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spaces/1toTree/lora_test/ppdiffusers/pipelines/ddim/pipeline_ddim.py

@@ -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)

spaces/801artistry/RVC801/Applio-RVC-Fork/utils/backups.py

@@ -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.

spaces/A00001/bingothoo/src/components/chat-panel.tsx

@@ -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>
-  )
-}

spaces/AIConsultant/MusicGen/CHANGELOG.md

@@ -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.

spaces/AIFILMS/generate_human_motion/VQ-Trans/dataset/prepare/download_extractor.sh

@@ -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!"

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/diffusionmodules/model.py

@@ -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
-

spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/models/autoencoder_multi.py

@@ -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

spaces/AP123/dreamgaussian/grid_put.py

@@ -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}")    

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/README.md

@@ -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}
-}
-```

spaces/Ababababababbababa/Ashaar/poetry_diacritizer/tester.py

@@ -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}")

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Liaobots.py

@@ -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})"

spaces/Aditya757864/SentimentAnalysis/app.py

@@ -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()

spaces/Aditya9790/yolo7-object-tracking/train.py

@@ -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, [email protected], [email protected], 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, [email protected], [email protected]]
-            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}')

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/confirmdialog/methods/Methods.js

@@ -1,9 +0,0 @@
-import ResetDisplayContent from './ResetDisplayContent.js';
-import Modal from './Modal.js';
-
-var Methods = {
-    resetDisplayContent: ResetDisplayContent,
-    modal: Modal,
-}
-
-export default Methods;

spaces/AlexWang/lama/bin/paper_runfiles/generate_test_paris.sh

@@ -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

spaces/AlexWang/lama/models/ade20k/segm_lib/nn/modules/tests/test_numeric_batchnorm.py

@@ -1,56 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : test_numeric_batchnorm.py
-# Author : Jiayuan Mao
-# Email  : [email protected]
-# 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()

spaces/AlexWang/lama/models/ade20k/segm_lib/nn/modules/unittest.py

@@ -1,29 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : unittest.py
-# Author : Jiayuan Mao
-# Email  : [email protected]
-# 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())
-        )

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/unidiffuser.md

@@ -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

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/using-diffusers/using_safetensors.md

@@ -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.

spaces/Andy1621/IAT_enhancement/model/IAT.py

@@ -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)

spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py

@@ -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)))

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py

@@ -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

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/pspnet_r50-d8.py

@@ -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'))

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './deeplabv3_r50-d8_769x769_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/AndyCer/TehVenom-MPT-7b-Chat-Instruct-LongCTX-Merge/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge").launch()

spaces/AnishKumbhar/DogDiseasePredictor/Dockerfile

@@ -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"]

spaces/AnjaneyuluChinni/AnjiChinniGenAIAvatar/README.md

@@ -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

spaces/Artrajz/vits-simple-api/config.py

@@ -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

spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/logger.py

@@ -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")

spaces/Asahi402/White-box-Cartoonization/README.md

@@ -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

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/gb2312freq.py

@@ -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

spaces/Benson/text-generation/Examples/Arena Breakout Beta Global Descargar.md

@@ -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 />

spaces/Benson/text-generation/Examples/Caso Penal Pacfico Baha Mod Men Apk.md

@@ -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 />

spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/utils.py

@@ -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

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/version.py

@@ -1,6 +0,0 @@
-import pkg_resources
-
-try:
-    __version__ = pkg_resources.get_distribution('setuptools').version
-except Exception:
-    __version__ = 'unknown'

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/dev/linter.sh

@@ -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

spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter_mod/utils.py

@@ -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)))

spaces/CVPR/WALT/mmdet/core/bbox/assigners/max_iou_assigner.py

@@ -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)

spaces/CarlDennis/HYTTS/README.md

@@ -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

spaces/CjangCjengh/Sanskrit-TTS/README.md

@@ -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

spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/midas/midas/midas_net.py

@@ -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)

spaces/DaFujaTyping/hf-Chat-ui/src/lib/server/database.ts

@@ -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 });
-});

spaces/DaFujaTyping/hf-Chat-ui/src/lib/utils/sha256.ts

@@ -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;
-}

spaces/DaleChen/AutoGPT/autogpt/token_counter.py

@@ -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))