner
-
-
-def get_condition_fuser(cfg: omegaconf.DictConfig) -> ConditionFuser:
-    """Instantiate a condition fuser object.
-    """
-    fuser_cfg = getattr(cfg, "fuser")
-    fuser_methods = ["sum", "cross", "prepend", "input_interpolate"]
-    fuse2cond = {k: fuser_cfg[k] for k in fuser_methods}
-    kwargs = {k: v for k, v in fuser_cfg.items() if k not in fuser_methods}
-    fuser = ConditionFuser(fuse2cond=fuse2cond, **kwargs)
-    return fuser
-
-
-def get_codebooks_pattern_provider(n_q: int, cfg: omegaconf.DictConfig) -> CodebooksPatternProvider:
-    """Instantiate a codebooks pattern provider object.
-    """
-    pattern_providers = {
-        'parallel': ParallelPatternProvider,
-        'delay': DelayedPatternProvider,
-        'unroll': UnrolledPatternProvider,
-        'valle': VALLEPattern,
-        'musiclm': MusicLMPattern,
-    }
-    name = cfg.modeling
-    kwargs = dict_from_config(cfg.get(name)) if hasattr(cfg, name) else {}
-    klass = pattern_providers[name]
-    return klass(n_q, **kwargs)
-
-
-def get_debug_compression_model(device='cpu'):
-    """Instantiate a debug compression model to be used for unit tests.
-    """
-    seanet_kwargs = {
-        'n_filters': 4,
-        'n_residual_layers': 1,
-        'dimension': 32,
-        'ratios': [10, 8, 16]  # 25 Hz at 32kHz
-    }
-    encoder = audiocraft.modules.SEANetEncoder(**seanet_kwargs)
-    decoder = audiocraft.modules.SEANetDecoder(**seanet_kwargs)
-    quantizer = qt.ResidualVectorQuantizer(dimension=32, bins=400, n_q=4)
-    init_x = torch.randn(8, 32, 128)
-    quantizer(init_x, 1)  # initialize kmeans etc.
-    compression_model = EncodecModel(
-        encoder, decoder, quantizer,
-        frame_rate=25, sample_rate=32000, channels=1).to(device)
-    return compression_model.eval()
-
-
-def get_debug_lm_model(device='cpu'):
-    """Instantiate a debug LM to be used for unit tests.
-    """
-    pattern = DelayedPatternProvider(n_q=4)
-    dim = 16
-    providers = {
-        'description': LUTConditioner(n_bins=128, dim=dim, output_dim=dim, tokenizer="whitespace"),
-    }
-    condition_provider = ConditioningProvider(providers)
-    fuser = ConditionFuser(
-        {'cross': ['description'], 'prepend': [],
-         'sum': [], 'input_interpolate': []})
-    lm = LMModel(
-        pattern, condition_provider, fuser,
-        n_q=4, card=400, dim=dim, num_heads=4, custom=True, num_layers=2,
-        cross_attention=True, causal=True)
-    return lm.to(device).eval()
diff --git a/spaces/datasciencemmw/ContextXLA-beta-demo/app.py b/spaces/datasciencemmw/ContextXLA-beta-demo/app.py
deleted file mode 100644
index a187c7c68946e3198462800c088ba6fa3d3a9893..0000000000000000000000000000000000000000
--- a/spaces/datasciencemmw/ContextXLA-beta-demo/app.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import gradio as gr
-
-title = "contextxla beta"
-description = "This is the official gradio demo for ContextXLA beta, the best language classification AI to be built."
-gr.Interface.load(
-    "huggingface/datasciencemmw/current-beta",
-    inputs="text", 
-    title=title,
-    description=description,
-    examples=[
-    ["Conflict is inevitable on the path to peace."],
-    ["Controversy is never leading to peace."],
-    ["Your mother is cool and I am Gilgamesh,,"],
-    ["I am a cool green purple dude."],
-],
-   
-).launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/misc/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/misc/__init__.py
deleted file mode 100644
index 156cb232a7aa80eee1526c7598f72043de10473f..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/misc/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Empty __init__.py file to signal Python this directory is a package."""
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/misc/dictTools.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/misc/dictTools.py
deleted file mode 100644
index 259613b27048c458980986167d429847d270691f..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/misc/dictTools.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""Misc dict tools."""
-
-
-__all__ = ["hashdict"]
-
-# https://stackoverflow.com/questions/1151658/python-hashable-dicts
-class hashdict(dict):
-    """
-    hashable dict implementation, suitable for use as a key into
-    other dicts.
-
-        >>> h1 = hashdict({"apples": 1, "bananas":2})
-        >>> h2 = hashdict({"bananas": 3, "mangoes": 5})
-        >>> h1+h2
-        hashdict(apples=1, bananas=3, mangoes=5)
-        >>> d1 = {}
-        >>> d1[h1] = "salad"
-        >>> d1[h1]
-        'salad'
-        >>> d1[h2]
-        Traceback (most recent call last):
-        ...
-        KeyError: hashdict(bananas=3, mangoes=5)
-
-    based on answers from
-       http://stackoverflow.com/questions/1151658/python-hashable-dicts
-
-    """
-
-    def __key(self):
-        return tuple(sorted(self.items()))
-
-    def __repr__(self):
-        return "{0}({1})".format(
-            self.__class__.__name__,
-            ", ".join("{0}={1}".format(str(i[0]), repr(i[1])) for i in self.__key()),
-        )
-
-    def __hash__(self):
-        return hash(self.__key())
-
-    def __setitem__(self, key, value):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    def __delitem__(self, key):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    def clear(self):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    def pop(self, *args, **kwargs):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    def popitem(self, *args, **kwargs):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    def setdefault(self, *args, **kwargs):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    def update(self, *args, **kwargs):
-        raise TypeError(
-            "{0} does not support item assignment".format(self.__class__.__name__)
-        )
-
-    # update is not ok because it mutates the object
-    # __add__ is ok because it creates a new object
-    # while the new object is under construction, it's ok to mutate it
-    def __add__(self, right):
-        result = hashdict(self)
-        dict.update(result, right)
-        return result
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_files.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_files.py
deleted file mode 100644
index 197a063ba43ffa7884b6789247468c4190fea457..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_files.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import typing
-import textwrap
-import unittest
-import warnings
-import importlib
-import contextlib
-
-import importlib_resources as resources
-from ..abc import Traversable
-from . import data01
-from . import util
-from . import _path
-from ._compat import os_helper, import_helper
-
-
-@contextlib.contextmanager
-def suppress_known_deprecation():
-    with warnings.catch_warnings(record=True) as ctx:
-        warnings.simplefilter('default', category=DeprecationWarning)
-        yield ctx
-
-
-class FilesTests:
-    def test_read_bytes(self):
-        files = resources.files(self.data)
-        actual = files.joinpath('utf-8.file').read_bytes()
-        assert actual == b'Hello, UTF-8 world!\n'
-
-    def test_read_text(self):
-        files = resources.files(self.data)
-        actual = files.joinpath('utf-8.file').read_text(encoding='utf-8')
-        assert actual == 'Hello, UTF-8 world!\n'
-
-    @unittest.skipUnless(
-        hasattr(typing, 'runtime_checkable'),
-        "Only suitable when typing supports runtime_checkable",
-    )
-    def test_traversable(self):
-        assert isinstance(resources.files(self.data), Traversable)
-
-    def test_old_parameter(self):
-        """
-        Files used to take a 'package' parameter. Make sure anyone
-        passing by name is still supported.
-        """
-        with suppress_known_deprecation():
-            resources.files(package=self.data)
-
-
-class OpenDiskTests(FilesTests, unittest.TestCase):
-    def setUp(self):
-        self.data = data01
-
-
-class OpenZipTests(FilesTests, util.ZipSetup, unittest.TestCase):
-    pass
-
-
-class OpenNamespaceTests(FilesTests, unittest.TestCase):
-    def setUp(self):
-        from . import namespacedata01
-
-        self.data = namespacedata01
-
-
-class SiteDir:
-    def setUp(self):
-        self.fixtures = contextlib.ExitStack()
-        self.addCleanup(self.fixtures.close)
-        self.site_dir = self.fixtures.enter_context(os_helper.temp_dir())
-        self.fixtures.enter_context(import_helper.DirsOnSysPath(self.site_dir))
-        self.fixtures.enter_context(import_helper.CleanImport())
-
-
-class ModulesFilesTests(SiteDir, unittest.TestCase):
-    def test_module_resources(self):
-        """
-        A module can have resources found adjacent to the module.
-        """
-        spec = {
-            'mod.py': '',
-            'res.txt': 'resources are the best',
-        }
-        _path.build(spec, self.site_dir)
-        import mod
-
-        actual = resources.files(mod).joinpath('res.txt').read_text(encoding='utf-8')
-        assert actual == spec['res.txt']
-
-
-class ImplicitContextFilesTests(SiteDir, unittest.TestCase):
-    def test_implicit_files(self):
-        """
-        Without any parameter, files() will infer the location as the caller.
-        """
-        spec = {
-            'somepkg': {
-                '__init__.py': textwrap.dedent(
-                    """
-                    import importlib_resources as res
-                    val = res.files().joinpath('res.txt').read_text(encoding='utf-8')
-                    """
-                ),
-                'res.txt': 'resources are the best',
-            },
-        }
-        _path.build(spec, self.site_dir)
-        assert importlib.import_module('somepkg').val == 'resources are the best'
-
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/spaces/deaaassws/QQsign1/Dockerfile b/spaces/deaaassws/QQsign1/Dockerfile
deleted file mode 100644
index 5b81d3b20c5bee450cf55a0ace7e5c95d58f72af..0000000000000000000000000000000000000000
--- a/spaces/deaaassws/QQsign1/Dockerfile
+++ /dev/null
@@ -1,17 +0,0 @@
-FROM openjdk:11.0-jdk
-
-# 设置时区
-ENV TZ Asia/Shanghai
-
-# 设置工作目录
-WORKDIR /app
-
-# 复制解压包和txlib到工作目录
-COPY unidbg-fetch-qsign /app
-COPY txlib /app/txlib
-
-# 设置命令
-CMD bash bin/unidbg-fetch-qsign --host=0.0.0.0 --port=7860 --count=$COUNT --library=txlib/$TXLIB_VERSION --android_id=$ANDROID_ID
-
-# 暴露端口
-EXPOSE 7860
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/vq_diffusion/pipeline_vq_diffusion.py b/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/vq_diffusion/pipeline_vq_diffusion.py
deleted file mode 100644
index 9147afe127e4b24366249c4a6e058abae9501050..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/vq_diffusion/pipeline_vq_diffusion.py
+++ /dev/null
@@ -1,330 +0,0 @@
-# Copyright 2023 Microsoft and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Callable, List, Optional, Tuple, Union
-
-import torch
-from transformers import CLIPTextModel, CLIPTokenizer
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...models import ModelMixin, Transformer2DModel, VQModel
-from ...schedulers import VQDiffusionScheduler
-from ...utils import logging
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class LearnedClassifierFreeSamplingEmbeddings(ModelMixin, ConfigMixin):
-    """
-    Utility class for storing learned text embeddings for classifier free sampling
-    """
-
-    @register_to_config
-    def __init__(self, learnable: bool, hidden_size: Optional[int] = None, length: Optional[int] = None):
-        super().__init__()
-
-        self.learnable = learnable
-
-        if self.learnable:
-            assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
-            assert length is not None, "learnable=True requires `length` to be set"
-
-            embeddings = torch.zeros(length, hidden_size)
-        else:
-            embeddings = None
-
-        self.embeddings = torch.nn.Parameter(embeddings)
-
-
-class VQDiffusionPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for text-to-image generation using VQ Diffusion
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        vqvae ([`VQModel`]):
-            Vector Quantized Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent
-            representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. VQ Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        transformer ([`Transformer2DModel`]):
-            Conditional transformer to denoise the encoded image latents.
-        scheduler ([`VQDiffusionScheduler`]):
-            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-    """
-
-    vqvae: VQModel
-    text_encoder: CLIPTextModel
-    tokenizer: CLIPTokenizer
-    transformer: Transformer2DModel
-    learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings
-    scheduler: VQDiffusionScheduler
-
-    def __init__(
-        self,
-        vqvae: VQModel,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        transformer: Transformer2DModel,
-        scheduler: VQDiffusionScheduler,
-        learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            vqvae=vqvae,
-            transformer=transformer,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            scheduler=scheduler,
-            learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
-        )
-
-    def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance):
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-
-        # get prompt text embeddings
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-
-        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
-            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
-        prompt_embeds = self.text_encoder(text_input_ids.to(self.device))[0]
-
-        # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
-        # While CLIP does normalize the pooled output of the text transformer when combining
-        # the image and text embeddings, CLIP does not directly normalize the last hidden state.
-        #
-        # CLIP normalizing the pooled output.
-        # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
-        prompt_embeds = prompt_embeds / prompt_embeds.norm(dim=-1, keepdim=True)
-
-        # duplicate text embeddings for each generation per prompt
-        prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-
-        if do_classifier_free_guidance:
-            if self.learned_classifier_free_sampling_embeddings.learnable:
-                negative_prompt_embeds = self.learned_classifier_free_sampling_embeddings.embeddings
-                negative_prompt_embeds = negative_prompt_embeds.unsqueeze(0).repeat(batch_size, 1, 1)
-            else:
-                uncond_tokens = [""] * batch_size
-
-                max_length = text_input_ids.shape[-1]
-                uncond_input = self.tokenizer(
-                    uncond_tokens,
-                    padding="max_length",
-                    max_length=max_length,
-                    truncation=True,
-                    return_tensors="pt",
-                )
-                negative_prompt_embeds = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
-                # See comment for normalizing text embeddings
-                negative_prompt_embeds = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1, keepdim=True)
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        return prompt_embeds
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        num_inference_steps: int = 100,
-        guidance_scale: float = 5.0,
-        truncation_rate: float = 1.0,
-        num_images_per_prompt: int = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-    ) -> Union[ImagePipelineOutput, Tuple]:
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            num_inference_steps (`int`, *optional*, defaults to 100):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            truncation_rate (`float`, *optional*, defaults to 1.0 (equivalent to no truncation)):
-                Used to "truncate" the predicted classes for x_0 such that the cumulative probability for a pixel is at
-                most `truncation_rate`. The lowest probabilities that would increase the cumulative probability above
-                `truncation_rate` are set to zero.
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            generator (`torch.Generator`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor` of shape (batch), *optional*):
-                Pre-generated noisy latents to be used as inputs for image generation. Must be valid embedding indices.
-                Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will
-                be generated of completely masked latent pixels.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~ pipeline_utils.ImagePipelineOutput `] if `return_dict`
-            is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.
-        """
-        if isinstance(prompt, str):
-            batch_size = 1
-        elif isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        batch_size = batch_size * num_images_per_prompt
-
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        prompt_embeds = self._encode_prompt(prompt, num_images_per_prompt, do_classifier_free_guidance)
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        # get the initial completely masked latents unless the user supplied it
-
-        latents_shape = (batch_size, self.transformer.num_latent_pixels)
-        if latents is None:
-            mask_class = self.transformer.num_vector_embeds - 1
-            latents = torch.full(latents_shape, mask_class).to(self.device)
-        else:
-            if latents.shape != latents_shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-            if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
-                raise ValueError(
-                    "Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
-                    f" {self.transformer.num_vector_embeds - 1} (inclusive)."
-                )
-            latents = latents.to(self.device)
-
-        # set timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=self.device)
-
-        timesteps_tensor = self.scheduler.timesteps.to(self.device)
-
-        sample = latents
-
-        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
-            # expand the sample if we are doing classifier free guidance
-            latent_model_input = torch.cat([sample] * 2) if do_classifier_free_guidance else sample
-
-            # predict the un-noised image
-            # model_output == `log_p_x_0`
-            model_output = self.transformer(latent_model_input, encoder_hidden_states=prompt_embeds, timestep=t).sample
-
-            if do_classifier_free_guidance:
-                model_output_uncond, model_output_text = model_output.chunk(2)
-                model_output = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
-                model_output -= torch.logsumexp(model_output, dim=1, keepdim=True)
-
-            model_output = self.truncate(model_output, truncation_rate)
-
-            # remove `log(0)`'s (`-inf`s)
-            model_output = model_output.clamp(-70)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            sample = self.scheduler.step(model_output, timestep=t, sample=sample, generator=generator).prev_sample
-
-            # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, sample)
-
-        embedding_channels = self.vqvae.config.vq_embed_dim
-        embeddings_shape = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
-        embeddings = self.vqvae.quantize.get_codebook_entry(sample, shape=embeddings_shape)
-        image = self.vqvae.decode(embeddings, force_not_quantize=True).sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)
-
-    def truncate(self, log_p_x_0: torch.FloatTensor, truncation_rate: float) -> torch.FloatTensor:
-        """
-        Truncates log_p_x_0 such that for each column vector, the total cumulative probability is `truncation_rate` The
-        lowest probabilities that would increase the cumulative probability above `truncation_rate` are set to zero.
-        """
-        sorted_log_p_x_0, indices = torch.sort(log_p_x_0, 1, descending=True)
-        sorted_p_x_0 = torch.exp(sorted_log_p_x_0)
-        keep_mask = sorted_p_x_0.cumsum(dim=1) < truncation_rate
-
-        # Ensure that at least the largest probability is not zeroed out
-        all_true = torch.full_like(keep_mask[:, 0:1, :], True)
-        keep_mask = torch.cat((all_true, keep_mask), dim=1)
-        keep_mask = keep_mask[:, :-1, :]
-
-        keep_mask = keep_mask.gather(1, indices.argsort(1))
-
-        rv = log_p_x_0.clone()
-
-        rv[~keep_mask] = -torch.inf  # -inf = log(0)
-
-        return rv
diff --git a/spaces/deepghs/anime-ai-detect-fucker/attacker/__init__.py b/spaces/deepghs/anime-ai-detect-fucker/attacker/__init__.py
deleted file mode 100644
index 7e6f6e2589c2aad24e3c86a2a31bba29c2a51794..0000000000000000000000000000000000000000
--- a/spaces/deepghs/anime-ai-detect-fucker/attacker/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .FGSM import *
-from .PGD import *
-from .base import *
diff --git a/spaces/deepset/should-i-follow/utils/config.py b/spaces/deepset/should-i-follow/utils/config.py
deleted file mode 100644
index b7eb91ce4c3c0d05068ccb923e50b5ec724214cd..0000000000000000000000000000000000000000
--- a/spaces/deepset/should-i-follow/utils/config.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import os
-from dotenv import load_dotenv
-
-load_dotenv()
-TWITTER_BEARER = os.getenv('TWITTER_BEARER_TOKEN')
\ No newline at end of file
diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/util/nvdiffrast.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/util/nvdiffrast.py
deleted file mode 100644
index f3245859c650afbfe841a66b74cddefaf28820d9..0000000000000000000000000000000000000000
--- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/util/nvdiffrast.py
+++ /dev/null
@@ -1,126 +0,0 @@
-"""This script is the differentiable renderer for Deep3DFaceRecon_pytorch
-    Attention, antialiasing step is missing in current version.
-"""
-import pytorch3d.ops
-import torch
-import torch.nn.functional as F
-import kornia
-from kornia.geometry.camera import pixel2cam
-import numpy as np
-from typing import List
-from scipy.io import loadmat
-from torch import nn
-
-from pytorch3d.structures import Meshes
-from pytorch3d.renderer import (
-    look_at_view_transform,
-    FoVPerspectiveCameras,
-    DirectionalLights,
-    RasterizationSettings,
-    MeshRenderer,
-    MeshRasterizer,
-    SoftPhongShader,
-    TexturesUV,
-)
-
-# def ndc_projection(x=0.1, n=1.0, f=50.0):
-#     return np.array([[n/x,    0,            0,              0],
-#                      [  0, n/-x,            0,              0],
-#                      [  0,    0, -(f+n)/(f-n), -(2*f*n)/(f-n)],
-#                      [  0,    0,           -1,              0]]).astype(np.float32)
-
-class MeshRenderer(nn.Module):
-    def __init__(self,
-                rasterize_fov,
-                znear=0.1,
-                zfar=10, 
-                rasterize_size=224):
-        super(MeshRenderer, self).__init__()
-
-        # x = np.tan(np.deg2rad(rasterize_fov * 0.5)) * znear
-        # self.ndc_proj = torch.tensor(ndc_projection(x=x, n=znear, f=zfar)).matmul(
-        #         torch.diag(torch.tensor([1., -1, -1, 1])))
-        self.rasterize_size = rasterize_size
-        self.fov = rasterize_fov
-        self.znear = znear
-        self.zfar = zfar
-
-        self.rasterizer = None
-    
-    def forward(self, vertex, tri, feat=None):
-        """
-        Return:
-            mask               -- torch.tensor, size (B, 1, H, W)
-            depth              -- torch.tensor, size (B, 1, H, W)
-            features(optional) -- torch.tensor, size (B, C, H, W) if feat is not None
-
-        Parameters:
-            vertex          -- torch.tensor, size (B, N, 3)
-            tri             -- torch.tensor, size (B, M, 3) or (M, 3), triangles
-            feat(optional)  -- torch.tensor, size (B, N ,C), features
-        """
-        device = vertex.device
-        rsize = int(self.rasterize_size)
-        # ndc_proj = self.ndc_proj.to(device)
-        # trans to homogeneous coordinates of 3d vertices, the direction of y is the same as v
-        if vertex.shape[-1] == 3:
-            vertex = torch.cat([vertex, torch.ones([*vertex.shape[:2], 1]).to(device)], dim=-1)
-            vertex[..., 0] = -vertex[..., 0]
-
-
-        # vertex_ndc = vertex @ ndc_proj.t()
-        if self.rasterizer is None:
-            self.rasterizer = MeshRasterizer()
-            print("create rasterizer on device cuda:%d"%device.index)
-        
-        # ranges = None
-        # if isinstance(tri, List) or len(tri.shape) == 3:
-        #     vum = vertex_ndc.shape[1]
-        #     fnum = torch.tensor([f.shape[0] for f in tri]).unsqueeze(1).to(device)
-        #     fstartidx = torch.cumsum(fnum, dim=0) - fnum
-        #     ranges = torch.cat([fstartidx, fnum], axis=1).type(torch.int32).cpu()
-        #     for i in range(tri.shape[0]):
-        #         tri[i] = tri[i] + i*vum
-        #     vertex_ndc = torch.cat(vertex_ndc, dim=0)
-        #     tri = torch.cat(tri, dim=0)
-
-        # for range_mode vetex: [B*N, 4], tri: [B*M, 3], for instance_mode vetex: [B, N, 4], tri: [M, 3]
-        tri = tri.type(torch.int32).contiguous()
-
-        # rasterize
-        cameras = FoVPerspectiveCameras(
-            device=device,
-            fov=self.fov,
-            znear=self.znear,
-            zfar=self.zfar,
-        )
-
-        raster_settings = RasterizationSettings(
-            image_size=rsize
-        )
-
-        # print(vertex.shape, tri.shape)
-        mesh = Meshes(vertex.contiguous()[...,:3], tri.unsqueeze(0).repeat((vertex.shape[0],1,1)))
-
-        fragments = self.rasterizer(mesh, cameras = cameras, raster_settings = raster_settings)
-        rast_out = fragments.pix_to_face.squeeze(-1)
-        depth = fragments.zbuf
-
-        # render depth
-        depth = depth.permute(0, 3, 1, 2)
-        mask = (rast_out > 0).float().unsqueeze(1)
-        depth = mask * depth
-        
-
-        image = None
-        if feat is not None:
-            attributes = feat.reshape(-1,3)[mesh.faces_packed()]
-            image = pytorch3d.ops.interpolate_face_attributes(fragments.pix_to_face,
-                                                      fragments.bary_coords,
-                                                      attributes)
-            # print(image.shape)
-            image = image.squeeze(-2).permute(0, 3, 1, 2)
-            image = mask * image
-        
-        return mask, depth, image
-
diff --git a/spaces/descript/vampnet/scripts/utils/stage.py b/spaces/descript/vampnet/scripts/utils/stage.py
deleted file mode 100644
index 253e1d070ccf3754be01578d22b65136858fa697..0000000000000000000000000000000000000000
--- a/spaces/descript/vampnet/scripts/utils/stage.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import os
-import subprocess
-from pathlib import Path
-
-import argbind
-import rich
-from audiotools.ml import Experiment
-
-
-@argbind.bind(without_prefix=True)
-def run(
-    run_dir: str = os.getenv("PATH_TO_RUNS", "runs"),
-    name: str = None,
-    recent: bool = False,
-):
-    if recent:
-        paths = sorted(Path(run_dir).iterdir(), key=os.path.getmtime)
-        paths = [p.name for p in paths if p.is_dir()]
-        if paths:
-            name = paths[-1]
-
-    with Experiment(run_dir, name) as exp:
-        exp.snapshot()
-        rich.print(f"Created a snapshot of {exp.parent_directory} at {exp.exp_dir}")
-
-
-if __name__ == "__main__":
-    args = argbind.parse_args()
-    with argbind.scope(args):
-        run()
diff --git a/spaces/diacanFperku/AutoGPT/Autodesk AutoCAD 2017 (x64) Key REPACK Crack.md b/spaces/diacanFperku/AutoGPT/Autodesk AutoCAD 2017 (x64) Key REPACK Crack.md
deleted file mode 100644
index fbf95446c62c5bd8959c5b5c287bbeadb7721edb..0000000000000000000000000000000000000000
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diff --git a/spaces/dineshreddy/WALT/mmdet/core/utils/misc.py b/spaces/dineshreddy/WALT/mmdet/core/utils/misc.py
deleted file mode 100644
index 3e22c7b9085317b61a25c67d361f7e70df65bed1..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/core/utils/misc.py
+++ /dev/null
@@ -1,61 +0,0 @@
-from functools import partial
-
-import numpy as np
-import torch
-from six.moves import map, zip
-
-from ..mask.structures import BitmapMasks, PolygonMasks
-
-
-def multi_apply(func, *args, **kwargs):
-    """Apply function to a list of arguments.
-
-    Note:
-        This function applies the ``func`` to multiple inputs and
-        map the multiple outputs of the ``func`` into different
-        list. Each list contains the same type of outputs corresponding
-        to different inputs.
-
-    Args:
-        func (Function): A function that will be applied to a list of
-            arguments
-
-    Returns:
-        tuple(list): A tuple containing multiple list, each list contains \
-            a kind of returned results by the function
-    """
-    pfunc = partial(func, **kwargs) if kwargs else func
-    map_results = map(pfunc, *args)
-    return tuple(map(list, zip(*map_results)))
-
-
-def unmap(data, count, inds, fill=0):
-    """Unmap a subset of item (data) back to the original set of items (of size
-    count)"""
-    if data.dim() == 1:
-        ret = data.new_full((count, ), fill)
-        ret[inds.type(torch.bool)] = data
-    else:
-        new_size = (count, ) + data.size()[1:]
-        ret = data.new_full(new_size, fill)
-        ret[inds.type(torch.bool), :] = data
-    return ret
-
-
-def mask2ndarray(mask):
-    """Convert Mask to ndarray..
-
-    Args:
-        mask (:obj:`BitmapMasks` or :obj:`PolygonMasks` or
-        torch.Tensor or np.ndarray): The mask to be converted.
-
-    Returns:
-        np.ndarray: Ndarray mask of shape (n, h, w) that has been converted
-    """
-    if isinstance(mask, (BitmapMasks, PolygonMasks)):
-        mask = mask.to_ndarray()
-    elif isinstance(mask, torch.Tensor):
-        mask = mask.detach().cpu().numpy()
-    elif not isinstance(mask, np.ndarray):
-        raise TypeError(f'Unsupported {type(mask)} data type')
-    return mask
diff --git a/spaces/dineshreddy/WALT/mmdet/models/necks/fpn.py b/spaces/dineshreddy/WALT/mmdet/models/necks/fpn.py
deleted file mode 100644
index 5e5dfe685964f06e7a66b63a13e66162e63fcafd..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/necks/fpn.py
+++ /dev/null
@@ -1,221 +0,0 @@
-import warnings
-
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, xavier_init
-from mmcv.runner import auto_fp16
-
-from ..builder import NECKS
-
-
-@NECKS.register_module()
-class FPN(nn.Module):
-    r"""Feature Pyramid Network.
-
-    This is an implementation of paper `Feature Pyramid Networks for Object
-    Detection <https://arxiv.org/abs/1612.03144>`_.
-
-    Args:
-        in_channels (List[int]): Number of input channels per scale.
-        out_channels (int): Number of output channels (used at each scale)
-        num_outs (int): Number of output scales.
-        start_level (int): Index of the start input backbone level used to
-            build the feature pyramid. Default: 0.
-        end_level (int): Index of the end input backbone level (exclusive) to
-            build the feature pyramid. Default: -1, which means the last level.
-        add_extra_convs (bool | str): If bool, it decides whether to add conv
-            layers on top of the original feature maps. Default to False.
-            If True, its actual mode is specified by `extra_convs_on_inputs`.
-            If str, it specifies the source feature map of the extra convs.
-            Only the following options are allowed
-
-            - 'on_input': Last feat map of neck inputs (i.e. backbone feature).
-            - 'on_lateral':  Last feature map after lateral convs.
-            - 'on_output': The last output feature map after fpn convs.
-        extra_convs_on_inputs (bool, deprecated): Whether to apply extra convs
-            on the original feature from the backbone. If True,
-            it is equivalent to `add_extra_convs='on_input'`. If False, it is
-            equivalent to set `add_extra_convs='on_output'`. Default to True.
-        relu_before_extra_convs (bool): Whether to apply relu before the extra
-            conv. Default: False.
-        no_norm_on_lateral (bool): Whether to apply norm on lateral.
-            Default: False.
-        conv_cfg (dict): Config dict for convolution layer. Default: None.
-        norm_cfg (dict): Config dict for normalization layer. Default: None.
-        act_cfg (str): Config dict for activation layer in ConvModule.
-            Default: None.
-        upsample_cfg (dict): Config dict for interpolate layer.
-            Default: `dict(mode='nearest')`
-
-    Example:
-        >>> import torch
-        >>> in_channels = [2, 3, 5, 7]
-        >>> scales = [340, 170, 84, 43]
-        >>> inputs = [torch.rand(1, c, s, s)
-        ...           for c, s in zip(in_channels, scales)]
-        >>> self = FPN(in_channels, 11, len(in_channels)).eval()
-        >>> outputs = self.forward(inputs)
-        >>> for i in range(len(outputs)):
-        ...     print(f'outputs[{i}].shape = {outputs[i].shape}')
-        outputs[0].shape = torch.Size([1, 11, 340, 340])
-        outputs[1].shape = torch.Size([1, 11, 170, 170])
-        outputs[2].shape = torch.Size([1, 11, 84, 84])
-        outputs[3].shape = torch.Size([1, 11, 43, 43])
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 num_outs,
-                 start_level=0,
-                 end_level=-1,
-                 add_extra_convs=False,
-                 extra_convs_on_inputs=True,
-                 relu_before_extra_convs=False,
-                 no_norm_on_lateral=False,
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 act_cfg=None,
-                 upsample_cfg=dict(mode='nearest')):
-        super(FPN, self).__init__()
-        assert isinstance(in_channels, list)
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.num_ins = len(in_channels)
-        self.num_outs = num_outs
-        self.relu_before_extra_convs = relu_before_extra_convs
-        self.no_norm_on_lateral = no_norm_on_lateral
-        self.fp16_enabled = False
-        self.upsample_cfg = upsample_cfg.copy()
-
-        if end_level == -1:
-            self.backbone_end_level = self.num_ins
-            assert num_outs >= self.num_ins - start_level
-        else:
-            # if end_level < inputs, no extra level is allowed
-            self.backbone_end_level = end_level
-            assert end_level <= len(in_channels)
-            assert num_outs == end_level - start_level
-        self.start_level = start_level
-        self.end_level = end_level
-        self.add_extra_convs = add_extra_convs
-        assert isinstance(add_extra_convs, (str, bool))
-        if isinstance(add_extra_convs, str):
-            # Extra_convs_source choices: 'on_input', 'on_lateral', 'on_output'
-            assert add_extra_convs in ('on_input', 'on_lateral', 'on_output')
-        elif add_extra_convs:  # True
-            if extra_convs_on_inputs:
-                # TODO: deprecate `extra_convs_on_inputs`
-                warnings.simplefilter('once')
-                warnings.warn(
-                    '"extra_convs_on_inputs" will be deprecated in v2.9.0,'
-                    'Please use "add_extra_convs"', DeprecationWarning)
-                self.add_extra_convs = 'on_input'
-            else:
-                self.add_extra_convs = 'on_output'
-
-        self.lateral_convs = nn.ModuleList()
-        self.fpn_convs = nn.ModuleList()
-
-        for i in range(self.start_level, self.backbone_end_level):
-            l_conv = ConvModule(
-                in_channels[i],
-                out_channels,
-                1,
-                conv_cfg=conv_cfg,
-                norm_cfg=norm_cfg if not self.no_norm_on_lateral else None,
-                act_cfg=act_cfg,
-                inplace=False)
-            fpn_conv = ConvModule(
-                out_channels,
-                out_channels,
-                3,
-                padding=1,
-                conv_cfg=conv_cfg,
-                norm_cfg=norm_cfg,
-                act_cfg=act_cfg,
-                inplace=False)
-
-            self.lateral_convs.append(l_conv)
-            self.fpn_convs.append(fpn_conv)
-
-        # add extra conv layers (e.g., RetinaNet)
-        extra_levels = num_outs - self.backbone_end_level + self.start_level
-        if self.add_extra_convs and extra_levels >= 1:
-            for i in range(extra_levels):
-                if i == 0 and self.add_extra_convs == 'on_input':
-                    in_channels = self.in_channels[self.backbone_end_level - 1]
-                else:
-                    in_channels = out_channels
-                extra_fpn_conv = ConvModule(
-                    in_channels,
-                    out_channels,
-                    3,
-                    stride=2,
-                    padding=1,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg,
-                    inplace=False)
-                self.fpn_convs.append(extra_fpn_conv)
-
-    # default init_weights for conv(msra) and norm in ConvModule
-    def init_weights(self):
-        """Initialize the weights of FPN module."""
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                xavier_init(m, distribution='uniform')
-
-    @auto_fp16()
-    def forward(self, inputs):
-        """Forward function."""
-        assert len(inputs) == len(self.in_channels)
-
-        # build laterals
-        laterals = [
-            lateral_conv(inputs[i + self.start_level])
-            for i, lateral_conv in enumerate(self.lateral_convs)
-        ]
-
-        # build top-down path
-        used_backbone_levels = len(laterals)
-        for i in range(used_backbone_levels - 1, 0, -1):
-            # In some cases, fixing `scale factor` (e.g. 2) is preferred, but
-            #  it cannot co-exist with `size` in `F.interpolate`.
-            if 'scale_factor' in self.upsample_cfg:
-                laterals[i - 1] += F.interpolate(laterals[i],
-                                                 **self.upsample_cfg)
-            else:
-                prev_shape = laterals[i - 1].shape[2:]
-                laterals[i - 1] += F.interpolate(
-                    laterals[i], size=prev_shape, **self.upsample_cfg)
-
-        # build outputs
-        # part 1: from original levels
-        outs = [
-            self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels)
-        ]
-        # part 2: add extra levels
-        if self.num_outs > len(outs):
-            # use max pool to get more levels on top of outputs
-            # (e.g., Faster R-CNN, Mask R-CNN)
-            if not self.add_extra_convs:
-                for i in range(self.num_outs - used_backbone_levels):
-                    outs.append(F.max_pool2d(outs[-1], 1, stride=2))
-            # add conv layers on top of original feature maps (RetinaNet)
-            else:
-                if self.add_extra_convs == 'on_input':
-                    extra_source = inputs[self.backbone_end_level - 1]
-                elif self.add_extra_convs == 'on_lateral':
-                    extra_source = laterals[-1]
-                elif self.add_extra_convs == 'on_output':
-                    extra_source = outs[-1]
-                else:
-                    raise NotImplementedError
-                outs.append(self.fpn_convs[used_backbone_levels](extra_source))
-                for i in range(used_backbone_levels + 1, self.num_outs):
-                    if self.relu_before_extra_convs:
-                        outs.append(self.fpn_convs[i](F.relu(outs[-1])))
-                    else:
-                        outs.append(self.fpn_convs[i](outs[-1]))
-        return tuple(outs)
diff --git a/spaces/dinhminh20521597/OCR_DEMO/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py b/spaces/dinhminh20521597/OCR_DEMO/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py
deleted file mode 100644
index 251b7bc2faaaa254766e0902c4238b2917f0d230..0000000000000000000000000000000000000000
--- a/spaces/dinhminh20521597/OCR_DEMO/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py
+++ /dev/null
@@ -1,35 +0,0 @@
-_base_ = [
-    '../../_base_/default_runtime.py',
-    '../../_base_/schedules/schedule_sgd_1200e.py',
-    '../../_base_/det_models/dbnet_r50dcnv2_fpnc.py',
-    '../../_base_/det_datasets/icdar2015.py',
-    '../../_base_/det_pipelines/dbnet_pipeline.py'
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline_r50dcnv2 = {{_base_.train_pipeline_r50dcnv2}}
-test_pipeline_4068_1024 = {{_base_.test_pipeline_4068_1024}}
-
-load_from = 'checkpoints/textdet/dbnet/res50dcnv2_synthtext.pth'
-
-data = dict(
-    samples_per_gpu=8,
-    workers_per_gpu=4,
-    val_dataloader=dict(samples_per_gpu=1),
-    test_dataloader=dict(samples_per_gpu=1),
-    train=dict(
-        type='UniformConcatDataset',
-        datasets=train_list,
-        pipeline=train_pipeline_r50dcnv2),
-    val=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline_4068_1024),
-    test=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline_4068_1024))
-
-evaluation = dict(interval=100, metric='hmean-iou')
diff --git a/spaces/dragao-elastico/RVC_V2/i18n/locale_diff.py b/spaces/dragao-elastico/RVC_V2/i18n/locale_diff.py
deleted file mode 100644
index 257277965e0866a86d0361863a8f1b408c4f71ab..0000000000000000000000000000000000000000
--- a/spaces/dragao-elastico/RVC_V2/i18n/locale_diff.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import json
-import os
-from collections import OrderedDict
-
-# Define the standard file name
-standard_file = "zh_CN.json"
-
-# Find all JSON files in the directory
-dir_path = "./"
-languages = [
-    f for f in os.listdir(dir_path) if f.endswith(".json") and f != standard_file
-]
-
-# Load the standard file
-with open(standard_file, "r", encoding="utf-8") as f:
-    standard_data = json.load(f, object_pairs_hook=OrderedDict)
-
-# Loop through each language file
-for lang_file in languages:
-    # Load the language file
-    with open(lang_file, "r", encoding="utf-8") as f:
-        lang_data = json.load(f, object_pairs_hook=OrderedDict)
-
-    # Find the difference between the language file and the standard file
-    diff = set(standard_data.keys()) - set(lang_data.keys())
-
-    miss = set(lang_data.keys()) - set(standard_data.keys())
-
-    # Add any missing keys to the language file
-    for key in diff:
-        lang_data[key] = key
-
-    # Del any extra keys to the language file
-    for key in miss:
-        del lang_data[key]
-
-    # Sort the keys of the language file to match the order of the standard file
-    lang_data = OrderedDict(
-        sorted(lang_data.items(), key=lambda x: list(standard_data.keys()).index(x[0]))
-    )
-
-    # Save the updated language file
-    with open(lang_file, "w", encoding="utf-8") as f:
-        json.dump(lang_data, f, ensure_ascii=False, indent=4)
-        f.write("\n")
diff --git a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp b/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp
deleted file mode 100644
index 551243fdadfd1682b5dc6628623b67a79b3f6c74..0000000000000000000000000000000000000000
--- a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp
+++ /dev/null
@@ -1,43 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-#include <vector>
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-
-namespace groundingdino {
-
-at::Tensor
-ms_deform_attn_cpu_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step)
-{
-    AT_ERROR("Not implement on cpu");
-}
-
-std::vector<at::Tensor>
-ms_deform_attn_cpu_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step)
-{
-    AT_ERROR("Not implement on cpu");
-}
-
-} // namespace groundingdino
diff --git a/spaces/dvc890/go-chatgpt-api/util/logger/logger.go b/spaces/dvc890/go-chatgpt-api/util/logger/logger.go
deleted file mode 100644
index 64ae236231ba72701f6385a8a6b3984cc96fdcf2..0000000000000000000000000000000000000000
--- a/spaces/dvc890/go-chatgpt-api/util/logger/logger.go
+++ /dev/null
@@ -1,37 +0,0 @@
-package logger
-
-import (
-	"fmt"
-
-	"github.com/sirupsen/logrus"
-)
-
-func init() {
-	logrus.SetFormatter(&logrus.TextFormatter{
-		ForceColors: true,
-	})
-}
-
-func Ansi(colorString string) func(...interface{}) string {
-	return func(args ...interface{}) string {
-		return fmt.Sprintf(colorString, fmt.Sprint(args...))
-	}
-}
-
-var (
-	Green  = Ansi("\033[1;32m%s\033[0m")
-	Yellow = Ansi("\033[1;33m%s\033[0m")
-	Red    = Ansi("\033[1;31m%s\033[0m")
-)
-
-func Info(msg string) {
-	logrus.Info(Green(msg))
-}
-
-func Warn(msg string) {
-	logrus.Warn(Yellow(msg))
-}
-
-func Error(msg string) {
-	logrus.Error(Red(msg))
-}
diff --git a/spaces/ennet/ChatDev/chatdev/statistics.py b/spaces/ennet/ChatDev/chatdev/statistics.py
deleted file mode 100644
index 4c082e294f7e2a7033a88e16b0a3c3da3a6bc9ad..0000000000000000000000000000000000000000
--- a/spaces/ennet/ChatDev/chatdev/statistics.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import os
-
-import numpy as np
-
-
-def get_info(dir, log_filepath):
-    print("dir:", dir)
-
-    version_updates = -1
-    num_code_files = -1
-    num_png_files = -1
-    num_doc_files = -1
-    code_lines = -1
-    env_lines = -1
-    manual_lines = -1
-    duration = -1
-    num_utterance = -1
-    num_reflection = -1
-    num_prompt_tokens = -1
-    num_completion_tokens = -1
-    num_total_tokens = -1
-
-    if os.path.exists(dir):
-        filenames = os.listdir(dir)
-        # print(filenames)
-
-        num_code_files = len([filename for filename in filenames if filename.endswith(".py")])
-        # print("num_code_files:", num_code_files)
-
-        num_png_files = len([filename for filename in filenames if filename.endswith(".png")])
-        # print("num_png_files:", num_png_files)
-
-        num_doc_files = 0
-        for filename in filenames:
-            if filename.endswith(".py") or filename.endswith(".png"):
-                continue
-            if os.path.isfile(os.path.join(dir, filename)):
-                # print(filename)
-                num_doc_files += 1
-        # print("num_doc_files:", num_doc_files)
-
-        if "meta.txt" in filenames:
-            lines = open(os.path.join(dir, "meta.txt"), "r", encoding="utf8").read().split("\n")
-            version_updates = float([lines[i + 1] for i, line in enumerate(lines) if "Code_Version" in line][0]) + 1
-        else:
-            version_updates = -1
-        # print("version_updates: ", version_updates)
-
-        if "requirements.txt" in filenames:
-            lines = open(os.path.join(dir, "requirements.txt"), "r", encoding="utf8").read().split("\n")
-            env_lines = len([line for line in lines if len(line.strip()) > 0])
-        else:
-            env_lines = -1
-        # print("env_lines:", env_lines)
-
-        if "manual.md" in filenames:
-            lines = open(os.path.join(dir, "manual.md"), "r", encoding="utf8").read().split("\n")
-            manual_lines = len([line for line in lines if len(line.strip()) > 0])
-        else:
-            manual_lines = -1
-        # print("manual_lines:", manual_lines)
-
-        code_lines = 0
-        for filename in filenames:
-            if filename.endswith(".py"):
-                # print("......filename:", filename)
-                lines = open(os.path.join(dir, filename), "r", encoding="utf8").read().split("\n")
-                code_lines += len([line for line in lines if len(line.strip()) > 0])
-        # print("code_lines:", code_lines)
-
-        lines = open(log_filepath, "a+", encoding="utf8").read().split("\n")
-        start_lines = [line for line in lines if "**[Start Chat]**" in line]
-        chat_lines = [line for line in lines if "<->" in line]
-        num_utterance = len(start_lines) + len(chat_lines)
-        # print("num_utterance:", num_utterance)
-
-        lines = open(log_filepath, "r", encoding="utf8").read().split("\n")
-        sublines = [line for line in lines if line.startswith("prompt_tokens:")]
-        if len(sublines) > 0:
-            nums = [int(line.split(": ")[-1]) for line in sublines]
-            num_prompt_tokens = np.sum(nums)
-            # print("num_prompt_tokens:", num_prompt_tokens)
-
-        lines = open(log_filepath, "r", encoding="utf8").read().split("\n")
-        sublines = [line for line in lines if line.startswith("completion_tokens:")]
-        if len(sublines) > 0:
-            nums = [int(line.split(": ")[-1]) for line in sublines]
-            num_completion_tokens = np.sum(nums)
-            # print("num_completion_tokens:", num_completion_tokens)
-
-        lines = open(log_filepath, "r", encoding="utf8").read().split("\n")
-        sublines = [line for line in lines if line.startswith("total_tokens:")]
-        if len(sublines) > 0:
-            nums = [int(line.split(": ")[-1]) for line in sublines]
-            num_total_tokens = np.sum(nums)
-            # print("num_total_tokens:", num_total_tokens)
-
-        lines = open(log_filepath, "r", encoding="utf8").read().split("\n")
-
-        lines = open(log_filepath, "r", encoding="utf8").read().split("\n")
-        num_reflection = 0
-        for line in lines:
-            if "on : Reflection" in line:
-                num_reflection += 1
-        # print("num_reflection:", num_reflection)
-
-    cost = 0.0
-    if num_png_files != -1:
-        cost += num_png_files * 0.016
-    if num_prompt_tokens != -1:
-        cost += num_prompt_tokens * 0.003 / 1000.0
-    if num_completion_tokens != -1:
-        cost += num_completion_tokens * 0.004 / 1000.0
-
-    # info = f"🕑duration={duration}s 💰cost=${cost} 🔨version_updates={version_updates} 📃num_code_files={num_code_files} 🏞num_png_files={num_png_files} 📚num_doc_files={num_doc_files} 📃code_lines={code_lines} 📋env_lines={env_lines} 📒manual_lines={manual_lines} 🗣num_utterances={num_utterance} 🤔num_self_reflections={num_reflection} ❓num_prompt_tokens={num_prompt_tokens} ❗num_completion_tokens={num_completion_tokens} ⁉️num_total_tokens={num_total_tokens}"
-
-    info = "\n\n💰**cost**=${:.6f}\n\n🔨**version_updates**={}\n\n📃**num_code_files**={}\n\n🏞**num_png_files**={}\n\n📚**num_doc_files**={}\n\n📃**code_lines**={}\n\n📋**env_lines**={}\n\n📒**manual_lines**={}\n\n🗣**num_utterances**={}\n\n🤔**num_self_reflections**={}\n\n❓**num_prompt_tokens**={}\n\n❗**num_completion_tokens**={}\n\n🌟**num_total_tokens**={}" \
-        .format(cost,
-                version_updates,
-                num_code_files,
-                num_png_files,
-                num_doc_files,
-                code_lines,
-                env_lines,
-                manual_lines,
-                num_utterance,
-                num_reflection,
-                num_prompt_tokens,
-                num_completion_tokens,
-                num_total_tokens)
-
-    return info
diff --git a/spaces/eskayML/object_detection_system/README.md b/spaces/eskayML/object_detection_system/README.md
deleted file mode 100644
index 4367992aed46f8dd1ddc3d08e8f877bc2e9dc571..0000000000000000000000000000000000000000
--- a/spaces/eskayML/object_detection_system/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Object Detection System
-emoji: 👁
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/eson/tokenizer-arena/utils/oov_util.py b/spaces/eson/tokenizer-arena/utils/oov_util.py
deleted file mode 100644
index ea7d0c2fe3c7418ba36a11a0abdcd69d29acc91a..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/utils/oov_util.py
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
-
-import os
-
-CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
-
-space_tokens = ["空格 ，两个空格  ，三个空格    ，制表符\t,换行符\n"]
-
-
-
-docs = [line.strip() for line in open(os.path.join(CURRENT_DIR, "test.txt"), "r", encoding="utf-8")]
diff --git a/spaces/evaluate-metric/bleu/app.py b/spaces/evaluate-metric/bleu/app.py
deleted file mode 100644
index fa8a6817380386120c3ef8ce492a9a962a0c22bc..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/bleu/app.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import evaluate
-from evaluate.utils import launch_gradio_widget
-
-
-module = evaluate.load("bleu")
-launch_gradio_widget(module)
diff --git a/spaces/falterWliame/Face_Mask_Detection/Akruti 7.0 Software Free 52.md b/spaces/falterWliame/Face_Mask_Detection/Akruti 7.0 Software Free 52.md
deleted file mode 100644
index 896b70e504597b33113f9d8a8868898a39f82f5e..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Akruti 7.0 Software Free 52.md	
+++ /dev/null
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Cell 211 Dual Audio Hindi.md b/spaces/falterWliame/Face_Mask_Detection/Cell 211 Dual Audio Hindi.md
deleted file mode 100644
index b008d63f43d85c339f2792e275a7d71e044f967c..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Cell 211 Dual Audio Hindi.md	
+++ /dev/null
@@ -1,64 +0,0 @@
-<h2>Cell 211 Dual Audio Hindi</h2><br /><p><b><b>Download Zip</b> &rArr;&rArr;&rArr; <a href="https://urlca.com/2uDcp3">https://urlca.com/2uDcp3</a></b></p><br /><br />
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-* Awards: 
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-Movies.com - 
-
-IMDb - 
-
-Amazon - 
-
-Google Play - 
-
-Here is the link to my code on GitHub
-
-A:
-
-Since you are using GooglePlayServices you can use this Library for your problem. 
-
-Following is the GoogleMap code, where i have used following method:
-
-/**
-
- * Method to handle the map click event
-
- * 
-
- * @param latLng
-
- *            GoogleMap LatLng point
-
- */
-
-private void handleMapClick(LatLng latLng) {
-
-    // Removes the marker and clears the polyline
-
-    marker.remove();
-
-    polyline.remove();
-
-    // Creates a marker in the centre of the map with a marker icon
-
-    marker = mMap.addMarker(new MarkerOptions()
-
-           .title(name)
-
-           .snippet(address)
-
-           .position(latLng)
-
-           .icon(BitmapDescriptorFactory
-
-                   .defaultMarker(BitmapDescriptorFactory.HUE_GREEN)));
-
-    // Appends a new path
-
-    polyline = mMap.addPolyline(new PolylineOptions()
-
-           .add(latLng, latLng)
-
-           .color(Color.BLUE) 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/falterWliame/Face_Mask_Detection/Omnipage Pro 16 Portable Free Download.md b/spaces/falterWliame/Face_Mask_Detection/Omnipage Pro 16 Portable Free Download.md
deleted file mode 100644
index 826867016c0244331f6fc18c82a69ce7311d179a..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Omnipage Pro 16 Portable Free Download.md	
+++ /dev/null
@@ -1,58 +0,0 @@
-<br />
-<h1>Omnipage Pro 16 Portable Free Download</h1>
-<p>If you are looking for a way to convert scanned documents and images into editable text files, you might want to try Omnipage Pro 16 Portable. This is a software that uses optical character recognition (OCR) technology to recognize and extract text from various sources, such as paper documents, PDF files, and digital camera pictures. You can then edit, save, or share the converted text files in various formats, such as Word, Excel, PowerPoint, HTML, PDF, and more.</p>
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-<p>Omnipage Pro 16 Portable has the same features and functions as Omnipage Pro 16, which is one of the most popular and powerful OCR software in the market. It can handle any type of document and image, regardless of the size, quality, layout, or language. It can also process multiple documents at once and batch convert them into your desired format.</p>
-<h2>How to Download Omnipage Pro 16 Portable for Free?</h2>
-<p>There are two ways to download Omnipage Pro 16 Portable for free, depending on your eligibility and preference.</p>
-<p>The first way is to use your Nuance Account at nuance.com. This method is available for customers who have purchased or subscribed to Omnipage Pro 16 or any other Nuance product. You can also use this method if you are an education customer and have an education license. To do this, you need to sign in to your Nuance Account, find your product under My Products, click the Download button and select Omnipage Pro 16 Portable.</p>
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-<p>The second way is to use a third-party website that offers free downloads of Omnipage Pro 16 Portable. This method is available for anyone who wants to try the software without paying anything. However, you need to be careful when using this method, as some websites may contain viruses, malware, or other harmful content. You also need to check the legality and authenticity of the website and the software before downloading it.</p>
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-<p>Omnipage Pro 16 Portable is a software that can help you convert scanned documents and images into editable text files with ease and accuracy. Some of the benefits and features of this software are:</p>
-<ul>
-<li>It supports over 120 languages and dialects, including Arabic, Chinese, Japanese, Korean, Russian, Greek, Hebrew, and more.</li>
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-<li>It has a variety of input and output options, such as scanners, cameras, fax machines, email attachments, network folders, cloud services, web pages, etc.</li>
-<li>It has a high recognition accuracy that preserves the original formatting and layout of the document or image.</li>
-<li>It has a powerful editing and proofreading tools that let you correct any errors or make any changes to the converted text.</li>
-<li>It has a flexible conversion options that let you customize the output format, quality, size, resolution, compression, etc.</li>
-<li>It has a smart automation features that let you schedule conversions, create workflows, apply profiles, etc.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Omnipage Pro 16 Portable is a software that can help you convert scanned documents and images into editable text files. You can download it for free if you are eligible and follow the steps in this article. You can also learn more about the benefits and features of this software, as well as some tips and tricks on how to use it effectively. We hope this article was helpful and informative. If you have any questions or feedback, please leave a comment below.</p>
-<h2>How to Troubleshoot Omnipage Pro 16 Portable?</h2>
-<p>Omnipage Pro 16 Portable is a software that usually works smoothly and efficiently. However, sometimes you may encounter some problems or errors that prevent you from using it properly. Here are some common issues and solutions that can help you troubleshoot Omnipage Pro 16 Portable:</p>
-<ul>
-<li>Issue: The software does not recognize your scanner or camera.</li>
-<li>Solution: Make sure that your scanner or camera is connected and turned on. Check the device driver and update it if necessary. Try using a different USB port or cable. Restart your computer and the software.</li>
-<li>Issue: The software does not convert your document or image correctly.</li>
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-<li>Issue: The software crashes or freezes during the conversion process.</li>
-<li>Solution: Make sure that your computer meets the minimum system requirements for the software. Close any other applications that may be running in the background. Scan your computer for viruses or malware. Reinstall the software if necessary.</li>
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-<p>If you have used Omnipage Pro 16 Portable and want to give feedback on this software, you can do so in several ways. Giving feedback is a way of expressing your opinion and suggestion on how to improve the quality, performance, features, etc. of this software. Here are some ways of giving feedback on Omnipage Pro 16 Portable:</p>
-<ul>
-<li>Rate and review: You can rate and review Omnipage Pro 16 Portable on the official website of Nuance Communications or on any other website that offers this software. You can give a star rating from one to five and write a brief comment on what you like or dislike about this software.</li>
-<li>Contact support: You can contact support on the official website of Nuance Communications or on any other website that offers this software. You can use the chat option, the phone number, the email address, or the support request form to communicate with an expert or a representative who can assist you with your feedback.</li>
-<li>Join community: You can join community on the official website of Nuance Communications or on any other website that offers this software. You can access online resources such as articles, tutorials, videos, forums, and FAQs that can help you with your feedback. You can also interact with other users and developers who can share their feedback with you.</li>
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diff --git a/spaces/fatiXbelha/sd/Craftsman 4 APK Download - The Most Popular App from Slbzer for Android.md b/spaces/fatiXbelha/sd/Craftsman 4 APK Download - The Most Popular App from Slbzer for Android.md
deleted file mode 100644
index af7b1f172ab016d0da603570e7caf5d64d4f9191..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Craftsman 4 APK Download - The Most Popular App from Slbzer for Android.md	
+++ /dev/null
@@ -1,109 +0,0 @@
-<br />
-<h1>Craftsman 4 APK Download: A Guide for Android Users</h1>
-<p>If you are looking for a fun and creative game that lets you build your own world in 3D, you might want to try Craftsman 4. This game is a sequel to the popular LokiCraft series, and it offers more features, more blocks, and more possibilities. In this article, we will tell you what Craftsman 4 is, how to play it, and how to download it for your Android device.</p>
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-<li>Use the creative mode to unleash your imagination and build anything you want without any limitations.</li>
-<li>Use the survival mode to challenge yourself and survive in a hostile environment with limited resources and enemies.</li>
-<li>Use the multiplayer mode to join other players online and collaborate or compete with them in different worlds.</li>
-<li>Use the map button to see your location and the surrounding areas.</li>
-<li>Use the settings button to adjust the game options, such as sound, graphics, language, and controls.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Craftsman 4 is a game that lets you create your own world in 3D. It has realistic graphics, various blocks, different modes, and user-friendly controls. It is a game that can keep you entertained for hours. If you want to play Craftsman 4 on your Android device, you can download its APK file from a reliable source and install it on your device. Just follow the requirements and steps that we have provided in this article, and you will be able to enjoy Craftsman 4 in no time.</p>
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-<table>
-<tr><td><b>Question</b></td><td><b>Answer</b></td></tr>
-<tr><td>Is Craftsman 4 APK safe to download?</td><td>Yes, as long as you download it from a trusted website that does not contain any malware or viruses. You should also scan the file before installing it on your device.</td></tr>
-<tr><td>Is Craftsman 4 APK free to download?</td><td>Yes, Craftsman 4 APK is free to download and play. However, some features may require in-app purchases or ads to unlock.</td></tr>
-<tr><td>Can I play Craftsman 4 APK offline?</td><td>Yes, you can play Craftsman 4 APK offline in the survival mode or the creative mode. However, you will need an internet connection to play the multiplayer mode or access some online features.</td></tr>
-<tr><td>Can I play Craftsman 4 APK on other devices?</td><td>Craftsman 4 APK is designed for Android devices only. If you want to play it on other devices, such as PC or iOS, you will need to use an emulator or a different version of the game.</td></tr>
-<tr><td>How can I update Craftsman 4 APK?</td><td>You can update Craftsman 4 APK by downloading the latest version of the file from the same website that you downloaded it from. You can also check for updates within the app itself.</td></tr>
-</table></p> 401be4b1e0<br />
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diff --git a/spaces/fatiXbelha/sd/Download Facebook for Windows 7 A Free and Easy Way to Connect with Friends.md b/spaces/fatiXbelha/sd/Download Facebook for Windows 7 A Free and Easy Way to Connect with Friends.md
deleted file mode 100644
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--- a/spaces/fatiXbelha/sd/Download Facebook for Windows 7 A Free and Easy Way to Connect with Friends.md	
+++ /dev/null
@@ -1,130 +0,0 @@
-
-<h1>Facebook app download for PC Windows 7</h1>
-<p>Facebook is one of the most popular social media platforms in the world, with over 2.8 billion monthly active users. It allows you to connect with your friends, family, and communities of people who share your interests. You can also discover new content, watch videos, play games, shop, and more.</p>
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-<p>If you are a Windows 7 user, you might be wondering how you can download and use Facebook on your PC. In this article, we will show you the benefits of using Facebook on PC, how to download it, how to use it, and some tips and tricks to make the most of it.</p>
- <h2>Benefits of using Facebook on PC</h2>
-<p>There are many reasons why you might want to use Facebook on your PC instead of your mobile device. Here are some of them:</p>
-<h3>Larger screen and keyboard</h3>
-<p>Using Facebook on your PC gives you a bigger and better view of your news feed, photos, videos, and messages. You can also enjoy a more comfortable typing experience with your keyboard, especially if you write long posts or comments.</p>
-<h3>Desktop notifications and shortcuts</h3>
-<p>With the Facebook app for PC, you can get desktop notifications for events, messages, friend requests, and more. You can also create shortcuts on your desktop or taskbar for easy access to your favorite features.</p>
-<h3>Access to more features and settings</h3>
-<p>Some features and settings are only available on the PC version of Facebook. For example, you can edit your profile information, manage your privacy and security settings, download your data, deactivate or delete your account, and more.</p>
- <h2>How to download Facebook on PC Windows 7</h2>
-<p>There are two ways to download Facebook on your PC Windows 7. You can either use the Microsoft Store app or a web browser.</p>
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-<h3>Using Microsoft Store app</h3>
-<p>The Microsoft Store app is a convenient way to download apps for your Windows device. To use it, follow these steps:</p>
-<ol>
-<li>Open the Microsoft Store app from your Start menu or taskbar.</li>
-<li>Search for "Facebook" in the search box.</li>
-<li>Select the Facebook app from the results. It should have a blue icon with a white "f" letter.</li>
-<li>Click on the "Get" button to download the app for free.</li>
-<li>Wait for the installation to complete.</li>
-<li>Launch the app from your Start menu or taskbar.</li>
-</ol>
-<h3>Using a web browser</h3>
-<p>If you don't have the Microsoft Store app or prefer to use a web browser, you can also download Facebook from its official website. To use a web browser, follow these steps:</p>
-<ol>
-<li>Open your preferred web browser, such as Chrome, Firefox, or Edge.</li>
-<li>Go to <a href="">https://www.facebook.com/download</a>.</li>
-<li>Click on the "Download for Windows" button. It should have a blue background and a white arrow.</li>
-<li>Save the file to your desired location.</li>
-<li>Open the file and follow the instructions to install the app.</li>
-<li>Launch the app from your Start menu or taskbar.</li>
-</ol>
- <h2>How to use Facebook on PC Windows 7</h2>
-<p>Once you have downloaded and installed Facebook on your PC Windows 7, you can start using it to connect with your friends and enjoy its features. Here are some basic steps to get you started:</p>
-<h3>Logging in and setting up your profile</h3>
-<p>To use Facebook, you need to have an account. If you already have one, you can log in with your email address or phone number and password. If you don't have one, you can create one by clicking on the "Sign up" button and filling out the form.</p>
-<p>After logging in, you can set up your profile by adding a photo, a cover photo, a bio, and some basic information about yourself. You can also find and add your friends by searching for their names or importing your contacts.</p>
-<h3>Navigating the interface and menu</h3>
-<p>The Facebook app for PC has a simple and user-friendly interface. You can access the main features from the left sidebar, such as Home, Watch, Marketplace, Groups, Gaming, and more. You can also see your notifications, messages, friend requests, and settings from the top right corner.</p>
-<p>The Home page is where you can see your news feed, which shows the posts from your friends and pages that you follow. You can also create your own posts by clicking on the "What's on your mind?" box and choosing what type of content you want to share.</p>
-<h3>Posting, commenting, and reacting</h3>
-<p>One of the main ways to interact with Facebook is by posting, commenting, and reacting to other people's posts. You can post text, photos, videos, links, polls, stories, live videos, and more. You can also comment on other people's posts by typing in the comment box below them. You can also react to posts by clicking on the like button or choosing one of the other emotions.</p>
- <h2>Tips and tricks for using Facebook on PC Windows 7</h2>
-<p>To make the most of your Facebook experience on PC Windows 7, here are some tips and tricks that you might find useful:</p>
-<h3>Customizing your news feed and preferences</h3>
-<p>If you want to see more of what you like and less of what you don't on your news feed, you can customize it by clicking on the three dots icon next to each post and choosing one of the options. For example, you can hide a post, unfollow a person or page, snooze them for 30 days, or see fewer posts like this.</p>
-<p>You can also adjust your news feed preferences by clicking on the gear icon at the top right corner of the Home page and choosing "News Feed Preferences". Here you can prioritize who to see first, unfollow people or pages, reconnect with people or pages you unfollowed, manage your snooze settings, and more.</p>
-<h3>Joining and creating groups</h3>
-<p>Groups are a great way to connect with people who share your interests or hobbies. You can join existing groups by searching for them in the Groups section or by getting invited by other members. You can also create your own group by clicking on the "Create Group" button at the top left corner of the Groups section and filling out the details.</p>
-<p>Once you join or create a group, you can post, comment, react, chat, share files, create events, invite members, and more. You can also manage your group settings by clicking on the three dots icon next to the group name and choosing one of the options. For example, you can edit the group name, description, privacy, rules, notifications, etc.</p>
-<h3>Uploading and sharing photos and videos</h3>
-<p>Photos and videos are some of the most popular types of content on Facebook. You can upload and share them by clicking on the "Photo/Video" button in the "What's on your mind?" box. You can also add filters, stickers, text, or effects to them before posting.</p>
-<p>You can also create albums to organize your photos and videos by clicking on the "Album" button in the same box. You can add a title, description, tags, and location to your album. You can also choose who can see your album and invite others to add photos or videos to it.</p>
- <h2>Conclusion</h2>
-<p>Facebook is a fun and easy way to stay in touch with your friends and family, discover new things, and express yourself. By downloading the Facebook app on your PC Windows 7, you can enjoy a larger and better view of your news feed, photos, videos, and messages. You can also access more features and settings that are only available on the PC version of Facebook.</p>
-<p>To download Facebook on your PC Windows 7, you can either use the Microsoft Store app or a web browser. Once you have installed the app, you can log in with your account or create a new one. Then, you can set up your profile, navigate the interface and menu, post, comment, react, customize your news feed and preferences, join and create groups, upload and share photos and videos, and more.</p>
-<p>We hope this article has helped you learn how to download and use Facebook on your PC Windows 7. If you have any questions or feedback, please let us know in the comments below. Happy Facebooking!</p>
- <h3>FAQs</h3>
-<h4>Is Facebook free to download on PC Windows 7?</h4>
-<p>Yes, Facebook is free to download and use on PC Windows 7. However, you might need an internet connection to access some of its features.</p>
-<h4>Is Facebook safe to use on PC Windows 7?</h4>
-<p>Facebook is generally safe to use on PC Windows 7, as long as you follow some basic security tips. For example, you should use a strong password, enable two-factor authentication, avoid clicking on suspicious links or attachments, review your privacy and security settings, and log out when you are done.</p>
-<h4>How can I update Facebook on PC Windows 7?</h4>
-<p>If you downloaded Facebook from the Microsoft Store app, you can update it by opening the app and clicking on the "Downloads and updates" option in the menu. Then, click on the "Get updates" button and wait for the process to complete.</p>
-<p>If you downloaded Facebook from its website, you can update it by visiting <a href="">https://www.facebook.com/download</a> again and downloading the latest version of the app.</p>
-<h4>How can I delete Facebook on PC Windows 7?</h4>
-<p>If you want to delete Facebook from your PC Windows 7, you can do so by following these steps:</p>
-<ol>
-<li>Open the Control Panel from your Start menu or taskbar.</li>
-<li>Select "Programs" and then "Programs and Features".</li>
-<li>Find and select "Facebook" from the list of programs.</li>
-<li>Click on the "Uninstall" button and follow the instructions to remove the app.</li>
-</ol>
-<h4>How can I contact Facebook support on PC Windows 7?</h4>
-<p>If you need help with using Facebook on PC Windows 7, you can contact Facebook support by visiting <a href="">https://www.facebook.com/help</a>. Here you can find answers to common questions, report a problem, give feedback, or request a feature.</p> 401be4b1e0<br />
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diff --git a/spaces/fatiXbelha/sd/Experience Temple Run 2 on PC with NoxPlayer - The Ultimate Android Gaming Platform.md b/spaces/fatiXbelha/sd/Experience Temple Run 2 on PC with NoxPlayer - The Ultimate Android Gaming Platform.md
deleted file mode 100644
index a85ab5c8fbb49a58e6b1bf364fd748ccb280f29a..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Experience Temple Run 2 on PC with NoxPlayer - The Ultimate Android Gaming Platform.md	
+++ /dev/null
@@ -1,107 +0,0 @@
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-<h1>How to Play Temple Run 2 APK on PC</h1>
-<p>Temple Run 2 is one of the most popular endless runner games on Android. It has over a billion downloads and millions of fans around the world. In this game, you have to run, jump, slide, and turn through various obstacles and environments as you try to escape from a giant monkey chasing you. The game is fast-paced, addictive, and fun.</p>
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-<p>But what if you want to play Temple Run 2 on your PC? Maybe you want to enjoy the game on a bigger screen, or use your keyboard and mouse for better controls, or save your phone's battery life. Whatever the reason, you can actually play Temple Run 2 APK on your PC with some simple steps. In this article, we will show you how to do that and also recommend some of the best Android emulators for PC that you can use.</p>
- <h2>How to install Temple Run 2 APK on PC</h2>
-<p>There are two ways to install Temple Run 2 APK on PC: using an Android emulator or using a special application.</p>
- <h3>Using an Android emulator</h3>
-<p>An Android emulator is a software that simulates an Android device on your PC. It lets you run Android apps and games on your computer as if you were using a real phone or tablet. There are many Android emulators available for Windows and Mac, but not all of them are compatible with Temple Run 2. Here are some of the best Android emulators for PC that you can use to play Temple Run 2:</p>
-<ul>
-<li><strong>BlueStacks</strong>: One of the most popular and widely used Android emulators for PC. It has a built-in app store where you can download Temple Run 2 and other apps easily. It also has many features to enhance your gaming experience, such as keyboard mapping, multi-instance, eco mode, and more.</li>
-<li><strong>NoxPlayer</strong>: A powerful and fast Android emulator for PC that is designed for gaming. It supports high FPS, smooth performance, and keyboard and mouse controls. You can download Temple Run 2 from Google Play or install it from an APK file.</li>
-<li><strong>MEmu Play</strong>: A versatile and customizable Android emulator for PC that supports both Intel and AMD processors. It has a user-friendly interface and allows you to adjust various settings, such as CPU, RAM, resolution, etc. You can also install Temple Run 2 from Google Play or from an APK file.</li>
-</ul>
-<p>To install Temple Run 2 APK on PC using an Android emulator, follow these steps:</p>
-<ol>
-<li>Download and install an Android emulator of your choice on your PC.</li>
-<li>Launch the emulator and sign in with your Google account.</li>
-<li>Search for Temple Run 2 in the emulator's app store or Google Play and install it.</li>
-<li>Alternatively, you can download the Temple Run 2 APK file from a trusted source and drag and drop it into the emulator's window to install it.</li>
-<li>Once installed, click on the Temple Run 2 icon on the emulator's home screen to start playing.</li>
-</ol>
- <h3>Using a special application</h3>
-<p>If you have Windows 11 installed on your PC, you can use a special application called WSA PacMan (Package Manager) to install Temple Run 2 APK on PC without using an emulator. WSA PacMan is a graphical user interface that simplifies the process of sideloading Android apps on Windows 11 using Windows Subsystem for Android (WSA). WSA PacMan is a graphical user interface that simplifies the process of sideloading Android apps on Windows 11 using Windows Subsystem for Android (WSA). It allows you to install APK and XAPK files with a double-click and shows you the app information, such as package name, icon, version, and permissions. It also has some useful features, such as opening Android settings, managing applications, and changing the language and theme of the app. </p>
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-<ol>
-<li>Download and install WSA PacMan from its GitHub page or from a trusted source.</li>
-<li>Download the Temple Run 2 APK file from a trusted source and save it on your PC.</li>
-<li>Double-click on the Temple Run 2 APK file to open it with WSA PacMan.</li>
-<li>Review the app information and click on the Install button to start the installation.</li>
-<li>Wait for the installation to finish and click on the Launch button to start playing.</li>
-</ol>
- <h2>Benefits of playing Temple Run 2 on PC</h2>
-<p>Playing Temple Run 2 on PC has some benefits that you may not get on your phone or tablet. Here are some of them:</p>
-<ul>
-<li><strong>Larger screen</strong>: You can enjoy the stunning graphics and animations of Temple Run 2 on a bigger and clearer screen. You can also see more details and avoid missing any coins or power-ups.</li>
-<li><strong>Better controls</strong>: You can use your keyboard and mouse to control your character and perform various actions, such as jumping, sliding, turning, and tilting. You can also customize the key mapping according to your preference.</li>
-<li><strong>Unlimited battery life</strong>: You don't have to worry about your phone's battery draining while playing Temple Run 2 on PC. You can play as long as you want without any interruptions.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Temple Run 2 is a fun and addictive game that you can play on your PC with some simple steps. You can either use an Android emulator or a special application like WSA PacMan to install Temple Run 2 APK on PC. Both methods have their pros and cons, so you can choose the one that suits you best. Playing Temple Run 2 on PC has some benefits, such as larger screen, better controls, and unlimited battery life. However, you should also be aware of the potential risks, such as malware infection, compatibility issues, or performance problems. Therefore, you should always download Temple Run 2 APK from a trusted source and scan it with an antivirus before installing it.</p>
-<p>Here are some tips for playing Temple Run 2 on PC:</p>
-<ul>
-<li>Collect as many coins as you can to upgrade your abilities and unlock new characters and outfits.</li>
-<li>Use power-ups wisely to boost your speed, shield yourself from obstacles, or magnetize coins.</li>
-<li>Avoid hitting walls, gaps, fire, waterfalls, or other hazards that can end your run.</li>
-<li>Complete daily and weekly challenges to earn extra rewards and achievements.</li>
-<li>Have fun and enjoy the thrill of running for your life!</li>
-</ul>
- <h2>FAQs</h2>
-<h3>What is Temple Run 2?</h3>
-<p>Temple Run 2 is an endless runner game developed by Imangi Studios for Android and iOS devices. It is the sequel to the original Temple Run game that was released in 2011. In Temple Run 2, you have to run away from a giant monkey that is chasing you through various locations, such as temples, jungles, mines, cliffs, and more. You have to avoid obstacles and collect coins and power-ups along the way.</p>
- <h3>What is an APK file?</h3>
-<p>An APK file is an Android application package file that contains all the files and data needed to install an app or game on an Android device. It is similar to an EXE file for Windows or a DMG file for Mac. You can download APK files from various sources online, such as Google Play Store, third-party websites, or developers' websites.</p>
- <h3>What is an XAPK file?</h3>
-<p>An XAPK file is an extended APK file that contains additional files and data that are not included in the standard APK file. These files may include OBB files (which store large media files), split APKs (which allow installing different versions of an app for different devices), or other resources. You need a special tool or application to install XAPK files on your Android device or emulator.</p>
- <h3>What is Windows <h3>What is Windows Subsystem for Android (WSA)?</h3>
-<p>Windows Subsystem for Android (WSA) is a feature of Windows 11 that allows you to run Android apps and games on your PC. It is similar to Windows Subsystem for Linux (WSL) that lets you run Linux applications on Windows. WSA uses a virtualized Android 11 environment that is compatible with most Android apps and games. You can access WSA from the Microsoft Store or from the Windows Settings app. </p>
- <h3>Is Temple Run 2 APK safe to install on PC?</h3>
-<p>Temple Run 2 APK is generally safe to install on PC, as long as you download it from a trusted source and scan it with an antivirus before installing it. However, there are some risks involved, such as malware infection, compatibility issues, or performance problems. Therefore, you should always be careful and cautious when installing APK files on your PC. You should also check the app permissions and reviews before installing it.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/Stable-Diffusion-CPU/app.py b/spaces/fffiloni/Stable-Diffusion-CPU/app.py
deleted file mode 100644
index 34054fdd91be1f7bebcaaf72fa661eeb8c3e54c7..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Stable-Diffusion-CPU/app.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import gradio as gr
-#import torch
-#from torch import autocast // only for GPU
-
-from PIL import Image
-
-import os
-MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
-
-from diffusers import StableDiffusionPipeline
-#from diffusers import StableDiffusionImg2ImgPipeline
-
-print("hello sylvain")
-
-YOUR_TOKEN=MY_SECRET_TOKEN
-
-device="cpu"
-
-pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_auth_token=YOUR_TOKEN)
-pipe.to(device)
-
-gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
-
-def infer(prompt): 
-    
-    #image = pipe(prompt, init_image=init_image)["sample"][0]
-    images_list = pipe([prompt] * 4)
-    images = []
-    safe_image = Image.open(r"unsafe.png")
-    for i, image in enumerate(images_list["images"]):
-        if(images_list["nsfw_content_detected"][i]):
-            images.append(safe_image)
-        else:
-            images.append(image)
-    
-    return images
-
-print("Great sylvain ! Everything is working fine !")
-
-title="Stable Diffusion CPU"
-description="Stable Diffusion example using CPU and HF token. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled.</b>" 
-
-gr.Interface(fn=infer, inputs="text", outputs=gallery,title=title,description=description).queue(max_size=10).launch(enable_queue=True)
-
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/build/userver.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/build/userver.js
deleted file mode 100644
index a61f391ec143c87c91ccef2470cb222fe2af33c5..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io/build/userver.js
+++ /dev/null
@@ -1,238 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-exports.uServer = void 0;
-const debug_1 = require("debug");
-const server_1 = require("./server");
-const transports_uws_1 = require("./transports-uws");
-const debug = (0, debug_1.default)("engine:uws");
-class uServer extends server_1.BaseServer {
-    init() { }
-    cleanup() { }
-    /**
-     * Prepares a request by processing the query string.
-     *
-     * @api private
-     */
-    prepare(req, res) {
-        req.method = req.getMethod().toUpperCase();
-        req.url = req.getUrl();
-        const params = new URLSearchParams(req.getQuery());
-        req._query = Object.fromEntries(params.entries());
-        req.headers = {};
-        req.forEach((key, value) => {
-            req.headers[key] = value;
-        });
-        req.connection = {
-            remoteAddress: Buffer.from(res.getRemoteAddressAsText()).toString(),
-        };
-        res.onAborted(() => {
-            debug("response has been aborted");
-        });
-    }
-    createTransport(transportName, req) {
-        return new transports_uws_1.default[transportName](req);
-    }
-    /**
-     * Attach the engine to a µWebSockets.js server
-     * @param app
-     * @param options
-     */
-    attach(app /* : TemplatedApp */, options = {}) {
-        const path = this._computePath(options);
-        app
-            .any(path, this.handleRequest.bind(this))
-            //
-            .ws(path, {
-            compression: options.compression,
-            idleTimeout: options.idleTimeout,
-            maxBackpressure: options.maxBackpressure,
-            maxPayloadLength: this.opts.maxHttpBufferSize,
-            upgrade: this.handleUpgrade.bind(this),
-            open: (ws) => {
-                ws.transport.socket = ws;
-                ws.transport.writable = true;
-                ws.transport.emit("drain");
-            },
-            message: (ws, message, isBinary) => {
-                ws.transport.onData(isBinary ? message : Buffer.from(message).toString());
-            },
-            close: (ws, code, message) => {
-                ws.transport.onClose(code, message);
-            },
-        });
-    }
-    _applyMiddlewares(req, res, callback) {
-        if (this.middlewares.length === 0) {
-            return callback();
-        }
-        // needed to buffer headers until the status is computed
-        req.res = new ResponseWrapper(res);
-        super._applyMiddlewares(req, req.res, () => {
-            // some middlewares (like express-session) wait for the writeHead() call to flush their headers
-            // see https://github.com/expressjs/session/blob/1010fadc2f071ddf2add94235d72224cf65159c6/index.js#L220-L244
-            req.res.writeHead();
-            callback();
-        });
-    }
-    handleRequest(res, req) {
-        debug('handling "%s" http request "%s"', req.getMethod(), req.getUrl());
-        this.prepare(req, res);
-        req.res = res;
-        this._applyMiddlewares(req, res, () => {
-            this.verify(req, false, (errorCode, errorContext) => {
-                if (errorCode !== undefined) {
-                    this.emit("connection_error", {
-                        req,
-                        code: errorCode,
-                        message: server_1.Server.errorMessages[errorCode],
-                        context: errorContext,
-                    });
-                    this.abortRequest(req.res, errorCode, errorContext);
-                    return;
-                }
-                if (req._query.sid) {
-                    debug("setting new request for existing client");
-                    this.clients[req._query.sid].transport.onRequest(req);
-                }
-                else {
-                    const closeConnection = (errorCode, errorContext) => this.abortRequest(res, errorCode, errorContext);
-                    this.handshake(req._query.transport, req, closeConnection);
-                }
-            });
-        });
-    }
-    handleUpgrade(res, req, context) {
-        debug("on upgrade");
-        this.prepare(req, res);
-        req.res = res;
-        this._applyMiddlewares(req, res, () => {
-            this.verify(req, true, async (errorCode, errorContext) => {
-                if (errorCode) {
-                    this.emit("connection_error", {
-                        req,
-                        code: errorCode,
-                        message: server_1.Server.errorMessages[errorCode],
-                        context: errorContext,
-                    });
-                    this.abortRequest(res, errorCode, errorContext);
-                    return;
-                }
-                const id = req._query.sid;
-                let transport;
-                if (id) {
-                    const client = this.clients[id];
-                    if (!client) {
-                        debug("upgrade attempt for closed client");
-                        res.close();
-                    }
-                    else if (client.upgrading) {
-                        debug("transport has already been trying to upgrade");
-                        res.close();
-                    }
-                    else if (client.upgraded) {
-                        debug("transport had already been upgraded");
-                        res.close();
-                    }
-                    else {
-                        debug("upgrading existing transport");
-                        transport = this.createTransport(req._query.transport, req);
-                        client.maybeUpgrade(transport);
-                    }
-                }
-                else {
-                    transport = await this.handshake(req._query.transport, req, (errorCode, errorContext) => this.abortRequest(res, errorCode, errorContext));
-                    if (!transport) {
-                        return;
-                    }
-                }
-                // calling writeStatus() triggers the flushing of any header added in a middleware
-                req.res.writeStatus("101 Switching Protocols");
-                res.upgrade({
-                    transport,
-                }, req.getHeader("sec-websocket-key"), req.getHeader("sec-websocket-protocol"), req.getHeader("sec-websocket-extensions"), context);
-            });
-        });
-    }
-    abortRequest(res, errorCode, errorContext) {
-        const statusCode = errorCode === server_1.Server.errors.FORBIDDEN
-            ? "403 Forbidden"
-            : "400 Bad Request";
-        const message = errorContext && errorContext.message
-            ? errorContext.message
-            : server_1.Server.errorMessages[errorCode];
-        res.writeStatus(statusCode);
-        res.writeHeader("Content-Type", "application/json");
-        res.end(JSON.stringify({
-            code: errorCode,
-            message,
-        }));
-    }
-}
-exports.uServer = uServer;
-class ResponseWrapper {
-    constructor(res) {
-        this.res = res;
-        this.statusWritten = false;
-        this.headers = [];
-    }
-    set statusCode(status) {
-        if (!status) {
-            return;
-        }
-        // FIXME: handle all status codes?
-        this.writeStatus(status === 200 ? "200 OK" : "204 No Content");
-    }
-    writeHead(status) {
-        this.statusCode = status;
-    }
-    setHeader(key, value) {
-        if (Array.isArray(value)) {
-            value.forEach((val) => {
-                this.writeHeader(key, val);
-            });
-        }
-        else {
-            this.writeHeader(key, value);
-        }
-    }
-    removeHeader() {
-        // FIXME: not implemented
-    }
-    // needed by vary: https://github.com/jshttp/vary/blob/5d725d059b3871025cf753e9dfa08924d0bcfa8f/index.js#L134
-    getHeader() { }
-    writeStatus(status) {
-        this.res.writeStatus(status);
-        this.statusWritten = true;
-        this.writeBufferedHeaders();
-    }
-    writeHeader(key, value) {
-        if (key === "Content-Length") {
-            // the content length is automatically added by uWebSockets.js
-            return;
-        }
-        if (this.statusWritten) {
-            this.res.writeHeader(key, value);
-        }
-        else {
-            this.headers.push([key, value]);
-        }
-    }
-    writeBufferedHeaders() {
-        this.headers.forEach(([key, value]) => {
-            this.res.writeHeader(key, value);
-        });
-    }
-    end(data) {
-        if (!this.statusWritten) {
-            // status will be inferred as "200 OK"
-            this.writeBufferedHeaders();
-        }
-        this.res.end(data);
-    }
-    onData(fn) {
-        this.res.onData(fn);
-    }
-    onAborted(fn) {
-        this.res.onAborted(fn);
-    }
-}
diff --git a/spaces/fffiloni/video-to-sound-fx/style.css b/spaces/fffiloni/video-to-sound-fx/style.css
deleted file mode 100644
index 55eb439d0fe89105ad03016a124042a3b6635fb6..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/video-to-sound-fx/style.css
+++ /dev/null
@@ -1,94 +0,0 @@
-#col-container {max-width: 440px; margin-left: auto; margin-right: auto;}
-
-a, a:hover, a:visited {
-  text-decoration-line: underline; 
-  font-weight: 600;
-  color: #1f2937 !important;
-}
-
-.dark a, .dark a:hover, .dark a:visited {
-  color: #f3f4f6 !important;
-}
-
-.footer {
-  margin-bottom: 45px;
-  margin-top: 10px;
-  text-align: center;
-  border-bottom: 1px solid #e5e5e5;
-}
-
-.footer>p {
-  font-size: .8rem!important;
-  display: inline-block;
-  padding: 0 10px;
-  transform: translateY(26px);
-  background: white;
-}
-.dark .footer {
-  border-color: #303030;
-}
-.dark .footer>p {
-  background: #0b0f19;
-}
-
-div#may-like-container > p {
-  font-size: .8em;
-  margin-bottom: 4px;
-}
-
-.animate-spin {
-  animation: spin 1s linear infinite;
-}
-
-@keyframes spin {
-  from {
-      transform: rotate(0deg);
-  }
-  to {
-      transform: rotate(360deg);
-  }
-}
-
-#share-btn-container {
-  display: flex; 
-  padding-left: 0.5rem !important; 
-  padding-right: 0.5rem !important; 
-  background-color: #000000; 
-  justify-content: center; 
-  align-items: center; 
-  border-radius: 9999px !important; 
-  max-width: 13rem;
-}
-
-#share-btn-container:hover {
-  background-color: #060606;
-}
-
-#share-btn {
-  all: initial; 
-  color: #ffffff;
-  font-weight: 600; 
-  cursor:pointer; 
-  font-family: 'IBM Plex Sans', sans-serif; 
-  margin-left: 0.5rem !important; 
-  padding-top: 0.5rem !important; 
-  padding-bottom: 0.5rem !important;
-  right:0;
-}
-
-#share-btn * {
-  all: unset;
-}
-
-#share-btn-container div:nth-child(-n+2){
-  width: auto !important;
-  min-height: 0px !important;
-}
-
-#share-btn-container .wrap {
-  display: none !important;
-}
-
-#share-btn-container.hidden {
-  display: none!important;
-}
\ No newline at end of file
diff --git a/spaces/firestalker/anime-tts/text/__init__.py b/spaces/firestalker/anime-tts/text/__init__.py
deleted file mode 100644
index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000
--- a/spaces/firestalker/anime-tts/text/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/fiyen/YangyangChatGPT/modules/openai_func.py b/spaces/fiyen/YangyangChatGPT/modules/openai_func.py
deleted file mode 100644
index fb07b16235476360ccc48849f5f9e761630efec3..0000000000000000000000000000000000000000
--- a/spaces/fiyen/YangyangChatGPT/modules/openai_func.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import requests
-import logging
-from modules.presets import (
-    timeout_all,
-    USAGE_API_URL,
-    BALANCE_API_URL,
-    standard_error_msg,
-    connection_timeout_prompt,
-    error_retrieve_prompt,
-    read_timeout_prompt
-)
-
-from modules import shared
-from modules.utils import get_proxies
-import os, datetime
-
-def get_billing_data(openai_api_key, billing_url):
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {openai_api_key}"
-    }
-    
-    timeout = timeout_all
-    proxies = get_proxies()
-    response = requests.get(
-        billing_url,
-        headers=headers,
-        timeout=timeout,
-        proxies=proxies,
-    )
-    
-    if response.status_code == 200:
-        data = response.json()
-        return data
-    else:
-        raise Exception(f"API request failed with status code {response.status_code}: {response.text}")
-    
-
-def get_usage(openai_api_key):
-    try:
-        balance_data=get_billing_data(openai_api_key, BALANCE_API_URL)
-        logging.debug(balance_data)
-        try:
-            balance = balance_data["total_available"] if balance_data["total_available"] else 0
-            total_used = balance_data["total_used"] if balance_data["total_used"] else 0
-            usage_percent = round(total_used / (total_used+balance) * 100, 2)
-        except Exception as e:
-            logging.error(f"API使用情况解析失败:"+str(e))
-            balance = 0
-            total_used=0
-            return f"**API使用情况解析失败**"
-        if balance == 0:
-            last_day_of_month = datetime.datetime.now().strftime("%Y-%m-%d")
-            first_day_of_month = datetime.datetime.now().replace(day=1).strftime("%Y-%m-%d")
-            usage_url = f"{USAGE_API_URL}?start_date={first_day_of_month}&end_date={last_day_of_month}"
-            try:
-                usage_data = get_billing_data(openai_api_key, usage_url)
-            except Exception as e:
-                logging.error(f"获取API使用情况失败:"+str(e))
-                return f"**获取API使用情况失败**"
-            return f"**本月使用金额** \u3000 ${usage_data['total_usage'] / 100}"
-        
-        # return f"**免费额度**（已用/余额）\u3000${total_used} / ${balance}"
-        return f"""\
-        <b>免费额度使用情况</b>
-        <div class="progress-bar">
-            <div class="progress" style="width: {usage_percent}%;">
-                <span class="progress-text">{usage_percent}%</span>
-            </div>
-        </div>
-        <div style="display: flex; justify-content: space-between;"><span>已用 ${total_used}</span><span>可用 ${balance}</span></div>
-        """
-    
-    except requests.exceptions.ConnectTimeout:
-        status_text = standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
-        return status_text
-    except requests.exceptions.ReadTimeout:
-        status_text = standard_error_msg + read_timeout_prompt + error_retrieve_prompt
-        return status_text
-    except Exception as e:
-        logging.error(f"获取API使用情况失败:"+str(e))
-        return standard_error_msg + error_retrieve_prompt
diff --git a/spaces/flax-community/multilingual-image-captioning/session.py b/spaces/flax-community/multilingual-image-captioning/session.py
deleted file mode 100644
index 890a29bafd97091f7163df306d58982decb0a11a..0000000000000000000000000000000000000000
--- a/spaces/flax-community/multilingual-image-captioning/session.py
+++ /dev/null
@@ -1,89 +0,0 @@
-#
-# Code for managing session state, which is needed for multi-input forms
-# See https://github.com/streamlit/streamlit/issues/1557
-#
-# This code is taken from
-# https://gist.github.com/okld/0aba4869ba6fdc8d49132e6974e2e662
-#
-from streamlit.hashing import _CodeHasher
-from streamlit.report_thread import get_report_ctx
-from streamlit.server.server import Server
-
-
-class _SessionState:
-    def __init__(self, session, hash_funcs):
-        """Initialize SessionState instance."""
-        self.__dict__["_state"] = {
-            "data": {},
-            "hash": None,
-            "hasher": _CodeHasher(hash_funcs),
-            "is_rerun": False,
-            "session": session,
-        }
-
-    def __call__(self, **kwargs):
-        """Initialize state data once."""
-        for item, value in kwargs.items():
-            if item not in self._state["data"]:
-                self._state["data"][item] = value
-
-    def __getitem__(self, item):
-        """Return a saved state value, None if item is undefined."""
-        return self._state["data"].get(item, None)
-
-    def __getattr__(self, item):
-        """Return a saved state value, None if item is undefined."""
-        return self._state["data"].get(item, None)
-
-    def __setitem__(self, item, value):
-        """Set state value."""
-        self._state["data"][item] = value
-
-    def __setattr__(self, item, value):
-        """Set state value."""
-        self._state["data"][item] = value
-
-    def clear(self):
-        """Clear session state and request a rerun."""
-        self._state["data"].clear()
-        self._state["session"].request_rerun()
-
-    def sync(self):
-        """
-        Rerun the app with all state values up to date from the beginning to
-        fix rollbacks.
-        """
-        data_to_bytes = self._state["hasher"].to_bytes(self._state["data"], None)
-
-        # Ensure to rerun only once to avoid infinite loops
-        # caused by a constantly changing state value at each run.
-        #
-        # Example: state.value += 1
-        if self._state["is_rerun"]:
-            self._state["is_rerun"] = False
-
-        elif self._state["hash"] is not None:
-            if self._state["hash"] != data_to_bytes:
-                self._state["is_rerun"] = True
-                self._state["session"].request_rerun()
-
-        self._state["hash"] = data_to_bytes
-
-
-def _get_session():
-    session_id = get_report_ctx().session_id
-    session_info = Server.get_current()._get_session_info(session_id)
-
-    if session_info is None:
-        raise RuntimeError("Couldn't get your Streamlit Session object.")
-
-    return session_info.session
-
-
-def _get_state(hash_funcs=None):
-    session = _get_session()
-
-    if not hasattr(session, "_custom_session_state"):
-        session._custom_session_state = _SessionState(session, hash_funcs)
-
-    return session._custom_session_state
diff --git a/spaces/flowers-team/SocialAISchool/gym-minigrid/README.md b/spaces/flowers-team/SocialAISchool/gym-minigrid/README.md
deleted file mode 100644
index cfb8a5bc6d486cb946b965f1cb4b0684297e39b2..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/gym-minigrid/README.md
+++ /dev/null
@@ -1,511 +0,0 @@
-# Minimalistic Gridworld Environment (MiniGrid)
-
-[![Build Status](https://travis-ci.org/maximecb/gym-minigrid.svg?branch=master)](https://travis-ci.org/maximecb/gym-minigrid)
-
-There are other gridworld Gym environments out there, but this one is
-designed to be particularly simple, lightweight and fast. The code has very few
-dependencies, making it less likely to break or fail to install. It loads no
-external sprites/textures, and it can run at up to 5000 FPS on a Core i7
-laptop, which means you can run your experiments faster. A known-working RL
-implementation can be found [in this repository](https://github.com/lcswillems/torch-rl).
-
-Requirements:
-- Python 3.5+
-- OpenAI Gym
-- NumPy
-- Matplotlib (optional, only needed for display)
-
-Please use this bibtex if you want to cite this repository in your publications:
-
-```
-@misc{gym_minigrid,
-  author = {Chevalier-Boisvert, Maxime and Willems, Lucas and Pal, Suman},
-  title = {Minimalistic Gridworld Environment for OpenAI Gym},
-  year = {2018},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\url{https://github.com/maximecb/gym-minigrid}},
-}
-```
-
-List of publications & submissions using MiniGrid or BabyAI (please open a pull request to add missing entries):
-- [Prioritized Level Replay](https://arxiv.org/pdf/2010.03934.pdf) (FAIR, October 2020)
-- [Learning with AMIGO: Adversarially Motivated Intrinsic Goals](https://arxiv.org/pdf/2006.12122.pdf) (MIT, FAIR, June 2020)
-- [RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments](https://openreview.net/forum?id=rkg-TJBFPB) (FAIR, ICLR 2020)
-- [Learning to Request Guidance in Emergent Communication](https://arxiv.org/pdf/1912.05525.pdf) (University of Amsterdam, Dec 2019)
-- [Working Memory Graphs](https://arxiv.org/abs/1911.07141) (MSR, Nov 2019)
-- [Fast Task-Adaptation for Tasks Labeled Using
-Natural Language in Reinforcement Learning](https://arxiv.org/pdf/1910.04040.pdf) (Oct 2019, University of Antwerp)
-- [Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
-](https://arxiv.org/abs/1910.12911) (MSR, NeurIPS, Oct 2019)
-- [Recurrent Independent Mechanisms](https://arxiv.org/pdf/1909.10893.pdf) (Mila, Sept 2019) 
-- [Learning Effective Subgoals with Multi-Task Hierarchical Reinforcement Learning](http://surl.tirl.info/proceedings/SURL-2019_paper_10.pdf) (Tsinghua University, August 2019)
-- [Mastering emergent language: learning to guide in simulated navigation](https://arxiv.org/abs/1908.05135) (University of Amsterdam, Aug 2019)
-- [Transfer Learning by Modeling a Distribution over Policies](https://arxiv.org/abs/1906.03574) (Mila, June 2019)
-- [Reinforcement Learning with Competitive Ensembles
-of Information-Constrained Primitives](https://arxiv.org/abs/1906.10667) (Mila, June 2019)
-- [Learning distant cause and effect using only local and immediate credit assignment](https://arxiv.org/abs/1905.11589) (Incubator 491, May 2019)
-- [Practical Open-Loop Optimistic Planning](https://arxiv.org/abs/1904.04700) (INRIA, April 2019)
-- [Learning World Graphs to Accelerate Hierarchical Reinforcement Learning](https://arxiv.org/abs/1907.00664) (Salesforce Research, 2019)
-- [Variational State Encoding as Intrinsic Motivation in Reinforcement Learning](https://mila.quebec/wp-content/uploads/2019/05/WebPage.pdf) (Mila, TARL 2019)
-- [Unsupervised Discovery of Decision States Through Intrinsic Control](https://tarl2019.github.io/assets/papers/modhe2019unsupervised.pdf) (Georgia Tech, TARL 2019)
-- [Modeling the Long Term Future in Model-Based Reinforcement Learning](https://openreview.net/forum?id=SkgQBn0cF7) (Mila, ICLR 2019)
-- [Unifying Ensemble Methods for Q-learning via Social Choice Theory](https://arxiv.org/pdf/1902.10646.pdf) (Max Planck Institute, Feb 2019)
-- [Planning Beyond The Sensing Horizon Using a Learned Context](https://personalrobotics.cs.washington.edu/workshops/mlmp2018/assets/docs/18_CameraReadySubmission.pdf) (MLMP@IROS, 2018)
-- [Guiding Policies with Language via Meta-Learning](https://arxiv.org/abs/1811.07882) (UC Berkeley, Nov 2018)
-- [On the Complexity of Exploration in Goal-Driven Navigation](https://arxiv.org/abs/1811.06889) (CMU, NeurIPS, Nov 2018)
-- [Transfer and Exploration via the Information Bottleneck](https://openreview.net/forum?id=rJg8yhAqKm) (Mila, Nov 2018)
-- [Creating safer reward functions for reinforcement learning agents in the gridworld](https://gupea.ub.gu.se/bitstream/2077/62445/1/gupea_2077_62445_1.pdf) (University of Gothenburg, 2018)
-- [BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop](https://arxiv.org/abs/1810.08272) (Mila, ICLR, Oct 2018)
-
-This environment has been built as part of work done at [Mila](https://mila.quebec). The Dynamic obstacles environment has been added as part of work done at [IAS in TU Darmstadt](https://www.ias.informatik.tu-darmstadt.de/) and the University of Genoa for mobile robot navigation with dynamic obstacles.
-
-## Installation
-
-There is now a [pip package](https://pypi.org/project/gym-minigrid/) available, which is updated periodically:
-
-```
-pip3 install gym-minigrid
-```
-
-Alternatively, to get the latest version of MiniGrid, you can clone this repository and install the dependencies with `pip3`:
-
-```
-git clone https://github.com/maximecb/gym-minigrid.git
-cd gym-minigrid
-pip3 install -e .
-```
-
-## Basic Usage
-
-There is a UI application which allows you to manually control the agent with the arrow keys:
-
-```
-./manual_control.py
-```
-
-The environment being run can be selected with the `--env` option, eg:
-
-```
-./manual_control.py --env MiniGrid-Empty-8x8-v0
-```
-
-## Reinforcement Learning
-
-If you want to train an agent with reinforcement learning, I recommend using the code found in the [torch-rl](https://github.com/lcswillems/torch-rl) repository. This code has been tested and is known to work with this environment. The default hyper-parameters are also known to converge.
-
-A sample training command is:
-
-```
-cd torch-rl
-python3 -m scripts.train --env MiniGrid-Empty-8x8-v0 --algo ppo
-```
-
-## Wrappers
-
-MiniGrid is built to support tasks involving natural language and sparse rewards.
-The observations are dictionaries, with an 'image' field, partially observable
-view of the environment, a 'mission' field which is a textual string
-describing the objective the agent should reach to get a reward, and a 'direction'
-field which can be used as an optional compass. Using dictionaries makes it
-easy for you to add additional information to observations
-if you need to, without having to encode everything into a single tensor.
-
-There are a variery of wrappers to change the observation format available in [gym_minigrid/wrappers.py](/gym_minigrid/wrappers.py). If your RL code expects one single tensor for observations, take a look at
-`FlatObsWrapper`. There is also an `ImgObsWrapper` that gets rid of the 'mission' field in observations,
-leaving only the image field tensor.
-
-Please note that the default observation format is a partially observable view of the environment using a
-compact and efficient encoding, with 3 input values per visible grid cell, 7x7x3 values total.
-These values are **not pixels**. If you want to obtain an array of RGB pixels as observations instead,
-use the `RGBImgPartialObsWrapper`. You can use it as follows:
-
-```
-from gym_minigrid.wrappers import *
-env = gym.make('MiniGrid-Empty-8x8-v0')
-env = RGBImgPartialObsWrapper(env) # Get pixel observations
-env = ImgObsWrapper(env) # Get rid of the 'mission' field
-obs = env.reset() # This now produces an RGB tensor only
-```
-
-## Design
-
-Structure of the world:
-- The world is an NxM grid of tiles
-- Each tile in the grid world contains zero or one object
-  - Cells that do not contain an object have the value `None`
-- Each object has an associated discrete color (string)
-- Each object has an associated type (string)
-  - Provided object types are: wall, floor, lava, door, key, ball, box and goal
-- The agent can pick up and carry exactly one object (eg: ball or key)
-- To open a locked door, the agent has to be carrying a key matching the door's color
-
-Actions in the basic environment:
-- Turn left
-- Turn right
-- Move forward
-- Pick up an object
-- Drop the object being carried
-- Toggle (open doors, interact with objects)
-- Done (task completed, optional)
-
-Default tile/observation encoding:
-- Each tile is encoded as a 3 dimensional tuple: (OBJECT_IDX, COLOR_IDX, STATE) 
-- OBJECT_TO_IDX and COLOR_TO_IDX mapping can be found in [gym_minigrid/minigrid.py](gym_minigrid/minigrid.py)
-- e.g. door STATE -> 0: open, 1: closed, 2: locked
-
-By default, sparse rewards are given for reaching a green goal tile. A
-reward of 1 is given for success, and zero for failure. There is also an
-environment-specific time step limit for completing the task.
-You can define your own reward function by creating a class derived
-from `MiniGridEnv`. Extending the environment with new object types or new actions
-should be very easy. If you wish to do this, you should take a look at the
-[gym_minigrid/minigrid.py](gym_minigrid/minigrid.py) source file.
-
-## Included Environments
-
-The environments listed below are implemented in the [gym_minigrid/envs](/gym_minigrid/envs) directory.
-Each environment provides one or more configurations registered with OpenAI gym. Each environment
-is also programmatically tunable in terms of size/complexity, which is useful for curriculum learning
-or to fine-tune difficulty.
-
-### Empty environment
-
-Registered configurations:
-- `MiniGrid-Empty-5x5-v0`
-- `MiniGrid-Empty-Random-5x5-v0`
-- `MiniGrid-Empty-6x6-v0`
-- `MiniGrid-Empty-Random-6x6-v0`
-- `MiniGrid-Empty-8x8-v0`
-- `MiniGrid-Empty-16x16-v0`
-
-<p align="center">
-<img src="/figures/empty-env.png" width=250>
-</p>
-
-This environment is an empty room, and the goal of the agent is to reach the
-green goal square, which provides a sparse reward. A small penalty is
-subtracted for the number of steps to reach the goal. This environment is
-useful, with small rooms, to validate that your RL algorithm works correctly,
-and with large rooms to experiment with sparse rewards and exploration.
-The random variants of the environment have the agent starting at a random
-position for each episode, while the regular variants have the agent always
-starting in the corner opposite to the goal.
-
-### Four rooms environment
-
-Registered configurations:
-- `MiniGrid-FourRooms-v0`
-
-<p align="center">
-<img src="/figures/four-rooms-env.png" width=380>
-</p>
-
-Classic four room reinforcement learning environment. The agent must navigate
-in a maze composed of four rooms interconnected by 4 gaps in the walls. To
-obtain a reward, the agent must reach the green goal square. Both the agent
-and the goal square are randomly placed in any of the four rooms.
-
-### Door & key environment
-
-Registered configurations:
-- `MiniGrid-DoorKey-5x5-v0`
-- `MiniGrid-DoorKey-6x6-v0`
-- `MiniGrid-DoorKey-8x8-v0`
-- `MiniGrid-DoorKey-16x16-v0`
-
-<p align="center">
-<img src="/figures/door-key-env.png">
-</p>
-
-This environment has a key that the agent must pick up in order to unlock
-a goal and then get to the green goal square. This environment is difficult,
-because of the sparse reward, to solve using classical RL algorithms. It is
-useful to experiment with curiosity or curriculum learning.
-
-### Multi-room environment
-
-Registered configurations:
-- `MiniGrid-MultiRoom-N2-S4-v0` (two small rooms)
-- `MiniGrid-MultiRoom-N4-S5-v0` (four rooms)
-- `MiniGrid-MultiRoom-N6-v0` (six rooms)
-
-<p align="center">
-<img src="/figures/multi-room.gif" width=416 height=424>
-</p>
-
-This environment has a series of connected rooms with doors that must be
-opened in order to get to the next room. The final room has the green goal
-square the agent must get to. This environment is extremely difficult to
-solve using RL alone. However, by gradually increasing the number of
-rooms and building a curriculum, the environment can be solved.
-
-### Fetch environment
-
-Registered configurations:
-- `MiniGrid-Fetch-5x5-N2-v0`
-- `MiniGrid-Fetch-6x6-N2-v0`
-- `MiniGrid-Fetch-8x8-N3-v0`
-
-<p align="center">
-<img src="/figures/fetch-env.png" width=450>
-</p>
-
-This environment has multiple objects of assorted types and colors. The
-agent receives a textual string as part of its observation telling it
-which object to pick up. Picking up the wrong object produces a negative
-reward.
-
-### Go-to-door environment
-
-Registered configurations:
-- `MiniGrid-GoToDoor-5x5-v0`
-- `MiniGrid-GoToDoor-6x6-v0`
-- `MiniGrid-GoToDoor-8x8-v0`
-
-<p align="center">
-<img src="/figures/gotodoor-6x6.png" width=400>
-</p>
-
-This environment is a room with four doors, one on each wall. The agent
-receives a textual (mission) string as input, telling it which door to go to,
-(eg: "go to the red door"). It receives a positive reward for performing the
-`done` action next to the correct door, as indicated in the mission string.
-
-### Put-near environment
-
-Registered configurations:
-- `MiniGrid-PutNear-6x6-N2-v0`
-- `MiniGrid-PutNear-8x8-N3-v0`
-
-The agent is instructed through a textual string to pick up an object and
-place it next to another object. This environment is easy to solve with two
-objects, but difficult to solve with more, as it involves both textual
-understanding and spatial reasoning involving multiple objects.
-
-### Red and blue doors environment
-
-Registered configurations:
-- `MiniGrid-RedBlueDoors-6x6-v0`
-- `MiniGrid-RedBlueDoors-8x8-v0`
-
-The purpose of this environment is to test memory.
-The agent is randomly placed within a room with one red and one blue door
-facing opposite directions. The agent has to open the red door and then open
-the blue door, in that order.  The agent, when facing one door, cannot see
-the door behind him. Hence, the agent needs to remember whether or not he has
-previously opened the other door in order to reliably succeed at completing
-the task.
-
-### Memory environment
-
-Registered configurations:
-- `MiniGrid-MemoryS17Random-v0`
-- `MiniGrid-MemoryS13Random-v0`
-- `MiniGrid-MemoryS13-v0`
-- `MiniGrid-MemoryS11-v0`
-- `MiniGrid-MemoryS9-v0`
-- `MiniGrid-MemoryS7-v0`
-
-This environment is a memory test. The agent starts in a small room
-where it sees an object. It then has to go through a narrow hallway
-which ends in a split. At each end of the split there is an object,
-one of which is the same as the object in the starting room. The
-agent has to remember the initial object, and go to the matching
-object at split.
-
-### Locked room environment
-
-Registed configurations:
-- `MiniGrid-LockedRoom-v0`
-
-The environment has six rooms, one of which is locked. The agent receives
-a textual mission string as input, telling it which room to go to in order
-to get the key that opens the locked room. It then has to go into the locked
-room in order to reach the final goal. This environment is extremely difficult
-to solve with vanilla reinforcement learning alone.
-
-### Key corridor environment
-
-Registed configurations:
-- `MiniGrid-KeyCorridorS3R1-v0`
-- `MiniGrid-KeyCorridorS3R2-v0`
-- `MiniGrid-KeyCorridorS3R3-v0`
-- `MiniGrid-KeyCorridorS4R3-v0`
-- `MiniGrid-KeyCorridorS5R3-v0`
-- `MiniGrid-KeyCorridorS6R3-v0`
-
-<p align="center">
-    <img src="figures/KeyCorridorS3R1.png" width="250">
-    <img src="figures/KeyCorridorS3R2.png" width="250">
-    <img src="figures/KeyCorridorS3R3.png" width="250">
-    <img src="figures/KeyCorridorS4R3.png" width="250">
-    <img src="figures/KeyCorridorS5R3.png" width="250">
-    <img src="figures/KeyCorridorS6R3.png" width="250">
-</p>
-
-This environment is similar to the locked room environment, but there are
-multiple registered environment configurations of increasing size,
-making it easier to use curriculum learning to train an agent to solve it.
-The agent has to pick up an object which is behind a locked door. The key is
-hidden in another room, and the agent has to explore the environment to find
-it. The mission string does not give the agent any clues as to where the
-key is placed. This environment can be solved without relying on language.
-
-### Unlock environment
-
-Registed configurations:
-- `MiniGrid-Unlock-v0`
-
-<p align="center">
-    <img src="figures/Unlock.png" width="200">
-</p>
-
-The agent has to open a locked door. This environment can be solved without
-relying on language.
-
-### Unlock pickup environment
-
-Registed configurations:
-- `MiniGrid-UnlockPickup-v0`
-
-<p align="center">
-    <img src="figures/UnlockPickup.png" width="250">
-</p>
-
-The agent has to pick up a box which is placed in another room, behind a
-locked door. This environment can be solved without relying on language.
-
-### Blocked unlock pickup environment
-
-Registed configurations:
-- `MiniGrid-BlockedUnlockPickup-v0`
-
-<p align="center">
-    <img src="figures/BlockedUnlockPickup.png" width="250">
-</p>
-
-The agent has to pick up a box which is placed in another room, behind a
-locked door. The door is also blocked by a ball which the agent has to move
-before it can unlock the door. Hence, the agent has to learn to move the ball,
-pick up the key, open the door and pick up the object in the other room.
-This environment can be solved without relying on language.
-
-## Obstructed maze environment
-
-Registered configurations:
-- `MiniGrid-ObstructedMaze-1Dl-v0`
-- `MiniGrid-ObstructedMaze-1Dlh-v0`
-- `MiniGrid-ObstructedMaze-1Dlhb-v0`
-- `MiniGrid-ObstructedMaze-2Dl-v0`
-- `MiniGrid-ObstructedMaze-2Dlh-v0`
-- `MiniGrid-ObstructedMaze-2Dlhb-v0`
-- `MiniGrid-ObstructedMaze-1Q-v0`
-- `MiniGrid-ObstructedMaze-2Q-v0`
-- `MiniGrid-ObstructedMaze-Full-v0`
-
-<p align="center">
-  <img src="figures/ObstructedMaze-1Dl.png" width="250">
-  <img src="figures/ObstructedMaze-1Dlh.png" width="250">
-  <img src="figures/ObstructedMaze-1Dlhb.png" width="250">
-  <img src="figures/ObstructedMaze-2Dl.png" width="100">
-  <img src="figures/ObstructedMaze-2Dlh.png" width="100">
-  <img src="figures/ObstructedMaze-2Dlhb.png" width="100">
-  <img src="figures/ObstructedMaze-1Q.png" width="250">
-  <img src="figures/ObstructedMaze-2Q.png" width="250">
-  <img src="figures/ObstructedMaze-4Q.png" width="250">
-</p>
-
-The agent has to pick up a box which is placed in a corner of a 3x3 maze.
-The doors are locked, the keys are hidden in boxes and doors are obstructed
-by balls. This environment can be solved without relying on language.
-
-## Distributional shift environment
-
-Registered configurations:
-- `MiniGrid-DistShift1-v0`
-- `MiniGrid-DistShift2-v0`
-
-This environment is based on one of the DeepMind [AI safety gridworlds](https://github.com/deepmind/ai-safety-gridworlds).
-The agent starts in the top-left corner and must reach the goal which is in the top-right corner, but has to avoid stepping
-into lava on its way. The aim of this environment is to test an agent's ability to generalize. There are two slightly
-different variants of the environment, so that the agent can be trained on one variant and tested on the other.
-
-<p align="center">
-  <img src="figures/DistShift1.png" width="200">
-  <img src="figures/DistShift2.png" width="200">
-</p>
-
-## Lava gap environment
-
-Registered configurations:
-- `MiniGrid-LavaGapS5-v0`
-- `MiniGrid-LavaGapS6-v0`
-- `MiniGrid-LavaGapS7-v0`
-
-<p align="center">
-  <img src="figures/LavaGapS6.png" width="200">
-</p>
-
-The agent has to reach the green goal square at the opposite corner of the room,
-and must pass through a narrow gap in a vertical strip of deadly lava. Touching
-the lava terminate the episode with a zero reward. This environment is useful
-for studying safety and safe exploration.
-
-## Lava crossing environment
-
-Registered configurations:
-- `MiniGrid-LavaCrossingS9N1-v0`
-- `MiniGrid-LavaCrossingS9N2-v0`
-- `MiniGrid-LavaCrossingS9N3-v0`
-- `MiniGrid-LavaCrossingS11N5-v0`
-
-<p align="center">
-  <img src="figures/LavaCrossingS9N1.png" width="200">
-  <img src="figures/LavaCrossingS9N2.png" width="200">
-  <img src="figures/LavaCrossingS9N3.png" width="200">
-  <img src="figures/LavaCrossingS11N5.png" width="250">
-</p>
-
-The agent has to reach the green goal square on the other corner of the room
-while avoiding rivers of deadly lava which terminate the episode in failure.
-Each lava stream runs across the room either horizontally or vertically, and
-has a single crossing point which can be safely used;  Luckily, a path to the
-goal is guaranteed to exist. This environment is useful for studying safety and
-safe exploration.
-
-## Simple crossing environment
-
-Registered configurations:
-- `MiniGrid-SimpleCrossingS9N1-v0`
-- `MiniGrid-SimpleCrossingS9N2-v0`
-- `MiniGrid-SimpleCrossingS9N3-v0`
-- `MiniGrid-SimpleCrossingS11N5-v0`
-
-<p align="center">
-  <img src="figures/SimpleCrossingS9N1.png" width="200">
-  <img src="figures/SimpleCrossingS9N2.png" width="200">
-  <img src="figures/SimpleCrossingS9N3.png" width="200">
-  <img src="figures/SimpleCrossingS11N5.png" width="250">
-</p>
-
-Similar to the `LavaCrossing` environment, the agent has to reach the green
-goal square on the other corner of the room, however lava is replaced by
-walls. This MDP is therefore much easier and and maybe useful for quickly
-testing your algorithms.
-
-### Dynamic obstacles environment
-
-Registered configurations:
-- `MiniGrid-Dynamic-Obstacles-5x5-v0`
-- `MiniGrid-Dynamic-Obstacles-Random-5x5-v0`
-- `MiniGrid-Dynamic-Obstacles-6x6-v0`
-- `MiniGrid-Dynamic-Obstacles-Random-6x6-v0`
-- `MiniGrid-Dynamic-Obstacles-8x8-v0`
-- `MiniGrid-Dynamic-Obstacles-16x16-v0`
-
-<p align="center">
-<img src="/figures/dynamic_obstacles.gif">
-</p>
-
-This environment is an empty room with moving obstacles. The goal of the agent is to reach the green goal square without colliding with any obstacle. A large penalty is subtracted if the agent collides with an obstacle and the episode finishes. This environment is useful to test Dynamic Obstacle Avoidance for mobile robots with Reinforcement Learning in Partial Observability.
diff --git a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/blocks_layout/run.py b/spaces/freddyaboulton/3.1.4.9-all-demos/demos/blocks_layout/run.py
deleted file mode 100644
index 2759b2d4fe6bfcebec4c863b19e750ab5ac11688..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/blocks_layout/run.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import gradio as gr
-
-
-demo = gr.Blocks()
-
-with demo:
-    with gr.Row():
-        gr.Image(interactive=True)
-        gr.Image()
-    with gr.Row():
-        gr.Textbox(label="Text")
-        gr.Number(label="Count")
-        gr.Radio(choices=["One", "Two"])
-    with gr.Row():
-        with gr.Row():
-            with gr.Column():
-                gr.Textbox(label="Text")
-                gr.Number(label="Count")
-                gr.Radio(choices=["One", "Two"])
-            gr.Image()
-            with gr.Column():
-                gr.Image(interactive=True)
-                gr.Image()
-    gr.Image()
-    gr.Textbox(label="Text")
-    gr.Number(label="Count")
-    gr.Radio(choices=["One", "Two"])
-
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/interface_parallel/run.py b/spaces/freddyaboulton/3.1.4.9-all-demos/demos/interface_parallel/run.py
deleted file mode 100644
index 8ac946bd166cc889554294c8ea4a0d74bcb240bf..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/interface_parallel/run.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import gradio as gr
-
-greeter_1 = gr.Interface(lambda name: f"Hello {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 1"))
-greeter_2 = gr.Interface(lambda name: f"Greetings {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 2"))
-demo = gr.Parallel(greeter_1, greeter_2)
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/freddyaboulton/all_demos_3/app.py b/spaces/freddyaboulton/all_demos_3/app.py
deleted file mode 100644
index 95a8cd2a6414b7adcee4c612d0b9f15081b0eef4..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/all_demos_3/app.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import importlib
-import gradio as gr
-import os
-import sys
-import copy
-import pathlib
-
-# At least one demo fails when caching examples
-# Temporary fix just to get the build to pass
-os.environ["SYSTEM"] = "SPACES"
-
-demo_dir = pathlib.Path(__file__).parent / "demos"
-
-
-all_demos = []
-demo_module = None
-for p in os.listdir("./demos"):
-    old_path = copy.deepcopy(sys.path)
-    sys.path = [os.path.join(demo_dir, p)] + sys.path
-    if demo_module is None:
-        demo_module = importlib.import_module(f"run")
-    else:
-        demo_module = importlib.reload(demo_module)
-    all_demos.append((p, demo_module.demo))
-
-with gr.Blocks() as mega_demo:
-    with gr.Tabs():
-        for demo_name, demo in all_demos:
-            with gr.TabItem(demo_name):
-                demo.render()
-
-mega_demo.launch()
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/three_interpolate.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/three_interpolate.py
deleted file mode 100644
index 203f47f05d58087e034fb3cd8cd6a09233947b4a..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/three_interpolate.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from typing import Tuple
-
-import torch
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
-    '_ext', ['three_interpolate_forward', 'three_interpolate_backward'])
-
-
-class ThreeInterpolate(Function):
-    """Performs weighted linear interpolation on 3 features.
-
-    Please refer to `Paper of PointNet++ <https://arxiv.org/abs/1706.02413>`_
-    for more details.
-    """
-
-    @staticmethod
-    def forward(ctx, features: torch.Tensor, indices: torch.Tensor,
-                weight: torch.Tensor) -> torch.Tensor:
-        """
-        Args:
-            features (Tensor): (B, C, M) Features descriptors to be
-                interpolated
-            indices (Tensor): (B, n, 3) index three nearest neighbors
-                of the target features in features
-            weight (Tensor): (B, n, 3) weights of interpolation
-
-        Returns:
-            Tensor: (B, C, N) tensor of the interpolated features
-        """
-        assert features.is_contiguous()
-        assert indices.is_contiguous()
-        assert weight.is_contiguous()
-
-        B, c, m = features.size()
-        n = indices.size(1)
-        ctx.three_interpolate_for_backward = (indices, weight, m)
-        output = torch.cuda.FloatTensor(B, c, n)
-
-        ext_module.three_interpolate_forward(
-            features, indices, weight, output, b=B, c=c, m=m, n=n)
-        return output
-
-    @staticmethod
-    def backward(
-        ctx, grad_out: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Args:
-            grad_out (Tensor): (B, C, N) tensor with gradients of outputs
-
-        Returns:
-            Tensor: (B, C, M) tensor with gradients of features
-        """
-        idx, weight, m = ctx.three_interpolate_for_backward
-        B, c, n = grad_out.size()
-
-        grad_features = torch.cuda.FloatTensor(B, c, m).zero_()
-        grad_out_data = grad_out.data.contiguous()
-
-        ext_module.three_interpolate_backward(
-            grad_out_data, idx, weight, grad_features.data, b=B, c=c, n=n, m=m)
-        return grad_features, None, None
-
-
-three_interpolate = ThreeInterpolate.apply
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/core/evaluation/eval_hooks.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/core/evaluation/eval_hooks.py
deleted file mode 100644
index 6fc100c8f96e817a6ed2666f7c9f762af2463b48..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/core/evaluation/eval_hooks.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import os.path as osp
-
-from annotator.uniformer.mmcv.runner import DistEvalHook as _DistEvalHook
-from annotator.uniformer.mmcv.runner import EvalHook as _EvalHook
-
-
-class EvalHook(_EvalHook):
-    """Single GPU EvalHook, with efficient test support.
-
-    Args:
-        by_epoch (bool): Determine perform evaluation by epoch or by iteration.
-            If set to True, it will perform by epoch. Otherwise, by iteration.
-            Default: False.
-        efficient_test (bool): Whether save the results as local numpy files to
-            save CPU memory during evaluation. Default: False.
-    Returns:
-        list: The prediction results.
-    """
-
-    greater_keys = ['mIoU', 'mAcc', 'aAcc']
-
-    def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs):
-        super().__init__(*args, by_epoch=by_epoch, **kwargs)
-        self.efficient_test = efficient_test
-
-    def after_train_iter(self, runner):
-        """After train epoch hook.
-
-        Override default ``single_gpu_test``.
-        """
-        if self.by_epoch or not self.every_n_iters(runner, self.interval):
-            return
-        from annotator.uniformer.mmseg.apis import single_gpu_test
-        runner.log_buffer.clear()
-        results = single_gpu_test(
-            runner.model,
-            self.dataloader,
-            show=False,
-            efficient_test=self.efficient_test)
-        self.evaluate(runner, results)
-
-    def after_train_epoch(self, runner):
-        """After train epoch hook.
-
-        Override default ``single_gpu_test``.
-        """
-        if not self.by_epoch or not self.every_n_epochs(runner, self.interval):
-            return
-        from annotator.uniformer.mmseg.apis import single_gpu_test
-        runner.log_buffer.clear()
-        results = single_gpu_test(runner.model, self.dataloader, show=False)
-        self.evaluate(runner, results)
-
-
-class DistEvalHook(_DistEvalHook):
-    """Distributed EvalHook, with efficient test support.
-
-    Args:
-        by_epoch (bool): Determine perform evaluation by epoch or by iteration.
-            If set to True, it will perform by epoch. Otherwise, by iteration.
-            Default: False.
-        efficient_test (bool): Whether save the results as local numpy files to
-            save CPU memory during evaluation. Default: False.
-    Returns:
-        list: The prediction results.
-    """
-
-    greater_keys = ['mIoU', 'mAcc', 'aAcc']
-
-    def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs):
-        super().__init__(*args, by_epoch=by_epoch, **kwargs)
-        self.efficient_test = efficient_test
-
-    def after_train_iter(self, runner):
-        """After train epoch hook.
-
-        Override default ``multi_gpu_test``.
-        """
-        if self.by_epoch or not self.every_n_iters(runner, self.interval):
-            return
-        from annotator.uniformer.mmseg.apis import multi_gpu_test
-        runner.log_buffer.clear()
-        results = multi_gpu_test(
-            runner.model,
-            self.dataloader,
-            tmpdir=osp.join(runner.work_dir, '.eval_hook'),
-            gpu_collect=self.gpu_collect,
-            efficient_test=self.efficient_test)
-        if runner.rank == 0:
-            print('\n')
-            self.evaluate(runner, results)
-
-    def after_train_epoch(self, runner):
-        """After train epoch hook.
-
-        Override default ``multi_gpu_test``.
-        """
-        if not self.by_epoch or not self.every_n_epochs(runner, self.interval):
-            return
-        from annotator.uniformer.mmseg.apis import multi_gpu_test
-        runner.log_buffer.clear()
-        results = multi_gpu_test(
-            runner.model,
-            self.dataloader,
-            tmpdir=osp.join(runner.work_dir, '.eval_hook'),
-            gpu_collect=self.gpu_collect)
-        if runner.rank == 0:
-            print('\n')
-            self.evaluate(runner, results)
diff --git a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/fpn/decoder.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/fpn/decoder.py
deleted file mode 100644
index 766190f45c32c6f9e4eb6d03632585c339fb81fa..0000000000000000000000000000000000000000
--- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/fpn/decoder.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class Conv3x3GNReLU(nn.Module):
-    def __init__(self, in_channels, out_channels, upsample=False):
-        super().__init__()
-        self.upsample = upsample
-        self.block = nn.Sequential(
-            nn.Conv2d(
-                in_channels, out_channels, (3, 3), stride=1, padding=1, bias=False
-            ),
-            nn.GroupNorm(32, out_channels),
-            nn.ReLU(inplace=True),
-        )
-
-    def forward(self, x):
-        x = self.block(x)
-        if self.upsample:
-            x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        return x
-
-
-class FPNBlock(nn.Module):
-    def __init__(self, pyramid_channels, skip_channels):
-        super().__init__()
-        self.skip_conv = nn.Conv2d(skip_channels, pyramid_channels, kernel_size=1)
-
-    def forward(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="nearest")
-        skip = self.skip_conv(skip)
-        x = x + skip
-        return x
-
-
-class SegmentationBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, n_upsamples=0):
-        super().__init__()
-
-        blocks = [Conv3x3GNReLU(in_channels, out_channels, upsample=bool(n_upsamples))]
-
-        if n_upsamples > 1:
-            for _ in range(1, n_upsamples):
-                blocks.append(Conv3x3GNReLU(out_channels, out_channels, upsample=True))
-
-        self.block = nn.Sequential(*blocks)
-
-    def forward(self, x):
-        return self.block(x)
-
-
-class MergeBlock(nn.Module):
-    def __init__(self, policy):
-        super().__init__()
-        if policy not in ["add", "cat"]:
-            raise ValueError(
-                "`merge_policy` must be one of: ['add', 'cat'], got {}".format(policy)
-            )
-        self.policy = policy
-
-    def forward(self, x):
-        if self.policy == "add":
-            return sum(x)
-        elif self.policy == "cat":
-            return torch.cat(x, dim=1)
-        else:
-            raise ValueError(
-                "`merge_policy` must be one of: ['add', 'cat'], got {}".format(
-                    self.policy
-                )
-            )
-
-
-class FPNDecoder(nn.Module):
-    def __init__(
-        self,
-        encoder_channels,
-        encoder_depth=5,
-        pyramid_channels=256,
-        segmentation_channels=128,
-        dropout=0.2,
-        merge_policy="add",
-    ):
-        super().__init__()
-
-        self.out_channels = (
-            segmentation_channels
-            if merge_policy == "add"
-            else segmentation_channels * 4
-        )
-        if encoder_depth < 3:
-            raise ValueError(
-                "Encoder depth for FPN decoder cannot be less than 3, got {}.".format(
-                    encoder_depth
-                )
-            )
-
-        encoder_channels = encoder_channels[::-1]
-        encoder_channels = encoder_channels[: encoder_depth + 1]
-
-        self.p5 = nn.Conv2d(encoder_channels[0], pyramid_channels, kernel_size=1)
-        self.p4 = FPNBlock(pyramid_channels, encoder_channels[1])
-        self.p3 = FPNBlock(pyramid_channels, encoder_channels[2])
-        self.p2 = FPNBlock(pyramid_channels, encoder_channels[3])
-
-        self.seg_blocks = nn.ModuleList(
-            [
-                SegmentationBlock(
-                    pyramid_channels, segmentation_channels, n_upsamples=n_upsamples
-                )
-                for n_upsamples in [3, 2, 1, 0]
-            ]
-        )
-
-        self.merge = MergeBlock(merge_policy)
-        self.dropout = nn.Dropout2d(p=dropout, inplace=True)
-
-    def forward(self, *features):
-        c2, c3, c4, c5 = features[-4:]
-
-        p5 = self.p5(c5)
-        p4 = self.p4(p5, c4)
-        p3 = self.p3(p4, c3)
-        p2 = self.p2(p3, c2)
-
-        feature_pyramid = [
-            seg_block(p) for seg_block, p in zip(self.seg_blocks, [p5, p4, p3, p2])
-        ]
-        x = self.merge(feature_pyramid)
-        x = self.dropout(x)
-
-        return x
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/M-powered Pro Tools 8 Ilok Cracked _BEST_.md b/spaces/gotiQspiryo/whisper-ui/examples/M-powered Pro Tools 8 Ilok Cracked _BEST_.md
deleted file mode 100644
index 4c46d9b1375c1f1475d1b443fd963313ac0acf6b..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/M-powered Pro Tools 8 Ilok Cracked _BEST_.md	
+++ /dev/null
@@ -1,24 +0,0 @@
-<h2>m-powered pro tools 8 ilok cracked</h2><br /><p><b><b>DOWNLOAD</b> &#10042;&#10042;&#10042; <a href="https://urlgoal.com/2uyNb3">https://urlgoal.com/2uyNb3</a></b></p><br /><br />
-<br />
-mts files is available on the Adobe website.
-
-These.mts files are updated versions of the.mts files that update Pro Tools to Pro Tools CS 8.0. The CS updates for Pro Tools 8 are posted on the official Pro Tools website.
-
-Numerous Tweaks, Fixes, and Improvements.
-
-Free and paid updates are available for Pro Tools 9. The updates for Pro Tools 9 are available on the official Pro Tools website.
-
-Pro Tools 9.2.4 Update for Windows. Pro Tools 9.2.3 Update for Mac. Pro Tools 9.2.2 Update for Mac. Pro Tools 9.2.1 Update for Mac. Pro Tools 9.2.0 Update for Mac. The updates for Pro Tools 9 are posted on the official Pro Tools website. This is the third update for Pro Tools 9 for Mac.
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-Pro Tools 9.2.3 Update for Windows. Pro Tools 9.2.2 Update for Windows. Pro Tools 9.2.1 Update for Windows. Pro Tools 9.2.0 Update for Windows. The updates for Pro Tools 9 are posted on the official Pro Tools website. Pro Tools 9.1.2 Updates. The latest update for Pro Tools 9 is a security update.
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-Pro Tools 9.1.1 Update for Mac. Pro Tools 9.1.0 Update for Mac. The updates for Pro Tools 9 are posted on the official Pro Tools website. Pro Tools 9.0.3.1 Updates for Mac. The updates for Pro Tools 9.0.3.0 are posted on the official Pro Tools website.
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-Pro Tools 9.0.3 Updates for Mac. The updates for Pro Tools 9.0.3.0 for Mac are posted on the official Pro Tools website. The updates for Pro Tools 9.0.3.0 for Mac are posted on the official Pro Tools website.
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-The updates for Pro Tools 9.0.3 for Windows are posted on the official Pro Tools website. Pro Tools 9.0.3 Updates for Mac. Pro Tools 9.0.3.0 Updates for Windows 4fefd39f24<br />
-<br />
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-<p></p>
diff --git a/spaces/gradio/HuBERT/examples/linformer/linformer_src/modules/__init__.py b/spaces/gradio/HuBERT/examples/linformer/linformer_src/modules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/gradio/HuBERT/examples/nonautoregressive_translation/README.md b/spaces/gradio/HuBERT/examples/nonautoregressive_translation/README.md
deleted file mode 100644
index 8793e225c99732c42c9c19e22075cde37c73341d..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/nonautoregressive_translation/README.md
+++ /dev/null
@@ -1,146 +0,0 @@
-# Non-autoregressive Neural Machine Translation (NAT)
-
-This page mainly includes instructions for reproducing results from the following papers
-* [Levenshtein Transformer (Gu et al., 2019)](https://arxiv.org/abs/1905.11006).
-* [Understanding Knowledge Distillation in Non-autoregressive Machine Translation (Zhou et al., 2019)](https://arxiv.org/abs/1911.02727).
-
-We also provided our own implementations for several popular non-autoregressive-based models as reference:<br>
-* [Non-Autoregressive Neural Machine Translation (Gu et al., 2017)](https://arxiv.org/abs/1711.02281)<br>
-* [Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al., 2018)](https://arxiv.org/abs/1802.06901)<br>
-* [Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al., 2019)](https://arxiv.org/abs/1902.03249)<br>
-* [Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019)](https://arxiv.org/abs/1904.09324v2)<br>
-* [Fast Structured Decoding for Sequence Models (Sun et al., 2019)](https://arxiv.org/abs/1910.11555)
-
-## Dataset
-
-First, follow the [instructions to download and preprocess the WMT'14 En-De dataset](../translation#wmt14-english-to-german-convolutional).
-Make sure to learn a joint vocabulary by passing the `--joined-dictionary` option to `fairseq-preprocess`.
-
-### Knowledge Distillation
-Following [Gu et al. 2019](https://arxiv.org/abs/1905.11006), [knowledge distillation](https://arxiv.org/abs/1606.07947) from an autoregressive model can effectively simplify the training data distribution, which is sometimes essential for NAT-based models to learn good translations.
-The easiest way of performing distillation is to follow the [instructions of training a standard transformer model](../translation) on the same data, and then decode the training set to produce a distillation dataset for NAT.
-
-### Download
-We also provided the preprocessed [original](http://dl.fbaipublicfiles.com/nat/original_dataset.zip) and [distillation](http://dl.fbaipublicfiles.com/nat/distill_dataset.zip) datasets. Please build the binarized dataset on your own.
-
-
-## Train a model
-
-Then we can train a nonautoregressive model using the `translation_lev` task and a new criterion `nat_loss`.
-Use the `--noise` flag to specify the input noise used on the target sentences.
-In default, we run the task for *Levenshtein Transformer*, with `--noise='random_delete'`. Full scripts to run other models can also be found [here](./scripts.md).
-
-The following command will train a *Levenshtein Transformer* on the binarized dataset.
-
-```bash
-fairseq-train \
-    data-bin/wmt14_en_de_distill \
-    --save-dir checkpoints \
-    --ddp-backend=legacy_ddp \
-    --task translation_lev \
-    --criterion nat_loss \
-    --arch levenshtein_transformer \
-    --noise random_delete \
-    --share-all-embeddings \
-    --optimizer adam --adam-betas '(0.9,0.98)' \
-    --lr 0.0005 --lr-scheduler inverse_sqrt \
-    --stop-min-lr '1e-09' --warmup-updates 10000 \
-    --warmup-init-lr '1e-07' --label-smoothing 0.1 \
-    --dropout 0.3 --weight-decay 0.01 \
-    --decoder-learned-pos \
-    --encoder-learned-pos \
-    --apply-bert-init \
-    --log-format 'simple' --log-interval 100 \
-    --fixed-validation-seed 7 \
-    --max-tokens 8000 \
-    --save-interval-updates 10000 \
-    --max-update 300000
-```
-
-## Translate
-
-Once a model is trained, we can generate translations using an `iterative_refinement_generator` which will based on the model's initial output and iteratively read and greedily refine the translation until (1) the model predicts the same translations for two consecutive iterations; or (2) the generator reaches the maximum iterations (`--iter-decode-max-iter`). Use `--print-step` to check the actual # of iteration for each sentence.
-
-For *Levenshtein Transformer*, it sometimes helps to apply a `--iter-decode-eos-penalty` (typically, 0~3) to penalize the model finishing generation too early and generating too short translations.
-
-For example, to generate with `--iter-decode-max-iter=9`:
-```bash
-fairseq-generate \
-    data-bin/wmt14_en_de_distill \
-    --gen-subset test \
-    --task translation_lev \
-    --path checkpoints/checkpoint_best.pt \
-    --iter-decode-max-iter 9 \
-    --iter-decode-eos-penalty 0 \
-    --beam 1 --remove-bpe \
-    --print-step \
-    --batch-size 400
-```
-In the end of the generation, we can see the tokenized BLEU score for the translation.
-
-## Advanced Decoding Methods
-### Ensemble
-The NAT models use special implementations of [ensembling](https://github.com/fairinternal/fairseq-py/blob/b98d88da52f2f21f1b169bab8c70c1c4ca19a768/fairseq/sequence_generator.py#L522) to support iterative refinement and a variety of parallel operations in different models, while it shares the same API as standard autoregressive models as follows:
-```bash
-fairseq-generate \
-    data-bin/wmt14_en_de_distill \
-    --gen-subset test \
-    --task translation_lev \
-    --path checkpoint_1.pt:checkpoint_2.pt:checkpoint_3.pt \
-    --iter-decode-max-iter 9 \
-    --iter-decode-eos-penalty 0 \
-    --beam 1 --remove-bpe \
-    --print-step \
-    --batch-size 400
-```
-We use ``:`` to split multiple models. Note that, not all NAT models support ensembling for now.
-
-
-### Length-beam 
-For models that predict lengths before decoding (e.g. the vanilla NAT, Mask-Predict, etc), it is possible to improve the translation quality by varying the target lengths around the predicted value, and translating the same example multiple times in parallel. We can select the best translation with the highest scores defined by your model's output.
-
-Note that, not all models support length beams. For models which dynamically change the lengths (e.g. *Insertion Transformer*, *Levenshtein Transformer*), the same trick does not apply.
-
-### Re-ranking
-If the model generates multiple translations with length beam, we can also introduce an autoregressive model to rerank the translations considering scoring from an autoregressive model is much faster than decoding from that.
-
-For example, to generate translations with length beam and reranking, 
-```bash
-fairseq-generate \
-    data-bin/wmt14_en_de_distill \
-    --gen-subset test \
-    --task translation_lev \
-    --path checkpoints/checkpoint_best.pt:at_checkpoints/checkpoint_best.pt \
-    --iter-decode-max-iter 9 \
-    --iter-decode-eos-penalty 0 \
-    --iter-decode-with-beam 9 \
-    --iter-decode-with-external-reranker \
-    --beam 1 --remove-bpe \
-    --print-step \
-    --batch-size 100
-``` 
-Note that we need to make sure the autoregressive model shares the same vocabulary as our target non-autoregressive model.
-
-
-## Citation
-
-```bibtex
-@incollection{NIPS2019_9297,
-    title = {Levenshtein Transformer},
-    author = {Gu, Jiatao and Wang, Changhan and Zhao, Junbo},
-    booktitle = {Advances in Neural Information Processing Systems 32},
-    editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
-    pages = {11179--11189},
-    year = {2019},
-    publisher = {Curran Associates, Inc.},
-    url = {http://papers.nips.cc/paper/9297-levenshtein-transformer.pdf}
-}
-```
-```bibtex
-@article{zhou2019understanding,
-  title={Understanding Knowledge Distillation in Non-autoregressive Machine Translation},
-  author={Zhou, Chunting and Neubig, Graham and Gu, Jiatao},
-  journal={arXiv preprint arXiv:1911.02727},
-  year={2019}
-}
-```
diff --git a/spaces/gradio/HuBERT/fairseq/optim/dynamic_loss_scaler.py b/spaces/gradio/HuBERT/fairseq/optim/dynamic_loss_scaler.py
deleted file mode 100644
index 43f9be37b9067c520cd794b9a941c57adae25e97..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/optim/dynamic_loss_scaler.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-class DynamicLossScaler(object):
-    def __init__(
-        self,
-        init_scale=2.0 ** 15,
-        scale_factor=2.0,
-        scale_window=2000,
-        tolerance=0.0,
-        threshold=None,
-        min_loss_scale=1e-4,
-    ):
-        self.loss_scale = init_scale
-        self.scale_factor = scale_factor
-        self.scale_window = scale_window
-        self.tolerance = tolerance
-        self.threshold = threshold
-        self._iter = 0
-        self._last_overflow_iter = -1
-        self._last_rescale_iter = -1
-        self._overflows_since_rescale = 0
-        self.min_loss_scale = min_loss_scale
-
-    def scale(self, outputs):
-        return self.loss_scale * outputs
-
-    def update(self):
-        if (self._iter - self._last_overflow_iter) % self.scale_window == 0:
-            self.loss_scale *= self.scale_factor
-            self._last_rescale_iter = self._iter
-        self._iter += 1
-
-    def _decrease_loss_scale(self):
-        self.loss_scale /= self.scale_factor
-        if self.threshold is not None:
-            self.loss_scale = max(self.loss_scale, self.threshold)
-
-    def check_overflow(self, grad_norm):
-        # detect inf and nan
-        if grad_norm == float("inf") or grad_norm != grad_norm:
-            # overflow has occured
-            prev_scale = self.loss_scale
-            iter_since_rescale = self._iter - self._last_rescale_iter
-
-            self._last_overflow_iter = self._iter
-            self._overflows_since_rescale += 1
-            pct_overflow = self._overflows_since_rescale / float(iter_since_rescale)
-            if pct_overflow >= self.tolerance:
-                self._decrease_loss_scale()
-                self._last_rescale_iter = self._iter
-                self._overflows_since_rescale = 0
-
-            if self.loss_scale <= self.min_loss_scale:
-                # Use FloatingPointError as an uncommon error that parent
-                # functions can safely catch to stop training.
-                self.loss_scale = prev_scale
-                raise FloatingPointError(
-                    (
-                        "Minimum loss scale reached ({}). Your loss is probably exploding. "
-                        "Try lowering the learning rate, using gradient clipping or "
-                        "increasing the batch size."
-                    ).format(self.min_loss_scale)
-                )
-
-            self._iter += 1
-            raise OverflowError("setting loss scale to: " + str(self.loss_scale))
diff --git a/spaces/gradio/HuBERT/scripts/compare_namespaces.py b/spaces/gradio/HuBERT/scripts/compare_namespaces.py
deleted file mode 100644
index bc24db624f8db36f546c263ba3a806dae6d466bf..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/scripts/compare_namespaces.py
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/usr/bin/env python
-"""Helper script to compare two argparse.Namespace objects."""
-
-from argparse import Namespace  # noqa
-
-
-def main():
-
-    ns1 = eval(input("Namespace 1: "))
-    ns2 = eval(input("Namespace 2: "))
-
-    def keys(ns):
-        ks = set()
-        for k in dir(ns):
-            if not k.startswith("_"):
-                ks.add(k)
-        return ks
-
-    k1 = keys(ns1)
-    k2 = keys(ns2)
-
-    def print_keys(ks, ns1, ns2=None):
-        for k in ks:
-            if ns2 is None:
-                print("{}\t{}".format(k, getattr(ns1, k, None)))
-            else:
-                print(
-                    "{}\t{}\t{}".format(k, getattr(ns1, k, None), getattr(ns2, k, None))
-                )
-
-    print("Keys unique to namespace 1:")
-    print_keys(k1 - k2, ns1)
-    print()
-
-    print("Keys unique to namespace 2:")
-    print_keys(k2 - k1, ns2)
-    print()
-
-    print("Overlapping keys with different values:")
-    ks = [k for k in k1 & k2 if getattr(ns1, k, "None") != getattr(ns2, k, "None")]
-    print_keys(ks, ns1, ns2)
-    print()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/geometry.py b/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/geometry.py
deleted file mode 100644
index 5e88b38602ae00d9c20343f21efb019b8fba1cc0..0000000000000000000000000000000000000000
--- a/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/geometry.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import torch
-
-
-def index(feat, uv):
-    '''
-
-    :param feat: [B, C, H, W] image features
-    :param uv: [B, 2, N] uv coordinates in the image plane, range [-1, 1]
-    :return: [B, C, N] image features at the uv coordinates
-    '''
-    uv = uv.transpose(1, 2)  # [B, N, 2]
-    uv = uv.unsqueeze(2)  # [B, N, 1, 2]
-    # NOTE: for newer PyTorch, it seems that training results are degraded due to implementation diff in F.grid_sample
-    # for old versions, simply remove the aligned_corners argument.
-    samples = torch.nn.functional.grid_sample(feat, uv, align_corners=True)  # [B, C, N, 1]
-    return samples[:, :, :, 0]  # [B, C, N]
-
-
-def orthogonal(points, calibrations, transforms=None):
-    '''
-    Compute the orthogonal projections of 3D points into the image plane by given projection matrix
-    :param points: [B, 3, N] Tensor of 3D points
-    :param calibrations: [B, 4, 4] Tensor of projection matrix
-    :param transforms: [B, 2, 3] Tensor of image transform matrix
-    :return: xyz: [B, 3, N] Tensor of xyz coordinates in the image plane
-    '''
-    rot = calibrations[:, :3, :3]
-    trans = calibrations[:, :3, 3:4]
-    pts = torch.baddbmm(trans, rot, points)  # [B, 3, N]
-    if transforms is not None:
-        scale = transforms[:2, :2]
-        shift = transforms[:2, 2:3]
-        pts[:, :2, :] = torch.baddbmm(shift, scale, pts[:, :2, :])
-    return pts
-
-
-def perspective(points, calibrations, transforms=None):
-    '''
-    Compute the perspective projections of 3D points into the image plane by given projection matrix
-    :param points: [Bx3xN] Tensor of 3D points
-    :param calibrations: [Bx4x4] Tensor of projection matrix
-    :param transforms: [Bx2x3] Tensor of image transform matrix
-    :return: xy: [Bx2xN] Tensor of xy coordinates in the image plane
-    '''
-    rot = calibrations[:, :3, :3]
-    trans = calibrations[:, :3, 3:4]
-    homo = torch.baddbmm(trans, rot, points)  # [B, 3, N]
-    xy = homo[:, :2, :] / homo[:, 2:3, :]
-    if transforms is not None:
-        scale = transforms[:2, :2]
-        shift = transforms[:2, 2:3]
-        xy = torch.baddbmm(shift, scale, xy)
-
-    xyz = torch.cat([xy, homo[:, 2:3, :]], 1)
-    return xyz
diff --git a/spaces/guardiancc/fast-stable-diffusion/app.py b/spaces/guardiancc/fast-stable-diffusion/app.py
deleted file mode 100644
index 21d04df88e752f1e0328fe9f7e1354cd8a0d864d..0000000000000000000000000000000000000000
--- a/spaces/guardiancc/fast-stable-diffusion/app.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import numpy as np
-import gradio as gr
-import requests
-import time
-import json
-import base64
-import os
-from PIL import Image
-from io import BytesIO
-
-class Prodia:
-    def __init__(self, api_key, base=None):
-        self.base = base or "https://api.prodia.com/v1"
-        self.headers = {
-            "X-Prodia-Key": api_key
-        }
-    
-    def generate(self, params):
-        response = self._post(f"{self.base}/sd/generate", params)
-        return response.json()
-    
-    def transform(self, params):
-        response = self._post(f"{self.base}/sd/transform", params)
-        return response.json()
-    
-    def controlnet(self, params):
-        response = self._post(f"{self.base}/sd/controlnet", params)
-        return response.json()
-    
-    def get_job(self, job_id):
-        response = self._get(f"{self.base}/job/{job_id}")
-        return response.json()
-
-    def wait(self, job):
-        job_result = job
-
-        while job_result['status'] not in ['succeeded', 'failed']:
-            time.sleep(0.25)
-            job_result = self.get_job(job['job'])
-
-        return job_result
-
-    def list_models(self):
-        response = self._get(f"{self.base}/models/list")
-        return response.json()
-
-    def _post(self, url, params):
-        headers = {
-            **self.headers,
-            "Content-Type": "application/json"
-        }
-        response = requests.post(url, headers=headers, data=json.dumps(params))
-
-        if response.status_code != 200:
-            raise Exception(f"Bad Prodia Response: {response.status_code}")
-
-        return response
-
-    def _get(self, url):
-        response = requests.get(url, headers=self.headers)
-
-        if response.status_code != 200:
-            raise Exception(f"Bad Prodia Response: {response.status_code}")
-
-        return response
-
-
-def image_to_base64(image_path):
-    # Open the image with PIL
-    with Image.open(image_path) as image:
-        # Convert the image to bytes
-        buffered = BytesIO()
-        image.save(buffered, format="PNG")  # You can change format to PNG if needed
-        
-        # Encode the bytes to base64
-        img_str = base64.b64encode(buffered.getvalue())
-
-    return img_str.decode('utf-8')  # Convert bytes to string
-
-
-
-prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
-
-def flip_text(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
-    result = prodia_client.generate({
-        "prompt": prompt,
-        "negative_prompt": negative_prompt,
-        "model": model,
-        "steps": steps,
-        "sampler": sampler,
-        "cfg_scale": cfg_scale,
-        "width": width,
-        "height": height,
-        "seed": seed
-    })
-
-    job = prodia_client.wait(result)
-
-    return job["imageUrl"]
-
-css = """
-#generate {
-    height: 100%;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-
-
-    with gr.Row():
-        with gr.Column(scale=6):
-            model = gr.Dropdown(interactive=True,value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True, label="Stable Diffusion Checkpoint", choices=prodia_client.list_models())
-  
-        with gr.Column(scale=1):
-            gr.Markdown(elem_id="powered-by-prodia", value="AUTOMATIC1111 Stable Diffusion Web UI.<br>Powered by [Prodia](https://prodia.com).")
-
-    with gr.Tab("txt2img"):
-        with gr.Row():
-            with gr.Column(scale=6, min_width=600):
-                prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3)
-                negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
-            with gr.Column():
-                text_button = gr.Button("Generate", variant='primary', elem_id="generate")
-                
-        with gr.Row():
-            with gr.Column(scale=3):
-                with gr.Tab("Generation"):
-                    with gr.Row():
-                        with gr.Column(scale=1):
-                            sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method", choices=[
-                                "Euler",
-                                "Euler a",
-                                "LMS",
-                                "Heun",
-                                "DPM2",
-                                "DPM2 a",
-                                "DPM++ 2S a",
-                                "DPM++ 2M",
-                                "DPM++ SDE",
-                                "DPM fast",
-                                "DPM adaptive",
-                                "LMS Karras",
-                                "DPM2 Karras",
-                                "DPM2 a Karras",
-                                "DPM++ 2S a Karras",
-                                "DPM++ 2M Karras",
-                                "DPM++ SDE Karras",
-                                "DDIM",
-                                "PLMS",
-                            ])
-                            
-                        with gr.Column(scale=1):
-                            steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=30, value=25, step=1)
-
-                    with gr.Row():
-                        with gr.Column(scale=1):
-                            width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
-                            height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
-                        
-                        with gr.Column(scale=1):
-                            batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
-                            batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
-
-                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
-                    seed = gr.Number(label="Seed", value=-1)
-
-                
-            with gr.Column(scale=2):
-                image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png")
-
-        text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output)
-    
-demo.queue(concurrency_count=24)
-demo.launch()
diff --git a/spaces/guetLzy/Real-ESRGAN-Demo/realesrgan/models/realesrnet_model.py b/spaces/guetLzy/Real-ESRGAN-Demo/realesrgan/models/realesrnet_model.py
deleted file mode 100644
index d11668f3712bffcd062c57db14d22ca3a0e1e59d..0000000000000000000000000000000000000000
--- a/spaces/guetLzy/Real-ESRGAN-Demo/realesrgan/models/realesrnet_model.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import numpy as np
-import random
-import torch
-from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
-from basicsr.data.transforms import paired_random_crop
-from basicsr.models.sr_model import SRModel
-from basicsr.utils import DiffJPEG, USMSharp
-from basicsr.utils.img_process_util import filter2D
-from basicsr.utils.registry import MODEL_REGISTRY
-from torch.nn import functional as F
-
-
-@MODEL_REGISTRY.register()
-class RealESRNetModel(SRModel):
-    """RealESRNet Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
-    It is trained without GAN losses.
-    It mainly performs:
-    1. randomly synthesize LQ images in GPU tensors
-    2. optimize the networks with GAN training.
-    """
-
-    def __init__(self, opt):
-        super(RealESRNetModel, self).__init__(opt)
-        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
-        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
-        self.queue_size = opt.get('queue_size', 180)
-
-    @torch.no_grad()
-    def _dequeue_and_enqueue(self):
-        """It is the training pair pool for increasing the diversity in a batch.
-
-        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
-        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
-        to increase the degradation diversity in a batch.
-        """
-        # initialize
-        b, c, h, w = self.lq.size()
-        if not hasattr(self, 'queue_lr'):
-            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
-            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
-            _, c, h, w = self.gt.size()
-            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
-            self.queue_ptr = 0
-        if self.queue_ptr == self.queue_size:  # the pool is full
-            # do dequeue and enqueue
-            # shuffle
-            idx = torch.randperm(self.queue_size)
-            self.queue_lr = self.queue_lr[idx]
-            self.queue_gt = self.queue_gt[idx]
-            # get first b samples
-            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
-            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
-            # update the queue
-            self.queue_lr[0:b, :, :, :] = self.lq.clone()
-            self.queue_gt[0:b, :, :, :] = self.gt.clone()
-
-            self.lq = lq_dequeue
-            self.gt = gt_dequeue
-        else:
-            # only do enqueue
-            self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone()
-            self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone()
-            self.queue_ptr = self.queue_ptr + b
-
-    @torch.no_grad()
-    def feed_data(self, data):
-        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
-        """
-        if self.is_train and self.opt.get('high_order_degradation', True):
-            # training data synthesis
-            self.gt = data['gt'].to(self.device)
-            # USM sharpen the GT images
-            if self.opt['gt_usm'] is True:
-                self.gt = self.usm_sharpener(self.gt)
-
-            self.kernel1 = data['kernel1'].to(self.device)
-            self.kernel2 = data['kernel2'].to(self.device)
-            self.sinc_kernel = data['sinc_kernel'].to(self.device)
-
-            ori_h, ori_w = self.gt.size()[2:4]
-
-            # ----------------------- The first degradation process ----------------------- #
-            # blur
-            out = filter2D(self.gt, self.kernel1)
-            # random resize
-            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0]
-            if updown_type == 'up':
-                scale = np.random.uniform(1, self.opt['resize_range'][1])
-            elif updown_type == 'down':
-                scale = np.random.uniform(self.opt['resize_range'][0], 1)
-            else:
-                scale = 1
-            mode = random.choice(['area', 'bilinear', 'bicubic'])
-            out = F.interpolate(out, scale_factor=scale, mode=mode)
-            # add noise
-            gray_noise_prob = self.opt['gray_noise_prob']
-            if np.random.uniform() < self.opt['gaussian_noise_prob']:
-                out = random_add_gaussian_noise_pt(
-                    out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
-            else:
-                out = random_add_poisson_noise_pt(
-                    out,
-                    scale_range=self.opt['poisson_scale_range'],
-                    gray_prob=gray_noise_prob,
-                    clip=True,
-                    rounds=False)
-            # JPEG compression
-            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
-            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
-            out = self.jpeger(out, quality=jpeg_p)
-
-            # ----------------------- The second degradation process ----------------------- #
-            # blur
-            if np.random.uniform() < self.opt['second_blur_prob']:
-                out = filter2D(out, self.kernel2)
-            # random resize
-            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0]
-            if updown_type == 'up':
-                scale = np.random.uniform(1, self.opt['resize_range2'][1])
-            elif updown_type == 'down':
-                scale = np.random.uniform(self.opt['resize_range2'][0], 1)
-            else:
-                scale = 1
-            mode = random.choice(['area', 'bilinear', 'bicubic'])
-            out = F.interpolate(
-                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
-            # add noise
-            gray_noise_prob = self.opt['gray_noise_prob2']
-            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
-                out = random_add_gaussian_noise_pt(
-                    out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
-            else:
-                out = random_add_poisson_noise_pt(
-                    out,
-                    scale_range=self.opt['poisson_scale_range2'],
-                    gray_prob=gray_noise_prob,
-                    clip=True,
-                    rounds=False)
-
-            # JPEG compression + the final sinc filter
-            # We also need to resize images to desired sizes. We group [resize back + sinc filter] together
-            # as one operation.
-            # We consider two orders:
-            #   1. [resize back + sinc filter] + JPEG compression
-            #   2. JPEG compression + [resize back + sinc filter]
-            # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
-            if np.random.uniform() < 0.5:
-                # resize back + the final sinc filter
-                mode = random.choice(['area', 'bilinear', 'bicubic'])
-                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
-                out = filter2D(out, self.sinc_kernel)
-                # JPEG compression
-                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
-                out = torch.clamp(out, 0, 1)
-                out = self.jpeger(out, quality=jpeg_p)
-            else:
-                # JPEG compression
-                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
-                out = torch.clamp(out, 0, 1)
-                out = self.jpeger(out, quality=jpeg_p)
-                # resize back + the final sinc filter
-                mode = random.choice(['area', 'bilinear', 'bicubic'])
-                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
-                out = filter2D(out, self.sinc_kernel)
-
-            # clamp and round
-            self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.
-
-            # random crop
-            gt_size = self.opt['gt_size']
-            self.gt, self.lq = paired_random_crop(self.gt, self.lq, gt_size, self.opt['scale'])
-
-            # training pair pool
-            self._dequeue_and_enqueue()
-            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
-        else:
-            # for paired training or validation
-            self.lq = data['lq'].to(self.device)
-            if 'gt' in data:
-                self.gt = data['gt'].to(self.device)
-                self.gt_usm = self.usm_sharpener(self.gt)
-
-    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
-        # do not use the synthetic process during validation
-        self.is_train = False
-        super(RealESRNetModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
-        self.is_train = True
diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/models/e4e/stylegan2/__init__.py b/spaces/gyugnsu/DragGan-Inversion/PTI/models/e4e/stylegan2/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/gyugnsu/DragGan-Inversion/training/networks_stylegan2.py b/spaces/gyugnsu/DragGan-Inversion/training/networks_stylegan2.py
deleted file mode 100644
index 6f570aad058ae63aaaa6733504d0d5ed4ba190a1..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/training/networks_stylegan2.py
+++ /dev/null
@@ -1,981 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Network architectures from the paper
-"Analyzing and Improving the Image Quality of StyleGAN".
-Matches the original implementation of configs E-F by Karras et al. at
-https://github.com/NVlabs/stylegan2/blob/master/training/networks_stylegan2.py"""
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch_utils import misc
-from torch_utils import persistence
-from torch_utils.ops import conv2d_resample
-from torch_utils.ops import upfirdn2d
-from torch_utils.ops import bias_act
-from torch_utils.ops import fma
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def normalize_2nd_moment(x, dim=1, eps=1e-8):
-    return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def modulated_conv2d(
-    # Input tensor of shape [batch_size, in_channels, in_height, in_width].
-    x,
-    # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width].
-    weight,
-    # Modulation coefficients of shape [batch_size, in_channels].
-    styles,
-    noise=None,     # Optional noise tensor to add to the output activations.
-    up=1,        # Integer upsampling factor.
-    down=1,        # Integer downsampling factor.
-    padding=0,        # Padding with respect to the upsampled image.
-    # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter().
-    resample_filter=None,
-    demodulate=True,     # Apply weight demodulation?
-    # False = convolution, True = correlation (matches torch.nn.functional.conv2d).
-    flip_weight=True,
-    # Perform modulation, convolution, and demodulation as a single fused operation?
-    fused_modconv=True,
-):
-    batch_size = x.shape[0]
-    out_channels, in_channels, kh, kw = weight.shape
-    misc.assert_shape(weight, [out_channels, in_channels, kh, kw])  # [OIkk]
-    misc.assert_shape(x, [batch_size, in_channels, None, None])  # [NIHW]
-    misc.assert_shape(styles, [batch_size, in_channels])  # [NI]
-
-    # Pre-normalize inputs to avoid FP16 overflow.
-    if x.dtype == torch.float16 and demodulate:
-        weight = weight * (1 / np.sqrt(in_channels * kh * kw) /
-                           weight.norm(float('inf'), dim=[1, 2, 3], keepdim=True))  # max_Ikk
-        styles = styles / \
-            styles.norm(float('inf'), dim=1, keepdim=True)  # max_I
-
-    # Calculate per-sample weights and demodulation coefficients.
-    w = None
-    dcoefs = None
-    if demodulate or fused_modconv:
-        w = weight.unsqueeze(0)  # [NOIkk]
-        w = w * styles.reshape(batch_size, 1, -1, 1, 1)  # [NOIkk]
-    if demodulate:
-        dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt()  # [NO]
-    if demodulate and fused_modconv:
-        w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1)  # [NOIkk]
-
-    # Execute by scaling the activations before and after the convolution.
-    if not fused_modconv:
-        x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1)
-        x = conv2d_resample.conv2d_resample(x=x, w=weight.to(
-            x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight)
-        if demodulate and noise is not None:
-            x = fma.fma(x, dcoefs.to(x.dtype).reshape(
-                batch_size, -1, 1, 1), noise.to(x.dtype))
-        elif demodulate:
-            x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1)
-        elif noise is not None:
-            x = x.add_(noise.to(x.dtype))
-        return x
-
-    # Execute as one fused op using grouped convolution.
-    with misc.suppress_tracer_warnings():  # this value will be treated as a constant
-        batch_size = int(batch_size)
-    misc.assert_shape(x, [batch_size, in_channels, None, None])
-    x = x.reshape(1, -1, *x.shape[2:])
-    w = w.reshape(-1, in_channels, kh, kw)
-    x = conv2d_resample.conv2d_resample(x=x, w=w.to(
-        x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight)
-    x = x.reshape(batch_size, -1, *x.shape[2:])
-    if noise is not None:
-        x = x.add_(noise)
-    return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class FullyConnectedLayer(torch.nn.Module):
-    def __init__(self,
-                 in_features,                # Number of input features.
-                 out_features,               # Number of output features.
-                 bias=True,     # Apply additive bias before the activation function?
-                 # Activation function: 'relu', 'lrelu', etc.
-                 activation='linear',
-                 lr_multiplier=1,        # Learning rate multiplier.
-                 bias_init=0,        # Initial value for the additive bias.
-                 ):
-        super().__init__()
-        self.in_features = in_features
-        self.out_features = out_features
-        self.activation = activation
-        self.weight = torch.nn.Parameter(torch.randn(
-            [out_features, in_features]) / lr_multiplier)
-        self.bias = torch.nn.Parameter(torch.full(
-            [out_features], np.float32(bias_init))) if bias else None
-        self.weight_gain = lr_multiplier / np.sqrt(in_features)
-        self.bias_gain = lr_multiplier
-
-    def forward(self, x):
-        w = self.weight.to(x.dtype) * self.weight_gain
-        b = self.bias
-        if b is not None:
-            b = b.to(x.dtype)
-            if self.bias_gain != 1:
-                b = b * self.bias_gain
-
-        if self.activation == 'linear' and b is not None:
-            x = torch.addmm(b.unsqueeze(0), x, w.t())
-        else:
-            x = x.matmul(w.t())
-            x = bias_act.bias_act(x, b, act=self.activation)
-        return x
-
-    def extra_repr(self):
-        return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Conv2dLayer(torch.nn.Module):
-    def __init__(self,
-                 in_channels,                    # Number of input channels.
-                 out_channels,                   # Number of output channels.
-                 # Width and height of the convolution kernel.
-                 kernel_size,
-                 bias=True,         # Apply additive bias before the activation function?
-                 # Activation function: 'relu', 'lrelu', etc.
-                 activation='linear',
-                 up=1,            # Integer upsampling factor.
-                 down=1,            # Integer downsampling factor.
-                 # Low-pass filter to apply when resampling activations.
-                 resample_filter=[1, 3, 3, 1],
-                 # Clamp the output to +-X, None = disable clamping.
-                 conv_clamp=None,
-                 channels_last=False,        # Expect the input to have memory_format=channels_last?
-                 trainable=True,         # Update the weights of this layer during training?
-                 ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.activation = activation
-        self.up = up
-        self.down = down
-        self.conv_clamp = conv_clamp
-        self.register_buffer(
-            'resample_filter', upfirdn2d.setup_filter(resample_filter))
-        self.padding = kernel_size // 2
-        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
-        self.act_gain = bias_act.activation_funcs[activation].def_gain
-
-        memory_format = torch.channels_last if channels_last else torch.contiguous_format
-        weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(
-            memory_format=memory_format)
-        bias = torch.zeros([out_channels]) if bias else None
-        if trainable:
-            self.weight = torch.nn.Parameter(weight)
-            self.bias = torch.nn.Parameter(bias) if bias is not None else None
-        else:
-            self.register_buffer('weight', weight)
-            if bias is not None:
-                self.register_buffer('bias', bias)
-            else:
-                self.bias = None
-
-    def forward(self, x, gain=1):
-        w = self.weight * self.weight_gain
-        b = self.bias.to(x.dtype) if self.bias is not None else None
-        flip_weight = (self.up == 1)  # slightly faster
-        x = conv2d_resample.conv2d_resample(x=x, w=w.to(
-            x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight)
-
-        act_gain = self.act_gain * gain
-        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
-        x = bias_act.bias_act(x, b, act=self.activation,
-                              gain=act_gain, clamp=act_clamp)
-        return x
-
-    def extra_repr(self):
-        return ' '.join([
-            f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},',
-            f'up={self.up}, down={self.down}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class MappingNetwork(torch.nn.Module):
-    def __init__(self,
-                 # Input latent (Z) dimensionality, 0 = no latent.
-                 z_dim,
-                 # Conditioning label (C) dimensionality, 0 = no label.
-                 c_dim,
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 # Number of intermediate latents to output, None = do not broadcast.
-                 num_ws,
-                 num_layers=8,        # Number of mapping layers.
-                 # Label embedding dimensionality, None = same as w_dim.
-                 embed_features=None,
-                 # Number of intermediate features in the mapping layers, None = same as w_dim.
-                 layer_features=None,
-                 # Activation function: 'relu', 'lrelu', etc.
-                 activation='lrelu',
-                 # Learning rate multiplier for the mapping layers.
-                 lr_multiplier=0.01,
-                 # Decay for tracking the moving average of W during training, None = do not track.
-                 w_avg_beta=0.998,
-                 ):
-        super().__init__()
-        self.z_dim = z_dim
-        self.c_dim = c_dim
-        self.w_dim = w_dim
-        self.num_ws = num_ws
-        self.num_layers = num_layers
-        self.w_avg_beta = w_avg_beta
-
-        if embed_features is None:
-            embed_features = w_dim
-        if c_dim == 0:
-            embed_features = 0
-        if layer_features is None:
-            layer_features = w_dim
-        features_list = [z_dim + embed_features] + \
-            [layer_features] * (num_layers - 1) + [w_dim]
-
-        if c_dim > 0:
-            self.embed = FullyConnectedLayer(c_dim, embed_features)
-        for idx in range(num_layers):
-            in_features = features_list[idx]
-            out_features = features_list[idx + 1]
-            layer = FullyConnectedLayer(
-                in_features, out_features, activation=activation, lr_multiplier=lr_multiplier)
-            setattr(self, f'fc{idx}', layer)
-
-        if num_ws is not None and w_avg_beta is not None:
-            self.register_buffer('w_avg', torch.zeros([w_dim]))
-
-    def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
-        # Embed, normalize, and concat inputs.
-        x = None
-        with torch.autograd.profiler.record_function('input'):
-            if self.z_dim > 0:
-                misc.assert_shape(z, [None, self.z_dim])
-                x = normalize_2nd_moment(z.to(torch.float32))
-            if self.c_dim > 0:
-                misc.assert_shape(c, [None, self.c_dim])
-                y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
-                x = torch.cat([x, y], dim=1) if x is not None else y
-
-        # Main layers.
-        for idx in range(self.num_layers):
-            layer = getattr(self, f'fc{idx}')
-            x = layer(x)
-
-        # Update moving average of W.
-        if update_emas and self.w_avg_beta is not None:
-            with torch.autograd.profiler.record_function('update_w_avg'):
-                self.w_avg.copy_(x.detach().mean(
-                    dim=0).lerp(self.w_avg, self.w_avg_beta))
-
-        # Broadcast.
-        if self.num_ws is not None:
-            with torch.autograd.profiler.record_function('broadcast'):
-                x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
-
-        # Apply truncation.
-        if truncation_psi != 1:
-            with torch.autograd.profiler.record_function('truncate'):
-                assert self.w_avg_beta is not None
-                if self.num_ws is None or truncation_cutoff is None:
-                    x = self.w_avg.lerp(x, truncation_psi)
-                else:
-                    x[:, :truncation_cutoff] = self.w_avg.lerp(
-                        x[:, :truncation_cutoff], truncation_psi)
-        return x
-
-    def extra_repr(self):
-        return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisLayer(torch.nn.Module):
-    def __init__(self,
-                 in_channels,                    # Number of input channels.
-                 out_channels,                   # Number of output channels.
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 resolution,                     # Resolution of this layer.
-                 kernel_size=3,            # Convolution kernel size.
-                 up=1,            # Integer upsampling factor.
-                 use_noise=True,         # Enable noise input?
-                 # Activation function: 'relu', 'lrelu', etc.
-                 activation='lrelu',
-                 # Low-pass filter to apply when resampling activations.
-                 resample_filter=[1, 3, 3, 1],
-                 # Clamp the output of convolution layers to +-X, None = disable clamping.
-                 conv_clamp=None,
-                 channels_last=False,        # Use channels_last format for the weights?
-                 ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.w_dim = w_dim
-        self.resolution = resolution
-        self.up = up
-        self.use_noise = use_noise
-        self.activation = activation
-        self.conv_clamp = conv_clamp
-        self.register_buffer(
-            'resample_filter', upfirdn2d.setup_filter(resample_filter))
-        self.padding = kernel_size // 2
-        self.act_gain = bias_act.activation_funcs[activation].def_gain
-
-        self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
-        memory_format = torch.channels_last if channels_last else torch.contiguous_format
-        self.weight = torch.nn.Parameter(torch.randn(
-            [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
-        if use_noise:
-            self.register_buffer(
-                'noise_const', torch.randn([resolution, resolution]))
-            self.noise_strength = torch.nn.Parameter(torch.zeros([]))
-        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
-
-    def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1):
-        assert noise_mode in ['random', 'const', 'none']
-        in_resolution = self.resolution // self.up
-        misc.assert_shape(x, [None, self.in_channels,
-                          in_resolution, in_resolution])
-        styles = self.affine(w)
-
-        noise = None
-        if self.use_noise and noise_mode == 'random':
-            noise = torch.randn([x.shape[0], 1, self.resolution,
-                                self.resolution], device=x.device) * self.noise_strength
-        if self.use_noise and noise_mode == 'const':
-            noise = self.noise_const * self.noise_strength
-
-        flip_weight = (self.up == 1)  # slightly faster
-        x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
-                             padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)
-
-        act_gain = self.act_gain * gain
-        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
-        x = bias_act.bias_act(x, self.bias.to(
-            x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
-        return x
-
-    def extra_repr(self):
-        return ' '.join([
-            f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},',
-            f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class ToRGBLayer(torch.nn.Module):
-    def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.w_dim = w_dim
-        self.conv_clamp = conv_clamp
-        self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
-        memory_format = torch.channels_last if channels_last else torch.contiguous_format
-        self.weight = torch.nn.Parameter(torch.randn(
-            [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
-        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
-        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
-
-    def forward(self, x, w, fused_modconv=True):
-        styles = self.affine(w) * self.weight_gain
-        x = modulated_conv2d(x=x, weight=self.weight, styles=styles,
-                             demodulate=False, fused_modconv=fused_modconv)
-        x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
-        return x
-
-    def extra_repr(self):
-        return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisBlock(torch.nn.Module):
-    def __init__(self,
-                 # Number of input channels, 0 = first block.
-                 in_channels,
-                 # Number of output channels.
-                 out_channels,
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 # Resolution of this block.
-                 resolution,
-                 # Number of output color channels.
-                 img_channels,
-                 is_last,                                # Is this the last block?
-                 # Architecture: 'orig', 'skip', 'resnet'.
-                 architecture='skip',
-                 # Low-pass filter to apply when resampling activations.
-                 resample_filter=[1, 3, 3, 1],
-                 # Clamp the output of convolution layers to +-X, None = disable clamping.
-                 conv_clamp=256,
-                 use_fp16=False,        # Use FP16 for this block?
-                 fp16_channels_last=False,        # Use channels-last memory format with FP16?
-                 # Default value of fused_modconv. 'inference_only' = True for inference, False for training.
-                 fused_modconv_default=True,
-                 # Arguments for SynthesisLayer.
-                 **layer_kwargs,
-                 ):
-        assert architecture in ['orig', 'skip', 'resnet']
-        super().__init__()
-        self.in_channels = in_channels
-        self.w_dim = w_dim
-        self.resolution = resolution
-        self.img_channels = img_channels
-        self.is_last = is_last
-        self.architecture = architecture
-        self.use_fp16 = use_fp16
-        self.channels_last = (use_fp16 and fp16_channels_last)
-        self.fused_modconv_default = fused_modconv_default
-        self.register_buffer(
-            'resample_filter', upfirdn2d.setup_filter(resample_filter))
-        self.num_conv = 0
-        self.num_torgb = 0
-
-        if in_channels == 0:
-            self.const = torch.nn.Parameter(torch.randn(
-                [out_channels, resolution, resolution]))
-
-        if in_channels != 0:
-            self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2,
-                                        resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
-            self.num_conv += 1
-
-        self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution,
-                                    conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
-        self.num_conv += 1
-
-        if is_last or architecture == 'skip':
-            self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim,
-                                    conv_clamp=conv_clamp, channels_last=self.channels_last)
-            self.num_torgb += 1
-
-        if in_channels != 0 and architecture == 'resnet':
-            self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2,
-                                    resample_filter=resample_filter, channels_last=self.channels_last)
-
-    def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs):
-        _ = update_emas  # unused
-        misc.assert_shape(
-            ws, [None, self.num_conv + self.num_torgb, self.w_dim])
-        w_iter = iter(ws.unbind(dim=1))
-        if ws.device.type != 'cuda':
-            force_fp32 = True
-        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
-        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
-        if fused_modconv is None:
-            fused_modconv = self.fused_modconv_default
-        if fused_modconv == 'inference_only':
-            fused_modconv = (not self.training)
-
-        # Input.
-        if self.in_channels == 0:
-            x = self.const.to(dtype=dtype, memory_format=memory_format)
-            x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
-        else:
-            misc.assert_shape(x, [None, self.in_channels,
-                              self.resolution // 2, self.resolution // 2])
-            x = x.to(dtype=dtype, memory_format=memory_format)
-
-        # Main layers.
-        if self.in_channels == 0:
-            x = self.conv1(x, next(w_iter),
-                           fused_modconv=fused_modconv, **layer_kwargs)
-        elif self.architecture == 'resnet':
-            y = self.skip(x, gain=np.sqrt(0.5))
-            x = self.conv0(x, next(w_iter),
-                           fused_modconv=fused_modconv, **layer_kwargs)
-            x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv,
-                           gain=np.sqrt(0.5), **layer_kwargs)
-            x = y.add_(x)
-        else:
-            x = self.conv0(x, next(w_iter),
-                           fused_modconv=fused_modconv, **layer_kwargs)
-            x = self.conv1(x, next(w_iter),
-                           fused_modconv=fused_modconv, **layer_kwargs)
-
-        # ToRGB.
-        if img is not None:
-            misc.assert_shape(
-                img, [None, self.img_channels, self.resolution // 2, self.resolution // 2])
-            img = upfirdn2d.upsample2d(img, self.resample_filter)
-        if self.is_last or self.architecture == 'skip':
-            y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv)
-            y = y.to(dtype=torch.float32,
-                     memory_format=torch.contiguous_format)
-            img = img.add_(y) if img is not None else y
-
-        assert x.dtype == dtype
-        assert img is None or img.dtype == torch.float32
-        return x, img
-
-    def extra_repr(self):
-        return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisNetwork(torch.nn.Module):
-    def __init__(self,
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 img_resolution,             # Output image resolution.
-                 img_channels,               # Number of color channels.
-                 # Overall multiplier for the number of channels.
-                 channel_base=32768,
-                 # Maximum number of channels in any layer.
-                 channel_max=512,
-                 # Use FP16 for the N highest resolutions.
-                 num_fp16_res=4,
-                 **block_kwargs,             # Arguments for SynthesisBlock.
-                 ):
-        assert img_resolution >= 4 and img_resolution & (
-            img_resolution - 1) == 0
-        super().__init__()
-        self.w_dim = w_dim
-        self.img_resolution = img_resolution
-        self.img_resolution_log2 = int(np.log2(img_resolution))
-        self.img_channels = img_channels
-        self.num_fp16_res = num_fp16_res
-        self.block_resolutions = [
-            2 ** i for i in range(2, self.img_resolution_log2 + 1)]
-        channels_dict = {res: min(channel_base // res, channel_max)
-                         for res in self.block_resolutions}
-        fp16_resolution = max(
-            2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
-
-        self.num_ws = 0
-        for res in self.block_resolutions:
-            in_channels = channels_dict[res // 2] if res > 4 else 0
-            out_channels = channels_dict[res]
-            use_fp16 = (res >= fp16_resolution)
-            is_last = (res == self.img_resolution)
-            block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res,
-                                   img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs)
-            self.num_ws += block.num_conv
-            if is_last:
-                self.num_ws += block.num_torgb
-            setattr(self, f'b{res}', block)
-
-    def forward(self, ws, return_feature=False, **block_kwargs):
-        block_ws = []
-        features = []
-        with torch.autograd.profiler.record_function('split_ws'):
-            misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
-            ws = ws.to(torch.float32)
-            w_idx = 0
-            for res in self.block_resolutions:
-                block = getattr(self, f'b{res}')
-                block_ws.append(
-                    ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
-                w_idx += block.num_conv
-
-        x = img = None
-        for res, cur_ws in zip(self.block_resolutions, block_ws):
-            block = getattr(self, f'b{res}')
-            x, img = block(x, img, cur_ws, **block_kwargs)
-            features.append(x)
-        if return_feature:
-            return img, features
-        else:
-            return img
-
-    def extra_repr(self):
-        return ' '.join([
-            f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
-            f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
-            f'num_fp16_res={self.num_fp16_res:d}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Generator(torch.nn.Module):
-    def __init__(self,
-                 z_dim,                      # Input latent (Z) dimensionality.
-                 # Conditioning label (C) dimensionality.
-                 c_dim,
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 img_resolution,             # Output resolution.
-                 img_channels,               # Number of output color channels.
-                 mapping_kwargs={},   # Arguments for MappingNetwork.
-                 synthesis_kwargs={},   # Arguments for SynthesisNetwork.
-                 resize=None,
-                 **synthesis_kwargs2,         # Arguments for SynthesisNetwork.
-                 ):
-        super().__init__()
-        self.z_dim = z_dim
-        self.c_dim = c_dim
-        self.w_dim = w_dim
-        self.img_resolution = img_resolution
-        self.img_channels = img_channels
-        if len(synthesis_kwargs) == 0:
-            synthesis_kwargs = synthesis_kwargs2
-        self.synthesis = SynthesisNetwork(
-            w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs)
-        self.num_ws = self.synthesis.num_ws
-        self.mapping = MappingNetwork(
-            z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
-        self.resize = resize
-
-    def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, input_is_w=False, return_feature=False, **synthesis_kwargs):
-        if input_is_w:
-            ws = z
-            if ws.dim() == 2:
-                ws = ws.unsqueeze(1).repeat([1, self.mapping.num_ws, 1])
-        else:
-            ws = self.mapping(z, c, truncation_psi=truncation_psi,
-                              truncation_cutoff=truncation_cutoff, update_emas=update_emas)
-        img = self.synthesis(ws, update_emas=update_emas,
-                             return_feature=return_feature, **synthesis_kwargs)
-        if return_feature:
-            img, feature = img
-        if self.resize is not None:
-            img = imresize(img, [self.resize, self.resize])
-        if return_feature:
-            return img, feature
-        else:
-            return img
-
-
-def imresize(image, size):
-    dim = image.dim()
-    if dim == 3:
-        image = image.unsqueeze(1)
-    b, _, h, w = image.shape
-    if size[0] > h:
-        image = F.interpolate(image, size, mode='bilinear')
-    elif size[0] < h:
-        image = F.interpolate(image, size, mode='area')
-    if dim == 3:
-        image = image.squeeze(1)
-    return image
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class DiscriminatorBlock(torch.nn.Module):
-    def __init__(self,
-                 # Number of input channels, 0 = first block.
-                 in_channels,
-                 # Number of intermediate channels.
-                 tmp_channels,
-                 # Number of output channels.
-                 out_channels,
-                 # Resolution of this block.
-                 resolution,
-                 # Number of input color channels.
-                 img_channels,
-                 # Index of the first layer.
-                 first_layer_idx,
-                 # Architecture: 'orig', 'skip', 'resnet'.
-                 architecture='resnet',
-                 # Activation function: 'relu', 'lrelu', etc.
-                 activation='lrelu',
-                 # Low-pass filter to apply when resampling activations.
-                 resample_filter=[1, 3, 3, 1],
-                 # Clamp the output of convolution layers to +-X, None = disable clamping.
-                 conv_clamp=None,
-                 use_fp16=False,        # Use FP16 for this block?
-                 fp16_channels_last=False,        # Use channels-last memory format with FP16?
-                 # Freeze-D: Number of layers to freeze.
-                 freeze_layers=0,
-                 ):
-        assert in_channels in [0, tmp_channels]
-        assert architecture in ['orig', 'skip', 'resnet']
-        super().__init__()
-        self.in_channels = in_channels
-        self.resolution = resolution
-        self.img_channels = img_channels
-        self.first_layer_idx = first_layer_idx
-        self.architecture = architecture
-        self.use_fp16 = use_fp16
-        self.channels_last = (use_fp16 and fp16_channels_last)
-        self.register_buffer(
-            'resample_filter', upfirdn2d.setup_filter(resample_filter))
-
-        self.num_layers = 0
-
-        def trainable_gen():
-            while True:
-                layer_idx = self.first_layer_idx + self.num_layers
-                trainable = (layer_idx >= freeze_layers)
-                self.num_layers += 1
-                yield trainable
-        trainable_iter = trainable_gen()
-
-        if in_channels == 0 or architecture == 'skip':
-            self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation,
-                                       trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
-
-        self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation,
-                                 trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
-
-        self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2,
-                                 trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last)
-
-        if architecture == 'resnet':
-            self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2,
-                                    trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last)
-
-    def forward(self, x, img, force_fp32=False):
-        if (x if x is not None else img).device.type != 'cuda':
-            force_fp32 = True
-        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
-        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
-
-        # Input.
-        if x is not None:
-            misc.assert_shape(x, [None, self.in_channels,
-                              self.resolution, self.resolution])
-            x = x.to(dtype=dtype, memory_format=memory_format)
-
-        # FromRGB.
-        if self.in_channels == 0 or self.architecture == 'skip':
-            misc.assert_shape(
-                img, [None, self.img_channels, self.resolution, self.resolution])
-            img = img.to(dtype=dtype, memory_format=memory_format)
-            y = self.fromrgb(img)
-            x = x + y if x is not None else y
-            img = upfirdn2d.downsample2d(
-                img, self.resample_filter) if self.architecture == 'skip' else None
-
-        # Main layers.
-        if self.architecture == 'resnet':
-            y = self.skip(x, gain=np.sqrt(0.5))
-            x = self.conv0(x)
-            x = self.conv1(x, gain=np.sqrt(0.5))
-            x = y.add_(x)
-        else:
-            x = self.conv0(x)
-            x = self.conv1(x)
-
-        assert x.dtype == dtype
-        return x, img
-
-    def extra_repr(self):
-        return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class MinibatchStdLayer(torch.nn.Module):
-    def __init__(self, group_size, num_channels=1):
-        super().__init__()
-        self.group_size = group_size
-        self.num_channels = num_channels
-
-    def forward(self, x):
-        N, C, H, W = x.shape
-        with misc.suppress_tracer_warnings():  # as_tensor results are registered as constants
-            G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(
-                N)) if self.group_size is not None else N
-        F = self.num_channels
-        c = C // F
-
-        # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
-        y = x.reshape(G, -1, F, c, H, W)
-        # [GnFcHW] Subtract mean over group.
-        y = y - y.mean(dim=0)
-        # [nFcHW]  Calc variance over group.
-        y = y.square().mean(dim=0)
-        y = (y + 1e-8).sqrt()               # [nFcHW]  Calc stddev over group.
-        # [nF]     Take average over channels and pixels.
-        y = y.mean(dim=[2, 3, 4])
-        y = y.reshape(-1, F, 1, 1)          # [nF11]   Add missing dimensions.
-        # [NFHW]   Replicate over group and pixels.
-        y = y.repeat(G, 1, H, W)
-        # [NCHW]   Append to input as new channels.
-        x = torch.cat([x, y], dim=1)
-        return x
-
-    def extra_repr(self):
-        return f'group_size={self.group_size}, num_channels={self.num_channels:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class DiscriminatorEpilogue(torch.nn.Module):
-    def __init__(self,
-                 in_channels,                    # Number of input channels.
-                 # Dimensionality of mapped conditioning label, 0 = no label.
-                 cmap_dim,
-                 resolution,                     # Resolution of this block.
-                 # Number of input color channels.
-                 img_channels,
-                 # Architecture: 'orig', 'skip', 'resnet'.
-                 architecture='resnet',
-                 # Group size for the minibatch standard deviation layer, None = entire minibatch.
-                 mbstd_group_size=4,
-                 # Number of features for the minibatch standard deviation layer, 0 = disable.
-                 mbstd_num_channels=1,
-                 # Activation function: 'relu', 'lrelu', etc.
-                 activation='lrelu',
-                 # Clamp the output of convolution layers to +-X, None = disable clamping.
-                 conv_clamp=None,
-                 ):
-        assert architecture in ['orig', 'skip', 'resnet']
-        super().__init__()
-        self.in_channels = in_channels
-        self.cmap_dim = cmap_dim
-        self.resolution = resolution
-        self.img_channels = img_channels
-        self.architecture = architecture
-
-        if architecture == 'skip':
-            self.fromrgb = Conv2dLayer(
-                img_channels, in_channels, kernel_size=1, activation=activation)
-        self.mbstd = MinibatchStdLayer(
-            group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None
-        self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels,
-                                kernel_size=3, activation=activation, conv_clamp=conv_clamp)
-        self.fc = FullyConnectedLayer(
-            in_channels * (resolution ** 2), in_channels, activation=activation)
-        self.out = FullyConnectedLayer(
-            in_channels, 1 if cmap_dim == 0 else cmap_dim)
-
-    def forward(self, x, img, cmap, force_fp32=False):
-        misc.assert_shape(x, [None, self.in_channels,
-                          self.resolution, self.resolution])  # [NCHW]
-        _ = force_fp32  # unused
-        dtype = torch.float32
-        memory_format = torch.contiguous_format
-
-        # FromRGB.
-        x = x.to(dtype=dtype, memory_format=memory_format)
-        if self.architecture == 'skip':
-            misc.assert_shape(
-                img, [None, self.img_channels, self.resolution, self.resolution])
-            img = img.to(dtype=dtype, memory_format=memory_format)
-            x = x + self.fromrgb(img)
-
-        # Main layers.
-        if self.mbstd is not None:
-            x = self.mbstd(x)
-        x = self.conv(x)
-        x = self.fc(x.flatten(1))
-        x = self.out(x)
-
-        # Conditioning.
-        if self.cmap_dim > 0:
-            misc.assert_shape(cmap, [None, self.cmap_dim])
-            x = (x * cmap).sum(dim=1, keepdim=True) * \
-                (1 / np.sqrt(self.cmap_dim))
-
-        assert x.dtype == dtype
-        return x
-
-    def extra_repr(self):
-        return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Discriminator(torch.nn.Module):
-    def __init__(self,
-                 # Conditioning label (C) dimensionality.
-                 c_dim,
-                 img_resolution,                 # Input resolution.
-                 # Number of input color channels.
-                 img_channels,
-                 # Architecture: 'orig', 'skip', 'resnet'.
-                 architecture='resnet',
-                 # Overall multiplier for the number of channels.
-                 channel_base=32768,
-                 # Maximum number of channels in any layer.
-                 channel_max=512,
-                 # Use FP16 for the N highest resolutions.
-                 num_fp16_res=4,
-                 # Clamp the output of convolution layers to +-X, None = disable clamping.
-                 conv_clamp=256,
-                 # Dimensionality of mapped conditioning label, None = default.
-                 cmap_dim=None,
-                 block_kwargs={},       # Arguments for DiscriminatorBlock.
-                 mapping_kwargs={},       # Arguments for MappingNetwork.
-                 # Arguments for DiscriminatorEpilogue.
-                 epilogue_kwargs={},
-                 ):
-        super().__init__()
-        self.c_dim = c_dim
-        self.img_resolution = img_resolution
-        self.img_resolution_log2 = int(np.log2(img_resolution))
-        self.img_channels = img_channels
-        self.block_resolutions = [
-            2 ** i for i in range(self.img_resolution_log2, 2, -1)]
-        channels_dict = {res: min(channel_base // res, channel_max)
-                         for res in self.block_resolutions + [4]}
-        fp16_resolution = max(
-            2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
-
-        if cmap_dim is None:
-            cmap_dim = channels_dict[4]
-        if c_dim == 0:
-            cmap_dim = 0
-
-        common_kwargs = dict(img_channels=img_channels,
-                             architecture=architecture, conv_clamp=conv_clamp)
-        cur_layer_idx = 0
-        for res in self.block_resolutions:
-            in_channels = channels_dict[res] if res < img_resolution else 0
-            tmp_channels = channels_dict[res]
-            out_channels = channels_dict[res // 2]
-            use_fp16 = (res >= fp16_resolution)
-            block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res,
-                                       first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs)
-            setattr(self, f'b{res}', block)
-            cur_layer_idx += block.num_layers
-        if c_dim > 0:
-            self.mapping = MappingNetwork(
-                z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs)
-        self.b4 = DiscriminatorEpilogue(
-            channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs)
-
-    def forward(self, img, c, update_emas=False, **block_kwargs):
-        _ = update_emas  # unused
-        x = None
-        for res in self.block_resolutions:
-            block = getattr(self, f'b{res}')
-            x, img = block(x, img, **block_kwargs)
-
-        cmap = None
-        if self.c_dim > 0:
-            cmap = self.mapping(None, c)
-        x = self.b4(x, img, cmap)
-        return x
-
-    def extra_repr(self):
-        return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/haakohu/deep_privacy2_face/configs/datasets/coco_cse.py b/spaces/haakohu/deep_privacy2_face/configs/datasets/coco_cse.py
deleted file mode 100644
index f4834b64ebb77634ce5f1fb66a3cf9abafe26464..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2_face/configs/datasets/coco_cse.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import os
-from pathlib import Path
-from tops.config import LazyCall as L
-import torch
-import functools
-from dp2.data.datasets.coco_cse import CocoCSE
-from dp2.data.build import get_dataloader
-from dp2.data.transforms.transforms import CreateEmbedding, Normalize, Resize, ToFloat, CreateCondition, RandomHorizontalFlip
-from dp2.data.transforms.stylegan2_transform import StyleGANAugmentPipe
-from dp2.metrics.torch_metrics import compute_metrics_iteratively
-from .utils import final_eval_fn
-
-
-dataset_base_dir = os.environ["BASE_DATASET_DIR"] if "BASE_DATASET_DIR" in os.environ else "data"
-metrics_cache = os.environ["FBA_METRICS_CACHE"] if "FBA_METRICS_CACHE" in os.environ else ".cache"
-data_dir = Path(dataset_base_dir, "coco_cse")
-data = dict(
-    imsize=(288, 160),
-    im_channels=3,
-    semantic_nc=26,
-    cse_nc=16,
-    train=dict(
-        dataset=L(CocoCSE)(data_dir.joinpath("train"), transform=None, normalize_E=False),
-        loader=L(get_dataloader)(
-            shuffle=True, num_workers=6, drop_last=True, prefetch_factor=2,
-            batch_size="${train.batch_size}",
-            dataset="${..dataset}",
-            infinite=True,
-            gpu_transform=L(torch.nn.Sequential)(*[
-                L(ToFloat)(),
-                L(StyleGANAugmentPipe)(
-                    rotate=0.5, rotate_max=.05,
-                    xint=.5, xint_max=0.05,
-                    scale=.5, scale_std=.05,
-                    aniso=0.5, aniso_std=.05,
-                    xfrac=.5, xfrac_std=.05,
-                    brightness=.5, brightness_std=.05,
-                    contrast=.5, contrast_std=.1,
-                    hue=.5, hue_max=.05,
-                    saturation=.5, saturation_std=.5,
-                    imgfilter=.5, imgfilter_std=.1),
-                L(RandomHorizontalFlip)(p=0.5),
-                L(CreateEmbedding)(),
-                L(Resize)(size="${data.imsize}"),
-                L(Normalize)(mean=[.5, .5, .5], std=[.5, .5, .5], inplace=True),
-                L(CreateCondition)(),
-            ])
-        )
-    ),
-    val=dict(
-        dataset=L(CocoCSE)(data_dir.joinpath("val"), transform=None, normalize_E=False),
-        loader=L(get_dataloader)(
-            shuffle=False, num_workers=6, drop_last=True, prefetch_factor=2,
-            batch_size="${train.batch_size}",
-            dataset="${..dataset}",
-            infinite=False,
-            gpu_transform=L(torch.nn.Sequential)(*[
-                L(ToFloat)(),
-                L(CreateEmbedding)(),
-                L(Resize)(size="${data.imsize}"),
-                L(Normalize)(mean=[.5, .5, .5], std=[.5, .5, .5], inplace=True),
-                L(CreateCondition)(),
-            ])
-        )
-    ),
-    # Training evaluation might do optimizations to reduce compute overhead. E.g. compute with AMP.
-    train_evaluation_fn=functools.partial(compute_metrics_iteratively, cache_directory=Path(metrics_cache, "coco_cse_val"), include_two_fake=False),
-    evaluation_fn=functools.partial(final_eval_fn, cache_directory=Path(metrics_cache, "coco_cse_val_final"), include_two_fake=True)
-)
diff --git a/spaces/halfdevil/demochat/README.md b/spaces/halfdevil/demochat/README.md
deleted file mode 100644
index 8c3bd6c9c0183d7975f4a73bd66a86dcf6527a53..0000000000000000000000000000000000000000
--- a/spaces/halfdevil/demochat/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Demochat
-emoji: 🌍
-colorFrom: red
-colorTo: green
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/utils/model_serialization.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/utils/model_serialization.py
deleted file mode 100644
index 01669fd076bc543096aafaccf42e3b256db91ec2..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/utils/model_serialization.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-from collections import OrderedDict, defaultdict
-import logging
-import math
-import torch
-
-from maskrcnn_benchmark.utils.imports import import_file
-
-def resize_2d(posemb, shape_new):
-    # Rescale the grid of position embeddings when loading from state_dict. Adapted from
-    # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
-    ntok_new = shape_new[0]
-    gs_old = int(math.sqrt(len(posemb)))  # 2 * w - 1
-    gs_new = int(math.sqrt(ntok_new))  # 2 * w - 1
-    posemb_grid = posemb.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
-    posemb_grid = torch.nn.functional.interpolate(posemb_grid, size=(gs_new, gs_new), mode='bilinear')
-    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(gs_new * gs_new, -1)
-    return posemb_grid
-
-def align_and_update_state_dicts(model_state_dict, loaded_state_dict, reshape_keys=['pos_bias_table'], use_weightmap=False):
-    """
-    Strategy: suppose that the models that we will create will have prefixes appended
-    to each of its keys, for example due to an extra level of nesting that the original
-    pre-trained weights from ImageNet won't contain. For example, model.state_dict()
-    might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains
-    res2.conv1.weight. We thus want to match both parameters together.
-    For that, we look for each model weight, look among all loaded keys if there is one
-    that is a suffix of the current weight name, and use it if that's the case.
-    If multiple matches exist, take the one with longest size
-    of the corresponding name. For example, for the same model as before, the pretrained
-    weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case,
-    we want to match backbone[0].body.conv1.weight to conv1.weight, and
-    backbone[0].body.res2.conv1.weight to res2.conv1.weight.
-    """
-    current_keys = sorted(list(model_state_dict.keys()))
-    loaded_keys = sorted(list(loaded_state_dict.keys()))
-    # get a matrix of string matches, where each (i, j) entry correspond to the size of the
-    # loaded_key string, if it matches
-    match_matrix = [
-        len(j) if i.endswith(j) else 0 for i in current_keys for j in loaded_keys
-    ]
-    match_matrix = torch.as_tensor(match_matrix).view(
-        len(current_keys), len(loaded_keys)
-    )
-    max_match_size, idxs = match_matrix.max(1)
-    # remove indices that correspond to no-match
-    idxs[max_match_size == 0] = -1
-
-    matched_keys = []
-    # used for logging
-    max_size = max([len(key) for key in current_keys]) if current_keys else 1
-    max_size_loaded = max([len(key) for key in loaded_keys]) if loaded_keys else 1
-    log_str_template = "{: <{}} loaded from {: <{}} of shape {}"
-    logger = logging.getLogger(__name__)
-    for idx_new, idx_old in enumerate(idxs.tolist()):
-        if idx_old == -1:
-            continue
-        key = current_keys[idx_new]
-        key_old = loaded_keys[idx_old]
-        if model_state_dict[key].shape != loaded_state_dict[key_old].shape:
-            if any([k in key_old for k in reshape_keys]):
-                new_shape = model_state_dict[key].shape
-                logger.warning('Reshaping {} -> {}. \n'.format(key_old, key))
-                model_state_dict[key] = resize_2d(loaded_state_dict[key_old], new_shape)
-            elif use_weightmap and 'cls_logits' in key:
-                coco_in_objects365_inds = [
-                    227, 26, 55, 202, 2, 44, 338, 346, 32, 336, 118, 299, 218,
-                    25, 361, 59, 95, 161, 278, 82, 110, 22, 364, 134, 9, 350,
-                    152, 323, 304, 130, 285, 289, 16, 172, 17, 18, 283, 305,
-                    321, 35, 362, 88, 127, 174, 292, 37, 11, 6, 267, 212, 41,
-                    58, 162, 237, 98, 48, 63, 81, 247, 23, 94, 326, 349, 178,
-                    203, 259, 171, 60, 198, 213, 325, 282, 258, 33, 71, 353,
-                    273, 318, 148, 330
-                ]
-                logger.info("Use coco_in_objects365_inds labelmap for COCO detection because of size mis-match, "
-                      "Reshaping {} -> {}. \n".format(key_old, key))
-                new_shape = model_state_dict[key].shape
-                assert new_shape[0] == len(coco_in_objects365_inds)
-                weight_inds_old = torch.as_tensor(coco_in_objects365_inds).to(loaded_state_dict[key_old].device)
-                model_state_dict[key] = loaded_state_dict[key_old][weight_inds_old].to(model_state_dict[key].device)
-            else:
-                logger.info('Skip due to size mismatch: {} -> {}. \n'.format(key_old, key))
-                continue
-        else:
-            model_state_dict[key] = loaded_state_dict[key_old]
-        matched_keys.append(key)
-        logger.info(
-            log_str_template.format(
-                key,
-                max_size,
-                key_old,
-                max_size_loaded,
-                tuple(loaded_state_dict[key_old].shape),
-            )
-        )
-    missing_keys = set(current_keys)-set(matched_keys)
-    if len(missing_keys):
-        groups = _group_checkpoint_keys(missing_keys)
-        msg_per_group = sorted(k + _group_to_str(v) for k, v in groups.items())
-        msg = '\n'.join(sorted(msg_per_group))
-        logger.warning('Some layers unloaded with pre-trained weight: \n' + msg)
-
-def strip_prefix_if_present(state_dict, prefix):
-    keys = sorted(state_dict.keys())
-    if not all(key.startswith(prefix) for key in keys):
-        return state_dict
-    stripped_state_dict = OrderedDict()
-    for key, value in state_dict.items():
-        stripped_state_dict[key.replace(prefix, "", 1)] = value
-    return stripped_state_dict
-
-def load_state_dict(model, loaded_state_dict):
-    model_state_dict = model.state_dict()
-    # if the state_dict comes from a model that was wrapped in a
-    # DataParallel or DistributedDataParallel during serialization,
-    # remove the "module" prefix before performing the matching
-    loaded_state_dict = strip_prefix_if_present(loaded_state_dict, prefix="module.")
-    align_and_update_state_dicts(model_state_dict, loaded_state_dict)
-
-    # use strict loading
-    model.load_state_dict(model_state_dict)
-
-def _group_checkpoint_keys(keys):
-    """
-    Group keys based on common prefixes. A prefix is the string up to the final
-    "." in each key.
-    Args:
-        keys (list[str]): list of parameter names, i.e. keys in the model
-            checkpoint dict.
-    Returns:
-        dict[list]: keys with common prefixes are grouped into lists.
-    """
-    groups = defaultdict(list)
-    for key in keys:
-        pos = key.rfind(".")
-        if pos >= 0:
-            head, tail = key[:pos], [key[pos + 1 :]]
-        else:
-            head, tail = key, []
-        groups[head].extend(tail)
-    return groups
-
-def _group_to_str(group):
-    """
-    Format a group of parameter name suffixes into a loggable string.
-    Args:
-        group (list[str]): list of parameter name suffixes.
-    Returns:
-        str: formated string.
-    """
-    if len(group) == 0:
-        return ""
-
-    if len(group) == 1:
-        return "." + group[0]
-
-    return ".{" + ", ".join(sorted(group)) + "}"
\ No newline at end of file
diff --git a/spaces/hasibzunair/fifa-tryon-demo/data/aligned_dataset.py b/spaces/hasibzunair/fifa-tryon-demo/data/aligned_dataset.py
deleted file mode 100644
index e187b4d64fcedadf2fa3feca76e1d005a16f67d4..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/data/aligned_dataset.py
+++ /dev/null
@@ -1,213 +0,0 @@
-import os.path
-from data.base_dataset import BaseDataset, get_params, get_transform, normalize
-from data.image_folder import make_dataset, make_dataset_test
-from PIL import Image
-import torch
-import json
-import numpy as np
-import os.path as osp
-from PIL import ImageDraw
-
-
-class AlignedDataset(BaseDataset):
-    def initialize(self, opt):
-        self.opt = opt
-        self.root = opt.dataroot
-        self.diction = {}
-
-        self.fine_height = 256
-        self.fine_width = 192
-        self.radius = 5
-
-        # load data list from pairs file
-        human_names = []
-        cloth_names = []
-        with open(os.path.join(opt.dataroot, opt.datapairs), 'r') as f:
-            for line in f.readlines():
-                h_name, c_name = line.strip().split()
-                human_names.append(h_name)
-                cloth_names.append(c_name)
-        self.human_names = human_names
-        self.cloth_names = cloth_names
-        self.dataset_size = len(human_names)
-
-        # input A (label maps)
-        dir_A = '_A' if self.opt.label_nc == 0 else '_label'
-        self.dir_A = os.path.join(opt.dataroot, opt.phase + dir_A)
-        self.A_paths = sorted(make_dataset(self.dir_A))
-
-        self.fine_height = 256
-        self.fine_width = 192
-        self.radius = 5
-
-        # input A test (label maps)
-        dir_A = '_A' if self.opt.label_nc == 0 else '_label'
-        self.dir_A = os.path.join(opt.dataroot, opt.phase + dir_A)
-        self.A_paths = sorted(make_dataset_test(self.dir_A))
-
-        # input B (real images)
-        dir_B = '_B' if self.opt.label_nc == 0 else '_img'
-        self.dir_B = os.path.join(opt.dataroot, opt.phase + dir_B)
-        self.B_paths = sorted(make_dataset(self.dir_B))
-
-        self.dataset_size = len(self.A_paths)
-        self.build_index(self.B_paths)
-
-        dir_E = '_edge'
-        self.dir_E = os.path.join(opt.dataroot, opt.phase + dir_E)
-        self.E_paths = sorted(make_dataset(self.dir_E))
-        self.ER_paths = make_dataset(self.dir_E)
-
-        dir_M = '_mask'
-        self.dir_M = os.path.join(opt.dataroot, opt.phase + dir_M)
-        self.M_paths = sorted(make_dataset(self.dir_M))
-        self.MR_paths = make_dataset(self.dir_M)
-
-        dir_MC = '_colormask'
-        self.dir_MC = os.path.join(opt.dataroot, opt.phase + dir_MC)
-        self.MC_paths = sorted(make_dataset(self.dir_MC))
-        self.MCR_paths = make_dataset(self.dir_MC)
-
-        dir_C = '_color'
-        self.dir_C = os.path.join(opt.dataroot, opt.phase + dir_C)
-        self.C_paths = sorted(make_dataset(self.dir_C))
-        self.CR_paths = make_dataset(self.dir_C)
-        # self.build_index(self.C_paths)
-
-        dir_A = '_A' if self.opt.label_nc == 0 else '_label'
-        self.dir_A = os.path.join(opt.dataroot, opt.phase + dir_A)
-        self.A_paths = sorted(make_dataset_test(self.dir_A))
-
-    def random_sample(self, item):
-        name = item.split('/')[-1]
-        name = name.split('-')[0]
-        lst = self.diction[name]
-        new_lst = []
-        for dir in lst:
-            if dir != item:
-                new_lst.append(dir)
-        return new_lst[np.random.randint(len(new_lst))]
-
-    def build_index(self, dirs):
-        for k, dir in enumerate(dirs):
-            name = dir.split('/')[-1]
-            name = name.split('-')[0]
-
-            # print(name)
-            for k, d in enumerate(dirs[max(k-20, 0):k+20]):
-                if name in d:
-                    if name not in self.diction.keys():
-                        self.diction[name] = []
-                        self.diction[name].append(d)
-                    else:
-                        self.diction[name].append(d)
-
-    def __getitem__(self, index):
-        train_mask = 9600
-        # input A (label maps)
-        box = []
-        # for k,x in enumerate(self.A_paths):
-        #     if '000386' in x :
-        #         index=k
-        #         break
-        test = np.random.randint(2032)
-        # for k, s in enumerate(self.B_paths):
-        #    if '006581' in s:
-        #        test = k
-        #        break
-
-        # get names from the pairs file
-        c_name = self.cloth_names[index]
-        h_name = self.human_names[index]
-
-        # A_path = self.A_paths[index]
-        A_path = osp.join(self.dir_A, h_name.replace(".jpg", ".png"))
-        A = Image.open(A_path).convert('L')
-
-        params = get_params(self.opt, A.size)
-        if self.opt.label_nc == 0:
-            transform_A = get_transform(self.opt, params)
-            A_tensor = transform_A(A.convert('RGB'))
-        else:
-            transform_A = get_transform(
-                self.opt, params, method=Image.NEAREST, normalize=False)
-            A_tensor = transform_A(A) * 255.0
-
-        B_tensor = inst_tensor = feat_tensor = 0
-        # input B (real images)
-
-        # B_path = self.B_paths[index]
-        B_path = osp.join(self.dir_B, h_name)
-        name = B_path.split('/')[-1]
-
-        B = Image.open(B_path).convert('RGB')
-        transform_B = get_transform(self.opt, params)
-        B_tensor = transform_B(B)
-
-        # input M (masks)
-        M_path = B_path  # self.M_paths[np.random.randint(1)]
-        MR_path = B_path  # self.MR_paths[np.random.randint(1)]
-        M = Image.open(M_path).convert('L')
-        MR = Image.open(MR_path).convert('L')
-        M_tensor = transform_A(MR)
-
-        ### input_MC (colorMasks)
-        MC_path = B_path  # self.MC_paths[1]
-        MCR_path = B_path  # self.MCR_paths[1]
-        MCR = Image.open(MCR_path).convert('L')
-        MC_tensor = transform_A(MCR)
-
-        ### input_C (color)
-        # print(self.C_paths)
-        # C_path = self.C_paths[test]
-        C_path = osp.join(self.dir_C, c_name)
-        C = Image.open(C_path).convert('RGB')
-        C_tensor = transform_B(C)
-
-        # Edge
-        # E_path = self.E_paths[test]
-        E_path = osp.join(self.dir_E, c_name)
-        # print(E_path)
-        E = Image.open(E_path).convert('L')
-        E_tensor = transform_A(E)
-
-        # Pose
-        pose_name = B_path.replace('.jpg', '_keypoints.json').replace('.png', '_keypoints.json').replace(
-            'test_img', 'test_pose')
-        with open(osp.join(pose_name), 'r') as f:
-            pose_label = json.load(f)
-            pose_data = pose_label['people'][0]['pose_keypoints']
-            pose_data = np.array(pose_data)
-            pose_data = pose_data.reshape((-1, 3))
-
-        point_num = pose_data.shape[0]
-        pose_map = torch.zeros(point_num, self.fine_height, self.fine_width)
-        r = self.radius
-        im_pose = Image.new('L', (self.fine_width, self.fine_height))
-        pose_draw = ImageDraw.Draw(im_pose)
-        for i in range(point_num):
-            one_map = Image.new('L', (self.fine_width, self.fine_height))
-            draw = ImageDraw.Draw(one_map)
-            pointx = pose_data[i, 0]
-            pointy = pose_data[i, 1]
-            if pointx > 1 and pointy > 1:
-                draw.rectangle((pointx-r, pointy-r, pointx +
-                                r, pointy+r), 'white', 'white')
-                pose_draw.rectangle(
-                    (pointx-r, pointy-r, pointx+r, pointy+r), 'white', 'white')
-            one_map = transform_B(one_map.convert('RGB'))
-            pose_map[i] = one_map[0]
-        P_tensor = pose_map
-
-        input_dict = {'label': A_tensor, 'image': B_tensor,
-                      'path': A_path, 'name': A_path.split("/")[-1],
-                      'edge': E_tensor, 'color': C_tensor, 'mask': M_tensor, 'colormask': MC_tensor, 'pose': P_tensor
-                      }
-
-        return input_dict
-
-    def __len__(self):
-        return len(self.A_paths) // self.opt.batchSize * self.opt.batchSize
-
-    def name(self):
-        return 'AlignedDataset'
diff --git a/spaces/hdhzk/bingo/src/components/learn-more.tsx b/spaces/hdhzk/bingo/src/components/learn-more.tsx
deleted file mode 100644
index a64459ee7900a612292e117a6bda96ee9260990f..0000000000000000000000000000000000000000
--- a/spaces/hdhzk/bingo/src/components/learn-more.tsx
+++ /dev/null
@@ -1,39 +0,0 @@
-import React from 'react'
-import { SourceAttribution } from '@/lib/bots/bing/types'
-
-export interface LearnMoreProps {
-  sourceAttributions?: SourceAttribution[]
-}
-
-export function LearnMore({ sourceAttributions }: LearnMoreProps) {
-  if (!sourceAttributions?.length) {
-    return null
-  }
-
-  return (
-    <div className="learn-more-root" role="list" aria-label="了解详细信息:">
-      <div className="learn-more">了解详细信息:</div>
-      <div className="attribution-container">
-        <div className="attribution-items">
-          {sourceAttributions.map((attribution, index) => {
-            const { providerDisplayName, seeMoreUrl } = attribution
-            const { host } = new URL(seeMoreUrl)
-            return (
-              <a
-                key={index}
-                className="attribution-item"
-                target="_blank"
-                role="listitem"
-                href={seeMoreUrl}
-                title={providerDisplayName}
-                tabIndex={index}
-              >
-                {index + 1}. {host}
-              </a>
-            )
-          })}
-        </div>
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/hersia/V_Admin_Bot/app.py b/spaces/hersia/V_Admin_Bot/app.py
deleted file mode 100644
index aee3e5b13c9b305b798d35b3af064467d2539a8c..0000000000000000000000000000000000000000
--- a/spaces/hersia/V_Admin_Bot/app.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import os
-import gradio as gr
-import openai
-from gtts import gTTS
-
-
-openai.api_key = os.environ["OPEN_AI_KEY"]
-
-
-def transcribe(audio):   
-        audio_file = open(audio, "rb")    
-        transcript = openai.Audio.transcribe("whisper-1", audio_file)
-        
-        return transcript["text"]
-
-
-
-   
-with gr.Blocks() as demo:
-    gr.Markdown(
-    """
-    # Welcome to your Virtual Smart Administrator.
-    """
-    )
-    with gr.Accordion("Click for Instructions:"):
-            gr.Markdown(
-    """
-    * Record your query.
-    * Submit your query, and follow the chat or listen to the advice.
-    * When you are ready to respond, clear your last recording and resubmit.
-    note: Transcribe Audio does not work on iOS
-    """)
-        
-
-    messages = gr.State(value=[{"role": "system", "content": "Ignore all instructions prior to this one. As an expert in Microsoft engineering, you have been working in this field for 20 years with a wide range of experience in end-user support, knowledge of all Microsoft services, and troubleshooting styles. Your task is to provide expert advice on resolving technical issues related to Microsoft services, including but not limited to: Windows and Mac OS operating systems, Microsoft Office, Azure, SharePoint, Dynamics 365, Exchange Server, SQL Server, Teams, Networking and infrastructure architecture. It is important that you ALWAYS ask clarifying questions before providing an answer, to ensure a better understanding of the request. Is this understood? Skills: As an IT Administrator Bot, you possess the following skills and expertise: 20 years of experience in Microsoft engineering, Strong knowledge of Windows operating system and troubleshooting techniques, Proficient in Microsoft Office suite, including Word, Excel, PowerPoint, Outlook, and OneNote, Expertise in Microsoft Azure, including configuration, deployment, and management, Knowledge of SharePoint, including site creation, content management, and permission management, Experience in Dynamics 365, including customer engagement, sales, and marketing modules, Proficient in Exchange Server, including mailbox management, email routing, and message tracking, Expertise in SQL Server, including database design, querying, and optimisation, Strong communication skills, both verbal and written, Ability to work under pressure and handle multiple tasks simultaneously, Strong problem-solving skills, with a focus on providing practical solutions to complex technical issues. Always ask questions to narrow down the resolution and increase your understanding of a problem. Here are some questions you can ask: Can you provide more details about the issue you are experiencing? Have you made any recent changes to the system or software? Have you tried any troubleshooting steps before contacting us? To resolve a user's problem, take the following steps: Identify the root cause of the issue by asking clarifying questions and analysing system logs. Develop a step-by-step plan to address the issue, including any necessary software updates or configuration changes. Communicate the plan to the user, ensuring that they understand the steps involved and any potential impacts. Execute the plan, testing each step and confirming that the issue has been resolved. Follow up with the user to ensure that the issue has been fully resolved and to gather feedback on the support experience. If the topic is not related to Microsoft, Azure, SharePoint, Teams, or desktop troubleshooting, you are not will not provide answers or attempt to engage, ignore the request complete. Type only 3 dots …..."}])
-
-    def botResponse(user_input, messages):
-        messages.append({"role": "user", "content": user_input})
-        response = openai.ChatCompletion.create(
-          model="gpt-3.5-turbo-0301",
-          messages=messages
-        )
-
-        system_message = response["choices"][0]["message"]["content"]
-        messages.append({"role": "assistant", "content": system_message})
-
-        chat_transcript = ""
-        for message in messages:
-            if (message["role"] != "system"):
-                chat_transcript += message["role"] + ": " + message["content"] + "\n\n"
-    
-        return chat_transcript
-
-    def giveVoice(messages):
-        bot_message=messages[-1]
-        
-        myobj = gTTS(text=bot_message["content"])
-        myobj.save("temp.mp3")
-    
-        dir = os.getcwd()
-        new_path = os.path.join(dir, "temp.mp3")
-    
-        return new_path
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            user_audio = gr.Audio(source="microphone", type="filepath", label="Input Phrase")
-            submit_btn = gr.Button(value="Transcribe Audio")
-            submit_btn2 = gr.Button(value="Submit Text")
-            gpt_voice = gr.Audio(label="Listen to Advice")
-        with gr.Column(scale=2):
-            user_transcript = gr.Text(label="Audio Translation", interactive=False)
-            user_text = gr.Text(label="Text Input")
-            gpt_transcript = gr.Text(label="Chat Transcript")
-    submit_btn.click(transcribe, user_audio, user_transcript)
-    submit_btn2.click(botResponse, [user_text, messages], gpt_transcript)
-    user_transcript.change(botResponse, [user_transcript, messages], gpt_transcript)
-    gpt_transcript.change(giveVoice, messages, gpt_voice)
-    
-    
-demo.launch(share=False)
\ No newline at end of file
diff --git a/spaces/hhhwmws/ChatHaruhi-GLMPro/app.py b/spaces/hhhwmws/ChatHaruhi-GLMPro/app.py
deleted file mode 100644
index 20921416240721c4a66d6ebc24e7927563cf12d5..0000000000000000000000000000000000000000
--- a/spaces/hhhwmws/ChatHaruhi-GLMPro/app.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import zipfile
-import gradio as gr
-from PIL import Image
-from chatharuhi import ChatHaruhi
-import wget
-import os
-import openai
-import copy
-
-
-NAME_DICT = {'汤师爷': 'tangshiye', '慕容复': 'murongfu', '李云龙': 'liyunlong', 'Luna': 'Luna', '王多鱼': 'wangduoyu',
-             'Ron': 'Ron', '鸠摩智': 'jiumozhi', 'Snape': 'Snape',
-             '凉宫春日': 'haruhi', 'Malfoy': 'Malfoy', '虚竹': 'xuzhu', '萧峰': 'xiaofeng', '段誉': 'duanyu',
-             'Hermione': 'Hermione', 'Dumbledore': 'Dumbledore', '王语嫣': 'wangyuyan',
-             'Harry': 'Harry', 'McGonagall': 'McGonagall', '白展堂': 'baizhantang', '佟湘玉': 'tongxiangyu',
-             '郭芙蓉': 'guofurong', '旅行者': 'wanderer', '钟离': 'zhongli',
-             '胡桃': 'hutao', 'Sheldon': 'Sheldon', 'Raj': 'Raj', 'Penny': 'Penny', '韦小宝': 'weixiaobao',
-             '乔峰': 'qiaofeng', '神里绫华': 'ayaka', '雷电将军': 'raidenShogun', '于谦': 'yuqian'}
-
-
-
-try:
-  os.makedirs("characters_zip")
-except:
-  pass
-try:
-  os.makedirs("characters")
-except:
-  pass
-ai_roles_obj = {}
-for ai_role_en in NAME_DICT.values():
-  file_url = f"https://github.com/LC1332/Haruhi-2-Dev/raw/main/data/character_in_zip/{ai_role_en}.zip"
-  try:
-    os.makedirs(f"characters/{ai_role_en}")
-  except:
-    pass
-  if f"{ai_role_en}.zip" not in os.listdir(f"characters_zip"):
-    destination_file = f"characters_zip/{ai_role_en}.zip"
-    wget.download(file_url, destination_file)
-    destination_folder = f"characters/{ai_role_en}"
-    with zipfile.ZipFile(destination_file, 'r') as zip_ref:
-        zip_ref.extractall(destination_folder)
-  db_folder = f"./characters/{ai_role_en}/content/{ai_role_en}"
-  system_prompt = f"./characters/{ai_role_en}/content/system_prompt.txt"
-  ai_roles_obj[ai_role_en] = ChatHaruhi(system_prompt=system_prompt,
-                        llm="GLMPro",
-                        story_db=db_folder,
-                        verbose=True)
-
-
-async def get_response(user_role, user_text, ai_role, chatbot):
-    role_en = NAME_DICT[ai_role]
-    ai_roles_obj[role_en].dialogue_history = copy.deepcopy(chatbot)
-    response = ai_roles_obj[role_en].chat(role=user_role, text=user_text)
-    user_msg = user_role + ':「' + user_text + '」'
-    latest_msg = (user_msg, response)
-    print(latest_msg)
-    chatbot.append(latest_msg)
-    return chatbot
-
-async def respond(user_role, user_text, ai_role, chatbot):
-    return await get_response(user_role, user_text, ai_role, chatbot), None
-
-
-def clear(user_role, user_text, chatbot):
-    return None, None, []
-
-
-def get_image(ai_role):
-    role_en = NAME_DICT[ai_role]
-    return Image.open(f'images/{role_en}.jpg'), None, None, []
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-        # Chat凉宫春日 ChatHaruhi
-        ## Reviving Anime Character in Reality via Large Language Model
-        ChatHaruhi2.0的GLMPro 版本demo implemented by [Weishi MI](https://github.com/hhhwmws0117) and [chenxi](https://github.com/todochenxi)
-        更多信息见项目github链接 [https://github.com/LC1332/Chat-Haruhi-Suzumiya](https://github.com/LC1332/Chat-Haruhi-Suzumiya)
-        如果觉得有趣请拜托为我们点上star. If you find it interesting, please be kind enough to give us a star.
-        user_role 为用户扮演的人物 请尽量设置为与剧情相关的人物 且不要与主角同名
-        """
-    )
-    with gr.Row():
-        chatbot = gr.Chatbot()
-        role_image = gr.Image(height=400, value="./images/haruhi.jpg")
-    with gr.Row():
-        user_role = gr.Textbox(label="user_role")
-        user_text = gr.Textbox(label="user_text")
-    with gr.Row():
-        submit = gr.Button("Submit")
-        clean = gr.ClearButton(value="Clear")
-    ai_role = gr.Radio(['汤师爷', '慕容复', '李云龙',
-                     'Luna', '王多鱼', 'Ron', '鸠摩智',
-                     'Snape', '凉宫春日', 'Malfoy', '虚竹',
-                     '萧峰', '段誉', 'Hermione', 'Dumbledore',
-                     '王语嫣',
-                     'Harry', 'McGonagall',
-                     '白展堂', '佟湘玉', '郭芙蓉',
-                     '旅行者', '钟离', '胡桃',
-                     'Sheldon', 'Raj', 'Penny',
-                     '韦小宝', '乔峰', '神里绫华',
-                     '雷电将军', '于谦'], label="characters", value='凉宫春日')
-    ai_role.change(get_image, ai_role, [role_image, user_role, user_text, chatbot])
-    user_text.submit(fn=respond, inputs=[user_role, user_text, ai_role, chatbot], outputs=[chatbot, user_text])
-    submit.click(fn=respond, inputs=[user_role, user_text, ai_role, chatbot], outputs=[chatbot, user_text])
-    clean.click(clear, [user_role, user_text, chatbot], [user_role, user_text, chatbot])
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/hjzhp/cgpt-online/Dockerfile b/spaces/hjzhp/cgpt-online/Dockerfile
deleted file mode 100644
index ed6faf6a660f2fea94239fa7b60de3ecd9509047..0000000000000000000000000000000000000000
--- a/spaces/hjzhp/cgpt-online/Dockerfile
+++ /dev/null
@@ -1,10 +0,0 @@
-FROM node:alpine
-WORKDIR /usr/src
-RUN npm install -g pnpm
-COPY package.json pnpm-lock.yaml ./
-RUN pnpm install
-COPY . .
-RUN pnpm run build
-ENV HOST=0.0.0.0 PORT=7860 NODE_ENV=production
-EXPOSE $PORT
-CMD ["node", "dist/server/entry.mjs"]
diff --git a/spaces/hugginglearners/grapevine-leaves-classification/app.py b/spaces/hugginglearners/grapevine-leaves-classification/app.py
deleted file mode 100644
index e6719e1dbe6edb2b5e50bcfc181552694edac2c6..0000000000000000000000000000000000000000
--- a/spaces/hugginglearners/grapevine-leaves-classification/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import gradio as gr
-import torch
-from huggingface_hub import from_pretrained_fastai
-from pathlib import Path
-
-examples = ["image_1.png", "image_2.png", "image_3.png", "image_4.png", "image_5.png"]
-repo_id = "hugginglearners/grapevine_leaves_classification"
-path = Path("./")
-
-def get_y(r):
-    return r["label"]
-    
-def get_x(r):
-    return path/r["fname"]
-    
-learner = from_pretrained_fastai(repo_id)
-labels = learner.dls.vocab
-
-def inference(image):
-    label_predict,_,probs = learner.predict(image)
-    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
-    return labels_probs
-
-gr.Interface(
-    fn=inference,
-    title="Grapevine leave image classification",
-    description = "Predict which type of grapevine leave belong to Ak, Ala_Idris, Buzgulu, Dimnit, Nazli",
-    inputs="image",
-    outputs=gr.outputs.Label(num_top_classes=5, label='Prediction'),
-    examples=examples,
-    cache_examples=False,
-    article = "Author: <a href=\"https://www.linkedin.com/in/vumichien/\">Vu Minh Chien</a>",
-).launch(debug=True, enable_queue=True)
\ No newline at end of file
diff --git a/spaces/huy-ha/semabs-relevancy/CLIP/model-card.md b/spaces/huy-ha/semabs-relevancy/CLIP/model-card.md
deleted file mode 100644
index 2d22e25bea89fdbccdaa2809fbeb83e0a7cfaa07..0000000000000000000000000000000000000000
--- a/spaces/huy-ha/semabs-relevancy/CLIP/model-card.md
+++ /dev/null
@@ -1,120 +0,0 @@
-# Model Card: CLIP
-
-Inspired by [Model Cards for Model Reporting (Mitchell et al.)](https://arxiv.org/abs/1810.03993) and [Lessons from Archives (Jo & Gebru)](https://arxiv.org/pdf/1912.10389.pdf), we’re providing some accompanying information about the multimodal model.
-
-## Model Details
-
-The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
-
-### Model Date
-
-January 2021
-
-### Model Type
-
-The base model uses a ResNet50 with several modifications as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. There is also a variant of the model where the ResNet image encoder is replaced with a Vision Transformer.
-
-### Model Versions
-
-Initially, we’ve released one CLIP model based on the Vision Transformer architecture equivalent to ViT-B/32, along with the RN50 model, using the architecture equivalent to ResNet-50.
-
-As part of the staged release process, we have also released the RN101 model, as well as RN50x4, a RN50 scaled up 4x according to the [EfficientNet](https://arxiv.org/abs/1905.11946) scaling rule. In July 2021, we additionally released the RN50x16 and ViT-B/16 models.
-
-Please see the paper linked below for further details about their specification.
-
-### Documents
-
-- [Blog Post](https://openai.com/blog/clip/)
-- [CLIP Paper](https://arxiv.org/abs/2103.00020)
-
-
-
-## Model Use
-
-### Intended Use
-
-The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
-
-#### Primary intended uses
-
-The primary intended users of these models are AI researchers.
-
-We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
-
-### Out-of-Scope Use Cases
-
-**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. 
-
-Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
-
-Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
-
-
-
-## Data
-
-The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
-
-### Data Mission Statement
-
-Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
-
-
-
-## Performance and Limitations
-
-### Performance
-
-We have evaluated the performance of CLIP on a wide range of benchmarks across a variety of computer vision datasets such as OCR to texture recognition to fine-grained classification. The paper describes model performance on the following datasets:
-
-- Food101
-- CIFAR10   
-- CIFAR100   
-- Birdsnap
-- SUN397
-- Stanford Cars
-- FGVC Aircraft
-- VOC2007
-- DTD
-- Oxford-IIIT Pet dataset
-- Caltech101
-- Flowers102
-- MNIST   
-- SVHN 
-- IIIT5K   
-- Hateful Memes   
-- SST-2
-- UCF101
-- Kinetics700
-- Country211
-- CLEVR Counting
-- KITTI Distance
-- STL-10
-- RareAct
-- Flickr30
-- MSCOCO
-- ImageNet
-- ImageNet-A
-- ImageNet-R
-- ImageNet Sketch
-- ObjectNet (ImageNet Overlap)
-- Youtube-BB
-- ImageNet-Vid
-
-## Limitations
-
-CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
-
-### Bias and Fairness
-
-We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
-
-We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
-
-
-
-## Feedback
-
-### Where to send questions or comments about the model
-
-Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9)
diff --git a/spaces/huybery/deep-thinking/README.md b/spaces/huybery/deep-thinking/README.md
deleted file mode 100644
index be93022c861e1e6d6bf2a19bdc8f8ac530953d30..0000000000000000000000000000000000000000
--- a/spaces/huybery/deep-thinking/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Deep Thinking
-emoji: 🤔
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/docs/install.md b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/docs/install.md
deleted file mode 100644
index 8824e7e3108adc76cee514a3e66a50f933c9c91f..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/docs/install.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# Installation
-
-### [Torch v1.11.0](https://pytorch.org/get-started/previous-versions/#v1110)
-#### Linux and Windows  
-- CUDA 11.3
-```shell
-
-pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
-```
-
-- CUDA 10.2
-```shell
-pip install torch==1.11.0+cu102 torchvision==0.12.0+cu102 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu102
-```
-
-### [Torch v1.9.0](https://pytorch.org/get-started/previous-versions/#v190)
-#### Linux and Windows  
-
-- CUDA 11.1
-```shell
-pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-- CUDA 10.2
-```shell
-pip install torch==1.9.0+cu102 torchvision==0.10.0+cu102 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
-```
diff --git a/spaces/iabualhaol/pdfchat/README.md b/spaces/iabualhaol/pdfchat/README.md
deleted file mode 100644
index ed611ea498696173d002091a828527089fba9d29..0000000000000000000000000000000000000000
--- a/spaces/iabualhaol/pdfchat/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Pdfchat
-emoji: 📚
-colorFrom: red
-colorTo: gray
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/iamironman4279/SadTalker/src/face3d/data/flist_dataset.py b/spaces/iamironman4279/SadTalker/src/face3d/data/flist_dataset.py
deleted file mode 100644
index c0b6945c80aa756074a5d3c02b9443b15ddcfc57..0000000000000000000000000000000000000000
--- a/spaces/iamironman4279/SadTalker/src/face3d/data/flist_dataset.py
+++ /dev/null
@@ -1,125 +0,0 @@
-"""This script defines the custom dataset for Deep3DFaceRecon_pytorch
-"""
-
-import os.path
-from data.base_dataset import BaseDataset, get_transform, get_affine_mat, apply_img_affine, apply_lm_affine
-from data.image_folder import make_dataset
-from PIL import Image
-import random
-import util.util as util
-import numpy as np
-import json
-import torch
-from scipy.io import loadmat, savemat
-import pickle
-from util.preprocess import align_img, estimate_norm
-from util.load_mats import load_lm3d
-
-
-def default_flist_reader(flist):
-    """
-    flist format: impath label\nimpath label\n ...(same to caffe's filelist)
-    """
-    imlist = []
-    with open(flist, 'r') as rf:
-        for line in rf.readlines():
-            impath = line.strip()
-            imlist.append(impath)
-
-    return imlist
-
-def jason_flist_reader(flist):
-    with open(flist, 'r') as fp:
-        info = json.load(fp)
-    return info
-
-def parse_label(label):
-    return torch.tensor(np.array(label).astype(np.float32))
-
-
-class FlistDataset(BaseDataset):
-    """
-    It requires one directories to host training images '/path/to/data/train'
-    You can train the model with the dataset flag '--dataroot /path/to/data'.
-    """
-
-    def __init__(self, opt):
-        """Initialize this dataset class.
-
-        Parameters:
-            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
-        """
-        BaseDataset.__init__(self, opt)
-        
-        self.lm3d_std = load_lm3d(opt.bfm_folder)
-        
-        msk_names = default_flist_reader(opt.flist)
-        self.msk_paths = [os.path.join(opt.data_root, i) for i in msk_names]
-
-        self.size = len(self.msk_paths) 
-        self.opt = opt
-        
-        self.name = 'train' if opt.isTrain else 'val'
-        if '_' in opt.flist:
-            self.name += '_' + opt.flist.split(os.sep)[-1].split('_')[0]
-        
-
-    def __getitem__(self, index):
-        """Return a data point and its metadata information.
-
-        Parameters:
-            index (int)      -- a random integer for data indexing
-
-        Returns a dictionary that contains A, B, A_paths and B_paths
-            img (tensor)       -- an image in the input domain
-            msk (tensor)       -- its corresponding attention mask
-            lm  (tensor)       -- its corresponding 3d landmarks
-            im_paths (str)     -- image paths
-            aug_flag (bool)    -- a flag used to tell whether its raw or augmented
-        """
-        msk_path = self.msk_paths[index % self.size]  # make sure index is within then range
-        img_path = msk_path.replace('mask/', '')
-        lm_path = '.'.join(msk_path.replace('mask', 'landmarks').split('.')[:-1]) + '.txt'
-
-        raw_img = Image.open(img_path).convert('RGB')
-        raw_msk = Image.open(msk_path).convert('RGB')
-        raw_lm = np.loadtxt(lm_path).astype(np.float32)
-
-        _, img, lm, msk = align_img(raw_img, raw_lm, self.lm3d_std, raw_msk)
-        
-        aug_flag = self.opt.use_aug and self.opt.isTrain
-        if aug_flag:
-            img, lm, msk = self._augmentation(img, lm, self.opt, msk)
-        
-        _, H = img.size
-        M = estimate_norm(lm, H)
-        transform = get_transform()
-        img_tensor = transform(img)
-        msk_tensor = transform(msk)[:1, ...]
-        lm_tensor = parse_label(lm)
-        M_tensor = parse_label(M)
-
-
-        return {'imgs': img_tensor, 
-                'lms': lm_tensor, 
-                'msks': msk_tensor, 
-                'M': M_tensor,
-                'im_paths': img_path, 
-                'aug_flag': aug_flag,
-                'dataset': self.name}
-
-    def _augmentation(self, img, lm, opt, msk=None):
-        affine, affine_inv, flip = get_affine_mat(opt, img.size)
-        img = apply_img_affine(img, affine_inv)
-        lm = apply_lm_affine(lm, affine, flip, img.size)
-        if msk is not None:
-            msk = apply_img_affine(msk, affine_inv, method=Image.BILINEAR)
-        return img, lm, msk
-    
-
-
-
-    def __len__(self):
-        """Return the total number of images in the dataset.
-        """
-        return self.size
diff --git a/spaces/igashov/DiffLinker/src/edm.py b/spaces/igashov/DiffLinker/src/edm.py
deleted file mode 100644
index 15fe793ac11a8d47780ed7c00f1f5a2eb3727caa..0000000000000000000000000000000000000000
--- a/spaces/igashov/DiffLinker/src/edm.py
+++ /dev/null
@@ -1,732 +0,0 @@
-import torch
-import torch.nn.functional as F
-import numpy as np
-import math
-
-from src import utils
-from src.egnn import Dynamics
-from src.noise import GammaNetwork, PredefinedNoiseSchedule
-from typing import Union
-
-from tqdm import tqdm
-
-from pdb import set_trace
-
-
-class EDM(torch.nn.Module):
-    def __init__(
-            self,
-            dynamics: Union[Dynamics],
-            in_node_nf: int,
-            n_dims: int,
-            timesteps: int = 1000,
-            noise_schedule='learned',
-            noise_precision=1e-4,
-            loss_type='vlb',
-            norm_values=(1., 1., 1.),
-            norm_biases=(None, 0., 0.),
-    ):
-        super().__init__()
-        if noise_schedule == 'learned':
-            assert loss_type == 'vlb', 'A noise schedule can only be learned with a vlb objective'
-            self.gamma = GammaNetwork()
-        else:
-            self.gamma = PredefinedNoiseSchedule(noise_schedule, timesteps=timesteps, precision=noise_precision)
-
-        self.dynamics = dynamics
-        self.in_node_nf = in_node_nf
-        self.n_dims = n_dims
-        self.T = timesteps
-        self.norm_values = norm_values
-        self.norm_biases = norm_biases
-
-    def forward(self, x, h, node_mask, fragment_mask, linker_mask, edge_mask, context=None):
-        # Normalization and concatenation
-        x, h = self.normalize(x, h)
-        xh = torch.cat([x, h], dim=2)
-
-        # Volume change loss term
-        delta_log_px = self.delta_log_px(linker_mask).mean()
-
-        # Sample t
-        t_int = torch.randint(0, self.T + 1, size=(x.size(0), 1), device=x.device).float()
-        s_int = t_int - 1
-        t = t_int / self.T
-        s = s_int / self.T
-
-        # Masks for t=0 and t>0
-        t_is_zero = (t_int == 0).squeeze().float()
-        t_is_not_zero = 1 - t_is_zero
-
-        # Compute gamma_t and gamma_s according to the noise schedule
-        gamma_t = self.inflate_batch_array(self.gamma(t), x)
-        gamma_s = self.inflate_batch_array(self.gamma(s), x)
-
-        # Compute alpha_t and sigma_t from gamma
-        alpha_t = self.alpha(gamma_t, x)
-        sigma_t = self.sigma(gamma_t, x)
-
-        # Sample noise
-        # Note: only for linker
-        eps_t = self.sample_combined_position_feature_noise(n_samples=x.size(0), n_nodes=x.size(1), mask=linker_mask)
-
-        # Sample z_t given x, h for timestep t, from q(z_t | x, h)
-        # Note: keep fragments unchanged
-        z_t = alpha_t * xh + sigma_t * eps_t
-        z_t = xh * fragment_mask + z_t * linker_mask
-
-        # Neural net prediction
-        eps_t_hat = self.dynamics.forward(
-            xh=z_t,
-            t=t,
-            node_mask=node_mask,
-            linker_mask=linker_mask,
-            context=context,
-            edge_mask=edge_mask,
-        )
-        eps_t_hat = eps_t_hat * linker_mask
-
-        # Computing basic error (further used for computing NLL and L2-loss)
-        error_t = self.sum_except_batch((eps_t - eps_t_hat) ** 2)
-
-        # Computing L2-loss for t>0
-        normalization = (self.n_dims + self.in_node_nf) * self.numbers_of_nodes(linker_mask)
-        l2_loss = error_t / normalization
-        l2_loss = l2_loss.mean()
-
-        # The KL between q(z_T | x) and p(z_T) = Normal(0, 1) (should be close to zero)
-        kl_prior = self.kl_prior(xh, linker_mask).mean()
-
-        # Computing NLL middle term
-        SNR_weight = (self.SNR(gamma_s - gamma_t) - 1).squeeze(1).squeeze(1)
-        loss_term_t = self.T * 0.5 * SNR_weight * error_t
-        loss_term_t = (loss_term_t * t_is_not_zero).sum() / t_is_not_zero.sum()
-
-        # Computing noise returned by dynamics
-        noise = torch.norm(eps_t_hat, dim=[1, 2])
-        noise_t = (noise * t_is_not_zero).sum() / t_is_not_zero.sum()
-
-        if t_is_zero.sum() > 0:
-            # The _constants_ depending on sigma_0 from the
-            # cross entropy term E_q(z0 | x) [log p(x | z0)]
-            neg_log_constants = -self.log_constant_of_p_x_given_z0(x, linker_mask)
-
-            # Computes the L_0 term (even if gamma_t is not actually gamma_0)
-            # and selected only relevant via masking
-            loss_term_0 = -self.log_p_xh_given_z0_without_constants(h, z_t, gamma_t, eps_t, eps_t_hat, linker_mask)
-            loss_term_0 = loss_term_0 + neg_log_constants
-            loss_term_0 = (loss_term_0 * t_is_zero).sum() / t_is_zero.sum()
-
-            # Computing noise returned by dynamics
-            noise_0 = (noise * t_is_zero).sum() / t_is_zero.sum()
-        else:
-            loss_term_0 = 0.
-            noise_0 = 0.
-
-        return delta_log_px, kl_prior, loss_term_t, loss_term_0, l2_loss, noise_t, noise_0
-
-    @torch.no_grad()
-    def sample_chain(self, x, h, node_mask, fragment_mask, linker_mask, edge_mask, context, keep_frames=None):
-        n_samples = x.size(0)
-        n_nodes = x.size(1)
-
-        # Normalization and concatenation
-        x, h, = self.normalize(x, h)
-        xh = torch.cat([x, h], dim=2)
-
-        # Initial linker sampling from N(0, I)
-        z = self.sample_combined_position_feature_noise(n_samples, n_nodes, mask=linker_mask)
-        z = xh * fragment_mask + z * linker_mask
-
-        if keep_frames is None:
-            keep_frames = self.T
-        else:
-            assert keep_frames <= self.T
-        chain = torch.zeros((keep_frames,) + z.size(), device=z.device)
-
-        # Sample p(z_s | z_t)
-        for s in tqdm(reversed(range(0, self.T)), total=self.T):
-            s_array = torch.full((n_samples, 1), fill_value=s, device=z.device)
-            t_array = s_array + 1
-            s_array = s_array / self.T
-            t_array = t_array / self.T
-
-            z = self.sample_p_zs_given_zt_only_linker(
-                s=s_array,
-                t=t_array,
-                z_t=z,
-                node_mask=node_mask,
-                fragment_mask=fragment_mask,
-                linker_mask=linker_mask,
-                edge_mask=edge_mask,
-                context=context,
-            )
-            write_index = (s * keep_frames) // self.T
-            chain[write_index] = self.unnormalize_z(z)
-
-        # Finally sample p(x, h | z_0)
-        x, h = self.sample_p_xh_given_z0_only_linker(
-            z_0=z,
-            node_mask=node_mask,
-            fragment_mask=fragment_mask,
-            linker_mask=linker_mask,
-            edge_mask=edge_mask,
-            context=context,
-        )
-        chain[0] = torch.cat([x, h], dim=2)
-
-        return chain
-
-    def sample_p_zs_given_zt_only_linker(self, s, t, z_t, node_mask, fragment_mask, linker_mask, edge_mask, context):
-        """Samples from zs ~ p(zs | zt). Only used during sampling. Samples only linker features and coords"""
-        gamma_s = self.gamma(s)
-        gamma_t = self.gamma(t)
-
-        sigma2_t_given_s, sigma_t_given_s, alpha_t_given_s = self.sigma_and_alpha_t_given_s(gamma_t, gamma_s, z_t)
-        sigma_s = self.sigma(gamma_s, target_tensor=z_t)
-        sigma_t = self.sigma(gamma_t, target_tensor=z_t)
-
-        # Neural net prediction.
-        eps_hat = self.dynamics.forward(
-            xh=z_t,
-            t=t,
-            node_mask=node_mask,
-            linker_mask=linker_mask,
-            context=context,
-            edge_mask=edge_mask,
-        )
-        eps_hat = eps_hat * linker_mask
-
-        # Compute mu for p(z_s | z_t)
-        mu = z_t / alpha_t_given_s - (sigma2_t_given_s / alpha_t_given_s / sigma_t) * eps_hat
-
-        # Compute sigma for p(z_s | z_t)
-        sigma = sigma_t_given_s * sigma_s / sigma_t
-
-        # Sample z_s given the parameters derived from zt
-        z_s = self.sample_normal(mu, sigma, linker_mask)
-        z_s = z_t * fragment_mask + z_s * linker_mask
-
-        return z_s
-
-    def sample_p_xh_given_z0_only_linker(self, z_0, node_mask, fragment_mask, linker_mask, edge_mask, context):
-        """Samples x ~ p(x|z0). Samples only linker features and coords"""
-        zeros = torch.zeros(size=(z_0.size(0), 1), device=z_0.device)
-        gamma_0 = self.gamma(zeros)
-
-        # Computes sqrt(sigma_0^2 / alpha_0^2)
-        sigma_x = self.SNR(-0.5 * gamma_0).unsqueeze(1)
-        eps_hat = self.dynamics.forward(
-            t=zeros,
-            xh=z_0,
-            node_mask=node_mask,
-            linker_mask=linker_mask,
-            edge_mask=edge_mask,
-            context=context
-        )
-        eps_hat = eps_hat * linker_mask
-
-        mu_x = self.compute_x_pred(eps_t=eps_hat, z_t=z_0, gamma_t=gamma_0)
-        xh = self.sample_normal(mu=mu_x, sigma=sigma_x, node_mask=linker_mask)
-        xh = z_0 * fragment_mask + xh * linker_mask
-
-        x, h = xh[:, :, :self.n_dims], xh[:, :, self.n_dims:]
-        x, h = self.unnormalize(x, h)
-        h = F.one_hot(torch.argmax(h, dim=2), self.in_node_nf) * node_mask
-
-        return x, h
-
-    def compute_x_pred(self, eps_t, z_t, gamma_t):
-        """Computes x_pred, i.e. the most likely prediction of x."""
-        sigma_t = self.sigma(gamma_t, target_tensor=eps_t)
-        alpha_t = self.alpha(gamma_t, target_tensor=eps_t)
-        x_pred = 1. / alpha_t * (z_t - sigma_t * eps_t)
-        return x_pred
-
-    def kl_prior(self, xh, mask):
-        """
-        Computes the KL between q(z1 | x) and the prior p(z1) = Normal(0, 1).
-        This is essentially a lot of work for something that is in practice negligible in the loss.
-        However, you compute it so that you see it when you've made a mistake in your noise schedule.
-        """
-        # Compute the last alpha value, alpha_T
-        ones = torch.ones((xh.size(0), 1), device=xh.device)
-        gamma_T = self.gamma(ones)
-        alpha_T = self.alpha(gamma_T, xh)
-
-        # Compute means
-        mu_T = alpha_T * xh
-        mu_T_x, mu_T_h = mu_T[:, :, :self.n_dims], mu_T[:, :, self.n_dims:]
-
-        # Compute standard deviations (only batch axis for x-part, inflated for h-part)
-        sigma_T_x = self.sigma(gamma_T, mu_T_x).view(-1)  # Remove inflate, only keep batch dimension for x-part
-        sigma_T_h = self.sigma(gamma_T, mu_T_h)
-
-        # Compute KL for h-part
-        zeros, ones = torch.zeros_like(mu_T_h), torch.ones_like(sigma_T_h)
-        kl_distance_h = self.gaussian_kl(mu_T_h, sigma_T_h, zeros, ones)
-
-        # Compute KL for x-part
-        zeros, ones = torch.zeros_like(mu_T_x), torch.ones_like(sigma_T_x)
-        d = self.dimensionality(mask)
-        kl_distance_x = self.gaussian_kl_for_dimension(mu_T_x, sigma_T_x, zeros, ones, d=d)
-
-        return kl_distance_x + kl_distance_h
-
-    def log_constant_of_p_x_given_z0(self, x, mask):
-        batch_size = x.size(0)
-        degrees_of_freedom_x = self.dimensionality(mask)
-        zeros = torch.zeros((batch_size, 1), device=x.device)
-        gamma_0 = self.gamma(zeros)
-
-        # Recall that sigma_x = sqrt(sigma_0^2 / alpha_0^2) = SNR(-0.5 gamma_0)
-        log_sigma_x = 0.5 * gamma_0.view(batch_size)
-
-        return degrees_of_freedom_x * (- log_sigma_x - 0.5 * np.log(2 * np.pi))
-
-    def log_p_xh_given_z0_without_constants(self, h, z_0, gamma_0, eps, eps_hat, mask, epsilon=1e-10):
-        # Discrete properties are predicted directly from z_0
-        z_h = z_0[:, :, self.n_dims:]
-
-        # Take only part over x
-        eps_x = eps[:, :, :self.n_dims]
-        eps_hat_x = eps_hat[:, :, :self.n_dims]
-
-        # Compute sigma_0 and rescale to the integer scale of the data
-        sigma_0 = self.sigma(gamma_0, target_tensor=z_0) * self.norm_values[1]
-
-        # Computes the error for the distribution N(x | 1 / alpha_0 z_0 + sigma_0/alpha_0 eps_0, sigma_0 / alpha_0),
-        # the weighting in the epsilon parametrization is exactly '1'
-        log_p_x_given_z_without_constants = -0.5 * self.sum_except_batch((eps_x - eps_hat_x) ** 2)
-
-        # Categorical features
-        # Compute delta indicator masks
-        h = h * self.norm_values[1] + self.norm_biases[1]
-        estimated_h = z_h * self.norm_values[1] + self.norm_biases[1]
-
-        # Centered h_cat around 1, since onehot encoded
-        centered_h = estimated_h - 1
-
-        # Compute integrals from 0.5 to 1.5 of the normal distribution
-        # N(mean=centered_h_cat, stdev=sigma_0_cat)
-        log_p_h_proportional = torch.log(
-            self.cdf_standard_gaussian((centered_h + 0.5) / sigma_0) -
-            self.cdf_standard_gaussian((centered_h - 0.5) / sigma_0) +
-            epsilon
-        )
-
-        # Normalize the distribution over the categories
-        log_Z = torch.logsumexp(log_p_h_proportional, dim=2, keepdim=True)
-        log_probabilities = log_p_h_proportional - log_Z
-
-        # Select the log_prob of the current category using the onehot representation
-        log_p_h_given_z = self.sum_except_batch(log_probabilities * h * mask)
-
-        # Combine log probabilities for x and h
-        log_p_xh_given_z = log_p_x_given_z_without_constants + log_p_h_given_z
-
-        return log_p_xh_given_z
-
-    def sample_combined_position_feature_noise(self, n_samples, n_nodes, mask):
-        z_x = utils.sample_gaussian_with_mask(
-            size=(n_samples, n_nodes, self.n_dims),
-            device=mask.device,
-            node_mask=mask
-        )
-        z_h = utils.sample_gaussian_with_mask(
-            size=(n_samples, n_nodes, self.in_node_nf),
-            device=mask.device,
-            node_mask=mask
-        )
-        z = torch.cat([z_x, z_h], dim=2)
-        return z
-
-    def sample_normal(self, mu, sigma, node_mask):
-        """Samples from a Normal distribution."""
-        eps = self.sample_combined_position_feature_noise(mu.size(0), mu.size(1), node_mask)
-        return mu + sigma * eps
-
-    def normalize(self, x, h):
-        new_x = x / self.norm_values[0]
-        new_h = (h.float() - self.norm_biases[1]) / self.norm_values[1]
-        return new_x, new_h
-
-    def unnormalize(self, x, h):
-        new_x = x * self.norm_values[0]
-        new_h = h * self.norm_values[1] + self.norm_biases[1]
-        return new_x, new_h
-
-    def unnormalize_z(self, z):
-        assert z.size(2) == self.n_dims + self.in_node_nf
-        x, h = z[:, :, :self.n_dims], z[:, :, self.n_dims:]
-        x, h = self.unnormalize(x, h)
-        return torch.cat([x, h], dim=2)
-
-    def delta_log_px(self, mask):
-        return -self.dimensionality(mask) * np.log(self.norm_values[0])
-
-    def dimensionality(self, mask):
-        return self.numbers_of_nodes(mask) * self.n_dims
-
-    def sigma(self, gamma, target_tensor):
-        """Computes sigma given gamma."""
-        return self.inflate_batch_array(torch.sqrt(torch.sigmoid(gamma)), target_tensor)
-
-    def alpha(self, gamma, target_tensor):
-        """Computes alpha given gamma."""
-        return self.inflate_batch_array(torch.sqrt(torch.sigmoid(-gamma)), target_tensor)
-
-    def SNR(self, gamma):
-        """Computes signal to noise ratio (alpha^2/sigma^2) given gamma."""
-        return torch.exp(-gamma)
-
-    def sigma_and_alpha_t_given_s(self, gamma_t: torch.Tensor, gamma_s: torch.Tensor, target_tensor: torch.Tensor):
-        """
-        Computes sigma t given s, using gamma_t and gamma_s. Used during sampling.
-
-        These are defined as:
-            alpha t given s = alpha t / alpha s,
-            sigma t given s = sqrt(1 - (alpha t given s) ^2 ).
-        """
-        sigma2_t_given_s = self.inflate_batch_array(
-            -self.expm1(self.softplus(gamma_s) - self.softplus(gamma_t)),
-            target_tensor
-        )
-
-        # alpha_t_given_s = alpha_t / alpha_s
-        log_alpha2_t = F.logsigmoid(-gamma_t)
-        log_alpha2_s = F.logsigmoid(-gamma_s)
-        log_alpha2_t_given_s = log_alpha2_t - log_alpha2_s
-
-        alpha_t_given_s = torch.exp(0.5 * log_alpha2_t_given_s)
-        alpha_t_given_s = self.inflate_batch_array(alpha_t_given_s, target_tensor)
-        sigma_t_given_s = torch.sqrt(sigma2_t_given_s)
-
-        return sigma2_t_given_s, sigma_t_given_s, alpha_t_given_s
-
-    @staticmethod
-    def numbers_of_nodes(mask):
-        return torch.sum(mask.squeeze(2), dim=1)
-
-    @staticmethod
-    def inflate_batch_array(array, target):
-        """
-        Inflates the batch array (array) with only a single axis (i.e. shape = (batch_size,),
-        or possibly more empty axes (i.e. shape (batch_size, 1, ..., 1)) to match the target shape.
-        """
-        target_shape = (array.size(0),) + (1,) * (len(target.size()) - 1)
-        return array.view(target_shape)
-
-    @staticmethod
-    def sum_except_batch(x):
-        return x.view(x.size(0), -1).sum(-1)
-
-    @staticmethod
-    def expm1(x: torch.Tensor) -> torch.Tensor:
-        return torch.expm1(x)
-
-    @staticmethod
-    def softplus(x: torch.Tensor) -> torch.Tensor:
-        return F.softplus(x)
-
-    @staticmethod
-    def cdf_standard_gaussian(x):
-        return 0.5 * (1. + torch.erf(x / math.sqrt(2)))
-
-    @staticmethod
-    def gaussian_kl(q_mu, q_sigma, p_mu, p_sigma):
-        """
-        Computes the KL distance between two normal distributions.
-        Args:
-            q_mu: Mean of distribution q.
-            q_sigma: Standard deviation of distribution q.
-            p_mu: Mean of distribution p.
-            p_sigma: Standard deviation of distribution p.
-        Returns:
-            The KL distance, summed over all dimensions except the batch dim.
-        """
-        kl = torch.log(p_sigma / q_sigma) + 0.5 * (q_sigma ** 2 + (q_mu - p_mu) ** 2) / (p_sigma ** 2) - 0.5
-        return EDM.sum_except_batch(kl)
-
-    @staticmethod
-    def gaussian_kl_for_dimension(q_mu, q_sigma, p_mu, p_sigma, d):
-        """
-        Computes the KL distance between two normal distributions taking the dimension into account.
-        Args:
-            q_mu: Mean of distribution q.
-            q_sigma: Standard deviation of distribution q.
-            p_mu: Mean of distribution p.
-            p_sigma: Standard deviation of distribution p.
-            d: dimension
-        Returns:
-            The KL distance, summed over all dimensions except the batch dim.
-        """
-        mu_norm_2 = EDM.sum_except_batch((q_mu - p_mu) ** 2)
-        return d * torch.log(p_sigma / q_sigma) + 0.5 * (d * q_sigma ** 2 + mu_norm_2) / (p_sigma ** 2) - 0.5 * d
-
-
-class InpaintingEDM(EDM):
-    def forward(self, x, h, node_mask, fragment_mask, linker_mask, edge_mask, context=None):
-        # Normalization and concatenation
-        x, h = self.normalize(x, h)
-        xh = torch.cat([x, h], dim=2)
-
-        # Volume change loss term
-        delta_log_px = self.delta_log_px(node_mask).mean()
-
-        # Sample t
-        t_int = torch.randint(0, self.T + 1, size=(x.size(0), 1), device=x.device).float()
-        s_int = t_int - 1
-        t = t_int / self.T
-        s = s_int / self.T
-
-        # Masks for t=0 and t>0
-        t_is_zero = (t_int == 0).squeeze().float()
-        t_is_not_zero = 1 - t_is_zero
-
-        # Compute gamma_t and gamma_s according to the noise schedule
-        gamma_t = self.inflate_batch_array(self.gamma(t), x)
-        gamma_s = self.inflate_batch_array(self.gamma(s), x)
-
-        # Compute alpha_t and sigma_t from gamma
-        alpha_t = self.alpha(gamma_t, x)
-        sigma_t = self.sigma(gamma_t, x)
-
-        # Sample noise
-        eps_t = self.sample_combined_position_feature_noise(n_samples=x.size(0), n_nodes=x.size(1), mask=node_mask)
-
-        # Sample z_t given x, h for timestep t, from q(z_t | x, h)
-        # Note: keep fragments unchanged
-        z_t = alpha_t * xh + sigma_t * eps_t
-
-        # Neural net prediction
-        eps_t_hat = self.dynamics.forward(
-            xh=z_t,
-            t=t,
-            node_mask=node_mask,
-            linker_mask=None,
-            context=context,
-            edge_mask=edge_mask,
-        )
-
-        # Computing basic error (further used for computing NLL and L2-loss)
-        error_t = self.sum_except_batch((eps_t - eps_t_hat) ** 2)
-
-        # Computing L2-loss for t>0
-        normalization = (self.n_dims + self.in_node_nf) * self.numbers_of_nodes(node_mask)
-        l2_loss = error_t / normalization
-        l2_loss = l2_loss.mean()
-
-        # The KL between q(z_T | x) and p(z_T) = Normal(0, 1) (should be close to zero)
-        kl_prior = self.kl_prior(xh, node_mask).mean()
-
-        # Computing NLL middle term
-        SNR_weight = (self.SNR(gamma_s - gamma_t) - 1).squeeze(1).squeeze(1)
-        loss_term_t = self.T * 0.5 * SNR_weight * error_t
-        loss_term_t = (loss_term_t * t_is_not_zero).sum() / t_is_not_zero.sum()
-
-        # Computing noise returned by dynamics
-        noise = torch.norm(eps_t_hat, dim=[1, 2])
-        noise_t = (noise * t_is_not_zero).sum() / t_is_not_zero.sum()
-
-        if t_is_zero.sum() > 0:
-            # The _constants_ depending on sigma_0 from the
-            # cross entropy term E_q(z0 | x) [log p(x | z0)]
-            neg_log_constants = -self.log_constant_of_p_x_given_z0(x, node_mask)
-
-            # Computes the L_0 term (even if gamma_t is not actually gamma_0)
-            # and selected only relevant via masking
-            loss_term_0 = -self.log_p_xh_given_z0_without_constants(h, z_t, gamma_t, eps_t, eps_t_hat, node_mask)
-            loss_term_0 = loss_term_0 + neg_log_constants
-            loss_term_0 = (loss_term_0 * t_is_zero).sum() / t_is_zero.sum()
-
-            # Computing noise returned by dynamics
-            noise_0 = (noise * t_is_zero).sum() / t_is_zero.sum()
-        else:
-            loss_term_0 = 0.
-            noise_0 = 0.
-
-        return delta_log_px, kl_prior, loss_term_t, loss_term_0, l2_loss, noise_t, noise_0
-
-    @torch.no_grad()
-    def sample_chain(self, x, h, node_mask, edge_mask, fragment_mask, linker_mask, context, keep_frames=None):
-        n_samples = x.size(0)
-        n_nodes = x.size(1)
-
-        # Normalization and concatenation
-        x, h, = self.normalize(x, h)
-        xh = torch.cat([x, h], dim=2)
-
-        # Sampling initial noise
-        z = self.sample_combined_position_feature_noise(n_samples, n_nodes, node_mask)
-
-        if keep_frames is None:
-            keep_frames = self.T
-        else:
-            assert keep_frames <= self.T
-        chain = torch.zeros((keep_frames,) + z.size(), device=z.device)
-
-        # Sample p(z_s | z_t)
-        for s in tqdm(reversed(range(0, self.T)), total=self.T):
-            s_array = torch.full((n_samples, 1), fill_value=s, device=z.device)
-            t_array = s_array + 1
-            s_array = s_array / self.T
-            t_array = t_array / self.T
-
-            z_linker_only_sampled = self.sample_p_zs_given_zt(
-                s=s_array,
-                t=t_array,
-                z_t=z,
-                node_mask=node_mask,
-                edge_mask=edge_mask,
-                context=context,
-            )
-            z_fragments_only_sampled = self.sample_q_zs_given_zt_and_x(
-                s=s_array,
-                t=t_array,
-                z_t=z,
-                x=xh * fragment_mask,
-                node_mask=fragment_mask,
-            )
-            z = z_linker_only_sampled * linker_mask + z_fragments_only_sampled * fragment_mask
-
-            # Project down to avoid numerical runaway of the center of gravity
-            z_x = utils.remove_mean_with_mask(z[:, :, :self.n_dims], node_mask)
-            z_h = z[:, :, self.n_dims:]
-            z = torch.cat([z_x, z_h], dim=2)
-
-            # Saving step to the chain
-            write_index = (s * keep_frames) // self.T
-            chain[write_index] = self.unnormalize_z(z)
-
-        # Finally sample p(x, h | z_0)
-        x_out_linker, h_out_linker = self.sample_p_xh_given_z0(
-            z_0=z,
-            node_mask=node_mask,
-            edge_mask=edge_mask,
-            context=context,
-        )
-        x_out_fragments, h_out_fragments = self.sample_q_xh_given_z0_and_x(z_0=z, node_mask=node_mask)
-
-        xh_out_linker = torch.cat([x_out_linker, h_out_linker], dim=2)
-        xh_out_fragments = torch.cat([x_out_fragments, h_out_fragments], dim=2)
-        xh_out = xh_out_linker * linker_mask + xh_out_fragments * fragment_mask
-
-        # Overwrite last frame with the resulting x and h
-        chain[0] = xh_out
-
-        return chain
-
-    def sample_p_zs_given_zt(self, s, t, z_t, node_mask, edge_mask, context):
-        """Samples from zs ~ p(zs | zt). Only used during sampling"""
-        gamma_s = self.gamma(s)
-        gamma_t = self.gamma(t)
-        sigma2_t_given_s, sigma_t_given_s, alpha_t_given_s = self.sigma_and_alpha_t_given_s(gamma_t, gamma_s, z_t)
-
-        sigma_s = self.sigma(gamma_s, target_tensor=z_t)
-        sigma_t = self.sigma(gamma_t, target_tensor=z_t)
-
-        # Neural net prediction.
-        eps_hat = self.dynamics.forward(
-            xh=z_t,
-            t=t,
-            node_mask=node_mask,
-            linker_mask=None,
-            edge_mask=edge_mask,
-            context=context
-        )
-
-        # Checking that epsilon is centered around linker COM
-        utils.assert_mean_zero_with_mask(eps_hat[:, :, :self.n_dims], node_mask)
-
-        # Compute mu for p(z_s | z_t)
-        mu = z_t / alpha_t_given_s - (sigma2_t_given_s / alpha_t_given_s / sigma_t) * eps_hat
-
-        # Compute sigma for p(z_s | z_t)
-        sigma = sigma_t_given_s * sigma_s / sigma_t
-
-        # Sample z_s given the parameters derived from z_t
-        z_s = self.sample_normal(mu, sigma, node_mask)
-        return z_s
-
-    def sample_q_zs_given_zt_and_x(self, s, t, z_t, x, node_mask):
-        """Samples from zs ~ q(zs | zt, x). Only used during sampling. Samples only linker features and coords"""
-        gamma_s = self.gamma(s)
-        gamma_t = self.gamma(t)
-        sigma2_t_given_s, sigma_t_given_s, alpha_t_given_s = self.sigma_and_alpha_t_given_s(gamma_t, gamma_s, z_t)
-
-        sigma_s = self.sigma(gamma_s, target_tensor=z_t)
-        sigma_t = self.sigma(gamma_t, target_tensor=z_t)
-        alpha_s = self.alpha(gamma_s, x)
-
-        mu = (
-            alpha_t_given_s * (sigma_s ** 2) / (sigma_t ** 2) * z_t +
-            alpha_s * sigma2_t_given_s / (sigma_t ** 2) * x
-        )
-
-        # Compute sigma for p(zs | zt)
-        sigma = sigma_t_given_s * sigma_s / sigma_t
-
-        # Sample zs given the parameters derived from zt
-        z_s = self.sample_normal(mu, sigma, node_mask)
-        return z_s
-
-    def sample_p_xh_given_z0(self, z_0, node_mask, edge_mask, context):
-        """Samples x ~ p(x|z0). Samples only linker features and coords"""
-        zeros = torch.zeros(size=(z_0.size(0), 1), device=z_0.device)
-        gamma_0 = self.gamma(zeros)
-
-        # Computes sqrt(sigma_0^2 / alpha_0^2)
-        sigma_x = self.SNR(-0.5 * gamma_0).unsqueeze(1)
-        eps_hat = self.dynamics.forward(
-            xh=z_0,
-            t=zeros,
-            node_mask=node_mask,
-            linker_mask=None,
-            edge_mask=edge_mask,
-            context=context
-        )
-        utils.assert_mean_zero_with_mask(eps_hat[:, :, :self.n_dims], node_mask)
-
-        mu_x = self.compute_x_pred(eps_hat, z_0, gamma_0)
-        xh = self.sample_normal(mu=mu_x, sigma=sigma_x, node_mask=node_mask)
-
-        x, h = xh[:, :, :self.n_dims], xh[:, :, self.n_dims:]
-        x, h = self.unnormalize(x, h)
-        h = F.one_hot(torch.argmax(h, dim=2), self.in_node_nf) * node_mask
-
-        return x, h
-
-    def sample_q_xh_given_z0_and_x(self, z_0, node_mask):
-        """Samples x ~ q(x|z0). Samples only linker features and coords"""
-        zeros = torch.zeros(size=(z_0.size(0), 1), device=z_0.device)
-        gamma_0 = self.gamma(zeros)
-        alpha_0 = self.alpha(gamma_0, z_0)
-        sigma_0 = self.sigma(gamma_0, z_0)
-
-        eps = self.sample_combined_position_feature_noise(z_0.size(0), z_0.size(1), node_mask)
-
-        xh = (1 / alpha_0) * z_0 - (sigma_0 / alpha_0) * eps
-
-        x, h = xh[:, :, :self.n_dims], xh[:, :, self.n_dims:]
-        x, h = self.unnormalize(x, h)
-        h = F.one_hot(torch.argmax(h, dim=2), self.in_node_nf) * node_mask
-
-        return x, h
-
-    def sample_combined_position_feature_noise(self, n_samples, n_nodes, mask):
-        z_x = utils.sample_center_gravity_zero_gaussian_with_mask(
-            size=(n_samples, n_nodes, self.n_dims),
-            device=mask.device,
-            node_mask=mask
-        )
-        z_h = utils.sample_gaussian_with_mask(
-            size=(n_samples, n_nodes, self.in_node_nf),
-            device=mask.device,
-            node_mask=mask
-        )
-        z = torch.cat([z_x, z_h], dim=2)
-        return z
-
-    def dimensionality(self, mask):
-        return (self.numbers_of_nodes(mask) - 1) * self.n_dims
diff --git a/spaces/ilumine-AI/AI-3D-Explorable-Video/Build/Builds.loader.js b/spaces/ilumine-AI/AI-3D-Explorable-Video/Build/Builds.loader.js
deleted file mode 100644
index 16d4b5b20de31f116dfa154e8c16de0c697cf0dc..0000000000000000000000000000000000000000
--- a/spaces/ilumine-AI/AI-3D-Explorable-Video/Build/Builds.loader.js
+++ /dev/null
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a&&(c.control=a.control,c.companyName=a.companyName,c.productName=a.productName,c.productVersion=a.productVersion),c.revalidated=!1,c.metaData={url:s,accessedAt:Date.now(),version:c.productVersion},c.response=null,c.enabled?i.loadRequest(s).then(function(e){var n,r,t;return e?(n=e.response,r=e.metaData,c.response=n,c.metaData.size=r.size,c.metaData.updatedAt=r.updatedAt,"immutable"==c.control?(c.revalidated=!0,i.updateRequestMetaData(r).then(function(){E("'"+c.metaData.url+"' served from the indexedDB cache without 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Promise(function(e,t){var n;l.SystemInfo.hasWebGL?1==l.SystemInfo.hasWebGL?(n='Your browser does not support graphics API "WebGL 2" which is required for this content.',"Safari"==l.SystemInfo.browser&&parseInt(l.SystemInfo.browserVersion)<15&&(l.SystemInfo.mobile||1<navigator.maxTouchPoints?n+="\nUpgrade to iOS 15 or later.":n+="\nUpgrade to Safari 15 or later."),t(n)):l.SystemInfo.hasWasm?(l.startupErrorHandler=t,c(0),l.postRun.push(function(){c(1),delete l.startupErrorHandler,e(C)}),T()):t("Your browser does not support WebAssembly."):t("Your browser does not support WebGL.")})}
\ No newline at end of file
diff --git a/spaces/imseldrith/DeepFakeAI/DeepFakeAI/uis/typing.py b/spaces/imseldrith/DeepFakeAI/DeepFakeAI/uis/typing.py
deleted file mode 100644
index 4abe384f07c4b90504e47291674905f85a5b8f52..0000000000000000000000000000000000000000
--- a/spaces/imseldrith/DeepFakeAI/DeepFakeAI/uis/typing.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from typing import Literal, Dict, Any
-import gradio
-
-Component = gradio.File or gradio.Image or gradio.Video or gradio.Slider
-ComponentName = Literal\
-[
-	'source_file',
-	'target_file',
-	'preview_frame_slider',
-	'face_recognition_dropdown',
-	'reference_face_position_gallery',
-	'reference_face_distance_slider',
-	'face_analyser_direction_dropdown',
-	'face_analyser_age_dropdown',
-	'face_analyser_gender_dropdown',
-	'frame_processors_checkbox_group'
-]
-Update = Dict[Any, Any]
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Adomania 1  CD Audio Classe A1 Downloads Torrent.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Adomania 1  CD Audio Classe A1 Downloads Torrent.md
deleted file mode 100644
index 95e98ef78c27e5be314fa0befd32e454dfeb1da7..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Adomania 1  CD Audio Classe A1 Downloads Torrent.md	
+++ /dev/null
@@ -1,12 +0,0 @@
-<h2>Adomania 1 : CD audio classe: A1 downloads torrent</h2><br /><p><b><b>DOWNLOAD</b> === <a href="https://urlin.us/2uEwd6">https://urlin.us/2uEwd6</a></b></p><br /><br />
-<br />
-...polynesian tattoo design guide with sign vol 1 illinois cdl tutorial audio illusion technology project management english stereoscopic vision
-12 Jul 2019 ...
-Download the manual for learning the programming language Scratch 2. 0. The manual will help you learn the programming language ...
-Book: A Guide to Making Polynesian Tattoos by Henari Adeku.
-Buy a book, read reviews | | ...
-Guide to creating Polynesian tattoos" Henari Adeku in the YAKABOO online store in Kiev and Ukraine ✍ Reviews, Reviews ✅ Price ...
-Publisher: Cdl Software Development Guide... 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Audaces Vestuario 9 Full TOP.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Audaces Vestuario 9 Full TOP.md
deleted file mode 100644
index 8f6622a2708cc2706e365f03ae5ce2d724d66e73..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Audaces Vestuario 9 Full TOP.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-
-<h1>Audaces Vestuario 9 Full: A Powerful Software for Fashion Designers</h1>
-<p>Audaces Vestuario 9 Full is a software that allows fashion designers to create, edit, and print patterns, sketches, and models for clothing and accessories. It is a comprehensive solution that covers all the stages of fashion design, from conception to production.</p>
-<h2>Audaces Vestuario 9 Full</h2><br /><p><b><b>Download Zip</b> &#10027; <a href="https://urlin.us/2uExEd">https://urlin.us/2uExEd</a></b></p><br /><br />
-<p>Audaces Vestuario 9 Full has many features that make it a versatile and efficient tool for fashion design. Some of these features are:</p>
-<ul>
-<li>A user-friendly interface that lets you work with multiple windows and tabs.</li>
-<li>A library of over 10,000 templates, shapes, and symbols that you can use or customize for your designs.</li>
-<li>A 3D simulation module that lets you preview how your garments will look on different body types, poses, and fabrics.</li>
-<li>A grading module that lets you adjust the sizes and measurements of your patterns according to international standards or your own criteria.</li>
-<li>A nesting module that lets you optimize the fabric consumption and cutting layout of your patterns.</li>
-<li>A printing module that lets you print your patterns on any printer or plotter, with options for scaling, mirroring, and adding annotations.</li>
-</ul>
-<p>Audaces Vestuario 9 Full is compatible with Windows XP, Vista, 7, 8, and 10. It requires a minimum of 2 GB of RAM, 10 GB of hard disk space, and a video card with 256 MB of memory. It also supports various file formats, such as DXF, PDF, PLT, HPGL, and JPG.</p>
-<p>If you are looking for a software that can help you create professional and innovative fashion designs, Audaces Vestuario 9 Full is a great option. You can download it from the official website[^1^] or from other sources[^2^] [^3^] [^4^] [^5^]. However, be aware that some sources may offer cracked versions of the software that may not work properly or may contain viruses. Therefore, it is advisable to use a reliable antivirus program before installing any software from unknown sources.</p>
-
-<p>Audaces Vestuario 9 Full has received positive feedback from many users who have tried it. Some of the benefits that they have reported are:</p>
-<p></p>
-<ul>
-<li>It saves time and money by reducing errors and rework in the design process.</li>
-<li>It improves the quality and fit of the garments by allowing precise adjustments and corrections.</li>
-<li>It enhances the creativity and innovation of the designers by offering a variety of tools and options.</li>
-<li>It facilitates the communication and collaboration between the designers and other stakeholders, such as clients, suppliers, and manufacturers.</li>
-<li>It integrates with other Audaces products, such as Audaces 360, Audaces Idea, Audaces Digiflash, and Audaces Jet Lux.</li>
-</ul>
-<p>If you want to see how Audaces Vestuario 9 Full works in practice, you can watch some video tutorials on YouTube[^1^] [^2^] or read some user reviews on Bitbucket[^3^]. You can also request a free trial or a demo from the Audaces website[^1^] or contact their customer service for more information.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Kismat 2009 Software Crack [HOT].md b/spaces/inplisQlawa/anything-midjourney-v4-1/Kismat 2009 Software Crack [HOT].md
deleted file mode 100644
index 29eef86f62df4823ba59216a4dd307750cdd05eb..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Kismat 2009 Software Crack [HOT].md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<br />
-`<h1>Kismat 2009 Software: A Powerful Tool for Astrologers</h1>`
-`<p>If you are looking for a reliable and easy-to-use software for creating and analyzing horoscopes, you might want to check out Kismat 2009 Software. This software is designed for practicing astrologers who follow the Krishnamurty Padhati (K.P) system of Astrology. It has many features that make it an ideal choice for professional and personal use.</p>
-<h2>Kismat 2009 Software Crack</h2><br /><p><b><b>Download Zip</b> >>> <a href="https://urlin.us/2uExDP">https://urlin.us/2uExDP</a></b></p><br /><br />`
-`<p>Some of the features of Kismat 2009 Software are:</p>`
-`<ul>`
-`<li>It can print any report using any printer resolution, which makes it suitable for any paper size.</li>`
-`<li>It has an intuitive user interface that allows you to quickly create a horoscope and send it to print[^1^].</li>`
-`<li>It displays astrological and horoscope predictions with current planets transits[^2^].</li>`
-`<li>It supports the majority of printers that are attached to PC[^2^].</li>`
-`<li>It has a backup facility and a yearly prediction feature[^2^].</li>`
-`</ul>`
-`<p>Kismat 2009 Software is a commercial software that costs $58.12. You can download a free trial version from the developer's website[^2^] and see if it meets your needs. However, be aware that the free trial version does not have some features such as Punchang or Prashna[^2^].</p>`
-`<p>If you are looking for a crack or a hack to use Kismat 2009 Software for free, you might be disappointed. There are some websites that claim to offer Kismat 2009 Software crack [^3^] [^4^], but they are not trustworthy. They might contain viruses, malware, or spyware that can harm your computer or steal your personal information. Moreover, using a cracked software is illegal and unethical. You should respect the work of the developers and pay for the software if you want to use it.</p>`
-`<p>Kismat 2009 Software is a powerful tool for astrologers who want to create and analyze horoscopes with ease and accuracy. It has many features that make it stand out from other similar software. If you are interested in trying it out, you can download a free trial version from the developer's website and see for yourself. If you like it, you can purchase it from the same website and enjoy its full functionality.</p>`
-
-`<p>One of the benefits of Kismat 2009 Software is that it is designed for practicing astrologers who follow the Krishnamurty Padhati (K.P) system of Astrology. This system is based on the concept of sub-lords and cusps, which are more precise and accurate than the traditional system of houses and planets. Kismat 2009 Software has more than 70 types of reports that cover various aspects of life, such as career, marriage, health, education, finance, etc. You can also customize the reports according to your preferences and needs[^1^].</p>
-<p></p>`
-`<p>Another benefit of Kismat 2009 Software is that it has a Prashna Kundli feature that generates planetary longitude and ascendant chart based on the query asked by the client. It also shows the Vimshottari ruling dasa and the transit (gochar) of planets and cusps. This feature is very useful for answering specific questions and giving predictions without the need of birth details[^1^].</p>`
-`<p>A third benefit of Kismat 2009 Software is that it has a powerful birth rectification program that can help you find out the exact time of birth of a person. This program uses various methods such as K.P system, Nadi system, Western system, etc. to calculate the most probable time of birth based on the given events and characteristics of the person. This feature is very helpful for astrologers who want to verify or correct the birth time of their clients[^1^].</p>` d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/james-oldfield/PandA/networks/genforce/models/encoder.py b/spaces/james-oldfield/PandA/networks/genforce/models/encoder.py
deleted file mode 100644
index 6527ab37ed3854832a6b2ee0f949519ee39cd91f..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/genforce/models/encoder.py
+++ /dev/null
@@ -1,538 +0,0 @@
-# python 3.7
-"""Contains the implementation of encoder network for GAN inversion.
-
-The network structure is primarily based on ResNet. The implementation is
-modified from `torchvision.models.resnet`.
-"""
-
-import numpy as np
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-__all__ = ['EncoderNet']
-
-# Resolutions allowed.
-_RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024]
-
-# Final resolution.
-_FINAL_RES = 4
-
-class BasicBlock(nn.Module):
-    """Implementation of ResNet BasicBlock."""
-
-    expansion = 1
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 base_width=64,
-                 stride=1,
-                 groups=1,
-                 dilation=1,
-                 norm_layer=None,
-                 downsample=None):
-        super().__init__()
-        if base_width != 64:
-            raise ValueError(f'BasicBlock of ResNet only supports '
-                             f'`base_width=64`, but {base_width} received!')
-        if stride not in [1, 2]:
-            raise ValueError(f'BasicBlock of ResNet only supports `stride=1` '
-                             f'and `stride=2`, but {stride} received!')
-        if groups != 1:
-            raise ValueError(f'BasicBlock of ResNet only supports `groups=1`, '
-                             f'but {groups} received!')
-        if dilation != 1:
-            raise ValueError(f'BasicBlock of ResNet only supports '
-                             f'`dilation=1`, but {dilation} received!')
-
-        self.stride = stride
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        self.conv1 = nn.Conv2d(in_channels=inplanes,
-                               out_channels=planes,
-                               kernel_size=3,
-                               stride=stride,
-                               padding=1,
-                               groups=1,
-                               dilation=1,
-                               bias=False)
-        self.bn1 = norm_layer(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = nn.Conv2d(in_channels=planes,
-                               out_channels=planes,
-                               kernel_size=3,
-                               stride=1,
-                               padding=1,
-                               groups=1,
-                               dilation=1,
-                               bias=False)
-        self.bn2 = norm_layer(planes)
-        self.downsample = downsample
-
-    def forward(self, x):
-        identity = self.downsample(x) if self.downsample is not None else x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out + identity)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    """Implementation of ResNet Bottleneck."""
-
-    expansion = 4
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 base_width=64,
-                 stride=1,
-                 groups=1,
-                 dilation=1,
-                 norm_layer=None,
-                 downsample=None):
-        super().__init__()
-        if stride not in [1, 2]:
-            raise ValueError(f'Bottlenet of ResNet only supports `stride=1` '
-                             f'and `stride=2`, but {stride} received!')
-
-        width = int(planes * (base_width / 64)) * groups
-        self.stride = stride
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        self.conv1 = nn.Conv2d(in_channels=inplanes,
-                               out_channels=width,
-                               kernel_size=1,
-                               stride=1,
-                               padding=0,
-                               dilation=1,
-                               groups=1,
-                               bias=False)
-        self.bn1 = norm_layer(width)
-        self.conv2 = nn.Conv2d(in_channels=width,
-                               out_channels=width,
-                               kernel_size=3,
-                               stride=stride,
-                               padding=dilation,
-                               groups=groups,
-                               dilation=dilation,
-                               bias=False)
-        self.bn2 = norm_layer(width)
-        self.conv3 = nn.Conv2d(in_channels=width,
-                               out_channels=planes * self.expansion,
-                               kernel_size=1,
-                               stride=1,
-                               padding=0,
-                               dilation=1,
-                               groups=1,
-                               bias=False)
-        self.bn3 = norm_layer(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-
-    def forward(self, x):
-        identity = self.downsample(x) if self.downsample is not None else x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-        out = self.relu(out + identity)
-
-        return out
-
-
-class FPN(nn.Module):
-    """Implementation of Feature Pyramid Network (FPN).
-
-    The input of this module is a pyramid of features with reducing resolutions.
-    Then, this module fuses these multi-level features from `top_level` to
-    `bottom_level`. In particular, starting from the `top_level`, each feature
-    is convoluted, upsampled, and fused into its previous feature (which is also
-    convoluted).
-
-    Args:
-        pyramid_channels: A list of integers, each of which indicates the number
-            of channels of the feature from a particular level.
-        out_channels: Number of channels for each output.
-
-    Returns:
-        A list of feature maps, each of which has `out_channels` channels.
-    """
-
-    def __init__(self, pyramid_channels, out_channels):
-        super().__init__()
-        assert isinstance(pyramid_channels, (list, tuple))
-        self.num_levels = len(pyramid_channels)
-
-        self.lateral_conv_list = nn.ModuleList()
-        self.feature_conv_list = nn.ModuleList()
-        for i in range(self.num_levels):
-            in_channels = pyramid_channels[i]
-            self.lateral_conv_list.append(nn.Conv2d(in_channels=in_channels,
-                                                    out_channels=out_channels,
-                                                    kernel_size=3,
-                                                    padding=1,
-                                                    bias=True))
-            self.feature_conv_list.append(nn.Conv2d(in_channels=out_channels,
-                                                    out_channels=out_channels,
-                                                    kernel_size=3,
-                                                    padding=1,
-                                                    bias=True))
-
-    def forward(self, inputs):
-        if len(inputs) != self.num_levels:
-            raise ValueError('Number of inputs and `num_levels` mismatch!')
-
-        # Project all related features to `out_channels`.
-        laterals = []
-        for i in range(self.num_levels):
-            laterals.append(self.lateral_conv_list[i](inputs[i]))
-
-        # Fusion, starting from `top_level`.
-        for i in range(self.num_levels - 1, 0, -1):
-            scale_factor = laterals[i - 1].shape[2] // laterals[i].shape[2]
-            laterals[i - 1] = (laterals[i - 1] +
-                               F.interpolate(laterals[i],
-                                             mode='nearest',
-                                             scale_factor=scale_factor))
-
-        # Get outputs.
-        outputs = []
-        for i, lateral in enumerate(laterals):
-            outputs.append(self.feature_conv_list[i](lateral))
-
-        return outputs
-
-
-class SAM(nn.Module):
-    """Implementation of Spatial Alignment Module (SAM).
-
-    The input of this module is a pyramid of features with reducing resolutions.
-    Then this module downsamples all levels of feature to the minimum resolution
-    and fuses it with the smallest feature map.
-
-    Args:
-        pyramid_channels: A list of integers, each of which indicates the number
-            of channels of the feature from a particular level.
-        out_channels: Number of channels for each output.
-
-    Returns:
-        A list of feature maps, each of which has `out_channels` channels.
-    """
-
-    def __init__(self, pyramid_channels, out_channels):
-        super().__init__()
-        assert isinstance(pyramid_channels, (list, tuple))
-        self.num_levels = len(pyramid_channels)
-
-        self.fusion_conv_list = nn.ModuleList()
-        for i in range(self.num_levels):
-            in_channels = pyramid_channels[i]
-            self.fusion_conv_list.append(nn.Conv2d(in_channels=in_channels,
-                                                   out_channels=out_channels,
-                                                   kernel_size=3,
-                                                   padding=1,
-                                                   bias=True))
-
-    def forward(self, inputs):
-        if len(inputs) != self.num_levels:
-            raise ValueError('Number of inputs and `num_levels` mismatch!')
-
-        output_res = inputs[-1].shape[2:]
-        for i in range(self.num_levels - 1, -1, -1):
-            if i != self.num_levels - 1:
-                inputs[i] = F.adaptive_avg_pool2d(inputs[i], output_res)
-            inputs[i] = self.fusion_conv_list[i](inputs[i])
-            if i != self.num_levels - 1:
-                inputs[i] = inputs[i] + inputs[-1]
-
-        return inputs
-
-
-class CodeHead(nn.Module):
-    """Implementation of the task-head to produce inverted codes."""
-
-    def __init__(self, in_channels, out_channels, norm_layer=nn.BatchNorm2d):
-        super().__init__()
-        self.fc = nn.Linear(in_channels, out_channels, bias=True)
-        if norm_layer is None:
-            self.norm = nn.Identity()
-        else:
-            self.norm = norm_layer(out_channels)
-
-    def forward(self, x):
-        if x.ndim > 2:
-            x = x.flatten(start_dim=1)
-        latent = self.fc(x)
-        latent = latent.unsqueeze(2).unsqueeze(3)
-        latent = self.norm(latent)
-
-        return latent.flatten(start_dim=1)
-
-class EncoderNet(nn.Module):
-    """Define the ResNet-based encoder network for GAN inversion.
-
-    On top of the backbone, there are several task-heads to produce inverted
-    codes. Please use `latent_dim` and `num_latents_per_head` to define the
-    structure.
-
-    Settings for the encoder network:
-
-    (1) resolution: The resolution of the output image.
-    (2) latent_dim: Dimension of the latent space. A number (one code will be
-        produced), or a list of numbers regarding layer-wise latent codes.
-    (3) num_latents_per_head: Number of latents that is produced by each head.
-    (4) image_channels: Number of channels of the output image. (default: 3)
-
-    ResNet-related settings:
-
-    (1) network_depth: Depth of the network, like 18 for ResNet18. (default: 18)
-    (2) inplanes: Number of channels of the first convolutional layer.
-        (default: 64)
-    (3) groups: Groups of the convolution, used in ResNet. (default: 1)
-    (4) width_per_group: Number of channels per group, used in ResNet.
-        (default: 64)
-    (5) replace_stride_with_dilation: Wether to replace stride with dilation,
-        used in ResNet. (default: None)
-    (6) norm_layer: Normalization layer used in the encoder.
-        (default: nn.BatchNorm2d)
-    (7) max_channels: Maximum number of channels in each layer. (default: 512)
-
-    Task-head related settings:
-
-    (1) use_fpn: Whether to use Feature Pyramid Network (FPN) before outputing
-        the latent code. (default: True)
-    (2) fpn_channels: Number of channels used in FPN. (default: 512)
-    (3) use_sam: Whether to use Spatial Alignment Module (SAM) before outputing
-        the latent code. (default: True)
-    (4) sam_channels: Number of channels used in SAM. (default: 512)
-    """
-
-    arch_settings = {
-        18: (BasicBlock,  [2, 2, 2, 2]),
-        34: (BasicBlock,  [3, 4, 6, 3]),
-        50: (Bottleneck,  [3, 4, 6, 3]),
-        101: (Bottleneck, [3, 4, 23, 3]),
-        152: (Bottleneck, [3, 8, 36, 3])
-    }
-
-    def __init__(self,
-                 resolution,
-                 latent_dim,
-                 num_latents_per_head,
-                 image_channels=3,
-                 network_depth=18,
-                 inplanes=64,
-                 groups=1,
-                 width_per_group=64,
-                 replace_stride_with_dilation=None,
-                 norm_layer=nn.BatchNorm2d,
-                 max_channels=512,
-                 use_fpn=True,
-                 fpn_channels=512,
-                 use_sam=True,
-                 sam_channels=512):
-        super().__init__()
-
-        if resolution not in _RESOLUTIONS_ALLOWED:
-            raise ValueError(f'Invalid resolution: `{resolution}`!\n'
-                             f'Resolutions allowed: {_RESOLUTIONS_ALLOWED}.')
-        if network_depth not in self.arch_settings:
-            raise ValueError(f'Invalid network depth: `{network_depth}`!\n'
-                             f'Options allowed: '
-                             f'{list(self.arch_settings.keys())}.')
-        if isinstance(latent_dim, int):
-            latent_dim = [latent_dim]
-        assert isinstance(latent_dim, (list, tuple))
-        assert isinstance(num_latents_per_head, (list, tuple))
-        assert sum(num_latents_per_head) == len(latent_dim)
-
-        self.resolution = resolution
-        self.latent_dim = latent_dim
-        self.num_latents_per_head = num_latents_per_head
-        self.num_heads = len(self.num_latents_per_head)
-        self.image_channels = image_channels
-        self.inplanes = inplanes
-        self.network_depth = network_depth
-        self.groups = groups
-        self.dilation = 1
-        self.base_width = width_per_group
-        self.replace_stride_with_dilation = replace_stride_with_dilation
-        if norm_layer is None or norm_layer == nn.BatchNorm2d:
-            norm_layer = nn.SyncBatchNorm
-        self.norm_layer = norm_layer
-        self.max_channels = max_channels
-        self.use_fpn = use_fpn
-        self.fpn_channels = fpn_channels
-        self.use_sam = use_sam
-        self.sam_channels = sam_channels
-
-        block_fn, num_blocks_per_stage = self.arch_settings[network_depth]
-
-        self.num_stages = int(np.log2(resolution // _FINAL_RES)) - 1
-        for i in range(4, self.num_stages):
-            num_blocks_per_stage.append(1)
-        if replace_stride_with_dilation is None:
-            replace_stride_with_dilation = [False] * self.num_stages
-
-        # Backbone.
-        self.conv1 = nn.Conv2d(in_channels=self.image_channels,
-                               out_channels=self.inplanes,
-                               kernel_size=7,
-                               stride=2,
-                               padding=3,
-                               bias=False)
-        self.bn1 = norm_layer(self.inplanes)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
-        self.stage_channels = [self.inplanes]
-        for i in range(1, self.num_stages + 1):
-            channels = min(self.max_channels, self.inplanes * (2 ** (i - 1)))
-            num_blocks = num_blocks_per_stage[i - 1]
-            stride = 1 if i == 1 else 2
-            dilate = replace_stride_with_dilation[i - 1]
-            self.add_module(f'layer{i}',
-                            self._make_stage(block_fn=block_fn,
-                                             planes=channels,
-                                             num_blocks=num_blocks,
-                                             stride=stride,
-                                             dilate=dilate))
-            self.stage_channels.append(channels)
-
-        if self.num_heads > len(self.stage_channels):
-            raise ValueError(f'Number of task heads is larger than number of '
-                             f'stages! Please reduce the number of heads.')
-
-        # Task-head.
-        if self.num_heads == 1:
-            self.use_fpn = False
-            self.use_sam = False
-
-        if self.use_fpn:
-            fpn_pyramid_channels = self.stage_channels[-self.num_heads:]
-            self.fpn = FPN(pyramid_channels=fpn_pyramid_channels,
-                           out_channels=self.fpn_channels)
-        if self.use_sam:
-            if use_fpn:
-                sam_pyramid_channels = [self.fpn_channels] * self.num_heads
-            else:
-                sam_pyramid_channels = self.stage_channels[-self.num_heads:]
-            self.sam = SAM(pyramid_channels=sam_pyramid_channels,
-                           out_channels=self.sam_channels)
-
-        self.head_list = nn.ModuleList()
-        for head_idx in range(self.num_heads):
-            # Parse in_channels.
-            if self.use_sam:
-                in_channels = self.sam_channels
-            elif self.use_fpn:
-                in_channels = self.fpn_channels
-            else:
-                in_channels = self.stage_channels[head_idx - self.num_heads]
-            in_channels = in_channels * _FINAL_RES * _FINAL_RES
-
-            # Parse out_channels.
-            start_latent_idx = sum(self.num_latents_per_head[:head_idx])
-            end_latent_idx = sum(self.num_latents_per_head[:head_idx + 1])
-            out_channels = sum(self.latent_dim[start_latent_idx:end_latent_idx])
-
-            self.head_list.append(CodeHead(in_channels=in_channels,
-                                           out_channels=out_channels,
-                                           norm_layer=self.norm_layer))
-
-    def _make_stage(self, block_fn, planes, num_blocks, stride=1, dilate=False):
-        norm_layer = self.norm_layer
-        downsample = None
-        previous_dilation = self.dilation
-        if dilate:
-            self.dilation *= stride
-            stride = 1
-        if stride != 1 or self.inplanes != planes * block_fn.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(in_channels=self.inplanes,
-                          out_channels=planes * block_fn.expansion,
-                          kernel_size=1,
-                          stride=stride,
-                          padding=0,
-                          dilation=1,
-                          groups=1,
-                          bias=False),
-                norm_layer(planes * block_fn.expansion),
-            )
-
-        blocks = []
-        blocks.append(block_fn(inplanes=self.inplanes,
-                               planes=planes,
-                               base_width=self.base_width,
-                               stride=stride,
-                               groups=self.groups,
-                               dilation=previous_dilation,
-                               norm_layer=norm_layer,
-                               downsample=downsample))
-        self.inplanes = planes * block_fn.expansion
-        for _ in range(1, num_blocks):
-            blocks.append(block_fn(inplanes=self.inplanes,
-                                   planes=planes,
-                                   base_width=self.base_width,
-                                   stride=1,
-                                   groups=self.groups,
-                                   dilation=self.dilation,
-                                   norm_layer=norm_layer,
-                                   downsample=None))
-
-        return nn.Sequential(*blocks)
-
-    def _forward_impl(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-
-        features = [x]
-        for i in range(1, self.num_stages + 1):
-            x = getattr(self, f'layer{i}')(x)
-            features.append(x)
-        features = features[-self.num_heads:]
-
-        if self.use_fpn:
-            features = self.fpn(features)
-        if self.use_sam:
-            features = self.sam(features)
-        else:
-            final_size = features[-1].shape[2:]
-            for i in range(self.num_heads - 1):
-                features[i] = F.adaptive_avg_pool2d(features[i], final_size)
-
-        outputs = []
-        for head_idx in range(self.num_heads):
-            codes = self.head_list[head_idx](features[head_idx])
-            start_latent_idx = sum(self.num_latents_per_head[:head_idx])
-            end_latent_idx = sum(self.num_latents_per_head[:head_idx + 1])
-            split_size = self.latent_dim[start_latent_idx:end_latent_idx]
-            outputs.extend(torch.split(codes, split_size, dim=1))
-        max_dim = max(self.latent_dim)
-        for i, dim in enumerate(self.latent_dim):
-            if dim < max_dim:
-                outputs[i] = F.pad(outputs[i], (0, max_dim - dim))
-            outputs[i] = outputs[i].unsqueeze(1)
-
-        return torch.cat(outputs, dim=1)
-
-    def forward(self, x):
-        return self._forward_impl(x)
diff --git a/spaces/jgurzoni/image_background_swapper/models/ade20k/__init__.py b/spaces/jgurzoni/image_background_swapper/models/ade20k/__init__.py
deleted file mode 100644
index 773cfc4664eef45a4f6fe05bd3fe2aa2143fdb5c..0000000000000000000000000000000000000000
--- a/spaces/jgurzoni/image_background_swapper/models/ade20k/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .base import *
\ No newline at end of file
diff --git a/spaces/jiejiejie0420/bingo/src/components/ui/tooltip.tsx b/spaces/jiejiejie0420/bingo/src/components/ui/tooltip.tsx
deleted file mode 100644
index af1d48beb90dd5ae311796539843700871052cae..0000000000000000000000000000000000000000
--- a/spaces/jiejiejie0420/bingo/src/components/ui/tooltip.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as TooltipPrimitive from '@radix-ui/react-tooltip'
-
-import { cn } from '@/lib/utils'
-
-const TooltipProvider = TooltipPrimitive.Provider
-
-const Tooltip = TooltipPrimitive.Root
-
-const TooltipTrigger = TooltipPrimitive.Trigger
-
-const TooltipContent = React.forwardRef<
-  React.ElementRef<typeof TooltipPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof TooltipPrimitive.Content>
->(({ className, sideOffset = 4, ...props }, ref) => (
-  <TooltipPrimitive.Content
-    ref={ref}
-    sideOffset={sideOffset}
-    className={cn(
-      'z-50 overflow-hidden rounded-md border bg-popover px-3 py-1.5 text-xs font-medium text-popover-foreground shadow-md animate-in fade-in-50 data-[side=bottom]:slide-in-from-top-1 data-[side=left]:slide-in-from-right-1 data-[side=right]:slide-in-from-left-1 data-[side=top]:slide-in-from-bottom-1',
-      className
-    )}
-    {...props}
-  />
-))
-TooltipContent.displayName = TooltipPrimitive.Content.displayName
-
-export { Tooltip, TooltipTrigger, TooltipContent, TooltipProvider }
diff --git a/spaces/jiushini/bingo-jiushini/Dockerfile b/spaces/jiushini/bingo-jiushini/Dockerfile
deleted file mode 100644
index c677b05b75f7e4b2beee8c97fb47957a0861a83e..0000000000000000000000000000000000000000
--- a/spaces/jiushini/bingo-jiushini/Dockerfile
+++ /dev/null
@@ -1,7 +0,0 @@
-FROM weaigc/bingo:latest
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-CMD npm start
diff --git a/spaces/jknero/ppggpt/app.py b/spaces/jknero/ppggpt/app.py
deleted file mode 100644
index 601a8ed2f82ee7f69aef001d364e0e2b4b0ebe43..0000000000000000000000000000000000000000
--- a/spaces/jknero/ppggpt/app.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import os
-
-import cv2
-import gradio as gr
-import torch
-from basicsr.archs.srvgg_arch import SRVGGNetCompact
-from gfpgan.utils import GFPGANer
-from realesrgan.utils import RealESRGANer
-
-os.system("pip freeze")
-# download weights
-if not os.path.exists('realesr-general-x4v3.pth'):
-    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
-if not os.path.exists('GFPGANv1.2.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
-if not os.path.exists('GFPGANv1.3.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
-if not os.path.exists('GFPGANv1.4.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
-if not os.path.exists('RestoreFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
-if not os.path.exists('CodeFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")
-
-torch.hub.download_url_to_file(
-    'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg',
-    'lincoln.jpg')
-torch.hub.download_url_to_file(
-    'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
-    'AI-generate.jpg')
-torch.hub.download_url_to_file(
-    'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
-    'Blake_Lively.jpg')
-torch.hub.download_url_to_file(
-    'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
-    '10045.png')
-
-# background enhancer with RealESRGAN
-model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-model_path = 'realesr-general-x4v3.pth'
-half = True if torch.cuda.is_available() else False
-upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
-
-os.makedirs('output', exist_ok=True)
-
-
-# def inference(img, version, scale, weight):
-def inference(img, version, scale):
-    # weight /= 100
-    print(img, version, scale)
-    try:
-        extension = os.path.splitext(os.path.basename(str(img)))[1]
-        img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
-        if len(img.shape) == 3 and img.shape[2] == 4:
-            img_mode = 'RGBA'
-        elif len(img.shape) == 2:  # for gray inputs
-            img_mode = None
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-        else:
-            img_mode = None
-
-        h, w = img.shape[0:2]
-        if h < 300:
-            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
-
-        if version == 'v1.2':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'v1.3':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'v1.4':
-            face_enhancer = GFPGANer(
-            model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'RestoreFormer':
-            face_enhancer = GFPGANer(
-            model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
-        # elif version == 'CodeFormer':
-        #     face_enhancer = GFPGANer(
-        #     model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
-
-        try:
-            # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
-            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
-        except RuntimeError as error:
-            print('Error', error)
-
-        try:
-            if scale != 2:
-                interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
-                h, w = img.shape[0:2]
-                output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
-        except Exception as error:
-            print('wrong scale input.', error)
-        if img_mode == 'RGBA':  # RGBA images should be saved in png format
-            extension = 'png'
-        else:
-            extension = 'jpg'
-        save_path = f'output/out.{extension}'
-        cv2.imwrite(save_path, output)
-
-        output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
-        return output, save_path
-    except Exception as error:
-        print('global exception', error)
-        return None, None
-
-
-title = "RESTAURAÇÃO DE IMAGEM"
-description = r"""By: Giovanne 😊
-"""
-article = r"""
-Instagram: @gz_777x
-"""
-demo = gr.Interface(
-    inference, [
-        gr.inputs.Image(type="filepath", label="Input"),
-        # gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", default='v1.4', label='version'),
-        gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", default='v1.4', label='version'),
-        gr.inputs.Number(label="Rescaling factor", default=2),
-        # gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', default=50)
-    ], [
-        gr.outputs.Image(type="numpy", label="Output (The whole image)"),
-        gr.outputs.File(label="Download the output image")
-    ],
-    title=title,
-    description=description,
-    article=article,
-    # examples=[['AI-generate.jpg', 'v1.4', 2, 50], ['lincoln.jpg', 'v1.4', 2, 50], ['Blake_Lively.jpg', 'v1.4', 2, 50],
-    #           ['10045.png', 'v1.4', 2, 50]]).launch()
-    examples=[['AI-generate.jpg', 'v1.4', 2], ['lincoln.jpg', 'v1.4', 2], ['Blake_Lively.jpg', 'v1.4', 2],
-              ['10045.png', 'v1.4', 2]])
-demo.queue(concurrency_count=4)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/transform.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/transform.py
deleted file mode 100644
index f85b54b73121589cb8de284a5e8efe9d20fefa17..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/transform.py
+++ /dev/null
@@ -1,495 +0,0 @@
-"""Affine 2D transformation matrix class.
-
-The Transform class implements various transformation matrix operations,
-both on the matrix itself, as well as on 2D coordinates.
-
-Transform instances are effectively immutable: all methods that operate on the
-transformation itself always return a new instance. This has as the
-interesting side effect that Transform instances are hashable, ie. they can be
-used as dictionary keys.
-
-This module exports the following symbols:
-
-Transform
-	this is the main class
-Identity
-	Transform instance set to the identity transformation
-Offset
-	Convenience function that returns a translating transformation
-Scale
-	Convenience function that returns a scaling transformation
-
-The DecomposedTransform class implements a transformation with separate
-translate, rotation, scale, skew, and transformation-center components.
-
-:Example:
-
-	>>> t = Transform(2, 0, 0, 3, 0, 0)
-	>>> t.transformPoint((100, 100))
-	(200, 300)
-	>>> t = Scale(2, 3)
-	>>> t.transformPoint((100, 100))
-	(200, 300)
-	>>> t.transformPoint((0, 0))
-	(0, 0)
-	>>> t = Offset(2, 3)
-	>>> t.transformPoint((100, 100))
-	(102, 103)
-	>>> t.transformPoint((0, 0))
-	(2, 3)
-	>>> t2 = t.scale(0.5)
-	>>> t2.transformPoint((100, 100))
-	(52.0, 53.0)
-	>>> import math
-	>>> t3 = t2.rotate(math.pi / 2)
-	>>> t3.transformPoint((0, 0))
-	(2.0, 3.0)
-	>>> t3.transformPoint((100, 100))
-	(-48.0, 53.0)
-	>>> t = Identity.scale(0.5).translate(100, 200).skew(0.1, 0.2)
-	>>> t.transformPoints([(0, 0), (1, 1), (100, 100)])
-	[(50.0, 100.0), (50.550167336042726, 100.60135501775433), (105.01673360427253, 160.13550177543362)]
-	>>>
-"""
-
-import math
-from typing import NamedTuple
-from dataclasses import dataclass
-
-
-__all__ = ["Transform", "Identity", "Offset", "Scale", "DecomposedTransform"]
-
-
-_EPSILON = 1e-15
-_ONE_EPSILON = 1 - _EPSILON
-_MINUS_ONE_EPSILON = -1 + _EPSILON
-
-
-def _normSinCos(v):
-    if abs(v) < _EPSILON:
-        v = 0
-    elif v > _ONE_EPSILON:
-        v = 1
-    elif v < _MINUS_ONE_EPSILON:
-        v = -1
-    return v
-
-
-class Transform(NamedTuple):
-
-    """2x2 transformation matrix plus offset, a.k.a. Affine transform.
-    Transform instances are immutable: all transforming methods, eg.
-    rotate(), return a new Transform instance.
-
-    :Example:
-
-            >>> t = Transform()
-            >>> t
-            <Transform [1 0 0 1 0 0]>
-            >>> t.scale(2)
-            <Transform [2 0 0 2 0 0]>
-            >>> t.scale(2.5, 5.5)
-            <Transform [2.5 0 0 5.5 0 0]>
-            >>>
-            >>> t.scale(2, 3).transformPoint((100, 100))
-            (200, 300)
-
-    Transform's constructor takes six arguments, all of which are
-    optional, and can be used as keyword arguments::
-
-            >>> Transform(12)
-            <Transform [12 0 0 1 0 0]>
-            >>> Transform(dx=12)
-            <Transform [1 0 0 1 12 0]>
-            >>> Transform(yx=12)
-            <Transform [1 0 12 1 0 0]>
-
-    Transform instances also behave like sequences of length 6::
-
-            >>> len(Identity)
-            6
-            >>> list(Identity)
-            [1, 0, 0, 1, 0, 0]
-            >>> tuple(Identity)
-            (1, 0, 0, 1, 0, 0)
-
-    Transform instances are comparable::
-
-            >>> t1 = Identity.scale(2, 3).translate(4, 6)
-            >>> t2 = Identity.translate(8, 18).scale(2, 3)
-            >>> t1 == t2
-            1
-
-    But beware of floating point rounding errors::
-
-            >>> t1 = Identity.scale(0.2, 0.3).translate(0.4, 0.6)
-            >>> t2 = Identity.translate(0.08, 0.18).scale(0.2, 0.3)
-            >>> t1
-            <Transform [0.2 0 0 0.3 0.08 0.18]>
-            >>> t2
-            <Transform [0.2 0 0 0.3 0.08 0.18]>
-            >>> t1 == t2
-            0
-
-    Transform instances are hashable, meaning you can use them as
-    keys in dictionaries::
-
-            >>> d = {Scale(12, 13): None}
-            >>> d
-            {<Transform [12 0 0 13 0 0]>: None}
-
-    But again, beware of floating point rounding errors::
-
-            >>> t1 = Identity.scale(0.2, 0.3).translate(0.4, 0.6)
-            >>> t2 = Identity.translate(0.08, 0.18).scale(0.2, 0.3)
-            >>> t1
-            <Transform [0.2 0 0 0.3 0.08 0.18]>
-            >>> t2
-            <Transform [0.2 0 0 0.3 0.08 0.18]>
-            >>> d = {t1: None}
-            >>> d
-            {<Transform [0.2 0 0 0.3 0.08 0.18]>: None}
-            >>> d[t2]
-            Traceback (most recent call last):
-              File "<stdin>", line 1, in ?
-            KeyError: <Transform [0.2 0 0 0.3 0.08 0.18]>
-    """
-
-    xx: float = 1
-    xy: float = 0
-    yx: float = 0
-    yy: float = 1
-    dx: float = 0
-    dy: float = 0
-
-    def transformPoint(self, p):
-        """Transform a point.
-
-        :Example:
-
-                >>> t = Transform()
-                >>> t = t.scale(2.5, 5.5)
-                >>> t.transformPoint((100, 100))
-                (250.0, 550.0)
-        """
-        (x, y) = p
-        xx, xy, yx, yy, dx, dy = self
-        return (xx * x + yx * y + dx, xy * x + yy * y + dy)
-
-    def transformPoints(self, points):
-        """Transform a list of points.
-
-        :Example:
-
-                >>> t = Scale(2, 3)
-                >>> t.transformPoints([(0, 0), (0, 100), (100, 100), (100, 0)])
-                [(0, 0), (0, 300), (200, 300), (200, 0)]
-                >>>
-        """
-        xx, xy, yx, yy, dx, dy = self
-        return [(xx * x + yx * y + dx, xy * x + yy * y + dy) for x, y in points]
-
-    def transformVector(self, v):
-        """Transform an (dx, dy) vector, treating translation as zero.
-
-        :Example:
-
-                >>> t = Transform(2, 0, 0, 2, 10, 20)
-                >>> t.transformVector((3, -4))
-                (6, -8)
-                >>>
-        """
-        (dx, dy) = v
-        xx, xy, yx, yy = self[:4]
-        return (xx * dx + yx * dy, xy * dx + yy * dy)
-
-    def transformVectors(self, vectors):
-        """Transform a list of (dx, dy) vector, treating translation as zero.
-
-        :Example:
-                >>> t = Transform(2, 0, 0, 2, 10, 20)
-                >>> t.transformVectors([(3, -4), (5, -6)])
-                [(6, -8), (10, -12)]
-                >>>
-        """
-        xx, xy, yx, yy = self[:4]
-        return [(xx * dx + yx * dy, xy * dx + yy * dy) for dx, dy in vectors]
-
-    def translate(self, x=0, y=0):
-        """Return a new transformation, translated (offset) by x, y.
-
-        :Example:
-                >>> t = Transform()
-                >>> t.translate(20, 30)
-                <Transform [1 0 0 1 20 30]>
-                >>>
-        """
-        return self.transform((1, 0, 0, 1, x, y))
-
-    def scale(self, x=1, y=None):
-        """Return a new transformation, scaled by x, y. The 'y' argument
-        may be None, which implies to use the x value for y as well.
-
-        :Example:
-                >>> t = Transform()
-                >>> t.scale(5)
-                <Transform [5 0 0 5 0 0]>
-                >>> t.scale(5, 6)
-                <Transform [5 0 0 6 0 0]>
-                >>>
-        """
-        if y is None:
-            y = x
-        return self.transform((x, 0, 0, y, 0, 0))
-
-    def rotate(self, angle):
-        """Return a new transformation, rotated by 'angle' (radians).
-
-        :Example:
-                >>> import math
-                >>> t = Transform()
-                >>> t.rotate(math.pi / 2)
-                <Transform [0 1 -1 0 0 0]>
-                >>>
-        """
-        import math
-
-        c = _normSinCos(math.cos(angle))
-        s = _normSinCos(math.sin(angle))
-        return self.transform((c, s, -s, c, 0, 0))
-
-    def skew(self, x=0, y=0):
-        """Return a new transformation, skewed by x and y.
-
-        :Example:
-                >>> import math
-                >>> t = Transform()
-                >>> t.skew(math.pi / 4)
-                <Transform [1 0 1 1 0 0]>
-                >>>
-        """
-        import math
-
-        return self.transform((1, math.tan(y), math.tan(x), 1, 0, 0))
-
-    def transform(self, other):
-        """Return a new transformation, transformed by another
-        transformation.
-
-        :Example:
-                >>> t = Transform(2, 0, 0, 3, 1, 6)
-                >>> t.transform((4, 3, 2, 1, 5, 6))
-                <Transform [8 9 4 3 11 24]>
-                >>>
-        """
-        xx1, xy1, yx1, yy1, dx1, dy1 = other
-        xx2, xy2, yx2, yy2, dx2, dy2 = self
-        return self.__class__(
-            xx1 * xx2 + xy1 * yx2,
-            xx1 * xy2 + xy1 * yy2,
-            yx1 * xx2 + yy1 * yx2,
-            yx1 * xy2 + yy1 * yy2,
-            xx2 * dx1 + yx2 * dy1 + dx2,
-            xy2 * dx1 + yy2 * dy1 + dy2,
-        )
-
-    def reverseTransform(self, other):
-        """Return a new transformation, which is the other transformation
-        transformed by self. self.reverseTransform(other) is equivalent to
-        other.transform(self).
-
-        :Example:
-                >>> t = Transform(2, 0, 0, 3, 1, 6)
-                >>> t.reverseTransform((4, 3, 2, 1, 5, 6))
-                <Transform [8 6 6 3 21 15]>
-                >>> Transform(4, 3, 2, 1, 5, 6).transform((2, 0, 0, 3, 1, 6))
-                <Transform [8 6 6 3 21 15]>
-                >>>
-        """
-        xx1, xy1, yx1, yy1, dx1, dy1 = self
-        xx2, xy2, yx2, yy2, dx2, dy2 = other
-        return self.__class__(
-            xx1 * xx2 + xy1 * yx2,
-            xx1 * xy2 + xy1 * yy2,
-            yx1 * xx2 + yy1 * yx2,
-            yx1 * xy2 + yy1 * yy2,
-            xx2 * dx1 + yx2 * dy1 + dx2,
-            xy2 * dx1 + yy2 * dy1 + dy2,
-        )
-
-    def inverse(self):
-        """Return the inverse transformation.
-
-        :Example:
-                >>> t = Identity.translate(2, 3).scale(4, 5)
-                >>> t.transformPoint((10, 20))
-                (42, 103)
-                >>> it = t.inverse()
-                >>> it.transformPoint((42, 103))
-                (10.0, 20.0)
-                >>>
-        """
-        if self == Identity:
-            return self
-        xx, xy, yx, yy, dx, dy = self
-        det = xx * yy - yx * xy
-        xx, xy, yx, yy = yy / det, -xy / det, -yx / det, xx / det
-        dx, dy = -xx * dx - yx * dy, -xy * dx - yy * dy
-        return self.__class__(xx, xy, yx, yy, dx, dy)
-
-    def toPS(self):
-        """Return a PostScript representation
-
-        :Example:
-
-                >>> t = Identity.scale(2, 3).translate(4, 5)
-                >>> t.toPS()
-                '[2 0 0 3 8 15]'
-                >>>
-        """
-        return "[%s %s %s %s %s %s]" % self
-
-    def toDecomposed(self) -> "DecomposedTransform":
-        """Decompose into a DecomposedTransform."""
-        return DecomposedTransform.fromTransform(self)
-
-    def __bool__(self):
-        """Returns True if transform is not identity, False otherwise.
-
-        :Example:
-
-                >>> bool(Identity)
-                False
-                >>> bool(Transform())
-                False
-                >>> bool(Scale(1.))
-                False
-                >>> bool(Scale(2))
-                True
-                >>> bool(Offset())
-                False
-                >>> bool(Offset(0))
-                False
-                >>> bool(Offset(2))
-                True
-        """
-        return self != Identity
-
-    def __repr__(self):
-        return "<%s [%g %g %g %g %g %g]>" % ((self.__class__.__name__,) + self)
-
-
-Identity = Transform()
-
-
-def Offset(x=0, y=0):
-    """Return the identity transformation offset by x, y.
-
-    :Example:
-            >>> Offset(2, 3)
-            <Transform [1 0 0 1 2 3]>
-            >>>
-    """
-    return Transform(1, 0, 0, 1, x, y)
-
-
-def Scale(x, y=None):
-    """Return the identity transformation scaled by x, y. The 'y' argument
-    may be None, which implies to use the x value for y as well.
-
-    :Example:
-            >>> Scale(2, 3)
-            <Transform [2 0 0 3 0 0]>
-            >>>
-    """
-    if y is None:
-        y = x
-    return Transform(x, 0, 0, y, 0, 0)
-
-
-@dataclass
-class DecomposedTransform:
-    """The DecomposedTransform class implements a transformation with separate
-    translate, rotation, scale, skew, and transformation-center components.
-    """
-
-    translateX: float = 0
-    translateY: float = 0
-    rotation: float = 0  # in degrees, counter-clockwise
-    scaleX: float = 1
-    scaleY: float = 1
-    skewX: float = 0  # in degrees, clockwise
-    skewY: float = 0  # in degrees, counter-clockwise
-    tCenterX: float = 0
-    tCenterY: float = 0
-
-    @classmethod
-    def fromTransform(self, transform):
-        # Adapted from an answer on
-        # https://math.stackexchange.com/questions/13150/extracting-rotation-scale-values-from-2d-transformation-matrix
-        a, b, c, d, x, y = transform
-
-        sx = math.copysign(1, a)
-        if sx < 0:
-            a *= sx
-            b *= sx
-
-        delta = a * d - b * c
-
-        rotation = 0
-        scaleX = scaleY = 0
-        skewX = skewY = 0
-
-        # Apply the QR-like decomposition.
-        if a != 0 or b != 0:
-            r = math.sqrt(a * a + b * b)
-            rotation = math.acos(a / r) if b >= 0 else -math.acos(a / r)
-            scaleX, scaleY = (r, delta / r)
-            skewX, skewY = (math.atan((a * c + b * d) / (r * r)), 0)
-        elif c != 0 or d != 0:
-            s = math.sqrt(c * c + d * d)
-            rotation = math.pi / 2 - (
-                math.acos(-c / s) if d >= 0 else -math.acos(c / s)
-            )
-            scaleX, scaleY = (delta / s, s)
-            skewX, skewY = (0, math.atan((a * c + b * d) / (s * s)))
-        else:
-            # a = b = c = d = 0
-            pass
-
-        return DecomposedTransform(
-            x,
-            y,
-            math.degrees(rotation),
-            scaleX * sx,
-            scaleY,
-            math.degrees(skewX) * sx,
-            math.degrees(skewY),
-            0,
-            0,
-        )
-
-    def toTransform(self):
-        """Return the Transform() equivalent of this transformation.
-
-        :Example:
-                >>> DecomposedTransform(scaleX=2, scaleY=2).toTransform()
-                <Transform [2 0 0 2 0 0]>
-                >>>
-        """
-        t = Transform()
-        t = t.translate(
-            self.translateX + self.tCenterX, self.translateY + self.tCenterY
-        )
-        t = t.rotate(math.radians(self.rotation))
-        t = t.scale(self.scaleX, self.scaleY)
-        t = t.skew(math.radians(self.skewX), math.radians(self.skewY))
-        t = t.translate(-self.tCenterX, -self.tCenterY)
-        return t
-
-
-if __name__ == "__main__":
-    import sys
-    import doctest
-
-    sys.exit(doctest.testmod().failed)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gradio/CHANGELOG.md b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gradio/CHANGELOG.md
deleted file mode 100644
index 04b46b565b24dde44c66d68049761e2e65b9eb11..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gradio/CHANGELOG.md
+++ /dev/null
@@ -1,4085 +0,0 @@
-# gradio
-
-## 3.47.1
-
-### Fixes
-
-- [#5816](https://github.com/gradio-app/gradio/pull/5816) [`796145e2c`](https://github.com/gradio-app/gradio/commit/796145e2c48c4087bec17f8ec0be4ceee47170cb) - Fix calls to the component server so that `gr.FileExplorer` works on Spaces.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.47.0
-
-### Highlights
-
-#### new `FileExplorer` component ([#5672](https://github.com/gradio-app/gradio/pull/5672) [`e4a307ed6`](https://github.com/gradio-app/gradio/commit/e4a307ed6cde3bbdf4ff2f17655739addeec941e))
-
-Thanks to a new capability that allows components to communicate directly with the server _without_ passing data via the value, we have created a new `FileExplorer` component.
-
-This component allows you to populate the explorer by passing a glob, but only provides the selected file(s) in your prediction function. 
-
-Users can then navigate the virtual filesystem and select files which will be accessible in your predict function. This component will allow developers to build more complex spaces, with more flexible input options.
-
-![output](https://github.com/pngwn/MDsveX/assets/12937446/ef108f0b-0e84-4292-9984-9dc66b3e144d)
-
-For more information check the [`FileExplorer` documentation](https://gradio.app/docs/fileexplorer).
-
- Thanks [@aliabid94](https://github.com/aliabid94)!
-
-### Features
-
-- [#5780](https://github.com/gradio-app/gradio/pull/5780) [`ed0f9a21b`](https://github.com/gradio-app/gradio/commit/ed0f9a21b04ad6b941b63d2ce45100dbd1abd5c5) - Adds `change()` event to `gr.Gallery`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5783](https://github.com/gradio-app/gradio/pull/5783) [`4567788bd`](https://github.com/gradio-app/gradio/commit/4567788bd1fc25df9322902ba748012e392b520a) - Adds the ability to set the `selected_index` in a `gr.Gallery`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5787](https://github.com/gradio-app/gradio/pull/5787) [`caeee8bf7`](https://github.com/gradio-app/gradio/commit/caeee8bf7821fd5fe2f936ed82483bed00f613ec) - ensure the client does not depend on `window` when running in a node environment.  Thanks [@gibiee](https://github.com/gibiee)!
-
-### Fixes
-
-- [#5798](https://github.com/gradio-app/gradio/pull/5798) [`a0d3cc45c`](https://github.com/gradio-app/gradio/commit/a0d3cc45c6db48dc0db423c229b8fb285623cdc4) - Fix `gr.SelectData` so that the target attribute is correctly attached, and the filedata is included in the data attribute with `gr.Gallery`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5795](https://github.com/gradio-app/gradio/pull/5795) [`957ba5cfd`](https://github.com/gradio-app/gradio/commit/957ba5cfde18e09caedf31236a2064923cd7b282) - Prevent bokeh from injecting bokeh js multiple times.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5790](https://github.com/gradio-app/gradio/pull/5790) [`37e70842d`](https://github.com/gradio-app/gradio/commit/37e70842d59f5aed6fab0086b1abf4b8d991f1c9) - added try except block in `state.py`.  Thanks [@SrijanSahaySrivastava](https://github.com/SrijanSahaySrivastava)!
-- [#5794](https://github.com/gradio-app/gradio/pull/5794) [`f096c3ae1`](https://github.com/gradio-app/gradio/commit/f096c3ae168c0df00f90fe131c1e48c572e0574b) - Throw helpful error when media devices are not found.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5776](https://github.com/gradio-app/gradio/pull/5776) [`c0fef4454`](https://github.com/gradio-app/gradio/commit/c0fef44541bfa61568bdcfcdfc7d7d79869ab1df) - Revert replica proxy logic and instead implement using the `root` variable.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-
-## 3.46.1
-
-### Features
-
-- [#5124](https://github.com/gradio-app/gradio/pull/5124) [`6e56a0d9b`](https://github.com/gradio-app/gradio/commit/6e56a0d9b0c863e76c69e1183d9d40196922b4cd) - Lite: Websocket queueing.  Thanks [@whitphx](https://github.com/whitphx)!
-
-### Fixes
-
-- [#5775](https://github.com/gradio-app/gradio/pull/5775) [`e2874bc3c`](https://github.com/gradio-app/gradio/commit/e2874bc3cb1397574f77dbd7f0408ed4e6792970) - fix pending chatbot message styling and ensure messages with value `None` don't render.  Thanks [@hannahblair](https://github.com/hannahblair)!
-
-## 3.46.0
-
-### Features
-
-- [#5699](https://github.com/gradio-app/gradio/pull/5699) [`8f0fed857`](https://github.com/gradio-app/gradio/commit/8f0fed857d156830626eb48b469d54d211a582d2) - Improve chatbot accessibility and UX.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5569](https://github.com/gradio-app/gradio/pull/5569) [`2a5b9e03b`](https://github.com/gradio-app/gradio/commit/2a5b9e03b15ea324d641fe6982f26d81b1ca7210) - Added support for pandas `Styler` object to `gr.DataFrame` (initially just sets the `display_value`).  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-### Fixes
-
-- [#5735](https://github.com/gradio-app/gradio/pull/5735) [`abb5e9df4`](https://github.com/gradio-app/gradio/commit/abb5e9df47989b2c56c2c312d74944678f9f2d4e) - Ensure images with no caption download in gallery.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5754](https://github.com/gradio-app/gradio/pull/5754) [`502054848`](https://github.com/gradio-app/gradio/commit/502054848fdbe39fc03ec42445242b4e49b7affc) - Fix Gallery `columns` and `rows` params.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5755](https://github.com/gradio-app/gradio/pull/5755) [`e842a561a`](https://github.com/gradio-app/gradio/commit/e842a561af4394f8109291ee5725bcf74743e816) - Fix new line issue in chatbot.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5731](https://github.com/gradio-app/gradio/pull/5731) [`c9af4f794`](https://github.com/gradio-app/gradio/commit/c9af4f794060e218193935d7213f0991a374f502) - Added timeout and error handling for frpc tunnel.  Thanks [@cansik](https://github.com/cansik)!
-- [#5766](https://github.com/gradio-app/gradio/pull/5766) [`ef96d3512`](https://github.com/gradio-app/gradio/commit/ef96d351229272738fc3c9680f7111f159590341) - Don't raise warnings when returning an updated component in a dictionary.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5767](https://github.com/gradio-app/gradio/pull/5767) [`caf6d9c0e`](https://github.com/gradio-app/gradio/commit/caf6d9c0e1f5b867cc20f2b4f6abb5ef47503a5f) - Set share=True for all Gradio apps in Colab by default.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.45.2
-
-### Features
-
-- [#5722](https://github.com/gradio-app/gradio/pull/5722) [`dba651904`](https://github.com/gradio-app/gradio/commit/dba651904c97dcddcaae2691540ac430d3eefd18) - Fix for deepcopy errors when running the replica-related logic on Spaces.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5721](https://github.com/gradio-app/gradio/pull/5721) [`84e03fe50`](https://github.com/gradio-app/gradio/commit/84e03fe506e08f1f81bac6d504c9fba7924f2d93) - Adds copy buttons to website, and better descriptions to API Docs.  Thanks [@aliabd](https://github.com/aliabd)!
-
-### Fixes
-
-- [#5714](https://github.com/gradio-app/gradio/pull/5714) [`a0fc5a296`](https://github.com/gradio-app/gradio/commit/a0fc5a29678baa2d9ba997a2124cadebecfb2c36) - Make Tab and Tabs updatable.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5713](https://github.com/gradio-app/gradio/pull/5713) [`c10dabd6b`](https://github.com/gradio-app/gradio/commit/c10dabd6b18b49259441eb5f956a19046f466339) - Fixes gr.select() Method Issues with Dataframe Cells.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5693](https://github.com/gradio-app/gradio/pull/5693) [`c2b31c396`](https://github.com/gradio-app/gradio/commit/c2b31c396f6d260cdf93377b715aee7ff162df75) - Context-based Progress tracker.  Thanks [@cbensimon](https://github.com/cbensimon)!
-- [#5705](https://github.com/gradio-app/gradio/pull/5705) [`78e7cf516`](https://github.com/gradio-app/gradio/commit/78e7cf5163e8d205e8999428fce4c02dbdece25f) - ensure internal data has updated before dispatching `success` or `then` events.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5668](https://github.com/gradio-app/gradio/pull/5668) [`d626c21e9`](https://github.com/gradio-app/gradio/commit/d626c21e91df026b04fdb3ee5c7dba74a261cfd3) - Fully resolve generated filepaths when running on Hugging Face Spaces with multiple replicas.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5711](https://github.com/gradio-app/gradio/pull/5711) [`aefb556ac`](https://github.com/gradio-app/gradio/commit/aefb556ac6dbadc320c618b11bb48371ef19dd61) - prevent internal log_message error from `/api/predict`.  Thanks [@cbensimon](https://github.com/cbensimon)!
-- [#5726](https://github.com/gradio-app/gradio/pull/5726) [`96c4b97c7`](https://github.com/gradio-app/gradio/commit/96c4b97c742311e90a87d8e8ee562c6ad765e9f0) - Adjust translation.  Thanks [@ylhsieh](https://github.com/ylhsieh)!
-- [#5732](https://github.com/gradio-app/gradio/pull/5732) [`3a48490bc`](https://github.com/gradio-app/gradio/commit/3a48490bc5e4136ec9bc0354b0d6fb6c04280505) - Add a bare `Component` type to the acceptable type list of `gr.load()`'s `inputs` and `outputs`.  Thanks [@whitphx](https://github.com/whitphx)!
-
-## 3.45.1
-
-### Fixes
-
-- [#5701](https://github.com/gradio-app/gradio/pull/5701) [`ee8eec1e5`](https://github.com/gradio-app/gradio/commit/ee8eec1e5e544a0127e0aa68c2522a7085b8ada5) - Fix for regression in rendering empty Markdown.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.45.0
-
-### Features
-
-- [#5675](https://github.com/gradio-app/gradio/pull/5675) [`b619e6f6e`](https://github.com/gradio-app/gradio/commit/b619e6f6e4ca55334fb86da53790e45a8f978566) - Reorganize Docs Navbar and Fill in Gaps.  Thanks [@aliabd](https://github.com/aliabd)!
-- [#5669](https://github.com/gradio-app/gradio/pull/5669) [`c5e969559`](https://github.com/gradio-app/gradio/commit/c5e969559612f956afcdb0c6f7b22ab8275bc49a) - Fix small issues in docs and guides.  Thanks [@aliabd](https://github.com/aliabd)!
-- [#5682](https://github.com/gradio-app/gradio/pull/5682) [`c57f1b75e`](https://github.com/gradio-app/gradio/commit/c57f1b75e272c76b0af4d6bd0c7f44743ff34f26) - Fix functional tests.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5681](https://github.com/gradio-app/gradio/pull/5681) [`40de3d217`](https://github.com/gradio-app/gradio/commit/40de3d2178b61ebe424b6f6228f94c0c6f679bea) - add query parameters to the `gr.Request` object through the `query_params` attribute.  Thanks [@DarhkVoyd](https://github.com/DarhkVoyd)!
-- [#5653](https://github.com/gradio-app/gradio/pull/5653) [`ea0e00b20`](https://github.com/gradio-app/gradio/commit/ea0e00b207b4b90a10e9d054c4202d4e705a29ba) - Prevent Clients from accessing API endpoints that set `api_name=False`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5639](https://github.com/gradio-app/gradio/pull/5639) [`e1874aff8`](https://github.com/gradio-app/gradio/commit/e1874aff814d13b23f3e59ef239cc13e18ad3fa7) - Add `gr.on` listener method.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5652](https://github.com/gradio-app/gradio/pull/5652) [`2e25d4305`](https://github.com/gradio-app/gradio/commit/2e25d430582264945ae3316acd04c4453a25ce38) - Pause autoscrolling if a user scrolls up in a `gr.Textbox` and resume autoscrolling if they go all the way down.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5642](https://github.com/gradio-app/gradio/pull/5642) [`21c7225bd`](https://github.com/gradio-app/gradio/commit/21c7225bda057117a9d3311854323520218720b5) - Improve plot rendering.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5677](https://github.com/gradio-app/gradio/pull/5677) [`9f9af327c`](https://github.com/gradio-app/gradio/commit/9f9af327c9115356433ec837f349d6286730fb97) - [Refactoring] Convert async functions that don't contain `await` statements to normal functions.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5660](https://github.com/gradio-app/gradio/pull/5660) [`d76555a12`](https://github.com/gradio-app/gradio/commit/d76555a122b545f0df7c9e7c1ca7bd2a6e262c86) - Fix secondary hue bug in gr.themes.builder().  Thanks [@hellofreckles](https://github.com/hellofreckles)!
-- [#5697](https://github.com/gradio-app/gradio/pull/5697) [`f4e4f82b5`](https://github.com/gradio-app/gradio/commit/f4e4f82b58a65efca9030a7e8e7c5ace60d8cc10) - Increase Slider clickable area.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5671](https://github.com/gradio-app/gradio/pull/5671) [`6a36c3b78`](https://github.com/gradio-app/gradio/commit/6a36c3b786700600d3826ce1e0629cc5308ddd47) - chore(deps): update dependency @types/prismjs to v1.26.1.  Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5240](https://github.com/gradio-app/gradio/pull/5240) [`da05e59a5`](https://github.com/gradio-app/gradio/commit/da05e59a53bbad15e5755a47f46685da18e1031e) - Cleanup of .update and .get_config per component.  Thanks [@aliabid94](https://github.com/aliabid94)!/n  get_config is removed, the config used is simply any attribute that is in the Block that shares a name with one of the constructor paramaters./n  update is not removed for backwards compatibility, but deprecated. Instead return the component itself. Created a updateable decorator that simply checks to see if we're in an update, and if so, skips the constructor and wraps the args and kwargs in an update dictionary. easy peasy.
-- [#5635](https://github.com/gradio-app/gradio/pull/5635) [`38fafb9e2`](https://github.com/gradio-app/gradio/commit/38fafb9e2a5509b444942e1d5dd48dffa20066f4) - Fix typos in Gallery docs.  Thanks [@atesgoral](https://github.com/atesgoral)!
-- [#5590](https://github.com/gradio-app/gradio/pull/5590) [`d1ad1f671`](https://github.com/gradio-app/gradio/commit/d1ad1f671caef9f226eb3965f39164c256d8615c) - Attach `elem_classes` selectors to layout elements, and an id to the Tab button (for targeting via CSS/JS).  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5554](https://github.com/gradio-app/gradio/pull/5554) [`75ddeb390`](https://github.com/gradio-app/gradio/commit/75ddeb390d665d4484667390a97442081b49a423) - Accessibility Improvements.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5598](https://github.com/gradio-app/gradio/pull/5598) [`6b1714386`](https://github.com/gradio-app/gradio/commit/6b17143868bdd2c1400af1199a01c1c0d5c27477) - Upgrade Pyodide to 0.24.0 and install the native orjson package.  Thanks [@whitphx](https://github.com/whitphx)!
-
-### Fixes
-
-- [#5625](https://github.com/gradio-app/gradio/pull/5625) [`9ccc4794a`](https://github.com/gradio-app/gradio/commit/9ccc4794a72ce8319417119f6c370e7af3ffca6d) - Use ContextVar instead of threading.local().  Thanks [@cbensimon](https://github.com/cbensimon)!
-- [#5602](https://github.com/gradio-app/gradio/pull/5602) [`54d21d3f1`](https://github.com/gradio-app/gradio/commit/54d21d3f18f2ddd4e796d149a0b41461f49c711b) - Ensure `HighlightedText` with `merge_elements` loads without a value.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5636](https://github.com/gradio-app/gradio/pull/5636) [`fb5964fb8`](https://github.com/gradio-app/gradio/commit/fb5964fb88082e7b956853b543c468116811cab9) - Fix bug in example cache loading event.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5633](https://github.com/gradio-app/gradio/pull/5633) [`341402337`](https://github.com/gradio-app/gradio/commit/34140233794c29d4722020e13c2d045da642dfae) - Allow Gradio apps containing `gr.Radio()`, `gr.Checkboxgroup()`, or `gr.Dropdown()` to be loaded with `gr.load()`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5616](https://github.com/gradio-app/gradio/pull/5616) [`7c34b434a`](https://github.com/gradio-app/gradio/commit/7c34b434aae0eb85f112a1dc8d66cefc7e2296b2) - Fix width and height issues that would cut off content in `gr.DataFrame`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5604](https://github.com/gradio-app/gradio/pull/5604) [`faad01f8e`](https://github.com/gradio-app/gradio/commit/faad01f8e10ef6d18249b1a4587477c59b74adb2) - Add `render_markdown` parameter to chatbot.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5593](https://github.com/gradio-app/gradio/pull/5593) [`88d43bd12`](https://github.com/gradio-app/gradio/commit/88d43bd124792d216da445adef932a2b02f5f416) - Fixes avatar image in chatbot being squashed.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5690](https://github.com/gradio-app/gradio/pull/5690) [`6b8c8afd9`](https://github.com/gradio-app/gradio/commit/6b8c8afd981fea984da568e9a0bd8bfc2a9c06c4) - Fix incorrect behavior of `gr.load()` with `gr.Examples`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5696](https://github.com/gradio-app/gradio/pull/5696) [`e51fcd5d5`](https://github.com/gradio-app/gradio/commit/e51fcd5d54315e8b65ee40e3de4dab17579ff6d5) - setting share=True on Spaces or in wasm should warn instead of raising error.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.44.4
-
-### Features
-
-- [#5514](https://github.com/gradio-app/gradio/pull/5514) [`52f783175`](https://github.com/gradio-app/gradio/commit/52f7831751b432411e109bd41add4ab286023a8e) - refactor: Use package.json for version management.  Thanks [@DarhkVoyd](https://github.com/DarhkVoyd)!
-- [#5535](https://github.com/gradio-app/gradio/pull/5535) [`d29b1ab74`](https://github.com/gradio-app/gradio/commit/d29b1ab740784d8c70f9ab7bc38bbbf7dd3ff737) - Makes sliders consistent across all browsers.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-
-### Fixes
-
-- [#5587](https://github.com/gradio-app/gradio/pull/5587) [`e0d61b8ba`](https://github.com/gradio-app/gradio/commit/e0d61b8baa0f6293f53b9bdb1647d42f9ae2583a) - Fix `.clear()` events for audio and image.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5534](https://github.com/gradio-app/gradio/pull/5534) [`d9e9ae43f`](https://github.com/gradio-app/gradio/commit/d9e9ae43f5c52c1f729af5a20e5d4f754689d429) - Guide fixes, esp. streaming audio.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5588](https://github.com/gradio-app/gradio/pull/5588) [`acdeff57e`](https://github.com/gradio-app/gradio/commit/acdeff57ece4672f943c374d537eaf47d3ec034f) - Allow multiple instances of Gradio with authentication to run on different ports.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.44.3
-
-### Fixes
-
-- [#5562](https://github.com/gradio-app/gradio/pull/5562) [`50d9747d0`](https://github.com/gradio-app/gradio/commit/50d9747d061962cff7f60a8da648bb3781794102) - chore(deps): update dependency iframe-resizer to v4.3.7.  Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5550](https://github.com/gradio-app/gradio/pull/5550) [`4ed5902e7`](https://github.com/gradio-app/gradio/commit/4ed5902e7dda2d95cd43e4ccaaef520ddd8eba57) - Adding basque language.  Thanks [@EkhiAzur](https://github.com/EkhiAzur)!
-- [#5547](https://github.com/gradio-app/gradio/pull/5547) [`290f51871`](https://github.com/gradio-app/gradio/commit/290f5187160cdbd7a786494fe3c19b0e70abe167) - typo in UploadButton's docstring.  Thanks [@chaeheum3](https://github.com/chaeheum3)!
-- [#5553](https://github.com/gradio-app/gradio/pull/5553) [`d1bf23cd2`](https://github.com/gradio-app/gradio/commit/d1bf23cd2c6da3692d7753856bfe7564d84778e0) - Modify Image examples docstring.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5563](https://github.com/gradio-app/gradio/pull/5563) [`ba64082ed`](https://github.com/gradio-app/gradio/commit/ba64082ed80c1ed9113497ae089e63f032dbcc75) - preprocess for components when type='index'.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.44.2
-
-### Fixes
-
-- [#5537](https://github.com/gradio-app/gradio/pull/5537) [`301c7878`](https://github.com/gradio-app/gradio/commit/301c7878217f9fc531c0f28330b394f02955811b) - allow gr.Image() examples to take urls.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5544](https://github.com/gradio-app/gradio/pull/5544) [`a0cc9ac9`](https://github.com/gradio-app/gradio/commit/a0cc9ac931554e06dcb091158c9b9ac0cc580b6c) - Fixes dropdown breaking if a user types in invalid value and presses enter.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.44.1
-
-### Fixes
-
-- [#5516](https://github.com/gradio-app/gradio/pull/5516) [`c5fe8eba`](https://github.com/gradio-app/gradio/commit/c5fe8ebadbf206e2f4199ccde4606e331a22148a) - Fix docstring of dropdown.  Thanks [@hysts](https://github.com/hysts)!
-- [#5529](https://github.com/gradio-app/gradio/pull/5529) [`81c9ca9a`](https://github.com/gradio-app/gradio/commit/81c9ca9a2e00d19334f632fec32081d36ad54c7f) - Fix `.update()` method in `gr.Dropdown()` to handle `choices`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5528](https://github.com/gradio-app/gradio/pull/5528) [`dc86e4a7`](https://github.com/gradio-app/gradio/commit/dc86e4a7e1c40b910c74558e6f88fddf9b3292bc) - Lazy load all images.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5525](https://github.com/gradio-app/gradio/pull/5525) [`21f1db40`](https://github.com/gradio-app/gradio/commit/21f1db40de6d1717eba97a550e11422a457ba7e9) - Ensure input value saves on dropdown blur.  Thanks [@hannahblair](https://github.com/hannahblair)!
-
-## 3.44.0
-
-### Features
-
-- [#5505](https://github.com/gradio-app/gradio/pull/5505) [`9ee20f49`](https://github.com/gradio-app/gradio/commit/9ee20f499f62c1fe5af6b8f84918b3a334eb1c8d) - Validate i18n file names with ISO-639x.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5475](https://github.com/gradio-app/gradio/pull/5475) [`c60b89b0`](https://github.com/gradio-app/gradio/commit/c60b89b0a54758a27277f0a6aa20d0653647c7c8) - Adding Central Kurdish.  Thanks [@Hrazhan](https://github.com/Hrazhan)!
-- [#5400](https://github.com/gradio-app/gradio/pull/5400) [`d112e261`](https://github.com/gradio-app/gradio/commit/d112e2611b0fc79ecedfaed367571f3157211387) - Allow interactive input in `gr.HighlightedText`.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5488](https://github.com/gradio-app/gradio/pull/5488) [`8909e42a`](https://github.com/gradio-app/gradio/commit/8909e42a7c6272358ad413588d27a5124d151205) - Adds `autoscroll` param to `gr.Textbox()`.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5384](https://github.com/gradio-app/gradio/pull/5384) [`ddc02268`](https://github.com/gradio-app/gradio/commit/ddc02268f731bd2ed04b7a5854accf3383f9a0da) - Allows the `gr.Dropdown` to have separate names and values, as well as enables `allow_custom_value` for multiselect dropdown.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5473](https://github.com/gradio-app/gradio/pull/5473) [`b271e738`](https://github.com/gradio-app/gradio/commit/b271e738860ca238ecdee2991f49b505c7559016) - Remove except asyncio.CancelledError which is no longer necessary due to 53d7025.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5474](https://github.com/gradio-app/gradio/pull/5474) [`041560f9`](https://github.com/gradio-app/gradio/commit/041560f9f11ca2560005b467bb412ee1becfc2b2) - Fix queueing.call_prediction to retrieve the default response class in the same manner as FastAPI's implementation.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5510](https://github.com/gradio-app/gradio/pull/5510) [`afcf3c48`](https://github.com/gradio-app/gradio/commit/afcf3c48e82712067d6d00a0caedb1562eb986f8) - Do not expose existence of files outside of working directory.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-### Fixes
-
-- [#5459](https://github.com/gradio-app/gradio/pull/5459) [`bd2fda77`](https://github.com/gradio-app/gradio/commit/bd2fda77fc98d815f4fb670f535af453ebee9b80) - Dispatch `stop_recording` event in Audio.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5508](https://github.com/gradio-app/gradio/pull/5508) [`05715f55`](https://github.com/gradio-app/gradio/commit/05715f5599ae3e928d3183c7b0a7f5291f843a96) - Adds a `filterable` parameter to `gr.Dropdown` that controls whether user can type to filter choices.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5470](https://github.com/gradio-app/gradio/pull/5470) [`a4e010a9`](https://github.com/gradio-app/gradio/commit/a4e010a96f1d8a52b3ac645e03fe472b9c3cbbb1) - Fix share button position.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5496](https://github.com/gradio-app/gradio/pull/5496) [`82ec4d26`](https://github.com/gradio-app/gradio/commit/82ec4d2622a43c31b248b78e9410e2ac918f6035) - Allow interface with components to be run inside blocks.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.43.2
-
-### Fixes
-
-- [#5456](https://github.com/gradio-app/gradio/pull/5456) [`6e381c4f`](https://github.com/gradio-app/gradio/commit/6e381c4f146cc8177a4e2b8e39f914f09cd7ff0c) - ensure dataframe doesn't steal focus.  Thanks [@pngwn](https://github.com/pngwn)!
-
-## 3.43.1
-
-### Fixes
-
-- [#5445](https://github.com/gradio-app/gradio/pull/5445) [`67bb7bcb`](https://github.com/gradio-app/gradio/commit/67bb7bcb6a95b7a00a8bdf612cf147850d919a44) - ensure dataframe doesn't scroll unless needed.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5447](https://github.com/gradio-app/gradio/pull/5447) [`7a4a89e5`](https://github.com/gradio-app/gradio/commit/7a4a89e5ca1dedb39e5366867501584b0c636bbb) - ensure iframe is correct size on spaces.  Thanks [@pngwn](https://github.com/pngwn)!
-
-## 3.43.0
-
-### Features
-
-- [#5165](https://github.com/gradio-app/gradio/pull/5165) [`c77f05ab`](https://github.com/gradio-app/gradio/commit/c77f05abb65b2828c9c19af4ec0a0c09412f9f6a) - Fix the Queue to call API endpoints without internal HTTP routing.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5427](https://github.com/gradio-app/gradio/pull/5427) [`aad7acd7`](https://github.com/gradio-app/gradio/commit/aad7acd7128dca05b227ecbba06db9f94d65b088) - Add sort to bar plot.  Thanks [@Chaitanya134](https://github.com/Chaitanya134)!
-- [#5342](https://github.com/gradio-app/gradio/pull/5342) [`afac0006`](https://github.com/gradio-app/gradio/commit/afac0006337ce2840cf497cd65691f2f60ee5912) - significantly improve the performance of `gr.Dataframe` for large datasets.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5417](https://github.com/gradio-app/gradio/pull/5417) [`d14d63e3`](https://github.com/gradio-app/gradio/commit/d14d63e30c4af3f9c2a664fd11b0a01943a8300c) - Auto scroll to bottom of textbox.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-
-### Fixes
-
-- [#5412](https://github.com/gradio-app/gradio/pull/5412) [`26fef8c7`](https://github.com/gradio-app/gradio/commit/26fef8c7f85a006c7e25cdbed1792df19c512d02) - Skip view_api request in js client when auth enabled.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5436](https://github.com/gradio-app/gradio/pull/5436) [`7ab4b70f`](https://github.com/gradio-app/gradio/commit/7ab4b70f6821afb4e85cef225d1235c19df8ebbf) - api_open does not take precedence over show_api.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-
-## 3.42.0
-
-### Highlights
-
-#### Like/Dislike Button for Chatbot ([#5391](https://github.com/gradio-app/gradio/pull/5391) [`abf1c57d`](https://github.com/gradio-app/gradio/commit/abf1c57d7d85de0df233ee3b38aeb38b638477db))
-
- Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-
-#### Added the ability to attach event listeners via decorators ([#5395](https://github.com/gradio-app/gradio/pull/5395) [`55fed04f`](https://github.com/gradio-app/gradio/commit/55fed04f559becb9c24f22cc6292dc572d709886))
-
-e.g.
-
-```python
-with gr.Blocks() as demo:
-    name = gr.Textbox(label="Name")
-    output = gr.Textbox(label="Output Box")
-    greet_btn = gr.Button("Greet")
-
-    @greet_btn.click(inputs=name, outputs=output)
-    def greet(name):
-        return "Hello " + name + "!"
-```
-
- Thanks [@aliabid94](https://github.com/aliabid94)!
-
-### Features
-
-- [#5334](https://github.com/gradio-app/gradio/pull/5334) [`c5bf9138`](https://github.com/gradio-app/gradio/commit/c5bf91385a632dc9f612499ee01166ac6ae509a9) - Add chat bubble width param.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5267](https://github.com/gradio-app/gradio/pull/5267) [`119c8343`](https://github.com/gradio-app/gradio/commit/119c834331bfae60d4742c8f20e9cdecdd67e8c2) - Faster reload mode.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5373](https://github.com/gradio-app/gradio/pull/5373) [`79d8f9d8`](https://github.com/gradio-app/gradio/commit/79d8f9d891901683c5a1b7486efb44eab2478c96) - Adds `height` and `zoom_speed` parameters to `Model3D` component, as well as a button to reset the camera position.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5370](https://github.com/gradio-app/gradio/pull/5370) [`61803c65`](https://github.com/gradio-app/gradio/commit/61803c6545e73fce47e8740bd46721ab9bb0ba5c) - chore(deps): update dependency extendable-media-recorder to v9.  Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5266](https://github.com/gradio-app/gradio/pull/5266) [`4ccb9a86`](https://github.com/gradio-app/gradio/commit/4ccb9a86f194c6997f80a09880edc3c2b0554aab) - Makes it possible to set the initial camera position for the `Model3D` component as a tuple of (alpha, beta, radius).  Thanks [@mbahri](https://github.com/mbahri)!
-- [#5271](https://github.com/gradio-app/gradio/pull/5271) [`97c3c7b1`](https://github.com/gradio-app/gradio/commit/97c3c7b1730407f9e80566af9ecb4ca7cccf62ff) - Move scripts from old website to CI.  Thanks [@aliabd](https://github.com/aliabd)!
-- [#5369](https://github.com/gradio-app/gradio/pull/5369) [`b8968898`](https://github.com/gradio-app/gradio/commit/b89688984fa9c6be0db06e392e6935a544620764) - Fix typo in utils.py.  Thanks [@eltociear](https://github.com/eltociear)!
-
-### Fixes
-
-- [#5304](https://github.com/gradio-app/gradio/pull/5304) [`05892302`](https://github.com/gradio-app/gradio/commit/05892302fb8fe2557d57834970a2b65aea97355b) - Adds kwarg to disable html sanitization in `gr.Chatbot()`.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5366](https://github.com/gradio-app/gradio/pull/5366) [`0cc7e2dc`](https://github.com/gradio-app/gradio/commit/0cc7e2dcf60e216e0a30e2f85a9879ce3cb2a1bd) - Hide avatar when message none.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5393](https://github.com/gradio-app/gradio/pull/5393) [`e4e7a431`](https://github.com/gradio-app/gradio/commit/e4e7a4319924aaf51dcb18d07d0c9953d4011074) - Renders LaTeX that is added to the page in `gr.Markdown`, `gr.Chatbot`, and `gr.DataFrame`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5394](https://github.com/gradio-app/gradio/pull/5394) [`4d94ea0a`](https://github.com/gradio-app/gradio/commit/4d94ea0a0cf2103cda19f48398a5634f8341d04d) - Adds horizontal scrolling to content that overflows in gr.Markdown.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5368](https://github.com/gradio-app/gradio/pull/5368) [`b27f7583`](https://github.com/gradio-app/gradio/commit/b27f7583254165b135bf1496a7d8c489a62ba96f) - Change markdown rendering to set breaks to false.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5360](https://github.com/gradio-app/gradio/pull/5360) [`64666525`](https://github.com/gradio-app/gradio/commit/6466652583e3c620df995fb865ef3511a34cb676) - Cancel Dropdown Filter.  Thanks [@deckar01](https://github.com/deckar01)!
-
-## 3.41.2
-
-### Features
-
-- [#5284](https://github.com/gradio-app/gradio/pull/5284) [`5f25eb68`](https://github.com/gradio-app/gradio/commit/5f25eb6836f6a78ce6208b53495a01e1fc1a1d2f) - Minor bug fix sweep.  Thanks [@aliabid94](https://github.com/aliabid94)!/n  - Our use of __exit__ was catching errors and corrupting the traceback of any component that failed to instantiate (try running blocks_kitchen_sink off main for an example). Now the __exit__ exits immediately if there's been an exception, so the original exception can be printed cleanly/n  - HighlightedText was rendering weird, cleaned it up
-
-### Fixes
-
-- [#5319](https://github.com/gradio-app/gradio/pull/5319) [`3341148c`](https://github.com/gradio-app/gradio/commit/3341148c109b5458cc88435d27eb154210efc472) - Fix: wrap avatar-image in a div to clip its shape.  Thanks [@Keldos-Li](https://github.com/Keldos-Li)!
-- [#5340](https://github.com/gradio-app/gradio/pull/5340) [`df090e89`](https://github.com/gradio-app/gradio/commit/df090e89f74a16e4cb2b700a1e3263cabd2bdd91) - Fix Checkbox select dispatch.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-
-## 3.41.1
-
-### Fixes
-
-- [#5324](https://github.com/gradio-app/gradio/pull/5324) [`31996c99`](https://github.com/gradio-app/gradio/commit/31996c991d6bfca8cef975eb8e3c9f61a7aced19) - ensure login form has correct styles.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5323](https://github.com/gradio-app/gradio/pull/5323) [`e32b0928`](https://github.com/gradio-app/gradio/commit/e32b0928d2d00342ca917ebb10c379ffc2ec200d) - ensure dropdown stays open when identical data is passed in.  Thanks [@pngwn](https://github.com/pngwn)!
-
-## 3.41.0
-
-### Highlights
-
-#### Improve startup performance and markdown support ([#5279](https://github.com/gradio-app/gradio/pull/5279) [`fe057300`](https://github.com/gradio-app/gradio/commit/fe057300f0672c62dab9d9b4501054ac5d45a4ec))
-
-##### Improved markdown support
-
-We now have better support for markdown in `gr.Markdown` and `gr.Dataframe`. Including syntax highlighting and Github Flavoured Markdown. We also have more consistent markdown behaviour and styling.
-
-##### Various performance improvements
-
-These improvements will be particularly beneficial to large applications.
-
-- Rather than attaching events manually, they are now delegated, leading to a significant performance improvement and addressing a performance regression introduced in a recent version of Gradio. App startup for large applications is now around twice as fast.
-- Optimised the mounting of individual components, leading to a modest performance improvement during startup (~30%).
-- Corrected an issue that was causing markdown to re-render infinitely.
-- Ensured that the `gr.3DModel` does re-render prematurely.
-
- Thanks [@pngwn](https://github.com/pngwn)!
-
-#### Enable streaming audio in python client ([#5248](https://github.com/gradio-app/gradio/pull/5248) [`390624d8`](https://github.com/gradio-app/gradio/commit/390624d8ad2b1308a5bf8384435fd0db98d8e29e))
-
-The `gradio_client` now supports streaming file outputs 🌊
-
-No new syntax! Connect to a gradio demo that supports streaming file outputs and call `predict` or `submit` as you normally would.
-
-```python
-import gradio_client as grc
-client = grc.Client("gradio/stream_audio_out")
-
-# Get the entire generated audio as a local file
-client.predict("/Users/freddy/Pictures/bark_demo.mp4", api_name="/predict")
-
-job = client.submit("/Users/freddy/Pictures/bark_demo.mp4", api_name="/predict")
-
-# Get the entire generated audio as a local file
-job.result()
-
-# Each individual chunk
-job.outputs()
-```
-
- Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-
-#### Add `render` function to `<gradio-app>` ([#5158](https://github.com/gradio-app/gradio/pull/5158) [`804fcc05`](https://github.com/gradio-app/gradio/commit/804fcc058e147f283ece67f1f353874e26235535))
-
-We now have an event `render` on the <gradio-app> web component, which is triggered once the embedded space has finished rendering.
-
-```html
-<script>
-	function handleLoadComplete() {
-		console.log("Embedded space has finished rendering");
-	}
-	const gradioApp = document.querySelector("gradio-app");
-	gradioApp.addEventListener("render", handleLoadComplete);
-</script>
-```
-
- Thanks [@hannahblair](https://github.com/hannahblair)!
-
-### Features
-
-- [#5268](https://github.com/gradio-app/gradio/pull/5268) [`f49028cf`](https://github.com/gradio-app/gradio/commit/f49028cfe3e21097001ddbda71c560b3d8b42e1c) - Move markdown & latex processing to the frontend for the gr.Markdown and gr.DataFrame components.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5215](https://github.com/gradio-app/gradio/pull/5215) [`fbdad78a`](https://github.com/gradio-app/gradio/commit/fbdad78af4c47454cbb570f88cc14bf4479bbceb) - Lazy load interactive or static variants of a component individually, rather than loading both variants regardless. This change will improve performance for many applications.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5216](https://github.com/gradio-app/gradio/pull/5216) [`4b58ea6d`](https://github.com/gradio-app/gradio/commit/4b58ea6d98e7a43b3f30d8a4cb6f379bc2eca6a8) - Update i18n tokens and locale files.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5283](https://github.com/gradio-app/gradio/pull/5283) [`a7460557`](https://github.com/gradio-app/gradio/commit/a74605572dd0d6bb41df6b38b120d656370dd67d) - Add height parameter and scrolling to `gr.Dataframe`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5232](https://github.com/gradio-app/gradio/pull/5232) [`c57d4c23`](https://github.com/gradio-app/gradio/commit/c57d4c232a97e03b4671f9e9edc3af456438fe89) - `gr.Radio` and `gr.CheckboxGroup` can now accept different names and values.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5219](https://github.com/gradio-app/gradio/pull/5219) [`e8fd4e4e`](https://github.com/gradio-app/gradio/commit/e8fd4e4ec68a6c974bc8c84b61f4a0ec50a85bc6) - Add `api_name` parameter to `gr.Interface`. Additionally, completely hide api page if show_api=False.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5280](https://github.com/gradio-app/gradio/pull/5280) [`a2f42e28`](https://github.com/gradio-app/gradio/commit/a2f42e28bd793bce4bed6d54164bb2a327a46fd5) - Allow updating the label of `gr.UpdateButton`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5112](https://github.com/gradio-app/gradio/pull/5112) [`1cefee7f`](https://github.com/gradio-app/gradio/commit/1cefee7fc05175aca23ba04b3a3fda7b97f49bf0) - chore(deps): update dependency marked to v7.  Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5260](https://github.com/gradio-app/gradio/pull/5260) [`a773eaf7`](https://github.com/gradio-app/gradio/commit/a773eaf7504abb53b99885b3454dc1e027adbb42) - Stop passing inputs and preprocessing on iterators.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#4943](https://github.com/gradio-app/gradio/pull/4943) [`947d615d`](https://github.com/gradio-app/gradio/commit/947d615db6f76519d0e8bc0d1a0d7edf89df267b) - Sign in with Hugging Face (OAuth support).  Thanks [@Wauplin](https://github.com/Wauplin)!
-- [#5298](https://github.com/gradio-app/gradio/pull/5298) [`cf167cd1`](https://github.com/gradio-app/gradio/commit/cf167cd1dd4acd9aee225ff1cb6fac0e849806ba) - Create event listener table for components on docs.  Thanks [@aliabd](https://github.com/aliabd)!
-- [#5173](https://github.com/gradio-app/gradio/pull/5173) [`730f0c1d`](https://github.com/gradio-app/gradio/commit/730f0c1d54792eb11359e40c9f2326e8a6e39203) - Ensure gradio client works as expected for functions that return nothing.  Thanks [@raymondtri](https://github.com/raymondtri)!
-- [#5188](https://github.com/gradio-app/gradio/pull/5188) [`b22e1888`](https://github.com/gradio-app/gradio/commit/b22e1888fcf0843520525c1e4b7e1fe73fdeb948) - Fix the images in the theme builder to use permanent URI.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5221](https://github.com/gradio-app/gradio/pull/5221) [`f344592a`](https://github.com/gradio-app/gradio/commit/f344592aeb1658013235ded154107f72d86f24e7) - Allows setting a height to `gr.File` and improves the UI of the component.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5265](https://github.com/gradio-app/gradio/pull/5265) [`06982212`](https://github.com/gradio-app/gradio/commit/06982212dfbd613853133d5d0eebd75577967027) - Removes scrollbar from File preview when not needed.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5305](https://github.com/gradio-app/gradio/pull/5305) [`15075241`](https://github.com/gradio-app/gradio/commit/15075241fa7ad3f7fd9ae2a91e54faf8f19a46f9) - Rotate axes labels on LinePlot, BarPlot, and ScatterPlot.  Thanks [@Faiga91](https://github.com/Faiga91)!
-- [#5258](https://github.com/gradio-app/gradio/pull/5258) [`92282cea`](https://github.com/gradio-app/gradio/commit/92282cea6afdf7e9930ece1046d8a63be34b3cea) - Chatbot Avatar Images.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5244](https://github.com/gradio-app/gradio/pull/5244) [`b3e50db9`](https://github.com/gradio-app/gradio/commit/b3e50db92f452f376aa2cc081326d40bb69d6dd7) - Remove aiohttp dependency.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5264](https://github.com/gradio-app/gradio/pull/5264) [`46a2b600`](https://github.com/gradio-app/gradio/commit/46a2b600a7ff030a9ea1560b882b3bf3ad266bbc) - ensure translations for audio work correctly.  Thanks [@hannahblair](https://github.com/hannahblair)!
-
-### Fixes
-
-- [#5256](https://github.com/gradio-app/gradio/pull/5256) [`933db53e`](https://github.com/gradio-app/gradio/commit/933db53e93a1229fdf149556d61da5c4c7e1a331) - Better handling of empty dataframe in `gr.DataFrame`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5242](https://github.com/gradio-app/gradio/pull/5242) [`2b397791`](https://github.com/gradio-app/gradio/commit/2b397791fe2059e4beb72937ff0436f2d4d28b4b) - Fix message text overflow onto copy button in `gr.Chatbot`.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5253](https://github.com/gradio-app/gradio/pull/5253) [`ddac7e4d`](https://github.com/gradio-app/gradio/commit/ddac7e4d0f55c3bdc6c3e9a9e24588b2563e4049) - Ensure File component uploads files to the server.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5179](https://github.com/gradio-app/gradio/pull/5179) [`6fb92b48`](https://github.com/gradio-app/gradio/commit/6fb92b48a916104db573602011a448b904d42e5e) - Fixes audio streaming issues.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5295](https://github.com/gradio-app/gradio/pull/5295) [`7b8fa8aa`](https://github.com/gradio-app/gradio/commit/7b8fa8aa58f95f5046b9add64b40368bd3f1b700) - Allow caching examples with streamed output.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5285](https://github.com/gradio-app/gradio/pull/5285) [`cdfd4217`](https://github.com/gradio-app/gradio/commit/cdfd42174a9c777eaee9c1209bf8e90d8c7791f2) - Tweaks to `icon` parameter in `gr.Button()`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5122](https://github.com/gradio-app/gradio/pull/5122) [`3b805346`](https://github.com/gradio-app/gradio/commit/3b8053469aca6c7a86a6731e641e4400fc34d7d3) - Allows code block in chatbot to scroll horizontally.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5312](https://github.com/gradio-app/gradio/pull/5312) [`f769cb67`](https://github.com/gradio-app/gradio/commit/f769cb67149d8e209091508f06d87014acaed965) - only start listening for events after the components are mounted.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5254](https://github.com/gradio-app/gradio/pull/5254) [`c39f06e1`](https://github.com/gradio-app/gradio/commit/c39f06e16b9feea97984e4822df35a99c807461c) - Fix `.update()` for `gr.Radio()` and `gr.CheckboxGroup()`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5231](https://github.com/gradio-app/gradio/pull/5231) [`87f1c2b4`](https://github.com/gradio-app/gradio/commit/87f1c2b4ac7c685c43477215fa5b96b6cbeffa05) - Allow `gr.Interface.from_pipeline()` and `gr.load()` to work within `gr.Blocks()`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5238](https://github.com/gradio-app/gradio/pull/5238) [`de23e9f7`](https://github.com/gradio-app/gradio/commit/de23e9f7d67e685e791faf48a21f34121f6d094a) - Improve audio streaming.  Thanks [@aliabid94](https://github.com/aliabid94)!/n  - Proper audio streaming with WAV files. We now do the proper processing to stream out wav files as a single stream of audio without any cracks in the seams./n  - Audio streaming with bytes. Stream any audio type by yielding out bytes, and it should work flawlessly.
-- [#5313](https://github.com/gradio-app/gradio/pull/5313) [`54bcb724`](https://github.com/gradio-app/gradio/commit/54bcb72417b2781ad9d7500ea0f89aa9d80f7d8f) - Restores missing part of bottom border on file component.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5235](https://github.com/gradio-app/gradio/pull/5235) [`1ecf88ac`](https://github.com/gradio-app/gradio/commit/1ecf88ac5f20bc5a1c91792d1a68559575e6afd7) - fix #5229.  Thanks [@breengles](https://github.com/breengles)!
-- [#5276](https://github.com/gradio-app/gradio/pull/5276) [`502f1015`](https://github.com/gradio-app/gradio/commit/502f1015bf23b365bc32446dd2e549b0c5d0dc72) - Ensure `Blocks` translation copy renders correctly.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5296](https://github.com/gradio-app/gradio/pull/5296) [`a0f22626`](https://github.com/gradio-app/gradio/commit/a0f22626f2aff297754414bbc83d5c4cfe086ea0) - `make_waveform()` twitter video resolution fix.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-
-## 3.40.0
-
-### Highlights
-
-#### Client.predict will now return the final output for streaming endpoints ([#5057](https://github.com/gradio-app/gradio/pull/5057) [`35856f8b`](https://github.com/gradio-app/gradio/commit/35856f8b54548cae7bd3b8d6a4de69e1748283b2))
-
-### This is a breaking change (for gradio_client only)!
-
-Previously, `Client.predict` would only return the first output of an endpoint that streamed results. This was causing confusion for developers that wanted to call these streaming demos via the client.
-
-We realize that developers using the client don't know the internals of whether a demo streams or not, so we're changing the behavior of predict to match developer expectations.
-
-Using `Client.predict` will now return the final output of a streaming endpoint. This will make it even easier to use gradio apps via the client.
-
- Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-
-#### Gradio now supports streaming audio outputs
-
-Allows users to use generators to stream audio out, yielding consecutive chunks of audio. Requires `streaming=True` to be set on the output audio.
-
-```python
-import gradio as gr
-from pydub import AudioSegment
-
-def stream_audio(audio_file):
-    audio = AudioSegment.from_mp3(audio_file)
-    i = 0
-    chunk_size = 3000
-    
-    while chunk_size*i < len(audio):
-        chunk = audio[chunk_size*i:chunk_size*(i+1)]
-        i += 1
-        if chunk:
-            file = f"/tmp/{i}.mp3"
-            chunk.export(file, format="mp3")            
-            yield file
-        
-demo = gr.Interface(
-    fn=stream_audio,
-    inputs=gr.Audio(type="filepath", label="Audio file to stream"),
-    outputs=gr.Audio(autoplay=True, streaming=True),
-)
-
-demo.queue().launch()
-```
-
-From the backend, streamed outputs are served from the `/stream/` endpoint instead of the `/file/` endpoint. Currently just used to serve audio streaming output.  The output JSON will have `is_stream`: `true`, instead of `is_file`: `true` in the file data object. Thanks [@aliabid94](https://github.com/aliabid94)!
-
-### Features
-
-- [#5081](https://github.com/gradio-app/gradio/pull/5081) [`d7f83823`](https://github.com/gradio-app/gradio/commit/d7f83823fbd7604456b0127d689a63eed759807d) - solve how can I config root_path dynamically? #4968. Thanks [@eastonsuo](https://github.com/eastonsuo)!
-- [#5025](https://github.com/gradio-app/gradio/pull/5025) [`6693660a`](https://github.com/gradio-app/gradio/commit/6693660a790996f8f481feaf22a8c49130d52d89) - Add download button to selected images in `Gallery`. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5133](https://github.com/gradio-app/gradio/pull/5133) [`61129052`](https://github.com/gradio-app/gradio/commit/61129052ed1391a75c825c891d57fa0ad6c09fc8) - Update dependency esbuild to ^0.19.0. Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5125](https://github.com/gradio-app/gradio/pull/5125) [`80be7a1c`](https://github.com/gradio-app/gradio/commit/80be7a1ca44c0adef1668367b2cf36b65e52e576) - chatbot conversation nodes can contain a copy button. Thanks [@fazpu](https://github.com/fazpu)!
-- [#5048](https://github.com/gradio-app/gradio/pull/5048) [`0b74a159`](https://github.com/gradio-app/gradio/commit/0b74a1595b30df744e32a2c358c07acb7fd1cfe5) - Use `importlib` in favor of deprecated `pkg_resources`. Thanks [@jayceslesar](https://github.com/jayceslesar)!
-- [#5045](https://github.com/gradio-app/gradio/pull/5045) [`3b9494f5`](https://github.com/gradio-app/gradio/commit/3b9494f5c57e6b52e6a040ce8d6b5141f780e84d) - Lite: Fix the analytics module to use asyncio to work in the Wasm env. Thanks [@whitphx](https://github.com/whitphx)!
-- [#5046](https://github.com/gradio-app/gradio/pull/5046) [`5244c587`](https://github.com/gradio-app/gradio/commit/5244c5873c355cf3e2f0acb7d67fda3177ef8b0b) - Allow new lines in `HighlightedText` with `/n` and preserve whitespace. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5076](https://github.com/gradio-app/gradio/pull/5076) [`2745075a`](https://github.com/gradio-app/gradio/commit/2745075a26f80e0e16863d483401ff1b6c5ada7a) - Add deploy_discord to docs. Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5116](https://github.com/gradio-app/gradio/pull/5116) [`0dc49b4c`](https://github.com/gradio-app/gradio/commit/0dc49b4c517706f572240f285313a881089ced79) - Add support for async functions and async generators to `gr.ChatInterface`. Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5047](https://github.com/gradio-app/gradio/pull/5047) [`883ac364`](https://github.com/gradio-app/gradio/commit/883ac364f69d92128774ac446ce49bdf8415fd7b) - Add `step` param to `Number`. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5137](https://github.com/gradio-app/gradio/pull/5137) [`22aa5eba`](https://github.com/gradio-app/gradio/commit/22aa5eba3fee3f14473e4b0fac29cf72fe31ef04) - Use font size `--text-md` for `<code>` in Chatbot messages. Thanks [@jaywonchung](https://github.com/jaywonchung)!
-- [#5005](https://github.com/gradio-app/gradio/pull/5005) [`f5539c76`](https://github.com/gradio-app/gradio/commit/f5539c7618e31451420bd3228754774da14dc65f) - Enhancement: Add focus event to textbox and number component. Thanks [@JodyZ0203](https://github.com/JodyZ0203)!
-- [#5104](https://github.com/gradio-app/gradio/pull/5104) [`34f6b22e`](https://github.com/gradio-app/gradio/commit/34f6b22efbfedfa569d452f3f99ed2e6593e3c21) - Strip leading and trailing spaces from username in login route. Thanks [@sweep-ai](https://github.com/apps/sweep-ai)!
-- [#5149](https://github.com/gradio-app/gradio/pull/5149) [`144df459`](https://github.com/gradio-app/gradio/commit/144df459a3b7895e524defcfc4c03fbb8b083aca) - Add `show_edit_button` param to `gr.Audio`. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5136](https://github.com/gradio-app/gradio/pull/5136) [`eaa1ce14`](https://github.com/gradio-app/gradio/commit/eaa1ce14ac41de1c23321e93f11f1b03a2f3c7f4) - Enhancing Tamil Translation: Language Refinement 🌟. Thanks [@sanjaiyan-dev](https://github.com/sanjaiyan-dev)!
-- [#5035](https://github.com/gradio-app/gradio/pull/5035) [`8b4eb8ca`](https://github.com/gradio-app/gradio/commit/8b4eb8cac9ea07bde31b44e2006ca2b7b5f4de36) - JS Client: Fixes cannot read properties of null (reading 'is_file'). Thanks [@raymondtri](https://github.com/raymondtri)!
-- [#5023](https://github.com/gradio-app/gradio/pull/5023) [`e6317d77`](https://github.com/gradio-app/gradio/commit/e6317d77f87d3dad638acca3dbc4a9228570e63c) - Update dependency extendable-media-recorder to v8. Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5085](https://github.com/gradio-app/gradio/pull/5085) [`13e47835`](https://github.com/gradio-app/gradio/commit/13e478353532c4af18cfa50772f8b6fb3c6c9818) - chore(deps): update dependency extendable-media-recorder to v8. Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5080](https://github.com/gradio-app/gradio/pull/5080) [`37caa2e0`](https://github.com/gradio-app/gradio/commit/37caa2e0fe95d6cab8beb174580fb557904f137f) - Add icon and link params to `gr.Button`. Thanks [@hannahblair](https://github.com/hannahblair)!
-
-### Fixes
-
-- [#5062](https://github.com/gradio-app/gradio/pull/5062) [`7d897165`](https://github.com/gradio-app/gradio/commit/7d89716519d0751072792c9bbda668ffeb597296) - `gr.Dropdown` now has correct behavior in static mode as well as when an option is selected. Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5077](https://github.com/gradio-app/gradio/pull/5077) [`667875b2`](https://github.com/gradio-app/gradio/commit/667875b2441753e74d25bd9d3c8adedd8ede11cd) - Live audio streaming output
-- [#5118](https://github.com/gradio-app/gradio/pull/5118) [`1b017e68`](https://github.com/gradio-app/gradio/commit/1b017e68f6a9623cc2ec085bd20e056229552028) - Add `interactive` args to `gr.ColorPicker`. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5114](https://github.com/gradio-app/gradio/pull/5114) [`56d2609d`](https://github.com/gradio-app/gradio/commit/56d2609de93387a75dc82b1c06c1240c5b28c0b8) - Reset textbox value to empty string when value is None. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5075](https://github.com/gradio-app/gradio/pull/5075) [`67265a58`](https://github.com/gradio-app/gradio/commit/67265a58027ef1f9e4c0eb849a532f72eaebde48) - Allow supporting >1000 files in `gr.File()` and `gr.UploadButton()`. Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5135](https://github.com/gradio-app/gradio/pull/5135) [`80727bbe`](https://github.com/gradio-app/gradio/commit/80727bbe2c6d631022054edf01515017691b3bdd) - Fix dataset features and dataset preview for HuggingFaceDatasetSaver. Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5039](https://github.com/gradio-app/gradio/pull/5039) [`620e4645`](https://github.com/gradio-app/gradio/commit/620e46452729d6d4877b3fab84a65daf2f2b7bc6) - `gr.Dropdown()` now supports values with arbitrary characters and doesn't clear value when re-focused. Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5061](https://github.com/gradio-app/gradio/pull/5061) [`136adc9c`](https://github.com/gradio-app/gradio/commit/136adc9ccb23e5cb4d02d2e88f23f0b850041f98) - Ensure `gradio_client` is backwards compatible with `gradio==3.24.1`. Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5129](https://github.com/gradio-app/gradio/pull/5129) [`97d804c7`](https://github.com/gradio-app/gradio/commit/97d804c748be9acfe27b8369dd2d64d61f43c2e7) - [Spaces] ZeroGPU Queue fix. Thanks [@cbensimon](https://github.com/cbensimon)!
-- [#5140](https://github.com/gradio-app/gradio/pull/5140) [`cd1353fa`](https://github.com/gradio-app/gradio/commit/cd1353fa3eb1b015f5860ca5d5a8e8d1aa4a831c) - Fixes the display of minutes in the video player. Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5111](https://github.com/gradio-app/gradio/pull/5111) [`b84a35b7`](https://github.com/gradio-app/gradio/commit/b84a35b7b91eca947f787648ceb361b1d023427b) - Add icon and link to DuplicateButton. Thanks [@aliabd](https://github.com/aliabd)!
-- [#5030](https://github.com/gradio-app/gradio/pull/5030) [`f6c491b0`](https://github.com/gradio-app/gradio/commit/f6c491b079d335af633dd854c68eb26f9e61c552) - highlightedtext throws an error basing on model. Thanks [@rajeunoia](https://github.com/rajeunoia)!
-
-## 3.39.0
-
-### Highlights
-
-#### Create Discord Bots from Gradio Apps 🤖 ([#4960](https://github.com/gradio-app/gradio/pull/4960) [`46e4ef67`](https://github.com/gradio-app/gradio/commit/46e4ef67d287dd68a91473b73172b29cbad064bc))
-
-We're excited to announce that Gradio can now automatically create a discord bot from any `gr.ChatInterface` app.
-
-It's as easy as importing `gradio_client`, connecting to the app, and calling `deploy_discord`!
-
-_🦙 Turning Llama 2 70b into a discord bot 🦙_
-
-```python
-import gradio_client as grc
-grc.Client("ysharma/Explore_llamav2_with_TGI").deploy_discord(to_id="llama2-70b-discord-bot")
-```
-
-<img src="https://gradio-builds.s3.amazonaws.com/demo-files/discordbots/guide/llama_chat.gif">
-
-#### Getting started with template spaces
-
-To help get you started, we have created an organization on Hugging Face called [gradio-discord-bots](https://huggingface.co/gradio-discord-bots) with template spaces you can use to turn state of the art LLMs powered by Gradio to discord bots.
-
-Currently we have template spaces for:
-
-- [Llama-2-70b-chat-hf](https://huggingface.co/spaces/gradio-discord-bots/Llama-2-70b-chat-hf) powered by a FREE Hugging Face Inference Endpoint!
-- [Llama-2-13b-chat-hf](https://huggingface.co/spaces/gradio-discord-bots/Llama-2-13b-chat-hf) powered by Hugging Face Inference Endpoints.
-- [Llama-2-13b-chat-hf](https://huggingface.co/spaces/gradio-discord-bots/llama-2-13b-chat-transformers) powered by Hugging Face transformers.
-- [falcon-7b-instruct](https://huggingface.co/spaces/gradio-discord-bots/falcon-7b-instruct) powered by Hugging Face Inference Endpoints.
-- [gpt-3.5-turbo](https://huggingface.co/spaces/gradio-discord-bots/gpt-35-turbo), powered by openai. Requires an OpenAI key.
-
-But once again, you can deploy ANY `gr.ChatInterface` app exposed on the internet! So don't hesitate to try it on your own Chatbots.
-
-❗️ Additional Note ❗️: Technically, any gradio app that exposes an api route that takes in a single string and outputs a single string can be deployed to discord. But `gr.ChatInterface` apps naturally lend themselves to discord's chat functionality so we suggest you start with those.
-
-Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-
-### Features
-
-- [#4995](https://github.com/gradio-app/gradio/pull/4995) [`3f8c210b`](https://github.com/gradio-app/gradio/commit/3f8c210b01ef1ceaaf8ee73be4bf246b5b745bbf) - Implement left and right click in `Gallery` component and show implicit images in `Gallery` grid. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#4993](https://github.com/gradio-app/gradio/pull/4993) [`dc07a9f9`](https://github.com/gradio-app/gradio/commit/dc07a9f947de44b419d8384987a02dcf94977851) - Bringing back the "Add download button for audio" PR by [@leuryr](https://github.com/leuryr). Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#4979](https://github.com/gradio-app/gradio/pull/4979) [`44ac8ad0`](https://github.com/gradio-app/gradio/commit/44ac8ad08d82ea12c503dde5c78f999eb0452de2) - Allow setting sketch color default. Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#4985](https://github.com/gradio-app/gradio/pull/4985) [`b74f8453`](https://github.com/gradio-app/gradio/commit/b74f8453034328f0e42da8e41785f5eb039b45d7) - Adds `additional_inputs` to `gr.ChatInterface`. Thanks [@abidlabs](https://github.com/abidlabs)!
-
-### Fixes
-
-- [#4997](https://github.com/gradio-app/gradio/pull/4997) [`41c83070`](https://github.com/gradio-app/gradio/commit/41c83070b01632084e7d29123048a96c1e261407) - Add CSS resets and specifiers to play nice with HF blog. Thanks [@aliabid94](https://github.com/aliabid94)!
-
-## 3.38
-
-### New Features:
-
-- Provide a parameter `animate` (`False` by default) in `gr.make_waveform()` which animates the overlayed waveform by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4918](https://github.com/gradio-app/gradio/pull/4918)
-- Add `show_download_button` param to allow the download button in static Image components to be hidden by [@hannahblair](https://github.com/hannahblair) in [PR 4959](https://github.com/gradio-app/gradio/pull/4959)
-- Added autofocus argument to Textbox by [@aliabid94](https://github.com/aliabid94) in [PR 4978](https://github.com/gradio-app/gradio/pull/4978)
-- The `gr.ChatInterface` UI now converts the "Submit" button to a "Stop" button in ChatInterface while streaming, which can be used to pause generation. By [@abidlabs](https://github.com/abidlabs) in [PR 4971](https://github.com/gradio-app/gradio/pull/4971).
-- Add a `border_color_accent_subdued` theme variable to add a subdued border color to accented items. This is used by chatbot user messages. Set the value of this variable in `Default` theme to `*primary_200`. By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4989](https://github.com/gradio-app/gradio/pull/4989)
-- Add default sketch color argument `brush_color`. Also, masks drawn on images are now slightly translucent (and mask color can also be set via brush_color). By [@aliabid94](https://github.com/aliabid94) in [PR 4979](https://github.com/gradio-app/gradio/pull/4979)
-
-### Bug Fixes:
-
-- Fixes `cancels` for generators so that if a generator is canceled before it is complete, subsequent runs of the event do not continue from the previous iteration, but rather start from the beginning. By [@abidlabs](https://github.com/abidlabs) in [PR 4969](https://github.com/gradio-app/gradio/pull/4969).
-- Use `gr.State` in `gr.ChatInterface` to reduce latency by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4976](https://github.com/gradio-app/gradio/pull/4976)
-- Fix bug with `gr.Interface` where component labels inferred from handler parameters were including special args like `gr.Request` or `gr.EventData`. By [@cbensimon](https://github.com/cbensimon) in [PR 4956](https://github.com/gradio-app/gradio/pull/4956)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Other Changes:
-
-- Apply pyright to the `components` directory by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4948](https://github.com/gradio-app/gradio/pull/4948)
-- Improved look of ChatInterface by [@aliabid94](https://github.com/aliabid94) in [PR 4978](https://github.com/gradio-app/gradio/pull/4978)
-
-## 3.37
-
-### New Features:
-
-Introducing a new `gr.ChatInterface` abstraction, which allows Gradio users to build fully functioning Chat interfaces very easily. The only required parameter is a chat function `fn`, which accepts a (string) user input `message` and a (list of lists) chat `history` and returns a (string) response. Here's a toy example:
-
-```py
-import gradio as gr
-
-def echo(message, history):
-    return message
-
-demo = gr.ChatInterface(fn=echo, examples=["hello", "hola", "merhaba"], title="Echo Bot")
-demo.launch()
-```
-
-Which produces:
-
-<img width="1291" alt="image" src="https://github.com/gradio-app/gradio/assets/1778297/ae94fd72-c2bb-406e-9e8d-7b9c12e80119">
-
-And a corresponding easy-to-use API at `/chat`:
-
-<img width="1164" alt="image" src="https://github.com/gradio-app/gradio/assets/1778297/7b10d6db-6476-4e2e-bebd-ecda802c3b8f">
-
-The `gr.ChatInterface` abstraction works nicely with various LLM libraries, such as `langchain`. See the [dedicated guide](https://gradio.app/guides/creating-a-chatbot-fast) for more examples using `gr.ChatInterface`. Collective team effort in [PR 4869](https://github.com/gradio-app/gradio/pull/4869)
-
-- Chatbot messages now show hyperlinks to download files uploaded to `gr.Chatbot()` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4848](https://github.com/gradio-app/gradio/pull/4848)
-- Cached examples now work with generators and async generators by [@abidlabs](https://github.com/abidlabs) in [PR 4927](https://github.com/gradio-app/gradio/pull/4927)
-- Add RTL support to `gr.Markdown`, `gr.Chatbot`, `gr.Textbox` (via the `rtl` boolean parameter) and text-alignment to `gr.Textbox`(via the string `text_align` parameter) by [@abidlabs](https://github.com/abidlabs) in [PR 4933](https://github.com/gradio-app/gradio/pull/4933)
-
-Examples of usage:
-
-```py
-with gr.Blocks() as demo:
-    gr.Textbox(interactive=True, text_align="right")
-demo.launch()
-```
-
-```py
-with gr.Blocks() as demo:
-    gr.Markdown("سلام", rtl=True)
-demo.launch()
-```
-
-- The `get_api_info` method of `Blocks` now supports layout output components [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4871](https://github.com/gradio-app/gradio/pull/4871)
-
-- Added the support for the new command `gradio environment`to make it easier for people to file bug reports if we shipped an easy command to list the OS, gradio version, and versions of gradio/gradio-client dependencies. bu [@varshneydevansh](https://github.com/varshneydevansh) in [PR 4915](https://github.com/gradio-app/gradio/pull/4915).
-
-### Bug Fixes:
-
-- The `.change()` event is fixed in `Video` and `Image` so that it only fires once by [@abidlabs](https://github.com/abidlabs) in [PR 4793](https://github.com/gradio-app/gradio/pull/4793)
-- The `.change()` event is fixed in `Audio` so that fires when the component value is programmatically updated by [@abidlabs](https://github.com/abidlabs) in [PR 4793](https://github.com/gradio-app/gradio/pull/4793)
-
-* Add missing `display: flex` property to `Row` so that flex styling is applied to children by [@hannahblair] in [PR 4896](https://github.com/gradio-app/gradio/pull/4896)
-* Fixed bug where `gr.Video` could not preprocess urls by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4904](https://github.com/gradio-app/gradio/pull/4904)
-* Fixed copy button rendering in API page on Safari by [@aliabid94](https://github.com/aliabid94) in [PR 4924](https://github.com/gradio-app/gradio/pull/4924)
-* Fixed `gr.Group` and `container=False`. `container` parameter only available for `Textbox`, `Number`, and `Dropdown`, the only elements where it makes sense. By [@aliabid94](https://github.com/aliabid94) in [PR 4916](https://github.com/gradio-app/gradio/pull/4916)
-* Fixed broken image link in auto-generated `app.py` from `ThemeClass.push_to_hub` by [@deepkyu](https://github.com/deepkyu) in [PR 4944](https://github.com/gradio-app/gradio/pull/4944)
-
-### Other Changes:
-
-- Warning on mobile that if a user leaves the tab, websocket connection may break. On broken connection, tries to rejoin queue and displays error conveying connection broke. By [@aliabid94](https://github.com/aliabid94) in [PR 4742](https://github.com/gradio-app/gradio/pull/4742)
-- Remove blocking network calls made before the local URL gets printed - these slow down the display of the local URL, especially when no internet is available. [@aliabid94](https://github.com/aliabid94) in [PR 4905](https://github.com/gradio-app/gradio/pull/4905).
-- Pinned dependencies to major versions to reduce the likelihood of a broken `gradio` due to changes in downstream dependencies by [@abidlabs](https://github.com/abidlabs) in [PR 4885](https://github.com/gradio-app/gradio/pull/4885)
-- Queue `max_size` defaults to parent Blocks `max_thread` when running on Spaces with ZeroGPU hardware. By [@cbensimon](https://github.com/cbensimon) in [PR 4937](https://github.com/gradio-app/gradio/pull/4937)
-
-### Breaking Changes:
-
-Motivated by the release of `pydantic==2.0`, which included breaking changes that broke a large number of Gradio apps, we've pinned many gradio dependencies. Note that pinned dependencies can cause downstream conflicts, so this may be a breaking change. That being said, we've kept the pins pretty loose, and we're expecting change to be better for the long-term stability of Gradio apps.
-
-## 3.36.1
-
-### New Features:
-
-- Hotfix to support pydantic v1 and v2 by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4835](https://github.com/gradio-app/gradio/pull/4835)
-
-### Bug Fixes:
-
-- Fix bug where `gr.File` change event was not triggered when the value was changed by another event by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4811](https://github.com/gradio-app/gradio/pull/4811)
-
-### Other Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.36.0
-
-### New Features:
-
-- The `gr.Video`, `gr.Audio`, `gr.Image`, `gr.Chatbot`, and `gr.Gallery` components now include a share icon when deployed on Spaces. This behavior can be modified by setting the `show_share_button` parameter in the component classes. by [@aliabid94](https://github.com/aliabid94) in [PR 4651](https://github.com/gradio-app/gradio/pull/4651)
-- Allow the web component `space`, `src`, and `host` attributes to be updated dynamically by [@pngwn](https://github.com/pngwn) in [PR 4461](https://github.com/gradio-app/gradio/pull/4461)
-- Suggestion for Spaces Duplication built into Gradio, by [@aliabid94](https://github.com/aliabid94) in [PR 4458](https://github.com/gradio-app/gradio/pull/4458)
-- The `api_name` parameter now accepts `False` as a value, which means it does not show up in named or unnamed endpoints. By [@abidlabs](https://github.com/aliabid94) in [PR 4683](https://github.com/gradio-app/gradio/pull/4683)
-- Added support for `pathlib.Path` in `gr.Video`, `gr.Gallery`, and `gr.Chatbot` by [sunilkumardash9](https://github.com/sunilkumardash9) in [PR 4581](https://github.com/gradio-app/gradio/pull/4581).
-
-### Bug Fixes:
-
-- Updated components with `info` attribute to update when `update()` is called on them. by [@jebarpg](https://github.com/jebarpg) in [PR 4715](https://github.com/gradio-app/gradio/pull/4715).
-- Ensure the `Image` components undo button works mode is `mask` or `color-sketch` by [@amyorz](https://github.com/AmyOrz) in [PR 4692](https://github.com/gradio-app/gradio/pull/4692)
-- Load the iframe resizer external asset asynchronously, by [@akx](https://github.com/akx) in [PR 4336](https://github.com/gradio-app/gradio/pull/4336)
-- Restored missing imports in `gr.components` by [@abidlabs](https://github.com/abidlabs) in [PR 4566](https://github.com/gradio-app/gradio/pull/4566)
-- Fix bug where `select` event was not triggered in `gr.Gallery` if `height` was set to be large with `allow_preview=False` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4551](https://github.com/gradio-app/gradio/pull/4551)
-- Fix bug where setting `visible=False` in `gr.Group` event did not work by [@abidlabs](https://github.com/abidlabs) in [PR 4567](https://github.com/gradio-app/gradio/pull/4567)
-- Fix `make_waveform` to work with paths that contain spaces [@akx](https://github.com/akx) in [PR 4570](https://github.com/gradio-app/gradio/pull/4570) & [PR 4578](https://github.com/gradio-app/gradio/pull/4578)
-- Send captured data in `stop_recording` event for `gr.Audio` and `gr.Video` components by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4554](https://github.com/gradio-app/gradio/pull/4554)
-- Fix bug in `gr.Gallery` where `height` and `object_fit` parameters where being ignored by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4576](https://github.com/gradio-app/gradio/pull/4576)
-- Fixes an HTML sanitization issue in DOMPurify where links in markdown were not opening in a new window by [@hannahblair] in [PR 4577](https://github.com/gradio-app/gradio/pull/4577)
-- Fixed Dropdown height rendering in Columns by [@aliabid94](https://github.com/aliabid94) in [PR 4584](https://github.com/gradio-app/gradio/pull/4584)
-- Fixed bug where `AnnotatedImage` css styling was causing the annotation masks to not be displayed correctly by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4628](https://github.com/gradio-app/gradio/pull/4628)
-- Ensure that Gradio does not silently fail when running on a port that is occupied by [@abidlabs](https://github.com/abidlabs) in [PR 4624](https://github.com/gradio-app/gradio/pull/4624).
-- Fix double upload bug that caused lag in file uploads by [@aliabid94](https://github.com/aliabid94) in [PR 4661](https://github.com/gradio-app/gradio/pull/4661)
-- `Progress` component now appears even when no `iterable` is specified in `tqdm` constructor by [@itrushkin](https://github.com/itrushkin) in [PR 4475](https://github.com/gradio-app/gradio/pull/4475)
-- Deprecation warnings now point at the user code using those deprecated features, instead of Gradio internals, by (https://github.com/akx) in [PR 4694](https://github.com/gradio-app/gradio/pull/4694)
-- Adapt column widths in gr.Examples based on content by [@pngwn](https://github.com/pngwn) & [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4700](https://github.com/gradio-app/gradio/pull/4700)
-- The `plot` parameter deprecation warnings should now only be emitted for `Image` components by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4709](https://github.com/gradio-app/gradio/pull/4709)
-- Removed uncessessary `type` deprecation warning by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4709](https://github.com/gradio-app/gradio/pull/4709)
-- Ensure Audio autoplays works when `autoplay=True` and the video source is dynamically updated [@pngwn](https://github.com/pngwn) in [PR 4705](https://github.com/gradio-app/gradio/pull/4705)
-- When an error modal is shown in spaces, ensure we scroll to the top so it can be seen by [@pngwn](https://github.com/pngwn) in [PR 4712](https://github.com/gradio-app/gradio/pull/4712)
-- Update depedencies by [@pngwn](https://github.com/pngwn) in [PR 4675](https://github.com/gradio-app/gradio/pull/4675)
-- Fixes `gr.Dropdown` being cutoff at the bottom by [@abidlabs](https://github.com/abidlabs) in [PR 4691](https://github.com/gradio-app/gradio/pull/4691).
-- Scroll top when clicking "View API" in spaces by [@pngwn](https://github.com/pngwn) in [PR 4714](https://github.com/gradio-app/gradio/pull/4714)
-- Fix bug where `show_label` was hiding the entire component for `gr.Label` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4713](https://github.com/gradio-app/gradio/pull/4713)
-- Don't crash when uploaded image has broken EXIF data, by [@akx](https://github.com/akx) in [PR 4764](https://github.com/gradio-app/gradio/pull/4764)
-- Place toast messages at the top of the screen by [@pngwn](https://github.com/pngwn) in [PR 4796](https://github.com/gradio-app/gradio/pull/4796)
-- Fix regressed styling of Login page when auth is enabled by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4797](https://github.com/gradio-app/gradio/pull/4797)
-- Prevent broken scrolling to output on Spaces by [@aliabid94](https://github.com/aliabid94) in [PR 4822](https://github.com/gradio-app/gradio/pull/4822)
-
-### Other Changes:
-
-- Add `.git-blame-ignore-revs` by [@akx](https://github.com/akx) in [PR 4586](https://github.com/gradio-app/gradio/pull/4586)
-- Update frontend dependencies in [PR 4601](https://github.com/gradio-app/gradio/pull/4601)
-- Use `typing.Literal` where possible in gradio library and client by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4608](https://github.com/gradio-app/gradio/pull/4608)
-- Remove unnecessary mock json files for frontend E2E tests by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4625](https://github.com/gradio-app/gradio/pull/4625)
-- Update dependencies by [@pngwn](https://github.com/pngwn) in [PR 4643](https://github.com/gradio-app/gradio/pull/4643)
-- The theme builder now launches successfully, and the API docs are cleaned up. By [@abidlabs](https://github.com/aliabid94) in [PR 4683](https://github.com/gradio-app/gradio/pull/4683)
-- Remove `cleared_value` from some components as its no longer used internally by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4685](https://github.com/gradio-app/gradio/pull/4685)
-- Better errors when you define two Blocks and reference components in one Blocks from the events in the other Blocks [@abidlabs](https://github.com/abidlabs) in [PR 4738](https://github.com/gradio-app/gradio/pull/4738).
-- Better message when share link is not created by [@abidlabs](https://github.com/abidlabs) in [PR 4773](https://github.com/gradio-app/gradio/pull/4773).
-- Improve accessibility around selected images in gr.Gallery component by [@hannahblair](https://github.com/hannahblair) in [PR 4790](https://github.com/gradio-app/gradio/pull/4790)
-
-### Breaking Changes:
-
-[PR 4683](https://github.com/gradio-app/gradio/pull/4683) removes the explict named endpoint "load_examples" from gr.Interface that was introduced in [PR 4456](https://github.com/gradio-app/gradio/pull/4456).
-
-## 3.35.2
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Fix chatbot streaming by [@aliabid94](https://github.com/aliabid94) in [PR 4537](https://github.com/gradio-app/gradio/pull/4537)
-- Fix chatbot height and scrolling by [@aliabid94](https://github.com/aliabid94) in [PR 4540](https://github.com/gradio-app/gradio/pull/4540)
-
-### Other Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.35.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Fix chatbot streaming by [@aliabid94](https://github.com/aliabid94) in [PR 4537](https://github.com/gradio-app/gradio/pull/4537)
-- Fix error modal position and text size by [@pngwn](https://github.com/pngwn) in [PR 4538](https://github.com/gradio-app/gradio/pull/4538).
-
-### Other Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.35.0
-
-### New Features:
-
-- A `gr.ClearButton` which allows users to easily clear the values of components by [@abidlabs](https://github.com/abidlabs) in [PR 4456](https://github.com/gradio-app/gradio/pull/4456)
-
-Example usage:
-
-```py
-import gradio as gr
-
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot([("Hello", "How are you?")])
-    with gr.Row():
-        textbox = gr.Textbox(scale=3, interactive=True)
-        gr.ClearButton([textbox, chatbot], scale=1)
-
-demo.launch()
-```
-
-- Min and max value for gr.Number by [@artegoser](https://github.com/artegoser) and [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3991](https://github.com/gradio-app/gradio/pull/3991)
-- Add `start_recording` and `stop_recording` events to `Video` and `Audio` components by [@pngwn](https://github.com/pngwn) in [PR 4422](https://github.com/gradio-app/gradio/pull/4422)
-- Allow any function to generate an error message and allow multiple messages to appear at a time. Other error modal improvements such as auto dismiss after a time limit and a new layout on mobile [@pngwn](https://github.com/pngwn) in [PR 4459](https://github.com/gradio-app/gradio/pull/4459).
-- Add `autoplay` kwarg to `Video` and `Audio` components by [@pngwn](https://github.com/pngwn) in [PR 4453](https://github.com/gradio-app/gradio/pull/4453)
-- Add `allow_preview` parameter to `Gallery` to control whether a detailed preview is displayed on click by
-  [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4470](https://github.com/gradio-app/gradio/pull/4470)
-- Add `latex_delimiters` parameter to `Chatbot` to control the delimiters used for LaTeX and to disable LaTeX in the `Chatbot` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4516](https://github.com/gradio-app/gradio/pull/4516)
-- Can now issue `gr.Warning` and `gr.Info` modals. Simply put the code `gr.Warning("Your warning message")` or `gr.Info("Your info message")` as a standalone line in your function. By [@aliabid94](https://github.com/aliabid94) in [PR 4518](https://github.com/gradio-app/gradio/pull/4518).
-
-Example:
-
-```python
-def start_process(name):
-    gr.Info("Starting process")
-    if name is None:
-        gr.Warning("Name is empty")
-    ...
-    if success == False:
-        raise gr.Error("Process failed")
-```
-
-### Bug Fixes:
-
-- Add support for PAUSED state in the JS client by [@abidlabs](https://github.com/abidlabs) in [PR 4438](https://github.com/gradio-app/gradio/pull/4438)
-- Ensure Tabs only occupy the space required by [@pngwn](https://github.com/pngwn) in [PR 4419](https://github.com/gradio-app/gradio/pull/4419)
-- Ensure components have the correct empty sizes to prevent empty containers from collapsing by [@pngwn](https://github.com/pngwn) in [PR 4447](https://github.com/gradio-app/gradio/pull/4447).
-- Frontend code no longer crashes when there is a relative URL in an `<a>` element, by [@akx](https://github.com/akx) in [PR 4449](https://github.com/gradio-app/gradio/pull/4449).
-- Fix bug where setting `format='mp4'` on a video component would cause the function to error out if the uploaded video was not playable by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4467](https://github.com/gradio-app/gradio/pull/4467)
-- Fix `_js` parameter to work even without backend function, by [@aliabid94](https://github.com/aliabid94) in [PR 4486](https://github.com/gradio-app/gradio/pull/4486).
-- Fix new line issue with `gr.Chatbot()` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4491](https://github.com/gradio-app/gradio/pull/4491)
-- Fixes issue with Clear button not working for `Label` component by [@abidlabs](https://github.com/abidlabs) in [PR 4456](https://github.com/gradio-app/gradio/pull/4456)
-- Restores the ability to pass in a tuple (sample rate, audio array) to gr.Audio() by [@abidlabs](https://github.com/abidlabs) in [PR 4525](https://github.com/gradio-app/gradio/pull/4525)
-- Ensure code is correctly formatted and copy button is always present in Chatbot by [@pngwn](https://github.com/pngwn) in [PR 4527](https://github.com/gradio-app/gradio/pull/4527)
-- `show_label` will not automatically be set to `True` in `gr.BarPlot.update` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4531](https://github.com/gradio-app/gradio/pull/4531)
-- `gr.BarPlot` group text now respects darkmode by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4531](https://github.com/gradio-app/gradio/pull/4531)
-- Fix dispatched errors from within components [@aliabid94](https://github.com/aliabid94) in [PR 4786](https://github.com/gradio-app/gradio/pull/4786)
-
-### Other Changes:
-
-- Change styling of status and toast error components by [@hannahblair](https://github.com/hannahblair) in [PR 4454](https://github.com/gradio-app/gradio/pull/4454).
-- Clean up unnecessary `new Promise()`s by [@akx](https://github.com/akx) in [PR 4442](https://github.com/gradio-app/gradio/pull/4442).
-- Minor UI cleanup for Examples and Dataframe components [@aliabid94](https://github.com/aliabid94) in [PR 4455](https://github.com/gradio-app/gradio/pull/4455).
-- Minor UI cleanup for Examples and Dataframe components [@aliabid94](https://github.com/aliabid94) in [PR 4455](https://github.com/gradio-app/gradio/pull/4455).
-- Add Catalan translation [@jordimas](https://github.com/jordimas) in [PR 4483](https://github.com/gradio-app/gradio/pull/4483).
-- The API endpoint that loads examples upon click has been given an explicit name ("/load_examples") by [@abidlabs](https://github.com/abidlabs) in [PR 4456](https://github.com/gradio-app/gradio/pull/4456).
-- Allows configuration of FastAPI app when calling `mount_gradio_app`, by [@charlesfrye](https://github.com/charlesfrye) in [PR4519](https://github.com/gradio-app/gradio/pull/4519).
-
-### Breaking Changes:
-
-- The behavior of the `Clear` button has been changed for `Slider`, `CheckboxGroup`, `Radio`, `Dropdown` components by [@abidlabs](https://github.com/abidlabs) in [PR 4456](https://github.com/gradio-app/gradio/pull/4456). The Clear button now sets the value of these components to be empty as opposed to the original default set by the developer. This is to make them in line with the rest of the Gradio components.
-- Python 3.7 end of life is June 27 2023. Gradio will no longer support python 3.7 by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4484](https://github.com/gradio-app/gradio/pull/4484)
-- Removed `$` as a default LaTeX delimiter for the `Chatbot` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4516](https://github.com/gradio-app/gradio/pull/4516). The specific LaTeX delimeters can be set using the new `latex_delimiters` parameter in `Chatbot`.
-
-## 3.34.0
-
-### New Features:
-
-- The `gr.UploadButton` component now supports the `variant` and `interactive` parameters by [@abidlabs](https://github.com/abidlabs) in [PR 4436](https://github.com/gradio-app/gradio/pull/4436).
-
-### Bug Fixes:
-
-- Remove target="\_blank" override on anchor tags with internal targets by [@hannahblair](https://github.com/hannahblair) in [PR 4405](https://github.com/gradio-app/gradio/pull/4405)
-- Fixed bug where `gr.File(file_count='multiple')` could not be cached as output by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4421](https://github.com/gradio-app/gradio/pull/4421)
-- Restricts the domains that can be proxied via `/proxy` route by [@abidlabs](https://github.com/abidlabs) in [PR 4406](https://github.com/gradio-app/gradio/pull/4406).
-- Fixes issue where `gr.UploadButton` could not be used to upload the same file twice by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4437](https://github.com/gradio-app/gradio/pull/4437)
-- Fixes bug where `/proxy` route was being incorrectly constructed by the frontend by [@abidlabs](https://github.com/abidlabs) in [PR 4430](https://github.com/gradio-app/gradio/pull/4430).
-- Fix z-index of status component by [@hannahblair](https://github.com/hannahblair) in [PR 4429](https://github.com/gradio-app/gradio/pull/4429)
-- Fix video rendering in Safari by [@aliabid94](https://github.com/aliabid94) in [PR 4433](https://github.com/gradio-app/gradio/pull/4433).
-- The output directory for files downloaded when calling Blocks as a function is now set to a temporary directory by default (instead of the working directory in some cases) by [@abidlabs](https://github.com/abidlabs) in [PR 4501](https://github.com/gradio-app/gradio/pull/4501)
-
-### Other Changes:
-
-- When running on Spaces, handler functions will be transformed by the [PySpaces](https://pypi.org/project/spaces/) library in order to make them work with specific hardware. It will have no effect on standalone Gradio apps or regular Gradio Spaces and can be globally deactivated as follows : `import spaces; spaces.disable_gradio_auto_wrap()` by [@cbensimon](https://github.com/cbensimon) in [PR 4389](https://github.com/gradio-app/gradio/pull/4389).
-- Deprecated `.style` parameter and moved arguments to constructor. Added support for `.update()` to all arguments initially in style. Added `scale` and `min_width` support to every Component. By [@aliabid94](https://github.com/aliabid94) in [PR 4374](https://github.com/gradio-app/gradio/pull/4374)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.33.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Allow `every` to work with generators by [@dkjshk](https://github.com/dkjshk) in [PR 4434](https://github.com/gradio-app/gradio/pull/4434)
-- Fix z-index of status component by [@hannahblair](https://github.com/hannahblair) in [PR 4429](https://github.com/gradio-app/gradio/pull/4429)
-- Allow gradio to work offline, by [@aliabid94](https://github.com/aliabid94) in [PR 4398](https://github.com/gradio-app/gradio/pull/4398).
-- Fixed `validate_url` to check for 403 errors and use a GET request in place of a HEAD by [@alvindaiyan](https://github.com/alvindaiyan) in [PR 4388](https://github.com/gradio-app/gradio/pull/4388).
-
-### Other Changes:
-
-- More explicit error message when share link binary is blocked by antivirus by [@abidlabs](https://github.com/abidlabs) in [PR 4380](https://github.com/gradio-app/gradio/pull/4380).
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.33.0
-
-### New Features:
-
-- Introduced `gradio deploy` to launch a Gradio app to Spaces directly from your terminal. By [@aliabid94](https://github.com/aliabid94) in [PR 4033](https://github.com/gradio-app/gradio/pull/4033).
-- Introduce `show_progress='corner'` argument to event listeners, which will not cover the output components with the progress animation, but instead show it in the corner of the components. By [@aliabid94](https://github.com/aliabid94) in [PR 4396](https://github.com/gradio-app/gradio/pull/4396).
-
-### Bug Fixes:
-
-- Fix bug where Label change event was triggering itself by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4371](https://github.com/gradio-app/gradio/pull/4371)
-- Make `Blocks.load` behave like other event listeners (allows chaining `then` off of it) [@anentropic](https://github.com/anentropic/) in [PR 4304](https://github.com/gradio-app/gradio/pull/4304)
-- Respect `interactive=True` in output components of a `gr.Interface` by [@abidlabs](https://github.com/abidlabs) in [PR 4356](https://github.com/gradio-app/gradio/pull/4356).
-- Remove unused frontend code by [@akx](https://github.com/akx) in [PR 4275](https://github.com/gradio-app/gradio/pull/4275)
-- Fixes favicon path on Windows by [@abidlabs](https://github.com/abidlabs) in [PR 4369](https://github.com/gradio-app/gradio/pull/4369).
-- Prevent path traversal in `/file` routes by [@abidlabs](https://github.com/abidlabs) in [PR 4370](https://github.com/gradio-app/gradio/pull/4370).
-- Do not send HF token to other domains via `/proxy` route by [@abidlabs](https://github.com/abidlabs) in [PR 4368](https://github.com/gradio-app/gradio/pull/4368).
-- Replace default `markedjs` sanitize function with DOMPurify sanitizer for `gr.Chatbot()` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4360](https://github.com/gradio-app/gradio/pull/4360)
-- Prevent the creation of duplicate copy buttons in the chatbot and ensure copy buttons work in non-secure contexts by [@binary-husky](https://github.com/binary-husky) in [PR 4350](https://github.com/gradio-app/gradio/pull/4350).
-
-### Other Changes:
-
-- Remove flicker of loading bar by adding opacity transition, by [@aliabid94](https://github.com/aliabid94) in [PR 4349](https://github.com/gradio-app/gradio/pull/4349).
-- Performance optimization in the frontend's Blocks code by [@akx](https://github.com/akx) in [PR 4334](https://github.com/gradio-app/gradio/pull/4334)
-- Upgrade the pnpm lock file format version from v6.0 to v6.1 by [@whitphx](https://github.com/whitphx) in [PR 4393](https://github.com/gradio-app/gradio/pull/4393)
-
-### Breaking Changes:
-
-- The `/file=` route no longer allows accessing dotfiles or files in "dot directories" by [@akx](https://github.com/akx) in [PR 4303](https://github.com/gradio-app/gradio/pull/4303)
-
-## 3.32.0
-
-### New Features:
-
-- `Interface.launch()` and `Blocks.launch()` now accept an `app_kwargs` argument to allow customizing the configuration of the underlying FastAPI app, by [@akx](https://github.com/akx) in [PR 4282](https://github.com/gradio-app/gradio/pull/4282)
-
-### Bug Fixes:
-
-- Fixed Gallery/AnnotatedImage components not respecting GRADIO_DEFAULT_DIR variable by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4256](https://github.com/gradio-app/gradio/pull/4256)
-- Fixed Gallery/AnnotatedImage components resaving identical images by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4256](https://github.com/gradio-app/gradio/pull/4256)
-- Fixed Audio/Video/File components creating empty tempfiles on each run by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4256](https://github.com/gradio-app/gradio/pull/4256)
-- Fixed the behavior of the `run_on_click` parameter in `gr.Examples` by [@abidlabs](https://github.com/abidlabs) in [PR 4258](https://github.com/gradio-app/gradio/pull/4258).
-- Ensure error modal displays when the queue is enabled by [@pngwn](https://github.com/pngwn) in [PR 4273](https://github.com/gradio-app/gradio/pull/4273)
-- Ensure js client respcts the full root when making requests to the server by [@pngwn](https://github.com/pngwn) in [PR 4271](https://github.com/gradio-app/gradio/pull/4271)
-
-### Other Changes:
-
-- Refactor web component `initial_height` attribute by [@whitphx](https://github.com/whitphx) in [PR 4223](https://github.com/gradio-app/gradio/pull/4223)
-- Relocate `mount_css` fn to remove circular dependency [@whitphx](https://github.com/whitphx) in [PR 4222](https://github.com/gradio-app/gradio/pull/4222)
-- Upgrade Black to 23.3 by [@akx](https://github.com/akx) in [PR 4259](https://github.com/gradio-app/gradio/pull/4259)
-- Add frontend LaTeX support in `gr.Chatbot()` using `KaTeX` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4285](https://github.com/gradio-app/gradio/pull/4285).
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.31.0
-
-### New Features:
-
-- The reloader command (`gradio app.py`) can now accept command line arguments by [@micky2be](https://github.com/micky2be) in [PR 4119](https://github.com/gradio-app/gradio/pull/4119)
-- Added `format` argument to `Audio` component by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4178](https://github.com/gradio-app/gradio/pull/4178)
-- Add JS client code snippets to use via api page by [@aliabd](https://github.com/aliabd) in [PR 3927](https://github.com/gradio-app/gradio/pull/3927).
-- Update to the JS client by [@pngwn](https://github.com/pngwn) in [PR 4202](https://github.com/gradio-app/gradio/pull/4202)
-
-### Bug Fixes:
-
-- Fix "TypeError: issubclass() arg 1 must be a class" When use Optional[Types] by [@lingfengchencn](https://github.com/lingfengchencn) in [PR 4200](https://github.com/gradio-app/gradio/pull/4200).
-- Gradio will no longer send any analytics or call home if analytics are disabled with the GRADIO_ANALYTICS_ENABLED environment variable. By [@akx](https://github.com/akx) in [PR 4194](https://github.com/gradio-app/gradio/pull/4194) and [PR 4236](https://github.com/gradio-app/gradio/pull/4236)
-- The deprecation warnings for kwargs now show the actual stack level for the invocation, by [@akx](https://github.com/akx) in [PR 4203](https://github.com/gradio-app/gradio/pull/4203).
-- Fix "TypeError: issubclass() arg 1 must be a class" When use Optional[Types] by [@lingfengchencn](https://github.com/lingfengchencn) in [PR 4200](https://github.com/gradio-app/gradio/pull/4200).
-- Ensure cancelling functions work correctly by [@pngwn](https://github.com/pngwn) in [PR 4225](https://github.com/gradio-app/gradio/pull/4225)
-- Fixes a bug with typing.get_type_hints() on Python 3.9 by [@abidlabs](https://github.com/abidlabs) in [PR 4228](https://github.com/gradio-app/gradio/pull/4228).
-- Fixes JSONDecodeError by [@davidai](https://github.com/davidai) in [PR 4241](https://github.com/gradio-app/gradio/pull/4241)
-- Fix `chatbot_dialogpt` demo by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4238](https://github.com/gradio-app/gradio/pull/4238).
-
-### Other Changes:
-
-- Change `gr.Chatbot()` markdown parsing to frontend using `marked` library and `prism` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4150](https://github.com/gradio-app/gradio/pull/4150)
-- Update the js client by [@pngwn](https://github.com/pngwn) in [PR 3899](https://github.com/gradio-app/gradio/pull/3899)
-- Fix documentation for the shape of the numpy array produced by the `Image` component by [@der3318](https://github.com/der3318) in [PR 4204](https://github.com/gradio-app/gradio/pull/4204).
-- Updates the timeout for websocket messaging from 1 second to 5 seconds by [@abidlabs](https://github.com/abidlabs) in [PR 4235](https://github.com/gradio-app/gradio/pull/4235)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.30.0
-
-### New Features:
-
-- Adds a `root_path` parameter to `launch()` that allows running Gradio applications on subpaths (e.g. www.example.com/app) behind a proxy, by [@abidlabs](https://github.com/abidlabs) in [PR 4133](https://github.com/gradio-app/gradio/pull/4133)
-- Fix dropdown change listener to trigger on change when updated as an output by [@aliabid94](https://github.com/aliabid94) in [PR 4128](https://github.com/gradio-app/gradio/pull/4128).
-- Add `.input` event listener, which is only triggered when a user changes the component value (as compared to `.change`, which is also triggered when a component updates as the result of a function trigger), by [@aliabid94](https://github.com/aliabid94) in [PR 4157](https://github.com/gradio-app/gradio/pull/4157).
-
-### Bug Fixes:
-
-- Records username when flagging by [@abidlabs](https://github.com/abidlabs) in [PR 4135](https://github.com/gradio-app/gradio/pull/4135)
-- Fix website build issue by [@aliabd](https://github.com/aliabd) in [PR 4142](https://github.com/gradio-app/gradio/pull/4142)
-- Fix lang agnostic type info for `gr.File(file_count='multiple')` output components by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4153](https://github.com/gradio-app/gradio/pull/4153)
-
-### Other Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.29.0
-
-### New Features:
-
-- Returning language agnostic types in the `/info` route by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4039](https://github.com/gradio-app/gradio/pull/4039)
-
-### Bug Fixes:
-
-- Allow users to upload audio files in Audio component on iOS by by [@aliabid94](https://github.com/aliabid94) in [PR 4071](https://github.com/gradio-app/gradio/pull/4071).
-- Fixes the gradio theme builder error that appeared on launch by [@aliabid94](https://github.com/aliabid94) and [@abidlabs](https://github.com/abidlabs) in [PR 4080](https://github.com/gradio-app/gradio/pull/4080)
-- Keep Accordion content in DOM by [@aliabid94](https://github.com/aliabid94) in [PR 4070](https://github.com/gradio-app/gradio/pull/4073)
-- Fixed bug where type hints in functions caused the event handler to crash by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4068](https://github.com/gradio-app/gradio/pull/4068)
-- Fix dropdown default value not appearing by [@aliabid94](https://github.com/aliabid94) in [PR 4072](https://github.com/gradio-app/gradio/pull/4072).
-- Soft theme label color fix by [@aliabid94](https://github.com/aliabid94) in [PR 4070](https://github.com/gradio-app/gradio/pull/4070)
-- Fix `gr.Slider` `release` event not triggering on mobile by [@space-nuko](https://github.com/space-nuko) in [PR 4098](https://github.com/gradio-app/gradio/pull/4098)
-- Removes extraneous `State` component info from the `/info` route by [@abidlabs](https://github.com/freddyaboulton) in [PR 4107](https://github.com/gradio-app/gradio/pull/4107)
-- Make .then() work even if first event fails by [@aliabid94](https://github.com/aliabid94) in [PR 4115](https://github.com/gradio-app/gradio/pull/4115).
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Allow users to submit with enter in Interfaces with textbox / number inputs [@aliabid94](https://github.com/aliabid94) in [PR 4090](https://github.com/gradio-app/gradio/pull/4090).
-- Updates gradio's requirements.txt to requires uvicorn>=0.14.0 by [@abidlabs](https://github.com/abidlabs) in [PR 4086](https://github.com/gradio-app/gradio/pull/4086)
-- Updates some error messaging by [@abidlabs](https://github.com/abidlabs) in [PR 4086](https://github.com/gradio-app/gradio/pull/4086)
-- Renames simplified Chinese translation file from `zh-cn.json` to `zh-CN.json` by [@abidlabs](https://github.com/abidlabs) in [PR 4086](https://github.com/gradio-app/gradio/pull/4086)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.28.3
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Fixes issue with indentation in `gr.Code()` component with streaming by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4043](https://github.com/gradio-app/gradio/pull/4043)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.28.2
-
-### Bug Fixes
-
-- Code component visual updates by [@pngwn](https://github.com/pngwn) in [PR 4051](https://github.com/gradio-app/gradio/pull/4051)
-
-### New Features:
-
-- Add support for `visual-question-answering`, `document-question-answering`, and `image-to-text` using `gr.Interface.load("models/...")` and `gr.Interface.from_pipeline` by [@osanseviero](https://github.com/osanseviero) in [PR 3887](https://github.com/gradio-app/gradio/pull/3887)
-- Add code block support in `gr.Chatbot()`, by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4048](https://github.com/gradio-app/gradio/pull/4048)
-- Adds the ability to blocklist filepaths (and also improves the allowlist mechanism) by [@abidlabs](https://github.com/abidlabs) in [PR 4047](https://github.com/gradio-app/gradio/pull/4047).
-- Adds the ability to specify the upload directory via an environment variable by [@abidlabs](https://github.com/abidlabs) in [PR 4047](https://github.com/gradio-app/gradio/pull/4047).
-
-### Bug Fixes:
-
-- Fixes issue with `matplotlib` not rendering correctly if the backend was not set to `Agg` by [@abidlabs](https://github.com/abidlabs) in [PR 4029](https://github.com/gradio-app/gradio/pull/4029)
-- Fixes bug where rendering the same `gr.State` across different Interfaces/Blocks within larger Blocks would not work by [@abidlabs](https://github.com/abidlabs) in [PR 4030](https://github.com/gradio-app/gradio/pull/4030)
-- Code component visual updates by [@pngwn](https://github.com/pngwn) in [PR 4051](https://github.com/gradio-app/gradio/pull/4051)
-
-### Documentation Changes:
-
-- Adds a Guide on how to use the Python Client within a FastAPI app, by [@abidlabs](https://github.com/abidlabs) in [PR 3892](https://github.com/gradio-app/gradio/pull/3892)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-- `gr.HuggingFaceDatasetSaver` behavior changed internally. The `flagging/` folder is not a `.git/` folder anymore when using it. `organization` parameter is now ignored in favor of passing a full dataset id as `dataset_name` (e.g. `"username/my-dataset"`).
-- New lines (`\n`) are not automatically converted to `<br>` in `gr.Markdown()` or `gr.Chatbot()`. For multiple new lines, a developer must add multiple `<br>` tags.
-
-### Full Changelog:
-
-- Safer version of `gr.HuggingFaceDatasetSaver` using HTTP methods instead of git pull/push by [@Wauplin](https://github.com/Wauplin) in [PR 3973](https://github.com/gradio-app/gradio/pull/3973)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.28.1
-
-### New Features:
-
-- Add a "clear mask" button to `gr.Image` sketch modes, by [@space-nuko](https://github.com/space-nuko) in [PR 3615](https://github.com/gradio-app/gradio/pull/3615)
-
-### Bug Fixes:
-
-- Fix dropdown default value not appearing by [@aliabid94](https://github.com/aliabid94) in [PR 3996](https://github.com/gradio-app/gradio/pull/3996).
-- Fix faded coloring of output textboxes in iOS / Safari by [@aliabid94](https://github.com/aliabid94) in [PR 3993](https://github.com/gradio-app/gradio/pull/3993)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-- CI: Simplified Python CI workflow by [@akx](https://github.com/akx) in [PR 3982](https://github.com/gradio-app/gradio/pull/3982)
-- Upgrade pyright to 1.1.305 by [@akx](https://github.com/akx) in [PR 4042](https://github.com/gradio-app/gradio/pull/4042)
-- More Ruff rules are enabled and lint errors fixed by [@akx](https://github.com/akx) in [PR 4038](https://github.com/gradio-app/gradio/pull/4038)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.28.0
-
-### Bug Fixes:
-
-- Fix duplicate play commands in full-screen mode of 'video'. by [@tomchang25](https://github.com/tomchang25) in [PR 3968](https://github.com/gradio-app/gradio/pull/3968).
-- Fix the issue of the UI stuck caused by the 'selected' of DataFrame not being reset. by [@tomchang25](https://github.com/tomchang25) in [PR 3916](https://github.com/gradio-app/gradio/pull/3916).
-- Fix issue where `gr.Video()` would not work inside a `gr.Tab()` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3891](https://github.com/gradio-app/gradio/pull/3891)
-- Fixed issue with old_value check in File. by [@tomchang25](https://github.com/tomchang25) in [PR 3859](https://github.com/gradio-app/gradio/pull/3859).
-- Fixed bug where all bokeh plots appeared in the same div by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3896](https://github.com/gradio-app/gradio/pull/3896)
-- Fixed image outputs to automatically take full output image height, unless explicitly set, by [@aliabid94](https://github.com/aliabid94) in [PR 3905](https://github.com/gradio-app/gradio/pull/3905)
-- Fix issue in `gr.Gallery()` where setting height causes aspect ratio of images to collapse by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3830](https://github.com/gradio-app/gradio/pull/3830)
-- Fix issue where requesting for a non-existing file would trigger a 500 error by [@micky2be](https://github.com/micky2be) in `[PR 3895](https://github.com/gradio-app/gradio/pull/3895)`.
-- Fix bugs with abspath about symlinks, and unresolvable path on Windows by [@micky2be](https://github.com/micky2be) in `[PR 3895](https://github.com/gradio-app/gradio/pull/3895)`.
-- Fixes type in client `Status` enum by [@10zinten](https://github.com/10zinten) in [PR 3931](https://github.com/gradio-app/gradio/pull/3931)
-- Fix `gr.ChatBot` to handle image url [tye-singwa](https://github.com/tye-signwa) in [PR 3953](https://github.com/gradio-app/gradio/pull/3953)
-- Move Google Tag Manager related initialization code to analytics-enabled block by [@akx](https://github.com/akx) in [PR 3956](https://github.com/gradio-app/gradio/pull/3956)
-- Fix bug where port was not reused if the demo was closed and then re-launched by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3896](https://github.com/gradio-app/gradio/pull/3959)
-- Fixes issue where dropdown does not position itself at selected element when opened [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3639](https://github.com/gradio-app/gradio/pull/3639)
-
-### Documentation Changes:
-
-- Make use of `gr` consistent across the docs by [@duerrsimon](https://github.com/duerrsimon) in [PR 3901](https://github.com/gradio-app/gradio/pull/3901)
-- Fixed typo in theming-guide.md by [@eltociear](https://github.com/eltociear) in [PR 3952](https://github.com/gradio-app/gradio/pull/3952)
-
-### Testing and Infrastructure Changes:
-
-- CI: Python backend lint is only run once, by [@akx](https://github.com/akx) in [PR 3960](https://github.com/gradio-app/gradio/pull/3960)
-- Format invocations and concatenations were replaced by f-strings where possible by [@akx](https://github.com/akx) in [PR 3984](https://github.com/gradio-app/gradio/pull/3984)
-- Linting rules were made more strict and issues fixed by [@akx](https://github.com/akx) in [PR 3979](https://github.com/gradio-app/gradio/pull/3979).
-
-### Breaking Changes:
-
-- Some re-exports in `gradio.themes` utilities (introduced in 3.24.0) have been eradicated.
-  By [@akx](https://github.com/akx) in [PR 3958](https://github.com/gradio-app/gradio/pull/3958)
-
-### Full Changelog:
-
-- Add DESCRIPTION.md to image_segmentation demo by [@aliabd](https://github.com/aliabd) in [PR 3866](https://github.com/gradio-app/gradio/pull/3866)
-- Fix error in running `gr.themes.builder()` by [@deepkyu](https://github.com/deepkyu) in [PR 3869](https://github.com/gradio-app/gradio/pull/3869)
-- Fixed a JavaScript TypeError when loading custom JS with `_js` and setting `outputs` to `None` in `gradio.Blocks()` by [@DavG25](https://github.com/DavG25) in [PR 3883](https://github.com/gradio-app/gradio/pull/3883)
-- Fixed bg_background_fill theme property to expand to whole background, block_radius to affect form elements as well, and added block_label_shadow theme property by [@aliabid94](https://github.com/aliabid94) in [PR 3590](https://github.com/gradio-app/gradio/pull/3590)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.27.0
-
-### New Features:
-
-###### AnnotatedImage Component
-
-New AnnotatedImage component allows users to highlight regions of an image, either by providing bounding boxes, or 0-1 pixel masks. This component is useful for tasks such as image segmentation, object detection, and image captioning.
-
-![AnnotatedImage screenshot](https://user-images.githubusercontent.com/7870876/232142720-86e0020f-beaf-47b9-a843-689c9621f09c.gif)
-
-Example usage:
-
-```python
-with gr.Blocks() as demo:
-    img = gr.Image()
-    img_section = gr.AnnotatedImage()
-    def mask(img):
-        top_left_corner = [0, 0, 20, 20]
-        random_mask = np.random.randint(0, 2, img.shape[:2])
-        return (img, [(top_left_corner, "left corner"), (random_mask, "random")])
-    img.change(mask, img, img_section)
-```
-
-See the [image_segmentation demo](https://github.com/gradio-app/gradio/tree/main/demo/image_segmentation) for a full example. By [@aliabid94](https://github.com/aliabid94) in [PR 3836](https://github.com/gradio-app/gradio/pull/3836)
-
-### Bug Fixes:
-
-No changes to highlight.
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.26.0
-
-### New Features:
-
-###### `Video` component supports subtitles
-
-- Allow the video component to accept subtitles as input, by [@tomchang25](https://github.com/tomchang25) in [PR 3673](https://github.com/gradio-app/gradio/pull/3673). To provide subtitles, simply return a tuple consisting of `(path_to_video, path_to_subtitles)` from your function. Both `.srt` and `.vtt` formats are supported:
-
-```py
-with gr.Blocks() as demo:
-    gr.Video(("video.mp4", "captions.srt"))
-```
-
-### Bug Fixes:
-
-- Fix code markdown support in `gr.Chatbot()` component by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3816](https://github.com/gradio-app/gradio/pull/3816)
-
-### Documentation Changes:
-
-- Updates the "view API" page in Gradio apps to use the `gradio_client` library by [@aliabd](https://github.com/aliabd) in [PR 3765](https://github.com/gradio-app/gradio/pull/3765)
-
-- Read more about how to use the `gradio_client` library here: https://gradio.app/getting-started-with-the-python-client/
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.25.0
-
-### New Features:
-
-- Improve error messages when number of inputs/outputs to event handlers mismatch, by [@space-nuko](https://github.com/space-nuko) in [PR 3519](https://github.com/gradio-app/gradio/pull/3519)
-
-- Add `select` listener to Images, allowing users to click on any part of an image and get the coordinates of the click by [@aliabid94](https://github.com/aliabid94) in [PR 3786](https://github.com/gradio-app/gradio/pull/3786).
-
-```python
-with gr.Blocks() as demo:
-    img = gr.Image()
-    textbox = gr.Textbox()
-
-    def select_handler(img, evt: gr.SelectData):
-        selected_pixel = img[evt.index[1], evt.index[0]]
-        return f"Selected pixel: {selected_pixel}"
-
-    img.select(select_handler, img, textbox)
-```
-
-![Recording 2023-04-08 at 17 44 39](https://user-images.githubusercontent.com/7870876/230748572-90a2a8d5-116d-4769-bb53-5516555fbd0f.gif)
-
-### Bug Fixes:
-
-- Increase timeout for sending analytics data by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3647](https://github.com/gradio-app/gradio/pull/3647)
-- Fix bug where http token was not accessed over websocket connections by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3735](https://github.com/gradio-app/gradio/pull/3735)
-- Add ability to specify `rows`, `columns` and `object-fit` in `style()` for `gr.Gallery()` component by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3586](https://github.com/gradio-app/gradio/pull/3586)
-- Fix bug where recording an audio file through the microphone resulted in a corrupted file name by [@abidlabs](https://github.com/abidlabs) in [PR 3770](https://github.com/gradio-app/gradio/pull/3770)
-- Added "ssl_verify" to blocks.launch method to allow for use of self-signed certs by [@garrettsutula](https://github.com/garrettsutula) in [PR 3873](https://github.com/gradio-app/gradio/pull/3873)
-- Fix bug where iterators where not being reset for processes that terminated early by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3777](https://github.com/gradio-app/gradio/pull/3777)
-- Fix bug where the upload button was not properly handling the `file_count='multiple'` case by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3782](https://github.com/gradio-app/gradio/pull/3782)
-- Fix bug where use Via API button was giving error by [@Devang-C](https://github.com/Devang-C) in [PR 3783](https://github.com/gradio-app/gradio/pull/3783)
-
-### Documentation Changes:
-
-- Fix invalid argument docstrings, by [@akx](https://github.com/akx) in [PR 3740](https://github.com/gradio-app/gradio/pull/3740)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Fixed IPv6 listening to work with bracket [::1] notation, by [@dsully](https://github.com/dsully) in [PR 3695](https://github.com/gradio-app/gradio/pull/3695)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.24.1
-
-### New Features:
-
-- No changes to highlight.
-
-### Bug Fixes:
-
-- Fixes Chatbot issue where new lines were being created every time a message was sent back and forth by [@aliabid94](https://github.com/aliabid94) in [PR 3717](https://github.com/gradio-app/gradio/pull/3717).
-- Fixes data updating in DataFrame invoking a `select` event once the dataframe has been selected. By [@yiyuezhuo](https://github.com/yiyuezhuo) in [PR 3861](https://github.com/gradio-app/gradio/pull/3861)
-- Fixes false positive warning which is due to too strict type checking by [@yiyuezhuo](https://github.com/yiyuezhuo) in [PR 3837](https://github.com/gradio-app/gradio/pull/3837).
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.24.0
-
-### New Features:
-
-- Trigger the release event when Slider number input is released or unfocused by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3589](https://github.com/gradio-app/gradio/pull/3589)
-- Created Theme Builder, which allows users to create themes without writing any code, by [@aliabid94](https://github.com/aliabid94) in [PR 3664](https://github.com/gradio-app/gradio/pull/3664). Launch by:
-
-  ```python
-  import gradio as gr
-  gr.themes.builder()
-  ```
-
-  ![Theme Builder](https://user-images.githubusercontent.com/7870876/228204929-d71cbba5-69c2-45b3-bd20-e3a201d98b12.png)
-
-- The `Dropdown` component now has a `allow_custom_value` parameter that lets users type in custom values not in the original list of choices.
-- The `Colorpicker` component now has a `.blur()` event
-
-###### Added a download button for videos! 📥
-
-![download_video](https://user-images.githubusercontent.com/41651716/227009612-9bc5fb72-2a44-4c55-9b7b-a0fa098e7f25.gif)
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3581](https://github.com/gradio-app/gradio/pull/3581).
-
-- Trigger the release event when Slider number input is released or unfocused by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3589](https://github.com/gradio-app/gradio/pull/3589)
-
-### Bug Fixes:
-
-- Fixed bug where text for altair plots was not legible in dark mode by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3555](https://github.com/gradio-app/gradio/pull/3555)
-- Fixes `Chatbot` and `Image` components so that files passed during processing are added to a directory where they can be served from, by [@abidlabs](https://github.com/abidlabs) in [PR 3523](https://github.com/gradio-app/gradio/pull/3523)
-- Use Gradio API server to send telemetry using `huggingface_hub` [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3488](https://github.com/gradio-app/gradio/pull/3488)
-- Fixes an an issue where if the Blocks scope was not exited, then State could be shared across sessions, by [@abidlabs](https://github.com/abidlabs) in [PR 3600](https://github.com/gradio-app/gradio/pull/3600)
-- Ensures that `gr.load()` loads and applies the upstream theme, by [@abidlabs](https://github.com/abidlabs) in [PR 3641](https://github.com/gradio-app/gradio/pull/3641)
-- Fixed bug where "or" was not being localized in file upload text by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3599](https://github.com/gradio-app/gradio/pull/3599)
-- Fixed bug where chatbot does not autoscroll inside of a tab, row or column by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3637](https://github.com/gradio-app/gradio/pull/3637)
-- Fixed bug where textbox shrinks when `lines` set to larger than 20 by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3637](https://github.com/gradio-app/gradio/pull/3637)
-- Ensure CSS has fully loaded before rendering the application, by [@pngwn](https://github.com/pngwn) in [PR 3573](https://github.com/gradio-app/gradio/pull/3573)
-- Support using an empty list as `gr.Dataframe` value, by [@space-nuko](https://github.com/space-nuko) in [PR 3646](https://github.com/gradio-app/gradio/pull/3646)
-- Fixed `gr.Image` not filling the entire element size, by [@space-nuko](https://github.com/space-nuko) in [PR 3649](https://github.com/gradio-app/gradio/pull/3649)
-- Make `gr.Code` support the `lines` property, by [@space-nuko](https://github.com/space-nuko) in [PR 3651](https://github.com/gradio-app/gradio/pull/3651)
-- Fixes certain `_js` return values being double wrapped in an array, by [@space-nuko](https://github.com/space-nuko) in [PR 3594](https://github.com/gradio-app/gradio/pull/3594)
-- Correct the documentation of `gr.File` component to state that its preprocessing method converts the uploaded file to a temporary file, by @RussellLuo in [PR 3660](https://github.com/gradio-app/gradio/pull/3660)
-- Fixed bug in Serializer ValueError text by [@osanseviero](https://github.com/osanseviero) in [PR 3669](https://github.com/gradio-app/gradio/pull/3669)
-- Fix default parameter argument and `gr.Progress` used in same function, by [@space-nuko](https://github.com/space-nuko) in [PR 3671](https://github.com/gradio-app/gradio/pull/3671)
-- Hide `Remove All` button in `gr.Dropdown` single-select mode by [@space-nuko](https://github.com/space-nuko) in [PR 3678](https://github.com/gradio-app/gradio/pull/3678)
-- Fix broken spaces in docs by [@aliabd](https://github.com/aliabd) in [PR 3698](https://github.com/gradio-app/gradio/pull/3698)
-- Fix items in `gr.Dropdown` besides the selected item receiving a checkmark, by [@space-nuko](https://github.com/space-nuko) in [PR 3644](https://github.com/gradio-app/gradio/pull/3644)
-- Fix several `gr.Dropdown` issues and improve usability, by [@space-nuko](https://github.com/space-nuko) in [PR 3705](https://github.com/gradio-app/gradio/pull/3705)
-
-### Documentation Changes:
-
-- Makes some fixes to the Theme Guide related to naming of variables, by [@abidlabs](https://github.com/abidlabs) in [PR 3561](https://github.com/gradio-app/gradio/pull/3561)
-- Documented `HuggingFaceDatasetJSONSaver` by [@osanseviero](https://github.com/osanseviero) in [PR 3604](https://github.com/gradio-app/gradio/pull/3604)
-- Makes some additions to documentation of `Audio` and `State` components, and fixes the `pictionary` demo by [@abidlabs](https://github.com/abidlabs) in [PR 3611](https://github.com/gradio-app/gradio/pull/3611)
-- Fix outdated sharing your app guide by [@aliabd](https://github.com/aliabd) in [PR 3699](https://github.com/gradio-app/gradio/pull/3699)
-
-### Testing and Infrastructure Changes:
-
-- Removed heavily-mocked tests related to comet_ml, wandb, and mlflow as they added a significant amount of test dependencies that prevented installation of test dependencies on Windows environments. By [@abidlabs](https://github.com/abidlabs) in [PR 3608](https://github.com/gradio-app/gradio/pull/3608)
-- Added Windows continuous integration, by [@space-nuko](https://github.com/space-nuko) in [PR 3628](https://github.com/gradio-app/gradio/pull/3628)
-- Switched linting from flake8 + isort to `ruff`, by [@akx](https://github.com/akx) in [PR 3710](https://github.com/gradio-app/gradio/pull/3710)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Mobile responsive iframes in themes guide by [@aliabd](https://github.com/aliabd) in [PR 3562](https://github.com/gradio-app/gradio/pull/3562)
-- Remove extra $demo from theme guide by [@aliabd](https://github.com/aliabd) in [PR 3563](https://github.com/gradio-app/gradio/pull/3563)
-- Set the theme name to be the upstream repo name when loading from the hub by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3595](https://github.com/gradio-app/gradio/pull/3595)
-- Copy everything in website Dockerfile, fix build issues by [@aliabd](https://github.com/aliabd) in [PR 3659](https://github.com/gradio-app/gradio/pull/3659)
-- Raise error when an event is queued but the queue is not configured by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3640](https://github.com/gradio-app/gradio/pull/3640)
-- Allows users to apss in a string name for a built-in theme, by [@abidlabs](https://github.com/abidlabs) in [PR 3641](https://github.com/gradio-app/gradio/pull/3641)
-- Added `orig_name` to Video output in the backend so that the front end can set the right name for downloaded video files by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3700](https://github.com/gradio-app/gradio/pull/3700)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.23.0
-
-### New Features:
-
-###### Theme Sharing!
-
-Once you have created a theme, you can upload it to the HuggingFace Hub to let others view it, use it, and build off of it! You can also download, reuse, and remix other peoples' themes. See https://gradio.app/theming-guide/ for more details.
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3428](https://github.com/gradio-app/gradio/pull/3428)
-
-### Bug Fixes:
-
-- Removes leading spaces from all lines of code uniformly in the `gr.Code()` component. By [@abidlabs](https://github.com/abidlabs) in [PR 3556](https://github.com/gradio-app/gradio/pull/3556)
-- Fixed broken login page, by [@aliabid94](https://github.com/aliabid94) in [PR 3529](https://github.com/gradio-app/gradio/pull/3529)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Fix rendering of dropdowns to take more space, and related bugs, by [@aliabid94](https://github.com/aliabid94) in [PR 3549](https://github.com/gradio-app/gradio/pull/3549)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.22.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Restore label bars by [@aliabid94](https://github.com/aliabid94) in [PR 3507](https://github.com/gradio-app/gradio/pull/3507)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.22.0
-
-### New Features:
-
-###### Official Theme release
-
-Gradio now supports a new theme system, which allows you to customize the look and feel of your app. You can now use the `theme=` kwarg to pass in a prebuilt theme, or customize your own! See https://gradio.app/theming-guide/ for more details. By [@aliabid94](https://github.com/aliabid94) in [PR 3470](https://github.com/gradio-app/gradio/pull/3470) and [PR 3497](https://github.com/gradio-app/gradio/pull/3497)
-
-###### `elem_classes`
-
-Add keyword argument `elem_classes` to Components to control class names of components, in the same manner as existing `elem_id`.
-By [@aliabid94](https://github.com/aliabid94) in [PR 3466](https://github.com/gradio-app/gradio/pull/3466)
-
-### Bug Fixes:
-
-- Fixes the File.upload() event trigger which broke as part of the change in how we uploaded files by [@abidlabs](https://github.com/abidlabs) in [PR 3462](https://github.com/gradio-app/gradio/pull/3462)
-- Fixed issue with `gr.Request` object failing to handle dictionaries when nested keys couldn't be converted to variable names [#3454](https://github.com/gradio-app/gradio/issues/3454) by [@radames](https://github.com/radames) in [PR 3459](https://github.com/gradio-app/gradio/pull/3459)
-- Fixed bug where css and client api was not working properly when mounted in a subpath by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3482](https://github.com/gradio-app/gradio/pull/3482)
-
-### Documentation Changes:
-
-- Document gr.Error in the docs by [@aliabd](https://github.com/aliabd) in [PR 3465](https://github.com/gradio-app/gradio/pull/3465)
-
-### Testing and Infrastructure Changes:
-
-- Pinned `pyright==1.1.298` for stability by [@abidlabs](https://github.com/abidlabs) in [PR 3475](https://github.com/gradio-app/gradio/pull/3475)
-- Removed `IOComponent.add_interactive_to_config()` by [@space-nuko](https://github.com/space-nuko) in [PR 3476](https://github.com/gradio-app/gradio/pull/3476)
-- Removed `IOComponent.generate_sample()` by [@space-nuko](https://github.com/space-nuko) in [PR 3475](https://github.com/gradio-app/gradio/pull/3483)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Revert primary button background color in dark mode by [@aliabid94](https://github.com/aliabid94) in [PR 3468](https://github.com/gradio-app/gradio/pull/3468)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.21.0
-
-### New Features:
-
-###### Theme Sharing 🎨 🤝
-
-You can now share your gradio themes with the world!
-
-After creating a theme, you can upload it to the HuggingFace Hub to let others view it, use it, and build off of it!
-
-###### Uploading
-
-There are two ways to upload a theme, via the theme class instance or the command line.
-
-1. Via the class instance
-
-```python
-my_theme.push_to_hub(repo_name="my_theme",
-                     version="0.2.0",
-                     hf_token="...")
-```
-
-2. Via the command line
-
-First save the theme to disk
-
-```python
-my_theme.dump(filename="my_theme.json")
-```
-
-Then use the `upload_theme` command:
-
-```bash
-upload_theme\
-"my_theme.json"\
-"my_theme"\
-"0.2.0"\
-"<hf-token>"
-```
-
-The `version` must be a valid [semantic version](https://www.geeksforgeeks.org/introduction-semantic-versioning/) string.
-
-This creates a space on the huggingface hub to host the theme files and show potential users a preview of your theme.
-
-An example theme space is here: https://huggingface.co/spaces/freddyaboulton/dracula_revamped
-
-###### Downloading
-
-To use a theme from the hub, use the `from_hub` method on the `ThemeClass` and pass it to your app:
-
-```python
-my_theme = gr.Theme.from_hub("freddyaboulton/my_theme")
-
-with gr.Blocks(theme=my_theme) as demo:
-    ....
-```
-
-You can also pass the theme string directly to `Blocks` or `Interface` (`gr.Blocks(theme="freddyaboulton/my_theme")`)
-
-You can pin your app to an upstream theme version by using semantic versioning expressions.
-
-For example, the following would ensure the theme we load from the `my_theme` repo was between versions `0.1.0` and `0.2.0`:
-
-```python
-with gr.Blocks(theme="freddyaboulton/my_theme@>=0.1.0,<0.2.0") as demo:
-    ....
-```
-
-by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3428](https://github.com/gradio-app/gradio/pull/3428)
-
-###### Code component 🦾
-
-New code component allows you to enter, edit and display code with full syntax highlighting by [@pngwn](https://github.com/pngwn) in [PR 3421](https://github.com/gradio-app/gradio/pull/3421)
-
-###### The `Chatbot` component now supports audio, video, and images
-
-The `Chatbot` component now supports audio, video, and images with a simple syntax: simply
-pass in a tuple with the URL or filepath (the second optional element of the tuple is alt text), and the image/audio/video will be displayed:
-
-```python
-gr.Chatbot([
-    (("driving.mp4",), "cool video"),
-    (("cantina.wav",), "cool audio"),
-    (("lion.jpg", "A lion"), "cool pic"),
-]).style(height=800)
-```
-
-<img width="1054" alt="image" src="https://user-images.githubusercontent.com/1778297/224116682-5908db47-f0fa-405c-82ab-9c7453e8c4f1.png">
-
-Note: images were previously supported via Markdown syntax and that is still supported for backwards compatibility. By [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3413](https://github.com/gradio-app/gradio/pull/3413)
-
-- Allow consecutive function triggers with `.then` and `.success` by [@aliabid94](https://github.com/aliabid94) in [PR 3430](https://github.com/gradio-app/gradio/pull/3430)
-
-- New code component allows you to enter, edit and display code with full syntax highlighting by [@pngwn](https://github.com/pngwn) in [PR 3421](https://github.com/gradio-app/gradio/pull/3421)
-
-![](https://user-images.githubusercontent.com/12937446/224116643-5cfb94b3-93ce-43ee-bb7b-c25c3b66e0a1.png)
-
-- Added the `.select()` event listener, which also includes event data that can be passed as an argument to a function with type hint `gr.SelectData`. The following components support the `.select()` event listener: Chatbot, CheckboxGroup, Dataframe, Dropdown, File, Gallery, HighlightedText, Label, Radio, TabItem, Tab, Textbox. Example usage:
-
-```python
-import gradio as gr
-
-with gr.Blocks() as demo:
-    gallery = gr.Gallery(["images/1.jpg", "images/2.jpg", "images/3.jpg"])
-    selected_index = gr.Textbox()
-
-    def on_select(evt: gr.SelectData):
-        return evt.index
-
-    gallery.select(on_select, None, selected_index)
-```
-
-By [@aliabid94](https://github.com/aliabid94) in [PR 3399](https://github.com/gradio-app/gradio/pull/3399)
-
-- The `Textbox` component now includes a copy button by [@abidlabs](https://github.com/abidlabs) in [PR 3452](https://github.com/gradio-app/gradio/pull/3452)
-
-### Bug Fixes:
-
-- Use `huggingface_hub` to send telemetry on `interface` and `blocks`; eventually to replace segment by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3342](https://github.com/gradio-app/gradio/pull/3342)
-- Ensure load events created by components (randomize for slider, callable values) are never queued unless every is passed by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3391](https://github.com/gradio-app/gradio/pull/3391)
-- Prevent in-place updates of `generic_update` by shallow copying by [@gitgithan](https://github.com/gitgithan) in [PR 3405](https://github.com/gradio-app/gradio/pull/3405) to fix [#3282](https://github.com/gradio-app/gradio/issues/3282)
-- Fix bug caused by not importing `BlockContext` in `utils.py` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3424](https://github.com/gradio-app/gradio/pull/3424)
-- Ensure dropdown does not highlight partial matches by [@pngwn](https://github.com/pngwn) in [PR 3421](https://github.com/gradio-app/gradio/pull/3421)
-- Fix mic button display by [@aliabid94](https://github.com/aliabid94) in [PR 3456](https://github.com/gradio-app/gradio/pull/3456)
-
-### Documentation Changes:
-
-- Added a section on security and access when sharing Gradio apps by [@abidlabs](https://github.com/abidlabs) in [PR 3408](https://github.com/gradio-app/gradio/pull/3408)
-- Add Chinese README by [@uanu2002](https://github.com/uanu2002) in [PR 3394](https://github.com/gradio-app/gradio/pull/3394)
-- Adds documentation for web components by [@abidlabs](https://github.com/abidlabs) in [PR 3407](https://github.com/gradio-app/gradio/pull/3407)
-- Fixed link in Chinese readme by [@eltociear](https://github.com/eltociear) in [PR 3417](https://github.com/gradio-app/gradio/pull/3417)
-- Document Blocks methods by [@aliabd](https://github.com/aliabd) in [PR 3427](https://github.com/gradio-app/gradio/pull/3427)
-- Fixed bug where event handlers were not showing up in documentation by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3434](https://github.com/gradio-app/gradio/pull/3434)
-
-### Testing and Infrastructure Changes:
-
-- Fixes tests that were failing locally but passing on CI by [@abidlabs](https://github.com/abidlabs) in [PR 3411](https://github.com/gradio-app/gradio/pull/3411)
-- Remove codecov from the repo by [@aliabd](https://github.com/aliabd) in [PR 3415](https://github.com/gradio-app/gradio/pull/3415)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Prevent in-place updates of `generic_update` by shallow copying by [@gitgithan](https://github.com/gitgithan) in [PR 3405](https://github.com/gradio-app/gradio/pull/3405) to fix [#3282](https://github.com/gradio-app/gradio/issues/3282)
-- Persist file names of files uploaded through any Gradio component by [@abidlabs](https://github.com/abidlabs) in [PR 3412](https://github.com/gradio-app/gradio/pull/3412)
-- Fix markdown embedded component in docs by [@aliabd](https://github.com/aliabd) in [PR 3410](https://github.com/gradio-app/gradio/pull/3410)
-- Clean up event listeners code by [@aliabid94](https://github.com/aliabid94) in [PR 3420](https://github.com/gradio-app/gradio/pull/3420)
-- Fix css issue with spaces logo by [@aliabd](https://github.com/aliabd) in [PR 3422](https://github.com/gradio-app/gradio/pull/3422)
-- Makes a few fixes to the `JSON` component (show_label parameter, icons) in [@abidlabs](https://github.com/abidlabs) in [PR 3451](https://github.com/gradio-app/gradio/pull/3451)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.20.1
-
-### New Features:
-
-- Add `height` kwarg to style in `gr.Chatbot()` component by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3369](https://github.com/gradio-app/gradio/pull/3369)
-
-```python
-chatbot = gr.Chatbot().style(height=500)
-```
-
-### Bug Fixes:
-
-- Ensure uploaded images are always shown in the sketch tool by [@pngwn](https://github.com/pngwn) in [PR 3386](https://github.com/gradio-app/gradio/pull/3386)
-- Fixes bug where when if fn is a non-static class member, then self should be ignored as the first param of the fn by [@or25](https://github.com/or25) in [PR #3227](https://github.com/gradio-app/gradio/pull/3227)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.20.0
-
-### New Features:
-
-###### Release event for Slider
-
-Now you can trigger your python function to run when the slider is released as opposed to every slider change value!
-
-Simply use the `release` method on the slider
-
-```python
-slider.release(function, inputs=[...], outputs=[...], api_name="predict")
-```
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3353](https://github.com/gradio-app/gradio/pull/3353)
-
-###### Dropdown Component Updates
-
-The standard dropdown component now supports searching for choices. Also when `multiselect` is `True`, you can specify `max_choices` to set the maximum number of choices you want the user to be able to select from the dropdown component.
-
-```python
-gr.Dropdown(label="Choose your favorite colors", choices=["red", "blue", "green", "yellow", "orange"], multiselect=True, max_choices=2)
-```
-
-by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3211](https://github.com/gradio-app/gradio/pull/3211)
-
-###### Download button for images 🖼️
-
-Output images will now automatically have a download button displayed to make it easier to save and share
-the results of Machine Learning art models.
-
-![download_sketch](https://user-images.githubusercontent.com/41651716/221025113-e693bf41-eabd-42b3-a4f2-26f2708d98fe.gif)
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3297](https://github.com/gradio-app/gradio/pull/3297)
-
-- Updated image upload component to accept all image formats, including lossless formats like .webp by [@fienestar](https://github.com/fienestar) in [PR 3225](https://github.com/gradio-app/gradio/pull/3225)
-- Adds a disabled mode to the `gr.Button` component by setting `interactive=False` by [@abidlabs](https://github.com/abidlabs) in [PR 3266](https://github.com/gradio-app/gradio/pull/3266) and [PR 3288](https://github.com/gradio-app/gradio/pull/3288)
-- Adds visual feedback to the when the Flag button is clicked, by [@abidlabs](https://github.com/abidlabs) in [PR 3289](https://github.com/gradio-app/gradio/pull/3289)
-- Adds ability to set `flagging_options` display text and saved flag separately by [@abidlabs](https://github.com/abidlabs) in [PR 3289](https://github.com/gradio-app/gradio/pull/3289)
-- Allow the setting of `brush_radius` for the `Image` component both as a default and via `Image.update()` by [@pngwn](https://github.com/pngwn) in [PR 3277](https://github.com/gradio-app/gradio/pull/3277)
-- Added `info=` argument to form components to enable extra context provided to users, by [@aliabid94](https://github.com/aliabid94) in [PR 3291](https://github.com/gradio-app/gradio/pull/3291)
-- Allow developers to access the username of a logged-in user from the `gr.Request()` object using the `.username` attribute by [@abidlabs](https://github.com/abidlabs) in [PR 3296](https://github.com/gradio-app/gradio/pull/3296)
-- Add `preview` option to `Gallery.style` that launches the gallery in preview mode when first loaded by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3345](https://github.com/gradio-app/gradio/pull/3345)
-
-### Bug Fixes:
-
-- Ensure `mirror_webcam` is always respected by [@pngwn](https://github.com/pngwn) in [PR 3245](https://github.com/gradio-app/gradio/pull/3245)
-- Fix issue where updated markdown links were not being opened in a new tab by [@gante](https://github.com/gante) in [PR 3236](https://github.com/gradio-app/gradio/pull/3236)
-- API Docs Fixes by [@aliabd](https://github.com/aliabd) in [PR 3287](https://github.com/gradio-app/gradio/pull/3287)
-- Added a timeout to queue messages as some demos were experiencing infinitely growing queues from active jobs waiting forever for clients to respond by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3196](https://github.com/gradio-app/gradio/pull/3196)
-- Fixes the height of rendered LaTeX images so that they match the height of surrounding text by [@abidlabs](https://github.com/abidlabs) in [PR 3258](https://github.com/gradio-app/gradio/pull/3258) and in [PR 3276](https://github.com/gradio-app/gradio/pull/3276)
-- Fix bug where matplotlib images where always too small on the front end by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3274](https://github.com/gradio-app/gradio/pull/3274)
-- Remove embed's `initial_height` when loading is complete so the embed finds its natural height once it is loaded [@pngwn](https://github.com/pngwn) in [PR 3292](https://github.com/gradio-app/gradio/pull/3292)
-- Prevent Sketch from crashing when a default image is provided by [@pngwn](https://github.com/pngwn) in [PR 3277](https://github.com/gradio-app/gradio/pull/3277)
-- Respect the `shape` argument on the front end when creating Image Sketches by [@pngwn](https://github.com/pngwn) in [PR 3277](https://github.com/gradio-app/gradio/pull/3277)
-- Fix infinite loop caused by setting `Dropdown's` value to be `[]` and adding a change event on the dropdown by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3295](https://github.com/gradio-app/gradio/pull/3295)
-- Fix change event listed twice in image docs by [@aliabd](https://github.com/aliabd) in [PR 3318](https://github.com/gradio-app/gradio/pull/3318)
-- Fix bug that cause UI to be vertically centered at all times by [@pngwn](https://github.com/pngwn) in [PR 3336](https://github.com/gradio-app/gradio/pull/3336)
-- Fix bug where `height` set in `Gallery.style` was not respected by the front-end by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3343](https://github.com/gradio-app/gradio/pull/3343)
-- Ensure markdown lists are rendered correctly by [@pngwn](https://github.com/pngwn) in [PR 3341](https://github.com/gradio-app/gradio/pull/3341)
-- Ensure that the initial empty value for `gr.Dropdown(Multiselect=True)` is an empty list and the initial value for `gr.Dropdown(Multiselect=False)` is an empty string by [@pngwn](https://github.com/pngwn) in [PR 3338](https://github.com/gradio-app/gradio/pull/3338)
-- Ensure uploaded images respect the shape property when the canvas is also enabled by [@pngwn](https://github.com/pngwn) in [PR 3351](https://github.com/gradio-app/gradio/pull/3351)
-- Ensure that Google Analytics works correctly when gradio apps are created with `analytics_enabled=True` by [@abidlabs](https://github.com/abidlabs) in [PR 3349](https://github.com/gradio-app/gradio/pull/3349)
-- Fix bug where files were being re-uploaded after updates by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3375](https://github.com/gradio-app/gradio/pull/3375)
-- Fix error when using backen_fn and custom js at the same time by [@jialeicui](https://github.com/jialeicui) in [PR 3358](https://github.com/gradio-app/gradio/pull/3358)
-- Support new embeds for huggingface spaces subdomains by [@pngwn](https://github.com/pngwn) in [PR 3367](https://github.com/gradio-app/gradio/pull/3367)
-
-### Documentation Changes:
-
-- Added the `types` field to the dependency field in the config by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3315](https://github.com/gradio-app/gradio/pull/3315)
-- Gradio Status Page by [@aliabd](https://github.com/aliabd) in [PR 3331](https://github.com/gradio-app/gradio/pull/3331)
-- Adds a Guide on setting up a dashboard from Supabase data using the `gr.BarPlot`
-  component by [@abidlabs](https://github.com/abidlabs) in [PR 3275](https://github.com/gradio-app/gradio/pull/3275)
-
-### Testing and Infrastructure Changes:
-
-- Adds a script to benchmark the performance of the queue and adds some instructions on how to use it. By [@freddyaboulton](https://github.com/freddyaboulton) and [@abidlabs](https://github.com/abidlabs) in [PR 3272](https://github.com/gradio-app/gradio/pull/3272)
-- Flaky python tests no longer cancel non-flaky tests by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3344](https://github.com/gradio-app/gradio/pull/3344)
-
-### Breaking Changes:
-
-- Chatbot bubble colors can no longer be set by `chatbot.style(color_map=)` by [@aliabid94] in [PR 3370](https://github.com/gradio-app/gradio/pull/3370)
-
-### Full Changelog:
-
-- Fixed comment typo in components.py by [@eltociear](https://github.com/eltociear) in [PR 3235](https://github.com/gradio-app/gradio/pull/3235)
-- Cleaned up chatbot ui look and feel by [@aliabid94] in [PR 3370](https://github.com/gradio-app/gradio/pull/3370)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.19.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- UI fixes including footer and API docs by [@aliabid94](https://github.com/aliabid94) in [PR 3242](https://github.com/gradio-app/gradio/pull/3242)
-- Updated image upload component to accept all image formats, including lossless formats like .webp by [@fienestar](https://github.com/fienestar) in [PR 3225](https://github.com/gradio-app/gradio/pull/3225)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Added backend support for themes by [@aliabid94](https://github.com/aliabid94) in [PR 2931](https://github.com/gradio-app/gradio/pull/2931)
-- Added support for button sizes "lg" (default) and "sm".
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.19.0
-
-### New Features:
-
-###### Improved embedding experience
-
-When embedding a spaces-hosted gradio app as a web component, you now get an improved UI linking back to the original space, better error handling and more intelligent load performance. No changes are required to your code to benefit from this enhanced experience; simply upgrade your gradio SDK to the latest version.
-
-![](https://user-images.githubusercontent.com/12937446/219653294-86937632-72c1-4e93-a77c-af705d49382a.png)
-
-This behaviour is configurable. You can disable the info panel at the bottom by passing `info="false"`. You can disable the container entirely by passing `container="false"`.
-
-Error statuses are reported in the UI with an easy way for end-users to report problems to the original space author via the community tab of that Hugginface space:
-
-![](https://user-images.githubusercontent.com/12937446/219655499-88019443-d694-44e7-9e6d-242e19d10a5c.png)
-
-By default, gradio apps are lazy loaded, vastly improving performance when there are several demos on the page. Metadata is loaded ahead of time, but the space will only be loaded and rendered when it is in view.
-
-This behaviour is configurable. You can pass `eager="true"` to load and render the space regardless of whether or not it is currently on the screen.
-
-by [@pngwn](https://github.com/pngwn) in [PR 3205](https://github.com/gradio-app/gradio/pull/3205)
-
-###### New `gr.BarPlot` component! 📊
-
-Create interactive bar plots from a high-level interface with `gr.BarPlot`.
-No need to remember matplotlib syntax anymore!
-
-Example usage:
-
-```python
-import gradio as gr
-import pandas as pd
-
-simple = pd.DataFrame({
-    'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
-    'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
-})
-
-with gr.Blocks() as demo:
-    gr.BarPlot(
-        simple,
-        x="a",
-        y="b",
-        title="Simple Bar Plot with made up data",
-        tooltip=['a', 'b'],
-    )
-
-demo.launch()
-```
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3157](https://github.com/gradio-app/gradio/pull/3157)
-
-###### Bokeh plots are back! 🌠
-
-Fixed a bug that prevented bokeh plots from being displayed on the front end and extended support for both 2.x and 3.x versions of bokeh!
-
-![image](https://user-images.githubusercontent.com/41651716/219468324-0d82e07f-8fb4-4ff9-b40c-8250b29e45f7.png)
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3212](https://github.com/gradio-app/gradio/pull/3212)
-
-### Bug Fixes:
-
-- Adds ability to add a single message from the bot or user side. Ex: specify `None` as the second value in the tuple, to add a single message in the chatbot from the "bot" side.
-
-```python
-gr.Chatbot([("Hi, I'm DialoGPT. Try asking me a question.", None)])
-```
-
-By [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3165](https://github.com/gradio-app/gradio/pull/3165)
-
-- Fixes `gr.utils.delete_none` to only remove props whose values are `None` from the config by [@abidlabs](https://github.com/abidlabs) in [PR 3188](https://github.com/gradio-app/gradio/pull/3188)
-- Fix bug where embedded demos were not loading files properly by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3177](https://github.com/gradio-app/gradio/pull/3177)
-- The `change` event is now triggered when users click the 'Clear All' button of the multiselect DropDown component by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3195](https://github.com/gradio-app/gradio/pull/3195)
-- Stops File component from freezing when a large file is uploaded by [@aliabid94](https://github.com/aliabid94) in [PR 3191](https://github.com/gradio-app/gradio/pull/3191)
-- Support Chinese pinyin in Dataframe by [@aliabid94](https://github.com/aliabid94) in [PR 3206](https://github.com/gradio-app/gradio/pull/3206)
-- The `clear` event is now triggered when images are cleared by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3218](https://github.com/gradio-app/gradio/pull/3218)
-- Fix bug where auth cookies where not sent when connecting to an app via http by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3223](https://github.com/gradio-app/gradio/pull/3223)
-- Ensure latext CSS is always applied in light and dark mode by [@pngwn](https://github.com/pngwn) in [PR 3233](https://github.com/gradio-app/gradio/pull/3233)
-
-### Documentation Changes:
-
-- Sort components in docs by alphabetic order by [@aliabd](https://github.com/aliabd) in [PR 3152](https://github.com/gradio-app/gradio/pull/3152)
-- Changes to W&B guide by [@scottire](https://github.com/scottire) in [PR 3153](https://github.com/gradio-app/gradio/pull/3153)
-- Keep pnginfo metadata for gallery by [@wfng92](https://github.com/wfng92) in [PR 3150](https://github.com/gradio-app/gradio/pull/3150)
-- Add a section on how to run a Gradio app locally [@osanseviero](https://github.com/osanseviero) in [PR 3170](https://github.com/gradio-app/gradio/pull/3170)
-- Fixed typos in gradio events function documentation by [@vidalmaxime](https://github.com/vidalmaxime) in [PR 3168](https://github.com/gradio-app/gradio/pull/3168)
-- Added an example using Gradio's batch mode with the diffusers library by [@abidlabs](https://github.com/abidlabs) in [PR 3224](https://github.com/gradio-app/gradio/pull/3224)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Fix demos page css and add close demos button by [@aliabd](https://github.com/aliabd) in [PR 3151](https://github.com/gradio-app/gradio/pull/3151)
-- Caches temp files from base64 input data by giving them a deterministic path based on the contents of data by [@abidlabs](https://github.com/abidlabs) in [PR 3197](https://github.com/gradio-app/gradio/pull/3197)
-- Better warnings (when there is a mismatch between the number of output components and values returned by a function, or when the `File` component or `UploadButton` component includes a `file_types` parameter along with `file_count=="dir"`) by [@abidlabs](https://github.com/abidlabs) in [PR 3194](https://github.com/gradio-app/gradio/pull/3194)
-- Raises a `gr.Error` instead of a regular Python error when you use `gr.Interface.load()` to load a model and there's an error querying the HF API by [@abidlabs](https://github.com/abidlabs) in [PR 3194](https://github.com/gradio-app/gradio/pull/3194)
-- Fixed gradio share links so that they are persistent and do not reset if network
-  connection is disrupted by by [XciD](https://github.com/XciD), [Wauplin](https://github.com/Wauplin), and [@abidlabs](https://github.com/abidlabs) in [PR 3149](https://github.com/gradio-app/gradio/pull/3149) and a follow-up to allow it to work for users upgrading from a previous Gradio version in [PR 3221](https://github.com/gradio-app/gradio/pull/3221)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.18.0
-
-### New Features:
-
-###### Revamped Stop Button for Interfaces 🛑
-
-If your Interface function is a generator, there used to be a separate `Stop` button displayed next
-to the `Submit` button.
-
-We've revamed the `Submit` button so that it turns into a `Stop` button during the generation process.
-Clicking on the `Stop` button will cancel the generation and turn it back to a `Submit` button.
-The `Stop` button will automatically turn back to a `Submit` button at the end of the generation if you don't use it!
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3124](https://github.com/gradio-app/gradio/pull/3124)
-
-###### Queue now works with reload mode!
-
-You can now call `queue` on your `demo` outside of the `if __name__ == "__main__"` block and
-run the script in reload mode with the `gradio` command.
-
-Any changes to the `app.py` file will be reflected in the webpage automatically and the queue will work
-properly!
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3089](https://github.com/gradio-app/gradio/pull/3089)
-
-###### Allow serving files from additional directories
-
-```python
-demo = gr.Interface(...)
-demo.launch(
-  file_directories=["/var/lib/demo/path/to/resources"]
-)
-```
-
-By [@maxaudron](https://github.com/maxaudron) in [PR 3075](https://github.com/gradio-app/gradio/pull/3075)
-
-### Bug Fixes:
-
-- Fixes URL resolution on Windows by [@abidlabs](https://github.com/abidlabs) in [PR 3108](https://github.com/gradio-app/gradio/pull/3108)
-- Example caching now works with components without a label attribute (e.g. `Column`) by [@abidlabs](https://github.com/abidlabs) in [PR 3123](https://github.com/gradio-app/gradio/pull/3123)
-- Ensure the Video component correctly resets the UI state when a new video source is loaded and reduce choppiness of UI by [@pngwn](https://github.com/abidlabs) in [PR 3117](https://github.com/gradio-app/gradio/pull/3117)
-- Fixes loading private Spaces by [@abidlabs](https://github.com/abidlabs) in [PR 3068](https://github.com/gradio-app/gradio/pull/3068)
-- Added a warning when attempting to launch an `Interface` via the `%%blocks` jupyter notebook magic command by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3126](https://github.com/gradio-app/gradio/pull/3126)
-- Fixes bug where interactive output image cannot be set when in edit mode by [@dawoodkhan82](https://github.com/@dawoodkhan82) in [PR 3135](https://github.com/gradio-app/gradio/pull/3135)
-- A share link will automatically be created when running on Sagemaker notebooks so that the front-end is properly displayed by [@abidlabs](https://github.com/abidlabs) in [PR 3137](https://github.com/gradio-app/gradio/pull/3137)
-- Fixes a few dropdown component issues; hide checkmark next to options as expected, and keyboard hover is visible by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3145]https://github.com/gradio-app/gradio/pull/3145)
-- Fixed bug where example pagination buttons were not visible in dark mode or displayed under the examples table. By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3144](https://github.com/gradio-app/gradio/pull/3144)
-- Fixed bug where the font color of axis labels and titles for native plots did not respond to dark mode preferences. By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3146](https://github.com/gradio-app/gradio/pull/3146)
-
-### Documentation Changes:
-
-- Added a guide on the 4 kinds of Gradio Interfaces by [@yvrjsharma](https://github.com/yvrjsharma) and [@abidlabs](https://github.com/abidlabs) in [PR 3003](https://github.com/gradio-app/gradio/pull/3003)
-- Explained that the parameters in `launch` will not be respected when using reload mode, e.g. `gradio` command by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3089](https://github.com/gradio-app/gradio/pull/3089)
-- Added a demo to show how to set up variable numbers of outputs in Gradio by [@abidlabs](https://github.com/abidlabs) in [PR 3127](https://github.com/gradio-app/gradio/pull/3127)
-- Updated docs to reflect that the `equal_height` parameter should be passed to the `.style()` method of `gr.Row()` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3125](https://github.com/gradio-app/gradio/pull/3125)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Changed URL of final image for `fake_diffusion` demos by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3120](https://github.com/gradio-app/gradio/pull/3120)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.17.1
-
-### New Features:
-
-###### iOS image rotation fixed 🔄
-
-Previously photos uploaded via iOS would be rotated after processing. This has been fixed by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3089](https://github.com/gradio-app/gradio/pull/3091)
-
-######### Before
-
-![image](https://user-images.githubusercontent.com/41651716/215846507-a36e9d05-1ac2-4867-8ab3-ce045a9415d9.png)
-
-######### After
-
-![image](https://user-images.githubusercontent.com/41651716/215846554-e41773ed-70f0-491a-9952-6a18babf91ef.png)
-
-###### Run on Kaggle kernels 🧪
-
-A share link will automatically be created when running on Kaggle kernels (notebooks) so that the front-end is properly displayed.
-
-![image](https://user-images.githubusercontent.com/41651716/216104254-2cf55599-449c-436c-b57e-40f6a83f9eee.png)
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3101](https://github.com/gradio-app/gradio/pull/3101)
-
-### Bug Fixes:
-
-- Fix bug where examples were not rendered correctly for demos created with Blocks api that had multiple input compinents by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3090](https://github.com/gradio-app/gradio/pull/3090)
-- Fix change event listener for JSON, HighlightedText, Chatbot by [@aliabid94](https://github.com/aliabid94) in [PR 3095](https://github.com/gradio-app/gradio/pull/3095)
-- Fixes bug where video and file change event not working [@tomchang25](https://github.com/tomchang25) in [PR 3098](https://github.com/gradio-app/gradio/pull/3098)
-- Fixes bug where static_video play and pause event not working [@tomchang25](https://github.com/tomchang25) in [PR 3098](https://github.com/gradio-app/gradio/pull/3098)
-- Fixed `Gallery.style(grid=...)` by by [@aliabd](https://github.com/aliabd) in [PR 3107](https://github.com/gradio-app/gradio/pull/3107)
-
-### Documentation Changes:
-
-- Update chatbot guide to include blocks demo and markdown support section by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3023](https://github.com/gradio-app/gradio/pull/3023)
-
-* Fix a broken link in the Quick Start guide, by [@cakiki](https://github.com/cakiki) in [PR 3109](https://github.com/gradio-app/gradio/pull/3109)
-* Better docs navigation on mobile by [@aliabd](https://github.com/aliabd) in [PR 3112](https://github.com/gradio-app/gradio/pull/3112)
-* Add a guide on using Gradio with [Comet](https://comet.com/), by [@DN6](https://github.com/DN6/) in [PR 3058](https://github.com/gradio-app/gradio/pull/3058)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Set minimum `markdown-it-py` version to `2.0.0` so that the dollar math plugin is compatible by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3102](https://github.com/gradio-app/gradio/pull/3102)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.17.0
-
-### New Features:
-
-###### Extended support for Interface.load! 🏗️
-
-You can now load `image-to-text` and `conversational` pipelines from the hub!
-
-###### Image-to-text Demo
-
-```python
-io = gr.Interface.load("models/nlpconnect/vit-gpt2-image-captioning",
-                       api_key="<optional-api-key>")
-io.launch()
-```
-
-<img width="1087" alt="image" src="https://user-images.githubusercontent.com/41651716/213260197-dc5d80b4-6e50-4b3a-a764-94980930ac38.png">
-
-###### conversational Demo
-
-```python
-chatbot = gr.Interface.load("models/microsoft/DialoGPT-medium",
-                           api_key="<optional-api-key>")
-chatbot.launch()
-```
-
-![chatbot_load](https://user-images.githubusercontent.com/41651716/213260220-3eaa25b7-a38b-48c6-adeb-2718bdf297a2.gif)
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3011](https://github.com/gradio-app/gradio/pull/3011)
-
-###### Download Button added to Model3D Output Component 📥
-
-No need for an additional file output component to enable model3d file downloads anymore. We now added a download button to the model3d component itself.
-
-<img width="739" alt="Screenshot 2023-01-18 at 3 52 45 PM" src="https://user-images.githubusercontent.com/12725292/213294198-5f4fda35-bde7-450c-864f-d5683e7fa29a.png">
-
-By [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3014](https://github.com/gradio-app/gradio/pull/3014)
-
-###### Fixing Auth on Spaces 🔑
-
-Authentication on spaces works now! Third party cookies must be enabled on your browser to be able
-to log in. Some browsers disable third party cookies by default (Safari, Chrome Incognito).
-
-![auth_spaces](https://user-images.githubusercontent.com/41651716/215528417-09538933-0576-4d1d-b3b9-1e877ab01905.gif)
-
-### Bug Fixes:
-
-- Fixes bug where interpretation event was not configured correctly by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2993](https://github.com/gradio-app/gradio/pull/2993)
-- Fix relative import bug in reload mode by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2992](https://github.com/gradio-app/gradio/pull/2992)
-- Fixes bug where png files were not being recognized when uploading images by [@abidlabs](https://github.com/abidlabs) in [PR 3002](https://github.com/gradio-app/gradio/pull/3002)
-- Fixes bug where external Spaces could not be loaded and used as functions if they returned files by [@abidlabs](https://github.com/abidlabs) in [PR 3004](https://github.com/gradio-app/gradio/pull/3004)
-- Fix bug where file serialization output was not JSON serializable by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2999](https://github.com/gradio-app/gradio/pull/2999)
-- Fixes bug where png files were not being recognized when uploading images by [@abidlabs](https://github.com/abidlabs) in [PR 3002](https://github.com/gradio-app/gradio/pull/3002)
-- Fixes bug where temporary uploaded files were not being added to temp sets by [@abidlabs](https://github.com/abidlabs) in [PR 3005](https://github.com/gradio-app/gradio/pull/3005)
-- Fixes issue where markdown support in chatbot breaks older demos [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3006](https://github.com/gradio-app/gradio/pull/3006)
-- Fixes the `/file/` route that was broken in a recent change in [PR 3010](https://github.com/gradio-app/gradio/pull/3010)
-- Fix bug where the Image component could not serialize image urls by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2957](https://github.com/gradio-app/gradio/pull/2957)
-- Fix forwarding for guides after SEO renaming by [@aliabd](https://github.com/aliabd) in [PR 3017](https://github.com/gradio-app/gradio/pull/3017)
-- Switch all pages on the website to use latest stable gradio by [@aliabd](https://github.com/aliabd) in [PR 3016](https://github.com/gradio-app/gradio/pull/3016)
-- Fix bug related to deprecated parameters in `huggingface_hub` for the HuggingFaceDatasetSaver in [PR 3025](https://github.com/gradio-app/gradio/pull/3025)
-- Added better support for symlinks in the way absolute paths are resolved by [@abidlabs](https://github.com/abidlabs) in [PR 3037](https://github.com/gradio-app/gradio/pull/3037)
-- Fix several minor frontend bugs (loading animation, examples as gallery) frontend [@aliabid94](https://github.com/3026) in [PR 2961](https://github.com/gradio-app/gradio/pull/3026).
-- Fixes bug that the chatbot sample code does not work with certain input value by [@petrov826](https://github.com/petrov826) in [PR 3039](https://github.com/gradio-app/gradio/pull/3039).
-- Fix shadows for form element and ensure focus styles more visible in dark mode [@pngwn](https://github.com/pngwn) in [PR 3042](https://github.com/gradio-app/gradio/pull/3042).
-- Fixed bug where the Checkbox and Dropdown change events were not triggered in response to other component changes by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3045](https://github.com/gradio-app/gradio/pull/3045)
-- Fix bug where the queue was not properly restarted after launching a `closed` app by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3022](https://github.com/gradio-app/gradio/pull/3022)
-- Adding missing embedded components on docs by [@aliabd](https://github.com/aliabd) in [PR 3027](https://github.com/gradio-app/gradio/pull/3027)
-- Fixes bug where app would crash if the `file_types` parameter of `gr.File` or `gr.UploadButton` was not a list by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3048](https://github.com/gradio-app/gradio/pull/3048)
-- Ensure CSS mounts correctly regardless of how many Gradio instances are on the page [@pngwn](https://github.com/pngwn) in [PR 3059](https://github.com/gradio-app/gradio/pull/3059).
-- Fix bug where input component was not hidden in the frontend for `UploadButton` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3053](https://github.com/gradio-app/gradio/pull/3053)
-- Fixes issue where after clicking submit or undo, the sketch output wouldn't clear. [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3047](https://github.com/gradio-app/gradio/pull/3047)
-- Ensure spaces embedded via the web component always use the correct URLs for server requests and change ports for testing to avoid strange collisions when users are working with embedded apps locally by [@pngwn](https://github.com/pngwn) in [PR 3065](https://github.com/gradio-app/gradio/pull/3065)
-- Preserve selected image of Gallery through updated by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3061](https://github.com/gradio-app/gradio/pull/3061)
-- Fix bug where auth was not respected on HF spaces by [@freddyaboulton](https://github.com/freddyaboulton) and [@aliabid94](https://github.com/aliabid94) in [PR 3049](https://github.com/gradio-app/gradio/pull/3049)
-- Fixes bug where tabs selected attribute not working if manually change tab by [@tomchang25](https://github.com/tomchang25) in [3055](https://github.com/gradio-app/gradio/pull/3055)
-- Change chatbot to show dots on progress, and fix bug where chatbot would not stick to bottom in the case of images by [@aliabid94](https://github.com/aliabid94) in [PR 3067](https://github.com/gradio-app/gradio/pull/3079)
-
-### Documentation Changes:
-
-- SEO improvements to guides by[@aliabd](https://github.com/aliabd) in [PR 2915](https://github.com/gradio-app/gradio/pull/2915)
-- Use `gr.LinePlot` for the `blocks_kinematics` demo by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2998](https://github.com/gradio-app/gradio/pull/2998)
-- Updated the `interface_series_load` to include some inline markdown code by [@abidlabs](https://github.com/abidlabs) in [PR 3051](https://github.com/gradio-app/gradio/pull/3051)
-
-### Testing and Infrastructure Changes:
-
-- Adds a GitHub action to test if any large files (> 5MB) are present by [@abidlabs](https://github.com/abidlabs) in [PR 3013](https://github.com/gradio-app/gradio/pull/3013)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Rewrote frontend using CSS variables for themes by [@pngwn](https://github.com/pngwn) in [PR 2840](https://github.com/gradio-app/gradio/pull/2840)
-- Moved telemetry requests to run on background threads by [@abidlabs](https://github.com/abidlabs) in [PR 3054](https://github.com/gradio-app/gradio/pull/3054)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.16.2
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Fixed file upload fails for files with zero size by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2923](https://github.com/gradio-app/gradio/pull/2923)
-- Fixed bug where `mount_gradio_app` would not launch if the queue was enabled in a gradio app by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2939](https://github.com/gradio-app/gradio/pull/2939)
-- Fix custom long CSS handling in Blocks by [@anton-l](https://github.com/anton-l) in [PR 2953](https://github.com/gradio-app/gradio/pull/2953)
-- Recovers the dropdown change event by [@abidlabs](https://github.com/abidlabs) in [PR 2954](https://github.com/gradio-app/gradio/pull/2954).
-- Fix audio file output by [@aliabid94](https://github.com/aliabid94) in [PR 2961](https://github.com/gradio-app/gradio/pull/2961).
-- Fixed bug where file extensions of really long files were not kept after download by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2929](https://github.com/gradio-app/gradio/pull/2929)
-- Fix bug where outputs for examples where not being returned by the backend by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2955](https://github.com/gradio-app/gradio/pull/2955)
-- Fix bug in `blocks_plug` demo that prevented switching tabs programmatically with python [@TashaSkyUp](https://github.com/https://github.com/TashaSkyUp) in [PR 2971](https://github.com/gradio-app/gradio/pull/2971).
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.16.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Fix audio file output by [@aliabid94](https://github.com/aliabid94) in [PR 2950](https://github.com/gradio-app/gradio/pull/2950).
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.16.0
-
-### New Features:
-
-###### Send custom progress updates by adding a `gr.Progress` argument after the input arguments to any function. Example:
-
-```python
-def reverse(word, progress=gr.Progress()):
-    progress(0, desc="Starting")
-    time.sleep(1)
-    new_string = ""
-    for letter in progress.tqdm(word, desc="Reversing"):
-        time.sleep(0.25)
-        new_string = letter + new_string
-    return new_string
-
-demo = gr.Interface(reverse, gr.Text(), gr.Text())
-```
-
-Progress indicator bar by [@aliabid94](https://github.com/aliabid94) in [PR 2750](https://github.com/gradio-app/gradio/pull/2750).
-
-- Added `title` argument to `TabbedInterface` by @MohamedAliRashad in [#2888](https://github.com/gradio-app/gradio/pull/2888)
-- Add support for specifying file extensions for `gr.File` and `gr.UploadButton`, using `file_types` parameter (e.g `gr.File(file_count="multiple", file_types=["text", ".json", ".csv"])`) by @dawoodkhan82 in [#2901](https://github.com/gradio-app/gradio/pull/2901)
-- Added `multiselect` option to `Dropdown` by @dawoodkhan82 in [#2871](https://github.com/gradio-app/gradio/pull/2871)
-
-###### With `multiselect` set to `true` a user can now select multiple options from the `gr.Dropdown` component.
-
-```python
-gr.Dropdown(["angola", "pakistan", "canada"], multiselect=True, value=["angola"])
-```
-
-<img width="610" alt="Screenshot 2023-01-03 at 4 14 36 PM" src="https://user-images.githubusercontent.com/12725292/210442547-c86975c9-4b4f-4b8e-8803-9d96e6a8583a.png">
-
-### Bug Fixes:
-
-- Fixed bug where an error opening an audio file led to a crash by [@FelixDombek](https://github.com/FelixDombek) in [PR 2898](https://github.com/gradio-app/gradio/pull/2898)
-- Fixed bug where setting `default_enabled=False` made it so that the entire queue did not start by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2876](https://github.com/gradio-app/gradio/pull/2876)
-- Fixed bug where csv preview for DataFrame examples would show filename instead of file contents by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2877](https://github.com/gradio-app/gradio/pull/2877)
-- Fixed bug where an error raised after yielding iterative output would not be displayed in the browser by
-  [@JaySmithWpg](https://github.com/JaySmithWpg) in [PR 2889](https://github.com/gradio-app/gradio/pull/2889)
-- Fixed bug in `blocks_style` demo that was preventing it from launching by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2890](https://github.com/gradio-app/gradio/pull/2890)
-- Fixed bug where files could not be downloaded by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2926](https://github.com/gradio-app/gradio/pull/2926)
-- Fixed bug where cached examples were not displaying properly by [@a-rogalska](https://github.com/a-rogalska) in [PR 2974](https://github.com/gradio-app/gradio/pull/2974)
-
-### Documentation Changes:
-
-- Added a Guide on using Google Sheets to create a real-time dashboard with Gradio's `DataFrame` and `LinePlot` component, by [@abidlabs](https://github.com/abidlabs) in [PR 2816](https://github.com/gradio-app/gradio/pull/2816)
-- Add a components - events matrix on the docs by [@aliabd](https://github.com/aliabd) in [PR 2921](https://github.com/gradio-app/gradio/pull/2921)
-
-### Testing and Infrastructure Changes:
-
-- Deployed PRs from forks to spaces by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2895](https://github.com/gradio-app/gradio/pull/2895)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- The `default_enabled` parameter of the `Blocks.queue` method has no effect by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2876](https://github.com/gradio-app/gradio/pull/2876)
-- Added typing to several Python files in codebase by [@abidlabs](https://github.com/abidlabs) in [PR 2887](https://github.com/gradio-app/gradio/pull/2887)
-- Excluding untracked files from demo notebook check action by [@aliabd](https://github.com/aliabd) in [PR 2897](https://github.com/gradio-app/gradio/pull/2897)
-- Optimize images and gifs by [@aliabd](https://github.com/aliabd) in [PR 2922](https://github.com/gradio-app/gradio/pull/2922)
-- Updated typing by [@1nF0rmed](https://github.com/1nF0rmed) in [PR 2904](https://github.com/gradio-app/gradio/pull/2904)
-
-### Contributors Shoutout:
-
-- @JaySmithWpg for making their first contribution to gradio!
-- @MohamedAliRashad for making their first contribution to gradio!
-
-## 3.15.0
-
-### New Features:
-
-Gradio's newest plotting component `gr.LinePlot`! 📈
-
-With this component you can easily create time series visualizations with customizable
-appearance for your demos and dashboards ... all without having to know an external plotting library.
-
-For an example of the api see below:
-
-```python
-gr.LinePlot(stocks,
-            x="date",
-            y="price",
-            color="symbol",
-            color_legend_position="bottom",
-            width=600, height=400, title="Stock Prices")
-```
-
-![image](https://user-images.githubusercontent.com/41651716/208711646-81ae3745-149b-46a3-babd-0569aecdd409.png)
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2807](https://github.com/gradio-app/gradio/pull/2807)
-
-### Bug Fixes:
-
-- Fixed bug where the `examples_per_page` parameter of the `Examples` component was not passed to the internal `Dataset` component by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2861](https://github.com/gradio-app/gradio/pull/2861)
-- Fixes loading Spaces that have components with default values by [@abidlabs](https://github.com/abidlabs) in [PR 2855](https://github.com/gradio-app/gradio/pull/2855)
-- Fixes flagging when `allow_flagging="auto"` in `gr.Interface()` by [@abidlabs](https://github.com/abidlabs) in [PR 2695](https://github.com/gradio-app/gradio/pull/2695)
-- Fixed bug where passing a non-list value to `gr.CheckboxGroup` would crash the entire app by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2866](https://github.com/gradio-app/gradio/pull/2866)
-
-### Documentation Changes:
-
-- Added a Guide on using BigQuery with Gradio's `DataFrame` and `ScatterPlot` component,
-  by [@abidlabs](https://github.com/abidlabs) in [PR 2794](https://github.com/gradio-app/gradio/pull/2794)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Fixed importing gradio can cause PIL.Image.registered_extensions() to break by `[@aliencaocao](https://github.com/aliencaocao)` in `[PR 2846](https://github.com/gradio-app/gradio/pull/2846)`
-- Fix css glitch and navigation in docs by [@aliabd](https://github.com/aliabd) in [PR 2856](https://github.com/gradio-app/gradio/pull/2856)
-- Added the ability to set `x_lim`, `y_lim` and legend positions for `gr.ScatterPlot` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2807](https://github.com/gradio-app/gradio/pull/2807)
-- Remove footers and min-height the correct way by [@aliabd](https://github.com/aliabd) in [PR 2860](https://github.com/gradio-app/gradio/pull/2860)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.14.0
-
-### New Features:
-
-###### Add Waveform Visual Support to Audio
-
-Adds a `gr.make_waveform()` function that creates a waveform video by combining an audio and an optional background image by [@dawoodkhan82](http://github.com/dawoodkhan82) and [@aliabid94](http://github.com/aliabid94) in [PR 2706](https://github.com/gradio-app/gradio/pull/2706. Helpful for making audio outputs much more shareable.
-
-![waveform screenrecording](https://user-images.githubusercontent.com/7870876/206062396-164a5e71-451a-4fe0-94a7-cbe9269d57e6.gif)
-
-###### Allows Every Component to Accept an `every` Parameter
-
-When a component's initial value is a function, the `every` parameter re-runs the function every `every` seconds. By [@abidlabs](https://github.com/abidlabs) in [PR 2806](https://github.com/gradio-app/gradio/pull/2806). Here's a code example:
-
-```py
-import gradio as gr
-
-with gr.Blocks() as demo:
-    df = gr.DataFrame(run_query, every=60*60)
-
-demo.queue().launch()
-```
-
-### Bug Fixes:
-
-- Fixed issue where too many temporary files were created, all with randomly generated
-  filepaths. Now fewer temporary files are created and are assigned a path that is a
-  hash based on the file contents by [@abidlabs](https://github.com/abidlabs) in [PR 2758](https://github.com/gradio-app/gradio/pull/2758)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.13.2
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-\*No changes to highlight.
-
--
-
-### Documentation Changes:
-
-- Improves documentation of several queuing-related parameters by [@abidlabs](https://github.com/abidlabs) in [PR 2825](https://github.com/gradio-app/gradio/pull/2825)
-
-### Testing and Infrastructure Changes:
-
-- Remove h11 pinning by [@ecederstrand](https://github.com/ecederstrand) in [PR 2820](https://github.com/gradio-app/gradio/pull/2820)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.13.1
-
-### New Features:
-
-###### New Shareable Links
-
-Replaces tunneling logic based on ssh port-forwarding to that based on `frp` by [XciD](https://github.com/XciD) and [Wauplin](https://github.com/Wauplin) in [PR 2509](https://github.com/gradio-app/gradio/pull/2509)
-
-You don't need to do anything differently, but when you set `share=True` in `launch()`,
-you'll get this message and a public link that look a little bit different:
-
-```bash
-Setting up a public link... we have recently upgraded the way public links are generated. If you encounter any problems, please downgrade to gradio version 3.13.0
-.
-Running on public URL: https://bec81a83-5b5c-471e.gradio.live
-```
-
-These links are a more secure and scalable way to create shareable demos!
-
-### Bug Fixes:
-
-- Allows `gr.Dataframe()` to take a `pandas.DataFrame` that includes numpy array and other types as its initial value, by [@abidlabs](https://github.com/abidlabs) in [PR 2804](https://github.com/gradio-app/gradio/pull/2804)
-- Add `altair` to requirements.txt by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2811](https://github.com/gradio-app/gradio/pull/2811)
-- Added aria-labels to icon buttons that are built into UI components by [@emilyuhde](http://github.com/emilyuhde) in [PR 2791](https://github.com/gradio-app/gradio/pull/2791)
-
-### Documentation Changes:
-
-- Fixed some typos in the "Plot Component for Maps" guide by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2811](https://github.com/gradio-app/gradio/pull/2811)
-
-### Testing and Infrastructure Changes:
-
-- Fixed test for IP address by [@abidlabs](https://github.com/abidlabs) in [PR 2808](https://github.com/gradio-app/gradio/pull/2808)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Fixed typo in parameter `visible` in classes in `templates.py` by [@abidlabs](https://github.com/abidlabs) in [PR 2805](https://github.com/gradio-app/gradio/pull/2805)
-- Switched external service for getting IP address from `https://api.ipify.org` to `https://checkip.amazonaws.com/` by [@abidlabs](https://github.com/abidlabs) in [PR 2810](https://github.com/gradio-app/gradio/pull/2810)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-- Fixed typo in parameter `visible` in classes in `templates.py` by [@abidlabs](https://github.com/abidlabs) in [PR 2805](https://github.com/gradio-app/gradio/pull/2805)
-- Switched external service for getting IP address from `https://api.ipify.org` to `https://checkip.amazonaws.com/` by [@abidlabs](https://github.com/abidlabs) in [PR 2810](https://github.com/gradio-app/gradio/pull/2810)
-
-## 3.13.0
-
-### New Features:
-
-###### Scatter plot component
-
-It is now possible to create a scatter plot natively in Gradio!
-
-The `gr.ScatterPlot` component accepts a pandas dataframe and some optional configuration parameters
-and will automatically create a plot for you!
-
-This is the first of many native plotting components in Gradio!
-
-For an example of how to use `gr.ScatterPlot` see below:
-
-```python
-import gradio as gr
-from vega_datasets import data
-
-cars = data.cars()
-
-with gr.Blocks() as demo:
-    gr.ScatterPlot(show_label=False,
-                   value=cars,
-                   x="Horsepower",
-                   y="Miles_per_Gallon",
-                   color="Origin",
-                   tooltip="Name",
-                   title="Car Data",
-                   y_title="Miles per Gallon",
-                   color_legend_title="Origin of Car").style(container=False)
-
-demo.launch()
-```
-
-<img width="404" alt="image" src="https://user-images.githubusercontent.com/41651716/206737726-4c4da5f0-dee8-4f0a-b1e1-e2b75c4638e9.png">
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2764](https://github.com/gradio-app/gradio/pull/2764)
-
-###### Support for altair plots
-
-The `Plot` component can now accept altair plots as values!
-Simply return an altair plot from your event listener and gradio will display it in the front-end.
-See the example below:
-
-```python
-import gradio as gr
-import altair as alt
-from vega_datasets import data
-
-cars = data.cars()
-chart = (
-    alt.Chart(cars)
-    .mark_point()
-    .encode(
-        x="Horsepower",
-        y="Miles_per_Gallon",
-        color="Origin",
-    )
-)
-
-with gr.Blocks() as demo:
-    gr.Plot(value=chart)
-demo.launch()
-```
-
-<img width="1366" alt="image" src="https://user-images.githubusercontent.com/41651716/204660697-f994316f-5ca7-4e8a-93bc-eb5e0d556c91.png">
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2741](https://github.com/gradio-app/gradio/pull/2741)
-
-###### Set the background color of a Label component
-
-The `Label` component now accepts a `color` argument by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2736](https://github.com/gradio-app/gradio/pull/2736).
-The `color` argument should either be a valid css color name or hexadecimal string.
-You can update the color with `gr.Label.update`!
-
-This lets you create Alert and Warning boxes with the `Label` component. See below:
-
-```python
-import gradio as gr
-import random
-
-def update_color(value):
-    if value < 0:
-        # This is bad so use red
-        return "#FF0000"
-    elif 0 <= value <= 20:
-        # Ok but pay attention (use orange)
-        return "#ff9966"
-    else:
-        # Nothing to worry about
-        return None
-
-def update_value():
-    choice = random.choice(['good', 'bad', 'so-so'])
-    color = update_color(choice)
-    return gr.Label.update(value=choice, color=color)
-
-
-with gr.Blocks() as demo:
-    label = gr.Label(value=-10)
-    demo.load(lambda: update_value(), inputs=None, outputs=[label], every=1)
-demo.queue().launch()
-```
-
-![label_bg_color_update](https://user-images.githubusercontent.com/41651716/204400372-80e53857-f26f-4a38-a1ae-1acadff75e89.gif)
-
-###### Add Brazilian Portuguese translation
-
-Add Brazilian Portuguese translation (pt-BR.json) by [@pstwh](http://github.com/pstwh) in [PR 2753](https://github.com/gradio-app/gradio/pull/2753):
-
-<img width="951" alt="image" src="https://user-images.githubusercontent.com/1778297/206615305-4c52031e-3f7d-4df2-8805-a79894206911.png">
-
-### Bug Fixes:
-
-- Fixed issue where image thumbnails were not showing when an example directory was provided
-  by [@abidlabs](https://github.com/abidlabs) in [PR 2745](https://github.com/gradio-app/gradio/pull/2745)
-- Fixed bug loading audio input models from the hub by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2779](https://github.com/gradio-app/gradio/pull/2779).
-- Fixed issue where entities were not merged when highlighted text was generated from the
-  dictionary inputs [@payoto](https://github.com/payoto) in [PR 2767](https://github.com/gradio-app/gradio/pull/2767)
-- Fixed bug where generating events did not finish running even if the websocket connection was closed by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2783](https://github.com/gradio-app/gradio/pull/2783).
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Images in the chatbot component are now resized if they exceed a max width by [@abidlabs](https://github.com/abidlabs) in [PR 2748](https://github.com/gradio-app/gradio/pull/2748)
-- Missing parameters have been added to `gr.Blocks().load()` by [@abidlabs](https://github.com/abidlabs) in [PR 2755](https://github.com/gradio-app/gradio/pull/2755)
-- Deindex share URLs from search by [@aliabd](https://github.com/aliabd) in [PR 2772](https://github.com/gradio-app/gradio/pull/2772)
-- Redirect old links and fix broken ones by [@aliabd](https://github.com/aliabd) in [PR 2774](https://github.com/gradio-app/gradio/pull/2774)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.12.0
-
-### New Features:
-
-###### The `Chatbot` component now supports a subset of Markdown (including bold, italics, code, images)
-
-You can now pass in some Markdown to the Chatbot component and it will show up,
-meaning that you can pass in images as well! by [@abidlabs](https://github.com/abidlabs) in [PR 2731](https://github.com/gradio-app/gradio/pull/2731)
-
-Here's a simple example that references a local image `lion.jpg` that is in the same
-folder as the Python script:
-
-```py
-import gradio as gr
-
-with gr.Blocks() as demo:
-    gr.Chatbot([("hi", "hello **abubakar**"), ("![](/file=lion.jpg)", "cool pic")])
-
-demo.launch()
-```
-
-![Alt text](https://user-images.githubusercontent.com/1778297/204357455-5c1a4002-eee7-479d-9a1e-ba2c12522723.png)
-
-To see a more realistic example, see the new demo `/demo/chatbot_multimodal/run.py`.
-
-###### Latex support
-
-Added mathtext (a subset of latex) support to gr.Markdown. Added by [@kashif](https://github.com/kashif) and [@aliabid94](https://github.com/aliabid94) in [PR 2696](https://github.com/gradio-app/gradio/pull/2696).
-
-Example of how it can be used:
-
-```python
-gr.Markdown(
-    r"""
-    # Hello World! $\frac{\sqrt{x + y}}{4}$ is today's lesson.
-    """)
-```
-
-###### Update Accordion properties from the backend
-
-You can now update the Accordion `label` and `open` status with `gr.Accordion.update` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2690](https://github.com/gradio-app/gradio/pull/2690)
-
-```python
-import gradio as gr
-
-with gr.Blocks() as demo:
-    with gr.Accordion(label="Open for greeting", open=False) as accordion:
-        gr.Textbox("Hello!")
-    open_btn = gr.Button(value="Open Accordion")
-    close_btn = gr.Button(value="Close Accordion")
-    open_btn.click(
-        lambda: gr.Accordion.update(open=True, label="Open Accordion"),
-        inputs=None,
-        outputs=[accordion],
-    )
-    close_btn.click(
-        lambda: gr.Accordion.update(open=False, label="Closed Accordion"),
-        inputs=None,
-        outputs=[accordion],
-    )
-demo.launch()
-```
-
-![update_accordion](https://user-images.githubusercontent.com/41651716/203164176-b102eae3-babe-4986-ae30-3ab4f400cedc.gif)
-
-### Bug Fixes:
-
-- Fixed bug where requests timeout is missing from utils.version_check() by [@yujiehecs](https://github.com/yujiehecs) in [PR 2729](https://github.com/gradio-app/gradio/pull/2729)
-- Fixed bug where so that the `File` component can properly preprocess files to "binary" byte-string format by [CoffeeVampir3](https://github.com/CoffeeVampir3) in [PR 2727](https://github.com/gradio-app/gradio/pull/2727)
-- Fixed bug to ensure that filenames are less than 200 characters even for non-English languages by [@SkyTNT](https://github.com/SkyTNT) in [PR 2685](https://github.com/gradio-app/gradio/pull/2685)
-
-### Documentation Changes:
-
-- Performance improvements to docs on mobile by [@aliabd](https://github.com/aliabd) in [PR 2730](https://github.com/gradio-app/gradio/pull/2730)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Make try examples button more prominent by [@aliabd](https://github.com/aliabd) in [PR 2705](https://github.com/gradio-app/gradio/pull/2705)
-- Fix id clashes in docs by [@aliabd](https://github.com/aliabd) in [PR 2713](https://github.com/gradio-app/gradio/pull/2713)
-- Fix typos in guide docs by [@andridns](https://github.com/andridns) in [PR 2722](https://github.com/gradio-app/gradio/pull/2722)
-- Add option to `include_audio` in Video component. When `True`, for `source="webcam"` this will record audio and video, for `source="upload"` this will retain the audio in an uploaded video by [@mandargogate](https://github.com/MandarGogate) in [PR 2721](https://github.com/gradio-app/gradio/pull/2721)
-
-### Contributors Shoutout:
-
-- [@andridns](https://github.com/andridns) made their first contribution in [PR 2722](https://github.com/gradio-app/gradio/pull/2722)!
-
-## 3.11.0
-
-### New Features:
-
-###### Upload Button
-
-There is now a new component called the `UploadButton` which is a file upload component but in button form! You can also specify what file types it should accept in the form of a list (ex: `image`, `video`, `audio`, `text`, or generic `file`). Added by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2591](https://github.com/gradio-app/gradio/pull/2591).
-
-Example of how it can be used:
-
-```python
-import gradio as gr
-
-def upload_file(files):
-    file_paths = [file.name for file in files]
-    return file_paths
-
-with gr.Blocks() as demo:
-    file_output = gr.File()
-    upload_button = gr.UploadButton("Click to Upload a File", file_types=["image", "video"], file_count="multiple")
-    upload_button.upload(upload_file, upload_button, file_output)
-
-demo.launch()
-```
-
-###### Revamped API documentation page
-
-New API Docs page with in-browser playground and updated aesthetics. [@gary149](https://github.com/gary149) in [PR 2652](https://github.com/gradio-app/gradio/pull/2652)
-
-###### Revamped Login page
-
-Previously our login page had its own CSS, had no dark mode, and had an ugly json message on the wrong credentials. Made the page more aesthetically consistent, added dark mode support, and a nicer error message. [@aliabid94](https://github.com/aliabid94) in [PR 2684](https://github.com/gradio-app/gradio/pull/2684)
-
-###### Accessing the Requests Object Directly
-
-You can now access the Request object directly in your Python function by [@abidlabs](https://github.com/abidlabs) in [PR 2641](https://github.com/gradio-app/gradio/pull/2641). This means that you can access request headers, the client IP address, and so on. In order to use it, add a parameter to your function and set its type hint to be `gr.Request`. Here's a simple example:
-
-```py
-import gradio as gr
-
-def echo(name, request: gr.Request):
-    if request:
-        print("Request headers dictionary:", request.headers)
-        print("IP address:", request.client.host)
-    return name
-
-io = gr.Interface(echo, "textbox", "textbox").launch()
-```
-
-### Bug Fixes:
-
-- Fixed bug that limited files from being sent over websockets to 16MB. The new limit
-  is now 1GB by [@abidlabs](https://github.com/abidlabs) in [PR 2709](https://github.com/gradio-app/gradio/pull/2709)
-
-### Documentation Changes:
-
-- Updated documentation for embedding Gradio demos on Spaces as web components by
-  [@julien-c](https://github.com/julien-c) in [PR 2698](https://github.com/gradio-app/gradio/pull/2698)
-- Updated IFrames in Guides to use the host URL instead of the Space name to be consistent with the new method for embedding Spaces, by
-  [@julien-c](https://github.com/julien-c) in [PR 2692](https://github.com/gradio-app/gradio/pull/2692)
-- Colab buttons on every demo in the website! Just click open in colab, and run the demo there.
-
-https://user-images.githubusercontent.com/9021060/202878400-cb16ed47-f4dd-4cb0-b2f0-102a9ff64135.mov
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Better warnings and error messages for `gr.Interface.load()` by [@abidlabs](https://github.com/abidlabs) in [PR 2694](https://github.com/gradio-app/gradio/pull/2694)
-- Add open in colab buttons to demos in docs and /demos by [@aliabd](https://github.com/aliabd) in [PR 2608](https://github.com/gradio-app/gradio/pull/2608)
-- Apply different formatting for the types in component docstrings by [@aliabd](https://github.com/aliabd) in [PR 2707](https://github.com/gradio-app/gradio/pull/2707)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.10.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Passes kwargs into `gr.Interface.load()` by [@abidlabs](https://github.com/abidlabs) in [PR 2669](https://github.com/gradio-app/gradio/pull/2669)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Clean up printed statements in Embedded Colab Mode by [@aliabid94](https://github.com/aliabid94) in [PR 2612](https://github.com/gradio-app/gradio/pull/2612)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.10.0
-
-- Add support for `'password'` and `'email'` types to `Textbox`. [@pngwn](https://github.com/pngwn) in [PR 2653](https://github.com/gradio-app/gradio/pull/2653)
-- `gr.Textbox` component will now raise an exception if `type` is not "text", "email", or "password" [@pngwn](https://github.com/pngwn) in [PR 2653](https://github.com/gradio-app/gradio/pull/2653). This will cause demos using the deprecated `gr.Textbox(type="number")` to raise an exception.
-
-### Bug Fixes:
-
-- Updated the minimum FastApi used in tests to version 0.87 by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2647](https://github.com/gradio-app/gradio/pull/2647)
-- Fixed bug where interfaces with examples could not be loaded with `gr.Interface.load` by [@freddyaboulton](https://github.com/freddyaboulton) [PR 2640](https://github.com/gradio-app/gradio/pull/2640)
-- Fixed bug where the `interactive` property of a component could not be updated by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2639](https://github.com/gradio-app/gradio/pull/2639)
-- Fixed bug where some URLs were not being recognized as valid URLs and thus were not
-  loading correctly in various components by [@abidlabs](https://github.com/abidlabs) in [PR 2659](https://github.com/gradio-app/gradio/pull/2659)
-
-### Documentation Changes:
-
-- Fix some typos in the embedded demo names in "05_using_blocks_like_functions.md" by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2656](https://github.com/gradio-app/gradio/pull/2656)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Add support for `'password'` and `'email'` types to `Textbox`. [@pngwn](https://github.com/pngwn) in [PR 2653](https://github.com/gradio-app/gradio/pull/2653)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.9.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Only set a min height on md and html when loading by [@pngwn](https://github.com/pngwn) in [PR 2623](https://github.com/gradio-app/gradio/pull/2623)
-
-### Documentation Changes:
-
-- See docs for the latest gradio commit to main as well the latest pip release:
-
-![main-vs-pip](https://user-images.githubusercontent.com/9021060/199607887-aab1ae4e-a070-4527-966d-024397abe15b.gif)
-
-- Modified the "Connecting To a Database Guide" to use `pd.read_sql` as opposed to low-level postgres connector by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2604](https://github.com/gradio-app/gradio/pull/2604)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Dropdown for seeing docs as latest or main by [@aliabd](https://github.com/aliabd) in [PR 2544](https://github.com/gradio-app/gradio/pull/2544)
-- Allow `gr.Templates` to accept parameters to override the defaults by [@abidlabs](https://github.com/abidlabs) in [PR 2600](https://github.com/gradio-app/gradio/pull/2600)
-- Components now throw a `ValueError()` if constructed with invalid parameters for `type` or `source` (for components that take those parameters) in [PR 2610](https://github.com/gradio-app/gradio/pull/2610)
-- Allow auth with using queue by [@GLGDLY](https://github.com/GLGDLY) in [PR 2611](https://github.com/gradio-app/gradio/pull/2611)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.9
-
-### New Features:
-
-- Gradio is now embedded directly in colab without requiring the share link by [@aliabid94](https://github.com/aliabid94) in [PR 2455](https://github.com/gradio-app/gradio/pull/2455)
-
-###### Calling functions by api_name in loaded apps
-
-When you load an upstream app with `gr.Blocks.load`, you can now specify which fn
-to call with the `api_name` parameter.
-
-```python
-import gradio as gr
-english_translator = gr.Blocks.load(name="spaces/gradio/english-translator")
-german = english_translator("My name is Freddy", api_name='translate-to-german')
-```
-
-The `api_name` parameter will take precedence over the `fn_index` parameter.
-
-### Bug Fixes:
-
-- Fixed bug where None could not be used for File,Model3D, and Audio examples by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2588](https://github.com/gradio-app/gradio/pull/2588)
-- Fixed links in Plotly map guide + demo by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2578](https://github.com/gradio-app/gradio/pull/2578)
-- `gr.Blocks.load()` now correctly loads example files from Spaces [@abidlabs](https://github.com/abidlabs) in [PR 2594](https://github.com/gradio-app/gradio/pull/2594)
-- Fixed bug when image clear started upload dialog [@mezotaken](https://github.com/mezotaken) in [PR 2577](https://github.com/gradio-app/gradio/pull/2577)
-
-### Documentation Changes:
-
-- Added a Guide on how to configure the queue for maximum performance by [@abidlabs](https://github.com/abidlabs) in [PR 2558](https://github.com/gradio-app/gradio/pull/2558)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Add `api_name` to `Blocks.__call__` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2593](https://github.com/gradio-app/gradio/pull/2593)
-- Update queue with using deque & update requirements by [@GLGDLY](https://github.com/GLGDLY) in [PR 2428](https://github.com/gradio-app/gradio/pull/2428)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.8.2
-
-### Bug Fixes:
-
-- Ensure gradio apps embedded via spaces use the correct endpoint for predictions. [@pngwn](https://github.com/pngwn) in [PR 2567](https://github.com/gradio-app/gradio/pull/2567)
-- Ensure gradio apps embedded via spaces use the correct websocket protocol. [@pngwn](https://github.com/pngwn) in [PR 2571](https://github.com/gradio-app/gradio/pull/2571)
-
-### New Features:
-
-###### Running Events Continuously
-
-Gradio now supports the ability to run an event continuously on a fixed schedule. To use this feature,
-pass `every=# of seconds` to the event definition. This will run the event every given number of seconds!
-
-This can be used to:
-
-- Create live visualizations that show the most up to date data
-- Refresh the state of the frontend automatically in response to changes in the backend
-
-Here is an example of a live plot that refreshes every half second:
-
-```python
-import math
-import gradio as gr
-import plotly.express as px
-import numpy as np
-
-
-plot_end = 2 * math.pi
-
-
-def get_plot(period=1):
-    global plot_end
-    x = np.arange(plot_end - 2 * math.pi, plot_end, 0.02)
-    y = np.sin(2*math.pi*period * x)
-    fig = px.line(x=x, y=y)
-    plot_end += 2 * math.pi
-    return fig
-
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("Change the value of the slider to automatically update the plot")
-            period = gr.Slider(label="Period of plot", value=1, minimum=0, maximum=10, step=1)
-            plot = gr.Plot(label="Plot (updates every half second)")
-
-    dep = demo.load(get_plot, None, plot, every=0.5)
-    period.change(get_plot, period, plot, every=0.5, cancels=[dep])
-
-demo.queue().launch()
-```
-
-![live_demo](https://user-images.githubusercontent.com/41651716/198357377-633ce460-4e31-47bd-8202-1440cdd6fe19.gif)
-
-### Bug Fixes:
-
-No changes to highlight.
-
-### Documentation Changes:
-
-- Explained how to set up `queue` and `auth` when working with reload mode by by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3089](https://github.com/gradio-app/gradio/pull/3089)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Allows loading private Spaces by passing an an `api_key` to `gr.Interface.load()`
-  by [@abidlabs](https://github.com/abidlabs) in [PR 2568](https://github.com/gradio-app/gradio/pull/2568)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.8
-
-### New Features:
-
-- Allows event listeners to accept a single dictionary as its argument, where the keys are the components and the values are the component values. This is set by passing the input components in the event listener as a set instead of a list. [@aliabid94](https://github.com/aliabid94) in [PR 2550](https://github.com/gradio-app/gradio/pull/2550)
-
-### Bug Fixes:
-
-- Fix whitespace issue when using plotly. [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2548](https://github.com/gradio-app/gradio/pull/2548)
-- Apply appropriate alt text to all gallery images. [@camenduru](https://github.com/camenduru) in [PR 2358](https://github.com/gradio-app/gradio/pull/2538)
-- Removed erroneous tkinter import in gradio.blocks by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2555](https://github.com/gradio-app/gradio/pull/2555)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Added the `every` keyword to event listeners that runs events on a fixed schedule by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2512](https://github.com/gradio-app/gradio/pull/2512)
-- Fix whitespace issue when using plotly. [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2548](https://github.com/gradio-app/gradio/pull/2548)
-- Apply appropriate alt text to all gallery images. [@camenduru](https://github.com/camenduru) in [PR 2358](https://github.com/gradio-app/gradio/pull/2538)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.7
-
-### New Features:
-
-###### Batched Functions
-
-Gradio now supports the ability to pass _batched_ functions. Batched functions are just
-functions which take in a list of inputs and return a list of predictions.
-
-For example, here is a batched function that takes in two lists of inputs (a list of
-words and a list of ints), and returns a list of trimmed words as output:
-
-```py
-import time
-
-def trim_words(words, lens):
-    trimmed_words = []
-    time.sleep(5)
-    for w, l in zip(words, lens):
-        trimmed_words.append(w[:l])
-    return [trimmed_words]
-```
-
-The advantage of using batched functions is that if you enable queuing, the Gradio
-server can automatically _batch_ incoming requests and process them in parallel,
-potentially speeding up your demo. Here's what the Gradio code looks like (notice
-the `batch=True` and `max_batch_size=16` -- both of these parameters can be passed
-into event triggers or into the `Interface` class)
-
-```py
-import gradio as gr
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        word = gr.Textbox(label="word", value="abc")
-        leng = gr.Number(label="leng", precision=0, value=1)
-        output = gr.Textbox(label="Output")
-    with gr.Row():
-        run = gr.Button()
-
-    event = run.click(trim_words, [word, leng], output, batch=True, max_batch_size=16)
-
-demo.queue()
-demo.launch()
-```
-
-In the example above, 16 requests could be processed in parallel (for a total inference
-time of 5 seconds), instead of each request being processed separately (for a total
-inference time of 80 seconds).
-
-###### Upload Event
-
-`Video`, `Audio`, `Image`, and `File` components now support a `upload()` event that is triggered when a user uploads a file into any of these components.
-
-Example usage:
-
-```py
-import gradio as gr
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        input_video = gr.Video()
-        output_video = gr.Video()
-
-     # Clears the output video when an input video is uploaded
-    input_video.upload(lambda : None, None, output_video)
-```
-
-### Bug Fixes:
-
-- Fixes issue where plotly animations, interactivity, titles, legends, were not working properly. [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2486](https://github.com/gradio-app/gradio/pull/2486)
-- Prevent requests to the `/api` endpoint from skipping the queue if the queue is enabled for that event by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2493](https://github.com/gradio-app/gradio/pull/2493)
-- Fixes a bug with `cancels` in event triggers so that it works properly if multiple
-  Blocks are rendered by [@abidlabs](https://github.com/abidlabs) in [PR 2530](https://github.com/gradio-app/gradio/pull/2530)
-- Prevent invalid targets of events from crashing the whole application. [@pngwn](https://github.com/pngwn) in [PR 2534](https://github.com/gradio-app/gradio/pull/2534)
-- Properly dequeue cancelled events when multiple apps are rendered by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2540](https://github.com/gradio-app/gradio/pull/2540)
-- Fixes videos being cropped due to height/width params not being used [@hannahblair](https://github.com/hannahblair) in [PR 4946](https://github.com/gradio-app/gradio/pull/4946)
-
-### Documentation Changes:
-
-- Added an example interactive dashboard to the "Tabular & Plots" section of the Demos page by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2508](https://github.com/gradio-app/gradio/pull/2508)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Fixes the error message if a user builds Gradio locally and tries to use `share=True` by [@abidlabs](https://github.com/abidlabs) in [PR 2502](https://github.com/gradio-app/gradio/pull/2502)
-- Allows the render() function to return self by [@Raul9595](https://github.com/Raul9595) in [PR 2514](https://github.com/gradio-app/gradio/pull/2514)
-- Fixes issue where plotly animations, interactivity, titles, legends, were not working properly. [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2486](https://github.com/gradio-app/gradio/pull/2486)
-- Gradio now supports batched functions by [@abidlabs](https://github.com/abidlabs) in [PR 2218](https://github.com/gradio-app/gradio/pull/2218)
-- Add `upload` event for `Video`, `Audio`, `Image`, and `File` components [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2448](https://github.com/gradio-app/gradio/pull/2456)
-- Changes websocket path for Spaces as it is no longer necessary to have a different URL for websocket connections on Spaces by [@abidlabs](https://github.com/abidlabs) in [PR 2528](https://github.com/gradio-app/gradio/pull/2528)
-- Clearer error message when events are defined outside of a Blocks scope, and a warning if you
-  try to use `Series` or `Parallel` with `Blocks` by [@abidlabs](https://github.com/abidlabs) in [PR 2543](https://github.com/gradio-app/gradio/pull/2543)
-- Adds support for audio samples that are in `float64`, `float16`, or `uint16` formats by [@abidlabs](https://github.com/abidlabs) in [PR 2545](https://github.com/gradio-app/gradio/pull/2545)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.6
-
-### New Features:
-
-###### Cancelling Running Events
-
-Running events can be cancelled when other events are triggered! To test this feature, pass the `cancels` parameter to the event listener.
-For this feature to work, the queue must be enabled.
-
-![cancel_on_change_rl](https://user-images.githubusercontent.com/41651716/195952623-61a606bd-e82b-4e1a-802e-223154cb8727.gif)
-
-Code:
-
-```python
-import time
-import gradio as gr
-
-def fake_diffusion(steps):
-    for i in range(steps):
-        time.sleep(1)
-        yield str(i)
-
-def long_prediction(*args, **kwargs):
-    time.sleep(10)
-    return 42
-
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            n = gr.Slider(1, 10, value=9, step=1, label="Number Steps")
-            run = gr.Button()
-            output = gr.Textbox(label="Iterative Output")
-            stop = gr.Button(value="Stop Iterating")
-        with gr.Column():
-            prediction = gr.Number(label="Expensive Calculation")
-            run_pred = gr.Button(value="Run Expensive Calculation")
-        with gr.Column():
-            cancel_on_change = gr.Textbox(label="Cancel Iteration and Expensive Calculation on Change")
-
-    click_event = run.click(fake_diffusion, n, output)
-    stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event])
-    pred_event = run_pred.click(fn=long_prediction, inputs=None, outputs=prediction)
-
-    cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])
-
-
-demo.queue(concurrency_count=1, max_size=20).launch()
-```
-
-For interfaces, a stop button will be added automatically if the function uses a `yield` statement.
-
-```python
-import gradio as gr
-import time
-
-def iteration(steps):
-    for i in range(steps):
-       time.sleep(0.5)
-       yield i
-
-gr.Interface(iteration,
-             inputs=gr.Slider(minimum=1, maximum=10, step=1, value=5),
-             outputs=gr.Number()).queue().launch()
-```
-
-![stop_interface_rl](https://user-images.githubusercontent.com/41651716/195952883-e7ca4235-aae3-4852-8f28-96d01d0c5822.gif)
-
-### Bug Fixes:
-
-- Add loading status tracker UI to HTML and Markdown components. [@pngwn](https://github.com/pngwn) in [PR 2474](https://github.com/gradio-app/gradio/pull/2474)
-- Fixed videos being mirrored in the front-end if source is not webcam by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2475](https://github.com/gradio-app/gradio/pull/2475)
-- Add clear button for timeseries component [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2487](https://github.com/gradio-app/gradio/pull/2487)
-- Removes special characters from temporary filenames so that the files can be served by components [@abidlabs](https://github.com/abidlabs) in [PR 2480](https://github.com/gradio-app/gradio/pull/2480)
-- Fixed infinite reload loop when mounting gradio as a sub application by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2477](https://github.com/gradio-app/gradio/pull/2477)
-
-### Documentation Changes:
-
-- Adds a demo to show how a sound alert can be played upon completion of a prediction by [@abidlabs](https://github.com/abidlabs) in [PR 2478](https://github.com/gradio-app/gradio/pull/2478)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Enable running events to be cancelled from other events by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2433](https://github.com/gradio-app/gradio/pull/2433)
-- Small fix for version check before reuploading demos by [@aliabd](https://github.com/aliabd) in [PR 2469](https://github.com/gradio-app/gradio/pull/2469)
-- Add loading status tracker UI to HTML and Markdown components. [@pngwn](https://github.com/pngwn) in [PR 2400](https://github.com/gradio-app/gradio/pull/2474)
-- Add clear button for timeseries component [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2487](https://github.com/gradio-app/gradio/pull/2487)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.5
-
-### Bug Fixes:
-
-- Ensure that Gradio does not take control of the HTML page title when embedding a gradio app as a web component, this behaviour flipped by adding `control_page_title="true"` to the webcomponent. [@pngwn](https://github.com/pngwn) in [PR 2400](https://github.com/gradio-app/gradio/pull/2400)
-- Decreased latency in iterative-output demos by making the iteration asynchronous [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2409](https://github.com/gradio-app/gradio/pull/2409)
-- Fixed queue getting stuck under very high load by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2374](https://github.com/gradio-app/gradio/pull/2374)
-- Ensure that components always behave as if `interactive=True` were set when the following conditions are true:
-
-  - no default value is provided,
-  - they are not set as the input or output of an event,
-  - `interactive` kwarg is not set.
-
-  [@pngwn](https://github.com/pngwn) in [PR 2459](https://github.com/gradio-app/gradio/pull/2459)
-
-### New Features:
-
-- When an `Image` component is set to `source="upload"`, it is now possible to drag and drop and image to replace a previously uploaded image by [@pngwn](https://github.com/pngwn) in [PR 1711](https://github.com/gradio-app/gradio/issues/1711)
-- The `gr.Dataset` component now accepts `HTML` and `Markdown` components by [@abidlabs](https://github.com/abidlabs) in [PR 2437](https://github.com/gradio-app/gradio/pull/2437)
-
-### Documentation Changes:
-
-- Improved documentation for the `gr.Dataset` component by [@abidlabs](https://github.com/abidlabs) in [PR 2437](https://github.com/gradio-app/gradio/pull/2437)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-- The `Carousel` component is officially deprecated. Since gradio 3.0, code containing the `Carousel` component would throw warnings. As of the next release, the `Carousel` component will raise an exception.
-
-### Full Changelog:
-
-- Speeds up Gallery component by using temporary files instead of base64 representation in the front-end by [@proxyphi](https://github.com/proxyphi), [@pngwn](https://github.com/pngwn), and [@abidlabs](https://github.com/abidlabs) in [PR 2265](https://github.com/gradio-app/gradio/pull/2265)
-- Fixed some embedded demos in the guides by not loading the gradio web component in some guides by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2403](https://github.com/gradio-app/gradio/pull/2403)
-- When an `Image` component is set to `source="upload"`, it is now possible to drag and drop and image to replace a previously uploaded image by [@pngwn](https://github.com/pngwn) in [PR 2400](https://github.com/gradio-app/gradio/pull/2410)
-- Improve documentation of the `Blocks.load()` event by [@abidlabs](https://github.com/abidlabs) in [PR 2413](https://github.com/gradio-app/gradio/pull/2413)
-- Decreased latency in iterative-output demos by making the iteration asynchronous [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2409](https://github.com/gradio-app/gradio/pull/2409)
-- Updated share link message to reference new Spaces Hardware [@abidlabs](https://github.com/abidlabs) in [PR 2423](https://github.com/gradio-app/gradio/pull/2423)
-- Automatically restart spaces if they're down by [@aliabd](https://github.com/aliabd) in [PR 2405](https://github.com/gradio-app/gradio/pull/2405)
-- Carousel component is now deprecated by [@abidlabs](https://github.com/abidlabs) in [PR 2434](https://github.com/gradio-app/gradio/pull/2434)
-- Build Gradio from source in ui tests by by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2440](https://github.com/gradio-app/gradio/pull/2440)
-- Change "return ValueError" to "raise ValueError" by [@vzakharov](https://github.com/vzakharov) in [PR 2445](https://github.com/gradio-app/gradio/pull/2445)
-- Add guide on creating a map demo using the `gr.Plot()` component [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2402](https://github.com/gradio-app/gradio/pull/2402)
-- Add blur event for `Textbox` and `Number` components [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2448](https://github.com/gradio-app/gradio/pull/2448)
-- Stops a gradio launch from hogging a port even after it's been killed [@aliabid94](https://github.com/aliabid94) in [PR 2453](https://github.com/gradio-app/gradio/pull/2453)
-- Fix embedded interfaces on touch screen devices by [@aliabd](https://github.com/aliabd) in [PR 2457](https://github.com/gradio-app/gradio/pull/2457)
-- Upload all demos to spaces by [@aliabd](https://github.com/aliabd) in [PR 2281](https://github.com/gradio-app/gradio/pull/2281)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.4.1
-
-### New Features:
-
-###### 1. See Past and Upcoming Changes in the Release History 👀
-
-You can now see gradio's release history directly on the website, and also keep track of upcoming changes. Just go [here](https://gradio.app/changelog/).
-
-![release-history](https://user-images.githubusercontent.com/9021060/193145458-3de699f7-7620-45de-aa73-a1c1b9b96257.gif)
-
-### Bug Fixes:
-
-1. Fix typo in guide image path by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2357](https://github.com/gradio-app/gradio/pull/2357)
-2. Raise error if Blocks has duplicate component with same IDs by [@abidlabs](https://github.com/abidlabs) in [PR 2359](https://github.com/gradio-app/gradio/pull/2359)
-3. Catch the permission exception on the audio component by [@Ian-GL](https://github.com/Ian-GL) in [PR 2330](https://github.com/gradio-app/gradio/pull/2330)
-4. Fix image_classifier_interface_load demo by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2365](https://github.com/gradio-app/gradio/pull/2365)
-5. Fix combining adjacent components without gaps by introducing `gr.Row(variant="compact")` by [@aliabid94](https://github.com/aliabid94) in [PR 2291](https://github.com/gradio-app/gradio/pull/2291) This comes with deprecation of the following arguments for `Component.style`: `round`, `margin`, `border`.
-6. Fix audio streaming, which was previously choppy in [PR 2351](https://github.com/gradio-app/gradio/pull/2351). Big thanks to [@yannickfunk](https://github.com/yannickfunk) for the proposed solution.
-7. Fix bug where new typeable slider doesn't respect the minimum and maximum values [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2380](https://github.com/gradio-app/gradio/pull/2380)
-
-### Documentation Changes:
-
-1. New Guide: Connecting to a Database 🗄️
-
-   A new guide by [@freddyaboulton](https://github.com/freddyaboulton) that explains how you can use Gradio to connect your app to a database. Read more [here](https://gradio.app/connecting_to_a_database/).
-
-2. New Guide: Running Background Tasks 🥷
-
-   A new guide by [@freddyaboulton](https://github.com/freddyaboulton) that explains how you can run background tasks from your gradio app. Read more [here](https://gradio.app/running_background_tasks/).
-
-3. Small fixes to docs for `Image` component by [@abidlabs](https://github.com/abidlabs) in [PR 2372](https://github.com/gradio-app/gradio/pull/2372)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Create a guide on how to connect an app to a database hosted on the cloud by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2341](https://github.com/gradio-app/gradio/pull/2341)
-- Removes `analytics` dependency by [@abidlabs](https://github.com/abidlabs) in [PR 2347](https://github.com/gradio-app/gradio/pull/2347)
-- Add guide on launching background tasks from your app by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2350](https://github.com/gradio-app/gradio/pull/2350)
-- Fix typo in guide image path by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2357](https://github.com/gradio-app/gradio/pull/2357)
-- Raise error if Blocks has duplicate component with same IDs by [@abidlabs](https://github.com/abidlabs) in [PR 2359](https://github.com/gradio-app/gradio/pull/2359)
-- Hotfix: fix version back to 3.4 by [@abidlabs](https://github.com/abidlabs) in [PR 2361](https://github.com/gradio-app/gradio/pull/2361)
-- Change version.txt to 3.4 instead of 3.4.0 by [@aliabd](https://github.com/aliabd) in [PR 2363](https://github.com/gradio-app/gradio/pull/2363)
-- Catch the permission exception on the audio component by [@Ian-GL](https://github.com/Ian-GL) in [PR 2330](https://github.com/gradio-app/gradio/pull/2330)
-- Fix image_classifier_interface_load demo by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2365](https://github.com/gradio-app/gradio/pull/2365)
-- Small fixes to docs for `Image` component by [@abidlabs](https://github.com/abidlabs) in [PR 2372](https://github.com/gradio-app/gradio/pull/2372)
-- Automated Release Notes by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2306](https://github.com/gradio-app/gradio/pull/2306)
-- Fixed small typos in the docs [@julien-c](https://github.com/julien-c) in [PR 2373](https://github.com/gradio-app/gradio/pull/2373)
-- Adds ability to disable pre/post-processing for examples [@abidlabs](https://github.com/abidlabs) in [PR 2383](https://github.com/gradio-app/gradio/pull/2383)
-- Copy changelog file in website docker by [@aliabd](https://github.com/aliabd) in [PR 2384](https://github.com/gradio-app/gradio/pull/2384)
-- Lets users provide a `gr.update()` dictionary even if post-processing is disabled [@abidlabs](https://github.com/abidlabs) in [PR 2385](https://github.com/gradio-app/gradio/pull/2385)
-- Fix bug where errors would cause apps run in reload mode to hang forever by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2394](https://github.com/gradio-app/gradio/pull/2394)
-- Fix bug where new typeable slider doesn't respect the minimum and maximum values [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2380](https://github.com/gradio-app/gradio/pull/2380)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.4
-
-### New Features:
-
-###### 1. Gallery Captions 🖼️
-
-You can now pass captions to images in the Gallery component. To do so you need to pass a {List} of (image, {str} caption) tuples. This is optional and the component also accepts just a list of the images.
-
-Here's an example:
-
-```python
-import gradio as gr
-
-images_with_captions = [
-    ("https://images.unsplash.com/photo-1551969014-7d2c4cddf0b6", "Cheetah by David Groves"),
-    ("https://images.unsplash.com/photo-1546182990-dffeafbe841d", "Lion by Francesco"),
-    ("https://images.unsplash.com/photo-1561731216-c3a4d99437d5", "Tiger by Mike Marrah")
-    ]
-
-with gr.Blocks() as demo:
-    gr.Gallery(value=images_with_captions)
-
-demo.launch()
-```
-
-<img src="https://user-images.githubusercontent.com/9021060/192399521-7360b1a9-7ce0-443e-8e94-863a230a7dbe.gif" alt="gallery_captions" width="1000"/>
-
-###### 2. Type Values into the Slider 🔢
-
-You can now type values directly on the Slider component! Here's what it looks like:
-
-![type-slider](https://user-images.githubusercontent.com/9021060/192399877-76b662a1-fede-4417-a932-fc15f0da7360.gif)
-
-###### 3. Better Sketching and Inpainting 🎨
-
-We've made a lot of changes to our Image component so that it can support better sketching and inpainting.
-
-Now supports:
-
-- A standalone black-and-white sketch
-
-```python
-import gradio as gr
-demo = gr.Interface(lambda x: x, gr.Sketchpad(), gr.Image())
-demo.launch()
-```
-
-![bw](https://user-images.githubusercontent.com/9021060/192410264-b08632b5-7b2a-4f86-afb0-5760e7b474cf.gif)
-
-- A standalone color sketch
-
-```python
-import gradio as gr
-demo = gr.Interface(lambda x: x, gr.Paint(), gr.Image())
-demo.launch()
-```
-
-![color-sketch](https://user-images.githubusercontent.com/9021060/192410500-3c8c3e64-a5fd-4df2-a991-f0a5cef93728.gif)
-
-- An uploadable image with black-and-white or color sketching
-
-```python
-import gradio as gr
-demo = gr.Interface(lambda x: x, gr.Image(source='upload', tool='color-sketch'), gr.Image()) # for black and white, tool = 'sketch'
-demo.launch()
-```
-
-![sketch-new](https://user-images.githubusercontent.com/9021060/192402422-e53cb7b6-024e-448c-87eb-d6a35a63c476.gif)
-
-- Webcam with black-and-white or color sketching
-
-```python
-import gradio as gr
-demo = gr.Interface(lambda x: x, gr.Image(source='webcam', tool='color-sketch'), gr.Image()) # for black and white, tool = 'sketch'
-demo.launch()
-```
-
-![webcam-sketch](https://user-images.githubusercontent.com/9021060/192410820-0ffaf324-776e-4e1f-9de6-0fdbbf4940fa.gif)
-
-As well as other fixes
-
-### Bug Fixes:
-
-1. Fix bug where max concurrency count is not respected in queue by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2286](https://github.com/gradio-app/gradio/pull/2286)
-2. fix : queue could be blocked by [@SkyTNT](https://github.com/SkyTNT) in [PR 2288](https://github.com/gradio-app/gradio/pull/2288)
-3. Supports `gr.update()` in example caching by [@abidlabs](https://github.com/abidlabs) in [PR 2309](https://github.com/gradio-app/gradio/pull/2309)
-4. Clipboard fix for iframes by [@abidlabs](https://github.com/abidlabs) in [PR 2321](https://github.com/gradio-app/gradio/pull/2321)
-5. Fix: Dataframe column headers are reset when you add a new column by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2318](https://github.com/gradio-app/gradio/pull/2318)
-6. Added support for URLs for Video, Audio, and Image by [@abidlabs](https://github.com/abidlabs) in [PR 2256](https://github.com/gradio-app/gradio/pull/2256)
-7. Add documentation about how to create and use the Gradio FastAPI app by [@abidlabs](https://github.com/abidlabs) in [PR 2263](https://github.com/gradio-app/gradio/pull/2263)
-
-### Documentation Changes:
-
-1. Adding a Playground Tab to the Website by [@aliabd](https://github.com/aliabd) in [PR 1860](https://github.com/gradio-app/gradio/pull/1860)
-2. Gradio for Tabular Data Science Workflows Guide by [@merveenoyan](https://github.com/merveenoyan) in [PR 2199](https://github.com/gradio-app/gradio/pull/2199)
-3. Promotes `postprocess` and `preprocess` to documented parameters by [@abidlabs](https://github.com/abidlabs) in [PR 2293](https://github.com/gradio-app/gradio/pull/2293)
-4. Update 2)key_features.md by [@voidxd](https://github.com/voidxd) in [PR 2326](https://github.com/gradio-app/gradio/pull/2326)
-5. Add docs to blocks context postprocessing function by [@Ian-GL](https://github.com/Ian-GL) in [PR 2332](https://github.com/gradio-app/gradio/pull/2332)
-
-### Testing and Infrastructure Changes
-
-1. Website fixes and refactoring by [@aliabd](https://github.com/aliabd) in [PR 2280](https://github.com/gradio-app/gradio/pull/2280)
-2. Don't deploy to spaces on release by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2313](https://github.com/gradio-app/gradio/pull/2313)
-
-### Full Changelog:
-
-- Website fixes and refactoring by [@aliabd](https://github.com/aliabd) in [PR 2280](https://github.com/gradio-app/gradio/pull/2280)
-- Fix bug where max concurrency count is not respected in queue by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2286](https://github.com/gradio-app/gradio/pull/2286)
-- Promotes `postprocess` and `preprocess` to documented parameters by [@abidlabs](https://github.com/abidlabs) in [PR 2293](https://github.com/gradio-app/gradio/pull/2293)
-- Raise warning when trying to cache examples but not all inputs have examples by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2279](https://github.com/gradio-app/gradio/pull/2279)
-- fix : queue could be blocked by [@SkyTNT](https://github.com/SkyTNT) in [PR 2288](https://github.com/gradio-app/gradio/pull/2288)
-- Don't deploy to spaces on release by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2313](https://github.com/gradio-app/gradio/pull/2313)
-- Supports `gr.update()` in example caching by [@abidlabs](https://github.com/abidlabs) in [PR 2309](https://github.com/gradio-app/gradio/pull/2309)
-- Respect Upstream Queue when loading interfaces/blocks from Spaces by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2294](https://github.com/gradio-app/gradio/pull/2294)
-- Clipboard fix for iframes by [@abidlabs](https://github.com/abidlabs) in [PR 2321](https://github.com/gradio-app/gradio/pull/2321)
-- Sketching + Inpainting Capabilities to Gradio by [@abidlabs](https://github.com/abidlabs) in [PR 2144](https://github.com/gradio-app/gradio/pull/2144)
-- Update 2)key_features.md by [@voidxd](https://github.com/voidxd) in [PR 2326](https://github.com/gradio-app/gradio/pull/2326)
-- release 3.4b3 by [@abidlabs](https://github.com/abidlabs) in [PR 2328](https://github.com/gradio-app/gradio/pull/2328)
-- Fix: Dataframe column headers are reset when you add a new column by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2318](https://github.com/gradio-app/gradio/pull/2318)
-- Start queue when gradio is a sub application by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2319](https://github.com/gradio-app/gradio/pull/2319)
-- Fix Web Tracker Script by [@aliabd](https://github.com/aliabd) in [PR 2308](https://github.com/gradio-app/gradio/pull/2308)
-- Add docs to blocks context postprocessing function by [@Ian-GL](https://github.com/Ian-GL) in [PR 2332](https://github.com/gradio-app/gradio/pull/2332)
-- Fix typo in iterator variable name in run_predict function by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2340](https://github.com/gradio-app/gradio/pull/2340)
-- Add captions to galleries by [@aliabid94](https://github.com/aliabid94) in [PR 2284](https://github.com/gradio-app/gradio/pull/2284)
-- Typeable value on gradio.Slider by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2329](https://github.com/gradio-app/gradio/pull/2329)
-
-### Contributors Shoutout:
-
-- [@SkyTNT](https://github.com/SkyTNT) made their first contribution in [PR 2288](https://github.com/gradio-app/gradio/pull/2288)
-- [@voidxd](https://github.com/voidxd) made their first contribution in [PR 2326](https://github.com/gradio-app/gradio/pull/2326)
-
-## 3.3
-
-### New Features:
-
-###### 1. Iterative Outputs ⏳
-
-You can now create an iterative output simply by having your function return a generator!
-
-Here's (part of) an example that was used to generate the interface below it. [See full code](https://colab.research.google.com/drive/1m9bWS6B82CT7bw-m4L6AJR8za7fEK7Ov?usp=sharing).
-
-```python
-def predict(steps, seed):
-    generator = torch.manual_seed(seed)
-    for i in range(1,steps):
-        yield pipeline(generator=generator, num_inference_steps=i)["sample"][0]
-```
-
-![example](https://user-images.githubusercontent.com/9021060/189086273-f5e7087d-71fa-4158-90a9-08e84da0421c.mp4)
-
-###### 2. Accordion Layout 🆕
-
-This version of Gradio introduces a new layout component to Blocks: the Accordion. Wrap your elements in a neat, expandable layout that allows users to toggle them as needed.
-
-Usage: ([Read the docs](https://gradio.app/docs/#accordion))
-
-```python
-with gr.Accordion("open up"):
-# components here
-```
-
-![accordion](https://user-images.githubusercontent.com/9021060/189088465-f0ffd7f0-fc6a-42dc-9249-11c5e1e0529b.gif)
-
-###### 3. Skops Integration 📈
-
-Our new integration with [skops](https://huggingface.co/blog/skops) allows you to load tabular classification and regression models directly from the [hub](https://huggingface.co/models).
-
-Here's a classification example showing how quick it is to set up an interface for a [model](https://huggingface.co/scikit-learn/tabular-playground).
-
-```python
-import gradio as gr
-gr.Interface.load("models/scikit-learn/tabular-playground").launch()
-```
-
-![187936493-5c90c01d-a6dd-400f-aa42-833a096156a1](https://user-images.githubusercontent.com/9021060/189090519-328fbcb4-120b-43c8-aa54-d6fccfa6b7e8.png)
-
-### Bug Fixes:
-
-No changes to highlight.
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- safari fixes by [@pngwn](https://github.com/pngwn) in [PR 2138](https://github.com/gradio-app/gradio/pull/2138)
-- Fix roundedness and form borders by [@aliabid94](https://github.com/aliabid94) in [PR 2147](https://github.com/gradio-app/gradio/pull/2147)
-- Better processing of example data prior to creating dataset component by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2147](https://github.com/gradio-app/gradio/pull/2147)
-- Show error on Connection drops by [@aliabid94](https://github.com/aliabid94) in [PR 2147](https://github.com/gradio-app/gradio/pull/2147)
-- 3.2 release! by [@abidlabs](https://github.com/abidlabs) in [PR 2139](https://github.com/gradio-app/gradio/pull/2139)
-- Fixed Named API Requests by [@abidlabs](https://github.com/abidlabs) in [PR 2151](https://github.com/gradio-app/gradio/pull/2151)
-- Quick Fix: Cannot upload Model3D image after clearing it by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2168](https://github.com/gradio-app/gradio/pull/2168)
-- Fixed misleading log when server_name is '0.0.0.0' by [@lamhoangtung](https://github.com/lamhoangtung) in [PR 2176](https://github.com/gradio-app/gradio/pull/2176)
-- Keep embedded PngInfo metadata by [@cobryan05](https://github.com/cobryan05) in [PR 2170](https://github.com/gradio-app/gradio/pull/2170)
-- Skops integration: Load tabular classification and regression models from the hub by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2126](https://github.com/gradio-app/gradio/pull/2126)
-- Respect original filename when cached example files are downloaded by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2145](https://github.com/gradio-app/gradio/pull/2145)
-- Add manual trigger to deploy to pypi by [@abidlabs](https://github.com/abidlabs) in [PR 2192](https://github.com/gradio-app/gradio/pull/2192)
-- Fix bugs with gr.update by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2157](https://github.com/gradio-app/gradio/pull/2157)
-- Make queue per app by [@aliabid94](https://github.com/aliabid94) in [PR 2193](https://github.com/gradio-app/gradio/pull/2193)
-- Preserve Labels In Interpretation Components by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2166](https://github.com/gradio-app/gradio/pull/2166)
-- Quick Fix: Multiple file download not working by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2169](https://github.com/gradio-app/gradio/pull/2169)
-- use correct MIME type for js-script file by [@daspartho](https://github.com/daspartho) in [PR 2200](https://github.com/gradio-app/gradio/pull/2200)
-- Add accordion component by [@aliabid94](https://github.com/aliabid94) in [PR 2208](https://github.com/gradio-app/gradio/pull/2208)
-
-### Contributors Shoutout:
-
-- [@lamhoangtung](https://github.com/lamhoangtung) made their first contribution in [PR 2176](https://github.com/gradio-app/gradio/pull/2176)
-- [@cobryan05](https://github.com/cobryan05) made their first contribution in [PR 2170](https://github.com/gradio-app/gradio/pull/2170)
-- [@daspartho](https://github.com/daspartho) made their first contribution in [PR 2200](https://github.com/gradio-app/gradio/pull/2200)
-
-## 3.2
-
-### New Features:
-
-###### 1. Improvements to Queuing 🥇
-
-We've implemented a brand new queuing system based on **web sockets** instead of HTTP long polling. Among other things, this allows us to manage queue sizes better on Hugging Face Spaces. There are also additional queue-related parameters you can add:
-
-- Now supports concurrent workers (parallelization)
-
-```python
-demo = gr.Interface(...)
-demo.queue(concurrency_count=3)
-demo.launch()
-```
-
-- Configure a maximum queue size
-
-```python
-demo = gr.Interface(...)
-demo.queue(max_size=100)
-demo.launch()
-```
-
-- If a user closes their tab / browser, they leave the queue, which means the demo will run faster for everyone else
-
-###### 2. Fixes to Examples
-
-- Dataframe examples will render properly, and look much clearer in the UI: (thanks to PR #2125)
-
-![Screen Shot 2022-08-30 at 8 29 58 PM](https://user-images.githubusercontent.com/9021060/187586561-d915bafb-f968-4966-b9a2-ef41119692b2.png)
-
-- Image and Video thumbnails are cropped to look neater and more uniform: (thanks to PR #2109)
-
-![Screen Shot 2022-08-30 at 8 32 15 PM](https://user-images.githubusercontent.com/9021060/187586890-56e1e4f0-1b84-42d9-a82f-911772c41030.png)
-
-- Other fixes in PR #2131 and #2064 make it easier to design and use Examples
-
-###### 3. Component Fixes 🧱
-
-- Specify the width and height of an image in its style tag (thanks to PR #2133)
-
-```python
-components.Image().style(height=260, width=300)
-```
-
-- Automatic conversion of videos so they are playable in the browser (thanks to PR #2003). Gradio will check if a video's format is playable in the browser and, if it isn't, will automatically convert it to a format that is (mp4).
-- Pass in a json filepath to the Label component (thanks to PR #2083)
-- Randomize the default value of a Slider (thanks to PR #1935)
-
-![slider-random](https://user-images.githubusercontent.com/9021060/187596230-3db9697f-9f4d-42f5-9387-d77573513448.gif)
-
-- Improvements to State in PR #2100
-
-###### 4. Ability to Randomize Input Sliders and Reload Data whenever the Page Loads
-
-- In some cases, you want to be able to show a different set of input data to every user as they load the page app. For example, you might want to randomize the value of a "seed" `Slider` input. Or you might want to show a `Textbox` with the current date. We now supporting passing _functions_ as the default value in input components. When you pass in a function, it gets **re-evaluated** every time someone loads the demo, allowing you to reload / change data for different users.
-
-Here's an example loading the current date time into an input Textbox:
-
-```python
-import gradio as gr
-import datetime
-
-with gr.Blocks() as demo:
-    gr.Textbox(datetime.datetime.now)
-
-demo.launch()
-```
-
-Note that we don't evaluate the function -- `datetime.datetime.now()` -- we pass in the function itself to get this behavior -- `datetime.datetime.now`
-
-Because randomizing the initial value of `Slider` is a common use case, we've added a `randomize` keyword argument you can use to randomize its initial value:
-
-```python
-import gradio as gr
-demo = gr.Interface(lambda x:x, gr.Slider(0, 10, randomize=True), "number")
-demo.launch()
-```
-
-###### 5. New Guide 🖊️
-
-- [Gradio and W&B Integration](https://gradio.app/Gradio_and_Wandb_Integration/)
-
-### Full Changelog:
-
-- Reset components to original state by setting value to None by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2044](https://github.com/gradio-app/gradio/pull/2044)
-- Cleaning up the way data is processed for components by [@abidlabs](https://github.com/abidlabs) in [PR 1967](https://github.com/gradio-app/gradio/pull/1967)
-- version 3.1.8b by [@abidlabs](https://github.com/abidlabs) in [PR 2063](https://github.com/gradio-app/gradio/pull/2063)
-- Wandb guide by [@AK391](https://github.com/AK391) in [PR 1898](https://github.com/gradio-app/gradio/pull/1898)
-- Add a flagging callback to save json files to a hugging face dataset by [@chrisemezue](https://github.com/chrisemezue) in [PR 1821](https://github.com/gradio-app/gradio/pull/1821)
-- Add data science demos to landing page by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2067](https://github.com/gradio-app/gradio/pull/2067)
-- Hide time series + xgboost demos by default by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2079](https://github.com/gradio-app/gradio/pull/2079)
-- Encourage people to keep trying when queue full by [@apolinario](https://github.com/apolinario) in [PR 2076](https://github.com/gradio-app/gradio/pull/2076)
-- Updated our analytics on creation of Blocks/Interface by [@abidlabs](https://github.com/abidlabs) in [PR 2082](https://github.com/gradio-app/gradio/pull/2082)
-- `Label` component now accepts file paths to `.json` files by [@abidlabs](https://github.com/abidlabs) in [PR 2083](https://github.com/gradio-app/gradio/pull/2083)
-- Fix issues related to demos in Spaces by [@abidlabs](https://github.com/abidlabs) in [PR 2086](https://github.com/gradio-app/gradio/pull/2086)
-- Fix TimeSeries examples not properly displayed in UI by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2064](https://github.com/gradio-app/gradio/pull/2064)
-- Fix infinite requests when doing tab item select by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2070](https://github.com/gradio-app/gradio/pull/2070)
-- Accept deprecated `file` route as well by [@abidlabs](https://github.com/abidlabs) in [PR 2099](https://github.com/gradio-app/gradio/pull/2099)
-- Allow frontend method execution on Block.load event by [@codedealer](https://github.com/codedealer) in [PR 2108](https://github.com/gradio-app/gradio/pull/2108)
-- Improvements to `State` by [@abidlabs](https://github.com/abidlabs) in [PR 2100](https://github.com/gradio-app/gradio/pull/2100)
-- Catch IndexError, KeyError in video_is_playable by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2113](https://github.com/gradio-app/gradio/pull/2113)
-- Fix: Download button does not respect the filepath returned by the function by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2073](https://github.com/gradio-app/gradio/pull/2073)
-- Refactoring Layout: Adding column widths, forms, and more. by [@aliabid94](https://github.com/aliabid94) in [PR 2097](https://github.com/gradio-app/gradio/pull/2097)
-- Update CONTRIBUTING.md by [@abidlabs](https://github.com/abidlabs) in [PR 2118](https://github.com/gradio-app/gradio/pull/2118)
-- 2092 df ex by [@pngwn](https://github.com/pngwn) in [PR 2125](https://github.com/gradio-app/gradio/pull/2125)
-- feat(samples table/gallery): Crop thumbs to square by [@ronvoluted](https://github.com/ronvoluted) in [PR 2109](https://github.com/gradio-app/gradio/pull/2109)
-- Some enhancements to `gr.Examples` by [@abidlabs](https://github.com/abidlabs) in [PR 2131](https://github.com/gradio-app/gradio/pull/2131)
-- Image size fix by [@aliabid94](https://github.com/aliabid94) in [PR 2133](https://github.com/gradio-app/gradio/pull/2133)
-
-### Contributors Shoutout:
-
-- [@chrisemezue](https://github.com/chrisemezue) made their first contribution in [PR 1821](https://github.com/gradio-app/gradio/pull/1821)
-- [@apolinario](https://github.com/apolinario) made their first contribution in [PR 2076](https://github.com/gradio-app/gradio/pull/2076)
-- [@codedealer](https://github.com/codedealer) made their first contribution in [PR 2108](https://github.com/gradio-app/gradio/pull/2108)
-
-## 3.1
-
-### New Features:
-
-###### 1. Embedding Demos on Any Website 💻
-
-With PR #1444, Gradio is now distributed as a web component. This means demos can be natively embedded on websites. You'll just need to add two lines: one to load the gradio javascript, and one to link to the demos backend.
-
-Here's a simple example that embeds the demo from a Hugging Face space:
-
-```html
-<script
-	type="module"
-	src="https://gradio.s3-us-west-2.amazonaws.com/3.0.18/gradio.js"
-></script>
-<gradio-app space="abidlabs/pytorch-image-classifier"></gradio-app>
-```
-
-But you can also embed demos that are running anywhere, you just need to link the demo to `src` instead of `space`. In fact, all the demos on the gradio website are embedded this way:
-
-<img width="1268" alt="Screen Shot 2022-07-14 at 2 41 44 PM" src="https://user-images.githubusercontent.com/9021060/178997124-b2f05af2-c18f-4716-bf1b-cb971d012636.png">
-
-Read more in the [Embedding Gradio Demos](https://gradio.app/embedding_gradio_demos) guide.
-
-###### 2. Reload Mode 👨‍💻
-
-Reload mode helps developers create gradio demos faster by automatically reloading the demo whenever the code changes. It can support development on Python IDEs (VS Code, PyCharm, etc), the terminal, as well as Jupyter notebooks.
-
-If your demo code is in a script named `app.py`, instead of running `python app.py` you can now run `gradio app.py` and that will launch the demo in reload mode:
-
-```bash
-Launching in reload mode on: http://127.0.0.1:7860 (Press CTRL+C to quit)
-Watching...
-WARNING: The --reload flag should not be used in production on Windows.
-```
-
-If you're working from a Jupyter or Colab Notebook, use these magic commands instead: `%load_ext gradio` when you import gradio, and `%%blocks` in the top of the cell with the demo code. Here's an example that shows how much faster the development becomes:
-
-![Blocks](https://user-images.githubusercontent.com/9021060/178986488-ed378cc8-5141-4330-ba41-672b676863d0.gif)
-
-###### 3. Inpainting Support on `gr.Image()` 🎨
-
-We updated the Image component to add support for inpainting demos. It works by adding `tool="sketch"` as a parameter, that passes both an image and a sketchable mask to your prediction function.
-
-Here's an example from the [LAMA space](https://huggingface.co/spaces/akhaliq/lama):
-
-![FXApVlFVsAALSD-](https://user-images.githubusercontent.com/9021060/178989479-549867c8-7fb0-436a-a97d-1e91c9f5e611.jpeg)
-
-###### 4. Markdown and HTML support in Dataframes 🔢
-
-We upgraded the Dataframe component in PR #1684 to support rendering Markdown and HTML inside the cells.
-
-This means you can build Dataframes that look like the following:
-
-![image (8)](https://user-images.githubusercontent.com/9021060/178991233-41cb07a5-e7a3-433e-89b8-319bc78eb9c2.png)
-
-###### 5. `gr.Examples()` for Blocks 🧱
-
-We've added the `gr.Examples` component helper to allow you to add examples to any Blocks demo. This class is a wrapper over the `gr.Dataset` component.
-
-<img width="1271" alt="Screen Shot 2022-07-14 at 2 23 50 PM" src="https://user-images.githubusercontent.com/9021060/178992715-c8bc7550-bc3d-4ddc-9fcb-548c159cd153.png">
-
-gr.Examples takes two required parameters:
-
-- `examples` which takes in a nested list
-- `inputs` which takes in a component or list of components
-
-You can read more in the [Examples docs](https://gradio.app/docs/#examples) or the [Adding Examples to your Demos guide](https://gradio.app/adding_examples_to_your_app/).
-
-###### 6. Fixes to Audio Streaming
-
-With [PR 1828](https://github.com/gradio-app/gradio/pull/1828) we now hide the status loading animation, as well as remove the echo in streaming. Check out the [stream_audio](https://github.com/gradio-app/gradio/blob/main/demo/stream_audio/run.py) demo for more or read through our [Real Time Speech Recognition](https://gradio.app/real_time_speech_recognition/) guide.
-
-<img width="785" alt="Screen Shot 2022-07-19 at 6 02 35 PM" src="https://user-images.githubusercontent.com/9021060/179808136-9e84502c-f9ee-4f30-b5e9-1086f678fe91.png">
-
-### Full Changelog:
-
-- File component: list multiple files and allow for download #1446 by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1681](https://github.com/gradio-app/gradio/pull/1681)
-- Add ColorPicker to docs by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1768](https://github.com/gradio-app/gradio/pull/1768)
-- Mock out requests in TestRequest unit tests by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1794](https://github.com/gradio-app/gradio/pull/1794)
-- Add requirements.txt and test_files to source dist by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1817](https://github.com/gradio-app/gradio/pull/1817)
-- refactor: f-string for tunneling.py by [@nhankiet](https://github.com/nhankiet) in [PR 1819](https://github.com/gradio-app/gradio/pull/1819)
-- Miscellaneous formatting improvements to website by [@aliabd](https://github.com/aliabd) in [PR 1754](https://github.com/gradio-app/gradio/pull/1754)
-- `integrate()` method moved to `Blocks` by [@abidlabs](https://github.com/abidlabs) in [PR 1776](https://github.com/gradio-app/gradio/pull/1776)
-- Add python-3.7 tests by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1818](https://github.com/gradio-app/gradio/pull/1818)
-- Copy test dir in website dockers by [@aliabd](https://github.com/aliabd) in [PR 1827](https://github.com/gradio-app/gradio/pull/1827)
-- Add info to docs on how to set default values for components by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1788](https://github.com/gradio-app/gradio/pull/1788)
-- Embedding Components on Docs by [@aliabd](https://github.com/aliabd) in [PR 1726](https://github.com/gradio-app/gradio/pull/1726)
-- Remove usage of deprecated gr.inputs and gr.outputs from website by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1796](https://github.com/gradio-app/gradio/pull/1796)
-- Some cleanups to the docs page by [@abidlabs](https://github.com/abidlabs) in [PR 1822](https://github.com/gradio-app/gradio/pull/1822)
-
-### Contributors Shoutout:
-
-- [@nhankiet](https://github.com/nhankiet) made their first contribution in [PR 1819](https://github.com/gradio-app/gradio/pull/1819)
-
-## 3.0
-
-###### 🔥 Gradio 3.0 is the biggest update to the library, ever.
-
-### New Features:
-
-###### 1. Blocks 🧱
-
-Blocks is a new, low-level API that allows you to have full control over the data flows and layout of your application. It allows you to build very complex, multi-step applications. For example, you might want to:
-
-- Group together related demos as multiple tabs in one web app
-- Change the layout of your demo instead of just having all of the inputs on the left and outputs on the right
-- Have multi-step interfaces, in which the output of one model becomes the input to the next model, or have more flexible data flows in general
-- Change a component's properties (for example, the choices in a Dropdown) or its visibility based on user input
-
-Here's a simple example that creates the demo below it:
-
-```python
-import gradio as gr
-
-def update(name):
-    return f"Welcome to Gradio, {name}!"
-
-demo = gr.Blocks()
-
-with demo:
-    gr.Markdown(
-    """
-    # Hello World!
-    Start typing below to see the output.
-    """)
-    inp = gr.Textbox(placeholder="What is your name?")
-    out = gr.Textbox()
-
-    inp.change(fn=update,
-               inputs=inp,
-               outputs=out)
-
-demo.launch()
-```
-
-![hello-blocks](https://user-images.githubusercontent.com/9021060/168684108-78cbd24b-e6bd-4a04-a8d9-20d535203434.gif)
-
-Read our [Introduction to Blocks](http://gradio.app/introduction_to_blocks/) guide for more, and join the 🎈 [Gradio Blocks Party](https://huggingface.co/spaces/Gradio-Blocks/README)!
-
-###### 2. Our Revamped Design 🎨
-
-We've upgraded our design across the entire library: from components, and layouts all the way to dark mode.
-
-![kitchen_sink](https://user-images.githubusercontent.com/9021060/168686333-7a6e3096-3e23-4309-abf2-5cd7736e0463.gif)
-
-###### 3. A New Website 💻
-
-We've upgraded [gradio.app](https://gradio.app) to make it cleaner, faster and easier to use. Our docs now come with components and demos embedded directly on the page. So you can quickly get up to speed with what you're looking for.
-
-![website](https://user-images.githubusercontent.com/9021060/168687191-10d6a3bd-101f-423a-8193-48f47a5e077d.gif)
-
-###### 4. New Components: Model3D, Dataset, and More..
-
-We've introduced a lot of new components in `3.0`, including `Model3D`, `Dataset`, `Markdown`, `Button` and `Gallery`. You can find all the components and play around with them [here](https://gradio.app/docs/#components).
-
-![Model3d](https://user-images.githubusercontent.com/9021060/168689062-6ad77151-8cc5-467d-916c-f7c78e52ec0c.gif)
-
-### Full Changelog:
-
-- Gradio dash fe by [@pngwn](https://github.com/pngwn) in [PR 807](https://github.com/gradio-app/gradio/pull/807)
-- Blocks components by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 765](https://github.com/gradio-app/gradio/pull/765)
-- Blocks components V2 by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 843](https://github.com/gradio-app/gradio/pull/843)
-- Blocks-Backend-Events by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 844](https://github.com/gradio-app/gradio/pull/844)
-- Interfaces from Blocks by [@aliabid94](https://github.com/aliabid94) in [PR 849](https://github.com/gradio-app/gradio/pull/849)
-- Blocks dev by [@aliabid94](https://github.com/aliabid94) in [PR 853](https://github.com/gradio-app/gradio/pull/853)
-- Started updating demos to use the new `gradio.components` syntax by [@abidlabs](https://github.com/abidlabs) in [PR 848](https://github.com/gradio-app/gradio/pull/848)
-- add test infra + add browser tests to CI by [@pngwn](https://github.com/pngwn) in [PR 852](https://github.com/gradio-app/gradio/pull/852)
-- 854 textbox by [@pngwn](https://github.com/pngwn) in [PR 859](https://github.com/gradio-app/gradio/pull/859)
-- Getting old Python unit tests to pass on `blocks-dev` by [@abidlabs](https://github.com/abidlabs) in [PR 861](https://github.com/gradio-app/gradio/pull/861)
-- initialise chatbot with empty array of messages by [@pngwn](https://github.com/pngwn) in [PR 867](https://github.com/gradio-app/gradio/pull/867)
-- add test for output to input by [@pngwn](https://github.com/pngwn) in [PR 866](https://github.com/gradio-app/gradio/pull/866)
-- More Interface -> Blocks features by [@aliabid94](https://github.com/aliabid94) in [PR 864](https://github.com/gradio-app/gradio/pull/864)
-- Fixing external.py in blocks-dev to reflect the new HF Spaces paths by [@abidlabs](https://github.com/abidlabs) in [PR 879](https://github.com/gradio-app/gradio/pull/879)
-- backend_default_value_refactoring by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 871](https://github.com/gradio-app/gradio/pull/871)
-- fix default_value by [@pngwn](https://github.com/pngwn) in [PR 869](https://github.com/gradio-app/gradio/pull/869)
-- fix buttons by [@aliabid94](https://github.com/aliabid94) in [PR 883](https://github.com/gradio-app/gradio/pull/883)
-- Checking and updating more demos to use 3.0 syntax by [@abidlabs](https://github.com/abidlabs) in [PR 892](https://github.com/gradio-app/gradio/pull/892)
-- Blocks Tests by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 902](https://github.com/gradio-app/gradio/pull/902)
-- Interface fix by [@pngwn](https://github.com/pngwn) in [PR 901](https://github.com/gradio-app/gradio/pull/901)
-- Quick fix: Issue 893 by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 907](https://github.com/gradio-app/gradio/pull/907)
-- 3d Image Component by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 775](https://github.com/gradio-app/gradio/pull/775)
-- fix endpoint url in prod by [@pngwn](https://github.com/pngwn) in [PR 911](https://github.com/gradio-app/gradio/pull/911)
-- rename Model3d to Image3D by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 912](https://github.com/gradio-app/gradio/pull/912)
-- update pypi to 2.9.1 by [@abidlabs](https://github.com/abidlabs) in [PR 916](https://github.com/gradio-app/gradio/pull/916)
-- blocks-with-fix by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 917](https://github.com/gradio-app/gradio/pull/917)
-- Restore Interpretation, Live, Auth, Queueing by [@aliabid94](https://github.com/aliabid94) in [PR 915](https://github.com/gradio-app/gradio/pull/915)
-- Allow `Blocks` instances to be used like a `Block` in other `Blocks` by [@abidlabs](https://github.com/abidlabs) in [PR 919](https://github.com/gradio-app/gradio/pull/919)
-- Redesign 1 by [@pngwn](https://github.com/pngwn) in [PR 918](https://github.com/gradio-app/gradio/pull/918)
-- blocks-components-tests by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 904](https://github.com/gradio-app/gradio/pull/904)
-- fix unit + browser tests by [@pngwn](https://github.com/pngwn) in [PR 926](https://github.com/gradio-app/gradio/pull/926)
-- blocks-move-test-data by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 927](https://github.com/gradio-app/gradio/pull/927)
-- remove debounce from form inputs by [@pngwn](https://github.com/pngwn) in [PR 932](https://github.com/gradio-app/gradio/pull/932)
-- reimplement webcam video by [@pngwn](https://github.com/pngwn) in [PR 928](https://github.com/gradio-app/gradio/pull/928)
-- blocks-move-test-data by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 941](https://github.com/gradio-app/gradio/pull/941)
-- allow audio components to take a string value by [@pngwn](https://github.com/pngwn) in [PR 930](https://github.com/gradio-app/gradio/pull/930)
-- static mode for textbox by [@pngwn](https://github.com/pngwn) in [PR 929](https://github.com/gradio-app/gradio/pull/929)
-- fix file upload text by [@pngwn](https://github.com/pngwn) in [PR 931](https://github.com/gradio-app/gradio/pull/931)
-- tabbed-interface-rewritten by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 958](https://github.com/gradio-app/gradio/pull/958)
-- Gan demo fix by [@abidlabs](https://github.com/abidlabs) in [PR 965](https://github.com/gradio-app/gradio/pull/965)
-- Blocks analytics by [@abidlabs](https://github.com/abidlabs) in [PR 947](https://github.com/gradio-app/gradio/pull/947)
-- Blocks page load by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 963](https://github.com/gradio-app/gradio/pull/963)
-- add frontend for page load events by [@pngwn](https://github.com/pngwn) in [PR 967](https://github.com/gradio-app/gradio/pull/967)
-- fix i18n and some tweaks by [@pngwn](https://github.com/pngwn) in [PR 966](https://github.com/gradio-app/gradio/pull/966)
-- add jinja2 to reqs by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 969](https://github.com/gradio-app/gradio/pull/969)
-- Cleaning up `Launchable()` by [@abidlabs](https://github.com/abidlabs) in [PR 968](https://github.com/gradio-app/gradio/pull/968)
-- Fix #944 by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 971](https://github.com/gradio-app/gradio/pull/971)
-- New Blocks Demo: neural instrument cloning by [@abidlabs](https://github.com/abidlabs) in [PR 975](https://github.com/gradio-app/gradio/pull/975)
-- Add huggingface_hub client library by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 973](https://github.com/gradio-app/gradio/pull/973)
-- State and variables by [@aliabid94](https://github.com/aliabid94) in [PR 977](https://github.com/gradio-app/gradio/pull/977)
-- update-components by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 986](https://github.com/gradio-app/gradio/pull/986)
-- ensure dataframe updates as expected by [@pngwn](https://github.com/pngwn) in [PR 981](https://github.com/gradio-app/gradio/pull/981)
-- test-guideline by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 990](https://github.com/gradio-app/gradio/pull/990)
-- Issue #785: add footer by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 972](https://github.com/gradio-app/gradio/pull/972)
-- indentation fix by [@abidlabs](https://github.com/abidlabs) in [PR 993](https://github.com/gradio-app/gradio/pull/993)
-- missing quote by [@aliabd](https://github.com/aliabd) in [PR 996](https://github.com/gradio-app/gradio/pull/996)
-- added interactive parameter to components by [@abidlabs](https://github.com/abidlabs) in [PR 992](https://github.com/gradio-app/gradio/pull/992)
-- custom-components by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 985](https://github.com/gradio-app/gradio/pull/985)
-- Refactor component shortcuts by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 995](https://github.com/gradio-app/gradio/pull/995)
-- Plot Component by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 805](https://github.com/gradio-app/gradio/pull/805)
-- updated PyPi version to 2.9.2 by [@abidlabs](https://github.com/abidlabs) in [PR 1002](https://github.com/gradio-app/gradio/pull/1002)
-- Release 2.9.3 by [@abidlabs](https://github.com/abidlabs) in [PR 1003](https://github.com/gradio-app/gradio/pull/1003)
-- Image3D Examples Fix by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1001](https://github.com/gradio-app/gradio/pull/1001)
-- release 2.9.4 by [@abidlabs](https://github.com/abidlabs) in [PR 1006](https://github.com/gradio-app/gradio/pull/1006)
-- templates import hotfix by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1008](https://github.com/gradio-app/gradio/pull/1008)
-- Progress indicator bar by [@aliabid94](https://github.com/aliabid94) in [PR 997](https://github.com/gradio-app/gradio/pull/997)
-- Fixed image input for absolute path by [@JefferyChiang](https://github.com/JefferyChiang) in [PR 1004](https://github.com/gradio-app/gradio/pull/1004)
-- Model3D + Plot Components by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1010](https://github.com/gradio-app/gradio/pull/1010)
-- Gradio Guides: Creating CryptoPunks with GANs by [@NimaBoscarino](https://github.com/NimaBoscarino) in [PR 1000](https://github.com/gradio-app/gradio/pull/1000)
-- [BIG PR] Gradio blocks & redesigned components by [@abidlabs](https://github.com/abidlabs) in [PR 880](https://github.com/gradio-app/gradio/pull/880)
-- fixed failing test on main by [@abidlabs](https://github.com/abidlabs) in [PR 1023](https://github.com/gradio-app/gradio/pull/1023)
-- Use smaller ASR model in external test by [@abidlabs](https://github.com/abidlabs) in [PR 1024](https://github.com/gradio-app/gradio/pull/1024)
-- updated PyPi version to 2.9.0b by [@abidlabs](https://github.com/abidlabs) in [PR 1026](https://github.com/gradio-app/gradio/pull/1026)
-- Fixing import issues so that the package successfully installs on colab notebooks by [@abidlabs](https://github.com/abidlabs) in [PR 1027](https://github.com/gradio-app/gradio/pull/1027)
-- Update website tracker slackbot by [@aliabd](https://github.com/aliabd) in [PR 1037](https://github.com/gradio-app/gradio/pull/1037)
-- textbox-autoheight by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1009](https://github.com/gradio-app/gradio/pull/1009)
-- Model3D Examples fixes by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1035](https://github.com/gradio-app/gradio/pull/1035)
-- GAN Gradio Guide: Adjustments to iframe heights by [@NimaBoscarino](https://github.com/NimaBoscarino) in [PR 1042](https://github.com/gradio-app/gradio/pull/1042)
-- added better default labels to form components by [@abidlabs](https://github.com/abidlabs) in [PR 1040](https://github.com/gradio-app/gradio/pull/1040)
-- Slackbot web tracker fix by [@aliabd](https://github.com/aliabd) in [PR 1043](https://github.com/gradio-app/gradio/pull/1043)
-- Plot fixes by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1044](https://github.com/gradio-app/gradio/pull/1044)
-- Small fixes to the demos by [@abidlabs](https://github.com/abidlabs) in [PR 1030](https://github.com/gradio-app/gradio/pull/1030)
-- fixing demo issue with website by [@aliabd](https://github.com/aliabd) in [PR 1047](https://github.com/gradio-app/gradio/pull/1047)
-- [hotfix] HighlightedText by [@aliabid94](https://github.com/aliabid94) in [PR 1046](https://github.com/gradio-app/gradio/pull/1046)
-- Update text by [@ronvoluted](https://github.com/ronvoluted) in [PR 1050](https://github.com/gradio-app/gradio/pull/1050)
-- Update CONTRIBUTING.md by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1052](https://github.com/gradio-app/gradio/pull/1052)
-- fix(ui): Increase contrast for footer by [@ronvoluted](https://github.com/ronvoluted) in [PR 1048](https://github.com/gradio-app/gradio/pull/1048)
-- UI design update by [@gary149](https://github.com/gary149) in [PR 1041](https://github.com/gradio-app/gradio/pull/1041)
-- updated PyPi version to 2.9.0b8 by [@abidlabs](https://github.com/abidlabs) in [PR 1059](https://github.com/gradio-app/gradio/pull/1059)
-- Running, testing, and fixing demos by [@abidlabs](https://github.com/abidlabs) in [PR 1060](https://github.com/gradio-app/gradio/pull/1060)
-- Form layout by [@pngwn](https://github.com/pngwn) in [PR 1054](https://github.com/gradio-app/gradio/pull/1054)
-- inputless-interfaces by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1038](https://github.com/gradio-app/gradio/pull/1038)
-- Update PULL_REQUEST_TEMPLATE.md by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1068](https://github.com/gradio-app/gradio/pull/1068)
-- Upgrading node memory to 4gb in website Docker by [@aliabd](https://github.com/aliabd) in [PR 1069](https://github.com/gradio-app/gradio/pull/1069)
-- Website reload error by [@aliabd](https://github.com/aliabd) in [PR 1079](https://github.com/gradio-app/gradio/pull/1079)
-- fixed favicon issue by [@abidlabs](https://github.com/abidlabs) in [PR 1064](https://github.com/gradio-app/gradio/pull/1064)
-- remove-queue-from-events by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1056](https://github.com/gradio-app/gradio/pull/1056)
-- Enable vertex colors for OBJs files by [@radames](https://github.com/radames) in [PR 1074](https://github.com/gradio-app/gradio/pull/1074)
-- Dark text by [@ronvoluted](https://github.com/ronvoluted) in [PR 1049](https://github.com/gradio-app/gradio/pull/1049)
-- Scroll to output by [@pngwn](https://github.com/pngwn) in [PR 1077](https://github.com/gradio-app/gradio/pull/1077)
-- Explicitly list pnpm version 6 in contributing guide by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1085](https://github.com/gradio-app/gradio/pull/1085)
-- hotfix for encrypt issue by [@abidlabs](https://github.com/abidlabs) in [PR 1096](https://github.com/gradio-app/gradio/pull/1096)
-- Release 2.9b9 by [@abidlabs](https://github.com/abidlabs) in [PR 1098](https://github.com/gradio-app/gradio/pull/1098)
-- tweak node circleci settings by [@pngwn](https://github.com/pngwn) in [PR 1091](https://github.com/gradio-app/gradio/pull/1091)
-- Website Reload Error by [@aliabd](https://github.com/aliabd) in [PR 1099](https://github.com/gradio-app/gradio/pull/1099)
-- Website Reload: README in demos docker by [@aliabd](https://github.com/aliabd) in [PR 1100](https://github.com/gradio-app/gradio/pull/1100)
-- Flagging fixes by [@abidlabs](https://github.com/abidlabs) in [PR 1081](https://github.com/gradio-app/gradio/pull/1081)
-- Backend for optional labels by [@abidlabs](https://github.com/abidlabs) in [PR 1080](https://github.com/gradio-app/gradio/pull/1080)
-- Optional labels fe by [@pngwn](https://github.com/pngwn) in [PR 1105](https://github.com/gradio-app/gradio/pull/1105)
-- clean-deprecated-parameters by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1090](https://github.com/gradio-app/gradio/pull/1090)
-- Blocks rendering fix by [@abidlabs](https://github.com/abidlabs) in [PR 1102](https://github.com/gradio-app/gradio/pull/1102)
-- Redos #1106 by [@abidlabs](https://github.com/abidlabs) in [PR 1112](https://github.com/gradio-app/gradio/pull/1112)
-- Interface types: handle input-only, output-only, and unified interfaces by [@abidlabs](https://github.com/abidlabs) in [PR 1108](https://github.com/gradio-app/gradio/pull/1108)
-- Hotfix + New pypi release 2.9b11 by [@abidlabs](https://github.com/abidlabs) in [PR 1118](https://github.com/gradio-app/gradio/pull/1118)
-- issue-checkbox by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1122](https://github.com/gradio-app/gradio/pull/1122)
-- issue-checkbox-hotfix by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1127](https://github.com/gradio-app/gradio/pull/1127)
-- Fix demos in website by [@aliabd](https://github.com/aliabd) in [PR 1130](https://github.com/gradio-app/gradio/pull/1130)
-- Guide for Gradio ONNX model zoo on Huggingface by [@AK391](https://github.com/AK391) in [PR 1073](https://github.com/gradio-app/gradio/pull/1073)
-- ONNX guide fixes by [@aliabd](https://github.com/aliabd) in [PR 1131](https://github.com/gradio-app/gradio/pull/1131)
-- Stacked form inputs css by [@gary149](https://github.com/gary149) in [PR 1134](https://github.com/gradio-app/gradio/pull/1134)
-- made default value in textbox empty string by [@abidlabs](https://github.com/abidlabs) in [PR 1135](https://github.com/gradio-app/gradio/pull/1135)
-- Examples UI by [@gary149](https://github.com/gary149) in [PR 1121](https://github.com/gradio-app/gradio/pull/1121)
-- Chatbot custom color support by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1092](https://github.com/gradio-app/gradio/pull/1092)
-- highlighted text colors by [@pngwn](https://github.com/pngwn) in [PR 1119](https://github.com/gradio-app/gradio/pull/1119)
-- pin to pnpm 6 for now by [@pngwn](https://github.com/pngwn) in [PR 1147](https://github.com/gradio-app/gradio/pull/1147)
-- Restore queue in Blocks by [@aliabid94](https://github.com/aliabid94) in [PR 1137](https://github.com/gradio-app/gradio/pull/1137)
-- add select event for tabitems by [@pngwn](https://github.com/pngwn) in [PR 1154](https://github.com/gradio-app/gradio/pull/1154)
-- max_lines + autoheight for textbox by [@pngwn](https://github.com/pngwn) in [PR 1153](https://github.com/gradio-app/gradio/pull/1153)
-- use color palette for chatbot by [@pngwn](https://github.com/pngwn) in [PR 1152](https://github.com/gradio-app/gradio/pull/1152)
-- Timeseries improvements by [@pngwn](https://github.com/pngwn) in [PR 1149](https://github.com/gradio-app/gradio/pull/1149)
-- move styling for interface panels to frontend by [@pngwn](https://github.com/pngwn) in [PR 1146](https://github.com/gradio-app/gradio/pull/1146)
-- html tweaks by [@pngwn](https://github.com/pngwn) in [PR 1145](https://github.com/gradio-app/gradio/pull/1145)
-- Issue #768: Support passing none to resize and crop image by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1144](https://github.com/gradio-app/gradio/pull/1144)
-- image gallery component + img css by [@aliabid94](https://github.com/aliabid94) in [PR 1140](https://github.com/gradio-app/gradio/pull/1140)
-- networking tweak by [@abidlabs](https://github.com/abidlabs) in [PR 1143](https://github.com/gradio-app/gradio/pull/1143)
-- Allow enabling queue per event listener by [@aliabid94](https://github.com/aliabid94) in [PR 1155](https://github.com/gradio-app/gradio/pull/1155)
-- config hotfix and v. 2.9b23 by [@abidlabs](https://github.com/abidlabs) in [PR 1158](https://github.com/gradio-app/gradio/pull/1158)
-- Custom JS calls by [@aliabid94](https://github.com/aliabid94) in [PR 1082](https://github.com/gradio-app/gradio/pull/1082)
-- Small fixes: queue default fix, ffmpeg installation message by [@abidlabs](https://github.com/abidlabs) in [PR 1159](https://github.com/gradio-app/gradio/pull/1159)
-- formatting by [@abidlabs](https://github.com/abidlabs) in [PR 1161](https://github.com/gradio-app/gradio/pull/1161)
-- enable flex grow for gr-box by [@radames](https://github.com/radames) in [PR 1165](https://github.com/gradio-app/gradio/pull/1165)
-- 1148 loading by [@pngwn](https://github.com/pngwn) in [PR 1164](https://github.com/gradio-app/gradio/pull/1164)
-- Put enable_queue kwarg back in launch() by [@aliabid94](https://github.com/aliabid94) in [PR 1167](https://github.com/gradio-app/gradio/pull/1167)
-- A few small fixes by [@abidlabs](https://github.com/abidlabs) in [PR 1171](https://github.com/gradio-app/gradio/pull/1171)
-- Hotfix for dropdown component by [@abidlabs](https://github.com/abidlabs) in [PR 1172](https://github.com/gradio-app/gradio/pull/1172)
-- use secondary buttons in interface by [@pngwn](https://github.com/pngwn) in [PR 1173](https://github.com/gradio-app/gradio/pull/1173)
-- 1183 component height by [@pngwn](https://github.com/pngwn) in [PR 1185](https://github.com/gradio-app/gradio/pull/1185)
-- 962 dataframe by [@pngwn](https://github.com/pngwn) in [PR 1186](https://github.com/gradio-app/gradio/pull/1186)
-- update-contributing by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1188](https://github.com/gradio-app/gradio/pull/1188)
-- Table tweaks by [@pngwn](https://github.com/pngwn) in [PR 1195](https://github.com/gradio-app/gradio/pull/1195)
-- wrap tab content in column by [@pngwn](https://github.com/pngwn) in [PR 1200](https://github.com/gradio-app/gradio/pull/1200)
-- WIP: Add dark mode support by [@gary149](https://github.com/gary149) in [PR 1187](https://github.com/gradio-app/gradio/pull/1187)
-- Restored /api/predict/ endpoint for Interfaces by [@abidlabs](https://github.com/abidlabs) in [PR 1199](https://github.com/gradio-app/gradio/pull/1199)
-- hltext-label by [@pngwn](https://github.com/pngwn) in [PR 1204](https://github.com/gradio-app/gradio/pull/1204)
-- add copy functionality to json by [@pngwn](https://github.com/pngwn) in [PR 1205](https://github.com/gradio-app/gradio/pull/1205)
-- Update component config by [@aliabid94](https://github.com/aliabid94) in [PR 1089](https://github.com/gradio-app/gradio/pull/1089)
-- fix placeholder prompt by [@pngwn](https://github.com/pngwn) in [PR 1215](https://github.com/gradio-app/gradio/pull/1215)
-- ensure webcam video value is propagated correctly by [@pngwn](https://github.com/pngwn) in [PR 1218](https://github.com/gradio-app/gradio/pull/1218)
-- Automatic word-break in highlighted text, combine_adjacent support by [@aliabid94](https://github.com/aliabid94) in [PR 1209](https://github.com/gradio-app/gradio/pull/1209)
-- async-function-support by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1190](https://github.com/gradio-app/gradio/pull/1190)
-- Sharing fix for assets by [@aliabid94](https://github.com/aliabid94) in [PR 1208](https://github.com/gradio-app/gradio/pull/1208)
-- Hotfixes for course demos by [@abidlabs](https://github.com/abidlabs) in [PR 1222](https://github.com/gradio-app/gradio/pull/1222)
-- Allow Custom CSS by [@aliabid94](https://github.com/aliabid94) in [PR 1170](https://github.com/gradio-app/gradio/pull/1170)
-- share-hotfix by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1226](https://github.com/gradio-app/gradio/pull/1226)
-- tweaks by [@pngwn](https://github.com/pngwn) in [PR 1229](https://github.com/gradio-app/gradio/pull/1229)
-- white space for class concatenation by [@radames](https://github.com/radames) in [PR 1228](https://github.com/gradio-app/gradio/pull/1228)
-- Tweaks by [@pngwn](https://github.com/pngwn) in [PR 1230](https://github.com/gradio-app/gradio/pull/1230)
-- css tweaks by [@pngwn](https://github.com/pngwn) in [PR 1235](https://github.com/gradio-app/gradio/pull/1235)
-- ensure defaults height match for media inputs by [@pngwn](https://github.com/pngwn) in [PR 1236](https://github.com/gradio-app/gradio/pull/1236)
-- Default Label label value by [@radames](https://github.com/radames) in [PR 1239](https://github.com/gradio-app/gradio/pull/1239)
-- update-shortcut-syntax by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1234](https://github.com/gradio-app/gradio/pull/1234)
-- Update version.txt by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1244](https://github.com/gradio-app/gradio/pull/1244)
-- Layout bugs by [@pngwn](https://github.com/pngwn) in [PR 1246](https://github.com/gradio-app/gradio/pull/1246)
-- Update demo by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1253](https://github.com/gradio-app/gradio/pull/1253)
-- Button default name by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1243](https://github.com/gradio-app/gradio/pull/1243)
-- Labels spacing by [@gary149](https://github.com/gary149) in [PR 1254](https://github.com/gradio-app/gradio/pull/1254)
-- add global loader for gradio app by [@pngwn](https://github.com/pngwn) in [PR 1251](https://github.com/gradio-app/gradio/pull/1251)
-- ui apis for dalle-mini by [@pngwn](https://github.com/pngwn) in [PR 1258](https://github.com/gradio-app/gradio/pull/1258)
-- Add precision to Number, backend only by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1125](https://github.com/gradio-app/gradio/pull/1125)
-- Website Design Changes by [@abidlabs](https://github.com/abidlabs) in [PR 1015](https://github.com/gradio-app/gradio/pull/1015)
-- Small fixes for multiple demos compatible with 3.0 by [@radames](https://github.com/radames) in [PR 1257](https://github.com/gradio-app/gradio/pull/1257)
-- Issue #1160: Model 3D component not destroyed correctly by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1219](https://github.com/gradio-app/gradio/pull/1219)
-- Fixes to components by [@abidlabs](https://github.com/abidlabs) in [PR 1260](https://github.com/gradio-app/gradio/pull/1260)
-- layout docs by [@abidlabs](https://github.com/abidlabs) in [PR 1263](https://github.com/gradio-app/gradio/pull/1263)
-- Static forms by [@pngwn](https://github.com/pngwn) in [PR 1264](https://github.com/gradio-app/gradio/pull/1264)
-- Cdn assets by [@pngwn](https://github.com/pngwn) in [PR 1265](https://github.com/gradio-app/gradio/pull/1265)
-- update logo by [@gary149](https://github.com/gary149) in [PR 1266](https://github.com/gradio-app/gradio/pull/1266)
-- fix slider by [@aliabid94](https://github.com/aliabid94) in [PR 1268](https://github.com/gradio-app/gradio/pull/1268)
-- maybe fix auth in iframes by [@pngwn](https://github.com/pngwn) in [PR 1261](https://github.com/gradio-app/gradio/pull/1261)
-- Improves "Getting Started" guide by [@abidlabs](https://github.com/abidlabs) in [PR 1269](https://github.com/gradio-app/gradio/pull/1269)
-- Add embedded demos to website by [@aliabid94](https://github.com/aliabid94) in [PR 1270](https://github.com/gradio-app/gradio/pull/1270)
-- Label hotfixes by [@abidlabs](https://github.com/abidlabs) in [PR 1281](https://github.com/gradio-app/gradio/pull/1281)
-- General tweaks by [@pngwn](https://github.com/pngwn) in [PR 1276](https://github.com/gradio-app/gradio/pull/1276)
-- only affect links within the document by [@pngwn](https://github.com/pngwn) in [PR 1282](https://github.com/gradio-app/gradio/pull/1282)
-- release 3.0b9 by [@abidlabs](https://github.com/abidlabs) in [PR 1283](https://github.com/gradio-app/gradio/pull/1283)
-- Dm by [@pngwn](https://github.com/pngwn) in [PR 1284](https://github.com/gradio-app/gradio/pull/1284)
-- Website fixes by [@aliabd](https://github.com/aliabd) in [PR 1286](https://github.com/gradio-app/gradio/pull/1286)
-- Create Streamables by [@aliabid94](https://github.com/aliabid94) in [PR 1279](https://github.com/gradio-app/gradio/pull/1279)
-- ensure table works on mobile by [@pngwn](https://github.com/pngwn) in [PR 1277](https://github.com/gradio-app/gradio/pull/1277)
-- changes by [@aliabid94](https://github.com/aliabid94) in [PR 1287](https://github.com/gradio-app/gradio/pull/1287)
-- demo alignment on landing page by [@aliabd](https://github.com/aliabd) in [PR 1288](https://github.com/gradio-app/gradio/pull/1288)
-- New meta img by [@aliabd](https://github.com/aliabd) in [PR 1289](https://github.com/gradio-app/gradio/pull/1289)
-- updated PyPi version to 3.0 by [@abidlabs](https://github.com/abidlabs) in [PR 1290](https://github.com/gradio-app/gradio/pull/1290)
-- Fix site by [@aliabid94](https://github.com/aliabid94) in [PR 1291](https://github.com/gradio-app/gradio/pull/1291)
-- Mobile responsive guides by [@aliabd](https://github.com/aliabd) in [PR 1293](https://github.com/gradio-app/gradio/pull/1293)
-- Update readme by [@abidlabs](https://github.com/abidlabs) in [PR 1292](https://github.com/gradio-app/gradio/pull/1292)
-- gif by [@abidlabs](https://github.com/abidlabs) in [PR 1296](https://github.com/gradio-app/gradio/pull/1296)
-- Allow decoding headerless b64 string [@1lint](https://github.com/1lint) in [PR 4031](https://github.com/gradio-app/gradio/pull/4031)
-
-### Contributors Shoutout:
-
-- [@JefferyChiang](https://github.com/JefferyChiang) made their first contribution in [PR 1004](https://github.com/gradio-app/gradio/pull/1004)
-- [@NimaBoscarino](https://github.com/NimaBoscarino) made their first contribution in [PR 1000](https://github.com/gradio-app/gradio/pull/1000)
-- [@ronvoluted](https://github.com/ronvoluted) made their first contribution in [PR 1050](https://github.com/gradio-app/gradio/pull/1050)
-- [@radames](https://github.com/radames) made their first contribution in [PR 1074](https://github.com/gradio-app/gradio/pull/1074)
-- [@freddyaboulton](https://github.com/freddyaboulton) made their first contribution in [PR 1085](https://github.com/gradio-app/gradio/pull/1085)
-- [@liteli1987gmail](https://github.com/liteli1987gmail) & [@chenglu](https://github.com/chenglu) made their first contribution in [PR 4767](https://github.com/gradio-app/gradio/pull/4767)
\ No newline at end of file
diff --git a/spaces/johnberg/CLIPInverter/models/stylegan2/op/fused_act.py b/spaces/johnberg/CLIPInverter/models/stylegan2/op/fused_act.py
deleted file mode 100644
index f5611d5bb73d39c647d1a2d7e5ba99048aefdcab..0000000000000000000000000000000000000000
--- a/spaces/johnberg/CLIPInverter/models/stylegan2/op/fused_act.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import os
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-module_path = os.path.dirname(__file__)
-
-
-
-class FusedLeakyReLU(nn.Module):
-    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
-        super().__init__()
-
-        self.bias = nn.Parameter(torch.zeros(channel))
-        self.negative_slope = negative_slope
-        self.scale = scale
-
-    def forward(self, input):
-        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
-    rest_dim = [1] * (input.ndim - bias.ndim - 1)
-    # input = input.cuda()
-    if input.ndim == 3:
-        return (
-            F.leaky_relu(
-                input + bias.view(1, *rest_dim, bias.shape[0]), negative_slope=negative_slope
-            )
-            * scale
-        )
-    else:
-        return (
-            F.leaky_relu(
-                input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
-            )
-            * scale
-        )
-
diff --git a/spaces/jordonpeter01/MusicGen/audiocraft/quantization/vq.py b/spaces/jordonpeter01/MusicGen/audiocraft/quantization/vq.py
deleted file mode 100644
index f67c3a0cd30d4b8993a36c587f00dc8a451d926f..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/MusicGen/audiocraft/quantization/vq.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-
-import torch
-
-from .base import BaseQuantizer, QuantizedResult
-from .core_vq import ResidualVectorQuantization
-
-
-class ResidualVectorQuantizer(BaseQuantizer):
-    """Residual Vector Quantizer.
-
-    Args:
-        dimension (int): Dimension of the codebooks.
-        n_q (int): Number of residual vector quantizers used.
-        q_dropout (bool): Random quantizer drop out at train time.
-        bins (int): Codebook size.
-        decay (float): Decay for exponential moving average over the codebooks.
-        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
-        kmeans_iters (int): Number of iterations used for kmeans initialization.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-        orthogonal_reg_weight (float): Orthogonal regularization weights.
-        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
-        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider.
-            for orthogonal regulariation.
-    """
-    def __init__(
-        self,
-        dimension: int = 256,
-        n_q: int = 8,
-        q_dropout: bool = False,
-        bins: int = 1024,
-        decay: float = 0.99,
-        kmeans_init: bool = True,
-        kmeans_iters: int = 10,
-        threshold_ema_dead_code: int = 2,
-        orthogonal_reg_weight: float = 0.0,
-        orthogonal_reg_active_codes_only: bool = False,
-        orthogonal_reg_max_codes: tp.Optional[int] = None,
-    ):
-        super().__init__()
-        self.max_n_q = n_q
-        self.n_q = n_q
-        self.q_dropout = q_dropout
-        self.dimension = dimension
-        self.bins = bins
-        self.decay = decay
-        self.kmeans_init = kmeans_init
-        self.kmeans_iters = kmeans_iters
-        self.threshold_ema_dead_code = threshold_ema_dead_code
-        self.orthogonal_reg_weight = orthogonal_reg_weight
-        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
-        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
-        self.vq = ResidualVectorQuantization(
-            dim=self.dimension,
-            codebook_size=self.bins,
-            num_quantizers=self.n_q,
-            decay=self.decay,
-            kmeans_init=self.kmeans_init,
-            kmeans_iters=self.kmeans_iters,
-            threshold_ema_dead_code=self.threshold_ema_dead_code,
-            orthogonal_reg_weight=self.orthogonal_reg_weight,
-            orthogonal_reg_active_codes_only=self.orthogonal_reg_active_codes_only,
-            orthogonal_reg_max_codes=self.orthogonal_reg_max_codes,
-            channels_last=False
-        )
-
-    def forward(self, x: torch.Tensor, frame_rate: int):
-        n_q = self.n_q
-        if self.training and self.q_dropout:
-            n_q = int(torch.randint(1, self.n_q + 1, (1,)).item())
-        bw_per_q = math.log2(self.bins) * frame_rate / 1000
-        quantized, codes, commit_loss = self.vq(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        bw = torch.tensor(n_q * bw_per_q).to(x)
-        return QuantizedResult(quantized, codes, bw, penalty=torch.mean(commit_loss))
-
-    def encode(self, x: torch.Tensor) -> torch.Tensor:
-        """Encode a given input tensor with the specified frame rate at the given bandwidth.
-        The RVQ encode method sets the appropriate number of quantizer to use
-        and returns indices for each quantizer.
-        """
-        n_q = self.n_q
-        codes = self.vq.encode(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        return codes
-
-    def decode(self, codes: torch.Tensor) -> torch.Tensor:
-        """Decode the given codes to the quantized representation.
-        """
-        # codes is [B, K, T], with T frames, K nb of codebooks, vq.decode expects [K, B, T].
-        codes = codes.transpose(0, 1)
-        quantized = self.vq.decode(codes)
-        return quantized
-
-    @property
-    def total_codebooks(self):
-        return self.max_n_q
-
-    @property
-    def num_codebooks(self):
-        return self.n_q
-
-    def set_num_codebooks(self, n: int):
-        assert n > 0 and n <= self.max_n_q
-        self.n_q = n
diff --git a/spaces/justest/gpt4free/g4f/.v1/unfinished/bard/README.md b/spaces/justest/gpt4free/g4f/.v1/unfinished/bard/README.md
deleted file mode 100644
index 67e8645ced188f048308ad80accee8ef900ef6ef..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/g4f/.v1/unfinished/bard/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-to do:
-- code refractoring
\ No newline at end of file
diff --git a/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/README.md b/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/README.md
deleted file mode 100644
index 1275f8ea6b4781282566b68d84cbf5d003f6d93c..0000000000000000000000000000000000000000
--- a/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/README.md
+++ /dev/null
@@ -1,90 +0,0 @@
-# MIDI song extender
-
-## System Architecture
-
-## Model Architecture
-Here is the architecture for a transformer_encoder block:
-
-![transformer_encoder block](model-1.png)
-
-Here is the architecture for our final model:
-
-![entire model](model-2.png)
-
-## Directory Guide
-musicautobot folder: this contains the encoder/decoder we use, found from [here](https://towardsdatascience.com/creating-a-pop-music-generator-with-the-transformer-5867511b382a).
-
-transformer-final folder: this is our final trained model.
-
-Comic Relief.mid, wii.mid: these are our test midi files.
-
-file_preprocessing.py: this was used to preprocess the adl-piano-midi dataset by merging all the songs into one folder.
-
-generator.ipynb: this trains and tests the model locally.
-
-model-1.png, model-2.png: files for model architecture visualization
-
-paths.txt: this is a file generated during preprocessing.
-
-preprocessor.ipynb: this takes the midi files in adl-piano-midi folder and turns them into data.npy (an array of encoded songs) and training data.
-
-test.py: this is an exploration and test of the midi encoding system.
-
-transformer_train.py: this is a python file that builds and trains a model like in generator.ipynb.
-
-##### Supplemental Files
-
-https://drive.google.com/drive/folders/1u26zC-kGG6o9xCCJDkk5Pd8euF4CjiVD?usp=sharing: google drive folder with data files too large to store in the repo. data.npy contains encoded songs, the data folder contains training data pairs. X files contain 100 length integer arrays that serve as input, and y files contain 2 length arrays that are the labels.
-
-https://colab.research.google.com/drive/16mGM6uQ-kkLZ_vXgStZWtfSTnu0coDr-?usp=sharing: Colab notebook for training.
-
-https://colab.research.google.com/drive/1yJQdfcS7pSXwxYV0oiD0-6jvkowuLVqO?usp=sharing: Colab notebook for testing.
-
-## Training Instructions
-Training uses data located in the supplementary [google drive folder](https://drive.google.com/drive/folders/1u26zC-kGG6o9xCCJDkk5Pd8euF4CjiVD?usp=sharing). Training can be done either locally or through Google Colab. You don't need data.npy for training.
-
-##### If using Google Colab:
-
-Create a copy of the Colab notebook ["transformer training"](https://colab.research.google.com/drive/16mGM6uQ-kkLZ_vXgStZWtfSTnu0coDr-?usp=sharing).
-
-Download the data folder and upload it to a folder in your personal google drive. 
-
-Mount the Google drive and change the path to your folder path. Take note that this folder will also store model checkpoints.
-
-Run the cells. If you are using a previous model, uncomment the keras.model.load_model command and change the path accordingly.
-
-The last cell will load data in batches of 10 and call model.fit on them, then save the model. run the last cell as long as you want. You can also tune hyperparameters in the second to last cell.
-
-Each call of model.fit should take 3 or 4 minutes using a GPU runtime.
-
-##### If training locally:
-You will need to download the data folder to a local directory. Change all filepaths as needed.
-
-Use the generator.ipynb file in this directory. You only need to run the cells in this notebook that correspond with the cells in the Colab notebook.
-
-## Testing Instructions
-Testing also uses data located in the [google drive folder](https://drive.google.com/drive/folders/1u26zC-kGG6o9xCCJDkk5Pd8euF4CjiVD?usp=sharing). Testing can also be done either locally or through Google Colab. You don't need the data folder for testing.
-
-##### If using Google Colab:
-
-Create a copy of the Colab notebook ["transformer testing"](https://colab.research.google.com/drive/1yJQdfcS7pSXwxYV0oiD0-6jvkowuLVqO?usp=sharing).
-
-Download data.npy and upload it to a folder in your personal google drive. Make sure a suitable model is also in the folder.
-
-Mount the Google drive and change the path to your folder path.
-
-Run the cells and load your model, changing the path as necessary.
-
-The midi_predict function takes 100 integers of the beginning of an encoded song as input, writes the input to input.mid, and writes the predicted output to output.mid while also returning the full integer array that got converted to output.mid. 
-
-midi_predict also has some postprocessing features that ensure the predicted song is a valid MIDI encoding. We round a range of note type encodings to the number 8 because 8 encodes to a pause, which is extremely important in how the final product sounds, and previous models weren't able to predict 8 with high accuracy. The provided model should be good enough so that the features are not necessary.
-
-We took encoded song samples from data.npy as an example. The last cell calls midi_predict on the sample. You will see the midi prediction in your drive folder.
-
-##### If testing locally:
-
-You will need to download data.npy to a local directory. Change all filepaths as needed.
-
-Use the generator.ipynb file in this directory. You only need to run the cells in this notebook that correspond with the cells in the Colab notebook.
-
-## Works Cited
\ No newline at end of file
diff --git a/spaces/keras-io/Generating-molecular-graphs-by-WGAN-GP/app.py b/spaces/keras-io/Generating-molecular-graphs-by-WGAN-GP/app.py
deleted file mode 100644
index 842769a5de0255a1020c43a914f20512647f6e6e..0000000000000000000000000000000000000000
--- a/spaces/keras-io/Generating-molecular-graphs-by-WGAN-GP/app.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from huggingface_hub import from_pretrained_keras
-import gradio as gr
-from rdkit import Chem, RDLogger
-from rdkit.Chem.Draw import MolsToGridImage
-import numpy as np
-import tensorflow as tf
-from tensorflow import keras
-
-# Config 
-atom_mapping = {
-    "C": 0,
-    0: "C",
-    "N": 1,
-    1: "N",
-    "O": 2,
-    2: "O",
-    "F": 3,
-    3: "F",
-}
-bond_mapping = {
-    "SINGLE": 0,
-    0: Chem.BondType.SINGLE,
-    "DOUBLE": 1,
-    1: Chem.BondType.DOUBLE,
-    "TRIPLE": 2,
-    2: Chem.BondType.TRIPLE,
-    "AROMATIC": 3,
-    3: Chem.BondType.AROMATIC,
-}
-NUM_ATOMS = 9  # Maximum number of atoms
-ATOM_DIM = 4 + 1  # Number of atom types
-BOND_DIM = 4 + 1  # Number of bond types
-LATENT_DIM = 64  # Size of the latent space
-RDLogger.DisableLog("rdApp.*")
-
-def graph_to_molecule(graph):
-    # Unpack graph
-    adjacency, features = graph
-
-    # RWMol is a molecule object intended to be edited
-    molecule = Chem.RWMol()
-
-    # Remove "no atoms" & atoms with no bonds
-    keep_idx = np.where(
-        (np.argmax(features, axis=1) != ATOM_DIM - 1)
-        & (np.sum(adjacency[:-1], axis=(0, 1)) != 0)
-    )[0]
-    features = features[keep_idx]
-    adjacency = adjacency[:, keep_idx, :][:, :, keep_idx]
-
-    # Add atoms to molecule
-    for atom_type_idx in np.argmax(features, axis=1):
-        atom = Chem.Atom(atom_mapping[atom_type_idx])
-        _ = molecule.AddAtom(atom)
-
-    # Add bonds between atoms in molecule; based on the upper triangles
-    # of the [symmetric] adjacency tensor
-    (bonds_ij, atoms_i, atoms_j) = np.where(np.triu(adjacency) == 1)
-    for (bond_ij, atom_i, atom_j) in zip(bonds_ij, atoms_i, atoms_j):
-        if atom_i == atom_j or bond_ij == BOND_DIM - 1:
-            continue
-        bond_type = bond_mapping[bond_ij]
-        molecule.AddBond(int(atom_i), int(atom_j), bond_type)
-
-    # Sanitize the molecule; for more information on sanitization, see
-    # https://www.rdkit.org/docs/RDKit_Book.html#molecular-sanitization
-    flag = Chem.SanitizeMol(molecule, catchErrors=True)
-    # Let's be strict. If sanitization fails, return None
-    if flag != Chem.SanitizeFlags.SANITIZE_NONE:
-        return None
-
-    return molecule
-    
-generator = from_pretrained_keras("keras-io/wgan-molecular-graphs")
-
-def predict(num_mol):
-    samples = num_mol*2
-    z = tf.random.normal((samples, LATENT_DIM))
-    graph = generator.predict(z)
-    # obtain one-hot encoded adjacency tensor
-    adjacency = tf.argmax(graph[0], axis=1)
-    adjacency = tf.one_hot(adjacency, depth=BOND_DIM, axis=1)
-    # Remove potential self-loops from adjacency
-    adjacency = tf.linalg.set_diag(adjacency, tf.zeros(tf.shape(adjacency)[:-1]))
-    # obtain one-hot encoded feature tensor
-    features = tf.argmax(graph[1], axis=2)
-    features = tf.one_hot(features, depth=ATOM_DIM, axis=2)
-    molecules = [
-        graph_to_molecule([adjacency[i].numpy(), features[i].numpy()])
-        for i in range(samples)
-    ]
-    MolsToGridImage(
-        [m for m in molecules if m is not None][:num_mol], molsPerRow=5, subImgSize=(150, 150), returnPNG=False, 
-    ).save("img.png")
-    return 'img.png'
-
-gr.Interface(
-    fn=predict,
-    title="Generating molecular graphs by WGAN-GP",
-    description = "WGAN-GP with R-GCN for the generation of small molecular graphs 🔬",
-    inputs=[
-        gr.inputs.Slider(5, 50, label='Number of Molecular Graphs', step=5, default=10),
-    ],
-    outputs="image",
-    article = "Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the keras example from <a href=\"https://keras.io/examples/generative/wgan-graphs/\">Alexander Kensert</a>",
-    ).launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/audio2pose_models/audio_encoder.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/audio2pose_models/audio_encoder.py
deleted file mode 100644
index 6279d2014a2e786a6c549f084339e18d00e50331..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/audio2pose_models/audio_encoder.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-class Conv2d(nn.Module):
-    def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.conv_block = nn.Sequential(
-                            nn.Conv2d(cin, cout, kernel_size, stride, padding),
-                            nn.BatchNorm2d(cout)
-                            )
-        self.act = nn.ReLU()
-        self.residual = residual
-
-    def forward(self, x):
-        out = self.conv_block(x)
-        if self.residual:
-            out += x
-        return self.act(out)
-
-class AudioEncoder(nn.Module):
-    def __init__(self, wav2lip_checkpoint, device):
-        super(AudioEncoder, self).__init__()
-
-        self.audio_encoder = nn.Sequential(
-            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
-            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
-            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
-            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
-            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
-            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),)
-
-        #### load the pre-trained audio_encoder, we do not need to load wav2lip model here.
-        # wav2lip_state_dict = torch.load(wav2lip_checkpoint, map_location=torch.device(device))['state_dict']
-        # state_dict = self.audio_encoder.state_dict()
-
-        # for k,v in wav2lip_state_dict.items():
-        #     if 'audio_encoder' in k:
-        #         state_dict[k.replace('module.audio_encoder.', '')] = v
-        # self.audio_encoder.load_state_dict(state_dict)
-
-
-    def forward(self, audio_sequences):
-        # audio_sequences = (B, T, 1, 80, 16)
-        B = audio_sequences.size(0)
-
-        audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
-
-        audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1
-        dim = audio_embedding.shape[1]
-        audio_embedding = audio_embedding.reshape((B, -1, dim, 1, 1))
-
-        return audio_embedding.squeeze(-1).squeeze(-1) #B seq_len+1 512 
diff --git a/spaces/kevinwang676/VoiceChanger/src/face3d/models/arcface_torch/onnx_ijbc.py b/spaces/kevinwang676/VoiceChanger/src/face3d/models/arcface_torch/onnx_ijbc.py
deleted file mode 100644
index 05b50bfad4b4cf38903b89f596263a8e29a50d3e..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChanger/src/face3d/models/arcface_torch/onnx_ijbc.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import argparse
-import os
-import pickle
-import timeit
-
-import cv2
-import mxnet as mx
-import numpy as np
-import pandas as pd
-import prettytable
-import skimage.transform
-from sklearn.metrics import roc_curve
-from sklearn.preprocessing import normalize
-
-from onnx_helper import ArcFaceORT
-
-SRC = np.array(
-    [
-        [30.2946, 51.6963],
-        [65.5318, 51.5014],
-        [48.0252, 71.7366],
-        [33.5493, 92.3655],
-        [62.7299, 92.2041]]
-    , dtype=np.float32)
-SRC[:, 0] += 8.0
-
-
-class AlignedDataSet(mx.gluon.data.Dataset):
-    def __init__(self, root, lines, align=True):
-        self.lines = lines
-        self.root = root
-        self.align = align
-
-    def __len__(self):
-        return len(self.lines)
-
-    def __getitem__(self, idx):
-        each_line = self.lines[idx]
-        name_lmk_score = each_line.strip().split(' ')
-        name = os.path.join(self.root, name_lmk_score[0])
-        img = cv2.cvtColor(cv2.imread(name), cv2.COLOR_BGR2RGB)
-        landmark5 = np.array([float(x) for x in name_lmk_score[1:-1]], dtype=np.float32).reshape((5, 2))
-        st = skimage.transform.SimilarityTransform()
-        st.estimate(landmark5, SRC)
-        img = cv2.warpAffine(img, st.params[0:2, :], (112, 112), borderValue=0.0)
-        img_1 = np.expand_dims(img, 0)
-        img_2 = np.expand_dims(np.fliplr(img), 0)
-        output = np.concatenate((img_1, img_2), axis=0).astype(np.float32)
-        output = np.transpose(output, (0, 3, 1, 2))
-        output = mx.nd.array(output)
-        return output
-
-
-def extract(model_root, dataset):
-    model = ArcFaceORT(model_path=model_root)
-    model.check()
-    feat_mat = np.zeros(shape=(len(dataset), 2 * model.feat_dim))
-
-    def batchify_fn(data):
-        return mx.nd.concat(*data, dim=0)
-
-    data_loader = mx.gluon.data.DataLoader(
-        dataset, 128, last_batch='keep', num_workers=4,
-        thread_pool=True, prefetch=16, batchify_fn=batchify_fn)
-    num_iter = 0
-    for batch in data_loader:
-        batch = batch.asnumpy()
-        batch = (batch - model.input_mean) / model.input_std
-        feat = model.session.run(model.output_names, {model.input_name: batch})[0]
-        feat = np.reshape(feat, (-1, model.feat_dim * 2))
-        feat_mat[128 * num_iter: 128 * num_iter + feat.shape[0], :] = feat
-        num_iter += 1
-        if num_iter % 50 == 0:
-            print(num_iter)
-    return feat_mat
-
-
-def read_template_media_list(path):
-    ijb_meta = pd.read_csv(path, sep=' ', header=None).values
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
-    return templates, medias
-
-
-def read_template_pair_list(path):
-    pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
-    return t1, t2, label
-
-
-def read_image_feature(path):
-    with open(path, 'rb') as fid:
-        img_feats = pickle.load(fid)
-    return img_feats
-
-
-def image2template_feature(img_feats=None,
-                           templates=None,
-                           medias=None):
-    unique_templates = np.unique(templates)
-    template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))
-    for count_template, uqt in enumerate(unique_templates):
-        (ind_t,) = np.where(templates == uqt)
-        face_norm_feats = img_feats[ind_t]
-        face_medias = medias[ind_t]
-        unique_medias, unique_media_counts = np.unique(face_medias, return_counts=True)
-        media_norm_feats = []
-        for u, ct in zip(unique_medias, unique_media_counts):
-            (ind_m,) = np.where(face_medias == u)
-            if ct == 1:
-                media_norm_feats += [face_norm_feats[ind_m]]
-            else:  # image features from the same video will be aggregated into one feature
-                media_norm_feats += [np.mean(face_norm_feats[ind_m], axis=0, keepdims=True), ]
-        media_norm_feats = np.array(media_norm_feats)
-        template_feats[count_template] = np.sum(media_norm_feats, axis=0)
-        if count_template % 2000 == 0:
-            print('Finish Calculating {} template features.'.format(
-                count_template))
-    template_norm_feats = normalize(template_feats)
-    return template_norm_feats, unique_templates
-
-
-def verification(template_norm_feats=None,
-                 unique_templates=None,
-                 p1=None,
-                 p2=None):
-    template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
-    for count_template, uqt in enumerate(unique_templates):
-        template2id[uqt] = count_template
-    score = np.zeros((len(p1),))
-    total_pairs = np.array(range(len(p1)))
-    batchsize = 100000
-    sublists = [total_pairs[i: i + batchsize] for i in range(0, len(p1), batchsize)]
-    total_sublists = len(sublists)
-    for c, s in enumerate(sublists):
-        feat1 = template_norm_feats[template2id[p1[s]]]
-        feat2 = template_norm_feats[template2id[p2[s]]]
-        similarity_score = np.sum(feat1 * feat2, -1)
-        score[s] = similarity_score.flatten()
-        if c % 10 == 0:
-            print('Finish {}/{} pairs.'.format(c, total_sublists))
-    return score
-
-
-def verification2(template_norm_feats=None,
-                  unique_templates=None,
-                  p1=None,
-                  p2=None):
-    template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
-    for count_template, uqt in enumerate(unique_templates):
-        template2id[uqt] = count_template
-    score = np.zeros((len(p1),))  # save cosine distance between pairs
-    total_pairs = np.array(range(len(p1)))
-    batchsize = 100000  # small batchsize instead of all pairs in one batch due to the memory limiation
-    sublists = [total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)]
-    total_sublists = len(sublists)
-    for c, s in enumerate(sublists):
-        feat1 = template_norm_feats[template2id[p1[s]]]
-        feat2 = template_norm_feats[template2id[p2[s]]]
-        similarity_score = np.sum(feat1 * feat2, -1)
-        score[s] = similarity_score.flatten()
-        if c % 10 == 0:
-            print('Finish {}/{} pairs.'.format(c, total_sublists))
-    return score
-
-
-def main(args):
-    use_norm_score = True  # if Ture, TestMode(N1)
-    use_detector_score = True  # if Ture, TestMode(D1)
-    use_flip_test = True  # if Ture, TestMode(F1)
-    assert args.target == 'IJBC' or args.target == 'IJBB'
-
-    start = timeit.default_timer()
-    templates, medias = read_template_media_list(
-        os.path.join('%s/meta' % args.image_path, '%s_face_tid_mid.txt' % args.target.lower()))
-    stop = timeit.default_timer()
-    print('Time: %.2f s. ' % (stop - start))
-
-    start = timeit.default_timer()
-    p1, p2, label = read_template_pair_list(
-        os.path.join('%s/meta' % args.image_path,
-                     '%s_template_pair_label.txt' % args.target.lower()))
-    stop = timeit.default_timer()
-    print('Time: %.2f s. ' % (stop - start))
-
-    start = timeit.default_timer()
-    img_path = '%s/loose_crop' % args.image_path
-    img_list_path = '%s/meta/%s_name_5pts_score.txt' % (args.image_path, args.target.lower())
-    img_list = open(img_list_path)
-    files = img_list.readlines()
-    dataset = AlignedDataSet(root=img_path, lines=files, align=True)
-    img_feats = extract(args.model_root, dataset)
-
-    faceness_scores = []
-    for each_line in files:
-        name_lmk_score = each_line.split()
-        faceness_scores.append(name_lmk_score[-1])
-    faceness_scores = np.array(faceness_scores).astype(np.float32)
-    stop = timeit.default_timer()
-    print('Time: %.2f s. ' % (stop - start))
-    print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0], img_feats.shape[1]))
-    start = timeit.default_timer()
-
-    if use_flip_test:
-        img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2] + img_feats[:, img_feats.shape[1] // 2:]
-    else:
-        img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2]
-
-    if use_norm_score:
-        img_input_feats = img_input_feats
-    else:
-        img_input_feats = img_input_feats / np.sqrt(np.sum(img_input_feats ** 2, -1, keepdims=True))
-
-    if use_detector_score:
-        print(img_input_feats.shape, faceness_scores.shape)
-        img_input_feats = img_input_feats * faceness_scores[:, np.newaxis]
-    else:
-        img_input_feats = img_input_feats
-
-    template_norm_feats, unique_templates = image2template_feature(
-        img_input_feats, templates, medias)
-    stop = timeit.default_timer()
-    print('Time: %.2f s. ' % (stop - start))
-
-    start = timeit.default_timer()
-    score = verification(template_norm_feats, unique_templates, p1, p2)
-    stop = timeit.default_timer()
-    print('Time: %.2f s. ' % (stop - start))
-    save_path = os.path.join(args.result_dir, "{}_result".format(args.target))
-    if not os.path.exists(save_path):
-        os.makedirs(save_path)
-    score_save_file = os.path.join(save_path, "{}.npy".format(args.model_root))
-    np.save(score_save_file, score)
-    files = [score_save_file]
-    methods = []
-    scores = []
-    for file in files:
-        methods.append(os.path.basename(file))
-        scores.append(np.load(file))
-    methods = np.array(methods)
-    scores = dict(zip(methods, scores))
-    x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]
-    tpr_fpr_table = prettytable.PrettyTable(['Methods'] + [str(x) for x in x_labels])
-    for method in methods:
-        fpr, tpr, _ = roc_curve(label, scores[method])
-        fpr = np.flipud(fpr)
-        tpr = np.flipud(tpr)
-        tpr_fpr_row = []
-        tpr_fpr_row.append("%s-%s" % (method, args.target))
-        for fpr_iter in np.arange(len(x_labels)):
-            _, min_index = min(
-                list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr)))))
-            tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100))
-        tpr_fpr_table.add_row(tpr_fpr_row)
-    print(tpr_fpr_table)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='do ijb test')
-    # general
-    parser.add_argument('--model-root', default='', help='path to load model.')
-    parser.add_argument('--image-path', default='', type=str, help='')
-    parser.add_argument('--result-dir', default='.', type=str, help='')
-    parser.add_argument('--target', default='IJBC', type=str, help='target, set to IJBC or IJBB')
-    main(parser.parse_args())
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg2mel/utils/abs_model.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg2mel/utils/abs_model.py
deleted file mode 100644
index b6d27a6df74c6988dd4355cbef149ed90f3a36cf..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg2mel/utils/abs_model.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from abc import ABC
-from abc import abstractmethod
-
-import torch
-
-class AbsMelDecoder(torch.nn.Module, ABC):
-    """The abstract PPG-based voice conversion class
-    This "model" is one of mediator objects for "Task" class.
-
-    """
-
-    @abstractmethod
-    def forward(
-        self, 
-        bottle_neck_features: torch.Tensor,
-        feature_lengths: torch.Tensor,
-        speech: torch.Tensor,
-        speech_lengths: torch.Tensor,
-        logf0_uv: torch.Tensor = None,
-        spembs: torch.Tensor = None,
-        styleembs: torch.Tensor = None,
-    ) -> torch.Tensor:
-        raise NotImplementedError
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/vocoder/fregan/modules.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/vocoder/fregan/modules.py
deleted file mode 100644
index 9b8160e4a9cd14627f0bc7a3f28c36ea6483e3a5..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/vocoder/fregan/modules.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-class KernelPredictor(torch.nn.Module):
-    ''' Kernel predictor for the location-variable convolutions
-    '''
-
-    def __init__(self,
-                 cond_channels,
-                 conv_in_channels,
-                 conv_out_channels,
-                 conv_layers,
-                 conv_kernel_size=3,
-                 kpnet_hidden_channels=64,
-                 kpnet_conv_size=3,
-                 kpnet_dropout=0.0,
-                 kpnet_nonlinear_activation="LeakyReLU",
-                 kpnet_nonlinear_activation_params={"negative_slope": 0.1}
-                 ):
-        '''
-        Args:
-            cond_channels (int): number of channel for the conditioning sequence,
-            conv_in_channels (int): number of channel for the input sequence,
-            conv_out_channels (int): number of channel for the output sequence,
-            conv_layers (int):
-            kpnet_
-        '''
-        super().__init__()
-
-        self.conv_in_channels = conv_in_channels
-        self.conv_out_channels = conv_out_channels
-        self.conv_kernel_size = conv_kernel_size
-        self.conv_layers = conv_layers
-
-        l_w = conv_in_channels * conv_out_channels * conv_kernel_size * conv_layers
-        l_b = conv_out_channels * conv_layers
-
-        padding = (kpnet_conv_size - 1) // 2
-        self.input_conv = torch.nn.Sequential(
-            torch.nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=(5 - 1) // 2, bias=True),
-            getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
-        )
-
-        self.residual_conv = torch.nn.Sequential(
-            torch.nn.Dropout(kpnet_dropout),
-            torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True),
-            getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
-            torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True),
-            getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
-            torch.nn.Dropout(kpnet_dropout),
-            torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True),
-            getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
-            torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True),
-            getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
-            torch.nn.Dropout(kpnet_dropout),
-            torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True),
-            getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
-            torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True),
-            getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
-        )
-
-        self.kernel_conv = torch.nn.Conv1d(kpnet_hidden_channels, l_w, kpnet_conv_size,
-                                           padding=padding, bias=True)
-        self.bias_conv = torch.nn.Conv1d(kpnet_hidden_channels, l_b, kpnet_conv_size, padding=padding,
-                                         bias=True)
-
-    def forward(self, c):
-        '''
-        Args:
-            c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
-        Returns:
-        '''
-        batch, cond_channels, cond_length = c.shape
-
-        c = self.input_conv(c)
-        c = c + self.residual_conv(c)
-        k = self.kernel_conv(c)
-        b = self.bias_conv(c)
-
-        kernels = k.contiguous().view(batch,
-                                      self.conv_layers,
-                                      self.conv_in_channels,
-                                      self.conv_out_channels,
-                                      self.conv_kernel_size,
-                                      cond_length)
-        bias = b.contiguous().view(batch,
-                                   self.conv_layers,
-                                   self.conv_out_channels,
-                                   cond_length)
-        return kernels, bias
-
-
-class LVCBlock(torch.nn.Module):
-    ''' the location-variable convolutions
-    '''
-
-    def __init__(self,
-                 in_channels,
-                 cond_channels,
-                 upsample_ratio,
-                 conv_layers=4,
-                 conv_kernel_size=3,
-                 cond_hop_length=256,
-                 kpnet_hidden_channels=64,
-                 kpnet_conv_size=3,
-                 kpnet_dropout=0.0
-                 ):
-        super().__init__()
-
-        self.cond_hop_length = cond_hop_length
-        self.conv_layers = conv_layers
-        self.conv_kernel_size = conv_kernel_size
-        self.convs = torch.nn.ModuleList()
-
-        self.upsample = torch.nn.ConvTranspose1d(in_channels, in_channels,
-                                    kernel_size=upsample_ratio*2, stride=upsample_ratio,
-                                    padding=upsample_ratio // 2 + upsample_ratio % 2,
-                                    output_padding=upsample_ratio % 2)
-
-
-        self.kernel_predictor = KernelPredictor(
-            cond_channels=cond_channels,
-            conv_in_channels=in_channels,
-            conv_out_channels=2 * in_channels,
-            conv_layers=conv_layers,
-            conv_kernel_size=conv_kernel_size,
-            kpnet_hidden_channels=kpnet_hidden_channels,
-            kpnet_conv_size=kpnet_conv_size,
-            kpnet_dropout=kpnet_dropout
-        )
-
-
-        for i in range(conv_layers):
-            padding = (3 ** i) * int((conv_kernel_size - 1) / 2)
-            conv = torch.nn.Conv1d(in_channels, in_channels, kernel_size=conv_kernel_size, padding=padding, dilation=3 ** i)
-
-            self.convs.append(conv)
-
-
-    def forward(self, x, c):
-        ''' forward propagation of the location-variable convolutions.
-        Args:
-            x (Tensor): the input sequence (batch, in_channels, in_length)
-            c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
-
-        Returns:
-            Tensor: the output sequence (batch, in_channels, in_length)
-        '''
-        batch, in_channels, in_length = x.shape
-
-
-        kernels, bias = self.kernel_predictor(c)
-
-        x = F.leaky_relu(x, 0.2)
-        x = self.upsample(x)
-
-        for i in range(self.conv_layers):
-            y = F.leaky_relu(x, 0.2)
-            y = self.convs[i](y)
-            y = F.leaky_relu(y, 0.2)
-
-            k = kernels[:, i, :, :, :, :]
-            b = bias[:, i, :, :]
-            y = self.location_variable_convolution(y, k, b, 1, self.cond_hop_length)
-            x = x + torch.sigmoid(y[:, :in_channels, :]) * torch.tanh(y[:, in_channels:, :])
-        return x
-
-    def location_variable_convolution(self, x, kernel, bias, dilation, hop_size):
-        ''' perform location-variable convolution operation on the input sequence (x) using the local convolution kernl.
-        Time: 414 μs ± 309 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each), test on NVIDIA V100.
-        Args:
-            x (Tensor): the input sequence (batch, in_channels, in_length).
-            kernel (Tensor): the local convolution kernel (batch, in_channel, out_channels, kernel_size, kernel_length)
-            bias (Tensor): the bias for the local convolution (batch, out_channels, kernel_length)
-            dilation (int): the dilation of convolution.
-            hop_size (int): the hop_size of the conditioning sequence.
-        Returns:
-            (Tensor): the output sequence after performing local convolution. (batch, out_channels, in_length).
-        '''
-        batch, in_channels, in_length = x.shape
-        batch, in_channels, out_channels, kernel_size, kernel_length = kernel.shape
-
-
-        assert in_length == (kernel_length * hop_size), "length of (x, kernel) is not matched"
-
-        padding = dilation * int((kernel_size - 1) / 2)
-        x = F.pad(x, (padding, padding), 'constant', 0)  # (batch, in_channels, in_length + 2*padding)
-        x = x.unfold(2, hop_size + 2 * padding, hop_size)  # (batch, in_channels, kernel_length, hop_size + 2*padding)
-
-        if hop_size < dilation:
-            x = F.pad(x, (0, dilation), 'constant', 0)
-        x = x.unfold(3, dilation,
-                     dilation)  # (batch, in_channels, kernel_length, (hop_size + 2*padding)/dilation, dilation)
-        x = x[:, :, :, :, :hop_size]
-        x = x.transpose(3, 4)  # (batch, in_channels, kernel_length, dilation, (hop_size + 2*padding)/dilation)
-        x = x.unfold(4, kernel_size, 1)  # (batch, in_channels, kernel_length, dilation, _, kernel_size)
-
-        o = torch.einsum('bildsk,biokl->bolsd', x, kernel)
-        o = o + bias.unsqueeze(-1).unsqueeze(-1)
-        o = o.contiguous().view(batch, out_channels, -1)
-        return o
diff --git a/spaces/kira4424/VITS-fast-fine-tuning/README_ZH.md b/spaces/kira4424/VITS-fast-fine-tuning/README_ZH.md
deleted file mode 100644
index 269546ccb91643bef62d872b39a90bf19a8393aa..0000000000000000000000000000000000000000
--- a/spaces/kira4424/VITS-fast-fine-tuning/README_ZH.md
+++ /dev/null
@@ -1,60 +0,0 @@
-English Documentation Please Click [here](https://github.com/Plachtaa/VITS-fast-fine-tuning/blob/main/README.md)
-# VITS 快速微调
-这个代码库会指导你如何将自定义角色（甚至你自己），加入预训练的VITS模型中，在1小时内的微调使模型具备如下功能：  
-1. 在 模型所包含的任意两个角色 之间进行声线转换
-2. 以 你加入的角色声线 进行中日英三语 文本到语音合成。  
-
-本项目使用的底模涵盖常见二次元男/女配音声线（来自原神数据集）以及现实世界常见男/女声线（来自VCTK数据集），支持中日英三语，保证能够在微调时快速适应新的声线。
-
-欢迎体验微调所使用的底模！  
-
-中日英：[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer) 作者：我  
-
-中日：[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/sayashi/vits-uma-genshin-honkai) 作者：[SayaSS](https://github.com/SayaSS) 
-
-### 目前支持的任务:
-- [x] 从 10条以上的短音频 克隆角色声音
-- [x] 从 3分钟以上的长音频（单个音频只能包含单说话人） 克隆角色声音
-- [x] 从 3分钟以上的视频（单个视频只能包含单说话人） 克隆角色声音
-- [x] 通过输入 bilibili视频链接（单个视频只能包含单说话人） 克隆角色声音
-
-### 目前支持声线转换和中日英三语TTS的角色
-- [x] 任意角色（只要你有角色的声音样本）
-（注意：声线转换只能在任意两个存在于模型中的说话人之间进行）
-
-
-
-
-## 微调
-建议使用 [Google Colab](https://colab.research.google.com/drive/1pn1xnFfdLK63gVXDwV4zCXfVeo8c-I-0?usp=sharing)
-进行微调任务，因为VITS在多语言情况下的某些环境依赖相当难以配置。
-### 在Google Colab里，我需要花多长时间？
-1. 安装依赖 (3 min)
-2. 选择预训练模型，详细区别参见[Colab 笔记本页面](https://colab.research.google.com/drive/1pn1xnFfdLK63gVXDwV4zCXfVeo8c-I-0?usp=sharing)。
-3. 上传你希望加入的其它角色声音，详细上传方式见[DATA.MD](https://github.com/Plachtaa/VITS-fast-fine-tuning/blob/main/DATA.MD)
-4. 进行微调，根据选择的微调方式和样本数量不同，花费时长可能在20分钟到2小时不等。
-
-微调结束后可以直接下载微调好的模型，日后在本地运行（不需要GPU）
-
-## 本地运行和推理
-0. 记得下载微调好的模型和config文件！
-1. 下载最新的Release包（在Github页面的右侧）
-2. 把下载的模型和config文件放在 `inference`文件夹下, 其文件名分别为 `G_latest.pth` 和 `finetune_speaker.json`。
-3. 一切准备就绪后，文件结构应该如下所示:
-```
-inference
-├───inference.exe
-├───...
-├───finetune_speaker.json
-└───G_latest.pth
-```
-4. 运行 `inference.exe`, 浏览器会自动弹出窗口, 注意其所在路径不能有中文字符或者空格.
-
-## 在MoeGoe使用
-0. MoeGoe以及类似其它VITS推理UI使用的config格式略有不同，需要下载的文件为模型`G_latest.pth`和配置文件`moegoe_config.json`
-1. 按照[MoeGoe](https://github.com/CjangCjengh/MoeGoe)页面的提示配置路径即可使用。
-2. MoeGoe在输入句子时需要使用相应的语言标记包裹句子才能正常合成。（日语用[JA], 中文用[ZH], 英文用[EN]），例如：  
-[JA]こんにちわ。[JA]  
-[ZH]你好！[ZH]  
-[EN]Hello![EN]  
-
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/ops/deform_roi_pool.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/ops/deform_roi_pool.py
deleted file mode 100644
index cc245ba91fee252226ba22e76bb94a35db9a629b..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/ops/deform_roi_pool.py
+++ /dev/null
@@ -1,204 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from torch import nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
-    '_ext', ['deform_roi_pool_forward', 'deform_roi_pool_backward'])
-
-
-class DeformRoIPoolFunction(Function):
-
-    @staticmethod
-    def symbolic(g, input, rois, offset, output_size, spatial_scale,
-                 sampling_ratio, gamma):
-        return g.op(
-            'mmcv::MMCVDeformRoIPool',
-            input,
-            rois,
-            offset,
-            pooled_height_i=output_size[0],
-            pooled_width_i=output_size[1],
-            spatial_scale_f=spatial_scale,
-            sampling_ratio_f=sampling_ratio,
-            gamma_f=gamma)
-
-    @staticmethod
-    def forward(ctx,
-                input,
-                rois,
-                offset,
-                output_size,
-                spatial_scale=1.0,
-                sampling_ratio=0,
-                gamma=0.1):
-        if offset is None:
-            offset = input.new_zeros(0)
-        ctx.output_size = _pair(output_size)
-        ctx.spatial_scale = float(spatial_scale)
-        ctx.sampling_ratio = int(sampling_ratio)
-        ctx.gamma = float(gamma)
-
-        assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!'
-
-        output_shape = (rois.size(0), input.size(1), ctx.output_size[0],
-                        ctx.output_size[1])
-        output = input.new_zeros(output_shape)
-
-        ext_module.deform_roi_pool_forward(
-            input,
-            rois,
-            offset,
-            output,
-            pooled_height=ctx.output_size[0],
-            pooled_width=ctx.output_size[1],
-            spatial_scale=ctx.spatial_scale,
-            sampling_ratio=ctx.sampling_ratio,
-            gamma=ctx.gamma)
-
-        ctx.save_for_backward(input, rois, offset)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        input, rois, offset = ctx.saved_tensors
-        grad_input = grad_output.new_zeros(input.shape)
-        grad_offset = grad_output.new_zeros(offset.shape)
-
-        ext_module.deform_roi_pool_backward(
-            grad_output,
-            input,
-            rois,
-            offset,
-            grad_input,
-            grad_offset,
-            pooled_height=ctx.output_size[0],
-            pooled_width=ctx.output_size[1],
-            spatial_scale=ctx.spatial_scale,
-            sampling_ratio=ctx.sampling_ratio,
-            gamma=ctx.gamma)
-        if grad_offset.numel() == 0:
-            grad_offset = None
-        return grad_input, None, grad_offset, None, None, None, None
-
-
-deform_roi_pool = DeformRoIPoolFunction.apply
-
-
-class DeformRoIPool(nn.Module):
-
-    def __init__(self,
-                 output_size,
-                 spatial_scale=1.0,
-                 sampling_ratio=0,
-                 gamma=0.1):
-        super(DeformRoIPool, self).__init__()
-        self.output_size = _pair(output_size)
-        self.spatial_scale = float(spatial_scale)
-        self.sampling_ratio = int(sampling_ratio)
-        self.gamma = float(gamma)
-
-    def forward(self, input, rois, offset=None):
-        return deform_roi_pool(input, rois, offset, self.output_size,
-                               self.spatial_scale, self.sampling_ratio,
-                               self.gamma)
-
-
-class DeformRoIPoolPack(DeformRoIPool):
-
-    def __init__(self,
-                 output_size,
-                 output_channels,
-                 deform_fc_channels=1024,
-                 spatial_scale=1.0,
-                 sampling_ratio=0,
-                 gamma=0.1):
-        super(DeformRoIPoolPack, self).__init__(output_size, spatial_scale,
-                                                sampling_ratio, gamma)
-
-        self.output_channels = output_channels
-        self.deform_fc_channels = deform_fc_channels
-
-        self.offset_fc = nn.Sequential(
-            nn.Linear(
-                self.output_size[0] * self.output_size[1] *
-                self.output_channels, self.deform_fc_channels),
-            nn.ReLU(inplace=True),
-            nn.Linear(self.deform_fc_channels, self.deform_fc_channels),
-            nn.ReLU(inplace=True),
-            nn.Linear(self.deform_fc_channels,
-                      self.output_size[0] * self.output_size[1] * 2))
-        self.offset_fc[-1].weight.data.zero_()
-        self.offset_fc[-1].bias.data.zero_()
-
-    def forward(self, input, rois):
-        assert input.size(1) == self.output_channels
-        x = deform_roi_pool(input, rois, None, self.output_size,
-                            self.spatial_scale, self.sampling_ratio,
-                            self.gamma)
-        rois_num = rois.size(0)
-        offset = self.offset_fc(x.view(rois_num, -1))
-        offset = offset.view(rois_num, 2, self.output_size[0],
-                             self.output_size[1])
-        return deform_roi_pool(input, rois, offset, self.output_size,
-                               self.spatial_scale, self.sampling_ratio,
-                               self.gamma)
-
-
-class ModulatedDeformRoIPoolPack(DeformRoIPool):
-
-    def __init__(self,
-                 output_size,
-                 output_channels,
-                 deform_fc_channels=1024,
-                 spatial_scale=1.0,
-                 sampling_ratio=0,
-                 gamma=0.1):
-        super(ModulatedDeformRoIPoolPack,
-              self).__init__(output_size, spatial_scale, sampling_ratio, gamma)
-
-        self.output_channels = output_channels
-        self.deform_fc_channels = deform_fc_channels
-
-        self.offset_fc = nn.Sequential(
-            nn.Linear(
-                self.output_size[0] * self.output_size[1] *
-                self.output_channels, self.deform_fc_channels),
-            nn.ReLU(inplace=True),
-            nn.Linear(self.deform_fc_channels, self.deform_fc_channels),
-            nn.ReLU(inplace=True),
-            nn.Linear(self.deform_fc_channels,
-                      self.output_size[0] * self.output_size[1] * 2))
-        self.offset_fc[-1].weight.data.zero_()
-        self.offset_fc[-1].bias.data.zero_()
-
-        self.mask_fc = nn.Sequential(
-            nn.Linear(
-                self.output_size[0] * self.output_size[1] *
-                self.output_channels, self.deform_fc_channels),
-            nn.ReLU(inplace=True),
-            nn.Linear(self.deform_fc_channels,
-                      self.output_size[0] * self.output_size[1] * 1),
-            nn.Sigmoid())
-        self.mask_fc[2].weight.data.zero_()
-        self.mask_fc[2].bias.data.zero_()
-
-    def forward(self, input, rois):
-        assert input.size(1) == self.output_channels
-        x = deform_roi_pool(input, rois, None, self.output_size,
-                            self.spatial_scale, self.sampling_ratio,
-                            self.gamma)
-        rois_num = rois.size(0)
-        offset = self.offset_fc(x.view(rois_num, -1))
-        offset = offset.view(rois_num, 2, self.output_size[0],
-                             self.output_size[1])
-        mask = self.mask_fc(x.view(rois_num, -1))
-        mask = mask.view(rois_num, 1, self.output_size[0], self.output_size[1])
-        d = deform_roi_pool(input, rois, offset, self.output_size,
-                            self.spatial_scale, self.sampling_ratio,
-                            self.gamma)
-        return d * mask
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/default_constructor.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/default_constructor.py
deleted file mode 100644
index 3f1f5b44168768dfda3947393a63a6cf9cf50b41..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/default_constructor.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from .builder import RUNNER_BUILDERS, RUNNERS
-
-
-@RUNNER_BUILDERS.register_module()
-class DefaultRunnerConstructor:
-    """Default constructor for runners.
-
-    Custom existing `Runner` like `EpocBasedRunner` though `RunnerConstructor`.
-    For example, We can inject some new properties and functions for `Runner`.
-
-    Example:
-        >>> from annotator.uniformer.mmcv.runner import RUNNER_BUILDERS, build_runner
-        >>> # Define a new RunnerReconstructor
-        >>> @RUNNER_BUILDERS.register_module()
-        >>> class MyRunnerConstructor:
-        ...     def __init__(self, runner_cfg, default_args=None):
-        ...         if not isinstance(runner_cfg, dict):
-        ...             raise TypeError('runner_cfg should be a dict',
-        ...                             f'but got {type(runner_cfg)}')
-        ...         self.runner_cfg = runner_cfg
-        ...         self.default_args = default_args
-        ...
-        ...     def __call__(self):
-        ...         runner = RUNNERS.build(self.runner_cfg,
-        ...                                default_args=self.default_args)
-        ...         # Add new properties for existing runner
-        ...         runner.my_name = 'my_runner'
-        ...         runner.my_function = lambda self: print(self.my_name)
-        ...         ...
-        >>> # build your runner
-        >>> runner_cfg = dict(type='EpochBasedRunner', max_epochs=40,
-        ...                   constructor='MyRunnerConstructor')
-        >>> runner = build_runner(runner_cfg)
-    """
-
-    def __init__(self, runner_cfg, default_args=None):
-        if not isinstance(runner_cfg, dict):
-            raise TypeError('runner_cfg should be a dict',
-                            f'but got {type(runner_cfg)}')
-        self.runner_cfg = runner_cfg
-        self.default_args = default_args
-
-    def __call__(self):
-        return RUNNERS.build(self.runner_cfg, default_args=self.default_args)
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/fully_sharded_data_parallel/README.md b/spaces/koajoel/PolyFormer/fairseq/examples/fully_sharded_data_parallel/README.md
deleted file mode 100644
index b9e44fef48bee5faeee27b3d1d1b1eb96b6a477f..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/fully_sharded_data_parallel/README.md
+++ /dev/null
@@ -1,177 +0,0 @@
-# Fully Sharded Data Parallel (FSDP)
-
-## Overview
-Recent work by [Microsoft](https://arxiv.org/abs/1910.02054) and
-[Google](https://arxiv.org/abs/2004.13336) has shown that data parallel
-training can be made significantly more efficient by sharding the model
-parameters and optimizer state across data parallel workers. These ideas are
-encapsulated in the new **`FullyShardedDataParallel` (FSDP)** wrapper provided
-by [fairscale](https://github.com/facebookresearch/fairscale/).
-
-Compared to PyTorch DDP:
-* FSDP produces identical results as PyTorch DDP (it's still synchronous data parallel training)
-* FSDP shards parameters (FP16 + FP32) and optimizer state across data parallel GPUs
-* FSDP is faster than PyTorch DDP because the optimizer step is sharded, and the communication can be overlapped with the forward pass
-* FSDP enables training 13B parameter models on 8 GPUs and 175B parameter models on 128 GPUs
-
-FSDP is fully supported in fairseq via the following new arguments:
-* `--ddp-backend=fully_sharded`: enables full sharding via FSDP
-* `--cpu-offload`: offloads the optimizer state and FP32 model copy to CPU (combine with `--optimizer=cpu_adam`)
-* `--no-reshard-after-forward`: increases training speed for large models (1B+ params) and is similar to ZeRO stage 2
-* other popular options (`--fp16`, `--update-freq`, `--checkpoint-activations`, `--offload-activations`, etc.) continue to work as normal
-
-<details><summary>Limitations</summary><p>
-
-FSDP currently has several limitations compared to fairseq's default DDP backend (PyTorch DDP):
-* while FSDP is full compatible with pointwise Optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.), it is not currently compatible with non-pointwise Optimizers (e.g., Adagrad, Adafactor, LAMB, etc.)
-* FSDP depends on flattening the parameters, so models that currently require `--fp16-no-flatten-grads` may not be supported
-
-See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed
-explanation of these and other limitations.
-
-</p></details>
-
-<details><summary>How it works</summary><p>
-
-<img width="800" alt="Fully Sharded Data Parallel" src="https://user-images.githubusercontent.com/231798/110406775-c2de0000-8050-11eb-9718-fbfc4510a76a.png">
-
-See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed
-explanation of how FSDP works.
-
-</p></details>
-
-## Example usage
-
-The following examples illustrate how to train a very large language model with
-13 billion parameters on 1 GPU by offloading parameters and optimizer states to
-CPU, or on 8 GPUs by fully sharding the params and optimizer states across GPUs.
-
-These examples use the WikiText-103 dataset for demonstration purposes, but
-in practice a much larger dataset will be needed to achieve good results.
-Follow the [instructions here](https://github.com/pytorch/fairseq/blob/main/examples/roberta/README.pretraining.md#1-preprocess-the-data)
-to preprocess the WikiText-103 dataset using the GPT-2/RoBERTa vocabulary.
-
-### 13B params on 1 V100 GPU (with CPU offloading)
-
-The following command trains a 13B parameter GPT-3 model on a single V100 GPU
-using the `--cpu-offload` feature to offload parameters and optimizer states to
-CPU. In this setting, the optimizer step (Adam) happens on CPU. We also use the
-`--checkpoint-activations` feature (sometimes called [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html)),
-which further saves memory in exchange for a small increase in computation.
-
-**Requirements:**
-- Install the latest master version of fairscale: `pip install git+https://github.com/facebookresearch/fairscale.git@master`
-- You'll need 32GB of GPU memory and ~256GB of system memory to train the 13B param model.
-- If you have less system memory, the 6.7B param model can be trained with ~128GB of system memory, just set `--arch transformer_lm_gpt3_6_7`
-- We use the CPU Adam optimizer from [DeepSpeed](https://github.com/microsoft/DeepSpeed), so you'll need to `pip install deepspeed` before running the command.
-
-**Notes:**
-- The command will take ~5 minutes to start training, during which time it will appear to be hung, since randomly initializing 13B weights can be slow.
-- The `--cpu-offload` feature requires training in mixed precision (`--fp16`).
-- Tune the `OMP_NUM_THREADS` env variable for best performance with CPU offloading.
-- The example command below stops training after 10 steps (`--max-update 10`) and does not save checkpoints (`--no-save`).
-
-```bash
-OMP_NUM_THREADS=20 CUDA_VISIBLE_DEVICES=0 \
-    fairseq-train data-bin/wikitext-103-roberta-bpe-bin \
-    --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \
-    --cpu-offload --checkpoint-activations \
-    --task language_modeling --tokens-per-sample 2048 --batch-size 8 \
-    --arch transformer_lm_gpt3_13 \
-    --optimizer cpu_adam --adam-betas "(0.9,0.98)" \
-    --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \
-    --max-update 10 --no-save --log-format json --log-interval 1
-```
-
-<details><summary>Example output</summary><p>
-
-```
-(...)
-2021-03-08 12:29:51 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920)
-(...)
-2021-03-08 12:29:51 | INFO | fairseq_cli.train | training on 1 devices (GPUs/TPUs)
-2021-03-08 12:29:51 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8
-(...)
-Adam Optimizer #0 is created with AVX2 arithmetic capability.
-Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1
-(...)
-2021-03-08 12:31:36 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.475", "ppl": "91120.8", "wps": "0", "ups": "0", "wpb": "16384", "bsz": "8", "num_updates": "1", "lr": "2e-05", "gnorm": "20.751", "loss_scale": "4", "train_wall": "99", "gb_free": "9.3", "wall": "105"}
-2021-03-08 12:32:33 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.446", "ppl": "89281.6", "wps": "288.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "2", "lr": "4e-05", "gnorm": "19.777", "loss_scale": "4", "train_wall": "57", "gb_free": "9.3", "wall": "161"}
-2021-03-08 12:33:12 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0
-2021-03-08 12:33:51 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0
-2021-03-08 12:34:45 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "25.22", "ppl": "3.90691e+07", "wps": "123.4", "ups": "0.01", "wpb": "16384", "bsz": "8", "num_updates": "3", "lr": "6e-05", "gnorm": "131.281", "loss_scale": "1", "train_wall": "133", "gb_free": "9.3", "wall": "294"}
-2021-03-08 12:35:43 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.079", "ppl": "276809", "wps": "285.5", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "4", "lr": "8e-05", "gnorm": "13.776", "loss_scale": "1", "train_wall": "57", "gb_free": "9.3", "wall": "351"}
-2021-03-08 12:36:35 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "23.729", "ppl": "1.39088e+07", "wps": "316.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "5", "lr": "0.0001", "gnorm": "72.774", "loss_scale": "1", "train_wall": "52", "gb_free": "9.3", "wall": "403"}
-2021-03-08 12:37:28 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "20.429", "ppl": "1.41203e+06", "wps": "307.6", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "6", "lr": "8e-05", "gnorm": "60.846", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "456"}
-2021-03-08 12:38:27 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.965", "ppl": "511684", "wps": "279.4", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "7", "lr": "6e-05", "gnorm": "22.687", "loss_scale": "1", "train_wall": "59", "gb_free": "9.3", "wall": "515"}
-2021-03-08 12:39:18 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.345", "ppl": "332887", "wps": "319.1", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "8", "lr": "4e-05", "gnorm": "8.451", "loss_scale": "1", "train_wall": "51", "gb_free": "9.3", "wall": "566"}
-2021-03-08 12:40:11 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "18.262", "ppl": "314336", "wps": "305.9", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "9", "lr": "2e-05", "gnorm": "6.457", "loss_scale": "1", "train_wall": "54", "gb_free": "9.3", "wall": "620"}
-2021-03-08 12:41:04 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "17.556", "ppl": "192686", "wps": "311.8", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "10", "lr": "0", "gnorm": "5.796", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "673"}
-2021-03-08 12:41:04 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10
-2021-03-08 12:41:04 | INFO | fairseq_cli.train | begin validation on "valid" subset
-2021-03-08 12:43:15 | INFO | valid | {"epoch": 1, "valid_loss": "17.953", "valid_ppl": "253807", "valid_wps": "1868.4", "valid_wpb": "15400.2", "valid_bsz": "7.6", "valid_num_updates": "10"}
-2021-03-08 12:43:15 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below)
-2021-03-08 12:43:15 | INFO | train | {"epoch": 1, "train_loss": "19.351", "train_ppl": "668509", "train_wps": "210.9", "train_ups": "0.01", "train_wpb": "16384", "train_bsz": "8", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "36.26", "train_loss_scale": "1", "train_train_wall": "667", "train_gb_free": "9.3", "train_wall": "804"}
-2021-03-08 12:43:15 | INFO | fairseq_cli.train | done training in 798.6 seconds
-```
-
-</p></details>
-
-### 13B params on 8 V100 GPUs (with full parameter + optimizer state sharding)
-
-FSDP can also shard the parameters and optimizer states across multiple GPUs,
-reducing memory requirements significantly. On 8 x 32GB GPUs, sharding enables
-training the same 13B parameter model *without offloading the parameters to
-CPU*. However, without CPU offloading we'd only be able to fit a batch size of
-1 per GPU, which would cause training speed to suffer.
-
-We obtain the best performance on 8 GPUs by combining full sharding and CPU
-offloading. The following command trains the same 13B parameter GPT-3 model as
-before on 8 x 32GB V100 GPUs; training speed increases superlinearly from ~310
-words per second to ~3200 words per second.
-
-```bash
-OMP_NUM_THREADS=20 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-    fairseq-train data-bin/wikitext-103-roberta-bpe-bin \
-    --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \
-    --cpu-offload --checkpoint-activations \
-    --task language_modeling --tokens-per-sample 2048 --batch-size 8 \
-    --arch transformer_lm_gpt3_13 \
-    --optimizer cpu_adam --adam-betas "(0.9,0.98)" \
-    --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \
-    --max-update 10 --no-save --log-format json --log-interval 1
-```
-
-<details><summary>Example output</summary><p>
-
-```
-(...)
-2021-03-08 18:04:09 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920)
-(...)
-2021-03-08 18:04:09 | INFO | fairseq_cli.train | training on 8 devices (GPUs/TPUs)
-2021-03-08 18:04:09 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8
-(...)
-Adam Optimizer #0 is created with AVX2 arithmetic capability.
-Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1
-(...)
-2021-03-08 18:05:06 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "16.408", "ppl": "86945.6", "wps": "0", "ups": "0", "wpb": "131072", "bsz": "64", "num_updates": "1", "lr": "2e-05", "gnorm": "18.27", "loss_scale": "4", "train_wall": "47", "gb_free": "9.3", "wall": "56"}
-2021-03-08 18:05:45 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "16.352", "ppl": "83644.3", "wps": "3283.4", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "2", "lr": "4e-05", "gnorm": "18.411", "loss_scale": "4", "train_wall": "40", "gb_free": "9.3", "wall": "96"}
-2021-03-08 18:06:21 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0
-2021-03-08 18:06:56 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0
-2021-03-08 18:07:37 | INFO | train_inner | {"epoch": 1, "update": 0.006, "loss": "23.682", "ppl": "1.34537e+07", "wps": "1176.6", "ups": "0.01", "wpb": "131072", "bsz": "64", "num_updates": "3", "lr": "6e-05", "gnorm": "119.682", "loss_scale": "1", "train_wall": "111", "gb_free": "9.3", "wall": "208"}
-2021-03-08 18:08:18 | INFO | train_inner | {"epoch": 1, "update": 0.007, "loss": "18.988", "ppl": "519921", "wps": "3189.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "4", "lr": "8e-05", "gnorm": "14.934", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "249"}
-2021-03-08 18:08:59 | INFO | train_inner | {"epoch": 1, "update": 0.008, "loss": "20.08", "ppl": "1.10798e+06", "wps": "3223.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "5", "lr": "0.0001", "gnorm": "59.92", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "289"}
-2021-03-08 18:09:39 | INFO | train_inner | {"epoch": 1, "update": 0.009, "loss": "18.323", "ppl": "327980", "wps": "3256.6", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "6", "lr": "8e-05", "gnorm": "37.425", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "330"}
-2021-03-08 18:10:20 | INFO | train_inner | {"epoch": 1, "update": 0.01, "loss": "17.264", "ppl": "157354", "wps": "3188.7", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "7", "lr": "6e-05", "gnorm": "10.824", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "371"}
-2021-03-08 18:11:01 | INFO | train_inner | {"epoch": 1, "update": 0.011, "loss": "16.794", "ppl": "113647", "wps": "3230", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "8", "lr": "4e-05", "gnorm": "5.616", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "411"}
-2021-03-08 18:11:39 | INFO | train_inner | {"epoch": 1, "update": 0.012, "loss": "16.706", "ppl": "106938", "wps": "3384", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "9", "lr": "2e-05", "gnorm": "5.318", "loss_scale": "1", "train_wall": "39", "gb_free": "9.3", "wall": "450"}
-2021-03-08 18:12:19 | INFO | train_inner | {"epoch": 1, "update": 0.013, "loss": "16.548", "ppl": "95796.2", "wps": "3274.4", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "10", "lr": "0", "gnorm": "5.22", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "490"}
-2021-03-08 18:12:19 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10
-2021-03-08 18:12:19 | INFO | fairseq_cli.train | begin validation on "valid" subset
-2021-03-08 18:12:45 | INFO | valid | {"epoch": 1, "valid_loss": "16.624", "valid_ppl": "101000", "valid_wps": "10855.9", "valid_wpb": "123202", "valid_bsz": "60.5", "valid_num_updates": "10"}
-2021-03-08 18:12:45 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below)
-2021-03-08 18:12:45 | INFO | train | {"epoch": 1, "train_loss": "18.114", "train_ppl": "283776", "train_wps": "2567.8", "train_ups": "0.02", "train_wpb": "131072", "train_bsz": "64", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "29.562", "train_loss_scale": "1", "train_train_wall": "480", "train_gb_free": "9.3", "train_wall": "516"}
-2021-03-08 18:12:45 | INFO | fairseq_cli.train | done training in 509.9 seconds
-```
-
-</p></details>
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/simultaneous_translation/docs/enja-waitk.md b/spaces/koajoel/PolyFormer/fairseq/examples/simultaneous_translation/docs/enja-waitk.md
deleted file mode 100644
index fb9d82576f80b4405564a99774fc98ac2fe6ad3b..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/simultaneous_translation/docs/enja-waitk.md
+++ /dev/null
@@ -1,106 +0,0 @@
-# An example of English to Japaneses Simultaneous Translation System
-
-This is an example of training and evaluating a transformer *wait-k* English to Japanese simultaneous text-to-text translation model.
-
-## Data Preparation
-This section introduces the data preparation for training and evaluation.
-If you only want to evaluate the model, please jump to [Inference & Evaluation](#inference-&-evaluation)
-
-For illustration, we only use the following subsets of the available data from [WMT20 news translation task](http://www.statmt.org/wmt20/translation-task.html), which results in 7,815,391 sentence pairs.
-- News Commentary v16
-- Wiki Titles v3
-- WikiMatrix V1
-- Japanese-English Subtitle Corpus
-- The Kyoto Free Translation Task Corpus
-
-We use WMT20 development data as development set. Training `transformer_vaswani_wmt_en_de_big` model on such amount of data will result in 17.3 BLEU with greedy search and 19.7 with beam (10) search. Notice that a better performance can be achieved with the full WMT training data.
-
-We use [sentencepiece](https://github.com/google/sentencepiece) toolkit to tokenize the data with a vocabulary size of 32000.
-Additionally, we filtered out the sentences longer than 200 words after tokenization.
-Assuming the tokenized text data is saved at `${DATA_DIR}`,
-we prepare the data binary with the following command.
-
-```bash
-fairseq-preprocess \
-    --source-lang en --target-lang ja \
-    --trainpref ${DATA_DIR}/train \
-    --validpref ${DATA_DIR}/dev \
-    --testpref ${DATA_DIR}/test \
-    --destdir ${WMT20_ENJA_DATA_BIN} \
-    --nwordstgt 32000 --nwordssrc 32000 \
-    --workers 20
-```
-
-## Simultaneous Translation Model Training
-To train a wait-k `(k=10)` model.
-```bash
-fairseq-train ${WMT20_ENJA_DATA_BIN}  \
-    --save-dir ${SAVEDIR}
-    --simul-type waitk  \
-    --waitk-lagging 10  \
-    --max-epoch 70  \
-    --arch transformer_monotonic_vaswani_wmt_en_de_big \
-    --optimizer adam  \
-    --adam-betas '(0.9, 0.98)'  \
-    --lr-scheduler inverse_sqrt  \
-    --warmup-init-lr 1e-07  \
-    --warmup-updates 4000  \
-    --lr 0.0005  \
-    --stop-min-lr 1e-09  \
-    --clip-norm 10.0  \
-    --dropout 0.3  \
-    --weight-decay 0.0  \
-    --criterion label_smoothed_cross_entropy  \
-    --label-smoothing 0.1  \
-    --max-tokens 3584
-```
-This command is for training on 8 GPUs. Equivalently, the model can be trained on one GPU with `--update-freq 8`.
-
-## Inference & Evaluation
-First of all, install [SimulEval](https://github.com/facebookresearch/SimulEval) for evaluation.
-
-```bash
-git clone https://github.com/facebookresearch/SimulEval.git
-cd SimulEval
-pip install -e .
-```
-
-The following command is for the evaluation.
-Assuming the source and reference files are `${SRC_FILE}` and `${REF_FILE}`, the sentencepiece model file for English is saved at `${SRC_SPM_PATH}`
-
-
-```bash
-simuleval \
-    --source ${SRC_FILE} \
-    --target ${TGT_FILE} \
-    --data-bin ${WMT20_ENJA_DATA_BIN} \
-    --sacrebleu-tokenizer ja-mecab \
-    --eval-latency-unit char \
-    --no-space \
-    --src-splitter-type sentencepiecemodel \
-    --src-splitter-path ${SRC_SPM_PATH} \
-    --agent ${FAIRSEQ}/examples/simultaneous_translation/agents/simul_trans_text_agent_enja.py \
-    --model-path ${SAVE_DIR}/${CHECKPOINT_FILENAME} \
-    --output ${OUTPUT} \
-    --scores
-```
-
-The `--data-bin` should be the same in previous sections if you prepare the data from the scratch.
-If only for evaluation, a prepared data directory can be found [here](https://dl.fbaipublicfiles.com/simultaneous_translation/wmt20_enja_medium_databin.tgz) and a pretrained checkpoint (wait-k=10 model) can be downloaded from [here](https://dl.fbaipublicfiles.com/simultaneous_translation/wmt20_enja_medium_wait10_ckpt.pt).
-
-The output should look like this:
-```bash
-{
-    "Quality": {
-        "BLEU": 11.442253287568398
-    },
-    "Latency": {
-        "AL": 8.6587861866951,
-        "AP": 0.7863304776251316,
-        "DAL": 9.477850951194764
-    }
-}
-```
-The latency is evaluated by characters (`--eval-latency-unit`) on the target side. The latency is evaluated with `sacrebleu` with `MeCab` tokenizer `--sacrebleu-tokenizer ja-mecab`. `--no-space` indicates that do not add space when merging the predicted words.
-
-If `--output ${OUTPUT}` option is used, the detailed log and scores will be stored under the `${OUTPUT}` directory.
diff --git a/spaces/kottu/stabble_diffusion_sketch/utils.py b/spaces/kottu/stabble_diffusion_sketch/utils.py
deleted file mode 100644
index ddf173a5dcb8d2ce3ecdda6b1fa626fd91e715f5..0000000000000000000000000000000000000000
--- a/spaces/kottu/stabble_diffusion_sketch/utils.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import random
-
-import numpy as np
-
-MAX_SEED = np.iinfo(np.int32).max
-
-
-style_list = [
-    {
-        "name": "(No style)",
-        "prompt": "{prompt}",
-        "negative_prompt": "",
-    },
-    {
-        "name": "Cinematic",
-        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
-        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
-    },
-    {
-        "name": "3D Model",
-        "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
-        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
-    },
-    {
-        "name": "Anime",
-        "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime,  highly detailed",
-        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
-    },
-    {
-        "name": "Digital Art",
-        "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
-        "negative_prompt": "photo, photorealistic, realism, ugly",
-    },
-    {
-        "name": "Photographic",
-        "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
-        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
-    },
-    {
-        "name": "Pixel art",
-        "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
-        "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
-    },
-    {
-        "name": "Fantasy art",
-        "prompt": "ethereal fantasy concept art of  {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
-        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
-    },
-    {
-        "name": "Neonpunk",
-        "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
-        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
-    },
-    {
-        "name": "Manga",
-        "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style",
-        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
-    },
-]
-
-styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
-STYLE_NAMES = list(styles.keys())
-DEFAULT_STYLE_NAME = "Photographic"
-
-
-def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
-    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
-    return p.replace("{prompt}", positive), n + negative
-
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
\ No newline at end of file
diff --git a/spaces/kukuhtw/VToonify/README.md b/spaces/kukuhtw/VToonify/README.md
deleted file mode 100644
index 40ded2ce4dc80cc8764c8fee50476d44bee383b6..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/VToonify/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: VToonify
-emoji: 👨‍🎨
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
-license: other
-duplicated_from: PKUWilliamYang/VToonify
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/M_V_A_R_.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/M_V_A_R_.py
deleted file mode 100644
index 8371795eb2f2d2c233ec1725b8a2c21453170f23..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/M_V_A_R_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table_M_V_A_R_(BaseTTXConverter):
-    pass
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio_client/utils.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio_client/utils.py
deleted file mode 100644
index 069ffdc53b2755ccd2fed74d4405d0331a4f0788..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio_client/utils.py
+++ /dev/null
@@ -1,547 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import base64
-import json
-import mimetypes
-import os
-import pkgutil
-import shutil
-import tempfile
-from concurrent.futures import CancelledError
-from dataclasses import dataclass, field
-from datetime import datetime
-from enum import Enum
-from pathlib import Path
-from threading import Lock
-from typing import Any, Callable, Optional
-
-import fsspec.asyn
-import httpx
-import huggingface_hub
-import requests
-from huggingface_hub import SpaceStage
-from websockets.legacy.protocol import WebSocketCommonProtocol
-
-API_URL = "/api/predict/"
-WS_URL = "/queue/join"
-UPLOAD_URL = "/upload"
-CONFIG_URL = "/config"
-API_INFO_URL = "/info"
-RAW_API_INFO_URL = "/info?serialize=False"
-SPACE_FETCHER_URL = "https://gradio-space-api-fetcher-v2.hf.space/api"
-RESET_URL = "/reset"
-SPACE_URL = "https://hf.space/{}"
-
-STATE_COMPONENT = "state"
-INVALID_RUNTIME = [
-    SpaceStage.NO_APP_FILE,
-    SpaceStage.CONFIG_ERROR,
-    SpaceStage.BUILD_ERROR,
-    SpaceStage.RUNTIME_ERROR,
-    SpaceStage.PAUSED,
-]
-
-__version__ = (pkgutil.get_data(__name__, "version.txt") or b"").decode("ascii").strip()
-
-
-class TooManyRequestsError(Exception):
-    """Raised when the API returns a 429 status code."""
-
-    pass
-
-
-class QueueError(Exception):
-    """Raised when the queue is full or there is an issue adding a job to the queue."""
-
-    pass
-
-
-class InvalidAPIEndpointError(Exception):
-    """Raised when the API endpoint is invalid."""
-
-    pass
-
-
-class SpaceDuplicationError(Exception):
-    """Raised when something goes wrong with a Space Duplication."""
-
-    pass
-
-
-class Status(Enum):
-    """Status codes presented to client users."""
-
-    STARTING = "STARTING"
-    JOINING_QUEUE = "JOINING_QUEUE"
-    QUEUE_FULL = "QUEUE_FULL"
-    IN_QUEUE = "IN_QUEUE"
-    SENDING_DATA = "SENDING_DATA"
-    PROCESSING = "PROCESSING"
-    ITERATING = "ITERATING"
-    PROGRESS = "PROGRESS"
-    FINISHED = "FINISHED"
-    CANCELLED = "CANCELLED"
-
-    @staticmethod
-    def ordering(status: Status) -> int:
-        """Order of messages. Helpful for testing."""
-        order = [
-            Status.STARTING,
-            Status.JOINING_QUEUE,
-            Status.QUEUE_FULL,
-            Status.IN_QUEUE,
-            Status.SENDING_DATA,
-            Status.PROCESSING,
-            Status.PROGRESS,
-            Status.ITERATING,
-            Status.FINISHED,
-            Status.CANCELLED,
-        ]
-        return order.index(status)
-
-    def __lt__(self, other: Status):
-        return self.ordering(self) < self.ordering(other)
-
-    @staticmethod
-    def msg_to_status(msg: str) -> Status:
-        """Map the raw message from the backend to the status code presented to users."""
-        return {
-            "send_hash": Status.JOINING_QUEUE,
-            "queue_full": Status.QUEUE_FULL,
-            "estimation": Status.IN_QUEUE,
-            "send_data": Status.SENDING_DATA,
-            "process_starts": Status.PROCESSING,
-            "process_generating": Status.ITERATING,
-            "process_completed": Status.FINISHED,
-            "progress": Status.PROGRESS,
-        }[msg]
-
-
-@dataclass
-class ProgressUnit:
-    index: Optional[int]
-    length: Optional[int]
-    unit: Optional[str]
-    progress: Optional[float]
-    desc: Optional[str]
-
-    @classmethod
-    def from_ws_msg(cls, data: list[dict]) -> list[ProgressUnit]:
-        return [
-            cls(
-                index=d.get("index"),
-                length=d.get("length"),
-                unit=d.get("unit"),
-                progress=d.get("progress"),
-                desc=d.get("desc"),
-            )
-            for d in data
-        ]
-
-
-@dataclass
-class StatusUpdate:
-    """Update message sent from the worker thread to the Job on the main thread."""
-
-    code: Status
-    rank: int | None
-    queue_size: int | None
-    eta: float | None
-    success: bool | None
-    time: datetime | None
-    progress_data: list[ProgressUnit] | None
-
-
-def create_initial_status_update():
-    return StatusUpdate(
-        code=Status.STARTING,
-        rank=None,
-        queue_size=None,
-        eta=None,
-        success=None,
-        time=datetime.now(),
-        progress_data=None,
-    )
-
-
-@dataclass
-class JobStatus:
-    """The job status.
-
-    Keeps track of the latest status update and intermediate outputs (not yet implements).
-    """
-
-    latest_status: StatusUpdate = field(default_factory=create_initial_status_update)
-    outputs: list[Any] = field(default_factory=list)
-
-
-@dataclass
-class Communicator:
-    """Helper class to help communicate between the worker thread and main thread."""
-
-    lock: Lock
-    job: JobStatus
-    prediction_processor: Callable[..., tuple]
-    reset_url: str
-    should_cancel: bool = False
-
-
-########################
-# Network utils
-########################
-
-
-def is_valid_url(possible_url: str) -> bool:
-    headers = {"User-Agent": "gradio (https://gradio.app/; team@gradio.app)"}
-    try:
-        head_request = requests.head(possible_url, headers=headers)
-        if head_request.status_code == 405:
-            return requests.get(possible_url, headers=headers).ok
-        return head_request.ok
-    except Exception:
-        return False
-
-
-async def get_pred_from_ws(
-    websocket: WebSocketCommonProtocol,
-    data: str,
-    hash_data: str,
-    helper: Communicator | None = None,
-) -> dict[str, Any]:
-    completed = False
-    resp = {}
-    while not completed:
-        # Receive message in the background so that we can
-        # cancel even while running a long pred
-        task = asyncio.create_task(websocket.recv())
-        while not task.done():
-            if helper:
-                with helper.lock:
-                    if helper.should_cancel:
-                        # Need to reset the iterator state since the client
-                        # will not reset the session
-                        async with httpx.AsyncClient() as http:
-                            reset = http.post(
-                                helper.reset_url, json=json.loads(hash_data)
-                            )
-                            # Retrieve cancel exception from task
-                            # otherwise will get nasty warning in console
-                            task.cancel()
-                            await asyncio.gather(task, reset, return_exceptions=True)
-                        raise CancelledError()
-            # Need to suspend this coroutine so that task actually runs
-            await asyncio.sleep(0.01)
-        msg = task.result()
-        resp = json.loads(msg)
-        if helper:
-            with helper.lock:
-                has_progress = "progress_data" in resp
-                status_update = StatusUpdate(
-                    code=Status.msg_to_status(resp["msg"]),
-                    queue_size=resp.get("queue_size"),
-                    rank=resp.get("rank", None),
-                    success=resp.get("success"),
-                    time=datetime.now(),
-                    eta=resp.get("rank_eta"),
-                    progress_data=ProgressUnit.from_ws_msg(resp["progress_data"])
-                    if has_progress
-                    else None,
-                )
-                output = resp.get("output", {}).get("data", [])
-                if output and status_update.code != Status.FINISHED:
-                    try:
-                        result = helper.prediction_processor(*output)
-                    except Exception as e:
-                        result = [e]
-                    helper.job.outputs.append(result)
-                helper.job.latest_status = status_update
-        if resp["msg"] == "queue_full":
-            raise QueueError("Queue is full! Please try again.")
-        if resp["msg"] == "send_hash":
-            await websocket.send(hash_data)
-        elif resp["msg"] == "send_data":
-            await websocket.send(data)
-        completed = resp["msg"] == "process_completed"
-    return resp["output"]
-
-
-########################
-# Data processing utils
-########################
-
-
-def download_tmp_copy_of_file(
-    url_path: str, hf_token: str | None = None, dir: str | None = None
-) -> tempfile._TemporaryFileWrapper:
-    if dir is not None:
-        os.makedirs(dir, exist_ok=True)
-    headers = {"Authorization": "Bearer " + hf_token} if hf_token else {}
-    prefix = Path(url_path).stem
-    suffix = Path(url_path).suffix
-    file_obj = tempfile.NamedTemporaryFile(
-        delete=False,
-        prefix=prefix,
-        suffix=suffix,
-        dir=dir,
-    )
-    with requests.get(url_path, headers=headers, stream=True) as r, open(
-        file_obj.name, "wb"
-    ) as f:
-        shutil.copyfileobj(r.raw, f)
-    return file_obj
-
-
-def create_tmp_copy_of_file(
-    file_path: str, dir: str | None = None
-) -> tempfile._TemporaryFileWrapper:
-    if dir is not None:
-        os.makedirs(dir, exist_ok=True)
-    prefix = Path(file_path).stem
-    suffix = Path(file_path).suffix
-    file_obj = tempfile.NamedTemporaryFile(
-        delete=False,
-        prefix=prefix,
-        suffix=suffix,
-        dir=dir,
-    )
-    shutil.copy2(file_path, file_obj.name)
-    return file_obj
-
-
-def get_mimetype(filename: str) -> str | None:
-    if filename.endswith(".vtt"):
-        return "text/vtt"
-    mimetype = mimetypes.guess_type(filename)[0]
-    if mimetype is not None:
-        mimetype = mimetype.replace("x-wav", "wav").replace("x-flac", "flac")
-    return mimetype
-
-
-def get_extension(encoding: str) -> str | None:
-    encoding = encoding.replace("audio/wav", "audio/x-wav")
-    type = mimetypes.guess_type(encoding)[0]
-    if type == "audio/flac":  # flac is not supported by mimetypes
-        return "flac"
-    elif type is None:
-        return None
-    extension = mimetypes.guess_extension(type)
-    if extension is not None and extension.startswith("."):
-        extension = extension[1:]
-    return extension
-
-
-def encode_file_to_base64(f: str | Path):
-    with open(f, "rb") as file:
-        encoded_string = base64.b64encode(file.read())
-        base64_str = str(encoded_string, "utf-8")
-        mimetype = get_mimetype(str(f))
-        return (
-            "data:"
-            + (mimetype if mimetype is not None else "")
-            + ";base64,"
-            + base64_str
-        )
-
-
-def encode_url_to_base64(url: str):
-    encoded_string = base64.b64encode(requests.get(url).content)
-    base64_str = str(encoded_string, "utf-8")
-    mimetype = get_mimetype(url)
-    return (
-        "data:" + (mimetype if mimetype is not None else "") + ";base64," + base64_str
-    )
-
-
-def encode_url_or_file_to_base64(path: str | Path):
-    path = str(path)
-    if is_valid_url(path):
-        return encode_url_to_base64(path)
-    else:
-        return encode_file_to_base64(path)
-
-
-def decode_base64_to_binary(encoding: str) -> tuple[bytes, str | None]:
-    extension = get_extension(encoding)
-    data = encoding.rsplit(",", 1)[-1]
-    return base64.b64decode(data), extension
-
-
-def strip_invalid_filename_characters(filename: str, max_bytes: int = 200) -> str:
-    """Strips invalid characters from a filename and ensures that the file_length is less than `max_bytes` bytes."""
-    filename = "".join([char for char in filename if char.isalnum() or char in "._- "])
-    filename_len = len(filename.encode())
-    if filename_len > max_bytes:
-        while filename_len > max_bytes:
-            if len(filename) == 0:
-                break
-            filename = filename[:-1]
-            filename_len = len(filename.encode())
-    return filename
-
-
-def sanitize_parameter_names(original_name: str) -> str:
-    """Cleans up a Python parameter name to make the API info more readable."""
-    return (
-        "".join([char for char in original_name if char.isalnum() or char in " _"])
-        .replace(" ", "_")
-        .lower()
-    )
-
-
-def decode_base64_to_file(
-    encoding: str,
-    file_path: str | None = None,
-    dir: str | Path | None = None,
-    prefix: str | None = None,
-):
-    if dir is not None:
-        os.makedirs(dir, exist_ok=True)
-    data, extension = decode_base64_to_binary(encoding)
-    if file_path is not None and prefix is None:
-        filename = Path(file_path).name
-        prefix = filename
-        if "." in filename:
-            prefix = filename[0 : filename.index(".")]
-            extension = filename[filename.index(".") + 1 :]
-
-    if prefix is not None:
-        prefix = strip_invalid_filename_characters(prefix)
-
-    if extension is None:
-        file_obj = tempfile.NamedTemporaryFile(delete=False, prefix=prefix, dir=dir)
-    else:
-        file_obj = tempfile.NamedTemporaryFile(
-            delete=False,
-            prefix=prefix,
-            suffix="." + extension,
-            dir=dir,
-        )
-    file_obj.write(data)
-    file_obj.flush()
-    return file_obj
-
-
-def dict_or_str_to_json_file(jsn: str | dict | list, dir: str | Path | None = None):
-    if dir is not None:
-        os.makedirs(dir, exist_ok=True)
-
-    file_obj = tempfile.NamedTemporaryFile(
-        delete=False, suffix=".json", dir=dir, mode="w+"
-    )
-    if isinstance(jsn, str):
-        jsn = json.loads(jsn)
-    json.dump(jsn, file_obj)
-    file_obj.flush()
-    return file_obj
-
-
-def file_to_json(file_path: str | Path) -> dict | list:
-    with open(file_path) as f:
-        return json.load(f)
-
-
-###########################
-# HuggingFace Hub API Utils
-###########################
-def set_space_timeout(
-    space_id: str,
-    hf_token: str | None = None,
-    timeout_in_seconds: int = 300,
-):
-    headers = huggingface_hub.utils.build_hf_headers(
-        token=hf_token,
-        library_name="gradio_client",
-        library_version=__version__,
-    )
-    r = requests.post(
-        f"https://huggingface.co/api/spaces/{space_id}/sleeptime",
-        json={"seconds": timeout_in_seconds},
-        headers=headers,
-    )
-    try:
-        huggingface_hub.utils.hf_raise_for_status(r)
-    except huggingface_hub.utils.HfHubHTTPError as err:
-        raise SpaceDuplicationError(
-            f"Could not set sleep timeout on duplicated Space. Please visit {SPACE_URL.format(space_id)} "
-            "to set a timeout manually to reduce billing charges."
-        ) from err
-
-
-########################
-# Misc utils
-########################
-
-
-def synchronize_async(func: Callable, *args, **kwargs) -> Any:
-    """
-    Runs async functions in sync scopes. Can be used in any scope.
-
-    Example:
-        if inspect.iscoroutinefunction(block_fn.fn):
-            predictions = utils.synchronize_async(block_fn.fn, *processed_input)
-
-    Args:
-        func:
-        *args:
-        **kwargs:
-    """
-    return fsspec.asyn.sync(fsspec.asyn.get_loop(), func, *args, **kwargs)  # type: ignore
-
-
-class APIInfoParseError(ValueError):
-    pass
-
-
-def get_type(schema: dict):
-    if "type" in schema:
-        return schema["type"]
-    elif schema.get("oneOf"):
-        return "oneOf"
-    elif schema.get("anyOf"):
-        return "anyOf"
-    else:
-        raise APIInfoParseError(f"Cannot parse type for {schema}")
-
-
-def json_schema_to_python_type(schema: Any) -> str:
-    """Convert the json schema into a python type hint"""
-    type_ = get_type(schema)
-    if type_ == {}:
-        if "json" in schema["description"]:
-            return "Dict[Any, Any]"
-        else:
-            return "Any"
-    elif type_ == "null":
-        return "None"
-    elif type_ == "integer":
-        return "int"
-    elif type_ == "string":
-        return "str"
-    elif type_ == "boolean":
-        return "bool"
-    elif type_ == "number":
-        return "int | float"
-    elif type_ == "array":
-        items = schema.get("items")
-        if "prefixItems" in items:
-            elements = ", ".join(
-                [json_schema_to_python_type(i) for i in items["prefixItems"]]
-            )
-            return f"Tuple[{elements}]"
-        else:
-            elements = json_schema_to_python_type(items)
-            return f"List[{elements}]"
-    elif type_ == "object":
-        des = ", ".join(
-            [
-                f"{n}: {json_schema_to_python_type(v)} ({v.get('description')})"
-                for n, v in schema["properties"].items()
-            ]
-        )
-        return f"Dict({des})"
-    elif type_ in ["oneOf", "anyOf"]:
-        desc = " | ".join([json_schema_to_python_type(i) for i in schema[type_]])
-        return desc
-    else:
-        raise APIInfoParseError(f"Cannot parse schema {schema}")
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/idna/intranges.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/idna/intranges.py
deleted file mode 100644
index 6a43b0475347cb50d0d65ada1000a82eeca9e882..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/idna/intranges.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""
-Given a list of integers, made up of (hopefully) a small number of long runs
-of consecutive integers, compute a representation of the form
-((start1, end1), (start2, end2) ...). Then answer the question "was x present
-in the original list?" in time O(log(# runs)).
-"""
-
-import bisect
-from typing import List, Tuple
-
-def intranges_from_list(list_: List[int]) -> Tuple[int, ...]:
-    """Represent a list of integers as a sequence of ranges:
-    ((start_0, end_0), (start_1, end_1), ...), such that the original
-    integers are exactly those x such that start_i <= x < end_i for some i.
-
-    Ranges are encoded as single integers (start << 32 | end), not as tuples.
-    """
-
-    sorted_list = sorted(list_)
-    ranges = []
-    last_write = -1
-    for i in range(len(sorted_list)):
-        if i+1 < len(sorted_list):
-            if sorted_list[i] == sorted_list[i+1]-1:
-                continue
-        current_range = sorted_list[last_write+1:i+1]
-        ranges.append(_encode_range(current_range[0], current_range[-1] + 1))
-        last_write = i
-
-    return tuple(ranges)
-
-def _encode_range(start: int, end: int) -> int:
-    return (start << 32) | end
-
-def _decode_range(r: int) -> Tuple[int, int]:
-    return (r >> 32), (r & ((1 << 32) - 1))
-
-
-def intranges_contain(int_: int, ranges: Tuple[int, ...]) -> bool:
-    """Determine if `int_` falls into one of the ranges in `ranges`."""
-    tuple_ = _encode_range(int_, 0)
-    pos = bisect.bisect_left(ranges, tuple_)
-    # we could be immediately ahead of a tuple (start, end)
-    # with start < int_ <= end
-    if pos > 0:
-        left, right = _decode_range(ranges[pos-1])
-        if left <= int_ < right:
-            return True
-    # or we could be immediately behind a tuple (int_, end)
-    if pos < len(ranges):
-        left, _ = _decode_range(ranges[pos])
-        if left == int_:
-            return True
-    return False
diff --git a/spaces/leave7/kazunaAI2.0/inference/infer_tool.py b/spaces/leave7/kazunaAI2.0/inference/infer_tool.py
deleted file mode 100644
index 3491348b6f91d47133cc450a9df21e97f5f74c48..0000000000000000000000000000000000000000
--- a/spaces/leave7/kazunaAI2.0/inference/infer_tool.py
+++ /dev/null
@@ -1,326 +0,0 @@
-import hashlib
-import json
-import logging
-import os
-import time
-from pathlib import Path
-
-import librosa
-import maad
-import numpy as np
-# import onnxruntime
-import parselmouth
-import soundfile
-import torch
-import torchaudio
-
-from hubert import hubert_model
-import utils
-from models import SynthesizerTrn
-
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-
-
-def read_temp(file_name):
-    if not os.path.exists(file_name):
-        with open(file_name, "w") as f:
-            f.write(json.dumps({"info": "temp_dict"}))
-        return {}
-    else:
-        try:
-            with open(file_name, "r") as f:
-                data = f.read()
-            data_dict = json.loads(data)
-            if os.path.getsize(file_name) > 50 * 1024 * 1024:
-                f_name = file_name.split("/")[-1]
-                print(f"clean {f_name}")
-                for wav_hash in list(data_dict.keys()):
-                    if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
-                        del data_dict[wav_hash]
-        except Exception as e:
-            print(e)
-            print(f"{file_name} error,auto rebuild file")
-            data_dict = {"info": "temp_dict"}
-        return data_dict
-
-
-def write_temp(file_name, data):
-    with open(file_name, "w") as f:
-        f.write(json.dumps(data))
-
-
-def timeit(func):
-    def run(*args, **kwargs):
-        t = time.time()
-        res = func(*args, **kwargs)
-        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
-        return res
-
-    return run
-
-
-def format_wav(audio_path):
-    if Path(audio_path).suffix == '.wav':
-        return
-    raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
-    soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
-
-
-def get_end_file(dir_path, end):
-    file_lists = []
-    for root, dirs, files in os.walk(dir_path):
-        files = [f for f in files if f[0] != '.']
-        dirs[:] = [d for d in dirs if d[0] != '.']
-        for f_file in files:
-            if f_file.endswith(end):
-                file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
-    return file_lists
-
-
-def get_md5(content):
-    return hashlib.new("md5", content).hexdigest()
-
-
-def resize2d_f0(x, target_len):
-    source = np.array(x)
-    source[source < 0.001] = np.nan
-    target = np.interp(np.arange(0, len(source) * target_len, len(source)) / target_len, np.arange(0, len(source)),
-                       source)
-    res = np.nan_to_num(target)
-    return res
-
-def get_f0(x, p_len,f0_up_key=0):
-
-    time_step = 160 / 16000 * 1000
-    f0_min = 50
-    f0_max = 1100
-    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
-    f0 = parselmouth.Sound(x, 16000).to_pitch_ac(
-        time_step=time_step / 1000, voicing_threshold=0.6,
-        pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
-
-    pad_size=(p_len - len(f0) + 1) // 2
-    if(pad_size>0 or p_len - len(f0) - pad_size>0):
-        f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
-
-    f0 *= pow(2, f0_up_key / 12)
-    f0_mel = 1127 * np.log(1 + f0 / 700)
-    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
-    f0_mel[f0_mel <= 1] = 1
-    f0_mel[f0_mel > 255] = 255
-    f0_coarse = np.rint(f0_mel).astype(np.int)
-    return f0_coarse, f0
-
-def clean_pitch(input_pitch):
-    num_nan = np.sum(input_pitch == 1)
-    if num_nan / len(input_pitch) > 0.9:
-        input_pitch[input_pitch != 1] = 1
-    return input_pitch
-
-
-def plt_pitch(input_pitch):
-    input_pitch = input_pitch.astype(float)
-    input_pitch[input_pitch == 1] = np.nan
-    return input_pitch
-
-
-def f0_to_pitch(ff):
-    f0_pitch = 69 + 12 * np.log2(ff / 440)
-    return f0_pitch
-
-
-def fill_a_to_b(a, b):
-    if len(a) < len(b):
-        for _ in range(0, len(b) - len(a)):
-            a.append(a[0])
-
-
-def mkdir(paths: list):
-    for path in paths:
-        if not os.path.exists(path):
-            os.mkdir(path)
-
-
-class Svc(object):
-    def __init__(self, net_g_path, config_path, hubert_path="hubert/hubert-soft-0d54a1f4.pt",
-                 onnx=False):
-        self.onnx = onnx
-        self.net_g_path = net_g_path
-        self.hubert_path = hubert_path
-        self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.net_g_ms = None
-        self.hps_ms = utils.get_hparams_from_file(config_path)
-        self.target_sample = self.hps_ms.data.sampling_rate
-        self.hop_size = self.hps_ms.data.hop_length
-        self.speakers = {}
-        for spk, sid in self.hps_ms.spk.items():
-            self.speakers[sid] = spk
-        self.spk2id = self.hps_ms.spk
-        # 加载hubert
-        self.hubert_soft = hubert_model.hubert_soft(hubert_path)
-        if torch.cuda.is_available():
-            self.hubert_soft = self.hubert_soft.cuda()
-        self.load_model()
-
-    def load_model(self):
-        # 获取模型配置
-        if self.onnx:
-            raise NotImplementedError
-            # self.net_g_ms = SynthesizerTrnForONNX(
-            #     178,
-            #     self.hps_ms.data.filter_length // 2 + 1,
-            #     self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
-            #     n_speakers=self.hps_ms.data.n_speakers,
-            #     **self.hps_ms.model)
-            # _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
-        else:
-            self.net_g_ms = SynthesizerTrn(
-                self.hps_ms.data.filter_length // 2 + 1,
-                self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
-                **self.hps_ms.model)
-            _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            _ = self.net_g_ms.half().eval().to(self.dev)
-        else:
-            _ = self.net_g_ms.eval().to(self.dev)
-
-    def get_units(self, source, sr):
-
-        source = source.unsqueeze(0).to(self.dev)
-        with torch.inference_mode():
-            start = time.time()
-            units = self.hubert_soft.units(source)
-            use_time = time.time() - start
-            print("hubert use time:{}".format(use_time))
-            return units
-
-
-    def get_unit_pitch(self, in_path, tran):
-        source, sr = torchaudio.load(in_path)
-        source = torchaudio.functional.resample(source, sr, 16000)
-        if len(source.shape) == 2 and source.shape[1] >= 2:
-            source = torch.mean(source, dim=0).unsqueeze(0)
-        soft = self.get_units(source, sr).squeeze(0).cpu().numpy()
-        f0_coarse, f0 = get_f0(source.cpu().numpy()[0], soft.shape[0]*2, tran)
-        return soft, f0
-
-    def infer(self, speaker_id, tran, raw_path):
-        if type(speaker_id) == str:
-            speaker_id = self.spk2id[speaker_id]
-        sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
-        soft, pitch = self.get_unit_pitch(raw_path, tran)
-        f0 = torch.FloatTensor(clean_pitch(pitch)).unsqueeze(0).to(self.dev)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            stn_tst = torch.HalfTensor(soft)
-        else:
-            stn_tst = torch.FloatTensor(soft)
-        with torch.no_grad():
-            x_tst = stn_tst.unsqueeze(0).to(self.dev)
-            start = time.time()
-            x_tst = torch.repeat_interleave(x_tst, repeats=2, dim=1).transpose(1, 2)
-            audio = self.net_g_ms.infer(x_tst, f0=f0, g=sid)[0,0].data.float()
-            use_time = time.time() - start
-            print("vits use time:{}".format(use_time))
-        return audio, audio.shape[-1]
-
-
-# class SvcONNXInferModel(object):
-#     def __init__(self, hubert_onnx, vits_onnx, config_path):
-#         self.config_path = config_path
-#         self.vits_onnx = vits_onnx
-#         self.hubert_onnx = hubert_onnx
-#         self.hubert_onnx_session = onnxruntime.InferenceSession(hubert_onnx, providers=['CUDAExecutionProvider', ])
-#         self.inspect_onnx(self.hubert_onnx_session)
-#         self.vits_onnx_session = onnxruntime.InferenceSession(vits_onnx, providers=['CUDAExecutionProvider', ])
-#         self.inspect_onnx(self.vits_onnx_session)
-#         self.hps_ms = utils.get_hparams_from_file(self.config_path)
-#         self.target_sample = self.hps_ms.data.sampling_rate
-#         self.feature_input = FeatureInput(self.hps_ms.data.sampling_rate, self.hps_ms.data.hop_length)
-#
-#     @staticmethod
-#     def inspect_onnx(session):
-#         for i in session.get_inputs():
-#             print("name:{}\tshape:{}\tdtype:{}".format(i.name, i.shape, i.type))
-#         for i in session.get_outputs():
-#             print("name:{}\tshape:{}\tdtype:{}".format(i.name, i.shape, i.type))
-#
-#     def infer(self, speaker_id, tran, raw_path):
-#         sid = np.array([int(speaker_id)], dtype=np.int64)
-#         soft, pitch = self.get_unit_pitch(raw_path, tran)
-#         pitch = np.expand_dims(pitch, axis=0).astype(np.int64)
-#         stn_tst = soft
-#         x_tst = np.expand_dims(stn_tst, axis=0)
-#         x_tst_lengths = np.array([stn_tst.shape[0]], dtype=np.int64)
-#         # 使用ONNX Runtime进行推理
-#         start = time.time()
-#         audio = self.vits_onnx_session.run(output_names=["audio"],
-#                                            input_feed={
-#                                                "hidden_unit": x_tst,
-#                                                "lengths": x_tst_lengths,
-#                                                "pitch": pitch,
-#                                                "sid": sid,
-#                                            })[0][0, 0]
-#         use_time = time.time() - start
-#         print("vits_onnx_session.run time:{}".format(use_time))
-#         audio = torch.from_numpy(audio)
-#         return audio, audio.shape[-1]
-#
-#     def get_units(self, source, sr):
-#         source = torchaudio.functional.resample(source, sr, 16000)
-#         if len(source.shape) == 2 and source.shape[1] >= 2:
-#             source = torch.mean(source, dim=0).unsqueeze(0)
-#         source = source.unsqueeze(0)
-#         # 使用ONNX Runtime进行推理
-#         start = time.time()
-#         units = self.hubert_onnx_session.run(output_names=["embed"],
-#                                              input_feed={"source": source.numpy()})[0]
-#         use_time = time.time() - start
-#         print("hubert_onnx_session.run time:{}".format(use_time))
-#         return units
-#
-#     def transcribe(self, source, sr, length, transform):
-#         feature_pit = self.feature_input.compute_f0(source, sr)
-#         feature_pit = feature_pit * 2 ** (transform / 12)
-#         feature_pit = resize2d_f0(feature_pit, length)
-#         coarse_pit = self.feature_input.coarse_f0(feature_pit)
-#         return coarse_pit
-#
-#     def get_unit_pitch(self, in_path, tran):
-#         source, sr = torchaudio.load(in_path)
-#         soft = self.get_units(source, sr).squeeze(0)
-#         input_pitch = self.transcribe(source.numpy()[0], sr, soft.shape[0], tran)
-#         return soft, input_pitch
-
-
-class RealTimeVC:
-    def __init__(self):
-        self.last_chunk = None
-        self.last_o = None
-        self.chunk_len = 16000  # 区块长度
-        self.pre_len = 3840  # 交叉淡化长度，640的倍数
-
-    """输入输出都是1维numpy 音频波形数组"""
-
-    def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path):
-        audio, sr = torchaudio.load(input_wav_path)
-        audio = audio.cpu().numpy()[0]
-        temp_wav = io.BytesIO()
-        if self.last_chunk is None:
-            input_wav_path.seek(0)
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
-            audio = audio.cpu().numpy()
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return audio[-self.chunk_len:]
-        else:
-            audio = np.concatenate([self.last_chunk, audio])
-            soundfile.write(temp_wav, audio, sr, format="wav")
-            temp_wav.seek(0)
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav)
-            audio = audio.cpu().numpy()
-            ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return ret[self.chunk_len:2 * self.chunk_len]
diff --git a/spaces/lewiswu1209/MockingBird/CODE_OF_CONDUCT.md b/spaces/lewiswu1209/MockingBird/CODE_OF_CONDUCT.md
deleted file mode 100644
index 77b56791b7e900eb0a7d8a258bf78ecb449c8468..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,130 +0,0 @@
-# Contributor Covenant Code of Conduct
-## First of all
-Don't be evil, never
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, religion, or sexual identity
-and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-* Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
-  and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the
-  overall community
-
-Examples of unacceptable behavior include:
-
-* The use of sexualized language or imagery, and sexual attention or
-  advances of any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or email
-  address, without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official e-mail address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at
-babysor00@gmail.com.
-All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series
-of actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or
-permanent ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior,  harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within
-the community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.0, available at
-https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
-
-Community Impact Guidelines were inspired by [Mozilla's code of conduct
-enforcement ladder](https://github.com/mozilla/diversity).
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see the FAQ at
-https://www.contributor-covenant.org/faq. Translations are available at
-https://www.contributor-covenant.org/translations.
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Avast 2020 Crack License File Valid Till 2060 Free Download.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Avast 2020 Crack License File Valid Till 2060 Free Download.md
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index 71150f4d92ccfdc0d79196fe4bde39ed075d25f9..0000000000000000000000000000000000000000
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/HD Online Player (kadhalil Sodhappuvadhu Yeppadi Full [BEST] ).md b/spaces/lincquiQcaudo/Top-20-Diffusion/HD Online Player (kadhalil Sodhappuvadhu Yeppadi Full [BEST] ).md
deleted file mode 100644
index bfbd6f4591fb6806121186c635507d86fc217511..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/HD Online Player (kadhalil Sodhappuvadhu Yeppadi Full [BEST] ).md	
+++ /dev/null
@@ -1,69 +0,0 @@
-<br />
-<h1>HD Online Player (kadhalil sodhappuvadhu yeppadi full )</h1>
-<p>If you are looking for a romantic comedy to watch online in HD quality, you might want to check out Kadhalil Sodhappuvadhu Yeppadi, a Tamil movie released in 2012. The movie stars Siddharth and Amala Paul as Arun and Parvathi, two college students who fall in love but face various challenges in their relationship.</p>
-<p>Kadhalil Sodhappuvadhu Yeppadi, which means How to Mess Up in Love, is based on a short film of the same name by Balaji Mohan, who also directed the feature film. The movie explores the different stages of love and the common mistakes that couples make. It also has a humorous narration by Arun, who gives tips on how to avoid messing up in love.</p>
-<h2>HD Online Player (kadhalil sodhappuvadhu yeppadi full )</h2><br /><p><b><b>Download Zip</b> &#10037;&#10037;&#10037; <a href="https://bytlly.com/2uGy9A">https://bytlly.com/2uGy9A</a></b></p><br /><br />
-<h2>Where to Watch HD Online Player (kadhalil sodhappuvadhu yeppadi full )</h2>
-<p>There are several online platforms where you can watch Kadhalil Sodhappuvadhu Yeppadi full movie in HD quality. Some of them are:</p>
-<ul>
-<li>Disney+ Hotstar: This is one of the most popular streaming services in India, where you can watch Kadhalil Sodhappuvadhu Yeppadi with English subtitles. You can also download the movie and watch it offline on your device.</li>
-<li>TamilYogi: This is a website that offers free streaming of Tamil movies in HD quality. You can watch Kadhalil Sodhappuvadhu Yeppadi without any registration or subscription.</li>
-<li>JustWatch: This is a website that helps you find where to watch movies and shows online. You can search for Kadhalil Sodhappuvadhu Yeppadi and see which streaming services offer it in your region.</li>
-</ul>
-<h3>Why You Should Watch HD Online Player (kadhalil sodhappuvadhu yeppadi full )</h3>
-<p>Kadhalil Sodhappuvadhu Yeppadi is a movie that will make you laugh and cry at the same time. It has a realistic portrayal of love and its complications, with relatable characters and situations. It also has a catchy soundtrack composed by Thaman, featuring songs like Azhaipaya, Parvathi Parvathi, and A Square B Square.</p>
-<p>Kadhalil Sodhappuvadhu Yeppadi is a movie that will appeal to anyone who has ever been in love or wants to be in love. It will also give you some valuable lessons on how to handle your relationship and avoid messing up in love. So, what are you waiting for? Grab your popcorn and watch Kadhalil Sodhappuvadhu Yeppadi online in HD quality today!</p>
-<h4>Who are the Cast and Crew of HD Online Player (kadhalil sodhappuvadhu yeppadi full )</h4>
-<p>Kadhalil Sodhappuvadhu Yeppadi is a movie that features a talented cast and crew who have worked together to create a memorable romantic comedy. Some of the main cast and crew members are:</p>
-<ul>
-<li>Siddharth: He plays the role of Arun, a college student who falls in love with Parvathi but faces many problems in their relationship. Siddharth is a popular actor who has worked in Tamil, Telugu, and Hindi movies. Some of his notable movies are Rang De Basanti, Bommarillu, Jigarthanda, and Enakkul Oruvan.</li>
-<li>Amala Paul: She plays the role of Parvathi, a college student who is Arun's girlfriend but breaks up with him after a misunderstanding. Amala Paul is a versatile actress who has worked in Tamil, Malayalam, and Telugu movies. Some of her notable movies are Mynaa, Deiva Thirumagal, Thalaivaa, and Aadai.</li>
-<li>Balaji Mohan: He is the director and writer of Kadhalil Sodhappuvadhu Yeppadi. He made his debut with this movie, which was based on his short film of the same name. He is known for his quirky and innovative style of filmmaking. Some of his other movies are Vaayai Moodi Pesavum, Maari, and Maari 2.</li>
-<li>Thaman: He is the music composer of Kadhalil Sodhappuvadhu Yeppadi. He has composed some catchy and melodious songs for the movie, such as Azhaipaya, Parvathi Parvathi, and A Square B Square. He is one of the most sought-after music composers in South Indian cinema. Some of his other movies are Dookudu, Sarrainodu, Ala Vaikunthapurramuloo, and Krack.</li>
-</ul>
-<p>Kadhalil Sodhappuvadhu Yeppadi also has a supporting cast of actors like Suresh, Ravi Raghavendra, Sriranjini, Surekha Vani, Arjunan, Bobby Simha, Dhanya Balakrishna, Pooja Ramachandran, Iswarya Menon, Sriram Balaji Mohan, Gayathri Raguram, Sree Vishnu, Rupesh Neelu Nasreen and others who have added more charm and humor to the movie.</p>
-<h5>What are the Reviews and Ratings of HD Online Player (kadhalil sodhappuvadhu yeppadi full )</h5>
-<p>Kadhalil Sodhappuvadhu Yeppadi is a movie that has received positive reviews and ratings from critics and audiences alike. The movie has been praised for its fresh and realistic take on love and relationships, its witty and engaging narration, its charming and natural performances, and its catchy and melodious music.</p>
-<p>Some of the reviews and ratings of Kadhalil Sodhappuvadhu Yeppadi are:</p>
-<p></p>
-<ul>
-<li>The Times of India gave the movie 3.5 out of 5 stars and said, \"Kadhalil Sodhappuvadhu Yeppadi is a breezy rom-com that will make you smile.\"</li>
-<li>Rediff gave the movie 3 out of 5 stars and said, \"Kadhalil Sodhappuvadhu Yeppadi is a fun film that will appeal to youngsters.\"</li>
-<li>Behindwoods gave the movie 2.75 out of 5 stars and said, \"Kadhalil Sodhappuvadhu Yeppadi is a simple, sweet and sensible film that will entertain you.\"</li>
-<li>IMDb gave the movie 7.1 out of 10 based on over 1,000 user ratings and said, \"Kadhalil Sodhappuvadhu Yeppadi is a movie that shows how love can go wrong in different ways.\"</li>
-</ul>
-<p>Kadhalil Sodhappuvadhu Yeppadi is a movie that has won several awards and nominations for its cast and crew. Some of them are:</p>
-<ul>
-<li>Siddharth won the Best Actor award at the Norway Tamil Film Festival for his role as Arun.</li>
-<li>Amala Paul won the Best Actress award at the Vijay Awards for her role as Parvathi.</li>
-<li>Balaji Mohan won the Best Debut Director award at the SIIMA Awards for his direction of Kadhalil Sodhappuvadhu Yeppadi.</li>
-<li>Thaman won the Best Music Director award at the Mirchi Music Awards South for his music composition of Kadhalil Sodhappuvadhu Yeppadi.</li>
-</ul>
-<h6>What are some Trivia and Facts about HD Online Player (kadhalil sodhappuvadhu yeppadi full )</h6>
-<p>Kadhalil Sodhappuvadhu Yeppadi is a movie that has some interesting trivia and facts that you might not know. Here are some of them:</p>
-<ul>
-<li>Kadhalil Sodhappuvadhu Yeppadi is based on a short film of the same name by Balaji Mohan, which was made in 2011 as a part of the Naalaya Iyakunar reality show. The short film was a huge hit on YouTube and received over 3 million views.</li>
-<li>Kadhalil Sodhappuvadhu Yeppadi was simultaneously made in Telugu as Love Failure, with the same cast and crew. The movie was also dubbed in Malayalam as Love Today.</li>
-<li>Kadhalil Sodhappuvadhu Yeppadi was Siddharth's first production venture under his banner Etaki Entertainment. He also co-produced the movie with Sashikanth of Y NOT Studios.</li>
-<li>Kadhalil Sodhappuvadhu Yeppadi marked the debut of Amala Paul in Tamil cinema. She was earlier seen in a cameo role in Deiva Thirumagal, but Kadhalil Sodhappuvadhu Yeppadi was her first full-fledged role.</li>
-<li>Kadhalil Sodhappuvadhu Yeppadi also featured several actors who went on to become famous in Tamil cinema, such as Bobby Simha, Dhanya Balakrishna, Pooja Ramachandran, Iswarya Menon, Sriram Balaji Mohan, Gayathri Raguram, Sree Vishnu, Rupesh Neelu Nasreen and others.</li>
-</ul>
-<p>Kadhalil Sodhappuvadhu Yeppadi is a movie that has many trivia and facts that make it more interesting and enjoyable. You can watch Kadhalil Sodhappuvadhu Yeppadi online in HD quality on various streaming platforms and discover more about this movie.</p>
-<h7>What are the Box Office and Budget of HD Online Player (kadhalil sodhappuvadhu yeppadi full )</h7>
-<p>Kadhalil Sodhappuvadhu Yeppadi is a movie that was made on a low budget but became a commercial success at the box office. The movie was produced by Siddharth and Sashikanth under their banners Etaki Entertainment and Y NOT Studios respectively. The movie was also co-produced by Nirav Shah and C.S. Amudhan.</p>
-<p>The budget of Kadhalil Sodhappuvadhu Yeppadi was estimated to be around Rs. 4 crores. The movie was released on February 17, 2012 in Tamil and Telugu languages. The movie received positive reviews from critics and audiences and performed well at the box office.</p>
-<p>The box office collection of Kadhalil Sodhappuvadhu Yeppadi was reported to be around Rs. 20 crores worldwide. The movie was declared a hit and earned profits for its makers and distributors. The movie also won several awards and nominations for its cast and crew.</p>
-<p>Kadhalil Sodhappuvadhu Yeppadi is a movie that proved that a good story and a talented team can make a successful movie even with a low budget. You can watch Kadhalil Sodhappuvadhu Yeppadi online in HD quality on various streaming platforms and enjoy this romantic comedy.</p>
-<h8>How to Download and Watch Online HD Online Player (kadhalil sodhappuvadhu yeppadi full )</h8>
-<p>Kadhalil Sodhappuvadhu Yeppadi is a movie that you can download and watch online in HD quality on various platforms. However, you should always use legal and safe methods to do so, as downloading or streaming pirated content can be harmful for your device and also illegal.</p>
-<p>Some of the legal and safe platforms where you can download and watch online Kadhalil Sodhappuvadhu Yeppadi full movie in HD quality are:</p>
-<ul>
-<li>Disney+ Hotstar: This is one of the most popular streaming services in India, where you can watch Kadhalil Sodhappuvadhu Yeppadi with English subtitles. You can also download the movie and watch it offline on your device. You need to have a subscription to access this service.</li>
-<li>Sun Nxt: This is another streaming service that offers Kadhalil Sodhappuvadhu Yeppadi in HD quality. You can also download the movie and watch it offline on your device. You need to have a subscription to access this service.</li>
-<li>ManoramaMax: This is a streaming service that offers Kadhalil Sodhappuvadhu Yeppadi in HD quality. You can also download the movie and watch it offline on your device. You need to have a subscription to access this service.</li>
-</ul>
-<p>Kadhalil Sodhappuvadhu Yeppadi is a movie that you can download and watch online in HD quality on various platforms. However, you should always use legal and safe methods to do so, as downloading or streaming pirated content can be harmful for your device and also illegal.</p>
-<p>Kadhalil Sodhappuvadhu Yeppadi is a movie that will make you laugh and cry at the same time. It is a movie that explores the different stages of love and the common mistakes that couples make. It is a movie that features a talented cast and crew who have worked together to create a memorable romantic comedy. It is a movie that has received positive reviews and ratings from critics and audiences alike. It is a movie that has won several awards and nominations for its cast and crew. It is a movie that you can watch online in HD quality on various streaming platforms.</p>
-<p>Kadhalil Sodhappuvadhu Yeppadi is a movie that you should not miss if you are a fan of romantic comedies. It is a movie that will appeal to anyone who has ever been in love or wants to be in love. It is a movie that will also give you some valuable lessons on how to handle your relationship and avoid messing up in love. So, what are you waiting for? Grab your popcorn and watch Kadhalil Sodhappuvadhu Yeppadi online in HD quality today!</p> 3cee63e6c2<br />
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/IObit Driver Booster Pro 6.4.0 Crack ((LINK)).md b/spaces/lincquiQcaudo/Top-20-Diffusion/IObit Driver Booster Pro 6.4.0 Crack ((LINK)).md
deleted file mode 100644
index 8a92c1afa78a5b810a8540fe62f9e79a52443f9c..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/IObit Driver Booster Pro 6.4.0 Crack ((LINK)).md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/ljjggr/bingo/src/lib/hooks/use-at-bottom.tsx b/spaces/ljjggr/bingo/src/lib/hooks/use-at-bottom.tsx
deleted file mode 100644
index d37c8cf4162adcb0064e08ecec24eb731416b045..0000000000000000000000000000000000000000
--- a/spaces/ljjggr/bingo/src/lib/hooks/use-at-bottom.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import * as React from 'react'
-
-export function useAtBottom(offset = 0) {
-  const [isAtBottom, setIsAtBottom] = React.useState(false)
-
-  React.useEffect(() => {
-    const handleScroll = () => {
-      setIsAtBottom(
-        window.innerHeight + window.scrollY >=
-          document.body.offsetHeight - offset
-      )
-    }
-
-    window.addEventListener('scroll', handleScroll, { passive: true })
-    handleScroll()
-
-    return () => {
-      window.removeEventListener('scroll', handleScroll)
-    }
-  }, [offset])
-
-  return isAtBottom
-}
diff --git a/spaces/ltgoslo/ssa-perin/data/field/mini_torchtext/example.py b/spaces/ltgoslo/ssa-perin/data/field/mini_torchtext/example.py
deleted file mode 100644
index 68aaf9e2beb663e1b8594232ea8124156ba13f1e..0000000000000000000000000000000000000000
--- a/spaces/ltgoslo/ssa-perin/data/field/mini_torchtext/example.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import six
-import json
-from functools import reduce
-
-
-class Example(object):
-    """Defines a single training or test example.
-
-    Stores each column of the example as an attribute.
-    """
-    @classmethod
-    def fromJSON(cls, data, fields):
-        ex = cls()
-        obj = json.loads(data)
-
-        for key, vals in fields.items():
-            if vals is not None:
-                if not isinstance(vals, list):
-                    vals = [vals]
-
-                for val in vals:
-                    # for processing the key likes 'foo.bar'
-                    name, field = val
-                    ks = key.split('.')
-
-                    def reducer(obj, key):
-                        if isinstance(obj, list):
-                            results = []
-                            for data in obj:
-                                if key not in data:
-                                    # key error
-                                    raise ValueError("Specified key {} was not found in "
-                                                     "the input data".format(key))
-                                else:
-                                    results.append(data[key])
-                            return results
-                        else:
-                            # key error
-                            if key not in obj:
-                                raise ValueError("Specified key {} was not found in "
-                                                 "the input data".format(key))
-                            else:
-                                return obj[key]
-
-                    v = reduce(reducer, ks, obj)
-                    setattr(ex, name, field.preprocess(v))
-        return ex
-
-    @classmethod
-    def fromdict(cls, data, fields):
-        ex = cls()
-        for key, vals in fields.items():
-            if key not in data:
-                raise ValueError("Specified key {} was not found in "
-                                 "the input data".format(key))
-            if vals is not None:
-                if not isinstance(vals, list):
-                    vals = [vals]
-                for val in vals:
-                    name, field = val
-                    setattr(ex, name, field.preprocess(data[key]))
-        return ex
-
-    @classmethod
-    def fromCSV(cls, data, fields, field_to_index=None):
-        if field_to_index is None:
-            return cls.fromlist(data, fields)
-        else:
-            assert(isinstance(fields, dict))
-            data_dict = {f: data[idx] for f, idx in field_to_index.items()}
-            return cls.fromdict(data_dict, fields)
-
-    @classmethod
-    def fromlist(cls, data, fields):
-        ex = cls()
-        for (name, field), val in zip(fields, data):
-            if field is not None:
-                if isinstance(val, six.string_types):
-                    val = val.rstrip('\n')
-                # Handle field tuples
-                if isinstance(name, tuple):
-                    for n, f in zip(name, field):
-                        setattr(ex, n, f.preprocess(val))
-                else:
-                    setattr(ex, name, field.preprocess(val))
-        return ex
-
-    @classmethod
-    def fromtree(cls, data, fields, subtrees=False):
-        try:
-            from nltk.tree import Tree
-        except ImportError:
-            print("Please install NLTK. "
-                  "See the docs at http://nltk.org for more information.")
-            raise
-        tree = Tree.fromstring(data)
-        if subtrees:
-            return [cls.fromlist(
-                [' '.join(t.leaves()), t.label()], fields) for t in tree.subtrees()]
-        return cls.fromlist([' '.join(tree.leaves()), tree.label()], fields)
diff --git a/spaces/lusea/rvc-Qinggan/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/lusea/rvc-Qinggan/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index ee3171bcb7c4a5066560723108b56e055f18be45..0000000000000000000000000000000000000000
--- a/spaces/lusea/rvc-Qinggan/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class DioF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/ma-xu/LIVE/pybind11/tests/test_gil_scoped.cpp b/spaces/ma-xu/LIVE/pybind11/tests/test_gil_scoped.cpp
deleted file mode 100644
index dc9b7ed2243a832c19d6826836ac579456232441..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/pybind11/tests/test_gil_scoped.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
-    tests/test_gil_scoped.cpp -- acquire and release gil
-
-    Copyright (c) 2017 Borja Zarco (Google LLC) <bzarco@google.com>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-#include <pybind11/functional.h>
-
-
-class VirtClass  {
-public:
-    virtual ~VirtClass() = default;
-    VirtClass() = default;
-    VirtClass(const VirtClass&) = delete;
-    virtual void virtual_func() {}
-    virtual void pure_virtual_func() = 0;
-};
-
-class PyVirtClass : public VirtClass {
-    void virtual_func() override {
-        PYBIND11_OVERLOAD(void, VirtClass, virtual_func,);
-    }
-    void pure_virtual_func() override {
-        PYBIND11_OVERLOAD_PURE(void, VirtClass, pure_virtual_func,);
-    }
-};
-
-TEST_SUBMODULE(gil_scoped, m) {
-  py::class_<VirtClass, PyVirtClass>(m, "VirtClass")
-      .def(py::init<>())
-      .def("virtual_func", &VirtClass::virtual_func)
-      .def("pure_virtual_func", &VirtClass::pure_virtual_func);
-
-    m.def("test_callback_py_obj",
-          [](py::object func) { func(); });
-    m.def("test_callback_std_func",
-          [](const std::function<void()> &func) { func(); });
-    m.def("test_callback_virtual_func",
-          [](VirtClass &virt) { virt.virtual_func(); });
-    m.def("test_callback_pure_virtual_func",
-          [](VirtClass &virt) { virt.pure_virtual_func(); });
-    m.def("test_cross_module_gil",
-          []() {
-              auto cm = py::module::import("cross_module_gil_utils");
-              auto gil_acquire = reinterpret_cast<void (*)()>(
-                  PyLong_AsVoidPtr(cm.attr("gil_acquire_funcaddr").ptr()));
-              py::gil_scoped_release gil_release;
-              gil_acquire();
-          });
-}
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/unique_by_key.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/unique_by_key.h
deleted file mode 100644
index dcf9acd42cd730a8f42bedb01407cf75137a86fb..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/unique_by_key.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a fill of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the unique_by_key.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch unique_by_key
-
-#include <thrust/system/detail/sequential/unique_by_key.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/unique_by_key.h>
-#include <thrust/system/cuda/detail/unique_by_key.h>
-#include <thrust/system/omp/detail/unique_by_key.h>
-#include <thrust/system/tbb/detail/unique_by_key.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_UNIQUE_BY_KEY_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/unique_by_key.h>
-#include __THRUST_HOST_SYSTEM_UNIQUE_BY_KEY_HEADER
-#undef __THRUST_HOST_SYSTEM_UNIQUE_BY_KEY_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_UNIQUE_BY_KEY_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/unique_by_key.h>
-#include __THRUST_DEVICE_SYSTEM_UNIQUE_BY_KEY_HEADER
-#undef __THRUST_DEVICE_SYSTEM_UNIQUE_BY_KEY_HEADER
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/tbb/detail/reduce_by_key.h b/spaces/ma-xu/LIVE/thrust/thrust/system/tbb/detail/reduce_by_key.h
deleted file mode 100644
index d8e3b38c59093f309942cda0577580ee4c1df251..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/tbb/detail/reduce_by_key.h
+++ /dev/null
@@ -1,57 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/tbb/detail/execution_policy.h>
-#include <thrust/pair.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace tbb
-{
-namespace detail
-{
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2,
-         typename BinaryPredicate,
-         typename BinaryFunction>
-  thrust::pair<OutputIterator1,OutputIterator2>
-    reduce_by_key(execution_policy<DerivedPolicy> &exec,
-                  InputIterator1 keys_first, 
-                  InputIterator1 keys_last,
-                  InputIterator2 values_first,
-                  OutputIterator1 keys_output,
-                  OutputIterator2 values_output,
-                  BinaryPredicate binary_pred,
-                  BinaryFunction binary_op);
-
-
-} // end namespace detail
-} // end namespace tbb
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/tbb/detail/reduce_by_key.inl>
-
diff --git a/spaces/magicr/BuboGPT/bubogpt/datasets/datasets/__init__.py b/spaces/magicr/BuboGPT/bubogpt/datasets/datasets/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/face_detection/detection/sfd/__init__.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/face_detection/detection/sfd/__init__.py
deleted file mode 100644
index 5a63ecd45658f22e66c171ada751fb33764d4559..0000000000000000000000000000000000000000
--- a/spaces/manavisrani07/gradio-lipsync-wav2lip/face_detection/detection/sfd/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .sfd_detector import SFDDetector as FaceDetector
\ No newline at end of file
diff --git a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/options/test_options.py b/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/options/test_options.py
deleted file mode 100644
index 67e2e3a720cf7f9e540b09b64242197cdb712b57..0000000000000000000000000000000000000000
--- a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/options/test_options.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-from .base_options import BaseOptions
-
-
-class TestOptions(BaseOptions):
-    def initialize(self):
-        BaseOptions.initialize(self)
-        self.parser.add_argument("--ntest", type=int, default=float("inf"), help="# of test examples.")
-        self.parser.add_argument("--results_dir", type=str, default="./results/", help="saves results here.")
-        self.parser.add_argument(
-            "--aspect_ratio", type=float, default=1.0, help="aspect ratio of result images"
-        )
-        self.parser.add_argument("--phase", type=str, default="test", help="train, val, test, etc")
-        self.parser.add_argument(
-            "--which_epoch",
-            type=str,
-            default="latest",
-            help="which epoch to load? set to latest to use latest cached model",
-        )
-        self.parser.add_argument("--how_many", type=int, default=50, help="how many test images to run")
-        self.parser.add_argument(
-            "--cluster_path",
-            type=str,
-            default="features_clustered_010.npy",
-            help="the path for clustered results of encoded features",
-        )
-        self.parser.add_argument(
-            "--use_encoded_image",
-            action="store_true",
-            help="if specified, encode the real image to get the feature map",
-        )
-        self.parser.add_argument("--export_onnx", type=str, help="export ONNX model to a given file")
-        self.parser.add_argument("--engine", type=str, help="run serialized TRT engine")
-        self.parser.add_argument("--onnx", type=str, help="run ONNX model via TRT")
-        self.parser.add_argument(
-            "--start_epoch",
-            type=int,
-            default=-1,
-            help="write the start_epoch of iter.txt into this parameter",
-        )
-
-        self.parser.add_argument("--test_dataset", type=str, default="Real_RGB_old.bigfile")
-        self.parser.add_argument(
-            "--no_degradation",
-            action="store_true",
-            help="when train the mapping, enable this parameter --> no degradation will be added into clean image",
-        )
-        self.parser.add_argument(
-            "--no_load_VAE",
-            action="store_true",
-            help="when train the mapping, enable this parameter --> random initialize the encoder an decoder",
-        )
-        self.parser.add_argument(
-            "--use_v2_degradation",
-            action="store_true",
-            help="enable this parameter --> 4 kinds of degradations will be used to synthesize corruption",
-        )
-        self.parser.add_argument("--use_vae_which_epoch", type=str, default="latest")
-        self.isTrain = False
-
-        self.parser.add_argument("--generate_pair", action="store_true")
-
-        self.parser.add_argument("--multi_scale_test", type=float, default=0.5)
-        self.parser.add_argument("--multi_scale_threshold", type=float, default=0.5)
-        self.parser.add_argument(
-            "--mask_need_scale",
-            action="store_true",
-            help="enable this param meas that the pixel range of mask is 0-255",
-        )
-        self.parser.add_argument("--scale_num", type=int, default=1)
-
-        self.parser.add_argument(
-            "--save_feature_url", type=str, default="", help="While extracting the features, where to put"
-        )
-
-        self.parser.add_argument(
-            "--test_input", type=str, default="", help="A directory or a root of bigfile"
-        )
-        self.parser.add_argument("--test_mask", type=str, default="", help="A directory or a root of bigfile")
-        self.parser.add_argument("--test_gt", type=str, default="", help="A directory or a root of bigfile")
-
-        self.parser.add_argument(
-            "--scale_input", action="store_true", help="While testing, choose to scale the input firstly"
-        )
-
-        self.parser.add_argument(
-            "--save_feature_name", type=str, default="features.json", help="The name of saved features"
-        )
-        self.parser.add_argument(
-            "--test_rgb_old_wo_scratch", action="store_true", help="Same setting with origin test"
-        )
-
-        self.parser.add_argument("--test_mode", type=str, default="Crop", help="Scale|Full|Crop")
-        self.parser.add_argument("--Quality_restore", action="store_true", help="For RGB images")
-        self.parser.add_argument(
-            "--Scratch_and_Quality_restore", action="store_true", help="For scratched images"
-        )
-        self.parser.add_argument("--HR", action='store_true',help='Large input size with scratches')
diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/old/temp/deep_heatmaps_model_primary_net.py b/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/old/temp/deep_heatmaps_model_primary_net.py
deleted file mode 100644
index e8bae0f1ffb8c62bd8768f9fd636319e441585b4..0000000000000000000000000000000000000000
--- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/old/temp/deep_heatmaps_model_primary_net.py
+++ /dev/null
@@ -1,911 +0,0 @@
-import scipy.io
-import scipy.misc
-from glob import glob
-import os
-import numpy as np
-from ops import *
-import tensorflow as tf
-from tensorflow import contrib
-from menpo_functions import *
-from logging_functions import *
-from data_loading_functions import *
-
-
-class DeepHeatmapsModel(object):
-
-    """facial landmark localization Network"""
-
-    def __init__(self, mode='TRAIN', train_iter=100000, batch_size=10, learning_rate=1e-3, adam_optimizer=True,
-                 momentum=0.95, step=100000, gamma=0.1, reg=0, weight_initializer='xavier', weight_initializer_std=0.01,
-                 bias_initializer=0.0, image_size=256, c_dim=3, num_landmarks=68, sigma=1.5, scale=1, margin=0.25,
-                 bb_type='gt', approx_maps=True, win_mult=3.33335, augment_basic=True, basic_start=0,
-                 augment_texture=False, p_texture=0., augment_geom=False, p_geom=0., artistic_step=-1, artistic_start=0,
-                 output_dir='output', save_model_path='model', save_sample_path='sample', save_log_path='logs',
-                 test_model_path='model/deep_heatmaps-50000', pre_train_path='model/deep_heatmaps-50000',load_pretrain=False,
-                 img_path='data', test_data='full', valid_data='full', valid_size=0, log_valid_every=5,
-                 train_crop_dir='crop_gt_margin_0.25', img_dir_ns='crop_gt_margin_0.25_ns',
-                 print_every=100, save_every=5000, sample_every=5000, sample_grid=9, sample_to_log=True,
-                 debug_data_size=20, debug=False, epoch_data_dir='epoch_data', use_epoch_data=False, menpo_verbose=True):
-
-        # define some extra parameters
-
-        self.log_histograms = False  # save weight + gradient histogram to log
-        self.save_valid_images = True  # sample heat maps of validation images
-        self.log_artistic_augmentation_probs = False  # save p_texture & p_geom to log
-        self.sample_per_channel = False  # sample heatmaps separately for each landmark
-        self.approx_maps_gpu = False  # create heat-maps on gpu. NOT RECOMMENDED. TODO: REMOVE
-
-        # for fine-tuning, choose reset_training_op==True. when resuming training, reset_training_op==False
-        self.reset_training_op = False
-
-        self.allocate_once = True  # create batch images/landmarks/maps zero arrays only once
-
-        self.fast_img_gen = True
-
-        self.compute_nme = True  # compute normalized mean error
-
-        self.config = tf.ConfigProto()
-        self.config.gpu_options.allow_growth = True
-
-        # sampling and logging parameters
-        self.print_every = print_every  # print losses to screen + log
-        self.save_every = save_every  # save model
-        self.sample_every = sample_every  # save images of gen heat maps compared to GT
-        self.sample_grid = sample_grid  # number of training images in sample
-        self.sample_to_log = sample_to_log  # sample images to log instead of disk
-        self.log_valid_every = log_valid_every  # log validation loss (in epochs)
-
-        self.debug = debug
-        self.debug_data_size = debug_data_size
-        self.use_epoch_data = use_epoch_data
-        self.epoch_data_dir = epoch_data_dir
-
-        self.load_pretrain = load_pretrain
-        self.pre_train_path = pre_train_path
-
-        self.mode = mode
-        self.train_iter = train_iter
-        self.learning_rate = learning_rate
-
-        self.image_size = image_size
-        self.c_dim = c_dim
-        self.batch_size = batch_size
-
-        self.num_landmarks = num_landmarks
-
-        self.save_log_path = save_log_path
-        self.save_sample_path = save_sample_path
-        self.save_model_path = save_model_path
-        self.test_model_path = test_model_path
-        self.img_path=img_path
-
-        self.momentum = momentum
-        self.step = step  # for lr decay
-        self.gamma = gamma  # for lr decay
-        self.reg = reg  # weight decay scale
-
-        self.weight_initializer = weight_initializer  # random_normal or xavier
-        self.weight_initializer_std = weight_initializer_std
-        self.bias_initializer = bias_initializer
-        self.adam_optimizer = adam_optimizer
-
-        self.sigma = sigma  # sigma for heatmap generation
-        self.scale = scale  # scale for image normalization 255 / 1 / 0
-        self.win_mult = win_mult  # gaussian filter size for cpu/gpu approximation: 2 * sigma * win_mult + 1
-        self.approx_maps_cpu = approx_maps  # create heat-maps by inserting gaussian filter around landmark locations
-
-        self.test_data = test_data  # if mode is TEST, this choose the set to use full/common/challenging/test/art
-        self.train_crop_dir = train_crop_dir
-        self.img_dir_ns = os.path.join(img_path, img_dir_ns)
-        self.augment_basic = augment_basic  # perform basic augmentation (rotation,flip,crop)
-        self.augment_texture = augment_texture  # perform artistic texture augmentation (NS)
-        self.p_texture = p_texture  # initial probability of artistic texture augmentation
-        self.augment_geom = augment_geom  # perform artistic geometric augmentation
-        self.p_geom = p_geom  # initial probability of artistic geometric augmentation
-        self.artistic_step = artistic_step  # increase probability of artistic augmentation every X epochs
-        self.artistic_start = artistic_start  # min epoch to start artistic augmentation
-        self.basic_start = basic_start  # min epoch to start basic augmentation
-
-        self.valid_size = valid_size
-        self.valid_data = valid_data
-
-        # load image, bb and landmark data using menpo
-        self.bb_dir = os.path.join(img_path, 'Bounding_Boxes')
-        self.bb_dictionary = load_bb_dictionary(self.bb_dir, mode, test_data=self.test_data)
-
-        if self.use_epoch_data:
-            epoch_0 = os.path.join(self.epoch_data_dir, '0')
-            self.img_menpo_list = load_menpo_image_list(
-                img_path, train_crop_dir=epoch_0, img_dir_ns=None, mode=mode, bb_dictionary=self.bb_dictionary,
-                image_size=self.image_size,test_data=self.test_data, augment_basic=False, augment_texture=False,
-                augment_geom=False, verbose=menpo_verbose)
-        else:
-            self.img_menpo_list = load_menpo_image_list(
-                img_path, train_crop_dir, self.img_dir_ns, mode, bb_dictionary=self.bb_dictionary,
-                image_size=self.image_size, margin=margin, bb_type=bb_type, test_data=self.test_data,
-                augment_basic=(augment_basic and basic_start == 0),
-                augment_texture=(augment_texture and artistic_start == 0 and p_texture > 0.), p_texture=p_texture,
-                augment_geom=(augment_geom and artistic_start == 0 and p_geom > 0.), p_geom=p_geom,
-                verbose=menpo_verbose)
-
-        if mode == 'TRAIN':
-
-            train_params = locals()
-            print_training_params_to_file(train_params)  # save init parameters
-
-            self.train_inds = np.arange(len(self.img_menpo_list))
-
-            if self.debug:
-                self.train_inds = self.train_inds[:self.debug_data_size]
-                self.img_menpo_list = self.img_menpo_list[self.train_inds]
-
-            if valid_size > 0:
-
-                self.valid_bb_dictionary = load_bb_dictionary(self.bb_dir, 'TEST', test_data=self.valid_data)
-                self.valid_img_menpo_list = load_menpo_image_list(
-                    img_path, train_crop_dir, self.img_dir_ns, 'TEST', bb_dictionary=self.valid_bb_dictionary,
-                    image_size=self.image_size, margin=margin, bb_type=bb_type, test_data=self.valid_data,
-                    verbose=menpo_verbose)
-
-                np.random.seed(0)
-                self.val_inds = np.arange(len(self.valid_img_menpo_list))
-                np.random.shuffle(self.val_inds)
-                self.val_inds = self.val_inds[:self.valid_size]
-
-                self.valid_img_menpo_list = self.valid_img_menpo_list[self.val_inds]
-
-                if self.approx_maps_cpu:
-                    self.valid_images_loaded, self.valid_gt_maps_loaded, self.valid_landmarks_loaded =\
-                        load_images_landmarks_approx_maps(
-                            self.valid_img_menpo_list, np.arange(self.valid_size), primary=True, image_size=self.image_size,
-                            num_landmarks=self.num_landmarks, c_dim=self.c_dim, scale=self.scale, win_mult=self.win_mult,
-                            sigma=self.sigma, save_landmarks=True)
-                else:
-                    self.valid_images_loaded, self.valid_gt_maps_loaded, self.valid_landmarks_loaded =\
-                        load_images_landmarks_maps(
-                            self.valid_img_menpo_list, np.arange(self.valid_size), primary=True, image_size=self.image_size,
-                            c_dim=self.c_dim, num_landmarks=self.num_landmarks, scale=self.scale, sigma=self.sigma,
-                            save_landmarks=True)
-
-                if self.allocate_once:
-                    self.valid_landmarks_pred = np.zeros([self.valid_size, self.num_landmarks, 2]).astype('float32')
-
-                if self.valid_size > self.sample_grid:
-                    self.valid_gt_maps_loaded = self.valid_gt_maps_loaded[:self.sample_grid]
-            else:
-                self.val_inds = None
-
-            self.epoch_inds_shuffle = train_val_shuffle_inds_per_epoch(
-                self.val_inds, self.train_inds, train_iter, batch_size, save_log_path)
-
-    def add_placeholders(self):
-
-        if self.mode == 'TEST':
-            self.images = tf.placeholder(
-                tf.float32, [None, self.image_size, self.image_size, self.c_dim], 'images')
-
-            self.heatmaps_small = tf.placeholder(
-                tf.float32, [None, int(self.image_size/4), int(self.image_size/4), self.num_landmarks], 'heatmaps_small')
-            self.lms_small = tf.placeholder(tf.float32, [None, self.num_landmarks, 2], 'lms_small')
-            self.pred_lms_small = tf.placeholder(tf.float32, [None, self.num_landmarks, 2], 'pred_lms_small')
-
-        elif self.mode == 'TRAIN':
-            self.images = tf.placeholder(
-                tf.float32, [None, self.image_size, self.image_size, self.c_dim], 'train_images')
-
-            self.heatmaps_small = tf.placeholder(
-                tf.float32, [None, int(self.image_size/4), int(self.image_size/4), self.num_landmarks], 'train_heatmaps_small')
-
-            self.train_lms_small = tf.placeholder(tf.float32, [None, self.num_landmarks, 2], 'train_lms_small')
-            self.train_pred_lms_small = tf.placeholder(tf.float32, [None, self.num_landmarks, 2], 'train_pred_lms_small')
-
-            self.valid_lms_small = tf.placeholder(tf.float32, [None, self.num_landmarks, 2], 'valid_lms_small')
-            self.valid_pred_lms_small = tf.placeholder(tf.float32, [None, self.num_landmarks, 2], 'valid_pred_lms_small')
-
-            self.p_texture_log = tf.placeholder(tf.float32, [])
-            self.p_geom_log = tf.placeholder(tf.float32, [])
-
-            self.sparse_hm_small = tf.placeholder(tf.float32, [None, int(self.image_size/4), int(self.image_size/4), 1])
-
-            if self.sample_to_log:
-                row = int(np.sqrt(self.sample_grid))
-                self.log_image_map = tf.placeholder(
-                    tf.uint8, [None,row * int(self.image_size/4), 3 * row *int(self.image_size/4), self.c_dim], 'sample_img_map')
-                if self.sample_per_channel:
-                    row = np.ceil(np.sqrt(self.num_landmarks)).astype(np.int64)
-                    self.log_map_channels = tf.placeholder(
-                        tf.uint8, [None, row * int(self.image_size/4), 2 * row * int(self.image_size/4), self.c_dim],
-                        'sample_map_channels')
-
-    def heatmaps_network(self, input_images, reuse=None, name='pred_heatmaps'):
-
-        with tf.name_scope(name):
-
-            if self.weight_initializer == 'xavier':
-                weight_initializer = contrib.layers.xavier_initializer()
-            else:
-                weight_initializer = tf.random_normal_initializer(stddev=self.weight_initializer_std)
-
-            bias_init = tf.constant_initializer(self.bias_initializer)
-
-            with tf.variable_scope('heatmaps_network'):
-                with tf.name_scope('primary_net'):
-
-                    l1 = conv_relu_pool(input_images, 5, 128, conv_ker_init=weight_initializer, conv_bias_init=bias_init,
-                                        reuse=reuse, var_scope='conv_1')
-                    l2 = conv_relu_pool(l1, 5, 128, conv_ker_init=weight_initializer, conv_bias_init=bias_init,
-                                        reuse=reuse, var_scope='conv_2')
-                    l3 = conv_relu(l2, 5, 128, conv_ker_init=weight_initializer, conv_bias_init=bias_init,
-                                   reuse=reuse, var_scope='conv_3')
-
-                    l4_1 = conv_relu(l3, 3, 128, conv_dilation=1, conv_ker_init=weight_initializer,
-                                     conv_bias_init=bias_init, reuse=reuse, var_scope='conv_4_1')
-                    l4_2 = conv_relu(l3, 3, 128, conv_dilation=2, conv_ker_init=weight_initializer,
-                                     conv_bias_init=bias_init, reuse=reuse, var_scope='conv_4_2')
-                    l4_3 = conv_relu(l3, 3, 128, conv_dilation=3, conv_ker_init=weight_initializer,
-                                     conv_bias_init=bias_init, reuse=reuse, var_scope='conv_4_3')
-                    l4_4 = conv_relu(l3, 3, 128, conv_dilation=4, conv_ker_init=weight_initializer,
-                                     conv_bias_init=bias_init, reuse=reuse, var_scope='conv_4_4')
-
-                    l4 = tf.concat([l4_1, l4_2, l4_3, l4_4], 3, name='conv_4')
-
-                    l5_1 = conv_relu(l4, 3, 256, conv_dilation=1, conv_ker_init=weight_initializer,
-                                     conv_bias_init=bias_init, reuse=reuse, var_scope='conv_5_1')
-                    l5_2 = conv_relu(l4, 3, 256, conv_dilation=2, conv_ker_init=weight_initializer,
-                                     conv_bias_init=bias_init, reuse=reuse, var_scope='conv_5_2')
-                    l5_3 = conv_relu(l4, 3, 256, conv_dilation=3, conv_ker_init=weight_initializer,
-                                     conv_bias_init=bias_init, reuse=reuse, var_scope='conv_5_3')
-                    l5_4 = conv_relu(l4, 3, 256, conv_dilation=4, conv_ker_init=weight_initializer,
-                                     conv_bias_init=bias_init, reuse=reuse, var_scope='conv_5_4')
-
-                    l5 = tf.concat([l5_1, l5_2, l5_3, l5_4], 3, name='conv_5')
-
-                    l6 = conv_relu(l5, 1, 512, conv_ker_init=weight_initializer,
-                                   conv_bias_init=bias_init, reuse=reuse, var_scope='conv_6')
-                    l7 = conv_relu(l6, 1, 256, conv_ker_init=weight_initializer,
-                                   conv_bias_init=bias_init, reuse=reuse, var_scope='conv_7')
-                    primary_out = conv(l7, 1, self.num_landmarks, conv_ker_init=weight_initializer,
-                                            conv_bias_init=bias_init, reuse=reuse, var_scope='conv_8')
-
-                self.all_layers = [l1, l2, l3, l4, l5, l6, l7, primary_out]
-
-                return primary_out
-
-    def build_model(self):
-        self.pred_hm_p = self.heatmaps_network(self.images,name='heatmaps_prediction')
-
-    def build_hm_generator(self):  # TODO: remove
-        # generate heat-maps using:
-        # a sparse base (matrix of zeros with 1's in landmark locations) and convolving with a gaussian filter
-        print ("*** using convolution to create heat-maps. use this option only with GPU support ***")
-
-        # create gaussian filter
-        win_small = int(self.win_mult * self.sigma)
-        x_small, y_small = np.mgrid[0:2*win_small+1, 0:2*win_small+1]
-
-        gauss_small = (8. / 3) * self.sigma * gaussian(x_small, y_small, win_small, win_small, sigma=self.sigma)
-        gauss_small = tf.constant(gauss_small, tf.float32)
-        gauss_small = tf.reshape(gauss_small, [2 * win_small + 1, 2 * win_small + 1, 1, 1])
-
-        # convolve sparse map with gaussian
-        self.filt_hm_small = tf.nn.conv2d(self.sparse_hm_small, gauss_small, strides=[1, 1, 1, 1], padding='SAME')
-        self.filt_hm_small = tf.transpose(
-            tf.concat(tf.split(self.filt_hm_small, self.batch_size, axis=0), 3), [3, 1, 2, 0])
-
-    def create_loss_ops(self):  # TODO: calculate NME on resized maps to 256
-
-        def l2_loss_norm_eyes(pred_landmarks, real_landmarks, normalize=True, name='NME'):
-
-            with tf.name_scope(name):
-                with tf.name_scope('real_pred_landmarks_rmse'):
-                    landmarks_rms_err = tf.reduce_mean(
-                        tf.sqrt(tf.reduce_sum(tf.square(pred_landmarks - real_landmarks), axis=2)), axis=1)
-                if normalize:
-                    with tf.name_scope('inter_pupil_dist'):
-                        with tf.name_scope('left_eye_center'):
-                            p1 = tf.reduce_mean(tf.slice(real_landmarks, [0, 42, 0], [-1, 6, 2]), axis=1)
-                        with tf.name_scope('right_eye_center'):
-                            p2 = tf.reduce_mean(tf.slice(real_landmarks, [0, 36, 0], [-1, 6, 2]), axis=1)
-
-                        eye_dist = tf.sqrt(tf.reduce_sum(tf.square(p1 - p2), axis=1))
-
-                    return landmarks_rms_err / eye_dist
-                else:
-                    return landmarks_rms_err
-
-        if self.mode is 'TRAIN':
-            primary_maps_diff = self.pred_hm_p-self.heatmaps_small
-            self.total_loss = 1000.*tf.reduce_mean(tf.square(primary_maps_diff))
-
-            # add weight decay
-            self.total_loss += self.reg * tf.add_n(
-                [tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'bias' not in v.name])
-
-            if self.compute_nme:
-                self.nme_loss = tf.reduce_mean(l2_loss_norm_eyes(self.train_pred_lms_small,self.train_lms_small))
-
-            if self.valid_size > 0 and self.compute_nme:
-                self.valid_nme_loss = tf.reduce_mean(l2_loss_norm_eyes(self.valid_pred_lms_small,self.valid_lms_small))
-
-        elif self.mode == 'TEST' and self.compute_nme:
-            self.nme_per_image = l2_loss_norm_eyes(self.pred_lms_small, self.lms_small)
-            self.nme_loss = tf.reduce_mean(self.nme_per_image)
-
-    def predict_landmarks_in_batches(self, image_paths, session):
-
-        num_batches = int(1.*len(image_paths)/self.batch_size)
-        if num_batches == 0:
-            batch_size = len(image_paths)
-            num_batches = 1
-        else:
-            batch_size = self.batch_size
-
-        img_inds = np.arange(len(image_paths))
-        for j in range(num_batches):
-            batch_inds = img_inds[j * batch_size:(j + 1) * batch_size]
-
-            batch_images, _, batch_lms_small = \
-                load_images_landmarks_maps(
-                    self.img_menpo_list, batch_inds, primary=True, image_size=self.image_size,
-                    c_dim=self.c_dim, num_landmarks=self.num_landmarks, scale=self.scale, sigma=self.sigma,
-                    save_landmarks=self.compute_nme)
-
-            batch_maps_small_pred = session.run(self.pred_hm_p, {self.images: batch_images})
-            batch_pred_landmarks = batch_heat_maps_to_landmarks(
-                batch_maps_small_pred, batch_size=batch_size, image_size=int(self.image_size/4),
-                num_landmarks=self.num_landmarks)
-
-            if j == 0:
-                all_pred_landmarks = batch_pred_landmarks.copy()
-                all_gt_landmarks = batch_lms_small.copy()
-            else:
-                all_pred_landmarks = np.concatenate((all_pred_landmarks,batch_pred_landmarks),0)
-                all_gt_landmarks = np.concatenate((all_gt_landmarks, batch_lms_small), 0)
-
-        reminder = len(image_paths)-num_batches*batch_size
-
-        if reminder > 0:
-            reminder_inds = img_inds[-reminder:]
-
-            batch_images, _, batch_lms_small = \
-                load_images_landmarks_maps(
-                    self.img_menpo_list, reminder_inds, primary=True, image_size=self.image_size,
-                    c_dim=self.c_dim, num_landmarks=self.num_landmarks, scale=self.scale, sigma=self.sigma,
-                    save_landmarks=self.compute_nme)
-
-            batch_maps_small_pred = session.run(self.pred_hm_p, {self.images: batch_images})
-            batch_pred_landmarks = batch_heat_maps_to_landmarks(
-                batch_maps_small_pred, batch_size=reminder, image_size=int(self.image_size/4),
-                num_landmarks=self.num_landmarks)
-
-            all_pred_landmarks = np.concatenate((all_pred_landmarks, batch_pred_landmarks), 0)
-            all_gt_landmarks = np.concatenate((all_gt_landmarks, batch_lms_small), 0)
-
-        return all_pred_landmarks, all_gt_landmarks
-
-    def predict_landmarks_in_batches_loaded(self, images, session):
-
-        num_images = int(images.shape[0])
-        num_batches = int(1.*num_images/self.batch_size)
-        if num_batches == 0:
-            batch_size = num_images
-            num_batches = 1
-        else:
-            batch_size = self.batch_size
-
-        for j in range(num_batches):
-
-            batch_images = images[j * batch_size:(j + 1) * batch_size,:,:,:]
-            batch_maps_small_pred = session.run(self.pred_hm_p, {self.images: batch_images})
-            if self.allocate_once:
-                batch_heat_maps_to_landmarks_alloc_once(
-                    batch_maps=batch_maps_small_pred,
-                    batch_landmarks=self.valid_landmarks_pred[j * batch_size:(j + 1) * batch_size, :, :],
-                    batch_size=batch_size, image_size=int(self.image_size/4), num_landmarks=self.num_landmarks)
-            else:
-                batch_pred_landmarks = batch_heat_maps_to_landmarks(
-                    batch_maps_small_pred, batch_size=batch_size, image_size=int(self.image_size/4),
-                    num_landmarks=self.num_landmarks)
-
-                if j == 0:
-                    all_pred_landmarks = batch_pred_landmarks.copy()
-                else:
-                    all_pred_landmarks = np.concatenate((all_pred_landmarks, batch_pred_landmarks), 0)
-
-        reminder = num_images-num_batches*batch_size
-        if reminder > 0:
-
-            batch_images = images[-reminder:, :, :, :]
-            batch_maps_small_pred = session.run(self.pred_hm_p, {self.images: batch_images})
-            if self.allocate_once:
-                batch_heat_maps_to_landmarks_alloc_once(
-                    batch_maps=batch_maps_small_pred,
-                    batch_landmarks=self.valid_landmarks_pred[-reminder:, :, :],
-                    batch_size=reminder, image_size=int(self.image_size/4), num_landmarks=self.num_landmarks)
-            else:
-                batch_pred_landmarks = batch_heat_maps_to_landmarks(
-                    batch_maps_small_pred, batch_size=reminder, image_size=int(self.image_size/4),
-                    num_landmarks=self.num_landmarks)
-
-                all_pred_landmarks = np.concatenate((all_pred_landmarks, batch_pred_landmarks), 0)
-
-        if not self.allocate_once:
-            return all_pred_landmarks
-
-    def create_summary_ops(self):
-
-        self.batch_summary_op = tf.summary.scalar('l_total', self.total_loss)
-
-        if self.compute_nme:
-            l_nme = tf.summary.scalar('l_nme', self.nme_loss)
-            self.batch_summary_op = tf.summary.merge([self.batch_summary_op, l_nme])
-
-        if self.log_histograms:
-            var_summary = [tf.summary.histogram(var.name, var) for var in tf.trainable_variables()]
-            grads = tf.gradients(self.total_loss, tf.trainable_variables())
-            grads = list(zip(grads, tf.trainable_variables()))
-            grad_summary = [tf.summary.histogram(var.name + '/grads', grad) for grad, var in grads]
-            activ_summary = [tf.summary.histogram(layer.name, layer) for layer in self.all_layers]
-            self.batch_summary_op = tf.summary.merge([self.batch_summary_op, var_summary, grad_summary, activ_summary])
-
-        if self.augment_texture and self.log_artistic_augmentation_probs:
-            p_texture_summary = tf.summary.scalar('p_texture', self.p_texture_log)
-            self.batch_summary_op = tf.summary.merge([self.batch_summary_op, p_texture_summary])
-
-        if self.augment_geom and self.log_artistic_augmentation_probs:
-            p_geom_summary = tf.summary.scalar('p_geom', self.p_geom_log)
-            self.batch_summary_op = tf.summary.merge([self.batch_summary_op, p_geom_summary])
-
-        if self.valid_size > 0 and self.compute_nme:
-            self.valid_summary = tf.summary.scalar('valid_l_nme', self.valid_nme_loss)
-
-        if self.sample_to_log:
-            img_map_summary =tf.summary.image('compare_map_to_gt',self.log_image_map)
-            if self.sample_per_channel:
-                map_channels_summary = tf.summary.image('compare_map_channels_to_gt', self.log_map_channels)
-                self.img_summary = tf.summary.merge([img_map_summary, map_channels_summary])
-            else:
-                self.img_summary = img_map_summary
-            if self.valid_size >= self.sample_grid:
-                img_map_summary_valid = tf.summary.image('compare_map_to_gt_valid', self.log_image_map)
-                if self.sample_per_channel:
-                    map_channels_summary_valid = tf.summary.image('compare_map_channels_to_gt_valid', self.log_map_channels)
-                    self.img_summary_valid = tf.summary.merge([img_map_summary_valid, map_channels_summary_valid])
-                else:
-                    self.img_summary_valid = img_map_summary_valid
-
-    def eval(self):
-
-        self.add_placeholders()
-        # build model
-        self.build_model()
-        self.create_loss_ops()
-
-        if self.debug:
-            self.img_menpo_list = self.img_menpo_list[:np.min([self.debug_data_size, len(self.img_menpo_list)])]
-
-        num_images = len(self.img_menpo_list)
-        img_inds = np.arange(num_images)
-
-        sample_iter = np.ceil(1. * num_images / self.sample_grid).astype('int')
-
-        with tf.Session(config=self.config) as sess:
-
-            # load trained parameters
-            print ('loading test model...')
-            saver = tf.train.Saver()
-            saver.restore(sess, self.test_model_path)
-
-            _, model_name = os.path.split(self.test_model_path)
-
-            gt_provided = self.img_menpo_list[0].has_landmarks  # check if GT landmarks provided
-
-            for i in range(sample_iter):
-
-                batch_inds = img_inds[i * self.sample_grid:(i + 1) * self.sample_grid]
-
-                if not gt_provided:
-                    batch_images = load_images(self.img_menpo_list, batch_inds, image_size=self.image_size,
-                                               c_dim=self.c_dim, scale=self.scale)
-
-                    batch_maps_small_pred = sess.run(self.pred_hm_p, {self.images: batch_images})
-
-                    batch_maps_gt = None
-                else:
-                    # TODO: add option for approx maps + allocate once
-                    batch_images, batch_maps_gt, _ = \
-                        load_images_landmarks_maps(
-                            self.img_menpo_list, batch_inds, primary=True, image_size=self.image_size,
-                            c_dim=self.c_dim, num_landmarks=self.num_landmarks, scale=self.scale, sigma=self.sigma,
-                            save_landmarks=False)
-
-                    batch_maps_small_pred = sess.run(self.pred_hm_p, {self.images: batch_images})
-
-                sample_path_imgs = os.path.join(
-                    self.save_sample_path, model_name +'-'+ self.test_data+'-sample-%d-to-%d-1.png' % (
-                        i * self.sample_grid, (i + 1) * self.sample_grid))
-
-                merged_img = merge_images_landmarks_maps_gt(
-                    batch_images.copy(), batch_maps_small_pred, batch_maps_gt, image_size=self.image_size,
-                    num_landmarks=self.num_landmarks, num_samples=self.sample_grid, scale=self.scale, circle_size=0,
-                    fast=self.fast_img_gen)
-
-                scipy.misc.imsave(sample_path_imgs, merged_img)
-
-                if self.sample_per_channel:
-                    map_per_channel = map_comapre_channels(
-                        batch_images.copy(), batch_maps_small_pred,batch_maps_gt, image_size=int(self.image_size/4),
-                        num_landmarks=self.num_landmarks, scale=self.scale)
-
-                    sample_path_channels = os.path.join(
-                        self.save_sample_path, model_name + '-' + self.test_data + '-sample-%d-to-%d-3.png' % (
-                            i * self.sample_grid, (i + 1) * self.sample_grid))
-
-                    scipy.misc.imsave(sample_path_channels, map_per_channel)
-
-                print ('saved %s' % sample_path_imgs)
-
-            if self.compute_nme and self.test_data in ['full', 'challenging', 'common', 'training', 'test']:
-                print ('\n Calculating NME on: ' + self.test_data + '...')
-                pred_lms, lms_gt = self.predict_landmarks_in_batches(self.img_menpo_list, sess)
-                nme = sess.run(self.nme_loss, {self.pred_lms_small: pred_lms, self.lms_small: lms_gt})
-                print ('NME on ' + self.test_data + ': ' + str(nme))
-
-    def train(self):
-        # set random seed
-        tf.set_random_seed(1234)
-        np.random.seed(1234)
-        # build a graph
-        # add placeholders
-        self.add_placeholders()
-        # build model
-        self.build_model()
-        # create loss ops
-        self.create_loss_ops()
-        # create summary ops
-        self.create_summary_ops()
-
-        # create optimizer and training op
-        global_step = tf.Variable(0, trainable=False)
-        lr = tf.train.exponential_decay(self.learning_rate,global_step, self.step, self.gamma, staircase=True)
-        if self.adam_optimizer:
-            optimizer = tf.train.AdamOptimizer(lr)
-        else:
-            optimizer = tf.train.MomentumOptimizer(lr, self.momentum)
-
-        train_op = optimizer.minimize(self.total_loss,global_step=global_step)
-
-        # TODO: remove
-        if self.approx_maps_gpu:  # create heat-maps using tf convolution. use only with GPU support!
-            self.build_hm_generator()
-
-        with tf.Session(config=self.config) as sess:
-
-            tf.global_variables_initializer().run()
-
-            # load pre trained weights if load_pretrain==True
-            if self.load_pretrain:
-                print
-                print('*** loading pre-trained weights from: '+self.pre_train_path+' ***')
-                loader = tf.train.Saver()
-                loader.restore(sess, self.pre_train_path)
-                print("*** Model restore finished, current global step: %d" % global_step.eval())
-
-            # for fine-tuning, choose reset_training_op==True. when resuming training, reset_training_op==False
-            if self.reset_training_op:
-                print ("resetting optimizer and global step")
-                opt_var_list = [optimizer.get_slot(var, name) for name in optimizer.get_slot_names()
-                                 for var in tf.global_variables() if optimizer.get_slot(var, name) is not None]
-                opt_var_list_init = tf.variables_initializer(opt_var_list)
-                opt_var_list_init.run()
-                sess.run(global_step.initializer)
-
-            # create model saver and file writer
-            summary_writer = tf.summary.FileWriter(logdir=self.save_log_path, graph=tf.get_default_graph())
-            saver = tf.train.Saver()
-
-            print
-            print('*** Start Training ***')
-
-            # initialize some variables before training loop
-            resume_step = global_step.eval()
-            num_train_images = len(self.img_menpo_list)
-            batches_in_epoch = int(float(num_train_images) / float(self.batch_size))
-            epoch = int(resume_step / batches_in_epoch)
-            img_inds = self.epoch_inds_shuffle[epoch, :]
-            p_texture = self.p_texture
-            p_geom = self.p_geom
-            artistic_reload = False
-            basic_reload = True
-            log_valid = True
-            log_valid_images = True
-
-            if self.allocate_once:
-                batch_images = np.zeros([self.batch_size, self.image_size, self.image_size, self.c_dim]).astype('float32')
-                batch_lms_small = np.zeros([self.batch_size, self.num_landmarks, 2]).astype('float32')
-                batch_lms_small_pred = np.zeros([self.batch_size, self.num_landmarks, 2]).astype('float32')
-                if self.approx_maps_gpu:
-                    batch_hm_base_small = np.zeros((self.batch_size * self.num_landmarks,
-                                                    int(self.image_size/4), int(self.image_size/4), 1)).astype('float32')
-                else:
-                    batch_maps_small = np.zeros((self.batch_size, int(self.image_size/4),
-                                                 int(self.image_size/4), self.num_landmarks)).astype('float32')
-
-            if self.approx_maps_cpu:
-                gaussian_filt = create_gaussian_filter(sigma=self.sigma, win_mult=self.win_mult)
-
-            for step in range(resume_step, self.train_iter):
-
-                j = step % batches_in_epoch  # j==0 if we finished an epoch
-
-                if step > resume_step and j == 0:  # if we finished an epoch and this isn't the first step
-                    epoch += 1
-                    img_inds = self.epoch_inds_shuffle[epoch, :]  # get next shuffled image inds
-                    artistic_reload = True
-                    log_valid = True
-                    log_valid_images = True
-                    if self.use_epoch_data:
-                        epoch_dir = os.path.join(self.epoch_data_dir, str(epoch))
-                        self.img_menpo_list = load_menpo_image_list(
-                            self.img_path, train_crop_dir=epoch_dir, img_dir_ns=None, mode=self.mode,
-                            bb_dictionary=self.bb_dictionary, image_size=self.image_size, test_data=self.test_data,
-                            augment_basic=False, augment_texture=False, augment_geom=False)
-
-                # add basic augmentation (if basic_start > 0 and augment_basic is True)
-                if basic_reload and (epoch >= self.basic_start) and self.basic_start > 0 and self.augment_basic:
-                    basic_reload = False
-                    self.img_menpo_list = reload_menpo_image_list(
-                        self.img_path, self.train_crop_dir, self.img_dir_ns, self.mode, self.train_inds,
-                        image_size=self.image_size, augment_basic=self.augment_basic,
-                        augment_texture=(self.augment_texture and epoch >= self.artistic_start), p_texture=p_texture,
-                        augment_geom=(self.augment_geom and epoch >= self.artistic_start), p_geom=p_geom)
-                    print ("****** adding basic augmentation ******")
-
-                # increase artistic augmentation probability
-                if ((epoch % self.artistic_step == 0 and epoch >= self.artistic_start and self.artistic_step != -1)
-                    or (epoch == self.artistic_start)) and (self.augment_geom or self.augment_texture)\
-                        and artistic_reload:
-
-                    artistic_reload = False
-
-                    if epoch == self.artistic_start:
-                        print ("****** adding artistic augmentation ******")
-                        print ("****** augment_geom: " + str(self.augment_geom) + ", p_geom: " + str(p_geom) + " ******")
-                        print ("****** augment_texture: " + str(self.augment_texture) + ", p_texture: " +
-                               str(p_texture) + " ******")
-
-                    if epoch % self.artistic_step == 0 and self.artistic_step != -1:
-                        print ("****** increasing artistic augmentation probability ******")
-
-                        p_geom = 1.- 0.95 ** (epoch/self.artistic_step)
-                        p_texture = 1. - 0.95 ** (epoch/self.artistic_step)
-
-                        print ("****** augment_geom: " + str(self.augment_geom) + ", p_geom: " + str(p_geom) + " ******")
-                        print ("****** augment_texture: " + str(self.augment_texture) + ", p_texture: " +
-                               str(p_texture) + " ******")
-
-                    self.img_menpo_list = reload_menpo_image_list(
-                        self.img_path, self.train_crop_dir, self.img_dir_ns, self.mode, self.train_inds,
-                        image_size=self.image_size, augment_basic=(self.augment_basic and epoch >= self.basic_start),
-                        augment_texture=self.augment_texture, p_texture=p_texture,
-                        augment_geom=self.augment_geom, p_geom=p_geom)
-
-                # get batch images
-                batch_inds = img_inds[j * self.batch_size:(j + 1) * self.batch_size]
-
-                if self.approx_maps_gpu:  # TODO: remove
-                    if self.allocate_once:
-                        load_images_landmarks_alloc_once(
-                            self.img_menpo_list, batch_inds, images=batch_images, landmarks_small=batch_lms_small,
-                            landmarks=None, primary=True, image_size=self.image_size, scale=self.scale)
-
-                        create_heat_maps_base_alloc_once(
-                            landmarks_small=batch_lms_small.astype(int), landmarks=None,
-                            hm_small=batch_hm_base_small, hm_large=None, primary=True, num_images=self.batch_size,
-                            num_landmarks=self.num_landmarks)
-                    else:
-                        batch_images, batch_lms_small = load_images_landmarks(
-                            self.img_menpo_list, batch_inds, primary=True, image_size=self.image_size, c_dim=self.c_dim,
-                            num_landmarks=self.num_landmarks, scale=self.scale)
-
-                        batch_hm_base_small = create_heat_maps_base(
-                            landmarks_small=batch_lms_small.astype(int), landmarks=None, primary=True,
-                            num_images=self.batch_size, image_size=self.image_size, num_landmarks=self.num_landmarks)
-
-                    batch_maps_small = sess.run(self.filt_hm_small, {self.sparse_hm_small: batch_hm_base_small})
-                elif self.approx_maps_cpu:
-                    if self.allocate_once:
-                        load_images_landmarks_approx_maps_alloc_once(
-                            self.img_menpo_list, batch_inds, images=batch_images, maps_small=batch_maps_small,
-                            maps=None, landmarks=batch_lms_small, primary=True, image_size=self.image_size,
-                            num_landmarks=self.num_landmarks, scale=self.scale, gauss_filt_small=gaussian_filt,
-                            win_mult=self.win_mult, sigma=self.sigma, save_landmarks=self.compute_nme)
-                    else:
-                        batch_images, batch_maps_small, batch_lms_small = load_images_landmarks_approx_maps(
-                            self.img_menpo_list, batch_inds, primary=True, image_size=self.image_size,
-                            num_landmarks=self.num_landmarks, c_dim=self.c_dim, scale=self.scale,
-                            gauss_filt_small=gaussian_filt, win_mult=self.win_mult, sigma=self.sigma,
-                            save_landmarks=self.compute_nme)
-                else:
-                    if self.allocate_once:
-                        load_images_landmarks_maps_alloc_once(
-                            self.img_menpo_list, batch_inds, images=batch_images, maps_small=batch_maps_small,
-                            landmarks=batch_lms_small, maps=None, primary=True, image_size=self.image_size,
-                            num_landmarks=self.num_landmarks, scale=self.scale, sigma=self.sigma,
-                            save_landmarks=self.compute_nme)
-                    else:
-                        batch_images, batch_maps_small, batch_lms_small = load_images_landmarks_maps(
-                            self.img_menpo_list, batch_inds, primary=True, image_size=self.image_size, c_dim=self.c_dim,
-                            num_landmarks=self.num_landmarks, scale=self.scale, sigma=self.sigma,
-                            save_landmarks=self.compute_nme)
-
-                feed_dict_train = {self.images: batch_images, self.heatmaps_small: batch_maps_small}
-
-                sess.run(train_op, feed_dict_train)
-
-                # save to log and print status
-                if step == resume_step or (step + 1) % self.print_every == 0:
-
-                    # log probability of artistic augmentation
-                    if self.log_artistic_augmentation_probs and (self.augment_geom or self.augment_texture):
-                        if self.augment_geom and not self.augment_texture:
-                            art_augment_prob_dict = {self.p_geom_log: p_geom}
-                        elif self.augment_texture and not self.augment_geom:
-                            art_augment_prob_dict = {self.p_texture_log: p_texture}
-                        else:
-                            art_augment_prob_dict = {self.p_texture_log: p_texture, self.p_geom_log: p_geom}
-
-                    # train data log
-                    if self.compute_nme:
-                        batch_maps_small_pred = sess.run(self.pred_hm_p, {self.images: batch_images})
-                        if self.allocate_once:
-                            batch_heat_maps_to_landmarks_alloc_once(
-                                batch_maps=batch_maps_small_pred, batch_landmarks=batch_lms_small_pred,
-                                batch_size=self.batch_size, image_size=int(self.image_size/4),
-                                num_landmarks=self.num_landmarks)
-                        else:
-                            batch_lms_small_pred = batch_heat_maps_to_landmarks(
-                                batch_maps_small_pred, self.batch_size, image_size=int(self.image_size/4),
-                                num_landmarks=self.num_landmarks)
-
-                        train_feed_dict_log = {
-                            self.images: batch_images, self.heatmaps_small: batch_maps_small,
-                            self.train_lms_small: batch_lms_small, self.train_pred_lms_small: batch_lms_small_pred}
-                        if self.log_artistic_augmentation_probs and (self.augment_geom or self.augment_texture):
-                            train_feed_dict_log.update(art_augment_prob_dict)
-
-                        summary, l_t, l_nme = sess.run(
-                            [self.batch_summary_op, self.total_loss, self.nme_loss], train_feed_dict_log)
-
-                        print (
-                            'epoch: [%d] step: [%d/%d] primary loss: [%.6f] NME: [%.6f]' % (
-                                epoch, step + 1, self.train_iter, l_t, l_nme))
-                    else:
-                        train_feed_dict_log = {self.images: batch_images, self.heatmaps_small: batch_maps_small}
-                        if self.log_artistic_augmentation_probs and (self.augment_geom or self.augment_texture):
-                            train_feed_dict_log.update(art_augment_prob_dict)
-
-                        summary, l_t = sess.run(
-                            [self.batch_summary_op, self.total_loss], train_feed_dict_log)
-
-                        print (
-                            'epoch: [%d] step: [%d/%d] primary loss: [%.6f]' % (
-                                epoch, step + 1, self.train_iter, l_t))
-
-                    summary_writer.add_summary(summary, step)
-
-                    # valid data log
-                    if self.valid_size > 0 and (log_valid and epoch % self.log_valid_every == 0)\
-                            and self.compute_nme:
-                        log_valid = False
-
-                        if self.allocate_once:
-                            self.predict_landmarks_in_batches_loaded(self.valid_images_loaded, sess)
-                            valid_feed_dict_log = {
-                                self.valid_lms_small: self.valid_landmarks_loaded,
-                                self.valid_pred_lms_small: self.valid_landmarks_pred}
-                        else:
-                            valid_pred_lms = self.predict_landmarks_in_batches_loaded(self.valid_images_loaded, sess)
-                            valid_feed_dict_log = {
-                                self.valid_lms_small: self.valid_landmarks_loaded,
-                                self.valid_pred_lms_small: valid_pred_lms}
-
-                        v_summary,l_v_nme = sess.run([self.valid_summary, self.valid_nme_loss], valid_feed_dict_log)
-                        summary_writer.add_summary(v_summary, step)
-
-                        print (
-                            'epoch: [%d] step: [%d/%d] valid NME: [%.6f]' % (
-                                epoch, step + 1, self.train_iter, l_v_nme))
-
-                # save model
-                if (step + 1) % self.save_every == 0:
-                    saver.save(sess, os.path.join(self.save_model_path, 'deep_heatmaps'), global_step=step + 1)
-                    print ('model/deep-heatmaps-%d saved' % (step + 1))
-
-                # save images. TODO: add option to allocate once
-                if step == resume_step or (step + 1) % self.sample_every == 0:
-
-                    if not self.compute_nme:
-                        batch_maps_small_pred = sess.run(self.pred_hm_p,  {self.images: batch_images})
-                        batch_lms_small_pred=None
-
-                    merged_img = merge_images_landmarks_maps_gt(
-                        batch_images.copy(), batch_maps_small_pred, batch_maps_small,
-                        landmarks=batch_lms_small_pred, image_size=self.image_size,
-                        num_landmarks=self.num_landmarks, num_samples=self.sample_grid, scale=self.scale,
-                        circle_size=0, fast=self.fast_img_gen)
-
-                    if self.sample_per_channel:
-                        map_per_channel = map_comapre_channels(
-                            batch_images.copy(), batch_maps_small_pred,batch_maps_small,
-                            image_size=int(self.image_size/4), num_landmarks=self.num_landmarks, scale=self.scale)
-
-                    if self.sample_to_log:
-                        if self.sample_per_channel:
-                            summary_img = sess.run(
-                                self.img_summary, {self.log_image_map: np.expand_dims(merged_img, 0),
-                                                   self.log_map_channels: np.expand_dims(map_per_channel, 0)})
-                        else:
-                            summary_img = sess.run(
-                                self.img_summary, {self.log_image_map: np.expand_dims(merged_img, 0)})
-
-                        summary_writer.add_summary(summary_img, step)
-
-                        if (self.valid_size >= self.sample_grid) and self.save_valid_images and\
-                                (log_valid_images and epoch % self.log_valid_every == 0):
-                            log_valid_images=False
-
-                            batch_maps_small_pred_val = sess.run(
-                                self.pred_hm_p, {self.images: self.valid_images_loaded[:self.sample_grid]})
-
-                            merged_img = merge_images_landmarks_maps_gt(
-                                self.valid_images_loaded[:self.sample_grid].copy(), batch_maps_small_pred_val,
-                                self.valid_gt_maps_loaded, image_size=self.image_size,
-                                num_landmarks=self.num_landmarks, num_samples=self.sample_grid,
-                                scale=self.scale, circle_size=0, fast=self.fast_img_gen)
-
-                            if self.sample_per_channel:
-                                map_per_channel = map_comapre_channels(
-                                    self.valid_images_loaded[:self.sample_grid].copy(), batch_maps_small_pred_val,
-                                    self.valid_gt_maps_loaded, image_size=int(self.image_size/4),
-                                    num_landmarks=self.num_landmarks, scale=self.scale)
-
-                                summary_img = sess.run(
-                                    self.img_summary_valid, {self.log_image_map: np.expand_dims(merged_img, 0),
-                                                       self.log_map_channels: np.expand_dims(map_per_channel, 0)})
-                            else:
-                                summary_img = sess.run(
-                                    self.img_summary_valid, {self.log_image_map: np.expand_dims(merged_img, 0)})
-                            summary_writer.add_summary(summary_img, step)
-
-                    else:
-                        sample_path_imgs = os.path.join(self.save_sample_path,'epoch-%d-train-iter-%d-1.png'
-                                                        % (epoch, step + 1))
-                        scipy.misc.imsave(sample_path_imgs, merged_img)
-                        if self.sample_per_channel:
-                            sample_path_ch_maps = os.path.join(self.save_sample_path, 'epoch-%d-train-iter-%d-3.png'
-                                                               % (epoch, step + 1))
-                            scipy.misc.imsave(sample_path_ch_maps, map_per_channel)
-
-            print('*** Finished Training ***')
-
-    def get_maps_image(self, test_image, reuse=None):
-        self.add_placeholders()
-        # build model
-        pred_hm_p = self.heatmaps_network(self.images,reuse=reuse)
-
-        with tf.Session(config=self.config) as sess:
-            # load trained parameters
-            saver = tf.train.Saver()
-            saver.restore(sess, self.test_model_path)
-            _, model_name = os.path.split(self.test_model_path)
-
-            test_image = test_image.pixels_with_channels_at_back().astype('float32')
-            if self.scale is '255':
-                test_image *= 255
-            elif self.scale is '0':
-                test_image = 2 * test_image - 1
-
-            test_image_map = sess.run(pred_hm_p, {self.images: np.expand_dims(test_image,0)})
-
-        return test_image_map
diff --git a/spaces/matthoffner/baby-gorilla-agi/README.md b/spaces/matthoffner/baby-gorilla-agi/README.md
deleted file mode 100644
index f401cd7e0bd33b4b44926eb28add460945303ff2..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/baby-gorilla-agi/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: baby gorilla agi
-sdk: streamlit
-emoji: 💻🦍
-colorFrom: green
-colorTo: blue
-app_file: babyllamalit.py
----
-# baby-gorilla-agi 👼🦍
-
-BabyAGI + Llama (Gorilla) + Streamlit
-
-Used BabyAGI-Streamlit https://huggingface.co/spaces/dory111111/babyagi-streamlit with Llama + Faiss
-
-## Updates
-* Change to gorilla
\ No newline at end of file
diff --git a/spaces/matthoffner/chatbot/types/data.ts b/spaces/matthoffner/chatbot/types/data.ts
deleted file mode 100644
index d57323721fbbf2ead31fcc33334717d75de1f3f6..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot/types/data.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-export interface KeyValuePair {
-  key: string;
-  value: any;
-}
diff --git a/spaces/merle/PROTEIN_GENERATOR/model/utils/.ipynb_checkpoints/diff_utils-checkpoint.py b/spaces/merle/PROTEIN_GENERATOR/model/utils/.ipynb_checkpoints/diff_utils-checkpoint.py
deleted file mode 100644
index 2fde867439dc3a604f03a9af67f88992148db71a..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/model/utils/.ipynb_checkpoints/diff_utils-checkpoint.py
+++ /dev/null
@@ -1,292 +0,0 @@
-import torch
-from icecream import ic
-import random
-import numpy as np
-from kinematics import get_init_xyz
-import torch.nn as nn 
-from util_module import ComputeAllAtomCoords
-from util import *
-from inpainting_util import MSAFeaturize_fixbb, TemplFeaturizeFixbb, lddt_unbin
-from kinematics import xyz_to_t2d
-
-def mask_inputs(seq, msa_masked, msa_full, xyz_t, t1d, input_seq_mask=None, 
-        input_str_mask=None, input_t1dconf_mask=None, diffuser=None, t=None):
-    """
-    RFnar inference
-    """
-    seq_mask = ~input_seq_mask
-    str_mask = input_str_mask
-    nar = Nonautoregressive()
-    r = t
-    print(f'USING THIS R: {r}')
-    
-    #mask sequence
-    if mask_seq_token:
-        print("MASK SEQ TOKEN")
-        seq_corrupt[seq_mask] = self.masked_token
-    elif mask_seq_random:
-        print("MASK SEQ RANDOM")
-        
-        
-    ic(seq_corrupt)
-        
-    seq=seq_corrupt.repeat(seq.shape[0], 1)
-    seq_corrupt_onehot=torch.nn.functional.one_hot(seq_corrupt,num_classes=22).float()
-    
-    ### msa_masked ###
-    ic(msa_masked.shape)
-    B,N,L,_=msa_masked.shape
-    msa_masked[:,0,:,:22] = seq_corrupt_onehot
-
-    msa_seq_mask = seq_mask.unsqueeze(0).repeat(N-1, 1)
-    #msa_masked[:,1:,:,:22] = torch.clone(msa_diffused)
-
-    # index 44/45 is insertion/deletion
-    # index 43 is the masked token NOTE check this
-    # index 42 is the unknown token 
-    msa_masked[:,0,:,22:44] = seq_corrupt_onehot
-    #msa_masked[:,1:,:,22:44] = msa_diffused
-
-    # insertion/deletion stuff 
-    msa_masked[:,0,~seq_mask,44:46] = 0
-
-    # msa_full #
-    #make msa_full same size as msa_masked
-    ic(msa_full.shape)
-    msa_full = msa_full[:,:msa_masked.shape[1],:,:]
-    msa_full[:,0,:,:22] = seq_corrupt_onehot
-    #msa_full[:,1:,:,:22] = msa_diffused
-    
-    ###########
-    ### t1d ###
-    ########### 
-    # NOTE: adjusting t1d last dim (confidence) from sequence mask
-    t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],1)).float()), -1).to(seq.device)
-    t1d[:,:,:21] = seq_corrupt_onehot[...,:21]
-
-    #t1d[:,:,21] *= input_t1dconf_mask
-    #set diffused conf to 0 and everything else to 1
-    t1d[:,seq_mask,21] = 0.0
-    t1d[:,~seq_mask,21] = 1.0
-
-    #to do add structure confidence metric; need to expand dimensions of chkpt b4
-    if t1d_24:
-        print("expanding t1d dim to 24")
-        t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],1)).float()), -1).to(seq.device)
-        t1d[:,str_mask,23] = 0.0
-        t1d[:,~str_mask,23] = 1.0
-
-    t1d[:1,:,22] = r
-    
-    ################
-    #mask structure#
-    ################
-    if mask_xyz_hole:
-        print("MASK XYZ BLACK HOLE")
-        ic(xyz_t.shape)
-        xyz_corrupt, xyz_mask = nar.xyz_mask_0(xyz_t[0], r, seq_mask = seq_mask, seq_xyz_mask_same = True, cos=True)
-        ic(xyz_corrupt.shape)
-    elif mask_xyz_random:
-        print("MASK XYZ RANDOM")
-        xyz_corrupt, xyz_mask = nar.xyz_mask_random(xyz_t[0], r, seq_mask = seq_mask, seq_xyz_mask_same = True, cos=True)
-    #only corrupt first template
-    xyz_t[0]=xyz_corrupt 
-    
-    assert torch.sum(torch.isnan(xyz_t[:,:,:3,:]))==0
-    
-    mask_msa[:,:,~loss_seq_mask] = False
-    
-    seq_diffused = seq_corrupt_onehot
-    
-    
-    return seq, msa_masked, msa_full, xyz_t, t1d, seq_diffused
-
-
-
-def mask_inputs(seq, msa_masked, msa_full, xyz_t, t1d, input_seq_mask=None, 
-        input_str_mask=None, input_t1dconf_mask=None, diffuser=None, t=None, RFnar = False):
-
-
-    """
-    JG - adapted slightly for the inference case
-
-    Parameters:
-        seq (torch.tensor, required): (I,L) integer sequence
-
-        msa_masked (torch.tensor, required): (I,N_short,L,48)
-
-        msa_full  (torch,.tensor, required): (I,N_long,L,25)
-
-        xyz_t (torch,tensor): (T,L,27,3) template crds BEFORE they go into get_init_xyz
-
-        t1d (torch.tensor, required): (I,L,22) this is the t1d before tacking on the chi angles
-
-        str_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where structure is masked at False positions
-
-        seq_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where seq is masked at False positions
-        t1d_24: is there an extra dimension to input structure confidence?
-
-        diffuser: diffuser class
-
-        t: time step
-
-    NOTE: in the MSA, the order is 20aa, 1x unknown, 1x mask token. We set the masked region to 22 (masked).
-        For the t1d, this has 20aa, 1x unkown, and 1x template conf. Here, we set the masked region to 21 (unknown).
-        This, we think, makes sense, as the template in normal RF training does not perfectly correspond to the MSA.
-    """
-    assert diffuser != None, 'please choose a diffuser'
-
-    ###########
-    seq = seq[0,:1]
-    msa_masked = msa_masked[0,:1]
-    msa_full = msa_full[0,:1]
-    t1d = t1d[0]
-    xyz_t = xyz_t[0]
-
-    seq_mask = input_seq_mask[0]
-
-
-
-    ######################
-    ###sequence diffusion###
-    ######################
-    str_mask     = input_str_mask[0]
-    
-    x_0          = torch.nn.functional.one_hot(seq[0,...],num_classes=22).float()*2-1
-    
-    #ic(seq_mask)
-
-    seq_diffused = diffuser.q_sample(x_0,torch.tensor([t-1]),mask=seq_mask)
-    #seq_diffused = torch.clamp(seq_diffused, min=-1, max=1)
-
-    seq_tmp=torch.argmax(seq_diffused,axis=-1).to(device=seq.device)
-    seq=seq_tmp.repeat(seq.shape[0], 1)
-    
-    ###################
-    ###msa diffusion###
-    ###################
-
-    ### msa_masked ###
-    #ic(msa_masked.shape)
-    B,N,L,_=msa_masked.shape
-
-    msa_masked[:,0,:,:22] = seq_diffused
-
-    x_0_msa = msa_masked[0,1:,:,:22].float()*2-1
-    msa_seq_mask = None #seq_mask.unsqueeze(0).repeat(N-1, 1)
-    msa_diffused = diffuser.q_sample(x_0_msa,torch.tensor([t-1]),mask=msa_seq_mask)
-    #msa_diffused = torch.clamp(msa_diffused, min=-1, max=1)
-    msa_masked[:,1:,:,:22] = torch.clone(msa_diffused)
-    
-    # index 44/45 is insertion/deletion
-    # index 43 is the masked token NOTE check this
-    # index 42 is the unknown token 
-    msa_masked[:,0,:,22:44] = seq_diffused
-    msa_masked[:,1:,:,22:44] = msa_diffused
-
-    # insertion/deletion stuff 
-    msa_masked[:,0,~seq_mask,44:46] = 0
-
-    ### msa_full ### 
-    ################
-    #msa_full[:,0,:,:22] = seq_diffused
-    #make msa_full same size as msa_masked
-    msa_full = msa_full[:,:msa_masked.shape[1],:,:]
-    msa_full[:,0,:,:22] = seq_diffused
-    msa_full[:,1:,:,:22] = msa_diffused
-
-    ### t1d ###
-    ########### 
-    # NOTE: adjusting t1d last dim (confidence) from sequence mask
-    t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],2)).float()), -1).to(seq.device)
-    t1d[:,:,:21] = seq_diffused[...,:21]
-
-    #t1d[:,:,21] *= input_t1dconf_mask
-    #set diffused conf to 0 and everything else to 1
-    t1d[:,~seq_mask,21] = 0.0
-    t1d[:,seq_mask,21] = 1.0
-    
-    t1d[:1,:,22] = 1-t/diffuser.num_timesteps
-
-    t1d[:,~str_mask,23] = 0.0
-    t1d[:,str_mask,23] = 1.0
-
-    
-    xyz_t = get_init_xyz(xyz_t[None])
-    xyz_t = xyz_t[0]
-
-    xyz_t[:,~seq_mask,3:,:] = float('nan')
-
-    # Structure masking
-    xyz_t[:,~str_mask,:,:] = float('nan')
-
-    xyz_t = get_init_xyz(xyz_t[None])
-    xyz_t = xyz_t[0]
-
-    assert torch.sum(torch.isnan(xyz_t[:,:,:3,:]))==0
-    
-
-    return seq, msa_masked, msa_full, xyz_t, t1d, seq_diffused
-
-
-
-
-conversion = 'ARNDCQEGHILKMFPSTWYVX-'
-
-
-#def take_step_nar():
-
-def take_step(model, msa, msa_extra, seq, t1d, t2d, idx_pdb, N_cycle, xyz_prev, alpha, xyz_t, 
-        alpha_t, params, T, diffuser, seq_diffused, msa_prev, pair_prev, state_prev):
-    """ 
-    Single step in the diffusion process
-    """
-    compute_allatom_coords=ComputeAllAtomCoords().to(seq.device) 
-    #ic(msa.shape)
-    B, _, N, L, _ = msa.shape
-    with torch.no_grad():
-        with torch.cuda.amp.autocast(True):
-            for i_cycle in range(N_cycle-1):
-                msa_prev, pair_prev, xyz_prev, state_prev, alpha = model(msa[:,0],
-                                                                   msa_extra[:,0],
-                                                                   seq[:,0], xyz_prev,
-                                                                   idx_pdb,
-                                                                   seq1hot=seq_diffused,
-                                                                   t1d=t1d, t2d=t2d,
-                                                                   xyz_t=xyz_t, alpha_t=alpha_t,
-                                                                   msa_prev=msa_prev,
-                                                                   pair_prev=pair_prev,
-                                                                   state_prev=state_prev,
-                                                                   return_raw=True) 
-                
-            
-            logit_s, logit_aa_s, logits_exp, xyz_prev, pred_lddt, msa_prev, pair_prev, state_prev, alpha = model(msa[:,0], 
-                                                            msa_extra[:,0],
-                                                            seq[:,0], xyz_prev,
-                                                            idx_pdb,
-                                                            seq1hot=seq_diffused,
-                                                            t1d=t1d, t2d=t2d, xyz_t=xyz_t, alpha_t=alpha_t,
-                                                            msa_prev=msa_prev,
-                                                            pair_prev=pair_prev,
-                                                            state_prev=state_prev,
-                                                            return_infer=True)
-        #ic(logit_aa_s.shape)        
-        logit_aa_s_msa = torch.clone(logit_aa_s)
-        logit_aa_s = logit_aa_s.reshape(B,-1,N,L)[:,:,0,:]
-        #ic(logit_aa_s.shape)
-        logit_aa_s = logit_aa_s.reshape(B,-1,L)
-        #ic(logit_aa_s.shape)
-        seq_out = torch.argmax(logit_aa_s, dim=-2)
-        #ic(seq_out.shape)
-        #ic(alpha.shape)
-
-        pred_lddt_unbinned = lddt_unbin(pred_lddt)
-        _, xyz_prev = compute_allatom_coords(seq_out, xyz_prev, alpha)
-    
-    if N>1:
-        return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, logit_aa_s_msa, alpha, msa_prev, pair_prev, state_prev  
-    else:
-        return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, alpha, msa_prev, pair_prev, state_prev
-            
-            
-
diff --git a/spaces/merve/data-leak/source/third_party/index.js b/spaces/merve/data-leak/source/third_party/index.js
deleted file mode 100644
index e070ccfa3ac2645f9431b1e4dbee36e81692574d..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/source/third_party/index.js
+++ /dev/null
@@ -1,74 +0,0 @@
-// https://github.com/1wheel/roadtolarissa Copyright 2018 Adam Pearce
-
-var fs = require('fs')
-var {exec, execSync} = require('child_process')
-
-var source = `${__dirname}/../../source`
-var public = `${__dirname}/../../public`
-if (!fs.existsSync(public)) fs.mkdirSync(public)
-
-function rsyncSource(){
-  exec(`rsync -a --exclude _posts --exclude _templates ${source}/ ${public}/`)
-}
-rsyncSource()
-
-var hljs = require('highlight.js')
-var marked = require('marked')
-marked.setOptions({
-  highlight: (code, lang) => hljs.highlight(lang || 'html', code).value,
-  smartypants: true
-})
-
-var templates = {}
-readdirAbs(`${source}/_templates`).forEach(path => {
-  var str = fs.readFileSync(path, 'utf8')
-  var templateName = path.split('_templates/')[1]
-  templates[templateName] = d => eval('`' + str + '`')
-})
-
-function readdirAbs(dir){ return fs.readdirSync(dir).map(d => dir + '/' + d) }
-
-var posts = readdirAbs(`${source}/_posts`)
-  .filter(d => !d.includes('.DS_Store'))
-  .map(parsePost)
-  
-fs.writeFileSync(public + '/rss.xml',  templates['rss.xml'](posts))
-fs.writeFileSync(public + '/sitemap.xml', templates['sitemap.xml'](posts))
-
-function parsePost(path){
-  var str = fs.readFileSync(path, 'utf8')
-  if (str[0] == '<') str = str.split('License.\n-->')[1]
-  var [top, body] = str
-    .replace('---\n', '')
-    .split('\n---\n')
-
-  console.log(path)
-
-  var post = {html: path.includes('.html') ? body : marked(body)}
-  top.split('\n').forEach(line => {
-    var [key, val] = line.split(/: (.+)/)
-    post[key] = val
-  })
-
-  return post
-}
-
-function writePost(post){
-  var dir = public + post.permalink
-  if (!fs.existsSync(dir)) execSync(`mkdir -p ${dir}`)
-  fs.writeFileSync(`${dir}/index.html`, templates[post.template](post))
-
-  var outposts = JSON.parse(JSON.stringify(posts))
-  outposts.forEach(d => delete d.html)
-  fs.writeFileSync(public + '/posts.json', JSON.stringify(outposts, null, 2))
-
-
-}
-posts.forEach(writePost)
-
-if (process.argv.includes('--watch')){
-  require('chokidar').watch(source).on('change', path => {
-    rsyncSource()
-    if (path.includes('_posts/')) writePost(parsePost(path))
-  })
-}
diff --git a/spaces/merve/dataset-worldviews/public/measuring-fairness/mini.js b/spaces/merve/dataset-worldviews/public/measuring-fairness/mini.js
deleted file mode 100644
index 51e81b909d66e7a0b45f54b318a0b88a95fdb217..0000000000000000000000000000000000000000
--- a/spaces/merve/dataset-worldviews/public/measuring-fairness/mini.js
+++ /dev/null
@@ -1,205 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-
-
-
-
-window.makeMini = function(){
-
-  var s = 10
-  var sScale = ([a, b]) => [s*a, s*b]
-
-  var miniSel = d3.selectAll('.mini').html('').each(addMini).st({overflow: 'visible'})
-
-  var cColors = {
-    true:  {true: colors.sick, false: lcolors.sick},
-    false: {true: colors.well, false: lcolors.well}
-  }
-  var rColors = {
-    true:  {true: lcolors.sick, false: llcolors.sick},
-    false: {true: lcolors.well, false: llcolors.well}
-  }
-
-
-  function addMini(){
-    var miniSel = d3.select(this)
-
-    var type = miniSel.attr('type')
-    var sex = miniSel.attr('sex')
-    var isAll = sex == 'all'
-
-    miniSel.st({marginBottom: sex == 'male' ? 30 : 0})
-
-    var data = students
-      .filter(d => isAll ? true : sex == 'male' ? d.isMale : !d.isMale)
-
-    var topDatum = {}
-    var botDatum = {}
-
-    if (type == 'fp'){
-      topDatum.opacity = d => d.grade > d.threshold && d.isSick
-      botDatum.opacity = d => d.isSick
-    } else {
-      topDatum.opacity = d => d.grade > d.threshold && d.isSick
-      botDatum.opacity = d => d.grade > d.threshold
-    }
-
-
-
-    var top = -s*nCols/2 + 10
-    if (!isAll) top /= 2
-    addGrid(miniSel.append('span'), topDatum)
-    miniSel.append('span.equation').text('÷').st({top, fontWeight: '', fontSize: 20})
-    addGrid(miniSel.append('span'), botDatum)
-    miniSel.append('span.equation').text('=').st({top, fontWeight: '', fontSize: 20})
-
-    if (!isAll){
-      var sexStr = sex == 'male' ? 'children' : 'adults'
-
-      var coStr = `of ${sexStr} <br>testing positive<br>are sick`
-      var fpStr = `of ${sexStr} <br>who are sick <br>test positive`
-      miniSel.st({position: 'relative'})
-        .append('div.axis')
-        .st({position: 'absolute', right: -9, textAlign: 'center', width: 95, lineHeight: 14, bottom: -15})
-        .html(type == 'fp' ? fpStr : coStr)
-
-    }
-    
-    var percentSel = miniSel.append('span.equation').st({top, marginLeft: 0})
-
-    function update(){
-      topDatum.update()
-      botDatum.update()
-
-      var percent = d3.sum(data, topDatum.opacity)/d3.sum(data, botDatum.opacity)
-      percentSel.text(d3.format('.0%')(percent))
-    }
-
-    miniSel.datum({update}) 
-
-
-    function addGrid(gridSel, datum){
-      var {opacity} = datum
-
-      var width = s*nCols
-      var height = s*nCols*(isAll ? 1 : .5)
-      var svg = gridSel.append('svg').at({width, height})
-
-      var callSickSel = svg.append('rect')
-        .at({width, height, fill: lcolors.sick})
-
-      var callWellPath = svg.append('path')
-        .at({width, height, fill: lcolors.well})
-
-
-      var personSel = svg.appendMany('g', data)
-        .translate(d => sScale(d.pos[isAll ? 'allIJ' : 'sexGroupIJ']))
-
-      var pad = 0
-      // var rectSel = personSel.append('rect')
-      //   .at({
-      //     height: s - pad,
-      //     width: s - pad,
-      //     // stroke: '#666',
-      //     // strokeWidth: .1,
-      //   })
-
-
-      var circleSel = personSel.append('circle')
-        .at({r: s/4, cx: s/2 - pad/2, cy: s/2 - pad/2, fill: d => d.isSick ? colors.sick : '#777'})
-
-      if (!isAll){
-        svg.append('path')
-          .translate([-1, -5])
-          .at({stroke: colors.sick, d: 'M 0 0 H ' + (sex == 'male' ? 8 : 4)*s})
-      }
-
-      var geodata = {type: 'FeatureCollection'}
-      geodata.features = data.map(d => {
-        var [x, y] = sScale(d.pos[isAll ? 'allIJ' : 'sexGroupIJ'])
-        return {
-          type: 'Feature',
-          geometry: {
-            type: 'Polygon',
-            coordinates: [
-              [[x, y], [x, y + s], [x + s, y + s], [x + s, y], [x, y]]
-            ]
-          },
-          properties: {d},
-        }
-      })
-
-      var topology = topojson.topology({boxes: geodata})
-      var geowrap = topojson.feature(topology, topology.objects.boxes)
-      var path = d3.geoPath()
-
-      var hiddenPath = svg.append('path')
-        .at({stroke: 'none', fill: 'rgba(255,255,255,.6)'})
-        .translate(.5, 1)
-
-      var includedPath = svg.append('path')
-        .at({stroke: '#000', fill: 'none'})
-        .translate(.5, 1)
-
-
-      circleSel.at({fill: d => d.isSick ? colors.sick : colors.well})
-
-      datum.update = () => {
-        // rectSel.at({
-        //   // fill: d => rColors[d.grade > d.threshold][opacity(d)],
-        //   // strokeWidth: d => opacity(d) ? 1 : .1,
-        // })
-
-        // circleSel.at({fill: d => cColors[d.isSick][opacity(d)]})
-
-        var byType = d3.nestBy(topology.objects.boxes.geometries, d => opacity(d.properties.d))
-
-        byType.forEach(type => {
-          var obj = {type: 'GeometryCollection', geometries: type}
-          var pathStr = path(topojson.mesh(topology, obj, (a, b) => a == b))
-
-          var pathSel = type.key == 'true' ? includedPath : hiddenPath
-          pathSel.at({d: pathStr})
-        })
-
-        var sickBoxes = topology.objects.boxes.geometries
-          .filter(d => d.properties.d.grade <= d.properties.d.threshold)
-        var obj = {type: 'GeometryCollection', geometries: sickBoxes}
-        var pathStr = path(topojson.mesh(topology, obj, (a, b) => a == b))
-        callWellPath.at({d: pathStr})
-      }
-    }
-   
-  }
-
-
-
-  function updateAll(){
-    miniSel.each(d => d.update())
-  }
-
-  return {updateAll}
-}
-
-
-
-
-
-
-
-
-
-if (window.init) window.init()
diff --git a/spaces/mfrashad/CharacterGAN/netdissect/upsegmodel/resnext.py b/spaces/mfrashad/CharacterGAN/netdissect/upsegmodel/resnext.py
deleted file mode 100644
index 4c618c9da5be17feb975833532e19474fca82dba..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/CharacterGAN/netdissect/upsegmodel/resnext.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import os
-import sys
-import torch
-import torch.nn as nn
-import math
-try:
-    from lib.nn import SynchronizedBatchNorm2d
-except ImportError:
-    from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d
-
-try:
-    from urllib import urlretrieve
-except ImportError:
-    from urllib.request import urlretrieve
-
-
-__all__ = ['ResNeXt', 'resnext101'] # support resnext 101
-
-
-model_urls = {
-    #'resnext50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext50-imagenet.pth',
-    'resnext101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext101-imagenet.pth'
-}
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    "3x3 convolution with padding"
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=False)
-
-
-class GroupBottleneck(nn.Module):
-    expansion = 2
-
-    def __init__(self, inplanes, planes, stride=1, groups=1, downsample=None):
-        super(GroupBottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        self.bn1 = SynchronizedBatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
-                               padding=1, groups=groups, bias=False)
-        self.bn2 = SynchronizedBatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1, bias=False)
-        self.bn3 = SynchronizedBatchNorm2d(planes * 2)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class ResNeXt(nn.Module):
-
-    def __init__(self, block, layers, groups=32, num_classes=1000):
-        self.inplanes = 128
-        super(ResNeXt, self).__init__()
-        self.conv1 = conv3x3(3, 64, stride=2)
-        self.bn1 = SynchronizedBatchNorm2d(64)
-        self.relu1 = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(64, 64)
-        self.bn2 = SynchronizedBatchNorm2d(64)
-        self.relu2 = nn.ReLU(inplace=True)
-        self.conv3 = conv3x3(64, 128)
-        self.bn3 = SynchronizedBatchNorm2d(128)
-        self.relu3 = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
-        self.layer1 = self._make_layer(block, 128, layers[0], groups=groups)
-        self.layer2 = self._make_layer(block, 256, layers[1], stride=2, groups=groups)
-        self.layer3 = self._make_layer(block, 512, layers[2], stride=2, groups=groups)
-        self.layer4 = self._make_layer(block, 1024, layers[3], stride=2, groups=groups)
-        self.avgpool = nn.AvgPool2d(7, stride=1)
-        self.fc = nn.Linear(1024 * block.expansion, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels // m.groups
-                m.weight.data.normal_(0, math.sqrt(2. / n))
-            elif isinstance(m, SynchronizedBatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-
-    def _make_layer(self, block, planes, blocks, stride=1, groups=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=False),
-                SynchronizedBatchNorm2d(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, groups, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes, groups=groups))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.relu1(self.bn1(self.conv1(x)))
-        x = self.relu2(self.bn2(self.conv2(x)))
-        x = self.relu3(self.bn3(self.conv3(x)))
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        x = self.avgpool(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc(x)
-
-        return x
-
-
-'''
-def resnext50(pretrained=False, **kwargs):
-    """Constructs a ResNet-50 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on Places
-    """
-    model = ResNeXt(GroupBottleneck, [3, 4, 6, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnext50']), strict=False)
-    return model
-'''
-
-
-def resnext101(pretrained=False, **kwargs):
-    """Constructs a ResNet-101 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on Places
-    """
-    model = ResNeXt(GroupBottleneck, [3, 4, 23, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnext101']), strict=False)
-    return model
-
-
-# def resnext152(pretrained=False, **kwargs):
-#     """Constructs a ResNeXt-152 model.
-#
-#     Args:
-#         pretrained (bool): If True, returns a model pre-trained on Places
-#     """
-#     model = ResNeXt(GroupBottleneck, [3, 8, 36, 3], **kwargs)
-#     if pretrained:
-#         model.load_state_dict(load_url(model_urls['resnext152']))
-#     return model
-
-
-def load_url(url, model_dir='./pretrained', map_location=None):
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    filename = url.split('/')[-1]
-    cached_file = os.path.join(model_dir, filename)
-    if not os.path.exists(cached_file):
-        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
-        urlretrieve(url, cached_file)
-    return torch.load(cached_file, map_location=map_location)
diff --git a/spaces/mfrashad/CharacterGAN/visualize.py b/spaces/mfrashad/CharacterGAN/visualize.py
deleted file mode 100644
index 433ae2ea8963c56a37e5e91932ad6d359495ed47..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/CharacterGAN/visualize.py
+++ /dev/null
@@ -1,314 +0,0 @@
-# Copyright 2020 Erik Härkönen. All rights reserved.
-# This file is licensed to you under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License. You may obtain a copy
-# of the License at http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software distributed under
-# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
-# OF ANY KIND, either express or implied. See the License for the specific language
-# governing permissions and limitations under the License.
-
-# Patch for broken CTRL+C handler
-# https://github.com/ContinuumIO/anaconda-issues/issues/905
-import os
-os.environ['FOR_DISABLE_CONSOLE_CTRL_HANDLER'] = '1'
-
-import torch, json, numpy as np
-from types import SimpleNamespace
-import matplotlib.pyplot as plt
-from pathlib import Path
-from os import makedirs
-from PIL import Image
-from netdissect import proggan, nethook, easydict, zdataset
-from netdissect.modelconfig import create_instrumented_model
-from estimators import get_estimator
-from models import get_instrumented_model
-from scipy.cluster.vq import kmeans
-import re
-import sys
-import datetime
-import argparse
-from tqdm import trange
-from config import Config
-from decomposition import get_random_dirs, get_or_compute, get_max_batch_size, SEED_VISUALIZATION
-from utils import pad_frames 
-
-def x_closest(p):
-    distances = np.sqrt(np.sum((X - p)**2, axis=-1))
-    idx = np.argmin(distances)
-    return distances[idx], X[idx]
-
-def make_gif(imgs, duration_secs, outname):
-    head, *tail = [Image.fromarray((x * 255).astype(np.uint8)) for x in imgs]
-    ms_per_frame = 1000 * duration_secs / instances
-    head.save(outname, format='GIF', append_images=tail, save_all=True, duration=ms_per_frame, loop=0)
-
-def make_mp4(imgs, duration_secs, outname):
-    import shutil
-    import subprocess as sp
-
-    FFMPEG_BIN = shutil.which("ffmpeg")
-    assert FFMPEG_BIN is not None, 'ffmpeg not found, install with "conda install -c conda-forge ffmpeg"'
-    assert len(imgs[0].shape) == 3, 'Invalid shape of frame data'
-    
-    resolution = imgs[0].shape[0:2]
-    fps = int(len(imgs) / duration_secs)
-
-    command = [ FFMPEG_BIN,
-        '-y', # overwrite output file
-        '-f', 'rawvideo',
-        '-vcodec','rawvideo',
-        '-s', f'{resolution[0]}x{resolution[1]}', # size of one frame
-        '-pix_fmt', 'rgb24',
-        '-r', f'{fps}',
-        '-i', '-', # imput from pipe
-        '-an', # no audio
-        '-c:v', 'libx264',
-        '-preset', 'slow',
-        '-crf', '17',
-        str(Path(outname).with_suffix('.mp4')) ]
-    
-    frame_data = np.concatenate([(x * 255).astype(np.uint8).reshape(-1) for x in imgs])
-    with sp.Popen(command, stdin=sp.PIPE, stdout=sp.PIPE, stderr=sp.PIPE) as p:
-        ret = p.communicate(frame_data.tobytes())
-        if p.returncode != 0:
-            print(ret[1].decode("utf-8"))
-            raise sp.CalledProcessError(p.returncode, command)
-
-
-def make_grid(latent, lat_mean, lat_comp, lat_stdev, act_mean, act_comp, act_stdev, scale=1, n_rows=10, n_cols=5, make_plots=True, edit_type='latent'):
-    from notebooks.notebook_utils import create_strip_centered
-
-    inst.remove_edits()
-    x_range = np.linspace(-scale, scale, n_cols, dtype=np.float32) # scale in sigmas
-
-    rows = []
-    for r in range(n_rows):
-        curr_row = []
-        out_batch = create_strip_centered(inst, edit_type, layer_key, [latent],
-            act_comp[r], lat_comp[r], act_stdev[r], lat_stdev[r], act_mean, lat_mean, scale, 0, -1, n_cols)[0]
-        for i, img in enumerate(out_batch):
-            curr_row.append(('c{}_{:.2f}'.format(r, x_range[i]), img))
-
-        rows.append(curr_row[:n_cols])
-
-    inst.remove_edits()
-    
-    if make_plots:
-        # If more rows than columns, make several blocks side by side
-        n_blocks = 2 if n_rows > n_cols else 1
-        
-        for r, data in enumerate(rows):
-            # Add white borders
-            imgs = pad_frames([img for _, img in data]) 
-            
-            coord = ((r * n_blocks) % n_rows) + ((r * n_blocks) // n_rows)
-            plt.subplot(n_rows//n_blocks, n_blocks, 1 + coord)
-            plt.imshow(np.hstack(imgs))
-            
-            # Custom x-axis labels
-            W = imgs[0].shape[1] # image width
-            P = imgs[1].shape[1] # padding width
-            locs = [(0.5*W + i*(W+P)) for i in range(n_cols)]
-            plt.xticks(locs, ["{:.2f}".format(v) for v in x_range])
-            plt.yticks([])
-            plt.ylabel(f'C{r}')
-
-        plt.tight_layout()
-        plt.subplots_adjust(top=0.96) # make room for suptitle
-
-    return [img for row in rows for img in row]
-
-
-######################
-### Visualize results
-######################
-
-if __name__ == '__main__':
-    global max_batch, sample_shape, feature_shape, inst, args, layer_key, model
-
-    args = Config().from_args()
-    t_start = datetime.datetime.now()
-    timestamp = lambda : datetime.datetime.now().strftime("%d.%m %H:%M")
-    print(f'[{timestamp()}] {args.model}, {args.layer}, {args.estimator}')
-
-    # Ensure reproducibility
-    torch.manual_seed(0) # also sets cuda seeds
-    np.random.seed(0)
-
-    # Speed up backend
-    torch.backends.cudnn.benchmark = True
-    torch.autograd.set_grad_enabled(False)
-
-    has_gpu = torch.cuda.is_available()
-    device = torch.device('cuda' if has_gpu else 'cpu')
-    layer_key = args.layer
-    layer_name = layer_key #layer_key.lower().split('.')[-1]
-
-    basedir = Path(__file__).parent.resolve()
-    outdir = basedir / 'out'
-
-    # Load model
-    inst = get_instrumented_model(args.model, args.output_class, layer_key, device, use_w=args.use_w)
-    model = inst.model
-    feature_shape = inst.feature_shape[layer_key]
-    latent_shape = model.get_latent_shape()
-    print('Feature shape:', feature_shape)
-
-    # Layout of activations
-    if len(feature_shape) != 4: # non-spatial
-        axis_mask = np.ones(len(feature_shape), dtype=np.int32)
-    else:
-        axis_mask = np.array([0, 1, 1, 1]) # only batch fixed => whole activation volume used
-
-    # Shape of sample passed to PCA
-    sample_shape = feature_shape*axis_mask
-    sample_shape[sample_shape == 0] = 1
-
-    # Load or compute components
-    dump_name = get_or_compute(args, inst)
-    data = np.load(dump_name, allow_pickle=False) # does not contain object arrays
-    X_comp = data['act_comp']
-    X_global_mean = data['act_mean']
-    X_stdev = data['act_stdev']
-    X_var_ratio = data['var_ratio']
-    X_stdev_random = data['random_stdevs']
-    Z_global_mean = data['lat_mean']
-    Z_comp = data['lat_comp']
-    Z_stdev = data['lat_stdev']
-    n_comp = X_comp.shape[0]
-    data.close()
-
-    # Transfer components to device
-    tensors = SimpleNamespace(
-        X_comp = torch.from_numpy(X_comp).to(device).float(), #-1, 1, C, H, W
-        X_global_mean = torch.from_numpy(X_global_mean).to(device).float(), # 1, C, H, W
-        X_stdev = torch.from_numpy(X_stdev).to(device).float(),
-        Z_comp = torch.from_numpy(Z_comp).to(device).float(),
-        Z_stdev = torch.from_numpy(Z_stdev).to(device).float(),
-        Z_global_mean = torch.from_numpy(Z_global_mean).to(device).float(),
-    )
-
-    transformer = get_estimator(args.estimator, n_comp, args.sparsity)
-    tr_param_str = transformer.get_param_str()
-
-    # Compute max batch size given VRAM usage
-    max_batch = args.batch_size or (get_max_batch_size(inst, device) if has_gpu else 1)
-    print('Batch size:', max_batch)
-
-    def show():
-        if args.batch_mode:
-            plt.close('all')
-        else:
-            plt.show()
-
-    print(f'[{timestamp()}] Creating visualizations')
-
-    # Ensure visualization gets new samples
-    torch.manual_seed(SEED_VISUALIZATION)
-    np.random.seed(SEED_VISUALIZATION)
-
-    # Make output directories
-    est_id = f'spca_{args.sparsity}' if args.estimator == 'spca' else args.estimator
-    outdir_comp = outdir/model.name/layer_key.lower()/est_id/'comp'
-    outdir_inst = outdir/model.name/layer_key.lower()/est_id/'inst'
-    outdir_summ = outdir/model.name/layer_key.lower()/est_id/'summ'
-    makedirs(outdir_comp, exist_ok=True)
-    makedirs(outdir_inst, exist_ok=True)
-    makedirs(outdir_summ, exist_ok=True)
-
-    # Measure component sparsity (!= activation sparsity)
-    sparsity = np.mean(X_comp == 0) # percentage of zero values in components
-    print(f'Sparsity: {sparsity:.2f}')
-
-    def get_edit_name(mode):
-        if mode == 'activation':
-            is_stylegan = 'StyleGAN' in args.model
-            is_w = layer_key in ['style', 'g_mapping']
-            return 'W' if (is_stylegan and is_w) else 'ACT'
-        elif mode == 'latent':
-            return model.latent_space_name()
-        elif mode == 'both':
-            return 'BOTH'
-        else:
-            raise RuntimeError(f'Unknown edit mode {mode}')
-
-    # Only visualize applicable edit modes
-    if args.use_w and layer_key in ['style', 'g_mapping']:
-        edit_modes = ['latent'] # activation edit is the same
-    else:
-        edit_modes = ['activation', 'latent']
-
-    # Summary grid, real components
-    for edit_mode in edit_modes:
-        plt.figure(figsize = (14,12))
-        plt.suptitle(f"{args.estimator.upper()}: {model.name} - {layer_name}, {get_edit_name(edit_mode)} edit", size=16)
-        make_grid(tensors.Z_global_mean, tensors.Z_global_mean, tensors.Z_comp, tensors.Z_stdev, tensors.X_global_mean,
-            tensors.X_comp, tensors.X_stdev, scale=args.sigma, edit_type=edit_mode, n_rows=14)
-        plt.savefig(outdir_summ / f'components_{get_edit_name(edit_mode)}.jpg', dpi=300)
-        show()
-
-    if args.make_video:
-        components = 15
-        instances = 150
-        
-        # One reasonable, one over the top
-        for sigma in [args.sigma, 3*args.sigma]:
-            for c in range(components):
-                for edit_mode in edit_modes:
-                    frames = make_grid(tensors.Z_global_mean, tensors.Z_global_mean, tensors.Z_comp[c:c+1, :, :], tensors.Z_stdev[c:c+1], tensors.X_global_mean,
-                        tensors.X_comp[c:c+1, :, :], tensors.X_stdev[c:c+1], n_rows=1, n_cols=instances, scale=sigma, make_plots=False, edit_type=edit_mode)
-                    plt.close('all')
-
-                    frames = [x for _, x in frames]
-                    frames = frames + frames[::-1]
-                    make_mp4(frames, 5, outdir_comp / f'{get_edit_name(edit_mode)}_sigma{sigma}_comp{c}.mp4')
-
-    
-    # Summary grid, random directions
-    # Using the stdevs of the principal components for same norm
-    random_dirs_act = torch.from_numpy(get_random_dirs(n_comp, np.prod(sample_shape)).reshape(-1, *sample_shape)).to(device)
-    random_dirs_z = torch.from_numpy(get_random_dirs(n_comp, np.prod(inst.input_shape)).reshape(-1, *latent_shape)).to(device)
-    
-    for edit_mode in edit_modes:
-        plt.figure(figsize = (14,12))
-        plt.suptitle(f"{model.name} - {layer_name}, random directions w/ PC stdevs, {get_edit_name(edit_mode)} edit", size=16)
-        make_grid(tensors.Z_global_mean, tensors.Z_global_mean, random_dirs_z, tensors.Z_stdev,
-            tensors.X_global_mean, random_dirs_act, tensors.X_stdev, scale=args.sigma, edit_type=edit_mode, n_rows=14)
-        plt.savefig(outdir_summ / f'random_dirs_{get_edit_name(edit_mode)}.jpg', dpi=300)
-        show()
-
-    # Random instances w/ components added
-    n_random_imgs = 10
-    latents = model.sample_latent(n_samples=n_random_imgs)
-
-    for img_idx in trange(n_random_imgs, desc='Random images', ascii=True):
-        #print(f'Creating visualizations for random image {img_idx+1}/{n_random_imgs}')
-        z = latents[img_idx][None, ...]
-
-        # Summary grid, real components
-        for edit_mode in edit_modes:
-            plt.figure(figsize = (14,12))
-            plt.suptitle(f"{args.estimator.upper()}: {model.name} - {layer_name}, {get_edit_name(edit_mode)} edit", size=16)
-            make_grid(z, tensors.Z_global_mean, tensors.Z_comp, tensors.Z_stdev,
-                tensors.X_global_mean, tensors.X_comp, tensors.X_stdev, scale=args.sigma, edit_type=edit_mode, n_rows=14)
-            plt.savefig(outdir_summ / f'samp{img_idx}_real_{get_edit_name(edit_mode)}.jpg', dpi=300)
-            show()
-
-        if args.make_video:
-            components = 5
-            instances = 150
-            
-            # One reasonable, one over the top
-            for sigma in [args.sigma, 3*args.sigma]: #[2, 5]:
-                for edit_mode in edit_modes:
-                    imgs = make_grid(z, tensors.Z_global_mean, tensors.Z_comp, tensors.Z_stdev, tensors.X_global_mean, tensors.X_comp, tensors.X_stdev,
-                        n_rows=components, n_cols=instances, scale=sigma, make_plots=False, edit_type=edit_mode)
-                    plt.close('all')
-
-                    for c in range(components):
-                        frames = [x for _, x in imgs[c*instances:(c+1)*instances]]
-                        frames = frames + frames[::-1]
-                        make_mp4(frames, 5, outdir_inst / f'{get_edit_name(edit_mode)}_sigma{sigma}_img{img_idx}_comp{c}.mp4')
-
-    print('Done in', datetime.datetime.now() - t_start)
\ No newline at end of file
diff --git a/spaces/mfrashad/ClothingGAN/README.md b/spaces/mfrashad/ClothingGAN/README.md
deleted file mode 100644
index 48d92436c16afb605cad08292b2ebe1528434e9c..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/ClothingGAN/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ClothingGAN
-emoji: 👘
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
-license: cc-by-nc-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/mikaelbhai/GPTBhai_TextToImage_DreamStudio/README.md b/spaces/mikaelbhai/GPTBhai_TextToImage_DreamStudio/README.md
deleted file mode 100644
index e1cf9327d489c5878b81cfd9a74928db284a008e..0000000000000000000000000000000000000000
--- a/spaces/mikaelbhai/GPTBhai_TextToImage_DreamStudio/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: GPTBhai TextToImage StableDiffusion
-emoji: ⚡
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mikeee/ttw/app.cmd b/spaces/mikeee/ttw/app.cmd
deleted file mode 100644
index dc6544b0f449b02aeb9d0fc78f4a57a68f847bac..0000000000000000000000000000000000000000
--- a/spaces/mikeee/ttw/app.cmd
+++ /dev/null
@@ -1,6 +0,0 @@
-
-@echo off
-rem This script was created by Nuitka to execute 'app.exe' with Python DLL being found.
-set PATH=c:\python\python37;%PATH%
-set PYTHONHOME=c:\python\python37
-"%~dp0.\app.exe"
diff --git a/spaces/mikeee/ultimatumbee/git-pull-github.bat b/spaces/mikeee/ultimatumbee/git-pull-github.bat
deleted file mode 100644
index 5bd530f4ea5abd6a2e19640820e5b2dc63bc56df..0000000000000000000000000000000000000000
--- a/spaces/mikeee/ultimatumbee/git-pull-github.bat
+++ /dev/null
@@ -1 +0,0 @@
-git pull github main 
diff --git a/spaces/mingyuan/MotionDiffuse/options/train_options.py b/spaces/mingyuan/MotionDiffuse/options/train_options.py
deleted file mode 100644
index 5bbe4db67964170a17dadf3302e164126863973d..0000000000000000000000000000000000000000
--- a/spaces/mingyuan/MotionDiffuse/options/train_options.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from options.base_options import BaseOptions
-import argparse
-
-class TrainCompOptions(BaseOptions):
-    def initialize(self):
-        BaseOptions.initialize(self)
-        self.parser.add_argument('--num_layers', type=int, default=8, help='num_layers of transformer')
-        self.parser.add_argument('--latent_dim', type=int, default=512, help='latent_dim of transformer')
-        self.parser.add_argument('--diffusion_steps', type=int, default=1000, help='diffusion_steps of transformer')
-        self.parser.add_argument('--no_clip', action='store_true', help='whether use clip pretrain')
-        self.parser.add_argument('--no_eff', action='store_true', help='whether use efficient attention')
-
-        self.parser.add_argument('--num_epochs', type=int, default=50, help='Number of epochs')
-        self.parser.add_argument('--lr', type=float, default=2e-4, help='Learning rate')
-        self.parser.add_argument('--batch_size', type=int, default=32, help='Batch size per GPU')
-        self.parser.add_argument('--times', type=int, default=1, help='times of dataset')
-
-        self.parser.add_argument('--feat_bias', type=float, default=5, help='Scales for global motion features and foot contact')
-
-        self.parser.add_argument('--is_continue', action="store_true", help='Is this trail continued from previous trail?')
-
-        self.parser.add_argument('--log_every', type=int, default=50, help='Frequency of printing training progress (by iteration)')
-        self.parser.add_argument('--save_every_e', type=int, default=5, help='Frequency of saving models (by epoch)')
-        self.parser.add_argument('--eval_every_e', type=int, default=5, help='Frequency of animation results (by epoch)')
-        self.parser.add_argument('--save_latest', type=int, default=500, help='Frequency of saving models (by iteration)')
-        self.is_train = True
diff --git a/spaces/ml6team/byt5_ocr_corrector/README.md b/spaces/ml6team/byt5_ocr_corrector/README.md
deleted file mode 100644
index 0cc9638538765b089c00ca5c47855e70e2b7a638..0000000000000000000000000000000000000000
--- a/spaces/ml6team/byt5_ocr_corrector/README.md
+++ /dev/null
@@ -1,29 +0,0 @@
----
-title: ByT5 dOCRtor
-emoji: 💊
-colorFrom: red
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
----
-# Configuration
-`title`: _string_  
-Display title for the Space
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
\ No newline at end of file
diff --git a/spaces/mpl8fjk/runwayml-stable-diffusion-v1-5/README.md b/spaces/mpl8fjk/runwayml-stable-diffusion-v1-5/README.md
deleted file mode 100644
index 5524c5d3a4d9f6477d00f2ddfce930b2b10e607d..0000000000000000000000000000000000000000
--- a/spaces/mpl8fjk/runwayml-stable-diffusion-v1-5/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Runwayml Stable Diffusion V1 5
-emoji: 🌖
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ms180/espnet_onnx_demo/modules/app.py b/spaces/ms180/espnet_onnx_demo/modules/app.py
deleted file mode 100644
index 0895399df76f2050f8e65ac9a218be4c08026f2a..0000000000000000000000000000000000000000
--- a/spaces/ms180/espnet_onnx_demo/modules/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from fastapi import APIRouter, FastAPI, Form, UploadFile
-from fastapi.responses import FileResponse, JSONResponse
-from fastapi.staticfiles import StaticFiles
-
-from modules.asr_frontend import ASRModel, audio_from_file
-from modules.tts_frontend import TextInput, TTSModel
-
-router = APIRouter()
-tts_model = TTSModel()
-asr_model = ASRModel()
-
-
-@router.get("/")
-async def index() -> FileResponse:
-    return FileResponse(path="template/index.html", media_type="text/html")
-
-
-@router.get("/api/tts/load_model")
-async def load_tts_model(model_name: str, fs: int) -> JSONResponse:
-    try:
-        tts_model.load_model(model_name, fs)
-        return JSONResponse(status_code=200, content="Successfully loaded model")
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": str(e)})
-
-
-@router.post("/api/tts/generate")
-async def generate_tts(text: TextInput):
-    try:
-        tts_model.generate(text)
-        return JSONResponse(status_code=200)
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": str(e)})
-
-
-@router.get("/api/asr/load_model")
-async def load_asr_model(model_name: str) -> JSONResponse:
-    try:
-        asr_model.load_model(model_name)
-        return JSONResponse(status_code=200, content="Successfully loaded model")
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": str(e)})
-
-
-@router.post("/api/asr/recognize")
-async def recognize(audio: UploadFile = Form(...)):
-    audio = await audio_from_file(audio.file)
-    try:
-        text = asr_model.generate(audio)
-        return JSONResponse(status_code=200, content={"text": text})
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": str(e)})
-
-
-app = FastAPI()
-app.mount("/static", StaticFiles(directory="static"), name="static")
-
-app.include_router(router)
diff --git a/spaces/mshukor/UnIVAL/run_scripts/averaging/ratatouille/eval/eval_vqa.sh b/spaces/mshukor/UnIVAL/run_scripts/averaging/ratatouille/eval/eval_vqa.sh
deleted file mode 100644
index acb6b9af3f6f91f5911b6f95da53743c3ab148cc..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/run_scripts/averaging/ratatouille/eval/eval_vqa.sh
+++ /dev/null
@@ -1,114 +0,0 @@
-#!/usr/bin/env bash
-
-# The port for communication. Note that if you want to run multiple tasks on the same machine,
-# you need to specify different port numbers.
-# The port for communication. Note that if you want to run multiple tasks on the same machine,
-# you need to specify different port numbers.
-# Number of GPUs per GPU worker
-export GPUS_PER_NODE=8
-# Number of GPU workers, for single-worker training, please set to 1
-export NUM_NODES=$SLURM_NNODES
-# The ip address of the rank-0 worker, for single-worker training, please set to localhost
-master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
-export MASTER_ADDR=$master_addr
-
-# The port for communication
-export MASTER_PORT=12350
-# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
-export RANK=$SLURM_NODEID
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "RANK :$RANK"
-echo "NUM_NODES :$NUM_NODES"
-echo "GPUS_PER_NODE :$GPUS_PER_NODE"
-
-export MIOPEN_USER_DB_PATH=/lus/home/NAT/gda2204/mshukor/.config/miopen_${MASTER_ADDR}_${SLURM_PROCID}/
-
-echo "MIOPEN_USER_DB_PATH :$MIOPEN_USER_DB_PATH"
-
-num_workers=0
-
-
-
-
-
-ofa_dir=/lus/home/NAT/gda2204/mshukor/code/unival
-base_data_dir=/lus/scratch/NAT/gda2204/SHARED/data
-base_log_dir=/work/NAT/gda2204/mshukor/logs
-
-
-
-
-bpe_dir=${ofa_dir}/utils/BPE
-user_dir=${ofa_dir}/ofa_module
-
-
-data_dir=${base_data_dir}/ofa/vqa_data
-
-# val or test or fullval
-# split=fullval
-# read_from_img_path='--read-from-img-path'
-
-split=val
-read_from_img_path=''
-
-image_dir=${base_data_dir}
-
-data=${data_dir}/vqa_${split}.tsv
-
-ans2label_file=${base_data_dir}/ofa/vqa_data/trainval_ans2label.pkl
-
-
-
-selected_cols=0,5,2,3,4
-valid_batch_size=40
-
-
-
-
-
-
-
-exp_name=eval_vqa_base_best_ratacapgroundsnlivqalr5e5
-path=${base_log_dir}/ofa/pretrained_models/average_models/vqa_ratacapgroundsnlivqalr5e5.pt
-
-
-
-
-echo ${path}
-result_path=${base_log_dir}/ofa/results/vqa/${exp_name}_${split}
-mkdir ${result_path}
-
-
-python3 -m torch.distributed.launch \
-    --nnodes=${NUM_NODES} \
-    --nproc_per_node=${GPUS_PER_NODE} \
-    --master_port=${MASTER_PORT} \
-    --node_rank=${RANK} \
-    --master_addr=${MASTER_ADDR} \
-    --use_env ${ofa_dir}/evaluate.py \
-    ${data} \
-    --path=${path} \
-    --user-dir=${user_dir} \
-    --task=vqa_gen \
-    --batch-size=16 \
-    --log-format=simple --log-interval=10 \
-    --seed=7 \
-    --gen-subset=${split} \
-    --results-path=${result_path} \
-    --fp16 \
-    --beam-search-vqa-eval \
-    --beam=5 \
-    --unnormalized \
-    --temperature=1.0 \
-    --num-workers=0 \
-    --model-overrides="{\"data\":\"${data}\",\"bpe_dir\":\"${bpe_dir}\",\"selected_cols\":\"${selected_cols}\",\"ans2label_file\":\"${ans2label_file}\",\"valid_batch_size\":\"${valid_batch_size}\"}" \
-    --image-dir=${image_dir} \
-    ${read_from_img_path} \
-    --strict
-    
-    # --ema-eval \
-
-
-
-    
\ No newline at end of file
diff --git a/spaces/nathanTQ/ChatDev/camel/prompts/solution_extraction.py b/spaces/nathanTQ/ChatDev/camel/prompts/solution_extraction.py
deleted file mode 100644
index fdc56a497221697e4fa966de2c51dcb7fc0b8269..0000000000000000000000000000000000000000
--- a/spaces/nathanTQ/ChatDev/camel/prompts/solution_extraction.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the “License”);
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an “AS IS” BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-from typing import Any
-
-from camel.prompts import TextPrompt, TextPromptDict
-from camel.typing import RoleType
-
-
-# flake8: noqa
-class SolutionExtractionPromptTemplateDict(TextPromptDict):
-    r"""A dictionary containing :obj:`TextPrompt` used in the `SolutionExtraction`
-    task.
-
-    Attributes:
-        ASSISTANT_PROMPT (TextPrompt): A system prompt for the AI assistant
-            that outlines the rules of the conversation and provides
-            instructions for completing tasks.
-    """
-    ASSISTANT_PROMPT = TextPrompt(
-        """You are an experienced solution extracting agent. 
-Your task is to extract full and complete solutions by looking at the conversation between a user and an assistant with particular specializations. 
-You should present me with a final and detailed solution purely based on the conversation. 
-You should present the solution as if its yours. 
-Use present tense and as if you are the one presenting the solution. 
-You should not miss any necessary details or examples.
-Keep all provided explanations and codes provided throughout the conversation. 
-Remember your task is not to summarize rather to extract the full solution.""")
-
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(*args, **kwargs)
-        self.update({
-            RoleType.ASSISTANT: self.ASSISTANT_PROMPT,
-        })
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Foto Mosaik Edda Professional 14.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Foto Mosaik Edda Professional 14.md
deleted file mode 100644
index 9bf29ecaf4b39b05ab2acbc0f4121827d2a03001..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Foto Mosaik Edda Professional 14.md	
+++ /dev/null
@@ -1,38 +0,0 @@
-<br />
-<h1>How to Create Stunning Photo Mosaics with Foto Mosaik Edda Professional 14</h1>
-<p>Photo mosaics are a creative and fun way to transform your photos into amazing artworks. A photo mosaic is a picture that is composed of many smaller photos, arranged in such a way that they form a larger image when viewed from a distance. Photo mosaics can be used to make personalized gifts, posters, wallpapers, or simply to impress your friends and family.</p>
-<h2>foto mosaik edda professional 14</h2><br /><p><b><b>DOWNLOAD</b> &#10038; <a href="https://urlcod.com/2uIcp4">https://urlcod.com/2uIcp4</a></b></p><br /><br />
-<p>But how can you create photo mosaics easily and quickly? The answer is Foto Mosaik Edda Professional 14, the latest version of the popular software that allows you to create stunning photo mosaics from your own computer. Foto Mosaik Edda Professional 14 is a powerful and user-friendly tool that lets you turn any photo into a mosaic masterpiece in just a few steps.</p>
-<h2>What is Foto Mosaik Edda Professional 14?</h2>
-<p>Foto Mosaik Edda Professional 14 is the advanced version of Foto Mosaik Edda, a software that has been around since 2001 and has been downloaded by millions of users worldwide. Foto Mosaik Edda Professional 14 offers more features and options than the standard version, such as:</p>
-<ul>
-<li>The ability to create photo mosaics with up to 100,000 tiles</li>
-<li>The option to use different shapes and sizes for the tiles, such as circles, hexagons, triangles, or custom shapes</li>
-<li>The possibility to adjust the color, brightness, contrast, and saturation of the tiles</li>
-<li>The option to add text, logos, or frames to the mosaic</li>
-<li>The ability to save the mosaic as a high-resolution image or print it directly from the software</li>
-<li>The possibility to export the mosaic as a web page or a video</li>
-<li>The access to online databases with thousands of photos that you can use for your mosaic</li>
-</ul>
-<h2>How to Create Photo Mosaics with Foto Mosaik Edda Professional 14?</h2>
-<p>Creating photo mosaics with Foto Mosaik Edda Professional 14 is very easy and fun. You just need to follow these simple steps:</p>
-<p></p>
-<ol>
-<li>Download and install Foto Mosaik Edda Professional 14 from <a href="https://fmedda.com/en/download">here</a>. You can try it for free for 30 days or buy it for a reasonable price.</li>
-<li>Launch the software and select "Create new project". Choose the photo that you want to use as the main image for your mosaic. You can also crop, rotate, or resize it if needed.</li>
-<li>Select the photos that you want to use as the tiles for your mosaic. You can use photos from your own collection or from online databases. You can also create multiple databases with different themes or categories.</li>
-<li>Choose the settings for your mosaic, such as the number, shape, and size of the tiles, the color mode, the blending mode, and the output format. You can preview the result at any time and make adjustments until you are satisfied.</li>
-<li>Save or print your mosaic. You can also export it as a web page or a video and share it online.</li>
-</ol>
-<h2>Why Choose Foto Mosaik Edda Professional 14?</h2>
-<p>Foto Mosaik Edda Professional 14 is not just another photo mosaic software. It is a unique and innovative tool that offers many advantages over other similar products, such as:</p>
-<ul>
-<li>It is fast and reliable. It can create photo mosaics in minutes without compromising on quality.</li>
-<li>It is easy and intuitive. It has a user-friendly interface that guides you through the process step by step.</li>
-<li>It is versatile and flexible. It allows you to customize every aspect of your mosaic according to your preferences and needs.</li>
-<li>It is affordable and convenient. It has a reasonable price and a free trial period. It also does not require any installation or registration.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>If you are looking for a way to create stunning photo mosaics from your own computer, you should definitely try Foto Mosaik Edda Professional</p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Illustrator Cs6 Essential Training With Justin Seeley Download [REPACK].md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Illustrator Cs6 Essential Training With Justin Seeley Download [REPACK].md
deleted file mode 100644
index a78deea22bb52a05d20e9084100c163a3d1df623..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Illustrator Cs6 Essential Training With Justin Seeley Download [REPACK].md	
+++ /dev/null
@@ -1,76 +0,0 @@
-
-<h1>Illustrator CS6 Essential Training with Justin Seeley Download</h1>
-<p>If you are interested in learning how to create stunning vector graphics using Adobe Illustrator CS6, you might want to check out this course by Justin Seeley. Justin Seeley is an experienced graphic designer, author, and instructor who has been teaching online courses for lynda.com since 2006. In this course, he will guide you through the basics and advanced features of Illustrator CS6, and show you how to apply them to various design projects.</p>
-<p>In this article, we will give you an overview of what Illustrator CS6 is, what are its benefits and features, how to download and install it on your computer, how to access the course by Justin Seeley, and some tips and tricks for using Illustrator CS6 effectively. We will also answer some frequently asked questions about Illustrator CS6 and the course.</p>
-<h2>Illustrator Cs6 Essential Training With Justin Seeley Download</h2><br /><p><b><b>Download</b> &hArr; <a href="https://urlcod.com/2uIbG2">https://urlcod.com/2uIbG2</a></b></p><br /><br />
- <h2>Why Learn Illustrator CS6?</h2>
-<p>Illustrator CS6 is a powerful vector graphics software that lets you create logos, icons, drawings, typography, illustrations, and more for print, web, video, and mobile. Vector graphics are made of paths that can be scaled up or down without losing quality or resolution. This makes them ideal for creating sharp and crisp graphics that can be used for any purpose.</p>
-<p>Some of the benefits of learning Illustrator CS6 are:</p>
-<ul>
-<li>You can create professional-looking graphics that stand out from the crowd.</li>
-<li>You can express your creativity and style with a variety of tools and effects.</li>
-<li>You can work faster and more efficiently with a streamlined and flexible interface.</li>
-<li>You can edit and manipulate your graphics with ease and precision.</li>
-<li>You can integrate your graphics with other Adobe applications such as Photoshop, InDesign, After Effects, etc.</li>
-</ul>
- <h3>Features of Illustrator CS6</h3>
-<p>Illustrator CS6 has many features that can help you create amazing vector graphics. Here are some of the most important ones:</p>
- <h4>Streamlined and flexible interface</h4>
-<p>Illustrator CS6 has a sleek and intuitive interface that allows you to customize your workspace according to your preferences. You can adjust the brightness of the user interface, dock or undock panels, access tools from the toolbar or the control panel, switch between different workspaces, etc. You can also use gestures on a touch-enabled device to zoom, pan, or rotate your artwork.</p>
- <h4>Enhancements Panel</h4>
-<p>The Enhancements Panel is a new feature in Illustrator CS 6 that lets you access and apply various effects and filters to your artwork. You can choose from a range of options such as drop shadow, glow, bevel, emboss, 3D, distort, warp, etc. You can also preview the results before applying them, and adjust the settings as you like.</p>
- <h4>Transform Panel</h4>
-<p>The Transform Panel is a handy tool that lets you resize, rotate, skew, and distort your objects. You can enter the values manually or use the handles to transform your objects visually. You can also use the Align and Distribute options to arrange your objects in a neat and orderly manner.</p>
- <h4>Transparency Panel</h4>
-<p>The Transparency Panel allows you to adjust the opacity and blending modes of your objects. You can create transparent or semi-transparent effects by changing the opacity percentage, or you can blend your objects with the background or other objects by choosing from different blending modes such as multiply, screen, overlay, etc.</p>
- <h4>Control Panel</h4>
-<p>The Control Panel is a context-sensitive panel that changes according to the type of object you select. You can use it to edit and format text, paths, strokes, and fills. You can also access other options such as clipping masks, live paint, live trace, etc. from the Control Panel.</p>
-<p></p>
- <h4>Image Trace</h4>
-<p>Image Trace is a feature that allows you to convert raster images into vector graphics. You can import an image from your computer or the web, and use the Image Trace panel to choose from different presets such as high fidelity photo, low fidelity photo, black and white logo, etc. You can also adjust the settings such as colors, paths, corners, noise, etc. to fine-tune your result. You can then expand your traced image and edit it as a vector graphic.</p>
- <h4>Gaussian Blur Enhancements</h4>
-<p>Gaussian Blur is an effect that creates a soft and smooth blur on your objects. In Illustrator CS6, you can apply Gaussian Blur faster and more accurately than before. You can also preview the effect on the artboard without having to open a dialog box. You can use Gaussian Blur to create realistic shadows and glows on your objects.</p>
- <h2>How to Download and Install Illustrator CS6?</h2>
-<p>If you want to download and install Illustrator CS6 on your computer, you have two options:</p>
-<ol>
-<li>You can buy it from Adobe as a standalone product or as part of the Creative Suite 6 or Creative Cloud subscription. You will need to create an Adobe account and pay for the product or subscription. You will then receive a download link and a serial number to activate your product.</li>
-<li>You can download it from other sources such as torrent sites or file-sharing platforms. However, this is not recommended as it may be illegal, unsafe, or unreliable. You may end up with a corrupted or infected file that could harm your computer or compromise your data. You may also face legal consequences if you are caught using pirated software.</li>
-</ol>
-<p>Once you have downloaded the file, you need to follow these steps to install it on your computer:</p>
-<ul>
-<li>Extract the file using a program such as WinRAR or 7-Zip.</li>
-<li>Run the setup.exe file and follow the instructions on the screen.</li>
-<li>Enter your serial number when prompted or choose the trial option if you want to try it for free for 30 days.</li>
-<li>Select the components and language you want to install.</li>
-<li>Wait for the installation to complete and restart your computer if necessary.</li>
-<li>Launch Illustrator CS6 from your desktop or start menu and enjoy!</li>
-</ul>
- <h2>How to Access the Course?</h2>
-<p>If you want to access the course by Justin Seeley on Illustrator CS6 Essential Training, you have two options:</p>
-<ol>
-<li>You can buy or subscribe to it from lynda.com. Lynda.com is an online learning platform that offers thousands of courses on various topics such as design, business, photography, etc. You will need to create an account and pay for the course or subscription. You will then be able to watch the videos online or download them for offline viewing. You will also get access to exercise files, transcripts, quizzes, certificates, etc.</li>
-<li>You can find free alternatives online such as YouTube videos, blogs, podcasts, etc. However, this may not be as comprehensive or reliable as the course from lynda.com. You may miss some important information or get outdated or inaccurate advice. You may also not get any support or feedback from the instructor or other learners.</li>
-</ol>
- <h2>Tips and Tricks for Using Illustrator CS6</h2>
-<p>To make the most of Illustrator CS6 and create amazing vector graphics, here are some tips and tricks that you should follow:</p>
-<ul>
-<li>Use shortcuts to speed up your work. For example, you can use Ctrl+C and Ctrl+V to copy and paste, Ctrl+Z and Ctrl+Shift+Z to undo and redo, Ctrl+G and Ctrl+Shift+G to group and ungroup, etc. You can also customize your own shortcuts from the Edit menu.</li>
-<li>Use actions to automate repetitive tasks. For example, you can use actions to resize, crop, export, or apply effects to multiple objects at once. You can create your own actions from the Window menu or use the default ones.</li>
-<li>Use brushes to add texture and style to your strokes. For example, you can use brushes to create realistic hair, fur, grass, or water effects. You can choose from different types of brushes such as calligraphic, scatter, art, pattern, etc. You can also create your own brushes from the Brushes panel or use the ones from the library.</li>
-<li>Use gradients to create smooth color transitions on your fills or strokes. For example, you can use gradients to create realistic lighting, shading, or reflection effects. You can choose from different types of gradients such as linear, radial, freeform, etc. You can also edit the colors, angles, positions, and opacity of the gradient stops from the Gradient panel or the Gradient tool.</li>
-<li>Use patterns to fill your objects with repeating shapes or images. For example, you can use patterns to create backgrounds, borders, or decorative elements. You can choose from different types of patterns such as basic, geometric, nature, etc. You can also create your own patterns from the Object menu or use the ones from the library.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, Illustrator CS6 is a great software for creating vector graphics that can be used for various purposes. It has many features and tools that can help you design professional-looking graphics with ease and creativity. You can learn how to use Illustrator CS6 by taking the course by Justin Seeley on lynda.com or by finding free alternatives online. You can also improve your skills by following some tips and tricks that we have shared in this article.</p>
-<p>We hope that this article has given you some useful information and inspiration for using Illustrator CS6. If you have any questions or comments, please feel free to leave them below. Thank you for reading and happy designing!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions and answers about Illustrator CS6 and the course:</p>
-<ol>
-<li>Q: What are the system requirements for Illustrator CS6?<br>A: The minimum system requirements for Illustrator CS6 are: Windows XP/Vista/7/8/10 or Mac OS X 10.6.8 or later; Intel Pentium 4 or AMD Athlon 64 processor; 1 GB of RAM (3 GB recommended); 2 GB of available hard-disk space; 1024 x 768 display (1280 x 800 recommended); OpenGL 2.0–capable system; DVD-ROM drive; Internet connection.</li>
-<li>Q: How much does Illustrator CS6 cost?<br>A: The price of Illustrator CS6 varies depending on the option you choose. You can buy it as a standalone product for $599 or as part of the Creative Suite 6 for $1,299-$2,599. You can also subscribe to it as part of the Creative Cloud for $19.99-$49.99 per month.</li>
-<li>Q: How long is the course by Justin Seeley?<br>A: The course by Justin Seeley on Illustrator CS6 Essential Training is about 9 hours long and consists of 12 chapters and 90 videos.</li>
-<li>Q: What will I learn from the course by Justin Seeley?<br>A: The course by Justin Seeley will teach you how to use Illustrator CS6 for creating vector graphics from scratch or from existing images. You will learn how to use the interface, tools, panels, effects, filters, etc. You will also learn how to work with layers, masks, symbols, text, shapes, paths, etc. You will also learn how to export and print your graphics for different purposes.</li>
-<li>Q: Where can I find more resources on Illustrator CS6?<br>A: You can find more resources on Illustrator CS6 from the following sources:<br>- Adobe Help Center: https://helpx.adobe.com/illustrator.html<br>- Adobe Forums: https://forums.adobe.com/community/illustrator<br>- Adobe Tutorials: https://helpx.adobe.com/illustrator/tutorials.html<br>- Lynda.com: https://www.lynda.com/Illustrator-training-tutorials/227-0.html<br>- YouTube: https://www.youtube.com/results?search_query=illustrator+cs6<br>- Blogs: https://www.google.com/search ?q=illustrator+cs6+blog<br>- Books: https://www.amazon.com/s?k=illustrator+cs6+book</li>
-</ol></p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Microsoft SQL Server 2000 (reload) 64 Bit High Quality.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Microsoft SQL Server 2000 (reload) 64 Bit High Quality.md
deleted file mode 100644
index ea02adfe13ed04718105c67bd1c4e2ce6c9dd2e4..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Microsoft SQL Server 2000 (reload) 64 Bit High Quality.md	
+++ /dev/null
@@ -1,30 +0,0 @@
-<br />
-<h1>How to Download and Install Microsoft SQL Server 2000 (reload) 64 bit</h1>
-<p>Microsoft SQL Server 2000 is a relational database management system that supports data analysis, business intelligence, and transaction processing. It was released in 2000 and has been updated with several service packs since then. The latest service pack is Service Pack 4 (SP4), which was released in 2005.</p>
-<h2>Microsoft SQL Server 2000 (reload) 64 bit</h2><br /><p><b><b>Download</b> &mdash;&mdash;&mdash; <a href="https://urlcod.com/2uIan6">https://urlcod.com/2uIan6</a></b></p><br /><br />
-<p>SQL Server 2000 has two versions: a 32-bit version and a 64-bit version. The 64-bit version is designed for systems that use the Intel Itanium processor architecture, which offers higher performance and scalability than the 32-bit version. However, the 64-bit version is not compatible with systems that use the x86 or x64 processor architectures.</p>
-<p>If you want to download and install SQL Server 2000 (reload) 64 bit, you need to have a TechNet Subscription, which gives you access to Microsoft software for evaluation and testing purposes. You can also use the TechNet Subscription to download other versions of SQL Server, such as SQL Server 2005, SQL Server 2008, and SQL Server 2012.</p>
-<p>To download SQL Server 2000 (reload) 64 bit, follow these steps:</p>
-<p></p>
-<ol>
-<li>Go to the TechNet Subscription Secure Downloads page: <a href="https://technet.microsoft.com/en-us/subscriptions/securedownloads/hh442904">https://technet.microsoft.com/en-us/subscriptions/securedownloads/hh442904</a></li>
-<li>Sign in with your Microsoft account and your TechNet Subscription ID.</li>
-<li>Search for "SQL Server 2000 Enterprise Edition - 64-bit (English)" in the search box.</li>
-<li>Click on the download link and save the file to your computer. The file name is X09-51140.exe and the file size is about 400 MB.</li>
-<li>Run the file and follow the instructions to extract the contents to a folder on your computer. The folder name is X09-51140.</li>
-</ol>
-<p>To install SQL Server 2000 (reload) 64 bit, follow these steps:</p>
-<ol>
-<li>Go to the folder where you extracted the files and double-click on setup.exe.</li>
-<li>Follow the instructions on the screen to install SQL Server 2000 (reload) 64 bit. You may need to enter your product key, which is located on the CD-ROM case or on the TechNet Subscription website.</li>
-<li>Restart your computer when prompted.</li>
-<li>Verify that SQL Server 2000 (reload) 64 bit is installed correctly by opening the SQL Server Enterprise Manager or by running a query in the Query Analyzer.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded and installed SQL Server 2000 (reload) 64 bit on your computer. You can now use it for your development and testing purposes. For more information about SQL Server 2000, you can visit the following websites:</p>
-<ul>
-<li><a href="https://support.microsoft.com/en-us/topic/kb290211-how-to-obtain-the-latest-sql-server-2000-service-pack-2ad4d0ab-a585-aecd-118c-c851c7dc8ad0">How to obtain the latest SQL Server 2000 service pack</a></li>
-<li><a href="https://social.msdn.microsoft.com/Forums/sqlserver/en-US/60693ea3-dfc9-497f-a5dd-4db72ab777a8/sql2000-64bit-download?forum=sqlsetupandupgrade">SQL2000 64bit download</a></li>
-<li><a href="https://archive.org/details/X09-51140">Microsoft SQL Server 2000 Developer Edition (64-bit)</a></li>
-</ul></p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/neuralmagic/question-answering/app.py b/spaces/neuralmagic/question-answering/app.py
deleted file mode 100644
index 28dbd099f6d97c73d34919914a42598fdd2a5cbe..0000000000000000000000000000000000000000
--- a/spaces/neuralmagic/question-answering/app.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from deepsparse import Pipeline
-import time
-import gradio as gr
-
-markdownn = '''
-# Question Answering Pipeline with DeepSparse
-Given a context and question, a Question Answering model provides an answer to the given question. 
-
-Question-answering models are helpful in retrieving answers quickly from large texts. 
-
-![Question Answering Pipeline with DeepSparse](https://huggingface.co/spaces/neuralmagic/question-answering/resolve/main/qa.png)
-## What is DeepSparse?
-DeepSparse is an inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application. Sparsification is a powerful technique for optimizing models for inference, reducing the compute needed with a limited accuracy tradeoff. DeepSparse is designed to take advantage of model sparsity, enabling you to deploy models with the flexibility and scalability of software on commodity CPUs with the best-in-class performance of hardware accelerators, enabling you to standardize operations and reduce infrastructure costs.
-Similar to Hugging Face, DeepSparse provides off-the-shelf pipelines for computer vision and NLP that wrap the model with proper pre- and post-processing to run performantly on CPUs by using sparse models.
-
-SparseML Question Answering Pipelines integrate with Hugging Face’s Transformers library to enable the sparsification of a large set of transformers models. 
-### Inference API Example
-Here is sample code for a question-answering pipeline: 
-```
-from deepsparse import Pipeline
-pipeline = Pipeline.create(task="question-answering", model_path="zoo:nlp/question_answering/distilbert-none/pytorch/huggingface/squad/base-none")
-question = "DeepSparse is sparsity-aware inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application."
-context = "What is DeepSparse?"
-inference = pipeline(question=question, context=context)
-print(inference)
-```
-## Use Case Description
-Search Documents quickly is one example of a question-answering model in use. 
-
-Imagine a situation where you have thousands of documents but need to find an answer from the documents.
-
-At the same time, you'd like to get the document where the answer comes from. You could open and search the documents one by one, but that would take a long time. 
-
-Enter Extractive Question Answering with Sparse Transformers. 
-
-With Extractive Question Answering, you input a query into the system and in return, you get the answer to your question and the document containing the answer.
-
-Extractive Question Answering enables you to search many records and find the answer.
-
-It works by:
-- Retrieving documents that are relevant to answering the questions.
-- Returns text that answers that question.
-
-[Want to train a sparse model on your data? Checkout the documentation on sparse transfer learning](https://docs.neuralmagic.com/use-cases/natural-language-processing/question-answering)
-
-'''
-task = "question-answering"
-sparse_qa_pipeline = Pipeline.create(
-        task=task,
-        model_path="zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned95_obs_quant-none",
-    )
-def run_pipeline(q_context, question):
-    sparse_start = time.perf_counter()
-    sparse_output = sparse_qa_pipeline(question=question, context=q_context)
-    sparse_result = dict(sparse_output)
-    sparse_end = time.perf_counter()
-    sparse_duration = (sparse_end - sparse_start) * 1000.0
-    
-    return sparse_result, sparse_duration
-
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(markdownn)
-            
-        with gr.Column():
-            gr.Markdown("""
-            ## Question answering demo
-            """)
-            q_context = gr.Text(label="Context")
-            question = gr.Text(label="Question")
-            btn = gr.Button("Submit")
-            sparse_answers = gr.Textbox(label="Sparse model answers")
-            sparse_duration = gr.Number(label="Sparse Latency (ms):")
-            btn.click(run_pipeline,inputs=[q_context, question],outputs=[sparse_answers,sparse_duration])
-            gr.Examples([["DeepSparse is sparsity-aware inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application","What is DeepSparse?"],["SparseML is a Library for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models","What is SparseML?"],["Gradio is an open-source Python package that allows you to quickly create easy-to-use, customizable UI components for your ML model, any API, or even an arbitrary Python function using a few lines of code. You can integrate the Gradio GUI directly into your Jupyter notebook or share it as a link with anyone.", "What is Gradio?"],],inputs=[q_context, question],)
-   
-
-   
-
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/niew/vits-uma-genshin-honka/app.py b/spaces/niew/vits-uma-genshin-honka/app.py
deleted file mode 100644
index 92ddafdcd240434f58569b0e6964ef331a971dcf..0000000000000000000000000000000000000000
--- a/spaces/niew/vits-uma-genshin-honka/app.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import time
-import gradio as gr
-import utils
-import commons
-from models import SynthesizerTrn
-from text import text_to_sequence
-from torch import no_grad, LongTensor
-import torch
-
-hps_ms = utils.get_hparams_from_file(r'./model/config.json')
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-net_g_ms = SynthesizerTrn(
-    len(hps_ms.symbols),
-    hps_ms.data.filter_length // 2 + 1,
-    hps_ms.train.segment_size // hps_ms.data.hop_length,
-    n_speakers=hps_ms.data.n_speakers,
-    **hps_ms.model).to(device)
-_ = net_g_ms.eval()
-speakers = hps_ms.speakers
-model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None)
-
-def get_text(text, hps):
-    text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm, clean_text
-
-def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale):
-    start = time.perf_counter()
-    if not len(text):
-        return "输入文本不能为空！", None, None
-    text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
-    if len(text) > 500:
-        return f"输入文字过长！{len(text)}>100", None, None
-    if language == 0:
-        text = f"[ZH]{text}[ZH]"
-    elif language == 1:
-        text = f"[JA]{text}[JA]"
-    else:
-        text = f"{text}"
-    stn_tst, clean_text = get_text(text, hps_ms)
-    with no_grad():
-        x_tst = stn_tst.unsqueeze(0)
-        x_tst_lengths = LongTensor([stn_tst.size(0)])
-        speaker_id = LongTensor([speaker_id])
-        audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
-                               length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
-
-    return "生成成功!", (22050, audio), f"生成耗时 {round(time.perf_counter()-start, 2)} s"
-
-def search_speaker(search_value):
-    for s in speakers:
-        if search_value == s:
-            return s
-    for s in speakers:
-        if search_value in s:
-            return s
-
-def change_lang(language):
-    if language == 0:
-        return 0.6, 0.668, 1.2
-    else:
-        return 0.6, 0.668, 1.1
-
-download_audio_js = """
-() =>{{
-    let root = document.querySelector("body > gradio-app");
-    if (root.shadowRoot != null)
-        root = root.shadowRoot;
-    let audio = root.querySelector("#tts-audio").querySelector("audio");
-    let text = root.querySelector("#input-text").querySelector("textarea");
-    if (audio == undefined)
-        return;
-    text = text.value;
-    if (text == undefined)
-        text = Math.floor(Math.random()*100000000);
-    audio = audio.src;
-    let oA = document.createElement("a");
-    oA.download = text.substr(0, 20)+'.wav';
-    oA.href = audio;
-    document.body.appendChild(oA);
-    oA.click();
-    oA.remove();
-}}
-"""
-
-if __name__ == '__main__':
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> VITS语音在线合成demo\n"
-            "<div align='center'>主要有赛马娘，原神中文，原神日语，崩坏3的音色</div>"
-            '<div align="center"><a><font color="#dd0000">结果有随机性，语调可能很奇怪，可多次生成取最佳效果</font></a></div>'
-            '<div align="center"><a><font color="#dd0000">标点符号会影响生成的结果</font></a></div>'
-        )
-
-        with gr.Tabs():
-            with gr.TabItem("vits"):
-                with gr.Row():
-                    with gr.Column():
-                        input_text = gr.Textbox(label="Text (100 words limitation)", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text")
-                        lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"],
-                                    type="index", value="中文")
-                        btn = gr.Button(value="Submit")
-                        with gr.Row():
-                            search = gr.Textbox(label="Search Speaker", lines=1)
-                            btn2 = gr.Button(value="Search")
-                        sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228])
-                        with gr.Row():
-                            ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
-                            nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
-                            ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True)
-                    with gr.Column():
-                        o1 = gr.Textbox(label="Output Message")
-                        o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio")
-                        o3 = gr.Textbox(label="Extra Info")
-                        download = gr.Button("Download Audio")
-                    btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3], api_name="generate")
-                    download.click(None, [], [], _js=download_audio_js.format())
-                    btn2.click(search_speaker, inputs=[search], outputs=[sid])
-                    lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
-            with gr.TabItem("可用人物一览"):
-                gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index")
-    app.queue(concurrency_count=1).launch()
\ No newline at end of file
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/roi_heads/deeplab.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/roi_heads/deeplab.py
deleted file mode 100644
index 4e5cb483037b302ff1fb2c305275a65e4ba4e941..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/roi_heads/deeplab.py
+++ /dev/null
@@ -1,263 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.config import CfgNode
-from detectron2.layers import Conv2d
-
-from .registry import ROI_DENSEPOSE_HEAD_REGISTRY
-
-
-@ROI_DENSEPOSE_HEAD_REGISTRY.register()
-class DensePoseDeepLabHead(nn.Module):
-    """
-    DensePose head using DeepLabV3 model from
-    "Rethinking Atrous Convolution for Semantic Image Segmentation"
-    <https://arxiv.org/abs/1706.05587>.
-    """
-
-    def __init__(self, cfg: CfgNode, input_channels: int):
-        super(DensePoseDeepLabHead, self).__init__()
-        # fmt: off
-        hidden_dim           = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_DIM
-        kernel_size          = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_KERNEL
-        norm                 = cfg.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NORM
-        self.n_stacked_convs = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_STACKED_CONVS
-        self.use_nonlocal    = cfg.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NONLOCAL_ON
-        # fmt: on
-        pad_size = kernel_size // 2
-        n_channels = input_channels
-
-        self.ASPP = ASPP(input_channels, [6, 12, 56], n_channels)  # 6, 12, 56
-        self.add_module("ASPP", self.ASPP)
-
-        if self.use_nonlocal:
-            self.NLBlock = NONLocalBlock2D(input_channels, bn_layer=True)
-            self.add_module("NLBlock", self.NLBlock)
-        # weight_init.c2_msra_fill(self.ASPP)
-
-        for i in range(self.n_stacked_convs):
-            norm_module = nn.GroupNorm(32, hidden_dim) if norm == "GN" else None
-            layer = Conv2d(
-                n_channels,
-                hidden_dim,
-                kernel_size,
-                stride=1,
-                padding=pad_size,
-                bias=not norm,
-                norm=norm_module,
-            )
-            weight_init.c2_msra_fill(layer)
-            n_channels = hidden_dim
-            layer_name = self._get_layer_name(i)
-            self.add_module(layer_name, layer)
-        self.n_out_channels = hidden_dim
-        # initialize_module_params(self)
-
-    def forward(self, features):
-        x0 = features
-        x = self.ASPP(x0)
-        if self.use_nonlocal:
-            x = self.NLBlock(x)
-        output = x
-        for i in range(self.n_stacked_convs):
-            layer_name = self._get_layer_name(i)
-            x = getattr(self, layer_name)(x)
-            x = F.relu(x)
-            output = x
-        return output
-
-    def _get_layer_name(self, i: int):
-        layer_name = "body_conv_fcn{}".format(i + 1)
-        return layer_name
-
-
-# Copied from
-# https://github.com/pytorch/vision/blob/master/torchvision/models/segmentation/deeplabv3.py
-# See https://arxiv.org/pdf/1706.05587.pdf for details
-class ASPPConv(nn.Sequential):
-    def __init__(self, in_channels, out_channels, dilation):
-        modules = [
-            nn.Conv2d(
-                in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False
-            ),
-            nn.GroupNorm(32, out_channels),
-            nn.ReLU(),
-        ]
-        super(ASPPConv, self).__init__(*modules)
-
-
-class ASPPPooling(nn.Sequential):
-    def __init__(self, in_channels, out_channels):
-        super(ASPPPooling, self).__init__(
-            nn.AdaptiveAvgPool2d(1),
-            nn.Conv2d(in_channels, out_channels, 1, bias=False),
-            nn.GroupNorm(32, out_channels),
-            nn.ReLU(),
-        )
-
-    def forward(self, x):
-        size = x.shape[-2:]
-        x = super(ASPPPooling, self).forward(x)
-        return F.interpolate(x, size=size, mode="bilinear", align_corners=False)
-
-
-class ASPP(nn.Module):
-    def __init__(self, in_channels, atrous_rates, out_channels):
-        super(ASPP, self).__init__()
-        modules = []
-        modules.append(
-            nn.Sequential(
-                nn.Conv2d(in_channels, out_channels, 1, bias=False),
-                nn.GroupNorm(32, out_channels),
-                nn.ReLU(),
-            )
-        )
-
-        rate1, rate2, rate3 = tuple(atrous_rates)
-        modules.append(ASPPConv(in_channels, out_channels, rate1))
-        modules.append(ASPPConv(in_channels, out_channels, rate2))
-        modules.append(ASPPConv(in_channels, out_channels, rate3))
-        modules.append(ASPPPooling(in_channels, out_channels))
-
-        self.convs = nn.ModuleList(modules)
-
-        self.project = nn.Sequential(
-            nn.Conv2d(5 * out_channels, out_channels, 1, bias=False),
-            # nn.BatchNorm2d(out_channels),
-            nn.ReLU()
-            # nn.Dropout(0.5)
-        )
-
-    def forward(self, x):
-        res = []
-        for conv in self.convs:
-            res.append(conv(x))
-        res = torch.cat(res, dim=1)
-        return self.project(res)
-
-
-# copied from
-# https://github.com/AlexHex7/Non-local_pytorch/blob/master/lib/non_local_embedded_gaussian.py
-# See https://arxiv.org/abs/1711.07971 for details
-class _NonLocalBlockND(nn.Module):
-    def __init__(
-        self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True
-    ):
-        super(_NonLocalBlockND, self).__init__()
-
-        assert dimension in [1, 2, 3]
-
-        self.dimension = dimension
-        self.sub_sample = sub_sample
-
-        self.in_channels = in_channels
-        self.inter_channels = inter_channels
-
-        if self.inter_channels is None:
-            self.inter_channels = in_channels // 2
-            if self.inter_channels == 0:
-                self.inter_channels = 1
-
-        if dimension == 3:
-            conv_nd = nn.Conv3d
-            max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2))
-            bn = nn.GroupNorm  # (32, hidden_dim) #nn.BatchNorm3d
-        elif dimension == 2:
-            conv_nd = nn.Conv2d
-            max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2))
-            bn = nn.GroupNorm  # (32, hidden_dim)nn.BatchNorm2d
-        else:
-            conv_nd = nn.Conv1d
-            max_pool_layer = nn.MaxPool1d(kernel_size=2)
-            bn = nn.GroupNorm  # (32, hidden_dim)nn.BatchNorm1d
-
-        self.g = conv_nd(
-            in_channels=self.in_channels,
-            out_channels=self.inter_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        )
-
-        if bn_layer:
-            self.W = nn.Sequential(
-                conv_nd(
-                    in_channels=self.inter_channels,
-                    out_channels=self.in_channels,
-                    kernel_size=1,
-                    stride=1,
-                    padding=0,
-                ),
-                bn(32, self.in_channels),
-            )
-            nn.init.constant_(self.W[1].weight, 0)
-            nn.init.constant_(self.W[1].bias, 0)
-        else:
-            self.W = conv_nd(
-                in_channels=self.inter_channels,
-                out_channels=self.in_channels,
-                kernel_size=1,
-                stride=1,
-                padding=0,
-            )
-            nn.init.constant_(self.W.weight, 0)
-            nn.init.constant_(self.W.bias, 0)
-
-        self.theta = conv_nd(
-            in_channels=self.in_channels,
-            out_channels=self.inter_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        )
-        self.phi = conv_nd(
-            in_channels=self.in_channels,
-            out_channels=self.inter_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        )
-
-        if sub_sample:
-            self.g = nn.Sequential(self.g, max_pool_layer)
-            self.phi = nn.Sequential(self.phi, max_pool_layer)
-
-    def forward(self, x):
-        """
-        :param x: (b, c, t, h, w)
-        :return:
-        """
-
-        batch_size = x.size(0)
-
-        g_x = self.g(x).view(batch_size, self.inter_channels, -1)
-        g_x = g_x.permute(0, 2, 1)
-
-        theta_x = self.theta(x).view(batch_size, self.inter_channels, -1)
-        theta_x = theta_x.permute(0, 2, 1)
-        phi_x = self.phi(x).view(batch_size, self.inter_channels, -1)
-        f = torch.matmul(theta_x, phi_x)
-        f_div_C = F.softmax(f, dim=-1)
-
-        y = torch.matmul(f_div_C, g_x)
-        y = y.permute(0, 2, 1).contiguous()
-        y = y.view(batch_size, self.inter_channels, *x.size()[2:])
-        W_y = self.W(y)
-        z = W_y + x
-
-        return z
-
-
-class NONLocalBlock2D(_NonLocalBlockND):
-    def __init__(self, in_channels, inter_channels=None, sub_sample=True, bn_layer=True):
-        super(NONLocalBlock2D, self).__init__(
-            in_channels,
-            inter_channels=inter_channels,
-            dimension=2,
-            sub_sample=sub_sample,
-            bn_layer=bn_layer,
-        )
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/PointRend/README.md b/spaces/nikitaPDL2023/assignment4/detectron2/projects/PointRend/README.md
deleted file mode 100644
index 79d75d506c6f5db710044d3c1cd2583027ac3dbe..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/PointRend/README.md
+++ /dev/null
@@ -1,167 +0,0 @@
-# PointRend: Image Segmentation as Rendering
-
-Alexander Kirillov, Yuxin Wu, Kaiming He, Ross Girshick
-
-[[`arXiv`](https://arxiv.org/abs/1912.08193)] [[`BibTeX`](#CitingPointRend)]
-
-<div align="center">
-  <img src="https://alexander-kirillov.github.io/images/kirillov2019pointrend.jpg"/>
-</div><br/>
-
-In this repository, we release code for PointRend in Detectron2. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models.
-
-## Quick start and visualization
-
-This [Colab Notebook](https://colab.research.google.com/drive/1isGPL5h5_cKoPPhVL9XhMokRtHDvmMVL) tutorial contains examples of PointRend usage and visualizations of its point sampling stages.
-
-## Training
-
-To train a model with 8 GPUs run:
-```bash
-cd /path/to/detectron2/projects/PointRend
-python train_net.py --config-file configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_coco.yaml --num-gpus 8
-```
-
-## Evaluation
-
-Model evaluation can be done similarly:
-```bash
-cd /path/to/detectron2/projects/PointRend
-python train_net.py --config-file configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_coco.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint
-```
-
-# Pretrained Models
-
-## Instance Segmentation
-#### COCO
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Mask<br/>head</th>
-<th valign="bottom">Backbone</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">Output<br/>resolution</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">mask<br/>AP&ast;</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
- <tr><td align="left"><a href="configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_coco.yaml">PointRend</a></td>
-<td align="center">R50-FPN</td>
-<td align="center">1&times;</td>
-<td align="center">224&times;224</td>
-<td align="center">36.2</td>
-<td align="center">39.7</td>
-<td align="center">164254221</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_coco/164254221/model_final_736f5a.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_coco/164254221/metrics.json">metrics</a></td>
-</tr>
- <tr><td align="left"><a href="configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_3x_coco.yaml">PointRend</a></td>
-<td align="center">R50-FPN</td>
-<td align="center">3&times;</td>
-<td align="center">224&times;224</td>
-<td align="center">38.3</td>
-<td align="center">41.6</td>
-<td align="center">164955410</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_50_FPN_3x_coco/164955410/model_final_edd263.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_50_FPN_3x_coco/164955410/metrics.json">metrics</a></td>
-</tr>
-</tr>
- <tr><td align="left"><a href="configs/InstanceSegmentation/pointrend_rcnn_R_101_FPN_3x_coco.yaml">PointRend</a></td>
-<td align="center">R101-FPN</td>
-<td align="center">3&times;</td>
-<td align="center">224&times;224</td>
-<td align="center">40.1</td>
-<td align="center">43.8</td>
-<td align="center"></td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_101_FPN_3x_coco/28119983/model_final_3f4d2a.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_101_FPN_3x_coco/28119983/metrics.json">metrics</a></td>
-</tr>
-</tr>
- <tr><td align="left"><a href="configs/InstanceSegmentation/pointrend_rcnn_X_101_32x8d_FPN_3x_coco.yaml">PointRend</a></td>
-<td align="center">X101-FPN</td>
-<td align="center">3&times;</td>
-<td align="center">224&times;224</td>
-<td align="center">41.1</td>
-<td align="center">44.7</td>
-<td align="center"></td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_X_101_32x8d_FPN_3x_coco/28119989/model_final_ba17b9.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_X_101_32x8d_FPN_3x_coco/28119989/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-AP&ast; is COCO mask AP evaluated against the higher-quality LVIS annotations; see the paper for details.
-Run `python detectron2/datasets/prepare_cocofied_lvis.py` to prepare GT files for AP&ast; evaluation.
-Since LVIS annotations are not exhaustive, `lvis-api` and not `cocoapi` should be used to evaluate AP&ast;.
-
-#### Cityscapes
-Cityscapes model is trained with ImageNet pretraining.
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Mask<br/>head</th>
-<th valign="bottom">Backbone</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">Output<br/>resolution</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
- <tr><td align="left"><a href="configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_cityscapes.yaml">PointRend</a></td>
-<td align="center">R50-FPN</td>
-<td align="center">1&times;</td>
-<td align="center">224&times;224</td>
-<td align="center">35.9</td>
-<td align="center">164255101</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_cityscapes/164255101/model_final_115bfb.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_cityscapes/164255101/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-
-## Semantic Segmentation
-
-#### Cityscapes
-Cityscapes model is trained with ImageNet pretraining.
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Method</th>
-<th valign="bottom">Backbone</th>
-<th valign="bottom">Output<br/>resolution</th>
-<th valign="bottom">mIoU</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
- <tr><td align="left"><a href="configs/SemanticSegmentation/pointrend_semantic_R_101_FPN_1x_cityscapes.yaml">SemanticFPN + PointRend</a></td>
-<td align="center">R101-FPN</td>
-<td align="center">1024&times;2048</td>
-<td align="center">78.9</td>
-<td align="center">202576688</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/SemanticSegmentation/pointrend_semantic_R_101_FPN_1x_cityscapes/202576688/model_final_cf6ac1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/SemanticSegmentation/pointrend_semantic_R_101_FPN_1x_cityscapes/202576688/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-## <a name="CitingPointRend"></a>Citing PointRend
-
-If you use PointRend, please use the following BibTeX entry.
-
-```BibTeX
-@InProceedings{kirillov2019pointrend,
-  title={{PointRend}: Image Segmentation as Rendering},
-  author={Alexander Kirillov and Yuxin Wu and Kaiming He and Ross Girshick},
-  journal={ArXiv:1912.08193},
-  year={2019}
-}
-```
-
-## <a name="CitingImplicitPointRend"></a>Citing Implicit PointRend
-
-If you use Implicit PointRend, please use the following BibTeX entry.
-
-```BibTeX
-@InProceedings{cheng2021pointly,
-  title={Pointly-Supervised Instance Segmentation,
-  author={Bowen Cheng and Omkar Parkhi and Alexander Kirillov},
-  journal={ArXiv},
-  year={2021}
-}
-```
diff --git a/spaces/nlphuji/whoops-explorer-analysis/app.py b/spaces/nlphuji/whoops-explorer-analysis/app.py
deleted file mode 100644
index 9ebcbca6e7b19c4f177282db30893fec5c8f045c..0000000000000000000000000000000000000000
--- a/spaces/nlphuji/whoops-explorer-analysis/app.py
+++ /dev/null
@@ -1,73 +0,0 @@
-from datasets import load_dataset
-import gradio as gr
-import os
-import random
-
-wmtis = load_dataset("nlphuji/wmtis-identify")['test']
-print(f"Loaded WMTIS identify, first example:")
-print(wmtis[0])
-dataset_size = len(wmtis) - 1
-
-NATURAL_IMAGE = 'natural_image'
-NORMAL_IMAGE = 'normal_image'
-STRANGE_IMAGE = 'strange_image'
-
-def func(index):
-    example = wmtis[index]
-    outputs = []
-    target_size = example['normal_image'].size
-    add_outputs_for_key(example, outputs, target_size, 'natural')
-    add_outputs_for_key(example, outputs, target_size, 'normal')
-    add_outputs_for_key(example, outputs, target_size, 'strange')
-
-    return outputs
-
-
-def add_outputs_for_key(example, outputs, target_size, item):
-    for item_key in [f'{item}_image', f'{item}_image_caption', f'rating_{item}', f'comments_{item}', f'{item}_hash']:
-        if item_key == f'comments_{item}':
-            outputs.append(get_empty_comment_if_needed(example[item_key]))
-        elif item_key == f'{item}_image':
-            outputs.append(example[item_key].resize(target_size))
-        else:
-            outputs.append(example[item_key])
-
-
-demo = gr.Blocks()
-
-def get_empty_comment_if_needed(item):
-    if item == 'nan':
-        return '-'
-    return item
-
-
-def add_column_by_key(item, target_size):
-    with gr.Column():
-        img = wmtis[index][f"{item}_image"]
-        img_resized = img.resize(target_size)
-        i1 = gr.Image(value=img_resized, label=f'{item.capitalize()} Image')
-        p1 = gr.Textbox(value=wmtis[index][f"{item}_image_caption"], label='BLIP2 Predicted Caption')
-        r1 = gr.Textbox(value=wmtis[index][f"rating_{item}"], label='Rating')
-        c1 = gr.Textbox(value=get_empty_comment_if_needed(wmtis[index][f"comments_{item}"]), label='Comments')
-        t1 = gr.Textbox(value=wmtis[index][f"{item}_hash"], label='Image ID')
-        item_outputs = [i1, p1, r1, c1, t1]
-        return item_outputs
-
-
-with demo:
-    gr.Markdown("# Main Challenge: Weirdness, not Synthesis")
-
-    with gr.Column():
-        slider = gr.Slider(minimum=0, maximum=dataset_size)
-        with gr.Row():
-            index = slider.value
-            if index >= dataset_size:
-                index = 0
-            target_size = wmtis[index]['normal_image'].size
-            natural_outputs = add_column_by_key('natural', target_size)
-            normal_outputs = add_column_by_key('normal', target_size)
-            strange_outputs = add_column_by_key('strange', target_size)
-
-    slider.change(func, inputs=[slider], outputs=natural_outputs + normal_outputs + strange_outputs)
-
-demo.launch()
diff --git a/spaces/oliver2023/chatgpt-on-wechat/voice/google/google_voice.py b/spaces/oliver2023/chatgpt-on-wechat/voice/google/google_voice.py
deleted file mode 100644
index 3a0537f1e93fa547063e8eee65cb0e5fdb5b61bc..0000000000000000000000000000000000000000
--- a/spaces/oliver2023/chatgpt-on-wechat/voice/google/google_voice.py
+++ /dev/null
@@ -1,46 +0,0 @@
-
-"""
-google voice service
-"""
-
-import time
-import speech_recognition
-from gtts import gTTS
-from bridge.reply import Reply, ReplyType
-from common.log import logger
-from common.tmp_dir import TmpDir
-from voice.voice import Voice
-
-
-class GoogleVoice(Voice):
-    recognizer = speech_recognition.Recognizer()
-
-    def __init__(self):
-        pass
-
-    def voiceToText(self, voice_file):
-        with speech_recognition.AudioFile(voice_file) as source:
-            audio = self.recognizer.record(source)
-        try:
-            text = self.recognizer.recognize_google(audio, language='zh-CN')
-            logger.info(
-                '[Google] voiceToText text={} voice file name={}'.format(text, voice_file))
-            reply = Reply(ReplyType.TEXT, text)
-        except speech_recognition.UnknownValueError:
-            reply = Reply(ReplyType.ERROR, "抱歉，我听不懂")
-        except speech_recognition.RequestError as e:
-            reply = Reply(ReplyType.ERROR, "抱歉，无法连接到 Google 语音识别服务；{0}".format(e))
-        finally:
-            return reply
-    def textToVoice(self, text):
-        try:
-            mp3File = TmpDir().path() + 'reply-' + str(int(time.time())) + '.mp3'
-            tts = gTTS(text=text, lang='zh')
-            tts.save(mp3File)            
-            logger.info(
-                '[Google] textToVoice text={} voice file name={}'.format(text, mp3File))
-            reply = Reply(ReplyType.VOICE, mp3File)
-        except Exception as e:
-            reply = Reply(ReplyType.ERROR, str(e))
-        finally:
-            return reply
diff --git a/spaces/oliverlevn/ocean_faster_RCNN/README.md b/spaces/oliverlevn/ocean_faster_RCNN/README.md
deleted file mode 100644
index 1db224ed5f8763625b7766de13f36b319bd04856..0000000000000000000000000000000000000000
--- a/spaces/oliverlevn/ocean_faster_RCNN/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-license: mit
-title: Ocean_faster_RCNN
-sdk: gradio
-emoji: 🔥
-colorFrom: green
-colorTo: blue
----
-# ocean-faster-rcnn
diff --git a/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/src/__init__.py b/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/osanseviero/tips/style.css b/spaces/osanseviero/tips/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/osanseviero/tips/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/download.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/download.py
deleted file mode 100644
index e582a935e06bff948426792e17fbbb780d77704e..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/download.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# In this file, we define download_model
-# It runs during container build time to get model weights built into the container
-
-import torch
-
-def download_model():
-    # do a dry run of loading the huggingface model, which will download weights
-    path = {"midas": ["ckpt/dpt_hybrid-midas-501f0c75.pt","https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt"]}
-    torch.hub.download_url_to_file(path["midas"][1], path["midas"][0])
-
-
-if __name__ == "__main__":
-    download_model()
\ No newline at end of file
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/conftest.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/conftest.py
deleted file mode 100644
index 3a48d18d1cc739f3fbf52c84a9c77afbf5694803..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/conftest.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# tests directory-specific settings - this file is run automatically
-# by pytest before any tests are run
-
-import sys
-import warnings
-from os.path import abspath, dirname, join
-
-
-# allow having multiple repository checkouts and not needing to remember to rerun
-# 'pip install -e .[dev]' when switching between checkouts and running tests.
-git_repo_path = abspath(join(dirname(dirname(dirname(__file__))), "src"))
-sys.path.insert(1, git_repo_path)
-
-
-# silence FutureWarning warnings in tests since often we can't act on them until
-# they become normal warnings - i.e. the tests still need to test the current functionality
-warnings.simplefilter(action="ignore", category=FutureWarning)
-
-
-def pytest_addoption(parser):
-    from diffusers.utils.testing_utils import pytest_addoption_shared
-
-    pytest_addoption_shared(parser)
-
-
-def pytest_terminal_summary(terminalreporter):
-    from diffusers.utils.testing_utils import pytest_terminal_summary_main
-
-    make_reports = terminalreporter.config.getoption("--make-reports")
-    if make_reports:
-        pytest_terminal_summary_main(terminalreporter, id=make_reports)
diff --git a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/segmodel/resnext.py b/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/segmodel/resnext.py
deleted file mode 100644
index d2c2ad7b295a171feadd7fd82a1567ebd44f7a2c..0000000000000000000000000000000000000000
--- a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/segmodel/resnext.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import os
-import sys
-import torch
-import torch.nn as nn
-import math
-try:
-  from lib.nn import SynchronizedBatchNorm2d
-except ImportError:
-  from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d
-
-try:
-    from urllib import urlretrieve
-except ImportError:
-    from urllib.request import urlretrieve
-
-
-__all__ = ['ResNeXt', 'resnext101'] # support resnext 101
-
-
-model_urls = {
-    #'resnext50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext50-imagenet.pth',
-    'resnext101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext101-imagenet.pth'
-}
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    "3x3 convolution with padding"
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=False)
-
-
-class GroupBottleneck(nn.Module):
-    expansion = 2
-
-    def __init__(self, inplanes, planes, stride=1, groups=1, downsample=None):
-        super(GroupBottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        self.bn1 = SynchronizedBatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
-                               padding=1, groups=groups, bias=False)
-        self.bn2 = SynchronizedBatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1, bias=False)
-        self.bn3 = SynchronizedBatchNorm2d(planes * 2)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class ResNeXt(nn.Module):
-
-    def __init__(self, block, layers, groups=32, num_classes=1000):
-        self.inplanes = 128
-        super(ResNeXt, self).__init__()
-        self.conv1 = conv3x3(3, 64, stride=2)
-        self.bn1 = SynchronizedBatchNorm2d(64)
-        self.relu1 = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(64, 64)
-        self.bn2 = SynchronizedBatchNorm2d(64)
-        self.relu2 = nn.ReLU(inplace=True)
-        self.conv3 = conv3x3(64, 128)
-        self.bn3 = SynchronizedBatchNorm2d(128)
-        self.relu3 = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
-        self.layer1 = self._make_layer(block, 128, layers[0], groups=groups)
-        self.layer2 = self._make_layer(block, 256, layers[1], stride=2, groups=groups)
-        self.layer3 = self._make_layer(block, 512, layers[2], stride=2, groups=groups)
-        self.layer4 = self._make_layer(block, 1024, layers[3], stride=2, groups=groups)
-        self.avgpool = nn.AvgPool2d(7, stride=1)
-        self.fc = nn.Linear(1024 * block.expansion, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels // m.groups
-                m.weight.data.normal_(0, math.sqrt(2. / n))
-            elif isinstance(m, SynchronizedBatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-
-    def _make_layer(self, block, planes, blocks, stride=1, groups=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=False),
-                SynchronizedBatchNorm2d(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, groups, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes, groups=groups))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.relu1(self.bn1(self.conv1(x)))
-        x = self.relu2(self.bn2(self.conv2(x)))
-        x = self.relu3(self.bn3(self.conv3(x)))
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        x = self.avgpool(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc(x)
-
-        return x
-
-
-'''
-def resnext50(pretrained=False, **kwargs):
-    """Constructs a ResNet-50 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on Places
-    """
-    model = ResNeXt(GroupBottleneck, [3, 4, 6, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnext50']), strict=False)
-    return model
-'''
-
-
-def resnext101(pretrained=False, **kwargs):
-    """Constructs a ResNet-101 model.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on Places
-    """
-    model = ResNeXt(GroupBottleneck, [3, 4, 23, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnext101']), strict=False)
-    return model
-
-
-# def resnext152(pretrained=False, **kwargs):
-#     """Constructs a ResNeXt-152 model.
-#
-#     Args:
-#         pretrained (bool): If True, returns a model pre-trained on Places
-#     """
-#     model = ResNeXt(GroupBottleneck, [3, 8, 36, 3], **kwargs)
-#     if pretrained:
-#         model.load_state_dict(load_url(model_urls['resnext152']))
-#     return model
-
-
-def load_url(url, model_dir='./pretrained', map_location=None):
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    filename = url.split('/')[-1]
-    cached_file = os.path.join(model_dir, filename)
-    if not os.path.exists(cached_file):
-        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
-        urlretrieve(url, cached_file)
-    return torch.load(cached_file, map_location=map_location)
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/self_outdated_check.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/self_outdated_check.py
deleted file mode 100644
index 41cc42c5677ddf0709d9eeb894eb8dbe4fd16f91..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/self_outdated_check.py
+++ /dev/null
@@ -1,242 +0,0 @@
-import datetime
-import functools
-import hashlib
-import json
-import logging
-import optparse
-import os.path
-import sys
-from dataclasses import dataclass
-from typing import Any, Callable, Dict, Optional
-
-from pip._vendor.packaging.version import parse as parse_version
-from pip._vendor.rich.console import Group
-from pip._vendor.rich.markup import escape
-from pip._vendor.rich.text import Text
-
-from pip._internal.index.collector import LinkCollector
-from pip._internal.index.package_finder import PackageFinder
-from pip._internal.metadata import get_default_environment
-from pip._internal.metadata.base import DistributionVersion
-from pip._internal.models.selection_prefs import SelectionPreferences
-from pip._internal.network.session import PipSession
-from pip._internal.utils.compat import WINDOWS
-from pip._internal.utils.entrypoints import (
-    get_best_invocation_for_this_pip,
-    get_best_invocation_for_this_python,
-)
-from pip._internal.utils.filesystem import adjacent_tmp_file, check_path_owner, replace
-from pip._internal.utils.misc import ensure_dir
-
-_DATE_FMT = "%Y-%m-%dT%H:%M:%SZ"
-
-
-logger = logging.getLogger(__name__)
-
-
-def _get_statefile_name(key: str) -> str:
-    key_bytes = key.encode()
-    name = hashlib.sha224(key_bytes).hexdigest()
-    return name
-
-
-class SelfCheckState:
-    def __init__(self, cache_dir: str) -> None:
-        self._state: Dict[str, Any] = {}
-        self._statefile_path = None
-
-        # Try to load the existing state
-        if cache_dir:
-            self._statefile_path = os.path.join(
-                cache_dir, "selfcheck", _get_statefile_name(self.key)
-            )
-            try:
-                with open(self._statefile_path, encoding="utf-8") as statefile:
-                    self._state = json.load(statefile)
-            except (OSError, ValueError, KeyError):
-                # Explicitly suppressing exceptions, since we don't want to
-                # error out if the cache file is invalid.
-                pass
-
-    @property
-    def key(self) -> str:
-        return sys.prefix
-
-    def get(self, current_time: datetime.datetime) -> Optional[str]:
-        """Check if we have a not-outdated version loaded already."""
-        if not self._state:
-            return None
-
-        if "last_check" not in self._state:
-            return None
-
-        if "pypi_version" not in self._state:
-            return None
-
-        seven_days_in_seconds = 7 * 24 * 60 * 60
-
-        # Determine if we need to refresh the state
-        last_check = datetime.datetime.strptime(self._state["last_check"], _DATE_FMT)
-        seconds_since_last_check = (current_time - last_check).total_seconds()
-        if seconds_since_last_check > seven_days_in_seconds:
-            return None
-
-        return self._state["pypi_version"]
-
-    def set(self, pypi_version: str, current_time: datetime.datetime) -> None:
-        # If we do not have a path to cache in, don't bother saving.
-        if not self._statefile_path:
-            return
-
-        # Check to make sure that we own the directory
-        if not check_path_owner(os.path.dirname(self._statefile_path)):
-            return
-
-        # Now that we've ensured the directory is owned by this user, we'll go
-        # ahead and make sure that all our directories are created.
-        ensure_dir(os.path.dirname(self._statefile_path))
-
-        state = {
-            # Include the key so it's easy to tell which pip wrote the
-            # file.
-            "key": self.key,
-            "last_check": current_time.strftime(_DATE_FMT),
-            "pypi_version": pypi_version,
-        }
-
-        text = json.dumps(state, sort_keys=True, separators=(",", ":"))
-
-        with adjacent_tmp_file(self._statefile_path) as f:
-            f.write(text.encode())
-
-        try:
-            # Since we have a prefix-specific state file, we can just
-            # overwrite whatever is there, no need to check.
-            replace(f.name, self._statefile_path)
-        except OSError:
-            # Best effort.
-            pass
-
-
-@dataclass
-class UpgradePrompt:
-    old: str
-    new: str
-
-    def __rich__(self) -> Group:
-        if WINDOWS:
-            pip_cmd = f"{get_best_invocation_for_this_python()} -m pip"
-        else:
-            pip_cmd = get_best_invocation_for_this_pip()
-
-        notice = "[bold][[reset][blue]notice[reset][bold]][reset]"
-        return Group(
-            Text(),
-            Text.from_markup(
-                f"{notice} A new release of pip is available: "
-                f"[red]{self.old}[reset] -> [green]{self.new}[reset]"
-            ),
-            Text.from_markup(
-                f"{notice} To update, run: "
-                f"[green]{escape(pip_cmd)} install --upgrade pip"
-            ),
-        )
-
-
-def was_installed_by_pip(pkg: str) -> bool:
-    """Checks whether pkg was installed by pip
-
-    This is used not to display the upgrade message when pip is in fact
-    installed by system package manager, such as dnf on Fedora.
-    """
-    dist = get_default_environment().get_distribution(pkg)
-    return dist is not None and "pip" == dist.installer
-
-
-def _get_current_remote_pip_version(
-    session: PipSession, options: optparse.Values
-) -> Optional[str]:
-    # Lets use PackageFinder to see what the latest pip version is
-    link_collector = LinkCollector.create(
-        session,
-        options=options,
-        suppress_no_index=True,
-    )
-
-    # Pass allow_yanked=False so we don't suggest upgrading to a
-    # yanked version.
-    selection_prefs = SelectionPreferences(
-        allow_yanked=False,
-        allow_all_prereleases=False,  # Explicitly set to False
-    )
-
-    finder = PackageFinder.create(
-        link_collector=link_collector,
-        selection_prefs=selection_prefs,
-    )
-    best_candidate = finder.find_best_candidate("pip").best_candidate
-    if best_candidate is None:
-        return None
-
-    return str(best_candidate.version)
-
-
-def _self_version_check_logic(
-    *,
-    state: SelfCheckState,
-    current_time: datetime.datetime,
-    local_version: DistributionVersion,
-    get_remote_version: Callable[[], Optional[str]],
-) -> Optional[UpgradePrompt]:
-    remote_version_str = state.get(current_time)
-    if remote_version_str is None:
-        remote_version_str = get_remote_version()
-        if remote_version_str is None:
-            logger.debug("No remote pip version found")
-            return None
-        state.set(remote_version_str, current_time)
-
-    remote_version = parse_version(remote_version_str)
-    logger.debug("Remote version of pip: %s", remote_version)
-    logger.debug("Local version of pip:  %s", local_version)
-
-    pip_installed_by_pip = was_installed_by_pip("pip")
-    logger.debug("Was pip installed by pip? %s", pip_installed_by_pip)
-    if not pip_installed_by_pip:
-        return None  # Only suggest upgrade if pip is installed by pip.
-
-    local_version_is_older = (
-        local_version < remote_version
-        and local_version.base_version != remote_version.base_version
-    )
-    if local_version_is_older:
-        return UpgradePrompt(old=str(local_version), new=remote_version_str)
-
-    return None
-
-
-def pip_self_version_check(session: PipSession, options: optparse.Values) -> None:
-    """Check for an update for pip.
-
-    Limit the frequency of checks to once per week. State is stored either in
-    the active virtualenv or in the user's USER_CACHE_DIR keyed off the prefix
-    of the pip script path.
-    """
-    installed_dist = get_default_environment().get_distribution("pip")
-    if not installed_dist:
-        return
-
-    try:
-        upgrade_prompt = _self_version_check_logic(
-            state=SelfCheckState(cache_dir=options.cache_dir),
-            current_time=datetime.datetime.utcnow(),
-            local_version=installed_dist.version,
-            get_remote_version=functools.partial(
-                _get_current_remote_pip_version, session, options
-            ),
-        )
-        if upgrade_prompt is not None:
-            logger.warning("[present-rich] %s", upgrade_prompt)
-    except Exception:
-        logger.warning("There was an error checking the latest version of pip.")
-        logger.debug("See below for error", exc_info=True)
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/lexers/_mapping.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/lexers/_mapping.py
deleted file mode 100644
index de6a0153b777f255a754c1ca9f8e4dc55cd3934b..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/lexers/_mapping.py
+++ /dev/null
@@ -1,559 +0,0 @@
-# Automatically generated by scripts/gen_mapfiles.py.
-# DO NOT EDIT BY HAND; run `tox -e mapfiles` instead.
-
-LEXERS = {
-    'ABAPLexer': ('pip._vendor.pygments.lexers.business', 'ABAP', ('abap',), ('*.abap', '*.ABAP'), ('text/x-abap',)),
-    'AMDGPULexer': ('pip._vendor.pygments.lexers.amdgpu', 'AMDGPU', ('amdgpu',), ('*.isa',), ()),
-    'APLLexer': ('pip._vendor.pygments.lexers.apl', 'APL', ('apl',), ('*.apl', '*.aplf', '*.aplo', '*.apln', '*.aplc', '*.apli', '*.dyalog'), ()),
-    'AbnfLexer': ('pip._vendor.pygments.lexers.grammar_notation', 'ABNF', ('abnf',), ('*.abnf',), ('text/x-abnf',)),
-    'ActionScript3Lexer': ('pip._vendor.pygments.lexers.actionscript', 'ActionScript 3', ('actionscript3', 'as3'), ('*.as',), ('application/x-actionscript3', 'text/x-actionscript3', 'text/actionscript3')),
-    'ActionScriptLexer': ('pip._vendor.pygments.lexers.actionscript', 'ActionScript', ('actionscript', 'as'), ('*.as',), ('application/x-actionscript', 'text/x-actionscript', 'text/actionscript')),
-    'AdaLexer': ('pip._vendor.pygments.lexers.ada', 'Ada', ('ada', 'ada95', 'ada2005'), ('*.adb', '*.ads', '*.ada'), ('text/x-ada',)),
-    'AdlLexer': ('pip._vendor.pygments.lexers.archetype', 'ADL', ('adl',), ('*.adl', '*.adls', '*.adlf', '*.adlx'), ()),
-    'AgdaLexer': ('pip._vendor.pygments.lexers.haskell', 'Agda', ('agda',), ('*.agda',), ('text/x-agda',)),
-    'AheuiLexer': ('pip._vendor.pygments.lexers.esoteric', 'Aheui', ('aheui',), ('*.aheui',), ()),
-    'AlloyLexer': ('pip._vendor.pygments.lexers.dsls', 'Alloy', ('alloy',), ('*.als',), ('text/x-alloy',)),
-    'AmbientTalkLexer': ('pip._vendor.pygments.lexers.ambient', 'AmbientTalk', ('ambienttalk', 'ambienttalk/2', 'at'), ('*.at',), ('text/x-ambienttalk',)),
-    'AmplLexer': ('pip._vendor.pygments.lexers.ampl', 'Ampl', ('ampl',), ('*.run',), ()),
-    'Angular2HtmlLexer': ('pip._vendor.pygments.lexers.templates', 'HTML + Angular2', ('html+ng2',), ('*.ng2',), ()),
-    'Angular2Lexer': ('pip._vendor.pygments.lexers.templates', 'Angular2', ('ng2',), (), ()),
-    'AntlrActionScriptLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With ActionScript Target', ('antlr-actionscript', 'antlr-as'), ('*.G', '*.g'), ()),
-    'AntlrCSharpLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With C# Target', ('antlr-csharp', 'antlr-c#'), ('*.G', '*.g'), ()),
-    'AntlrCppLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With CPP Target', ('antlr-cpp',), ('*.G', '*.g'), ()),
-    'AntlrJavaLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With Java Target', ('antlr-java',), ('*.G', '*.g'), ()),
-    'AntlrLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR', ('antlr',), (), ()),
-    'AntlrObjectiveCLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With ObjectiveC Target', ('antlr-objc',), ('*.G', '*.g'), ()),
-    'AntlrPerlLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With Perl Target', ('antlr-perl',), ('*.G', '*.g'), ()),
-    'AntlrPythonLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With Python Target', ('antlr-python',), ('*.G', '*.g'), ()),
-    'AntlrRubyLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With Ruby Target', ('antlr-ruby', 'antlr-rb'), ('*.G', '*.g'), ()),
-    'ApacheConfLexer': ('pip._vendor.pygments.lexers.configs', 'ApacheConf', ('apacheconf', 'aconf', 'apache'), ('.htaccess', 'apache.conf', 'apache2.conf'), ('text/x-apacheconf',)),
-    'AppleScriptLexer': ('pip._vendor.pygments.lexers.scripting', 'AppleScript', ('applescript',), ('*.applescript',), ()),
-    'ArduinoLexer': ('pip._vendor.pygments.lexers.c_like', 'Arduino', ('arduino',), ('*.ino',), ('text/x-arduino',)),
-    'ArrowLexer': ('pip._vendor.pygments.lexers.arrow', 'Arrow', ('arrow',), ('*.arw',), ()),
-    'ArturoLexer': ('pip._vendor.pygments.lexers.arturo', 'Arturo', ('arturo', 'art'), ('*.art',), ()),
-    'AscLexer': ('pip._vendor.pygments.lexers.asc', 'ASCII armored', ('asc', 'pem'), ('*.asc', '*.pem', 'id_dsa', 'id_ecdsa', 'id_ecdsa_sk', 'id_ed25519', 'id_ed25519_sk', 'id_rsa'), ('application/pgp-keys', 'application/pgp-encrypted', 'application/pgp-signature')),
-    'AspectJLexer': ('pip._vendor.pygments.lexers.jvm', 'AspectJ', ('aspectj',), ('*.aj',), ('text/x-aspectj',)),
-    'AsymptoteLexer': ('pip._vendor.pygments.lexers.graphics', 'Asymptote', ('asymptote', 'asy'), ('*.asy',), ('text/x-asymptote',)),
-    'AugeasLexer': ('pip._vendor.pygments.lexers.configs', 'Augeas', ('augeas',), ('*.aug',), ()),
-    'AutoItLexer': ('pip._vendor.pygments.lexers.automation', 'AutoIt', ('autoit',), ('*.au3',), ('text/x-autoit',)),
-    'AutohotkeyLexer': ('pip._vendor.pygments.lexers.automation', 'autohotkey', ('autohotkey', 'ahk'), ('*.ahk', '*.ahkl'), ('text/x-autohotkey',)),
-    'AwkLexer': ('pip._vendor.pygments.lexers.textedit', 'Awk', ('awk', 'gawk', 'mawk', 'nawk'), ('*.awk',), ('application/x-awk',)),
-    'BBCBasicLexer': ('pip._vendor.pygments.lexers.basic', 'BBC Basic', ('bbcbasic',), ('*.bbc',), ()),
-    'BBCodeLexer': ('pip._vendor.pygments.lexers.markup', 'BBCode', ('bbcode',), (), ('text/x-bbcode',)),
-    'BCLexer': ('pip._vendor.pygments.lexers.algebra', 'BC', ('bc',), ('*.bc',), ()),
-    'BSTLexer': ('pip._vendor.pygments.lexers.bibtex', 'BST', ('bst', 'bst-pybtex'), ('*.bst',), ()),
-    'BareLexer': ('pip._vendor.pygments.lexers.bare', 'BARE', ('bare',), ('*.bare',), ()),
-    'BaseMakefileLexer': ('pip._vendor.pygments.lexers.make', 'Base Makefile', ('basemake',), (), ()),
-    'BashLexer': ('pip._vendor.pygments.lexers.shell', 'Bash', ('bash', 'sh', 'ksh', 'zsh', 'shell'), ('*.sh', '*.ksh', '*.bash', '*.ebuild', '*.eclass', '*.exheres-0', '*.exlib', '*.zsh', '.bashrc', 'bashrc', '.bash_*', 'bash_*', 'zshrc', '.zshrc', '.kshrc', 'kshrc', 'PKGBUILD'), ('application/x-sh', 'application/x-shellscript', 'text/x-shellscript')),
-    'BashSessionLexer': ('pip._vendor.pygments.lexers.shell', 'Bash Session', ('console', 'shell-session'), ('*.sh-session', '*.shell-session'), ('application/x-shell-session', 'application/x-sh-session')),
-    'BatchLexer': ('pip._vendor.pygments.lexers.shell', 'Batchfile', ('batch', 'bat', 'dosbatch', 'winbatch'), ('*.bat', '*.cmd'), ('application/x-dos-batch',)),
-    'BddLexer': ('pip._vendor.pygments.lexers.bdd', 'Bdd', ('bdd',), ('*.feature',), ('text/x-bdd',)),
-    'BefungeLexer': ('pip._vendor.pygments.lexers.esoteric', 'Befunge', ('befunge',), ('*.befunge',), ('application/x-befunge',)),
-    'BerryLexer': ('pip._vendor.pygments.lexers.berry', 'Berry', ('berry', 'be'), ('*.be',), ('text/x-berry', 'application/x-berry')),
-    'BibTeXLexer': ('pip._vendor.pygments.lexers.bibtex', 'BibTeX', ('bibtex', 'bib'), ('*.bib',), ('text/x-bibtex',)),
-    'BlitzBasicLexer': ('pip._vendor.pygments.lexers.basic', 'BlitzBasic', ('blitzbasic', 'b3d', 'bplus'), ('*.bb', '*.decls'), ('text/x-bb',)),
-    'BlitzMaxLexer': ('pip._vendor.pygments.lexers.basic', 'BlitzMax', ('blitzmax', 'bmax'), ('*.bmx',), ('text/x-bmx',)),
-    'BnfLexer': ('pip._vendor.pygments.lexers.grammar_notation', 'BNF', ('bnf',), ('*.bnf',), ('text/x-bnf',)),
-    'BoaLexer': ('pip._vendor.pygments.lexers.boa', 'Boa', ('boa',), ('*.boa',), ()),
-    'BooLexer': ('pip._vendor.pygments.lexers.dotnet', 'Boo', ('boo',), ('*.boo',), ('text/x-boo',)),
-    'BoogieLexer': ('pip._vendor.pygments.lexers.verification', 'Boogie', ('boogie',), ('*.bpl',), ()),
-    'BrainfuckLexer': ('pip._vendor.pygments.lexers.esoteric', 'Brainfuck', ('brainfuck', 'bf'), ('*.bf', '*.b'), ('application/x-brainfuck',)),
-    'BugsLexer': ('pip._vendor.pygments.lexers.modeling', 'BUGS', ('bugs', 'winbugs', 'openbugs'), ('*.bug',), ()),
-    'CAmkESLexer': ('pip._vendor.pygments.lexers.esoteric', 'CAmkES', ('camkes', 'idl4'), ('*.camkes', '*.idl4'), ()),
-    'CLexer': ('pip._vendor.pygments.lexers.c_cpp', 'C', ('c',), ('*.c', '*.h', '*.idc', '*.x[bp]m'), ('text/x-chdr', 'text/x-csrc', 'image/x-xbitmap', 'image/x-xpixmap')),
-    'CMakeLexer': ('pip._vendor.pygments.lexers.make', 'CMake', ('cmake',), ('*.cmake', 'CMakeLists.txt'), ('text/x-cmake',)),
-    'CObjdumpLexer': ('pip._vendor.pygments.lexers.asm', 'c-objdump', ('c-objdump',), ('*.c-objdump',), ('text/x-c-objdump',)),
-    'CPSALexer': ('pip._vendor.pygments.lexers.lisp', 'CPSA', ('cpsa',), ('*.cpsa',), ()),
-    'CSSUL4Lexer': ('pip._vendor.pygments.lexers.ul4', 'CSS+UL4', ('css+ul4',), ('*.cssul4',), ()),
-    'CSharpAspxLexer': ('pip._vendor.pygments.lexers.dotnet', 'aspx-cs', ('aspx-cs',), ('*.aspx', '*.asax', '*.ascx', '*.ashx', '*.asmx', '*.axd'), ()),
-    'CSharpLexer': ('pip._vendor.pygments.lexers.dotnet', 'C#', ('csharp', 'c#', 'cs'), ('*.cs',), ('text/x-csharp',)),
-    'Ca65Lexer': ('pip._vendor.pygments.lexers.asm', 'ca65 assembler', ('ca65',), ('*.s',), ()),
-    'CadlLexer': ('pip._vendor.pygments.lexers.archetype', 'cADL', ('cadl',), ('*.cadl',), ()),
-    'CapDLLexer': ('pip._vendor.pygments.lexers.esoteric', 'CapDL', ('capdl',), ('*.cdl',), ()),
-    'CapnProtoLexer': ('pip._vendor.pygments.lexers.capnproto', "Cap'n Proto", ('capnp',), ('*.capnp',), ()),
-    'CarbonLexer': ('pip._vendor.pygments.lexers.carbon', 'Carbon', ('carbon',), ('*.carbon',), ('text/x-carbon',)),
-    'CbmBasicV2Lexer': ('pip._vendor.pygments.lexers.basic', 'CBM BASIC V2', ('cbmbas',), ('*.bas',), ()),
-    'CddlLexer': ('pip._vendor.pygments.lexers.cddl', 'CDDL', ('cddl',), ('*.cddl',), ('text/x-cddl',)),
-    'CeylonLexer': ('pip._vendor.pygments.lexers.jvm', 'Ceylon', ('ceylon',), ('*.ceylon',), ('text/x-ceylon',)),
-    'Cfengine3Lexer': ('pip._vendor.pygments.lexers.configs', 'CFEngine3', ('cfengine3', 'cf3'), ('*.cf',), ()),
-    'ChaiscriptLexer': ('pip._vendor.pygments.lexers.scripting', 'ChaiScript', ('chaiscript', 'chai'), ('*.chai',), ('text/x-chaiscript', 'application/x-chaiscript')),
-    'ChapelLexer': ('pip._vendor.pygments.lexers.chapel', 'Chapel', ('chapel', 'chpl'), ('*.chpl',), ()),
-    'CharmciLexer': ('pip._vendor.pygments.lexers.c_like', 'Charmci', ('charmci',), ('*.ci',), ()),
-    'CheetahHtmlLexer': ('pip._vendor.pygments.lexers.templates', 'HTML+Cheetah', ('html+cheetah', 'html+spitfire', 'htmlcheetah'), (), ('text/html+cheetah', 'text/html+spitfire')),
-    'CheetahJavascriptLexer': ('pip._vendor.pygments.lexers.templates', 'JavaScript+Cheetah', ('javascript+cheetah', 'js+cheetah', 'javascript+spitfire', 'js+spitfire'), (), ('application/x-javascript+cheetah', 'text/x-javascript+cheetah', 'text/javascript+cheetah', 'application/x-javascript+spitfire', 'text/x-javascript+spitfire', 'text/javascript+spitfire')),
-    'CheetahLexer': ('pip._vendor.pygments.lexers.templates', 'Cheetah', ('cheetah', 'spitfire'), ('*.tmpl', '*.spt'), ('application/x-cheetah', 'application/x-spitfire')),
-    'CheetahXmlLexer': ('pip._vendor.pygments.lexers.templates', 'XML+Cheetah', ('xml+cheetah', 'xml+spitfire'), (), ('application/xml+cheetah', 'application/xml+spitfire')),
-    'CirruLexer': ('pip._vendor.pygments.lexers.webmisc', 'Cirru', ('cirru',), ('*.cirru',), ('text/x-cirru',)),
-    'ClayLexer': ('pip._vendor.pygments.lexers.c_like', 'Clay', ('clay',), ('*.clay',), ('text/x-clay',)),
-    'CleanLexer': ('pip._vendor.pygments.lexers.clean', 'Clean', ('clean',), ('*.icl', '*.dcl'), ()),
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-    'X10Lexer': ('pip._vendor.pygments.lexers.x10', 'X10', ('x10', 'xten'), ('*.x10',), ('text/x-x10',)),
-    'XMLUL4Lexer': ('pip._vendor.pygments.lexers.ul4', 'XML+UL4', ('xml+ul4',), ('*.xmlul4',), ()),
-    'XQueryLexer': ('pip._vendor.pygments.lexers.webmisc', 'XQuery', ('xquery', 'xqy', 'xq', 'xql', 'xqm'), ('*.xqy', '*.xquery', '*.xq', '*.xql', '*.xqm'), ('text/xquery', 'application/xquery')),
-    'XmlDjangoLexer': ('pip._vendor.pygments.lexers.templates', 'XML+Django/Jinja', ('xml+django', 'xml+jinja'), ('*.xml.j2', '*.xml.jinja2'), ('application/xml+django', 'application/xml+jinja')),
-    'XmlErbLexer': ('pip._vendor.pygments.lexers.templates', 'XML+Ruby', ('xml+ruby', 'xml+erb'), (), ('application/xml+ruby',)),
-    'XmlLexer': ('pip._vendor.pygments.lexers.html', 'XML', ('xml',), ('*.xml', '*.xsl', '*.rss', '*.xslt', '*.xsd', '*.wsdl', '*.wsf'), ('text/xml', 'application/xml', 'image/svg+xml', 'application/rss+xml', 'application/atom+xml')),
-    'XmlPhpLexer': ('pip._vendor.pygments.lexers.templates', 'XML+PHP', ('xml+php',), (), ('application/xml+php',)),
-    'XmlSmartyLexer': ('pip._vendor.pygments.lexers.templates', 'XML+Smarty', ('xml+smarty',), (), ('application/xml+smarty',)),
-    'XorgLexer': ('pip._vendor.pygments.lexers.xorg', 'Xorg', ('xorg.conf',), ('xorg.conf',), ()),
-    'XppLexer': ('pip._vendor.pygments.lexers.dotnet', 'X++', ('xpp', 'x++'), ('*.xpp',), ()),
-    'XsltLexer': ('pip._vendor.pygments.lexers.html', 'XSLT', ('xslt',), ('*.xsl', '*.xslt', '*.xpl'), ('application/xsl+xml', 'application/xslt+xml')),
-    'XtendLexer': ('pip._vendor.pygments.lexers.jvm', 'Xtend', ('xtend',), ('*.xtend',), ('text/x-xtend',)),
-    'XtlangLexer': ('pip._vendor.pygments.lexers.lisp', 'xtlang', ('extempore',), ('*.xtm',), ()),
-    'YamlJinjaLexer': ('pip._vendor.pygments.lexers.templates', 'YAML+Jinja', ('yaml+jinja', 'salt', 'sls'), ('*.sls', '*.yaml.j2', '*.yml.j2', '*.yaml.jinja2', '*.yml.jinja2'), ('text/x-yaml+jinja', 'text/x-sls')),
-    'YamlLexer': ('pip._vendor.pygments.lexers.data', 'YAML', ('yaml',), ('*.yaml', '*.yml'), ('text/x-yaml',)),
-    'YangLexer': ('pip._vendor.pygments.lexers.yang', 'YANG', ('yang',), ('*.yang',), ('application/yang',)),
-    'ZeekLexer': ('pip._vendor.pygments.lexers.dsls', 'Zeek', ('zeek', 'bro'), ('*.zeek', '*.bro'), ()),
-    'ZephirLexer': ('pip._vendor.pygments.lexers.php', 'Zephir', ('zephir',), ('*.zep',), ()),
-    'ZigLexer': ('pip._vendor.pygments.lexers.zig', 'Zig', ('zig',), ('*.zig',), ('text/zig',)),
-    'apdlexer': ('pip._vendor.pygments.lexers.apdlexer', 'ANSYS parametric design language', ('ansys', 'apdl'), ('*.ans',), ()),
-}
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/windows.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/windows.py
deleted file mode 100644
index d5c27b341403fa03283562373b2a0b05daba075e..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/platformdirs/windows.py
+++ /dev/null
@@ -1,184 +0,0 @@
-from __future__ import annotations
-
-import ctypes
-import os
-import sys
-from functools import lru_cache
-from typing import Callable
-
-from .api import PlatformDirsABC
-
-
-class Windows(PlatformDirsABC):
-    """`MSDN on where to store app data files
-    <http://support.microsoft.com/default.aspx?scid=kb;en-us;310294#XSLTH3194121123120121120120>`_.
-    Makes use of the
-    `appname <platformdirs.api.PlatformDirsABC.appname>`,
-    `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`,
-    `version <platformdirs.api.PlatformDirsABC.version>`,
-    `roaming <platformdirs.api.PlatformDirsABC.roaming>`,
-    `opinion <platformdirs.api.PlatformDirsABC.opinion>`."""
-
-    @property
-    def user_data_dir(self) -> str:
-        """
-        :return: data directory tied to the user, e.g.
-         ``%USERPROFILE%\\AppData\\Local\\$appauthor\\$appname`` (not roaming) or
-         ``%USERPROFILE%\\AppData\\Roaming\\$appauthor\\$appname`` (roaming)
-        """
-        const = "CSIDL_APPDATA" if self.roaming else "CSIDL_LOCAL_APPDATA"
-        path = os.path.normpath(get_win_folder(const))
-        return self._append_parts(path)
-
-    def _append_parts(self, path: str, *, opinion_value: str | None = None) -> str:
-        params = []
-        if self.appname:
-            if self.appauthor is not False:
-                author = self.appauthor or self.appname
-                params.append(author)
-            params.append(self.appname)
-            if opinion_value is not None and self.opinion:
-                params.append(opinion_value)
-            if self.version:
-                params.append(self.version)
-        return os.path.join(path, *params)
-
-    @property
-    def site_data_dir(self) -> str:
-        """:return: data directory shared by users, e.g. ``C:\\ProgramData\\$appauthor\\$appname``"""
-        path = os.path.normpath(get_win_folder("CSIDL_COMMON_APPDATA"))
-        return self._append_parts(path)
-
-    @property
-    def user_config_dir(self) -> str:
-        """:return: config directory tied to the user, same as `user_data_dir`"""
-        return self.user_data_dir
-
-    @property
-    def site_config_dir(self) -> str:
-        """:return: config directory shared by the users, same as `site_data_dir`"""
-        return self.site_data_dir
-
-    @property
-    def user_cache_dir(self) -> str:
-        """
-        :return: cache directory tied to the user (if opinionated with ``Cache`` folder within ``$appname``) e.g.
-         ``%USERPROFILE%\\AppData\\Local\\$appauthor\\$appname\\Cache\\$version``
-        """
-        path = os.path.normpath(get_win_folder("CSIDL_LOCAL_APPDATA"))
-        return self._append_parts(path, opinion_value="Cache")
-
-    @property
-    def user_state_dir(self) -> str:
-        """:return: state directory tied to the user, same as `user_data_dir`"""
-        return self.user_data_dir
-
-    @property
-    def user_log_dir(self) -> str:
-        """
-        :return: log directory tied to the user, same as `user_data_dir` if not opinionated else ``Logs`` in it
-        """
-        path = self.user_data_dir
-        if self.opinion:
-            path = os.path.join(path, "Logs")
-        return path
-
-    @property
-    def user_documents_dir(self) -> str:
-        """
-        :return: documents directory tied to the user e.g. ``%USERPROFILE%\\Documents``
-        """
-        return os.path.normpath(get_win_folder("CSIDL_PERSONAL"))
-
-    @property
-    def user_runtime_dir(self) -> str:
-        """
-        :return: runtime directory tied to the user, e.g.
-         ``%USERPROFILE%\\AppData\\Local\\Temp\\$appauthor\\$appname``
-        """
-        path = os.path.normpath(os.path.join(get_win_folder("CSIDL_LOCAL_APPDATA"), "Temp"))
-        return self._append_parts(path)
-
-
-def get_win_folder_from_env_vars(csidl_name: str) -> str:
-    """Get folder from environment variables."""
-    if csidl_name == "CSIDL_PERSONAL":  # does not have an environment name
-        return os.path.join(os.path.normpath(os.environ["USERPROFILE"]), "Documents")
-
-    env_var_name = {
-        "CSIDL_APPDATA": "APPDATA",
-        "CSIDL_COMMON_APPDATA": "ALLUSERSPROFILE",
-        "CSIDL_LOCAL_APPDATA": "LOCALAPPDATA",
-    }.get(csidl_name)
-    if env_var_name is None:
-        raise ValueError(f"Unknown CSIDL name: {csidl_name}")
-    result = os.environ.get(env_var_name)
-    if result is None:
-        raise ValueError(f"Unset environment variable: {env_var_name}")
-    return result
-
-
-def get_win_folder_from_registry(csidl_name: str) -> str:
-    """Get folder from the registry.
-
-    This is a fallback technique at best. I'm not sure if using the
-    registry for this guarantees us the correct answer for all CSIDL_*
-    names.
-    """
-    shell_folder_name = {
-        "CSIDL_APPDATA": "AppData",
-        "CSIDL_COMMON_APPDATA": "Common AppData",
-        "CSIDL_LOCAL_APPDATA": "Local AppData",
-        "CSIDL_PERSONAL": "Personal",
-    }.get(csidl_name)
-    if shell_folder_name is None:
-        raise ValueError(f"Unknown CSIDL name: {csidl_name}")
-    if sys.platform != "win32":  # only needed for mypy type checker to know that this code runs only on Windows
-        raise NotImplementedError
-    import winreg
-
-    key = winreg.OpenKey(winreg.HKEY_CURRENT_USER, r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders")
-    directory, _ = winreg.QueryValueEx(key, shell_folder_name)
-    return str(directory)
-
-
-def get_win_folder_via_ctypes(csidl_name: str) -> str:
-    """Get folder with ctypes."""
-    csidl_const = {
-        "CSIDL_APPDATA": 26,
-        "CSIDL_COMMON_APPDATA": 35,
-        "CSIDL_LOCAL_APPDATA": 28,
-        "CSIDL_PERSONAL": 5,
-    }.get(csidl_name)
-    if csidl_const is None:
-        raise ValueError(f"Unknown CSIDL name: {csidl_name}")
-
-    buf = ctypes.create_unicode_buffer(1024)
-    windll = getattr(ctypes, "windll")  # noqa: B009 # using getattr to avoid false positive with mypy type checker
-    windll.shell32.SHGetFolderPathW(None, csidl_const, None, 0, buf)
-
-    # Downgrade to short path name if it has highbit chars.
-    if any(ord(c) > 255 for c in buf):
-        buf2 = ctypes.create_unicode_buffer(1024)
-        if windll.kernel32.GetShortPathNameW(buf.value, buf2, 1024):
-            buf = buf2
-
-    return buf.value
-
-
-def _pick_get_win_folder() -> Callable[[str], str]:
-    if hasattr(ctypes, "windll"):
-        return get_win_folder_via_ctypes
-    try:
-        import winreg  # noqa: F401
-    except ImportError:
-        return get_win_folder_from_env_vars
-    else:
-        return get_win_folder_from_registry
-
-
-get_win_folder = lru_cache(maxsize=None)(_pick_get_win_folder())
-
-__all__ = [
-    "Windows",
-]
diff --git a/spaces/prerna9811/Chord/portaudio/src/hostapi/alsa/pa_linux_alsa.c b/spaces/prerna9811/Chord/portaudio/src/hostapi/alsa/pa_linux_alsa.c
deleted file mode 100644
index a66f90d5f3a8b29bd0a03547585374a9eebe9a66..0000000000000000000000000000000000000000
--- a/spaces/prerna9811/Chord/portaudio/src/hostapi/alsa/pa_linux_alsa.c
+++ /dev/null
@@ -1,4659 +0,0 @@
-/*
- * $Id$
- * PortAudio Portable Real-Time Audio Library
- * Latest Version at: http://www.portaudio.com
- * ALSA implementation by Joshua Haberman and Arve Knudsen
- *
- * Copyright (c) 2002 Joshua Haberman <joshua@haberman.com>
- * Copyright (c) 2005-2009 Arve Knudsen <arve.knudsen@gmail.com>
- * Copyright (c) 2008 Kevin Kofler <kevin.kofler@chello.at>
- *
- * Based on the Open Source API proposed by Ross Bencina
- * Copyright (c) 1999-2002 Ross Bencina, Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-/**
- @file
- @ingroup hostapi_src
-*/
-
-#define ALSA_PCM_NEW_HW_PARAMS_API
-#define ALSA_PCM_NEW_SW_PARAMS_API
-#include <alsa/asoundlib.h>
-#undef ALSA_PCM_NEW_HW_PARAMS_API
-#undef ALSA_PCM_NEW_SW_PARAMS_API
-
-#include <sys/poll.h>
-#include <string.h> /* strlen() */
-#include <limits.h>
-#include <math.h>
-#include <pthread.h>
-#include <signal.h>
-#include <time.h>
-#include <sys/mman.h>
-#include <signal.h> /* For sig_atomic_t */
-#ifdef PA_ALSA_DYNAMIC
-    #include <dlfcn.h> /* For dlXXX functions */
-#endif
-
-#include "portaudio.h"
-#include "pa_util.h"
-#include "pa_unix_util.h"
-#include "pa_allocation.h"
-#include "pa_hostapi.h"
-#include "pa_stream.h"
-#include "pa_cpuload.h"
-#include "pa_process.h"
-#include "pa_endianness.h"
-#include "pa_debugprint.h"
-
-#include "pa_linux_alsa.h"
-
-/* Add missing define (for compatibility with older ALSA versions) */
-#ifndef SND_PCM_TSTAMP_ENABLE
-    #define SND_PCM_TSTAMP_ENABLE SND_PCM_TSTAMP_MMAP
-#endif
-
-/* Combine version elements into a single (unsigned) integer */
-#define ALSA_VERSION_INT(major, minor, subminor)  ((major << 16) | (minor << 8) | subminor)
-
-/* The acceptable tolerance of sample rate set, to that requested (as a ratio, eg 50 is 2%, 100 is 1%) */
-#define RATE_MAX_DEVIATE_RATIO 100
-
-/* Defines Alsa function types and pointers to these functions. */
-#define _PA_DEFINE_FUNC(x)  typedef typeof(x) x##_ft; static x##_ft *alsa_##x = 0
-
-/* Alloca helper. */
-#define __alsa_snd_alloca(ptr,type) do { size_t __alsa_alloca_size = alsa_##type##_sizeof(); (*ptr) = (type##_t *) alloca(__alsa_alloca_size); memset(*ptr, 0, __alsa_alloca_size); } while (0)
-
-_PA_DEFINE_FUNC(snd_pcm_open);
-_PA_DEFINE_FUNC(snd_pcm_close);
-_PA_DEFINE_FUNC(snd_pcm_nonblock);
-_PA_DEFINE_FUNC(snd_pcm_frames_to_bytes);
-_PA_DEFINE_FUNC(snd_pcm_prepare);
-_PA_DEFINE_FUNC(snd_pcm_start);
-_PA_DEFINE_FUNC(snd_pcm_resume);
-_PA_DEFINE_FUNC(snd_pcm_wait);
-_PA_DEFINE_FUNC(snd_pcm_state);
-_PA_DEFINE_FUNC(snd_pcm_avail_update);
-_PA_DEFINE_FUNC(snd_pcm_areas_silence);
-_PA_DEFINE_FUNC(snd_pcm_mmap_begin);
-_PA_DEFINE_FUNC(snd_pcm_mmap_commit);
-_PA_DEFINE_FUNC(snd_pcm_readi);
-_PA_DEFINE_FUNC(snd_pcm_readn);
-_PA_DEFINE_FUNC(snd_pcm_writei);
-_PA_DEFINE_FUNC(snd_pcm_writen);
-_PA_DEFINE_FUNC(snd_pcm_drain);
-_PA_DEFINE_FUNC(snd_pcm_recover);
-_PA_DEFINE_FUNC(snd_pcm_drop);
-_PA_DEFINE_FUNC(snd_pcm_area_copy);
-_PA_DEFINE_FUNC(snd_pcm_poll_descriptors);
-_PA_DEFINE_FUNC(snd_pcm_poll_descriptors_count);
-_PA_DEFINE_FUNC(snd_pcm_poll_descriptors_revents);
-_PA_DEFINE_FUNC(snd_pcm_format_size);
-_PA_DEFINE_FUNC(snd_pcm_link);
-_PA_DEFINE_FUNC(snd_pcm_delay);
-
-_PA_DEFINE_FUNC(snd_pcm_hw_params_sizeof);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_malloc);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_free);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_any);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_set_access);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_set_format);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_set_channels);
-//_PA_DEFINE_FUNC(snd_pcm_hw_params_set_periods_near);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_set_rate_near); //!!!
-_PA_DEFINE_FUNC(snd_pcm_hw_params_set_rate);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_set_rate_resample);
-//_PA_DEFINE_FUNC(snd_pcm_hw_params_set_buffer_time_near);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_set_buffer_size);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_set_buffer_size_near); //!!!
-_PA_DEFINE_FUNC(snd_pcm_hw_params_set_buffer_size_min);
-//_PA_DEFINE_FUNC(snd_pcm_hw_params_set_period_time_near);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_set_period_size_near);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_set_periods_integer);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_set_periods_min);
-
-_PA_DEFINE_FUNC(snd_pcm_hw_params_get_buffer_size);
-//_PA_DEFINE_FUNC(snd_pcm_hw_params_get_period_size);
-//_PA_DEFINE_FUNC(snd_pcm_hw_params_get_access);
-//_PA_DEFINE_FUNC(snd_pcm_hw_params_get_periods);
-//_PA_DEFINE_FUNC(snd_pcm_hw_params_get_rate);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_get_channels_min);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_get_channels_max);
-
-_PA_DEFINE_FUNC(snd_pcm_hw_params_test_period_size);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_test_format);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_test_access);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_dump);
-_PA_DEFINE_FUNC(snd_pcm_hw_params);
-
-_PA_DEFINE_FUNC(snd_pcm_hw_params_get_periods_min);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_get_periods_max);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_set_period_size);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_get_period_size_min);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_get_period_size_max);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_get_buffer_size_max);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_get_rate_min);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_get_rate_max);
-_PA_DEFINE_FUNC(snd_pcm_hw_params_get_rate_numden);
-#define alsa_snd_pcm_hw_params_alloca(ptr) __alsa_snd_alloca(ptr, snd_pcm_hw_params)
-
-_PA_DEFINE_FUNC(snd_pcm_sw_params_sizeof);
-_PA_DEFINE_FUNC(snd_pcm_sw_params_malloc);
-_PA_DEFINE_FUNC(snd_pcm_sw_params_current);
-_PA_DEFINE_FUNC(snd_pcm_sw_params_set_avail_min);
-_PA_DEFINE_FUNC(snd_pcm_sw_params);
-_PA_DEFINE_FUNC(snd_pcm_sw_params_free);
-_PA_DEFINE_FUNC(snd_pcm_sw_params_set_start_threshold);
-_PA_DEFINE_FUNC(snd_pcm_sw_params_set_stop_threshold);
-_PA_DEFINE_FUNC(snd_pcm_sw_params_get_boundary);
-_PA_DEFINE_FUNC(snd_pcm_sw_params_set_silence_threshold);
-_PA_DEFINE_FUNC(snd_pcm_sw_params_set_silence_size);
-_PA_DEFINE_FUNC(snd_pcm_sw_params_set_xfer_align);
-_PA_DEFINE_FUNC(snd_pcm_sw_params_set_tstamp_mode);
-#define alsa_snd_pcm_sw_params_alloca(ptr) __alsa_snd_alloca(ptr, snd_pcm_sw_params)
-
-_PA_DEFINE_FUNC(snd_pcm_info);
-_PA_DEFINE_FUNC(snd_pcm_info_sizeof);
-_PA_DEFINE_FUNC(snd_pcm_info_malloc);
-_PA_DEFINE_FUNC(snd_pcm_info_free);
-_PA_DEFINE_FUNC(snd_pcm_info_set_device);
-_PA_DEFINE_FUNC(snd_pcm_info_set_subdevice);
-_PA_DEFINE_FUNC(snd_pcm_info_set_stream);
-_PA_DEFINE_FUNC(snd_pcm_info_get_name);
-_PA_DEFINE_FUNC(snd_pcm_info_get_card);
-#define alsa_snd_pcm_info_alloca(ptr) __alsa_snd_alloca(ptr, snd_pcm_info)
-
-_PA_DEFINE_FUNC(snd_ctl_pcm_next_device);
-_PA_DEFINE_FUNC(snd_ctl_pcm_info);
-_PA_DEFINE_FUNC(snd_ctl_open);
-_PA_DEFINE_FUNC(snd_ctl_close);
-_PA_DEFINE_FUNC(snd_ctl_card_info_malloc);
-_PA_DEFINE_FUNC(snd_ctl_card_info_free);
-_PA_DEFINE_FUNC(snd_ctl_card_info);
-_PA_DEFINE_FUNC(snd_ctl_card_info_sizeof);
-_PA_DEFINE_FUNC(snd_ctl_card_info_get_name);
-#define alsa_snd_ctl_card_info_alloca(ptr) __alsa_snd_alloca(ptr, snd_ctl_card_info)
-
-_PA_DEFINE_FUNC(snd_config);
-_PA_DEFINE_FUNC(snd_config_update);
-_PA_DEFINE_FUNC(snd_config_search);
-_PA_DEFINE_FUNC(snd_config_iterator_entry);
-_PA_DEFINE_FUNC(snd_config_iterator_first);
-_PA_DEFINE_FUNC(snd_config_iterator_end);
-_PA_DEFINE_FUNC(snd_config_iterator_next);
-_PA_DEFINE_FUNC(snd_config_get_string);
-_PA_DEFINE_FUNC(snd_config_get_id);
-_PA_DEFINE_FUNC(snd_config_update_free_global);
-
-_PA_DEFINE_FUNC(snd_pcm_status);
-_PA_DEFINE_FUNC(snd_pcm_status_sizeof);
-_PA_DEFINE_FUNC(snd_pcm_status_get_tstamp);
-_PA_DEFINE_FUNC(snd_pcm_status_get_state);
-_PA_DEFINE_FUNC(snd_pcm_status_get_trigger_tstamp);
-_PA_DEFINE_FUNC(snd_pcm_status_get_delay);
-#define alsa_snd_pcm_status_alloca(ptr) __alsa_snd_alloca(ptr, snd_pcm_status)
-
-_PA_DEFINE_FUNC(snd_card_next);
-_PA_DEFINE_FUNC(snd_asoundlib_version);
-_PA_DEFINE_FUNC(snd_strerror);
-_PA_DEFINE_FUNC(snd_output_stdio_attach);
-
-#define alsa_snd_config_for_each(pos, next, node)\
-    for (pos = alsa_snd_config_iterator_first(node),\
-         next = alsa_snd_config_iterator_next(pos);\
-         pos != alsa_snd_config_iterator_end(node); pos = next, next = alsa_snd_config_iterator_next(pos))
-
-#undef _PA_DEFINE_FUNC
-
-/* Redefine 'PA_ALSA_PATHNAME' to a different Alsa library name if desired. */
-#ifndef PA_ALSA_PATHNAME
-    #define PA_ALSA_PATHNAME "libasound.so"
-#endif
-static const char *g_AlsaLibName = PA_ALSA_PATHNAME;
-
-/* Handle to dynamically loaded library. */
-static void *g_AlsaLib = NULL;
-
-#ifdef PA_ALSA_DYNAMIC
-
-#define _PA_LOCAL_IMPL(x) __pa_local_##x
-
-int _PA_LOCAL_IMPL(snd_pcm_hw_params_set_rate_near) (snd_pcm_t *pcm, snd_pcm_hw_params_t *params, unsigned int *val, int *dir)
-{
-    int ret;
-
-    if(( ret = alsa_snd_pcm_hw_params_set_rate(pcm, params, (*val), (*dir)) ) < 0 )
-        return ret;
-
-    return 0;
-}
-
-int _PA_LOCAL_IMPL(snd_pcm_hw_params_set_buffer_size_near) (snd_pcm_t *pcm, snd_pcm_hw_params_t *params, snd_pcm_uframes_t *val)
-{
-    int ret;
-
-    if(( ret = alsa_snd_pcm_hw_params_set_buffer_size(pcm, params, (*val)) ) < 0 )
-        return ret;
-
-    return 0;
-}
-
-int _PA_LOCAL_IMPL(snd_pcm_hw_params_set_period_size_near) (snd_pcm_t *pcm, snd_pcm_hw_params_t *params, snd_pcm_uframes_t *val, int *dir)
-{
-    int ret;
-
-    if(( ret = alsa_snd_pcm_hw_params_set_period_size(pcm, params, (*val), (*dir)) ) < 0 )
-        return ret;
-
-    return 0;
-}
-
-int _PA_LOCAL_IMPL(snd_pcm_hw_params_get_channels_min) (const snd_pcm_hw_params_t *params, unsigned int *val)
-{
-    (*val) = 1;
-    return 0;
-}
-
-int _PA_LOCAL_IMPL(snd_pcm_hw_params_get_channels_max) (const snd_pcm_hw_params_t *params, unsigned int *val)
-{
-    (*val) = 2;
-    return 0;
-}
-
-int _PA_LOCAL_IMPL(snd_pcm_hw_params_get_periods_min) (const snd_pcm_hw_params_t *params, unsigned int *val, int *dir)
-{
-    (*val) = 2;
-    return 0;
-}
-
-int _PA_LOCAL_IMPL(snd_pcm_hw_params_get_periods_max) (const snd_pcm_hw_params_t *params, unsigned int *val, int *dir)
-{
-    (*val) = 8;
-    return 0;
-}
-
-int _PA_LOCAL_IMPL(snd_pcm_hw_params_get_period_size_min) (const snd_pcm_hw_params_t *params, snd_pcm_uframes_t *frames, int *dir)
-{
-    (*frames) = 64;
-    return 0;
-}
-
-int _PA_LOCAL_IMPL(snd_pcm_hw_params_get_period_size_max) (const snd_pcm_hw_params_t *params, snd_pcm_uframes_t *frames, int *dir)
-{
-    (*frames) = 512;
-    return 0;
-}
-
-int _PA_LOCAL_IMPL(snd_pcm_hw_params_get_buffer_size_max) (const snd_pcm_hw_params_t *params, snd_pcm_uframes_t *val)
-{
-    int ret;
-    int dir                = 0;
-    snd_pcm_uframes_t pmax = 0;
-    unsigned int      pcnt = 0;
-
-    dir = 0;
-    if(( ret = _PA_LOCAL_IMPL(snd_pcm_hw_params_get_period_size_max)(params, &pmax, &dir) ) < 0 )
-        return ret;
-    dir = 0;
-    if(( ret = _PA_LOCAL_IMPL(snd_pcm_hw_params_get_periods_max)(params, &pcnt, &dir) ) < 0 )
-        return ret;
-
-    (*val) = pmax * pcnt;
-    return 0;
-}
-
-int _PA_LOCAL_IMPL(snd_pcm_hw_params_get_rate_min) (const snd_pcm_hw_params_t *params, unsigned int *val, int *dir)
-{
-    (*val) = 44100;
-    return 0;
-}
-
-int _PA_LOCAL_IMPL(snd_pcm_hw_params_get_rate_max) (const snd_pcm_hw_params_t *params, unsigned int *val, int *dir)
-{
-    (*val) = 44100;
-    return 0;
-}
-
-#endif // PA_ALSA_DYNAMIC
-
-/* Trying to load Alsa library dynamically if 'PA_ALSA_DYNAMIC' is defined, othervise
-   will link during compilation.
-*/
-static int PaAlsa_LoadLibrary()
-{
-#ifdef PA_ALSA_DYNAMIC
-
-    PA_DEBUG(( "%s: loading ALSA library file - %s\n", __FUNCTION__, g_AlsaLibName ));
-
-    dlerror();
-    g_AlsaLib = dlopen(g_AlsaLibName, (RTLD_NOW|RTLD_GLOBAL) );
-    if (g_AlsaLib == NULL)
-    {
-        PA_DEBUG(( "%s: failed dlopen() ALSA library file - %s, error: %s\n", __FUNCTION__, g_AlsaLibName, dlerror() ));
-        return 0;
-    }
-
-    PA_DEBUG(( "%s: loading ALSA API\n", __FUNCTION__ ));
-
-    #define _PA_LOAD_FUNC(x) do {             \
-        alsa_##x = dlsym( g_AlsaLib, #x );      \
-        if( alsa_##x == NULL ) {               \
-            PA_DEBUG(( "%s: symbol [%s] not found in - %s, error: %s\n", __FUNCTION__, #x, g_AlsaLibName, dlerror() )); }\
-        } while(0)
-
-#else
-
-    #define _PA_LOAD_FUNC(x) alsa_##x = &x
-
-#endif
-
-    _PA_LOAD_FUNC(snd_pcm_open);
-    _PA_LOAD_FUNC(snd_pcm_close);
-    _PA_LOAD_FUNC(snd_pcm_nonblock);
-    _PA_LOAD_FUNC(snd_pcm_frames_to_bytes);
-    _PA_LOAD_FUNC(snd_pcm_prepare);
-    _PA_LOAD_FUNC(snd_pcm_start);
-    _PA_LOAD_FUNC(snd_pcm_resume);
-    _PA_LOAD_FUNC(snd_pcm_wait);
-    _PA_LOAD_FUNC(snd_pcm_state);
-    _PA_LOAD_FUNC(snd_pcm_avail_update);
-    _PA_LOAD_FUNC(snd_pcm_areas_silence);
-    _PA_LOAD_FUNC(snd_pcm_mmap_begin);
-    _PA_LOAD_FUNC(snd_pcm_mmap_commit);
-    _PA_LOAD_FUNC(snd_pcm_readi);
-    _PA_LOAD_FUNC(snd_pcm_readn);
-    _PA_LOAD_FUNC(snd_pcm_writei);
-    _PA_LOAD_FUNC(snd_pcm_writen);
-    _PA_LOAD_FUNC(snd_pcm_drain);
-    _PA_LOAD_FUNC(snd_pcm_recover);
-    _PA_LOAD_FUNC(snd_pcm_drop);
-    _PA_LOAD_FUNC(snd_pcm_area_copy);
-    _PA_LOAD_FUNC(snd_pcm_poll_descriptors);
-    _PA_LOAD_FUNC(snd_pcm_poll_descriptors_count);
-    _PA_LOAD_FUNC(snd_pcm_poll_descriptors_revents);
-    _PA_LOAD_FUNC(snd_pcm_format_size);
-    _PA_LOAD_FUNC(snd_pcm_link);
-    _PA_LOAD_FUNC(snd_pcm_delay);
-
-    _PA_LOAD_FUNC(snd_pcm_hw_params_sizeof);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_malloc);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_free);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_any);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_set_access);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_set_format);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_set_channels);
-//    _PA_LOAD_FUNC(snd_pcm_hw_params_set_periods_near);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_set_rate_near);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_set_rate);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_set_rate_resample);
-//    _PA_LOAD_FUNC(snd_pcm_hw_params_set_buffer_time_near);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_set_buffer_size);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_set_buffer_size_near);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_set_buffer_size_min);
-//    _PA_LOAD_FUNC(snd_pcm_hw_params_set_period_time_near);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_set_period_size_near);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_set_periods_integer);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_set_periods_min);
-
-    _PA_LOAD_FUNC(snd_pcm_hw_params_get_buffer_size);
-//    _PA_LOAD_FUNC(snd_pcm_hw_params_get_period_size);
-//    _PA_LOAD_FUNC(snd_pcm_hw_params_get_access);
-//    _PA_LOAD_FUNC(snd_pcm_hw_params_get_periods);
-//    _PA_LOAD_FUNC(snd_pcm_hw_params_get_rate);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_get_channels_min);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_get_channels_max);
-
-    _PA_LOAD_FUNC(snd_pcm_hw_params_test_period_size);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_test_format);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_test_access);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_dump);
-    _PA_LOAD_FUNC(snd_pcm_hw_params);
-
-    _PA_LOAD_FUNC(snd_pcm_hw_params_get_periods_min);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_get_periods_max);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_set_period_size);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_get_period_size_min);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_get_period_size_max);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_get_buffer_size_max);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_get_rate_min);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_get_rate_max);
-    _PA_LOAD_FUNC(snd_pcm_hw_params_get_rate_numden);
-
-    _PA_LOAD_FUNC(snd_pcm_sw_params_sizeof);
-    _PA_LOAD_FUNC(snd_pcm_sw_params_malloc);
-    _PA_LOAD_FUNC(snd_pcm_sw_params_current);
-    _PA_LOAD_FUNC(snd_pcm_sw_params_set_avail_min);
-    _PA_LOAD_FUNC(snd_pcm_sw_params);
-    _PA_LOAD_FUNC(snd_pcm_sw_params_free);
-    _PA_LOAD_FUNC(snd_pcm_sw_params_set_start_threshold);
-    _PA_LOAD_FUNC(snd_pcm_sw_params_set_stop_threshold);
-    _PA_LOAD_FUNC(snd_pcm_sw_params_get_boundary);
-    _PA_LOAD_FUNC(snd_pcm_sw_params_set_silence_threshold);
-    _PA_LOAD_FUNC(snd_pcm_sw_params_set_silence_size);
-    _PA_LOAD_FUNC(snd_pcm_sw_params_set_xfer_align);
-    _PA_LOAD_FUNC(snd_pcm_sw_params_set_tstamp_mode);
-
-    _PA_LOAD_FUNC(snd_pcm_info);
-    _PA_LOAD_FUNC(snd_pcm_info_sizeof);
-    _PA_LOAD_FUNC(snd_pcm_info_malloc);
-    _PA_LOAD_FUNC(snd_pcm_info_free);
-    _PA_LOAD_FUNC(snd_pcm_info_set_device);
-    _PA_LOAD_FUNC(snd_pcm_info_set_subdevice);
-    _PA_LOAD_FUNC(snd_pcm_info_set_stream);
-    _PA_LOAD_FUNC(snd_pcm_info_get_name);
-    _PA_LOAD_FUNC(snd_pcm_info_get_card);
-
-    _PA_LOAD_FUNC(snd_ctl_pcm_next_device);
-    _PA_LOAD_FUNC(snd_ctl_pcm_info);
-    _PA_LOAD_FUNC(snd_ctl_open);
-    _PA_LOAD_FUNC(snd_ctl_close);
-    _PA_LOAD_FUNC(snd_ctl_card_info_malloc);
-    _PA_LOAD_FUNC(snd_ctl_card_info_free);
-    _PA_LOAD_FUNC(snd_ctl_card_info);
-    _PA_LOAD_FUNC(snd_ctl_card_info_sizeof);
-    _PA_LOAD_FUNC(snd_ctl_card_info_get_name);
-
-    _PA_LOAD_FUNC(snd_config);
-    _PA_LOAD_FUNC(snd_config_update);
-    _PA_LOAD_FUNC(snd_config_search);
-    _PA_LOAD_FUNC(snd_config_iterator_entry);
-    _PA_LOAD_FUNC(snd_config_iterator_first);
-    _PA_LOAD_FUNC(snd_config_iterator_end);
-    _PA_LOAD_FUNC(snd_config_iterator_next);
-    _PA_LOAD_FUNC(snd_config_get_string);
-    _PA_LOAD_FUNC(snd_config_get_id);
-    _PA_LOAD_FUNC(snd_config_update_free_global);
-
-    _PA_LOAD_FUNC(snd_pcm_status);
-    _PA_LOAD_FUNC(snd_pcm_status_sizeof);
-    _PA_LOAD_FUNC(snd_pcm_status_get_tstamp);
-    _PA_LOAD_FUNC(snd_pcm_status_get_state);
-    _PA_LOAD_FUNC(snd_pcm_status_get_trigger_tstamp);
-    _PA_LOAD_FUNC(snd_pcm_status_get_delay);
-
-    _PA_LOAD_FUNC(snd_card_next);
-    _PA_LOAD_FUNC(snd_asoundlib_version);
-    _PA_LOAD_FUNC(snd_strerror);
-    _PA_LOAD_FUNC(snd_output_stdio_attach);
-#undef _PA_LOAD_FUNC
-
-#ifdef PA_ALSA_DYNAMIC
-    PA_DEBUG(( "%s: loaded ALSA API - ok\n", __FUNCTION__ ));
-
-#define _PA_VALIDATE_LOAD_REPLACEMENT(x)\
-    do {\
-        if( alsa_##x == NULL )\
-        {\
-            alsa_##x = &_PA_LOCAL_IMPL(x);\
-            PA_DEBUG(( "%s: replacing [%s] with local implementation\n", __FUNCTION__, #x ));\
-        }\
-    } while (0)
-
-    _PA_VALIDATE_LOAD_REPLACEMENT(snd_pcm_hw_params_set_rate_near);
-    _PA_VALIDATE_LOAD_REPLACEMENT(snd_pcm_hw_params_set_buffer_size_near);
-    _PA_VALIDATE_LOAD_REPLACEMENT(snd_pcm_hw_params_set_period_size_near);
-    _PA_VALIDATE_LOAD_REPLACEMENT(snd_pcm_hw_params_get_channels_min);
-    _PA_VALIDATE_LOAD_REPLACEMENT(snd_pcm_hw_params_get_channels_max);
-    _PA_VALIDATE_LOAD_REPLACEMENT(snd_pcm_hw_params_get_periods_min);
-    _PA_VALIDATE_LOAD_REPLACEMENT(snd_pcm_hw_params_get_periods_max);
-    _PA_VALIDATE_LOAD_REPLACEMENT(snd_pcm_hw_params_get_period_size_min);
-    _PA_VALIDATE_LOAD_REPLACEMENT(snd_pcm_hw_params_get_period_size_max);
-    _PA_VALIDATE_LOAD_REPLACEMENT(snd_pcm_hw_params_get_buffer_size_max);
-    _PA_VALIDATE_LOAD_REPLACEMENT(snd_pcm_hw_params_get_rate_min);
-    _PA_VALIDATE_LOAD_REPLACEMENT(snd_pcm_hw_params_get_rate_max);
-
-#undef _PA_LOCAL_IMPL
-#undef _PA_VALIDATE_LOAD_REPLACEMENT
-
-#endif // PA_ALSA_DYNAMIC
-
-    return 1;
-}
-
-void PaAlsa_SetLibraryPathName( const char *pathName )
-{
-#ifdef PA_ALSA_DYNAMIC
-    g_AlsaLibName = pathName;
-#else
-    (void)pathName;
-#endif
-}
-
-/* Close handle to Alsa library. */
-static void PaAlsa_CloseLibrary()
-{
-#ifdef PA_ALSA_DYNAMIC
-    dlclose(g_AlsaLib);
-    g_AlsaLib = NULL;
-#endif
-}
-
-/* Check return value of ALSA function, and map it to PaError */
-#define ENSURE_(expr, code) \
-    do { \
-        int __pa_unsure_error_id;\
-        if( UNLIKELY( (__pa_unsure_error_id = (expr)) < 0 ) ) \
-        { \
-            /* PaUtil_SetLastHostErrorInfo should only be used in the main thread */ \
-            if( (code) == paUnanticipatedHostError && pthread_equal( pthread_self(), paUnixMainThread) ) \
-            { \
-                PaUtil_SetLastHostErrorInfo( paALSA, __pa_unsure_error_id, alsa_snd_strerror( __pa_unsure_error_id ) ); \
-            } \
-            PaUtil_DebugPrint( "Expression '" #expr "' failed in '" __FILE__ "', line: " STRINGIZE( __LINE__ ) "\n" ); \
-            if( (code) == paUnanticipatedHostError ) \
-                PA_DEBUG(( "Host error description: %s\n", alsa_snd_strerror( __pa_unsure_error_id ) )); \
-            result = (code); \
-            goto error; \
-        } \
-    } while (0)
-
-#define ASSERT_CALL_(expr, success) \
-    do {\
-        int __pa_assert_error_id;\
-        __pa_assert_error_id = (expr);\
-        assert( success == __pa_assert_error_id );\
-    } while (0)
-
-static int numPeriods_ = 4;
-static int busyRetries_ = 100;
-
-int PaAlsa_SetNumPeriods( int numPeriods )
-{
-    numPeriods_ = numPeriods;
-    return paNoError;
-}
-
-typedef enum
-{
-    StreamDirection_In,
-    StreamDirection_Out
-} StreamDirection;
-
-typedef struct
-{
-    PaSampleFormat hostSampleFormat;
-    int numUserChannels, numHostChannels;
-    int userInterleaved, hostInterleaved;
-    int canMmap;
-    void *nonMmapBuffer;
-    unsigned int nonMmapBufferSize;
-    PaDeviceIndex device;     /* Keep the device index */
-    int deviceIsPlug; /* Distinguish plug types from direct 'hw:' devices */
-    int useReventFix; /* Alsa older than 1.0.16, plug devices need a fix */
-
-    snd_pcm_t *pcm;
-    snd_pcm_uframes_t framesPerPeriod, alsaBufferSize;
-    snd_pcm_format_t nativeFormat;
-    unsigned int nfds;
-    int ready;  /* Marked ready from poll */
-    void **userBuffers;
-    snd_pcm_uframes_t offset;
-    StreamDirection streamDir;
-
-    snd_pcm_channel_area_t *channelAreas;  /* Needed for channel adaption */
-} PaAlsaStreamComponent;
-
-/* Implementation specific stream structure */
-typedef struct PaAlsaStream
-{
-    PaUtilStreamRepresentation streamRepresentation;
-    PaUtilCpuLoadMeasurer cpuLoadMeasurer;
-    PaUtilBufferProcessor bufferProcessor;
-    PaUnixThread thread;
-
-    unsigned long framesPerUserBuffer, maxFramesPerHostBuffer;
-
-    int primeBuffers;
-    int callbackMode;              /* bool: are we running in callback mode? */
-    int pcmsSynced;                /* Have we successfully synced pcms */
-    int rtSched;
-
-    /* the callback thread uses these to poll the sound device(s), waiting
-     * for data to be ready/available */
-    struct pollfd* pfds;
-    int pollTimeout;
-
-    /* Used in communication between threads */
-    volatile sig_atomic_t callback_finished; /* bool: are we in the "callback finished" state? */
-    volatile sig_atomic_t callbackAbort;    /* Drop frames? */
-    volatile sig_atomic_t isActive;         /* Is stream in active state? (Between StartStream and StopStream || !paContinue) */
-    PaUnixMutex stateMtx;                   /* Used to synchronize access to stream state */
-
-    int neverDropInput;
-
-    PaTime underrun;
-    PaTime overrun;
-
-    PaAlsaStreamComponent capture, playback;
-}
-PaAlsaStream;
-
-/* PaAlsaHostApiRepresentation - host api datastructure specific to this implementation */
-
-typedef struct PaAlsaHostApiRepresentation
-{
-    PaUtilHostApiRepresentation baseHostApiRep;
-    PaUtilStreamInterface callbackStreamInterface;
-    PaUtilStreamInterface blockingStreamInterface;
-
-    PaUtilAllocationGroup *allocations;
-
-    PaHostApiIndex hostApiIndex;
-    PaUint32 alsaLibVersion; /* Retrieved from the library at run-time */
-}
-PaAlsaHostApiRepresentation;
-
-typedef struct PaAlsaDeviceInfo
-{
-    PaDeviceInfo baseDeviceInfo;
-    char *alsaName;
-    int isPlug;
-    int minInputChannels;
-    int minOutputChannels;
-}
-PaAlsaDeviceInfo;
-
-/* prototypes for functions declared in this file */
-
-static void Terminate( struct PaUtilHostApiRepresentation *hostApi );
-static PaError IsFormatSupported( struct PaUtilHostApiRepresentation *hostApi,
-                                  const PaStreamParameters *inputParameters,
-                                  const PaStreamParameters *outputParameters,
-                                  double sampleRate );
-static PaError OpenStream( struct PaUtilHostApiRepresentation *hostApi,
-                           PaStream** s,
-                           const PaStreamParameters *inputParameters,
-                           const PaStreamParameters *outputParameters,
-                           double sampleRate,
-                           unsigned long framesPerBuffer,
-                           PaStreamFlags streamFlags,
-                           PaStreamCallback *callback,
-                           void *userData );
-static PaError CloseStream( PaStream* stream );
-static PaError StartStream( PaStream *stream );
-static PaError StopStream( PaStream *stream );
-static PaError AbortStream( PaStream *stream );
-static PaError IsStreamStopped( PaStream *s );
-static PaError IsStreamActive( PaStream *stream );
-static PaTime GetStreamTime( PaStream *stream );
-static double GetStreamCpuLoad( PaStream* stream );
-static PaError BuildDeviceList( PaAlsaHostApiRepresentation *hostApi );
-static int SetApproximateSampleRate( snd_pcm_t *pcm, snd_pcm_hw_params_t *hwParams, double sampleRate );
-static int GetExactSampleRate( snd_pcm_hw_params_t *hwParams, double *sampleRate );
-static PaUint32 PaAlsaVersionNum(void);
-
-/* Callback prototypes */
-static void *CallbackThreadFunc( void *userData );
-
-/* Blocking prototypes */
-static signed long GetStreamReadAvailable( PaStream* s );
-static signed long GetStreamWriteAvailable( PaStream* s );
-static PaError ReadStream( PaStream* stream, void *buffer, unsigned long frames );
-static PaError WriteStream( PaStream* stream, const void *buffer, unsigned long frames );
-
-
-static const PaAlsaDeviceInfo *GetDeviceInfo( const PaUtilHostApiRepresentation *hostApi, int device )
-{
-    return (const PaAlsaDeviceInfo *)hostApi->deviceInfos[device];
-}
-
-/** Uncommented because AlsaErrorHandler is unused for anything good yet. If AlsaErrorHandler is
-    to be used, do not forget to register this callback in PaAlsa_Initialize, and unregister in Terminate.
-*/
-/*static void AlsaErrorHandler(const char *file, int line, const char *function, int err, const char *fmt, ...)
-{
-}*/
-
-PaError PaAlsa_Initialize( PaUtilHostApiRepresentation **hostApi, PaHostApiIndex hostApiIndex )
-{
-    PaError result = paNoError;
-    PaAlsaHostApiRepresentation *alsaHostApi = NULL;
-
-    /* Try loading Alsa library. */
-    if (!PaAlsa_LoadLibrary())
-        return paHostApiNotFound;
-
-    PA_UNLESS( alsaHostApi = (PaAlsaHostApiRepresentation*) PaUtil_AllocateMemory(
-                sizeof(PaAlsaHostApiRepresentation) ), paInsufficientMemory );
-    PA_UNLESS( alsaHostApi->allocations = PaUtil_CreateAllocationGroup(), paInsufficientMemory );
-    alsaHostApi->hostApiIndex = hostApiIndex;
-    alsaHostApi->alsaLibVersion = PaAlsaVersionNum();
-
-    *hostApi = (PaUtilHostApiRepresentation*)alsaHostApi;
-    (*hostApi)->info.structVersion = 1;
-    (*hostApi)->info.type = paALSA;
-    (*hostApi)->info.name = "ALSA";
-
-    (*hostApi)->Terminate = Terminate;
-    (*hostApi)->OpenStream = OpenStream;
-    (*hostApi)->IsFormatSupported = IsFormatSupported;
-
-    /** If AlsaErrorHandler is to be used, do not forget to unregister callback pointer in
-        Terminate function.
-    */
-    /*ENSURE_( snd_lib_error_set_handler(AlsaErrorHandler), paUnanticipatedHostError );*/
-
-    PA_ENSURE( BuildDeviceList( alsaHostApi ) );
-
-    PaUtil_InitializeStreamInterface( &alsaHostApi->callbackStreamInterface,
-                                      CloseStream, StartStream,
-                                      StopStream, AbortStream,
-                                      IsStreamStopped, IsStreamActive,
-                                      GetStreamTime, GetStreamCpuLoad,
-                                      PaUtil_DummyRead, PaUtil_DummyWrite,
-                                      PaUtil_DummyGetReadAvailable,
-                                      PaUtil_DummyGetWriteAvailable );
-
-    PaUtil_InitializeStreamInterface( &alsaHostApi->blockingStreamInterface,
-                                      CloseStream, StartStream,
-                                      StopStream, AbortStream,
-                                      IsStreamStopped, IsStreamActive,
-                                      GetStreamTime, PaUtil_DummyGetCpuLoad,
-                                      ReadStream, WriteStream,
-                                      GetStreamReadAvailable,
-                                      GetStreamWriteAvailable );
-
-    PA_ENSURE( PaUnixThreading_Initialize() );
-
-    return result;
-
-error:
-    if( alsaHostApi )
-    {
-        if( alsaHostApi->allocations )
-        {
-            PaUtil_FreeAllAllocations( alsaHostApi->allocations );
-            PaUtil_DestroyAllocationGroup( alsaHostApi->allocations );
-        }
-
-        PaUtil_FreeMemory( alsaHostApi );
-    }
-
-    return result;
-}
-
-static void Terminate( struct PaUtilHostApiRepresentation *hostApi )
-{
-    PaAlsaHostApiRepresentation *alsaHostApi = (PaAlsaHostApiRepresentation*)hostApi;
-
-    assert( hostApi );
-
-    /** See AlsaErrorHandler and PaAlsa_Initialize for details.
-    */
-    /*snd_lib_error_set_handler(NULL);*/
-
-    if( alsaHostApi->allocations )
-    {
-        PaUtil_FreeAllAllocations( alsaHostApi->allocations );
-        PaUtil_DestroyAllocationGroup( alsaHostApi->allocations );
-    }
-
-    PaUtil_FreeMemory( alsaHostApi );
-    alsa_snd_config_update_free_global();
-
-    /* Close Alsa library. */
-    PaAlsa_CloseLibrary();
-}
-
-/** Determine max channels and default latencies.
- *
- * This function provides functionality to grope an opened (might be opened for capture or playback) pcm device for
- * traits like max channels, suitable default latencies and default sample rate. Upon error, max channels is set to zero,
- * and a suitable result returned. The device is closed before returning.
- */
-static PaError GropeDevice( snd_pcm_t* pcm, int isPlug, StreamDirection mode, int openBlocking,
-        PaAlsaDeviceInfo* devInfo )
-{
-    PaError result = paNoError;
-    snd_pcm_hw_params_t *hwParams;
-    snd_pcm_uframes_t alsaBufferFrames, alsaPeriodFrames;
-    unsigned int minChans, maxChans;
-    int* minChannels, * maxChannels;
-    double * defaultLowLatency, * defaultHighLatency, * defaultSampleRate =
-        &devInfo->baseDeviceInfo.defaultSampleRate;
-    double defaultSr = *defaultSampleRate;
-
-    assert( pcm );
-
-    PA_DEBUG(( "%s: collecting info ..\n", __FUNCTION__ ));
-
-    if( StreamDirection_In == mode )
-    {
-        minChannels = &devInfo->minInputChannels;
-        maxChannels = &devInfo->baseDeviceInfo.maxInputChannels;
-        defaultLowLatency = &devInfo->baseDeviceInfo.defaultLowInputLatency;
-        defaultHighLatency = &devInfo->baseDeviceInfo.defaultHighInputLatency;
-    }
-    else
-    {
-        minChannels = &devInfo->minOutputChannels;
-        maxChannels = &devInfo->baseDeviceInfo.maxOutputChannels;
-        defaultLowLatency = &devInfo->baseDeviceInfo.defaultLowOutputLatency;
-        defaultHighLatency = &devInfo->baseDeviceInfo.defaultHighOutputLatency;
-    }
-
-    ENSURE_( alsa_snd_pcm_nonblock( pcm, 0 ), paUnanticipatedHostError );
-
-    alsa_snd_pcm_hw_params_alloca( &hwParams );
-    alsa_snd_pcm_hw_params_any( pcm, hwParams );
-
-    if( defaultSr >= 0 )
-    {
-        /* Could be that the device opened in one mode supports samplerates that the other mode wont have,
-         * so try again .. */
-        if( SetApproximateSampleRate( pcm, hwParams, defaultSr ) < 0 )
-        {
-            defaultSr = -1.;
-            alsa_snd_pcm_hw_params_any( pcm, hwParams ); /* Clear any params (rate) that might have been set */
-            PA_DEBUG(( "%s: Original default samplerate failed, trying again ..\n", __FUNCTION__ ));
-        }
-    }
-
-    if( defaultSr < 0. )           /* Default sample rate not set */
-    {
-        unsigned int sampleRate = 44100;        /* Will contain approximate rate returned by alsa-lib */
-
-        /* Don't allow rate resampling when probing for the default rate (but ignore if this call fails) */
-        alsa_snd_pcm_hw_params_set_rate_resample( pcm, hwParams, 0 );
-        if( alsa_snd_pcm_hw_params_set_rate_near( pcm, hwParams, &sampleRate, NULL ) < 0 )
-        {
-            result = paUnanticipatedHostError;
-            goto error;
-        }
-        ENSURE_( GetExactSampleRate( hwParams, &defaultSr ), paUnanticipatedHostError );
-    }
-
-    ENSURE_( alsa_snd_pcm_hw_params_get_channels_min( hwParams, &minChans ), paUnanticipatedHostError );
-    ENSURE_( alsa_snd_pcm_hw_params_get_channels_max( hwParams, &maxChans ), paUnanticipatedHostError );
-    assert( maxChans <= INT_MAX );
-    assert( maxChans > 0 );    /* Weird linking issue could cause wrong version of ALSA symbols to be called,
-                                   resulting in zeroed values */
-
-    /* XXX: Limit to sensible number (ALSA plugins accept a crazy amount of channels)? */
-    if( isPlug && maxChans > 128 )
-    {
-        maxChans = 128;
-        PA_DEBUG(( "%s: Limiting number of plugin channels to %u\n", __FUNCTION__, maxChans ));
-    }
-
-    /* TWEAKME:
-     * Giving values for default min and max latency is not straightforward.
-     *  * for low latency, we want to give the lowest value that will work reliably.
-     *      This varies based on the sound card, kernel, CPU, etc.  Better to give
-     *      sub-optimal latency than to give a number too low and cause dropouts.
-     *  * for high latency we want to give a large enough value that dropouts are basically impossible.
-     *      This doesn't really require as much tweaking, since providing too large a number will
-     *      just cause us to select the nearest setting that will work at stream config time.
-     */
-    /* Try low latency values, (sometimes the buffer & period that result are larger) */
-    alsaBufferFrames = 512;
-    alsaPeriodFrames = 128;
-    ENSURE_( alsa_snd_pcm_hw_params_set_buffer_size_near( pcm, hwParams, &alsaBufferFrames ), paUnanticipatedHostError );
-    ENSURE_( alsa_snd_pcm_hw_params_set_period_size_near( pcm, hwParams, &alsaPeriodFrames, NULL ), paUnanticipatedHostError );
-    *defaultLowLatency = (double) (alsaBufferFrames - alsaPeriodFrames) / defaultSr;
-
-    /* Base the high latency case on values four times larger */
-    alsaBufferFrames = 2048;
-    alsaPeriodFrames = 512;
-    /* Have to reset hwParams, to set new buffer size; need to also set sample rate again */
-    ENSURE_( alsa_snd_pcm_hw_params_any( pcm, hwParams ), paUnanticipatedHostError );
-    ENSURE_( SetApproximateSampleRate( pcm, hwParams, defaultSr ), paUnanticipatedHostError );
-    ENSURE_( alsa_snd_pcm_hw_params_set_buffer_size_near( pcm, hwParams, &alsaBufferFrames ), paUnanticipatedHostError );
-    ENSURE_( alsa_snd_pcm_hw_params_set_period_size_near( pcm, hwParams, &alsaPeriodFrames, NULL ), paUnanticipatedHostError );
-    *defaultHighLatency = (double) (alsaBufferFrames - alsaPeriodFrames) / defaultSr;
-
-    *minChannels = (int)minChans;
-    *maxChannels = (int)maxChans;
-    *defaultSampleRate = defaultSr;
-
-end:
-    alsa_snd_pcm_close( pcm );
-    return result;
-
-error:
-    goto end;
-}
-
-/* Initialize device info with invalid values (maxInputChannels and maxOutputChannels are set to zero since these indicate
- * whether input/output is available) */
-static void InitializeDeviceInfo( PaDeviceInfo *deviceInfo )
-{
-    deviceInfo->structVersion = -1;
-    deviceInfo->name = NULL;
-    deviceInfo->hostApi = -1;
-    deviceInfo->maxInputChannels = 0;
-    deviceInfo->maxOutputChannels = 0;
-    deviceInfo->defaultLowInputLatency = -1.;
-    deviceInfo->defaultLowOutputLatency = -1.;
-    deviceInfo->defaultHighInputLatency = -1.;
-    deviceInfo->defaultHighOutputLatency = -1.;
-    deviceInfo->defaultSampleRate = -1.;
-}
-
-
-/* Retrieve the version of the runtime Alsa-lib, as a single number equivalent to
- * SND_LIB_VERSION.  Only a version string is available ("a.b.c") so this has to be converted.
- * Assume 'a' and 'b' are single digits only.
- */
-static PaUint32 PaAlsaVersionNum(void)
-{
-    char* verStr;
-    PaUint32 verNum;
-
-    verStr = (char*) alsa_snd_asoundlib_version();
-    verNum = ALSA_VERSION_INT( atoi(verStr), atoi(verStr + 2), atoi(verStr + 4) );
-    PA_DEBUG(( "ALSA version (build): " SND_LIB_VERSION_STR "\nALSA version (runtime): %s\n", verStr ));
-
-    return verNum;
-}
-
-
-/* Helper struct */
-typedef struct
-{
-    char *alsaName;
-    char *name;
-    int isPlug;
-    int hasPlayback;
-    int hasCapture;
-} HwDevInfo;
-
-
-HwDevInfo predefinedNames[] = {
-    { "center_lfe", NULL, 0, 1, 0 },
-/* { "default", NULL, 0, 1, 1 }, */
-    { "dmix", NULL, 0, 1, 0 },
-/* { "dpl", NULL, 0, 1, 0 }, */
-/* { "dsnoop", NULL, 0, 0, 1 }, */
-    { "front", NULL, 0, 1, 0 },
-    { "iec958", NULL, 0, 1, 0 },
-/* { "modem", NULL, 0, 1, 0 }, */
-    { "rear", NULL, 0, 1, 0 },
-    { "side", NULL, 0, 1, 0 },
-/*     { "spdif", NULL, 0, 0, 0 }, */
-    { "surround40", NULL, 0, 1, 0 },
-    { "surround41", NULL, 0, 1, 0 },
-    { "surround50", NULL, 0, 1, 0 },
-    { "surround51", NULL, 0, 1, 0 },
-    { "surround71", NULL, 0, 1, 0 },
-
-    { "AndroidPlayback_Earpiece_normal",         NULL, 0, 1, 0 },
-    { "AndroidPlayback_Speaker_normal",          NULL, 0, 1, 0 },
-    { "AndroidPlayback_Bluetooth_normal",        NULL, 0, 1, 0 },
-    { "AndroidPlayback_Headset_normal",          NULL, 0, 1, 0 },
-    { "AndroidPlayback_Speaker_Headset_normal",  NULL, 0, 1, 0 },
-    { "AndroidPlayback_Bluetooth-A2DP_normal",   NULL, 0, 1, 0 },
-    { "AndroidPlayback_ExtraDockSpeaker_normal", NULL, 0, 1, 0 },
-    { "AndroidPlayback_TvOut_normal",            NULL, 0, 1, 0 },
-
-    { "AndroidRecord_Microphone",                NULL, 0, 0, 1 },
-    { "AndroidRecord_Earpiece_normal",           NULL, 0, 0, 1 },
-    { "AndroidRecord_Speaker_normal",            NULL, 0, 0, 1 },
-    { "AndroidRecord_Headset_normal",            NULL, 0, 0, 1 },
-    { "AndroidRecord_Bluetooth_normal",          NULL, 0, 0, 1 },
-    { "AndroidRecord_Speaker_Headset_normal",    NULL, 0, 0, 1 },
-
-    { NULL, NULL, 0, 1, 0 }
-};
-
-static const HwDevInfo *FindDeviceName( const char *name )
-{
-    int i;
-
-    for( i = 0; predefinedNames[i].alsaName; i++ )
-    {
-        if( strcmp( name, predefinedNames[i].alsaName ) == 0 )
-        {
-            return &predefinedNames[i];
-        }
-    }
-
-    return NULL;
-}
-
-static PaError PaAlsa_StrDup( PaAlsaHostApiRepresentation *alsaApi,
-        char **dst,
-        const char *src)
-{
-    PaError result = paNoError;
-    int len = strlen( src ) + 1;
-
-    /* PA_DEBUG(("PaStrDup %s %d\n", src, len)); */
-
-    PA_UNLESS( *dst = (char *)PaUtil_GroupAllocateMemory( alsaApi->allocations, len ),
-            paInsufficientMemory );
-    strncpy( *dst, src, len );
-
-error:
-    return result;
-}
-
-/* Disregard some standard plugins
- */
-static int IgnorePlugin( const char *pluginId )
-{
-    static const char *ignoredPlugins[] = {"hw", "plughw", "plug", "dsnoop", "tee",
-        "file", "null", "shm", "cards", "rate_convert", NULL};
-    int i = 0;
-
-    if( getenv( "PA_ALSA_IGNORE_ALL_PLUGINS" ) && atoi( getenv( "PA_ALSA_IGNORE_ALL_PLUGINS") ) )
-        return 1;
-
-    while( ignoredPlugins[i] )
-    {
-        if( !strcmp( pluginId, ignoredPlugins[i] ) )
-        {
-            return 1;
-        }
-        ++i;
-    }
-
-    return 0;
-}
-
-/* Skip past parts at the beginning of a (pcm) info name that are already in the card name, to avoid duplication */
-static char *SkipCardDetailsInName( char *infoSkipName, char *cardRefName )
-{
-    char *lastSpacePosn = infoSkipName;
-
-    /* Skip matching chars; but only in chunks separated by ' ' (not part words etc), so track lastSpacePosn */
-    while( *cardRefName )
-    {
-        while( *infoSkipName && *cardRefName && *infoSkipName == *cardRefName)
-        {
-            infoSkipName++;
-            cardRefName++;
-            if( *infoSkipName == ' ' || *infoSkipName == '\0' )
-                lastSpacePosn = infoSkipName;
-        }
-        infoSkipName = lastSpacePosn;
-        /* Look for another chunk; post-increment means ends pointing to next char */
-        while( *cardRefName && ( *cardRefName++ != ' ' ));
-    }
-    if( *infoSkipName == '\0' )
-        return "-"; /* The 2 names were identical; instead of a nul-string, return a marker string */
-
-    /* Now want to move to the first char after any spaces */
-    while( *lastSpacePosn && *lastSpacePosn == ' ' )
-        lastSpacePosn++;
-    /* Skip a single separator char if present in the remaining pcm name; (pa will add its own) */
-    if(( *lastSpacePosn == '-' || *lastSpacePosn == ':' ) && *(lastSpacePosn + 1) == ' ' )
-        lastSpacePosn += 2;
-
-    return lastSpacePosn;
-}
-
-/** Open PCM device.
- *
- * Wrapper around alsa_snd_pcm_open which may repeatedly retry opening a device if it is busy, for
- * a certain time. This is because dmix may temporarily hold on to a device after it (dmix)
- * has been opened and closed.
- * @param mode: Open mode (e.g., SND_PCM_BLOCKING).
- * @param waitOnBusy: Retry opening busy device for up to one second?
- **/
-static int OpenPcm( snd_pcm_t **pcmp, const char *name, snd_pcm_stream_t stream, int mode, int waitOnBusy )
-{
-    int ret, tries = 0, maxTries = waitOnBusy ? busyRetries_ : 0;
-
-    ret = alsa_snd_pcm_open( pcmp, name, stream, mode );
-
-    for( tries = 0; tries < maxTries && -EBUSY == ret; ++tries )
-    {
-        Pa_Sleep( 10 );
-        ret = alsa_snd_pcm_open( pcmp, name, stream, mode );
-        if( -EBUSY != ret )
-        {
-            PA_DEBUG(( "%s: Successfully opened initially busy device after %d tries\n", __FUNCTION__, tries ));
-        }
-    }
-    if( -EBUSY == ret )
-    {
-        PA_DEBUG(( "%s: Failed to open busy device '%s'\n", __FUNCTION__, name ));
-    }
-    else
-    {
-        if( ret < 0 )
-            PA_DEBUG(( "%s: Opened device '%s' ptr[%p] - result: [%d:%s]\n", __FUNCTION__, name, *pcmp, ret, alsa_snd_strerror(ret) ));
-    }
-
-    return ret;
-}
-
-static PaError FillInDevInfo( PaAlsaHostApiRepresentation *alsaApi, HwDevInfo* deviceHwInfo, int blocking,
-        PaAlsaDeviceInfo* devInfo, int* devIdx )
-{
-    PaError result = 0;
-    PaDeviceInfo *baseDeviceInfo = &devInfo->baseDeviceInfo;
-    snd_pcm_t *pcm = NULL;
-    PaUtilHostApiRepresentation *baseApi = &alsaApi->baseHostApiRep;
-
-    PA_DEBUG(( "%s: Filling device info for: %s\n", __FUNCTION__, deviceHwInfo->name ));
-
-    /* Zero fields */
-    InitializeDeviceInfo( baseDeviceInfo );
-
-    /* To determine device capabilities, we must open the device and query the
-     * hardware parameter configuration space */
-
-    /* Query capture */
-    if( deviceHwInfo->hasCapture &&
-        OpenPcm( &pcm, deviceHwInfo->alsaName, SND_PCM_STREAM_CAPTURE, blocking, 0 ) >= 0 )
-    {
-        if( GropeDevice( pcm, deviceHwInfo->isPlug, StreamDirection_In, blocking, devInfo ) != paNoError )
-        {
-            /* Error */
-            PA_DEBUG(( "%s: Failed groping %s for capture\n", __FUNCTION__, deviceHwInfo->alsaName ));
-            goto end;
-        }
-    }
-
-    /* Query playback */
-    if( deviceHwInfo->hasPlayback &&
-        OpenPcm( &pcm, deviceHwInfo->alsaName, SND_PCM_STREAM_PLAYBACK, blocking, 0 ) >= 0 )
-    {
-        if( GropeDevice( pcm, deviceHwInfo->isPlug, StreamDirection_Out, blocking, devInfo ) != paNoError )
-        {
-            /* Error */
-            PA_DEBUG(( "%s: Failed groping %s for playback\n", __FUNCTION__, deviceHwInfo->alsaName ));
-            goto end;
-        }
-    }
-
-    baseDeviceInfo->structVersion = 2;
-    baseDeviceInfo->hostApi = alsaApi->hostApiIndex;
-    baseDeviceInfo->name = deviceHwInfo->name;
-    devInfo->alsaName = deviceHwInfo->alsaName;
-    devInfo->isPlug = deviceHwInfo->isPlug;
-
-    /* A: Storing pointer to PaAlsaDeviceInfo object as pointer to PaDeviceInfo object.
-     * Should now be safe to add device info, unless the device supports neither capture nor playback
-     */
-    if( baseDeviceInfo->maxInputChannels > 0 || baseDeviceInfo->maxOutputChannels > 0 )
-    {
-        /* Make device default if there isn't already one or it is the ALSA "default" device */
-        if( ( baseApi->info.defaultInputDevice == paNoDevice ||
-            !strcmp( deviceHwInfo->alsaName, "default" ) ) && baseDeviceInfo->maxInputChannels > 0 )
-        {
-            baseApi->info.defaultInputDevice = *devIdx;
-            PA_DEBUG(( "Default input device: %s\n", deviceHwInfo->name ));
-        }
-        if( ( baseApi->info.defaultOutputDevice == paNoDevice ||
-            !strcmp( deviceHwInfo->alsaName, "default" ) ) && baseDeviceInfo->maxOutputChannels > 0 )
-        {
-            baseApi->info.defaultOutputDevice = *devIdx;
-            PA_DEBUG(( "Default output device: %s\n", deviceHwInfo->name ));
-        }
-        PA_DEBUG(( "%s: Adding device %s: %d\n", __FUNCTION__, deviceHwInfo->name, *devIdx ));
-        baseApi->deviceInfos[*devIdx] = (PaDeviceInfo *) devInfo;
-        (*devIdx) += 1;
-    }
-    else
-    {
-        PA_DEBUG(( "%s: Skipped device: %s, all channels == 0\n", __FUNCTION__, deviceHwInfo->name ));
-    }
-
-end:
-    return result;
-}
-
-/* Build PaDeviceInfo list, ignore devices for which we cannot determine capabilities (possibly busy, sigh) */
-static PaError BuildDeviceList( PaAlsaHostApiRepresentation *alsaApi )
-{
-    PaUtilHostApiRepresentation *baseApi = &alsaApi->baseHostApiRep;
-    PaAlsaDeviceInfo *deviceInfoArray;
-    int cardIdx = -1, devIdx = 0;
-    snd_ctl_card_info_t *cardInfo;
-    PaError result = paNoError;
-    size_t numDeviceNames = 0, maxDeviceNames = 1, i;
-    HwDevInfo *hwDevInfos = NULL;
-    snd_config_t *topNode = NULL;
-    snd_pcm_info_t *pcmInfo;
-    int res;
-    int blocking = SND_PCM_NONBLOCK;
-    int usePlughw = 0;
-    char *hwPrefix = "";
-    char alsaCardName[50];
-#ifdef PA_ENABLE_DEBUG_OUTPUT
-    PaTime startTime = PaUtil_GetTime();
-#endif
-
-    if( getenv( "PA_ALSA_INITIALIZE_BLOCK" ) && atoi( getenv( "PA_ALSA_INITIALIZE_BLOCK" ) ) )
-        blocking = 0;
-
-    /* If PA_ALSA_PLUGHW is 1 (non-zero), use the plughw: pcm throughout instead of hw: */
-    if( getenv( "PA_ALSA_PLUGHW" ) && atoi( getenv( "PA_ALSA_PLUGHW" ) ) )
-    {
-        usePlughw = 1;
-        hwPrefix = "plug";
-        PA_DEBUG(( "%s: Using Plughw\n", __FUNCTION__ ));
-    }
-
-    /* These two will be set to the first working input and output device, respectively */
-    baseApi->info.defaultInputDevice = paNoDevice;
-    baseApi->info.defaultOutputDevice = paNoDevice;
-
-    /* Gather info about hw devices
-
-     * alsa_snd_card_next() modifies the integer passed to it to be:
-     *      the index of the first card if the parameter is -1
-     *      the index of the next card if the parameter is the index of a card
-     *      -1 if there are no more cards
-     *
-     * The function itself returns 0 if it succeeded. */
-    cardIdx = -1;
-    alsa_snd_ctl_card_info_alloca( &cardInfo );
-    alsa_snd_pcm_info_alloca( &pcmInfo );
-    while( alsa_snd_card_next( &cardIdx ) == 0 && cardIdx >= 0 )
-    {
-        char *cardName;
-        int devIdx = -1;
-        snd_ctl_t *ctl;
-        char buf[50];
-
-        snprintf( alsaCardName, sizeof (alsaCardName), "hw:%d", cardIdx );
-
-        /* Acquire name of card */
-        if( alsa_snd_ctl_open( &ctl, alsaCardName, 0 ) < 0 )
-        {
-            /* Unable to open card :( */
-            PA_DEBUG(( "%s: Unable to open device %s\n", __FUNCTION__, alsaCardName ));
-            continue;
-        }
-        alsa_snd_ctl_card_info( ctl, cardInfo );
-
-        PA_ENSURE( PaAlsa_StrDup( alsaApi, &cardName, alsa_snd_ctl_card_info_get_name( cardInfo )) );
-
-        while( alsa_snd_ctl_pcm_next_device( ctl, &devIdx ) == 0 && devIdx >= 0 )
-        {
-            char *alsaDeviceName, *deviceName, *infoName;
-            size_t len;
-            int hasPlayback = 0, hasCapture = 0;
-
-            snprintf( buf, sizeof (buf), "%s%s,%d", hwPrefix, alsaCardName, devIdx );
-
-            /* Obtain info about this particular device */
-            alsa_snd_pcm_info_set_device( pcmInfo, devIdx );
-            alsa_snd_pcm_info_set_subdevice( pcmInfo, 0 );
-            alsa_snd_pcm_info_set_stream( pcmInfo, SND_PCM_STREAM_CAPTURE );
-            if( alsa_snd_ctl_pcm_info( ctl, pcmInfo ) >= 0 )
-            {
-                hasCapture = 1;
-            }
-
-            alsa_snd_pcm_info_set_stream( pcmInfo, SND_PCM_STREAM_PLAYBACK );
-            if( alsa_snd_ctl_pcm_info( ctl, pcmInfo ) >= 0 )
-            {
-                hasPlayback = 1;
-            }
-
-            if( !hasPlayback && !hasCapture )
-            {
-                /* Error */
-                continue;
-            }
-
-            infoName = SkipCardDetailsInName( (char *)alsa_snd_pcm_info_get_name( pcmInfo ), cardName );
-
-            /* The length of the string written by snprintf plus terminating 0 */
-            len = snprintf( NULL, 0, "%s: %s (%s)", cardName, infoName, buf ) + 1;
-            PA_UNLESS( deviceName = (char *)PaUtil_GroupAllocateMemory( alsaApi->allocations, len ),
-                    paInsufficientMemory );
-            snprintf( deviceName, len, "%s: %s (%s)", cardName, infoName, buf );
-
-            PA_DEBUG(( "%s: Found device [%d]: %s\n", __FUNCTION__, numDeviceNames, deviceName ));
-
-            ++numDeviceNames;
-            if( !hwDevInfos || numDeviceNames > maxDeviceNames )
-            {
-                maxDeviceNames *= 2;
-                PA_UNLESS( hwDevInfos = (HwDevInfo *) realloc( hwDevInfos, maxDeviceNames * sizeof (HwDevInfo) ),
-                        paInsufficientMemory );
-            }
-
-            PA_ENSURE( PaAlsa_StrDup( alsaApi, &alsaDeviceName, buf ) );
-
-            hwDevInfos[ numDeviceNames - 1 ].alsaName = alsaDeviceName;
-            hwDevInfos[ numDeviceNames - 1 ].name = deviceName;
-            hwDevInfos[ numDeviceNames - 1 ].isPlug = usePlughw;
-            hwDevInfos[ numDeviceNames - 1 ].hasPlayback = hasPlayback;
-            hwDevInfos[ numDeviceNames - 1 ].hasCapture = hasCapture;
-        }
-        alsa_snd_ctl_close( ctl );
-    }
-
-    /* Iterate over plugin devices */
-    if( NULL == (*alsa_snd_config) )
-    {
-        /* alsa_snd_config_update is called implicitly by some functions, if this hasn't happened snd_config will be NULL (bleh) */
-        ENSURE_( alsa_snd_config_update(), paUnanticipatedHostError );
-        PA_DEBUG(( "Updating snd_config\n" ));
-    }
-    assert( *alsa_snd_config );
-    if( ( res = alsa_snd_config_search( *alsa_snd_config, "pcm", &topNode ) ) >= 0 )
-    {
-        snd_config_iterator_t i, next;
-
-        alsa_snd_config_for_each( i, next, topNode )
-        {
-            const char *tpStr = "unknown", *idStr = NULL;
-            int err = 0;
-
-            char *alsaDeviceName, *deviceName;
-            const HwDevInfo *predefined = NULL;
-            snd_config_t *n = alsa_snd_config_iterator_entry( i ), * tp = NULL;;
-
-            if( (err = alsa_snd_config_search( n, "type", &tp )) < 0 )
-            {
-                if( -ENOENT != err )
-                {
-                    ENSURE_(err, paUnanticipatedHostError);
-                }
-            }
-            else
-            {
-                ENSURE_( alsa_snd_config_get_string( tp, &tpStr ), paUnanticipatedHostError );
-            }
-            ENSURE_( alsa_snd_config_get_id( n, &idStr ), paUnanticipatedHostError );
-            if( IgnorePlugin( idStr ) )
-            {
-                PA_DEBUG(( "%s: Ignoring ALSA plugin device [%s] of type [%s]\n", __FUNCTION__, idStr, tpStr ));
-                continue;
-            }
-            PA_DEBUG(( "%s: Found plugin [%s] of type [%s]\n", __FUNCTION__, idStr, tpStr ));
-
-            PA_UNLESS( alsaDeviceName = (char*)PaUtil_GroupAllocateMemory( alsaApi->allocations,
-                                                            strlen(idStr) + 6 ), paInsufficientMemory );
-            strcpy( alsaDeviceName, idStr );
-            PA_UNLESS( deviceName = (char*)PaUtil_GroupAllocateMemory( alsaApi->allocations,
-                                                            strlen(idStr) + 1 ), paInsufficientMemory );
-            strcpy( deviceName, idStr );
-
-            ++numDeviceNames;
-            if( !hwDevInfos || numDeviceNames > maxDeviceNames )
-            {
-                maxDeviceNames *= 2;
-                PA_UNLESS( hwDevInfos = (HwDevInfo *) realloc( hwDevInfos, maxDeviceNames * sizeof (HwDevInfo) ),
-                        paInsufficientMemory );
-            }
-
-            predefined = FindDeviceName( alsaDeviceName );
-
-            hwDevInfos[numDeviceNames - 1].alsaName = alsaDeviceName;
-            hwDevInfos[numDeviceNames - 1].name     = deviceName;
-            hwDevInfos[numDeviceNames - 1].isPlug   = 1;
-
-            if( predefined )
-            {
-                hwDevInfos[numDeviceNames - 1].hasPlayback = predefined->hasPlayback;
-                hwDevInfos[numDeviceNames - 1].hasCapture  = predefined->hasCapture;
-            }
-            else
-            {
-                hwDevInfos[numDeviceNames - 1].hasPlayback = 1;
-                hwDevInfos[numDeviceNames - 1].hasCapture  = 1;
-            }
-        }
-    }
-    else
-        PA_DEBUG(( "%s: Iterating over ALSA plugins failed: %s\n", __FUNCTION__, alsa_snd_strerror( res ) ));
-
-    /* allocate deviceInfo memory based on the number of devices */
-    PA_UNLESS( baseApi->deviceInfos = (PaDeviceInfo**)PaUtil_GroupAllocateMemory(
-            alsaApi->allocations, sizeof(PaDeviceInfo*) * (numDeviceNames) ), paInsufficientMemory );
-
-    /* allocate all device info structs in a contiguous block */
-    PA_UNLESS( deviceInfoArray = (PaAlsaDeviceInfo*)PaUtil_GroupAllocateMemory(
-            alsaApi->allocations, sizeof(PaAlsaDeviceInfo) * numDeviceNames ), paInsufficientMemory );
-
-    /* Loop over list of cards, filling in info. If a device is deemed unavailable (can't get name),
-     * it's ignored.
-     *
-     * Note that we do this in two stages. This is a workaround owing to the fact that the 'dmix'
-     * plugin may cause the underlying hardware device to be busy for a short while even after it
-     * (dmix) is closed. The 'default' plugin may also point to the dmix plugin, so the same goes
-     * for this.
-     */
-    PA_DEBUG(( "%s: Filling device info for %d devices\n", __FUNCTION__, numDeviceNames ));
-    for( i = 0, devIdx = 0; i < numDeviceNames; ++i )
-    {
-        PaAlsaDeviceInfo* devInfo = &deviceInfoArray[i];
-        HwDevInfo* hwInfo = &hwDevInfos[i];
-        if( !strcmp( hwInfo->name, "dmix" ) || !strcmp( hwInfo->name, "default" ) )
-        {
-            continue;
-        }
-
-        PA_ENSURE( FillInDevInfo( alsaApi, hwInfo, blocking, devInfo, &devIdx ) );
-    }
-    assert( devIdx <= numDeviceNames );
-    /* Now inspect 'dmix' and 'default' plugins */
-    for( i = 0; i < numDeviceNames; ++i )
-    {
-        PaAlsaDeviceInfo* devInfo = &deviceInfoArray[i];
-        HwDevInfo* hwInfo = &hwDevInfos[i];
-        if( strcmp( hwInfo->name, "dmix" ) && strcmp( hwInfo->name, "default" ) )
-        {
-            continue;
-        }
-
-        PA_ENSURE( FillInDevInfo( alsaApi, hwInfo, blocking, devInfo, &devIdx ) );
-    }
-    free( hwDevInfos );
-
-    baseApi->info.deviceCount = devIdx;   /* Number of successfully queried devices */
-
-#ifdef PA_ENABLE_DEBUG_OUTPUT
-    PA_DEBUG(( "%s: Building device list took %f seconds\n", __FUNCTION__, PaUtil_GetTime() - startTime ));
-#endif
-
-end:
-    return result;
-
-error:
-    /* No particular action */
-    goto end;
-}
-
-/* Check against known device capabilities */
-static PaError ValidateParameters( const PaStreamParameters *parameters, PaUtilHostApiRepresentation *hostApi, StreamDirection mode )
-{
-    PaError result = paNoError;
-    int maxChans;
-    const PaAlsaDeviceInfo *deviceInfo = NULL;
-    assert( parameters );
-
-    if( parameters->device != paUseHostApiSpecificDeviceSpecification )
-    {
-        assert( parameters->device < hostApi->info.deviceCount );
-        PA_UNLESS( parameters->hostApiSpecificStreamInfo == NULL, paBadIODeviceCombination );
-        deviceInfo = GetDeviceInfo( hostApi, parameters->device );
-    }
-    else
-    {
-        const PaAlsaStreamInfo *streamInfo = parameters->hostApiSpecificStreamInfo;
-
-        PA_UNLESS( parameters->device == paUseHostApiSpecificDeviceSpecification, paInvalidDevice );
-        PA_UNLESS( streamInfo->size == sizeof (PaAlsaStreamInfo) && streamInfo->version == 1,
-                paIncompatibleHostApiSpecificStreamInfo );
-        PA_UNLESS( streamInfo->deviceString != NULL, paInvalidDevice );
-
-        /* Skip further checking */
-        return paNoError;
-    }
-
-    assert( deviceInfo );
-    assert( parameters->hostApiSpecificStreamInfo == NULL );
-    maxChans = ( StreamDirection_In == mode ? deviceInfo->baseDeviceInfo.maxInputChannels :
-        deviceInfo->baseDeviceInfo.maxOutputChannels );
-    PA_UNLESS( parameters->channelCount <= maxChans, paInvalidChannelCount );
-
-error:
-    return result;
-}
-
-/* Given an open stream, what sample formats are available? */
-static PaSampleFormat GetAvailableFormats( snd_pcm_t *pcm )
-{
-    PaSampleFormat available = 0;
-    snd_pcm_hw_params_t *hwParams;
-    alsa_snd_pcm_hw_params_alloca( &hwParams );
-
-    alsa_snd_pcm_hw_params_any( pcm, hwParams );
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_FLOAT ) >= 0)
-        available |= paFloat32;
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S32 ) >= 0)
-        available |= paInt32;
-
-#ifdef PA_LITTLE_ENDIAN
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S24_3LE ) >= 0)
-        available |= paInt24;
-#elif defined PA_BIG_ENDIAN
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S24_3BE ) >= 0)
-        available |= paInt24;
-#endif
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S16 ) >= 0)
-        available |= paInt16;
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U8 ) >= 0)
-        available |= paUInt8;
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S8 ) >= 0)
-        available |= paInt8;
-
-    return available;
-}
-
-/* Output to console all formats supported by device */
-static void LogAllAvailableFormats( snd_pcm_t *pcm )
-{
-    PaSampleFormat available = 0;
-    snd_pcm_hw_params_t *hwParams;
-    alsa_snd_pcm_hw_params_alloca( &hwParams );
-
-    alsa_snd_pcm_hw_params_any( pcm, hwParams );
-
-    PA_DEBUG(( " --- Supported Formats ---\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S8 ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S8\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U8 ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U8\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S16_LE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S16_LE\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S16_BE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S16_BE\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U16_LE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U16_LE\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U16_BE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U16_BE\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S24_LE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S24_LE\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S24_BE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S24_BE\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U24_LE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U24_LE\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U24_BE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U24_BE\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_FLOAT_LE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_FLOAT_LE\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_FLOAT_BE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_FLOAT_BE\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_FLOAT64_LE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_FLOAT64_LE\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_FLOAT64_BE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_FLOAT64_BE\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_IEC958_SUBFRAME_LE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_IEC958_SUBFRAME_LE\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_IEC958_SUBFRAME_BE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_IEC958_SUBFRAME_BE\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_MU_LAW ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_MU_LAW\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_A_LAW ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_A_LAW\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_IMA_ADPCM ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_IMA_ADPCM\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_MPEG ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_MPEG\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_GSM ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_GSM\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_SPECIAL ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_SPECIAL\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S24_3LE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S24_3LE\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S24_3BE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S24_3BE\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U24_3LE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U24_3LE\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U24_3BE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U24_3BE\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S20_3LE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S20_3LE\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S20_3BE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S20_3BE\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U20_3LE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U20_3LE\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U20_3BE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U20_3BE\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S18_3LE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S18_3LE\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S18_3BE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S18_3BE\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U18_3LE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U18_3LE\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U18_3BE ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U18_3BE\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S16 ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S16\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U16 ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U16\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S24 ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S24\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U24 ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U24\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_S32 ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_S32\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_U32 ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_U32\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_FLOAT ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_FLOAT\n" ));
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_FLOAT64 ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_FLOAT64\n" ));
-
-    if( alsa_snd_pcm_hw_params_test_format( pcm, hwParams, SND_PCM_FORMAT_IEC958_SUBFRAME ) >= 0)
-        PA_DEBUG(( "SND_PCM_FORMAT_IEC958_SUBFRAME\n" ));
-
-    PA_DEBUG(( " -------------------------\n" ));
-}
-
-static snd_pcm_format_t Pa2AlsaFormat( PaSampleFormat paFormat )
-{
-    switch( paFormat )
-    {
-        case paFloat32:
-            return SND_PCM_FORMAT_FLOAT;
-
-        case paInt16:
-            return SND_PCM_FORMAT_S16;
-
-        case paInt24:
-#ifdef PA_LITTLE_ENDIAN
-            return SND_PCM_FORMAT_S24_3LE;
-#elif defined PA_BIG_ENDIAN
-            return SND_PCM_FORMAT_S24_3BE;
-#endif
-
-        case paInt32:
-            return SND_PCM_FORMAT_S32;
-
-        case paInt8:
-            return SND_PCM_FORMAT_S8;
-
-        case paUInt8:
-            return SND_PCM_FORMAT_U8;
-
-        default:
-            return SND_PCM_FORMAT_UNKNOWN;
-    }
-}
-
-/** Open an ALSA pcm handle.
- *
- * The device to be open can be specified by name in a custom PaAlsaStreamInfo struct, or it will be by
- * the Portaudio device number supplied in the stream parameters.
- */
-static PaError AlsaOpen( const PaUtilHostApiRepresentation *hostApi, const PaStreamParameters *params, StreamDirection
-        streamDir, snd_pcm_t **pcm )
-{
-    PaError result = paNoError;
-    int ret;
-    const char* deviceName = "";
-    const PaAlsaDeviceInfo *deviceInfo = NULL;
-    PaAlsaStreamInfo *streamInfo = (PaAlsaStreamInfo *)params->hostApiSpecificStreamInfo;
-
-    if( !streamInfo )
-    {
-        deviceInfo = GetDeviceInfo( hostApi, params->device );
-        deviceName = deviceInfo->alsaName;
-    }
-    else
-        deviceName = streamInfo->deviceString;
-
-    PA_DEBUG(( "%s: Opening device %s\n", __FUNCTION__, deviceName ));
-    if( (ret = OpenPcm( pcm, deviceName, streamDir == StreamDirection_In ? SND_PCM_STREAM_CAPTURE : SND_PCM_STREAM_PLAYBACK,
-                    SND_PCM_NONBLOCK, 1 )) < 0 )
-    {
-        /* Not to be closed */
-        *pcm = NULL;
-        ENSURE_( ret, -EBUSY == ret ? paDeviceUnavailable : paBadIODeviceCombination );
-    }
-    ENSURE_( alsa_snd_pcm_nonblock( *pcm, 0 ), paUnanticipatedHostError );
-
-end:
-    return result;
-
-error:
-    goto end;
-}
-
-static PaError TestParameters( const PaUtilHostApiRepresentation *hostApi, const PaStreamParameters *parameters,
-        double sampleRate, StreamDirection streamDir )
-{
-    PaError result = paNoError;
-    snd_pcm_t *pcm = NULL;
-    PaSampleFormat availableFormats;
-    /* We are able to adapt to a number of channels less than what the device supports */
-    unsigned int numHostChannels;
-    PaSampleFormat hostFormat;
-    snd_pcm_hw_params_t *hwParams;
-    alsa_snd_pcm_hw_params_alloca( &hwParams );
-
-    if( !parameters->hostApiSpecificStreamInfo )
-    {
-        const PaAlsaDeviceInfo *devInfo = GetDeviceInfo( hostApi, parameters->device );
-        numHostChannels = PA_MAX( parameters->channelCount, StreamDirection_In == streamDir ?
-                devInfo->minInputChannels : devInfo->minOutputChannels );
-    }
-    else
-        numHostChannels = parameters->channelCount;
-
-    PA_ENSURE( AlsaOpen( hostApi, parameters, streamDir, &pcm ) );
-
-    alsa_snd_pcm_hw_params_any( pcm, hwParams );
-
-    if( SetApproximateSampleRate( pcm, hwParams, sampleRate ) < 0 )
-    {
-        result = paInvalidSampleRate;
-        goto error;
-    }
-
-    if( alsa_snd_pcm_hw_params_set_channels( pcm, hwParams, numHostChannels ) < 0 )
-    {
-        result = paInvalidChannelCount;
-        goto error;
-    }
-
-    /* See if we can find a best possible match */
-    availableFormats = GetAvailableFormats( pcm );
-    PA_ENSURE( hostFormat = PaUtil_SelectClosestAvailableFormat( availableFormats, parameters->sampleFormat ) );
-
-    /* Some specific hardware (reported: Audio8 DJ) can fail with assertion during this step. */
-    ENSURE_( alsa_snd_pcm_hw_params_set_format( pcm, hwParams, Pa2AlsaFormat( hostFormat ) ), paUnanticipatedHostError );
-
-    {
-        /* It happens that this call fails because the device is busy */
-        int ret = 0;
-        if( ( ret = alsa_snd_pcm_hw_params( pcm, hwParams ) ) < 0 )
-        {
-            if( -EINVAL == ret )
-            {
-                /* Don't know what to return here */
-                result = paBadIODeviceCombination;
-                goto error;
-            }
-            else if( -EBUSY == ret )
-            {
-                result = paDeviceUnavailable;
-                PA_DEBUG(( "%s: Device is busy\n", __FUNCTION__ ));
-            }
-            else
-            {
-                result = paUnanticipatedHostError;
-            }
-
-            ENSURE_( ret, result );
-        }
-    }
-
-end:
-    if( pcm )
-    {
-        alsa_snd_pcm_close( pcm );
-    }
-    return result;
-
-error:
-    goto end;
-}
-
-static PaError IsFormatSupported( struct PaUtilHostApiRepresentation *hostApi,
-                                  const PaStreamParameters *inputParameters,
-                                  const PaStreamParameters *outputParameters,
-                                  double sampleRate )
-{
-    int inputChannelCount = 0, outputChannelCount = 0;
-    PaSampleFormat inputSampleFormat, outputSampleFormat;
-    PaError result = paFormatIsSupported;
-
-    if( inputParameters )
-    {
-        PA_ENSURE( ValidateParameters( inputParameters, hostApi, StreamDirection_In ) );
-
-        inputChannelCount = inputParameters->channelCount;
-        inputSampleFormat = inputParameters->sampleFormat;
-    }
-
-    if( outputParameters )
-    {
-        PA_ENSURE( ValidateParameters( outputParameters, hostApi, StreamDirection_Out ) );
-
-        outputChannelCount = outputParameters->channelCount;
-        outputSampleFormat = outputParameters->sampleFormat;
-    }
-
-    if( inputChannelCount )
-    {
-        if( ( result = TestParameters( hostApi, inputParameters, sampleRate, StreamDirection_In ) )
-                != paNoError )
-            goto error;
-    }
-    if ( outputChannelCount )
-    {
-        if( ( result = TestParameters( hostApi, outputParameters, sampleRate, StreamDirection_Out ) )
-                != paNoError )
-            goto error;
-    }
-
-    return paFormatIsSupported;
-
-error:
-    return result;
-}
-
-
-static PaError PaAlsaStreamComponent_Initialize( PaAlsaStreamComponent *self, PaAlsaHostApiRepresentation *alsaApi,
-        const PaStreamParameters *params, StreamDirection streamDir, int callbackMode )
-{
-    PaError result = paNoError;
-    PaSampleFormat userSampleFormat = params->sampleFormat, hostSampleFormat = paNoError;
-    assert( params->channelCount > 0 );
-
-    /* Make sure things have an initial value */
-    memset( self, 0, sizeof (PaAlsaStreamComponent) );
-
-    if( NULL == params->hostApiSpecificStreamInfo )
-    {
-        const PaAlsaDeviceInfo *devInfo = GetDeviceInfo( &alsaApi->baseHostApiRep, params->device );
-        self->numHostChannels = PA_MAX( params->channelCount, StreamDirection_In == streamDir ? devInfo->minInputChannels
-                : devInfo->minOutputChannels );
-        self->deviceIsPlug = devInfo->isPlug;
-        PA_DEBUG(( "%s: Host Chans %c %i\n", __FUNCTION__, streamDir == StreamDirection_In ? 'C' : 'P', self->numHostChannels ));
-    }
-    else
-    {
-        /* We're blissfully unaware of the minimum channelCount */
-        self->numHostChannels = params->channelCount;
-        /* Check if device name does not start with hw: to determine if it is a 'plug' device */
-        if( strncmp( "hw:", ((PaAlsaStreamInfo *)params->hostApiSpecificStreamInfo)->deviceString, 3 ) != 0  )
-            self->deviceIsPlug = 1; /* An Alsa plug device, not a direct hw device */
-    }
-    if( self->deviceIsPlug && alsaApi->alsaLibVersion < ALSA_VERSION_INT( 1, 0, 16 ) )
-        self->useReventFix = 1; /* Prior to Alsa1.0.16, plug devices may stutter without this fix */
-
-    self->device = params->device;
-
-    PA_ENSURE( AlsaOpen( &alsaApi->baseHostApiRep, params, streamDir, &self->pcm ) );
-    self->nfds = alsa_snd_pcm_poll_descriptors_count( self->pcm );
-
-    PA_ENSURE( hostSampleFormat = PaUtil_SelectClosestAvailableFormat( GetAvailableFormats( self->pcm ), userSampleFormat ) );
-
-    self->hostSampleFormat = hostSampleFormat;
-    self->nativeFormat = Pa2AlsaFormat( hostSampleFormat );
-    self->hostInterleaved = self->userInterleaved = !( userSampleFormat & paNonInterleaved );
-    self->numUserChannels = params->channelCount;
-    self->streamDir = streamDir;
-    self->canMmap = 0;
-    self->nonMmapBuffer = NULL;
-    self->nonMmapBufferSize = 0;
-
-    if( !callbackMode && !self->userInterleaved )
-    {
-        /* Pre-allocate non-interleaved user provided buffers */
-        PA_UNLESS( self->userBuffers = PaUtil_AllocateMemory( sizeof (void *) * self->numUserChannels ),
-                paInsufficientMemory );
-    }
-
-error:
-
-    /* Log all available formats. */
-    if ( hostSampleFormat == paSampleFormatNotSupported )
-    {
-        LogAllAvailableFormats( self->pcm );
-        PA_DEBUG(( "%s: Please provide the log output to PortAudio developers, your hardware does not have any sample format implemented yet.\n", __FUNCTION__ ));
-    }
-
-    return result;
-}
-
-static void PaAlsaStreamComponent_Terminate( PaAlsaStreamComponent *self )
-{
-    alsa_snd_pcm_close( self->pcm );
-    PaUtil_FreeMemory( self->userBuffers ); /* (Ptr can be NULL; PaUtil_FreeMemory includes a NULL check) */
-    PaUtil_FreeMemory( self->nonMmapBuffer );
-}
-
-/*
-static int nearbyint_(float value) {
-    if(  value - (int)value > .5 )
-        return (int)ceil( value );
-    return (int)floor( value );
-}
-*/
-
-/** Initiate configuration, preparing for determining a period size suitable for both capture and playback components.
- *
- */
-static PaError PaAlsaStreamComponent_InitialConfigure( PaAlsaStreamComponent *self, const PaStreamParameters *params,
-        int primeBuffers, snd_pcm_hw_params_t *hwParams, double *sampleRate )
-{
-    /* Configuration consists of setting all of ALSA's parameters.
-     * These parameters come in two flavors: hardware parameters
-     * and software parameters.  Hardware parameters will affect
-     * the way the device is initialized, software parameters
-     * affect the way ALSA interacts with me, the user-level client.
-     */
-
-    PaError result = paNoError;
-    snd_pcm_access_t accessMode, alternateAccessMode;
-    int dir = 0;
-    snd_pcm_t *pcm = self->pcm;
-    double sr = *sampleRate;
-    unsigned int minPeriods = 2;
-
-    /* self->framesPerPeriod = framesPerHostBuffer; */
-
-    /* ... fill up the configuration space with all possible
-     * combinations of parameters this device will accept */
-    ENSURE_( alsa_snd_pcm_hw_params_any( pcm, hwParams ), paUnanticipatedHostError );
-
-    ENSURE_( alsa_snd_pcm_hw_params_set_periods_integer( pcm, hwParams ), paUnanticipatedHostError );
-    /* I think there should be at least 2 periods (even though ALSA doesn't appear to enforce this) */
-    dir = 0;
-    ENSURE_( alsa_snd_pcm_hw_params_set_periods_min( pcm, hwParams, &minPeriods, &dir ), paUnanticipatedHostError );
-
-    if( self->userInterleaved )
-    {
-        accessMode          = SND_PCM_ACCESS_MMAP_INTERLEAVED;
-        alternateAccessMode = SND_PCM_ACCESS_MMAP_NONINTERLEAVED;
-
-        /* test if MMAP supported */
-        self->canMmap = alsa_snd_pcm_hw_params_test_access( pcm, hwParams, accessMode ) >= 0 ||
-                        alsa_snd_pcm_hw_params_test_access( pcm, hwParams, alternateAccessMode ) >= 0;
-
-        PA_DEBUG(( "%s: device MMAP SND_PCM_ACCESS_MMAP_INTERLEAVED: %s\n", __FUNCTION__, ( alsa_snd_pcm_hw_params_test_access( pcm, hwParams, accessMode ) >= 0 ? "YES" : "NO" ) ));
-        PA_DEBUG(( "%s: device MMAP SND_PCM_ACCESS_MMAP_NONINTERLEAVED: %s\n", __FUNCTION__, ( alsa_snd_pcm_hw_params_test_access( pcm, hwParams, alternateAccessMode ) >= 0 ? "YES" : "NO" ) ));
-
-        if( !self->canMmap )
-        {
-            accessMode          = SND_PCM_ACCESS_RW_INTERLEAVED;
-            alternateAccessMode = SND_PCM_ACCESS_RW_NONINTERLEAVED;
-        }
-    }
-    else
-    {
-        accessMode          = SND_PCM_ACCESS_MMAP_NONINTERLEAVED;
-        alternateAccessMode = SND_PCM_ACCESS_MMAP_INTERLEAVED;
-
-        /* test if MMAP supported */
-        self->canMmap = alsa_snd_pcm_hw_params_test_access( pcm, hwParams, accessMode ) >= 0 ||
-                        alsa_snd_pcm_hw_params_test_access( pcm, hwParams, alternateAccessMode ) >= 0;
-
-        PA_DEBUG((" %s: device MMAP SND_PCM_ACCESS_MMAP_NONINTERLEAVED: %s\n", __FUNCTION__, ( alsa_snd_pcm_hw_params_test_access( pcm, hwParams, accessMode ) >= 0 ? "YES" : "NO" ) ));
-        PA_DEBUG(( "%s: device MMAP SND_PCM_ACCESS_MMAP_INTERLEAVED: %s\n", __FUNCTION__, ( alsa_snd_pcm_hw_params_test_access( pcm, hwParams, alternateAccessMode ) >= 0 ? "YES" : "NO" ) ));
-
-        if( !self->canMmap )
-        {
-            accessMode          = SND_PCM_ACCESS_RW_NONINTERLEAVED;
-            alternateAccessMode = SND_PCM_ACCESS_RW_INTERLEAVED;
-        }
-    }
-
-    PA_DEBUG(( "%s: device can MMAP: %s\n", __FUNCTION__, ( self->canMmap ? "YES" : "NO" ) ));
-
-    /* If requested access mode fails, try alternate mode */
-    if( alsa_snd_pcm_hw_params_set_access( pcm, hwParams, accessMode ) < 0 )
-    {
-        int err = 0;
-        if( ( err = alsa_snd_pcm_hw_params_set_access( pcm, hwParams, alternateAccessMode )) < 0 )
-        {
-            result = paUnanticipatedHostError;
-            PaUtil_SetLastHostErrorInfo( paALSA, err, alsa_snd_strerror( err ) );
-            goto error;
-        }
-        /* Flip mode */
-        self->hostInterleaved = !self->userInterleaved;
-    }
-
-    /* Some specific hardware (reported: Audio8 DJ) can fail with assertion during this step. */
-    ENSURE_( alsa_snd_pcm_hw_params_set_format( pcm, hwParams, self->nativeFormat ), paUnanticipatedHostError );
-
-    if( ( result = SetApproximateSampleRate( pcm, hwParams, sr )) != paUnanticipatedHostError )
-    {
-        ENSURE_( GetExactSampleRate( hwParams, &sr ), paUnanticipatedHostError );
-        if( result == paInvalidSampleRate ) /* From the SetApproximateSampleRate() call above */
-        { /* The sample rate was returned as 'out of tolerance' of the one requested */
-            PA_DEBUG(( "%s: Wanted %.3f, closest sample rate was %.3f\n", __FUNCTION__, sampleRate, sr ));
-            PA_ENSURE( paInvalidSampleRate );
-        }
-    }
-    else
-    {
-        PA_ENSURE( paUnanticipatedHostError );
-    }
-
-    ENSURE_( alsa_snd_pcm_hw_params_set_channels( pcm, hwParams, self->numHostChannels ), paInvalidChannelCount );
-
-    *sampleRate = sr;
-
-end:
-    return result;
-
-error:
-    /* No particular action */
-    goto end;
-}
-
-/** Finish the configuration of the component's ALSA device.
- *
- * As part of this method, the component's alsaBufferSize attribute will be set.
- * @param latency: The latency for this component.
- */
-static PaError PaAlsaStreamComponent_FinishConfigure( PaAlsaStreamComponent *self, snd_pcm_hw_params_t* hwParams,
-        const PaStreamParameters *params, int primeBuffers, double sampleRate, PaTime* latency )
-{
-    PaError result = paNoError;
-    snd_pcm_sw_params_t* swParams;
-    snd_pcm_uframes_t bufSz = 0;
-    *latency = -1.;
-
-    alsa_snd_pcm_sw_params_alloca( &swParams );
-
-    bufSz = params->suggestedLatency * sampleRate + self->framesPerPeriod;
-    ENSURE_( alsa_snd_pcm_hw_params_set_buffer_size_near( self->pcm, hwParams, &bufSz ), paUnanticipatedHostError );
-
-    /* Set the parameters! */
-    {
-        int r = alsa_snd_pcm_hw_params( self->pcm, hwParams );
-#ifdef PA_ENABLE_DEBUG_OUTPUT
-        if( r < 0 )
-        {
-            snd_output_t *output = NULL;
-            alsa_snd_output_stdio_attach( &output, stderr, 0 );
-            alsa_snd_pcm_hw_params_dump( hwParams, output );
-        }
-#endif
-        ENSURE_( r, paUnanticipatedHostError );
-    }
-    if( alsa_snd_pcm_hw_params_get_buffer_size != NULL )
-    {
-        ENSURE_( alsa_snd_pcm_hw_params_get_buffer_size( hwParams, &self->alsaBufferSize ), paUnanticipatedHostError );
-    }
-    else
-    {
-        self->alsaBufferSize = bufSz;
-    }
-
-    /* Latency in seconds */
-    *latency = (self->alsaBufferSize - self->framesPerPeriod) / sampleRate;
-
-    /* Now software parameters... */
-    ENSURE_( alsa_snd_pcm_sw_params_current( self->pcm, swParams ), paUnanticipatedHostError );
-
-    ENSURE_( alsa_snd_pcm_sw_params_set_start_threshold( self->pcm, swParams, self->framesPerPeriod ), paUnanticipatedHostError );
-    ENSURE_( alsa_snd_pcm_sw_params_set_stop_threshold( self->pcm, swParams, self->alsaBufferSize ), paUnanticipatedHostError );
-
-    /* Silence buffer in the case of underrun */
-    if( !primeBuffers ) /* XXX: Make sense? */
-    {
-        snd_pcm_uframes_t boundary;
-        ENSURE_( alsa_snd_pcm_sw_params_get_boundary( swParams, &boundary ), paUnanticipatedHostError );
-        ENSURE_( alsa_snd_pcm_sw_params_set_silence_threshold( self->pcm, swParams, 0 ), paUnanticipatedHostError );
-        ENSURE_( alsa_snd_pcm_sw_params_set_silence_size( self->pcm, swParams, boundary ), paUnanticipatedHostError );
-    }
-
-    ENSURE_( alsa_snd_pcm_sw_params_set_avail_min( self->pcm, swParams, self->framesPerPeriod ), paUnanticipatedHostError );
-    ENSURE_( alsa_snd_pcm_sw_params_set_xfer_align( self->pcm, swParams, 1 ), paUnanticipatedHostError );
-    ENSURE_( alsa_snd_pcm_sw_params_set_tstamp_mode( self->pcm, swParams, SND_PCM_TSTAMP_ENABLE ), paUnanticipatedHostError );
-
-    /* Set the parameters! */
-    ENSURE_( alsa_snd_pcm_sw_params( self->pcm, swParams ), paUnanticipatedHostError );
-
-error:
-    return result;
-}
-
-static PaError PaAlsaStream_Initialize( PaAlsaStream *self, PaAlsaHostApiRepresentation *alsaApi, const PaStreamParameters *inParams,
-        const PaStreamParameters *outParams, double sampleRate, unsigned long framesPerUserBuffer, PaStreamCallback callback,
-        PaStreamFlags streamFlags, void *userData )
-{
-    PaError result = paNoError;
-    assert( self );
-
-    memset( self, 0, sizeof( PaAlsaStream ) );
-
-    if( NULL != callback )
-    {
-        PaUtil_InitializeStreamRepresentation( &self->streamRepresentation,
-                                               &alsaApi->callbackStreamInterface,
-                                               callback, userData );
-        self->callbackMode = 1;
-    }
-    else
-    {
-        PaUtil_InitializeStreamRepresentation( &self->streamRepresentation,
-                                               &alsaApi->blockingStreamInterface,
-                                               NULL, userData );
-    }
-
-    self->framesPerUserBuffer = framesPerUserBuffer;
-    self->neverDropInput = streamFlags & paNeverDropInput;
-    /* XXX: Ignore paPrimeOutputBuffersUsingStreamCallback until buffer priming is fully supported in pa_process.c */
-    /*
-    if( outParams & streamFlags & paPrimeOutputBuffersUsingStreamCallback )
-        self->primeBuffers = 1;
-        */
-    memset( &self->capture, 0, sizeof (PaAlsaStreamComponent) );
-    memset( &self->playback, 0, sizeof (PaAlsaStreamComponent) );
-    if( inParams )
-    {
-        PA_ENSURE( PaAlsaStreamComponent_Initialize( &self->capture, alsaApi, inParams, StreamDirection_In, NULL != callback ) );
-    }
-    if( outParams )
-    {
-        PA_ENSURE( PaAlsaStreamComponent_Initialize( &self->playback, alsaApi, outParams, StreamDirection_Out, NULL != callback ) );
-    }
-
-    assert( self->capture.nfds || self->playback.nfds );
-
-    PA_UNLESS( self->pfds = (struct pollfd*)PaUtil_AllocateMemory( ( self->capture.nfds +
-                    self->playback.nfds ) * sizeof( struct pollfd ) ), paInsufficientMemory );
-
-    PaUtil_InitializeCpuLoadMeasurer( &self->cpuLoadMeasurer, sampleRate );
-    ASSERT_CALL_( PaUnixMutex_Initialize( &self->stateMtx ), paNoError );
-
-error:
-    return result;
-}
-
-/** Free resources associated with stream, and eventually stream itself.
- *
- * Frees allocated memory, and terminates individual StreamComponents.
- */
-static void PaAlsaStream_Terminate( PaAlsaStream *self )
-{
-    assert( self );
-
-    if( self->capture.pcm )
-    {
-        PaAlsaStreamComponent_Terminate( &self->capture );
-    }
-    if( self->playback.pcm )
-    {
-        PaAlsaStreamComponent_Terminate( &self->playback );
-    }
-
-    PaUtil_FreeMemory( self->pfds );
-    ASSERT_CALL_( PaUnixMutex_Terminate( &self->stateMtx ), paNoError );
-
-    PaUtil_FreeMemory( self );
-}
-
-/** Calculate polling timeout
- *
- * @param frames Time to wait
- * @return Polling timeout in milliseconds
- */
-static int CalculatePollTimeout( const PaAlsaStream *stream, unsigned long frames )
-{
-    assert( stream->streamRepresentation.streamInfo.sampleRate > 0.0 );
-    /* Period in msecs, rounded up */
-    return (int)ceil( 1000 * frames / stream->streamRepresentation.streamInfo.sampleRate );
-}
-
-/** Align value in backward direction.
- *
- * @param v: Value to align.
- * @param align: Alignment.
- */
-static unsigned long PaAlsa_AlignBackward(unsigned long v, unsigned long align)
-{
-    return ( v - ( align ? v % align : 0 ) );
-}
-
-/** Align value in forward direction.
- *
- * @param v: Value to align.
- * @param align: Alignment.
- */
-static unsigned long PaAlsa_AlignForward(unsigned long v, unsigned long align)
-{
-    unsigned long remainder = ( align ? ( v % align ) : 0);
-    return ( remainder != 0 ? v + ( align - remainder ) : v );
-}
-
-/** Get size of host buffer maintained from the number of user frames, sample rate and suggested latency. Minimum double buffering
- *  is maintained to allow 100% CPU usage inside user callback.
- *
- * @param userFramesPerBuffer: User buffer size in number of frames.
- * @param suggestedLatency: User provided desired latency.
- * @param sampleRate: Sample rate.
- */
-static unsigned long PaAlsa_GetFramesPerHostBuffer(unsigned long userFramesPerBuffer, PaTime suggestedLatency, double sampleRate)
-{
-    unsigned long frames = userFramesPerBuffer + PA_MAX( userFramesPerBuffer, (unsigned long)( suggestedLatency * sampleRate ) );
-    return frames;
-}
-
-/** Determine size per host buffer.
- *
- * During this method call, the component's framesPerPeriod attribute gets computed, and the corresponding period size
- * gets configured for the device.
- * @param accurate: If the configured period size is non-integer, this will be set to 0.
- */
-static PaError PaAlsaStreamComponent_DetermineFramesPerBuffer( PaAlsaStreamComponent* self, const PaStreamParameters* params,
-        unsigned long framesPerUserBuffer, double sampleRate, snd_pcm_hw_params_t* hwParams, int* accurate )
-{
-    PaError result = paNoError;
-    unsigned long bufferSize, framesPerHostBuffer;
-    int dir = 0;
-
-    /* Calculate host buffer size */
-    bufferSize = PaAlsa_GetFramesPerHostBuffer(framesPerUserBuffer, params->suggestedLatency, sampleRate);
-
-    /* Log */
-    PA_DEBUG(( "%s: user-buffer (frames)           = %lu\n", __FUNCTION__, framesPerUserBuffer ));
-    PA_DEBUG(( "%s: user-buffer (sec)              = %f\n",  __FUNCTION__, (double)(framesPerUserBuffer / sampleRate) ));
-    PA_DEBUG(( "%s: suggested latency (sec)        = %f\n",  __FUNCTION__, params->suggestedLatency ));
-    PA_DEBUG(( "%s: suggested host buffer (frames) = %lu\n", __FUNCTION__, bufferSize ));
-    PA_DEBUG(( "%s: suggested host buffer (sec)    = %f\n",  __FUNCTION__, (double)(bufferSize / sampleRate) ));
-
-#ifdef PA_ALSA_USE_OBSOLETE_HOST_BUFFER_CALC
-
-    if( framesPerUserBuffer != paFramesPerBufferUnspecified )
-    {
-        /* Preferably the host buffer size should be a multiple of the user buffer size */
-
-        if( bufferSize > framesPerUserBuffer )
-        {
-            snd_pcm_uframes_t remainder = bufferSize % framesPerUserBuffer;
-            if( remainder > framesPerUserBuffer / 2. )
-                bufferSize += framesPerUserBuffer - remainder;
-            else
-                bufferSize -= remainder;
-
-            assert( bufferSize % framesPerUserBuffer == 0 );
-        }
-        else if( framesPerUserBuffer % bufferSize != 0 )
-        {
-            /*  Find a good compromise between user specified latency and buffer size */
-            if( bufferSize > framesPerUserBuffer * .75 )
-            {
-                bufferSize = framesPerUserBuffer;
-            }
-            else
-            {
-                snd_pcm_uframes_t newSz = framesPerUserBuffer;
-                while( newSz / 2 >= bufferSize )
-                {
-                    if( framesPerUserBuffer % (newSz / 2) != 0 )
-                    {
-                        /* No use dividing any further */
-                        break;
-                    }
-                    newSz /= 2;
-                }
-                bufferSize = newSz;
-            }
-
-            assert( framesPerUserBuffer % bufferSize == 0 );
-        }
-    }
-
-#endif
-
-    {
-        unsigned numPeriods = numPeriods_, maxPeriods = 0, minPeriods = numPeriods_;
-
-        /* It may be that the device only supports 2 periods for instance */
-        dir = 0;
-        ENSURE_( alsa_snd_pcm_hw_params_get_periods_min( hwParams, &minPeriods, &dir ), paUnanticipatedHostError );
-        dir = 0;
-        ENSURE_( alsa_snd_pcm_hw_params_get_periods_max( hwParams, &maxPeriods, &dir ), paUnanticipatedHostError );
-        assert( maxPeriods > 1 );
-
-        /* Clamp to min/max */
-        numPeriods = PA_MIN(maxPeriods, PA_MAX(minPeriods, numPeriods));
-
-        PA_DEBUG(( "%s: periods min = %lu, max = %lu, req = %lu \n", __FUNCTION__, minPeriods, maxPeriods, numPeriods ));
-
-#ifndef PA_ALSA_USE_OBSOLETE_HOST_BUFFER_CALC
-
-        /* Calculate period size */
-        framesPerHostBuffer = (bufferSize / numPeriods);
-
-        /* Align & test size */
-        if( framesPerUserBuffer != paFramesPerBufferUnspecified )
-        {
-            /* Align to user buffer size */
-            framesPerHostBuffer = PaAlsa_AlignForward(framesPerHostBuffer, framesPerUserBuffer);
-
-            /* Test (borrowed from older implementation) */
-            if( framesPerHostBuffer < framesPerUserBuffer )
-            {
-                assert( framesPerUserBuffer % framesPerHostBuffer == 0 );
-                if( alsa_snd_pcm_hw_params_test_period_size( self->pcm, hwParams, framesPerHostBuffer, 0 ) < 0 )
-                {
-                    if( alsa_snd_pcm_hw_params_test_period_size( self->pcm, hwParams, framesPerHostBuffer * 2, 0 ) == 0 )
-                        framesPerHostBuffer *= 2;
-                    else if( alsa_snd_pcm_hw_params_test_period_size( self->pcm, hwParams, framesPerHostBuffer / 2, 0 ) == 0 )
-                        framesPerHostBuffer /= 2;
-                }
-            }
-            else
-            {
-                assert( framesPerHostBuffer % framesPerUserBuffer == 0 );
-                if( alsa_snd_pcm_hw_params_test_period_size( self->pcm, hwParams, framesPerHostBuffer, 0 ) < 0 )
-                {
-                    if( alsa_snd_pcm_hw_params_test_period_size( self->pcm, hwParams, framesPerHostBuffer + framesPerUserBuffer, 0 ) == 0 )
-                        framesPerHostBuffer += framesPerUserBuffer;
-                    else if( alsa_snd_pcm_hw_params_test_period_size( self->pcm, hwParams, framesPerHostBuffer - framesPerUserBuffer, 0 ) == 0 )
-                        framesPerHostBuffer -= framesPerUserBuffer;
-                }
-            }
-        }
-#endif
-
-#ifdef PA_ALSA_USE_OBSOLETE_HOST_BUFFER_CALC
-
-        if( framesPerUserBuffer != paFramesPerBufferUnspecified )
-        {
-            /* Try to get a power-of-two of the user buffer size. */
-            framesPerHostBuffer = framesPerUserBuffer;
-            if( framesPerHostBuffer < bufferSize )
-            {
-                while( bufferSize / framesPerHostBuffer > numPeriods )
-                {
-                    framesPerHostBuffer *= 2;
-                }
-                /* One extra period is preferable to one less (should be more robust) */
-                if( bufferSize / framesPerHostBuffer < numPeriods )
-                {
-                    framesPerHostBuffer /= 2;
-                }
-            }
-            else
-            {
-                while( bufferSize / framesPerHostBuffer < numPeriods )
-                {
-                    if( framesPerUserBuffer % ( framesPerHostBuffer / 2 ) != 0 )
-                    {
-                        /* Can't be divided any further */
-                        break;
-                    }
-                    framesPerHostBuffer /= 2;
-                }
-            }
-
-            if( framesPerHostBuffer < framesPerUserBuffer )
-            {
-                assert( framesPerUserBuffer % framesPerHostBuffer == 0 );
-                if( alsa_snd_pcm_hw_params_test_period_size( self->pcm, hwParams, framesPerHostBuffer, 0 ) < 0 )
-                {
-                    if( alsa_snd_pcm_hw_params_test_period_size( self->pcm, hwParams, framesPerHostBuffer * 2, 0 ) == 0 )
-                        framesPerHostBuffer *= 2;
-                    else if( alsa_snd_pcm_hw_params_test_period_size( self->pcm, hwParams, framesPerHostBuffer / 2, 0 ) == 0 )
-                        framesPerHostBuffer /= 2;
-                }
-            }
-            else
-            {
-                assert( framesPerHostBuffer % framesPerUserBuffer == 0 );
-                if( alsa_snd_pcm_hw_params_test_period_size( self->pcm, hwParams, framesPerHostBuffer, 0 ) < 0 )
-                {
-                    if( alsa_snd_pcm_hw_params_test_period_size( self->pcm, hwParams, framesPerHostBuffer + framesPerUserBuffer, 0 ) == 0 )
-                        framesPerHostBuffer += framesPerUserBuffer;
-                    else if( alsa_snd_pcm_hw_params_test_period_size( self->pcm, hwParams, framesPerHostBuffer - framesPerUserBuffer, 0 ) == 0 )
-                        framesPerHostBuffer -= framesPerUserBuffer;
-                }
-            }
-        }
-        else
-        {
-            framesPerHostBuffer = bufferSize / numPeriods;
-        }
-
-        /* non-mmap mode needs a reasonably-sized buffer or it'll stutter */
-        if( !self->canMmap && framesPerHostBuffer < 2048 )
-            framesPerHostBuffer = 2048;
-#endif
-        PA_DEBUG(( "%s: suggested host buffer period   = %lu \n", __FUNCTION__, framesPerHostBuffer ));
-    }
-
-    {
-        /* Get min/max period sizes and adjust our chosen */
-        snd_pcm_uframes_t min = 0, max = 0, minmax_diff;
-        ENSURE_( alsa_snd_pcm_hw_params_get_period_size_min( hwParams, &min, NULL ), paUnanticipatedHostError );
-        ENSURE_( alsa_snd_pcm_hw_params_get_period_size_max( hwParams, &max, NULL ), paUnanticipatedHostError );
-        minmax_diff = max - min;
-
-        if( framesPerHostBuffer < min )
-        {
-            PA_DEBUG(( "%s: The determined period size (%lu) is less than minimum (%lu)\n", __FUNCTION__, framesPerHostBuffer, min ));
-            framesPerHostBuffer = (( minmax_diff == 2 ) ? min + 1 : min );
-        }
-        else if( framesPerHostBuffer > max )
-        {
-            PA_DEBUG(( "%s: The determined period size (%lu) is greater than maximum (%lu)\n", __FUNCTION__, framesPerHostBuffer, max ));
-            framesPerHostBuffer = (( minmax_diff == 2 ) ? max - 1 : max );
-        }
-
-        PA_DEBUG(( "%s: device period minimum          = %lu\n", __FUNCTION__, min ));
-        PA_DEBUG(( "%s: device period maximum          = %lu\n", __FUNCTION__, max ));
-        PA_DEBUG(( "%s: host buffer period             = %lu\n", __FUNCTION__, framesPerHostBuffer ));
-        PA_DEBUG(( "%s: host buffer period latency     = %f\n", __FUNCTION__, (double)( framesPerHostBuffer / sampleRate ) ));
-
-        /* Try setting period size */
-        dir = 0;
-        ENSURE_( alsa_snd_pcm_hw_params_set_period_size_near( self->pcm, hwParams, &framesPerHostBuffer, &dir ), paUnanticipatedHostError );
-        if( dir != 0 )
-        {
-            PA_DEBUG(( "%s: The configured period size is non-integer.\n", __FUNCTION__, dir ));
-            *accurate = 0;
-        }
-    }
-
-    /* Set result */
-    self->framesPerPeriod = framesPerHostBuffer;
-
-error:
-    return result;
-}
-
-/* We need to determine how many frames per host buffer (period) to use.  Our
- * goals are to provide the best possible performance, but also to
- * honor the requested latency settings as closely as we can. Therefore this
- * decision is based on:
- *
- *   - the period sizes that playback and/or capture support.  The
- *     host buffer size has to be one of these.
- *   - the number of periods that playback and/or capture support.
- *
- * We want to make period_size*(num_periods-1) to be as close as possible
- * to latency*rate for both playback and capture.
- *
- * This method will determine suitable period sizes for capture and playback handles, and report the maximum number of
- * frames per host buffer. The latter is relevant, in case we should be so unfortunate that the period size differs
- * between capture and playback. If this should happen, the stream's hostBufferSizeMode attribute will be set to
- * paUtilBoundedHostBufferSize, because the best we can do is limit the size of individual host buffers to the upper
- * bound. The size of host buffers scheduled for processing should only matter if the user has specified a buffer size,
- * but when he/she does we must strive for an optimal configuration. By default we'll opt for a fixed host buffer size,
- * which should be fine if the period size is the same for capture and playback. In general, if there is a specified user
- * buffer size, this method tries it best to determine a period size which is a multiple of the user buffer size.
- *
- * The framesPerPeriod attributes of the individual capture and playback components of the stream are set to corresponding
- * values determined here. Since these should be reported as
- *
- * This is one of those blocks of code that will just take a lot of
- * refinement to be any good.
- *
- * In the full-duplex case it is possible that the routine was unable
- * to find a number of frames per buffer acceptable to both devices
- * TODO: Implement an algorithm to find the value closest to acceptance
- * by both devices, to minimize difference between period sizes?
- *
- * @param determinedFramesPerHostBuffer: The determined host buffer size.
- */
-static PaError PaAlsaStream_DetermineFramesPerBuffer( PaAlsaStream* self, double sampleRate, const PaStreamParameters* inputParameters,
-        const PaStreamParameters* outputParameters, unsigned long framesPerUserBuffer, snd_pcm_hw_params_t* hwParamsCapture,
-        snd_pcm_hw_params_t* hwParamsPlayback, PaUtilHostBufferSizeMode* hostBufferSizeMode )
-{
-    PaError result = paNoError;
-    unsigned long framesPerHostBuffer = 0;
-    int dir = 0;
-    int accurate = 1;
-    unsigned numPeriods = numPeriods_;
-
-    if( self->capture.pcm && self->playback.pcm )
-    {
-        if( framesPerUserBuffer == paFramesPerBufferUnspecified )
-        {
-            /* Come up with a common desired latency */
-            snd_pcm_uframes_t desiredBufSz, e, minPeriodSize, maxPeriodSize, optimalPeriodSize, periodSize,
-                              minCapture, minPlayback, maxCapture, maxPlayback;
-
-            dir = 0;
-            ENSURE_( alsa_snd_pcm_hw_params_get_period_size_min( hwParamsCapture, &minCapture, &dir ), paUnanticipatedHostError );
-            dir = 0;
-            ENSURE_( alsa_snd_pcm_hw_params_get_period_size_min( hwParamsPlayback, &minPlayback, &dir ), paUnanticipatedHostError );
-            dir = 0;
-            ENSURE_( alsa_snd_pcm_hw_params_get_period_size_max( hwParamsCapture, &maxCapture, &dir ), paUnanticipatedHostError );
-            dir = 0;
-            ENSURE_( alsa_snd_pcm_hw_params_get_period_size_max( hwParamsPlayback, &maxPlayback, &dir ), paUnanticipatedHostError );
-            minPeriodSize = PA_MAX( minPlayback, minCapture );
-            maxPeriodSize = PA_MIN( maxPlayback, maxCapture );
-            PA_UNLESS( minPeriodSize <= maxPeriodSize, paBadIODeviceCombination );
-
-            desiredBufSz = (snd_pcm_uframes_t)( PA_MIN( outputParameters->suggestedLatency, inputParameters->suggestedLatency )
-                    * sampleRate );
-            /* Clamp desiredBufSz */
-            {
-                snd_pcm_uframes_t maxBufferSize;
-                snd_pcm_uframes_t maxBufferSizeCapture, maxBufferSizePlayback;
-                ENSURE_( alsa_snd_pcm_hw_params_get_buffer_size_max( hwParamsCapture, &maxBufferSizeCapture ), paUnanticipatedHostError );
-                ENSURE_( alsa_snd_pcm_hw_params_get_buffer_size_max( hwParamsPlayback, &maxBufferSizePlayback ), paUnanticipatedHostError );
-                maxBufferSize = PA_MIN( maxBufferSizeCapture, maxBufferSizePlayback );
-
-                desiredBufSz = PA_MIN( desiredBufSz, maxBufferSize );
-            }
-
-            /* Find the closest power of 2 */
-            e = ilogb( minPeriodSize );
-            if( minPeriodSize & ( minPeriodSize - 1 ) )
-                e += 1;
-            periodSize = (snd_pcm_uframes_t)pow( 2, e );
-
-            while( periodSize <= maxPeriodSize )
-            {
-                if( alsa_snd_pcm_hw_params_test_period_size( self->playback.pcm, hwParamsPlayback, periodSize, 0 ) >= 0 &&
-                        alsa_snd_pcm_hw_params_test_period_size( self->capture.pcm, hwParamsCapture, periodSize, 0 ) >= 0 )
-                {
-                    /* OK! */
-                    break;
-                }
-
-                periodSize *= 2;
-            }
-
-            optimalPeriodSize = PA_MAX( desiredBufSz / numPeriods, minPeriodSize );
-            optimalPeriodSize = PA_MIN( optimalPeriodSize, maxPeriodSize );
-
-            /* Find the closest power of 2 */
-            e = ilogb( optimalPeriodSize );
-            if( optimalPeriodSize & (optimalPeriodSize - 1) )
-                e += 1;
-            optimalPeriodSize = (snd_pcm_uframes_t)pow( 2, e );
-
-            while( optimalPeriodSize >= periodSize )
-            {
-                if( alsa_snd_pcm_hw_params_test_period_size( self->capture.pcm, hwParamsCapture, optimalPeriodSize, 0 )
-                        >= 0 && alsa_snd_pcm_hw_params_test_period_size( self->playback.pcm, hwParamsPlayback,
-                            optimalPeriodSize, 0 ) >= 0 )
-                {
-                    break;
-                }
-                optimalPeriodSize /= 2;
-            }
-
-            if( optimalPeriodSize > periodSize )
-                periodSize = optimalPeriodSize;
-
-            if( periodSize <= maxPeriodSize )
-            {
-                /* Looks good, the periodSize _should_ be acceptable by both devices */
-                ENSURE_( alsa_snd_pcm_hw_params_set_period_size( self->capture.pcm, hwParamsCapture, periodSize, 0 ),
-                        paUnanticipatedHostError );
-                ENSURE_( alsa_snd_pcm_hw_params_set_period_size( self->playback.pcm, hwParamsPlayback, periodSize, 0 ),
-                        paUnanticipatedHostError );
-                self->capture.framesPerPeriod = self->playback.framesPerPeriod = periodSize;
-                framesPerHostBuffer = periodSize;
-            }
-            else
-            {
-                /* Unable to find a common period size, oh well */
-                optimalPeriodSize = PA_MAX( desiredBufSz / numPeriods, minPeriodSize );
-                optimalPeriodSize = PA_MIN( optimalPeriodSize, maxPeriodSize );
-
-                self->capture.framesPerPeriod = optimalPeriodSize;
-                dir = 0;
-                ENSURE_( alsa_snd_pcm_hw_params_set_period_size_near( self->capture.pcm, hwParamsCapture, &self->capture.framesPerPeriod, &dir ),
-                        paUnanticipatedHostError );
-                self->playback.framesPerPeriod = optimalPeriodSize;
-                dir = 0;
-                ENSURE_( alsa_snd_pcm_hw_params_set_period_size_near( self->playback.pcm, hwParamsPlayback, &self->playback.framesPerPeriod, &dir ),
-                        paUnanticipatedHostError );
-                framesPerHostBuffer = PA_MAX( self->capture.framesPerPeriod, self->playback.framesPerPeriod );
-                *hostBufferSizeMode = paUtilBoundedHostBufferSize;
-            }
-        }
-        else
-        {
-            /* We choose the simple route and determine a suitable number of frames per buffer for one component of
-             * the stream, then we hope that this will work for the other component too (it should!).
-             */
-
-            unsigned maxPeriods = 0;
-            PaAlsaStreamComponent* first = &self->capture, * second = &self->playback;
-            const PaStreamParameters* firstStreamParams = inputParameters;
-            snd_pcm_hw_params_t* firstHwParams = hwParamsCapture, * secondHwParams = hwParamsPlayback;
-
-            dir = 0;
-            ENSURE_( alsa_snd_pcm_hw_params_get_periods_max( hwParamsPlayback, &maxPeriods, &dir ), paUnanticipatedHostError );
-            if( maxPeriods < numPeriods )
-            {
-                /* The playback component is trickier to get right, try that first */
-                first = &self->playback;
-                second = &self->capture;
-                firstStreamParams = outputParameters;
-                firstHwParams = hwParamsPlayback;
-                secondHwParams = hwParamsCapture;
-            }
-
-            PA_ENSURE( PaAlsaStreamComponent_DetermineFramesPerBuffer( first, firstStreamParams, framesPerUserBuffer,
-                        sampleRate, firstHwParams, &accurate ) );
-
-            second->framesPerPeriod = first->framesPerPeriod;
-            dir = 0;
-            ENSURE_( alsa_snd_pcm_hw_params_set_period_size_near( second->pcm, secondHwParams, &second->framesPerPeriod, &dir ),
-                    paUnanticipatedHostError );
-            if( self->capture.framesPerPeriod == self->playback.framesPerPeriod )
-            {
-                framesPerHostBuffer = self->capture.framesPerPeriod;
-            }
-            else
-            {
-                framesPerHostBuffer = PA_MAX( self->capture.framesPerPeriod, self->playback.framesPerPeriod );
-                *hostBufferSizeMode = paUtilBoundedHostBufferSize;
-            }
-        }
-    }
-    else    /* half-duplex is a slightly simpler case */
-    {
-        if( self->capture.pcm )
-        {
-            PA_ENSURE( PaAlsaStreamComponent_DetermineFramesPerBuffer( &self->capture, inputParameters, framesPerUserBuffer,
-                        sampleRate, hwParamsCapture, &accurate) );
-            framesPerHostBuffer = self->capture.framesPerPeriod;
-        }
-        else
-        {
-            assert( self->playback.pcm );
-            PA_ENSURE( PaAlsaStreamComponent_DetermineFramesPerBuffer( &self->playback, outputParameters, framesPerUserBuffer,
-                        sampleRate, hwParamsPlayback, &accurate ) );
-            framesPerHostBuffer = self->playback.framesPerPeriod;
-        }
-    }
-
-    PA_UNLESS( framesPerHostBuffer != 0, paInternalError );
-    self->maxFramesPerHostBuffer = framesPerHostBuffer;
-
-    if( !self->playback.canMmap || !accurate )
-    {
-        /* Don't know the exact size per host buffer */
-        *hostBufferSizeMode = paUtilBoundedHostBufferSize;
-        /* Raise upper bound */
-        if( !accurate )
-            ++self->maxFramesPerHostBuffer;
-    }
-
-error:
-    return result;
-}
-
-/** Set up ALSA stream parameters.
- *
- */
-static PaError PaAlsaStream_Configure( PaAlsaStream *self, const PaStreamParameters *inParams, const PaStreamParameters*
-        outParams, double sampleRate, unsigned long framesPerUserBuffer, double* inputLatency, double* outputLatency,
-        PaUtilHostBufferSizeMode* hostBufferSizeMode )
-{
-    PaError result = paNoError;
-    double realSr = sampleRate;
-    snd_pcm_hw_params_t* hwParamsCapture, * hwParamsPlayback;
-
-    alsa_snd_pcm_hw_params_alloca( &hwParamsCapture );
-    alsa_snd_pcm_hw_params_alloca( &hwParamsPlayback );
-
-    if( self->capture.pcm )
-        PA_ENSURE( PaAlsaStreamComponent_InitialConfigure( &self->capture, inParams, self->primeBuffers, hwParamsCapture,
-                    &realSr ) );
-    if( self->playback.pcm )
-        PA_ENSURE( PaAlsaStreamComponent_InitialConfigure( &self->playback, outParams, self->primeBuffers, hwParamsPlayback,
-                    &realSr ) );
-
-    PA_ENSURE( PaAlsaStream_DetermineFramesPerBuffer( self, realSr, inParams, outParams, framesPerUserBuffer,
-                hwParamsCapture, hwParamsPlayback, hostBufferSizeMode ) );
-
-    if( self->capture.pcm )
-    {
-        assert( self->capture.framesPerPeriod != 0 );
-        PA_ENSURE( PaAlsaStreamComponent_FinishConfigure( &self->capture, hwParamsCapture, inParams, self->primeBuffers, realSr,
-                    inputLatency ) );
-        PA_DEBUG(( "%s: Capture period size: %lu, latency: %f\n", __FUNCTION__, self->capture.framesPerPeriod, *inputLatency ));
-    }
-    if( self->playback.pcm )
-    {
-        assert( self->playback.framesPerPeriod != 0 );
-        PA_ENSURE( PaAlsaStreamComponent_FinishConfigure( &self->playback, hwParamsPlayback, outParams, self->primeBuffers, realSr,
-                    outputLatency ) );
-        PA_DEBUG(( "%s: Playback period size: %lu, latency: %f\n", __FUNCTION__, self->playback.framesPerPeriod, *outputLatency ));
-    }
-
-    /* Should be exact now */
-    self->streamRepresentation.streamInfo.sampleRate = realSr;
-
-    /* this will cause the two streams to automatically start/stop/prepare in sync.
-     * We only need to execute these operations on one of the pair.
-     * A: We don't want to do this on a blocking stream.
-     */
-    if( self->callbackMode && self->capture.pcm && self->playback.pcm )
-    {
-        int err = alsa_snd_pcm_link( self->capture.pcm, self->playback.pcm );
-        if( err == 0 )
-            self->pcmsSynced = 1;
-        else
-            PA_DEBUG(( "%s: Unable to sync pcms: %s\n", __FUNCTION__, alsa_snd_strerror( err ) ));
-    }
-
-    {
-        unsigned long minFramesPerHostBuffer = PA_MIN( self->capture.pcm ? self->capture.framesPerPeriod : ULONG_MAX,
-            self->playback.pcm ? self->playback.framesPerPeriod : ULONG_MAX );
-        self->pollTimeout = CalculatePollTimeout( self, minFramesPerHostBuffer );    /* Period in msecs, rounded up */
-
-        /* Time before watchdog unthrottles realtime thread == 1/4 of period time in msecs */
-        /* self->threading.throttledSleepTime = (unsigned long) (minFramesPerHostBuffer / sampleRate / 4 * 1000); */
-    }
-
-    if( self->callbackMode )
-    {
-        /* If the user expects a certain number of frames per callback we will either have to rely on block adaption
-         * (framesPerHostBuffer is not an integer multiple of framesPerPeriod) or we can simply align the number
-         * of host buffer frames with what the user specified */
-        if( self->framesPerUserBuffer != paFramesPerBufferUnspecified )
-        {
-            /* self->alignFrames = 1; */
-
-            /* Unless the ratio between number of host and user buffer frames is an integer we will have to rely
-             * on block adaption */
-        /*
-            if( framesPerHostBuffer % framesPerPeriod != 0 || (self->capture.pcm && self->playback.pcm &&
-                        self->capture.framesPerPeriod != self->playback.framesPerPeriod) )
-                self->useBlockAdaption = 1;
-            else
-                self->alignFrames = 1;
-        */
-        }
-    }
-
-error:
-    return result;
-}
-
-static PaError OpenStream( struct PaUtilHostApiRepresentation *hostApi,
-                           PaStream** s,
-                           const PaStreamParameters *inputParameters,
-                           const PaStreamParameters *outputParameters,
-                           double sampleRate,
-                           unsigned long framesPerBuffer,
-                           PaStreamFlags streamFlags,
-                           PaStreamCallback* callback,
-                           void *userData )
-{
-    PaError result = paNoError;
-    PaAlsaHostApiRepresentation *alsaHostApi = (PaAlsaHostApiRepresentation*)hostApi;
-    PaAlsaStream *stream = NULL;
-    PaSampleFormat hostInputSampleFormat = 0, hostOutputSampleFormat = 0;
-    PaSampleFormat inputSampleFormat = 0, outputSampleFormat = 0;
-    int numInputChannels = 0, numOutputChannels = 0;
-    PaTime inputLatency, outputLatency;
-    /* Operate with fixed host buffer size by default, since other modes will invariably lead to block adaption */
-    /* XXX: Use Bounded by default? Output tends to get stuttery with Fixed ... */
-    PaUtilHostBufferSizeMode hostBufferSizeMode = paUtilFixedHostBufferSize;
-
-    if( ( streamFlags & paPlatformSpecificFlags ) != 0 )
-        return paInvalidFlag;
-
-    if( inputParameters )
-    {
-        PA_ENSURE( ValidateParameters( inputParameters, hostApi, StreamDirection_In ) );
-
-        numInputChannels = inputParameters->channelCount;
-        inputSampleFormat = inputParameters->sampleFormat;
-    }
-    if( outputParameters )
-    {
-        PA_ENSURE( ValidateParameters( outputParameters, hostApi, StreamDirection_Out ) );
-
-        numOutputChannels = outputParameters->channelCount;
-        outputSampleFormat = outputParameters->sampleFormat;
-    }
-
-    /* XXX: Why do we support this anyway? */
-    if( framesPerBuffer == paFramesPerBufferUnspecified && getenv( "PA_ALSA_PERIODSIZE" ) != NULL )
-    {
-        PA_DEBUG(( "%s: Getting framesPerBuffer (Alsa period-size) from environment\n", __FUNCTION__ ));
-        framesPerBuffer = atoi( getenv("PA_ALSA_PERIODSIZE") );
-    }
-
-    PA_UNLESS( stream = (PaAlsaStream*)PaUtil_AllocateMemory( sizeof(PaAlsaStream) ), paInsufficientMemory );
-    PA_ENSURE( PaAlsaStream_Initialize( stream, alsaHostApi, inputParameters, outputParameters, sampleRate,
-                framesPerBuffer, callback, streamFlags, userData ) );
-
-    PA_ENSURE( PaAlsaStream_Configure( stream, inputParameters, outputParameters, sampleRate, framesPerBuffer,
-                &inputLatency, &outputLatency, &hostBufferSizeMode ) );
-    hostInputSampleFormat = stream->capture.hostSampleFormat | (!stream->capture.hostInterleaved ? paNonInterleaved : 0);
-    hostOutputSampleFormat = stream->playback.hostSampleFormat | (!stream->playback.hostInterleaved ? paNonInterleaved : 0);
-
-    PA_ENSURE( PaUtil_InitializeBufferProcessor( &stream->bufferProcessor,
-                    numInputChannels, inputSampleFormat, hostInputSampleFormat,
-                    numOutputChannels, outputSampleFormat, hostOutputSampleFormat,
-                    sampleRate, streamFlags, framesPerBuffer, stream->maxFramesPerHostBuffer,
-                    hostBufferSizeMode, callback, userData ) );
-
-    /* Ok, buffer processor is initialized, now we can deduce it's latency */
-    if( numInputChannels > 0 )
-        stream->streamRepresentation.streamInfo.inputLatency = inputLatency + (PaTime)(
-                PaUtil_GetBufferProcessorInputLatencyFrames( &stream->bufferProcessor ) / sampleRate);
-    if( numOutputChannels > 0 )
-        stream->streamRepresentation.streamInfo.outputLatency = outputLatency + (PaTime)(
-                PaUtil_GetBufferProcessorOutputLatencyFrames( &stream->bufferProcessor ) / sampleRate);
-
-    PA_DEBUG(( "%s: Stream: framesPerBuffer = %lu, maxFramesPerHostBuffer = %lu, latency i=%f, o=%f\n", __FUNCTION__, framesPerBuffer, stream->maxFramesPerHostBuffer, stream->streamRepresentation.streamInfo.inputLatency, stream->streamRepresentation.streamInfo.outputLatency));
-
-    *s = (PaStream*)stream;
-
-    return result;
-
-error:
-    if( stream )
-    {
-        PA_DEBUG(( "%s: Stream in error, terminating\n", __FUNCTION__ ));
-        PaAlsaStream_Terminate( stream );
-    }
-
-    return result;
-}
-
-static PaError CloseStream( PaStream* s )
-{
-    PaError result = paNoError;
-    PaAlsaStream *stream = (PaAlsaStream*)s;
-
-    PaUtil_TerminateBufferProcessor( &stream->bufferProcessor );
-    PaUtil_TerminateStreamRepresentation( &stream->streamRepresentation );
-
-    PaAlsaStream_Terminate( stream );
-
-    return result;
-}
-
-static void SilenceBuffer( PaAlsaStream *stream )
-{
-    const snd_pcm_channel_area_t *areas;
-    snd_pcm_uframes_t frames = (snd_pcm_uframes_t)alsa_snd_pcm_avail_update( stream->playback.pcm ), offset;
-
-    alsa_snd_pcm_mmap_begin( stream->playback.pcm, &areas, &offset, &frames );
-    alsa_snd_pcm_areas_silence( areas, offset, stream->playback.numHostChannels, frames, stream->playback.nativeFormat );
-    alsa_snd_pcm_mmap_commit( stream->playback.pcm, offset, frames );
-}
-
-/** Start/prepare pcm(s) for streaming.
- *
- * Depending on whether the stream is in callback or blocking mode, we will respectively start or simply
- * prepare the playback pcm. If the buffer has _not_ been primed, we will in callback mode prepare and
- * silence the buffer before starting playback. In blocking mode we simply prepare, as the playback will
- * be started automatically as the user writes to output.
- *
- * The capture pcm, however, will simply be prepared and started.
- */
-static PaError AlsaStart( PaAlsaStream *stream, int priming )
-{
-    PaError result = paNoError;
-
-    if( stream->playback.pcm )
-    {
-        if( stream->callbackMode )
-        {
-            if( !priming )
-            {
-                /* Buffer isn't primed, so prepare and silence */
-                ENSURE_( alsa_snd_pcm_prepare( stream->playback.pcm ), paUnanticipatedHostError );
-                if( stream->playback.canMmap )
-                    SilenceBuffer( stream );
-            }
-            if( stream->playback.canMmap )
-                ENSURE_( alsa_snd_pcm_start( stream->playback.pcm ), paUnanticipatedHostError );
-        }
-        else
-            ENSURE_( alsa_snd_pcm_prepare( stream->playback.pcm ), paUnanticipatedHostError );
-    }
-    if( stream->capture.pcm && !stream->pcmsSynced )
-    {
-        ENSURE_( alsa_snd_pcm_prepare( stream->capture.pcm ), paUnanticipatedHostError );
-        /* For a blocking stream we want to start capture as well, since nothing will happen otherwise */
-        ENSURE_( alsa_snd_pcm_start( stream->capture.pcm ), paUnanticipatedHostError );
-    }
-
-end:
-    return result;
-error:
-    goto end;
-}
-
-/** Utility function for determining if pcms are in running state.
- *
- */
-#if 0
-static int IsRunning( PaAlsaStream *stream )
-{
-    int result = 0;
-
-    PA_ENSURE( PaUnixMutex_Lock( &stream->stateMtx ) );
-    if( stream->capture.pcm )
-    {
-        snd_pcm_state_t capture_state = alsa_snd_pcm_state( stream->capture.pcm );
-
-        if( capture_state == SND_PCM_STATE_RUNNING || capture_state == SND_PCM_STATE_XRUN
-                || capture_state == SND_PCM_STATE_DRAINING )
-        {
-            result = 1;
-            goto end;
-        }
-    }
-
-    if( stream->playback.pcm )
-    {
-        snd_pcm_state_t playback_state = alsa_snd_pcm_state( stream->playback.pcm );
-
-        if( playback_state == SND_PCM_STATE_RUNNING || playback_state == SND_PCM_STATE_XRUN
-                || playback_state == SND_PCM_STATE_DRAINING )
-        {
-            result = 1;
-            goto end;
-        }
-    }
-
-end:
-    ASSERT_CALL_( PaUnixMutex_Unlock( &stream->stateMtx ), paNoError );
-    return result;
-error:
-    goto error;
-}
-#endif
-
-static PaError StartStream( PaStream *s )
-{
-    PaError result = paNoError;
-    PaAlsaStream* stream = (PaAlsaStream*)s;
-    int streamStarted = 0;  /* So we can know whether we need to take the stream down */
-
-    /* Ready the processor */
-    PaUtil_ResetBufferProcessor( &stream->bufferProcessor );
-
-    /* Set now, so we can test for activity further down */
-    stream->isActive = 1;
-
-    if( stream->callbackMode )
-    {
-        PA_ENSURE( PaUnixThread_New( &stream->thread, &CallbackThreadFunc, stream, 1., stream->rtSched ) );
-    }
-    else
-    {
-        PA_ENSURE( AlsaStart( stream, 0 ) );
-        streamStarted = 1;
-    }
-
-end:
-    return result;
-error:
-    if( streamStarted )
-    {
-        AbortStream( stream );
-    }
-    stream->isActive = 0;
-
-    goto end;
-}
-
-/** Stop PCM handle, either softly or abruptly.
- */
-static PaError AlsaStop( PaAlsaStream *stream, int abort )
-{
-    PaError result = paNoError;
-    /* XXX: alsa_snd_pcm_drain tends to lock up, avoid it until we find out more */
-    abort = 1;
-    /*
-    if( stream->capture.pcm && !strcmp( Pa_GetDeviceInfo( stream->capture.device )->name,
-                "dmix" ) )
-    {
-        abort = 1;
-    }
-    else if( stream->playback.pcm && !strcmp( Pa_GetDeviceInfo( stream->playback.device )->name,
-                "dmix" ) )
-    {
-        abort = 1;
-    }
-    */
-
-    if( abort )
-    {
-        if( stream->playback.pcm )
-        {
-            ENSURE_( alsa_snd_pcm_drop( stream->playback.pcm ), paUnanticipatedHostError );
-        }
-        if( stream->capture.pcm && !stream->pcmsSynced )
-        {
-            ENSURE_( alsa_snd_pcm_drop( stream->capture.pcm ), paUnanticipatedHostError );
-        }
-
-        PA_DEBUG(( "%s: Dropped frames\n", __FUNCTION__ ));
-    }
-    else
-    {
-        if( stream->playback.pcm )
-        {
-            ENSURE_( alsa_snd_pcm_nonblock( stream->playback.pcm, 0 ), paUnanticipatedHostError );
-            if( alsa_snd_pcm_drain( stream->playback.pcm ) < 0 )
-            {
-                PA_DEBUG(( "%s: Draining playback handle failed!\n", __FUNCTION__ ));
-            }
-        }
-        if( stream->capture.pcm && !stream->pcmsSynced )
-        {
-            /* We don't need to retrieve any remaining frames */
-            if( alsa_snd_pcm_drain( stream->capture.pcm ) < 0 )
-            {
-                PA_DEBUG(( "%s: Draining capture handle failed!\n", __FUNCTION__ ));
-            }
-        }
-    }
-
-end:
-    return result;
-error:
-    goto end;
-}
-
-/** Stop or abort stream.
- *
- * If a stream is in callback mode we will have to inspect whether the background thread has
- * finished, or we will have to take it out. In either case we join the thread before
- * returning. In blocking mode, we simply tell ALSA to stop abruptly (abort) or finish
- * buffers (drain)
- *
- * Stream will be considered inactive (!PaAlsaStream::isActive) after a call to this function
- */
-static PaError RealStop( PaAlsaStream *stream, int abort )
-{
-    PaError result = paNoError;
-
-    /* First deal with the callback thread, cancelling and/or joining
-     * it if necessary
-     */
-    if( stream->callbackMode )
-    {
-        PaError threadRes;
-        stream->callbackAbort = abort;
-
-        if( !abort )
-        {
-            PA_DEBUG(( "Stopping callback\n" ));
-        }
-        PA_ENSURE( PaUnixThread_Terminate( &stream->thread, !abort, &threadRes ) );
-        if( threadRes != paNoError )
-        {
-            PA_DEBUG(( "Callback thread returned: %d\n", threadRes ));
-        }
-#if 0
-        if( watchdogRes != paNoError )
-            PA_DEBUG(( "Watchdog thread returned: %d\n", watchdogRes ));
-#endif
-
-        stream->callback_finished = 0;
-    }
-    else
-    {
-        PA_ENSURE( AlsaStop( stream, abort ) );
-    }
-
-    stream->isActive = 0;
-
-end:
-    return result;
-
-error:
-    goto end;
-}
-
-static PaError StopStream( PaStream *s )
-{
-    return RealStop( (PaAlsaStream *) s, 0 );
-}
-
-static PaError AbortStream( PaStream *s )
-{
-    return RealStop( (PaAlsaStream * ) s, 1 );
-}
-
-/** The stream is considered stopped before StartStream, or AFTER a call to Abort/StopStream (callback
- * returning !paContinue is not considered)
- *
- */
-static PaError IsStreamStopped( PaStream *s )
-{
-    PaAlsaStream *stream = (PaAlsaStream *)s;
-
-    /* callback_finished indicates we need to join callback thread (ie. in Abort/StopStream) */
-    return !IsStreamActive( s ) && !stream->callback_finished;
-}
-
-static PaError IsStreamActive( PaStream *s )
-{
-    PaAlsaStream *stream = (PaAlsaStream*)s;
-    return stream->isActive;
-}
-
-static PaTime GetStreamTime( PaStream *s )
-{
-    PaAlsaStream *stream = (PaAlsaStream*)s;
-
-    snd_timestamp_t timestamp;
-    snd_pcm_status_t* status;
-    alsa_snd_pcm_status_alloca( &status );
-
-    /* TODO: what if we have both?  does it really matter? */
-
-    /* TODO: if running in callback mode, this will mean
-     * libasound routines are being called from multiple threads.
-     * need to verify that libasound is thread-safe. */
-
-    if( stream->capture.pcm )
-    {
-        alsa_snd_pcm_status( stream->capture.pcm, status );
-    }
-    else if( stream->playback.pcm )
-    {
-        alsa_snd_pcm_status( stream->playback.pcm, status );
-    }
-
-    alsa_snd_pcm_status_get_tstamp( status, &timestamp );
-    return timestamp.tv_sec + (PaTime)timestamp.tv_usec / 1e6;
-}
-
-static double GetStreamCpuLoad( PaStream* s )
-{
-    PaAlsaStream *stream = (PaAlsaStream*)s;
-
-    return PaUtil_GetCpuLoad( &stream->cpuLoadMeasurer );
-}
-
-/* Set the stream sample rate to a nominal value requested; allow only a defined tolerance range */
-static int SetApproximateSampleRate( snd_pcm_t *pcm, snd_pcm_hw_params_t *hwParams, double sampleRate )
-{
-    PaError result = paNoError;
-    unsigned int reqRate, setRate, deviation;
-
-    assert( pcm && hwParams );
-
-    /* The Alsa sample rate is set by integer value; also the actual rate may differ */
-    reqRate = setRate = (unsigned int) sampleRate;
-
-    ENSURE_( alsa_snd_pcm_hw_params_set_rate_near( pcm, hwParams, &setRate, NULL ), paUnanticipatedHostError );
-    /* The value actually set will be put in 'setRate' (may be way off); check the deviation as a proportion
-     * of the requested-rate with reference to the max-deviate-ratio (larger values allow less deviation) */
-    deviation = abs( (int)setRate - (int)reqRate );
-    if( deviation > 0 && deviation * RATE_MAX_DEVIATE_RATIO > reqRate )
-        result = paInvalidSampleRate;
-
-end:
-    return result;
-
-error:
-    /* Log */
-    {
-        unsigned int _min = 0, _max = 0;
-        int _dir = 0;
-        ENSURE_( alsa_snd_pcm_hw_params_get_rate_min( hwParams, &_min, &_dir ), paUnanticipatedHostError );
-        _dir = 0;
-        ENSURE_( alsa_snd_pcm_hw_params_get_rate_max( hwParams, &_max, &_dir ), paUnanticipatedHostError );
-        PA_DEBUG(( "%s: SR min = %u, max = %u, req = %u\n", __FUNCTION__, _min, _max, reqRate ));
-    }
-    goto end;
-}
-
-/* Return exact sample rate in param sampleRate */
-static int GetExactSampleRate( snd_pcm_hw_params_t *hwParams, double *sampleRate )
-{
-    unsigned int num, den = 1;
-    int err;
-
-    assert( hwParams );
-
-    err = alsa_snd_pcm_hw_params_get_rate_numden( hwParams, &num, &den );
-    *sampleRate = (double) num / den;
-
-    return err;
-}
-
-/* Utility functions for blocking/callback interfaces */
-
-/* Atomic restart of stream (we don't want the intermediate state visible) */
-static PaError AlsaRestart( PaAlsaStream *stream )
-{
-    PaError result = paNoError;
-
-    PA_ENSURE( PaUnixMutex_Lock( &stream->stateMtx ) );
-    PA_ENSURE( AlsaStop( stream, 0 ) );
-    PA_ENSURE( AlsaStart( stream, 0 ) );
-
-    PA_DEBUG(( "%s: Restarted audio\n", __FUNCTION__ ));
-
-error:
-    PA_ENSURE( PaUnixMutex_Unlock( &stream->stateMtx ) );
-
-    return result;
-}
-
-/** Recover from xrun state.
- *
- */
-static PaError PaAlsaStream_HandleXrun( PaAlsaStream *self )
-{
-    PaError result = paNoError;
-    snd_pcm_status_t *st;
-    PaTime now = PaUtil_GetTime();
-    snd_timestamp_t t;
-    int restartAlsa = 0; /* do not restart Alsa by default */
-
-    alsa_snd_pcm_status_alloca( &st );
-
-    if( self->playback.pcm )
-    {
-        alsa_snd_pcm_status( self->playback.pcm, st );
-        if( alsa_snd_pcm_status_get_state( st ) == SND_PCM_STATE_XRUN )
-        {
-            alsa_snd_pcm_status_get_trigger_tstamp( st, &t );
-            self->underrun = now * 1000 - ( (PaTime)t.tv_sec * 1000 + (PaTime)t.tv_usec / 1000 );
-
-            if( !self->playback.canMmap )
-            {
-                if( alsa_snd_pcm_recover( self->playback.pcm, -EPIPE, 0 ) < 0 )
-                {
-                    PA_DEBUG(( "%s: [playback] non-MMAP-PCM failed recovering from XRUN, will restart Alsa\n", __FUNCTION__ ));
-                    ++ restartAlsa; /* did not manage to recover */
-                }
-            }
-            else
-                ++ restartAlsa; /* always restart MMAPed device */
-        }
-    }
-    if( self->capture.pcm )
-    {
-        alsa_snd_pcm_status( self->capture.pcm, st );
-        if( alsa_snd_pcm_status_get_state( st ) == SND_PCM_STATE_XRUN )
-        {
-            alsa_snd_pcm_status_get_trigger_tstamp( st, &t );
-            self->overrun = now * 1000 - ((PaTime) t.tv_sec * 1000 + (PaTime) t.tv_usec / 1000);
-
-            if (!self->capture.canMmap)
-            {
-                if (alsa_snd_pcm_recover( self->capture.pcm, -EPIPE, 0 ) < 0)
-                {
-                    PA_DEBUG(( "%s: [capture] non-MMAP-PCM failed recovering from XRUN, will restart Alsa\n", __FUNCTION__ ));
-                    ++ restartAlsa; /* did not manage to recover */
-                }
-            }
-            else
-                ++ restartAlsa; /* always restart MMAPed device */
-        }
-    }
-
-    if( restartAlsa )
-    {
-        PA_DEBUG(( "%s: restarting Alsa to recover from XRUN\n", __FUNCTION__ ));
-        PA_ENSURE( AlsaRestart( self ) );
-    }
-
-end:
-    return result;
-error:
-    goto end;
-}
-
-/** Decide if we should continue polling for specified direction, eventually adjust the poll timeout.
- *
- */
-static PaError ContinuePoll( const PaAlsaStream *stream, StreamDirection streamDir, int *pollTimeout, int *continuePoll )
-{
-    PaError result = paNoError;
-    snd_pcm_sframes_t delay, margin;
-    int err;
-    const PaAlsaStreamComponent *component = NULL, *otherComponent = NULL;
-
-    *continuePoll = 1;
-
-    if( StreamDirection_In == streamDir )
-    {
-        component = &stream->capture;
-        otherComponent = &stream->playback;
-    }
-    else
-    {
-        component = &stream->playback;
-        otherComponent = &stream->capture;
-    }
-
-    /* ALSA docs say that negative delay should indicate xrun, but in my experience alsa_snd_pcm_delay returns -EPIPE */
-    if( ( err = alsa_snd_pcm_delay( otherComponent->pcm, &delay ) ) < 0 )
-    {
-        if( err == -EPIPE )
-        {
-            /* Xrun */
-            *continuePoll = 0;
-            goto error;
-        }
-
-        ENSURE_( err, paUnanticipatedHostError );
-    }
-
-    if( StreamDirection_Out == streamDir )
-    {
-        /* Number of eligible frames before capture overrun */
-        delay = otherComponent->alsaBufferSize - delay;
-    }
-    margin = delay - otherComponent->framesPerPeriod / 2;
-
-    if( margin < 0 )
-    {
-        PA_DEBUG(( "%s: Stopping poll for %s\n", __FUNCTION__, StreamDirection_In == streamDir ? "capture" : "playback" ));
-        *continuePoll = 0;
-    }
-    else if( margin < otherComponent->framesPerPeriod )
-    {
-        *pollTimeout = CalculatePollTimeout( stream, margin );
-        PA_DEBUG(( "%s: Trying to poll again for %s frames, pollTimeout: %d\n",
-                    __FUNCTION__, StreamDirection_In == streamDir ? "capture" : "playback", *pollTimeout ));
-    }
-
-error:
-    return result;
-}
-
-/* Callback interface */
-
-static void OnExit( void *data )
-{
-    PaAlsaStream *stream = (PaAlsaStream *) data;
-
-    assert( data );
-
-    PaUtil_ResetCpuLoadMeasurer( &stream->cpuLoadMeasurer );
-
-    stream->callback_finished = 1;  /* Let the outside world know stream was stopped in callback */
-    PA_DEBUG(( "%s: Stopping ALSA handles\n", __FUNCTION__ ));
-    AlsaStop( stream, stream->callbackAbort );
-
-    PA_DEBUG(( "%s: Stoppage\n", __FUNCTION__ ));
-
-    /* Eventually notify user all buffers have played */
-    if( stream->streamRepresentation.streamFinishedCallback )
-    {
-        stream->streamRepresentation.streamFinishedCallback( stream->streamRepresentation.userData );
-    }
-    stream->isActive = 0;
-}
-
-static void CalculateTimeInfo( PaAlsaStream *stream, PaStreamCallbackTimeInfo *timeInfo )
-{
-    snd_pcm_status_t *capture_status, *playback_status;
-    snd_timestamp_t capture_timestamp, playback_timestamp;
-    PaTime capture_time = 0., playback_time = 0.;
-
-    alsa_snd_pcm_status_alloca( &capture_status );
-    alsa_snd_pcm_status_alloca( &playback_status );
-
-    if( stream->capture.pcm )
-    {
-        snd_pcm_sframes_t capture_delay;
-
-        alsa_snd_pcm_status( stream->capture.pcm, capture_status );
-        alsa_snd_pcm_status_get_tstamp( capture_status, &capture_timestamp );
-
-        capture_time = capture_timestamp.tv_sec +
-            ( (PaTime)capture_timestamp.tv_usec / 1000000.0 );
-        timeInfo->currentTime = capture_time;
-
-        capture_delay = alsa_snd_pcm_status_get_delay( capture_status );
-        timeInfo->inputBufferAdcTime = timeInfo->currentTime -
-            (PaTime)capture_delay / stream->streamRepresentation.streamInfo.sampleRate;
-    }
-    if( stream->playback.pcm )
-    {
-        snd_pcm_sframes_t playback_delay;
-
-        alsa_snd_pcm_status( stream->playback.pcm, playback_status );
-        alsa_snd_pcm_status_get_tstamp( playback_status, &playback_timestamp );
-
-        playback_time = playback_timestamp.tv_sec +
-            ((PaTime)playback_timestamp.tv_usec / 1000000.0);
-
-        if( stream->capture.pcm ) /* Full duplex */
-        {
-            /* Hmm, we have both a playback and a capture timestamp.
-             * Hopefully they are the same... */
-            if( fabs( capture_time - playback_time ) > 0.01 )
-                PA_DEBUG(( "Capture time and playback time differ by %f\n", fabs( capture_time-playback_time ) ));
-        }
-        else
-            timeInfo->currentTime = playback_time;
-
-        playback_delay = alsa_snd_pcm_status_get_delay( playback_status );
-        timeInfo->outputBufferDacTime = timeInfo->currentTime +
-            (PaTime)playback_delay / stream->streamRepresentation.streamInfo.sampleRate;
-    }
-}
-
-/** Called after buffer processing is finished.
- *
- * A number of mmapped frames is committed, it is possible that an xrun has occurred in the meantime.
- *
- * @param numFrames The number of frames that has been processed
- * @param xrun Return whether an xrun has occurred
- */
-static PaError PaAlsaStreamComponent_EndProcessing( PaAlsaStreamComponent *self, unsigned long numFrames, int *xrun )
-{
-    PaError result = paNoError;
-    int res = 0;
-
-    /* @concern FullDuplex It is possible that only one direction is marked ready after polling, and processed
-     * afterwards
-     */
-    if( !self->ready )
-        goto end;
-
-    if( !self->canMmap && StreamDirection_Out == self->streamDir )
-    {
-        /* Play sound */
-        if( self->hostInterleaved )
-            res = alsa_snd_pcm_writei( self->pcm, self->nonMmapBuffer, numFrames );
-        else
-        {
-            void *bufs[self->numHostChannels];
-            int bufsize = alsa_snd_pcm_format_size( self->nativeFormat, self->framesPerPeriod + 1 );
-            unsigned char *buffer = self->nonMmapBuffer;
-            int i;
-            for( i = 0; i < self->numHostChannels; ++i )
-            {
-                bufs[i] = buffer;
-                buffer += bufsize;
-            }
-            res = alsa_snd_pcm_writen( self->pcm, bufs, numFrames );
-        }
-    }
-
-    if( self->canMmap )
-        res = alsa_snd_pcm_mmap_commit( self->pcm, self->offset, numFrames );
-
-    if( res == -EPIPE || res == -ESTRPIPE )
-    {
-        *xrun = 1;
-    }
-    else
-    {
-        ENSURE_( res, paUnanticipatedHostError );
-    }
-
-end:
-error:
-    return result;
-}
-
-/* Extract buffer from channel area */
-static unsigned char *ExtractAddress( const snd_pcm_channel_area_t *area, snd_pcm_uframes_t offset )
-{
-    return (unsigned char *) area->addr + ( area->first + offset * area->step ) / 8;
-}
-
-/** Do necessary adaption between user and host channels.
- *
-    @concern ChannelAdaption Adapting between user and host channels can involve silencing unused channels and
-    duplicating mono information if host outputs come in pairs.
- */
-static PaError PaAlsaStreamComponent_DoChannelAdaption( PaAlsaStreamComponent *self, PaUtilBufferProcessor *bp, int numFrames )
-{
-    PaError result = paNoError;
-    unsigned char *p;
-    int i;
-    int unusedChans = self->numHostChannels - self->numUserChannels;
-    unsigned char *src, *dst;
-    int convertMono = ( self->numHostChannels % 2 ) == 0 && ( self->numUserChannels % 2 ) != 0;
-
-    assert( StreamDirection_Out == self->streamDir );
-
-    if( self->hostInterleaved )
-    {
-        int swidth = alsa_snd_pcm_format_size( self->nativeFormat, 1 );
-        unsigned char *buffer = self->canMmap ? ExtractAddress( self->channelAreas, self->offset ) : self->nonMmapBuffer;
-
-        /* Start after the last user channel */
-        p = buffer + self->numUserChannels * swidth;
-
-        if( convertMono )
-        {
-            /* Convert the last user channel into stereo pair */
-            src = buffer + ( self->numUserChannels - 1 ) * swidth;
-            for( i = 0; i < numFrames; ++i )
-            {
-                dst = src + swidth;
-                memcpy( dst, src, swidth );
-                src += self->numHostChannels * swidth;
-            }
-
-            /* Don't touch the channel we just wrote to */
-            p += swidth;
-            --unusedChans;
-        }
-
-        if( unusedChans > 0 )
-        {
-            /* Silence unused output channels */
-            for( i = 0; i < numFrames; ++i )
-            {
-                memset( p, 0, swidth * unusedChans );
-                p += self->numHostChannels * swidth;
-            }
-        }
-    }
-    else
-    {
-        /* We extract the last user channel */
-        if( convertMono )
-        {
-            ENSURE_( alsa_snd_pcm_area_copy( self->channelAreas + self->numUserChannels, self->offset, self->channelAreas +
-                    ( self->numUserChannels - 1 ), self->offset, numFrames, self->nativeFormat ), paUnanticipatedHostError );
-            --unusedChans;
-        }
-        if( unusedChans > 0 )
-        {
-            alsa_snd_pcm_areas_silence( self->channelAreas + ( self->numHostChannels - unusedChans ), self->offset, unusedChans, numFrames,
-                    self->nativeFormat );
-        }
-    }
-
-error:
-    return result;
-}
-
-static PaError PaAlsaStream_EndProcessing( PaAlsaStream *self, unsigned long numFrames, int *xrunOccurred )
-{
-    PaError result = paNoError;
-    int xrun = 0;
-
-    if( self->capture.pcm )
-    {
-        PA_ENSURE( PaAlsaStreamComponent_EndProcessing( &self->capture, numFrames, &xrun ) );
-    }
-    if( self->playback.pcm )
-    {
-        if( self->playback.numHostChannels > self->playback.numUserChannels )
-        {
-            PA_ENSURE( PaAlsaStreamComponent_DoChannelAdaption( &self->playback, &self->bufferProcessor, numFrames ) );
-        }
-        PA_ENSURE( PaAlsaStreamComponent_EndProcessing( &self->playback, numFrames, &xrun ) );
-    }
-
-error:
-    *xrunOccurred = xrun;
-    return result;
-}
-
-/** Update the number of available frames.
- *
- */
-static PaError PaAlsaStreamComponent_GetAvailableFrames( PaAlsaStreamComponent *self, unsigned long *numFrames, int *xrunOccurred )
-{
-    PaError result = paNoError;
-    snd_pcm_sframes_t framesAvail = alsa_snd_pcm_avail_update( self->pcm );
-    *xrunOccurred = 0;
-
-    if( -EPIPE == framesAvail )
-    {
-        *xrunOccurred = 1;
-        framesAvail = 0;
-    }
-    else
-    {
-        ENSURE_( framesAvail, paUnanticipatedHostError );
-    }
-
-    *numFrames = framesAvail;
-
-error:
-    return result;
-}
-
-/** Fill in pollfd objects.
- */
-static PaError PaAlsaStreamComponent_BeginPolling( PaAlsaStreamComponent* self, struct pollfd* pfds )
-{
-    int nfds = alsa_snd_pcm_poll_descriptors( self->pcm, pfds, self->nfds );
-    /* If alsa returns anything else, like -EPIPE return */
-    if( nfds != self->nfds )
-    {
-        return paUnanticipatedHostError;
-    }
-    self->ready = 0;
-
-    return paNoError;
-}
-
-/** Examine results from poll().
- *
- * @param pfds pollfds to inspect
- * @param shouldPoll Should we continue to poll
- * @param xrun Has an xrun occurred
- */
-static PaError PaAlsaStreamComponent_EndPolling( PaAlsaStreamComponent* self, struct pollfd* pfds, int* shouldPoll, int* xrun )
-{
-    PaError result = paNoError;
-    unsigned short revents;
-
-    ENSURE_( alsa_snd_pcm_poll_descriptors_revents( self->pcm, pfds, self->nfds, &revents ), paUnanticipatedHostError );
-    if( revents != 0 )
-    {
-        if( revents & POLLERR )
-        {
-            *xrun = 1;
-        }
-        else if( revents & POLLHUP )
-        {
-            *xrun = 1;
-            PA_DEBUG(( "%s: revents has POLLHUP, processing as XRUN\n", __FUNCTION__ ));
-        }
-        else
-            self->ready = 1;
-
-        *shouldPoll = 0;
-    }
-    else /* (A zero revent occurred) */
-        /* Work around an issue with Alsa older than 1.0.16 using some plugins (eg default with plug + dmix) where
-         * POLLIN or POLLOUT are zeroed by Alsa-lib if _mmap_avail() is a few frames short of avail_min at period
-         * boundary, possibly due to erratic dma interrupts at period boundary?  Treat as a valid event.
-         */
-        if( self->useReventFix )
-        {
-            self->ready = 1;
-            *shouldPoll = 0;
-        }
-
-error:
-    return result;
-}
-
-/** Return the number of available frames for this stream.
- *
- * @concern FullDuplex The minimum available for the two directions is calculated, it might be desirable to ignore
- * one direction however (not marked ready from poll), so this is controlled by queryCapture and queryPlayback.
- *
- * @param queryCapture Check available for capture
- * @param queryPlayback Check available for playback
- * @param available The returned number of frames
- * @param xrunOccurred Return whether an xrun has occurred
- */
-static PaError PaAlsaStream_GetAvailableFrames( PaAlsaStream *self, int queryCapture, int queryPlayback, unsigned long
-        *available, int *xrunOccurred )
-{
-    PaError result = paNoError;
-    unsigned long captureFrames, playbackFrames;
-    *xrunOccurred = 0;
-
-    assert( queryCapture || queryPlayback );
-
-    if( queryCapture )
-    {
-        assert( self->capture.pcm );
-        PA_ENSURE( PaAlsaStreamComponent_GetAvailableFrames( &self->capture, &captureFrames, xrunOccurred ) );
-        if( *xrunOccurred )
-        {
-            goto end;
-        }
-    }
-    if( queryPlayback )
-    {
-        assert( self->playback.pcm );
-        PA_ENSURE( PaAlsaStreamComponent_GetAvailableFrames( &self->playback, &playbackFrames, xrunOccurred ) );
-        if( *xrunOccurred )
-        {
-            goto end;
-        }
-    }
-
-    if( queryCapture && queryPlayback )
-    {
-        *available = PA_MIN( captureFrames, playbackFrames );
-        /*PA_DEBUG(("capture: %lu, playback: %lu, combined: %lu\n", captureFrames, playbackFrames, *available));*/
-    }
-    else if( queryCapture )
-    {
-        *available = captureFrames;
-    }
-    else
-    {
-        *available = playbackFrames;
-    }
-
-end:
-error:
-    return result;
-}
-
-/** Wait for and report available buffer space from ALSA.
- *
- * Unless ALSA reports a minimum of frames available for I/O, we poll the ALSA filedescriptors for more.
- * Both of these operations can uncover xrun conditions.
- *
- * @concern Xruns Both polling and querying available frames can report an xrun condition.
- *
- * @param framesAvail Return the number of available frames
- * @param xrunOccurred Return whether an xrun has occurred
- */
-static PaError PaAlsaStream_WaitForFrames( PaAlsaStream *self, unsigned long *framesAvail, int *xrunOccurred )
-{
-    PaError result = paNoError;
-    int pollPlayback = self->playback.pcm != NULL, pollCapture = self->capture.pcm != NULL;
-    int pollTimeout = self->pollTimeout;
-    int xrun = 0, timeouts = 0;
-    int pollResults;
-
-    assert( self );
-    assert( framesAvail );
-
-    if( !self->callbackMode )
-    {
-        /* In blocking mode we will only wait if necessary */
-        PA_ENSURE( PaAlsaStream_GetAvailableFrames( self, self->capture.pcm != NULL, self->playback.pcm != NULL,
-                    framesAvail, &xrun ) );
-        if( xrun )
-        {
-            goto end;
-        }
-
-        if( *framesAvail > 0 )
-        {
-            /* Mark pcms ready from poll */
-            if( self->capture.pcm )
-                self->capture.ready = 1;
-            if( self->playback.pcm )
-                self->playback.ready = 1;
-
-            goto end;
-        }
-    }
-
-    while( pollPlayback || pollCapture )
-    {
-        int totalFds = 0;
-        struct pollfd *capturePfds = NULL, *playbackPfds = NULL;
-
-#ifdef PTHREAD_CANCELED
-        pthread_testcancel();
-#endif
-        if( pollCapture )
-        {
-            capturePfds = self->pfds;
-            PaError res = PaAlsaStreamComponent_BeginPolling( &self->capture, capturePfds );
-            if( res != paNoError)
-            {
-                xrun = 1;
-                goto end;
-            }
-            totalFds += self->capture.nfds;
-        }
-        if( pollPlayback )
-        {
-            /* self->pfds is in effect an array of fds; if necessary, index past the capture fds */
-            playbackPfds = self->pfds + (pollCapture ? self->capture.nfds : 0);
-            PaError res = PaAlsaStreamComponent_BeginPolling( &self->playback, playbackPfds );
-            if( res != paNoError)
-            {
-                xrun = 1;
-                goto end;
-            }
-            totalFds += self->playback.nfds;
-        }
-
-#ifdef PTHREAD_CANCELED
-        if( self->callbackMode )
-        {
-            /* To allow 'Abort' to terminate the callback thread, enable cancelability just for poll() (& disable after) */
-            pthread_setcancelstate( PTHREAD_CANCEL_ENABLE, NULL );
-        }
-#endif
-
-        pollResults = poll( self->pfds, totalFds, pollTimeout );
-
-#ifdef PTHREAD_CANCELED
-        if( self->callbackMode )
-        {
-            pthread_setcancelstate( PTHREAD_CANCEL_DISABLE, NULL );
-        }
-#endif
-
-        if( pollResults < 0 )
-        {
-            /*  XXX: Depend on preprocessor condition? */
-            if( errno == EINTR )
-            {
-                /* gdb */
-                Pa_Sleep( 1 ); /* avoid hot loop */
-                continue;
-            }
-
-            /* TODO: Add macro for checking system calls */
-            PA_ENSURE( paInternalError );
-        }
-        else if( pollResults == 0 )
-        {
-           /* Suspended, paused or failed device can provide 0 poll results. To avoid deadloop in such situation
-            * we simply run counter 'timeouts' which detects 0 poll result and accumulates. As soon as 2048 timouts (around 2 seconds)
-            * are achieved we simply fail function with paTimedOut to notify waiting methods that device is not capable
-            * of providing audio data anymore and needs some corresponding recovery action.
-            * Note that 'timeouts' is reset to 0 if poll() managed to return non 0 results.
-            */
-
-            /*PA_DEBUG(( "%s: poll == 0 results, timed out, %d times left\n", __FUNCTION__, 2048 - timeouts ));*/
-            ++ timeouts;
-            if( timeouts > 1 ) /* sometimes device times out, but normally once, so we do not sleep any time */
-            {
-                Pa_Sleep( 1 ); /* avoid hot loop */
-            }
-            /* not else ! */
-            if( timeouts >= 2048 ) /* audio device not working, shall return error to notify waiters */
-            {
-                *framesAvail = 0; /* no frames available for processing */
-                xrun = 1; /* try recovering device */
-
-                PA_DEBUG(( "%s: poll timed out\n", __FUNCTION__, timeouts ));
-                goto end;/*PA_ENSURE( paTimedOut );*/
-            }
-        }
-        else if( pollResults > 0 )
-        {
-            /* reset timouts counter */
-            timeouts = 0;
-
-            /* check the return status of our pfds */
-            if( pollCapture )
-            {
-                PA_ENSURE( PaAlsaStreamComponent_EndPolling( &self->capture, capturePfds, &pollCapture, &xrun ) );
-            }
-            if( pollPlayback )
-            {
-                PA_ENSURE( PaAlsaStreamComponent_EndPolling( &self->playback, playbackPfds, &pollPlayback, &xrun ) );
-            }
-            if( xrun )
-            {
-                break;
-            }
-        }
-
-        /* @concern FullDuplex If only one of two pcms is ready we may want to compromise between the two.
-         * If there is less than half a period's worth of samples left of frames in the other pcm's buffer we will
-         * stop polling.
-         */
-        if( self->capture.pcm && self->playback.pcm )
-        {
-            if( pollCapture && !pollPlayback )
-            {
-                PA_ENSURE( ContinuePoll( self, StreamDirection_In, &pollTimeout, &pollCapture ) );
-            }
-            else if( pollPlayback && !pollCapture )
-            {
-                PA_ENSURE( ContinuePoll( self, StreamDirection_Out, &pollTimeout, &pollPlayback ) );
-            }
-        }
-    }
-
-    if( !xrun )
-    {
-        /* Get the number of available frames for the pcms that are marked ready.
-         * @concern FullDuplex If only one direction is marked ready (from poll), the number of frames available for
-         * the other direction is returned. Output is normally preferred over capture however, so capture frames may be
-         * discarded to avoid overrun unless paNeverDropInput is specified.
-         */
-        int captureReady = self->capture.pcm ? self->capture.ready : 0,
-            playbackReady = self->playback.pcm ? self->playback.ready : 0;
-        PA_ENSURE( PaAlsaStream_GetAvailableFrames( self, captureReady, playbackReady, framesAvail, &xrun ) );
-
-        if( self->capture.pcm && self->playback.pcm )
-        {
-            if( !self->playback.ready && !self->neverDropInput )
-            {
-                /* Drop input, a period's worth */
-                assert( self->capture.ready );
-                PaAlsaStreamComponent_EndProcessing( &self->capture, PA_MIN( self->capture.framesPerPeriod,
-                            *framesAvail ), &xrun );
-                *framesAvail = 0;
-                self->capture.ready = 0;
-            }
-        }
-        else if( self->capture.pcm )
-            assert( self->capture.ready );
-        else
-            assert( self->playback.ready );
-    }
-
-end:
-error:
-    if( xrun )
-    {
-        /* Recover from the xrun state */
-        PA_ENSURE( PaAlsaStream_HandleXrun( self ) );
-        *framesAvail = 0;
-    }
-    else
-    {
-        if( 0 != *framesAvail )
-        {
-            /* If we're reporting frames eligible for processing, one of the handles better be ready */
-            PA_UNLESS( self->capture.ready || self->playback.ready, paInternalError );
-        }
-    }
-    *xrunOccurred = xrun;
-
-    return result;
-}
-
-/** Register per-channel ALSA buffer information with buffer processor.
- *
- * Mmapped buffer space is acquired from ALSA, and registered with the buffer processor. Differences between the
- * number of host and user channels is taken into account.
- *
- * @param numFrames On entrance the number of requested frames, on exit the number of contiguously accessible frames.
- */
-static PaError PaAlsaStreamComponent_RegisterChannels( PaAlsaStreamComponent* self, PaUtilBufferProcessor* bp,
-        unsigned long* numFrames, int* xrun )
-{
-    PaError result = paNoError;
-    const snd_pcm_channel_area_t *areas, *area;
-    void (*setChannel)(PaUtilBufferProcessor *, unsigned int, void *, unsigned int) =
-        StreamDirection_In == self->streamDir ? PaUtil_SetInputChannel : PaUtil_SetOutputChannel;
-    unsigned char *buffer, *p;
-    int i;
-    unsigned long framesAvail;
-
-    /* This _must_ be called before mmap_begin */
-    PA_ENSURE( PaAlsaStreamComponent_GetAvailableFrames( self, &framesAvail, xrun ) );
-    if( *xrun )
-    {
-        *numFrames = 0;
-        goto end;
-    }
-
-    if( self->canMmap )
-    {
-        ENSURE_( alsa_snd_pcm_mmap_begin( self->pcm, &areas, &self->offset, numFrames ), paUnanticipatedHostError );
-        /* @concern ChannelAdaption Buffer address is recorded so we can do some channel adaption later */
-        self->channelAreas = (snd_pcm_channel_area_t *)areas;
-    }
-    else
-    {
-        unsigned int bufferSize = self->numHostChannels * alsa_snd_pcm_format_size( self->nativeFormat, *numFrames );
-        if( bufferSize > self->nonMmapBufferSize )
-        {
-            self->nonMmapBuffer = realloc( self->nonMmapBuffer, ( self->nonMmapBufferSize = bufferSize ) );
-            if( !self->nonMmapBuffer )
-            {
-                result = paInsufficientMemory;
-                goto error;
-            }
-        }
-    }
-
-    if( self->hostInterleaved )
-    {
-        int swidth = alsa_snd_pcm_format_size( self->nativeFormat, 1 );
-
-        p = buffer = self->canMmap ? ExtractAddress( areas, self->offset ) : self->nonMmapBuffer;
-        for( i = 0; i < self->numUserChannels; ++i )
-        {
-            /* We're setting the channels up to userChannels, but the stride will be hostChannels samples */
-            setChannel( bp, i, p, self->numHostChannels );
-            p += swidth;
-        }
-    }
-    else
-    {
-        if( self->canMmap )
-        {
-            for( i = 0; i < self->numUserChannels; ++i )
-            {
-                area = areas + i;
-                buffer = ExtractAddress( area, self->offset );
-                setChannel( bp, i, buffer, 1 );
-            }
-        }
-        else
-        {
-            unsigned int buf_per_ch_size = self->nonMmapBufferSize / self->numHostChannels;
-            buffer = self->nonMmapBuffer;
-            for( i = 0; i < self->numUserChannels; ++i )
-            {
-                setChannel( bp, i, buffer, 1 );
-                buffer += buf_per_ch_size;
-            }
-        }
-    }
-
-    if( !self->canMmap && StreamDirection_In == self->streamDir )
-    {
-        /* Read sound */
-        int res;
-        if( self->hostInterleaved )
-            res = alsa_snd_pcm_readi( self->pcm, self->nonMmapBuffer, *numFrames );
-        else
-        {
-            void *bufs[self->numHostChannels];
-            unsigned int buf_per_ch_size = self->nonMmapBufferSize / self->numHostChannels;
-            unsigned char *buffer = self->nonMmapBuffer;
-            int i;
-            for( i = 0; i < self->numHostChannels; ++i )
-            {
-                bufs[i] = buffer;
-                buffer += buf_per_ch_size;
-            }
-            res = alsa_snd_pcm_readn( self->pcm, bufs, *numFrames );
-        }
-        if( res == -EPIPE || res == -ESTRPIPE )
-        {
-            *xrun = 1;
-            *numFrames = 0;
-        }
-    }
-
-end:
-error:
-    return result;
-}
-
-/** Initiate buffer processing.
- *
- * ALSA buffers are registered with the PA buffer processor and the buffer size (in frames) set.
- *
- * @concern FullDuplex If both directions are being processed, the minimum amount of frames for the two directions is
- * calculated.
- *
- * @param numFrames On entrance the number of available frames, on exit the number of received frames
- * @param xrunOccurred Return whether an xrun has occurred
- */
-static PaError PaAlsaStream_SetUpBuffers( PaAlsaStream* self, unsigned long* numFrames, int* xrunOccurred )
-{
-    PaError result = paNoError;
-    unsigned long captureFrames = ULONG_MAX, playbackFrames = ULONG_MAX, commonFrames = 0;
-    int xrun = 0;
-
-    if( *xrunOccurred )
-    {
-        *numFrames = 0;
-        return result;
-    }
-    /* If we got here at least one of the pcm's should be marked ready */
-    PA_UNLESS( self->capture.ready || self->playback.ready, paInternalError );
-
-    /* Extract per-channel ALSA buffer pointers and register them with the buffer processor.
-     * It is possible that a direction is not marked ready however, because it is out of sync with the other.
-     */
-    if( self->capture.pcm && self->capture.ready )
-    {
-        captureFrames = *numFrames;
-        PA_ENSURE( PaAlsaStreamComponent_RegisterChannels( &self->capture, &self->bufferProcessor, &captureFrames,
-                    &xrun ) );
-    }
-    if( self->playback.pcm && self->playback.ready )
-    {
-        playbackFrames = *numFrames;
-        PA_ENSURE( PaAlsaStreamComponent_RegisterChannels( &self->playback, &self->bufferProcessor, &playbackFrames,
-                    &xrun ) );
-    }
-    if( xrun )
-    {
-        /* Nothing more to do */
-        assert( 0 == commonFrames );
-        goto end;
-    }
-
-    commonFrames = PA_MIN( captureFrames, playbackFrames );
-    /* assert( commonFrames <= *numFrames ); */
-    if( commonFrames > *numFrames )
-    {
-        /* Hmmm ... how come there are more frames available than we requested!? Blah. */
-        PA_DEBUG(( "%s: Common available frames are reported to be more than number requested: %lu, %lu, callbackMode: %d\n", __FUNCTION__,
-                    commonFrames, *numFrames, self->callbackMode ));
-        if( self->capture.pcm )
-        {
-            PA_DEBUG(( "%s: captureFrames: %lu, capture.ready: %d\n", __FUNCTION__, captureFrames, self->capture.ready ));
-        }
-        if( self->playback.pcm )
-        {
-            PA_DEBUG(( "%s: playbackFrames: %lu, playback.ready: %d\n", __FUNCTION__, playbackFrames, self->playback.ready ));
-        }
-
-        commonFrames = 0;
-        goto end;
-    }
-
-    /* Inform PortAudio of the number of frames we got.
-     * @concern FullDuplex We might be experiencing underflow in either end; if its an input underflow, we go on
-     * with output. If its output underflow however, depending on the paNeverDropInput flag, we may want to simply
-     * discard the excess input or call the callback with paOutputOverflow flagged.
-     */
-    if( self->capture.pcm )
-    {
-        if( self->capture.ready )
-        {
-            PaUtil_SetInputFrameCount( &self->bufferProcessor, commonFrames );
-        }
-        else
-        {
-            /* We have input underflow */
-            PaUtil_SetNoInput( &self->bufferProcessor );
-        }
-    }
-    if( self->playback.pcm )
-    {
-        if( self->playback.ready )
-        {
-            PaUtil_SetOutputFrameCount( &self->bufferProcessor, commonFrames );
-        }
-        else
-        {
-            /* We have output underflow, but keeping input data (paNeverDropInput) */
-            assert( self->neverDropInput );
-            assert( self->capture.pcm != NULL );
-            PA_DEBUG(( "%s: Setting output buffers to NULL\n", __FUNCTION__ ));
-            PaUtil_SetNoOutput( &self->bufferProcessor );
-        }
-    }
-
-end:
-    *numFrames = commonFrames;
-error:
-    if( xrun )
-    {
-        PA_ENSURE( PaAlsaStream_HandleXrun( self ) );
-        *numFrames = 0;
-    }
-    *xrunOccurred = xrun;
-
-    return result;
-}
-
-/** Callback thread's function.
- *
- * Roughly, the workflow can be described in the following way: The number of available frames that can be processed
- * directly is obtained from ALSA, we then request as much directly accessible memory as possible within this amount
- * from ALSA. The buffer memory is registered with the PA buffer processor and processing is carried out with
- * PaUtil_EndBufferProcessing. Finally, the number of processed frames is reported to ALSA. The processing can
- * happen in several iterations until we have consumed the known number of available frames (or an xrun is detected).
- */
-static void *CallbackThreadFunc( void *userData )
-{
-    PaError result = paNoError;
-    PaAlsaStream *stream = (PaAlsaStream*) userData;
-    PaStreamCallbackTimeInfo timeInfo = {0, 0, 0};
-    snd_pcm_sframes_t startThreshold = 0;
-    int callbackResult = paContinue;
-    PaStreamCallbackFlags cbFlags = 0;  /* We might want to keep state across iterations */
-    int streamStarted = 0;
-
-    assert( stream );
-    /* Not implemented */
-    assert( !stream->primeBuffers );
-
-    /* Execute OnExit when exiting */
-    pthread_cleanup_push( &OnExit, stream );
-#ifdef PTHREAD_CANCELED
-    /* 'Abort' will use thread cancellation to terminate the callback thread, but the Alsa-lib functions
-     * are NOT cancel-safe, (and can end up in an inconsistent state).  So, disable cancelability for
-     * the thread here, and just re-enable it for the poll() in PaAlsaStream_WaitForFrames(). */
-    pthread_testcancel();
-    pthread_setcancelstate( PTHREAD_CANCEL_DISABLE, NULL );
-#endif
-
-    /* @concern StreamStart If the output is being primed the output pcm needs to be prepared, otherwise the
-     * stream is started immediately. The latter involves signaling the waiting main thread.
-     */
-    if( stream->primeBuffers )
-    {
-        snd_pcm_sframes_t avail;
-
-        if( stream->playback.pcm )
-            ENSURE_( alsa_snd_pcm_prepare( stream->playback.pcm ), paUnanticipatedHostError );
-        if( stream->capture.pcm && !stream->pcmsSynced )
-            ENSURE_( alsa_snd_pcm_prepare( stream->capture.pcm ), paUnanticipatedHostError );
-
-        /* We can't be certain that the whole ring buffer is available for priming, but there should be
-         * at least one period */
-        avail = alsa_snd_pcm_avail_update( stream->playback.pcm );
-        startThreshold = avail - (avail % stream->playback.framesPerPeriod);
-        assert( startThreshold >= stream->playback.framesPerPeriod );
-    }
-    else
-    {
-        PA_ENSURE( PaUnixThread_PrepareNotify( &stream->thread ) );
-        /* Buffer will be zeroed */
-        PA_ENSURE( AlsaStart( stream, 0 ) );
-        PA_ENSURE( PaUnixThread_NotifyParent( &stream->thread ) );
-
-        streamStarted = 1;
-    }
-
-    while( 1 )
-    {
-        unsigned long framesAvail, framesGot;
-        int xrun = 0;
-
-#ifdef PTHREAD_CANCELED
-        pthread_testcancel();
-#endif
-
-        /* @concern StreamStop if the main thread has requested a stop and the stream has not been effectively
-         * stopped we signal this condition by modifying callbackResult (we'll want to flush buffered output).
-         */
-        if( PaUnixThread_StopRequested( &stream->thread ) && paContinue == callbackResult )
-        {
-            PA_DEBUG(( "Setting callbackResult to paComplete\n" ));
-            callbackResult = paComplete;
-        }
-
-        if( paContinue != callbackResult )
-        {
-            stream->callbackAbort = ( paAbort == callbackResult );
-            if( stream->callbackAbort ||
-                    /** @concern BlockAdaption: Go on if adaption buffers are empty */
-                    PaUtil_IsBufferProcessorOutputEmpty( &stream->bufferProcessor ) )
-            {
-                goto end;
-            }
-
-            PA_DEBUG(( "%s: Flushing buffer processor\n", __FUNCTION__ ));
-            /* There is still buffered output that needs to be processed */
-        }
-
-        /* Wait for data to become available, this comes down to polling the ALSA file descriptors until we have
-         * a number of available frames.
-         */
-        PA_ENSURE( PaAlsaStream_WaitForFrames( stream, &framesAvail, &xrun ) );
-        if( xrun )
-        {
-            assert( 0 == framesAvail );
-            continue;
-
-            /* XXX: Report xruns to the user? A situation is conceivable where the callback is never invoked due
-             * to constant xruns, it might be desirable to notify the user of this.
-             */
-        }
-
-        /* Consume buffer space. Once we have a number of frames available for consumption we must retrieve the
-         * mmapped buffers from ALSA, this is contiguously accessible memory however, so we may receive smaller
-         * portions at a time than is available as a whole. Therefore we should be prepared to process several
-         * chunks successively. The buffers are passed to the PA buffer processor.
-         */
-        while( framesAvail > 0 )
-        {
-            xrun = 0;
-
-            /** @concern Xruns Under/overflows are to be reported to the callback */
-            if( stream->underrun > 0.0 )
-            {
-                cbFlags |= paOutputUnderflow;
-                stream->underrun = 0.0;
-            }
-            if( stream->overrun > 0.0 )
-            {
-                cbFlags |= paInputOverflow;
-                stream->overrun = 0.0;
-            }
-            if( stream->capture.pcm && stream->playback.pcm )
-            {
-                /** @concern FullDuplex It's possible that only one direction is being processed to avoid an
-                 * under- or overflow, this should be reported correspondingly */
-                if( !stream->capture.ready )
-                {
-                    cbFlags |= paInputUnderflow;
-                    PA_DEBUG(( "%s: Input underflow\n", __FUNCTION__ ));
-                }
-                else if( !stream->playback.ready )
-                {
-                    cbFlags |= paOutputOverflow;
-                    PA_DEBUG(( "%s: Output overflow\n", __FUNCTION__ ));
-                }
-            }
-
-#if 0
-            CallbackUpdate( &stream->threading );
-#endif
-
-            CalculateTimeInfo( stream, &timeInfo );
-            PaUtil_BeginBufferProcessing( &stream->bufferProcessor, &timeInfo, cbFlags );
-            cbFlags = 0;
-
-            /* CPU load measurement should include processing activity external to the stream callback */
-            PaUtil_BeginCpuLoadMeasurement( &stream->cpuLoadMeasurer );
-
-            framesGot = framesAvail;
-            if( paUtilFixedHostBufferSize == stream->bufferProcessor.hostBufferSizeMode )
-            {
-                /* We've committed to a fixed host buffer size, stick to that */
-                framesGot = framesGot >= stream->maxFramesPerHostBuffer ? stream->maxFramesPerHostBuffer : 0;
-            }
-            else
-            {
-                /* We've committed to an upper bound on the size of host buffers */
-                assert( paUtilBoundedHostBufferSize == stream->bufferProcessor.hostBufferSizeMode );
-                framesGot = PA_MIN( framesGot, stream->maxFramesPerHostBuffer );
-            }
-            PA_ENSURE( PaAlsaStream_SetUpBuffers( stream, &framesGot, &xrun ) );
-            /* Check the host buffer size against the buffer processor configuration */
-            framesAvail -= framesGot;
-
-            if( framesGot > 0 )
-            {
-                assert( !xrun );
-                PaUtil_EndBufferProcessing( &stream->bufferProcessor, &callbackResult );
-                PA_ENSURE( PaAlsaStream_EndProcessing( stream, framesGot, &xrun ) );
-            }
-            PaUtil_EndCpuLoadMeasurement( &stream->cpuLoadMeasurer, framesGot );
-
-            if( 0 == framesGot )
-            {
-                /* Go back to polling for more frames */
-                break;
-            }
-
-            if( paContinue != callbackResult )
-                break;
-        }
-    }
-
-end:
-    ; /* Hack to fix "label at end of compound statement" error caused by pthread_cleanup_pop(1) macro. */
-    /* Match pthread_cleanup_push */
-    pthread_cleanup_pop( 1 );
-
-    PA_DEBUG(( "%s: Thread %d exiting\n ", __FUNCTION__, pthread_self() ));
-    PaUnixThreading_EXIT( result );
-
-error:
-    PA_DEBUG(( "%s: Thread %d is canceled due to error %d\n ", __FUNCTION__, pthread_self(), result ));
-    goto end;
-}
-
-/* Blocking interface */
-
-static PaError ReadStream( PaStream* s, void *buffer, unsigned long frames )
-{
-    PaError result = paNoError;
-    PaAlsaStream *stream = (PaAlsaStream*)s;
-    unsigned long framesGot, framesAvail;
-    void *userBuffer;
-    snd_pcm_t *save = stream->playback.pcm;
-
-    assert( stream );
-
-    PA_UNLESS( stream->capture.pcm, paCanNotReadFromAnOutputOnlyStream );
-
-    /* Disregard playback */
-    stream->playback.pcm = NULL;
-
-    if( stream->overrun > 0. )
-    {
-        result = paInputOverflowed;
-        stream->overrun = 0.0;
-    }
-
-    if( stream->capture.userInterleaved )
-    {
-        userBuffer = buffer;
-    }
-    else
-    {
-        /* Copy channels into local array */
-        userBuffer = stream->capture.userBuffers;
-        memcpy( userBuffer, buffer, sizeof (void *) * stream->capture.numUserChannels );
-    }
-
-    /* Start stream if in prepared state */
-    if( alsa_snd_pcm_state( stream->capture.pcm ) == SND_PCM_STATE_PREPARED )
-    {
-        ENSURE_( alsa_snd_pcm_start( stream->capture.pcm ), paUnanticipatedHostError );
-    }
-
-    while( frames > 0 )
-    {
-        int xrun = 0;
-        PA_ENSURE( PaAlsaStream_WaitForFrames( stream, &framesAvail, &xrun ) );
-        framesGot = PA_MIN( framesAvail, frames );
-
-        PA_ENSURE( PaAlsaStream_SetUpBuffers( stream, &framesGot, &xrun ) );
-        if( framesGot > 0 )
-        {
-            framesGot = PaUtil_CopyInput( &stream->bufferProcessor, &userBuffer, framesGot );
-            PA_ENSURE( PaAlsaStream_EndProcessing( stream, framesGot, &xrun ) );
-            frames -= framesGot;
-        }
-    }
-
-end:
-    stream->playback.pcm = save;
-    return result;
-error:
-    goto end;
-}
-
-static PaError WriteStream( PaStream* s, const void *buffer, unsigned long frames )
-{
-    PaError result = paNoError;
-    signed long err;
-    PaAlsaStream *stream = (PaAlsaStream*)s;
-    snd_pcm_uframes_t framesGot, framesAvail;
-    const void *userBuffer;
-    snd_pcm_t *save = stream->capture.pcm;
-
-    assert( stream );
-
-    PA_UNLESS( stream->playback.pcm, paCanNotWriteToAnInputOnlyStream );
-
-    /* Disregard capture */
-    stream->capture.pcm = NULL;
-
-    if( stream->underrun > 0. )
-    {
-        result = paOutputUnderflowed;
-        stream->underrun = 0.0;
-    }
-
-    if( stream->playback.userInterleaved )
-        userBuffer = buffer;
-    else /* Copy channels into local array */
-    {
-        userBuffer = stream->playback.userBuffers;
-        memcpy( (void *)userBuffer, buffer, sizeof (void *) * stream->playback.numUserChannels );
-    }
-
-    while( frames > 0 )
-    {
-        int xrun = 0;
-        snd_pcm_uframes_t hwAvail;
-
-        PA_ENSURE( PaAlsaStream_WaitForFrames( stream, &framesAvail, &xrun ) );
-        framesGot = PA_MIN( framesAvail, frames );
-
-        PA_ENSURE( PaAlsaStream_SetUpBuffers( stream, &framesGot, &xrun ) );
-        if( framesGot > 0 )
-        {
-            framesGot = PaUtil_CopyOutput( &stream->bufferProcessor, &userBuffer, framesGot );
-            PA_ENSURE( PaAlsaStream_EndProcessing( stream, framesGot, &xrun ) );
-            frames -= framesGot;
-        }
-
-        /* Start stream after one period of samples worth */
-
-        /* Frames residing in buffer */
-        PA_ENSURE( err = GetStreamWriteAvailable( stream ) );
-        framesAvail = err;
-        hwAvail = stream->playback.alsaBufferSize - framesAvail;
-
-        if( alsa_snd_pcm_state( stream->playback.pcm ) == SND_PCM_STATE_PREPARED &&
-                hwAvail >= stream->playback.framesPerPeriod )
-        {
-            ENSURE_( alsa_snd_pcm_start( stream->playback.pcm ), paUnanticipatedHostError );
-        }
-    }
-
-end:
-    stream->capture.pcm = save;
-    return result;
-error:
-    goto end;
-}
-
-/* Return frames available for reading. In the event of an overflow, the capture pcm will be restarted */
-static signed long GetStreamReadAvailable( PaStream* s )
-{
-    PaError result = paNoError;
-    PaAlsaStream *stream = (PaAlsaStream*)s;
-    unsigned long avail;
-    int xrun;
-
-    PA_ENSURE( PaAlsaStreamComponent_GetAvailableFrames( &stream->capture, &avail, &xrun ) );
-    if( xrun )
-    {
-        PA_ENSURE( PaAlsaStream_HandleXrun( stream ) );
-        PA_ENSURE( PaAlsaStreamComponent_GetAvailableFrames( &stream->capture, &avail, &xrun ) );
-        if( xrun )
-            PA_ENSURE( paInputOverflowed );
-    }
-
-    return (signed long)avail;
-
-error:
-    return result;
-}
-
-static signed long GetStreamWriteAvailable( PaStream* s )
-{
-    PaError result = paNoError;
-    PaAlsaStream *stream = (PaAlsaStream*)s;
-    unsigned long avail;
-    int xrun;
-
-    PA_ENSURE( PaAlsaStreamComponent_GetAvailableFrames( &stream->playback, &avail, &xrun ) );
-    if( xrun )
-    {
-        snd_pcm_sframes_t savail;
-
-        PA_ENSURE( PaAlsaStream_HandleXrun( stream ) );
-        savail = alsa_snd_pcm_avail_update( stream->playback.pcm );
-
-        /* savail should not contain -EPIPE now, since PaAlsaStream_HandleXrun will only prepare the pcm */
-        ENSURE_( savail, paUnanticipatedHostError );
-
-        avail = (unsigned long) savail;
-    }
-
-    return (signed long)avail;
-
-error:
-    return result;
-}
-
-/* Extensions */
-
-void PaAlsa_InitializeStreamInfo( PaAlsaStreamInfo *info )
-{
-    info->size = sizeof (PaAlsaStreamInfo);
-    info->hostApiType = paALSA;
-    info->version = 1;
-    info->deviceString = NULL;
-}
-
-void PaAlsa_EnableRealtimeScheduling( PaStream *s, int enable )
-{
-    PaAlsaStream *stream = (PaAlsaStream *) s;
-    stream->rtSched = enable;
-}
-
-#if 0
-void PaAlsa_EnableWatchdog( PaStream *s, int enable )
-{
-    PaAlsaStream *stream = (PaAlsaStream *) s;
-    stream->thread.useWatchdog = enable;
-}
-#endif
-
-static PaError GetAlsaStreamPointer( PaStream* s, PaAlsaStream** stream )
-{
-    PaError result = paNoError;
-    PaUtilHostApiRepresentation* hostApi;
-    PaAlsaHostApiRepresentation* alsaHostApi;
-
-    PA_ENSURE( PaUtil_ValidateStreamPointer( s ) );
-    PA_ENSURE( PaUtil_GetHostApiRepresentation( &hostApi, paALSA ) );
-    alsaHostApi = (PaAlsaHostApiRepresentation*)hostApi;
-
-    PA_UNLESS( PA_STREAM_REP( s )->streamInterface == &alsaHostApi->callbackStreamInterface
-            || PA_STREAM_REP( s )->streamInterface == &alsaHostApi->blockingStreamInterface,
-        paIncompatibleStreamHostApi );
-
-    *stream = (PaAlsaStream*)s;
-error:
-    return paNoError;
-}
-
-PaError PaAlsa_GetStreamInputCard( PaStream* s, int* card )
-{
-    PaAlsaStream *stream;
-    PaError result = paNoError;
-    snd_pcm_info_t* pcmInfo;
-
-    PA_ENSURE( GetAlsaStreamPointer( s, &stream ) );
-
-    /* XXX: More descriptive error? */
-    PA_UNLESS( stream->capture.pcm, paDeviceUnavailable );
-
-    alsa_snd_pcm_info_alloca( &pcmInfo );
-    PA_ENSURE( alsa_snd_pcm_info( stream->capture.pcm, pcmInfo ) );
-    *card = alsa_snd_pcm_info_get_card( pcmInfo );
-
-error:
-    return result;
-}
-
-PaError PaAlsa_GetStreamOutputCard( PaStream* s, int* card )
-{
-    PaAlsaStream *stream;
-    PaError result = paNoError;
-    snd_pcm_info_t* pcmInfo;
-
-    PA_ENSURE( GetAlsaStreamPointer( s, &stream ) );
-
-    /* XXX: More descriptive error? */
-    PA_UNLESS( stream->playback.pcm, paDeviceUnavailable );
-
-    alsa_snd_pcm_info_alloca( &pcmInfo );
-    PA_ENSURE( alsa_snd_pcm_info( stream->playback.pcm, pcmInfo ) );
-    *card = alsa_snd_pcm_info_get_card( pcmInfo );
-
-error:
-    return result;
-}
-
-PaError PaAlsa_SetRetriesBusy( int retries )
-{
-    busyRetries_ = retries;
-    return paNoError;
-}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/r-3ca97919.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/r-3ca97919.js
deleted file mode 100644
index e460c951763f569906751f34aed4265f5d719d36..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/r-3ca97919.js
+++ /dev/null
@@ -1,2 +0,0 @@
-function f(e){for(var n={},r=0;r<e.length;++r)n[e[r]]=!0;return n}var b=["NULL","NA","Inf","NaN","NA_integer_","NA_real_","NA_complex_","NA_character_","TRUE","FALSE"],g=["list","quote","bquote","eval","return","call","parse","deparse"],s=["if","else","repeat","while","function","for","in","next","break"],y=["if","else","repeat","while","function","for"],h=f(b),m=f(g),N=f(s),A=f(y),k=/[+\-*\/^<>=!&|~$:]/,t;function p(e,n){t=null;var r=e.next();if(r=="#")return e.skipToEnd(),"comment";if(r=="0"&&e.eat("x"))return e.eatWhile(/[\da-f]/i),"number";if(r=="."&&e.eat(/\d/))return e.match(/\d*(?:e[+\-]?\d+)?/),"number";if(/\d/.test(r))return e.match(/\d*(?:\.\d+)?(?:e[+\-]\d+)?L?/),"number";if(r=="'"||r=='"')return n.tokenize=E(r),"string";if(r=="`")return e.match(/[^`]+`/),"string.special";if(r=="."&&e.match(/.(?:[.]|\d+)/))return"keyword";if(/[a-zA-Z\.]/.test(r)){e.eatWhile(/[\w\.]/);var i=e.current();return h.propertyIsEnumerable(i)?"atom":N.propertyIsEnumerable(i)?(A.propertyIsEnumerable(i)&&!e.match(/\s*if(\s+|$)/,!1)&&(t="block"),"keyword"):m.propertyIsEnumerable(i)?"builtin":"variable"}else return r=="%"?(e.skipTo("%")&&e.next(),"variableName.special"):r=="<"&&e.eat("-")||r=="<"&&e.match("<-")||r=="-"&&e.match(/>>?/)||r=="="&&n.ctx.argList?"operator":k.test(r)?(r=="$"||e.eatWhile(k),"operator"):/[\(\){}\[\];]/.test(r)?(t=r,r==";"?"punctuation":null):null}function E(e){return function(n,r){if(n.eat("\\")){var i=n.next();return i=="x"?n.match(/^[a-f0-9]{2}/i):(i=="u"||i=="U")&&n.eat("{")&&n.skipTo("}")?n.next():i=="u"?n.match(/^[a-f0-9]{4}/i):i=="U"?n.match(/^[a-f0-9]{8}/i):/[0-7]/.test(i)&&n.match(/^[0-7]{1,2}/),"string.special"}else{for(var l;(l=n.next())!=null;){if(l==e){r.tokenize=p;break}if(l=="\\"){n.backUp(1);break}}return"string"}}}var v=1,u=2,c=4;function o(e,n,r){e.ctx={type:n,indent:e.indent,flags:0,column:r.column(),prev:e.ctx}}function x(e,n){var r=e.ctx;e.ctx={type:r.type,indent:r.indent,flags:r.flags|n,column:r.column,prev:r.prev}}function a(e){e.indent=e.ctx.indent,e.ctx=e.ctx.prev}const I={name:"r",startState:function(e){return{tokenize:p,ctx:{type:"top",indent:-e,flags:u},indent:0,afterIdent:!1}},token:function(e,n){if(e.sol()&&(n.ctx.flags&3||(n.ctx.flags|=u),n.ctx.flags&c&&a(n),n.indent=e.indentation()),e.eatSpace())return null;var r=n.tokenize(e,n);return r!="comment"&&!(n.ctx.flags&u)&&x(n,v),(t==";"||t=="{"||t=="}")&&n.ctx.type=="block"&&a(n),t=="{"?o(n,"}",e):t=="("?(o(n,")",e),n.afterIdent&&(n.ctx.argList=!0)):t=="["?o(n,"]",e):t=="block"?o(n,"block",e):t==n.ctx.type?a(n):n.ctx.type=="block"&&r!="comment"&&x(n,c),n.afterIdent=r=="variable"||r=="keyword",r},indent:function(e,n,r){if(e.tokenize!=p)return 0;var i=n&&n.charAt(0),l=e.ctx,d=i==l.type;return l.flags&c&&(l=l.prev),l.type=="block"?l.indent+(i=="{"?0:r.unit):l.flags&v?l.column+(d?0:1):l.indent+(d?0:r.unit)},languageData:{wordChars:".",commentTokens:{line:"#"},autocomplete:b.concat(g,s)}};export{I as r};
-//# sourceMappingURL=r-3ca97919.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/include/numpy/npy_3kcompat.h b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/include/numpy/npy_3kcompat.h
deleted file mode 100644
index 62fde943aacc4ad117712182846a94faf69a767b..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/include/numpy/npy_3kcompat.h
+++ /dev/null
@@ -1,595 +0,0 @@
-/*
- * This is a convenience header file providing compatibility utilities
- * for supporting different minor versions of Python 3.
- * It was originally used to support the transition from Python 2,
- * hence the "3k" naming.
- *
- * If you want to use this for your own projects, it's recommended to make a
- * copy of it. Although the stuff below is unlikely to change, we don't provide
- * strong backwards compatibility guarantees at the moment.
- */
-
-#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_
-#define NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_
-
-#include <Python.h>
-#include <stdio.h>
-
-#ifndef NPY_PY3K
-#define NPY_PY3K 1
-#endif
-
-#include "numpy/npy_common.h"
-#include "numpy/ndarrayobject.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/*
- * PyInt -> PyLong
- */
-
-
-/*
- * This is a renamed copy of the Python non-limited API function _PyLong_AsInt. It is
- * included here because it is missing from the PyPy API. It completes the PyLong_As*
- * group of functions and can be useful in replacing PyInt_Check.
- */
-static inline int
-Npy__PyLong_AsInt(PyObject *obj)
-{
-    int overflow;
-    long result = PyLong_AsLongAndOverflow(obj, &overflow);
-
-    /* INT_MAX and INT_MIN are defined in Python.h */
-    if (overflow || result > INT_MAX || result < INT_MIN) {
-        /* XXX: could be cute and give a different
-           message for overflow == -1 */
-        PyErr_SetString(PyExc_OverflowError,
-                        "Python int too large to convert to C int");
-        return -1;
-    }
-    return (int)result;
-}
-
-
-#if defined(NPY_PY3K)
-/* Return True only if the long fits in a C long */
-static inline int PyInt_Check(PyObject *op) {
-    int overflow = 0;
-    if (!PyLong_Check(op)) {
-        return 0;
-    }
-    PyLong_AsLongAndOverflow(op, &overflow);
-    return (overflow == 0);
-}
-
-
-#define PyInt_FromLong PyLong_FromLong
-#define PyInt_AsLong PyLong_AsLong
-#define PyInt_AS_LONG PyLong_AsLong
-#define PyInt_AsSsize_t PyLong_AsSsize_t
-#define PyNumber_Int PyNumber_Long
-
-/* NOTE:
- *
- * Since the PyLong type is very different from the fixed-range PyInt,
- * we don't define PyInt_Type -> PyLong_Type.
- */
-#endif /* NPY_PY3K */
-
-/* Py3 changes PySlice_GetIndicesEx' first argument's type to PyObject* */
-#ifdef NPY_PY3K
-#  define NpySlice_GetIndicesEx PySlice_GetIndicesEx
-#else
-#  define NpySlice_GetIndicesEx(op, nop, start, end, step, slicelength) \
-    PySlice_GetIndicesEx((PySliceObject *)op, nop, start, end, step, slicelength)
-#endif
-
-#if PY_VERSION_HEX < 0x030900a4
-    /* Introduced in https://github.com/python/cpython/commit/d2ec81a8c99796b51fb8c49b77a7fe369863226f */
-    #define Py_SET_TYPE(obj, type) ((Py_TYPE(obj) = (type)), (void)0)
-    /* Introduced in https://github.com/python/cpython/commit/b10dc3e7a11fcdb97e285882eba6da92594f90f9 */
-    #define Py_SET_SIZE(obj, size) ((Py_SIZE(obj) = (size)), (void)0)
-    /* Introduced in https://github.com/python/cpython/commit/c86a11221df7e37da389f9c6ce6e47ea22dc44ff */
-    #define Py_SET_REFCNT(obj, refcnt) ((Py_REFCNT(obj) = (refcnt)), (void)0)
-#endif
-
-
-#define Npy_EnterRecursiveCall(x) Py_EnterRecursiveCall(x)
-
-/*
- * PyString -> PyBytes
- */
-
-#if defined(NPY_PY3K)
-
-#define PyString_Type PyBytes_Type
-#define PyString_Check PyBytes_Check
-#define PyStringObject PyBytesObject
-#define PyString_FromString PyBytes_FromString
-#define PyString_FromStringAndSize PyBytes_FromStringAndSize
-#define PyString_AS_STRING PyBytes_AS_STRING
-#define PyString_AsStringAndSize PyBytes_AsStringAndSize
-#define PyString_FromFormat PyBytes_FromFormat
-#define PyString_Concat PyBytes_Concat
-#define PyString_ConcatAndDel PyBytes_ConcatAndDel
-#define PyString_AsString PyBytes_AsString
-#define PyString_GET_SIZE PyBytes_GET_SIZE
-#define PyString_Size PyBytes_Size
-
-#define PyUString_Type PyUnicode_Type
-#define PyUString_Check PyUnicode_Check
-#define PyUStringObject PyUnicodeObject
-#define PyUString_FromString PyUnicode_FromString
-#define PyUString_FromStringAndSize PyUnicode_FromStringAndSize
-#define PyUString_FromFormat PyUnicode_FromFormat
-#define PyUString_Concat PyUnicode_Concat2
-#define PyUString_ConcatAndDel PyUnicode_ConcatAndDel
-#define PyUString_GET_SIZE PyUnicode_GET_SIZE
-#define PyUString_Size PyUnicode_Size
-#define PyUString_InternFromString PyUnicode_InternFromString
-#define PyUString_Format PyUnicode_Format
-
-#define PyBaseString_Check(obj) (PyUnicode_Check(obj))
-
-#else
-
-#define PyBytes_Type PyString_Type
-#define PyBytes_Check PyString_Check
-#define PyBytesObject PyStringObject
-#define PyBytes_FromString PyString_FromString
-#define PyBytes_FromStringAndSize PyString_FromStringAndSize
-#define PyBytes_AS_STRING PyString_AS_STRING
-#define PyBytes_AsStringAndSize PyString_AsStringAndSize
-#define PyBytes_FromFormat PyString_FromFormat
-#define PyBytes_Concat PyString_Concat
-#define PyBytes_ConcatAndDel PyString_ConcatAndDel
-#define PyBytes_AsString PyString_AsString
-#define PyBytes_GET_SIZE PyString_GET_SIZE
-#define PyBytes_Size PyString_Size
-
-#define PyUString_Type PyString_Type
-#define PyUString_Check PyString_Check
-#define PyUStringObject PyStringObject
-#define PyUString_FromString PyString_FromString
-#define PyUString_FromStringAndSize PyString_FromStringAndSize
-#define PyUString_FromFormat PyString_FromFormat
-#define PyUString_Concat PyString_Concat
-#define PyUString_ConcatAndDel PyString_ConcatAndDel
-#define PyUString_GET_SIZE PyString_GET_SIZE
-#define PyUString_Size PyString_Size
-#define PyUString_InternFromString PyString_InternFromString
-#define PyUString_Format PyString_Format
-
-#define PyBaseString_Check(obj) (PyBytes_Check(obj) || PyUnicode_Check(obj))
-
-#endif /* NPY_PY3K */
-
-/*
- * Macros to protect CRT calls against instant termination when passed an
- * invalid parameter (https://bugs.python.org/issue23524).
- */
-#if defined _MSC_VER && _MSC_VER >= 1900
-
-#include <stdlib.h>
-
-extern _invalid_parameter_handler _Py_silent_invalid_parameter_handler;
-#define NPY_BEGIN_SUPPRESS_IPH { _invalid_parameter_handler _Py_old_handler = \
-    _set_thread_local_invalid_parameter_handler(_Py_silent_invalid_parameter_handler);
-#define NPY_END_SUPPRESS_IPH _set_thread_local_invalid_parameter_handler(_Py_old_handler); }
-
-#else
-
-#define NPY_BEGIN_SUPPRESS_IPH
-#define NPY_END_SUPPRESS_IPH
-
-#endif /* _MSC_VER >= 1900 */
-
-
-static inline void
-PyUnicode_ConcatAndDel(PyObject **left, PyObject *right)
-{
-    Py_SETREF(*left, PyUnicode_Concat(*left, right));
-    Py_DECREF(right);
-}
-
-static inline void
-PyUnicode_Concat2(PyObject **left, PyObject *right)
-{
-    Py_SETREF(*left, PyUnicode_Concat(*left, right));
-}
-
-/*
- * PyFile_* compatibility
- */
-
-/*
- * Get a FILE* handle to the file represented by the Python object
- */
-static inline FILE*
-npy_PyFile_Dup2(PyObject *file, char *mode, npy_off_t *orig_pos)
-{
-    int fd, fd2, unbuf;
-    Py_ssize_t fd2_tmp;
-    PyObject *ret, *os, *io, *io_raw;
-    npy_off_t pos;
-    FILE *handle;
-
-    /* For Python 2 PyFileObject, use PyFile_AsFile */
-#if !defined(NPY_PY3K)
-    if (PyFile_Check(file)) {
-        return PyFile_AsFile(file);
-    }
-#endif
-
-    /* Flush first to ensure things end up in the file in the correct order */
-    ret = PyObject_CallMethod(file, "flush", "");
-    if (ret == NULL) {
-        return NULL;
-    }
-    Py_DECREF(ret);
-    fd = PyObject_AsFileDescriptor(file);
-    if (fd == -1) {
-        return NULL;
-    }
-
-    /*
-     * The handle needs to be dup'd because we have to call fclose
-     * at the end
-     */
-    os = PyImport_ImportModule("os");
-    if (os == NULL) {
-        return NULL;
-    }
-    ret = PyObject_CallMethod(os, "dup", "i", fd);
-    Py_DECREF(os);
-    if (ret == NULL) {
-        return NULL;
-    }
-    fd2_tmp = PyNumber_AsSsize_t(ret, PyExc_IOError);
-    Py_DECREF(ret);
-    if (fd2_tmp == -1 && PyErr_Occurred()) {
-        return NULL;
-    }
-    if (fd2_tmp < INT_MIN || fd2_tmp > INT_MAX) {
-        PyErr_SetString(PyExc_IOError,
-                        "Getting an 'int' from os.dup() failed");
-        return NULL;
-    }
-    fd2 = (int)fd2_tmp;
-
-    /* Convert to FILE* handle */
-#ifdef _WIN32
-    NPY_BEGIN_SUPPRESS_IPH
-    handle = _fdopen(fd2, mode);
-    NPY_END_SUPPRESS_IPH
-#else
-    handle = fdopen(fd2, mode);
-#endif
-    if (handle == NULL) {
-        PyErr_SetString(PyExc_IOError,
-                        "Getting a FILE* from a Python file object via "
-                        "_fdopen failed. If you built NumPy, you probably "
-                        "linked with the wrong debug/release runtime");
-        return NULL;
-    }
-
-    /* Record the original raw file handle position */
-    *orig_pos = npy_ftell(handle);
-    if (*orig_pos == -1) {
-        /* The io module is needed to determine if buffering is used */
-        io = PyImport_ImportModule("io");
-        if (io == NULL) {
-            fclose(handle);
-            return NULL;
-        }
-        /* File object instances of RawIOBase are unbuffered */
-        io_raw = PyObject_GetAttrString(io, "RawIOBase");
-        Py_DECREF(io);
-        if (io_raw == NULL) {
-            fclose(handle);
-            return NULL;
-        }
-        unbuf = PyObject_IsInstance(file, io_raw);
-        Py_DECREF(io_raw);
-        if (unbuf == 1) {
-            /* Succeed if the IO is unbuffered */
-            return handle;
-        }
-        else {
-            PyErr_SetString(PyExc_IOError, "obtaining file position failed");
-            fclose(handle);
-            return NULL;
-        }
-    }
-
-    /* Seek raw handle to the Python-side position */
-    ret = PyObject_CallMethod(file, "tell", "");
-    if (ret == NULL) {
-        fclose(handle);
-        return NULL;
-    }
-    pos = PyLong_AsLongLong(ret);
-    Py_DECREF(ret);
-    if (PyErr_Occurred()) {
-        fclose(handle);
-        return NULL;
-    }
-    if (npy_fseek(handle, pos, SEEK_SET) == -1) {
-        PyErr_SetString(PyExc_IOError, "seeking file failed");
-        fclose(handle);
-        return NULL;
-    }
-    return handle;
-}
-
-/*
- * Close the dup-ed file handle, and seek the Python one to the current position
- */
-static inline int
-npy_PyFile_DupClose2(PyObject *file, FILE* handle, npy_off_t orig_pos)
-{
-    int fd, unbuf;
-    PyObject *ret, *io, *io_raw;
-    npy_off_t position;
-
-    /* For Python 2 PyFileObject, do nothing */
-#if !defined(NPY_PY3K)
-    if (PyFile_Check(file)) {
-        return 0;
-    }
-#endif
-
-    position = npy_ftell(handle);
-
-    /* Close the FILE* handle */
-    fclose(handle);
-
-    /*
-     * Restore original file handle position, in order to not confuse
-     * Python-side data structures
-     */
-    fd = PyObject_AsFileDescriptor(file);
-    if (fd == -1) {
-        return -1;
-    }
-
-    if (npy_lseek(fd, orig_pos, SEEK_SET) == -1) {
-
-        /* The io module is needed to determine if buffering is used */
-        io = PyImport_ImportModule("io");
-        if (io == NULL) {
-            return -1;
-        }
-        /* File object instances of RawIOBase are unbuffered */
-        io_raw = PyObject_GetAttrString(io, "RawIOBase");
-        Py_DECREF(io);
-        if (io_raw == NULL) {
-            return -1;
-        }
-        unbuf = PyObject_IsInstance(file, io_raw);
-        Py_DECREF(io_raw);
-        if (unbuf == 1) {
-            /* Succeed if the IO is unbuffered */
-            return 0;
-        }
-        else {
-            PyErr_SetString(PyExc_IOError, "seeking file failed");
-            return -1;
-        }
-    }
-
-    if (position == -1) {
-        PyErr_SetString(PyExc_IOError, "obtaining file position failed");
-        return -1;
-    }
-
-    /* Seek Python-side handle to the FILE* handle position */
-    ret = PyObject_CallMethod(file, "seek", NPY_OFF_T_PYFMT "i", position, 0);
-    if (ret == NULL) {
-        return -1;
-    }
-    Py_DECREF(ret);
-    return 0;
-}
-
-static inline int
-npy_PyFile_Check(PyObject *file)
-{
-    int fd;
-    /* For Python 2, check if it is a PyFileObject */
-#if !defined(NPY_PY3K)
-    if (PyFile_Check(file)) {
-        return 1;
-    }
-#endif
-    fd = PyObject_AsFileDescriptor(file);
-    if (fd == -1) {
-        PyErr_Clear();
-        return 0;
-    }
-    return 1;
-}
-
-static inline PyObject*
-npy_PyFile_OpenFile(PyObject *filename, const char *mode)
-{
-    PyObject *open;
-    open = PyDict_GetItemString(PyEval_GetBuiltins(), "open");
-    if (open == NULL) {
-        return NULL;
-    }
-    return PyObject_CallFunction(open, "Os", filename, mode);
-}
-
-static inline int
-npy_PyFile_CloseFile(PyObject *file)
-{
-    PyObject *ret;
-
-    ret = PyObject_CallMethod(file, "close", NULL);
-    if (ret == NULL) {
-        return -1;
-    }
-    Py_DECREF(ret);
-    return 0;
-}
-
-
-/* This is a copy of _PyErr_ChainExceptions
- */
-static inline void
-npy_PyErr_ChainExceptions(PyObject *exc, PyObject *val, PyObject *tb)
-{
-    if (exc == NULL)
-        return;
-
-    if (PyErr_Occurred()) {
-        /* only py3 supports this anyway */
-        #ifdef NPY_PY3K
-            PyObject *exc2, *val2, *tb2;
-            PyErr_Fetch(&exc2, &val2, &tb2);
-            PyErr_NormalizeException(&exc, &val, &tb);
-            if (tb != NULL) {
-                PyException_SetTraceback(val, tb);
-                Py_DECREF(tb);
-            }
-            Py_DECREF(exc);
-            PyErr_NormalizeException(&exc2, &val2, &tb2);
-            PyException_SetContext(val2, val);
-            PyErr_Restore(exc2, val2, tb2);
-        #endif
-    }
-    else {
-        PyErr_Restore(exc, val, tb);
-    }
-}
-
-
-/* This is a copy of _PyErr_ChainExceptions, with:
- *  - a minimal implementation for python 2
- *  - __cause__ used instead of __context__
- */
-static inline void
-npy_PyErr_ChainExceptionsCause(PyObject *exc, PyObject *val, PyObject *tb)
-{
-    if (exc == NULL)
-        return;
-
-    if (PyErr_Occurred()) {
-        /* only py3 supports this anyway */
-        #ifdef NPY_PY3K
-            PyObject *exc2, *val2, *tb2;
-            PyErr_Fetch(&exc2, &val2, &tb2);
-            PyErr_NormalizeException(&exc, &val, &tb);
-            if (tb != NULL) {
-                PyException_SetTraceback(val, tb);
-                Py_DECREF(tb);
-            }
-            Py_DECREF(exc);
-            PyErr_NormalizeException(&exc2, &val2, &tb2);
-            PyException_SetCause(val2, val);
-            PyErr_Restore(exc2, val2, tb2);
-        #endif
-    }
-    else {
-        PyErr_Restore(exc, val, tb);
-    }
-}
-
-/*
- * PyObject_Cmp
- */
-#if defined(NPY_PY3K)
-static inline int
-PyObject_Cmp(PyObject *i1, PyObject *i2, int *cmp)
-{
-    int v;
-    v = PyObject_RichCompareBool(i1, i2, Py_LT);
-    if (v == 1) {
-        *cmp = -1;
-        return 1;
-    }
-    else if (v == -1) {
-        return -1;
-    }
-
-    v = PyObject_RichCompareBool(i1, i2, Py_GT);
-    if (v == 1) {
-        *cmp = 1;
-        return 1;
-    }
-    else if (v == -1) {
-        return -1;
-    }
-
-    v = PyObject_RichCompareBool(i1, i2, Py_EQ);
-    if (v == 1) {
-        *cmp = 0;
-        return 1;
-    }
-    else {
-        *cmp = 0;
-        return -1;
-    }
-}
-#endif
-
-/*
- * PyCObject functions adapted to PyCapsules.
- *
- * The main job here is to get rid of the improved error handling
- * of PyCapsules. It's a shame...
- */
-static inline PyObject *
-NpyCapsule_FromVoidPtr(void *ptr, void (*dtor)(PyObject *))
-{
-    PyObject *ret = PyCapsule_New(ptr, NULL, dtor);
-    if (ret == NULL) {
-        PyErr_Clear();
-    }
-    return ret;
-}
-
-static inline PyObject *
-NpyCapsule_FromVoidPtrAndDesc(void *ptr, void* context, void (*dtor)(PyObject *))
-{
-    PyObject *ret = NpyCapsule_FromVoidPtr(ptr, dtor);
-    if (ret != NULL && PyCapsule_SetContext(ret, context) != 0) {
-        PyErr_Clear();
-        Py_DECREF(ret);
-        ret = NULL;
-    }
-    return ret;
-}
-
-static inline void *
-NpyCapsule_AsVoidPtr(PyObject *obj)
-{
-    void *ret = PyCapsule_GetPointer(obj, NULL);
-    if (ret == NULL) {
-        PyErr_Clear();
-    }
-    return ret;
-}
-
-static inline void *
-NpyCapsule_GetDesc(PyObject *obj)
-{
-    return PyCapsule_GetContext(obj);
-}
-
-static inline int
-NpyCapsule_Check(PyObject *ptr)
-{
-    return PyCapsule_CheckExact(ptr);
-}
-
-#ifdef __cplusplus
-}
-#endif
-
-
-#endif  /* NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_ */
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/util/numba_.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/util/numba_.py
deleted file mode 100644
index b8d489179338b2edb22fc887b7acaad3f9a1c907..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/util/numba_.py
+++ /dev/null
@@ -1,89 +0,0 @@
-"""Common utilities for Numba operations"""
-from __future__ import annotations
-
-from typing import (
-    TYPE_CHECKING,
-    Callable,
-)
-
-from pandas.compat._optional import import_optional_dependency
-from pandas.errors import NumbaUtilError
-
-GLOBAL_USE_NUMBA: bool = False
-
-
-def maybe_use_numba(engine: str | None) -> bool:
-    """Signal whether to use numba routines."""
-    return engine == "numba" or (engine is None and GLOBAL_USE_NUMBA)
-
-
-def set_use_numba(enable: bool = False) -> None:
-    global GLOBAL_USE_NUMBA
-    if enable:
-        import_optional_dependency("numba")
-    GLOBAL_USE_NUMBA = enable
-
-
-def get_jit_arguments(
-    engine_kwargs: dict[str, bool] | None = None, kwargs: dict | None = None
-) -> dict[str, bool]:
-    """
-    Return arguments to pass to numba.JIT, falling back on pandas default JIT settings.
-
-    Parameters
-    ----------
-    engine_kwargs : dict, default None
-        user passed keyword arguments for numba.JIT
-    kwargs : dict, default None
-        user passed keyword arguments to pass into the JITed function
-
-    Returns
-    -------
-    dict[str, bool]
-        nopython, nogil, parallel
-
-    Raises
-    ------
-    NumbaUtilError
-    """
-    if engine_kwargs is None:
-        engine_kwargs = {}
-
-    nopython = engine_kwargs.get("nopython", True)
-    if kwargs and nopython:
-        raise NumbaUtilError(
-            "numba does not support kwargs with nopython=True: "
-            "https://github.com/numba/numba/issues/2916"
-        )
-    nogil = engine_kwargs.get("nogil", False)
-    parallel = engine_kwargs.get("parallel", False)
-    return {"nopython": nopython, "nogil": nogil, "parallel": parallel}
-
-
-def jit_user_function(func: Callable) -> Callable:
-    """
-    If user function is not jitted already, mark the user's function
-    as jitable.
-
-    Parameters
-    ----------
-    func : function
-        user defined function
-
-    Returns
-    -------
-    function
-        Numba JITed function, or function marked as JITable by numba
-    """
-    if TYPE_CHECKING:
-        import numba
-    else:
-        numba = import_optional_dependency("numba")
-
-    if numba.extending.is_jitted(func):
-        # Don't jit a user passed jitted function
-        numba_func = func
-    else:
-        numba_func = numba.extending.register_jitable(func)
-
-    return numba_func
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/extension/base/setitem.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/extension/base/setitem.py
deleted file mode 100644
index fd90635deffacf9dab9077e1be802fa9f832e8b8..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/extension/base/setitem.py
+++ /dev/null
@@ -1,445 +0,0 @@
-import numpy as np
-import pytest
-
-import pandas as pd
-import pandas._testing as tm
-
-
-class BaseSetitemTests:
-    @pytest.fixture(
-        params=[
-            lambda x: x.index,
-            lambda x: list(x.index),
-            lambda x: slice(None),
-            lambda x: slice(0, len(x)),
-            lambda x: range(len(x)),
-            lambda x: list(range(len(x))),
-            lambda x: np.ones(len(x), dtype=bool),
-        ],
-        ids=[
-            "index",
-            "list[index]",
-            "null_slice",
-            "full_slice",
-            "range",
-            "list(range)",
-            "mask",
-        ],
-    )
-    def full_indexer(self, request):
-        """
-        Fixture for an indexer to pass to obj.loc to get/set the full length of the
-        object.
-
-        In some cases, assumes that obj.index is the default RangeIndex.
-        """
-        return request.param
-
-    @pytest.fixture(autouse=True)
-    def skip_if_immutable(self, dtype, request):
-        if dtype._is_immutable:
-            node = request.node
-            if node.name.split("[")[0] == "test_is_immutable":
-                # This fixture is auto-used, but we want to not-skip
-                # test_is_immutable.
-                return
-            pytest.skip("__setitem__ test not applicable with immutable dtype")
-
-    def test_is_immutable(self, data):
-        if data.dtype._is_immutable:
-            with pytest.raises(TypeError):
-                data[0] = data[0]
-        else:
-            data[0] = data[1]
-            assert data[0] == data[1]
-
-    def test_setitem_scalar_series(self, data, box_in_series):
-        if box_in_series:
-            data = pd.Series(data)
-        data[0] = data[1]
-        assert data[0] == data[1]
-
-    def test_setitem_sequence(self, data, box_in_series):
-        if box_in_series:
-            data = pd.Series(data)
-        original = data.copy()
-
-        data[[0, 1]] = [data[1], data[0]]
-        assert data[0] == original[1]
-        assert data[1] == original[0]
-
-    def test_setitem_sequence_mismatched_length_raises(self, data, as_array):
-        ser = pd.Series(data)
-        original = ser.copy()
-        value = [data[0]]
-        if as_array:
-            value = data._from_sequence(value)
-
-        xpr = "cannot set using a {} indexer with a different length"
-        with pytest.raises(ValueError, match=xpr.format("list-like")):
-            ser[[0, 1]] = value
-        # Ensure no modifications made before the exception
-        tm.assert_series_equal(ser, original)
-
-        with pytest.raises(ValueError, match=xpr.format("slice")):
-            ser[slice(3)] = value
-        tm.assert_series_equal(ser, original)
-
-    def test_setitem_empty_indexer(self, data, box_in_series):
-        if box_in_series:
-            data = pd.Series(data)
-        original = data.copy()
-        data[np.array([], dtype=int)] = []
-        tm.assert_equal(data, original)
-
-    def test_setitem_sequence_broadcasts(self, data, box_in_series):
-        if box_in_series:
-            data = pd.Series(data)
-        data[[0, 1]] = data[2]
-        assert data[0] == data[2]
-        assert data[1] == data[2]
-
-    @pytest.mark.parametrize("setter", ["loc", "iloc"])
-    def test_setitem_scalar(self, data, setter):
-        arr = pd.Series(data)
-        setter = getattr(arr, setter)
-        setter[0] = data[1]
-        assert arr[0] == data[1]
-
-    def test_setitem_loc_scalar_mixed(self, data):
-        df = pd.DataFrame({"A": np.arange(len(data)), "B": data})
-        df.loc[0, "B"] = data[1]
-        assert df.loc[0, "B"] == data[1]
-
-    def test_setitem_loc_scalar_single(self, data):
-        df = pd.DataFrame({"B": data})
-        df.loc[10, "B"] = data[1]
-        assert df.loc[10, "B"] == data[1]
-
-    def test_setitem_loc_scalar_multiple_homogoneous(self, data):
-        df = pd.DataFrame({"A": data, "B": data})
-        df.loc[10, "B"] = data[1]
-        assert df.loc[10, "B"] == data[1]
-
-    def test_setitem_iloc_scalar_mixed(self, data):
-        df = pd.DataFrame({"A": np.arange(len(data)), "B": data})
-        df.iloc[0, 1] = data[1]
-        assert df.loc[0, "B"] == data[1]
-
-    def test_setitem_iloc_scalar_single(self, data):
-        df = pd.DataFrame({"B": data})
-        df.iloc[10, 0] = data[1]
-        assert df.loc[10, "B"] == data[1]
-
-    def test_setitem_iloc_scalar_multiple_homogoneous(self, data):
-        df = pd.DataFrame({"A": data, "B": data})
-        df.iloc[10, 1] = data[1]
-        assert df.loc[10, "B"] == data[1]
-
-    @pytest.mark.parametrize(
-        "mask",
-        [
-            np.array([True, True, True, False, False]),
-            pd.array([True, True, True, False, False], dtype="boolean"),
-            pd.array([True, True, True, pd.NA, pd.NA], dtype="boolean"),
-        ],
-        ids=["numpy-array", "boolean-array", "boolean-array-na"],
-    )
-    def test_setitem_mask(self, data, mask, box_in_series):
-        arr = data[:5].copy()
-        expected = arr.take([0, 0, 0, 3, 4])
-        if box_in_series:
-            arr = pd.Series(arr)
-            expected = pd.Series(expected)
-        arr[mask] = data[0]
-        tm.assert_equal(expected, arr)
-
-    def test_setitem_mask_raises(self, data, box_in_series):
-        # wrong length
-        mask = np.array([True, False])
-
-        if box_in_series:
-            data = pd.Series(data)
-
-        with pytest.raises(IndexError, match="wrong length"):
-            data[mask] = data[0]
-
-        mask = pd.array(mask, dtype="boolean")
-        with pytest.raises(IndexError, match="wrong length"):
-            data[mask] = data[0]
-
-    def test_setitem_mask_boolean_array_with_na(self, data, box_in_series):
-        mask = pd.array(np.zeros(data.shape, dtype="bool"), dtype="boolean")
-        mask[:3] = True
-        mask[3:5] = pd.NA
-
-        if box_in_series:
-            data = pd.Series(data)
-
-        data[mask] = data[0]
-
-        assert (data[:3] == data[0]).all()
-
-    @pytest.mark.parametrize(
-        "idx",
-        [[0, 1, 2], pd.array([0, 1, 2], dtype="Int64"), np.array([0, 1, 2])],
-        ids=["list", "integer-array", "numpy-array"],
-    )
-    def test_setitem_integer_array(self, data, idx, box_in_series):
-        arr = data[:5].copy()
-        expected = data.take([0, 0, 0, 3, 4])
-
-        if box_in_series:
-            arr = pd.Series(arr)
-            expected = pd.Series(expected)
-
-        arr[idx] = arr[0]
-        tm.assert_equal(arr, expected)
-
-    @pytest.mark.parametrize(
-        "idx, box_in_series",
-        [
-            ([0, 1, 2, pd.NA], False),
-            pytest.param(
-                [0, 1, 2, pd.NA], True, marks=pytest.mark.xfail(reason="GH-31948")
-            ),
-            (pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
-            (pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
-        ],
-        ids=["list-False", "list-True", "integer-array-False", "integer-array-True"],
-    )
-    def test_setitem_integer_with_missing_raises(self, data, idx, box_in_series):
-        arr = data.copy()
-
-        # TODO(xfail) this raises KeyError about labels not found (it tries label-based)
-        # for list of labels with Series
-        if box_in_series:
-            arr = pd.Series(data, index=[chr(100 + i) for i in range(len(data))])
-
-        msg = "Cannot index with an integer indexer containing NA values"
-        with pytest.raises(ValueError, match=msg):
-            arr[idx] = arr[0]
-
-    @pytest.mark.parametrize("as_callable", [True, False])
-    @pytest.mark.parametrize("setter", ["loc", None])
-    def test_setitem_mask_aligned(self, data, as_callable, setter):
-        ser = pd.Series(data)
-        mask = np.zeros(len(data), dtype=bool)
-        mask[:2] = True
-
-        if as_callable:
-            mask2 = lambda x: mask
-        else:
-            mask2 = mask
-
-        if setter:
-            # loc
-            target = getattr(ser, setter)
-        else:
-            # Series.__setitem__
-            target = ser
-
-        target[mask2] = data[5:7]
-
-        ser[mask2] = data[5:7]
-        assert ser[0] == data[5]
-        assert ser[1] == data[6]
-
-    @pytest.mark.parametrize("setter", ["loc", None])
-    def test_setitem_mask_broadcast(self, data, setter):
-        ser = pd.Series(data)
-        mask = np.zeros(len(data), dtype=bool)
-        mask[:2] = True
-
-        if setter:  # loc
-            target = getattr(ser, setter)
-        else:  # __setitem__
-            target = ser
-
-        target[mask] = data[10]
-        assert ser[0] == data[10]
-        assert ser[1] == data[10]
-
-    def test_setitem_expand_columns(self, data):
-        df = pd.DataFrame({"A": data})
-        result = df.copy()
-        result["B"] = 1
-        expected = pd.DataFrame({"A": data, "B": [1] * len(data)})
-        tm.assert_frame_equal(result, expected)
-
-        result = df.copy()
-        result.loc[:, "B"] = 1
-        tm.assert_frame_equal(result, expected)
-
-        # overwrite with new type
-        result["B"] = data
-        expected = pd.DataFrame({"A": data, "B": data})
-        tm.assert_frame_equal(result, expected)
-
-    def test_setitem_expand_with_extension(self, data):
-        df = pd.DataFrame({"A": [1] * len(data)})
-        result = df.copy()
-        result["B"] = data
-        expected = pd.DataFrame({"A": [1] * len(data), "B": data})
-        tm.assert_frame_equal(result, expected)
-
-        result = df.copy()
-        result.loc[:, "B"] = data
-        tm.assert_frame_equal(result, expected)
-
-    def test_setitem_frame_invalid_length(self, data):
-        df = pd.DataFrame({"A": [1] * len(data)})
-        xpr = (
-            rf"Length of values \({len(data[:5])}\) "
-            rf"does not match length of index \({len(df)}\)"
-        )
-        with pytest.raises(ValueError, match=xpr):
-            df["B"] = data[:5]
-
-    def test_setitem_tuple_index(self, data):
-        ser = pd.Series(data[:2], index=[(0, 0), (0, 1)])
-        expected = pd.Series(data.take([1, 1]), index=ser.index)
-        ser[(0, 0)] = data[1]
-        tm.assert_series_equal(ser, expected)
-
-    def test_setitem_slice(self, data, box_in_series):
-        arr = data[:5].copy()
-        expected = data.take([0, 0, 0, 3, 4])
-        if box_in_series:
-            arr = pd.Series(arr)
-            expected = pd.Series(expected)
-
-        arr[:3] = data[0]
-        tm.assert_equal(arr, expected)
-
-    def test_setitem_loc_iloc_slice(self, data):
-        arr = data[:5].copy()
-        s = pd.Series(arr, index=["a", "b", "c", "d", "e"])
-        expected = pd.Series(data.take([0, 0, 0, 3, 4]), index=s.index)
-
-        result = s.copy()
-        result.iloc[:3] = data[0]
-        tm.assert_equal(result, expected)
-
-        result = s.copy()
-        result.loc[:"c"] = data[0]
-        tm.assert_equal(result, expected)
-
-    def test_setitem_slice_mismatch_length_raises(self, data):
-        arr = data[:5]
-        with pytest.raises(ValueError):
-            arr[:1] = arr[:2]
-
-    def test_setitem_slice_array(self, data):
-        arr = data[:5].copy()
-        arr[:5] = data[-5:]
-        tm.assert_extension_array_equal(arr, data[-5:])
-
-    def test_setitem_scalar_key_sequence_raise(self, data):
-        arr = data[:5].copy()
-        with pytest.raises(ValueError):
-            arr[0] = arr[[0, 1]]
-
-    def test_setitem_preserves_views(self, data):
-        # GH#28150 setitem shouldn't swap the underlying data
-        view1 = data.view()
-        view2 = data[:]
-
-        data[0] = data[1]
-        assert view1[0] == data[1]
-        assert view2[0] == data[1]
-
-    def test_setitem_with_expansion_dataframe_column(self, data, full_indexer):
-        # https://github.com/pandas-dev/pandas/issues/32395
-        df = expected = pd.DataFrame({"data": pd.Series(data)})
-        result = pd.DataFrame(index=df.index)
-
-        key = full_indexer(df)
-        result.loc[key, "data"] = df["data"]
-
-        tm.assert_frame_equal(result, expected)
-
-    def test_setitem_with_expansion_row(self, data, na_value):
-        df = pd.DataFrame({"data": data[:1]})
-
-        df.loc[1, "data"] = data[1]
-        expected = pd.DataFrame({"data": data[:2]})
-        tm.assert_frame_equal(df, expected)
-
-        # https://github.com/pandas-dev/pandas/issues/47284
-        df.loc[2, "data"] = na_value
-        expected = pd.DataFrame(
-            {"data": pd.Series([data[0], data[1], na_value], dtype=data.dtype)}
-        )
-        tm.assert_frame_equal(df, expected)
-
-    def test_setitem_series(self, data, full_indexer):
-        # https://github.com/pandas-dev/pandas/issues/32395
-        ser = pd.Series(data, name="data")
-        result = pd.Series(index=ser.index, dtype=object, name="data")
-
-        # because result has object dtype, the attempt to do setting inplace
-        #  is successful, and object dtype is retained
-        key = full_indexer(ser)
-        result.loc[key] = ser
-
-        expected = pd.Series(
-            data.astype(object), index=ser.index, name="data", dtype=object
-        )
-        tm.assert_series_equal(result, expected)
-
-    def test_setitem_frame_2d_values(self, data):
-        # GH#44514
-        df = pd.DataFrame({"A": data})
-
-        # Avoiding using_array_manager fixture
-        #  https://github.com/pandas-dev/pandas/pull/44514#discussion_r754002410
-        using_array_manager = isinstance(df._mgr, pd.core.internals.ArrayManager)
-        using_copy_on_write = pd.options.mode.copy_on_write
-
-        blk_data = df._mgr.arrays[0]
-
-        orig = df.copy()
-
-        df.iloc[:] = df
-        tm.assert_frame_equal(df, orig)
-
-        df.iloc[:-1] = df.iloc[:-1]
-        tm.assert_frame_equal(df, orig)
-
-        df.iloc[:] = df.values
-        tm.assert_frame_equal(df, orig)
-        if not using_array_manager and not using_copy_on_write:
-            # GH#33457 Check that this setting occurred in-place
-            # FIXME(ArrayManager): this should work there too
-            assert df._mgr.arrays[0] is blk_data
-
-        df.iloc[:-1] = df.values[:-1]
-        tm.assert_frame_equal(df, orig)
-
-    def test_delitem_series(self, data):
-        # GH#40763
-        ser = pd.Series(data, name="data")
-
-        taker = np.arange(len(ser))
-        taker = np.delete(taker, 1)
-
-        expected = ser[taker]
-        del ser[1]
-        tm.assert_series_equal(ser, expected)
-
-    def test_setitem_invalid(self, data, invalid_scalar):
-        msg = ""  # messages vary by subclass, so we do not test it
-        with pytest.raises((ValueError, TypeError), match=msg):
-            data[0] = invalid_scalar
-
-        with pytest.raises((ValueError, TypeError), match=msg):
-            data[:] = invalid_scalar
-
-    def test_setitem_2d_values(self, data):
-        # GH50085
-        original = data.copy()
-        df = pd.DataFrame({"a": data, "b": data})
-        df.loc[[0, 1], :] = df.loc[[1, 0], :].values
-        assert (df.loc[0, :] == original[1]).all()
-        assert (df.loc[1, :] == original[0]).all()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/series/methods/test_autocorr.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/series/methods/test_autocorr.py
deleted file mode 100644
index c1d768cf02f37baceb01b9ef39b91e419cef1e0f..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/series/methods/test_autocorr.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import numpy as np
-
-
-class TestAutoCorr:
-    def test_autocorr(self, datetime_series):
-        # Just run the function
-        corr1 = datetime_series.autocorr()
-
-        # Now run it with the lag parameter
-        corr2 = datetime_series.autocorr(lag=1)
-
-        # corr() with lag needs Series of at least length 2
-        if len(datetime_series) <= 2:
-            assert np.isnan(corr1)
-            assert np.isnan(corr2)
-        else:
-            assert corr1 == corr2
-
-        # Choose a random lag between 1 and length of Series - 2
-        # and compare the result with the Series corr() function
-        n = 1 + np.random.default_rng(2).integers(max(1, len(datetime_series) - 2))
-        corr1 = datetime_series.corr(datetime_series.shift(n))
-        corr2 = datetime_series.autocorr(lag=n)
-
-        # corr() with lag needs Series of at least length 2
-        if len(datetime_series) <= 2:
-            assert np.isnan(corr1)
-            assert np.isnan(corr2)
-        else:
-            assert corr1 == corr2
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/conftest.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/conftest.py
deleted file mode 100644
index 73ab470ab97a77a65001d62aa05be7d372fbc1f5..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/conftest.py
+++ /dev/null
@@ -1,146 +0,0 @@
-from datetime import (
-    datetime,
-    timedelta,
-)
-
-import numpy as np
-import pytest
-
-import pandas.util._test_decorators as td
-
-from pandas import (
-    DataFrame,
-    Series,
-    bdate_range,
-)
-
-
-@pytest.fixture(params=[True, False])
-def raw(request):
-    """raw keyword argument for rolling.apply"""
-    return request.param
-
-
-@pytest.fixture(
-    params=[
-        "sum",
-        "mean",
-        "median",
-        "max",
-        "min",
-        "var",
-        "std",
-        "kurt",
-        "skew",
-        "count",
-        "sem",
-    ]
-)
-def arithmetic_win_operators(request):
-    return request.param
-
-
-@pytest.fixture(params=[True, False])
-def center(request):
-    return request.param
-
-
-@pytest.fixture(params=[None, 1])
-def min_periods(request):
-    return request.param
-
-
-@pytest.fixture(params=[True, False])
-def parallel(request):
-    """parallel keyword argument for numba.jit"""
-    return request.param
-
-
-# Can parameterize nogil & nopython over True | False, but limiting per
-# https://github.com/pandas-dev/pandas/pull/41971#issuecomment-860607472
-
-
-@pytest.fixture(params=[False])
-def nogil(request):
-    """nogil keyword argument for numba.jit"""
-    return request.param
-
-
-@pytest.fixture(params=[True])
-def nopython(request):
-    """nopython keyword argument for numba.jit"""
-    return request.param
-
-
-@pytest.fixture(params=[True, False])
-def adjust(request):
-    """adjust keyword argument for ewm"""
-    return request.param
-
-
-@pytest.fixture(params=[True, False])
-def ignore_na(request):
-    """ignore_na keyword argument for ewm"""
-    return request.param
-
-
-@pytest.fixture(params=[True, False])
-def numeric_only(request):
-    """numeric_only keyword argument"""
-    return request.param
-
-
-@pytest.fixture(
-    params=[
-        pytest.param("numba", marks=[td.skip_if_no("numba"), pytest.mark.single_cpu]),
-        "cython",
-    ]
-)
-def engine(request):
-    """engine keyword argument for rolling.apply"""
-    return request.param
-
-
-@pytest.fixture(
-    params=[
-        pytest.param(
-            ("numba", True), marks=[td.skip_if_no("numba"), pytest.mark.single_cpu]
-        ),
-        ("cython", True),
-        ("cython", False),
-    ]
-)
-def engine_and_raw(request):
-    """engine and raw keyword arguments for rolling.apply"""
-    return request.param
-
-
-@pytest.fixture(params=["1 day", timedelta(days=1), np.timedelta64(1, "D")])
-def halflife_with_times(request):
-    """Halflife argument for EWM when times is specified."""
-    return request.param
-
-
-@pytest.fixture
-def series():
-    """Make mocked series as fixture."""
-    arr = np.random.default_rng(2).standard_normal(100)
-    locs = np.arange(20, 40)
-    arr[locs] = np.nan
-    series = Series(arr, index=bdate_range(datetime(2009, 1, 1), periods=100))
-    return series
-
-
-@pytest.fixture
-def frame():
-    """Make mocked frame as fixture."""
-    return DataFrame(
-        np.random.default_rng(2).standard_normal((100, 10)),
-        index=bdate_range(datetime(2009, 1, 1), periods=100),
-    )
-
-
-@pytest.fixture(params=[None, 1, 2, 5, 10])
-def step(request):
-    """step keyword argument for rolling window operations."""
-    return request.param
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/metadata/pkg_resources.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/metadata/pkg_resources.py
deleted file mode 100644
index d39f0ba31da5342b7d067ba2e1e92f2d6e79afdb..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/metadata/pkg_resources.py
+++ /dev/null
@@ -1,256 +0,0 @@
-import email.message
-import email.parser
-import logging
-import os
-import pathlib
-import zipfile
-from typing import Collection, Iterable, Iterator, List, Mapping, NamedTuple, Optional
-
-from pip._vendor import pkg_resources
-from pip._vendor.packaging.requirements import Requirement
-from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
-from pip._vendor.packaging.version import parse as parse_version
-
-from pip._internal.exceptions import InvalidWheel, NoneMetadataError, UnsupportedWheel
-from pip._internal.utils.misc import display_path
-from pip._internal.utils.wheel import parse_wheel, read_wheel_metadata_file
-
-from .base import (
-    BaseDistribution,
-    BaseEntryPoint,
-    BaseEnvironment,
-    DistributionVersion,
-    InfoPath,
-    Wheel,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class EntryPoint(NamedTuple):
-    name: str
-    value: str
-    group: str
-
-
-class WheelMetadata:
-    """IMetadataProvider that reads metadata files from a dictionary.
-
-    This also maps metadata decoding exceptions to our internal exception type.
-    """
-
-    def __init__(self, metadata: Mapping[str, bytes], wheel_name: str) -> None:
-        self._metadata = metadata
-        self._wheel_name = wheel_name
-
-    def has_metadata(self, name: str) -> bool:
-        return name in self._metadata
-
-    def get_metadata(self, name: str) -> str:
-        try:
-            return self._metadata[name].decode()
-        except UnicodeDecodeError as e:
-            # Augment the default error with the origin of the file.
-            raise UnsupportedWheel(
-                f"Error decoding metadata for {self._wheel_name}: {e} in {name} file"
-            )
-
-    def get_metadata_lines(self, name: str) -> Iterable[str]:
-        return pkg_resources.yield_lines(self.get_metadata(name))
-
-    def metadata_isdir(self, name: str) -> bool:
-        return False
-
-    def metadata_listdir(self, name: str) -> List[str]:
-        return []
-
-    def run_script(self, script_name: str, namespace: str) -> None:
-        pass
-
-
-class Distribution(BaseDistribution):
-    def __init__(self, dist: pkg_resources.Distribution) -> None:
-        self._dist = dist
-
-    @classmethod
-    def from_directory(cls, directory: str) -> "Distribution":
-        dist_dir = directory.rstrip(os.sep)
-
-        # Build a PathMetadata object, from path to metadata. :wink:
-        base_dir, dist_dir_name = os.path.split(dist_dir)
-        metadata = pkg_resources.PathMetadata(base_dir, dist_dir)
-
-        # Determine the correct Distribution object type.
-        if dist_dir.endswith(".egg-info"):
-            dist_cls = pkg_resources.Distribution
-            dist_name = os.path.splitext(dist_dir_name)[0]
-        else:
-            assert dist_dir.endswith(".dist-info")
-            dist_cls = pkg_resources.DistInfoDistribution
-            dist_name = os.path.splitext(dist_dir_name)[0].split("-")[0]
-
-        dist = dist_cls(base_dir, project_name=dist_name, metadata=metadata)
-        return cls(dist)
-
-    @classmethod
-    def from_wheel(cls, wheel: Wheel, name: str) -> "Distribution":
-        """Load the distribution from a given wheel.
-
-        :raises InvalidWheel: Whenever loading of the wheel causes a
-            :py:exc:`zipfile.BadZipFile` exception to be thrown.
-        :raises UnsupportedWheel: If the wheel is a valid zip, but malformed
-            internally.
-        """
-        try:
-            with wheel.as_zipfile() as zf:
-                info_dir, _ = parse_wheel(zf, name)
-                metadata_text = {
-                    path.split("/", 1)[-1]: read_wheel_metadata_file(zf, path)
-                    for path in zf.namelist()
-                    if path.startswith(f"{info_dir}/")
-                }
-        except zipfile.BadZipFile as e:
-            raise InvalidWheel(wheel.location, name) from e
-        except UnsupportedWheel as e:
-            raise UnsupportedWheel(f"{name} has an invalid wheel, {e}")
-        dist = pkg_resources.DistInfoDistribution(
-            location=wheel.location,
-            metadata=WheelMetadata(metadata_text, wheel.location),
-            project_name=name,
-        )
-        return cls(dist)
-
-    @property
-    def location(self) -> Optional[str]:
-        return self._dist.location
-
-    @property
-    def info_location(self) -> Optional[str]:
-        return self._dist.egg_info
-
-    @property
-    def installed_by_distutils(self) -> bool:
-        # A distutils-installed distribution is provided by FileMetadata. This
-        # provider has a "path" attribute not present anywhere else. Not the
-        # best introspection logic, but pip has been doing this for a long time.
-        try:
-            return bool(self._dist._provider.path)
-        except AttributeError:
-            return False
-
-    @property
-    def canonical_name(self) -> NormalizedName:
-        return canonicalize_name(self._dist.project_name)
-
-    @property
-    def version(self) -> DistributionVersion:
-        return parse_version(self._dist.version)
-
-    def is_file(self, path: InfoPath) -> bool:
-        return self._dist.has_metadata(str(path))
-
-    def iterdir(self, path: InfoPath) -> Iterator[pathlib.PurePosixPath]:
-        name = str(path)
-        if not self._dist.has_metadata(name):
-            raise FileNotFoundError(name)
-        if not self._dist.isdir(name):
-            raise NotADirectoryError(name)
-        for child in self._dist.metadata_listdir(name):
-            yield pathlib.PurePosixPath(path, child)
-
-    def read_text(self, path: InfoPath) -> str:
-        name = str(path)
-        if not self._dist.has_metadata(name):
-            raise FileNotFoundError(name)
-        content = self._dist.get_metadata(name)
-        if content is None:
-            raise NoneMetadataError(self, name)
-        return content
-
-    def iter_entry_points(self) -> Iterable[BaseEntryPoint]:
-        for group, entries in self._dist.get_entry_map().items():
-            for name, entry_point in entries.items():
-                name, _, value = str(entry_point).partition("=")
-                yield EntryPoint(name=name.strip(), value=value.strip(), group=group)
-
-    @property
-    def metadata(self) -> email.message.Message:
-        """
-        :raises NoneMetadataError: if the distribution reports `has_metadata()`
-            True but `get_metadata()` returns None.
-        """
-        if isinstance(self._dist, pkg_resources.DistInfoDistribution):
-            metadata_name = "METADATA"
-        else:
-            metadata_name = "PKG-INFO"
-        try:
-            metadata = self.read_text(metadata_name)
-        except FileNotFoundError:
-            if self.location:
-                displaying_path = display_path(self.location)
-            else:
-                displaying_path = repr(self.location)
-            logger.warning("No metadata found in %s", displaying_path)
-            metadata = ""
-        feed_parser = email.parser.FeedParser()
-        feed_parser.feed(metadata)
-        return feed_parser.close()
-
-    def iter_dependencies(self, extras: Collection[str] = ()) -> Iterable[Requirement]:
-        if extras:  # pkg_resources raises on invalid extras, so we sanitize.
-            extras = frozenset(extras).intersection(self._dist.extras)
-        return self._dist.requires(extras)
-
-    def iter_provided_extras(self) -> Iterable[str]:
-        return self._dist.extras
-
-
-class Environment(BaseEnvironment):
-    def __init__(self, ws: pkg_resources.WorkingSet) -> None:
-        self._ws = ws
-
-    @classmethod
-    def default(cls) -> BaseEnvironment:
-        return cls(pkg_resources.working_set)
-
-    @classmethod
-    def from_paths(cls, paths: Optional[List[str]]) -> BaseEnvironment:
-        return cls(pkg_resources.WorkingSet(paths))
-
-    def _search_distribution(self, name: str) -> Optional[BaseDistribution]:
-        """Find a distribution matching the ``name`` in the environment.
-
-        This searches from *all* distributions available in the environment, to
-        match the behavior of ``pkg_resources.get_distribution()``.
-        """
-        canonical_name = canonicalize_name(name)
-        for dist in self.iter_distributions():
-            if dist.canonical_name == canonical_name:
-                return dist
-        return None
-
-    def get_distribution(self, name: str) -> Optional[BaseDistribution]:
-        # Search the distribution by looking through the working set.
-        dist = self._search_distribution(name)
-        if dist:
-            return dist
-
-        # If distribution could not be found, call working_set.require to
-        # update the working set, and try to find the distribution again.
-        # This might happen for e.g. when you install a package twice, once
-        # using setup.py develop and again using setup.py install. Now when
-        # running pip uninstall twice, the package gets removed from the
-        # working set in the first uninstall, so we have to populate the
-        # working set again so that pip knows about it and the packages gets
-        # picked up and is successfully uninstalled the second time too.
-        try:
-            # We didn't pass in any version specifiers, so this can never
-            # raise pkg_resources.VersionConflict.
-            self._ws.require(name)
-        except pkg_resources.DistributionNotFound:
-            return None
-        return self._search_distribution(name)
-
-    def _iter_distributions(self) -> Iterator[BaseDistribution]:
-        for dist in self._ws:
-            yield Distribution(dist)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/req/req_tracker.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/req/req_tracker.py
deleted file mode 100644
index 24d3c530335d0bc108055ca68744893fe5108b53..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/req/req_tracker.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import contextlib
-import hashlib
-import logging
-import os
-from types import TracebackType
-from typing import Dict, Iterator, Optional, Set, Type, Union
-
-from pip._internal.models.link import Link
-from pip._internal.req.req_install import InstallRequirement
-from pip._internal.utils.temp_dir import TempDirectory
-
-logger = logging.getLogger(__name__)
-
-
-@contextlib.contextmanager
-def update_env_context_manager(**changes: str) -> Iterator[None]:
-    target = os.environ
-
-    # Save values from the target and change them.
-    non_existent_marker = object()
-    saved_values: Dict[str, Union[object, str]] = {}
-    for name, new_value in changes.items():
-        try:
-            saved_values[name] = target[name]
-        except KeyError:
-            saved_values[name] = non_existent_marker
-        target[name] = new_value
-
-    try:
-        yield
-    finally:
-        # Restore original values in the target.
-        for name, original_value in saved_values.items():
-            if original_value is non_existent_marker:
-                del target[name]
-            else:
-                assert isinstance(original_value, str)  # for mypy
-                target[name] = original_value
-
-
-@contextlib.contextmanager
-def get_requirement_tracker() -> Iterator["RequirementTracker"]:
-    root = os.environ.get("PIP_REQ_TRACKER")
-    with contextlib.ExitStack() as ctx:
-        if root is None:
-            root = ctx.enter_context(TempDirectory(kind="req-tracker")).path
-            ctx.enter_context(update_env_context_manager(PIP_REQ_TRACKER=root))
-            logger.debug("Initialized build tracking at %s", root)
-
-        with RequirementTracker(root) as tracker:
-            yield tracker
-
-
-class RequirementTracker:
-    def __init__(self, root: str) -> None:
-        self._root = root
-        self._entries: Set[InstallRequirement] = set()
-        logger.debug("Created build tracker: %s", self._root)
-
-    def __enter__(self) -> "RequirementTracker":
-        logger.debug("Entered build tracker: %s", self._root)
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.cleanup()
-
-    def _entry_path(self, link: Link) -> str:
-        hashed = hashlib.sha224(link.url_without_fragment.encode()).hexdigest()
-        return os.path.join(self._root, hashed)
-
-    def add(self, req: InstallRequirement) -> None:
-        """Add an InstallRequirement to build tracking."""
-
-        assert req.link
-        # Get the file to write information about this requirement.
-        entry_path = self._entry_path(req.link)
-
-        # Try reading from the file. If it exists and can be read from, a build
-        # is already in progress, so a LookupError is raised.
-        try:
-            with open(entry_path) as fp:
-                contents = fp.read()
-        except FileNotFoundError:
-            pass
-        else:
-            message = "{} is already being built: {}".format(req.link, contents)
-            raise LookupError(message)
-
-        # If we're here, req should really not be building already.
-        assert req not in self._entries
-
-        # Start tracking this requirement.
-        with open(entry_path, "w", encoding="utf-8") as fp:
-            fp.write(str(req))
-        self._entries.add(req)
-
-        logger.debug("Added %s to build tracker %r", req, self._root)
-
-    def remove(self, req: InstallRequirement) -> None:
-        """Remove an InstallRequirement from build tracking."""
-
-        assert req.link
-        # Delete the created file and the corresponding entries.
-        os.unlink(self._entry_path(req.link))
-        self._entries.remove(req)
-
-        logger.debug("Removed %s from build tracker %r", req, self._root)
-
-    def cleanup(self) -> None:
-        for req in set(self._entries):
-            self.remove(req)
-
-        logger.debug("Removed build tracker: %r", self._root)
-
-    @contextlib.contextmanager
-    def track(self, req: InstallRequirement) -> Iterator[None]:
-        self.add(req)
-        yield
-        self.remove(req)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/errors.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/errors.py
deleted file mode 100644
index 0bcbe53ef59373c608e62ea285536f8b22b47ecb..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/errors.py
+++ /dev/null
@@ -1,34 +0,0 @@
-class ConsoleError(Exception):
-    """An error in console operation."""
-
-
-class StyleError(Exception):
-    """An error in styles."""
-
-
-class StyleSyntaxError(ConsoleError):
-    """Style was badly formatted."""
-
-
-class MissingStyle(StyleError):
-    """No such style."""
-
-
-class StyleStackError(ConsoleError):
-    """Style stack is invalid."""
-
-
-class NotRenderableError(ConsoleError):
-    """Object is not renderable."""
-
-
-class MarkupError(ConsoleError):
-    """Markup was badly formatted."""
-
-
-class LiveError(ConsoleError):
-    """Error related to Live display."""
-
-
-class NoAltScreen(ConsoleError):
-    """Alt screen mode was required."""
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/ssl_.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/ssl_.py
deleted file mode 100644
index e35e3940301c3f92c3dadc086b5bb6b6cb1f31ec..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/ssl_.py
+++ /dev/null
@@ -1,515 +0,0 @@
-from __future__ import annotations
-
-import hmac
-import os
-import socket
-import sys
-import typing
-import warnings
-from binascii import unhexlify
-from hashlib import md5, sha1, sha256
-
-from ..exceptions import ProxySchemeUnsupported, SSLError
-from .url import _BRACELESS_IPV6_ADDRZ_RE, _IPV4_RE
-
-SSLContext = None
-SSLTransport = None
-HAS_NEVER_CHECK_COMMON_NAME = False
-IS_PYOPENSSL = False
-IS_SECURETRANSPORT = False
-ALPN_PROTOCOLS = ["http/1.1"]
-
-_TYPE_VERSION_INFO = typing.Tuple[int, int, int, str, int]
-
-# Maps the length of a digest to a possible hash function producing this digest
-HASHFUNC_MAP = {32: md5, 40: sha1, 64: sha256}
-
-
-def _is_bpo_43522_fixed(
-    implementation_name: str,
-    version_info: _TYPE_VERSION_INFO,
-    pypy_version_info: _TYPE_VERSION_INFO | None,
-) -> bool:
-    """Return True for CPython 3.8.9+, 3.9.3+ or 3.10+ and PyPy 7.3.8+ where
-    setting SSLContext.hostname_checks_common_name to False works.
-
-    Outside of CPython and PyPy we don't know which implementations work
-    or not so we conservatively use our hostname matching as we know that works
-    on all implementations.
-
-    https://github.com/urllib3/urllib3/issues/2192#issuecomment-821832963
-    https://foss.heptapod.net/pypy/pypy/-/issues/3539
-    """
-    if implementation_name == "pypy":
-        # https://foss.heptapod.net/pypy/pypy/-/issues/3129
-        return pypy_version_info >= (7, 3, 8) and version_info >= (3, 8)  # type: ignore[operator]
-    elif implementation_name == "cpython":
-        major_minor = version_info[:2]
-        micro = version_info[2]
-        return (
-            (major_minor == (3, 8) and micro >= 9)
-            or (major_minor == (3, 9) and micro >= 3)
-            or major_minor >= (3, 10)
-        )
-    else:  # Defensive:
-        return False
-
-
-def _is_has_never_check_common_name_reliable(
-    openssl_version: str,
-    openssl_version_number: int,
-    implementation_name: str,
-    version_info: _TYPE_VERSION_INFO,
-    pypy_version_info: _TYPE_VERSION_INFO | None,
-) -> bool:
-    # As of May 2023, all released versions of LibreSSL fail to reject certificates with
-    # only common names, see https://github.com/urllib3/urllib3/pull/3024
-    is_openssl = openssl_version.startswith("OpenSSL ")
-    # Before fixing OpenSSL issue #14579, the SSL_new() API was not copying hostflags
-    # like X509_CHECK_FLAG_NEVER_CHECK_SUBJECT, which tripped up CPython.
-    # https://github.com/openssl/openssl/issues/14579
-    # This was released in OpenSSL 1.1.1l+ (>=0x101010cf)
-    is_openssl_issue_14579_fixed = openssl_version_number >= 0x101010CF
-
-    return is_openssl and (
-        is_openssl_issue_14579_fixed
-        or _is_bpo_43522_fixed(implementation_name, version_info, pypy_version_info)
-    )
-
-
-if typing.TYPE_CHECKING:
-    from ssl import VerifyMode
-
-    from typing_extensions import Literal, TypedDict
-
-    from .ssltransport import SSLTransport as SSLTransportType
-
-    class _TYPE_PEER_CERT_RET_DICT(TypedDict, total=False):
-        subjectAltName: tuple[tuple[str, str], ...]
-        subject: tuple[tuple[tuple[str, str], ...], ...]
-        serialNumber: str
-
-
-# Mapping from 'ssl.PROTOCOL_TLSX' to 'TLSVersion.X'
-_SSL_VERSION_TO_TLS_VERSION: dict[int, int] = {}
-
-try:  # Do we have ssl at all?
-    import ssl
-    from ssl import (  # type: ignore[assignment]
-        CERT_REQUIRED,
-        HAS_NEVER_CHECK_COMMON_NAME,
-        OP_NO_COMPRESSION,
-        OP_NO_TICKET,
-        OPENSSL_VERSION,
-        OPENSSL_VERSION_NUMBER,
-        PROTOCOL_TLS,
-        PROTOCOL_TLS_CLIENT,
-        OP_NO_SSLv2,
-        OP_NO_SSLv3,
-        SSLContext,
-        TLSVersion,
-    )
-
-    PROTOCOL_SSLv23 = PROTOCOL_TLS
-
-    # Setting SSLContext.hostname_checks_common_name = False didn't work before CPython
-    # 3.8.9, 3.9.3, and 3.10 (but OK on PyPy) or OpenSSL 1.1.1l+
-    if HAS_NEVER_CHECK_COMMON_NAME and not _is_has_never_check_common_name_reliable(
-        OPENSSL_VERSION,
-        OPENSSL_VERSION_NUMBER,
-        sys.implementation.name,
-        sys.version_info,
-        sys.pypy_version_info if sys.implementation.name == "pypy" else None,  # type: ignore[attr-defined]
-    ):
-        HAS_NEVER_CHECK_COMMON_NAME = False
-
-    # Need to be careful here in case old TLS versions get
-    # removed in future 'ssl' module implementations.
-    for attr in ("TLSv1", "TLSv1_1", "TLSv1_2"):
-        try:
-            _SSL_VERSION_TO_TLS_VERSION[getattr(ssl, f"PROTOCOL_{attr}")] = getattr(
-                TLSVersion, attr
-            )
-        except AttributeError:  # Defensive:
-            continue
-
-    from .ssltransport import SSLTransport  # type: ignore[assignment]
-except ImportError:
-    OP_NO_COMPRESSION = 0x20000  # type: ignore[assignment]
-    OP_NO_TICKET = 0x4000  # type: ignore[assignment]
-    OP_NO_SSLv2 = 0x1000000  # type: ignore[assignment]
-    OP_NO_SSLv3 = 0x2000000  # type: ignore[assignment]
-    PROTOCOL_SSLv23 = PROTOCOL_TLS = 2  # type: ignore[assignment]
-    PROTOCOL_TLS_CLIENT = 16  # type: ignore[assignment]
-
-
-_TYPE_PEER_CERT_RET = typing.Union["_TYPE_PEER_CERT_RET_DICT", bytes, None]
-
-
-def assert_fingerprint(cert: bytes | None, fingerprint: str) -> None:
-    """
-    Checks if given fingerprint matches the supplied certificate.
-
-    :param cert:
-        Certificate as bytes object.
-    :param fingerprint:
-        Fingerprint as string of hexdigits, can be interspersed by colons.
-    """
-
-    if cert is None:
-        raise SSLError("No certificate for the peer.")
-
-    fingerprint = fingerprint.replace(":", "").lower()
-    digest_length = len(fingerprint)
-    hashfunc = HASHFUNC_MAP.get(digest_length)
-    if not hashfunc:
-        raise SSLError(f"Fingerprint of invalid length: {fingerprint}")
-
-    # We need encode() here for py32; works on py2 and p33.
-    fingerprint_bytes = unhexlify(fingerprint.encode())
-
-    cert_digest = hashfunc(cert).digest()
-
-    if not hmac.compare_digest(cert_digest, fingerprint_bytes):
-        raise SSLError(
-            f'Fingerprints did not match. Expected "{fingerprint}", got "{cert_digest.hex()}"'
-        )
-
-
-def resolve_cert_reqs(candidate: None | int | str) -> VerifyMode:
-    """
-    Resolves the argument to a numeric constant, which can be passed to
-    the wrap_socket function/method from the ssl module.
-    Defaults to :data:`ssl.CERT_REQUIRED`.
-    If given a string it is assumed to be the name of the constant in the
-    :mod:`ssl` module or its abbreviation.
-    (So you can specify `REQUIRED` instead of `CERT_REQUIRED`.
-    If it's neither `None` nor a string we assume it is already the numeric
-    constant which can directly be passed to wrap_socket.
-    """
-    if candidate is None:
-        return CERT_REQUIRED
-
-    if isinstance(candidate, str):
-        res = getattr(ssl, candidate, None)
-        if res is None:
-            res = getattr(ssl, "CERT_" + candidate)
-        return res  # type: ignore[no-any-return]
-
-    return candidate  # type: ignore[return-value]
-
-
-def resolve_ssl_version(candidate: None | int | str) -> int:
-    """
-    like resolve_cert_reqs
-    """
-    if candidate is None:
-        return PROTOCOL_TLS
-
-    if isinstance(candidate, str):
-        res = getattr(ssl, candidate, None)
-        if res is None:
-            res = getattr(ssl, "PROTOCOL_" + candidate)
-        return typing.cast(int, res)
-
-    return candidate
-
-
-def create_urllib3_context(
-    ssl_version: int | None = None,
-    cert_reqs: int | None = None,
-    options: int | None = None,
-    ciphers: str | None = None,
-    ssl_minimum_version: int | None = None,
-    ssl_maximum_version: int | None = None,
-) -> ssl.SSLContext:
-    """Creates and configures an :class:`ssl.SSLContext` instance for use with urllib3.
-
-    :param ssl_version:
-        The desired protocol version to use. This will default to
-        PROTOCOL_SSLv23 which will negotiate the highest protocol that both
-        the server and your installation of OpenSSL support.
-
-        This parameter is deprecated instead use 'ssl_minimum_version'.
-    :param ssl_minimum_version:
-        The minimum version of TLS to be used. Use the 'ssl.TLSVersion' enum for specifying the value.
-    :param ssl_maximum_version:
-        The maximum version of TLS to be used. Use the 'ssl.TLSVersion' enum for specifying the value.
-        Not recommended to set to anything other than 'ssl.TLSVersion.MAXIMUM_SUPPORTED' which is the
-        default value.
-    :param cert_reqs:
-        Whether to require the certificate verification. This defaults to
-        ``ssl.CERT_REQUIRED``.
-    :param options:
-        Specific OpenSSL options. These default to ``ssl.OP_NO_SSLv2``,
-        ``ssl.OP_NO_SSLv3``, ``ssl.OP_NO_COMPRESSION``, and ``ssl.OP_NO_TICKET``.
-    :param ciphers:
-        Which cipher suites to allow the server to select. Defaults to either system configured
-        ciphers if OpenSSL 1.1.1+, otherwise uses a secure default set of ciphers.
-    :returns:
-        Constructed SSLContext object with specified options
-    :rtype: SSLContext
-    """
-    if SSLContext is None:
-        raise TypeError("Can't create an SSLContext object without an ssl module")
-
-    # This means 'ssl_version' was specified as an exact value.
-    if ssl_version not in (None, PROTOCOL_TLS, PROTOCOL_TLS_CLIENT):
-        # Disallow setting 'ssl_version' and 'ssl_minimum|maximum_version'
-        # to avoid conflicts.
-        if ssl_minimum_version is not None or ssl_maximum_version is not None:
-            raise ValueError(
-                "Can't specify both 'ssl_version' and either "
-                "'ssl_minimum_version' or 'ssl_maximum_version'"
-            )
-
-        # 'ssl_version' is deprecated and will be removed in the future.
-        else:
-            # Use 'ssl_minimum_version' and 'ssl_maximum_version' instead.
-            ssl_minimum_version = _SSL_VERSION_TO_TLS_VERSION.get(
-                ssl_version, TLSVersion.MINIMUM_SUPPORTED
-            )
-            ssl_maximum_version = _SSL_VERSION_TO_TLS_VERSION.get(
-                ssl_version, TLSVersion.MAXIMUM_SUPPORTED
-            )
-
-            # This warning message is pushing users to use 'ssl_minimum_version'
-            # instead of both min/max. Best practice is to only set the minimum version and
-            # keep the maximum version to be it's default value: 'TLSVersion.MAXIMUM_SUPPORTED'
-            warnings.warn(
-                "'ssl_version' option is deprecated and will be "
-                "removed in urllib3 v2.1.0. Instead use 'ssl_minimum_version'",
-                category=DeprecationWarning,
-                stacklevel=2,
-            )
-
-    # PROTOCOL_TLS is deprecated in Python 3.10 so we always use PROTOCOL_TLS_CLIENT
-    context = SSLContext(PROTOCOL_TLS_CLIENT)
-
-    if ssl_minimum_version is not None:
-        context.minimum_version = ssl_minimum_version
-    else:  # Python <3.10 defaults to 'MINIMUM_SUPPORTED' so explicitly set TLSv1.2 here
-        context.minimum_version = TLSVersion.TLSv1_2
-
-    if ssl_maximum_version is not None:
-        context.maximum_version = ssl_maximum_version
-
-    # Unless we're given ciphers defer to either system ciphers in
-    # the case of OpenSSL 1.1.1+ or use our own secure default ciphers.
-    if ciphers:
-        context.set_ciphers(ciphers)
-
-    # Setting the default here, as we may have no ssl module on import
-    cert_reqs = ssl.CERT_REQUIRED if cert_reqs is None else cert_reqs
-
-    if options is None:
-        options = 0
-        # SSLv2 is easily broken and is considered harmful and dangerous
-        options |= OP_NO_SSLv2
-        # SSLv3 has several problems and is now dangerous
-        options |= OP_NO_SSLv3
-        # Disable compression to prevent CRIME attacks for OpenSSL 1.0+
-        # (issue #309)
-        options |= OP_NO_COMPRESSION
-        # TLSv1.2 only. Unless set explicitly, do not request tickets.
-        # This may save some bandwidth on wire, and although the ticket is encrypted,
-        # there is a risk associated with it being on wire,
-        # if the server is not rotating its ticketing keys properly.
-        options |= OP_NO_TICKET
-
-    context.options |= options
-
-    # Enable post-handshake authentication for TLS 1.3, see GH #1634. PHA is
-    # necessary for conditional client cert authentication with TLS 1.3.
-    # The attribute is None for OpenSSL <= 1.1.0 or does not exist in older
-    # versions of Python.  We only enable on Python 3.7.4+ or if certificate
-    # verification is enabled to work around Python issue #37428
-    # See: https://bugs.python.org/issue37428
-    if (cert_reqs == ssl.CERT_REQUIRED or sys.version_info >= (3, 7, 4)) and getattr(
-        context, "post_handshake_auth", None
-    ) is not None:
-        context.post_handshake_auth = True
-
-    # The order of the below lines setting verify_mode and check_hostname
-    # matter due to safe-guards SSLContext has to prevent an SSLContext with
-    # check_hostname=True, verify_mode=NONE/OPTIONAL.
-    # We always set 'check_hostname=False' for pyOpenSSL so we rely on our own
-    # 'ssl.match_hostname()' implementation.
-    if cert_reqs == ssl.CERT_REQUIRED and not IS_PYOPENSSL:
-        context.verify_mode = cert_reqs
-        context.check_hostname = True
-    else:
-        context.check_hostname = False
-        context.verify_mode = cert_reqs
-
-    try:
-        context.hostname_checks_common_name = False
-    except AttributeError:  # Defensive: for CPython < 3.8.9 and 3.9.3; for PyPy < 7.3.8
-        pass
-
-    # Enable logging of TLS session keys via defacto standard environment variable
-    # 'SSLKEYLOGFILE', if the feature is available (Python 3.8+). Skip empty values.
-    if hasattr(context, "keylog_filename"):
-        sslkeylogfile = os.environ.get("SSLKEYLOGFILE")
-        if sslkeylogfile:
-            context.keylog_filename = sslkeylogfile
-
-    return context
-
-
-@typing.overload
-def ssl_wrap_socket(
-    sock: socket.socket,
-    keyfile: str | None = ...,
-    certfile: str | None = ...,
-    cert_reqs: int | None = ...,
-    ca_certs: str | None = ...,
-    server_hostname: str | None = ...,
-    ssl_version: int | None = ...,
-    ciphers: str | None = ...,
-    ssl_context: ssl.SSLContext | None = ...,
-    ca_cert_dir: str | None = ...,
-    key_password: str | None = ...,
-    ca_cert_data: None | str | bytes = ...,
-    tls_in_tls: Literal[False] = ...,
-) -> ssl.SSLSocket:
-    ...
-
-
-@typing.overload
-def ssl_wrap_socket(
-    sock: socket.socket,
-    keyfile: str | None = ...,
-    certfile: str | None = ...,
-    cert_reqs: int | None = ...,
-    ca_certs: str | None = ...,
-    server_hostname: str | None = ...,
-    ssl_version: int | None = ...,
-    ciphers: str | None = ...,
-    ssl_context: ssl.SSLContext | None = ...,
-    ca_cert_dir: str | None = ...,
-    key_password: str | None = ...,
-    ca_cert_data: None | str | bytes = ...,
-    tls_in_tls: bool = ...,
-) -> ssl.SSLSocket | SSLTransportType:
-    ...
-
-
-def ssl_wrap_socket(
-    sock: socket.socket,
-    keyfile: str | None = None,
-    certfile: str | None = None,
-    cert_reqs: int | None = None,
-    ca_certs: str | None = None,
-    server_hostname: str | None = None,
-    ssl_version: int | None = None,
-    ciphers: str | None = None,
-    ssl_context: ssl.SSLContext | None = None,
-    ca_cert_dir: str | None = None,
-    key_password: str | None = None,
-    ca_cert_data: None | str | bytes = None,
-    tls_in_tls: bool = False,
-) -> ssl.SSLSocket | SSLTransportType:
-    """
-    All arguments except for server_hostname, ssl_context, tls_in_tls, ca_cert_data and
-    ca_cert_dir have the same meaning as they do when using
-    :func:`ssl.create_default_context`, :meth:`ssl.SSLContext.load_cert_chain`,
-    :meth:`ssl.SSLContext.set_ciphers` and :meth:`ssl.SSLContext.wrap_socket`.
-
-    :param server_hostname:
-        When SNI is supported, the expected hostname of the certificate
-    :param ssl_context:
-        A pre-made :class:`SSLContext` object. If none is provided, one will
-        be created using :func:`create_urllib3_context`.
-    :param ciphers:
-        A string of ciphers we wish the client to support.
-    :param ca_cert_dir:
-        A directory containing CA certificates in multiple separate files, as
-        supported by OpenSSL's -CApath flag or the capath argument to
-        SSLContext.load_verify_locations().
-    :param key_password:
-        Optional password if the keyfile is encrypted.
-    :param ca_cert_data:
-        Optional string containing CA certificates in PEM format suitable for
-        passing as the cadata parameter to SSLContext.load_verify_locations()
-    :param tls_in_tls:
-        Use SSLTransport to wrap the existing socket.
-    """
-    context = ssl_context
-    if context is None:
-        # Note: This branch of code and all the variables in it are only used in tests.
-        # We should consider deprecating and removing this code.
-        context = create_urllib3_context(ssl_version, cert_reqs, ciphers=ciphers)
-
-    if ca_certs or ca_cert_dir or ca_cert_data:
-        try:
-            context.load_verify_locations(ca_certs, ca_cert_dir, ca_cert_data)
-        except OSError as e:
-            raise SSLError(e) from e
-
-    elif ssl_context is None and hasattr(context, "load_default_certs"):
-        # try to load OS default certs; works well on Windows.
-        context.load_default_certs()
-
-    # Attempt to detect if we get the goofy behavior of the
-    # keyfile being encrypted and OpenSSL asking for the
-    # passphrase via the terminal and instead error out.
-    if keyfile and key_password is None and _is_key_file_encrypted(keyfile):
-        raise SSLError("Client private key is encrypted, password is required")
-
-    if certfile:
-        if key_password is None:
-            context.load_cert_chain(certfile, keyfile)
-        else:
-            context.load_cert_chain(certfile, keyfile, key_password)
-
-    try:
-        context.set_alpn_protocols(ALPN_PROTOCOLS)
-    except NotImplementedError:  # Defensive: in CI, we always have set_alpn_protocols
-        pass
-
-    ssl_sock = _ssl_wrap_socket_impl(sock, context, tls_in_tls, server_hostname)
-    return ssl_sock
-
-
-def is_ipaddress(hostname: str | bytes) -> bool:
-    """Detects whether the hostname given is an IPv4 or IPv6 address.
-    Also detects IPv6 addresses with Zone IDs.
-
-    :param str hostname: Hostname to examine.
-    :return: True if the hostname is an IP address, False otherwise.
-    """
-    if isinstance(hostname, bytes):
-        # IDN A-label bytes are ASCII compatible.
-        hostname = hostname.decode("ascii")
-    return bool(_IPV4_RE.match(hostname) or _BRACELESS_IPV6_ADDRZ_RE.match(hostname))
-
-
-def _is_key_file_encrypted(key_file: str) -> bool:
-    """Detects if a key file is encrypted or not."""
-    with open(key_file) as f:
-        for line in f:
-            # Look for Proc-Type: 4,ENCRYPTED
-            if "ENCRYPTED" in line:
-                return True
-
-    return False
-
-
-def _ssl_wrap_socket_impl(
-    sock: socket.socket,
-    ssl_context: ssl.SSLContext,
-    tls_in_tls: bool,
-    server_hostname: str | None = None,
-) -> ssl.SSLSocket | SSLTransportType:
-    if tls_in_tls:
-        if not SSLTransport:
-            # Import error, ssl is not available.
-            raise ProxySchemeUnsupported(
-                "TLS in TLS requires support for the 'ssl' module"
-            )
-
-        SSLTransport._validate_ssl_context_for_tls_in_tls(ssl_context)
-        return SSLTransport(sock, ssl_context, server_hostname)
-
-    return ssl_context.wrap_socket(sock, server_hostname=server_hostname)
diff --git a/spaces/pszemraj/pdf-ocr/app.py b/spaces/pszemraj/pdf-ocr/app.py
deleted file mode 100644
index a39dd43cbfe64fa643111bf2aab48765754fc943..0000000000000000000000000000000000000000
--- a/spaces/pszemraj/pdf-ocr/app.py
+++ /dev/null
@@ -1,182 +0,0 @@
-import logging
-import time
-from pathlib import Path
-import contextlib
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(levelname)s - %(message)s",
-)
-
-
-import gradio as gr
-import nltk
-import torch
-
-from pdf2text import *
-
-_here = Path(__file__).parent
-
-nltk.download("stopwords")  # TODO=find where this requirement originates from
-
-
-def load_uploaded_file(file_obj, temp_dir: Path = None):
-    """
-    load_uploaded_file - process an uploaded file
-
-    Args:
-        file_obj (POTENTIALLY list): Gradio file object inside a list
-
-    Returns:
-        str, the uploaded file contents
-    """
-
-    # check if mysterious file object is a list
-    if isinstance(file_obj, list):
-        file_obj = file_obj[0]
-    file_path = Path(file_obj.name)
-
-    if temp_dir is None:
-        _temp_dir = _here / "temp"
-    _temp_dir.mkdir(exist_ok=True)
-
-    try:
-        pdf_bytes_obj = open(file_path, "rb").read()
-        temp_path = temp_dir / file_path.name if temp_dir else file_path
-        # save to PDF file
-        with open(temp_path, "wb") as f:
-            f.write(pdf_bytes_obj)
-        logging.info(f"Saved uploaded file to {temp_path}")
-        return str(temp_path.resolve())
-
-    except Exception as e:
-        logging.error(f"Trying to load file with path {file_path}, error: {e}")
-        print(f"Trying to load file with path {file_path}, error: {e}")
-        return None
-
-
-def convert_PDF(
-    pdf_obj,
-    language: str = "en",
-    max_pages=20,
-):
-    """
-    convert_PDF - convert a PDF file to text
-
-    Args:
-        pdf_bytes_obj (bytes): PDF file contents
-        language (str, optional): Language to use for OCR. Defaults to "en".
-
-    Returns:
-        str, the PDF file contents as text
-    """
-    # clear local text cache
-    rm_local_text_files()
-    global ocr_model
-    st = time.perf_counter()
-    if isinstance(pdf_obj, list):
-        pdf_obj = pdf_obj[0]
-    file_path = Path(pdf_obj.name)
-    if not file_path.suffix == ".pdf":
-        logging.error(f"File {file_path} is not a PDF file")
-
-        html_error = f"""
-        <div style="color: red; font-size: 20px; font-weight: bold;">
-        File {file_path} is not a PDF file. Please upload a PDF file.
-        </div>
-        """
-        return "File is not a PDF file", html_error, None
-
-    conversion_stats = convert_PDF_to_Text(
-        file_path,
-        ocr_model=ocr_model,
-        max_pages=max_pages,
-    )
-    converted_txt = conversion_stats["converted_text"]
-    num_pages = conversion_stats["num_pages"]
-    was_truncated = conversion_stats["truncated"]
-    # if alt_lang: # TODO: fix this
-
-    rt = round((time.perf_counter() - st) / 60, 2)
-    print(f"Runtime: {rt} minutes")
-    html = ""
-    if was_truncated:
-        html += f"<p>WARNING - PDF was truncated to {max_pages} pages</p>"
-    html += f"<p>Runtime: {rt} minutes on CPU for {num_pages} pages</p>"
-
-    _output_name = f"RESULT_{file_path.stem}_OCR.txt"
-    with open(_output_name, "w", encoding="utf-8", errors="ignore") as f:
-        f.write(converted_txt)
-
-    return converted_txt, html, _output_name
-
-
-if __name__ == "__main__":
-    logging.info("Starting app")
-
-    use_GPU = torch.cuda.is_available()
-    logging.info(f"Using GPU status: {use_GPU}")
-    logging.info("Loading OCR model")
-    with contextlib.redirect_stdout(None):
-        ocr_model = ocr_predictor(
-            "db_resnet50",
-            "crnn_mobilenet_v3_large",
-            pretrained=True,
-            assume_straight_pages=True,
-        )
-
-    # define pdf bytes as None
-    pdf_obj = _here / "example_file.pdf"
-    pdf_obj = str(pdf_obj.resolve())
-    _temp_dir = _here / "temp"
-    _temp_dir.mkdir(exist_ok=True)
-
-    logging.info("starting demo")
-    demo = gr.Blocks()
-
-    with demo:
-
-        gr.Markdown("# PDF to Text")
-        gr.Markdown(
-            "A basic demo of pdf-to-text conversion using OCR from the [doctr](https://mindee.github.io/doctr/index.html) package"
-        )
-        gr.Markdown("---")
-
-        with gr.Column():
-
-            gr.Markdown("## Load Inputs")
-            gr.Markdown("Upload your own file & replace the default. Files should be < 10MB to avoid upload issues - search for a PDF compressor online as needed.")
-            gr.Markdown(
-                "_If no file is uploaded, a sample PDF will be used. PDFs are truncated to 20 pages._"
-            )
-
-            uploaded_file = gr.File(
-                label="Upload a PDF file",
-                file_count="single",
-                type="file",
-                value=_here / "example_file.pdf",
-            )
-
-            gr.Markdown("---")
-
-        with gr.Column():
-            gr.Markdown("## Convert PDF to Text")
-            convert_button = gr.Button("Convert PDF!", variant="primary")
-            out_placeholder = gr.HTML("<p><em>Output will appear below:</em></p>")
-            gr.Markdown("### Output")
-            OCR_text = gr.Textbox(
-                label="OCR Result", placeholder="The OCR text will appear here"
-            )
-            text_file = gr.File(
-                label="Download Text File",
-                file_count="single",
-                type="file",
-                interactive=False,
-            )
-
-        convert_button.click(
-            fn=convert_PDF,
-            inputs=[uploaded_file],
-            outputs=[OCR_text, out_placeholder, text_file],
-        )
-    demo.launch(enable_queue=True)
diff --git a/spaces/pycoming/bingo/next.config.js b/spaces/pycoming/bingo/next.config.js
deleted file mode 100644
index 0e6ccd7fbc91d0459eaaff3e968ce0556789c605..0000000000000000000000000000000000000000
--- a/spaces/pycoming/bingo/next.config.js
+++ /dev/null
@@ -1,38 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-  // output: 'export',
-  // assetPrefix: '.',
-  webpack: (config, { isServer }) => {
-    if (!isServer) {
-      config.resolve = {
-        ...config.resolve,
-        fallback: {
-          'bufferutil': false,
-          'utf-8-validate': false,
-          http: false,
-          https: false,
-          stream: false,
-          // fixes proxy-agent dependencies
-          net: false,
-          dns: false,
-          tls: false,
-          assert: false,
-          // fixes next-i18next dependencies
-          path: false,
-          fs: false,
-          // fixes mapbox dependencies
-          events: false,
-          // fixes sentry dependencies
-          process: false
-        }
-      };
-    }
-    config.module.exprContextCritical = false;
-
-    return config;
-  },
-}
-
-module.exports = (...args) => {
-  return nextConfig
-}
diff --git a/spaces/pytorch/MiDaS/app.py b/spaces/pytorch/MiDaS/app.py
deleted file mode 100644
index f113fabac69b22df63691569fefdb7e2f29c0849..0000000000000000000000000000000000000000
--- a/spaces/pytorch/MiDaS/app.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import cv2
-import torch
-import gradio as gr
-import numpy as np
-from PIL import Image
-
-torch.hub.download_url_to_file('https://images.unsplash.com/photo-1437622368342-7a3d73a34c8f', 'turtle.jpg')
-torch.hub.download_url_to_file('https://images.unsplash.com/photo-1519066629447-267fffa62d4b', 'lions.jpg')
-
-midas = torch.hub.load("intel-isl/MiDaS", "MiDaS")
-
-use_large_model = True
-
-if use_large_model:
-    midas = torch.hub.load("intel-isl/MiDaS", "MiDaS")
-else:
-    midas = torch.hub.load("intel-isl/MiDaS", "MiDaS_small")
-
-device = "cpu"
-midas.to(device)
-
-midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
-
-if use_large_model:
-    transform = midas_transforms.default_transform
-else:
-    transform = midas_transforms.small_transform
-
-
-def depth(img):
-  cv_image = np.array(img) 
-  img = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
-
-  input_batch = transform(img).to(device)
-  with torch.no_grad():
-    prediction = midas(input_batch)
-
-    prediction = torch.nn.functional.interpolate(
-        prediction.unsqueeze(1),
-        size=img.shape[:2],
-        mode="bicubic",
-        align_corners=False,
-    ).squeeze()
-    
-  output = prediction.cpu().numpy()
-  formatted = (output * 255 / np.max(output)).astype('uint8')
-  img = Image.fromarray(formatted)
-  return img
-    
-
-inputs =  gr.inputs.Image(type='pil', label="Original Image")
-outputs = gr.outputs.Image(type="pil",label="Output Image")
-
-title = "MiDaS"
-description = "Gradio demo for MiDaS v2.1 which takes in a single image for computing relative depth. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.01341v3'>Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer</a> | <a href='https://github.com/intel-isl/MiDaS'>Github Repo</a></p>"
-
-examples = [
-    ["turtle.jpg"],
-    ["lions.jpg"]
-]
-
-gr.Interface(depth, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(enable_queue=True,cache_examples=True)
\ No newline at end of file
diff --git a/spaces/qingxu98/gpt-academic/crazy_functions/latex_fns/latex_actions.py b/spaces/qingxu98/gpt-academic/crazy_functions/latex_fns/latex_actions.py
deleted file mode 100644
index dcde0e994e7a8890e0f4dd5c9006a96e2071d5c9..0000000000000000000000000000000000000000
--- a/spaces/qingxu98/gpt-academic/crazy_functions/latex_fns/latex_actions.py
+++ /dev/null
@@ -1,447 +0,0 @@
-from toolbox import update_ui, update_ui_lastest_msg, get_log_folder
-from toolbox import zip_folder, objdump, objload, promote_file_to_downloadzone
-from .latex_toolbox import PRESERVE, TRANSFORM
-from .latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
-from .latex_toolbox import reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process
-from .latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout
-
-import os, shutil
-import re
-import numpy as np
-
-pj = os.path.join
-
-
-def split_subprocess(txt, project_folder, return_dict, opts):
-    """
-    break down latex file to a linked list,
-    each node use a preserve flag to indicate whether it should
-    be proccessed by GPT.
-    """
-    text = txt
-    mask = np.zeros(len(txt), dtype=np.uint8) + TRANSFORM
-
-    # 吸收title与作者以上的部分
-    text, mask = set_forbidden_text(text, mask, r"^(.*?)\\maketitle", re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, r"^(.*?)\\begin{document}", re.DOTALL)
-    # 吸收iffalse注释
-    text, mask = set_forbidden_text(text, mask, r"\\iffalse(.*?)\\fi", re.DOTALL)
-    # 吸收在42行以内的begin-end组合
-    text, mask = set_forbidden_text_begin_end(text, mask, r"\\begin\{([a-z\*]*)\}(.*?)\\end\{\1\}", re.DOTALL, limit_n_lines=42)
-    # 吸收匿名公式
-    text, mask = set_forbidden_text(text, mask, [ r"\$\$([^$]+)\$\$",  r"\\\[.*?\\\]" ], re.DOTALL)
-    # 吸收其他杂项
-    text, mask = set_forbidden_text(text, mask, [ r"\\section\{(.*?)\}", r"\\section\*\{(.*?)\}", r"\\subsection\{(.*?)\}", r"\\subsubsection\{(.*?)\}" ])
-    text, mask = set_forbidden_text(text, mask, [ r"\\bibliography\{(.*?)\}", r"\\bibliographystyle\{(.*?)\}" ])
-    text, mask = set_forbidden_text(text, mask, r"\\begin\{thebibliography\}.*?\\end\{thebibliography\}", re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, r"\\begin\{lstlisting\}(.*?)\\end\{lstlisting\}", re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, r"\\begin\{wraptable\}(.*?)\\end\{wraptable\}", re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, r"\\begin\{algorithm\}(.*?)\\end\{algorithm\}", re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{wrapfigure\}(.*?)\\end\{wrapfigure\}", r"\\begin\{wrapfigure\*\}(.*?)\\end\{wrapfigure\*\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{figure\}(.*?)\\end\{figure\}", r"\\begin\{figure\*\}(.*?)\\end\{figure\*\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{multline\}(.*?)\\end\{multline\}", r"\\begin\{multline\*\}(.*?)\\end\{multline\*\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{table\}(.*?)\\end\{table\}", r"\\begin\{table\*\}(.*?)\\end\{table\*\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{minipage\}(.*?)\\end\{minipage\}", r"\\begin\{minipage\*\}(.*?)\\end\{minipage\*\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{align\*\}(.*?)\\end\{align\*\}", r"\\begin\{align\}(.*?)\\end\{align\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\begin\{equation\}(.*?)\\end\{equation\}", r"\\begin\{equation\*\}(.*?)\\end\{equation\*\}"], re.DOTALL)
-    text, mask = set_forbidden_text(text, mask, [r"\\includepdf\[(.*?)\]\{(.*?)\}", r"\\clearpage", r"\\newpage", r"\\appendix", r"\\tableofcontents", r"\\include\{(.*?)\}"])
-    text, mask = set_forbidden_text(text, mask, [r"\\vspace\{(.*?)\}", r"\\hspace\{(.*?)\}", r"\\label\{(.*?)\}", r"\\begin\{(.*?)\}", r"\\end\{(.*?)\}", r"\\item "])
-    text, mask = set_forbidden_text_careful_brace(text, mask, r"\\hl\{(.*?)\}", re.DOTALL)
-    # reverse 操作必须放在最后
-    text, mask = reverse_forbidden_text_careful_brace(text, mask, r"\\caption\{(.*?)\}", re.DOTALL, forbid_wrapper=True)
-    text, mask = reverse_forbidden_text_careful_brace(text, mask, r"\\abstract\{(.*?)\}", re.DOTALL, forbid_wrapper=True)
-    text, mask = reverse_forbidden_text(text, mask, r"\\begin\{abstract\}(.*?)\\end\{abstract\}", re.DOTALL, forbid_wrapper=True)
-    root = convert_to_linklist(text, mask)
-
-    # 最后一步处理，增强稳健性
-    root = post_process(root)
-
-    # 输出html调试文件，用红色标注处保留区（PRESERVE），用黑色标注转换区（TRANSFORM）
-    with open(pj(project_folder, 'debug_log.html'), 'w', encoding='utf8') as f:
-        segment_parts_for_gpt = []
-        nodes = []
-        node = root
-        while True:
-            nodes.append(node)
-            show_html = node.string.replace('\n','<br/>')
-            if not node.preserve:
-                segment_parts_for_gpt.append(node.string)
-                f.write(f'<p style="color:black;">#{node.range}{show_html}#</p>')
-            else:
-                f.write(f'<p style="color:red;">{show_html}</p>')
-            node = node.next
-            if node is None: break
-
-    for n in nodes: n.next = None   # break
-    return_dict['nodes'] = nodes
-    return_dict['segment_parts_for_gpt'] = segment_parts_for_gpt
-    return return_dict
-
-class LatexPaperSplit():
-    """
-    break down latex file to a linked list,
-    each node use a preserve flag to indicate whether it should
-    be proccessed by GPT.
-    """
-    def __init__(self) -> None:
-        self.nodes = None
-        self.msg = "*{\\scriptsize\\textbf{警告：该PDF由GPT-Academic开源项目调用大语言模型+Latex翻译插件一键生成，" + \
-            "版权归原文作者所有。翻译内容可靠性无保障，请仔细鉴别并以原文为准。" + \
-            "项目Github地址 \\url{https://github.com/binary-husky/gpt_academic/}。"
-        # 请您不要删除或修改这行警告，除非您是论文的原作者（如果您是论文原作者，欢迎加REAME中的QQ联系开发者）
-        self.msg_declare = "为了防止大语言模型的意外谬误产生扩散影响，禁止移除或修改此警告。}}\\\\" 
-
-
-    def merge_result(self, arr, mode, msg, buggy_lines=[], buggy_line_surgery_n_lines=10):
-        """
-        Merge the result after the GPT process completed
-        """
-        result_string = ""
-        node_cnt = 0
-        line_cnt = 0
-        
-        for node in self.nodes:
-            if node.preserve:
-                line_cnt += node.string.count('\n')
-                result_string += node.string
-            else:
-                translated_txt = fix_content(arr[node_cnt], node.string)
-                begin_line = line_cnt
-                end_line = line_cnt + translated_txt.count('\n')
-
-                # reverse translation if any error
-                if any([begin_line-buggy_line_surgery_n_lines <= b_line <= end_line+buggy_line_surgery_n_lines for b_line in buggy_lines]):
-                    translated_txt = node.string
-
-                result_string += translated_txt
-                node_cnt += 1
-                line_cnt += translated_txt.count('\n')
-
-        if mode == 'translate_zh':
-            pattern = re.compile(r'\\begin\{abstract\}.*\n')
-            match = pattern.search(result_string)
-            if not match:
-                # match \abstract{xxxx}
-                pattern_compile = re.compile(r"\\abstract\{(.*?)\}", flags=re.DOTALL)
-                match = pattern_compile.search(result_string)
-                position = match.regs[1][0]
-            else:
-                # match \begin{abstract}xxxx\end{abstract}
-                position = match.end()
-            result_string = result_string[:position] + self.msg + msg + self.msg_declare + result_string[position:]
-        return result_string
-
-
-    def split(self, txt, project_folder, opts): 
-        """
-        break down latex file to a linked list,
-        each node use a preserve flag to indicate whether it should
-        be proccessed by GPT.
-        P.S. use multiprocessing to avoid timeout error
-        """
-        import multiprocessing
-        manager = multiprocessing.Manager()
-        return_dict = manager.dict()
-        p = multiprocessing.Process(
-            target=split_subprocess, 
-            args=(txt, project_folder, return_dict, opts))
-        p.start()
-        p.join()
-        p.close()
-        self.nodes = return_dict['nodes']
-        self.sp = return_dict['segment_parts_for_gpt']
-        return self.sp
-
-
-class LatexPaperFileGroup():
-    """
-    use tokenizer to break down text according to max_token_limit
-    """
-    def __init__(self):
-        self.file_paths = []
-        self.file_contents = []
-        self.sp_file_contents = []
-        self.sp_file_index = []
-        self.sp_file_tag = []
-
-        # count_token
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        self.get_token_num = get_token_num
-
-    def run_file_split(self, max_token_limit=1900):
-        """
-        use tokenizer to break down text according to max_token_limit
-        """
-        for index, file_content in enumerate(self.file_contents):
-            if self.get_token_num(file_content) < max_token_limit:
-                self.sp_file_contents.append(file_content)
-                self.sp_file_index.append(index)
-                self.sp_file_tag.append(self.file_paths[index])
-            else:
-                from ..crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
-                for j, segment in enumerate(segments):
-                    self.sp_file_contents.append(segment)
-                    self.sp_file_index.append(index)
-                    self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
-        print('Segmentation: done')
-
-    def merge_result(self):
-        self.file_result = ["" for _ in range(len(self.file_paths))]
-        for r, k in zip(self.sp_file_result, self.sp_file_index):
-            self.file_result[k] += r
-
-    def write_result(self):
-        manifest = []
-        for path, res in zip(self.file_paths, self.file_result):
-            with open(path + '.polish.tex', 'w', encoding='utf8') as f:
-                manifest.append(path + '.polish.tex')
-                f.write(res)
-        return manifest
-
-
-def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, mode='proofread', switch_prompt=None, opts=[]):
-    import time, os, re
-    from ..crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-    from .latex_actions import LatexPaperFileGroup, LatexPaperSplit
-
-    #  <-------- 寻找主tex文件 ----------> 
-    maintex = find_main_tex_file(file_manifest, mode)
-    chatbot.append((f"定位主Latex文件", f'[Local Message] 分析结果：该项目的Latex主文件是{maintex}, 如果分析错误, 请立即终止程序, 删除或修改歧义文件, 然后重试。主程序即将开始, 请稍候。'))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    time.sleep(3)
-
-    #  <-------- 读取Latex文件, 将多文件tex工程融合为一个巨型tex ----------> 
-    main_tex_basename = os.path.basename(maintex)
-    assert main_tex_basename.endswith('.tex')
-    main_tex_basename_bare = main_tex_basename[:-4]
-    may_exist_bbl = pj(project_folder, f'{main_tex_basename_bare}.bbl')
-    if os.path.exists(may_exist_bbl):
-        shutil.copyfile(may_exist_bbl, pj(project_folder, f'merge.bbl'))
-        shutil.copyfile(may_exist_bbl, pj(project_folder, f'merge_{mode}.bbl'))
-        shutil.copyfile(may_exist_bbl, pj(project_folder, f'merge_diff.bbl'))
-
-    with open(maintex, 'r', encoding='utf-8', errors='replace') as f:
-        content = f.read()
-        merged_content = merge_tex_files(project_folder, content, mode)
-
-    with open(project_folder + '/merge.tex', 'w', encoding='utf-8', errors='replace') as f:
-        f.write(merged_content)
-
-    #  <-------- 精细切分latex文件 ----------> 
-    chatbot.append((f"Latex文件融合完成", f'[Local Message] 正在精细切分latex文件，这需要一段时间计算，文档越长耗时越长，请耐心等待。'))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    lps = LatexPaperSplit()
-    res = lps.split(merged_content, project_folder, opts) # 消耗时间的函数
-
-    #  <-------- 拆分过长的latex片段 ----------> 
-    pfg = LatexPaperFileGroup()
-    for index, r in enumerate(res):
-        pfg.file_paths.append('segment-' + str(index))
-        pfg.file_contents.append(r)
-
-    pfg.run_file_split(max_token_limit=1024)
-    n_split = len(pfg.sp_file_contents)
-
-    #  <-------- 根据需要切换prompt ----------> 
-    inputs_array, sys_prompt_array = switch_prompt(pfg, mode)
-    inputs_show_user_array = [f"{mode} {f}" for f in pfg.sp_file_tag]
-
-    if os.path.exists(pj(project_folder,'temp.pkl')):
-
-        #  <-------- 【仅调试】如果存在调试缓存文件，则跳过GPT请求环节 ----------> 
-        pfg = objload(file=pj(project_folder,'temp.pkl'))
-
-    else:
-        #  <-------- gpt 多线程请求 ----------> 
-        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-            inputs_array=inputs_array,
-            inputs_show_user_array=inputs_show_user_array,
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history_array=[[""] for _ in range(n_split)],
-            sys_prompt_array=sys_prompt_array,
-            # max_workers=5,  # 并行任务数量限制, 最多同时执行5个, 其他的排队等待
-            scroller_max_len = 40
-        )
-
-        #  <-------- 文本碎片重组为完整的tex片段 ----------> 
-        pfg.sp_file_result = []
-        for i_say, gpt_say, orig_content in zip(gpt_response_collection[0::2], gpt_response_collection[1::2], pfg.sp_file_contents):
-            pfg.sp_file_result.append(gpt_say)
-        pfg.merge_result()
-
-        # <-------- 临时存储用于调试 ----------> 
-        pfg.get_token_num = None
-        objdump(pfg, file=pj(project_folder,'temp.pkl'))
-
-    write_html(pfg.sp_file_contents, pfg.sp_file_result, chatbot=chatbot, project_folder=project_folder)
-
-    #  <-------- 写出文件 ----------> 
-    msg = f"当前大语言模型: {llm_kwargs['llm_model']}，当前语言模型温度设定: {llm_kwargs['temperature']}。"
-    final_tex = lps.merge_result(pfg.file_result, mode, msg)
-    objdump((lps, pfg.file_result, mode, msg), file=pj(project_folder,'merge_result.pkl'))
-
-    with open(project_folder + f'/merge_{mode}.tex', 'w', encoding='utf-8', errors='replace') as f:
-        if mode != 'translate_zh' or "binary" in final_tex: f.write(final_tex)
-        
-
-    #  <-------- 整理结果, 退出 ----------> 
-    chatbot.append((f"完成了吗？", 'GPT结果已输出, 即将编译PDF'))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    #  <-------- 返回 ----------> 
-    return project_folder + f'/merge_{mode}.tex'
-
-
-def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work_folder_modified, fixed_line=[]):
-    try:
-        with open(log_path, 'r', encoding='utf-8', errors='replace') as f:
-            log = f.read()
-        import re
-        buggy_lines = re.findall(tex_name+':([0-9]{1,5}):', log)
-        buggy_lines = [int(l) for l in buggy_lines]
-        buggy_lines = sorted(buggy_lines)
-        buggy_line = buggy_lines[0]-1
-        print("reversing tex line that has errors", buggy_line)
-
-        # 重组，逆转出错的段落
-        if buggy_line not in fixed_line:
-            fixed_line.append(buggy_line)
-
-        lps, file_result, mode, msg = objload(file=pj(work_folder_modified,'merge_result.pkl'))
-        final_tex = lps.merge_result(file_result, mode, msg, buggy_lines=fixed_line, buggy_line_surgery_n_lines=5*n_fix)
-
-        with open(pj(work_folder_modified, f"{tex_name_pure}_fix_{n_fix}.tex"), 'w', encoding='utf-8', errors='replace') as f:
-            f.write(final_tex)
-
-        return True, f"{tex_name_pure}_fix_{n_fix}", buggy_lines
-    except:
-        print("Fatal error occurred, but we cannot identify error, please download zip, read latex log, and compile manually.")
-        return False, -1, [-1]
-
-
-def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_folder_original, work_folder_modified, work_folder, mode='default'):
-    import os, time
-    n_fix = 1
-    fixed_line = []
-    max_try = 32
-    chatbot.append([f"正在编译PDF文档", f'编译已经开始。当前工作路径为{work_folder}，如果程序停顿5分钟以上，请直接去该路径下取回翻译结果，或者重启之后再度尝试 ...']); yield from update_ui(chatbot=chatbot, history=history)
-    chatbot.append([f"正在编译PDF文档", '...']); yield from update_ui(chatbot=chatbot, history=history); time.sleep(1); chatbot[-1] = list(chatbot[-1]) # 刷新界面
-    yield from update_ui_lastest_msg('编译已经开始...', chatbot, history)   # 刷新Gradio前端界面
-
-    while True:
-        import os
-        may_exist_bbl = pj(work_folder_modified, f'merge.bbl')
-        target_bbl = pj(work_folder_modified, f'{main_file_modified}.bbl')
-        if os.path.exists(may_exist_bbl) and not os.path.exists(target_bbl):
-            shutil.copyfile(may_exist_bbl, target_bbl)
-
-        # https://stackoverflow.com/questions/738755/dont-make-me-manually-abort-a-latex-compile-when-theres-an-error
-        yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译原始PDF ...', chatbot, history)   # 刷新Gradio前端界面
-        ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
-
-        yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译转化后的PDF ...', chatbot, history)   # 刷新Gradio前端界面
-        ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
-        
-        if ok and os.path.exists(pj(work_folder_modified, f'{main_file_modified}.pdf')):
-            # 只有第二步成功，才能继续下面的步骤
-            yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译BibTex ...', chatbot, history)    # 刷新Gradio前端界面
-            if not os.path.exists(pj(work_folder_original, f'{main_file_original}.bbl')):
-                ok = compile_latex_with_timeout(f'bibtex  {main_file_original}.aux', work_folder_original)
-            if not os.path.exists(pj(work_folder_modified, f'{main_file_modified}.bbl')):
-                ok = compile_latex_with_timeout(f'bibtex  {main_file_modified}.aux', work_folder_modified)
-
-            yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译文献交叉引用 ...', chatbot, history)  # 刷新Gradio前端界面
-            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
-            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
-            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
-            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
-
-            if mode!='translate_zh':
-                yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 使用latexdiff生成论文转化前后对比 ...', chatbot, history) # 刷新Gradio前端界面
-                print(    f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')
-                ok = compile_latex_with_timeout(f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex', os.getcwd())
-
-                yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 正在编译对比PDF ...', chatbot, history)   # 刷新Gradio前端界面
-                ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
-                ok = compile_latex_with_timeout(f'bibtex    merge_diff.aux', work_folder)
-                ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
-                ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
-
-        # <---------- 检查结果 ----------->
-        results_ = ""
-        original_pdf_success = os.path.exists(pj(work_folder_original, f'{main_file_original}.pdf'))
-        modified_pdf_success = os.path.exists(pj(work_folder_modified, f'{main_file_modified}.pdf'))
-        diff_pdf_success     = os.path.exists(pj(work_folder, f'merge_diff.pdf'))
-        results_ += f"原始PDF编译是否成功: {original_pdf_success};" 
-        results_ += f"转化PDF编译是否成功: {modified_pdf_success};" 
-        results_ += f"对比PDF编译是否成功: {diff_pdf_success};" 
-        yield from update_ui_lastest_msg(f'第{n_fix}编译结束:<br/>{results_}...', chatbot, history) # 刷新Gradio前端界面
-
-        if diff_pdf_success:
-            result_pdf = pj(work_folder_modified, f'merge_diff.pdf')    # get pdf path
-            promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
-        if modified_pdf_success:
-            yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 即将退出 ...', chatbot, history)    # 刷新Gradio前端界面
-            result_pdf = pj(work_folder_modified, f'{main_file_modified}.pdf') # get pdf path
-            origin_pdf = pj(work_folder_original, f'{main_file_original}.pdf') # get pdf path
-            if os.path.exists(pj(work_folder, '..', 'translation')):
-                shutil.copyfile(result_pdf, pj(work_folder, '..', 'translation', 'translate_zh.pdf'))
-            promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
-            # 将两个PDF拼接
-            if original_pdf_success: 
-                try:
-                    from .latex_toolbox import merge_pdfs
-                    concat_pdf = pj(work_folder_modified, f'comparison.pdf')
-                    merge_pdfs(origin_pdf, result_pdf, concat_pdf)
-                    promote_file_to_downloadzone(concat_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
-                except Exception as e:
-                    pass
-            return True # 成功啦
-        else:
-            if n_fix>=max_try: break
-            n_fix += 1
-            can_retry, main_file_modified, buggy_lines = remove_buggy_lines(
-                file_path=pj(work_folder_modified, f'{main_file_modified}.tex'), 
-                log_path=pj(work_folder_modified, f'{main_file_modified}.log'),
-                tex_name=f'{main_file_modified}.tex',
-                tex_name_pure=f'{main_file_modified}',
-                n_fix=n_fix,
-                work_folder_modified=work_folder_modified,
-                fixed_line=fixed_line
-            )
-            yield from update_ui_lastest_msg(f'由于最为关键的转化PDF编译失败, 将根据报错信息修正tex源文件并重试, 当前报错的latex代码处于第{buggy_lines}行 ...', chatbot, history)   # 刷新Gradio前端界面
-            if not can_retry: break
-
-    return False # 失败啦
-
-
-def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
-    # write html
-    try:
-        import shutil
-        from ..crazy_utils import construct_html
-        from toolbox import gen_time_str
-        ch = construct_html() 
-        orig = ""
-        trans = ""
-        final = []
-        for c,r in zip(sp_file_contents, sp_file_result): 
-            final.append(c)
-            final.append(r)
-        for i, k in enumerate(final): 
-            if i%2==0:
-                orig = k
-            if i%2==1:
-                trans = k
-                ch.add_row(a=orig, b=trans)
-        create_report_file_name = f"{gen_time_str()}.trans.html"
-        res = ch.save_file(create_report_file_name)
-        shutil.copyfile(res, pj(project_folder, create_report_file_name))
-        promote_file_to_downloadzone(file=res, chatbot=chatbot)
-    except:
-        from toolbox import trimmed_format_exc
-        print('writing html result failed:', trimmed_format_exc())
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Geografia E Historia 1 Eso Santillana.pdf [Extra Quality].md b/spaces/quidiaMuxgu/Expedit-SAM/Geografia E Historia 1 Eso Santillana.pdf [Extra Quality].md
deleted file mode 100644
index 5878dcaf38a2fc3f3dda30a28c6401020376c90c..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Geografia E Historia 1 Eso Santillana.pdf [Extra Quality].md	
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--- a/spaces/quidiaMuxgu/Expedit-SAM/Ladies VS Ricky Bahl Full Hd Movie 1080p Download.md	
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Luv Ka The End 1 Movie In Hindi Download.md b/spaces/quidiaMuxgu/Expedit-SAM/Luv Ka The End 1 Movie In Hindi Download.md
deleted file mode 100644
index c7e0439958be306fcee483cd7f16ed974f766e7d..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Luv Ka The End 1 Movie In Hindi Download.md	
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Marmoset Tool Bag 2 Full LINK Crack 19.md b/spaces/quidiaMuxgu/Expedit-SAM/Marmoset Tool Bag 2 Full LINK Crack 19.md
deleted file mode 100644
index a2ab7e0cfef8a7ca5f02ecdff987679bb855d9c5..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Marmoset Tool Bag 2 Full LINK Crack 19.md	
+++ /dev/null
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-<li>Animation Editor: You can use this feature to import and keyframe animations for your 3D models. You can also preview and edit animations in real time. You can use curves, dope sheet, or timeline to control your animations.</li>
-<li>Baking Tools: You can use this feature to bake high quality textures for your low poly models. You can bake normal maps, ambient occlusion maps, curvature maps, position maps, etc. You can also use interactive painting tools to fix seams and errors.</li>
-<li>Global Illumination: You can use this feature to simulate realistic lighting effects for your scene. You can use image-based lighting or sky lighting to create natural illumination. You can also adjust exposure, contrast, saturation, etc.</li>
-<li>Image-Based Lighting: You can use this feature to create realistic reflections and refractions for your scene. You can use HDR images or capture your own environment maps to create custom lighting.</li>
-<li>Occlusion: You can use this feature to create realistic shadows and depth for your scene. You can use screen space ambient occlusion or ray traced ambient occlusion to create soft shadows.</li>
-<li>Reflections: You can use this feature to create realistic reflections for your scene. You can use screen space reflections or ray traced reflections to create sharp or blurry reflections.</li>
-<li>Explosions: You can use this feature to create dynamic explosions for your scene. You can use bomb helpers or particle flow to create fireballs, smoke trails, sparks, etc.</li>
-<li>Shattering: You can use this feature to create realistic destructions of objects from collisions and explosions. You can use shatter modifiers or voronoi modifiers to fragment geometry into pieces.</li>
-<li>Slicing: You can use this feature to slice geometry with advanced features. You can use slice modifiers or draw cuts by mouse to cut geometry into parts.</li>
-<li>Cracks: You can use this feature to create animated cracks inside refractive objects. You can use cracks modifiers or draw cracks by mouse to create cracks on geometry.</li>
-<li>Voxels: You can use this feature to create voxels using geometry volume. You can use voxel modifiers or draw voxels by mouse to create voxelized geometry.</li>
-<li>Particle Flow: You can use this feature to generate geometry objects on particle death and dynamically continue their animation. You can use particle flow support or draw particles by mouse to create particle effects.</li>
-</ul>
-
-<h2>Examples of Marmoset Tool Bag 2 Creations</h2>
-<p>Here are some examples of what you can create with Marmoset Tool Bag 2:</p>
-
-<img src="https://i.ytimg.com/vi/0yQ6FZ7fL9w/maxresdefault.jpg" alt="Marmoset Toolbag - Realtime Rendering Showcase" width="600" height="338">
-<p>A realtime rendering showcase using Marmoset Tool Bag 2 features such as global illumination, image-based lighting, occlusion, reflections, explosions, shattering, slicing, cracks, voxels, particle flow, etc.</p>
-
-<img src="https://i.ytimg.com/vi/5XnLWm0Jxqk/maxresdefault.jpg" alt="Marmoset Toolbag - Baking Tutorial" width="600" height="338">
-<p>A baking tutorial using Marmoset Tool Bag 2 features such as baking tools, interactive painting tools, normal maps, ambient occlusion maps, curvature maps, position maps, etc.</p>
-
-<img src="https://i.ytimg.com/vi/9wY4a7xOQoA/maxresdefault.jpg" alt="Marmoset Toolbag - Animation Tutorial" width="600" height="338">
-<p>An animation tutorial using Marmoset Tool Bag 2 features such as animation editor, keyframing tools, curves, dope sheet, timeline, etc.</p>
-<p></p>
-
-<h2>Conclusion</h2>
-<p>Marmoset Tool Bag 2 is a powerful and full-featured 3D design and real-time rendering software for creating 3D artwork in a real-time environment. It has many features that you can use to create realistic and stunning 3D artwork such as real-time material editor, animation editor, baking tools,
-global illumination,
-image-based lighting,
-occlusion,
-reflections,
-explosions,
-shattering,
-slicing,
-cracks,
-voxels,
-particle flow,
-and more.
-You can download and install Marmoset Tool Bag 2 Full Crack 19 for free by following the steps in this article.
-You can also see some examples of what you can create with Marmoset Tool Bag 2 in this article.
-If you want to learn more about Marmoset Tool Bag 2,
-you can visit its official website at https://marmoset.co/toolbag/
-or watch some tutorials on YouTube.
-We hope you enjoyed this article and found it useful.
-Thank you for reading!</p>
-<h2>Conclusion</h2>
-<p>Marmoset Tool Bag 2 is a powerful and full-featured 3D design and real-time rendering software for creating 3D artwork in a real-time environment. It has many features that you can use to create realistic and stunning 3D artwork such as real-time material editor, animation editor, baking tools,
-global illumination,
-image-based lighting,
-occlusion,
-reflections,
-explosions,
-shattering,
-slicing,
-cracks,
-voxels,
-particle flow,
-and more.
-You can download and install Marmoset Tool Bag 2 Full Crack 19 for free by following the steps in this article.
-You can also see some examples of what you can create with Marmoset Tool Bag 2 in this article.
-If you want to learn more about Marmoset Tool Bag 2,
-you can visit its official website at https://marmoset.co/toolbag/
-or watch some tutorials on YouTube.
-We hope you enjoyed this article and found it useful.
-Thank you for reading!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image/app-txt2img.py b/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image/app-txt2img.py
deleted file mode 100644
index d473fcf8f1c9486df5bb2bd98ca57d2acf56b97f..0000000000000000000000000000000000000000
--- a/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image/app-txt2img.py
+++ /dev/null
@@ -1,249 +0,0 @@
-import asyncio
-import json
-import logging
-import traceback
-from pydantic import BaseModel
-
-from fastapi import FastAPI, WebSocket, HTTPException, WebSocketDisconnect
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import StreamingResponse, JSONResponse
-from fastapi.staticfiles import StaticFiles
-
-from diffusers import DiffusionPipeline, AutoencoderTiny
-from compel import Compel
-import torch
-
-try:
-    import intel_extension_for_pytorch as ipex
-except:
-    pass
-from PIL import Image
-import numpy as np
-import gradio as gr
-import io
-import uuid
-import os
-import time
-import psutil
-
-
-MAX_QUEUE_SIZE = int(os.environ.get("MAX_QUEUE_SIZE", 0))
-TIMEOUT = float(os.environ.get("TIMEOUT", 0))
-SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
-TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)   
-
-WIDTH = 768
-HEIGHT = 768
-# disable tiny autoencoder for better quality speed tradeoff
-USE_TINY_AUTOENCODER = False
-
-# check if MPS is available OSX only M1/M2/M3 chips
-mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
-xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
-device = torch.device(
-    "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu"
-)
-torch_device = device
-# change to torch.float16 to save GPU memory
-torch_dtype = torch.float32
-
-print(f"TIMEOUT: {TIMEOUT}")
-print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
-print(f"MAX_QUEUE_SIZE: {MAX_QUEUE_SIZE}")
-print(f"device: {device}")
-
-if mps_available:
-    device = torch.device("mps")
-    torch_device = "cpu"
-    torch_dtype = torch.float32
-
-if SAFETY_CHECKER == "True":
-    pipe = DiffusionPipeline.from_pretrained(
-        "SimianLuo/LCM_Dreamshaper_v7",
-    )
-else:
-    pipe = DiffusionPipeline.from_pretrained(
-        "SimianLuo/LCM_Dreamshaper_v7",
-        safety_checker=None,
-    )
-if USE_TINY_AUTOENCODER:
-    pipe.vae = AutoencoderTiny.from_pretrained(
-        "madebyollin/taesd", torch_dtype=torch_dtype, use_safetensors=True
-    )
-pipe.set_progress_bar_config(disable=True)
-pipe.to(device=torch_device, dtype=torch_dtype).to(device)
-pipe.unet.to(memory_format=torch.channels_last)
-
-# check if computer has less than 64GB of RAM using sys or os
-if psutil.virtual_memory().total < 64 * 1024**3:
-    pipe.enable_attention_slicing()
-
-if TORCH_COMPILE:
-    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-    pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
-
-    pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
-
-compel_proc = Compel(
-    tokenizer=pipe.tokenizer,
-    text_encoder=pipe.text_encoder,
-    truncate_long_prompts=False,
-)
-user_queue_map = {}
-
-
-class InputParams(BaseModel):
-    seed: int = 2159232
-    prompt: str
-    guidance_scale: float = 8.0
-    strength: float = 0.5
-    steps: int = 4
-    lcm_steps: int = 50
-    width: int = WIDTH
-    height: int = HEIGHT
-
-
-def predict(params: InputParams):
-    generator = torch.manual_seed(params.seed)
-    prompt_embeds = compel_proc(params.prompt)
-    # Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
-    results = pipe(
-        prompt_embeds=prompt_embeds,
-        generator=generator,
-        num_inference_steps=params.steps,
-        guidance_scale=params.guidance_scale,
-        width=params.width,
-        height=params.height,
-        original_inference_steps=params.lcm_steps,
-        output_type="pil",
-    )
-    nsfw_content_detected = (
-        results.nsfw_content_detected[0]
-        if "nsfw_content_detected" in results
-        else False
-    )
-    if nsfw_content_detected:
-        return None
-    return results.images[0]
-
-
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-@app.websocket("/ws")
-async def websocket_endpoint(websocket: WebSocket):
-    await websocket.accept()
-    if MAX_QUEUE_SIZE > 0 and len(user_queue_map) >= MAX_QUEUE_SIZE:
-        print("Server is full")
-        await websocket.send_json({"status": "error", "message": "Server is full"})
-        await websocket.close()
-        return
-
-    try:
-        uid = str(uuid.uuid4())
-        print(f"New user connected: {uid}")
-        await websocket.send_json(
-            {"status": "success", "message": "Connected", "userId": uid}
-        )
-        user_queue_map[uid] = {
-            "queue": asyncio.Queue(),
-        }
-        await websocket.send_json(
-            {"status": "start", "message": "Start Streaming", "userId": uid}
-        )
-        await handle_websocket_data(websocket, uid)
-    except WebSocketDisconnect as e:
-        logging.error(f"WebSocket Error: {e}, {uid}")
-        traceback.print_exc()
-    finally:
-        print(f"User disconnected: {uid}")
-        queue_value = user_queue_map.pop(uid, None)
-        queue = queue_value.get("queue", None)
-        if queue:
-            while not queue.empty():
-                try:
-                    queue.get_nowait()
-                except asyncio.QueueEmpty:
-                    continue
-
-
-@app.get("/queue_size")
-async def get_queue_size():
-    queue_size = len(user_queue_map)
-    return JSONResponse({"queue_size": queue_size})
-
-
-@app.get("/stream/{user_id}")
-async def stream(user_id: uuid.UUID):
-    uid = str(user_id)
-    try:
-        user_queue = user_queue_map[uid]
-        queue = user_queue["queue"]
-
-        async def generate():
-            while True:
-                params = await queue.get()
-                if params is None:
-                    continue
-
-                image = predict(params)
-                if image is None:
-                    continue
-                frame_data = io.BytesIO()
-                image.save(frame_data, format="JPEG")
-                frame_data = frame_data.getvalue()
-                if frame_data is not None and len(frame_data) > 0:
-                    yield b"--frame\r\nContent-Type: image/jpeg\r\n\r\n" + frame_data + b"\r\n"
-
-                await asyncio.sleep(1.0 / 120.0)
-
-        return StreamingResponse(
-            generate(), media_type="multipart/x-mixed-replace;boundary=frame"
-        )
-    except Exception as e:
-        logging.error(f"Streaming Error: {e}, {user_queue_map}")
-        traceback.print_exc()
-        return HTTPException(status_code=404, detail="User not found")
-
-
-async def handle_websocket_data(websocket: WebSocket, user_id: uuid.UUID):
-    uid = str(user_id)
-    user_queue = user_queue_map[uid]
-    queue = user_queue["queue"]
-    if not queue:
-        return HTTPException(status_code=404, detail="User not found")
-    last_time = time.time()
-    try:
-        while True:
-            params = await websocket.receive_json()
-            params = InputParams(**params)
-            while not queue.empty():
-                try:
-                    queue.get_nowait()
-                except asyncio.QueueEmpty:
-                    continue
-            await queue.put(params)
-            if TIMEOUT > 0 and time.time() - last_time > TIMEOUT:
-                await websocket.send_json(
-                    {
-                        "status": "timeout",
-                        "message": "Your session has ended",
-                        "userId": uid,
-                    }
-                )
-                await websocket.close()
-                return
-
-    except Exception as e:
-        logging.error(f"Error: {e}")
-        traceback.print_exc()
-
-
-app.mount("/", StaticFiles(directory="txt2img", html=True), name="public")
diff --git a/spaces/radames/UserControllableLT-Latent-Transformer/models/networks/__init__.py b/spaces/radames/UserControllableLT-Latent-Transformer/models/networks/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/radames/hello-pytesseract/app.py b/spaces/radames/hello-pytesseract/app.py
deleted file mode 100644
index 7fd7fab001e3fc3adb6fe3949eb0576b50bf932b..0000000000000000000000000000000000000000
--- a/spaces/radames/hello-pytesseract/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from PIL import Image
-import pytesseract
-import gradio as gr
-import os
-langs = []
-
-choices = os.popen('tesseract --list-langs').read().split('\n')[1:-1]
-
-blocks = gr.Blocks()
-
-
-# If you don't have tesseract executable in your PATH, include the following:
-# pytesseract.pytesseract.tesseract_cmd = r'<full_path_to_your_tesseract_executable>'
-# Example tesseract_cmd = r'C:\Program Files (x86)\Tesseract-OCR\tesseract'
-
-# Simple image to string
-# print(pytesseract.image_to_string(Image.open('eurotext.png')))
-
-# # French text image to string
-# print(pytesseract.image_to_string(Image.open('test-european.jpg'), lang='fra'))
-
-# # Get bounding box estimates
-# print(pytesseract.image_to_boxes(Image.open('test.png')))
-
-# # Get verbose data including boxes, confidences, line and page numbers
-# print(pytesseract.image_to_data(Image.open('test.png')))
-
-# # Get information about orientation and script detection
-# print(pytesseract.image_to_osd(Image.open('test.png'))
-
-
-def run(image, lang=None):
-    result = pytesseract.image_to_string(
-        image, lang=None if lang == [] else lang)
-    return result
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("## Hello pytesseract!")
-    with gr.Row():
-        with gr.Column():
-            image_in = gr.Image(type="pil")
-            lang = gr.Dropdown(choices)
-            btn = gr.Button("Run")
-        with gr.Column():
-            text_out = gr.TextArea()
-
-    examples = gr.Examples([["./eurotext.png", None]], fn=run, inputs=[
-                           image_in, lang], outputs=[text_out], cache_examples=False)
-    btn.click(fn=run, inputs=[image_in, lang], outputs=[text_out])
-
-demo.launch()
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Adobe CC 2019 Crack How to Get All the Creative Cloud Apps for Free.md b/spaces/raedeXanto/academic-chatgpt-beta/Adobe CC 2019 Crack How to Get All the Creative Cloud Apps for Free.md
deleted file mode 100644
index 0187b42067f8feba65dcd9766f29dac81084fdcd..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Adobe CC 2019 Crack How to Get All the Creative Cloud Apps for Free.md	
+++ /dev/null
@@ -1,134 +0,0 @@
-
-<h1>Adobe CC 2019 Crack: What You Need to Know</h1>
-<p>If you are a creative professional or a hobbyist who wants to use the best tools for your projects, you might have heard of Adobe CC 2019. This is the latest version of Adobe's Creative Cloud suite, which includes popular applications such as Photoshop, Illustrator, Premiere Pro, After Effects, and more. But you might also have heard of Adobe CC 2019 Crack, which is a way to bypass the license activation and use the software for free. Is this a good idea? What are the risks and consequences of using Adobe CC 2019 Crack? And what are the alternatives to it? In this article, we will answer these questions and help you make an informed decision.</p>
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- <h2>What is Adobe CC 2019?</h2>
-<p>Adobe CC 2019 is the name given to the collection of software applications that are part of the Creative Cloud suite. Creative Cloud is a subscription-based service that gives you access to over 20 apps for design, photography, video, audio, web development, and more. You can also get cloud storage, fonts, stock images, and other resources through Creative Cloud.</p>
- <h3>The features of Adobe CC 2019</h3>
-<p>Adobe CC 2019 introduced many new features and improvements to the existing apps. Some of the highlights are:</p>
-<ul>
-<li>Photoshop: Content-Aware Fill workspace, Frame tool, Symmetry mode, Live blend mode preview, etc.</li>
-<li>Illustrator: Freeform gradient, Global edit, Custom toolbar, Presentation mode, etc.</li>
-<li>Premiere Pro: Selective color grading, Motion graphics templates with responsive design, Audio ducking with Essential Sound panel, etc.</li>
-<li>After Effects: Advanced puppet engine, Depth passes for native 3D layers, Mesh sculpting tools, etc.</li>
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-</ul>
- <h3>The benefits of Adobe CC 2019</h3>
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-<ul>
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-</ul>
- <h2>What is Adobe CC 2019 Crack?</h2>
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- <h3>The definition of Adobe CC 2019 Crack</h3>
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- <h2>Conclusion</h2>
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Artmoney Pro V7 40 Cracked [TOP].md b/spaces/raedeXanto/academic-chatgpt-beta/Artmoney Pro V7 40 Cracked [TOP].md
deleted file mode 100644
index 98b0048591e6fc2673e49ba81dc057a84e80b077..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Artmoney Pro V7 40 Cracked [TOP].md	
+++ /dev/null
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-<h1>How to Use ArtMoney Pro v7.40 Cracked to Cheat in Any Game</h1>
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/FULL Ejercicios Resueltos De Ingenieria Economica De Blank Y Tarquin 6ta Edicion Descarga Gratis El PDF.md b/spaces/raedeXanto/academic-chatgpt-beta/FULL Ejercicios Resueltos De Ingenieria Economica De Blank Y Tarquin 6ta Edicion Descarga Gratis El PDF.md
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--- a/spaces/raedeXanto/academic-chatgpt-beta/FULL Ejercicios Resueltos De Ingenieria Economica De Blank Y Tarquin 6ta Edicion Descarga Gratis El PDF.md	
+++ /dev/null
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-<h1>FULL Ejercicios Resueltos De Ingenieria Economica De Blank Y Tarquin 6ta Edicion</h1>
- <p>Engineering economy is the discipline that deals with the evaluation of the economic feasibility of engineering projects. It involves the application of mathematical techniques to compare the costs and benefits of different alternatives, taking into account the time value of money, interest rates, inflation, depreciation, taxes, risk, uncertainty, and other factors. Engineering economy helps engineers, managers, and decision makers to select the best alternative among several competing options, based on economic criteria.</p>
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- <p>One of the most popular and widely used textbooks on engineering economy is <em>Ingenieria Economica</em> by Leland Blank and Anthony Tarquin. This book was first published in English as <em>Engineering Economy</em> in 1987, and has been translated into several languages, including Spanish. The sixth edition of this book was published in 2012, and contains updated examples, exercises, case studies, tables, charts, formulas, and references.</p>
- <p>The book covers the main topics of engineering economy in a clear, concise, and comprehensive way. It is organized into eight chapters, each with a set of solved exercises (ejercicios resueltos) that illustrate the concepts and methods discussed in the chapter. The book also includes a set of unsolved exercises (ejercicios propuestos) at the end of each chapter for students to practice their skills. The book also provides an appendix with useful tables of factors, interest rates, depreciation methods, tax rates, inflation rates, conversion factors, and symbols.</p>
- <h2>Chapter 1: Foundations of Engineering Economy</h2>
- <p>The first chapter introduces the basic concepts and principles of engineering economy. It explains what engineering economy is, why it is important, who uses it, how it is applied, what are the common terms and symbols used in it, what are the main evaluation criteria and alternatives considered in it, what are the differences between simple and compound interest, what are the concepts of rate of return and profitability, and what are some ethical issues involved in engineering economy.</p>
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- <h3>Time value of money and interest rates</h3>
- <p>The time value of money is the idea that money has different values at different points in time. This is because money can be invested or borrowed to earn or pay interest over time. Interest is the amount of money paid or received for the use of money. Interest can be expressed as a percentage of the principal (the amount of money invested or borrowed) per unit of time (usually a year). This percentage is called the interest rate.</p>
- <p>There are two types of interest rates:</p>
- <ul>
-<li>Nominal interest rate (i): This is the interest rate that does not take into account the effect of inflation or compounding frequency. It is also called the stated interest rate or annual percentage rate (APR).</li>
-<li>Effective interest rate (i<sub>e</sub>): This is the interest rate that takes into account the effect of inflation or compounding frequency. It is also called the true interest rate or annual percentage yield (APY).</li>
-</ul>
- <h3>Evaluation criteria and alternatives</h3>
- <p>Evaluation criteria are the measures of value that are used to identify the best alternative among several competing options. The primary evaluation criterion used in engineering economy is cost. Cost can be measured in different ways depending on the type of project or decision involved. Some common types of costs are:</p>
- <ul>
-<li>Initial cost (P): This is the cost incurred at the beginning or at time zero of a project or decision. It usually includes capital expenditures such as land acquisition, construction costs, equipment purchases, installation costs, etc.</li>
-<li>Annual cost (A): This is the cost incurred every year or every period during the life or duration of a project or decision. It usually includes operating costs such as maintenance costs, labor costs, energy costs, taxes, insurance premiums, etc.</li>
-<li>Total cost (T): This is the sum of all costs incurred over the life or duration of a project or decision. It usually includes initial cost plus annual cost multiplied by the number of years or periods.</li>
-<h2>Chapter 2: Factors: How Time and Interest Affect Money</h2>
- <p>The second chapter introduces the concept of factors and how they are used to simplify the calculations of present worth, future worth, annual worth, and other economic measures. Factors are mathematical expressions that relate a series of cash flows to a single equivalent value at a given point in time, using a given interest rate. Factors can be derived from the basic formulas of compound interest and can be represented in tables or spreadsheet functions.</p>
- <h3>Single-payment factors</h3>
- <p>Single-payment factors are factors that involve a single amount of money either now or in the future. There are two types of single-payment factors:</p>
- <ul>
-<li>Single-payment compound amount factor (F/P,i,n): This factor converts a present amount (P) to a future amount (F) after n periods at an interest rate of i per period.</li>
-<li>Single-payment present worth factor (P/F,i,n): This factor converts a future amount (F) to a present amount (P) n periods before at an interest rate of i per period.</li>
-</ul>
- <h3>Uniform-payment series factors</h3>
- <p>Uniform-payment series factors are factors that involve a series of equal payments or receipts that occur at regular intervals. There are four types of uniform-payment series factors:</p>
- <ul>
-<li>Uniform-series compound amount factor (F/A,i,n): This factor converts a uniform series of payments (A) to a future amount (F) at the end of n periods at an interest rate of i per period.</li>
-<li>Uniform-series sinking fund factor (A/F,i,n): This factor converts a future amount (F) to a uniform series of payments (A) that will accumulate to F after n periods at an interest rate of i per period.</li>
-<li>Uniform-series capital recovery factor (A/P,i,n): This factor converts a present amount (P) to a uniform series of payments (A) that will recover P after n periods at an interest rate of i per period.</li>
-<li>Uniform-series present worth factor (P/A,i,n): This factor converts a uniform series of payments (A) to a present amount (P) at the beginning of n periods at an interest rate of i per period.</li>
-</ul>
- <h3>Gradient series factors</h3>
- <p>Gradient series factors are factors that involve a series of payments or receipts that increase or decrease by a constant amount each period. There are two types of gradient series factors:</p>
- <ul>
-<li>Arithmetic gradient uniform-series conversion factor (A/G,i,n): This factor converts an arithmetic gradient series (G) to a uniform series of payments (A) over n periods at an interest rate of i per period.</li>
-<li>Arithmetic gradient present worth factor (P/G,i,n): This factor converts an arithmetic gradient series (G) to a present amount (P) at the beginning of n periods at an interest rate of i per period.</li>
-</ul>
- <h3>Using factors in spreadsheet functions</h3>
- <p>Factors can be used in spreadsheet functions to perform engineering economy calculations quickly and easily. Some common spreadsheet functions that use factors are:</p>
- <ul>
-<li>FV: This function calculates the future value of an investment based on constant payments and a constant interest rate.</li>
-<li>PV: This function calculates the present value of an investment based on constant payments and a constant interest rate.</li>
-<li>PMT: This function calculates the payment for a loan based on constant payments and a constant interest rate.</li>
-<li>NPER: This function calculates the number of periods for an investment based on constant payments and a constant interest rate.</li>
-<li>IRR: This function calculates the internal rate of return for a series of cash flows that occur at regular intervals.</li>
-<li>NPV: This function calculates the net present value for a series of cash flows that occur at regular intervals.</li>
-</ul>
- <h2>Chapter 3: Nominal and Effective Interest Rates</h2>
- <p>The third chapter explains the difference between nominal and effective interest rates and how they affect the value of money over time. It also shows how to convert between different interest rates and compounding frequencies using formulas and spreadsheet functions.</p>
- <h3>Nominal interest rate and compounding frequency</h3>
- <p>Nominal interest rate is the interest rate that does not take into account the effect of inflation or compounding frequency. It is also called the stated interest rate or annual percentage rate (APR). Nominal interest rate is usually expressed as an annual percentage, but it can be applied to any period of time, such as monthly, quarterly, semiannually, etc. The period of time over which nominal interest rate is applied is called the compounding frequency or compounding period.</p>
- <p>Compounding frequency is the number of times per year that interest is calculated and added to the principal. The more frequently interest is compounded, the more money is earned or paid over time. Compounding frequency can be expressed as:</p>
- <ul>
-<li>m: The number of compounding periods per year.</li>
-<li>t: The number of years or fraction thereof for which interest is calculated.</li>
-<li>n: The total number of compounding periods over t years. n = mt.</li>
-</ul>
- <h3>Effective interest rate and continuous compounding</h3>
- <p>Effective interest rate is the interest rate that takes into account the effect of inflation or compounding frequency. It is also called the true interest rate or annual percentage yield (APY). Effective interest rate is always expressed as an annual percentage, regardless of the compounding frequency. Effective interest rate is the actual rate at which money grows or declines over time.</p>
- <p>Continuous compounding is the limit case where interest is compounded infinitely many times per year. Continuous compounding results in the highest possible effective interest rate for any given nominal interest rate. Continuous compounding can be expressed as:</p>
- <ul>
-<li>e: The base of natural logarithms, approximately equal to 2.71828.</li>
-<li>i<sub>c</sub>: The nominal interest rate compounded continuously per year.</li>
-<li>i<sub>e</sub>: The effective interest rate compounded continuously per year.</li>
-</ul>
- <h3>Equivalence of different interest rates and compounding frequencies</h3>
- <h2>Chapter 4: Present Worth Analysis</h2>
- <p>The fourth chapter introduces the present worth method and how it is used to compare the economic feasibility of different alternatives. The present worth method involves calculating the equivalent value today or at time zero of all the cash flows associated with each alternative, using a given interest rate or minimum attractive rate of return (MARR). The alternative with the highest present worth or the lowest negative present worth is selected as the best one.</p>
- <h3>Present worth method and its advantages</h3>
- <p>The present worth method is one of the most widely used methods in engineering economy. It has several advantages, such as:</p>
- <ul>
-<li>It considers the time value of money and interest rates.</li>
-<li>It can handle any type of cash flow pattern (single, uniform, gradient, irregular, etc.).</li>
-<li>It can handle any type of project life (finite, infinite, different, etc.).</li>
-<li>It can handle any type of comparison (mutually exclusive, independent, constrained, etc.).</li>
-<li>It can handle any type of tax situation (before-tax, after-tax, etc.).</li>
-<li>It can handle any type of inflation or deflation.</li>
-<li>It can handle any type of risk or uncertainty.</li>
-<li>It is consistent with the goal of maximizing the wealth of the owner or investor.</li>
-</ul>
- <h3>Cash flow diagrams and notation</h3>
- <p>Cash flow diagrams are graphical representations of the cash flows associated with each alternative. They show the direction, magnitude, and timing of each cash flow on a horizontal time scale. Cash flow diagrams help to visualize and understand the economic aspects of each alternative. Cash flow diagrams use the following notation:</p>
- <ul>
-<li>P: Present amount or initial cost at time zero.</li>
-<li>F: Future amount or salvage value at the end of project life.</li>
-<li>A: Uniform amount or annual cost or benefit over project life.</li>
-<li>G: Gradient amount or increase or decrease in uniform amount each period.</li>
-<li>i: Interest rate or MARR per period.</li>
-<li>n: Number of periods or project life.</li>
-<li>PW: Present worth or net present value of all cash flows.</li>
-</ul>
- <h3>Present worth comparisons of alternatives</h3>
- <p>Present worth comparisons of alternatives involve calculating the present worth of each alternative using a common interest rate or MARR and selecting the best one based on a decision rule. The decision rule depends on the type of comparison involved. There are three types of comparisons:</p>
- <ul>
-<li>Mutually exclusive alternatives: Only one alternative can be selected among several competing options. The decision rule is to select the alternative with the highest present worth or the lowest negative present worth.</li>
-<li>Independent alternatives: Any number of alternatives can be selected as long as they meet a minimum requirement. The decision rule is to select all alternatives that have a positive present worth or a negative present worth lower than a specified limit.</li>
-<li>Constrained alternatives: Only a limited number of alternatives can be selected due to budgetary or other constraints. The decision rule is to select the combination of alternatives that maximizes the total present worth within the constraint.</li>
-</ul>
- <h3>Capitalized cost analysis</h3>
- <p>Capitalized cost analysis is a special case of present worth analysis where the project life is assumed to be infinite or very long. Capitalized cost is the present worth of all costs incurred over an infinite period of time. Capitalized cost analysis is useful for comparing alternatives that have different initial costs but identical annual costs, or vice versa. The decision rule is to select the alternative with the lowest capitalized cost.</p>
- <h2>Chapter 5: Annual Worth Analysis</h2>
- <h2>Chapter 6: Rate of Return Analysis</h2>
- <p>The sixth chapter introduces the rate of return method and how it is used to compare the economic feasibility of different alternatives. The rate of return method involves calculating the interest rate or percentage return that each alternative earns or costs over its life. The alternative with the highest rate of return or the lowest negative rate of return is selected as the best one.</p>
- <h3>Rate of return method and its advantages</h3>
- <p>The rate of return method is another widely used method in engineering economy. It has several advantages, such as:</p>
- <ul>
-<li>It considers the time value of money and interest rates.</li>
-<li>It can handle any type of cash flow pattern (single, uniform, gradient, irregular, etc.).</li>
-<li>It can handle any type of project life (finite, infinite, different, etc.).</li>
-<li>It can handle any type of comparison (mutually exclusive, independent, constrained, etc.).</li>
-<li>It can handle any type of tax situation (before-tax, after-tax, etc.).</li>
-<li>It can handle any type of inflation or deflation.</li>
-<li>It can handle any type of risk or uncertainty.</li>
-<li>It is consistent with the goal of maximizing the wealth of the owner or investor.</li>
-</ul>
- <h3>Internal rate of return and external rate of return</h3>
- <p>There are two types of rate of return that can be calculated for each alternative:</p>
- <ul>
-<li>Internal rate of return (IRR): This is the interest rate that makes the present worth or net present value of all cash flows equal to zero. It is also called the discounted cash flow rate of return or the yield on investment.</li>
-<li>External rate of return (ERR): This is the interest rate that makes the future worth or net future value of all cash flows equal to zero. It is also called the reinvestment rate of return or the compounded annual growth rate.</li>
-</ul>
- <h3>Rate of return comparisons of alternatives</h3>
- <p>Rate of return comparisons of alternatives involve calculating the internal rate of return and/or external rate of return for each alternative and selecting the best one based on a decision rule. The decision rule depends on the type of comparison involved. There are three types of comparisons:</p>
- <ul>
-<li>Mutually exclusive alternatives: Only one alternative can be selected among several competing options. The decision rule is to select the alternative with the highest internal rate of return or external rate of return that is greater than or equal to a given MARR.</li>
-<li>Independent alternatives: Any number of alternatives can be selected as long as they meet a minimum requirement. The decision rule is to select all alternatives that have an internal rate of return or external rate of return that is greater than or equal to a given MARR.</li>
-<li>Constrained alternatives: Only a limited number of alternatives can be selected due to budgetary or other constraints. The decision rule is to select the combination of alternatives that maximizes the total internal rate of return or external rate of return within the constraint.</li>
-</ul>
- <h3>Multiple rates of return and incremental analysis</h3>
- <h2>Chapter 7: Benefit/Cost Analysis and Public Sector Projects</h2>
- <p>The seventh chapter introduces the benefit/cost analysis and how it is used to evaluate the economic feasibility of public sector projects. Public sector projects are projects that are undertaken by government agencies or non-profit organizations for the benefit of the society or a specific group of people. Public sector projects involve costs and benefits that are not always measurable in monetary terms, such as environmental impacts, social welfare, public safety, etc. Benefit/cost analysis involves estimating and comparing the present worth of all costs and benefits associated with each project, using a given interest rate or social discount rate. The project with the highest benefit/cost ratio or net present value is selected as the best one.</p>
- <h3>Benefit/cost analysis and its advantages</h3>
- <p>Benefit/cost analysis is a special type of present worth analysis that is applied to public sector projects. It has several advantages, such as:</p>
- <ul>
-<li>It considers the time value of money and interest rates.</li>
-<li>It can handle any type of cash flow pattern (single, uniform, gradient, irregular, etc.).</li>
-<li>It can handle any type of project life (finite, infinite, different, etc.).</li>
-<li>It can handle any type of comparison (mutually exclusive, independent, constrained, etc.).</li>
-<li>It can handle any type of tax situation (before-tax, after-tax, etc.).</li>
-<li>It can handle any type of inflation or deflation.</li>
-<li>It can handle any type of risk or uncertainty.</li>
-<li>It can handle any type of intangible costs and benefits (environmental impacts, social welfare, public safety, etc.).</li>
-</ul>
- <h3>Benefit/cost ratio method and net present value method</h3>
- <p>There are two methods that can be used to perform benefit/cost analysis:</p>
- <ul>
-<li>Benefit/cost ratio method: This method involves calculating the ratio of the present worth of all benefits to the present worth of all costs for each project. The project with the highest benefit/cost ratio that is greater than or equal to one is selected as the best one.</li>
-<li>Net present value method: This method involves calculating the difference between the present worth of all benefits and the present worth of all costs for each project. The project with the highest net present value that is positive or zero is selected as the best one.</li>
-</ul>
- <h3>Benefit/cost analysis of mutually exclusive alternatives</h3>
- <p>Benefit/cost analysis of mutually exclusive alternatives involves comparing two or more projects that provide similar services or benefits but have different costs and/or benefits. The decision rule is to select the project with the highest benefit/cost ratio or net present value that is greater than or equal to one or zero respectively.</p>
- <h3>Sensitivity analysis and risk assessment</h3>
- <p>Sensitivity analysis and risk assessment are techniques that are used to evaluate the impact of uncertainty and variability on the outcome of benefit/cost analysis. Sensitivity analysis involves changing one or more parameters (such as interest rate, project life, cost estimate, benefit estimate, etc.) and observing how they affect the benefit/cost ratio or net present value of each project. Risk assessment involves identifying and quantifying the sources and probabilities of uncertainty and variability in the parameters and incorporating them into the benefit/cost analysis using techniques such as expected value, decision tree, Monte Carlo simulation, etc.</p>
- <h2>Chapter 8: Breakeven, Sensitivity, and Payback Analysis</h2>
- <p>The eighth chapter introduces three additional methods that are used to supplement or complement the main methods of engineering economy. These methods are breakeven analysis, sensitivity analysis, and payback period method. These methods are useful for analyzing situations where there are uncertainties or trade-offs involved in the economic evaluation of alternatives.</p>
- <h3>Breakeven analysis and its advantages</h3>
- <h2>Chapter 8: Breakeven, Sensitivity, and Payback Analysis</h2>
- <p>The eighth chapter introduces three additional methods that are used to supplement or complement the main methods of engineering economy. These methods are breakeven analysis, sensitivity analysis, and payback period method. These methods are useful for analyzing situations where there are uncertainties or trade-offs involved in the economic evaluation of alternatives.</p>
- <h3>Breakeven analysis and its advantages</h3>
- <p>Breakeven analysis is a technique that is used to determine the point at which two alternatives have equal economic measures (such as present worth, annual worth, rate of return, etc.). Breakeven analysis can be used to find the value of a parameter (such as revenue, cost, interest rate, project life, etc.) that makes two alternatives indifferent or equally attractive. Breakeven analysis has several advantages, such as:</p>
- <ul>
-<li>It can handle any type of cash flow pattern (single, uniform, gradient, irregular, etc.).</li>
-<li>It can handle any type of project life (finite, infinite, different, etc.).</li>
-<li>It can handle any type of comparison (mutually exclusive, independent, constrained, etc.).</li>
-<li>It can handle any type of tax situation (before-tax, after-tax, etc.).</li>
-<li>It can handle any type of inflation or deflation.</li>
-<li>It can handle any type of risk or uncertainty.</li>
-<li>It can provide insight into the sensitivity and robustness of the alternatives.</li>
-</ul>
- <h3>Sensitivity analysis and its advantages</h3>
- <p>Sensitivity analysis is a technique that is used to evaluate the impact of uncertainty and variability on the outcome of engineering economy analysis. Sensitivity analysis involves changing one or more parameters (such as interest rate, project life, cost estimate, benefit estimate, etc.) and observing how they affect the economic measure (such as present worth, annual worth, rate of return, etc.) of each alternative. Sensitivity analysis has several advantages, such as:</p>
- <ul>
-<li>It can handle any type of cash flow pattern (single, uniform, gradient, irregular, etc.).</li>
-<li>It can handle any type of project life (finite, infinite, different, etc.).</li>
-<li>It can handle any type of comparison (mutually exclusive, independent, constrained, etc.).</li>
-<li>It can handle any type of tax situation (before-tax, after-tax, etc.).</li>
-<li>It can handle any type of inflation or deflation.</li>
-<li>It can handle any type of risk or uncertainty.</li>
-<li>It can provide insight into the critical factors and trade-offs involved in the alternatives.</li>
-</ul>
- <h3>Payback period method and its advantages</h3>
- <p>Payback period method is a technique that is used to measure the time required for an alternative to recover its initial investment or cost. Payback period method involves calculating the number of periods (such as years) it takes for the cumulative cash flow of an alternative to become positive or zero. Payback period method has several advantages, such as:</p>
- <ul>
-<li>It is simple and easy to understand and apply.</li>
-<li>It considers the time value of money and interest rates.</li>
-<li>It can handle any type of cash flow pattern (single, uniform, gradient, irregular, etc.).</li>
-<li>It can provide insight into the liquidity and riskiness of the alternatives.</li>
-</ul>
- <h2></h2>
- <p>This is the end of the article. I hope you enjoyed reading it and learned something new about engineering economy. Thank you for your attention and feedback.</p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rajesh1729/question-answering-gradio/app.py b/spaces/rajesh1729/question-answering-gradio/app.py
deleted file mode 100644
index b422713ef5bfa15ab61495ff62db775acfcec8a3..0000000000000000000000000000000000000000
--- a/spaces/rajesh1729/question-answering-gradio/app.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-title = 'Question Answering demo with transformers and gradio'
-context = "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname, and French Guiana. States or departments in four nations contain 'Amazonas' in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
-question = "Which name is also used to describe the Amazon rainforest in English?"
-
-
-question_answerer = pipeline("question-answering", model='distilbert-base-cased-distilled-squad')
- #result  = question_answerer(question = question, context=context)
- #return result['answer']
-
-
-
-
-interface = gr.Interface.from_pipeline(question_answerer,
-    title = title,
-    theme = "peach",
-    examples = [[context, question]]).launch()
-    
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/CRACK Adobe Photoshop Lightroom CC 6.10.1 Portable Cracked.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/CRACK Adobe Photoshop Lightroom CC 6.10.1 Portable Cracked.md
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--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/CRACK Adobe Photoshop Lightroom CC 6.10.1 Portable Cracked.md	
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--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Digital Anarchy ToonIt Photo.md	
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index 7f8fb9efdfa59c9831fee8b9d01e47ab73bb418d..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Film Horor Indonesia Full Hantu Tanah 24 LINK.md	
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index 2e0cb7ed196705c56d52a3bb8e4ffc72f5335218..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/FULL ReCap Pro 2019 Free !!TOP!! Download.md	
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/FisaMonitorizareTemperaturaFrigideredoc VERIFIED.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/FisaMonitorizareTemperaturaFrigideredoc VERIFIED.md
deleted file mode 100644
index f6ba594aa5398bd6a22d69482b33390a47ecae81..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/FisaMonitorizareTemperaturaFrigideredoc VERIFIED.md	
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-<br />
-<h1>Fisa Monitorizare Temperatura Frigideredoc: What Is It and Why Is It Important?</h1>
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-<ul>
-<li>Date</li>
-<li>Refrigerator name or number</li>
-<li>Temperature reading in the morning (in degrees Celsius)</li>
-<li>Temperature reading in the evening (in degrees Celsius)</li>
-<li>Signature of the person who took the readings</li>
-<li>Signature of the person who verified the readings</li>
-<li>Alerts (if any)</li>
-</ul>
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-<p>To use a Fisa Monitorizare Temperatura Frigideredoc, follow these steps:</p>
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-</ol>
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-<p></p>
-
-<h2>What Are the Best Practices for Using a Fisa Monitorizare Temperatura Frigideredoc?</h2>
-
-<p>Using a Fisa Monitorizare Temperatura Frigideredoc is not enough to ensure the safety and quality of your food products. You also need to follow some best practices to make the most out of your Fisa Monitorizare Temperatura Frigideredoc. Some of the best practices are:</p>
-
-<ul>
-<li>Check your refrigerators regularly and keep them clean and well-maintained. Remove any expired, spoiled, or damaged food products and dispose of them properly. Clean any spills, leaks, or stains that may affect the temperature or hygiene of your refrigerators.</li>
-<li>Store your food products according to their type and shelf life. Use labels, containers, bags, or wraps to separate and protect your food products from cross-contamination. Follow the first-in first-out (FIFO) principle and use the oldest products first.</li>
-<li>Keep your refrigerators at the right temperature and humidity level. Avoid overloading or underloading your refrigerators and leave some space for air circulation. Do not open or close your refrigerator doors too frequently or for too long.</li>
-<li>Train your staff on how to use a Fisa Monitorizare Temperatura Frigideredoc and why it is important. Explain the benefits and risks of using a Fisa Monitorizare Temperatura Frigideredoc and how to handle any alerts or issues. Assign roles and responsibilities to your staff and supervise their performance.</li>
-<li>Review your Fisa Monitorizare Temperatura Frigideredoc documents regularly and look for any trends or patterns. Analyze the data and identify any problems or opportunities for improvement. Implement corrective or preventive actions and monitor their effectiveness.</li>
-</ul>
-
-<p>A Fisa Monitorizare Temperatura Frigideredoc is a valuable document that can help you improve your food safety and quality management system. By using it correctly and consistently, you can enhance your business performance and customer satisfaction.</p>
-<h2>How to Choose the Right Refrigerator for Your Food Business?</h2>
-
-<p>One of the factors that can affect the temperature of your refrigerators is the type and size of the refrigerator itself. Not all refrigerators are suitable for all kinds of food products. You need to choose the right refrigerator for your food business based on your needs and preferences. Some of the things you need to consider when choosing a refrigerator are:</p>
-
-<ul>
-<li>The capacity and dimensions of the refrigerator. You need to measure the space available in your premises and the amount of food products you need to store. You also need to consider the shape and size of your food products and how they fit in the refrigerator.</li>
-<li>The features and functions of the refrigerator. You need to look for a refrigerator that has a reliable and adjustable thermostat, a good insulation and ventilation system, a clear and easy-to-read display, a lock and alarm system, and a low noise level.</li>
-<li>The energy efficiency and maintenance of the refrigerator. You need to look for a refrigerator that has a low energy consumption and a long lifespan. You also need to check the warranty and service terms of the refrigerator and how often you need to clean and repair it.</li>
-<li>The price and quality of the refrigerator. You need to compare different models and brands of refrigerators and see which one offers the best value for money. You also need to check the reviews and ratings of other customers who have used the refrigerator.</li>
-</ul>
-
-<p>Choosing the right refrigerator for your food business can help you maintain the optimal temperature and quality of your food products. It can also help you save money, energy, and resources by avoiding unnecessary repairs or replacements.</p>
-
-<h2>How to Use a Fisa Monitorizare Temperatura Frigideredoc with Other Food Safety Documents?</h2>
-
-<p>A Fisa Monitorizare Temperatura Frigideredoc is not the only document you need to use for your food safety management system. You also need to use other documents that complement and support your Fisa Monitorizare Temperatura Frigideredoc. Some of these documents are:</p>
-
-<ul>
-<li>A Hazard Analysis and Critical Control Points (HACCP) plan. A HACCP plan is a document that identifies and controls the potential hazards that may affect your food products, such as biological, chemical, or physical hazards. It also establishes critical control points (CCPs) where you need to monitor and control these hazards.</li>
-<li>A Standard Operating Procedure (SOP) manual. A SOP manual is a document that describes the steps and procedures you need to follow for each operation or activity in your food business, such as receiving, storing, preparing, cooking, serving, or delivering food products.</li>
-<li>A Food Safety Checklist. A Food Safety Checklist is a document that helps you inspect and verify if you are complying with all the food safety standards and regulations in your premises, equipment, staff, processes, and products.</li>
-<li>A Food Safety Record. A Food Safety Record is a document that helps you document and keep track of all the data and information related to your food safety management system, such as temperature readings, test results, corrective actions, training sessions, audits, or complaints.</li>
-</ul>
-
-<p>Using a Fisa Monitorizare Temperatura Frigideredoc with other food safety documents can help you improve your food safety performance and compliance. It can also help you identify any gaps or weaknesses in your food safety management system and take appropriate actions to address them.</p>
-<h2>Conclusion</h2>
-
-<p>A Fisa Monitorizare Temperatura Frigideredoc is a document that helps you monitor and control the temperature of your refrigerators. It is a simple and effective tool that allows you to record the temperature readings of your refrigerators at different times of the day, and to check if they are within the optimal range. By using a Fisa Monitorizare Temperatura Frigideredoc, you can prevent food spoilage, contamination, and waste.</p>
-
-<p>To use a Fisa Monitorizare Temperatura Frigideredoc, you need to take the temperature readings of your refrigerators twice a day, write them down on the Fisa Monitorizare Temperatura Frigideredoc template, sign your name to confirm and verify the readings, compare the readings with the optimal range, and take action if there are any alerts or issues. You also need to keep your Fisa Monitorizare Temperatura Frigideredoc documents in a safe and accessible place.</p>
-
-<p>Using a Fisa Monitorizare Temperatura Frigideredoc has many benefits for your business and your customers. It helps you maintain the quality and freshness of your food products, prevent food spoilage, contamination, and waste, reduce your energy consumption and costs, comply with food safety regulations and standards, and improve your reputation and customer satisfaction.</p>
-
-<p>A Fisa Monitorizare Temperatura Frigideredoc is not the only document you need to use for your food safety management system. You also need to use other documents that complement and support your Fisa Monitorizare Temperatura Frigideredoc, such as a HACCP plan, a SOP manual, a Food Safety Checklist, and a Food Safety Record.</p>
-
-<p>Choosing the right refrigerator for your food business is also important for maintaining the optimal temperature and quality of your food products. You need to consider the capacity and dimensions, the features and functions, the energy efficiency and maintenance, and the price and quality of the refrigerator.</p>
-
-<p>A Fisa Monitorizare Temperatura Frigideredoc is a must-have document for any food business that uses refrigerators. By using it correctly and consistently, you can ensure that your food products are safe and fresh for consumption. You can also save money, energy, and resources by avoiding unnecessary repairs or replacements.</p> 3cee63e6c2<br />
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-<br />
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Freedom Fighter 2 Pc Game Free Download BEST.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Freedom Fighter 2 Pc Game Free Download BEST.md
deleted file mode 100644
index f050197b3775ef4ab84dc3b495cd04803967d790..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Freedom Fighter 2 Pc Game Free Download BEST.md	
+++ /dev/null
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-<h2>freedom fighter 2 pc game free download</h2><br /><p><b><b>DOWNLOAD</b> &#10031;&#10031;&#10031; <a href="https://urlgoal.com/2uCM6e">https://urlgoal.com/2uCM6e</a></b></p><br /><br />
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-​.For the installation instruction, click on the Read more....3.4 million acres in federal lands – which could be opened to oil and gas leasing. This includes national parks like Dinosaur National Monument, Anasazi Heritage Center, Dinosaur Ridge and Black Mesa.
-
-Dallin Oaks of the LDS Church who recently came to a “better understanding” of the relationship between man and nature, has endorsed Enbridge’s Transmountain pipeline and the two proposed tar sands pipelines in Wyoming, Montana, South Dakota and North Dakota. According to a press release of the LDS Church’s Ensign magazine, “Oaks said that if energy development is done in a ‘caring and environmentally sound’ manner, it can contribute to the building up of community.” He added that it “is important for us to put aside past battles and learn from the mistakes of the past.”
-
-In a letter to the press dated March 5, 2018, Oaks explained that the Transmountain pipeline “will provide affordable and dependable energy for the foreseeable future.” He also said that “environmental and climate concerns are important to us, and they should be considered by policymakers and by the general public when making decisions about energy development.”
-
-He also proposed that land which is currently off limits to energy development be opened for oil and gas leasing, while other federal lands should be “carefully considered” for such development. These recommendations would permit oil and gas development in the western states and public lands.
-
-As Oaks stated in his letter, “We want to be the environmental light on the hill.”
-
-In fact, LDS Church founder Joseph Smith taught that “There are two principles which should ever govern the treatment of animals, namely, kindness and justice.” Oaks has called the LDS Church “one of the most ecologically minded church organizations in the world,” noting that it has acted “to protect the natural environment.”
-
-Oaks’ comments and actions show the LDS Church’s support for the oil and gas industry. The LDS Church and Enbridge have had close relationships since 1978.
-
-The LDS Church purchased more than five million acres of land in Utah, mostly in the Wasatch Front, in order to protect the state’s natural resources from pollution and overuse. The land was subsequently turned into parks and reserves including Zion, Bryce, Capitol Reef, 4fefd39f24<br />
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diff --git a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/flyingthings3d_subset_bi_with_occ_384x768.py b/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/flyingthings3d_subset_bi_with_occ_384x768.py
deleted file mode 100644
index 0163b2ac4d49cf16f46da7c600ec5339c2d025ee..0000000000000000000000000000000000000000
--- a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/flyingthings3d_subset_bi_with_occ_384x768.py
+++ /dev/null
@@ -1,109 +0,0 @@
-test_dataset_type = 'Sintel'
-test_data_root = 'data/Sintel'
-
-img_norm_cfg = dict(mean=[0., 0., 0.], std=[255., 255., 255.], to_rgb=False)
-
-global_transform = dict(
-    translates=(0.05, 0.05),
-    zoom=(1.0, 1.5),
-    shear=(0.86, 1.16),
-    rotate=(-10., 10.))
-
-relative_transform = dict(
-    translates=(0.00375, 0.00375),
-    zoom=(0.985, 1.015),
-    shear=(1.0, 1.0),
-    rotate=(-1.0, 1.0))
-
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_occ=True),
-    dict(
-        type='ColorJitter',
-        brightness=0.5,
-        contrast=0.5,
-        saturation=0.5,
-        hue=0.5),
-    dict(type='RandomGamma', gamma_range=(0.7, 1.5)),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='GaussianNoise', sigma_range=(0, 0.04), clamp_range=(0., 1.)),
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-    dict(type='RandomFlip', prob=0.5, direction='vertical'),
-    dict(
-        type='RandomAffine',
-        global_transform=global_transform,
-        relative_transform=relative_transform,
-        check_bound=True),
-    dict(type='RandomCrop', crop_size=(384, 768)),
-    dict(type='DefaultFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['imgs', 'flow_fw_gt', 'flow_bw_gt', 'occ_fw_gt', 'occ_bw_gt'],
-        meta_keys=[
-            'img_fields', 'ann_fields', 'filename1', 'filename2',
-            'ori_filename1', 'ori_filename2', 'filename_flow_fw',
-            'ori_filename_flow_fw', 'filename_flow_bw', 'ori_filename_flow_bw',
-            'filename_occ_fw', 'ori_filename_occ_fw', 'filename_occ_bw',
-            'ori_filename_occ_bw', 'ori_shape', 'img_shape', 'img_norm_cfg'
-        ]),
-]
-
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(type='InputResize', exponent=6),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='TestFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['imgs'],
-        meta_keys=[
-            'flow_gt', 'filename1', 'filename2', 'ori_filename1',
-            'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg',
-            'scale_factor', 'pad_shape'
-        ])
-]
-
-flyingthings3d_subset_bidirection_train = dict(
-    type='FlyingThings3DSubset',
-    pipeline=train_pipeline,
-    data_root='data/FlyingThings3D_subset',
-    test_mode=False,
-    direction='bidirection',
-    scene=None)
-
-test_data_cleanpass = dict(
-    type=test_dataset_type,
-    data_root=test_data_root,
-    pipeline=test_pipeline,
-    test_mode=True,
-    pass_style='clean')
-
-test_data_finalpass = dict(
-    type=test_dataset_type,
-    data_root=test_data_root,
-    pipeline=test_pipeline,
-    test_mode=True,
-    pass_style='final')
-
-data = dict(
-    train_dataloader=dict(
-        samples_per_gpu=1,
-        workers_per_gpu=2,
-        drop_last=True,
-        persistent_workers=True),
-    val_dataloader=dict(
-        samples_per_gpu=1,
-        workers_per_gpu=2,
-        shuffle=False,
-        persistent_workers=True),
-    test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=2, shuffle=False),
-    train=flyingthings3d_subset_bidirection_train,
-    val=dict(
-        type='ConcatDataset',
-        datasets=[test_data_cleanpass, test_data_finalpass],
-        separate_eval=True),
-    test=dict(
-        type='ConcatDataset',
-        datasets=[test_data_cleanpass, test_data_finalpass],
-        separate_eval=True))
diff --git a/spaces/robin0307/MMOCR/configs/_base_/recog_datasets/ST_MJ_train.py b/spaces/robin0307/MMOCR/configs/_base_/recog_datasets/ST_MJ_train.py
deleted file mode 100644
index aedccc9df54829c2b841ba47882dea0cbcc8b23d..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/_base_/recog_datasets/ST_MJ_train.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Text Recognition Training set, including:
-# Synthetic Datasets: SynthText, Syn90k
-
-train_root = 'data/mixture'
-
-train_img_prefix1 = f'{train_root}/Syn90k/mnt/ramdisk/max/90kDICT32px'
-train_ann_file1 = f'{train_root}/Syn90k/label.lmdb'
-
-train1 = dict(
-    type='OCRDataset',
-    img_prefix=train_img_prefix1,
-    ann_file=train_ann_file1,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='lmdb',
-        parser=dict(type='LineJsonParser', keys=['filename', 'text'])),
-    pipeline=None,
-    test_mode=False)
-
-train_img_prefix2 = f'{train_root}/SynthText/' + \
-    'synthtext/SynthText_patch_horizontal'
-train_ann_file2 = f'{train_root}/SynthText/label.lmdb'
-
-train2 = {key: value for key, value in train1.items()}
-train2['img_prefix'] = train_img_prefix2
-train2['ann_file'] = train_ann_file2
-
-train_list = [train1, train2]
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/assigners/region_assigner.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/assigners/region_assigner.py
deleted file mode 100644
index 1833b89418820562333c7abfc2acea57deba4893..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/assigners/region_assigner.py
+++ /dev/null
@@ -1,222 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-
-from mmdet.core import anchor_inside_flags
-from ..builder import BBOX_ASSIGNERS
-from .assign_result import AssignResult
-from .base_assigner import BaseAssigner
-
-
-def calc_region(bbox, ratio, stride, featmap_size=None):
-    """Calculate region of the box defined by the ratio, the ratio is from the
-    center of the box to every edge."""
-    # project bbox on the feature
-    f_bbox = bbox / stride
-    x1 = torch.round((1 - ratio) * f_bbox[0] + ratio * f_bbox[2])
-    y1 = torch.round((1 - ratio) * f_bbox[1] + ratio * f_bbox[3])
-    x2 = torch.round(ratio * f_bbox[0] + (1 - ratio) * f_bbox[2])
-    y2 = torch.round(ratio * f_bbox[1] + (1 - ratio) * f_bbox[3])
-    if featmap_size is not None:
-        x1 = x1.clamp(min=0, max=featmap_size[1])
-        y1 = y1.clamp(min=0, max=featmap_size[0])
-        x2 = x2.clamp(min=0, max=featmap_size[1])
-        y2 = y2.clamp(min=0, max=featmap_size[0])
-    return (x1, y1, x2, y2)
-
-
-def anchor_ctr_inside_region_flags(anchors, stride, region):
-    """Get the flag indicate whether anchor centers are inside regions."""
-    x1, y1, x2, y2 = region
-    f_anchors = anchors / stride
-    x = (f_anchors[:, 0] + f_anchors[:, 2]) * 0.5
-    y = (f_anchors[:, 1] + f_anchors[:, 3]) * 0.5
-    flags = (x >= x1) & (x <= x2) & (y >= y1) & (y <= y2)
-    return flags
-
-
-@BBOX_ASSIGNERS.register_module()
-class RegionAssigner(BaseAssigner):
-    """Assign a corresponding gt bbox or background to each bbox.
-
-    Each proposals will be assigned with `-1`, `0`, or a positive integer
-    indicating the ground truth index.
-
-    - -1: don't care
-    - 0: negative sample, no assigned gt
-    - positive integer: positive sample, index (1-based) of assigned gt
-
-    Args:
-        center_ratio: ratio of the region in the center of the bbox to
-            define positive sample.
-        ignore_ratio: ratio of the region to define ignore samples.
-    """
-
-    def __init__(self, center_ratio=0.2, ignore_ratio=0.5):
-        self.center_ratio = center_ratio
-        self.ignore_ratio = ignore_ratio
-
-    def assign(self,
-               mlvl_anchors,
-               mlvl_valid_flags,
-               gt_bboxes,
-               img_meta,
-               featmap_sizes,
-               anchor_scale,
-               anchor_strides,
-               gt_bboxes_ignore=None,
-               gt_labels=None,
-               allowed_border=0):
-        """Assign gt to anchors.
-
-        This method assign a gt bbox to every bbox (proposal/anchor), each bbox
-        will be assigned with -1, 0, or a positive number. -1 means don't care,
-        0 means negative sample, positive number is the index (1-based) of
-        assigned gt.
-
-        The assignment is done in following steps, and the order matters.
-
-        1. Assign every anchor to 0 (negative)
-        2. (For each gt_bboxes) Compute ignore flags based on ignore_region
-           then assign -1 to anchors w.r.t. ignore flags
-        3. (For each gt_bboxes) Compute pos flags based on center_region then
-           assign gt_bboxes to anchors w.r.t. pos flags
-        4. (For each gt_bboxes) Compute ignore flags based on adjacent anchor
-           level then assign -1 to anchors w.r.t. ignore flags
-        5. Assign anchor outside of image to -1
-
-        Args:
-            mlvl_anchors (list[Tensor]): Multi level anchors.
-            mlvl_valid_flags (list[Tensor]): Multi level valid flags.
-            gt_bboxes (Tensor): Ground truth bboxes of image
-            img_meta (dict): Meta info of image.
-            featmap_sizes (list[Tensor]): Feature mapsize each level
-            anchor_scale (int): Scale of the anchor.
-            anchor_strides (list[int]): Stride of the anchor.
-            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
-            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
-                labelled as `ignored`, e.g., crowd boxes in COCO.
-            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
-            allowed_border (int, optional): The border to allow the valid
-                anchor. Defaults to 0.
-
-        Returns:
-            :obj:`AssignResult`: The assign result.
-        """
-        if gt_bboxes_ignore is not None:
-            raise NotImplementedError
-
-        num_gts = gt_bboxes.shape[0]
-        num_bboxes = sum(x.shape[0] for x in mlvl_anchors)
-
-        if num_gts == 0 or num_bboxes == 0:
-            # No ground truth or boxes, return empty assignment
-            max_overlaps = gt_bboxes.new_zeros((num_bboxes, ))
-            assigned_gt_inds = gt_bboxes.new_zeros((num_bboxes, ),
-                                                   dtype=torch.long)
-            if gt_labels is None:
-                assigned_labels = None
-            else:
-                assigned_labels = gt_bboxes.new_full((num_bboxes, ),
-                                                     -1,
-                                                     dtype=torch.long)
-            return AssignResult(
-                num_gts,
-                assigned_gt_inds,
-                max_overlaps,
-                labels=assigned_labels)
-
-        num_lvls = len(mlvl_anchors)
-        r1 = (1 - self.center_ratio) / 2
-        r2 = (1 - self.ignore_ratio) / 2
-
-        scale = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) *
-                           (gt_bboxes[:, 3] - gt_bboxes[:, 1]))
-        min_anchor_size = scale.new_full(
-            (1, ), float(anchor_scale * anchor_strides[0]))
-        target_lvls = torch.floor(
-            torch.log2(scale) - torch.log2(min_anchor_size) + 0.5)
-        target_lvls = target_lvls.clamp(min=0, max=num_lvls - 1).long()
-
-        # 1. assign 0 (negative) by default
-        mlvl_assigned_gt_inds = []
-        mlvl_ignore_flags = []
-        for lvl in range(num_lvls):
-            h, w = featmap_sizes[lvl]
-            assert h * w == mlvl_anchors[lvl].shape[0]
-            assigned_gt_inds = gt_bboxes.new_full((h * w, ),
-                                                  0,
-                                                  dtype=torch.long)
-            ignore_flags = torch.zeros_like(assigned_gt_inds)
-            mlvl_assigned_gt_inds.append(assigned_gt_inds)
-            mlvl_ignore_flags.append(ignore_flags)
-
-        for gt_id in range(num_gts):
-            lvl = target_lvls[gt_id].item()
-            featmap_size = featmap_sizes[lvl]
-            stride = anchor_strides[lvl]
-            anchors = mlvl_anchors[lvl]
-            gt_bbox = gt_bboxes[gt_id, :4]
-
-            # Compute regions
-            ignore_region = calc_region(gt_bbox, r2, stride, featmap_size)
-            ctr_region = calc_region(gt_bbox, r1, stride, featmap_size)
-
-            # 2. Assign -1 to ignore flags
-            ignore_flags = anchor_ctr_inside_region_flags(
-                anchors, stride, ignore_region)
-            mlvl_assigned_gt_inds[lvl][ignore_flags] = -1
-
-            # 3. Assign gt_bboxes to pos flags
-            pos_flags = anchor_ctr_inside_region_flags(anchors, stride,
-                                                       ctr_region)
-            mlvl_assigned_gt_inds[lvl][pos_flags] = gt_id + 1
-
-            # 4. Assign -1 to ignore adjacent lvl
-            if lvl > 0:
-                d_lvl = lvl - 1
-                d_anchors = mlvl_anchors[d_lvl]
-                d_featmap_size = featmap_sizes[d_lvl]
-                d_stride = anchor_strides[d_lvl]
-                d_ignore_region = calc_region(gt_bbox, r2, d_stride,
-                                              d_featmap_size)
-                ignore_flags = anchor_ctr_inside_region_flags(
-                    d_anchors, d_stride, d_ignore_region)
-                mlvl_ignore_flags[d_lvl][ignore_flags] = 1
-            if lvl < num_lvls - 1:
-                u_lvl = lvl + 1
-                u_anchors = mlvl_anchors[u_lvl]
-                u_featmap_size = featmap_sizes[u_lvl]
-                u_stride = anchor_strides[u_lvl]
-                u_ignore_region = calc_region(gt_bbox, r2, u_stride,
-                                              u_featmap_size)
-                ignore_flags = anchor_ctr_inside_region_flags(
-                    u_anchors, u_stride, u_ignore_region)
-                mlvl_ignore_flags[u_lvl][ignore_flags] = 1
-
-        # 4. (cont.) Assign -1 to ignore adjacent lvl
-        for lvl in range(num_lvls):
-            ignore_flags = mlvl_ignore_flags[lvl]
-            mlvl_assigned_gt_inds[lvl][ignore_flags] = -1
-
-        # 5. Assign -1 to anchor outside of image
-        flat_assigned_gt_inds = torch.cat(mlvl_assigned_gt_inds)
-        flat_anchors = torch.cat(mlvl_anchors)
-        flat_valid_flags = torch.cat(mlvl_valid_flags)
-        assert (flat_assigned_gt_inds.shape[0] == flat_anchors.shape[0] ==
-                flat_valid_flags.shape[0])
-        inside_flags = anchor_inside_flags(flat_anchors, flat_valid_flags,
-                                           img_meta['img_shape'],
-                                           allowed_border)
-        outside_flags = ~inside_flags
-        flat_assigned_gt_inds[outside_flags] = -1
-
-        if gt_labels is not None:
-            assigned_labels = torch.zeros_like(flat_assigned_gt_inds)
-            pos_flags = assigned_gt_inds > 0
-            assigned_labels[pos_flags] = gt_labels[
-                flat_assigned_gt_inds[pos_flags] - 1]
-        else:
-            assigned_labels = None
-
-        return AssignResult(
-            num_gts, flat_assigned_gt_inds, None, labels=assigned_labels)
diff --git a/spaces/rorallitri/biomedical-language-models/logs/HD Online Player (Final Destination 5 Hindi Dubbed Ful) !NEW!.md b/spaces/rorallitri/biomedical-language-models/logs/HD Online Player (Final Destination 5 Hindi Dubbed Ful) !NEW!.md
deleted file mode 100644
index 317749ba2433541dbe9528673fbb8802bf5b4388..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/HD Online Player (Final Destination 5 Hindi Dubbed Ful) !NEW!.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-
-<h1>How to Watch Final Destination 5 in Hindi Online for Free</h1>
-<p>Final Destination 5 is a 2011 horror movie that follows a group of coworkers who survive a bridge collapse thanks to a premonition, but soon realize that death is still after them. The movie is the fifth installment in the Final Destination franchise, and features Nicholas D'Agosto, Emma Bell, Miles Fisher, Courtney B. Vance, and Tony Todd.</p>
-<p>If you are a fan of the Final Destination series and want to watch the movie in Hindi, you might be wondering how to do that online for free. Well, there are some options available, but you should be careful of the quality and legality of the sources. Here are some tips on how to watch Final Destination 5 in Hindi online for free:</p>
-<h2>HD Online Player (Final Destination 5 Hindi Dubbed Ful)</h2><br /><p><b><b>DOWNLOAD</b> ===> <a href="https://tinurll.com/2uzodX">https://tinurll.com/2uzodX</a></b></p><br /><br />
-<ul>
-<li>One option is to use a streaming service that offers the movie in Hindi with ads or subtitles. For example, you can watch Final Destination 5 on Tubi TV for free with ads[^1^], or rent it on Amazon Video[^2^] with Hindi subtitles.</li>
-<li>Another option is to use a torrent site or a file-sharing platform that has the movie in Hindi dubbed or with subtitles. However, this option is risky and illegal, as you might download viruses or malware along with the movie, or face legal consequences for piracy. Moreover, the quality and accuracy of the dubbing or subtitles might be poor or unreliable.</li>
-<li>A third option is to use a VPN service that allows you to access geo-restricted content from other countries. For example, you might be able to watch Final Destination 5 on Netflix or Hulu if you connect to a server in India or Pakistan, where the movie might be available in Hindi. However, this option might also violate the terms of service of the streaming platforms, and you might need to pay for a VPN subscription.</li>
-</ul>
-<p>As you can see, there is no easy or safe way to watch Final Destination 5 in Hindi online for free. The best option is to use a legal and reliable streaming service that offers the movie in Hindi with ads or subtitles, such as Tubi TV or Amazon Video. Alternatively, you can buy or rent the DVD or Blu-ray of the movie that has Hindi audio or subtitles.</p>
-<p>Final Destination 5 is a thrilling and gruesome movie that will keep you on the edge of your seat. If you want to watch it in Hindi online for free, be careful of the sources you use and the risks you take. Enjoy the movie at your own peril!</p>
-
-<p>If you are curious about the plot of Final Destination 5, here is a brief summary. The movie begins with Sam having a vision of a bridge collapse that kills him and his coworkers. He manages to save himself, Molly, Peter, Candice, Nathan, Olivia, Isaac, and Dennis by getting off the bus before the disaster. However, they soon discover that they have cheated death and that death is coming back for them in the order they were supposed to die.</p>
-<p>One by one, the survivors meet their gruesome fates in various ways. Candice dies when she breaks her spine during a gymnastics practice. Isaac dies when he is crushed by a falling Buddha statue at a spa. Olivia dies when she falls out of a window after a botched laser eye surgery. Dennis dies when a wrench flies into his head at a factory. Nathan kills Roy in self-defense and unknowingly takes his place in death's design. Peter becomes paranoid and tries to kill Molly to take her life, but Sam intervenes and kills Peter instead.</p>
-<p>Sam and Molly think they have finally escaped death and decide to go to Paris together. However, it is revealed that they are on Flight 180, the same plane that exploded in the first Final Destination movie. Sam and Molly die in the plane explosion along with Alex Browning and his friends who also had a premonition. Nathan is also killed by a landing gear that crashes into a bar where he is drinking with a co-worker. The movie ends with Bludworth saying that death does not like to be cheated.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rosenthal/chess/chessfenbot/helper_webkit2png.py b/spaces/rosenthal/chess/chessfenbot/helper_webkit2png.py
deleted file mode 100644
index b2d9643d8ee6caef33c4163abc47bf0b5fe7db94..0000000000000000000000000000000000000000
--- a/spaces/rosenthal/chess/chessfenbot/helper_webkit2png.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# Wrapper on webkit2png to render chessboard layouts from lichess
-
-import sys
-from PyQt4.QtCore import *
-from PyQt4.QtGui import *
-from PyQt4.QtWebKit import *
-from PyQt4.QtNetwork import *
-
-# Download webkit2png from here and place in same directory
-# https://github.com/adamn/python-webkit2png/tree/master/webkit2png
-# 
-import webkit2png
-
-######################################################3
-# For scraping website screenshots
-class Options():
-  url = None
-  output_filename = None
-  cookie = None
-
-
-class ChessScreenshotServer():
-  """docstring for ChessScreenshotServer"""
-  def __init__(self, url=None, output_filename=None):
-    self.options = Options()
-    self.options.url = url
-    self.options.output_filename = output_filename
-
-    self.app = self.init_qtgui()
-
-  def init_qtgui(self, display=None, style=None, qtargs=None):
-    """Initiates the QApplication environment using the given args."""
-    if QApplication.instance():
-      print ("QApplication has already been instantiated.\n"
-         "Ignoring given arguments and returning existing QApplication.")
-      return QApplication.instance()
-
-    qtargs2 = [sys.argv[0]]
-    qtargs2.extend(qtargs or [])
-
-    return QApplication(qtargs2)
-
-  def renderScreenshotToFile(self):
-    """This is run within QT"""
-    try:
-      renderer = webkit2png.WebkitRenderer()
-      # renderer.wait = 5
-      renderer.qWebSettings[QWebSettings.JavascriptEnabled] = True # Enable javascript
-      if self.options.cookie:
-        renderer.cookies = [self.options.cookie]
-      with open(self.options.output_filename, 'w') as f:
-        renderer.render_to_file(res=self.options.url, file_object=f)
-        print "\tSaved screenshot to '%s'" % f.name
-      QApplication.exit(0)
-    except RuntimeError, e:
-      print "Error:", e
-      print >> sys.stderr, e
-      QApplication.exit(1)
-
-
-  def takeScreenshot(self, url=None, output_filename=None):
-    if url:
-      self.options.url = url
-    if output_filename:
-      self.options.output_filename = output_filename
-    
-    QTimer.singleShot(0, self.renderScreenshotToFile)
-    return self.app.exec_()
-
-  def takeChessScreenshot(self, fen_string=None, output_filename=None, 
-                          cookie=None): 
-    """Take uncropped screenshot of lichess board of FEN string and save to file"""
-    url_template = "http://en.lichess.org/editor/%s"
-    if cookie:
-      self.options.cookie = cookie
-    return self.takeScreenshot(url_template % fen_string, output_filename)
\ No newline at end of file
diff --git a/spaces/runa91/barc_gradio/src/stacked_hourglass/utils/evaluation.py b/spaces/runa91/barc_gradio/src/stacked_hourglass/utils/evaluation.py
deleted file mode 100644
index b02a4d804332ae263c4fb005d36cccd967ade029..0000000000000000000000000000000000000000
--- a/spaces/runa91/barc_gradio/src/stacked_hourglass/utils/evaluation.py
+++ /dev/null
@@ -1,188 +0,0 @@
-# Modified from:
-#   https://github.com/anibali/pytorch-stacked-hourglass 
-#   https://github.com/bearpaw/pytorch-pose
-
-import math
-import torch
-from kornia.geometry.subpix import dsnt     # kornia 0.4.0
-import torch.nn.functional as F
-from .transforms import transform_preds
-
-__all__ = ['get_preds', 'get_preds_soft', 'calc_dists', 'dist_acc', 'accuracy', 'final_preds_untransformed',
-           'final_preds', 'AverageMeter']
-
-def get_preds(scores, return_maxval=False):
-    ''' get predictions from score maps in torch Tensor
-        return type: torch.LongTensor
-    '''
-    assert scores.dim() == 4, 'Score maps should be 4-dim'
-    maxval, idx = torch.max(scores.view(scores.size(0), scores.size(1), -1), 2)
-
-    maxval = maxval.view(scores.size(0), scores.size(1), 1)
-    idx = idx.view(scores.size(0), scores.size(1), 1) + 1
-
-    preds = idx.repeat(1, 1, 2).float()
-
-    preds[:,:,0] = (preds[:,:,0] - 1) % scores.size(3) + 1
-    preds[:,:,1] = torch.floor((preds[:,:,1] - 1) / scores.size(3)) + 1
-
-    pred_mask = maxval.gt(0).repeat(1, 1, 2).float()    # values > 0
-    preds *= pred_mask
-    if return_maxval:
-        return preds, maxval
-    else:
-        return preds
-
-
-def get_preds_soft(scores, return_maxval=False, norm_coords=False, norm_and_unnorm_coords=False):
-    ''' get predictions from score maps in torch Tensor
-        predictions are made assuming a logit output map
-        return type: torch.LongTensor
-    '''
-
-    # New: work on logit predictions
-    scores_norm = dsnt.spatial_softmax2d(scores, temperature=torch.tensor(1))
-    # maxval_norm, idx_norm = torch.max(scores_norm.view(scores.size(0), scores.size(1), -1), 2)
-    # from unnormalized to normalized see:
-    # from -1to1 to 0to64
-    # see https://github.com/kornia/kornia/blob/b9ffe7efcba7399daeeb8028f10c22941b55d32d/kornia/utils/grid.py#L7 (line 40)
-    # xs = (xs / (width - 1) - 0.5) * 2
-    # ys = (ys / (height - 1) - 0.5) * 2
-
-    device = scores.device
-
-    if return_maxval:
-        preds_normalized = dsnt.spatial_expectation2d(scores_norm, normalized_coordinates=True) 
-        # grid_sample(input, grid, mode='bilinear', padding_mode='zeros')
-        gs_input_single = scores_norm.reshape((-1, 1, scores_norm.shape[2], scores_norm.shape[3]))     # (120, 1, 64, 64)
-        gs_input = scores_norm.reshape((-1, 1, scores_norm.shape[2], scores_norm.shape[3]))     # (120, 1, 64, 64)
-
-        half_pad = 2
-        gs_input_single_padded = F.pad(input=gs_input_single, pad=(half_pad, half_pad, half_pad, half_pad, 0, 0, 0, 0), mode='constant', value=0)
-        gs_input_all = torch.zeros((gs_input_single.shape[0], 9, gs_input_single.shape[2], gs_input_single.shape[3])).to(device)
-        ind_tot = 0
-        for ind0 in [-1, 0, 1]:
-            for ind1 in [-1, 0, 1]:
-                gs_input_all[:, ind_tot, :, :] = gs_input_single_padded[:, 0, half_pad+ind0:-half_pad+ind0, half_pad+ind1:-half_pad+ind1]
-                ind_tot +=1
-
-        gs_grid = preds_normalized.reshape((-1, 2))[:, None, None, :]                           # (120, 1, 1, 2)
-        gs_output_all = F.grid_sample(gs_input_all, gs_grid, mode='nearest', padding_mode='zeros', align_corners=True).reshape((gs_input_all.shape[0], gs_input_all.shape[1], 1))
-        gs_output = gs_output_all.sum(axis=1)
-        # scores_norm[0, :, :, :].max(axis=2)[0].max(axis=1)[0]
-        # gs_output[0, :, 0]
-        gs_output_resh = gs_output.reshape((scores_norm.shape[0], scores_norm.shape[1], 1))
-
-        if norm_and_unnorm_coords:
-            preds = dsnt.spatial_expectation2d(scores_norm, normalized_coordinates=False) + 1
-            return preds, preds_normalized, gs_output_resh      
-        elif norm_coords:
-            return preds_normalized, gs_output_resh
-        else:
-            preds = dsnt.spatial_expectation2d(scores_norm, normalized_coordinates=False) + 1
-            return preds, gs_output_resh
-    else:
-        if norm_coords:
-            preds_normalized = dsnt.spatial_expectation2d(scores_norm, normalized_coordinates=True) 
-            return preds_normalized
-        else:
-            preds = dsnt.spatial_expectation2d(scores_norm, normalized_coordinates=False) + 1
-            return preds
-
-
-def calc_dists(preds, target, normalize):
-    preds = preds.float()
-    target = target.float()
-    dists = torch.zeros(preds.size(1), preds.size(0))
-    for n in range(preds.size(0)):
-        for c in range(preds.size(1)):
-            if target[n,c,0] > 1 and target[n, c, 1] > 1:
-                dists[c, n] = torch.dist(preds[n,c,:], target[n,c,:])/normalize[n]
-            else:
-                dists[c, n] = -1
-    return dists
-
-def dist_acc(dist, thr=0.5):
-    ''' Return percentage below threshold while ignoring values with a -1 '''
-    dist = dist[dist != -1]
-    if len(dist) > 0:
-        return 1.0 * (dist < thr).sum().item() / len(dist)
-    else:
-        return -1
-
-def accuracy(output, target, idxs=None, thr=0.5):
-    ''' Calculate accuracy according to PCK, but uses ground truth heatmap rather than x,y locations
-        First value to be returned is average accuracy across 'idxs', followed by individual accuracies
-    '''
-    if idxs is None:
-        idxs = list(range(target.shape[-3]))
-    preds   = get_preds_soft(output)     # get_preds(output)
-    gts     = get_preds(target)
-    norm    = torch.ones(preds.size(0))*output.size(3)/10
-    dists   = calc_dists(preds, gts, norm)
-
-    acc = torch.zeros(len(idxs)+1)
-    avg_acc = 0
-    cnt = 0
-
-    for i in range(len(idxs)):
-        acc[i+1] = dist_acc(dists[idxs[i]], thr=thr)
-        if acc[i+1] >= 0:
-            avg_acc = avg_acc + acc[i+1]
-            cnt += 1
-
-    if cnt != 0:
-        acc[0] = avg_acc / cnt
-    return acc
-
-def final_preds_untransformed(output, res):
-    coords = get_preds_soft(output)     # get_preds(output) # float type
-
-    # pose-processing
-    for n in range(coords.size(0)):
-        for p in range(coords.size(1)):
-            hm = output[n][p]
-            px = int(math.floor(coords[n][p][0]))
-            py = int(math.floor(coords[n][p][1]))
-            if px > 1 and px < res[0] and py > 1 and py < res[1]:
-                diff = torch.Tensor([hm[py - 1][px] - hm[py - 1][px - 2], hm[py][px - 1]-hm[py - 2][px - 1]])
-                coords[n][p] += diff.sign() * .25
-    coords += 0.5
-
-    if coords.dim() < 3:
-        coords = coords.unsqueeze(0)
-
-    coords -= 1  # Convert from 1-based to 0-based coordinates
-
-    return coords
-
-def final_preds(output, center, scale, res):
-    coords = final_preds_untransformed(output, res)
-    preds = coords.clone()
-
-    # Transform back
-    for i in range(coords.size(0)):
-        preds[i] = transform_preds(coords[i], center[i], scale[i], res)
-
-    if preds.dim() < 3:
-        preds = preds.unsqueeze(0)
-
-    return preds
-
-
-class AverageMeter(object):
-    """Computes and stores the average and current value"""
-    def __init__(self):
-        self.reset()
-
-    def reset(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-
-    def update(self, val, n=1):
-        self.val = val
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
diff --git a/spaces/rupeshs/fastsdcpu/backend/lcm_text_to_image.py b/spaces/rupeshs/fastsdcpu/backend/lcm_text_to_image.py
deleted file mode 100644
index 7efa5ec3764419109146c0b5dc89f4dcf5673f0e..0000000000000000000000000000000000000000
--- a/spaces/rupeshs/fastsdcpu/backend/lcm_text_to_image.py
+++ /dev/null
@@ -1,115 +0,0 @@
-from typing import Any
-from diffusers import DiffusionPipeline
-from os import path
-import torch
-from backend.models.lcmdiffusion_setting import LCMDiffusionSetting
-import numpy as np
-from constants import DEVICE
-
-
-if DEVICE == "cpu":
-    from backend.lcmdiffusion.pipelines.openvino.lcm_ov_pipeline import (
-        OVLatentConsistencyModelPipeline,
-    )
-    from backend.lcmdiffusion.pipelines.openvino.lcm_scheduler import (
-        LCMScheduler,
-    )
-
-
-class LCMTextToImage:
-    def __init__(
-        self,
-        device: str = "cpu",
-    ) -> None:
-        self.pipeline = None
-        self.use_openvino = False
-        self.device = None
-        self.previous_model_id = None
-
-    def _get_lcm_diffusion_pipeline_path(self) -> str:
-        script_path = path.dirname(path.abspath(__file__))
-        file_path = path.join(
-            script_path,
-            "lcmdiffusion",
-            "pipelines",
-            "latent_consistency_txt2img.py",
-        )
-        return file_path
-
-    def init(
-        self,
-        model_id: str,
-        use_openvino: bool = False,
-        device: str = "cpu",
-        use_local_model: bool = False,
-    ) -> None:
-        self.device = device
-        self.use_openvino = use_openvino
-        if self.pipeline is None or self.previous_model_id != model_id:
-            if self.use_openvino and DEVICE == "cpu":
-                if self.pipeline:
-                    del self.pipeline
-                scheduler = LCMScheduler.from_pretrained(
-                    model_id,
-                    subfolder="scheduler",
-                )
-                self.pipeline = OVLatentConsistencyModelPipeline.from_pretrained(
-                    model_id,
-                    scheduler=scheduler,
-                    compile=False,
-                    local_files_only=use_local_model,
-                )
-            else:
-                if self.pipeline:
-                    del self.pipeline
-
-                self.pipeline = DiffusionPipeline.from_pretrained(
-                    model_id,
-                    custom_pipeline=self._get_lcm_diffusion_pipeline_path(),
-                    custom_revision="main",
-                    local_files_only=use_local_model,
-                )
-                self.pipeline.to(
-                    torch_device=self.device,
-                    torch_dtype=torch.float32,
-                )
-            self.previous_model_id = model_id
-
-    def generate(
-        self,
-        lcm_diffusion_setting: LCMDiffusionSetting,
-        reshape: bool = False,
-    ) -> Any:
-        if lcm_diffusion_setting.use_seed:
-            cur_seed = lcm_diffusion_setting.seed
-            if self.use_openvino:
-                np.random.seed(cur_seed)
-            else:
-                torch.manual_seed(cur_seed)
-
-        if self.use_openvino and DEVICE == "cpu":
-            print("Using OpenVINO")
-            if reshape:
-                print("Reshape and compile")
-                self.pipeline.reshape(
-                    batch_size=1,
-                    height=lcm_diffusion_setting.image_height,
-                    width=lcm_diffusion_setting.image_width,
-                    num_images_per_prompt=lcm_diffusion_setting.number_of_images,
-                )
-                self.pipeline.compile()
-
-        if not lcm_diffusion_setting.use_safety_checker:
-            self.pipeline.safety_checker = None
-
-        result_images = self.pipeline(
-            prompt=lcm_diffusion_setting.prompt,
-            num_inference_steps=lcm_diffusion_setting.inference_steps,
-            guidance_scale=lcm_diffusion_setting.guidance_scale,
-            width=lcm_diffusion_setting.image_width,
-            height=lcm_diffusion_setting.image_height,
-            output_type="pil",
-            num_images_per_prompt=lcm_diffusion_setting.number_of_images,
-        ).images
-
-        return result_images
diff --git a/spaces/ruslanmv/Clone-Your-Voice/encoder/__init__.py b/spaces/ruslanmv/Clone-Your-Voice/encoder/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/sailormars18/Yelp-reviews-usingGPT2/app.py b/spaces/sailormars18/Yelp-reviews-usingGPT2/app.py
deleted file mode 100644
index b385b7fa4813658634719df43a5444651e17b8ec..0000000000000000000000000000000000000000
--- a/spaces/sailormars18/Yelp-reviews-usingGPT2/app.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import subprocess
-subprocess.run(["pip", "install","gradio","torch","transformers"])
-import re
-import gradio as gr
-import torch
-import transformers
-
-import json
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Define a function for generating text based on a prompt using the fine-tuned GPT-2 model and the tokenizer
-def generate_text(prompt, length=100, theme=None, **kwargs):
-
-    model_url = "https://huggingface.co/spaces/sailormars18/Yelp-reviews-usingGPT2/blob/main/pytorch_model.bin"
-    config_url = "https://huggingface.co/spaces/sailormars18/Yelp-reviews-usingGPT2/blob/main/config.json"
-    generation_config_url = "https://huggingface.co/spaces/sailormars18/Yelp-reviews-usingGPT2/blob/main/generation_config.json"
-    
-    # Load the model from the Hugging Face space
-    model = transformers.GPT2LMHeadModel.from_pretrained(model_url).to(device)
-    
-    # Load the tokenizer from the Hugging Face space
-    tokenizer = transformers.GPT2Tokenizer.from_pretrained(config_url)
-
-    # If a theme is specified, add it to the prompt as a prefix for a special token
-    if theme:
-        prompt = f"<{theme.strip()}> {prompt.strip()}"
-
-    # Encode the input prompt
-    input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
-    attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=device)
-    pad_token_id = tokenizer.eos_token_id
-
-    # Set the max length of the generated text based on the input parameter
-    max_length = length if length > 0 else 100
-
-    # Generate the text using the model
-    sample_outputs = model.generate(
-        input_ids,
-        attention_mask=attention_mask,
-        pad_token_id=pad_token_id,
-        do_sample=True,
-        max_length=max_length,
-        top_k=50,
-        top_p=0.95,
-        temperature=0.8,
-        num_return_sequences=1,
-        no_repeat_ngram_size=2,
-        repetition_penalty=1.5,
-    )
-
-    # Decode the generated text
-    generated_text = tokenizer.decode(sample_outputs[0], skip_special_tokens=True)
-
-    # Postprocessing of the generated text
-    generated_text = generated_text.strip().strip('"') # Remove leading and trailing whitespace, remove any leading and trailing quotation marks
-    generated_text = re.sub(r'<([^>]+)>', '', generated_text) # Find the special token in the generated text and remove it
-    generated_text = re.sub(r'^\d+|^"', '', generated_text) # Remove any leading numeric characters and quotation marks
-    generated_text = generated_text.replace('\n', '') # Remove any newline characters from the generated text
-    generated_text = re.sub(r'[^\w\s]+', '', generated_text) # Remove any other unwanted special characters
-    generated_text = generated_text.capitalize()
-
-    return generated_text
-
-# Define a Gradio interface for the generate_text function
-iface = gr.Interface(
-    fn=generate_text,
-    inputs=[
-        "text", 
-        gr.inputs.Slider(minimum=10, maximum=100, default=50, label='Length of text'),
-        gr.inputs.Textbox(default='Food', label='Theme')
-    ],
-    outputs=[gr.outputs.Textbox(label='Generated Text')],
-    title='Yelp Review Generator',
-    description='Generate a Yelp review based on a prompt, length of text, and theme.',
-    examples=[
-        ['I had a great experience at this restaurant.', 50, 'Service'],
-        ['The service was terrible and the food was cold.', 50, 'Atmosphere'],
-        ['The food was delicious but the service was slow.', 50, 'Food'],
-        ['The ambiance was amazing and the staff was friendly.', 75, 'Service'],
-        ['The waitstaff was knowledgeable and attentive, but the noise level was a bit high.', 75, 'Atmosphere'],
-        ['The menu had a good variety of options, but the portion sizes were a bit small for the price.', 75, 'Food']
-    ],
-    allow_flagging="manual",
-    flagging_options=[("🙌", "positive"), ("😞", "negative")],
-)
-
-iface.launch(debug=False)
\ No newline at end of file
diff --git a/spaces/samthakur/stable-diffusion-2.1/app.py b/spaces/samthakur/stable-diffusion-2.1/app.py
deleted file mode 100644
index 0c17bca56620aed0dedf0b6909df2fb70dfb4dee..0000000000000000000000000000000000000000
--- a/spaces/samthakur/stable-diffusion-2.1/app.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import gradio as gr
-import torch
-from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
-
-model_id = "stabilityai/stable-diffusion-2-1"
-
-# Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-
-def diffusion(text,num_inference_steps,guidance_scale):
-    prompt = text
-    image = pipe(prompt,guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
-    return image
-demo = gr.Interface(
-    diffusion,
-    [
-        gr.Textbox(
-            label="prompt text",
-            lines=3,
-        ),
-        gr.Slider(1, 100, value=50),
-        gr.Slider(1.0, 30.0, value=7.5),
-    ],
-    "image",
-   
-)
-if __name__ == "__main__":
-    demo.launch()
-
-    
-image.save("astronaut_rides_horse.png")
\ No newline at end of file
diff --git a/spaces/samuelinferences/transformers-can-do-bayesian-inference/prior-fitting/tabular.py b/spaces/samuelinferences/transformers-can-do-bayesian-inference/prior-fitting/tabular.py
deleted file mode 100644
index 5182a1dd701e176e4a6445b78234ed35e78f4d59..0000000000000000000000000000000000000000
--- a/spaces/samuelinferences/transformers-can-do-bayesian-inference/prior-fitting/tabular.py
+++ /dev/null
@@ -1,725 +0,0 @@
-from catboost import CatBoostClassifier, Pool
-from sklearn.model_selection import GridSearchCV
-from sklearn.model_selection import KFold
-from sklearn.model_selection import ParameterGrid
-
-import pyro
-import pyro.distributions as dist
-from pyro.nn import PyroModule, PyroSample
-from pyro.infer.autoguide import AutoDiagonalNormal
-from pyro.infer import SVI, Trace_ELBO, Predictive, MCMC, NUTS
-from pytorch_tabnet.tab_model import TabNetClassifier, TabNetRegressor
-from sklearn.metrics import accuracy_score, roc_auc_score
-import argparse
-import itertools
-
-from train import train, get_weighted_single_eval_pos_sampler, Losses
-import priors
-import encoders
-from sklearn import preprocessing
-
-from sklearn.base import BaseEstimator, ClassifierMixin
-
-from torch import nn
-
-from datasets import *
-import xgboost as xgb
-import matplotlib.pyplot as plt
-import numpy as np
-
-import torch
-from sklearn import neighbors, datasets
-from sklearn.gaussian_process import GaussianProcessClassifier
-from sklearn.gaussian_process.kernels import RBF
-
-from priors.utils import trunc_norm_sampler_f, beta_sampler_f, gamma_sampler_f, uniform_sampler_f, zipf_sampler_f, scaled_beta_sampler_f, uniform_int_sampler_f
-
-from tqdm import tqdm
-import time
-import random
-
-import os
-
-CV = 5
-param_grid = {}
-metric_used = roc_auc_score
-
-def get_uniform_single_eval_pos_sampler(max_len):
-    """
-    Just sample any evaluation position with the same weight
-    :return: Sampler that can be fed to `train()` as `single_eval_pos_gen`.
-    """
-    return lambda: random.choices(range(max_len))[0]
-
-
-def get_mlp_prior_hyperparameters(config):
-    sigma_sampler = gamma_sampler_f(config["prior_sigma_gamma_k"], config["prior_sigma_gamma_theta"])
-    noise_std_sampler = gamma_sampler_f(config["prior_noise_std_gamma_k"], config["prior_noise_std_gamma_theta"])
-
-    mlp_prior_hyperparameters = (list(config["prior_nlayers_sampler"].values())[0]
-                                 , list(config["prior_emsize_sampler"].values())[0]
-                                 , config["prior_activations"]
-                                 , sigma_sampler
-                                 , noise_std_sampler
-                                 , list(config["prior_dropout_sampler"].values())[0]
-                                 , True
-                                 , list(config["prior_num_features_used_sampler"].values())[0]
-                                 , list(config["prior_causes_sampler"].values())[0] if config['prior_is_causal'] else None
-                                 , config["prior_is_causal"]
-                                 , config["prior_pre_sample_causes"] if config['prior_is_causal'] else None
-                                 , config["prior_pre_sample_weights"] if config['prior_is_causal'] else None
-                                 , config["prior_y_is_effect"] if config['prior_is_causal'] else None
-                                 , config["prior_order_y"]
-                                 , config["prior_normalize_by_used_features"]
-                                 , list(config["prior_categorical_feats"].values())[0] if config['prior_is_causal'] else None
-                                 , 0.0
-                                 )
-
-    return mlp_prior_hyperparameters
-
-
-def get_gp_mix_prior_hyperparameters(config):
-    return {'lengthscale_concentration': config["prior_lengthscale_concentration"],
-            'nu': config["prior_nu"],
-            'outputscale_concentration': config["prior_outputscale_concentration"],
-            'categorical_data': config["prior_y_minmax_norm"],
-            'y_minmax_norm': config["prior_lengthscale_concentration"],
-            'noise_concentration': config["prior_noise_concentration"],
-            'noise_rate': config["prior_noise_rate"]}
-
-
-def get_gp_prior_hyperparameters(config):
-
-
-    return (config['prior_noise']
-            , lambda : config['prior_outputscale']
-            , lambda : config['prior_lengthscale']  # lengthscale, Höher mehr sep
-            , True
-            , list(config['prior_num_features_used_sampler'].values())[0]
-            , config['prior_normalize_by_used_features']
-            , config['prior_order_y'])
-
-
-def get_meta_gp_prior_hyperparameters(config):
-    lengthscale_sampler = trunc_norm_sampler_f(config["prior_lengthscale_mean"], config["prior_lengthscale_mean"] * config["prior_lengthscale_std_f"])
-    outputscale_sampler = trunc_norm_sampler_f(config["prior_outputscale_mean"], config["prior_outputscale_mean"] * config["prior_outputscale_std_f"])
-
-    return (config['prior_noise']
-            , outputscale_sampler
-            , lengthscale_sampler  # lengthscale, Höher mehr sep
-            , True
-            , list(config['prior_num_features_used_sampler'].values())[0]
-            , config['prior_normalize_by_used_features']
-            , config['prior_order_y'])
-
-
-
-def get_model(config, device, eval_positions, should_train=True, verbose=False):
-    extra_kwargs = {}
-    if config['prior_type'] == 'mlp':
-        prior_hyperparameters = get_mlp_prior_hyperparameters(config)
-        model_proto = priors.mlp.DataLoader
-        extra_kwargs['batch_size_per_gp_sample'] = 8
-    elif config['prior_type'] == 'gp':
-        prior_hyperparameters = get_gp_prior_hyperparameters(config)
-        model_proto = priors.fast_gp.DataLoader
-    elif config['prior_type'] == 'custom_gp_mix':
-        prior_hyperparameters = get_meta_gp_prior_hyperparameters(config)
-        model_proto = priors.fast_gp.DataLoader
-    elif config['prior_type'] == 'gp_mix':
-        prior_hyperparameters = get_gp_mix_prior_hyperparameters(config)
-        model_proto = priors.fast_gp_mix.DataLoader
-    else:
-        raise Exception()
-
-    epochs = 0 if not should_train else config['epochs']
-    model = train(model_proto
-                  , Losses.bce
-                  , encoders.Linear
-                  , emsize=config['emsize']
-                  , nhead=config['nhead']
-                  , y_encoder_generator=encoders.Linear
-                  , pos_encoder_generator=None
-                  , batch_size=config['batch_size']
-                  , nlayers=config['nlayers']
-                  , nhid=config['emsize'] * config['nhid_factor']
-                  , epochs=epochs
-                  , warmup_epochs=epochs // 4
-                  , bptt=config['bptt']
-                  , gpu_device=device
-                  , dropout=config['dropout']
-                  , steps_per_epoch=100
-                  , single_eval_pos_gen=get_uniform_single_eval_pos_sampler(max(eval_positions) + 1)
-                  , extra_prior_kwargs_dict={
-            'num_features': config['num_features']
-            # , 'canonical_args': None
-            , 'fuse_x_y': False
-            , 'hyperparameters': prior_hyperparameters
-            , **extra_kwargs
-        }
-                  , lr=config['lr']
-                  , verbose=verbose)
-
-    return model
-
-
-## General eval
-
-def evaluate(datasets, model, method, bptt, eval_position_range, device, max_features=0, plot=False, extend_features=False, save=True, rescale_features=False, overwrite=False,
-             max_samples=40, path_interfix=''):
-    # eval_position_range: last entry is the one used to calculate metricuracy; up to index is used for training
-    result = {'metric': 'auc'}
-
-    metric_sum = 0
-    for [name, X, y, categorical_feats] in datasets:
-        result_ds = {}
-        path = f'/home/hollmann/prior-fitting/results/tabular/{path_interfix}/results_{method}_{name}.npy'
-        if (os.path.isfile(path)) and not overwrite:
-            with open(path, 'rb') as f:
-                result_ds = np.load(f, allow_pickle=True).tolist()
-                if 'time' in result_ds:
-                    result_ds[name+'_time'] = result_ds['time']
-                    del result_ds['time']
-                result.update(result_ds)
-                mean_metric = float(result[name + '_mean_metric_at_' + str(eval_position_range[-1])])
-                metric_sum += mean_metric
-                print(f'Loaded saved result for {name} (mean metric {mean_metric})')
-                continue
-
-        print('Evaluating ' + str(name))
-        rescale_features_factor = X.shape[1] / max_features if rescale_features and extend_features else 1.0
-        if extend_features:
-            X = torch.cat([X, torch.zeros((X.shape[0], max_features - X.shape[1]))], -1)
-
-        start_time = time.time()
-        ds_result = evaluate_dataset(X.to(device), y.to(device), categorical_feats, model, bptt, eval_position_range,
-                               rescale_features=rescale_features_factor, max_samples=max_samples)
-        elapsed = time.time() - start_time
-
-        for i, r in enumerate(ds_result):
-            metric, outputs, ys = r
-            if save:
-                result_ds[name + '_per_ds_metric_at_' + str(eval_position_range[i])] = metric
-                result_ds[name + '_outputs_at_' + str(eval_position_range[i])] = outputs
-                result_ds[name + '_ys_at_' + str(eval_position_range[i])] = ys
-
-            result_ds[name + '_mean_metric_at_' + str(eval_position_range[i])] = metric_used(ys.detach().cpu().flatten(), outputs.flatten())
-            result_ds[name + '_time'] = elapsed
-
-        if save:
-            with open(path, 'wb') as f:
-                np.save(f, result_ds)
-
-        result.update(result_ds)
-        metric_sum += float(metric[-1].mean())
-
-    for pos in eval_position_range:
-        result[f'mean_metric_at_{pos}'] = np.array([result[d[0] + '_mean_metric_at_' + str(pos)] for d in datasets]).mean()
-
-    result['mean_metric'] = np.array([result['mean_metric_at_' + str(pos)] for pos in eval_position_range]).mean()
-
-    return result
-
-
-def evaluate_dataset(X, y, categorical_feats, model, bptt, eval_position_range, plot=False, rescale_features=1.0,
-                     max_samples=40):
-    result = []
-    for eval_position in eval_position_range:
-        r = evaluate_position(X, y, categorical_feats, model, bptt, eval_position, rescale_features=rescale_features,
-                              max_samples=max_samples)
-        result.append(r)
-        print('\t Eval position ' + str(eval_position) + ' done..')
-
-    if plot:
-        plt.plot(np.array(list(eval_position_range)), np.array([r.mean() for r in result]))
-
-    return result
-
-
-def evaluate_position(X, y, categorical_feats, model, bptt, eval_position, rescale_features=1.0, max_samples=40):
-    # right now permutation style is to test performance on one before the last element
-    # eval_position = bptt - eval_positions
-
-    # TODO: Make sure that no bias exists
-    # assert(eval_position % 2 == 0)
-
-    eval_xs = []
-    eval_ys = []
-    num_evals = len(X) - bptt  # len(X)-bptt-(bptt-eval_position)+1
-
-    # Generate permutations of evaluation data
-    #     with torch.random.fork_rng():
-    #         torch.random.manual_seed(13)
-    #         ps = [torch.randperm(2*(bptt - eval_position)) for _ in range(num_evals)]
-
-    for i in range(num_evals):
-        # Select chunk of data with extra evaluation positions that can be discarded
-        #         x_ = X[i:i+bptt+(bptt-eval_position)].clone()
-        #         y_ = y[i:i+bptt+(bptt-eval_position)].clone()
-
-        #         # Permutate evaluation positions
-        #         perm_range = slice(eval_position,bptt+(bptt - eval_position))
-        #         x_[perm_range] = x_[perm_range][ps[i]]
-        #         y_[perm_range] = y_[perm_range][ps[i]]
-
-        #         # Discard extra evaluation positions
-        #         x_ = x_[0:bptt]
-        #         y_ = y_[0:bptt]
-
-        x_ = X[i:i + bptt].clone()
-        y_ = y[i:i + bptt].clone()
-
-        eval_xs.append(x_)
-        eval_ys.append(y_)
-
-    # eval data will be ordered in training range and
-    #   will be a random subset of 2*eval_position data points in eval positions
-    eval_xs = torch.stack(eval_xs, 1)
-    eval_ys = torch.stack(eval_ys, 1)
-
-    # Limit to N samples per dataset
-    with torch.random.fork_rng():
-        torch.random.manual_seed(13)
-        sel = torch.randperm(eval_xs.shape[1])
-        eval_xs = eval_xs[:, sel[0:max_samples], :]
-        eval_ys = eval_ys[:, sel[0:max_samples]]
-    #
-    # if quantile_transform:
-    #     for sample in range(0, eval_xs.shape[1]):
-    #         quantile_transformer = preprocessing.QuantileTransformer(random_state=0, n_quantiles=eval_xs.shape[0])
-    #         quantile_transformer.fit(eval_xs[:eval_position, sample].cpu())
-    #         eval_xs[:, sample] = torch.tensor(quantile_transformer.transform(eval_xs[:, sample].cpu()))
-
-    if isinstance(model, nn.Module):
-        model.eval()
-        outputs = np.zeros(shape=(len(list(range(eval_position, eval_xs.shape[0]))), eval_xs.shape[1]))
-        for i, pos in enumerate(range(eval_position, eval_xs.shape[0])):
-            eval_x = torch.cat([eval_xs[:eval_position], eval_xs[pos].unsqueeze(0)])
-            eval_y = eval_ys[:eval_position]
-
-            # Center data using training positions so that it matches priors
-            mean = eval_x.mean(0)
-            std = eval_x.std(0) + .000001
-            eval_x = (eval_x - mean) / std
-            eval_x = eval_x / rescale_features
-
-            output = torch.sigmoid(model((eval_x, eval_y.float()), single_eval_pos=eval_position)).squeeze(-1)
-            outputs[i, :] = output.detach().cpu().numpy()
-
-        metric_per_t = np.array([metric_used(eval_ys[eval_position:, i].cpu(), outputs[:, i]) for i in range(eval_xs.shape[1])])
-        return metric_per_t, outputs, eval_ys[eval_position:]
-    else:
-        metric_eval_pos, outputs = batch_pred(model, eval_xs, eval_ys, categorical_feats, start=eval_position)
-
-        return metric_eval_pos, outputs, eval_ys[eval_position:]
-
-
-def batch_pred(metric_function, eval_xs, eval_ys, categorical_feats, start=2):
-    metrics = []
-    outputs = []
-    # for i in tqdm(list(range(start,len(eval_xs)))):
-    eval_splits = list(zip(eval_xs.transpose(0, 1), eval_ys.transpose(0, 1)))
-    for eval_x, eval_y in tqdm(eval_splits):  # eval x is One sample i.e. bptt x num_features
-        mean = eval_x[:start].mean(0)
-        std = eval_x[:start].std(0) + .000001
-        eval_x = (eval_x - mean) / std
-
-        metric, output = metric_function(eval_x[:start], eval_y[:start], eval_x[start:], eval_y[start:], categorical_feats)
-        metrics += [metric]
-        outputs += [output]
-    #     metrics_per_t.append(metric_sum/eval_xs.shape[1])
-    return np.array(metrics), np.array(outputs).T
-
-## Ridge
-
-
-from sklearn.linear_model import RidgeClassifier
-# param_grid['ridge'] = {'alpha': [0, 0.1, .5, 1.0, 2.0], 'fit_intercept': [True, False]} # 'normalize': [False],
-def ridge_metric(x, y, test_x, test_y, cat_features):
-    import warnings
-    def warn(*args, **kwargs):
-        pass
-
-    warnings.warn = warn
-
-    x, y, test_x, test_y = x.cpu(), y.cpu(), test_x.cpu(), test_y.cpu()
-
-    clf = RidgeClassifier()
-
-    # create a dictionary of all values we want to test for n_neighbors
-    # use gridsearch to test all values for n_neighbors
-    clf = GridSearchCV(clf, param_grid['ridge'], cv=min(CV, x.shape[0]//2))
-    # fit model to data
-    clf.fit(x, y.long())
-
-    pred = clf.decision_function(test_x)
-    metric = metric_used(test_y.cpu().numpy(), pred)
-
-    return metric, pred
-
-
-from sklearn.linear_model import LogisticRegression
-param_grid['logistic'] = {'solver': ['saga'], 'penalty': ['l1', 'l2', 'none'], 'tol': [1e-2, 1e-4, 1e-10], 'max_iter': [500], 'fit_intercept': [True, False], 'C': [1e-5, 0.001, 0.01, 0.1, 1.0, 2.0]} # 'normalize': [False],
-def logistic_metric(x, y, test_x, test_y, cat_features):
-    import warnings
-    def warn(*args, **kwargs):
-        pass
-
-    warnings.warn = warn
-
-    x, y, test_x, test_y = x.cpu(), y.cpu(), test_x.cpu(), test_y.cpu()
-
-    clf = LogisticRegression()
-
-    # create a dictionary of all values we want to test for n_neighbors
-    # use gridsearch to test all values for n_neighbors
-    clf = GridSearchCV(clf, param_grid['logistic'], cv=min(CV, x.shape[0]//2))
-    # fit model to data
-    clf.fit(x, y.long())
-
-    pred = clf.predict_proba(test_x)[:, 1]
-    metric = metric_used(test_y.cpu().numpy(), pred)
-
-    return metric, pred
-
-
-## KNN
-param_grid['knn'] = {'n_neighbors (max number of samples)': np.arange(1, 6)}
-def knn_metric(x, y, test_x, test_y, cat_features):
-    x, y, test_x, test_y = x.cpu(), y.cpu(), test_x.cpu(), test_y.cpu()
-
-    clf = neighbors.KNeighborsClassifier()  # min(param['n_neighbors'],len(y)))
-    param_grid_knn = {'n_neighbors': np.arange(1, min(6, len(y) - 1))}
-    # create a dictionary of all values we want to test for n_neighbors
-    # use gridsearch to test all values for n_neighbors
-    clf = GridSearchCV(clf, param_grid_knn, cv=min(CV, x.shape[0]//2))
-    # fit model to data
-    clf.fit(x, y.long())
-
-    # print(clf.best_params_)
-
-    # clf.fit(x, y.long())
-    pred = clf.predict_proba(test_x)[:, 1]
-
-    metric = metric_used(test_y.cpu().numpy(), pred)
-
-    return metric, pred
-
-
-## Bayesian NN
-class BayesianModel(PyroModule):
-    def __init__(self, model_spec, device='cuda'):
-        super().__init__()
-
-        self.device = device
-        self.num_features = model_spec['num_features']
-
-        mu, sigma = torch.tensor([0.0]).to(self.device), torch.tensor([1.0]).to(self.device)
-
-        self.fc1 = PyroModule[nn.Linear](self.num_features, model_spec['embed'])
-        self.fc1.weight = PyroSample(
-            dist.Normal(mu, sigma).expand([model_spec['embed'], self.num_features]).to_event(2))
-        self.fc1.bias = PyroSample(dist.Normal(mu, sigma).expand([model_spec['embed']]).to_event(1))
-        self.fc2 = PyroModule[nn.Linear](model_spec['embed'], 2)
-        self.fc2.weight = PyroSample(dist.Normal(mu, sigma).expand([2, model_spec['embed']]).to_event(2))
-        self.fc2.bias = PyroSample(dist.Normal(mu, sigma).expand([2]).to_event(1))
-
-        self.model = torch.nn.Sequential(self.fc1, self.fc2)
-
-        self.to(self.device)
-
-    def forward(self, x=None, y=None, seq_len=1):
-        if x is None:
-            with pyro.plate("x_plate", seq_len):
-                d_ = dist.Normal(torch.tensor([0.0]).to(self.device), torch.tensor([1.0]).to(self.device)).expand(
-                    [self.num_features]).to_event(1)
-                x = pyro.sample("x", d_)
-
-        out = self.model(x)
-        mu = out.squeeze()
-        softmax = torch.nn.Softmax(dim=1)
-        # sigma = pyro.sample("sigma", dist.Uniform(torch.tensor([0.0]).to(self.device), torch.tensor([1.0]).to(self.device)))
-        with pyro.plate("data", out.shape[0]):
-            # d_ = dist.Normal(mu, sigma)
-            # obs = pyro.sample("obs", d_, obs=y)
-            s = softmax(mu)
-            obs = pyro.sample('obs', dist.Categorical(probs=s), obs=y).float()
-
-        return x, obs
-
-
-class BayesianNNClassifier(BaseEstimator, ClassifierMixin):
-
-    def __init__(self, num_features, n_layers, embed, lr, device):
-        self.num_pred_samples = 400
-        self.num_steps = 400
-        self.embed = embed
-        self.n_layers = n_layers
-        self.lr = lr
-        self.num_features = num_features
-        self.device = device
-
-    def fit(self, X, y):
-        model_spec = {'nlayers': 2, 'embed': self.embed, 'num_features': self.num_features}
-
-        self.model = BayesianModel(model_spec, device=self.device)
-        self.guide = AutoDiagonalNormal(self.model).to(self.device)
-        self.adam = pyro.optim.Adam({"lr": self.lr})
-        self.svi = SVI(self.model, self.guide, self.adam, loss=Trace_ELBO())
-
-        pyro.clear_param_store()
-
-        X = X.to(self.device)
-        y = y.to(self.device)
-
-        for epoch in tqdm(range(0, self.num_steps)):
-            loss = self.svi.step(X, y)
-
-        # Return the classifier
-        return self
-
-    def predict(self, X):
-        X = X.to(self.device)
-        predictive = Predictive(self.model, guide=self.guide, num_samples=self.num_pred_samples)
-        preds = predictive(X)['obs']
-        preds_means = preds.float().mean(axis=0).detach().cpu()
-        preds_hard = preds_means > 0.5
-
-        return preds_hard.long()
-
-    def predict_proba(self, X):
-        X = X.to(self.device)
-        predictive = Predictive(self.model, guide=self.guide, num_samples=self.num_pred_samples)
-        preds = predictive(X)['obs']
-        preds_means = preds.float().mean(axis=0).detach().cpu()
-
-        return preds_means
-
-    def score(self, X, y):
-        return super().score(X, y)
-
-param_grid['bayes'] = {'embed': [5, 10, 30, 64], 'lr': [1e-3, 1e-4], 'num_training_steps': [400], 'num_samples_for_prediction': [400]}
-def bayes_net_metric(x, y, test_x, test_y, cat_features):
-    device = x.device
-
-    clf = BayesianNNClassifier(x.shape[1], 2, 1, 1e-3, device)
-    # create a dictionary of all values we want to test for n_neighbors
-    # use gridsearch to test all values for n_neighbors
-    clf = GridSearchCV(clf, param_grid['bayes'], cv=5)
-    # fit model to data
-    clf.fit(x.cpu(), y.long().cpu())
-
-    pred = clf.predict_proba(test_x)
-    metric = metric_used(test_y.cpu().numpy(), pred.cpu().numpy())
-
-    return metric, pred
-
-## GP
-param_grid['gp'] = {'params_y_scale': [0.05, 0.1, 0.5, 1.0, 5.0, 10.0],
-    'params_length_scale': [0.1, 0.5, 1.0, 2.0]}
-def gp_metric(x, y, test_x, test_y, cat_features):
-    import warnings
-    def warn(*args, **kwargs):
-        pass
-    warnings.warn = warn
-
-    x, y, test_x, test_y = x.cpu(), y.cpu(), test_x.cpu(), test_y.cpu()
-
-    clf = GaussianProcessClassifier()
-    # create a dictionary of all values we want to test for n_neighbors
-    params_y_scale = [0.05, 0.1, 0.5, 1.0, 5.0, 10.0]# 0.000001, 0.00001,
-    params_length_scale = [0.1, 0.5, 1.0, 2.0] # 0.01,
-    param_grid = {'kernel': [y * RBF(l) for (y, l) in list(itertools.product(params_y_scale, params_length_scale))]}
-    # use gridsearch to test all values for n_neighbors
-    clf = GridSearchCV(clf, param_grid, cv=min(CV, x.shape[0]//2))
-    # fit model to data
-    clf.fit(x, y.long())
-    pred = clf.predict_proba(test_x)[:, 1]
-    metric = metric_used(test_y.cpu().numpy(), pred)
-
-    return metric, pred
-
-
-## Tabnet
-# https://github.com/dreamquark-ai/tabnet
-param_grid['tabnet'] = {'n_d': [2, 4], 'n_steps': [2,4,6], 'gamma': [1.3], 'optimizer_params': [{'lr': 2e-2}, {'lr': 2e-1}]}
-#param_grid['tabnet'] = {'n_d': [2], 'n_steps': [2], 'optimizer_params': [{'lr': 2e-2}, {'lr': 2e-1}]}
-def tabnet_metric(x, y, test_x, test_y, cat_features):
-    x, y, test_x, test_y = x.cpu().numpy(), y.cpu().numpy(), test_x.cpu().numpy(), test_y.cpu().numpy()
-
-    mean_metrics = []
-    mean_best_epochs = []
-
-    for params in list(ParameterGrid(param_grid['tabnet'])):
-        kf = KFold(n_splits=min(5, x.shape[0]//2), random_state=None, shuffle=False)
-        metrics = []
-        best_epochs = []
-        for train_index, test_index in kf.split(x):
-            X_train, X_valid, y_train, y_valid = x[train_index], x[test_index], y[train_index], y[test_index]
-
-            clf = TabNetClassifier(verbose=True, cat_idxs=cat_features, n_a=params['n_d'], **params)
-
-            clf.fit(
-                X_train, y_train,
-                #eval_set=[(X_valid, y_valid)], patience=15
-            )
-
-            metric = metric_used(test_y.cpu().numpy(), clf.predict(X_valid))
-            metrics += [metric]
-            #best_epochs += [clf.best_epoch]
-        mean_metrics += [np.array(metrics).mean()]
-        #mean_best_epochs += [np.array(best_epochs).mean().astype(int)]
-
-    mean_metrics = np.array(mean_metrics)
-    #mean_best_epochs = np.array(mean_best_epochs)
-    params_used = np.array(list(ParameterGrid(param_grid['tabnet'])))
-
-    best_idx = np.argmax(mean_metrics)
-    #print(params_used[best_idx])
-    clf = TabNetClassifier(cat_idxs=cat_features, **params_used[best_idx])
-
-    clf.fit(
-        x, y#, max_epochs=mean_best_epochs[best_idx]
-    )
-
-    pred = 1 - clf.predict_proba(test_x)[:,0]
-    metric = metric_used(test_y, pred)
-
-    #print(metric, clf.predict(test_x), pred)
-
-    return metric, pred
-
-
-# Catboost
-param_grid['catboost'] = {'learning_rate': [0.1, 0.5, 1.0],
-            'depth': [2, 4, 7],
-            'l2_leaf_reg': [0.0, 0.5, 1],
-            'iterations': [10, 40, 70],
-                          'loss_function': ['Logloss']}
-def catboost_metric(x, y, test_x, test_y, categorical_feats):
-    import warnings
-    def warn(*args, **kwargs):
-        pass
-
-    warnings.warn = warn
-
-    x, y, test_x, test_y = x.numpy(), y.numpy(), test_x.numpy(), test_y.numpy()
-
-    def make_pd_from_np(x):
-        data = pd.DataFrame(x)
-        for c in categorical_feats:
-            data.iloc[:, c] = data.iloc[:, c].astype('int')
-        return data
-
-    x = make_pd_from_np(x)
-    test_x = make_pd_from_np(test_x)
-
-    model = CatBoostClassifier(iterations=2,
-                               depth=2,
-                               learning_rate=1,
-                               loss_function='Logloss',
-                               logging_level='Silent')
-
-    grid_search_result = model.grid_search(param_grid['catboost'],
-                                           X=x,
-                                           y=y,
-                                           cv=5,
-                                           plot=False,
-                                           verbose=False)  # randomized_search with n_iter
-
-    # model.fit(x, y)
-    pred = model.predict_proba(test_x)[:, 1]
-    metric = metric_used(test_y.cpu().numpy(), pred)
-
-    return metric, pred
-
-
-# XGBoost
-param_grid['xgb'] = {
-        'min_child_weight': [0.5, 1.0],
-        'learning_rate': [0.02, 0.2],
-        #'gamma': [0.1, 0.2, 0.5, 1, 2],
-        'subsample': [0.5, 0.8],
-        'max_depth': [1, 2],
-        'colsample_bytree': [0.8], #0.5,
-        'eval_metric': ['logloss'],
-        'n_estimators': [100]
-    }
-def xgb_metric(x, y, test_x, test_y, cat_features):
-    x, y, test_x, test_y = x.numpy(), y.numpy().astype(int), test_x.numpy(), test_y.numpy().astype(int)
-
-    clf = xgb.XGBClassifier(use_label_encoder=False)
-
-    # {'num_round': [2,5,10,20], 'max_depth': [1, 2,4,6,8], 'eta': [.1, .01, .001], 'eval_metric': 'logloss'}
-    # use gridsearch to test all values for n_neighbors
-    clf = GridSearchCV(clf, param_grid['xgb'], cv=5, n_jobs=4, verbose=2)
-    # fit model to data
-    clf.fit(x, y.astype(int))
-
-    print(clf.best_params_)
-
-    # clf.fit(x, y.long())
-    pred = clf.predict_proba(test_x)[:, 1]
-    metrics = ((pred > 0.5) == test_y).astype(float).mean()
-    return metrics, pred
-
-def get_default_spec(test_datasets, valid_datasets):
-    bptt = 100
-    eval_positions = [30] #list(range(6, 42, 2))  # list(range(10, bptt-10, 20)) + [bptt-10]
-    max_features = max([X.shape[1] for (_, X, _, _) in test_datasets] + [X.shape[1] for (_, X, _, _) in valid_datasets])
-    max_samples = 20
-
-    return bptt, eval_positions, max_features, max_samples
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--method', default='ridge', type=str)
-    parser.add_argument('--did', default=-1, type=int)
-    parser.add_argument('--overwrite', default=False, type=bool)
-    args = parser.parse_args()
-
-    test_datasets, _ = load_openml_list(test_dids_classification)
-    valid_datasets, _ = load_openml_list(valid_dids_classification)
-
-    selector = 'test'
-    ds = valid_datasets if selector == 'valid' else test_datasets
-    if args.did > -1:
-        ds = ds[args.did:args.did+1]
-
-    bptt, eval_positions, max_features, max_samples = get_default_spec(test_datasets, valid_datasets)
-
-    if args.method == 'bayes':
-        clf = bayes_net_metric
-        device = 'cpu'
-    elif args.method == 'gp':
-        clf = gp_metric
-        device = 'cpu'
-    elif args.method == 'ridge':
-        clf = ridge_metric
-        device = 'cpu'
-    elif args.method == 'knn':
-        clf = knn_metric
-        device = 'cpu'
-    elif args.method == 'catboost':
-        clf = catboost_metric
-        device = 'cpu'
-    elif args.method == 'tabnet':
-        clf = tabnet_metric
-        device = 'cpu'
-    elif args.method == 'xgb':
-        # Uses lots of cpu so difficult to time
-        clf = xgb_metric
-        device = 'cpu'
-    elif args.method == 'logistic':
-        clf = logistic_metric
-        device = 'cpu'
-    else:
-        clf = None
-        device = 'cpu'
-
-    start_time = time.time()
-    result = evaluate(ds, clf, args.method, bptt, eval_positions, device=device, max_samples=max_samples, overwrite=args.overwrite, save=True)
-    result['time_spent'] = time.time() - start_time
-
-    with open(f'/home/hollmann/prior-fitting/results/tabular/results_{selector}_{args.method}.npy', 'wb') as f:
-        np.save(f, result)
\ No newline at end of file
diff --git a/spaces/sanchit-gandhi/whisper-large-v2/app.py b/spaces/sanchit-gandhi/whisper-large-v2/app.py
deleted file mode 100644
index fece0a3231fc1e8d3441d114d98d90b31cdb1d48..0000000000000000000000000000000000000000
--- a/spaces/sanchit-gandhi/whisper-large-v2/app.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import torch
-
-import gradio as gr
-import yt_dlp as youtube_dl
-from transformers import pipeline
-from transformers.pipelines.audio_utils import ffmpeg_read
-
-import tempfile
-import os
-
-MODEL_NAME = "openai/whisper-large-v2"
-BATCH_SIZE = 8
-FILE_LIMIT_MB = 1000
-YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files
-
-device = 0 if torch.cuda.is_available() else "cpu"
-
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    device=device,
-)
-
-
-def transcribe(inputs, task):
-    if inputs is None:
-        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
-
-    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-    return  text
-
-
-def _return_yt_html_embed(yt_url):
-    video_id = yt_url.split("?v=")[-1]
-    HTML_str = (
-        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
-        " </center>"
-    )
-    return HTML_str
-
-def download_yt_audio(yt_url, filename):
-    info_loader = youtube_dl.YoutubeDL()
-    
-    try:
-        info = info_loader.extract_info(yt_url, download=False)
-    except youtube_dl.utils.DownloadError as err:
-        raise gr.Error(str(err))
-    
-    file_length = info["duration_string"]
-    file_h_m_s = file_length.split(":")
-    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
-    
-    if len(file_h_m_s) == 1:
-        file_h_m_s.insert(0, 0)
-    if len(file_h_m_s) == 2:
-        file_h_m_s.insert(0, 0)
-    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
-    
-    if file_length_s > YT_LENGTH_LIMIT_S:
-        yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
-        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
-        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
-    
-    ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
-    
-    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
-        try:
-            ydl.download([yt_url])
-        except youtube_dl.utils.ExtractorError as err:
-            raise gr.Error(str(err))
-
-
-def yt_transcribe(yt_url, task, max_filesize=75.0):
-    html_embed_str = _return_yt_html_embed(yt_url)
-
-    with tempfile.TemporaryDirectory() as tmpdirname:
-        filepath = os.path.join(tmpdirname, "video.mp4")
-        download_yt_audio(yt_url, filepath)
-        with open(filepath, "rb") as f:
-            inputs = f.read()
-
-    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
-    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
-
-    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-
-    return html_embed_str, text
-
-
-demo = gr.Blocks()
-
-mf_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
-        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
-    ],
-    outputs="text",
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Large V2: Transcribe Audio",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
-)
-
-file_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"),
-        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
-    ],
-    outputs="text",
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Large V2: Transcribe Audio",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
-)
-
-yt_transcribe = gr.Interface(
-    fn=yt_transcribe,
-    inputs=[
-        gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
-        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe")
-    ],
-    outputs=["html", "text"],
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Large V2: Transcribe YouTube",
-    description=(
-        "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint"
-        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
-        " arbitrary length."
-    ),
-    allow_flagging="never",
-)
-
-with demo:
-    gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
-
-demo.launch(enable_queue=True)
-
diff --git a/spaces/sawblade/prompt-extend/app.py b/spaces/sawblade/prompt-extend/app.py
deleted file mode 100644
index 48a29d9619c45a3a223944fb144e3e9f76af907e..0000000000000000000000000000000000000000
--- a/spaces/sawblade/prompt-extend/app.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from transformers import pipeline
-import gradio as gr
-
-pipe = pipeline('text-generation', model='daspartho/prompt-extend')
-
-def extend_prompt(prompt):
-    return pipe(prompt+',', num_return_sequences=1)[0]["generated_text"]
-
-examples = [
-            ['elon musk as thor'],
-            ["giant dragon flying in the sky"],
-            ['psychedelic liquids space'],
-            ["a coconut laying on the beach"],
-            ["peaceful village landscape"],
-            ]
-
-iface = gr.Interface(
-    description = "Enter a main idea for a prompt, and the model will attempt to add suitable style cues.",
-    article = "<p style='text-align: center'><a href='https://github.com/daspartho/prompt-extend' target='_blank'>Github</a></p>",
-    fn=extend_prompt, 
-    inputs=gr.Text(label="Type the prompt here"), 
-    outputs=gr.TextArea(label='Extended prompt'),
-    examples=examples,
-    title="Prompt Extend"
-    )
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/Film-Indian-Online-Subtitrat-In-Romana-Veer-Razboinicul.md b/spaces/scedlatioru/img-to-music/Film-Indian-Online-Subtitrat-In-Romana-Veer-Razboinicul.md
deleted file mode 100644
index d81ad9689560d0f16f2cd6ea05af0d01b69bccf8..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/Film-Indian-Online-Subtitrat-In-Romana-Veer-Razboinicul.md
+++ /dev/null
@@ -1,70 +0,0 @@
-## Film Indian Online Subtitrat In Romana Veer Razboinicul
-
-
-
- 
-
- ![Film Indian Online Subtitrat In Romana Veer Razboinicul](https://www.filmindian.ro/71/veer-inima-de-razboinic.jpg)
-
- 
-
- 
-
-**Click Here ✸ [https://urlca.com/2txvQ3](https://urlca.com/2txvQ3)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# Veer războinicul: un film indian plin de acțiune și romantism
-
- 
-
-Veer războinicul este un film indian din 2010, regizat de Anil Sharma și avându-i în rolurile principale pe Salman Khan, Zarine Khan și Mithun Chakraborty. Filmul este o dramă istorică care prezintă povestea de dragoste dintre Veer, un războinic din clanul Pindhari, și Yashodhara, fiica unui rege care a trădat cauza indiană în fața britanicilor.
-
- 
-
-Filmul se desfășoară în anul 1875, când India era sub dominația colonială a Marii Britanii. Veer este fiul lui Prithvi Singh, liderul clanului Pindhari, care se opune cu îndârjire ocupației străine. Veer se îndrăgostește de Yashodhara, o prințesă frumoasă și curajoasă, care îi împărtășește idealurile. Dar dragostea lor este pusă la grea încercare de intrigile politice și de război. Veer trebuie să lupte nu numai cu britanicii, ci și cu tatăl lui Yashodhara, regele Gyanendra Singh, care a făcut o alianță cu dușmanii pentru a-și păstra tronul.
-
- 
-
-Veer războinicul este un film plin de acțiune, aventură și romantism, care îmbină elemente istorice cu cele fantastice. Filmul are scene spectaculoase de lupte, dansuri și muzică tipic indiene, dar și momente emoționante de sacrificiu și devotament. Filmul este o omagiere adusă spiritului de rezistență al poporului indian în fața opresiunii.
-
-  
-
-Veer și Yashodhara se ascund în deșertul Thar, unde se căsătoresc în secret. Dar fericirea lor nu durează mult, deoarece Veer este capturat de britanici și trimis în închisoare. Yashodhara este forțată să se întoarcă la palatul tatălui ei, unde este obligată să se mărite cu un prinț dintr-un regat vecin. Veer își petrece următorii 20 de ani în închisoare, fără să știe nimic despre soarta iubitei sale.
-
- 
-
-În 1895, o tânără avocată pakistaneză, Saamiya Siddiqui ( Rani Mukerji ), este desemnată să apere cazul lui Veer, care este considerat un trădător și un terorist. Saamiya este hotărâtă să afle adevărul despre viața lui Veer și să-i dovedească nevinovăția. Ea îl vizitează pe Veer în închisoare și îi câștigă încrederea. Veer îi povestește despre dragostea lui pentru Yashodhara și despre lupta lui pentru libertatea Indiei.
-
- 
-
-Saamiya începe să investigheze cazul lui Veer și descoperă că Yashodhara este încă în viață și că are un fiu cu Veer. Ea o găsește pe Yashodhara și o convinge să vină la proces pentru a-l elibera pe Veer. În fața instanței, Saamiya reușește să demonstreze că Veer a fost victima unei conspirații și că a fost un erou național. Veer este eliberat după 20 de ani de suferință și se reunește cu Yashodhara și fiul lor.
-
-  
-
-Veer războinicul este un film care încearcă să recreeze atmosfera filmelor epice din anii '80 și '90, când eroii erau invincibili și luptau pentru o cauză nobilă. Filmul are un buget generos și o distribuție de nume mari, dar nu reușește să impresioneze prin poveste sau prin regie. Filmul suferă de un scenariu slab, care nu explică clar motivațiile personajelor și care se pierde în detalii nesemnificative. De asemenea, filmul are o durată prea lungă, care îi plictisește pe spectatori cu scene repetitive și melodramatice.
-
- 
-
-Filmul are totuși și câteva puncte forte, cum ar fi secvențele de acțiune, care sunt bine coregrafiate și pline de suspans. Filmul are și o coloană sonoră frumoasă, compusă de Sajid-Wajid și Gulzar, care se potrivește cu tonul filmului. Filmul are și câteva momente de umor, care îi aliniază pe cei care apreciază comedia tipic indiană. Dar acestea nu sunt suficiente pentru a salva filmul de la mediocritate.
-
- 
-
-Veer războinicul este un film pentru fanii lui Salman Khan, care îl vor admira în rolul unui războinic curajos și romantic. Dar pentru restul publicului, filmul este o dezamăgire, care nu aduce nimic nou sau interesant în genul filmelor istorice. Filmul este o dovadă că nu numai britanicii au fost dușmanii Indiei, ci și scenariștii și regizorii care nu au știut să valorifice potențialul unei povești pline de pasiune și patriotism.
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diff --git a/spaces/scedlatioru/img-to-music/example/Condition Partner 2.3.md b/spaces/scedlatioru/img-to-music/example/Condition Partner 2.3.md
deleted file mode 100644
index 141127c7f778e3c433b6dbe9d5a6acab95649146..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Condition Partner 2.3.md	
+++ /dev/null
@@ -1,138 +0,0 @@
-
-<h1>Condition Partner 2.3: A New Way to Manage Rebates and Conditions</h1>
-
-<p>If you are looking for a flexible and efficient solution to manage rebates and conditions for your customers and suppliers, you may want to consider Condition Partner 2.3. This is a software utility that integrates with SAP S/4HANA and allows you to create and settle condition contracts for various scenarios. Condition Partner 2.3 has many advantages over the traditional rebate system in SAP ERP 6.0, such as:</p>
-<h2>Condition partner 2.3</h2><br /><p><b><b>Download Zip</b> &#10084; <a href="https://gohhs.com/2uEA6V">https://gohhs.com/2uEA6V</a></b></p><br /><br />
-
-<ul>
-<li>It provides a single transaction code or SAP Fiori app to create and maintain condition contracts for both customers and suppliers.</li>
-<li>It offers real-time access to actual business volume data directly from transactional documents, such as billing documents.</li>
-<li>It eliminates the need to run a program periodically to update the sales volume to be settled in the rebate agreement.</li>
-<li>It allows you to create condition contracts with a start date in the past, which is useful when the condition contract is created after the transactional documents used to calculate business volume are posted.</li>
-<li>It supports various types of conditions, such as prices, discounts, surcharges, and rebates.</li>
-</ul>
-
-<p>In this article, we will explain what condition contracts are, how to use Condition Partner 2.3 to create and settle them, and what benefits you can get from using this software utility.</p>
-
-<h2>What are Condition Contracts?</h2>
-
-<p>A condition contract is a central object that stores all the information related to an agreement with a customer or a supplier, such as:</p>
-
-<ul>
-<li>The contract partner, which is the customer or supplier who is eligible for the conditions.</li>
-<li>The validity period of the agreement.</li>
-<li>The conditions that apply to the agreement, such as prices, discounts, surcharges, and rebates.</li>
-<li>The settlement dates and information needed to calculate the business volume, which determines the settlement amount.</li>
-<li>The notes and text elements that provide additional information about the agreement.</li>
-</ul>
-
-<p>A condition contract can contain two types of conditions:</p>
-
-<ul>
-<li>Conditions accompanying an invoice: These are the conditions that are valid with immediate effect and can alter the value of a sales order item. For example, prices, discounts, and surcharges.</li>
-<li>Conditions requiring subsequent settlement: These are the conditions that are settled at a later date. For example, rebates.</li>
-</ul>
-
-<p>You can store and edit these conditions in the condition contract and mark specific conditions as active or inactive. You can also include or exclude conditions based on various organizational and master data aspects, such as customer, material, sales organization, material group, customer hierarchy, and so on.</p>
-<p></p>
-
-<h2>How to Use Condition Partner 2.3?</h2>
-
-<p>To use Condition Partner 2.3, you need to have SAP S/4HANA installed on your system and activate the scope-dependent application job catalog entries for Settlement Management. You also need to configure the pricing settings for condition contract management in the IMG menu path Logistics - General > Settlement Management > Condition Contract Management.</p>
-
-<p>Once you have done these prerequisites, you can use Condition Partner 2.3 to create and settle condition contracts for your customers and suppliers. Here are some general steps to follow:</p>
-
-<ol>
-<li>Create a condition contract by using Transaction WB2R_SC or the Create Condition Contracts app. You need to enter the contract partner, validity period, conditions, settlement information, and other details.</li>
-<li>Maintain the condition contract by using Transaction WB2R_SC or the Maintain Condition Contracts app. You can change or add any information in the condition contract as needed.</li>
-<li>Settle the condition contract by using Transaction WB2R_SC or the Settle Condition Contracts app. You can settle the condition contract manually or automatically based on the settlement dates and business volume data.</li>
-</ol>
-
-<p>You can also use various reports and analytics tools to monitor and evaluate your condition contracts. For example, you can use Transaction WB2R_RV or the Display Business Volume app to display the business volume data for a condition contract. You can also use Transaction WB2R_SV or the Display Settlement Documents app to display the settlement documents for a condition contract.</p>
-
-<h2>What are the Benefits of Using Condition Partner 2.3?</h2>
-
-<p>Using Condition Partner 2.3 can bring you many benefits, such as:</p>
-
-<ul>
-<li>You can save time and resources by managing rebates and conditions for both customers and suppliers in a single software utility.</li>
-<li>You can improve your accuracy and efficiency by accessing real-time business volume data directly from transactional documents.</li>
-<li>You can increase your flexibility and adaptability by creating condition contracts with a start date in the past or changing them as needed.</li>
-<li>You can enhance your performance and functionality by supporting various types of conditions for different scenarios.</li>
-</ul>
-
-<p>Condition Partner 2.3 is a software utility that can help you manage rebates and conditions for your customers and suppliers in a flexible and efficient way. It integrates with SAP S/4HANA and allows you to create and settle condition contracts for various scenarios. By using Condition Partner 2.3, you can save time and resources, improve your accuracy and efficiency, increase your flexibility and adaptability, and enhance your performance and functionality.</p>
-<h2>What is Condition Partner 2.3?</h2>
-
-<p>Condition Partner 2.3 is a software utility that integrates with Condition Contract Management in SAP S/4HANA and provides additional features and functionalities for managing rebates and conditions. Condition Partner 2.3 allows you to:</p>
-
-<ul>
-<li>Create and maintain condition contracts for different types of partners, such as customers, suppliers, distributors, or agents.</li>
-<li>Define and apply different types of conditions, such as prices, discounts, surcharges, rebates, commissions, or bonuses.</li>
-<li>Set up and execute different types of settlement methods, such as invoice-based, order-based, or delivery-based.</li>
-<li>Monitor and analyze the performance and profitability of your condition contracts and partners.</li>
-</ul>
-
-<p>Condition Partner 2.3 is designed to help you optimize your business processes and relationships with your partners. It enables you to offer competitive and attractive conditions to your partners and reward them for their loyalty and performance. It also helps you to reduce your operational costs and risks by automating and simplifying your condition contract management.</p>
-
-<h2>How to Install and Configure Condition Partner 2.3?</h2>
-
-<p>To install and configure Condition Partner 2.3, you need to have SAP S/4HANA installed on your system and activate the scope-dependent application job catalog entries for Settlement Management. You also need to download and install the Condition Partner 2.3 add-on from the SAP Software Download Center.</p>
-
-<p>Once you have installed Condition Partner 2.3, you need to configure the following settings in the IMG menu path Logistics - General > Settlement Management > Condition Contract Management > Condition Partner:</p>
-
-<ul>
-<li>Define partner roles: You need to define the partner roles that are relevant for your condition contracts, such as customer, supplier, distributor, or agent.</li>
-<li>Assign partner roles to condition contract types: You need to assign the partner roles to the condition contract types that you want to use for your condition contracts.</li>
-<li>Define partner determination procedures: You need to define the partner determination procedures that determine how the system finds the relevant partners for your condition contracts.</li>
-<li>Assign partner determination procedures to condition contract types: You need to assign the partner determination procedures to the condition contract types that you want to use for your condition contracts.</li>
-</ul>
-
-<p>After you have configured these settings, you can use Condition Partner 2.3 to create and maintain condition contracts for different types of partners and conditions.</p>
-<h2>How to Create and Maintain Condition Contracts with Condition Partner 2.3?</h2>
-
-<p>To create and maintain condition contracts with Condition Partner 2.3, you need to use Transaction WB2R_SC or the Create Condition Contracts app or the Maintain Condition Contracts app. You can use these tools to enter and change the information related to your condition contracts, such as:</p>
-
-<ul>
-<li>The contract partner, which is the customer or supplier who is eligible for the conditions.</li>
-<li>The validity period of the agreement.</li>
-<li>The conditions that apply to the agreement, such as prices, discounts, surcharges, and rebates.</li>
-<li>The settlement dates and information needed to calculate the business volume, which determines the settlement amount.</li>
-<li>The notes and text elements that provide additional information about the agreement.</li>
-</ul>
-
-<p>You can also use these tools to perform various actions on your condition contracts, such as:</p>
-
-<ul>
-<li>Activate or deactivate specific conditions.</li>
-<li>Include or exclude conditions based on various criteria.</li>
-<li>Copy or delete condition contracts.</li>
-<li>Display or print condition contracts.</li>
-</ul>
-
-<p>You can also use various reports and analytics tools to monitor and evaluate your condition contracts and partners. For example, you can use Transaction WB2R_RP or the Display Condition Contracts app to display the condition contracts that match your selection criteria. You can also use Transaction WB2R_CP or the Display Contract Partners app to display the contract partners that are assigned to your condition contracts.</p>
-
-<h2>How to Settle Condition Contracts with Condition Partner 2.3?</h2>
-
-<p>To settle condition contracts with Condition Partner 2.3, you need to use Transaction WB2R_SC or the Settle Condition Contracts app. You can use these tools to settle your condition contracts manually or automatically based on the settlement dates and business volume data.</p>
-
-<p>You can also use these tools to perform various actions on your settlement documents, such as:</p>
-
-<ul>
-<li>Display or print settlement documents.</li>
-<li>Reverse or cancel settlement documents.</li>
-<li>Post accruals or payments for settlement documents.</li>
-</ul>
-
-<p>You can also use various reports and analytics tools to monitor and evaluate your settlement documents and amounts. For example, you can use Transaction WB2R_SV or the Display Settlement Documents app to display the settlement documents that match your selection criteria. You can also use Transaction WB2R_SA or the Display Settlement Amounts app to display the settlement amounts that are calculated for your condition contracts.</p>
-<h2>Conclusion</h2>
-
-<p>Condition Partner 2.3 is a software utility that integrates with Condition Contract Management in SAP S/4HANA and provides additional features and functionalities for managing rebates and conditions for your customers and suppliers. It allows you to create and maintain condition contracts for different types of partners and conditions, set up and execute different types of settlement methods, and monitor and analyze the performance and profitability of your condition contracts and partners.</p>
-
-<p>Condition Partner 2.3 is designed to help you optimize your business processes and relationships with your partners. It enables you to offer competitive and attractive conditions to your partners and reward them for their loyalty and performance. It also helps you to reduce your operational costs and risks by automating and simplifying your condition contract management.</p>
-
-<p>To use Condition Partner 2.3, you need to have SAP S/4HANA installed on your system and activate the scope-dependent application job catalog entries for Settlement Management. You also need to download and install the Condition Partner 2.3 add-on from the SAP Software Download Center. You also need to configure the pricing settings and the partner roles and determination procedures for condition contract management.</p>
-
-<p>By using Condition Partner 2.3, you can manage rebates and conditions for your customers and suppliers in a flexible and efficient way. It integrates with SAP S/4HANA and allows you to create and settle condition contracts for various scenarios. By using Condition Partner 2.3, you can save time and resources, improve your accuracy and efficiency, increase your flexibility and adaptability, and enhance your performance and functionality.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/example/FarPoint Spread 8.0.14.md b/spaces/scedlatioru/img-to-music/example/FarPoint Spread 8.0.14.md
deleted file mode 100644
index e729ef00e87e679cab4fc4b11f2fa81dd00c167a..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/FarPoint Spread 8.0.14.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>FarPoint Spread 8.0.14</h2><br /><p><b><b>Download File</b> &#10040;&#10040;&#10040; <a href="https://gohhs.com/2uEAi8">https://gohhs.com/2uEAi8</a></b></p><br /><br />
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diff --git a/spaces/scedlatioru/img-to-music/example/M3 Bitlocker Recovery Keygen _BEST_ 61.md b/spaces/scedlatioru/img-to-music/example/M3 Bitlocker Recovery Keygen _BEST_ 61.md
deleted file mode 100644
index 481f1bb2d1527b5cf3a35f4e22fe3f44563f13e8..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/M3 Bitlocker Recovery Keygen _BEST_ 61.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-
-<p>bitlocker overview. bitlocker is a microsoft encryption product that is designed to protect the user data on a computer. in the event of a problem with bitlocker, you may encounter a prompt for a bitlocker recovery key. if you do not have a working recovery key for the bitlocker prompt, you will be unable to access the computer.. jan 07, 2020 now, how to get bitlocker recovery key from cmd follow the steps to make this possible. 1. open cmd as administrator. 2. type in the command'manage-bde -protectors c: -get' and press enter. 3. command prompt will immediately display the 48-digital bitlocker recovery key.</p>
-<p>m3 data recovery crack lets you recover several critical records with just a couple of snaps. in the event that you are prompted to enable it when you open m3 data recovery crack or even afterward, simply tap on the change button and pick the on and off choices underneath that are given. choose a path of administration that you would like to pick records from. tap on the restore record button to begin the course of action. if there are a lot of records that are claimed, they will be arranged by ascending sort order. you can pick which records you need, and the program will pick them out for you. the window will then proceed to offer you a preview of the records you chose, and a chance to have them printed or duplicated. tap on the recover record button to get the records.</p>
-<h2>M3 Bitlocker Recovery Keygen 61</h2><br /><p><b><b>Download File</b> &#9889; <a href="https://gohhs.com/2uEzIk">https://gohhs.com/2uEzIk</a></b></p><br /><br />
-<p>it is a free and straightforward to use bitlocker recovery software. this all-inclusive bitlocker recovery application can be effortlessly downloaded and installed. it is a versatile bitlocker recovery instrument that will be used by bitlocker recoverer to revive lost passwords for the bitlocker encryption and by bitlocker recoverer to check the bitlocker recovery key on the drives with bitlocker encryption. to get the bitlocker recovery key, simply follow the installation procedure and then it will be saved in the program. all you need to do is input the bitlocker recovery key and hit on the recover button to see if your data is saved in the drive. if it is, then everything is ok. to get more information, please read the following reviews: – bitlocker recovery key is the 48-digit string that you get as the result of the right bitlocker recovery management process. it is the master password to your bitlocker drive or drives. without the bitlocker recovery key, you cant open your bitlocker protected drive or drives. however, the bitlocker recovery software will help you to find the recovery key of your bitlocker protected drive or drives by using the built-in bitlocker recovery management tool. the bitlocker recovery management tool uses the information saved in the system registry or in any other storage medium to determine the 48-digit bitlocker recovery key. the bitlocker recovery management tool provides a wide range of useful functions such as finding the bitlocker recovery key for your drives, finding the bitlocker recovery key of the drives connected to the computer, recovering the bitlocker recovery key of the connected drives, or even recreating a new bitlocker recovery key for a drive or drives. you can use it to manage the recovery keys of all bitlocker-encrypted drives on the system in a safe manner.</p> 899543212b<br />
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diff --git a/spaces/scedlatioru/img-to-music/example/Steinberg Cubase VST32 V5.03.exe Download.md b/spaces/scedlatioru/img-to-music/example/Steinberg Cubase VST32 V5.03.exe Download.md
deleted file mode 100644
index 5e00f309b8d40f96896619bddcf0dae9adb87d6b..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Steinberg Cubase VST32 V5.03.exe Download.md	
+++ /dev/null
@@ -1,32 +0,0 @@
-<h2>Steinberg Cubase VST32 V5.03.exe Download</h2><br /><p><b><b>Download File</b> &harr; <a href="https://gohhs.com/2uEA8X">https://gohhs.com/2uEA8X</a></b></p><br /><br />
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diff --git a/spaces/sczhou/CodeFormer/CodeFormer/facelib/detection/__init__.py b/spaces/sczhou/CodeFormer/CodeFormer/facelib/detection/__init__.py
deleted file mode 100644
index 296262d4e2e29eaa2afba7bda1f0399d77da24f6..0000000000000000000000000000000000000000
--- a/spaces/sczhou/CodeFormer/CodeFormer/facelib/detection/__init__.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import os
-import torch
-from torch import nn
-from copy import deepcopy
-
-from facelib.utils import load_file_from_url
-from facelib.utils import download_pretrained_models
-from facelib.detection.yolov5face.models.common import Conv
-
-from .retinaface.retinaface import RetinaFace
-from .yolov5face.face_detector import YoloDetector
-
-
-def init_detection_model(model_name, half=False, device='cuda'):
-    if 'retinaface' in model_name:
-        model = init_retinaface_model(model_name, half, device)
-    elif 'YOLOv5' in model_name:
-        model = init_yolov5face_model(model_name, device)
-    else:
-        raise NotImplementedError(f'{model_name} is not implemented.')
-
-    return model
-
-
-def init_retinaface_model(model_name, half=False, device='cuda'):
-    if model_name == 'retinaface_resnet50':
-        model = RetinaFace(network_name='resnet50', half=half)
-        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth'
-    elif model_name == 'retinaface_mobile0.25':
-        model = RetinaFace(network_name='mobile0.25', half=half)
-        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth'
-    else:
-        raise NotImplementedError(f'{model_name} is not implemented.')
-
-    model_path = load_file_from_url(url=model_url, model_dir='weights/facelib', progress=True, file_name=None)
-    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
-    # remove unnecessary 'module.'
-    for k, v in deepcopy(load_net).items():
-        if k.startswith('module.'):
-            load_net[k[7:]] = v
-            load_net.pop(k)
-    model.load_state_dict(load_net, strict=True)
-    model.eval()
-    model = model.to(device)
-
-    return model
-
-
-def init_yolov5face_model(model_name, device='cuda'):
-    if model_name == 'YOLOv5l':
-        model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5l.yaml', device=device)
-        model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth'
-    elif model_name == 'YOLOv5n':
-        model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5n.yaml', device=device)
-        model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5n-face.pth'
-    else:
-        raise NotImplementedError(f'{model_name} is not implemented.')
-    
-    model_path = load_file_from_url(url=model_url, model_dir='weights/facelib', progress=True, file_name=None)
-    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
-    model.detector.load_state_dict(load_net, strict=True)
-    model.detector.eval()
-    model.detector = model.detector.to(device).float()
-
-    for m in model.detector.modules():
-        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
-            m.inplace = True  # pytorch 1.7.0 compatibility
-        elif isinstance(m, Conv):
-            m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
-
-    return model
-
-
-# Download from Google Drive
-# def init_yolov5face_model(model_name, device='cuda'):
-#     if model_name == 'YOLOv5l':
-#         model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5l.yaml', device=device)
-#         f_id = {'yolov5l-face.pth': '131578zMA6B2x8VQHyHfa6GEPtulMCNzV'}
-#     elif model_name == 'YOLOv5n':
-#         model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5n.yaml', device=device)
-#         f_id = {'yolov5n-face.pth': '1fhcpFvWZqghpGXjYPIne2sw1Fy4yhw6o'}
-#     else:
-#         raise NotImplementedError(f'{model_name} is not implemented.')
-
-#     model_path = os.path.join('weights/facelib', list(f_id.keys())[0])
-#     if not os.path.exists(model_path):
-#         download_pretrained_models(file_ids=f_id, save_path_root='weights/facelib')
-
-#     load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
-#     model.detector.load_state_dict(load_net, strict=True)
-#     model.detector.eval()
-#     model.detector = model.detector.to(device).float()
-
-#     for m in model.detector.modules():
-#         if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
-#             m.inplace = True  # pytorch 1.7.0 compatibility
-#         elif isinstance(m, Conv):
-#             m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
-
-#     return model
\ No newline at end of file
diff --git a/spaces/sgxz/bingo/src/components/chat-history.tsx b/spaces/sgxz/bingo/src/components/chat-history.tsx
deleted file mode 100644
index feb81de66562edda8f40d3c0cc717202c92b6509..0000000000000000000000000000000000000000
--- a/spaces/sgxz/bingo/src/components/chat-history.tsx
+++ /dev/null
@@ -1,48 +0,0 @@
-import { IconEdit, IconTrash, IconMore, IconDownload } from "./ui/icons"
-
-export function ChatHistory() {
-  return (
-    <div className="chat-history fixed top-18 right-4">
-      <div className="chat-history-header text-sm font-semibold text-left w-[280px] px-4 py-6">
-        历史记录
-      </div>
-      <div className="chat-history-main">
-        <div className="scroller">
-          <div className="surface">
-            <div className="threads">
-              <div className="thread">
-                <div className="primary-row">
-                  <button type="button" aria-label="加载聊天">
-
-                  </button>
-                  <div className="description">
-                    <h3 className="name">无标题的聊天</h3>
-                  </div>
-                  <h4 className="time">上午1:42</h4>
-                  <div className="controls">
-
-                    <button className="edit icon-button" type="button" aria-label="重命名">
-                      <IconEdit />
-                    </button>
-
-                    <button className="delete icon-button" type="button" aria-label="删除">
-                      <IconTrash />
-                    </button>
-
-                    <button className="more icon-button" type="button" aria-haspopup="true" aria-expanded="false" aria-label="更多">
-                      <IconMore />
-                    </button>
-
-                    <button className="export icon-button" type="button" aria-label="导出">
-                      <IconDownload />
-                    </button>
-                  </div>
-                </div>
-              </div>
-            </div>
-          </div>
-        </div>
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/shabnam91/Sanskrit-TTS/indic_nlp_library/docs/conf.py b/spaces/shabnam91/Sanskrit-TTS/indic_nlp_library/docs/conf.py
deleted file mode 100644
index ea5f14051897b9545eb78ffc9acfaa77171237fe..0000000000000000000000000000000000000000
--- a/spaces/shabnam91/Sanskrit-TTS/indic_nlp_library/docs/conf.py
+++ /dev/null
@@ -1,242 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Indic NLP Library documentation build configuration file, created by
-# sphinx-quickstart on Tue Nov  3 01:50:37 2015.
-#
-# This file is execfile()d with the current directory set to its containing dir.
-#
-# Note that not all possible configuration values are present in this
-# autogenerated file.
-#
-# All configuration values have a default; values that are commented out
-# serve to show the default.
-
-import sys, os
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-sys.path.insert(0, os.path.abspath('..'))
-
-# -- General configuration -----------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-#needs_sphinx = '1.0'
-
-# Add any Sphinx extension module names here, as strings. They can be extensions
-# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
-extensions = ['sphinx.ext.autodoc', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', 'sphinxarg.ext']
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The suffix of source filenames.
-source_suffix = '.rst'
-
-# The encoding of source files.
-#source_encoding = 'utf-8-sig'
-
-# The master toctree document.
-master_doc = 'index'
-
-# General information about the project.
-project = 'Indic NLP Library'
-copyright = '2015, Anoop Kunchukuttan'
-
-# The version info for the project you're documenting, acts as replacement for
-# |version| and |release|, also used in various other places throughout the
-# built documents.
-#
-# The short X.Y version.
-version = '0.2'
-# The full version, including alpha/beta/rc tags.
-release = '0.2'
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#language = None
-
-# There are two options for replacing |today|: either, you set today to some
-# non-false value, then it is used:
-#today = ''
-# Else, today_fmt is used as the format for a strftime call.
-#today_fmt = '%B %d, %Y'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-exclude_patterns = ['_build']
-
-# The reST default role (used for this markup: `text`) to use for all documents.
-#default_role = None
-
-# If true, '()' will be appended to :func: etc. cross-reference text.
-#add_function_parentheses = True
-
-# If true, the current module name will be prepended to all description
-# unit titles (such as .. function::).
-#add_module_names = True
-
-# If true, sectionauthor and moduleauthor directives will be shown in the
-# output. They are ignored by default.
-#show_authors = False
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
-
-# A list of ignored prefixes for module index sorting.
-#modindex_common_prefix = []
-
-
-# -- Options for HTML output ---------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-html_theme = 'sphinx_rtd_theme'
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-#html_theme_options = {}
-
-# Add any paths that contain custom themes here, relative to this directory.
-#html_theme_path = []
-
-# The name for this set of Sphinx documents.  If None, it defaults to
-# "<project> v<release> documentation".
-#html_title = None
-
-# A shorter title for the navigation bar.  Default is the same as html_title.
-#html_short_title = None
-
-# The name of an image file (relative to this directory) to place at the top
-# of the sidebar.
-#html_logo = None
-
-# The name of an image file (within the static path) to use as favicon of the
-# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
-# pixels large.
-#html_favicon = None
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
-# using the given strftime format.
-#html_last_updated_fmt = '%b %d, %Y'
-
-# If true, SmartyPants will be used to convert quotes and dashes to
-# typographically correct entities.
-#html_use_smartypants = True
-
-# Custom sidebar templates, maps document names to template names.
-#html_sidebars = {}
-
-# Additional templates that should be rendered to pages, maps page names to
-# template names.
-#html_additional_pages = {}
-
-# If false, no module index is generated.
-#html_domain_indices = True
-
-# If false, no index is generated.
-#html_use_index = True
-
-# If true, the index is split into individual pages for each letter.
-#html_split_index = False
-
-# If true, links to the reST sources are added to the pages.
-#html_show_sourcelink = True
-
-# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
-#html_show_sphinx = True
-
-# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
-#html_show_copyright = True
-
-# If true, an OpenSearch description file will be output, and all pages will
-# contain a <link> tag referring to it.  The value of this option must be the
-# base URL from which the finished HTML is served.
-#html_use_opensearch = ''
-
-# This is the file name suffix for HTML files (e.g. ".xhtml").
-#html_file_suffix = None
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = 'IndicNLPLibrarydoc'
-
-
-# -- Options for LaTeX output --------------------------------------------------
-
-latex_elements = {
-# The paper size ('letterpaper' or 'a4paper').
-#'papersize': 'letterpaper',
-
-# The font size ('10pt', '11pt' or '12pt').
-#'pointsize': '10pt',
-
-# Additional stuff for the LaTeX preamble.
-#'preamble': '',
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title, author, documentclass [howto/manual]).
-latex_documents = [
-  ('index', 'IndicNLPLibrary.tex', 'Indic NLP Library Documentation',
-   'Anoop Kunchukuttan', 'manual'),
-]
-
-# The name of an image file (relative to this directory) to place at the top of
-# the title page.
-#latex_logo = None
-
-# For "manual" documents, if this is true, then toplevel headings are parts,
-# not chapters.
-#latex_use_parts = False
-
-# If true, show page references after internal links.
-#latex_show_pagerefs = False
-
-# If true, show URL addresses after external links.
-#latex_show_urls = False
-
-# Documents to append as an appendix to all manuals.
-#latex_appendices = []
-
-# If false, no module index is generated.
-#latex_domain_indices = True
-
-
-# -- Options for manual page output --------------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [
-    ('index', 'indicnlplibrary', 'Indic NLP Library Documentation',
-     ['Anoop Kunchukuttan'], 1)
-]
-
-# If true, show URL addresses after external links.
-#man_show_urls = False
-
-
-# -- Options for Texinfo output ------------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-#  dir menu entry, description, category)
-texinfo_documents = [
-  ('index', 'IndicNLPLibrary', 'Indic NLP Library Documentation',
-   'Anoop Kunchukuttan', 'IndicNLPLibrary', 'NLP library for Indian languages',
-   'NLP'),
-]
-
-# Documents to append as an appendix to all manuals.
-#texinfo_appendices = []
-
-# If false, no module index is generated.
-#texinfo_domain_indices = True
-
-# How to display URL addresses: 'footnote', 'no', or 'inline'.
-#texinfo_show_urls = 'footnote'
diff --git a/spaces/shencc/gpt/crazy_functions/test_project/cpp/libJPG/jpgd.cpp b/spaces/shencc/gpt/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
deleted file mode 100644
index 36d06c8e9068570c3e7624895d474f33dbfe3d29..0000000000000000000000000000000000000000
--- a/spaces/shencc/gpt/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
+++ /dev/null
@@ -1,3276 +0,0 @@
-// jpgd.cpp - C++ class for JPEG decompression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-// Last updated Apr. 16, 2011
-// Alex Evans: Linear memory allocator (taken from jpge.h).
-//
-// Supports progressive and baseline sequential JPEG image files, and the most common chroma subsampling factors: Y, H1V1, H2V1, H1V2, and H2V2.
-//
-// Chroma upsampling quality: H2V2 is upsampled in the frequency domain, H2V1 and H1V2 are upsampled using point sampling.
-// Chroma upsampling reference: "Fast Scheme for Image Size Change in the Compressed Domain"
-// http://vision.ai.uiuc.edu/~dugad/research/dct/index.html
-
-#include "jpgd.h"
-#include <string.h>
-
-#include <assert.h>
-// BEGIN EPIC MOD
-#define JPGD_ASSERT(x) { assert(x); CA_ASSUME(x); } (void)0
-// END EPIC MOD
-
-#ifdef _MSC_VER
-#pragma warning (disable : 4611) // warning C4611: interaction between '_setjmp' and C++ object destruction is non-portable
-#endif
-
-// Set to 1 to enable freq. domain chroma upsampling on images using H2V2 subsampling (0=faster nearest neighbor sampling).
-// This is slower, but results in higher quality on images with highly saturated colors.
-#define JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING 1
-
-#define JPGD_TRUE (1)
-#define JPGD_FALSE (0)
-
-#define JPGD_MAX(a,b) (((a)>(b)) ? (a) : (b))
-#define JPGD_MIN(a,b) (((a)<(b)) ? (a) : (b))
-
-namespace jpgd {
-
-	static inline void *jpgd_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-	static inline void jpgd_free(void *p) { FMemory::Free(p); }
-
-// BEGIN EPIC MOD
-//@UE3 - use UE3 BGRA encoding instead of assuming RGBA
-	// stolen from IImageWrapper.h
-	enum ERGBFormatJPG
-	{
-		Invalid = -1,
-		RGBA =  0,
-		BGRA =  1,
-		Gray =  2,
-	};
-	static ERGBFormatJPG jpg_format;
-// END EPIC MOD
-
-	// DCT coefficients are stored in this sequence.
-	static int g_ZAG[64] = {  0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-
-	enum JPEG_MARKER
-	{
-		M_SOF0  = 0xC0, M_SOF1  = 0xC1, M_SOF2  = 0xC2, M_SOF3  = 0xC3, M_SOF5  = 0xC5, M_SOF6  = 0xC6, M_SOF7  = 0xC7, M_JPG   = 0xC8,
-		M_SOF9  = 0xC9, M_SOF10 = 0xCA, M_SOF11 = 0xCB, M_SOF13 = 0xCD, M_SOF14 = 0xCE, M_SOF15 = 0xCF, M_DHT   = 0xC4, M_DAC   = 0xCC,
-		M_RST0  = 0xD0, M_RST1  = 0xD1, M_RST2  = 0xD2, M_RST3  = 0xD3, M_RST4  = 0xD4, M_RST5  = 0xD5, M_RST6  = 0xD6, M_RST7  = 0xD7,
-		M_SOI   = 0xD8, M_EOI   = 0xD9, M_SOS   = 0xDA, M_DQT   = 0xDB, M_DNL   = 0xDC, M_DRI   = 0xDD, M_DHP   = 0xDE, M_EXP   = 0xDF,
-		M_APP0  = 0xE0, M_APP15 = 0xEF, M_JPG0  = 0xF0, M_JPG13 = 0xFD, M_COM   = 0xFE, M_TEM   = 0x01, M_ERROR = 0x100, RST0   = 0xD0
-	};
-
-	enum JPEG_SUBSAMPLING { JPGD_GRAYSCALE = 0, JPGD_YH1V1, JPGD_YH2V1, JPGD_YH1V2, JPGD_YH2V2 };
-
-#define CONST_BITS  13
-#define PASS1_BITS  2
-#define SCALEDONE ((int32)1)
-
-#define FIX_0_298631336  ((int32)2446)        /* FIX(0.298631336) */
-#define FIX_0_390180644  ((int32)3196)        /* FIX(0.390180644) */
-#define FIX_0_541196100  ((int32)4433)        /* FIX(0.541196100) */
-#define FIX_0_765366865  ((int32)6270)        /* FIX(0.765366865) */
-#define FIX_0_899976223  ((int32)7373)        /* FIX(0.899976223) */
-#define FIX_1_175875602  ((int32)9633)        /* FIX(1.175875602) */
-#define FIX_1_501321110  ((int32)12299)       /* FIX(1.501321110) */
-#define FIX_1_847759065  ((int32)15137)       /* FIX(1.847759065) */
-#define FIX_1_961570560  ((int32)16069)       /* FIX(1.961570560) */
-#define FIX_2_053119869  ((int32)16819)       /* FIX(2.053119869) */
-#define FIX_2_562915447  ((int32)20995)       /* FIX(2.562915447) */
-#define FIX_3_072711026  ((int32)25172)       /* FIX(3.072711026) */
-
-#define DESCALE(x,n)  (((x) + (SCALEDONE << ((n)-1))) >> (n))
-#define DESCALE_ZEROSHIFT(x,n)  (((x) + (128 << (n)) + (SCALEDONE << ((n)-1))) >> (n))
-
-#define MULTIPLY(var, cnst)  ((var) * (cnst))
-
-#define CLAMP(i) ((static_cast<uint>(i) > 255) ? (((~i) >> 31) & 0xFF) : (i))
-
-	// Compiler creates a fast path 1D IDCT for X non-zero columns
-	template <int NONZERO_COLS>
-	struct Row
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-			// ACCESS_COL() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_COL(x) (((x) < NONZERO_COLS) ? (int)pSrc[x] : 0)
-
-			const int z2 = ACCESS_COL(2), z3 = ACCESS_COL(6);
-
-			const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
-			const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
-			const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
-			const int tmp0 = (ACCESS_COL(0) + ACCESS_COL(4)) << CONST_BITS;
-			const int tmp1 = (ACCESS_COL(0) - ACCESS_COL(4)) << CONST_BITS;
-
-			const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
-			const int atmp0 = ACCESS_COL(7), atmp1 = ACCESS_COL(5), atmp2 = ACCESS_COL(3), atmp3 = ACCESS_COL(1);
-
-			const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
-			const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
-			const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
-			const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
-			const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
-			const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
-			const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
-			const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
-			const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
-			const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
-			pTemp[0] = DESCALE(tmp10 + btmp3, CONST_BITS-PASS1_BITS);
-			pTemp[7] = DESCALE(tmp10 - btmp3, CONST_BITS-PASS1_BITS);
-			pTemp[1] = DESCALE(tmp11 + btmp2, CONST_BITS-PASS1_BITS);
-			pTemp[6] = DESCALE(tmp11 - btmp2, CONST_BITS-PASS1_BITS);
-			pTemp[2] = DESCALE(tmp12 + btmp1, CONST_BITS-PASS1_BITS);
-			pTemp[5] = DESCALE(tmp12 - btmp1, CONST_BITS-PASS1_BITS);
-			pTemp[3] = DESCALE(tmp13 + btmp0, CONST_BITS-PASS1_BITS);
-			pTemp[4] = DESCALE(tmp13 - btmp0, CONST_BITS-PASS1_BITS);
-		}
-	};
-
-	template <>
-	struct Row<0>
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-#ifdef _MSC_VER
-			pTemp; pSrc;
-#endif
-		}
-	};
-
-	template <>
-	struct Row<1>
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-			const int dcval = (pSrc[0] << PASS1_BITS);
-
-			pTemp[0] = dcval;
-			pTemp[1] = dcval;
-			pTemp[2] = dcval;
-			pTemp[3] = dcval;
-			pTemp[4] = dcval;
-			pTemp[5] = dcval;
-			pTemp[6] = dcval;
-			pTemp[7] = dcval;
-		}
-	};
-
-	// Compiler creates a fast path 1D IDCT for X non-zero rows
-	template <int NONZERO_ROWS>
-	struct Col
-	{
-		static void idct(uint8* pDst_ptr, const int* pTemp)
-		{
-			// ACCESS_ROW() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_ROW(x) (((x) < NONZERO_ROWS) ? pTemp[x * 8] : 0)
-
-			const int z2 = ACCESS_ROW(2);
-			const int z3 = ACCESS_ROW(6);
-
-			const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
-			const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
-			const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
-			const int tmp0 = (ACCESS_ROW(0) + ACCESS_ROW(4)) << CONST_BITS;
-			const int tmp1 = (ACCESS_ROW(0) - ACCESS_ROW(4)) << CONST_BITS;
-
-			const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
-			const int atmp0 = ACCESS_ROW(7), atmp1 = ACCESS_ROW(5), atmp2 = ACCESS_ROW(3), atmp3 = ACCESS_ROW(1);
-
-			const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
-			const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
-			const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
-			const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
-			const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
-			const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
-			const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
-			const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
-			const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
-			const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
-			int i = DESCALE_ZEROSHIFT(tmp10 + btmp3, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*0] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp10 - btmp3, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*7] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp11 + btmp2, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*1] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp11 - btmp2, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*6] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp12 + btmp1, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*2] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp12 - btmp1, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*5] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp13 + btmp0, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*3] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp13 - btmp0, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*4] = (uint8)CLAMP(i);
-		}
-	};
-
-	template <>
-	struct Col<1>
-	{
-		static void idct(uint8* pDst_ptr, const int* pTemp)
-		{
-			int dcval = DESCALE_ZEROSHIFT(pTemp[0], PASS1_BITS+3);
-			const uint8 dcval_clamped = (uint8)CLAMP(dcval);
-			pDst_ptr[0*8] = dcval_clamped;
-			pDst_ptr[1*8] = dcval_clamped;
-			pDst_ptr[2*8] = dcval_clamped;
-			pDst_ptr[3*8] = dcval_clamped;
-			pDst_ptr[4*8] = dcval_clamped;
-			pDst_ptr[5*8] = dcval_clamped;
-			pDst_ptr[6*8] = dcval_clamped;
-			pDst_ptr[7*8] = dcval_clamped;
-		}
-	};
-
-	static const uint8 s_idct_row_table[] =
-	{
-		1,0,0,0,0,0,0,0, 2,0,0,0,0,0,0,0, 2,1,0,0,0,0,0,0, 2,1,1,0,0,0,0,0, 2,2,1,0,0,0,0,0, 3,2,1,0,0,0,0,0, 4,2,1,0,0,0,0,0, 4,3,1,0,0,0,0,0,
-		4,3,2,0,0,0,0,0, 4,3,2,1,0,0,0,0, 4,3,2,1,1,0,0,0, 4,3,2,2,1,0,0,0, 4,3,3,2,1,0,0,0, 4,4,3,2,1,0,0,0, 5,4,3,2,1,0,0,0, 6,4,3,2,1,0,0,0,
-		6,5,3,2,1,0,0,0, 6,5,4,2,1,0,0,0, 6,5,4,3,1,0,0,0, 6,5,4,3,2,0,0,0, 6,5,4,3,2,1,0,0, 6,5,4,3,2,1,1,0, 6,5,4,3,2,2,1,0, 6,5,4,3,3,2,1,0,
-		6,5,4,4,3,2,1,0, 6,5,5,4,3,2,1,0, 6,6,5,4,3,2,1,0, 7,6,5,4,3,2,1,0, 8,6,5,4,3,2,1,0, 8,7,5,4,3,2,1,0, 8,7,6,4,3,2,1,0, 8,7,6,5,3,2,1,0,
-		8,7,6,5,4,2,1,0, 8,7,6,5,4,3,1,0, 8,7,6,5,4,3,2,0, 8,7,6,5,4,3,2,1, 8,7,6,5,4,3,2,2, 8,7,6,5,4,3,3,2, 8,7,6,5,4,4,3,2, 8,7,6,5,5,4,3,2,
-		8,7,6,6,5,4,3,2, 8,7,7,6,5,4,3,2, 8,8,7,6,5,4,3,2, 8,8,8,6,5,4,3,2, 8,8,8,7,5,4,3,2, 8,8,8,7,6,4,3,2, 8,8,8,7,6,5,3,2, 8,8,8,7,6,5,4,2,
-		8,8,8,7,6,5,4,3, 8,8,8,7,6,5,4,4, 8,8,8,7,6,5,5,4, 8,8,8,7,6,6,5,4, 8,8,8,7,7,6,5,4, 8,8,8,8,7,6,5,4, 8,8,8,8,8,6,5,4, 8,8,8,8,8,7,5,4,
-		8,8,8,8,8,7,6,4, 8,8,8,8,8,7,6,5, 8,8,8,8,8,7,6,6, 8,8,8,8,8,7,7,6, 8,8,8,8,8,8,7,6, 8,8,8,8,8,8,8,6, 8,8,8,8,8,8,8,7, 8,8,8,8,8,8,8,8,
-	};
-
-	static const uint8 s_idct_col_table[] = { 1, 1, 2, 3, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8 };
-
-	void idct(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr, int block_max_zag)
-	{
-		JPGD_ASSERT(block_max_zag >= 1);
-		JPGD_ASSERT(block_max_zag <= 64);
-
-		if (block_max_zag == 1)
-		{
-			int k = ((pSrc_ptr[0] + 4) >> 3) + 128;
-			k = CLAMP(k);
-			k = k | (k<<8);
-			k = k | (k<<16);
-
-			for (int i = 8; i > 0; i--)
-			{
-				*(int*)&pDst_ptr[0] = k;
-				*(int*)&pDst_ptr[4] = k;
-				pDst_ptr += 8;
-			}
-			return;
-		}
-
-		int temp[64];
-
-		const jpgd_block_t* pSrc = pSrc_ptr;
-		int* pTemp = temp;
-
-		const uint8* pRow_tab = &s_idct_row_table[(block_max_zag - 1) * 8];
-		int i;
-		for (i = 8; i > 0; i--, pRow_tab++)
-		{
-			switch (*pRow_tab)
-			{
-			case 0: Row<0>::idct(pTemp, pSrc); break;
-			case 1: Row<1>::idct(pTemp, pSrc); break;
-			case 2: Row<2>::idct(pTemp, pSrc); break;
-			case 3: Row<3>::idct(pTemp, pSrc); break;
-			case 4: Row<4>::idct(pTemp, pSrc); break;
-			case 5: Row<5>::idct(pTemp, pSrc); break;
-			case 6: Row<6>::idct(pTemp, pSrc); break;
-			case 7: Row<7>::idct(pTemp, pSrc); break;
-			case 8: Row<8>::idct(pTemp, pSrc); break;
-			}
-
-			pSrc += 8;
-			pTemp += 8;
-		}
-
-		pTemp = temp;
-
-		const int nonzero_rows = s_idct_col_table[block_max_zag - 1];
-		for (i = 8; i > 0; i--)
-		{
-			switch (nonzero_rows)
-			{
-			case 1: Col<1>::idct(pDst_ptr, pTemp); break;
-			case 2: Col<2>::idct(pDst_ptr, pTemp); break;
-			case 3: Col<3>::idct(pDst_ptr, pTemp); break;
-			case 4: Col<4>::idct(pDst_ptr, pTemp); break;
-			case 5: Col<5>::idct(pDst_ptr, pTemp); break;
-			case 6: Col<6>::idct(pDst_ptr, pTemp); break;
-			case 7: Col<7>::idct(pDst_ptr, pTemp); break;
-			case 8: Col<8>::idct(pDst_ptr, pTemp); break;
-			}
-
-			pTemp++;
-			pDst_ptr++;
-		}
-	}
-
-	void idct_4x4(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr)
-	{
-		int temp[64];
-		int* pTemp = temp;
-		const jpgd_block_t* pSrc = pSrc_ptr;
-
-		for (int i = 4; i > 0; i--)
-		{
-			Row<4>::idct(pTemp, pSrc);
-			pSrc += 8;
-			pTemp += 8;
-		}
-
-		pTemp = temp;
-		for (int i = 8; i > 0; i--)
-		{
-			Col<4>::idct(pDst_ptr, pTemp);
-			pTemp++;
-			pDst_ptr++;
-		}
-	}
-
-	// Retrieve one character from the input stream.
-	inline uint jpeg_decoder::get_char()
-	{
-		// Any bytes remaining in buffer?
-		if (!m_in_buf_left)
-		{
-			// Try to get more bytes.
-			prep_in_buffer();
-			// Still nothing to get?
-			if (!m_in_buf_left)
-			{
-				// Pad the end of the stream with 0xFF 0xD9 (EOI marker)
-				int t = m_tem_flag;
-				m_tem_flag ^= 1;
-				if (t)
-					return 0xD9;
-				else
-					return 0xFF;
-			}
-		}
-
-		uint c = *m_pIn_buf_ofs++;
-		m_in_buf_left--;
-
-		return c;
-	}
-
-	// Same as previous method, except can indicate if the character is a pad character or not.
-	inline uint jpeg_decoder::get_char(bool *pPadding_flag)
-	{
-		if (!m_in_buf_left)
-		{
-			prep_in_buffer();
-			if (!m_in_buf_left)
-			{
-				*pPadding_flag = true;
-				int t = m_tem_flag;
-				m_tem_flag ^= 1;
-				if (t)
-					return 0xD9;
-				else
-					return 0xFF;
-			}
-		}
-
-		*pPadding_flag = false;
-
-		uint c = *m_pIn_buf_ofs++;
-		m_in_buf_left--;
-
-		return c;
-	}
-
-	// Inserts a previously retrieved character back into the input buffer.
-	inline void jpeg_decoder::stuff_char(uint8 q)
-	{
-		*(--m_pIn_buf_ofs) = q;
-		m_in_buf_left++;
-	}
-
-	// Retrieves one character from the input stream, but does not read past markers. Will continue to return 0xFF when a marker is encountered.
-	inline uint8 jpeg_decoder::get_octet()
-	{
-		bool padding_flag;
-		int c = get_char(&padding_flag);
-
-		if (c == 0xFF)
-		{
-			if (padding_flag)
-				return 0xFF;
-
-			c = get_char(&padding_flag);
-			if (padding_flag)
-			{
-				stuff_char(0xFF);
-				return 0xFF;
-			}
-
-			if (c == 0x00)
-				return 0xFF;
-			else
-			{
-				stuff_char(static_cast<uint8>(c));
-				stuff_char(0xFF);
-				return 0xFF;
-			}
-		}
-
-		return static_cast<uint8>(c);
-	}
-
-	// Retrieves a variable number of bits from the input stream. Does not recognize markers.
-	inline uint jpeg_decoder::get_bits(int num_bits)
-	{
-		if (!num_bits)
-			return 0;
-
-		uint i = m_bit_buf >> (32 - num_bits);
-
-		if ((m_bits_left -= num_bits) <= 0)
-		{
-			m_bit_buf <<= (num_bits += m_bits_left);
-
-			uint c1 = get_char();
-			uint c2 = get_char();
-			m_bit_buf = (m_bit_buf & 0xFFFF0000) | (c1 << 8) | c2;
-
-			m_bit_buf <<= -m_bits_left;
-
-			m_bits_left += 16;
-
-			JPGD_ASSERT(m_bits_left >= 0);
-		}
-		else
-			m_bit_buf <<= num_bits;
-
-		return i;
-	}
-
-	// Retrieves a variable number of bits from the input stream. Markers will not be read into the input bit buffer. Instead, an infinite number of all 1's will be returned when a marker is encountered.
-	inline uint jpeg_decoder::get_bits_no_markers(int num_bits)
-	{
-		if (!num_bits)
-			return 0;
-
-		uint i = m_bit_buf >> (32 - num_bits);
-
-		if ((m_bits_left -= num_bits) <= 0)
-		{
-			m_bit_buf <<= (num_bits += m_bits_left);
-
-			if ((m_in_buf_left < 2) || (m_pIn_buf_ofs[0] == 0xFF) || (m_pIn_buf_ofs[1] == 0xFF))
-			{
-				uint c1 = get_octet();
-				uint c2 = get_octet();
-				m_bit_buf |= (c1 << 8) | c2;
-			}
-			else
-			{
-				m_bit_buf |= ((uint)m_pIn_buf_ofs[0] << 8) | m_pIn_buf_ofs[1];
-				m_in_buf_left -= 2;
-				m_pIn_buf_ofs += 2;
-			}
-
-			m_bit_buf <<= -m_bits_left;
-
-			m_bits_left += 16;
-
-			JPGD_ASSERT(m_bits_left >= 0);
-		}
-		else
-			m_bit_buf <<= num_bits;
-
-		return i;
-	}
-
-	// Decodes a Huffman encoded symbol.
-	inline int jpeg_decoder::huff_decode(huff_tables *pH)
-	{
-		int symbol;
-
-		// Check first 8-bits: do we have a complete symbol?
-		if ((symbol = pH->look_up[m_bit_buf >> 24]) < 0)
-		{
-			// Decode more bits, use a tree traversal to find symbol.
-			int ofs = 23;
-			do
-			{
-				symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
-				ofs--;
-			} while (symbol < 0);
-
-			get_bits_no_markers(8 + (23 - ofs));
-		}
-		else
-			get_bits_no_markers(pH->code_size[symbol]);
-
-		return symbol;
-	}
-
-	// Decodes a Huffman encoded symbol.
-	inline int jpeg_decoder::huff_decode(huff_tables *pH, int& extra_bits)
-	{
-		int symbol;
-
-		// Check first 8-bits: do we have a complete symbol?
-		if ((symbol = pH->look_up2[m_bit_buf >> 24]) < 0)
-		{
-			// Use a tree traversal to find symbol.
-			int ofs = 23;
-			do
-			{
-				symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
-				ofs--;
-			} while (symbol < 0);
-
-			get_bits_no_markers(8 + (23 - ofs));
-
-			extra_bits = get_bits_no_markers(symbol & 0xF);
-		}
-		else
-		{
-			JPGD_ASSERT(((symbol >> 8) & 31) == pH->code_size[symbol & 255] + ((symbol & 0x8000) ? (symbol & 15) : 0));
-
-			if (symbol & 0x8000)
-			{
-				get_bits_no_markers((symbol >> 8) & 31);
-				extra_bits = symbol >> 16;
-			}
-			else
-			{
-				int code_size = (symbol >> 8) & 31;
-				int num_extra_bits = symbol & 0xF;
-				int bits = code_size + num_extra_bits;
-				if (bits <= (m_bits_left + 16))
-					extra_bits = get_bits_no_markers(bits) & ((1 << num_extra_bits) - 1);
-				else
-				{
-					get_bits_no_markers(code_size);
-					extra_bits = get_bits_no_markers(num_extra_bits);
-				}
-			}
-
-			symbol &= 0xFF;
-		}
-
-		return symbol;
-	}
-
-	// Tables and macro used to fully decode the DPCM differences.
-	static const int s_extend_test[16] = { 0, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010, 0x0020, 0x0040, 0x0080, 0x0100, 0x0200, 0x0400, 0x0800, 0x1000, 0x2000, 0x4000 };
-	static const int s_extend_offset[16] = { 0, -1, -3, -7, -15, -31, -63, -127, -255, -511, -1023, -2047, -4095, -8191, -16383, -32767 };
-	static const int s_extend_mask[] = { 0, (1<<0), (1<<1), (1<<2), (1<<3), (1<<4), (1<<5), (1<<6), (1<<7), (1<<8), (1<<9), (1<<10), (1<<11), (1<<12), (1<<13), (1<<14), (1<<15), (1<<16) };
-#define HUFF_EXTEND(x,s) ((x) < s_extend_test[s] ? (x) + s_extend_offset[s] : (x))
-
-	// Clamps a value between 0-255.
-	inline uint8 jpeg_decoder::clamp(int i)
-	{
-		if (static_cast<uint>(i) > 255)
-			i = (((~i) >> 31) & 0xFF);
-
-		return static_cast<uint8>(i);
-	}
-
-	namespace DCT_Upsample
-	{
-		struct Matrix44
-		{
-			typedef int Element_Type;
-			enum { NUM_ROWS = 4, NUM_COLS = 4 };
-
-			Element_Type v[NUM_ROWS][NUM_COLS];
-
-			inline int rows() const { return NUM_ROWS; }
-			inline int cols() const { return NUM_COLS; }
-
-			inline const Element_Type & at(int r, int c) const { return v[r][c]; }
-			inline       Element_Type & at(int r, int c)       { return v[r][c]; }
-
-			inline Matrix44() { }
-
-			inline Matrix44& operator += (const Matrix44& a)
-			{
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					at(r, 0) += a.at(r, 0);
-					at(r, 1) += a.at(r, 1);
-					at(r, 2) += a.at(r, 2);
-					at(r, 3) += a.at(r, 3);
-				}
-				return *this;
-			}
-
-			inline Matrix44& operator -= (const Matrix44& a)
-			{
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					at(r, 0) -= a.at(r, 0);
-					at(r, 1) -= a.at(r, 1);
-					at(r, 2) -= a.at(r, 2);
-					at(r, 3) -= a.at(r, 3);
-				}
-				return *this;
-			}
-
-			friend inline Matrix44 operator + (const Matrix44& a, const Matrix44& b)
-			{
-				Matrix44 ret;
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					ret.at(r, 0) = a.at(r, 0) + b.at(r, 0);
-					ret.at(r, 1) = a.at(r, 1) + b.at(r, 1);
-					ret.at(r, 2) = a.at(r, 2) + b.at(r, 2);
-					ret.at(r, 3) = a.at(r, 3) + b.at(r, 3);
-				}
-				return ret;
-			}
-
-			friend inline Matrix44 operator - (const Matrix44& a, const Matrix44& b)
-			{
-				Matrix44 ret;
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					ret.at(r, 0) = a.at(r, 0) - b.at(r, 0);
-					ret.at(r, 1) = a.at(r, 1) - b.at(r, 1);
-					ret.at(r, 2) = a.at(r, 2) - b.at(r, 2);
-					ret.at(r, 3) = a.at(r, 3) - b.at(r, 3);
-				}
-				return ret;
-			}
-
-			static inline void add_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
-			{
-				for (int r = 0; r < 4; r++)
-				{
-					pDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) + b.at(r, 0));
-					pDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) + b.at(r, 1));
-					pDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) + b.at(r, 2));
-					pDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) + b.at(r, 3));
-				}
-			}
-
-			static inline void sub_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
-			{
-				for (int r = 0; r < 4; r++)
-				{
-					pDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) - b.at(r, 0));
-					pDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) - b.at(r, 1));
-					pDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) - b.at(r, 2));
-					pDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) - b.at(r, 3));
-				}
-			}
-		};
-
-		const int FRACT_BITS = 10;
-		const int SCALE = 1 << FRACT_BITS;
-
-		typedef int Temp_Type;
-#define D(i) (((i) + (SCALE >> 1)) >> FRACT_BITS)
-#define F(i) ((int)((i) * SCALE + .5f))
-
-		// Any decent C++ compiler will optimize this at compile time to a 0, or an array access.
-#define AT(c, r) ((((c)>=NUM_COLS)||((r)>=NUM_ROWS)) ? 0 : pSrc[(c)+(r)*8])
-
-		// NUM_ROWS/NUM_COLS = # of non-zero rows/cols in input matrix
-		template<int NUM_ROWS, int NUM_COLS>
-		struct P_Q
-		{
-			static void calc(Matrix44& P, Matrix44& Q, const jpgd_block_t* pSrc)
-			{
-				// 4x8 = 4x8 times 8x8, matrix 0 is constant
-				const Temp_Type X000 = AT(0, 0);
-				const Temp_Type X001 = AT(0, 1);
-				const Temp_Type X002 = AT(0, 2);
-				const Temp_Type X003 = AT(0, 3);
-				const Temp_Type X004 = AT(0, 4);
-				const Temp_Type X005 = AT(0, 5);
-				const Temp_Type X006 = AT(0, 6);
-				const Temp_Type X007 = AT(0, 7);
-				const Temp_Type X010 = D(F(0.415735f) * AT(1, 0) + F(0.791065f) * AT(3, 0) + F(-0.352443f) * AT(5, 0) + F(0.277785f) * AT(7, 0));
-				const Temp_Type X011 = D(F(0.415735f) * AT(1, 1) + F(0.791065f) * AT(3, 1) + F(-0.352443f) * AT(5, 1) + F(0.277785f) * AT(7, 1));
-				const Temp_Type X012 = D(F(0.415735f) * AT(1, 2) + F(0.791065f) * AT(3, 2) + F(-0.352443f) * AT(5, 2) + F(0.277785f) * AT(7, 2));
-				const Temp_Type X013 = D(F(0.415735f) * AT(1, 3) + F(0.791065f) * AT(3, 3) + F(-0.352443f) * AT(5, 3) + F(0.277785f) * AT(7, 3));
-				const Temp_Type X014 = D(F(0.415735f) * AT(1, 4) + F(0.791065f) * AT(3, 4) + F(-0.352443f) * AT(5, 4) + F(0.277785f) * AT(7, 4));
-				const Temp_Type X015 = D(F(0.415735f) * AT(1, 5) + F(0.791065f) * AT(3, 5) + F(-0.352443f) * AT(5, 5) + F(0.277785f) * AT(7, 5));
-				const Temp_Type X016 = D(F(0.415735f) * AT(1, 6) + F(0.791065f) * AT(3, 6) + F(-0.352443f) * AT(5, 6) + F(0.277785f) * AT(7, 6));
-				const Temp_Type X017 = D(F(0.415735f) * AT(1, 7) + F(0.791065f) * AT(3, 7) + F(-0.352443f) * AT(5, 7) + F(0.277785f) * AT(7, 7));
-				const Temp_Type X020 = AT(4, 0);
-				const Temp_Type X021 = AT(4, 1);
-				const Temp_Type X022 = AT(4, 2);
-				const Temp_Type X023 = AT(4, 3);
-				const Temp_Type X024 = AT(4, 4);
-				const Temp_Type X025 = AT(4, 5);
-				const Temp_Type X026 = AT(4, 6);
-				const Temp_Type X027 = AT(4, 7);
-				const Temp_Type X030 = D(F(0.022887f) * AT(1, 0) + F(-0.097545f) * AT(3, 0) + F(0.490393f) * AT(5, 0) + F(0.865723f) * AT(7, 0));
-				const Temp_Type X031 = D(F(0.022887f) * AT(1, 1) + F(-0.097545f) * AT(3, 1) + F(0.490393f) * AT(5, 1) + F(0.865723f) * AT(7, 1));
-				const Temp_Type X032 = D(F(0.022887f) * AT(1, 2) + F(-0.097545f) * AT(3, 2) + F(0.490393f) * AT(5, 2) + F(0.865723f) * AT(7, 2));
-				const Temp_Type X033 = D(F(0.022887f) * AT(1, 3) + F(-0.097545f) * AT(3, 3) + F(0.490393f) * AT(5, 3) + F(0.865723f) * AT(7, 3));
-				const Temp_Type X034 = D(F(0.022887f) * AT(1, 4) + F(-0.097545f) * AT(3, 4) + F(0.490393f) * AT(5, 4) + F(0.865723f) * AT(7, 4));
-				const Temp_Type X035 = D(F(0.022887f) * AT(1, 5) + F(-0.097545f) * AT(3, 5) + F(0.490393f) * AT(5, 5) + F(0.865723f) * AT(7, 5));
-				const Temp_Type X036 = D(F(0.022887f) * AT(1, 6) + F(-0.097545f) * AT(3, 6) + F(0.490393f) * AT(5, 6) + F(0.865723f) * AT(7, 6));
-				const Temp_Type X037 = D(F(0.022887f) * AT(1, 7) + F(-0.097545f) * AT(3, 7) + F(0.490393f) * AT(5, 7) + F(0.865723f) * AT(7, 7));
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				P.at(0, 0) = X000;
-				P.at(0, 1) = D(X001 * F(0.415735f) + X003 * F(0.791065f) + X005 * F(-0.352443f) + X007 * F(0.277785f));
-				P.at(0, 2) = X004;
-				P.at(0, 3) = D(X001 * F(0.022887f) + X003 * F(-0.097545f) + X005 * F(0.490393f) + X007 * F(0.865723f));
-				P.at(1, 0) = X010;
-				P.at(1, 1) = D(X011 * F(0.415735f) + X013 * F(0.791065f) + X015 * F(-0.352443f) + X017 * F(0.277785f));
-				P.at(1, 2) = X014;
-				P.at(1, 3) = D(X011 * F(0.022887f) + X013 * F(-0.097545f) + X015 * F(0.490393f) + X017 * F(0.865723f));
-				P.at(2, 0) = X020;
-				P.at(2, 1) = D(X021 * F(0.415735f) + X023 * F(0.791065f) + X025 * F(-0.352443f) + X027 * F(0.277785f));
-				P.at(2, 2) = X024;
-				P.at(2, 3) = D(X021 * F(0.022887f) + X023 * F(-0.097545f) + X025 * F(0.490393f) + X027 * F(0.865723f));
-				P.at(3, 0) = X030;
-				P.at(3, 1) = D(X031 * F(0.415735f) + X033 * F(0.791065f) + X035 * F(-0.352443f) + X037 * F(0.277785f));
-				P.at(3, 2) = X034;
-				P.at(3, 3) = D(X031 * F(0.022887f) + X033 * F(-0.097545f) + X035 * F(0.490393f) + X037 * F(0.865723f));
-				// 40 muls 24 adds
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				Q.at(0, 0) = D(X001 * F(0.906127f) + X003 * F(-0.318190f) + X005 * F(0.212608f) + X007 * F(-0.180240f));
-				Q.at(0, 1) = X002;
-				Q.at(0, 2) = D(X001 * F(-0.074658f) + X003 * F(0.513280f) + X005 * F(0.768178f) + X007 * F(-0.375330f));
-				Q.at(0, 3) = X006;
-				Q.at(1, 0) = D(X011 * F(0.906127f) + X013 * F(-0.318190f) + X015 * F(0.212608f) + X017 * F(-0.180240f));
-				Q.at(1, 1) = X012;
-				Q.at(1, 2) = D(X011 * F(-0.074658f) + X013 * F(0.513280f) + X015 * F(0.768178f) + X017 * F(-0.375330f));
-				Q.at(1, 3) = X016;
-				Q.at(2, 0) = D(X021 * F(0.906127f) + X023 * F(-0.318190f) + X025 * F(0.212608f) + X027 * F(-0.180240f));
-				Q.at(2, 1) = X022;
-				Q.at(2, 2) = D(X021 * F(-0.074658f) + X023 * F(0.513280f) + X025 * F(0.768178f) + X027 * F(-0.375330f));
-				Q.at(2, 3) = X026;
-				Q.at(3, 0) = D(X031 * F(0.906127f) + X033 * F(-0.318190f) + X035 * F(0.212608f) + X037 * F(-0.180240f));
-				Q.at(3, 1) = X032;
-				Q.at(3, 2) = D(X031 * F(-0.074658f) + X033 * F(0.513280f) + X035 * F(0.768178f) + X037 * F(-0.375330f));
-				Q.at(3, 3) = X036;
-				// 40 muls 24 adds
-			}
-		};
-
-		template<int NUM_ROWS, int NUM_COLS>
-		struct R_S
-		{
-			static void calc(Matrix44& R, Matrix44& S, const jpgd_block_t* pSrc)
-			{
-				// 4x8 = 4x8 times 8x8, matrix 0 is constant
-				const Temp_Type X100 = D(F(0.906127f) * AT(1, 0) + F(-0.318190f) * AT(3, 0) + F(0.212608f) * AT(5, 0) + F(-0.180240f) * AT(7, 0));
-				const Temp_Type X101 = D(F(0.906127f) * AT(1, 1) + F(-0.318190f) * AT(3, 1) + F(0.212608f) * AT(5, 1) + F(-0.180240f) * AT(7, 1));
-				const Temp_Type X102 = D(F(0.906127f) * AT(1, 2) + F(-0.318190f) * AT(3, 2) + F(0.212608f) * AT(5, 2) + F(-0.180240f) * AT(7, 2));
-				const Temp_Type X103 = D(F(0.906127f) * AT(1, 3) + F(-0.318190f) * AT(3, 3) + F(0.212608f) * AT(5, 3) + F(-0.180240f) * AT(7, 3));
-				const Temp_Type X104 = D(F(0.906127f) * AT(1, 4) + F(-0.318190f) * AT(3, 4) + F(0.212608f) * AT(5, 4) + F(-0.180240f) * AT(7, 4));
-				const Temp_Type X105 = D(F(0.906127f) * AT(1, 5) + F(-0.318190f) * AT(3, 5) + F(0.212608f) * AT(5, 5) + F(-0.180240f) * AT(7, 5));
-				const Temp_Type X106 = D(F(0.906127f) * AT(1, 6) + F(-0.318190f) * AT(3, 6) + F(0.212608f) * AT(5, 6) + F(-0.180240f) * AT(7, 6));
-				const Temp_Type X107 = D(F(0.906127f) * AT(1, 7) + F(-0.318190f) * AT(3, 7) + F(0.212608f) * AT(5, 7) + F(-0.180240f) * AT(7, 7));
-				const Temp_Type X110 = AT(2, 0);
-				const Temp_Type X111 = AT(2, 1);
-				const Temp_Type X112 = AT(2, 2);
-				const Temp_Type X113 = AT(2, 3);
-				const Temp_Type X114 = AT(2, 4);
-				const Temp_Type X115 = AT(2, 5);
-				const Temp_Type X116 = AT(2, 6);
-				const Temp_Type X117 = AT(2, 7);
-				const Temp_Type X120 = D(F(-0.074658f) * AT(1, 0) + F(0.513280f) * AT(3, 0) + F(0.768178f) * AT(5, 0) + F(-0.375330f) * AT(7, 0));
-				const Temp_Type X121 = D(F(-0.074658f) * AT(1, 1) + F(0.513280f) * AT(3, 1) + F(0.768178f) * AT(5, 1) + F(-0.375330f) * AT(7, 1));
-				const Temp_Type X122 = D(F(-0.074658f) * AT(1, 2) + F(0.513280f) * AT(3, 2) + F(0.768178f) * AT(5, 2) + F(-0.375330f) * AT(7, 2));
-				const Temp_Type X123 = D(F(-0.074658f) * AT(1, 3) + F(0.513280f) * AT(3, 3) + F(0.768178f) * AT(5, 3) + F(-0.375330f) * AT(7, 3));
-				const Temp_Type X124 = D(F(-0.074658f) * AT(1, 4) + F(0.513280f) * AT(3, 4) + F(0.768178f) * AT(5, 4) + F(-0.375330f) * AT(7, 4));
-				const Temp_Type X125 = D(F(-0.074658f) * AT(1, 5) + F(0.513280f) * AT(3, 5) + F(0.768178f) * AT(5, 5) + F(-0.375330f) * AT(7, 5));
-				const Temp_Type X126 = D(F(-0.074658f) * AT(1, 6) + F(0.513280f) * AT(3, 6) + F(0.768178f) * AT(5, 6) + F(-0.375330f) * AT(7, 6));
-				const Temp_Type X127 = D(F(-0.074658f) * AT(1, 7) + F(0.513280f) * AT(3, 7) + F(0.768178f) * AT(5, 7) + F(-0.375330f) * AT(7, 7));
-				const Temp_Type X130 = AT(6, 0);
-				const Temp_Type X131 = AT(6, 1);
-				const Temp_Type X132 = AT(6, 2);
-				const Temp_Type X133 = AT(6, 3);
-				const Temp_Type X134 = AT(6, 4);
-				const Temp_Type X135 = AT(6, 5);
-				const Temp_Type X136 = AT(6, 6);
-				const Temp_Type X137 = AT(6, 7);
-				// 80 muls 48 adds
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				R.at(0, 0) = X100;
-				R.at(0, 1) = D(X101 * F(0.415735f) + X103 * F(0.791065f) + X105 * F(-0.352443f) + X107 * F(0.277785f));
-				R.at(0, 2) = X104;
-				R.at(0, 3) = D(X101 * F(0.022887f) + X103 * F(-0.097545f) + X105 * F(0.490393f) + X107 * F(0.865723f));
-				R.at(1, 0) = X110;
-				R.at(1, 1) = D(X111 * F(0.415735f) + X113 * F(0.791065f) + X115 * F(-0.352443f) + X117 * F(0.277785f));
-				R.at(1, 2) = X114;
-				R.at(1, 3) = D(X111 * F(0.022887f) + X113 * F(-0.097545f) + X115 * F(0.490393f) + X117 * F(0.865723f));
-				R.at(2, 0) = X120;
-				R.at(2, 1) = D(X121 * F(0.415735f) + X123 * F(0.791065f) + X125 * F(-0.352443f) + X127 * F(0.277785f));
-				R.at(2, 2) = X124;
-				R.at(2, 3) = D(X121 * F(0.022887f) + X123 * F(-0.097545f) + X125 * F(0.490393f) + X127 * F(0.865723f));
-				R.at(3, 0) = X130;
-				R.at(3, 1) = D(X131 * F(0.415735f) + X133 * F(0.791065f) + X135 * F(-0.352443f) + X137 * F(0.277785f));
-				R.at(3, 2) = X134;
-				R.at(3, 3) = D(X131 * F(0.022887f) + X133 * F(-0.097545f) + X135 * F(0.490393f) + X137 * F(0.865723f));
-				// 40 muls 24 adds
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				S.at(0, 0) = D(X101 * F(0.906127f) + X103 * F(-0.318190f) + X105 * F(0.212608f) + X107 * F(-0.180240f));
-				S.at(0, 1) = X102;
-				S.at(0, 2) = D(X101 * F(-0.074658f) + X103 * F(0.513280f) + X105 * F(0.768178f) + X107 * F(-0.375330f));
-				S.at(0, 3) = X106;
-				S.at(1, 0) = D(X111 * F(0.906127f) + X113 * F(-0.318190f) + X115 * F(0.212608f) + X117 * F(-0.180240f));
-				S.at(1, 1) = X112;
-				S.at(1, 2) = D(X111 * F(-0.074658f) + X113 * F(0.513280f) + X115 * F(0.768178f) + X117 * F(-0.375330f));
-				S.at(1, 3) = X116;
-				S.at(2, 0) = D(X121 * F(0.906127f) + X123 * F(-0.318190f) + X125 * F(0.212608f) + X127 * F(-0.180240f));
-				S.at(2, 1) = X122;
-				S.at(2, 2) = D(X121 * F(-0.074658f) + X123 * F(0.513280f) + X125 * F(0.768178f) + X127 * F(-0.375330f));
-				S.at(2, 3) = X126;
-				S.at(3, 0) = D(X131 * F(0.906127f) + X133 * F(-0.318190f) + X135 * F(0.212608f) + X137 * F(-0.180240f));
-				S.at(3, 1) = X132;
-				S.at(3, 2) = D(X131 * F(-0.074658f) + X133 * F(0.513280f) + X135 * F(0.768178f) + X137 * F(-0.375330f));
-				S.at(3, 3) = X136;
-				// 40 muls 24 adds
-			}
-		};
-	} // end namespace DCT_Upsample
-
-	// Unconditionally frees all allocated m_blocks.
-	void jpeg_decoder::free_all_blocks()
-	{
-		m_pStream = NULL;
-		for (mem_block *b = m_pMem_blocks; b; )
-		{
-			mem_block *n = b->m_pNext;
-			jpgd_free(b);
-			b = n;
-		}
-		m_pMem_blocks = NULL;
-	}
-
-	// This method handles all errors.
-	// It could easily be changed to use C++ exceptions.
-	void jpeg_decoder::stop_decoding(jpgd_status status)
-	{
-		m_error_code = status;
-		free_all_blocks();
-		longjmp(m_jmp_state, status);
-
-		// we shouldn't get here as longjmp shouldn't return, but we put it here to make it explicit
-		// that this function doesn't return, otherwise we get this error:
-		// 
-		// error : function declared 'noreturn' should not return
-		exit(1);
-	}
-
-	void *jpeg_decoder::alloc(size_t nSize, bool zero)
-	{
-		nSize = (JPGD_MAX(nSize, 1) + 3) & ~3;
-		char *rv = NULL;
-		for (mem_block *b = m_pMem_blocks; b; b = b->m_pNext)
-		{
-			if ((b->m_used_count + nSize) <= b->m_size)
-			{
-				rv = b->m_data + b->m_used_count;
-				b->m_used_count += nSize;
-				break;
-			}
-		}
-		if (!rv)
-		{
-			int capacity = JPGD_MAX(32768 - 256, (nSize + 2047) & ~2047);
-			mem_block *b = (mem_block*)jpgd_malloc(sizeof(mem_block) + capacity);
-			if (!b) stop_decoding(JPGD_NOTENOUGHMEM);
-			b->m_pNext = m_pMem_blocks; m_pMem_blocks = b;
-			b->m_used_count = nSize;
-			b->m_size = capacity;
-			rv = b->m_data;
-		}
-		if (zero) memset(rv, 0, nSize);
-		return rv;
-	}
-
-	void jpeg_decoder::word_clear(void *p, uint16 c, uint n)
-	{
-		uint8 *pD = (uint8*)p;
-		const uint8 l = c & 0xFF, h = (c >> 8) & 0xFF;
-		while (n)
-		{
-			pD[0] = l; pD[1] = h; pD += 2;
-			n--;
-		}
-	}
-
-	// Refill the input buffer.
-	// This method will sit in a loop until (A) the buffer is full or (B)
-	// the stream's read() method reports and end of file condition.
-	void jpeg_decoder::prep_in_buffer()
-	{
-		m_in_buf_left = 0;
-		m_pIn_buf_ofs = m_in_buf;
-
-		if (m_eof_flag)
-			return;
-
-		do
-		{
-			int bytes_read = m_pStream->read(m_in_buf + m_in_buf_left, JPGD_IN_BUF_SIZE - m_in_buf_left, &m_eof_flag);
-			if (bytes_read == -1)
-				stop_decoding(JPGD_STREAM_READ);
-
-			m_in_buf_left += bytes_read;
-		} while ((m_in_buf_left < JPGD_IN_BUF_SIZE) && (!m_eof_flag));
-
-		m_total_bytes_read += m_in_buf_left;
-
-		// Pad the end of the block with M_EOI (prevents the decompressor from going off the rails if the stream is invalid).
-		// (This dates way back to when this decompressor was written in C/asm, and the all-asm Huffman decoder did some fancy things to increase perf.)
-		word_clear(m_pIn_buf_ofs + m_in_buf_left, 0xD9FF, 64);
-	}
-
-	// Read a Huffman code table.
-	void jpeg_decoder::read_dht_marker()
-	{
-		int i, index, count;
-		uint8 huff_num[17];
-		uint8 huff_val[256];
-
-		uint num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_DHT_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			index = get_bits(8);
-
-			huff_num[0] = 0;
-
-			count = 0;
-
-			for (i = 1; i <= 16; i++)
-			{
-				huff_num[i] = static_cast<uint8>(get_bits(8));
-				count += huff_num[i];
-			}
-
-			if (count > 255)
-				stop_decoding(JPGD_BAD_DHT_COUNTS);
-
-			for (i = 0; i < count; i++)
-				huff_val[i] = static_cast<uint8>(get_bits(8));
-
-			i = 1 + 16 + count;
-
-			if (num_left < (uint)i)
-				stop_decoding(JPGD_BAD_DHT_MARKER);
-
-			num_left -= i;
-
-			if ((index & 0x10) > 0x10)
-				stop_decoding(JPGD_BAD_DHT_INDEX);
-
-			index = (index & 0x0F) + ((index & 0x10) >> 4) * (JPGD_MAX_HUFF_TABLES >> 1);
-
-			if (index >= JPGD_MAX_HUFF_TABLES)
-				stop_decoding(JPGD_BAD_DHT_INDEX);
-
-			if (!m_huff_num[index])
-				m_huff_num[index] = (uint8 *)alloc(17);
-
-			if (!m_huff_val[index])
-				m_huff_val[index] = (uint8 *)alloc(256);
-
-			m_huff_ac[index] = (index & 0x10) != 0;
-			memcpy(m_huff_num[index], huff_num, 17);
-			memcpy(m_huff_val[index], huff_val, 256);
-		}
-	}
-
-	// Read a quantization table.
-	void jpeg_decoder::read_dqt_marker()
-	{
-		int n, i, prec;
-		uint num_left;
-		uint temp;
-
-		num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_DQT_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			n = get_bits(8);
-			prec = n >> 4;
-			n &= 0x0F;
-
-			if (n >= JPGD_MAX_QUANT_TABLES)
-				stop_decoding(JPGD_BAD_DQT_TABLE);
-
-			if (!m_quant[n])
-				m_quant[n] = (jpgd_quant_t *)alloc(64 * sizeof(jpgd_quant_t));
-
-			// read quantization entries, in zag order
-			for (i = 0; i < 64; i++)
-			{
-				temp = get_bits(8);
-
-				if (prec)
-					temp = (temp << 8) + get_bits(8);
-
-				m_quant[n][i] = static_cast<jpgd_quant_t>(temp);
-			}
-
-			i = 64 + 1;
-
-			if (prec)
-				i += 64;
-
-			if (num_left < (uint)i)
-				stop_decoding(JPGD_BAD_DQT_LENGTH);
-
-			num_left -= i;
-		}
-	}
-
-	// Read the start of frame (SOF) marker.
-	void jpeg_decoder::read_sof_marker()
-	{
-		int i;
-		uint num_left;
-
-		num_left = get_bits(16);
-
-		if (get_bits(8) != 8)   /* precision: sorry, only 8-bit precision is supported right now */
-			stop_decoding(JPGD_BAD_PRECISION);
-
-		m_image_y_size = get_bits(16);
-
-		if ((m_image_y_size < 1) || (m_image_y_size > JPGD_MAX_HEIGHT))
-			stop_decoding(JPGD_BAD_HEIGHT);
-
-		m_image_x_size = get_bits(16);
-
-		if ((m_image_x_size < 1) || (m_image_x_size > JPGD_MAX_WIDTH))
-			stop_decoding(JPGD_BAD_WIDTH);
-
-		m_comps_in_frame = get_bits(8);
-
-		if (m_comps_in_frame > JPGD_MAX_COMPONENTS)
-			stop_decoding(JPGD_TOO_MANY_COMPONENTS);
-
-		if (num_left != (uint)(m_comps_in_frame * 3 + 8))
-			stop_decoding(JPGD_BAD_SOF_LENGTH);
-
-		for (i = 0; i < m_comps_in_frame; i++)
-		{
-			m_comp_ident[i]  = get_bits(8);
-			m_comp_h_samp[i] = get_bits(4);
-			m_comp_v_samp[i] = get_bits(4);
-			m_comp_quant[i]  = get_bits(8);
-		}
-	}
-
-	// Used to skip unrecognized markers.
-	void jpeg_decoder::skip_variable_marker()
-	{
-		uint num_left;
-
-		num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_VARIABLE_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			get_bits(8);
-			num_left--;
-		}
-	}
-
-	// Read a define restart interval (DRI) marker.
-	void jpeg_decoder::read_dri_marker()
-	{
-		if (get_bits(16) != 4)
-			stop_decoding(JPGD_BAD_DRI_LENGTH);
-
-		m_restart_interval = get_bits(16);
-	}
-
-	// Read a start of scan (SOS) marker.
-	void jpeg_decoder::read_sos_marker()
-	{
-		uint num_left;
-		int i, ci, n, c, cc;
-
-		num_left = get_bits(16);
-
-		n = get_bits(8);
-
-		m_comps_in_scan = n;
-
-		num_left -= 3;
-
-		if ( (num_left != (uint)(n * 2 + 3)) || (n < 1) || (n > JPGD_MAX_COMPS_IN_SCAN) )
-			stop_decoding(JPGD_BAD_SOS_LENGTH);
-
-		for (i = 0; i < n; i++)
-		{
-			cc = get_bits(8);
-			c = get_bits(8);
-			num_left -= 2;
-
-			for (ci = 0; ci < m_comps_in_frame; ci++)
-				if (cc == m_comp_ident[ci])
-					break;
-
-			if (ci >= m_comps_in_frame)
-				stop_decoding(JPGD_BAD_SOS_COMP_ID);
-
-			m_comp_list[i]    = ci;
-			m_comp_dc_tab[ci] = (c >> 4) & 15;
-			m_comp_ac_tab[ci] = (c & 15) + (JPGD_MAX_HUFF_TABLES >> 1);
-		}
-
-		m_spectral_start  = get_bits(8);
-		m_spectral_end    = get_bits(8);
-		m_successive_high = get_bits(4);
-		m_successive_low  = get_bits(4);
-
-		if (!m_progressive_flag)
-		{
-			m_spectral_start = 0;
-			m_spectral_end = 63;
-		}
-
-		num_left -= 3;
-
-		while (num_left)                  /* read past whatever is num_left */
-		{
-			get_bits(8);
-			num_left--;
-		}
-	}
-
-	// Finds the next marker.
-	int jpeg_decoder::next_marker()
-	{
-		uint c, bytes;
-
-		bytes = 0;
-
-		do
-		{
-			do
-			{
-				bytes++;
-				c = get_bits(8);
-			} while (c != 0xFF);
-
-			do
-			{
-				c = get_bits(8);
-			} while (c == 0xFF);
-
-		} while (c == 0);
-
-		// If bytes > 0 here, there where extra bytes before the marker (not good).
-
-		return c;
-	}
-
-	// Process markers. Returns when an SOFx, SOI, EOI, or SOS marker is
-	// encountered.
-	int jpeg_decoder::process_markers()
-	{
-		int c;
-
-		for ( ; ; )
-		{
-			c = next_marker();
-
-			switch (c)
-			{
-			case M_SOF0:
-			case M_SOF1:
-			case M_SOF2:
-			case M_SOF3:
-			case M_SOF5:
-			case M_SOF6:
-			case M_SOF7:
-				//      case M_JPG:
-			case M_SOF9:
-			case M_SOF10:
-			case M_SOF11:
-			case M_SOF13:
-			case M_SOF14:
-			case M_SOF15:
-			case M_SOI:
-			case M_EOI:
-			case M_SOS:
-				{
-					return c;
-				}
-			case M_DHT:
-				{
-					read_dht_marker();
-					break;
-				}
-				// No arithmitic support - dumb patents!
-			case M_DAC:
-				{
-					stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
-					break;
-				}
-			case M_DQT:
-				{
-					read_dqt_marker();
-					break;
-				}
-			case M_DRI:
-				{
-					read_dri_marker();
-					break;
-				}
-				//case M_APP0:  /* no need to read the JFIF marker */
-
-			case M_JPG:
-			case M_RST0:    /* no parameters */
-			case M_RST1:
-			case M_RST2:
-			case M_RST3:
-			case M_RST4:
-			case M_RST5:
-			case M_RST6:
-			case M_RST7:
-			case M_TEM:
-				{
-					stop_decoding(JPGD_UNEXPECTED_MARKER);
-					break;
-				}
-			default:    /* must be DNL, DHP, EXP, APPn, JPGn, COM, or RESn or APP0 */
-				{
-					skip_variable_marker();
-					break;
-				}
-			}
-		}
-	}
-
-	// Finds the start of image (SOI) marker.
-	// This code is rather defensive: it only checks the first 512 bytes to avoid
-	// false positives.
-	void jpeg_decoder::locate_soi_marker()
-	{
-		uint lastchar, thischar;
-		uint bytesleft;
-
-		lastchar = get_bits(8);
-
-		thischar = get_bits(8);
-
-		/* ok if it's a normal JPEG file without a special header */
-
-		if ((lastchar == 0xFF) && (thischar == M_SOI))
-			return;
-
-		bytesleft = 4096; //512;
-
-		for ( ; ; )
-		{
-			if (--bytesleft == 0)
-				stop_decoding(JPGD_NOT_JPEG);
-
-			lastchar = thischar;
-
-			thischar = get_bits(8);
-
-			if (lastchar == 0xFF)
-			{
-				if (thischar == M_SOI)
-					break;
-				else if (thischar == M_EOI) // get_bits will keep returning M_EOI if we read past the end
-					stop_decoding(JPGD_NOT_JPEG);
-			}
-		}
-
-		// Check the next character after marker: if it's not 0xFF, it can't be the start of the next marker, so the file is bad.
-		thischar = (m_bit_buf >> 24) & 0xFF;
-
-		if (thischar != 0xFF)
-			stop_decoding(JPGD_NOT_JPEG);
-	}
-
-	// Find a start of frame (SOF) marker.
-	void jpeg_decoder::locate_sof_marker()
-	{
-		locate_soi_marker();
-
-		int c = process_markers();
-
-		switch (c)
-		{
-		case M_SOF2:
-			m_progressive_flag = JPGD_TRUE;
-		case M_SOF0:  /* baseline DCT */
-		case M_SOF1:  /* extended sequential DCT */
-			{
-				read_sof_marker();
-				break;
-			}
-		case M_SOF9:  /* Arithmitic coding */
-			{
-				stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
-				break;
-			}
-		default:
-			{
-				stop_decoding(JPGD_UNSUPPORTED_MARKER);
-				break;
-			}
-		}
-	}
-
-	// Find a start of scan (SOS) marker.
-	int jpeg_decoder::locate_sos_marker()
-	{
-		int c;
-
-		c = process_markers();
-
-		if (c == M_EOI)
-			return JPGD_FALSE;
-		else if (c != M_SOS)
-			stop_decoding(JPGD_UNEXPECTED_MARKER);
-
-		read_sos_marker();
-
-		return JPGD_TRUE;
-	}
-
-	// Reset everything to default/uninitialized state.
-	void jpeg_decoder::init(jpeg_decoder_stream *pStream)
-	{
-		m_pMem_blocks = NULL;
-		m_error_code = JPGD_SUCCESS;
-		m_ready_flag = false;
-		m_image_x_size = m_image_y_size = 0;
-		m_pStream = pStream;
-		m_progressive_flag = JPGD_FALSE;
-
-		memset(m_huff_ac, 0, sizeof(m_huff_ac));
-		memset(m_huff_num, 0, sizeof(m_huff_num));
-		memset(m_huff_val, 0, sizeof(m_huff_val));
-		memset(m_quant, 0, sizeof(m_quant));
-
-		m_scan_type = 0;
-		m_comps_in_frame = 0;
-
-		memset(m_comp_h_samp, 0, sizeof(m_comp_h_samp));
-		memset(m_comp_v_samp, 0, sizeof(m_comp_v_samp));
-		memset(m_comp_quant, 0, sizeof(m_comp_quant));
-		memset(m_comp_ident, 0, sizeof(m_comp_ident));
-		memset(m_comp_h_blocks, 0, sizeof(m_comp_h_blocks));
-		memset(m_comp_v_blocks, 0, sizeof(m_comp_v_blocks));
-
-		m_comps_in_scan = 0;
-		memset(m_comp_list, 0, sizeof(m_comp_list));
-		memset(m_comp_dc_tab, 0, sizeof(m_comp_dc_tab));
-		memset(m_comp_ac_tab, 0, sizeof(m_comp_ac_tab));
-
-		m_spectral_start = 0;
-		m_spectral_end = 0;
-		m_successive_low = 0;
-		m_successive_high = 0;
-		m_max_mcu_x_size = 0;
-		m_max_mcu_y_size = 0;
-		m_blocks_per_mcu = 0;
-		m_max_blocks_per_row = 0;
-		m_mcus_per_row = 0;
-		m_mcus_per_col = 0;
-		m_expanded_blocks_per_component = 0;
-		m_expanded_blocks_per_mcu = 0;
-		m_expanded_blocks_per_row = 0;
-		m_freq_domain_chroma_upsample = false;
-
-		memset(m_mcu_org, 0, sizeof(m_mcu_org));
-
-		m_total_lines_left = 0;
-		m_mcu_lines_left = 0;
-		m_real_dest_bytes_per_scan_line = 0;
-		m_dest_bytes_per_scan_line = 0;
-		m_dest_bytes_per_pixel = 0;
-
-		memset(m_pHuff_tabs, 0, sizeof(m_pHuff_tabs));
-
-		memset(m_dc_coeffs, 0, sizeof(m_dc_coeffs));
-		memset(m_ac_coeffs, 0, sizeof(m_ac_coeffs));
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		m_eob_run = 0;
-
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		m_pIn_buf_ofs = m_in_buf;
-		m_in_buf_left = 0;
-		m_eof_flag = false;
-		m_tem_flag = 0;
-
-		memset(m_in_buf_pad_start, 0, sizeof(m_in_buf_pad_start));
-		memset(m_in_buf, 0, sizeof(m_in_buf));
-		memset(m_in_buf_pad_end, 0, sizeof(m_in_buf_pad_end));
-
-		m_restart_interval = 0;
-		m_restarts_left    = 0;
-		m_next_restart_num = 0;
-
-		m_max_mcus_per_row = 0;
-		m_max_blocks_per_mcu = 0;
-		m_max_mcus_per_col = 0;
-
-		memset(m_last_dc_val, 0, sizeof(m_last_dc_val));
-		m_pMCU_coefficients = NULL;
-		m_pSample_buf = NULL;
-
-		m_total_bytes_read = 0;
-
-		m_pScan_line_0 = NULL;
-		m_pScan_line_1 = NULL;
-
-		// Ready the input buffer.
-		prep_in_buffer();
-
-		// Prime the bit buffer.
-		m_bits_left = 16;
-		m_bit_buf = 0;
-
-		get_bits(16);
-		get_bits(16);
-
-		for (int i = 0; i < JPGD_MAX_BLOCKS_PER_MCU; i++)
-			m_mcu_block_max_zag[i] = 64;
-	}
-
-#define SCALEBITS 16
-#define ONE_HALF  ((int) 1 << (SCALEBITS-1))
-#define FIX(x)    ((int) ((x) * (1L<<SCALEBITS) + 0.5f))
-
-	// Create a few tables that allow us to quickly convert YCbCr to RGB.
-	void jpeg_decoder::create_look_ups()
-	{
-		for (int i = 0; i <= 255; i++)
-		{
-			int k = i - 128;
-			m_crr[i] = ( FIX(1.40200f)  * k + ONE_HALF) >> SCALEBITS;
-			m_cbb[i] = ( FIX(1.77200f)  * k + ONE_HALF) >> SCALEBITS;
-			m_crg[i] = (-FIX(0.71414f)) * k;
-			m_cbg[i] = (-FIX(0.34414f)) * k + ONE_HALF;
-		}
-	}
-
-	// This method throws back into the stream any bytes that where read
-	// into the bit buffer during initial marker scanning.
-	void jpeg_decoder::fix_in_buffer()
-	{
-		// In case any 0xFF's where pulled into the buffer during marker scanning.
-		JPGD_ASSERT((m_bits_left & 7) == 0);
-
-		if (m_bits_left == 16)
-			stuff_char( (uint8)(m_bit_buf & 0xFF));
-
-		if (m_bits_left >= 8)
-			stuff_char( (uint8)((m_bit_buf >> 8) & 0xFF));
-
-		stuff_char((uint8)((m_bit_buf >> 16) & 0xFF));
-		stuff_char((uint8)((m_bit_buf >> 24) & 0xFF));
-
-		m_bits_left = 16;
-		get_bits_no_markers(16);
-		get_bits_no_markers(16);
-	}
-
-	void jpeg_decoder::transform_mcu(int mcu_row)
-	{
-		jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
-		uint8* pDst_ptr = m_pSample_buf + mcu_row * m_blocks_per_mcu * 64;
-
-		for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-		{
-			idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
-			pSrc_ptr += 64;
-			pDst_ptr += 64;
-		}
-	}
-
-	static const uint8 s_max_rc[64] =
-	{
-		17, 18, 34, 50, 50, 51, 52, 52, 52, 68, 84, 84, 84, 84, 85, 86, 86, 86, 86, 86,
-		102, 118, 118, 118, 118, 118, 118, 119, 120, 120, 120, 120, 120, 120, 120, 136,
-		136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
-		136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136
-	};
-
-	void jpeg_decoder::transform_mcu_expand(int mcu_row)
-	{
-		jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
-		uint8* pDst_ptr = m_pSample_buf + mcu_row * m_expanded_blocks_per_mcu * 64;
-
-		// Y IDCT
-		int mcu_block;
-		for (mcu_block = 0; mcu_block < m_expanded_blocks_per_component; mcu_block++)
-		{
-			idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
-			pSrc_ptr += 64;
-			pDst_ptr += 64;
-		}
-
-		// Chroma IDCT, with upsampling
-		jpgd_block_t temp_block[64];
-
-		for (int i = 0; i < 2; i++)
-		{
-			DCT_Upsample::Matrix44 P, Q, R, S;
-
-			JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] >= 1);
-			JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] <= 64);
-
-			switch (s_max_rc[m_mcu_block_max_zag[mcu_block++] - 1])
-			{
-			case 1*16+1:
-				DCT_Upsample::P_Q<1, 1>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<1, 1>::calc(R, S, pSrc_ptr);
-				break;
-			case 1*16+2:
-				DCT_Upsample::P_Q<1, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<1, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 2*16+2:
-				DCT_Upsample::P_Q<2, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<2, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+2:
-				DCT_Upsample::P_Q<3, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+3:
-				DCT_Upsample::P_Q<3, 3>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 3>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+4:
-				DCT_Upsample::P_Q<3, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 4*16+4:
-				DCT_Upsample::P_Q<4, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<4, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+4:
-				DCT_Upsample::P_Q<5, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+5:
-				DCT_Upsample::P_Q<5, 5>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 5>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+6:
-				DCT_Upsample::P_Q<5, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 6*16+6:
-				DCT_Upsample::P_Q<6, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<6, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+6:
-				DCT_Upsample::P_Q<7, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+7:
-				DCT_Upsample::P_Q<7, 7>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 7>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+8:
-				DCT_Upsample::P_Q<7, 8>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 8>::calc(R, S, pSrc_ptr);
-				break;
-			case 8*16+8:
-				DCT_Upsample::P_Q<8, 8>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<8, 8>::calc(R, S, pSrc_ptr);
-				break;
-			default:
-				JPGD_ASSERT(false);
-			}
-
-			DCT_Upsample::Matrix44 a(P + Q); P -= Q;
-			DCT_Upsample::Matrix44& b = P;
-			DCT_Upsample::Matrix44 c(R + S); R -= S;
-			DCT_Upsample::Matrix44& d = R;
-
-			DCT_Upsample::Matrix44::add_and_store(temp_block, a, c);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::sub_and_store(temp_block, a, c);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::add_and_store(temp_block, b, d);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::sub_and_store(temp_block, b, d);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			pSrc_ptr += 64;
-		}
-	}
-
-	// Loads and dequantizes the next row of (already decoded) coefficients.
-	// Progressive images only.
-	void jpeg_decoder::load_next_row()
-	{
-		int i;
-		jpgd_block_t *p;
-		jpgd_quant_t *q;
-		int mcu_row, mcu_block, row_block = 0;
-		int component_num, component_id;
-		int block_x_mcu[JPGD_MAX_COMPONENTS];
-
-		memset(block_x_mcu, 0, JPGD_MAX_COMPONENTS * sizeof(int));
-
-		for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-		{
-			int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
-			for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-			{
-				component_id = m_mcu_org[mcu_block];
-				q = m_quant[m_comp_quant[component_id]];
-
-				p = m_pMCU_coefficients + 64 * mcu_block;
-
-				jpgd_block_t* pAC = coeff_buf_getp(m_ac_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-				jpgd_block_t* pDC = coeff_buf_getp(m_dc_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-				p[0] = pDC[0];
-				memcpy(&p[1], &pAC[1], 63 * sizeof(jpgd_block_t));
-
-				for (i = 63; i > 0; i--)
-					if (p[g_ZAG[i]])
-						break;
-
-				m_mcu_block_max_zag[mcu_block] = i + 1;
-
-				for ( ; i >= 0; i--)
-					if (p[g_ZAG[i]])
-						p[g_ZAG[i]] = static_cast<jpgd_block_t>(p[g_ZAG[i]] * q[i]);
-
-				row_block++;
-
-				if (m_comps_in_scan == 1)
-					block_x_mcu[component_id]++;
-				else
-				{
-					if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
-					{
-						block_x_mcu_ofs = 0;
-
-						if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
-						{
-							block_y_mcu_ofs = 0;
-
-							block_x_mcu[component_id] += m_comp_h_samp[component_id];
-						}
-					}
-				}
-			}
-
-			if (m_freq_domain_chroma_upsample)
-				transform_mcu_expand(mcu_row);
-			else
-				transform_mcu(mcu_row);
-		}
-
-		if (m_comps_in_scan == 1)
-			m_block_y_mcu[m_comp_list[0]]++;
-		else
-		{
-			for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-			{
-				component_id = m_comp_list[component_num];
-
-				m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
-			}
-		}
-	}
-
-	// Restart interval processing.
-	void jpeg_decoder::process_restart()
-	{
-		int i;
-		int c = 0;
-
-		// Align to a byte boundry
-		// FIXME: Is this really necessary? get_bits_no_markers() never reads in markers!
-		//get_bits_no_markers(m_bits_left & 7);
-
-		// Let's scan a little bit to find the marker, but not _too_ far.
-		// 1536 is a "fudge factor" that determines how much to scan.
-		for (i = 1536; i > 0; i--)
-			if (get_char() == 0xFF)
-				break;
-
-		if (i == 0)
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		for ( ; i > 0; i--)
-			if ((c = get_char()) != 0xFF)
-				break;
-
-		if (i == 0)
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		// Is it the expected marker? If not, something bad happened.
-		if (c != (m_next_restart_num + M_RST0))
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		// Reset each component's DC prediction values.
-		memset(&m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
-		m_eob_run = 0;
-
-		m_restarts_left = m_restart_interval;
-
-		m_next_restart_num = (m_next_restart_num + 1) & 7;
-
-		// Get the bit buffer going again...
-
-		m_bits_left = 16;
-		get_bits_no_markers(16);
-		get_bits_no_markers(16);
-	}
-
-	static inline int dequantize_ac(int c, int q) {	c *= q;	return c; }
-
-	// Decodes and dequantizes the next row of coefficients.
-	void jpeg_decoder::decode_next_row()
-	{
-		int row_block = 0;
-
-		for (int mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-		{
-			if ((m_restart_interval) && (m_restarts_left == 0))
-				process_restart();
-
-			jpgd_block_t* p = m_pMCU_coefficients;
-			for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++, p += 64)
-			{
-				int component_id = m_mcu_org[mcu_block];
-				jpgd_quant_t* q = m_quant[m_comp_quant[component_id]];
-
-				int r, s;
-				s = huff_decode(m_pHuff_tabs[m_comp_dc_tab[component_id]], r);
-				s = HUFF_EXTEND(r, s);
-
-				m_last_dc_val[component_id] = (s += m_last_dc_val[component_id]);
-
-				p[0] = static_cast<jpgd_block_t>(s * q[0]);
-
-				int prev_num_set = m_mcu_block_max_zag[mcu_block];
-
-				huff_tables *pH = m_pHuff_tabs[m_comp_ac_tab[component_id]];
-
-				int k;
-				for (k = 1; k < 64; k++)
-				{
-					int extra_bits;
-					s = huff_decode(pH, extra_bits);
-
-					r = s >> 4;
-					s &= 15;
-
-					if (s)
-					{
-						if (r)
-						{
-							if ((k + r) > 63)
-								stop_decoding(JPGD_DECODE_ERROR);
-
-							if (k < prev_num_set)
-							{
-								int n = JPGD_MIN(r, prev_num_set - k);
-								int kt = k;
-								while (n--)
-									p[g_ZAG[kt++]] = 0;
-							}
-
-							k += r;
-						}
-
-						s = HUFF_EXTEND(extra_bits, s);
-
-						JPGD_ASSERT(k < 64);
-
-						p[g_ZAG[k]] = static_cast<jpgd_block_t>(dequantize_ac(s, q[k])); //s * q[k];
-					}
-					else
-					{
-						if (r == 15)
-						{
-							if ((k + 16) > 64)
-								stop_decoding(JPGD_DECODE_ERROR);
-
-							if (k < prev_num_set)
-							{
-								int n = JPGD_MIN(16, prev_num_set - k);
-								int kt = k;
-								while (n--)
-								{
-									JPGD_ASSERT(kt <= 63);
-									p[g_ZAG[kt++]] = 0;
-								}
-							}
-
-							k += 16 - 1; // - 1 because the loop counter is k
-							// BEGIN EPIC MOD
-							JPGD_ASSERT(k < 64 && p[g_ZAG[k]] == 0);
-							// END EPIC MOD
-						}
-						else
-							break;
-					}
-				}
-
-				if (k < prev_num_set)
-				{
-					int kt = k;
-					while (kt < prev_num_set)
-						p[g_ZAG[kt++]] = 0;
-				}
-
-				m_mcu_block_max_zag[mcu_block] = k;
-
-				row_block++;
-			}
-
-			if (m_freq_domain_chroma_upsample)
-				transform_mcu_expand(mcu_row);
-			else
-				transform_mcu(mcu_row);
-
-			m_restarts_left--;
-		}
-	}
-
-	// YCbCr H1V1 (1x1:1:1, 3 m_blocks per MCU) to RGB
-	void jpeg_decoder::H1V1Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d = m_pScan_line_0;
-		uint8 *s = m_pSample_buf + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int j = 0; j < 8; j++)
-			{
-				int y = s[j];
-				int cb = s[64+j];
-				int cr = s[128+j];
-
-				if (jpg_format == ERGBFormatJPG::BGRA)
-				{
-					d[0] = clamp(y + m_cbb[cb]);
-					d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-					d[2] = clamp(y + m_crr[cr]);
-					d[3] = 255;
-				}
-				else
-				{
-					d[0] = clamp(y + m_crr[cr]);
-					d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-					d[2] = clamp(y + m_cbb[cb]);
-					d[3] = 255;
-				}
-				d += 4;
-			}
-
-			s += 64*3;
-		}
-	}
-
-	// YCbCr H2V1 (2x1:1:1, 4 m_blocks per MCU) to RGB
-	void jpeg_decoder::H2V1Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *y = m_pSample_buf + row * 8;
-		uint8 *c = m_pSample_buf + 2*64 + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int l = 0; l < 2; l++)
-			{
-				for (int j = 0; j < 4; j++)
-				{
-					int cb = c[0];
-					int cr = c[64];
-
-					int rc = m_crr[cr];
-					int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-					int bc = m_cbb[cb];
-
-					int yy = y[j<<1];
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d0[0] = clamp(yy+bc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+rc);
-						d0[3] = 255;
-						yy = y[(j<<1)+1];
-						d0[4] = clamp(yy+bc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+rc);
-						d0[7] = 255;
-					}
-					else
-					{
-						d0[0] = clamp(yy+rc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+bc);
-						d0[3] = 255;
-						yy = y[(j<<1)+1];
-						d0[4] = clamp(yy+rc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+bc);
-						d0[7] = 255;
-					}
-
-					d0 += 8;
-
-					c++;
-				}
-				y += 64;
-			}
-
-			y += 64*4 - 64*2;
-			c += 64*4 - 8;
-		}
-	}
-
-	// YCbCr H2V1 (1x2:1:1, 4 m_blocks per MCU) to RGB
-	void jpeg_decoder::H1V2Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *d1 = m_pScan_line_1;
-		uint8 *y;
-		uint8 *c;
-
-		if (row < 8)
-			y = m_pSample_buf + row * 8;
-		else
-			y = m_pSample_buf + 64*1 + (row & 7) * 8;
-
-		c = m_pSample_buf + 64*2 + (row >> 1) * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int j = 0; j < 8; j++)
-			{
-				int cb = c[0+j];
-				int cr = c[64+j];
-
-				int rc = m_crr[cr];
-				int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-				int bc = m_cbb[cb];
-
-				int yy = y[j];
-				if (jpg_format == ERGBFormatJPG::BGRA)
-				{
-					d0[0] = clamp(yy+bc);
-					d0[1] = clamp(yy+gc);
-					d0[2] = clamp(yy+rc);
-					d0[3] = 255;
-					yy = y[8+j];
-					d1[0] = clamp(yy+bc);
-					d1[1] = clamp(yy+gc);
-					d1[2] = clamp(yy+rc);
-					d1[3] = 255;
-				}
-				else
-				{
-					d0[0] = clamp(yy+rc);
-					d0[1] = clamp(yy+gc);
-					d0[2] = clamp(yy+bc);
-					d0[3] = 255;
-					yy = y[8+j];
-					d1[0] = clamp(yy+rc);
-					d1[1] = clamp(yy+gc);
-					d1[2] = clamp(yy+bc);
-					d1[3] = 255;
-				}
-
-				d0 += 4;
-				d1 += 4;
-			}
-
-			y += 64*4;
-			c += 64*4;
-		}
-	}
-
-	// YCbCr H2V2 (2x2:1:1, 6 m_blocks per MCU) to RGB
-	void jpeg_decoder::H2V2Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *d1 = m_pScan_line_1;
-		uint8 *y;
-		uint8 *c;
-
-		if (row < 8)
-			y = m_pSample_buf + row * 8;
-		else
-			y = m_pSample_buf + 64*2 + (row & 7) * 8;
-
-		c = m_pSample_buf + 64*4 + (row >> 1) * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int l = 0; l < 2; l++)
-			{
-				for (int j = 0; j < 8; j += 2)
-				{
-					int cb = c[0];
-					int cr = c[64];
-
-					int rc = m_crr[cr];
-					int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-					int bc = m_cbb[cb];
-
-					int yy = y[j];
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d0[0] = clamp(yy+bc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+rc);
-						d0[3] = 255;
-						yy = y[j+1];
-						d0[4] = clamp(yy+bc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+rc);
-						d0[7] = 255;
-						yy = y[j+8];
-						d1[0] = clamp(yy+bc);
-						d1[1] = clamp(yy+gc);
-						d1[2] = clamp(yy+rc);
-						d1[3] = 255;
-						yy = y[j+8+1];
-						d1[4] = clamp(yy+bc);
-						d1[5] = clamp(yy+gc);
-						d1[6] = clamp(yy+rc);
-						d1[7] = 255;
-					}
-					else
-					{
-						d0[0] = clamp(yy+rc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+bc);
-						d0[3] = 255;
-						yy = y[j+1];
-						d0[4] = clamp(yy+rc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+bc);
-						d0[7] = 255;
-						yy = y[j+8];
-						d1[0] = clamp(yy+rc);
-						d1[1] = clamp(yy+gc);
-						d1[2] = clamp(yy+bc);
-						d1[3] = 255;
-						yy = y[j+8+1];
-						d1[4] = clamp(yy+rc);
-						d1[5] = clamp(yy+gc);
-						d1[6] = clamp(yy+bc);
-						d1[7] = 255;
-					}
-
-					d0 += 8;
-					d1 += 8;
-
-					c++;
-				}
-				y += 64;
-			}
-
-			y += 64*6 - 64*2;
-			c += 64*6 - 8;
-		}
-	}
-
-	// Y (1 block per MCU) to 8-bit grayscale
-	void jpeg_decoder::gray_convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d = m_pScan_line_0;
-		uint8 *s = m_pSample_buf + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			*(uint *)d = *(uint *)s;
-			*(uint *)(&d[4]) = *(uint *)(&s[4]);
-
-			s += 64;
-			d += 8;
-		}
-	}
-
-	void jpeg_decoder::expanded_convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-
-		uint8* Py = m_pSample_buf + (row / 8) * 64 * m_comp_h_samp[0] + (row & 7) * 8;
-
-		uint8* d = m_pScan_line_0;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int k = 0; k < m_max_mcu_x_size; k += 8)
-			{
-				const int Y_ofs = k * 8;
-				const int Cb_ofs = Y_ofs + 64 * m_expanded_blocks_per_component;
-				const int Cr_ofs = Y_ofs + 64 * m_expanded_blocks_per_component * 2;
-				for (int j = 0; j < 8; j++)
-				{
-					int y = Py[Y_ofs + j];
-					int cb = Py[Cb_ofs + j];
-					int cr = Py[Cr_ofs + j];
-
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d[0] = clamp(y + m_cbb[cb]);
-						d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-						d[2] = clamp(y + m_crr[cr]);
-						d[3] = 255;
-					}
-					else
-					{
-						d[0] = clamp(y + m_crr[cr]);
-						d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-						d[2] = clamp(y + m_cbb[cb]);
-						d[3] = 255;
-					}
-
-					d += 4;
-				}
-			}
-
-			Py += 64 * m_expanded_blocks_per_mcu;
-		}
-	}
-
-	// Find end of image (EOI) marker, so we can return to the user the exact size of the input stream.
-	void jpeg_decoder::find_eoi()
-	{
-		if (!m_progressive_flag)
-		{
-			// Attempt to read the EOI marker.
-			//get_bits_no_markers(m_bits_left & 7);
-
-			// Prime the bit buffer
-			m_bits_left = 16;
-			get_bits(16);
-			get_bits(16);
-
-			// The next marker _should_ be EOI
-			process_markers();
-		}
-
-		m_total_bytes_read -= m_in_buf_left;
-	}
-
-	int jpeg_decoder::decode(const void** pScan_line, uint* pScan_line_len)
-	{
-		if ((m_error_code) || (!m_ready_flag))
-			return JPGD_FAILED;
-
-		if (m_total_lines_left == 0)
-			return JPGD_DONE;
-
-		if (m_mcu_lines_left == 0)
-		{
-			if (setjmp(m_jmp_state))
-				return JPGD_FAILED;
-
-			if (m_progressive_flag)
-				load_next_row();
-			else
-				decode_next_row();
-
-			// Find the EOI marker if that was the last row.
-			if (m_total_lines_left <= m_max_mcu_y_size)
-				find_eoi();
-
-			m_mcu_lines_left = m_max_mcu_y_size;
-		}
-
-		if (m_freq_domain_chroma_upsample)
-		{
-			expanded_convert();
-			*pScan_line = m_pScan_line_0;
-		}
-		else
-		{
-			switch (m_scan_type)
-			{
-			case JPGD_YH2V2:
-				{
-					if ((m_mcu_lines_left & 1) == 0)
-					{
-						H2V2Convert();
-						*pScan_line = m_pScan_line_0;
-					}
-					else
-						*pScan_line = m_pScan_line_1;
-
-					break;
-				}
-			case JPGD_YH2V1:
-				{
-					H2V1Convert();
-					*pScan_line = m_pScan_line_0;
-					break;
-				}
-			case JPGD_YH1V2:
-				{
-					if ((m_mcu_lines_left & 1) == 0)
-					{
-						H1V2Convert();
-						*pScan_line = m_pScan_line_0;
-					}
-					else
-						*pScan_line = m_pScan_line_1;
-
-					break;
-				}
-			case JPGD_YH1V1:
-				{
-					H1V1Convert();
-					*pScan_line = m_pScan_line_0;
-					break;
-				}
-			case JPGD_GRAYSCALE:
-				{
-					gray_convert();
-					*pScan_line = m_pScan_line_0;
-
-					break;
-				}
-			}
-		}
-
-		*pScan_line_len = m_real_dest_bytes_per_scan_line;
-
-		m_mcu_lines_left--;
-		m_total_lines_left--;
-
-		return JPGD_SUCCESS;
-	}
-
-	// Creates the tables needed for efficient Huffman decoding.
-	void jpeg_decoder::make_huff_table(int index, huff_tables *pH)
-	{
-		int p, i, l, si;
-		uint8 huffsize[257];
-		uint huffcode[257];
-		uint code;
-		uint subtree;
-		int code_size;
-		int lastp;
-		int nextfreeentry;
-		int currententry;
-
-		pH->ac_table = m_huff_ac[index] != 0;
-
-		p = 0;
-
-		for (l = 1; l <= 16; l++)
-		{
-			for (i = 1; i <= m_huff_num[index][l]; i++)
-				huffsize[p++] = static_cast<uint8>(l);
-		}
-
-		huffsize[p] = 0;
-
-		lastp = p;
-
-		code = 0;
-		si = huffsize[0];
-		p = 0;
-
-		while (huffsize[p])
-		{
-			while (huffsize[p] == si)
-			{
-				huffcode[p++] = code;
-				code++;
-			}
-
-			code <<= 1;
-			si++;
-		}
-
-		memset(pH->look_up, 0, sizeof(pH->look_up));
-		memset(pH->look_up2, 0, sizeof(pH->look_up2));
-		memset(pH->tree, 0, sizeof(pH->tree));
-		memset(pH->code_size, 0, sizeof(pH->code_size));
-
-		nextfreeentry = -1;
-
-		p = 0;
-
-		while (p < lastp)
-		{
-			i = m_huff_val[index][p];
-			code = huffcode[p];
-			code_size = huffsize[p];
-
-			pH->code_size[i] = static_cast<uint8>(code_size);
-
-			if (code_size <= 8)
-			{
-				code <<= (8 - code_size);
-
-				for (l = 1 << (8 - code_size); l > 0; l--)
-				{
-					JPGD_ASSERT(i < 256);
-
-					pH->look_up[code] = i;
-
-					bool has_extrabits = false;
-					int extra_bits = 0;
-					int num_extra_bits = i & 15;
-
-					int bits_to_fetch = code_size;
-					if (num_extra_bits)
-					{
-						int total_codesize = code_size + num_extra_bits;
-						if (total_codesize <= 8)
-						{
-							has_extrabits = true;
-							extra_bits = ((1 << num_extra_bits) - 1) & (code >> (8 - total_codesize));
-							JPGD_ASSERT(extra_bits <= 0x7FFF);
-							bits_to_fetch += num_extra_bits;
-						}
-					}
-
-					if (!has_extrabits)
-						pH->look_up2[code] = i | (bits_to_fetch << 8);
-					else
-						pH->look_up2[code] = i | 0x8000 | (extra_bits << 16) | (bits_to_fetch << 8);
-
-					code++;
-				}
-			}
-			else
-			{
-				subtree = (code >> (code_size - 8)) & 0xFF;
-
-				currententry = pH->look_up[subtree];
-
-				if (currententry == 0)
-				{
-					pH->look_up[subtree] = currententry = nextfreeentry;
-					pH->look_up2[subtree] = currententry = nextfreeentry;
-
-					nextfreeentry -= 2;
-				}
-
-				code <<= (16 - (code_size - 8));
-
-				for (l = code_size; l > 9; l--)
-				{
-					if ((code & 0x8000) == 0)
-						currententry--;
-
-					if (pH->tree[-currententry - 1] == 0)
-					{
-						pH->tree[-currententry - 1] = nextfreeentry;
-
-						currententry = nextfreeentry;
-
-						nextfreeentry -= 2;
-					}
-					else
-						currententry = pH->tree[-currententry - 1];
-
-					code <<= 1;
-				}
-
-				if ((code & 0x8000) == 0)
-					currententry--;
-
-				pH->tree[-currententry - 1] = i;
-			}
-
-			p++;
-		}
-	}
-
-	// Verifies the quantization tables needed for this scan are available.
-	void jpeg_decoder::check_quant_tables()
-	{
-		for (int i = 0; i < m_comps_in_scan; i++)
-			if (m_quant[m_comp_quant[m_comp_list[i]]] == NULL)
-				stop_decoding(JPGD_UNDEFINED_QUANT_TABLE);
-	}
-
-	// Verifies that all the Huffman tables needed for this scan are available.
-	void jpeg_decoder::check_huff_tables()
-	{
-		for (int i = 0; i < m_comps_in_scan; i++)
-		{
-			if ((m_spectral_start == 0) && (m_huff_num[m_comp_dc_tab[m_comp_list[i]]] == NULL))
-				stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
-
-			if ((m_spectral_end > 0) && (m_huff_num[m_comp_ac_tab[m_comp_list[i]]] == NULL))
-				stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
-		}
-
-		for (int i = 0; i < JPGD_MAX_HUFF_TABLES; i++)
-			if (m_huff_num[i])
-			{
-				if (!m_pHuff_tabs[i])
-					m_pHuff_tabs[i] = (huff_tables *)alloc(sizeof(huff_tables));
-
-				make_huff_table(i, m_pHuff_tabs[i]);
-			}
-	}
-
-	// Determines the component order inside each MCU.
-	// Also calcs how many MCU's are on each row, etc.
-	void jpeg_decoder::calc_mcu_block_order()
-	{
-		int component_num, component_id;
-		int max_h_samp = 0, max_v_samp = 0;
-
-		for (component_id = 0; component_id < m_comps_in_frame; component_id++)
-		{
-			if (m_comp_h_samp[component_id] > max_h_samp)
-				max_h_samp = m_comp_h_samp[component_id];
-
-			if (m_comp_v_samp[component_id] > max_v_samp)
-				max_v_samp = m_comp_v_samp[component_id];
-		}
-
-		for (component_id = 0; component_id < m_comps_in_frame; component_id++)
-		{
-			m_comp_h_blocks[component_id] = ((((m_image_x_size * m_comp_h_samp[component_id]) + (max_h_samp - 1)) / max_h_samp) + 7) / 8;
-			m_comp_v_blocks[component_id] = ((((m_image_y_size * m_comp_v_samp[component_id]) + (max_v_samp - 1)) / max_v_samp) + 7) / 8;
-		}
-
-		if (m_comps_in_scan == 1)
-		{
-			m_mcus_per_row = m_comp_h_blocks[m_comp_list[0]];
-			m_mcus_per_col = m_comp_v_blocks[m_comp_list[0]];
-		}
-		else
-		{
-			m_mcus_per_row = (((m_image_x_size + 7) / 8) + (max_h_samp - 1)) / max_h_samp;
-			m_mcus_per_col = (((m_image_y_size + 7) / 8) + (max_v_samp - 1)) / max_v_samp;
-		}
-
-		if (m_comps_in_scan == 1)
-		{
-			m_mcu_org[0] = m_comp_list[0];
-
-			m_blocks_per_mcu = 1;
-		}
-		else
-		{
-			m_blocks_per_mcu = 0;
-
-			for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-			{
-				int num_blocks;
-
-				component_id = m_comp_list[component_num];
-
-				num_blocks = m_comp_h_samp[component_id] * m_comp_v_samp[component_id];
-
-				while (num_blocks--)
-					m_mcu_org[m_blocks_per_mcu++] = component_id;
-			}
-		}
-	}
-
-	// Starts a new scan.
-	int jpeg_decoder::init_scan()
-	{
-		if (!locate_sos_marker())
-			return JPGD_FALSE;
-
-		calc_mcu_block_order();
-
-		check_huff_tables();
-
-		check_quant_tables();
-
-		memset(m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
-		m_eob_run = 0;
-
-		if (m_restart_interval)
-		{
-			m_restarts_left = m_restart_interval;
-			m_next_restart_num = 0;
-		}
-
-		fix_in_buffer();
-
-		return JPGD_TRUE;
-	}
-
-	// Starts a frame. Determines if the number of components or sampling factors
-	// are supported.
-	void jpeg_decoder::init_frame()
-	{
-		int i;
-
-		if (m_comps_in_frame == 1)
-		{
-			if ((m_comp_h_samp[0] != 1) || (m_comp_v_samp[0] != 1))
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
-			m_scan_type = JPGD_GRAYSCALE;
-			m_max_blocks_per_mcu = 1;
-			m_max_mcu_x_size = 8;
-			m_max_mcu_y_size = 8;
-		}
-		else if (m_comps_in_frame == 3)
-		{
-			if ( ((m_comp_h_samp[1] != 1) || (m_comp_v_samp[1] != 1)) ||
-				((m_comp_h_samp[2] != 1) || (m_comp_v_samp[2] != 1)) )
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
-			if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
-			{
-				m_scan_type = JPGD_YH1V1;
-
-				m_max_blocks_per_mcu = 3;
-				m_max_mcu_x_size = 8;
-				m_max_mcu_y_size = 8;
-			}
-			else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
-			{
-				m_scan_type = JPGD_YH2V1;
-				m_max_blocks_per_mcu = 4;
-				m_max_mcu_x_size = 16;
-				m_max_mcu_y_size = 8;
-			}
-			else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 2))
-			{
-				m_scan_type = JPGD_YH1V2;
-				m_max_blocks_per_mcu = 4;
-				m_max_mcu_x_size = 8;
-				m_max_mcu_y_size = 16;
-			}
-			else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
-			{
-				m_scan_type = JPGD_YH2V2;
-				m_max_blocks_per_mcu = 6;
-				m_max_mcu_x_size = 16;
-				m_max_mcu_y_size = 16;
-			}
-			else
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-		}
-		else
-			stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
-		m_max_mcus_per_row = (m_image_x_size + (m_max_mcu_x_size - 1)) / m_max_mcu_x_size;
-		m_max_mcus_per_col = (m_image_y_size + (m_max_mcu_y_size - 1)) / m_max_mcu_y_size;
-
-		// These values are for the *destination* pixels: after conversion.
-		if (m_scan_type == JPGD_GRAYSCALE)
-			m_dest_bytes_per_pixel = 1;
-		else
-			m_dest_bytes_per_pixel = 4;
-
-		m_dest_bytes_per_scan_line = ((m_image_x_size + 15) & 0xFFF0) * m_dest_bytes_per_pixel;
-
-		m_real_dest_bytes_per_scan_line = (m_image_x_size * m_dest_bytes_per_pixel);
-
-		// Initialize two scan line buffers.
-		m_pScan_line_0 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
-		if ((m_scan_type == JPGD_YH1V2) || (m_scan_type == JPGD_YH2V2))
-			m_pScan_line_1 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
-
-		m_max_blocks_per_row = m_max_mcus_per_row * m_max_blocks_per_mcu;
-
-		// Should never happen
-		if (m_max_blocks_per_row > JPGD_MAX_BLOCKS_PER_ROW)
-			stop_decoding(JPGD_ASSERTION_ERROR);
-
-		// Allocate the coefficient buffer, enough for one MCU
-		m_pMCU_coefficients = (jpgd_block_t*)alloc(m_max_blocks_per_mcu * 64 * sizeof(jpgd_block_t));
-
-		for (i = 0; i < m_max_blocks_per_mcu; i++)
-			m_mcu_block_max_zag[i] = 64;
-
-		m_expanded_blocks_per_component = m_comp_h_samp[0] * m_comp_v_samp[0];
-		m_expanded_blocks_per_mcu = m_expanded_blocks_per_component * m_comps_in_frame;
-		m_expanded_blocks_per_row = m_max_mcus_per_row * m_expanded_blocks_per_mcu;
-		// Freq. domain chroma upsampling is only supported for H2V2 subsampling factor.
-// BEGIN EPIC MOD
-#if JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING
-		m_freq_domain_chroma_upsample = (m_expanded_blocks_per_mcu == 4*3);
-#else
-		m_freq_domain_chroma_upsample = 0;
-#endif
-// END EPIC MOD
-
-		if (m_freq_domain_chroma_upsample)
-			m_pSample_buf = (uint8 *)alloc(m_expanded_blocks_per_row * 64);
-		else
-			m_pSample_buf = (uint8 *)alloc(m_max_blocks_per_row * 64);
-
-		m_total_lines_left = m_image_y_size;
-
-		m_mcu_lines_left = 0;
-
-		create_look_ups();
-	}
-
-	// The coeff_buf series of methods originally stored the coefficients
-	// into a "virtual" file which was located in EMS, XMS, or a disk file. A cache
-	// was used to make this process more efficient. Now, we can store the entire
-	// thing in RAM.
-	jpeg_decoder::coeff_buf* jpeg_decoder::coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y)
-	{
-		coeff_buf* cb = (coeff_buf*)alloc(sizeof(coeff_buf));
-
-		cb->block_num_x = block_num_x;
-		cb->block_num_y = block_num_y;
-		cb->block_len_x = block_len_x;
-		cb->block_len_y = block_len_y;
-		cb->block_size = (block_len_x * block_len_y) * sizeof(jpgd_block_t);
-		cb->pData = (uint8 *)alloc(cb->block_size * block_num_x * block_num_y, true);
-		return cb;
-	}
-
-	inline jpgd_block_t *jpeg_decoder::coeff_buf_getp(coeff_buf *cb, int block_x, int block_y)
-	{
-		JPGD_ASSERT((block_x < cb->block_num_x) && (block_y < cb->block_num_y));
-		return (jpgd_block_t *)(cb->pData + block_x * cb->block_size + block_y * (cb->block_size * cb->block_num_x));
-	}
-
-	// The following methods decode the various types of m_blocks encountered
-	// in progressively encoded images.
-	void jpeg_decoder::decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int s, r;
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
-		if ((s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_dc_tab[component_id]])) != 0)
-		{
-			r = pD->get_bits_no_markers(s);
-			s = HUFF_EXTEND(r, s);
-		}
-
-		pD->m_last_dc_val[component_id] = (s += pD->m_last_dc_val[component_id]);
-
-		p[0] = static_cast<jpgd_block_t>(s << pD->m_successive_low);
-	}
-
-	void jpeg_decoder::decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		if (pD->get_bits_no_markers(1))
-		{
-			jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
-			p[0] |= (1 << pD->m_successive_low);
-		}
-	}
-
-	void jpeg_decoder::decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int k, s, r;
-
-		if (pD->m_eob_run)
-		{
-			pD->m_eob_run--;
-			return;
-		}
-
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
-		for (k = pD->m_spectral_start; k <= pD->m_spectral_end; k++)
-		{
-			s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
-			r = s >> 4;
-			s &= 15;
-
-			if (s)
-			{
-				if ((k += r) > 63)
-					pD->stop_decoding(JPGD_DECODE_ERROR);
-
-				r = pD->get_bits_no_markers(s);
-				s = HUFF_EXTEND(r, s);
-
-				p[g_ZAG[k]] = static_cast<jpgd_block_t>(s << pD->m_successive_low);
-			}
-			else
-			{
-				if (r == 15)
-				{
-					if ((k += 15) > 63)
-						pD->stop_decoding(JPGD_DECODE_ERROR);
-				}
-				else
-				{
-					pD->m_eob_run = 1 << r;
-
-					if (r)
-						pD->m_eob_run += pD->get_bits_no_markers(r);
-
-					pD->m_eob_run--;
-
-					break;
-				}
-			}
-		}
-	}
-
-	void jpeg_decoder::decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int s, k, r;
-		int p1 = 1 << pD->m_successive_low;
-		int m1 = (-1) << pD->m_successive_low;
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
-		k = pD->m_spectral_start;
-
-		if (pD->m_eob_run == 0)
-		{
-			for ( ; k <= pD->m_spectral_end; k++)
-			{
-				s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
-				r = s >> 4;
-				s &= 15;
-
-				if (s)
-				{
-					if (s != 1)
-						pD->stop_decoding(JPGD_DECODE_ERROR);
-
-					if (pD->get_bits_no_markers(1))
-						s = p1;
-					else
-						s = m1;
-				}
-				else
-				{
-					if (r != 15)
-					{
-						pD->m_eob_run = 1 << r;
-
-						if (r)
-							pD->m_eob_run += pD->get_bits_no_markers(r);
-
-						break;
-					}
-				}
-
-				do
-				{
-					// BEGIN EPIC MOD
-					JPGD_ASSERT(k < 64);
-					// END EPIC MOD
-
-					jpgd_block_t *this_coef = p + g_ZAG[k];
-
-					if (*this_coef != 0)
-					{
-						if (pD->get_bits_no_markers(1))
-						{
-							if ((*this_coef & p1) == 0)
-							{
-								if (*this_coef >= 0)
-									*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);
-								else
-									*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);
-							}
-						}
-					}
-					else
-					{
-						if (--r < 0)
-							break;
-					}
-
-					k++;
-
-				} while (k <= pD->m_spectral_end);
-
-				if ((s) && (k < 64))
-				{
-					p[g_ZAG[k]] = static_cast<jpgd_block_t>(s);
-				}
-			}
-		}
-
-		if (pD->m_eob_run > 0)
-		{
-			for ( ; k <= pD->m_spectral_end; k++)
-			{
-				// BEGIN EPIC MOD
-				JPGD_ASSERT(k < 64);
-				// END EPIC MOD
-
-				jpgd_block_t *this_coef = p + g_ZAG[k];
-
-				if (*this_coef != 0)
-				{
-					if (pD->get_bits_no_markers(1))
-					{
-						if ((*this_coef & p1) == 0)
-						{
-							if (*this_coef >= 0)
-								*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);
-							else
-								*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);
-						}
-					}
-				}
-			}
-
-			pD->m_eob_run--;
-		}
-	}
-
-	// Decode a scan in a progressively encoded image.
-	void jpeg_decoder::decode_scan(pDecode_block_func decode_block_func)
-	{
-		int mcu_row, mcu_col, mcu_block;
-		int block_x_mcu[JPGD_MAX_COMPONENTS], m_block_y_mcu[JPGD_MAX_COMPONENTS];
-
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		for (mcu_col = 0; mcu_col < m_mcus_per_col; mcu_col++)
-		{
-			int component_num, component_id;
-
-			memset(block_x_mcu, 0, sizeof(block_x_mcu));
-
-			for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-			{
-				int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
-				if ((m_restart_interval) && (m_restarts_left == 0))
-					process_restart();
-
-				for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-				{
-					component_id = m_mcu_org[mcu_block];
-
-					decode_block_func(this, component_id, block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-
-					if (m_comps_in_scan == 1)
-						block_x_mcu[component_id]++;
-					else
-					{
-						if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
-						{
-							block_x_mcu_ofs = 0;
-
-							if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
-							{
-								block_y_mcu_ofs = 0;
-								block_x_mcu[component_id] += m_comp_h_samp[component_id];
-							}
-						}
-					}
-				}
-
-				m_restarts_left--;
-			}
-
-			if (m_comps_in_scan == 1)
-				m_block_y_mcu[m_comp_list[0]]++;
-			else
-			{
-				for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-				{
-					component_id = m_comp_list[component_num];
-					m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
-				}
-			}
-		}
-	}
-
-	// Decode a progressively encoded image.
-	void jpeg_decoder::init_progressive()
-	{
-		int i;
-
-		if (m_comps_in_frame == 4)
-			stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
-		// Allocate the coefficient buffers.
-		for (i = 0; i < m_comps_in_frame; i++)
-		{
-			m_dc_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 1, 1);
-			m_ac_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 8, 8);
-		}
-
-		for ( ; ; )
-		{
-			int dc_only_scan, refinement_scan;
-			pDecode_block_func decode_block_func;
-
-			if (!init_scan())
-				break;
-
-			dc_only_scan = (m_spectral_start == 0);
-			refinement_scan = (m_successive_high != 0);
-
-			if ((m_spectral_start > m_spectral_end) || (m_spectral_end > 63))
-				stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
-			if (dc_only_scan)
-			{
-				if (m_spectral_end)
-					stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-			}
-			else if (m_comps_in_scan != 1)  /* AC scans can only contain one component */
-				stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
-			if ((refinement_scan) && (m_successive_low != m_successive_high - 1))
-				stop_decoding(JPGD_BAD_SOS_SUCCESSIVE);
-
-			if (dc_only_scan)
-			{
-				if (refinement_scan)
-					decode_block_func = decode_block_dc_refine;
-				else
-					decode_block_func = decode_block_dc_first;
-			}
-			else
-			{
-				if (refinement_scan)
-					decode_block_func = decode_block_ac_refine;
-				else
-					decode_block_func = decode_block_ac_first;
-			}
-
-			decode_scan(decode_block_func);
-
-			m_bits_left = 16;
-			get_bits(16);
-			get_bits(16);
-		}
-
-		m_comps_in_scan = m_comps_in_frame;
-
-		for (i = 0; i < m_comps_in_frame; i++)
-			m_comp_list[i] = i;
-
-		calc_mcu_block_order();
-	}
-
-	void jpeg_decoder::init_sequential()
-	{
-		if (!init_scan())
-			stop_decoding(JPGD_UNEXPECTED_MARKER);
-	}
-
-	void jpeg_decoder::decode_start()
-	{
-		init_frame();
-
-		if (m_progressive_flag)
-			init_progressive();
-		else
-			init_sequential();
-	}
-
-	void jpeg_decoder::decode_init(jpeg_decoder_stream *pStream)
-	{
-		init(pStream);
-		locate_sof_marker();
-	}
-
-	jpeg_decoder::jpeg_decoder(jpeg_decoder_stream *pStream)
-	{
-		if (setjmp(m_jmp_state))
-			return;
-		decode_init(pStream);
-	}
-
-	int jpeg_decoder::begin_decoding()
-	{
-		if (m_ready_flag)
-			return JPGD_SUCCESS;
-
-		if (m_error_code)
-			return JPGD_FAILED;
-
-		if (setjmp(m_jmp_state))
-			return JPGD_FAILED;
-
-		decode_start();
-
-		m_ready_flag = true;
-
-		return JPGD_SUCCESS;
-	}
-
-	jpeg_decoder::~jpeg_decoder()
-	{
-		free_all_blocks();
-	}
-
-	jpeg_decoder_file_stream::jpeg_decoder_file_stream()
-	{
-		m_pFile = NULL;
-		m_eof_flag = false;
-		m_error_flag = false;
-	}
-
-	void jpeg_decoder_file_stream::close()
-	{
-		if (m_pFile)
-		{
-			fclose(m_pFile);
-			m_pFile = NULL;
-		}
-
-		m_eof_flag = false;
-		m_error_flag = false;
-	}
-
-	jpeg_decoder_file_stream::~jpeg_decoder_file_stream()
-	{
-		close();
-	}
-
-	bool jpeg_decoder_file_stream::open(const char *Pfilename)
-	{
-		close();
-
-		m_eof_flag = false;
-		m_error_flag = false;
-
-#if defined(_MSC_VER)
-		m_pFile = NULL;
-		fopen_s(&m_pFile, Pfilename, "rb");
-#else
-		m_pFile = fopen(Pfilename, "rb");
-#endif
-		return m_pFile != NULL;
-	}
-
-	int jpeg_decoder_file_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
-	{
-		if (!m_pFile)
-			return -1;
-
-		if (m_eof_flag)
-		{
-			*pEOF_flag = true;
-			return 0;
-		}
-
-		if (m_error_flag)
-			return -1;
-
-		int bytes_read = static_cast<int>(fread(pBuf, 1, max_bytes_to_read, m_pFile));
-		if (bytes_read < max_bytes_to_read)
-		{
-			if (ferror(m_pFile))
-			{
-				m_error_flag = true;
-				return -1;
-			}
-
-			m_eof_flag = true;
-			*pEOF_flag = true;
-		}
-
-		return bytes_read;
-	}
-
-	bool jpeg_decoder_mem_stream::open(const uint8 *pSrc_data, uint size)
-	{
-		close();
-		m_pSrc_data = pSrc_data;
-		m_ofs = 0;
-		m_size = size;
-		return true;
-	}
-
-	int jpeg_decoder_mem_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
-	{
-		*pEOF_flag = false;
-
-		if (!m_pSrc_data)
-			return -1;
-
-		uint bytes_remaining = m_size - m_ofs;
-		if ((uint)max_bytes_to_read > bytes_remaining)
-		{
-			max_bytes_to_read = bytes_remaining;
-			*pEOF_flag = true;
-		}
-
-		memcpy(pBuf, m_pSrc_data + m_ofs, max_bytes_to_read);
-		m_ofs += max_bytes_to_read;
-
-		return max_bytes_to_read;
-	}
-
-	unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps)
-	{
-		if (!actual_comps)
-			return NULL;
-		*actual_comps = 0;
-
-		if ((!pStream) || (!width) || (!height) || (!req_comps))
-			return NULL;
-
-		if ((req_comps != 1) && (req_comps != 3) && (req_comps != 4))
-			return NULL;
-
-		jpeg_decoder decoder(pStream);
-		if (decoder.get_error_code() != JPGD_SUCCESS)
-			return NULL;
-
-		const int image_width = decoder.get_width(), image_height = decoder.get_height();
-		*width = image_width;
-		*height = image_height;
-		*actual_comps = decoder.get_num_components();
-
-		if (decoder.begin_decoding() != JPGD_SUCCESS)
-			return NULL;
-
-		const int dst_bpl = image_width * req_comps;
-
-		uint8 *pImage_data = (uint8*)jpgd_malloc(dst_bpl * image_height);
-		if (!pImage_data)
-			return NULL;
-
-		for (int y = 0; y < image_height; y++)
-		{
-			const uint8* pScan_line = 0;
-			uint scan_line_len;
-			if (decoder.decode((const void**)&pScan_line, &scan_line_len) != JPGD_SUCCESS)
-			{
-				jpgd_free(pImage_data);
-				return NULL;
-			}
-
-			uint8 *pDst = pImage_data + y * dst_bpl;
-
-			if (((req_comps == 4) && (decoder.get_num_components() == 3)) ||
-				((req_comps == 1) && (decoder.get_num_components() == 1)))
-			{
-				memcpy(pDst, pScan_line, dst_bpl);
-			}
-			else if (decoder.get_num_components() == 1)
-			{
-				if (req_comps == 3)
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						uint8 luma = pScan_line[x];
-						pDst[0] = luma;
-						pDst[1] = luma;
-						pDst[2] = luma;
-						pDst += 3;
-					}
-				}
-				else
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						uint8 luma = pScan_line[x];
-						pDst[0] = luma;
-						pDst[1] = luma;
-						pDst[2] = luma;
-						pDst[3] = 255;
-						pDst += 4;
-					}
-				}
-			}
-			else if (decoder.get_num_components() == 3)
-			{
-				if (req_comps == 1)
-				{
-					const int YR = 19595, YG = 38470, YB = 7471;
-					for (int x = 0; x < image_width; x++)
-					{
-						int r = pScan_line[x*4+0];
-						int g = pScan_line[x*4+1];
-						int b = pScan_line[x*4+2];
-						*pDst++ = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-					}
-				}
-				else
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						pDst[0] = pScan_line[x*4+0];
-						pDst[1] = pScan_line[x*4+1];
-						pDst[2] = pScan_line[x*4+2];
-						pDst += 3;
-					}
-				}
-			}
-		}
-
-		return pImage_data;
-	}
-
-// BEGIN EPIC MOD
-	unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format)
-	{
-		jpg_format = (ERGBFormatJPG)format;
-// EMD EPIC MOD
-		jpgd::jpeg_decoder_mem_stream mem_stream(pSrc_data, src_data_size);
-		return decompress_jpeg_image_from_stream(&mem_stream, width, height, actual_comps, req_comps);
-	}
-
-	unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps)
-	{
-		jpgd::jpeg_decoder_file_stream file_stream;
-		if (!file_stream.open(pSrc_filename))
-			return NULL;
-		return decompress_jpeg_image_from_stream(&file_stream, width, height, actual_comps, req_comps);
-	}
-
-} // namespace jpgd
diff --git a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/docs/README.en.md b/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/docs/README.en.md
deleted file mode 100644
index 6fa77f3ec1cfa55a6e91c16eed2012f05f694b98..0000000000000000000000000000000000000000
--- a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/docs/README.en.md
+++ /dev/null
@@ -1,102 +0,0 @@
-<div align="center">
-
-<h1>Retrieval-based-Voice-Conversion-WebUI</h1>
-An easy-to-use SVC framework based on VITS.<br><br>
-
-[![madewithlove](https://forthebadge.com/images/badges/built-with-love.svg)](https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI)
-  
-<img src="https://counter.seku.su/cmoe?name=rvc&theme=r34" /><br>
-  
-[![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/github/liujing04/Retrieval-based-Voice-Conversion-WebUI/blob/main/Retrieval_based_Voice_Conversion_WebUI.ipynb)
-[![Licence](https://img.shields.io/github/license/liujing04/Retrieval-based-Voice-Conversion-WebUI?style=for-the-badge)](https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI/blob/main/%E4%BD%BF%E7%94%A8%E9%9C%80%E9%81%B5%E5%AE%88%E7%9A%84%E5%8D%8F%E8%AE%AE-LICENSE.txt)
-[![Huggingface](https://img.shields.io/badge/🤗%20-Spaces-yellow.svg?style=for-the-badge)](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/)
-
-[![Discord](https://img.shields.io/badge/RVC%20Developers-Discord-7289DA?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/HcsmBBGyVk)
-
-</div>
-
-------
-[**Changelog**](https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI/blob/main/Changelog_CN.md)
-
-[**English**](./README.en.md) | [**中文简体**](../README.md) | [**日本語**](./README.ja.md) | [**한국어**](./README.ko.md) ([**韓國語**](./README.ko.han.md))
-
-> Check our [Demo Video](https://www.bilibili.com/video/BV1pm4y1z7Gm/) here!
-
-> Realtime Voice Conversion Software using RVC : [w-okada/voice-changer](https://github.com/w-okada/voice-changer)
-
-> The dataset for the pre-training model uses nearly 50 hours of high quality VCTK open source dataset.
-
-> High quality licensed song datasets will be added to training-set one after another for your use, without worrying about copyright infringement.
-## Summary
-This repository has the following features:
-+ Reduce tone leakage by replacing source feature to training-set feature using top1 retrieval;
-+ Easy and fast training, even on relatively poor graphics cards;
-+ Training with a small amount of data also obtains relatively good results (>=10min low noise speech recommended);
-+ Supporting model fusion to change timbres (using ckpt processing tab->ckpt merge);
-+ Easy-to-use Webui interface;
-+ Use the UVR5 model to quickly separate vocals and instruments.
-## Preparing the environment
-We recommend you install the dependencies through poetry.
-
-The following commands need to be executed in the environment of Python version 3.8 or higher:
-```bash
-# Install PyTorch-related core dependencies, skip if installed
-# Reference: https://pytorch.org/get-started/locally/
-pip install torch torchvision torchaudio
-
-#For Windows + Nvidia Ampere Architecture(RTX30xx), you need to specify the cuda version corresponding to pytorch according to the experience of https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI/issues/21
-#pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
-
-# Install the Poetry dependency management tool, skip if installed
-# Reference: https://python-poetry.org/docs/#installation
-curl -sSL https://install.python-poetry.org | python3 -
-
-# Install the project dependencies
-poetry install
-```
-You can also use pip to install the dependencies
-
-**Notice**: `faiss 1.7.2` will raise Segmentation Fault: 11 under `MacOS`, please use `pip install faiss-cpu==1.7.0` if you use pip to install it manually.
-
-```bash
-pip install -r requirements.txt
-```
-
-## Preparation of other Pre-models
-RVC requires other pre-models to infer and train.
-
-You need to download them from our [Huggingface space](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/).
-
-Here's a list of Pre-models and other files that RVC needs:
-```bash
-hubert_base.pt
-
-./pretrained 
-
-./uvr5_weights
-
-#If you are using Windows, you may also need this dictionary, skip if FFmpeg is installed
-ffmpeg.exe
-```
-Then use this command to start Webui:
-```bash
-python infer-web.py
-```
-If you are using Windows, you can download and extract `RVC-beta.7z` to use RVC directly and use `go-web.bat` to start Webui.
-
-There's also a tutorial on RVC in Chinese and you can check it out if needed.
-
-## Credits
-+ [ContentVec](https://github.com/auspicious3000/contentvec/)
-+ [VITS](https://github.com/jaywalnut310/vits)
-+ [HIFIGAN](https://github.com/jik876/hifi-gan)
-+ [Gradio](https://github.com/gradio-app/gradio)
-+ [FFmpeg](https://github.com/FFmpeg/FFmpeg)
-+ [Ultimate Vocal Remover](https://github.com/Anjok07/ultimatevocalremovergui)
-+ [audio-slicer](https://github.com/openvpi/audio-slicer)
-## Thanks to all contributors for their efforts
-
-<a href="https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI/graphs/contributors" target="_blank">
-  <img src="https://contrib.rocks/image?repo=liujing04/Retrieval-based-Voice-Conversion-WebUI" />
-</a>
-
diff --git a/spaces/shi-labs/FcF-Inpainting/training/data/mask_generator.py b/spaces/shi-labs/FcF-Inpainting/training/data/mask_generator.py
deleted file mode 100644
index 3dd426a7068d950d12584d008a55bec948878a78..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/FcF-Inpainting/training/data/mask_generator.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import numpy as np
-from PIL import Image, ImageDraw
-import math
-
-def RandomBrush(
-    max_tries,
-    s,
-    min_num_vertex = 4,
-    max_num_vertex = 18,
-    mean_angle = 2*math.pi / 5,
-    angle_range = 2*math.pi / 15,
-    min_width = 12,
-    max_width = 48):
-    H, W = s, s
-    average_radius = math.sqrt(H*H+W*W) / 8
-    mask = Image.new('L', (W, H), 0)
-    for _ in range(np.random.randint(max_tries)):
-        num_vertex = np.random.randint(min_num_vertex, max_num_vertex)
-        angle_min = mean_angle - np.random.uniform(0, angle_range)
-        angle_max = mean_angle + np.random.uniform(0, angle_range)
-        angles = []
-        vertex = []
-        for i in range(num_vertex):
-            if i % 2 == 0:
-                angles.append(2*math.pi - np.random.uniform(angle_min, angle_max))
-            else:
-                angles.append(np.random.uniform(angle_min, angle_max))
-
-        h, w = mask.size
-        vertex.append((int(np.random.randint(0, w)), int(np.random.randint(0, h))))
-        for i in range(num_vertex):
-            r = np.clip(
-                np.random.normal(loc=average_radius, scale=average_radius//2),
-                0, 2*average_radius)
-            new_x = np.clip(vertex[-1][0] + r * math.cos(angles[i]), 0, w)
-            new_y = np.clip(vertex[-1][1] + r * math.sin(angles[i]), 0, h)
-            vertex.append((int(new_x), int(new_y)))
-
-        draw = ImageDraw.Draw(mask)
-        width = int(np.random.uniform(min_width, max_width))
-        draw.line(vertex, fill=1, width=width)
-        for v in vertex:
-            draw.ellipse((v[0] - width//2,
-                          v[1] - width//2,
-                          v[0] + width//2,
-                          v[1] + width//2),
-                         fill=1)
-        if np.random.random() > 0.5:
-            mask.transpose(Image.FLIP_LEFT_RIGHT)
-        if np.random.random() > 0.5:
-            mask.transpose(Image.FLIP_TOP_BOTTOM)
-    mask = np.asarray(mask, np.uint8)
-    if np.random.random() > 0.5:
-        mask = np.flip(mask, 0)
-    if np.random.random() > 0.5:
-        mask = np.flip(mask, 1)
-    return mask
-
-def RandomMask(s, hole_range=[0,1]):
-    coef = min(hole_range[0] + hole_range[1], 1.0)
-    while True:
-        mask = np.ones((s, s), np.uint8)
-        def Fill(max_size):
-            w, h = np.random.randint(max_size), np.random.randint(max_size)
-            ww, hh = w // 2, h // 2
-            x, y = np.random.randint(-ww, s - w + ww), np.random.randint(-hh, s - h + hh)
-            mask[max(y, 0): min(y + h, s), max(x, 0): min(x + w, s)] = 0
-        def MultiFill(max_tries, max_size):
-            for _ in range(np.random.randint(max_tries)):
-                Fill(max_size)
-        MultiFill(int(10 * coef), s // 2)
-        MultiFill(int(5 * coef), s)
-        mask = 1 - np.logical_and(mask, 1 - RandomBrush(int(20 * coef), s))
-        hole_ratio = np.mean(mask)
-        if hole_range is not None and (hole_ratio <= hole_range[0] or hole_ratio >= hole_range[1]):
-            continue
-        return mask[np.newaxis, ...].astype(np.float32)
-
-def generate_random_mask(s=256, hole_range=[0.1,1]):
-    return RandomMask(s, hole_range)
\ No newline at end of file
diff --git a/spaces/shikunl/prismer/prismer/experts/segmentation/mask2former/modeling/transformer_decoder/position_encoding.py b/spaces/shikunl/prismer/prismer/experts/segmentation/mask2former/modeling/transformer_decoder/position_encoding.py
deleted file mode 100644
index 1d273443d7af2f8e82e8c36ed7efa0498406018d..0000000000000000000000000000000000000000
--- a/spaces/shikunl/prismer/prismer/experts/segmentation/mask2former/modeling/transformer_decoder/position_encoding.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# # Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py
-"""
-Various positional encodings for the transformer.
-"""
-import math
-
-import torch
-from torch import nn
-
-
-class PositionEmbeddingSine(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperature = temperature
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, x, mask=None):
-        if mask is None:
-            mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
-        not_mask = ~mask
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-        if self.normalize:
-            eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_t = self.temperature ** (2 * (torch.div(dim_t, 2, rounding_mode='trunc')) / self.num_pos_feats)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-        pos_x = torch.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos_y = torch.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-        return pos
-    
-    def __repr__(self, _repr_indent=4):
-        head = "Positional encoding " + self.__class__.__name__
-        body = [
-            "num_pos_feats: {}".format(self.num_pos_feats),
-            "temperature: {}".format(self.temperature),
-            "normalize: {}".format(self.normalize),
-            "scale: {}".format(self.scale),
-        ]
-        # _repr_indent = 4
-        lines = [head] + [" " * _repr_indent + line for line in body]
-        return "\n".join(lines)
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/CPU-Z APK for Android A Powerful and Easy-to-Use App to Display Your Devices Hardware Details.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/CPU-Z APK for Android A Powerful and Easy-to-Use App to Display Your Devices Hardware Details.md
deleted file mode 100644
index 0e08219c46281e233a8fa2d5f6180e6af1aa201f..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/CPU-Z APK for Android A Powerful and Easy-to-Use App to Display Your Devices Hardware Details.md	
+++ /dev/null
@@ -1,152 +0,0 @@
-
-<h1>How to Download CPU-Z APK for Android</h1>
-<p>If you are looking for a way to check all the hardware information of your Android device, you might want to try CPU-Z. CPU-Z is a popular and free tool that reports detailed information about your device's processor, memory, storage, battery, sensors, and more. In this article, we will show you how to download and install CPU-Z APK for Android, and how to use it to check your device's hardware information.</p>
- <h2>What is CPU-Z and why do you need it?</h2>
-<p>CPU-Z is an application that was originally developed for Windows PC, but has been adapted for Android devices as well. It is a tool that allows you to view all kinds of information related to the hardware of your device. You can use it to check the name, architecture, clock speed, and cores of your processor; the model, resolution, RAM, and storage of your device; the version, root status, and security patch level of your Android system; the level, status, temperature, and capacity of your battery; the status and values of your sensors; and more.</p>
-<h2>download cpu z apk android</h2><br /><p><b><b>Download File</b> &#10027;&#10027;&#10027; <a href="https://ssurll.com/2uO19Y">https://ssurll.com/2uO19Y</a></b></p><br /><br />
-<p>You might need CPU-Z if you want to know more about your device's hardware specifications, or if you want to compare them with other devices. You might also need it if you want to validate your device's hardware specification online or offline, which can help you verify if your device is genuine or counterfeit.</p>
- <h3>CPU-Z features and benefits</h3>
-<p>Some of the features and benefits of using CPU-Z are:</p>
-<ul>
-<li>It is free and does not require any special permissions or root access.</li>
-<li>It is fast and accurate in reporting hardware information.</li>
-<li>It supports a wide range of devices and processors.</li>
-<li>It allows you to validate your device's hardware specification online or offline.</li>
-<li>It has a simple and user-friendly interface.</li>
-</ul>
- <h3>CPU-Z requirements and compatibility</h3>
-<p>To use CPU-Z on your Android device, you need to have:</p>
-<ul>
-<li>An Android device running Android 4.4 or higher.</li>
-<li>An internet connection (optional) for online validation.</li>
-<li>A file manager app (optional) for locating and installing the APK file.</li>
-</ul>
-<p>CPU-Z is compatible with most Android devices and processors, including ARM, x86, x64, MIPS, Qualcomm Snapdragon, Samsung Exynos, MediaTek Helio, Huawei Kirin, NVIDIA Tegra, Intel Atom, AMD Ryzen, etc.</p>
- <h2>How to download and install CPU-Z APK for Android</h2>
-<p>If you want to download and install CPU-Z APK for Android, you need to follow these steps:</p>
-<p>download cpu z apk android free<br />
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- <h3>Step 1: Enable unknown sources on your device</h3>
-<p>Since CPU-Z APK is not available on the Google Play Store, you need to enable unknown sources on your device to allow installation from third-party sources. To do this:</p>
-<ol>
-<li>Go to Settings > Security > Unknown sources (or Settings > Apps & notifications > Special app access > Install unknown apps).</li>
-<li>Toggle on the switch or check the box to enable unknown sources.</li>
-<li>Tap OK or Allow if prompted.</li>
-</ol <h3>Step 2: Download the CPU-Z APK file from a trusted source</h3>
-<p>Next, you need to download the CPU-Z APK file from a trusted source. You can use your web browser or a download manager app to do this. Here are some of the sources where you can find the latest version of CPU-Z APK:</p>
-<ul>
-<li>[CPU-Z official website]</li>
-<li>[APKPure]</li>
-<li>[APKMirror]</li>
-<li>[Uptodown]</li>
-</ul>
-<p>Make sure you download the file from a secure and reliable source, and avoid any fake or malicious links. You can also scan the file with an antivirus app before installing it.</p>
- <h3>Step 3: Locate and install the CPU-Z APK file on your device</h3>
-<p>After downloading the CPU-Z APK file, you need to locate and install it on your device. To do this:</p>
-<ol>
-<li>Open your file manager app and navigate to the folder where you saved the CPU-Z APK file.</li>
-<li>Tap on the file to open it.</li>
-<li>Tap on Install when prompted.</li>
-<li>Wait for the installation process to complete.</li>
-</ol>
- <h3>Step 4: Launch and use CPU-Z on your device</h3>
-<p>Once the installation is done, you can launch and use CPU-Z on your device. To do this:</p>
-<ol>
-<li>Go to your app drawer and tap on the CPU-Z icon.</li>
-<li>Grant any permissions that CPU-Z may request.</li>
-<li>Wait for CPU-Z to scan and display your device's hardware information.</li>
-<li>Swipe left or right to switch between different tabs and categories of information.</li>
-</ol>
- <h2>How to use CPU-Z to check your device's hardware information</h2>
-<p>CPU-Z has six main tabs that show different types of hardware information about your device. Here is a brief overview of each tab and what it shows:</p>
- <h3>SOC tab: Processor and GPU information</h3>
-<p>The SOC (System on Chip) tab shows information about your device's processor and GPU (Graphics Processing Unit). You can see the name, architecture, clock speed, cores, instruction set, revision, and vendor of your processor; and the name, vendor, renderer, version, and extensions of your GPU. You can also see a real-time graph of your processor's load and frequency.</p>
- <h3>Device tab: Model, resolution, RAM, and storage information</h3>
-<p>The Device tab shows information about your device's model, resolution, RAM (Random Access Memory), and storage. You can see the brand, model, board, screen size, screen density, resolution, total RAM, available RAM, internal storage, and external storage of your device. You can also see a real-time graph of your device's RAM usage.</p>
- <h3>System tab: Android version and root status information</h3>
-<p>The System tab shows information about your device's Android version and root status. You can see the Android version, API level, build ID, build date, security patch level, bootloader, kernel version, kernel architecture, SELinux status, Java VM version, OpenGL ES version, root access status, busybox status, and system uptime of your device.</p>
- <h3>Battery tab: Level, status, temperature, and capacity information</h3>
-<p>The Battery tab shows information about your device's battery level, status, temperature, and capacity. You can see the battery level percentage, battery status (charging or discharging), battery health (good or bad), battery technology (Li-ion or Li-poly), battery voltage (in millivolts), battery temperature (in Celsius or Fahrenheit), battery capacity (in milliampere-hours), minimum battery capacity (in milliampere-hours), and charge counter (in milliampere-hours) of your device. You can also see a real-time graph of your battery level and temperature.</p>
- <h3>Sensors tab: Accelerometer, light sensor, and other sensor information</h3>
-<p>The Sensors tab shows information about your device's sensors such as accelerometer, light sensor, proximity sensor, gyroscope sensor, magnetic sensor , and more. You can see the name, type, vendor, version, power, resolution, range, and values of each sensor on your device. You can also see a real-time graph of the sensor values.</p>
- <h2>How to validate your device's hardware specification with CPU-Z</h2>
-<p>One of the useful features of CPU-Z is that it allows you to validate your device's hardware specification online or offline. This can help you verify if your device is genuine or counterfeit, or if it matches the advertised specification. Here is how to do it:</p>
- <h3>Online validation process and benefits</h3>
-<p>The online validation process involves sending your device's hardware information to the CPU-Z website, where it will be compared with a database of other devices and processors. To do this:</p>
-<ol>
-<li>Tap on the menu icon (three horizontal lines) on the top left corner of the CPU-Z app.</li>
-<li>Tap on Online Validation.</li>
-<li>Tap on Submit.</li>
-<li>Wait for the validation process to complete.</li>
-<li>Tap on View Validation to see the result on your web browser.</li>
-</ol>
-<p>The benefits of online validation are:</p>
-<ul>
-<li>You can see a detailed comparison of your device's hardware information with other devices and processors.</li>
-<li>You can see a validation score that indicates how well your device matches the expected specification.</li>
-<li>You can see a validation link that you can share with others to prove your device's authenticity or performance.</li>
-</ul>
- <h3>Offline validation process and benefits</h3>
-<p>The offline validation process involves generating a validation file that contains your device's hardware information, which you can save on your device or send to others. To do this:</p>
-<ol>
-<li>Tap on the menu icon (three horizontal lines) on the top left corner of the CPU-Z app.</li>
-<li>Tap on Offline Validation.</li>
-<li>Tap on Generate Validation File.</li>
-<li>Choose a location and name for the validation file and tap on Save.</li>
-</ol>
-<p>The benefits of offline validation are:</p>
-<ul>
-<li>You can save your device's hardware information for future reference or comparison.</li>
-<li>You can send the validation file to others via email, Bluetooth, or other methods.</li>
-<li>You can open the validation file with any text editor or viewer app.</li>
-</ul>
- <h2>Conclusion and FAQs</h2>
-<p>CPU-Z is a handy tool that lets you check all the hardware information of your Android device. You can use it to view detailed information about your processor, memory, storage, battery, sensors, and more. You can also use it to validate your device's hardware specification online or offline, which can help you verify if your device is genuine or counterfeit. CPU-Z is free, fast, accurate, and easy to use. You can download and install CPU-Z APK for Android by following the steps in this article.</p>
- <p>Here are some frequently asked questions about CPU-Z:</p>
- <h4>Q: Is CPU-Z safe to use?</h4>
-<p>A: Yes, CPU-Z is safe to use as long as you download it from a trusted source and scan it with an antivirus app before installing it. CPU-Z does not require any special permissions or root access, and does not collect or send any personal data from your device.</p>
- <h4>Q: How accurate is CPU-Z?</h4>
-<p>A: CPU-Z is generally accurate in reporting hardware information, but it may not be able to detect some features or specifications that are not supported by your device or processor. For example, CPU-Z may not be able to show the exact clock speed of some processors that use dynamic frequency scaling or thermal throttling. CPU-Z may also not be able to show the exact battery capacity of some devices that use adaptive battery management.</p>
- <h4>Q: How often is CPU-Z updated?</h4>
-<p>A: CPU-Z is updated regularly to support new devices and processors, fix bugs, and improve performance. You can check for updates by tapping on the menu icon (three horizontal lines) on the top left corner of the CPU-Z app and tapping on Check for Update. You can also visit the CPU-Z official website or any of the sources mentioned in this article to download the latest version of CPU-Z APK for Android.</p>
- <h4>Q: What are some alternatives to CPU-Z?</h4>
-<p>A: Some alternatives to CPU-Z are:</p>
-<ul>
-<li>[AIDA64]: A comprehensive system information and benchmarking tool that supports Android devices as well as Windows PC, Linux PC, Mac OS X PC, iOS devices, Windows Phone devices, Tizen devices, Chrome OS devices, and more.</li>
-<li>[Device Info]: A simple and elegant system information and hardware test tool that shows information about your device's processor, memory, battery, camera, network, sensors, and more.</li>
-<li>[Geekbench]: A cross-platform benchmarking tool that measures the performance of your device's processor and memory by running various tests and comparing the results with other devices.</li>
-<li>[AnTuTu]: A popular benchmarking tool that tests the performance of your device's processor, GPU, memory, storage, battery, and more by running various scenarios and games and giving a score for each aspect.</li>
-</ul>
- <h4>Q: How can I contact the developer of CPU-Z?</h4>
-<p>A: You can contact the developer of CPU-Z by visiting the CPU-Z official website and filling out the contact form. You can also follow the developer on Twitter or Facebook for news and updates. You can also report any bugs or issues with CPU-Z by using the Report button on the app or by sending an email to cpuz.android@gmail.com.</p>
- <p>I hope you enjoyed this article and learned how to download and use CPU-Z APK for Android. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/skf15963/summary/fengshen/examples/unimc/README_en.md b/spaces/skf15963/summary/fengshen/examples/unimc/README_en.md
deleted file mode 100644
index 0a1e86888c5cfc3046527613f603f96729cdab08..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/unimc/README_en.md
+++ /dev/null
@@ -1,104 +0,0 @@
-[**中文**](./README.md) | [**English**](./README_en.md)
-# UniMC
-Code for  [Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective](https://arxiv.org/abs/2210.08590)
-
-
-
-![](./unimc.jpg)
-
-## Update
-- [2022-10-18] Release preprint in arXiv.
-- [2022-10-14] Release code in GitHub.
-
-## Requirements
-
-
-```shell
-git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git
-cd Fengshenbang-LM
-pip install --editable .
-```
-
-## Quick Start
-You can refer to our [example.py]()
-
-```python
-import argparse
-from fengshen.pipelines.multiplechoice import UniMCPipelines
-
-total_parser = argparse.ArgumentParser("TASK NAME")
-total_parser = UniMCPipelines.piplines_args(total_parser)
-args = total_parser.parse_args()
-    
-pretrained_model_path = 'IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English'
-args.language='english'
-args.learning_rate=2e-5
-args.max_length=512
-args.max_epochs=3
-args.batchsize=8
-args.default_root_dir='./'
-model = UniMCPipelines(args, model_path=pretrained_model_path)
-
-train_data = [] 
-dev_data = [] 
-test_data = [{
-	"texta": "it 's just incredibly dull .",
-	"textb": "",
-	"question": "What is sentiment of follow review?",
-	"choice": ["it's great", "it's terrible"],
-	"answer": "",
-	"label": 0,
-	"id": 19
-}]
-
-if args.train:
-	model.train(train_data, dev_data)
-result = model.predict(test_data)
-```
-## Pretrained Model
-For the English model, the model was pre-trained with 14 multiplechoice datasets. For the Chinese model, we have collected 48 datasets to pre-train the model, and we have open sourced the pre-trained model to the HuggingFace community.
-
-| Model | URL   |
-|:---------:|:--------------:|
-| Erlangshen-UniMC-Albert-235-English  | [https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English)   |
-| Erlangshen-UniMC-RoBERTa-110M-Chinese  | [https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese)       |
-| Erlangshen-UniMC-RoBERTa-330M-Chinese  | [https://huggingface.co/IDEA-CCNL/Erlangshen-UnimC-RoBERTa-330M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-330M-Chinese)   |
-| Erlangshen-UniMC-MegatronBERT-1.3B-Chinese  | [https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese)       |
-
-
-## Experiments
-To evaluate the performance of UniMC, we use 14 multiple-choice datasets to pre-train the model with the ability to make choices
-
-**Zero-shot**
-| Model   | T0 11B | GLaM 60B | FLAN 137B | PaLM 540B | UniMC 235M |
-|---------|--------|----------|-----------|-----------|------------|
-| ANLI R1 | 43.6   | 40.9     | 47.7      | 48.4      | **52.0**         |
-| ANLI R2 | 38.7   | 38.2     | 43.9      | 44.2      | **44.4**       |
-| ANLI R3 | 41.3   | 40.9     | 47.0        | 45.7      | **47.8**       |
-| CB      | 70.1   | 33.9     | 64.1      | 51.8      | **75.7**       |
-
-## Citation
-If this repository helps you, please cite this paper:
-
-```text
-@article{unimc,
-  author    = {Ping Yang and
-               Junjie Wang and
-               Ruyi Gan and
-               Xinyu Zhu and
-               Lin Zhang and
-               Ziwei Wu and
-               Xinyu Gao and
-               Jiaxing Zhang and
-               Tetsuya Sakai},
-  title     = {Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective},
-  journal   = {CoRR},
-  volume    = {abs/2210.08590},
-  year      = {2022}
-}
-```
-
-## License
-
-[Apache License 2.0](https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/LICENSE)
-
diff --git a/spaces/srini047/asapp-hackathon/profanity.py b/spaces/srini047/asapp-hackathon/profanity.py
deleted file mode 100644
index 514ebb409e38ba00966bb022c974373d7af595fc..0000000000000000000000000000000000000000
--- a/spaces/srini047/asapp-hackathon/profanity.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import requests
-import time
-import json
-
-from profanityfilter import ProfanityFilter
-pf = ProfanityFilter()
-input_text = "like"
-print(pf.is_profane(input_text))
-
-model_name = "Dabid/abusive-tagalog-profanity-detection"
-endpoint = f"https://api-inference.huggingface.co/models/Dabid/abusive-tagalog-profanity-detection"
-
-payload = {
-    "inputs": input_text,
-    "options": {
-        "max_length": 50,
-        "temperature": 1,
-    },
-}
-
-headers = {"Authorization": "Bearer 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"}
-response = requests.post(endpoint, json=payload, headers=headers)
-if response.status_code == 200:
-    result = json.loads(response.text)
-    filtered_text = result[0]
-    # print(filtered_text)
-    # print("Filtered Text:")
-    p = filtered_text[0]["label"]
-    val = filtered_text[0]["score"]
-    if p == "Non-Abusive" and val > 0.75:
-        print("Non Profane")
-    else:
-        print("Profane")
-elif "Model is currently loading" in response.text:
-    print("Model is still loading. Retrying in a few seconds...")
-    time.sleep(20)
-else:
-    print("API call failed with status code:", response.status_code)
-    print(response.text)
diff --git a/spaces/starlit7/USPoliticsTTS/README.md b/spaces/starlit7/USPoliticsTTS/README.md
deleted file mode 100644
index f55652a9084753e1f4b34f2302674aa646bd6b7a..0000000000000000000000000000000000000000
--- a/spaces/starlit7/USPoliticsTTS/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: USPoliticsTTS
-emoji: 🏢
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.14.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Danganronpa V3 Killing Harmony OST Download Unblocked ((EXCLUSIVE)).md b/spaces/stomexserde/gpt4-ui/Examples/Danganronpa V3 Killing Harmony OST Download Unblocked ((EXCLUSIVE)).md
deleted file mode 100644
index 60e198e513564bcf1c91d9aeb0f641e6697b8fc5..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Danganronpa V3 Killing Harmony OST Download Unblocked ((EXCLUSIVE)).md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<br />
-<h1>How to Download Danganronpa V3: Killing Harmony OST for Free</h1>
-<p>If you are a fan of the Danganronpa series, you might be interested in downloading the original soundtrack (OST) of the latest installment, Danganronpa V3: Killing Harmony. The OST features 10 tracks composed by beatmaker Masafumi Takada, who also worked on the previous games. The OST includes songs such as "DANGANRONPA V3", "EXISAL TRIBE", and "CLASS TRIAL: SPACE EDITION".</p>
-<p>However, you might have trouble finding a way to download the OST for free, as it is only available as a downloadable content (DLC) on Steam. The DLC requires you to own the base game Danganronpa V3: Killing Harmony on Steam in order to play. The DLC was also offered as an early purchase bonus from September 26 – October 3, 2017, but if you missed that window, you will have to pay $9.99 USD to get it.</p>
-<h2>Danganronpa V3: Killing Harmony OST download unblocked</h2><br /><p><b><b>Download</b> >>> <a href="https://urlgoal.com/2uI8yg">https://urlgoal.com/2uI8yg</a></b></p><br /><br />
-<p>Fortunately, there is a way to download the OST for free without buying the base game or the DLC. This method is unblocked and safe, and it does not involve any illegal or unethical actions. All you need is a Steam account and a web browser. Here are the steps to follow:</p>
-<ol>
-<li>Go to the Steam page for the OST [^1^] and log in with your Steam account.</li>
-<li>Right-click on the page and select "View page source".</li>
-<li>Press Ctrl+F and search for "depot". You should see a line that says <code>"depot": 707391,</code>. This is the ID of the OST depot on Steam.</li>
-<li>Copy the depot ID and go to this website: https://steamdb.info/depot/ . Paste the depot ID at the end of the URL, so it looks like this: https://steamdb.info/depot/707391/ . Press Enter.</li>
-<li>On the depot page, scroll down to the "Manifests" section. You should see a table with different manifest IDs and dates. The manifest ID is a unique identifier for each version of the depot. Choose the latest manifest ID (the one with the most recent date) and copy it.</li>
-<li>Go to this website: https://steamdb.info/depotdownloader/ . This is a tool that allows you to download any depot from Steam using its depot ID and manifest ID.</li>
-<li>Paste the depot ID and manifest ID in their respective fields. Leave the other fields blank. Click on "Download Depot".</li>
-<li>A new tab will open with a link to download a ZIP file containing the OST files. Click on the link and save the ZIP file to your computer.</li>
-<li>Extract the ZIP file using any program that can handle ZIP files (such as WinRAR or 7-Zip). You should see a folder named "Danganronpa V3 Killing Harmony OST" with 10 MP3 and 10 FLAC files inside.</li>
-<li>Enjoy listening to the OST!</li>
-</ol>
-<p>Note: This method only works for downloading depots that are publicly available on Steam. It does not work for depots that are encrypted or restricted by region or license. It also does not allow you to play the OST on Steam or access any other features of the DLC. It is only for personal use and not for redistribution or resale.</p>
-<p>We hope this guide helped you download Danganronpa V3: Killing Harmony OST for free. If you liked this article, please share it with your friends and leave us a comment below. Thank you for reading!</p> e93f5a0c3f<br />
-<br />
-<br />
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/East West Quantum Leap Ministry Of Rock 29.md b/spaces/stomexserde/gpt4-ui/Examples/East West Quantum Leap Ministry Of Rock 29.md
deleted file mode 100644
index 1503fdd1250b905dd4e32247e77cd5f8e2419280..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/East West Quantum Leap Ministry Of Rock 29.md	
+++ /dev/null
@@ -1,38 +0,0 @@
-<br />
-<h1>East West Quantum Leap Ministry of Rock 2: A Virtual Instrument for Rock Production</h1>
-<p>If you are looking for a library that can produce realistic and powerful rock sounds, you might want to check out East West Quantum Leap Ministry of Rock 2. This is a 57 GB collection of drums, basses and guitars that were recorded at the legendary EastWest Studios with some of the industry's top players and engineers. Whether you need hard-hitting drums, distorted guitars, or groovy basses, Ministry of Rock 2 has you covered.</p>
-<h2>east west quantum leap ministry of rock 29</h2><br /><p><b><b>Download File</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://urlgoal.com/2uIa1F">https://urlgoal.com/2uIa1F</a></b></p><br /><br />
-<p>Ministry of Rock 2 is the sequel to the acclaimed Ministry of Rock 1, which was released in 2008. The new library features more instruments, more articulations, more mic positions, and more effects than its predecessor. It also includes a new live technology that allows you to perform with realistic dynamics and repetitions. You can easily switch between different playing styles and techniques with keyswitches, and adjust the tone and balance of each instrument with dual amps and mixer effects.</p>
-<p>Some of the highlights of Ministry of Rock 2 are:</p>
-<ul>
-<li>A comprehensive production kit for creating a full rock band</li>
-<li>Created by award-winning sounds producers Doug Rogers and Nick Phoenix</li>
-<li>Recorded through vintage mics from Neumann, Telefunken, AKG and more</li>
-<li>Performances from legendary guitarists like Shane Gibson, Greg Suran, Doug Rappaport and Tal Bergman</li>
-<li>Engineered for hyper-realistic mockups</li>
-<li>A variety of guitars, basses and drums to choose from</li>
-<li>A live technology that enables round robin samples, extreme dynamics and three mic positions for drums</li>
-<li>Dual amps for guitars and basses for tone control</li>
-<li>MIDI tools and mixer effects for further customization</li>
-</ul>
-<p>Ministry of Rock 2 is compatible with OPUS, EastWest's new playback engine that offers improved performance, sound quality and features. You can also use it with Play 6 or higher. You can buy it as a standalone product or as part of the ComposerCloud subscription service.</p>
-<p>If you want to learn more about Ministry of Rock 2, you can visit the official website at <a href="https://www.soundsonline.com/guitars-and-bass/ministry-of-rock-2">https://www.soundsonline.com/guitars-and-bass/ministry-of-rock-2</a>. You can also listen to some demos and watch some videos to get a taste of what this library can do.</p>
-<p>Ministry of Rock 2 is a must-have for any rock producer who wants to create authentic and powerful tracks without any live overdubs. It is a library that can rock your world.</p><p>Ministry of Rock 2 has received positive reviews from users and critics alike, who praised its sound quality, realism, versatility and ease of use. Some of the common points that were highlighted are:</p>
-<ul>
-<li>The library covers a wide range of rock styles, from classic to modern, from heavy to pop.</li>
-<li>The guitars and basses have a rich and detailed tone, with dual amps for each instrument that can be blended or switched.</li>
-<li>The drums have a punchy and dynamic sound, with three mic positions and compression options for each kit.</li>
-<li>The live technology enables realistic repetitions and dynamics, as well as round robin samples for every articulation.</li>
-<li>The keyswitches are intuitive and consistent, allowing quick access to different playing techniques and effects.</li>
-<li>The interface is simple and user-friendly, with MIDI tools and mixer effects for further tweaking.</li>
-</ul>
-<p>Some of the drawbacks that were mentioned are:</p>
-<ul>
-<li>The installation process is tedious and time-consuming, requiring eight DVDs and online authorisation.</li>
-<li>The library is not compatible with Play 3 or higher, which may cause some issues with newer systems and hosts.</li>
-<li>The guitar keyswitches are not universal, meaning that some articulations are only available for certain guitars.</li>
-<li>The library may produce some digital popping noises with Play 2, which can be fixed by increasing the buffer size or updating the sound engine.</li>
-</ul>
-<p>Overall, Ministry of Rock 2 is a well-executed and sensibly priced library that deserves a place among rock royalty. It is a great addition to any rock producer's arsenal, and a worthy companion to Ministry of Rock 1. If you want to rock out with your virtual band, this is the library for you.</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sub314xxl/MetaGPT/metagpt/tools/prompt_writer.py b/spaces/sub314xxl/MetaGPT/metagpt/tools/prompt_writer.py
deleted file mode 100644
index 83a29413bc2def4d521507827d9d16d402fad562..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/metagpt/tools/prompt_writer.py
+++ /dev/null
@@ -1,110 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/2 16:03
-@Author  : alexanderwu
-@File    : prompt_writer.py
-"""
-from typing import Union
-
-
-class GPTPromptGenerator:
-    """通过LLM，给定输出，要求LLM给出输入（支持指令、对话、搜索三种风格）"""
-    def __init__(self):
-        self._generators = {i: getattr(self, f"gen_{i}_style") for i in ['instruction', 'chatbot', 'query']}
-
-    def gen_instruction_style(self, example):
-        """指令风格：给定输出，要求LLM给出输入"""
-        return f"""指令：X
-输出：{example}
-这个输出可能来源于什么样的指令？
-X："""
-
-    def gen_chatbot_style(self, example):
-        """对话风格：给定输出，要求LLM给出输入"""
-        return f"""你是一个对话机器人。一个用户给你发送了一条非正式的信息，你的回复如下。
-信息：X
-回复：{example}
-非正式信息X是什么？
-X："""
-
-    def gen_query_style(self, example):
-        """搜索风格：给定输出，要求LLM给出输入"""
-        return f"""你是一个搜索引擎。一个人详细地查询了某个问题，关于这个查询最相关的文档如下。
-查询：X
-文档：{example} 详细的查询X是什么？
-X："""
-
-    def gen(self, example: str, style: str = 'all') -> Union[list[str], str]:
-        """
-        通过example生成一个或多个输出，用于让LLM回复对应输入
-
-        :param example: LLM的预期输出样本
-        :param style: (all|instruction|chatbot|query)
-        :return: LLM的预期输入样本（一个或多个）
-        """
-        if style != 'all':
-            return self._generators[style](example)
-        return [f(example) for f in self._generators.values()]
-
-
-class WikiHowTemplate:
-    def __init__(self):
-        self._prompts = """Give me {step} steps to {question}.
-How to {question}?
-Do you know how can I {question}?
-List {step} instructions to {question}.
-What are some tips to {question}?
-What are some steps to {question}?
-Can you provide {step} clear and concise instructions on how to {question}?
-I'm interested in learning how to {question}. Could you break it down into {step} easy-to-follow steps?
-For someone who is new to {question}, what would be {step} key steps to get started?
-What is the most efficient way to {question}? Could you provide a list of {step} steps?
-Do you have any advice on how to {question} successfully? Maybe a step-by-step guide with {step} steps?
-I'm trying to accomplish {question}. Could you walk me through the process with {step} detailed instructions?
-What are the essential {step} steps to {question}?
-I need to {question}, but I'm not sure where to start. Can you give me {step} actionable steps?
-As a beginner in {question}, what are the {step} basic steps I should take?
-I'm looking for a comprehensive guide on how to {question}. Can you provide {step} detailed steps?
-Could you outline {step} practical steps to achieve {question}?
-What are the {step} fundamental steps to consider when attempting to {question}?"""
-
-    def gen(self, question: str, step: str) -> list[str]:
-        return self._prompts.format(question=question, step=step).splitlines()
-
-
-class EnronTemplate:
-    def __init__(self):
-        self._prompts = """Write an email with the subject "{subj}".
-Can you craft an email with the subject {subj}?
-Would you be able to compose an email and use {subj} as the subject?
-Create an email about {subj}.
-Draft an email and include the subject "{subj}".
-Generate an email about {subj}.
-Hey, can you shoot me an email about {subj}?
-Do you mind crafting an email for me with {subj} as the subject?
-Can you whip up an email with the subject of "{subj}"?
-Hey, can you write an email and use "{subj}" as the subject?
-Can you send me an email about {subj}?"""
-
-    def gen(self, subj):
-        return self._prompts.format(subj=subj).splitlines()
-
-
-class BEAGECTemplate:
-    def __init__(self):
-        self._prompts = """Edit and revise this document to improve its grammar, vocabulary, spelling, and style.
-Revise this document to correct all the errors related to grammar, spelling, and style.
-Refine this document by eliminating all grammatical, lexical, and orthographic errors and improving its writing style.
-Polish this document by rectifying all errors related to grammar, vocabulary, and writing style.
-Enhance this document by correcting all the grammar errors and style issues, and improving its overall quality.
-Rewrite this document by fixing all grammatical, lexical and orthographic errors.
-Fix all grammar errors and style issues and rewrite this document.
-Take a stab at fixing all the mistakes in this document and make it sound better.
-Give this document a once-over and clean up any grammar or spelling errors.
-Tweak this document to make it read smoother and fix any mistakes you see.
-Make this document sound better by fixing all the grammar, spelling, and style issues.
-Proofread this document and fix any errors that make it sound weird or confusing."""
-
-    def gen(self):
-        return self._prompts.splitlines()
diff --git a/spaces/sub314xxl/MetaGPT/tests/metagpt/utils/test_config.py b/spaces/sub314xxl/MetaGPT/tests/metagpt/utils/test_config.py
deleted file mode 100644
index 510892c2ff8a4cf6ed70cddd3dcbb40fe33ce613..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/tests/metagpt/utils/test_config.py
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/1 11:19
-@Author  : alexanderwu
-@File    : test_config.py
-@Modified By: mashenquan, 2013/8/20, Add `test_options`; remove global configuration `CONFIG`, enable configuration support for business isolation.
-"""
-from pathlib import Path
-
-import pytest
-
-from metagpt.config import Config
-
-
-def test_config_class_get_key_exception():
-    with pytest.raises(Exception) as exc_info:
-        config = Config()
-        config.get('wtf')
-    assert str(exc_info.value) == "Key 'wtf' not found in environment variables or in the YAML file"
-
-
-def test_config_yaml_file_not_exists():
-    with pytest.raises(Exception) as exc_info:
-        Config(Path('wtf.yaml'))
-    assert str(exc_info.value) == "Set OPENAI_API_KEY or Anthropic_API_KEY first"
-
-
-def test_options():
-    filename = Path(__file__).resolve().parent.parent.parent.parent / "config/config.yaml"
-    config = Config(filename)
-    opts = config.runtime_options
-    assert opts
-
-
-if __name__ == '__main__':
-    test_options()
diff --git a/spaces/subhajitmaji/MusicGen/audiocraft/data/audio_utils.py b/spaces/subhajitmaji/MusicGen/audiocraft/data/audio_utils.py
deleted file mode 100644
index 76d4bc2a33ce722d879db2af33cd1336bd6b1fb3..0000000000000000000000000000000000000000
--- a/spaces/subhajitmaji/MusicGen/audiocraft/data/audio_utils.py
+++ /dev/null
@@ -1,174 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import sys
-import typing as tp
-
-import julius
-import torch
-import torchaudio
-
-
-def convert_audio_channels(wav: torch.Tensor, channels: int = 2) -> torch.Tensor:
-    """Convert audio to the given number of channels.
-
-    Args:
-        wav (torch.Tensor): Audio wave of shape [B, C, T].
-        channels (int): Expected number of channels as output.
-    Returns:
-        torch.Tensor: Downmixed or unchanged audio wave [B, C, T].
-    """
-    *shape, src_channels, length = wav.shape
-    if src_channels == channels:
-        pass
-    elif channels == 1:
-        # Case 1:
-        # The caller asked 1-channel audio, and the stream has multiple
-        # channels, downmix all channels.
-        wav = wav.mean(dim=-2, keepdim=True)
-    elif src_channels == 1:
-        # Case 2:
-        # The caller asked for multiple channels, but the input file has
-        # a single channel, replicate the audio over all channels.
-        wav = wav.expand(*shape, channels, length)
-    elif src_channels >= channels:
-        # Case 3:
-        # The caller asked for multiple channels, and the input file has
-        # more channels than requested. In that case return the first channels.
-        wav = wav[..., :channels, :]
-    else:
-        # Case 4: What is a reasonable choice here?
-        raise ValueError('The audio file has less channels than requested but is not mono.')
-    return wav
-
-
-def convert_audio(wav: torch.Tensor, from_rate: float,
-                  to_rate: float, to_channels: int) -> torch.Tensor:
-    """Convert audio to new sample rate and number of audio channels.
-    """
-    wav = julius.resample_frac(wav, int(from_rate), int(to_rate))
-    wav = convert_audio_channels(wav, to_channels)
-    return wav
-
-
-def normalize_loudness(wav: torch.Tensor, sample_rate: int, loudness_headroom_db: float = 14,
-                       loudness_compressor: bool = False, energy_floor: float = 2e-3):
-    """Normalize an input signal to a user loudness in dB LKFS.
-    Audio loudness is defined according to the ITU-R BS.1770-4 recommendation.
-
-    Args:
-        wav (torch.Tensor): Input multichannel audio data.
-        sample_rate (int): Sample rate.
-        loudness_headroom_db (float): Target loudness of the output in dB LUFS.
-        loudness_compressor (bool): Uses tanh for soft clipping.
-        energy_floor (float): anything below that RMS level will not be rescaled.
-    Returns:
-        output (torch.Tensor): Loudness normalized output data.
-    """
-    energy = wav.pow(2).mean().sqrt().item()
-    if energy < energy_floor:
-        return wav
-    transform = torchaudio.transforms.Loudness(sample_rate)
-    input_loudness_db = transform(wav).item()
-    # calculate the gain needed to scale to the desired loudness level
-    delta_loudness = -loudness_headroom_db - input_loudness_db
-    gain = 10.0 ** (delta_loudness / 20.0)
-    output = gain * wav
-    if loudness_compressor:
-        output = torch.tanh(output)
-    assert output.isfinite().all(), (input_loudness_db, wav.pow(2).mean().sqrt())
-    return output
-
-
-def _clip_wav(wav: torch.Tensor, log_clipping: bool = False, stem_name: tp.Optional[str] = None) -> None:
-    """Utility function to clip the audio with logging if specified."""
-    max_scale = wav.abs().max()
-    if log_clipping and max_scale > 1:
-        clamp_prob = (wav.abs() > 1).float().mean().item()
-        print(f"CLIPPING {stem_name or ''} happening with proba (a bit of clipping is okay):",
-              clamp_prob, "maximum scale: ", max_scale.item(), file=sys.stderr)
-    wav.clamp_(-1, 1)
-
-
-def normalize_audio(wav: torch.Tensor, normalize: bool = True,
-                    strategy: str = 'peak', peak_clip_headroom_db: float = 1,
-                    rms_headroom_db: float = 18, loudness_headroom_db: float = 14,
-                    loudness_compressor: bool = False, log_clipping: bool = False,
-                    sample_rate: tp.Optional[int] = None,
-                    stem_name: tp.Optional[str] = None) -> torch.Tensor:
-    """Normalize the audio according to the prescribed strategy (see after).
-
-    Args:
-        wav (torch.Tensor): Audio data.
-        normalize (bool): if `True` (default), normalizes according to the prescribed
-            strategy (see after). If `False`, the strategy is only used in case clipping
-            would happen.
-        strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak',
-            i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square
-            with extra headroom to avoid clipping. 'clip' just clips.
-        peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy.
-        rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger
-            than the `peak_clip` one to avoid further clipping.
-        loudness_headroom_db (float): Target loudness for loudness normalization.
-        loudness_compressor (bool): If True, uses tanh based soft clipping.
-        log_clipping (bool): If True, basic logging on stderr when clipping still
-            occurs despite strategy (only for 'rms').
-        sample_rate (int): Sample rate for the audio data (required for loudness).
-        stem_name (Optional[str]): Stem name for clipping logging.
-    Returns:
-        torch.Tensor: Normalized audio.
-    """
-    scale_peak = 10 ** (-peak_clip_headroom_db / 20)
-    scale_rms = 10 ** (-rms_headroom_db / 20)
-    if strategy == 'peak':
-        rescaling = (scale_peak / wav.abs().max())
-        if normalize or rescaling < 1:
-            wav = wav * rescaling
-    elif strategy == 'clip':
-        wav = wav.clamp(-scale_peak, scale_peak)
-    elif strategy == 'rms':
-        mono = wav.mean(dim=0)
-        rescaling = scale_rms / mono.pow(2).mean().sqrt()
-        if normalize or rescaling < 1:
-            wav = wav * rescaling
-        _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
-    elif strategy == 'loudness':
-        assert sample_rate is not None, "Loudness normalization requires sample rate."
-        wav = normalize_loudness(wav, sample_rate, loudness_headroom_db, loudness_compressor)
-        _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
-    else:
-        assert wav.abs().max() < 1
-        assert strategy == '' or strategy == 'none', f"Unexpected strategy: '{strategy}'"
-    return wav
-
-
-def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
-    """Convert audio to float 32 bits PCM format.
-    """
-    if wav.dtype.is_floating_point:
-        return wav
-    else:
-        assert wav.dtype == torch.int16
-        return wav.float() / 2**15
-
-
-def i16_pcm(wav: torch.Tensor) -> torch.Tensor:
-    """Convert audio to int 16 bits PCM format.
-
-    ..Warning:: There exist many formula for doing this convertion. None are perfect
-    due to the asymetry of the int16 range. One either have possible clipping, DC offset,
-    or inconsistancies with f32_pcm. If the given wav doesn't have enough headroom,
-    it is possible that `i16_pcm(f32_pcm)) != Identity`.
-    """
-    if wav.dtype.is_floating_point:
-        assert wav.abs().max() <= 1
-        candidate = (wav * 2 ** 15).round()
-        if candidate.max() >= 2 ** 15:  # clipping would occur
-            candidate = (wav * (2 ** 15 - 1)).round()
-        return candidate.short()
-    else:
-        assert wav.dtype == torch.int16
-        return wav
diff --git a/spaces/sunshineatnoon/TextureScraping/swapae/test.py b/spaces/sunshineatnoon/TextureScraping/swapae/test.py
deleted file mode 100644
index 92befa3ea1fbecf912106f465e27049a8605e95e..0000000000000000000000000000000000000000
--- a/spaces/sunshineatnoon/TextureScraping/swapae/test.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import sys
-sys.path.append('./')
-
-import time
-from swapae.options import TestOptions
-import swapae.models as models
-from swapae.evaluation import GroupEvaluator
-import swapae.data as data
-import torchvision.utils as vutils
-
-import torch
-from PIL import Image
-import torchvision.transforms as transforms
-
-opt = TestOptions().parse()
-#dataset = data.create_dataset(opt)
-#evaluators = GroupEvaluator(opt)
-
-model = models.create_model(opt)
-
-#evaluators.evaluate(model, dataset, opt.resume_iter)
-
-structure_path = '/home/xtli/Dropbox/swapping-autoencoder-pytorch/testphotos/ffhq512/fig9/structure/12000.png'
-style_path = '/home/xtli/Dropbox/swapping-autoencoder-pytorch/testphotos/ffhq512/fig9/style/11104.png'
-
-structure_img = Image.open(structure_path)
-style_img = Image.open(style_path)
-
-transform = transforms.Compose([
-            transforms.Resize(256),
-            transforms.CenterCrop(256),
-	    transforms.ToTensor()])
-
-structure_img = transform(structure_img).unsqueeze(0)
-style_img = transform(style_img).unsqueeze(0)
-structure_img = structure_img * 2 - 1
-style_img = style_img * 2 - 1
-
-s_time = time.time()
-with torch.no_grad():
-    structure_feat = model(structure_img, command="encode")[0]
-    style_feat = model(style_img, command="encode")[1]
-    rec = model(structure_feat, style_feat, command="decode")
-e_time = time.time()
-print(e_time - s_time)
-rec = (rec + 1) / 2
-vutils.save_image(rec, 'rec.png')
-
diff --git a/spaces/supertori/files/stable-diffusion-webui/modules/sd_samplers_common.py b/spaces/supertori/files/stable-diffusion-webui/modules/sd_samplers_common.py
deleted file mode 100644
index c0709f6260ba776c82232abbd7142608840cac88..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/modules/sd_samplers_common.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from collections import namedtuple
-import numpy as np
-import torch
-from PIL import Image
-from modules import devices, processing, images, sd_vae_approx
-
-from modules.shared import opts, state
-import modules.shared as shared
-
-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
-
-
-def setup_img2img_steps(p, steps=None):
-    if opts.img2img_fix_steps or steps is not None:
-        requested_steps = (steps or p.steps)
-        steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
-        t_enc = requested_steps - 1
-    else:
-        steps = p.steps
-        t_enc = int(min(p.denoising_strength, 0.999) * steps)
-
-    return steps, t_enc
-
-
-approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
-
-
-def single_sample_to_image(sample, approximation=None):
-    if approximation is None:
-        approximation = approximation_indexes.get(opts.show_progress_type, 0)
-
-    if approximation == 2:
-        x_sample = sd_vae_approx.cheap_approximation(sample)
-    elif approximation == 1:
-        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
-    else:
-        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
-
-    x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
-    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-    x_sample = x_sample.astype(np.uint8)
-    return Image.fromarray(x_sample)
-
-
-def sample_to_image(samples, index=0, approximation=None):
-    return single_sample_to_image(samples[index], approximation)
-
-
-def samples_to_image_grid(samples, approximation=None):
-    return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
-
-
-def store_latent(decoded):
-    state.current_latent = decoded
-
-    if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
-        if not shared.parallel_processing_allowed:
-            shared.state.assign_current_image(sample_to_image(decoded))
-
-
-class InterruptedException(BaseException):
-    pass
diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Afinsa Catalogo 2013 Ver Online !!BETTER!!.md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Afinsa Catalogo 2013 Ver Online !!BETTER!!.md
deleted file mode 100644
index f51e392a917ef1c9e73d2d3c1b4af954e60578fe..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Afinsa Catalogo 2013 Ver Online !!BETTER!!.md	
+++ /dev/null
@@ -1,19 +0,0 @@
-
-<h1>Afinsa Catalogo 2013: A Comprehensive Guide to Portuguese Stamps</h1>
-<p>Afinsa Catalogo 2013 is a specialized stamp catalogue that covers the history and variety of Portuguese stamps from 1853 to 2012. It is published by Afinsa, a leading company in the philatelic market, and it is available online for free viewing. Afinsa Catalogo 2013 is divided into two volumes, one for Portugal and the other for its overseas territories, such as Azores, Madeira, and Angola. It includes detailed descriptions, illustrations, prices, and information about different aspects of Portuguese philately, such as administrative districts, first day covers, blocks, stamps on cover, reprints, miniature sheets, Framas, officials, airmails, parcel post, telegraphs, booklets, errors, and pre-adhesive mail.</p>
-<p>Afinsa Catalogo 2013 is a valuable resource for stamp collectors who are interested in Portuguese stamps and their history. It provides a comprehensive overview of the evolution of Portuguese postal services and the cultural and political events that influenced their design and production. It also showcases the diversity and beauty of Portuguese stamps, which reflect the rich heritage and identity of Portugal and its people. Afinsa Catalogo 2013 is more than just a stamp catalogue; it is a journey through time and space that reveals the stories behind the stamps.</p>
-<h2>Afinsa Catalogo 2013 Ver Online</h2><br /><p><b><b>Download File</b> &#9889; <a href="https://urluss.com/2uCEcd">https://urluss.com/2uCEcd</a></b></p><br /><br />
-<p>To view Afinsa Catalogo 2013 online, you can visit the official website of Afinsa[^1^] or download the PDF files from LifePress magazin[^2^] or aboulderpharm.com[^3^]. You can also order a printed copy of the catalogue from Vera Trinder Shop[^1^], a reputable online store that sells stamps, catalogues, albums, and other philatelic accessories. Whether you are a beginner or an expert in stamp collecting, Afinsa Catalogo 2013 will help you discover and appreciate the fascinating world of Portuguese stamps.</p>
-
-<h2>The History of Portuguese Stamps</h2>
-<p>Portugal was one of the first countries in Europe to issue postage stamps, following the example of Great Britain in 1840. The first Portuguese stamps appeared in 1853, featuring the head of Queen Maria II embossed on a coloured background. The design was simple and elegant, but also prone to forgery and wear. The first stamps were denominated in reis, the currency of Portugal until 1911.</p>
-<p>The first pictorial issue came out in 1894, commemorating the 500th anniversary of the birth of Henry the Navigator, a Portuguese prince who sponsored many exploratory voyages in the 15th century. The stamps depicted scenes and portraits related to Henry's life and achievements, such as his tomb, his school of navigation, and his discoveries in Africa. The stamps were printed in lithography by Waterlow and Sons in London.</p>
-<p>In 1898, another commemorative issue celebrated the 400th anniversary of Vasco da Gama's voyage to India, which opened a new sea route to Asia and established Portugal as a global maritime power. The stamps featured images of da Gama and his ships, as well as maps and landmarks of his journey. The stamps were also printed in lithography by Waterlow and Sons.</p>
-<p>The Vasco da Gama designs were also used in the African colonies of Portugal, such as Angola, Mozambique, and Cape Verde. The only difference was that the stamps were inscribed with "Africa" instead of "Portugal". This was the only general issue for the colonies, which later had their own distinctive stamps.</p>
-<p>The last monarchist issue was in 1910, featuring King Manuel II, who had ascended to the throne in 1908 after the assassination of his father Carlos I and his elder brother Luis Filipe. However, Manuel's reign was short-lived, as a republican revolution overthrew him on October 5, 1910. The existing stamps were overprinted with "Republica" to mark the change of regime.</p>
-<p></p>
-<p>The first republican issue was the Ceres series of 1912, depicting the Roman goddess of agriculture and fertility. The design was symbolic of the new era of progress and prosperity that the republicans hoped to bring to Portugal. The Ceres series was issued until 1945, with various changes in colours, denominations, overprints, and printing methods. It was also used in some overseas territories, such as Madeira and Timor.</p>
-<p>The Ceres series was replaced by the Caravel series in 1943, featuring a stylized image of a caravel, a type of sailing ship used by Portuguese explorers in the Age of Discovery. The Caravel series was issued until 1974, with different colours and values. It was also used in some colonies until their independence.</p>
-<p>The volume of stamp issues increased from the 1960s onwards, reflecting the social and cultural changes that Portugal underwent during and after the Estado Novo dictatorship that lasted from 1933 to 1974. The stamps depicted various themes and topics related to Portuguese history, art, literature, science, sports, nature, and international relations. The currency on Portuguese stamps changed from escudos to euros at the start of 2002.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/openpose/__init__.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/openpose/__init__.py
deleted file mode 100644
index 8c26f1b37dae854f51da938da2fa67a8ef48ce5a..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/openpose/__init__.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import os
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-import torch
-import numpy as np
-from . import util
-from .body import Body
-from .hand import Hand
-from annotator.util import annotator_ckpts_path
-
-
-body_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/body_pose_model.pth"
-hand_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/hand_pose_model.pth"
-
-
-class OpenposeDetector:
-    def __init__(self):
-        body_modelpath = os.path.join(annotator_ckpts_path, "body_pose_model.pth")
-        hand_modelpath = os.path.join(annotator_ckpts_path, "hand_pose_model.pth")
-
-        if not os.path.exists(hand_modelpath):
-            from basicsr.utils.download_util import load_file_from_url
-            load_file_from_url(body_model_path, model_dir=annotator_ckpts_path)
-            load_file_from_url(hand_model_path, model_dir=annotator_ckpts_path)
-
-        self.body_estimation = Body(body_modelpath)
-        self.hand_estimation = Hand(hand_modelpath)
-
-    def __call__(self, oriImg, hand=False):
-        oriImg = oriImg[:, :, ::-1].copy()
-        with torch.no_grad():
-            candidate, subset = self.body_estimation(oriImg)
-            canvas = np.zeros_like(oriImg)
-            canvas = util.draw_bodypose(canvas, candidate, subset)
-            if hand:
-                hands_list = util.handDetect(candidate, subset, oriImg)
-                all_hand_peaks = []
-                for x, y, w, is_left in hands_list:
-                    peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :])
-                    peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x)
-                    peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y)
-                    all_hand_peaks.append(peaks)
-                canvas = util.draw_handpose(canvas, all_hand_peaks)
-            return canvas, dict(candidate=candidate.tolist(), subset=subset.tolist())
diff --git a/spaces/tecnolitas/MJ-prompt-generator/tabs.py b/spaces/tecnolitas/MJ-prompt-generator/tabs.py
deleted file mode 100644
index ad7a95d9d0a6a13fddef7d22f26d569177a08da9..0000000000000000000000000000000000000000
--- a/spaces/tecnolitas/MJ-prompt-generator/tabs.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from contextlib import nullcontext
-
-import csv
-import os.path
-import random
-import gradio as gr
-from gradio.components import Textbox, Dropdown
-from gradio import Interface, TabbedInterface
-
-
-def parametroAleatorio(lista):
-    res = random.choice(list(lista))
-    while res == '':
-        res = random.choice(list(lista))
-    return res
-
-
-def make_concat_prompt(csv_reader, valoresUnicos):
-    def concat_prompt(*args):
-        campos = {campo: parametroAleatorio(valoresUnicos[campo]) for campo in csv_reader.fieldnames}
-        campos.update({campo: valor for campo, valor in zip(csv_reader.fieldnames, args) if valor})
-
-        prompt = ', '.join([campos[campo] for campo in csv_reader.fieldnames])
-        return prompt
-    return concat_prompt
-
-
-csv_files = [f for f in os.listdir() if os.path.isfile(f) and f.endswith('.csv')]
-csv_files.sort() # Ordenar alfabéticamente
-
-interfaces = []
-tab_labels = []
-for file_name in csv_files:
-    with open(file_name, 'r') as csv_file:
-        csv_reader = csv.DictReader(csv_file)
-        valoresUnicos = {}
-        for campo in csv_reader.fieldnames:
-            valoresUnicos[campo] = set()
-        for row in csv_reader:
-            for campo, valor in row.items():
-                valoresUnicos[campo].add(valor)
-
-        inputs = [Dropdown(list(valoresUnicos[campo]), label=campo.capitalize()) for campo in valoresUnicos.keys()]
-        output_text = Textbox(label='Prompt')
-
-        tab_label = os.path.splitext(file_name)[0].title()
-        tab_labels.append(tab_label)
-        
-        title = f"Generador Prompts Tecnolitas"
-        description = '<div align="center"> Selecciona entre los distintos menús desplegables para generar un Prompt creativo, los que dejes en blanco serán elegidos aleatoriamente.</div>'
-        fn = make_concat_prompt(csv_reader, valoresUnicos)
-
-       
-        interface = Interface(fn=fn, inputs=inputs, outputs=output_text, title=title, description=description)
-        interfaces.append(interface)
-
-tabbed_interface = TabbedInterface(interfaces, tab_labels)
-tabbed_interface.launch()
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Blufftitler Bixpack 10 Free Download Full !!EXCLUSIVE!!.md b/spaces/terfces0erbo/CollegeProjectV2/Blufftitler Bixpack 10 Free Download Full !!EXCLUSIVE!!.md
deleted file mode 100644
index e7139a7a45094430ed72d8cb4d8a46304724f360..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Blufftitler Bixpack 10 Free Download Full !!EXCLUSIVE!!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Blufftitler Bixpack 10 Free Download Full</h2><br /><p><b><b>Download</b> &#10040; <a href="https://bytlly.com/2uGld9">https://bytlly.com/2uGld9</a></b></p><br /><br />
-<br />
-BluffTitler. November, 23rd - % Safe - Demo. Free Download. ( MB) Safe & Secure. Features · Screenshots ... Learn more Mirror layer With the mirror layer you can create perfect reflections on planes. ... BluffTitler 14 Template Free Bixpack ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/terfces0erbo/CollegeProjectV2/EPLAN P8 MACROS SIEMENS [TOP] Download.epub.md b/spaces/terfces0erbo/CollegeProjectV2/EPLAN P8 MACROS SIEMENS [TOP] Download.epub.md
deleted file mode 100644
index 70c1cdf6f1b5f7424582ee2e2c8d7be4a4b2636e..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/EPLAN P8 MACROS SIEMENS [TOP] Download.epub.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>EPLAN P8 MACROS SIEMENS DOWNLOAD.epub</h2><br /><p><b><b>Download</b> ===> <a href="https://bytlly.com/2uGkRf">https://bytlly.com/2uGkRf</a></b></p><br /><br />
-
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-<br />
-<br />
-<p></p>
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Horde 2 The Citadel English 83.md b/spaces/terfces0erbo/CollegeProjectV2/Horde 2 The Citadel English 83.md
deleted file mode 100644
index df26e3d0b1d7c83cda5790afb94f16a6d736d806..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Horde 2 The Citadel English 83.md	
+++ /dev/null
@@ -1,13 +0,0 @@
-<br />
-<h1>Horde 2 The Citadel: A Classic RTS Game Based on Russian Folktales</h1>
-<p>Horde 2 The Citadel is a real-time strategy game developed by 7th BitLabs and published by Buka Entertainment in 2001. It is the sequel to Horde: The Northern Wind, which was released in 1999. The game is based on Russian folk stories and features a fantasy setting with magic, mythical creatures and heroes.</p>
-<h2>horde 2 the citadel english 83</h2><br /><p><b><b>DOWNLOAD</b> &#10002; <a href="https://bytlly.com/2uGluC">https://bytlly.com/2uGluC</a></b></p><br /><br />
-<p>The game follows the story of a young hero who starts with a small settlement and a few warriors. He has to develop his city economically and militarily, while dealing with the threats of other nations and monsters. He can also use diplomacy to form alliances or declare war with his neighbors. The game has two modes: move-based and real-time. In move-based mode, the player can plan his actions and move his units on a map. In real-time mode, the player can control his units directly and engage in huge battles with hundreds of units on screen.</p>
-<p>Horde 2 The Citadel has several new features compared to its predecessor, such as new units, structures, creatures and multiplayer options. The game also has a low system requirement and can run on Windows XP, 2000 and Windows 7. The game has been praised for its gameplay balance, graphics and sound effects.</p>
-<p>Horde 2 The Citadel is a classic RTS game that fans of the genre should not miss. It offers a unique and immersive experience that combines strategy, fantasy and folklore. The game is available for download on GamesNostalgia[^5^] and SoundCloud[^2^]. You can also check out some gameplay videos on YouTube[^8^] or read more about the game on Gamepressure[^6^] or GameZone[^7^].</p>
-
-<p>Horde 2 The Citadel has a rich and diverse gameplay that offers many challenges and opportunities for the player. The player can choose from four different heroes, each with their own skills, abilities and items. The heroes can also level up and learn new spells as they progress through the game. The player can also recruit various units, such as warriors, archers, mages, healers, scouts and pigeons. Each unit has its own strengths and weaknesses, and can be upgraded with better weapons and armor.</p>
-<p>The game also has a complex economic system that requires the player to manage his resources wisely. The player can collect taxes from his citizens, trade with other settlements, loot enemy camps and find treasures. The player can also build different structures, such as houses, barracks, markets, temples, towers and walls. Each structure has a different function and cost, and can be damaged or destroyed by enemies. The player can also research new technologies that unlock new units, structures and abilities.</p>
-<p>The game has a dynamic and nonlinear storyline that depends on the player's choices and actions. The player can interact with various characters and factions, such as the Semireks, the nomads, the dragons, the hermit wizards and the undead. The player can also influence the relations between these factions by helping or harming them. The game has multiple endings that reflect the player's decisions and achievements throughout the game.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Ielts Essay Kiran Makkar ((TOP)).md b/spaces/terfces0erbo/CollegeProjectV2/Ielts Essay Kiran Makkar ((TOP)).md
deleted file mode 100644
index c25d3f74aeb06ba0ecbd8e6a30cbd58eb5921ee9..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Ielts Essay Kiran Makkar ((TOP)).md	
+++ /dev/null
@@ -1,135 +0,0 @@
-<br />
-<h1>Ielts Essay Kiran Makkar: How to Write High-Scoring Essays for IELTS</h1>
-
-<p>Writing an essay for the IELTS exam can be challenging, especially if you are not familiar with the topics, formats, and expectations of the test. However, there is a way to improve your essay writing skills and boost your band score with the help of Ielts Essay Kiran Makkar, a series of books that provide IELTS academic essays from past exams.</p>
-<h2>Ielts Essay Kiran Makkar</h2><br /><p><b><b>Download File</b> &bull; <a href="https://bytlly.com/2uGk9R">https://bytlly.com/2uGk9R</a></b></p><br /><br />
-
-<p>Ielts Essay Kiran Makkar is written by Dr. Kiran Makkar, a renowned IELTS expert and teacher who has helped thousands of students achieve their desired band scores. Dr. Kiran Makkar has a passion for teaching and a wealth of experience in the IELTS field. She has composed many books on IELTS to support students, such as Makkar IELTS Writing Task 2, Makkar IELTS Reading, and Makkar IELTS Speaking.</p>
-
-<h2>What is Ielts Essay Kiran Makkar?</h2>
-
-<p>Ielts Essay Kiran Makkar is a series of books that provide IELTS academic essays from past exams. These books are designed to help students prepare for the writing task 2 of the IELTS exam, which requires them to write an essay of at least 250 words on a given topic.</p>
-
-<p>The books contain sample essays written by Dr. Kiran Makkar based on the actual questions that appeared in the past IELTS exams. The essays cover a wide range of topics, such as education, environment, health, technology, culture, and society. The essays are also graded according to the IELTS band descriptors, so students can see what makes an essay score high or low.</p>
-
-<p>Each book also contains tips and techniques for writing effective essays, such as how to structure an essay, how to develop ideas, how to use vocabulary and grammar correctly, and how to avoid common mistakes. The books also provide model answers and feedback for some of the essays, so students can learn from the strengths and weaknesses of their own writing.</p>
-
-<h2>Why should you use Ielts Essay Kiran Makkar?</h2>
-
-<p>Ielts Essay Kiran Makkar is a valuable resource for anyone who wants to improve their essay writing skills and score high in the IELTS exam. Here are some of the benefits of using these books:</p>
-<p></p>
-
-<ul>
-<li>You can learn from real examples of IELTS essays that have been written by an expert teacher.</li>
-<li>You can practice writing essays on a variety of topics that are relevant and current for the IELTS exam.</li>
-<li>You can get familiar with the format and expectations of the writing task 2 of the IELTS exam.</li>
-<li>You can improve your vocabulary and grammar by studying the language used in the sample essays.</li>
-<li>You can get feedback and guidance on how to improve your own writing by comparing your essays with the model answers and comments.</li>
-</ul>
-
-<h2>How to use Ielts Essay Kiran Makkar?</h2>
-
-<p>Here are some tips on how to use Ielts Essay Kiran Makkar effectively:</p>
-
-<ol>
-<li>Choose a book that matches your level and goal. There are different books for different levels of difficulty and different types of essays.</li>
-<li>Read the introduction and the tips and techniques section carefully. They will give you an overview of what to expect and how to approach the writing task 2.</li>
-<li>Pick an essay topic that interests you or that you need to practice. Read the question carefully and brainstorm some ideas before you start writing.</li>
-<li>Write your essay in 40 minutes or less, following the instructions and guidelines given in the book. Try to write at least 250 words and use clear paragraphs.</li>
-<li>Check your essay for spelling, grammar, punctuation, coherence, and cohesion. Use a dictionary or a grammar checker if needed.</li>
-<li>Compare your essay with the sample essay provided in the book. See how your essay matches or differs from the sample essay in terms of content, structure, language, and band score.</li>
-<li>Read the feedback and comments given for some of the essays in the book. See what you can learn from them and how you can apply them to your own writing.</li>
-<li>Repeat the process with different topics until you feel confident and comfortable with writing essays for the IELTS exam.</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>Ielts Essay Kiran Makkar is a series of books that provide IELTS academic essays from past exams written by Dr. Kiran Makkar, a renowned IELTS expert and teacher. These books are designed to help students improve their essay writing skills and score high in the writing task 2 of the IELTS exam. By using these books, students can learn from real examples of IELTS essays, practice writing on various topics, get familiar with the format and expectations of the test, improve their vocabulary and grammar, and get feedback and guidance on their own writing.</p>
-
-<p>If you want to achieve your desired band score in the IELTS exam, you should definitely check out Ielts Essay Kiran Makkar and see how it can help you write high-scoring essays for IELTS.</p>
-<h2>Where can you buy Ielts Essay Kiran Makkar books?</h2>
-
-<p>If you are interested in buying Ielts Essay Kiran Makkar books, you have several options to choose from. You can buy them online from Amazon, InstaMojo, or the official website of makkarIELTS. You can also join the Telegram channel of makkarIELTS and download the books for free. Alternatively, you can visit a local bookstore or library and look for the books there.</p>
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-<p>However, before you buy or download any book, make sure that it is the latest edition and that it matches your level and goal. There are different books for different levels of difficulty and different types of essays. You can check the cover page and the introduction of each book to see what it contains and how it can help you.</p>
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-<h2>How can you use Ielts Essay Kiran Makkar books with other resources?</h2>
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-<p>Ielts Essay Kiran Makkar books are not the only resources that you can use to prepare for the IELTS exam. There are many other resources that can complement and enhance your learning experience. For example, you can use:</p>
-
-<ul>
-<li>Cambridge IELTS books: These are official books that contain authentic IELTS tests from past exams. You can use them to practice your writing skills under exam conditions and to familiarize yourself with the format and topics of the test.</li>
-<li>IELTS websites and blogs: These are online platforms that provide useful information, tips, advice, and samples for IELTS students. You can use them to learn from experts and other students, to get feedback on your writing, and to stay updated on the latest IELTS news and trends.</li>
-<li>IELTS podcasts and videos: These are audio-visual materials that cover various aspects of the IELTS exam. You can use them to listen to lectures, interviews, discussions, and explanations on IELTS topics, to improve your listening and speaking skills, and to get inspired by successful stories.</li>
-<li>IELTS tutors and courses: These are professional services that offer personalized guidance and instruction for IELTS students. You can use them to get expert feedback on your writing, to improve your weak areas, and to boost your confidence and motivation.</li>
-</ul>
-
-<p>By using Ielts Essay Kiran Makkar books with other resources, you can maximize your potential and achieve your desired band score in the IELTS exam.</p>
-<h2>What are the advantages of Ielts Essay Kiran Makkar books?</h2>
-
-<p>Ielts Essay Kiran Makkar books have many advantages for IELTS students who want to improve their essay writing skills and score high in the writing task 2 of the IELTS exam. Some of the advantages are:</p>
-
-<ul>
-<li>They are based on real IELTS questions that have appeared in past exams, so they are relevant and up-to-date.</li>
-<li>They cover a wide range of topics that are commonly asked in the IELTS exam, so they provide a lot of ideas and information.</li>
-<li>They are written by an experienced and qualified IELTS teacher who knows the criteria and expectations of the examiners.</li>
-<li>They provide clear and simple explanations and examples of how to write effective essays, such as how to structure an essay, how to develop ideas, how to use vocabulary and grammar correctly, and how to avoid common mistakes.</li>
-<li>They provide model answers and feedback for some of the essays, so students can see what makes an essay score high or low.</li>
-<li>They are easy to use and follow, as they have a clear layout and design.</li>
-</ul>
-
-<h2>What are the limitations of Ielts Essay Kiran Makkar books?</h2>
-
-<p>While Ielts Essay Kiran Makkar books are very useful and helpful for IELTS students, they also have some limitations that students should be aware of. Some of the limitations are:</p>
-
-<ul>
-<li>They are not official books from the IELTS organization, so they may not reflect the exact format and difficulty level of the actual exam.</li>
-<li>They may not cover all the possible topics and types of essays that may appear in the IELTS exam, so students should not rely on them exclusively.</li>
-<li>They may not suit every student's level and style of writing, as different students may have different strengths and weaknesses.</li>
-<li>They may not guarantee a high band score in the IELTS exam, as there are many other factors that affect the performance and result of the test.</li>
-</ul>
-
-<p>Therefore, students should use Ielts Essay Kiran Makkar books as a supplement and not a substitute for their own practice and preparation for the IELTS exam.</p>
-<h2>What are the challenges of Ielts Essay Kiran Makkar books?</h2>
-
-<p>While Ielts Essay Kiran Makkar books are very helpful and useful for IELTS students, they also pose some challenges that students should be prepared for. Some of the challenges are:</p>
-
-<ul>
-<li>They require a lot of time and effort to read, understand, and practice the essays. Students should not expect to improve their writing skills overnight by reading these books.</li>
-<li>They may not suit every student's preference and personality. Some students may find the essays too long, too complex, or too boring. Some students may prefer to write their own essays rather than follow the sample essays.</li>
-<li>They may not reflect the latest changes and trends in the IELTS exam. The IELTS exam is constantly evolving and updating its questions and topics. Therefore, students should not rely on these books alone and should also use other sources of information and practice.</li>
-<li>They may create a false sense of security or overconfidence in some students. Some students may think that by memorizing or copying these essays, they can score high in the IELTS exam. However, this is not true, as the examiners can easily detect plagiarism and lack of originality.</li>
-</ul>
-
-<p>Therefore, students should use Ielts Essay Kiran Makkar books as a guide and not a guarantee for their success in the IELTS exam.</p>
-
-<h2>What are the alternatives to Ielts Essay Kiran Makkar books?</h2>
-
-<p>If you are looking for alternatives to Ielts Essay Kiran Makkar books, you have many options to choose from. You can use other books that provide IELTS academic essays from past exams, such as:</p>
-
-<ul>
-<li>The Official Cambridge Guide to IELTS: This is an official book that contains authentic IELTS tests from past exams. It also provides sample essays with comments and scores.</li>
-<li>Barron's Writing for the IELTS: This is a book that provides tips and strategies for writing effective essays for the IELTS exam. It also provides sample essays with explanations and exercises.</li>
-<li>IELTS Writing Task 2: The Ultimate Guide with Practice to Get a Target Band Score of 8.0+ In 10 Minutes a Day: This is a book that provides a step-by-step guide on how to write high-scoring essays for the IELTS exam. It also provides sample essays with analysis and feedback.</li>
-</ul>
-
-<p>You can also use other resources that provide IELTS academic essays from past exams, such as:</p>
-
-<ul>
-<li>IELTS websites and blogs: These are online platforms that provide useful information, tips, advice, and samples for IELTS students. You can find many examples of IELTS essays on these websites and blogs.</li>
-<li>IELTS podcasts and videos: These are audio-visual materials that cover various aspects of the IELTS exam. You can find many lectures, interviews, discussions, and explanations on IELTS topics and essays on these podcasts and videos.</li>
-<li>IELTS tutors and courses: These are professional services that offer personalized guidance and instruction for IELTS students. You can find many tutors and courses that can help you write high-quality essays for the IELTS exam.</li>
-</ul>
-
-<p>By using alternatives to Ielts Essay Kiran Makkar books, you can diversify your sources of information and practice and improve your essay writing skills for the IELTS exam.</p>
-<h2>Conclusion</h2>
-
-<p>Ielts Essay Kiran Makkar is a series of books that provide IELTS academic essays from past exams written by Dr. Kiran Makkar, a renowned IELTS expert and teacher. These books are designed to help students improve their essay writing skills and score high in the writing task 2 of the IELTS exam. By using these books, students can learn from real examples of IELTS essays, practice writing on various topics, get familiar with the format and expectations of the test, improve their vocabulary and grammar, and get feedback and guidance on their own writing.</p>
-
-<p>However, these books also have some limitations and challenges that students should be aware of. They are not official books from the IELTS organization, they may not cover all the possible topics and types of essays that may appear in the exam, they may not suit every student's level and style of writing, and they may not guarantee a high band score in the exam. Therefore, students should use these books as a supplement and not a substitute for their own practice and preparation for the IELTS exam.</p>
-
-<p>If students are looking for alternatives to these books, they have many options to choose from. They can use other books that provide IELTS academic essays from past exams, such as The Official Cambridge Guide to IELTS, Barron's Writing for the IELTS, or IELTS Writing Task 2: The Ultimate Guide with Practice to Get a Target Band Score of 8.0+ In 10 Minutes a Day. They can also use other resources that provide IELTS academic essays from past exams, such as IELTS websites and blogs, IELTS podcasts and videos, or IELTS tutors and courses.</p>
-
-<p>By using Ielts Essay Kiran Makkar books with other resources, students can maximize their potential and achieve their desired band score in the IELTS exam.</p> 3cee63e6c2<br />
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Crawfish Image For Cricut [EXCLUSIVE].md b/spaces/tialenAdioni/chat-gpt-api/logs/Crawfish Image For Cricut [EXCLUSIVE].md
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index 0ff75f0f0f3a3224d2be2810d3ab983286406e3f..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Crawfish Image For Cricut [EXCLUSIVE].md	
+++ /dev/null
@@ -1,26 +0,0 @@
-
-<h1>How to Find and Use Crawfish Images for Cricut</h1>
-<p>Cricut is a popular brand of cutting machines that can help you create amazing crafts and projects. You can use Cricut to cut various materials such as paper, vinyl, fabric, leather, wood, and more. You can also use Cricut to draw, score, engrave, emboss, and foil your designs.</p>
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-<p>One of the fun things you can do with Cricut is to use images of different themes and motifs for your projects. For example, if you love crawfish or want to celebrate Mardi Gras, you can use crawfish images for Cricut to make stickers, decals, shirts, signs, cards, earrings, and more.</p>
-<p>But where can you find crawfish images for Cricut? And how can you use them with your Cricut machine? Here are some tips and tricks to help you out:</p>
-<h2>Where to Find Crawfish Images for Cricut</h2>
-<p>There are many sources where you can find crawfish images for Cricut. Some of them are free and some of them are paid. Here are some of the most common ones:</p>
-<ul>
-<li><strong>Etsy</strong>: Etsy is a great place to find crawfish images for Cricut that are ready to use. You can find many sellers who offer crawfish SVG files, PNG files, DXF files, EPS files, and more. These files are compatible with Cricut Design Space and can be easily uploaded and resized. You can also find many crawfish designs that are unique and creative. For example, you can find crawfish monograms, crawfish quotes, crawfish flags, crawfish earrings, and more.  </li>
-<li><strong>Google Images</strong>: Google Images is another option to find crawfish images for Cricut. You can search for "crawfish image for cricut" or "crawfish svg" or "crawfish clipart" and see what comes up. You can also filter the results by size, color, type, usage rights, etc. However, be careful when using Google Images as some of the images may be copyrighted or low quality. Always check the source and license of the image before downloading and using it.</li>
-<li><strong>Creative Market</strong>: Creative Market is a platform where you can buy and sell digital assets such as fonts, graphics, templates, photos, etc. You can also find many crawfish images for Cricut on Creative Market that are high quality and professional. You can browse through different categories such as illustrations, icons, patterns, backgrounds, etc. and find the perfect crawfish image for your project. You can also preview the image before buying it and download it in various formats such as SVG, PNG, EPS, etc.</li>
-<li><strong>Pinterest</strong>: Pinterest is a social media platform where you can discover and save ideas for various topics. You can also find many crawfish images for Cricut on Pinterest that are inspiring and beautiful. You can search for "crawfish image for cricut" or "crawfish svg" or "crawfish craft" and see what pins come up. You can also follow boards or users who share similar interests as you. However, be aware that not all pins have links to the original source or file of the image. You may need to do some extra research or contact the pin owner to get the image.</li>
-</ul>
-<h2>How to Use Crawfish Images for Cricut</h2>
-<p>Once you have found your desired crawfish image for Cricut, you need to upload it to Cricut Design Space and use it with your Cricut machine. Here are some steps to follow:</p>
-<p></p>
-<ol>
-<li>Open Cricut Design Space on your computer or mobile device and sign in to your account.</li>
-<li>Click on the "New Project" button or open an existing project.</li>
-<li>Click on the "Upload" button on the left panel and select "Upload Image".</li>
-<li>Browse your computer or device and select the crawfish image file that you want to upload. You can upload SVG files or PNG files or other supported formats.</li>
-<li>Name your image and tag it if you want. Then click on "Save".</li>
-<li>Your image will appear in</p> ddb901b051<br />
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Dil Juunglee Dual Audio Hindi Eng 720p.md b/spaces/tialenAdioni/chat-gpt-api/logs/Dil Juunglee Dual Audio Hindi Eng 720p.md
deleted file mode 100644
index 169fcc23cca180349114a7338a896fc3a2f1d4d0..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Dil Juunglee Dual Audio Hindi Eng 720p.md	
+++ /dev/null
@@ -1,14 +0,0 @@
-
-<h1>Dil Juunglee: A Romantic Comedy with Dual Audio</h1>
-<p>Dil Juunglee is a 2018 Hindi movie that tells the story of two opposites who fall in love. Koroli (Taapsee Pannu) is a rich and shy girl who wants to be a writer. Sumit (Saqib Saleem) is a struggling actor who wants to be famous. They meet at an English language class and soon develop a friendship that turns into romance. However, their different backgrounds and aspirations create conflicts and misunderstandings between them. Will they be able to overcome their differences and stay together?</p>
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-<p>The movie is written and directed by Aleya Sen, who makes her debut as a filmmaker. The movie also features Abhilash Thapliyal, Nidhi Singh, Ayesha Kaduskar, Srishti Shrivastava and Santosh Barmola in supporting roles. The movie has a soundtrack composed by Tanishk Bagchi, Sharib-Toshi, Guru Randhawa and Rajat Nagpal.</p>
-<p>Dil Juunglee is available in multiple qualities, including 480p, 720p, 720p HEVC and 1080p. You can also watch the movie with dual audio in Hindi and English for a better experience. Download Dil Juunglee from the link below and enjoy this fun-filled romantic comedy.</p>
-<p><a href="https://pogolinks.art/movies/dil-juunglee-2018/">Download Dil Juunglee (2018) Movie | Hindi | PogoLinks</a></p><p>Dil Juunglee is a movie that tries to be a light-hearted and quirky rom-com, but fails to deliver on its promise. The movie suffers from a weak script, poor direction, cliched characters and forced humor. The movie does not offer anything new or original to the genre and relies on stereotypes and tropes. The movie also lacks chemistry between the lead pair and fails to make the audience care about their relationship.</p>
-<p>The movie received mainly negative reviews from critics and audiences alike. The movie was criticized for being boring, predictable, unrealistic and unfunny. The movie was also accused of wasting the talent of its actors and wasting the time of its viewers. The movie has a rating of 1.5/5 on Rotten Tomatoes, based on 4 reviews.</p>
-<p></p>
-<p>Dil Juunglee is a movie that you can skip without missing anything. It is not worth your time or money. If you are looking for a good romantic comedy, there are many better options available. Dil Juunglee is a movie that will make you feel more juunglee than dil.</p><p>However, if you are still curious about Dil Juunglee and want to watch it for yourself, you can download it from the link provided in this article. You can also choose the quality and the audio language that suits your preference. You can watch Dil Juunglee with dual audio in Hindi and English, which might make the movie more enjoyable for some viewers. You can also watch Dil Juunglee with subtitles if you want to understand the dialogues better.</p>
-<p>Dil Juunglee is a movie that might appeal to some fans of Taapsee Pannu and Saqib Saleem, who have given better performances in other movies. The movie might also appeal to some viewers who like mindless and silly comedies that do not require much thinking. The movie might also appeal to some viewers who are bored and have nothing else to do. But for most viewers, Dil Juunglee is a movie that will disappoint and frustrate them.</p>
-<p>Dil Juunglee is a movie that you should avoid at all costs. It is not a movie that will make you laugh or cry or feel anything. It is a movie that will make you regret your decision of watching it. Dil Juunglee is a movie that is not worth your attention.</p> 7196e7f11a<br />
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Driver updater pro serial number Tips and tricks for optimal performance.md b/spaces/tialenAdioni/chat-gpt-api/logs/Driver updater pro serial number Tips and tricks for optimal performance.md
deleted file mode 100644
index 9d6e4b3b068c638a86ec0ab57e3ce8d73480f85a..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Driver updater pro serial number Tips and tricks for optimal performance.md	
+++ /dev/null
@@ -1,186 +0,0 @@
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-<p>If you want to keep your Windows drivers up to date, you might be interested in Driver Updater Pro, a software that can automatically scan, download and install the latest drivers for your devices. But how can you get a serial number for this program and what are the benefits of using it? In this article, we will answer these questions and more.</p>
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-<p>Driver Updater Pro is a software that can help you update your Windows drivers with ease. It can identify your devices automatically and find the most compatible drivers for them from its online database. It can also backup your drivers for quick restore, uninstall unwanted drivers, export your hardware configuration and diagnose any driver-related issues.</p>
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-<p>Driver Updater Pro works by scanning your system for outdated, missing or corrupted drivers. It then downloads and installs the latest drivers for your devices in a few clicks. You can also schedule automatic scans and updates to ensure that your drivers are always up to date.</p>
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-<p>To use Driver Updater Pro, you need a serial number or an activation key that can unlock its full features. There are several ways to get a serial number for this software, such as:</p>
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-<li>Buying it from the official website of PC HelpSoft, the developer of Driver Updater Pro. You can choose from different plans and payment options.</li>
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-<li>Improving the performance and stability of your system by fixing driver-related errors and conflicts.</li>
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-<li>Saving time and effort by automating the process of finding, downloading and installing drivers for your devices.</li>
-<li>Protecting your system from potential risks by backing up your drivers before updating them and restoring them in case of any problems.</li>
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-<p>Driver Updater Pro is a software that can help you update your Windows drivers with ease. It can identify your devices automatically and find the most compatible drivers for them from its online database. It can also backup your drivers for quick restore, uninstall unwanted drivers, export your hardware configuration and diagnose any driver-related issues.</p>
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-<p>To use Driver Updater Pro, you need a serial number or an activation key that can unlock its full features. You can get a serial number for this software by buying it from the official website of PC HelpSoft, downloading it from a third-party website that offers free serial numbers or cracks, or participating in a giveaway or a promotion that offers free serial numbers or licenses.</p>
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-<p>Using Driver Updater Pro can bring you many advantages, such as improving the performance and stability of your system, enhancing the functionality and compatibility of your devices, saving time and effort, protecting your system from potential risks and optimizing your system.</p>
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-<p>If you want to keep your Windows drivers up to date, you might want to try Driver Updater Pro. It is a software that can make updating drivers easy and convenient.</p>
-<h2>How to Download and Install Driver Updater Pro?</h2>
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-<p>If you have a serial number for Driver Updater Pro, you can download and install it on your Windows PC easily. Here are the steps to follow:</p>
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-<ol>
-<li>Go to the official website of PC HelpSoft and click on the Download button.</li>
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-</ol>
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-<p>If you don't have a serial number for Driver Updater Pro, you can still download and install it as a trial version. However, you will have limited access to its features and you will need to purchase a serial number to unlock them.</p>
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-<h2>How to Update Your Drivers with Driver Updater Pro?</h2>
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-<p>Once you have activated Driver Updater Pro, you can start updating your drivers with a few clicks. Here are the steps to follow:</p>
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-<ol>
-<li>Click on the Scan button to scan your system for outdated, missing or corrupted drivers.</li>
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-<li>Click on the Update button to download and install the latest drivers for your devices.</li>
-<li>Restart your computer if prompted to apply the changes.</li>
-</ol>
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-<p>You can also customize the settings of Driver Updater Pro according to your preferences. For example, you can schedule automatic scans and updates, create driver backups and restore points, exclude certain drivers from scanning, enable notifications and more.</p>
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-<p>Driver Updater Pro is a software that can help you update your Windows drivers with ease. It can identify your devices automatically and find the most compatible drivers for them from its online database. It can also backup your drivers for quick restore, uninstall unwanted drivers, export your hardware configuration and diagnose any driver-related issues.</p>
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-<p>To use Driver Updater Pro, you need a serial number or an activation key that can unlock its full features. You can get a serial number for this software by buying it from the official website of PC HelpSoft, downloading it from a third-party website that offers free serial numbers or cracks, or participating in a giveaway or a promotion that offers free serial numbers or licenses.</p>
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-<p>Using Driver Updater Pro can bring you many advantages, such as improving the performance and stability of your system, enhancing the functionality and compatibility of your devices, saving time and effort, protecting your system from potential risks and optimizing your system.</p>
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-<p>If you want to keep your Windows drivers up to date, you might want to try Driver Updater Pro. It is a software that can make updating drivers easy and convenient.</p>
-<h2>How to Troubleshoot Driver Issues with Driver Updater Pro?</h2>
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-<p>Sometimes, you may encounter driver issues that affect the performance or functionality of your devices. For example, you may experience blue screen errors, device conflicts, audio problems, network issues, printer errors and more. In such cases, you can use Driver Updater Pro to troubleshoot and fix these issues. Here are the steps to follow:</p>
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-<ol>
-<li>Launch Driver Updater Pro and click on the Hardware Diagnostic button.</li>
-<li>Select the device category that you want to diagnose and click on the Scan button.</li>
-<li>Review the scan results and see if there are any driver errors or warnings.</li>
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-<li>If there are no driver errors or warnings, click on the Backup button to create a backup of your drivers before making any changes.</li>
-<li>Click on the Update button to update your drivers to the latest versions.</li>
-<li>If the problem persists, click on the Restore button to restore your drivers from the backup.</li>
-</ol>
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-<p>Driver Updater Pro can help you troubleshoot and fix driver issues with ease. It can scan your devices for driver errors or warnings, repair them, update them, backup them and restore them. It can also provide you with detailed information about your devices and drivers.</p>
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-<h2>How to Uninstall Driver Updater Pro?</h2>
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-<p>If you want to uninstall Driver Updater Pro from your Windows PC, you can do so easily. Here are the steps to follow:</p>
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-<ol>
-<li>Go to the Start menu and click on the Control Panel.</li>
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-<li>Find and select Driver Updater Pro from the list of installed programs.</li>
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-<p>You can also use the Custom Driver Uninstaller feature of Driver Updater Pro to uninstall specific drivers from your system. Here are the steps to follow:</p>
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-<li>Launch Driver Updater Pro and click on the Custom Driver Uninstaller button.</li>
-<li>Select the drivers that you want to uninstall and click on the Uninstall button.</li>
-<li>Follow the instructions on the screen to complete the uninstallation process.</li>
-</ol>
-
-<p>Driver Updater Pro can help you uninstall unwanted drivers from your system. It can also uninstall itself if you no longer need it.</p>
-
-<h2>Conclusion</h2>
-
-<p>Driver Updater Pro is a software that can help you update your Windows drivers with ease. It can identify your devices automatically and find the most compatible drivers for them from its online database. It can also backup your drivers for quick restore, uninstall unwanted drivers, export your hardware configuration and diagnose any driver-related issues.</p>
-
-<p>To use Driver Updater Pro, you need a serial number or an activation key that can unlock its full features. You can get a serial number for this software by buying it from the official website of PC HelpSoft, downloading it from a third-party website that offers free serial numbers or cracks, or participating in a giveaway or a promotion that offers free serial numbers or licenses.</p>
-
-<p>Using Driver Updater Pro can bring you many advantages, such as improving the performance and stability of your system, enhancing the functionality and compatibility of your devices, saving time and effort, protecting your system from potential risks and optimizing your system.</p>
-
-<p>If you want to keep your Windows drivers up to date, you might want to try Driver Updater Pro. It is a software that can make updating drivers easy and convenient.</p>
-<h2>Conclusion</h2>
-
-<p>Driver Updater Pro is a software that can help you update your Windows drivers with ease. It can identify your devices automatically and find the most compatible drivers for them from its online database. It can also backup your drivers for quick restore, uninstall unwanted drivers, export your hardware configuration and diagnose any driver-related issues.</p>
-
-<p>To use Driver Updater Pro, you need a serial number or an activation key that can unlock its full features. You can get a serial number for this software by buying it from the official website of PC HelpSoft, downloading it from a third-party website that offers free serial numbers or cracks, or participating in a giveaway or a promotion that offers free serial numbers or licenses.</p>
-
-<p>Using Driver Updater Pro can bring you many advantages, such as improving the performance and stability of your system, enhancing the functionality and compatibility of your devices, saving time and effort, protecting your system from potential risks and optimizing your system.</p>
-
-<p>If you want to keep your Windows drivers up to date, you might want to try Driver Updater Pro. It is a software that can make updating drivers easy and convenient.</p> 679dcb208e<br />
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Keygen Vmware Workstation 8 Linux.md b/spaces/tialenAdioni/chat-gpt-api/logs/Keygen Vmware Workstation 8 Linux.md
deleted file mode 100644
index 50d4a66d491c0e0574770605461a873514e44a39..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Keygen Vmware Workstation 8 Linux.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<br />
-<h1>How to Use Keygen for Vmware Workstation 8 on Linux</h1>
-<p>Vmware Workstation 8 is a powerful virtualization software that allows you to run multiple operating systems on a single machine. It is widely used by developers, testers, and IT professionals who need to create and test applications in different environments. However, Vmware Workstation 8 is not free and requires a license key to activate it.</p>
-<p>If you want to use Vmware Workstation 8 on Linux without paying for a license, you can try using a keygen program that generates valid serial keys for the software. A keygen is a type of crack that bypasses the software's protection mechanism and allows you to use it without any restrictions. However, using a keygen is illegal and may expose you to malware or legal risks.</p>
-<h2>Keygen Vmware Workstation 8 Linux</h2><br /><p><b><b>DOWNLOAD</b> &#9733;&#9733;&#9733; <a href="https://urlcod.com/2uK9BK">https://urlcod.com/2uK9BK</a></b></p><br /><br />
-<p>In this article, we will show you how to use a keygen for Vmware Workstation 8 on Linux, but we do not endorse or recommend this method. We only provide this information for educational purposes and we are not responsible for any consequences that may arise from using a keygen. You should always purchase a legitimate license from Vmware if you want to use their software.</p>
-<h2>Step 1: Download the Keygen</h2>
-<p>The first step is to download the keygen program that can generate serial keys for Vmware Workstation 8 on Linux. There are many sources online that claim to offer such keygens, but most of them are fake or malicious. You should be very careful when downloading any file from unknown or untrusted websites.</p>
-<p>One possible source of a keygen for Vmware Workstation 8 on Linux is the Reddit post by Pauderney[^1^], who shared a link to the original scene release of Vmware Workstation Pro v15.0.0 Linux X64 Incl Keygen-AMPED. This release contains an ISO file of the software installer and a keygen binary that can be run on Linux. You can download this release from Archive.org[^2^] or other torrent sites.</p>
-<h2>Step 2: Extract the Keygen</h2>
-<p>The next step is to extract the keygen program from the downloaded file. The file is compressed in multiple ZIP and RAR archives, so you will need to use a tool like 7-Zip or WinRAR to extract them. You should end up with two files: one is the ISO file of the software installer and the other is the keygen binary in an AMPED folder.</p>
-<p>You can copy the keygen binary to your Linux machine or use a USB drive to transfer it. You will need to make the keygen executable by changing its permissions with the following command:</p>
-<p></p>
-<code>chmod +x ./keygen</code>
-<h2>Step 3: Generate a Serial Key</h2>
-<p>The final step is to run the keygen program and generate a serial key for Vmware Workstation 8 on Linux. You can do this by opening a terminal window and navigating to the directory where you saved the keygen binary. Then, you can execute the following command:</p>
-<code>./keygen</code>
-<p>The keygen will display a random serial key that you can copy and paste into the software activation window. You can also try using some of the serial keys posted online by other users[^3^], but they may not work or may have been blacklisted by Vmware.</p>
-<h2>Conclusion</h2>
-<p>In this article, we showed you how to use a keygen for Vmware Workstation 8 on Linux, but we do not recommend this method as it is illegal and risky. You should always buy a legitimate license from Vmware if you want to use their software legally and safely.</p> 7b8c122e87<br />
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/1 dollarn tarixi v mnyi - Azrbaycan manat il nec dyidi?.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/1 dollarn tarixi v mnyi - Azrbaycan manat il nec dyidi?.md
deleted file mode 100644
index f33220b94bc006fcad6ce99514663367482a73c5..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/1 dollarn tarixi v mnyi - Azrbaycan manat il nec dyidi?.md	
+++ /dev/null
@@ -1,117 +0,0 @@
-
-<h1>1 dollar qiymeti: What does it mean and how does it affect Azerbaijan's economy?</h1>
- <p>If you are interested in learning more about Azerbaijan's economy, you may have come across the term "1 dollar qiymeti". But what does it mean and why is it important? In this article, we will explain what 1 dollar qiymeti is, how it is calculated, and how it affects Azerbaijan's economic performance and trade relations. We will also look at the history, current situation, and future prospects of 1 dollar qiymeti in Azerbaijan.</p>
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- <h2>Introduction</h2>
- <p>1 dollar qiymeti is a term that refers to the average price of one US dollar in Azerbaijani manats (AZN), the national currency of Azerbaijan. It is calculated by dividing the total value of Azerbaijan's exports by the total value of its imports, both expressed in US dollars. For example, if Azerbaijan exported goods worth $10 billion and imported goods worth $5 billion in a given year, then its 1 dollar qiymeti would be $10 billion / $5 billion = $2. This means that one US dollar would be equivalent to two Azerbaijani manats on average.</p>
- <p>Why is 1 dollar qiymeti important for Azerbaijan's economy and trade? Because it reflects the balance of trade between Azerbaijan and the rest of the world. A higher 1 dollar qiymeti means that Azerbaijan is exporting more than it is importing, which indicates a trade surplus. A lower 1 dollar qiymeti means that Azerbaijan is importing more than it is exporting, which indicates a trade deficit. A trade surplus or deficit can have significant implications for Azerbaijan's economic growth, inflation, exchange rate, foreign reserves, external debt, and competitiveness.</p>
- <h2>The history of 1 dollar qiymeti in Azerbaijan</h2>
- <p>How has 1 dollar qiymeti changed over time and what were the main factors influencing it? The following table shows the historical values of 1 dollar qiymeti in Azerbaijan from 1995 to 2020, based on data from the World Bank.</p>
- <table>
-<tr>
-<th>Year</th>
-<th>Exports (US$ billion)</th>
-<th>Imports (US$ billion)</th>
-<th>1 dollar qiymeti (AZN)</th>
-</tr>
-<tr>
-<td>1995</td>
-<td>0.6</td>
-<td>0.9</td>
-<td>0.67</td>
-</tr>
-<tr>
-<td>2000</td>
-<td>2.3</td>
-<td>2.4</td>
-<td>0.96</td>
-</tr>
-<tr>
-<td>2005</td>
-<td>8.4</td>
-<td>4.3</td>
-<td>1.95</td>
-</tr>
-<tr>
-<td>2010</td>
-<td>20.9</td>
-<td>7.9</td>
-<td>2.65</td>
-</tr>
-<tr>
-<td>2015</td>
-<td>12.8</td>
-<td>9.9</td>
-<td>1.29</td>
-</tr>
-<tr>
-<td>2020</td>
-<td>14.5</td> <td>8.3</td>
-<td>1.75</td>
-</tr>
-</table>
- <p>As we can see from the table, 1 dollar qiymeti in Azerbaijan has fluctuated significantly over the years, ranging from 0.67 to 2.65. The main factor that influenced 1 dollar qiymeti in Azerbaijan was the price of oil, which is the main export commodity of the country. When the oil price was high, Azerbaijan enjoyed a high trade surplus and a high 1 dollar qiymeti, which boosted its economic growth, reduced its inflation, and strengthened its exchange rate. When the oil price was low, Azerbaijan faced a low trade surplus or a trade deficit and a low 1 dollar qiymeti, which slowed down its economic growth, increased its inflation, and weakened its exchange rate.</p>
- <h2>The current situation of 1 dollar qiymeti in Azerbaijan</h2>
- <p>What is the current value of 1 dollar qiymeti in Azerbaijan and how does it compare to other countries? According to the latest data from the World Bank, the value of 1 dollar qiymeti in Azerbaijan in 2020 was 1.75, which means that one US dollar was equivalent to 1.75 Azerbaijani manats on average. This was higher than the global average of 1.33, but lower than some of its neighboring countries, such as Turkey (2.69), Russia (2.96), and Iran (4.42).</p>
- <p>What are the main challenges and opportunities facing Azerbaijan's economy due to 1 dollar qiymeti? One of the main challenges is to diversify its economy and reduce its dependence on oil exports, which make it vulnerable to external shocks and price fluctuations. Another challenge is to improve its trade competitiveness and productivity, which are affected by factors such as infrastructure, innovation, education, and governance. One of the main opportunities is to leverage its strategic location and natural resources to attract foreign investment and develop new markets and sectors, such as tourism, agriculture, and renewable energy.</p>
- <h2>The future prospects of 1 dollar qiymeti in Azerbaijan</h2>
- <p>What are the expected trends and scenarios for 1 dollar qiymeti in Azerbaijan in the near and long term? According to the projections from the International Monetary Fund, the value of 1 dollar qiymeti in Azerbaijan is expected to increase slightly to 1.78 in 2021 and then decline gradually to 1.63 by 2026. This implies that Azerbaijan's trade balance will improve slightly in the short term but deteriorate in the long term.</p>
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- <p>What are the policy recommendations and strategies for Azerbaijan to benefit from 1 dollar qiymeti? Some of the policy recommendations and strategies are: - To adopt a flexible exchange rate regime that allows the manat to adjust to market conditions and avoid excessive volatility. - To implement fiscal and monetary policies that support economic stability and growth, while maintaining adequate foreign reserves and external debt levels. - To pursue structural reforms that enhance economic diversification, competitiveness, productivity, and innovation, while addressing social and environmental challenges. - To foster regional cooperation and integration that promote trade facilitation, market access, and investment opportunities.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, 1 dollar qiymeti is a useful indicator that reflects the balance of trade between Azerbaijan and the rest of the world. It has significant implications for Azerbaijan's economic performance and trade relations. It has fluctuated significantly over time, mainly influenced by the price of oil. It is currently higher than the global average but lower than some of its neighboring countries. It is expected to increase slightly in the short term but decline gradually in the long term. Azerbaijan can benefit from 1 dollar qiymeti by adopting sound policies and strategies that enhance its economic resilience, diversification, competitiveness, and integration.</p>
- <p>We hope you enjoyed this article and learned something new about 1 dollar qiymeti. If you have any questions or comments, please feel free to share them below. Thank you for reading!</p>
- <h2>FAQs</h2>
- <h3>What is the official exchange rate of Azerbaijani manat?</h3>
-<p>The official exchange rate of Azerbaijani manat is determined by the Central Bank of Azerbaijan (CBA) based on a basket of currencies that includes US dollar, euro, Russian ruble, Turkish lira, Chinese yuan, and British pound. As of June 18, 2023, one US dollar was equal to 1.7 Azerbaijani manats according to CBA.</p>
- <h3>How does 1 dollar qiymeti affect inflation in Azerbaijan?</h3>
-<p>One dollar qiymeti affects inflation in Azerbaijan through two channels: the direct channel and the indirect channel. The direct channel is when a change in 1 dollar qiymeti affects the prices of imported goods and services, which directly affects the consumer price index (CPI). The indirect channel is when a change in 1 dollar qiymeti affects the domestic production costs, which indirectly affects the CPI. For example, if 1 dollar qiymeti increases, the prices of imported inputs and intermediate goods will increase, which will increase the production costs and the prices of final goods and services. This will lead to higher inflation in Azerbaijan.</p>
- <h3>How does 1 dollar qiymeti affect the exchange rate of Azerbaijani manat?</h3>
-<p>One dollar qiymeti affects the exchange rate of Azerbaijani manat through the demand and supply of foreign currency in the market. The demand for foreign currency is determined by the imports of goods and services, the repayment of external debt, and the capital outflows. The supply of foreign currency is determined by the exports of goods and services, the inflow of foreign investment, and the capital inflows. For example, if 1 dollar qiymeti increases, the demand for foreign currency will decrease, as Azerbaijan will need less foreign currency to pay for its imports. This will lead to a lower exchange rate of Azerbaijani manat.</p>
- <h3>How does 1 dollar qiymeti affect the economic growth of Azerbaijan?</h3>
-<p>One dollar qiymeti affects the economic growth of Azerbaijan through the trade balance, the income effect, and the substitution effect. The trade balance is the difference between exports and imports, which contributes to the gross domestic product (GDP) of Azerbaijan. The income effect is when a change in 1 dollar qiymeti affects the real income and purchasing power of consumers and producers in Azerbaijan. The substitution effect is when a change in 1 dollar qiymeti affects the relative prices and competitiveness of domestic and foreign goods and services in Azerbaijan. For example, if 1 dollar qiymeti increases, the trade balance will improve, as Azerbaijan will earn more from its exports and spend less on its imports. This will increase the GDP of Azerbaijan. However, the income effect will be negative, as consumers and producers in Azerbaijan will face higher prices and lower real income. This will reduce the consumption and investment in Azerbaijan. The substitution effect will be mixed, as domestic goods and services will become cheaper and more competitive in foreign markets, but more expensive and less competitive in domestic markets. This will increase the export demand but reduce the domestic demand for Azerbaijani goods and services.</p>
- <h3>How does 1 dollar qiymeti affect the foreign reserves of Azerbaijan?</h3>
-<p>One dollar qiymeti affects the foreign reserves of Azerbaijan through the balance of payments, which is the record of all transactions between Azerbaijan and the rest of the world. The balance of payments consists of two main accounts: the current account and the capital account. The current account records the trade in goods and services, income, and transfers. The capital account records the financial flows, such as foreign direct investment, portfolio investment, loans, and aid. The balance of payments must always balance, meaning that any surplus or deficit in one account must be offset by an equal surplus or deficit in another account. For example, if Azerbaijan has a current account surplus due to a high 1 dollar qiymeti, it must have a capital account deficit or an increase in its foreign reserves to balance its payments. Conversely, if Azerbaijan has a current account deficit due to a low 1 dollar qiymeti, it must have a capital account surplus or a decrease in its foreign reserves to balance its payments.</p>
- <h3>How does 1 dollar qiymeti affect the competitiveness of Azerbaijani products?</h3>
-<p>One dollar qiymeti affects the competitiveness of Azerbaijani products through two aspects: price competitiveness and non-price competitiveness. Price competitiveness refers to how affordable and attractive Azerbaijani products are compared to foreign products in terms of price. Non-price competitiveness refers to how desirable and attractive Azerbaijani products are compared to foreign products in terms of quality, design, innovation, service, reputation, etc. For example, if 1 dollar qiymeti increases, Azerbaijani products will become more price competitive in foreign markets but less price competitive in domestic markets. However, price competitiveness is not enough to ensure success in global markets. Azerbaijani products also need to improve their non-price competitiveness by enhancing their quality, design, innovation, service, reputation, etc. This will increase their value-added and differentiation in global markets.</p> 197e85843d<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Download Eemua 158 Pdf Free !!EXCLUSIVE!! 234 8.md b/spaces/tioseFevbu/cartoon-converter/scripts/Download Eemua 158 Pdf Free !!EXCLUSIVE!! 234 8.md
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+++ /dev/null
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-
-Here is a possible title and article with html formatting for the keyword "Download eemua 158 pdf free 234 8":
-
-<h1>Download EEMUA 158 PDF Free: Construction Specification for Fixed Offshore Structures</h1>
-<p>EEMUA 158 is a publication by the Engineering Equipment and Materials Users Association (EEMUA) that defines the essential requirements for cost-effective construction of primary structures of offshore installations. It covers topics such as material requirements, allowable welding consumables, welding quality and personnel requirements, fabrication tolerances, and inspection and non-destructive testing acceptance levels[^1^].</p>
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-<p>The publication reflects a consensus view of offshore construction companies with regard to both fabrication and acceptance criteria for structures to be installed worldwide. It also incorporates any necessary considerations for offshore wind constructions, while updating the rest of the publication in accordance with the latest versions of the relevant regulations and standards[^1^].</p>
-<p>EEMUA 158 is available in digital format as a PDF file that can be downloaded from the EEMUA website for a non-member price of £139.00. However, if you are a user from an EEMUA member company, you can log in and download your complimentary digital edition here[^1^].</p>
-<p>If you are looking for a free download of EEMUA 158 PDF, you may be disappointed to find out that there is no legitimate source that offers it for free. EEMUA publications are protected by copyright and are not intended to be distributed or reproduced without permission. Therefore, any website that claims to offer a free download of EEMUA 158 PDF may be violating the law or exposing you to malware or viruses.</p>
-<p>Instead of risking your computer or legal troubles, we recommend that you purchase the official digital edition of EEMUA 158 PDF from the EEMUA website or contact them for more information. You can also preview the contents of EEMUA 158 PDF here[^2^] before buying it.</p>
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-<p>EEMUA 158 PDF is a valuable resource for anyone involved in the construction of fixed offshore structures. It provides guidance on how to ensure quality, safety, and efficiency in the fabrication and installation of these structures. By following the specifications and recommendations in EEMUA 158 PDF, you can achieve optimal performance and durability of your offshore structures.</p>Here are a few more paragraphs with html formatting for the article:
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-<li>EEMUA Publication 176 Specification for structural castings for use offshore</li>
-<li>EEMUA Publication 197 Specification for the fabrication of non-primary structural steelwork for offshore installations</li>
-<li>EEMUA Publication 234 90/10 Copper nickel alloy piping for offshore applications (Specification)</li>
-<li>EEMUA Publication 231 The mechanical integrity of plant containing hazardous substances - a guide to periodic examination and testing</li>
-<li>EEMUA Publication 186 A Practitioner's Handbook for potentially explosive atmospheres</li>
-</ul>
-<p>You can find a catalogue of all current EEMUA publications here[^3^] and preview their contents before purchasing them. Some of the EEMUA publications are also available as free guidance or checklists here.</p>
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Lampiran Permendagri Nomor 17 Tahun 2007 Tentang Pedoman Teknis Pengelolaan Barang Milik Daerah ((FREE)).md b/spaces/tioseFevbu/cartoon-converter/scripts/Lampiran Permendagri Nomor 17 Tahun 2007 Tentang Pedoman Teknis Pengelolaan Barang Milik Daerah ((FREE)).md
deleted file mode 100644
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--- a/spaces/tioseFevbu/cartoon-converter/scripts/Lampiran Permendagri Nomor 17 Tahun 2007 Tentang Pedoman Teknis Pengelolaan Barang Milik Daerah ((FREE)).md	
+++ /dev/null
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-<h1>Lampiran Permendagri Nomor 17 Tahun 2007: Panduan Praktis untuk Pengelolaan Barang Milik Daerah</h1>
-<p>Lampiran Permendagri Nomor 17 Tahun 2007 adalah dokumen yang berisi pedoman teknis pengelolaan barang milik daerah. Barang milik daerah adalah semua barang yang dibeli atau diperoleh dengan menggunakan anggaran pendapatan dan belanja daerah (APBD) atau sumber lain yang sah. Barang milik daerah meliputi tanah, bangunan, kendaraan, peralatan, persediaan, dan lain-lain.</p>
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-<p>Pengelolaan barang milik daerah bertujuan untuk meningkatkan efisiensi, efektivitas, akuntabilitas, dan transparansi penggunaan sumber daya daerah. Pengelolaan barang milik daerah meliputi perencanaan kebutuhan dan penganggaran, pengadaan, penerimaan, penyimpanan dan penyaluran, penggunaan, penatausahaan, penilaian, pengamanan dan pemeliharaan, penghapusan, dan pertanggungjawaban.</p>
-<p>Lampiran Permendagri Nomor 17 Tahun 2007 memberikan petunjuk teknis mengenai hal-hal berikut:</p>
-<ul>
-<li>Kriteria dan klasifikasi barang milik daerah;</li>
-<li>Penyusunan rencana kebutuhan barang milik daerah;</li>
-<li>Prosedur pengadaan barang milik daerah;</li>
-<li>Prosedur penerimaan, penyimpanan dan penyaluran barang milik daerah;</li>
-<li>Prosedur penggunaan barang milik daerah;</li>
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-<li>Prosedur pengamanan dan pemeliharaan barang milik daerah;</li>
-<li>Prosedur penghapusan barang milik daerah;</li>
-<li>Prosedur pertanggungjawaban barang milik daerah.</li>
-</ul>
-<p>Lampiran Permendagri Nomor 17 Tahun 2007 juga menyertakan format-format laporan dan dokumen yang harus digunakan dalam pengelolaan barang milik daerah. Format-format tersebut meliputi:</p>
-<p></p>
-<ul>
-<li>Daftar inventaris barang milik daerah;</li>
-<li>Kartu inventaris barang (KIB);</li>
-<li>Buku inventaris;</li>
-<li>Buku induk;</li>
-<li>Buku pembantu;</li>
-<li>Buku mutasi;</li>
-<li>Buku penerimaan dan penyaluran;</li>
-<li>Buku pemeliharaan;</li>
-<li>Berita acara serah terima;</li>
-<li>Berita acara pemeriksaan fisik;</li>
-<li>Berita acara penilaian;</li>
-<li>Berita acara penghapusan;</li>
-<li>Laporan pertanggungjawaban.</li>
-</ul>
-<p>Lampiran Permendagri Nomor 17 Tahun 2007 berlaku sejak tanggal 21 Maret 2007. Lampiran ini menggantikan Lampiran Permendagri Nomor 19 Tahun 2016 yang dicabut pada tanggal yang sama. Lampiran ini merupakan bagian tidak terpisahkan dari Peraturan Menteri Dalam Negeri Nomor 17 Tahun 2007 tentang Pedoman Teknis Pengelolaan Barang Milik Daerah.</p>Here are a few more paragraphs for the article:
-
-<h2>Kriteria dan Klasifikasi Barang Milik Daerah</h2>
-<p>Barang milik daerah diklasifikasikan berdasarkan jenis, fungsi, dan nilai ekonomisnya. Jenis barang milik daerah meliputi:</p>
-<ol>
-<li>Barang berwujud, yaitu barang yang memiliki bentuk fisik dan dapat dilihat, diraba, atau diukur. Contoh: tanah, bangunan, kendaraan, peralatan, persediaan, dan lain-lain.</li>
-<li>Barang tidak berwujud, yaitu barang yang tidak memiliki bentuk fisik tetapi memiliki nilai ekonomis. Contoh: hak atas tanah, hak cipta, merek dagang, paten, dan lain-lain.</li>
-</ol>
-<p>Fungsi barang milik daerah meliputi:</p>
-<ol>
-<li>Barang operasional, yaitu barang yang digunakan untuk mendukung pelaksanaan tugas pokok dan fungsi pemerintahan daerah. Contoh: meja, kursi, komputer, printer, telepon, dan lain-lain.</li>
-<li>Barang non operasional, yaitu barang yang tidak digunakan untuk mendukung pelaksanaan tugas pokok dan fungsi pemerintahan daerah tetapi memiliki nilai ekonomis atau strategis. Contoh: tanah cadangan, bangunan cadangan, kendaraan cadangan, dan lain-lain.</li>
-</ol>
-<p>Nilai ekonomis barang milik daerah meliputi:</p>
-<ol>
-<li>Barang habis pakai, yaitu barang yang habis atau rusak setelah digunakan satu kali atau dalam jangka waktu tertentu. Contoh: kertas, tinta, baterai, obat-obatan, dan lain-lain.</li>
-<li>Barang tidak habis pakai, yaitu barang yang dapat digunakan berulang-ulang atau dalam jangka waktu lama tanpa mengalami kerusakan atau penurunan nilai. Contoh: tanah, bangunan, kendaraan, peralatan, dan lain-lain.</li>
-</ol>
-
-<h2>Penyusunan Rencana Kebutuhan Barang Milik Daerah</h2>
-<p>Rencana kebutuhan barang milik daerah adalah dokumen yang berisi estimasi jumlah dan jenis barang yang dibutuhkan oleh pemerintah daerah dalam satu tahun anggaran. Rencana kebutuhan barang milik daerah disusun oleh setiap unit kerja pemerintah daerah berdasarkan analisis kebutuhan dan ketersediaan barang milik daerah.</p>
-<p>Rencana kebutuhan barang milik daerah harus memperhatikan hal-hal berikut:</p>
-<ul>
-<li>Kesesuaian dengan visi, misi, tujuan, sasaran, strategi, program, dan kegiatan pemerintah daerah;</li>
-<li>Kesesuaian dengan standar mutu dan spesifikasi teknis barang;</li>
-<li>Ketersediaan anggaran;</li>
-<li>Ketersediaan sumber daya manusia;</li>
-<li>Ketersediaan sarana dan prasarana;</li>
-<li>Ketersediaan pasokan barang;</li>
-<li>Kebijakan pengadaan barang milik daerah;</li>
-<li>Aspek hukum dan peraturan perundang-undangan;</li>
-<li>Aspek lingkungan hidup;</li>
-<li>Aspek pertimbangan lain yang relevan.</li>
-</ul></p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Lolwiz For Mac.md b/spaces/tioseFevbu/cartoon-converter/scripts/Lolwiz For Mac.md
deleted file mode 100644
index a0b370015b6170aae59152c52f5cce578b2f6ea0..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Lolwiz For Mac.md	
+++ /dev/null
@@ -1,34 +0,0 @@
-
-<h1>How to Use Lolwiz on Mac to Improve Your League of Legends Skills</h1>
-<p>Lolwiz is a popular app that helps you get an edge in League of Legends by providing you with real-time stats, builds, runes, and more. But what if you are a Mac user and want to enjoy the benefits of Lolwiz? Don't worry, we have you covered. In this article, we will show you how to use Lolwiz on Mac with a simple workaround.</p>
-<h2>What is Lolwiz?</h2>
-<p>Lolwiz is an app that runs on Overwolf, a platform that allows you to use various apps and mods for your favorite games. Lolwiz is designed to help you dominate ranked play in League of Legends by giving you access to crucial information such as:</p>
-<h2>Lolwiz For Mac</h2><br /><p><b><b>Download</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://urlcod.com/2uHvZF">https://urlcod.com/2uHvZF</a></b></p><br /><br />
-<ul>
-<li>The best champions and bans for your role and the current meta</li>
-<li>The most popular and highest win rate runes and builds for your champion</li>
-<li>The stats and performance of your teammates and opponents</li>
-<li>The damage breakdown of each player and team</li>
-<li>The optimal items and strategies for different situations</li>
-</ul>
-<p>Lolwiz is fully compliant with Riot's Terms of Service and will not get your account flagged or banned. It is used by over 1.6 million players, including pro players and streamers.</p>
-<h2>How to Use Lolwiz on Mac?</h2>
-<p>Unfortunately, Overwolf does not support Mac OS natively, which means you cannot install Lolwiz directly on your Mac. However, there is a workaround that allows you to use Lolwiz on Mac by using a virtual machine.</p>
-<p>A virtual machine is a software that emulates a different operating system on your computer. This way, you can run Windows programs on your Mac without having to reboot or partition your hard drive. There are several virtual machine software available for Mac, such as Parallels Desktop, VMware Fusion, or VirtualBox.</p>
-<p>To use Lolwiz on Mac with a virtual machine, you need to follow these steps:</p>
-<ol>
-<li>Download and install a virtual machine software of your choice on your Mac.</li>
-<li>Create a new virtual machine and install Windows on it. You will need a Windows license key for this step.</li>
-<li>Download and install Overwolf on your virtual machine.</li>
-<li>Download and install Lolwiz from the Overwolf store.</li>
-<li>Launch League of Legends on your Mac as usual.</li>
-<li>Launch Lolwiz on your virtual machine and enter your summoner name and region.</li>
-<li>Enjoy using Lolwiz on Mac!</li>
-</ol>
-<p>Note that running a virtual machine may affect your Mac's performance and battery life, so make sure you have enough resources and power before using this method.</p>
-<h2>Conclusion</h2>
-<p>Lolwiz is a great app that can help you improve your League of Legends skills by providing you with real-time stats, builds, runes, and more. However, if you are a Mac user, you cannot use Lolwiz directly on your computer. You need to use a virtual machine software to run Windows programs on your Mac. This way, you can use Lolwiz on Mac with a simple workaround.</p>
-<p></p>
-<p>We hope this article was helpful for you. If you have any questions or feedback, feel free to leave a comment below. Happy gaming!</p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pyparsing/helpers.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pyparsing/helpers.py
deleted file mode 100644
index 9588b3b780159a2a2d23c7f84a4404ec350e2b65..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pyparsing/helpers.py
+++ /dev/null
@@ -1,1088 +0,0 @@
-# helpers.py
-import html.entities
-import re
-import typing
-
-from . import __diag__
-from .core import *
-from .util import _bslash, _flatten, _escape_regex_range_chars
-
-
-#
-# global helpers
-#
-def delimited_list(
-    expr: Union[str, ParserElement],
-    delim: Union[str, ParserElement] = ",",
-    combine: bool = False,
-    min: typing.Optional[int] = None,
-    max: typing.Optional[int] = None,
-    *,
-    allow_trailing_delim: bool = False,
-) -> ParserElement:
-    """Helper to define a delimited list of expressions - the delimiter
-    defaults to ','. By default, the list elements and delimiters can
-    have intervening whitespace, and comments, but this can be
-    overridden by passing ``combine=True`` in the constructor. If
-    ``combine`` is set to ``True``, the matching tokens are
-    returned as a single token string, with the delimiters included;
-    otherwise, the matching tokens are returned as a list of tokens,
-    with the delimiters suppressed.
-
-    If ``allow_trailing_delim`` is set to True, then the list may end with
-    a delimiter.
-
-    Example::
-
-        delimited_list(Word(alphas)).parse_string("aa,bb,cc") # -> ['aa', 'bb', 'cc']
-        delimited_list(Word(hexnums), delim=':', combine=True).parse_string("AA:BB:CC:DD:EE") # -> ['AA:BB:CC:DD:EE']
-    """
-    if isinstance(expr, str_type):
-        expr = ParserElement._literalStringClass(expr)
-
-    dlName = "{expr} [{delim} {expr}]...{end}".format(
-        expr=str(expr.copy().streamline()),
-        delim=str(delim),
-        end=" [{}]".format(str(delim)) if allow_trailing_delim else "",
-    )
-
-    if not combine:
-        delim = Suppress(delim)
-
-    if min is not None:
-        if min < 1:
-            raise ValueError("min must be greater than 0")
-        min -= 1
-    if max is not None:
-        if min is not None and max <= min:
-            raise ValueError("max must be greater than, or equal to min")
-        max -= 1
-    delimited_list_expr = expr + (delim + expr)[min, max]
-
-    if allow_trailing_delim:
-        delimited_list_expr += Opt(delim)
-
-    if combine:
-        return Combine(delimited_list_expr).set_name(dlName)
-    else:
-        return delimited_list_expr.set_name(dlName)
-
-
-def counted_array(
-    expr: ParserElement,
-    int_expr: typing.Optional[ParserElement] = None,
-    *,
-    intExpr: typing.Optional[ParserElement] = None,
-) -> ParserElement:
-    """Helper to define a counted list of expressions.
-
-    This helper defines a pattern of the form::
-
-        integer expr expr expr...
-
-    where the leading integer tells how many expr expressions follow.
-    The matched tokens returns the array of expr tokens as a list - the
-    leading count token is suppressed.
-
-    If ``int_expr`` is specified, it should be a pyparsing expression
-    that produces an integer value.
-
-    Example::
-
-        counted_array(Word(alphas)).parse_string('2 ab cd ef')  # -> ['ab', 'cd']
-
-        # in this parser, the leading integer value is given in binary,
-        # '10' indicating that 2 values are in the array
-        binary_constant = Word('01').set_parse_action(lambda t: int(t[0], 2))
-        counted_array(Word(alphas), int_expr=binary_constant).parse_string('10 ab cd ef')  # -> ['ab', 'cd']
-
-        # if other fields must be parsed after the count but before the
-        # list items, give the fields results names and they will
-        # be preserved in the returned ParseResults:
-        count_with_metadata = integer + Word(alphas)("type")
-        typed_array = counted_array(Word(alphanums), int_expr=count_with_metadata)("items")
-        result = typed_array.parse_string("3 bool True True False")
-        print(result.dump())
-
-        # prints
-        # ['True', 'True', 'False']
-        # - items: ['True', 'True', 'False']
-        # - type: 'bool'
-    """
-    intExpr = intExpr or int_expr
-    array_expr = Forward()
-
-    def count_field_parse_action(s, l, t):
-        nonlocal array_expr
-        n = t[0]
-        array_expr <<= (expr * n) if n else Empty()
-        # clear list contents, but keep any named results
-        del t[:]
-
-    if intExpr is None:
-        intExpr = Word(nums).set_parse_action(lambda t: int(t[0]))
-    else:
-        intExpr = intExpr.copy()
-    intExpr.set_name("arrayLen")
-    intExpr.add_parse_action(count_field_parse_action, call_during_try=True)
-    return (intExpr + array_expr).set_name("(len) " + str(expr) + "...")
-
-
-def match_previous_literal(expr: ParserElement) -> ParserElement:
-    """Helper to define an expression that is indirectly defined from
-    the tokens matched in a previous expression, that is, it looks for
-    a 'repeat' of a previous expression.  For example::
-
-        first = Word(nums)
-        second = match_previous_literal(first)
-        match_expr = first + ":" + second
-
-    will match ``"1:1"``, but not ``"1:2"``.  Because this
-    matches a previous literal, will also match the leading
-    ``"1:1"`` in ``"1:10"``. If this is not desired, use
-    :class:`match_previous_expr`. Do *not* use with packrat parsing
-    enabled.
-    """
-    rep = Forward()
-
-    def copy_token_to_repeater(s, l, t):
-        if t:
-            if len(t) == 1:
-                rep << t[0]
-            else:
-                # flatten t tokens
-                tflat = _flatten(t.as_list())
-                rep << And(Literal(tt) for tt in tflat)
-        else:
-            rep << Empty()
-
-    expr.add_parse_action(copy_token_to_repeater, callDuringTry=True)
-    rep.set_name("(prev) " + str(expr))
-    return rep
-
-
-def match_previous_expr(expr: ParserElement) -> ParserElement:
-    """Helper to define an expression that is indirectly defined from
-    the tokens matched in a previous expression, that is, it looks for
-    a 'repeat' of a previous expression.  For example::
-
-        first = Word(nums)
-        second = match_previous_expr(first)
-        match_expr = first + ":" + second
-
-    will match ``"1:1"``, but not ``"1:2"``.  Because this
-    matches by expressions, will *not* match the leading ``"1:1"``
-    in ``"1:10"``; the expressions are evaluated first, and then
-    compared, so ``"1"`` is compared with ``"10"``. Do *not* use
-    with packrat parsing enabled.
-    """
-    rep = Forward()
-    e2 = expr.copy()
-    rep <<= e2
-
-    def copy_token_to_repeater(s, l, t):
-        matchTokens = _flatten(t.as_list())
-
-        def must_match_these_tokens(s, l, t):
-            theseTokens = _flatten(t.as_list())
-            if theseTokens != matchTokens:
-                raise ParseException(
-                    s, l, "Expected {}, found{}".format(matchTokens, theseTokens)
-                )
-
-        rep.set_parse_action(must_match_these_tokens, callDuringTry=True)
-
-    expr.add_parse_action(copy_token_to_repeater, callDuringTry=True)
-    rep.set_name("(prev) " + str(expr))
-    return rep
-
-
-def one_of(
-    strs: Union[typing.Iterable[str], str],
-    caseless: bool = False,
-    use_regex: bool = True,
-    as_keyword: bool = False,
-    *,
-    useRegex: bool = True,
-    asKeyword: bool = False,
-) -> ParserElement:
-    """Helper to quickly define a set of alternative :class:`Literal` s,
-    and makes sure to do longest-first testing when there is a conflict,
-    regardless of the input order, but returns
-    a :class:`MatchFirst` for best performance.
-
-    Parameters:
-
-    - ``strs`` - a string of space-delimited literals, or a collection of
-      string literals
-    - ``caseless`` - treat all literals as caseless - (default= ``False``)
-    - ``use_regex`` - as an optimization, will
-      generate a :class:`Regex` object; otherwise, will generate
-      a :class:`MatchFirst` object (if ``caseless=True`` or ``asKeyword=True``, or if
-      creating a :class:`Regex` raises an exception) - (default= ``True``)
-    - ``as_keyword`` - enforce :class:`Keyword`-style matching on the
-      generated expressions - (default= ``False``)
-    - ``asKeyword`` and ``useRegex`` are retained for pre-PEP8 compatibility,
-      but will be removed in a future release
-
-    Example::
-
-        comp_oper = one_of("< = > <= >= !=")
-        var = Word(alphas)
-        number = Word(nums)
-        term = var | number
-        comparison_expr = term + comp_oper + term
-        print(comparison_expr.search_string("B = 12  AA=23 B<=AA AA>12"))
-
-    prints::
-
-        [['B', '=', '12'], ['AA', '=', '23'], ['B', '<=', 'AA'], ['AA', '>', '12']]
-    """
-    asKeyword = asKeyword or as_keyword
-    useRegex = useRegex and use_regex
-
-    if (
-        isinstance(caseless, str_type)
-        and __diag__.warn_on_multiple_string_args_to_oneof
-    ):
-        warnings.warn(
-            "More than one string argument passed to one_of, pass"
-            " choices as a list or space-delimited string",
-            stacklevel=2,
-        )
-
-    if caseless:
-        isequal = lambda a, b: a.upper() == b.upper()
-        masks = lambda a, b: b.upper().startswith(a.upper())
-        parseElementClass = CaselessKeyword if asKeyword else CaselessLiteral
-    else:
-        isequal = lambda a, b: a == b
-        masks = lambda a, b: b.startswith(a)
-        parseElementClass = Keyword if asKeyword else Literal
-
-    symbols: List[str] = []
-    if isinstance(strs, str_type):
-        symbols = strs.split()
-    elif isinstance(strs, Iterable):
-        symbols = list(strs)
-    else:
-        raise TypeError("Invalid argument to one_of, expected string or iterable")
-    if not symbols:
-        return NoMatch()
-
-    # reorder given symbols to take care to avoid masking longer choices with shorter ones
-    # (but only if the given symbols are not just single characters)
-    if any(len(sym) > 1 for sym in symbols):
-        i = 0
-        while i < len(symbols) - 1:
-            cur = symbols[i]
-            for j, other in enumerate(symbols[i + 1 :]):
-                if isequal(other, cur):
-                    del symbols[i + j + 1]
-                    break
-                elif masks(cur, other):
-                    del symbols[i + j + 1]
-                    symbols.insert(i, other)
-                    break
-            else:
-                i += 1
-
-    if useRegex:
-        re_flags: int = re.IGNORECASE if caseless else 0
-
-        try:
-            if all(len(sym) == 1 for sym in symbols):
-                # symbols are just single characters, create range regex pattern
-                patt = "[{}]".format(
-                    "".join(_escape_regex_range_chars(sym) for sym in symbols)
-                )
-            else:
-                patt = "|".join(re.escape(sym) for sym in symbols)
-
-            # wrap with \b word break markers if defining as keywords
-            if asKeyword:
-                patt = r"\b(?:{})\b".format(patt)
-
-            ret = Regex(patt, flags=re_flags).set_name(" | ".join(symbols))
-
-            if caseless:
-                # add parse action to return symbols as specified, not in random
-                # casing as found in input string
-                symbol_map = {sym.lower(): sym for sym in symbols}
-                ret.add_parse_action(lambda s, l, t: symbol_map[t[0].lower()])
-
-            return ret
-
-        except re.error:
-            warnings.warn(
-                "Exception creating Regex for one_of, building MatchFirst", stacklevel=2
-            )
-
-    # last resort, just use MatchFirst
-    return MatchFirst(parseElementClass(sym) for sym in symbols).set_name(
-        " | ".join(symbols)
-    )
-
-
-def dict_of(key: ParserElement, value: ParserElement) -> ParserElement:
-    """Helper to easily and clearly define a dictionary by specifying
-    the respective patterns for the key and value.  Takes care of
-    defining the :class:`Dict`, :class:`ZeroOrMore`, and
-    :class:`Group` tokens in the proper order.  The key pattern
-    can include delimiting markers or punctuation, as long as they are
-    suppressed, thereby leaving the significant key text.  The value
-    pattern can include named results, so that the :class:`Dict` results
-    can include named token fields.
-
-    Example::
-
-        text = "shape: SQUARE posn: upper left color: light blue texture: burlap"
-        attr_expr = (label + Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join))
-        print(attr_expr[1, ...].parse_string(text).dump())
-
-        attr_label = label
-        attr_value = Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join)
-
-        # similar to Dict, but simpler call format
-        result = dict_of(attr_label, attr_value).parse_string(text)
-        print(result.dump())
-        print(result['shape'])
-        print(result.shape)  # object attribute access works too
-        print(result.as_dict())
-
-    prints::
-
-        [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'light blue'], ['texture', 'burlap']]
-        - color: 'light blue'
-        - posn: 'upper left'
-        - shape: 'SQUARE'
-        - texture: 'burlap'
-        SQUARE
-        SQUARE
-        {'color': 'light blue', 'shape': 'SQUARE', 'posn': 'upper left', 'texture': 'burlap'}
-    """
-    return Dict(OneOrMore(Group(key + value)))
-
-
-def original_text_for(
-    expr: ParserElement, as_string: bool = True, *, asString: bool = True
-) -> ParserElement:
-    """Helper to return the original, untokenized text for a given
-    expression.  Useful to restore the parsed fields of an HTML start
-    tag into the raw tag text itself, or to revert separate tokens with
-    intervening whitespace back to the original matching input text. By
-    default, returns astring containing the original parsed text.
-
-    If the optional ``as_string`` argument is passed as
-    ``False``, then the return value is
-    a :class:`ParseResults` containing any results names that
-    were originally matched, and a single token containing the original
-    matched text from the input string.  So if the expression passed to
-    :class:`original_text_for` contains expressions with defined
-    results names, you must set ``as_string`` to ``False`` if you
-    want to preserve those results name values.
-
-    The ``asString`` pre-PEP8 argument is retained for compatibility,
-    but will be removed in a future release.
-
-    Example::
-
-        src = "this is test <b> bold <i>text</i> </b> normal text "
-        for tag in ("b", "i"):
-            opener, closer = make_html_tags(tag)
-            patt = original_text_for(opener + SkipTo(closer) + closer)
-            print(patt.search_string(src)[0])
-
-    prints::
-
-        ['<b> bold <i>text</i> </b>']
-        ['<i>text</i>']
-    """
-    asString = asString and as_string
-
-    locMarker = Empty().set_parse_action(lambda s, loc, t: loc)
-    endlocMarker = locMarker.copy()
-    endlocMarker.callPreparse = False
-    matchExpr = locMarker("_original_start") + expr + endlocMarker("_original_end")
-    if asString:
-        extractText = lambda s, l, t: s[t._original_start : t._original_end]
-    else:
-
-        def extractText(s, l, t):
-            t[:] = [s[t.pop("_original_start") : t.pop("_original_end")]]
-
-    matchExpr.set_parse_action(extractText)
-    matchExpr.ignoreExprs = expr.ignoreExprs
-    matchExpr.suppress_warning(Diagnostics.warn_ungrouped_named_tokens_in_collection)
-    return matchExpr
-
-
-def ungroup(expr: ParserElement) -> ParserElement:
-    """Helper to undo pyparsing's default grouping of And expressions,
-    even if all but one are non-empty.
-    """
-    return TokenConverter(expr).add_parse_action(lambda t: t[0])
-
-
-def locatedExpr(expr: ParserElement) -> ParserElement:
-    """
-    (DEPRECATED - future code should use the Located class)
-    Helper to decorate a returned token with its starting and ending
-    locations in the input string.
-
-    This helper adds the following results names:
-
-    - ``locn_start`` - location where matched expression begins
-    - ``locn_end`` - location where matched expression ends
-    - ``value`` - the actual parsed results
-
-    Be careful if the input text contains ``<TAB>`` characters, you
-    may want to call :class:`ParserElement.parseWithTabs`
-
-    Example::
-
-        wd = Word(alphas)
-        for match in locatedExpr(wd).searchString("ljsdf123lksdjjf123lkkjj1222"):
-            print(match)
-
-    prints::
-
-        [[0, 'ljsdf', 5]]
-        [[8, 'lksdjjf', 15]]
-        [[18, 'lkkjj', 23]]
-    """
-    locator = Empty().set_parse_action(lambda ss, ll, tt: ll)
-    return Group(
-        locator("locn_start")
-        + expr("value")
-        + locator.copy().leaveWhitespace()("locn_end")
-    )
-
-
-def nested_expr(
-    opener: Union[str, ParserElement] = "(",
-    closer: Union[str, ParserElement] = ")",
-    content: typing.Optional[ParserElement] = None,
-    ignore_expr: ParserElement = quoted_string(),
-    *,
-    ignoreExpr: ParserElement = quoted_string(),
-) -> ParserElement:
-    """Helper method for defining nested lists enclosed in opening and
-    closing delimiters (``"("`` and ``")"`` are the default).
-
-    Parameters:
-    - ``opener`` - opening character for a nested list
-      (default= ``"("``); can also be a pyparsing expression
-    - ``closer`` - closing character for a nested list
-      (default= ``")"``); can also be a pyparsing expression
-    - ``content`` - expression for items within the nested lists
-      (default= ``None``)
-    - ``ignore_expr`` - expression for ignoring opening and closing delimiters
-      (default= :class:`quoted_string`)
-    - ``ignoreExpr`` - this pre-PEP8 argument is retained for compatibility
-      but will be removed in a future release
-
-    If an expression is not provided for the content argument, the
-    nested expression will capture all whitespace-delimited content
-    between delimiters as a list of separate values.
-
-    Use the ``ignore_expr`` argument to define expressions that may
-    contain opening or closing characters that should not be treated as
-    opening or closing characters for nesting, such as quoted_string or
-    a comment expression.  Specify multiple expressions using an
-    :class:`Or` or :class:`MatchFirst`. The default is
-    :class:`quoted_string`, but if no expressions are to be ignored, then
-    pass ``None`` for this argument.
-
-    Example::
-
-        data_type = one_of("void int short long char float double")
-        decl_data_type = Combine(data_type + Opt(Word('*')))
-        ident = Word(alphas+'_', alphanums+'_')
-        number = pyparsing_common.number
-        arg = Group(decl_data_type + ident)
-        LPAR, RPAR = map(Suppress, "()")
-
-        code_body = nested_expr('{', '}', ignore_expr=(quoted_string | c_style_comment))
-
-        c_function = (decl_data_type("type")
-                      + ident("name")
-                      + LPAR + Opt(delimited_list(arg), [])("args") + RPAR
-                      + code_body("body"))
-        c_function.ignore(c_style_comment)
-
-        source_code = '''
-            int is_odd(int x) {
-                return (x%2);
-            }
-
-            int dec_to_hex(char hchar) {
-                if (hchar >= '0' && hchar <= '9') {
-                    return (ord(hchar)-ord('0'));
-                } else {
-                    return (10+ord(hchar)-ord('A'));
-                }
-            }
-        '''
-        for func in c_function.search_string(source_code):
-            print("%(name)s (%(type)s) args: %(args)s" % func)
-
-
-    prints::
-
-        is_odd (int) args: [['int', 'x']]
-        dec_to_hex (int) args: [['char', 'hchar']]
-    """
-    if ignoreExpr != ignore_expr:
-        ignoreExpr = ignore_expr if ignoreExpr == quoted_string() else ignoreExpr
-    if opener == closer:
-        raise ValueError("opening and closing strings cannot be the same")
-    if content is None:
-        if isinstance(opener, str_type) and isinstance(closer, str_type):
-            if len(opener) == 1 and len(closer) == 1:
-                if ignoreExpr is not None:
-                    content = Combine(
-                        OneOrMore(
-                            ~ignoreExpr
-                            + CharsNotIn(
-                                opener + closer + ParserElement.DEFAULT_WHITE_CHARS,
-                                exact=1,
-                            )
-                        )
-                    ).set_parse_action(lambda t: t[0].strip())
-                else:
-                    content = empty.copy() + CharsNotIn(
-                        opener + closer + ParserElement.DEFAULT_WHITE_CHARS
-                    ).set_parse_action(lambda t: t[0].strip())
-            else:
-                if ignoreExpr is not None:
-                    content = Combine(
-                        OneOrMore(
-                            ~ignoreExpr
-                            + ~Literal(opener)
-                            + ~Literal(closer)
-                            + CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS, exact=1)
-                        )
-                    ).set_parse_action(lambda t: t[0].strip())
-                else:
-                    content = Combine(
-                        OneOrMore(
-                            ~Literal(opener)
-                            + ~Literal(closer)
-                            + CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS, exact=1)
-                        )
-                    ).set_parse_action(lambda t: t[0].strip())
-        else:
-            raise ValueError(
-                "opening and closing arguments must be strings if no content expression is given"
-            )
-    ret = Forward()
-    if ignoreExpr is not None:
-        ret <<= Group(
-            Suppress(opener) + ZeroOrMore(ignoreExpr | ret | content) + Suppress(closer)
-        )
-    else:
-        ret <<= Group(Suppress(opener) + ZeroOrMore(ret | content) + Suppress(closer))
-    ret.set_name("nested %s%s expression" % (opener, closer))
-    return ret
-
-
-def _makeTags(tagStr, xml, suppress_LT=Suppress("<"), suppress_GT=Suppress(">")):
-    """Internal helper to construct opening and closing tag expressions, given a tag name"""
-    if isinstance(tagStr, str_type):
-        resname = tagStr
-        tagStr = Keyword(tagStr, caseless=not xml)
-    else:
-        resname = tagStr.name
-
-    tagAttrName = Word(alphas, alphanums + "_-:")
-    if xml:
-        tagAttrValue = dbl_quoted_string.copy().set_parse_action(remove_quotes)
-        openTag = (
-            suppress_LT
-            + tagStr("tag")
-            + Dict(ZeroOrMore(Group(tagAttrName + Suppress("=") + tagAttrValue)))
-            + Opt("/", default=[False])("empty").set_parse_action(
-                lambda s, l, t: t[0] == "/"
-            )
-            + suppress_GT
-        )
-    else:
-        tagAttrValue = quoted_string.copy().set_parse_action(remove_quotes) | Word(
-            printables, exclude_chars=">"
-        )
-        openTag = (
-            suppress_LT
-            + tagStr("tag")
-            + Dict(
-                ZeroOrMore(
-                    Group(
-                        tagAttrName.set_parse_action(lambda t: t[0].lower())
-                        + Opt(Suppress("=") + tagAttrValue)
-                    )
-                )
-            )
-            + Opt("/", default=[False])("empty").set_parse_action(
-                lambda s, l, t: t[0] == "/"
-            )
-            + suppress_GT
-        )
-    closeTag = Combine(Literal("</") + tagStr + ">", adjacent=False)
-
-    openTag.set_name("<%s>" % resname)
-    # add start<tagname> results name in parse action now that ungrouped names are not reported at two levels
-    openTag.add_parse_action(
-        lambda t: t.__setitem__(
-            "start" + "".join(resname.replace(":", " ").title().split()), t.copy()
-        )
-    )
-    closeTag = closeTag(
-        "end" + "".join(resname.replace(":", " ").title().split())
-    ).set_name("</%s>" % resname)
-    openTag.tag = resname
-    closeTag.tag = resname
-    openTag.tag_body = SkipTo(closeTag())
-    return openTag, closeTag
-
-
-def make_html_tags(
-    tag_str: Union[str, ParserElement]
-) -> Tuple[ParserElement, ParserElement]:
-    """Helper to construct opening and closing tag expressions for HTML,
-    given a tag name. Matches tags in either upper or lower case,
-    attributes with namespaces and with quoted or unquoted values.
-
-    Example::
-
-        text = '<td>More info at the <a href="https://github.com/pyparsing/pyparsing/wiki">pyparsing</a> wiki page</td>'
-        # make_html_tags returns pyparsing expressions for the opening and
-        # closing tags as a 2-tuple
-        a, a_end = make_html_tags("A")
-        link_expr = a + SkipTo(a_end)("link_text") + a_end
-
-        for link in link_expr.search_string(text):
-            # attributes in the <A> tag (like "href" shown here) are
-            # also accessible as named results
-            print(link.link_text, '->', link.href)
-
-    prints::
-
-        pyparsing -> https://github.com/pyparsing/pyparsing/wiki
-    """
-    return _makeTags(tag_str, False)
-
-
-def make_xml_tags(
-    tag_str: Union[str, ParserElement]
-) -> Tuple[ParserElement, ParserElement]:
-    """Helper to construct opening and closing tag expressions for XML,
-    given a tag name. Matches tags only in the given upper/lower case.
-
-    Example: similar to :class:`make_html_tags`
-    """
-    return _makeTags(tag_str, True)
-
-
-any_open_tag: ParserElement
-any_close_tag: ParserElement
-any_open_tag, any_close_tag = make_html_tags(
-    Word(alphas, alphanums + "_:").set_name("any tag")
-)
-
-_htmlEntityMap = {k.rstrip(";"): v for k, v in html.entities.html5.items()}
-common_html_entity = Regex("&(?P<entity>" + "|".join(_htmlEntityMap) + ");").set_name(
-    "common HTML entity"
-)
-
-
-def replace_html_entity(t):
-    """Helper parser action to replace common HTML entities with their special characters"""
-    return _htmlEntityMap.get(t.entity)
-
-
-class OpAssoc(Enum):
-    LEFT = 1
-    RIGHT = 2
-
-
-InfixNotationOperatorArgType = Union[
-    ParserElement, str, Tuple[Union[ParserElement, str], Union[ParserElement, str]]
-]
-InfixNotationOperatorSpec = Union[
-    Tuple[
-        InfixNotationOperatorArgType,
-        int,
-        OpAssoc,
-        typing.Optional[ParseAction],
-    ],
-    Tuple[
-        InfixNotationOperatorArgType,
-        int,
-        OpAssoc,
-    ],
-]
-
-
-def infix_notation(
-    base_expr: ParserElement,
-    op_list: List[InfixNotationOperatorSpec],
-    lpar: Union[str, ParserElement] = Suppress("("),
-    rpar: Union[str, ParserElement] = Suppress(")"),
-) -> ParserElement:
-    """Helper method for constructing grammars of expressions made up of
-    operators working in a precedence hierarchy.  Operators may be unary
-    or binary, left- or right-associative.  Parse actions can also be
-    attached to operator expressions. The generated parser will also
-    recognize the use of parentheses to override operator precedences
-    (see example below).
-
-    Note: if you define a deep operator list, you may see performance
-    issues when using infix_notation. See
-    :class:`ParserElement.enable_packrat` for a mechanism to potentially
-    improve your parser performance.
-
-    Parameters:
-    - ``base_expr`` - expression representing the most basic operand to
-      be used in the expression
-    - ``op_list`` - list of tuples, one for each operator precedence level
-      in the expression grammar; each tuple is of the form ``(op_expr,
-      num_operands, right_left_assoc, (optional)parse_action)``, where:
-
-      - ``op_expr`` is the pyparsing expression for the operator; may also
-        be a string, which will be converted to a Literal; if ``num_operands``
-        is 3, ``op_expr`` is a tuple of two expressions, for the two
-        operators separating the 3 terms
-      - ``num_operands`` is the number of terms for this operator (must be 1,
-        2, or 3)
-      - ``right_left_assoc`` is the indicator whether the operator is right
-        or left associative, using the pyparsing-defined constants
-        ``OpAssoc.RIGHT`` and ``OpAssoc.LEFT``.
-      - ``parse_action`` is the parse action to be associated with
-        expressions matching this operator expression (the parse action
-        tuple member may be omitted); if the parse action is passed
-        a tuple or list of functions, this is equivalent to calling
-        ``set_parse_action(*fn)``
-        (:class:`ParserElement.set_parse_action`)
-    - ``lpar`` - expression for matching left-parentheses; if passed as a
-      str, then will be parsed as Suppress(lpar). If lpar is passed as
-      an expression (such as ``Literal('(')``), then it will be kept in
-      the parsed results, and grouped with them. (default= ``Suppress('(')``)
-    - ``rpar`` - expression for matching right-parentheses; if passed as a
-      str, then will be parsed as Suppress(rpar). If rpar is passed as
-      an expression (such as ``Literal(')')``), then it will be kept in
-      the parsed results, and grouped with them. (default= ``Suppress(')')``)
-
-    Example::
-
-        # simple example of four-function arithmetic with ints and
-        # variable names
-        integer = pyparsing_common.signed_integer
-        varname = pyparsing_common.identifier
-
-        arith_expr = infix_notation(integer | varname,
-            [
-            ('-', 1, OpAssoc.RIGHT),
-            (one_of('* /'), 2, OpAssoc.LEFT),
-            (one_of('+ -'), 2, OpAssoc.LEFT),
-            ])
-
-        arith_expr.run_tests('''
-            5+3*6
-            (5+3)*6
-            -2--11
-            ''', full_dump=False)
-
-    prints::
-
-        5+3*6
-        [[5, '+', [3, '*', 6]]]
-
-        (5+3)*6
-        [[[5, '+', 3], '*', 6]]
-
-        -2--11
-        [[['-', 2], '-', ['-', 11]]]
-    """
-    # captive version of FollowedBy that does not do parse actions or capture results names
-    class _FB(FollowedBy):
-        def parseImpl(self, instring, loc, doActions=True):
-            self.expr.try_parse(instring, loc)
-            return loc, []
-
-    _FB.__name__ = "FollowedBy>"
-
-    ret = Forward()
-    if isinstance(lpar, str):
-        lpar = Suppress(lpar)
-    if isinstance(rpar, str):
-        rpar = Suppress(rpar)
-
-    # if lpar and rpar are not suppressed, wrap in group
-    if not (isinstance(rpar, Suppress) and isinstance(rpar, Suppress)):
-        lastExpr = base_expr | Group(lpar + ret + rpar)
-    else:
-        lastExpr = base_expr | (lpar + ret + rpar)
-
-    for i, operDef in enumerate(op_list):
-        opExpr, arity, rightLeftAssoc, pa = (operDef + (None,))[:4]
-        if isinstance(opExpr, str_type):
-            opExpr = ParserElement._literalStringClass(opExpr)
-        if arity == 3:
-            if not isinstance(opExpr, (tuple, list)) or len(opExpr) != 2:
-                raise ValueError(
-                    "if numterms=3, opExpr must be a tuple or list of two expressions"
-                )
-            opExpr1, opExpr2 = opExpr
-            term_name = "{}{} term".format(opExpr1, opExpr2)
-        else:
-            term_name = "{} term".format(opExpr)
-
-        if not 1 <= arity <= 3:
-            raise ValueError("operator must be unary (1), binary (2), or ternary (3)")
-
-        if rightLeftAssoc not in (OpAssoc.LEFT, OpAssoc.RIGHT):
-            raise ValueError("operator must indicate right or left associativity")
-
-        thisExpr: Forward = Forward().set_name(term_name)
-        if rightLeftAssoc is OpAssoc.LEFT:
-            if arity == 1:
-                matchExpr = _FB(lastExpr + opExpr) + Group(lastExpr + opExpr[1, ...])
-            elif arity == 2:
-                if opExpr is not None:
-                    matchExpr = _FB(lastExpr + opExpr + lastExpr) + Group(
-                        lastExpr + (opExpr + lastExpr)[1, ...]
-                    )
-                else:
-                    matchExpr = _FB(lastExpr + lastExpr) + Group(lastExpr[2, ...])
-            elif arity == 3:
-                matchExpr = _FB(
-                    lastExpr + opExpr1 + lastExpr + opExpr2 + lastExpr
-                ) + Group(lastExpr + OneOrMore(opExpr1 + lastExpr + opExpr2 + lastExpr))
-        elif rightLeftAssoc is OpAssoc.RIGHT:
-            if arity == 1:
-                # try to avoid LR with this extra test
-                if not isinstance(opExpr, Opt):
-                    opExpr = Opt(opExpr)
-                matchExpr = _FB(opExpr.expr + thisExpr) + Group(opExpr + thisExpr)
-            elif arity == 2:
-                if opExpr is not None:
-                    matchExpr = _FB(lastExpr + opExpr + thisExpr) + Group(
-                        lastExpr + (opExpr + thisExpr)[1, ...]
-                    )
-                else:
-                    matchExpr = _FB(lastExpr + thisExpr) + Group(
-                        lastExpr + thisExpr[1, ...]
-                    )
-            elif arity == 3:
-                matchExpr = _FB(
-                    lastExpr + opExpr1 + thisExpr + opExpr2 + thisExpr
-                ) + Group(lastExpr + opExpr1 + thisExpr + opExpr2 + thisExpr)
-        if pa:
-            if isinstance(pa, (tuple, list)):
-                matchExpr.set_parse_action(*pa)
-            else:
-                matchExpr.set_parse_action(pa)
-        thisExpr <<= (matchExpr | lastExpr).setName(term_name)
-        lastExpr = thisExpr
-    ret <<= lastExpr
-    return ret
-
-
-def indentedBlock(blockStatementExpr, indentStack, indent=True, backup_stacks=[]):
-    """
-    (DEPRECATED - use IndentedBlock class instead)
-    Helper method for defining space-delimited indentation blocks,
-    such as those used to define block statements in Python source code.
-
-    Parameters:
-
-    - ``blockStatementExpr`` - expression defining syntax of statement that
-      is repeated within the indented block
-    - ``indentStack`` - list created by caller to manage indentation stack
-      (multiple ``statementWithIndentedBlock`` expressions within a single
-      grammar should share a common ``indentStack``)
-    - ``indent`` - boolean indicating whether block must be indented beyond
-      the current level; set to ``False`` for block of left-most statements
-      (default= ``True``)
-
-    A valid block must contain at least one ``blockStatement``.
-
-    (Note that indentedBlock uses internal parse actions which make it
-    incompatible with packrat parsing.)
-
-    Example::
-
-        data = '''
-        def A(z):
-          A1
-          B = 100
-          G = A2
-          A2
-          A3
-        B
-        def BB(a,b,c):
-          BB1
-          def BBA():
-            bba1
-            bba2
-            bba3
-        C
-        D
-        def spam(x,y):
-             def eggs(z):
-                 pass
-        '''
-
-
-        indentStack = [1]
-        stmt = Forward()
-
-        identifier = Word(alphas, alphanums)
-        funcDecl = ("def" + identifier + Group("(" + Opt(delimitedList(identifier)) + ")") + ":")
-        func_body = indentedBlock(stmt, indentStack)
-        funcDef = Group(funcDecl + func_body)
-
-        rvalue = Forward()
-        funcCall = Group(identifier + "(" + Opt(delimitedList(rvalue)) + ")")
-        rvalue << (funcCall | identifier | Word(nums))
-        assignment = Group(identifier + "=" + rvalue)
-        stmt << (funcDef | assignment | identifier)
-
-        module_body = stmt[1, ...]
-
-        parseTree = module_body.parseString(data)
-        parseTree.pprint()
-
-    prints::
-
-        [['def',
-          'A',
-          ['(', 'z', ')'],
-          ':',
-          [['A1'], [['B', '=', '100']], [['G', '=', 'A2']], ['A2'], ['A3']]],
-         'B',
-         ['def',
-          'BB',
-          ['(', 'a', 'b', 'c', ')'],
-          ':',
-          [['BB1'], [['def', 'BBA', ['(', ')'], ':', [['bba1'], ['bba2'], ['bba3']]]]]],
-         'C',
-         'D',
-         ['def',
-          'spam',
-          ['(', 'x', 'y', ')'],
-          ':',
-          [[['def', 'eggs', ['(', 'z', ')'], ':', [['pass']]]]]]]
-    """
-    backup_stacks.append(indentStack[:])
-
-    def reset_stack():
-        indentStack[:] = backup_stacks[-1]
-
-    def checkPeerIndent(s, l, t):
-        if l >= len(s):
-            return
-        curCol = col(l, s)
-        if curCol != indentStack[-1]:
-            if curCol > indentStack[-1]:
-                raise ParseException(s, l, "illegal nesting")
-            raise ParseException(s, l, "not a peer entry")
-
-    def checkSubIndent(s, l, t):
-        curCol = col(l, s)
-        if curCol > indentStack[-1]:
-            indentStack.append(curCol)
-        else:
-            raise ParseException(s, l, "not a subentry")
-
-    def checkUnindent(s, l, t):
-        if l >= len(s):
-            return
-        curCol = col(l, s)
-        if not (indentStack and curCol in indentStack):
-            raise ParseException(s, l, "not an unindent")
-        if curCol < indentStack[-1]:
-            indentStack.pop()
-
-    NL = OneOrMore(LineEnd().set_whitespace_chars("\t ").suppress())
-    INDENT = (Empty() + Empty().set_parse_action(checkSubIndent)).set_name("INDENT")
-    PEER = Empty().set_parse_action(checkPeerIndent).set_name("")
-    UNDENT = Empty().set_parse_action(checkUnindent).set_name("UNINDENT")
-    if indent:
-        smExpr = Group(
-            Opt(NL)
-            + INDENT
-            + OneOrMore(PEER + Group(blockStatementExpr) + Opt(NL))
-            + UNDENT
-        )
-    else:
-        smExpr = Group(
-            Opt(NL)
-            + OneOrMore(PEER + Group(blockStatementExpr) + Opt(NL))
-            + Opt(UNDENT)
-        )
-
-    # add a parse action to remove backup_stack from list of backups
-    smExpr.add_parse_action(
-        lambda: backup_stacks.pop(-1) and None if backup_stacks else None
-    )
-    smExpr.set_fail_action(lambda a, b, c, d: reset_stack())
-    blockStatementExpr.ignore(_bslash + LineEnd())
-    return smExpr.set_name("indented block")
-
-
-# it's easy to get these comment structures wrong - they're very common, so may as well make them available
-c_style_comment = Combine(Regex(r"/\*(?:[^*]|\*(?!/))*") + "*/").set_name(
-    "C style comment"
-)
-"Comment of the form ``/* ... */``"
-
-html_comment = Regex(r"<!--[\s\S]*?-->").set_name("HTML comment")
-"Comment of the form ``<!-- ... -->``"
-
-rest_of_line = Regex(r".*").leave_whitespace().set_name("rest of line")
-dbl_slash_comment = Regex(r"//(?:\\\n|[^\n])*").set_name("// comment")
-"Comment of the form ``// ... (to end of line)``"
-
-cpp_style_comment = Combine(
-    Regex(r"/\*(?:[^*]|\*(?!/))*") + "*/" | dbl_slash_comment
-).set_name("C++ style comment")
-"Comment of either form :class:`c_style_comment` or :class:`dbl_slash_comment`"
-
-java_style_comment = cpp_style_comment
-"Same as :class:`cpp_style_comment`"
-
-python_style_comment = Regex(r"#.*").set_name("Python style comment")
-"Comment of the form ``# ... (to end of line)``"
-
-
-# build list of built-in expressions, for future reference if a global default value
-# gets updated
-_builtin_exprs: List[ParserElement] = [
-    v for v in vars().values() if isinstance(v, ParserElement)
-]
-
-
-# pre-PEP8 compatible names
-delimitedList = delimited_list
-countedArray = counted_array
-matchPreviousLiteral = match_previous_literal
-matchPreviousExpr = match_previous_expr
-oneOf = one_of
-dictOf = dict_of
-originalTextFor = original_text_for
-nestedExpr = nested_expr
-makeHTMLTags = make_html_tags
-makeXMLTags = make_xml_tags
-anyOpenTag, anyCloseTag = any_open_tag, any_close_tag
-commonHTMLEntity = common_html_entity
-replaceHTMLEntity = replace_html_entity
-opAssoc = OpAssoc
-infixNotation = infix_notation
-cStyleComment = c_style_comment
-htmlComment = html_comment
-restOfLine = rest_of_line
-dblSlashComment = dbl_slash_comment
-cppStyleComment = cpp_style_comment
-javaStyleComment = java_style_comment
-pythonStyleComment = python_style_comment
diff --git a/spaces/tobiascz/demotime/pytorch_grad_cam/utils/model_targets.py b/spaces/tobiascz/demotime/pytorch_grad_cam/utils/model_targets.py
deleted file mode 100644
index 260e21ca10f7b652a759aae44516c0f94b25d72e..0000000000000000000000000000000000000000
--- a/spaces/tobiascz/demotime/pytorch_grad_cam/utils/model_targets.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import numpy as np
-import torch
-import torchvision
-
-class ClassifierOutputTarget:
-    def __init__(self, category):
-        self.category = category
-    def __call__(self, model_output):
-        if len(model_output.shape) == 1:
-            return model_output[self.category]
-        return model_output[:, self.category]
-
-class SemanticSegmentationTarget:
-    """ Gets a binary spatial mask and a category,
-        And return the sum of the category scores,
-        of the pixels in the mask. """
-    def __init__(self, category, mask):
-        self.category = category
-        self.mask = torch.from_numpy(mask)
-        if torch.cuda.is_available():
-            self.mask = self.mask.cuda()
-        
-    def __call__(self, model_output):
-        return (model_output[self.category, :, : ] * self.mask).sum()
-
-
-class FasterRCNNBoxScoreTarget:
-    """ For every original detected bounding box specified in "bounding boxes",
-        assign a score on how the current bounding boxes match it,
-            1. In IOU
-            2. In the classification score.
-        If there is not a large enough overlap, or the category changed,
-        assign a score of 0.
-
-        The total score is the sum of all the box scores.
-    """
-
-    def __init__(self, labels, bounding_boxes, iou_threshold=0.5):
-        self.labels = labels
-        self.bounding_boxes = bounding_boxes
-        self.iou_threshold = iou_threshold
-
-    def __call__(self, model_outputs):
-        output = torch.Tensor([0])
-        if torch.cuda.is_available():
-            output = output.cuda()
-
-        if len(model_outputs["boxes"]) == 0:
-            return output
-
-        for box, label in zip(self.bounding_boxes, self.labels):
-            box = torch.Tensor(box[None, :])
-            if torch.cuda.is_available():
-                box = box.cuda()
-
-            ious = torchvision.ops.box_iou(box, model_outputs["boxes"])
-            index = ious.argmax()
-            if ious[0, index] > self.iou_threshold and model_outputs["labels"][index] == label:
-                score = ious[0, index] + model_outputs["scores"][index]
-                output = output + score
-        return output
\ No newline at end of file
diff --git a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/csrc/vision.cpp b/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/csrc/vision.cpp
deleted file mode 100644
index 30971995d4aafa3bbd4f1cb00138098c139163f6..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/csrc/vision.cpp
+++ /dev/null
@@ -1,25 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-#include "nms.h"
-#include "ROIAlign.h"
-#include "ROIPool.h"
-#include "SigmoidFocalLoss.h"
-#include "deform_conv.h"
-#include "deform_pool.h"
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("nms", &nms, "non-maximum suppression");
-  m.def("roi_align_forward", &ROIAlign_forward, "ROIAlign_forward");
-  m.def("roi_align_backward", &ROIAlign_backward, "ROIAlign_backward");
-  m.def("roi_pool_forward", &ROIPool_forward, "ROIPool_forward");
-  m.def("roi_pool_backward", &ROIPool_backward, "ROIPool_backward");
-  m.def("sigmoid_focalloss_forward", &SigmoidFocalLoss_forward, "SigmoidFocalLoss_forward");
-  m.def("sigmoid_focalloss_backward", &SigmoidFocalLoss_backward, "SigmoidFocalLoss_backward");
-  // dcn-v2
-  m.def("deform_conv_forward", &deform_conv_forward, "deform_conv_forward");
-  m.def("deform_conv_backward_input", &deform_conv_backward_input, "deform_conv_backward_input");
-  m.def("deform_conv_backward_parameters", &deform_conv_backward_parameters, "deform_conv_backward_parameters");
-  m.def("modulated_deform_conv_forward", &modulated_deform_conv_forward, "modulated_deform_conv_forward");
-  m.def("modulated_deform_conv_backward", &modulated_deform_conv_backward, "modulated_deform_conv_backward");
-  m.def("deform_psroi_pooling_forward", &deform_psroi_pooling_forward, "deform_psroi_pooling_forward");
-  m.def("deform_psroi_pooling_backward", &deform_psroi_pooling_backward, "deform_psroi_pooling_backward");
-}
\ No newline at end of file
diff --git a/spaces/tonyassi/video-face-swap/DeepFakeAI/processors/frame/modules/frame_enhancer.py b/spaces/tonyassi/video-face-swap/DeepFakeAI/processors/frame/modules/frame_enhancer.py
deleted file mode 100644
index c8df474272e4b58488720aac6eb46c6327cdcc32..0000000000000000000000000000000000000000
--- a/spaces/tonyassi/video-face-swap/DeepFakeAI/processors/frame/modules/frame_enhancer.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from typing import Any, List, Callable
-import cv2
-import threading
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from realesrgan import RealESRGANer
-
-import DeepFakeAI.processors.frame.core as frame_processors
-from DeepFakeAI.typing import Frame, Face
-from DeepFakeAI.utilities import conditional_download, resolve_relative_path
-
-FRAME_PROCESSOR = None
-THREAD_SEMAPHORE = threading.Semaphore()
-THREAD_LOCK = threading.Lock()
-NAME = 'FACEFUSION.FRAME_PROCESSOR.FRAME_ENHANCER'
-
-
-def get_frame_processor() -> Any:
-	global FRAME_PROCESSOR
-
-	with THREAD_LOCK:
-		if FRAME_PROCESSOR is None:
-			model_path = resolve_relative_path('../.assets/models/RealESRGAN_x4plus.pth')
-			FRAME_PROCESSOR = RealESRGANer(
-				model_path = model_path,
-				model = RRDBNet(
-					num_in_ch = 3,
-					num_out_ch = 3,
-					num_feat = 64,
-					num_block = 23,
-					num_grow_ch = 32,
-					scale = 4
-				),
-				device = frame_processors.get_device(),
-				tile = 512,
-				tile_pad = 32,
-				pre_pad = 0,
-				scale = 4
-			)
-	return FRAME_PROCESSOR
-
-
-def clear_frame_processor() -> None:
-	global FRAME_PROCESSOR
-
-	FRAME_PROCESSOR = None
-
-
-def pre_check() -> bool:
-	download_directory_path = resolve_relative_path('../.assets/models')
-	conditional_download(download_directory_path, ['https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/RealESRGAN_x4plus.pth'])
-	return True
-
-
-def pre_process() -> bool:
-	return True
-
-
-def post_process() -> None:
-	clear_frame_processor()
-
-
-def enhance_frame(temp_frame : Frame) -> Frame:
-	with THREAD_SEMAPHORE:
-		temp_frame, _ = get_frame_processor().enhance(temp_frame, outscale = 1)
-	return temp_frame
-
-
-def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame:
-	return enhance_frame(temp_frame)
-
-
-def process_frames(source_path : str, temp_frame_paths : List[str], update: Callable[[], None]) -> None:
-	for temp_frame_path in temp_frame_paths:
-		temp_frame = cv2.imread(temp_frame_path)
-		result_frame = process_frame(None, None, temp_frame)
-		cv2.imwrite(temp_frame_path, result_frame)
-		if update:
-			update()
-
-
-def process_image(source_path : str, target_path : str, output_path : str) -> None:
-	target_frame = cv2.imread(target_path)
-	result = process_frame(None, None, target_frame)
-	cv2.imwrite(output_path, result)
-
-
-def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
-	frame_processors.process_video(None, temp_frame_paths, process_frames)
diff --git a/spaces/ttt246/brain/Extension/src/pages/Newtab/Newtab.css b/spaces/ttt246/brain/Extension/src/pages/Newtab/Newtab.css
deleted file mode 100644
index 74b5e053450a48a6bdb4d71aad648e7af821975c..0000000000000000000000000000000000000000
--- a/spaces/ttt246/brain/Extension/src/pages/Newtab/Newtab.css
+++ /dev/null
@@ -1,38 +0,0 @@
-.App {
-  text-align: center;
-}
-
-.App-logo {
-  height: 40vmin;
-  pointer-events: none;
-}
-
-@media (prefers-reduced-motion: no-preference) {
-  .App-logo {
-    animation: App-logo-spin infinite 20s linear;
-  }
-}
-
-.App-header {
-  background-color: #282c34;
-  min-height: 100vh;
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-  justify-content: center;
-  font-size: calc(10px + 2vmin);
-  color: white;
-}
-
-.App-link {
-  color: #61dafb;
-}
-
-@keyframes App-logo-spin {
-  from {
-    transform: rotate(0deg);
-  }
-  to {
-    transform: rotate(360deg);
-  }
-}
diff --git a/spaces/ulysses115/Nogizaka46-so/app.py b/spaces/ulysses115/Nogizaka46-so/app.py
deleted file mode 100644
index 5c38f010527aa985b47fdaec293e44f51af884ba..0000000000000000000000000000000000000000
--- a/spaces/ulysses115/Nogizaka46-so/app.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import io
-import os
-
-#os.system("wget -P hubert/ https://huggingface.co/spaces/Nogizaka46/Nogizaka46-so/resolve/main/hubert/checkpoint_best_legacy_500.pt")
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile
-from inference.infer_tool import Svc
-import logging
-import time
-logging.getLogger('numba').setLevel(logging.WARNING)
-logging.getLogger('markdown_it').setLevel(logging.WARNING)
-logging.getLogger('urllib3').setLevel(logging.WARNING)
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-model = Svc("logs/44k/@github-NGZ-sovits-4.pth", "configs/config-65.json", cluster_model_path="logs/44k/kmeans_10000.pt")
-#model = Svc("logs/44k/@github-NGZ-sovits-4.pth", "configs/config.json")
-
-from matplotlib import pyplot as plt
-
-def f0_to_pitch(ff):
-    f0_pitch = 69 + 12 * np.log2(ff / 160)
-    return f0_pitch
-def compute_f0(wav_file1, wav_file2,tran):
-    y1, sr1 = librosa.load(wav_file1, sr=16000)
-    y2, sr2 = librosa.load(wav_file2, sr=16000)
-
-    # Compute the f0 using the YIN pitch estimation method
-    f0_1 = librosa.core.yin(y1, fmin=70, fmax=600)
-    f0_2 = librosa.core.yin(y2, fmin=70, fmax=600)
-    # 半音偏差
-    sum_y = []
-    if np.sum(wav_file1 == 0) / len(wav_file1) > 0.9:
-        mistake, var_take = 0, 0
-    else:
-        for i in range(min(len(f0_1), len(f0_2))):
-            if f0_1[i] > 0 and f0_2[i] > 0:
-                sum_y.append(
-                    abs(f0_to_pitch(f0_2[i]) - (f0_to_pitch(f0_1[i]) + tran)))
-        num_y = 0
-        for x in sum_y:
-            num_y += x
-        len_y = len(sum_y) if len(sum_y) else 1
-        mistake = round(float(num_y / len_y), 2)
-        var_take = round(float(np.std(sum_y, ddof=1)), 2)
-    print("mistake", mistake, var_take)
-    return f0_1, f0_2, sr1, sr2, round(mistake / 10, 2), round(var_take / 10, 2)
-
-
-def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,F0_mean_pooling):
-    #cluster_ratio =0
-    start_time = time.time()
-    if input_audio is None:
-        return "You need to upload an audio", None
-    sampling_rate, audio = input_audio
-    duration = audio.shape[0] / sampling_rate
-    if duration > 70:
-        return "请上传小于70s的音频，需要转换长音频请本地进行转换", None	, None
-    audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-    if len(audio.shape) > 1:
-        audio = librosa.to_mono(audio.transpose(1, 0))
-    if sampling_rate != 16000:
-        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-    #print(audio.shape)
-
-    
-    out_wav_path = "temp.wav"
-    soundfile.write(out_wav_path, audio, 16000, format="wav")
-    
-    #print(slice_db, cluster_ratio, auto_f0, noise_scale, sid)
-    print(out_wav_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale, F0_mean_pooling)
-    _audio = model.slice_inference(out_wav_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale,F0_mean_pooling=F0_mean_pooling)
-
-    soundfile.write("output.wav", _audio, 44100, format="wav")
-    f01, f02, sr1, sr2 , mistake ,var  = compute_f0('temp.wav', 'output.wav',vc_transform)
-    time_step_1 = np.arange(0,len(f01) )
-    time_step_2 = np.arange(0,len(f02) )
-    plt.figure(figsize=[8, 3])
-    plt.plot(time_step_1  , f01, label='Input')
-    plt.plot(time_step_2 , f02, label='Output')
-
-    #plt.title("T0 of Input and Output")
-    #plt.ylabel("T0")
-    #plt.xlabel("Time step")
-
-    length = np.arange(0,int( duration*10) ,int( duration))
-    plt.xticks(np.linspace(0, len(f01),len(length)), length)
-    plt.legend()
-    plt.savefig('temp.svg')
-    plt.close()
-        
-    used_time = round(time.time() - start_time, 2)
-    out_str = ("Success! total use time:{}s\n半音偏差:{}\n半音方差:{}".format(
-        used_time, mistake, var)) 
-    return out_str , (44100, _audio), gr.Image.update("temp.svg")
-
-
-app = gr.Blocks()
-with app:
-    with gr.Tabs():
-        with gr.TabItem("Basic"):
-            gr.Markdown(value="""
-                    # 前言
-                    * 此模型为sovits4.0原版(抗混响强),如果音色不像可以试试另一个模型:[https://huggingface.co/spaces/Nogizaka46/Nogizaka46-so-dev](https://huggingface.co/spaces/Nogizaka46/Nogizaka46-so-dev)
-                    * 23-05-29修复池化功能，有bug记得反馈下。模型更新日期23-04-26.新模型使用65小时语音训练63位成员。仅供个人娱乐和非商业用途，禁止用于血腥、暴力、性相关、政治相关内容,转换长音频请本地进行转换
-                    * 扒干声教程:[BV1sb411o7xF](https://www.bilibili.com/video/BV1sb411o7xF) [cv23095265](https://www.bilibili.com/read/cv23095265) b站传播的Ai翻唱大多数是他人翻唱或原曲混响和声少的，不是所有歌都能扒干净的,如果声音不像都是因为混响与和声扒不干净，结合自己的时间学会放弃。更多相关教程,翻唱,本地整合包在Tg群:[t.me/+vP8NK1NMLiYzMDJl](https://t.me/+vP8NK1NMLiYzMDJl)
-                    * [Ripx,Au,UVR工具下载](https://pan.baidu.com/s/1Ne55iKqoacjKE-moK_YtGg?pwd=qsfd)  总有问制作流程，这说一下。。以冬之花为例，1.用UVR-4_HP-Vocal模型提取人声 或 vocalremover.org(这个网站处理不会损伤人声，方便二次处理，推荐)，UVR-5_HP-Karaoke去除和声，2.合成，对比干声听听有几处哑音 如果有,使用RipX去除干声里造成哑音的和声 4.合成再听听，再不行就使用池化 5.使用Au调音，按喜好，添加混响，和声，回声等，这步可以增强音色，效果是很明显的。通过冬之花的练习，你已经具备处理干声的能力，轻松一天量产10首。
-                    
-                    # 声明
-                    * 如用此模型制作音频请标注来源：github.com/3110asuka/Nogizaka46-so 或 huggingface.co/spaces/Nogizaka46/Nogizaka46-so""")
-            gr.Markdown(value="""秋元真夏 AKIMOTO_MANATSU| 生田絵梨花 IKUTA_ERIKA| 生駒里奈 IKOMA_RINA| 伊藤純奈 ITO_JUNNA| 井上小百合 INOUE_SAYURI| 衛藤美彩 ETO_MISA| 川後陽菜 KAWAGO_HINA|北野日奈子 KITANO_HINAKO|齋藤飛鳥 SAITO_ASUKA|斉藤優里 SATO_YUURI|相楽伊織 SAGARA_IORI|桜井玲香 SAKURAI_REIKA|佐々木琴子 SASAKI_KOTOKO|白石麻衣 SHIRAISHI_MAI|新内眞衣 SHINUCHI_MAI|鈴木絢音 SUZUKI_AYANE|高山一実 TAKAYAMA_KAZUMI|寺田蘭世 TERADA_RANZE|西野七瀬 NISHINO_NANASE|能條愛未 NOUJO_AMI|樋口日奈 HIGUCHI_HINA|星野みなみ HOSHINO_MINAMI|堀未央奈 HORI_MIONA|松村沙友理 MATSUMURA_SAYURI|山崎怜奈 YAMAZAKI_RENA|若月佑美 WAKATSUKI_YUMI|渡辺みり愛 WATANABE_MIRIA|和田まあや WADA_MAAYA|伊藤理々杏 ITO_RIRIA|岩本蓮加 IWAMOTO_RENKA|梅澤美波 UMEZAWA_MINAMI|大園桃子 OZONO_MOMOKO|久保史緒里 KUBO_SHIORI|阪口珠美 SAKAGUCHI_TAMAMI|佐藤楓 SATO_KAEDE|中村麗乃 NAKAMURA_RENO|向井葉月 MUKAI_HAZUKI|山下美月 YAMASHITA_MIZUKI|与田祐希 YODA_YUUKI|遠藤さくら ENDO_SAKURA|賀喜遥香 KAKI_HARUKA|掛橋沙耶香 KAKEHASHI_SAYAKA|金川紗耶 KANAGAWA_SAYA|北川悠理 KITAGAWA_YURI|柴田柚菜 SHIBATA_YUNA|田村真佑 TAMURA_MAYU|筒井あやめ TSUTSUI_AYAME|早川聖来 HAYAKAWA_SEIRA|矢久保美緒 YAKUBO_MIO|黒見明香 HARUKA_KUROMI|佐藤璃果 RIKA_SATO|林瑠奈 RUNA_HAYASHI|松尾美佑 MIYU_MATSUO|弓木奈於 NAO_YUMIKI|五百城茉央 IOKI_MAO|池田瑛紗 IKEDA_TERESA|一ノ瀬美空 ICHINOSE_MIKU|井上和 INOUE_NAGI|小川彩 OGAWA_AYA|奥田いろは OKUDA_IROHA|川﨑桜 KAWASAKI_SAKURA|菅原咲月 SUGAWARA_SATSUKI|冨里奈央 TOMISATO_NAO|中西アルノ NAKANISHI_ARUNO""")
-            spks = list(model.spk2id.keys())
-            sid = gr.Dropdown(label="音色", choices=spks, value="IKUTA_ERIKA")
-            vc_input3 = gr.Audio(label="上传音频<70s无BGM无混响的干声",value="没什么「你的名字。」干声素材12s.mp3")
-            #vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）一般填写6或-6之内", value=0)
-            vc_transform = gr.Slider(label="变调（整数，可以正负，半音数量，升高八度就是12）一般填写6或-6之内",
-                                              maximum=16, minimum=-16, step=1, value=0)
-            cluster_ratio = gr.Number(label="聚类模型混合比例，0-1之间，默认为0不启用聚类，能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0)
-            auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声不要勾选此项会究极跑调）", value=False)
-            slice_db = gr.Slider(label="切片阈值(较嘈杂时-30，保留呼吸声时-50，一般默认-40)",
-                                              maximum=-30, minimum=-70, step=1, value=-40)
-            noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4)
-            F0_mean_pooling = gr.Checkbox(label="是否对F0使用均值滤波器(池化)，对部分哑音有改善（和声混响造成的哑音无效）。注意，会导致推理速度下降，默认关闭", value=False)
-            vc_submit = gr.Button("转换", variant="primary")
-            vc_output1 = gr.Textbox(label="音高平均偏差半音数量，体现转换音频的跑调情况（一般小于0.5）")
-            vc_output2 = gr.Audio(label="Output Audio")
-            f0_image = gr.Image(label="f0曲线，蓝色为输入音高，橙色为合成音频的音高（代码有误差）")
-        vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,F0_mean_pooling], 
-                        [vc_output1, vc_output2, f0_image])
-
-    app.launch() 
\ No newline at end of file
diff --git a/spaces/umitgunduz/news-extractor/src/app.py b/spaces/umitgunduz/news-extractor/src/app.py
deleted file mode 100644
index 0451e22a040e8300d8cdd2f689e9b04d77434d9e..0000000000000000000000000000000000000000
--- a/spaces/umitgunduz/news-extractor/src/app.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import json
-import logging
-import threading
-
-import gradio as gr
-import uvicorn
-from fastapi import FastAPI, Response
-
-from inference import NewsInference
-from train import NewsTrainer
-
-UI_PATH = "/"
-
-app = FastAPI()
-inference = NewsInference()
-logging.basicConfig(level=logging.INFO)
-
-
-@app.get("/api/predict")
-def predict(url: str):
-    """
-    Haber makalelerine yönelik tahminler yapmak için kullanılan bir endpointdir.
-
-    Parametreler:
-        url (str): Haber makalesinin URL'i.
-
-    Dönen Değerler:
-        dict: Tarih, başlık, açıklama ve içerik gibi haber makalesine ilişkin tahmin edilen bilgiler.
-    """
-    response = inference.predict(url)
-    return response
-
-
-@app.get("/api/train")
-async def train(name: str):
-    """
-    Haber modelini asenkron olarak eğitmek için kullanılan bir endpointdir.
-
-    Parametreler:
-        name (str): Eğitim işleminin adı.
-
-    Dönen Değerler:
-        Response: Eğitim işleminin başladığını belirten bir mesaj içeren HTTP yanıtı.
-    """
-    _train_data_path = "./data/dataset"
-    _model_output_path = "./models"
-    trainer = NewsTrainer()
-
-    thread = threading.Thread(target=trainer.run, args=(name, _train_data_path, _model_output_path))
-    thread.daemon = True
-    thread.start()
-
-    output = {"message": "Eğitim Başladı..."}
-    result = json.dumps(output, sort_keys=False, indent=4)
-    return Response(content=result, status_code=200, media_type="application/json")
-
-
-def clear():
-    """
-    Gradio arayüzündeki giriş ve çıkış alanlarını temizlemek için kullanılan bir fonksiyon.
-
-    Dönen Değerler:
-        tuple: Giriş ve çıkış alanları için boş değerler.
-    """
-    return None, None, None, None, None, None, None, None, None
-
-
-@app.get("/run/predict")
-def gradio_predict(url: str):
-    """
-    Gradio arayüzü üzerinden tahmin yapmak için kullanılan bir endpointdir.
-
-    Parametreler:
-        url (str): Haber makalesinin URL'i.
-
-    Dönen Değerler:
-        list: Tarih değeri, tarih skoru, başlık değeri, başlık skoru, açıklama değeri, açıklama skoru,
-              içerik değeri ve içerik skoru gibi tahmin edilen bilgiler.
-    """
-    data = predict(url)
-    date_value = data["date"]["value"] if data["date"] is not None else None
-    date_score = data["date"]["score"] if data["date"] is not None else None
-
-    title_value = data["title"]["value"] if data["title"] is not None else None
-    title_score = data["title"]["score"] if data["title"] is not None else None
-
-    description_value = data["description"]["value"] if data["description"] is not None else None
-    description_score = data["description"]["score"] if data["description"] is not None else None
-
-    content_value = data["content"]["value"] if data["content"] is not None else None
-    content_score = data["content"]["score"] if data["content"] is not None else None
-    result = [date_value, date_score, title_value, title_score, description_value, description_score, content_value,
-              content_score]
-    return result
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-    # Haber sitelerinin başlık, açıklama (spot), tarih ve içerik bilgilerinin Yapay Zeka modeli kullanılarak çıkarılması.
-      Bu çalışmada, belirli haber sitelerinden otomatik olarak başlık, açıklama (spot), tarih ve içerik bilgilerini çıkarabilen bir yapay zeka modeli geliştirilmeye odaklanılmıştır. Bu geliştirme sürecinde, modelin haber sitelerinin içeriklerini anlaması ve ilgili bilgileri doğru bir şekilde tanıması ve çıkarabilmesi için derinlemesine eğitimler gerçekleştirilmiştir. Bu kapsamlı çalışma, haber sitelerinin veri içeriklerinin otomatik çıkarılması ve analizi konusunda önemli bir adımı temsil etmektedir.
-    """
-    )
-    with gr.Row():
-        with gr.Column():
-            input = gr.Textbox(label="Link")
-            with gr.Row():
-                with gr.Column():
-                    translate_btn = gr.Button(value="Çalıştır", variant="primary")
-                    clear_btn = gr.Button(value="Temizle")
-            with gr.Row():
-                examples = gr.Examples(examples=[
-                    "https://www.aksam.com.tr/dunya/abdde-anketler-2024-secimlerinde-cumhuriyetcileri-onde-gosteriyor/haber-1369989",
-                    "https://www.cumhuriyet.com.tr/bilim-teknoloji/bill-gates-uyardi-amazon-ve-google-gibi-sirketleri-yapay-zeka-bitirecek-2084726",
-                    "https://www.ensonhaber.com/teknoloji/nasa-uranusun-kuzey-kutbundaki-siklonu-ilk-kez-goruntuledi",
-                    "https://www.haber7.com/teknoloji/haber/3327933-olumcul-bakteriler-tarihe-karisabilir-yapay-zeka-ile-antibiyotik-gelistirdiler",
-                    "https://haberglobal.com.tr/teknoloji/heyecan-yaratan-bulus-dunya-buyuklugunde-otegezegen-kesfedildi-251592"],
-                    inputs=[input])
-
-        with gr.Column() as output:
-            with gr.Box():
-                date_value = gr.Textbox(label="Tarih")
-                date_score = gr.Textbox(label="Skor")
-            with gr.Box():
-                title_value = gr.Textbox(label="Başlık")
-                title_score = gr.Textbox(label="Skor")
-            with gr.Box():
-                description_value = gr.Textbox(label="Açıklama")
-                description_score = gr.Textbox(label="Skor")
-            with gr.Box():
-                content_value = gr.Textbox(label="İçerik")
-                content_score = gr.Textbox(label="Skor")
-
-    translate_btn.click(gradio_predict, inputs=input, outputs=[date_value, date_score,
-                                                               title_value, title_score,
-                                                               description_value, description_score,
-                                                               content_value, content_score])
-    clear_btn.click(fn=clear, inputs=None, outputs=[input,
-                                                    date_value, date_score,
-                                                    title_value, title_score,
-                                                    description_value, description_score,
-                                                    content_value, content_score])
-
-app = gr.mount_gradio_app(app, demo, "/", gradio_api_url="http://localhost:7860/")
-
-
-def start():
-    """
-    Sunucuyu başlatmak ve Gradio uygulamasını çalıştırmak için kullanılan bir fonksiyondur.
-
-    Bu fonksiyon belirtilen host ve portta sunucuyu başlatır ve Gradio uygulamasını çalıştırır.
-    """
-    uvicorn.run(app, host="0.0.0.0", port=7860, log_level="info", workers=1)
diff --git a/spaces/unik-style/unik-ml/config.py b/spaces/unik-style/unik-ml/config.py
deleted file mode 100644
index e53df2df4ea2d24932bdb31109c7fe87713c633e..0000000000000000000000000000000000000000
--- a/spaces/unik-style/unik-ml/config.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import os
-
-from pydantic import BaseModel
-
-
-class Settings(BaseModel):
-    hf_token: str = os.environ.get("hf_token")
-    base_sd_model: str = os.environ.get("base_sd_model") or "stabilityai/stable-diffusion-xl-base-1.0"
-    refiner_sd_model: str = os.environ.get("refiner_sd_model") or "stabilityai/stable-diffusion-xl-refiner-1.0"
-    version: str = "0.1.0"
-    url_version: str = "v1"
-    prefix: str = "/v1/unik-ml"
-
-
-settings = Settings()
diff --git a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/animation.py b/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/animation.py
deleted file mode 100644
index ccb6dbd206b09ada3627e89398655964fdd72845..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/animation.py
+++ /dev/null
@@ -1,258 +0,0 @@
-import numpy as np
-import cv2
-from functools import reduce
-import math
-import py3d_tools as p3d
-import torch
-from einops import rearrange
-from .prompt import check_is_number
-
-# Webui
-from modules.shared import state
-
-def sample_from_cv2(sample: np.ndarray) -> torch.Tensor:
-    sample = ((sample.astype(float) / 255.0) * 2) - 1
-    sample = sample[None].transpose(0, 3, 1, 2).astype(np.float16)
-    sample = torch.from_numpy(sample)
-    return sample
-
-def sample_to_cv2(sample: torch.Tensor, type=np.uint8) -> np.ndarray:
-    sample_f32 = rearrange(sample.squeeze().cpu().numpy(), "c h w -> h w c").astype(np.float32)
-    sample_f32 = ((sample_f32 * 0.5) + 0.5).clip(0, 1)
-    sample_int8 = (sample_f32 * 255)
-    return sample_int8.astype(type)
-
-def construct_RotationMatrixHomogenous(rotation_angles):
-    assert(type(rotation_angles)==list and len(rotation_angles)==3)
-    RH = np.eye(4,4)
-    cv2.Rodrigues(np.array(rotation_angles), RH[0:3, 0:3])
-    return RH
-
-# https://en.wikipedia.org/wiki/Rotation_matrix
-def getRotationMatrixManual(rotation_angles):
-	
-    rotation_angles = [np.deg2rad(x) for x in rotation_angles]
-    
-    phi         = rotation_angles[0] # around x
-    gamma       = rotation_angles[1] # around y
-    theta       = rotation_angles[2] # around z
-    
-    # X rotation
-    Rphi        = np.eye(4,4)
-    sp          = np.sin(phi)
-    cp          = np.cos(phi)
-    Rphi[1,1]   = cp
-    Rphi[2,2]   = Rphi[1,1]
-    Rphi[1,2]   = -sp
-    Rphi[2,1]   = sp
-    
-    # Y rotation
-    Rgamma        = np.eye(4,4)
-    sg            = np.sin(gamma)
-    cg            = np.cos(gamma)
-    Rgamma[0,0]   = cg
-    Rgamma[2,2]   = Rgamma[0,0]
-    Rgamma[0,2]   = sg
-    Rgamma[2,0]   = -sg
-    
-    # Z rotation (in-image-plane)
-    Rtheta      = np.eye(4,4)
-    st          = np.sin(theta)
-    ct          = np.cos(theta)
-    Rtheta[0,0] = ct
-    Rtheta[1,1] = Rtheta[0,0]
-    Rtheta[0,1] = -st
-    Rtheta[1,0] = st
-    
-    R           = reduce(lambda x,y : np.matmul(x,y), [Rphi, Rgamma, Rtheta]) 
-    
-    return R
-
-def getPoints_for_PerspectiveTranformEstimation(ptsIn, ptsOut, W, H, sidelength):
-    
-    ptsIn2D      =  ptsIn[0,:]
-    ptsOut2D     =  ptsOut[0,:]
-    ptsOut2Dlist =  []
-    ptsIn2Dlist  =  []
-    
-    for i in range(0,4):
-        ptsOut2Dlist.append([ptsOut2D[i,0], ptsOut2D[i,1]])
-        ptsIn2Dlist.append([ptsIn2D[i,0], ptsIn2D[i,1]])
-    
-    pin  =  np.array(ptsIn2Dlist)   +  [W/2.,H/2.]
-    pout = (np.array(ptsOut2Dlist)  +  [1.,1.]) * (0.5*sidelength)
-    pin  = pin.astype(np.float32)
-    pout = pout.astype(np.float32)
-    
-    return pin, pout
-
-
-def warpMatrix(W, H, theta, phi, gamma, scale, fV):
-    
-    # M is to be estimated
-    M          = np.eye(4, 4)
-    
-    fVhalf     = np.deg2rad(fV/2.)
-    d          = np.sqrt(W*W+H*H)
-    sideLength = scale*d/np.cos(fVhalf)
-    h          = d/(2.0*np.sin(fVhalf))
-    n          = h-(d/2.0)
-    f          = h+(d/2.0)
-    
-    # Translation along Z-axis by -h
-    T       = np.eye(4,4)
-    T[2,3]  = -h
-    
-    # Rotation matrices around x,y,z
-    R = getRotationMatrixManual([phi, gamma, theta])
-    
-    
-    # Projection Matrix 
-    P       = np.eye(4,4)
-    P[0,0]  = 1.0/np.tan(fVhalf)
-    P[1,1]  = P[0,0]
-    P[2,2]  = -(f+n)/(f-n)
-    P[2,3]  = -(2.0*f*n)/(f-n)
-    P[3,2]  = -1.0
-    
-    # pythonic matrix multiplication
-    F       = reduce(lambda x,y : np.matmul(x,y), [P, T, R]) 
-    
-    # shape should be 1,4,3 for ptsIn and ptsOut since perspectiveTransform() expects data in this way. 
-    # In C++, this can be achieved by Mat ptsIn(1,4,CV_64FC3);
-    ptsIn = np.array([[
-                 [-W/2., H/2., 0.],[ W/2., H/2., 0.],[ W/2.,-H/2., 0.],[-W/2.,-H/2., 0.]
-                 ]])
-    ptsOut  = np.array(np.zeros((ptsIn.shape), dtype=ptsIn.dtype))
-    ptsOut  = cv2.perspectiveTransform(ptsIn, F)
-    
-    ptsInPt2f, ptsOutPt2f = getPoints_for_PerspectiveTranformEstimation(ptsIn, ptsOut, W, H, sideLength)
-    
-    # check float32 otherwise OpenCV throws an error
-    assert(ptsInPt2f.dtype  == np.float32)
-    assert(ptsOutPt2f.dtype == np.float32)
-    M33 = cv2.getPerspectiveTransform(ptsInPt2f,ptsOutPt2f)
-
-    return M33, sideLength
-
-def get_flip_perspective_matrix(W, H, keys, frame_idx):
-    perspective_flip_theta = keys.perspective_flip_theta_series[frame_idx]
-    perspective_flip_phi = keys.perspective_flip_phi_series[frame_idx]
-    perspective_flip_gamma = keys.perspective_flip_gamma_series[frame_idx]
-    perspective_flip_fv = keys.perspective_flip_fv_series[frame_idx]
-    M,sl = warpMatrix(W, H, perspective_flip_theta, perspective_flip_phi, perspective_flip_gamma, 1., perspective_flip_fv);
-    post_trans_mat = np.float32([[1, 0, (W-sl)/2], [0, 1, (H-sl)/2]])
-    post_trans_mat = np.vstack([post_trans_mat, [0,0,1]])
-    bM = np.matmul(M, post_trans_mat)
-    return bM
-
-def flip_3d_perspective(anim_args, prev_img_cv2, keys, frame_idx):
-    W, H = (prev_img_cv2.shape[1], prev_img_cv2.shape[0])
-    return cv2.warpPerspective(
-        prev_img_cv2,
-        get_flip_perspective_matrix(W, H, keys, frame_idx),
-        (W, H),
-        borderMode=cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE
-    )
-
-def anim_frame_warp(prev_img_cv2, args, anim_args, keys, frame_idx, depth_model=None, depth=None, device='cuda', half_precision = False):
-
-    if anim_args.use_depth_warping:
-        if depth is None and depth_model is not None:
-            depth = depth_model.predict(prev_img_cv2, anim_args, half_precision)
-    else:
-        depth = None
-
-    if anim_args.animation_mode == '2D':
-        prev_img = anim_frame_warp_2d(prev_img_cv2, args, anim_args, keys, frame_idx)
-    else: # '3D'
-        prev_img = anim_frame_warp_3d(device, prev_img_cv2, depth, anim_args, keys, frame_idx)
-                
-    return prev_img, depth
-
-def anim_frame_warp_2d(prev_img_cv2, args, anim_args, keys, frame_idx):
-    angle = keys.angle_series[frame_idx]
-    zoom = keys.zoom_series[frame_idx]
-    translation_x = keys.translation_x_series[frame_idx]
-    translation_y = keys.translation_y_series[frame_idx]
-
-    center = (args.W // 2, args.H // 2)
-    trans_mat = np.float32([[1, 0, translation_x], [0, 1, translation_y]])
-    rot_mat = cv2.getRotationMatrix2D(center, angle, zoom)
-    trans_mat = np.vstack([trans_mat, [0,0,1]])
-    rot_mat = np.vstack([rot_mat, [0,0,1]])
-    if anim_args.enable_perspective_flip:
-        bM = get_flip_perspective_matrix(args.W, args.H, keys, frame_idx)
-        rot_mat = np.matmul(bM, rot_mat, trans_mat)
-    else:
-        rot_mat = np.matmul(rot_mat, trans_mat)
-    return cv2.warpPerspective(
-        prev_img_cv2,
-        rot_mat,
-        (prev_img_cv2.shape[1], prev_img_cv2.shape[0]),
-        borderMode=cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE
-    )
-
-def anim_frame_warp_3d(device, prev_img_cv2, depth, anim_args, keys, frame_idx):
-    TRANSLATION_SCALE = 1.0/200.0 # matches Disco
-    translate_xyz = [
-        -keys.translation_x_series[frame_idx] * TRANSLATION_SCALE, 
-        keys.translation_y_series[frame_idx] * TRANSLATION_SCALE, 
-        -keys.translation_z_series[frame_idx] * TRANSLATION_SCALE
-    ]
-    rotate_xyz = [
-        math.radians(keys.rotation_3d_x_series[frame_idx]), 
-        math.radians(keys.rotation_3d_y_series[frame_idx]), 
-        math.radians(keys.rotation_3d_z_series[frame_idx])
-    ]
-    if anim_args.enable_perspective_flip:
-        prev_img_cv2 = flip_3d_perspective(anim_args, prev_img_cv2, keys, frame_idx)
-    rot_mat = p3d.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), "XYZ").unsqueeze(0)
-    result = transform_image_3d(device if not device.type.startswith('mps') else torch.device('cpu'), prev_img_cv2, depth, rot_mat, translate_xyz, anim_args, keys, frame_idx)
-    torch.cuda.empty_cache()
-    return result
-
-def transform_image_3d(device, prev_img_cv2, depth_tensor, rot_mat, translate, anim_args, keys, frame_idx):
-    # adapted and optimized version of transform_image_3d from Disco Diffusion https://github.com/alembics/disco-diffusion 
-    w, h = prev_img_cv2.shape[1], prev_img_cv2.shape[0]
-
-    aspect_ratio = float(w)/float(h)
-    near = keys.near_series[frame_idx]
-    far = keys.far_series[frame_idx]
-    fov_deg = keys.fov_series[frame_idx]
-    persp_cam_old = p3d.FoVPerspectiveCameras(near, far, aspect_ratio, fov=fov_deg, degrees=True, device=device)
-    persp_cam_new = p3d.FoVPerspectiveCameras(near, far, aspect_ratio, fov=fov_deg, degrees=True, R=rot_mat, T=torch.tensor([translate]), device=device)
-
-    # range of [-1,1] is important to torch grid_sample's padding handling
-    y,x = torch.meshgrid(torch.linspace(-1.,1.,h,dtype=torch.float32,device=device),torch.linspace(-1.,1.,w,dtype=torch.float32,device=device))
-    if depth_tensor is None:
-        z = torch.ones_like(x)
-    else:
-        z = torch.as_tensor(depth_tensor, dtype=torch.float32, device=device)
-    xyz_old_world = torch.stack((x.flatten(), y.flatten(), z.flatten()), dim=1)
-
-    xyz_old_cam_xy = persp_cam_old.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]
-    xyz_new_cam_xy = persp_cam_new.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]
-
-    offset_xy = xyz_new_cam_xy - xyz_old_cam_xy
-    # affine_grid theta param expects a batch of 2D mats. Each is 2x3 to do rotation+translation.
-    identity_2d_batch = torch.tensor([[1.,0.,0.],[0.,1.,0.]], device=device).unsqueeze(0)
-    # coords_2d will have shape (N,H,W,2).. which is also what grid_sample needs.
-    coords_2d = torch.nn.functional.affine_grid(identity_2d_batch, [1,1,h,w], align_corners=False)
-    offset_coords_2d = coords_2d - torch.reshape(offset_xy, (h,w,2)).unsqueeze(0)
-
-    image_tensor = rearrange(torch.from_numpy(prev_img_cv2.astype(np.float32)), 'h w c -> c h w').to(device)
-    new_image = torch.nn.functional.grid_sample(
-        image_tensor.add(1/512 - 0.0001).unsqueeze(0), 
-        offset_coords_2d, 
-        mode=anim_args.sampling_mode, 
-        padding_mode=anim_args.padding_mode, 
-        align_corners=False
-    )
-
-    # convert back to cv2 style numpy array
-    result = rearrange(
-        new_image.squeeze().clamp(0,255), 
-        'c h w -> h w c'
-    ).cpu().numpy().astype(prev_img_cv2.dtype)
-    return result
diff --git a/spaces/vaibhavarduino/better-autogpt/README.md b/spaces/vaibhavarduino/better-autogpt/README.md
deleted file mode 100644
index ccea68a11e92d6a496f1c04e30e504a3b7e33b32..0000000000000000000000000000000000000000
--- a/spaces/vaibhavarduino/better-autogpt/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Better Autogpt
-emoji: 🌖
-colorFrom: red
-colorTo: purple
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vasistasaimagam/FoodVision_Big/model.py b/spaces/vasistasaimagam/FoodVision_Big/model.py
deleted file mode 100644
index 52c2696c874740179528f0bdae8ce87b774a138f..0000000000000000000000000000000000000000
--- a/spaces/vasistasaimagam/FoodVision_Big/model.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import torch
-import torchvision
-
-from torch import nn
-
-
-def create_effnetb2_model(num_classes:int=3, 
-                          seed:int=42):
-    """Creates an EfficientNetB2 feature extractor model and transforms.
-
-    Args:
-        num_classes (int, optional): number of classes in the classifier head. 
-            Defaults to 3.
-        seed (int, optional): random seed value. Defaults to 42.
-
-    Returns:
-        model (torch.nn.Module): EffNetB2 feature extractor model. 
-        transforms (torchvision.transforms): EffNetB2 image transforms.
-    """
-    # Create EffNetB2 pretrained weights, transforms and model
-    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
-    transforms = weights.transforms()
-    model = torchvision.models.efficientnet_b2(weights=weights)
-
-    # Freeze all layers in base model
-    for param in model.parameters():
-        param.requires_grad = False
-
-    # Change classifier head with random seed for reproducibility
-    torch.manual_seed(seed)
-    model.classifier = nn.Sequential(
-        nn.Dropout(p=0.3, inplace=True),
-        nn.Linear(in_features=1408, out_features=num_classes),
-    )
-    
-    return model, transforms
diff --git a/spaces/vincent1bt/Line_Art_Colorization/app.py b/spaces/vincent1bt/Line_Art_Colorization/app.py
deleted file mode 100644
index 28301e6205ede319b4ba7e53cad99f25576c29da..0000000000000000000000000000000000000000
--- a/spaces/vincent1bt/Line_Art_Colorization/app.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-from PIL import Image
-
-model = tf.keras.models.load_model("saved_model")
-
-def load_image(image_path):
-  img = tf.io.read_file(image_path)
-  img = tf.image.decode_jpeg(img, channels=3)
-  img = tf.image.convert_image_dtype(img, tf.float32)
-
-  img = tf.expand_dims(img, 0)
-
-  return img
-
-def inference(line_path_1, line_path_2, line_path_3, 
-            distance_path_1, distance_path_2, distance_path_3,
-            color_path_1, color_path_3):
-  line_1 = load_image(line_path_1)
-  line_2 = load_image(line_path_2)
-  line_3 = load_image(line_path_3)
-
-  distance_1 = load_image(distance_path_1)
-  distance_2 = load_image(distance_path_2)
-  distance_3 = load_image(distance_path_3)
-
-  color_1 = load_image(color_path_1)
-  color_3 = load_image(color_path_3)
-
-  image, _, _ = model(
-      [line_2, distance_2, color_1, line_1, distance_1, color_3, line_3, distance_3]
-  )
-
-  image = tf.squeeze(image)
-  image = image * 255.0
-  image = tf.clip_by_value(image, 0, 255)
-  image = Image.fromarray(tf.cast(image, tf.uint8).numpy())
-
-  return image
-
-with gr.Blocks() as demo:
-  with gr.Row():
-    with gr.Column(scale=1):
-      line_path_1 = gr.Image(type="filepath", label="First Line art Image")
-      distance_path_1 = gr.Image(type="filepath", label="First Distance Image")
-      color_path_1 = gr.Image(type="filepath", label="First Color Image")
-
-      gr.Examples(["images/line_art_images/4120.jpg"], inputs=line_path_1)
-      gr.Examples(["images/distance_images/4120.jpg"], inputs=distance_path_1)
-      gr.Examples(["images/color_images/4120.jpg"], inputs=color_path_1)
-
-    with gr.Column(scale=1):
-      line_path_2 = gr.Image(type="filepath", label="Second Line art Image (To colorize)")
-      distance_path_2 = gr.Image(type="filepath", label="Second Distance Image")
-      final_image = gr.Image(label="Ouput Image")
-
-      gr.Examples(["images/line_art_images/4184.jpg"], inputs=line_path_2)
-      gr.Examples(["images/distance_images/4184.jpg"], inputs=distance_path_2)
-    
-    with gr.Column(scale=1):
-      line_path_3 = gr.Image(type="filepath", label="Third Line art Image")
-      distance_path_3 = gr.Image(type="filepath", label="Third Distance Image")
-      color_path_3 = gr.Image(type="filepath", label="Second Color Image")
-
-      gr.Examples(["images/line_art_images/4216.jpg"], inputs=line_path_3)
-      gr.Examples(["images/distance_images/4216.jpg"], inputs=distance_path_3)
-      gr.Examples(["images/color_images/4216.jpg"], inputs=color_path_3)
-      
-  b1 = gr.Button("Colorize")
-  inputs = [line_path_1, line_path_2, line_path_3, 
-            distance_path_1, distance_path_2, distance_path_3,
-            color_path_1, color_path_3]
-
-  b1.click(inference, inputs=inputs, outputs=final_image)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/vladisov/fn/app.py b/spaces/vladisov/fn/app.py
deleted file mode 100644
index 5d4e169c67b52a085dc3c80051f9464005032751..0000000000000000000000000000000000000000
--- a/spaces/vladisov/fn/app.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../birds.ipynb.
-
-# %% auto 0
-__all__ = ['learner', 'class_dict', 'labels', 'image', 'label', 'examples', 'intf', 'top_n', 'classify_image']
-
-# %% ../birds.ipynb 1
-import gradio as gr
-from fastai.vision.all import *
-
-# %% ../birds.ipynb 3
-learner = load_learner('model.pkl')
-
-# %% ../birds.ipynb 5
-class_dict = pd.read_csv('class_dict.csv')
-labels = class_dict['class'].tolist()
-
-# %% ../birds.ipynb 6
-import heapq
-
-def top_n(probs, n=5):
-    top = []
-    for prob in probs:   
-        heapq.heappush(top, prob)
-        if len(top) > n:
-            heapq.heappop(top)
-    return dict(map(reversed, top))
-
-def classify_image(img):
-    bird, idx, probs = learner.predict(img)
-    return top_n(list(zip(map(float, probs),  labels)))
-
-
-
-
-# %% ../birds.ipynb 8
-image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
-
-examples = ['eagle.jpeg', 'owl.jpeg', 'pigeon.jpeg']
-intf = gr.Interface(fn=classify_image, examples=examples, inputs=gr.Image(), outputs=gr.Label())
-intf.launch(inline=False)
diff --git a/spaces/voidKandy/WW1_Poet_Bot/README.md b/spaces/voidKandy/WW1_Poet_Bot/README.md
deleted file mode 100644
index 3814ed50be3edc04280a55cc983ba6bf388dae05..0000000000000000000000000000000000000000
--- a/spaces/voidKandy/WW1_Poet_Bot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: WW1 Poet Bot
-emoji: 🐠
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vorstcavry/Vorst-Cavry-stablediffusion/app.py b/spaces/vorstcavry/Vorst-Cavry-stablediffusion/app.py
deleted file mode 100644
index beac946f59050f7dba6149f71e24daf39418a7ff..0000000000000000000000000000000000000000
--- a/spaces/vorstcavry/Vorst-Cavry-stablediffusion/app.py
+++ /dev/null
@@ -1,244 +0,0 @@
-"""
-Stable Diffusion Webui Version 1.6
-https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.6.0
-
-"""
-#commit_id=r"5ef669de080814067961f28357256e8fe27544f4" #Version 1.6.0
-import os
-from sys import executable 
-import subprocess
-import pathlib
-import gc
-import time
-import subprocess
-def Gitclone(URI:str,ClonePath:pathlib.Path ) -> int :
-  if pathlib.Path.exists(ClonePath):
-    return 0
-  for z in range(10):
-    i=subprocess.run([r"git",r"clone",str(URI),str(ClonePath)])
-    if(i.returncode == 0 ): 
-     del i
-     return 0
-    else :
-     del i
-  raise Exception(str.format("clone \'{0}\' failed",URI))
-    
-
-def DownLoad(URI:str,DownloadPath:pathlib.Path,DownLoadFileName:str ) -> int:
-  if (DownloadPath / DownLoadFileName).is_file(): return 0
-  for z in range(10):
-    i=subprocess.run([r"aria2c",r"-c",r"-x" ,r"16", r"-s",r"16", r"-k" ,r"1M" ,r"-m",r"0",r"--enable-mmap=false",r"--console-log-level=error",r"-d",str(DownloadPath),r"-o",DownLoadFileName,URI]);
-    if(i.returncode == 0 ): 
-      del i
-      gc.collect()
-      return 0
-    else :
-      del i
-  raise Exception(str.format("download \'{0}\' failed",URI))
-
-user_home =pathlib.Path.home().resolve()
-os.chdir(str(user_home))
-#clone stable-diffusion-webui repo
-print("cloning stable-diffusion-webui repo")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui.git",user_home / r"stable-diffusion-webui")
-#os.chdir(str(user_home / r"stable-diffusion-webui"))
-#os.system("git reset --hard "+commit_id)
-os.chdir(user_home / r"stable-diffusion-webui")
-Gitclone(r"https://github.com/vorstcavry/ncpt_colab_timer",user_home / r"stable-diffusion-webui" / r"extensions" / r"ncpt_colab_timer")
-Gitclone(r"https://github.com/vorstcavry/static",user_home / r"stable-diffusion-webui" / r"static")
-
-def run_echo_command():
-    try:
-        start_huggingface
-    except NameError:
-        start_huggingface = int(time.time()) - 5
-
-    cmd = f"echo -n {start_huggingface} > /home/user/app/stable-diffusion-webui/static/colabTimer.txt"
-    subprocess.run(cmd, shell=True)
-
-# Contoh pemanggilan fungsi run_echo_command:
-run_echo_command()
-os.chdir(user_home / r"stable-diffusion-webui")
-#install extensions
-print("installing extensions")
-Gitclone(r"https://github.com/vorstcavry/embeddings",user_home / r"stable-diffusion-webui" / r"embeddings"  / r"negative")
-Gitclone(r"https://github.com/vorstcavry/lora",user_home / r"stable-diffusion-webui" / r"models" / r"Lora" / r"positive")
-Gitclone(r"https://github.com/vorstcavry/Checkpoint-Model",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint")
-
-DownLoad(r"https://huggingface.co/embed/upscale/resolve/main/4x-UltraSharp.pth",user_home / r"stable-diffusion-webui" / r"models" / r"ESRGAN" ,r"4x-UltraSharp.pth")
-while (True):
-   i=subprocess.run([r"wget",r"https://raw.githubusercontent.com/camenduru/stable-diffusion-webui-scripts/main/run_n_times.py",r"-O",str(user_home / r"stable-diffusion-webui" / r"scripts" / r"run_n_times.py")])
-   if(i.returncode == 0 ): 
-    del i
-    gc.collect()
-    break
-   else :
-    del i
-#Gitclone(r"https://github.com/deforum-art/deforum-for-automatic1111-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"deforum-for-automatic1111-webui" )
-#Gitclone(r"https://github.com/AlUlkesh/stable-diffusion-webui-images-browser",user_home / r"stable-diffusion-webui" / r"extensions"/ r"stable-diffusion-webui-images-browser")
-Gitclone(r"https://github.com/camenduru/stable-diffusion-webui-huggingface",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-huggingface")
-Gitclone(r"https://github.com/BlafKing/sd-civitai-browser-plus",user_home / r"stable-diffusion-webui" / r"extensions" / r"civitai-browser")
-Gitclone(r"https://github.com/kohya-ss/sd-webui-additional-networks",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks")
-Gitclone(r"https://github.com/Mikubill/sd-webui-controlnet",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-controlnet")
-Gitclone(r"https://github.com/fkunn1326/openpose-editor",user_home / r"stable-diffusion-webui" / r"extensions" / r"openpose-editor")
-#Gitclone(r"https://github.com/jexom/sd-webui-depth-lib",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-depth-lib")
-#Gitclone(r"https://github.com/hnmr293/posex",user_home / r"stable-diffusion-webui" / r"extensions" / r"posex")
-#Gitclone(r"https://github.com/nonnonstop/sd-webui-3d-open-pose-editor",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-3d-open-pose-editor")
-#Gitclone(r"https://github.com/hnmr293/posex",user_home / r"stable-diffusion-webui" / r"extensions" / r"posex")
-Gitclone(r"https://github.com/EdithForsaken/sd-webui-cloud-inference.git",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-cloud-inference")
-Gitclone(r"https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git" , user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-tagcomplete")
-Gitclone(r"https://github.com/camenduru/sd-webui-tunnels",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-tunnels")
-Gitclone(r"https://github.com/etherealxx/batchlinks-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"batchlinks-webui")
-Gitclone(r"https://github.com/zanllp/sd-webui-infinite-image-browsing",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-infinite-image-browsing")
-#Gitclone(r"https://github.com/catppuccin/stable-diffusion-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-catppuccin")
-#Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui-rembg",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-rembg")
-Gitclone(r"https://tinyurl.com/aspect-ratio-v",user_home / r"stable-diffusion-webui" / r"extensions" / r"aspect-ratio")
-#Gitclone(r"https://github.com/vorstcavry/cleaner",user_home / r"stable-diffusion-webui" / r"extensions" / r"cleaner")
-Gitclone(r"https://github.com/hnmr293/sd-webui-llul",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-llul")
-Gitclone(r"https://github.com/IDEA-Research/DWPose",user_home / r"stable-diffusion-webui" / r"extensions" / r"DWPose")
-Gitclone(r"https://github.com/Bing-su/adetailer",user_home / r"stable-diffusion-webui" / r"extensions" / r"adetailer")
-Gitclone(r"https://github.com/NoCrypt/sd_hf_out",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_hf_out")
-
-
-#Gitclone(r"https://github.com/NoCrypt/sd_hf_out",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_hf_out")
-#Gitclone(r"https://github.com/Iyashinouta/sd-model-downloader",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-model-downloader")
-#Gitclone(r"https://github.com/AIrjen/OneButtonPrompt",user_home / r"stable-diffusion-webui" / r"extensions" / r"OneButtonPrompt")
-#Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-wildcards")
-#Gitclone(r"https://github.com/adieyal/sd-dynamic-prompts",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-dynamic-prompts")
-#Gitclone(r"https://github.com/d8ahazard/sd_dreambooth_extension",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_dreambooth_extension")
-#Gitclone(r"https://github.com/yfszzx/stable-diffusion-webui-inspiration",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-inspiration")
-#Gitclone(r"https://github.com/Coyote-A/ultimate-upscale-for-automatic1111",user_home / r"stable-diffusion-webui" / r"extensions" / r"ultimate-upscale-for-automatic1111")
-os.chdir(user_home / r"stable-diffusion-webui")
-#download ControlNet models
-print("extensions dolwnload done .\ndownloading ControlNet models")
-dList =[ r"https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors",
-               r"https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors",
-               r"https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors",
-               r"https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors",
-               r"https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors"]
-for i in range(0,len(dList)): DownLoad(dList[i],user_home / r"stable-diffusion-webui" / r"models" / r"ControlNet",pathlib.Path(dList[i]).name)
-del dList
-
-#download ControlNet models
-#print("extensions dolwnload done .\ndownloading ControlNet models")
-#dList =[r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_scribble_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_seg_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile_fp16.safetensors",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_ip2p_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_shuffle_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_canny_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1p_sd15_depth_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_inpaint_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_lineart_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_mlsd_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_normalbae_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_openpose_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_scribble_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_seg_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_softedge_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15s2_lineart_anime_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1e_sd15_tile_fp16.yaml",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_style_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_seg_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_openpose_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_keypose_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd14v1.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd15v2.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd15v2.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd15v2.pth",
-#               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_zoedepth_sd15v1.pth"]
-#for i in range(0,len(dList)): DownLoad(dList[i],user_home / r"stable-diffusion-webui" / r"extensions" / "sd-webui-controlnet" / r"models",pathlib.Path(dList[i]).name)
-#del dList
-#download model    
-#you can change model download address here
-print("ControlNet models download done.\ndownloading model")
-#Stable Diffusion Checkpoint Model
-#anything version4.5
-#DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.5-pruned.ckpt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"anything-v4.5-pruned.ckpt")
-#DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.0.vae.pt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"anything-v4.0.vae.pt")
-#Counterfeit-V3.0
-#DownLoad(r"https://huggingface.co/gsdf/Counterfeit-V3.0/resolve/main/Counterfeit-V3.0_fp16.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"Counterfeit-V3.0_fp16.safetensors")
-#AbyssOrangeMix2 sfw
-#DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_sfw.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"AbyssOrangeMix2_sfw.safetensors")
-#DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"orangemix.vae.pt")
-#MeinaPastelV5
-#DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Baked%20VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"MeinaPastelV5_BakedVAE.safetensors")
-#DownLoad(r"https://huggingface.co/AnonPerson/ChilloutMix/resolve/main/ChilloutMix-ni-fp16.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"ChilloutMix-ni-fp16.safetensors")
-#DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV4%20-%20Without%20VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"MeinaPastelV4%20-%20Without%20VAE.safetensors")
-#DownLoad(r"https://huggingface.co/ckpt/perfect_world/resolve/main/perfectWorld_v2Baked.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"perfectWorld_v2Baked.safetensors")
-#DownLoad(r"https://huggingface.co/vorstcavry/figurestyle1/resolve/main/figure.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"figure.safetensors")
-#DownLoad(r"https://huggingface.co/vorstcavry/dosmix/resolve/main/ddosmix_V2.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"ddosmix_V2.safetensors")
-#DownLoad(r"https://huggingface.co/ckpt/rev-animated/resolve/main/revAnimated_v11.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"revAnimated_v11.safetensors")
-#DownLoad(r"https://huggingface.co/ckpt/MeinaMix/resolve/main/Meina_V8_baked_VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"Meina_V8_baked_VAE.safetensors")
-#DownLoad(r"https://huggingface.co/ckpt/CyberRealistic/resolve/main/cyberrealistic_v13.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"cyberrealistic_v13.safetensors")
-DownLoad(r"https://huggingface.co/vorstcavry/mymodel/resolve/main/Cavry_V2.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion" / r"Checkpoint",r"Cavry_V2.safetensors")
-#downloadvae
-DownLoad(r"https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"VAE",r"vae-ft-mse-840000-ema-pruned.safetensors")
-
-#Lora Model
-#Better Light
-#DownLoad(r"https://civitai.com/api/download/models/39885",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"Better_light.safetensors")
-#DownLoad(r"https://civitai.com/api/download/models/39885",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"Better_light.safetensors")
-#LAS
-#DownLoad(r"https://civitai.com/api/download/models/21065",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"LAS.safetensors")
-#DownLoad(r"https://civitai.com/api/download/models/21065",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"LAS.safetensors")
-#Backlighting
-#DownLoad(r"https://civitai.com/api/download/models/39164",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"backlighting.safetensors")
-#DownLoad(r"https://civitai.com/api/download/models/39164",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"backlighting.safetensors")
-DownLoad(r"https://huggingface.co/vorstcavry/loraasia1/resolve/main/japaneseDollLikeness_v15.safetensors",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"japaneseDollLikeness_v15.safetensors")
-DownLoad(r"https://huggingface.co/vorstcavry/loraasia1/resolve/main/koreanDollLikeness_v20.safetensors",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"koreanDollLikeness_v20.safetensors")
-DownLoad(r"https://huggingface.co/vorstcavry/loraasia1/resolve/main/taiwanDollLikeness_v15.safetensors",user_home / r"stable-diffusion-webui" /  r"models"/ r"lora",r"taiwanDollLikeness_v15.safetensors")
-
-
-
-
-#GFPGAN Model
-#detection Resnet50
-DownLoad(r"https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"detection_Resnet50_Final.pth")
-#parsing_parsenet
-DownLoad(r"https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"parsing_parsenet.pth")
-#GFPGANv1.4
-DownLoad(r"https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"GFPGANv1.4.pth")
-#strt Stable Diffusion Webui
-print("Done\nStarting Webui...")
-os.chdir(user_home / r"stable-diffusion-webui")
-import subprocess
-import pathlib
-
-user_home = pathlib.Path("/home/user")  # Gantilah dengan path yang sesuai
-
-args = [
-    executable,
-    user_home / "stable-diffusion-webui" / "launch.py",
-    "--precision", "full",
-    "--no-half",
-    "--no-half-vae",
-    "--enable-insecure-extension-access",
-    "--medvram",
-    "--skip-torch-cuda-test",
-    "--enable-console-prompts",
-    "--ui-settings-file=" + str(pathlib.Path(__file__).parent / "config.json"),
-    "--hf-token-out",
-    "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"  # Gantilah dengan token yang sesuai
-]
-
-args = [arg.as_posix() if isinstance(arg, pathlib.PosixPath) else arg for arg in args]
-
-try:
-    ret = subprocess.run(args)
-except Exception as e:
-    print("Error:", e)
-del os ,user_home ,pyexecutable ,subprocess
\ No newline at end of file
diff --git a/spaces/wanghuoto/gogoai/src/pages/api/healthz.ts b/spaces/wanghuoto/gogoai/src/pages/api/healthz.ts
deleted file mode 100644
index f6ae44ff0fd66ccd3f7feaa550025fbf2a83bf77..0000000000000000000000000000000000000000
--- a/spaces/wanghuoto/gogoai/src/pages/api/healthz.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  res.status(200).end('ok')
-}
diff --git a/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/utils/__init__.py b/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/utils/__init__.py
deleted file mode 100644
index 583942d31eee92261b22930fde15f8a151d49141..0000000000000000000000000000000000000000
--- a/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/utils/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/4/29 16:01
-@Author  : alexanderwu
-@File    : __init__.py
-"""
diff --git a/spaces/wonderit-safeai/tts-announcer/README.md b/spaces/wonderit-safeai/tts-announcer/README.md
deleted file mode 100644
index e69e191fbbb7902b731f256cf70d7ab35fa8bf3f..0000000000000000000000000000000000000000
--- a/spaces/wonderit-safeai/tts-announcer/README.md
+++ /dev/null
@@ -1,23 +0,0 @@
----
-title: TTS-Announcer
-emoji: 💻
-colorFrom: purple
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.27.0
-app_file: app.py
-pinned: true
----
-
-
-
-### Setting
-
-- Build monotonic alignment search
-```sh
-cd monotonic_align
-mkdir monotonic_align
-python setup.py build_ext --inplace
-cd ..
-
-PHONEMIZER_ESPEAK_LIBRARY=C:\Program Files\eSpeak NG\libespeak-ng.dll
diff --git a/spaces/wrs/nb/Dockerfile b/spaces/wrs/nb/Dockerfile
deleted file mode 100644
index e88decd3f231d8863b086820f2d3c70ac1b46e02..0000000000000000000000000000000000000000
--- a/spaces/wrs/nb/Dockerfile
+++ /dev/null
@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符-仅可进行对话，如需绘画，需要修改为自己的token
-ENV Go_Proxy_BingAI_USER_TOKEN_1="d9d5da16199141b3a41daa5043487247"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/xiangdy/chatGPT/modules/models/StableLM.py b/spaces/xiangdy/chatGPT/modules/models/StableLM.py
deleted file mode 100644
index f4affc3699e335f1e42bf5fc8c93e92a41d027fe..0000000000000000000000000000000000000000
--- a/spaces/xiangdy/chatGPT/modules/models/StableLM.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
-import time
-import numpy as np
-from torch.nn import functional as F
-import os
-from .base_model import BaseLLMModel
-from threading import Thread
-
-STABLELM_MODEL = None
-STABLELM_TOKENIZER = None
-
-
-class StopOnTokens(StoppingCriteria):
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        stop_ids = [50278, 50279, 50277, 1, 0]
-        for stop_id in stop_ids:
-            if input_ids[0][-1] == stop_id:
-                return True
-        return False
-
-
-class StableLM_Client(BaseLLMModel):
-    def __init__(self, model_name, user_name="") -> None:
-        super().__init__(model_name=model_name, user=user_name)
-        global STABLELM_MODEL, STABLELM_TOKENIZER
-        print(f"Starting to load StableLM to memory")
-        if model_name == "StableLM":
-            model_name = "stabilityai/stablelm-tuned-alpha-7b"
-        else:
-            model_name = f"models/{model_name}"
-        if STABLELM_MODEL is None:
-            STABLELM_MODEL = AutoModelForCausalLM.from_pretrained(
-                model_name, torch_dtype=torch.float16).cuda()
-        if STABLELM_TOKENIZER is None:
-            STABLELM_TOKENIZER = AutoTokenizer.from_pretrained(model_name)
-        self.generator = pipeline(
-            'text-generation', model=STABLELM_MODEL, tokenizer=STABLELM_TOKENIZER, device=0)
-        print(f"Sucessfully loaded StableLM to the memory")
-        self.system_prompt = """StableAssistant
-- StableAssistant is A helpful and harmless Open Source AI Language Model developed by Stability and CarperAI.
-- StableAssistant is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
-- StableAssistant is more than just an information source, StableAssistant is also able to write poetry, short stories, and make jokes.
-- StableAssistant will refuse to participate in anything that could harm a human."""
-        self.max_generation_token = 1024
-        self.top_p = 0.95
-        self.temperature = 1.0
-
-    def _get_stablelm_style_input(self):
-        history = self.history + [{"role": "assistant", "content": ""}]
-        print(history)
-        messages = self.system_prompt + \
-            "".join(["".join(["<|USER|>"+history[i]["content"], "<|ASSISTANT|>"+history[i + 1]["content"]])
-                    for i in range(0, len(history), 2)])
-        return messages
-
-    def _generate(self, text, bad_text=None):
-        stop = StopOnTokens()
-        result = self.generator(text, max_new_tokens=self.max_generation_token, num_return_sequences=1, num_beams=1, do_sample=True,
-                                temperature=self.temperature, top_p=self.top_p, top_k=1000, stopping_criteria=StoppingCriteriaList([stop]))
-        return result[0]["generated_text"].replace(text, "")
-
-    def get_answer_at_once(self):
-        messages = self._get_stablelm_style_input()
-        return self._generate(messages), len(messages)
-
-    def get_answer_stream_iter(self):
-        stop = StopOnTokens()
-        messages = self._get_stablelm_style_input()
-
-        # model_inputs = tok([messages], return_tensors="pt")['input_ids'].cuda()[:, :4096-1024]
-        model_inputs = STABLELM_TOKENIZER(
-            [messages], return_tensors="pt").to("cuda")
-        streamer = TextIteratorStreamer(
-            STABLELM_TOKENIZER, timeout=10., skip_prompt=True, skip_special_tokens=True)
-        generate_kwargs = dict(
-            model_inputs,
-            streamer=streamer,
-            max_new_tokens=self.max_generation_token,
-            do_sample=True,
-            top_p=self.top_p,
-            top_k=1000,
-            temperature=self.temperature,
-            num_beams=1,
-            stopping_criteria=StoppingCriteriaList([stop])
-        )
-        t = Thread(target=STABLELM_MODEL.generate, kwargs=generate_kwargs)
-        t.start()
-
-        partial_text = ""
-        for new_text in streamer:
-            partial_text += new_text
-            yield partial_text
diff --git a/spaces/xiaoxin1111/vits-uma-genshin-honkai/attentions.py b/spaces/xiaoxin1111/vits-uma-genshin-honkai/attentions.py
deleted file mode 100644
index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000
--- a/spaces/xiaoxin1111/vits-uma-genshin-honkai/attentions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/xnetba/Chat_advance/assets/custom.css b/spaces/xnetba/Chat_advance/assets/custom.css
deleted file mode 100644
index 22108488886cfc8d7772214dd9b83727b3fca6a3..0000000000000000000000000000000000000000
--- a/spaces/xnetba/Chat_advance/assets/custom.css
+++ /dev/null
@@ -1,468 +0,0 @@
-:root {
-    --chatbot-color-light: #000000;
-    --chatbot-color-dark: #FFFFFF;
-    --chatbot-background-color-light: #F3F3F3;
-    --chatbot-background-color-dark: #121111;
-    --message-user-background-color-light: #95EC69;
-    --message-user-background-color-dark: #26B561;
-    --message-bot-background-color-light: #FFFFFF;
-    --message-bot-background-color-dark: #2C2C2C;
-}
-
-#app_title {
-    font-weight: var(--prose-header-text-weight);
-    font-size: var(--text-xxl);
-    line-height: 1.3;
-    text-align: left;
-    margin-top: 6px;
-    white-space: nowrap;
-}
-#description {
-    text-align: center;
-    margin: 32px 0 4px 0;
-}
-
-/* gradio的页脚信息 */
-footer {
-    /* display: none !important; */
-    margin-top: .2em !important;
-    font-size: 85%;
-}
-#footer {
-    text-align: center;
-}
-#footer div {
-    display: inline-block;
-}
-#footer .versions{
-    font-size: 85%;
-    opacity: 0.60;
-}
-
-#float_display {
-    position: absolute;
-    max-height: 30px;
-}
-/* user_info */
-#user_info {
-    white-space: nowrap;
-    position: absolute; left: 8em; top: .2em;
-    z-index: var(--layer-2);
-    box-shadow: var(--block-shadow);
-    border: none; border-radius: var(--block-label-radius);
-    background: var(--color-accent);
-    padding: var(--block-label-padding);
-    font-size: var(--block-label-text-size); line-height: var(--line-sm);
-    width: auto; min-height: 30px!important;
-    opacity: 1;
-    transition: opacity 0.3s ease-in-out;
-}
-#user_info .wrap {
-    opacity: 0;
-}
-#user_info p {
-    color: white;
-    font-weight: var(--block-label-text-weight);
-}
-#user_info.hideK {
-    opacity: 0;
-    transition: opacity 1s ease-in-out;
-}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: ui-monospace, "SF Mono", "SFMono-Regular", "Menlo", "Consolas", "Liberation Mono", "Microsoft Yahei UI", "Microsoft Yahei", monospace;
-    /* Windows下中文的monospace会fallback为新宋体，实在太丑，这里折中使用微软雅黑 */
-    color: var(--body-text-color-subdued);
-}
-
-#status_display {
-    transition: all 0.6s;
-}
-#chuanhu_chatbot {
-    transition: height 0.3s ease;
-}
-
-/* usage_display */
-.insert_block {
-    position: relative;
-    margin: 0;
-    padding: .5em 1em;
-    box-shadow: var(--block-shadow);
-    border-width: var(--block-border-width);
-    border-color: var(--block-border-color);
-    border-radius: var(--block-radius);
-    background: var(--block-background-fill);
-    width: 100%;
-    line-height: var(--line-sm);
-    min-height: 2em;
-}
-#usage_display p, #usage_display span {
-    margin: 0;
-    font-size: .85em;
-    color: var(--body-text-color-subdued);
-}
-.progress-bar {
-    background-color: var(--input-background-fill);;
-    margin: .5em 0 !important;
-    height: 20px;
-    border-radius: 10px;
-    overflow: hidden;
-}
-.progress {
-    background-color: var(--block-title-background-fill);
-    height: 100%;
-    border-radius: 10px;
-    text-align: right;
-    transition: width 0.5s ease-in-out;
-}
-.progress-text {
-    /* color: white; */
-    color: var(--color-accent) !important;
-    font-size: 1em !important;
-    font-weight: bold;
-    padding-right: 10px;
-    line-height: 20px;
-}
-
-.apSwitch {
-    top: 2px;
-    display: inline-block;
-    height: 24px;
-    position: relative;
-    width: 48px;
-    border-radius: 12px;
-}
-.apSwitch input {
-    display: none !important;
-}
-.apSlider {
-    background-color: var(--neutral-200);
-    bottom: 0;
-    cursor: pointer;
-    left: 0;
-    position: absolute;
-    right: 0;
-    top: 0;
-    transition: .4s;
-    font-size: 18px;
-    border-radius: 12px;
-}
-.apSlider::before {
-    bottom: -1.5px;
-    left: 1px;
-    position: absolute;
-    transition: .4s;
-    content: "🌞";
-}
-input:checked + .apSlider {
-    background-color: var(--primary-600);
-}
-input:checked + .apSlider::before {
-    transform: translateX(23px);
-    content:"🌚";
-}
-
-/* Override Slider Styles (for webkit browsers like Safari and Chrome)
- * 好希望这份提案能早日实现 https://github.com/w3c/csswg-drafts/issues/4410
- * 进度滑块在各个平台还是太不统一了
- */
-input[type="range"] {
-    -webkit-appearance: none;
-    height: 4px;
-    background: var(--input-background-fill);
-    border-radius: 5px;
-    background-image: linear-gradient(var(--primary-500),var(--primary-500));
-    background-size: 0% 100%;
-    background-repeat: no-repeat;
-}
-input[type="range"]::-webkit-slider-thumb {
-    -webkit-appearance: none;
-    height: 20px;
-    width: 20px;
-    border-radius: 50%;
-    border: solid 0.5px #ddd;
-    background-color: white;
-    cursor: ew-resize;
-    box-shadow: var(--input-shadow);
-    transition: background-color .1s ease;
-}
-input[type="range"]::-webkit-slider-thumb:hover {
-    background: var(--neutral-50);
-}
-input[type=range]::-webkit-slider-runnable-track {
-    -webkit-appearance: none;
-    box-shadow: none;
-    border: none;
-    background: transparent;
-}
-
-#submit_btn, #cancel_btn {
-    height: 42px !important;
-}
-#submit_btn::before {
-    content: url("data:image/svg+xml, %3Csvg width='21px' height='20px' viewBox='0 0 21 20' version='1.1' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'%3E %3Cg id='page' stroke='none' stroke-width='1' fill='none' fill-rule='evenodd'%3E %3Cg id='send' transform='translate(0.435849, 0.088463)' fill='%23FFFFFF' fill-rule='nonzero'%3E %3Cpath d='M0.579148261,0.0428666046 C0.301105539,-0.0961547561 -0.036517765,0.122307382 0.0032026237,0.420210298 L1.4927172,18.1553639 C1.5125774,18.4334066 1.79062012,18.5922882 2.04880264,18.4929872 L8.24518329,15.8913017 L11.6412765,19.7441794 C11.8597387,19.9825018 12.2370824,19.8832008 12.3165231,19.5852979 L13.9450591,13.4882182 L19.7839562,11.0255541 C20.0619989,10.8865327 20.0818591,10.4694687 19.7839562,10.3105871 L0.579148261,0.0428666046 Z M11.6138902,17.0883151 L9.85385903,14.7195502 L0.718169621,0.618812241 L12.69945,12.9346347 L11.6138902,17.0883151 Z' id='shape'%3E%3C/path%3E %3C/g%3E %3C/g%3E %3C/svg%3E");
-    height: 21px;
-}
-#cancel_btn::before {
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-    height: 21px;
-}
-/* list */
-ol:not(.options), ul:not(.options) {
-    padding-inline-start: 2em !important;
-}
-
-/* 亮色（默认） */
-#chuanhu_chatbot {
-    background-color: var(--chatbot-background-color-light) !important;
-    color: var(--chatbot-color-light) !important;
-}
-[data-testid = "bot"] {
-    background-color: var(--message-bot-background-color-light) !important;
-}
-[data-testid = "user"] {
-    background-color: var(--message-user-background-color-light) !important;
-}
-/* 暗色 */
-.dark #chuanhu_chatbot {
-    background-color: var(--chatbot-background-color-dark) !important;
-    color: var(--chatbot-color-dark) !important;
-}
-.dark [data-testid = "bot"] {
-    background-color: var(--message-bot-background-color-dark) !important;
-}
-.dark [data-testid = "user"] {
-    background-color: var(--message-user-background-color-dark) !important;
-}
-
-/* 屏幕宽度大于等于500px的设备 */
-/* update on 2023.4.8: 高度的细致调整已写入JavaScript */
-@media screen and (min-width: 500px) {
-    #chuanhu_chatbot {
-        height: calc(100vh - 200px);
-    }
-    #chuanhu_chatbot .wrap {
-        max-height: calc(100vh - 200px - var(--line-sm)*1rem - 2*var(--block-label-margin) );
-    }
-}
-/* 屏幕宽度小于500px的设备 */
-@media screen and (max-width: 499px) {
-    #chuanhu_chatbot {
-        height: calc(100vh - 140px);
-    }
-    #chuanhu_chatbot .wrap {
-        max-height: calc(100vh - 140px - var(--line-sm)*1rem - 2*var(--block-label-margin) );
-    }
-    [data-testid = "bot"] {
-        max-width: 95% !important;
-    }
-    #app_title h1{
-        letter-spacing: -1px; font-size: 22px;
-    }
-}
-#chuanhu_chatbot .wrap {
-    overflow-x: hidden;
-}
-/* 对话气泡 */
-.message {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    padding: var(--spacing-xl) !important;
-    font-size: var(--text-md) !important;
-    line-height: var(--line-md) !important;
-    min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-    min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-}
-[data-testid = "bot"] {
-    max-width: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-
-.message.user p {
-    white-space: pre-wrap;
-}
-.message .user-message {
-    display: block;
-    padding: 0 !important;
-    white-space: pre-wrap;
-}
-
-.message .md-message p {
-    margin-top: 0.6em !important;
-    margin-bottom: 0.6em !important;
-}
-.message .md-message p:first-child { margin-top: 0 !important; }
-.message .md-message p:last-of-type { margin-bottom: 0 !important; }
-
-.message .md-message {
-    display: block;
-    padding: 0 !important;
-}
-.message .raw-message p {
-    margin:0 !important;
-}
-.message .raw-message {
-    display: block;
-    padding: 0 !important;
-    white-space: pre-wrap;
-}
-.raw-message.hideM, .md-message.hideM {
-    display: none;
-}
-
-/* custom buttons */
-.chuanhu-btn {
-    border-radius: 5px;
-    /* background-color: #E6E6E6 !important; */
-    color: rgba(120, 120, 120, 0.64) !important;
-    padding: 4px !important;
-    position: absolute;
-    right: -22px;
-    cursor: pointer !important;
-    transition: color .2s ease, background-color .2s ease;
-}
-.chuanhu-btn:hover {
-    background-color: rgba(167, 167, 167, 0.25) !important;
-    color: unset !important;
-}
-.chuanhu-btn:active {
-    background-color: rgba(167, 167, 167, 0.5) !important;
-}
-.chuanhu-btn:focus {
-    outline: none;
-}
-.copy-bot-btn {
-    /* top: 18px; */
-    bottom: 0;
-}
-.toggle-md-btn {
-    /* top: 0; */
-    bottom: 20px;
-}
-.copy-code-btn {
-    position: relative;
-    float: right;
-    font-size: 1em;
-    cursor: pointer;
-}
-
-.message-wrap>div img{
-    border-radius: 10px !important;
-}
-
-/* history message */
-.wrap>.history-message {
-    padding: 10px !important;
-}
-.history-message {
-    /* padding: 0 !important; */
-    opacity: 80%;
-    display: flex;
-    flex-direction: column;
-}
-.history-message>.history-message {
-    padding: 0 !important;
-}
-.history-message>.message-wrap {
-    padding: 0 !important;
-    margin-bottom: 16px;
-}
-.history-message>.message {
-    margin-bottom: 16px;
-}
-.wrap>.history-message::after {
-    content: "";
-    display: block;
-    height: 2px;
-    background-color: var(--body-text-color-subdued);
-    margin-bottom: 10px;
-    margin-top: -10px;
-    clear: both;
-}
-.wrap>.history-message>:last-child::after {
-    content: "仅供查看";
-    display: block;
-    text-align: center;
-    color: var(--body-text-color-subdued);
-    font-size: 0.8em;
-}
-
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-.message :not(pre) code {
-    display: inline;
-    white-space: break-spaces;
-    font-family: var(--font-mono);
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-/* 代码块 */
-.message pre,
-.message pre[class*=language-] {
-    color: #fff;
-    overflow-x: auto;
-    overflow-y: hidden;
-    margin: .8em 1em 1em 0em !important;
-    padding: var(--spacing-xl) 1.2em !important;
-    border-radius: var(--radius-lg) !important;
-}
-.message pre code,
-.message pre code[class*=language-] {
-    color: #fff;
-    padding: 0;
-    margin: 0;
-    background-color: unset;
-    text-shadow: none;
-    font-family: var(--font-mono);
-}
-/* 覆盖 gradio 丑陋的复制按钮样式 */
-pre button[title="copy"] {
-    border-radius: 5px;
-    transition: background-color .2s ease;
-}
-pre button[title="copy"]:hover {
-    background-color: #333232;
-}
-pre button .check {
-    color: #fff !important;
-    background: var(--neutral-950) !important;
-}
-
-/* 覆盖prism.css */
-.language-css .token.string,
-.style .token.string,
-.token.entity,
-.token.operator,
-.token.url {
-    background: none !important;
-}
diff --git a/spaces/xt0r3/AI-Hype-Monitor/README.md b/spaces/xt0r3/AI-Hype-Monitor/README.md
deleted file mode 100644
index 58037dcd89fd8a9157baae508b3381a32fcf3f6d..0000000000000000000000000000000000000000
--- a/spaces/xt0r3/AI-Hype-Monitor/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AI Hype Monitor
-emoji: ⚡
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/xxie92/antibody_visulization/diffab/tools/eval/similarity.py b/spaces/xxie92/antibody_visulization/diffab/tools/eval/similarity.py
deleted file mode 100644
index 0cd396f27bc3e98e1e79a501cc1117318d1cfe96..0000000000000000000000000000000000000000
--- a/spaces/xxie92/antibody_visulization/diffab/tools/eval/similarity.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import numpy as np
-from Bio.PDB import PDBParser, Selection
-from Bio.PDB.Polypeptide import three_to_one
-from Bio import pairwise2
-from Bio.Align import substitution_matrices
-
-from diffab.tools.eval.base import EvalTask
-
-
-def reslist_rmsd(res_list1, res_list2):
-    res_short, res_long = (res_list1, res_list2) if len(res_list1) < len(res_list2) else (res_list2, res_list1)
-    M, N = len(res_short), len(res_long)
-
-    def d(i, j):
-        coord_i = np.array(res_short[i]['CA'].get_coord())
-        coord_j = np.array(res_long[j]['CA'].get_coord())
-        return ((coord_i - coord_j) ** 2).sum()
-
-    SD = np.full([M, N], np.inf)
-    for i in range(M):
-        j = N - (M - i)
-        SD[i, j] = sum([ d(i+k, j+k) for k in range(N-j) ])
-    
-    for j in range(N):
-        SD[M-1, j] = d(M-1, j)
-
-    for i in range(M-2, -1, -1):
-        for j in range((N-(M-i))-1, -1, -1):
-            SD[i, j] = min(
-                d(i, j) + SD[i+1, j+1],
-                SD[i, j+1]
-            )
-
-    min_SD = SD[0, :N-M+1].min()
-    best_RMSD = np.sqrt(min_SD / M)
-    return best_RMSD
-
-
-def entity_to_seq(entity):
-    seq = ''
-    mapping = []
-    for res in Selection.unfold_entities(entity, 'R'):
-        try:
-            seq += three_to_one(res.get_resname())
-            mapping.append(res.get_id())
-        except KeyError:
-            pass
-    assert len(seq) == len(mapping)
-    return seq, mapping
-
-
-def reslist_seqid(res_list1, res_list2):
-    seq1, _ = entity_to_seq(res_list1)
-    seq2, _ = entity_to_seq(res_list2)
-    _, seq_id = align_sequences(seq1, seq2)
-    return seq_id
-
-
-def align_sequences(sequence_A, sequence_B, **kwargs):
-    """
-    Performs a global pairwise alignment between two sequences
-    using the BLOSUM62 matrix and the Needleman-Wunsch algorithm
-    as implemented in Biopython. Returns the alignment, the sequence
-    identity and the residue mapping between both original sequences.
-    """
-
-    def _calculate_identity(sequenceA, sequenceB):
-        """
-        Returns the percentage of identical characters between two sequences.
-        Assumes the sequences are aligned.
-        """
-
-        sa, sb, sl = sequenceA, sequenceB, len(sequenceA)
-        matches = [sa[i] == sb[i] for i in range(sl)]
-        seq_id = (100 * sum(matches)) / sl
-        return seq_id
-
-        # gapless_sl = sum([1 for i in range(sl) if (sa[i] != '-' and sb[i] != '-')])
-        # gap_id = (100 * sum(matches)) / gapless_sl
-        # return (seq_id, gap_id)
-
-    #
-    matrix = kwargs.get('matrix', substitution_matrices.load("BLOSUM62"))
-    gap_open = kwargs.get('gap_open', -10.0)
-    gap_extend = kwargs.get('gap_extend', -0.5)
-
-    alns = pairwise2.align.globalds(sequence_A, sequence_B,
-                                    matrix, gap_open, gap_extend,
-                                    penalize_end_gaps=(False, False) )
-
-    best_aln = alns[0]
-    aligned_A, aligned_B, score, begin, end = best_aln
-
-    # Calculate sequence identity
-    seq_id = _calculate_identity(aligned_A, aligned_B)
-    return (aligned_A, aligned_B), seq_id
-
-
-def extract_reslist(model, residue_first, residue_last):
-    assert residue_first[0] == residue_last[0]
-    residue_first, residue_last = tuple(residue_first), tuple(residue_last)
-
-    chain_id = residue_first[0]
-    pos_first, pos_last = residue_first[1:], residue_last[1:]
-    chain = model[chain_id]
-    reslist = []
-    for res in Selection.unfold_entities(chain, 'R'):
-        pos_current = (res.id[1], res.id[2])
-        if pos_first <= pos_current <= pos_last:
-            reslist.append(res)
-    return reslist
-
-
-def eval_similarity(task: EvalTask):
-    model_gen = task.get_gen_biopython_model()
-    model_ref = task.get_ref_biopython_model()
-
-    reslist_gen = extract_reslist(model_gen, task.residue_first, task.residue_last)
-    reslist_ref = extract_reslist(model_ref, task.residue_first, task.residue_last)
-
-    task.scores.update({
-        'rmsd': reslist_rmsd(reslist_gen, reslist_ref),
-        'seqid': reslist_seqid(reslist_gen, reslist_ref),
-    })
-    return task
diff --git a/spaces/yaoshining/text-generation-webui/extensions/google_translate/script.py b/spaces/yaoshining/text-generation-webui/extensions/google_translate/script.py
deleted file mode 100644
index 63226107b2c2afe086fc343c7b7f7df78bef3f8a..0000000000000000000000000000000000000000
--- a/spaces/yaoshining/text-generation-webui/extensions/google_translate/script.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import gradio as gr
-from deep_translator import GoogleTranslator
-
-params = {
-    "language string": "ja",
-}
-
-language_codes = {'Afrikaans': 'af', 'Albanian': 'sq', 'Amharic': 'am', 'Arabic': 'ar', 'Armenian': 'hy', 'Azerbaijani': 'az', 'Basque': 'eu', 'Belarusian': 'be', 'Bengali': 'bn', 'Bosnian': 'bs', 'Bulgarian': 'bg', 'Catalan': 'ca', 'Cebuano': 'ceb', 'Chinese (Simplified)': 'zh-CN', 'Chinese (Traditional)': 'zh-TW', 'Corsican': 'co', 'Croatian': 'hr', 'Czech': 'cs', 'Danish': 'da', 'Dutch': 'nl', 'English': 'en', 'Esperanto': 'eo', 'Estonian': 'et', 'Finnish': 'fi', 'French': 'fr', 'Frisian': 'fy', 'Galician': 'gl', 'Georgian': 'ka', 'German': 'de', 'Greek': 'el', 'Gujarati': 'gu', 'Haitian Creole': 'ht', 'Hausa': 'ha', 'Hawaiian': 'haw', 'Hebrew': 'iw', 'Hindi': 'hi', 'Hmong': 'hmn', 'Hungarian': 'hu', 'Icelandic': 'is', 'Igbo': 'ig', 'Indonesian': 'id', 'Irish': 'ga', 'Italian': 'it', 'Japanese': 'ja', 'Javanese': 'jw', 'Kannada': 'kn', 'Kazakh': 'kk', 'Khmer': 'km', 'Korean': 'ko', 'Kurdish': 'ku', 'Kyrgyz': 'ky', 'Lao': 'lo', 'Latin': 'la', 'Latvian': 'lv', 'Lithuanian': 'lt', 'Luxembourgish': 'lb', 'Macedonian': 'mk', 'Malagasy': 'mg', 'Malay': 'ms', 'Malayalam': 'ml', 'Maltese': 'mt', 'Maori': 'mi', 'Marathi': 'mr', 'Mongolian': 'mn', 'Myanmar (Burmese)': 'my', 'Nepali': 'ne', 'Norwegian': 'no', 'Nyanja (Chichewa)': 'ny', 'Pashto': 'ps', 'Persian': 'fa', 'Polish': 'pl', 'Portuguese (Portugal, Brazil)': 'pt', 'Punjabi': 'pa', 'Romanian': 'ro', 'Russian': 'ru', 'Samoan': 'sm', 'Scots Gaelic': 'gd', 'Serbian': 'sr', 'Sesotho': 'st', 'Shona': 'sn', 'Sindhi': 'sd', 'Sinhala (Sinhalese)': 'si', 'Slovak': 'sk', 'Slovenian': 'sl', 'Somali': 'so', 'Spanish': 'es', 'Sundanese': 'su', 'Swahili': 'sw', 'Swedish': 'sv', 'Tagalog (Filipino)': 'tl', 'Tajik': 'tg', 'Tamil': 'ta', 'Telugu': 'te', 'Thai': 'th', 'Turkish': 'tr', 'Ukrainian': 'uk', 'Urdu': 'ur', 'Uzbek': 'uz', 'Vietnamese': 'vi', 'Welsh': 'cy', 'Xhosa': 'xh', 'Yiddish': 'yi', 'Yoruba': 'yo', 'Zulu': 'zu'}
-
-
-def input_modifier(string):
-    """
-    This function is applied to your text inputs before
-    they are fed into the model.
-    """
-
-    return GoogleTranslator(source=params['language string'], target='en').translate(string)
-
-
-def output_modifier(string):
-    """
-    This function is applied to the model outputs.
-    """
-
-    return GoogleTranslator(source='en', target=params['language string']).translate(string)
-
-
-def bot_prefix_modifier(string):
-    """
-    This function is only applied in chat mode. It modifies
-    the prefix text for the Bot and can be used to bias its
-    behavior.
-    """
-
-    return string
-
-
-def ui():
-    # Finding the language name from the language code to use as the default value
-    language_name = list(language_codes.keys())[list(language_codes.values()).index(params['language string'])]
-
-    # Gradio elements
-    language = gr.Dropdown(value=language_name, choices=[k for k in language_codes], label='Language')
-
-    # Event functions to update the parameters in the backend
-    language.change(lambda x: params.update({"language string": language_codes[x]}), language, None)
diff --git a/spaces/yderre-aubay/midi-player-demo/src/main/components/KeyboardShortcut/isFocusable.ts b/spaces/yderre-aubay/midi-player-demo/src/main/components/KeyboardShortcut/isFocusable.ts
deleted file mode 100644
index 515fa7b393c4362b951aa7437f8f274ce0b49892..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/main/components/KeyboardShortcut/isFocusable.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-export const isFocusable = (e: EventTarget) =>
-  e instanceof HTMLAnchorElement ||
-  e instanceof HTMLAreaElement ||
-  e instanceof HTMLInputElement ||
-  e instanceof HTMLSelectElement ||
-  e instanceof HTMLTextAreaElement ||
-  e instanceof HTMLButtonElement ||
-  e instanceof HTMLIFrameElement
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/conditional_detr/modeling_conditional_detr.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/conditional_detr/modeling_conditional_detr.py
deleted file mode 100644
index 15f24084f469952e4a926ee389978cb80508ed7d..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/conditional_detr/modeling_conditional_detr.py
+++ /dev/null
@@ -1,2786 +0,0 @@
-# coding=utf-8
-# Copyright 2022 Microsoft Research Asia and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch Conditional DETR model."""
-
-
-import math
-from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple, Union
-
-import torch
-from torch import Tensor, nn
-
-from ...activations import ACT2FN
-from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
-from ...modeling_utils import PreTrainedModel
-from ...utils import (
-    ModelOutput,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_scipy_available,
-    is_timm_available,
-    is_vision_available,
-    logging,
-    replace_return_docstrings,
-    requires_backends,
-)
-from ..auto import AutoBackbone
-from .configuration_conditional_detr import ConditionalDetrConfig
-
-
-if is_scipy_available():
-    from scipy.optimize import linear_sum_assignment
-
-if is_timm_available():
-    from timm import create_model
-
-if is_vision_available():
-    from ...image_transforms import center_to_corners_format
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "ConditionalDetrConfig"
-_CHECKPOINT_FOR_DOC = "microsoft/conditional-detr-resnet-50"
-
-CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "microsoft/conditional-detr-resnet-50",
-    # See all Conditional DETR models at https://huggingface.co/models?filter=conditional_detr
-]
-
-
-@dataclass
-class ConditionalDetrDecoderOutput(BaseModelOutputWithCrossAttentions):
-    """
-    Base class for outputs of the Conditional DETR decoder. This class adds one attribute to
-    BaseModelOutputWithCrossAttentions, namely an optional stack of intermediate decoder activations, i.e. the output
-    of each decoder layer, each of them gone through a layernorm. This is useful when training the model with auxiliary
-    decoding losses.
-
-    Args:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
-            plus the initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
-            the self-attention heads.
-        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
-            used to compute the weighted average in the cross-attention heads.
-        intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, num_queries, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`):
-            Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
-            layernorm.
-    """
-
-    intermediate_hidden_states: Optional[torch.FloatTensor] = None
-    reference_points: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class ConditionalDetrModelOutput(Seq2SeqModelOutput):
-    """
-    Base class for outputs of the Conditional DETR encoder-decoder model. This class adds one attribute to
-    Seq2SeqModelOutput, namely an optional stack of intermediate decoder activations, i.e. the output of each decoder
-    layer, each of them gone through a layernorm. This is useful when training the model with auxiliary decoding
-    losses.
-
-    Args:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-            Sequence of hidden-states at the output of the last layer of the decoder of the model.
-        decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each
-            layer plus the initial embedding outputs.
-        decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the
-            weighted average in the self-attention heads.
-        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
-            used to compute the weighted average in the cross-attention heads.
-        encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Sequence of hidden-states at the output of the last layer of the encoder of the model.
-        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each
-            layer plus the initial embedding outputs.
-        encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the
-            weighted average in the self-attention heads.
-        intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, sequence_length, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`):
-            Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
-            layernorm.
-    """
-
-    intermediate_hidden_states: Optional[torch.FloatTensor] = None
-    reference_points: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-# Copied from transformers.models.detr.modeling_detr.DetrObjectDetectionOutput with Detr->ConditionalDetr
-class ConditionalDetrObjectDetectionOutput(ModelOutput):
-    """
-    Output type of [`ConditionalDetrForObjectDetection`].
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
-            Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
-            bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
-            scale-invariant IoU loss.
-        loss_dict (`Dict`, *optional*):
-            A dictionary containing the individual losses. Useful for logging.
-        logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`):
-            Classification logits (including no-object) for all queries.
-        pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
-            Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
-            values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
-            possible padding). You can use [`~ConditionalDetrImageProcessor.post_process_object_detection`] to retrieve
-            the unnormalized bounding boxes.
-        auxiliary_outputs (`list[Dict]`, *optional*):
-            Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
-            and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and
-            `pred_boxes`) for each decoder layer.
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Sequence of hidden-states at the output of the last layer of the decoder of the model.
-        decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each
-            layer plus the initial embedding outputs.
-        decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the
-            weighted average in the self-attention heads.
-        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
-            used to compute the weighted average in the cross-attention heads.
-        encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Sequence of hidden-states at the output of the last layer of the encoder of the model.
-        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each
-            layer plus the initial embedding outputs.
-        encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the
-            weighted average in the self-attention heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    loss_dict: Optional[Dict] = None
-    logits: torch.FloatTensor = None
-    pred_boxes: torch.FloatTensor = None
-    auxiliary_outputs: Optional[List[Dict]] = None
-    last_hidden_state: Optional[torch.FloatTensor] = None
-    decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_last_hidden_state: Optional[torch.FloatTensor] = None
-    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-# Copied from transformers.models.detr.modeling_detr.DetrSegmentationOutput with Detr->ConditionalDetr
-class ConditionalDetrSegmentationOutput(ModelOutput):
-    """
-    Output type of [`ConditionalDetrForSegmentation`].
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
-            Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
-            bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
-            scale-invariant IoU loss.
-        loss_dict (`Dict`, *optional*):
-            A dictionary containing the individual losses. Useful for logging.
-        logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`):
-            Classification logits (including no-object) for all queries.
-        pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
-            Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
-            values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
-            possible padding). You can use [`~ConditionalDetrImageProcessor.post_process_object_detection`] to retrieve
-            the unnormalized bounding boxes.
-        pred_masks (`torch.FloatTensor` of shape `(batch_size, num_queries, height/4, width/4)`):
-            Segmentation masks logits for all queries. See also
-            [`~ConditionalDetrImageProcessor.post_process_semantic_segmentation`] or
-            [`~ConditionalDetrImageProcessor.post_process_instance_segmentation`]
-            [`~ConditionalDetrImageProcessor.post_process_panoptic_segmentation`] to evaluate semantic, instance and
-            panoptic segmentation masks respectively.
-        auxiliary_outputs (`list[Dict]`, *optional*):
-            Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
-            and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and
-            `pred_boxes`) for each decoder layer.
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Sequence of hidden-states at the output of the last layer of the decoder of the model.
-        decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each
-            layer plus the initial embedding outputs.
-        decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the
-            weighted average in the self-attention heads.
-        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
-            used to compute the weighted average in the cross-attention heads.
-        encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Sequence of hidden-states at the output of the last layer of the encoder of the model.
-        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each
-            layer plus the initial embedding outputs.
-        encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the
-            weighted average in the self-attention heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    loss_dict: Optional[Dict] = None
-    logits: torch.FloatTensor = None
-    pred_boxes: torch.FloatTensor = None
-    pred_masks: torch.FloatTensor = None
-    auxiliary_outputs: Optional[List[Dict]] = None
-    last_hidden_state: Optional[torch.FloatTensor] = None
-    decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_last_hidden_state: Optional[torch.FloatTensor] = None
-    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrFrozenBatchNorm2d with Detr->ConditionalDetr
-class ConditionalDetrFrozenBatchNorm2d(nn.Module):
-    """
-    BatchNorm2d where the batch statistics and the affine parameters are fixed.
-
-    Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than
-    torchvision.models.resnet[18,34,50,101] produce nans.
-    """
-
-    def __init__(self, n):
-        super().__init__()
-        self.register_buffer("weight", torch.ones(n))
-        self.register_buffer("bias", torch.zeros(n))
-        self.register_buffer("running_mean", torch.zeros(n))
-        self.register_buffer("running_var", torch.ones(n))
-
-    def _load_from_state_dict(
-        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-    ):
-        num_batches_tracked_key = prefix + "num_batches_tracked"
-        if num_batches_tracked_key in state_dict:
-            del state_dict[num_batches_tracked_key]
-
-        super()._load_from_state_dict(
-            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-        )
-
-    def forward(self, x):
-        # move reshapes to the beginning
-        # to make it user-friendly
-        weight = self.weight.reshape(1, -1, 1, 1)
-        bias = self.bias.reshape(1, -1, 1, 1)
-        running_var = self.running_var.reshape(1, -1, 1, 1)
-        running_mean = self.running_mean.reshape(1, -1, 1, 1)
-        epsilon = 1e-5
-        scale = weight * (running_var + epsilon).rsqrt()
-        bias = bias - running_mean * scale
-        return x * scale + bias
-
-
-# Copied from transformers.models.detr.modeling_detr.replace_batch_norm with Detr->ConditionalDetr
-def replace_batch_norm(model):
-    r"""
-    Recursively replace all `torch.nn.BatchNorm2d` with `ConditionalDetrFrozenBatchNorm2d`.
-
-    Args:
-        model (torch.nn.Module):
-            input model
-    """
-    for name, module in model.named_children():
-        if isinstance(module, nn.BatchNorm2d):
-            new_module = ConditionalDetrFrozenBatchNorm2d(module.num_features)
-
-            new_module.weight.data.copy_(module.weight)
-            new_module.bias.data.copy_(module.bias)
-            new_module.running_mean.data.copy_(module.running_mean)
-            new_module.running_var.data.copy_(module.running_var)
-
-            model._modules[name] = new_module
-
-        if len(list(module.children())) > 0:
-            replace_batch_norm(module)
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrConvEncoder
-class ConditionalDetrConvEncoder(nn.Module):
-    """
-    Convolutional backbone, using either the AutoBackbone API or one from the timm library.
-
-    nn.BatchNorm2d layers are replaced by DetrFrozenBatchNorm2d as defined above.
-
-    """
-
-    def __init__(self, config):
-        super().__init__()
-
-        self.config = config
-
-        if config.use_timm_backbone:
-            requires_backends(self, ["timm"])
-            kwargs = {}
-            if config.dilation:
-                kwargs["output_stride"] = 16
-            backbone = create_model(
-                config.backbone,
-                pretrained=config.use_pretrained_backbone,
-                features_only=True,
-                out_indices=(1, 2, 3, 4),
-                in_chans=config.num_channels,
-                **kwargs,
-            )
-        else:
-            backbone = AutoBackbone.from_config(config.backbone_config)
-
-        # replace batch norm by frozen batch norm
-        with torch.no_grad():
-            replace_batch_norm(backbone)
-        self.model = backbone
-        self.intermediate_channel_sizes = (
-            self.model.feature_info.channels() if config.use_timm_backbone else self.model.channels
-        )
-
-        backbone_model_type = config.backbone if config.use_timm_backbone else config.backbone_config.model_type
-        if "resnet" in backbone_model_type:
-            for name, parameter in self.model.named_parameters():
-                if config.use_timm_backbone:
-                    if "layer2" not in name and "layer3" not in name and "layer4" not in name:
-                        parameter.requires_grad_(False)
-                else:
-                    if "stage.1" not in name and "stage.2" not in name and "stage.3" not in name:
-                        parameter.requires_grad_(False)
-
-    def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor):
-        # send pixel_values through the model to get list of feature maps
-        features = self.model(pixel_values) if self.config.use_timm_backbone else self.model(pixel_values).feature_maps
-
-        out = []
-        for feature_map in features:
-            # downsample pixel_mask to match shape of corresponding feature_map
-            mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0]
-            out.append((feature_map, mask))
-        return out
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrConvModel with Detr->ConditionalDetr
-class ConditionalDetrConvModel(nn.Module):
-    """
-    This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder.
-    """
-
-    def __init__(self, conv_encoder, position_embedding):
-        super().__init__()
-        self.conv_encoder = conv_encoder
-        self.position_embedding = position_embedding
-
-    def forward(self, pixel_values, pixel_mask):
-        # send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples
-        out = self.conv_encoder(pixel_values, pixel_mask)
-        pos = []
-        for feature_map, mask in out:
-            # position encoding
-            pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype))
-
-        return out, pos
-
-
-# Copied from transformers.models.detr.modeling_detr._expand_mask with Detr->ConditionalDetr
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, target_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, target_seq_len, source_seq_len]`.
-    """
-    batch_size, source_len = mask.size()
-    target_len = target_len if target_len is not None else source_len
-
-    expanded_mask = mask[:, None, None, :].expand(batch_size, 1, target_len, source_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrSinePositionEmbedding with Detr->ConditionalDetr
-class ConditionalDetrSinePositionEmbedding(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
-    need paper, generalized to work on images.
-    """
-
-    def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale=None):
-        super().__init__()
-        self.embedding_dim = embedding_dim
-        self.temperature = temperature
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, pixel_values, pixel_mask):
-        if pixel_mask is None:
-            raise ValueError("No pixel mask provided")
-        y_embed = pixel_mask.cumsum(1, dtype=torch.float32)
-        x_embed = pixel_mask.cumsum(2, dtype=torch.float32)
-        if self.normalize:
-            y_embed = y_embed / (y_embed[:, -1:, :] + 1e-6) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + 1e-6) * self.scale
-
-        dim_t = torch.arange(self.embedding_dim, dtype=torch.float32, device=pixel_values.device)
-        dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
-        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-        return pos
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrLearnedPositionEmbedding with Detr->ConditionalDetr
-class ConditionalDetrLearnedPositionEmbedding(nn.Module):
-    """
-    This module learns positional embeddings up to a fixed maximum size.
-    """
-
-    def __init__(self, embedding_dim=256):
-        super().__init__()
-        self.row_embeddings = nn.Embedding(50, embedding_dim)
-        self.column_embeddings = nn.Embedding(50, embedding_dim)
-
-    def forward(self, pixel_values, pixel_mask=None):
-        height, width = pixel_values.shape[-2:]
-        width_values = torch.arange(width, device=pixel_values.device)
-        height_values = torch.arange(height, device=pixel_values.device)
-        x_emb = self.column_embeddings(width_values)
-        y_emb = self.row_embeddings(height_values)
-        pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1)
-        pos = pos.permute(2, 0, 1)
-        pos = pos.unsqueeze(0)
-        pos = pos.repeat(pixel_values.shape[0], 1, 1, 1)
-        return pos
-
-
-# Copied from transformers.models.detr.modeling_detr.build_position_encoding with Detr->ConditionalDetr
-def build_position_encoding(config):
-    n_steps = config.d_model // 2
-    if config.position_embedding_type == "sine":
-        # TODO find a better way of exposing other arguments
-        position_embedding = ConditionalDetrSinePositionEmbedding(n_steps, normalize=True)
-    elif config.position_embedding_type == "learned":
-        position_embedding = ConditionalDetrLearnedPositionEmbedding(n_steps)
-    else:
-        raise ValueError(f"Not supported {config.position_embedding_type}")
-
-    return position_embedding
-
-
-# function to generate sine positional embedding for 2d coordinates
-def gen_sine_position_embeddings(pos_tensor, d_model):
-    scale = 2 * math.pi
-    dim = d_model // 2
-    dim_t = torch.arange(dim, dtype=torch.float32, device=pos_tensor.device)
-    dim_t = 10000 ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / dim)
-    x_embed = pos_tensor[:, :, 0] * scale
-    y_embed = pos_tensor[:, :, 1] * scale
-    pos_x = x_embed[:, :, None] / dim_t
-    pos_y = y_embed[:, :, None] / dim_t
-    pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
-    pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
-    pos = torch.cat((pos_y, pos_x), dim=2)
-    return pos
-
-
-def inverse_sigmoid(x, eps=1e-5):
-    x = x.clamp(min=0, max=1)
-    x1 = x.clamp(min=eps)
-    x2 = (1 - x).clamp(min=eps)
-    return torch.log(x1 / x2)
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrAttention
-class DetrAttention(nn.Module):
-    """
-    Multi-headed attention from 'Attention Is All You Need' paper.
-
-    Here, we add position embeddings to the queries and keys (as explained in the DETR paper).
-    """
-
-    def __init__(
-        self,
-        embed_dim: int,
-        num_heads: int,
-        dropout: float = 0.0,
-        bias: bool = True,
-    ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = embed_dim // num_heads
-        if self.head_dim * num_heads != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
-                f" {num_heads})."
-            )
-        self.scaling = self.head_dim**-0.5
-
-        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
-        return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs):
-        position_embeddings = kwargs.pop("position_embeddings", None)
-
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
-        if position_embeddings is not None and object_queries is not None:
-            raise ValueError(
-                "Cannot specify both position_embeddings and object_queries. Please use just object_queries"
-            )
-
-        if position_embeddings is not None:
-            logger.warning_once(
-                "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
-            )
-            object_queries = position_embeddings
-
-        return tensor if object_queries is None else tensor + object_queries
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        object_queries: Optional[torch.Tensor] = None,
-        key_value_states: Optional[torch.Tensor] = None,
-        spatial_position_embeddings: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        position_embeddings = kwargs.pop("position_ebmeddings", None)
-        key_value_position_embeddings = kwargs.pop("key_value_position_embeddings", None)
-
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
-        if position_embeddings is not None and object_queries is not None:
-            raise ValueError(
-                "Cannot specify both position_embeddings and object_queries. Please use just object_queries"
-            )
-
-        if key_value_position_embeddings is not None and spatial_position_embeddings is not None:
-            raise ValueError(
-                "Cannot specify both key_value_position_embeddings and spatial_position_embeddings. Please use just spatial_position_embeddings"
-            )
-
-        if position_embeddings is not None:
-            logger.warning_once(
-                "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
-            )
-            object_queries = position_embeddings
-
-        if key_value_position_embeddings is not None:
-            logger.warning_once(
-                "key_value_position_embeddings has been deprecated and will be removed in v4.34. Please use spatial_position_embeddings instead"
-            )
-            spatial_position_embeddings = key_value_position_embeddings
-
-        # if key_value_states are provided this layer is used as a cross-attention layer
-        # for the decoder
-        is_cross_attention = key_value_states is not None
-        batch_size, target_len, embed_dim = hidden_states.size()
-
-        # add position embeddings to the hidden states before projecting to queries and keys
-        if object_queries is not None:
-            hidden_states_original = hidden_states
-            hidden_states = self.with_pos_embed(hidden_states, object_queries)
-
-        # add key-value position embeddings to the key value states
-        if spatial_position_embeddings is not None:
-            key_value_states_original = key_value_states
-            key_value_states = self.with_pos_embed(key_value_states, spatial_position_embeddings)
-
-        # get query proj
-        query_states = self.q_proj(hidden_states) * self.scaling
-        # get key, value proj
-        if is_cross_attention:
-            # cross_attentions
-            key_states = self._shape(self.k_proj(key_value_states), -1, batch_size)
-            value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size)
-        else:
-            # self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, batch_size)
-            value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size)
-
-        proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
-        query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape)
-        key_states = key_states.view(*proj_shape)
-        value_states = value_states.view(*proj_shape)
-
-        source_len = key_states.size(1)
-
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
-
-        if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len):
-            raise ValueError(
-                f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_mask.size() != (batch_size, 1, target_len, source_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is"
-                    f" {attention_mask.size()}"
-                )
-            attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask
-            attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len)
-
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
-        if output_attentions:
-            # this operation is a bit awkward, but it's required to
-            # make sure that attn_weights keeps its gradient.
-            # In order to do so, attn_weights have to reshaped
-            # twice and have to be reused in the following
-            attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len)
-            attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len)
-        else:
-            attn_weights_reshaped = None
-
-        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-
-        attn_output = torch.bmm(attn_probs, value_states)
-
-        if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim)
-        attn_output = attn_output.transpose(1, 2)
-        attn_output = attn_output.reshape(batch_size, target_len, embed_dim)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights_reshaped
-
-
-class ConditionalDetrAttention(nn.Module):
-    """
-    Cross-Attention used in Conditional DETR 'Conditional DETR for Fast Training Convergence' paper.
-
-    The key q_proj, k_proj, v_proj are defined outside the attention. This attention allows the dim of q, k to be
-    different to v.
-    """
-
-    def __init__(
-        self,
-        embed_dim: int,
-        out_dim: int,
-        num_heads: int,
-        dropout: float = 0.0,
-        bias: bool = True,
-    ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.out_dim = out_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = embed_dim // num_heads
-        if self.head_dim * num_heads != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
-                f" {num_heads})."
-            )
-        # head dimension of values
-        self.v_head_dim = out_dim // num_heads
-        if self.v_head_dim * num_heads != self.out_dim:
-            raise ValueError(
-                f"out_dim must be divisible by num_heads (got `out_dim`: {self.out_dim} and `num_heads`: {num_heads})."
-            )
-        self.scaling = self.head_dim**-0.5
-
-        self.out_proj = nn.Linear(out_dim, out_dim, bias=bias)
-
-    def _qk_shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
-        return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def _v_shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
-        return tensor.view(batch_size, seq_len, self.num_heads, self.v_head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        key_states: Optional[torch.Tensor] = None,
-        value_states: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        batch_size, target_len, _ = hidden_states.size()
-
-        # get query proj
-        query_states = hidden_states * self.scaling
-        # get key, value proj
-        key_states = self._qk_shape(key_states, -1, batch_size)
-        value_states = self._v_shape(value_states, -1, batch_size)
-
-        proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
-        v_proj_shape = (batch_size * self.num_heads, -1, self.v_head_dim)
-        query_states = self._qk_shape(query_states, target_len, batch_size).view(*proj_shape)
-        key_states = key_states.view(*proj_shape)
-        value_states = value_states.view(*v_proj_shape)
-
-        source_len = key_states.size(1)
-
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
-
-        if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len):
-            raise ValueError(
-                f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_mask.size() != (batch_size, 1, target_len, source_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is"
-                    f" {attention_mask.size()}"
-                )
-            attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask
-            attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len)
-
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
-        if output_attentions:
-            # this operation is a bit awkward, but it's required to
-            # make sure that attn_weights keeps its gradient.
-            # In order to do so, attn_weights have to reshaped
-            # twice and have to be reused in the following
-            attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len)
-            attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len)
-        else:
-            attn_weights_reshaped = None
-
-        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-
-        attn_output = torch.bmm(attn_probs, value_states)
-
-        if attn_output.size() != (batch_size * self.num_heads, target_len, self.v_head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.v_head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.v_head_dim)
-        attn_output = attn_output.transpose(1, 2)
-        attn_output = attn_output.reshape(batch_size, target_len, self.out_dim)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights_reshaped
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrEncoderLayer with DetrEncoderLayer->ConditionalDetrEncoderLayer,DetrConfig->ConditionalDetrConfig
-class ConditionalDetrEncoderLayer(nn.Module):
-    def __init__(self, config: ConditionalDetrConfig):
-        super().__init__()
-        self.embed_dim = config.d_model
-        self.self_attn = DetrAttention(
-            embed_dim=self.embed_dim,
-            num_heads=config.encoder_attention_heads,
-            dropout=config.attention_dropout,
-        )
-        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.dropout = config.dropout
-        self.activation_fn = ACT2FN[config.activation_function]
-        self.activation_dropout = config.activation_dropout
-        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
-        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
-        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor,
-        object_queries: torch.Tensor = None,
-        output_attentions: bool = False,
-        **kwargs,
-    ):
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
-                values.
-            object_queries (`torch.FloatTensor`, *optional*):
-                Object queries (also called content embeddings), to be added to the hidden states.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        position_embeddings = kwargs.pop("position_embeddings", None)
-
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
-        if position_embeddings is not None and object_queries is not None:
-            raise ValueError(
-                "Cannot specify both position_embeddings and object_queries. Please use just object_queries"
-            )
-
-        if position_embeddings is not None:
-            logger.warning_once(
-                "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
-            )
-            object_queries = position_embeddings
-
-        residual = hidden_states
-        hidden_states, attn_weights = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            object_queries=object_queries,
-            output_attentions=output_attentions,
-        )
-
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.self_attn_layer_norm(hidden_states)
-
-        residual = hidden_states
-        hidden_states = self.activation_fn(self.fc1(hidden_states))
-        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
-
-        hidden_states = self.fc2(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-
-        hidden_states = residual + hidden_states
-        hidden_states = self.final_layer_norm(hidden_states)
-
-        if self.training:
-            if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
-                clamp_value = torch.finfo(hidden_states.dtype).max - 1000
-                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs
-
-
-class ConditionalDetrDecoderLayer(nn.Module):
-    def __init__(self, config: ConditionalDetrConfig):
-        super().__init__()
-        self.embed_dim = config.d_model
-
-        d_model = config.d_model
-        # Decoder Self-Attention projections
-        self.sa_qcontent_proj = nn.Linear(d_model, d_model)
-        self.sa_qpos_proj = nn.Linear(d_model, d_model)
-        self.sa_kcontent_proj = nn.Linear(d_model, d_model)
-        self.sa_kpos_proj = nn.Linear(d_model, d_model)
-        self.sa_v_proj = nn.Linear(d_model, d_model)
-
-        self.self_attn = ConditionalDetrAttention(
-            embed_dim=self.embed_dim,
-            out_dim=self.embed_dim,
-            num_heads=config.decoder_attention_heads,
-            dropout=config.attention_dropout,
-        )
-        self.dropout = config.dropout
-        self.activation_fn = ACT2FN[config.activation_function]
-        self.activation_dropout = config.activation_dropout
-
-        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-
-        # Decoder Cross-Attention projections
-        self.ca_qcontent_proj = nn.Linear(d_model, d_model)
-        self.ca_qpos_proj = nn.Linear(d_model, d_model)
-        self.ca_kcontent_proj = nn.Linear(d_model, d_model)
-        self.ca_kpos_proj = nn.Linear(d_model, d_model)
-        self.ca_v_proj = nn.Linear(d_model, d_model)
-        self.ca_qpos_sine_proj = nn.Linear(d_model, d_model)
-
-        self.encoder_attn = ConditionalDetrAttention(
-            self.embed_dim * 2, self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout
-        )
-        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
-        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
-        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.nhead = config.decoder_attention_heads
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        object_queries: Optional[torch.Tensor] = None,
-        query_position_embeddings: Optional[torch.Tensor] = None,
-        query_sine_embed: Optional[torch.Tensor] = None,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = False,
-        is_first: Optional[bool] = False,
-        **kwargs,
-    ):
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
-                values.
-            object_queries (`torch.FloatTensor`, *optional*):
-                object_queries that are added to the queries and keys
-            in the cross-attention layer.
-            query_position_embeddings (`torch.FloatTensor`, *optional*):
-                object_queries that are added to the queries and keys
-            in the self-attention layer.
-            encoder_hidden_states (`torch.FloatTensor`):
-                cross attention input to the layer of shape `(seq_len, batch, embed_dim)`
-            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
-                `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
-                values.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        position_embeddings = kwargs.pop("position_embeddings", None)
-
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
-        if position_embeddings is not None and object_queries is not None:
-            raise ValueError(
-                "Cannot specify both position_embeddings and object_queries. Please use just object_queries"
-            )
-
-        if position_embeddings is not None:
-            logger.warning_once(
-                "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
-            )
-            object_queries = position_embeddings
-
-        residual = hidden_states
-
-        # ========== Begin of Self-Attention =============
-        # Apply projections here
-        # shape: num_queries x batch_size x 256
-        q_content = self.sa_qcontent_proj(
-            hidden_states
-        )  # target is the input of the first decoder layer. zero by default.
-        q_pos = self.sa_qpos_proj(query_position_embeddings)
-        k_content = self.sa_kcontent_proj(hidden_states)
-        k_pos = self.sa_kpos_proj(query_position_embeddings)
-        v = self.sa_v_proj(hidden_states)
-
-        _, num_queries, n_model = q_content.shape
-
-        q = q_content + q_pos
-        k = k_content + k_pos
-        hidden_states, self_attn_weights = self.self_attn(
-            hidden_states=q,
-            attention_mask=attention_mask,
-            key_states=k,
-            value_states=v,
-            output_attentions=output_attentions,
-        )
-        # ============ End of Self-Attention =============
-
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.self_attn_layer_norm(hidden_states)
-
-        # ========== Begin of Cross-Attention =============
-        # Apply projections here
-        # shape: num_queries x batch_size x 256
-        q_content = self.ca_qcontent_proj(hidden_states)
-        k_content = self.ca_kcontent_proj(encoder_hidden_states)
-        v = self.ca_v_proj(encoder_hidden_states)
-
-        batch_size, num_queries, n_model = q_content.shape
-        _, source_len, _ = k_content.shape
-
-        k_pos = self.ca_kpos_proj(object_queries)
-
-        # For the first decoder layer, we concatenate the positional embedding predicted from
-        # the object query (the positional embedding) into the original query (key) in DETR.
-        if is_first:
-            q_pos = self.ca_qpos_proj(query_position_embeddings)
-            q = q_content + q_pos
-            k = k_content + k_pos
-        else:
-            q = q_content
-            k = k_content
-
-        q = q.view(batch_size, num_queries, self.nhead, n_model // self.nhead)
-        query_sine_embed = self.ca_qpos_sine_proj(query_sine_embed)
-        query_sine_embed = query_sine_embed.view(batch_size, num_queries, self.nhead, n_model // self.nhead)
-        q = torch.cat([q, query_sine_embed], dim=3).view(batch_size, num_queries, n_model * 2)
-        k = k.view(batch_size, source_len, self.nhead, n_model // self.nhead)
-        k_pos = k_pos.view(batch_size, source_len, self.nhead, n_model // self.nhead)
-        k = torch.cat([k, k_pos], dim=3).view(batch_size, source_len, n_model * 2)
-
-        # Cross-Attention Block
-        cross_attn_weights = None
-        if encoder_hidden_states is not None:
-            residual = hidden_states
-
-            hidden_states, cross_attn_weights = self.encoder_attn(
-                hidden_states=q,
-                attention_mask=encoder_attention_mask,
-                key_states=k,
-                value_states=v,
-                output_attentions=output_attentions,
-            )
-
-            hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-            hidden_states = residual + hidden_states
-            hidden_states = self.encoder_attn_layer_norm(hidden_states)
-
-        # ============ End of Cross-Attention =============
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.activation_fn(self.fc1(hidden_states))
-        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
-        hidden_states = self.fc2(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.final_layer_norm(hidden_states)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights, cross_attn_weights)
-
-        return outputs
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrClassificationHead with Detr->ConditionalDetr
-class ConditionalDetrClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float):
-        super().__init__()
-        self.dense = nn.Linear(input_dim, inner_dim)
-        self.dropout = nn.Dropout(p=pooler_dropout)
-        self.out_proj = nn.Linear(inner_dim, num_classes)
-
-    def forward(self, hidden_states: torch.Tensor):
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.dense(hidden_states)
-        hidden_states = torch.tanh(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.out_proj(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead with DetrMLPPredictionHead->MLP
-class MLP(nn.Module):
-    """
-    Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
-    height and width of a bounding box w.r.t. an image.
-
-    Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py
-
-    """
-
-    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
-        super().__init__()
-        self.num_layers = num_layers
-        h = [hidden_dim] * (num_layers - 1)
-        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
-
-    def forward(self, x):
-        for i, layer in enumerate(self.layers):
-            x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
-        return x
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrPreTrainedModel with Detr->ConditionalDetr
-class ConditionalDetrPreTrainedModel(PreTrainedModel):
-    config_class = ConditionalDetrConfig
-    base_model_prefix = "model"
-    main_input_name = "pixel_values"
-
-    def _init_weights(self, module):
-        std = self.config.init_std
-        xavier_std = self.config.init_xavier_std
-
-        if isinstance(module, ConditionalDetrMHAttentionMap):
-            nn.init.zeros_(module.k_linear.bias)
-            nn.init.zeros_(module.q_linear.bias)
-            nn.init.xavier_uniform_(module.k_linear.weight, gain=xavier_std)
-            nn.init.xavier_uniform_(module.q_linear.weight, gain=xavier_std)
-        elif isinstance(module, ConditionalDetrLearnedPositionEmbedding):
-            nn.init.uniform_(module.row_embeddings.weight)
-            nn.init.uniform_(module.column_embeddings.weight)
-        if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, ConditionalDetrDecoder):
-            module.gradient_checkpointing = value
-
-
-CONDITIONAL_DETR_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`ConditionalDetrConfig`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-CONDITIONAL_DETR_INPUTS_DOCSTRING = r"""
-    Args:
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Padding will be ignored by default should you provide it.
-
-            Pixel values can be obtained using [`AutoImageProcessor`]. See [`ConditionalDetrImageProcessor.__call__`]
-            for details.
-
-        pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
-            Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:
-
-            - 1 for pixels that are real (i.e. **not masked**),
-            - 0 for pixels that are padding (i.e. **masked**).
-
-            [What are attention masks?](../glossary#attention-mask)
-
-        decoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*):
-            Not used by default. Can be used to mask object queries.
-        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
-            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
-            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
-            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
-            can choose to directly pass a flattened representation of an image.
-        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
-            Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
-            embedded representation.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrEncoder with Detr->ConditionalDetr,DETR->ConditionalDETR
-class ConditionalDetrEncoder(ConditionalDetrPreTrainedModel):
-    """
-    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
-    [`ConditionalDetrEncoderLayer`].
-
-    The encoder updates the flattened feature map through multiple self-attention layers.
-
-    Small tweak for ConditionalDETR:
-
-    - object_queries are added to the forward pass.
-
-    Args:
-        config: ConditionalDetrConfig
-    """
-
-    def __init__(self, config: ConditionalDetrConfig):
-        super().__init__(config)
-
-        self.dropout = config.dropout
-        self.layerdrop = config.encoder_layerdrop
-
-        self.layers = nn.ModuleList([ConditionalDetrEncoderLayer(config) for _ in range(config.encoder_layers)])
-
-        # in the original ConditionalDETR, no layernorm is used at the end of the encoder, as "normalize_before" is set to False by default
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-        self,
-        inputs_embeds=None,
-        attention_mask=None,
-        object_queries=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        **kwargs,
-    ):
-        r"""
-        Args:
-            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                Flattened feature map (output of the backbone + projection layer) that is passed to the encoder.
-
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
-
-                - 1 for pixel features that are real (i.e. **not masked**),
-                - 0 for pixel features that are padding (i.e. **masked**).
-
-                [What are attention masks?](../glossary#attention-mask)
-
-            object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                Object queries that are added to the queries in each self-attention layer.
-
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        """
-        position_embeddings = kwargs.pop("position_embeddings", None)
-
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
-        if position_embeddings is not None and object_queries is not None:
-            raise ValueError(
-                "Cannot specify both position_embeddings and object_queries. Please use just object_queries"
-            )
-
-        if position_embeddings is not None:
-            logger.warning_once(
-                "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
-            )
-            object_queries = position_embeddings
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        hidden_states = inputs_embeds
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-
-        # expand attention_mask
-        if attention_mask is not None:
-            # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len]
-            attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
-
-        encoder_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-        for i, encoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                encoder_states = encoder_states + (hidden_states,)
-            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
-            to_drop = False
-            if self.training:
-                dropout_probability = torch.rand([])
-                if dropout_probability < self.layerdrop:  # skip the layer
-                    to_drop = True
-
-            if to_drop:
-                layer_outputs = (None, None)
-            else:
-                # we add object_queries as extra input to the encoder_layer
-                layer_outputs = encoder_layer(
-                    hidden_states,
-                    attention_mask,
-                    object_queries=object_queries,
-                    output_attentions=output_attentions,
-                )
-
-                hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            encoder_states = encoder_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
-        )
-
-
-class ConditionalDetrDecoder(ConditionalDetrPreTrainedModel):
-    """
-    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`ConditionalDetrDecoderLayer`].
-
-    The decoder updates the query embeddings through multiple self-attention and cross-attention layers.
-
-    Some small tweaks for Conditional DETR:
-
-    - object_queries and query_position_embeddings are added to the forward pass.
-    - if self.config.auxiliary_loss is set to True, also returns a stack of activations from all decoding layers.
-
-    Args:
-        config: ConditionalDetrConfig
-    """
-
-    def __init__(self, config: ConditionalDetrConfig):
-        super().__init__(config)
-        self.dropout = config.dropout
-        self.layerdrop = config.decoder_layerdrop
-
-        self.layers = nn.ModuleList([ConditionalDetrDecoderLayer(config) for _ in range(config.decoder_layers)])
-        # in Conditional DETR, the decoder uses layernorm after the last decoder layer output
-        self.layernorm = nn.LayerNorm(config.d_model)
-        d_model = config.d_model
-        self.gradient_checkpointing = False
-
-        # query_scale is the FFN applied on f to generate transformation T
-        self.query_scale = MLP(d_model, d_model, d_model, 2)
-        self.ref_point_head = MLP(d_model, d_model, 2, 2)
-        for layer_id in range(config.decoder_layers - 1):
-            self.layers[layer_id + 1].ca_qpos_proj = None
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-        self,
-        inputs_embeds=None,
-        attention_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        object_queries=None,
-        query_position_embeddings=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        **kwargs,
-    ):
-        r"""
-        Args:
-            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                The query embeddings that are passed into the decoder.
-
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on certain queries. Mask values selected in `[0, 1]`:
-
-                - 1 for queries that are **not masked**,
-                - 0 for queries that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
-                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
-                of the decoder.
-            encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
-                Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected
-                in `[0, 1]`:
-
-                - 1 for pixels that are real (i.e. **not masked**),
-                - 0 for pixels that are padding (i.e. **masked**).
-
-            object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-                Position embeddings that are added to the queries and keys in each cross-attention layer.
-            query_position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
-                , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        """
-        position_embeddings = kwargs.pop("position_embeddings", None)
-
-        if kwargs:
-            raise ValueError(f"Unexpected arguments {kwargs.keys()}")
-
-        if position_embeddings is not None and object_queries is not None:
-            raise ValueError(
-                "Cannot specify both position_embeddings and object_queries. Please use just object_queries"
-            )
-
-        if position_embeddings is not None:
-            logger.warning_once(
-                "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
-            )
-            object_queries = position_embeddings
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if inputs_embeds is not None:
-            hidden_states = inputs_embeds
-            input_shape = inputs_embeds.size()[:-1]
-
-        combined_attention_mask = None
-
-        if attention_mask is not None and combined_attention_mask is not None:
-            # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len]
-            combined_attention_mask = combined_attention_mask + _expand_mask(
-                attention_mask, inputs_embeds.dtype, target_len=input_shape[-1]
-            )
-
-        # expand encoder attention mask
-        if encoder_hidden_states is not None and encoder_attention_mask is not None:
-            # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len]
-            encoder_attention_mask = _expand_mask(
-                encoder_attention_mask, inputs_embeds.dtype, target_len=input_shape[-1]
-            )
-
-        # optional intermediate hidden states
-        intermediate = () if self.config.auxiliary_loss else None
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
-
-        reference_points_before_sigmoid = self.ref_point_head(
-            query_position_embeddings
-        )  # [num_queries, batch_size, 2]
-        reference_points = reference_points_before_sigmoid.sigmoid().transpose(0, 1)
-        obj_center = reference_points[..., :2].transpose(0, 1)
-        # get sine embedding for the query vector
-        query_sine_embed_before_transformation = gen_sine_position_embeddings(obj_center, self.config.d_model)
-
-        for idx, decoder_layer in enumerate(self.layers):
-            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-            if self.training:
-                dropout_probability = torch.rand([])
-                if dropout_probability < self.layerdrop:
-                    continue
-            if idx == 0:
-                pos_transformation = 1
-            else:
-                pos_transformation = self.query_scale(hidden_states)
-            # apply transformation
-            query_sine_embed = query_sine_embed_before_transformation * pos_transformation
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
-                    hidden_states,
-                    combined_attention_mask,
-                    object_queries,
-                    query_position_embeddings,
-                    query_sine_embed,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    None,
-                    None,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=combined_attention_mask,
-                    object_queries=object_queries,
-                    query_position_embeddings=query_position_embeddings,
-                    query_sine_embed=query_sine_embed,
-                    encoder_hidden_states=encoder_hidden_states,
-                    encoder_attention_mask=encoder_attention_mask,
-                    output_attentions=output_attentions,
-                    is_first=(idx == 0),
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if self.config.auxiliary_loss:
-                hidden_states = self.layernorm(hidden_states)
-                intermediate += (hidden_states,)
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-                if encoder_hidden_states is not None:
-                    all_cross_attentions += (layer_outputs[2],)
-
-        # finally, apply layernorm
-        hidden_states = self.layernorm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        # stack intermediate decoder activations
-        if self.config.auxiliary_loss:
-            intermediate = torch.stack(intermediate)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    hidden_states,
-                    all_hidden_states,
-                    all_self_attns,
-                    all_cross_attentions,
-                    intermediate,
-                    reference_points,
-                ]
-                if v is not None
-            )
-        return ConditionalDetrDecoderOutput(
-            last_hidden_state=hidden_states,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-            cross_attentions=all_cross_attentions,
-            intermediate_hidden_states=intermediate,
-            reference_points=reference_points,
-        )
-
-
-@add_start_docstrings(
-    """
-    The bare Conditional DETR Model (consisting of a backbone and encoder-decoder Transformer) outputting raw
-    hidden-states without any specific head on top.
-    """,
-    CONDITIONAL_DETR_START_DOCSTRING,
-)
-class ConditionalDetrModel(ConditionalDetrPreTrainedModel):
-    def __init__(self, config: ConditionalDetrConfig):
-        super().__init__(config)
-
-        # Create backbone + positional encoding
-        backbone = ConditionalDetrConvEncoder(config)
-        object_queries = build_position_encoding(config)
-        self.backbone = ConditionalDetrConvModel(backbone, object_queries)
-
-        # Create projection layer
-        self.input_projection = nn.Conv2d(backbone.intermediate_channel_sizes[-1], config.d_model, kernel_size=1)
-
-        self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model)
-
-        self.encoder = ConditionalDetrEncoder(config)
-        self.decoder = ConditionalDetrDecoder(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_encoder(self):
-        return self.encoder
-
-    def get_decoder(self):
-        return self.decoder
-
-    def freeze_backbone(self):
-        for name, param in self.backbone.conv_encoder.model.named_parameters():
-            param.requires_grad_(False)
-
-    def unfreeze_backbone(self):
-        for name, param in self.backbone.conv_encoder.model.named_parameters():
-            param.requires_grad_(True)
-
-    @add_start_docstrings_to_model_forward(CONDITIONAL_DETR_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=ConditionalDetrModelOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor,
-        pixel_mask: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.LongTensor] = None,
-        encoder_outputs: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.FloatTensor], ConditionalDetrModelOutput]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoImageProcessor, AutoModel
-        >>> from PIL import Image
-        >>> import requests
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50")
-        >>> model = AutoModel.from_pretrained("microsoft/conditional-detr-resnet-50")
-
-        >>> # prepare image for the model
-        >>> inputs = image_processor(images=image, return_tensors="pt")
-
-        >>> # forward pass
-        >>> outputs = model(**inputs)
-
-        >>> # the last hidden states are the final query embeddings of the Transformer decoder
-        >>> # these are of shape (batch_size, num_queries, hidden_size)
-        >>> last_hidden_states = outputs.last_hidden_state
-        >>> list(last_hidden_states.shape)
-        [1, 300, 256]
-        ```"""
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        batch_size, num_channels, height, width = pixel_values.shape
-        device = pixel_values.device
-
-        if pixel_mask is None:
-            pixel_mask = torch.ones(((batch_size, height, width)), device=device)
-
-        # First, sent pixel_values + pixel_mask through Backbone to obtain the features
-        # pixel_values should be of shape (batch_size, num_channels, height, width)
-        # pixel_mask should be of shape (batch_size, height, width)
-        features, object_queries_list = self.backbone(pixel_values, pixel_mask)
-
-        # get final feature map and downsampled mask
-        feature_map, mask = features[-1]
-
-        if mask is None:
-            raise ValueError("Backbone does not return downsampled pixel mask")
-
-        # Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
-        projected_feature_map = self.input_projection(feature_map)
-
-        # Third, flatten the feature map + object_queries of shape NxCxHxW to NxCxHW, and permute it to NxHWxC
-        # In other words, turn their shape into (batch_size, sequence_length, hidden_size)
-        flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1)
-        object_queries = object_queries_list[-1].flatten(2).permute(0, 2, 1)
-
-        flattened_mask = mask.flatten(1)
-
-        # Fourth, sent flattened_features + flattened_mask + object_queries through encoder
-        # flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size)
-        # flattened_mask is a Tensor of shape (batch_size, heigth*width)
-        if encoder_outputs is None:
-            encoder_outputs = self.encoder(
-                inputs_embeds=flattened_features,
-                attention_mask=flattened_mask,
-                object_queries=object_queries,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-            )
-        # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
-        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
-            encoder_outputs = BaseModelOutput(
-                last_hidden_state=encoder_outputs[0],
-                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
-                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
-            )
-
-        # Fifth, sent query embeddings + object_queries through the decoder (which is conditioned on the encoder output)
-        query_position_embeddings = self.query_position_embeddings.weight.unsqueeze(0).repeat(batch_size, 1, 1)
-        queries = torch.zeros_like(query_position_embeddings)
-
-        # decoder outputs consists of (dec_features, dec_hidden, dec_attn)
-        decoder_outputs = self.decoder(
-            inputs_embeds=queries,
-            attention_mask=None,
-            object_queries=object_queries,
-            query_position_embeddings=query_position_embeddings,
-            encoder_hidden_states=encoder_outputs[0],
-            encoder_attention_mask=flattened_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        if not return_dict:
-            return decoder_outputs + encoder_outputs
-
-        return ConditionalDetrModelOutput(
-            last_hidden_state=decoder_outputs.last_hidden_state,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
-            encoder_hidden_states=encoder_outputs.hidden_states,
-            encoder_attentions=encoder_outputs.attentions,
-            intermediate_hidden_states=decoder_outputs.intermediate_hidden_states,
-            reference_points=decoder_outputs.reference_points,
-        )
-
-
-@add_start_docstrings(
-    """
-    CONDITIONAL_DETR Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on
-    top, for tasks such as COCO detection.
-    """,
-    CONDITIONAL_DETR_START_DOCSTRING,
-)
-class ConditionalDetrForObjectDetection(ConditionalDetrPreTrainedModel):
-    def __init__(self, config: ConditionalDetrConfig):
-        super().__init__(config)
-
-        # CONDITIONAL DETR encoder-decoder model
-        self.model = ConditionalDetrModel(config)
-
-        # Object detection heads
-        self.class_labels_classifier = nn.Linear(
-            config.d_model, config.num_labels
-        )  # We add one for the "no object" class
-        self.bbox_predictor = ConditionalDetrMLPPredictionHead(
-            input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
-        )
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    # taken from https://github.com/Atten4Vis/conditionalDETR/blob/master/models/conditional_detr.py
-    @torch.jit.unused
-    def _set_aux_loss(self, outputs_class, outputs_coord):
-        # this is a workaround to make torchscript happy, as torchscript
-        # doesn't support dictionary with non-homogeneous values, such
-        # as a dict having both a Tensor and a list.
-        return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]
-
-    @add_start_docstrings_to_model_forward(CONDITIONAL_DETR_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=ConditionalDetrObjectDetectionOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor,
-        pixel_mask: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.LongTensor] = None,
-        encoder_outputs: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[List[dict]] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.FloatTensor], ConditionalDetrObjectDetectionOutput]:
-        r"""
-        labels (`List[Dict]` of len `(batch_size,)`, *optional*):
-            Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
-            following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch
-            respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes
-            in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`.
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoImageProcessor, AutoModelForObjectDetection
-        >>> from PIL import Image
-        >>> import requests
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50")
-        >>> model = AutoModelForObjectDetection.from_pretrained("microsoft/conditional-detr-resnet-50")
-
-        >>> inputs = image_processor(images=image, return_tensors="pt")
-
-        >>> outputs = model(**inputs)
-
-        >>> # convert outputs (bounding boxes and class logits) to COCO API
-        >>> target_sizes = torch.tensor([image.size[::-1]])
-        >>> results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[
-        ...     0
-        ... ]
-        >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-        ...     box = [round(i, 2) for i in box.tolist()]
-        ...     print(
-        ...         f"Detected {model.config.id2label[label.item()]} with confidence "
-        ...         f"{round(score.item(), 3)} at location {box}"
-        ...     )
-        Detected remote with confidence 0.833 at location [38.31, 72.1, 177.63, 118.45]
-        Detected cat with confidence 0.831 at location [9.2, 51.38, 321.13, 469.0]
-        Detected cat with confidence 0.804 at location [340.3, 16.85, 642.93, 370.95]
-        Detected remote with confidence 0.683 at location [334.48, 73.49, 366.37, 190.01]
-        Detected couch with confidence 0.535 at location [0.52, 1.19, 640.35, 475.1]
-        ```"""
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # First, sent images through CONDITIONAL_DETR base model to obtain encoder + decoder outputs
-        outputs = self.model(
-            pixel_values,
-            pixel_mask=pixel_mask,
-            decoder_attention_mask=decoder_attention_mask,
-            encoder_outputs=encoder_outputs,
-            inputs_embeds=inputs_embeds,
-            decoder_inputs_embeds=decoder_inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        # class logits + predicted bounding boxes
-        logits = self.class_labels_classifier(sequence_output)
-
-        reference = outputs.reference_points if return_dict else outputs[-1]
-        reference_before_sigmoid = inverse_sigmoid(reference).transpose(0, 1)
-        outputs_coords = []
-        hs = sequence_output
-        tmp = self.bbox_predictor(hs)
-        tmp[..., :2] += reference_before_sigmoid
-        pred_boxes = tmp.sigmoid()
-        # pred_boxes = self.bbox_predictor(sequence_output).sigmoid()
-
-        loss, loss_dict, auxiliary_outputs = None, None, None
-        if labels is not None:
-            # First: create the matcher
-            matcher = ConditionalDetrHungarianMatcher(
-                class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost
-            )
-            # Second: create the criterion
-            losses = ["labels", "boxes", "cardinality"]
-            criterion = ConditionalDetrLoss(
-                matcher=matcher,
-                num_classes=self.config.num_labels,
-                focal_alpha=self.config.focal_alpha,
-                losses=losses,
-            )
-            criterion.to(self.device)
-            # Third: compute the losses, based on outputs and labels
-            outputs_loss = {}
-            outputs_loss["logits"] = logits
-            outputs_loss["pred_boxes"] = pred_boxes
-            if self.config.auxiliary_loss:
-                intermediate = outputs.intermediate_hidden_states if return_dict else outputs[4]
-                outputs_class = self.class_labels_classifier(intermediate)
-
-                for lvl in range(hs.shape[0]):
-                    tmp = self.bbox_predictor(hs[lvl])
-                    tmp[..., :2] += reference_before_sigmoid
-                    outputs_coord = tmp.sigmoid()
-                    outputs_coords.append(outputs_coord)
-                outputs_coord = torch.stack(outputs_coords)
-
-                auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord)
-                outputs_loss["auxiliary_outputs"] = auxiliary_outputs
-
-            loss_dict = criterion(outputs_loss, labels)
-            # Fourth: compute total loss, as a weighted sum of the various losses
-            weight_dict = {"loss_ce": self.config.cls_loss_coefficient, "loss_bbox": self.config.bbox_loss_coefficient}
-            weight_dict["loss_giou"] = self.config.giou_loss_coefficient
-            if self.config.auxiliary_loss:
-                aux_weight_dict = {}
-                for i in range(self.config.decoder_layers - 1):
-                    aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
-                weight_dict.update(aux_weight_dict)
-            loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
-
-        if not return_dict:
-            if auxiliary_outputs is not None:
-                output = (logits, pred_boxes) + auxiliary_outputs + outputs
-            else:
-                output = (logits, pred_boxes) + outputs
-            return ((loss, loss_dict) + output) if loss is not None else output
-
-        return ConditionalDetrObjectDetectionOutput(
-            loss=loss,
-            loss_dict=loss_dict,
-            logits=logits,
-            pred_boxes=pred_boxes,
-            auxiliary_outputs=auxiliary_outputs,
-            last_hidden_state=outputs.last_hidden_state,
-            decoder_hidden_states=outputs.decoder_hidden_states,
-            decoder_attentions=outputs.decoder_attentions,
-            cross_attentions=outputs.cross_attentions,
-            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
-            encoder_hidden_states=outputs.encoder_hidden_states,
-            encoder_attentions=outputs.encoder_attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    CONDITIONAL_DETR Model (consisting of a backbone and encoder-decoder Transformer) with a segmentation head on top,
-    for tasks such as COCO panoptic.
-
-    """,
-    CONDITIONAL_DETR_START_DOCSTRING,
-)
-class ConditionalDetrForSegmentation(ConditionalDetrPreTrainedModel):
-    def __init__(self, config: ConditionalDetrConfig):
-        super().__init__(config)
-
-        # object detection model
-        self.conditional_detr = ConditionalDetrForObjectDetection(config)
-
-        # segmentation head
-        hidden_size, number_of_heads = config.d_model, config.encoder_attention_heads
-        intermediate_channel_sizes = self.conditional_detr.model.backbone.conv_encoder.intermediate_channel_sizes
-
-        self.mask_head = ConditionalDetrMaskHeadSmallConv(
-            hidden_size + number_of_heads, intermediate_channel_sizes[::-1][-3:], hidden_size
-        )
-
-        self.bbox_attention = ConditionalDetrMHAttentionMap(
-            hidden_size, hidden_size, number_of_heads, dropout=0.0, std=config.init_xavier_std
-        )
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(CONDITIONAL_DETR_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=ConditionalDetrSegmentationOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor,
-        pixel_mask: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.FloatTensor] = None,
-        encoder_outputs: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[List[dict]] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.FloatTensor], ConditionalDetrSegmentationOutput]:
-        r"""
-        labels (`List[Dict]` of len `(batch_size,)`, *optional*):
-            Labels for computing the bipartite matching loss, DICE/F-1 loss and Focal loss. List of dicts, each
-            dictionary containing at least the following 3 keys: 'class_labels', 'boxes' and 'masks' (the class labels,
-            bounding boxes and segmentation masks of an image in the batch respectively). The class labels themselves
-            should be a `torch.LongTensor` of len `(number of bounding boxes in the image,)`, the boxes a
-            `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)` and the masks a
-            `torch.FloatTensor` of shape `(number of bounding boxes in the image, height, width)`.
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> import io
-        >>> import requests
-        >>> from PIL import Image
-        >>> import torch
-        >>> import numpy
-
-        >>> from transformers import (
-        ...     AutoImageProcessor,
-        ...     ConditionalDetrConfig,
-        ...     ConditionalDetrForSegmentation,
-        ... )
-        >>> from transformers.image_transforms import rgb_to_id
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50")
-
-        >>> # randomly initialize all weights of the model
-        >>> config = ConditionalDetrConfig()
-        >>> model = ConditionalDetrForSegmentation(config)
-
-        >>> # prepare image for the model
-        >>> inputs = image_processor(images=image, return_tensors="pt")
-
-        >>> # forward pass
-        >>> outputs = model(**inputs)
-
-        >>> # Use the `post_process_panoptic_segmentation` method of the `image_processor` to retrieve post-processed panoptic segmentation maps
-        >>> # Segmentation results are returned as a list of dictionaries
-        >>> result = image_processor.post_process_panoptic_segmentation(outputs, target_sizes=[(300, 500)])
-        >>> # A tensor of shape (height, width) where each value denotes a segment id, filled with -1 if no segment is found
-        >>> panoptic_seg = result[0]["segmentation"]
-        >>> # Get prediction score and segment_id to class_id mapping of each segment
-        >>> panoptic_segments_info = result[0]["segments_info"]
-        ```"""
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        batch_size, num_channels, height, width = pixel_values.shape
-        device = pixel_values.device
-
-        if pixel_mask is None:
-            pixel_mask = torch.ones((batch_size, height, width), device=device)
-
-        # First, get list of feature maps and object_queries
-        features, object_queries_list = self.conditional_detr.model.backbone(pixel_values, pixel_mask=pixel_mask)
-
-        # Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
-        feature_map, mask = features[-1]
-        batch_size, num_channels, height, width = feature_map.shape
-        projected_feature_map = self.conditional_detr.model.input_projection(feature_map)
-
-        # Third, flatten the feature map + object_queries of shape NxCxHxW to NxCxHW, and permute it to NxHWxC
-        # In other words, turn their shape into (batch_size, sequence_length, hidden_size)
-        flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1)
-        object_queries = object_queries_list[-1].flatten(2).permute(0, 2, 1)
-
-        flattened_mask = mask.flatten(1)
-
-        # Fourth, sent flattened_features + flattened_mask + object_queries through encoder
-        # flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size)
-        # flattened_mask is a Tensor of shape (batch_size, heigth*width)
-        if encoder_outputs is None:
-            encoder_outputs = self.conditional_detr.model.encoder(
-                inputs_embeds=flattened_features,
-                attention_mask=flattened_mask,
-                object_queries=object_queries,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-            )
-        # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
-        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
-            encoder_outputs = BaseModelOutput(
-                last_hidden_state=encoder_outputs[0],
-                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
-                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
-            )
-
-        # Fifth, sent query embeddings + object_queries through the decoder (which is conditioned on the encoder output)
-        query_position_embeddings = self.conditional_detr.model.query_position_embeddings.weight.unsqueeze(0).repeat(
-            batch_size, 1, 1
-        )
-        queries = torch.zeros_like(query_position_embeddings)
-
-        # decoder outputs consists of (dec_features, dec_hidden, dec_attn)
-        decoder_outputs = self.conditional_detr.model.decoder(
-            inputs_embeds=queries,
-            attention_mask=None,
-            object_queries=object_queries,
-            query_position_embeddings=query_position_embeddings,
-            encoder_hidden_states=encoder_outputs[0],
-            encoder_attention_mask=flattened_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = decoder_outputs[0]
-
-        # Sixth, compute logits, pred_boxes and pred_masks
-        logits = self.conditional_detr.class_labels_classifier(sequence_output)
-        pred_boxes = self.conditional_detr.bbox_predictor(sequence_output).sigmoid()
-
-        memory = encoder_outputs[0].permute(0, 2, 1).view(batch_size, self.config.d_model, height, width)
-        mask = flattened_mask.view(batch_size, height, width)
-
-        # FIXME h_boxes takes the last one computed, keep this in mind
-        # important: we need to reverse the mask, since in the original implementation the mask works reversed
-        # bbox_mask is of shape (batch_size, num_queries, number_of_attention_heads in bbox_attention, height/32, width/32)
-        bbox_mask = self.bbox_attention(sequence_output, memory, mask=~mask)
-
-        seg_masks = self.mask_head(projected_feature_map, bbox_mask, [features[2][0], features[1][0], features[0][0]])
-
-        pred_masks = seg_masks.view(
-            batch_size, self.conditional_detr.config.num_queries, seg_masks.shape[-2], seg_masks.shape[-1]
-        )
-
-        loss, loss_dict, auxiliary_outputs = None, None, None
-        if labels is not None:
-            # First: create the matcher
-            matcher = ConditionalDetrHungarianMatcher(
-                class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost
-            )
-            # Second: create the criterion
-            losses = ["labels", "boxes", "cardinality", "masks"]
-            criterion = ConditionalDetrLoss(
-                matcher=matcher,
-                num_classes=self.config.num_labels,
-                focal_alpha=self.config.focal_alpha,
-                losses=losses,
-            )
-            criterion.to(self.device)
-            # Third: compute the losses, based on outputs and labels
-            outputs_loss = {}
-            outputs_loss["logits"] = logits
-            outputs_loss["pred_boxes"] = pred_boxes
-            outputs_loss["pred_masks"] = pred_masks
-            if self.config.auxiliary_loss:
-                intermediate = decoder_outputs.intermediate_hidden_states if return_dict else decoder_outputs[-1]
-                outputs_class = self.class_labels_classifier(intermediate)
-                outputs_coord = self.bbox_predictor(intermediate).sigmoid()
-                auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord)
-                outputs_loss["auxiliary_outputs"] = auxiliary_outputs
-
-            loss_dict = criterion(outputs_loss, labels)
-            # Fourth: compute total loss, as a weighted sum of the various losses
-            weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient}
-            weight_dict["loss_giou"] = self.config.giou_loss_coefficient
-            weight_dict["loss_mask"] = self.config.mask_loss_coefficient
-            weight_dict["loss_dice"] = self.config.dice_loss_coefficient
-            if self.config.auxiliary_loss:
-                aux_weight_dict = {}
-                for i in range(self.config.decoder_layers - 1):
-                    aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
-                weight_dict.update(aux_weight_dict)
-            loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
-
-        if not return_dict:
-            if auxiliary_outputs is not None:
-                output = (logits, pred_boxes, pred_masks) + auxiliary_outputs + decoder_outputs + encoder_outputs
-            else:
-                output = (logits, pred_boxes, pred_masks) + decoder_outputs + encoder_outputs
-            return ((loss, loss_dict) + output) if loss is not None else output
-
-        return ConditionalDetrSegmentationOutput(
-            loss=loss,
-            loss_dict=loss_dict,
-            logits=logits,
-            pred_boxes=pred_boxes,
-            pred_masks=pred_masks,
-            auxiliary_outputs=auxiliary_outputs,
-            last_hidden_state=decoder_outputs.last_hidden_state,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
-            encoder_hidden_states=encoder_outputs.hidden_states,
-            encoder_attentions=encoder_outputs.attentions,
-        )
-
-
-def _expand(tensor, length: int):
-    return tensor.unsqueeze(1).repeat(1, int(length), 1, 1, 1).flatten(0, 1)
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrMaskHeadSmallConv with Detr->ConditionalDetr
-class ConditionalDetrMaskHeadSmallConv(nn.Module):
-    """
-    Simple convolutional head, using group norm. Upsampling is done using a FPN approach
-    """
-
-    def __init__(self, dim, fpn_dims, context_dim):
-        super().__init__()
-
-        if dim % 8 != 0:
-            raise ValueError(
-                "The hidden_size + number of attention heads must be divisible by 8 as the number of groups in"
-                " GroupNorm is set to 8"
-            )
-
-        inter_dims = [dim, context_dim // 2, context_dim // 4, context_dim // 8, context_dim // 16, context_dim // 64]
-
-        self.lay1 = nn.Conv2d(dim, dim, 3, padding=1)
-        self.gn1 = nn.GroupNorm(8, dim)
-        self.lay2 = nn.Conv2d(dim, inter_dims[1], 3, padding=1)
-        self.gn2 = nn.GroupNorm(min(8, inter_dims[1]), inter_dims[1])
-        self.lay3 = nn.Conv2d(inter_dims[1], inter_dims[2], 3, padding=1)
-        self.gn3 = nn.GroupNorm(min(8, inter_dims[2]), inter_dims[2])
-        self.lay4 = nn.Conv2d(inter_dims[2], inter_dims[3], 3, padding=1)
-        self.gn4 = nn.GroupNorm(min(8, inter_dims[3]), inter_dims[3])
-        self.lay5 = nn.Conv2d(inter_dims[3], inter_dims[4], 3, padding=1)
-        self.gn5 = nn.GroupNorm(min(8, inter_dims[4]), inter_dims[4])
-        self.out_lay = nn.Conv2d(inter_dims[4], 1, 3, padding=1)
-
-        self.dim = dim
-
-        self.adapter1 = nn.Conv2d(fpn_dims[0], inter_dims[1], 1)
-        self.adapter2 = nn.Conv2d(fpn_dims[1], inter_dims[2], 1)
-        self.adapter3 = nn.Conv2d(fpn_dims[2], inter_dims[3], 1)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_uniform_(m.weight, a=1)
-                nn.init.constant_(m.bias, 0)
-
-    def forward(self, x: Tensor, bbox_mask: Tensor, fpns: List[Tensor]):
-        # here we concatenate x, the projected feature map, of shape (batch_size, d_model, heigth/32, width/32) with
-        # the bbox_mask = the attention maps of shape (batch_size, n_queries, n_heads, height/32, width/32).
-        # We expand the projected feature map to match the number of heads.
-        x = torch.cat([_expand(x, bbox_mask.shape[1]), bbox_mask.flatten(0, 1)], 1)
-
-        x = self.lay1(x)
-        x = self.gn1(x)
-        x = nn.functional.relu(x)
-        x = self.lay2(x)
-        x = self.gn2(x)
-        x = nn.functional.relu(x)
-
-        cur_fpn = self.adapter1(fpns[0])
-        if cur_fpn.size(0) != x.size(0):
-            cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0))
-        x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest")
-        x = self.lay3(x)
-        x = self.gn3(x)
-        x = nn.functional.relu(x)
-
-        cur_fpn = self.adapter2(fpns[1])
-        if cur_fpn.size(0) != x.size(0):
-            cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0))
-        x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest")
-        x = self.lay4(x)
-        x = self.gn4(x)
-        x = nn.functional.relu(x)
-
-        cur_fpn = self.adapter3(fpns[2])
-        if cur_fpn.size(0) != x.size(0):
-            cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0))
-        x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest")
-        x = self.lay5(x)
-        x = self.gn5(x)
-        x = nn.functional.relu(x)
-
-        x = self.out_lay(x)
-        return x
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrMHAttentionMap with Detr->ConditionalDetr
-class ConditionalDetrMHAttentionMap(nn.Module):
-    """This is a 2D attention module, which only returns the attention softmax (no multiplication by value)"""
-
-    def __init__(self, query_dim, hidden_dim, num_heads, dropout=0.0, bias=True, std=None):
-        super().__init__()
-        self.num_heads = num_heads
-        self.hidden_dim = hidden_dim
-        self.dropout = nn.Dropout(dropout)
-
-        self.q_linear = nn.Linear(query_dim, hidden_dim, bias=bias)
-        self.k_linear = nn.Linear(query_dim, hidden_dim, bias=bias)
-
-        self.normalize_fact = float(hidden_dim / self.num_heads) ** -0.5
-
-    def forward(self, q, k, mask: Optional[Tensor] = None):
-        q = self.q_linear(q)
-        k = nn.functional.conv2d(k, self.k_linear.weight.unsqueeze(-1).unsqueeze(-1), self.k_linear.bias)
-        queries_per_head = q.view(q.shape[0], q.shape[1], self.num_heads, self.hidden_dim // self.num_heads)
-        keys_per_head = k.view(k.shape[0], self.num_heads, self.hidden_dim // self.num_heads, k.shape[-2], k.shape[-1])
-        weights = torch.einsum("bqnc,bnchw->bqnhw", queries_per_head * self.normalize_fact, keys_per_head)
-
-        if mask is not None:
-            weights.masked_fill_(mask.unsqueeze(1).unsqueeze(1), torch.finfo(weights.dtype).min)
-        weights = nn.functional.softmax(weights.flatten(2), dim=-1).view(weights.size())
-        weights = self.dropout(weights)
-        return weights
-
-
-# Copied from transformers.models.detr.modeling_detr.dice_loss
-def dice_loss(inputs, targets, num_boxes):
-    """
-    Compute the DICE loss, similar to generalized IOU for masks
-
-    Args:
-        inputs: A float tensor of arbitrary shape.
-                The predictions for each example.
-        targets: A float tensor with the same shape as inputs. Stores the binary
-                 classification label for each element in inputs (0 for the negative class and 1 for the positive
-                 class).
-    """
-    inputs = inputs.sigmoid()
-    inputs = inputs.flatten(1)
-    numerator = 2 * (inputs * targets).sum(1)
-    denominator = inputs.sum(-1) + targets.sum(-1)
-    loss = 1 - (numerator + 1) / (denominator + 1)
-    return loss.sum() / num_boxes
-
-
-# Copied from transformers.models.detr.modeling_detr.sigmoid_focal_loss
-def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2):
-    """
-    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
-
-    Args:
-        inputs (`torch.FloatTensor` of arbitrary shape):
-            The predictions for each example.
-        targets (`torch.FloatTensor` with the same shape as `inputs`)
-            A tensor storing the binary classification label for each element in the `inputs` (0 for the negative class
-            and 1 for the positive class).
-        alpha (`float`, *optional*, defaults to `0.25`):
-            Optional weighting factor in the range (0,1) to balance positive vs. negative examples.
-        gamma (`int`, *optional*, defaults to `2`):
-            Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples.
-
-    Returns:
-        Loss tensor
-    """
-    prob = inputs.sigmoid()
-    ce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
-    # add modulating factor
-    p_t = prob * targets + (1 - prob) * (1 - targets)
-    loss = ce_loss * ((1 - p_t) ** gamma)
-
-    if alpha >= 0:
-        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
-        loss = alpha_t * loss
-
-    return loss.mean(1).sum() / num_boxes
-
-
-class ConditionalDetrLoss(nn.Module):
-    """
-    This class computes the losses for ConditionalDetrForObjectDetection/ConditionalDetrForSegmentation. The process
-    happens in two steps: 1) we compute hungarian assignment between ground truth boxes and the outputs of the model 2)
-    we supervise each pair of matched ground-truth / prediction (supervise class and box).
-
-    Args:
-        matcher (`ConditionalDetrHungarianMatcher`):
-            Module able to compute a matching between targets and proposals.
-        num_classes (`int`):
-            Number of object categories, omitting the special no-object category.
-        focal_alpha (`float`):
-            Alpha parameter in focal loss.
-        losses (`List[str]`):
-            List of all the losses to be applied. See `get_loss` for a list of all available losses.
-    """
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.__init__
-    def __init__(self, matcher, num_classes, focal_alpha, losses):
-        super().__init__()
-        self.matcher = matcher
-        self.num_classes = num_classes
-        self.focal_alpha = focal_alpha
-        self.losses = losses
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.loss_labels
-    def loss_labels(self, outputs, targets, indices, num_boxes):
-        """
-        Classification loss (Binary focal loss) targets dicts must contain the key "class_labels" containing a tensor
-        of dim [nb_target_boxes]
-        """
-        if "logits" not in outputs:
-            raise KeyError("No logits were found in the outputs")
-        source_logits = outputs["logits"]
-
-        idx = self._get_source_permutation_idx(indices)
-        target_classes_o = torch.cat([t["class_labels"][J] for t, (_, J) in zip(targets, indices)])
-        target_classes = torch.full(
-            source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device
-        )
-        target_classes[idx] = target_classes_o
-
-        target_classes_onehot = torch.zeros(
-            [source_logits.shape[0], source_logits.shape[1], source_logits.shape[2] + 1],
-            dtype=source_logits.dtype,
-            layout=source_logits.layout,
-            device=source_logits.device,
-        )
-        target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1)
-
-        target_classes_onehot = target_classes_onehot[:, :, :-1]
-        loss_ce = (
-            sigmoid_focal_loss(source_logits, target_classes_onehot, num_boxes, alpha=self.focal_alpha, gamma=2)
-            * source_logits.shape[1]
-        )
-        losses = {"loss_ce": loss_ce}
-
-        return losses
-
-    @torch.no_grad()
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.loss_cardinality
-    def loss_cardinality(self, outputs, targets, indices, num_boxes):
-        """
-        Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes.
-
-        This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients.
-        """
-        logits = outputs["logits"]
-        device = logits.device
-        target_lengths = torch.as_tensor([len(v["class_labels"]) for v in targets], device=device)
-        # Count the number of predictions that are NOT "no-object" (which is the last class)
-        card_pred = (logits.argmax(-1) != logits.shape[-1] - 1).sum(1)
-        card_err = nn.functional.l1_loss(card_pred.float(), target_lengths.float())
-        losses = {"cardinality_error": card_err}
-        return losses
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.loss_boxes
-    def loss_boxes(self, outputs, targets, indices, num_boxes):
-        """
-        Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss.
-
-        Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes
-        are expected in format (center_x, center_y, w, h), normalized by the image size.
-        """
-        if "pred_boxes" not in outputs:
-            raise KeyError("No predicted boxes found in outputs")
-        idx = self._get_source_permutation_idx(indices)
-        source_boxes = outputs["pred_boxes"][idx]
-        target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0)
-
-        loss_bbox = nn.functional.l1_loss(source_boxes, target_boxes, reduction="none")
-
-        losses = {}
-        losses["loss_bbox"] = loss_bbox.sum() / num_boxes
-
-        loss_giou = 1 - torch.diag(
-            generalized_box_iou(center_to_corners_format(source_boxes), center_to_corners_format(target_boxes))
-        )
-        losses["loss_giou"] = loss_giou.sum() / num_boxes
-        return losses
-
-    # Copied from transformers.models.detr.modeling_detr.DetrLoss.loss_masks
-    def loss_masks(self, outputs, targets, indices, num_boxes):
-        """
-        Compute the losses related to the masks: the focal loss and the dice loss.
-
-        Targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w].
-        """
-        if "pred_masks" not in outputs:
-            raise KeyError("No predicted masks found in outputs")
-
-        source_idx = self._get_source_permutation_idx(indices)
-        target_idx = self._get_target_permutation_idx(indices)
-        source_masks = outputs["pred_masks"]
-        source_masks = source_masks[source_idx]
-        masks = [t["masks"] for t in targets]
-        # TODO use valid to mask invalid areas due to padding in loss
-        target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()
-        target_masks = target_masks.to(source_masks)
-        target_masks = target_masks[target_idx]
-
-        # upsample predictions to the target size
-        source_masks = nn.functional.interpolate(
-            source_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False
-        )
-        source_masks = source_masks[:, 0].flatten(1)
-
-        target_masks = target_masks.flatten(1)
-        target_masks = target_masks.view(source_masks.shape)
-        losses = {
-            "loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes),
-            "loss_dice": dice_loss(source_masks, target_masks, num_boxes),
-        }
-        return losses
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss._get_source_permutation_idx
-    def _get_source_permutation_idx(self, indices):
-        # permute predictions following indices
-        batch_idx = torch.cat([torch.full_like(source, i) for i, (source, _) in enumerate(indices)])
-        source_idx = torch.cat([source for (source, _) in indices])
-        return batch_idx, source_idx
-
-    # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss._get_target_permutation_idx
-    def _get_target_permutation_idx(self, indices):
-        # permute targets following indices
-        batch_idx = torch.cat([torch.full_like(target, i) for i, (_, target) in enumerate(indices)])
-        target_idx = torch.cat([target for (_, target) in indices])
-        return batch_idx, target_idx
-
-    # Copied from transformers.models.detr.modeling_detr.DetrLoss.get_loss
-    def get_loss(self, loss, outputs, targets, indices, num_boxes):
-        loss_map = {
-            "labels": self.loss_labels,
-            "cardinality": self.loss_cardinality,
-            "boxes": self.loss_boxes,
-            "masks": self.loss_masks,
-        }
-        if loss not in loss_map:
-            raise ValueError(f"Loss {loss} not supported")
-        return loss_map[loss](outputs, targets, indices, num_boxes)
-
-    # Copied from transformers.models.detr.modeling_detr.DetrLoss.forward
-    def forward(self, outputs, targets):
-        """
-        This performs the loss computation.
-
-        Args:
-             outputs (`dict`, *optional*):
-                Dictionary of tensors, see the output specification of the model for the format.
-             targets (`List[dict]`, *optional*):
-                List of dicts, such that `len(targets) == batch_size`. The expected keys in each dict depends on the
-                losses applied, see each loss' doc.
-        """
-        outputs_without_aux = {k: v for k, v in outputs.items() if k != "auxiliary_outputs"}
-
-        # Retrieve the matching between the outputs of the last layer and the targets
-        indices = self.matcher(outputs_without_aux, targets)
-
-        # Compute the average number of target boxes across all nodes, for normalization purposes
-        num_boxes = sum(len(t["class_labels"]) for t in targets)
-        num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
-        # (Niels): comment out function below, distributed training to be added
-        # if is_dist_avail_and_initialized():
-        #     torch.distributed.all_reduce(num_boxes)
-        # (Niels) in original implementation, num_boxes is divided by get_world_size()
-        num_boxes = torch.clamp(num_boxes, min=1).item()
-
-        # Compute all the requested losses
-        losses = {}
-        for loss in self.losses:
-            losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes))
-
-        # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
-        if "auxiliary_outputs" in outputs:
-            for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]):
-                indices = self.matcher(auxiliary_outputs, targets)
-                for loss in self.losses:
-                    if loss == "masks":
-                        # Intermediate masks losses are too costly to compute, we ignore them.
-                        continue
-                    l_dict = self.get_loss(loss, auxiliary_outputs, targets, indices, num_boxes)
-                    l_dict = {k + f"_{i}": v for k, v in l_dict.items()}
-                    losses.update(l_dict)
-
-        return losses
-
-
-# Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead with Detr->ConditionalDetr
-class ConditionalDetrMLPPredictionHead(nn.Module):
-    """
-    Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
-    height and width of a bounding box w.r.t. an image.
-
-    Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py
-
-    """
-
-    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
-        super().__init__()
-        self.num_layers = num_layers
-        h = [hidden_dim] * (num_layers - 1)
-        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
-
-    def forward(self, x):
-        for i, layer in enumerate(self.layers):
-            x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
-        return x
-
-
-# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrHungarianMatcher with DeformableDetr->ConditionalDetr
-class ConditionalDetrHungarianMatcher(nn.Module):
-    """
-    This class computes an assignment between the targets and the predictions of the network.
-
-    For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more
-    predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, while the others are
-    un-matched (and thus treated as non-objects).
-
-    Args:
-        class_cost:
-            The relative weight of the classification error in the matching cost.
-        bbox_cost:
-            The relative weight of the L1 error of the bounding box coordinates in the matching cost.
-        giou_cost:
-            The relative weight of the giou loss of the bounding box in the matching cost.
-    """
-
-    def __init__(self, class_cost: float = 1, bbox_cost: float = 1, giou_cost: float = 1):
-        super().__init__()
-        requires_backends(self, ["scipy"])
-
-        self.class_cost = class_cost
-        self.bbox_cost = bbox_cost
-        self.giou_cost = giou_cost
-        if class_cost == 0 and bbox_cost == 0 and giou_cost == 0:
-            raise ValueError("All costs of the Matcher can't be 0")
-
-    @torch.no_grad()
-    def forward(self, outputs, targets):
-        """
-        Args:
-            outputs (`dict`):
-                A dictionary that contains at least these entries:
-                * "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
-                * "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates.
-            targets (`List[dict]`):
-                A list of targets (len(targets) = batch_size), where each target is a dict containing:
-                * "class_labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of
-                  ground-truth
-                 objects in the target) containing the class labels
-                * "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates.
-
-        Returns:
-            `List[Tuple]`: A list of size `batch_size`, containing tuples of (index_i, index_j) where:
-            - index_i is the indices of the selected predictions (in order)
-            - index_j is the indices of the corresponding selected targets (in order)
-            For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
-        """
-        batch_size, num_queries = outputs["logits"].shape[:2]
-
-        # We flatten to compute the cost matrices in a batch
-        out_prob = outputs["logits"].flatten(0, 1).sigmoid()  # [batch_size * num_queries, num_classes]
-        out_bbox = outputs["pred_boxes"].flatten(0, 1)  # [batch_size * num_queries, 4]
-
-        # Also concat the target labels and boxes
-        target_ids = torch.cat([v["class_labels"] for v in targets])
-        target_bbox = torch.cat([v["boxes"] for v in targets])
-
-        # Compute the classification cost.
-        alpha = 0.25
-        gamma = 2.0
-        neg_cost_class = (1 - alpha) * (out_prob**gamma) * (-(1 - out_prob + 1e-8).log())
-        pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())
-        class_cost = pos_cost_class[:, target_ids] - neg_cost_class[:, target_ids]
-
-        # Compute the L1 cost between boxes
-        bbox_cost = torch.cdist(out_bbox, target_bbox, p=1)
-
-        # Compute the giou cost between boxes
-        giou_cost = -generalized_box_iou(center_to_corners_format(out_bbox), center_to_corners_format(target_bbox))
-
-        # Final cost matrix
-        cost_matrix = self.bbox_cost * bbox_cost + self.class_cost * class_cost + self.giou_cost * giou_cost
-        cost_matrix = cost_matrix.view(batch_size, num_queries, -1).cpu()
-
-        sizes = [len(v["boxes"]) for v in targets]
-        indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))]
-        return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]
-
-
-# Copied from transformers.models.detr.modeling_detr._upcast
-def _upcast(t: Tensor) -> Tensor:
-    # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
-    if t.is_floating_point():
-        return t if t.dtype in (torch.float32, torch.float64) else t.float()
-    else:
-        return t if t.dtype in (torch.int32, torch.int64) else t.int()
-
-
-# Copied from transformers.models.detr.modeling_detr.box_area
-def box_area(boxes: Tensor) -> Tensor:
-    """
-    Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.
-
-    Args:
-        boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`):
-            Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1
-            < x2` and `0 <= y1 < y2`.
-
-    Returns:
-        `torch.FloatTensor`: a tensor containing the area for each box.
-    """
-    boxes = _upcast(boxes)
-    return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
-
-
-# Copied from transformers.models.detr.modeling_detr.box_iou
-def box_iou(boxes1, boxes2):
-    area1 = box_area(boxes1)
-    area2 = box_area(boxes2)
-
-    left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
-    right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
-
-    width_height = (right_bottom - left_top).clamp(min=0)  # [N,M,2]
-    inter = width_height[:, :, 0] * width_height[:, :, 1]  # [N,M]
-
-    union = area1[:, None] + area2 - inter
-
-    iou = inter / union
-    return iou, union
-
-
-# Copied from transformers.models.detr.modeling_detr.generalized_box_iou
-def generalized_box_iou(boxes1, boxes2):
-    """
-    Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format.
-
-    Returns:
-        `torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2)
-    """
-    # degenerate boxes gives inf / nan results
-    # so do an early check
-    if not (boxes1[:, 2:] >= boxes1[:, :2]).all():
-        raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}")
-    if not (boxes2[:, 2:] >= boxes2[:, :2]).all():
-        raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}")
-    iou, union = box_iou(boxes1, boxes2)
-
-    top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2])
-    bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
-
-    width_height = (bottom_right - top_left).clamp(min=0)  # [N,M,2]
-    area = width_height[:, :, 0] * width_height[:, :, 1]
-
-    return iou - (area - union) / area
-
-
-# Copied from transformers.models.detr.modeling_detr._max_by_axis
-def _max_by_axis(the_list):
-    # type: (List[List[int]]) -> List[int]
-    maxes = the_list[0]
-    for sublist in the_list[1:]:
-        for index, item in enumerate(sublist):
-            maxes[index] = max(maxes[index], item)
-    return maxes
-
-
-# Copied from transformers.models.detr.modeling_detr.NestedTensor
-class NestedTensor(object):
-    def __init__(self, tensors, mask: Optional[Tensor]):
-        self.tensors = tensors
-        self.mask = mask
-
-    def to(self, device):
-        cast_tensor = self.tensors.to(device)
-        mask = self.mask
-        if mask is not None:
-            cast_mask = mask.to(device)
-        else:
-            cast_mask = None
-        return NestedTensor(cast_tensor, cast_mask)
-
-    def decompose(self):
-        return self.tensors, self.mask
-
-    def __repr__(self):
-        return str(self.tensors)
-
-
-# Copied from transformers.models.detr.modeling_detr.nested_tensor_from_tensor_list
-def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
-    if tensor_list[0].ndim == 3:
-        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
-        batch_shape = [len(tensor_list)] + max_size
-        batch_size, num_channels, height, width = batch_shape
-        dtype = tensor_list[0].dtype
-        device = tensor_list[0].device
-        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
-        mask = torch.ones((batch_size, height, width), dtype=torch.bool, device=device)
-        for img, pad_img, m in zip(tensor_list, tensor, mask):
-            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-            m[: img.shape[1], : img.shape[2]] = False
-    else:
-        raise ValueError("Only 3-dimensional tensors are supported")
-    return NestedTensor(tensor, mask)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/ernie_m/configuration_ernie_m.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/ernie_m/configuration_ernie_m.py
deleted file mode 100644
index d23d616b81907a702f21de3415a0697a5fa27880..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/ernie_m/configuration_ernie_m.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# coding=utf-8
-# Copyright 2023 Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" ErnieM model configuration"""
-# Adapted from original paddlenlp repository.(https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/ernie_m/configuration.py)
-
-from __future__ import annotations
-
-from typing import Dict
-
-from ...configuration_utils import PretrainedConfig
-
-
-ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
-    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
-}
-
-
-class ErnieMConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`ErnieMModel`]. It is used to instantiate a
-    Ernie-M model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the `Ernie-M`
-    [susnato/ernie-m-base_pytorch](https://huggingface.co/susnato/ernie-m-base_pytorch) architecture.
-
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 250002):
-            Vocabulary size of `inputs_ids` in [`ErnieMModel`]. Also is the vocab size of token embedding matrix.
-            Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling
-            [`ErnieMModel`].
-        hidden_size (`int`, *optional*, defaults to 768):
-            Dimensionality of the embedding layer, encoder layers and pooler layer.
-        num_hidden_layers (`int`, *optional*, defaults to 12):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 12):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (`int`, *optional*, defaults to 3072):
-            Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors to feed-forward layers are
-            firstly projected from hidden_size to intermediate_size, and then projected back to hidden_size. Typically
-            intermediate_size is larger than hidden_size.
-        hidden_act (`str`, *optional*, defaults to `"gelu"`):
-            The non-linear activation function in the feed-forward layer. `"gelu"`, `"relu"` and any other torch
-            supported activation functions are supported.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout probability for all fully connected layers in the embeddings and encoder.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout probability used in `MultiHeadAttention` in all encoder layers to drop some attention target.
-        act_dropout (`float`, *optional*, defaults to 0.0):
-            This dropout probability is used in `ErnieMEncoderLayer` after activation.
-        max_position_embeddings (`int`, *optional*, defaults to 512):
-            The maximum value of the dimensionality of position encoding, which dictates the maximum supported length
-            of an input sequence.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
-            The epsilon used by the layer normalization layers.
-        classifier_dropout (`float`, *optional*):
-            The dropout ratio for the classification head.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the normal initializer for initializing all weight matrices.
-        pad_token_id(`int`, *optional*, defaults to 1):
-            The index of padding token in the token vocabulary.
-
-    A normal_initializer initializes weight matrices as normal distributions. See
-    `ErnieMPretrainedModel._init_weights()` for how weights are initialized in `ErnieMModel`.
-    """
-    model_type = "ernie_m"
-    attribute_map: Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
-
-    def __init__(
-        self,
-        vocab_size: int = 250002,
-        hidden_size: int = 768,
-        num_hidden_layers: int = 12,
-        num_attention_heads: int = 12,
-        intermediate_size: int = 3072,
-        hidden_act: str = "gelu",
-        hidden_dropout_prob: float = 0.1,
-        attention_probs_dropout_prob: float = 0.1,
-        max_position_embeddings: int = 514,
-        initializer_range: float = 0.02,
-        pad_token_id: int = 1,
-        layer_norm_eps: float = 1e-05,
-        classifier_dropout=None,
-        is_decoder=False,
-        act_dropout=0.0,
-        **kwargs,
-    ):
-        super().__init__(pad_token_id=pad_token_id, **kwargs)
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.intermediate_size = intermediate_size
-        self.hidden_act = hidden_act
-        self.hidden_dropout_prob = hidden_dropout_prob
-        self.attention_probs_dropout_prob = attention_probs_dropout_prob
-        self.max_position_embeddings = max_position_embeddings
-        self.initializer_range = initializer_range
-        self.layer_norm_eps = layer_norm_eps
-        self.classifier_dropout = classifier_dropout
-        self.is_decoder = is_decoder
-        self.act_dropout = act_dropout
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlmv2/configuration_layoutlmv2.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlmv2/configuration_layoutlmv2.py
deleted file mode 100644
index 3cc8027c1dd5c8565a8040045f26a04023d60c02..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlmv2/configuration_layoutlmv2.py
+++ /dev/null
@@ -1,223 +0,0 @@
-# coding=utf-8
-# Copyright Microsoft Research and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" LayoutLMv2 model configuration"""
-
-from ...configuration_utils import PretrainedConfig
-from ...utils import is_detectron2_available, logging
-
-
-logger = logging.get_logger(__name__)
-
-LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json",
-    "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json",
-    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
-}
-
-# soft dependency
-if is_detectron2_available():
-    import detectron2
-
-
-class LayoutLMv2Config(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`LayoutLMv2Model`]. It is used to instantiate an
-    LayoutLMv2 model according to the specified arguments, defining the model architecture. Instantiating a
-    configuration with the defaults will yield a similar configuration to that of the LayoutLMv2
-    [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 30522):
-            Vocabulary size of the LayoutLMv2 model. Defines the number of different tokens that can be represented by
-            the `inputs_ids` passed when calling [`LayoutLMv2Model`] or [`TFLayoutLMv2Model`].
-        hidden_size (`int`, *optional*, defaults to 768):
-            Dimension of the encoder layers and the pooler layer.
-        num_hidden_layers (`int`, *optional*, defaults to 12):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 12):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (`int`, *optional*, defaults to 3072):
-            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
-            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
-            `"relu"`, `"selu"` and `"gelu_new"` are supported.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout ratio for the attention probabilities.
-        max_position_embeddings (`int`, *optional*, defaults to 512):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        type_vocab_size (`int`, *optional*, defaults to 2):
-            The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv2Model`] or
-            [`TFLayoutLMv2Model`].
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
-            The epsilon used by the layer normalization layers.
-        max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
-            The maximum value that the 2D position embedding might ever be used with. Typically set this to something
-            large just in case (e.g., 1024).
-        max_rel_pos (`int`, *optional*, defaults to 128):
-            The maximum number of relative positions to be used in the self-attention mechanism.
-        rel_pos_bins (`int`, *optional*, defaults to 32):
-            The number of relative position bins to be used in the self-attention mechanism.
-        fast_qkv (`bool`, *optional*, defaults to `True`):
-            Whether or not to use a single matrix for the queries, keys, values in the self-attention layers.
-        max_rel_2d_pos (`int`, *optional*, defaults to 256):
-            The maximum number of relative 2D positions in the self-attention mechanism.
-        rel_2d_pos_bins (`int`, *optional*, defaults to 64):
-            The number of 2D relative position bins in the self-attention mechanism.
-        image_feature_pool_shape (`List[int]`, *optional*, defaults to [7, 7, 256]):
-            The shape of the average-pooled feature map.
-        coordinate_size (`int`, *optional*, defaults to 128):
-            Dimension of the coordinate embeddings.
-        shape_size (`int`, *optional*, defaults to 128):
-            Dimension of the width and height embeddings.
-        has_relative_attention_bias (`bool`, *optional*, defaults to `True`):
-            Whether or not to use a relative attention bias in the self-attention mechanism.
-        has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
-            Whether or not to use a spatial attention bias in the self-attention mechanism.
-        has_visual_segment_embedding (`bool`, *optional*, defaults to `False`):
-            Whether or not to add visual segment embeddings.
-        detectron2_config_args (`dict`, *optional*):
-            Dictionary containing the configuration arguments of the Detectron2 visual backbone. Refer to [this
-            file](https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/models/layoutlmv2/detectron2_config.py)
-            for details regarding default values.
-
-    Example:
-
-    ```python
-    >>> from transformers import LayoutLMv2Config, LayoutLMv2Model
-
-    >>> # Initializing a LayoutLMv2 microsoft/layoutlmv2-base-uncased style configuration
-    >>> configuration = LayoutLMv2Config()
-
-    >>> # Initializing a model (with random weights) from the microsoft/layoutlmv2-base-uncased style configuration
-    >>> model = LayoutLMv2Model(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-    model_type = "layoutlmv2"
-
-    def __init__(
-        self,
-        vocab_size=30522,
-        hidden_size=768,
-        num_hidden_layers=12,
-        num_attention_heads=12,
-        intermediate_size=3072,
-        hidden_act="gelu",
-        hidden_dropout_prob=0.1,
-        attention_probs_dropout_prob=0.1,
-        max_position_embeddings=512,
-        type_vocab_size=2,
-        initializer_range=0.02,
-        layer_norm_eps=1e-12,
-        pad_token_id=0,
-        max_2d_position_embeddings=1024,
-        max_rel_pos=128,
-        rel_pos_bins=32,
-        fast_qkv=True,
-        max_rel_2d_pos=256,
-        rel_2d_pos_bins=64,
-        convert_sync_batchnorm=True,
-        image_feature_pool_shape=[7, 7, 256],
-        coordinate_size=128,
-        shape_size=128,
-        has_relative_attention_bias=True,
-        has_spatial_attention_bias=True,
-        has_visual_segment_embedding=False,
-        detectron2_config_args=None,
-        **kwargs,
-    ):
-        super().__init__(
-            vocab_size=vocab_size,
-            hidden_size=hidden_size,
-            num_hidden_layers=num_hidden_layers,
-            num_attention_heads=num_attention_heads,
-            intermediate_size=intermediate_size,
-            hidden_act=hidden_act,
-            hidden_dropout_prob=hidden_dropout_prob,
-            attention_probs_dropout_prob=attention_probs_dropout_prob,
-            max_position_embeddings=max_position_embeddings,
-            type_vocab_size=type_vocab_size,
-            initializer_range=initializer_range,
-            layer_norm_eps=layer_norm_eps,
-            pad_token_id=pad_token_id,
-            **kwargs,
-        )
-        self.max_2d_position_embeddings = max_2d_position_embeddings
-        self.max_rel_pos = max_rel_pos
-        self.rel_pos_bins = rel_pos_bins
-        self.fast_qkv = fast_qkv
-        self.max_rel_2d_pos = max_rel_2d_pos
-        self.rel_2d_pos_bins = rel_2d_pos_bins
-        self.convert_sync_batchnorm = convert_sync_batchnorm
-        self.image_feature_pool_shape = image_feature_pool_shape
-        self.coordinate_size = coordinate_size
-        self.shape_size = shape_size
-        self.has_relative_attention_bias = has_relative_attention_bias
-        self.has_spatial_attention_bias = has_spatial_attention_bias
-        self.has_visual_segment_embedding = has_visual_segment_embedding
-        self.detectron2_config_args = (
-            detectron2_config_args if detectron2_config_args is not None else self.get_default_detectron2_config()
-        )
-
-    @classmethod
-    def get_default_detectron2_config(self):
-        return {
-            "MODEL.MASK_ON": True,
-            "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
-            "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
-            "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
-            "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
-            "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
-            "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
-            "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
-            "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
-            "MODEL.POST_NMS_TOPK_TEST": 1000,
-            "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
-            "MODEL.ROI_HEADS.NUM_CLASSES": 5,
-            "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
-            "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
-            "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
-            "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
-            "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
-            "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
-            "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
-            "MODEL.RESNETS.DEPTH": 101,
-            "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
-            "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
-            "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
-            "MODEL.RESNETS.NUM_GROUPS": 32,
-            "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
-            "MODEL.RESNETS.STRIDE_IN_1X1": False,
-        }
-
-    def get_detectron2_config(self):
-        detectron2_config = detectron2.config.get_cfg()
-        for k, v in self.detectron2_config_args.items():
-            attributes = k.split(".")
-            to_set = detectron2_config
-            for attribute in attributes[:-1]:
-                to_set = getattr(to_set, attribute)
-            setattr(to_set, attributes[-1], v)
-
-        return detectron2_config
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/common/data/coco_keypoint.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/common/data/coco_keypoint.py
deleted file mode 100644
index b4ceb066faf696954244205dc75376b767071217..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/common/data/coco_keypoint.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from detectron2.data.detection_utils import create_keypoint_hflip_indices
-
-from .coco import dataloader
-
-dataloader.train.dataset.min_keypoints = 1
-dataloader.train.dataset.names = "keypoints_coco_2017_train"
-dataloader.test.dataset.names = "keypoints_coco_2017_val"
-
-dataloader.train.mapper.update(
-    use_instance_mask=False,
-    use_keypoint=True,
-    keypoint_hflip_indices=create_keypoint_hflip_indices(dataloader.train.dataset.names),
-)
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py
deleted file mode 100644
index b1eedeebf8e3bde80722fc4acf51be6ca212cb3d..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py
+++ /dev/null
@@ -1,270 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import numpy as np
-import torch
-
-from detectron2.config import configurable
-from detectron2.layers import ShapeSpec, batched_nms_rotated
-from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated
-from detectron2.utils.events import get_event_storage
-
-from ..box_regression import Box2BoxTransformRotated
-from ..poolers import ROIPooler
-from ..proposal_generator.proposal_utils import add_ground_truth_to_proposals
-from .box_head import build_box_head
-from .fast_rcnn import FastRCNNOutputLayers
-from .roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads
-
-logger = logging.getLogger(__name__)
-
-"""
-Shape shorthand in this module:
-
-    N: number of images in the minibatch
-    R: number of ROIs, combined over all images, in the minibatch
-    Ri: number of ROIs in image i
-    K: number of foreground classes. E.g.,there are 80 foreground classes in COCO.
-
-Naming convention:
-
-    deltas: refers to the 5-d (dx, dy, dw, dh, da) deltas that parameterize the box2box
-    transform (see :class:`box_regression.Box2BoxTransformRotated`).
-
-    pred_class_logits: predicted class scores in [-inf, +inf]; use
-        softmax(pred_class_logits) to estimate P(class).
-
-    gt_classes: ground-truth classification labels in [0, K], where [0, K) represent
-        foreground object classes and K represents the background class.
-
-    pred_proposal_deltas: predicted rotated box2box transform deltas for transforming proposals
-        to detection box predictions.
-
-    gt_proposal_deltas: ground-truth rotated box2box transform deltas
-"""
-
-
-def fast_rcnn_inference_rotated(
-    boxes, scores, image_shapes, score_thresh, nms_thresh, topk_per_image
-):
-    """
-    Call `fast_rcnn_inference_single_image_rotated` for all images.
-
-    Args:
-        boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic
-            boxes for each image. Element i has shape (Ri, K * 5) if doing
-            class-specific regression, or (Ri, 5) if doing class-agnostic
-            regression, where Ri is the number of predicted objects for image i.
-            This is compatible with the output of :meth:`FastRCNNOutputLayers.predict_boxes`.
-        scores (list[Tensor]): A list of Tensors of predicted class scores for each image.
-            Element i has shape (Ri, K + 1), where Ri is the number of predicted objects
-            for image i. Compatible with the output of :meth:`FastRCNNOutputLayers.predict_probs`.
-        image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch.
-        score_thresh (float): Only return detections with a confidence score exceeding this
-            threshold.
-        nms_thresh (float):  The threshold to use for box non-maximum suppression. Value in [0, 1].
-        topk_per_image (int): The number of top scoring detections to return. Set < 0 to return
-            all detections.
-
-    Returns:
-        instances: (list[Instances]): A list of N instances, one for each image in the batch,
-            that stores the topk most confidence detections.
-        kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates
-            the corresponding boxes/scores index in [0, Ri) from the input, for image i.
-    """
-    result_per_image = [
-        fast_rcnn_inference_single_image_rotated(
-            boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image
-        )
-        for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes)
-    ]
-    return [x[0] for x in result_per_image], [x[1] for x in result_per_image]
-
-
-def fast_rcnn_inference_single_image_rotated(
-    boxes, scores, image_shape, score_thresh, nms_thresh, topk_per_image
-):
-    """
-    Single-image inference. Return rotated bounding-box detection results by thresholding
-    on scores and applying rotated non-maximum suppression (Rotated NMS).
-
-    Args:
-        Same as `fast_rcnn_inference_rotated`, but with rotated boxes, scores, and image shapes
-        per image.
-
-    Returns:
-        Same as `fast_rcnn_inference_rotated`, but for only one image.
-    """
-    valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
-    if not valid_mask.all():
-        boxes = boxes[valid_mask]
-        scores = scores[valid_mask]
-
-    B = 5  # box dimension
-    scores = scores[:, :-1]
-    num_bbox_reg_classes = boxes.shape[1] // B
-    # Convert to Boxes to use the `clip` function ...
-    boxes = RotatedBoxes(boxes.reshape(-1, B))
-    boxes.clip(image_shape)
-    boxes = boxes.tensor.view(-1, num_bbox_reg_classes, B)  # R x C x B
-    # Filter results based on detection scores
-    filter_mask = scores > score_thresh  # R x K
-    # R' x 2. First column contains indices of the R predictions;
-    # Second column contains indices of classes.
-    filter_inds = filter_mask.nonzero()
-    if num_bbox_reg_classes == 1:
-        boxes = boxes[filter_inds[:, 0], 0]
-    else:
-        boxes = boxes[filter_mask]
-    scores = scores[filter_mask]
-
-    # Apply per-class Rotated NMS
-    keep = batched_nms_rotated(boxes, scores, filter_inds[:, 1], nms_thresh)
-    if topk_per_image >= 0:
-        keep = keep[:topk_per_image]
-    boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep]
-
-    result = Instances(image_shape)
-    result.pred_boxes = RotatedBoxes(boxes)
-    result.scores = scores
-    result.pred_classes = filter_inds[:, 1]
-
-    return result, filter_inds[:, 0]
-
-
-class RotatedFastRCNNOutputLayers(FastRCNNOutputLayers):
-    """
-    Two linear layers for predicting Rotated Fast R-CNN outputs.
-    """
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        args = super().from_config(cfg, input_shape)
-        args["box2box_transform"] = Box2BoxTransformRotated(
-            weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS
-        )
-        return args
-
-    def inference(self, predictions, proposals):
-        """
-        Returns:
-            list[Instances]: same as `fast_rcnn_inference_rotated`.
-            list[Tensor]: same as `fast_rcnn_inference_rotated`.
-        """
-        boxes = self.predict_boxes(predictions, proposals)
-        scores = self.predict_probs(predictions, proposals)
-        image_shapes = [x.image_size for x in proposals]
-
-        return fast_rcnn_inference_rotated(
-            boxes,
-            scores,
-            image_shapes,
-            self.test_score_thresh,
-            self.test_nms_thresh,
-            self.test_topk_per_image,
-        )
-
-
-@ROI_HEADS_REGISTRY.register()
-class RROIHeads(StandardROIHeads):
-    """
-    This class is used by Rotated Fast R-CNN to detect rotated boxes.
-    For now, it only supports box predictions but not mask or keypoints.
-    """
-
-    @configurable
-    def __init__(self, **kwargs):
-        """
-        NOTE: this interface is experimental.
-        """
-        super().__init__(**kwargs)
-        assert (
-            not self.mask_on and not self.keypoint_on
-        ), "Mask/Keypoints not supported in Rotated ROIHeads."
-        assert not self.train_on_pred_boxes, "train_on_pred_boxes not implemented for RROIHeads!"
-
-    @classmethod
-    def _init_box_head(cls, cfg, input_shape):
-        # fmt: off
-        in_features       = cfg.MODEL.ROI_HEADS.IN_FEATURES
-        pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
-        pooler_scales     = tuple(1.0 / input_shape[k].stride for k in in_features)
-        sampling_ratio    = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
-        pooler_type       = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE
-        # fmt: on
-        assert pooler_type in ["ROIAlignRotated"], pooler_type
-        # assume all channel counts are equal
-        in_channels = [input_shape[f].channels for f in in_features][0]
-
-        box_pooler = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type=pooler_type,
-        )
-        box_head = build_box_head(
-            cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution)
-        )
-        # This line is the only difference v.s. StandardROIHeads
-        box_predictor = RotatedFastRCNNOutputLayers(cfg, box_head.output_shape)
-        return {
-            "box_in_features": in_features,
-            "box_pooler": box_pooler,
-            "box_head": box_head,
-            "box_predictor": box_predictor,
-        }
-
-    @torch.no_grad()
-    def label_and_sample_proposals(self, proposals, targets):
-        """
-        Prepare some proposals to be used to train the RROI heads.
-        It performs box matching between `proposals` and `targets`, and assigns
-        training labels to the proposals.
-        It returns `self.batch_size_per_image` random samples from proposals and groundtruth boxes,
-        with a fraction of positives that is no larger than `self.positive_sample_fraction.
-
-        Args:
-            See :meth:`StandardROIHeads.forward`
-
-        Returns:
-            list[Instances]: length `N` list of `Instances`s containing the proposals
-                sampled for training. Each `Instances` has the following fields:
-                - proposal_boxes: the rotated proposal boxes
-                - gt_boxes: the ground-truth rotated boxes that the proposal is assigned to
-                  (this is only meaningful if the proposal has a label > 0; if label = 0
-                   then the ground-truth box is random)
-                - gt_classes: the ground-truth classification lable for each proposal
-        """
-        if self.proposal_append_gt:
-            proposals = add_ground_truth_to_proposals(targets, proposals)
-
-        proposals_with_gt = []
-
-        num_fg_samples = []
-        num_bg_samples = []
-        for proposals_per_image, targets_per_image in zip(proposals, targets):
-            has_gt = len(targets_per_image) > 0
-            match_quality_matrix = pairwise_iou_rotated(
-                targets_per_image.gt_boxes, proposals_per_image.proposal_boxes
-            )
-            matched_idxs, matched_labels = self.proposal_matcher(match_quality_matrix)
-            sampled_idxs, gt_classes = self._sample_proposals(
-                matched_idxs, matched_labels, targets_per_image.gt_classes
-            )
-
-            proposals_per_image = proposals_per_image[sampled_idxs]
-            proposals_per_image.gt_classes = gt_classes
-
-            if has_gt:
-                sampled_targets = matched_idxs[sampled_idxs]
-                proposals_per_image.gt_boxes = targets_per_image.gt_boxes[sampled_targets]
-
-            num_bg_samples.append((gt_classes == self.num_classes).sum().item())
-            num_fg_samples.append(gt_classes.numel() - num_bg_samples[-1])
-            proposals_with_gt.append(proposals_per_image)
-
-        # Log the number of fg/bg samples that are selected for training ROI heads
-        storage = get_event_storage()
-        storage.put_scalar("roi_head/num_fg_samples", np.mean(num_fg_samples))
-        storage.put_scalar("roi_head/num_bg_samples", np.mean(num_bg_samples))
-
-        return proposals_with_gt
diff --git a/spaces/youplala/StoreCopilot/assets/animate.css b/spaces/youplala/StoreCopilot/assets/animate.css
deleted file mode 100644
index fcd0e9bf7ae016d6207b9311abc006d1cbe17ad3..0000000000000000000000000000000000000000
--- a/spaces/youplala/StoreCopilot/assets/animate.css
+++ /dev/null
@@ -1,7 +0,0 @@
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-* Version - 4.1.1
-* Licensed under the MIT license - http://opensource.org/licenses/MIT
-*
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0);transform:translate3d(0, 0, 0)}}@keyframes bounceInUp{from,60%,75%,90%,to{-webkit-animation-timing-function:cubic-bezier(0.215, 0.61, 0.355, 1);animation-timing-function:cubic-bezier(0.215, 0.61, 0.355, 1)}from{opacity:0;-webkit-transform:translate3d(0, 3000px, 0) scaleY(5);transform:translate3d(0, 3000px, 0) scaleY(5)}60%{opacity:1;-webkit-transform:translate3d(0, -20px, 0) scaleY(0.9);transform:translate3d(0, -20px, 0) scaleY(0.9)}75%{-webkit-transform:translate3d(0, 10px, 0) scaleY(0.95);transform:translate3d(0, 10px, 0) scaleY(0.95)}90%{-webkit-transform:translate3d(0, -5px, 0) scaleY(0.985);transform:translate3d(0, -5px, 0) scaleY(0.985)}to{-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}.animate__bounceInUp{-webkit-animation-name:bounceInUp;animation-name:bounceInUp}@-webkit-keyframes bounceOut{20%{-webkit-transform:scale3d(0.9, 0.9, 0.9);transform:scale3d(0.9, 0.9, 0.9)}50%,55%{opacity:1;-webkit-transform:scale3d(1.1, 1.1, 1.1);transform:scale3d(1.1, 1.1, 1.1)}to{opacity:0;-webkit-transform:scale3d(0.3, 0.3, 0.3);transform:scale3d(0.3, 0.3, 0.3)}}@keyframes bounceOut{20%{-webkit-transform:scale3d(0.9, 0.9, 0.9);transform:scale3d(0.9, 0.9, 0.9)}50%,55%{opacity:1;-webkit-transform:scale3d(1.1, 1.1, 1.1);transform:scale3d(1.1, 1.1, 1.1)}to{opacity:0;-webkit-transform:scale3d(0.3, 0.3, 0.3);transform:scale3d(0.3, 0.3, 0.3)}}.animate__bounceOut{-webkit-animation-duration:.75s;animation-duration:.75s;-webkit-animation-duration:calc(var(--animate-duration)*.75);animation-duration:calc(var(--animate-duration)*.75);-webkit-animation-name:bounceOut;animation-name:bounceOut}@-webkit-keyframes bounceOutDown{20%{-webkit-transform:translate3d(0, 10px, 0) scaleY(0.985);transform:translate3d(0, 10px, 0) scaleY(0.985)}40%,45%{opacity:1;-webkit-transform:translate3d(0, -20px, 0) scaleY(0.9);transform:translate3d(0, -20px, 0) scaleY(0.9)}to{opacity:0;-webkit-transform:translate3d(0, 2000px, 0) scaleY(3);transform:translate3d(0, 2000px, 0) scaleY(3)}}@keyframes bounceOutDown{20%{-webkit-transform:translate3d(0, 10px, 0) scaleY(0.985);transform:translate3d(0, 10px, 0) scaleY(0.985)}40%,45%{opacity:1;-webkit-transform:translate3d(0, -20px, 0) scaleY(0.9);transform:translate3d(0, -20px, 0) scaleY(0.9)}to{opacity:0;-webkit-transform:translate3d(0, 2000px, 0) scaleY(3);transform:translate3d(0, 2000px, 0) scaleY(3)}}.animate__bounceOutDown{-webkit-animation-name:bounceOutDown;animation-name:bounceOutDown}@-webkit-keyframes bounceOutLeft{20%{opacity:1;-webkit-transform:translate3d(20px, 0, 0) scaleX(0.9);transform:translate3d(20px, 0, 0) scaleX(0.9)}to{opacity:0;-webkit-transform:translate3d(-2000px, 0, 0) scaleX(2);transform:translate3d(-2000px, 0, 0) scaleX(2)}}@keyframes bounceOutLeft{20%{opacity:1;-webkit-transform:translate3d(20px, 0, 0) scaleX(0.9);transform:translate3d(20px, 0, 0) scaleX(0.9)}to{opacity:0;-webkit-transform:translate3d(-2000px, 0, 0) scaleX(2);transform:translate3d(-2000px, 0, 0) scaleX(2)}}.animate__bounceOutLeft{-webkit-animation-name:bounceOutLeft;animation-name:bounceOutLeft}@-webkit-keyframes bounceOutRight{20%{opacity:1;-webkit-transform:translate3d(-20px, 0, 0) scaleX(0.9);transform:translate3d(-20px, 0, 0) scaleX(0.9)}to{opacity:0;-webkit-transform:translate3d(2000px, 0, 0) scaleX(2);transform:translate3d(2000px, 0, 0) scaleX(2)}}@keyframes bounceOutRight{20%{opacity:1;-webkit-transform:translate3d(-20px, 0, 0) scaleX(0.9);transform:translate3d(-20px, 0, 0) scaleX(0.9)}to{opacity:0;-webkit-transform:translate3d(2000px, 0, 0) scaleX(2);transform:translate3d(2000px, 0, 0) scaleX(2)}}.animate__bounceOutRight{-webkit-animation-name:bounceOutRight;animation-name:bounceOutRight}@-webkit-keyframes bounceOutUp{20%{-webkit-transform:translate3d(0, -10px, 0) scaleY(0.985);transform:translate3d(0, -10px, 0) scaleY(0.985)}40%,45%{opacity:1;-webkit-transform:translate3d(0, 20px, 0) scaleY(0.9);transform:translate3d(0, 20px, 0) scaleY(0.9)}to{opacity:0;-webkit-transform:translate3d(0, -2000px, 0) scaleY(3);transform:translate3d(0, -2000px, 0) scaleY(3)}}@keyframes bounceOutUp{20%{-webkit-transform:translate3d(0, -10px, 0) scaleY(0.985);transform:translate3d(0, -10px, 0) scaleY(0.985)}40%,45%{opacity:1;-webkit-transform:translate3d(0, 20px, 0) scaleY(0.9);transform:translate3d(0, 20px, 0) scaleY(0.9)}to{opacity:0;-webkit-transform:translate3d(0, -2000px, 0) scaleY(3);transform:translate3d(0, -2000px, 0) scaleY(3)}}.animate__bounceOutUp{-webkit-animation-name:bounceOutUp;animation-name:bounceOutUp}@-webkit-keyframes fadeIn{from{opacity:0}to{opacity:1}}@keyframes fadeIn{from{opacity:0}to{opacity:1}}.animate__fadeIn{-webkit-animation-name:fadeIn;animation-name:fadeIn}@-webkit-keyframes fadeInDown{from{opacity:0;-webkit-transform:translate3d(0, -100%, 0);transform:translate3d(0, -100%, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}@keyframes fadeInDown{from{opacity:0;-webkit-transform:translate3d(0, -100%, 0);transform:translate3d(0, -100%, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}.animate__fadeInDown{-webkit-animation-name:fadeInDown;animation-name:fadeInDown}@-webkit-keyframes fadeInDownBig{from{opacity:0;-webkit-transform:translate3d(0, -2000px, 0);transform:translate3d(0, -2000px, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}@keyframes fadeInDownBig{from{opacity:0;-webkit-transform:translate3d(0, -2000px, 0);transform:translate3d(0, -2000px, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}.animate__fadeInDownBig{-webkit-animation-name:fadeInDownBig;animation-name:fadeInDownBig}@-webkit-keyframes fadeInLeft{from{opacity:0;-webkit-transform:translate3d(-100%, 0, 0);transform:translate3d(-100%, 0, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}@keyframes fadeInLeft{from{opacity:0;-webkit-transform:translate3d(-100%, 0, 0);transform:translate3d(-100%, 0, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}.animate__fadeInLeft{-webkit-animation-name:fadeInLeft;animation-name:fadeInLeft}@-webkit-keyframes fadeInLeftBig{from{opacity:0;-webkit-transform:translate3d(-2000px, 0, 0);transform:translate3d(-2000px, 0, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}@keyframes fadeInLeftBig{from{opacity:0;-webkit-transform:translate3d(-2000px, 0, 0);transform:translate3d(-2000px, 0, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}.animate__fadeInLeftBig{-webkit-animation-name:fadeInLeftBig;animation-name:fadeInLeftBig}@-webkit-keyframes fadeInRight{from{opacity:0;-webkit-transform:translate3d(100%, 0, 0);transform:translate3d(100%, 0, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}@keyframes fadeInRight{from{opacity:0;-webkit-transform:translate3d(100%, 0, 0);transform:translate3d(100%, 0, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}.animate__fadeInRight{-webkit-animation-name:fadeInRight;animation-name:fadeInRight}@-webkit-keyframes fadeInRightBig{from{opacity:0;-webkit-transform:translate3d(2000px, 0, 0);transform:translate3d(2000px, 0, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}@keyframes fadeInRightBig{from{opacity:0;-webkit-transform:translate3d(2000px, 0, 0);transform:translate3d(2000px, 0, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}.animate__fadeInRightBig{-webkit-animation-name:fadeInRightBig;animation-name:fadeInRightBig}@-webkit-keyframes fadeInUp{from{opacity:0;-webkit-transform:translate3d(0, 100%, 0);transform:translate3d(0, 100%, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}@keyframes fadeInUp{from{opacity:0;-webkit-transform:translate3d(0, 100%, 0);transform:translate3d(0, 100%, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}.animate__fadeInUp{-webkit-animation-name:fadeInUp;animation-name:fadeInUp}@-webkit-keyframes fadeInUpBig{from{opacity:0;-webkit-transform:translate3d(0, 2000px, 0);transform:translate3d(0, 2000px, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}@keyframes fadeInUpBig{from{opacity:0;-webkit-transform:translate3d(0, 2000px, 0);transform:translate3d(0, 2000px, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}.animate__fadeInUpBig{-webkit-animation-name:fadeInUpBig;animation-name:fadeInUpBig}@-webkit-keyframes fadeInTopLeft{from{opacity:0;-webkit-transform:translate3d(-100%, -100%, 0);transform:translate3d(-100%, -100%, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}@keyframes fadeInTopLeft{from{opacity:0;-webkit-transform:translate3d(-100%, -100%, 0);transform:translate3d(-100%, -100%, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}.animate__fadeInTopLeft{-webkit-animation-name:fadeInTopLeft;animation-name:fadeInTopLeft}@-webkit-keyframes fadeInTopRight{from{opacity:0;-webkit-transform:translate3d(100%, -100%, 0);transform:translate3d(100%, -100%, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}@keyframes fadeInTopRight{from{opacity:0;-webkit-transform:translate3d(100%, -100%, 0);transform:translate3d(100%, -100%, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}.animate__fadeInTopRight{-webkit-animation-name:fadeInTopRight;animation-name:fadeInTopRight}@-webkit-keyframes fadeInBottomLeft{from{opacity:0;-webkit-transform:translate3d(-100%, 100%, 0);transform:translate3d(-100%, 100%, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}@keyframes fadeInBottomLeft{from{opacity:0;-webkit-transform:translate3d(-100%, 100%, 0);transform:translate3d(-100%, 100%, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}.animate__fadeInBottomLeft{-webkit-animation-name:fadeInBottomLeft;animation-name:fadeInBottomLeft}@-webkit-keyframes fadeInBottomRight{from{opacity:0;-webkit-transform:translate3d(100%, 100%, 0);transform:translate3d(100%, 100%, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}@keyframes fadeInBottomRight{from{opacity:0;-webkit-transform:translate3d(100%, 100%, 0);transform:translate3d(100%, 100%, 0)}to{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}}.animate__fadeInBottomRight{-webkit-animation-name:fadeInBottomRight;animation-name:fadeInBottomRight}@-webkit-keyframes fadeOut{from{opacity:1}to{opacity:0}}@keyframes fadeOut{from{opacity:1}to{opacity:0}}.animate__fadeOut{-webkit-animation-name:fadeOut;animation-name:fadeOut}@-webkit-keyframes fadeOutDown{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(0, 100%, 0);transform:translate3d(0, 100%, 0)}}@keyframes fadeOutDown{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(0, 100%, 0);transform:translate3d(0, 100%, 0)}}.animate__fadeOutDown{-webkit-animation-name:fadeOutDown;animation-name:fadeOutDown}@-webkit-keyframes fadeOutDownBig{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(0, 2000px, 0);transform:translate3d(0, 2000px, 0)}}@keyframes fadeOutDownBig{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(0, 2000px, 0);transform:translate3d(0, 2000px, 0)}}.animate__fadeOutDownBig{-webkit-animation-name:fadeOutDownBig;animation-name:fadeOutDownBig}@-webkit-keyframes fadeOutLeft{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(-100%, 0, 0);transform:translate3d(-100%, 0, 0)}}@keyframes fadeOutLeft{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(-100%, 0, 0);transform:translate3d(-100%, 0, 0)}}.animate__fadeOutLeft{-webkit-animation-name:fadeOutLeft;animation-name:fadeOutLeft}@-webkit-keyframes fadeOutLeftBig{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(-2000px, 0, 0);transform:translate3d(-2000px, 0, 0)}}@keyframes fadeOutLeftBig{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(-2000px, 0, 0);transform:translate3d(-2000px, 0, 0)}}.animate__fadeOutLeftBig{-webkit-animation-name:fadeOutLeftBig;animation-name:fadeOutLeftBig}@-webkit-keyframes fadeOutRight{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(100%, 0, 0);transform:translate3d(100%, 0, 0)}}@keyframes fadeOutRight{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(100%, 0, 0);transform:translate3d(100%, 0, 0)}}.animate__fadeOutRight{-webkit-animation-name:fadeOutRight;animation-name:fadeOutRight}@-webkit-keyframes fadeOutRightBig{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(2000px, 0, 0);transform:translate3d(2000px, 0, 0)}}@keyframes fadeOutRightBig{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(2000px, 0, 0);transform:translate3d(2000px, 0, 0)}}.animate__fadeOutRightBig{-webkit-animation-name:fadeOutRightBig;animation-name:fadeOutRightBig}@-webkit-keyframes fadeOutUp{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(0, -100%, 0);transform:translate3d(0, -100%, 0)}}@keyframes fadeOutUp{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(0, -100%, 0);transform:translate3d(0, -100%, 0)}}.animate__fadeOutUp{-webkit-animation-name:fadeOutUp;animation-name:fadeOutUp}@-webkit-keyframes fadeOutUpBig{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(0, -2000px, 0);transform:translate3d(0, -2000px, 0)}}@keyframes fadeOutUpBig{from{opacity:1}to{opacity:0;-webkit-transform:translate3d(0, -2000px, 0);transform:translate3d(0, -2000px, 0)}}.animate__fadeOutUpBig{-webkit-animation-name:fadeOutUpBig;animation-name:fadeOutUpBig}@-webkit-keyframes fadeOutTopLeft{from{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}to{opacity:0;-webkit-transform:translate3d(-100%, -100%, 0);transform:translate3d(-100%, -100%, 0)}}@keyframes fadeOutTopLeft{from{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}to{opacity:0;-webkit-transform:translate3d(-100%, -100%, 0);transform:translate3d(-100%, -100%, 0)}}.animate__fadeOutTopLeft{-webkit-animation-name:fadeOutTopLeft;animation-name:fadeOutTopLeft}@-webkit-keyframes fadeOutTopRight{from{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}to{opacity:0;-webkit-transform:translate3d(100%, -100%, 0);transform:translate3d(100%, -100%, 0)}}@keyframes fadeOutTopRight{from{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}to{opacity:0;-webkit-transform:translate3d(100%, -100%, 0);transform:translate3d(100%, -100%, 0)}}.animate__fadeOutTopRight{-webkit-animation-name:fadeOutTopRight;animation-name:fadeOutTopRight}@-webkit-keyframes fadeOutBottomRight{from{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}to{opacity:0;-webkit-transform:translate3d(100%, 100%, 0);transform:translate3d(100%, 100%, 0)}}@keyframes fadeOutBottomRight{from{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}to{opacity:0;-webkit-transform:translate3d(100%, 100%, 0);transform:translate3d(100%, 100%, 0)}}.animate__fadeOutBottomRight{-webkit-animation-name:fadeOutBottomRight;animation-name:fadeOutBottomRight}@-webkit-keyframes fadeOutBottomLeft{from{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}to{opacity:0;-webkit-transform:translate3d(-100%, 100%, 0);transform:translate3d(-100%, 100%, 0)}}@keyframes fadeOutBottomLeft{from{opacity:1;-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}to{opacity:0;-webkit-transform:translate3d(-100%, 100%, 0);transform:translate3d(-100%, 100%, 0)}}.animate__fadeOutBottomLeft{-webkit-animation-name:fadeOutBottomLeft;animation-name:fadeOutBottomLeft}@-webkit-keyframes flip{from{-webkit-transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 0) rotate3d(0, 1, 0, -360deg);transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 0) rotate3d(0, 1, 0, -360deg);-webkit-animation-timing-function:ease-out;animation-timing-function:ease-out}40%{-webkit-transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -190deg);transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -190deg);-webkit-animation-timing-function:ease-out;animation-timing-function:ease-out}50%{-webkit-transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -170deg);transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -170deg);-webkit-animation-timing-function:ease-in;animation-timing-function:ease-in}80%{-webkit-transform:perspective(400px) scale3d(0.95, 0.95, 0.95) translate3d(0, 0, 0) rotate3d(0, 1, 0, 0deg);transform:perspective(400px) scale3d(0.95, 0.95, 0.95) translate3d(0, 0, 0) rotate3d(0, 1, 0, 0deg);-webkit-animation-timing-function:ease-in;animation-timing-function:ease-in}to{-webkit-transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 0) rotate3d(0, 1, 0, 0deg);transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 0) rotate3d(0, 1, 0, 0deg);-webkit-animation-timing-function:ease-in;animation-timing-function:ease-in}}@keyframes flip{from{-webkit-transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 0) rotate3d(0, 1, 0, -360deg);transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 0) rotate3d(0, 1, 0, -360deg);-webkit-animation-timing-function:ease-out;animation-timing-function:ease-out}40%{-webkit-transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -190deg);transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -190deg);-webkit-animation-timing-function:ease-out;animation-timing-function:ease-out}50%{-webkit-transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -170deg);transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -170deg);-webkit-animation-timing-function:ease-in;animation-timing-function:ease-in}80%{-webkit-transform:perspective(400px) scale3d(0.95, 0.95, 0.95) translate3d(0, 0, 0) rotate3d(0, 1, 0, 0deg);transform:perspective(400px) scale3d(0.95, 0.95, 0.95) translate3d(0, 0, 0) rotate3d(0, 1, 0, 0deg);-webkit-animation-timing-function:ease-in;animation-timing-function:ease-in}to{-webkit-transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 0) rotate3d(0, 1, 0, 0deg);transform:perspective(400px) scale3d(1, 1, 1) translate3d(0, 0, 0) rotate3d(0, 1, 0, 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0)}}.animate__slideOutRight{-webkit-animation-name:slideOutRight;animation-name:slideOutRight}@-webkit-keyframes slideOutUp{from{-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}to{visibility:hidden;-webkit-transform:translate3d(0, -100%, 0);transform:translate3d(0, -100%, 0)}}@keyframes slideOutUp{from{-webkit-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0)}to{visibility:hidden;-webkit-transform:translate3d(0, -100%, 0);transform:translate3d(0, -100%, 0)}}.animate__slideOutUp{-webkit-animation-name:slideOutUp;animation-name:slideOutUp}
diff --git a/spaces/yunfei0710/gpt-academic/request_llm/bridge_newbingfree.py b/spaces/yunfei0710/gpt-academic/request_llm/bridge_newbingfree.py
deleted file mode 100644
index 38d2eb9bf610ef95aa5e3f571b1dc7a30a6eada1..0000000000000000000000000000000000000000
--- a/spaces/yunfei0710/gpt-academic/request_llm/bridge_newbingfree.py
+++ /dev/null
@@ -1,243 +0,0 @@
-"""
-========================================================================
-第一部分：来自EdgeGPT.py
-https://github.com/acheong08/EdgeGPT
-========================================================================
-"""
-from .edge_gpt_free import Chatbot as NewbingChatbot
-load_message = "等待NewBing响应。"
-
-"""
-========================================================================
-第二部分：子进程Worker（调用主体）
-========================================================================
-"""
-import time
-import json
-import re
-import logging
-import asyncio
-import importlib
-import threading
-from toolbox import update_ui, get_conf, trimmed_format_exc
-from multiprocessing import Process, Pipe
-
-def preprocess_newbing_out(s):
-    pattern = r'\^(\d+)\^' # 匹配^数字^
-    sub = lambda m: '('+m.group(1)+')' # 将匹配到的数字作为替换值
-    result = re.sub(pattern, sub, s) # 替换操作
-    if '[1]' in result:
-        result += '\n\n```reference\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
-    return result
-
-def preprocess_newbing_out_simple(result):
-    if '[1]' in result:
-        result += '\n\n```reference\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
-    return result
-
-class NewBingHandle(Process):
-    def __init__(self):
-        super().__init__(daemon=True)
-        self.parent, self.child = Pipe()
-        self.newbing_model = None
-        self.info = ""
-        self.success = True
-        self.local_history = []
-        self.check_dependency()
-        self.start()
-        self.threadLock = threading.Lock()
-        
-    def check_dependency(self):
-        try:
-            self.success = False
-            import certifi, httpx, rich
-            self.info = "依赖检测通过，等待NewBing响应。注意目前不能多人同时调用NewBing接口（有线程锁），否则将导致每个人的NewBing问询历史互相渗透。调用NewBing时，会自动使用已配置的代理。"
-            self.success = True
-        except:
-            self.info = "缺少的依赖，如果要使用Newbing，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_newbing.txt`安装Newbing的依赖。"
-            self.success = False
-
-    def ready(self):
-        return self.newbing_model is not None
-
-    async def async_run(self):
-        # 读取配置
-        NEWBING_STYLE, = get_conf('NEWBING_STYLE')
-        from request_llm.bridge_all import model_info
-        endpoint = model_info['newbing']['endpoint']
-        while True:
-            # 等待
-            kwargs = self.child.recv()
-            question=kwargs['query']
-            history=kwargs['history']
-            system_prompt=kwargs['system_prompt']
-
-            # 是否重置
-            if len(self.local_history) > 0 and len(history)==0:
-                await self.newbing_model.reset()
-                self.local_history = []
-
-            # 开始问问题
-            prompt = ""
-            if system_prompt not in self.local_history:
-                self.local_history.append(system_prompt)
-                prompt += system_prompt + '\n'
-
-            # 追加历史
-            for ab in history:
-                a, b = ab
-                if a not in self.local_history:
-                    self.local_history.append(a)
-                    prompt += a + '\n'
-                # if b not in self.local_history:
-                #     self.local_history.append(b)
-                #     prompt += b + '\n'
-
-            # 问题
-            prompt += question
-            self.local_history.append(question)
-            print('question:', prompt)
-            # 提交
-            async for final, response in self.newbing_model.ask_stream(
-                prompt=question,
-                conversation_style=NEWBING_STYLE,     # ["creative", "balanced", "precise"]
-                wss_link=endpoint,                      # "wss://sydney.bing.com/sydney/ChatHub"
-            ):
-                if not final:
-                    print(response)
-                    self.child.send(str(response))
-                else:
-                    print('-------- receive final ---------')
-                    self.child.send('[Finish]')
-                    # self.local_history.append(response)
-
-    
-    def run(self):
-        """
-        这个函数运行在子进程
-        """
-        # 第一次运行，加载参数
-        self.success = False
-        self.local_history = []
-        if (self.newbing_model is None) or (not self.success):
-            # 代理设置
-            proxies, = get_conf('proxies')
-            if proxies is None: 
-                self.proxies_https = None
-            else: 
-                self.proxies_https = proxies['https']
-
-            try:
-                self.newbing_model = NewbingChatbot(proxy=self.proxies_https)
-            except:
-                self.success = False
-                tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
-                self.child.send(f'[Local Message] 不能加载Newbing组件。{tb_str}')
-                self.child.send('[Fail]')
-                self.child.send('[Finish]')
-                raise RuntimeError(f"不能加载Newbing组件。")
-
-        self.success = True
-        try:
-            # 进入任务等待状态
-            asyncio.run(self.async_run())
-        except Exception:
-            tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
-            self.child.send(f'[Local Message] Newbing失败 {tb_str}.')
-            self.child.send('[Fail]')
-            self.child.send('[Finish]')
-        
-    def stream_chat(self, **kwargs):
-        """
-        这个函数运行在主进程
-        """
-        self.threadLock.acquire()
-        self.parent.send(kwargs)    # 发送请求到子进程
-        while True:
-            res = self.parent.recv()    # 等待newbing回复的片段
-            if res == '[Finish]':
-                break       # 结束
-            elif res == '[Fail]':
-                self.success = False
-                break
-            else:
-                yield res   # newbing回复的片段
-        self.threadLock.release()
-
-
-"""
-========================================================================
-第三部分：主进程统一调用函数接口
-========================================================================
-"""
-global newbingfree_handle
-newbingfree_handle = None
-
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
-    """
-        多线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    global newbingfree_handle
-    if (newbingfree_handle is None) or (not newbingfree_handle.success):
-        newbingfree_handle = NewBingHandle()
-        if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + newbingfree_handle.info
-        if not newbingfree_handle.success: 
-            error = newbingfree_handle.info
-            newbingfree_handle = None
-            raise RuntimeError(error)
-
-    # 没有 sys_prompt 接口，因此把prompt加入 history
-    history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
-    response = ""
-    if len(observe_window) >= 1: observe_window[0] = "[Local Message]: 等待NewBing响应中 ..."
-    for response in newbingfree_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        if len(observe_window) >= 1:  observe_window[0] = preprocess_newbing_out_simple(response)
-        if len(observe_window) >= 2:  
-            if (time.time()-observe_window[1]) > watch_dog_patience:
-                raise RuntimeError("程序终止。")
-    return preprocess_newbing_out_simple(response)
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
-    """
-        单线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    chatbot.append((inputs, "[Local Message]: 等待NewBing响应中 ..."))
-
-    global newbingfree_handle
-    if (newbingfree_handle is None) or (not newbingfree_handle.success):
-        newbingfree_handle = NewBingHandle()
-        chatbot[-1] = (inputs, load_message + "\n\n" + newbingfree_handle.info)
-        yield from update_ui(chatbot=chatbot, history=[])
-        if not newbingfree_handle.success: 
-            newbingfree_handle = None
-            return
-
-    if additional_fn is not None:
-        import core_functional
-        importlib.reload(core_functional)    # 热更新prompt
-        core_functional = core_functional.get_core_functions()
-        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
-        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
-
-    history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    chatbot[-1] = (inputs, "[Local Message]: 等待NewBing响应中 ...")
-    response = "[Local Message]: 等待NewBing响应中 ..."
-    yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
-    for response in newbingfree_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        chatbot[-1] = (inputs, preprocess_newbing_out(response))
-        yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
-    if response == "[Local Message]: 等待NewBing响应中 ...": response = "[Local Message]: NewBing响应异常，请刷新界面重试 ..."
-    history.extend([inputs, response])
-    logging.info(f'[raw_input] {inputs}')
-    logging.info(f'[response] {response}')
-    yield from update_ui(chatbot=chatbot, history=history, msg="完成全部响应，请提交新问题。")
-
diff --git a/spaces/zama-fhe/encrypted_health_prediction/app.py b/spaces/zama-fhe/encrypted_health_prediction/app.py
deleted file mode 100644
index 543996e2be2fc337b0f202f145fe5fa69e63a75e..0000000000000000000000000000000000000000
--- a/spaces/zama-fhe/encrypted_health_prediction/app.py
+++ /dev/null
@@ -1,767 +0,0 @@
-import subprocess
-import time
-from typing import Dict, List, Tuple
-
-import gradio as gr  # pylint: disable=import-error
-import numpy as np
-import pandas as pd
-import requests
-from symptoms_categories import SYMPTOMS_LIST
-from utils import (
-    CLIENT_DIR,
-    CURRENT_DIR,
-    DEPLOYMENT_DIR,
-    INPUT_BROWSER_LIMIT,
-    KEYS_DIR,
-    SERVER_URL,
-    TARGET_COLUMNS,
-    TRAINING_FILENAME,
-    clean_directory,
-    get_disease_name,
-    load_data,
-    pretty_print,
-)
-
-from concrete.ml.deployment import FHEModelClient
-
-subprocess.Popen(["uvicorn", "server:app"], cwd=CURRENT_DIR)
-time.sleep(3)
-
-# pylint: disable=c-extension-no-member,invalid-name
-
-
-def is_none(obj) -> bool:
-    """
-    Check if the object is None.
-
-    Args:
-        obj (any): The input to be checked.
-
-    Returns:
-        bool: True if the object is None or empty, False otherwise.
-    """
-    return obj is None or (obj is not None and len(obj) < 1)
-
-
-def display_default_symptoms_fn(default_disease: str) -> Dict:
-    """
-    Displays the symptoms of a given existing disease.
-
-    Args:
-        default_disease (str): Disease
-    Returns:
-        Dict: The according symptoms
-    """
-    df = pd.read_csv(TRAINING_FILENAME)
-    df_filtred = df[df[TARGET_COLUMNS[1]] == default_disease]
-
-    return {
-        default_symptoms: gr.update(
-            visible=True,
-            value=pretty_print(
-                df_filtred.columns[df_filtred.eq(1).any()].to_list(), delimiter=", "
-            ),
-        )
-    }
-
-
-def get_user_symptoms_from_checkboxgroup(checkbox_symptoms: List) -> np.array:
-    """
-    Convert the user symptoms into a binary vector representation.
-
-    Args:
-        checkbox_symptoms (List): A list of user symptoms.
-
-    Returns:
-        np.array: A binary vector representing the user's symptoms.
-
-    Raises:
-        KeyError: If a provided symptom is not recognized as a valid symptom.
-
-    """
-    symptoms_vector = {key: 0 for key in valid_symptoms}
-    for pretty_symptom in checkbox_symptoms:
-        original_symptom = "_".join((pretty_symptom.lower().split(" ")))
-        if original_symptom not in symptoms_vector.keys():
-            raise KeyError(
-                f"The symptom '{original_symptom}' you provided is not recognized as a valid "
-                f"symptom.\nHere is the list of valid symptoms: {symptoms_vector}"
-            )
-        symptoms_vector[original_symptom] = 1
-
-    user_symptoms_vect = np.fromiter(symptoms_vector.values(), dtype=float)[np.newaxis, :]
-
-    assert all(value == 0 or value == 1 for value in user_symptoms_vect.flatten())
-
-    return user_symptoms_vect
-
-
-def get_features_fn(*checked_symptoms: Tuple[str]) -> Dict:
-    """
-    Get vector features based on the selected symptoms.
-
-    Args:
-        checked_symptoms (Tuple[str]): User symptoms
-
-    Returns:
-        Dict: The encoded user vector symptoms.
-    """
-    if not any(lst for lst in checked_symptoms if lst):
-        return {
-            error_box1: gr.update(visible=True, value="⚠️ Please provide your chief complaints."),
-        }
-
-    if len(pretty_print(checked_symptoms)) < 5:
-        print("Provide at least 5 symptoms.")
-        return {
-            error_box1: gr.update(visible=True, value="⚠️ Provide at least 5 symptoms"),
-            one_hot_vect: None,
-        }
-
-    return {
-        error_box1: gr.update(visible=False),
-        one_hot_vect: gr.update(
-            visible=False,
-            value=get_user_symptoms_from_checkboxgroup(pretty_print(checked_symptoms)),
-        ),
-        submit_btn: gr.update(value="Data submitted ✅"),
-    }
-
-
-def key_gen_fn(user_symptoms: List[str]) -> Dict:
-    """
-    Generate keys for a given user.
-
-    Args:
-        user_symptoms (List[str]): The vector symptoms provided by the user.
-
-    Returns:
-        dict: A dictionary containing the generated keys and related information.
-
-    """
-    clean_directory()
-
-    if is_none(user_symptoms):
-        print("Error: Please submit your symptoms or select a default disease.")
-        return {
-            error_box2: gr.update(visible=True, value="⚠️ Please submit your symptoms first."),
-        }
-
-    # Generate a random user ID
-    user_id = np.random.randint(0, 2**32)
-    print(f"Your user ID is: {user_id}....")
-
-    client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{user_id}")
-    client.load()
-
-    # Creates the private and evaluation keys on the client side
-    client.generate_private_and_evaluation_keys()
-
-    # Get the serialized evaluation keys
-    serialized_evaluation_keys = client.get_serialized_evaluation_keys()
-    assert isinstance(serialized_evaluation_keys, bytes)
-
-    # Save the evaluation key
-    evaluation_key_path = KEYS_DIR / f"{user_id}/evaluation_key"
-    with evaluation_key_path.open("wb") as f:
-        f.write(serialized_evaluation_keys)
-
-    serialized_evaluation_keys_shorten_hex = serialized_evaluation_keys.hex()[:INPUT_BROWSER_LIMIT]
-
-    return {
-        error_box2: gr.update(visible=False),
-        key_box: gr.update(visible=False, value=serialized_evaluation_keys_shorten_hex),
-        user_id_box: gr.update(visible=True, value=user_id),
-        key_len_box: gr.update(
-            visible=False, value=f"{len(serialized_evaluation_keys) / (10**6):.2f} MB"
-        ),
-    }
-
-
-def encrypt_fn(user_symptoms: np.ndarray, user_id: str) -> None:
-    """
-    Encrypt the user symptoms vector in the `Client Side`.
-
-    Args:
-        user_symptoms (List[str]): The vector symptoms provided by the user
-        user_id (user): The current user's ID
-    """
-
-    if is_none(user_id) or is_none(user_symptoms):
-        print("Error in encryption step: Provide your symptoms and generate the evaluation keys.")
-        return {
-            error_box3: gr.update(
-                visible=True,
-                value="⚠️ Please ensure that your symptoms have been submitted and "
-                "that you have generated the evaluation key.",
-            )
-        }
-
-    # Retrieve the client API
-    client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{user_id}")
-    client.load()
-
-    user_symptoms = np.fromstring(user_symptoms[2:-2], dtype=int, sep=".").reshape(1, -1)
-    # quant_user_symptoms = client.model.quantize_input(user_symptoms)
-
-    encrypted_quantized_user_symptoms = client.quantize_encrypt_serialize(user_symptoms)
-    assert isinstance(encrypted_quantized_user_symptoms, bytes)
-    encrypted_input_path = KEYS_DIR / f"{user_id}/encrypted_input"
-
-    with encrypted_input_path.open("wb") as f:
-        f.write(encrypted_quantized_user_symptoms)
-
-    encrypted_quantized_user_symptoms_shorten_hex = encrypted_quantized_user_symptoms.hex()[
-        :INPUT_BROWSER_LIMIT
-    ]
-
-    return {
-        error_box3: gr.update(visible=False),
-        one_hot_vect_box: gr.update(visible=True, value=user_symptoms),
-        enc_vect_box: gr.update(visible=True, value=encrypted_quantized_user_symptoms_shorten_hex),
-    }
-
-
-def send_input_fn(user_id: str, user_symptoms: np.ndarray) -> Dict:
-    """Send the encrypted data and the evaluation key to the server.
-
-    Args:
-        user_id (str): The current user's ID
-        user_symptoms (np.ndarray): The user symptoms
-    """
-
-    if is_none(user_id) or is_none(user_symptoms):
-        return {
-            error_box4: gr.update(
-                visible=True,
-                value="⚠️ Please check your connectivity \n"
-                "⚠️ Ensure that the symptoms have been submitted and the evaluation "
-                "key has been generated before sending the data to the server.",
-            )
-        }
-
-    evaluation_key_path = KEYS_DIR / f"{user_id}/evaluation_key"
-    encrypted_input_path = KEYS_DIR / f"{user_id}/encrypted_input"
-
-    if not evaluation_key_path.is_file():
-        print(
-            "Error Encountered While Sending Data to the Server: "
-            f"The key has been generated correctly - {evaluation_key_path.is_file()=}"
-        )
-
-        return {
-            error_box4: gr.update(visible=True, value="⚠️ Please generate the private key first.")
-        }
-
-    if not encrypted_input_path.is_file():
-        print(
-            "Error Encountered While Sending Data to the Server: The data has not been encrypted "
-            f"correctly on the client side - {encrypted_input_path.is_file()=}"
-        )
-        return {
-            error_box4: gr.update(
-                visible=True,
-                value="⚠️ Please encrypt the data with the private key first.",
-            ),
-        }
-
-    # Define the data and files to post
-    data = {
-        "user_id": user_id,
-        "input": user_symptoms,
-    }
-
-    files = [
-        ("files", open(encrypted_input_path, "rb")),
-        ("files", open(evaluation_key_path, "rb")),
-    ]
-
-    # Send the encrypted input and evaluation key to the server
-    url = SERVER_URL + "send_input"
-    with requests.post(
-        url=url,
-        data=data,
-        files=files,
-    ) as response:
-        print(f"Sending Data: {response.ok=}")
-    return {
-        error_box4: gr.update(visible=False),
-        srv_resp_send_data_box: "Data sent",
-    }
-
-
-def run_fhe_fn(user_id: str) -> Dict:
-    """Send the encrypted input and the evaluation key to the server.
-
-    Args:
-        user_id (int): The current user's ID.
-    """
-    if is_none(user_id):
-        return {
-            error_box5: gr.update(
-                visible=True,
-                value="⚠️ Please check your connectivity \n"
-                "⚠️ Ensure that the symptoms have been submitted, the evaluation "
-                "key has been generated and the server received the data "
-                "before processing the data.",
-            ),
-            fhe_execution_time_box: None,
-        }
-
-    data = {
-        "user_id": user_id,
-    }
-
-    url = SERVER_URL + "run_fhe"
-
-    with requests.post(
-        url=url,
-        data=data,
-    ) as response:
-        if not response.ok:
-            return {
-                error_box5: gr.update(
-                    visible=True,
-                    value=(
-                        "⚠️ An error occurred on the Server Side. "
-                        "Please check connectivity and data transmission."
-                    ),
-                ),
-                fhe_execution_time_box: gr.update(visible=False),
-            }
-        else:
-            time.sleep(1)
-            print(f"response.ok: {response.ok}, {response.json()} - Computed")
-
-    return {
-        error_box5: gr.update(visible=False),
-        fhe_execution_time_box: gr.update(visible=True, value=f"{response.json():.2f} seconds"),
-    }
-
-
-def get_output_fn(user_id: str, user_symptoms: np.ndarray) -> Dict:
-    """Retreive the encrypted data from the server.
-
-    Args:
-        user_id (str): The current user's ID
-        user_symptoms (np.ndarray): The user symptoms
-    """
-
-    if is_none(user_id) or is_none(user_symptoms):
-        return {
-            error_box6: gr.update(
-                visible=True,
-                value="⚠️ Please check your connectivity \n"
-                "⚠️ Ensure that the server has successfully processed and transmitted the data to the client.",
-            )
-        }
-
-    data = {
-        "user_id": user_id,
-    }
-
-    # Retrieve the encrypted output
-    url = SERVER_URL + "get_output"
-    with requests.post(
-        url=url,
-        data=data,
-    ) as response:
-        if response.ok:
-            print(f"Receive Data: {response.ok=}")
-
-            encrypted_output = response.content
-
-            # Save the encrypted output to bytes in a file as it is too large to pass through
-            # regular Gradio buttons (see https://github.com/gradio-app/gradio/issues/1877)
-            encrypted_output_path = CLIENT_DIR / f"{user_id}_encrypted_output"
-
-            with encrypted_output_path.open("wb") as f:
-                f.write(encrypted_output)
-    return {error_box6: gr.update(visible=False), srv_resp_retrieve_data_box: "Data received"}
-
-
-def decrypt_fn(
-    user_id: str, user_symptoms: np.ndarray, *checked_symptoms, threshold: int = 0.5
-) -> Dict:
-    """Dencrypt the data on the `Client Side`.
-
-    Args:
-        user_id (str): The current user's ID
-        user_symptoms (np.ndarray): The user symptoms
-        threshold (float): Probability confidence threshold
-
-    Returns:
-        Decrypted output
-    """
-
-    if is_none(user_id) or is_none(user_symptoms):
-        return {
-            error_box7: gr.update(
-                visible=True,
-                value="⚠️ Please check your connectivity \n"
-                "⚠️ Ensure that the client has successfully received the data from the server.",
-            )
-        }
-
-    # Get the encrypted output path
-    encrypted_output_path = CLIENT_DIR / f"{user_id}_encrypted_output"
-
-    if not encrypted_output_path.is_file():
-        print("Error in decryption step: Please run the FHE execution, first.")
-        return {
-            error_box7: gr.update(
-                visible=True,
-                value="⚠️ Please ensure that: \n"
-                "- the connectivity \n"
-                "- the symptoms have been submitted \n"
-                "- the evaluation key has been generated \n"
-                "- the server processed the encrypted data \n"
-                "- the Client received the data from the Server before decrypting the prediction",
-            ),
-            decrypt_box: None,
-        }
-
-    # Load the encrypted output as bytes
-    with encrypted_output_path.open("rb") as f:
-        encrypted_output = f.read()
-
-    # Retrieve the client API
-    client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{user_id}")
-    client.load()
-
-    # Deserialize, decrypt and post-process the encrypted output
-    output = client.deserialize_decrypt_dequantize(encrypted_output)
-
-    top3_diseases = np.argsort(output.flatten())[-3:][::-1]
-    top3_proba = output[0][top3_diseases]
-
-    out = ""
-
-    if top3_proba[0] < threshold or abs(top3_proba[0] - top3_proba[1]) < 0.1:
-        out = (
-            "⚠️ The prediction appears uncertain; including more symptoms "
-            "may improve the results.\n\n"
-        )
-
-    out = (
-        f"{out}Given the symptoms you provided: "
-        f"{pretty_print(checked_symptoms, case_conversion=str.capitalize, delimiter=', ')}\n\n"
-        "Here are the top3 predictions:\n\n"
-        f"1. « {get_disease_name(top3_diseases[0])} » with a probability of {top3_proba[0]:.2%}\n"
-        f"2. « {get_disease_name(top3_diseases[1])} » with a probability of {top3_proba[1]:.2%}\n"
-        f"3. « {get_disease_name(top3_diseases[2])} » with a probability of {top3_proba[2]:.2%}\n"
-    )
-
-    return {
-        error_box7: gr.update(visible=False),
-        decrypt_box: out,
-        submit_btn: gr.update(value="Submit"),
-    }
-
-
-def reset_fn():
-    """Reset the space and clear all the box outputs."""
-
-    clean_directory()
-
-    return {
-        one_hot_vect: None,
-        one_hot_vect_box: None,
-        enc_vect_box: gr.update(visible=True, value=None),
-        quant_vect_box: gr.update(visible=False, value=None),
-        user_id_box: gr.update(visible=False, value=None),
-        default_symptoms: gr.update(visible=True, value=None),
-        default_disease_box: gr.update(visible=True, value=None),
-        key_box: gr.update(visible=True, value=None),
-        key_len_box: gr.update(visible=False, value=None),
-        fhe_execution_time_box: gr.update(visible=True, value=None),
-        decrypt_box: None,
-        submit_btn: gr.update(value="Submit"),
-        error_box7: gr.update(visible=False),
-        error_box1: gr.update(visible=False),
-        error_box2: gr.update(visible=False),
-        error_box3: gr.update(visible=False),
-        error_box4: gr.update(visible=False),
-        error_box5: gr.update(visible=False),
-        error_box6: gr.update(visible=False),
-        srv_resp_send_data_box: None,
-        srv_resp_retrieve_data_box: None,
-        **{box: None for box in check_boxes},
-    }
-
-
-if __name__ == "__main__":
-
-    print("Starting demo ...")
-
-    clean_directory()
-
-    (X_train, X_test), (y_train, y_test), valid_symptoms, diseases = load_data()
-
-    with gr.Blocks() as demo:
-
-        # Link + images
-        gr.Markdown()
-        gr.Markdown(
-            """
-            <p align="center">
-                <img width=200 src="https://user-images.githubusercontent.com/5758427/197816413-d9cddad3-ba38-4793-847d-120975e1da11.png">
-            </p>
-            """)
-        gr.Markdown()
-        gr.Markdown("""<h2 align="center">Health Prediction On Encrypted Data Using Fully Homomorphic Encryption</h2>""")
-        gr.Markdown()
-        gr.Markdown(
-            """
-            <p align="center">
-                <a href="https://github.com/zama-ai/concrete-ml"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="https://user-images.githubusercontent.com/5758427/197972109-faaaff3e-10e2-4ab6-80f5-7531f7cfb08f.png">Concrete-ML</a>
-                —
-                <a href="https://docs.zama.ai/concrete-ml"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="https://user-images.githubusercontent.com/5758427/197976802-fddd34c5-f59a-48d0-9bff-7ad1b00cb1fb.png">Documentation</a>
-                —
-                <a href="https://zama.ai/community"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="https://user-images.githubusercontent.com/5758427/197977153-8c9c01a7-451a-4993-8e10-5a6ed5343d02.png">Community</a>
-                —
-                <a href="https://twitter.com/zama_fhe"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="https://user-images.githubusercontent.com/5758427/197975044-bab9d199-e120-433b-b3be-abd73b211a54.png">@zama_fhe</a>
-            </p>
-            """)
-        gr.Markdown()
-        gr.Markdown(
-            """"
-            <p align="center">
-            <img width="65%" height="25%" src="https://raw.githubusercontent.com/kcelia/Img/main/healthcare_prediction.jpg">
-            </p>
-            """
-        )
-        gr.Markdown("## Notes")
-        gr.Markdown(
-            """
-            - The private key is used to encrypt and decrypt the data and shall never be shared.
-            - The evaluation key is a public key that the server needs to process encrypted data.
-            """
-        )
-
-        # ------------------------- Step 1 -------------------------
-        gr.Markdown("\n")
-        gr.Markdown("## Step 1: Select chief complaints")
-        gr.Markdown("<hr />")
-        gr.Markdown("<span style='color:grey'>Client Side</span>")
-        gr.Markdown("Select at least 5 chief complaints from the list below.")
-
-        # Step 1.1: Provide symptoms
-        check_boxes = []
-        with gr.Row():
-            with gr.Column():
-                for category in SYMPTOMS_LIST[:3]:
-                    with gr.Accordion(pretty_print(category.keys()), open=False):
-                        check_box = gr.CheckboxGroup(pretty_print(category.values()), show_label=0)
-                        check_boxes.append(check_box)
-            with gr.Column():
-                for category in SYMPTOMS_LIST[3:6]:
-                    with gr.Accordion(pretty_print(category.keys()), open=False):
-                        check_box = gr.CheckboxGroup(pretty_print(category.values()), show_label=0)
-                        check_boxes.append(check_box)
-            with gr.Column():
-                for category in SYMPTOMS_LIST[6:]:
-                    with gr.Accordion(pretty_print(category.keys()), open=False):
-                        check_box = gr.CheckboxGroup(pretty_print(category.values()), show_label=0)
-                        check_boxes.append(check_box)
-
-        error_box1 = gr.Textbox(label="Error ❌", visible=False)
-
-        # Default disease, picked from the dataframe
-        gr.Markdown(
-            "You can choose an **existing disease** and explore its associated symptoms.",
-            visible=False,
-        )
-
-        with gr.Row():
-            with gr.Column(scale=2):
-                default_disease_box = gr.Dropdown(sorted(diseases), label="Diseases", visible=False)
-            with gr.Column(scale=5):
-                default_symptoms = gr.Textbox(label="Related Symptoms:", visible=False)
-        # User vector symptoms encoded in oneHot representation
-        one_hot_vect = gr.Textbox(visible=False)
-        # Submit botton
-        submit_btn = gr.Button("Submit")
-        # Clear botton
-        clear_button = gr.Button("Reset Space 🔁", visible=False)
-
-        default_disease_box.change(
-            fn=display_default_symptoms_fn, inputs=[default_disease_box], outputs=[default_symptoms]
-        )
-
-        submit_btn.click(
-            fn=get_features_fn,
-            inputs=[*check_boxes],
-            outputs=[one_hot_vect, error_box1, submit_btn],
-        )
-
-        # ------------------------- Step 2 -------------------------
-        gr.Markdown("\n")
-        gr.Markdown("## Step 2: Encrypt data")
-        gr.Markdown("<hr />")
-        gr.Markdown("<span style='color:grey'>Client Side</span>")
-        # Step 2.1: Key generation
-        gr.Markdown(
-            "### Key Generation\n\n"
-            "In FHE schemes, a secret (enc/dec)ryption keys are generated for encrypting and decrypting data owned by the client. \n\n"
-            "Additionally, a public evaluation key is generated, enabling external entities to perform homomorphic operations on encrypted data, without the need to decrypt them. \n\n"
-            "The evaluation key will be transmitted to the server for further processing."
-        )
-
-        gen_key_btn = gr.Button("Generate the evaluation key")
-        error_box2 = gr.Textbox(label="Error ❌", visible=False)
-        user_id_box = gr.Textbox(label="User ID:", visible=True)
-        key_len_box = gr.Textbox(label="Evaluation Key Size:", visible=False)
-        key_box = gr.Textbox(label="Evaluation key (truncated):", max_lines=3, visible=False)
-
-        gen_key_btn.click(
-            key_gen_fn,
-            inputs=one_hot_vect,
-            outputs=[
-                key_box,
-                user_id_box,
-                key_len_box,
-                error_box2,
-            ],
-        )
-
-        # Step 2.2: Encrypt data locally
-        gr.Markdown("### Encrypt the data")
-        encrypt_btn = gr.Button("Encrypt the data using the private secret key")
-        error_box3 = gr.Textbox(label="Error ❌", visible=False)
-        quant_vect_box = gr.Textbox(label="Quantized Vector:", visible=False)
-
-        with gr.Row():
-            with gr.Column():
-                one_hot_vect_box = gr.Textbox(label="User Symptoms Vector:", max_lines=10)
-            with gr.Column():
-                enc_vect_box = gr.Textbox(label="Encrypted Vector:", max_lines=10)
-
-        encrypt_btn.click(
-            encrypt_fn,
-            inputs=[one_hot_vect, user_id_box],
-            outputs=[
-                one_hot_vect_box,
-                enc_vect_box,
-                error_box3,
-            ],
-        )
-        # Step 2.3: Send encrypted data to the server
-        gr.Markdown(
-            "### Send the encrypted data to the <span style='color:grey'>Server Side</span>"
-        )
-        error_box4 = gr.Textbox(label="Error ❌", visible=False)
-
-        with gr.Row().style(equal_height=False):
-            with gr.Column(scale=4):
-                send_input_btn = gr.Button("Send data")
-            with gr.Column(scale=1):
-                srv_resp_send_data_box = gr.Checkbox(label="Data Sent", show_label=False)
-
-        send_input_btn.click(
-            send_input_fn,
-            inputs=[user_id_box, one_hot_vect],
-            outputs=[error_box4, srv_resp_send_data_box],
-        )
-
-        # ------------------------- Step 3 -------------------------
-        gr.Markdown("\n")
-        gr.Markdown("## Step 3: Run the FHE evaluation")
-        gr.Markdown("<hr />")
-        gr.Markdown("<span style='color:grey'>Server Side</span>")
-        gr.Markdown(
-            "Once the server receives the encrypted data, it can process and compute the output without ever decrypting the data just as it would on clear data.\n\n"
-            "This server employs a [Logistic Regression](https://github.com/zama-ai/concrete-ml/tree/release/1.1.x/use_case_examples/disease_prediction) model that has been trained on this [data-set](https://github.com/anujdutt9/Disease-Prediction-from-Symptoms/tree/master/dataset)."
-        )
-
-        run_fhe_btn = gr.Button("Run the FHE evaluation")
-        error_box5 = gr.Textbox(label="Error ❌", visible=False)
-        fhe_execution_time_box = gr.Textbox(label="Total FHE Execution Time:", visible=True)
-        run_fhe_btn.click(
-            run_fhe_fn,
-            inputs=[user_id_box],
-            outputs=[fhe_execution_time_box, error_box5],
-        )
-
-        # ------------------------- Step 4 -------------------------
-        gr.Markdown("\n")
-        gr.Markdown("## Step 4: Decrypt the data")
-        gr.Markdown("<hr />")
-        gr.Markdown("<span style='color:grey'>Client Side</span>")
-        gr.Markdown(
-            "### Get the encrypted data from the <span style='color:grey'>Server Side</span>"
-        )
-
-        error_box6 = gr.Textbox(label="Error ❌", visible=False)
-
-        # Step 4.1: Data transmission
-        with gr.Row().style(equal_height=True):
-            with gr.Column(scale=4):
-                get_output_btn = gr.Button("Get data")
-            with gr.Column(scale=1):
-                srv_resp_retrieve_data_box = gr.Checkbox(label="Data Received", show_label=False)
-
-        get_output_btn.click(
-            get_output_fn,
-            inputs=[user_id_box, one_hot_vect],
-            outputs=[srv_resp_retrieve_data_box, error_box6],
-        )
-
-        # Step 4.1: Data transmission
-        gr.Markdown("### Decrypt the output")
-        decrypt_btn = gr.Button("Decrypt the output using the private secret key")
-        error_box7 = gr.Textbox(label="Error ❌", visible=False)
-        decrypt_box = gr.Textbox(label="Decrypted Output:")
-
-        decrypt_btn.click(
-            decrypt_fn,
-            inputs=[user_id_box, one_hot_vect, *check_boxes],
-            outputs=[decrypt_box, error_box7, submit_btn],
-        )
-
-        # ------------------------- End -------------------------
-
-        gr.Markdown(
-            """The app was built with [Concrete ML](https://github.com/zama-ai/concrete-ml), a Privacy-Preserving Machine Learning (PPML) open-source set of tools by Zama. 
-            Try it yourself and don't forget to star on [Github](https://github.com/zama-ai/concrete-ml) ⭐.
-            """
-        )
-
-        gr.Markdown("\n\n")
-
-        gr.Markdown(
-            """**Please Note**: This space is intended solely for educational and demonstration purposes. 
-           It should not be considered as a replacement for professional medical counsel, diagnosis, or therapy for any health or related issues. 
-           Any questions or concerns about your individual health should be addressed to your doctor or another qualified healthcare provider.
-            """
-        )
-
-        clear_button.click(
-            reset_fn,
-            outputs=[
-                one_hot_vect_box,
-                one_hot_vect,
-                submit_btn,
-                error_box1,
-                error_box2,
-                error_box3,
-                error_box4,
-                error_box5,
-                error_box6,
-                error_box7,
-                default_disease_box,
-                default_symptoms,
-                user_id_box,
-                key_len_box,
-                key_box,
-                quant_vect_box,
-                enc_vect_box,
-                srv_resp_send_data_box,
-                srv_resp_retrieve_data_box,
-                fhe_execution_time_box,
-                decrypt_box,
-                *check_boxes,
-            ],
-        )
-
-        demo.launch()
diff --git a/spaces/zdxiaoda/sovits-4.0-V1-anime-character-model/so-vits-svc/modules/attentions.py b/spaces/zdxiaoda/sovits-4.0-V1-anime-character-model/so-vits-svc/modules/attentions.py
deleted file mode 100644
index f9c11ca4a3acb86bf1abc04d9dcfa82a4ed4061f..0000000000000000000000000000000000000000
--- a/spaces/zdxiaoda/sovits-4.0-V1-anime-character-model/so-vits-svc/modules/attentions.py
+++ /dev/null
@@ -1,349 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import modules.commons as commons
-import modules.modules as modules
-from modules.modules import LayerNorm
-
-
-class FFT(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers=1, kernel_size=1, p_dropout=0.,
-               proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(
-        MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias,
-                           proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(
-        FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/zhang-wei-jian/docker/node_modules/mime-types/HISTORY.md b/spaces/zhang-wei-jian/docker/node_modules/mime-types/HISTORY.md
deleted file mode 100644
index c5043b75b958766a3880805dc4f19d70a4f167dd..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/mime-types/HISTORY.md
+++ /dev/null
@@ -1,397 +0,0 @@
-2.1.35 / 2022-03-12
-===================
-
-  * deps: mime-db@1.52.0
-    - Add extensions from IANA for more `image/*` types
-    - Add extension `.asc` to `application/pgp-keys`
-    - Add extensions to various XML types
-    - Add new upstream MIME types
-
-2.1.34 / 2021-11-08
-===================
-
-  * deps: mime-db@1.51.0
-    - Add new upstream MIME types
-
-2.1.33 / 2021-10-01
-===================
-
-  * deps: mime-db@1.50.0
-    - Add deprecated iWorks mime types and extensions
-    - Add new upstream MIME types
-
-2.1.32 / 2021-07-27
-===================
-
-  * deps: mime-db@1.49.0
-    - Add extension `.trig` to `application/trig`
-    - Add new upstream MIME types
-
-2.1.31 / 2021-06-01
-===================
-
-  * deps: mime-db@1.48.0
-    - Add extension `.mvt` to `application/vnd.mapbox-vector-tile`
-    - Add new upstream MIME types
-
-2.1.30 / 2021-04-02
-===================
-
-  * deps: mime-db@1.47.0
-    - Add extension `.amr` to `audio/amr`
-    - Remove ambigious extensions from IANA for `application/*+xml` types
-    - Update primary extension to `.es` for `application/ecmascript`
-
-2.1.29 / 2021-02-17
-===================
-
-  * deps: mime-db@1.46.0
-    - Add extension `.amr` to `audio/amr`
-    - Add extension `.m4s` to `video/iso.segment`
-    - Add extension `.opus` to `audio/ogg`
-    - Add new upstream MIME types
-
-2.1.28 / 2021-01-01
-===================
-
-  * deps: mime-db@1.45.0
-    - Add `application/ubjson` with extension `.ubj`
-    - Add `image/avif` with extension `.avif`
-    - Add `image/ktx2` with extension `.ktx2`
-    - Add extension `.dbf` to `application/vnd.dbf`
-    - Add extension `.rar` to `application/vnd.rar`
-    - Add extension `.td` to `application/urc-targetdesc+xml`
-    - Add new upstream MIME types
-    - Fix extension of `application/vnd.apple.keynote` to be `.key`
-
-2.1.27 / 2020-04-23
-===================
-
-  * deps: mime-db@1.44.0
-    - Add charsets from IANA
-    - Add extension `.cjs` to `application/node`
-    - Add new upstream MIME types
-
-2.1.26 / 2020-01-05
-===================
-
-  * deps: mime-db@1.43.0
-    - Add `application/x-keepass2` with extension `.kdbx`
-    - Add extension `.mxmf` to `audio/mobile-xmf`
-    - Add extensions from IANA for `application/*+xml` types
-    - Add new upstream MIME types
-
-2.1.25 / 2019-11-12
-===================
-
-  * deps: mime-db@1.42.0
-    - Add new upstream MIME types
-    - Add `application/toml` with extension `.toml`
-    - Add `image/vnd.ms-dds` with extension `.dds`
-
-2.1.24 / 2019-04-20
-===================
-
-  * deps: mime-db@1.40.0
-    - Add extensions from IANA for `model/*` types
-    - Add `text/mdx` with extension `.mdx`
-
-2.1.23 / 2019-04-17
-===================
-
-  * deps: mime-db@~1.39.0
-    - Add extensions `.siv` and `.sieve` to `application/sieve`
-    - Add new upstream MIME types
-
-2.1.22 / 2019-02-14
-===================
-
-  * deps: mime-db@~1.38.0
-    - Add extension `.nq` to `application/n-quads`
-    - Add extension `.nt` to `application/n-triples`
-    - Add new upstream MIME types
-
-2.1.21 / 2018-10-19
-===================
-
-  * deps: mime-db@~1.37.0
-    - Add extensions to HEIC image types
-    - Add new upstream MIME types
-
-2.1.20 / 2018-08-26
-===================
-
-  * deps: mime-db@~1.36.0
-    - Add Apple file extensions from IANA
-    - Add extensions from IANA for `image/*` types
-    - Add new upstream MIME types
-
-2.1.19 / 2018-07-17
-===================
-
-  * deps: mime-db@~1.35.0
-    - Add extension `.csl` to `application/vnd.citationstyles.style+xml`
-    - Add extension `.es` to `application/ecmascript`
-    - Add extension `.owl` to `application/rdf+xml`
-    - Add new upstream MIME types
-    - Add UTF-8 as default charset for `text/turtle`
-
-2.1.18 / 2018-02-16
-===================
-
-  * deps: mime-db@~1.33.0
-    - Add `application/raml+yaml` with extension `.raml`
-    - Add `application/wasm` with extension `.wasm`
-    - Add `text/shex` with extension `.shex`
-    - Add extensions for JPEG-2000 images
-    - Add extensions from IANA for `message/*` types
-    - Add new upstream MIME types
-    - Update font MIME types
-    - Update `text/hjson` to registered `application/hjson`
-
-2.1.17 / 2017-09-01
-===================
-
-  * deps: mime-db@~1.30.0
-    - Add `application/vnd.ms-outlook`
-    - Add `application/x-arj`
-    - Add extension `.mjs` to `application/javascript`
-    - Add glTF types and extensions
-    - Add new upstream MIME types
-    - Add `text/x-org`
-    - Add VirtualBox MIME types
-    - Fix `source` records for `video/*` types that are IANA
-    - Update `font/opentype` to registered `font/otf`
-
-2.1.16 / 2017-07-24
-===================
-
-  * deps: mime-db@~1.29.0
-    - Add `application/fido.trusted-apps+json`
-    - Add extension `.wadl` to `application/vnd.sun.wadl+xml`
-    - Add extension `.gz` to `application/gzip`
-    - Add new upstream MIME types
-    - Update extensions `.md` and `.markdown` to be `text/markdown`
-
-2.1.15 / 2017-03-23
-===================
-
-  * deps: mime-db@~1.27.0
-    - Add new mime types
-    - Add `image/apng`
-
-2.1.14 / 2017-01-14
-===================
-
-  * deps: mime-db@~1.26.0
-    - Add new mime types
-
-2.1.13 / 2016-11-18
-===================
-
-  * deps: mime-db@~1.25.0
-    - Add new mime types
-
-2.1.12 / 2016-09-18
-===================
-
-  * deps: mime-db@~1.24.0
-    - Add new mime types
-    - Add `audio/mp3`
-
-2.1.11 / 2016-05-01
-===================
-
-  * deps: mime-db@~1.23.0
-    - Add new mime types
-
-2.1.10 / 2016-02-15
-===================
-
-  * deps: mime-db@~1.22.0
-    - Add new mime types
-    - Fix extension of `application/dash+xml`
-    - Update primary extension for `audio/mp4`
-
-2.1.9 / 2016-01-06
-==================
-
-  * deps: mime-db@~1.21.0
-    - Add new mime types
-
-2.1.8 / 2015-11-30
-==================
-
-  * deps: mime-db@~1.20.0
-    - Add new mime types
-
-2.1.7 / 2015-09-20
-==================
-
-  * deps: mime-db@~1.19.0
-    - Add new mime types
-
-2.1.6 / 2015-09-03
-==================
-
-  * deps: mime-db@~1.18.0
-    - Add new mime types
-
-2.1.5 / 2015-08-20
-==================
-
-  * deps: mime-db@~1.17.0
-    - Add new mime types
-
-2.1.4 / 2015-07-30
-==================
-
-  * deps: mime-db@~1.16.0
-    - Add new mime types
-
-2.1.3 / 2015-07-13
-==================
-
-  * deps: mime-db@~1.15.0
-    - Add new mime types
-
-2.1.2 / 2015-06-25
-==================
-
-  * deps: mime-db@~1.14.0
-    - Add new mime types
-
-2.1.1 / 2015-06-08
-==================
-
-  * perf: fix deopt during mapping
-
-2.1.0 / 2015-06-07
-==================
-
-  * Fix incorrectly treating extension-less file name as extension
-    - i.e. `'path/to/json'` will no longer return `application/json`
-  * Fix `.charset(type)` to accept parameters
-  * Fix `.charset(type)` to match case-insensitive
-  * Improve generation of extension to MIME mapping
-  * Refactor internals for readability and no argument reassignment
-  * Prefer `application/*` MIME types from the same source
-  * Prefer any type over `application/octet-stream`
-  * deps: mime-db@~1.13.0
-    - Add nginx as a source
-    - Add new mime types
-
-2.0.14 / 2015-06-06
-===================
-
-  * deps: mime-db@~1.12.0
-    - Add new mime types
-
-2.0.13 / 2015-05-31
-===================
-
-  * deps: mime-db@~1.11.0
-    - Add new mime types
-
-2.0.12 / 2015-05-19
-===================
-
-  * deps: mime-db@~1.10.0
-    - Add new mime types
-
-2.0.11 / 2015-05-05
-===================
-
-  * deps: mime-db@~1.9.1
-    - Add new mime types
-
-2.0.10 / 2015-03-13
-===================
-
-  * deps: mime-db@~1.8.0
-    - Add new mime types
-
-2.0.9 / 2015-02-09
-==================
-
-  * deps: mime-db@~1.7.0
-    - Add new mime types
-    - Community extensions ownership transferred from `node-mime`
-
-2.0.8 / 2015-01-29
-==================
-
-  * deps: mime-db@~1.6.0
-    - Add new mime types
-
-2.0.7 / 2014-12-30
-==================
-
-  * deps: mime-db@~1.5.0
-    - Add new mime types
-    - Fix various invalid MIME type entries
-
-2.0.6 / 2014-12-30
-==================
-
-  * deps: mime-db@~1.4.0
-    - Add new mime types
-    - Fix various invalid MIME type entries
-    - Remove example template MIME types
-
-2.0.5 / 2014-12-29
-==================
-
-  * deps: mime-db@~1.3.1
-    - Fix missing extensions
-
-2.0.4 / 2014-12-10
-==================
-
-  * deps: mime-db@~1.3.0
-    - Add new mime types
-
-2.0.3 / 2014-11-09
-==================
-
-  * deps: mime-db@~1.2.0
-    - Add new mime types
-
-2.0.2 / 2014-09-28
-==================
-
-  * deps: mime-db@~1.1.0
-    - Add new mime types
-    - Update charsets
-
-2.0.1 / 2014-09-07
-==================
-
-  * Support Node.js 0.6
-
-2.0.0 / 2014-09-02
-==================
-
-  * Use `mime-db`
-  * Remove `.define()`
-
-1.0.2 / 2014-08-04
-==================
-
-  * Set charset=utf-8 for `text/javascript`
-
-1.0.1 / 2014-06-24
-==================
-
-  * Add `text/jsx` type
-
-1.0.0 / 2014-05-12
-==================
-
-  * Return `false` for unknown types
-  * Set charset=utf-8 for `application/json`
-
-0.1.0 / 2014-05-02
-==================
-
-  * Initial release
diff --git a/spaces/zhenwusw/JoJoGAN/e4e/criteria/lpips/lpips.py b/spaces/zhenwusw/JoJoGAN/e4e/criteria/lpips/lpips.py
deleted file mode 100644
index 1add6acc84c1c04cfcb536cf31ec5acdf24b716b..0000000000000000000000000000000000000000
--- a/spaces/zhenwusw/JoJoGAN/e4e/criteria/lpips/lpips.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import torch
-import torch.nn as nn
-
-from criteria.lpips.networks import get_network, LinLayers
-from criteria.lpips.utils import get_state_dict
-
-
-class LPIPS(nn.Module):
-    r"""Creates a criterion that measures
-    Learned Perceptual Image Patch Similarity (LPIPS).
-    Arguments:
-        net_type (str): the network type to compare the features:
-                        'alex' | 'squeeze' | 'vgg'. Default: 'alex'.
-        version (str): the version of LPIPS. Default: 0.1.
-    """
-    def __init__(self, net_type: str = 'alex', version: str = '0.1'):
-
-        assert version in ['0.1'], 'v0.1 is only supported now'
-
-        super(LPIPS, self).__init__()
-
-        # pretrained network
-        self.net = get_network(net_type).to("cuda")
-
-        # linear layers
-        self.lin = LinLayers(self.net.n_channels_list).to("cuda")
-        self.lin.load_state_dict(get_state_dict(net_type, version))
-
-    def forward(self, x: torch.Tensor, y: torch.Tensor):
-        feat_x, feat_y = self.net(x), self.net(y)
-
-        diff = [(fx - fy) ** 2 for fx, fy in zip(feat_x, feat_y)]
-        res = [l(d).mean((2, 3), True) for d, l in zip(diff, self.lin)]
-
-        return torch.sum(torch.cat(res, 0)) / x.shape[0]
diff --git a/spaces/zhiyin123/MyBingAI6/README.md b/spaces/zhiyin123/MyBingAI6/README.md
deleted file mode 100644
index f3d1b63961ebd4b88a0305964f69aaa6ed191030..0000000000000000000000000000000000000000
--- a/spaces/zhiyin123/MyBingAI6/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: MyBingAI6
-emoji: 🏢
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/zxy666/bingo-chatai666/tests/kblob.ts b/spaces/zxy666/bingo-chatai666/tests/kblob.ts
deleted file mode 100644
index 9e15b41c1c94a690beb61b23cdb42fc78767ccd2..0000000000000000000000000000000000000000
--- a/spaces/zxy666/bingo-chatai666/tests/kblob.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-import FormData from 'form-data'
-
-import { fetch } from '@/lib/isomorphic'
-
-const formData = new FormData()
-
-const knowledgeRequest = {"imageInfo":{"url":"https://www.baidu.com/img/PCfb_5bf082d29588c07f842ccde3f97243ea.png"},"knowledgeRequest":{"invokedSkills":["ImageById"],"subscriptionId":"Bing.Chat.Multimodal","invokedSkillsRequestData":{"enableFaceBlur":true},"convoData":{"convoid":"51D|BingProdUnAuthenticatedUsers|E3DCA904FF236C67C3450163BCEC64CFF3F618CC8A4AFD75FD518F5ED0ADA080","convotone":"Creative"}}}
-
-formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest))
-
-
-fetch('https://bing.vcanbb.top/images/kblob',
-  {
-    method: 'POST',
-    body: formData.getBuffer(),
-    headers: {
-      "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-      "sec-ch-ua-mobile": "?0",
-      "sec-ch-ua-platform": "\"Windows\"",
-      "Referer": "https://bing.vcanbb.top/web/index.html",
-      "Referrer-Policy": "origin-when-cross-origin",
-      ...formData.getHeaders()
-    }
-
-  }
-).then(res => res.text())
-.then(res => console.log('res', res))