diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Call Of Duty Black Ops II [UPD] Crack Only-SKIDROW Torrent.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Call Of Duty Black Ops II [UPD] Crack Only-SKIDROW Torrent.md deleted file mode 100644 index 8f0ac7842f0a5ba0865c75e048efc90713fe3036..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Call Of Duty Black Ops II [UPD] Crack Only-SKIDROW Torrent.md +++ /dev/null @@ -1,25 +0,0 @@ -
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Call of Duty Black Ops II is a first-person shooter video game developed by Treyarch and published by Activision. It is the ninth game in the Call of Duty series and a sequel to the 2010 game Call of Duty: Black Ops. The game was released worldwide on November 13, 2012 for Microsoft Windows, PlayStation 3, Xbox 360, and Wii U.

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If you want to play the game without buying it, you can download and install a crack file that bypasses the game's protection and allows you to run it without a valid license. One of the most popular crack files for Call of Duty Black Ops II is the one released by SKIDROW, a group of hackers who specialize in cracking video games. In this article, we will show you how to download and install Call of Duty Black Ops II Crack Only-SKIDROW torrent using a torrent client.

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Step 1: Download a torrent client

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A torrent client is a software that enables you to download files from other users who are sharing them on a peer-to-peer network. There are many torrent clients available online, such as uTorrent, BitTorrent, qBittorrent, etc. You can choose any one that suits your preferences and system requirements. Download and install the torrent client on your computer.

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Step 2: Download Call of Duty Black Ops II Crack Only-SKIDROW torrent

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Once you have installed the torrent client, you need to find and download the Call of Duty Black Ops II Crack Only-SKIDROW torrent file. A torrent file is a small file that contains information about the files you want to download, such as their names, sizes, locations, etc. You can find the Call of Duty Black Ops II Crack Only-SKIDROW torrent file on various websites that host torrents, such as LimeTorrents.to[^2^], MegaGames.com[^1^], Archive.org[^4^], etc. You can also use a search engine like Google or Bing to look for the torrent file.

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Once you have found the torrent file, click on it to open it with your torrent client. The torrent client will start downloading the crack file from other users who are sharing it. The download speed may vary depending on your internet connection and the number of seeders (users who have the complete file) and leechers (users who are downloading the file) available. Wait until the download is complete.

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Step 3: Install Call of Duty Black Ops II Crack Only-SKIDROW

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After downloading the crack file, you need to install it on your computer. The crack file is usually compressed in a ZIP or RAR archive that you need to extract first using a software like WinRAR or 7-Zip. After extracting the archive, you will find a folder named SKIDROW that contains several files, such as Call.of.Duty.Black.Ops.II.Update.1.and.2.exe, SKIDROW.ini, steam_api.dll, etc.

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To install the crack file, follow these steps:

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  4. Play the game by launching it from its executable file or from a shortcut on your desktop.
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Conclusion

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In this article, we have shown you how to download and install Call of Duty Black Ops II Crack Only-SKIDROW torrent using a torrent client. This method allows you to play the game without purchasing it, but it may also expose you to some risks, such as viruses, malware, legal issues, etc. Therefore, we do not condone or encourage piracy and we advise you to use this method

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\ No newline at end of file diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (welcome 2007 hindi movie 720p torren) - Enjoy the best quality of the Indian blockbuster Welcome.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (welcome 2007 hindi movie 720p torren) - Enjoy the best quality of the Indian blockbuster Welcome.md deleted file mode 100644 index 84ab8d3c0f25bb524c8bfeaba48c4bc4edcc3067..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (welcome 2007 hindi movie 720p torren) - Enjoy the best quality of the Indian blockbuster Welcome.md +++ /dev/null @@ -1,119 +0,0 @@ - -

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\ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Cherish Model 11.md b/spaces/1gistliPinn/ChatGPT4/Examples/Cherish Model 11.md deleted file mode 100644 index 428fc82e9f32c068700289122f95d56d1887f877..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Cherish Model 11.md +++ /dev/null @@ -1,6 +0,0 @@ - -

the goal of the cherish consortium is to support and advance hiv-related research by identifying, recruiting, and providing funding for young investigators with a strong commitment to the study of hiv/aids. the consortium is a collaboration of academic, government, and community partners. the consortium has developed a cherish national steering committee and has been awarded funding to support the cherish pilot study. over the next two years, the consortium will evaluate and refine the cherish protocol, conduct a pilot study to test the efficacy of the cherish intervention on hiv-related clinical outcomes, and assess the feasibility and acceptability of the cherish intervention. consortium members are: susan cohan, m.d., assistant professor of medicine, division of infectious diseases, department of medicine, massachusetts general hospital; megan curtis, m., clinical fellow, infectious diseases division, boston medical center; j.t. kapsimalis, m., infectious diseases division, brigham and women's hospital; steve kowdley, m., associate professor of medicine, division of infectious diseases, department of medicine, massachusetts general hospital; douglas o'malley, m., professor of medicine, division of infectious diseases, department of medicine, harvard medical school; michael perzanowski, m., assistant professor of medicine, division of infectious diseases, department of medicine, harvard medical school; and dennis shusterman, m., assistant professor of medicine, division of infectious diseases, department of medicine, massachusetts general hospital.

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\ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/FIFA-14-crack [NEW]-V6-FINAL-3DM-exe.md b/spaces/1gistliPinn/ChatGPT4/Examples/FIFA-14-crack [NEW]-V6-FINAL-3DM-exe.md deleted file mode 100644 index 7bbdac499900327cc8008824631d674637dfd052..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/FIFA-14-crack [NEW]-V6-FINAL-3DM-exe.md +++ /dev/null @@ -1,9 +0,0 @@ -

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diff --git a/spaces/1toTree/lora_test/ppdiffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py b/spaces/1toTree/lora_test/ppdiffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py deleted file mode 100644 index 6159ae89f5251a647afdd42d99132914a33e891f..0000000000000000000000000000000000000000 --- a/spaces/1toTree/lora_test/ppdiffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py +++ /dev/null @@ -1,253 +0,0 @@ -# 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 math import acos, sin -from typing import List, Tuple, Union - -import numpy as np -import paddle -from PIL import Image - -from ...models import AutoencoderKL, UNet2DConditionModel -from ...pipeline_utils import ( - AudioPipelineOutput, - BaseOutput, - DiffusionPipeline, - ImagePipelineOutput, -) -from ...schedulers import DDIMScheduler, DDPMScheduler -from .mel import Mel - - -class AudioDiffusionPipeline(DiffusionPipeline): - """ - This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the - library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) - - Parameters: - vqae ([`AutoencoderKL`]): Variational AutoEncoder for Latent Audio Diffusion or None - unet ([`UNet2DConditionModel`]): UNET model - mel ([`Mel`]): transform audio <-> spectrogram - scheduler ([`DDIMScheduler` or `DDPMScheduler`]): de-noising scheduler - """ - - _optional_components = ["vqvae"] - - def __init__( - self, - vqvae: AutoencoderKL, - unet: UNet2DConditionModel, - mel: Mel, - scheduler: Union[DDIMScheduler, DDPMScheduler], - ): - super().__init__() - self.register_modules(unet=unet, scheduler=scheduler, mel=mel, vqvae=vqvae) - - def get_input_dims(self) -> Tuple: - """Returns dimension of input image - - Returns: - `Tuple`: (height, width) - """ - input_module = self.vqvae if self.vqvae is not None else self.unet - # For backwards compatibility - sample_size = ( - (input_module.sample_size, input_module.sample_size) - if type(input_module.sample_size) == int - else input_module.sample_size - ) - return sample_size - - def get_default_steps(self) -> int: - """Returns default number of steps recommended for inference - - Returns: - `int`: number of steps - """ - return 50 if isinstance(self.scheduler, DDIMScheduler) else 1000 - - @paddle.no_grad() - def __call__( - self, - batch_size: int = 1, - audio_file: str = None, - raw_audio: np.ndarray = None, - slice: int = 0, - start_step: int = 0, - steps: int = None, - generator: paddle.Generator = None, - mask_start_secs: float = 0, - mask_end_secs: float = 0, - step_generator: paddle.Generator = None, - eta: float = 0, - noise: paddle.Tensor = None, - return_dict=True, - ) -> Union[ - Union[AudioPipelineOutput, ImagePipelineOutput], Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]] - ]: - """Generate random mel spectrogram from audio input and convert to audio. - - Args: - batch_size (`int`): number of samples to generate - audio_file (`str`): must be a file on disk due to Librosa limitation or - raw_audio (`np.ndarray`): audio as numpy array - slice (`int`): slice number of audio to convert - start_step (int): step to start from - steps (`int`): number of de-noising steps (defaults to 50 for DDIM, 1000 for DDPM) - generator (`paddle.Generator`): random number generator or None - mask_start_secs (`float`): number of seconds of audio to mask (not generate) at start - mask_end_secs (`float`): number of seconds of audio to mask (not generate) at end - step_generator (`paddle.Generator`): random number generator used to de-noise or None - eta (`float`): parameter between 0 and 1 used with DDIM scheduler - noise (`paddle.Tensor`): noise tensor of shape (batch_size, 1, height, width) or None - return_dict (`bool`): if True return AudioPipelineOutput, ImagePipelineOutput else Tuple - - Returns: - `List[PIL Image]`: mel spectrograms (`float`, `List[np.ndarray]`): sample rate and raw audios - """ - - steps = steps or self.get_default_steps() - self.scheduler.set_timesteps(steps) - step_generator = step_generator or generator - # For backwards compatibility - if type(self.unet.sample_size) == int: - self.unet.sample_size = (self.unet.sample_size, self.unet.sample_size) - input_dims = self.get_input_dims() - self.mel.set_resolution(x_res=input_dims[1], y_res=input_dims[0]) - if noise is None: - noise = paddle.randn( - (batch_size, self.unet.in_channels, self.unet.sample_size[0], self.unet.sample_size[1]), - generator=generator, - ) - images = noise - mask = None - - if audio_file is not None or raw_audio is not None: - self.mel.load_audio(audio_file, raw_audio) - input_image = self.mel.audio_slice_to_image(slice) - input_image = np.frombuffer(input_image.tobytes(), dtype="uint8").reshape( - (input_image.height, input_image.width) - ) - input_image = (input_image / 255) * 2 - 1 - input_images = paddle.to_tensor(input_image[np.newaxis, :, :], dtype=paddle.float32) - - if self.vqvae is not None: - input_images = self.vqvae.encode(paddle.unsqueeze(input_images, 0)).latent_dist.sample( - generator=generator - )[0] - input_images = 0.18215 * input_images - - if start_step > 0: - images[0, 0] = self.scheduler.add_noise(input_images, noise, self.scheduler.timesteps[start_step - 1]) - - pixels_per_second = ( - self.unet.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length - ) - mask_start = int(mask_start_secs * pixels_per_second) - mask_end = int(mask_end_secs * pixels_per_second) - mask = self.scheduler.add_noise( - input_images, noise, paddle.to_tensor(self.scheduler.timesteps[start_step:]) - ) - - for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])): - model_output = self.unet(images, t)["sample"] - - if isinstance(self.scheduler, DDIMScheduler): - images = self.scheduler.step( - model_output=model_output, timestep=t, sample=images, eta=eta, generator=step_generator - )["prev_sample"] - else: - images = self.scheduler.step( - model_output=model_output, timestep=t, sample=images, generator=step_generator - )["prev_sample"] - - if mask is not None: - if mask_start > 0: - images[:, :, :, :mask_start] = mask[:, step, :, :mask_start] - if mask_end > 0: - images[:, :, :, -mask_end:] = mask[:, step, :, -mask_end:] - - if self.vqvae is not None: - # 0.18215 was scaling factor used in training to ensure unit variance - images = 1 / 0.18215 * images - images = self.vqvae.decode(images)["sample"] - - images = (images / 2 + 0.5).clip(0, 1) - images = images.transpose([0, 2, 3, 1]).cast("float32").numpy() - images = (images * 255).round().astype("uint8") - images = list( - map(lambda _: Image.fromarray(_[:, :, 0]), images) - if images.shape[3] == 1 - else map(lambda _: Image.fromarray(_, mode="RGB").convert("L"), images) - ) - - audios = list(map(lambda _: self.mel.image_to_audio(_), images)) - if not return_dict: - return images, (self.mel.get_sample_rate(), audios) - - return BaseOutput(**AudioPipelineOutput(np.array(audios)[:, np.newaxis, :]), **ImagePipelineOutput(images)) - - @paddle.no_grad() - def encode(self, images: List[Image.Image], steps: int = 50) -> np.ndarray: - """Reverse step process: recover noisy image from generated image. - - Args: - images (`List[PIL Image]`): list of images to encode - steps (`int`): number of encoding steps to perform (defaults to 50) - - Returns: - `np.ndarray`: noise tensor of shape (batch_size, 1, height, width) - """ - - # Only works with DDIM as this method is deterministic - assert isinstance(self.scheduler, DDIMScheduler) - self.scheduler.set_timesteps(steps) - sample = np.array( - [np.frombuffer(image.tobytes(), dtype="uint8").reshape((1, image.height, image.width)) for image in images] - ) - sample = (sample / 255) * 2 - 1 - sample = paddle.to_tensor(sample) - - for t in self.progress_bar(paddle.flip(self.scheduler.timesteps, (0,))): - prev_timestep = t - self.scheduler.num_train_timesteps // self.scheduler.num_inference_steps - alpha_prod_t = self.scheduler.alphas_cumprod[t] - alpha_prod_t_prev = ( - self.scheduler.alphas_cumprod[prev_timestep] - if prev_timestep >= 0 - else self.scheduler.final_alpha_cumprod - ) - beta_prod_t = 1 - alpha_prod_t - model_output = self.unet(sample, t)["sample"] - pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * model_output - sample = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5) - sample = sample * alpha_prod_t ** (0.5) + beta_prod_t ** (0.5) * model_output - - return sample - - @staticmethod - def slerp(x0: paddle.Tensor, x1: paddle.Tensor, alpha: float) -> paddle.Tensor: - """Spherical Linear intERPolation - - Args: - x0 (`paddle.Tensor`): first tensor to interpolate between - x1 (`paddle.Tensor`): seconds tensor to interpolate between - alpha (`float`): interpolation between 0 and 1 - - Returns: - `paddle.Tensor`: interpolated tensor - """ - - theta = acos(paddle.dot(paddle.flatten(x0), paddle.flatten(x1)) / paddle.norm(x0) / paddle.norm(x1)) - return sin((1 - alpha) * theta) * x0 / sin(theta) + sin(alpha * theta) * x1 / sin(theta) diff --git a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py b/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py deleted file mode 100644 index eb4e0d31f1aedf4590628d394e1606920fefb5c9..0000000000000000000000000000000000000000 --- a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py +++ /dev/null @@ -1,26 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "arcface" -config.network = "r18" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 1.0 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 5e-4 -config.batch_size = 128 -config.lr = 0.1 # batch size is 512 - -config.rec = "/train_tmp/ms1m-retinaface-t1" -config.num_classes = 93431 -config.num_image = 5179510 -config.num_epoch = 25 -config.warmup_epoch = -1 -config.decay_epoch = [10, 16, 22] -config.val_targets = ["lfw", "cfp_fp", "agedb_30"] diff --git a/spaces/AI-Hobbyist/Hoyo-RVC/gui.py b/spaces/AI-Hobbyist/Hoyo-RVC/gui.py deleted file mode 100644 index 1e5e5d90b87e88929a308d51274855db99d2c376..0000000000000000000000000000000000000000 --- a/spaces/AI-Hobbyist/Hoyo-RVC/gui.py +++ /dev/null @@ -1,698 +0,0 @@ -""" -0416后的更新： - 引入config中half - 重建npy而不用填写 - v2支持 - 无f0模型支持 - 修复 - - int16： - 增加无索引支持 - f0算法改harvest(怎么看就只有这个会影响CPU占用)，但是不这么改效果不好 -""" -import os, sys, traceback, re - -import json - -now_dir = os.getcwd() -sys.path.append(now_dir) -from config import Config - -Config = Config() -import PySimpleGUI as sg -import sounddevice as sd -import noisereduce as nr -import numpy as np -from fairseq import checkpoint_utils -import librosa, torch, pyworld, faiss, time, threading -import torch.nn.functional as F -import torchaudio.transforms as tat -import scipy.signal as signal - - -# import matplotlib.pyplot as plt -from infer_pack.models import ( - SynthesizerTrnMs256NSFsid, - SynthesizerTrnMs256NSFsid_nono, - SynthesizerTrnMs768NSFsid, - SynthesizerTrnMs768NSFsid_nono, -) -from i18n import I18nAuto - -i18n = I18nAuto() -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -current_dir = os.getcwd() - - -class RVC: - def __init__( - self, key, hubert_path, pth_path, index_path, npy_path, index_rate - ) -> None: - """ - 初始化 - """ - try: - self.f0_up_key = key - self.time_step = 160 / 16000 * 1000 - self.f0_min = 50 - self.f0_max = 1100 - self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700) - self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700) - self.sr = 16000 - self.window = 160 - if index_rate != 0: - self.index = faiss.read_index(index_path) - # self.big_npy = np.load(npy_path) - self.big_npy = self.index.reconstruct_n(0, self.index.ntotal) - print("index search enabled") - self.index_rate = index_rate - model_path = hubert_path - print("load model(s) from {}".format(model_path)) - models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( - [model_path], - suffix="", - ) - self.model = models[0] - self.model = self.model.to(device) - if Config.is_half: - self.model = self.model.half() - else: - self.model = self.model.float() - self.model.eval() - cpt = torch.load(pth_path, map_location="cpu") - self.tgt_sr = cpt["config"][-1] - cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk - self.if_f0 = cpt.get("f0", 1) - self.version = cpt.get("version", "v1") - if self.version == "v1": - if self.if_f0 == 1: - self.net_g = SynthesizerTrnMs256NSFsid( - *cpt["config"], is_half=Config.is_half - ) - else: - self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) - elif self.version == "v2": - if self.if_f0 == 1: - self.net_g = SynthesizerTrnMs768NSFsid( - *cpt["config"], is_half=Config.is_half - ) - else: - self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) - del self.net_g.enc_q - print(self.net_g.load_state_dict(cpt["weight"], strict=False)) - self.net_g.eval().to(device) - if Config.is_half: - self.net_g = self.net_g.half() - else: - self.net_g = self.net_g.float() - except: - print(traceback.format_exc()) - - def get_f0(self, x, f0_up_key, inp_f0=None): - x_pad = 1 - f0_min = 50 - f0_max = 1100 - f0_mel_min = 1127 * np.log(1 + f0_min / 700) - f0_mel_max = 1127 * np.log(1 + f0_max / 700) - f0, t = pyworld.harvest( - x.astype(np.double), - fs=self.sr, - f0_ceil=f0_max, - f0_floor=f0_min, - frame_period=10, - ) - f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr) - f0 = signal.medfilt(f0, 3) - f0 *= pow(2, f0_up_key / 12) - # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) - tf0 = self.sr // self.window # 每秒f0点数 - if inp_f0 is not None: - delta_t = np.round( - (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1 - ).astype("int16") - replace_f0 = np.interp( - list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1] - ) - shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0] - f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape] - # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) - f0bak = f0.copy() - f0_mel = 1127 * np.log(1 + f0 / 700) - f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / ( - f0_mel_max - f0_mel_min - ) + 1 - f0_mel[f0_mel <= 1] = 1 - f0_mel[f0_mel > 255] = 255 - f0_coarse = np.rint(f0_mel).astype(np.int) - return f0_coarse, f0bak # 1-0 - - def infer(self, feats: torch.Tensor) -> np.ndarray: - """ - 推理函数 - """ - audio = feats.clone().cpu().numpy() - assert feats.dim() == 1, feats.dim() - feats = feats.view(1, -1) - padding_mask = torch.BoolTensor(feats.shape).fill_(False) - if Config.is_half: - feats = feats.half() - else: - feats = feats.float() - inputs = { - "source": feats.to(device), - "padding_mask": padding_mask.to(device), - "output_layer": 9 if self.version == "v1" else 12, - } - torch.cuda.synchronize() - with torch.no_grad(): - logits = self.model.extract_features(**inputs) - feats = ( - self.model.final_proj(logits[0]) if self.version == "v1" else logits[0] - ) - - ####索引优化 - try: - if ( - hasattr(self, "index") - and hasattr(self, "big_npy") - and self.index_rate != 0 - ): - npy = feats[0].cpu().numpy().astype("float32") - score, ix = self.index.search(npy, k=8) - weight = np.square(1 / score) - weight /= weight.sum(axis=1, keepdims=True) - npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1) - if Config.is_half: - npy = npy.astype("float16") - feats = ( - torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate - + (1 - self.index_rate) * feats - ) - else: - print("index search FAIL or disabled") - except: - traceback.print_exc() - print("index search FAIL") - feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) - torch.cuda.synchronize() - print(feats.shape) - if self.if_f0 == 1: - pitch, pitchf = self.get_f0(audio, self.f0_up_key) - p_len = min(feats.shape[1], 13000, pitch.shape[0]) # 太大了爆显存 - else: - pitch, pitchf = None, None - p_len = min(feats.shape[1], 13000) # 太大了爆显存 - torch.cuda.synchronize() - # print(feats.shape,pitch.shape) - feats = feats[:, :p_len, :] - if self.if_f0 == 1: - pitch = pitch[:p_len] - pitchf = pitchf[:p_len] - pitch = torch.LongTensor(pitch).unsqueeze(0).to(device) - pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device) - p_len = torch.LongTensor([p_len]).to(device) - ii = 0 # sid - sid = torch.LongTensor([ii]).to(device) - with torch.no_grad(): - if self.if_f0 == 1: - infered_audio = ( - self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] - .data.cpu() - .float() - ) - else: - infered_audio = ( - self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float() - ) - torch.cuda.synchronize() - return infered_audio - - -class GUIConfig: - def __init__(self) -> None: - self.hubert_path: str = "" - self.pth_path: str = "" - self.index_path: str = "" - self.npy_path: str = "" - self.pitch: int = 12 - self.samplerate: int = 44100 - self.block_time: float = 1.0 # s - self.buffer_num: int = 1 - self.threhold: int = -30 - self.crossfade_time: float = 0.08 - self.extra_time: float = 0.04 - self.I_noise_reduce = False - self.O_noise_reduce = False - self.index_rate = 0.3 - - -class GUI: - def __init__(self) -> None: - self.config = GUIConfig() - self.flag_vc = False - - self.launcher() - - def load(self): - input_devices, output_devices, _, _ = self.get_devices() - try: - with open("values1.json", "r") as j: - data = json.load(j) - except: - with open("values1.json", "w") as j: - data = { - "pth_path": " ", - "index_path": " ", - "sg_input_device": input_devices[sd.default.device[0]], - "sg_output_device": output_devices[sd.default.device[1]], - "threhold": "-45", - "pitch": "0", - "index_rate": "0", - "block_time": "1", - "crossfade_length": "0.04", - "extra_time": "1", - } - return data - - def launcher(self): - data = self.load() - sg.theme("LightBlue3") - input_devices, output_devices, _, _ = self.get_devices() - layout = [ - [ - sg.Frame( - title=i18n("加载模型"), - layout=[ - [ - sg.Input( - default_text="hubert_base.pt", - key="hubert_path", - disabled=True, - ), - sg.FileBrowse( - i18n("Hubert模型"), - initial_folder=os.path.join(os.getcwd()), - file_types=((". pt"),), - ), - ], - [ - sg.Input( - default_text=data.get("pth_path", ""), - key="pth_path", - ), - sg.FileBrowse( - i18n("选择.pth文件"), - initial_folder=os.path.join(os.getcwd(), "weights"), - file_types=((". pth"),), - ), - ], - [ - sg.Input( - default_text=data.get("index_path", ""), - key="index_path", - ), - sg.FileBrowse( - i18n("选择.index文件"), - initial_folder=os.path.join(os.getcwd(), "logs"), - file_types=((". index"),), - ), - ], - [ - sg.Input( - default_text="你不需要填写这个You don't need write this.", - key="npy_path", - disabled=True, - ), - sg.FileBrowse( - i18n("选择.npy文件"), - initial_folder=os.path.join(os.getcwd(), "logs"), - file_types=((". npy"),), - ), - ], - ], - ) - ], - [ - sg.Frame( - layout=[ - [ - sg.Text(i18n("输入设备")), - sg.Combo( - input_devices, - key="sg_input_device", - default_value=data.get("sg_input_device", ""), - ), - ], - [ - sg.Text(i18n("输出设备")), - sg.Combo( - output_devices, - key="sg_output_device", - default_value=data.get("sg_output_device", ""), - ), - ], - ], - title=i18n("音频设备(请使用同种类驱动)"), - ) - ], - [ - sg.Frame( - layout=[ - [ - sg.Text(i18n("响应阈值")), - sg.Slider( - range=(-60, 0), - key="threhold", - resolution=1, - orientation="h", - default_value=data.get("threhold", ""), - ), - ], - [ - sg.Text(i18n("音调设置")), - sg.Slider( - range=(-24, 24), - key="pitch", - resolution=1, - orientation="h", - default_value=data.get("pitch", ""), - ), - ], - [ - sg.Text(i18n("Index Rate")), - sg.Slider( - range=(0.0, 1.0), - key="index_rate", - resolution=0.01, - orientation="h", - default_value=data.get("index_rate", ""), - ), - ], - ], - title=i18n("常规设置"), - ), - sg.Frame( - layout=[ - [ - sg.Text(i18n("采样长度")), - sg.Slider( - range=(0.1, 3.0), - key="block_time", - resolution=0.1, - orientation="h", - default_value=data.get("block_time", ""), - ), - ], - [ - sg.Text(i18n("淡入淡出长度")), - sg.Slider( - range=(0.01, 0.15), - key="crossfade_length", - resolution=0.01, - orientation="h", - default_value=data.get("crossfade_length", ""), - ), - ], - [ - sg.Text(i18n("额外推理时长")), - sg.Slider( - range=(0.05, 3.00), - key="extra_time", - resolution=0.01, - orientation="h", - default_value=data.get("extra_time", ""), - ), - ], - [ - sg.Checkbox(i18n("输入降噪"), key="I_noise_reduce"), - sg.Checkbox(i18n("输出降噪"), key="O_noise_reduce"), - ], - ], - title=i18n("性能设置"), - ), - ], - [ - sg.Button(i18n("开始音频转换"), key="start_vc"), - sg.Button(i18n("停止音频转换"), key="stop_vc"), - sg.Text(i18n("推理时间(ms):")), - sg.Text("0", key="infer_time"), - ], - ] - self.window = sg.Window("RVC - GUI", layout=layout) - self.event_handler() - - def event_handler(self): - while True: - event, values = self.window.read() - if event == sg.WINDOW_CLOSED: - self.flag_vc = False - exit() - if event == "start_vc" and self.flag_vc == False: - if self.set_values(values) == True: - print("using_cuda:" + str(torch.cuda.is_available())) - self.start_vc() - settings = { - "pth_path": values["pth_path"], - "index_path": values["index_path"], - "sg_input_device": values["sg_input_device"], - "sg_output_device": values["sg_output_device"], - "threhold": values["threhold"], - "pitch": values["pitch"], - "index_rate": values["index_rate"], - "block_time": values["block_time"], - "crossfade_length": values["crossfade_length"], - "extra_time": values["extra_time"], - } - with open("values1.json", "w") as j: - json.dump(settings, j) - if event == "stop_vc" and self.flag_vc == True: - self.flag_vc = False - - def set_values(self, values): - if len(values["pth_path"].strip()) == 0: - sg.popup(i18n("请选择pth文件")) - return False - if len(values["index_path"].strip()) == 0: - sg.popup(i18n("请选择index文件")) - return False - pattern = re.compile("[^\x00-\x7F]+") - if pattern.findall(values["hubert_path"]): - sg.popup(i18n("hubert模型路径不可包含中文")) - return False - if pattern.findall(values["pth_path"]): - sg.popup(i18n("pth文件路径不可包含中文")) - return False - if pattern.findall(values["index_path"]): - sg.popup(i18n("index文件路径不可包含中文")) - return False - self.set_devices(values["sg_input_device"], values["sg_output_device"]) - self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt") - self.config.pth_path = values["pth_path"] - self.config.index_path = values["index_path"] - self.config.npy_path = values["npy_path"] - self.config.threhold = values["threhold"] - self.config.pitch = values["pitch"] - self.config.block_time = values["block_time"] - self.config.crossfade_time = values["crossfade_length"] - self.config.extra_time = values["extra_time"] - self.config.I_noise_reduce = values["I_noise_reduce"] - self.config.O_noise_reduce = values["O_noise_reduce"] - self.config.index_rate = values["index_rate"] - return True - - def start_vc(self): - torch.cuda.empty_cache() - self.flag_vc = True - self.block_frame = int(self.config.block_time * self.config.samplerate) - self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate) - self.sola_search_frame = int(0.012 * self.config.samplerate) - self.delay_frame = int(0.01 * self.config.samplerate) # 往前预留0.02s - self.extra_frame = int(self.config.extra_time * self.config.samplerate) - self.rvc = None - self.rvc = RVC( - self.config.pitch, - self.config.hubert_path, - self.config.pth_path, - self.config.index_path, - self.config.npy_path, - self.config.index_rate, - ) - self.input_wav: np.ndarray = np.zeros( - self.extra_frame - + self.crossfade_frame - + self.sola_search_frame - + self.block_frame, - dtype="float32", - ) - self.output_wav: torch.Tensor = torch.zeros( - self.block_frame, device=device, dtype=torch.float32 - ) - self.sola_buffer: torch.Tensor = torch.zeros( - self.crossfade_frame, device=device, dtype=torch.float32 - ) - self.fade_in_window: torch.Tensor = torch.linspace( - 0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32 - ) - self.fade_out_window: torch.Tensor = 1 - self.fade_in_window - self.resampler1 = tat.Resample( - orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32 - ) - self.resampler2 = tat.Resample( - orig_freq=self.rvc.tgt_sr, - new_freq=self.config.samplerate, - dtype=torch.float32, - ) - thread_vc = threading.Thread(target=self.soundinput) - thread_vc.start() - - def soundinput(self): - """ - 接受音频输入 - """ - with sd.Stream( - callback=self.audio_callback, - blocksize=self.block_frame, - samplerate=self.config.samplerate, - dtype="float32", - ): - while self.flag_vc: - time.sleep(self.config.block_time) - print("Audio block passed.") - print("ENDing VC") - - def audio_callback( - self, indata: np.ndarray, outdata: np.ndarray, frames, times, status - ): - """ - 音频处理 - """ - start_time = time.perf_counter() - indata = librosa.to_mono(indata.T) - if self.config.I_noise_reduce: - indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate) - - """noise gate""" - frame_length = 2048 - hop_length = 1024 - rms = librosa.feature.rms( - y=indata, frame_length=frame_length, hop_length=hop_length - ) - db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold - # print(rms.shape,db.shape,db) - for i in range(db_threhold.shape[0]): - if db_threhold[i]: - indata[i * hop_length : (i + 1) * hop_length] = 0 - self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata) - - # infer - print("input_wav:" + str(self.input_wav.shape)) - # print('infered_wav:'+str(infer_wav.shape)) - infer_wav: torch.Tensor = self.resampler2( - self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav))) - )[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to( - device - ) - print("infer_wav:" + str(infer_wav.shape)) - - # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC - cor_nom = F.conv1d( - infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame], - self.sola_buffer[None, None, :], - ) - cor_den = torch.sqrt( - F.conv1d( - infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame] - ** 2, - torch.ones(1, 1, self.crossfade_frame, device=device), - ) - + 1e-8 - ) - sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0]) - print("sola offset: " + str(int(sola_offset))) - - # crossfade - self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame] - self.output_wav[: self.crossfade_frame] *= self.fade_in_window - self.output_wav[: self.crossfade_frame] += self.sola_buffer[:] - if sola_offset < self.sola_search_frame: - self.sola_buffer[:] = ( - infer_wav[ - -self.sola_search_frame - - self.crossfade_frame - + sola_offset : -self.sola_search_frame - + sola_offset - ] - * self.fade_out_window - ) - else: - self.sola_buffer[:] = ( - infer_wav[-self.crossfade_frame :] * self.fade_out_window - ) - - if self.config.O_noise_reduce: - outdata[:] = np.tile( - nr.reduce_noise( - y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate - ), - (2, 1), - ).T - else: - outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy() - total_time = time.perf_counter() - start_time - self.window["infer_time"].update(int(total_time * 1000)) - print("infer time:" + str(total_time)) - - def get_devices(self, update: bool = True): - """获取设备列表""" - if update: - sd._terminate() - sd._initialize() - devices = sd.query_devices() - hostapis = sd.query_hostapis() - for hostapi in hostapis: - for device_idx in hostapi["devices"]: - devices[device_idx]["hostapi_name"] = hostapi["name"] - input_devices = [ - f"{d['name']} ({d['hostapi_name']})" - for d in devices - if d["max_input_channels"] > 0 - ] - output_devices = [ - f"{d['name']} ({d['hostapi_name']})" - for d in devices - if d["max_output_channels"] > 0 - ] - input_devices_indices = [ - d["index"] if "index" in d else d["name"] - for d in devices - if d["max_input_channels"] > 0 - ] - output_devices_indices = [ - d["index"] if "index" in d else d["name"] - for d in devices - if d["max_output_channels"] > 0 - ] - return ( - input_devices, - output_devices, - input_devices_indices, - output_devices_indices, - ) - - def set_devices(self, input_device, output_device): - """设置输出设备""" - ( - input_devices, - output_devices, - input_device_indices, - output_device_indices, - ) = self.get_devices() - sd.default.device[0] = input_device_indices[input_devices.index(input_device)] - sd.default.device[1] = output_device_indices[ - output_devices.index(output_device) - ] - print("input device:" + str(sd.default.device[0]) + ":" + str(input_device)) - print("output device:" + str(sd.default.device[1]) + ":" + str(output_device)) - - -gui = GUI() diff --git a/spaces/AI-Hobbyist/Hoyo-RVC/slicer2.py b/spaces/AI-Hobbyist/Hoyo-RVC/slicer2.py deleted file mode 100644 index 5b29ee262aa54045e807be2cffeb41687499ba58..0000000000000000000000000000000000000000 --- a/spaces/AI-Hobbyist/Hoyo-RVC/slicer2.py +++ /dev/null @@ -1,260 +0,0 @@ -import numpy as np - - -# This function is obtained from librosa. -def get_rms( - y, - frame_length=2048, - hop_length=512, - pad_mode="constant", -): - padding = (int(frame_length // 2), int(frame_length // 2)) - y = np.pad(y, padding, mode=pad_mode) - - axis = -1 - # put our new within-frame axis at the end for now - out_strides = y.strides + tuple([y.strides[axis]]) - # Reduce the shape on the framing axis - x_shape_trimmed = list(y.shape) - x_shape_trimmed[axis] -= frame_length - 1 - out_shape = tuple(x_shape_trimmed) + tuple([frame_length]) - xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides) - if axis < 0: - target_axis = axis - 1 - else: - target_axis = axis + 1 - xw = np.moveaxis(xw, -1, target_axis) - # Downsample along the target axis - slices = [slice(None)] * xw.ndim - slices[axis] = slice(0, None, hop_length) - x = xw[tuple(slices)] - - # Calculate power - power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True) - - return np.sqrt(power) - - -class Slicer: - def __init__( - self, - sr: int, - threshold: float = -40.0, - min_length: int = 5000, - min_interval: int = 300, - hop_size: int = 20, - max_sil_kept: int = 5000, - ): - if not min_length >= min_interval >= hop_size: - raise ValueError( - "The following condition must be satisfied: min_length >= min_interval >= hop_size" - ) - if not max_sil_kept >= hop_size: - raise ValueError( - "The following condition must be satisfied: max_sil_kept >= hop_size" - ) - min_interval = sr * min_interval / 1000 - self.threshold = 10 ** (threshold / 20.0) - self.hop_size = round(sr * hop_size / 1000) - self.win_size = min(round(min_interval), 4 * self.hop_size) - self.min_length = round(sr * min_length / 1000 / self.hop_size) - self.min_interval = round(min_interval / self.hop_size) - self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size) - - def _apply_slice(self, waveform, begin, end): - if len(waveform.shape) > 1: - return waveform[ - :, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size) - ] - else: - return waveform[ - begin * self.hop_size : min(waveform.shape[0], end * self.hop_size) - ] - - # @timeit - def slice(self, waveform): - if len(waveform.shape) > 1: - samples = waveform.mean(axis=0) - else: - samples = waveform - if samples.shape[0] <= self.min_length: - return [waveform] - rms_list = get_rms( - y=samples, frame_length=self.win_size, hop_length=self.hop_size - ).squeeze(0) - sil_tags = [] - silence_start = None - clip_start = 0 - for i, rms in enumerate(rms_list): - # Keep looping while frame is silent. - if rms < self.threshold: - # Record start of silent frames. - if silence_start is None: - silence_start = i - continue - # Keep looping while frame is not silent and silence start has not been recorded. - if silence_start is None: - continue - # Clear recorded silence start if interval is not enough or clip is too short - is_leading_silence = silence_start == 0 and i > self.max_sil_kept - need_slice_middle = ( - i - silence_start >= self.min_interval - and i - clip_start >= self.min_length - ) - if not is_leading_silence and not need_slice_middle: - silence_start = None - continue - # Need slicing. Record the range of silent frames to be removed. - if i - silence_start <= self.max_sil_kept: - pos = rms_list[silence_start : i + 1].argmin() + silence_start - if silence_start == 0: - sil_tags.append((0, pos)) - else: - sil_tags.append((pos, pos)) - clip_start = pos - elif i - silence_start <= self.max_sil_kept * 2: - pos = rms_list[ - i - self.max_sil_kept : silence_start + self.max_sil_kept + 1 - ].argmin() - pos += i - self.max_sil_kept - pos_l = ( - rms_list[ - silence_start : silence_start + self.max_sil_kept + 1 - ].argmin() - + silence_start - ) - pos_r = ( - rms_list[i - self.max_sil_kept : i + 1].argmin() - + i - - self.max_sil_kept - ) - if silence_start == 0: - sil_tags.append((0, pos_r)) - clip_start = pos_r - else: - sil_tags.append((min(pos_l, pos), max(pos_r, pos))) - clip_start = max(pos_r, pos) - else: - pos_l = ( - rms_list[ - silence_start : silence_start + self.max_sil_kept + 1 - ].argmin() - + silence_start - ) - pos_r = ( - rms_list[i - self.max_sil_kept : i + 1].argmin() - + i - - self.max_sil_kept - ) - if silence_start == 0: - sil_tags.append((0, pos_r)) - else: - sil_tags.append((pos_l, pos_r)) - clip_start = pos_r - silence_start = None - # Deal with trailing silence. - total_frames = rms_list.shape[0] - if ( - silence_start is not None - and total_frames - silence_start >= self.min_interval - ): - silence_end = min(total_frames, silence_start + self.max_sil_kept) - pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start - sil_tags.append((pos, total_frames + 1)) - # Apply and return slices. - if len(sil_tags) == 0: - return [waveform] - else: - chunks = [] - if sil_tags[0][0] > 0: - chunks.append(self._apply_slice(waveform, 0, sil_tags[0][0])) - for i in range(len(sil_tags) - 1): - chunks.append( - self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]) - ) - if sil_tags[-1][1] < total_frames: - chunks.append( - self._apply_slice(waveform, sil_tags[-1][1], total_frames) - ) - return chunks - - -def main(): - import os.path - from argparse import ArgumentParser - - import librosa - import soundfile - - parser = ArgumentParser() - parser.add_argument("audio", type=str, help="The audio to be sliced") - parser.add_argument( - "--out", type=str, help="Output directory of the sliced audio clips" - ) - parser.add_argument( - "--db_thresh", - type=float, - required=False, - default=-40, - help="The dB threshold for silence detection", - ) - parser.add_argument( - "--min_length", - type=int, - required=False, - default=5000, - help="The minimum milliseconds required for each sliced audio clip", - ) - parser.add_argument( - "--min_interval", - type=int, - required=False, - default=300, - help="The minimum milliseconds for a silence part to be sliced", - ) - parser.add_argument( - "--hop_size", - type=int, - required=False, - default=10, - help="Frame length in milliseconds", - ) - parser.add_argument( - "--max_sil_kept", - type=int, - required=False, - default=500, - help="The maximum silence length kept around the sliced clip, presented in milliseconds", - ) - args = parser.parse_args() - out = args.out - if out is None: - out = os.path.dirname(os.path.abspath(args.audio)) - audio, sr = librosa.load(args.audio, sr=None, mono=False) - slicer = Slicer( - sr=sr, - threshold=args.db_thresh, - min_length=args.min_length, - min_interval=args.min_interval, - hop_size=args.hop_size, - max_sil_kept=args.max_sil_kept, - ) - chunks = slicer.slice(audio) - if not os.path.exists(out): - os.makedirs(out) - for i, chunk in enumerate(chunks): - if len(chunk.shape) > 1: - chunk = chunk.T - soundfile.write( - os.path.join( - out, - f"%s_%d.wav" - % (os.path.basename(args.audio).rsplit(".", maxsplit=1)[0], i), - ), - chunk, - sr, - ) - - -if __name__ == "__main__": - main() diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/data_gen_utils.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/data_gen_utils.py deleted file mode 100644 index 57ccb7c0200de5124908db2cba0347baf2663adc..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/data_gen_utils.py +++ /dev/null @@ -1,357 +0,0 @@ -import warnings - -warnings.filterwarnings("ignore") - -import parselmouth -import os -import torch -from skimage.transform import resize -from utils.text_encoder import TokenTextEncoder -from utils.pitch_utils import f0_to_coarse -import struct -import webrtcvad -from scipy.ndimage.morphology import binary_dilation -import librosa -import numpy as np -from utils import audio -import pyloudnorm as pyln -import re -import json -from collections import OrderedDict - -PUNCS = '!,.?;:' - -int16_max = (2 ** 15) - 1 - - -def trim_long_silences(path, sr=None, return_raw_wav=False, norm=True, vad_max_silence_length=12): - """ - Ensures that segments without voice in the waveform remain no longer than a - threshold determined by the VAD parameters in params.py. - :param wav: the raw waveform as a numpy array of floats - :param vad_max_silence_length: Maximum number of consecutive silent frames a segment can have. - :return: the same waveform with silences trimmed away (length <= original wav length) - """ - - ## Voice Activation Detection - # Window size of the VAD. Must be either 10, 20 or 30 milliseconds. - # This sets the granularity of the VAD. Should not need to be changed. - sampling_rate = 16000 - wav_raw, sr = librosa.core.load(path, sr=sr) - - if norm: - meter = pyln.Meter(sr) # create BS.1770 meter - loudness = meter.integrated_loudness(wav_raw) - wav_raw = pyln.normalize.loudness(wav_raw, loudness, -20.0) - if np.abs(wav_raw).max() > 1.0: - wav_raw = wav_raw / np.abs(wav_raw).max() - - wav = librosa.resample(wav_raw, sr, sampling_rate, res_type='kaiser_best') - - vad_window_length = 30 # In milliseconds - # Number of frames to average together when performing the moving average smoothing. - # The larger this value, the larger the VAD variations must be to not get smoothed out. - vad_moving_average_width = 8 - - # Compute the voice detection window size - samples_per_window = (vad_window_length * sampling_rate) // 1000 - - # Trim the end of the audio to have a multiple of the window size - wav = wav[:len(wav) - (len(wav) % samples_per_window)] - - # Convert the float waveform to 16-bit mono PCM - pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16)) - - # Perform voice activation detection - voice_flags = [] - vad = webrtcvad.Vad(mode=3) - for window_start in range(0, len(wav), samples_per_window): - window_end = window_start + samples_per_window - voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2], - sample_rate=sampling_rate)) - voice_flags = np.array(voice_flags) - - # Smooth the voice detection with a moving average - def moving_average(array, width): - array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2))) - ret = np.cumsum(array_padded, dtype=float) - ret[width:] = ret[width:] - ret[:-width] - return ret[width - 1:] / width - - audio_mask = moving_average(voice_flags, vad_moving_average_width) - audio_mask = np.round(audio_mask).astype(np.bool) - - # Dilate the voiced regions - audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1)) - audio_mask = np.repeat(audio_mask, samples_per_window) - audio_mask = resize(audio_mask, (len(wav_raw),)) > 0 - if return_raw_wav: - return wav_raw, audio_mask, sr - return wav_raw[audio_mask], audio_mask, sr - - -def process_utterance(wav_path, - fft_size=1024, - hop_size=256, - win_length=1024, - window="hann", - num_mels=80, - fmin=80, - fmax=7600, - eps=1e-6, - sample_rate=22050, - loud_norm=False, - min_level_db=-100, - return_linear=False, - trim_long_sil=False, vocoder='pwg'): - if isinstance(wav_path, str): - if trim_long_sil: - wav, _, _ = trim_long_silences(wav_path, sample_rate) - else: - wav, _ = librosa.core.load(wav_path, sr=sample_rate) - else: - wav = wav_path - - if loud_norm: - meter = pyln.Meter(sample_rate) # create BS.1770 meter - loudness = meter.integrated_loudness(wav) - wav = pyln.normalize.loudness(wav, loudness, -22.0) - if np.abs(wav).max() > 1: - wav = wav / np.abs(wav).max() - - # get amplitude spectrogram - x_stft = librosa.stft(wav, n_fft=fft_size, hop_length=hop_size, - win_length=win_length, window=window, pad_mode="constant") - spc = np.abs(x_stft) # (n_bins, T) - - # get mel basis - fmin = 0 if fmin == -1 else fmin - fmax = sample_rate / 2 if fmax == -1 else fmax - mel_basis = librosa.filters.mel(sample_rate, fft_size, num_mels, fmin, fmax) - mel = mel_basis @ spc - - if vocoder == 'pwg': - mel = np.log10(np.maximum(eps, mel)) # (n_mel_bins, T) - else: - assert False, f'"{vocoder}" is not in ["pwg"].' - - l_pad, r_pad = audio.librosa_pad_lr(wav, fft_size, hop_size, 1) - wav = np.pad(wav, (l_pad, r_pad), mode='constant', constant_values=0.0) - wav = wav[:mel.shape[1] * hop_size] - - if not return_linear: - return wav, mel - else: - spc = audio.amp_to_db(spc) - spc = audio.normalize(spc, {'min_level_db': min_level_db}) - return wav, mel, spc - - -def get_pitch(wav_data, mel, hparams): - """ - - :param wav_data: [T] - :param mel: [T, 80] - :param hparams: - :return: - """ - time_step = hparams['hop_size'] / hparams['audio_sample_rate'] * 1000 - f0_min = 80 - f0_max = 750 - - if hparams['hop_size'] == 128: - pad_size = 4 - elif hparams['hop_size'] == 256: - pad_size = 2 - else: - assert False - - f0 = parselmouth.Sound(wav_data, hparams['audio_sample_rate']).to_pitch_ac( - time_step=time_step / 1000, voicing_threshold=0.6, - pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency'] - lpad = pad_size * 2 - rpad = len(mel) - len(f0) - lpad - f0 = np.pad(f0, [[lpad, rpad]], mode='constant') - # mel and f0 are extracted by 2 different libraries. we should force them to have the same length. - # Attention: we find that new version of some libraries could cause ``rpad'' to be a negetive value... - # Just to be sure, we recommend users to set up the same environments as them in requirements_auto.txt (by Anaconda) - delta_l = len(mel) - len(f0) - assert np.abs(delta_l) <= 8 - if delta_l > 0: - f0 = np.concatenate([f0, [f0[-1]] * delta_l], 0) - f0 = f0[:len(mel)] - pitch_coarse = f0_to_coarse(f0) - return f0, pitch_coarse - - -def remove_empty_lines(text): - """remove empty lines""" - assert (len(text) > 0) - assert (isinstance(text, list)) - text = [t.strip() for t in text] - if "" in text: - text.remove("") - return text - - -class TextGrid(object): - def __init__(self, text): - text = remove_empty_lines(text) - self.text = text - self.line_count = 0 - self._get_type() - self._get_time_intval() - self._get_size() - self.tier_list = [] - self._get_item_list() - - def _extract_pattern(self, pattern, inc): - """ - Parameters - ---------- - pattern : regex to extract pattern - inc : increment of line count after extraction - Returns - ------- - group : extracted info - """ - try: - group = re.match(pattern, self.text[self.line_count]).group(1) - self.line_count += inc - except AttributeError: - raise ValueError("File format error at line %d:%s" % (self.line_count, self.text[self.line_count])) - return group - - def _get_type(self): - self.file_type = self._extract_pattern(r"File type = \"(.*)\"", 2) - - def _get_time_intval(self): - self.xmin = self._extract_pattern(r"xmin = (.*)", 1) - self.xmax = self._extract_pattern(r"xmax = (.*)", 2) - - def _get_size(self): - self.size = int(self._extract_pattern(r"size = (.*)", 2)) - - def _get_item_list(self): - """Only supports IntervalTier currently""" - for itemIdx in range(1, self.size + 1): - tier = OrderedDict() - item_list = [] - tier_idx = self._extract_pattern(r"item \[(.*)\]:", 1) - tier_class = self._extract_pattern(r"class = \"(.*)\"", 1) - if tier_class != "IntervalTier": - raise NotImplementedError("Only IntervalTier class is supported currently") - tier_name = self._extract_pattern(r"name = \"(.*)\"", 1) - tier_xmin = self._extract_pattern(r"xmin = (.*)", 1) - tier_xmax = self._extract_pattern(r"xmax = (.*)", 1) - tier_size = self._extract_pattern(r"intervals: size = (.*)", 1) - for i in range(int(tier_size)): - item = OrderedDict() - item["idx"] = self._extract_pattern(r"intervals \[(.*)\]", 1) - item["xmin"] = self._extract_pattern(r"xmin = (.*)", 1) - item["xmax"] = self._extract_pattern(r"xmax = (.*)", 1) - item["text"] = self._extract_pattern(r"text = \"(.*)\"", 1) - item_list.append(item) - tier["idx"] = tier_idx - tier["class"] = tier_class - tier["name"] = tier_name - tier["xmin"] = tier_xmin - tier["xmax"] = tier_xmax - tier["size"] = tier_size - tier["items"] = item_list - self.tier_list.append(tier) - - def toJson(self): - _json = OrderedDict() - _json["file_type"] = self.file_type - _json["xmin"] = self.xmin - _json["xmax"] = self.xmax - _json["size"] = self.size - _json["tiers"] = self.tier_list - return json.dumps(_json, ensure_ascii=False, indent=2) - - -def get_mel2ph(tg_fn, ph, mel, hparams): - ph_list = ph.split(" ") - with open(tg_fn, "r") as f: - tg = f.readlines() - tg = remove_empty_lines(tg) - tg = TextGrid(tg) - tg = json.loads(tg.toJson()) - split = np.ones(len(ph_list) + 1, np.float) * -1 - tg_idx = 0 - ph_idx = 0 - tg_align = [x for x in tg['tiers'][-1]['items']] - tg_align_ = [] - for x in tg_align: - x['xmin'] = float(x['xmin']) - x['xmax'] = float(x['xmax']) - if x['text'] in ['sil', 'sp', '', 'SIL', 'PUNC']: - x['text'] = '' - if len(tg_align_) > 0 and tg_align_[-1]['text'] == '': - tg_align_[-1]['xmax'] = x['xmax'] - continue - tg_align_.append(x) - tg_align = tg_align_ - tg_len = len([x for x in tg_align if x['text'] != '']) - ph_len = len([x for x in ph_list if not is_sil_phoneme(x)]) - assert tg_len == ph_len, (tg_len, ph_len, tg_align, ph_list, tg_fn) - while tg_idx < len(tg_align) or ph_idx < len(ph_list): - if tg_idx == len(tg_align) and is_sil_phoneme(ph_list[ph_idx]): - split[ph_idx] = 1e8 - ph_idx += 1 - continue - x = tg_align[tg_idx] - if x['text'] == '' and ph_idx == len(ph_list): - tg_idx += 1 - continue - assert ph_idx < len(ph_list), (tg_len, ph_len, tg_align, ph_list, tg_fn) - ph = ph_list[ph_idx] - if x['text'] == '' and not is_sil_phoneme(ph): - assert False, (ph_list, tg_align) - if x['text'] != '' and is_sil_phoneme(ph): - ph_idx += 1 - else: - assert (x['text'] == '' and is_sil_phoneme(ph)) \ - or x['text'].lower() == ph.lower() \ - or x['text'].lower() == 'sil', (x['text'], ph) - split[ph_idx] = x['xmin'] - if ph_idx > 0 and split[ph_idx - 1] == -1 and is_sil_phoneme(ph_list[ph_idx - 1]): - split[ph_idx - 1] = split[ph_idx] - ph_idx += 1 - tg_idx += 1 - assert tg_idx == len(tg_align), (tg_idx, [x['text'] for x in tg_align]) - assert ph_idx >= len(ph_list) - 1, (ph_idx, ph_list, len(ph_list), [x['text'] for x in tg_align], tg_fn) - mel2ph = np.zeros([mel.shape[0]], np.int) - split[0] = 0 - split[-1] = 1e8 - for i in range(len(split) - 1): - assert split[i] != -1 and split[i] <= split[i + 1], (split[:-1],) - split = [int(s * hparams['audio_sample_rate'] / hparams['hop_size'] + 0.5) for s in split] - for ph_idx in range(len(ph_list)): - mel2ph[split[ph_idx]:split[ph_idx + 1]] = ph_idx + 1 - mel2ph_torch = torch.from_numpy(mel2ph) - T_t = len(ph_list) - dur = mel2ph_torch.new_zeros([T_t + 1]).scatter_add(0, mel2ph_torch, torch.ones_like(mel2ph_torch)) - dur = dur[1:].numpy() - return mel2ph, dur - - -def build_phone_encoder(data_dir): - phone_list_file = os.path.join(data_dir, 'phone_set.json') - phone_list = json.load(open(phone_list_file)) - return TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',') - - -def build_word_encoder(data_dir): - word_list_file = os.path.join(data_dir, 'word_set.json') - word_list = json.load(open(word_list_file)) - return TokenTextEncoder(None, vocab_list=word_list, replace_oov=',') - -def is_sil_phoneme(p): - return not p[0].isalpha() - - -def build_token_encoder(token_list_file): - token_list = json.load(open(token_list_file)) - return TokenTextEncoder(None, vocab_list=token_list, replace_oov='') diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/emotion/params_model.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/emotion/params_model.py deleted file mode 100644 index 48f8e564e772649e3207c7a90bff1bee9e6b3a47..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/emotion/params_model.py +++ /dev/null @@ -1,11 +0,0 @@ - -## Model parameters -model_hidden_size = 256 -model_embedding_size = 256 -model_num_layers = 3 - - -## Training parameters -learning_rate_init = 1e-4 -speakers_per_batch = 6 -utterances_per_speaker = 20 diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/custom_dataset/.ipynb_checkpoints/yolov6_s_fast-checkpoint.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/custom_dataset/.ipynb_checkpoints/yolov6_s_fast-checkpoint.py deleted file mode 100644 index 5e04123bb59ed5b29bbea891f3456a81a5ed4a9f..0000000000000000000000000000000000000000 --- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/custom_dataset/.ipynb_checkpoints/yolov6_s_fast-checkpoint.py +++ /dev/null @@ -1,124 +0,0 @@ -_base_ = '../yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco.py' - -max_epochs = 100 # 训练的最大 epoch -data_root = './data-df2/' # 数据集目录的绝对路径 - -# 结果保存的路径，可以省略，省略保存的文件名位于 work_dirs 下 config 同名的文件夹中 -# 如果某个 config 只是修改了部分参数，修改这个变量就可以将新的训练文件保存到其他地方 -work_dir = './work_dirs/yolov6_s_df2' - -# 根据自己的 GPU 情况，修改 batch size，YOLOv5-s 默认为 8卡 x 16bs -train_batch_size_per_gpu = 32 -train_num_workers = 4 # 推荐使用 train_num_workers = nGPU x 4 - -save_epoch_intervals = 2 # 每 interval 轮迭代进行一次保存一次权重 - -# 根据自己的 GPU 情况，修改 base_lr，修改的比例是 base_lr_default * (your_bs / default_bs) -base_lr = _base_.base_lr / 4 - -class_name = ('short_sleeved_shirt', - 'long_sleeved_shirt', - 'short_sleeved_outwear', - 'long_sleeved_outwear', - 'vest', - 'sling', - 'shorts', - 'trousers', - 'skirt', - 'short_sleeved_dress', - 'long_sleeved_dress', - 'vest_dress', - 'sling_dress') # 根据 class_with_id.txt 类别信息，设置 class_name - -num_classes = len(class_name) -metainfo = dict( - classes=class_name, - palette=[(255, 0, 0), - (255, 128, 0), - (255, 255, 0), - (128, 255, 0), - (0, 255, 0), - (0, 255, 128), - (0, 255, 255), - (0, 128, 255), - (0, 0, 255), - (127, 0, 255), - (255, 0, 255), - (255, 0, 127), - (128, 128, 128)] # 画图时候的颜色，随便设置即可 -) - -train_cfg = dict( - max_epochs=max_epochs, - val_begin=20, # 第几个 epoch 后验证，这里设置 20 是因为前 20 个 epoch 精度不高，测试意义不大，故跳过 - val_interval=save_epoch_intervals, # 每 val_interval 轮迭代进行一次测试评估 - dynamic_intervals=[(max_epochs-_base_.num_last_epochs, 1)] -) - -model = dict( - bbox_head=dict( - head_module=dict(num_classes=num_classes)), - train_cfg=dict( - initial_assigner=dict(num_classes=num_classes), - assigner=dict(num_classes=num_classes) - ) -) - -train_dataloader = dict( - batch_size=train_batch_size_per_gpu, - num_workers=train_num_workers, - dataset=dict( - _delete_=True, - type='RepeatDataset', - # 数据量太少的话，可以使用 RepeatDataset ，在每个 epoch 内重复当前数据集 n 次，这里设置 5 是重复 5 次 - times=2, - dataset=dict( - type=_base_.dataset_type, - data_root=data_root, - metainfo=metainfo, - ann_file='annotations/trainval.json', - data_prefix=dict(img='smaller-dataset/'), - filter_cfg=dict(filter_empty_gt=False, min_size=32), - pipeline=_base_.train_pipeline))) - -val_dataloader = dict( - dataset=dict( - metainfo=metainfo, - data_root=data_root, - ann_file='annotations/trainval.json', - data_prefix=dict(img='smaller-dataset/'))) - -test_dataloader = val_dataloader - -val_evaluator = dict(ann_file=data_root + 'annotations/trainval.json') -test_evaluator = val_evaluator - -optim_wrapper = dict(optimizer=dict(lr=base_lr)) - -default_hooks = dict( - # 设置间隔多少个 epoch 保存模型，以及保存模型最多几个，`save_best` 是另外保存最佳模型（推荐） - checkpoint=dict( - type='CheckpointHook', - interval=save_epoch_intervals, - max_keep_ckpts=5, - save_best='auto'), - param_scheduler=dict(max_epochs=max_epochs), - # logger 输出的间隔 - logger=dict(type='LoggerHook', interval=10)) - -custom_hooks = [ - dict( - type="EMAHook", - ema_type="ExpMomentumEMA", - momentum=0.0001, - update_buffers=True, - strict_load=False, - priority=49), - dict( - type="mmdet.PipelineSwitchHook", - switch_epoch=max_epochs-max_epochs-_base_.num_last_epochs, - switch_pipeline=_base_.train_pipeline_stage2 - ) -] - -visualizer = dict(vis_backends=[dict(type='LocalVisBackend'), dict(type='WandbVisBackend')]) \ No newline at end of file diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Better.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Better.py deleted file mode 100644 index 07d6a04f1e092073365ce016debb2a170d95e891..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Better.py +++ /dev/null @@ -1,57 +0,0 @@ -import os -import json -import requests -from typing import Dict, get_type_hints - -url = 'https://openai-proxy-api.vercel.app/v1/' -model = [ - 'gpt-3.5-turbo', - 'gpt-3.5-turbo-0613', - 'gpt-3.5-turbo-16k', - 'gpt-3.5-turbo-16k-0613', - 'gpt-4', -] - -supports_stream = True -needs_auth = False - - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - headers = { - 'Content-Type': 'application/json', - 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36 Edg/114.0.1823.58', - 'Referer': 'https://chat.ylokh.xyz/', - 'Origin': 'https://chat.ylokh.xyz', - 'Connection': 'keep-alive', - } - - json_data = { - 'messages': messages, - 'temperature': 1.0, - 'model': model, - 'stream': stream, - } - - response = requests.post( - 'https://openai-proxy-api.vercel.app/v1/chat/completions', headers=headers, json=json_data, stream=True - ) - - for token in response.iter_lines(): - decoded = token.decode('utf-8') - if decoded.startswith('data: '): - data_str = decoded.replace('data: ', '') - data = json.loads(data_str) - if 'choices' in data and 'delta' in data['choices'][0]: - delta = data['choices'][0]['delta'] - content = delta.get('content', '') - finish_reason = delta.get('finish_reason', '') - - if finish_reason == 'stop': - break - if content: - yield content - - -params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + '(%s)' % ', '.join( - [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]]) - diff --git a/spaces/Alcedo/yunmedia/resources/chatgpt-plugin/live2d/live2dcubismcore.min.js b/spaces/Alcedo/yunmedia/resources/chatgpt-plugin/live2d/live2dcubismcore.min.js deleted file mode 100644 index 6ff22a9ef5a05e2c81afaaa216d74149a4b3d2f8..0000000000000000000000000000000000000000 --- a/spaces/Alcedo/yunmedia/resources/chatgpt-plugin/live2d/live2dcubismcore.min.js +++ /dev/null @@ -1,9 +0,0 @@ -/** - * Live2D Cubism Core - * (C) 2019 Live2D Inc. 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Float32Array(_em.HEAPF32.buffer,_keyValues[i],length2[i])}),Parts=(Live2DCubismCore.Parameters=Parameters,function(modelPtr){this.count=_csm.getPartCount(modelPtr),length=_csm.getPartCount(modelPtr),this.ids=new Array(length);for(var length,_ids=new Uint32Array(_em.HEAPU32.buffer,_csm.getPartIds(modelPtr),length),i=0;i<_ids.length;i++)this.ids[i]=_em.UTF8ToString(_ids[i]);length=_csm.getPartCount(modelPtr),this.opacities=new Float32Array(_em.HEAPF32.buffer,_csm.getPartOpacities(modelPtr),length),length=_csm.getPartCount(modelPtr),this.parentIndices=new Int32Array(_em.HEAP32.buffer,_csm.getPartParentPartIndices(modelPtr),length)}),Drawables=(Live2DCubismCore.Parts=Parts,function(){function Drawables(modelPtr){this._modelPtr=modelPtr;for(var length,length2=null,_ids=(this.count=_csm.getDrawableCount(modelPtr),length=_csm.getDrawableCount(modelPtr),this.ids=new Array(length),new Uint32Array(_em.HEAPU32.buffer,_csm.getDrawableIds(modelPtr),length)),i=0;i<_ids.length;i++)this.ids[i]=_em.UTF8ToString(_ids[i]);length=_csm.getDrawableCount(modelPtr),this.constantFlags=new Uint8Array(_em.HEAPU8.buffer,_csm.getDrawableConstantFlags(modelPtr),length),length=_csm.getDrawableCount(modelPtr),this.dynamicFlags=new Uint8Array(_em.HEAPU8.buffer,_csm.getDrawableDynamicFlags(modelPtr),length),length=_csm.getDrawableCount(modelPtr),this.textureIndices=new Int32Array(_em.HEAP32.buffer,_csm.getDrawableTextureIndices(modelPtr),length),length=_csm.getDrawableCount(modelPtr),this.drawOrders=new Int32Array(_em.HEAP32.buffer,_csm.getDrawableDrawOrders(modelPtr),length),length=_csm.getDrawableCount(modelPtr),this.renderOrders=new Int32Array(_em.HEAP32.buffer,_csm.getDrawableRenderOrders(modelPtr),length),length=_csm.getDrawableCount(modelPtr),this.opacities=new Float32Array(_em.HEAPF32.buffer,_csm.getDrawableOpacities(modelPtr),length),length=_csm.getDrawableCount(modelPtr),this.maskCounts=new Int32Array(_em.HEAP32.buffer,_csm.getDrawableMaskCounts(modelPtr),length),length=_csm.getDrawableCount(modelPtr),this.vertexCounts=new Int32Array(_em.HEAP32.buffer,_csm.getDrawableVertexCounts(modelPtr),length),length=_csm.getDrawableCount(modelPtr),this.indexCounts=new Int32Array(_em.HEAP32.buffer,_csm.getDrawableIndexCounts(modelPtr),length),length=_csm.getDrawableCount(modelPtr),this.multiplyColors=new Float32Array(_em.HEAPF32.buffer,_csm.getDrawableMultiplyColors(modelPtr),4*length),length=_csm.getDrawableCount(modelPtr),this.screenColors=new Float32Array(_em.HEAPF32.buffer,_csm.getDrawableScreenColors(modelPtr),4*length),length=_csm.getDrawableCount(modelPtr),this.parentPartIndices=new Int32Array(_em.HEAP32.buffer,_csm.getDrawableParentPartIndices(modelPtr),length),length=_csm.getDrawableCount(modelPtr),length2=new Int32Array(_em.HEAP32.buffer,_csm.getDrawableMaskCounts(modelPtr),length),this.masks=new Array(length);for(var _masks=new Uint32Array(_em.HEAPU32.buffer,_csm.getDrawableMasks(modelPtr),length),i=0;i<_masks.length;i++)this.masks[i]=new Int32Array(_em.HEAP32.buffer,_masks[i],length2[i]);length=_csm.getDrawableCount(modelPtr),length2=new Int32Array(_em.HEAP32.buffer,_csm.getDrawableVertexCounts(modelPtr),length),this.vertexPositions=new Array(length);for(var _vertexPositions=new Uint32Array(_em.HEAPU32.buffer,_csm.getDrawableVertexPositions(modelPtr),length),i=0;i<_vertexPositions.length;i++)this.vertexPositions[i]=new Float32Array(_em.HEAPF32.buffer,_vertexPositions[i],2*length2[i]);length=_csm.getDrawableCount(modelPtr),length2=new Int32Array(_em.HEAP32.buffer,_csm.getDrawableVertexCounts(modelPtr),length),this.vertexUvs=new Array(length);for(var _vertexUvs=new Uint32Array(_em.HEAPU32.buffer,_csm.getDrawableVertexUvs(modelPtr),length),i=0;i<_vertexUvs.length;i++)this.vertexUvs[i]=new Float32Array(_em.HEAPF32.buffer,_vertexUvs[i],2*length2[i]);length=_csm.getDrawableCount(modelPtr),length2=new Int32Array(_em.HEAP32.buffer,_csm.getDrawableIndexCounts(modelPtr),length),this.indices=new Array(length);for(var _indices=new Uint32Array(_em.HEAPU32.buffer,_csm.getDrawableIndices(modelPtr),length),i=0;i<_indices.length;i++)this.indices[i]=new Uint16Array(_em.HEAPU16.buffer,_indices[i],length2[i])}return Drawables.prototype.resetDynamicFlags=function(){_csm.resetDrawableDynamicFlags(this._modelPtr)},Drawables}()),Version=(Live2DCubismCore.Drawables=Drawables,function(){function Utils(){}return Utils.hasBlendAdditiveBit=function(bitfield){return 1==(1&bitfield)},Utils.hasBlendMultiplicativeBit=function(bitfield){return 2==(2&bitfield)},Utils.hasIsDoubleSidedBit=function(bitfield){return 4==(4&bitfield)},Utils.hasIsInvertedMaskBit=function(bitfield){return 8==(8&bitfield)},Utils.hasIsVisibleBit=function(bitfield){return 1==(1&bitfield)},Utils.hasVisibilityDidChangeBit=function(bitfield){return 2==(2&bitfield)},Utils.hasOpacityDidChangeBit=function(bitfield){return 4==(4&bitfield)},Utils.hasDrawOrderDidChangeBit=function(bitfield){return 8==(8&bitfield)},Utils.hasRenderOrderDidChangeBit=function(bitfield){return 16==(16&bitfield)},Utils.hasVertexPositionsDidChangeBit=function(bitfield){return 32==(32&bitfield)},Utils.hasBlendColorDidChangeBit=function(bitfield){return 64==(64&bitfield)},Utils}()),Version=(Live2DCubismCore.Utils=Version,function(){function Memory(){}return Memory.initializeAmountOfMemory=function(size){16777216>10,56320|1023&g)))):f+=String.fromCharCode(g)}return f}function da(a,c){return a?ca(M,a,c):""}function ea(a){return 0>>16)*f+d*(c>>>16)<<16)|0}),Math.clz32||(Math.clz32=function(a){var c=32,d=a>>16;return d&&(c-=16,a=d),(d=a>>8)&&(c-=8,a=d),(d=a>>4)&&(c-=4,a=d),(d=a>>2)&&(c-=2,a=d),a>>1?c-2:c-a}),Math.trunc||(Math.trunc=function(a){return 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D(a){for(var c=[],d=0;d>4,g=(15&g)<<4|h>>2,k=(3&h)<<6|p}while(c+=String.fromCharCode(f),64!==h&&(c+=String.fromCharCode(g)),64!==p&&(c+=String.fromCharCode(k)),d>>0<1280)return ia(0,993,f+576|0),S=Na,(Ma=0)|Ma;if(0|yc(b))return ia(0,1057,f+584|0),S=Na,(Ma=0)|Ma;if(g=255&(f=0|a[(C=b+4|0)>>0]),!(f<<24>>24))return c[h>>2]=g,ia(0,1110,h),S=Na,(Ma=0)|Ma;if(4<(255&f))return c[i>>2]=4,c[i+4>>2]=g,ia(0,1177,i),S=Na,(Ma=0)|Ma;(y=0!=(0|a[(x=b+5|0)>>0]))&&(sb(C,1),tb(b+64|0,4,160)),$c(0|Ka,0,576),pa(b,Ka),F=0|a[C>>0],w=b+d|0,f=128+(z=0|c[Ka>>2])|0;a:do{if(z>>>0>>0|w>>>0>>0||f>>>0>>0|w>>>0>>0||(o=(m=0|c[Ka+4>>2])+64|0,m>>>0>>0|w>>>0>>0)||m>>>0>>0|o>>>0>>0|w>>>0>>0||!(-1<(0|($=0|c[z>>2])))||(p=(n=0|c[Ka+8>>2])+(u=$<<2)|0,n>>>0>>0|w>>>0>>0)||n>>>0>>0|p>>>0>>0|w>>>0

>>0||(q=(aa=0|c[(ba=Ka+12|0)>>2])+($<<6)|0,aa>>>0>>0|w>>>0>>0)||aa>>>0

>>0|q>>>0>>0|w>>>0>>0||(r=(j=0|c[(ua=Ka+16|0)>>2])+u|0,j>>>0>>0|w>>>0>>0)||j>>>0>>0|r>>>0>>0|w>>>0>>0||(s=(k=0|c[(Ca=Ka+20|0)>>2])+u|0,k>>>0>>0|w>>>0>>0)||k>>>0>>0|s>>>0>>0|w>>>0>>0||(t=(l=0|c[(Ea=Ka+24|0)>>2])+u|0,l>>>0>>0|w>>>0>>0))Ma=319;else{if(l>>>0>>0|t>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(Y=Ka+28|0)>>2])+u|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(o=Ka+32|0)>>2])+u|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(i=(f=0|c[(Z=Ka+36|0)>>2])+u|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|i>>>0>>0|w>>>0>>0){Ma=319;break}if((0|(g=0|c[z+4>>2]))<=-1){Ma=319;break}if(h=(f=0|c[Ka+40>>2])+(d=g<<2)|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(t=Ka+44|0)>>2])+(g<<6)|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(n=Ka+48|0)>>2])+d|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(A=Ka+52|0)>>2])+d|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(u=Ka+56|0)>>2])+d|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(D=Ka+60|0)>>2])+d|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(B=Ka+64|0)>>2])+d|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(p=Ka+68|0)>>2])+d|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(E=Ka+72|0)>>2])+d|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if((0|(f=0|c[z+8>>2]))<=-1){Ma=319;break}if(i=(g=0|c[Ka+76>>2])+(m=f<<2)|0,g>>>0>>0|w>>>0>>0){Ma=319;break}if(g>>>0>>0|i>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(ga=Ka+80|0)>>2])+m|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(Ja=Ka+84|0)>>2])+m|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(Ga=Ka+92|0)>>2])+m|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(sa=Ka+96|0)>>2])+m|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(i=(f=0|c[(ea=Ka+100|0)>>2])+m|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|i>>>0>>0|w>>>0>>0){Ma=319;break}if((0|(f=0|c[z+12>>2]))<=-1){Ma=319;break}if(h=(g=0|c[Ka+108>>2])+(l=f<<2)|0,g>>>0>>0|w>>>0>>0){Ma=319;break}if(g>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(ya=Ka+112|0)>>2])+l|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(Ia=Ka+116|0)>>2])+l|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[Ka+124>>2])+l|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if((0|(i=0|c[z+16>>2]))<=-1){Ma=319;break}if(h=(f=0|c[Ka+128>>2])+(k=i<<2)|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[Ka+132>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[Ka+136>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[Ka+140>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(ca=Ka+144|0)>>2])+(i<<6)|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(fa=Ka+148|0)>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(ha=Ka+152|0)>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(Ha=Ka+156|0)>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(xa=Ka+164|0)>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(wa=Ka+168|0)>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(Aa=Ka+172|0)>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(za=Ka+176|0)>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(Ba=Ka+180|0)>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[Ka+184>>2])+i|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(Fa=Ka+188|0)>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(la=Ka+192|0)>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(qa=Ka+196|0)>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(g=(f=0|c[(ka=Ka+200|0)>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break}if(h=(f=0|c[(va=Ka+204|0)>>2])+k|0,f>>>0>>0|w>>>0>>0){Ma=319;break}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break}if(i=(f=0|c[(ta=Ka+208|0)>>2])+k|0,f>>>0>>0|w>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a}if(g=(f=0|c[Ka+384>>2])+d|0,f>>>0>>0|w>>>0>>0){Ma=319;break a}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break a}if(i=(f=0|c[Ka+388>>2])+d|0,f>>>0>>0|w>>>0>>0){Ma=319;break a}if(f>>>0>>0|i>>>0>>0|w>>>0>>0){Ma=319;break a}if((0|(f=0|c[z+112>>2]))<=-1){Ma=319;break a}if(h=(g=0|c[Ka+392>>2])+(d=f<<2)|0,g>>>0>>0|w>>>0>>0){Ma=319;break a}if(g>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break a}if(g=(f=0|c[Ka+396>>2])+d|0,f>>>0>>0|w>>>0>>0){Ma=319;break a}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break a}if(h=(f=0|c[Ka+400>>2])+d|0,f>>>0>>0|w>>>0>>0){Ma=319;break a}if(f>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break a}if((0|(f=0|c[z+116>>2]))<=-1){Ma=319;break a}if(i=(g=0|c[Ka+404>>2])+(f<<2)|0,g>>>0>>0|w>>>0>>0){Ma=319;break a}if(g>>>0>>0|i>>>0>>0|w>>>0>>0){Ma=319;break a}if((0|(f=0|c[z+120>>2]))<=-1){Ma=319;break a}if(h=(g=0|c[Ka+408>>2])+(d=f<<2)|0,g>>>0>>0|w>>>0>>0){Ma=319;break a}if(g>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break a}if(g=(f=0|c[Ka+412>>2])+d|0,f>>>0>>0|w>>>0>>0){Ma=319;break a}if(f>>>0>>0|g>>>0>>0|w>>>0>>0){Ma=319;break a}if(i=(f=0|c[Ka+416>>2])+d|0,f>>>0>>0|w>>>0>>0){Ma=319;break a}if(f>>>0>>0|i>>>0>>0|w>>>0>>0){Ma=319;break a}if((0|(f=0|c[z+124>>2]))<=-1){Ma=319;break a}if(h=(g=0|c[Ka+420>>2])+(f<<=2)|0,g>>>0>>0|w>>>0>>0){Ma=319;break a}if(g>>>0>>0|h>>>0>>0|w>>>0>>0){Ma=319;break a}if(f=(g=0|c[Ka+424>>2])+f|0,g>>>0>>0|w>>>0>>0){Ma=319;break a}if(g>>>0>>0|f>>>0>>0|w>>>0>>0){Ma=319;break a}}}while(0);C=y?(ra(b),aa=(a[x>>0]=0)|c[Ka>>2],F=0|a[C>>0],q=0|c[aa>>2],b=aa,g=0|c[ba>>2],aa):(q=$,g=aa,b=z);b:do{if(0<(0|q)){f=0;do{if(63<(0|Ac(g+(f<<6)|0))>>>0)break b}while((0|(f=f+1|0))<(0|q));f=0|c[ua>>2],g=0|c[(h=b+48|0)>>2],i=0;do{if(ua=0|c[f+(i<<2)>>2],i=i+1|0,(0|ua)<0|(0|g)<=(0|ua))break b}while((0|i)<(0|q));k=0|c[Ca>>2],f=0|c[Ea>>2],g=0|c[b+24>>2],j=0;do{if(0|(i=0|c[f+(j<<2)>>2])){if((0|i)<0|(0|g)<(0|i))break b;if(!(-1<(0|(d=0|c[k+(j<<2)>>2]))&(0|d)<(0|g)))break b;if((Ea=d+i|0)>>>31|(0|g)<(0|Ea)|0)break b}}while((0|(j=j+1|0))<(0|q));f=0|c[Y>>2],g=0;do{if(1<(0|c[f+(g<<2)>>2])>>>0)break b}while((0|(g=g+1|0))<(0|q));f=0|c[o>>2],g=0;do{if(1<(0|c[f+(g<<2)>>2])>>>0)break b}while((0|(g=g+1|0))<(0|q));for(f=0|c[Z>>2],g=0;;){if(Ea=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ea)<-1|(0|q)<=(0|Ea))break b;if((0|q)<=(0|g)){Ma=345;break}}}else h=b+48|0,Ma=345}while(0);c:do{if(345==(0|Ma)){o=0|c[b+4>>2],f=0|c[t>>2];d:do{if(0<(0|o)){g=0;do{if(63<(0|Ac(f+(g<<6)|0))>>>0)break c}while((0|(g=g+1|0))<(0|o));g=0|c[n>>2],l=0|c[h>>2],f=0;do{if(Ea=0|c[g+(f<<2)>>2],f=f+1|0,(0|Ea)<0|(0|l)<=(0|Ea))break c}while((0|f)<(0|o));f=0|c[A>>2],g=0;do{if(1<(0|c[f+(g<<2)>>2])>>>0)break c}while((0|(g=g+1|0))<(0|o));f=0|c[u>>2],g=0;do{if(1<(0|c[f+(g<<2)>>2])>>>0)break c}while((0|(g=g+1|0))<(0|o));f=0|c[D>>2],g=0;do{if(Ea=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ea)<-1|(0|q)<=(0|Ea))break c}while((0|g)<(0|o));f=0|c[B>>2],g=0;do{if(Ea=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ea)<-1|(0|o)<=(0|Ea))break c}while((0|g)<(0|o));k=0|c[p>>2],f=0;do{if(1<(0|c[k+(f<<2)>>2])>>>0)break c}while((0|(f=f+1|0))<(0|o));for(g=0|c[E>>2],f=b+8|0,i=b+12|0,j=0;;){switch(h=0|c[g+(j<<2)>>2],0|c[k+(j<<2)>>2]){case 0:d=f;break;case 1:d=i;break;default:break c}if((0|h)<=-1)break c;if(j=j+1|0,(0|h)>=(0|c[d>>2]))break c;if((0|o)<=(0|j)){w=l;break d}}}else w=0|c[h>>2],f=b+8|0}while(0);if(E=0|c[f>>2],f=0|c[Ka+76>>2],B=0<(0|E)){g=0;do{if(Ea=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ea)<0|(0|w)<=(0|Ea))break c}while((0|g)<(0|E));f=0|c[ga>>2],g=0|c[Ja>>2],h=0|c[(k=b+28|0)>>2],j=0;do{if(0|(i=0|c[g+(j<<2)>>2])){if((0|i)<0|(0|h)<(0|i))break c;if(!(-1<(0|(d=0|c[f+(j<<2)>>2]))&(0|d)<(0|h)))break c;if((Ea=d+i|0)>>>31|(0|h)<(0|Ea)|0)break c}}while((0|(j=j+1|0))<(0|E));for(i=0|c[sa>>2],f=0|c[ea>>2],g=0|c[Ga>>2],h=0;;){if(ua=0|c[i+(h<<2)>>2],Ca=0|c[f+(h<<2)>>2],Ea=0|c[g+(h<<2)>>2],h=h+1|0,!((0|v(Ca+1|0,ua+1|0))==(0|Ea)&0<(0|ua)&0<(0|Ca)&0<(0|Ea)))break c;if((0|E)<=(0|h)){D=k;break}}}else D=b+28|0;if(z=0|c[b+12>>2],f=0|c[Ka+108>>2],y=0<(0|z)){g=0;do{if(Ea=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ea)<0|(0|w)<=(0|Ea))break c}while((0|g)<(0|z));for(f=0|c[ya>>2],g=0|c[Ia>>2],h=0|c[(j=b+32|0)>>2],k=0;;){if(0|(i=0|c[g+(k<<2)>>2])){if((0|i)<0|(0|h)<(0|i))break c;if(!(-1<(0|(d=0|c[f+(k<<2)>>2]))&(0|d)<(0|h)))break c;if((Ea=d+i|0)>>>31|(0|h)<(0|Ea)|0)break c}if((0|z)<=(0|(k=k+1|0))){m=j;break}}}else m=b+32|0;if(p=0|c[(u=b+16|0)>>2],f=0|c[ca>>2],n=0<(0|p)){g=0;do{if(63<(0|Ac(f+(g<<6)|0))>>>0)break c}while((0|(g=g+1|0))<(0|p));f=0|c[fa>>2],g=0;do{if(Ea=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ea)<0|(0|w)<=(0|Ea))break c}while((0|g)<(0|p));f=0|c[ha>>2],g=0|c[Ha>>2],h=0|c[(l=b+36|0)>>2],j=0;do{if(0|(i=0|c[g+(j<<2)>>2])){if((0|i)<0|(0|h)<(0|i))break c;if(!(-1<(0|(d=0|c[f+(j<<2)>>2]))&(0|d)<(0|h)))break c;if((Ea=d+i|0)>>>31|(0|h)<(0|Ea)|0)break c}}while((0|(j=j+1|0))<(0|p));f=0|c[xa>>2],g=0;do{if(1<(0|c[f+(g<<2)>>2])>>>0)break c}while((0|(g=g+1|0))<(0|p));f=0|c[wa>>2],g=0;do{if(1<(0|c[f+(g<<2)>>2])>>>0)break c}while((0|(g=g+1|0))<(0|p));f=0|c[Aa>>2],g=0;do{if(Ea=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ea)<-1|(0|q)<=(0|Ea))break c}while((0|g)<(0|p));f=0|c[za>>2],g=0;do{if(Ea=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ea)<-1|(0|o)<=(0|Ea))break c}while((0|g)<(0|p));f=0|c[Ba>>2],g=0;do{if((0|c[f+(g<<2)>>2])<0)break c}while((0|(g=g+1|0))<(0|p));i=0|c[Fa>>2],f=0;do{if((0|c[i+(f<<2)>>2])<0)break c}while((0|(f=f+1|0))<(0|p));f=0|c[la>>2],g=0|c[b+60>>2],h=0;do{if(Ea=(0|c[f+(h<<2)>>2])+(c[i+(h<<2)>>2]<<1)|0,h=h+1|0,Ea>>>31|(0|g)<(0|Ea)|0)break c}while((0|h)<(0|p));j=0|c[qa>>2],f=0|c[ka>>2],g=0|c[b+64>>2],d=0;do{if(0|(h=0|c[f+(d<<2)>>2])){if((0|h)<0|(0|g)<(0|h))break c;if(!(-1<(0|(i=0|c[j+(d<<2)>>2]))&(0|i)<(0|g)))break c;if((Ea=i+h|0)>>>31|(0|g)<(0|Ea)|0)break c}}while((0|(d=d+1|0))<(0|p));for(j=0|c[va>>2],f=0|c[ta>>2],g=0|c[(k=b+68|0)>>2],d=0;;){if(0|(h=0|c[f+(d<<2)>>2])){if((0|h)<0|(0|g)<(0|h))break c;if(!(-1<(0|(i=0|c[j+(d<<2)>>2]))&(0|i)<(0|g)))break c;if((Ea=i+h|0)>>>31|(0|g)<(0|Ea)|0)break c}if((0|p)<=(0|(d=d+1|0))){x=l;break}}}else x=b+36|0,k=b+68|0;if(A=0|c[b+20>>2],f=0|c[Da>>2],t=0<(0|A)){g=0;do{if(63<(0|Ac(f+(g<<6)|0))>>>0)break c}while((0|(g=g+1|0))<(0|A));f=0|c[na>>2],g=0;do{if(1<(0|c[f+(g<<2)>>2])>>>0)break c}while((0|(g=g+1|0))<(0|A));f=0|c[ma>>2],g=0;do{if((0|c[f+(g<<2)>>2])<0)break c}while((0|(g=g+1|0))<(0|A));for(j=0|c[oa>>2],f=0|c[ja>>2],g=0|c[b+52>>2],d=0;;){if(0|(h=0|c[f+(d<<2)>>2])){if((0|h)<0|(0|g)<(0|h))break c;if(!(-1<(0|(i=0|c[j+(d<<2)>>2]))&(0|i)<(0|g)))break c;if((Ea=i+h|0)>>>31|(0|g)<(0|Ea)|0)break c}if((0|A)<=(0|(d=d+1|0))){l=g;break}}}else l=0|c[b+52>>2];if(h=0|c[Ga>>2],f=0|c[da>>2],i=0|c[b+40>>2],B){g=0;do{if(Ga=(0|c[f+(g<<2)>>2])+(c[h+(g<<2)>>2]<<1)|0,g=g+1|0,Ga>>>31|(0|i)<(0|Ga)|0)break c}while((0|g)<(0|E))}if(h=0|c[m>>2],f=0|c[r>>2],0<(0|h)){g=0;do{if(1<(0|c[f+(g<<2)>>2])>>>0)break c}while((0|(g=g+1|0))<(0|h));f=0|c[X>>2],g=0;do{if(1<(0|c[f+(g<<2)>>2])>>>0)break c}while((0|(g=g+1|0))<(0|h))}if(r=0|c[Fa>>2],g=0|c[W>>2],n){f=0;do{if(Ga=(0|c[g+(f<<2)>>2])+(c[r+(f<<2)>>2]<<1)|0,f=f+1|0,Ga>>>31|(0|i)<(0|Ga)|0)break c}while((0|f)<(0|p))}if(j=0|c[b+44>>2],f=0|c[V>>2],0<(0|j)){g=0;do{if(Ga=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ga)<0|(0|l)<=(0|Ga))break c}while((0|g)<(0|j))}if(d=0|c[Ka+340>>2],f=0|c[U>>2],0<(0|w)){i=0;do{if(0|(g=0|c[f+(i<<2)>>2])){if((0|g)<0|(0|j)<(0|g))break c;if(!(-1<(0|(h=0|c[d+(i<<2)>>2]))&(0|h)<(0|j)))break c;if((Ga=h+g|0)>>>31|(0|j)<(0|Ga)|0)break c}}while((0|(i=i+1|0))<(0|w))}if(d=0|c[Ka+328>>2],f=0|c[s>>2],s=0|c[b+56>>2],0<(0|l)){i=0;do{if(0|(g=0|c[f+(i<<2)>>2])){if((0|g)<0|(0|s)<(0|g))break c;if(!(-1<(0|(h=0|c[d+(i<<2)>>2]))&(0|h)<(0|s)))break c;if((Ga=h+g|0)>>>31|(0|s)<(0|Ga)|0)break c}}while((0|(i=i+1|0))<(0|l))}if(h=0|c[k>>2],f=0|c[T>>2],0<(0|h)){g=0;do{if(Ga=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ga)<-1|(0|p)<=(0|Ga))break c}while((0|g)<(0|h))}if(k=0|c[b+72>>2],d=0|c[Ka+444>>2],f=0|c[R>>2],j=0|c[b+76>>2],0<(0|k)){i=0;do{if(0|(g=0|c[f+(i<<2)>>2])){if((0|g)<0|(0|j)<(0|g))break c;if(!(-1<(0|(h=0|c[d+(i<<2)>>2]))&(0|h)<(0|j)))break c;if((Ga=h+g|0)>>>31|(0|j)<(0|Ga)|0)break c}}while((0|(i=i+1|0))<(0|k))}if(d=0|c[Ka+464>>2],0<(0|j)){f=0;do{if(1<(0|c[d+(f<<2)>>2])>>>0)break c}while((0|(f=f+1|0))<(0|j));f=0|c[K>>2],i=0;do{switch(g=0|c[f+(i<<2)>>2],0|c[d+(i<<2)>>2]){case 0:h=u;break;case 1:h=C;break;default:break c}if((0|g)<=-1)break c;if(i=i+1|0,(0|g)>=(0|c[h>>2]))break c}while((0|i)<(0|j));f=0|c[Q>>2],g=0;do{if(Ga=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ga)<-1|(0|k)<=(0|Ga))break c}while((0|g)<(0|j))}if(q=0|c[b+80>>2],f=0|c[J>>2],0<(0|q)){g=0;do{if(63<(0|Ac(f+(g<<6)|0))>>>0)break c}while((0|(g=g+1|0))<(0|q));f=0|c[H>>2],g=0;do{if(Ga=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ga)<0|(0|w)<=(0|Ga))break c}while((0|g)<(0|q));j=0|c[I>>2],f=0|c[G>>2],g=0|c[b+88>>2],d=0;do{if(0|(h=0|c[f+(d<<2)>>2])){if((0|h)<0|(0|g)<(0|h))break c;if(!(-1<(0|(i=0|c[j+(d<<2)>>2]))&(0|i)<(0|g)))break c;if((Ga=i+h|0)>>>31|(0|g)<(0|Ga)|0)break c}}while((0|(d=d+1|0))<(0|q));p=0|c[L>>2],o=0|c[u>>2],f=0;do{if(Ga=0|c[p+(f<<2)>>2],f=f+1|0,(0|Ga)<0|(0|o)<=(0|Ga))break c}while((0|f)<(0|q));n=0|c[O>>2],f=0;do{if(Ga=0|c[n+(f<<2)>>2],f=f+1|0,(0|Ga)<0|(0|o)<=(0|Ga))break c}while((0|f)<(0|q));m=0|c[P>>2],l=0|c[M>>2],f=0|c[b+84>>2],i=0;do{if(0|(g=0|c[l+(i<<2)>>2])){if((0|g)<0|(0|f)<(0|g))break c;if(!(-1<(0|(h=0|c[m+(i<<2)>>2]))&(0|h)<(0|f)))break c;if((Ga=h+g|0)>>>31|(0|f)<(0|Ga)|0)break c}}while((0|(i=i+1|0))<(0|q));f=0|c[N>>2],j=0;do{if(g=0|c[r+(c[p+(j<<2)>>2]<<2)>>2],h=0|c[r+(c[n+(j<<2)>>2]<<2)>>2],i=0|c[l+(j<<2)>>2],d=f+(c[m+(j<<2)>>2]<<1)|0,0<(0|i)){k=0;do{if(!((0|g)>(0|e[d+(k<<1)>>1])&&(0|h)>(0|e[d+((1|k)<<1)>>1])))break c}while((0|(k=k+2|0))<(0|i))}}while((0|(j=j+1|0))<(0|q))}else o=0|c[u>>2];if((255&F)<=1){f=1;break a}if(f=0|c[Ka+104>>2],B){g=0;do{if(1<(0|c[f+(g<<2)>>2])>>>0)break c}while((0|(g=g+1|0))<(0|E))}if((255&F)<=3){f=1;break a}if(f=0|c[Ka+264>>2],g=0|c[Ka+268>>2],t){d=0;do{if(0|(h=0|c[g+(d<<2)>>2])){if((0|h)<0|(0|s)<(0|h))break c;if(!(-1<(0|(i=0|c[f+(d<<2)>>2]))&(0|i)<(0|s)))break c;if((Ga=i+h|0)>>>31|(0|s)<(0|Ga)|0)break c}}while((0|(d=d+1|0))<(0|A))}if((0|(j=0|c[b+92>>2]))!=(0|c[b+96>>2]))break;if(d=0|c[Ka+88>>2],i=0|c[Ja>>2],B){h=0;do{if(0|(f=0|c[i+(h<<2)>>2])){if((0|f)<0|(0|j)<(0|f))break c;if(!(-1<(0|(g=0|c[d+(h<<2)>>2]))&(0|g)<(0|j)))break c;if((Ja=g+f|0)>>>31|(0|j)<(0|Ja)|0)break c}}while((0|(h=h+1|0))<(0|E))}if(d=0|c[Ka+120>>2],i=0|c[Ia>>2],y){h=0;do{if(0|(f=0|c[i+(h<<2)>>2])){if((0|f)<0|(0|j)<(0|f))break c;if(!(-1<(0|(g=0|c[d+(h<<2)>>2]))&(0|g)<(0|j)))break c;if((Ja=g+f|0)>>>31|(0|j)<(0|Ja)|0)break c}}while((0|(h=h+1|0))<(0|z))}if(d=0|c[Ka+160>>2],f=0|c[Ha>>2],0<(0|o)){i=0;do{if(0|(g=0|c[f+(i<<2)>>2])){if((0|g)<0|(0|j)<(0|g))break c;if(!(-1<(0|(h=0|c[d+(i<<2)>>2]))&(0|h)<(0|j)))break c;if((Ja=h+g|0)>>>31|(0|j)<(0|Ja)|0)break c}}while((0|(i=i+1|0))<(0|o))}if(g=0|c[Ka+240>>2],t){f=0;do{if(1<(0|c[g+(f<<2)>>2])>>>0)break c}while((0|(f=f+1|0))<(0|A));for(f=0|c[Ka+252>>2],g=0|c[Ka+256>>2],h=0|c[b+100>>2],j=0;;){if(0|(i=0|c[g+(j<<2)>>2])){if((0|i)<0|(0|h)<(0|i))break c;if(!(-1<(0|(d=0|c[f+(j<<2)>>2]))&(0|d)<(0|h)))break c;if((Ja=d+i|0)>>>31|(0|h)<(0|Ja)|0)break c}if((0|A)<=(0|(j=j+1|0))){j=h;break}}}else j=0|c[b+100>>2];if(f=0|c[Ka+348>>2],d=0|c[Ka+352>>2],0<(0|j)){i=0;do{if(0|(g=0|c[d+(i<<2)>>2])){if((0|g)<0|(0|s)<(0|g))break c;if(!(-1<(0|(h=0|c[f+(i<<2)>>2]))&(0|h)<(0|s)))break c;if((Ja=h+g|0)>>>31|(0|s)<(0|Ja)|0)break c}}while((0|(i=i+1|0))<(0|j));f=0|c[Ka+356>>2],h=0;do{if((0|(g=0|c[f+(h<<2)>>2]))<=-1)break c;if((0|g)>=(0|c[d+(h<<2)>>2]))break c}while((0|(h=h+1|0))<(0|j))}if(n=0|c[b+104>>2],f=0|c[Ka+360>>2],0<(0|n)){g=0;do{if(Ja=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ja)<0|(0|j)<=(0|Ja))break c}while((0|g)<(0|n));for(f=0|c[Ka+372>>2],g=0|c[Ka+376>>2],h=0|c[b+116>>2],j=0;;){if(0|(i=0|c[g+(j<<2)>>2])){if((0|i)<0|(0|h)<(0|i))break c;if(!(-1<(0|(d=0|c[f+(j<<2)>>2]))&(0|d)<(0|h)))break c;if((Ja=d+i|0)>>>31|(0|h)<(0|Ja)|0)break c}if((0|n)<=(0|(j=j+1|0))){m=h;break}}}else m=0|c[b+116>>2];if(l=0|c[b+108>>2],f=0|c[Ka+380>>2],0<(0|l)){g=0;do{if(Ja=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ja)<0|(0|E)<=(0|Ja))break c}while((0|g)<(0|l));k=0|c[Ka+384>>2],f=0|c[Ka+388>>2],i=0;do{if(0|(g=0|c[f+(i<<2)>>2])){if((0|g)<0|(0|n)<(0|g))break c;if(!(-1<(0|(h=0|c[k+(i<<2)>>2]))&(0|h)<(0|n)))break c;if((Ja=h+g|0)>>>31|(0|n)<(0|Ja)|0)break c}}while((0|(i=i+1|0))<(0|l));j=0|c[Ka+364>>2],d=0|c[Ka+368>>2],f=0|c[D>>2],i=0;do{if(g=0|c[k+(i<<2)>>2],0|(h=0|c[d+(g<<2)>>2])){if((0|h)<0|(0|f)<(0|h))break c;if(!(-1<(0|(Ia=0|c[j+(g<<2)>>2]))&(0|Ia)<(0|f)&0==((Ja=Ia+h|0)>>>31|(0|f)<(0|Ja)|0)))break c}}while((0|(i=i+1|0))<(0|l))}else d=0|c[Ka+368>>2],j=0|c[Ka+364>>2];if(l=0|c[b+112>>2],f=0|c[Ka+392>>2],0<(0|l)){g=0;do{if(Ja=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ja)<0|(0|o)<=(0|Ja))break c}while((0|g)<(0|l));k=0|c[Ka+396>>2],f=0|c[Ka+400>>2],i=0;do{if(0|(g=0|c[f+(i<<2)>>2])){if((0|g)<0|(0|n)<(0|g))break c;if(!(-1<(0|(h=0|c[k+(i<<2)>>2]))&(0|h)<(0|n)))break c;if((Ja=h+g|0)>>>31|(0|n)<(0|Ja)|0)break c}}while((0|(i=i+1|0))<(0|l));f=0|c[x>>2],i=0;do{if(g=0|c[k+(i<<2)>>2],0|(h=0|c[d+(g<<2)>>2])){if((0|h)<0|(0|f)<(0|h))break c;if(!(-1<(0|(Ia=0|c[j+(g<<2)>>2]))&(0|Ia)<(0|f)&0==((Ja=Ia+h|0)>>>31|(0|f)<(0|Ja)|0)))break c}}while((0|(i=i+1|0))<(0|l))}if(f=0|c[Ka+404>>2],k=0|c[b+120>>2],0<(0|m)){g=0;do{if(Ja=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ja)<0|(0|k)<=(0|Ja))break c}while((0|g)<(0|m))}if(f=0|c[Ka+408>>2],(0|k)<=0){f=1;break a}g=0;do{if(Ja=0|c[f+(g<<2)>>2],g=g+1|0,(0|Ja)<-1|(0|A)<=(0|Ja))break c}while((0|g)<(0|k));for(j=0|c[Ka+412>>2],d=0|c[Ka+416>>2],f=0|c[b+124>>2],i=0;;){if(0|(g=0|c[d+(i<<2)>>2])){if((0|g)<0|(0|f)<(0|g))break c;if(!(-1<(0|(h=0|c[j+(i<<2)>>2]))&(0|h)<(0|f)))break c;if((Ka=h+g|0)>>>31|(0|f)<(0|Ka)|0)break c}if((0|k)<=(0|(i=i+1|0))){f=1;break a}}}}while(0);ia(0,1336,La),f=0}}while(0);return 319==(0|Ma)&&(ia(0,1277,_),sb(C,1),tb(b+64|0,4,160),f=0),S=Na,0|f}function ra(b){var f,e,d=0|a[4+(b|=0)>>0];tb(0|c[(e=b+704|0)>>2],4,32),sb(0|c[(f=b+708|0)>>2],4),sb(4+(0|c[f>>2])|0,4),sb(8+(0|c[f>>2])|0,4),sb(12+(0|c[f>>2])|0,4),sb(16+(0|c[f>>2])|0,4),sb(20+(0|c[f>>2])|0,1),tb(0|c[b+720>>2],4,0|c[c[e>>2]>>2]),tb(0|c[b+724>>2],4,0|c[c[e>>2]>>2]),tb(0|c[b+728>>2],4,0|c[c[e>>2]>>2]),tb(0|c[b+732>>2],4,0|c[c[e>>2]>>2]),tb(0|c[b+736>>2],4,0|c[c[e>>2]>>2]),tb(0|c[b+740>>2],4,0|c[c[e>>2]>>2]),tb(0|c[b+752>>2],4,0|c[4+(0|c[e>>2])>>2]),tb(0|c[b+756>>2],4,0|c[4+(0|c[e>>2])>>2]),tb(0|c[b+760>>2],4,0|c[4+(0|c[e>>2])>>2]),tb(0|c[b+764>>2],4,0|c[4+(0|c[e>>2])>>2]),tb(0|c[b+768>>2],4,0|c[4+(0|c[e>>2])>>2]),tb(0|c[b+772>>2],4,0|c[4+(0|c[e>>2])>>2]),tb(0|c[b+776>>2],4,0|c[4+(0|c[e>>2])>>2]),tb(0|c[b+780>>2],4,0|c[8+(0|c[e>>2])>>2]),tb(0|c[b+784>>2],4,0|c[8+(0|c[e>>2])>>2]),tb(0|c[b+788>>2],4,0|c[8+(0|c[e>>2])>>2]),tb(0|c[b+796>>2],4,0|c[8+(0|c[e>>2])>>2]),tb(0|c[b+800>>2],4,0|c[8+(0|c[e>>2])>>2]),tb(0|c[b+804>>2],4,0|c[8+(0|c[e>>2])>>2]),tb(0|c[b+812>>2],4,0|c[12+(0|c[e>>2])>>2]),tb(0|c[b+816>>2],4,0|c[12+(0|c[e>>2])>>2]),tb(0|c[b+820>>2],4,0|c[12+(0|c[e>>2])>>2]),tb(0|c[b+828>>2],4,0|c[12+(0|c[e>>2])>>2]),tb(0|c[b+852>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+856>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+860>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+868>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+872>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+876>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+880>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+884>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+888>>2],1,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+892>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+896>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+900>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+904>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+908>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+912>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+924>>2],4,0|c[20+(0|c[e>>2])>>2]),tb(0|c[b+928>>2],4,0|c[20+(0|c[e>>2])>>2]),tb(0|c[b+932>>2],4,0|c[20+(0|c[e>>2])>>2]),tb(0|c[b+936>>2],4,0|c[20+(0|c[e>>2])>>2]),tb(0|c[b+940>>2],4,0|c[20+(0|c[e>>2])>>2]),tb(0|c[b+948>>2],4,0|c[20+(0|c[e>>2])>>2]),tb(0|c[b+952>>2],4,0|c[20+(0|c[e>>2])>>2]),tb(0|c[b+976>>2],4,0|c[24+(0|c[e>>2])>>2]),tb(0|c[b+980>>2],4,0|c[28+(0|c[e>>2])>>2]),tb(0|c[b+984>>2],4,0|c[28+(0|c[e>>2])>>2]),tb(0|c[b+988>>2],4,0|c[32+(0|c[e>>2])>>2]),tb(0|c[b+992>>2],4,0|c[32+(0|c[e>>2])>>2]),tb(0|c[b+996>>2],4,0|c[32+(0|c[e>>2])>>2]),tb(0|c[b+1e3>>2],4,0|c[32+(0|c[e>>2])>>2]),tb(0|c[b+1004>>2],4,0|c[32+(0|c[e>>2])>>2]),tb(0|c[b+1008>>2],4,0|c[32+(0|c[e>>2])>>2]),tb(0|c[b+1012>>2],4,0|c[32+(0|c[e>>2])>>2]),tb(0|c[b+1016>>2],4,0|c[36+(0|c[e>>2])>>2]),tb(0|c[b+1020>>2],4,0|c[36+(0|c[e>>2])>>2]),tb(0|c[b+1024>>2],4,0|c[36+(0|c[e>>2])>>2]),tb(0|c[b+1028>>2],4,0|c[40+(0|c[e>>2])>>2]),tb(0|c[b+1040>>2],4,0|c[44+(0|c[e>>2])>>2]),tb(0|c[b+1044>>2],4,0|c[48+(0|c[e>>2])>>2]),tb(0|c[b+1048>>2],4,0|c[48+(0|c[e>>2])>>2]),tb(0|c[b+1032>>2],4,0|c[52+(0|c[e>>2])>>2]),tb(0|c[b+1036>>2],4,0|c[52+(0|c[e>>2])>>2]),tb(0|c[b+1132>>2],4,0|c[56+(0|c[e>>2])>>2]),tb(0|c[b+1136>>2],4,0|c[60+(0|c[e>>2])>>2]),tb(0|c[b+1140>>2],2,0|c[64+(0|c[e>>2])>>2]),tb(0|c[b+1144>>2],4,0|c[68+(0|c[e>>2])>>2]),tb(0|c[b+1148>>2],4,0|c[72+(0|c[e>>2])>>2]),tb(0|c[b+1152>>2],4,0|c[72+(0|c[e>>2])>>2]),tb(0|c[b+1156>>2],4,0|c[72+(0|c[e>>2])>>2]),tb(0|c[b+1160>>2],4,0|c[72+(0|c[e>>2])>>2]),tb(0|c[b+1164>>2],4,0|c[72+(0|c[e>>2])>>2]),tb(0|c[b+1168>>2],4,0|c[76+(0|c[e>>2])>>2]),tb(0|c[b+1172>>2],4,0|c[76+(0|c[e>>2])>>2]),tb(0|c[b+1176>>2],4,0|c[76+(0|c[e>>2])>>2]),tb(0|c[b+1188>>2],4,0|c[80+(0|c[e>>2])>>2]),tb(0|c[b+1192>>2],4,0|c[80+(0|c[e>>2])>>2]),tb(0|c[b+1196>>2],4,0|c[80+(0|c[e>>2])>>2]),tb(0|c[b+1200>>2],4,0|c[80+(0|c[e>>2])>>2]),tb(0|c[b+1204>>2],4,0|c[80+(0|c[e>>2])>>2]),tb(0|c[b+1208>>2],4,0|c[80+(0|c[e>>2])>>2]),tb(0|c[b+1212>>2],4,0|c[80+(0|c[e>>2])>>2]),tb(0|c[b+1216>>2],4,0|c[84+(0|c[e>>2])>>2]),tb(0|c[b+1220>>2],2,0|c[84+(0|c[e>>2])>>2]),tb(0|c[b+1224>>2],4,0|c[88+(0|c[e>>2])>>2]),(255&d)<=1||(tb(0|c[b+808>>2],4,0|c[8+(0|c[e>>2])>>2]),(255&d)<=3||(tb(0|c[b+968>>2],4,0|c[20+(0|c[e>>2])>>2]),tb(0|c[b+972>>2],4,0|c[20+(0|c[e>>2])>>2]),tb(0|c[b+792>>2],4,0|c[8+(0|c[e>>2])>>2]),tb(0|c[b+824>>2],4,0|c[12+(0|c[e>>2])>>2]),tb(0|c[b+864>>2],4,0|c[16+(0|c[e>>2])>>2]),tb(0|c[b+1228>>2],4,0|c[92+(0|c[e>>2])>>2]),tb(0|c[b+1232>>2],4,0|c[92+(0|c[e>>2])>>2]),tb(0|c[b+1236>>2],4,0|c[92+(0|c[e>>2])>>2]),tb(0|c[b+1240>>2],4,0|c[96+(0|c[e>>2])>>2]),tb(0|c[b+1244>>2],4,0|c[96+(0|c[e>>2])>>2]),tb(0|c[b+1248>>2],4,0|c[96+(0|c[e>>2])>>2]),tb(0|c[b+944>>2],4,0|c[20+(0|c[e>>2])>>2]),tb(0|c[b+956>>2],4,0|c[20+(0|c[e>>2])>>2]),tb(0|c[b+960>>2],4,0|c[20+(0|c[e>>2])>>2]),tb(0|c[b+1052>>2],4,0|c[100+(0|c[e>>2])>>2]),tb(0|c[b+1056>>2],4,0|c[100+(0|c[e>>2])>>2]),tb(0|c[b+1060>>2],4,0|c[100+(0|c[e>>2])>>2]),tb(0|c[b+1064>>2],4,0|c[104+(0|c[e>>2])>>2]),tb(0|c[b+1068>>2],4,0|c[104+(0|c[e>>2])>>2]),tb(0|c[b+1072>>2],4,0|c[104+(0|c[e>>2])>>2]),tb(0|c[b+1076>>2],4,0|c[104+(0|c[e>>2])>>2]),tb(0|c[b+1080>>2],4,0|c[104+(0|c[e>>2])>>2]),tb(0|c[b+1084>>2],4,0|c[108+(0|c[e>>2])>>2]),tb(0|c[b+1088>>2],4,0|c[108+(0|c[e>>2])>>2]),tb(0|c[b+1092>>2],4,0|c[108+(0|c[e>>2])>>2]),tb(0|c[b+1096>>2],4,0|c[112+(0|c[e>>2])>>2]),tb(0|c[b+1100>>2],4,0|c[112+(0|c[e>>2])>>2]),tb(0|c[b+1104>>2],4,0|c[112+(0|c[e>>2])>>2]),tb(0|c[b+1108>>2],4,0|c[116+(0|c[e>>2])>>2]),tb(0|c[b+1112>>2],4,0|c[120+(0|c[e>>2])>>2]),tb(0|c[b+1116>>2],4,0|c[120+(0|c[e>>2])>>2]),tb(0|c[b+1120>>2],4,0|c[120+(0|c[e>>2])>>2]),tb(0|c[b+1124>>2],4,0|c[124+(0|c[e>>2])>>2]),tb(0|c[b+1128>>2],4,0|c[124+(0|c[e>>2])>>2])))}function sa(d){d|=0;var o,p,e=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,q=0,r=0,s=0,t=S,e=S=S+63&-64;if(S=S+32|0,h=e+24|0,c[(s=e)>>2]=4,c[s+4>>2]=2,c[s+8>>2]=4,function(a,b){var d,e=S=(a=S)+63&-64;S=S+272|0,d=16+e|0,c[e>>2]=b|=0,cc(d,1393,e),function(a){var b,d=S=(b=S)+63&-64;S=S+16|0,c[d>>2]=a|=0,function(a,b,d){a=S=(b=S)+63&-64,S=S+16|0,c[a>>2]=d|=0,dc(496,2934,a),S=b}(0,0,d),S=b}(d),S=a}(0,s),0|yc(d))return ia(0,1433,e+16|0),S=t,(s=0)|s;if(4<(255&(e=0|a[(g=d+4|0)>>0])))return c[h>>2]=4,c[h+4>>2]=255&e,ia(0,1496,h),S=t,(s=0)|s;if(0|a[(e=d+5|0)>>0]?(sb(g,1),tb(d+64|0,4,160),pa(d,s=d+704|(a[e>>0]=0)),ra(d)):pa(d,s=d+704|0),0|c[1009]||(c[1009]=1),e=0|c[s>>2],r=0|c[e+16>>2],n=(g=0|c[d+912>>2])+(r<<2)|0,0<(0|r)){for(l=d+1144|0,m=0|c[d+908>>2];;){k=(0|c[l>>2])+(c[m>>2]<<2)|0,h=(e=0|c[g>>2])-1|0;a:do{if(1<(0|e))for(i=0,j=h;;){for(;!((0|c[(h=k+(i<<2)|0)>>2])<0);){if(!((0|(h=i+1|0))<(0|j))){h=j;break a}i=h}if(_c(0|h,h+4|0,(e-i<<2)-4|0),!((0|i)<(0|(h=j+-1|0)))){e=j;break}e=j,j=h}}while(0);if(0<(0|e)&&(e=(0|c[k+(h<<2)>>2])<0?h:e),c[g>>2]=e,n>>>0<=(g=g+4|0)>>>0)break;m=m+4|0}e=0|c[s>>2]}if(0<(0|c[e>>2]))for(g=d+716|0,h=d+712|0,i=0;c[(0|c[h>>2])+(i<<2)>>2]=(0|c[g>>2])+(i<<6),e=0|c[s>>2],(0|(i=i+1|0))<(0|c[e>>2]););if(0<(0|c[e+4>>2]))for(g=d+748|0,h=d+744|0,i=0;c[(0|c[h>>2])+(i<<2)>>2]=(0|c[g>>2])+(i<<6),e=0|c[s>>2],(0|(i=i+1|0))<(0|c[e+4>>2]););if(0<(0|c[e+16>>2]))for(j=d+848|0,k=d+832|0,q=d+1136|0,o=d+896|0,p=d+836|0,l=d+1140|0,m=d+900|0,n=d+840|0,g=d+1144|0,h=d+908|0,i=d+844|0,r=0;c[(0|c[k>>2])+(r<<2)>>2]=(0|c[j>>2])+(r<<6),c[(0|c[p>>2])+(r<<2)>>2]=(0|c[q>>2])+(c[(0|c[o>>2])+(r<<2)>>2]<<2),c[(0|c[n>>2])+(r<<2)>>2]=(0|c[l>>2])+(c[(0|c[m>>2])+(r<<2)>>2]<<1),c[(0|c[i>>2])+(r<<2)>>2]=(0|c[g>>2])+(c[(0|c[h>>2])+(r<<2)>>2]<<2),e=0|c[s>>2],(0|(r=r+1|0))<(0|c[e+16>>2]););if(0<(0|c[e+20>>2]))for(g=d+920|0,h=d+916|0,i=0;c[(0|c[h>>2])+(i<<2)>>2]=(0|c[g>>2])+(i<<6),e=0|c[s>>2],(0|(i=i+1|0))<(0|c[e+20>>2]););if(0<(0|c[e+80>>2]))for(g=d+1184|0,h=d+1180|0,i=0;c[(0|c[h>>2])+(i<<2)>>2]=(0|c[g>>2])+(i<<6),e=0|c[s>>2],(0|(i=i+1|0))<(0|c[e+80>>2]););if(!(1&a[20+(0|c[d+708>>2])>>0]||(0|(m=0|c[e+16>>2]))<=0)){e=0|c[d+1140>>2],g=0|c[d+900>>2],h=0|c[d+904>>2],j=0;do{if(i=e+(c[g+(j<<2)>>2]<<1)|0,l=(s=0|c[h+(j<<2)>>2])-1|0,1<(0|s))for(k=0;r=0|b[(q=i+(k<<1)|0)>>1],b[q>>1]=0|b[(s=i+(k+2<<1)|0)>>1],b[s>>1]=r,(0|(k=k+3|0))<(0|l););}while((0|(j=j+1|0))!=(0|m));g=0|c[d+1136>>2],h=0|c[d+896>>2],i=0|c[d+892>>2],k=0;do{if(j=(e=g+(c[h+(k<<2)>>2]<<2)|0)+((s=0|c[i+(k<<2)>>2])<<1<<2)|0,0<(0|s))for(e=e+4|0;f[e>>2]=1-+f[e>>2],(e=e+8|0)>>>0>>0;);}while((0|(k=k+1|0))!=(0|m))}return S=t,0|d}function ta(a){var c=0,b=S,c=S=S+63&-64;return S=S+16|0,0|yc(a|=0)?(ia(0,1605,c),S=b,(c=0)|c):(c=0|d[a+4>>0],S=b,0|c)}function va(a){var j,k,l,m,b=0,e=0,f=0,g=0,h=0,i=0,e=64+(a|=0)|0,b=a+144|0;if(za(e,0|c[a+88>>2],0|c[a+148>>2],0|c[b>>2]),Ba(e,0|c[a+92>>2],0|c[a+152>>2],0|c[796+(0|c[a>>2])>>2],2,0|c[b>>2]),!((0|d[4+(0|c[a>>2])>>0])<4||(za(e,0|c[a+96>>2],0|c[(m=a+120|0)>>2],0|c[b>>2]),za(e,0|c[a+100>>2],0|c[(f=a+124|0)>>2],0|c[b>>2]),za(e,0|c[a+104>>2],0|c[(g=a+128|0)>>2],0|c[b>>2]),za(e,0|c[a+108>>2],0|c[(j=a+132|0)>>2],0|c[b>>2]),za(e,0|c[a+112>>2],0|c[(k=a+136|0)>>2],0|c[b>>2]),za(e,0|c[a+116>>2],0|c[(l=a+140|0)>>2],0|c[b>>2]),b=0|c[a+156>>2],e=0|c[m>>2],f=0|c[f>>2],g=0|c[g>>2],(0|(m=0|c[a+56>>2]))<=0))){for(i=h=0;c[b+(h<<2)>>2]=c[e+(i<<2)>>2],c[b+((1|h)<<2)>>2]=c[f+(i<<2)>>2],c[b+((2|h)<<2)>>2]=c[g+(i<<2)>>2],(0|(i=i+1|0))!=(0|m);)h=h+4|0;for(i=0|c[a+160>>2],h=0|c[j>>2],g=0|c[k>>2],b=0|c[l>>2],f=e=0;c[i+(e<<2)>>2]=c[h+(f<<2)>>2],c[i+((1|e)<<2)>>2]=c[g+(f<<2)>>2],c[i+((2|e)<<2)>>2]=c[b+(f<<2)>>2],(0|(f=f+1|0))!=(0|m);)e=e+4|0}}function wa(a){var j,k,l,m,b=0,e=0,f=0,g=0,h=0,i=0,e=172+(a|=0)|0,b=a+264|0;if(za(e,0|c[a+196>>2],0|c[a+268>>2],0|c[b>>2]),za(e,0|c[a+200>>2],0|c[a+284>>2],0|c[b>>2]),za(e,0|c[a+204>>2],0|c[a+276>>2],0|c[b>>2]),za(e,0|c[a+208>>2],0|c[a+280>>2],0|c[b>>2]),za(e,0|c[a+212>>2],0|c[a+272>>2],0|c[b>>2]),!((0|d[4+(0|c[a>>2])>>0])<4||(za(e,0|c[a+216>>2],0|c[(m=a+240|0)>>2],0|c[b>>2]),za(e,0|c[a+220>>2],0|c[(f=a+244|0)>>2],0|c[b>>2]),za(e,0|c[a+224>>2],0|c[(g=a+248|0)>>2],0|c[b>>2]),za(e,0|c[a+228>>2],0|c[(j=a+252|0)>>2],0|c[b>>2]),za(e,0|c[a+232>>2],0|c[(k=a+256|0)>>2],0|c[b>>2]),za(e,0|c[a+236>>2],0|c[(l=a+260|0)>>2],0|c[b>>2]),b=0|c[a+296>>2],e=0|c[m>>2],f=0|c[f>>2],g=0|c[g>>2],(0|(m=0|c[a+164>>2]))<=0))){for(i=h=0;c[b+(h<<2)>>2]=c[e+(i<<2)>>2],c[b+((1|h)<<2)>>2]=c[f+(i<<2)>>2],c[b+((2|h)<<2)>>2]=c[g+(i<<2)>>2],(0|(i=i+1|0))!=(0|m);)h=h+4|0;for(i=0|c[a+300>>2],h=0|c[j>>2],g=0|c[k>>2],b=0|c[l>>2],f=e=0;c[i+(e<<2)>>2]=c[h+(f<<2)>>2],c[i+((1|e)<<2)>>2]=c[g+(f<<2)>>2],c[i+((2|e)<<2)>>2]=c[b+(f<<2)>>2],(0|(f=f+1|0))!=(0|m);)e=e+4|0}}function xa(a){var j,k,l,m,b=0,e=0,f=0,g=0,h=0,i=0,e=340+(a|=0)|0,b=a+424|0;if(za(e,0|c[a+364>>2],0|c[a+448>>2],0|c[b>>2]),Aa(e,0|c[a+368>>2],0|c[a+440>>2],0|c[b>>2]),Ba(e,0|c[a+372>>2],0|c[a+444>>2],0|c[892+(0|c[a>>2])>>2],2,0|c[b>>2]),!((0|d[4+(0|c[a>>2])>>0])<4||(za(e,0|c[a+376>>2],0|c[(m=a+400|0)>>2],0|c[b>>2]),za(e,0|c[a+380>>2],0|c[(f=a+404|0)>>2],0|c[b>>2]),za(e,0|c[a+384>>2],0|c[(g=a+408|0)>>2],0|c[b>>2]),za(e,0|c[a+388>>2],0|c[(j=a+412|0)>>2],0|c[b>>2]),za(e,0|c[a+392>>2],0|c[(k=a+416|0)>>2],0|c[b>>2]),za(e,0|c[a+396>>2],0|c[(l=a+420|0)>>2],0|c[b>>2]),b=0|c[a+452>>2],e=0|c[m>>2],f=0|c[f>>2],g=0|c[g>>2],(0|(m=0|c[a+332>>2]))<=0))){for(i=h=0;c[b+(h<<2)>>2]=c[e+(i<<2)>>2],c[b+((1|h)<<2)>>2]=c[f+(i<<2)>>2],c[b+((2|h)<<2)>>2]=c[g+(i<<2)>>2],(0|(i=i+1|0))!=(0|m);)h=h+4|0;for(i=0|c[a+456>>2],h=0|c[j>>2],g=0|c[k>>2],b=0|c[l>>2],f=e=0;c[i+(e<<2)>>2]=c[h+(f<<2)>>2],c[i+((1|e)<<2)>>2]=c[g+(f<<2)>>2],c[i+((2|e)<<2)>>2]=c[b+(f<<2)>>2],(0|(f=f+1|0))!=(0|m);)e=e+4|0}}function za(a,b,d,e){b|=0,d|=0,e|=0;var m,n,o,g=0,h=0,i=0,j=0,k=0,l=0;if(0<(0|(g=0|c[8+(a|=0)>>2])))for(h=0|c[a+20>>2],i=0|c[a+12>>2],j=0;f[i+(j<<2)>>2]=+f[b+(j<<2)>>2]*+f[h+(j<<2)>>2],(0|(j=j+1|0))!=(0|g););if(!((0|(n=0|c[a>>2]))<=0))if(l=a+16|0,o=0|c[a+4>>2],m=a+12|0,e)for(b=j=0;;){if(0|c[e>>2]){if(h=(a=0|c[(0|c[l>>2])+(b<<2)>>2])+j|0,0<(0|a))for(g=0|c[m>>2],i=j,k=0;k+=+f[g+(i<<2)>>2],(0|(i=i+1|0))<(0|h););else k=0;f[d+(b<<2)>>2]=k}if((0|(g=b+1|0))==(0|n))break;e=e+4|0,j=(0|c[o+(b<<2)>>2])+j|0,b=g}else for(j=0|c[l>>2],a=b=0;;){if(h=(l=0|c[j+(a<<2)>>2])+b|0,0<(0|l))for(g=0|c[m>>2],i=b,k=0;k+=+f[g+(i<<2)>>2],(0|(i=i+1|0))<(0|h););else k=0;if(f[d+(a<<2)>>2]=k,(0|(g=a+1|0))==(0|n))break;b=(0|c[o+(a<<2)>>2])+b|0,a=g}}function Aa(a,b,d,e){b|=0,d|=0,e|=0;var m,n,g=0,h=0,i=0,j=0,k=0,l=0;if(0<(0|(g=0|c[8+(a|=0)>>2])))for(h=0|c[a+20>>2],i=0|c[a+12>>2],j=0;f[i+(j<<2)>>2]=+f[b+(j<<2)>>2]*+f[h+(j<<2)>>2],(0|(j=j+1|0))!=(0|g););if(!((0|(g=0|c[a>>2]))<=0))if(l=a+16|0,m=0|c[a+4>>2],n=a+12|0,e)for(b=j=0;;){if(0|c[e>>2]){if(h=(i=0|c[(0|c[l>>2])+(b<<2)>>2])+j|0,0<(0|i))for(g=0|c[n>>2],i=j,k=0;k+=+f[g+(i<<2)>>2],(0|(i=i+1|0))<(0|h););else k=0;c[d+(b<<2)>>2]=~~(k+.0010000000474974513),g=0|c[a>>2]}if(!((0|(h=b+1|0))<(0|g)))break;e=e+4|0,j=(0|c[m+(b<<2)>>2])+j|0,b=h}else for(j=0|c[l>>2],e=b=0;;){if(h=(l=0|c[j+(e<<2)>>2])+b|0,0<(0|l))for(g=0|c[n>>2],i=b,k=0;k+=+f[g+(i<<2)>>2],(0|(i=i+1|0))<(0|h););else k=0;if(c[d+(e<<2)>>2]=~~(k+.0010000000474974513),!((0|(g=e+1|0))<(0|c[a>>2])))break;b=(0|c[m+(e<<2)>>2])+b|0,e=g}}function Ba(a,b,d,e,g,h){b|=0,d|=0,e|=0,g|=0,h|=0;var r,s,u,w,x,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,t=0|c[(a|=0)>>2];if(!((0|t)<=0))if(w=a+16|0,u=0|c[a+4>>2],s=a+20|0,r=g<<2,h)for(p=o=0;;){if(0|c[h>>2]&&(l=0|c[d+(p<<2)>>2],a=0|c[e+(p<<2)>>2],n=0|v(a,g),m=(i=0|c[(0|c[w>>2])+(p<<2)>>2])+o|0,(k=0<(0|n))&&$c(0|l,0,0|v(r,a)),0<(0|i)&&(q=0|c[s>>2],k))){i=o;do{for(a=0|c[b+(i<<2)>>2],j=+f[q+(i<<2)>>2],k=0;f[(x=l+(k<<2)|0)>>2]=+f[x>>2]+j*+f[a+(k<<2)>>2],(0|(k=k+1|0))!=(0|n););}while((0|(i=i+1|0))<(0|m))}if((0|(a=p+1|0))==(0|t))break;h=h+4|0,o=(0|c[u+(p<<2)>>2])+o|0,p=a}else for(o=n=0;;){if(k=0|c[d+(o<<2)>>2],a=0|c[e+(o<<2)>>2],m=0|v(a,g),l=(h=0|c[(0|c[w>>2])+(o<<2)>>2])+n|0,(i=0<(0|m))&&$c(0|k,0,0|v(r,a)),0<(0|h)&&(p=0|c[s>>2],i)){h=n;do{for(a=0|c[b+(h<<2)>>2],j=+f[p+(h<<2)>>2],i=0;f[(q=k+(i<<2)|0)>>2]=+f[q>>2]+j*+f[a+(i<<2)>>2],(0|(i=i+1|0))!=(0|m););}while((0|(h=h+1|0))<(0|l))}if((0|(a=o+1|0))==(0|t))break;n=(0|c[u+(o<<2)>>2])+n|0,o=a}}function Ka(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|function(a){a|=0;var b,d,e;return S=(d=S)+560|0,b=(e=d)+548|0,$c(0|e,0,548),ma(a,e,b),S=d,0|c[b>>2]}(a),S=d,0|b):(c[b>>2]=1931,c[b+4>>2]=1949,ia(0,1664,b),S=d,(b=0)|b)}function La(a,b,d){b|=0,d|=0;var g,f=0,h=0,i=S,e=S=S+63&-64;return S=S+32|0,h=24+e|0,g=16+e|0,f=8+e|0,(a|=0)?b?((f=b)+15&-16|0)!=(0|f)?(c[g>>2]=1967,c[4+g>>2]=1738,ia(0,1664,g),S=i,(h=0)|h):(b=0|na(a,b,d))?(S=i,0|b):(c[h>>2]=1967,c[h+4>>2]=1993,ia(0,1664,h),S=i,(h=0)|h):(c[f>>2]=1967,c[f+4>>2]=1698,ia(0,1664,f),S=i,(h=0)|h):(c[e>>2]=1967,c[4+e>>2]=1949,ia(0,1664,e),S=i,(h=0)|h)}function rb(a,b,c,d){if(c=+c,d=+d,(b=+u(b=+b,a=+a)-+u(+d,+c))<-3.1415927410125732)for(;(b+=6.2831854820251465)<-3.1415927410125732;);if(3.1415927410125732>>0<=b>>>0))for(;d=0|a[b>>0],a[b>>0]=0|a[c>>0],b=b+1|0,a[c>>0]=d,b>>>0<(c=c+-1|0)>>>0;);}function tb(b,c,d){b|=0,c|=0;var g,e=0,f=0;if(d|=0)do{if(d=d+-1|0,(f=b)>>>0<(e=(b=b+c|0)-1|0)>>>0)for(;g=0|a[f>>0],a[f>>0]=0|a[e>>0],f=f+1|0,a[e>>0]=g,f>>>0<(e=e+-1|0)>>>0;);}while(0!=(0|d))}function Ab(a){var s,l,o,b=0,e=0,g=0,h=0,i=0,j=0,k=0,n=0,p=0,q=0,r=0,t=0,u=0,v=0,m=(b=0|c[(l=336+(a|=0)|0)>>2])+(20*(n=0|c[(o=a+332|0)>>2])|0)|0,p=a+424|0,i=0|c[a+48>>2],j=0|c[a+316>>2],k=0|c[a+308>>2];if(0<(0|n))for(n=0|c[p>>2],g=0|c[a+448>>2],h=0|c[a+444>>2];0|c[n>>2]&&(-1!=(0|(e=0|c[b+4>>2]))&&(f[g>>2]=+f[i+(e<<2)>>2]*+f[g>>2]),-1!=(0|(e=0|c[b+8>>2]))&&(f[g>>2]=+f[j+(e<<2)>>2]*+f[g>>2],r=0|c[h>>2],$[3&c[k+(e<<5)+24>>2]](a,e,r,r,0|c[b+16>>2]))),!(m>>>0<=(b=b+20|0)>>>0);)n=n+4|0,g=g+4|0,h=h+4|0;if(!((0|d[4+(0|c[a>>2])>>0])<4||(k=(b=0|c[l>>2])+(20*(r=0|c[o>>2])|0)|0,i=0|c[a+324>>2],j=0|c[a+328>>2],(0|r)<=0)))for(h=0|c[p>>2],e=b,g=0|c[a+452>>2],b=0|c[a+456>>2];0|c[h>>2]&&-1!=(0|(q=0|c[e+8>>2]))&&(t=+f[g>>2]*+f[(o=i+((p=q<<2)<<2)|0)>>2],f[g>>2]=t,u=+f[(r=g+4|0)>>2]*+f[4+o>>2],f[r>>2]=u,v=+f[(a=g+8|0)>>2]*+f[8+o>>2],f[g>>2]=t<0?0:1>2]=u<0?0:1>2]=v<0?0:1>2]=1,v=+f[b>>2],u=+f[(p=j+(p<<2)|0)>>2],f[b>>2]=u=v+u-v*u,v=+f[(a=b+4|0)>>2],t=+f[p+4>>2],f[a>>2]=t=v+t-v*t,s=(v=+f[(r=b+8|0)>>2])+(s=+f[p+8>>2])-v*s,f[b>>2]=u<0?0:1>2]=t<0?0:1>2]=s<0?0:1>2]=1),!(k>>>0<=(e=e+20|0)>>>0);)h=h+4|0,g=g+16|0,b=b+16|0}function Bb(a){var e,b=0|c[332+(a|=0)>>2];0|c[a+428>>2]&&(Zc(0|c[a+460>>2],0|c[a+436>>2],0|(e=b<<2)),Zc(0|c[a+464>>2],0|c[a+440>>2],0|e),Zc(0|c[a+468>>2],0|c[a+448>>2],0|e),(0|d[4+(0|c[a>>2])>>0])<=3||(Zc(0|c[a+472>>2],0|c[a+452>>2],0|(e=b<<4)),Zc(0|c[a+476>>2],0|c[a+456>>2],0|e)))}function Cb(b){var j,k,m,n,o,p,q,r,s,t,y,d=0,e=0,g=0,h=0,i=0,l=0,u=0,v=0,x=0,e=0|a[4+(0|c[(b|=0)>>2])>>0],w=0|c[b+332>>2],d=b+428|0;if(0|c[b+620>>2]){if(!(((c[d>>2]=0)|w)<=0))for(h=b+424|0,i=b+432|0,d=b+448|0,e=0;g=0!=(0|c[(0|c[h>>2])+(e<<2)>>2])&&0!=+f[(0|c[d>>2])+(e<<2)>>2]?127:126,a[(0|c[i>>2])+e>>0]=g,(0|(e=e+1|0))!=(0|w););}else if(0|c[d>>2]){if(!(((c[d>>2]=0)|w)<=0))if(m=b+424|0,p=b+432|0,o=b+448|0,r=b+468|0,n=b+440|0,q=b+464|0,t=b+436|0,s=b+460|0,j=b+452|0,l=b+472|0,k=b+456|0,i=b+476|0,(255&e)<=3)for(d=0;l=0==(0|c[(0|c[m>>2])+(d<<2)>>2]),x=+f[(0|c[o>>2])+(d<<2)>>2],v=(0|c[p>>2])+d|0,u=(1&a[v>>0])==(u=0!=x&(1^l)&1)<<24>>24?u:2|u,u=x!=+f[(0|c[r>>2])+(d<<2)>>2]?4|u:u,u=(0|c[(0|c[n>>2])+(d<<2)>>2])==(0|c[(0|c[q>>2])+(d<<2)>>2])?u:8|u,u=(0|c[(0|c[t>>2])+(d<<2)>>2])==(0|c[(0|c[s>>2])+(d<<2)>>2])?u:16|u,a[v>>0]=l?u:32|u,(0|(d=d+1|0))!=(0|w););else for(h=g=0;y=0==(0|c[(0|c[m>>2])+(g<<2)>>2]),x=+f[(0|c[o>>2])+(g<<2)>>2],e=(0|c[p>>2])+g|0,d=(1&a[e>>0])==(d=0!=x&(1^y)&1)<<24>>24?d:2|d,d=x!=+f[(0|c[r>>2])+(g<<2)>>2]?4|d:d,d=(0|c[(0|c[n>>2])+(g<<2)>>2])==(0|c[(0|c[q>>2])+(g<<2)>>2])?d:8|d,d=(0|c[(0|c[t>>2])+(g<<2)>>2])==(0|c[(0|c[s>>2])+(g<<2)>>2])?d:16|d,d=y?d:32|d,y=(0|c[j>>2])+(h<<2)|0,b=(0|c[l>>2])+(h<<2)|0,+f[y>>2]==+f[b>>2]&&+f[4+y>>2]==+f[b+4>>2]&&+f[8+y>>2]==+f[b+8>>2]&&+f[12+y>>2]==+f[b+12>>2]&&(u=(0|c[k>>2])+(h<<2)|0,v=(0|c[i>>2])+(h<<2)|0,+f[u>>2]==+f[v>>2])&&+f[u+4>>2]==+f[v+4>>2]&&+f[u+8>>2]==+f[v+8>>2]&&+f[u+12>>2]==+f[v+12>>2]||(d|=64),a[e>>0]=d,(0|(g=g+1|0))!=(0|w);)h=h+4|0}else if(!((0|w)<=0))for(g=b+424|0,h=b+432|0,d=b+448|0,e=0;0!=(0|c[(0|c[g>>2])+(e<<2)>>2])&&0!=+f[(0|c[d>>2])+(e<<2)>>2]?(v=(0|c[h>>2])+e|0,a[v>>0]=1|a[v>>0]):(v=(0|c[h>>2])+e|0,a[v>>0]=-2&a[v>>0]),(0|(e=e+1|0))!=(0|w););}function Eb(a){var C,D,E,F,G,A,B,b=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,H=0,I=0,J=0,K=0,k=0|c[(A=60+(a|=0)|0)>>2],b=0|c[a>>2],l=0|c[b+784>>2],m=0|c[(B=a+56|0)>>2],n=0|c[b+1028>>2];if(0<(0|m)){for(s=a+80|0,v=a+92|0,u=a+88|0,w=a+84|0,t=b+984|0,h=b+980|0,j=i=0;;){if(g=0|c[k+(24*i|0)>>2],15==(0|(z=0!=(0|c[(b=g+24|0)>>2])||0|c[g+28>>2]?15:z))&&(c[((z=0)|c[s>>2])+(i<<2)>>2]=c[(r=g+12|0)>>2],0|c[b>>2])&&(o=0|c[l+(i<<2)>>2],x=(p=0|c[g+16>>2])+((H=0|c[r>>2])<<2)|0,0<(0|H)))for(b=p,e=(0|c[u>>2])+(j<<2)|0,f=(0|c[v>>2])+(j<<2)|0;H=(0|c[b>>2])+o|0,c[f>>2]=n+(c[(0|c[t>>2])+(H<<2)>>2]<<2),c[e>>2]=c[(0|c[h>>2])+(H<<2)>>2],!(x>>>0<=(b=b+4|0)>>>0);)e=e+4|0,f=f+4|0;if(0|c[g+28>>2]&&(y=(q=0|c[g+20>>2])+((H=0|c[g+12>>2])<<2)|0,0<(0|H)))for(b=(0|c[w>>2])+(j<<2)|0,e=q;c[b>>2]=c[e>>2],!(y>>>0<=(e=e+4|0)>>>0);)b=b+4|0;if((0|(i=i+1|0))==(0|m))break;j=(0|c[g+8>>2])+j|0}b=0|c[a>>2]}if(!((0|d[b+4>>0])<4||(G=0|c[A>>2],H=0|c[b+792>>2],(0|(A=0|c[B>>2]))<=0)))for(D=a+96|0,C=a+100|0,B=a+104|0,F=a+108|0,E=a+112|0,z=a+116|0,x=b+1228|0,v=b+1232|0,u=b+1236|0,y=b+1240|0,w=b+1244|0,k=b+1248|0,m=l=0;;){if(b=0|c[G+(24*l|0)>>2],0|c[b+24>>2]&&(I=0|c[H+(l<<2)>>2],K=(J=0|c[b+16>>2])+((a=0|c[b+12>>2])<<2)|0,0<(0|a)))for(e=0|c[x>>2],f=0|c[v>>2],g=0|c[u>>2],h=0|c[y>>2],i=0|c[w>>2],j=0|c[k>>2],n=J,o=(0|c[B>>2])+(m<<2)|0,p=(0|c[C>>2])+(m<<2)|0,q=(0|c[D>>2])+(m<<2)|0,r=(0|c[z>>2])+(m<<2)|0,s=(0|c[E>>2])+(m<<2)|0,t=(0|c[F>>2])+(m<<2)|0;a=(0|c[n>>2])+I|0,c[q>>2]=c[e+(a<<2)>>2],c[p>>2]=c[f+(a<<2)>>2],c[o>>2]=c[g+(a<<2)>>2],c[t>>2]=c[h+(a<<2)>>2],c[s>>2]=c[i+(a<<2)>>2],c[r>>2]=c[j+(a<<2)>>2],!(K>>>0<=(n=n+4|0)>>>0);)o=o+4|0,p=p+4|0,q=q+4|0,r=r+4|0,s=s+4|0,t=t+4|0;if((0|(l=l+1|0))==(0|A))break;m=(0|c[b+8>>2])+m|0}}function Fb(a){var H,I,J,K,L,Q,b=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,D=0,E=0,F=0,G=0,M=0,N=0,O=0,P=0,T=0,U=0,V=0,W=0,S=0|c[168+(a|=0)>>2],R=0|c[a>>2],r=0|c[816+R>>2];if(0<(0|(s=0|c[(Q=a+164|0)>>2])))for(A=a+188|0,w=a+200|0,E=a+204|0,G=a+208|0,K=a+212|0,B=a+196|0,L=a+192|0,v=992+R|0,D=996+R|0,F=1e3+R|0,J=1004+R|0,C=988+R|0,H=1008+R|0,x=a+288|0,I=1012+R|0,y=a+292|0,O=N=0;;){if(t=0|c[S+(12*N|0)>>2],14==(0|(P=0!=(0|c[(b=t+24|0)>>2])||0|c[t+28>>2]?14:P))&&(c[((P=0)|c[A>>2])+(N<<2)>>2]=c[(z=t+12|0)>>2],0|c[b>>2])){if(b=0|c[r+(N<<2)>>2],k=(e=0|c[t+16>>2])+((q=0|c[z>>2])<<2)|0,0<(0|q))for(f=0|c[v>>2],g=0|c[D>>2],h=0|c[F>>2],i=0|c[J>>2],j=0|c[C>>2],l=(0|c[w>>2])+(O<<2)|0,m=e,n=(0|c[B>>2])+(O<<2)|0,o=(0|c[E>>2])+(O<<2)|0,p=(0|c[G>>2])+(O<<2)|0,q=(0|c[K>>2])+(O<<2)|0;W=(0|c[m>>2])+b|0,c[l>>2]=c[f+(W<<2)>>2],c[o>>2]=c[g+(W<<2)>>2],c[p>>2]=c[h+(W<<2)>>2],c[q>>2]=c[i+(W<<2)>>2],c[n>>2]=c[j+(W<<2)>>2],!(k>>>0<=(m=m+4|0)>>>0);)l=l+4|0,n=n+4|0,o=o+4|0,p=p+4|0,q=q+4|0;W=(0|c[e>>2])+b|0,c[(0|c[x>>2])+(N<<2)>>2]=c[(0|c[H>>2])+(W<<2)>>2],c[(0|c[y>>2])+(N<<2)>>2]=c[(0|c[I>>2])+(W<<2)>>2]}if(0|c[t+28>>2]&&(M=(u=0|c[t+20>>2])+((W=0|c[t+12>>2])<<2)|0,0<(0|W)))for(b=(0|c[L>>2])+(O<<2)|0,e=u;c[b>>2]=c[e>>2],!(M>>>0<=(e=e+4|0)>>>0);)b=b+4|0;if((0|(N=N+1|0))==(0|s))break;O=(0|c[t+8>>2])+O|0}if(!((0|d[4+R>>0])<4||(G=0|c[824+R>>2],(0|(A=0|c[Q>>2]))<=0)))for(D=a+216|0,C=a+220|0,B=a+224|0,F=a+228|0,E=a+232|0,z=a+236|0,x=1228+R|0,v=1232+R|0,u=1236+R|0,y=1240+R|0,w=1244+R|0,k=1248+R|0,m=l=0;;){if(j=0|c[S+(12*l|0)>>2],0|c[j+24>>2]&&(U=0|c[G+(l<<2)>>2],V=(T=0|c[j+16>>2])+((W=0|c[j+12>>2])<<2)|0,0<(0|W)))for(b=0|c[x>>2],e=0|c[v>>2],f=0|c[u>>2],g=0|c[y>>2],h=0|c[w>>2],i=0|c[k>>2],n=T,o=(0|c[B>>2])+(m<<2)|0,p=(0|c[C>>2])+(m<<2)|0,q=(0|c[D>>2])+(m<<2)|0,r=(0|c[z>>2])+(m<<2)|0,s=(0|c[E>>2])+(m<<2)|0,t=(0|c[F>>2])+(m<<2)|0;W=(0|c[n>>2])+U|0,c[q>>2]=c[b+(W<<2)>>2],c[p>>2]=c[e+(W<<2)>>2],c[o>>2]=c[f+(W<<2)>>2],c[t>>2]=c[g+(W<<2)>>2],c[s>>2]=c[h+(W<<2)>>2],c[r>>2]=c[i+(W<<2)>>2],!(V>>>0<=(n=n+4|0)>>>0);)o=o+4|0,p=p+4|0,q=q+4|0,r=r+4|0,s=s+4|0,t=t+4|0;if((0|(l=l+1|0))==(0|A))break;m=(0|c[j+8>>2])+m|0}}function Gb(a){var F,G,D,E,b=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,C=0,H=0,I=0,J=0,K=0,l=0|c[(D=336+(a|=0)|0)>>2],b=0|c[a>>2],m=0|c[b+856>>2],n=0|c[(E=a+332|0)>>2],o=0|c[b+1028>>2];if(0<(0|n)){for(t=a+356|0,y=a+372|0,w=a+364|0,u=a+368|0,z=a+360|0,v=b+1024|0,x=b+1016|0,i=b+1020|0,k=j=0;;){if(h=0|c[l+(20*j|0)>>2],15==(0|(C=0!=(0|c[(b=h+24|0)>>2])||0|c[h+28>>2]?15:C))&&(c[((C=0)|c[t>>2])+(j<<2)>>2]=c[(s=h+12|0)>>2],0|c[b>>2])&&(p=0|c[m+(j<<2)>>2],A=(q=0|c[h+16>>2])+((H=0|c[s>>2])<<2)|0,0<(0|H)))for(b=(0|c[u>>2])+(k<<2)|0,e=q,f=(0|c[w>>2])+(k<<2)|0,g=(0|c[y>>2])+(k<<2)|0;H=(0|c[e>>2])+p|0,c[g>>2]=o+(c[(0|c[v>>2])+(H<<2)>>2]<<2),c[f>>2]=c[(0|c[x>>2])+(H<<2)>>2],c[b>>2]=c[(0|c[i>>2])+(H<<2)>>2],!(A>>>0<=(e=e+4|0)>>>0);)b=b+4|0,f=f+4|0,g=g+4|0;if(0|c[h+28>>2]&&(B=(r=0|c[h+20>>2])+((H=0|c[h+12>>2])<<2)|0,0<(0|H)))for(b=(0|c[z>>2])+(k<<2)|0,e=r;c[b>>2]=c[e>>2],!(B>>>0<=(e=e+4|0)>>>0);)b=b+4|0;if((0|(j=j+1|0))==(0|n))break;k=(0|c[h+8>>2])+k|0}b=0|c[a>>2]}if(!((0|d[b+4>>0])<4||(G=0|c[D>>2],H=0|c[b+864>>2],(0|(A=0|c[E>>2]))<=0)))for(D=a+376|0,C=a+380|0,B=a+384|0,F=a+388|0,E=a+392|0,z=a+396|0,x=b+1228|0,v=b+1232|0,u=b+1236|0,y=b+1240|0,w=b+1244|0,k=b+1248|0,m=l=0;;){if(b=0|c[G+(20*l|0)>>2],0|c[b+24>>2]&&(I=0|c[H+(l<<2)>>2],K=(J=0|c[b+16>>2])+((a=0|c[b+12>>2])<<2)|0,0<(0|a)))for(e=0|c[x>>2],f=0|c[v>>2],g=0|c[u>>2],h=0|c[y>>2],i=0|c[w>>2],j=0|c[k>>2],n=J,o=(0|c[B>>2])+(m<<2)|0,p=(0|c[C>>2])+(m<<2)|0,q=(0|c[D>>2])+(m<<2)|0,r=(0|c[z>>2])+(m<<2)|0,s=(0|c[E>>2])+(m<<2)|0,t=(0|c[F>>2])+(m<<2)|0;a=(0|c[n>>2])+I|0,c[q>>2]=c[e+(a<<2)>>2],c[p>>2]=c[f+(a<<2)>>2],c[o>>2]=c[g+(a<<2)>>2],c[t>>2]=c[h+(a<<2)>>2],c[s>>2]=c[i+(a<<2)>>2],c[r>>2]=c[j+(a<<2)>>2],!(K>>>0<=(n=n+4|0)>>>0);)o=o+4|0,p=p+4|0,q=q+4|0,r=r+4|0,s=s+4|0,t=t+4|0;if((0|(l=l+1|0))==(0|A))break;m=(0|c[b+8>>2])+m|0}}function Jb(a){var d,e,g,b=0;if(Bb(a|=0),function(a){var e,g,b=0,d=0,h=0,j=0,k=0,i=(b=0|c[(a|=0)+4>>2])+(52*(h=0|c[a>>2])|0)|0;if(!((0|h)<=0))for(h=0|c[a+12>>2];d=+f[h>>2],a=b+4|0,d=(g=0==(0|c[b+16>>2]))?(k=+f[a>>2],j=+f[b+8>>2],d>2],(j=+f[a>>2])+k*((d=(d-j)/k)-(0|~~+q(+d)))),a=b+48|0,+f[(e=b+44|0)>>2]!=d?(c[a>>2]=1,f[e>>2]=d):c[a>>2]=0,g&&(f[h>>2]=d),!(i>>>0<=(b=b+52|0)>>>0);)h=h+4|0}(a+540|0),function(a){var n,b=0,d=0,e=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,o=0,p=0,q=0,r=0,t=0,u=0,s=(b=0|c[(a|=0)+544>>2])+(52*(n=0|c[a+540>>2])|0)|0;if(!((0|n)<=0)){n=0==(0|c[a+620>>2]);do{a:do{if(0==(0|c[b>>2])&&(o=+f[b+44>>2],p=+f[b+20>>2],q=+f[b+24>>2],t=(r=0|c[b+28>>2])+(28*(l=0|c[b+32>>2])|0)|0,0<(0|l))){if(!n)for(l=r;;){h=0|c[l>>2];do{if(1<=(0|h)){if(g=0|c[l+4>>2],d=(e=+f[g>>2])-p,1==(0|h)){h=!(o>2]));){if(!((0|(a=a+1|0))<(0|h))){m=40;break}e=d}if(40==(0|m)){g=l+16|0,a=h+-(i=1)|0,d=0,m=43;break}d=d-p>2])?(i=1&h,m=43):(c[l+24>>2]=1,c[l+20>>2]=1,k=l+8|0,j=l+12|0,h=0)),43==(0|m)&&(e=+f[(j=l+12|(m=0))>>2],h=1&((0|c[(k=l+8|0)>>2])!=(0|a)|(u=e!=d)&(0==d|0==e)),c[l+24>>2]=1&u,c[l+20>>2]=h,h=i),f[j>>2]=d,c[k>>2]=a,c[g>>2]=h,t>>>0<=(l=l+28|0)>>>0)break a}if(!(0|c[b+48>>2]))for(a=r;;)if(c[a+24>>2]=0,t>>>(c[a+20>>2]=0)<=(a=a+28|0)>>>0)break a;l=r;do{h=0|c[l>>2];do{if(1<=(0|h)){if(g=0|c[l+4>>2],d=(e=+f[g>>2])-p,1==(0|h)){h=!(o>2]));){if(!((0|(a=a+1|0))<(0|h))){m=21;break}e=d}if(21==(0|m)){g=l+16|0,a=h+-(i=1)|0,d=0,m=26;break}d=d-p>2])?(i=1&h,m=26):(c[l+24>>2]=1,c[l+20>>2]=1,k=l+8|0,j=l+12|0,h=0)),26==(0|m)&&(e=+f[(j=l+12|(m=0))>>2],h=1&((0|c[(k=l+8|0)>>2])!=(0|a)|(u=e!=d)&(0==d|0==e)),c[l+24>>2]=1&u,c[l+20>>2]=h,h=i),f[j>>2]=d,c[k>>2]=a,c[g>>2]=h,(l=l+28|0)>>>0>>0)}}while(0)}while((b=b+52|0)>>>0>>0)}}(a),Qb(a),function(a){var l,o,y,z,p,b=0,d=0,e=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,q=0,r=0,s=0,u=0,w=0,x=0,t=(b=0|c[(a|=0)+568>>2])+(36*(p=0|c[a+564>>2])|0)|0;if(!((0|p)<=0)){p=0==(0|c[a+620>>2]);do{o=0|c[b+4>>2];a:do{if(0<(0|o))for(i=0|c[b>>2],e=a=d=j=0;;){if(g=0|c[i+(j<<2)>>2],0|c[g+16>>2]){e=1,a=d=0;break a}if(e=e||0|c[g+24>>2],a=a||0|c[g+20>>2],d=d+(0!=+f[g+12>>2]&1)|0,(0|o)<=(0|(j=j+1|0))){i=d,x=11;break}}else e=a=i=0,x=11}while(0);b:do{if(11==(0|x))if(d=p?a:1,(x=0)!=((a=p?e:1)|d|0)&&(c[b+12>>2]=w=1<>2],r=0|c[b+16>>2],s=0|c[b+20>>2],u=r+(w<<2)|0,31!=(0|i))){for($c(0|r,0,((e=r+4|0)>>>0>>0?u:e)+~r+4&-4|0),e=s+(w<<2)|0,g=s;f[g>>2]=1,(g=g+4|0)>>>0>>0;);if(1<=(0|o)){if(!i)for(i=0,k=1;;){if(g=0|c[q+(i<<2)>>2],j=0|v(0|c[g+8>>2],k),0==(h=+f[g+12>>2]))for(e=0;c[(n=r+(e<<2)|0)>>2]=(0|c[n>>2])+j,(0|(e=e+1|0))!=(0|w););else c[r>>2]=(0|c[r>>2])+j,f[s>>2]=(1-h)*+f[s>>2];if(k=0|v(0|c[g>>2],k),(0|(i=i+1|0))==(0|o)){e=0;break b}}m=0,n=e=1;do{if(l=0|c[q+(m<<2)>>2],g=0|c[8+l>>2],k=0|v(g,n),0==(h=+f[(j=12+l|0)>>2]))for(g=0;c[(j=r+(g<<2)|0)>>2]=(0|c[j>>2])+k,(0|(g=g+1|0))!=(0|w););else{for(i=0|v(g+1|0,n),c[r>>2]=(0|c[r>>2])+k,f[s>>2]=(1-h)*+f[s>>2],g=1;h=+f[j>>2],c[(y=r+(g<<2)|0)>>2]=((z=0!=(g&e|0))?i:k)+(0|c[y>>2]),f[(y=s+(g<<2)|0)>>2]=(z?h:1-h)*+f[y>>2],(0|(g=g+1|0))!=(0|w););e<<=1}}while(n=0|v(0|c[l>>2],n),(0|(m=m+1|0))!=(0|o));e=0}else e=0}else e=0}while(0)}while(c[b+28>>2]=a,c[b+24>>2]=d,c[b+32>>2]=e,(b=b+36|0)>>>0>>0)}}(a),Rb(a),e=0|c[a+4>>2],d=(b=0|c[a+52>>2])+(e<<2)|0,0<(0|e))for(;g=+f[b>>2],f[b>>2]=g<0?0:1>>0>>0;);!function(a,b,d){d|=0;var e=0,f=(b|=0)+(12*(a|=0)|0)|0;if(!((0|a)<=0))for(e=d;;){do{if(0|c[b+8>>2]){if(-1!=(0|(a=0|c[b+4>>2]))&&0==(0|c[d+(a<<2)>>2])){a=0;break}a=0==(0|c[32+(0|c[b>>2])>>2])}else a=0}while(0);if(c[e>>2]=1&a,f>>>0<=(b=b+12|0)>>>0)break;e=e+4|0}}(e,0|c[a+8>>2],0|c[a+40>>2]),function(a){var e,f,i,q,r,b=0,d=0,g=0,h=0,k=0,l=0,o=0,p=0,s=0,t=0,u=0,j=0|c[8+(a|=0)>>2],b=0|c[a>>2],m=0|c[b+724>>2],n=0|c[a+4>>2];if(!((0|n)<=0))for(q=a+28|0,r=a+36|0,i=a+32|0,f=b+976|0,h=g=0;;){if(e=0|c[j+(12*g|0)>>2],6==(0|(u=0!=(0|c[(a=24+e|0)>>2])||0|c[28+e>>2]?6:u))&&(c[((u=0)|c[q>>2])+(g<<2)>>2]=c[(p=12+e|0)>>2],0|c[a>>2])&&(o=0|c[m+(g<<2)>>2],s=(k=0|c[16+e>>2])+((d=0|c[p>>2])<<2)|0,0<(0|d)))for(a=0|c[f>>2],b=(0|c[r>>2])+(h<<2)|0,d=k;c[b>>2]=c[a+((0|c[d>>2])+o<<2)>>2],!(s>>>0<=(d=d+4|0)>>>0);)b=b+4|0;if(0|c[28+e>>2]&&(t=(l=0|c[20+e>>2])+((d=0|c[12+e>>2])<<2)|0,0<(0|d)))for(a=(0|c[i>>2])+(h<<2)|0,b=l;c[a>>2]=c[b>>2],!(t>>>0<=(b=b+4|0)>>>0);)a=a+4|0;if((0|(g=g+1|0))==(0|n))break;h=(0|c[8+e>>2])+h|0}}(a),function(a){Aa(12+(a|=0)|0,0|c[36+a>>2],0|c[44+a>>2],0|c[40+a>>2])}(a),function(a){var d,f,g,h,i,k,b=0,e=0,j=S=(k=S)+63&-64;if(S=S+16|0,i=(b=0|c[308+(a|=0)>>2])+((e=0|c[a+304>>2])<<5)|0,f=0|c[a+40>>2],g=0|c[a+312>>2],h=0|c[a+144>>2],d=0|c[a+264>>2],(0|e)<=0)S=k;else{for(e=g;;){do{if(0|c[b+28>>2]){if(-1!=(0|(a=0|c[b+4>>2]))&&0==(0|c[f+(a<<2)>>2])){a=0;break}if(-1!=(0|(a=0|c[b+8>>2]))&&0==(0|c[g+(a<<2)>>2])){a=0;break}a=0==(0|c[32+(0|c[b>>2])>>2])}else a=0}while(0);switch(a&=1,c[e>>2]=a,0|c[b+12>>2]){case 0:c[h+(c[b+16>>2]<<2)>>2]=a;break;case 1:c[d+(c[b+16>>2]<<2)>>2]=a;break;default:ia(0,2874,j)}if(i>>>0<=(b=b+32|0)>>>0)break;e=e+4|0}S=k}}(a),Eb(a),Fb(a),va(a),wa(a),function(a){var b=0,d=0,g=(b=0|c[(a|=0)+336>>2])+(20*(d=0|c[a+332>>2])|0)|0,e=0|c[a+40>>2],f=0|c[a+312>>2];if(!((0|d)<=0))for(d=0|c[a+424>>2];;){do{if(0|c[b+12>>2]){if(-1!=(0|(a=0|c[b+4>>2]))&&0==(0|c[e+(a<<2)>>2])){a=0;break}if(-1!=(0|(a=0|c[b+8>>2]))&&0==(0|c[f+(a<<2)>>2])){a=0;break}a=0==(0|c[32+(0|c[b>>2])>>2])}else a=0}while(0);if(c[d>>2]=1&a,g>>>0<=(b=b+20|0)>>>0)break;d=d+4|0}}(a),Gb(a),xa(a),function(a){var e,f,i,q,r,b=0,d=0,g=0,h=0,k=0,l=0,o=0,p=0,s=0,t=0,u=0,j=0|c[504+(a|=0)>>2],b=0|c[a>>2],m=0|c[b+1192>>2],n=0|c[a+500>>2];if(!((0|n)<=0))for(q=a+524|0,r=a+532|0,i=a+528|0,f=b+1224|0,h=g=0;;){if(e=0|c[j+(24*g|0)>>2],6==(0|(u=0!=(0|c[(a=24+e|0)>>2])||0|c[28+e>>2]?6:u))&&(c[((u=0)|c[q>>2])+(g<<2)>>2]=c[(p=12+e|0)>>2],0|c[a>>2])&&(o=0|c[m+(g<<2)>>2],s=(k=0|c[16+e>>2])+((d=0|c[p>>2])<<2)|0,0<(0|d)))for(a=0|c[f>>2],b=(0|c[r>>2])+(h<<2)|0,d=k;c[b>>2]=c[a+((0|c[d>>2])+o<<2)>>2],!(s>>>0<=(d=d+4|0)>>>0);)b=b+4|0;if(0|c[28+e>>2]&&(t=(l=0|c[20+e>>2])+((d=0|c[12+e>>2])<<2)|0,0<(0|d)))for(a=(0|c[i>>2])+(h<<2)|0,b=l;c[a>>2]=c[b>>2],!(t>>>0<=(b=b+4|0)>>>0);)a=a+4|0;if((0|(g=g+1|0))==(0|n))break;h=(0|c[8+e>>2])+h|0}}(a),function(a){za(508+(a|=0)|0,0|c[532+a>>2],0|c[536+a>>2],0)}(a),fa(a),ha(a),function(a){var b=0,d=0,g=0,h=0,j=0,i=(b=0|c[8+(a|=0)>>2])+(12*(j=0|c[a+4>>2])|0)|0,e=0|c[a+48>>2];if(!((0|j)<=0))for(j=0|c[a+40>>2],d=e,a=0|c[a+52>>2];0|c[j>>2]&&(g=+f[a>>2],f[d>>2]=g,-1!=(0|(h=0|c[b+4>>2])))&&(f[d>>2]=g*+f[e+(h<<2)>>2]),!(i>>>0<=(b=b+12|0)>>>0);)j=j+4|0,d=d+4|0,a=a+4|0}(a),function(a){var d=0,e=0,f=0,b=0|c[304+(a|=0)>>2];if(!((0|b)<=0))for(d=0|c[312+a>>2],e=0|c[308+a>>2],f=0;0|c[d>>2]&&_[3&c[e+20>>2]](a,f),(0|(f=f+1|0))!=(0|b);)d=d+4|0,e=e+32|0}(a),Ab(a),ea(a),function(a){var g,j,b=0,d=0,e=0,h=0,i=0;if(!(0|c[(a|=0)+624>>2]||(g=(b=0|c[a+336>>2])+(20*(h=0|c[a+332>>2])|0)|0,(0|h)<=0)))for(h=0|c[a+424>>2],a=0|c[a+444>>2];;){if(0|c[h>>2]&&(e=0|c[a>>2],i=(d=0|c[b+16>>2])<<1,0<(0|d)))for(d=1;f[(j=e+(d<<2)|0)>>2]=-+f[j>>2],(0|(d=d+2|0))<(0|i););if(g>>>0<=(b=b+20|0)>>>0)break;h=h+4|0,a=a+4|0}}(a),function(a){var n,r,b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,o=0,p=0,q=0|c[(a|=0)+480>>2],j=(r=0|c[a+484>>2])+(28*q|0)|0,b=0|c[a+440>>2],d=0|c[a+44>>2],e=0|c[a+40>>2],f=0|c[a+424>>2];if(!((0|q)<=0)){k=r;do{if(0<(0|(g=0|c[k+4>>2])))for(h=0|c[k+12>>2],i=k+20|0,l=0;p=0|c[h+(l<<4)+4>>2],o=1==(0|c[h+(l<<4)>>2]),c[h+(l<<4)+12>>2]=c[(0==(0|c[(o?e:f)+(p<<2)>>2])?i:(o?d:b)+(p<<2)|0)>>2],(0|(l=l+1|0))!=(0|g););}while((k=k+28|0)>>>0>>0);n=0|c[a+436>>2],o=a+488|0,p=a+492|0,l=a+496|0,m=0;do{if(0<(0|c[(a=r+(28*m|0)+24|0)>>2])){for(b=0|c[o>>2],e=0;c[b+(e<<2)>>2]=-1,(0|(e=e+1|0))<(0|(d=0|c[a>>2])););if(0<(0|d))for(b=0|c[l>>2],d=0;c[b+(d<<2)>>2]=-1,(0|(d=d+1|0))<(0|c[a>>2]););}if(0<(0|c[(k=r+(28*m|0)+4|0)>>2])){for(j=0|c[p>>2],d=0;c[j+(d<<2)>>2]=-1,(0|(d=d+1|0))<(0|(b=0|c[k>>2])););if(0<(0|b))for(e=0|c[r+(28*m|0)+12>>2],g=r+(28*m|0)+20|0,f=0|c[l>>2],i=0;d=(0|c[e+(i<<4)+12>>2])-(0|c[g>>2])|0,b=-1==(0|(b=0|c[(h=f+(d<<2)|0)>>2]))?(0|c[o>>2])+(d<<2)|0:j+(b<<2)|0,c[b>>2]=i,(0|(i=(c[h>>2]=i)+1|0))<(0|c[k>>2]););}if(0<(0|(b=0|c[a>>2]))){i=0|c[o>>2],j=r+(28*m|0)+12|0,d=(h=0)|c[r+(28*m|0)+8>>2];do{if(-1!=(0|(e=0|c[i+(h<<2)>>2]))){for(f=0|c[j>>2],g=0|c[p>>2];d=(b=1==(0|c[f+(e<<4)>>2])?(b=0|c[f+(e<<4)+8>>2],c[r+(28*b|0)+8>>2]=d,0|c[r+(28*b|0)>>2]):(c[n+(c[f+(e<<4)+4>>2]<<2)>>2]=d,1))+d|0,!((0|(e=0|c[g+((k=e)<<2)>>2]))<=(0|k)|-1==(0|e)););b=0|c[a>>2]}}while((0|(h=h+1|0))<(0|b))}}while((0|(m=m+1|0))!=(0|q))}}(a),Cb(a),c[a+620>>2]=0}function Qb(a){var l,s,b=0,e=0,g=0,h=0,i=0,j=0,k=0,m=0,n=0,o=0,p=0,q=0,r=0,t=0,u=0;if(!((0|d[4+(0|c[(a|=0)>>2])>>0])<4||(s=(b=0|c[a+544>>2])+(52*(l=0|c[a+540>>2])|0)|0,(0|l)<=0))){l=0==(0|c[a+620>>2]);do{a:do{if(1==(0|c[b>>2])&&(q=+f[b+44>>2],t=(r=0|c[b+36>>2])+(28*(k=0|c[b+40>>2])|0)|0,0<(0|k))){if(!l)for(j=r;;){g=0|c[j>>2];do{if(2<=(0|g)&&(o=0|c[j+4>>2],!(q<=(p=+f[o>>2])))){for(h=1,i=p;;){if(a=h+1|0,q<(e=+f[o+(h<<2)>>2])){k=25;break}if(!((0|a)<(0|g))){k=26;break}h=a,i=e}if(25==(0|k)){a=h+-1|0,e=(q-i)/(e-i);break}if(26==(0|k)){a=g+-1|0,e=0;break}}else e=a=0}while(0);if(i=+f[(h=j+16|0)>>2],g=1&((0|c[(k=j+12|0)>>2])!=(0|a)|(u=i!=e)&(0==e|0==i)),c[j+24>>2]=1&u,c[j+20>>2]=g,f[h>>2]=e,c[k>>2]=a,t>>>0<=(j=j+28|0)>>>0)break a}if(!(0|c[b+48>>2]))for(a=r;;)if(c[a+24>>2]=0,t>>>(c[a+20>>2]=0)<=(a=a+28|0)>>>0)break a;j=r;do{g=0|c[j>>2];do{if(2<=(0|g)&&(m=0|c[j+4>>2],!(q<=(n=+f[m>>2])))){for(h=1,i=n;;){if(a=h+1|0,q<(e=+f[m+(h<<2)>>2])){k=17;break}if(!((0|a)<(0|g))){k=18;break}h=a,i=e}if(17==(0|k)){a=h+-1|0,e=(q-i)/(e-i);break}if(18==(0|k)){a=g+-1|0,e=0;break}}else e=a=0}while(0)}while(i=+f[(k=j+16|0)>>2],h=1&((0|c[(u=j+12|0)>>2])!=(0|a)|(g=i!=e)&(0==e|0==i)),c[j+24>>2]=1&g,c[j+20>>2]=h,f[k>>2]=e,c[u>>2]=a,(j=j+28|0)>>>0>>0)}}while(0)}while((b=b+52|0)>>>0>>0)}}function Rb(a){var n,o,q,r,t,y,b=0,e=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,p=0,s=0,u=0,v=0,w=0,x=0;if(!((0|d[4+(0|c[(a|=0)>>2])>>0])<4||(y=(b=0|c[a+592>>2])+(48*(t=0|c[a+588>>2])|0)|0,(0|t)<=0))){t=0==(0|c[a+620>>2]);do{if(a=0|c[b>>2],g=t?0|c[a+24>>2]:1,(m=t?0|c[a+20>>2]:1)|g){k=0|c[a+12>>2],e=+f[a+16>>2],j=(0|k)==(0|(a=0|c[a+8>>2]));do{if(0!=e){if(i=k+1|0,j){e=(g=a=c[b+8>>2]=1)-e,s=11;break}a=(0|i)==(0|a)?1:2,s=10;break}}while(a=1&(1^j),s=10,0);10==(0|s)&&(s=0,c[b+8>>2]=a,g?(i=k,a=m,s=11):(i=k,a=m,g=0)),11==(0|s)&&(f[b+20>>2]=1-e,f[b+24>>2]=e),a?(c[b+12>>2]=i,c[b+16>>2]=i+1):a=0}else a=m;q=0|c[b+36>>2];a:do{if(0<(0|q)){if(r=0|c[b+40>>2],!t)for(k=0,e=1;;){j=0|c[r+(k<<2)>>2],i=0|c[j>>2];do{if(0!=(0|i)&&(u=0|c[j+4>>2],v=0|c[j+8>>2],w=0|c[j+12>>2],x=+f[i+44>>2],1<=(0|w))){if(1==(0|w)){h=+f[v>>2];break}if(x<=(h=+f[u>>2])){h=+f[v>>2];break}for(i=1;;){if(x<(l=+f[u+(i<<2)>>2])){s=43;break}if(!((0|(i=i+1|0))<(0|w))){s=44;break}h=l}if(43==(0|s)){h=(1-(h=(x-h)/(l-h)))*+f[v+(i+-1<<2)>>2]+h*+f[v+(i<<2)>>2];break}if(44==(0|s)){h=+f[v+(w+-1<<2)>>2];break}}else h=1}while(0);if(e=e<(f[j+16>>2]=h)?e:h,(0|(k=k+1|0))==(0|q))break a}p=0,e=1;do{o=0|c[r+(p<<2)>>2],i=0|c[o>>2];do{if(i){if(!(0|c[i+48>>2])){h=+f[16+o>>2];break}j=0|c[4+o>>2],k=0|c[8+o>>2],m=0|c[12+o>>2],n=+f[i+44>>2];do{if(1<=(0|m)){if(1==(0|m)){h=+f[k>>2];break}if(n<=(h=+f[j>>2])){h=+f[k>>2];break}for(i=1;;){if(n<(l=+f[j+(i<<2)>>2])){s=29;break}if(!((0|(i=i+1|0))<(0|m))){s=28;break}h=l}if(28==(0|s)){h=+f[k+(m+-1<<2)>>2];break}if(29==(0|s)){h=(1-(h=(n-h)/(l-h)))*+f[k+(i+-1<<2)>>2]+h*+f[k+(i<<2)>>2];break}}else h=1}while(0);f[16+o>>2]=h}else h=f[16+o>>2]=1}while(0)}while(e=e>2]=e,c[b+28>>2]=a,c[b+32>>2]=g,(b=b+48|0)>>>0>>0)}}function cc(b,d,e){b|=0,d|=0,e|=0;var f,j,h=0,g=S,i=S=S+63&-64;S=S+144|0,Zc(0|i,640,144),c[48+i>>2]=h=(h=-2-b|0)>>>0<256?h:256,c[(f=20+i|0)>>2]=b,j=(c[44+i>>2]=b)+h|0,c[(b=16+i|0)>>2]=j,c[28+i>>2]=j,dc(i,d,e),0|h&&(j=0|c[f>>2],a[j+(((0|j)==(0|c[b>>2]))<<31>>31)>>0]=0),S=g}function dc(a,b,c){gc(a|=0,b|=0,c|=0)}function ec(b,e,f,g,h,i){b|=0,e=+e,f|=0,g|=0,h|=0,i|=0;var C,B,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,w=0,x=0,y=0,A=0,D=0,E=0,F=0,G=0,H=S,l=S=S+63&-64;S=S+560|0,m=l+32|0,t=l+536|0,E=12+(l=(F=G=l)+540|0)|(c[t>>2]=0),vc(e),B=(0|(j=0|z()))<0?(vc(e=-e),j=0|z(),D=1,2955):(D=0!=(2049&h|0)&1,0==(2048&h|0)?0==(1&h|0)?2956:2961:2958);do{if(!0&2146435072==(2146435072&j|0))G=0!=(32&i|0),qc(b,32,f,j=D+3|0,-65537&h),ic(b,B,D),ic(b,e!=e|!1?G?2982:2986:G?2974:2978,3),qc(b,32,f,j,8192^h);else{if((j=0!=(q=2*+wc(e,t)))&&(c[t>>2]=(0|c[t>>2])-1),97==(0|(w=32|i))){r=0==(0|(o=32&i))?B:9+B|0,p=2|D,j=12-g|0;do{if(!(11>>0|0==(0|j))){for(e=8;e*=16,0!=(0|(j=j+-1|0)););if(45==(0|a[r>>0])){e=-(e+(-q-e));break}e=q+e-e;break}}while(e=q,0);for((0|(j=0|oc(j=(0|(k=0|c[t>>2]))<0?0-k|0:k,((0|j)<0)<<31>>31,E)))==(0|E)&&(a[(j=l+11|0)>>0]=48),a[j+-1>>0]=43+(k>>31&2),a[(n=j+-2|0)>>0]=i+15,k=(0|g)<1,l=0==(8&h|0),j=G;m=j+1|0,a[j>>0]=o|d[480+(D=~~e)>>0],e=16*(e-(0|D)),1!=(m-F|0)||l&k&0==e||(a[m>>0]=46,m=j+2|0),0!=e;)j=m;qc(b,32,f,E=(j=0!=(0|g)&&(-2-F+m|0)<(0|g)?g+2+(k=E)-(l=n)|0:(k=E)-F-(l=n)+m|0)+p|0,h),ic(b,r,p),qc(b,48,f,E,65536^h),ic(b,G,F=m-F|0),qc(b,48,j-(F+(G=k-l|0))|0,0,0),ic(b,n,G),qc(b,32,f,E,8192^h),j=E;break}for(k=(0|g)<0?6:g,e=j?(l=(0|c[t>>2])-28|0,c[t>>2]=l,268435456*q):(l=0|c[t>>2],q),m=C=(0|l)<0?m:m+288|0;c[m>>2]=y=~~e>>>0,m=m+4|0,0!=(e=1e9*(e-(y>>>0))););if(y=C,0<(0|l)){j=C;do{if(o=(0|l)<29?l:29,j>>>0<=(l=m+-4|0)>>>0){for(n=0;s=0|Rc(0|(s=0|Xc(0|c[l>>2],0,0|o)),0|z(),0|n,0),x=0|Rc(0|s,0|(u=0|z()),0|(x=0|Qc(0|(n=0|Vc(0|s,0|u,1e9,0)),0|z(),-1e9,-1)),0|z()),z(),c[l>>2]=x,j>>>0<=(l=l+-4|0)>>>0;);n&&(c[(j=j+-4|0)>>2]=n)}a:do{if(j>>>0>>0)for(;;){if(0|c[(l=m+-4|0)>>2])break a;if(!(j>>>0>>0)){m=l;break}m=l}}while(0)}while(l=(0|c[t>>2])-o|0,0<(0|(c[t>>2]=l)))}else j=C;if((0|l)<0){g=1+((k+25|0)/9|0)|0,s=102==(0|w);do{if(r=(0|(r=0-l|0))<9?r:9,j>>>0>>0){for(o=(1<>>r:1e9,p=0,l=j;x=0|c[l>>2],c[l>>2]=(r?x>>>r:x)+p,p=0|v(x&o,n),(l=l+4|0)>>>0>>0;);j=0==(0|c[j>>2])?j+4|0:j,p&&(c[m>>2]=p,m=m+4|0)}else j=0==(0|c[j>>2])?j+4|0:j}while(m=(0|g)<((x=m-(l=s?C:j)|0)>>2|0)?l+(g<<2)|0:m,l=(0|c[t>>2])+r|0,(0|(c[t>>2]=l))<0);s=m}else s=m;if(j>>>0>>0){if(l=9*((y-j|0)>>2)|0,10<=(n=0|c[j>>2])>>>0)for(m=10;l=l+1|0,(m=10*m|0)>>>0<=n>>>0;);}else l=0;if((0|(m=k-(102==(0|w)?0:l)+(((u=0!=(0|k))&(t=103==(0|w)))<<31>>31)|0))<((9*((x=s-y|0)>>2)|0)-9|0)){if(g=C+4+((m=(0|(x=m+9216|0))/9|0)-1024<<2)|0,(0|(m=x+(0|v(m,-9))|0))<8)for(n=10;n=10*n|0,(0|m)<7;)m=m+1|0;else n=10;if((p=(g+4|0)==(0|s))&0==(0|(o=(o=0|c[g>>2])-(r=0|v(m=(o>>>0)/(n>>>0)|0,n))|0)))m=g;else if(q=0==(1&m|0)?9007199254740992:9007199254740994,e=o>>>0<(x=n>>>1)>>>0?.5:p&(0|o)==(0|x)?1:1.5,D&&(q=(x=45==(0|a[B>>0]))?-q:q,e=x?-e:e),c[g>>2]=r,q+e!=q){if(999999999<(c[g>>2]=x=r+n|0)>>>0)for(l=g;(m=l+-4|0)>>>(c[l>>2]=0)>>0&&(c[(j=j+-4|0)>>2]=0),x=1+(0|c[m>>2])|0,999999999<(c[m>>2]=x)>>>0;)l=m;else m=g;if(l=9*((y-j|0)>>2)|0,10<=(o=0|c[j>>2])>>>0)for(n=10;l=l+1|0,(n=10*n|0)>>>0<=o>>>0;);}else m=g;x=j,j=(w=m+4|0)>>>0>>0?w:s}else x=j,j=s;r=0-l|0;b:do{if(x>>>0>>0)for(;;){if(0|c[(m=j+-4|0)>>2]){s=1,w=j;break b}if(!(x>>>0>>0)){s=0,w=m;break}j=m}else s=0,w=j}while(0);do{if(t){if(n=(0|l)<(0|(j=k+(1&(1^u))|0))&-5<(0|l)?(k=j+-1-l|0,i+-1|0):(k=j+-1|0,i+-2|0),!(8&h)){if(s&&0!=(0|(A=0|c[w+-4>>2])))if((A>>>0)%10|0)m=0;else for(j=10,m=0;m=m+1|0,!((A>>>0)%((j=10*j|0)>>>0)|0););else m=9;if(j=(9*((w-y|0)>>2)|0)-9|0,102==(32|n)){k=(0|k)<(0|(i=0<(0|(i=j-m|0))?i:0))?k:i;break}k=(0|k)<(0|(i=0<(0|(i=j+l-m|0))?i:0))?k:i;break}}else n=i}while(0);if(o=(g=0!=(0|k))?1:h>>>3&1,p=102==(32|n))j=(u=0)<(0|l)?l:0;else{if(((m=E)-(j=0|oc(j=(0|l)<0?r:l,((0|j)<0)<<31>>31,E))|0)<2)for(;a[(j=j+-1|0)>>0]=48,(m-j|0)<2;);a[j+-1>>0]=43+(l>>31&2),a[(j=j+-2|0)>>0]=n,j=m-(u=j)|0}if(qc(b,32,f,j=D+1+k+o+j|0,h),ic(b,B,D),qc(b,48,f,j,65536^h),p){p=r=G+9|0,n=G+8|0,m=o=C>>>0>>0?C:x;do{if(l=0|oc(0|c[m>>2],0,r),(0|m)==(0|o))(0|l)==(0|r)&&(a[n>>0]=48,l=n);else if(G>>>0>>0)for($c(0|G,48,l-F|0);G>>>0<(l=l+-1|0)>>>0;);}while(ic(b,l,p-l|0),(m=m+4|0)>>>0<=C>>>0);if(0==(8&h|0)&(1^g)||ic(b,2990,1),m>>>0>>0&0<(0|k))for(;;){if(G>>>0<(l=0|oc(0|c[m>>2],0,r))>>>0)for($c(0|G,48,l-F|0);G>>>0<(l=l+-1|0)>>>0;);if(ic(b,l,(0|k)<9?k:9),l=k+-9|0,!((m=m+4|0)>>>0>>0&9<(0|k))){k=l;break}k=l}qc(b,48,k+9|0,9,0)}else{if(x>>>0<(g=s?w:x+4|0)>>>0&-1<(0|k)){t=0==(8&h|0),s=r=G+9|0,n=0-F|0,p=G+8|0,o=x;do{(0|(l=0|oc(0|c[o>>2],0,r)))==(0|r)&&(a[p>>0]=48,l=p);do{if((0|o)==(0|x)){if(m=l+1|0,ic(b,l,1),t&(0|k)<1){l=m;break}ic(b,2990,1),l=m}else{if(l>>>0<=G>>>0)break;for($c(0|G,48,l+n|0);G>>>0<(l=l+-1|0)>>>0;);}}while(0)}while(ic(b,l,(0|(F=s-l|0))<(0|k)?F:k),(o=o+4|0)>>>0>>0&-1<(0|(k=k-F|0)))}qc(b,48,k+18|0,18,0),ic(b,u,E-u|0)}qc(b,32,f,j,8192^h)}}while(0);return S=H,0|((0|j)<(0|f)?f:j)}function gc(b,d,e){b|=0,d|=0,e|=0;var h,i,j,k,l,n,o,f=0,g=0,p=S,m=S=S+63&-64;for(S=S+224|0,l=208+m|0,n=80+m|0,g=40+(f=o=160+m|0)|0;(0|(f=f+4|(c[f>>2]=0)))<(0|g););c[l>>2]=c[e>>2],0<=(0|hc(0,d,l,n,o))&&(k=32&(e=0|c[b>>2]),(0|a[b+74>>0])<1&&(c[b>>2]=-33&e),0|c[(g=b+48|0)>>2]?hc(b,d,l,n,o):(e=0|c[(f=b+44|0)>>2],c[f>>2]=m,c[(h=b+28|0)>>2]=m,c[(j=b+20|0)>>2]=m,c[g>>2]=80,c[(i=b+16|0)>>2]=80+m,hc(b,d,l,n,o),0|e&&(X[3&c[b+36>>2]](b,0,0),c[f>>2]=e,c[g>>2]=0,c[i>>2]=0,c[h>>2]=0,c[j>>2]=0)),c[b>>2]=c[b>>2]|k),S=p}function hc(d,e,f,h,i){d|=0,f|=0,h|=0,i|=0;var v,w,x,y,A,B,D,E,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,G=0,F=S,C=S=S+63&-64;S=S+64|0,D=56+C|0,B=40+C|0,E=48+(w=C)|0,C=60+C|0,c[D>>2]=e|=0,A=0!=(0|d),y=v=40+w|0,w=39+w|0,x=4+E|0,m=j=e=0;a:for(;;){do{do{if(-1<(0|e)){if((2147483647-e|0)<(0|j)){c[1026]=61,e=-1;break}e=j+e|0;break}}while(0);if(p=0|c[D>>2],!((j=0|a[p>>0])<<24>>24)){u=91;break a}k=p;b:for(;;){switch(j<<24>>24){case 37:u=10;break b;case 0:j=k;break b}c[D>>2]=t=k+1|0,j=0|a[t>>0],k=t}c:do{if(10==(0|u)){u=0,j=l=k;do{if(37!=(0|a[l+1>>0]))break c}while(j=j+1|0,c[D>>2]=l=l+2|0,37==(0|a[l>>0]))}}while(0)}while(j=j-p|0,A&&ic(d,p,j),0!=(0|j));if(l=0|c[D>>2],o=0|jc(j=0|a[l+1>>0])?(k=(o=36==(0|a[l+2>>0]))?3:1,r=o?j+-48|0:-1,o?1:m):(r=-(k=1),m),c[D>>2]=k=l+k|0,31<(l=((j=0|a[k>>0])<<24>>24)-32|0)>>>0|0==(1<>2]=k=k+1|0,31<(l=((j=0|a[k>>0])<<24>>24)-32|0)>>>0|0==(1<>24==42){if(0!=(0|jc(j=0|a[(l=k+1|0)>>0]))&&36==(0|a[k+2>>0]))c[i+(j+-48<<2)>>2]=10,m=1,j=k+3|0,k=0|c[h+((0|a[l>>0])-48<<3)>>2];else{if(0|o){e=-1;break}A?(m=3+(0|c[f>>2])&-4,k=0|c[m>>2],c[f>>2]=m+4,m=0,j=l):(j=l,k=m=0)}l=c[D>>2]=j,q=(s=(0|k)<0)?8192|n:n,t=m,s=s?0-k|0:k}else{if((0|(j=0|kc(D)))<0){e=-1;break}l=0|c[D>>2],q=n,t=o,s=j}do{if(46==(0|a[l>>0])){if(42!=(0|a[(j=l+1|0)>>0])){c[D>>2]=j,n=0|kc(D),j=0|c[D>>2];break}if(0|jc(k=0|a[(j=l+2|0)>>0])&&36==(0|a[l+3>>0])){c[i+(k+-48<<2)>>2]=10,n=0|c[h+((0|a[j>>0])-48<<3)>>2],c[D>>2]=j=l+4|0;break}if(0|t){e=-1;break a}A?(o=3+(0|c[f>>2])&-4,k=0|c[o>>2],c[f>>2]=o+4):k=0,c[D>>2]=j,n=k}else j=l,n=-1}while(0);for(o=0;;){if(57<((0|a[j>>0])-65|0)>>>0){e=-1;break a}if(c[D>>2]=k=j+1|0,!(((m=255&(l=0|a[(0|a[j>>0])-65+(16+(58*o|0))>>0]))-1|0)>>>0<8))break;j=k,o=m}if(!(l<<24>>24)){e=-1;break}k=-1<(0|r);do{if(l<<24>>24==19){if(k){e=-1;break a}u=52}else{if(k){c[i+(r<<2)>>2]=m,r=0|c[4+(m=h+(r<<3)|0)>>2],c[(u=B)>>2]=c[m>>2],c[u+4>>2]=r,u=52;break}if(!A){e=0;break a}lc(B,m,f),u=53}}while(0);52==(0|u)&&(u=0,A?u=53:j=0);d:do{if(53==(0|u)){l=(u=0)!=(0|o)&3==(15&(l=0|a[j>>0])|0)?-33&l:l,j=-65537&q,r=0==(8192&q|0)?q:j;e:do{switch(0|l){case 110:switch((255&o)<<24>>24){case 0:case 1:c[c[B>>2]>>2]=e,j=0;break d;case 2:j=0|c[B>>2],c[j>>2]=e,c[j+4>>2]=((0|e)<0)<<31>>31,j=0;break d;case 3:b[c[B>>2]>>1]=e,j=0;break d;case 4:a[c[B>>2]>>0]=e,j=0;break d;case 6:c[c[B>>2]>>2]=e,j=0;break d;case 7:j=0|c[B>>2],c[j>>2]=e,c[j+4>>2]=((0|e)<0)<<31>>31,j=0;break d;default:j=0;break d}case 112:j=8|r,k=8>>0?n:8,m=120,u=65;break;case 88:case 120:j=r,k=n,m=l,u=65;break;case 111:n=0==(8&(j=r)|0)|(0|(l=y-(o=0|function(b,c,d){if(d|=0,!(0==(0|(b|=0))&0==(0|(c|=0))))for(;a[(d=d+-1|0)>>0]=7&b|48,!(0==(0|(b=0|Wc(0|b,0|c,3)))&0==(0|(c=0|z()))););return 0|d}(p=0|c[(q=B)>>2],q=0|c[q+4>>2],v))|0))<(0|n)?n:l+1|0,l=0,k=2938,u=71;break;case 105:case 100:if(j=0|c[(k=B)>>2],(0|(k=0|c[k+4>>2]))<0){j=0|Sc(0,0,0|j,0|k),k=0|z(),c[(l=B)>>2]=j,c[l+4>>2]=k,l=1,m=2938,u=70;break e}l=0!=(2049&r|0)&1,m=0==(2048&r|0)?0==(1&r|0)?2938:2940:2939,u=70;break e;case 117:j=0|c[(k=B)>>2],k=0|c[k+4>>2],l=0,m=2938,u=70;break;case 99:a[w>>0]=c[B>>2],p=w,o=j,m=1,l=0,k=2938,j=y;break;case 115:p=q=0==(0|(q=0|c[B>>2]))?2948:q,o=j,m=(G=0==(0|(r=0|pc(q,n))))?n:r-q|0,l=0,k=2938,j=G?q+n|0:r;break;case 67:c[E>>2]=c[B>>2],c[x>>2]=0,l=c[B>>2]=E,n=-1,u=78;break;case 83:if(n){l=0|c[B>>2],u=78;break e}qc(d,32,s,0,r),j=0,u=88;break e;case 65:case 71:case 70:case 69:case 97:case 103:case 102:case 101:j=0|ec(d,+g[B>>3],s,n,r,l);break d;default:o=r,m=n,l=0,k=2938,j=y}}while(0);f:do{if(65==(0|u))o=0|mc(p=0|c[(q=B)>>2],q=0|c[q+4>>2],v,32&m),n=k,l=(G=0==(8&j|0)|0==(0|p)&0==(0|q))?0:2,k=G?2938:2938+(m>>>4)|0,u=71;else if(70==(0|u))o=0|oc(p=j,q=k,v),j=r,k=m,u=71;else if(78==(0|u)){for(j=u=0,o=l;k=0|c[o>>2];){if((m=(0|(k=0|rc(C,k)))<0)|(n-j|0)>>>0>>0){u=82;break}if(!((j=k+j|0)>>>0>>0))break;o=o+4|0}if(82==(0|u)&&(u=0,m)){e=-1;break a}if(qc(d,32,s,j,r),j)for(m=0;;){if(!(k=0|c[l>>2])){u=88;break f}if((0|j)<(0|(m=(k=0|rc(C,k))+m|0))){u=88;break f}if(ic(d,C,k),j>>>0<=m>>>0){u=88;break}l=l+4|0}else j=0,u=88}}while(0);if(71==(0|u))G=(u=0)!=(0|n)|(m=0!=(0|p)|0!=(0|q)),m=y-o+(1&(1^m))|0,p=G?o:v,o=-1<(0|n)?-65537&j:j,m=G?(0|m)<(0|n)?n:m:0,j=y;else if(88==(0|u)){u=0,qc(d,32,s,j,8192^r),j=(0|j)<(0|s)?s:j;break}qc(d,32,j=(0|s)<(0|(G=(q=(0|m)<(0|(r=j-p|0))?r:m)+l|0))?G:s,G,o),ic(d,k,l),qc(d,48,j,G,65536^o),qc(d,48,q,r,0),ic(d,p,r),qc(d,32,j,G,8192^o)}}while(0);m=t}g:do{if(91==(0|u)&&!d)if(m){for(e=1;j=0|c[i+(e<<2)>>2];)if(lc(h+(e<<3)|0,j,f),10<=(e=e+1|0)>>>0){e=1;break g}for(j=0;;){if(e=e+1|0,0|j){e=-1;break g}if(10<=e>>>0){e=1;break g}j=0|c[i+(e<<2)>>2]}}else e=0}while(0);return S=F,0|e}function ic(a,b,d){b|=0,d|=0,32&c[(a|=0)>>2]||tc(b,d,a)}function jc(a){return((0|a)-48|0)>>>0<10|0}function kc(b){var d=0,e=0,f=0,e=0|c[(b|=0)>>2];if(0|jc((d=0|a[e>>0])<<24>>24))for(f=d,d=0;d=(10*d|0)-48+(f<<24>>24)|0,c[b>>2]=e=e+1|0,0!=(0|jc((f=0|a[e>>0])<<24>>24)););else d=0;return 0|d}function lc(a,b,d){a|=0,b|=0,d|=0;var h,e=0,f=0;a:do{if(b>>>0<=20)switch(0|b){case 9:e=3+(0|c[d>>2])&-4,b=0|c[e>>2],c[d>>2]=e+4,c[a>>2]=b;break a;case 10:e=3+(0|c[d>>2])&-4,b=0|c[e>>2],c[d>>2]=e+4,c[(e=a)>>2]=b,c[e+4>>2]=((0|b)<0)<<31>>31;break a;case 11:e=3+(0|c[d>>2])&-4,b=0|c[e>>2],c[d>>2]=e+4,c[(e=a)>>2]=b,c[e+4>>2]=0;break a;case 12:e=7+(0|c[d>>2])&-8,f=0|c[(b=e)>>2],b=0|c[b+4>>2],c[d>>2]=e+8,c[(e=a)>>2]=f,c[e+4>>2]=b;break a;case 13:f=3+(0|c[d>>2])&-4,e=0|c[f>>2],c[d>>2]=f+4,c[(f=a)>>2]=e=(65535&e)<<16>>16,c[f+4>>2]=((0|e)<0)<<31>>31;break a;case 14:f=3+(0|c[d>>2])&-4,e=0|c[f>>2],c[d>>2]=f+4,c[(f=a)>>2]=65535&e,c[f+4>>2]=0;break a;case 15:f=3+(0|c[d>>2])&-4,e=0|c[f>>2],c[d>>2]=f+4,c[(f=a)>>2]=e=(255&e)<<24>>24,c[f+4>>2]=((0|e)<0)<<31>>31;break a;case 16:f=3+(0|c[d>>2])&-4,e=0|c[f>>2],c[d>>2]=f+4,c[(f=a)>>2]=255&e,c[f+4>>2]=0;break a;case 17:f=7+(0|c[d>>2])&-8,h=+g[f>>3],c[d>>2]=f+8,g[a>>3]=h;break a;case 18:!function(a,b){a|=0;var e=7+(0|c[(b|=0)>>2])&-8,d=+g[e>>3];c[b>>2]=8+e,g[a>>3]=d}(a,d);break a;default:break a}}while(0)}function mc(b,c,e,f){if(e|=0,f|=0,!(0==(0|(b|=0))&0==(0|(c|=0))))for(;a[(e=e+-1|0)>>0]=0|d[480+(15&b)>>0]|f,!(0==(0|(b=0|Wc(0|b,0|c,4)))&0==(0|(c=0|z()))););return 0|e}function oc(b,c,d){d|=0;var e,f=0,g=0;if(0<(c|=0)>>>0|0==(0|c)&4294967295<(b|=0)>>>0)for(;g=0|Rc(0|(e=b),0|(f=c),0|(g=0|Qc(0|(b=0|Vc(0|b,0|c,10,0)),0|(c=0|z()),-10,-1)),0|z()),z(),a[(d=d+-1|0)>>0]=255&g|48,9>>0|9==(0|f)&4294967295>>0;);if(b)for(;f=255&((g=b)+(0|v(b=(b>>>0)/10|0,-10))|48),a[(d=d+-1|0)>>0]=f,10<=g>>>0;);return 0|d}function pc(b,d){b|=0;var e=0,f=0,e=0!=(0|(d|=0));a:do{if(e&0!=(3&b|0))for(;;){if(!(0|a[b>>0]))break a;if(!((e=0!=(0|(d=d+-1|0)))&0!=(3&(b=b+1|0)|0))){f=5;break}}else f=5}while(0);b:do{if(5==(0|f)){do{if(e){if(!(0|a[b>>0])){if(d)break b;break}c:do{if(3>>0)for(;;){if((-2139062144&(e=0|c[b>>2])^-2139062144)&e+-16843009|0)break c;if(b=b+4|0,(d=d+-4|0)>>>0<=3){f=11;break}}else f=11}while(0);if(11==(0|f)&&!d)break;for(;;){if(!(0|a[b>>0]))break b;if(!(d=d+-1|0))break;b=b+1|0}}}while(0);b=0}}while(0);return 0|b}function qc(a,b,c,d,e){a|=0,b|=0;var g=S,f=S=S+63&-64;if(S=S+256|0,(0|(d|=0))<(0|(c|=0))&0==(73728&(e|=0)|0)){if($c(0|f,b<<24>>24|0,0|((e=c-d|0)>>>0<256?e:256)),255>>0){for(d=e;ic(a,f,256),255<(d=d+-256|0)>>>0;);e&=255}ic(a,f,e)}S=g}function rc(a,b){return b|=0,0|((a|=0)?0|sc(a,b):0)}function sc(b,d){b|=0,d|=0;do{if(b){if(d>>>0<128){a[b>>0]=d,b=1;break}if(!(0|c[1020])){if(57216==(-128&d|0)){a[b>>0]=d,b=1;break}c[1026]=25,b=-1;break}if(d>>>0<2048){a[b>>0]=d>>>6|192,a[b+1>>0]=63&d|128,b=2;break}if(d>>>0<55296|57344==(-8192&d|0)){a[b>>0]=d>>>12|224,a[b+1>>0]=d>>>6&63|128,a[b+2>>0]=63&d|128,b=3;break}if((d+-65536|0)>>>0<1048576){a[b>>0]=d>>>18|240,a[b+1>>0]=d>>>12&63|128,a[b+2>>0]=d>>>6&63|128,a[b+3>>0]=63&d|128,b=4;break}c[1026]=25,b=-1;break}}while(b=1,0);return 0|b}function tc(b,d,e){b|=0,d|=0;var i,f=0,g=0,h=0;(f=0|c[(g=16+(e|=0)|0)>>2])?h=5:0|function(b){var d=0,e=0|a[(d=74+(b|=0)|0)>>0];return a[d>>0]=255+e|e,0|(d=8&(d=0|c[b>>2])?(c[b>>2]=32|d,-1):(c[8+b>>2]=0,d=(c[4+b>>2]=0)|c[44+b>>2],c[28+b>>2]=d,c[20+b>>2]=d,c[16+b>>2]=d+(0|c[48+b>>2]),0))}(e)||(f=0|c[g>>2],h=5);a:do{if(5==(0|h)){if((f-(g=h=0|c[(i=e+20|0)>>2])|0)>>>0>>0){X[3&c[e+36>>2]](e,b,d);break}b:do{if((0|a[e+75>>0])<0|0==(0|d))f=d;else{for(h=d;10!=(0|a[b+(f=h+-1|0)>>0]);){if(!f){f=d;break b}h=f}if((0|X[3&c[e+36>>2]](e,b,h))>>>0>>0)break a;g=0|c[i>>2],f=d-h|0,b=b+h|0}}while(0);Zc(0|g,0|b,0|f),c[i>>2]=(0|c[i>>2])+f}}while(0)}function vc(a){g[h>>3]=a=+a,a=0|c[h>>2],y(0|c[h+4>>2])}function wc(a,b){b|=0;var e,f,d=0;switch(g[h>>3]=a=+a,f=0|Wc(0|(d=0|c[h>>2]),0|(e=0|c[h+4>>2]),52),z(),2047&f){case 0:d=0!=a?(a=+wc(0x10000000000000000*a,b),(0|c[b>>2])-64|0):0,c[b>>2]=d;break;case 2047:break;default:c[b>>2]=(2047&f)-1022,c[h>>2]=d,c[h+4>>2]=-2146435073&e|1071644672,a=+g[h>>3]}return+a}function yc(b){var 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f,d=0,e=S,d=S=S+63&-64;if(S=S+256|0,2<=(0|(b|=0)))for(Zc(0|(c[a+(b<<2)>>2]=d),0|c[a>>2],4),d=0;Zc(0|c[(f=a+(d<<2)|0)>>2],0|c[a+((d=d+1|0)<<2)>>2],4),c[f>>2]=4+(0|c[f>>2]),(0|d)!=(0|b););S=e}function Jc(a){a|=0;var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,v=0,w=S,n=S=S+63&-64;S=S+16|0;do{if(a>>>0<245){if(m=0|c[1027],3&(d=(a=(k=a>>>0<11?16:a+11&-8)>>>3)?m>>>a:m)|0)return a=0|c[(b=8+(f=4148+((e=(1&d^1)+a|0)<<1<<2)|0)|0)>>2],(0|(d=0|c[(g=a+8|0)>>2]))==(0|f)?c[1027]=m&~(1<>2]=f,c[b>>2]=d),c[a+4>>2]=3|(v=e<<3),c[(v=a+v+4|0)>>2]=1|c[v>>2],S=w,0|g;if((l=0|c[1029])>>>0>>0){if(0|d)return g=0|c[(b=8+(d=4148+((e=((a=(e=(i=(e=((e=d<>>12&16)?e>>>i:e)>>>5&8)|i|(g=(e=a?e>>>a:e)>>>2&4)|(b=(e=g?e>>>g:e)>>>1&2)|(d=(e=b?e>>>b:e)>>>1&1))+(d?e>>>d:e)|0)<<1<<2)|0)|0)>>2],(0|(a=0|c[(i=g+8|0)>>2]))==(0|d)?c[1027]=a=m&~(1<>2]=d,c[b>>2]=a,a=m),h=(v=e<<3)-k|0,c[g+4>>2]=3|k,c[4+(f=g+k|0)>>2]=1|h,c[g+v>>2]=h,0|l&&(e=0|c[1032],d=4148+((b=l>>>3)<<1<<2)|0,a=a&(b=1<>2]:(c[1027]=a|b,b=d+8|0,d),c[b>>2]=e,c[a+12>>2]=e,c[e+8>>2]=a,c[e+12>>2]=d),c[1029]=h,c[1032]=f,S=w,0|i;if(g=0|c[1028]){for(j=0|c[4412+(((e=(j=(f=(j=(g&0-g)-1|0)>>>12&16)?j>>>f:j)>>>5&8)|f|(h=(j=e?j>>>e:j)>>>2&4)|(d=(j=h?j>>>h:j)>>>1&2)|(i=(j=d?j>>>d:j)>>>1&1))+(i?j>>>i:j)<<2)>>2],i=(-8&c[j+4>>2])-k|0,d=j;(a=0|c[d+16>>2])||(a=0|c[d+20>>2]);)i=(h=(d=(-8&c[a+4>>2])-k|0)>>>0>>0)?d:i,d=a,j=h?a:j;if(j>>>0<(h=j+k|0)>>>0){f=0|c[j+24>>2],b=0|c[j+12>>2];do{if((0|b)==(0|j)){if(!(b=0|c[(a=j+20|0)>>2])&&!(b=0|c[(a=j+16|0)>>2])){d=0;break}for(;;)if(d=0|c[(e=b+20|0)>>2])b=d,a=e;else{if(!(d=0|c[(e=b+16|0)>>2]))break;b=d,a=e}c[a>>2]=0,d=b}else d=0|c[j+8>>2],c[d+12>>2]=b,c[b+8>>2]=d,d=b}while(0);do{if(0|f){if(b=0|c[j+28>>2],(0|j)==(0|c[(a=4412+(b<<2)|0)>>2])){if(!(c[a>>2]=d)){c[1028]=g&~(1<>2])==(0|j)?v:f+20|0)>>2]=d))break;c[d+24>>2]=f,0|(b=0|c[j+16>>2])&&(c[d+16>>2]=b,c[b+24>>2]=d),0|(b=0|c[j+20>>2])&&(c[d+20>>2]=b,c[b+24>>2]=d)}}while(0);return i>>>0<16?(c[j+4>>2]=3|(v=i+k|0),c[(v=j+v+4|0)>>2]=1|c[v>>2]):(c[j+4>>2]=3|k,c[h+4>>2]=1|i,c[h+i>>2]=i,0|l&&(e=0|c[1032],d=4148+((b=l>>>3)<<1<<2)|0,a=(b=1<>2]:(c[1027]=b|m,b=d+8|0,d),c[b>>2]=e,c[a+12>>2]=e,c[e+8>>2]=a,c[e+12>>2]=d),c[1029]=i,c[1032]=h),S=w,j+8|0}m=k}else m=k}else m=k}else if(a>>>0<=4294967231)if(k=-8&(a=a+11|0),e=0|c[1028]){d=0-k|0,j=(a>>>=8)?16777215>>0?31:1&((r=7+(j=14-((i=(520192+(j=a<<(m=(a+1048320|0)>>>16&8))|0)>>>16&4)|m|(r=(245760+(j<<=i)|0)>>>16&2))+(j<>>15)|0)|0)?k>>>r:k)|j<<1:0,a=0|c[4412+(j<<2)>>2];a:do{if(a)for(h=k<<(31==((f=0)|j)?0:25-(j>>>1)|0),i=a,a=0;;){if((g=(-8&c[i+4>>2])-k|0)>>>0>>0){if(!g){d=0,a=f=i,r=65;break a}d=g,a=i}if(f=0==(0|(r=0|c[i+20>>2]))|(0|r)==(0|(i=0|c[i+16+(h>>>31<<2)>>2]))?f:r,!i){r=61;break}h<<=1}else a=f=0,r=61}while(0);if(61==(0|r)){if(0==(0|f)&0==(0|a)){if(!(a=((a=2<>>12&16)?f>>>i:f)>>>5&8)|i|(j=(f=h?f>>>h:f)>>>2&4)|(m=(f=j?f>>>j:f)>>>1&2)|(a=(f=m?f>>>m:f)>>>1&1))+(a?f>>>a:f)<<2)>>2],a=0}f?r=65:(i=d,g=a)}if(65==(0|r))for(;;){if(d=(g=(m=(-8&c[f+4>>2])-k|0)>>>0>>0)?m:d,g=g?f:a,!(a=(a=0|c[f+16>>2])||0|c[f+20>>2])){i=d;break}f=a,a=g}if(0!=(0|g)&&i>>>0<((0|c[1029])-k|0)>>>0&&g>>>0<(l=g+k|0)>>>0){h=0|c[g+24>>2],b=0|c[g+12>>2];do{if((0|b)==(0|g)){if(!(b=0|c[(a=g+20|0)>>2])&&!(b=0|c[(a=g+16|0)>>2])){b=0;break}for(;;)if(d=0|c[(f=b+20|0)>>2])b=d,a=f;else{if(!(d=0|c[(f=b+16|0)>>2]))break;b=d,a=f}c[a>>2]=0}else v=0|c[g+8>>2],c[v+12>>2]=b,c[b+8>>2]=v}while(0);do{if(h){if(a=0|c[g+28>>2],(0|g)==(0|c[(d=4412+(a<<2)|0)>>2])){if(!(c[d>>2]=b)){e&=~(1<>2])==(0|g)?v:h+20|0)>>2]=b))break;c[b+24>>2]=h,0|(a=0|c[g+16>>2])&&(c[b+16>>2]=a,c[a+24>>2]=b),(a=0|c[g+20>>2])&&(c[b+20>>2]=a,c[a+24>>2]=b)}}while(0);b:do{if(i>>>0<16)c[g+4>>2]=3|(v=i+k|0),c[(v=g+v+4|0)>>2]=1|c[v>>2];else{if(c[g+4>>2]=3|k,c[l+4>>2]=1|i,b=(c[l+i>>2]=i)>>>3,i>>>0<256){d=4148+(b<<1<<2)|0,a=(a=0|c[1027])&(b=1<>2]:(c[1027]=a|b,b=d+8|0,d),c[b>>2]=l,c[a+12>>2]=l,c[l+8>>2]=a,c[l+12>>2]=d;break}if(b=4412+((d=(b=i>>>8)?16777215>>0?31:1&((v=7+(d=14-((t=(520192+(d=b<<(u=(b+1048320|0)>>>16&8))|0)>>>16&4)|u|(v=(245760+(d<<=t)|0)>>>16&2))+(d<>>15)|0)|0)?i>>>v:i)|d<<1:0)<<2)|0,c[l+28>>2]=d,c[4+(a=l+16|0)>>2]=0,c[a>>2]=0,!(e&(a=1<>2]=l,c[l+24>>2]=b,c[l+12>>2]=l,c[l+8>>2]=l;break}b=0|c[b>>2];c:do{if((-8&c[b+4>>2]|0)!=(0|i)){for(e=i<<(31==(0|d)?0:25-(d>>>1)|0);a=0|c[(d=b+16+(e>>>31<<2)|0)>>2];){if((-8&c[a+4>>2]|0)==(0|i)){b=a;break c}e<<=1,b=a}c[d>>2]=l,c[l+24>>2]=b,c[l+12>>2]=l,c[l+8>>2]=l;break b}}while(0);v=0|c[(u=b+8|0)>>2],c[v+12>>2]=l,c[u>>2]=l,c[l+8>>2]=v,c[l+12>>2]=b,c[l+24>>2]=0}}while(0);return S=w,g+8|0}m=k}else m=k;else m=-1}while(0);if(m>>>0<=(d=0|c[1029])>>>0)return b=0|c[1032],15<(a=d-m|0)>>>0?(c[1032]=v=b+m|0,c[1029]=a,c[v+4>>2]=1|a,c[b+d>>2]=a,c[b+4>>2]=3|m):(c[1029]=0,c[1032]=0,c[b+4>>2]=3|d,c[(v=b+d+4|0)>>2]=1|c[v>>2]),S=w,b+8|0;if(m>>>0<(h=0|c[1030])>>>0)return c[1030]=t=h-m|0,v=0|c[1033],c[1033]=u=v+m|0,c[u+4>>2]=1|t,c[v+4>>2]=3|m,S=w,v+8|0;if(i=m+48|0,(k=(g=(a=0|c[1145]?0|c[1147]:(c[1147]=4096,c[1146]=4096,c[1148]=-1,c[1149]=-1,c[1150]=0,c[1138]=0,c[1145]=-16&n^1431655768,4096))+(j=m+47|0)|0)&(e=0-a|0))>>>0<=m>>>0)return S=w,(v=0)|v;if(0|(a=0|c[1137])&&(n=(l=0|c[1135])+k|0)>>>0<=l>>>0|a>>>0>>0)return S=w,(v=0)|v;d:do{if(4&c[1138])b=0,r=143;else{d=0|c[1033];e:do{if(d){for(f=4556;!((a=0|c[f>>2])>>>0<=d>>>0&&(a+(0|c[(q=f+4|0)>>2])|0)>>>0>d>>>0);){if(!(a=0|c[f+8>>2])){r=128;break e}f=a}if((b=g-h&e)>>>0<2147483647)if((0|(a=0|Oc(b)))==((0|c[f>>2])+(0|c[q>>2])|0)){if(-1!=(0|a)){h=a,g=b,r=145;break d}}else e=a,r=136;else b=0}else r=128}while(0);do{if(128==(0|r))if(-1!=(0|(d=0|Oc(0)))&&(b=d,p=(b=(0==((p=(o=0|c[1146])-1|0)&b|0)?0:(p+b&0-o)-b|0)+k|0)+(o=0|c[1135])|0,m>>>0>>0&b>>>0<2147483647)){if(0|(q=0|c[1137])&&p>>>0<=o>>>0|q>>>0

>>0){b=0;break}if((0|(a=0|Oc(b)))==(0|d)){h=d,g=b,r=145;break d}e=a,r=136}else b=0}while(0);do{if(136==(0|r)){if(d=0-b|0,!(b>>>0>>0&b>>>0<2147483647&-1!=(0|e))){if(-1==(0|e)){b=0;break}h=e,g=b,r=145;break d}if(2147483647<=(a=j-b+(a=0|c[1147])&0-a)>>>0){h=e,g=b,r=145;break d}if(-1==(0|Oc(a))){Oc(d),b=0;break}h=e,g=a+b|0,r=145;break d}}while(0);c[1138]=4|c[1138],r=143}}while(0);if(143==(0|r)&&k>>>0<2147483647&&!(-1==(0|(s=0|Oc(k)))|1^(t=(m+40|0)>>>0<(u=(q=0|Oc(0))-s|0)>>>0)|s>>>0>>0&-1!=(0|s)&-1!=(0|q)^1)&&(h=s,g=t?u:b,r=145),145==(0|r)){b=(0|c[1135])+g|0,(c[1135]=b)>>>0>(0|c[1136])>>>0&&(c[1136]=b),j=0|c[1033];f:do{if(j){for(f=4556;;){if((0|h)==((b=0|c[f>>2])+(a=0|c[(e=f+4|0)>>2])|0)){r=154;break}if(!(d=0|c[f+8>>2]))break;f=d}if(154==(0|r)&&0==(8&c[f+12>>2]|0)&&j>>>0>>0&b>>>0<=j>>>0){c[e>>2]=a+g,u=j+(t=0==(7&(t=j+8|0)|0)?0:0-t&7)|0,t=(v=(0|c[1030])+g|0)-t|0,c[1033]=u,c[1030]=t,c[u+4>>2]=1|t,c[j+v+4>>2]=40,c[1034]=c[1149];break}for(h>>>0<(0|c[1031])>>>0&&(c[1031]=h),d=h+g|0,a=4556;;){if((0|c[a>>2])==(0|d)){r=162;break}if(!(b=0|c[a+8>>2]))break;a=b}if(162==(0|r)&&0==(8&c[a+12>>2]|0)){c[a>>2]=h,c[(l=a+4|0)>>2]=(0|c[l>>2])+g,k=(l=h+(0==(7&(l=h+8|0)|0)?0:0-l&7)|0)+m|0,i=(b=d+(0==(7&(b=d+8|0)|0)?0:0-b&7)|0)-l-m|0,c[l+4>>2]=3|m;g:do{if((0|j)==(0|b))v=(0|c[1030])+i|0,c[1030]=v,c[1033]=k,c[k+4>>2]=1|v;else{if((0|c[1032])==(0|b)){v=(0|c[1029])+i|0,c[1029]=v,c[1032]=k,c[k+4>>2]=1|v,c[k+v>>2]=v;break}if(1==(3&(a=0|c[b+4>>2])|0)){h=-8&a,e=a>>>3;h:do{if(a>>>0<256){if(a=0|c[b+8>>2],(0|(d=0|c[b+12>>2]))==(0|a)){c[1027]=c[1027]&~(1<>2]=d,c[d+8>>2]=a;break}g=0|c[b+24>>2],a=0|c[b+12>>2];do{if((0|a)==(0|b)){if(!(a=0|c[(d=4+(e=b+16|0)|0)>>2])){if(!(a=0|c[e>>2])){a=0;break}d=e}for(;;)if(e=0|c[(f=a+20|0)>>2])a=e,d=f;else{if(!(e=0|c[(f=a+16|0)>>2]))break;a=e,d=f}c[d>>2]=0}else v=0|c[b+8>>2],c[v+12>>2]=a,c[a+8>>2]=v}while(0);if(!g)break;e=4412+((d=0|c[b+28>>2])<<2)|0;do{if((0|c[e>>2])==(0|b)){if(0|(c[e>>2]=a))break;c[1028]=c[1028]&~(1<>2])==(0|b)?v:g+20|0)>>2]=a))break h}while(0)}while(c[a+24>>2]=g,0|(d=0|c[(e=b+16|0)>>2])&&(c[a+16>>2]=d,c[d+24>>2]=a),(d=0|c[e+4>>2])&&(c[a+20>>2]=d,c[d+24>>2]=a,0));b=b+h|0,f=h+i|0}else f=i;if(c[(b=b+4|0)>>2]=-2&c[b>>2],c[k+4>>2]=1|f,b=(c[k+f>>2]=f)>>>3,f>>>0<256){d=4148+(b<<1<<2)|0,a=(a=0|c[1027])&(b=1<>2]:(c[1027]=a|b,b=d+8|0,d),c[b>>2]=k,c[a+12>>2]=k,c[k+8>>2]=a,c[k+12>>2]=d;break}b=f>>>8;do{if(b){if(16777215>>0){e=31;break}e=1&((v=7+(e=14-((t=(520192+(e=b<<(u=(b+1048320|0)>>>16&8))|0)>>>16&4)|u|(v=(245760+(e<<=t)|0)>>>16&2))+(e<>>15)|0)|0)?f>>>v:f)|e<<1}else e=0}while(0);if(a=4412+(e<<2)|0,c[k+28>>2]=e,c[4+(b=k+16|0)>>2]=0,!((b=(c[b>>2]=0)|c[1028])&(d=1<>2]=k,c[k+24>>2]=a,c[k+12>>2]=k,c[k+8>>2]=k;break}b=0|c[a>>2];i:do{if((-8&c[b+4>>2]|0)!=(0|f)){for(e=f<<(31==(0|e)?0:25-(e>>>1)|0);a=0|c[(d=b+16+(e>>>31<<2)|0)>>2];){if((-8&c[a+4>>2]|0)==(0|f)){b=a;break i}e<<=1,b=a}c[d>>2]=k,c[k+24>>2]=b,c[k+12>>2]=k,c[k+8>>2]=k;break g}}while(0);v=0|c[(u=b+8|0)>>2],c[v+12>>2]=k,c[u>>2]=k,c[k+8>>2]=v,c[k+12>>2]=b,c[k+24>>2]=0}}while(0);return S=w,l+8|0}for(a=4556;!((b=0|c[a>>2])>>>0<=j>>>0&&j>>>0<(v=b+(0|c[a+4>>2])|0)>>>0);)a=0|c[a+8>>2];for(b=(a=(a=(f=v+-47|0)+(0==(7&(a=f+8|0)|0)?0:0-a&7)|0)>>>0<(f=j+16|0)>>>0?j:a)+8|0,u=h+(t=0==(7&(t=h+8|0)|0)?0:0-t&7)|0,t=(d=g+-40|0)-t|0,c[1033]=u,c[1030]=t,c[u+4>>2]=1|t,c[h+d+4>>2]=40,c[1034]=c[1149],c[(d=a+4|0)>>2]=27,c[b>>2]=c[1139],c[b+4>>2]=c[1140],c[b+8>>2]=c[1141],c[b+12>>2]=c[1142],c[1139]=h,c[1140]=g,c[1142]=0,c[1141]=b,b=a+24|0;c[(b=(u=b)+4|0)>>2]=7,(u+8|0)>>>0>>0;);if((0|a)!=(0|j)){if(g=a-j|0,c[d>>2]=-2&c[d>>2],c[j+4>>2]=1|g,b=(c[a>>2]=g)>>>3,g>>>0<256){d=4148+(b<<1<<2)|0,a=(a=0|c[1027])&(b=1<>2]:(c[1027]=a|b,b=d+8|0,d),c[b>>2]=j,c[a+12>>2]=j,c[j+8>>2]=a,c[j+12>>2]=d;break}if(d=4412+((e=(b=g>>>8)?16777215>>0?31:1&((v=7+(e=14-((t=(520192+(e=b<<(u=(b+1048320|0)>>>16&8))|0)>>>16&4)|u|(v=(245760+(e<<=t)|0)>>>16&2))+(e<>>15)|0)|0)?g>>>v:g)|e<<1:0)<<2)|0,c[j+28>>2]=e,c[j+20>>2]=0,!((b=(c[f>>2]=0)|c[1028])&(a=1<>2]=j,c[j+24>>2]=d,c[j+12>>2]=j,c[j+8>>2]=j;break}b=0|c[d>>2];j:do{if((-8&c[b+4>>2]|0)!=(0|g)){for(e=g<<(31==(0|e)?0:25-(e>>>1)|0);a=0|c[(d=b+16+(e>>>31<<2)|0)>>2];){if((-8&c[a+4>>2]|0)==(0|g)){b=a;break j}e<<=1,b=a}c[d>>2]=j,c[j+24>>2]=b,c[j+12>>2]=j,c[j+8>>2]=j;break f}}while(0);v=0|c[(u=b+8|0)>>2],c[v+12>>2]=j,c[u>>2]=j,c[j+8>>2]=v,c[j+12>>2]=b,c[j+24>>2]=0}}else 0==(0|(v=0|c[1031]))|h>>>0>>0&&(c[1031]=h),c[1139]=h,c[1140]=g,c[1142]=0,c[1036]=c[1145],c[1035]=-1,c[1040]=4148,c[1039]=4148,c[1042]=4156,c[1041]=4156,c[1044]=4164,c[1043]=4164,c[1046]=4172,c[1045]=4172,c[1048]=4180,c[1047]=4180,c[1050]=4188,c[1049]=4188,c[1052]=4196,c[1051]=4196,c[1054]=4204,c[1053]=4204,c[1056]=4212,c[1055]=4212,c[1058]=4220,c[1057]=4220,c[1060]=4228,c[1059]=4228,c[1062]=4236,c[1061]=4236,c[1064]=4244,c[1063]=4244,c[1066]=4252,c[1065]=4252,c[1068]=4260,c[1067]=4260,c[1070]=4268,c[1069]=4268,c[1072]=4276,c[1071]=4276,c[1074]=4284,c[1073]=4284,c[1076]=4292,c[1075]=4292,c[1078]=4300,c[1077]=4300,c[1080]=4308,c[1079]=4308,c[1082]=4316,c[1081]=4316,c[1084]=4324,c[1083]=4324,c[1086]=4332,c[1085]=4332,c[1088]=4340,c[1087]=4340,c[1090]=4348,c[1089]=4348,c[1092]=4356,c[1091]=4356,c[1094]=4364,c[1093]=4364,c[1096]=4372,c[1095]=4372,c[1098]=4380,c[1097]=4380,c[1100]=4388,c[1099]=4388,c[1102]=4396,c[1101]=4396,u=h+(t=0==(7&(t=h+8|0)|0)?0:0-t&7)|0,t=(v=g+-40|0)-t|0,c[1033]=u,c[1030]=t,c[u+4>>2]=1|t,c[h+v+4>>2]=40,c[1034]=c[1149]}while(0);if(m>>>0<(b=0|c[1030])>>>0)return c[1030]=t=b-m|0,v=0|c[1033],c[1033]=u=v+m|0,c[u+4>>2]=1|t,c[v+4>>2]=3|m,S=w,v+8|0}return c[1026]=48,S=w,(v=0)|v}function Kc(a){var b=0,d=0,e=0,f=0,g=0,h=0,i=0,j=0,k=0;if(a|=0){e=0|c[1031],k=(d=a+-8|0)+(b=-8&(a=0|c[a+-4>>2]))|0;do{if(1&a)j=i=d;else{if(f=0|c[d>>2],!(3&a))return;if(h=f+b|0,(g=d+(0-f)|0)>>>0>>0)return;if((0|c[1032])==(0|g)){if(3==(3&(a=0|c[(b=k+4|0)>>2])|0))return c[1029]=h,c[b>>2]=-2&a,c[g+4>>2]=1|h,void(c[g+h>>2]=h);j=i=g,b=h;break}if(d=f>>>3,f>>>0<256){if(a=0|c[g+8>>2],(0|(b=0|c[g+12>>2]))==(0|a)){c[1027]=c[1027]&~(1<>2]=b,c[b+8>>2]=a,j=i=g,b=h;break}f=0|c[g+24>>2],a=0|c[g+12>>2];do{if((0|a)==(0|g)){if(!(a=0|c[(b=4+(d=g+16|0)|0)>>2])){if(!(a=0|c[d>>2])){d=0;break}b=d}for(;;)if(d=0|c[(e=a+20|0)>>2])a=d,b=e;else{if(!(d=0|c[(e=a+16|0)>>2]))break;a=d,b=e}c[b>>2]=0,d=a}else d=0|c[g+8>>2],c[d+12>>2]=a,c[a+8>>2]=d,d=a}while(0);if(f){if(a=0|c[g+28>>2],(0|c[(b=4412+(a<<2)|0)>>2])==(0|g)){if(!(c[b>>2]=d)){c[1028]=c[1028]&~(1<>2])==(0|g)?j:f+20|0)>>2]=d)){j=i=g,b=h;break}c[d+24>>2]=f,0|(a=0|c[(b=g+16|0)>>2])&&(c[d+16>>2]=a,c[a+24>>2]=d),(a=0|c[b+4>>2])&&(c[d+20>>2]=a,c[a+24>>2]=d),j=i=g,b=h}else j=i=g,b=h}}while(0);if(!(k>>>0<=i>>>0)&&1&(d=0|c[(a=k+4|0)>>2])){if(2&d)c[a>>2]=-2&d,c[j+4>>2]=1|b,f=c[i+b>>2]=b;else{if((0|c[1033])==(0|k))return k=(0|c[1030])+b|0,c[1030]=k,c[1033]=j,c[j+4>>2]=1|k,void((0|j)==(0|c[1032])&&(c[1032]=0,c[1029]=0));if((0|c[1032])==(0|k))return k=(0|c[1029])+b|0,c[1029]=k,c[1032]=i,c[j+4>>2]=1|k,void(c[i+k>>2]=k);f=(-8&d)+b|0,e=d>>>3;do{if(d>>>0<256){if(b=0|c[k+8>>2],(0|(a=0|c[k+12>>2]))==(0|b)){c[1027]=c[1027]&~(1<>2]=a,c[a+8>>2]=b;break}g=0|c[k+24>>2],a=0|c[k+12>>2];do{if((0|a)==(0|k)){if(!(a=0|c[(b=4+(d=k+16|0)|0)>>2])){if(!(a=0|c[d>>2])){d=0;break}b=d}for(;;)if(d=0|c[(e=a+20|0)>>2])a=d,b=e;else{if(!(d=0|c[(e=a+16|0)>>2]))break;a=d,b=e}c[b>>2]=0,d=a}else d=0|c[k+8>>2],c[d+12>>2]=a,c[a+8>>2]=d,d=a}while(0);if(0|g){if(a=0|c[k+28>>2],(0|c[(b=4412+(a<<2)|0)>>2])==(0|k)){if(!(c[b>>2]=d)){c[1028]=c[1028]&~(1<>2])==(0|k)?h:g+20|0)>>2]=d))break;c[d+24>>2]=g,0|(a=0|c[(b=k+16|0)>>2])&&(c[d+16>>2]=a,c[a+24>>2]=d),0|(a=0|c[b+4>>2])&&(c[d+20>>2]=a,c[a+24>>2]=d)}}while(0);if(c[j+4>>2]=1|f,c[i+f>>2]=f,(0|j)==(0|c[1032]))return void(c[1029]=f)}if(a=f>>>3,f>>>0<256)return d=4148+(a<<1<<2)|0,b=(b=0|c[1027])&(a=1<>2]:(c[1027]=b|a,a=d+8|0,d),c[a>>2]=j,c[b+12>>2]=j,c[j+8>>2]=b,void(c[j+12>>2]=d);b=4412+((e=(a=f>>>8)?16777215>>0?31:1&((k=7+(e=14-((h=(520192+(e=a<<(i=(a+1048320|0)>>>16&8))|0)>>>16&4)|i|(k=(245760+(e<<=h)|0)>>>16&2))+(e<>>15)|0)|0)?f>>>k:f)|e<<1:0)<<2)|0,c[j+28>>2]=e,c[j+20>>2]=0,a=(c[j+16>>2]=0)|c[1028],d=1<>2];b:do{if((-8&c[a+4>>2]|0)!=(0|f)){for(e=f<<(31==(0|e)?0:25-(e>>>1)|0);b=0|c[(d=a+16+(e>>>31<<2)|0)>>2];){if((-8&c[b+4>>2]|0)==(0|f)){a=b;break b}e<<=1,a=b}c[d>>2]=j,c[j+24>>2]=a,c[j+12>>2]=j,c[j+8>>2]=j;break a}}while(0);k=0|c[(i=a+8|0)>>2],c[k+12>>2]=j,c[i>>2]=j,c[j+8>>2]=k,c[j+12>>2]=a,c[j+24>>2]=0}else c[1028]=a|d,c[b>>2]=j,c[j+24>>2]=b,c[j+12>>2]=j,c[j+8>>2]=j}while(0);if(k=(0|c[1035])-1|0,!(0|(c[1035]=k))){for(a=4564;a=0|c[a>>2];)a=a+8|0;c[1035]=-1}}}}function Lc(a,b){var d=0,e=0,f=0,g=0,h=0,i=0,j=0,j=(a|=0)+(b|=0)|0,d=0|c[a+4>>2];do{if(1&d)i=a,a=b;else{if(e=0|c[a>>2],!(3&d))return;if(h=e+b|0,(0|c[1032])==(0|(g=a+(0-e)|0))){if(3==(3&(d=0|c[(a=j+4|0)>>2])|0))return c[1029]=h,c[a>>2]=-2&d,c[g+4>>2]=1|h,c[j>>2]=h;i=g,a=h;break}if(b=e>>>3,e>>>0<256){if(d=0|c[g+8>>2],(0|(a=0|c[g+12>>2]))==(0|d)){c[1027]=c[1027]&~(1<>2]=a,c[a+8>>2]=d,i=g,a=h;break}f=0|c[g+24>>2],d=0|c[g+12>>2];do{if((0|d)==(0|g)){if(!(d=0|c[(a=4+(b=g+16|0)|0)>>2])){if(!(d=0|c[b>>2])){b=0;break}a=b}for(;;)if(b=0|c[(e=d+20|0)>>2])d=b,a=e;else{if(!(b=0|c[(e=d+16|0)>>2]))break;d=b,a=e}c[a>>2]=0,b=d}else b=0|c[g+8>>2],c[b+12>>2]=d,c[d+8>>2]=b,b=d}while(0);if(f){if(d=0|c[g+28>>2],(0|c[(a=4412+(d<<2)|0)>>2])==(0|g)){if(!(c[a>>2]=b)){c[1028]=c[1028]&~(1<>2])==(0|g)?i:f+20|0)>>2]=b)){i=g,a=h;break}c[b+24>>2]=f,0|(d=0|c[(a=g+16|0)>>2])&&(c[b+16>>2]=d,c[d+24>>2]=b),(d=0|c[a+4>>2])&&(c[b+20>>2]=d,c[d+24>>2]=b),i=g,a=h}else i=g,a=h}}while(0);if(2&(b=0|c[(d=j+4|0)>>2]))c[d>>2]=-2&b,c[i+4>>2]=1|a,c[i+a>>2]=a;else{if((0|c[1033])==(0|j))return j=(0|c[1030])+a|0,c[1030]=j,c[1033]=i,c[i+4>>2]=1|j,(0|i)==(0|c[1032])&&(c[1032]=0,c[1029]=0);if((0|c[1032])==(0|j))return j=(0|c[1029])+a|0,c[1029]=j,c[1032]=i,c[i+4>>2]=1|j,c[i+j>>2]=j;g=(-8&b)+a|0,e=b>>>3;do{if(b>>>0<256){if(a=0|c[j+8>>2],(0|(d=0|c[j+12>>2]))==(0|a)){c[1027]=c[1027]&~(1<>2]=d,c[d+8>>2]=a;break}f=0|c[j+24>>2],d=0|c[j+12>>2];do{if((0|d)==(0|j)){if(!(d=0|c[(a=4+(b=j+16|0)|0)>>2])){if(!(d=0|c[b>>2])){b=0;break}a=b}for(;;)if(b=0|c[(e=d+20|0)>>2])d=b,a=e;else{if(!(b=0|c[(e=d+16|0)>>2]))break;d=b,a=e}c[a>>2]=0,b=d}else b=0|c[j+8>>2],c[b+12>>2]=d,c[d+8>>2]=b,b=d}while(0);if(0|f){if(d=0|c[j+28>>2],(0|c[(a=4412+(d<<2)|0)>>2])==(0|j)){if(!(c[a>>2]=b)){c[1028]=c[1028]&~(1<>2])==(0|j)?h:f+20|0)>>2]=b))break;c[b+24>>2]=f,0|(d=0|c[(a=j+16|0)>>2])&&(c[b+16>>2]=d,c[d+24>>2]=b),0|(d=0|c[a+4>>2])&&(c[b+20>>2]=d,c[d+24>>2]=b)}}while(0);if(c[i+4>>2]=1|g,c[i+g>>2]=g,(0|i)==(0|c[1032]))return c[1029]=g;a=g}if(d=a>>>3,a>>>0<256)return b=4148+(d<<1<<2)|0,a=(a=0|c[1027])&(d=1<>2]:(c[1027]=a|d,d=b+8|0,b),c[d>>2]=i,c[a+12>>2]=i,c[i+8>>2]=a,c[i+12>>2]=b;if(b=4412+((f=(d=a>>>8)?16777215>>0?31:1&((j=7+(f=14-((g=(520192+(f=d<<(h=(d+1048320|0)>>>16&8))|0)>>>16&4)|h|(j=(245760+(f<<=g)|0)>>>16&2))+(f<>>15)|0)|0)?a>>>j:a)|f<<1:0)<<2)|0,c[i+28>>2]=f,c[i+20>>2]=0,!((d=(c[i+16>>2]=0)|c[1028])&(e=1<>2]=i,c[i+24>>2]=b,c[i+12>>2]=i,c[i+8>>2]=i;d=0|c[b>>2];a:do{if((-8&c[d+4>>2]|0)!=(0|a)){for(f=a<<(31==(0|f)?0:25-(f>>>1)|0);b=0|c[(e=d+16+(f>>>31<<2)|0)>>2];){if((-8&c[b+4>>2]|0)==(0|a)){d=b;break a}f<<=1,d=b}return c[e>>2]=i,c[i+24>>2]=d,c[i+12>>2]=i,c[i+8>>2]=i}}while(0);j=0|c[(h=d+8|0)>>2],c[j+12>>2]=i,c[h>>2]=i,c[i+8>>2]=j,c[i+12>>2]=d,c[i+24>>2]=0}function Nc(a,b,d){var e;return a|=0,0!=(3&(b|=0)|0)|0==(0|(e=b>>>2))||1073741823+e&e|0?28:(-64-b|0)>>>0<(d|=0)>>>0||!(b=0|function(a,b){b|=0;var g,d=0,e=0,f=0,h=0,i=0;if((d=16<(a|=0)>>>0?a:16)+-1&d)for(a=16;a>>>0>>0;)a<<=1;else a=d;if((-64-a|0)>>>0<=b>>>0)return c[1026]=48,(h=0)|h;if(!(d=0|Jc(12+(g=b>>>0<11?16:b+11&-8)+a|0)))return(h=0)|h;f=d+-8|0;do{if(a+-1&d){if(e=15<((e=(d+a+-1&0-a)-8|0)-(b=f)|0)>>>0?e:e+a|0,d=(-8&(i=0|c[(a=d+-4|0)>>2]))-(b=e-b|0)|0,3&i){c[(i=e+4|0)>>2]=d|1&c[i>>2]|2,c[(d=e+d+4|0)>>2]=1|c[d>>2],c[a>>2]=b|1&c[a>>2]|2,c[i>>2]=1|c[i>>2],Lc(f,b),a=b=e;break}c[e>>2]=(0|c[f>>2])+b,c[e+4>>2]=d,a=b=e;break}}while(a=b=f,0);return 3&(a=0|c[(d=a+4|0)>>2])|0&&(16+g|0)>>>0<(h=-8&a)>>>0&&(i=h-g|0,f=b+g|0,c[d>>2]=g|1&a|2,c[f+4>>2]=3|i,c[(h=b+h+4|0)>>2]=1|c[h>>2],Lc(f,i)),b+8|0}(16>>0?b:16,d))?48:(c[a>>2]=b,(a=0)|a)}function Oc(a){var b,d=0;return(a=(b=0|c[(d=4624)>>2])+(0|a)|0)>>>0>(0|H())>>>0&&0==(0|J(0|a))?(c[1026]=48,-1):(c[d>>2]=a,0|b)}function Qc(a,b,c,d){b|=0,d|=0;var e,f;return c=0|function(a,b){var f,e,c,d;return a=((c=0|v(e=65535&(b|=0),f=65535&(a|=0)))>>>16)+(0|v(e,d=a>>>16))|0,b=0|v(e=b>>>16,f),0|(y((a>>>16)+(0|v(e,d))+(((65535&a)+b|0)>>>16)|0),a+b<<16|65535&c|0)}(e=a|=0,f=c|=0),a=0|z(),0|(y((0|v(b,f))+(0|v(d,e))+a|0&a|0),0|c)}function Rc(a,b,c,d){return 0|(y((b|=0)+(d|=0)+((c=(a|=0)+(0|c)>>>0)>>>0>>0|0)>>>0|0),0|c)}function Sc(a,b,c,d){return 0|(y(0|(d=(b|=0)-(d|=0)-((a|=0)>>>0<(c|=0)>>>0|0)>>>0)),a-c>>>0|0)}function Tc(a){return 0|((a|=0)?31-(0|w(a^a-1))|0:32)}function Uc(a,b,d,e,f){f|=0;var g=0,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,l=a|=0,h=d|=0,i=n=e|=0;if(!(k=j=b|=0))return g=0!=(0|f),i?(g&&(c[f>>2]=0|a,c[f+4>>2]=0&b),(f=n=0)|(y(0|n),f)):(g&&(c[f>>2]=(l>>>0)%(h>>>0),c[f+4>>2]=0),f=(l>>>(n=0))/(h>>>0)>>>0,0|(y(0|n),f));g=0==(0|i);do{if(h){if(!g){if((g=(0|w(0|i))-(0|w(0|k))|0)>>>0<=31){a=l>>>((h=m=g+1|0)>>>0)&(b=g-31>>31)|k<<(i=31-g|0),b&=k>>>(m>>>0),g=0,i=l<>2]=0|a,c[f+4>>2]=j|0&b),(f=n=0)|(y(0|n),f)}if((g=h-1|0)&h|0){a=(m=32-(i=33+(0|w(0|h))-(0|w(0|k))|0)|0)-1>>31&k>>>((o=i-32|0)>>>0)|(k<>>((h=i)>>>0))&(b=o>>31),b&=k>>>(i>>>0),g=l<<(p=64-i|0)&(j=m>>31),i=(k<>>(o>>>0))&j|l<>31;break}return 0|f&&(c[f>>2]=g&l,c[f+4>>2]=0),1==(0|h)?(p=0|a,0|(y(0|(o=j|0&b)),p)):(o=k>>>((p=0|Tc(0|h))>>>0)|0,p=k<<32-p|l>>>(p>>>0)|0,0|(y(0|o),p))}if(g)return 0|f&&(c[f>>2]=(k>>>0)%(h>>>0),c[f+4>>2]=0),p=(k>>>(o=0))/(h>>>0)>>>0,0|(y(0|o),p);if(!l)return 0|f&&(c[f>>2]=0,c[f+4>>2]=(k>>>0)%(i>>>0)),p=(k>>>(o=0))/(i>>>0)>>>0,0|(y(0|o),p);if(!((g=i-1|0)&i))return 0|f&&(c[f>>2]=0|a,c[f+4>>2]=g&k|0&b),p=k>>>(((o=0)|Tc(0|i))>>>0),0|(y(0|o),p);if((g=(0|w(0|i))-(0|w(0|k))|0)>>>0<=30){a=k<<(i=31-g|0)|l>>>((h=b=g+1|0)>>>0),b=k>>>(b>>>0),g=0,i=l<>2]=0|a,c[f+4>>2]=j|0&b),(p=o=0)|(y(0|o),p)}while(0);if(h){for(k=0|Rc(0|(m=0|d),0|(l=n|0&e),-1,-1),d=0|z(),j=i,i=0;j=g>>>31|(e=j)<<1,g=i|g<<1,Sc(0|k,0|d,0|(e=a<<1|e>>>31|0),0|(n=a>>>31|b<<1|0)),i=1&(o=(p=0|z())>>31|((0|p)<0?-1:0)<<1),a=0|Sc(0|e,0|n,o&m|0,(((0|p)<0?-1:0)>>31|((0|p)<0?-1:0)<<1)&l|0),b=0|z(),0!=(0|(h=h-1|0)););k=j,j=0}else k=i,i=j=0;return(h=0)|f&&(c[f>>2]=a,c[f+4>>2]=b),p=-2&(g<<1|0)|i,0|(y(0|(o=(0|g)>>>31|(k|h)<<1|0&(h<<1|g>>>31)|j)),p)}function Vc(a,b,c,d){return 0|Uc(a|=0,b|=0,c|=0,d|=0,0)}function Wc(a,b,c){return a|=0,b|=0,(0|(c|=0))<32?(y(b>>>c|0),a>>>c|(b&(1<>>c-32|0)}function Xc(a,b,c){return a|=0,b|=0,(0|(c|=0))<32?(y(b<>>32-c|0),a<>0]=0|a[d>>0],b=b+1|0,d=d+1|0,e=e-1|0}for(f=(e=-4&g|0)-64|0;(0|b)<=(0|f);)c[b>>2]=c[d>>2],c[b+4>>2]=c[d+4>>2],c[b+8>>2]=c[d+8>>2],c[b+12>>2]=c[d+12>>2],c[b+16>>2]=c[d+16>>2],c[b+20>>2]=c[d+20>>2],c[b+24>>2]=c[d+24>>2],c[b+28>>2]=c[d+28>>2],c[b+32>>2]=c[d+32>>2],c[b+36>>2]=c[d+36>>2],c[b+40>>2]=c[d+40>>2],c[b+44>>2]=c[d+44>>2],c[b+48>>2]=c[d+48>>2],c[b+52>>2]=c[d+52>>2],c[b+56>>2]=c[d+56>>2],c[b+60>>2]=c[d+60>>2],b=b+64|0,d=d+64|0;for(;(0|b)<(0|e);)c[b>>2]=c[d>>2],b=b+4|0,d=d+4|0}else for(e=g-4|0;(0|b)<(0|e);)a[b>>0]=0|a[d>>0],a[b+1>>0]=0|a[d+1>>0],a[b+2>>0]=0|a[d+2>>0],a[b+3>>0]=0|a[d+3>>0],b=b+4|0,d=d+4|0;for(;(0|b)<(0|g);)a[b>>0]=0|a[d>>0],b=b+1|0,d=d+1|0;return 0|h}function _c(b,c,d){var e;if((0|(c|=0))<(0|(b|=0))&(0|b)<(c+(d|=0)|0)){for(c=c+d|0,b=(e=b)+d|0;0<(0|d);)d=d-1|0,a[(b=b-1|0)>>0]=0|a[(c=c-1|0)>>0];b=e}else Zc(b,c,d);return 0|b}function $c(b,d,e){var f,g,i,h=(b|=0)+(e|=0)|0;if(d=255&(0|d),67<=(0|e)){for(;3&b;)a[b>>0]=d,b=b+1|0;for(i=d|d<<8|d<<16|d<<24,g=(f=-4&h|0)-64|0;(0|b)<=(0|g);)c[b>>2]=i,c[b+4>>2]=i,c[b+8>>2]=i,c[b+12>>2]=i,c[b+16>>2]=i,c[b+20>>2]=i,c[b+24>>2]=i,c[b+28>>2]=i,c[b+32>>2]=i,c[b+36>>2]=i,c[b+40>>2]=i,c[b+44>>2]=i,c[b+48>>2]=i,c[b+52>>2]=i,c[b+56>>2]=i,c[b+60>>2]=i,b=b+64|0;for(;(0|b)<(0|f);)c[b>>2]=i,b=b+4|0}for(;(0|b)<(0|h);)a[b>>0]=d,b=b+1|0;return h-e|0}function md(a){return x(0),0}function od(a,b,c,d){return x(2),0}function ea(a){var b,d,g,h,i,j,n,o,p,q,r,s,t,u,v,w,x,y,k=0,l=0,m=0|c[500+(a|=0)>>2];if(!((0|m)<=0)){n=0|c[a+504>>2],o=0|c[a+444>>2],d=0|c[a+536>>2],l=0;do{if(g=0|c[n+(24*l|0)+12>>2],h=0|c[o+(c[n+(24*l|0)+4>>2]<<2)>>2],i=0|c[o+(c[n+(24*l|0)+8>>2]<<2)>>2],j=0|c[n+(24*l|0)+16>>2],a=0|c[n+(24*l|0)+20>>2],b=+f[d+(l<<2)>>2],0<(0|g))for(k=0;x=+f[j+(k<<2)>>2],s=+f[j+((p=1|k)<<2)>>2],w=(0|e[a+(k<<1)>>1])<<1&65534,v=+f[(y=h+(w<<2)|0)>>2],r=+f[(w=h+((1|w)<<2)|0)>>2],p=(0|e[a+(p<<1)>>1])<<1&65534,u=+f[(t=i+(p<<2)|0)>>2],q=+f[(p=i+((1|p)<<2)|0)>>2],f[y>>2]=v+x*(u-v)*b,f[w>>2]=r+x*(q-r)*b,f[t>>2]=u+s*(v-u)*b,f[p>>2]=q+s*(r-q)*b,(0|(k=k+2|0))<(0|g););}while((0|(l=l+1|0))!=(0|m))}}function fa(a){var b=0|c[(a|=0)>>2];(0|d[4+b>>0])<4||ga(a,0|c[a+596>>2],0|c[a+600>>2],0|c[984+b>>2],0|c[a+152>>2],0|c[796+b>>2])}function ga(a,b,d,e,g,h){a|=0,e|=0,g|=0,h|=0;var l,o,p,q,r,s,t,u,w,i=0,j=0,k=0,m=0,n=0,v=S;if(S=S+16|0,u=v+8|0,t=v,s=(d|=0)+(12*(b|=0)|0)|0,(0|b)<=0)S=v;else{do{b=0|c[d>>2],o=0|c[g+(b<<2)>>2],q=(b=0|c[h+(b<<2)>>2])<<1,n=0|c[d+4>>2],i=0|c[d+8>>2],p=0|c[1028+(0|c[a>>2])>>2],r=i+(48*n|0)|0;a:do{if(0<(0|n)){if((0|b)<=0)for(;;)if(3<=(b=0|c[i+8>>2])>>>0&&(c[u>>2]=b,ia(0,784,u)),r>>>0<=(i=i+48|0)>>>0)break a;do{j=e+(c[i+4>>2]<<2)|0,b=0|c[i+8>>2];b:do{if(0|b)switch(n=p+(c[j+(c[i+12>>2]<<2)>>2]<<2)|0,0|b){case 1:for(j=i+20|0,b=i+44|0,k=0;f[(m=o+(k<<2)|0)>>2]=+f[m>>2]+ +f[b>>2]*(+f[n+(k<<2)>>2]*+f[j>>2]),(0|(k=k+1|0))!=(0|q););break;case 2:for(j=p+(c[j+(c[i+16>>2]<<2)>>2]<<2)|0,k=i+20|0,l=i+24|0,b=i+44|0,m=0;f[(w=o+(m<<2)|0)>>2]=+f[w>>2]+ +f[b>>2]*(+f[n+(m<<2)>>2]*+f[k>>2]+ +f[j+(m<<2)>>2]*+f[l>>2]),(0|(m=m+1|0))!=(0|q););break;default:c[t>>2]=b,ia(0,784,t);break b}}while(0)}while((i=i+48|0)>>>0>>0)}}while(0)}while((d=d+12|0)>>>0>>0);S=v}}function ha(a){var b=0|c[(a|=0)>>2];(0|d[4+b>>0])<4||ga(a,0|c[a+604>>2],0|c[a+608>>2],0|c[1024+b>>2],0|c[a+444>>2],0|c[892+b>>2])}function ia(a,b,d){a|=0,b|=0,d|=0;var e,f,g=S;S=S+272|0,a=g+16|0,f=g,(e=0|c[1008])&&(c[f>>2]=d,cc(a,b,f),Z[1&e](a)),S=g}function ma(b,d,e){e|=0;var m,f=0,g=0,h=0,i=0,j=0,k=0,l=0,n=0,o=0,p=0,o=0|a[4+(b|=0)>>0];if(c[(d|=0)>>2]=628,p=0|c[(n=b+704|0)>>2],0<(0|(j=0|c[p>>2]))){for(f=0|c[b+1048>>2],g=0|c[b+720>>2],i=h=0;i=(1<>2]<<2)>>2])+i|0,(0|(h=h+1|0))!=(0|j););f=i<<2}else f=0;if(c[d+4>>2]=12*j,c[d+8>>2]=c[p>>2]<<2,c[d+12>>2]=c[p>>2]<<2,c[d+16>>2]=c[p>>2]<<2,c[d+20>>2]=c[p>>2]<<2,c[d+24>>2]=c[p>>2]<<2,c[d+28>>2]=f,c[d+32>>2]=c[p>>2]<<2,c[d+36>>2]=f,c[d+40>>2]=f,c[d+44>>2]=c[(m=p+4|0)>>2]<<5,c[d+48>>2]=c[m>>2]<<2,c[d+52>>2]=c[m>>2]<<2,c[d+56>>2]=c[m>>2]<<2,c[d+60>>2]=c[m>>2]<<4,c[d+64>>2]=c[m>>2]<<4,0<(0|(l=0|c[(m=p+8|0)>>2]))){for(g=0|c[b+796>>2],h=0|c[b+1048>>2],i=0|c[b+780>>2],f=k=j=0;f=(15+(c[g+(j<<2)>>2]<<3)&-16)+f|0,k=(1<>2]<<2)>>2])+k|0,(0|(j=j+1|0))!=(0|l););g=k<<2}else f=g=0;if(c[d+68>>2]=24*l,c[d+72>>2]=c[m>>2]<<2,c[d+76>>2]=c[m>>2]<<2,c[d+80>>2]=c[m>>2]<<2,c[d+84>>2]=f,c[d+88>>2]=c[m>>2]<<4,c[d+92>>2]=c[m>>2]<<4,c[d+96>>2]=c[m>>2]<<2,c[d+100>>2]=g,c[d+104>>2]=c[m>>2]<<2,c[d+108>>2]=g,c[d+112>>2]=g,c[d+116>>2]=g,c[d+120>>2]=g,c[d+124>>2]=g,c[d+128>>2]=g,c[d+132>>2]=g,c[d+136>>2]=g,c[d+140>>2]=g,c[d+144>>2]=c[m>>2]<<2,c[d+148>>2]=c[m>>2]<<2,c[d+152>>2]=c[m>>2]<<2,c[d+156>>2]=c[m>>2]<<2,c[d+160>>2]=c[m>>2]<<2,c[d+164>>2]=c[m>>2]<<2,0<(0|(j=0|c[(k=p+12|0)>>2]))){for(f=0|c[b+1048>>2],g=0|c[b+812>>2],i=h=0;i=(1<>2]<<2)>>2])+i|0,(0|(h=h+1|0))!=(0|j););f=i<<2}else f=0;if(c[d+168>>2]=12*j,c[d+172>>2]=c[k>>2]<<2,c[d+176>>2]=c[k>>2]<<2,c[d+180>>2]=c[k>>2]<<2,c[d+184>>2]=c[k>>2]<<2,c[d+188>>2]=c[k>>2]<<2,c[d+192>>2]=c[k>>2]<<2,c[d+196>>2]=c[k>>2]<<2,c[d+200>>2]=c[k>>2]<<2,c[d+204>>2]=c[k>>2]<<4,c[d+208>>2]=c[k>>2]<<4,c[d+212>>2]=c[k>>2]<<2,c[d+216>>2]=f,c[d+220>>2]=c[k>>2]<<2,c[d+224>>2]=f,c[d+228>>2]=f,c[d+232>>2]=f,c[d+236>>2]=f,c[d+240>>2]=f,c[d+244>>2]=f,c[d+248>>2]=f,c[d+252>>2]=f,c[d+256>>2]=f,c[d+260>>2]=f,c[d+264>>2]=f,c[d+268>>2]=f,c[d+272>>2]=c[k>>2]<<2,c[d+276>>2]=c[k>>2]<<2,c[d+280>>2]=c[k>>2]<<2,c[d+284>>2]=c[k>>2]<<2,c[d+288>>2]=c[k>>2]<<2,c[d+292>>2]=c[k>>2]<<2,0<(0|(l=0|c[(m=p+16|0)>>2]))){for(g=0|c[b+892>>2],h=0|c[b+1048>>2],i=0|c[b+852>>2],f=k=j=0;f=(15+(c[g+(j<<2)>>2]<<3)&-16)+f|0,k=(1<>2]<<2)>>2])+k|0,(0|(j=j+1|0))!=(0|l););g=k<<2}else f=g=0;if(c[d+296>>2]=20*l,c[d+300>>2]=c[m>>2]<<2,c[d+304>>2]=c[m>>2],c[d+308>>2]=c[m>>2]<<2,c[d+312>>2]=c[m>>2]<<2,c[d+316>>2]=c[m>>2]<<2,c[d+320>>2]=f,c[d+324>>2]=c[m>>2]<<2,c[d+328>>2]=c[m>>2]<<4,c[d+332>>2]=c[m>>2]<<4,c[d+336>>2]=c[m>>2]<<2,c[d+340>>2]=c[m>>2]<<2,c[d+344>>2]=c[m>>2]<<2,c[d+348>>2]=c[m>>2]<<4,c[d+352>>2]=c[m>>2]<<4,c[d+356>>2]=c[m>>2]<<2,c[d+360>>2]=g,c[d+364>>2]=c[m>>2]<<2,c[d+368>>2]=g,c[d+372>>2]=g,c[d+376>>2]=g,c[d+380>>2]=g,c[d+384>>2]=g,c[d+388>>2]=g,c[d+392>>2]=g,c[d+396>>2]=g,c[d+400>>2]=g,c[d+404>>2]=g,c[d+408>>2]=c[m>>2]<<2,c[d+412>>2]=c[m>>2]<<2,c[d+416>>2]=c[m>>2]<<2,c[d+420>>2]=c[m>>2]<<2,c[d+424>>2]=c[m>>2]<<2,c[d+428>>2]=c[m>>2]<<2,n=0|c[n>>2],c[d+432>>2]=52*(0|c[(g=n+20|0)>>2]),f=(m=3<(255&o))?0:c[g>>2]<<2,c[d+436>>2]=f,c[d+440>>2]=c[g>>2]<<2,c[d+444>>2]=28*(0|c[n+52>>2]),0<(0|(j=0|c[p+48>>2]))){for(f=0|c[b+1048>>2],i=h=g=0;h=(o=0|c[f+(g<<2)>>2])+h|0,i=(1<>2]=36*j,c[d+452>>2]=g,c[d+456>>2]=f,c[d+460>>2]=f,c[d+488>>2]=28*(0|c[(g=p+72|0)>>2]),0<(0|(g=0|c[g>>2]))){for(h=0|c[b+1152>>2],i=0|c[b+1160>>2],f=0|c[b+1164>>2],l=k=j=0;k=(0|k)<(0|(o=0|c[h+(j<<2)>>2]))?o:k,l=(0|(o=(0|c[i+(j<<2)>>2])-(0|c[f+(j<<2)>>2])|0))<(0|l)?l:o+1|0,(0|(j=j+1|0))!=(0|g););g=k<<2,f=l<<2}else f=g=0;if(c[d+492>>2]=c[p+76>>2]<<4,c[d+496>>2]=f,c[d+500>>2]=g,c[d+504>>2]=f,0<(0|(j=0|c[(k=p+80|0)>>2]))){for(f=0|c[b+1048>>2],g=0|c[b+1188>>2],i=h=0;i=(1<>2]<<2)>>2])+i|0,(0|(h=h+1|0))!=(0|j););f=i<<2}else f=0;if(c[d+508>>2]=24*j,c[d+512>>2]=c[k>>2]<<2,c[d+516>>2]=c[k>>2]<<2,c[d+520>>2]=f,c[d+524>>2]=c[k>>2]<<2,c[d+528>>2]=f,c[d+532>>2]=f,m){if(c[d+464>>2]=20*(0|c[n+120>>2]),c[d+468>>2]=28*(0|c[n+100>>2]),0<(0|(i=0|c[p+104>>2]))){for(f=0|c[b+1080>>2],h=g=0;h=(0|c[f+(g<<2)>>2])+h|0,(0|(g=g+1|0))!=(0|i););f=h<<2}else f=0;c[d+472>>2]=48*i,c[d+476>>2]=f,c[d+480>>2]=12*(0|c[n+108>>2]),c[d+484>>2]=12*(0|c[n+112>>2]),f=g=c[d+540>>2]=0}else{if(0<(0|(m=0|c[(n=p+20|0)>>2]))){k=0|c[b+948>>2],l=0|c[b+952>>2],i=0|c[b+1036>>2],f=j=0;do{if(b=0|c[l+(j<<2)>>2],h=(g=i+(c[k+(j<<2)>>2]<<2)|0)+(b<<2)|0,0<(0|b))for(;f=(0|c[g>>2])+f|0,(g=g+4|0)>>>0>>0;);}while((0|(j=j+1|0))!=(0|m))}else f=0;c[d+540>>2]=m<<2,g=f<<2,f=c[n>>2]<<2}for(c[d+536>>2]=f,c[d+544>>2]=g,g=f=0;g=(b=15+(0|c[(p=d+(f<<2)|0)>>2])&-16)+(c[p>>2]=g)|0,137!=(0|(f=f+1|0)););c[e>>2]=g}function na(b,e,g){b|=0,e|=0,g|=0;var C,D,G,J,M,V,h=0,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,s=0,t=0,u=0,v=0,w=0,x=0,y=0,z=0,A=0,B=0,E=0,F=0,H=0,I=0,K=0,L=0,N=0,O=0,P=0,Q=0,R=0,T=0,U=0,W=0,X=0,Y=0,Z=0,_=S;if(S=S+560|0,H=_+552|0,h=(p=_)+556|0,$c(0|p,0,548),ma(b,p,h),g>>>0<(h=0|c[h>>2])>>>0)return S=_,(Z=0)|Z;if($c(0|e,0,0|h),Z=e+(0|c[p>>2])|0,c[(D=Z+8|0)>>2]=e+(0|c[p+4>>2]),c[Z+40>>2]=e+(0|c[p+8>>2]),c[Z+44>>2]=e+(0|c[p+12>>2]),c[Z+48>>2]=e+(0|c[p+16>>2]),c[(E=Z+52|0)>>2]=e+(0|c[p+20>>2]),c[(F=Z+16|0)>>2]=e+(0|c[p+24>>2]),c[Z+24>>2]=e+(0|c[p+28>>2]),c[Z+28>>2]=e+(0|c[p+32>>2]),c[Z+32>>2]=e+(0|c[p+36>>2]),c[Z+36>>2]=e+(0|c[p+40>>2]),j=0|c[(Y=b+704|0)>>2],c[(G=Z+308|0)>>2]=e+(0|c[p+44>>2]),c[Z+312>>2]=e+(0|c[p+48>>2]),c[Z+316>>2]=e+(0|c[p+52>>2]),c[Z+320>>2]=e+(0|c[p+56>>2]),c[Z+324>>2]=e+(0|c[p+60>>2]),c[Z+328>>2]=e+(0|c[p+64>>2]),c[(L=Z+60|0)>>2]=e+(0|c[p+68>>2]),c[Z+144>>2]=e+(0|c[p+72>>2]),c[Z+148>>2]=e+(0|c[p+76>>2]),h=e+(0|c[p+80>>2])|0,c[(k=Z+152|0)>>2]=h,0<(0|(j=0|c[j+8>>2]))&&(l=e+(0|c[p+84>>2])|0,m=b+796|0,c[h>>2]=l,1!=(0|j)))for(h=0,i=1,g=l;g=g+(15+(c[(0|c[m>>2])+(h<<2)>>2]<<3)&-16)|0,c[(0|c[k>>2])+(i<<2)>>2]=g,!((0|j)<=(0|(h=i+1|0)));)W=i,i=h,h=W;if(c[Z+156>>2]=e+(0|c[p+88>>2]),c[Z+160>>2]=e+(0|c[p+92>>2]),c[(I=Z+68|0)>>2]=e+(0|c[p+96>>2]),c[Z+76>>2]=e+(0|c[p+100>>2]),c[Z+80>>2]=e+(0|c[p+104>>2]),c[Z+84>>2]=e+(0|c[p+108>>2]),c[Z+88>>2]=e+(0|c[p+112>>2]),c[Z+92>>2]=e+(0|c[p+116>>2]),c[Z+96>>2]=e+(0|c[p+120>>2]),c[Z+100>>2]=e+(0|c[p+124>>2]),c[Z+104>>2]=e+(0|c[p+128>>2]),c[Z+108>>2]=e+(0|c[p+132>>2]),c[Z+112>>2]=e+(0|c[p+136>>2]),c[Z+116>>2]=e+(0|c[p+140>>2]),c[Z+120>>2]=e+(0|c[p+144>>2]),c[Z+124>>2]=e+(0|c[p+148>>2]),c[Z+128>>2]=e+(0|c[p+152>>2]),c[Z+132>>2]=e+(0|c[p+156>>2]),c[Z+136>>2]=e+(0|c[p+160>>2]),c[Z+140>>2]=e+(0|c[p+164>>2]),c[(J=Z+168|0)>>2]=e+(0|c[p+168>>2]),c[Z+264>>2]=e+(0|c[p+172>>2]),c[Z+268>>2]=e+(0|c[p+176>>2]),c[Z+272>>2]=e+(0|c[p+180>>2]),c[Z+276>>2]=e+(0|c[p+184>>2]),c[Z+280>>2]=e+(0|c[p+188>>2]),c[Z+284>>2]=e+(0|c[p+192>>2]),c[Z+288>>2]=e+(0|c[p+196>>2]),c[Z+292>>2]=e+(0|c[p+200>>2]),c[Z+296>>2]=e+(0|c[p+204>>2]),c[Z+300>>2]=e+(0|c[p+208>>2]),c[(K=Z+176|0)>>2]=e+(0|c[p+212>>2]),c[Z+184>>2]=e+(0|c[p+216>>2]),c[Z+188>>2]=e+(0|c[p+220>>2]),c[Z+192>>2]=e+(0|c[p+224>>2]),c[Z+196>>2]=e+(0|c[p+228>>2]),c[Z+200>>2]=e+(0|c[p+232>>2]),c[Z+204>>2]=e+(0|c[p+236>>2]),c[Z+208>>2]=e+(0|c[p+240>>2]),c[Z+212>>2]=e+(0|c[p+244>>2]),c[Z+216>>2]=e+(0|c[p+248>>2]),c[Z+220>>2]=e+(0|c[p+252>>2]),c[Z+224>>2]=e+(0|c[p+256>>2]),c[Z+228>>2]=e+(0|c[p+260>>2]),c[Z+232>>2]=e+(0|c[p+264>>2]),c[Z+236>>2]=e+(0|c[p+268>>2]),c[Z+240>>2]=e+(0|c[p+272>>2]),c[Z+244>>2]=e+(0|c[p+276>>2]),c[Z+248>>2]=e+(0|c[p+280>>2]),c[Z+252>>2]=e+(0|c[p+284>>2]),c[Z+256>>2]=e+(0|c[p+288>>2]),c[Z+260>>2]=e+(0|c[p+292>>2]),j=0|c[Y>>2],c[(O=Z+336|0)>>2]=e+(0|c[p+296>>2]),c[Z+424>>2]=e+(0|c[p+300>>2]),c[Z+432>>2]=e+(0|c[p+304>>2]),c[Z+436>>2]=e+(0|c[p+308>>2]),c[Z+440>>2]=e+(0|c[p+312>>2]),h=e+(0|c[p+316>>2])|0,c[(k=Z+444|0)>>2]=h,0<(0|(j=0|c[j+16>>2]))&&(n=e+(0|c[p+320>>2])|0,o=b+892|0,c[h>>2]=n,1!=(0|j)))for(h=0,i=1,g=n;g=g+(15+(c[(0|c[o>>2])+(h<<2)>>2]<<3)&-16)|0,c[(0|c[k>>2])+(i<<2)>>2]=g,!((0|j)<=(0|(h=i+1|0)));)W=i,i=h,h=W;if(c[Z+448>>2]=e+(0|c[p+324>>2]),c[(P=Z+452|0)>>2]=e+(0|c[p+328>>2]),c[(Q=Z+456|0)>>2]=e+(0|c[p+332>>2]),c[Z+460>>2]=e+(0|c[p+336>>2]),c[Z+464>>2]=e+(0|c[p+340>>2]),c[Z+468>>2]=e+(0|c[p+344>>2]),c[Z+472>>2]=e+(0|c[p+348>>2]),c[Z+476>>2]=e+(0|c[p+352>>2]),c[(N=Z+344|0)>>2]=e+(0|c[p+356>>2]),c[Z+352>>2]=e+(0|c[p+360>>2]),c[Z+356>>2]=e+(0|c[p+364>>2]),c[Z+360>>2]=e+(0|c[p+368>>2]),c[Z+364>>2]=e+(0|c[p+372>>2]),c[Z+368>>2]=e+(0|c[p+376>>2]),c[Z+372>>2]=e+(0|c[p+380>>2]),c[Z+376>>2]=e+(0|c[p+384>>2]),c[Z+380>>2]=e+(0|c[p+388>>2]),c[Z+384>>2]=e+(0|c[p+392>>2]),c[Z+388>>2]=e+(0|c[p+396>>2]),c[Z+392>>2]=e+(0|c[p+400>>2]),c[Z+396>>2]=e+(0|c[p+404>>2]),c[Z+400>>2]=e+(0|c[p+408>>2]),c[Z+404>>2]=e+(0|c[p+412>>2]),c[Z+408>>2]=e+(0|c[p+416>>2]),c[Z+412>>2]=e+(0|c[p+420>>2]),c[Z+416>>2]=e+(0|c[p+424>>2]),c[Z+420>>2]=e+(0|c[p+428>>2]),A=0|c[p+436>>2],C=0|c[p+440>>2],c[(W=Z+544|0)>>2]=e+(0|c[p+432>>2]),c[(B=Z+548|0)>>2]=e+A,c[(A=Z+552|0)>>2]=e+C,c[(C=Z+560|0)>>2]=e+(0|c[p+444>>2]),h=0|c[Y>>2],j=e+(0|c[p+448>>2])|0,c[(U=Z+568|0)>>2]=j,0<(0|(h=0|c[h+48>>2])))for(g=0|c[b+1048>>2],k=e+(0|c[p+452>>2])|0,m=e+((l=0)|c[p+456>>2])|0,o=e+(0|c[p+460>>2])|0;c[j+(36*l|0)>>2]=k,c[j+(36*l|0)+16>>2]=m,c[j+(36*l|0)+20>>2]=o,n=1<<(i=0|c[g+(l<<2)>>2]),(0|(l=l+1|0))!=(0|h);)k=k+(i<<2)|0,m=m+(n<<2)|0,o=o+(n<<2)|0;if(h=0|c[Y>>2],i=e+(0|c[p+488>>2])|0,c[(M=Z+484|0)>>2]=i,0<(0|(h=0|c[h+72>>2])))for(g=0|c[b+1152>>2],k=e+((j=0)|c[p+492>>2])|0;k=(c[i+(28*j|0)+12>>2]=k)+(c[g+(j<<2)>>2]<<4)|0,(0|(j=j+1|0))!=(0|h););if(c[Z+488>>2]=e+(0|c[p+496>>2]),c[Z+492>>2]=e+(0|c[p+500>>2]),c[Z+496>>2]=e+(0|c[p+504>>2]),c[(R=Z+504|0)>>2]=e+(0|c[p+508>>2]),c[Z+536>>2]=e+(0|c[p+512>>2]),c[(T=Z+512|0)>>2]=e+(0|c[p+516>>2]),c[Z+520>>2]=e+(0|c[p+520>>2]),c[Z+524>>2]=e+(0|c[p+524>>2]),c[Z+528>>2]=e+(0|c[p+528>>2]),c[Z+532>>2]=e+(0|c[p+532>>2]),z=3<(0|d[(V=b+4|0)>>0])){if(c[Z+576>>2]=e+(0|c[p+464>>2]),c[Z+584>>2]=e+(0|c[p+468>>2]),h=0|c[p+476>>2],j=0|c[Y>>2],l=e+(0|c[p+472>>2])|0,c[Z+592>>2]=l,0<(0|(j=0|c[j+104>>2])))for(k=0|c[b+1080>>2],h=e+h|0,g=0;c[l+(48*g|0)+40>>2]=h,(0|(i=g+1|0))!=(0|j);)h=h+(c[k+(g<<2)>>2]<<2)|0,g=i;c[Z+600>>2]=e+(0|c[p+480>>2]),c[Z+608>>2]=e+(0|c[p+484>>2]),g=0|c[Y>>2],h=0|c[g+20>>2]}else if(g=0|c[p+536>>2],i=0|c[p+544>>2],c[Z+616>>2]=e+(0|c[p+540>>2]),c[(p=Z+612|0)>>2]=e+g,g=0|c[Y>>2],0<(0|(h=0|c[g+20>>2])))for(m=b+948|0,n=b+952|0,l=b+1036|0,k=e+i|(o=0);;){if(h=0|c[(0|c[m>>2])+(o<<2)>>2],i=(y=0|c[(0|c[n>>2])+(o<<2)>>2])+h|0,0<(0|y)){for(g=0|c[l>>2],j=0;j=(0|c[g+(h<<2)>>2])+j|0,(0|(h=h+1|0))<(0|i););h=(0|c[p>>2])+(o<<2)|0,j?(g=k,i=j):X=27}else h=(0|c[p>>2])+(o<<2)|0,X=27;if(27==(0|X)&&(i=g=X=0),c[h>>2]=g,g=0|c[Y>>2],(0|(h=0|c[g+20>>2]))<=(0|(o=o+1|0)))break;k=k+(i<<2)|0}c[Z>>2]=b,c[Z+620>>2]=1,c[Z+624>>2]=1&a[20+(0|c[b+708>>2])>>0],k=(c[Z+540>>2]=h)+-1|0;a:do{if(0<(0|h)){if(e=0|c[W>>2],q=0|c[b+928>>2],s=0|c[b+924>>2],u=0|c[b+936>>2],v=0|c[b+932>>2],w=0|c[b+940>>2],x=0|c[b+952>>2],y=b+948|0,p=0|c[A>>2],o=Z+584|0,n=b+956|0,!z)for(;;){if(c[e+(52*k|0)>>2]=0,c[e+(52*k|0)+4>>2]=c[(i=q+(k<<2)|0)>>2],c[e+(52*k|0)+8>>2]=c[(A=s+(k<<2)|0)>>2],f[e+(52*k|0)+12>>2]=+f[A>>2]-+f[i>>2],c[e+(52*k|0)+16>>2]=c[u+(k<<2)>>2],c[e+(52*k|0)+44>>2]=c[(i=v+(k<<2)|0)>>2],t=+r(.10000000149011612,0|c[w+(k<<2)>>2]),f[e+(52*k|0)+20>>2]=t,f[e+(52*k|0)+24>>2]=1.5*t,A=0|c[x+(k<<2)>>2],j=(c[e+(52*k|0)+32>>2]=A)?(0|c[C>>2])+(28*(0|c[(0|c[y>>2])+(k<<2)>>2])|0)|0:0,c[e+(52*k|0)+28>>2]=j,c[e+(52*k|0)+48>>2]=1,c[p+(k<<2)>>2]=c[i>>2],!(0<(0|k)))break a;k=k+-1|0}for(l=0|c[b+944>>2],m=0|c[b+960>>2];c[e+(52*k|0)>>2]=c[l+(k<<2)>>2],c[e+(52*k|0)+4>>2]=c[(j=q+(k<<2)|0)>>2],c[e+(52*k|0)+8>>2]=c[(A=s+(k<<2)|0)>>2],f[e+(52*k|0)+12>>2]=+f[A>>2]-+f[j>>2],c[e+(52*k|0)+16>>2]=c[u+(k<<2)>>2],c[e+(52*k|0)+44>>2]=c[(j=v+(k<<2)|0)>>2],t=+r(.10000000149011612,0|c[w+(k<<2)>>2]),f[e+(52*k|0)+20>>2]=t,f[e+(52*k|0)+24>>2]=1.5*t,A=0|c[x+(k<<2)>>2],i=(c[e+(52*k|0)+32>>2]=A)?(0|c[C>>2])+(28*(0|c[(0|c[y>>2])+(k<<2)>>2])|0)|0:0,c[e+(52*k|0)+28>>2]=i,A=0|c[m+(k<<2)>>2],i=(c[e+(52*k|0)+40>>2]=A)?(0|c[o>>2])+(28*(0|c[(0|c[n>>2])+(k<<2)>>2])|0)|0:0,c[e+(52*k|0)+36>>2]=i,c[e+(52*k|0)+48>>2]=1,c[p+(k<<2)>>2]=c[j>>2],0<(0|k);)k=k+-1|0}}while(0);if(z?(c[B>>2]=c[b+944>>2],l=g,u=b):($c(0|c[B>>2],0,h<<2|0),u=0|c[Z>>2],l=0|c[u+704>>2]),h=0|c[l+52>>2],0<(0|(c[Z+556>>2]=h)))for(g=0|c[C>>2],i=0|c[u+1036>>2],j=0|c[u+1132>>2],k=0|c[u+1032>>2];c[g+(28*(h=(B=h)+-1|0)|0)>>2]=c[i+(h<<2)>>2],c[g+(28*h|0)+4>>2]=j+(c[k+(h<<2)>>2]<<2),c[g+(28*h|0)+8>>2]=0,f[g+(28*h|0)+12>>2]=0,c[g+(28*h|0)+16>>2]=0,c[g+(28*h|0)+20>>2]=1,(c[g+(28*h|0)+24>>2]=1)<(0|B););if(h=0|c[l+48>>2],0<(0|(c[Z+564>>2]=h))){n=u+1048|0,o=u+1040|0,m=u+1044|0;do{if(l=h,g=0|c[U>>2],i=0|c[(0|c[n>>2])+((h=h+-1|0)<<2)>>2],0<(0|(c[g+(36*h|0)+4>>2]=i)))for(j=g+(36*h|0)|0,k=0;c[(0|c[j>>2])+(k<<2)>>2]=(0|c[C>>2])+(28*(0|c[(0|c[o>>2])+((0|c[(0|c[m>>2])+(h<<2)>>2])+k<<2)>>2])|0),(0|(k=k+1|0))!=(0|i););}while(c[g+(36*h|0)+8>>2]=1<>2]=1,(c[g+(36*h|0)+28>>2]=1)<(0|l));u=0|c[Z>>2],l=0|c[u+704>>2]}if(g=0|c[l>>2],0<(0|(c[(e=Z+4|0)>>2]=g))){for(j=0|c[D>>2],o=0|c[U>>2],p=0|c[u+720>>2],k=0|c[u+740>>2],m=0|c[u+736>>2],n=0|c[u+732>>2],h=0|c[E>>2],i=g;c[j+(12*(i=(E=i)+-1|0)|0)>>2]=o+(36*(0|c[p+(i<<2)>>2])|0),c[j+(12*i|0)+4>>2]=c[k+(i<<2)>>2],c[j+(12*i|0)+8>>2]=c[m+(i<<2)>>2],f[h+(i<<2)>>2]=0==(0|c[n+(i<<2)>>2])?0:1,1<(0|E););for(i=0|c[F>>2],h=0;E=0|c[o+(36*(0|c[p+((g=(F=g)+-1|0)<<2)>>2])|0)+8>>2],h=(c[i+(g<<2)>>2]=E)+h|0,1<(0|F););g=0|c[e>>2]}else h=0;if(c[Z+20>>2]=h,c[Z+12>>2]=g,h=0|c[l+4>>2],0<(0|(c[Z+304>>2]=h))){m=u+752|0,o=u+764|0,n=u+768|0,l=u+772|0,p=u+776|0,k=u+760|0;do{switch(j=h,g=0|c[G>>2],c[g+((h=h+-1|0)<<5)>>2]=(0|c[U>>2])+(36*(0|c[(0|c[m>>2])+(h<<2)>>2])|0),c[g+(h<<5)+4>>2]=c[(0|c[o>>2])+(h<<2)>>2],c[g+(h<<5)+8>>2]=c[(0|c[n>>2])+(h<<2)>>2],F=0|c[(0|c[l>>2])+(h<<2)>>2],c[g+(h<<5)+12>>2]=F,i=0|c[(0|c[p>>2])+(h<<2)>>2],c[g+(h<<5)+16>>2]=i,c[g+(h<<5)+28>>2]=c[(0|c[k>>2])+(h<<2)>>2],0|F){case 0:c[(0|c[L>>2])+(24*i|0)+20>>2]=h,c[g+(h<<5)+20>>2]=2,c[g+(h<<5)+24>>2]=2;break;case 1:c[(0|c[J>>2])+(12*i|0)+8>>2]=h,c[g+(h<<5)+20>>2]=3,c[g+(h<<5)+24>>2]=3;break;default:ia(0,937,H)}}while(1<(0|j));u=0|c[Z>>2],B=0|c[u+704>>2]}else B=l;if(i=0|c[B+8>>2],h=(c[(e=Z+56|0)>>2]=i)+-1|0,p=0<(0|i))if(j=0|c[L>>2],k=0|c[U>>2],l=0|c[u+780>>2],m=0|c[u+800>>2],n=0|c[u+804>>2],o=0|c[u+796>>2],1<(0|d[u+4>>0]))for(g=0|c[u+808>>2];c[j+(24*h|0)>>2]=k+(36*(0|c[l+(h<<2)>>2])|0),c[j+(24*h|0)+4>>2]=c[m+(h<<2)>>2],c[j+(24*h|0)+8>>2]=c[n+(h<<2)>>2],c[j+(24*h|0)+16>>2]=c[o+(h<<2)>>2],c[j+(24*h|0)+12>>2]=c[g+(h<<2)>>2],0<(0|h);)h=h+-1|0;else for(;c[j+(24*h|0)>>2]=k+(36*(0|c[l+(h<<2)>>2])|0),c[j+(24*h|0)+4>>2]=c[m+(h<<2)>>2],c[j+(24*h|0)+8>>2]=c[n+(h<<2)>>2],c[j+(24*h|0)+16>>2]=c[o+(h<<2)>>2],(c[j+(24*h|0)+12>>2]=0)<(0|h);)h=h+-1|0;if(g=0|c[B+12>>2],0<(0|(c[(n=Z+164|0)>>2]=g)))for(h=0|c[J>>2],j=0|c[U>>2],k=0|c[u+812>>2],l=0|c[u+828>>2],m=g;c[h+(12*(m=(H=m)+-1|0)|0)>>2]=j+(36*(0|c[k+(m<<2)>>2])|0),c[h+(12*m|0)+4>>2]=c[l+(m<<2)>>2],1<(0|H););if(p){for(j=0|c[L>>2],g=0|c[I>>2],h=0;I=0|c[8+(0|c[j+(24*(i=(L=i)+-1|0)|0)>>2])>>2],h=(c[g+(i<<2)>>2]=I)+h|0,1<(0|L););i=0|c[e>>2],g=0|c[n>>2]}else h=0;if(c[Z+72>>2]=h,c[Z+64>>2]=i,0<(0|g)){for(j=0|c[J>>2],i=0|c[K>>2],h=0;K=0|c[8+(0|c[j+(12*(g=(L=g)+-1|0)|0)>>2])>>2],h=(c[i+(g<<2)>>2]=K)+h|0,1<(0|L););g=0|c[n>>2]}else h=0;if(c[Z+180>>2]=h,c[Z+172>>2]=g,g=u+704|0,h=0|c[B+16>>2],0<(0|(c[(e=Z+332|0)>>2]=h))){for(i=0|c[O>>2],o=0|c[U>>2],p=0|c[u+852>>2],j=0|c[u+876>>2],k=0|c[u+880>>2],l=0|c[u+892>>2],m=0|c[u+872>>2],n=h;c[i+(20*(n=(O=n)+-1|0)|0)>>2]=o+(36*(0|c[p+(n<<2)>>2])|0),c[i+(20*n|0)+4>>2]=c[j+(n<<2)>>2],c[i+(20*n|0)+8>>2]=c[k+(n<<2)>>2],c[i+(20*n|0)+16>>2]=c[l+(n<<2)>>2],c[i+(20*n|0)+12>>2]=c[m+(n<<2)>>2],1<(0|O););for(i=0|c[N>>2],j=0;N=0|c[o+(36*(0|c[p+((h=(O=h)+-1|0)<<2)>>2])|0)+8>>2],j=(c[i+(h<<2)>>2]=N)+j|0,1<(0|O););if(h=0|c[e>>2],c[Z+348>>2]=j,0<(0|(c[Z+340>>2]=h)))for(k=0|c[P>>2],i=0|c[Q>>2],j=h<<2;f[k+((j=(Q=j)+-4|0)<<2)>>2]=1,f[k+((O=Q+-3|0)<<2)>>2]=1,f[k+((P=Q+-2|0)<<2)>>2]=1,f[k+((Q=Q+-1|0)<<2)>>2]=1,f[i+(j<<2)>>2]=0,f[i+(O<<2)>>2]=0,!(((f[i+(P<<2)>>2]=0)|h)<=(f[i+(Q<<2)>>2]=1));)h=h+-1|0}else c[Z+348>>2]=0,c[Z+340>>2]=h;if(A=0|c[B+72>>2],0<(0|(c[Z+480>>2]=A))){h=0|c[M>>2],i=0|c[u+1152>>2],j=0|c[u+1156>>2],k=0|c[u+1160>>2],l=0|c[u+1164>>2],m=0|c[u+1148>>2],v=u+1172|0,x=u+1168|0,w=u+1176|0,y=0;do{if(n=0|c[i+(y<<2)>>2],c[h+(28*y|0)+4>>2]=n,c[h+(28*y|0)>>2]=c[j+(y<<2)>>2],Q=0|c[k+(y<<2)>>2],c[h+(28*y|0)+16>>2]=Q,o=0|c[l+(y<<2)>>2],c[h+(28*y|0)+20>>2]=o,c[h+(28*y|0)+24>>2]=Q+1-o,o=(c[h+(28*y|0)+8>>2]=0)|c[m+(y<<2)>>2],0<(0|n))for(p=0|c[h+(28*y|0)+12>>2],e=0|c[v>>2],q=0|c[x>>2],s=0|c[w>>2],z=0;c[p+(z<<4)+4>>2]=c[e+((Q=z+o|0)<<2)>>2],c[p+(z<<4)>>2]=c[q+(Q<<2)>>2],c[p+(z<<4)+8>>2]=c[s+(Q<<2)>>2],(0|(z=z+1|(c[p+(z<<4)+12>>2]=0)))!=(0|n););}while((0|(y=y+1|0))!=(0|A))}i=0|c[B+80>>2],c[(q=Z+500|0)>>2]=i;do{if(0<(0|i)){for(o=0|c[R>>2],p=0|c[U>>2],e=0|c[u+1188>>2],h=0|c[u+1200>>2],j=0|c[u+1204>>2],k=0|c[u+1212>>2],l=0|c[u+1216>>2],m=0|c[u+1208>>2],n=0|c[u+1220>>2];c[o+(24*(i=(U=i)+-1|0)|0)>>2]=p+(36*(0|c[e+(i<<2)>>2])|0),c[o+(24*i|0)+4>>2]=c[h+(i<<2)>>2],c[o+(24*i|0)+8>>2]=c[j+(i<<2)>>2],c[o+(24*i|0)+12>>2]=c[k+(i<<2)>>2],R=0|c[m+(i<<2)>>2],c[o+(24*i|0)+16>>2]=l+(R<<2),c[o+(24*i|0)+20>>2]=n+(R<<1),1<(0|U););if((0|(i=0|c[q>>2]))<=0){h=0;break}for(j=0|c[T>>2],h=0;T=0|c[p+(36*(0|c[e+((i=(U=i)+-1|0)<<2)>>2])|0)+8>>2],h=(c[j+(i<<2)>>2]=T)+h|0,1<(0|U););i=0|c[q>>2]}else h=0}while(0);c[Z+516>>2]=h,c[Z+508>>2]=i;do{if(3<(0|d[V>>0])){if((255&(h=0|a[u+4>>0]))<4)break;if(i=0|c[B+120>>2],0<(0|(c[Z+572>>2]=i))){for(n=0|c[Z+576>>2],o=0|c[u+1112>>2],e=u+1124|0,p=u+1116|0,q=u+1128|0,m=u+1120|0;j=-1<(0|(h=0|c[o+((i=(l=i)+-1|0)<<2)>>2]))?(j=0|c[(0|c[p>>2])+(i<<2)>>2],k=0|c[(0|c[m>>2])+(i<<2)>>2],h=(0|c[W>>2])+(52*h|0)|0,g=(0|c[e>>2])+(j<<2)|0,(0|c[q>>2])+(j<<2)|0):g=h=k=0,c[n+(20*i|0)>>2]=h,c[n+(20*i|0)+4>>2]=g,c[n+(20*i|0)+8>>2]=j,c[n+(20*i|0)+12>>2]=k,1<(0|l););if(g=0|c[Z>>2],(255&(h=0|a[g+4>>0]))<4)break;g=(u=g)+704|0}if(i=0|c[g>>2],g=0|c[i+100>>2],0<(0|(c[Z+580>>2]=g)))for(j=0|c[Z+584>>2],k=0|c[u+1056>>2],l=0|c[u+1132>>2],m=0|c[u+1052>>2],n=0|c[u+1060>>2];c[j+(28*(g=(X=g)+-1|0)|0)>>2]=c[k+(g<<2)>>2],c[j+(28*g|0)+4>>2]=l+(c[m+(g<<2)>>2]<<2),c[j+(28*g|0)+8>>2]=c[n+(g<<2)>>2],c[j+(28*g|0)+12>>2]=0,f[j+(28*g|0)+16>>2]=0,c[j+(28*g|0)+20>>2]=1,(c[j+(28*g|0)+24>>2]=1)<(0|X););if(g=0|c[i+104>>2],0<(0|(c[Z+588>>2]=g))){p=Z+592|0,q=Z+584|0,o=u+1064|0,s=u+1068|0,m=u+1080|0,e=Z+576|0,n=u+1108|0,l=u+1076|0;do{if(k=g,h=0|c[p>>2],c[h+(48*(g=g+-1|0)|0)>>2]=(0|c[q>>2])+(28*(0|c[(0|c[o>>2])+(g<<2)>>2])|0),c[h+(48*g|0)+4>>2]=c[(0|c[s>>2])+(g<<2)>>2],c[h+(48*g|0)+8>>2]=0,c[h+(48*g|0)+28>>2]=1,c[h+(48*g|0)+32>>2]=1,j=0|c[(0|c[m>>2])+(g<<2)>>2],0<(0|(c[h+(48*g|0)+36>>2]=j)))for(h=h+(48*g|0)+40|0,i=0;c[(0|c[h>>2])+(i<<2)>>2]=(0|c[e>>2])+(20*(0|c[(0|c[n>>2])+((0|c[(0|c[l>>2])+(g<<2)>>2])+i<<2)>>2])|0),(0|(i=i+1|0))!=(0|j););}while(1<(0|k));o=0|c[Z>>2],h=0|a[o+4>>0]}else o=u;if(n=0|c[Y>>2],g=0|c[n+108>>2],(255&h)<4)break;if(0<(0|(c[Z+596>>2]=g)))for(i=0|c[Z+600>>2],j=0|c[b+1084>>2],k=0|c[b+1092>>2],l=0|c[Z+592>>2],m=0|c[b+1088>>2],h=g;c[i+(12*(h=(Y=h)+-1|0)|0)>>2]=c[j+(h<<2)>>2],c[i+(12*h|0)+4>>2]=c[k+(h<<2)>>2],c[i+(12*h|0)+8>>2]=l+(48*(0|c[m+(h<<2)>>2])|0),1<(0|Y););if(h=0|c[n+112>>2],0<(0|(c[Z+604>>2]=h)))for(i=0|c[Z+608>>2],j=0|c[b+1096>>2],k=0|c[b+1104>>2],l=0|c[Z+592>>2],g=0|c[b+1100>>2];c[i+(12*(h=(b=h)+-1|0)|0)>>2]=c[j+(h<<2)>>2],c[i+(12*h|0)+4>>2]=c[k+(h<<2)>>2],c[i+(12*h|0)+8>>2]=l+(48*(0|c[g+(h<<2)>>2])|0),1<(0|b););if(i=0|c[o+1132>>2],j=0|c[20+(0|c[o+704>>2])>>2],k=Z+612|0,c[Z+616>>2]=c[o+972>>2],h=0|c[o+964>>2],c[k>>2]=h,(0|j)<=0)break;if(c[h>>2]=i+(c[c[(g=o+968|0)>>2]>>2]<<2),1==(0|j))break;for(h=1;c[(0|c[k>>2])+(h<<2)>>2]=i+(c[(0|c[g>>2])+(h<<2)>>2]<<2),(0|(h=h+1|0))!=(0|j););}else{if((0|c[B+20>>2])<=0)break;z=u+948|0,A=u+952|0,w=Z+612|0,x=u+1032|0,y=u+1036|0,s=u+1132|0,u=Z+616|0,v=0;do{if(h=0|c[(0|c[z>>2])+(v<<2)>>2],e=(b=0|c[(0|c[A>>2])+(v<<2)>>2])+h|0,q=0|c[(0|c[w>>2])+(v<<2)>>2],0<(0|b)){o=0|c[x>>2],p=0|c[y>>2],n=h,h=0;do{if(i=0|c[o+(n<<2)>>2],m=(b=0|c[p+(n<<2)>>2])+i|0,0<(0|b)){l=0|c[s>>2];do{t=+f[l+(i<<2)>>2],j=q+(h<<2)|0;b:do{if(0<(0|h))for(k=q;;){if(+f[k>>2]==t)break b;if(j>>>0<=(k=k+4|0)>>>0){X=150;break}}else X=150}while(0)}while(150==(0|X)&&(X=0,f[j>>2]=t,h=h+1|0),(0|(i=i+1|0))<(0|m))}}while((0|(n=n+1|0))<(0|e))}else h=0;!function(a,b){a|=0;var h,j,d=0,e=0,f=0,g=0,i=0,l=0,m=S,k=S=S+63&-64;S=S+208|0,f=(b|=0)<<2,c[(i=k=192+(j=k)|0)>>2]=1,c[i+4>>2]=0;a:do{if(0|f){for(c[4+j>>2]=4,d=b=c[j>>2]=4,e=2;(c[j+(e<<2)>>2]=b=b+4+d|0)>>>0>>0;)i=d,d=b,e=e+1|0,b=i;if(a>>>0<(g=a+f+-4|0)>>>0){h=g,i=4+k|0,d=a,e=b=1;do{do{if(3!=(3&b|0)){if((0|c[j+((f=e+-1|0)<<2)>>2])>>>0<(h-d|0)>>>0?Cc(d,e,j):Ec(d,b,0|c[i>>2],e,0,j),1==(0|e)){Fc(k,1),e=0;break}Fc(k,f),e=1;break}}while(Cc(d,e,j),Dc(k,2),e=e+2|0,0)}while(b=1|c[k>>2],c[k>>2]=b,(d=d+4|0)>>>0>>0);f=0|c[(g=i)>>2]}else g=4+k|0,f=0,d=a,e=b=1;for(Ec(d,b,f,e,0,j),f=4+k|0;;){if(1==(0|e)&1==(0|b)){if(!(0|c[f>>2]))break a;l=21}else(0|e)<2?l=21:(Fc(k,2),a=e+-2|0,c[k>>2]=7^c[k>>2],Dc(k,1),Ec(d+(0-(0|c[j+(a<<2)>>2]))+-4|0,0|c[k>>2],0|c[g>>2],e+-1|0,1,j),Fc(k,1),b=1|c[k>>2],Ec(d=d+-4|0,c[k>>2]=b,0|c[g>>2],a,1,j),e=a);21==(0|l)&&(Dc(k,a=(l=0)|Gc(k)),b=0|c[k>>2],d=d+-4|0,e=a+e|0)}}}while(0);S=m}(q,h),c[(0|c[u>>2])+(v<<2)>>2]=h,v=v+1|0}while((0|v)<(0|c[20+(0|c[g>>2])>>2]))}}while(0);return Jb(Z),S=_,0|Z}function oa(a,b){return 0|((a=+f[(a|=0)>>2])<(b=+f[(b|=0)>>2])?-1:b>0];c[(d|=0)>>2]=b+(0|c[b+64>>2]),c[d+4>>2]=b+(0|c[b+68>>2]),c[d+8>>2]=b+(0|c[b+72>>2]),c[d+12>>2]=b+(0|c[b+76>>2]),c[d+16>>2]=b+(0|c[b+80>>2]),c[d+20>>2]=b+(0|c[b+84>>2]),c[d+24>>2]=b+(0|c[b+88>>2]),c[d+28>>2]=b+(0|c[b+92>>2]),c[d+32>>2]=b+(0|c[b+96>>2]),c[d+36>>2]=b+(0|c[b+100>>2]),c[d+40>>2]=b+(0|c[b+104>>2]),c[d+44>>2]=b+(0|c[b+108>>2]),c[d+48>>2]=b+(0|c[b+112>>2]),c[d+52>>2]=b+(0|c[b+116>>2]),c[d+56>>2]=b+(0|c[b+120>>2]),c[d+60>>2]=b+(0|c[b+124>>2]),c[d+64>>2]=b+(0|c[b+128>>2]),c[d+68>>2]=b+(0|c[b+132>>2]),c[d+72>>2]=b+(0|c[b+136>>2]),c[d+76>>2]=b+(0|c[b+140>>2]),c[d+80>>2]=b+(0|c[b+144>>2]),c[d+84>>2]=b+(0|c[b+148>>2]),c[d+92>>2]=b+(0|c[b+152>>2]),c[d+96>>2]=b+(0|c[b+156>>2]),c[d+100>>2]=b+(0|c[b+160>>2]),c[d+108>>2]=b+(0|c[b+164>>2]),c[d+112>>2]=b+(0|c[b+168>>2]),c[d+116>>2]=b+(0|c[b+172>>2]),c[d+124>>2]=b+(0|c[b+176>>2]),c[d+128>>2]=b+(0|c[b+180>>2]),c[d+132>>2]=b+(0|c[b+184>>2]),c[d+136>>2]=b+(0|c[b+188>>2]),c[d+140>>2]=b+(0|c[b+192>>2]),c[d+144>>2]=b+(0|c[b+196>>2]),c[d+148>>2]=b+(0|c[b+200>>2]),c[d+152>>2]=b+(0|c[b+204>>2]),c[d+156>>2]=b+(0|c[b+208>>2]),c[d+164>>2]=b+(0|c[b+212>>2]),c[d+168>>2]=b+(0|c[b+216>>2]),c[d+172>>2]=b+(0|c[b+220>>2]),c[d+176>>2]=b+(0|c[b+224>>2]),c[d+180>>2]=b+(0|c[b+228>>2]),c[d+184>>2]=b+(0|c[b+232>>2]),c[d+188>>2]=b+(0|c[b+236>>2]),c[d+192>>2]=b+(0|c[b+240>>2]),c[d+196>>2]=b+(0|c[b+244>>2]),c[d+200>>2]=b+(0|c[b+248>>2]),c[d+204>>2]=b+(0|c[b+252>>2]),c[d+208>>2]=b+(0|c[b+256>>2]),c[d+212>>2]=b+(0|c[b+260>>2]),c[d+216>>2]=b+(0|c[b+264>>2]),c[d+220>>2]=b+(0|c[b+268>>2]),c[d+224>>2]=b+(0|c[b+272>>2]),c[d+228>>2]=b+(0|c[b+276>>2]),c[d+232>>2]=b+(0|c[b+280>>2]),c[d+236>>2]=b+(0|c[b+284>>2]),c[d+244>>2]=b+(0|c[b+288>>2]),c[d+248>>2]=b+(0|c[b+292>>2]),c[d+272>>2]=b+(0|c[b+296>>2]),c[d+276>>2]=b+(0|c[b+300>>2]),c[d+280>>2]=b+(0|c[b+304>>2]),c[d+284>>2]=b+(0|c[b+308>>2]),c[d+288>>2]=b+(0|c[b+312>>2]),c[d+292>>2]=b+(0|c[b+316>>2]),c[d+296>>2]=b+(0|c[b+320>>2]),c[d+300>>2]=b+(0|c[b+324>>2]),c[d+304>>2]=b+(0|c[b+328>>2]),c[d+308>>2]=b+(0|c[b+332>>2]),c[d+312>>2]=b+(0|c[b+336>>2]),c[d+316>>2]=b+(0|c[b+340>>2]),c[d+320>>2]=b+(0|c[b+344>>2]),c[d+324>>2]=b+(0|c[b+348>>2]),c[d+336>>2]=b+(0|c[b+352>>2]),c[d+340>>2]=b+(0|c[b+356>>2]),c[d+344>>2]=b+(0|c[b+360>>2]),c[d+328>>2]=b+(0|c[b+364>>2]),c[d+332>>2]=b+(0|c[b+368>>2]),c[d+428>>2]=b+(0|c[b+372>>2]),c[d+432>>2]=b+(0|c[b+376>>2]),c[d+436>>2]=b+(0|c[b+380>>2]),c[d+440>>2]=b+(0|c[b+384>>2]),c[d+444>>2]=b+(0|c[b+388>>2]),c[d+448>>2]=b+(0|c[b+392>>2]),c[d+452>>2]=b+(0|c[b+396>>2]),c[d+456>>2]=b+(0|c[b+400>>2]),c[d+460>>2]=b+(0|c[b+404>>2]),c[d+464>>2]=b+(0|c[b+408>>2]),c[d+468>>2]=b+(0|c[b+412>>2]),c[d+472>>2]=b+(0|c[b+416>>2]),c[d+476>>2]=b+(0|c[b+420>>2]),c[d+480>>2]=b+(0|c[b+424>>2]),c[d+484>>2]=b+(0|c[b+428>>2]),c[d+488>>2]=b+(0|c[b+432>>2]),c[d+492>>2]=b+(0|c[b+436>>2]),c[d+496>>2]=b+(0|c[b+440>>2]),c[d+500>>2]=b+(0|c[b+444>>2]),c[d+504>>2]=b+(0|c[b+448>>2]),c[d+508>>2]=b+(0|c[b+452>>2]),c[d+512>>2]=b+(0|c[b+456>>2]),c[d+516>>2]=b+(0|c[b+460>>2]),c[d+520>>2]=b+(0|c[b+464>>2]),(255&e)<=1||(c[d+104>>2]=b+(0|c[b+468>>2]),(255&e)<=3||(c[d+260>>2]=b+(0|c[b+472>>2]),c[d+264>>2]=b+(0|c[b+476>>2]),c[d+268>>2]=b+(0|c[b+480>>2]),c[d+88>>2]=b+(0|c[b+484>>2]),c[d+120>>2]=b+(0|c[b+488>>2]),c[d+160>>2]=b+(0|c[b+492>>2]),c[d+524>>2]=b+(0|c[b+496>>2]),c[d+528>>2]=b+(0|c[b+500>>2]),c[d+532>>2]=b+(0|c[b+504>>2]),c[d+536>>2]=b+(0|c[b+508>>2]),c[d+540>>2]=b+(0|c[b+512>>2]),c[d+544>>2]=b+(0|c[b+516>>2]),c[d+240>>2]=b+(0|c[b+520>>2]),c[d+252>>2]=b+(0|c[b+524>>2]),c[d+256>>2]=b+(0|c[b+528>>2]),c[d+348>>2]=b+(0|c[b+532>>2]),c[d+352>>2]=b+(0|c[b+536>>2]),c[d+356>>2]=b+(0|c[b+540>>2]),c[d+360>>2]=b+(0|c[b+544>>2]),c[d+364>>2]=b+(0|c[b+548>>2]),c[d+368>>2]=b+(0|c[b+552>>2]),c[d+372>>2]=b+(0|c[b+556>>2]),c[d+376>>2]=b+(0|c[b+560>>2]),c[d+380>>2]=b+(0|c[b+564>>2]),c[d+384>>2]=b+(0|c[b+568>>2]),c[d+388>>2]=b+(0|c[b+572>>2]),c[d+392>>2]=b+(0|c[b+576>>2]),c[d+396>>2]=b+(0|c[b+580>>2]),c[d+400>>2]=b+(0|c[b+584>>2]),c[d+404>>2]=b+(0|c[b+588>>2]),c[d+408>>2]=b+(0|c[b+592>>2]),c[d+412>>2]=b+(0|c[b+596>>2]),c[d+416>>2]=b+(0|c[b+600>>2]),c[d+420>>2]=b+(0|c[b+604>>2]),c[d+424>>2]=b+(0|c[b+608>>2])))}var W=[md,function(a){return 0|A(0,0|(a|=0))},function(a){return 0},md],X=[function(a,b,c){return x(1),0},function(a,b,c){return 0|B(0,0|(a|=0),0|(b|=0),0|(c|=0))},function(a,b,d){b|=0,d|=0;var e,j,k,m,n,o,f=0,g=0,h=0,p=0,l=S,i=S=S+63&-64;for(S=S+32|0,i=16+(g=i)|0,f=0|c[(j=28+(a|=0)|0)>>2],c[g>>2]=f,f=(0|c[(k=a+20|0)>>2])-f|0,c[g+4>>2]=f,c[g+8>>2]=b,e=a+60|0,f=f+(c[g+12>>(h=2)]=d)|0;;){if((0|f)==(0|(b=0|function(a){return 0|((a|=0)<<16>>16?(c[1026]=65535&a,-1):0)}(0|G(0|c[e>>2],0|g,0|h,0|i))?c[i>>2]=-1:0|c[i>>2]))){b=6;break}if((0|b)<0){b=8;break}p=0|c[g+4>>2],c[(n=(m=p>>>0>>0)?g+8|0:g)>>2]=(0|c[n>>2])+(p=b-(m?p:0)|0),c[(o=n+4|0)>>2]=(0|c[o>>2])-p,g=n,h=h+(m<<31>>31)|0,f=f-b|0}return 6==(0|b)?(p=0|c[a+44>>2],c[a+16>>2]=p+(0|c[a+48>>2]),c[j>>2]=p,c[k>>2]=p):8==(0|b)&&(c[a+16>>2]=0,c[j>>2]=0,c[k>>2]=0,c[a>>2]=32|c[a>>2],d=2==(0|h)?0:d-(0|c[g+4>>2])|0),S=l,0|d},function(a,b,d){var e,f;return Zc(0|(f=0|c[(e=20+(a|=0)|0)>>2]),0|(b|=0),0|(a=(d|=0)>>>0<(a=(0|c[a+16>>2])-f|0)>>>0?d:a)),c[e>>2]=(0|c[e>>2])+a,0|d}],Y=[od,function(a,b,c,d){return 0|C(0,0|(a|=0),0|(b|=0),0|(c|=0),0|(d|=0))},function(a,b,c,d){return y(0),0},od],Z=[function(a){x(3)},function(a){D(0,0|(a|=0))}],_=[function(a,b){x(4)},function(a,b){E(0,0|(a|=0),0|(b|=0))},function(a,b){var j,e=0,g=0,h=0,i=0,k=0,l=0,m=0,n=0,e=0|c[(j=308+(a|=0)|0)>>2],g=0|c[a+316>>2],h=0|c[a+320>>2];-1==(0|(i=0|c[(k=e+((b|=0)<<5)+8|0)>>2]))?(c[g+(b<<2)>>2]=c[(0|c[a+148>>2])+(c[e+(b<<5)+16>>2]<<2)>>2],f[h+(b<<2)>>2]=1):(m=0|c[(l=e+(b<<5)+16|0)>>2],n=0|c[(0|c[a+152>>2])+(m<<2)>>2],$[3&c[e+(i<<5)+24>>2]](a,i,n,n,0|c[(0|c[a+60>>2])+(24*m|0)+16>>2]),k=0|c[k>>2],f[g+(b<<2)>>2]=+f[(0|c[a+148>>2])+(c[l>>2]<<2)>>2]*+f[g+(k<<2)>>2],c[h+(b<<2)>>2]=c[h+(k<<2)>>2]),(0|d[4+(0|c[a>>2])>>0])<4||(e=0|c[j>>2],g=0|c[a+324>>2],j=0|c[a+328>>2],h=b<<2,i=c[e+(b<<5)+16>>2]<<2,-1==(0|(e=0|c[e+(b<<5)+8>>2]))?(e=0|c[a+156>>2],c[g+(h<<2)>>2]=c[e+(i<<2)>>2],c[g+((b=1|h)<<2)>>2]=c[e+((k=1|i)<<2)>>2],c[g+((n=2|h)<<2)>>2]=c[e+((m=2|i)<<2)>>2],f[g+((e=3|h)<<2)>>2]=1,l=0|c[a+160>>2],c[j+(h<<2)>>2]=c[l+(i<<2)>>2],c[j+(b<<2)>>2]=c[l+(k<<2)>>2],c[j+(n<<2)>>2]=c[l+(m<<2)>>2]):(m=e<<2,l=(0|c[a+156>>2])+(i<<2)|0,f[(e=g+(h<<2)|0)>>2]=+f[l>>2]*+f[(n=g+(m<<2)|0)>>2],f[e+4>>2]=+f[l+4>>2]*+f[n+4>>2],f[e+8>>2]=+f[l+8>>2]*+f[n+8>>2],f[g+((e=3|h)<<2)>>2]=1,l=(0|c[a+160>>2])+(i<<2)|0,b=+f[l>>2],k=+f[(m=j+(m<<2)|0)>>2],f[(n=j+(h<<2)|0)>>2]=b+k-b*k,k=+f[l+4>>2],b=+f[m+4>>2],f[n+4>>2]=k+b-k*b,b=+f[l+8>>2],k=+f[m+8>>2],f[n+8>>2]=b+k-b*k),f[j+(e<<2)>>2]=1)},function(a,b){var n,p,q,r,s,t,u,v,w,x,y,z,A,B,C,e=0,g=0,h=0,i=0,j=0,k=0,l=0,m=0,o=0,D=0,E=0,F=0,G=0,H=S,F=S=S+63&-64;if(S=S+48|0,u=F+8|0,s=F+40|0,r=F+32|0,q=(t=F)+24|0,F=F+16|0,e=0|c[(y=308+(a|=0)|0)>>2],w=0|c[a+316>>2],x=0|c[a+320>>2],-1==(0|(p=0|c[(B=e+((b|=0)<<5)+8|0)>>2])))G=0|c[e+(b<<5)+16>>2],c[w+(b<<2)>>2]=c[(0|c[a+268>>2])+(G<<2)>>2],c[x+(b<<2)>>2]=c[(0|c[a+272>>2])+(G<<2)>>2];else{for(v=0|c[(C=e+(b<<5)+16|0)>>2],j=0|c[(0|c[(z=a+276|0)>>2])+(v<<2)>>2],c[F>>2]=j,v=0|c[(0|c[(A=a+280|0)>>2])+(v<<2)>>2],c[(D=F+4|0)>>2]=v,E=1==(0|c[e+(p<<5)+12>>2])?-10:-.10000000149011612,c[s>>2]=j,c[(j=4+s|0)>>2]=v,$[3&c[(v=e+(p<<5)+24|0)>>2]](a,p,s,r,1),h=4+q|0,e=4+r|0,g=t+4|0,i=9,o=1;f[q>>2]=(m=0*o)+ +f[s>>2],f[h>>2]=(n=E*o)+ +f[j>>2],$[3&c[v>>2]](a,p,q,t,1),l=+f[t>>2]-+f[r>>2],f[t>>2]=l,k=+f[g>>2]-+f[e>>2],!(0!=l|0!=(f[g>>2]=k));){if(f[q>>2]=+f[s>>2]-m,f[h>>2]=+f[j>>2]-n,$[3&c[v>>2]](a,p,q,t,1),k=+f[t>>2]-+f[r>>2],f[t>>2]=k,m=+f[g>>2]-+f[e>>2],0!=k|0!=(f[g>>2]=m)){G=6;break}if(!i){G=8;break}i=i+-1|0,o*=.10000000149011612}6==(0|G)?(l=-k,k=-m):8==(0|G)&&(ia(0,2813,u),k=l=0),E=180*+rb(0,E,l,k)/3.1415927410125732,$[3&c[v>>2]](a,0|c[B>>2],F,F,1),G=0|c[C>>2],c[(0|c[z>>2])+(G<<2)>>2]=c[F>>2],c[(0|c[A>>2])+(G<<2)>>2]=c[D>>2],F=(0|c[a+284>>2])+(G<<2)|0,f[F>>2]=+f[F>>2]-E,F=0|c[B>>2],f[w+(b<<2)>>2]=+f[(0|c[a+268>>2])+(G<<2)>>2]*+f[w+(F<<2)>>2],G=(0|c[a+272>>2])+(G<<2)|0,E=+f[G>>2]*+f[x+(F<<2)>>2],f[x+(b<<2)>>2]=E,f[G>>2]=E}(0|d[4+(0|c[a>>2])>>0])<4||(e=0|c[y>>2],g=0|c[a+324>>2],j=0|c[a+328>>2],h=b<<2,i=c[e+(b<<5)+16>>2]<<2,-1==(0|(e=0|c[e+(b<<5)+8>>2]))?(e=0|c[a+296>>2],c[g+(h<<2)>>2]=c[e+(i<<2)>>2],c[g+((F=1|h)<<2)>>2]=c[e+((D=1|i)<<2)>>2],c[g+((b=2|h)<<2)>>2]=c[e+((G=2|i)<<2)>>2],f[g+((e=3|h)<<2)>>2]=1,a=0|c[a+300>>2],c[j+(h<<2)>>2]=c[a+(i<<2)>>2],c[j+(F<<2)>>2]=c[a+(D<<2)>>2],c[j+(b<<2)>>2]=c[a+(G<<2)>>2]):(F=e<<2,G=(0|c[a+296>>2])+(i<<2)|0,f[(e=g+(h<<2)|0)>>2]=+f[G>>2]*+f[(b=g+(F<<2)|0)>>2],f[e+4>>2]=+f[G+4>>2]*+f[b+4>>2],f[e+8>>2]=+f[G+8>>2]*+f[b+8>>2],f[g+((e=3|h)<<2)>>2]=1,G=(0|c[a+300>>2])+(i<<2)|0,o=+f[G>>2],E=+f[(a=j+(F<<2)|0)>>2],f[(b=j+(h<<2)|0)>>2]=o+E-o*E,E=+f[G+4>>2],o=+f[a+4>>2],f[b+4>>2]=E+o-E*o,o=+f[G+8>>2],E=+f[a+8>>2],f[b+8>>2]=o+E-o*E),f[j+(e<<2)>>2]=1),S=H}],$=[function(a,b,c,d,e){x(5)},function(a,b,c,d,e){F(0,0|(a|=0),0|(b|=0),0|(c|=0),0|(d|=0),0|(e|=0))},function(a,b,d,e,h){d|=0,e|=0;var B,F,G,H,I,J,K,L,N,O,P,Q,R,T,U,V,W,X,Y,ba,ca,da,ea,i=0,j=0,k=0,l=0,m=0,n=0,o=0,p=0,q=0,r=0,s=0,t=0,u=0,w=0,x=0,y=0,z=0,A=0,C=0,D=0,E=0,M=0,_=0,Z=S;if(S=S+32|0,Y=Z,b=0|c[(0|c[308+(a|=0)>>2])+((b|=0)<<5)+16>>2],i=0|c[a+60>>2],R=0|c[(0|c[a+152>>2])+(b<<2)>>2],T=0|c[i+(24*b|0)+8>>2],U=0|c[i+(24*b|0)+4>>2],V=1+T|0,(0|(h|=0))<=0)S=Z;else{W=0|T,X=0|U,Q=0==(0|c[i+(24*b|0)+12>>2]),F=R+(T<<3)|0,G=R+((N=0|v(U,V))<<3)|0,H=R+((L=N+T|0)<<3)|0,I=4+R|0,J=R+(T<<3)+4|0,K=R+(N<<3)+4|0,L=R+(L<<3)+4|0,P=T-1|0,O=U-1|0,t=u=w=x=j=k=s=r=q=p=E=D=o=n=m=l=M=A=0;do{y=(B=+f[d+(M<<3)>>2])*W,C=(z=+f[d+(M<<3)+4>>2])*X,a=!(1<=B),i=!(1<=z);do{if(i&a&!(B<0)&!(z<0)){if(a=~~y,b=(0|v(V,i=~~C))+a|0,k=y-(0|a),j=C-(0|i),!Q){f[e+(M<<3)>>2]=(y=1-j)*((z=1-k)*+f[R+(b<<3)>>2])+y*(k*+f[R+((_=b+1|0)<<3)>>2])+j*(z*+f[R+((a=b+V|0)<<3)>>2])+j*(k*+f[R+((i=a+1|0)<<3)>>2]),z=y*(z*+f[R+(b<<3)+4>>2])+y*(k*+f[R+(_<<3)+4>>2])+j*(z*+f[R+(a<<3)+4>>2]),y=j*(k*+f[R+(i<<3)+4>>2]);break}if(k+j<=1){f[e+(M<<3)>>2]=(z=1-k-j)*+f[R+(b<<3)>>2]+k*+f[R+((i=b+1|0)<<3)>>2]+j*+f[R+((_=b+V|0)<<3)>>2],z=z*+f[R+(b<<3)+4>>2]+k*+f[R+(i<<3)+4>>2],y=j*+f[R+(_<<3)+4>>2];break}f[e+(M<<3)>>2]=(C=k+-1+j)*+f[R+((a=1+(i=b+V|0)|0)<<3)>>2]+(z=1-k)*+f[R+(i<<3)>>2]+(y=1-j)*+f[R+((_=b+1|0)<<3)>>2],z=C*+f[R+(a<<3)+4>>2]+z*+f[R+(i<<3)+4>>2],y*=+f[R+(_<<3)+4>>2];break}if(A||(ea=+f[R>>2],da=+f[F>>2],ca=+f[G>>2],D=+f[H>>2],ba=+f[I>>2],t=(w=+f[J>>2])-(x=+f[K>>2]),A=1,D=.25*(ea+da+ca+D)-.5*(ea=D-ea),E=.25*(ba+w+x+(E=+f[L>>2]))-.5*(ba=E-ba),x=.5*((u=da-ca)+ea),w=.5*(t+ba),u=.5*(ea-u),t=.5*(ba-t)),!(z<3&-2>2]=z*u+(B*x+D),z*=t,y=B*w+E;break}do{if(B<=0){if(z<=0){l=D-(n=2*u),m=E-(o=2*t),n=(r=D-2*x)-n,o=(s=E-2*w)-o,p=+f[R>>2],q=+f[I>>2],k=.5*(2+B),j=.5*(z+2);break}if(i){o=(j=0|(i=(0|U)==(0|(i=~~C))?O:i))/X,k=(0|(_=i+1|0))/X,i=0|v(i,V),_=0|v(_,V),l=+f[R+(i<<3)>>2],m=+f[R+(i<<3)+4>>2],n=o*u+(r=D-2*x),o=o*t+(s=E-2*w),p=+f[R+(_<<3)>>2],q=+f[R+(_<<3)+4>>2],r=k*u+r,s=k*t+s,k=.5*(2+B),j=C-j;break}l=+f[G>>2],m=+f[K>>2],n=u+(r=D-2*x),o=t+(s=E-2*w),p=(j=3*u)+D,q=(k=3*t)+E,r=j+r,s=k+s,k=.5*(2+B),j=.5*(z+-1);break}if(b=z<=0,a){if(b){l=(m=(0|(i=(_=(0|T)==(0|(_=~~y))?P:_)+1|0))/W)*x+D-(n=2*u),m=m*w+E-(o=2*t),n=(p=(k=0|_)/W)*x+D-n,o=p*w+E-o,p=+f[R+(i<<3)>>2],q=+f[R+(i<<3)+4>>2],r=+f[R+(_<<3)>>2],s=+f[R+(_<<3)+4>>2],k=y-k,j=.5*(z+2);break}if(i){c[Y>>2]=M,g[Y+8>>3]=B,g[Y+16>>3]=z,ia(0,865,Y);break}k=0|(_=(0|T)==(0|(_=~~y))?P:_),q=(0|(i=_+1|0))/W,l=+f[R+((i=i+N|0)<<3)>>2],m=+f[R+(i<<3)+4>>2],n=+f[R+((_=_+N|0)<<3)>>2],o=+f[R+(_<<3)+4>>2],p=(r=3*u)+(q*x+D),q=(j=3*t)+(q*w+E),r+=(s=k/W)*x+D,s=j+(s*w+E),k=y-k,j=.5*(z+-1);break}if(b){l=(p=3*x+D)-(n=2*u),m=(q=3*w+E)-(o=2*t),n=x+D-n,o=w+E-o,r=+f[F>>2],s=+f[J>>2],k=.5*(B-1),j=.5*(z+2);break}if(i){m=(j=0|(i=(0|U)==(0|(i=~~C))?O:i))/X,r=(0|(_=i+1|0))/X,i=(0|v(i,V))+T|0,_=(0|v(_,V))+T|0,l=m*u+(p=3*x+D),m=m*t+(q=3*w+E),n=+f[R+(i<<3)>>2],o=+f[R+(i<<3)+4>>2],p=r*u+p,q=r*t+q,r=+f[R+(_<<3)>>2],s=+f[R+(_<<3)+4>>2],k=.5*(B-1),j=C-j;break}l=u+(p=3*x+D),m=t+(q=3*w+E),n=+f[H>>2],o=+f[L>>2],p=(r=3*u)+p,q=(s=3*t)+q,r+=x+D,s+=w+E,k=.5*(B-1),j=.5*(z+-1);break}while(0);if(k+j<=1){f[e+(M<<3)>>2]=n+(l-n)*k+(r-n)*j,z=o+(m-o)*k,y=(s-o)*j;break}f[e+(M<<3)>>2]=p+(r-p)*(z=1-k)+(l-p)*(y=1-j),z=q+(s-q)*z,y*=m-q;break}while(0)}while(f[e+(M<<3)+4>>2]=z+y,(0|(M=M+1|0))!=(0|h));S=Z}},function(a,b,d,e,g){d|=0,e|=0,g|=0;var i,k,n,o,h,j,l,m;if(b=0|c[(0|c[(a|=0)+308>>2])+((b|=0)<<5)+16>>2],j=3.1415927410125732*(+f[(0|c[a+168>>2])+(12*b|0)+4>>2]+ +f[(0|c[a+284>>2])+(b<<2)>>2])/180,h=+t(j),j=+s(j),k=(j*=i=+f[(0|c[a+272>>2])+(b<<2)>>2])*(l=0==(0|c[(0|c[a+288>>2])+(b<<2)>>2])?1:-1),i=(h*=i)*(m=0==(0|c[(0|c[a+292>>2])+(b<<2)>>2])?1:-1),l*=h,m*=j,j=+f[(0|c[a+276>>2])+(b<<2)>>2],h=+f[(0|c[a+280>>2])+(b<<2)>>2],!((0|g)<=0))for(b=0;o=+f[d+(b<<3)>>2],n=+f[d+(b<<3)+4>>2],f[e+(b<<3)>>2]=k*o-i*n+j,f[e+(b<<3)+4>>2]=l*o+m*n+h,(0|(b=b+1|0))!=(0|g););}];return{___errno_location:function(){return 4104},___muldi3:Qc,___udivdi3:Vc,_bitshift64Lshr:Wc,_bitshift64Shl:Xc,_csmFree:function(a){Kc(a|=0)},_csmGetDrawableConstantFlags:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[888+(0|c[a>>2])>>2],S=d,0|b):(c[b>>2]=2312,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableCount:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[a+332>>2],S=d,0|b):(c[b>>2]=2274,c[b+4>>2]=1827,ia(0,1664,b),S=d,-1)},_csmGetDrawableDrawOrders:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[a+440>>2],S=d,0|b):(c[b>>2]=2396,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableDynamicFlags:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[a+432>>2],S=d,0|b):(c[b>>2]=2340,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableIds:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[832+(0|c[a>>2])>>2],S=d,0|b):(c[b>>2]=2294,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableIndexCounts:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[904+(0|c[a>>2])>>2],S=d,0|b):(c[b>>2]=2598,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableIndices:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[840+(0|c[a>>2])>>2],S=d,0|b):(c[b>>2]=2624,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableMaskCounts:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[912+(0|c[a>>2])>>2],S=d,0|b):(c[b>>2]=2472,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableMasks:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[844+(0|c[a>>2])>>2],S=d,0|b):(c[b>>2]=2497,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableMultiplyColors:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[a+452>>2],S=d,0|b):(c[b>>2]=2646,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableOpacities:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[a+448>>2],S=d,0|b):(c[b>>2]=2448,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableParentPartIndices:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[876+(0|c[a>>2])>>2],S=d,0|b):(c[b>>2]=2702,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableRenderOrders:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[a+436>>2],S=d,0|b):(c[b>>2]=2421,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableScreenColors:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[a+456>>2],S=d,0|b):(c[b>>2]=2675,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableTextureIndices:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[884+(0|c[a>>2])>>2],S=d,0|b):(c[b>>2]=2367,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableVertexCounts:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[892+(0|c[a>>2])>>2],S=d,0|b):(c[b>>2]=2517,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableVertexPositions:function(a){var b=0,d=S,b=S=S+63&-64;return S=S+16|0,(a|=0)?(b=0|c[a+444>>2],S=d,0|b):(c[b>>2]=2544,c[b+4>>2]=1827,ia(0,1664,b),S=d,(b=0)|b)},_csmGetDrawableVertexUvs:function(a){var b=0,d=S,b=S=S+63&-64;return 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imtype=np.uint8, cent=1., factor=255. / 2.): - # def im2tensor(image, imtype=np.uint8, cent=1., factor=1.): - return torch.Tensor((image / factor - cent) - [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) - - -############################################################ -# base_model.py # -############################################################ - - -class BaseModel(torch.nn.Module): - def __init__(self): - super().__init__() - - def name(self): - return 'BaseModel' - - def initialize(self, use_gpu=True): - self.use_gpu = use_gpu - - def forward(self): - pass - - def get_image_paths(self): - pass - - def optimize_parameters(self): - pass - - def get_current_visuals(self): - return self.input - - def get_current_errors(self): - return {} - - def save(self, label): - pass - - # helper saving function that can be used by subclasses - def save_network(self, network, path, network_label, epoch_label): - save_filename = '%s_net_%s.pth' % (epoch_label, network_label) - save_path = os.path.join(path, save_filename) - torch.save(network.state_dict(), save_path) - - # helper loading function that can be used by subclasses - def load_network(self, network, network_label, epoch_label): - save_filename = '%s_net_%s.pth' % (epoch_label, network_label) - save_path = os.path.join(self.save_dir, save_filename) - print('Loading network from %s' % save_path) - network.load_state_dict(torch.load(save_path, map_location='cpu')) - - def update_learning_rate(): - pass - - def get_image_paths(self): - return self.image_paths - - def save_done(self, flag=False): - np.save(os.path.join(self.save_dir, 'done_flag'), flag) - np.savetxt(os.path.join(self.save_dir, 'done_flag'), [flag, ], fmt='%i') - - -############################################################ -# dist_model.py # -############################################################ - -import os -from collections import OrderedDict -from scipy.ndimage import zoom -from tqdm import tqdm - - -class DistModel(BaseModel): - def name(self): - return self.model_name - - def initialize(self, model='net-lin', net='alex', colorspace='Lab', pnet_rand=False, pnet_tune=False, - model_path=None, - use_gpu=True, printNet=False, spatial=False, - is_train=False, lr=.0001, beta1=0.5, version='0.1'): - ''' - INPUTS - model - ['net-lin'] for linearly calibrated network - ['net'] for off-the-shelf network - ['L2'] for L2 distance in Lab colorspace - ['SSIM'] for ssim in RGB colorspace - net - ['squeeze','alex','vgg'] - model_path - if None, will look in weights/[NET_NAME].pth - colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM - use_gpu - bool - whether or not to use a GPU - printNet - bool - whether or not to print network architecture out - spatial - bool - whether to output an array containing varying distances across spatial dimensions - spatial_shape - if given, output spatial shape. if None then spatial shape is determined automatically via spatial_factor (see below). - spatial_factor - if given, specifies upsampling factor relative to the largest spatial extent of a convolutional layer. if None then resized to size of input images. - spatial_order - spline order of filter for upsampling in spatial mode, by default 1 (bilinear). - is_train - bool - [True] for training mode - lr - float - initial learning rate - beta1 - float - initial momentum term for adam - version - 0.1 for latest, 0.0 was original (with a bug) - ''' - BaseModel.initialize(self, use_gpu=use_gpu) - - self.model = model - self.net = net - self.is_train = is_train - self.spatial = spatial - self.model_name = '%s [%s]' % (model, net) - - if (self.model == 'net-lin'): # pretrained net + linear layer - self.net = PNetLin(pnet_rand=pnet_rand, pnet_tune=pnet_tune, pnet_type=net, - use_dropout=True, spatial=spatial, version=version, lpips=True) - kw = dict(map_location='cpu') - if (model_path is None): - import inspect - model_path = os.path.abspath( - os.path.join(os.path.dirname(__file__), '..', '..', '..', 'models', 'lpips_models', f'{net}.pth')) - - if (not is_train): - self.net.load_state_dict(torch.load(model_path, **kw), strict=False) - - elif (self.model == 'net'): # pretrained network - self.net = PNetLin(pnet_rand=pnet_rand, pnet_type=net, lpips=False) - elif (self.model in ['L2', 'l2']): - self.net = L2(use_gpu=use_gpu, colorspace=colorspace) # not really a network, only for testing - self.model_name = 'L2' - elif (self.model in ['DSSIM', 'dssim', 'SSIM', 'ssim']): - self.net = DSSIM(use_gpu=use_gpu, colorspace=colorspace) - self.model_name = 'SSIM' - else: - raise ValueError("Model [%s] not recognized." % self.model) - - self.trainable_parameters = list(self.net.parameters()) - - if self.is_train: # training mode - # extra network on top to go from distances (d0,d1) => predicted human judgment (h*) - self.rankLoss = BCERankingLoss() - self.trainable_parameters += list(self.rankLoss.net.parameters()) - self.lr = lr - self.old_lr = lr - self.optimizer_net = torch.optim.Adam(self.trainable_parameters, lr=lr, betas=(beta1, 0.999)) - else: # test mode - self.net.eval() - - # if (use_gpu): - # self.net.to(gpu_ids[0]) - # self.net = torch.nn.DataParallel(self.net, device_ids=gpu_ids) - # if (self.is_train): - # self.rankLoss = self.rankLoss.to(device=gpu_ids[0]) # just put this on GPU0 - - if (printNet): - print('---------- Networks initialized -------------') - print_network(self.net) - print('-----------------------------------------------') - - def forward(self, in0, in1, retPerLayer=False): - ''' Function computes the distance between image patches in0 and in1 - INPUTS - in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1] - OUTPUT - computed distances between in0 and in1 - ''' - - return self.net(in0, in1, retPerLayer=retPerLayer) - - # ***** TRAINING FUNCTIONS ***** - def optimize_parameters(self): - self.forward_train() - self.optimizer_net.zero_grad() - self.backward_train() - self.optimizer_net.step() - self.clamp_weights() - - def clamp_weights(self): - for module in self.net.modules(): - if (hasattr(module, 'weight') and module.kernel_size == (1, 1)): - module.weight.data = torch.clamp(module.weight.data, min=0) - - def set_input(self, data): - self.input_ref = data['ref'] - self.input_p0 = data['p0'] - self.input_p1 = data['p1'] - self.input_judge = data['judge'] - - # if (self.use_gpu): - # self.input_ref = self.input_ref.to(device=self.gpu_ids[0]) - # self.input_p0 = self.input_p0.to(device=self.gpu_ids[0]) - # self.input_p1 = self.input_p1.to(device=self.gpu_ids[0]) - # self.input_judge = self.input_judge.to(device=self.gpu_ids[0]) - - # self.var_ref = Variable(self.input_ref, requires_grad=True) - # self.var_p0 = Variable(self.input_p0, requires_grad=True) - # self.var_p1 = Variable(self.input_p1, requires_grad=True) - - def forward_train(self): # run forward pass - # print(self.net.module.scaling_layer.shift) - # print(torch.norm(self.net.module.net.slice1[0].weight).item(), torch.norm(self.net.module.lin0.model[1].weight).item()) - - assert False, "We shoud've not get here when using LPIPS as a metric" - - self.d0 = self(self.var_ref, self.var_p0) - self.d1 = self(self.var_ref, self.var_p1) - self.acc_r = self.compute_accuracy(self.d0, self.d1, self.input_judge) - - self.var_judge = Variable(1. * self.input_judge).view(self.d0.size()) - - self.loss_total = self.rankLoss(self.d0, self.d1, self.var_judge * 2. - 1.) - - return self.loss_total - - def backward_train(self): - torch.mean(self.loss_total).backward() - - def compute_accuracy(self, d0, d1, judge): - ''' d0, d1 are Variables, judge is a Tensor ''' - d1_lt_d0 = (d1 < d0).cpu().data.numpy().flatten() - judge_per = judge.cpu().numpy().flatten() - return d1_lt_d0 * judge_per + (1 - d1_lt_d0) * (1 - judge_per) - - def get_current_errors(self): - retDict = OrderedDict([('loss_total', self.loss_total.data.cpu().numpy()), - ('acc_r', self.acc_r)]) - - for key in retDict.keys(): - retDict[key] = np.mean(retDict[key]) - - return retDict - - def get_current_visuals(self): - zoom_factor = 256 / self.var_ref.data.size()[2] - - ref_img = tensor2im(self.var_ref.data) - p0_img = tensor2im(self.var_p0.data) - p1_img = tensor2im(self.var_p1.data) - - ref_img_vis = zoom(ref_img, [zoom_factor, zoom_factor, 1], order=0) - p0_img_vis = zoom(p0_img, [zoom_factor, zoom_factor, 1], order=0) - p1_img_vis = zoom(p1_img, [zoom_factor, zoom_factor, 1], order=0) - - return OrderedDict([('ref', ref_img_vis), - ('p0', p0_img_vis), - ('p1', p1_img_vis)]) - - def save(self, path, label): - if (self.use_gpu): - self.save_network(self.net.module, path, '', label) - else: - self.save_network(self.net, path, '', label) - self.save_network(self.rankLoss.net, path, 'rank', label) - - def update_learning_rate(self, nepoch_decay): - lrd = self.lr / nepoch_decay - lr = self.old_lr - lrd - - for param_group in self.optimizer_net.param_groups: - param_group['lr'] = lr - - print('update lr [%s] decay: %f -> %f' % (type, self.old_lr, lr)) - self.old_lr = lr - - -def score_2afc_dataset(data_loader, func, name=''): - ''' Function computes Two Alternative Forced Choice (2AFC) score using - distance function 'func' in dataset 'data_loader' - INPUTS - data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside - func - callable distance function - calling d=func(in0,in1) should take 2 - pytorch tensors with shape Nx3xXxY, and return numpy array of length N - OUTPUTS - [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators - [1] - dictionary with following elements - d0s,d1s - N arrays containing distances between reference patch to perturbed patches - gts - N array in [0,1], preferred patch selected by human evaluators - (closer to "0" for left patch p0, "1" for right patch p1, - "0.6" means 60pct people preferred right patch, 40pct preferred left) - scores - N array in [0,1], corresponding to what percentage function agreed with humans - CONSTS - N - number of test triplets in data_loader - ''' - - d0s = [] - d1s = [] - gts = [] - - for data in tqdm(data_loader.load_data(), desc=name): - d0s += func(data['ref'], data['p0']).data.cpu().numpy().flatten().tolist() - d1s += func(data['ref'], data['p1']).data.cpu().numpy().flatten().tolist() - gts += data['judge'].cpu().numpy().flatten().tolist() - - d0s = np.array(d0s) - d1s = np.array(d1s) - gts = np.array(gts) - scores = (d0s < d1s) * (1. - gts) + (d1s < d0s) * gts + (d1s == d0s) * .5 - - return (np.mean(scores), dict(d0s=d0s, d1s=d1s, gts=gts, scores=scores)) - - -def score_jnd_dataset(data_loader, func, name=''): - ''' Function computes JND score using distance function 'func' in dataset 'data_loader' - INPUTS - data_loader - CustomDatasetDataLoader object - contains a JNDDataset inside - func - callable distance function - calling d=func(in0,in1) should take 2 - pytorch tensors with shape Nx3xXxY, and return pytorch array of length N - OUTPUTS - [0] - JND score in [0,1], mAP score (area under precision-recall curve) - [1] - dictionary with following elements - ds - N array containing distances between two patches shown to human evaluator - sames - N array containing fraction of people who thought the two patches were identical - CONSTS - N - number of test triplets in data_loader - ''' - - ds = [] - gts = [] - - for data in tqdm(data_loader.load_data(), desc=name): - ds += func(data['p0'], data['p1']).data.cpu().numpy().tolist() - gts += data['same'].cpu().numpy().flatten().tolist() - - sames = np.array(gts) - ds = np.array(ds) - - sorted_inds = np.argsort(ds) - ds_sorted = ds[sorted_inds] - sames_sorted = sames[sorted_inds] - - TPs = np.cumsum(sames_sorted) - FPs = np.cumsum(1 - sames_sorted) - FNs = np.sum(sames_sorted) - TPs - - precs = TPs / (TPs + FPs) - recs = TPs / (TPs + FNs) - score = voc_ap(recs, precs) - - return (score, dict(ds=ds, sames=sames)) - - -############################################################ -# networks_basic.py # -############################################################ - -import torch.nn as nn -from torch.autograd import Variable -import numpy as np - - -def spatial_average(in_tens, keepdim=True): - return in_tens.mean([2, 3], keepdim=keepdim) - - -def upsample(in_tens, out_H=64): # assumes scale factor is same for H and W - in_H = in_tens.shape[2] - scale_factor = 1. * out_H / in_H - - return nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=False)(in_tens) - - -# Learned perceptual metric -class PNetLin(nn.Module): - def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, spatial=False, - version='0.1', lpips=True): - super(PNetLin, self).__init__() - - self.pnet_type = pnet_type - self.pnet_tune = pnet_tune - self.pnet_rand = pnet_rand - self.spatial = spatial - self.lpips = lpips - self.version = version - self.scaling_layer = ScalingLayer() - - if (self.pnet_type in ['vgg', 'vgg16']): - net_type = vgg16 - self.chns = [64, 128, 256, 512, 512] - elif (self.pnet_type == 'alex'): - net_type = alexnet - self.chns = [64, 192, 384, 256, 256] - elif (self.pnet_type == 'squeeze'): - net_type = squeezenet - self.chns = [64, 128, 256, 384, 384, 512, 512] - self.L = len(self.chns) - - self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune) - - if (lpips): - self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout) - self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout) - self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout) - self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout) - self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout) - self.lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4] - if (self.pnet_type == 'squeeze'): # 7 layers for squeezenet - self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout) - self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout) - self.lins += [self.lin5, self.lin6] - - def forward(self, in0, in1, retPerLayer=False): - # v0.0 - original release had a bug, where input was not scaled - in0_input, in1_input = (self.scaling_layer(in0), self.scaling_layer(in1)) if self.version == '0.1' else ( - in0, in1) - outs0, outs1 = self.net(in0_input), self.net(in1_input) - feats0, feats1, diffs = {}, {}, {} - - for kk in range(self.L): - feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk]) - diffs[kk] = (feats0[kk] - feats1[kk]) ** 2 - - if (self.lpips): - if (self.spatial): - res = [upsample(self.lins[kk].model(diffs[kk]), out_H=in0.shape[2]) for kk in range(self.L)] - else: - res = [spatial_average(self.lins[kk].model(diffs[kk]), keepdim=True) for kk in range(self.L)] - else: - if (self.spatial): - res = [upsample(diffs[kk].sum(dim=1, keepdim=True), out_H=in0.shape[2]) for kk in range(self.L)] - else: - res = [spatial_average(diffs[kk].sum(dim=1, keepdim=True), keepdim=True) for kk in range(self.L)] - - val = res[0] - for l in range(1, self.L): - val += res[l] - - if (retPerLayer): - return (val, res) - else: - return val - - -class ScalingLayer(nn.Module): - def __init__(self): - super(ScalingLayer, self).__init__() - self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None]) - self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None]) - - def forward(self, inp): - return (inp - self.shift) / self.scale - - -class NetLinLayer(nn.Module): - ''' A single linear layer which does a 1x1 conv ''' - - def __init__(self, chn_in, chn_out=1, use_dropout=False): - super(NetLinLayer, self).__init__() - - layers = [nn.Dropout(), ] if (use_dropout) else [] - layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ] - self.model = nn.Sequential(*layers) - - -class Dist2LogitLayer(nn.Module): - ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) ''' - - def __init__(self, chn_mid=32, use_sigmoid=True): - super(Dist2LogitLayer, self).__init__() - - layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True), ] - layers += [nn.LeakyReLU(0.2, True), ] - layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True), ] - layers += [nn.LeakyReLU(0.2, True), ] - layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True), ] - if (use_sigmoid): - layers += [nn.Sigmoid(), ] - self.model = nn.Sequential(*layers) - - def forward(self, d0, d1, eps=0.1): - return self.model(torch.cat((d0, d1, d0 - d1, d0 / (d1 + eps), d1 / (d0 + eps)), dim=1)) - - -class BCERankingLoss(nn.Module): - def __init__(self, chn_mid=32): - super(BCERankingLoss, self).__init__() - self.net = Dist2LogitLayer(chn_mid=chn_mid) - # self.parameters = list(self.net.parameters()) - self.loss = torch.nn.BCELoss() - - def forward(self, d0, d1, judge): - per = (judge + 1.) / 2. - self.logit = self.net(d0, d1) - return self.loss(self.logit, per) - - -# L2, DSSIM metrics -class FakeNet(nn.Module): - def __init__(self, use_gpu=True, colorspace='Lab'): - super(FakeNet, self).__init__() - self.use_gpu = use_gpu - self.colorspace = colorspace - - -class L2(FakeNet): - - def forward(self, in0, in1, retPerLayer=None): - assert (in0.size()[0] == 1) # currently only supports batchSize 1 - - if (self.colorspace == 'RGB'): - (N, C, X, Y) = in0.size() - value = torch.mean(torch.mean(torch.mean((in0 - in1) ** 2, dim=1).view(N, 1, X, Y), dim=2).view(N, 1, 1, Y), - dim=3).view(N) - return value - elif (self.colorspace == 'Lab'): - value = l2(tensor2np(tensor2tensorlab(in0.data, to_norm=False)), - tensor2np(tensor2tensorlab(in1.data, to_norm=False)), range=100.).astype('float') - ret_var = Variable(torch.Tensor((value,))) - # if (self.use_gpu): - # ret_var = ret_var.cuda() - return ret_var - - -class DSSIM(FakeNet): - - def forward(self, in0, in1, retPerLayer=None): - assert (in0.size()[0] == 1) # currently only supports batchSize 1 - - if (self.colorspace == 'RGB'): - value = dssim(1. * tensor2im(in0.data), 1. * tensor2im(in1.data), range=255.).astype('float') - elif (self.colorspace == 'Lab'): - value = dssim(tensor2np(tensor2tensorlab(in0.data, to_norm=False)), - tensor2np(tensor2tensorlab(in1.data, to_norm=False)), range=100.).astype('float') - ret_var = Variable(torch.Tensor((value,))) - # if (self.use_gpu): - # ret_var = ret_var.cuda() - return ret_var - - -def print_network(net): - num_params = 0 - for param in net.parameters(): - num_params += param.numel() - print('Network', net) - print('Total number of parameters: %d' % num_params) - - -############################################################ -# pretrained_networks.py # -############################################################ - -from collections import namedtuple -import torch -from torchvision import models as tv - - -class squeezenet(torch.nn.Module): - def __init__(self, requires_grad=False, pretrained=True): - super(squeezenet, self).__init__() - pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features - self.slice1 = torch.nn.Sequential() - self.slice2 = torch.nn.Sequential() - self.slice3 = torch.nn.Sequential() - self.slice4 = torch.nn.Sequential() - self.slice5 = torch.nn.Sequential() - self.slice6 = torch.nn.Sequential() - self.slice7 = torch.nn.Sequential() - self.N_slices = 7 - for x in range(2): - self.slice1.add_module(str(x), pretrained_features[x]) - for x in range(2, 5): - self.slice2.add_module(str(x), pretrained_features[x]) - for x in range(5, 8): - self.slice3.add_module(str(x), pretrained_features[x]) - for x in range(8, 10): - self.slice4.add_module(str(x), pretrained_features[x]) - for x in range(10, 11): - self.slice5.add_module(str(x), pretrained_features[x]) - for x in range(11, 12): - self.slice6.add_module(str(x), pretrained_features[x]) - for x in range(12, 13): - self.slice7.add_module(str(x), pretrained_features[x]) - if not requires_grad: - for param in self.parameters(): - param.requires_grad = False - - def forward(self, X): - h = self.slice1(X) - h_relu1 = h - h = self.slice2(h) - h_relu2 = h - h = self.slice3(h) - h_relu3 = h - h = self.slice4(h) - h_relu4 = h - h = self.slice5(h) - h_relu5 = h - h = self.slice6(h) - h_relu6 = h - h = self.slice7(h) - h_relu7 = h - vgg_outputs = namedtuple("SqueezeOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5', 'relu6', 'relu7']) - out = vgg_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5, h_relu6, h_relu7) - - return out - - -class alexnet(torch.nn.Module): - def __init__(self, requires_grad=False, pretrained=True): - super(alexnet, self).__init__() - alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features - self.slice1 = torch.nn.Sequential() - self.slice2 = torch.nn.Sequential() - self.slice3 = torch.nn.Sequential() - self.slice4 = torch.nn.Sequential() - self.slice5 = torch.nn.Sequential() - self.N_slices = 5 - for x in range(2): - self.slice1.add_module(str(x), alexnet_pretrained_features[x]) - for x in range(2, 5): - self.slice2.add_module(str(x), alexnet_pretrained_features[x]) - for x in range(5, 8): - self.slice3.add_module(str(x), alexnet_pretrained_features[x]) - for x in range(8, 10): - self.slice4.add_module(str(x), alexnet_pretrained_features[x]) - for x in range(10, 12): - self.slice5.add_module(str(x), alexnet_pretrained_features[x]) - if not requires_grad: - for param in self.parameters(): - param.requires_grad = False - - def forward(self, X): - h = self.slice1(X) - h_relu1 = h - h = self.slice2(h) - h_relu2 = h - h = self.slice3(h) - h_relu3 = h - h = self.slice4(h) - h_relu4 = h - h = self.slice5(h) - h_relu5 = h - alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5']) - out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5) - - return out - - -class vgg16(torch.nn.Module): - def __init__(self, requires_grad=False, pretrained=True): - super(vgg16, self).__init__() - vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features - self.slice1 = torch.nn.Sequential() - self.slice2 = torch.nn.Sequential() - self.slice3 = torch.nn.Sequential() - self.slice4 = torch.nn.Sequential() - self.slice5 = torch.nn.Sequential() - self.N_slices = 5 - for x in range(4): - self.slice1.add_module(str(x), vgg_pretrained_features[x]) - for x in range(4, 9): - self.slice2.add_module(str(x), vgg_pretrained_features[x]) - for x in range(9, 16): - self.slice3.add_module(str(x), vgg_pretrained_features[x]) - for x in range(16, 23): - self.slice4.add_module(str(x), vgg_pretrained_features[x]) - for x in range(23, 30): - self.slice5.add_module(str(x), vgg_pretrained_features[x]) - if not requires_grad: - for param in self.parameters(): - param.requires_grad = False - - def forward(self, X): - h = self.slice1(X) - h_relu1_2 = h - h = self.slice2(h) - h_relu2_2 = h - h = self.slice3(h) - h_relu3_3 = h - h = self.slice4(h) - h_relu4_3 = h - h = self.slice5(h) - h_relu5_3 = h - vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3']) - out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) - - return out - - -class resnet(torch.nn.Module): - def __init__(self, requires_grad=False, pretrained=True, num=18): - super(resnet, self).__init__() - if (num == 18): - self.net = tv.resnet18(pretrained=pretrained) - elif (num == 34): - self.net = tv.resnet34(pretrained=pretrained) - elif (num == 50): - self.net = tv.resnet50(pretrained=pretrained) - elif (num == 101): - self.net = tv.resnet101(pretrained=pretrained) - elif (num == 152): - self.net = tv.resnet152(pretrained=pretrained) - self.N_slices = 5 - - self.conv1 = self.net.conv1 - self.bn1 = self.net.bn1 - self.relu = self.net.relu - self.maxpool = self.net.maxpool - self.layer1 = self.net.layer1 - self.layer2 = self.net.layer2 - self.layer3 = self.net.layer3 - self.layer4 = self.net.layer4 - - def forward(self, X): - h = self.conv1(X) - h = self.bn1(h) - h = self.relu(h) - h_relu1 = h - h = self.maxpool(h) - h = self.layer1(h) - h_conv2 = h - h = self.layer2(h) - h_conv3 = h - h = self.layer3(h) - h_conv4 = h - h = self.layer4(h) - h_conv5 = h - - outputs = namedtuple("Outputs", ['relu1', 'conv2', 'conv3', 'conv4', 'conv5']) - out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5) - - return out diff --git a/spaces/Alpaca233/SadTalker/src/facerender/sync_batchnorm/unittest.py b/spaces/Alpaca233/SadTalker/src/facerender/sync_batchnorm/unittest.py deleted file mode 100644 index 0675c022e4ba85d38d1f813490f6740150909524..0000000000000000000000000000000000000000 --- a/spaces/Alpaca233/SadTalker/src/facerender/sync_batchnorm/unittest.py +++ /dev/null @@ -1,29 +0,0 @@ -# -*- coding: utf-8 -*- -# File : unittest.py -# Author : Jiayuan Mao -# Email : maojiayuan@gmail.com -# Date : 27/01/2018 -# -# This file is part of Synchronized-BatchNorm-PyTorch. -# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch -# Distributed under MIT License. - -import unittest - -import numpy as np -from torch.autograd import Variable - - -def as_numpy(v): - if isinstance(v, Variable): - v = v.data - return v.cpu().numpy() - - -class TorchTestCase(unittest.TestCase): - def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3): - npa, npb = as_numpy(a), as_numpy(b) - self.assertTrue( - np.allclose(npa, npb, atol=atol), - 'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max()) - ) diff --git a/spaces/Ameaou/academic-chatgpt3.1/main.py b/spaces/Ameaou/academic-chatgpt3.1/main.py deleted file mode 100644 index f7a3a79fede1c36c1151cfd76b02fefd5d278ae0..0000000000000000000000000000000000000000 --- a/spaces/Ameaou/academic-chatgpt3.1/main.py +++ /dev/null @@ -1,190 +0,0 @@ -import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染 - -def main(): - import gradio as gr - from request_llm.bridge_all import predict - from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith - # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到 - proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY, AVAIL_LLM_MODELS = \ - get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY', 'AVAIL_LLM_MODELS') - - # 如果WEB_PORT是-1, 则随机选取WEB端口 - PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT - if not AUTHENTICATION: AUTHENTICATION = None - - from check_proxy import get_current_version - initial_prompt = "Serve me as a writing and programming assistant." - title_html = f"

ChatGPT 学术优化 {get_current_version()}

" - description = """代码开源和更新[地址🚀](https://github.com/binary-husky/chatgpt_academic)，感谢热情的[开发者们❤️](https://github.com/binary-husky/chatgpt_academic/graphs/contributors)""" - - # 问询记录, python 版本建议3.9+（越新越好） - import logging - os.makedirs("gpt_log", exist_ok=True) - try:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, encoding="utf-8") - except:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO) - print("所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log, 请注意自我隐私保护哦！") - - # 一些普通功能模块 - from core_functional import get_core_functions - functional = get_core_functions() - - # 高级函数插件 - from crazy_functional import get_crazy_functions - crazy_fns = get_crazy_functions() - - # 处理markdown文本格式的转变 - gr.Chatbot.postprocess = format_io - - # 做一些外观色彩上的调整 - from theme import adjust_theme, advanced_css - set_theme = adjust_theme() - - # 代理与自动更新 - from check_proxy import check_proxy, auto_update, warm_up_modules - proxy_info = check_proxy(proxies) - - gr_L1 = lambda: gr.Row().style() - gr_L2 = lambda scale: gr.Column(scale=scale) - if LAYOUT == "TOP-DOWN": - gr_L1 = lambda: DummyWith() - gr_L2 = lambda scale: gr.Row() - CHATBOT_HEIGHT /= 2 - - cancel_handles = [] - with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo: - gr.HTML(title_html) - gr.HTML('''
Duplicate Space请您打开此页面后务必点击上方的“复制空间”（Duplicate Space）按钮！使用时，先在输入框填入API-KEY然后回车。
切忌在“复制空间”（Duplicate Space）之前填入API_KEY或进行提问，否则您的API_KEY将极可能被空间所有者攫取！
支持任意数量的OpenAI的密钥和API2D的密钥共存，例如输入"OpenAI密钥1,API2D密钥2"，然后提交，即可同时使用两种模型接口。
''') - cookies = gr.State({'api_key': API_KEY, 'llm_model': LLM_MODEL}) - with gr_L1(): - with gr_L2(scale=2): - chatbot = gr.Chatbot() - chatbot.style(height=CHATBOT_HEIGHT) - history = gr.State([]) - with gr_L2(scale=1): - with gr.Accordion("输入区", open=True) as area_input_primary: - with gr.Row(): - txt = gr.Textbox(show_label=False, lines=2, placeholder="输入问题或API密钥，输入多个密钥时，用英文逗号间隔。支持OpenAI密钥和API2D密钥共存。").style(container=False) - with gr.Row(): - submitBtn = gr.Button("提交", variant="primary") - with gr.Row(): - resetBtn = gr.Button("重置", variant="secondary"); resetBtn.style(size="sm") - stopBtn = gr.Button("停止", variant="secondary"); stopBtn.style(size="sm") - clearBtn = gr.Button("清除", variant="secondary", visible=False); clearBtn.style(size="sm") - with gr.Row(): - status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {proxy_info}") - with gr.Accordion("基础功能区", open=True) as area_basic_fn: - with gr.Row(): - for k in functional: - variant = functional[k]["Color"] if "Color" in functional[k] else "secondary" - functional[k]["Button"] = gr.Button(k, variant=variant) - with gr.Accordion("函数插件区", open=True) as area_crazy_fn: - with gr.Row(): - gr.Markdown("注意：以下“红颜色”标识的函数插件需从输入区读取路径作为参数.") - with gr.Row(): - for k in crazy_fns: - if not crazy_fns[k].get("AsButton", True): continue - variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary" - crazy_fns[k]["Button"] = gr.Button(k, variant=variant) - crazy_fns[k]["Button"].style(size="sm") - with gr.Row(): - with gr.Accordion("更多函数插件", open=True): - dropdown_fn_list = [k for k in crazy_fns.keys() if not crazy_fns[k].get("AsButton", True)] - with gr.Column(scale=1): - dropdown = gr.Dropdown(dropdown_fn_list, value=r"打开插件列表", label="").style(container=False) - with gr.Column(scale=1): - switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary") - with gr.Row(): - with gr.Accordion("点击展开“文件上传区”。上传本地文件可供红色函数插件调用。", open=False) as area_file_up: - file_upload = gr.Files(label="任何文件, 但推荐上传压缩文件(zip, tar)", file_count="multiple") - with gr.Accordion("更换模型 & SysPrompt & 交互界面布局", open=(LAYOUT == "TOP-DOWN")): - system_prompt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt) - top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",) - temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",) - max_length_sl = gr.Slider(minimum=256, maximum=4096, value=512, step=1, interactive=True, label="Local LLM MaxLength",) - checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "底部输入区", "输入清除键"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区") - md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False) - - gr.Markdown(description) - with gr.Accordion("备选输入区", open=True, visible=False) as area_input_secondary: - with gr.Row(): - txt2 = gr.Textbox(show_label=False, placeholder="Input question here.", label="输入区2").style(container=False) - with gr.Row(): - submitBtn2 = gr.Button("提交", variant="primary") - with gr.Row(): - resetBtn2 = gr.Button("重置", variant="secondary"); resetBtn2.style(size="sm") - stopBtn2 = gr.Button("停止", variant="secondary"); stopBtn2.style(size="sm") - clearBtn2 = gr.Button("清除", variant="secondary", visible=False); clearBtn.style(size="sm") - # 功能区显示开关与功能区的互动 - def fn_area_visibility(a): - ret = {} - ret.update({area_basic_fn: gr.update(visible=("基础功能区" in a))}) - ret.update({area_crazy_fn: gr.update(visible=("函数插件区" in a))}) - ret.update({area_input_primary: gr.update(visible=("底部输入区" not in a))}) - ret.update({area_input_secondary: gr.update(visible=("底部输入区" in a))}) - ret.update({clearBtn: gr.update(visible=("输入清除键" in a))}) - ret.update({clearBtn2: gr.update(visible=("输入清除键" in a))}) - if "底部输入区" in a: ret.update({txt: gr.update(value="")}) - return ret - checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2, clearBtn, clearBtn2] ) - # 整理反复出现的控件句柄组合 - input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt] - output_combo = [cookies, chatbot, history, status] - predict_args = dict(fn=ArgsGeneralWrapper(predict), inputs=input_combo, outputs=output_combo) - # 提交按钮、重置按钮 - cancel_handles.append(txt.submit(**predict_args)) - cancel_handles.append(txt2.submit(**predict_args)) - cancel_handles.append(submitBtn.click(**predict_args)) - cancel_handles.append(submitBtn2.click(**predict_args)) - resetBtn.click(lambda: ([], [], "已重置"), None, [chatbot, history, status]) - resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status]) - clearBtn.click(lambda: ("",""), None, [txt, txt2]) - clearBtn2.click(lambda: ("",""), None, [txt, txt2]) - # 基础功能区的回调函数注册 - for k in functional: - click_handle = functional[k]["Button"].click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(k)], outputs=output_combo) - cancel_handles.append(click_handle) - # 文件上传区，接收文件后与chatbot的互动 - file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes], [chatbot, txt, txt2]) - # 函数插件-固定按钮区 - for k in crazy_fns: - if not crazy_fns[k].get("AsButton", True): continue - click_handle = crazy_fns[k]["Button"].click(ArgsGeneralWrapper(crazy_fns[k]["Function"]), [*input_combo, gr.State(PORT)], output_combo) - click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot]) - cancel_handles.append(click_handle) - # 函数插件-下拉菜单与随变按钮的互动 - def on_dropdown_changed(k): - variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary" - return {switchy_bt: gr.update(value=k, variant=variant)} - dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt] ) - # 随变按钮的回调函数注册 - def route(k, *args, **kwargs): - if k in [r"打开插件列表", r"请先从插件列表中选择"]: return - yield from ArgsGeneralWrapper(crazy_fns[k]["Function"])(*args, **kwargs) - click_handle = switchy_bt.click(route,[switchy_bt, *input_combo, gr.State(PORT)], output_combo) - click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot]) - # def expand_file_area(file_upload, area_file_up): - # if len(file_upload)>0: return {area_file_up: gr.update(open=True)} - # click_handle.then(expand_file_area, [file_upload, area_file_up], [area_file_up]) - cancel_handles.append(click_handle) - # 终止按钮的回调函数注册 - stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles) - stopBtn2.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles) - - # gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数 - def auto_opentab_delay(): - import threading, webbrowser, time - print(f"如果浏览器没有自动打开，请复制并转到以下URL：") - print(f"\t（亮色主题）: http://localhost:{PORT}") - print(f"\t（暗色主题）: http://localhost:{PORT}/?__dark-theme=true") - def open(): - time.sleep(2) # 打开浏览器 - webbrowser.open_new_tab(f"http://localhost:{PORT}/?__dark-theme=true") - threading.Thread(target=open, name="open-browser", daemon=True).start() - threading.Thread(target=auto_update, name="self-upgrade", daemon=True).start() - threading.Thread(target=warm_up_modules, name="warm-up", daemon=True).start() - - auto_opentab_delay() - demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", share=False, favicon_path="docs/logo.png") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/models/facial_recognition/helpers.py b/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/models/facial_recognition/helpers.py deleted file mode 100644 index b51fdf97141407fcc1c9d249a086ddbfd042469f..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/models/facial_recognition/helpers.py +++ /dev/null @@ -1,119 +0,0 @@ -from collections import namedtuple -import torch -from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module - -""" -ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) -""" - - -class Flatten(Module): - def forward(self, input): - return input.view(input.size(0), -1) - - -def l2_norm(input, axis=1): - norm = torch.norm(input, 2, axis, True) - output = torch.div(input, norm) - return output - - -class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])): - """ A named tuple describing a ResNet block. """ - - -def get_block(in_channel, depth, num_units, stride=2): - return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)] - - -def get_blocks(num_layers): - if num_layers == 50: - blocks = [ - get_block(in_channel=64, depth=64, num_units=3), - get_block(in_channel=64, depth=128, num_units=4), - get_block(in_channel=128, depth=256, num_units=14), - get_block(in_channel=256, depth=512, num_units=3) - ] - elif num_layers == 100: - blocks = [ - get_block(in_channel=64, depth=64, num_units=3), - get_block(in_channel=64, depth=128, num_units=13), - get_block(in_channel=128, depth=256, num_units=30), - get_block(in_channel=256, depth=512, num_units=3) - ] - elif num_layers == 152: - blocks = [ - get_block(in_channel=64, depth=64, num_units=3), - get_block(in_channel=64, depth=128, num_units=8), - get_block(in_channel=128, depth=256, num_units=36), - get_block(in_channel=256, depth=512, num_units=3) - ] - else: - raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers)) - return blocks - - -class SEModule(Module): - def __init__(self, channels, reduction): - super(SEModule, self).__init__() - self.avg_pool = AdaptiveAvgPool2d(1) - self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False) - self.relu = ReLU(inplace=True) - self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False) - self.sigmoid = Sigmoid() - - def forward(self, x): - module_input = x - x = self.avg_pool(x) - x = self.fc1(x) - x = self.relu(x) - x = self.fc2(x) - x = self.sigmoid(x) - return module_input * x - - -class bottleneck_IR(Module): - def __init__(self, in_channel, depth, stride): - super(bottleneck_IR, self).__init__() - if in_channel == depth: - self.shortcut_layer = MaxPool2d(1, stride) - else: - self.shortcut_layer = Sequential( - Conv2d(in_channel, depth, (1, 1), stride, bias=False), - BatchNorm2d(depth) - ) - self.res_layer = Sequential( - BatchNorm2d(in_channel), - Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth), - Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth) - ) - - def forward(self, x): - shortcut = self.shortcut_layer(x) - res = self.res_layer(x) - return res + shortcut - - -class bottleneck_IR_SE(Module): - def __init__(self, in_channel, depth, stride): - super(bottleneck_IR_SE, self).__init__() - if in_channel == depth: - self.shortcut_layer = MaxPool2d(1, stride) - else: - self.shortcut_layer = Sequential( - Conv2d(in_channel, depth, (1, 1), stride, bias=False), - BatchNorm2d(depth) - ) - self.res_layer = Sequential( - BatchNorm2d(in_channel), - Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), - PReLU(depth), - Conv2d(depth, depth, (3, 3), stride, 1, bias=False), - BatchNorm2d(depth), - SEModule(depth, 16) - ) - - def forward(self, x): - shortcut = self.shortcut_layer(x) - res = self.res_layer(x) - return res + shortcut diff --git a/spaces/AndrewRWilliams/video-whisper/app.py b/spaces/AndrewRWilliams/video-whisper/app.py deleted file mode 100644 index 98b14ee3775b67bbc39f06d7aa26adb2c711f03e..0000000000000000000000000000000000000000 --- a/spaces/AndrewRWilliams/video-whisper/app.py +++ /dev/null @@ -1,82 +0,0 @@ -# https://huggingface.co/spaces/aadnk/whisper-webui/blob/main/app.py - -import gradio as gr -import os -import re -import unicodedata -import pathlib -import asyncio -import ffmpeg - -import whisper -from whisper.utils import write_srt - -MAX_FILE_PREFIX_LENGTH = 17 - -model = whisper.load_model("base") - -demo = gr.Blocks(cache_examples=False) - -def slugify(value, allow_unicode=False): - """ - Taken from https://github.com/django/django/blob/master/django/utils/text.py - Convert to ASCII if 'allow_unicode' is False. Convert spaces or repeated - dashes to single dashes. Remove characters that aren't alphanumerics, - underscores, or hyphens. Convert to lowercase. Also strip leading and - trailing whitespace, dashes, and underscores. - """ - value = str(value) - if allow_unicode: - value = unicodedata.normalize('NFKC', value) - else: - value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii') - value = re.sub(r'[^\w\s-]', '', value.lower()) - return re.sub(r'[-\s]+', '-', value).strip('-_') - -async def transcribe(file): - - print(type(file)) - audio = whisper.load_audio(file) - # transcribe_options = dict(beam_size=5, best_of=5, without_timestamps=False) - - # result = model.transcribe(file, **transcribe_options) - result = model.transcribe(audio) - - file_path = pathlib.Path(file) - sourceName = file_path.stem[:MAX_FILE_PREFIX_LENGTH] + file_path.suffix - filePrefix = slugify(sourceName, allow_unicode=True) - - #write to file - with open(filePrefix + "-transcript.txt", 'w', encoding="utf-8") as f: - f.write(result['text']) - - #subtitles - with open(filePrefix + "-subs.srt", 'w', encoding="utf-8") as srt: - write_srt(result["segments"], file=srt) - - download = [] - download.append(filePrefix + "-subs.srt"); - download.append(filePrefix + "-transcript.txt"); - - return download - -async def transcribe_video(video): - - print(type(video)) - -with demo: - - gr.Markdown("Choisir le type d'entrée: fichier audio ou fichier vidéo") - with gr.Tab("audio"): - audio_file = gr.Audio(type="filepath") - audio_button = gr.Button("Transcrire audio") - with gr.Tab("vidéo"): - video_file = gr.Video(type="filepath") - video_button = gr.Button("Transcrire vidéo") - - transcript = gr.File(label="transcript") - - audio_button.click(transcribe, inputs=audio_file, outputs=transcript) - video_button.click(transcribe_video, inputs=video_file, outputs=transcript) - -demo.launch() diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/README.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/README.md deleted file mode 100644 index 9307df83d7d622464093859e4074015619bfda22..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/README.md +++ /dev/null @@ -1,228 +0,0 @@ -

-
- -
-

-

- - GitHub - - - GitHub release - - - Contributor Covenant - -

- -🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](https://huggingface.co/docs/diffusers/conceptual/philosophy#usability-over-performance), [simple over easy](https://huggingface.co/docs/diffusers/conceptual/philosophy#simple-over-easy), and [customizability over abstractions](https://huggingface.co/docs/diffusers/conceptual/philosophy#tweakable-contributorfriendly-over-abstraction). - -🤗 Diffusers offers three core components: - -- State-of-the-art [diffusion pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) that can be run in inference with just a few lines of code. -- Interchangeable noise [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview) for different diffusion speeds and output quality. -- Pretrained [models](https://huggingface.co/docs/diffusers/api/models) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems. - -## Installation - -We recommend installing 🤗 Diffusers in a virtual environment from PyPi or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/#installation), please refer to their official documentation. - -### PyTorch - -With `pip` (official package): - -```bash -pip install --upgrade diffusers[torch] -``` - -With `conda` (maintained by the community): - -```sh -conda install -c conda-forge diffusers -``` - -### Flax - -With `pip` (official package): - -```bash -pip install --upgrade diffusers[flax] -``` - -### Apple Silicon (M1/M2) support - -Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide. - -## Quickstart - -Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 4000+ checkpoints): - -```python -from diffusers import DiffusionPipeline -import torch - -pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) -pipeline.to("cuda") -pipeline("An image of a squirrel in Picasso style").images[0] -``` - -You can also dig into the models and schedulers toolbox to build your own diffusion system: - -```python -from diffusers import DDPMScheduler, UNet2DModel -from PIL import Image -import torch -import numpy as np - -scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256") -model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda") -scheduler.set_timesteps(50) - -sample_size = model.config.sample_size -noise = torch.randn((1, 3, sample_size, sample_size)).to("cuda") -input = noise - -for t in scheduler.timesteps: - with torch.no_grad(): - noisy_residual = model(input, t).sample - prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample - input = prev_noisy_sample - -image = (input / 2 + 0.5).clamp(0, 1) -image = image.cpu().permute(0, 2, 3, 1).numpy()[0] -image = Image.fromarray((image * 255).round().astype("uint8")) -image -``` - -Check out the [Quickstart](https://huggingface.co/docs/diffusers/quicktour) to launch your diffusion journey today! - -## How to navigate the documentation - -| **Documentation** | **What can I learn?** | -|---------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| [Tutorial](https://huggingface.co/docs/diffusers/tutorials/tutorial_overview) | A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model. | -| [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading_overview) | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers. | -| [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/pipeline_overview) | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library. | -| [Optimization](https://huggingface.co/docs/diffusers/optimization/opt_overview) | Guides for how to optimize your diffusion model to run faster and consume less memory. | -| [Training](https://huggingface.co/docs/diffusers/training/overview) | Guides for how to train a diffusion model for different tasks with different training techniques. | -## Contribution - -We ❤️ contributions from the open-source community! -If you want to contribute to this library, please check out our [Contribution guide](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md). -You can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library. -- See [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) for general opportunities to contribute -- See [New model/pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) to contribute exciting new diffusion models / diffusion pipelines -- See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) - -Also, say 👋 in our public Discord channel Join us on Discord. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or -just hang out ☕. - - -## Popular Tasks & Pipelines - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
TaskPipeline🤗 Hub
Unconditional Image Generation DDPM google/ddpm-ema-church-256
Text-to-ImageStable Diffusion Text-to-Image runwayml/stable-diffusion-v1-5
Text-to-Imageunclip kakaobrain/karlo-v1-alpha
Text-to-ImageDeepFloyd IF DeepFloyd/IF-I-XL-v1.0
Text-to-ImageKandinsky kandinsky-community/kandinsky-2-2-decoder
Text-guided Image-to-ImageControlnet lllyasviel/sd-controlnet-canny
Text-guided Image-to-ImageInstruct Pix2Pix timbrooks/instruct-pix2pix
Text-guided Image-to-ImageStable Diffusion Image-to-Image runwayml/stable-diffusion-v1-5
Text-guided Image InpaintingStable Diffusion Inpaint runwayml/stable-diffusion-inpainting
Image VariationStable Diffusion Image Variation lambdalabs/sd-image-variations-diffusers
Super ResolutionStable Diffusion Upscale stabilityai/stable-diffusion-x4-upscaler
Super ResolutionStable Diffusion Latent Upscale stabilityai/sd-x2-latent-upscaler
- -## Popular libraries using 🧨 Diffusers - -- https://github.com/microsoft/TaskMatrix -- https://github.com/invoke-ai/InvokeAI -- https://github.com/apple/ml-stable-diffusion -- https://github.com/Sanster/lama-cleaner -- https://github.com/IDEA-Research/Grounded-Segment-Anything -- https://github.com/ashawkey/stable-dreamfusion -- https://github.com/deep-floyd/IF -- https://github.com/bentoml/BentoML -- https://github.com/bmaltais/kohya_ss -- +3000 other amazing GitHub repositories 💪 - -Thank you for using us ❤️ - -## Credits - -This library concretizes previous work by many different authors and would not have been possible without their great research and implementations. We'd like to thank, in particular, the following implementations which have helped us in our development and without which the API could not have been as polished today: - -- @CompVis' latent diffusion models library, available [here](https://github.com/CompVis/latent-diffusion) -- @hojonathanho original DDPM implementation, available [here](https://github.com/hojonathanho/diffusion) as well as the extremely useful translation into PyTorch by @pesser, available [here](https://github.com/pesser/pytorch_diffusion) -- @ermongroup's DDIM implementation, available [here](https://github.com/ermongroup/ddim) -- @yang-song's Score-VE and Score-VP implementations, available [here](https://github.com/yang-song/score_sde_pytorch) - -We also want to thank @heejkoo for the very helpful overview of papers, code and resources on diffusion models, available [here](https://github.com/heejkoo/Awesome-Diffusion-Models) as well as @crowsonkb and @rromb for useful discussions and insights. - -## Citation - -```bibtex -@misc{von-platen-etal-2022-diffusers, - author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf}, - title = {Diffusers: State-of-the-art diffusion models}, - year = {2022}, - publisher = {GitHub}, - journal = {GitHub repository}, - howpublished = {\url{https://github.com/huggingface/diffusers}} -} -``` diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/nasfcos_head.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/nasfcos_head.py deleted file mode 100644 index 994ce0455e1982110f237b3958a81394c319bb47..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/nasfcos_head.py +++ /dev/null @@ -1,75 +0,0 @@ -import copy - -import torch.nn as nn -from mmcv.cnn import (ConvModule, Scale, bias_init_with_prob, - caffe2_xavier_init, normal_init) - -from mmdet.models.dense_heads.fcos_head import FCOSHead -from ..builder import HEADS - - -@HEADS.register_module() -class NASFCOSHead(FCOSHead): - """Anchor-free head used in `NASFCOS `_. - - It is quite similar with FCOS head, except for the searched structure of - classification branch and bbox regression branch, where a structure of - "dconv3x3, conv3x3, dconv3x3, conv1x1" is utilized instead. - """ - - def _init_layers(self): - """Initialize layers of the head.""" - dconv3x3_config = dict( - type='DCNv2', - kernel_size=3, - use_bias=True, - deform_groups=2, - padding=1) - conv3x3_config = dict(type='Conv', kernel_size=3, padding=1) - conv1x1_config = dict(type='Conv', kernel_size=1) - - self.arch_config = [ - dconv3x3_config, conv3x3_config, dconv3x3_config, conv1x1_config - ] - self.cls_convs = nn.ModuleList() - self.reg_convs = nn.ModuleList() - for i, op_ in enumerate(self.arch_config): - op = copy.deepcopy(op_) - chn = self.in_channels if i == 0 else self.feat_channels - assert isinstance(op, dict) - use_bias = op.pop('use_bias', False) - padding = op.pop('padding', 0) - kernel_size = op.pop('kernel_size') - module = ConvModule( - chn, - self.feat_channels, - kernel_size, - stride=1, - padding=padding, - norm_cfg=self.norm_cfg, - bias=use_bias, - conv_cfg=op) - - self.cls_convs.append(copy.deepcopy(module)) - self.reg_convs.append(copy.deepcopy(module)) - - self.conv_cls = nn.Conv2d( - self.feat_channels, self.cls_out_channels, 3, padding=1) - self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1) - self.conv_centerness = nn.Conv2d(self.feat_channels, 1, 3, padding=1) - - self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides]) - - def init_weights(self): - """Initialize weights of the head.""" - # retinanet_bias_init - bias_cls = bias_init_with_prob(0.01) - normal_init(self.conv_reg, std=0.01) - normal_init(self.conv_centerness, std=0.01) - normal_init(self.conv_cls, std=0.01, bias=bias_cls) - - for branch in [self.cls_convs, self.reg_convs]: - for module in branch.modules(): - if isinstance(module, ConvModule) \ - and isinstance(module.conv, nn.Conv2d): - caffe2_xavier_init(module.conv) diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/utils/contextmanagers.py b/spaces/Andy1621/uniformer_image_detection/mmdet/utils/contextmanagers.py deleted file mode 100644 index 38a639262d949b5754dedf12f33fa814b030ea38..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/mmdet/utils/contextmanagers.py +++ /dev/null @@ -1,121 +0,0 @@ -import asyncio -import contextlib -import logging -import os -import time -from typing import List - -import torch - -logger = logging.getLogger(__name__) - -DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False)) - - -@contextlib.asynccontextmanager -async def completed(trace_name='', - name='', - sleep_interval=0.05, - streams: List[torch.cuda.Stream] = None): - """Async context manager that waits for work to complete on given CUDA - streams.""" - if not torch.cuda.is_available(): - yield - return - - stream_before_context_switch = torch.cuda.current_stream() - if not streams: - streams = [stream_before_context_switch] - else: - streams = [s if s else stream_before_context_switch for s in streams] - - end_events = [ - torch.cuda.Event(enable_timing=DEBUG_COMPLETED_TIME) for _ in streams - ] - - if DEBUG_COMPLETED_TIME: - start = torch.cuda.Event(enable_timing=True) - stream_before_context_switch.record_event(start) - - cpu_start = time.monotonic() - logger.debug('%s %s starting, streams: %s', trace_name, name, streams) - grad_enabled_before = torch.is_grad_enabled() - try: - yield - finally: - current_stream = torch.cuda.current_stream() - assert current_stream == stream_before_context_switch - - if DEBUG_COMPLETED_TIME: - cpu_end = time.monotonic() - for i, stream in enumerate(streams): - event = end_events[i] - stream.record_event(event) - - grad_enabled_after = torch.is_grad_enabled() - - # observed change of torch.is_grad_enabled() during concurrent run of - # async_test_bboxes code - assert (grad_enabled_before == grad_enabled_after - ), 'Unexpected is_grad_enabled() value change' - - are_done = [e.query() for e in end_events] - logger.debug('%s %s completed: %s streams: %s', trace_name, name, - are_done, streams) - with torch.cuda.stream(stream_before_context_switch): - while not all(are_done): - await asyncio.sleep(sleep_interval) - are_done = [e.query() for e in end_events] - logger.debug( - '%s %s completed: %s streams: %s', - trace_name, - name, - are_done, - streams, - ) - - current_stream = torch.cuda.current_stream() - assert current_stream == stream_before_context_switch - - if DEBUG_COMPLETED_TIME: - cpu_time = (cpu_end - cpu_start) * 1000 - stream_times_ms = '' - for i, stream in enumerate(streams): - elapsed_time = start.elapsed_time(end_events[i]) - stream_times_ms += f' {stream} {elapsed_time:.2f} ms' - logger.info('%s %s %.2f ms %s', trace_name, name, cpu_time, - stream_times_ms) - - -@contextlib.asynccontextmanager -async def concurrent(streamqueue: asyncio.Queue, - trace_name='concurrent', - name='stream'): - """Run code concurrently in different streams. - - :param streamqueue: asyncio.Queue instance. - - Queue tasks define the pool of streams used for concurrent execution. - """ - if not torch.cuda.is_available(): - yield - return - - initial_stream = torch.cuda.current_stream() - - with torch.cuda.stream(initial_stream): - stream = await streamqueue.get() - assert isinstance(stream, torch.cuda.Stream) - - try: - with torch.cuda.stream(stream): - logger.debug('%s %s is starting, stream: %s', trace_name, name, - stream) - yield - current = torch.cuda.current_stream() - assert current == stream - logger.debug('%s %s has finished, stream: %s', trace_name, - name, stream) - finally: - streamqueue.task_done() - streamqueue.put_nowait(stream) diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py deleted file mode 100644 index 96cbaa48d61ee208117d074e9f06bf4218407d78..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,5 +0,0 @@ -_base_ = [ - '../_base_/models/pointrend_r50.py', '../_base_/datasets/cityscapes.py', - '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' -] -lr_config = dict(warmup='linear', warmup_iters=200) diff --git a/spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/clipboard/clipboard.min.js b/spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/clipboard/clipboard.min.js deleted file mode 100644 index 1103f811ed80f17985ecf61e0d50e3359484244f..0000000000000000000000000000000000000000 --- a/spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/clipboard/clipboard.min.js +++ /dev/null @@ -1,7 +0,0 @@ -/*! - * clipboard.js v2.0.11 - * https://clipboardjs.com/ - * - * Licensed MIT © Zeno Rocha - */ -!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1.stApp {{ - background-image: url("https://images.unsplash.com/photo-1474540412665-1cdae210ae6b?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8Mnx8Y2FsbXxlbnwwfHwwfHx8MA%3D%3D&w=1000&q=80"); - background-attachment: fixed; - background-size: cover - }}""",unsafe_allow_html=True) - - # Add a heading. - st.header("Weather in **{}** ".format(city)) - - # Add a paragraph. - st.markdown("The weather in **{}** is **{}** and the temperature is **{}** Kelvin Unit.".format(city, weather_description, temperature)) - - col1, col2, col3 = st.columns(3) - - # Add a button to convert the temperature to Celsius. - with col1: - convert_to_celsius = st.button("Convert to Celsius") - - if convert_to_celsius: - temperature_in_celsius = float("{:.2f}".format(temperature - 273.15)) - st.markdown( - f""" - The temperature in **{city}** is **{weather_description}** and the temperature is **{temperature_in_celsius}** degrees Celsius. - """ - ) - - #Add button to convert the temperature to Fahrenheit - with col2: - convert_to_fahrenheit = st.button("Convert to Fahrenheit") - - if convert_to_fahrenheit: - temperature_in_fahrenheit = float("{:.2f}".format((temperature - 273.15) * 9 / 5 + 32)) - st.markdown( - f""" - The temperature in **{city}** is **{weather_description}** and the temperature is **{temperature_in_fahrenheit}** degrees Fahrenheit. - """ - ) - - #Add pressure and humidity - with col3: - p_and_h = st.button("Pressure and Humidity") - - if p_and_h: - st.markdown("The pressure is **{}** hPa and the humidity is **{}**%.".format(pressure, humidity)) \ No newline at end of file diff --git a/spaces/Aphrodite/stable-diffusion-2/app.py b/spaces/Aphrodite/stable-diffusion-2/app.py deleted file mode 100644 index b6b9c842eb9cb5913fe53569aa719ff333793bc9..0000000000000000000000000000000000000000 --- a/spaces/Aphrodite/stable-diffusion-2/app.py +++ /dev/null @@ -1,154 +0,0 @@ -from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler -import gradio as gr -import torch -from PIL import Image - -model_id = 'stabilityai/stable-diffusion-2' -prefix = '' - -scheduler = DPMSolverMultistepScheduler( - beta_start=0.00085, - beta_end=0.012, - beta_schedule="scaled_linear", - num_train_timesteps=1000, - trained_betas=None, - predict_epsilon=True, - thresholding=True, - algorithm_type="dpmsolver++", - solver_type="midpoint", - lower_order_final=True, -) - -pipe = StableDiffusionPipeline.from_pretrained( - model_id, - torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, - scheduler=scheduler) - -pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained( - model_id, - torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, - scheduler=scheduler) - -if torch.cuda.is_available(): - pipe = pipe.to("cuda") - pipe_i2i = pipe_i2i.to("cuda") - -def error_str(error, title="Error"): - return f"""#### {title} - {error}""" if error else "" - -def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=True): - - generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None - prompt = f"{prefix} {prompt}" if auto_prefix else prompt - - try: - if img is not None: - return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None - else: - return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None - except Exception as e: - return None, error_str(e) - -def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator): - - result = pipe( - prompt, - negative_prompt = neg_prompt, - num_inference_steps = int(steps), - guidance_scale = guidance, - width = width, - height = height, - generator = generator) - - return replace_nsfw_images(result) - -def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator): - - ratio = min(height / img.height, width / img.width) - img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS) - result = pipe_i2i( - prompt, - negative_prompt = neg_prompt, - init_image = img, - num_inference_steps = int(steps), - strength = strength, - guidance_scale = guidance, - width = width, - height = height, - generator = generator) - - return replace_nsfw_images(result) - -def replace_nsfw_images(results): - - for i in range(len(results.images)): - if results.nsfw_content_detected[i]: - results.images[i] = Image.open("nsfw.png") - return results.images[0] - -css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem} -""" -with gr.Blocks(css=css) as demo: - gr.HTML( - f""" -
-
-

Stable Diffusion 2

-
-

- Demo for Stable Diffusion 2 Stable Diffusion model.
- Add the following tokens to your prompts for the model to work properly: . -

- Running on {"GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"} -
- """ - ) - with gr.Row(): - - with gr.Column(scale=55): - with gr.Group(): - with gr.Row(): - prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False) - generate = gr.Button(value="Generate").style(rounded=(False, True, True, False)) - - image_out = gr.Image(height=512) - error_output = gr.Markdown() - - with gr.Column(scale=45): - with gr.Tab("Options"): - with gr.Group(): - neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image") - auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ()", value=True) - - with gr.Row(): - guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) - steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1) - - with gr.Row(): - width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8) - height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8) - - seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1) - - with gr.Tab("Image to image"): - with gr.Group(): - image = gr.Image(label="Image", height=256, tool="editor", type="pil") - strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5) - - auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False) - - inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix] - outputs = [image_out, error_output] - prompt.submit(inference, inputs=inputs, outputs=outputs) - generate.click(inference, inputs=inputs, outputs=outputs) - - gr.HTML(""" -
-
-

This space was created using SD Space Creator.

-
- """) - -demo.queue(concurrency_count=1) -demo.launch() diff --git a/spaces/AquaSuisei/ChatGPTXE/modules/utils.py b/spaces/AquaSuisei/ChatGPTXE/modules/utils.py deleted file mode 100644 index 23f47d688d9690c6c68ccacc765108ce68d62b76..0000000000000000000000000000000000000000 --- a/spaces/AquaSuisei/ChatGPTXE/modules/utils.py +++ /dev/null @@ -1,536 +0,0 @@ -# -*- coding:utf-8 -*- -from __future__ import annotations -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type -import logging -import json -import os -import datetime -import hashlib -import csv -import requests -import re -import html -import sys -import subprocess - -import gradio as gr -from pypinyin import lazy_pinyin -import tiktoken -import mdtex2html -from markdown import markdown -from pygments import highlight -from pygments.lexers import get_lexer_by_name -from pygments.formatters import HtmlFormatter -import pandas as pd - -from modules.presets import * -from . import shared -from modules.config import retrieve_proxy - -if TYPE_CHECKING: - from typing import TypedDict - - class DataframeData(TypedDict): - headers: List[str] - data: List[List[str | int | bool]] - - -def count_token(message): - encoding = tiktoken.get_encoding("cl100k_base") - input_str = f"role: {message['role']}, content: {message['content']}" - length = len(encoding.encode(input_str)) - return length - - -def markdown_to_html_with_syntax_highlight(md_str): - def replacer(match): - lang = match.group(1) or "text" - code = match.group(2) - - try: - lexer = get_lexer_by_name(lang, stripall=True) - except ValueError: - lexer = get_lexer_by_name("text", stripall=True) - - formatter = HtmlFormatter() - highlighted_code = highlight(code, lexer, formatter) - - return f'
{highlighted_code}
' - - code_block_pattern = r"```(\w+)?\n([\s\S]+?)\n```" - md_str = re.sub(code_block_pattern, replacer, md_str, flags=re.MULTILINE) - - html_str = markdown(md_str) - return html_str - - -def normalize_markdown(md_text: str) -> str: - lines = md_text.split("\n") - normalized_lines = [] - inside_list = False - - for i, line in enumerate(lines): - if re.match(r"^(\d+\.|-|\*|\+)\s", line.strip()): - if not inside_list and i > 0 and lines[i - 1].strip() != "": - normalized_lines.append("") - inside_list = True - normalized_lines.append(line) - elif inside_list and line.strip() == "": - if i < len(lines) - 1 and not re.match( - r"^(\d+\.|-|\*|\+)\s", lines[i + 1].strip() - ): - normalized_lines.append(line) - continue - else: - inside_list = False - normalized_lines.append(line) - - return "\n".join(normalized_lines) - - -def convert_mdtext(md_text): - code_block_pattern = re.compile(r"```(.*?)(?:```|$)", re.DOTALL) - inline_code_pattern = re.compile(r"`(.*?)`", re.DOTALL) - code_blocks = code_block_pattern.findall(md_text) - non_code_parts = code_block_pattern.split(md_text)[::2] - - result = [] - for non_code, code in zip(non_code_parts, code_blocks + [""]): - if non_code.strip(): - non_code = normalize_markdown(non_code) - if inline_code_pattern.search(non_code): - result.append(markdown(non_code, extensions=["tables"])) - else: - result.append(mdtex2html.convert(non_code, extensions=["tables"])) - if code.strip(): - # _, code = detect_language(code) # 暂时去除代码高亮功能，因为在大段代码的情况下会出现问题 - # code = code.replace("\n\n", "\n") # 暂时去除代码中的空行，因为在大段代码的情况下会出现问题 - code = f"\n```{code}\n\n```" - code = markdown_to_html_with_syntax_highlight(code) - result.append(code) - result = "".join(result) - result += ALREADY_CONVERTED_MARK - return result - - -def convert_asis(userinput): - return ( - f'

{html.escape(userinput)}

' - + ALREADY_CONVERTED_MARK - ) - - -def detect_converted_mark(userinput): - if userinput.endswith(ALREADY_CONVERTED_MARK): - return True - else: - return False - - -def detect_language(code): - if code.startswith("\n"): - first_line = "" - else: - first_line = code.strip().split("\n", 1)[0] - language = first_line.lower() if first_line else "" - code_without_language = code[len(first_line) :].lstrip() if first_line else code - return language, code_without_language - - -def construct_text(role, text): - return {"role": role, "content": text} - - -def construct_user(text): - return construct_text("user", text) - - -def construct_system(text): - return construct_text("system", text) - - -def construct_assistant(text): - return construct_text("assistant", text) - - -def construct_token_message(tokens: List[int]): - token_sum = 0 - for i in range(len(tokens)): - token_sum += sum(tokens[: i + 1]) - return f"Token 计数: {sum(tokens)}，本次对话累计消耗了 {token_sum} tokens" - - -def delete_first_conversation(history, previous_token_count): - if history: - del history[:2] - del previous_token_count[0] - return ( - history, - previous_token_count, - construct_token_message(previous_token_count), - ) - - -def delete_last_conversation(chatbot, history, previous_token_count): - if len(chatbot) > 0 and standard_error_msg in chatbot[-1][1]: - logging.info("由于包含报错信息，只删除chatbot记录") - chatbot.pop() - return chatbot, history - if len(history) > 0: - logging.info("删除了一组对话历史") - history.pop() - history.pop() - if len(chatbot) > 0: - logging.info("删除了一组chatbot对话") - chatbot.pop() - if len(previous_token_count) > 0: - logging.info("删除了一组对话的token计数记录") - previous_token_count.pop() - return ( - chatbot, - history, - previous_token_count, - construct_token_message(previous_token_count), - ) - - -def save_file(filename, system, history, chatbot, user_name): - logging.info(f"{user_name} 保存对话历史中……") - os.makedirs(HISTORY_DIR / user_name, exist_ok=True) - if filename.endswith(".json"): - json_s = {"system": system, "history": history, "chatbot": chatbot} - print(json_s) - with open(os.path.join(HISTORY_DIR / user_name, filename), "w") as f: - json.dump(json_s, f) - elif filename.endswith(".md"): - md_s = f"system: \n- {system} \n" - for data in history: - md_s += f"\n{data['role']}: \n- {data['content']} \n" - with open(os.path.join(HISTORY_DIR / user_name, filename), "w", encoding="utf8") as f: - f.write(md_s) - logging.info(f"{user_name} 保存对话历史完毕") - return os.path.join(HISTORY_DIR / user_name, filename) - - -def save_chat_history(filename, system, history, chatbot, user_name): - if filename == "": - return - if not filename.endswith(".json"): - filename += ".json" - return save_file(filename, system, history, chatbot, user_name) - - -def export_markdown(filename, system, history, chatbot, user_name): - if filename == "": - return - if not filename.endswith(".md"): - filename += ".md" - return save_file(filename, system, history, chatbot, user_name) - - -def load_chat_history(filename, system, history, chatbot, user_name): - logging.info(f"{user_name} 加载对话历史中……") - if type(filename) != str: - filename = filename.name - try: - with open(os.path.join(HISTORY_DIR / user_name, filename), "r") as f: - json_s = json.load(f) - try: - if type(json_s["history"][0]) == str: - logging.info("历史记录格式为旧版，正在转换……") - new_history = [] - for index, item in enumerate(json_s["history"]): - if index % 2 == 0: - new_history.append(construct_user(item)) - else: - new_history.append(construct_assistant(item)) - json_s["history"] = new_history - logging.info(new_history) - except: - # 没有对话历史 - pass - logging.info(f"{user_name} 加载对话历史完毕") - return filename, json_s["system"], json_s["history"], json_s["chatbot"] - except FileNotFoundError: - logging.info(f"{user_name} 没有找到对话历史文件，不执行任何操作") - return filename, system, history, chatbot - - -def sorted_by_pinyin(list): - return sorted(list, key=lambda char: lazy_pinyin(char)[0][0]) - - -def get_file_names(dir, plain=False, filetypes=[".json"]): - logging.info(f"获取文件名列表，目录为{dir}，文件类型为{filetypes}，是否为纯文本列表{plain}") - files = [] - try: - for type in filetypes: - files += [f for f in os.listdir(dir) if f.endswith(type)] - except FileNotFoundError: - files = [] - files = sorted_by_pinyin(files) - if files == []: - files = [""] - logging.debug(f"files are:{files}") - if plain: - return files - else: - return gr.Dropdown.update(choices=files) - - -def get_history_names(plain=False, user_name=""): - logging.info(f"从用户 {user_name} 中获取历史记录文件名列表") - return get_file_names(HISTORY_DIR / user_name, plain) - - -def load_template(filename, mode=0): - logging.info(f"加载模板文件{filename}，模式为{mode}（0为返回字典和下拉菜单，1为返回下拉菜单，2为返回字典）") - lines = [] - logging.info("Loading template...") - if filename.endswith(".json"): - with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as f: - lines = json.load(f) - lines = [[i["act"], i["prompt"]] for i in lines] - else: - with open( - os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8" - ) as csvfile: - reader = csv.reader(csvfile) - lines = list(reader) - lines = lines[1:] - if mode == 1: - return sorted_by_pinyin([row[0] for row in lines]) - elif mode == 2: - return {row[0]: row[1] for row in lines} - else: - choices = sorted_by_pinyin([row[0] for row in lines]) - return {row[0]: row[1] for row in lines}, gr.Dropdown.update( - choices=choices - ) - - -def get_template_names(plain=False): - logging.info("获取模板文件名列表") - return get_file_names(TEMPLATES_DIR, plain, filetypes=[".csv", "json"]) - - -def get_template_content(templates, selection, original_system_prompt): - logging.info(f"应用模板中，选择为{selection}，原始系统提示为{original_system_prompt}") - try: - return templates[selection] - except: - return original_system_prompt - - -def reset_state(): - logging.info("重置状态") - return [], [], [], construct_token_message([0]) - - -def reset_textbox(): - logging.debug("重置文本框") - return gr.update(value="") - - -def reset_default(): - default_host = shared.state.reset_api_host() - retrieve_proxy("") - return gr.update(value=default_host), gr.update(value=""), "API-Host 和代理已重置" - - -def change_api_host(host): - shared.state.set_api_host(host) - msg = f"API-Host更改为了{host}" - logging.info(msg) - return msg - - -def change_proxy(proxy): - retrieve_proxy(proxy) - os.environ["HTTPS_PROXY"] = proxy - msg = f"代理更改为了{proxy}" - logging.info(msg) - return msg - - -def hide_middle_chars(s): - if s is None: - return "" - if len(s) <= 8: - return s - else: - head = s[:4] - tail = s[-4:] - hidden = "*" * (len(s) - 8) - return head + hidden + tail - - -def submit_key(key): - key = key.strip() - msg = f"API密钥更改为了{hide_middle_chars(key)}" - logging.info(msg) - return key, msg - - -def replace_today(prompt): - today = datetime.datetime.today().strftime("%Y-%m-%d") - return prompt.replace("{current_date}", today) - - -def get_geoip(): - try: - with retrieve_proxy(): - response = requests.get("https://ipapi.co/json/", timeout=5) - data = response.json() - except: - data = {"error": True, "reason": "连接ipapi失败"} - if "error" in data.keys(): - logging.warning(f"无法获取IP地址信息。\n{data}") - if data["reason"] == "RateLimited": - return ( - f"获取IP地理位置失败，因为达到了检测IP的速率限制。聊天功能可能仍然可用。" - ) - else: - return f"获取IP地理位置失败。原因：{data['reason']}。你仍然可以使用聊天功能。" - else: - country = data["country_name"] - if country == "China": - text = "**您的IP区域：中国。请立即检查代理设置，在不受支持的地区使用API可能导致账号被封禁。**" - else: - text = f"您的IP区域：{country}。" - logging.info(text) - return text - - -def find_n(lst, max_num): - n = len(lst) - total = sum(lst) - - if total < max_num: - return n - - for i in range(len(lst)): - if total - lst[i] < max_num: - return n - i - 1 - total = total - lst[i] - return 1 - - -def start_outputing(): - logging.debug("显示取消按钮，隐藏发送按钮") - return gr.Button.update(visible=True), gr.Button.update(visible=False) - - -def end_outputing(): - return ( - gr.Button.update(visible=True), - gr.Button.update(visible=False), - ) - - -def cancel_outputing(): - logging.info("中止输出……") - shared.state.interrupt() - - -def transfer_input(inputs): - # 一次性返回，降低延迟 - textbox = reset_textbox() - outputing = start_outputing() - return ( - inputs, - gr.update(value=""), - gr.Button.update(visible=True), - gr.Button.update(visible=False), - ) - - - -def run(command, desc=None, errdesc=None, custom_env=None, live=False): - if desc is not None: - print(desc) - if live: - result = subprocess.run(command, shell=True, env=os.environ if custom_env is None else custom_env) - if result.returncode != 0: - raise RuntimeError(f"""{errdesc or 'Error running command'}. -Command: {command} -Error code: {result.returncode}""") - - return "" - result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env) - if result.returncode != 0: - message = f"""{errdesc or 'Error running command'}. -Command: {command} -Error code: {result.returncode} -stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout)>0 else ''} -stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.stderr)>0 else ''} -""" - raise RuntimeError(message) - return result.stdout.decode(encoding="utf8", errors="ignore") - -def versions_html(): - git = os.environ.get('GIT', "git") - python_version = ".".join([str(x) for x in sys.version_info[0:3]]) - try: - commit_hash = run(f"{git} rev-parse HEAD").strip() - except Exception: - commit_hash = "" - if commit_hash != "": - short_commit = commit_hash[0:7] - commit_info = f"{short_commit}" - else: - commit_info = "unknown \U0001F615" - return f""" -Python: {python_version} - •  -Gradio: {gr.__version__} - •  -Commit: {commit_info} -""" - -def add_source_numbers(lst, source_name = "Source", use_source = True): - if use_source: - return [f'[{idx+1}]\t "{item[0]}"\n{source_name}: {item[1]}' for idx, item in enumerate(lst)] - else: - return [f'[{idx+1}]\t "{item}"' for idx, item in enumerate(lst)] - -def add_details(lst): - nodes = [] - for index, txt in enumerate(lst): - brief = txt[:25].replace("\n", "") - nodes.append( - f"
{brief}...

{txt}

" - ) - return nodes - - -def sheet_to_string(sheet): - result = "" - for index, row in sheet.iterrows(): - row_string = "" - for column in sheet.columns: - row_string += f"{column}: {row[column]}, " - row_string = row_string.rstrip(", ") - row_string += "." - result += row_string + "\n" - return result - -def excel_to_string(file_path): - # 读取Excel文件中的所有工作表 - excel_file = pd.read_excel(file_path, engine='openpyxl', sheet_name=None) - - # 初始化结果字符串 - result = "" - - # 遍历每一个工作表 - for sheet_name, sheet_data in excel_file.items(): - # 将工作表名称添加到结果字符串 - result += f"Sheet: {sheet_name}\n" - - # 处理当前工作表并添加到结果字符串 - result += sheet_to_string(sheet_data) - - # 在不同工作表之间添加分隔符 - result += "\n" + ("-" * 20) + "\n\n" - - return result diff --git a/spaces/Armandoliv/cars-parts-segmentation-resnet18/README.md b/spaces/Armandoliv/cars-parts-segmentation-resnet18/README.md deleted file mode 100644 index 5a09ffb24ce6076fd67aea6e843e281f4282e4ae..0000000000000000000000000000000000000000 --- a/spaces/Armandoliv/cars-parts-segmentation-resnet18/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Cars Parts Segmentation Resnet18 -emoji: 💩 -colorFrom: red -colorTo: green -sdk: gradio -sdk_version: 3.3 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/slconfig.py b/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/slconfig.py deleted file mode 100644 index 672e72ed0b68a54c13ade66c9f146d2d542e97c6..0000000000000000000000000000000000000000 --- a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/slconfig.py +++ /dev/null @@ -1,427 +0,0 @@ -# ========================================================== -# Modified from mmcv -# ========================================================== -import ast -import os -import os.path as osp -import shutil -import sys -import tempfile -from argparse import Action -from importlib import import_module - -from addict import Dict -from yapf.yapflib.yapf_api import FormatCode - -BASE_KEY = "_base_" -DELETE_KEY = "_delete_" -RESERVED_KEYS = ["filename", "text", "pretty_text", "get", "dump", "merge_from_dict"] - - -def check_file_exist(filename, msg_tmpl='file "{}" does not exist'): - if not osp.isfile(filename): - raise FileNotFoundError(msg_tmpl.format(filename)) - - -class ConfigDict(Dict): - def __missing__(self, name): - raise KeyError(name) - - def __getattr__(self, name): - try: - value = super(ConfigDict, self).__getattr__(name) - except KeyError: - ex = AttributeError(f"'{self.__class__.__name__}' object has no " f"attribute '{name}'") - except Exception as e: - ex = e - else: - return value - raise ex - - -class SLConfig(object): - """ - config files. - only support .py file as config now. - - ref: mmcv.utils.config - - Example: - >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1]))) - >>> cfg.a - 1 - >>> cfg.b - {'b1': [0, 1]} - >>> cfg.b.b1 - [0, 1] - >>> cfg = Config.fromfile('tests/data/config/a.py') - >>> cfg.filename - "/home/kchen/projects/mmcv/tests/data/config/a.py" - >>> cfg.item4 - 'test' - >>> cfg - "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: " - "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}" - """ - - @staticmethod - def _validate_py_syntax(filename): - with open(filename) as f: - content = f.read() - try: - ast.parse(content) - except SyntaxError: - raise SyntaxError("There are syntax errors in config " f"file {filename}") - - @staticmethod - def _file2dict(filename): - filename = osp.abspath(osp.expanduser(filename)) - check_file_exist(filename) - if filename.lower().endswith(".py"): - with tempfile.TemporaryDirectory() as temp_config_dir: - temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=".py") - temp_config_name = osp.basename(temp_config_file.name) - if os.name == 'nt': - temp_config_file.close() - shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name)) - temp_module_name = osp.splitext(temp_config_name)[0] - sys.path.insert(0, temp_config_dir) - SLConfig._validate_py_syntax(filename) - mod = import_module(temp_module_name) - sys.path.pop(0) - cfg_dict = { - name: value for name, value in mod.__dict__.items() if not name.startswith("__") - } - # delete imported module - del sys.modules[temp_module_name] - # close temp file - temp_config_file.close() - elif filename.lower().endswith((".yml", ".yaml", ".json")): - from .slio import slload - - cfg_dict = slload(filename) - else: - raise IOError("Only py/yml/yaml/json type are supported now!") - - cfg_text = filename + "\n" - with open(filename, "r") as f: - cfg_text += f.read() - - # parse the base file - if BASE_KEY in cfg_dict: - cfg_dir = osp.dirname(filename) - base_filename = cfg_dict.pop(BASE_KEY) - base_filename = base_filename if isinstance(base_filename, list) else [base_filename] - - cfg_dict_list = list() - cfg_text_list = list() - for f in base_filename: - _cfg_dict, _cfg_text = SLConfig._file2dict(osp.join(cfg_dir, f)) - cfg_dict_list.append(_cfg_dict) - cfg_text_list.append(_cfg_text) - - base_cfg_dict = dict() - for c in cfg_dict_list: - if len(base_cfg_dict.keys() & c.keys()) > 0: - raise KeyError("Duplicate key is not allowed among bases") - # TODO Allow the duplicate key while warnning user - base_cfg_dict.update(c) - - base_cfg_dict = SLConfig._merge_a_into_b(cfg_dict, base_cfg_dict) - cfg_dict = base_cfg_dict - - # merge cfg_text - cfg_text_list.append(cfg_text) - cfg_text = "\n".join(cfg_text_list) - - return cfg_dict, cfg_text - - @staticmethod - def _merge_a_into_b(a, b): - """merge dict `a` into dict `b` (non-inplace). - values in `a` will overwrite `b`. - copy first to avoid inplace modification - - Args: - a ([type]): [description] - b ([type]): [description] - - Returns: - [dict]: [description] - """ - # import ipdb; ipdb.set_trace() - if not isinstance(a, dict): - return a - - b = b.copy() - for k, v in a.items(): - if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False): - - if not isinstance(b[k], dict) and not isinstance(b[k], list): - # if : - # import ipdb; ipdb.set_trace() - raise TypeError( - f"{k}={v} in child config cannot inherit from base " - f"because {k} is a dict in the child config but is of " - f"type {type(b[k])} in base config. You may set " - f"`{DELETE_KEY}=True` to ignore the base config" - ) - b[k] = SLConfig._merge_a_into_b(v, b[k]) - elif isinstance(b, list): - try: - _ = int(k) - except: - raise TypeError( - f"b is a list, " f"index {k} should be an int when input but {type(k)}" - ) - b[int(k)] = SLConfig._merge_a_into_b(v, b[int(k)]) - else: - b[k] = v - - return b - - @staticmethod - def fromfile(filename): - cfg_dict, cfg_text = SLConfig._file2dict(filename) - return SLConfig(cfg_dict, cfg_text=cfg_text, filename=filename) - - def __init__(self, cfg_dict=None, cfg_text=None, filename=None): - if cfg_dict is None: - cfg_dict = dict() - elif not isinstance(cfg_dict, dict): - raise TypeError("cfg_dict must be a dict, but " f"got {type(cfg_dict)}") - for key in cfg_dict: - if key in RESERVED_KEYS: - raise KeyError(f"{key} is reserved for config file") - - super(SLConfig, self).__setattr__("_cfg_dict", ConfigDict(cfg_dict)) - super(SLConfig, self).__setattr__("_filename", filename) - if cfg_text: - text = cfg_text - elif filename: - with open(filename, "r") as f: - text = f.read() - else: - text = "" - super(SLConfig, self).__setattr__("_text", text) - - @property - def filename(self): - return self._filename - - @property - def text(self): - return self._text - - @property - def pretty_text(self): - - indent = 4 - - def _indent(s_, num_spaces): - s = s_.split("\n") - if len(s) == 1: - return s_ - first = s.pop(0) - s = [(num_spaces * " ") + line for line in s] - s = "\n".join(s) - s = first + "\n" + s - return s - - def _format_basic_types(k, v, use_mapping=False): - if isinstance(v, str): - v_str = f"'{v}'" - else: - v_str = str(v) - - if use_mapping: - k_str = f"'{k}'" if isinstance(k, str) else str(k) - attr_str = f"{k_str}: {v_str}" - else: - attr_str = f"{str(k)}={v_str}" - attr_str = _indent(attr_str, indent) - - return attr_str - - def _format_list(k, v, use_mapping=False): - # check if all items in the list are dict - if all(isinstance(_, dict) for _ in v): - v_str = "[\n" - v_str += "\n".join( - f"dict({_indent(_format_dict(v_), indent)})," for v_ in v - ).rstrip(",") - if use_mapping: - k_str = f"'{k}'" if isinstance(k, str) else str(k) - attr_str = f"{k_str}: {v_str}" - else: - attr_str = f"{str(k)}={v_str}" - attr_str = _indent(attr_str, indent) + "]" - else: - attr_str = _format_basic_types(k, v, use_mapping) - return attr_str - - def _contain_invalid_identifier(dict_str): - contain_invalid_identifier = False - for key_name in dict_str: - contain_invalid_identifier |= not str(key_name).isidentifier() - return contain_invalid_identifier - - def _format_dict(input_dict, outest_level=False): - r = "" - s = [] - - use_mapping = _contain_invalid_identifier(input_dict) - if use_mapping: - r += "{" - for idx, (k, v) in enumerate(input_dict.items()): - is_last = idx >= len(input_dict) - 1 - end = "" if outest_level or is_last else "," - if isinstance(v, dict): - v_str = "\n" + _format_dict(v) - if use_mapping: - k_str = f"'{k}'" if isinstance(k, str) else str(k) - attr_str = f"{k_str}: dict({v_str}" - else: - attr_str = f"{str(k)}=dict({v_str}" - attr_str = _indent(attr_str, indent) + ")" + end - elif isinstance(v, list): - attr_str = _format_list(k, v, use_mapping) + end - else: - attr_str = _format_basic_types(k, v, use_mapping) + end - - s.append(attr_str) - r += "\n".join(s) - if use_mapping: - r += "}" - return r - - cfg_dict = self._cfg_dict.to_dict() - text = _format_dict(cfg_dict, outest_level=True) - # copied from setup.cfg - yapf_style = dict( - based_on_style="pep8", - blank_line_before_nested_class_or_def=True, - split_before_expression_after_opening_paren=True, - ) - text, _ = FormatCode(text, style_config=yapf_style, verify=True) - - return text - - def __repr__(self): - return f"Config (path: {self.filename}): {self._cfg_dict.__repr__()}" - - def __len__(self): - return len(self._cfg_dict) - - def __getattr__(self, name): - # # debug - # print('+'*15) - # print('name=%s' % name) - # print("addr:", id(self)) - # # print('type(self):', type(self)) - # print(self.__dict__) - # print('+'*15) - # if self.__dict__ == {}: - # raise ValueError - - return getattr(self._cfg_dict, name) - - def __getitem__(self, name): - return self._cfg_dict.__getitem__(name) - - def __setattr__(self, name, value): - if isinstance(value, dict): - value = ConfigDict(value) - self._cfg_dict.__setattr__(name, value) - - def __setitem__(self, name, value): - if isinstance(value, dict): - value = ConfigDict(value) - self._cfg_dict.__setitem__(name, value) - - def __iter__(self): - return iter(self._cfg_dict) - - def dump(self, file=None): - # import ipdb; ipdb.set_trace() - if file is None: - return self.pretty_text - else: - with open(file, "w") as f: - f.write(self.pretty_text) - - def merge_from_dict(self, options): - """Merge list into cfg_dict - - Merge the dict parsed by MultipleKVAction into this cfg. - - Examples: - >>> options = {'model.backbone.depth': 50, - ... 'model.backbone.with_cp':True} - >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet')))) - >>> cfg.merge_from_dict(options) - >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict') - >>> assert cfg_dict == dict( - ... model=dict(backbone=dict(depth=50, with_cp=True))) - - Args: - options (dict): dict of configs to merge from. - """ - option_cfg_dict = {} - for full_key, v in options.items(): - d = option_cfg_dict - key_list = full_key.split(".") - for subkey in key_list[:-1]: - d.setdefault(subkey, ConfigDict()) - d = d[subkey] - subkey = key_list[-1] - d[subkey] = v - - cfg_dict = super(SLConfig, self).__getattribute__("_cfg_dict") - super(SLConfig, self).__setattr__( - "_cfg_dict", SLConfig._merge_a_into_b(option_cfg_dict, cfg_dict) - ) - - # for multiprocess - def __setstate__(self, state): - self.__init__(state) - - def copy(self): - return SLConfig(self._cfg_dict.copy()) - - def deepcopy(self): - return SLConfig(self._cfg_dict.deepcopy()) - - -class DictAction(Action): - """ - argparse action to split an argument into KEY=VALUE form - on the first = and append to a dictionary. List options should - be passed as comma separated values, i.e KEY=V1,V2,V3 - """ - - @staticmethod - def _parse_int_float_bool(val): - try: - return int(val) - except ValueError: - pass - try: - return float(val) - except ValueError: - pass - if val.lower() in ["true", "false"]: - return True if val.lower() == "true" else False - if val.lower() in ["none", "null"]: - return None - return val - - def __call__(self, parser, namespace, values, option_string=None): - options = {} - for kv in values: - key, val = kv.split("=", maxsplit=1) - val = [self._parse_int_float_bool(v) for v in val.split(",")] - if len(val) == 1: - val = val[0] - options[key] = val - setattr(namespace, self.dest, options) diff --git a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/utils.py b/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/utils.py deleted file mode 100644 index 8cf83ae03a7865bc48493be16e8b1b2d53a1b09f..0000000000000000000000000000000000000000 --- a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/utils.py +++ /dev/null @@ -1,610 +0,0 @@ -import argparse -import json -import warnings -from collections import OrderedDict -from copy import deepcopy -from typing import Any, Dict, List - -import numpy as np -import torch -from transformers import AutoTokenizer - -from groundingdino.util.slconfig import SLConfig - - -def slprint(x, name="x"): - if isinstance(x, (torch.Tensor, np.ndarray)): - print(f"{name}.shape:", x.shape) - elif isinstance(x, (tuple, list)): - print("type x:", type(x)) - for i in range(min(10, len(x))): - slprint(x[i], f"{name}[{i}]") - elif isinstance(x, dict): - for k, v in x.items(): - slprint(v, f"{name}[{k}]") - else: - print(f"{name}.type:", type(x)) - - -def clean_state_dict(state_dict): - new_state_dict = OrderedDict() - for k, v in state_dict.items(): - if k[:7] == "module.": - k = k[7:] # remove `module.` - new_state_dict[k] = v - return new_state_dict - - -def renorm( - img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] -) -> torch.FloatTensor: - # img: tensor(3,H,W) or tensor(B,3,H,W) - # return: same as img - assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim() - if img.dim() == 3: - assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % ( - img.size(0), - str(img.size()), - ) - img_perm = img.permute(1, 2, 0) - mean = torch.Tensor(mean) - std = torch.Tensor(std) - img_res = img_perm * std + mean - return img_res.permute(2, 0, 1) - else: # img.dim() == 4 - assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % ( - img.size(1), - str(img.size()), - ) - img_perm = img.permute(0, 2, 3, 1) - mean = torch.Tensor(mean) - std = torch.Tensor(std) - img_res = img_perm * std + mean - return img_res.permute(0, 3, 1, 2) - - -class CocoClassMapper: - def __init__(self) -> None: - self.category_map_str = { - "1": 1, - "2": 2, - "3": 3, - "4": 4, - "5": 5, - "6": 6, - "7": 7, - "8": 8, - "9": 9, - "10": 10, - "11": 11, - "13": 12, - "14": 13, - "15": 14, - "16": 15, - "17": 16, - "18": 17, - "19": 18, - "20": 19, - "21": 20, - "22": 21, - "23": 22, - "24": 23, - "25": 24, - "27": 25, - "28": 26, - "31": 27, - "32": 28, - "33": 29, - "34": 30, - "35": 31, - "36": 32, - "37": 33, - "38": 34, - "39": 35, - "40": 36, - "41": 37, - "42": 38, - "43": 39, - "44": 40, - "46": 41, - "47": 42, - "48": 43, - "49": 44, - "50": 45, - "51": 46, - "52": 47, - "53": 48, - "54": 49, - "55": 50, - "56": 51, - "57": 52, - "58": 53, - "59": 54, - "60": 55, - "61": 56, - "62": 57, - "63": 58, - "64": 59, - "65": 60, - "67": 61, - "70": 62, - "72": 63, - "73": 64, - "74": 65, - "75": 66, - "76": 67, - "77": 68, - "78": 69, - "79": 70, - "80": 71, - "81": 72, - "82": 73, - "84": 74, - "85": 75, - "86": 76, - "87": 77, - "88": 78, - "89": 79, - "90": 80, - } - self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()} - self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()} - - def origin2compact(self, idx): - return self.origin2compact_mapper[int(idx)] - - def compact2origin(self, idx): - return self.compact2origin_mapper[int(idx)] - - -def to_device(item, device): - if isinstance(item, torch.Tensor): - return item.to(device) - elif isinstance(item, list): - return [to_device(i, device) for i in item] - elif isinstance(item, dict): - return {k: to_device(v, device) for k, v in item.items()} - else: - raise NotImplementedError( - "Call Shilong if you use other containers! type: {}".format(type(item)) - ) - - -# -def get_gaussian_mean(x, axis, other_axis, softmax=True): - """ - - Args: - x (float): Input images(BxCxHxW) - axis (int): The index for weighted mean - other_axis (int): The other index - - Returns: weighted index for axis, BxC - - """ - mat2line = torch.sum(x, axis=other_axis) - # mat2line = mat2line / mat2line.mean() * 10 - if softmax: - u = torch.softmax(mat2line, axis=2) - else: - u = mat2line / (mat2line.sum(2, keepdim=True) + 1e-6) - size = x.shape[axis] - ind = torch.linspace(0, 1, size).to(x.device) - batch = x.shape[0] - channel = x.shape[1] - index = ind.repeat([batch, channel, 1]) - mean_position = torch.sum(index * u, dim=2) - return mean_position - - -def get_expected_points_from_map(hm, softmax=True): - """get_gaussian_map_from_points - B,C,H,W -> B,N,2 float(0, 1) float(0, 1) - softargmax function - - Args: - hm (float): Input images(BxCxHxW) - - Returns: - weighted index for axis, BxCx2. float between 0 and 1. - - """ - # hm = 10*hm - B, C, H, W = hm.shape - y_mean = get_gaussian_mean(hm, 2, 3, softmax=softmax) # B,C - x_mean = get_gaussian_mean(hm, 3, 2, softmax=softmax) # B,C - # return torch.cat((x_mean.unsqueeze(-1), y_mean.unsqueeze(-1)), 2) - return torch.stack([x_mean, y_mean], dim=2) - - -# Positional encoding (section 5.1) -# borrow from nerf -class Embedder: - def __init__(self, **kwargs): - self.kwargs = kwargs - self.create_embedding_fn() - - def create_embedding_fn(self): - embed_fns = [] - d = self.kwargs["input_dims"] - out_dim = 0 - if self.kwargs["include_input"]: - embed_fns.append(lambda x: x) - out_dim += d - - max_freq = self.kwargs["max_freq_log2"] - N_freqs = self.kwargs["num_freqs"] - - if self.kwargs["log_sampling"]: - freq_bands = 2.0 ** torch.linspace(0.0, max_freq, steps=N_freqs) - else: - freq_bands = torch.linspace(2.0**0.0, 2.0**max_freq, steps=N_freqs) - - for freq in freq_bands: - for p_fn in self.kwargs["periodic_fns"]: - embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq)) - out_dim += d - - self.embed_fns = embed_fns - self.out_dim = out_dim - - def embed(self, inputs): - return torch.cat([fn(inputs) for fn in self.embed_fns], -1) - - -def get_embedder(multires, i=0): - import torch.nn as nn - - if i == -1: - return nn.Identity(), 3 - - embed_kwargs = { - "include_input": True, - "input_dims": 3, - "max_freq_log2": multires - 1, - "num_freqs": multires, - "log_sampling": True, - "periodic_fns": [torch.sin, torch.cos], - } - - embedder_obj = Embedder(**embed_kwargs) - embed = lambda x, eo=embedder_obj: eo.embed(x) - return embed, embedder_obj.out_dim - - -class APOPMeter: - def __init__(self) -> None: - self.tp = 0 - self.fp = 0 - self.tn = 0 - self.fn = 0 - - def update(self, pred, gt): - """ - Input: - pred, gt: Tensor() - """ - assert pred.shape == gt.shape - self.tp += torch.logical_and(pred == 1, gt == 1).sum().item() - self.fp += torch.logical_and(pred == 1, gt == 0).sum().item() - self.tn += torch.logical_and(pred == 0, gt == 0).sum().item() - self.tn += torch.logical_and(pred == 1, gt == 0).sum().item() - - def update_cm(self, tp, fp, tn, fn): - self.tp += tp - self.fp += fp - self.tn += tn - self.tn += fn - - -def inverse_sigmoid(x, eps=1e-5): - x = x.clamp(min=0, max=1) - x1 = x.clamp(min=eps) - x2 = (1 - x).clamp(min=eps) - return torch.log(x1 / x2) - - -def get_raw_dict(args): - """ - return the dicf contained in args. - - e.g: - >>> with open(path, 'w') as f: - json.dump(get_raw_dict(args), f, indent=2) - """ - if isinstance(args, argparse.Namespace): - return vars(args) - elif isinstance(args, dict): - return args - elif isinstance(args, SLConfig): - return args._cfg_dict - else: - raise NotImplementedError("Unknown type {}".format(type(args))) - - -def stat_tensors(tensor): - assert tensor.dim() == 1 - tensor_sm = tensor.softmax(0) - entropy = (tensor_sm * torch.log(tensor_sm + 1e-9)).sum() - - return { - "max": tensor.max(), - "min": tensor.min(), - "mean": tensor.mean(), - "var": tensor.var(), - "std": tensor.var() ** 0.5, - "entropy": entropy, - } - - -class NiceRepr: - """Inherit from this class and define ``__nice__`` to "nicely" print your - objects. - - Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function - Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``. - If the inheriting class has a ``__len__``, method then the default - ``__nice__`` method will return its length. - - Example: - >>> class Foo(NiceRepr): - ... def __nice__(self): - ... return 'info' - >>> foo = Foo() - >>> assert str(foo) == '' - >>> assert repr(foo).startswith('>> class Bar(NiceRepr): - ... pass - >>> bar = Bar() - >>> import pytest - >>> with pytest.warns(None) as record: - >>> assert 'object at' in str(bar) - >>> assert 'object at' in repr(bar) - - Example: - >>> class Baz(NiceRepr): - ... def __len__(self): - ... return 5 - >>> baz = Baz() - >>> assert str(baz) == '' - """ - - def __nice__(self): - """str: a "nice" summary string describing this module""" - if hasattr(self, "__len__"): - # It is a common pattern for objects to use __len__ in __nice__ - # As a convenience we define a default __nice__ for these objects - return str(len(self)) - else: - # In all other cases force the subclass to overload __nice__ - raise NotImplementedError(f"Define the __nice__ method for {self.__class__!r}") - - def __repr__(self): - """str: the string of the module""" - try: - nice = self.__nice__() - classname = self.__class__.__name__ - return f"<{classname}({nice}) at {hex(id(self))}>" - except NotImplementedError as ex: - warnings.warn(str(ex), category=RuntimeWarning) - return object.__repr__(self) - - def __str__(self): - """str: the string of the module""" - try: - classname = self.__class__.__name__ - nice = self.__nice__() - return f"<{classname}({nice})>" - except NotImplementedError as ex: - warnings.warn(str(ex), category=RuntimeWarning) - return object.__repr__(self) - - -def ensure_rng(rng=None): - """Coerces input into a random number generator. - - If the input is None, then a global random state is returned. - - If the input is a numeric value, then that is used as a seed to construct a - random state. Otherwise the input is returned as-is. - - Adapted from [1]_. - - Args: - rng (int | numpy.random.RandomState | None): - if None, then defaults to the global rng. Otherwise this can be an - integer or a RandomState class - Returns: - (numpy.random.RandomState) : rng - - a numpy random number generator - - References: - .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270 # noqa: E501 - """ - - if rng is None: - rng = np.random.mtrand._rand - elif isinstance(rng, int): - rng = np.random.RandomState(rng) - else: - rng = rng - return rng - - -def random_boxes(num=1, scale=1, rng=None): - """Simple version of ``kwimage.Boxes.random`` - - Returns: - Tensor: shape (n, 4) in x1, y1, x2, y2 format. - - References: - https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390 - - Example: - >>> num = 3 - >>> scale = 512 - >>> rng = 0 - >>> boxes = random_boxes(num, scale, rng) - >>> print(boxes) - tensor([[280.9925, 278.9802, 308.6148, 366.1769], - [216.9113, 330.6978, 224.0446, 456.5878], - [405.3632, 196.3221, 493.3953, 270.7942]]) - """ - rng = ensure_rng(rng) - - tlbr = rng.rand(num, 4).astype(np.float32) - - tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2]) - tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3]) - br_x = np.maximum(tlbr[:, 0], tlbr[:, 2]) - br_y = np.maximum(tlbr[:, 1], tlbr[:, 3]) - - tlbr[:, 0] = tl_x * scale - tlbr[:, 1] = tl_y * scale - tlbr[:, 2] = br_x * scale - tlbr[:, 3] = br_y * scale - - boxes = torch.from_numpy(tlbr) - return boxes - - -class ModelEma(torch.nn.Module): - def __init__(self, model, decay=0.9997, device=None): - super(ModelEma, self).__init__() - # make a copy of the model for accumulating moving average of weights - self.module = deepcopy(model) - self.module.eval() - - # import ipdb; ipdb.set_trace() - - self.decay = decay - self.device = device # perform ema on different device from model if set - if self.device is not None: - self.module.to(device=device) - - def _update(self, model, update_fn): - with torch.no_grad(): - for ema_v, model_v in zip( - self.module.state_dict().values(), model.state_dict().values() - ): - if self.device is not None: - model_v = model_v.to(device=self.device) - ema_v.copy_(update_fn(ema_v, model_v)) - - def update(self, model): - self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m) - - def set(self, model): - self._update(model, update_fn=lambda e, m: m) - - -class BestMetricSingle: - def __init__(self, init_res=0.0, better="large") -> None: - self.init_res = init_res - self.best_res = init_res - self.best_ep = -1 - - self.better = better - assert better in ["large", "small"] - - def isbetter(self, new_res, old_res): - if self.better == "large": - return new_res > old_res - if self.better == "small": - return new_res < old_res - - def update(self, new_res, ep): - if self.isbetter(new_res, self.best_res): - self.best_res = new_res - self.best_ep = ep - return True - return False - - def __str__(self) -> str: - return "best_res: {}\t best_ep: {}".format(self.best_res, self.best_ep) - - def __repr__(self) -> str: - return self.__str__() - - def summary(self) -> dict: - return { - "best_res": self.best_res, - "best_ep": self.best_ep, - } - - -class BestMetricHolder: - def __init__(self, init_res=0.0, better="large", use_ema=False) -> None: - self.best_all = BestMetricSingle(init_res, better) - self.use_ema = use_ema - if use_ema: - self.best_ema = BestMetricSingle(init_res, better) - self.best_regular = BestMetricSingle(init_res, better) - - def update(self, new_res, epoch, is_ema=False): - """ - return if the results is the best. - """ - if not self.use_ema: - return self.best_all.update(new_res, epoch) - else: - if is_ema: - self.best_ema.update(new_res, epoch) - return self.best_all.update(new_res, epoch) - else: - self.best_regular.update(new_res, epoch) - return self.best_all.update(new_res, epoch) - - def summary(self): - if not self.use_ema: - return self.best_all.summary() - - res = {} - res.update({f"all_{k}": v for k, v in self.best_all.summary().items()}) - res.update({f"regular_{k}": v for k, v in self.best_regular.summary().items()}) - res.update({f"ema_{k}": v for k, v in self.best_ema.summary().items()}) - return res - - def __repr__(self) -> str: - return json.dumps(self.summary(), indent=2) - - def __str__(self) -> str: - return self.__repr__() - - -def targets_to(targets: List[Dict[str, Any]], device): - """Moves the target dicts to the given device.""" - excluded_keys = [ - "questionId", - "tokens_positive", - "strings_positive", - "tokens", - "dataset_name", - "sentence_id", - "original_img_id", - "nb_eval", - "task_id", - "original_id", - "token_span", - "caption", - "dataset_type", - ] - return [ - {k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets - ] - - -def get_phrases_from_posmap( - posmap: torch.BoolTensor, tokenized: Dict, tokenizer: AutoTokenizer, left_idx: int = 0, right_idx: int = 255 -): - assert isinstance(posmap, torch.Tensor), "posmap must be torch.Tensor" - if posmap.dim() == 1: - posmap[0: left_idx + 1] = False - posmap[right_idx:] = False - non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist() - token_ids = [tokenized["input_ids"][i] for i in non_zero_idx] - return tokenizer.decode(token_ids) - else: - raise NotImplementedError("posmap must be 1-dim") diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/pyparsing/common.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/pyparsing/common.py deleted file mode 100644 index 1859fb79cc4e78850b69742fca56698041ce59f8..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/pyparsing/common.py +++ /dev/null @@ -1,424 +0,0 @@ -# common.py -from .core import * -from .helpers import delimited_list, any_open_tag, any_close_tag -from datetime import datetime - - -# some other useful expressions - using lower-case class name since we are really using this as a namespace -class pyparsing_common: - """Here are some common low-level expressions that may be useful in - jump-starting parser development: - - - numeric forms (:class:`integers`, :class:`reals`, - :class:`scientific notation`) - - common :class:`programming identifiers` - - network addresses (:class:`MAC`, - :class:`IPv4`, :class:`IPv6`) - - ISO8601 :class:`dates` and - :class:`datetime` - - :class:`UUID` - - :class:`comma-separated list` - - :class:`url` - - Parse actions: - - - :class:`convertToInteger` - - :class:`convertToFloat` - - :class:`convertToDate` - - :class:`convertToDatetime` - - :class:`stripHTMLTags` - - :class:`upcaseTokens` - - :class:`downcaseTokens` - - Example:: - - pyparsing_common.number.runTests(''' - # any int or real number, returned as the appropriate type - 100 - -100 - +100 - 3.14159 - 6.02e23 - 1e-12 - ''') - - pyparsing_common.fnumber.runTests(''' - # any int or real number, returned as float - 100 - -100 - +100 - 3.14159 - 6.02e23 - 1e-12 - ''') - - pyparsing_common.hex_integer.runTests(''' - # hex numbers - 100 - FF - ''') - - pyparsing_common.fraction.runTests(''' - # fractions - 1/2 - -3/4 - ''') - - pyparsing_common.mixed_integer.runTests(''' - # mixed fractions - 1 - 1/2 - -3/4 - 1-3/4 - ''') - - import uuid - pyparsing_common.uuid.setParseAction(tokenMap(uuid.UUID)) - pyparsing_common.uuid.runTests(''' - # uuid - 12345678-1234-5678-1234-567812345678 - ''') - - prints:: - - # any int or real number, returned as the appropriate type - 100 - [100] - - -100 - [-100] - - +100 - [100] - - 3.14159 - [3.14159] - - 6.02e23 - [6.02e+23] - - 1e-12 - [1e-12] - - # any int or real number, returned as float - 100 - [100.0] - - -100 - [-100.0] - - +100 - [100.0] - - 3.14159 - [3.14159] - - 6.02e23 - [6.02e+23] - - 1e-12 - [1e-12] - - # hex numbers - 100 - [256] - - FF - [255] - - # fractions - 1/2 - [0.5] - - -3/4 - [-0.75] - - # mixed fractions - 1 - [1] - - 1/2 - [0.5] - - -3/4 - [-0.75] - - 1-3/4 - [1.75] - - # uuid - 12345678-1234-5678-1234-567812345678 - [UUID('12345678-1234-5678-1234-567812345678')] - """ - - convert_to_integer = token_map(int) - """ - Parse action for converting parsed integers to Python int - """ - - convert_to_float = token_map(float) - """ - Parse action for converting parsed numbers to Python float - """ - - integer = Word(nums).set_name("integer").set_parse_action(convert_to_integer) - """expression that parses an unsigned integer, returns an int""" - - hex_integer = ( - Word(hexnums).set_name("hex integer").set_parse_action(token_map(int, 16)) - ) - """expression that parses a hexadecimal integer, returns an int""" - - signed_integer = ( - Regex(r"[+-]?\d+") - .set_name("signed integer") - .set_parse_action(convert_to_integer) - ) - """expression that parses an integer with optional leading sign, returns an int""" - - fraction = ( - signed_integer().set_parse_action(convert_to_float) - + "/" - + signed_integer().set_parse_action(convert_to_float) - ).set_name("fraction") - """fractional expression of an integer divided by an integer, returns a float""" - fraction.add_parse_action(lambda tt: tt[0] / tt[-1]) - - mixed_integer = ( - fraction | signed_integer + Opt(Opt("-").suppress() + fraction) - ).set_name("fraction or mixed integer-fraction") - """mixed integer of the form 'integer - fraction', with optional leading integer, returns float""" - mixed_integer.add_parse_action(sum) - - real = ( - Regex(r"[+-]?(?:\d+\.\d*|\.\d+)") - .set_name("real number") - .set_parse_action(convert_to_float) - ) - """expression that parses a floating point number and returns a float""" - - sci_real = ( - Regex(r"[+-]?(?:\d+(?:[eE][+-]?\d+)|(?:\d+\.\d*|\.\d+)(?:[eE][+-]?\d+)?)") - .set_name("real number with scientific notation") - .set_parse_action(convert_to_float) - ) - """expression that parses a floating point number with optional - scientific notation and returns a float""" - - # streamlining this expression makes the docs nicer-looking - number = (sci_real | real | signed_integer).setName("number").streamline() - """any numeric expression, returns the corresponding Python type""" - - fnumber = ( - Regex(r"[+-]?\d+\.?\d*([eE][+-]?\d+)?") - .set_name("fnumber") - .set_parse_action(convert_to_float) - ) - """any int or real number, returned as float""" - - identifier = Word(identchars, identbodychars).set_name("identifier") - """typical code identifier (leading alpha or '_', followed by 0 or more alphas, nums, or '_')""" - - ipv4_address = Regex( - r"(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})(\.(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})){3}" - ).set_name("IPv4 address") - "IPv4 address (``0.0.0.0 - 255.255.255.255``)" - - _ipv6_part = Regex(r"[0-9a-fA-F]{1,4}").set_name("hex_integer") - _full_ipv6_address = (_ipv6_part + (":" + _ipv6_part) * 7).set_name( - "full IPv6 address" - ) - _short_ipv6_address = ( - Opt(_ipv6_part + (":" + _ipv6_part) * (0, 6)) - + "::" - + Opt(_ipv6_part + (":" + _ipv6_part) * (0, 6)) - ).set_name("short IPv6 address") - _short_ipv6_address.add_condition( - lambda t: sum(1 for tt in t if pyparsing_common._ipv6_part.matches(tt)) < 8 - ) - _mixed_ipv6_address = ("::ffff:" + ipv4_address).set_name("mixed IPv6 address") - ipv6_address = Combine( - (_full_ipv6_address | _mixed_ipv6_address | _short_ipv6_address).set_name( - "IPv6 address" - ) - ).set_name("IPv6 address") - "IPv6 address (long, short, or mixed form)" - - mac_address = Regex( - r"[0-9a-fA-F]{2}([:.-])[0-9a-fA-F]{2}(?:\1[0-9a-fA-F]{2}){4}" - ).set_name("MAC address") - "MAC address xx:xx:xx:xx:xx (may also have '-' or '.' delimiters)" - - @staticmethod - def convert_to_date(fmt: str = "%Y-%m-%d"): - """ - Helper to create a parse action for converting parsed date string to Python datetime.date - - Params - - - fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%d"``) - - Example:: - - date_expr = pyparsing_common.iso8601_date.copy() - date_expr.setParseAction(pyparsing_common.convertToDate()) - print(date_expr.parseString("1999-12-31")) - - prints:: - - [datetime.date(1999, 12, 31)] - """ - - def cvt_fn(ss, ll, tt): - try: - return datetime.strptime(tt[0], fmt).date() - except ValueError as ve: - raise ParseException(ss, ll, str(ve)) - - return cvt_fn - - @staticmethod - def convert_to_datetime(fmt: str = "%Y-%m-%dT%H:%M:%S.%f"): - """Helper to create a parse action for converting parsed - datetime string to Python datetime.datetime - - Params - - - fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%dT%H:%M:%S.%f"``) - - Example:: - - dt_expr = pyparsing_common.iso8601_datetime.copy() - dt_expr.setParseAction(pyparsing_common.convertToDatetime()) - print(dt_expr.parseString("1999-12-31T23:59:59.999")) - - prints:: - - [datetime.datetime(1999, 12, 31, 23, 59, 59, 999000)] - """ - - def cvt_fn(s, l, t): - try: - return datetime.strptime(t[0], fmt) - except ValueError as ve: - raise ParseException(s, l, str(ve)) - - return cvt_fn - - iso8601_date = Regex( - r"(?P\d{4})(?:-(?P\d\d)(?:-(?P\d\d))?)?" - ).set_name("ISO8601 date") - "ISO8601 date (``yyyy-mm-dd``)" - - iso8601_datetime = Regex( - r"(?P\d{4})-(?P\d\d)-(?P\d\d)[T ](?P\d\d):(?P\d\d)(:(?P\d\d(\.\d*)?)?)?(?PZ|[+-]\d\d:?\d\d)?" - ).set_name("ISO8601 datetime") - "ISO8601 datetime (``yyyy-mm-ddThh:mm:ss.s(Z|+-00:00)``) - trailing seconds, milliseconds, and timezone optional; accepts separating ``'T'`` or ``' '``" - - uuid = Regex(r"[0-9a-fA-F]{8}(-[0-9a-fA-F]{4}){3}-[0-9a-fA-F]{12}").set_name("UUID") - "UUID (``xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx``)" - - _html_stripper = any_open_tag.suppress() | any_close_tag.suppress() - - @staticmethod - def strip_html_tags(s: str, l: int, tokens: ParseResults): - """Parse action to remove HTML tags from web page HTML source - - Example:: - - # strip HTML links from normal text - text = 'More info at the pyparsing wiki page' - td, td_end = makeHTMLTags("TD") - table_text = td + SkipTo(td_end).setParseAction(pyparsing_common.stripHTMLTags)("body") + td_end - print(table_text.parseString(text).body) - - Prints:: - - More info at the pyparsing wiki page - """ - return pyparsing_common._html_stripper.transform_string(tokens[0]) - - _commasepitem = ( - Combine( - OneOrMore( - ~Literal(",") - + ~LineEnd() - + Word(printables, exclude_chars=",") - + Opt(White(" \t") + ~FollowedBy(LineEnd() | ",")) - ) - ) - .streamline() - .set_name("commaItem") - ) - comma_separated_list = delimited_list( - Opt(quoted_string.copy() | _commasepitem, default="") - ).set_name("comma separated list") - """Predefined expression of 1 or more printable words or quoted strings, separated by commas.""" - - upcase_tokens = staticmethod(token_map(lambda t: t.upper())) - """Parse action to convert tokens to upper case.""" - - downcase_tokens = staticmethod(token_map(lambda t: t.lower())) - """Parse action to convert tokens to lower case.""" - - # fmt: off - url = Regex( - # https://mathiasbynens.be/demo/url-regex - # https://gist.github.com/dperini/729294 - r"^" + - # protocol identifier (optional) - # short syntax // still required - r"(?:(?:(?Phttps?|ftp):)?\/\/)" + - # user:pass BasicAuth (optional) - r"(?:(?P\S+(?::\S*)?)@)?" + - r"(?P" + - # IP address exclusion - # private & local networks - r"(?!(?:10|127)(?:\.\d{1,3}){3})" + - r"(?!(?:169\.254|192\.168)(?:\.\d{1,3}){2})" + - r"(?!172\.(?:1[6-9]|2\d|3[0-1])(?:\.\d{1,3}){2})" + - # IP address dotted notation octets - # excludes loopback network 0.0.0.0 - # excludes reserved space >= 224.0.0.0 - # excludes network & broadcast addresses - # (first & last IP address of each class) - r"(?:[1-9]\d?|1\d\d|2[01]\d|22[0-3])" + - r"(?:\.(?:1?\d{1,2}|2[0-4]\d|25[0-5])){2}" + - r"(?:\.(?:[1-9]\d?|1\d\d|2[0-4]\d|25[0-4]))" + - r"|" + - # host & domain names, may end with dot - # can be replaced by a shortest alternative - # (?![-_])(?:[-\w\u00a1-\uffff]{0,63}[^-_]\.)+ - r"(?:" + - r"(?:" + - r"[a-z0-9\u00a1-\uffff]" + - r"[a-z0-9\u00a1-\uffff_-]{0,62}" + - r")?" + - r"[a-z0-9\u00a1-\uffff]\." + - r")+" + - # TLD identifier name, may end with dot - r"(?:[a-z\u00a1-\uffff]{2,}\.?)" + - r")" + - # port number (optional) - r"(:(?P\d{2,5}))?" + - # resource path (optional) - r"(?P\/[^?# ]*)?" + - # query string (optional) - r"(\?(?P[^#]*))?" + - # fragment (optional) - r"(#(?P\S*))?" + - r"$" - ).set_name("url") - # fmt: on - - # pre-PEP8 compatibility names - convertToInteger = convert_to_integer - convertToFloat = convert_to_float - convertToDate = convert_to_date - convertToDatetime = convert_to_datetime - stripHTMLTags = strip_html_tags - upcaseTokens = upcase_tokens - downcaseTokens = downcase_tokens - - -_builtin_exprs = [ - v for v in vars(pyparsing_common).values() if isinstance(v, ParserElement) -] diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py deleted file mode 100644 index 63c54ee9a5ce2368494b775cc90fada1439feaa5..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py +++ /dev/null @@ -1,14 +0,0 @@ -from .mask_rcnn_R_101_FPN_100ep_LSJ import ( - dataloader, - lr_multiplier, - model, - optimizer, - train, -) - -train.max_iter *= 4 # 100ep -> 400ep - -lr_multiplier.scheduler.milestones = [ - milestone * 4 for milestone in lr_multiplier.scheduler.milestones -] -lr_multiplier.scheduler.num_updates = train.max_iter diff --git a/spaces/BartPoint/VoiceChange_Beta/util.py b/spaces/BartPoint/VoiceChange_Beta/util.py deleted file mode 100644 index 8d6bcff1135c2d97e4caad7922f03f05c98484da..0000000000000000000000000000000000000000 --- a/spaces/BartPoint/VoiceChange_Beta/util.py +++ /dev/null @@ -1,81 +0,0 @@ -import sys -import asyncio -from io import BytesIO - -from fairseq import checkpoint_utils - -import torch - -import edge_tts -import librosa - - -# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/config.py#L43-L55 # noqa -def has_mps() -> bool: - if sys.platform != "darwin": - return False - else: - if not getattr(torch, 'has_mps', False): - return False - - try: - torch.zeros(1).to(torch.device("mps")) - return True - except Exception: - return False - - -def is_half(device: str) -> bool: - if not device.startswith('cuda'): - return False - else: - gpu_name = torch.cuda.get_device_name( - int(device.split(':')[-1]) - ).upper() - - # ...regex? - if ( - ('16' in gpu_name and 'V100' not in gpu_name) - or 'P40' in gpu_name - or '1060' in gpu_name - or '1070' in gpu_name - or '1080' in gpu_name - ): - return False - - return True - - -def load_hubert_model(device: str, model_path: str = 'hubert_base.pt'): - model = checkpoint_utils.load_model_ensemble_and_task( - [model_path] - )[0][0].to(device) - - if is_half(device): - return model.half() - else: - return model.float() - - -async def call_edge_tts(speaker_name: str, text: str): - tts_com = edge_tts.Communicate(text, speaker_name) - tts_raw = b'' - - # Stream TTS audio to bytes - async for chunk in tts_com.stream(): - if chunk['type'] == 'audio': - tts_raw += chunk['data'] - - # Convert mp3 stream to wav - ffmpeg_proc = await asyncio.create_subprocess_exec( - 'ffmpeg', - '-f', 'mp3', - '-i', '-', - '-f', 'wav', - '-', - stdin=asyncio.subprocess.PIPE, - stdout=asyncio.subprocess.PIPE - ) - (tts_wav, _) = await ffmpeg_proc.communicate(tts_raw) - - return librosa.load(BytesIO(tts_wav)) diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/resolvelib/requirements.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/resolvelib/requirements.py deleted file mode 100644 index 06addc0ddce8d1fd1df15b26f8b45221a44737b6..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/resolvelib/requirements.py +++ /dev/null @@ -1,165 +0,0 @@ -from pip._vendor.packaging.specifiers import SpecifierSet -from pip._vendor.packaging.utils import NormalizedName, canonicalize_name - -from pip._internal.req.req_install import InstallRequirement - -from .base import Candidate, CandidateLookup, Requirement, format_name - - -class ExplicitRequirement(Requirement): - def __init__(self, candidate: Candidate) -> None: - self.candidate = candidate - - def __str__(self) -> str: - return str(self.candidate) - - def __repr__(self) -> str: - return "{class_name}({candidate!r})".format( - class_name=self.__class__.__name__, - candidate=self.candidate, - ) - - @property - def project_name(self) -> NormalizedName: - # No need to canonicalize - the candidate did this - return self.candidate.project_name - - @property - def name(self) -> str: - # No need to canonicalize - the candidate did this - return self.candidate.name - - def format_for_error(self) -> str: - return self.candidate.format_for_error() - - def get_candidate_lookup(self) -> CandidateLookup: - return self.candidate, None - - def is_satisfied_by(self, candidate: Candidate) -> bool: - return candidate == self.candidate - - -class SpecifierRequirement(Requirement): - def __init__(self, ireq: InstallRequirement) -> None: - assert ireq.link is None, "This is a link, not a specifier" - self._ireq = ireq - self._extras = frozenset(ireq.extras) - - def __str__(self) -> str: - return str(self._ireq.req) - - def __repr__(self) -> str: - return "{class_name}({requirement!r})".format( - class_name=self.__class__.__name__, - requirement=str(self._ireq.req), - ) - - @property - def project_name(self) -> NormalizedName: - assert self._ireq.req, "Specifier-backed ireq is always PEP 508" - return canonicalize_name(self._ireq.req.name) - - @property - def name(self) -> str: - return format_name(self.project_name, self._extras) - - def format_for_error(self) -> str: - # Convert comma-separated specifiers into "A, B, ..., F and G" - # This makes the specifier a bit more "human readable", without - # risking a change in meaning. (Hopefully! Not all edge cases have - # been checked) - parts = [s.strip() for s in str(self).split(",")] - if len(parts) == 0: - return "" - elif len(parts) == 1: - return parts[0] - - return ", ".join(parts[:-1]) + " and " + parts[-1] - - def get_candidate_lookup(self) -> CandidateLookup: - return None, self._ireq - - def is_satisfied_by(self, candidate: Candidate) -> bool: - assert candidate.name == self.name, ( - f"Internal issue: Candidate is not for this requirement " - f"{candidate.name} vs {self.name}" - ) - # We can safely always allow prereleases here since PackageFinder - # already implements the prerelease logic, and would have filtered out - # prerelease candidates if the user does not expect them. - assert self._ireq.req, "Specifier-backed ireq is always PEP 508" - spec = self._ireq.req.specifier - return spec.contains(candidate.version, prereleases=True) - - -class RequiresPythonRequirement(Requirement): - """A requirement representing Requires-Python metadata.""" - - def __init__(self, specifier: SpecifierSet, match: Candidate) -> None: - self.specifier = specifier - self._candidate = match - - def __str__(self) -> str: - return f"Python {self.specifier}" - - def __repr__(self) -> str: - return "{class_name}({specifier!r})".format( - class_name=self.__class__.__name__, - specifier=str(self.specifier), - ) - - @property - def project_name(self) -> NormalizedName: - return self._candidate.project_name - - @property - def name(self) -> str: - return self._candidate.name - - def format_for_error(self) -> str: - return str(self) - - def get_candidate_lookup(self) -> CandidateLookup: - if self.specifier.contains(self._candidate.version, prereleases=True): - return self._candidate, None - return None, None - - def is_satisfied_by(self, candidate: Candidate) -> bool: - assert candidate.name == self._candidate.name, "Not Python candidate" - # We can safely always allow prereleases here since PackageFinder - # already implements the prerelease logic, and would have filtered out - # prerelease candidates if the user does not expect them. - return self.specifier.contains(candidate.version, prereleases=True) - - -class UnsatisfiableRequirement(Requirement): - """A requirement that cannot be satisfied.""" - - def __init__(self, name: NormalizedName) -> None: - self._name = name - - def __str__(self) -> str: - return f"{self._name} (unavailable)" - - def __repr__(self) -> str: - return "{class_name}({name!r})".format( - class_name=self.__class__.__name__, - name=str(self._name), - ) - - @property - def project_name(self) -> NormalizedName: - return self._name - - @property - def name(self) -> str: - return self._name - - def format_for_error(self) -> str: - return str(self) - - def get_candidate_lookup(self) -> CandidateLookup: - return None, None - - def is_satisfied_by(self, candidate: Candidate) -> bool: - return False diff --git a/spaces/BisratWorku/Bear_classifier/README.md b/spaces/BisratWorku/Bear_classifier/README.md deleted file mode 100644 index 62731afeb42e23f003041a63ebab36a728a7f837..0000000000000000000000000000000000000000 --- a/spaces/BisratWorku/Bear_classifier/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Bear Classifier -emoji: 📊 -colorFrom: green -colorTo: yellow -sdk: gradio -sdk_version: 3.29.0 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/BlueRey/MendoBERT_QA/app.py b/spaces/BlueRey/MendoBERT_QA/app.py deleted file mode 100644 index 7bf388798c37be98dcb386c59c2553c2624fc386..0000000000000000000000000000000000000000 --- a/spaces/BlueRey/MendoBERT_QA/app.py +++ /dev/null @@ -1,40 +0,0 @@ -import streamlit as st -from transformers import pipeline - -model = pipeline("question-answering", model="/home/user/app/MendoBERT/", tokenizer="indolem/indobert-base-uncased") -basemodel = pipeline("question-answering", model="/home/user/app/IndoLEM/", tokenizer="indolem/indobert-base-uncased") - -st.title(':blue[MendoBERT] - Question Answering 🤔 💭') - -if 'context' not in st.session_state: - st.session_state['options'] = "" - -if 'question' not in st.session_state: - st.session_state['options'] = "" - -def button1_callback(): - st.session_state['context'] = "Acrokeratosis paraneoplastica (Sindrom Bazex) dengan karsinoma sel skuamosa orofaringeal. Seorang pria kulit putih berusia 65 tahun menunjukkan semua gambaran klinis akrokeratosis paraneoplastica dari Bazex, ditandai dengan eritema keunguan dan penskalaan hidung, heliks aural, jari tangan, dan kaki, dengan keratoderma dan distrofi kuku yang parah. Pemeriksaan pasien untuk kemungkinan keganasan terkait mengungkapkan karsinoma sel skuamosa asimtomatik di daerah orofaringeal. Lesi kulit sembuh hampir seluruhnya setelah terapi radiasi neoplasma, tetapi onikodistrofi tetap ada. Laporan kasus ini menggambarkan pentingnya pengenalan dini sindrom Bazex." - st.session_state['question'] = "Nama sinonim dari Acrokeratosis paraneoplastica." - -def button2_callback(): - st.session_state['context'] = "Hingga saat ini, jumlah faktor genetik molekuler yang secara tegas terkait dengan tumor hipofisis dapat dihitung dengan jari: (1) aktivasi GNAS1 pada akromegali; (2) mutasi MENIN dan p27Kip1 (CDKN1B) yang terkait dengan neoplasia endokrin multipel tipe 1; (3) mutasi PRKA1RA dengan hilangnya 17q22-24 di kompleks Carney, dan (4) mutasi gen reseptor hidrokarbon aril yang berinteraksi protein pada 15% adenoma hipofisis terisolasi familial dan 50% akromegali terisolasi familial" - st.session_state['question'] = "Mutasi gen mana yang terlibat dalam adenoma hipofisis terisolasi familial?" - -context_placeholder = st.empty() -with context_placeholder: - context = st.text_area('Enter context: ', key = 'context') - -question_placeholder = st.empty() -with question_placeholder: - question = st.text_area('Enter question: ', key = 'question') - -st.caption('_Examples_') -st.button('Context: \n\n Acrokeratosis paraneoplastica (Sindrom Bazex) dengan karsinoma sel skuamosa orofaringeal. Seorang pria kulit putih berusia 65 tahun menunjukkan semua gambaran klinis akrokeratosis paraneoplastica dari Bazex, ditandai dengan eritema keunguan dan penskalaan hidung, heliks aural, jari tangan, dan kaki, dengan keratoderma dan distrofi kuku yang parah. Pemeriksaan pasien untuk kemungkinan keganasan terkait mengungkapkan karsinoma sel skuamosa asimtomatik di daerah orofaringeal. Lesi kulit sembuh hampir seluruhnya setelah terapi radiasi neoplasma, tetapi onikodistrofi tetap ada. Laporan kasus ini menggambarkan pentingnya pengenalan dini sindrom Bazex. \n\n\n Question: \n\n Nama sinonim dari Acrokeratosis paraneoplastica. \n\n\n Expected Answer: \n\n Sindrom Bazex', use_container_width=True, on_click = button1_callback) -st.button('Context: \n\n Hingga saat ini, jumlah faktor genetik molekuler yang secara tegas terkait dengan tumor hipofisis dapat dihitung dengan jari: (1) aktivasi GNAS1 pada akromegali; (2) mutasi MENIN dan p27Kip1 (CDKN1B) yang terkait dengan neoplasia endokrin multipel tipe 1; (3) mutasi PRKA1RA dengan hilangnya 17q22-24 di kompleks Carney, dan (4) mutasi gen reseptor hidrokarbon aril yang berinteraksi protein pada 15% adenoma hipofisis terisolasi familial dan 50% akromegali terisolasi familial \n\n\n Question: \n\n Mutasi gen mana yang terlibat dalam adenoma hipofisis terisolasi familial? \n\n\n Expected Answer: \n\n reseptor hidrokarbon aril yang berinteraksi protein', use_container_width=True, on_click = button2_callback) - -if context and question: - st.subheader('MendoBERT') - st.write(model(context=context, question=question)) - st.write("\n") - st.subheader('IndoLEM') - st.write(basemodel(context=context, question=question)) \ No newline at end of file diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/events.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/events.py deleted file mode 100644 index 6ce6a483239ecb661254ea5c5cfe3b0d34109dbd..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/events.py +++ /dev/null @@ -1,385 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import datetime -import json -import logging -import os -import time -from collections import defaultdict -from contextlib import contextmanager -import torch -from fvcore.common.file_io import PathManager -from fvcore.common.history_buffer import HistoryBuffer - -_CURRENT_STORAGE_STACK = [] - - -def get_event_storage(): - """ - Returns: - The :class:`EventStorage` object that's currently being used. - Throws an error if no :class`EventStorage` is currently enabled. - """ - assert len( - _CURRENT_STORAGE_STACK - ), "get_event_storage() has to be called inside a 'with EventStorage(...)' context!" - return _CURRENT_STORAGE_STACK[-1] - - -class EventWriter: - """ - Base class for writers that obtain events from :class:`EventStorage` and process them. - """ - - def write(self): - raise NotImplementedError - - def close(self): - pass - - -class JSONWriter(EventWriter): - """ - Write scalars to a json file. - - It saves scalars as one json per line (instead of a big json) for easy parsing. - - Examples parsing such a json file: - - .. code-block:: none - - $ cat metrics.json | jq -s '.[0:2]' - [ - { - "data_time": 0.008433341979980469, - "iteration": 20, - "loss": 1.9228371381759644, - "loss_box_reg": 0.050025828182697296, - "loss_classifier": 0.5316952466964722, - "loss_mask": 0.7236229181289673, - "loss_rpn_box": 0.0856662318110466, - "loss_rpn_cls": 0.48198649287223816, - "lr": 0.007173333333333333, - "time": 0.25401854515075684 - }, - { - "data_time": 0.007216215133666992, - "iteration": 40, - "loss": 1.282649278640747, - "loss_box_reg": 0.06222952902317047, - "loss_classifier": 0.30682939291000366, - "loss_mask": 0.6970193982124329, - "loss_rpn_box": 0.038663312792778015, - "loss_rpn_cls": 0.1471673548221588, - "lr": 0.007706666666666667, - "time": 0.2490077018737793 - } - ] - - $ cat metrics.json | jq '.loss_mask' - 0.7126231789588928 - 0.689423680305481 - 0.6776131987571716 - ... - - """ - - def __init__(self, json_file, window_size=20): - """ - Args: - json_file (str): path to the json file. New data will be appended if the file exists. - window_size (int): the window size of median smoothing for the scalars whose - `smoothing_hint` are True. - """ - self._file_handle = PathManager.open(json_file, "a") - self._window_size = window_size - - def write(self): - storage = get_event_storage() - to_save = {"iteration": storage.iter} - to_save.update(storage.latest_with_smoothing_hint(self._window_size)) - self._file_handle.write(json.dumps(to_save, sort_keys=True) + "\n") - self._file_handle.flush() - try: - os.fsync(self._file_handle.fileno()) - except AttributeError: - pass - - def close(self): - self._file_handle.close() - - -class TensorboardXWriter(EventWriter): - """ - Write all scalars to a tensorboard file. - """ - - def __init__(self, log_dir: str, window_size: int = 20, **kwargs): - """ - Args: - log_dir (str): the directory to save the output events - window_size (int): the scalars will be median-smoothed by this window size - - kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)` - """ - self._window_size = window_size - from torch.utils.tensorboard import SummaryWriter - - self._writer = SummaryWriter(log_dir, **kwargs) - - def write(self): - storage = get_event_storage() - for k, v in storage.latest_with_smoothing_hint(self._window_size).items(): - self._writer.add_scalar(k, v, storage.iter) - - if len(storage.vis_data) >= 1: - for img_name, img, step_num in storage.vis_data: - self._writer.add_image(img_name, img, step_num) - storage.clear_images() - - def close(self): - if hasattr(self, "_writer"): # doesn't exist when the code fails at import - self._writer.close() - - -class CommonMetricPrinter(EventWriter): - """ - Print **common** metrics to the terminal, including - iteration time, ETA, memory, all losses, and the learning rate. - - To print something different, please implement a similar printer by yourself. - """ - - def __init__(self, max_iter): - """ - Args: - max_iter (int): the maximum number of iterations to train. - Used to compute ETA. - """ - self.logger = logging.getLogger(__name__) - self._max_iter = max_iter - self._last_write = None - - def write(self): - storage = get_event_storage() - iteration = storage.iter - - try: - data_time = storage.history("data_time").avg(20) - except KeyError: - # they may not exist in the first few iterations (due to warmup) - # or when SimpleTrainer is not used - data_time = None - - eta_string = "N/A" - try: - iter_time = storage.history("time").global_avg() - eta_seconds = storage.history("time").median(1000) * (self._max_iter - iteration) - storage.put_scalar("eta_seconds", eta_seconds, smoothing_hint=False) - eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) - except KeyError: - iter_time = None - # estimate eta on our own - more noisy - if self._last_write is not None: - estimate_iter_time = (time.perf_counter() - self._last_write[1]) / ( - iteration - self._last_write[0] - ) - eta_seconds = estimate_iter_time * (self._max_iter - iteration) - eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) - self._last_write = (iteration, time.perf_counter()) - - try: - lr = "{:.6f}".format(storage.history("lr").latest()) - except KeyError: - lr = "N/A" - - if torch.cuda.is_available(): - max_mem_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0 - else: - max_mem_mb = None - - # NOTE: max_mem is parsed by grep in "dev/parse_results.sh" - self.logger.info( - " eta: {eta} iter: {iter} {losses} {time}{data_time}lr: {lr} {memory}".format( - eta=eta_string, - iter=iteration, - losses=" ".join( - [ - "{}: {:.3f}".format(k, v.median(20)) - for k, v in storage.histories().items() - if "loss" in k - ] - ), - time="time: {:.4f} ".format(iter_time) if iter_time is not None else "", - data_time="data_time: {:.4f} ".format(data_time) if data_time is not None else "", - lr=lr, - memory="max_mem: {:.0f}M".format(max_mem_mb) if max_mem_mb is not None else "", - ) - ) - - -class EventStorage: - """ - The user-facing class that provides metric storage functionalities. - - In the future we may add support for storing / logging other types of data if needed. - """ - - def __init__(self, start_iter=0): - """ - Args: - start_iter (int): the iteration number to start with - """ - self._history = defaultdict(HistoryBuffer) - self._smoothing_hints = {} - self._latest_scalars = {} - self._iter = start_iter - self._current_prefix = "" - self._vis_data = [] - - def put_image(self, img_name, img_tensor): - """ - Add an `img_tensor` to the `_vis_data` associated with `img_name`. - - Args: - img_name (str): The name of the image to put into tensorboard. - img_tensor (torch.Tensor or numpy.array): An `uint8` or `float` - Tensor of shape `[channel, height, width]` where `channel` is - 3. The image format should be RGB. The elements in img_tensor - can either have values in [0, 1] (float32) or [0, 255] (uint8). - The `img_tensor` will be visualized in tensorboard. - """ - self._vis_data.append((img_name, img_tensor, self._iter)) - - def clear_images(self): - """ - Delete all the stored images for visualization. This should be called - after images are written to tensorboard. - """ - self._vis_data = [] - - def put_scalar(self, name, value, smoothing_hint=True): - """ - Add a scalar `value` to the `HistoryBuffer` associated with `name`. - - Args: - smoothing_hint (bool): a 'hint' on whether this scalar is noisy and should be - smoothed when logged. The hint will be accessible through - :meth:`EventStorage.smoothing_hints`. A writer may ignore the hint - and apply custom smoothing rule. - - It defaults to True because most scalars we save need to be smoothed to - provide any useful signal. - """ - name = self._current_prefix + name - history = self._history[name] - value = float(value) - history.update(value, self._iter) - self._latest_scalars[name] = value - - existing_hint = self._smoothing_hints.get(name) - if existing_hint is not None: - assert ( - existing_hint == smoothing_hint - ), "Scalar {} was put with a different smoothing_hint!".format(name) - else: - self._smoothing_hints[name] = smoothing_hint - - def put_scalars(self, *, smoothing_hint=True, **kwargs): - """ - Put multiple scalars from keyword arguments. - - Examples: - - storage.put_scalars(loss=my_loss, accuracy=my_accuracy, smoothing_hint=True) - """ - for k, v in kwargs.items(): - self.put_scalar(k, v, smoothing_hint=smoothing_hint) - - def history(self, name): - """ - Returns: - HistoryBuffer: the scalar history for name - """ - ret = self._history.get(name, None) - if ret is None: - raise KeyError("No history metric available for {}!".format(name)) - return ret - - def histories(self): - """ - Returns: - dict[name -> HistoryBuffer]: the HistoryBuffer for all scalars - """ - return self._history - - def latest(self): - """ - Returns: - dict[name -> number]: the scalars that's added in the current iteration. - """ - return self._latest_scalars - - def latest_with_smoothing_hint(self, window_size=20): - """ - Similar to :meth:`latest`, but the returned values - are either the un-smoothed original latest value, - or a median of the given window_size, - depend on whether the smoothing_hint is True. - - This provides a default behavior that other writers can use. - """ - result = {} - for k, v in self._latest_scalars.items(): - result[k] = self._history[k].median(window_size) if self._smoothing_hints[k] else v - return result - - def smoothing_hints(self): - """ - Returns: - dict[name -> bool]: the user-provided hint on whether the scalar - is noisy and needs smoothing. - """ - return self._smoothing_hints - - def step(self): - """ - User should call this function at the beginning of each iteration, to - notify the storage of the start of a new iteration. - The storage will then be able to associate the new data with the - correct iteration number. - """ - self._iter += 1 - self._latest_scalars = {} - - @property - def vis_data(self): - return self._vis_data - - @property - def iter(self): - return self._iter - - @property - def iteration(self): - # for backward compatibility - return self._iter - - def __enter__(self): - _CURRENT_STORAGE_STACK.append(self) - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - assert _CURRENT_STORAGE_STACK[-1] == self - _CURRENT_STORAGE_STACK.pop() - - @contextmanager - def name_scope(self, name): - """ - Yields: - A context within which all the events added to this storage - will be prefixed by the name scope. - """ - old_prefix = self._current_prefix - self._current_prefix = name.rstrip("/") + "/" - yield - self._current_prefix = old_prefix diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/memory_resource.h b/spaces/CVPR/LIVE/thrust/thrust/system/cpp/memory_resource.h deleted file mode 100644 index e89fd25fdecc1d0362e5c66c5866fc5eaa78d76c..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/memory_resource.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright 2018 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/*! \file cpp/memory_resource.h - * \brief Memory resources for the CPP system. - */ - -#pragma once - -#include -#include -#include - -#include - -namespace thrust -{ -namespace system -{ -namespace cpp -{ - -//! \cond -namespace detail -{ - typedef thrust::mr::fancy_pointer_resource< - thrust::mr::new_delete_resource, - thrust::cpp::pointer - > native_resource; -} -//! \endcond - -/*! \addtogroup memory_resources Memory Resources - * \ingroup memory_management_classes - */ - -/*! The memory resource for the CPP system. Uses \p mr::new_delete_resource and tags it with \p cpp::pointer. */ -typedef detail::native_resource memory_resource; -/*! An alias for \p cpp::memory_resource. */ -typedef detail::native_resource universal_memory_resource; -/*! An alias for \p cpp::memory_resource. */ -typedef detail::native_resource universal_host_pinned_memory_resource; - -/*! \} - */ - -} -} -} diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/async/sort.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/async/sort.h deleted file mode 100644 index c3a83ad404f8943d52dbeeca9a183997d8dff7c3..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/async/sort.h +++ /dev/null @@ -1,34 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// The purpose of this header is to #include the async/sort.h header of the -// sequential, host, and device systems. It should be #included in any code -// which uses ADL to dispatch async sort. - -#pragma once - -#include - -//#include - -//#define __THRUST_HOST_SYSTEM_ASYNC_SORT_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/async/sort.h> -//#include __THRUST_HOST_SYSTEM_ASYNC_SORT_HEADER -//#undef __THRUST_HOST_SYSTEM_ASYNC_SORT_HEADER - -#define __THRUST_DEVICE_SYSTEM_ASYNC_SORT_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/async/sort.h> -#include __THRUST_DEVICE_SYSTEM_ASYNC_SORT_HEADER -#undef __THRUST_DEVICE_SYSTEM_ASYNC_SORT_HEADER - diff --git a/spaces/CVPR/LIVE/thrust/thrust/type_traits/remove_cvref.h b/spaces/CVPR/LIVE/thrust/thrust/type_traits/remove_cvref.h deleted file mode 100644 index 4079bfe8e81df29bae686f1d4b8d30b598648aaa..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/type_traits/remove_cvref.h +++ /dev/null @@ -1,48 +0,0 @@ -/* - * Copyright 2018 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include -#include - -namespace thrust -{ - -#if THRUST_CPP_DIALECT >= 2020 - -using std::remove_cvref; -using std::remove_cvref_t; - -#else // Older than C++20. - -template -struct remove_cvref -{ - typedef typename detail::remove_cv< - typename detail::remove_reference::type - >::type type; -}; - -#if THRUST_CPP_DIALECT >= 2011 -template -using remove_cvref_t = typename remove_cvref::type; -#endif - -#endif // THRUST_CPP_DIALECT >= 2020 - -} // end namespace thrust - diff --git a/spaces/CikeyQI/Yunzai/Yunzai/lib/plugins/handler.js b/spaces/CikeyQI/Yunzai/Yunzai/lib/plugins/handler.js deleted file mode 100644 index a96ebade0a82b9018f94bd0d5060c3304e490657..0000000000000000000000000000000000000000 --- a/spaces/CikeyQI/Yunzai/Yunzai/lib/plugins/handler.js +++ /dev/null @@ -1,73 +0,0 @@ -import util from 'node:util' -import lodash from 'lodash' - -let events = {} -let Handler = { - add (cfg) { - let { ns, fn, self, property = 50 } = cfg - let key = cfg.key || cfg.event - if (!key || !fn) { - return - } - Handler.del(ns, key) - logger.mark(`[Handler][Reg]: [${ns}][${key}]`) - events[key] = events[key] || [] - events[key].push({ - property, - fn, - ns, - self, - key - }) - events[key] = lodash.orderBy(events[key], ['priority'], ['asc']) - }, - del (ns, key = '') { - if (!key) { - for (let key in events) { - Handler.del(ns, key) - } - return - } - if (!events[key]) { - return - } - for (let idx = 0; idx < events[key].length; idx++) { - let handler = events[key][idx] - if (handler.ns === ns) { - events[key].splice(idx, 1) - events[key] = lodash.orderBy(events[key], ['priority'], ['asc']) - } - } - }, - async callAll (key, e, args) { - // 暂时屏蔽调用 - // return Handler.call(key, e, args, true) - }, - async call (key, e, args, allHandler = false) { - let ret - for (let obj of events[key]) { - let fn = obj.fn - let done = true - let reject = (msg = '') => { - if (msg) { - logger.mark(`[Handler][Reject]: [${obj.ns}][${key}] ${msg}`) - } - done = false - } - ret = fn.call(obj.self, e, args, reject) - if (util.types.isPromise(ret)) { - ret = await ret - } - if (done && !allHandler) { - logger.mark(`[Handler][Done]: [${obj.ns}][${key}]`) - return ret - } - } - return ret - }, - has (key) { - return !!events[key] - } -} -export default Handler - diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/attr/_version_info.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/attr/_version_info.py deleted file mode 100644 index 51a1312f9759f21063caea779a62882d7f7c86ae..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/attr/_version_info.py +++ /dev/null @@ -1,86 +0,0 @@ -# SPDX-License-Identifier: MIT - - -from functools import total_ordering - -from ._funcs import astuple -from ._make import attrib, attrs - - -@total_ordering -@attrs(eq=False, order=False, slots=True, frozen=True) -class VersionInfo: - """ - A version object that can be compared to tuple of length 1--4: - - >>> attr.VersionInfo(19, 1, 0, "final") <= (19, 2) - True - >>> attr.VersionInfo(19, 1, 0, "final") < (19, 1, 1) - True - >>> vi = attr.VersionInfo(19, 2, 0, "final") - >>> vi < (19, 1, 1) - False - >>> vi < (19,) - False - >>> vi == (19, 2,) - True - >>> vi == (19, 2, 1) - False - - .. versionadded:: 19.2 - """ - - year = attrib(type=int) - minor = attrib(type=int) - micro = attrib(type=int) - releaselevel = attrib(type=str) - - @classmethod - def _from_version_string(cls, s): - """ - Parse *s* and return a _VersionInfo. - """ - v = s.split(".") - if len(v) == 3: - v.append("final") - - return cls( - year=int(v[0]), minor=int(v[1]), micro=int(v[2]), releaselevel=v[3] - ) - - def _ensure_tuple(self, other): - """ - Ensure *other* is a tuple of a valid length. - - Returns a possibly transformed *other* and ourselves as a tuple of - the same length as *other*. - """ - - if self.__class__ is other.__class__: - other = astuple(other) - - if not isinstance(other, tuple): - raise NotImplementedError - - if not (1 <= len(other) <= 4): - raise NotImplementedError - - return astuple(self)[: len(other)], other - - def __eq__(self, other): - try: - us, them = self._ensure_tuple(other) - except NotImplementedError: - return NotImplemented - - return us == them - - def __lt__(self, other): - try: - us, them = self._ensure_tuple(other) - except NotImplementedError: - return NotImplemented - - # Since alphabetically "dev0" < "final" < "post1" < "post2", we don't - # have to do anything special with releaselevel for now. - return us < them diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dotenv/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dotenv/__init__.py deleted file mode 100644 index 7f4c631ba11786bceebd22591f91bd378d8b232c..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dotenv/__init__.py +++ /dev/null @@ -1,49 +0,0 @@ -from typing import Any, Optional - -from .main import (dotenv_values, find_dotenv, get_key, load_dotenv, set_key, - unset_key) - - -def load_ipython_extension(ipython: Any) -> None: - from .ipython import load_ipython_extension - load_ipython_extension(ipython) - - -def get_cli_string( - path: Optional[str] = None, - action: Optional[str] = None, - key: Optional[str] = None, - value: Optional[str] = None, - quote: Optional[str] = None, -): - """Returns a string suitable for running as a shell script. - - Useful for converting a arguments passed to a fabric task - to be passed to a `local` or `run` command. - """ - command = ['dotenv'] - if quote: - command.append(f'-q {quote}') - if path: - command.append(f'-f {path}') - if action: - command.append(action) - if key: - command.append(key) - if value: - if ' ' in value: - command.append(f'"{value}"') - else: - command.append(value) - - return ' '.join(command).strip() - - -__all__ = ['get_cli_string', - 'load_dotenv', - 'dotenv_values', - 'get_key', - 'set_key', - 'unset_key', - 'find_dotenv', - 'load_ipython_extension'] diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-75764f1c.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-75764f1c.js deleted file mode 100644 index 7591352fe96b824c5995019fc1c3e313d05a6206..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-75764f1c.js +++ /dev/null @@ -1,2 +0,0 @@ -import{ae as s}from"./index-3370be2a.js";const o=["static"];export{s as Component,o as modes}; -//# sourceMappingURL=index-75764f1c.js.map diff --git a/spaces/DamarJati/DamarJati-NSFW-filter-DecentScan/app.py b/spaces/DamarJati/DamarJati-NSFW-filter-DecentScan/app.py deleted file mode 100644 index 5bcea22d01f529d57faea551d95d7d693693ec6f..0000000000000000000000000000000000000000 --- a/spaces/DamarJati/DamarJati-NSFW-filter-DecentScan/app.py +++ /dev/null @@ -1,11 +0,0 @@ -import gradio as gr -from transformers import pipeline -import os - -pipe = pipeline(task="image-classification", - model="DamarJati/NSFW-Filterization-DecentScan" -) -gr.Interface.from_pipeline(pipe, - title="Image Classification", - description="NSFW-filter-DecentScan", - ).launch() \ No newline at end of file diff --git a/spaces/Datasculptor/StyleGAN-NADA/e4e/models/__init__.py b/spaces/Datasculptor/StyleGAN-NADA/e4e/models/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Datasculptor/car-data/app.py b/spaces/Datasculptor/car-data/app.py deleted file mode 100644 index b3c9cbc536b556bf2420fbe5f45df2093b2e419c..0000000000000000000000000000000000000000 --- a/spaces/Datasculptor/car-data/app.py +++ /dev/null @@ -1,73 +0,0 @@ -import io - -import gradio as gr -import requests -import torch -import torch.nn.functional as F -import torchvision.transforms as transforms -from PIL import Image - -from constants import MAKES_MODELS, PRICE_BIN_LABELS, YEARS - -print("downloading checkpoint...") -data = requests.get( - "https://data.aqnichol.com/car-data/models/mobilenetv2_432000_calib_torchscript.pt", - stream=True, -).content - -print("creating model...") -model = torch.jit.load(io.BytesIO(data)) -model.eval() -transform = transforms.Compose( - [ - transforms.ToTensor(), - transforms.Normalize( - (0.48145466, 0.4578275, 0.40821073), - (0.26862954, 0.26130258, 0.27577711), - ), - ] -) - -print("done.") - - -def classify(img: Image.Image): - in_tensor = transform(img)[None] - outputs = model(in_tensor) - - price_bins = dict( - zip(PRICE_BIN_LABELS, F.softmax(outputs["price_bin"], dim=-1)[0].tolist()) - ) - years = dict( - zip( - [str(year) for year in YEARS] + ["Unknown"], - F.softmax(outputs["year"], dim=-1)[0].tolist(), - ) - ) - make_models = dict( - zip( - ([f"{make} {model}" for make, model in MAKES_MODELS] + ["Unknown"]), - F.softmax(outputs["make_model"], dim=-1)[0].tolist(), - ) - ) - return ( - f"${int(round(outputs['price_median'].item()))}", - price_bins, - years, - make_models, - img, - ) - - -iface = gr.Interface( - fn=classify, - inputs=gr.Image(shape=(224, 224), type="pil"), - outputs=[ - gr.Text(label="Price Prediction"), - gr.Label(label="Price Bin", num_top_classes=5), - gr.Label(label="Year", num_top_classes=5), - gr.Label(label="Make/Model", num_top_classes=10), - gr.Image(label="Cropped Input"), - ], -) -iface.queue(concurrency_count=2).launch() diff --git a/spaces/Duskfallcrew/darkstorm2150-Protogen_x5.8_Official_Release/app.py b/spaces/Duskfallcrew/darkstorm2150-Protogen_x5.8_Official_Release/app.py deleted file mode 100644 index 10e6c7418320c5b1fd9a99b78f207170b2443f82..0000000000000000000000000000000000000000 --- a/spaces/Duskfallcrew/darkstorm2150-Protogen_x5.8_Official_Release/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/darkstorm2150/Protogen_x5.8_Official_Release").launch() \ No newline at end of file diff --git a/spaces/ECCV2022/bytetrack/tutorials/centertrack/opts.py b/spaces/ECCV2022/bytetrack/tutorials/centertrack/opts.py deleted file mode 100644 index 5d54fe39ff696933e1391c531868f8b73865b690..0000000000000000000000000000000000000000 --- a/spaces/ECCV2022/bytetrack/tutorials/centertrack/opts.py +++ /dev/null @@ -1,406 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import argparse -import os -import sys - -class opts(object): - def __init__(self): - self.parser = argparse.ArgumentParser() - # basic experiment setting - self.parser.add_argument('task', default='', - help='ctdet | ddd | multi_pose ' - '| tracking or combined with ,') - self.parser.add_argument('--dataset', default='coco', - help='see lib/dataset/dataset_facotry for ' + - 'available datasets') - self.parser.add_argument('--test_dataset', default='', - help='coco | kitti | coco_hp | pascal') - self.parser.add_argument('--exp_id', default='default') - self.parser.add_argument('--test', action='store_true') - self.parser.add_argument('--debug', type=int, default=0, - help='level of visualization.' - '1: only show the final detection results' - '2: show the network output features' - '3: use matplot to display' # useful when lunching training with ipython notebook - '4: save all visualizations to disk') - self.parser.add_argument('--no_pause', action='store_true') - self.parser.add_argument('--demo', default='', - help='path to image/ image folders/ video. ' - 'or "webcam"') - self.parser.add_argument('--load_model', default='', - help='path to pretrained model') - self.parser.add_argument('--resume', action='store_true', - help='resume an experiment. ' - 'Reloaded the optimizer parameter and ' - 'set load_model to model_last.pth ' - 'in the exp dir if load_model is empty.') - - # system - self.parser.add_argument('--gpus', default='0', - help='-1 for CPU, use comma for multiple gpus') - self.parser.add_argument('--num_workers', type=int, default=4, - help='dataloader threads. 0 for single-thread.') - self.parser.add_argument('--not_cuda_benchmark', action='store_true', - help='disable when the input size is not fixed.') - self.parser.add_argument('--seed', type=int, default=317, - help='random seed') # from CornerNet - self.parser.add_argument('--not_set_cuda_env', action='store_true', - help='used when training in slurm clusters.') - - # log - self.parser.add_argument('--print_iter', type=int, default=0, - help='disable progress bar and print to screen.') - self.parser.add_argument('--save_all', action='store_true', - help='save model to disk every 5 epochs.') - self.parser.add_argument('--vis_thresh', type=float, default=0.3, - help='visualization threshold.') - self.parser.add_argument('--debugger_theme', default='white', - choices=['white', 'black']) - self.parser.add_argument('--eval_val', action='store_true') - self.parser.add_argument('--save_imgs', default='', help='') - self.parser.add_argument('--save_img_suffix', default='', help='') - self.parser.add_argument('--skip_first', type=int, default=-1, help='') - self.parser.add_argument('--save_video', action='store_true') - self.parser.add_argument('--save_framerate', type=int, default=30) - self.parser.add_argument('--resize_video', action='store_true') - self.parser.add_argument('--video_h', type=int, default=512, help='') - self.parser.add_argument('--video_w', type=int, default=512, help='') - self.parser.add_argument('--transpose_video', action='store_true') - self.parser.add_argument('--show_track_color', action='store_true') - self.parser.add_argument('--not_show_bbox', action='store_true') - self.parser.add_argument('--not_show_number', action='store_true') - self.parser.add_argument('--not_show_txt', action='store_true') - self.parser.add_argument('--qualitative', action='store_true') - self.parser.add_argument('--tango_color', action='store_true') - self.parser.add_argument('--only_show_dots', action='store_true') - self.parser.add_argument('--show_trace', action='store_true') - - # model - self.parser.add_argument('--arch', default='dla_34', - help='model architecture. Currently tested' - 'res_18 | res_101 | resdcn_18 | resdcn_101 |' - 'dlav0_34 | dla_34 | hourglass') - self.parser.add_argument('--dla_node', default='dcn') - self.parser.add_argument('--head_conv', type=int, default=-1, - help='conv layer channels for output head' - '0 for no conv layer' - '-1 for default setting: ' - '64 for resnets and 256 for dla.') - self.parser.add_argument('--num_head_conv', type=int, default=1) - self.parser.add_argument('--head_kernel', type=int, default=3, help='') - self.parser.add_argument('--down_ratio', type=int, default=4, - help='output stride. Currently only supports 4.') - self.parser.add_argument('--not_idaup', action='store_true') - self.parser.add_argument('--num_classes', type=int, default=-1) - self.parser.add_argument('--num_layers', type=int, default=101) - self.parser.add_argument('--backbone', default='dla34') - self.parser.add_argument('--neck', default='dlaup') - self.parser.add_argument('--msra_outchannel', type=int, default=256) - self.parser.add_argument('--efficient_level', type=int, default=0) - self.parser.add_argument('--prior_bias', type=float, default=-4.6) # -2.19 - - # input - self.parser.add_argument('--input_res', type=int, default=-1, - help='input height and width. -1 for default from ' - 'dataset. Will be overriden by input_h | input_w') - self.parser.add_argument('--input_h', type=int, default=-1, - help='input height. -1 for default from dataset.') - self.parser.add_argument('--input_w', type=int, default=-1, - help='input width. -1 for default from dataset.') - self.parser.add_argument('--dataset_version', default='') - - # train - self.parser.add_argument('--optim', default='adam') - self.parser.add_argument('--lr', type=float, default=1.25e-4, - help='learning rate for batch size 32.') - self.parser.add_argument('--lr_step', type=str, default='60', - help='drop learning rate by 10.') - self.parser.add_argument('--save_point', type=str, default='90', - help='when to save the model to disk.') - self.parser.add_argument('--num_epochs', type=int, default=70, - help='total training epochs.') - self.parser.add_argument('--batch_size', type=int, default=32, - help='batch size') - self.parser.add_argument('--master_batch_size', type=int, default=-1, - help='batch size on the master gpu.') - self.parser.add_argument('--num_iters', type=int, default=-1, - help='default: #samples / batch_size.') - self.parser.add_argument('--val_intervals', type=int, default=10000, - help='number of epochs to run validation.') - self.parser.add_argument('--trainval', action='store_true', - help='include validation in training and ' - 'test on test set') - self.parser.add_argument('--ltrb', action='store_true', - help='') - self.parser.add_argument('--ltrb_weight', type=float, default=0.1, - help='') - self.parser.add_argument('--reset_hm', action='store_true') - self.parser.add_argument('--reuse_hm', action='store_true') - self.parser.add_argument('--use_kpt_center', action='store_true') - self.parser.add_argument('--add_05', action='store_true') - self.parser.add_argument('--dense_reg', type=int, default=1, help='') - - # test - self.parser.add_argument('--flip_test', action='store_true', - help='flip data augmentation.') - self.parser.add_argument('--test_scales', type=str, default='1', - help='multi scale test augmentation.') - self.parser.add_argument('--nms', action='store_true', - help='run nms in testing.') - self.parser.add_argument('--K', type=int, default=100, - help='max number of output objects.') - self.parser.add_argument('--not_prefetch_test', action='store_true', - help='not use parallal data pre-processing.') - self.parser.add_argument('--fix_short', type=int, default=-1) - self.parser.add_argument('--keep_res', action='store_true', - help='keep the original resolution' - ' during validation.') - self.parser.add_argument('--map_argoverse_id', action='store_true', - help='if trained on nuscenes and eval on kitti') - self.parser.add_argument('--out_thresh', type=float, default=-1, - help='') - self.parser.add_argument('--depth_scale', type=float, default=1, - help='') - self.parser.add_argument('--save_results', action='store_true') - self.parser.add_argument('--load_results', default='') - self.parser.add_argument('--use_loaded_results', action='store_true') - self.parser.add_argument('--ignore_loaded_cats', default='') - self.parser.add_argument('--model_output_list', action='store_true', - help='Used when convert to onnx') - self.parser.add_argument('--non_block_test', action='store_true') - self.parser.add_argument('--vis_gt_bev', default='', help='') - self.parser.add_argument('--kitti_split', default='3dop', - help='different validation split for kitti: ' - '3dop | subcnn') - self.parser.add_argument('--test_focal_length', type=int, default=-1) - - # dataset - self.parser.add_argument('--not_rand_crop', action='store_true', - help='not use the random crop data augmentation' - 'from CornerNet.') - self.parser.add_argument('--not_max_crop', action='store_true', - help='used when the training dataset has' - 'inbalanced aspect ratios.') - self.parser.add_argument('--shift', type=float, default=0, - help='when not using random crop, 0.1' - 'apply shift augmentation.') - self.parser.add_argument('--scale', type=float, default=0, - help='when not using random crop, 0.4' - 'apply scale augmentation.') - self.parser.add_argument('--aug_rot', type=float, default=0, - help='probability of applying ' - 'rotation augmentation.') - self.parser.add_argument('--rotate', type=float, default=0, - help='when not using random crop' - 'apply rotation augmentation.') - self.parser.add_argument('--flip', type=float, default=0.5, - help='probability of applying flip augmentation.') - self.parser.add_argument('--no_color_aug', action='store_true', - help='not use the color augmenation ' - 'from CornerNet') - - # Tracking - self.parser.add_argument('--tracking', action='store_true') - self.parser.add_argument('--pre_hm', action='store_true') - self.parser.add_argument('--same_aug_pre', action='store_true') - self.parser.add_argument('--zero_pre_hm', action='store_true') - self.parser.add_argument('--hm_disturb', type=float, default=0) - self.parser.add_argument('--lost_disturb', type=float, default=0) - self.parser.add_argument('--fp_disturb', type=float, default=0) - self.parser.add_argument('--pre_thresh', type=float, default=-1) - self.parser.add_argument('--track_thresh', type=float, default=0.3) - self.parser.add_argument('--match_thresh', type=float, default=0.8) - self.parser.add_argument('--track_buffer', type=int, default=30) - self.parser.add_argument('--new_thresh', type=float, default=0.3) - self.parser.add_argument('--max_frame_dist', type=int, default=3) - self.parser.add_argument('--ltrb_amodal', action='store_true') - self.parser.add_argument('--ltrb_amodal_weight', type=float, default=0.1) - self.parser.add_argument('--public_det', action='store_true') - self.parser.add_argument('--no_pre_img', action='store_true') - self.parser.add_argument('--zero_tracking', action='store_true') - self.parser.add_argument('--hungarian', action='store_true') - self.parser.add_argument('--max_age', type=int, default=-1) - - - # loss - self.parser.add_argument('--tracking_weight', type=float, default=1) - self.parser.add_argument('--reg_loss', default='l1', - help='regression loss: sl1 | l1 | l2') - self.parser.add_argument('--hm_weight', type=float, default=1, - help='loss weight for keypoint heatmaps.') - self.parser.add_argument('--off_weight', type=float, default=1, - help='loss weight for keypoint local offsets.') - self.parser.add_argument('--wh_weight', type=float, default=0.1, - help='loss weight for bounding box size.') - self.parser.add_argument('--hp_weight', type=float, default=1, - help='loss weight for human pose offset.') - self.parser.add_argument('--hm_hp_weight', type=float, default=1, - help='loss weight for human keypoint heatmap.') - self.parser.add_argument('--amodel_offset_weight', type=float, default=1, - help='Please forgive the typo.') - self.parser.add_argument('--dep_weight', type=float, default=1, - help='loss weight for depth.') - self.parser.add_argument('--dim_weight', type=float, default=1, - help='loss weight for 3d bounding box size.') - self.parser.add_argument('--rot_weight', type=float, default=1, - help='loss weight for orientation.') - self.parser.add_argument('--nuscenes_att', action='store_true') - self.parser.add_argument('--nuscenes_att_weight', type=float, default=1) - self.parser.add_argument('--velocity', action='store_true') - self.parser.add_argument('--velocity_weight', type=float, default=1) - - # custom dataset - self.parser.add_argument('--custom_dataset_img_path', default='') - self.parser.add_argument('--custom_dataset_ann_path', default='') - self.parser.add_argument('--bird_view_world_size', type=int, default=64) - - def parse(self, args=''): - if args == '': - opt = self.parser.parse_args() - else: - opt = self.parser.parse_args(args) - - if opt.test_dataset == '': - opt.test_dataset = opt.dataset - - opt.gpus_str = opt.gpus - opt.gpus = [int(gpu) for gpu in opt.gpus.split(',')] - opt.gpus = [i for i in range(len(opt.gpus))] if opt.gpus[0] >=0 else [-1] - opt.lr_step = [int(i) for i in opt.lr_step.split(',')] - opt.save_point = [int(i) for i in opt.save_point.split(',')] - opt.test_scales = [float(i) for i in opt.test_scales.split(',')] - opt.save_imgs = [i for i in opt.save_imgs.split(',')] \ - if opt.save_imgs != '' else [] - opt.ignore_loaded_cats = \ - [int(i) for i in opt.ignore_loaded_cats.split(',')] \ - if opt.ignore_loaded_cats != '' else [] - - opt.num_workers = max(opt.num_workers, 2 * len(opt.gpus)) - opt.pre_img = False - if 'tracking' in opt.task: - print('Running tracking') - opt.tracking = True -# opt.out_thresh = max(opt.track_thresh, opt.out_thresh) -# opt.pre_thresh = max(opt.track_thresh, opt.pre_thresh) -# opt.new_thresh = max(opt.track_thresh, opt.new_thresh) - opt.pre_img = not opt.no_pre_img - print('Using tracking threshold for out threshold!', opt.track_thresh) - if 'ddd' in opt.task: - opt.show_track_color = True - - opt.fix_res = not opt.keep_res - print('Fix size testing.' if opt.fix_res else 'Keep resolution testing.') - - if opt.head_conv == -1: # init default head_conv - opt.head_conv = 256 if 'dla' in opt.arch else 64 - - opt.pad = 127 if 'hourglass' in opt.arch else 31 - opt.num_stacks = 2 if opt.arch == 'hourglass' else 1 - - if opt.master_batch_size == -1: - opt.master_batch_size = opt.batch_size // len(opt.gpus) - rest_batch_size = (opt.batch_size - opt.master_batch_size) - opt.chunk_sizes = [opt.master_batch_size] - for i in range(len(opt.gpus) - 1): - slave_chunk_size = rest_batch_size // (len(opt.gpus) - 1) - if i < rest_batch_size % (len(opt.gpus) - 1): - slave_chunk_size += 1 - opt.chunk_sizes.append(slave_chunk_size) - print('training chunk_sizes:', opt.chunk_sizes) - - if opt.debug > 0: - opt.num_workers = 0 - opt.batch_size = 1 - opt.gpus = [opt.gpus[0]] - opt.master_batch_size = -1 - - # log dirs - opt.root_dir = os.path.join(os.path.dirname(__file__), '..', '..') - opt.data_dir = os.path.join(opt.root_dir, 'data') - opt.exp_dir = os.path.join(opt.root_dir, 'exp', opt.task) - opt.save_dir = os.path.join(opt.exp_dir, opt.exp_id) - opt.debug_dir = os.path.join(opt.save_dir, 'debug') - - if opt.resume and opt.load_model == '': - opt.load_model = os.path.join(opt.save_dir, 'model_last.pth') - return opt - - - def update_dataset_info_and_set_heads(self, opt, dataset): - opt.num_classes = dataset.num_categories \ - if opt.num_classes < 0 else opt.num_classes - # input_h(w): opt.input_h overrides opt.input_res overrides dataset default - input_h, input_w = dataset.default_resolution - input_h = opt.input_res if opt.input_res > 0 else input_h - input_w = opt.input_res if opt.input_res > 0 else input_w - opt.input_h = opt.input_h if opt.input_h > 0 else input_h - opt.input_w = opt.input_w if opt.input_w > 0 else input_w - opt.output_h = opt.input_h // opt.down_ratio - opt.output_w = opt.input_w // opt.down_ratio - opt.input_res = max(opt.input_h, opt.input_w) - opt.output_res = max(opt.output_h, opt.output_w) - - opt.heads = {'hm': opt.num_classes, 'reg': 2, 'wh': 2} - - if 'tracking' in opt.task: - opt.heads.update({'tracking': 2}) - - if 'ddd' in opt.task: - opt.heads.update({'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2}) - - if 'multi_pose' in opt.task: - opt.heads.update({ - 'hps': dataset.num_joints * 2, 'hm_hp': dataset.num_joints, - 'hp_offset': 2}) - - if opt.ltrb: - opt.heads.update({'ltrb': 4}) - if opt.ltrb_amodal: - opt.heads.update({'ltrb_amodal': 4}) - if opt.nuscenes_att: - opt.heads.update({'nuscenes_att': 8}) - if opt.velocity: - opt.heads.update({'velocity': 3}) - - weight_dict = {'hm': opt.hm_weight, 'wh': opt.wh_weight, - 'reg': opt.off_weight, 'hps': opt.hp_weight, - 'hm_hp': opt.hm_hp_weight, 'hp_offset': opt.off_weight, - 'dep': opt.dep_weight, 'rot': opt.rot_weight, - 'dim': opt.dim_weight, - 'amodel_offset': opt.amodel_offset_weight, - 'ltrb': opt.ltrb_weight, - 'tracking': opt.tracking_weight, - 'ltrb_amodal': opt.ltrb_amodal_weight, - 'nuscenes_att': opt.nuscenes_att_weight, - 'velocity': opt.velocity_weight} - opt.weights = {head: weight_dict[head] for head in opt.heads} - for head in opt.weights: - if opt.weights[head] == 0: - del opt.heads[head] - opt.head_conv = {head: [opt.head_conv \ - for i in range(opt.num_head_conv if head != 'reg' else 1)] for head in opt.heads} - - print('input h w:', opt.input_h, opt.input_w) - print('heads', opt.heads) - print('weights', opt.weights) - print('head conv', opt.head_conv) - - return opt - - def init(self, args=''): - # only used in demo - default_dataset_info = { - 'ctdet': 'coco', 'multi_pose': 'coco_hp', 'ddd': 'nuscenes', - 'tracking,ctdet': 'coco', 'tracking,multi_pose': 'coco_hp', - 'tracking,ddd': 'nuscenes' - } - opt = self.parse() - from dataset.dataset_factory import dataset_factory - train_dataset = default_dataset_info[opt.task] \ - if opt.task in default_dataset_info else 'coco' - dataset = dataset_factory[train_dataset] - opt = self.update_dataset_info_and_set_heads(opt, dataset) - return opt diff --git a/spaces/ECCV2022/bytetrack/tutorials/cstrack/tracker.py b/spaces/ECCV2022/bytetrack/tutorials/cstrack/tracker.py deleted file mode 100644 index 67b7b49600993b016cc877abf0ceabd1a7942520..0000000000000000000000000000000000000000 --- a/spaces/ECCV2022/bytetrack/tutorials/cstrack/tracker.py +++ /dev/null @@ -1,542 +0,0 @@ -from collections import deque -import os -import cv2 -import numpy as np -import torch -import torch.nn.functional as F -from torchsummary import summary - -from core.mot.general import non_max_suppression_and_inds, non_max_suppression_jde, non_max_suppression, scale_coords -from core.mot.torch_utils import intersect_dicts -from models.mot.cstrack import Model - -from mot_online import matching -from mot_online.kalman_filter import KalmanFilter -from mot_online.log import logger -from mot_online.utils import * - -from mot_online.basetrack import BaseTrack, TrackState - - -class STrack(BaseTrack): - shared_kalman = KalmanFilter() - def __init__(self, tlwh, score, temp_feat, buffer_size=30): - - # wait activate - self._tlwh = np.asarray(tlwh, dtype=np.float) - self.kalman_filter = None - self.mean, self.covariance = None, None - self.is_activated = False - - self.score = score - self.tracklet_len = 0 - - self.smooth_feat = None - self.update_features(temp_feat) - self.features = deque([], maxlen=buffer_size) - self.alpha = 0.9 - - def update_features(self, feat): - feat /= np.linalg.norm(feat) - self.curr_feat = feat - if self.smooth_feat is None: - self.smooth_feat = feat - else: - self.smooth_feat = self.alpha * self.smooth_feat + (1 - self.alpha) * feat - self.features.append(feat) - self.smooth_feat /= np.linalg.norm(self.smooth_feat) - - def predict(self): - mean_state = self.mean.copy() - if self.state != TrackState.Tracked: - mean_state[7] = 0 - self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance) - - @staticmethod - def multi_predict(stracks): - if len(stracks) > 0: - multi_mean = np.asarray([st.mean.copy() for st in stracks]) - multi_covariance = np.asarray([st.covariance for st in stracks]) - for i, st in enumerate(stracks): - if st.state != TrackState.Tracked: - multi_mean[i][7] = 0 - multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance) - for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)): - stracks[i].mean = mean - stracks[i].covariance = cov - - def activate(self, kalman_filter, frame_id): - """Start a new tracklet""" - self.kalman_filter = kalman_filter - self.track_id = self.next_id() - self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh)) - - self.tracklet_len = 0 - self.state = TrackState.Tracked - #self.is_activated = True - self.frame_id = frame_id - self.start_frame = frame_id - - def re_activate(self, new_track, frame_id, new_id=False): - self.mean, self.covariance = self.kalman_filter.update( - self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh) - ) - - self.update_features(new_track.curr_feat) - self.tracklet_len = 0 - self.state = TrackState.Tracked - self.is_activated = True - self.frame_id = frame_id - if new_id: - self.track_id = self.next_id() - - def update(self, new_track, frame_id, update_feature=True): - """ - Update a matched track - :type new_track: STrack - :type frame_id: int - :type update_feature: bool - :return: - """ - self.frame_id = frame_id - self.tracklet_len += 1 - - new_tlwh = new_track.tlwh - self.mean, self.covariance = self.kalman_filter.update( - self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh)) - self.state = TrackState.Tracked - self.is_activated = True - - self.score = new_track.score - if update_feature: - self.update_features(new_track.curr_feat) - - @property - # @jit(nopython=True) - def tlwh(self): - """Get current position in bounding box format `(top left x, top left y, - width, height)`. - """ - if self.mean is None: - return self._tlwh.copy() - ret = self.mean[:4].copy() - ret[2] *= ret[3] - ret[:2] -= ret[2:] / 2 - return ret - - @property - # @jit(nopython=True) - def tlbr(self): - """Convert bounding box to format `(min x, min y, max x, max y)`, i.e., - `(top left, bottom right)`. - """ - ret = self.tlwh.copy() - ret[2:] += ret[:2] - return ret - - @staticmethod - # @jit(nopython=True) - def tlwh_to_xyah(tlwh): - """Convert bounding box to format `(center x, center y, aspect ratio, - height)`, where the aspect ratio is `width / height`. - """ - ret = np.asarray(tlwh).copy() - ret[:2] += ret[2:] / 2 - ret[2] /= ret[3] - return ret - - def to_xyah(self): - return self.tlwh_to_xyah(self.tlwh) - - @staticmethod - # @jit(nopython=True) - def tlbr_to_tlwh(tlbr): - ret = np.asarray(tlbr).copy() - ret[2:] -= ret[:2] - return ret - - @staticmethod - # @jit(nopython=True) - def tlwh_to_tlbr(tlwh): - ret = np.asarray(tlwh).copy() - ret[2:] += ret[:2] - return ret - - def __repr__(self): - return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame) - - -class JDETracker(object): - def __init__(self, opt, frame_rate=30): - self.opt = opt - if int(opt.gpus[0]) >= 0: - opt.device = torch.device('cuda') - else: - opt.device = torch.device('cpu') - print('Creating model...') - - ckpt = torch.load(opt.weights, map_location=opt.device) # load checkpoint - self.model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=1).to(opt.device) # create - exclude = ['anchor'] if opt.cfg else [] # exclude keys - if type(ckpt['model']).__name__ == "OrderedDict": - state_dict = ckpt['model'] - else: - state_dict = ckpt['model'].float().state_dict() # to FP32 - state_dict = intersect_dicts(state_dict, self.model.state_dict(), exclude=exclude) # intersect - self.model.load_state_dict(state_dict, strict=False) # load - self.model.cuda().eval() - total_params = sum(p.numel() for p in self.model.parameters()) - print(f'{total_params:,} total parameters.') - - - self.tracked_stracks = [] # type: list[STrack] - self.lost_stracks = [] # type: list[STrack] - self.removed_stracks = [] # type: list[STrack] - - self.frame_id = 0 - self.det_thresh = opt.conf_thres - self.buffer_size = int(frame_rate / 30.0 * opt.track_buffer) - self.max_time_lost = self.buffer_size - self.mean = np.array(opt.mean, dtype=np.float32).reshape(1, 1, 3) - self.std = np.array(opt.std, dtype=np.float32).reshape(1, 1, 3) - - self.kalman_filter = KalmanFilter() - self.low_thres = 0.2 - self.high_thres = self.opt.conf_thres + 0.1 - - def update(self, im_blob, img0,seq_num, save_dir): - self.frame_id += 1 - activated_starcks = [] - refind_stracks = [] - lost_stracks = [] - removed_stracks = [] - dets = [] - - ''' Step 1: Network forward, get detections & embeddings''' - with torch.no_grad(): - output = self.model(im_blob, augment=False) - pred, train_out = output[1] - - pred = pred[pred[:, :, 4] > self.low_thres] - detections = [] - if len(pred) > 0: - dets,x_inds,y_inds = non_max_suppression_and_inds(pred[:,:6].unsqueeze(0), 0.1, self.opt.nms_thres,method='cluster_diou') - if len(dets) != 0: - scale_coords(self.opt.img_size, dets[:, :4], img0.shape).round() - id_feature = output[0][0, y_inds, x_inds, :].cpu().numpy() - - remain_inds = dets[:, 4] > self.opt.conf_thres - inds_low = dets[:, 4] > self.low_thres - inds_high = dets[:, 4] < self.opt.conf_thres - inds_second = np.logical_and(inds_low, inds_high) - dets_second = dets[inds_second] - if id_feature.shape[0] == 1: - id_feature_second = id_feature - else: - id_feature_second = id_feature[inds_second] - dets = dets[remain_inds] - id_feature = id_feature[remain_inds] - - detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for - (tlbrs, f) in zip(dets[:, :5], id_feature)] - - else: - detections = [] - dets_second = [] - id_feature_second = [] - - ''' Add newly detected tracklets to tracked_stracks''' - unconfirmed = [] - tracked_stracks = [] # type: list[STrack] - for track in self.tracked_stracks: - if not track.is_activated: - unconfirmed.append(track) - else: - tracked_stracks.append(track) - - ''' Step 2: First association, with embedding''' - strack_pool = joint_stracks(tracked_stracks, self.lost_stracks) - # Predict the current location with KF - #for strack in strack_pool: - #strack.predict() - STrack.multi_predict(strack_pool) - dists = matching.embedding_distance(strack_pool, detections) - dists = matching.fuse_motion(self.kalman_filter, dists, strack_pool, detections) - #dists = matching.iou_distance(strack_pool, detections) - matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.4) - - for itracked, idet in matches: - track = strack_pool[itracked] - det = detections[idet] - if track.state == TrackState.Tracked: - track.update(detections[idet], self.frame_id) - activated_starcks.append(track) - else: - track.re_activate(det, self.frame_id, new_id=False) - refind_stracks.append(track) - - # vis - track_features, det_features, cost_matrix, cost_matrix_det, cost_matrix_track = [],[],[],[],[] - if self.opt.vis_state == 1 and self.frame_id % 20 == 0: - if len(dets) != 0: - for i in range(0, dets.shape[0]): - bbox = dets[i][0:4] - cv2.rectangle(img0, (int(bbox[0]), int(bbox[1])),(int(bbox[2]), int(bbox[3])),(0, 255, 0), 2) - track_features, det_features, cost_matrix, cost_matrix_det, cost_matrix_track = matching.vis_id_feature_A_distance(strack_pool, detections) - vis_feature(self.frame_id,seq_num,img0,track_features, - det_features, cost_matrix, cost_matrix_det, cost_matrix_track, max_num=5, out_path=save_dir) - - ''' Step 3: Second association, with IOU''' - detections = [detections[i] for i in u_detection] - r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] - dists = matching.iou_distance(r_tracked_stracks, detections) - matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) - - for itracked, idet in matches: - track = r_tracked_stracks[itracked] - det = detections[idet] - if track.state == TrackState.Tracked: - track.update(det, self.frame_id) - activated_starcks.append(track) - else: - track.re_activate(det, self.frame_id, new_id=False) - refind_stracks.append(track) - - # association the untrack to the low score detections - if len(dets_second) > 0: - detections_second = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for - (tlbrs, f) in zip(dets_second[:, :5], id_feature_second)] - else: - detections_second = [] - second_tracked_stracks = [r_tracked_stracks[i] for i in u_track if r_tracked_stracks[i].state == TrackState.Tracked] - dists = matching.iou_distance(second_tracked_stracks, detections_second) - matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.4) - for itracked, idet in matches: - track = second_tracked_stracks[itracked] - det = detections_second[idet] - if track.state == TrackState.Tracked: - track.update(det, self.frame_id) - activated_starcks.append(track) - else: - track.re_activate(det, self.frame_id, new_id=False) - refind_stracks.append(track) - - for it in u_track: - track = second_tracked_stracks[it] - if not track.state == TrackState.Lost: - track.mark_lost() - lost_stracks.append(track) - - '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' - detections = [detections[i] for i in u_detection] - dists = matching.iou_distance(unconfirmed, detections) - matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) - for itracked, idet in matches: - unconfirmed[itracked].update(detections[idet], self.frame_id) - activated_starcks.append(unconfirmed[itracked]) - for it in u_unconfirmed: - track = unconfirmed[it] - track.mark_removed() - removed_stracks.append(track) - - """ Step 4: Init new stracks""" - for inew in u_detection: - track = detections[inew] - if track.score < self.high_thres: - continue - track.activate(self.kalman_filter, self.frame_id) - activated_starcks.append(track) - """ Step 5: Update state""" - for track in self.lost_stracks: - if self.frame_id - track.end_frame > self.max_time_lost: - track.mark_removed() - removed_stracks.append(track) - - # print('Ramained match {} s'.format(t4-t3)) - - self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked] - self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks) - self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks) - self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks) - self.lost_stracks.extend(lost_stracks) - self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks) - self.removed_stracks.extend(removed_stracks) - self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks) - # get scores of lost tracks - output_stracks = [track for track in self.tracked_stracks if track.is_activated] - - logger.debug('===========Frame {}=========='.format(self.frame_id)) - logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks])) - logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks])) - logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks])) - logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks])) - - return output_stracks - - -def joint_stracks(tlista, tlistb): - exists = {} - res = [] - for t in tlista: - exists[t.track_id] = 1 - res.append(t) - for t in tlistb: - tid = t.track_id - if not exists.get(tid, 0): - exists[tid] = 1 - res.append(t) - return res - - -def sub_stracks(tlista, tlistb): - stracks = {} - for t in tlista: - stracks[t.track_id] = t - for t in tlistb: - tid = t.track_id - if stracks.get(tid, 0): - del stracks[tid] - return list(stracks.values()) - - -def remove_duplicate_stracks(stracksa, stracksb): - pdist = matching.iou_distance(stracksa, stracksb) - pairs = np.where(pdist < 0.15) - dupa, dupb = list(), list() - for p, q in zip(*pairs): - timep = stracksa[p].frame_id - stracksa[p].start_frame - timeq = stracksb[q].frame_id - stracksb[q].start_frame - if timep > timeq: - dupb.append(q) - else: - dupa.append(p) - resa = [t for i, t in enumerate(stracksa) if not i in dupa] - resb = [t for i, t in enumerate(stracksb) if not i in dupb] - return resa, resb - -def vis_feature(frame_id,seq_num,img,track_features, det_features, cost_matrix, cost_matrix_det, cost_matrix_track,max_num=5, out_path='/home/XX/'): - num_zero = ["0000","000","00","0"] - img = cv2.resize(img, (778, 435)) - - if len(det_features) != 0: - max_f = det_features.max() - min_f = det_features.min() - det_features = np.round((det_features - min_f) / (max_f - min_f) * 255) - det_features = det_features.astype(np.uint8) - d_F_M = [] - cutpff_line = [40]*512 - for d_f in det_features: - for row in range(45): - d_F_M += [[40]*3+d_f.tolist()+[40]*3] - for row in range(3): - d_F_M += [[40]*3+cutpff_line+[40]*3] - d_F_M = np.array(d_F_M) - d_F_M = d_F_M.astype(np.uint8) - det_features_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET) - feature_img2 = cv2.resize(det_features_img, (435, 435)) - #cv2.putText(feature_img2, "det_features", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) - else: - feature_img2 = np.zeros((435, 435)) - feature_img2 = feature_img2.astype(np.uint8) - feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET) - #cv2.putText(feature_img2, "det_features", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) - feature_img = np.concatenate((img, feature_img2), axis=1) - - if len(cost_matrix_det) != 0 and len(cost_matrix_det[0]) != 0: - max_f = cost_matrix_det.max() - min_f = cost_matrix_det.min() - cost_matrix_det = np.round((cost_matrix_det - min_f) / (max_f - min_f) * 255) - d_F_M = [] - cutpff_line = [40]*len(cost_matrix_det)*10 - for c_m in cost_matrix_det: - add = [] - for row in range(len(c_m)): - add += [255-c_m[row]]*10 - for row in range(10): - d_F_M += [[40]+add+[40]] - d_F_M = np.array(d_F_M) - d_F_M = d_F_M.astype(np.uint8) - cost_matrix_det_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET) - feature_img2 = cv2.resize(cost_matrix_det_img, (435, 435)) - #cv2.putText(feature_img2, "cost_matrix_det", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) - else: - feature_img2 = np.zeros((435, 435)) - feature_img2 = feature_img2.astype(np.uint8) - feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET) - #cv2.putText(feature_img2, "cost_matrix_det", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) - feature_img = np.concatenate((feature_img, feature_img2), axis=1) - - if len(track_features) != 0: - max_f = track_features.max() - min_f = track_features.min() - track_features = np.round((track_features - min_f) / (max_f - min_f) * 255) - track_features = track_features.astype(np.uint8) - d_F_M = [] - cutpff_line = [40]*512 - for d_f in track_features: - for row in range(45): - d_F_M += [[40]*3+d_f.tolist()+[40]*3] - for row in range(3): - d_F_M += [[40]*3+cutpff_line+[40]*3] - d_F_M = np.array(d_F_M) - d_F_M = d_F_M.astype(np.uint8) - track_features_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET) - feature_img2 = cv2.resize(track_features_img, (435, 435)) - #cv2.putText(feature_img2, "track_features", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) - else: - feature_img2 = np.zeros((435, 435)) - feature_img2 = feature_img2.astype(np.uint8) - feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET) - #cv2.putText(feature_img2, "track_features", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) - feature_img = np.concatenate((feature_img, feature_img2), axis=1) - - if len(cost_matrix_track) != 0 and len(cost_matrix_track[0]) != 0: - max_f = cost_matrix_track.max() - min_f = cost_matrix_track.min() - cost_matrix_track = np.round((cost_matrix_track - min_f) / (max_f - min_f) * 255) - d_F_M = [] - cutpff_line = [40]*len(cost_matrix_track)*10 - for c_m in cost_matrix_track: - add = [] - for row in range(len(c_m)): - add += [255-c_m[row]]*10 - for row in range(10): - d_F_M += [[40]+add+[40]] - d_F_M = np.array(d_F_M) - d_F_M = d_F_M.astype(np.uint8) - cost_matrix_track_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET) - feature_img2 = cv2.resize(cost_matrix_track_img, (435, 435)) - #cv2.putText(feature_img2, "cost_matrix_track", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) - else: - feature_img2 = np.zeros((435, 435)) - feature_img2 = feature_img2.astype(np.uint8) - feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET) - #cv2.putText(feature_img2, "cost_matrix_track", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) - feature_img = np.concatenate((feature_img, feature_img2), axis=1) - - if len(cost_matrix) != 0 and len(cost_matrix[0]) != 0: - max_f = cost_matrix.max() - min_f = cost_matrix.min() - cost_matrix = np.round((cost_matrix - min_f) / (max_f - min_f) * 255) - d_F_M = [] - cutpff_line = [40]*len(cost_matrix[0])*10 - for c_m in cost_matrix: - add = [] - for row in range(len(c_m)): - add += [255-c_m[row]]*10 - for row in range(10): - d_F_M += [[40]+add+[40]] - d_F_M = np.array(d_F_M) - d_F_M = d_F_M.astype(np.uint8) - cost_matrix_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET) - feature_img2 = cv2.resize(cost_matrix_img, (435, 435)) - #cv2.putText(feature_img2, "cost_matrix", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) - else: - feature_img2 = np.zeros((435, 435)) - feature_img2 = feature_img2.astype(np.uint8) - feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET) - #cv2.putText(feature_img2, "cost_matrix", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) - feature_img = np.concatenate((feature_img, feature_img2), axis=1) - - dst_path = out_path + "/" + seq_num + "_" + num_zero[len(str(frame_id))-1] + str(frame_id) + '.png' - cv2.imwrite(dst_path, feature_img) \ No newline at end of file diff --git a/spaces/EPFL-VILAB/MultiMAE/utils/cross_entropy.py b/spaces/EPFL-VILAB/MultiMAE/utils/cross_entropy.py deleted file mode 100644 index 3d47ce23bdd30da0474aac7f67c6cf5347de88f1..0000000000000000000000000000000000000000 --- a/spaces/EPFL-VILAB/MultiMAE/utils/cross_entropy.py +++ /dev/null @@ -1,43 +0,0 @@ -# -------------------------------------------------------- -# Based on the timm code base -# https://github.com/rwightman/pytorch-image-models/tree/master/timm -# -------------------------------------------------------- - - -""" Cross Entropy w/ smoothing or soft targets - -Hacked together by / Copyright 2021 Ross Wightman -""" - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class LabelSmoothingCrossEntropy(nn.Module): - """ NLL loss with label smoothing. - """ - - def __init__(self, smoothing=0.1): - super(LabelSmoothingCrossEntropy, self).__init__() - assert smoothing < 1.0 - self.smoothing = smoothing - self.confidence = 1. - smoothing - - def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: - logprobs = F.log_softmax(x, dim=-1) - nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) - nll_loss = nll_loss.squeeze(1) - smooth_loss = -logprobs.mean(dim=-1) - loss = self.confidence * nll_loss + self.smoothing * smooth_loss - return loss.mean() - - -class SoftTargetCrossEntropy(nn.Module): - - def __init__(self): - super(SoftTargetCrossEntropy, self).__init__() - - def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: - loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1) - return loss.mean() diff --git a/spaces/Eddevs/README/README.md b/spaces/Eddevs/README/README.md deleted file mode 100644 index b0ca81992d3fbf153e28091c32756f4691baae86..0000000000000000000000000000000000000000 --- a/spaces/Eddevs/README/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: README -emoji: 📚 -colorFrom: yellow -colorTo: indigo -sdk: static -pinned: false ---- - -TUM AI Makeathon Team: ed-devs - -After 48 hour deep-dive into great AI4SocialGood problem we came up with solution to help educators lower recognisability of the assessment questions, and enable students achieve higher learning goals. - -In addition to the private solution we submitted to Brian. We also built a Space in hopes that educators around the world may benefit from it, check it out. 📚 \ No newline at end of file diff --git a/spaces/EuroPython2022/Scratchpad-w-BLOOM/app.py b/spaces/EuroPython2022/Scratchpad-w-BLOOM/app.py deleted file mode 100644 index d7e00bb40d5378f6b86f8922d09304b1e3a00c58..0000000000000000000000000000000000000000 --- a/spaces/EuroPython2022/Scratchpad-w-BLOOM/app.py +++ /dev/null @@ -1,63 +0,0 @@ -import gradio as gr -import requests -import os - -##Bloom -API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom" -HF_TOKEN = os.environ["HF_TOKEN"] -headers = {"Authorization": f"Bearer {HF_TOKEN}"} - -def text_generate(prompt): - print(f"Prompt is :{prompt}") - p = prompt + " Solution: " - print(f"Final prompt is : {p}") - json_ = {"inputs": p, - "parameters": - { - "top_p": 0.9, - "temperature": 1.1, - "max_new_tokens": 250, - "return_full_text": True - }, "options": - { - "use_cache": True, - "wait_for_model":True - },} - response = requests.post(API_URL, headers=headers, json=json_) - print(f"Response is : {response}") - output = response.json() - print(f"output is : {output}") - output_tmp = output[0]['generated_text'] - print(f"output_tmp is: {output_tmp}") - solution = output_tmp.split("\nQ:")[0] - print(f"Final response after splits is: {solution}") - return solution - -demo = gr.Blocks() - -with demo: - gr.Markdown("

Length generalization (LG) With BLOOM🌸

") - gr.Markdown( - """ - We will examine large language models ability to extrapolate to longer problems! \n - Length generalization (LG) is important: Often, long examples are rare and intrinsically more difficult, yet are the ones we care more about. \n - Recent paper [Exploring Length Generalization in Large Language Models](https://arxiv.org/pdf/2207.04901) found that using few-shot [scratchpad](https://arxiv.org/abs/2112.00114), a combo behind many strong LLM results (eg. #Minerva ) \n - leads to **substantial improvements in length generalization!** \n - In-context learning enables variable length pattern matching, producing solutions of correct lengths. \n - This space is an attempt at inspecting this LLM behavior/capability in the new HuggingFace BigScienceW [Bloom](https://huggingface.co/bigscience/bloom) model. \n - This Space is created by [Muhtasham Oblokulov](https://twitter.com/muhtasham9) for EuroPython 2022 Demo. \n - This Space is work in progress, BLOOM doesn't support inference on long sequencess so you may try with shorter sequences. \n - """ - ) - with gr.Row(): - input_prompt = gr.Textbox(value="Q:The coin is heads up.(1) Then Austin flips. Is the coin still heads up? Solution: Coin is initially heads up. (1) After Austin flips, coin turns to heads. Q: The coin is heads up. (2) Then Austin doesn't flip. (1) Then Kara flips. Is the coin still heads up?", - label="Enter your examples zero-shot (few-shot is not supported due to API limit) followed by Query :") - generated_txt = gr.Textbox(lines=10, label="Generated Solution:") - - b1 = gr.Button("Generate Text") - b1.click(text_generate,inputs=[input_prompt], outputs=[generated_txt]) - - with gr.Row(): - gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=europython2022_scratchpad-w-bloom)") - -demo.launch(enable_queue=True, debug=True) \ No newline at end of file diff --git a/spaces/EuroPython2022/mmocr-demo/configs/_base_/schedules/schedule_adadelta_5e.py b/spaces/EuroPython2022/mmocr-demo/configs/_base_/schedules/schedule_adadelta_5e.py deleted file mode 100644 index ad996d65f8aca131023d34712e2d960bf6928cce..0000000000000000000000000000000000000000 --- a/spaces/EuroPython2022/mmocr-demo/configs/_base_/schedules/schedule_adadelta_5e.py +++ /dev/null @@ -1,8 +0,0 @@ -# optimizer -optimizer = dict(type='Adadelta', lr=1.0) -optimizer_config = dict(grad_clip=None) -# learning policy -lr_config = dict(policy='step', step=[]) -# running settings -runner = dict(type='EpochBasedRunner', max_epochs=5) -checkpoint_config = dict(interval=1) diff --git a/spaces/Farazquraishi/pendora/utils/utils.py b/spaces/Farazquraishi/pendora/utils/utils.py deleted file mode 100644 index beccaf08edc411529a66d4c11498cd6b43423d0d..0000000000000000000000000000000000000000 --- a/spaces/Farazquraishi/pendora/utils/utils.py +++ /dev/null @@ -1,377 +0,0 @@ -import json -from tensorflow.keras.models import model_from_json -from networks.layers import AdaIN, AdaptiveAttention -import tensorflow as tf - -import numpy as np -import cv2 -import math -from skimage import transform as trans -from scipy.signal import convolve2d -from skimage.color import rgb2yuv, yuv2rgb - -from PIL import Image - - -def save_model_internal(model, path, name, num): - json_model = model.to_json() - with open(path + name + '.json', "w") as json_file: - json_file.write(json_model) - - model.save_weights(path + name + '_' + str(num) + '.h5') - - -def load_model_internal(path, name, num): - with open(path + name + '.json', 'r') as json_file: - model_dict = json_file.read() - - mod = model_from_json(model_dict, custom_objects={'AdaIN': AdaIN, 'AdaptiveAttention': AdaptiveAttention}) - mod.load_weights(path + name + '_' + str(num) + '.h5') - - return mod - - -def save_training_meta(state_dict, path, num): - with open(path + str(num) + '.json', 'w') as json_file: - json.dump(state_dict, json_file, indent=2) - - -def load_training_meta(path, num): - with open(path + str(num) + '.json', 'r') as json_file: - state_dict = json.load(json_file) - return state_dict - - -def log_info(sw, results_dict, iteration): - with sw.as_default(): - for key in results_dict.keys(): - tf.summary.scalar(key, results_dict[key], step=iteration) - - -src1 = np.array([[51.642, 50.115], [57.617, 49.990], [35.740, 69.007], - [51.157, 89.050], [57.025, 89.702]], - dtype=np.float32) -# <--left -src2 = np.array([[45.031, 50.118], [65.568, 50.872], [39.677, 68.111], - [45.177, 86.190], [64.246, 86.758]], - dtype=np.float32) - -# ---frontal -src3 = np.array([[39.730, 51.138], [72.270, 51.138], [56.000, 68.493], - [42.463, 87.010], [69.537, 87.010]], - dtype=np.float32) - -# -->right -src4 = np.array([[46.845, 50.872], [67.382, 50.118], [72.737, 68.111], - [48.167, 86.758], [67.236, 86.190]], - dtype=np.float32) - -# -->right profile -src5 = np.array([[54.796, 49.990], [60.771, 50.115], [76.673, 69.007], - [55.388, 89.702], [61.257, 89.050]], - dtype=np.float32) - -src = np.array([src1, src2, src3, src4, src5]) -src_map = {112: src, 224: src * 2} - -# Left eye, right eye, nose, left mouth, right mouth -arcface_src = np.array( - [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], - [41.5493, 92.3655], [70.7299, 92.2041]], - dtype=np.float32) - -arcface_src = np.expand_dims(arcface_src, axis=0) - - -def extract_face(img, bb, absolute_center, mode='arcface', extention_rate=0.05, debug=False): - """Extract face from image given a bounding box""" - # bbox - x1, y1, x2, y2 = bb + 60 - adjusted_absolute_center = (absolute_center[0] + 60, absolute_center[1] + 60) - if debug: - print(bb + 60) - x1, y1, x2, y2 = bb - cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 3) - cv2.circle(img, absolute_center, 1, (255, 0, 255), 2) - Image.fromarray(img).show() - x1, y1, x2, y2 = bb + 60 - # Pad image in case face is out of frame - padded_img = np.zeros(shape=(248, 248, 3), dtype=np.uint8) - padded_img[60:-60, 60:-60, :] = img - - if debug: - cv2.rectangle(padded_img, (x1, y1), (x2, y2), (0, 255, 255), 3) - cv2.circle(padded_img, adjusted_absolute_center, 1, (255, 255, 255), 2) - Image.fromarray(padded_img).show() - - y_len = abs(y1 - y2) - x_len = abs(x1 - x2) - - new_len = (y_len + x_len) // 2 - - extension = int(new_len * extention_rate) - - x_adjust = (x_len - new_len) // 2 - y_adjust = (y_len - new_len) // 2 - - x_1_adjusted = x1 + x_adjust - extension - x_2_adjusted = x2 - x_adjust + extension - - if mode == 'arcface': - y_1_adjusted = y1 - extension - y_2_adjusted = y2 - 2 * y_adjust + extension - else: - y_1_adjusted = y1 + 2 * y_adjust - extension - y_2_adjusted = y2 + extension - - move_x = adjusted_absolute_center[0] - (x_1_adjusted + x_2_adjusted) // 2 - move_y = adjusted_absolute_center[1] - (y_1_adjusted + y_2_adjusted) // 2 - - x_1_adjusted = x_1_adjusted + move_x - x_2_adjusted = x_2_adjusted + move_x - y_1_adjusted = y_1_adjusted + move_y - y_2_adjusted = y_2_adjusted + move_y - - # print(y_1_adjusted, y_2_adjusted, x_1_adjusted, x_2_adjusted) - - return padded_img[y_1_adjusted:y_2_adjusted, x_1_adjusted:x_2_adjusted] - - -def distance(a, b): - return np.sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2) - - -def euclidean_distance(a, b): - x1 = a[0]; y1 = a[1] - x2 = b[0]; y2 = b[1] - return np.sqrt(((x2 - x1) * (x2 - x1)) + ((y2 - y1) * (y2 - y1))) - - -def align_face(img, landmarks, debug=False): - nose, right_eye, left_eye = landmarks - - left_eye_x = left_eye[0] - left_eye_y = left_eye[1] - - right_eye_x = right_eye[0] - right_eye_y = right_eye[1] - - center_eye = ((left_eye[0] + right_eye[0]) // 2, (left_eye[1] + right_eye[1]) // 2) - - if left_eye_y < right_eye_y: - point_3rd = (right_eye_x, left_eye_y) - direction = -1 - else: - point_3rd = (left_eye_x, right_eye_y) - direction = 1 - - if debug: - cv2.circle(img, point_3rd, 1, (255, 0, 0), 1) - cv2.circle(img, center_eye, 1, (255, 0, 0), 1) - - cv2.line(img, right_eye, left_eye, (0, 0, 0), 1) - cv2.line(img, left_eye, point_3rd, (0, 0, 0), 1) - cv2.line(img, right_eye, point_3rd, (0, 0, 0), 1) - - a = euclidean_distance(left_eye, point_3rd) - b = euclidean_distance(right_eye, left_eye) - c = euclidean_distance(right_eye, point_3rd) - - cos_a = (b * b + c * c - a * a) / (2 * b * c) - - angle = np.arccos(cos_a) - - angle = (angle * 180) / np.pi - - if direction == -1: - angle = 90 - angle - ang = math.radians(direction * angle) - else: - ang = math.radians(direction * angle) - angle = 0 - angle - - M = cv2.getRotationMatrix2D((64, 64), angle, 1) - new_img = cv2.warpAffine(img, M, (128, 128), - flags=cv2.INTER_CUBIC) - - rotated_nose = (int((nose[0] - 64) * np.cos(ang) - (nose[1] - 64) * np.sin(ang) + 64), - int((nose[0] - 64) * np.sin(ang) + (nose[1] - 64) * np.cos(ang) + 64)) - - rotated_center_eye = (int((center_eye[0] - 64) * np.cos(ang) - (center_eye[1] - 64) * np.sin(ang) + 64), - int((center_eye[0] - 64) * np.sin(ang) + (center_eye[1] - 64) * np.cos(ang) + 64)) - - abolute_center = (rotated_center_eye[0], (rotated_nose[1] + rotated_center_eye[1]) // 2) - - if debug: - cv2.circle(new_img, rotated_nose, 1, (0, 0, 255), 1) - cv2.circle(new_img, rotated_center_eye, 1, (0, 0, 255), 1) - cv2.circle(new_img, abolute_center, 1, (0, 0, 255), 1) - - return new_img, abolute_center - - -def estimate_norm(lmk, image_size=112, mode='arcface', shrink_factor=1.0): - assert lmk.shape == (5, 2) - tform = trans.SimilarityTransform() - lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1) - min_M = [] - min_index = [] - min_error = float('inf') - src_factor = image_size / 112 - if mode == 'arcface': - src = arcface_src * shrink_factor + (1 - shrink_factor) * 56 - src = src * src_factor - else: - src = src_map[image_size] * src_factor - for i in np.arange(src.shape[0]): - tform.estimate(lmk, src[i]) - M = tform.params[0:2, :] - results = np.dot(M, lmk_tran.T) - results = results.T - error = np.sum(np.sqrt(np.sum((results - src[i])**2, axis=1))) - # print(error) - if error < min_error: - min_error = error - min_M = M - min_index = i - return min_M, min_index - - -def inverse_estimate_norm(lmk, t_lmk, image_size=112, mode='arcface', shrink_factor=1.0): - assert lmk.shape == (5, 2) - tform = trans.SimilarityTransform() - lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1) - min_M = [] - min_index = [] - min_error = float('inf') - src_factor = image_size / 112 - if mode == 'arcface': - src = arcface_src * shrink_factor + (1 - shrink_factor) * 56 - src = src * src_factor - else: - src = src_map[image_size] * src_factor - for i in np.arange(src.shape[0]): - tform.estimate(t_lmk, lmk) - M = tform.params[0:2, :] - results = np.dot(M, lmk_tran.T) - results = results.T - error = np.sum(np.sqrt(np.sum((results - src[i])**2, axis=1))) - # print(error) - if error < min_error: - min_error = error - min_M = M - min_index = i - return min_M, min_index - - -def norm_crop(img, landmark, image_size=112, mode='arcface', shrink_factor=1.0): - """ - Align and crop the image based of the facial landmarks in the image. The alignment is done with - a similarity transformation based of source coordinates. - :param img: Image to transform. - :param landmark: Five landmark coordinates in the image. - :param image_size: Desired output size after transformation. - :param mode: 'arcface' aligns the face for the use of Arcface facial recognition model. Useful for - both facial recognition tasks and face swapping tasks. - :param shrink_factor: Shrink factor that shrinks the source landmark coordinates. This will include more border - information around the face. Useful when you want to include more background information when performing face swaps. - The lower the shrink factor the more of the face is included. Default value 1.0 will align the image to be ready - for the Arcface recognition model, but usually omits part of the chin. Value of 0.0 would transform all source points - to the middle of the image, probably rendering the alignment procedure useless. - - If you process the image with a shrink factor of 0.85 and then want to extract the identity embedding with arcface, - you simply do a central crop of factor 0.85 to yield same cropped result as using shrink factor 1.0. This will - reduce the resolution, the recommendation is to processed images to output resolutions higher than 112 is using - Arcface. This will make sure no information is lost by resampling the image after central crop. - :return: Returns the transformed image. - """ - M, pose_index = estimate_norm(landmark, image_size, mode, shrink_factor=shrink_factor) - warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0) - return warped - - -def transform_landmark_points(M, points): - lmk_tran = np.insert(points, 2, values=np.ones(5), axis=1) - transformed_lmk = np.dot(M, lmk_tran.T) - transformed_lmk = transformed_lmk.T - - return transformed_lmk - - -def multi_convolver(image, kernel, iterations): - if kernel == "Sharpen": - kernel = np.array([[0, -1, 0], - [-1, 5, -1], - [0, -1, 0]]) - elif kernel == "Unsharp_mask": - kernel = np.array([[1, 4, 6, 4, 1], - [4, 16, 24, 16, 1], - [6, 24, -476, 24, 1], - [4, 16, 24, 16, 1], - [1, 4, 6, 4, 1]]) * (-1 / 256) - elif kernel == "Blur": - kernel = (1 / 16.0) * np.array([[1., 2., 1.], - [2., 4., 2.], - [1., 2., 1.]]) - for i in range(iterations): - image = convolve2d(image, kernel, 'same', boundary='fill', fillvalue = 0) - return image - - -def convolve_rgb(image, kernel, iterations=1): - img_yuv = rgb2yuv(image) - img_yuv[:, :, 0] = multi_convolver(img_yuv[:, :, 0], kernel, - iterations) - final_image = yuv2rgb(img_yuv) - - return final_image.astype('float32') - - -def generate_mask_from_landmarks(lms, im_size): - blend_mask_lm = np.zeros(shape=(im_size, im_size, 3), dtype='float32') - - # EYES - blend_mask_lm = cv2.circle(blend_mask_lm, - (int(lms[0][0]), int(lms[0][1])), 12, (255, 255, 255), 30) - blend_mask_lm = cv2.circle(blend_mask_lm, - (int(lms[1][0]), int(lms[1][1])), 12, (255, 255, 255), 30) - blend_mask_lm = cv2.circle(blend_mask_lm, - (int((lms[0][0] + lms[1][0]) / 2), int((lms[0][1] + lms[1][1]) / 2)), - 16, (255, 255, 255), 65) - - # NOSE - blend_mask_lm = cv2.circle(blend_mask_lm, - (int(lms[2][0]), int(lms[2][1])), 5, (255, 255, 255), 5) - blend_mask_lm = cv2.circle(blend_mask_lm, - (int((lms[0][0] + lms[1][0]) / 2), int(lms[2][1])), 16, (255, 255, 255), 100) - - # MOUTH - blend_mask_lm = cv2.circle(blend_mask_lm, - (int(lms[3][0]), int(lms[3][1])), 6, (255, 255, 255), 30) - blend_mask_lm = cv2.circle(blend_mask_lm, - (int(lms[4][0]), int(lms[4][1])), 6, (255, 255, 255), 30) - - blend_mask_lm = cv2.circle(blend_mask_lm, - (int((lms[3][0] + lms[4][0]) / 2), int((lms[3][1] + lms[4][1]) / 2)), - 16, (255, 255, 255), 40) - return blend_mask_lm - - -def display_distance_text(im, distance, lms, im_w, im_h, scale=2): - blended_insert = cv2.putText(im, str(distance)[:4], - (int(lms[4] * im_w * 0.5), int(lms[5] * im_h * 0.8)), - cv2.FONT_HERSHEY_SIMPLEX, scale * 0.5, (0.08, 0.16, 0.08), int(scale * 2)) - blended_insert = cv2.putText(blended_insert, str(distance)[:4], - (int(lms[4] * im_w * 0.5), int(lms[5] * im_h * 0.8)), - cv2.FONT_HERSHEY_SIMPLEX, scale* 0.5, (0.3, 0.7, 0.32), int(scale * 1)) - return blended_insert - - -def get_lm(annotation, im_w, im_h): - lm_align = np.array([[annotation[4] * im_w, annotation[5] * im_h], - [annotation[6] * im_w, annotation[7] * im_h], - [annotation[8] * im_w, annotation[9] * im_h], - [annotation[10] * im_w, annotation[11] * im_h], - [annotation[12] * im_w, annotation[13] * im_h]], - dtype=np.float32) - return lm_align diff --git a/spaces/FlippFuzz/whisper-webui/src/whisper/fasterWhisperContainer.py b/spaces/FlippFuzz/whisper-webui/src/whisper/fasterWhisperContainer.py deleted file mode 100644 index ef3dccae5421a657028a6c2e95415767dd5147dd..0000000000000000000000000000000000000000 --- a/spaces/FlippFuzz/whisper-webui/src/whisper/fasterWhisperContainer.py +++ /dev/null @@ -1,190 +0,0 @@ -import os -from typing import List, Union - -from faster_whisper import WhisperModel, download_model -from src.config import ModelConfig -from src.hooks.progressListener import ProgressListener -from src.languages import get_language_from_name -from src.modelCache import ModelCache -from src.whisper.abstractWhisperContainer import AbstractWhisperCallback, AbstractWhisperContainer -from src.utils import format_timestamp - -class FasterWhisperContainer(AbstractWhisperContainer): - def __init__(self, model_name: str, device: str = None, compute_type: str = "float16", - download_root: str = None, - cache: ModelCache = None, models: List[ModelConfig] = []): - super().__init__(model_name, device, compute_type, download_root, cache, models) - - def ensure_downloaded(self): - """ - Ensure that the model is downloaded. This is useful if you want to ensure that the model is downloaded before - passing the container to a subprocess. - """ - model_config = self._get_model_config() - - if os.path.isdir(model_config.url): - model_config.path = model_config.url - else: - model_config.path = download_model(model_config.url, output_dir=self.download_root) - - def _get_model_config(self) -> ModelConfig: - """ - Get the model configuration for the model. - """ - for model in self.models: - if model.name == self.model_name: - return model - return None - - def _create_model(self): - print("Loading faster whisper model " + self.model_name + " for device " + str(self.device)) - model_config = self._get_model_config() - - if model_config.type == "whisper" and model_config.url not in ["tiny", "base", "small", "medium", "large", "large-v2"]: - raise Exception("FasterWhisperContainer does not yet support Whisper models. Use ct2-transformers-converter to convert the model to a faster-whisper model.") - - device = self.device - - if (device is None): - device = "auto" - - model = WhisperModel(model_config.url, device=device, compute_type=self.compute_type) - return model - - def create_callback(self, language: str = None, task: str = None, initial_prompt: str = None, **decodeOptions: dict): - """ - Create a WhisperCallback object that can be used to transcript audio files. - - Parameters - ---------- - language: str - The target language of the transcription. If not specified, the language will be inferred from the audio content. - task: str - The task - either translate or transcribe. - initial_prompt: str - The initial prompt to use for the transcription. - decodeOptions: dict - Additional options to pass to the decoder. Must be pickleable. - - Returns - ------- - A WhisperCallback object. - """ - return FasterWhisperCallback(self, language=language, task=task, initial_prompt=initial_prompt, **decodeOptions) - -class FasterWhisperCallback(AbstractWhisperCallback): - def __init__(self, model_container: FasterWhisperContainer, language: str = None, task: str = None, initial_prompt: str = None, **decodeOptions: dict): - self.model_container = model_container - self.language = language - self.task = task - self.initial_prompt = initial_prompt - self.decodeOptions = decodeOptions - - self._printed_warning = False - - def invoke(self, audio, segment_index: int, prompt: str, detected_language: str, progress_listener: ProgressListener = None): - """ - Peform the transcription of the given audio file or data. - - Parameters - ---------- - audio: Union[str, np.ndarray, torch.Tensor] - The audio file to transcribe, or the audio data as a numpy array or torch tensor. - segment_index: int - The target language of the transcription. If not specified, the language will be inferred from the audio content. - task: str - The task - either translate or transcribe. - progress_listener: ProgressListener - A callback to receive progress updates. - """ - model: WhisperModel = self.model_container.get_model() - language_code = self._lookup_language_code(self.language) if self.language else None - - # Copy decode options and remove options that are not supported by faster-whisper - decodeOptions = self.decodeOptions.copy() - verbose = decodeOptions.pop("verbose", None) - - logprob_threshold = decodeOptions.pop("logprob_threshold", None) - - patience = decodeOptions.pop("patience", None) - length_penalty = decodeOptions.pop("length_penalty", None) - suppress_tokens = decodeOptions.pop("suppress_tokens", None) - - if (decodeOptions.pop("fp16", None) is not None): - if not self._printed_warning: - print("WARNING: fp16 option is ignored by faster-whisper - use compute_type instead.") - self._printed_warning = True - - # Fix up decode options - if (logprob_threshold is not None): - decodeOptions["log_prob_threshold"] = logprob_threshold - - decodeOptions["patience"] = float(patience) if patience is not None else 1.0 - decodeOptions["length_penalty"] = float(length_penalty) if length_penalty is not None else 1.0 - - # See if supress_tokens is a string - if so, convert it to a list of ints - decodeOptions["suppress_tokens"] = self._split_suppress_tokens(suppress_tokens) - - segments_generator, info = model.transcribe(audio, \ - language=language_code if language_code else detected_language, task=self.task, \ - initial_prompt=self._concat_prompt(self.initial_prompt, prompt) if segment_index == 0 else prompt, \ - **decodeOptions - ) - - segments = [] - - for segment in segments_generator: - segments.append(segment) - - if progress_listener is not None: - progress_listener.on_progress(segment.end, info.duration) - if verbose: - print("[{}->{}] {}".format(format_timestamp(segment.start, True), format_timestamp(segment.end, True), - segment.text)) - - text = " ".join([segment.text for segment in segments]) - - # Convert the segments to a format that is easier to serialize - whisper_segments = [{ - "text": segment.text, - "start": segment.start, - "end": segment.end, - - # Extra fields added by faster-whisper - "words": [{ - "start": word.start, - "end": word.end, - "word": word.word, - "probability": word.probability - } for word in (segment.words if segment.words is not None else []) ] - } for segment in segments] - - result = { - "segments": whisper_segments, - "text": text, - "language": info.language if info else None, - - # Extra fields added by faster-whisper - "language_probability": info.language_probability if info else None, - "duration": info.duration if info else None - } - - if progress_listener is not None: - progress_listener.on_finished() - return result - - def _split_suppress_tokens(self, suppress_tokens: Union[str, List[int]]): - if (suppress_tokens is None): - return None - if (isinstance(suppress_tokens, list)): - return suppress_tokens - - return [int(token) for token in suppress_tokens.split(",")] - - def _lookup_language_code(self, language: str): - language = get_language_from_name(language) - - if language is None: - raise ValueError("Invalid language: " + language) - - return language.code diff --git a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/cleaners.py b/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/cleaners.py deleted file mode 100644 index 263df9c0f7c185290600454abfff464e7f774576..0000000000000000000000000000000000000000 --- a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/cleaners.py +++ /dev/null @@ -1,134 +0,0 @@ -import re -from text.japanese import japanese_to_romaji_with_accent, japanese_to_ipa, japanese_to_ipa2, japanese_to_ipa3 -from text.korean import latin_to_hangul, number_to_hangul, divide_hangul, korean_to_lazy_ipa, korean_to_ipa -from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo, chinese_to_romaji, chinese_to_lazy_ipa, chinese_to_ipa, chinese_to_ipa2 -from text.sanskrit import devanagari_to_ipa -from text.english import english_to_lazy_ipa, english_to_ipa2, english_to_lazy_ipa2 -from text.thai import num_to_thai, latin_to_thai -# from text.shanghainese import shanghainese_to_ipa -# from text.cantonese import cantonese_to_ipa -# from text.ngu_dialect import ngu_dialect_to_ipa - - -def japanese_cleaners(text): - text = japanese_to_romaji_with_accent(text) - text = re.sub(r'([A-Za-z])$', r'\1.', text) - return text - - -def japanese_cleaners2(text): - return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…') - - -def korean_cleaners(text): - '''Pipeline for Korean text''' - text = latin_to_hangul(text) - text = number_to_hangul(text) - text = divide_hangul(text) - text = re.sub(r'([\u3131-\u3163])$', r'\1.', text) - return text - - -# def chinese_cleaners(text): -# '''Pipeline for Chinese text''' -# text = number_to_chinese(text) -# text = chinese_to_bopomofo(text) -# text = latin_to_bopomofo(text) -# text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text) -# return text - -def chinese_cleaners(text): - from pypinyin import Style, pinyin - text = text.replace("[ZH]", "") - phones = [phone[0] for phone in pinyin(text, style=Style.TONE3)] - return ' '.join(phones) - - -def zh_ja_mixture_cleaners(text): - text = re.sub(r'\[ZH\](.*?)\[ZH\]', - lambda x: chinese_to_romaji(x.group(1))+' ', text) - text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_romaji_with_accent( - x.group(1)).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')+' ', text) - text = re.sub(r'\s+$', '', text) - text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) - return text - - -def sanskrit_cleaners(text): - text = text.replace('॥', '।').replace('ॐ', 'ओम्') - text = re.sub(r'([^।])$', r'\1।', text) - return text - - -def cjks_cleaners(text): - text = re.sub(r'\[ZH\](.*?)\[ZH\]', - lambda x: chinese_to_lazy_ipa(x.group(1))+' ', text) - text = re.sub(r'\[JA\](.*?)\[JA\]', - lambda x: japanese_to_ipa(x.group(1))+' ', text) - text = re.sub(r'\[KO\](.*?)\[KO\]', - lambda x: korean_to_lazy_ipa(x.group(1))+' ', text) - text = re.sub(r'\[SA\](.*?)\[SA\]', - lambda x: devanagari_to_ipa(x.group(1))+' ', text) - text = re.sub(r'\[EN\](.*?)\[EN\]', - lambda x: english_to_lazy_ipa(x.group(1))+' ', text) - text = re.sub(r'\s+$', '', text) - text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) - return text - - -def cjke_cleaners(text): - text = re.sub(r'\[ZH\](.*?)\[ZH\]', lambda x: chinese_to_lazy_ipa(x.group(1)).replace( - 'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn')+' ', text) - text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_ipa(x.group(1)).replace('ʧ', 'tʃ').replace( - 'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz')+' ', text) - text = re.sub(r'\[KO\](.*?)\[KO\]', - lambda x: korean_to_ipa(x.group(1))+' ', text) - text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: english_to_ipa2(x.group(1)).replace('ɑ', 'a').replace( - 'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u')+' ', text) - text = re.sub(r'\s+$', '', text) - text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) - return text - - -def cjke_cleaners2(text): - text = re.sub(r'\[ZH\](.*?)\[ZH\]', - lambda x: chinese_to_ipa(x.group(1))+' ', text) - text = re.sub(r'\[JA\](.*?)\[JA\]', - lambda x: japanese_to_ipa2(x.group(1))+' ', text) - text = re.sub(r'\[KO\](.*?)\[KO\]', - lambda x: korean_to_ipa(x.group(1))+' ', text) - text = re.sub(r'\[EN\](.*?)\[EN\]', - lambda x: english_to_ipa2(x.group(1))+' ', text) - text = re.sub(r'\s+$', '', text) - text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) - return text - - -def thai_cleaners(text): - text = num_to_thai(text) - text = latin_to_thai(text) - return text - - -# def shanghainese_cleaners(text): -# text = shanghainese_to_ipa(text) -# text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) -# return text - - -# def chinese_dialect_cleaners(text): -# text = re.sub(r'\[ZH\](.*?)\[ZH\]', -# lambda x: chinese_to_ipa2(x.group(1))+' ', text) -# text = re.sub(r'\[JA\](.*?)\[JA\]', -# lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ')+' ', text) -# text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5', -# '˧˧˦').replace('6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e')+' ', text) -# text = re.sub(r'\[GD\](.*?)\[GD\]', -# lambda x: cantonese_to_ipa(x.group(1))+' ', text) -# text = re.sub(r'\[EN\](.*?)\[EN\]', -# lambda x: english_to_lazy_ipa2(x.group(1))+' ', text) -# text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group( -# 1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ')+' ', text) -# text = re.sub(r'\s+$', '', text) -# text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) -# return text diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/color_ordered_container_arrangement.py b/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/color_ordered_container_arrangement.py deleted file mode 100644 index 5d3da86ee288f7f719c3f9c081aa8649267508e3..0000000000000000000000000000000000000000 --- a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/color_ordered_container_arrangement.py +++ /dev/null @@ -1,58 +0,0 @@ -import numpy as np -import os -import pybullet as p -import random -from cliport.tasks import primitives -from cliport.tasks.grippers import Spatula -from cliport.tasks.task import Task -from cliport.utils import utils -import numpy as np -from cliport.tasks.task import Task -from cliport.utils import utils - -class ColorOrderedContainerArrangement(Task): - """Arrange six containers with blocks of matching colors in a specific color order.""" - - def __init__(self): - super().__init__() - self.max_steps = 20 - self.lang_template = "arrange the containers in the color order: red, blue, green, yellow, orange, and purple" - self.task_completed_desc = "done arranging containers." - self.additional_reset() - - def reset(self, env): - super().reset(env) - - # Define color order - color_order = ['red', 'blue', 'green', 'yellow', 'orange', 'purple'] - - # Add containers and blocks - container_template = 'container/container-template.urdf' - container_size = (0.12, 0.12, 0.02) - replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)} - container_urdf = self.fill_template(container_template, replace) - - block_size = (0.04, 0.04, 0.04) - block_urdf = 'block/block.urdf' - containers = [] - blocks = [] - for color in color_order: - # Add container - container_pose = self.get_random_pose(env, container_size) - container_id = env.add_object(container_urdf, container_pose, color=utils.COLORS[color]) - containers.append(container_id) - - # Add block - block_pose = self.get_random_pose(env, block_size) - block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) - blocks.append(block_id) - - # Add subgoal to place block in container - self.add_goal(objs=[block_id], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False, - rotations=True, metric='pose', params=None, step_max_reward=1/6, - language_goal=self.lang_template) - - # Add final goal to arrange containers in color order - container_poses = [self.get_random_pose(env, container_size) for _ in color_order] - self.add_goal(objs=containers, matches=np.eye(len(color_order)), targ_poses=container_poses, replace=False, - rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template) \ No newline at end of file diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/models/resnet_lang.py b/spaces/Gen-Sim/Gen-Sim/cliport/models/resnet_lang.py deleted file mode 100644 index 0d28cf6b2b859c9750f7061b12cd6fec07176257..0000000000000000000000000000000000000000 --- a/spaces/Gen-Sim/Gen-Sim/cliport/models/resnet_lang.py +++ /dev/null @@ -1,118 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -import cliport.utils.utils as utils -from transformers import DistilBertTokenizer, DistilBertModel -from cliport.models.core import fusion -from cliport.models.resnet import ConvBlock, IdentityBlock - - -class ResNet43_8s_lang(nn.Module): - def __init__(self, input_shape, output_dim, cfg, device, preprocess): - super(ResNet43_8s_lang, self).__init__() - self.input_shape = input_shape - self.input_dim = input_shape[-1] - self.output_dim = output_dim - self.cfg = cfg - self.device = device - self.batchnorm = self.cfg['train']['batchnorm'] - self.lang_fusion_type = self.cfg['train']['lang_fusion_type'] - self.preprocess = preprocess - - self._make_layers() - - def _make_layers(self): - self.conv1 = nn.Sequential( - # conv1 - nn.Conv2d(self.input_dim, 64, stride=1, kernel_size=3, padding=1), - nn.BatchNorm2d(64) if self.batchnorm else nn.Identity(), - nn.ReLU(True), - - # fcn - ConvBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), - IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), - - ConvBlock(64, [128, 128, 128], kernel_size=3, stride=2, batchnorm=self.batchnorm), - IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), - - ConvBlock(128, [256, 256, 256], kernel_size=3, stride=2, batchnorm=self.batchnorm), - IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), - - ConvBlock(256, [512, 512, 512], kernel_size=3, stride=2, batchnorm=self.batchnorm), - IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm), - ) - - - # decoders - self.decoder1 = nn.Sequential( - ConvBlock(512, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), - IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm), - nn.UpsamplingBilinear2d(scale_factor=2), - ) - - self.decoder2 = nn.Sequential( - ConvBlock(256, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), - IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm), - nn.UpsamplingBilinear2d(scale_factor=2), - ) - - self.decoder3 = nn.Sequential( - ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), - IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), - nn.UpsamplingBilinear2d(scale_factor=2), - ) - - self.conv2 = nn.Sequential( - # conv2 - ConvBlock(64, [16, 16, self.output_dim], kernel_size=3, stride=1, - final_relu=False, batchnorm=self.batchnorm), - IdentityBlock(self.output_dim, [16, 16, self.output_dim], kernel_size=3, stride=1, - final_relu=False, batchnorm=self.batchnorm), - ) - - self.tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') - self.text_encoder = DistilBertModel.from_pretrained('distilbert-base-uncased') - self.text_fc = nn.Linear(768, 1024) - - self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2) - self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4) - self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8) - - self.proj_input_dim = 512 if 'word' in self.lang_fusion_type else 1024 - self.lang_proj1 = nn.Linear(self.proj_input_dim, 512) - self.lang_proj2 = nn.Linear(self.proj_input_dim, 256) - self.lang_proj3 = nn.Linear(self.proj_input_dim, 128) - - def encode_text(self, l): - with torch.no_grad(): - inputs = self.tokenizer(l, return_tensors='pt') - input_ids, attention_mask = inputs['input_ids'].to(self.device), inputs['attention_mask'].to(self.device) - text_embeddings = self.text_encoder(input_ids, attention_mask) - text_encodings = text_embeddings.last_hidden_state.mean(1) - text_feat = self.text_fc(text_encodings) - text_mask = torch.ones_like(input_ids) # [1, max_token_len] - return text_feat, text_embeddings.last_hidden_state, text_mask - - def forward(self, x, l): - x = self.preprocess(x, dist='transporter') - - # encode language - l_enc, l_emb, l_mask = self.encode_text(l) - l_input = l_emb if 'word' in self.lang_fusion_type else l_enc - l_input = l_input.to(dtype=x.dtype) - - x = self.conv1(x) - - x = self.lang_fuser1(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj1) - x = self.decoder1(x) - - x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2) - x = self.decoder2(x) - - x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3) - x = self.decoder3(x) - - out = self.conv2(x) - - return out \ No newline at end of file diff --git a/spaces/GoodStuff/Cool/README.md b/spaces/GoodStuff/Cool/README.md deleted file mode 100644 index a8d2e7ac02a0374267ead5310644b6a84129aeb9..0000000000000000000000000000000000000000 --- a/spaces/GoodStuff/Cool/README.md +++ /dev/null @@ -1,9 +0,0 @@ ---- -title: DALL·E mini -emoji: 🥑 -colorFrom: yellow -colorTo: green -sdk: static -pinned: True -license: apache-2.0 ---- diff --git a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/common/residue_constants_test.py b/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/common/residue_constants_test.py deleted file mode 100644 index 3a7981e0d1bb59b58274194b69e23afe9da89bf4..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/common/residue_constants_test.py +++ /dev/null @@ -1,190 +0,0 @@ -# Copyright 2021 DeepMind Technologies Limited -# -# 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. - -"""Test that residue_constants generates correct values.""" - -from absl.testing import absltest -from absl.testing import parameterized -from alphafold.common import residue_constants -import numpy as np - - -class ResidueConstantsTest(parameterized.TestCase): - - @parameterized.parameters( - ('ALA', 0), - ('CYS', 1), - ('HIS', 2), - ('MET', 3), - ('LYS', 4), - ('ARG', 4), - ) - def testChiAnglesAtoms(self, residue_name, chi_num): - chi_angles_atoms = residue_constants.chi_angles_atoms[residue_name] - self.assertLen(chi_angles_atoms, chi_num) - for chi_angle_atoms in chi_angles_atoms: - self.assertLen(chi_angle_atoms, 4) - - def testChiGroupsForAtom(self): - for k, chi_groups in residue_constants.chi_groups_for_atom.items(): - res_name, atom_name = k - for chi_group_i, atom_i in chi_groups: - self.assertEqual( - atom_name, - residue_constants.chi_angles_atoms[res_name][chi_group_i][atom_i]) - - @parameterized.parameters( - ('ALA', 5), ('ARG', 11), ('ASN', 8), ('ASP', 8), ('CYS', 6), ('GLN', 9), - ('GLU', 9), ('GLY', 4), ('HIS', 10), ('ILE', 8), ('LEU', 8), ('LYS', 9), - ('MET', 8), ('PHE', 11), ('PRO', 7), ('SER', 6), ('THR', 7), ('TRP', 14), - ('TYR', 12), ('VAL', 7) - ) - def testResidueAtoms(self, atom_name, num_residue_atoms): - residue_atoms = residue_constants.residue_atoms[atom_name] - self.assertLen(residue_atoms, num_residue_atoms) - - def testStandardAtomMask(self): - with self.subTest('Check shape'): - self.assertEqual(residue_constants.STANDARD_ATOM_MASK.shape, (21, 37,)) - - with self.subTest('Check values'): - str_to_row = lambda s: [c == '1' for c in s] # More clear/concise. - np.testing.assert_array_equal( - residue_constants.STANDARD_ATOM_MASK, - np.array([ - # NB This was defined by c+p but looks sane. - str_to_row('11111 '), # ALA - str_to_row('111111 1 1 11 1 '), # ARG - str_to_row('111111 11 '), # ASP - str_to_row('111111 11 '), # ASN - str_to_row('11111 1 '), # CYS - str_to_row('111111 1 11 '), # GLU - str_to_row('111111 1 11 '), # GLN - str_to_row('111 1 '), # GLY - str_to_row('111111 11 1 1 '), # HIS - str_to_row('11111 11 1 '), # ILE - str_to_row('111111 11 '), # LEU - str_to_row('111111 1 1 1 '), # LYS - str_to_row('111111 11 '), # MET - str_to_row('111111 11 11 1 '), # PHE - str_to_row('111111 1 '), # PRO - str_to_row('11111 1 '), # SER - str_to_row('11111 1 1 '), # THR - str_to_row('111111 11 11 1 1 11 '), # TRP - str_to_row('111111 11 11 11 '), # TYR - str_to_row('11111 11 '), # VAL - str_to_row(' '), # UNK - ])) - - with self.subTest('Check row totals'): - # Check each row has the right number of atoms. - for row, restype in enumerate(residue_constants.restypes): # A, R, ... - long_restype = residue_constants.restype_1to3[restype] # ALA, ARG, ... - atoms_names = residue_constants.residue_atoms[ - long_restype] # ['C', 'CA', 'CB', 'N', 'O'], ... - self.assertLen(atoms_names, - residue_constants.STANDARD_ATOM_MASK[row, :].sum(), - long_restype) - - def testAtomTypes(self): - self.assertEqual(residue_constants.atom_type_num, 37) - - self.assertEqual(residue_constants.atom_types[0], 'N') - self.assertEqual(residue_constants.atom_types[1], 'CA') - self.assertEqual(residue_constants.atom_types[2], 'C') - self.assertEqual(residue_constants.atom_types[3], 'CB') - self.assertEqual(residue_constants.atom_types[4], 'O') - - self.assertEqual(residue_constants.atom_order['N'], 0) - self.assertEqual(residue_constants.atom_order['CA'], 1) - self.assertEqual(residue_constants.atom_order['C'], 2) - self.assertEqual(residue_constants.atom_order['CB'], 3) - self.assertEqual(residue_constants.atom_order['O'], 4) - self.assertEqual(residue_constants.atom_type_num, 37) - - def testRestypes(self): - three_letter_restypes = [ - residue_constants.restype_1to3[r] for r in residue_constants.restypes] - for restype, exp_restype in zip( - three_letter_restypes, sorted(residue_constants.restype_1to3.values())): - self.assertEqual(restype, exp_restype) - self.assertEqual(residue_constants.restype_num, 20) - - def testSequenceToOneHotHHBlits(self): - one_hot = residue_constants.sequence_to_onehot( - 'ABCDEFGHIJKLMNOPQRSTUVWXYZ-', residue_constants.HHBLITS_AA_TO_ID) - exp_one_hot = np.array( - [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], - [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], - [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]) - np.testing.assert_array_equal(one_hot, exp_one_hot) - - def testSequenceToOneHotStandard(self): - one_hot = residue_constants.sequence_to_onehot( - 'ARNDCQEGHILKMFPSTWYV', residue_constants.restype_order) - np.testing.assert_array_equal(one_hot, np.eye(20)) - - def testSequenceToOneHotUnknownMapping(self): - seq = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' - expected_out = np.zeros([26, 21]) - for row, position in enumerate( - [0, 20, 4, 3, 6, 13, 7, 8, 9, 20, 11, 10, 12, 2, 20, 14, 5, 1, 15, 16, - 20, 19, 17, 20, 18, 20]): - expected_out[row, position] = 1 - aa_types = residue_constants.sequence_to_onehot( - sequence=seq, - mapping=residue_constants.restype_order_with_x, - map_unknown_to_x=True) - self.assertTrue((aa_types == expected_out).all()) - - @parameterized.named_parameters( - ('lowercase', 'aaa'), # Insertions in A3M. - ('gaps', '---'), # Gaps in A3M. - ('dots', '...'), # Gaps in A3M. - ('metadata', '>TEST'), # FASTA metadata line. - ) - def testSequenceToOneHotUnknownMappingError(self, seq): - with self.assertRaises(ValueError): - residue_constants.sequence_to_onehot( - sequence=seq, - mapping=residue_constants.restype_order_with_x, - map_unknown_to_x=True) - - -if __name__ == '__main__': - absltest.main() diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gcnet/README.md b/spaces/Gradio-Blocks/uniformer_image_detection/configs/gcnet/README.md deleted file mode 100644 index 0ef8db737743c63fbf2089e53d8f5302b52ee5e6..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gcnet/README.md +++ /dev/null @@ -1,59 +0,0 @@ -# GCNet for Object Detection - -By [Yue Cao](http://yue-cao.me), [Jiarui Xu](http://jerryxu.net), [Stephen Lin](https://scholar.google.com/citations?user=c3PYmxUAAAAJ&hl=en), Fangyun Wei, [Han Hu](https://sites.google.com/site/hanhushomepage/). - -We provide config files to reproduce the results in the paper for -["GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond"](https://arxiv.org/abs/1904.11492) on COCO object detection. - -## Introduction - -[ALGORITHM] - -**GCNet** is initially described in [arxiv](https://arxiv.org/abs/1904.11492). Via absorbing advantages of Non-Local Networks (NLNet) and Squeeze-Excitation Networks (SENet), GCNet provides a simple, fast and effective approach for global context modeling, which generally outperforms both NLNet and SENet on major benchmarks for various recognition tasks. - -## Citing GCNet - -```latex -@article{cao2019GCNet, - title={GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond}, - author={Cao, Yue and Xu, Jiarui and Lin, Stephen and Wei, Fangyun and Hu, Han}, - journal={arXiv preprint arXiv:1904.11492}, - year={2019} -} -``` - -## Results and models - -The results on COCO 2017val are shown in the below table. - -| Backbone | Model | Context | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | -| :-------: | :--------------: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | -| R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | | 39.7 | 35.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915.log.json) | -| R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.0 | 39.9 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204_024626.log.json) | -| R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 11.4 | 41.3 | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205_192835.log.json) | -| R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.6 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206_112128.log.json) | - -| Backbone | Model | Context | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | -| :-------: | :--------------: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :-------: | -| R-50-FPN | Mask | - | 1x | 4.4 | 16.6 | 38.4 | 34.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202_214122.log.json) | -| R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | 15.5 | 40.4 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202_174907.log.json) | -| R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.1 | 40.7 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) | -| R-101-FPN | Mask | - | 1x | 6.4 | 13.3 | 40.5 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210_220422.log.json) | -| R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 12.0 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207_015330.log.json) | -| R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.8 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) | -| X-101-FPN | Mask | - | 1x | 7.6 | 11.3 | 42.4 | 37.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211_054326.log.json) | -| X-101-FPN | Mask | GC(c3-c5, r16) | 1x | 8.8 | 9.8 | 43.5 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_164715.log.json) | -| X-101-FPN | Mask | GC(c3-c5, r4) | 1x | 9.0 | 9.7 | 43.9 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212_070942.log.json) | -| X-101-FPN | Cascade Mask | - | 1x | 9.2 | 8.4 | 44.7 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310_115217.log.json) | -| X-101-FPN | Cascade Mask | GC(c3-c5, r16) | 1x | 10.3 | 7.7 | 46.2 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_184154.log.json) | -| X-101-FPN | Cascade Mask | GC(c3-c5, r4) | 1x | 10.6 | | 46.4 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653.log.json) | -| X-101-FPN | DCN Cascade Mask | - | 1x | | | 44.9 | 38.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20200516_182249-680fc3f2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20200516_182249.log.json)| -| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r16) | 1x | | | 44.6 | |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20200516_015634-08f56b56.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20200516_015634.log.json) | -| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r4) | 1x | | | 45.7 | 39.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20200518_041145-24cabcfd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20200518_041145.log.json) | - -**Notes:** - -- The `SyncBN` is added in the backbone for all models in **Table 2**. -- `GC` denotes Global Context (GC) block is inserted after 1x1 conv of backbone. -- `DCN` denotes replace 3x3 conv with 3x3 Deformable Convolution in `c3-c5` stages of backbone. -- `r4` and `r16` denote ratio 4 and ratio 16 in GC block respectively. diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py deleted file mode 100644 index 0acd088a469e682011a90b770efa51116f6c42ca..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py +++ /dev/null @@ -1,13 +0,0 @@ -_base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py' -model = dict( - pretrained='open-mmlab://resnext101_64x4d', - backbone=dict( - type='ResNeXt', - depth=101, - groups=64, - base_width=4, - num_stages=4, - out_indices=(0, 1, 2, 3), - frozen_stages=1, - norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/dense_heads/retina_head.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/dense_heads/retina_head.py deleted file mode 100644 index b12416fa8332f02b9a04bbfc7926f6d13875e61b..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/dense_heads/retina_head.py +++ /dev/null @@ -1,114 +0,0 @@ -import torch.nn as nn -from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init - -from ..builder import HEADS -from .anchor_head import AnchorHead - - -@HEADS.register_module() -class RetinaHead(AnchorHead): - r"""An anchor-based head used in `RetinaNet - `_. - - The head contains two subnetworks. The first classifies anchor boxes and - the second regresses deltas for the anchors. - - Example: - >>> import torch - >>> self = RetinaHead(11, 7) - >>> x = torch.rand(1, 7, 32, 32) - >>> cls_score, bbox_pred = self.forward_single(x) - >>> # Each anchor predicts a score for each class except background - >>> cls_per_anchor = cls_score.shape[1] / self.num_anchors - >>> box_per_anchor = bbox_pred.shape[1] / self.num_anchors - >>> assert cls_per_anchor == (self.num_classes) - >>> assert box_per_anchor == 4 - """ - - def __init__(self, - num_classes, - in_channels, - stacked_convs=4, - conv_cfg=None, - norm_cfg=None, - anchor_generator=dict( - type='AnchorGenerator', - octave_base_scale=4, - scales_per_octave=3, - ratios=[0.5, 1.0, 2.0], - strides=[8, 16, 32, 64, 128]), - **kwargs): - self.stacked_convs = stacked_convs - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - super(RetinaHead, self).__init__( - num_classes, - in_channels, - anchor_generator=anchor_generator, - **kwargs) - - def _init_layers(self): - """Initialize layers of the head.""" - self.relu = nn.ReLU(inplace=True) - self.cls_convs = nn.ModuleList() - self.reg_convs = nn.ModuleList() - for i in range(self.stacked_convs): - chn = self.in_channels if i == 0 else self.feat_channels - self.cls_convs.append( - ConvModule( - chn, - self.feat_channels, - 3, - stride=1, - padding=1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg)) - self.reg_convs.append( - ConvModule( - chn, - self.feat_channels, - 3, - stride=1, - padding=1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg)) - self.retina_cls = nn.Conv2d( - self.feat_channels, - self.num_anchors * self.cls_out_channels, - 3, - padding=1) - self.retina_reg = nn.Conv2d( - self.feat_channels, self.num_anchors * 4, 3, padding=1) - - def init_weights(self): - """Initialize weights of the head.""" - for m in self.cls_convs: - normal_init(m.conv, std=0.01) - for m in self.reg_convs: - normal_init(m.conv, std=0.01) - bias_cls = bias_init_with_prob(0.01) - normal_init(self.retina_cls, std=0.01, bias=bias_cls) - normal_init(self.retina_reg, std=0.01) - - def forward_single(self, x): - """Forward feature of a single scale level. - - Args: - x (Tensor): Features of a single scale level. - - Returns: - tuple: - cls_score (Tensor): Cls scores for a single scale level - the channels number is num_anchors * num_classes. - bbox_pred (Tensor): Box energies / deltas for a single scale - level, the channels number is num_anchors * 4. - """ - cls_feat = x - reg_feat = x - for cls_conv in self.cls_convs: - cls_feat = cls_conv(cls_feat) - for reg_conv in self.reg_convs: - reg_feat = reg_conv(reg_feat) - cls_score = self.retina_cls(cls_feat) - bbox_pred = self.retina_reg(reg_feat) - return cls_score, bbox_pred diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py deleted file mode 100644 index c094391b1dfcef2fa6278f0c181fb50c303f7a4c..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py +++ /dev/null @@ -1,39 +0,0 @@ -_base_ = './ocrnet_hr18_512x1024_160k_cityscapes.py' -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w48', - backbone=dict( - extra=dict( - stage2=dict(num_channels=(48, 96)), - stage3=dict(num_channels=(48, 96, 192)), - stage4=dict(num_channels=(48, 96, 192, 384)))), - decode_head=[ - dict( - type='FCNHead', - in_channels=[48, 96, 192, 384], - channels=sum([48, 96, 192, 384]), - input_transform='resize_concat', - in_index=(0, 1, 2, 3), - kernel_size=1, - num_convs=1, - norm_cfg=norm_cfg, - concat_input=False, - dropout_ratio=-1, - num_classes=19, - align_corners=False, - loss_decode=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), - dict( - type='OCRHead', - in_channels=[48, 96, 192, 384], - channels=512, - ocr_channels=256, - input_transform='resize_concat', - in_index=(0, 1, 2, 3), - norm_cfg=norm_cfg, - dropout_ratio=-1, - num_classes=19, - align_corners=False, - loss_decode=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)) - ]) diff --git a/spaces/HReynaud/EchoDiffusionDemo/README.md b/spaces/HReynaud/EchoDiffusionDemo/README.md deleted file mode 100644 index 72b18dcdc118c6f5be1d45e6641f6578faf18571..0000000000000000000000000000000000000000 --- a/spaces/HReynaud/EchoDiffusionDemo/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: EchoNet Video Diffusion -emoji: 🖤 -colorFrom: gray -colorTo: purple -sdk: gradio -sdk_version: 3.17.0 -app_file: app.py -pinned: false -license: mit -duplicated_from: anon-SGXT/echocardiogram-video-diffusion ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py deleted file mode 100644 index 5f292528f80d6bb51f16a4324d97342d28fce942..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py +++ /dev/null @@ -1,447 +0,0 @@ -# Copyright (c) 2017-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the LICENSE file in -# the root directory of this source tree. An additional grant of patent rights -# can be found in the PATENTS file in the same directory. - -from dataclasses import dataclass, field -import logging -import math -import os -from typing import Optional -import torch - -from fairseq.logging import metrics -from fairseq.tasks import FairseqTask, register_task -from ..data import ExtractedFeaturesDataset, RandomInputDataset - -from fairseq.data import ( - Dictionary, - data_utils, - StripTokenDataset, -) -from fairseq.dataclass import FairseqDataclass -from fairseq.distributed.utils import get_data_parallel_world_size -from omegaconf import MISSING - -from examples.speech_recognition.kaldi.kaldi_decoder import ( - KaldiDecoder, - KaldiDecoderConfig, -) - - -logger = logging.getLogger(__name__) - - -@dataclass -class DecodingConfig(FairseqDataclass): - kenlm_path: Optional[str] = None - lm_weight: float = 0 - blank_weight: float = 0 - - -@dataclass -class UnpairedAudioTextConfig(FairseqDataclass): - data: str = field( - default=MISSING, metadata={"help": "path to data directory containing audio"} - ) - text_data: str = field( - default=MISSING, metadata={"help": "path to data directory containing text"} - ) - max_length: Optional[int] = None - labels: Optional[str] = field( - default=None, - metadata={"help": "extension of the label file to load, used for fine-tuning"}, - ) - unfiltered: bool = field( - default=False, metadata={"help": "load data with _unfiltered suffix"} - ) - ctc_eval: bool = field( - default=False, metadata={"help": "eval UER as if computed by CTC"} - ) - sort_by_length: bool = field( - default=True, metadata={"help": "sort examples by length of audio timesteps"} - ) - shuffle: bool = field(default=True, metadata={"help": "shuffle examples"}) - append_eos: bool = field(default=False, metadata={"help": "append eos"}) - uppercase: Optional[bool] = field( - default=False, metadata={"help": "uppercase for LM score computation"} - ) - skipwords: Optional[str] = field( - default="", - metadata={ - "help": "comma-separated words to be removed for LM score computation" - }, - ) - kenlm_path: Optional[str] = None - vocab_usage_power: float = 2 - - word_decoder_config: Optional[KaldiDecoderConfig] = None - word_kenlm_path: Optional[str] = None - - decoding_config: DecodingConfig = DecodingConfig() - - -@register_task("unpaired_audio_text", dataclass=UnpairedAudioTextConfig) -class UnpairedAudioText(FairseqTask): - """ """ - - cfg: UnpairedAudioTextConfig - - def __init__( - self, - cfg: UnpairedAudioTextConfig, - source_dictionary=None, - target_dictionary=None, - ): - super().__init__(cfg) - - self._target_dictionary = target_dictionary - self._source_dictionary = source_dictionary - self.num_symbols = ( - len([s for s in target_dictionary.symbols if not s.startswith("madeup")]) - - target_dictionary.nspecial - ) - self.sil_id = ( - target_dictionary.index("") if "" in target_dictionary else -1 - ) - self.kenlm = None - if cfg.kenlm_path is not None: - import kenlm - - self.kenlm = kenlm.Model(cfg.kenlm_path) - - self.word_kenlm = None - if cfg.word_kenlm_path is not None: - import kenlm - - self.word_kenlm = kenlm.Model(cfg.word_kenlm_path) - - self.uppercase = cfg.uppercase - self.skipwords = set(cfg.skipwords.split(",")) - - def str_postprocess(s): - s = " ".join(w for w in s.split() if w not in self.skipwords) - s = s.upper() if self.uppercase else s - return s - - self.str_postprocess = str_postprocess - self.compute_lm_score = lambda s: self.kenlm.score(self.str_postprocess(s)) - - self.compute_word_score = None - if cfg.word_decoder_config is not None: - self.kaldi_decoder = KaldiDecoder(cfg.word_decoder_config, beam=10) - - def compute_word_score(logits, padding): - res = self.kaldi_decoder.decode(logits, padding) - for r in res: - r = r.result() - assert len(r) == 1 - r = r[0] - yield r["score"], r["words"] - - self.compute_word_score = compute_word_score - - @classmethod - def setup_task(cls, cfg: UnpairedAudioTextConfig, **kwargs): - """Setup the task (e.g., load dictionaries). - - Args: - cfg (AudioPretrainingConfig): configuration of this task - """ - - dict_path = os.path.join(cfg.text_data, "dict.txt") - if os.path.exists(dict_path): - target_dictionary = Dictionary.load(dict_path) - else: - dict_path = os.path.join(cfg.data, f"dict.{cfg.labels}.txt") - target_dictionary = Dictionary.load(dict_path) - - return cls(cfg, target_dictionary=target_dictionary) - - def optimizer_step(self, optimizer, model, update_num): - if hasattr(model, "get_groups_for_update"): - groups = model.get_groups_for_update(update_num) - optimizer.step(groups={groups}) - else: - optimizer.step() - - def valid_step(self, sample, model, criterion): - res = model( - **sample["net_input"], - dense_x_only=True, - ) - - dense_x = res["logits"] - padding_mask = res["padding_mask"] - - word_scores = None - if self.compute_word_score is not None: - word_scores = self.compute_word_score(dense_x.cpu(), padding_mask.cpu()) - - z = dense_x.argmax(-1) - z[padding_mask] = self.target_dictionary.pad() - - vocab_seen = torch.zeros(self.num_symbols, dtype=torch.bool) - - import editdistance - - c_err = 0 - c_len = 0 - pred_c_len = 0 - lm_score_sum = 0 - for i, (x, t, id) in enumerate( - zip( - z, - sample["target"] if "target" in sample else [None] * len(z), - sample["id"], - ) - ): - - if t is not None: - t = t[(t >= self.target_dictionary.nspecial)] - x = x[ - (x >= self.target_dictionary.nspecial) - & (x < (self.num_symbols + self.target_dictionary.nspecial)) - ] - if self.sil_id >= 0: - x = x[x != self.sil_id] - - vocab_seen[x - self.target_dictionary.nspecial] = True - - pred_units_arr = x - if self.cfg.ctc_eval: - pred_units_arr = pred_units_arr.unique_consecutive() - pred_units_arr = pred_units_arr[pred_units_arr != 0] - - if id == 0: - if t is not None: - logger.info(f"REF: {self.target_dictionary.string(t)}") - logger.info(f"HYP: {self.target_dictionary.string(pred_units_arr)}") - - if self.kenlm is not None: - if t is not None: - ref_lm_s = self.compute_lm_score( - self.target_dictionary.string(t) - ) - logger.info( - f"LM [REF]: {ref_lm_s}, {math.pow(10, -ref_lm_s / (len(t) + 1))}" - ) - - hyp_lm_s = self.compute_lm_score( - self.target_dictionary.string(pred_units_arr) - ) - logger.info( - f"LM [HYP]: {hyp_lm_s}, {math.pow(10, -hyp_lm_s / (len(pred_units_arr) + 1))}" - ) - - pred_units_arr = pred_units_arr.tolist() - - pred_c_len += len(pred_units_arr) - - if t is not None: - t = t.tolist() - c_err += editdistance.eval(pred_units_arr, t) - c_len += len(t) - else: - c_len = pred_c_len - - if self.kenlm is not None: - pred_str = self.target_dictionary.string(pred_units_arr) - lm_score = self.compute_lm_score(pred_str) - lm_score_sum += lm_score - - kaldi_score_sum = 0 - word_lm_sum = 0 - num_words = 0 - if word_scores is not None: - for score, words in word_scores: - kaldi_score_sum += score - num_words += len(words) - if self.word_kenlm is not None: - word_lm_sum += self.kenlm.score(" ".join(words)) - - try: - world_size = get_data_parallel_world_size() - except: - world_size = 1 - - logging_output = { - "loss": c_err, - "_num_char_errors": c_err, - "_num_chars": c_len, - "_num_pred_chars": pred_c_len, - "ntokens": c_len, - "nsentences": z.size(0), - "sample_size": c_len, - "_world_size": world_size, - "_lm_score_sum": lm_score_sum, - "_kaldi_score_sum": kaldi_score_sum, - "_word_lm_sum": word_lm_sum, - "_num_words": num_words, - "_vocab_seen": vocab_seen, - } - - return c_err, c_len, logging_output - - def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): - data_path = self.cfg.data - task_cfg = task_cfg or self.cfg - - has_unpaired_text = os.path.exists( - os.path.join(self.cfg.text_data, f"{split}.idx") - ) - - self.datasets[split] = ExtractedFeaturesDataset( - path=data_path, - split=split, - min_length=3, - max_length=task_cfg.max_length, - labels=None if has_unpaired_text else task_cfg.labels, - label_dict=self.target_dictionary, - shuffle=getattr(task_cfg, "shuffle", True), - sort_by_length=task_cfg.sort_by_length, - ) - - logger.info(f"split {split} has unpaired text? {has_unpaired_text}") - if has_unpaired_text: - text_dataset = data_utils.load_indexed_dataset( - os.path.join(self.cfg.text_data, split), self.target_dictionary - ) - text_dataset = StripTokenDataset(text_dataset, self.target_dictionary.eos()) - self.datasets[split] = RandomInputDataset( - self.datasets[split], - text_dataset, - ["random_label"], - add_to_input=True, - pad_idx=self.target_dictionary.pad(), - ) - - @property - def source_dictionary(self): - return self._source_dictionary - - @property - def target_dictionary(self): - """Return the :class:`~fairseq.data.Dictionary` for the language - model.""" - return self._target_dictionary - - def max_positions(self): - """Maximum input length supported by the encoder.""" - return None - - def reduce_metrics(self, logging_outputs, criterion): - super().reduce_metrics(logging_outputs, criterion) - - zero = torch.scalar_tensor(0.0) - num_char_errors = sum( - log.get("_num_char_errors", zero) for log in logging_outputs - ) - num_chars = sum(log.get("_num_chars", zero) for log in logging_outputs) - num_word_errors = sum( - log.get("_num_word_errors", zero) for log in logging_outputs - ) - num_words = sum(log.get("_num_words", zero) for log in logging_outputs) - num_pred_chars = sum( - log.get("_num_pred_chars", zero) for log in logging_outputs - ) - - lm_score_sum = sum(log.get("_lm_score_sum", zero) for log in logging_outputs) - vocab_seen = ( - sum(log.get("_vocab_seen", zero) for log in logging_outputs) - .bool() - .sum() - .item() - ) - kaldi_score_sum = sum( - log.get("_kaldi_score_sum", zero) for log in logging_outputs - ) - word_lm_sum = sum(log.get("_word_lm_sum", zero) for log in logging_outputs) - - metrics.log_scalar_sum("_num_char_errors", num_char_errors) - metrics.log_scalar_sum("_num_chars", num_chars) - metrics.log_scalar_sum("_num_word_errors", num_word_errors) - metrics.log_scalar_sum("_num_words", num_words) - - metrics.log_scalar_sum("lm_score_sum", lm_score_sum) - metrics.log_scalar_sum("num_pred_chars", num_pred_chars) - - if self.cfg.word_kenlm_path is not None: - metrics.log_scalar_sum("kaldi_score_sum", kaldi_score_sum) - metrics.log_scalar_sum("word_lm_sum", word_lm_sum) - - if num_chars > 0: - metrics.log_derived( - "uer", - lambda meters: meters["_num_char_errors"].sum - * 100.0 - / meters["_num_chars"].sum - if meters["_num_chars"].sum > 0 - else float("nan"), - ) - - if lm_score_sum < 0 and vocab_seen > 0: - metrics.log_scalar("vocab_seen_pct", vocab_seen / self.num_symbols) - - metrics.log_derived( - "weighted_lm_ppl", - lambda meters: math.pow( - 10, - -meters["lm_score_sum"].sum - / ( - meters["num_pred_chars"].sum + meters["nsentences"].sum - ), # account for - ) - / meters["vocab_seen_pct"].avg ** self.cfg.vocab_usage_power, - ) - - metrics.log_derived( - "lm_ppl", - lambda meters: math.pow( - 10, - -meters["lm_score_sum"].sum - / ( - meters["num_pred_chars"].sum + meters["nsentences"].sum - ), # account for - ), - ) - else: - metrics.log_derived("weighted_lm_ppl", lambda meters: float("inf")) - - if num_words > 0: - if word_lm_sum != 0: - metrics.log_derived( - "word_lm_ppl", - lambda meters: math.pow( - 10, - -meters["word_lm_sum"].sum - / ( - meters["_num_words"].sum + meters["nsentences"].sum - ), # account for - ), - ) - metrics.log_derived( - "weighted_word_lm_ppl", - lambda meters: math.pow( - 10, - -meters["word_lm_sum"].sum - / ( - meters["_num_words"].sum + meters["nsentences"].sum - ), # account for - ) - / meters["vocab_seen_pct"].avg ** self.cfg.vocab_usage_power, - ) - - if self.cfg.word_kenlm_path is not None: - metrics.log_derived( - "kaldi_score", - lambda meters: meters["kaldi_score_sum"].sum - / meters["nsentences"].sum, - ) - - def build_model(self, cfg: FairseqDataclass): - model = super().build_model(cfg) - - return model diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/adagrad.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/adagrad.py deleted file mode 100644 index 4f539541c1c91d8c822f7ce624fa6eabf744f60e..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/adagrad.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.optim - -from . import LegacyFairseqOptimizer, register_optimizer - - -@register_optimizer("adagrad") -class Adagrad(LegacyFairseqOptimizer): - def __init__(self, args, params): - super().__init__(args) - self._optimizer = torch.optim.Adagrad(params, **self.optimizer_config) - - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', - help='weight decay') - # fmt: on - - @property - def optimizer_config(self): - """ - Return a kwarg dictionary that will be used to override optimizer - args stored in checkpoints. This allows us to load a checkpoint and - resume training using a different set of optimizer args, e.g., with a - different learning rate. - """ - return { - "lr": self.args.lr[0], - "weight_decay": self.args.weight_decay, - } - - @property - def supports_flat_params(self): - return False diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/test_dataclass_utils.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/test_dataclass_utils.py deleted file mode 100644 index 45fc391a979feb198b0a4ecea69c31f1340e87d2..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/test_dataclass_utils.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest -from argparse import ArgumentParser -from dataclasses import dataclass, field - -from fairseq.dataclass import FairseqDataclass -from fairseq.dataclass.utils import gen_parser_from_dataclass - - -@dataclass -class A(FairseqDataclass): - data: str = field(default="test", metadata={"help": "the data input"}) - num_layers: int = field(default=200, metadata={"help": "more layers is better?"}) - - -@dataclass -class B(FairseqDataclass): - bar: A = field(default=A()) - foo: int = field(default=0, metadata={"help": "not a bar"}) - - -@dataclass -class D(FairseqDataclass): - arch: A = field(default=A()) - foo: int = field(default=0, metadata={"help": "not a bar"}) - - -@dataclass -class C(FairseqDataclass): - data: str = field(default="test", metadata={"help": "root level data input"}) - encoder: D = field(default=D()) - decoder: A = field(default=A()) - lr: int = field(default=0, metadata={"help": "learning rate"}) - - -class TestDataclassUtils(unittest.TestCase): - def test_argparse_convert_basic(self): - parser = ArgumentParser() - gen_parser_from_dataclass(parser, A(), True) - args = parser.parse_args(["--num-layers", '10', "the/data/path"]) - self.assertEqual(args.num_layers, 10) - self.assertEqual(args.data, "the/data/path") - - def test_argparse_recursive(self): - parser = ArgumentParser() - gen_parser_from_dataclass(parser, B(), True) - args = parser.parse_args(["--num-layers", "10", "--foo", "10", "the/data/path"]) - self.assertEqual(args.num_layers, 10) - self.assertEqual(args.foo, 10) - self.assertEqual(args.data, "the/data/path") - - def test_argparse_recursive_prefixing(self): - self.maxDiff = None - parser = ArgumentParser() - gen_parser_from_dataclass(parser, C(), True, "") - args = parser.parse_args( - [ - "--encoder-arch-data", - "ENCODER_ARCH_DATA", - "--encoder-arch-num-layers", - "10", - "--encoder-foo", - "10", - "--decoder-data", - "DECODER_DATA", - "--decoder-num-layers", - "10", - "--lr", - "10", - "the/data/path", - ] - ) - self.assertEqual(args.encoder_arch_data, "ENCODER_ARCH_DATA") - self.assertEqual(args.encoder_arch_num_layers, 10) - self.assertEqual(args.encoder_foo, 10) - self.assertEqual(args.decoder_data, "DECODER_DATA") - self.assertEqual(args.decoder_num_layers, 10) - self.assertEqual(args.lr, 10) - self.assertEqual(args.data, "the/data/path") - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/src/glow_tts/hifi/models.py b/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/src/glow_tts/hifi/models.py deleted file mode 100644 index aaf911836119d69129abe22aa4fc875f2ba3d53c..0000000000000000000000000000000000000000 --- a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/src/glow_tts/hifi/models.py +++ /dev/null @@ -1,403 +0,0 @@ -import torch -import torch.nn.functional as F -import torch.nn as nn -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from .utils import init_weights, get_padding - -LRELU_SLOPE = 0.1 - - -class ResBlock1(torch.nn.Module): - def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.h = h - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - def forward(self, x): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - xt = c2(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.h = h - self.convs = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - ] - ) - self.convs.apply(init_weights) - - def forward(self, x): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Generator(torch.nn.Module): - def __init__(self, h): - super(Generator, self).__init__() - self.h = h - self.num_kernels = len(h.resblock_kernel_sizes) - self.num_upsamples = len(h.upsample_rates) - self.conv_pre = weight_norm( - Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3) - ) - resblock = ResBlock1 if h.resblock == "1" else ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): - self.ups.append( - weight_norm( - ConvTranspose1d( - h.upsample_initial_channel // (2 ** i), - h.upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = h.upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes) - ): - self.resblocks.append(resblock(h, ch, k, d)) - - self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) - self.ups.apply(init_weights) - self.conv_post.apply(init_weights) - - def forward(self, x): - x = self.conv_pre(x) - for i in range(self.num_upsamples): - x = F.leaky_relu(x, LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print("Removing weight norm...") - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - remove_weight_norm(self.conv_pre) - remove_weight_norm(self.conv_post) - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f( - Conv2d( - 1, - 32, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(5, 1), 0), - ) - ), - norm_f( - Conv2d( - 32, - 128, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(5, 1), 0), - ) - ), - norm_f( - Conv2d( - 128, - 512, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(5, 1), 0), - ) - ), - norm_f( - Conv2d( - 512, - 1024, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(5, 1), 0), - ) - ), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), - ] - ) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self): - super(MultiPeriodDiscriminator, self).__init__() - self.discriminators = nn.ModuleList( - [ - DiscriminatorP(2), - DiscriminatorP(3), - DiscriminatorP(5), - DiscriminatorP(7), - DiscriminatorP(11), - ] - ) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f(Conv1d(1, 128, 15, 1, padding=7)), - norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), - norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), - norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ] - ) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiScaleDiscriminator(torch.nn.Module): - def __init__(self): - super(MultiScaleDiscriminator, self).__init__() - self.discriminators = nn.ModuleList( - [ - DiscriminatorS(use_spectral_norm=True), - DiscriminatorS(), - DiscriminatorS(), - ] - ) - self.meanpools = nn.ModuleList( - [AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)] - ) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - if i != 0: - y = self.meanpools[i - 1](y) - y_hat = self.meanpools[i - 1](y_hat) - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -def feature_loss(fmap_r, fmap_g): - loss = 0 - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - loss += torch.mean(torch.abs(rl - gl)) - - return loss * 2 - - -def discriminator_loss(disc_real_outputs, disc_generated_outputs): - loss = 0 - r_losses = [] - g_losses = [] - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - r_loss = torch.mean((1 - dr) ** 2) - g_loss = torch.mean(dg ** 2) - loss += r_loss + g_loss - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - - return loss, r_losses, g_losses - - -def generator_loss(disc_outputs): - loss = 0 - gen_losses = [] - for dg in disc_outputs: - l = torch.mean((1 - dg) ** 2) - gen_losses.append(l) - loss += l - - return loss, gen_losses diff --git a/spaces/HemanthSai7/IntelligentQuestionGenerator/src/PreviousVersionCode/context.py b/spaces/HemanthSai7/IntelligentQuestionGenerator/src/PreviousVersionCode/context.py deleted file mode 100644 index 29f365b8990107c34ab1343a4dbef82e0b791d75..0000000000000000000000000000000000000000 --- a/spaces/HemanthSai7/IntelligentQuestionGenerator/src/PreviousVersionCode/context.py +++ /dev/null @@ -1,379 +0,0 @@ -# -*- coding: utf-8 -*- -"""context - -Automatically generated by Colaboratory. - -Original file is located at - https://colab.research.google.com/drive/1qLh1aASQj5HIENPZpHQltTuShZny_567 -""" - -# !pip install -q transformers - -# Import important libraries -# Commented out IPython magic to ensure Python compatibility. -import os -import json -import wanb -from pprint import pprint - -import torch -from torch.utils.data import Dataset -from torch.utils.data import DataLoader -from transformers import AdamW -from tqdm.notebook import tqdm -from transformers import BertForQuestionAnswering,BertTokenizer,BertTokenizerFast - -import numpy as np -import matplotlib.pyplot as plt -import seaborn as sns -import pandas as pd -# %matplotlib inline - -#connecting to wandb -wandb.login() - -#Sweep Configuration -PROJECT_NAME="context" -ENTITY=None - -sweep_config={ - 'method':'random' -} - -#set metric information --> we want to minimize the loss function. -metric = { - 'name': 'Validation accuracy', - 'goal': 'maximize' - } -sweep_config['metric'] = metric - -#set all other hyperparameters -parameters_dict = { - 'epochs':{ - 'values': [1] - }, - 'optimizer':{ - 'values': ['sgd','adam'] - }, - 'momentum':{ - 'distribution': 'uniform', - 'min': 0.5, - 'max': 0.99 - }, - 'batch_size':{ - 'distribution': 'q_log_uniform_values', - 'q': 8, - 'min': 16, - 'max': 256 - } - } -sweep_config['parameters'] = parameters_dict - -#print the configuration of the sweep -pprint(sweep_config) - -#initialize the sweep -sweep_id=wandb.sweep(sweep_config,project=PROJECT_NAME,entity=ENTITY) - -# Mount the Google Drive to save the model -from google.colab import drive -drive.mount('/content/drive') - -if not os.path.exists('/content/drive/MyDrive/BERT-SQuAD'): - os.mkdir('/content/drive/MyDrive/BERT-SQuAD') - -# Download SQuAD 2.0 data -# !wget -nc https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json -# !wget -nc https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json - -"""Load the training dataset and take a look at it""" -with open('train-v2.0.json','rb') as f: - squad=json.load(f) - -# Each 'data' dict has two keys (title and paragraphs) -squad['data'][150]['paragraphs'][0]['context'] - -"""Load the dev dataset and take a look at it""" -def read_data(path): - - with open(path,'rb') as f: - squad=json.load(f) - - contexts=[] - questions=[] - answers=[] - for group in squad['data']: - for passage in group['paragraphs']: - context=passage['context'] - for qna in passage['qas']: - question=qna['question'] - for answer in qna['answers']: - contexts.append(context) - questions.append(question) - answers.append(answer) - return contexts,questions,answers - - -#Put the contexts, questions and answers for training and validation into the appropriate lists. -""" -The answers are dictionaries whith the answer text and an integer which indicates the start index of the answer in the context. -""" -train_contexts,train_questions,train_answers=read_data('train-v2.0.json') -valid_contexts,valid_questions,valid_answers=read_data('dev-v2.0.json') -# print(train_contexts[:10]) - -# Create a dictionary to map the words to their indices -def end_idx(answers,contexts): - for answers,context in zip(answers,contexts): - gold_text=answers['text'] - start_idx=answers['answer_start'] - end_idx=start_idx+len(gold_text) - - # sometimes squad answers are off by a character or two so we fix this - if context[start_idx:end_idx] == gold_text: - answers['answer_end'] = end_idx - elif context[start_idx-1:end_idx-1] == gold_text: - answers['answer_start'] = start_idx - 1 - answers['answer_end'] = end_idx - 1 # When the gold label is off by one character - elif context[start_idx-2:end_idx-2] == gold_text: - answers['answer_start'] = start_idx - 2 - answers['answer_end'] = end_idx - 2 # When the gold label is off by two characters - - -""""Tokenization""" -tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') -train_encodings = tokenizer(train_contexts, train_questions, truncation=True, padding=True) -valid_encodings = tokenizer(valid_contexts, valid_questions, truncation=True, padding=True) - -# print(train_encodings.keys()) ---> dict_keys(['input_ids', 'token_type_ids', 'attention_mask']) - -# Positional encoding -def add_token_positions(encodings,answers): - start_positions=[] - end_positions=[] - for i in range(len(answers)): - start_positions.append(encodings.char_to_token(i,answers[i]['answer_start'])) - end_positions.append(encodings.char_to_token(i,answers[i]['answer_end'])) - - # if start position is None, the answer passage has been truncated - if start_positions[-1] is None: - start_positions[-1] = tokenizer.model_max_length - if end_positions[-1] is None: - end_positions[-1] = tokenizer.model_max_length - - encodings.update({'start_positions': start_positions, 'end_positions': end_positions}) - - -"""Dataloader for the training dataset""" -class DatasetRetriever(Dataset): - def __init__(self,encodings): - self.encodings=encodings - - def __getitem__(self,idx): - return {key:torch.tensor(val[idx]) for key,val in self.encodings.items()} - - def __len__(self): - return len(self.encodings.input_ids) - -#Split the dataset into train and validation -train_dataset=DatasetRetriever(train_encodings) -valid_dataset=DatasetRetriever(valid_encodings) -train_loader=DataLoader(train_dataset,batch_size=16,shuffle=True) -valid_loader=DataLoader(valid_dataset,batch_size=16) -model = BertForQuestionAnswering.from_pretrained("bert-base-uncased") -device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') - -#Training and testing Loop -def pipeline(): - epochs=1, - optimizer = torch.optim.AdamW(model.parameters(),lr=5e-5) - - with wandb.init(config=None): - config=wandb.config - model.to(device) - - #train the model - model.train() - for epoch in range(config.epochs): - loop = tqdm(train_loader, leave=True) - for batch in loop: - optimizer.zero_grad() - input_ids = batch['input_ids'].to(device) - attention_mask = batch['attention_mask'].to(device) - start_positions = batch['start_positions'].to(device) - end_positions = batch['end_positions'].to(device) - outputs = model(input_ids, attention_mask=attention_mask, start_positions=start_positions, end_positions=end_positions) - loss = outputs[0] - loss.backward() - optimizer.step() - - loop.set_description(f'Epoch {epoch+1}') - loop.set_postfix(loss=loss.item()) - wandb.log({'Validation Loss':loss}) - - #set the model to evaluation phase - model.eval() - acc=[] - for batch in tqdm(valid_loader): - with torch.no_grad(): - input_ids=batch['input_ids'].to(device) - attention_mask=batch['attention_mask'].to(device) - start_true=batch['start_positions'].to(device) - end_true=batch['end_positions'].to(device) - - outputs=model(input_ids,attention_mask=attention_mask) - - start_pred=torch.argmax(outputs['start_logits'],dim=1) - end_pred=torch.argmax(outputs['end_logits'],dim=1) - - acc.append(((start_pred == start_true).sum()/len(start_pred)).item()) - acc.append(((end_pred == end_true).sum()/len(end_pred)).item()) - - acc = sum(acc)/len(acc) - - print("\n\nT/P\tanswer_start\tanswer_end\n") - for i in range(len(start_true)): - print(f"true\t{start_true[i]}\t{end_true[i]}\n" - f"pred\t{start_pred[i]}\t{end_pred[i]}\n") - wandb.log({'Validation accuracy': acc}) - -#Run the pipeline -wandb.agent(sweep_id, pipeline, count = 4) - - -"""Save the model so we dont have to train it again""" -model_path = '/content/drive/MyDrive/BERT-SQuAD' -model.save_pretrained(model_path) -tokenizer.save_pretrained(model_path) - -"""Load the model""" -model_path = '/content/drive/MyDrive/BERT-SQuAD' -model = BertForQuestionAnswering.from_pretrained(model_path) -tokenizer = BertTokenizerFast.from_pretrained(model_path) -device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') -model = model.to(device) - - - -#Get predictions -def get_prediction(context,answer): - inputs=tokenizer.encode_plus(question,context,return_tensors='pt').to(device) - outputs=model(**inputs) - answer_start=torch.argmax(outputs[0]) # start position of the answer - answer_end=torch.argmax(outputs[1])+1 # end position of the answer - answer = tokenizer.convert_tokens_to_string(tokenizer. ## convert the tokens to string - convert_ids_to_tokens(inputs['input_ids'][0][answer_start:answer_end])) - return answer - - -""" -Question testing - -Official SQuAD evaluation script--> -https://colab.research.google.com/github/fastforwardlabs/ff14_blog/blob/master/_notebooks/2020-06-09-Evaluating_BERT_on_SQuAD.ipynb#scrollTo=MzPlHgWEBQ8D -""" - -def normalize_text(s): - """Removing articles and punctuation, and standardizing whitespace are all typical text processing steps.""" - import string, re - def remove_articles(text): - regex = re.compile(r"\b(a|an|the)\b", re.UNICODE) - return re.sub(regex, " ", text) - def white_space_fix(text): - return " ".join(text.split()) - def remove_punc(text): - exclude = set(string.punctuation) - return "".join(ch for ch in text if ch not in exclude) - def lower(text): - return text.lower() - - return white_space_fix(remove_articles(remove_punc(lower(s)))) - -def exact_match(prediction, truth): - return bool(normalize_text(prediction) == normalize_text(truth)) - -def compute_f1(prediction, truth): - pred_tokens = normalize_text(prediction).split() - truth_tokens = normalize_text(truth).split() - - # if either the prediction or the truth is no-answer then f1 = 1 if they agree, 0 otherwise - if len(pred_tokens) == 0 or len(truth_tokens) == 0: - return int(pred_tokens == truth_tokens) - - common_tokens = set(pred_tokens) & set(truth_tokens) - - # if there are no common tokens then f1 = 0 - if len(common_tokens) == 0: - return 0 - - prec = len(common_tokens) / len(pred_tokens) - rec = len(common_tokens) / len(truth_tokens) - - return round(2 * (prec * rec) / (prec + rec), 2) - -def question_answer(context, question,answer): - prediction = get_prediction(context,question) - em_score = exact_match(prediction, answer) - f1_score = compute_f1(prediction, answer) - - print(f'Question: {question}') - print(f'Prediction: {prediction}') - print(f'True Answer: {answer}') - print(f'Exact match: {em_score}') - print(f'F1 score: {f1_score}\n') - -context = """Space exploration is a very exciting field of research. It is the - frontier of Physics and no doubt will change the understanding of science. - However, it does come at a cost. A normal space shuttle costs about 1.5 billion dollars to make. - The annual budget of NASA, which is a premier space exploring organization is about 17 billion. - So the question that some people ask is that whether it is worth it.""" - - -questions =["What wil change the understanding of science?", - "What is the main idea in the paragraph?"] - -answers = ["Space Exploration", - "The cost of space exploration is too high"] - -""" -VISUALISATION IN PROGRESS - -for question, answer in zip(questions, answers): - question_answer(context, question, answer) - - #Visualize the start scores - plt.rcParams["figure.figsize"]=(20,10) - ax=sns.barplot(x=token_labels,y=start_scores) - ax.set_xticklabels(ax.get_xticklabels(),rotation=90,ha="center") - ax.grid(True) - plt.title("Start word scores") - plt.show() - - #Visualize the end scores - plt.rcParams["figure.figsize"]=(20,10) - ax=sns.barplot(x=token_labels,y=end_scores) - ax.set_xticklabels(ax.get_xticklabels(),rotation=90,ha="center") - ax.grid(True) - plt.title("End word scores") - plt.show() - - #Visualize both the scores - scores=[] - for (i,token_label) in enumerate(token_labels): - # Add the token's start score as one row. - scores.append({'token_label':token_label, - 'score':start_scores[i], - 'marker':'start'}) - - # Add the token's end score as another row. - scores.append({'token_label': token_label, - 'score': end_scores[i], - 'marker': 'end'}) - - df=pd.DataFrame(scores) - group_plot=sns.catplot(x="token_label",y="score",hue="marker",data=df, - kind="bar",height=6,aspect=4) - - group_plot.set_xticklabels(ax.get_xticklabels(),rotation=90,ha="center") - group_plot.ax.grid(True) -""" diff --git a/spaces/Hina4867/bingo/src/components/external-link.tsx b/spaces/Hina4867/bingo/src/components/external-link.tsx deleted file mode 100644 index 011265f364d5a64a770f4c7e9c65c5ade21d623a..0000000000000000000000000000000000000000 --- a/spaces/Hina4867/bingo/src/components/external-link.tsx +++ /dev/null @@ -1,30 +0,0 @@ -export function ExternalLink({ - href, - children -}: { - href: string - children: React.ReactNode -}) { - return ( - - {children} - - - ) -} diff --git a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step2.sh b/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step2.sh deleted file mode 100644 index 59a6cbb12539cf62658f8344f7be7cecf2e3380f..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step2.sh +++ /dev/null @@ -1,30 +0,0 @@ -#!/bin/bash - -# prepare a new data directory of HMM word output - -. ./path.sh - -set -eu - -out_dir= # same as in train.sh -dec_lmparam= # LM hyperparameters (e.g., 7.0.0) - -dec_exp=tri3b # what HMM stage to decode (e.g., tri3b) -dec_suffix=word -dec_splits="train valid" -dec_data_dir=$out_dir/dec_data_word # where to write HMM output - -data_dir=$out_dir/data -wrd_data_dir=$out_dir/data_word - -for x in $dec_splits; do - mkdir -p $dec_data_dir/$x - cp $data_dir/$x/{feats.scp,cmvn.scp,utt2spk,spk2utt} $dec_data_dir/$x/ - - tra=$out_dir/exp/$dec_exp/decode${dec_suffix}_${x}/scoring/${dec_lmparam}.tra - cat $tra | utils/int2sym.pl -f 2- $data_dir/lang_word/words.txt | \ - sed 's:::g' | sed 's:::g' > $dec_data_dir/$x/text - utils/fix_data_dir.sh $dec_data_dir/$x - echo "WER on $x is" $(compute-wer ark:$wrd_data_dir/${x}_gt/text ark:$dec_data_dir/$x/text | cut -d" " -f2-) -done - diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/modules/learned_positional_embedding.py b/spaces/ICML2022/OFA/fairseq/fairseq/modules/learned_positional_embedding.py deleted file mode 100644 index 378d0f707183dd344dbb9288dda394b11053acf0..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/fairseq/modules/learned_positional_embedding.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Dict, Optional - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import utils -from torch import Tensor - - -class LearnedPositionalEmbedding(nn.Embedding): - """ - This module learns positional embeddings up to a fixed maximum size. - Padding ids are ignored by either offsetting based on padding_idx - or by setting padding_idx to None and ensuring that the appropriate - position ids are passed to the forward function. - """ - - def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int): - super().__init__(num_embeddings, embedding_dim, padding_idx) - self.onnx_trace = False - if self.padding_idx is not None: - self.max_positions = self.num_embeddings - self.padding_idx - 1 - else: - self.max_positions = self.num_embeddings - - def forward( - self, - input: Tensor, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - positions: Optional[Tensor] = None, - ): - """Input is expected to be of size [bsz x seqlen].""" - assert (positions is None) or ( - self.padding_idx is None - ), "If positions is pre-computed then padding_idx should not be set." - - if positions is None: - if incremental_state is not None: - # positions is the same for every token when decoding a single step - # Without the int() cast, it doesn't work in some cases when exporting to ONNX - positions = torch.zeros( - (1, 1), device=input.device, dtype=input.dtype - ).fill_(int(self.padding_idx + input.size(1))) - else: - positions = utils.make_positions( - input, self.padding_idx, onnx_trace=self.onnx_trace - ) - return F.embedding( - positions, - self.weight, - self.padding_idx, - self.max_norm, - self.norm_type, - self.scale_grad_by_freq, - self.sparse, - ) diff --git a/spaces/ICML2022/resefa/third_party/stylegan3_official_ops/bias_act.h b/spaces/ICML2022/resefa/third_party/stylegan3_official_ops/bias_act.h deleted file mode 100644 index 60b81c6058d54638a6d74a13046fa388442d767d..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/resefa/third_party/stylegan3_official_ops/bias_act.h +++ /dev/null @@ -1,38 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -//------------------------------------------------------------------------ -// CUDA kernel parameters. - -struct bias_act_kernel_params -{ - const void* x; // [sizeX] - const void* b; // [sizeB] or NULL - const void* xref; // [sizeX] or NULL - const void* yref; // [sizeX] or NULL - const void* dy; // [sizeX] or NULL - void* y; // [sizeX] - - int grad; - int act; - float alpha; - float gain; - float clamp; - - int sizeX; - int sizeB; - int stepB; - int loopX; -}; - -//------------------------------------------------------------------------ -// CUDA kernel selection. - -template void* choose_bias_act_kernel(const bias_act_kernel_params& p); - -//------------------------------------------------------------------------ diff --git a/spaces/Iceclear/StableSR/StableSR/basicsr/data/video_test_dataset.py b/spaces/Iceclear/StableSR/StableSR/basicsr/data/video_test_dataset.py deleted file mode 100644 index 929f7d97472a0eb810e33e694d5362a6749ab4b6..0000000000000000000000000000000000000000 --- a/spaces/Iceclear/StableSR/StableSR/basicsr/data/video_test_dataset.py +++ /dev/null @@ -1,283 +0,0 @@ -import glob -import torch -from os import path as osp -from torch.utils import data as data - -from basicsr.data.data_util import duf_downsample, generate_frame_indices, read_img_seq -from basicsr.utils import get_root_logger, scandir -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class VideoTestDataset(data.Dataset): - """Video test dataset. - - Supported datasets: Vid4, REDS4, REDSofficial. - More generally, it supports testing dataset with following structures: - - :: - - dataroot - ├── subfolder1 - ├── frame000 - ├── frame001 - ├── ... - ├── subfolder2 - ├── frame000 - ├── frame001 - ├── ... - ├── ... - - For testing datasets, there is no need to prepare LMDB files. - - Args: - opt (dict): Config for train dataset. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - io_backend (dict): IO backend type and other kwarg. - cache_data (bool): Whether to cache testing datasets. - name (str): Dataset name. - meta_info_file (str): The path to the file storing the list of test folders. If not provided, all the folders - in the dataroot will be used. - num_frame (int): Window size for input frames. - padding (str): Padding mode. - """ - - def __init__(self, opt): - super(VideoTestDataset, self).__init__() - self.opt = opt - self.cache_data = opt['cache_data'] - self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq'] - self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []} - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.' - - logger = get_root_logger() - logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}') - self.imgs_lq, self.imgs_gt = {}, {} - if 'meta_info_file' in opt: - with open(opt['meta_info_file'], 'r') as fin: - subfolders = [line.split(' ')[0] for line in fin] - subfolders_lq = [osp.join(self.lq_root, key) for key in subfolders] - subfolders_gt = [osp.join(self.gt_root, key) for key in subfolders] - else: - subfolders_lq = sorted(glob.glob(osp.join(self.lq_root, '*'))) - subfolders_gt = sorted(glob.glob(osp.join(self.gt_root, '*'))) - - if opt['name'].lower() in ['vid4', 'reds4', 'redsofficial']: - for subfolder_lq, subfolder_gt in zip(subfolders_lq, subfolders_gt): - # get frame list for lq and gt - subfolder_name = osp.basename(subfolder_lq) - img_paths_lq = sorted(list(scandir(subfolder_lq, full_path=True))) - img_paths_gt = sorted(list(scandir(subfolder_gt, full_path=True))) - - max_idx = len(img_paths_lq) - assert max_idx == len(img_paths_gt), (f'Different number of images in lq ({max_idx})' - f' and gt folders ({len(img_paths_gt)})') - - self.data_info['lq_path'].extend(img_paths_lq) - self.data_info['gt_path'].extend(img_paths_gt) - self.data_info['folder'].extend([subfolder_name] * max_idx) - for i in range(max_idx): - self.data_info['idx'].append(f'{i}/{max_idx}') - border_l = [0] * max_idx - for i in range(self.opt['num_frame'] // 2): - border_l[i] = 1 - border_l[max_idx - i - 1] = 1 - self.data_info['border'].extend(border_l) - - # cache data or save the frame list - if self.cache_data: - logger.info(f'Cache {subfolder_name} for VideoTestDataset...') - self.imgs_lq[subfolder_name] = read_img_seq(img_paths_lq) - self.imgs_gt[subfolder_name] = read_img_seq(img_paths_gt) - else: - self.imgs_lq[subfolder_name] = img_paths_lq - self.imgs_gt[subfolder_name] = img_paths_gt - else: - raise ValueError(f'Non-supported video test dataset: {type(opt["name"])}') - - def __getitem__(self, index): - folder = self.data_info['folder'][index] - idx, max_idx = self.data_info['idx'][index].split('/') - idx, max_idx = int(idx), int(max_idx) - border = self.data_info['border'][index] - lq_path = self.data_info['lq_path'][index] - - select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding']) - - if self.cache_data: - imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx)) - img_gt = self.imgs_gt[folder][idx] - else: - img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx] - imgs_lq = read_img_seq(img_paths_lq) - img_gt = read_img_seq([self.imgs_gt[folder][idx]]) - img_gt.squeeze_(0) - - return { - 'lq': imgs_lq, # (t, c, h, w) - 'gt': img_gt, # (c, h, w) - 'folder': folder, # folder name - 'idx': self.data_info['idx'][index], # e.g., 0/99 - 'border': border, # 1 for border, 0 for non-border - 'lq_path': lq_path # center frame - } - - def __len__(self): - return len(self.data_info['gt_path']) - - -@DATASET_REGISTRY.register() -class VideoTestVimeo90KDataset(data.Dataset): - """Video test dataset for Vimeo90k-Test dataset. - - It only keeps the center frame for testing. - For testing datasets, there is no need to prepare LMDB files. - - Args: - opt (dict): Config for train dataset. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - io_backend (dict): IO backend type and other kwarg. - cache_data (bool): Whether to cache testing datasets. - name (str): Dataset name. - meta_info_file (str): The path to the file storing the list of test folders. If not provided, all the folders - in the dataroot will be used. - num_frame (int): Window size for input frames. - padding (str): Padding mode. - """ - - def __init__(self, opt): - super(VideoTestVimeo90KDataset, self).__init__() - self.opt = opt - self.cache_data = opt['cache_data'] - if self.cache_data: - raise NotImplementedError('cache_data in Vimeo90K-Test dataset is not implemented.') - self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq'] - self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []} - neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])] - - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.' - - logger = get_root_logger() - logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}') - with open(opt['meta_info_file'], 'r') as fin: - subfolders = [line.split(' ')[0] for line in fin] - for idx, subfolder in enumerate(subfolders): - gt_path = osp.join(self.gt_root, subfolder, 'im4.png') - self.data_info['gt_path'].append(gt_path) - lq_paths = [osp.join(self.lq_root, subfolder, f'im{i}.png') for i in neighbor_list] - self.data_info['lq_path'].append(lq_paths) - self.data_info['folder'].append('vimeo90k') - self.data_info['idx'].append(f'{idx}/{len(subfolders)}') - self.data_info['border'].append(0) - - def __getitem__(self, index): - lq_path = self.data_info['lq_path'][index] - gt_path = self.data_info['gt_path'][index] - imgs_lq = read_img_seq(lq_path) - img_gt = read_img_seq([gt_path]) - img_gt.squeeze_(0) - - return { - 'lq': imgs_lq, # (t, c, h, w) - 'gt': img_gt, # (c, h, w) - 'folder': self.data_info['folder'][index], # folder name - 'idx': self.data_info['idx'][index], # e.g., 0/843 - 'border': self.data_info['border'][index], # 0 for non-border - 'lq_path': lq_path[self.opt['num_frame'] // 2] # center frame - } - - def __len__(self): - return len(self.data_info['gt_path']) - - -@DATASET_REGISTRY.register() -class VideoTestDUFDataset(VideoTestDataset): - """ Video test dataset for DUF dataset. - - Args: - opt (dict): Config for train dataset. Most of keys are the same as VideoTestDataset. - It has the following extra keys: - use_duf_downsampling (bool): Whether to use duf downsampling to generate low-resolution frames. - scale (bool): Scale, which will be added automatically. - """ - - def __getitem__(self, index): - folder = self.data_info['folder'][index] - idx, max_idx = self.data_info['idx'][index].split('/') - idx, max_idx = int(idx), int(max_idx) - border = self.data_info['border'][index] - lq_path = self.data_info['lq_path'][index] - - select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding']) - - if self.cache_data: - if self.opt['use_duf_downsampling']: - # read imgs_gt to generate low-resolution frames - imgs_lq = self.imgs_gt[folder].index_select(0, torch.LongTensor(select_idx)) - imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale']) - else: - imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx)) - img_gt = self.imgs_gt[folder][idx] - else: - if self.opt['use_duf_downsampling']: - img_paths_lq = [self.imgs_gt[folder][i] for i in select_idx] - # read imgs_gt to generate low-resolution frames - imgs_lq = read_img_seq(img_paths_lq, require_mod_crop=True, scale=self.opt['scale']) - imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale']) - else: - img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx] - imgs_lq = read_img_seq(img_paths_lq) - img_gt = read_img_seq([self.imgs_gt[folder][idx]], require_mod_crop=True, scale=self.opt['scale']) - img_gt.squeeze_(0) - - return { - 'lq': imgs_lq, # (t, c, h, w) - 'gt': img_gt, # (c, h, w) - 'folder': folder, # folder name - 'idx': self.data_info['idx'][index], # e.g., 0/99 - 'border': border, # 1 for border, 0 for non-border - 'lq_path': lq_path # center frame - } - - -@DATASET_REGISTRY.register() -class VideoRecurrentTestDataset(VideoTestDataset): - """Video test dataset for recurrent architectures, which takes LR video - frames as input and output corresponding HR video frames. - - Args: - opt (dict): Same as VideoTestDataset. Unused opt: - padding (str): Padding mode. - - """ - - def __init__(self, opt): - super(VideoRecurrentTestDataset, self).__init__(opt) - # Find unique folder strings - self.folders = sorted(list(set(self.data_info['folder']))) - - def __getitem__(self, index): - folder = self.folders[index] - - if self.cache_data: - imgs_lq = self.imgs_lq[folder] - imgs_gt = self.imgs_gt[folder] - else: - raise NotImplementedError('Without cache_data is not implemented.') - - return { - 'lq': imgs_lq, - 'gt': imgs_gt, - 'folder': folder, - } - - def __len__(self): - return len(self.folders) diff --git a/spaces/JUNGU/VToonify/vtoonify/model/raft/train.py b/spaces/JUNGU/VToonify/vtoonify/model/raft/train.py deleted file mode 100644 index 307573097f13ee30c67bbe11658f457fdf1ead3c..0000000000000000000000000000000000000000 --- a/spaces/JUNGU/VToonify/vtoonify/model/raft/train.py +++ /dev/null @@ -1,247 +0,0 @@ -from __future__ import print_function, division -import sys -sys.path.append('core') - -import argparse -import os -import cv2 -import time -import numpy as np -import matplotlib.pyplot as plt - -import torch -import torch.nn as nn -import torch.optim as optim -import torch.nn.functional as F - -from torch.utils.data import DataLoader -from raft import RAFT -import evaluate -import datasets - -from torch.utils.tensorboard import SummaryWriter - -try: - from torch.cuda.amp import GradScaler -except: - # dummy GradScaler for PyTorch < 1.6 - class GradScaler: - def __init__(self): - pass - def scale(self, loss): - return loss - def unscale_(self, optimizer): - pass - def step(self, optimizer): - optimizer.step() - def update(self): - pass - - -# exclude extremly large displacements -MAX_FLOW = 400 -SUM_FREQ = 100 -VAL_FREQ = 5000 - - -def sequence_loss(flow_preds, flow_gt, valid, gamma=0.8, max_flow=MAX_FLOW): - """ Loss function defined over sequence of flow predictions """ - - n_predictions = len(flow_preds) - flow_loss = 0.0 - - # exlude invalid pixels and extremely large diplacements - mag = torch.sum(flow_gt**2, dim=1).sqrt() - valid = (valid >= 0.5) & (mag < max_flow) - - for i in range(n_predictions): - i_weight = gamma**(n_predictions - i - 1) - i_loss = (flow_preds[i] - flow_gt).abs() - flow_loss += i_weight * (valid[:, None] * i_loss).mean() - - epe = torch.sum((flow_preds[-1] - flow_gt)**2, dim=1).sqrt() - epe = epe.view(-1)[valid.view(-1)] - - metrics = { - 'epe': epe.mean().item(), - '1px': (epe < 1).float().mean().item(), - '3px': (epe < 3).float().mean().item(), - '5px': (epe < 5).float().mean().item(), - } - - return flow_loss, metrics - - -def count_parameters(model): - return sum(p.numel() for p in model.parameters() if p.requires_grad) - - -def fetch_optimizer(args, model): - """ Create the optimizer and learning rate scheduler """ - optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=args.epsilon) - - scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps+100, - pct_start=0.05, cycle_momentum=False, anneal_strategy='linear') - - return optimizer, scheduler - - -class Logger: - def __init__(self, model, scheduler): - self.model = model - self.scheduler = scheduler - self.total_steps = 0 - self.running_loss = {} - self.writer = None - - def _print_training_status(self): - metrics_data = [self.running_loss[k]/SUM_FREQ for k in sorted(self.running_loss.keys())] - training_str = "[{:6d}, {:10.7f}] ".format(self.total_steps+1, self.scheduler.get_last_lr()[0]) - metrics_str = ("{:10.4f}, "*len(metrics_data)).format(*metrics_data) - - # print the training status - print(training_str + metrics_str) - - if self.writer is None: - self.writer = SummaryWriter() - - for k in self.running_loss: - self.writer.add_scalar(k, self.running_loss[k]/SUM_FREQ, self.total_steps) - self.running_loss[k] = 0.0 - - def push(self, metrics): - self.total_steps += 1 - - for key in metrics: - if key not in self.running_loss: - self.running_loss[key] = 0.0 - - self.running_loss[key] += metrics[key] - - if self.total_steps % SUM_FREQ == SUM_FREQ-1: - self._print_training_status() - self.running_loss = {} - - def write_dict(self, results): - if self.writer is None: - self.writer = SummaryWriter() - - for key in results: - self.writer.add_scalar(key, results[key], self.total_steps) - - def close(self): - self.writer.close() - - -def train(args): - - model = nn.DataParallel(RAFT(args), device_ids=args.gpus) - print("Parameter Count: %d" % count_parameters(model)) - - if args.restore_ckpt is not None: - model.load_state_dict(torch.load(args.restore_ckpt), strict=False) - - model.cuda() - model.train() - - if args.stage != 'chairs': - model.module.freeze_bn() - - train_loader = datasets.fetch_dataloader(args) - optimizer, scheduler = fetch_optimizer(args, model) - - total_steps = 0 - scaler = GradScaler(enabled=args.mixed_precision) - logger = Logger(model, scheduler) - - VAL_FREQ = 5000 - add_noise = True - - should_keep_training = True - while should_keep_training: - - for i_batch, data_blob in enumerate(train_loader): - optimizer.zero_grad() - image1, image2, flow, valid = [x.cuda() for x in data_blob] - - if args.add_noise: - stdv = np.random.uniform(0.0, 5.0) - image1 = (image1 + stdv * torch.randn(*image1.shape).cuda()).clamp(0.0, 255.0) - image2 = (image2 + stdv * torch.randn(*image2.shape).cuda()).clamp(0.0, 255.0) - - flow_predictions = model(image1, image2, iters=args.iters) - - loss, metrics = sequence_loss(flow_predictions, flow, valid, args.gamma) - scaler.scale(loss).backward() - scaler.unscale_(optimizer) - torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip) - - scaler.step(optimizer) - scheduler.step() - scaler.update() - - logger.push(metrics) - - if total_steps % VAL_FREQ == VAL_FREQ - 1: - PATH = 'checkpoints/%d_%s.pth' % (total_steps+1, args.name) - torch.save(model.state_dict(), PATH) - - results = {} - for val_dataset in args.validation: - if val_dataset == 'chairs': - results.update(evaluate.validate_chairs(model.module)) - elif val_dataset == 'sintel': - results.update(evaluate.validate_sintel(model.module)) - elif val_dataset == 'kitti': - results.update(evaluate.validate_kitti(model.module)) - - logger.write_dict(results) - - model.train() - if args.stage != 'chairs': - model.module.freeze_bn() - - total_steps += 1 - - if total_steps > args.num_steps: - should_keep_training = False - break - - logger.close() - PATH = 'checkpoints/%s.pth' % args.name - torch.save(model.state_dict(), PATH) - - return PATH - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--name', default='raft', help="name your experiment") - parser.add_argument('--stage', help="determines which dataset to use for training") - parser.add_argument('--restore_ckpt', help="restore checkpoint") - parser.add_argument('--small', action='store_true', help='use small model') - parser.add_argument('--validation', type=str, nargs='+') - - parser.add_argument('--lr', type=float, default=0.00002) - parser.add_argument('--num_steps', type=int, default=100000) - parser.add_argument('--batch_size', type=int, default=6) - parser.add_argument('--image_size', type=int, nargs='+', default=[384, 512]) - parser.add_argument('--gpus', type=int, nargs='+', default=[0,1]) - parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision') - - parser.add_argument('--iters', type=int, default=12) - parser.add_argument('--wdecay', type=float, default=.00005) - parser.add_argument('--epsilon', type=float, default=1e-8) - parser.add_argument('--clip', type=float, default=1.0) - parser.add_argument('--dropout', type=float, default=0.0) - parser.add_argument('--gamma', type=float, default=0.8, help='exponential weighting') - parser.add_argument('--add_noise', action='store_true') - args = parser.parse_args() - - torch.manual_seed(1234) - np.random.seed(1234) - - if not os.path.isdir('checkpoints'): - os.mkdir('checkpoints') - - train(args) \ No newline at end of file diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/repaint/pipeline_repaint.py b/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/repaint/pipeline_repaint.py deleted file mode 100644 index 7af88f62755983ce41f4566a3a33a0e624d5e94f..0000000000000000000000000000000000000000 --- a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/repaint/pipeline_repaint.py +++ /dev/null @@ -1,140 +0,0 @@ -# Copyright 2022 ETH Zurich Computer Vision Lab and The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from typing import Optional, Tuple, Union - -import numpy as np -import torch - -import PIL -from tqdm.auto import tqdm - -from ...models import UNet2DModel -from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput -from ...schedulers import RePaintScheduler - - -def _preprocess_image(image: PIL.Image.Image): - image = np.array(image.convert("RGB")) - image = image[None].transpose(0, 3, 1, 2) - image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 - return image - - -def _preprocess_mask(mask: PIL.Image.Image): - mask = np.array(mask.convert("L")) - mask = mask.astype(np.float32) / 255.0 - mask = mask[None, None] - mask[mask < 0.5] = 0 - mask[mask >= 0.5] = 1 - mask = torch.from_numpy(mask) - return mask - - -class RePaintPipeline(DiffusionPipeline): - unet: UNet2DModel - scheduler: RePaintScheduler - - def __init__(self, unet, scheduler): - super().__init__() - self.register_modules(unet=unet, scheduler=scheduler) - - @torch.no_grad() - def __call__( - self, - original_image: Union[torch.FloatTensor, PIL.Image.Image], - mask_image: Union[torch.FloatTensor, PIL.Image.Image], - num_inference_steps: int = 250, - eta: float = 0.0, - jump_length: int = 10, - jump_n_sample: int = 10, - generator: Optional[torch.Generator] = None, - output_type: Optional[str] = "pil", - return_dict: bool = True, - ) -> Union[ImagePipelineOutput, Tuple]: - r""" - Args: - original_image (`torch.FloatTensor` or `PIL.Image.Image`): - The original image to inpaint on. - mask_image (`torch.FloatTensor` or `PIL.Image.Image`): - The mask_image where 0.0 values define which part of the original image to inpaint (change). - num_inference_steps (`int`, *optional*, defaults to 1000): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. - eta (`float`): - The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 - 0.0 is DDIM - and 1.0 is DDPM scheduler respectively. - jump_length (`int`, *optional*, defaults to 10): - The number of steps taken forward in time before going backward in time for a single jump ("j" in - RePaint paper). Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf. - jump_n_sample (`int`, *optional*, defaults to 10): - The number of times we will make forward time jump for a given chosen time sample. Take a look at - Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf. - generator (`torch.Generator`, *optional*): - A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation - deterministic. - 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. - """ - - if not isinstance(original_image, torch.FloatTensor): - original_image = _preprocess_image(original_image) - original_image = original_image.to(self.device) - if not isinstance(mask_image, torch.FloatTensor): - mask_image = _preprocess_mask(mask_image) - mask_image = mask_image.to(self.device) - - # sample gaussian noise to begin the loop - image = torch.randn( - original_image.shape, - generator=generator, - device=self.device, - ) - image = image.to(self.device) - - # set step values - self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, self.device) - self.scheduler.eta = eta - - t_last = self.scheduler.timesteps[0] + 1 - for i, t in enumerate(tqdm(self.scheduler.timesteps)): - if t < t_last: - # predict the noise residual - model_output = self.unet(image, t).sample - # compute previous image: x_t -> x_t-1 - image = self.scheduler.step(model_output, t, image, original_image, mask_image, generator).prev_sample - - else: - # compute the reverse: x_t-1 -> x_t - image = self.scheduler.undo_step(image, t_last, generator) - t_last = t - - image = (image / 2 + 0.5).clamp(0, 1) - image = image.cpu().permute(0, 2, 3, 1).numpy() - if output_type == "pil": - image = self.numpy_to_pil(image) - - if not return_dict: - return (image,) - - return ImagePipelineOutput(images=image) diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/vq_diffusion/__init__.py b/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/vq_diffusion/__init__.py deleted file mode 100644 index 8c9f14f000648347fe75a5bec0cb45d08c7d2ff9..0000000000000000000000000000000000000000 --- a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/vq_diffusion/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from ...utils import is_torch_available, is_transformers_available - - -if is_transformers_available() and is_torch_available(): - from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline diff --git a/spaces/Jamel887/Rv-percobaan887/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/Jamel887/Rv-percobaan887/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py deleted file mode 100644 index ee3171bcb7c4a5066560723108b56e055f18be45..0000000000000000000000000000000000000000 --- a/spaces/Jamel887/Rv-percobaan887/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py +++ /dev/null @@ -1,90 +0,0 @@ -from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor -import pyworld -import numpy as np - - -class DioF0Predictor(F0Predictor): - def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100): - self.hop_length = hop_length - self.f0_min = f0_min - self.f0_max = f0_max - self.sampling_rate = sampling_rate - - def interpolate_f0(self, f0): - """ - 对F0进行插值处理 - """ - - data = np.reshape(f0, (f0.size, 1)) - - vuv_vector = np.zeros((data.size, 1), dtype=np.float32) - vuv_vector[data > 0.0] = 1.0 - vuv_vector[data <= 0.0] = 0.0 - - ip_data = data - - frame_number = data.size - last_value = 0.0 - for i in range(frame_number): - if data[i] <= 0.0: - j = i + 1 - for j in range(i + 1, frame_number): - if data[j] > 0.0: - break - if j < frame_number - 1: - if last_value > 0.0: - step = (data[j] - data[i - 1]) / float(j - i) - for k in range(i, j): - ip_data[k] = data[i - 1] + step * (k - i + 1) - else: - for k in range(i, j): - ip_data[k] = data[j] - else: - for k in range(i, frame_number): - ip_data[k] = last_value - else: - ip_data[i] = data[i] # 这里可能存在一个没有必要的拷贝 - last_value = data[i] - - return ip_data[:, 0], vuv_vector[:, 0] - - def resize_f0(self, x, target_len): - source = np.array(x) - source[source < 0.001] = np.nan - target = np.interp( - np.arange(0, len(source) * target_len, len(source)) / target_len, - np.arange(0, len(source)), - source, - ) - res = np.nan_to_num(target) - return res - - def compute_f0(self, wav, p_len=None): - if p_len is None: - p_len = wav.shape[0] // self.hop_length - f0, t = pyworld.dio( - wav.astype(np.double), - fs=self.sampling_rate, - f0_floor=self.f0_min, - f0_ceil=self.f0_max, - frame_period=1000 * self.hop_length / self.sampling_rate, - ) - f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate) - for index, pitch in enumerate(f0): - f0[index] = round(pitch, 1) - return self.interpolate_f0(self.resize_f0(f0, p_len))[0] - - def compute_f0_uv(self, wav, p_len=None): - if p_len is None: - p_len = wav.shape[0] // self.hop_length - f0, t = pyworld.dio( - wav.astype(np.double), - fs=self.sampling_rate, - f0_floor=self.f0_min, - f0_ceil=self.f0_max, - frame_period=1000 * self.hop_length / self.sampling_rate, - ) - f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate) - for index, pitch in enumerate(f0): - f0[index] = round(pitch, 1) - return self.interpolate_f0(self.resize_f0(f0, p_len)) diff --git a/spaces/JeffJing/ZookChatBot/steamship/data/package/package.py b/spaces/JeffJing/ZookChatBot/steamship/data/package/package.py deleted file mode 100644 index f9519342128cf48deabde2d0cbf94d90b34c3181..0000000000000000000000000000000000000000 --- a/spaces/JeffJing/ZookChatBot/steamship/data/package/package.py +++ /dev/null @@ -1,73 +0,0 @@ -# -# This is the CLIENT-side abstraction for an invocable. -# -# If you are implementing a package, see: steamship.invocable.server.App -# - -from __future__ import annotations - -from typing import Any, Optional, Type - -from pydantic import BaseModel, Field - -from steamship.base.client import Client -from steamship.base.model import CamelModel -from steamship.base.request import CreateRequest, GetRequest, UpdateRequest -from steamship.data.manifest import Manifest - - -class PackageCreateRequest(CreateRequest): - is_public: bool = False - fetch_if_exists = False - profile: Optional[Manifest] = None - - -class PackageUpdateRequest(UpdateRequest): - id: Optional[str] = None - handle: Optional[str] = None - description: Optional[str] = None - profile: Optional[Manifest] = None - readme: Optional[str] = None - - -class Package(CamelModel): - client: Client = Field(None, exclude=True) - id: str = None - handle: str = None - user_id: str = None - profile: Optional[Manifest] = None - description: Optional[str] = None - readme: Optional[str] = None - is_public: bool = False - - @classmethod - def parse_obj(cls: Type[BaseModel], obj: Any) -> BaseModel: - # TODO (enias): This needs to be solved at the engine side - obj = obj["package"] if "package" in obj else obj - return super().parse_obj(obj) - - @staticmethod - def create( - client: Client, - handle: str = None, - profile: Manifest = None, - is_public=False, - fetch_if_exists=False, - ) -> Package: - req = PackageCreateRequest( - handle=handle, profile=profile, is_public=is_public, fetch_if_exists=fetch_if_exists - ) - return client.post("package/create", payload=req, expect=Package) - - @staticmethod - def get(client: Client, handle: str) -> Package: - return client.post("package/get", GetRequest(handle=handle), expect=Package) - - def update(self, client: Client) -> Package: - return client.post( - "package/update", - PackageUpdateRequest( - id=self.id, description=self.description, profile=self.profile, readme=self.readme - ), - expect=Package, - ) diff --git a/spaces/JeffJing/ZookChatBot/steamship/plugin/outputs/raw_block_and_tag_plugin_output.py b/spaces/JeffJing/ZookChatBot/steamship/plugin/outputs/raw_block_and_tag_plugin_output.py deleted file mode 100644 index f6d068a6af9d38148f6a44bcf30f2f367faa9d47..0000000000000000000000000000000000000000 --- a/spaces/JeffJing/ZookChatBot/steamship/plugin/outputs/raw_block_and_tag_plugin_output.py +++ /dev/null @@ -1,10 +0,0 @@ -from __future__ import annotations - -from typing import List - -from steamship.base.model import CamelModel -from steamship.data.file import Block - - -class RawBlockAndTagPluginOutput(CamelModel): - blocks: List[Block] diff --git a/spaces/Jo0xFF/4xArText/utils/net_interp.py b/spaces/Jo0xFF/4xArText/utils/net_interp.py deleted file mode 100644 index b746d0ab5dfd2de26c3aab36c148c1acdcc60e4c..0000000000000000000000000000000000000000 --- a/spaces/Jo0xFF/4xArText/utils/net_interp.py +++ /dev/null @@ -1,24 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import sys -import torch -from collections import OrderedDict - -alpha = float(sys.argv[1]) - -net_PSNR_path = "./models/RRDB_PSNR_x4_old_arch.pth" -net_ESRGAN_path = "./models/RRDB_ESRGAN_x4_old_arch.pth" -net_interp_path = "./models/interp_{:02d}.pth".format(int(alpha * 10)) - -net_PSNR = torch.load(net_PSNR_path) -net_ESRGAN = torch.load(net_ESRGAN_path) -net_interp = OrderedDict() - -print("Interpolating with alpha = ", alpha) - -for k, v_PSNR in net_PSNR.items(): - v_ESRGAN = net_ESRGAN[k] - net_interp[k] = (1 - alpha) * v_PSNR + alpha * v_ESRGAN - -torch.save(net_interp, net_interp_path) diff --git a/spaces/Juno360219/xlm-roberta-base/README.md b/spaces/Juno360219/xlm-roberta-base/README.md deleted file mode 100644 index 20fc8e95fcc80bc1b874f15df52f7b5593a6917d..0000000000000000000000000000000000000000 --- a/spaces/Juno360219/xlm-roberta-base/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Xlm Roberta Base -emoji: 🔥 -colorFrom: red -colorTo: green -sdk: streamlit -sdk_version: 1.19.0 -app_file: app.py -pinned: false -license: openrail ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/uvr5_pack/lib_v5/layers_new.py b/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/uvr5_pack/lib_v5/layers_new.py deleted file mode 100644 index 44153b6a23399c6938affc61c71919eaa172bcee..0000000000000000000000000000000000000000 --- a/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/uvr5_pack/lib_v5/layers_new.py +++ /dev/null @@ -1,125 +0,0 @@ -import torch -import torch.nn.functional as F -from torch import nn - -from . import spec_utils - - -class Conv2DBNActiv(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(Conv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, - nout, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - bias=False, - ), - nn.BatchNorm2d(nout), - activ(), - ) - - def __call__(self, x): - return self.conv(x) - - -class Encoder(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): - super(Encoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ) - self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ) - - def __call__(self, x): - h = self.conv1(x) - h = self.conv2(h) - - return h - - -class Decoder(nn.Module): - def __init__( - self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False - ): - super(Decoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - # self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def __call__(self, x, skip=None): - x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) - - if skip is not None: - skip = spec_utils.crop_center(skip, x) - x = torch.cat([x, skip], dim=1) - - h = self.conv1(x) - # h = self.conv2(h) - - if self.dropout is not None: - h = self.dropout(h) - - return h - - -class ASPPModule(nn.Module): - def __init__(self, nin, nout, dilations=(4, 8, 12), activ=nn.ReLU, dropout=False): - super(ASPPModule, self).__init__() - self.conv1 = nn.Sequential( - nn.AdaptiveAvgPool2d((1, None)), - Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ), - ) - self.conv2 = Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ) - self.conv3 = Conv2DBNActiv( - nin, nout, 3, 1, dilations[0], dilations[0], activ=activ - ) - self.conv4 = Conv2DBNActiv( - nin, nout, 3, 1, dilations[1], dilations[1], activ=activ - ) - self.conv5 = Conv2DBNActiv( - nin, nout, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.bottleneck = Conv2DBNActiv(nout * 5, nout, 1, 1, 0, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def forward(self, x): - _, _, h, w = x.size() - feat1 = F.interpolate( - self.conv1(x), size=(h, w), mode="bilinear", align_corners=True - ) - feat2 = self.conv2(x) - feat3 = self.conv3(x) - feat4 = self.conv4(x) - feat5 = self.conv5(x) - out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) - out = self.bottleneck(out) - - if self.dropout is not None: - out = self.dropout(out) - - return out - - -class LSTMModule(nn.Module): - def __init__(self, nin_conv, nin_lstm, nout_lstm): - super(LSTMModule, self).__init__() - self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0) - self.lstm = nn.LSTM( - input_size=nin_lstm, hidden_size=nout_lstm // 2, bidirectional=True - ) - self.dense = nn.Sequential( - nn.Linear(nout_lstm, nin_lstm), nn.BatchNorm1d(nin_lstm), nn.ReLU() - ) - - def forward(self, x): - N, _, nbins, nframes = x.size() - h = self.conv(x)[:, 0] # N, nbins, nframes - h = h.permute(2, 0, 1) # nframes, N, nbins - h, _ = self.lstm(h) - h = self.dense(h.reshape(-1, h.size()[-1])) # nframes * N, nbins - h = h.reshape(nframes, N, 1, nbins) - h = h.permute(1, 2, 3, 0) - - return h diff --git a/spaces/KarmKarma/rvc-models-genshinimpact/infer_pack/modules.py b/spaces/KarmKarma/rvc-models-genshinimpact/infer_pack/modules.py deleted file mode 100644 index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000 --- a/spaces/KarmKarma/rvc-models-genshinimpact/infer_pack/modules.py +++ /dev/null @@ -1,522 +0,0 @@ -import copy -import math -import numpy as np -import scipy -import torch -from torch import nn -from torch.nn import functional as F - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm - -from infer_pack import commons -from infer_pack.commons import init_weights, get_padding -from infer_pack.transforms import piecewise_rational_quadratic_transform - - -LRELU_SLOPE = 0.1 - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - -class ConvReluNorm(nn.Module): - def __init__( - self, - in_channels, - hidden_channels, - out_channels, - kernel_size, - n_layers, - p_dropout, - ): - super().__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - assert n_layers > 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append( - nn.Conv1d( - in_channels, hidden_channels, kernel_size, padding=kernel_size // 2 - ) - ) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout)) - for _ in range(n_layers - 1): - self.conv_layers.append( - nn.Conv1d( - hidden_channels, - hidden_channels, - kernel_size, - padding=kernel_size // 2, - ) - ) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size**i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append( - nn.Conv1d( - channels, - channels, - kernel_size, - groups=channels, - dilation=dilation, - padding=padding, - ) - ) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__( - self, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - p_dropout=0, - ): - super(WN, self).__init__() - assert kernel_size % 2 == 1 - self.hidden_channels = hidden_channels - self.kernel_size = (kernel_size,) - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - self.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d( - gin_channels, 2 * hidden_channels * n_layers, 1 - ) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight") - - for i in range(n_layers): - dilation = dilation_rate**i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d( - hidden_channels, - 2 * hidden_channels, - kernel_size, - dilation=dilation, - padding=padding, - ) - in_layer = torch.nn.utils.weight_norm(in_layer, name="weight") - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight") - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] - else: - g_l = torch.zeros_like(x_in) - - acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:, : self.hidden_channels, :] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:, self.hidden_channels :, :] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - ] - ) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels, 1)) - self.logs = nn.Parameter(torch.zeros(channels, 1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1, 2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False, - ): - assert channels % 2 == 0, "channels should be divisible by 2" - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=p_dropout, - gin_channels=gin_channels, - ) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels] * 2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels] * 2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1, 2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - def remove_weight_norm(self): - self.enc.remove_weight_norm() - - -class ConvFlow(nn.Module): - def __init__( - self, - in_channels, - filter_channels, - kernel_size, - n_layers, - num_bins=10, - tail_bound=5.0, - ): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.num_bins = num_bins - self.tail_bound = tail_bound - self.half_channels = in_channels // 2 - - self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) - self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0) - self.proj = nn.Conv1d( - filter_channels, self.half_channels * (num_bins * 3 - 1), 1 - ) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels] * 2, 1) - h = self.pre(x0) - h = self.convs(h, x_mask, g=g) - h = self.proj(h) * x_mask - - b, c, t = x0.shape - h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] - - unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt( - self.filter_channels - ) - unnormalized_derivatives = h[..., 2 * self.num_bins :] - - x1, logabsdet = piecewise_rational_quadratic_transform( - x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails="linear", - tail_bound=self.tail_bound, - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1, 2]) - if not reverse: - return x, logdet - else: - return x diff --git a/spaces/Kavinloll/text_generator/app.py b/spaces/Kavinloll/text_generator/app.py deleted file mode 100644 index 428b332e526f76af353668569c7aa1e2a01a0022..0000000000000000000000000000000000000000 --- a/spaces/Kavinloll/text_generator/app.py +++ /dev/null @@ -1,13 +0,0 @@ -import gradio as gr -from gradio.mix import Parallel - -title="My First Generator" -description="Input text and summit." - -model1=gr.Interface.load("huggingface/gpt2") -model2=gr.Interface.load("huggingface/EleutherAI/gpt-neo-1.3B") -model3=gr.Interface.load("huggingface/EleutherAI/gpt-j-6B") -model4=gr.Interface.load("huggingface/EleutherAI/gpt-neo-2.7B") - - -gr.Parallel(model1,model2,model3,model4, title=title, description=description).launch() \ No newline at end of file diff --git a/spaces/KoalaAI/Text-Moderation-Demo/app.py b/spaces/KoalaAI/Text-Moderation-Demo/app.py deleted file mode 100644 index a49f87861a2b54eba04116dd790b03b95c67b70e..0000000000000000000000000000000000000000 --- a/spaces/KoalaAI/Text-Moderation-Demo/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.load("models/KoalaAI/Text-Moderation").launch() \ No newline at end of file diff --git a/spaces/Kororinpa/Amadeus_Project/monotonic_align/__init__.py b/spaces/Kororinpa/Amadeus_Project/monotonic_align/__init__.py deleted file mode 100644 index 3d7009c40fea3a98168e3e3bc9ae061e91327422..0000000000000000000000000000000000000000 --- a/spaces/Kororinpa/Amadeus_Project/monotonic_align/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -import numpy as np -import torch -from .monotonic_align.core import maximum_path_c - - -def maximum_path(neg_cent, mask): - """ Cython optimized version. - neg_cent: [b, t_t, t_s] - mask: [b, t_t, t_s] - """ - device = neg_cent.device - dtype = neg_cent.dtype - neg_cent = neg_cent.data.cpu().numpy().astype(np.float32) - path = np.zeros(neg_cent.shape, dtype=np.int32) - - t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32) - t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32) - maximum_path_c(path, neg_cent, t_t_max, t_s_max) - return torch.from_numpy(path).to(device=device, dtype=dtype) diff --git a/spaces/Lamai/LAMAIGPT/autogpt/commands/times.py b/spaces/Lamai/LAMAIGPT/autogpt/commands/times.py deleted file mode 100644 index 3c9b8a4fc67a251c9e81a8c4a725cd1e25fcbebe..0000000000000000000000000000000000000000 --- a/spaces/Lamai/LAMAIGPT/autogpt/commands/times.py +++ /dev/null @@ -1,10 +0,0 @@ -from datetime import datetime - - -def get_datetime() -> str: - """Return the current date and time - - Returns: - str: The current date and time - """ - return "Current date and time: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S") diff --git a/spaces/LanguageBind/LanguageBind/scripts/audio_language/train.sh b/spaces/LanguageBind/LanguageBind/scripts/audio_language/train.sh deleted file mode 100644 index a3f58b42df72fff423b3034a35877c00eda680ee..0000000000000000000000000000000000000000 --- a/spaces/LanguageBind/LanguageBind/scripts/audio_language/train.sh +++ /dev/null @@ -1,22 +0,0 @@ - -CACHE_DIR="path/to/pretrained/weight" -TRAIN_DATA="path/to/data" -# this script is for 512 total batch_size (n(16) GPUs * batch_size(32) * accum_freq(1)) -cd /path/to/LanguageBind -TORCH_DISTRIBUTED_DEBUG=DETAIL HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 torchrun --nnodes=2 --nproc_per_node 8 \ - -m main \ - --train-data ${TRAIN_DATA} \ - --train-num-samples 413639 \ - --clip-type "al" --num_mel_bins 112 --target_length 1008 --audio_sample_rate 16000 \ - --lock-text --lock-image --text-type "polish_mplug" \ - --init-temp 0.07 --learn-temp \ - --model "ViT-L-14" --cache-dir ${CACHE_DIR} \ - --convert_to_lora --lora_r 8 \ - --lr 5e-4 --coef-lr 1e-3 \ - --beta1 0.9 --beta2 0.98 --wd 0.2 --eps 1e-6 \ - --num-frames 1 --force-patch-dropout 0.3 \ - --epochs 8 --batch-size 32 --accum-freq 1 --warmup 200 \ - --precision "amp" --workers 10 --video-decode-backend "imgs" \ - --save-frequency 1 --log-every-n-steps 20 --report-to "tensorboard" --resume "latest" \ - --do_eval --do_train \ - --val_a_cls_data "ESC50" diff --git a/spaces/Lianjd/stock_dashboard/backtrader/metabase.py b/spaces/Lianjd/stock_dashboard/backtrader/metabase.py deleted file mode 100644 index 9f507d723f251611c68031c29a359dd4bc4d3383..0000000000000000000000000000000000000000 --- a/spaces/Lianjd/stock_dashboard/backtrader/metabase.py +++ /dev/null @@ -1,331 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8; py-indent-offset:4 -*- -############################################################################### -# -# Copyright (C) 2015-2020 Daniel Rodriguez -# -# This program is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with this program. If not, see . -# -############################################################################### -from __future__ import (absolute_import, division, print_function, - unicode_literals) - -from collections import OrderedDict -import itertools -import sys - -import backtrader as bt -from .utils.py3 import zip, string_types, with_metaclass - - -def findbases(kls, topclass): - retval = list() - for base in kls.__bases__: - if issubclass(base, topclass): - retval.extend(findbases(base, topclass)) - retval.append(base) - - return retval - - -def findowner(owned, cls, startlevel=2, skip=None): - # skip this frame and the caller's -> start at 2 - for framelevel in itertools.count(startlevel): - try: - frame = sys._getframe(framelevel) - except ValueError: - # Frame depth exceeded ... no owner ... break away - break - - # 'self' in regular code - self_ = frame.f_locals.get('self', None) - if skip is not self_: - if self_ is not owned and isinstance(self_, cls): - return self_ - - # '_obj' in metaclasses - obj_ = frame.f_locals.get('_obj', None) - if skip is not obj_: - if obj_ is not owned and isinstance(obj_, cls): - return obj_ - - return None - - -class MetaBase(type): - def doprenew(cls, *args, **kwargs): - return cls, args, kwargs - - def donew(cls, *args, **kwargs): - _obj = cls.__new__(cls, *args, **kwargs) - return _obj, args, kwargs - - def dopreinit(cls, _obj, *args, **kwargs): - return _obj, args, kwargs - - def doinit(cls, _obj, *args, **kwargs): - _obj.__init__(*args, **kwargs) - return _obj, args, kwargs - - def dopostinit(cls, _obj, *args, **kwargs): - return _obj, args, kwargs - - def __call__(cls, *args, **kwargs): - cls, args, kwargs = cls.doprenew(*args, **kwargs) - _obj, args, kwargs = cls.donew(*args, **kwargs) - _obj, args, kwargs = cls.dopreinit(_obj, *args, **kwargs) - _obj, args, kwargs = cls.doinit(_obj, *args, **kwargs) - _obj, args, kwargs = cls.dopostinit(_obj, *args, **kwargs) - return _obj - - -class AutoInfoClass(object): - _getpairsbase = classmethod(lambda cls: OrderedDict()) - _getpairs = classmethod(lambda cls: OrderedDict()) - _getrecurse = classmethod(lambda cls: False) - - @classmethod - def _derive(cls, name, info, otherbases, recurse=False): - # collect the 3 set of infos - # info = OrderedDict(info) - baseinfo = cls._getpairs().copy() - obasesinfo = OrderedDict() - for obase in otherbases: - if isinstance(obase, (tuple, dict)): - obasesinfo.update(obase) - else: - obasesinfo.update(obase._getpairs()) - - # update the info of this class (base) with that from the other bases - baseinfo.update(obasesinfo) - - # The info of the new class is a copy of the full base info - # plus and update from parameter - clsinfo = baseinfo.copy() - clsinfo.update(info) - - # The new items to update/set are those from the otherbase plus the new - info2add = obasesinfo.copy() - info2add.update(info) - - clsmodule = sys.modules[cls.__module__] - newclsname = str(cls.__name__ + '_' + name) # str - Python 2/3 compat - - # This loop makes sure that if the name has already been defined, a new - # unique name is found. A collision example is in the plotlines names - # definitions of bt.indicators.MACD and bt.talib.MACD. Both end up - # definining a MACD_pl_macd and this makes it impossible for the pickle - # module to send results over a multiprocessing channel - namecounter = 1 - while hasattr(clsmodule, newclsname): - newclsname += str(namecounter) - namecounter += 1 - - newcls = type(newclsname, (cls,), {}) - setattr(clsmodule, newclsname, newcls) - - setattr(newcls, '_getpairsbase', - classmethod(lambda cls: baseinfo.copy())) - setattr(newcls, '_getpairs', classmethod(lambda cls: clsinfo.copy())) - setattr(newcls, '_getrecurse', classmethod(lambda cls: recurse)) - - for infoname, infoval in info2add.items(): - if recurse: - recursecls = getattr(newcls, infoname, AutoInfoClass) - infoval = recursecls._derive(name + '_' + infoname, - infoval, - []) - - setattr(newcls, infoname, infoval) - - return newcls - - def isdefault(self, pname): - return self._get(pname) == self._getkwargsdefault()[pname] - - def notdefault(self, pname): - return self._get(pname) != self._getkwargsdefault()[pname] - - def _get(self, name, default=None): - return getattr(self, name, default) - - @classmethod - def _getkwargsdefault(cls): - return cls._getpairs() - - @classmethod - def _getkeys(cls): - return cls._getpairs().keys() - - @classmethod - def _getdefaults(cls): - return list(cls._getpairs().values()) - - @classmethod - def _getitems(cls): - return cls._getpairs().items() - - @classmethod - def _gettuple(cls): - return tuple(cls._getpairs().items()) - - def _getkwargs(self, skip_=False): - l = [ - (x, getattr(self, x)) - for x in self._getkeys() if not skip_ or not x.startswith('_')] - return OrderedDict(l) - - def _getvalues(self): - return [getattr(self, x) for x in self._getkeys()] - - def __new__(cls, *args, **kwargs): - obj = super(AutoInfoClass, cls).__new__(cls, *args, **kwargs) - - if cls._getrecurse(): - for infoname in obj._getkeys(): - recursecls = getattr(cls, infoname) - setattr(obj, infoname, recursecls()) - - return obj - - -class MetaParams(MetaBase): - def __new__(meta, name, bases, dct): - # Remove params from class definition to avoid inheritance - # (and hence "repetition") - newparams = dct.pop('params', ()) - - packs = 'packages' - newpackages = tuple(dct.pop(packs, ())) # remove before creation - - fpacks = 'frompackages' - fnewpackages = tuple(dct.pop(fpacks, ())) # remove before creation - - # Create the new class - this pulls predefined "params" - cls = super(MetaParams, meta).__new__(meta, name, bases, dct) - - # Pulls the param class out of it - default is the empty class - params = getattr(cls, 'params', AutoInfoClass) - - # Pulls the packages class out of it - default is the empty class - packages = tuple(getattr(cls, packs, ())) - fpackages = tuple(getattr(cls, fpacks, ())) - - # get extra (to the right) base classes which have a param attribute - morebasesparams = [x.params for x in bases[1:] if hasattr(x, 'params')] - - # Get extra packages, add them to the packages and put all in the class - for y in [x.packages for x in bases[1:] if hasattr(x, packs)]: - packages += tuple(y) - - for y in [x.frompackages for x in bases[1:] if hasattr(x, fpacks)]: - fpackages += tuple(y) - - cls.packages = packages + newpackages - cls.frompackages = fpackages + fnewpackages - - # Subclass and store the newly derived params class - cls.params = params._derive(name, newparams, morebasesparams) - - return cls - - def donew(cls, *args, **kwargs): - clsmod = sys.modules[cls.__module__] - # import specified packages - for p in cls.packages: - if isinstance(p, (tuple, list)): - p, palias = p - else: - palias = p - - pmod = __import__(p) - - plevels = p.split('.') - if p == palias and len(plevels) > 1: # 'os.path' not aliased - setattr(clsmod, pmod.__name__, pmod) # set 'os' in module - - else: # aliased and/or dots - for plevel in plevels[1:]: # recurse down the mod - pmod = getattr(pmod, plevel) - - setattr(clsmod, palias, pmod) - - # import from specified packages - the 2nd part is a string or iterable - for p, frompackage in cls.frompackages: - if isinstance(frompackage, string_types): - frompackage = (frompackage,) # make it a tuple - - for fp in frompackage: - if isinstance(fp, (tuple, list)): - fp, falias = fp - else: - fp, falias = fp, fp # assumed is string - - # complain "not string" without fp (unicode vs bytes) - pmod = __import__(p, fromlist=[str(fp)]) - pattr = getattr(pmod, fp) - setattr(clsmod, falias, pattr) - for basecls in cls.__bases__: - setattr(sys.modules[basecls.__module__], falias, pattr) - - # Create params and set the values from the kwargs - params = cls.params() - for pname, pdef in cls.params._getitems(): - setattr(params, pname, kwargs.pop(pname, pdef)) - - # Create the object and set the params in place - _obj, args, kwargs = super(MetaParams, cls).donew(*args, **kwargs) - _obj.params = params - _obj.p = params # shorter alias - - # Parameter values have now been set before __init__ - return _obj, args, kwargs - - -class ParamsBase(with_metaclass(MetaParams, object)): - pass # stub to allow easy subclassing without metaclasses - - -class ItemCollection(object): - ''' - Holds a collection of items that can be reached by - - - Index - - Name (if set in the append operation) - ''' - def __init__(self): - self._items = list() - self._names = list() - - def __len__(self): - return len(self._items) - - def append(self, item, name=None): - setattr(self, name, item) - self._items.append(item) - if name: - self._names.append(name) - - def __getitem__(self, key): - return self._items[key] - - def getnames(self): - return self._names - - def getitems(self): - return zip(self._names, self._items) - - def getbyname(self, name): - idx = self._names.index(name) - return self._items[idx] diff --git a/spaces/Liu-LAB/GPT-academic/crazy_functions/test_project/cpp/cppipc/queue.h b/spaces/Liu-LAB/GPT-academic/crazy_functions/test_project/cpp/cppipc/queue.h deleted file mode 100644 index a21f3446e06b5826af7b554c8a7d9c5d80848b62..0000000000000000000000000000000000000000 --- a/spaces/Liu-LAB/GPT-academic/crazy_functions/test_project/cpp/cppipc/queue.h +++ /dev/null @@ -1,216 +0,0 @@ -#pragma once - -#include -#include -#include // [[since C++14]]: std::exchange -#include -#include -#include -#include -#include -#include -#include // assert - -#include "libipc/def.h" -#include "libipc/shm.h" -#include "libipc/rw_lock.h" - -#include "libipc/utility/log.h" -#include "libipc/platform/detail.h" -#include "libipc/circ/elem_def.h" - -namespace ipc { -namespace detail { - -class queue_conn { -protected: - circ::cc_t connected_ = 0; - shm::handle elems_h_; - - template - Elems* open(char const * name) { - if (name == nullptr || name[0] == '\0') { - ipc::error("fail open waiter: name is empty!\n"); - return nullptr; - } - if (!elems_h_.acquire(name, sizeof(Elems))) { - return nullptr; - } - auto elems = static_cast(elems_h_.get()); - if (elems == nullptr) { - ipc::error("fail acquire elems: %s\n", name); - return nullptr; - } - elems->init(); - return elems; - } - - void close() { - elems_h_.release(); - } - -public: - queue_conn() = default; - queue_conn(const queue_conn&) = delete; - queue_conn& operator=(const queue_conn&) = delete; - - bool connected() const noexcept { - return connected_ != 0; - } - - circ::cc_t connected_id() const noexcept { - return connected_; - } - - template - auto connect(Elems* elems) noexcept - /*needs 'optional' here*/ - -> std::tuple().cursor())> { - if (elems == nullptr) return {}; - // if it's already connected, just return - if (connected()) return {connected(), false, 0}; - connected_ = elems->connect_receiver(); - return {connected(), true, elems->cursor()}; - } - - template - bool disconnect(Elems* elems) noexcept { - if (elems == nullptr) return false; - // if it's already disconnected, just return false - if (!connected()) return false; - elems->disconnect_receiver(std::exchange(connected_, 0)); - return true; - } -}; - -template -class queue_base : public queue_conn { - using base_t = queue_conn; - -public: - using elems_t = Elems; - using policy_t = typename elems_t::policy_t; - -protected: - elems_t * elems_ = nullptr; - decltype(std::declval().cursor()) cursor_ = 0; - bool sender_flag_ = false; - -public: - using base_t::base_t; - - queue_base() = default; - - explicit queue_base(char const * name) - : queue_base{} { - elems_ = open(name); - } - - explicit queue_base(elems_t * elems) noexcept - : queue_base{} { - assert(elems != nullptr); - elems_ = elems; - } - - /* not virtual */ ~queue_base() { - base_t::close(); - } - - elems_t * elems() noexcept { return elems_; } - elems_t const * elems() const noexcept { return elems_; } - - bool ready_sending() noexcept { - if (elems_ == nullptr) return false; - return sender_flag_ || (sender_flag_ = elems_->connect_sender()); - } - - void shut_sending() noexcept { - if (elems_ == nullptr) return; - if (!sender_flag_) return; - elems_->disconnect_sender(); - } - - bool connect() noexcept { - auto tp = base_t::connect(elems_); - if (std::get<0>(tp) && std::get<1>(tp)) { - cursor_ = std::get<2>(tp); - return true; - } - return std::get<0>(tp); - } - - bool disconnect() noexcept { - return base_t::disconnect(elems_); - } - - std::size_t conn_count() const noexcept { - return (elems_ == nullptr) ? static_cast(invalid_value) : elems_->conn_count(); - } - - bool valid() const noexcept { - return elems_ != nullptr; - } - - bool empty() const noexcept { - return !valid() || (cursor_ == elems_->cursor()); - } - - template - bool push(F&& prep, P&&... params) { - if (elems_ == nullptr) return false; - return elems_->push(this, [&](void* p) { - if (prep(p)) ::new (p) T(std::forward

(params)...); - }); - } - - template - bool force_push(F&& prep, P&&... params) { - if (elems_ == nullptr) return false; - return elems_->force_push(this, [&](void* p) { - if (prep(p)) ::new (p) T(std::forward

(params)...); - }); - } - - template - bool pop(T& item, F&& out) { - if (elems_ == nullptr) { - return false; - } - return elems_->pop(this, &(this->cursor_), [&item](void* p) { - ::new (&item) T(std::move(*static_cast(p))); - }, std::forward(out)); - } -}; - -} // namespace detail - -template -class queue final : public detail::queue_base> { - using base_t = detail::queue_base>; - -public: - using value_t = T; - - using base_t::base_t; - - template - bool push(P&&... params) { - return base_t::template push(std::forward

(params)...); - } - - template - bool force_push(P&&... params) { - return base_t::template force_push(std::forward

(params)...); - } - - bool pop(T& item) { - return base_t::pop(item, [](bool) {}); - } - - template - bool pop(T& item, F&& out) { - return base_t::pop(item, std::forward(out)); - } -}; - -} // namespace ipc diff --git a/spaces/Liu-LAB/GPT-academic/request_llm/bridge_jittorllms_rwkv.py b/spaces/Liu-LAB/GPT-academic/request_llm/bridge_jittorllms_rwkv.py deleted file mode 100644 index ee4f592f5a1a3e6022b41c899e342bd7e55ed44f..0000000000000000000000000000000000000000 --- a/spaces/Liu-LAB/GPT-academic/request_llm/bridge_jittorllms_rwkv.py +++ /dev/null @@ -1,175 +0,0 @@ - -from transformers import AutoModel, AutoTokenizer -import time -import threading -import importlib -from toolbox import update_ui, get_conf -from multiprocessing import Process, Pipe - -load_message = "jittorllms尚未加载，加载需要一段时间。注意，请避免混用多种jittor模型，否则可能导致显存溢出而造成卡顿，取决于`config.py`的配置，jittorllms消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……" - -################################################################################# -class GetGLMHandle(Process): - def __init__(self): - super().__init__(daemon=True) - self.parent, self.child = Pipe() - self.jittorllms_model = None - self.info = "" - self.local_history = [] - self.success = True - self.check_dependency() - self.start() - self.threadLock = threading.Lock() - - def check_dependency(self): - try: - import pandas - self.info = "依赖检测通过" - self.success = True - except: - from toolbox import trimmed_format_exc - self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\ - r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\ - r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc() - self.success = False - - def ready(self): - return self.jittorllms_model is not None - - def run(self): - # 子进程执行 - # 第一次运行，加载参数 - def validate_path(): - import os, sys - dir_name = os.path.dirname(__file__) - env = os.environ.get("PATH", "") - os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin') - root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..') - os.chdir(root_dir_assume + '/request_llm/jittorllms') - sys.path.append(root_dir_assume + '/request_llm/jittorllms') - validate_path() # validate path so you can run from base directory - - def load_model(): - import types - try: - if self.jittorllms_model is None: - device, = get_conf('LOCAL_MODEL_DEVICE') - from .jittorllms.models import get_model - # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"] - args_dict = {'model': 'chatrwkv'} - print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))') - self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict)) - print('done get model') - except: - self.child.send('[Local Message] Call jittorllms fail 不能正常加载jittorllms的参数。') - raise RuntimeError("不能正常加载jittorllms的参数！") - print('load_model') - load_model() - - # 进入任务等待状态 - print('进入任务等待状态') - while True: - # 进入任务等待状态 - kwargs = self.child.recv() - query = kwargs['query'] - history = kwargs['history'] - # 是否重置 - if len(self.local_history) > 0 and len(history)==0: - print('触发重置') - self.jittorllms_model.reset() - self.local_history.append(query) - - print('收到消息，开始请求') - try: - for response in self.jittorllms_model.stream_chat(query, history): - print(response) - self.child.send(response) - except: - from toolbox import trimmed_format_exc - print(trimmed_format_exc()) - self.child.send('[Local Message] Call jittorllms fail.') - # 请求处理结束，开始下一个循环 - self.child.send('[Finish]') - - def stream_chat(self, **kwargs): - # 主进程执行 - self.threadLock.acquire() - self.parent.send(kwargs) - while True: - res = self.parent.recv() - if res != '[Finish]': - yield res - else: - break - self.threadLock.release() - -global rwkv_glm_handle -rwkv_glm_handle = None -################################################################################# -def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False): - """ - 多线程方法 - 函数的说明请见 request_llm/bridge_all.py - """ - global rwkv_glm_handle - if rwkv_glm_handle is None: - rwkv_glm_handle = GetGLMHandle() - if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + rwkv_glm_handle.info - if not rwkv_glm_handle.success: - error = rwkv_glm_handle.info - rwkv_glm_handle = None - raise RuntimeError(error) - - # jittorllms 没有 sys_prompt 接口，因此把prompt加入 history - history_feedin = [] - for i in range(len(history)//2): - history_feedin.append([history[2*i], history[2*i+1]] ) - - watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可 - response = "" - for response in rwkv_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']): - print(response) - if len(observe_window) >= 1: observe_window[0] = response - if len(observe_window) >= 2: - if (time.time()-observe_window[1]) > watch_dog_patience: - raise RuntimeError("程序终止。") - return response - - - -def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): - """ - 单线程方法 - 函数的说明请见 request_llm/bridge_all.py - """ - chatbot.append((inputs, "")) - - global rwkv_glm_handle - if rwkv_glm_handle is None: - rwkv_glm_handle = GetGLMHandle() - chatbot[-1] = (inputs, load_message + "\n\n" + rwkv_glm_handle.info) - yield from update_ui(chatbot=chatbot, history=[]) - if not rwkv_glm_handle.success: - rwkv_glm_handle = None - return - - if additional_fn is not None: - from core_functional import handle_core_functionality - inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot) - - # 处理历史信息 - history_feedin = [] - for i in range(len(history)//2): - history_feedin.append([history[2*i], history[2*i+1]] ) - - # 开始接收jittorllms的回复 - response = "[Local Message]: 等待jittorllms响应中 ..." - for response in rwkv_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']): - chatbot[-1] = (inputs, response) - yield from update_ui(chatbot=chatbot, history=history) - - # 总结输出 - if response == "[Local Message]: 等待jittorllms响应中 ...": - response = "[Local Message]: jittorllms响应异常 ..." - history.extend([inputs, response]) - yield from update_ui(chatbot=chatbot, history=history) diff --git a/spaces/LuxOAI/ChatGpt-Web/app/masks/typing.ts b/spaces/LuxOAI/ChatGpt-Web/app/masks/typing.ts deleted file mode 100644 index 5f39ccdc87cd2303744ccbd46ca79a73baf37cf6..0000000000000000000000000000000000000000 --- a/spaces/LuxOAI/ChatGpt-Web/app/masks/typing.ts +++ /dev/null @@ -1,3 +0,0 @@ -import { type Mask } from "../store/mask"; - -export type BuiltinMask = Omit; diff --git a/spaces/MMars/whisper-small-ar-demo/app.py b/spaces/MMars/whisper-small-ar-demo/app.py deleted file mode 100644 index d24c489bd17afd59689ea42047a222491287ee58..0000000000000000000000000000000000000000 --- a/spaces/MMars/whisper-small-ar-demo/app.py +++ /dev/null @@ -1,97 +0,0 @@ -import torch - -import gradio as gr -import pytube as pt -from transformers import pipeline -from huggingface_hub import model_info - -MODEL_NAME = "MMars/whisper-small-ar" #this always needs to stay in line 8 :D sorry for the hackiness -lang = "ar" - -device = 0 if torch.cuda.is_available() else "cpu" -pipe = pipeline( - task="automatic-speech-recognition", - model=MODEL_NAME, - chunk_length_s=30, - device=device, -) - -pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe") - -def transcribe(microphone, file_upload): - warn_output = "" - if (microphone is not None) and (file_upload is not None): - warn_output = ( - "WARNING: You've uploaded an audio file and used the microphone. " - "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" - ) - - elif (microphone is None) and (file_upload is None): - return "ERROR: You have to either use the microphone or upload an audio file" - - file = microphone if microphone is not None else file_upload - - text = pipe(file)["text"] - - return warn_output + text - - -def _return_yt_html_embed(yt_url): - video_id = yt_url.split("?v=")[-1] - HTML_str = ( - f'

' - "
" - ) - return HTML_str - - -def yt_transcribe(yt_url): - yt = pt.YouTube(yt_url) - html_embed_str = _return_yt_html_embed(yt_url) - stream = yt.streams.filter(only_audio=True)[0] - stream.download(filename="audio.mp3") - - text = pipe("audio.mp3")["text"] - - return html_embed_str, text - - -demo = gr.Blocks() - -mf_transcribe = gr.Interface( - fn=transcribe, - inputs=[ - gr.inputs.Audio(source="microphone", type="filepath", optional=True), - gr.inputs.Audio(source="upload", type="filepath", optional=True), - ], - outputs="text", - layout="horizontal", - theme="huggingface", - title="Whisper-small-ar Demo: Transcribe Audio", - description=( - "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned" - f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" - " of arbitrary length." - ), - allow_flagging="never", -) - -yt_transcribe = gr.Interface( - fn=yt_transcribe, - inputs=[gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")], - outputs=["html", "text"], - layout="horizontal", - theme="huggingface", - title="Whisper-small-ar Demo: Transcribe YouTube", - description=( - "Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:" - f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of" - " arbitrary length." - ), - allow_flagging="never", -) - -with demo: - gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"]) - -demo.launch(enable_queue=True) diff --git a/spaces/Ma5onic/MVSEP-MDX23-music-separation-model/demucs3/utils.py b/spaces/Ma5onic/MVSEP-MDX23-music-separation-model/demucs3/utils.py deleted file mode 100644 index 38ef120a50742e0332f0845231b9f540658ea5b1..0000000000000000000000000000000000000000 --- a/spaces/Ma5onic/MVSEP-MDX23-music-separation-model/demucs3/utils.py +++ /dev/null @@ -1,141 +0,0 @@ -# Copyright (c) Meta, Inc. and its affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from collections import defaultdict -from contextlib import contextmanager -import math -import os -import tempfile -import typing as tp - -import torch -from torch.nn import functional as F -from torch.utils.data import Subset - - -def unfold(a, kernel_size, stride): - """Given input of size [*OT, T], output Tensor of size [*OT, F, K] - with K the kernel size, by extracting frames with the given stride. - - This will pad the input so that `F = ceil(T / K)`. - - see https://github.com/pytorch/pytorch/issues/60466 - """ - *shape, length = a.shape - n_frames = math.ceil(length / stride) - tgt_length = (n_frames - 1) * stride + kernel_size - a = F.pad(a, (0, tgt_length - length)) - strides = list(a.stride()) - assert strides[-1] == 1, 'data should be contiguous' - strides = strides[:-1] + [stride, 1] - return a.as_strided([*shape, n_frames, kernel_size], strides) - - -def center_trim(tensor: torch.Tensor, reference: tp.Union[torch.Tensor, int]): - """ - Center trim `tensor` with respect to `reference`, along the last dimension. - `reference` can also be a number, representing the length to trim to. - If the size difference != 0 mod 2, the extra sample is removed on the right side. - """ - ref_size: int - if isinstance(reference, torch.Tensor): - ref_size = reference.size(-1) - else: - ref_size = reference - delta = tensor.size(-1) - ref_size - if delta < 0: - raise ValueError("tensor must be larger than reference. " f"Delta is {delta}.") - if delta: - tensor = tensor[..., delta // 2:-(delta - delta // 2)] - return tensor - - -def pull_metric(history: tp.List[dict], name: str): - out = [] - for metrics in history: - metric = metrics - for part in name.split("."): - metric = metric[part] - out.append(metric) - return out - - -def EMA(beta: float = 1): - """ - Exponential Moving Average callback. - Returns a single function that can be called to repeatidly update the EMA - with a dict of metrics. The callback will return - the new averaged dict of metrics. - - Note that for `beta=1`, this is just plain averaging. - """ - fix: tp.Dict[str, float] = defaultdict(float) - total: tp.Dict[str, float] = defaultdict(float) - - def _update(metrics: dict, weight: float = 1) -> dict: - nonlocal total, fix - for key, value in metrics.items(): - total[key] = total[key] * beta + weight * float(value) - fix[key] = fix[key] * beta + weight - return {key: tot / fix[key] for key, tot in total.items()} - return _update - - -def sizeof_fmt(num: float, suffix: str = 'B'): - """ - Given `num` bytes, return human readable size. - Taken from https://stackoverflow.com/a/1094933 - """ - for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']: - if abs(num) < 1024.0: - return "%3.1f%s%s" % (num, unit, suffix) - num /= 1024.0 - return "%.1f%s%s" % (num, 'Yi', suffix) - - -@contextmanager -def temp_filenames(count: int, delete=True): - names = [] - try: - for _ in range(count): - names.append(tempfile.NamedTemporaryFile(delete=False).name) - yield names - finally: - if delete: - for name in names: - os.unlink(name) - - -def random_subset(dataset, max_samples: int, seed: int = 42): - if max_samples >= len(dataset): - return dataset - - generator = torch.Generator().manual_seed(seed) - perm = torch.randperm(len(dataset), generator=generator) - return Subset(dataset, perm[:max_samples].tolist()) - - -class DummyPoolExecutor: - class DummyResult: - def __init__(self, func, *args, **kwargs): - self.func = func - self.args = args - self.kwargs = kwargs - - def result(self): - return self.func(*self.args, **self.kwargs) - - def __init__(self, workers=0): - pass - - def submit(self, func, *args, **kwargs): - return DummyPoolExecutor.DummyResult(func, *args, **kwargs) - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_value, exc_tb): - return diff --git a/spaces/Maaz66/GPT3-SPANISH-CHATBOT-PUBLIC/README.md b/spaces/Maaz66/GPT3-SPANISH-CHATBOT-PUBLIC/README.md deleted file mode 100644 index 729beb841d4591d513857eab6241fb90ba6e437a..0000000000000000000000000000000000000000 --- a/spaces/Maaz66/GPT3-SPANISH-CHATBOT-PUBLIC/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: GPT3 SPANISH CHATBOT -emoji: 😻 -colorFrom: red -colorTo: yellow -sdk: gradio -sdk_version: 3.15.0 -app_file: app.py -pinned: false -license: unknown -duplicated_from: Maaz66/GPT3-SPANISH-CHATBOT ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/text/symbols.py b/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/text/symbols.py deleted file mode 100644 index 3705de1c96d52d5643eab9bc80671fe9cb7e4363..0000000000000000000000000000000000000000 --- a/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/text/symbols.py +++ /dev/null @@ -1,67 +0,0 @@ -''' -Defines the set of symbols used in text input to the model. -''' -_pad = '_' -_punctuation = ',.!?-~…' -_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ ' -''' -# japanese_cleaners2 -_pad = '_' -_punctuation = ',.!?-~…' -_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ ' -''' - -'''# korean_cleaners -_pad = '_' -_punctuation = ',.!?…~' -_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ ' -''' - -'''# chinese_cleaners -_pad = '_' -_punctuation = '，。！？—…' -_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ ' -''' - - -'''# sanskrit_cleaners -_pad = '_' -_punctuation = '।' -_letters = 'ँंःअआइईउऊऋएऐओऔकखगघङचछजझञटठडढणतथदधनपफबभमयरलळवशषसहऽािीुूृॄेैोौ्ॠॢ ' -''' - -'''# cjks_cleaners -_pad = '_' -_punctuation = ',.!?-~…' -_letters = 'NQabdefghijklmnopstuvwxyzʃʧʥʦɯɹəɥçɸɾβŋɦː⁼ʰ`^#*=→↓↑ ' -''' - -'''# thai_cleaners -_pad = '_' -_punctuation = '.!? ' -_letters = 'กขฃคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลวศษสหฬอฮฯะัาำิีึืุูเแโใไๅๆ็่้๊๋์' -''' - -'''# cjke_cleaners2 -_pad = '_' -_punctuation = ',.!?-~…' -_letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ ' -''' - -'''# shanghainese_cleaners -_pad = '_' -_punctuation = ',.!?…' -_letters = 'abdfghiklmnopstuvyzøŋȵɑɔɕəɤɦɪɿʑʔʰ̩̃ᴀᴇ15678 ' -''' - -'''# chinese_dialect_cleaners -_pad = '_' -_punctuation = ',.!?~…─' -_letters = '#Nabdefghijklmnoprstuvwxyzæçøŋœȵɐɑɒɓɔɕɗɘəɚɛɜɣɤɦɪɭɯɵɷɸɻɾɿʂʅʊʋʌʏʑʔʦʮʰʷˀː˥˦˧˨˩̥̩̃̚αᴀᴇ↑↓∅ⱼ ' -''' - -# Export all symbols: -symbols = [_pad] + list(_punctuation) + list(_letters) - -# Special symbol ids -SPACE_ID = symbols.index(" ") diff --git a/spaces/Marshalls/testmtd/analysis/pymo/viz_tools.py b/spaces/Marshalls/testmtd/analysis/pymo/viz_tools.py deleted file mode 100644 index b936dc26b9324e3a120b6869a94c15549c238a0b..0000000000000000000000000000000000000000 --- a/spaces/Marshalls/testmtd/analysis/pymo/viz_tools.py +++ /dev/null @@ -1,311 +0,0 @@ -import pandas as pd -import numpy as np -import matplotlib.animation as animation -import matplotlib.colors as colors -import matplotlib.patheffects as pe -import matplotlib.pyplot as plt -#import IPython -import os - -def save_fig(fig_id, tight_layout=True): - if tight_layout: - plt.tight_layout() - plt.savefig(fig_id + '.png', format='png', dpi=300) - - -def draw_stickfigure(mocap_track, frame, data=None, joints=None, draw_names=False, ax=None, figsize=(8,8)): - if ax is None: - fig = plt.figure(figsize=figsize) - ax = fig.add_subplot(111) - - if joints is None: - joints_to_draw = mocap_track.skeleton.keys() - else: - joints_to_draw = joints - - if data is None: - df = mocap_track.values - else: - df = data - - for joint in joints_to_draw: - ax.scatter(x=df['%s_Xposition'%joint][frame], - y=df['%s_Yposition'%joint][frame], - alpha=0.6, c='b', marker='o') - - parent_x = df['%s_Xposition'%joint][frame] - parent_y = df['%s_Yposition'%joint][frame] - - children_to_draw = [c for c in mocap_track.skeleton[joint]['children'] if c in joints_to_draw] - - for c in children_to_draw: - child_x = df['%s_Xposition'%c][frame] - child_y = df['%s_Yposition'%c][frame] - ax.plot([parent_x, child_x], [parent_y, child_y], 'k-', lw=2) - - if draw_names: - ax.annotate(joint, - (df['%s_Xposition'%joint][frame] + 0.1, - df['%s_Yposition'%joint][frame] + 0.1)) - - return ax - -def draw_stickfigure3d(mocap_track, frame, data=None, joints=None, draw_names=False, ax=None, figsize=(8,8)): - from mpl_toolkits.mplot3d import Axes3D - - if ax is None: - fig = plt.figure(figsize=figsize) - ax = fig.add_subplot(111, projection='3d') - - if joints is None: - joints_to_draw = mocap_track.skeleton.keys() - else: - joints_to_draw = joints - - if data is None: - df = mocap_track.values - else: - df = data - - for joint in joints_to_draw: - parent_x = df['%s_Xposition'%joint][frame] - parent_y = df['%s_Zposition'%joint][frame] - parent_z = df['%s_Yposition'%joint][frame] - # ^ In mocaps, Y is the up-right axis - - ax.scatter(xs=parent_x, - ys=parent_y, - zs=parent_z, - alpha=0.6, c='b', marker='o') - - - children_to_draw = [c for c in mocap_track.skeleton[joint]['children'] if c in joints_to_draw] - - for c in children_to_draw: - child_x = df['%s_Xposition'%c][frame] - child_y = df['%s_Zposition'%c][frame] - child_z = df['%s_Yposition'%c][frame] - # ^ In mocaps, Y is the up-right axis - - ax.plot([parent_x, child_x], [parent_y, child_y], [parent_z, child_z], 'k-', lw=2, c='black') - - if draw_names: - ax.text(x=parent_x + 0.1, - y=parent_y + 0.1, - z=parent_z + 0.1, - s=joint, - color='rgba(0,0,0,0.9') - - return ax - - -def sketch_move(mocap_track, data=None, ax=None, figsize=(16,8)): - if ax is None: - fig = plt.figure(figsize=figsize) - ax = fig.add_subplot(111) - - if data is None: - data = mocap_track.values - - for frame in range(0, data.shape[0], 4): -# draw_stickfigure(mocap_track, f, data=data, ax=ax) - - for joint in mocap_track.skeleton.keys(): - children_to_draw = [c for c in mocap_track.skeleton[joint]['children']] - - parent_x = data['%s_Xposition'%joint][frame] - parent_y = data['%s_Yposition'%joint][frame] - - frame_alpha = frame/data.shape[0] - - for c in children_to_draw: - child_x = data['%s_Xposition'%c][frame] - child_y = data['%s_Yposition'%c][frame] - - ax.plot([parent_x, child_x], [parent_y, child_y], '-', lw=1, color='gray', alpha=frame_alpha) - -def render_mp4(mocap_track, filename, data=None, ax=None, axis_scale=50, elev=45, azim=45): - if ax is None: - fig = plt.figure(figsize=(10,10)) - ax = fig.add_subplot(111, projection='3d') - ax.set_xlim3d(-axis_scale, axis_scale) - ax.set_zlim3d( 0, axis_scale) - ax.set_ylim3d(-axis_scale, axis_scale) - ax.grid(True) - ax.set_axis_off() - - ax.view_init(elev=elev, azim=azim) - - xs = np.linspace(-200, 200, 50) - ys = np.linspace(-200, 200, 50) - X, Y = np.meshgrid(xs, ys) - Z = np.zeros(X.shape) - - wframe = ax.plot_wireframe(X, Y, Z, rstride=2, cstride=2, color='grey',lw=0.2) - - # fig = plt.figure(figsize=figsize) - # ax = fig.add_subplot(111) - - if data is None: - data = mocap_track.values - - fps=int(np.round(1/mocap_track.framerate)) - lines=[] - lines.append([plt.plot([0,0], [0,0], [0,0], color='red', - lw=2, path_effects=[pe.Stroke(linewidth=3, foreground='black'), pe.Normal()])[0] for _ in range(len(mocap_track.skeleton.keys()))]) - - def animate(frame): - - changed = [] - j=0 - for joint in mocap_track.skeleton.keys(): - children_to_draw = [c for c in mocap_track.skeleton[joint]['children']] - - parent_x = data['%s_Xposition'%joint][frame] - parent_y = data['%s_Yposition'%joint][frame] - parent_z = data['%s_Zposition'%joint][frame] - - #frame_alpha = frame/data.shape[0] - - for c in children_to_draw: - child_x = data['%s_Xposition'%c][frame] - child_y = data['%s_Yposition'%c][frame] - child_z = data['%s_Zposition'%c][frame] - - lines[0][j].set_data(np.array([[child_x, parent_x],[-child_z,-parent_z]])) - lines[0][j].set_3d_properties(np.array([ child_y,parent_y])) - - changed += lines - j+=1 - - return changed - - plt.tight_layout() - - ani = animation.FuncAnimation(fig, - animate, np.arange(data.shape[0]), interval=1000/fps) - - if filename != None: - ani.save(filename, fps=fps, bitrate=13934) - ani.event_source.stop() - del ani - plt.close() - try: - plt.show() - plt.save() - except AttributeError as e: - pass - - -def viz_cnn_filter(feature_to_viz, mocap_track, data, gap=25): - fig = plt.figure(figsize=(16,4)) - ax = plt.subplot2grid((1,8),(0,0)) - ax.imshow(feature_to_viz.T, aspect='auto', interpolation='nearest') - - ax = plt.subplot2grid((1,8),(0,1), colspan=7) - for frame in range(feature_to_viz.shape[0]): - frame_alpha = 0.2#frame/data.shape[0] * 2 + 0.2 - - for joint_i, joint in enumerate(mocap_track.skeleton.keys()): - children_to_draw = [c for c in mocap_track.skeleton[joint]['children']] - - parent_x = data['%s_Xposition'%joint][frame] + frame * gap - parent_y = data['%s_Yposition'%joint][frame] - - ax.scatter(x=parent_x, - y=parent_y, - alpha=0.6, - cmap='RdBu', - c=feature_to_viz[frame][joint_i] * 10000, - marker='o', - s = abs(feature_to_viz[frame][joint_i] * 10000)) - plt.axis('off') - for c in children_to_draw: - child_x = data['%s_Xposition'%c][frame] + frame * gap - child_y = data['%s_Yposition'%c][frame] - - ax.plot([parent_x, child_x], [parent_y, child_y], '-', lw=1, color='gray', alpha=frame_alpha) - - -def print_skel(X): - stack = [X.root_name] - tab=0 - while stack: - joint = stack.pop() - tab = len(stack) - print('%s- %s (%s)'%('| '*tab, joint, X.skeleton[joint]['parent'])) - for c in X.skeleton[joint]['children']: - stack.append(c) - - -# def nb_play_mocap_fromurl(mocap, mf, frame_time=1/30, scale=1, base_url='http://titan:8385'): - # if mf == 'bvh': - # bw = BVHWriter() - # with open('test.bvh', 'w') as ofile: - # bw.write(mocap, ofile) - - # filepath = '../notebooks/test.bvh' - # elif mf == 'pos': - # c = list(mocap.values.columns) - - # for cc in c: - # if 'rotation' in cc: - # c.remove(cc) - # mocap.values.to_csv('test.csv', index=False, columns=c) - - # filepath = '../notebooks/test.csv' - # else: - # return - - # url = '%s/mocapplayer/player.html?data_url=%s&scale=%f&cz=200&order=xzyi&frame_time=%f'%(base_url, filepath, scale, frame_time) - # iframe = '' - # link = 'New Window'%url - # return IPython.display.HTML(iframe+link) - -# def nb_play_mocap(mocap, mf, meta=None, frame_time=1/30, scale=1, camera_z=500, base_url=None): - # data_template = 'var dataBuffer = `$$DATA$$`;' - # data_template += 'var metadata = $$META$$;' - # data_template += 'start(dataBuffer, metadata, $$CZ$$, $$SCALE$$, $$FRAMETIME$$);' - # dir_path = os.path.dirname(os.path.realpath(__file__)) - - - # if base_url is None: - # base_url = os.path.join(dir_path, 'mocapplayer/playBuffer.html') - - # # print(dir_path) - - # if mf == 'bvh': - # pass - # elif mf == 'pos': - # cols = list(mocap.values.columns) - # for c in cols: - # if 'rotation' in c: - # cols.remove(c) - - # data_csv = mocap.values.to_csv(index=False, columns=cols) - - # if meta is not None: - # lines = [','.join(item) for item in meta.astype('str')] - # meta_csv = '[' + ','.join('[%s]'%l for l in lines) +']' - # else: - # meta_csv = '[]' - - # data_assigned = data_template.replace('$$DATA$$', data_csv) - # data_assigned = data_assigned.replace('$$META$$', meta_csv) - # data_assigned = data_assigned.replace('$$CZ$$', str(camera_z)) - # data_assigned = data_assigned.replace('$$SCALE$$', str(scale)) - # data_assigned = data_assigned.replace('$$FRAMETIME$$', str(frame_time)) - - # else: - # return - - - - # with open(os.path.join(dir_path, 'mocapplayer/data.js'), 'w') as oFile: - # oFile.write(data_assigned) - - # url = '%s?&cz=200&order=xzyi&frame_time=%f&scale=%f'%(base_url, frame_time, scale) - # iframe = '' - # link = 'New Window'%url - # return IPython.display.HTML(iframe+link) - \ No newline at end of file diff --git a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/data/datasets/lvis_22k_categories.py b/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/data/datasets/lvis_22k_categories.py deleted file mode 100644 index 9525f0873d68d84dd691979c32eaadd7860f59fe..0000000000000000000000000000000000000000 --- a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/data/datasets/lvis_22k_categories.py +++ /dev/null @@ -1 +0,0 @@ -CATEGORIES = [{'name': 'aerosol_can', 'id': 1, 'frequency': 'c', 'synset': 'aerosol.n.02'}, {'name': 'air_conditioner', 'id': 2, 'frequency': 'f', 'synset': 'air_conditioner.n.01'}, {'name': 'airplane', 'id': 3, 'frequency': 'f', 'synset': 'airplane.n.01'}, {'name': 'alarm_clock', 'id': 4, 'frequency': 'f', 'synset': 'alarm_clock.n.01'}, {'name': 'alcohol', 'id': 5, 'frequency': 'c', 'synset': 'alcohol.n.01'}, {'name': 'alligator', 'id': 6, 'frequency': 'c', 'synset': 'alligator.n.02'}, {'name': 'almond', 'id': 7, 'frequency': 'c', 'synset': 'almond.n.02'}, {'name': 'ambulance', 'id': 8, 'frequency': 'c', 'synset': 'ambulance.n.01'}, {'name': 'amplifier', 'id': 9, 'frequency': 'c', 'synset': 'amplifier.n.01'}, {'name': 'anklet', 'id': 10, 'frequency': 'c', 'synset': 'anklet.n.03'}, {'name': 'antenna', 'id': 11, 'frequency': 'f', 'synset': 'antenna.n.01'}, {'name': 'apple', 'id': 12, 'frequency': 'f', 'synset': 'apple.n.01'}, {'name': 'applesauce', 'id': 13, 'frequency': 'r', 'synset': 'applesauce.n.01'}, {'name': 'apricot', 'id': 14, 'frequency': 'r', 'synset': 'apricot.n.02'}, {'name': 'apron', 'id': 15, 'frequency': 'f', 'synset': 'apron.n.01'}, {'name': 'aquarium', 'id': 16, 'frequency': 'c', 'synset': 'aquarium.n.01'}, {'name': 'arctic_(type_of_shoe)', 'id': 17, 'frequency': 'r', 'synset': 'arctic.n.02'}, {'name': 'armband', 'id': 18, 'frequency': 'c', 'synset': 'armband.n.02'}, {'name': 'armchair', 'id': 19, 'frequency': 'f', 'synset': 'armchair.n.01'}, {'name': 'armoire', 'id': 20, 'frequency': 'r', 'synset': 'armoire.n.01'}, {'name': 'armor', 'id': 21, 'frequency': 'r', 'synset': 'armor.n.01'}, {'name': 'artichoke', 'id': 22, 'frequency': 'c', 'synset': 'artichoke.n.02'}, {'name': 'trash_can', 'id': 23, 'frequency': 'f', 'synset': 'ashcan.n.01'}, {'name': 'ashtray', 'id': 24, 'frequency': 'c', 'synset': 'ashtray.n.01'}, {'name': 'asparagus', 'id': 25, 'frequency': 'c', 'synset': 'asparagus.n.02'}, {'name': 'atomizer', 'id': 26, 'frequency': 'c', 'synset': 'atomizer.n.01'}, {'name': 'avocado', 'id': 27, 'frequency': 'f', 'synset': 'avocado.n.01'}, {'name': 'award', 'id': 28, 'frequency': 'c', 'synset': 'award.n.02'}, {'name': 'awning', 'id': 29, 'frequency': 'f', 'synset': 'awning.n.01'}, {'name': 'ax', 'id': 30, 'frequency': 'r', 'synset': 'ax.n.01'}, {'name': 'baboon', 'id': 31, 'frequency': 'r', 'synset': 'baboon.n.01'}, {'name': 'baby_buggy', 'id': 32, 'frequency': 'f', 'synset': 'baby_buggy.n.01'}, {'name': 'basketball_backboard', 'id': 33, 'frequency': 'c', 'synset': 'backboard.n.01'}, {'name': 'backpack', 'id': 34, 'frequency': 'f', 'synset': 'backpack.n.01'}, {'name': 'handbag', 'id': 35, 'frequency': 'f', 'synset': 'bag.n.04'}, {'name': 'suitcase', 'id': 36, 'frequency': 'f', 'synset': 'bag.n.06'}, {'name': 'bagel', 'id': 37, 'frequency': 'c', 'synset': 'bagel.n.01'}, {'name': 'bagpipe', 'id': 38, 'frequency': 'r', 'synset': 'bagpipe.n.01'}, {'name': 'baguet', 'id': 39, 'frequency': 'r', 'synset': 'baguet.n.01'}, {'name': 'bait', 'id': 40, 'frequency': 'r', 'synset': 'bait.n.02'}, {'name': 'ball', 'id': 41, 'frequency': 'f', 'synset': 'ball.n.06'}, {'name': 'ballet_skirt', 'id': 42, 'frequency': 'r', 'synset': 'ballet_skirt.n.01'}, {'name': 'balloon', 'id': 43, 'frequency': 'f', 'synset': 'balloon.n.01'}, {'name': 'bamboo', 'id': 44, 'frequency': 'c', 'synset': 'bamboo.n.02'}, {'name': 'banana', 'id': 45, 'frequency': 'f', 'synset': 'banana.n.02'}, {'name': 'Band_Aid', 'id': 46, 'frequency': 'c', 'synset': 'band_aid.n.01'}, {'name': 'bandage', 'id': 47, 'frequency': 'c', 'synset': 'bandage.n.01'}, {'name': 'bandanna', 'id': 48, 'frequency': 'f', 'synset': 'bandanna.n.01'}, {'name': 'banjo', 'id': 49, 'frequency': 'r', 'synset': 'banjo.n.01'}, {'name': 'banner', 'id': 50, 'frequency': 'f', 'synset': 'banner.n.01'}, {'name': 'barbell', 'id': 51, 'frequency': 'r', 'synset': 'barbell.n.01'}, {'name': 'barge', 'id': 52, 'frequency': 'r', 'synset': 'barge.n.01'}, {'name': 'barrel', 'id': 53, 'frequency': 'f', 'synset': 'barrel.n.02'}, {'name': 'barrette', 'id': 54, 'frequency': 'c', 'synset': 'barrette.n.01'}, {'name': 'barrow', 'id': 55, 'frequency': 'c', 'synset': 'barrow.n.03'}, {'name': 'baseball_base', 'id': 56, 'frequency': 'f', 'synset': 'base.n.03'}, {'name': 'baseball', 'id': 57, 'frequency': 'f', 'synset': 'baseball.n.02'}, {'name': 'baseball_bat', 'id': 58, 'frequency': 'f', 'synset': 'baseball_bat.n.01'}, {'name': 'baseball_cap', 'id': 59, 'frequency': 'f', 'synset': 'baseball_cap.n.01'}, {'name': 'baseball_glove', 'id': 60, 'frequency': 'f', 'synset': 'baseball_glove.n.01'}, {'name': 'basket', 'id': 61, 'frequency': 'f', 'synset': 'basket.n.01'}, {'name': 'basketball', 'id': 62, 'frequency': 'c', 'synset': 'basketball.n.02'}, {'name': 'bass_horn', 'id': 63, 'frequency': 'r', 'synset': 'bass_horn.n.01'}, {'name': 'bat_(animal)', 'id': 64, 'frequency': 'c', 'synset': 'bat.n.01'}, {'name': 'bath_mat', 'id': 65, 'frequency': 'f', 'synset': 'bath_mat.n.01'}, {'name': 'bath_towel', 'id': 66, 'frequency': 'f', 'synset': 'bath_towel.n.01'}, {'name': 'bathrobe', 'id': 67, 'frequency': 'c', 'synset': 'bathrobe.n.01'}, {'name': 'bathtub', 'id': 68, 'frequency': 'f', 'synset': 'bathtub.n.01'}, {'name': 'batter_(food)', 'id': 69, 'frequency': 'r', 'synset': 'batter.n.02'}, {'name': 'battery', 'id': 70, 'frequency': 'c', 'synset': 'battery.n.02'}, {'name': 'beachball', 'id': 71, 'frequency': 'r', 'synset': 'beach_ball.n.01'}, {'name': 'bead', 'id': 72, 'frequency': 'c', 'synset': 'bead.n.01'}, {'name': 'bean_curd', 'id': 73, 'frequency': 'c', 'synset': 'bean_curd.n.01'}, {'name': 'beanbag', 'id': 74, 'frequency': 'c', 'synset': 'beanbag.n.01'}, {'name': 'beanie', 'id': 75, 'frequency': 'f', 'synset': 'beanie.n.01'}, {'name': 'bear', 'id': 76, 'frequency': 'f', 'synset': 'bear.n.01'}, {'name': 'bed', 'id': 77, 'frequency': 'f', 'synset': 'bed.n.01'}, {'name': 'bedpan', 'id': 78, 'frequency': 'r', 'synset': 'bedpan.n.01'}, {'name': 'bedspread', 'id': 79, 'frequency': 'f', 'synset': 'bedspread.n.01'}, {'name': 'cow', 'id': 80, 'frequency': 'f', 'synset': 'beef.n.01'}, {'name': 'beef_(food)', 'id': 81, 'frequency': 'f', 'synset': 'beef.n.02'}, {'name': 'beeper', 'id': 82, 'frequency': 'r', 'synset': 'beeper.n.01'}, {'name': 'beer_bottle', 'id': 83, 'frequency': 'f', 'synset': 'beer_bottle.n.01'}, {'name': 'beer_can', 'id': 84, 'frequency': 'c', 'synset': 'beer_can.n.01'}, {'name': 'beetle', 'id': 85, 'frequency': 'r', 'synset': 'beetle.n.01'}, {'name': 'bell', 'id': 86, 'frequency': 'f', 'synset': 'bell.n.01'}, {'name': 'bell_pepper', 'id': 87, 'frequency': 'f', 'synset': 'bell_pepper.n.02'}, {'name': 'belt', 'id': 88, 'frequency': 'f', 'synset': 'belt.n.02'}, {'name': 'belt_buckle', 'id': 89, 'frequency': 'f', 'synset': 'belt_buckle.n.01'}, {'name': 'bench', 'id': 90, 'frequency': 'f', 'synset': 'bench.n.01'}, {'name': 'beret', 'id': 91, 'frequency': 'c', 'synset': 'beret.n.01'}, {'name': 'bib', 'id': 92, 'frequency': 'c', 'synset': 'bib.n.02'}, {'name': 'Bible', 'id': 93, 'frequency': 'r', 'synset': 'bible.n.01'}, {'name': 'bicycle', 'id': 94, 'frequency': 'f', 'synset': 'bicycle.n.01'}, {'name': 'visor', 'id': 95, 'frequency': 'f', 'synset': 'bill.n.09'}, {'name': 'billboard', 'id': 96, 'frequency': 'f', 'synset': 'billboard.n.01'}, {'name': 'binder', 'id': 97, 'frequency': 'c', 'synset': 'binder.n.03'}, {'name': 'binoculars', 'id': 98, 'frequency': 'c', 'synset': 'binoculars.n.01'}, {'name': 'bird', 'id': 99, 'frequency': 'f', 'synset': 'bird.n.01'}, {'name': 'birdfeeder', 'id': 100, 'frequency': 'c', 'synset': 'bird_feeder.n.01'}, {'name': 'birdbath', 'id': 101, 'frequency': 'c', 'synset': 'birdbath.n.01'}, {'name': 'birdcage', 'id': 102, 'frequency': 'c', 'synset': 'birdcage.n.01'}, {'name': 'birdhouse', 'id': 103, 'frequency': 'c', 'synset': 'birdhouse.n.01'}, {'name': 'birthday_cake', 'id': 104, 'frequency': 'f', 'synset': 'birthday_cake.n.01'}, {'name': 'birthday_card', 'id': 105, 'frequency': 'r', 'synset': 'birthday_card.n.01'}, {'name': 'pirate_flag', 'id': 106, 'frequency': 'r', 'synset': 'black_flag.n.01'}, {'name': 'black_sheep', 'id': 107, 'frequency': 'c', 'synset': 'black_sheep.n.02'}, {'name': 'blackberry', 'id': 108, 'frequency': 'c', 'synset': 'blackberry.n.01'}, {'name': 'blackboard', 'id': 109, 'frequency': 'f', 'synset': 'blackboard.n.01'}, {'name': 'blanket', 'id': 110, 'frequency': 'f', 'synset': 'blanket.n.01'}, {'name': 'blazer', 'id': 111, 'frequency': 'c', 'synset': 'blazer.n.01'}, {'name': 'blender', 'id': 112, 'frequency': 'f', 'synset': 'blender.n.01'}, {'name': 'blimp', 'id': 113, 'frequency': 'r', 'synset': 'blimp.n.02'}, {'name': 'blinker', 'id': 114, 'frequency': 'f', 'synset': 'blinker.n.01'}, {'name': 'blouse', 'id': 115, 'frequency': 'f', 'synset': 'blouse.n.01'}, {'name': 'blueberry', 'id': 116, 'frequency': 'f', 'synset': 'blueberry.n.02'}, {'name': 'gameboard', 'id': 117, 'frequency': 'r', 'synset': 'board.n.09'}, {'name': 'boat', 'id': 118, 'frequency': 'f', 'synset': 'boat.n.01'}, {'name': 'bob', 'id': 119, 'frequency': 'r', 'synset': 'bob.n.05'}, {'name': 'bobbin', 'id': 120, 'frequency': 'c', 'synset': 'bobbin.n.01'}, {'name': 'bobby_pin', 'id': 121, 'frequency': 'c', 'synset': 'bobby_pin.n.01'}, {'name': 'boiled_egg', 'id': 122, 'frequency': 'c', 'synset': 'boiled_egg.n.01'}, {'name': 'bolo_tie', 'id': 123, 'frequency': 'r', 'synset': 'bolo_tie.n.01'}, {'name': 'deadbolt', 'id': 124, 'frequency': 'c', 'synset': 'bolt.n.03'}, {'name': 'bolt', 'id': 125, 'frequency': 'f', 'synset': 'bolt.n.06'}, {'name': 'bonnet', 'id': 126, 'frequency': 'r', 'synset': 'bonnet.n.01'}, {'name': 'book', 'id': 127, 'frequency': 'f', 'synset': 'book.n.01'}, {'name': 'bookcase', 'id': 128, 'frequency': 'c', 'synset': 'bookcase.n.01'}, {'name': 'booklet', 'id': 129, 'frequency': 'c', 'synset': 'booklet.n.01'}, {'name': 'bookmark', 'id': 130, 'frequency': 'r', 'synset': 'bookmark.n.01'}, {'name': 'boom_microphone', 'id': 131, 'frequency': 'r', 'synset': 'boom.n.04'}, {'name': 'boot', 'id': 132, 'frequency': 'f', 'synset': 'boot.n.01'}, {'name': 'bottle', 'id': 133, 'frequency': 'f', 'synset': 'bottle.n.01'}, {'name': 'bottle_opener', 'id': 134, 'frequency': 'c', 'synset': 'bottle_opener.n.01'}, {'name': 'bouquet', 'id': 135, 'frequency': 'c', 'synset': 'bouquet.n.01'}, {'name': 'bow_(weapon)', 'id': 136, 'frequency': 'r', 'synset': 'bow.n.04'}, {'name': 'bow_(decorative_ribbons)', 'id': 137, 'frequency': 'f', 'synset': 'bow.n.08'}, {'name': 'bow-tie', 'id': 138, 'frequency': 'f', 'synset': 'bow_tie.n.01'}, {'name': 'bowl', 'id': 139, 'frequency': 'f', 'synset': 'bowl.n.03'}, {'name': 'pipe_bowl', 'id': 140, 'frequency': 'r', 'synset': 'bowl.n.08'}, {'name': 'bowler_hat', 'id': 141, 'frequency': 'c', 'synset': 'bowler_hat.n.01'}, {'name': 'bowling_ball', 'id': 142, 'frequency': 'r', 'synset': 'bowling_ball.n.01'}, {'name': 'box', 'id': 143, 'frequency': 'f', 'synset': 'box.n.01'}, {'name': 'boxing_glove', 'id': 144, 'frequency': 'r', 'synset': 'boxing_glove.n.01'}, {'name': 'suspenders', 'id': 145, 'frequency': 'c', 'synset': 'brace.n.06'}, {'name': 'bracelet', 'id': 146, 'frequency': 'f', 'synset': 'bracelet.n.02'}, {'name': 'brass_plaque', 'id': 147, 'frequency': 'r', 'synset': 'brass.n.07'}, {'name': 'brassiere', 'id': 148, 'frequency': 'c', 'synset': 'brassiere.n.01'}, {'name': 'bread-bin', 'id': 149, 'frequency': 'c', 'synset': 'bread-bin.n.01'}, {'name': 'bread', 'id': 150, 'frequency': 'f', 'synset': 'bread.n.01'}, {'name': 'breechcloth', 'id': 151, 'frequency': 'r', 'synset': 'breechcloth.n.01'}, {'name': 'bridal_gown', 'id': 152, 'frequency': 'f', 'synset': 'bridal_gown.n.01'}, {'name': 'briefcase', 'id': 153, 'frequency': 'c', 'synset': 'briefcase.n.01'}, {'name': 'broccoli', 'id': 154, 'frequency': 'f', 'synset': 'broccoli.n.01'}, {'name': 'broach', 'id': 155, 'frequency': 'r', 'synset': 'brooch.n.01'}, {'name': 'broom', 'id': 156, 'frequency': 'c', 'synset': 'broom.n.01'}, {'name': 'brownie', 'id': 157, 'frequency': 'c', 'synset': 'brownie.n.03'}, {'name': 'brussels_sprouts', 'id': 158, 'frequency': 'c', 'synset': 'brussels_sprouts.n.01'}, {'name': 'bubble_gum', 'id': 159, 'frequency': 'r', 'synset': 'bubble_gum.n.01'}, {'name': 'bucket', 'id': 160, 'frequency': 'f', 'synset': 'bucket.n.01'}, {'name': 'horse_buggy', 'id': 161, 'frequency': 'r', 'synset': 'buggy.n.01'}, {'name': 'bull', 'id': 162, 'frequency': 'c', 'synset': 'bull.n.11'}, {'name': 'bulldog', 'id': 163, 'frequency': 'c', 'synset': 'bulldog.n.01'}, {'name': 'bulldozer', 'id': 164, 'frequency': 'r', 'synset': 'bulldozer.n.01'}, {'name': 'bullet_train', 'id': 165, 'frequency': 'c', 'synset': 'bullet_train.n.01'}, {'name': 'bulletin_board', 'id': 166, 'frequency': 'c', 'synset': 'bulletin_board.n.02'}, {'name': 'bulletproof_vest', 'id': 167, 'frequency': 'r', 'synset': 'bulletproof_vest.n.01'}, {'name': 'bullhorn', 'id': 168, 'frequency': 'c', 'synset': 'bullhorn.n.01'}, {'name': 'bun', 'id': 169, 'frequency': 'f', 'synset': 'bun.n.01'}, {'name': 'bunk_bed', 'id': 170, 'frequency': 'c', 'synset': 'bunk_bed.n.01'}, {'name': 'buoy', 'id': 171, 'frequency': 'f', 'synset': 'buoy.n.01'}, {'name': 'burrito', 'id': 172, 'frequency': 'r', 'synset': 'burrito.n.01'}, {'name': 'bus_(vehicle)', 'id': 173, 'frequency': 'f', 'synset': 'bus.n.01'}, {'name': 'business_card', 'id': 174, 'frequency': 'c', 'synset': 'business_card.n.01'}, {'name': 'butter', 'id': 175, 'frequency': 'f', 'synset': 'butter.n.01'}, {'name': 'butterfly', 'id': 176, 'frequency': 'c', 'synset': 'butterfly.n.01'}, {'name': 'button', 'id': 177, 'frequency': 'f', 'synset': 'button.n.01'}, {'name': 'cab_(taxi)', 'id': 178, 'frequency': 'f', 'synset': 'cab.n.03'}, {'name': 'cabana', 'id': 179, 'frequency': 'r', 'synset': 'cabana.n.01'}, {'name': 'cabin_car', 'id': 180, 'frequency': 'c', 'synset': 'cabin_car.n.01'}, {'name': 'cabinet', 'id': 181, 'frequency': 'f', 'synset': 'cabinet.n.01'}, {'name': 'locker', 'id': 182, 'frequency': 'r', 'synset': 'cabinet.n.03'}, {'name': 'cake', 'id': 183, 'frequency': 'f', 'synset': 'cake.n.03'}, {'name': 'calculator', 'id': 184, 'frequency': 'c', 'synset': 'calculator.n.02'}, {'name': 'calendar', 'id': 185, 'frequency': 'f', 'synset': 'calendar.n.02'}, {'name': 'calf', 'id': 186, 'frequency': 'c', 'synset': 'calf.n.01'}, {'name': 'camcorder', 'id': 187, 'frequency': 'c', 'synset': 'camcorder.n.01'}, {'name': 'camel', 'id': 188, 'frequency': 'c', 'synset': 'camel.n.01'}, {'name': 'camera', 'id': 189, 'frequency': 'f', 'synset': 'camera.n.01'}, {'name': 'camera_lens', 'id': 190, 'frequency': 'c', 'synset': 'camera_lens.n.01'}, {'name': 'camper_(vehicle)', 'id': 191, 'frequency': 'c', 'synset': 'camper.n.02'}, {'name': 'can', 'id': 192, 'frequency': 'f', 'synset': 'can.n.01'}, {'name': 'can_opener', 'id': 193, 'frequency': 'c', 'synset': 'can_opener.n.01'}, {'name': 'candle', 'id': 194, 'frequency': 'f', 'synset': 'candle.n.01'}, {'name': 'candle_holder', 'id': 195, 'frequency': 'f', 'synset': 'candlestick.n.01'}, {'name': 'candy_bar', 'id': 196, 'frequency': 'r', 'synset': 'candy_bar.n.01'}, {'name': 'candy_cane', 'id': 197, 'frequency': 'c', 'synset': 'candy_cane.n.01'}, {'name': 'walking_cane', 'id': 198, 'frequency': 'c', 'synset': 'cane.n.01'}, {'name': 'canister', 'id': 199, 'frequency': 'c', 'synset': 'canister.n.02'}, {'name': 'canoe', 'id': 200, 'frequency': 'c', 'synset': 'canoe.n.01'}, {'name': 'cantaloup', 'id': 201, 'frequency': 'c', 'synset': 'cantaloup.n.02'}, {'name': 'canteen', 'id': 202, 'frequency': 'r', 'synset': 'canteen.n.01'}, {'name': 'cap_(headwear)', 'id': 203, 'frequency': 'f', 'synset': 'cap.n.01'}, {'name': 'bottle_cap', 'id': 204, 'frequency': 'f', 'synset': 'cap.n.02'}, {'name': 'cape', 'id': 205, 'frequency': 'c', 'synset': 'cape.n.02'}, {'name': 'cappuccino', 'id': 206, 'frequency': 'c', 'synset': 'cappuccino.n.01'}, {'name': 'car_(automobile)', 'id': 207, 'frequency': 'f', 'synset': 'car.n.01'}, {'name': 'railcar_(part_of_a_train)', 'id': 208, 'frequency': 'f', 'synset': 'car.n.02'}, {'name': 'elevator_car', 'id': 209, 'frequency': 'r', 'synset': 'car.n.04'}, {'name': 'car_battery', 'id': 210, 'frequency': 'r', 'synset': 'car_battery.n.01'}, {'name': 'identity_card', 'id': 211, 'frequency': 'c', 'synset': 'card.n.02'}, {'name': 'card', 'id': 212, 'frequency': 'c', 'synset': 'card.n.03'}, {'name': 'cardigan', 'id': 213, 'frequency': 'c', 'synset': 'cardigan.n.01'}, {'name': 'cargo_ship', 'id': 214, 'frequency': 'r', 'synset': 'cargo_ship.n.01'}, {'name': 'carnation', 'id': 215, 'frequency': 'r', 'synset': 'carnation.n.01'}, {'name': 'horse_carriage', 'id': 216, 'frequency': 'c', 'synset': 'carriage.n.02'}, {'name': 'carrot', 'id': 217, 'frequency': 'f', 'synset': 'carrot.n.01'}, {'name': 'tote_bag', 'id': 218, 'frequency': 'f', 'synset': 'carryall.n.01'}, {'name': 'cart', 'id': 219, 'frequency': 'c', 'synset': 'cart.n.01'}, {'name': 'carton', 'id': 220, 'frequency': 'c', 'synset': 'carton.n.02'}, {'name': 'cash_register', 'id': 221, 'frequency': 'c', 'synset': 'cash_register.n.01'}, {'name': 'casserole', 'id': 222, 'frequency': 'r', 'synset': 'casserole.n.01'}, {'name': 'cassette', 'id': 223, 'frequency': 'r', 'synset': 'cassette.n.01'}, {'name': 'cast', 'id': 224, 'frequency': 'c', 'synset': 'cast.n.05'}, {'name': 'cat', 'id': 225, 'frequency': 'f', 'synset': 'cat.n.01'}, {'name': 'cauliflower', 'id': 226, 'frequency': 'f', 'synset': 'cauliflower.n.02'}, {'name': 'cayenne_(spice)', 'id': 227, 'frequency': 'c', 'synset': 'cayenne.n.02'}, {'name': 'CD_player', 'id': 228, 'frequency': 'c', 'synset': 'cd_player.n.01'}, {'name': 'celery', 'id': 229, 'frequency': 'f', 'synset': 'celery.n.01'}, {'name': 'cellular_telephone', 'id': 230, 'frequency': 'f', 'synset': 'cellular_telephone.n.01'}, {'name': 'chain_mail', 'id': 231, 'frequency': 'r', 'synset': 'chain_mail.n.01'}, {'name': 'chair', 'id': 232, 'frequency': 'f', 'synset': 'chair.n.01'}, {'name': 'chaise_longue', 'id': 233, 'frequency': 'r', 'synset': 'chaise_longue.n.01'}, {'name': 'chalice', 'id': 234, 'frequency': 'r', 'synset': 'chalice.n.01'}, {'name': 'chandelier', 'id': 235, 'frequency': 'f', 'synset': 'chandelier.n.01'}, {'name': 'chap', 'id': 236, 'frequency': 'r', 'synset': 'chap.n.04'}, {'name': 'checkbook', 'id': 237, 'frequency': 'r', 'synset': 'checkbook.n.01'}, {'name': 'checkerboard', 'id': 238, 'frequency': 'r', 'synset': 'checkerboard.n.01'}, {'name': 'cherry', 'id': 239, 'frequency': 'c', 'synset': 'cherry.n.03'}, {'name': 'chessboard', 'id': 240, 'frequency': 'r', 'synset': 'chessboard.n.01'}, {'name': 'chicken_(animal)', 'id': 241, 'frequency': 'c', 'synset': 'chicken.n.02'}, {'name': 'chickpea', 'id': 242, 'frequency': 'c', 'synset': 'chickpea.n.01'}, {'name': 'chili_(vegetable)', 'id': 243, 'frequency': 'c', 'synset': 'chili.n.02'}, {'name': 'chime', 'id': 244, 'frequency': 'r', 'synset': 'chime.n.01'}, {'name': 'chinaware', 'id': 245, 'frequency': 'r', 'synset': 'chinaware.n.01'}, {'name': 'crisp_(potato_chip)', 'id': 246, 'frequency': 'c', 'synset': 'chip.n.04'}, {'name': 'poker_chip', 'id': 247, 'frequency': 'r', 'synset': 'chip.n.06'}, {'name': 'chocolate_bar', 'id': 248, 'frequency': 'c', 'synset': 'chocolate_bar.n.01'}, {'name': 'chocolate_cake', 'id': 249, 'frequency': 'c', 'synset': 'chocolate_cake.n.01'}, {'name': 'chocolate_milk', 'id': 250, 'frequency': 'r', 'synset': 'chocolate_milk.n.01'}, {'name': 'chocolate_mousse', 'id': 251, 'frequency': 'r', 'synset': 'chocolate_mousse.n.01'}, {'name': 'choker', 'id': 252, 'frequency': 'f', 'synset': 'choker.n.03'}, {'name': 'chopping_board', 'id': 253, 'frequency': 'f', 'synset': 'chopping_board.n.01'}, {'name': 'chopstick', 'id': 254, 'frequency': 'f', 'synset': 'chopstick.n.01'}, {'name': 'Christmas_tree', 'id': 255, 'frequency': 'f', 'synset': 'christmas_tree.n.05'}, {'name': 'slide', 'id': 256, 'frequency': 'c', 'synset': 'chute.n.02'}, {'name': 'cider', 'id': 257, 'frequency': 'r', 'synset': 'cider.n.01'}, {'name': 'cigar_box', 'id': 258, 'frequency': 'r', 'synset': 'cigar_box.n.01'}, {'name': 'cigarette', 'id': 259, 'frequency': 'f', 'synset': 'cigarette.n.01'}, {'name': 'cigarette_case', 'id': 260, 'frequency': 'c', 'synset': 'cigarette_case.n.01'}, {'name': 'cistern', 'id': 261, 'frequency': 'f', 'synset': 'cistern.n.02'}, {'name': 'clarinet', 'id': 262, 'frequency': 'r', 'synset': 'clarinet.n.01'}, {'name': 'clasp', 'id': 263, 'frequency': 'c', 'synset': 'clasp.n.01'}, {'name': 'cleansing_agent', 'id': 264, 'frequency': 'c', 'synset': 'cleansing_agent.n.01'}, {'name': 'cleat_(for_securing_rope)', 'id': 265, 'frequency': 'r', 'synset': 'cleat.n.02'}, {'name': 'clementine', 'id': 266, 'frequency': 'r', 'synset': 'clementine.n.01'}, {'name': 'clip', 'id': 267, 'frequency': 'c', 'synset': 'clip.n.03'}, {'name': 'clipboard', 'id': 268, 'frequency': 'c', 'synset': 'clipboard.n.01'}, {'name': 'clippers_(for_plants)', 'id': 269, 'frequency': 'r', 'synset': 'clipper.n.03'}, {'name': 'cloak', 'id': 270, 'frequency': 'r', 'synset': 'cloak.n.02'}, {'name': 'clock', 'id': 271, 'frequency': 'f', 'synset': 'clock.n.01'}, {'name': 'clock_tower', 'id': 272, 'frequency': 'f', 'synset': 'clock_tower.n.01'}, {'name': 'clothes_hamper', 'id': 273, 'frequency': 'c', 'synset': 'clothes_hamper.n.01'}, {'name': 'clothespin', 'id': 274, 'frequency': 'c', 'synset': 'clothespin.n.01'}, {'name': 'clutch_bag', 'id': 275, 'frequency': 'r', 'synset': 'clutch_bag.n.01'}, {'name': 'coaster', 'id': 276, 'frequency': 'f', 'synset': 'coaster.n.03'}, {'name': 'coat', 'id': 277, 'frequency': 'f', 'synset': 'coat.n.01'}, {'name': 'coat_hanger', 'id': 278, 'frequency': 'c', 'synset': 'coat_hanger.n.01'}, {'name': 'coatrack', 'id': 279, 'frequency': 'c', 'synset': 'coatrack.n.01'}, {'name': 'cock', 'id': 280, 'frequency': 'c', 'synset': 'cock.n.04'}, {'name': 'cockroach', 'id': 281, 'frequency': 'r', 'synset': 'cockroach.n.01'}, {'name': 'cocoa_(beverage)', 'id': 282, 'frequency': 'r', 'synset': 'cocoa.n.01'}, {'name': 'coconut', 'id': 283, 'frequency': 'c', 'synset': 'coconut.n.02'}, {'name': 'coffee_maker', 'id': 284, 'frequency': 'f', 'synset': 'coffee_maker.n.01'}, {'name': 'coffee_table', 'id': 285, 'frequency': 'f', 'synset': 'coffee_table.n.01'}, {'name': 'coffeepot', 'id': 286, 'frequency': 'c', 'synset': 'coffeepot.n.01'}, {'name': 'coil', 'id': 287, 'frequency': 'r', 'synset': 'coil.n.05'}, {'name': 'coin', 'id': 288, 'frequency': 'c', 'synset': 'coin.n.01'}, {'name': 'colander', 'id': 289, 'frequency': 'c', 'synset': 'colander.n.01'}, {'name': 'coleslaw', 'id': 290, 'frequency': 'c', 'synset': 'coleslaw.n.01'}, {'name': 'coloring_material', 'id': 291, 'frequency': 'r', 'synset': 'coloring_material.n.01'}, {'name': 'combination_lock', 'id': 292, 'frequency': 'r', 'synset': 'combination_lock.n.01'}, {'name': 'pacifier', 'id': 293, 'frequency': 'c', 'synset': 'comforter.n.04'}, {'name': 'comic_book', 'id': 294, 'frequency': 'r', 'synset': 'comic_book.n.01'}, {'name': 'compass', 'id': 295, 'frequency': 'r', 'synset': 'compass.n.01'}, {'name': 'computer_keyboard', 'id': 296, 'frequency': 'f', 'synset': 'computer_keyboard.n.01'}, {'name': 'condiment', 'id': 297, 'frequency': 'f', 'synset': 'condiment.n.01'}, {'name': 'cone', 'id': 298, 'frequency': 'f', 'synset': 'cone.n.01'}, {'name': 'control', 'id': 299, 'frequency': 'f', 'synset': 'control.n.09'}, {'name': 'convertible_(automobile)', 'id': 300, 'frequency': 'r', 'synset': 'convertible.n.01'}, {'name': 'sofa_bed', 'id': 301, 'frequency': 'r', 'synset': 'convertible.n.03'}, {'name': 'cooker', 'id': 302, 'frequency': 'r', 'synset': 'cooker.n.01'}, {'name': 'cookie', 'id': 303, 'frequency': 'f', 'synset': 'cookie.n.01'}, {'name': 'cooking_utensil', 'id': 304, 'frequency': 'r', 'synset': 'cooking_utensil.n.01'}, {'name': 'cooler_(for_food)', 'id': 305, 'frequency': 'f', 'synset': 'cooler.n.01'}, {'name': 'cork_(bottle_plug)', 'id': 306, 'frequency': 'f', 'synset': 'cork.n.04'}, {'name': 'corkboard', 'id': 307, 'frequency': 'r', 'synset': 'corkboard.n.01'}, {'name': 'corkscrew', 'id': 308, 'frequency': 'c', 'synset': 'corkscrew.n.01'}, {'name': 'edible_corn', 'id': 309, 'frequency': 'f', 'synset': 'corn.n.03'}, {'name': 'cornbread', 'id': 310, 'frequency': 'r', 'synset': 'cornbread.n.01'}, {'name': 'cornet', 'id': 311, 'frequency': 'c', 'synset': 'cornet.n.01'}, {'name': 'cornice', 'id': 312, 'frequency': 'c', 'synset': 'cornice.n.01'}, {'name': 'cornmeal', 'id': 313, 'frequency': 'r', 'synset': 'cornmeal.n.01'}, {'name': 'corset', 'id': 314, 'frequency': 'c', 'synset': 'corset.n.01'}, {'name': 'costume', 'id': 315, 'frequency': 'c', 'synset': 'costume.n.04'}, {'name': 'cougar', 'id': 316, 'frequency': 'r', 'synset': 'cougar.n.01'}, {'name': 'coverall', 'id': 317, 'frequency': 'r', 'synset': 'coverall.n.01'}, {'name': 'cowbell', 'id': 318, 'frequency': 'c', 'synset': 'cowbell.n.01'}, {'name': 'cowboy_hat', 'id': 319, 'frequency': 'f', 'synset': 'cowboy_hat.n.01'}, {'name': 'crab_(animal)', 'id': 320, 'frequency': 'c', 'synset': 'crab.n.01'}, {'name': 'crabmeat', 'id': 321, 'frequency': 'r', 'synset': 'crab.n.05'}, {'name': 'cracker', 'id': 322, 'frequency': 'c', 'synset': 'cracker.n.01'}, {'name': 'crape', 'id': 323, 'frequency': 'r', 'synset': 'crape.n.01'}, {'name': 'crate', 'id': 324, 'frequency': 'f', 'synset': 'crate.n.01'}, {'name': 'crayon', 'id': 325, 'frequency': 'c', 'synset': 'crayon.n.01'}, {'name': 'cream_pitcher', 'id': 326, 'frequency': 'r', 'synset': 'cream_pitcher.n.01'}, {'name': 'crescent_roll', 'id': 327, 'frequency': 'c', 'synset': 'crescent_roll.n.01'}, {'name': 'crib', 'id': 328, 'frequency': 'c', 'synset': 'crib.n.01'}, {'name': 'crock_pot', 'id': 329, 'frequency': 'c', 'synset': 'crock.n.03'}, {'name': 'crossbar', 'id': 330, 'frequency': 'f', 'synset': 'crossbar.n.01'}, {'name': 'crouton', 'id': 331, 'frequency': 'r', 'synset': 'crouton.n.01'}, {'name': 'crow', 'id': 332, 'frequency': 'c', 'synset': 'crow.n.01'}, {'name': 'crowbar', 'id': 333, 'frequency': 'r', 'synset': 'crowbar.n.01'}, {'name': 'crown', 'id': 334, 'frequency': 'c', 'synset': 'crown.n.04'}, {'name': 'crucifix', 'id': 335, 'frequency': 'c', 'synset': 'crucifix.n.01'}, {'name': 'cruise_ship', 'id': 336, 'frequency': 'c', 'synset': 'cruise_ship.n.01'}, {'name': 'police_cruiser', 'id': 337, 'frequency': 'c', 'synset': 'cruiser.n.01'}, {'name': 'crumb', 'id': 338, 'frequency': 'f', 'synset': 'crumb.n.03'}, {'name': 'crutch', 'id': 339, 'frequency': 'c', 'synset': 'crutch.n.01'}, {'name': 'cub_(animal)', 'id': 340, 'frequency': 'c', 'synset': 'cub.n.03'}, {'name': 'cube', 'id': 341, 'frequency': 'c', 'synset': 'cube.n.05'}, {'name': 'cucumber', 'id': 342, 'frequency': 'f', 'synset': 'cucumber.n.02'}, {'name': 'cufflink', 'id': 343, 'frequency': 'c', 'synset': 'cufflink.n.01'}, {'name': 'cup', 'id': 344, 'frequency': 'f', 'synset': 'cup.n.01'}, {'name': 'trophy_cup', 'id': 345, 'frequency': 'c', 'synset': 'cup.n.08'}, {'name': 'cupboard', 'id': 346, 'frequency': 'f', 'synset': 'cupboard.n.01'}, {'name': 'cupcake', 'id': 347, 'frequency': 'f', 'synset': 'cupcake.n.01'}, {'name': 'hair_curler', 'id': 348, 'frequency': 'r', 'synset': 'curler.n.01'}, {'name': 'curling_iron', 'id': 349, 'frequency': 'r', 'synset': 'curling_iron.n.01'}, {'name': 'curtain', 'id': 350, 'frequency': 'f', 'synset': 'curtain.n.01'}, {'name': 'cushion', 'id': 351, 'frequency': 'f', 'synset': 'cushion.n.03'}, {'name': 'cylinder', 'id': 352, 'frequency': 'r', 'synset': 'cylinder.n.04'}, {'name': 'cymbal', 'id': 353, 'frequency': 'r', 'synset': 'cymbal.n.01'}, {'name': 'dagger', 'id': 354, 'frequency': 'r', 'synset': 'dagger.n.01'}, {'name': 'dalmatian', 'id': 355, 'frequency': 'r', 'synset': 'dalmatian.n.02'}, {'name': 'dartboard', 'id': 356, 'frequency': 'c', 'synset': 'dartboard.n.01'}, {'name': 'date_(fruit)', 'id': 357, 'frequency': 'r', 'synset': 'date.n.08'}, {'name': 'deck_chair', 'id': 358, 'frequency': 'f', 'synset': 'deck_chair.n.01'}, {'name': 'deer', 'id': 359, 'frequency': 'c', 'synset': 'deer.n.01'}, {'name': 'dental_floss', 'id': 360, 'frequency': 'c', 'synset': 'dental_floss.n.01'}, {'name': 'desk', 'id': 361, 'frequency': 'f', 'synset': 'desk.n.01'}, {'name': 'detergent', 'id': 362, 'frequency': 'r', 'synset': 'detergent.n.01'}, {'name': 'diaper', 'id': 363, 'frequency': 'c', 'synset': 'diaper.n.01'}, {'name': 'diary', 'id': 364, 'frequency': 'r', 'synset': 'diary.n.01'}, {'name': 'die', 'id': 365, 'frequency': 'r', 'synset': 'die.n.01'}, {'name': 'dinghy', 'id': 366, 'frequency': 'r', 'synset': 'dinghy.n.01'}, {'name': 'dining_table', 'id': 367, 'frequency': 'f', 'synset': 'dining_table.n.01'}, {'name': 'tux', 'id': 368, 'frequency': 'r', 'synset': 'dinner_jacket.n.01'}, {'name': 'dish', 'id': 369, 'frequency': 'f', 'synset': 'dish.n.01'}, {'name': 'dish_antenna', 'id': 370, 'frequency': 'c', 'synset': 'dish.n.05'}, {'name': 'dishrag', 'id': 371, 'frequency': 'c', 'synset': 'dishrag.n.01'}, {'name': 'dishtowel', 'id': 372, 'frequency': 'f', 'synset': 'dishtowel.n.01'}, {'name': 'dishwasher', 'id': 373, 'frequency': 'f', 'synset': 'dishwasher.n.01'}, {'name': 'dishwasher_detergent', 'id': 374, 'frequency': 'r', 'synset': 'dishwasher_detergent.n.01'}, {'name': 'dispenser', 'id': 375, 'frequency': 'f', 'synset': 'dispenser.n.01'}, {'name': 'diving_board', 'id': 376, 'frequency': 'r', 'synset': 'diving_board.n.01'}, {'name': 'Dixie_cup', 'id': 377, 'frequency': 'f', 'synset': 'dixie_cup.n.01'}, {'name': 'dog', 'id': 378, 'frequency': 'f', 'synset': 'dog.n.01'}, {'name': 'dog_collar', 'id': 379, 'frequency': 'f', 'synset': 'dog_collar.n.01'}, {'name': 'doll', 'id': 380, 'frequency': 'f', 'synset': 'doll.n.01'}, {'name': 'dollar', 'id': 381, 'frequency': 'r', 'synset': 'dollar.n.02'}, {'name': 'dollhouse', 'id': 382, 'frequency': 'r', 'synset': 'dollhouse.n.01'}, {'name': 'dolphin', 'id': 383, 'frequency': 'c', 'synset': 'dolphin.n.02'}, {'name': 'domestic_ass', 'id': 384, 'frequency': 'c', 'synset': 'domestic_ass.n.01'}, {'name': 'doorknob', 'id': 385, 'frequency': 'f', 'synset': 'doorknob.n.01'}, {'name': 'doormat', 'id': 386, 'frequency': 'c', 'synset': 'doormat.n.02'}, {'name': 'doughnut', 'id': 387, 'frequency': 'f', 'synset': 'doughnut.n.02'}, {'name': 'dove', 'id': 388, 'frequency': 'r', 'synset': 'dove.n.01'}, {'name': 'dragonfly', 'id': 389, 'frequency': 'r', 'synset': 'dragonfly.n.01'}, {'name': 'drawer', 'id': 390, 'frequency': 'f', 'synset': 'drawer.n.01'}, {'name': 'underdrawers', 'id': 391, 'frequency': 'c', 'synset': 'drawers.n.01'}, {'name': 'dress', 'id': 392, 'frequency': 'f', 'synset': 'dress.n.01'}, {'name': 'dress_hat', 'id': 393, 'frequency': 'c', 'synset': 'dress_hat.n.01'}, {'name': 'dress_suit', 'id': 394, 'frequency': 'f', 'synset': 'dress_suit.n.01'}, {'name': 'dresser', 'id': 395, 'frequency': 'f', 'synset': 'dresser.n.05'}, {'name': 'drill', 'id': 396, 'frequency': 'c', 'synset': 'drill.n.01'}, {'name': 'drone', 'id': 397, 'frequency': 'r', 'synset': 'drone.n.04'}, {'name': 'dropper', 'id': 398, 'frequency': 'r', 'synset': 'dropper.n.01'}, {'name': 'drum_(musical_instrument)', 'id': 399, 'frequency': 'c', 'synset': 'drum.n.01'}, {'name': 'drumstick', 'id': 400, 'frequency': 'r', 'synset': 'drumstick.n.02'}, {'name': 'duck', 'id': 401, 'frequency': 'f', 'synset': 'duck.n.01'}, {'name': 'duckling', 'id': 402, 'frequency': 'c', 'synset': 'duckling.n.02'}, {'name': 'duct_tape', 'id': 403, 'frequency': 'c', 'synset': 'duct_tape.n.01'}, {'name': 'duffel_bag', 'id': 404, 'frequency': 'f', 'synset': 'duffel_bag.n.01'}, {'name': 'dumbbell', 'id': 405, 'frequency': 'r', 'synset': 'dumbbell.n.01'}, {'name': 'dumpster', 'id': 406, 'frequency': 'c', 'synset': 'dumpster.n.01'}, {'name': 'dustpan', 'id': 407, 'frequency': 'r', 'synset': 'dustpan.n.02'}, {'name': 'eagle', 'id': 408, 'frequency': 'c', 'synset': 'eagle.n.01'}, {'name': 'earphone', 'id': 409, 'frequency': 'f', 'synset': 'earphone.n.01'}, {'name': 'earplug', 'id': 410, 'frequency': 'r', 'synset': 'earplug.n.01'}, {'name': 'earring', 'id': 411, 'frequency': 'f', 'synset': 'earring.n.01'}, {'name': 'easel', 'id': 412, 'frequency': 'c', 'synset': 'easel.n.01'}, {'name': 'eclair', 'id': 413, 'frequency': 'r', 'synset': 'eclair.n.01'}, {'name': 'eel', 'id': 414, 'frequency': 'r', 'synset': 'eel.n.01'}, {'name': 'egg', 'id': 415, 'frequency': 'f', 'synset': 'egg.n.02'}, {'name': 'egg_roll', 'id': 416, 'frequency': 'r', 'synset': 'egg_roll.n.01'}, {'name': 'egg_yolk', 'id': 417, 'frequency': 'c', 'synset': 'egg_yolk.n.01'}, {'name': 'eggbeater', 'id': 418, 'frequency': 'c', 'synset': 'eggbeater.n.02'}, {'name': 'eggplant', 'id': 419, 'frequency': 'c', 'synset': 'eggplant.n.01'}, {'name': 'electric_chair', 'id': 420, 'frequency': 'r', 'synset': 'electric_chair.n.01'}, {'name': 'refrigerator', 'id': 421, 'frequency': 'f', 'synset': 'electric_refrigerator.n.01'}, {'name': 'elephant', 'id': 422, 'frequency': 'f', 'synset': 'elephant.n.01'}, {'name': 'elk', 'id': 423, 'frequency': 'c', 'synset': 'elk.n.01'}, {'name': 'envelope', 'id': 424, 'frequency': 'c', 'synset': 'envelope.n.01'}, {'name': 'eraser', 'id': 425, 'frequency': 'c', 'synset': 'eraser.n.01'}, {'name': 'escargot', 'id': 426, 'frequency': 'r', 'synset': 'escargot.n.01'}, {'name': 'eyepatch', 'id': 427, 'frequency': 'r', 'synset': 'eyepatch.n.01'}, {'name': 'falcon', 'id': 428, 'frequency': 'r', 'synset': 'falcon.n.01'}, {'name': 'fan', 'id': 429, 'frequency': 'f', 'synset': 'fan.n.01'}, {'name': 'faucet', 'id': 430, 'frequency': 'f', 'synset': 'faucet.n.01'}, {'name': 'fedora', 'id': 431, 'frequency': 'r', 'synset': 'fedora.n.01'}, {'name': 'ferret', 'id': 432, 'frequency': 'r', 'synset': 'ferret.n.02'}, {'name': 'Ferris_wheel', 'id': 433, 'frequency': 'c', 'synset': 'ferris_wheel.n.01'}, {'name': 'ferry', 'id': 434, 'frequency': 'c', 'synset': 'ferry.n.01'}, {'name': 'fig_(fruit)', 'id': 435, 'frequency': 'r', 'synset': 'fig.n.04'}, {'name': 'fighter_jet', 'id': 436, 'frequency': 'c', 'synset': 'fighter.n.02'}, {'name': 'figurine', 'id': 437, 'frequency': 'f', 'synset': 'figurine.n.01'}, {'name': 'file_cabinet', 'id': 438, 'frequency': 'c', 'synset': 'file.n.03'}, {'name': 'file_(tool)', 'id': 439, 'frequency': 'r', 'synset': 'file.n.04'}, {'name': 'fire_alarm', 'id': 440, 'frequency': 'f', 'synset': 'fire_alarm.n.02'}, {'name': 'fire_engine', 'id': 441, 'frequency': 'f', 'synset': 'fire_engine.n.01'}, {'name': 'fire_extinguisher', 'id': 442, 'frequency': 'f', 'synset': 'fire_extinguisher.n.01'}, {'name': 'fire_hose', 'id': 443, 'frequency': 'c', 'synset': 'fire_hose.n.01'}, {'name': 'fireplace', 'id': 444, 'frequency': 'f', 'synset': 'fireplace.n.01'}, {'name': 'fireplug', 'id': 445, 'frequency': 'f', 'synset': 'fireplug.n.01'}, {'name': 'first-aid_kit', 'id': 446, 'frequency': 'r', 'synset': 'first-aid_kit.n.01'}, {'name': 'fish', 'id': 447, 'frequency': 'f', 'synset': 'fish.n.01'}, {'name': 'fish_(food)', 'id': 448, 'frequency': 'c', 'synset': 'fish.n.02'}, {'name': 'fishbowl', 'id': 449, 'frequency': 'r', 'synset': 'fishbowl.n.02'}, {'name': 'fishing_rod', 'id': 450, 'frequency': 'c', 'synset': 'fishing_rod.n.01'}, {'name': 'flag', 'id': 451, 'frequency': 'f', 'synset': 'flag.n.01'}, {'name': 'flagpole', 'id': 452, 'frequency': 'f', 'synset': 'flagpole.n.02'}, {'name': 'flamingo', 'id': 453, 'frequency': 'c', 'synset': 'flamingo.n.01'}, {'name': 'flannel', 'id': 454, 'frequency': 'c', 'synset': 'flannel.n.01'}, {'name': 'flap', 'id': 455, 'frequency': 'c', 'synset': 'flap.n.01'}, {'name': 'flash', 'id': 456, 'frequency': 'r', 'synset': 'flash.n.10'}, {'name': 'flashlight', 'id': 457, 'frequency': 'c', 'synset': 'flashlight.n.01'}, {'name': 'fleece', 'id': 458, 'frequency': 'r', 'synset': 'fleece.n.03'}, {'name': 'flip-flop_(sandal)', 'id': 459, 'frequency': 'f', 'synset': 'flip-flop.n.02'}, {'name': 'flipper_(footwear)', 'id': 460, 'frequency': 'c', 'synset': 'flipper.n.01'}, {'name': 'flower_arrangement', 'id': 461, 'frequency': 'f', 'synset': 'flower_arrangement.n.01'}, {'name': 'flute_glass', 'id': 462, 'frequency': 'c', 'synset': 'flute.n.02'}, {'name': 'foal', 'id': 463, 'frequency': 'c', 'synset': 'foal.n.01'}, {'name': 'folding_chair', 'id': 464, 'frequency': 'c', 'synset': 'folding_chair.n.01'}, {'name': 'food_processor', 'id': 465, 'frequency': 'c', 'synset': 'food_processor.n.01'}, {'name': 'football_(American)', 'id': 466, 'frequency': 'c', 'synset': 'football.n.02'}, {'name': 'football_helmet', 'id': 467, 'frequency': 'r', 'synset': 'football_helmet.n.01'}, {'name': 'footstool', 'id': 468, 'frequency': 'c', 'synset': 'footstool.n.01'}, {'name': 'fork', 'id': 469, 'frequency': 'f', 'synset': 'fork.n.01'}, {'name': 'forklift', 'id': 470, 'frequency': 'c', 'synset': 'forklift.n.01'}, {'name': 'freight_car', 'id': 471, 'frequency': 'c', 'synset': 'freight_car.n.01'}, {'name': 'French_toast', 'id': 472, 'frequency': 'c', 'synset': 'french_toast.n.01'}, {'name': 'freshener', 'id': 473, 'frequency': 'c', 'synset': 'freshener.n.01'}, {'name': 'frisbee', 'id': 474, 'frequency': 'f', 'synset': 'frisbee.n.01'}, {'name': 'frog', 'id': 475, 'frequency': 'c', 'synset': 'frog.n.01'}, {'name': 'fruit_juice', 'id': 476, 'frequency': 'c', 'synset': 'fruit_juice.n.01'}, {'name': 'frying_pan', 'id': 477, 'frequency': 'f', 'synset': 'frying_pan.n.01'}, {'name': 'fudge', 'id': 478, 'frequency': 'r', 'synset': 'fudge.n.01'}, {'name': 'funnel', 'id': 479, 'frequency': 'r', 'synset': 'funnel.n.02'}, {'name': 'futon', 'id': 480, 'frequency': 'r', 'synset': 'futon.n.01'}, {'name': 'gag', 'id': 481, 'frequency': 'r', 'synset': 'gag.n.02'}, {'name': 'garbage', 'id': 482, 'frequency': 'r', 'synset': 'garbage.n.03'}, {'name': 'garbage_truck', 'id': 483, 'frequency': 'c', 'synset': 'garbage_truck.n.01'}, {'name': 'garden_hose', 'id': 484, 'frequency': 'c', 'synset': 'garden_hose.n.01'}, {'name': 'gargle', 'id': 485, 'frequency': 'c', 'synset': 'gargle.n.01'}, {'name': 'gargoyle', 'id': 486, 'frequency': 'r', 'synset': 'gargoyle.n.02'}, {'name': 'garlic', 'id': 487, 'frequency': 'c', 'synset': 'garlic.n.02'}, {'name': 'gasmask', 'id': 488, 'frequency': 'r', 'synset': 'gasmask.n.01'}, {'name': 'gazelle', 'id': 489, 'frequency': 'c', 'synset': 'gazelle.n.01'}, {'name': 'gelatin', 'id': 490, 'frequency': 'c', 'synset': 'gelatin.n.02'}, {'name': 'gemstone', 'id': 491, 'frequency': 'r', 'synset': 'gem.n.02'}, {'name': 'generator', 'id': 492, 'frequency': 'r', 'synset': 'generator.n.02'}, {'name': 'giant_panda', 'id': 493, 'frequency': 'c', 'synset': 'giant_panda.n.01'}, {'name': 'gift_wrap', 'id': 494, 'frequency': 'c', 'synset': 'gift_wrap.n.01'}, {'name': 'ginger', 'id': 495, 'frequency': 'c', 'synset': 'ginger.n.03'}, {'name': 'giraffe', 'id': 496, 'frequency': 'f', 'synset': 'giraffe.n.01'}, {'name': 'cincture', 'id': 497, 'frequency': 'c', 'synset': 'girdle.n.02'}, {'name': 'glass_(drink_container)', 'id': 498, 'frequency': 'f', 'synset': 'glass.n.02'}, {'name': 'globe', 'id': 499, 'frequency': 'c', 'synset': 'globe.n.03'}, {'name': 'glove', 'id': 500, 'frequency': 'f', 'synset': 'glove.n.02'}, {'name': 'goat', 'id': 501, 'frequency': 'c', 'synset': 'goat.n.01'}, {'name': 'goggles', 'id': 502, 'frequency': 'f', 'synset': 'goggles.n.01'}, {'name': 'goldfish', 'id': 503, 'frequency': 'r', 'synset': 'goldfish.n.01'}, {'name': 'golf_club', 'id': 504, 'frequency': 'c', 'synset': 'golf_club.n.02'}, {'name': 'golfcart', 'id': 505, 'frequency': 'c', 'synset': 'golfcart.n.01'}, {'name': 'gondola_(boat)', 'id': 506, 'frequency': 'r', 'synset': 'gondola.n.02'}, {'name': 'goose', 'id': 507, 'frequency': 'c', 'synset': 'goose.n.01'}, {'name': 'gorilla', 'id': 508, 'frequency': 'r', 'synset': 'gorilla.n.01'}, {'name': 'gourd', 'id': 509, 'frequency': 'r', 'synset': 'gourd.n.02'}, {'name': 'grape', 'id': 510, 'frequency': 'f', 'synset': 'grape.n.01'}, {'name': 'grater', 'id': 511, 'frequency': 'c', 'synset': 'grater.n.01'}, {'name': 'gravestone', 'id': 512, 'frequency': 'c', 'synset': 'gravestone.n.01'}, {'name': 'gravy_boat', 'id': 513, 'frequency': 'r', 'synset': 'gravy_boat.n.01'}, {'name': 'green_bean', 'id': 514, 'frequency': 'f', 'synset': 'green_bean.n.02'}, {'name': 'green_onion', 'id': 515, 'frequency': 'f', 'synset': 'green_onion.n.01'}, {'name': 'griddle', 'id': 516, 'frequency': 'r', 'synset': 'griddle.n.01'}, {'name': 'grill', 'id': 517, 'frequency': 'f', 'synset': 'grill.n.02'}, {'name': 'grits', 'id': 518, 'frequency': 'r', 'synset': 'grits.n.01'}, {'name': 'grizzly', 'id': 519, 'frequency': 'c', 'synset': 'grizzly.n.01'}, {'name': 'grocery_bag', 'id': 520, 'frequency': 'c', 'synset': 'grocery_bag.n.01'}, {'name': 'guitar', 'id': 521, 'frequency': 'f', 'synset': 'guitar.n.01'}, {'name': 'gull', 'id': 522, 'frequency': 'c', 'synset': 'gull.n.02'}, {'name': 'gun', 'id': 523, 'frequency': 'c', 'synset': 'gun.n.01'}, {'name': 'hairbrush', 'id': 524, 'frequency': 'f', 'synset': 'hairbrush.n.01'}, {'name': 'hairnet', 'id': 525, 'frequency': 'c', 'synset': 'hairnet.n.01'}, {'name': 'hairpin', 'id': 526, 'frequency': 'c', 'synset': 'hairpin.n.01'}, {'name': 'halter_top', 'id': 527, 'frequency': 'r', 'synset': 'halter.n.03'}, {'name': 'ham', 'id': 528, 'frequency': 'f', 'synset': 'ham.n.01'}, {'name': 'hamburger', 'id': 529, 'frequency': 'c', 'synset': 'hamburger.n.01'}, {'name': 'hammer', 'id': 530, 'frequency': 'c', 'synset': 'hammer.n.02'}, {'name': 'hammock', 'id': 531, 'frequency': 'c', 'synset': 'hammock.n.02'}, {'name': 'hamper', 'id': 532, 'frequency': 'r', 'synset': 'hamper.n.02'}, {'name': 'hamster', 'id': 533, 'frequency': 'c', 'synset': 'hamster.n.01'}, {'name': 'hair_dryer', 'id': 534, 'frequency': 'f', 'synset': 'hand_blower.n.01'}, {'name': 'hand_glass', 'id': 535, 'frequency': 'r', 'synset': 'hand_glass.n.01'}, {'name': 'hand_towel', 'id': 536, 'frequency': 'f', 'synset': 'hand_towel.n.01'}, {'name': 'handcart', 'id': 537, 'frequency': 'c', 'synset': 'handcart.n.01'}, {'name': 'handcuff', 'id': 538, 'frequency': 'r', 'synset': 'handcuff.n.01'}, {'name': 'handkerchief', 'id': 539, 'frequency': 'c', 'synset': 'handkerchief.n.01'}, {'name': 'handle', 'id': 540, 'frequency': 'f', 'synset': 'handle.n.01'}, {'name': 'handsaw', 'id': 541, 'frequency': 'r', 'synset': 'handsaw.n.01'}, {'name': 'hardback_book', 'id': 542, 'frequency': 'r', 'synset': 'hardback.n.01'}, {'name': 'harmonium', 'id': 543, 'frequency': 'r', 'synset': 'harmonium.n.01'}, {'name': 'hat', 'id': 544, 'frequency': 'f', 'synset': 'hat.n.01'}, {'name': 'hatbox', 'id': 545, 'frequency': 'r', 'synset': 'hatbox.n.01'}, {'name': 'veil', 'id': 546, 'frequency': 'c', 'synset': 'head_covering.n.01'}, {'name': 'headband', 'id': 547, 'frequency': 'f', 'synset': 'headband.n.01'}, {'name': 'headboard', 'id': 548, 'frequency': 'f', 'synset': 'headboard.n.01'}, {'name': 'headlight', 'id': 549, 'frequency': 'f', 'synset': 'headlight.n.01'}, {'name': 'headscarf', 'id': 550, 'frequency': 'c', 'synset': 'headscarf.n.01'}, {'name': 'headset', 'id': 551, 'frequency': 'r', 'synset': 'headset.n.01'}, {'name': 'headstall_(for_horses)', 'id': 552, 'frequency': 'c', 'synset': 'headstall.n.01'}, {'name': 'heart', 'id': 553, 'frequency': 'c', 'synset': 'heart.n.02'}, {'name': 'heater', 'id': 554, 'frequency': 'c', 'synset': 'heater.n.01'}, {'name': 'helicopter', 'id': 555, 'frequency': 'c', 'synset': 'helicopter.n.01'}, {'name': 'helmet', 'id': 556, 'frequency': 'f', 'synset': 'helmet.n.02'}, {'name': 'heron', 'id': 557, 'frequency': 'r', 'synset': 'heron.n.02'}, {'name': 'highchair', 'id': 558, 'frequency': 'c', 'synset': 'highchair.n.01'}, {'name': 'hinge', 'id': 559, 'frequency': 'f', 'synset': 'hinge.n.01'}, {'name': 'hippopotamus', 'id': 560, 'frequency': 'r', 'synset': 'hippopotamus.n.01'}, {'name': 'hockey_stick', 'id': 561, 'frequency': 'r', 'synset': 'hockey_stick.n.01'}, {'name': 'hog', 'id': 562, 'frequency': 'c', 'synset': 'hog.n.03'}, {'name': 'home_plate_(baseball)', 'id': 563, 'frequency': 'f', 'synset': 'home_plate.n.01'}, {'name': 'honey', 'id': 564, 'frequency': 'c', 'synset': 'honey.n.01'}, {'name': 'fume_hood', 'id': 565, 'frequency': 'f', 'synset': 'hood.n.06'}, {'name': 'hook', 'id': 566, 'frequency': 'f', 'synset': 'hook.n.05'}, {'name': 'hookah', 'id': 567, 'frequency': 'r', 'synset': 'hookah.n.01'}, {'name': 'hornet', 'id': 568, 'frequency': 'r', 'synset': 'hornet.n.01'}, {'name': 'horse', 'id': 569, 'frequency': 'f', 'synset': 'horse.n.01'}, {'name': 'hose', 'id': 570, 'frequency': 'f', 'synset': 'hose.n.03'}, {'name': 'hot-air_balloon', 'id': 571, 'frequency': 'r', 'synset': 'hot-air_balloon.n.01'}, {'name': 'hotplate', 'id': 572, 'frequency': 'r', 'synset': 'hot_plate.n.01'}, {'name': 'hot_sauce', 'id': 573, 'frequency': 'c', 'synset': 'hot_sauce.n.01'}, {'name': 'hourglass', 'id': 574, 'frequency': 'r', 'synset': 'hourglass.n.01'}, {'name': 'houseboat', 'id': 575, 'frequency': 'r', 'synset': 'houseboat.n.01'}, {'name': 'hummingbird', 'id': 576, 'frequency': 'c', 'synset': 'hummingbird.n.01'}, {'name': 'hummus', 'id': 577, 'frequency': 'r', 'synset': 'hummus.n.01'}, {'name': 'polar_bear', 'id': 578, 'frequency': 'f', 'synset': 'ice_bear.n.01'}, {'name': 'icecream', 'id': 579, 'frequency': 'c', 'synset': 'ice_cream.n.01'}, {'name': 'popsicle', 'id': 580, 'frequency': 'r', 'synset': 'ice_lolly.n.01'}, {'name': 'ice_maker', 'id': 581, 'frequency': 'c', 'synset': 'ice_maker.n.01'}, {'name': 'ice_pack', 'id': 582, 'frequency': 'r', 'synset': 'ice_pack.n.01'}, {'name': 'ice_skate', 'id': 583, 'frequency': 'r', 'synset': 'ice_skate.n.01'}, {'name': 'igniter', 'id': 584, 'frequency': 'c', 'synset': 'igniter.n.01'}, {'name': 'inhaler', 'id': 585, 'frequency': 'r', 'synset': 'inhaler.n.01'}, {'name': 'iPod', 'id': 586, 'frequency': 'f', 'synset': 'ipod.n.01'}, {'name': 'iron_(for_clothing)', 'id': 587, 'frequency': 'c', 'synset': 'iron.n.04'}, {'name': 'ironing_board', 'id': 588, 'frequency': 'c', 'synset': 'ironing_board.n.01'}, {'name': 'jacket', 'id': 589, 'frequency': 'f', 'synset': 'jacket.n.01'}, {'name': 'jam', 'id': 590, 'frequency': 'c', 'synset': 'jam.n.01'}, {'name': 'jar', 'id': 591, 'frequency': 'f', 'synset': 'jar.n.01'}, {'name': 'jean', 'id': 592, 'frequency': 'f', 'synset': 'jean.n.01'}, {'name': 'jeep', 'id': 593, 'frequency': 'c', 'synset': 'jeep.n.01'}, {'name': 'jelly_bean', 'id': 594, 'frequency': 'r', 'synset': 'jelly_bean.n.01'}, {'name': 'jersey', 'id': 595, 'frequency': 'f', 'synset': 'jersey.n.03'}, {'name': 'jet_plane', 'id': 596, 'frequency': 'c', 'synset': 'jet.n.01'}, {'name': 'jewel', 'id': 597, 'frequency': 'r', 'synset': 'jewel.n.01'}, {'name': 'jewelry', 'id': 598, 'frequency': 'c', 'synset': 'jewelry.n.01'}, {'name': 'joystick', 'id': 599, 'frequency': 'r', 'synset': 'joystick.n.02'}, {'name': 'jumpsuit', 'id': 600, 'frequency': 'c', 'synset': 'jump_suit.n.01'}, {'name': 'kayak', 'id': 601, 'frequency': 'c', 'synset': 'kayak.n.01'}, {'name': 'keg', 'id': 602, 'frequency': 'r', 'synset': 'keg.n.02'}, {'name': 'kennel', 'id': 603, 'frequency': 'r', 'synset': 'kennel.n.01'}, {'name': 'kettle', 'id': 604, 'frequency': 'c', 'synset': 'kettle.n.01'}, {'name': 'key', 'id': 605, 'frequency': 'f', 'synset': 'key.n.01'}, {'name': 'keycard', 'id': 606, 'frequency': 'r', 'synset': 'keycard.n.01'}, {'name': 'kilt', 'id': 607, 'frequency': 'c', 'synset': 'kilt.n.01'}, {'name': 'kimono', 'id': 608, 'frequency': 'c', 'synset': 'kimono.n.01'}, {'name': 'kitchen_sink', 'id': 609, 'frequency': 'f', 'synset': 'kitchen_sink.n.01'}, {'name': 'kitchen_table', 'id': 610, 'frequency': 'r', 'synset': 'kitchen_table.n.01'}, {'name': 'kite', 'id': 611, 'frequency': 'f', 'synset': 'kite.n.03'}, {'name': 'kitten', 'id': 612, 'frequency': 'c', 'synset': 'kitten.n.01'}, {'name': 'kiwi_fruit', 'id': 613, 'frequency': 'c', 'synset': 'kiwi.n.03'}, {'name': 'knee_pad', 'id': 614, 'frequency': 'f', 'synset': 'knee_pad.n.01'}, {'name': 'knife', 'id': 615, 'frequency': 'f', 'synset': 'knife.n.01'}, {'name': 'knitting_needle', 'id': 616, 'frequency': 'r', 'synset': 'knitting_needle.n.01'}, {'name': 'knob', 'id': 617, 'frequency': 'f', 'synset': 'knob.n.02'}, {'name': 'knocker_(on_a_door)', 'id': 618, 'frequency': 'r', 'synset': 'knocker.n.05'}, {'name': 'koala', 'id': 619, 'frequency': 'r', 'synset': 'koala.n.01'}, {'name': 'lab_coat', 'id': 620, 'frequency': 'r', 'synset': 'lab_coat.n.01'}, {'name': 'ladder', 'id': 621, 'frequency': 'f', 'synset': 'ladder.n.01'}, {'name': 'ladle', 'id': 622, 'frequency': 'c', 'synset': 'ladle.n.01'}, {'name': 'ladybug', 'id': 623, 'frequency': 'c', 'synset': 'ladybug.n.01'}, {'name': 'lamb_(animal)', 'id': 624, 'frequency': 'f', 'synset': 'lamb.n.01'}, {'name': 'lamb-chop', 'id': 625, 'frequency': 'r', 'synset': 'lamb_chop.n.01'}, {'name': 'lamp', 'id': 626, 'frequency': 'f', 'synset': 'lamp.n.02'}, {'name': 'lamppost', 'id': 627, 'frequency': 'f', 'synset': 'lamppost.n.01'}, {'name': 'lampshade', 'id': 628, 'frequency': 'f', 'synset': 'lampshade.n.01'}, {'name': 'lantern', 'id': 629, 'frequency': 'c', 'synset': 'lantern.n.01'}, {'name': 'lanyard', 'id': 630, 'frequency': 'f', 'synset': 'lanyard.n.02'}, {'name': 'laptop_computer', 'id': 631, 'frequency': 'f', 'synset': 'laptop.n.01'}, {'name': 'lasagna', 'id': 632, 'frequency': 'r', 'synset': 'lasagna.n.01'}, {'name': 'latch', 'id': 633, 'frequency': 'f', 'synset': 'latch.n.02'}, {'name': 'lawn_mower', 'id': 634, 'frequency': 'r', 'synset': 'lawn_mower.n.01'}, {'name': 'leather', 'id': 635, 'frequency': 'r', 'synset': 'leather.n.01'}, {'name': 'legging_(clothing)', 'id': 636, 'frequency': 'c', 'synset': 'legging.n.01'}, {'name': 'Lego', 'id': 637, 'frequency': 'c', 'synset': 'lego.n.01'}, {'name': 'legume', 'id': 638, 'frequency': 'r', 'synset': 'legume.n.02'}, {'name': 'lemon', 'id': 639, 'frequency': 'f', 'synset': 'lemon.n.01'}, {'name': 'lemonade', 'id': 640, 'frequency': 'r', 'synset': 'lemonade.n.01'}, {'name': 'lettuce', 'id': 641, 'frequency': 'f', 'synset': 'lettuce.n.02'}, {'name': 'license_plate', 'id': 642, 'frequency': 'f', 'synset': 'license_plate.n.01'}, {'name': 'life_buoy', 'id': 643, 'frequency': 'f', 'synset': 'life_buoy.n.01'}, {'name': 'life_jacket', 'id': 644, 'frequency': 'f', 'synset': 'life_jacket.n.01'}, {'name': 'lightbulb', 'id': 645, 'frequency': 'f', 'synset': 'light_bulb.n.01'}, {'name': 'lightning_rod', 'id': 646, 'frequency': 'r', 'synset': 'lightning_rod.n.02'}, {'name': 'lime', 'id': 647, 'frequency': 'f', 'synset': 'lime.n.06'}, {'name': 'limousine', 'id': 648, 'frequency': 'r', 'synset': 'limousine.n.01'}, {'name': 'lion', 'id': 649, 'frequency': 'c', 'synset': 'lion.n.01'}, {'name': 'lip_balm', 'id': 650, 'frequency': 'c', 'synset': 'lip_balm.n.01'}, {'name': 'liquor', 'id': 651, 'frequency': 'r', 'synset': 'liquor.n.01'}, {'name': 'lizard', 'id': 652, 'frequency': 'c', 'synset': 'lizard.n.01'}, {'name': 'log', 'id': 653, 'frequency': 'f', 'synset': 'log.n.01'}, {'name': 'lollipop', 'id': 654, 'frequency': 'c', 'synset': 'lollipop.n.02'}, {'name': 'speaker_(stero_equipment)', 'id': 655, 'frequency': 'f', 'synset': 'loudspeaker.n.01'}, {'name': 'loveseat', 'id': 656, 'frequency': 'c', 'synset': 'love_seat.n.01'}, {'name': 'machine_gun', 'id': 657, 'frequency': 'r', 'synset': 'machine_gun.n.01'}, {'name': 'magazine', 'id': 658, 'frequency': 'f', 'synset': 'magazine.n.02'}, {'name': 'magnet', 'id': 659, 'frequency': 'f', 'synset': 'magnet.n.01'}, {'name': 'mail_slot', 'id': 660, 'frequency': 'c', 'synset': 'mail_slot.n.01'}, {'name': 'mailbox_(at_home)', 'id': 661, 'frequency': 'f', 'synset': 'mailbox.n.01'}, {'name': 'mallard', 'id': 662, 'frequency': 'r', 'synset': 'mallard.n.01'}, {'name': 'mallet', 'id': 663, 'frequency': 'r', 'synset': 'mallet.n.01'}, {'name': 'mammoth', 'id': 664, 'frequency': 'r', 'synset': 'mammoth.n.01'}, {'name': 'manatee', 'id': 665, 'frequency': 'r', 'synset': 'manatee.n.01'}, {'name': 'mandarin_orange', 'id': 666, 'frequency': 'c', 'synset': 'mandarin.n.05'}, {'name': 'manger', 'id': 667, 'frequency': 'c', 'synset': 'manger.n.01'}, {'name': 'manhole', 'id': 668, 'frequency': 'f', 'synset': 'manhole.n.01'}, {'name': 'map', 'id': 669, 'frequency': 'f', 'synset': 'map.n.01'}, {'name': 'marker', 'id': 670, 'frequency': 'f', 'synset': 'marker.n.03'}, {'name': 'martini', 'id': 671, 'frequency': 'r', 'synset': 'martini.n.01'}, {'name': 'mascot', 'id': 672, 'frequency': 'r', 'synset': 'mascot.n.01'}, {'name': 'mashed_potato', 'id': 673, 'frequency': 'c', 'synset': 'mashed_potato.n.01'}, {'name': 'masher', 'id': 674, 'frequency': 'r', 'synset': 'masher.n.02'}, {'name': 'mask', 'id': 675, 'frequency': 'f', 'synset': 'mask.n.04'}, {'name': 'mast', 'id': 676, 'frequency': 'f', 'synset': 'mast.n.01'}, {'name': 'mat_(gym_equipment)', 'id': 677, 'frequency': 'c', 'synset': 'mat.n.03'}, {'name': 'matchbox', 'id': 678, 'frequency': 'r', 'synset': 'matchbox.n.01'}, {'name': 'mattress', 'id': 679, 'frequency': 'f', 'synset': 'mattress.n.01'}, {'name': 'measuring_cup', 'id': 680, 'frequency': 'c', 'synset': 'measuring_cup.n.01'}, {'name': 'measuring_stick', 'id': 681, 'frequency': 'c', 'synset': 'measuring_stick.n.01'}, {'name': 'meatball', 'id': 682, 'frequency': 'c', 'synset': 'meatball.n.01'}, {'name': 'medicine', 'id': 683, 'frequency': 'c', 'synset': 'medicine.n.02'}, {'name': 'melon', 'id': 684, 'frequency': 'c', 'synset': 'melon.n.01'}, {'name': 'microphone', 'id': 685, 'frequency': 'f', 'synset': 'microphone.n.01'}, {'name': 'microscope', 'id': 686, 'frequency': 'r', 'synset': 'microscope.n.01'}, {'name': 'microwave_oven', 'id': 687, 'frequency': 'f', 'synset': 'microwave.n.02'}, {'name': 'milestone', 'id': 688, 'frequency': 'r', 'synset': 'milestone.n.01'}, {'name': 'milk', 'id': 689, 'frequency': 'f', 'synset': 'milk.n.01'}, {'name': 'milk_can', 'id': 690, 'frequency': 'r', 'synset': 'milk_can.n.01'}, {'name': 'milkshake', 'id': 691, 'frequency': 'r', 'synset': 'milkshake.n.01'}, {'name': 'minivan', 'id': 692, 'frequency': 'f', 'synset': 'minivan.n.01'}, {'name': 'mint_candy', 'id': 693, 'frequency': 'r', 'synset': 'mint.n.05'}, {'name': 'mirror', 'id': 694, 'frequency': 'f', 'synset': 'mirror.n.01'}, {'name': 'mitten', 'id': 695, 'frequency': 'c', 'synset': 'mitten.n.01'}, {'name': 'mixer_(kitchen_tool)', 'id': 696, 'frequency': 'c', 'synset': 'mixer.n.04'}, {'name': 'money', 'id': 697, 'frequency': 'c', 'synset': 'money.n.03'}, {'name': 'monitor_(computer_equipment) computer_monitor', 'id': 698, 'frequency': 'f', 'synset': 'monitor.n.04'}, {'name': 'monkey', 'id': 699, 'frequency': 'c', 'synset': 'monkey.n.01'}, {'name': 'motor', 'id': 700, 'frequency': 'f', 'synset': 'motor.n.01'}, {'name': 'motor_scooter', 'id': 701, 'frequency': 'f', 'synset': 'motor_scooter.n.01'}, {'name': 'motor_vehicle', 'id': 702, 'frequency': 'r', 'synset': 'motor_vehicle.n.01'}, {'name': 'motorcycle', 'id': 703, 'frequency': 'f', 'synset': 'motorcycle.n.01'}, {'name': 'mound_(baseball)', 'id': 704, 'frequency': 'f', 'synset': 'mound.n.01'}, {'name': 'mouse_(computer_equipment)', 'id': 705, 'frequency': 'f', 'synset': 'mouse.n.04'}, {'name': 'mousepad', 'id': 706, 'frequency': 'f', 'synset': 'mousepad.n.01'}, {'name': 'muffin', 'id': 707, 'frequency': 'c', 'synset': 'muffin.n.01'}, {'name': 'mug', 'id': 708, 'frequency': 'f', 'synset': 'mug.n.04'}, {'name': 'mushroom', 'id': 709, 'frequency': 'f', 'synset': 'mushroom.n.02'}, {'name': 'music_stool', 'id': 710, 'frequency': 'r', 'synset': 'music_stool.n.01'}, {'name': 'musical_instrument', 'id': 711, 'frequency': 'c', 'synset': 'musical_instrument.n.01'}, {'name': 'nailfile', 'id': 712, 'frequency': 'r', 'synset': 'nailfile.n.01'}, {'name': 'napkin', 'id': 713, 'frequency': 'f', 'synset': 'napkin.n.01'}, {'name': 'neckerchief', 'id': 714, 'frequency': 'r', 'synset': 'neckerchief.n.01'}, {'name': 'necklace', 'id': 715, 'frequency': 'f', 'synset': 'necklace.n.01'}, {'name': 'necktie', 'id': 716, 'frequency': 'f', 'synset': 'necktie.n.01'}, {'name': 'needle', 'id': 717, 'frequency': 'c', 'synset': 'needle.n.03'}, {'name': 'nest', 'id': 718, 'frequency': 'c', 'synset': 'nest.n.01'}, {'name': 'newspaper', 'id': 719, 'frequency': 'f', 'synset': 'newspaper.n.01'}, {'name': 'newsstand', 'id': 720, 'frequency': 'c', 'synset': 'newsstand.n.01'}, {'name': 'nightshirt', 'id': 721, 'frequency': 'c', 'synset': 'nightwear.n.01'}, {'name': 'nosebag_(for_animals)', 'id': 722, 'frequency': 'r', 'synset': 'nosebag.n.01'}, {'name': 'noseband_(for_animals)', 'id': 723, 'frequency': 'c', 'synset': 'noseband.n.01'}, {'name': 'notebook', 'id': 724, 'frequency': 'f', 'synset': 'notebook.n.01'}, {'name': 'notepad', 'id': 725, 'frequency': 'c', 'synset': 'notepad.n.01'}, {'name': 'nut', 'id': 726, 'frequency': 'f', 'synset': 'nut.n.03'}, {'name': 'nutcracker', 'id': 727, 'frequency': 'r', 'synset': 'nutcracker.n.01'}, {'name': 'oar', 'id': 728, 'frequency': 'f', 'synset': 'oar.n.01'}, {'name': 'octopus_(food)', 'id': 729, 'frequency': 'r', 'synset': 'octopus.n.01'}, {'name': 'octopus_(animal)', 'id': 730, 'frequency': 'r', 'synset': 'octopus.n.02'}, {'name': 'oil_lamp', 'id': 731, 'frequency': 'c', 'synset': 'oil_lamp.n.01'}, {'name': 'olive_oil', 'id': 732, 'frequency': 'c', 'synset': 'olive_oil.n.01'}, {'name': 'omelet', 'id': 733, 'frequency': 'r', 'synset': 'omelet.n.01'}, {'name': 'onion', 'id': 734, 'frequency': 'f', 'synset': 'onion.n.01'}, {'name': 'orange_(fruit)', 'id': 735, 'frequency': 'f', 'synset': 'orange.n.01'}, {'name': 'orange_juice', 'id': 736, 'frequency': 'c', 'synset': 'orange_juice.n.01'}, {'name': 'ostrich', 'id': 737, 'frequency': 'c', 'synset': 'ostrich.n.02'}, {'name': 'ottoman', 'id': 738, 'frequency': 'f', 'synset': 'ottoman.n.03'}, {'name': 'oven', 'id': 739, 'frequency': 'f', 'synset': 'oven.n.01'}, {'name': 'overalls_(clothing)', 'id': 740, 'frequency': 'c', 'synset': 'overall.n.01'}, {'name': 'owl', 'id': 741, 'frequency': 'c', 'synset': 'owl.n.01'}, {'name': 'packet', 'id': 742, 'frequency': 'c', 'synset': 'packet.n.03'}, {'name': 'inkpad', 'id': 743, 'frequency': 'r', 'synset': 'pad.n.03'}, {'name': 'pad', 'id': 744, 'frequency': 'c', 'synset': 'pad.n.04'}, {'name': 'paddle', 'id': 745, 'frequency': 'f', 'synset': 'paddle.n.04'}, {'name': 'padlock', 'id': 746, 'frequency': 'c', 'synset': 'padlock.n.01'}, {'name': 'paintbrush', 'id': 747, 'frequency': 'c', 'synset': 'paintbrush.n.01'}, {'name': 'painting', 'id': 748, 'frequency': 'f', 'synset': 'painting.n.01'}, {'name': 'pajamas', 'id': 749, 'frequency': 'f', 'synset': 'pajama.n.02'}, {'name': 'palette', 'id': 750, 'frequency': 'c', 'synset': 'palette.n.02'}, {'name': 'pan_(for_cooking)', 'id': 751, 'frequency': 'f', 'synset': 'pan.n.01'}, {'name': 'pan_(metal_container)', 'id': 752, 'frequency': 'r', 'synset': 'pan.n.03'}, {'name': 'pancake', 'id': 753, 'frequency': 'c', 'synset': 'pancake.n.01'}, {'name': 'pantyhose', 'id': 754, 'frequency': 'r', 'synset': 'pantyhose.n.01'}, {'name': 'papaya', 'id': 755, 'frequency': 'r', 'synset': 'papaya.n.02'}, {'name': 'paper_plate', 'id': 756, 'frequency': 'f', 'synset': 'paper_plate.n.01'}, {'name': 'paper_towel', 'id': 757, 'frequency': 'f', 'synset': 'paper_towel.n.01'}, {'name': 'paperback_book', 'id': 758, 'frequency': 'r', 'synset': 'paperback_book.n.01'}, {'name': 'paperweight', 'id': 759, 'frequency': 'r', 'synset': 'paperweight.n.01'}, {'name': 'parachute', 'id': 760, 'frequency': 'c', 'synset': 'parachute.n.01'}, {'name': 'parakeet', 'id': 761, 'frequency': 'c', 'synset': 'parakeet.n.01'}, {'name': 'parasail_(sports)', 'id': 762, 'frequency': 'c', 'synset': 'parasail.n.01'}, {'name': 'parasol', 'id': 763, 'frequency': 'c', 'synset': 'parasol.n.01'}, {'name': 'parchment', 'id': 764, 'frequency': 'r', 'synset': 'parchment.n.01'}, {'name': 'parka', 'id': 765, 'frequency': 'c', 'synset': 'parka.n.01'}, {'name': 'parking_meter', 'id': 766, 'frequency': 'f', 'synset': 'parking_meter.n.01'}, {'name': 'parrot', 'id': 767, 'frequency': 'c', 'synset': 'parrot.n.01'}, {'name': 'passenger_car_(part_of_a_train)', 'id': 768, 'frequency': 'c', 'synset': 'passenger_car.n.01'}, {'name': 'passenger_ship', 'id': 769, 'frequency': 'r', 'synset': 'passenger_ship.n.01'}, {'name': 'passport', 'id': 770, 'frequency': 'c', 'synset': 'passport.n.02'}, {'name': 'pastry', 'id': 771, 'frequency': 'f', 'synset': 'pastry.n.02'}, {'name': 'patty_(food)', 'id': 772, 'frequency': 'r', 'synset': 'patty.n.01'}, {'name': 'pea_(food)', 'id': 773, 'frequency': 'c', 'synset': 'pea.n.01'}, {'name': 'peach', 'id': 774, 'frequency': 'c', 'synset': 'peach.n.03'}, {'name': 'peanut_butter', 'id': 775, 'frequency': 'c', 'synset': 'peanut_butter.n.01'}, {'name': 'pear', 'id': 776, 'frequency': 'f', 'synset': 'pear.n.01'}, {'name': 'peeler_(tool_for_fruit_and_vegetables)', 'id': 777, 'frequency': 'c', 'synset': 'peeler.n.03'}, {'name': 'wooden_leg', 'id': 778, 'frequency': 'r', 'synset': 'peg.n.04'}, {'name': 'pegboard', 'id': 779, 'frequency': 'r', 'synset': 'pegboard.n.01'}, {'name': 'pelican', 'id': 780, 'frequency': 'c', 'synset': 'pelican.n.01'}, {'name': 'pen', 'id': 781, 'frequency': 'f', 'synset': 'pen.n.01'}, {'name': 'pencil', 'id': 782, 'frequency': 'f', 'synset': 'pencil.n.01'}, {'name': 'pencil_box', 'id': 783, 'frequency': 'r', 'synset': 'pencil_box.n.01'}, {'name': 'pencil_sharpener', 'id': 784, 'frequency': 'r', 'synset': 'pencil_sharpener.n.01'}, {'name': 'pendulum', 'id': 785, 'frequency': 'r', 'synset': 'pendulum.n.01'}, {'name': 'penguin', 'id': 786, 'frequency': 'c', 'synset': 'penguin.n.01'}, {'name': 'pennant', 'id': 787, 'frequency': 'r', 'synset': 'pennant.n.02'}, {'name': 'penny_(coin)', 'id': 788, 'frequency': 'r', 'synset': 'penny.n.02'}, {'name': 'pepper', 'id': 789, 'frequency': 'f', 'synset': 'pepper.n.03'}, {'name': 'pepper_mill', 'id': 790, 'frequency': 'c', 'synset': 'pepper_mill.n.01'}, {'name': 'perfume', 'id': 791, 'frequency': 'c', 'synset': 'perfume.n.02'}, {'name': 'persimmon', 'id': 792, 'frequency': 'r', 'synset': 'persimmon.n.02'}, {'name': 'person', 'id': 793, 'frequency': 'f', 'synset': 'person.n.01'}, {'name': 'pet', 'id': 794, 'frequency': 'c', 'synset': 'pet.n.01'}, {'name': 'pew_(church_bench)', 'id': 795, 'frequency': 'c', 'synset': 'pew.n.01'}, {'name': 'phonebook', 'id': 796, 'frequency': 'r', 'synset': 'phonebook.n.01'}, {'name': 'phonograph_record', 'id': 797, 'frequency': 'c', 'synset': 'phonograph_record.n.01'}, {'name': 'piano', 'id': 798, 'frequency': 'f', 'synset': 'piano.n.01'}, {'name': 'pickle', 'id': 799, 'frequency': 'f', 'synset': 'pickle.n.01'}, {'name': 'pickup_truck', 'id': 800, 'frequency': 'f', 'synset': 'pickup.n.01'}, {'name': 'pie', 'id': 801, 'frequency': 'c', 'synset': 'pie.n.01'}, {'name': 'pigeon', 'id': 802, 'frequency': 'c', 'synset': 'pigeon.n.01'}, {'name': 'piggy_bank', 'id': 803, 'frequency': 'r', 'synset': 'piggy_bank.n.01'}, {'name': 'pillow', 'id': 804, 'frequency': 'f', 'synset': 'pillow.n.01'}, {'name': 'pin_(non_jewelry)', 'id': 805, 'frequency': 'r', 'synset': 'pin.n.09'}, {'name': 'pineapple', 'id': 806, 'frequency': 'f', 'synset': 'pineapple.n.02'}, {'name': 'pinecone', 'id': 807, 'frequency': 'c', 'synset': 'pinecone.n.01'}, {'name': 'ping-pong_ball', 'id': 808, 'frequency': 'r', 'synset': 'ping-pong_ball.n.01'}, {'name': 'pinwheel', 'id': 809, 'frequency': 'r', 'synset': 'pinwheel.n.03'}, {'name': 'tobacco_pipe', 'id': 810, 'frequency': 'r', 'synset': 'pipe.n.01'}, {'name': 'pipe', 'id': 811, 'frequency': 'f', 'synset': 'pipe.n.02'}, {'name': 'pistol', 'id': 812, 'frequency': 'r', 'synset': 'pistol.n.01'}, {'name': 'pita_(bread)', 'id': 813, 'frequency': 'c', 'synset': 'pita.n.01'}, {'name': 'pitcher_(vessel_for_liquid)', 'id': 814, 'frequency': 'f', 'synset': 'pitcher.n.02'}, {'name': 'pitchfork', 'id': 815, 'frequency': 'r', 'synset': 'pitchfork.n.01'}, {'name': 'pizza', 'id': 816, 'frequency': 'f', 'synset': 'pizza.n.01'}, {'name': 'place_mat', 'id': 817, 'frequency': 'f', 'synset': 'place_mat.n.01'}, {'name': 'plate', 'id': 818, 'frequency': 'f', 'synset': 'plate.n.04'}, {'name': 'platter', 'id': 819, 'frequency': 'c', 'synset': 'platter.n.01'}, {'name': 'playpen', 'id': 820, 'frequency': 'r', 'synset': 'playpen.n.01'}, {'name': 'pliers', 'id': 821, 'frequency': 'c', 'synset': 'pliers.n.01'}, {'name': 'plow_(farm_equipment)', 'id': 822, 'frequency': 'r', 'synset': 'plow.n.01'}, {'name': 'plume', 'id': 823, 'frequency': 'r', 'synset': 'plume.n.02'}, {'name': 'pocket_watch', 'id': 824, 'frequency': 'r', 'synset': 'pocket_watch.n.01'}, {'name': 'pocketknife', 'id': 825, 'frequency': 'c', 'synset': 'pocketknife.n.01'}, {'name': 'poker_(fire_stirring_tool)', 'id': 826, 'frequency': 'c', 'synset': 'poker.n.01'}, {'name': 'pole', 'id': 827, 'frequency': 'f', 'synset': 'pole.n.01'}, {'name': 'polo_shirt', 'id': 828, 'frequency': 'f', 'synset': 'polo_shirt.n.01'}, {'name': 'poncho', 'id': 829, 'frequency': 'r', 'synset': 'poncho.n.01'}, {'name': 'pony', 'id': 830, 'frequency': 'c', 'synset': 'pony.n.05'}, {'name': 'pool_table', 'id': 831, 'frequency': 'r', 'synset': 'pool_table.n.01'}, {'name': 'pop_(soda)', 'id': 832, 'frequency': 'f', 'synset': 'pop.n.02'}, {'name': 'postbox_(public)', 'id': 833, 'frequency': 'c', 'synset': 'postbox.n.01'}, {'name': 'postcard', 'id': 834, 'frequency': 'c', 'synset': 'postcard.n.01'}, {'name': 'poster', 'id': 835, 'frequency': 'f', 'synset': 'poster.n.01'}, {'name': 'pot', 'id': 836, 'frequency': 'f', 'synset': 'pot.n.01'}, {'name': 'flowerpot', 'id': 837, 'frequency': 'f', 'synset': 'pot.n.04'}, {'name': 'potato', 'id': 838, 'frequency': 'f', 'synset': 'potato.n.01'}, {'name': 'potholder', 'id': 839, 'frequency': 'c', 'synset': 'potholder.n.01'}, {'name': 'pottery', 'id': 840, 'frequency': 'c', 'synset': 'pottery.n.01'}, {'name': 'pouch', 'id': 841, 'frequency': 'c', 'synset': 'pouch.n.01'}, {'name': 'power_shovel', 'id': 842, 'frequency': 'c', 'synset': 'power_shovel.n.01'}, {'name': 'prawn', 'id': 843, 'frequency': 'c', 'synset': 'prawn.n.01'}, {'name': 'pretzel', 'id': 844, 'frequency': 'c', 'synset': 'pretzel.n.01'}, {'name': 'printer', 'id': 845, 'frequency': 'f', 'synset': 'printer.n.03'}, {'name': 'projectile_(weapon)', 'id': 846, 'frequency': 'c', 'synset': 'projectile.n.01'}, {'name': 'projector', 'id': 847, 'frequency': 'c', 'synset': 'projector.n.02'}, {'name': 'propeller', 'id': 848, 'frequency': 'f', 'synset': 'propeller.n.01'}, {'name': 'prune', 'id': 849, 'frequency': 'r', 'synset': 'prune.n.01'}, {'name': 'pudding', 'id': 850, 'frequency': 'r', 'synset': 'pudding.n.01'}, {'name': 'puffer_(fish)', 'id': 851, 'frequency': 'r', 'synset': 'puffer.n.02'}, {'name': 'puffin', 'id': 852, 'frequency': 'r', 'synset': 'puffin.n.01'}, {'name': 'pug-dog', 'id': 853, 'frequency': 'r', 'synset': 'pug.n.01'}, {'name': 'pumpkin', 'id': 854, 'frequency': 'c', 'synset': 'pumpkin.n.02'}, {'name': 'puncher', 'id': 855, 'frequency': 'r', 'synset': 'punch.n.03'}, {'name': 'puppet', 'id': 856, 'frequency': 'r', 'synset': 'puppet.n.01'}, {'name': 'puppy', 'id': 857, 'frequency': 'c', 'synset': 'puppy.n.01'}, {'name': 'quesadilla', 'id': 858, 'frequency': 'r', 'synset': 'quesadilla.n.01'}, {'name': 'quiche', 'id': 859, 'frequency': 'r', 'synset': 'quiche.n.02'}, {'name': 'quilt', 'id': 860, 'frequency': 'f', 'synset': 'quilt.n.01'}, {'name': 'rabbit', 'id': 861, 'frequency': 'c', 'synset': 'rabbit.n.01'}, {'name': 'race_car', 'id': 862, 'frequency': 'r', 'synset': 'racer.n.02'}, {'name': 'racket', 'id': 863, 'frequency': 'c', 'synset': 'racket.n.04'}, {'name': 'radar', 'id': 864, 'frequency': 'r', 'synset': 'radar.n.01'}, {'name': 'radiator', 'id': 865, 'frequency': 'f', 'synset': 'radiator.n.03'}, {'name': 'radio_receiver', 'id': 866, 'frequency': 'c', 'synset': 'radio_receiver.n.01'}, {'name': 'radish', 'id': 867, 'frequency': 'c', 'synset': 'radish.n.03'}, {'name': 'raft', 'id': 868, 'frequency': 'c', 'synset': 'raft.n.01'}, {'name': 'rag_doll', 'id': 869, 'frequency': 'r', 'synset': 'rag_doll.n.01'}, {'name': 'raincoat', 'id': 870, 'frequency': 'c', 'synset': 'raincoat.n.01'}, {'name': 'ram_(animal)', 'id': 871, 'frequency': 'c', 'synset': 'ram.n.05'}, {'name': 'raspberry', 'id': 872, 'frequency': 'c', 'synset': 'raspberry.n.02'}, {'name': 'rat', 'id': 873, 'frequency': 'r', 'synset': 'rat.n.01'}, {'name': 'razorblade', 'id': 874, 'frequency': 'c', 'synset': 'razorblade.n.01'}, {'name': 'reamer_(juicer)', 'id': 875, 'frequency': 'c', 'synset': 'reamer.n.01'}, {'name': 'rearview_mirror', 'id': 876, 'frequency': 'f', 'synset': 'rearview_mirror.n.01'}, {'name': 'receipt', 'id': 877, 'frequency': 'c', 'synset': 'receipt.n.02'}, {'name': 'recliner', 'id': 878, 'frequency': 'c', 'synset': 'recliner.n.01'}, {'name': 'record_player', 'id': 879, 'frequency': 'c', 'synset': 'record_player.n.01'}, {'name': 'reflector', 'id': 880, 'frequency': 'f', 'synset': 'reflector.n.01'}, {'name': 'remote_control', 'id': 881, 'frequency': 'f', 'synset': 'remote_control.n.01'}, {'name': 'rhinoceros', 'id': 882, 'frequency': 'c', 'synset': 'rhinoceros.n.01'}, {'name': 'rib_(food)', 'id': 883, 'frequency': 'r', 'synset': 'rib.n.03'}, {'name': 'rifle', 'id': 884, 'frequency': 'c', 'synset': 'rifle.n.01'}, {'name': 'ring', 'id': 885, 'frequency': 'f', 'synset': 'ring.n.08'}, {'name': 'river_boat', 'id': 886, 'frequency': 'r', 'synset': 'river_boat.n.01'}, {'name': 'road_map', 'id': 887, 'frequency': 'r', 'synset': 'road_map.n.02'}, {'name': 'robe', 'id': 888, 'frequency': 'c', 'synset': 'robe.n.01'}, {'name': 'rocking_chair', 'id': 889, 'frequency': 'c', 'synset': 'rocking_chair.n.01'}, {'name': 'rodent', 'id': 890, 'frequency': 'r', 'synset': 'rodent.n.01'}, {'name': 'roller_skate', 'id': 891, 'frequency': 'r', 'synset': 'roller_skate.n.01'}, {'name': 'Rollerblade', 'id': 892, 'frequency': 'r', 'synset': 'rollerblade.n.01'}, {'name': 'rolling_pin', 'id': 893, 'frequency': 'c', 'synset': 'rolling_pin.n.01'}, {'name': 'root_beer', 'id': 894, 'frequency': 'r', 'synset': 'root_beer.n.01'}, {'name': 'router_(computer_equipment)', 'id': 895, 'frequency': 'c', 'synset': 'router.n.02'}, {'name': 'rubber_band', 'id': 896, 'frequency': 'f', 'synset': 'rubber_band.n.01'}, {'name': 'runner_(carpet)', 'id': 897, 'frequency': 'c', 'synset': 'runner.n.08'}, {'name': 'plastic_bag', 'id': 898, 'frequency': 'f', 'synset': 'sack.n.01'}, {'name': 'saddle_(on_an_animal)', 'id': 899, 'frequency': 'f', 'synset': 'saddle.n.01'}, {'name': 'saddle_blanket', 'id': 900, 'frequency': 'f', 'synset': 'saddle_blanket.n.01'}, {'name': 'saddlebag', 'id': 901, 'frequency': 'c', 'synset': 'saddlebag.n.01'}, {'name': 'safety_pin', 'id': 902, 'frequency': 'r', 'synset': 'safety_pin.n.01'}, {'name': 'sail', 'id': 903, 'frequency': 'f', 'synset': 'sail.n.01'}, {'name': 'salad', 'id': 904, 'frequency': 'f', 'synset': 'salad.n.01'}, {'name': 'salad_plate', 'id': 905, 'frequency': 'r', 'synset': 'salad_plate.n.01'}, {'name': 'salami', 'id': 906, 'frequency': 'c', 'synset': 'salami.n.01'}, {'name': 'salmon_(fish)', 'id': 907, 'frequency': 'c', 'synset': 'salmon.n.01'}, {'name': 'salmon_(food)', 'id': 908, 'frequency': 'r', 'synset': 'salmon.n.03'}, {'name': 'salsa', 'id': 909, 'frequency': 'c', 'synset': 'salsa.n.01'}, {'name': 'saltshaker', 'id': 910, 'frequency': 'f', 'synset': 'saltshaker.n.01'}, {'name': 'sandal_(type_of_shoe)', 'id': 911, 'frequency': 'f', 'synset': 'sandal.n.01'}, {'name': 'sandwich', 'id': 912, 'frequency': 'f', 'synset': 'sandwich.n.01'}, {'name': 'satchel', 'id': 913, 'frequency': 'r', 'synset': 'satchel.n.01'}, {'name': 'saucepan', 'id': 914, 'frequency': 'r', 'synset': 'saucepan.n.01'}, {'name': 'saucer', 'id': 915, 'frequency': 'f', 'synset': 'saucer.n.02'}, {'name': 'sausage', 'id': 916, 'frequency': 'f', 'synset': 'sausage.n.01'}, {'name': 'sawhorse', 'id': 917, 'frequency': 'r', 'synset': 'sawhorse.n.01'}, {'name': 'saxophone', 'id': 918, 'frequency': 'r', 'synset': 'sax.n.02'}, {'name': 'scale_(measuring_instrument)', 'id': 919, 'frequency': 'f', 'synset': 'scale.n.07'}, {'name': 'scarecrow', 'id': 920, 'frequency': 'r', 'synset': 'scarecrow.n.01'}, {'name': 'scarf', 'id': 921, 'frequency': 'f', 'synset': 'scarf.n.01'}, {'name': 'school_bus', 'id': 922, 'frequency': 'c', 'synset': 'school_bus.n.01'}, {'name': 'scissors', 'id': 923, 'frequency': 'f', 'synset': 'scissors.n.01'}, {'name': 'scoreboard', 'id': 924, 'frequency': 'f', 'synset': 'scoreboard.n.01'}, {'name': 'scraper', 'id': 925, 'frequency': 'r', 'synset': 'scraper.n.01'}, {'name': 'screwdriver', 'id': 926, 'frequency': 'c', 'synset': 'screwdriver.n.01'}, {'name': 'scrubbing_brush', 'id': 927, 'frequency': 'f', 'synset': 'scrub_brush.n.01'}, {'name': 'sculpture', 'id': 928, 'frequency': 'c', 'synset': 'sculpture.n.01'}, {'name': 'seabird', 'id': 929, 'frequency': 'c', 'synset': 'seabird.n.01'}, {'name': 'seahorse', 'id': 930, 'frequency': 'c', 'synset': 'seahorse.n.02'}, {'name': 'seaplane', 'id': 931, 'frequency': 'r', 'synset': 'seaplane.n.01'}, {'name': 'seashell', 'id': 932, 'frequency': 'c', 'synset': 'seashell.n.01'}, {'name': 'sewing_machine', 'id': 933, 'frequency': 'c', 'synset': 'sewing_machine.n.01'}, {'name': 'shaker', 'id': 934, 'frequency': 'c', 'synset': 'shaker.n.03'}, {'name': 'shampoo', 'id': 935, 'frequency': 'c', 'synset': 'shampoo.n.01'}, {'name': 'shark', 'id': 936, 'frequency': 'c', 'synset': 'shark.n.01'}, {'name': 'sharpener', 'id': 937, 'frequency': 'r', 'synset': 'sharpener.n.01'}, {'name': 'Sharpie', 'id': 938, 'frequency': 'r', 'synset': 'sharpie.n.03'}, {'name': 'shaver_(electric)', 'id': 939, 'frequency': 'r', 'synset': 'shaver.n.03'}, {'name': 'shaving_cream', 'id': 940, 'frequency': 'c', 'synset': 'shaving_cream.n.01'}, {'name': 'shawl', 'id': 941, 'frequency': 'r', 'synset': 'shawl.n.01'}, {'name': 'shears', 'id': 942, 'frequency': 'r', 'synset': 'shears.n.01'}, {'name': 'sheep', 'id': 943, 'frequency': 'f', 'synset': 'sheep.n.01'}, {'name': 'shepherd_dog', 'id': 944, 'frequency': 'r', 'synset': 'shepherd_dog.n.01'}, {'name': 'sherbert', 'id': 945, 'frequency': 'r', 'synset': 'sherbert.n.01'}, {'name': 'shield', 'id': 946, 'frequency': 'c', 'synset': 'shield.n.02'}, {'name': 'shirt', 'id': 947, 'frequency': 'f', 'synset': 'shirt.n.01'}, {'name': 'shoe', 'id': 948, 'frequency': 'f', 'synset': 'shoe.n.01'}, {'name': 'shopping_bag', 'id': 949, 'frequency': 'f', 'synset': 'shopping_bag.n.01'}, {'name': 'shopping_cart', 'id': 950, 'frequency': 'c', 'synset': 'shopping_cart.n.01'}, {'name': 'short_pants', 'id': 951, 'frequency': 'f', 'synset': 'short_pants.n.01'}, {'name': 'shot_glass', 'id': 952, 'frequency': 'r', 'synset': 'shot_glass.n.01'}, {'name': 'shoulder_bag', 'id': 953, 'frequency': 'f', 'synset': 'shoulder_bag.n.01'}, {'name': 'shovel', 'id': 954, 'frequency': 'c', 'synset': 'shovel.n.01'}, {'name': 'shower_head', 'id': 955, 'frequency': 'f', 'synset': 'shower.n.01'}, {'name': 'shower_cap', 'id': 956, 'frequency': 'r', 'synset': 'shower_cap.n.01'}, {'name': 'shower_curtain', 'id': 957, 'frequency': 'f', 'synset': 'shower_curtain.n.01'}, {'name': 'shredder_(for_paper)', 'id': 958, 'frequency': 'r', 'synset': 'shredder.n.01'}, {'name': 'signboard', 'id': 959, 'frequency': 'f', 'synset': 'signboard.n.01'}, {'name': 'silo', 'id': 960, 'frequency': 'c', 'synset': 'silo.n.01'}, {'name': 'sink', 'id': 961, 'frequency': 'f', 'synset': 'sink.n.01'}, {'name': 'skateboard', 'id': 962, 'frequency': 'f', 'synset': 'skateboard.n.01'}, {'name': 'skewer', 'id': 963, 'frequency': 'c', 'synset': 'skewer.n.01'}, {'name': 'ski', 'id': 964, 'frequency': 'f', 'synset': 'ski.n.01'}, {'name': 'ski_boot', 'id': 965, 'frequency': 'f', 'synset': 'ski_boot.n.01'}, {'name': 'ski_parka', 'id': 966, 'frequency': 'f', 'synset': 'ski_parka.n.01'}, {'name': 'ski_pole', 'id': 967, 'frequency': 'f', 'synset': 'ski_pole.n.01'}, {'name': 'skirt', 'id': 968, 'frequency': 'f', 'synset': 'skirt.n.02'}, {'name': 'skullcap', 'id': 969, 'frequency': 'r', 'synset': 'skullcap.n.01'}, {'name': 'sled', 'id': 970, 'frequency': 'c', 'synset': 'sled.n.01'}, {'name': 'sleeping_bag', 'id': 971, 'frequency': 'c', 'synset': 'sleeping_bag.n.01'}, {'name': 'sling_(bandage)', 'id': 972, 'frequency': 'r', 'synset': 'sling.n.05'}, {'name': 'slipper_(footwear)', 'id': 973, 'frequency': 'c', 'synset': 'slipper.n.01'}, {'name': 'smoothie', 'id': 974, 'frequency': 'r', 'synset': 'smoothie.n.02'}, {'name': 'snake', 'id': 975, 'frequency': 'r', 'synset': 'snake.n.01'}, {'name': 'snowboard', 'id': 976, 'frequency': 'f', 'synset': 'snowboard.n.01'}, {'name': 'snowman', 'id': 977, 'frequency': 'c', 'synset': 'snowman.n.01'}, {'name': 'snowmobile', 'id': 978, 'frequency': 'c', 'synset': 'snowmobile.n.01'}, {'name': 'soap', 'id': 979, 'frequency': 'f', 'synset': 'soap.n.01'}, {'name': 'soccer_ball', 'id': 980, 'frequency': 'f', 'synset': 'soccer_ball.n.01'}, {'name': 'sock', 'id': 981, 'frequency': 'f', 'synset': 'sock.n.01'}, {'name': 'sofa', 'id': 982, 'frequency': 'f', 'synset': 'sofa.n.01'}, {'name': 'softball', 'id': 983, 'frequency': 'r', 'synset': 'softball.n.01'}, {'name': 'solar_array', 'id': 984, 'frequency': 'c', 'synset': 'solar_array.n.01'}, {'name': 'sombrero', 'id': 985, 'frequency': 'r', 'synset': 'sombrero.n.02'}, {'name': 'soup', 'id': 986, 'frequency': 'f', 'synset': 'soup.n.01'}, {'name': 'soup_bowl', 'id': 987, 'frequency': 'r', 'synset': 'soup_bowl.n.01'}, {'name': 'soupspoon', 'id': 988, 'frequency': 'c', 'synset': 'soupspoon.n.01'}, {'name': 'sour_cream', 'id': 989, 'frequency': 'c', 'synset': 'sour_cream.n.01'}, {'name': 'soya_milk', 'id': 990, 'frequency': 'r', 'synset': 'soya_milk.n.01'}, {'name': 'space_shuttle', 'id': 991, 'frequency': 'r', 'synset': 'space_shuttle.n.01'}, {'name': 'sparkler_(fireworks)', 'id': 992, 'frequency': 'r', 'synset': 'sparkler.n.02'}, {'name': 'spatula', 'id': 993, 'frequency': 'f', 'synset': 'spatula.n.02'}, {'name': 'spear', 'id': 994, 'frequency': 'r', 'synset': 'spear.n.01'}, {'name': 'spectacles', 'id': 995, 'frequency': 'f', 'synset': 'spectacles.n.01'}, {'name': 'spice_rack', 'id': 996, 'frequency': 'c', 'synset': 'spice_rack.n.01'}, {'name': 'spider', 'id': 997, 'frequency': 'c', 'synset': 'spider.n.01'}, {'name': 'crawfish', 'id': 998, 'frequency': 'r', 'synset': 'spiny_lobster.n.02'}, {'name': 'sponge', 'id': 999, 'frequency': 'c', 'synset': 'sponge.n.01'}, {'name': 'spoon', 'id': 1000, 'frequency': 'f', 'synset': 'spoon.n.01'}, {'name': 'sportswear', 'id': 1001, 'frequency': 'c', 'synset': 'sportswear.n.01'}, {'name': 'spotlight', 'id': 1002, 'frequency': 'c', 'synset': 'spotlight.n.02'}, {'name': 'squid_(food)', 'id': 1003, 'frequency': 'r', 'synset': 'squid.n.01'}, {'name': 'squirrel', 'id': 1004, 'frequency': 'c', 'synset': 'squirrel.n.01'}, {'name': 'stagecoach', 'id': 1005, 'frequency': 'r', 'synset': 'stagecoach.n.01'}, {'name': 'stapler_(stapling_machine)', 'id': 1006, 'frequency': 'c', 'synset': 'stapler.n.01'}, {'name': 'starfish', 'id': 1007, 'frequency': 'c', 'synset': 'starfish.n.01'}, {'name': 'statue_(sculpture)', 'id': 1008, 'frequency': 'f', 'synset': 'statue.n.01'}, {'name': 'steak_(food)', 'id': 1009, 'frequency': 'c', 'synset': 'steak.n.01'}, {'name': 'steak_knife', 'id': 1010, 'frequency': 'r', 'synset': 'steak_knife.n.01'}, {'name': 'steering_wheel', 'id': 1011, 'frequency': 'f', 'synset': 'steering_wheel.n.01'}, {'name': 'stepladder', 'id': 1012, 'frequency': 'r', 'synset': 'step_ladder.n.01'}, {'name': 'step_stool', 'id': 1013, 'frequency': 'c', 'synset': 'step_stool.n.01'}, {'name': 'stereo_(sound_system)', 'id': 1014, 'frequency': 'c', 'synset': 'stereo.n.01'}, {'name': 'stew', 'id': 1015, 'frequency': 'r', 'synset': 'stew.n.02'}, {'name': 'stirrer', 'id': 1016, 'frequency': 'r', 'synset': 'stirrer.n.02'}, {'name': 'stirrup', 'id': 1017, 'frequency': 'f', 'synset': 'stirrup.n.01'}, {'name': 'stool', 'id': 1018, 'frequency': 'f', 'synset': 'stool.n.01'}, {'name': 'stop_sign', 'id': 1019, 'frequency': 'f', 'synset': 'stop_sign.n.01'}, {'name': 'brake_light', 'id': 1020, 'frequency': 'f', 'synset': 'stoplight.n.01'}, {'name': 'stove', 'id': 1021, 'frequency': 'f', 'synset': 'stove.n.01'}, {'name': 'strainer', 'id': 1022, 'frequency': 'c', 'synset': 'strainer.n.01'}, {'name': 'strap', 'id': 1023, 'frequency': 'f', 'synset': 'strap.n.01'}, {'name': 'straw_(for_drinking)', 'id': 1024, 'frequency': 'f', 'synset': 'straw.n.04'}, {'name': 'strawberry', 'id': 1025, 'frequency': 'f', 'synset': 'strawberry.n.01'}, {'name': 'street_sign', 'id': 1026, 'frequency': 'f', 'synset': 'street_sign.n.01'}, {'name': 'streetlight', 'id': 1027, 'frequency': 'f', 'synset': 'streetlight.n.01'}, {'name': 'string_cheese', 'id': 1028, 'frequency': 'r', 'synset': 'string_cheese.n.01'}, {'name': 'stylus', 'id': 1029, 'frequency': 'r', 'synset': 'stylus.n.02'}, {'name': 'subwoofer', 'id': 1030, 'frequency': 'r', 'synset': 'subwoofer.n.01'}, {'name': 'sugar_bowl', 'id': 1031, 'frequency': 'r', 'synset': 'sugar_bowl.n.01'}, {'name': 'sugarcane_(plant)', 'id': 1032, 'frequency': 'r', 'synset': 'sugarcane.n.01'}, {'name': 'suit_(clothing)', 'id': 1033, 'frequency': 'f', 'synset': 'suit.n.01'}, {'name': 'sunflower', 'id': 1034, 'frequency': 'c', 'synset': 'sunflower.n.01'}, {'name': 'sunglasses', 'id': 1035, 'frequency': 'f', 'synset': 'sunglasses.n.01'}, {'name': 'sunhat', 'id': 1036, 'frequency': 'c', 'synset': 'sunhat.n.01'}, {'name': 'surfboard', 'id': 1037, 'frequency': 'f', 'synset': 'surfboard.n.01'}, {'name': 'sushi', 'id': 1038, 'frequency': 'c', 'synset': 'sushi.n.01'}, {'name': 'mop', 'id': 1039, 'frequency': 'c', 'synset': 'swab.n.02'}, {'name': 'sweat_pants', 'id': 1040, 'frequency': 'c', 'synset': 'sweat_pants.n.01'}, {'name': 'sweatband', 'id': 1041, 'frequency': 'c', 'synset': 'sweatband.n.02'}, {'name': 'sweater', 'id': 1042, 'frequency': 'f', 'synset': 'sweater.n.01'}, {'name': 'sweatshirt', 'id': 1043, 'frequency': 'f', 'synset': 'sweatshirt.n.01'}, {'name': 'sweet_potato', 'id': 1044, 'frequency': 'c', 'synset': 'sweet_potato.n.02'}, {'name': 'swimsuit', 'id': 1045, 'frequency': 'f', 'synset': 'swimsuit.n.01'}, {'name': 'sword', 'id': 1046, 'frequency': 'c', 'synset': 'sword.n.01'}, {'name': 'syringe', 'id': 1047, 'frequency': 'r', 'synset': 'syringe.n.01'}, {'name': 'Tabasco_sauce', 'id': 1048, 'frequency': 'r', 'synset': 'tabasco.n.02'}, {'name': 'table-tennis_table', 'id': 1049, 'frequency': 'r', 'synset': 'table-tennis_table.n.01'}, {'name': 'table', 'id': 1050, 'frequency': 'f', 'synset': 'table.n.02'}, {'name': 'table_lamp', 'id': 1051, 'frequency': 'c', 'synset': 'table_lamp.n.01'}, {'name': 'tablecloth', 'id': 1052, 'frequency': 'f', 'synset': 'tablecloth.n.01'}, {'name': 'tachometer', 'id': 1053, 'frequency': 'r', 'synset': 'tachometer.n.01'}, {'name': 'taco', 'id': 1054, 'frequency': 'r', 'synset': 'taco.n.02'}, {'name': 'tag', 'id': 1055, 'frequency': 'f', 'synset': 'tag.n.02'}, {'name': 'taillight', 'id': 1056, 'frequency': 'f', 'synset': 'taillight.n.01'}, {'name': 'tambourine', 'id': 1057, 'frequency': 'r', 'synset': 'tambourine.n.01'}, {'name': 'army_tank', 'id': 1058, 'frequency': 'r', 'synset': 'tank.n.01'}, {'name': 'tank_(storage_vessel)', 'id': 1059, 'frequency': 'f', 'synset': 'tank.n.02'}, {'name': 'tank_top_(clothing)', 'id': 1060, 'frequency': 'f', 'synset': 'tank_top.n.01'}, {'name': 'tape_(sticky_cloth_or_paper)', 'id': 1061, 'frequency': 'f', 'synset': 'tape.n.01'}, {'name': 'tape_measure', 'id': 1062, 'frequency': 'c', 'synset': 'tape.n.04'}, {'name': 'tapestry', 'id': 1063, 'frequency': 'c', 'synset': 'tapestry.n.02'}, {'name': 'tarp', 'id': 1064, 'frequency': 'f', 'synset': 'tarpaulin.n.01'}, {'name': 'tartan', 'id': 1065, 'frequency': 'c', 'synset': 'tartan.n.01'}, {'name': 'tassel', 'id': 1066, 'frequency': 'c', 'synset': 'tassel.n.01'}, {'name': 'tea_bag', 'id': 1067, 'frequency': 'c', 'synset': 'tea_bag.n.01'}, {'name': 'teacup', 'id': 1068, 'frequency': 'c', 'synset': 'teacup.n.02'}, {'name': 'teakettle', 'id': 1069, 'frequency': 'c', 'synset': 'teakettle.n.01'}, {'name': 'teapot', 'id': 1070, 'frequency': 'f', 'synset': 'teapot.n.01'}, {'name': 'teddy_bear', 'id': 1071, 'frequency': 'f', 'synset': 'teddy.n.01'}, {'name': 'telephone', 'id': 1072, 'frequency': 'f', 'synset': 'telephone.n.01'}, {'name': 'telephone_booth', 'id': 1073, 'frequency': 'c', 'synset': 'telephone_booth.n.01'}, {'name': 'telephone_pole', 'id': 1074, 'frequency': 'f', 'synset': 'telephone_pole.n.01'}, {'name': 'telephoto_lens', 'id': 1075, 'frequency': 'r', 'synset': 'telephoto_lens.n.01'}, {'name': 'television_camera', 'id': 1076, 'frequency': 'c', 'synset': 'television_camera.n.01'}, {'name': 'television_set', 'id': 1077, 'frequency': 'f', 'synset': 'television_receiver.n.01'}, {'name': 'tennis_ball', 'id': 1078, 'frequency': 'f', 'synset': 'tennis_ball.n.01'}, {'name': 'tennis_racket', 'id': 1079, 'frequency': 'f', 'synset': 'tennis_racket.n.01'}, {'name': 'tequila', 'id': 1080, 'frequency': 'r', 'synset': 'tequila.n.01'}, {'name': 'thermometer', 'id': 1081, 'frequency': 'c', 'synset': 'thermometer.n.01'}, {'name': 'thermos_bottle', 'id': 1082, 'frequency': 'c', 'synset': 'thermos.n.01'}, {'name': 'thermostat', 'id': 1083, 'frequency': 'f', 'synset': 'thermostat.n.01'}, {'name': 'thimble', 'id': 1084, 'frequency': 'r', 'synset': 'thimble.n.02'}, {'name': 'thread', 'id': 1085, 'frequency': 'c', 'synset': 'thread.n.01'}, {'name': 'thumbtack', 'id': 1086, 'frequency': 'c', 'synset': 'thumbtack.n.01'}, {'name': 'tiara', 'id': 1087, 'frequency': 'c', 'synset': 'tiara.n.01'}, {'name': 'tiger', 'id': 1088, 'frequency': 'c', 'synset': 'tiger.n.02'}, {'name': 'tights_(clothing)', 'id': 1089, 'frequency': 'c', 'synset': 'tights.n.01'}, {'name': 'timer', 'id': 1090, 'frequency': 'c', 'synset': 'timer.n.01'}, {'name': 'tinfoil', 'id': 1091, 'frequency': 'f', 'synset': 'tinfoil.n.01'}, {'name': 'tinsel', 'id': 1092, 'frequency': 'c', 'synset': 'tinsel.n.01'}, {'name': 'tissue_paper', 'id': 1093, 'frequency': 'f', 'synset': 'tissue.n.02'}, {'name': 'toast_(food)', 'id': 1094, 'frequency': 'c', 'synset': 'toast.n.01'}, {'name': 'toaster', 'id': 1095, 'frequency': 'f', 'synset': 'toaster.n.02'}, {'name': 'toaster_oven', 'id': 1096, 'frequency': 'f', 'synset': 'toaster_oven.n.01'}, {'name': 'toilet', 'id': 1097, 'frequency': 'f', 'synset': 'toilet.n.02'}, {'name': 'toilet_tissue', 'id': 1098, 'frequency': 'f', 'synset': 'toilet_tissue.n.01'}, {'name': 'tomato', 'id': 1099, 'frequency': 'f', 'synset': 'tomato.n.01'}, {'name': 'tongs', 'id': 1100, 'frequency': 'f', 'synset': 'tongs.n.01'}, {'name': 'toolbox', 'id': 1101, 'frequency': 'c', 'synset': 'toolbox.n.01'}, {'name': 'toothbrush', 'id': 1102, 'frequency': 'f', 'synset': 'toothbrush.n.01'}, {'name': 'toothpaste', 'id': 1103, 'frequency': 'f', 'synset': 'toothpaste.n.01'}, {'name': 'toothpick', 'id': 1104, 'frequency': 'f', 'synset': 'toothpick.n.01'}, {'name': 'cover', 'id': 1105, 'frequency': 'f', 'synset': 'top.n.09'}, {'name': 'tortilla', 'id': 1106, 'frequency': 'c', 'synset': 'tortilla.n.01'}, {'name': 'tow_truck', 'id': 1107, 'frequency': 'c', 'synset': 'tow_truck.n.01'}, {'name': 'towel', 'id': 1108, 'frequency': 'f', 'synset': 'towel.n.01'}, {'name': 'towel_rack', 'id': 1109, 'frequency': 'f', 'synset': 'towel_rack.n.01'}, {'name': 'toy', 'id': 1110, 'frequency': 'f', 'synset': 'toy.n.03'}, {'name': 'tractor_(farm_equipment)', 'id': 1111, 'frequency': 'c', 'synset': 'tractor.n.01'}, {'name': 'traffic_light', 'id': 1112, 'frequency': 'f', 'synset': 'traffic_light.n.01'}, {'name': 'dirt_bike', 'id': 1113, 'frequency': 'c', 'synset': 'trail_bike.n.01'}, {'name': 'trailer_truck', 'id': 1114, 'frequency': 'f', 'synset': 'trailer_truck.n.01'}, {'name': 'train_(railroad_vehicle)', 'id': 1115, 'frequency': 'f', 'synset': 'train.n.01'}, {'name': 'trampoline', 'id': 1116, 'frequency': 'r', 'synset': 'trampoline.n.01'}, {'name': 'tray', 'id': 1117, 'frequency': 'f', 'synset': 'tray.n.01'}, {'name': 'trench_coat', 'id': 1118, 'frequency': 'r', 'synset': 'trench_coat.n.01'}, {'name': 'triangle_(musical_instrument)', 'id': 1119, 'frequency': 'r', 'synset': 'triangle.n.05'}, {'name': 'tricycle', 'id': 1120, 'frequency': 'c', 'synset': 'tricycle.n.01'}, {'name': 'tripod', 'id': 1121, 'frequency': 'f', 'synset': 'tripod.n.01'}, {'name': 'trousers', 'id': 1122, 'frequency': 'f', 'synset': 'trouser.n.01'}, {'name': 'truck', 'id': 1123, 'frequency': 'f', 'synset': 'truck.n.01'}, {'name': 'truffle_(chocolate)', 'id': 1124, 'frequency': 'r', 'synset': 'truffle.n.03'}, {'name': 'trunk', 'id': 1125, 'frequency': 'c', 'synset': 'trunk.n.02'}, {'name': 'vat', 'id': 1126, 'frequency': 'r', 'synset': 'tub.n.02'}, {'name': 'turban', 'id': 1127, 'frequency': 'c', 'synset': 'turban.n.01'}, {'name': 'turkey_(food)', 'id': 1128, 'frequency': 'c', 'synset': 'turkey.n.04'}, {'name': 'turnip', 'id': 1129, 'frequency': 'r', 'synset': 'turnip.n.01'}, {'name': 'turtle', 'id': 1130, 'frequency': 'c', 'synset': 'turtle.n.02'}, {'name': 'turtleneck_(clothing)', 'id': 1131, 'frequency': 'c', 'synset': 'turtleneck.n.01'}, {'name': 'typewriter', 'id': 1132, 'frequency': 'c', 'synset': 'typewriter.n.01'}, {'name': 'umbrella', 'id': 1133, 'frequency': 'f', 'synset': 'umbrella.n.01'}, {'name': 'underwear', 'id': 1134, 'frequency': 'f', 'synset': 'underwear.n.01'}, {'name': 'unicycle', 'id': 1135, 'frequency': 'r', 'synset': 'unicycle.n.01'}, {'name': 'urinal', 'id': 1136, 'frequency': 'f', 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{'name': 'water_bottle', 'id': 1162, 'frequency': 'f', 'synset': 'water_bottle.n.01'}, {'name': 'water_cooler', 'id': 1163, 'frequency': 'c', 'synset': 'water_cooler.n.01'}, {'name': 'water_faucet', 'id': 1164, 'frequency': 'c', 'synset': 'water_faucet.n.01'}, {'name': 'water_heater', 'id': 1165, 'frequency': 'r', 'synset': 'water_heater.n.01'}, {'name': 'water_jug', 'id': 1166, 'frequency': 'c', 'synset': 'water_jug.n.01'}, {'name': 'water_gun', 'id': 1167, 'frequency': 'r', 'synset': 'water_pistol.n.01'}, {'name': 'water_scooter', 'id': 1168, 'frequency': 'c', 'synset': 'water_scooter.n.01'}, {'name': 'water_ski', 'id': 1169, 'frequency': 'c', 'synset': 'water_ski.n.01'}, {'name': 'water_tower', 'id': 1170, 'frequency': 'c', 'synset': 'water_tower.n.01'}, {'name': 'watering_can', 'id': 1171, 'frequency': 'c', 'synset': 'watering_can.n.01'}, {'name': 'watermelon', 'id': 1172, 'frequency': 'f', 'synset': 'watermelon.n.02'}, {'name': 'weathervane', 'id': 1173, 'frequency': 'f', 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'wristlet', 'id': 1198, 'frequency': 'f', 'synset': 'wristlet.n.01'}, {'name': 'yacht', 'id': 1199, 'frequency': 'c', 'synset': 'yacht.n.01'}, {'name': 'yogurt', 'id': 1200, 'frequency': 'c', 'synset': 'yogurt.n.01'}, {'name': 'yoke_(animal_equipment)', 'id': 1201, 'frequency': 'c', 'synset': 'yoke.n.07'}, {'name': 'zebra', 'id': 1202, 'frequency': 'f', 'synset': 'zebra.n.01'}, {'name': 'zucchini', 'id': 1203, 'frequency': 'c', 'synset': 'zucchini.n.02'}, {'id': 1204, 'synset': 'organism.n.01', 'name': 'organism'}, {'id': 1205, 'synset': 'benthos.n.02', 'name': 'benthos'}, {'id': 1206, 'synset': 'heterotroph.n.01', 'name': 'heterotroph'}, {'id': 1207, 'synset': 'cell.n.02', 'name': 'cell'}, {'id': 1208, 'synset': 'animal.n.01', 'name': 'animal'}, {'id': 1209, 'synset': 'plant.n.02', 'name': 'plant'}, {'id': 1210, 'synset': 'food.n.01', 'name': 'food'}, {'id': 1211, 'synset': 'artifact.n.01', 'name': 'artifact'}, {'id': 1212, 'synset': 'hop.n.01', 'name': 'hop'}, {'id': 1213, 'synset': 'check-in.n.01', 'name': 'check-in'}, {'id': 1214, 'synset': 'dressage.n.01', 'name': 'dressage'}, {'id': 1215, 'synset': 'curvet.n.01', 'name': 'curvet'}, {'id': 1216, 'synset': 'piaffe.n.01', 'name': 'piaffe'}, {'id': 1217, 'synset': 'funambulism.n.01', 'name': 'funambulism'}, {'id': 1218, 'synset': 'rock_climbing.n.01', 'name': 'rock_climbing'}, {'id': 1219, 'synset': 'contact_sport.n.01', 'name': 'contact_sport'}, {'id': 1220, 'synset': 'outdoor_sport.n.01', 'name': 'outdoor_sport'}, {'id': 1221, 'synset': 'gymnastics.n.01', 'name': 'gymnastics'}, {'id': 1222, 'synset': 'acrobatics.n.01', 'name': 'acrobatics'}, {'id': 1223, 'synset': 'track_and_field.n.01', 'name': 'track_and_field'}, {'id': 1224, 'synset': 'track.n.11', 'name': 'track'}, {'id': 1225, 'synset': 'jumping.n.01', 'name': 'jumping'}, {'id': 1226, 'synset': 'broad_jump.n.02', 'name': 'broad_jump'}, {'id': 1227, 'synset': 'high_jump.n.02', 'name': 'high_jump'}, {'id': 1228, 'synset': 'fosbury_flop.n.01', 'name': 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'jackknife.n.02', 'name': 'jackknife'}, {'id': 1245, 'synset': 'swan_dive.n.01', 'name': 'swan_dive'}, {'id': 1246, 'synset': 'skin_diving.n.01', 'name': 'skin_diving'}, {'id': 1247, 'synset': 'scuba_diving.n.01', 'name': 'scuba_diving'}, {'id': 1248, 'synset': 'snorkeling.n.01', 'name': 'snorkeling'}, {'id': 1249, 'synset': 'surfing.n.01', 'name': 'surfing'}, {'id': 1250, 'synset': 'water-skiing.n.01', 'name': 'water-skiing'}, {'id': 1251, 'synset': 'rowing.n.01', 'name': 'rowing'}, {'id': 1252, 'synset': 'sculling.n.01', 'name': 'sculling'}, {'id': 1253, 'synset': 'boxing.n.01', 'name': 'boxing'}, {'id': 1254, 'synset': 'professional_boxing.n.01', 'name': 'professional_boxing'}, {'id': 1255, 'synset': 'in-fighting.n.02', 'name': 'in-fighting'}, {'id': 1256, 'synset': 'fight.n.05', 'name': 'fight'}, {'id': 1257, 'synset': 'rope-a-dope.n.01', 'name': 'rope-a-dope'}, {'id': 1258, 'synset': 'spar.n.03', 'name': 'spar'}, {'id': 1259, 'synset': 'archery.n.01', 'name': 'archery'}, {'id': 1260, 'synset': 'sledding.n.01', 'name': 'sledding'}, {'id': 1261, 'synset': 'tobogganing.n.01', 'name': 'tobogganing'}, {'id': 1262, 'synset': 'luging.n.01', 'name': 'luging'}, {'id': 1263, 'synset': 'bobsledding.n.01', 'name': 'bobsledding'}, {'id': 1264, 'synset': 'wrestling.n.02', 'name': 'wrestling'}, {'id': 1265, 'synset': 'greco-roman_wrestling.n.01', 'name': 'Greco-Roman_wrestling'}, {'id': 1266, 'synset': 'professional_wrestling.n.01', 'name': 'professional_wrestling'}, {'id': 1267, 'synset': 'sumo.n.01', 'name': 'sumo'}, {'id': 1268, 'synset': 'skating.n.01', 'name': 'skating'}, {'id': 1269, 'synset': 'ice_skating.n.01', 'name': 'ice_skating'}, {'id': 1270, 'synset': 'figure_skating.n.01', 'name': 'figure_skating'}, {'id': 1271, 'synset': 'rollerblading.n.01', 'name': 'rollerblading'}, {'id': 1272, 'synset': 'roller_skating.n.01', 'name': 'roller_skating'}, {'id': 1273, 'synset': 'skateboarding.n.01', 'name': 'skateboarding'}, {'id': 1274, 'synset': 'speed_skating.n.01', 'name': 'speed_skating'}, {'id': 1275, 'synset': 'racing.n.01', 'name': 'racing'}, {'id': 1276, 'synset': 'auto_racing.n.01', 'name': 'auto_racing'}, {'id': 1277, 'synset': 'boat_racing.n.01', 'name': 'boat_racing'}, {'id': 1278, 'synset': 'hydroplane_racing.n.01', 'name': 'hydroplane_racing'}, {'id': 1279, 'synset': 'camel_racing.n.01', 'name': 'camel_racing'}, {'id': 1280, 'synset': 'greyhound_racing.n.01', 'name': 'greyhound_racing'}, {'id': 1281, 'synset': 'horse_racing.n.01', 'name': 'horse_racing'}, {'id': 1282, 'synset': 'riding.n.01', 'name': 'riding'}, {'id': 1283, 'synset': 'equestrian_sport.n.01', 'name': 'equestrian_sport'}, {'id': 1284, 'synset': 'pony-trekking.n.01', 'name': 'pony-trekking'}, {'id': 1285, 'synset': 'showjumping.n.01', 'name': 'showjumping'}, {'id': 1286, 'synset': 'cross-country_riding.n.01', 'name': 'cross-country_riding'}, {'id': 1287, 'synset': 'cycling.n.01', 'name': 'cycling'}, {'id': 1288, 'synset': 'bicycling.n.01', 'name': 'bicycling'}, {'id': 1289, 'synset': 'motorcycling.n.01', 'name': 'motorcycling'}, {'id': 1290, 'synset': 'dune_cycling.n.01', 'name': 'dune_cycling'}, {'id': 1291, 'synset': 'blood_sport.n.01', 'name': 'blood_sport'}, {'id': 1292, 'synset': 'bullfighting.n.01', 'name': 'bullfighting'}, {'id': 1293, 'synset': 'cockfighting.n.01', 'name': 'cockfighting'}, {'id': 1294, 'synset': 'hunt.n.08', 'name': 'hunt'}, {'id': 1295, 'synset': 'battue.n.01', 'name': 'battue'}, {'id': 1296, 'synset': 'beagling.n.01', 'name': 'beagling'}, {'id': 1297, 'synset': 'coursing.n.01', 'name': 'coursing'}, {'id': 1298, 'synset': 'deer_hunting.n.01', 'name': 'deer_hunting'}, {'id': 1299, 'synset': 'ducking.n.01', 'name': 'ducking'}, {'id': 1300, 'synset': 'fox_hunting.n.01', 'name': 'fox_hunting'}, {'id': 1301, 'synset': 'pigsticking.n.01', 'name': 'pigsticking'}, {'id': 1302, 'synset': 'fishing.n.01', 'name': 'fishing'}, {'id': 1303, 'synset': 'angling.n.01', 'name': 'angling'}, {'id': 1304, 'synset': 'fly-fishing.n.01', 'name': 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{'id': 1320, 'synset': 'medal_play.n.01', 'name': 'medal_play'}, {'id': 1321, 'synset': 'match_play.n.01', 'name': 'match_play'}, {'id': 1322, 'synset': 'miniature_golf.n.01', 'name': 'miniature_golf'}, {'id': 1323, 'synset': 'croquet.n.01', 'name': 'croquet'}, {'id': 1324, 'synset': 'quoits.n.01', 'name': 'quoits'}, {'id': 1325, 'synset': 'shuffleboard.n.01', 'name': 'shuffleboard'}, {'id': 1326, 'synset': 'field_game.n.01', 'name': 'field_game'}, {'id': 1327, 'synset': 'field_hockey.n.01', 'name': 'field_hockey'}, {'id': 1328, 'synset': 'shinny.n.01', 'name': 'shinny'}, {'id': 1329, 'synset': 'football.n.01', 'name': 'football'}, {'id': 1330, 'synset': 'american_football.n.01', 'name': 'American_football'}, {'id': 1331, 'synset': 'professional_football.n.01', 'name': 'professional_football'}, {'id': 1332, 'synset': 'touch_football.n.01', 'name': 'touch_football'}, {'id': 1333, 'synset': 'hurling.n.01', 'name': 'hurling'}, {'id': 1334, 'synset': 'rugby.n.01', 'name': 'rugby'}, {'id': 1335, 'synset': 'ball_game.n.01', 'name': 'ball_game'}, {'id': 1336, 'synset': 'baseball.n.01', 'name': 'baseball'}, {'id': 1337, 'synset': 'ball.n.11', 'name': 'ball'}, {'id': 1338, 'synset': 'professional_baseball.n.01', 'name': 'professional_baseball'}, {'id': 1339, 'synset': 'hardball.n.02', 'name': 'hardball'}, {'id': 1340, 'synset': 'perfect_game.n.01', 'name': 'perfect_game'}, {'id': 1341, 'synset': 'no-hit_game.n.01', 'name': 'no-hit_game'}, {'id': 1342, 'synset': 'one-hitter.n.01', 'name': 'one-hitter'}, {'id': 1343, 'synset': 'two-hitter.n.01', 'name': 'two-hitter'}, {'id': 1344, 'synset': 'three-hitter.n.01', 'name': 'three-hitter'}, {'id': 1345, 'synset': 'four-hitter.n.01', 'name': 'four-hitter'}, {'id': 1346, 'synset': 'five-hitter.n.01', 'name': 'five-hitter'}, {'id': 1347, 'synset': 'softball.n.02', 'name': 'softball'}, {'id': 1348, 'synset': 'rounders.n.01', 'name': 'rounders'}, {'id': 1349, 'synset': 'stickball.n.01', 'name': 'stickball'}, {'id': 1350, 'synset': 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'camping.n.01', 'name': 'camping'}, {'id': 1383, 'synset': 'pest.n.04', 'name': 'pest'}, {'id': 1384, 'synset': 'critter.n.01', 'name': 'critter'}, {'id': 1385, 'synset': 'creepy-crawly.n.01', 'name': 'creepy-crawly'}, {'id': 1386, 'synset': 'darter.n.02', 'name': 'darter'}, {'id': 1387, 'synset': 'peeper.n.03', 'name': 'peeper'}, {'id': 1388, 'synset': 'homeotherm.n.01', 'name': 'homeotherm'}, {'id': 1389, 'synset': 'poikilotherm.n.01', 'name': 'poikilotherm'}, {'id': 1390, 'synset': 'range_animal.n.01', 'name': 'range_animal'}, {'id': 1391, 'synset': 'scavenger.n.03', 'name': 'scavenger'}, {'id': 1392, 'synset': 'bottom-feeder.n.02', 'name': 'bottom-feeder'}, {'id': 1393, 'synset': 'bottom-feeder.n.01', 'name': 'bottom-feeder'}, {'id': 1394, 'synset': 'work_animal.n.01', 'name': 'work_animal'}, {'id': 1395, 'synset': 'beast_of_burden.n.01', 'name': 'beast_of_burden'}, {'id': 1396, 'synset': 'draft_animal.n.01', 'name': 'draft_animal'}, {'id': 1397, 'synset': 'pack_animal.n.01', 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'herbivore.n.01', 'name': 'herbivore'}, {'id': 1432, 'synset': 'insectivore.n.02', 'name': 'insectivore'}, {'id': 1433, 'synset': 'acrodont.n.01', 'name': 'acrodont'}, {'id': 1434, 'synset': 'pleurodont.n.01', 'name': 'pleurodont'}, {'id': 1435, 'synset': 'microorganism.n.01', 'name': 'microorganism'}, {'id': 1436, 'synset': 'monohybrid.n.01', 'name': 'monohybrid'}, {'id': 1437, 'synset': 'arbovirus.n.01', 'name': 'arbovirus'}, {'id': 1438, 'synset': 'adenovirus.n.01', 'name': 'adenovirus'}, {'id': 1439, 'synset': 'arenavirus.n.01', 'name': 'arenavirus'}, {'id': 1440, 'synset': 'marburg_virus.n.01', 'name': 'Marburg_virus'}, {'id': 1441, 'synset': 'arenaviridae.n.01', 'name': 'Arenaviridae'}, {'id': 1442, 'synset': 'vesiculovirus.n.01', 'name': 'vesiculovirus'}, {'id': 1443, 'synset': 'reoviridae.n.01', 'name': 'Reoviridae'}, {'id': 1444, 'synset': 'variola_major.n.02', 'name': 'variola_major'}, {'id': 1445, 'synset': 'viroid.n.01', 'name': 'viroid'}, {'id': 1446, 'synset': 'coliphage.n.01', 'name': 'coliphage'}, {'id': 1447, 'synset': 'paramyxovirus.n.01', 'name': 'paramyxovirus'}, {'id': 1448, 'synset': 'poliovirus.n.01', 'name': 'poliovirus'}, {'id': 1449, 'synset': 'herpes.n.02', 'name': 'herpes'}, {'id': 1450, 'synset': 'herpes_simplex_1.n.01', 'name': 'herpes_simplex_1'}, {'id': 1451, 'synset': 'herpes_zoster.n.02', 'name': 'herpes_zoster'}, {'id': 1452, 'synset': 'herpes_varicella_zoster.n.01', 'name': 'herpes_varicella_zoster'}, {'id': 1453, 'synset': 'cytomegalovirus.n.01', 'name': 'cytomegalovirus'}, {'id': 1454, 'synset': 'varicella_zoster_virus.n.01', 'name': 'varicella_zoster_virus'}, {'id': 1455, 'synset': 'polyoma.n.01', 'name': 'polyoma'}, {'id': 1456, 'synset': 'lyssavirus.n.01', 'name': 'lyssavirus'}, {'id': 1457, 'synset': 'reovirus.n.01', 'name': 'reovirus'}, {'id': 1458, 'synset': 'rotavirus.n.01', 'name': 'rotavirus'}, {'id': 1459, 'synset': 'moneran.n.01', 'name': 'moneran'}, {'id': 1460, 'synset': 'archaebacteria.n.01', 'name': 'archaebacteria'}, {'id': 1461, 'synset': 'bacteroid.n.01', 'name': 'bacteroid'}, {'id': 1462, 'synset': 'bacillus_anthracis.n.01', 'name': 'Bacillus_anthracis'}, {'id': 1463, 'synset': 'yersinia_pestis.n.01', 'name': 'Yersinia_pestis'}, {'id': 1464, 'synset': 'brucella.n.01', 'name': 'Brucella'}, {'id': 1465, 'synset': 'spirillum.n.02', 'name': 'spirillum'}, {'id': 1466, 'synset': 'botulinus.n.01', 'name': 'botulinus'}, {'id': 1467, 'synset': 'clostridium_perfringens.n.01', 'name': 'clostridium_perfringens'}, {'id': 1468, 'synset': 'cyanobacteria.n.01', 'name': 'cyanobacteria'}, {'id': 1469, 'synset': 'trichodesmium.n.01', 'name': 'trichodesmium'}, {'id': 1470, 'synset': 'nitric_bacteria.n.01', 'name': 'nitric_bacteria'}, {'id': 1471, 'synset': 'spirillum.n.01', 'name': 'spirillum'}, {'id': 1472, 'synset': 'francisella.n.01', 'name': 'Francisella'}, {'id': 1473, 'synset': 'gonococcus.n.01', 'name': 'gonococcus'}, {'id': 1474, 'synset': 'corynebacterium_diphtheriae.n.01', 'name': 'Corynebacterium_diphtheriae'}, {'id': 1475, 'synset': 'enteric_bacteria.n.01', 'name': 'enteric_bacteria'}, {'id': 1476, 'synset': 'klebsiella.n.01', 'name': 'klebsiella'}, {'id': 1477, 'synset': 'salmonella_typhimurium.n.01', 'name': 'Salmonella_typhimurium'}, {'id': 1478, 'synset': 'typhoid_bacillus.n.01', 'name': 'typhoid_bacillus'}, {'id': 1479, 'synset': 'nitrate_bacterium.n.01', 'name': 'nitrate_bacterium'}, {'id': 1480, 'synset': 'nitrite_bacterium.n.01', 'name': 'nitrite_bacterium'}, {'id': 1481, 'synset': 'actinomycete.n.01', 'name': 'actinomycete'}, {'id': 1482, 'synset': 'streptomyces.n.01', 'name': 'streptomyces'}, {'id': 1483, 'synset': 'streptomyces_erythreus.n.01', 'name': 'Streptomyces_erythreus'}, {'id': 1484, 'synset': 'streptomyces_griseus.n.01', 'name': 'Streptomyces_griseus'}, {'id': 1485, 'synset': 'tubercle_bacillus.n.01', 'name': 'tubercle_bacillus'}, {'id': 1486, 'synset': 'pus-forming_bacteria.n.01', 'name': 'pus-forming_bacteria'}, {'id': 1487, 'synset': 'streptobacillus.n.01', 'name': 'streptobacillus'}, {'id': 1488, 'synset': 'myxobacteria.n.01', 'name': 'myxobacteria'}, {'id': 1489, 'synset': 'staphylococcus.n.01', 'name': 'staphylococcus'}, {'id': 1490, 'synset': 'diplococcus.n.01', 'name': 'diplococcus'}, {'id': 1491, 'synset': 'pneumococcus.n.01', 'name': 'pneumococcus'}, {'id': 1492, 'synset': 'streptococcus.n.01', 'name': 'streptococcus'}, {'id': 1493, 'synset': 'spirochete.n.01', 'name': 'spirochete'}, {'id': 1494, 'synset': 'planktonic_algae.n.01', 'name': 'planktonic_algae'}, {'id': 1495, 'synset': 'zooplankton.n.01', 'name': 'zooplankton'}, {'id': 1496, 'synset': 'parasite.n.01', 'name': 'parasite'}, {'id': 1497, 'synset': 'endoparasite.n.01', 'name': 'endoparasite'}, {'id': 1498, 'synset': 'ectoparasite.n.01', 'name': 'ectoparasite'}, {'id': 1499, 'synset': 'pathogen.n.01', 'name': 'pathogen'}, {'id': 1500, 'synset': 'commensal.n.01', 'name': 'commensal'}, {'id': 1501, 'synset': 'myrmecophile.n.01', 'name': 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{'id': 1518, 'synset': 'golden_algae.n.01', 'name': 'golden_algae'}, {'id': 1519, 'synset': 'yellow-green_algae.n.01', 'name': 'yellow-green_algae'}, {'id': 1520, 'synset': 'brown_algae.n.01', 'name': 'brown_algae'}, {'id': 1521, 'synset': 'kelp.n.01', 'name': 'kelp'}, {'id': 1522, 'synset': 'fucoid.n.02', 'name': 'fucoid'}, {'id': 1523, 'synset': 'fucoid.n.01', 'name': 'fucoid'}, {'id': 1524, 'synset': 'fucus.n.01', 'name': 'fucus'}, {'id': 1525, 'synset': 'bladderwrack.n.01', 'name': 'bladderwrack'}, {'id': 1526, 'synset': 'green_algae.n.01', 'name': 'green_algae'}, {'id': 1527, 'synset': 'pond_scum.n.01', 'name': 'pond_scum'}, {'id': 1528, 'synset': 'chlorella.n.01', 'name': 'chlorella'}, {'id': 1529, 'synset': 'stonewort.n.01', 'name': 'stonewort'}, {'id': 1530, 'synset': 'desmid.n.01', 'name': 'desmid'}, {'id': 1531, 'synset': 'sea_moss.n.02', 'name': 'sea_moss'}, {'id': 1532, 'synset': 'eukaryote.n.01', 'name': 'eukaryote'}, {'id': 1533, 'synset': 'prokaryote.n.01', 'name': 'prokaryote'}, {'id': 1534, 'synset': 'zooid.n.01', 'name': 'zooid'}, {'id': 1535, 'synset': 'leishmania.n.01', 'name': 'Leishmania'}, {'id': 1536, 'synset': 'zoomastigote.n.01', 'name': 'zoomastigote'}, {'id': 1537, 'synset': 'polymastigote.n.01', 'name': 'polymastigote'}, {'id': 1538, 'synset': 'costia.n.01', 'name': 'costia'}, {'id': 1539, 'synset': 'giardia.n.01', 'name': 'giardia'}, {'id': 1540, 'synset': 'cryptomonad.n.01', 'name': 'cryptomonad'}, {'id': 1541, 'synset': 'sporozoan.n.01', 'name': 'sporozoan'}, {'id': 1542, 'synset': 'sporozoite.n.01', 'name': 'sporozoite'}, {'id': 1543, 'synset': 'trophozoite.n.01', 'name': 'trophozoite'}, {'id': 1544, 'synset': 'merozoite.n.01', 'name': 'merozoite'}, {'id': 1545, 'synset': 'coccidium.n.01', 'name': 'coccidium'}, {'id': 1546, 'synset': 'gregarine.n.01', 'name': 'gregarine'}, {'id': 1547, 'synset': 'plasmodium.n.02', 'name': 'plasmodium'}, {'id': 1548, 'synset': 'leucocytozoan.n.01', 'name': 'leucocytozoan'}, {'id': 1549, 'synset': 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1565, 'synset': 'rudd.n.01', 'name': 'rudd'}, {'id': 1566, 'synset': 'minnow.n.01', 'name': 'minnow'}, {'id': 1567, 'synset': 'gudgeon.n.02', 'name': 'gudgeon'}, {'id': 1568, 'synset': 'crucian_carp.n.01', 'name': 'crucian_carp'}, {'id': 1569, 'synset': 'electric_eel.n.01', 'name': 'electric_eel'}, {'id': 1570, 'synset': 'catostomid.n.01', 'name': 'catostomid'}, {'id': 1571, 'synset': 'buffalo_fish.n.01', 'name': 'buffalo_fish'}, {'id': 1572, 'synset': 'black_buffalo.n.01', 'name': 'black_buffalo'}, {'id': 1573, 'synset': 'hog_sucker.n.01', 'name': 'hog_sucker'}, {'id': 1574, 'synset': 'redhorse.n.01', 'name': 'redhorse'}, {'id': 1575, 'synset': 'cyprinodont.n.01', 'name': 'cyprinodont'}, {'id': 1576, 'synset': 'killifish.n.01', 'name': 'killifish'}, {'id': 1577, 'synset': 'mummichog.n.01', 'name': 'mummichog'}, {'id': 1578, 'synset': 'striped_killifish.n.01', 'name': 'striped_killifish'}, {'id': 1579, 'synset': 'rivulus.n.01', 'name': 'rivulus'}, {'id': 1580, 'synset': 'flagfish.n.01', 'name': 'flagfish'}, {'id': 1581, 'synset': 'swordtail.n.01', 'name': 'swordtail'}, {'id': 1582, 'synset': 'guppy.n.01', 'name': 'guppy'}, {'id': 1583, 'synset': 'topminnow.n.01', 'name': 'topminnow'}, {'id': 1584, 'synset': 'mosquitofish.n.01', 'name': 'mosquitofish'}, {'id': 1585, 'synset': 'platy.n.01', 'name': 'platy'}, {'id': 1586, 'synset': 'mollie.n.01', 'name': 'mollie'}, {'id': 1587, 'synset': 'squirrelfish.n.02', 'name': 'squirrelfish'}, {'id': 1588, 'synset': 'reef_squirrelfish.n.01', 'name': 'reef_squirrelfish'}, {'id': 1589, 'synset': 'deepwater_squirrelfish.n.01', 'name': 'deepwater_squirrelfish'}, {'id': 1590, 'synset': 'holocentrus_ascensionis.n.01', 'name': 'Holocentrus_ascensionis'}, {'id': 1591, 'synset': 'soldierfish.n.01', 'name': 'soldierfish'}, {'id': 1592, 'synset': 'anomalops.n.01', 'name': 'anomalops'}, {'id': 1593, 'synset': 'flashlight_fish.n.01', 'name': 'flashlight_fish'}, {'id': 1594, 'synset': 'john_dory.n.01', 'name': 'John_Dory'}, {'id': 1595, 'synset': 'boarfish.n.02', 'name': 'boarfish'}, {'id': 1596, 'synset': 'boarfish.n.01', 'name': 'boarfish'}, {'id': 1597, 'synset': 'cornetfish.n.01', 'name': 'cornetfish'}, {'id': 1598, 'synset': 'stickleback.n.01', 'name': 'stickleback'}, {'id': 1599, 'synset': 'three-spined_stickleback.n.01', 'name': 'three-spined_stickleback'}, {'id': 1600, 'synset': 'ten-spined_stickleback.n.01', 'name': 'ten-spined_stickleback'}, {'id': 1601, 'synset': 'pipefish.n.01', 'name': 'pipefish'}, {'id': 1602, 'synset': 'dwarf_pipefish.n.01', 'name': 'dwarf_pipefish'}, {'id': 1603, 'synset': 'deepwater_pipefish.n.01', 'name': 'deepwater_pipefish'}, {'id': 1604, 'synset': 'snipefish.n.01', 'name': 'snipefish'}, {'id': 1605, 'synset': 'shrimpfish.n.01', 'name': 'shrimpfish'}, {'id': 1606, 'synset': 'trumpetfish.n.01', 'name': 'trumpetfish'}, {'id': 1607, 'synset': 'pellicle.n.01', 'name': 'pellicle'}, {'id': 1608, 'synset': 'embryo.n.02', 'name': 'embryo'}, {'id': 1609, 'synset': 'fetus.n.01', 'name': 'fetus'}, {'id': 1610, 'synset': 'abortus.n.01', 'name': 'abortus'}, {'id': 1611, 'synset': 'spawn.n.01', 'name': 'spawn'}, {'id': 1612, 'synset': 'blastula.n.01', 'name': 'blastula'}, {'id': 1613, 'synset': 'blastocyst.n.01', 'name': 'blastocyst'}, {'id': 1614, 'synset': 'gastrula.n.01', 'name': 'gastrula'}, {'id': 1615, 'synset': 'morula.n.01', 'name': 'morula'}, {'id': 1616, 'synset': 'yolk.n.02', 'name': 'yolk'}, {'id': 1617, 'synset': 'chordate.n.01', 'name': 'chordate'}, {'id': 1618, 'synset': 'cephalochordate.n.01', 'name': 'cephalochordate'}, {'id': 1619, 'synset': 'lancelet.n.01', 'name': 'lancelet'}, {'id': 1620, 'synset': 'tunicate.n.01', 'name': 'tunicate'}, {'id': 1621, 'synset': 'ascidian.n.01', 'name': 'ascidian'}, {'id': 1622, 'synset': 'sea_squirt.n.01', 'name': 'sea_squirt'}, {'id': 1623, 'synset': 'salp.n.01', 'name': 'salp'}, {'id': 1624, 'synset': 'doliolum.n.01', 'name': 'doliolum'}, {'id': 1625, 'synset': 'larvacean.n.01', 'name': 'larvacean'}, {'id': 1626, 'synset': 'appendicularia.n.01', 'name': 'appendicularia'}, {'id': 1627, 'synset': 'ascidian_tadpole.n.01', 'name': 'ascidian_tadpole'}, {'id': 1628, 'synset': 'vertebrate.n.01', 'name': 'vertebrate'}, {'id': 1629, 'synset': 'amniota.n.01', 'name': 'Amniota'}, {'id': 1630, 'synset': 'amniote.n.01', 'name': 'amniote'}, {'id': 1631, 'synset': 'aquatic_vertebrate.n.01', 'name': 'aquatic_vertebrate'}, {'id': 1632, 'synset': 'jawless_vertebrate.n.01', 'name': 'jawless_vertebrate'}, {'id': 1633, 'synset': 'ostracoderm.n.01', 'name': 'ostracoderm'}, {'id': 1634, 'synset': 'heterostracan.n.01', 'name': 'heterostracan'}, {'id': 1635, 'synset': 'anaspid.n.01', 'name': 'anaspid'}, {'id': 1636, 'synset': 'conodont.n.02', 'name': 'conodont'}, {'id': 1637, 'synset': 'cyclostome.n.01', 'name': 'cyclostome'}, {'id': 1638, 'synset': 'lamprey.n.01', 'name': 'lamprey'}, {'id': 1639, 'synset': 'sea_lamprey.n.01', 'name': 'sea_lamprey'}, {'id': 1640, 'synset': 'hagfish.n.01', 'name': 'hagfish'}, {'id': 1641, 'synset': 'myxine_glutinosa.n.01', 'name': 'Myxine_glutinosa'}, {'id': 1642, 'synset': 'eptatretus.n.01', 'name': 'eptatretus'}, {'id': 1643, 'synset': 'gnathostome.n.01', 'name': 'gnathostome'}, {'id': 1644, 'synset': 'placoderm.n.01', 'name': 'placoderm'}, {'id': 1645, 'synset': 'cartilaginous_fish.n.01', 'name': 'cartilaginous_fish'}, {'id': 1646, 'synset': 'holocephalan.n.01', 'name': 'holocephalan'}, {'id': 1647, 'synset': 'chimaera.n.03', 'name': 'chimaera'}, {'id': 1648, 'synset': 'rabbitfish.n.01', 'name': 'rabbitfish'}, {'id': 1649, 'synset': 'elasmobranch.n.01', 'name': 'elasmobranch'}, {'id': 1650, 'synset': 'cow_shark.n.01', 'name': 'cow_shark'}, {'id': 1651, 'synset': 'mackerel_shark.n.01', 'name': 'mackerel_shark'}, {'id': 1652, 'synset': 'porbeagle.n.01', 'name': 'porbeagle'}, {'id': 1653, 'synset': 'mako.n.01', 'name': 'mako'}, {'id': 1654, 'synset': 'shortfin_mako.n.01', 'name': 'shortfin_mako'}, {'id': 1655, 'synset': 'longfin_mako.n.01', 'name': 'longfin_mako'}, {'id': 1656, 'synset': 'bonito_shark.n.01', 'name': 'bonito_shark'}, {'id': 1657, 'synset': 'great_white_shark.n.01', 'name': 'great_white_shark'}, {'id': 1658, 'synset': 'basking_shark.n.01', 'name': 'basking_shark'}, {'id': 1659, 'synset': 'thresher.n.02', 'name': 'thresher'}, {'id': 1660, 'synset': 'carpet_shark.n.01', 'name': 'carpet_shark'}, {'id': 1661, 'synset': 'nurse_shark.n.01', 'name': 'nurse_shark'}, {'id': 1662, 'synset': 'sand_tiger.n.01', 'name': 'sand_tiger'}, {'id': 1663, 'synset': 'whale_shark.n.01', 'name': 'whale_shark'}, {'id': 1664, 'synset': 'requiem_shark.n.01', 'name': 'requiem_shark'}, {'id': 1665, 'synset': 'bull_shark.n.01', 'name': 'bull_shark'}, {'id': 1666, 'synset': 'sandbar_shark.n.02', 'name': 'sandbar_shark'}, {'id': 1667, 'synset': 'blacktip_shark.n.01', 'name': 'blacktip_shark'}, {'id': 1668, 'synset': 'whitetip_shark.n.02', 'name': 'whitetip_shark'}, {'id': 1669, 'synset': 'dusky_shark.n.01', 'name': 'dusky_shark'}, {'id': 1670, 'synset': 'lemon_shark.n.01', 'name': 'lemon_shark'}, {'id': 1671, 'synset': 'blue_shark.n.01', 'name': 'blue_shark'}, {'id': 1672, 'synset': 'tiger_shark.n.01', 'name': 'tiger_shark'}, {'id': 1673, 'synset': 'soupfin_shark.n.01', 'name': 'soupfin_shark'}, {'id': 1674, 'synset': 'dogfish.n.02', 'name': 'dogfish'}, {'id': 1675, 'synset': 'smooth_dogfish.n.01', 'name': 'smooth_dogfish'}, {'id': 1676, 'synset': 'smoothhound.n.01', 'name': 'smoothhound'}, {'id': 1677, 'synset': 'american_smooth_dogfish.n.01', 'name': 'American_smooth_dogfish'}, {'id': 1678, 'synset': 'florida_smoothhound.n.01', 'name': 'Florida_smoothhound'}, {'id': 1679, 'synset': 'whitetip_shark.n.01', 'name': 'whitetip_shark'}, {'id': 1680, 'synset': 'spiny_dogfish.n.01', 'name': 'spiny_dogfish'}, {'id': 1681, 'synset': 'atlantic_spiny_dogfish.n.01', 'name': 'Atlantic_spiny_dogfish'}, {'id': 1682, 'synset': 'pacific_spiny_dogfish.n.01', 'name': 'Pacific_spiny_dogfish'}, {'id': 1683, 'synset': 'hammerhead.n.03', 'name': 'hammerhead'}, {'id': 1684, 'synset': 'smooth_hammerhead.n.01', 'name': 'smooth_hammerhead'}, {'id': 1685, 'synset': 'smalleye_hammerhead.n.01', 'name': 'smalleye_hammerhead'}, {'id': 1686, 'synset': 'shovelhead.n.01', 'name': 'shovelhead'}, {'id': 1687, 'synset': 'angel_shark.n.01', 'name': 'angel_shark'}, {'id': 1688, 'synset': 'ray.n.07', 'name': 'ray'}, {'id': 1689, 'synset': 'electric_ray.n.01', 'name': 'electric_ray'}, {'id': 1690, 'synset': 'sawfish.n.01', 'name': 'sawfish'}, {'id': 1691, 'synset': 'smalltooth_sawfish.n.01', 'name': 'smalltooth_sawfish'}, {'id': 1692, 'synset': 'guitarfish.n.01', 'name': 'guitarfish'}, {'id': 1693, 'synset': 'stingray.n.01', 'name': 'stingray'}, {'id': 1694, 'synset': 'roughtail_stingray.n.01', 'name': 'roughtail_stingray'}, {'id': 1695, 'synset': 'butterfly_ray.n.01', 'name': 'butterfly_ray'}, {'id': 1696, 'synset': 'eagle_ray.n.01', 'name': 'eagle_ray'}, {'id': 1697, 'synset': 'spotted_eagle_ray.n.01', 'name': 'spotted_eagle_ray'}, {'id': 1698, 'synset': 'cownose_ray.n.01', 'name': 'cownose_ray'}, {'id': 1699, 'synset': 'manta.n.02', 'name': 'manta'}, {'id': 1700, 'synset': 'atlantic_manta.n.01', 'name': 'Atlantic_manta'}, {'id': 1701, 'synset': 'devil_ray.n.01', 'name': 'devil_ray'}, {'id': 1702, 'synset': 'skate.n.02', 'name': 'skate'}, {'id': 1703, 'synset': 'grey_skate.n.01', 'name': 'grey_skate'}, {'id': 1704, 'synset': 'little_skate.n.01', 'name': 'little_skate'}, {'id': 1705, 'synset': 'thorny_skate.n.01', 'name': 'thorny_skate'}, {'id': 1706, 'synset': 'barndoor_skate.n.01', 'name': 'barndoor_skate'}, {'id': 1707, 'synset': 'dickeybird.n.01', 'name': 'dickeybird'}, {'id': 1708, 'synset': 'fledgling.n.02', 'name': 'fledgling'}, {'id': 1709, 'synset': 'nestling.n.01', 'name': 'nestling'}, {'id': 1710, 'synset': 'cock.n.05', 'name': 'cock'}, {'id': 1711, 'synset': 'gamecock.n.01', 'name': 'gamecock'}, {'id': 1712, 'synset': 'hen.n.02', 'name': 'hen'}, {'id': 1713, 'synset': 'nester.n.02', 'name': 'nester'}, {'id': 1714, 'synset': 'night_bird.n.01', 'name': 'night_bird'}, {'id': 1715, 'synset': 'night_raven.n.02', 'name': 'night_raven'}, {'id': 1716, 'synset': 'bird_of_passage.n.02', 'name': 'bird_of_passage'}, {'id': 1717, 'synset': 'archaeopteryx.n.01', 'name': 'archaeopteryx'}, {'id': 1718, 'synset': 'archaeornis.n.01', 'name': 'archaeornis'}, {'id': 1719, 'synset': 'ratite.n.01', 'name': 'ratite'}, {'id': 1720, 'synset': 'carinate.n.01', 'name': 'carinate'}, {'id': 1721, 'synset': 'cassowary.n.01', 'name': 'cassowary'}, {'id': 1722, 'synset': 'emu.n.02', 'name': 'emu'}, {'id': 1723, 'synset': 'kiwi.n.04', 'name': 'kiwi'}, {'id': 1724, 'synset': 'rhea.n.03', 'name': 'rhea'}, {'id': 1725, 'synset': 'rhea.n.02', 'name': 'rhea'}, {'id': 1726, 'synset': 'elephant_bird.n.01', 'name': 'elephant_bird'}, {'id': 1727, 'synset': 'moa.n.01', 'name': 'moa'}, {'id': 1728, 'synset': 'passerine.n.01', 'name': 'passerine'}, {'id': 1729, 'synset': 'nonpasserine_bird.n.01', 'name': 'nonpasserine_bird'}, {'id': 1730, 'synset': 'oscine.n.01', 'name': 'oscine'}, {'id': 1731, 'synset': 'songbird.n.01', 'name': 'songbird'}, {'id': 1732, 'synset': 'honey_eater.n.01', 'name': 'honey_eater'}, {'id': 1733, 'synset': 'accentor.n.01', 'name': 'accentor'}, {'id': 1734, 'synset': 'hedge_sparrow.n.01', 'name': 'hedge_sparrow'}, {'id': 1735, 'synset': 'lark.n.03', 'name': 'lark'}, {'id': 1736, 'synset': 'skylark.n.01', 'name': 'skylark'}, {'id': 1737, 'synset': 'wagtail.n.01', 'name': 'wagtail'}, {'id': 1738, 'synset': 'pipit.n.01', 'name': 'pipit'}, {'id': 1739, 'synset': 'meadow_pipit.n.01', 'name': 'meadow_pipit'}, {'id': 1740, 'synset': 'finch.n.01', 'name': 'finch'}, {'id': 1741, 'synset': 'chaffinch.n.01', 'name': 'chaffinch'}, {'id': 1742, 'synset': 'brambling.n.01', 'name': 'brambling'}, {'id': 1743, 'synset': 'goldfinch.n.02', 'name': 'goldfinch'}, {'id': 1744, 'synset': 'linnet.n.02', 'name': 'linnet'}, {'id': 1745, 'synset': 'siskin.n.01', 'name': 'siskin'}, {'id': 1746, 'synset': 'red_siskin.n.01', 'name': 'red_siskin'}, {'id': 1747, 'synset': 'redpoll.n.02', 'name': 'redpoll'}, {'id': 1748, 'synset': 'redpoll.n.01', 'name': 'redpoll'}, {'id': 1749, 'synset': 'new_world_goldfinch.n.01', 'name': 'New_World_goldfinch'}, {'id': 1750, 'synset': 'pine_siskin.n.01', 'name': 'pine_siskin'}, {'id': 1751, 'synset': 'house_finch.n.01', 'name': 'house_finch'}, {'id': 1752, 'synset': 'purple_finch.n.01', 'name': 'purple_finch'}, {'id': 1753, 'synset': 'canary.n.04', 'name': 'canary'}, {'id': 1754, 'synset': 'common_canary.n.01', 'name': 'common_canary'}, {'id': 1755, 'synset': 'serin.n.01', 'name': 'serin'}, {'id': 1756, 'synset': 'crossbill.n.01', 'name': 'crossbill'}, {'id': 1757, 'synset': 'bullfinch.n.02', 'name': 'bullfinch'}, {'id': 1758, 'synset': 'junco.n.01', 'name': 'junco'}, {'id': 1759, 'synset': 'dark-eyed_junco.n.01', 'name': 'dark-eyed_junco'}, {'id': 1760, 'synset': 'new_world_sparrow.n.01', 'name': 'New_World_sparrow'}, {'id': 1761, 'synset': 'vesper_sparrow.n.01', 'name': 'vesper_sparrow'}, {'id': 1762, 'synset': 'white-throated_sparrow.n.01', 'name': 'white-throated_sparrow'}, {'id': 1763, 'synset': 'white-crowned_sparrow.n.01', 'name': 'white-crowned_sparrow'}, {'id': 1764, 'synset': 'chipping_sparrow.n.01', 'name': 'chipping_sparrow'}, {'id': 1765, 'synset': 'field_sparrow.n.01', 'name': 'field_sparrow'}, {'id': 1766, 'synset': 'tree_sparrow.n.02', 'name': 'tree_sparrow'}, {'id': 1767, 'synset': 'song_sparrow.n.01', 'name': 'song_sparrow'}, {'id': 1768, 'synset': 'swamp_sparrow.n.01', 'name': 'swamp_sparrow'}, {'id': 1769, 'synset': 'bunting.n.02', 'name': 'bunting'}, {'id': 1770, 'synset': 'indigo_bunting.n.01', 'name': 'indigo_bunting'}, {'id': 1771, 'synset': 'ortolan.n.01', 'name': 'ortolan'}, {'id': 1772, 'synset': 'reed_bunting.n.01', 'name': 'reed_bunting'}, {'id': 1773, 'synset': 'yellowhammer.n.02', 'name': 'yellowhammer'}, {'id': 1774, 'synset': 'yellow-breasted_bunting.n.01', 'name': 'yellow-breasted_bunting'}, {'id': 1775, 'synset': 'snow_bunting.n.01', 'name': 'snow_bunting'}, {'id': 1776, 'synset': 'honeycreeper.n.02', 'name': 'honeycreeper'}, {'id': 1777, 'synset': 'banana_quit.n.01', 'name': 'banana_quit'}, {'id': 1778, 'synset': 'sparrow.n.01', 'name': 'sparrow'}, {'id': 1779, 'synset': 'english_sparrow.n.01', 'name': 'English_sparrow'}, {'id': 1780, 'synset': 'tree_sparrow.n.01', 'name': 'tree_sparrow'}, {'id': 1781, 'synset': 'grosbeak.n.01', 'name': 'grosbeak'}, {'id': 1782, 'synset': 'evening_grosbeak.n.01', 'name': 'evening_grosbeak'}, {'id': 1783, 'synset': 'hawfinch.n.01', 'name': 'hawfinch'}, {'id': 1784, 'synset': 'pine_grosbeak.n.01', 'name': 'pine_grosbeak'}, {'id': 1785, 'synset': 'cardinal.n.04', 'name': 'cardinal'}, {'id': 1786, 'synset': 'pyrrhuloxia.n.01', 'name': 'pyrrhuloxia'}, {'id': 1787, 'synset': 'towhee.n.01', 'name': 'towhee'}, {'id': 1788, 'synset': 'chewink.n.01', 'name': 'chewink'}, {'id': 1789, 'synset': 'green-tailed_towhee.n.01', 'name': 'green-tailed_towhee'}, {'id': 1790, 'synset': 'weaver.n.02', 'name': 'weaver'}, {'id': 1791, 'synset': 'baya.n.01', 'name': 'baya'}, {'id': 1792, 'synset': 'whydah.n.01', 'name': 'whydah'}, {'id': 1793, 'synset': 'java_sparrow.n.01', 'name': 'Java_sparrow'}, {'id': 1794, 'synset': 'avadavat.n.01', 'name': 'avadavat'}, {'id': 1795, 'synset': 'grassfinch.n.01', 'name': 'grassfinch'}, {'id': 1796, 'synset': 'zebra_finch.n.01', 'name': 'zebra_finch'}, {'id': 1797, 'synset': 'honeycreeper.n.01', 'name': 'honeycreeper'}, {'id': 1798, 'synset': 'lyrebird.n.01', 'name': 'lyrebird'}, {'id': 1799, 'synset': 'scrubbird.n.01', 'name': 'scrubbird'}, {'id': 1800, 'synset': 'broadbill.n.04', 'name': 'broadbill'}, {'id': 1801, 'synset': 'tyrannid.n.01', 'name': 'tyrannid'}, {'id': 1802, 'synset': 'new_world_flycatcher.n.01', 'name': 'New_World_flycatcher'}, {'id': 1803, 'synset': 'kingbird.n.01', 'name': 'kingbird'}, {'id': 1804, 'synset': 'arkansas_kingbird.n.01', 'name': 'Arkansas_kingbird'}, {'id': 1805, 'synset': "cassin's_kingbird.n.01", 'name': "Cassin's_kingbird"}, {'id': 1806, 'synset': 'eastern_kingbird.n.01', 'name': 'eastern_kingbird'}, {'id': 1807, 'synset': 'grey_kingbird.n.01', 'name': 'grey_kingbird'}, {'id': 1808, 'synset': 'pewee.n.01', 'name': 'pewee'}, {'id': 1809, 'synset': 'western_wood_pewee.n.01', 'name': 'western_wood_pewee'}, {'id': 1810, 'synset': 'phoebe.n.03', 'name': 'phoebe'}, {'id': 1811, 'synset': 'vermillion_flycatcher.n.01', 'name': 'vermillion_flycatcher'}, {'id': 1812, 'synset': 'cotinga.n.01', 'name': 'cotinga'}, {'id': 1813, 'synset': 'cock_of_the_rock.n.02', 'name': 'cock_of_the_rock'}, {'id': 1814, 'synset': 'cock_of_the_rock.n.01', 'name': 'cock_of_the_rock'}, {'id': 1815, 'synset': 'manakin.n.03', 'name': 'manakin'}, {'id': 1816, 'synset': 'bellbird.n.01', 'name': 'bellbird'}, {'id': 1817, 'synset': 'umbrella_bird.n.01', 'name': 'umbrella_bird'}, {'id': 1818, 'synset': 'ovenbird.n.02', 'name': 'ovenbird'}, {'id': 1819, 'synset': 'antbird.n.01', 'name': 'antbird'}, {'id': 1820, 'synset': 'ant_thrush.n.01', 'name': 'ant_thrush'}, {'id': 1821, 'synset': 'ant_shrike.n.01', 'name': 'ant_shrike'}, {'id': 1822, 'synset': 'spotted_antbird.n.01', 'name': 'spotted_antbird'}, {'id': 1823, 'synset': 'woodhewer.n.01', 'name': 'woodhewer'}, {'id': 1824, 'synset': 'pitta.n.01', 'name': 'pitta'}, {'id': 1825, 'synset': 'scissortail.n.01', 'name': 'scissortail'}, {'id': 1826, 'synset': 'old_world_flycatcher.n.01', 'name': 'Old_World_flycatcher'}, {'id': 1827, 'synset': 'spotted_flycatcher.n.01', 'name': 'spotted_flycatcher'}, {'id': 1828, 'synset': 'thickhead.n.01', 'name': 'thickhead'}, {'id': 1829, 'synset': 'thrush.n.03', 'name': 'thrush'}, {'id': 1830, 'synset': 'missel_thrush.n.01', 'name': 'missel_thrush'}, {'id': 1831, 'synset': 'song_thrush.n.01', 'name': 'song_thrush'}, {'id': 1832, 'synset': 'fieldfare.n.01', 'name': 'fieldfare'}, {'id': 1833, 'synset': 'redwing.n.02', 'name': 'redwing'}, {'id': 1834, 'synset': 'blackbird.n.02', 'name': 'blackbird'}, {'id': 1835, 'synset': 'ring_ouzel.n.01', 'name': 'ring_ouzel'}, {'id': 1836, 'synset': 'robin.n.02', 'name': 'robin'}, {'id': 1837, 'synset': 'clay-colored_robin.n.01', 'name': 'clay-colored_robin'}, {'id': 1838, 'synset': 'hermit_thrush.n.01', 'name': 'hermit_thrush'}, {'id': 1839, 'synset': 'veery.n.01', 'name': 'veery'}, {'id': 1840, 'synset': 'wood_thrush.n.01', 'name': 'wood_thrush'}, {'id': 1841, 'synset': 'nightingale.n.01', 'name': 'nightingale'}, {'id': 1842, 'synset': 'thrush_nightingale.n.01', 'name': 'thrush_nightingale'}, {'id': 1843, 'synset': 'bulbul.n.01', 'name': 'bulbul'}, {'id': 1844, 'synset': 'old_world_chat.n.01', 'name': 'Old_World_chat'}, {'id': 1845, 'synset': 'stonechat.n.01', 'name': 'stonechat'}, {'id': 1846, 'synset': 'whinchat.n.01', 'name': 'whinchat'}, {'id': 1847, 'synset': 'solitaire.n.03', 'name': 'solitaire'}, {'id': 1848, 'synset': 'redstart.n.02', 'name': 'redstart'}, {'id': 1849, 'synset': 'wheatear.n.01', 'name': 'wheatear'}, {'id': 1850, 'synset': 'bluebird.n.02', 'name': 'bluebird'}, {'id': 1851, 'synset': 'robin.n.01', 'name': 'robin'}, {'id': 1852, 'synset': 'bluethroat.n.01', 'name': 'bluethroat'}, {'id': 1853, 'synset': 'warbler.n.02', 'name': 'warbler'}, {'id': 1854, 'synset': 'gnatcatcher.n.01', 'name': 'gnatcatcher'}, {'id': 1855, 'synset': 'kinglet.n.01', 'name': 'kinglet'}, {'id': 1856, 'synset': 'goldcrest.n.01', 'name': 'goldcrest'}, {'id': 1857, 'synset': 'gold-crowned_kinglet.n.01', 'name': 'gold-crowned_kinglet'}, {'id': 1858, 'synset': 'ruby-crowned_kinglet.n.01', 'name': 'ruby-crowned_kinglet'}, {'id': 1859, 'synset': 'old_world_warbler.n.01', 'name': 'Old_World_warbler'}, {'id': 1860, 'synset': 'blackcap.n.04', 'name': 'blackcap'}, {'id': 1861, 'synset': 'greater_whitethroat.n.01', 'name': 'greater_whitethroat'}, {'id': 1862, 'synset': 'lesser_whitethroat.n.01', 'name': 'lesser_whitethroat'}, {'id': 1863, 'synset': 'wood_warbler.n.02', 'name': 'wood_warbler'}, {'id': 1864, 'synset': 'sedge_warbler.n.01', 'name': 'sedge_warbler'}, {'id': 1865, 'synset': 'wren_warbler.n.01', 'name': 'wren_warbler'}, {'id': 1866, 'synset': 'tailorbird.n.01', 'name': 'tailorbird'}, {'id': 1867, 'synset': 'babbler.n.02', 'name': 'babbler'}, {'id': 1868, 'synset': 'new_world_warbler.n.01', 'name': 'New_World_warbler'}, {'id': 1869, 'synset': 'parula_warbler.n.01', 'name': 'parula_warbler'}, {'id': 1870, 'synset': "wilson's_warbler.n.01", 'name': "Wilson's_warbler"}, {'id': 1871, 'synset': 'flycatching_warbler.n.01', 'name': 'flycatching_warbler'}, {'id': 1872, 'synset': 'american_redstart.n.01', 'name': 'American_redstart'}, {'id': 1873, 'synset': 'cape_may_warbler.n.01', 'name': 'Cape_May_warbler'}, {'id': 1874, 'synset': 'yellow_warbler.n.01', 'name': 'yellow_warbler'}, {'id': 1875, 'synset': 'blackburn.n.01', 'name': 'Blackburn'}, {'id': 1876, 'synset': "audubon's_warbler.n.01", 'name': "Audubon's_warbler"}, {'id': 1877, 'synset': 'myrtle_warbler.n.01', 'name': 'myrtle_warbler'}, {'id': 1878, 'synset': 'blackpoll.n.01', 'name': 'blackpoll'}, {'id': 1879, 'synset': 'new_world_chat.n.01', 'name': 'New_World_chat'}, {'id': 1880, 'synset': 'yellow-breasted_chat.n.01', 'name': 'yellow-breasted_chat'}, {'id': 1881, 'synset': 'ovenbird.n.01', 'name': 'ovenbird'}, {'id': 1882, 'synset': 'water_thrush.n.01', 'name': 'water_thrush'}, {'id': 1883, 'synset': 'yellowthroat.n.01', 'name': 'yellowthroat'}, {'id': 1884, 'synset': 'common_yellowthroat.n.01', 'name': 'common_yellowthroat'}, {'id': 1885, 'synset': 'riflebird.n.01', 'name': 'riflebird'}, {'id': 1886, 'synset': 'new_world_oriole.n.01', 'name': 'New_World_oriole'}, {'id': 1887, 'synset': 'northern_oriole.n.01', 'name': 'northern_oriole'}, {'id': 1888, 'synset': 'baltimore_oriole.n.01', 'name': 'Baltimore_oriole'}, {'id': 1889, 'synset': "bullock's_oriole.n.01", 'name': "Bullock's_oriole"}, {'id': 1890, 'synset': 'orchard_oriole.n.01', 'name': 'orchard_oriole'}, {'id': 1891, 'synset': 'meadowlark.n.01', 'name': 'meadowlark'}, {'id': 1892, 'synset': 'eastern_meadowlark.n.01', 'name': 'eastern_meadowlark'}, {'id': 1893, 'synset': 'western_meadowlark.n.01', 'name': 'western_meadowlark'}, {'id': 1894, 'synset': 'cacique.n.01', 'name': 'cacique'}, {'id': 1895, 'synset': 'bobolink.n.01', 'name': 'bobolink'}, {'id': 1896, 'synset': 'new_world_blackbird.n.01', 'name': 'New_World_blackbird'}, {'id': 1897, 'synset': 'grackle.n.02', 'name': 'grackle'}, {'id': 1898, 'synset': 'purple_grackle.n.01', 'name': 'purple_grackle'}, {'id': 1899, 'synset': 'rusty_blackbird.n.01', 'name': 'rusty_blackbird'}, {'id': 1900, 'synset': 'cowbird.n.01', 'name': 'cowbird'}, {'id': 1901, 'synset': 'red-winged_blackbird.n.01', 'name': 'red-winged_blackbird'}, {'id': 1902, 'synset': 'old_world_oriole.n.01', 'name': 'Old_World_oriole'}, {'id': 1903, 'synset': 'golden_oriole.n.01', 'name': 'golden_oriole'}, {'id': 1904, 'synset': 'fig-bird.n.01', 'name': 'fig-bird'}, {'id': 1905, 'synset': 'starling.n.01', 'name': 'starling'}, {'id': 1906, 'synset': 'common_starling.n.01', 'name': 'common_starling'}, {'id': 1907, 'synset': 'rose-colored_starling.n.01', 'name': 'rose-colored_starling'}, {'id': 1908, 'synset': 'myna.n.01', 'name': 'myna'}, {'id': 1909, 'synset': 'crested_myna.n.01', 'name': 'crested_myna'}, {'id': 1910, 'synset': 'hill_myna.n.01', 'name': 'hill_myna'}, {'id': 1911, 'synset': 'corvine_bird.n.01', 'name': 'corvine_bird'}, {'id': 1912, 'synset': 'american_crow.n.01', 'name': 'American_crow'}, {'id': 1913, 'synset': 'raven.n.01', 'name': 'raven'}, {'id': 1914, 'synset': 'rook.n.02', 'name': 'rook'}, {'id': 1915, 'synset': 'jackdaw.n.01', 'name': 'jackdaw'}, {'id': 1916, 'synset': 'chough.n.01', 'name': 'chough'}, {'id': 1917, 'synset': 'jay.n.02', 'name': 'jay'}, {'id': 1918, 'synset': 'old_world_jay.n.01', 'name': 'Old_World_jay'}, {'id': 1919, 'synset': 'common_european_jay.n.01', 'name': 'common_European_jay'}, {'id': 1920, 'synset': 'new_world_jay.n.01', 'name': 'New_World_jay'}, {'id': 1921, 'synset': 'blue_jay.n.01', 'name': 'blue_jay'}, {'id': 1922, 'synset': 'canada_jay.n.01', 'name': 'Canada_jay'}, {'id': 1923, 'synset': 'rocky_mountain_jay.n.01', 'name': 'Rocky_Mountain_jay'}, {'id': 1924, 'synset': 'nutcracker.n.03', 'name': 'nutcracker'}, {'id': 1925, 'synset': 'common_nutcracker.n.01', 'name': 'common_nutcracker'}, {'id': 1926, 'synset': "clark's_nutcracker.n.01", 'name': "Clark's_nutcracker"}, {'id': 1927, 'synset': 'magpie.n.01', 'name': 'magpie'}, {'id': 1928, 'synset': 'european_magpie.n.01', 'name': 'European_magpie'}, {'id': 1929, 'synset': 'american_magpie.n.01', 'name': 'American_magpie'}, {'id': 1930, 'synset': 'australian_magpie.n.01', 'name': 'Australian_magpie'}, {'id': 1931, 'synset': 'butcherbird.n.02', 'name': 'butcherbird'}, {'id': 1932, 'synset': 'currawong.n.01', 'name': 'currawong'}, {'id': 1933, 'synset': 'piping_crow.n.01', 'name': 'piping_crow'}, {'id': 1934, 'synset': 'wren.n.02', 'name': 'wren'}, {'id': 1935, 'synset': 'winter_wren.n.01', 'name': 'winter_wren'}, {'id': 1936, 'synset': 'house_wren.n.01', 'name': 'house_wren'}, {'id': 1937, 'synset': 'marsh_wren.n.01', 'name': 'marsh_wren'}, {'id': 1938, 'synset': 'long-billed_marsh_wren.n.01', 'name': 'long-billed_marsh_wren'}, {'id': 1939, 'synset': 'sedge_wren.n.01', 'name': 'sedge_wren'}, {'id': 1940, 'synset': 'rock_wren.n.02', 'name': 'rock_wren'}, {'id': 1941, 'synset': 'carolina_wren.n.01', 'name': 'Carolina_wren'}, {'id': 1942, 'synset': 'cactus_wren.n.01', 'name': 'cactus_wren'}, {'id': 1943, 'synset': 'mockingbird.n.01', 'name': 'mockingbird'}, {'id': 1944, 'synset': 'blue_mockingbird.n.01', 'name': 'blue_mockingbird'}, {'id': 1945, 'synset': 'catbird.n.02', 'name': 'catbird'}, {'id': 1946, 'synset': 'thrasher.n.02', 'name': 'thrasher'}, {'id': 1947, 'synset': 'brown_thrasher.n.01', 'name': 'brown_thrasher'}, {'id': 1948, 'synset': 'new_zealand_wren.n.01', 'name': 'New_Zealand_wren'}, {'id': 1949, 'synset': 'rock_wren.n.01', 'name': 'rock_wren'}, {'id': 1950, 'synset': 'rifleman_bird.n.01', 'name': 'rifleman_bird'}, {'id': 1951, 'synset': 'creeper.n.03', 'name': 'creeper'}, {'id': 1952, 'synset': 'brown_creeper.n.01', 'name': 'brown_creeper'}, {'id': 1953, 'synset': 'european_creeper.n.01', 'name': 'European_creeper'}, {'id': 1954, 'synset': 'wall_creeper.n.01', 'name': 'wall_creeper'}, {'id': 1955, 'synset': 'european_nuthatch.n.01', 'name': 'European_nuthatch'}, {'id': 1956, 'synset': 'red-breasted_nuthatch.n.01', 'name': 'red-breasted_nuthatch'}, {'id': 1957, 'synset': 'white-breasted_nuthatch.n.01', 'name': 'white-breasted_nuthatch'}, {'id': 1958, 'synset': 'titmouse.n.01', 'name': 'titmouse'}, {'id': 1959, 'synset': 'chickadee.n.01', 'name': 'chickadee'}, {'id': 1960, 'synset': 'black-capped_chickadee.n.01', 'name': 'black-capped_chickadee'}, {'id': 1961, 'synset': 'tufted_titmouse.n.01', 'name': 'tufted_titmouse'}, {'id': 1962, 'synset': 'carolina_chickadee.n.01', 'name': 'Carolina_chickadee'}, {'id': 1963, 'synset': 'blue_tit.n.01', 'name': 'blue_tit'}, {'id': 1964, 'synset': 'bushtit.n.01', 'name': 'bushtit'}, {'id': 1965, 'synset': 'wren-tit.n.01', 'name': 'wren-tit'}, {'id': 1966, 'synset': 'verdin.n.01', 'name': 'verdin'}, {'id': 1967, 'synset': 'fairy_bluebird.n.01', 'name': 'fairy_bluebird'}, {'id': 1968, 'synset': 'swallow.n.03', 'name': 'swallow'}, {'id': 1969, 'synset': 'barn_swallow.n.01', 'name': 'barn_swallow'}, {'id': 1970, 'synset': 'cliff_swallow.n.01', 'name': 'cliff_swallow'}, {'id': 1971, 'synset': 'tree_swallow.n.02', 'name': 'tree_swallow'}, {'id': 1972, 'synset': 'white-bellied_swallow.n.01', 'name': 'white-bellied_swallow'}, {'id': 1973, 'synset': 'martin.n.05', 'name': 'martin'}, {'id': 1974, 'synset': 'house_martin.n.01', 'name': 'house_martin'}, {'id': 1975, 'synset': 'bank_martin.n.01', 'name': 'bank_martin'}, {'id': 1976, 'synset': 'purple_martin.n.01', 'name': 'purple_martin'}, {'id': 1977, 'synset': 'wood_swallow.n.01', 'name': 'wood_swallow'}, {'id': 1978, 'synset': 'tanager.n.01', 'name': 'tanager'}, {'id': 1979, 'synset': 'scarlet_tanager.n.01', 'name': 'scarlet_tanager'}, {'id': 1980, 'synset': 'western_tanager.n.01', 'name': 'western_tanager'}, {'id': 1981, 'synset': 'summer_tanager.n.01', 'name': 'summer_tanager'}, {'id': 1982, 'synset': 'hepatic_tanager.n.01', 'name': 'hepatic_tanager'}, {'id': 1983, 'synset': 'shrike.n.01', 'name': 'shrike'}, {'id': 1984, 'synset': 'butcherbird.n.01', 'name': 'butcherbird'}, {'id': 1985, 'synset': 'european_shrike.n.01', 'name': 'European_shrike'}, {'id': 1986, 'synset': 'northern_shrike.n.01', 'name': 'northern_shrike'}, {'id': 1987, 'synset': 'white-rumped_shrike.n.01', 'name': 'white-rumped_shrike'}, {'id': 1988, 'synset': 'loggerhead_shrike.n.01', 'name': 'loggerhead_shrike'}, {'id': 1989, 'synset': 'migrant_shrike.n.01', 'name': 'migrant_shrike'}, {'id': 1990, 'synset': 'bush_shrike.n.01', 'name': 'bush_shrike'}, {'id': 1991, 'synset': 'black-fronted_bush_shrike.n.01', 'name': 'black-fronted_bush_shrike'}, {'id': 1992, 'synset': 'bowerbird.n.01', 'name': 'bowerbird'}, {'id': 1993, 'synset': 'satin_bowerbird.n.01', 'name': 'satin_bowerbird'}, {'id': 1994, 'synset': 'great_bowerbird.n.01', 'name': 'great_bowerbird'}, {'id': 1995, 'synset': 'water_ouzel.n.01', 'name': 'water_ouzel'}, {'id': 1996, 'synset': 'european_water_ouzel.n.01', 'name': 'European_water_ouzel'}, {'id': 1997, 'synset': 'american_water_ouzel.n.01', 'name': 'American_water_ouzel'}, {'id': 1998, 'synset': 'vireo.n.01', 'name': 'vireo'}, {'id': 1999, 'synset': 'red-eyed_vireo.n.01', 'name': 'red-eyed_vireo'}, {'id': 2000, 'synset': 'solitary_vireo.n.01', 'name': 'solitary_vireo'}, {'id': 2001, 'synset': 'blue-headed_vireo.n.01', 'name': 'blue-headed_vireo'}, {'id': 2002, 'synset': 'waxwing.n.01', 'name': 'waxwing'}, {'id': 2003, 'synset': 'cedar_waxwing.n.01', 'name': 'cedar_waxwing'}, {'id': 2004, 'synset': 'bohemian_waxwing.n.01', 'name': 'Bohemian_waxwing'}, {'id': 2005, 'synset': 'bird_of_prey.n.01', 'name': 'bird_of_prey'}, {'id': 2006, 'synset': 'accipitriformes.n.01', 'name': 'Accipitriformes'}, {'id': 2007, 'synset': 'hawk.n.01', 'name': 'hawk'}, {'id': 2008, 'synset': 'eyas.n.01', 'name': 'eyas'}, {'id': 2009, 'synset': 'tiercel.n.01', 'name': 'tiercel'}, {'id': 2010, 'synset': 'goshawk.n.01', 'name': 'goshawk'}, {'id': 2011, 'synset': 'sparrow_hawk.n.02', 'name': 'sparrow_hawk'}, {'id': 2012, 'synset': "cooper's_hawk.n.01", 'name': "Cooper's_hawk"}, {'id': 2013, 'synset': 'chicken_hawk.n.01', 'name': 'chicken_hawk'}, {'id': 2014, 'synset': 'buteonine.n.01', 'name': 'buteonine'}, {'id': 2015, 'synset': 'redtail.n.01', 'name': 'redtail'}, {'id': 2016, 'synset': 'rough-legged_hawk.n.01', 'name': 'rough-legged_hawk'}, {'id': 2017, 'synset': 'red-shouldered_hawk.n.01', 'name': 'red-shouldered_hawk'}, {'id': 2018, 'synset': 'buzzard.n.02', 'name': 'buzzard'}, {'id': 2019, 'synset': 'honey_buzzard.n.01', 'name': 'honey_buzzard'}, {'id': 2020, 'synset': 'kite.n.04', 'name': 'kite'}, {'id': 2021, 'synset': 'black_kite.n.01', 'name': 'black_kite'}, {'id': 2022, 'synset': 'swallow-tailed_kite.n.01', 'name': 'swallow-tailed_kite'}, {'id': 2023, 'synset': 'white-tailed_kite.n.01', 'name': 'white-tailed_kite'}, {'id': 2024, 'synset': 'harrier.n.03', 'name': 'harrier'}, {'id': 2025, 'synset': 'marsh_harrier.n.01', 'name': 'marsh_harrier'}, {'id': 2026, 'synset': "montagu's_harrier.n.01", 'name': "Montagu's_harrier"}, {'id': 2027, 'synset': 'marsh_hawk.n.01', 'name': 'marsh_hawk'}, {'id': 2028, 'synset': 'harrier_eagle.n.01', 'name': 'harrier_eagle'}, {'id': 2029, 'synset': 'peregrine.n.01', 'name': 'peregrine'}, {'id': 2030, 'synset': 'falcon-gentle.n.01', 'name': 'falcon-gentle'}, {'id': 2031, 'synset': 'gyrfalcon.n.01', 'name': 'gyrfalcon'}, {'id': 2032, 'synset': 'kestrel.n.02', 'name': 'kestrel'}, {'id': 2033, 'synset': 'sparrow_hawk.n.01', 'name': 'sparrow_hawk'}, {'id': 2034, 'synset': 'pigeon_hawk.n.01', 'name': 'pigeon_hawk'}, {'id': 2035, 'synset': 'hobby.n.03', 'name': 'hobby'}, {'id': 2036, 'synset': 'caracara.n.01', 'name': 'caracara'}, {'id': 2037, 'synset': "audubon's_caracara.n.01", 'name': "Audubon's_caracara"}, {'id': 2038, 'synset': 'carancha.n.01', 'name': 'carancha'}, {'id': 2039, 'synset': 'young_bird.n.01', 'name': 'young_bird'}, {'id': 2040, 'synset': 'eaglet.n.01', 'name': 'eaglet'}, {'id': 2041, 'synset': 'harpy.n.04', 'name': 'harpy'}, {'id': 2042, 'synset': 'golden_eagle.n.01', 'name': 'golden_eagle'}, {'id': 2043, 'synset': 'tawny_eagle.n.01', 'name': 'tawny_eagle'}, {'id': 2044, 'synset': 'bald_eagle.n.01', 'name': 'bald_eagle'}, {'id': 2045, 'synset': 'sea_eagle.n.02', 'name': 'sea_eagle'}, {'id': 2046, 'synset': 'kamchatkan_sea_eagle.n.01', 'name': 'Kamchatkan_sea_eagle'}, {'id': 2047, 'synset': 'ern.n.01', 'name': 'ern'}, {'id': 2048, 'synset': 'fishing_eagle.n.01', 'name': 'fishing_eagle'}, {'id': 2049, 'synset': 'osprey.n.01', 'name': 'osprey'}, {'id': 2050, 'synset': 'aegypiidae.n.01', 'name': 'Aegypiidae'}, {'id': 2051, 'synset': 'old_world_vulture.n.01', 'name': 'Old_World_vulture'}, {'id': 2052, 'synset': 'griffon_vulture.n.01', 'name': 'griffon_vulture'}, {'id': 2053, 'synset': 'bearded_vulture.n.01', 'name': 'bearded_vulture'}, {'id': 2054, 'synset': 'egyptian_vulture.n.01', 'name': 'Egyptian_vulture'}, {'id': 2055, 'synset': 'black_vulture.n.02', 'name': 'black_vulture'}, {'id': 2056, 'synset': 'secretary_bird.n.01', 'name': 'secretary_bird'}, {'id': 2057, 'synset': 'new_world_vulture.n.01', 'name': 'New_World_vulture'}, {'id': 2058, 'synset': 'buzzard.n.01', 'name': 'buzzard'}, {'id': 2059, 'synset': 'condor.n.01', 'name': 'condor'}, {'id': 2060, 'synset': 'andean_condor.n.01', 'name': 'Andean_condor'}, {'id': 2061, 'synset': 'california_condor.n.01', 'name': 'California_condor'}, {'id': 2062, 'synset': 'black_vulture.n.01', 'name': 'black_vulture'}, {'id': 2063, 'synset': 'king_vulture.n.01', 'name': 'king_vulture'}, {'id': 2064, 'synset': 'owlet.n.01', 'name': 'owlet'}, {'id': 2065, 'synset': 'little_owl.n.01', 'name': 'little_owl'}, {'id': 2066, 'synset': 'horned_owl.n.01', 'name': 'horned_owl'}, {'id': 2067, 'synset': 'great_horned_owl.n.01', 'name': 'great_horned_owl'}, {'id': 2068, 'synset': 'great_grey_owl.n.01', 'name': 'great_grey_owl'}, {'id': 2069, 'synset': 'tawny_owl.n.01', 'name': 'tawny_owl'}, {'id': 2070, 'synset': 'barred_owl.n.01', 'name': 'barred_owl'}, {'id': 2071, 'synset': 'screech_owl.n.02', 'name': 'screech_owl'}, {'id': 2072, 'synset': 'screech_owl.n.01', 'name': 'screech_owl'}, {'id': 2073, 'synset': 'scops_owl.n.01', 'name': 'scops_owl'}, {'id': 2074, 'synset': 'spotted_owl.n.01', 'name': 'spotted_owl'}, {'id': 2075, 'synset': 'old_world_scops_owl.n.01', 'name': 'Old_World_scops_owl'}, {'id': 2076, 'synset': 'oriental_scops_owl.n.01', 'name': 'Oriental_scops_owl'}, {'id': 2077, 'synset': 'hoot_owl.n.01', 'name': 'hoot_owl'}, {'id': 2078, 'synset': 'hawk_owl.n.01', 'name': 'hawk_owl'}, {'id': 2079, 'synset': 'long-eared_owl.n.01', 'name': 'long-eared_owl'}, {'id': 2080, 'synset': 'laughing_owl.n.01', 'name': 'laughing_owl'}, {'id': 2081, 'synset': 'barn_owl.n.01', 'name': 'barn_owl'}, {'id': 2082, 'synset': 'amphibian.n.03', 'name': 'amphibian'}, {'id': 2083, 'synset': 'ichyostega.n.01', 'name': 'Ichyostega'}, {'id': 2084, 'synset': 'urodele.n.01', 'name': 'urodele'}, {'id': 2085, 'synset': 'salamander.n.01', 'name': 'salamander'}, {'id': 2086, 'synset': 'european_fire_salamander.n.01', 'name': 'European_fire_salamander'}, {'id': 2087, 'synset': 'spotted_salamander.n.02', 'name': 'spotted_salamander'}, {'id': 2088, 'synset': 'alpine_salamander.n.01', 'name': 'alpine_salamander'}, {'id': 2089, 'synset': 'newt.n.01', 'name': 'newt'}, {'id': 2090, 'synset': 'common_newt.n.01', 'name': 'common_newt'}, {'id': 2091, 'synset': 'red_eft.n.01', 'name': 'red_eft'}, {'id': 2092, 'synset': 'pacific_newt.n.01', 'name': 'Pacific_newt'}, {'id': 2093, 'synset': 'rough-skinned_newt.n.01', 'name': 'rough-skinned_newt'}, {'id': 2094, 'synset': 'california_newt.n.01', 'name': 'California_newt'}, {'id': 2095, 'synset': 'eft.n.01', 'name': 'eft'}, {'id': 2096, 'synset': 'ambystomid.n.01', 'name': 'ambystomid'}, {'id': 2097, 'synset': 'mole_salamander.n.01', 'name': 'mole_salamander'}, {'id': 2098, 'synset': 'spotted_salamander.n.01', 'name': 'spotted_salamander'}, {'id': 2099, 'synset': 'tiger_salamander.n.01', 'name': 'tiger_salamander'}, {'id': 2100, 'synset': 'axolotl.n.01', 'name': 'axolotl'}, {'id': 2101, 'synset': 'waterdog.n.01', 'name': 'waterdog'}, {'id': 2102, 'synset': 'hellbender.n.01', 'name': 'hellbender'}, {'id': 2103, 'synset': 'giant_salamander.n.01', 'name': 'giant_salamander'}, {'id': 2104, 'synset': 'olm.n.01', 'name': 'olm'}, {'id': 2105, 'synset': 'mud_puppy.n.01', 'name': 'mud_puppy'}, {'id': 2106, 'synset': 'dicamptodon.n.01', 'name': 'dicamptodon'}, {'id': 2107, 'synset': 'pacific_giant_salamander.n.01', 'name': 'Pacific_giant_salamander'}, {'id': 2108, 'synset': 'olympic_salamander.n.01', 'name': 'olympic_salamander'}, {'id': 2109, 'synset': 'lungless_salamander.n.01', 'name': 'lungless_salamander'}, {'id': 2110, 'synset': 'eastern_red-backed_salamander.n.01', 'name': 'eastern_red-backed_salamander'}, {'id': 2111, 'synset': 'western_red-backed_salamander.n.01', 'name': 'western_red-backed_salamander'}, {'id': 2112, 'synset': 'dusky_salamander.n.01', 'name': 'dusky_salamander'}, {'id': 2113, 'synset': 'climbing_salamander.n.01', 'name': 'climbing_salamander'}, {'id': 2114, 'synset': 'arboreal_salamander.n.01', 'name': 'arboreal_salamander'}, {'id': 2115, 'synset': 'slender_salamander.n.01', 'name': 'slender_salamander'}, {'id': 2116, 'synset': 'web-toed_salamander.n.01', 'name': 'web-toed_salamander'}, {'id': 2117, 'synset': 'shasta_salamander.n.01', 'name': 'Shasta_salamander'}, {'id': 2118, 'synset': 'limestone_salamander.n.01', 'name': 'limestone_salamander'}, {'id': 2119, 'synset': 'amphiuma.n.01', 'name': 'amphiuma'}, {'id': 2120, 'synset': 'siren.n.05', 'name': 'siren'}, {'id': 2121, 'synset': 'true_frog.n.01', 'name': 'true_frog'}, {'id': 2122, 'synset': 'wood-frog.n.01', 'name': 'wood-frog'}, {'id': 2123, 'synset': 'leopard_frog.n.01', 'name': 'leopard_frog'}, {'id': 2124, 'synset': 'bullfrog.n.01', 'name': 'bullfrog'}, {'id': 2125, 'synset': 'green_frog.n.01', 'name': 'green_frog'}, {'id': 2126, 'synset': 'cascades_frog.n.01', 'name': 'cascades_frog'}, {'id': 2127, 'synset': 'goliath_frog.n.01', 'name': 'goliath_frog'}, {'id': 2128, 'synset': 'pickerel_frog.n.01', 'name': 'pickerel_frog'}, {'id': 2129, 'synset': 'tarahumara_frog.n.01', 'name': 'tarahumara_frog'}, {'id': 2130, 'synset': 'grass_frog.n.01', 'name': 'grass_frog'}, {'id': 2131, 'synset': 'leptodactylid_frog.n.01', 'name': 'leptodactylid_frog'}, {'id': 2132, 'synset': 'robber_frog.n.02', 'name': 'robber_frog'}, {'id': 2133, 'synset': 'barking_frog.n.01', 'name': 'barking_frog'}, {'id': 2134, 'synset': 'crapaud.n.01', 'name': 'crapaud'}, {'id': 2135, 'synset': 'tree_frog.n.02', 'name': 'tree_frog'}, {'id': 2136, 'synset': 'tailed_frog.n.01', 'name': 'tailed_frog'}, {'id': 2137, 'synset': 'liopelma_hamiltoni.n.01', 'name': 'Liopelma_hamiltoni'}, {'id': 2138, 'synset': 'true_toad.n.01', 'name': 'true_toad'}, {'id': 2139, 'synset': 'bufo.n.01', 'name': 'bufo'}, {'id': 2140, 'synset': 'agua.n.01', 'name': 'agua'}, {'id': 2141, 'synset': 'european_toad.n.01', 'name': 'European_toad'}, {'id': 2142, 'synset': 'natterjack.n.01', 'name': 'natterjack'}, {'id': 2143, 'synset': 'american_toad.n.01', 'name': 'American_toad'}, {'id': 2144, 'synset': 'eurasian_green_toad.n.01', 'name': 'Eurasian_green_toad'}, {'id': 2145, 'synset': 'american_green_toad.n.01', 'name': 'American_green_toad'}, {'id': 2146, 'synset': 'yosemite_toad.n.01', 'name': 'Yosemite_toad'}, {'id': 2147, 'synset': 'texas_toad.n.01', 'name': 'Texas_toad'}, {'id': 2148, 'synset': 'southwestern_toad.n.01', 'name': 'southwestern_toad'}, {'id': 2149, 'synset': 'western_toad.n.01', 'name': 'western_toad'}, {'id': 2150, 'synset': 'obstetrical_toad.n.01', 'name': 'obstetrical_toad'}, {'id': 2151, 'synset': 'midwife_toad.n.01', 'name': 'midwife_toad'}, {'id': 2152, 'synset': 'fire-bellied_toad.n.01', 'name': 'fire-bellied_toad'}, {'id': 2153, 'synset': 'spadefoot.n.01', 'name': 'spadefoot'}, {'id': 2154, 'synset': 'western_spadefoot.n.01', 'name': 'western_spadefoot'}, {'id': 2155, 'synset': 'southern_spadefoot.n.01', 'name': 'southern_spadefoot'}, {'id': 2156, 'synset': 'plains_spadefoot.n.01', 'name': 'plains_spadefoot'}, {'id': 2157, 'synset': 'tree_toad.n.01', 'name': 'tree_toad'}, {'id': 2158, 'synset': 'spring_peeper.n.01', 'name': 'spring_peeper'}, {'id': 2159, 'synset': 'pacific_tree_toad.n.01', 'name': 'Pacific_tree_toad'}, {'id': 2160, 'synset': 'canyon_treefrog.n.01', 'name': 'canyon_treefrog'}, {'id': 2161, 'synset': 'chameleon_tree_frog.n.01', 'name': 'chameleon_tree_frog'}, {'id': 2162, 'synset': 'cricket_frog.n.01', 'name': 'cricket_frog'}, {'id': 2163, 'synset': 'northern_cricket_frog.n.01', 'name': 'northern_cricket_frog'}, {'id': 2164, 'synset': 'eastern_cricket_frog.n.01', 'name': 'eastern_cricket_frog'}, {'id': 2165, 'synset': 'chorus_frog.n.01', 'name': 'chorus_frog'}, {'id': 2166, 'synset': 'lowland_burrowing_treefrog.n.01', 'name': 'lowland_burrowing_treefrog'}, {'id': 2167, 'synset': 'western_narrow-mouthed_toad.n.01', 'name': 'western_narrow-mouthed_toad'}, {'id': 2168, 'synset': 'eastern_narrow-mouthed_toad.n.01', 'name': 'eastern_narrow-mouthed_toad'}, {'id': 2169, 'synset': 'sheep_frog.n.01', 'name': 'sheep_frog'}, {'id': 2170, 'synset': 'tongueless_frog.n.01', 'name': 'tongueless_frog'}, {'id': 2171, 'synset': 'surinam_toad.n.01', 'name': 'Surinam_toad'}, {'id': 2172, 'synset': 'african_clawed_frog.n.01', 'name': 'African_clawed_frog'}, {'id': 2173, 'synset': 'south_american_poison_toad.n.01', 'name': 'South_American_poison_toad'}, {'id': 2174, 'synset': 'caecilian.n.01', 'name': 'caecilian'}, {'id': 2175, 'synset': 'reptile.n.01', 'name': 'reptile'}, {'id': 2176, 'synset': 'anapsid.n.01', 'name': 'anapsid'}, {'id': 2177, 'synset': 'diapsid.n.01', 'name': 'diapsid'}, {'id': 2178, 'synset': 'diapsida.n.01', 'name': 'Diapsida'}, {'id': 2179, 'synset': 'chelonian.n.01', 'name': 'chelonian'}, {'id': 2180, 'synset': 'sea_turtle.n.01', 'name': 'sea_turtle'}, {'id': 2181, 'synset': 'green_turtle.n.01', 'name': 'green_turtle'}, {'id': 2182, 'synset': 'loggerhead.n.02', 'name': 'loggerhead'}, {'id': 2183, 'synset': 'ridley.n.01', 'name': 'ridley'}, {'id': 2184, 'synset': 'atlantic_ridley.n.01', 'name': 'Atlantic_ridley'}, {'id': 2185, 'synset': 'pacific_ridley.n.01', 'name': 'Pacific_ridley'}, {'id': 2186, 'synset': 'hawksbill_turtle.n.01', 'name': 'hawksbill_turtle'}, {'id': 2187, 'synset': 'leatherback_turtle.n.01', 'name': 'leatherback_turtle'}, {'id': 2188, 'synset': 'snapping_turtle.n.01', 'name': 'snapping_turtle'}, {'id': 2189, 'synset': 'common_snapping_turtle.n.01', 'name': 'common_snapping_turtle'}, {'id': 2190, 'synset': 'alligator_snapping_turtle.n.01', 'name': 'alligator_snapping_turtle'}, {'id': 2191, 'synset': 'mud_turtle.n.01', 'name': 'mud_turtle'}, {'id': 2192, 'synset': 'musk_turtle.n.01', 'name': 'musk_turtle'}, {'id': 2193, 'synset': 'terrapin.n.01', 'name': 'terrapin'}, {'id': 2194, 'synset': 'diamondback_terrapin.n.01', 'name': 'diamondback_terrapin'}, {'id': 2195, 'synset': 'red-bellied_terrapin.n.01', 'name': 'red-bellied_terrapin'}, {'id': 2196, 'synset': 'slider.n.03', 'name': 'slider'}, {'id': 2197, 'synset': 'cooter.n.01', 'name': 'cooter'}, {'id': 2198, 'synset': 'box_turtle.n.01', 'name': 'box_turtle'}, {'id': 2199, 'synset': 'western_box_turtle.n.01', 'name': 'Western_box_turtle'}, {'id': 2200, 'synset': 'painted_turtle.n.01', 'name': 'painted_turtle'}, {'id': 2201, 'synset': 'tortoise.n.01', 'name': 'tortoise'}, {'id': 2202, 'synset': 'european_tortoise.n.01', 'name': 'European_tortoise'}, {'id': 2203, 'synset': 'giant_tortoise.n.01', 'name': 'giant_tortoise'}, {'id': 2204, 'synset': 'gopher_tortoise.n.01', 'name': 'gopher_tortoise'}, {'id': 2205, 'synset': 'desert_tortoise.n.01', 'name': 'desert_tortoise'}, {'id': 2206, 'synset': 'texas_tortoise.n.01', 'name': 'Texas_tortoise'}, {'id': 2207, 'synset': 'soft-shelled_turtle.n.01', 'name': 'soft-shelled_turtle'}, {'id': 2208, 'synset': 'spiny_softshell.n.01', 'name': 'spiny_softshell'}, {'id': 2209, 'synset': 'smooth_softshell.n.01', 'name': 'smooth_softshell'}, {'id': 2210, 'synset': 'tuatara.n.01', 'name': 'tuatara'}, {'id': 2211, 'synset': 'saurian.n.01', 'name': 'saurian'}, {'id': 2212, 'synset': 'gecko.n.01', 'name': 'gecko'}, {'id': 2213, 'synset': 'flying_gecko.n.01', 'name': 'flying_gecko'}, {'id': 2214, 'synset': 'banded_gecko.n.01', 'name': 'banded_gecko'}, {'id': 2215, 'synset': 'iguanid.n.01', 'name': 'iguanid'}, {'id': 2216, 'synset': 'common_iguana.n.01', 'name': 'common_iguana'}, {'id': 2217, 'synset': 'marine_iguana.n.01', 'name': 'marine_iguana'}, {'id': 2218, 'synset': 'desert_iguana.n.01', 'name': 'desert_iguana'}, {'id': 2219, 'synset': 'chuckwalla.n.01', 'name': 'chuckwalla'}, {'id': 2220, 'synset': 'zebra-tailed_lizard.n.01', 'name': 'zebra-tailed_lizard'}, {'id': 2221, 'synset': 'fringe-toed_lizard.n.01', 'name': 'fringe-toed_lizard'}, {'id': 2222, 'synset': 'earless_lizard.n.01', 'name': 'earless_lizard'}, {'id': 2223, 'synset': 'collared_lizard.n.01', 'name': 'collared_lizard'}, {'id': 2224, 'synset': 'leopard_lizard.n.01', 'name': 'leopard_lizard'}, {'id': 2225, 'synset': 'spiny_lizard.n.02', 'name': 'spiny_lizard'}, {'id': 2226, 'synset': 'fence_lizard.n.01', 'name': 'fence_lizard'}, {'id': 2227, 'synset': 'western_fence_lizard.n.01', 'name': 'western_fence_lizard'}, {'id': 2228, 'synset': 'eastern_fence_lizard.n.01', 'name': 'eastern_fence_lizard'}, {'id': 2229, 'synset': 'sagebrush_lizard.n.01', 'name': 'sagebrush_lizard'}, {'id': 2230, 'synset': 'side-blotched_lizard.n.01', 'name': 'side-blotched_lizard'}, {'id': 2231, 'synset': 'tree_lizard.n.01', 'name': 'tree_lizard'}, {'id': 2232, 'synset': 'horned_lizard.n.01', 'name': 'horned_lizard'}, {'id': 2233, 'synset': 'texas_horned_lizard.n.01', 'name': 'Texas_horned_lizard'}, {'id': 2234, 'synset': 'basilisk.n.03', 'name': 'basilisk'}, {'id': 2235, 'synset': 'american_chameleon.n.01', 'name': 'American_chameleon'}, {'id': 2236, 'synset': 'worm_lizard.n.01', 'name': 'worm_lizard'}, {'id': 2237, 'synset': 'night_lizard.n.01', 'name': 'night_lizard'}, {'id': 2238, 'synset': 'skink.n.01', 'name': 'skink'}, {'id': 2239, 'synset': 'western_skink.n.01', 'name': 'western_skink'}, {'id': 2240, 'synset': 'mountain_skink.n.01', 'name': 'mountain_skink'}, {'id': 2241, 'synset': 'teiid_lizard.n.01', 'name': 'teiid_lizard'}, {'id': 2242, 'synset': 'whiptail.n.01', 'name': 'whiptail'}, {'id': 2243, 'synset': 'racerunner.n.01', 'name': 'racerunner'}, {'id': 2244, 'synset': 'plateau_striped_whiptail.n.01', 'name': 'plateau_striped_whiptail'}, {'id': 2245, 'synset': 'chihuahuan_spotted_whiptail.n.01', 'name': 'Chihuahuan_spotted_whiptail'}, {'id': 2246, 'synset': 'western_whiptail.n.01', 'name': 'western_whiptail'}, {'id': 2247, 'synset': 'checkered_whiptail.n.01', 'name': 'checkered_whiptail'}, {'id': 2248, 'synset': 'teju.n.01', 'name': 'teju'}, {'id': 2249, 'synset': 'caiman_lizard.n.01', 'name': 'caiman_lizard'}, {'id': 2250, 'synset': 'agamid.n.01', 'name': 'agamid'}, {'id': 2251, 'synset': 'agama.n.01', 'name': 'agama'}, {'id': 2252, 'synset': 'frilled_lizard.n.01', 'name': 'frilled_lizard'}, {'id': 2253, 'synset': 'moloch.n.03', 'name': 'moloch'}, {'id': 2254, 'synset': 'mountain_devil.n.02', 'name': 'mountain_devil'}, {'id': 2255, 'synset': 'anguid_lizard.n.01', 'name': 'anguid_lizard'}, {'id': 2256, 'synset': 'alligator_lizard.n.01', 'name': 'alligator_lizard'}, {'id': 2257, 'synset': 'blindworm.n.01', 'name': 'blindworm'}, {'id': 2258, 'synset': 'glass_lizard.n.01', 'name': 'glass_lizard'}, {'id': 2259, 'synset': 'legless_lizard.n.01', 'name': 'legless_lizard'}, {'id': 2260, 'synset': 'lanthanotus_borneensis.n.01', 'name': 'Lanthanotus_borneensis'}, {'id': 2261, 'synset': 'venomous_lizard.n.01', 'name': 'venomous_lizard'}, {'id': 2262, 'synset': 'gila_monster.n.01', 'name': 'Gila_monster'}, {'id': 2263, 'synset': 'beaded_lizard.n.01', 'name': 'beaded_lizard'}, {'id': 2264, 'synset': 'lacertid_lizard.n.01', 'name': 'lacertid_lizard'}, {'id': 2265, 'synset': 'sand_lizard.n.01', 'name': 'sand_lizard'}, {'id': 2266, 'synset': 'green_lizard.n.01', 'name': 'green_lizard'}, {'id': 2267, 'synset': 'chameleon.n.03', 'name': 'chameleon'}, {'id': 2268, 'synset': 'african_chameleon.n.01', 'name': 'African_chameleon'}, {'id': 2269, 'synset': 'horned_chameleon.n.01', 'name': 'horned_chameleon'}, {'id': 2270, 'synset': 'monitor.n.07', 'name': 'monitor'}, {'id': 2271, 'synset': 'african_monitor.n.01', 'name': 'African_monitor'}, {'id': 2272, 'synset': 'komodo_dragon.n.01', 'name': 'Komodo_dragon'}, {'id': 2273, 'synset': 'crocodilian_reptile.n.01', 'name': 'crocodilian_reptile'}, {'id': 2274, 'synset': 'crocodile.n.01', 'name': 'crocodile'}, {'id': 2275, 'synset': 'african_crocodile.n.01', 'name': 'African_crocodile'}, {'id': 2276, 'synset': 'asian_crocodile.n.01', 'name': 'Asian_crocodile'}, {'id': 2277, 'synset': "morlett's_crocodile.n.01", 'name': "Morlett's_crocodile"}, {'id': 2278, 'synset': 'false_gavial.n.01', 'name': 'false_gavial'}, {'id': 2279, 'synset': 'american_alligator.n.01', 'name': 'American_alligator'}, {'id': 2280, 'synset': 'chinese_alligator.n.01', 'name': 'Chinese_alligator'}, {'id': 2281, 'synset': 'caiman.n.01', 'name': 'caiman'}, {'id': 2282, 'synset': 'spectacled_caiman.n.01', 'name': 'spectacled_caiman'}, {'id': 2283, 'synset': 'gavial.n.01', 'name': 'gavial'}, {'id': 2284, 'synset': 'armored_dinosaur.n.01', 'name': 'armored_dinosaur'}, {'id': 2285, 'synset': 'stegosaur.n.01', 'name': 'stegosaur'}, {'id': 2286, 'synset': 'ankylosaur.n.01', 'name': 'ankylosaur'}, {'id': 2287, 'synset': 'edmontonia.n.01', 'name': 'Edmontonia'}, {'id': 2288, 'synset': 'bone-headed_dinosaur.n.01', 'name': 'bone-headed_dinosaur'}, {'id': 2289, 'synset': 'pachycephalosaur.n.01', 'name': 'pachycephalosaur'}, {'id': 2290, 'synset': 'ceratopsian.n.01', 'name': 'ceratopsian'}, {'id': 2291, 'synset': 'protoceratops.n.01', 'name': 'protoceratops'}, {'id': 2292, 'synset': 'triceratops.n.01', 'name': 'triceratops'}, {'id': 2293, 'synset': 'styracosaur.n.01', 'name': 'styracosaur'}, {'id': 2294, 'synset': 'psittacosaur.n.01', 'name': 'psittacosaur'}, {'id': 2295, 'synset': 'ornithopod.n.01', 'name': 'ornithopod'}, {'id': 2296, 'synset': 'hadrosaur.n.01', 'name': 'hadrosaur'}, {'id': 2297, 'synset': 'trachodon.n.01', 'name': 'trachodon'}, {'id': 2298, 'synset': 'saurischian.n.01', 'name': 'saurischian'}, {'id': 2299, 'synset': 'sauropod.n.01', 'name': 'sauropod'}, {'id': 2300, 'synset': 'apatosaur.n.01', 'name': 'apatosaur'}, {'id': 2301, 'synset': 'barosaur.n.01', 'name': 'barosaur'}, {'id': 2302, 'synset': 'diplodocus.n.01', 'name': 'diplodocus'}, {'id': 2303, 'synset': 'argentinosaur.n.01', 'name': 'argentinosaur'}, {'id': 2304, 'synset': 'theropod.n.01', 'name': 'theropod'}, {'id': 2305, 'synset': 'ceratosaur.n.01', 'name': 'ceratosaur'}, {'id': 2306, 'synset': 'coelophysis.n.01', 'name': 'coelophysis'}, {'id': 2307, 'synset': 'tyrannosaur.n.01', 'name': 'tyrannosaur'}, {'id': 2308, 'synset': 'allosaur.n.01', 'name': 'allosaur'}, {'id': 2309, 'synset': 'ornithomimid.n.01', 'name': 'ornithomimid'}, {'id': 2310, 'synset': 'maniraptor.n.01', 'name': 'maniraptor'}, {'id': 2311, 'synset': 'oviraptorid.n.01', 'name': 'oviraptorid'}, {'id': 2312, 'synset': 'velociraptor.n.01', 'name': 'velociraptor'}, {'id': 2313, 'synset': 'deinonychus.n.01', 'name': 'deinonychus'}, {'id': 2314, 'synset': 'utahraptor.n.01', 'name': 'utahraptor'}, {'id': 2315, 'synset': 'synapsid.n.01', 'name': 'synapsid'}, {'id': 2316, 'synset': 'dicynodont.n.01', 'name': 'dicynodont'}, {'id': 2317, 'synset': 'pelycosaur.n.01', 'name': 'pelycosaur'}, {'id': 2318, 'synset': 'dimetrodon.n.01', 'name': 'dimetrodon'}, {'id': 2319, 'synset': 'pterosaur.n.01', 'name': 'pterosaur'}, {'id': 2320, 'synset': 'pterodactyl.n.01', 'name': 'pterodactyl'}, {'id': 2321, 'synset': 'ichthyosaur.n.01', 'name': 'ichthyosaur'}, {'id': 2322, 'synset': 'ichthyosaurus.n.01', 'name': 'ichthyosaurus'}, {'id': 2323, 'synset': 'stenopterygius.n.01', 'name': 'stenopterygius'}, {'id': 2324, 'synset': 'plesiosaur.n.01', 'name': 'plesiosaur'}, {'id': 2325, 'synset': 'nothosaur.n.01', 'name': 'nothosaur'}, {'id': 2326, 'synset': 'colubrid_snake.n.01', 'name': 'colubrid_snake'}, {'id': 2327, 'synset': 'hoop_snake.n.01', 'name': 'hoop_snake'}, {'id': 2328, 'synset': 'thunder_snake.n.01', 'name': 'thunder_snake'}, {'id': 2329, 'synset': 'ringneck_snake.n.01', 'name': 'ringneck_snake'}, {'id': 2330, 'synset': 'hognose_snake.n.01', 'name': 'hognose_snake'}, {'id': 2331, 'synset': 'leaf-nosed_snake.n.01', 'name': 'leaf-nosed_snake'}, {'id': 2332, 'synset': 'green_snake.n.02', 'name': 'green_snake'}, {'id': 2333, 'synset': 'smooth_green_snake.n.01', 'name': 'smooth_green_snake'}, {'id': 2334, 'synset': 'rough_green_snake.n.01', 'name': 'rough_green_snake'}, {'id': 2335, 'synset': 'green_snake.n.01', 'name': 'green_snake'}, {'id': 2336, 'synset': 'racer.n.04', 'name': 'racer'}, {'id': 2337, 'synset': 'blacksnake.n.02', 'name': 'blacksnake'}, {'id': 2338, 'synset': 'blue_racer.n.01', 'name': 'blue_racer'}, {'id': 2339, 'synset': 'horseshoe_whipsnake.n.01', 'name': 'horseshoe_whipsnake'}, {'id': 2340, 'synset': 'whip-snake.n.01', 'name': 'whip-snake'}, {'id': 2341, 'synset': 'coachwhip.n.02', 'name': 'coachwhip'}, {'id': 2342, 'synset': 'california_whipsnake.n.01', 'name': 'California_whipsnake'}, {'id': 2343, 'synset': 'sonoran_whipsnake.n.01', 'name': 'Sonoran_whipsnake'}, {'id': 2344, 'synset': 'rat_snake.n.01', 'name': 'rat_snake'}, {'id': 2345, 'synset': 'corn_snake.n.01', 'name': 'corn_snake'}, {'id': 2346, 'synset': 'black_rat_snake.n.01', 'name': 'black_rat_snake'}, {'id': 2347, 'synset': 'chicken_snake.n.01', 'name': 'chicken_snake'}, {'id': 2348, 'synset': 'indian_rat_snake.n.01', 'name': 'Indian_rat_snake'}, {'id': 2349, 'synset': 'glossy_snake.n.01', 'name': 'glossy_snake'}, {'id': 2350, 'synset': 'bull_snake.n.01', 'name': 'bull_snake'}, {'id': 2351, 'synset': 'gopher_snake.n.02', 'name': 'gopher_snake'}, {'id': 2352, 'synset': 'pine_snake.n.01', 'name': 'pine_snake'}, {'id': 2353, 'synset': 'king_snake.n.01', 'name': 'king_snake'}, {'id': 2354, 'synset': 'common_kingsnake.n.01', 'name': 'common_kingsnake'}, {'id': 2355, 'synset': 'milk_snake.n.01', 'name': 'milk_snake'}, {'id': 2356, 'synset': 'garter_snake.n.01', 'name': 'garter_snake'}, {'id': 2357, 'synset': 'common_garter_snake.n.01', 'name': 'common_garter_snake'}, {'id': 2358, 'synset': 'ribbon_snake.n.01', 'name': 'ribbon_snake'}, {'id': 2359, 'synset': 'western_ribbon_snake.n.01', 'name': 'Western_ribbon_snake'}, {'id': 2360, 'synset': 'lined_snake.n.01', 'name': 'lined_snake'}, {'id': 2361, 'synset': 'ground_snake.n.01', 'name': 'ground_snake'}, {'id': 2362, 'synset': 'eastern_ground_snake.n.01', 'name': 'eastern_ground_snake'}, {'id': 2363, 'synset': 'water_snake.n.01', 'name': 'water_snake'}, {'id': 2364, 'synset': 'common_water_snake.n.01', 'name': 'common_water_snake'}, {'id': 2365, 'synset': 'water_moccasin.n.02', 'name': 'water_moccasin'}, {'id': 2366, 'synset': 'grass_snake.n.01', 'name': 'grass_snake'}, {'id': 2367, 'synset': 'viperine_grass_snake.n.01', 'name': 'viperine_grass_snake'}, {'id': 2368, 'synset': 'red-bellied_snake.n.01', 'name': 'red-bellied_snake'}, {'id': 2369, 'synset': 'sand_snake.n.01', 'name': 'sand_snake'}, {'id': 2370, 'synset': 'banded_sand_snake.n.01', 'name': 'banded_sand_snake'}, {'id': 2371, 'synset': 'black-headed_snake.n.01', 'name': 'black-headed_snake'}, {'id': 2372, 'synset': 'vine_snake.n.01', 'name': 'vine_snake'}, {'id': 2373, 'synset': 'lyre_snake.n.01', 'name': 'lyre_snake'}, {'id': 2374, 'synset': 'sonoran_lyre_snake.n.01', 'name': 'Sonoran_lyre_snake'}, {'id': 2375, 'synset': 'night_snake.n.01', 'name': 'night_snake'}, {'id': 2376, 'synset': 'blind_snake.n.01', 'name': 'blind_snake'}, {'id': 2377, 'synset': 'western_blind_snake.n.01', 'name': 'western_blind_snake'}, {'id': 2378, 'synset': 'indigo_snake.n.01', 'name': 'indigo_snake'}, {'id': 2379, 'synset': 'eastern_indigo_snake.n.01', 'name': 'eastern_indigo_snake'}, {'id': 2380, 'synset': 'constrictor.n.01', 'name': 'constrictor'}, {'id': 2381, 'synset': 'boa.n.02', 'name': 'boa'}, {'id': 2382, 'synset': 'boa_constrictor.n.01', 'name': 'boa_constrictor'}, {'id': 2383, 'synset': 'rubber_boa.n.01', 'name': 'rubber_boa'}, {'id': 2384, 'synset': 'rosy_boa.n.01', 'name': 'rosy_boa'}, {'id': 2385, 'synset': 'anaconda.n.01', 'name': 'anaconda'}, {'id': 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{'id': 2430, 'synset': 'sidewinder.n.01', 'name': 'sidewinder'}, {'id': 2431, 'synset': 'western_diamondback.n.01', 'name': 'Western_diamondback'}, {'id': 2432, 'synset': 'rock_rattlesnake.n.01', 'name': 'rock_rattlesnake'}, {'id': 2433, 'synset': 'tiger_rattlesnake.n.01', 'name': 'tiger_rattlesnake'}, {'id': 2434, 'synset': 'mojave_rattlesnake.n.01', 'name': 'Mojave_rattlesnake'}, {'id': 2435, 'synset': 'speckled_rattlesnake.n.01', 'name': 'speckled_rattlesnake'}, {'id': 2436, 'synset': 'massasauga.n.02', 'name': 'massasauga'}, {'id': 2437, 'synset': 'ground_rattler.n.01', 'name': 'ground_rattler'}, {'id': 2438, 'synset': 'fer-de-lance.n.01', 'name': 'fer-de-lance'}, {'id': 2439, 'synset': 'carcase.n.01', 'name': 'carcase'}, {'id': 2440, 'synset': 'carrion.n.01', 'name': 'carrion'}, {'id': 2441, 'synset': 'arthropod.n.01', 'name': 'arthropod'}, {'id': 2442, 'synset': 'trilobite.n.01', 'name': 'trilobite'}, {'id': 2443, 'synset': 'arachnid.n.01', 'name': 'arachnid'}, {'id': 2444, 'synset': 'harvestman.n.01', 'name': 'harvestman'}, {'id': 2445, 'synset': 'scorpion.n.03', 'name': 'scorpion'}, {'id': 2446, 'synset': 'false_scorpion.n.01', 'name': 'false_scorpion'}, {'id': 2447, 'synset': 'book_scorpion.n.01', 'name': 'book_scorpion'}, {'id': 2448, 'synset': 'whip-scorpion.n.01', 'name': 'whip-scorpion'}, {'id': 2449, 'synset': 'vinegarroon.n.01', 'name': 'vinegarroon'}, {'id': 2450, 'synset': 'orb-weaving_spider.n.01', 'name': 'orb-weaving_spider'}, {'id': 2451, 'synset': 'black_and_gold_garden_spider.n.01', 'name': 'black_and_gold_garden_spider'}, {'id': 2452, 'synset': 'barn_spider.n.01', 'name': 'barn_spider'}, {'id': 2453, 'synset': 'garden_spider.n.01', 'name': 'garden_spider'}, {'id': 2454, 'synset': 'comb-footed_spider.n.01', 'name': 'comb-footed_spider'}, {'id': 2455, 'synset': 'black_widow.n.01', 'name': 'black_widow'}, {'id': 2456, 'synset': 'tarantula.n.02', 'name': 'tarantula'}, {'id': 2457, 'synset': 'wolf_spider.n.01', 'name': 'wolf_spider'}, {'id': 2458, 'synset': 'european_wolf_spider.n.01', 'name': 'European_wolf_spider'}, {'id': 2459, 'synset': 'trap-door_spider.n.01', 'name': 'trap-door_spider'}, {'id': 2460, 'synset': 'acarine.n.01', 'name': 'acarine'}, {'id': 2461, 'synset': 'tick.n.02', 'name': 'tick'}, {'id': 2462, 'synset': 'hard_tick.n.01', 'name': 'hard_tick'}, {'id': 2463, 'synset': 'ixodes_dammini.n.01', 'name': 'Ixodes_dammini'}, {'id': 2464, 'synset': 'ixodes_neotomae.n.01', 'name': 'Ixodes_neotomae'}, {'id': 2465, 'synset': 'ixodes_pacificus.n.01', 'name': 'Ixodes_pacificus'}, {'id': 2466, 'synset': 'ixodes_scapularis.n.01', 'name': 'Ixodes_scapularis'}, {'id': 2467, 'synset': 'sheep-tick.n.02', 'name': 'sheep-tick'}, {'id': 2468, 'synset': 'ixodes_persulcatus.n.01', 'name': 'Ixodes_persulcatus'}, {'id': 2469, 'synset': 'ixodes_dentatus.n.01', 'name': 'Ixodes_dentatus'}, {'id': 2470, 'synset': 'ixodes_spinipalpis.n.01', 'name': 'Ixodes_spinipalpis'}, {'id': 2471, 'synset': 'wood_tick.n.01', 'name': 'wood_tick'}, {'id': 2472, 'synset': 'soft_tick.n.01', 'name': 'soft_tick'}, {'id': 2473, 'synset': 'mite.n.02', 'name': 'mite'}, {'id': 2474, 'synset': 'web-spinning_mite.n.01', 'name': 'web-spinning_mite'}, {'id': 2475, 'synset': 'acarid.n.01', 'name': 'acarid'}, {'id': 2476, 'synset': 'trombidiid.n.01', 'name': 'trombidiid'}, {'id': 2477, 'synset': 'trombiculid.n.01', 'name': 'trombiculid'}, {'id': 2478, 'synset': 'harvest_mite.n.01', 'name': 'harvest_mite'}, {'id': 2479, 'synset': 'acarus.n.01', 'name': 'acarus'}, {'id': 2480, 'synset': 'itch_mite.n.01', 'name': 'itch_mite'}, {'id': 2481, 'synset': 'rust_mite.n.01', 'name': 'rust_mite'}, {'id': 2482, 'synset': 'spider_mite.n.01', 'name': 'spider_mite'}, {'id': 2483, 'synset': 'red_spider.n.01', 'name': 'red_spider'}, {'id': 2484, 'synset': 'myriapod.n.01', 'name': 'myriapod'}, {'id': 2485, 'synset': 'garden_centipede.n.01', 'name': 'garden_centipede'}, {'id': 2486, 'synset': 'tardigrade.n.01', 'name': 'tardigrade'}, {'id': 2487, 'synset': 'centipede.n.01', 'name': 'centipede'}, {'id': 2488, 'synset': 'house_centipede.n.01', 'name': 'house_centipede'}, {'id': 2489, 'synset': 'millipede.n.01', 'name': 'millipede'}, {'id': 2490, 'synset': 'sea_spider.n.01', 'name': 'sea_spider'}, {'id': 2491, 'synset': 'merostomata.n.01', 'name': 'Merostomata'}, {'id': 2492, 'synset': 'horseshoe_crab.n.01', 'name': 'horseshoe_crab'}, {'id': 2493, 'synset': 'asian_horseshoe_crab.n.01', 'name': 'Asian_horseshoe_crab'}, {'id': 2494, 'synset': 'eurypterid.n.01', 'name': 'eurypterid'}, {'id': 2495, 'synset': 'tongue_worm.n.01', 'name': 'tongue_worm'}, {'id': 2496, 'synset': 'gallinaceous_bird.n.01', 'name': 'gallinaceous_bird'}, {'id': 2497, 'synset': 'domestic_fowl.n.01', 'name': 'domestic_fowl'}, {'id': 2498, 'synset': 'dorking.n.01', 'name': 'Dorking'}, {'id': 2499, 'synset': 'plymouth_rock.n.02', 'name': 'Plymouth_Rock'}, {'id': 2500, 'synset': 'cornish.n.02', 'name': 'Cornish'}, {'id': 2501, 'synset': 'rock_cornish.n.01', 'name': 'Rock_Cornish'}, {'id': 2502, 'synset': 'game_fowl.n.01', 'name': 'game_fowl'}, {'id': 2503, 'synset': 'cochin.n.01', 'name': 'cochin'}, {'id': 2504, 'synset': 'jungle_fowl.n.01', 'name': 'jungle_fowl'}, {'id': 2505, 'synset': 'jungle_cock.n.01', 'name': 'jungle_cock'}, {'id': 2506, 'synset': 'jungle_hen.n.01', 'name': 'jungle_hen'}, {'id': 2507, 'synset': 'red_jungle_fowl.n.01', 'name': 'red_jungle_fowl'}, {'id': 2508, 'synset': 'bantam.n.01', 'name': 'bantam'}, {'id': 2509, 'synset': 'chick.n.01', 'name': 'chick'}, {'id': 2510, 'synset': 'cockerel.n.01', 'name': 'cockerel'}, {'id': 2511, 'synset': 'capon.n.02', 'name': 'capon'}, {'id': 2512, 'synset': 'hen.n.01', 'name': 'hen'}, {'id': 2513, 'synset': 'cackler.n.01', 'name': 'cackler'}, {'id': 2514, 'synset': 'brood_hen.n.01', 'name': 'brood_hen'}, {'id': 2515, 'synset': 'mother_hen.n.02', 'name': 'mother_hen'}, {'id': 2516, 'synset': 'layer.n.04', 'name': 'layer'}, {'id': 2517, 'synset': 'pullet.n.02', 'name': 'pullet'}, {'id': 2518, 'synset': 'spring_chicken.n.02', 'name': 'spring_chicken'}, {'id': 2519, 'synset': 'rhode_island_red.n.01', 'name': 'Rhode_Island_red'}, {'id': 2520, 'synset': 'dominique.n.01', 'name': 'Dominique'}, {'id': 2521, 'synset': 'orpington.n.01', 'name': 'Orpington'}, {'id': 2522, 'synset': 'turkey.n.01', 'name': 'turkey'}, {'id': 2523, 'synset': 'turkey_cock.n.01', 'name': 'turkey_cock'}, {'id': 2524, 'synset': 'ocellated_turkey.n.01', 'name': 'ocellated_turkey'}, {'id': 2525, 'synset': 'grouse.n.02', 'name': 'grouse'}, {'id': 2526, 'synset': 'black_grouse.n.01', 'name': 'black_grouse'}, {'id': 2527, 'synset': 'european_black_grouse.n.01', 'name': 'European_black_grouse'}, {'id': 2528, 'synset': 'asian_black_grouse.n.01', 'name': 'Asian_black_grouse'}, {'id': 2529, 'synset': 'blackcock.n.01', 'name': 'blackcock'}, {'id': 2530, 'synset': 'greyhen.n.01', 'name': 'greyhen'}, {'id': 2531, 'synset': 'ptarmigan.n.01', 'name': 'ptarmigan'}, {'id': 2532, 'synset': 'red_grouse.n.01', 'name': 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{'id': 2547, 'synset': 'texas_chachalaca.n.01', 'name': 'Texas_chachalaca'}, {'id': 2548, 'synset': 'megapode.n.01', 'name': 'megapode'}, {'id': 2549, 'synset': 'mallee_fowl.n.01', 'name': 'mallee_fowl'}, {'id': 2550, 'synset': 'mallee_hen.n.01', 'name': 'mallee_hen'}, {'id': 2551, 'synset': 'brush_turkey.n.01', 'name': 'brush_turkey'}, {'id': 2552, 'synset': 'maleo.n.01', 'name': 'maleo'}, {'id': 2553, 'synset': 'phasianid.n.01', 'name': 'phasianid'}, {'id': 2554, 'synset': 'pheasant.n.01', 'name': 'pheasant'}, {'id': 2555, 'synset': 'ring-necked_pheasant.n.01', 'name': 'ring-necked_pheasant'}, {'id': 2556, 'synset': 'afropavo.n.01', 'name': 'afropavo'}, {'id': 2557, 'synset': 'argus.n.02', 'name': 'argus'}, {'id': 2558, 'synset': 'golden_pheasant.n.01', 'name': 'golden_pheasant'}, {'id': 2559, 'synset': 'bobwhite.n.01', 'name': 'bobwhite'}, {'id': 2560, 'synset': 'northern_bobwhite.n.01', 'name': 'northern_bobwhite'}, {'id': 2561, 'synset': 'old_world_quail.n.01', 'name': 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'name': 'Greek_partridge'}, {'id': 2577, 'synset': 'mountain_quail.n.01', 'name': 'mountain_quail'}, {'id': 2578, 'synset': 'guinea_fowl.n.01', 'name': 'guinea_fowl'}, {'id': 2579, 'synset': 'guinea_hen.n.02', 'name': 'guinea_hen'}, {'id': 2580, 'synset': 'hoatzin.n.01', 'name': 'hoatzin'}, {'id': 2581, 'synset': 'tinamou.n.01', 'name': 'tinamou'}, {'id': 2582, 'synset': 'columbiform_bird.n.01', 'name': 'columbiform_bird'}, {'id': 2583, 'synset': 'dodo.n.02', 'name': 'dodo'}, {'id': 2584, 'synset': 'pouter_pigeon.n.01', 'name': 'pouter_pigeon'}, {'id': 2585, 'synset': 'rock_dove.n.01', 'name': 'rock_dove'}, {'id': 2586, 'synset': 'band-tailed_pigeon.n.01', 'name': 'band-tailed_pigeon'}, {'id': 2587, 'synset': 'wood_pigeon.n.01', 'name': 'wood_pigeon'}, {'id': 2588, 'synset': 'turtledove.n.02', 'name': 'turtledove'}, {'id': 2589, 'synset': 'streptopelia_turtur.n.01', 'name': 'Streptopelia_turtur'}, {'id': 2590, 'synset': 'ringdove.n.01', 'name': 'ringdove'}, {'id': 2591, 'synset': 'australian_turtledove.n.01', 'name': 'Australian_turtledove'}, {'id': 2592, 'synset': 'mourning_dove.n.01', 'name': 'mourning_dove'}, {'id': 2593, 'synset': 'domestic_pigeon.n.01', 'name': 'domestic_pigeon'}, {'id': 2594, 'synset': 'squab.n.03', 'name': 'squab'}, {'id': 2595, 'synset': 'fairy_swallow.n.01', 'name': 'fairy_swallow'}, {'id': 2596, 'synset': 'roller.n.07', 'name': 'roller'}, {'id': 2597, 'synset': 'homing_pigeon.n.01', 'name': 'homing_pigeon'}, {'id': 2598, 'synset': 'carrier_pigeon.n.01', 'name': 'carrier_pigeon'}, {'id': 2599, 'synset': 'passenger_pigeon.n.01', 'name': 'passenger_pigeon'}, {'id': 2600, 'synset': 'sandgrouse.n.01', 'name': 'sandgrouse'}, {'id': 2601, 'synset': 'painted_sandgrouse.n.01', 'name': 'painted_sandgrouse'}, {'id': 2602, 'synset': 'pin-tailed_sandgrouse.n.01', 'name': 'pin-tailed_sandgrouse'}, {'id': 2603, 'synset': "pallas's_sandgrouse.n.01", 'name': "pallas's_sandgrouse"}, {'id': 2604, 'synset': 'popinjay.n.02', 'name': 'popinjay'}, {'id': 2605, 'synset': 'poll.n.04', 'name': 'poll'}, {'id': 2606, 'synset': 'african_grey.n.01', 'name': 'African_grey'}, {'id': 2607, 'synset': 'amazon.n.04', 'name': 'amazon'}, {'id': 2608, 'synset': 'macaw.n.01', 'name': 'macaw'}, {'id': 2609, 'synset': 'kea.n.01', 'name': 'kea'}, {'id': 2610, 'synset': 'cockatoo.n.01', 'name': 'cockatoo'}, {'id': 2611, 'synset': 'sulphur-crested_cockatoo.n.01', 'name': 'sulphur-crested_cockatoo'}, {'id': 2612, 'synset': 'pink_cockatoo.n.01', 'name': 'pink_cockatoo'}, {'id': 2613, 'synset': 'cockateel.n.01', 'name': 'cockateel'}, {'id': 2614, 'synset': 'lovebird.n.02', 'name': 'lovebird'}, {'id': 2615, 'synset': 'lory.n.01', 'name': 'lory'}, {'id': 2616, 'synset': 'lorikeet.n.01', 'name': 'lorikeet'}, {'id': 2617, 'synset': 'varied_lorikeet.n.01', 'name': 'varied_Lorikeet'}, {'id': 2618, 'synset': 'rainbow_lorikeet.n.01', 'name': 'rainbow_lorikeet'}, {'id': 2619, 'synset': 'carolina_parakeet.n.01', 'name': 'Carolina_parakeet'}, {'id': 2620, 'synset': 'budgerigar.n.01', 'name': 'budgerigar'}, {'id': 2621, 'synset': 'ring-necked_parakeet.n.01', 'name': 'ring-necked_parakeet'}, {'id': 2622, 'synset': 'cuculiform_bird.n.01', 'name': 'cuculiform_bird'}, {'id': 2623, 'synset': 'cuckoo.n.02', 'name': 'cuckoo'}, {'id': 2624, 'synset': 'european_cuckoo.n.01', 'name': 'European_cuckoo'}, {'id': 2625, 'synset': 'black-billed_cuckoo.n.01', 'name': 'black-billed_cuckoo'}, {'id': 2626, 'synset': 'roadrunner.n.01', 'name': 'roadrunner'}, {'id': 2627, 'synset': 'ani.n.01', 'name': 'ani'}, {'id': 2628, 'synset': 'coucal.n.01', 'name': 'coucal'}, {'id': 2629, 'synset': 'crow_pheasant.n.01', 'name': 'crow_pheasant'}, {'id': 2630, 'synset': 'touraco.n.01', 'name': 'touraco'}, {'id': 2631, 'synset': 'coraciiform_bird.n.01', 'name': 'coraciiform_bird'}, {'id': 2632, 'synset': 'roller.n.06', 'name': 'roller'}, {'id': 2633, 'synset': 'european_roller.n.01', 'name': 'European_roller'}, {'id': 2634, 'synset': 'ground_roller.n.01', 'name': 'ground_roller'}, {'id': 2635, 'synset': 'kingfisher.n.01', 'name': 'kingfisher'}, {'id': 2636, 'synset': 'eurasian_kingfisher.n.01', 'name': 'Eurasian_kingfisher'}, {'id': 2637, 'synset': 'belted_kingfisher.n.01', 'name': 'belted_kingfisher'}, {'id': 2638, 'synset': 'kookaburra.n.01', 'name': 'kookaburra'}, {'id': 2639, 'synset': 'bee_eater.n.01', 'name': 'bee_eater'}, {'id': 2640, 'synset': 'hornbill.n.01', 'name': 'hornbill'}, {'id': 2641, 'synset': 'hoopoe.n.01', 'name': 'hoopoe'}, {'id': 2642, 'synset': 'euopean_hoopoe.n.01', 'name': 'Euopean_hoopoe'}, {'id': 2643, 'synset': 'wood_hoopoe.n.01', 'name': 'wood_hoopoe'}, {'id': 2644, 'synset': 'motmot.n.01', 'name': 'motmot'}, {'id': 2645, 'synset': 'tody.n.01', 'name': 'tody'}, {'id': 2646, 'synset': 'apodiform_bird.n.01', 'name': 'apodiform_bird'}, {'id': 2647, 'synset': 'swift.n.03', 'name': 'swift'}, {'id': 2648, 'synset': 'european_swift.n.01', 'name': 'European_swift'}, {'id': 2649, 'synset': 'chimney_swift.n.01', 'name': 'chimney_swift'}, {'id': 2650, 'synset': 'swiftlet.n.01', 'name': 'swiftlet'}, {'id': 2651, 'synset': 'tree_swift.n.01', 'name': 'tree_swift'}, {'id': 2652, 'synset': 'archilochus_colubris.n.01', 'name': 'Archilochus_colubris'}, {'id': 2653, 'synset': 'thornbill.n.01', 'name': 'thornbill'}, {'id': 2654, 'synset': 'goatsucker.n.01', 'name': 'goatsucker'}, {'id': 2655, 'synset': 'european_goatsucker.n.01', 'name': 'European_goatsucker'}, {'id': 2656, 'synset': "chuck-will's-widow.n.01", 'name': "chuck-will's-widow"}, {'id': 2657, 'synset': 'whippoorwill.n.01', 'name': 'whippoorwill'}, {'id': 2658, 'synset': 'poorwill.n.01', 'name': 'poorwill'}, {'id': 2659, 'synset': 'frogmouth.n.01', 'name': 'frogmouth'}, {'id': 2660, 'synset': 'oilbird.n.01', 'name': 'oilbird'}, {'id': 2661, 'synset': 'piciform_bird.n.01', 'name': 'piciform_bird'}, {'id': 2662, 'synset': 'woodpecker.n.01', 'name': 'woodpecker'}, {'id': 2663, 'synset': 'green_woodpecker.n.01', 'name': 'green_woodpecker'}, {'id': 2664, 'synset': 'downy_woodpecker.n.01', 'name': 'downy_woodpecker'}, {'id': 2665, 'synset': 'flicker.n.02', 'name': 'flicker'}, {'id': 2666, 'synset': 'yellow-shafted_flicker.n.01', 'name': 'yellow-shafted_flicker'}, {'id': 2667, 'synset': 'gilded_flicker.n.01', 'name': 'gilded_flicker'}, {'id': 2668, 'synset': 'red-shafted_flicker.n.01', 'name': 'red-shafted_flicker'}, {'id': 2669, 'synset': 'ivorybill.n.01', 'name': 'ivorybill'}, {'id': 2670, 'synset': 'redheaded_woodpecker.n.01', 'name': 'redheaded_woodpecker'}, {'id': 2671, 'synset': 'sapsucker.n.01', 'name': 'sapsucker'}, {'id': 2672, 'synset': 'yellow-bellied_sapsucker.n.01', 'name': 'yellow-bellied_sapsucker'}, {'id': 2673, 'synset': 'red-breasted_sapsucker.n.01', 'name': 'red-breasted_sapsucker'}, {'id': 2674, 'synset': 'wryneck.n.02', 'name': 'wryneck'}, {'id': 2675, 'synset': 'piculet.n.01', 'name': 'piculet'}, {'id': 2676, 'synset': 'barbet.n.01', 'name': 'barbet'}, {'id': 2677, 'synset': 'puffbird.n.01', 'name': 'puffbird'}, {'id': 2678, 'synset': 'honey_guide.n.01', 'name': 'honey_guide'}, {'id': 2679, 'synset': 'jacamar.n.01', 'name': 'jacamar'}, {'id': 2680, 'synset': 'toucan.n.01', 'name': 'toucan'}, {'id': 2681, 'synset': 'toucanet.n.01', 'name': 'toucanet'}, {'id': 2682, 'synset': 'trogon.n.01', 'name': 'trogon'}, {'id': 2683, 'synset': 'quetzal.n.02', 'name': 'quetzal'}, {'id': 2684, 'synset': 'resplendent_quetzel.n.01', 'name': 'resplendent_quetzel'}, {'id': 2685, 'synset': 'aquatic_bird.n.01', 'name': 'aquatic_bird'}, {'id': 2686, 'synset': 'waterfowl.n.01', 'name': 'waterfowl'}, {'id': 2687, 'synset': 'anseriform_bird.n.01', 'name': 'anseriform_bird'}, {'id': 2688, 'synset': 'drake.n.02', 'name': 'drake'}, {'id': 2689, 'synset': 'quack-quack.n.01', 'name': 'quack-quack'}, {'id': 2690, 'synset': 'diving_duck.n.01', 'name': 'diving_duck'}, {'id': 2691, 'synset': 'dabbling_duck.n.01', 'name': 'dabbling_duck'}, {'id': 2692, 'synset': 'black_duck.n.01', 'name': 'black_duck'}, {'id': 2693, 'synset': 'teal.n.02', 'name': 'teal'}, {'id': 2694, 'synset': 'greenwing.n.01', 'name': 'greenwing'}, {'id': 2695, 'synset': 'bluewing.n.01', 'name': 'bluewing'}, {'id': 2696, 'synset': 'garganey.n.01', 'name': 'garganey'}, {'id': 2697, 'synset': 'widgeon.n.01', 'name': 'widgeon'}, {'id': 2698, 'synset': 'american_widgeon.n.01', 'name': 'American_widgeon'}, {'id': 2699, 'synset': 'shoveler.n.02', 'name': 'shoveler'}, {'id': 2700, 'synset': 'pintail.n.01', 'name': 'pintail'}, {'id': 2701, 'synset': 'sheldrake.n.02', 'name': 'sheldrake'}, {'id': 2702, 'synset': 'shelduck.n.01', 'name': 'shelduck'}, {'id': 2703, 'synset': 'ruddy_duck.n.01', 'name': 'ruddy_duck'}, {'id': 2704, 'synset': 'bufflehead.n.01', 'name': 'bufflehead'}, {'id': 2705, 'synset': 'goldeneye.n.02', 'name': 'goldeneye'}, {'id': 2706, 'synset': "barrow's_goldeneye.n.01", 'name': "Barrow's_goldeneye"}, {'id': 2707, 'synset': 'canvasback.n.01', 'name': 'canvasback'}, {'id': 2708, 'synset': 'pochard.n.01', 'name': 'pochard'}, {'id': 2709, 'synset': 'redhead.n.02', 'name': 'redhead'}, {'id': 2710, 'synset': 'scaup.n.01', 'name': 'scaup'}, {'id': 2711, 'synset': 'greater_scaup.n.01', 'name': 'greater_scaup'}, {'id': 2712, 'synset': 'lesser_scaup.n.01', 'name': 'lesser_scaup'}, {'id': 2713, 'synset': 'wild_duck.n.01', 'name': 'wild_duck'}, {'id': 2714, 'synset': 'wood_duck.n.01', 'name': 'wood_duck'}, {'id': 2715, 'synset': 'wood_drake.n.01', 'name': 'wood_drake'}, {'id': 2716, 'synset': 'mandarin_duck.n.01', 'name': 'mandarin_duck'}, {'id': 2717, 'synset': 'muscovy_duck.n.01', 'name': 'muscovy_duck'}, {'id': 2718, 'synset': 'sea_duck.n.01', 'name': 'sea_duck'}, {'id': 2719, 'synset': 'eider.n.01', 'name': 'eider'}, {'id': 2720, 'synset': 'scoter.n.01', 'name': 'scoter'}, {'id': 2721, 'synset': 'common_scoter.n.01', 'name': 'common_scoter'}, {'id': 2722, 'synset': 'old_squaw.n.01', 'name': 'old_squaw'}, {'id': 2723, 'synset': 'merganser.n.01', 'name': 'merganser'}, {'id': 2724, 'synset': 'goosander.n.01', 'name': 'goosander'}, {'id': 2725, 'synset': 'american_merganser.n.01', 'name': 'American_merganser'}, {'id': 2726, 'synset': 'red-breasted_merganser.n.01', 'name': 'red-breasted_merganser'}, {'id': 2727, 'synset': 'smew.n.01', 'name': 'smew'}, {'id': 2728, 'synset': 'hooded_merganser.n.01', 'name': 'hooded_merganser'}, {'id': 2729, 'synset': 'gosling.n.01', 'name': 'gosling'}, {'id': 2730, 'synset': 'gander.n.01', 'name': 'gander'}, {'id': 2731, 'synset': 'chinese_goose.n.01', 'name': 'Chinese_goose'}, {'id': 2732, 'synset': 'greylag.n.01', 'name': 'greylag'}, {'id': 2733, 'synset': 'blue_goose.n.01', 'name': 'blue_goose'}, {'id': 2734, 'synset': 'snow_goose.n.01', 'name': 'snow_goose'}, {'id': 2735, 'synset': 'brant.n.01', 'name': 'brant'}, {'id': 2736, 'synset': 'common_brant_goose.n.01', 'name': 'common_brant_goose'}, {'id': 2737, 'synset': 'honker.n.03', 'name': 'honker'}, {'id': 2738, 'synset': 'barnacle_goose.n.01', 'name': 'barnacle_goose'}, {'id': 2739, 'synset': 'coscoroba.n.01', 'name': 'coscoroba'}, {'id': 2740, 'synset': 'swan.n.01', 'name': 'swan'}, {'id': 2741, 'synset': 'cob.n.04', 'name': 'cob'}, {'id': 2742, 'synset': 'pen.n.05', 'name': 'pen'}, {'id': 2743, 'synset': 'cygnet.n.01', 'name': 'cygnet'}, {'id': 2744, 'synset': 'mute_swan.n.01', 'name': 'mute_swan'}, {'id': 2745, 'synset': 'whooper.n.02', 'name': 'whooper'}, {'id': 2746, 'synset': 'tundra_swan.n.01', 'name': 'tundra_swan'}, {'id': 2747, 'synset': 'whistling_swan.n.01', 'name': 'whistling_swan'}, {'id': 2748, 'synset': "bewick's_swan.n.01", 'name': "Bewick's_swan"}, {'id': 2749, 'synset': 'trumpeter.n.04', 'name': 'trumpeter'}, {'id': 2750, 'synset': 'black_swan.n.01', 'name': 'black_swan'}, {'id': 2751, 'synset': 'screamer.n.03', 'name': 'screamer'}, {'id': 2752, 'synset': 'horned_screamer.n.01', 'name': 'horned_screamer'}, {'id': 2753, 'synset': 'crested_screamer.n.01', 'name': 'crested_screamer'}, {'id': 2754, 'synset': 'chaja.n.01', 'name': 'chaja'}, {'id': 2755, 'synset': 'mammal.n.01', 'name': 'mammal'}, {'id': 2756, 'synset': 'female_mammal.n.01', 'name': 'female_mammal'}, {'id': 2757, 'synset': 'tusker.n.01', 'name': 'tusker'}, {'id': 2758, 'synset': 'prototherian.n.01', 'name': 'prototherian'}, {'id': 2759, 'synset': 'monotreme.n.01', 'name': 'monotreme'}, {'id': 2760, 'synset': 'echidna.n.02', 'name': 'echidna'}, {'id': 2761, 'synset': 'echidna.n.01', 'name': 'echidna'}, {'id': 2762, 'synset': 'platypus.n.01', 'name': 'platypus'}, {'id': 2763, 'synset': 'marsupial.n.01', 'name': 'marsupial'}, {'id': 2764, 'synset': 'opossum.n.02', 'name': 'opossum'}, {'id': 2765, 'synset': 'common_opossum.n.01', 'name': 'common_opossum'}, {'id': 2766, 'synset': 'crab-eating_opossum.n.01', 'name': 'crab-eating_opossum'}, {'id': 2767, 'synset': 'opossum_rat.n.01', 'name': 'opossum_rat'}, {'id': 2768, 'synset': 'bandicoot.n.01', 'name': 'bandicoot'}, {'id': 2769, 'synset': 'rabbit-eared_bandicoot.n.01', 'name': 'rabbit-eared_bandicoot'}, {'id': 2770, 'synset': 'kangaroo.n.01', 'name': 'kangaroo'}, {'id': 2771, 'synset': 'giant_kangaroo.n.01', 'name': 'giant_kangaroo'}, {'id': 2772, 'synset': 'wallaby.n.01', 'name': 'wallaby'}, {'id': 2773, 'synset': 'common_wallaby.n.01', 'name': 'common_wallaby'}, {'id': 2774, 'synset': 'hare_wallaby.n.01', 'name': 'hare_wallaby'}, {'id': 2775, 'synset': 'nail-tailed_wallaby.n.01', 'name': 'nail-tailed_wallaby'}, {'id': 2776, 'synset': 'rock_wallaby.n.01', 'name': 'rock_wallaby'}, {'id': 2777, 'synset': 'pademelon.n.01', 'name': 'pademelon'}, {'id': 2778, 'synset': 'tree_wallaby.n.01', 'name': 'tree_wallaby'}, {'id': 2779, 'synset': 'musk_kangaroo.n.01', 'name': 'musk_kangaroo'}, {'id': 2780, 'synset': 'rat_kangaroo.n.01', 'name': 'rat_kangaroo'}, {'id': 2781, 'synset': 'potoroo.n.01', 'name': 'potoroo'}, {'id': 2782, 'synset': 'bettong.n.01', 'name': 'bettong'}, {'id': 2783, 'synset': 'jerboa_kangaroo.n.01', 'name': 'jerboa_kangaroo'}, {'id': 2784, 'synset': 'phalanger.n.01', 'name': 'phalanger'}, {'id': 2785, 'synset': 'cuscus.n.01', 'name': 'cuscus'}, {'id': 2786, 'synset': 'brush-tailed_phalanger.n.01', 'name': 'brush-tailed_phalanger'}, {'id': 2787, 'synset': 'flying_phalanger.n.01', 'name': 'flying_phalanger'}, {'id': 2788, 'synset': 'wombat.n.01', 'name': 'wombat'}, {'id': 2789, 'synset': 'dasyurid_marsupial.n.01', 'name': 'dasyurid_marsupial'}, {'id': 2790, 'synset': 'dasyure.n.01', 'name': 'dasyure'}, {'id': 2791, 'synset': 'eastern_dasyure.n.01', 'name': 'eastern_dasyure'}, {'id': 2792, 'synset': 'native_cat.n.01', 'name': 'native_cat'}, {'id': 2793, 'synset': 'thylacine.n.01', 'name': 'thylacine'}, {'id': 2794, 'synset': 'tasmanian_devil.n.01', 'name': 'Tasmanian_devil'}, {'id': 2795, 'synset': 'pouched_mouse.n.01', 'name': 'pouched_mouse'}, {'id': 2796, 'synset': 'numbat.n.01', 'name': 'numbat'}, {'id': 2797, 'synset': 'pouched_mole.n.01', 'name': 'pouched_mole'}, {'id': 2798, 'synset': 'placental.n.01', 'name': 'placental'}, {'id': 2799, 'synset': 'livestock.n.01', 'name': 'livestock'}, {'id': 2800, 'synset': 'cow.n.02', 'name': 'cow'}, {'id': 2801, 'synset': 'calf.n.04', 'name': 'calf'}, {'id': 2802, 'synset': 'yearling.n.03', 'name': 'yearling'}, {'id': 2803, 'synset': 'buck.n.05', 'name': 'buck'}, {'id': 2804, 'synset': 'doe.n.02', 'name': 'doe'}, {'id': 2805, 'synset': 'insectivore.n.01', 'name': 'insectivore'}, {'id': 2806, 'synset': 'mole.n.06', 'name': 'mole'}, {'id': 2807, 'synset': 'starnose_mole.n.01', 'name': 'starnose_mole'}, {'id': 2808, 'synset': "brewer's_mole.n.01", 'name': "brewer's_mole"}, {'id': 2809, 'synset': 'golden_mole.n.01', 'name': 'golden_mole'}, {'id': 2810, 'synset': 'shrew_mole.n.01', 'name': 'shrew_mole'}, {'id': 2811, 'synset': 'asiatic_shrew_mole.n.01', 'name': 'Asiatic_shrew_mole'}, {'id': 2812, 'synset': 'american_shrew_mole.n.01', 'name': 'American_shrew_mole'}, {'id': 2813, 'synset': 'shrew.n.02', 'name': 'shrew'}, {'id': 2814, 'synset': 'common_shrew.n.01', 'name': 'common_shrew'}, {'id': 2815, 'synset': 'masked_shrew.n.01', 'name': 'masked_shrew'}, {'id': 2816, 'synset': 'short-tailed_shrew.n.01', 'name': 'short-tailed_shrew'}, {'id': 2817, 'synset': 'water_shrew.n.01', 'name': 'water_shrew'}, {'id': 2818, 'synset': 'american_water_shrew.n.01', 'name': 'American_water_shrew'}, {'id': 2819, 'synset': 'european_water_shrew.n.01', 'name': 'European_water_shrew'}, {'id': 2820, 'synset': 'mediterranean_water_shrew.n.01', 'name': 'Mediterranean_water_shrew'}, {'id': 2821, 'synset': 'least_shrew.n.01', 'name': 'least_shrew'}, {'id': 2822, 'synset': 'hedgehog.n.02', 'name': 'hedgehog'}, {'id': 2823, 'synset': 'tenrec.n.01', 'name': 'tenrec'}, {'id': 2824, 'synset': 'tailless_tenrec.n.01', 'name': 'tailless_tenrec'}, {'id': 2825, 'synset': 'otter_shrew.n.01', 'name': 'otter_shrew'}, {'id': 2826, 'synset': 'eiderdown.n.02', 'name': 'eiderdown'}, {'id': 2827, 'synset': 'aftershaft.n.01', 'name': 'aftershaft'}, {'id': 2828, 'synset': 'sickle_feather.n.01', 'name': 'sickle_feather'}, {'id': 2829, 'synset': 'contour_feather.n.01', 'name': 'contour_feather'}, {'id': 2830, 'synset': 'bastard_wing.n.01', 'name': 'bastard_wing'}, {'id': 2831, 'synset': 'saddle_hackle.n.01', 'name': 'saddle_hackle'}, {'id': 2832, 'synset': 'encolure.n.01', 'name': 'encolure'}, {'id': 2833, 'synset': 'hair.n.06', 'name': 'hair'}, {'id': 2834, 'synset': 'squama.n.01', 'name': 'squama'}, {'id': 2835, 'synset': 'scute.n.01', 'name': 'scute'}, {'id': 2836, 'synset': 'sclerite.n.01', 'name': 'sclerite'}, {'id': 2837, 'synset': 'plastron.n.05', 'name': 'plastron'}, {'id': 2838, 'synset': 'scallop_shell.n.01', 'name': 'scallop_shell'}, {'id': 2839, 'synset': 'oyster_shell.n.01', 'name': 'oyster_shell'}, {'id': 2840, 'synset': 'theca.n.02', 'name': 'theca'}, {'id': 2841, 'synset': 'invertebrate.n.01', 'name': 'invertebrate'}, {'id': 2842, 'synset': 'sponge.n.04', 'name': 'sponge'}, {'id': 2843, 'synset': 'choanocyte.n.01', 'name': 'choanocyte'}, {'id': 2844, 'synset': 'glass_sponge.n.01', 'name': 'glass_sponge'}, {'id': 2845, 'synset': "venus's_flower_basket.n.01", 'name': "Venus's_flower_basket"}, {'id': 2846, 'synset': 'metazoan.n.01', 'name': 'metazoan'}, {'id': 2847, 'synset': 'coelenterate.n.01', 'name': 'coelenterate'}, {'id': 2848, 'synset': 'planula.n.01', 'name': 'planula'}, {'id': 2849, 'synset': 'polyp.n.02', 'name': 'polyp'}, {'id': 2850, 'synset': 'medusa.n.02', 'name': 'medusa'}, {'id': 2851, 'synset': 'jellyfish.n.02', 'name': 'jellyfish'}, {'id': 2852, 'synset': 'scyphozoan.n.01', 'name': 'scyphozoan'}, {'id': 2853, 'synset': 'chrysaora_quinquecirrha.n.01', 'name': 'Chrysaora_quinquecirrha'}, {'id': 2854, 'synset': 'hydrozoan.n.01', 'name': 'hydrozoan'}, {'id': 2855, 'synset': 'hydra.n.04', 'name': 'hydra'}, {'id': 2856, 'synset': 'siphonophore.n.01', 'name': 'siphonophore'}, {'id': 2857, 'synset': 'nanomia.n.01', 'name': 'nanomia'}, {'id': 2858, 'synset': 'portuguese_man-of-war.n.01', 'name': 'Portuguese_man-of-war'}, {'id': 2859, 'synset': 'praya.n.01', 'name': 'praya'}, {'id': 2860, 'synset': 'apolemia.n.01', 'name': 'apolemia'}, {'id': 2861, 'synset': 'anthozoan.n.01', 'name': 'anthozoan'}, {'id': 2862, 'synset': 'sea_anemone.n.01', 'name': 'sea_anemone'}, {'id': 2863, 'synset': 'actinia.n.02', 'name': 'actinia'}, {'id': 2864, 'synset': 'sea_pen.n.01', 'name': 'sea_pen'}, {'id': 2865, 'synset': 'coral.n.04', 'name': 'coral'}, {'id': 2866, 'synset': 'gorgonian.n.01', 'name': 'gorgonian'}, {'id': 2867, 'synset': 'sea_feather.n.01', 'name': 'sea_feather'}, {'id': 2868, 'synset': 'sea_fan.n.01', 'name': 'sea_fan'}, {'id': 2869, 'synset': 'red_coral.n.02', 'name': 'red_coral'}, {'id': 2870, 'synset': 'stony_coral.n.01', 'name': 'stony_coral'}, {'id': 2871, 'synset': 'brain_coral.n.01', 'name': 'brain_coral'}, {'id': 2872, 'synset': 'staghorn_coral.n.01', 'name': 'staghorn_coral'}, {'id': 2873, 'synset': 'mushroom_coral.n.01', 'name': 'mushroom_coral'}, {'id': 2874, 'synset': 'ctenophore.n.01', 'name': 'ctenophore'}, {'id': 2875, 'synset': 'beroe.n.01', 'name': 'beroe'}, {'id': 2876, 'synset': 'platyctenean.n.01', 'name': 'platyctenean'}, {'id': 2877, 'synset': 'sea_gooseberry.n.01', 'name': 'sea_gooseberry'}, {'id': 2878, 'synset': "venus's_girdle.n.01", 'name': "Venus's_girdle"}, {'id': 2879, 'synset': 'worm.n.01', 'name': 'worm'}, {'id': 2880, 'synset': 'helminth.n.01', 'name': 'helminth'}, {'id': 2881, 'synset': 'woodworm.n.01', 'name': 'woodworm'}, {'id': 2882, 'synset': 'woodborer.n.01', 'name': 'woodborer'}, {'id': 2883, 'synset': 'acanthocephalan.n.01', 'name': 'acanthocephalan'}, {'id': 2884, 'synset': 'arrowworm.n.01', 'name': 'arrowworm'}, {'id': 2885, 'synset': 'bladder_worm.n.01', 'name': 'bladder_worm'}, {'id': 2886, 'synset': 'flatworm.n.01', 'name': 'flatworm'}, {'id': 2887, 'synset': 'planarian.n.01', 'name': 'planarian'}, {'id': 2888, 'synset': 'fluke.n.05', 'name': 'fluke'}, {'id': 2889, 'synset': 'cercaria.n.01', 'name': 'cercaria'}, {'id': 2890, 'synset': 'liver_fluke.n.01', 'name': 'liver_fluke'}, {'id': 2891, 'synset': 'fasciolopsis_buski.n.01', 'name': 'Fasciolopsis_buski'}, {'id': 2892, 'synset': 'schistosome.n.01', 'name': 'schistosome'}, {'id': 2893, 'synset': 'tapeworm.n.01', 'name': 'tapeworm'}, {'id': 2894, 'synset': 'echinococcus.n.01', 'name': 'echinococcus'}, {'id': 2895, 'synset': 'taenia.n.02', 'name': 'taenia'}, {'id': 2896, 'synset': 'ribbon_worm.n.01', 'name': 'ribbon_worm'}, {'id': 2897, 'synset': 'beard_worm.n.01', 'name': 'beard_worm'}, {'id': 2898, 'synset': 'rotifer.n.01', 'name': 'rotifer'}, {'id': 2899, 'synset': 'nematode.n.01', 'name': 'nematode'}, {'id': 2900, 'synset': 'common_roundworm.n.01', 'name': 'common_roundworm'}, {'id': 2901, 'synset': 'chicken_roundworm.n.01', 'name': 'chicken_roundworm'}, {'id': 2902, 'synset': 'pinworm.n.01', 'name': 'pinworm'}, {'id': 2903, 'synset': 'eelworm.n.01', 'name': 'eelworm'}, {'id': 2904, 'synset': 'vinegar_eel.n.01', 'name': 'vinegar_eel'}, {'id': 2905, 'synset': 'trichina.n.01', 'name': 'trichina'}, {'id': 2906, 'synset': 'hookworm.n.01', 'name': 'hookworm'}, {'id': 2907, 'synset': 'filaria.n.02', 'name': 'filaria'}, {'id': 2908, 'synset': 'guinea_worm.n.02', 'name': 'Guinea_worm'}, {'id': 2909, 'synset': 'annelid.n.01', 'name': 'annelid'}, {'id': 2910, 'synset': 'archiannelid.n.01', 'name': 'archiannelid'}, {'id': 2911, 'synset': 'oligochaete.n.01', 'name': 'oligochaete'}, {'id': 2912, 'synset': 'earthworm.n.01', 'name': 'earthworm'}, {'id': 2913, 'synset': 'polychaete.n.01', 'name': 'polychaete'}, {'id': 2914, 'synset': 'lugworm.n.01', 'name': 'lugworm'}, {'id': 2915, 'synset': 'sea_mouse.n.01', 'name': 'sea_mouse'}, {'id': 2916, 'synset': 'bloodworm.n.01', 'name': 'bloodworm'}, {'id': 2917, 'synset': 'leech.n.01', 'name': 'leech'}, {'id': 2918, 'synset': 'medicinal_leech.n.01', 'name': 'medicinal_leech'}, {'id': 2919, 'synset': 'horseleech.n.01', 'name': 'horseleech'}, {'id': 2920, 'synset': 'mollusk.n.01', 'name': 'mollusk'}, {'id': 2921, 'synset': 'scaphopod.n.01', 'name': 'scaphopod'}, {'id': 2922, 'synset': 'tooth_shell.n.01', 'name': 'tooth_shell'}, {'id': 2923, 'synset': 'gastropod.n.01', 'name': 'gastropod'}, {'id': 2924, 'synset': 'abalone.n.01', 'name': 'abalone'}, {'id': 2925, 'synset': 'ormer.n.01', 'name': 'ormer'}, {'id': 2926, 'synset': 'scorpion_shell.n.01', 'name': 'scorpion_shell'}, {'id': 2927, 'synset': 'conch.n.01', 'name': 'conch'}, {'id': 2928, 'synset': 'giant_conch.n.01', 'name': 'giant_conch'}, {'id': 2929, 'synset': 'snail.n.01', 'name': 'snail'}, {'id': 2930, 'synset': 'edible_snail.n.01', 'name': 'edible_snail'}, {'id': 2931, 'synset': 'garden_snail.n.01', 'name': 'garden_snail'}, {'id': 2932, 'synset': 'brown_snail.n.01', 'name': 'brown_snail'}, {'id': 2933, 'synset': 'helix_hortensis.n.01', 'name': 'Helix_hortensis'}, {'id': 2934, 'synset': 'slug.n.07', 'name': 'slug'}, {'id': 2935, 'synset': 'seasnail.n.02', 'name': 'seasnail'}, {'id': 2936, 'synset': 'neritid.n.01', 'name': 'neritid'}, {'id': 2937, 'synset': 'nerita.n.01', 'name': 'nerita'}, {'id': 2938, 'synset': 'bleeding_tooth.n.01', 'name': 'bleeding_tooth'}, {'id': 2939, 'synset': 'neritina.n.01', 'name': 'neritina'}, {'id': 2940, 'synset': 'whelk.n.02', 'name': 'whelk'}, {'id': 2941, 'synset': 'moon_shell.n.01', 'name': 'moon_shell'}, {'id': 2942, 'synset': 'periwinkle.n.04', 'name': 'periwinkle'}, {'id': 2943, 'synset': 'limpet.n.02', 'name': 'limpet'}, {'id': 2944, 'synset': 'common_limpet.n.01', 'name': 'common_limpet'}, {'id': 2945, 'synset': 'keyhole_limpet.n.01', 'name': 'keyhole_limpet'}, {'id': 2946, 'synset': 'river_limpet.n.01', 'name': 'river_limpet'}, {'id': 2947, 'synset': 'sea_slug.n.01', 'name': 'sea_slug'}, {'id': 2948, 'synset': 'sea_hare.n.01', 'name': 'sea_hare'}, {'id': 2949, 'synset': 'hermissenda_crassicornis.n.01', 'name': 'Hermissenda_crassicornis'}, {'id': 2950, 'synset': 'bubble_shell.n.01', 'name': 'bubble_shell'}, {'id': 2951, 'synset': 'physa.n.01', 'name': 'physa'}, {'id': 2952, 'synset': 'cowrie.n.01', 'name': 'cowrie'}, {'id': 2953, 'synset': 'money_cowrie.n.01', 'name': 'money_cowrie'}, {'id': 2954, 'synset': 'tiger_cowrie.n.01', 'name': 'tiger_cowrie'}, {'id': 2955, 'synset': 'solenogaster.n.01', 'name': 'solenogaster'}, {'id': 2956, 'synset': 'chiton.n.02', 'name': 'chiton'}, {'id': 2957, 'synset': 'bivalve.n.01', 'name': 'bivalve'}, {'id': 2958, 'synset': 'spat.n.03', 'name': 'spat'}, {'id': 2959, 'synset': 'clam.n.01', 'name': 'clam'}, {'id': 2960, 'synset': 'soft-shell_clam.n.02', 'name': 'soft-shell_clam'}, {'id': 2961, 'synset': 'quahog.n.02', 'name': 'quahog'}, {'id': 2962, 'synset': 'littleneck.n.02', 'name': 'littleneck'}, {'id': 2963, 'synset': 'cherrystone.n.02', 'name': 'cherrystone'}, {'id': 2964, 'synset': 'geoduck.n.01', 'name': 'geoduck'}, {'id': 2965, 'synset': 'razor_clam.n.01', 'name': 'razor_clam'}, {'id': 2966, 'synset': 'giant_clam.n.01', 'name': 'giant_clam'}, {'id': 2967, 'synset': 'cockle.n.02', 'name': 'cockle'}, {'id': 2968, 'synset': 'edible_cockle.n.01', 'name': 'edible_cockle'}, {'id': 2969, 'synset': 'oyster.n.01', 'name': 'oyster'}, {'id': 2970, 'synset': 'japanese_oyster.n.01', 'name': 'Japanese_oyster'}, {'id': 2971, 'synset': 'virginia_oyster.n.01', 'name': 'Virginia_oyster'}, {'id': 2972, 'synset': 'pearl_oyster.n.01', 'name': 'pearl_oyster'}, {'id': 2973, 'synset': 'saddle_oyster.n.01', 'name': 'saddle_oyster'}, {'id': 2974, 'synset': 'window_oyster.n.01', 'name': 'window_oyster'}, {'id': 2975, 'synset': 'ark_shell.n.01', 'name': 'ark_shell'}, {'id': 2976, 'synset': 'blood_clam.n.01', 'name': 'blood_clam'}, {'id': 2977, 'synset': 'mussel.n.02', 'name': 'mussel'}, {'id': 2978, 'synset': 'marine_mussel.n.01', 'name': 'marine_mussel'}, {'id': 2979, 'synset': 'edible_mussel.n.01', 'name': 'edible_mussel'}, {'id': 2980, 'synset': 'freshwater_mussel.n.01', 'name': 'freshwater_mussel'}, {'id': 2981, 'synset': 'pearly-shelled_mussel.n.01', 'name': 'pearly-shelled_mussel'}, {'id': 2982, 'synset': 'thin-shelled_mussel.n.01', 'name': 'thin-shelled_mussel'}, {'id': 2983, 'synset': 'zebra_mussel.n.01', 'name': 'zebra_mussel'}, {'id': 2984, 'synset': 'scallop.n.04', 'name': 'scallop'}, {'id': 2985, 'synset': 'bay_scallop.n.02', 'name': 'bay_scallop'}, {'id': 2986, 'synset': 'sea_scallop.n.02', 'name': 'sea_scallop'}, {'id': 2987, 'synset': 'shipworm.n.01', 'name': 'shipworm'}, {'id': 2988, 'synset': 'teredo.n.01', 'name': 'teredo'}, {'id': 2989, 'synset': 'piddock.n.01', 'name': 'piddock'}, {'id': 2990, 'synset': 'cephalopod.n.01', 'name': 'cephalopod'}, {'id': 2991, 'synset': 'chambered_nautilus.n.01', 'name': 'chambered_nautilus'}, {'id': 2992, 'synset': 'octopod.n.01', 'name': 'octopod'}, {'id': 2993, 'synset': 'paper_nautilus.n.01', 'name': 'paper_nautilus'}, {'id': 2994, 'synset': 'decapod.n.02', 'name': 'decapod'}, {'id': 2995, 'synset': 'squid.n.02', 'name': 'squid'}, {'id': 2996, 'synset': 'loligo.n.01', 'name': 'loligo'}, {'id': 2997, 'synset': 'ommastrephes.n.01', 'name': 'ommastrephes'}, {'id': 2998, 'synset': 'architeuthis.n.01', 'name': 'architeuthis'}, {'id': 2999, 'synset': 'cuttlefish.n.01', 'name': 'cuttlefish'}, {'id': 3000, 'synset': 'spirula.n.01', 'name': 'spirula'}, {'id': 3001, 'synset': 'crustacean.n.01', 'name': 'crustacean'}, {'id': 3002, 'synset': 'malacostracan_crustacean.n.01', 'name': 'malacostracan_crustacean'}, {'id': 3003, 'synset': 'decapod_crustacean.n.01', 'name': 'decapod_crustacean'}, {'id': 3004, 'synset': 'brachyuran.n.01', 'name': 'brachyuran'}, {'id': 3005, 'synset': 'stone_crab.n.02', 'name': 'stone_crab'}, {'id': 3006, 'synset': 'hard-shell_crab.n.01', 'name': 'hard-shell_crab'}, {'id': 3007, 'synset': 'soft-shell_crab.n.02', 'name': 'soft-shell_crab'}, {'id': 3008, 'synset': 'dungeness_crab.n.02', 'name': 'Dungeness_crab'}, {'id': 3009, 'synset': 'rock_crab.n.01', 'name': 'rock_crab'}, {'id': 3010, 'synset': 'jonah_crab.n.01', 'name': 'Jonah_crab'}, {'id': 3011, 'synset': 'swimming_crab.n.01', 'name': 'swimming_crab'}, {'id': 3012, 'synset': 'english_lady_crab.n.01', 'name': 'English_lady_crab'}, {'id': 3013, 'synset': 'american_lady_crab.n.01', 'name': 'American_lady_crab'}, {'id': 3014, 'synset': 'blue_crab.n.02', 'name': 'blue_crab'}, {'id': 3015, 'synset': 'fiddler_crab.n.01', 'name': 'fiddler_crab'}, {'id': 3016, 'synset': 'pea_crab.n.01', 'name': 'pea_crab'}, {'id': 3017, 'synset': 'king_crab.n.03', 'name': 'king_crab'}, {'id': 3018, 'synset': 'spider_crab.n.01', 'name': 'spider_crab'}, {'id': 3019, 'synset': 'european_spider_crab.n.01', 'name': 'European_spider_crab'}, {'id': 3020, 'synset': 'giant_crab.n.01', 'name': 'giant_crab'}, {'id': 3021, 'synset': 'lobster.n.02', 'name': 'lobster'}, {'id': 3022, 'synset': 'true_lobster.n.01', 'name': 'true_lobster'}, {'id': 3023, 'synset': 'american_lobster.n.02', 'name': 'American_lobster'}, {'id': 3024, 'synset': 'european_lobster.n.02', 'name': 'European_lobster'}, {'id': 3025, 'synset': 'cape_lobster.n.01', 'name': 'Cape_lobster'}, {'id': 3026, 'synset': 'norway_lobster.n.01', 'name': 'Norway_lobster'}, {'id': 3027, 'synset': 'crayfish.n.03', 'name': 'crayfish'}, {'id': 3028, 'synset': 'old_world_crayfish.n.01', 'name': 'Old_World_crayfish'}, {'id': 3029, 'synset': 'american_crayfish.n.01', 'name': 'American_crayfish'}, {'id': 3030, 'synset': 'hermit_crab.n.01', 'name': 'hermit_crab'}, {'id': 3031, 'synset': 'shrimp.n.03', 'name': 'shrimp'}, {'id': 3032, 'synset': 'snapping_shrimp.n.01', 'name': 'snapping_shrimp'}, {'id': 3033, 'synset': 'prawn.n.02', 'name': 'prawn'}, {'id': 3034, 'synset': 'long-clawed_prawn.n.01', 'name': 'long-clawed_prawn'}, {'id': 3035, 'synset': 'tropical_prawn.n.01', 'name': 'tropical_prawn'}, {'id': 3036, 'synset': 'krill.n.01', 'name': 'krill'}, {'id': 3037, 'synset': 'euphausia_pacifica.n.01', 'name': 'Euphausia_pacifica'}, {'id': 3038, 'synset': 'opossum_shrimp.n.01', 'name': 'opossum_shrimp'}, {'id': 3039, 'synset': 'stomatopod.n.01', 'name': 'stomatopod'}, {'id': 3040, 'synset': 'mantis_shrimp.n.01', 'name': 'mantis_shrimp'}, {'id': 3041, 'synset': 'squilla.n.01', 'name': 'squilla'}, {'id': 3042, 'synset': 'isopod.n.01', 'name': 'isopod'}, {'id': 3043, 'synset': 'woodlouse.n.01', 'name': 'woodlouse'}, {'id': 3044, 'synset': 'pill_bug.n.01', 'name': 'pill_bug'}, {'id': 3045, 'synset': 'sow_bug.n.01', 'name': 'sow_bug'}, {'id': 3046, 'synset': 'sea_louse.n.01', 'name': 'sea_louse'}, {'id': 3047, 'synset': 'amphipod.n.01', 'name': 'amphipod'}, {'id': 3048, 'synset': 'skeleton_shrimp.n.01', 'name': 'skeleton_shrimp'}, {'id': 3049, 'synset': 'whale_louse.n.01', 'name': 'whale_louse'}, {'id': 3050, 'synset': 'daphnia.n.01', 'name': 'daphnia'}, {'id': 3051, 'synset': 'fairy_shrimp.n.01', 'name': 'fairy_shrimp'}, {'id': 3052, 'synset': 'brine_shrimp.n.01', 'name': 'brine_shrimp'}, {'id': 3053, 'synset': 'tadpole_shrimp.n.01', 'name': 'tadpole_shrimp'}, {'id': 3054, 'synset': 'copepod.n.01', 'name': 'copepod'}, {'id': 3055, 'synset': 'cyclops.n.02', 'name': 'cyclops'}, {'id': 3056, 'synset': 'seed_shrimp.n.01', 'name': 'seed_shrimp'}, {'id': 3057, 'synset': 'barnacle.n.01', 'name': 'barnacle'}, {'id': 3058, 'synset': 'acorn_barnacle.n.01', 'name': 'acorn_barnacle'}, {'id': 3059, 'synset': 'goose_barnacle.n.01', 'name': 'goose_barnacle'}, {'id': 3060, 'synset': 'onychophoran.n.01', 'name': 'onychophoran'}, {'id': 3061, 'synset': 'wading_bird.n.01', 'name': 'wading_bird'}, {'id': 3062, 'synset': 'stork.n.01', 'name': 'stork'}, {'id': 3063, 'synset': 'white_stork.n.01', 'name': 'white_stork'}, {'id': 3064, 'synset': 'black_stork.n.01', 'name': 'black_stork'}, {'id': 3065, 'synset': 'adjutant_bird.n.01', 'name': 'adjutant_bird'}, {'id': 3066, 'synset': 'marabou.n.01', 'name': 'marabou'}, {'id': 3067, 'synset': 'openbill.n.01', 'name': 'openbill'}, {'id': 3068, 'synset': 'jabiru.n.03', 'name': 'jabiru'}, {'id': 3069, 'synset': 'saddlebill.n.01', 'name': 'saddlebill'}, {'id': 3070, 'synset': 'policeman_bird.n.01', 'name': 'policeman_bird'}, {'id': 3071, 'synset': 'wood_ibis.n.02', 'name': 'wood_ibis'}, {'id': 3072, 'synset': 'shoebill.n.01', 'name': 'shoebill'}, {'id': 3073, 'synset': 'ibis.n.01', 'name': 'ibis'}, {'id': 3074, 'synset': 'wood_ibis.n.01', 'name': 'wood_ibis'}, {'id': 3075, 'synset': 'sacred_ibis.n.01', 'name': 'sacred_ibis'}, {'id': 3076, 'synset': 'spoonbill.n.01', 'name': 'spoonbill'}, {'id': 3077, 'synset': 'common_spoonbill.n.01', 'name': 'common_spoonbill'}, {'id': 3078, 'synset': 'roseate_spoonbill.n.01', 'name': 'roseate_spoonbill'}, {'id': 3079, 'synset': 'great_blue_heron.n.01', 'name': 'great_blue_heron'}, {'id': 3080, 'synset': 'great_white_heron.n.03', 'name': 'great_white_heron'}, {'id': 3081, 'synset': 'egret.n.01', 'name': 'egret'}, {'id': 3082, 'synset': 'little_blue_heron.n.01', 'name': 'little_blue_heron'}, {'id': 3083, 'synset': 'snowy_egret.n.01', 'name': 'snowy_egret'}, {'id': 3084, 'synset': 'little_egret.n.01', 'name': 'little_egret'}, {'id': 3085, 'synset': 'great_white_heron.n.02', 'name': 'great_white_heron'}, {'id': 3086, 'synset': 'american_egret.n.01', 'name': 'American_egret'}, {'id': 3087, 'synset': 'cattle_egret.n.01', 'name': 'cattle_egret'}, {'id': 3088, 'synset': 'night_heron.n.01', 'name': 'night_heron'}, {'id': 3089, 'synset': 'black-crowned_night_heron.n.01', 'name': 'black-crowned_night_heron'}, {'id': 3090, 'synset': 'yellow-crowned_night_heron.n.01', 'name': 'yellow-crowned_night_heron'}, {'id': 3091, 'synset': 'boatbill.n.01', 'name': 'boatbill'}, {'id': 3092, 'synset': 'bittern.n.01', 'name': 'bittern'}, {'id': 3093, 'synset': 'american_bittern.n.01', 'name': 'American_bittern'}, {'id': 3094, 'synset': 'european_bittern.n.01', 'name': 'European_bittern'}, {'id': 3095, 'synset': 'least_bittern.n.01', 'name': 'least_bittern'}, {'id': 3096, 'synset': 'crane.n.05', 'name': 'crane'}, {'id': 3097, 'synset': 'whooping_crane.n.01', 'name': 'whooping_crane'}, {'id': 3098, 'synset': 'courlan.n.01', 'name': 'courlan'}, {'id': 3099, 'synset': 'limpkin.n.01', 'name': 'limpkin'}, {'id': 3100, 'synset': 'crested_cariama.n.01', 'name': 'crested_cariama'}, {'id': 3101, 'synset': 'chunga.n.01', 'name': 'chunga'}, {'id': 3102, 'synset': 'rail.n.05', 'name': 'rail'}, {'id': 3103, 'synset': 'weka.n.01', 'name': 'weka'}, {'id': 3104, 'synset': 'crake.n.01', 'name': 'crake'}, {'id': 3105, 'synset': 'corncrake.n.01', 'name': 'corncrake'}, {'id': 3106, 'synset': 'spotted_crake.n.01', 'name': 'spotted_crake'}, {'id': 3107, 'synset': 'gallinule.n.01', 'name': 'gallinule'}, {'id': 3108, 'synset': 'florida_gallinule.n.01', 'name': 'Florida_gallinule'}, {'id': 3109, 'synset': 'moorhen.n.01', 'name': 'moorhen'}, {'id': 3110, 'synset': 'purple_gallinule.n.01', 'name': 'purple_gallinule'}, {'id': 3111, 'synset': 'european_gallinule.n.01', 'name': 'European_gallinule'}, {'id': 3112, 'synset': 'american_gallinule.n.01', 'name': 'American_gallinule'}, {'id': 3113, 'synset': 'notornis.n.01', 'name': 'notornis'}, {'id': 3114, 'synset': 'coot.n.01', 'name': 'coot'}, {'id': 3115, 'synset': 'american_coot.n.01', 'name': 'American_coot'}, {'id': 3116, 'synset': 'old_world_coot.n.01', 'name': 'Old_World_coot'}, {'id': 3117, 'synset': 'bustard.n.01', 'name': 'bustard'}, {'id': 3118, 'synset': 'great_bustard.n.01', 'name': 'great_bustard'}, {'id': 3119, 'synset': 'plain_turkey.n.01', 'name': 'plain_turkey'}, {'id': 3120, 'synset': 'button_quail.n.01', 'name': 'button_quail'}, {'id': 3121, 'synset': 'striped_button_quail.n.01', 'name': 'striped_button_quail'}, {'id': 3122, 'synset': 'plain_wanderer.n.01', 'name': 'plain_wanderer'}, {'id': 3123, 'synset': 'trumpeter.n.03', 'name': 'trumpeter'}, {'id': 3124, 'synset': 'brazilian_trumpeter.n.01', 'name': 'Brazilian_trumpeter'}, {'id': 3125, 'synset': 'shorebird.n.01', 'name': 'shorebird'}, {'id': 3126, 'synset': 'plover.n.01', 'name': 'plover'}, {'id': 3127, 'synset': 'piping_plover.n.01', 'name': 'piping_plover'}, {'id': 3128, 'synset': 'killdeer.n.01', 'name': 'killdeer'}, {'id': 3129, 'synset': 'dotterel.n.01', 'name': 'dotterel'}, {'id': 3130, 'synset': 'golden_plover.n.01', 'name': 'golden_plover'}, {'id': 3131, 'synset': 'lapwing.n.01', 'name': 'lapwing'}, {'id': 3132, 'synset': 'turnstone.n.01', 'name': 'turnstone'}, {'id': 3133, 'synset': 'ruddy_turnstone.n.01', 'name': 'ruddy_turnstone'}, {'id': 3134, 'synset': 'black_turnstone.n.01', 'name': 'black_turnstone'}, {'id': 3135, 'synset': 'sandpiper.n.01', 'name': 'sandpiper'}, {'id': 3136, 'synset': 'surfbird.n.01', 'name': 'surfbird'}, {'id': 3137, 'synset': 'european_sandpiper.n.01', 'name': 'European_sandpiper'}, {'id': 3138, 'synset': 'spotted_sandpiper.n.01', 'name': 'spotted_sandpiper'}, {'id': 3139, 'synset': 'least_sandpiper.n.01', 'name': 'least_sandpiper'}, {'id': 3140, 'synset': 'red-backed_sandpiper.n.01', 'name': 'red-backed_sandpiper'}, {'id': 3141, 'synset': 'greenshank.n.01', 'name': 'greenshank'}, {'id': 3142, 'synset': 'redshank.n.01', 'name': 'redshank'}, {'id': 3143, 'synset': 'yellowlegs.n.01', 'name': 'yellowlegs'}, {'id': 3144, 'synset': 'greater_yellowlegs.n.01', 'name': 'greater_yellowlegs'}, {'id': 3145, 'synset': 'lesser_yellowlegs.n.01', 'name': 'lesser_yellowlegs'}, {'id': 3146, 'synset': 'pectoral_sandpiper.n.01', 'name': 'pectoral_sandpiper'}, {'id': 3147, 'synset': 'knot.n.07', 'name': 'knot'}, {'id': 3148, 'synset': 'curlew_sandpiper.n.01', 'name': 'curlew_sandpiper'}, {'id': 3149, 'synset': 'sanderling.n.01', 'name': 'sanderling'}, {'id': 3150, 'synset': 'upland_sandpiper.n.01', 'name': 'upland_sandpiper'}, {'id': 3151, 'synset': 'ruff.n.03', 'name': 'ruff'}, {'id': 3152, 'synset': 'reeve.n.01', 'name': 'reeve'}, {'id': 3153, 'synset': 'tattler.n.02', 'name': 'tattler'}, {'id': 3154, 'synset': 'polynesian_tattler.n.01', 'name': 'Polynesian_tattler'}, {'id': 3155, 'synset': 'willet.n.01', 'name': 'willet'}, {'id': 3156, 'synset': 'woodcock.n.01', 'name': 'woodcock'}, {'id': 3157, 'synset': 'eurasian_woodcock.n.01', 'name': 'Eurasian_woodcock'}, {'id': 3158, 'synset': 'american_woodcock.n.01', 'name': 'American_woodcock'}, {'id': 3159, 'synset': 'snipe.n.01', 'name': 'snipe'}, {'id': 3160, 'synset': 'whole_snipe.n.01', 'name': 'whole_snipe'}, {'id': 3161, 'synset': "wilson's_snipe.n.01", 'name': "Wilson's_snipe"}, {'id': 3162, 'synset': 'great_snipe.n.01', 'name': 'great_snipe'}, {'id': 3163, 'synset': 'jacksnipe.n.01', 'name': 'jacksnipe'}, {'id': 3164, 'synset': 'dowitcher.n.01', 'name': 'dowitcher'}, {'id': 3165, 'synset': 'greyback.n.02', 'name': 'greyback'}, {'id': 3166, 'synset': 'red-breasted_snipe.n.01', 'name': 'red-breasted_snipe'}, {'id': 3167, 'synset': 'curlew.n.01', 'name': 'curlew'}, {'id': 3168, 'synset': 'european_curlew.n.01', 'name': 'European_curlew'}, {'id': 3169, 'synset': 'eskimo_curlew.n.01', 'name': 'Eskimo_curlew'}, {'id': 3170, 'synset': 'godwit.n.01', 'name': 'godwit'}, {'id': 3171, 'synset': 'hudsonian_godwit.n.01', 'name': 'Hudsonian_godwit'}, {'id': 3172, 'synset': 'stilt.n.04', 'name': 'stilt'}, {'id': 3173, 'synset': 'black-necked_stilt.n.01', 'name': 'black-necked_stilt'}, {'id': 3174, 'synset': 'black-winged_stilt.n.01', 'name': 'black-winged_stilt'}, {'id': 3175, 'synset': 'white-headed_stilt.n.01', 'name': 'white-headed_stilt'}, {'id': 3176, 'synset': 'kaki.n.02', 'name': 'kaki'}, {'id': 3177, 'synset': 'stilt.n.03', 'name': 'stilt'}, {'id': 3178, 'synset': 'banded_stilt.n.01', 'name': 'banded_stilt'}, {'id': 3179, 'synset': 'avocet.n.01', 'name': 'avocet'}, {'id': 3180, 'synset': 'oystercatcher.n.01', 'name': 'oystercatcher'}, {'id': 3181, 'synset': 'phalarope.n.01', 'name': 'phalarope'}, {'id': 3182, 'synset': 'red_phalarope.n.01', 'name': 'red_phalarope'}, {'id': 3183, 'synset': 'northern_phalarope.n.01', 'name': 'northern_phalarope'}, {'id': 3184, 'synset': "wilson's_phalarope.n.01", 'name': "Wilson's_phalarope"}, {'id': 3185, 'synset': 'pratincole.n.01', 'name': 'pratincole'}, {'id': 3186, 'synset': 'courser.n.04', 'name': 'courser'}, {'id': 3187, 'synset': 'cream-colored_courser.n.01', 'name': 'cream-colored_courser'}, {'id': 3188, 'synset': 'crocodile_bird.n.01', 'name': 'crocodile_bird'}, {'id': 3189, 'synset': 'stone_curlew.n.01', 'name': 'stone_curlew'}, {'id': 3190, 'synset': 'coastal_diving_bird.n.01', 'name': 'coastal_diving_bird'}, {'id': 3191, 'synset': 'larid.n.01', 'name': 'larid'}, {'id': 3192, 'synset': 'mew.n.02', 'name': 'mew'}, {'id': 3193, 'synset': 'black-backed_gull.n.01', 'name': 'black-backed_gull'}, {'id': 3194, 'synset': 'herring_gull.n.01', 'name': 'herring_gull'}, {'id': 3195, 'synset': 'laughing_gull.n.01', 'name': 'laughing_gull'}, {'id': 3196, 'synset': 'ivory_gull.n.01', 'name': 'ivory_gull'}, {'id': 3197, 'synset': 'kittiwake.n.01', 'name': 'kittiwake'}, {'id': 3198, 'synset': 'tern.n.01', 'name': 'tern'}, {'id': 3199, 'synset': 'sea_swallow.n.01', 'name': 'sea_swallow'}, {'id': 3200, 'synset': 'skimmer.n.04', 'name': 'skimmer'}, {'id': 3201, 'synset': 'jaeger.n.01', 'name': 'jaeger'}, {'id': 3202, 'synset': 'parasitic_jaeger.n.01', 'name': 'parasitic_jaeger'}, {'id': 3203, 'synset': 'skua.n.01', 'name': 'skua'}, {'id': 3204, 'synset': 'great_skua.n.01', 'name': 'great_skua'}, {'id': 3205, 'synset': 'auk.n.01', 'name': 'auk'}, {'id': 3206, 'synset': 'auklet.n.01', 'name': 'auklet'}, {'id': 3207, 'synset': 'razorbill.n.01', 'name': 'razorbill'}, {'id': 3208, 'synset': 'little_auk.n.01', 'name': 'little_auk'}, {'id': 3209, 'synset': 'guillemot.n.01', 'name': 'guillemot'}, {'id': 3210, 'synset': 'black_guillemot.n.01', 'name': 'black_guillemot'}, {'id': 3211, 'synset': 'pigeon_guillemot.n.01', 'name': 'pigeon_guillemot'}, {'id': 3212, 'synset': 'murre.n.01', 'name': 'murre'}, {'id': 3213, 'synset': 'common_murre.n.01', 'name': 'common_murre'}, {'id': 3214, 'synset': 'thick-billed_murre.n.01', 'name': 'thick-billed_murre'}, {'id': 3215, 'synset': 'atlantic_puffin.n.01', 'name': 'Atlantic_puffin'}, {'id': 3216, 'synset': 'horned_puffin.n.01', 'name': 'horned_puffin'}, {'id': 3217, 'synset': 'tufted_puffin.n.01', 'name': 'tufted_puffin'}, {'id': 3218, 'synset': 'gaviiform_seabird.n.01', 'name': 'gaviiform_seabird'}, {'id': 3219, 'synset': 'loon.n.02', 'name': 'loon'}, {'id': 3220, 'synset': 'podicipitiform_seabird.n.01', 'name': 'podicipitiform_seabird'}, {'id': 3221, 'synset': 'grebe.n.01', 'name': 'grebe'}, {'id': 3222, 'synset': 'great_crested_grebe.n.01', 'name': 'great_crested_grebe'}, {'id': 3223, 'synset': 'red-necked_grebe.n.01', 'name': 'red-necked_grebe'}, {'id': 3224, 'synset': 'black-necked_grebe.n.01', 'name': 'black-necked_grebe'}, {'id': 3225, 'synset': 'dabchick.n.01', 'name': 'dabchick'}, {'id': 3226, 'synset': 'pied-billed_grebe.n.01', 'name': 'pied-billed_grebe'}, {'id': 3227, 'synset': 'pelecaniform_seabird.n.01', 'name': 'pelecaniform_seabird'}, {'id': 3228, 'synset': 'white_pelican.n.01', 'name': 'white_pelican'}, {'id': 3229, 'synset': 'old_world_white_pelican.n.01', 'name': 'Old_world_white_pelican'}, {'id': 3230, 'synset': 'frigate_bird.n.01', 'name': 'frigate_bird'}, {'id': 3231, 'synset': 'gannet.n.01', 'name': 'gannet'}, {'id': 3232, 'synset': 'solan.n.01', 'name': 'solan'}, {'id': 3233, 'synset': 'booby.n.02', 'name': 'booby'}, {'id': 3234, 'synset': 'cormorant.n.01', 'name': 'cormorant'}, {'id': 3235, 'synset': 'snakebird.n.01', 'name': 'snakebird'}, {'id': 3236, 'synset': 'water_turkey.n.01', 'name': 'water_turkey'}, {'id': 3237, 'synset': 'tropic_bird.n.01', 'name': 'tropic_bird'}, {'id': 3238, 'synset': 'sphenisciform_seabird.n.01', 'name': 'sphenisciform_seabird'}, {'id': 3239, 'synset': 'adelie.n.01', 'name': 'Adelie'}, {'id': 3240, 'synset': 'king_penguin.n.01', 'name': 'king_penguin'}, {'id': 3241, 'synset': 'emperor_penguin.n.01', 'name': 'emperor_penguin'}, {'id': 3242, 'synset': 'jackass_penguin.n.01', 'name': 'jackass_penguin'}, {'id': 3243, 'synset': 'rock_hopper.n.01', 'name': 'rock_hopper'}, {'id': 3244, 'synset': 'pelagic_bird.n.01', 'name': 'pelagic_bird'}, {'id': 3245, 'synset': 'procellariiform_seabird.n.01', 'name': 'procellariiform_seabird'}, {'id': 3246, 'synset': 'albatross.n.02', 'name': 'albatross'}, {'id': 3247, 'synset': 'wandering_albatross.n.01', 'name': 'wandering_albatross'}, {'id': 3248, 'synset': 'black-footed_albatross.n.01', 'name': 'black-footed_albatross'}, {'id': 3249, 'synset': 'petrel.n.01', 'name': 'petrel'}, {'id': 3250, 'synset': 'white-chinned_petrel.n.01', 'name': 'white-chinned_petrel'}, {'id': 3251, 'synset': 'giant_petrel.n.01', 'name': 'giant_petrel'}, {'id': 3252, 'synset': 'fulmar.n.01', 'name': 'fulmar'}, {'id': 3253, 'synset': 'shearwater.n.01', 'name': 'shearwater'}, {'id': 3254, 'synset': 'manx_shearwater.n.01', 'name': 'Manx_shearwater'}, {'id': 3255, 'synset': 'storm_petrel.n.01', 'name': 'storm_petrel'}, {'id': 3256, 'synset': 'stormy_petrel.n.01', 'name': 'stormy_petrel'}, {'id': 3257, 'synset': "mother_carey's_chicken.n.01", 'name': "Mother_Carey's_chicken"}, {'id': 3258, 'synset': 'diving_petrel.n.01', 'name': 'diving_petrel'}, {'id': 3259, 'synset': 'aquatic_mammal.n.01', 'name': 'aquatic_mammal'}, {'id': 3260, 'synset': 'cetacean.n.01', 'name': 'cetacean'}, {'id': 3261, 'synset': 'whale.n.02', 'name': 'whale'}, {'id': 3262, 'synset': 'baleen_whale.n.01', 'name': 'baleen_whale'}, {'id': 3263, 'synset': 'right_whale.n.01', 'name': 'right_whale'}, {'id': 3264, 'synset': 'bowhead.n.01', 'name': 'bowhead'}, {'id': 3265, 'synset': 'rorqual.n.01', 'name': 'rorqual'}, {'id': 3266, 'synset': 'blue_whale.n.01', 'name': 'blue_whale'}, {'id': 3267, 'synset': 'finback.n.01', 'name': 'finback'}, {'id': 3268, 'synset': 'sei_whale.n.01', 'name': 'sei_whale'}, {'id': 3269, 'synset': 'lesser_rorqual.n.01', 'name': 'lesser_rorqual'}, {'id': 3270, 'synset': 'humpback.n.03', 'name': 'humpback'}, {'id': 3271, 'synset': 'grey_whale.n.01', 'name': 'grey_whale'}, {'id': 3272, 'synset': 'toothed_whale.n.01', 'name': 'toothed_whale'}, {'id': 3273, 'synset': 'sperm_whale.n.01', 'name': 'sperm_whale'}, {'id': 3274, 'synset': 'pygmy_sperm_whale.n.01', 'name': 'pygmy_sperm_whale'}, {'id': 3275, 'synset': 'dwarf_sperm_whale.n.01', 'name': 'dwarf_sperm_whale'}, {'id': 3276, 'synset': 'beaked_whale.n.01', 'name': 'beaked_whale'}, {'id': 3277, 'synset': 'bottle-nosed_whale.n.01', 'name': 'bottle-nosed_whale'}, {'id': 3278, 'synset': 'common_dolphin.n.01', 'name': 'common_dolphin'}, {'id': 3279, 'synset': 'bottlenose_dolphin.n.01', 'name': 'bottlenose_dolphin'}, {'id': 3280, 'synset': 'atlantic_bottlenose_dolphin.n.01', 'name': 'Atlantic_bottlenose_dolphin'}, {'id': 3281, 'synset': 'pacific_bottlenose_dolphin.n.01', 'name': 'Pacific_bottlenose_dolphin'}, {'id': 3282, 'synset': 'porpoise.n.01', 'name': 'porpoise'}, {'id': 3283, 'synset': 'harbor_porpoise.n.01', 'name': 'harbor_porpoise'}, {'id': 3284, 'synset': 'vaquita.n.01', 'name': 'vaquita'}, {'id': 3285, 'synset': 'grampus.n.02', 'name': 'grampus'}, {'id': 3286, 'synset': 'killer_whale.n.01', 'name': 'killer_whale'}, {'id': 3287, 'synset': 'pilot_whale.n.01', 'name': 'pilot_whale'}, {'id': 3288, 'synset': 'river_dolphin.n.01', 'name': 'river_dolphin'}, {'id': 3289, 'synset': 'narwhal.n.01', 'name': 'narwhal'}, {'id': 3290, 'synset': 'white_whale.n.01', 'name': 'white_whale'}, {'id': 3291, 'synset': 'sea_cow.n.01', 'name': 'sea_cow'}, {'id': 3292, 'synset': 'dugong.n.01', 'name': 'dugong'}, {'id': 3293, 'synset': "steller's_sea_cow.n.01", 'name': "Steller's_sea_cow"}, {'id': 3294, 'synset': 'carnivore.n.01', 'name': 'carnivore'}, {'id': 3295, 'synset': 'omnivore.n.02', 'name': 'omnivore'}, {'id': 3296, 'synset': 'pinniped_mammal.n.01', 'name': 'pinniped_mammal'}, {'id': 3297, 'synset': 'seal.n.09', 'name': 'seal'}, {'id': 3298, 'synset': 'crabeater_seal.n.01', 'name': 'crabeater_seal'}, {'id': 3299, 'synset': 'eared_seal.n.01', 'name': 'eared_seal'}, {'id': 3300, 'synset': 'fur_seal.n.02', 'name': 'fur_seal'}, {'id': 3301, 'synset': 'guadalupe_fur_seal.n.01', 'name': 'guadalupe_fur_seal'}, {'id': 3302, 'synset': 'fur_seal.n.01', 'name': 'fur_seal'}, {'id': 3303, 'synset': 'alaska_fur_seal.n.01', 'name': 'Alaska_fur_seal'}, {'id': 3304, 'synset': 'sea_lion.n.01', 'name': 'sea_lion'}, {'id': 3305, 'synset': 'south_american_sea_lion.n.01', 'name': 'South_American_sea_lion'}, {'id': 3306, 'synset': 'california_sea_lion.n.01', 'name': 'California_sea_lion'}, {'id': 3307, 'synset': 'australian_sea_lion.n.01', 'name': 'Australian_sea_lion'}, {'id': 3308, 'synset': 'steller_sea_lion.n.01', 'name': 'Steller_sea_lion'}, {'id': 3309, 'synset': 'earless_seal.n.01', 'name': 'earless_seal'}, {'id': 3310, 'synset': 'harbor_seal.n.01', 'name': 'harbor_seal'}, {'id': 3311, 'synset': 'harp_seal.n.01', 'name': 'harp_seal'}, {'id': 3312, 'synset': 'elephant_seal.n.01', 'name': 'elephant_seal'}, {'id': 3313, 'synset': 'bearded_seal.n.01', 'name': 'bearded_seal'}, {'id': 3314, 'synset': 'hooded_seal.n.01', 'name': 'hooded_seal'}, {'id': 3315, 'synset': 'atlantic_walrus.n.01', 'name': 'Atlantic_walrus'}, {'id': 3316, 'synset': 'pacific_walrus.n.01', 'name': 'Pacific_walrus'}, {'id': 3317, 'synset': 'fissipedia.n.01', 'name': 'Fissipedia'}, {'id': 3318, 'synset': 'fissiped_mammal.n.01', 'name': 'fissiped_mammal'}, {'id': 3319, 'synset': 'aardvark.n.01', 'name': 'aardvark'}, {'id': 3320, 'synset': 'canine.n.02', 'name': 'canine'}, {'id': 3321, 'synset': 'bitch.n.04', 'name': 'bitch'}, {'id': 3322, 'synset': 'brood_bitch.n.01', 'name': 'brood_bitch'}, {'id': 3323, 'synset': 'pooch.n.01', 'name': 'pooch'}, {'id': 3324, 'synset': 'cur.n.01', 'name': 'cur'}, {'id': 3325, 'synset': 'feist.n.01', 'name': 'feist'}, {'id': 3326, 'synset': 'pariah_dog.n.01', 'name': 'pariah_dog'}, {'id': 3327, 'synset': 'lapdog.n.01', 'name': 'lapdog'}, {'id': 3328, 'synset': 'toy_dog.n.01', 'name': 'toy_dog'}, {'id': 3329, 'synset': 'chihuahua.n.03', 'name': 'Chihuahua'}, {'id': 3330, 'synset': 'japanese_spaniel.n.01', 'name': 'Japanese_spaniel'}, {'id': 3331, 'synset': 'maltese_dog.n.01', 'name': 'Maltese_dog'}, {'id': 3332, 'synset': 'pekinese.n.01', 'name': 'Pekinese'}, {'id': 3333, 'synset': 'shih-tzu.n.01', 'name': 'Shih-Tzu'}, {'id': 3334, 'synset': 'toy_spaniel.n.01', 'name': 'toy_spaniel'}, {'id': 3335, 'synset': 'english_toy_spaniel.n.01', 'name': 'English_toy_spaniel'}, {'id': 3336, 'synset': 'blenheim_spaniel.n.01', 'name': 'Blenheim_spaniel'}, {'id': 3337, 'synset': 'king_charles_spaniel.n.01', 'name': 'King_Charles_spaniel'}, {'id': 3338, 'synset': 'papillon.n.01', 'name': 'papillon'}, {'id': 3339, 'synset': 'toy_terrier.n.01', 'name': 'toy_terrier'}, {'id': 3340, 'synset': 'hunting_dog.n.01', 'name': 'hunting_dog'}, {'id': 3341, 'synset': 'courser.n.03', 'name': 'courser'}, {'id': 3342, 'synset': 'rhodesian_ridgeback.n.01', 'name': 'Rhodesian_ridgeback'}, {'id': 3343, 'synset': 'hound.n.01', 'name': 'hound'}, {'id': 3344, 'synset': 'afghan_hound.n.01', 'name': 'Afghan_hound'}, {'id': 3345, 'synset': 'basset.n.01', 'name': 'basset'}, {'id': 3346, 'synset': 'beagle.n.01', 'name': 'beagle'}, {'id': 3347, 'synset': 'bloodhound.n.01', 'name': 'bloodhound'}, {'id': 3348, 'synset': 'bluetick.n.01', 'name': 'bluetick'}, {'id': 3349, 'synset': 'boarhound.n.01', 'name': 'boarhound'}, {'id': 3350, 'synset': 'coonhound.n.01', 'name': 'coonhound'}, {'id': 3351, 'synset': 'coondog.n.01', 'name': 'coondog'}, {'id': 3352, 'synset': 'black-and-tan_coonhound.n.01', 'name': 'black-and-tan_coonhound'}, {'id': 3353, 'synset': 'dachshund.n.01', 'name': 'dachshund'}, {'id': 3354, 'synset': 'sausage_dog.n.01', 'name': 'sausage_dog'}, {'id': 3355, 'synset': 'foxhound.n.01', 'name': 'foxhound'}, {'id': 3356, 'synset': 'american_foxhound.n.01', 'name': 'American_foxhound'}, {'id': 3357, 'synset': 'walker_hound.n.01', 'name': 'Walker_hound'}, {'id': 3358, 'synset': 'english_foxhound.n.01', 'name': 'English_foxhound'}, {'id': 3359, 'synset': 'harrier.n.02', 'name': 'harrier'}, {'id': 3360, 'synset': 'plott_hound.n.01', 'name': 'Plott_hound'}, {'id': 3361, 'synset': 'redbone.n.01', 'name': 'redbone'}, {'id': 3362, 'synset': 'wolfhound.n.01', 'name': 'wolfhound'}, {'id': 3363, 'synset': 'borzoi.n.01', 'name': 'borzoi'}, {'id': 3364, 'synset': 'irish_wolfhound.n.01', 'name': 'Irish_wolfhound'}, {'id': 3365, 'synset': 'greyhound.n.01', 'name': 'greyhound'}, {'id': 3366, 'synset': 'italian_greyhound.n.01', 'name': 'Italian_greyhound'}, {'id': 3367, 'synset': 'whippet.n.01', 'name': 'whippet'}, {'id': 3368, 'synset': 'ibizan_hound.n.01', 'name': 'Ibizan_hound'}, {'id': 3369, 'synset': 'norwegian_elkhound.n.01', 'name': 'Norwegian_elkhound'}, {'id': 3370, 'synset': 'otterhound.n.01', 'name': 'otterhound'}, {'id': 3371, 'synset': 'saluki.n.01', 'name': 'Saluki'}, {'id': 3372, 'synset': 'scottish_deerhound.n.01', 'name': 'Scottish_deerhound'}, {'id': 3373, 'synset': 'staghound.n.01', 'name': 'staghound'}, {'id': 3374, 'synset': 'weimaraner.n.01', 'name': 'Weimaraner'}, {'id': 3375, 'synset': 'terrier.n.01', 'name': 'terrier'}, {'id': 3376, 'synset': 'bullterrier.n.01', 'name': 'bullterrier'}, {'id': 3377, 'synset': 'staffordshire_bullterrier.n.01', 'name': 'Staffordshire_bullterrier'}, {'id': 3378, 'synset': 'american_staffordshire_terrier.n.01', 'name': 'American_Staffordshire_terrier'}, {'id': 3379, 'synset': 'bedlington_terrier.n.01', 'name': 'Bedlington_terrier'}, {'id': 3380, 'synset': 'border_terrier.n.01', 'name': 'Border_terrier'}, {'id': 3381, 'synset': 'kerry_blue_terrier.n.01', 'name': 'Kerry_blue_terrier'}, {'id': 3382, 'synset': 'irish_terrier.n.01', 'name': 'Irish_terrier'}, {'id': 3383, 'synset': 'norfolk_terrier.n.01', 'name': 'Norfolk_terrier'}, {'id': 3384, 'synset': 'norwich_terrier.n.01', 'name': 'Norwich_terrier'}, {'id': 3385, 'synset': 'yorkshire_terrier.n.01', 'name': 'Yorkshire_terrier'}, {'id': 3386, 'synset': 'rat_terrier.n.01', 'name': 'rat_terrier'}, {'id': 3387, 'synset': 'manchester_terrier.n.01', 'name': 'Manchester_terrier'}, {'id': 3388, 'synset': 'toy_manchester.n.01', 'name': 'toy_Manchester'}, {'id': 3389, 'synset': 'fox_terrier.n.01', 'name': 'fox_terrier'}, {'id': 3390, 'synset': 'smooth-haired_fox_terrier.n.01', 'name': 'smooth-haired_fox_terrier'}, {'id': 3391, 'synset': 'wire-haired_fox_terrier.n.01', 'name': 'wire-haired_fox_terrier'}, {'id': 3392, 'synset': 'wirehair.n.01', 'name': 'wirehair'}, {'id': 3393, 'synset': 'lakeland_terrier.n.01', 'name': 'Lakeland_terrier'}, {'id': 3394, 'synset': 'welsh_terrier.n.01', 'name': 'Welsh_terrier'}, {'id': 3395, 'synset': 'sealyham_terrier.n.01', 'name': 'Sealyham_terrier'}, {'id': 3396, 'synset': 'airedale.n.01', 'name': 'Airedale'}, {'id': 3397, 'synset': 'cairn.n.02', 'name': 'cairn'}, {'id': 3398, 'synset': 'australian_terrier.n.01', 'name': 'Australian_terrier'}, {'id': 3399, 'synset': 'dandie_dinmont.n.01', 'name': 'Dandie_Dinmont'}, {'id': 3400, 'synset': 'boston_bull.n.01', 'name': 'Boston_bull'}, {'id': 3401, 'synset': 'schnauzer.n.01', 'name': 'schnauzer'}, {'id': 3402, 'synset': 'miniature_schnauzer.n.01', 'name': 'miniature_schnauzer'}, {'id': 3403, 'synset': 'giant_schnauzer.n.01', 'name': 'giant_schnauzer'}, {'id': 3404, 'synset': 'standard_schnauzer.n.01', 'name': 'standard_schnauzer'}, {'id': 3405, 'synset': 'scotch_terrier.n.01', 'name': 'Scotch_terrier'}, {'id': 3406, 'synset': 'tibetan_terrier.n.01', 'name': 'Tibetan_terrier'}, {'id': 3407, 'synset': 'silky_terrier.n.01', 'name': 'silky_terrier'}, {'id': 3408, 'synset': 'skye_terrier.n.01', 'name': 'Skye_terrier'}, {'id': 3409, 'synset': 'clydesdale_terrier.n.01', 'name': 'Clydesdale_terrier'}, {'id': 3410, 'synset': 'soft-coated_wheaten_terrier.n.01', 'name': 'soft-coated_wheaten_terrier'}, {'id': 3411, 'synset': 'west_highland_white_terrier.n.01', 'name': 'West_Highland_white_terrier'}, {'id': 3412, 'synset': 'lhasa.n.02', 'name': 'Lhasa'}, {'id': 3413, 'synset': 'sporting_dog.n.01', 'name': 'sporting_dog'}, {'id': 3414, 'synset': 'bird_dog.n.01', 'name': 'bird_dog'}, {'id': 3415, 'synset': 'water_dog.n.02', 'name': 'water_dog'}, {'id': 3416, 'synset': 'retriever.n.01', 'name': 'retriever'}, {'id': 3417, 'synset': 'flat-coated_retriever.n.01', 'name': 'flat-coated_retriever'}, {'id': 3418, 'synset': 'curly-coated_retriever.n.01', 'name': 'curly-coated_retriever'}, {'id': 3419, 'synset': 'golden_retriever.n.01', 'name': 'golden_retriever'}, {'id': 3420, 'synset': 'labrador_retriever.n.01', 'name': 'Labrador_retriever'}, {'id': 3421, 'synset': 'chesapeake_bay_retriever.n.01', 'name': 'Chesapeake_Bay_retriever'}, {'id': 3422, 'synset': 'pointer.n.04', 'name': 'pointer'}, {'id': 3423, 'synset': 'german_short-haired_pointer.n.01', 'name': 'German_short-haired_pointer'}, {'id': 3424, 'synset': 'setter.n.02', 'name': 'setter'}, {'id': 3425, 'synset': 'vizsla.n.01', 'name': 'vizsla'}, {'id': 3426, 'synset': 'english_setter.n.01', 'name': 'English_setter'}, {'id': 3427, 'synset': 'irish_setter.n.01', 'name': 'Irish_setter'}, {'id': 3428, 'synset': 'gordon_setter.n.01', 'name': 'Gordon_setter'}, {'id': 3429, 'synset': 'spaniel.n.01', 'name': 'spaniel'}, {'id': 3430, 'synset': 'brittany_spaniel.n.01', 'name': 'Brittany_spaniel'}, {'id': 3431, 'synset': 'clumber.n.01', 'name': 'clumber'}, {'id': 3432, 'synset': 'field_spaniel.n.01', 'name': 'field_spaniel'}, {'id': 3433, 'synset': 'springer_spaniel.n.01', 'name': 'springer_spaniel'}, {'id': 3434, 'synset': 'english_springer.n.01', 'name': 'English_springer'}, {'id': 3435, 'synset': 'welsh_springer_spaniel.n.01', 'name': 'Welsh_springer_spaniel'}, {'id': 3436, 'synset': 'cocker_spaniel.n.01', 'name': 'cocker_spaniel'}, {'id': 3437, 'synset': 'sussex_spaniel.n.01', 'name': 'Sussex_spaniel'}, {'id': 3438, 'synset': 'water_spaniel.n.01', 'name': 'water_spaniel'}, {'id': 3439, 'synset': 'american_water_spaniel.n.01', 'name': 'American_water_spaniel'}, {'id': 3440, 'synset': 'irish_water_spaniel.n.01', 'name': 'Irish_water_spaniel'}, {'id': 3441, 'synset': 'griffon.n.03', 'name': 'griffon'}, {'id': 3442, 'synset': 'working_dog.n.01', 'name': 'working_dog'}, {'id': 3443, 'synset': 'watchdog.n.02', 'name': 'watchdog'}, {'id': 3444, 'synset': 'kuvasz.n.01', 'name': 'kuvasz'}, {'id': 3445, 'synset': 'attack_dog.n.01', 'name': 'attack_dog'}, {'id': 3446, 'synset': 'housedog.n.01', 'name': 'housedog'}, {'id': 3447, 'synset': 'schipperke.n.01', 'name': 'schipperke'}, {'id': 3448, 'synset': 'belgian_sheepdog.n.01', 'name': 'Belgian_sheepdog'}, {'id': 3449, 'synset': 'groenendael.n.01', 'name': 'groenendael'}, {'id': 3450, 'synset': 'malinois.n.01', 'name': 'malinois'}, {'id': 3451, 'synset': 'briard.n.01', 'name': 'briard'}, {'id': 3452, 'synset': 'kelpie.n.02', 'name': 'kelpie'}, {'id': 3453, 'synset': 'komondor.n.01', 'name': 'komondor'}, {'id': 3454, 'synset': 'old_english_sheepdog.n.01', 'name': 'Old_English_sheepdog'}, {'id': 3455, 'synset': 'shetland_sheepdog.n.01', 'name': 'Shetland_sheepdog'}, {'id': 3456, 'synset': 'collie.n.01', 'name': 'collie'}, {'id': 3457, 'synset': 'border_collie.n.01', 'name': 'Border_collie'}, {'id': 3458, 'synset': 'bouvier_des_flandres.n.01', 'name': 'Bouvier_des_Flandres'}, {'id': 3459, 'synset': 'rottweiler.n.01', 'name': 'Rottweiler'}, {'id': 3460, 'synset': 'german_shepherd.n.01', 'name': 'German_shepherd'}, {'id': 3461, 'synset': 'police_dog.n.01', 'name': 'police_dog'}, {'id': 3462, 'synset': 'pinscher.n.01', 'name': 'pinscher'}, {'id': 3463, 'synset': 'doberman.n.01', 'name': 'Doberman'}, {'id': 3464, 'synset': 'miniature_pinscher.n.01', 'name': 'miniature_pinscher'}, {'id': 3465, 'synset': 'sennenhunde.n.01', 'name': 'Sennenhunde'}, {'id': 3466, 'synset': 'greater_swiss_mountain_dog.n.01', 'name': 'Greater_Swiss_Mountain_dog'}, {'id': 3467, 'synset': 'bernese_mountain_dog.n.01', 'name': 'Bernese_mountain_dog'}, {'id': 3468, 'synset': 'appenzeller.n.01', 'name': 'Appenzeller'}, {'id': 3469, 'synset': 'entlebucher.n.01', 'name': 'EntleBucher'}, {'id': 3470, 'synset': 'boxer.n.04', 'name': 'boxer'}, {'id': 3471, 'synset': 'mastiff.n.01', 'name': 'mastiff'}, {'id': 3472, 'synset': 'bull_mastiff.n.01', 'name': 'bull_mastiff'}, {'id': 3473, 'synset': 'tibetan_mastiff.n.01', 'name': 'Tibetan_mastiff'}, {'id': 3474, 'synset': 'french_bulldog.n.01', 'name': 'French_bulldog'}, {'id': 3475, 'synset': 'great_dane.n.01', 'name': 'Great_Dane'}, {'id': 3476, 'synset': 'guide_dog.n.01', 'name': 'guide_dog'}, {'id': 3477, 'synset': 'seeing_eye_dog.n.01', 'name': 'Seeing_Eye_dog'}, {'id': 3478, 'synset': 'hearing_dog.n.01', 'name': 'hearing_dog'}, {'id': 3479, 'synset': 'saint_bernard.n.01', 'name': 'Saint_Bernard'}, {'id': 3480, 'synset': 'seizure-alert_dog.n.01', 'name': 'seizure-alert_dog'}, {'id': 3481, 'synset': 'sled_dog.n.01', 'name': 'sled_dog'}, {'id': 3482, 'synset': 'eskimo_dog.n.01', 'name': 'Eskimo_dog'}, {'id': 3483, 'synset': 'malamute.n.01', 'name': 'malamute'}, {'id': 3484, 'synset': 'siberian_husky.n.01', 'name': 'Siberian_husky'}, {'id': 3485, 'synset': 'liver-spotted_dalmatian.n.01', 'name': 'liver-spotted_dalmatian'}, {'id': 3486, 'synset': 'affenpinscher.n.01', 'name': 'affenpinscher'}, {'id': 3487, 'synset': 'basenji.n.01', 'name': 'basenji'}, {'id': 3488, 'synset': 'leonberg.n.01', 'name': 'Leonberg'}, {'id': 3489, 'synset': 'newfoundland.n.01', 'name': 'Newfoundland'}, {'id': 3490, 'synset': 'great_pyrenees.n.01', 'name': 'Great_Pyrenees'}, {'id': 3491, 'synset': 'spitz.n.01', 'name': 'spitz'}, {'id': 3492, 'synset': 'samoyed.n.03', 'name': 'Samoyed'}, {'id': 3493, 'synset': 'pomeranian.n.01', 'name': 'Pomeranian'}, {'id': 3494, 'synset': 'chow.n.03', 'name': 'chow'}, {'id': 3495, 'synset': 'keeshond.n.01', 'name': 'keeshond'}, {'id': 3496, 'synset': 'griffon.n.02', 'name': 'griffon'}, {'id': 3497, 'synset': 'brabancon_griffon.n.01', 'name': 'Brabancon_griffon'}, {'id': 3498, 'synset': 'corgi.n.01', 'name': 'corgi'}, {'id': 3499, 'synset': 'pembroke.n.01', 'name': 'Pembroke'}, {'id': 3500, 'synset': 'cardigan.n.02', 'name': 'Cardigan'}, {'id': 3501, 'synset': 'poodle.n.01', 'name': 'poodle'}, {'id': 3502, 'synset': 'toy_poodle.n.01', 'name': 'toy_poodle'}, {'id': 3503, 'synset': 'miniature_poodle.n.01', 'name': 'miniature_poodle'}, {'id': 3504, 'synset': 'standard_poodle.n.01', 'name': 'standard_poodle'}, {'id': 3505, 'synset': 'large_poodle.n.01', 'name': 'large_poodle'}, {'id': 3506, 'synset': 'mexican_hairless.n.01', 'name': 'Mexican_hairless'}, {'id': 3507, 'synset': 'timber_wolf.n.01', 'name': 'timber_wolf'}, {'id': 3508, 'synset': 'white_wolf.n.01', 'name': 'white_wolf'}, {'id': 3509, 'synset': 'red_wolf.n.01', 'name': 'red_wolf'}, {'id': 3510, 'synset': 'coyote.n.01', 'name': 'coyote'}, {'id': 3511, 'synset': 'coydog.n.01', 'name': 'coydog'}, {'id': 3512, 'synset': 'jackal.n.01', 'name': 'jackal'}, {'id': 3513, 'synset': 'wild_dog.n.01', 'name': 'wild_dog'}, {'id': 3514, 'synset': 'dingo.n.01', 'name': 'dingo'}, {'id': 3515, 'synset': 'dhole.n.01', 'name': 'dhole'}, {'id': 3516, 'synset': 'crab-eating_dog.n.01', 'name': 'crab-eating_dog'}, {'id': 3517, 'synset': 'raccoon_dog.n.01', 'name': 'raccoon_dog'}, {'id': 3518, 'synset': 'african_hunting_dog.n.01', 'name': 'African_hunting_dog'}, {'id': 3519, 'synset': 'hyena.n.01', 'name': 'hyena'}, {'id': 3520, 'synset': 'striped_hyena.n.01', 'name': 'striped_hyena'}, {'id': 3521, 'synset': 'brown_hyena.n.01', 'name': 'brown_hyena'}, {'id': 3522, 'synset': 'spotted_hyena.n.01', 'name': 'spotted_hyena'}, {'id': 3523, 'synset': 'aardwolf.n.01', 'name': 'aardwolf'}, {'id': 3524, 'synset': 'fox.n.01', 'name': 'fox'}, {'id': 3525, 'synset': 'vixen.n.02', 'name': 'vixen'}, {'id': 3526, 'synset': 'reynard.n.01', 'name': 'Reynard'}, {'id': 3527, 'synset': 'red_fox.n.03', 'name': 'red_fox'}, {'id': 3528, 'synset': 'black_fox.n.01', 'name': 'black_fox'}, {'id': 3529, 'synset': 'silver_fox.n.01', 'name': 'silver_fox'}, {'id': 3530, 'synset': 'red_fox.n.02', 'name': 'red_fox'}, {'id': 3531, 'synset': 'kit_fox.n.02', 'name': 'kit_fox'}, {'id': 3532, 'synset': 'kit_fox.n.01', 'name': 'kit_fox'}, {'id': 3533, 'synset': 'arctic_fox.n.01', 'name': 'Arctic_fox'}, {'id': 3534, 'synset': 'blue_fox.n.01', 'name': 'blue_fox'}, {'id': 3535, 'synset': 'grey_fox.n.01', 'name': 'grey_fox'}, {'id': 3536, 'synset': 'feline.n.01', 'name': 'feline'}, {'id': 3537, 'synset': 'domestic_cat.n.01', 'name': 'domestic_cat'}, {'id': 3538, 'synset': 'kitty.n.04', 'name': 'kitty'}, {'id': 3539, 'synset': 'mouser.n.01', 'name': 'mouser'}, {'id': 3540, 'synset': 'alley_cat.n.01', 'name': 'alley_cat'}, {'id': 3541, 'synset': 'stray.n.01', 'name': 'stray'}, {'id': 3542, 'synset': 'tom.n.02', 'name': 'tom'}, {'id': 3543, 'synset': 'gib.n.02', 'name': 'gib'}, {'id': 3544, 'synset': 'tabby.n.02', 'name': 'tabby'}, {'id': 3545, 'synset': 'tabby.n.01', 'name': 'tabby'}, {'id': 3546, 'synset': 'tiger_cat.n.02', 'name': 'tiger_cat'}, {'id': 3547, 'synset': 'tortoiseshell.n.03', 'name': 'tortoiseshell'}, {'id': 3548, 'synset': 'persian_cat.n.01', 'name': 'Persian_cat'}, {'id': 3549, 'synset': 'angora.n.04', 'name': 'Angora'}, {'id': 3550, 'synset': 'siamese_cat.n.01', 'name': 'Siamese_cat'}, {'id': 3551, 'synset': 'blue_point_siamese.n.01', 'name': 'blue_point_Siamese'}, {'id': 3552, 'synset': 'burmese_cat.n.01', 'name': 'Burmese_cat'}, {'id': 3553, 'synset': 'egyptian_cat.n.01', 'name': 'Egyptian_cat'}, {'id': 3554, 'synset': 'maltese.n.03', 'name': 'Maltese'}, {'id': 3555, 'synset': 'abyssinian.n.01', 'name': 'Abyssinian'}, {'id': 3556, 'synset': 'manx.n.02', 'name': 'Manx'}, {'id': 3557, 'synset': 'wildcat.n.03', 'name': 'wildcat'}, {'id': 3558, 'synset': 'sand_cat.n.01', 'name': 'sand_cat'}, {'id': 3559, 'synset': 'european_wildcat.n.01', 'name': 'European_wildcat'}, {'id': 3560, 'synset': 'ocelot.n.01', 'name': 'ocelot'}, {'id': 3561, 'synset': 'jaguarundi.n.01', 'name': 'jaguarundi'}, {'id': 3562, 'synset': 'kaffir_cat.n.01', 'name': 'kaffir_cat'}, {'id': 3563, 'synset': 'jungle_cat.n.01', 'name': 'jungle_cat'}, {'id': 3564, 'synset': 'serval.n.01', 'name': 'serval'}, {'id': 3565, 'synset': 'leopard_cat.n.01', 'name': 'leopard_cat'}, {'id': 3566, 'synset': 'margay.n.01', 'name': 'margay'}, {'id': 3567, 'synset': 'manul.n.01', 'name': 'manul'}, {'id': 3568, 'synset': 'lynx.n.02', 'name': 'lynx'}, {'id': 3569, 'synset': 'common_lynx.n.01', 'name': 'common_lynx'}, {'id': 3570, 'synset': 'canada_lynx.n.01', 'name': 'Canada_lynx'}, {'id': 3571, 'synset': 'bobcat.n.01', 'name': 'bobcat'}, {'id': 3572, 'synset': 'spotted_lynx.n.01', 'name': 'spotted_lynx'}, {'id': 3573, 'synset': 'caracal.n.01', 'name': 'caracal'}, {'id': 3574, 'synset': 'big_cat.n.01', 'name': 'big_cat'}, {'id': 3575, 'synset': 'leopard.n.02', 'name': 'leopard'}, {'id': 3576, 'synset': 'leopardess.n.01', 'name': 'leopardess'}, {'id': 3577, 'synset': 'panther.n.02', 'name': 'panther'}, {'id': 3578, 'synset': 'snow_leopard.n.01', 'name': 'snow_leopard'}, {'id': 3579, 'synset': 'jaguar.n.01', 'name': 'jaguar'}, {'id': 3580, 'synset': 'lioness.n.01', 'name': 'lioness'}, {'id': 3581, 'synset': 'lionet.n.01', 'name': 'lionet'}, {'id': 3582, 'synset': 'bengal_tiger.n.01', 'name': 'Bengal_tiger'}, {'id': 3583, 'synset': 'tigress.n.01', 'name': 'tigress'}, {'id': 3584, 'synset': 'liger.n.01', 'name': 'liger'}, {'id': 3585, 'synset': 'tiglon.n.01', 'name': 'tiglon'}, {'id': 3586, 'synset': 'cheetah.n.01', 'name': 'cheetah'}, {'id': 3587, 'synset': 'saber-toothed_tiger.n.01', 'name': 'saber-toothed_tiger'}, {'id': 3588, 'synset': 'smiledon_californicus.n.01', 'name': 'Smiledon_californicus'}, {'id': 3589, 'synset': 'brown_bear.n.01', 'name': 'brown_bear'}, {'id': 3590, 'synset': 'bruin.n.01', 'name': 'bruin'}, {'id': 3591, 'synset': 'syrian_bear.n.01', 'name': 'Syrian_bear'}, {'id': 3592, 'synset': 'alaskan_brown_bear.n.01', 'name': 'Alaskan_brown_bear'}, {'id': 3593, 'synset': 'american_black_bear.n.01', 'name': 'American_black_bear'}, {'id': 3594, 'synset': 'cinnamon_bear.n.01', 'name': 'cinnamon_bear'}, {'id': 3595, 'synset': 'asiatic_black_bear.n.01', 'name': 'Asiatic_black_bear'}, {'id': 3596, 'synset': 'sloth_bear.n.01', 'name': 'sloth_bear'}, {'id': 3597, 'synset': 'viverrine.n.01', 'name': 'viverrine'}, {'id': 3598, 'synset': 'civet.n.01', 'name': 'civet'}, {'id': 3599, 'synset': 'large_civet.n.01', 'name': 'large_civet'}, {'id': 3600, 'synset': 'small_civet.n.01', 'name': 'small_civet'}, {'id': 3601, 'synset': 'binturong.n.01', 'name': 'binturong'}, {'id': 3602, 'synset': 'cryptoprocta.n.01', 'name': 'Cryptoprocta'}, {'id': 3603, 'synset': 'fossa.n.03', 'name': 'fossa'}, {'id': 3604, 'synset': 'fanaloka.n.01', 'name': 'fanaloka'}, {'id': 3605, 'synset': 'genet.n.03', 'name': 'genet'}, {'id': 3606, 'synset': 'banded_palm_civet.n.01', 'name': 'banded_palm_civet'}, {'id': 3607, 'synset': 'mongoose.n.01', 'name': 'mongoose'}, {'id': 3608, 'synset': 'indian_mongoose.n.01', 'name': 'Indian_mongoose'}, {'id': 3609, 'synset': 'ichneumon.n.01', 'name': 'ichneumon'}, {'id': 3610, 'synset': 'palm_cat.n.01', 'name': 'palm_cat'}, {'id': 3611, 'synset': 'meerkat.n.01', 'name': 'meerkat'}, {'id': 3612, 'synset': 'slender-tailed_meerkat.n.01', 'name': 'slender-tailed_meerkat'}, {'id': 3613, 'synset': 'suricate.n.01', 'name': 'suricate'}, {'id': 3614, 'synset': 'fruit_bat.n.01', 'name': 'fruit_bat'}, {'id': 3615, 'synset': 'flying_fox.n.01', 'name': 'flying_fox'}, {'id': 3616, 'synset': 'pteropus_capestratus.n.01', 'name': 'Pteropus_capestratus'}, {'id': 3617, 'synset': 'pteropus_hypomelanus.n.01', 'name': 'Pteropus_hypomelanus'}, {'id': 3618, 'synset': 'harpy.n.03', 'name': 'harpy'}, {'id': 3619, 'synset': 'cynopterus_sphinx.n.01', 'name': 'Cynopterus_sphinx'}, {'id': 3620, 'synset': 'carnivorous_bat.n.01', 'name': 'carnivorous_bat'}, {'id': 3621, 'synset': 'mouse-eared_bat.n.01', 'name': 'mouse-eared_bat'}, {'id': 3622, 'synset': 'leafnose_bat.n.01', 'name': 'leafnose_bat'}, {'id': 3623, 'synset': 'macrotus.n.01', 'name': 'macrotus'}, {'id': 3624, 'synset': 'spearnose_bat.n.01', 'name': 'spearnose_bat'}, {'id': 3625, 'synset': 'phyllostomus_hastatus.n.01', 'name': 'Phyllostomus_hastatus'}, {'id': 3626, 'synset': 'hognose_bat.n.01', 'name': 'hognose_bat'}, {'id': 3627, 'synset': 'horseshoe_bat.n.02', 'name': 'horseshoe_bat'}, {'id': 3628, 'synset': 'horseshoe_bat.n.01', 'name': 'horseshoe_bat'}, {'id': 3629, 'synset': 'orange_bat.n.01', 'name': 'orange_bat'}, {'id': 3630, 'synset': 'false_vampire.n.01', 'name': 'false_vampire'}, {'id': 3631, 'synset': 'big-eared_bat.n.01', 'name': 'big-eared_bat'}, {'id': 3632, 'synset': 'vespertilian_bat.n.01', 'name': 'vespertilian_bat'}, {'id': 3633, 'synset': 'frosted_bat.n.01', 'name': 'frosted_bat'}, {'id': 3634, 'synset': 'red_bat.n.01', 'name': 'red_bat'}, {'id': 3635, 'synset': 'brown_bat.n.01', 'name': 'brown_bat'}, {'id': 3636, 'synset': 'little_brown_bat.n.01', 'name': 'little_brown_bat'}, {'id': 3637, 'synset': 'cave_myotis.n.01', 'name': 'cave_myotis'}, {'id': 3638, 'synset': 'big_brown_bat.n.01', 'name': 'big_brown_bat'}, {'id': 3639, 'synset': 'serotine.n.01', 'name': 'serotine'}, {'id': 3640, 'synset': 'pallid_bat.n.01', 'name': 'pallid_bat'}, {'id': 3641, 'synset': 'pipistrelle.n.01', 'name': 'pipistrelle'}, {'id': 3642, 'synset': 'eastern_pipistrel.n.01', 'name': 'eastern_pipistrel'}, {'id': 3643, 'synset': 'jackass_bat.n.01', 'name': 'jackass_bat'}, {'id': 3644, 'synset': 'long-eared_bat.n.01', 'name': 'long-eared_bat'}, {'id': 3645, 'synset': 'western_big-eared_bat.n.01', 'name': 'western_big-eared_bat'}, {'id': 3646, 'synset': 'freetail.n.01', 'name': 'freetail'}, {'id': 3647, 'synset': 'guano_bat.n.01', 'name': 'guano_bat'}, {'id': 3648, 'synset': 'pocketed_bat.n.01', 'name': 'pocketed_bat'}, {'id': 3649, 'synset': 'mastiff_bat.n.01', 'name': 'mastiff_bat'}, {'id': 3650, 'synset': 'vampire_bat.n.01', 'name': 'vampire_bat'}, {'id': 3651, 'synset': 'desmodus_rotundus.n.01', 'name': 'Desmodus_rotundus'}, {'id': 3652, 'synset': 'hairy-legged_vampire_bat.n.01', 'name': 'hairy-legged_vampire_bat'}, {'id': 3653, 'synset': 'predator.n.02', 'name': 'predator'}, {'id': 3654, 'synset': 'prey.n.02', 'name': 'prey'}, {'id': 3655, 'synset': 'game.n.04', 'name': 'game'}, {'id': 3656, 'synset': 'big_game.n.01', 'name': 'big_game'}, {'id': 3657, 'synset': 'game_bird.n.01', 'name': 'game_bird'}, {'id': 3658, 'synset': 'fossorial_mammal.n.01', 'name': 'fossorial_mammal'}, {'id': 3659, 'synset': 'tetrapod.n.01', 'name': 'tetrapod'}, {'id': 3660, 'synset': 'quadruped.n.01', 'name': 'quadruped'}, {'id': 3661, 'synset': 'hexapod.n.01', 'name': 'hexapod'}, {'id': 3662, 'synset': 'biped.n.01', 'name': 'biped'}, {'id': 3663, 'synset': 'insect.n.01', 'name': 'insect'}, {'id': 3664, 'synset': 'social_insect.n.01', 'name': 'social_insect'}, {'id': 3665, 'synset': 'holometabola.n.01', 'name': 'holometabola'}, {'id': 3666, 'synset': 'defoliator.n.01', 'name': 'defoliator'}, {'id': 3667, 'synset': 'pollinator.n.01', 'name': 'pollinator'}, {'id': 3668, 'synset': 'gallfly.n.03', 'name': 'gallfly'}, {'id': 3669, 'synset': 'scorpion_fly.n.01', 'name': 'scorpion_fly'}, {'id': 3670, 'synset': 'hanging_fly.n.01', 'name': 'hanging_fly'}, {'id': 3671, 'synset': 'collembolan.n.01', 'name': 'collembolan'}, {'id': 3672, 'synset': 'tiger_beetle.n.01', 'name': 'tiger_beetle'}, {'id': 3673, 'synset': 'two-spotted_ladybug.n.01', 'name': 'two-spotted_ladybug'}, {'id': 3674, 'synset': 'mexican_bean_beetle.n.01', 'name': 'Mexican_bean_beetle'}, {'id': 3675, 'synset': 'hippodamia_convergens.n.01', 'name': 'Hippodamia_convergens'}, {'id': 3676, 'synset': 'vedalia.n.01', 'name': 'vedalia'}, {'id': 3677, 'synset': 'ground_beetle.n.01', 'name': 'ground_beetle'}, {'id': 3678, 'synset': 'bombardier_beetle.n.01', 'name': 'bombardier_beetle'}, {'id': 3679, 'synset': 'calosoma.n.01', 'name': 'calosoma'}, {'id': 3680, 'synset': 'searcher.n.03', 'name': 'searcher'}, {'id': 3681, 'synset': 'firefly.n.02', 'name': 'firefly'}, {'id': 3682, 'synset': 'glowworm.n.01', 'name': 'glowworm'}, {'id': 3683, 'synset': 'long-horned_beetle.n.01', 'name': 'long-horned_beetle'}, {'id': 3684, 'synset': 'sawyer.n.02', 'name': 'sawyer'}, {'id': 3685, 'synset': 'pine_sawyer.n.01', 'name': 'pine_sawyer'}, {'id': 3686, 'synset': 'leaf_beetle.n.01', 'name': 'leaf_beetle'}, {'id': 3687, 'synset': 'flea_beetle.n.01', 'name': 'flea_beetle'}, {'id': 3688, 'synset': 'colorado_potato_beetle.n.01', 'name': 'Colorado_potato_beetle'}, {'id': 3689, 'synset': 'carpet_beetle.n.01', 'name': 'carpet_beetle'}, {'id': 3690, 'synset': 'buffalo_carpet_beetle.n.01', 'name': 'buffalo_carpet_beetle'}, {'id': 3691, 'synset': 'black_carpet_beetle.n.01', 'name': 'black_carpet_beetle'}, {'id': 3692, 'synset': 'clerid_beetle.n.01', 'name': 'clerid_beetle'}, {'id': 3693, 'synset': 'bee_beetle.n.01', 'name': 'bee_beetle'}, {'id': 3694, 'synset': 'lamellicorn_beetle.n.01', 'name': 'lamellicorn_beetle'}, {'id': 3695, 'synset': 'scarabaeid_beetle.n.01', 'name': 'scarabaeid_beetle'}, {'id': 3696, 'synset': 'dung_beetle.n.01', 'name': 'dung_beetle'}, {'id': 3697, 'synset': 'scarab.n.01', 'name': 'scarab'}, {'id': 3698, 'synset': 'tumblebug.n.01', 'name': 'tumblebug'}, {'id': 3699, 'synset': 'dorbeetle.n.01', 'name': 'dorbeetle'}, {'id': 3700, 'synset': 'june_beetle.n.01', 'name': 'June_beetle'}, {'id': 3701, 'synset': 'green_june_beetle.n.01', 'name': 'green_June_beetle'}, {'id': 3702, 'synset': 'japanese_beetle.n.01', 'name': 'Japanese_beetle'}, {'id': 3703, 'synset': 'oriental_beetle.n.01', 'name': 'Oriental_beetle'}, {'id': 3704, 'synset': 'rhinoceros_beetle.n.01', 'name': 'rhinoceros_beetle'}, {'id': 3705, 'synset': 'melolonthid_beetle.n.01', 'name': 'melolonthid_beetle'}, {'id': 3706, 'synset': 'cockchafer.n.01', 'name': 'cockchafer'}, {'id': 3707, 'synset': 'rose_chafer.n.02', 'name': 'rose_chafer'}, {'id': 3708, 'synset': 'rose_chafer.n.01', 'name': 'rose_chafer'}, {'id': 3709, 'synset': 'stag_beetle.n.01', 'name': 'stag_beetle'}, {'id': 3710, 'synset': 'elaterid_beetle.n.01', 'name': 'elaterid_beetle'}, {'id': 3711, 'synset': 'click_beetle.n.01', 'name': 'click_beetle'}, {'id': 3712, 'synset': 'firefly.n.01', 'name': 'firefly'}, {'id': 3713, 'synset': 'wireworm.n.01', 'name': 'wireworm'}, {'id': 3714, 'synset': 'water_beetle.n.01', 'name': 'water_beetle'}, {'id': 3715, 'synset': 'whirligig_beetle.n.01', 'name': 'whirligig_beetle'}, {'id': 3716, 'synset': 'deathwatch_beetle.n.01', 'name': 'deathwatch_beetle'}, {'id': 3717, 'synset': 'weevil.n.01', 'name': 'weevil'}, {'id': 3718, 'synset': 'snout_beetle.n.01', 'name': 'snout_beetle'}, {'id': 3719, 'synset': 'boll_weevil.n.01', 'name': 'boll_weevil'}, {'id': 3720, 'synset': 'blister_beetle.n.01', 'name': 'blister_beetle'}, {'id': 3721, 'synset': 'oil_beetle.n.01', 'name': 'oil_beetle'}, {'id': 3722, 'synset': 'spanish_fly.n.01', 'name': 'Spanish_fly'}, {'id': 3723, 'synset': 'dutch-elm_beetle.n.01', 'name': 'Dutch-elm_beetle'}, {'id': 3724, 'synset': 'bark_beetle.n.01', 'name': 'bark_beetle'}, {'id': 3725, 'synset': 'spruce_bark_beetle.n.01', 'name': 'spruce_bark_beetle'}, {'id': 3726, 'synset': 'rove_beetle.n.01', 'name': 'rove_beetle'}, {'id': 3727, 'synset': 'darkling_beetle.n.01', 'name': 'darkling_beetle'}, {'id': 3728, 'synset': 'mealworm.n.01', 'name': 'mealworm'}, {'id': 3729, 'synset': 'flour_beetle.n.01', 'name': 'flour_beetle'}, {'id': 3730, 'synset': 'seed_beetle.n.01', 'name': 'seed_beetle'}, {'id': 3731, 'synset': 'pea_weevil.n.01', 'name': 'pea_weevil'}, {'id': 3732, 'synset': 'bean_weevil.n.01', 'name': 'bean_weevil'}, {'id': 3733, 'synset': 'rice_weevil.n.01', 'name': 'rice_weevil'}, {'id': 3734, 'synset': 'asian_longhorned_beetle.n.01', 'name': 'Asian_longhorned_beetle'}, {'id': 3735, 'synset': 'web_spinner.n.01', 'name': 'web_spinner'}, {'id': 3736, 'synset': 'louse.n.01', 'name': 'louse'}, {'id': 3737, 'synset': 'common_louse.n.01', 'name': 'common_louse'}, {'id': 3738, 'synset': 'head_louse.n.01', 'name': 'head_louse'}, {'id': 3739, 'synset': 'body_louse.n.01', 'name': 'body_louse'}, {'id': 3740, 'synset': 'crab_louse.n.01', 'name': 'crab_louse'}, {'id': 3741, 'synset': 'bird_louse.n.01', 'name': 'bird_louse'}, {'id': 3742, 'synset': 'flea.n.01', 'name': 'flea'}, {'id': 3743, 'synset': 'pulex_irritans.n.01', 'name': 'Pulex_irritans'}, {'id': 3744, 'synset': 'dog_flea.n.01', 'name': 'dog_flea'}, {'id': 3745, 'synset': 'cat_flea.n.01', 'name': 'cat_flea'}, {'id': 3746, 'synset': 'chigoe.n.01', 'name': 'chigoe'}, {'id': 3747, 'synset': 'sticktight.n.02', 'name': 'sticktight'}, {'id': 3748, 'synset': 'dipterous_insect.n.01', 'name': 'dipterous_insect'}, {'id': 3749, 'synset': 'gall_midge.n.01', 'name': 'gall_midge'}, {'id': 3750, 'synset': 'hessian_fly.n.01', 'name': 'Hessian_fly'}, {'id': 3751, 'synset': 'fly.n.01', 'name': 'fly'}, {'id': 3752, 'synset': 'housefly.n.01', 'name': 'housefly'}, {'id': 3753, 'synset': 'tsetse_fly.n.01', 'name': 'tsetse_fly'}, {'id': 3754, 'synset': 'blowfly.n.01', 'name': 'blowfly'}, {'id': 3755, 'synset': 'bluebottle.n.02', 'name': 'bluebottle'}, {'id': 3756, 'synset': 'greenbottle.n.01', 'name': 'greenbottle'}, {'id': 3757, 'synset': 'flesh_fly.n.01', 'name': 'flesh_fly'}, {'id': 3758, 'synset': 'tachina_fly.n.01', 'name': 'tachina_fly'}, {'id': 3759, 'synset': 'gadfly.n.02', 'name': 'gadfly'}, {'id': 3760, 'synset': 'botfly.n.01', 'name': 'botfly'}, {'id': 3761, 'synset': 'human_botfly.n.01', 'name': 'human_botfly'}, {'id': 3762, 'synset': 'sheep_botfly.n.01', 'name': 'sheep_botfly'}, {'id': 3763, 'synset': 'warble_fly.n.01', 'name': 'warble_fly'}, {'id': 3764, 'synset': 'horsefly.n.02', 'name': 'horsefly'}, {'id': 3765, 'synset': 'bee_fly.n.01', 'name': 'bee_fly'}, {'id': 3766, 'synset': 'robber_fly.n.01', 'name': 'robber_fly'}, {'id': 3767, 'synset': 'fruit_fly.n.01', 'name': 'fruit_fly'}, {'id': 3768, 'synset': 'apple_maggot.n.01', 'name': 'apple_maggot'}, {'id': 3769, 'synset': 'mediterranean_fruit_fly.n.01', 'name': 'Mediterranean_fruit_fly'}, {'id': 3770, 'synset': 'drosophila.n.01', 'name': 'drosophila'}, {'id': 3771, 'synset': 'vinegar_fly.n.01', 'name': 'vinegar_fly'}, {'id': 3772, 'synset': 'leaf_miner.n.01', 'name': 'leaf_miner'}, {'id': 3773, 'synset': 'louse_fly.n.01', 'name': 'louse_fly'}, {'id': 3774, 'synset': 'horse_tick.n.01', 'name': 'horse_tick'}, {'id': 3775, 'synset': 'sheep_ked.n.01', 'name': 'sheep_ked'}, {'id': 3776, 'synset': 'horn_fly.n.01', 'name': 'horn_fly'}, {'id': 3777, 'synset': 'mosquito.n.01', 'name': 'mosquito'}, {'id': 3778, 'synset': 'wiggler.n.02', 'name': 'wiggler'}, {'id': 3779, 'synset': 'gnat.n.02', 'name': 'gnat'}, {'id': 3780, 'synset': 'yellow-fever_mosquito.n.01', 'name': 'yellow-fever_mosquito'}, {'id': 3781, 'synset': 'asian_tiger_mosquito.n.01', 'name': 'Asian_tiger_mosquito'}, {'id': 3782, 'synset': 'anopheline.n.01', 'name': 'anopheline'}, {'id': 3783, 'synset': 'malarial_mosquito.n.01', 'name': 'malarial_mosquito'}, {'id': 3784, 'synset': 'common_mosquito.n.01', 'name': 'common_mosquito'}, {'id': 3785, 'synset': 'culex_quinquefasciatus.n.01', 'name': 'Culex_quinquefasciatus'}, {'id': 3786, 'synset': 'gnat.n.01', 'name': 'gnat'}, {'id': 3787, 'synset': 'punkie.n.01', 'name': 'punkie'}, {'id': 3788, 'synset': 'midge.n.01', 'name': 'midge'}, {'id': 3789, 'synset': 'fungus_gnat.n.02', 'name': 'fungus_gnat'}, {'id': 3790, 'synset': 'psychodid.n.01', 'name': 'psychodid'}, {'id': 3791, 'synset': 'sand_fly.n.01', 'name': 'sand_fly'}, {'id': 3792, 'synset': 'fungus_gnat.n.01', 'name': 'fungus_gnat'}, {'id': 3793, 'synset': 'armyworm.n.03', 'name': 'armyworm'}, {'id': 3794, 'synset': 'crane_fly.n.01', 'name': 'crane_fly'}, {'id': 3795, 'synset': 'blackfly.n.02', 'name': 'blackfly'}, {'id': 3796, 'synset': 'hymenopterous_insect.n.01', 'name': 'hymenopterous_insect'}, {'id': 3797, 'synset': 'bee.n.01', 'name': 'bee'}, {'id': 3798, 'synset': 'drone.n.01', 'name': 'drone'}, {'id': 3799, 'synset': 'queen_bee.n.01', 'name': 'queen_bee'}, {'id': 3800, 'synset': 'worker.n.03', 'name': 'worker'}, {'id': 3801, 'synset': 'soldier.n.02', 'name': 'soldier'}, {'id': 3802, 'synset': 'worker_bee.n.01', 'name': 'worker_bee'}, {'id': 3803, 'synset': 'honeybee.n.01', 'name': 'honeybee'}, {'id': 3804, 'synset': 'africanized_bee.n.01', 'name': 'Africanized_bee'}, {'id': 3805, 'synset': 'black_bee.n.01', 'name': 'black_bee'}, {'id': 3806, 'synset': 'carniolan_bee.n.01', 'name': 'Carniolan_bee'}, {'id': 3807, 'synset': 'italian_bee.n.01', 'name': 'Italian_bee'}, {'id': 3808, 'synset': 'carpenter_bee.n.01', 'name': 'carpenter_bee'}, {'id': 3809, 'synset': 'bumblebee.n.01', 'name': 'bumblebee'}, {'id': 3810, 'synset': 'cuckoo-bumblebee.n.01', 'name': 'cuckoo-bumblebee'}, {'id': 3811, 'synset': 'andrena.n.01', 'name': 'andrena'}, {'id': 3812, 'synset': 'nomia_melanderi.n.01', 'name': 'Nomia_melanderi'}, {'id': 3813, 'synset': 'leaf-cutting_bee.n.01', 'name': 'leaf-cutting_bee'}, {'id': 3814, 'synset': 'mason_bee.n.01', 'name': 'mason_bee'}, {'id': 3815, 'synset': 'potter_bee.n.01', 'name': 'potter_bee'}, {'id': 3816, 'synset': 'wasp.n.02', 'name': 'wasp'}, {'id': 3817, 'synset': 'vespid.n.01', 'name': 'vespid'}, {'id': 3818, 'synset': 'paper_wasp.n.01', 'name': 'paper_wasp'}, {'id': 3819, 'synset': 'giant_hornet.n.01', 'name': 'giant_hornet'}, {'id': 3820, 'synset': 'common_wasp.n.01', 'name': 'common_wasp'}, {'id': 3821, 'synset': 'bald-faced_hornet.n.01', 'name': 'bald-faced_hornet'}, {'id': 3822, 'synset': 'yellow_jacket.n.02', 'name': 'yellow_jacket'}, {'id': 3823, 'synset': 'polistes_annularis.n.01', 'name': 'Polistes_annularis'}, {'id': 3824, 'synset': 'mason_wasp.n.02', 'name': 'mason_wasp'}, {'id': 3825, 'synset': 'potter_wasp.n.01', 'name': 'potter_wasp'}, {'id': 3826, 'synset': 'mutillidae.n.01', 'name': 'Mutillidae'}, {'id': 3827, 'synset': 'velvet_ant.n.01', 'name': 'velvet_ant'}, {'id': 3828, 'synset': 'sphecoid_wasp.n.01', 'name': 'sphecoid_wasp'}, {'id': 3829, 'synset': 'mason_wasp.n.01', 'name': 'mason_wasp'}, {'id': 3830, 'synset': 'digger_wasp.n.01', 'name': 'digger_wasp'}, {'id': 3831, 'synset': 'cicada_killer.n.01', 'name': 'cicada_killer'}, {'id': 3832, 'synset': 'mud_dauber.n.01', 'name': 'mud_dauber'}, {'id': 3833, 'synset': 'gall_wasp.n.01', 'name': 'gall_wasp'}, {'id': 3834, 'synset': 'chalcid_fly.n.01', 'name': 'chalcid_fly'}, {'id': 3835, 'synset': 'strawworm.n.02', 'name': 'strawworm'}, {'id': 3836, 'synset': 'chalcis_fly.n.01', 'name': 'chalcis_fly'}, {'id': 3837, 'synset': 'ichneumon_fly.n.01', 'name': 'ichneumon_fly'}, {'id': 3838, 'synset': 'sawfly.n.01', 'name': 'sawfly'}, {'id': 3839, 'synset': 'birch_leaf_miner.n.01', 'name': 'birch_leaf_miner'}, {'id': 3840, 'synset': 'ant.n.01', 'name': 'ant'}, {'id': 3841, 'synset': 'pharaoh_ant.n.01', 'name': 'pharaoh_ant'}, {'id': 3842, 'synset': 'little_black_ant.n.01', 'name': 'little_black_ant'}, {'id': 3843, 'synset': 'army_ant.n.01', 'name': 'army_ant'}, {'id': 3844, 'synset': 'carpenter_ant.n.01', 'name': 'carpenter_ant'}, {'id': 3845, 'synset': 'fire_ant.n.01', 'name': 'fire_ant'}, {'id': 3846, 'synset': 'wood_ant.n.01', 'name': 'wood_ant'}, {'id': 3847, 'synset': 'slave_ant.n.01', 'name': 'slave_ant'}, {'id': 3848, 'synset': 'formica_fusca.n.01', 'name': 'Formica_fusca'}, {'id': 3849, 'synset': 'slave-making_ant.n.01', 'name': 'slave-making_ant'}, {'id': 3850, 'synset': 'sanguinary_ant.n.01', 'name': 'sanguinary_ant'}, {'id': 3851, 'synset': 'bulldog_ant.n.01', 'name': 'bulldog_ant'}, {'id': 3852, 'synset': 'amazon_ant.n.01', 'name': 'Amazon_ant'}, {'id': 3853, 'synset': 'termite.n.01', 'name': 'termite'}, {'id': 3854, 'synset': 'dry-wood_termite.n.01', 'name': 'dry-wood_termite'}, {'id': 3855, 'synset': 'reticulitermes_lucifugus.n.01', 'name': 'Reticulitermes_lucifugus'}, {'id': 3856, 'synset': 'mastotermes_darwiniensis.n.01', 'name': 'Mastotermes_darwiniensis'}, {'id': 3857, 'synset': 'mastotermes_electrodominicus.n.01', 'name': 'Mastotermes_electrodominicus'}, {'id': 3858, 'synset': 'powder-post_termite.n.01', 'name': 'powder-post_termite'}, {'id': 3859, 'synset': 'orthopterous_insect.n.01', 'name': 'orthopterous_insect'}, {'id': 3860, 'synset': 'grasshopper.n.01', 'name': 'grasshopper'}, {'id': 3861, 'synset': 'short-horned_grasshopper.n.01', 'name': 'short-horned_grasshopper'}, {'id': 3862, 'synset': 'locust.n.01', 'name': 'locust'}, {'id': 3863, 'synset': 'migratory_locust.n.01', 'name': 'migratory_locust'}, {'id': 3864, 'synset': 'migratory_grasshopper.n.01', 'name': 'migratory_grasshopper'}, {'id': 3865, 'synset': 'long-horned_grasshopper.n.01', 'name': 'long-horned_grasshopper'}, {'id': 3866, 'synset': 'katydid.n.01', 'name': 'katydid'}, {'id': 3867, 'synset': 'mormon_cricket.n.01', 'name': 'mormon_cricket'}, {'id': 3868, 'synset': 'sand_cricket.n.01', 'name': 'sand_cricket'}, {'id': 3869, 'synset': 'cricket.n.01', 'name': 'cricket'}, {'id': 3870, 'synset': 'mole_cricket.n.01', 'name': 'mole_cricket'}, {'id': 3871, 'synset': 'european_house_cricket.n.01', 'name': 'European_house_cricket'}, {'id': 3872, 'synset': 'field_cricket.n.01', 'name': 'field_cricket'}, {'id': 3873, 'synset': 'tree_cricket.n.01', 'name': 'tree_cricket'}, {'id': 3874, 'synset': 'snowy_tree_cricket.n.01', 'name': 'snowy_tree_cricket'}, {'id': 3875, 'synset': 'phasmid.n.01', 'name': 'phasmid'}, {'id': 3876, 'synset': 'walking_stick.n.02', 'name': 'walking_stick'}, {'id': 3877, 'synset': 'diapheromera.n.01', 'name': 'diapheromera'}, {'id': 3878, 'synset': 'walking_leaf.n.02', 'name': 'walking_leaf'}, {'id': 3879, 'synset': 'oriental_cockroach.n.01', 'name': 'oriental_cockroach'}, {'id': 3880, 'synset': 'american_cockroach.n.01', 'name': 'American_cockroach'}, {'id': 3881, 'synset': 'australian_cockroach.n.01', 'name': 'Australian_cockroach'}, {'id': 3882, 'synset': 'german_cockroach.n.01', 'name': 'German_cockroach'}, {'id': 3883, 'synset': 'giant_cockroach.n.01', 'name': 'giant_cockroach'}, {'id': 3884, 'synset': 'mantis.n.01', 'name': 'mantis'}, {'id': 3885, 'synset': 'praying_mantis.n.01', 'name': 'praying_mantis'}, {'id': 3886, 'synset': 'bug.n.01', 'name': 'bug'}, {'id': 3887, 'synset': 'hemipterous_insect.n.01', 'name': 'hemipterous_insect'}, {'id': 3888, 'synset': 'leaf_bug.n.01', 'name': 'leaf_bug'}, {'id': 3889, 'synset': 'mirid_bug.n.01', 'name': 'mirid_bug'}, {'id': 3890, 'synset': 'four-lined_plant_bug.n.01', 'name': 'four-lined_plant_bug'}, {'id': 3891, 'synset': 'lygus_bug.n.01', 'name': 'lygus_bug'}, {'id': 3892, 'synset': 'tarnished_plant_bug.n.01', 'name': 'tarnished_plant_bug'}, {'id': 3893, 'synset': 'lace_bug.n.01', 'name': 'lace_bug'}, {'id': 3894, 'synset': 'lygaeid.n.01', 'name': 'lygaeid'}, {'id': 3895, 'synset': 'chinch_bug.n.01', 'name': 'chinch_bug'}, {'id': 3896, 'synset': 'coreid_bug.n.01', 'name': 'coreid_bug'}, {'id': 3897, 'synset': 'squash_bug.n.01', 'name': 'squash_bug'}, {'id': 3898, 'synset': 'leaf-footed_bug.n.01', 'name': 'leaf-footed_bug'}, {'id': 3899, 'synset': 'bedbug.n.01', 'name': 'bedbug'}, {'id': 3900, 'synset': 'backswimmer.n.01', 'name': 'backswimmer'}, {'id': 3901, 'synset': 'true_bug.n.01', 'name': 'true_bug'}, {'id': 3902, 'synset': 'heteropterous_insect.n.01', 'name': 'heteropterous_insect'}, {'id': 3903, 'synset': 'water_bug.n.01', 'name': 'water_bug'}, {'id': 3904, 'synset': 'giant_water_bug.n.01', 'name': 'giant_water_bug'}, {'id': 3905, 'synset': 'water_scorpion.n.01', 'name': 'water_scorpion'}, {'id': 3906, 'synset': 'water_boatman.n.01', 'name': 'water_boatman'}, {'id': 3907, 'synset': 'water_strider.n.01', 'name': 'water_strider'}, {'id': 3908, 'synset': 'common_pond-skater.n.01', 'name': 'common_pond-skater'}, {'id': 3909, 'synset': 'assassin_bug.n.01', 'name': 'assassin_bug'}, {'id': 3910, 'synset': 'conenose.n.01', 'name': 'conenose'}, {'id': 3911, 'synset': 'wheel_bug.n.01', 'name': 'wheel_bug'}, {'id': 3912, 'synset': 'firebug.n.02', 'name': 'firebug'}, {'id': 3913, 'synset': 'cotton_stainer.n.01', 'name': 'cotton_stainer'}, {'id': 3914, 'synset': 'homopterous_insect.n.01', 'name': 'homopterous_insect'}, {'id': 3915, 'synset': 'whitefly.n.01', 'name': 'whitefly'}, {'id': 3916, 'synset': 'citrus_whitefly.n.01', 'name': 'citrus_whitefly'}, {'id': 3917, 'synset': 'greenhouse_whitefly.n.01', 'name': 'greenhouse_whitefly'}, {'id': 3918, 'synset': 'sweet-potato_whitefly.n.01', 'name': 'sweet-potato_whitefly'}, {'id': 3919, 'synset': 'superbug.n.02', 'name': 'superbug'}, {'id': 3920, 'synset': 'cotton_strain.n.01', 'name': 'cotton_strain'}, {'id': 3921, 'synset': 'coccid_insect.n.01', 'name': 'coccid_insect'}, {'id': 3922, 'synset': 'scale_insect.n.01', 'name': 'scale_insect'}, {'id': 3923, 'synset': 'soft_scale.n.01', 'name': 'soft_scale'}, {'id': 3924, 'synset': 'brown_soft_scale.n.01', 'name': 'brown_soft_scale'}, {'id': 3925, 'synset': 'armored_scale.n.01', 'name': 'armored_scale'}, {'id': 3926, 'synset': 'san_jose_scale.n.01', 'name': 'San_Jose_scale'}, {'id': 3927, 'synset': 'cochineal_insect.n.01', 'name': 'cochineal_insect'}, {'id': 3928, 'synset': 'mealybug.n.01', 'name': 'mealybug'}, {'id': 3929, 'synset': 'citrophilous_mealybug.n.01', 'name': 'citrophilous_mealybug'}, {'id': 3930, 'synset': 'comstock_mealybug.n.01', 'name': 'Comstock_mealybug'}, {'id': 3931, 'synset': 'citrus_mealybug.n.01', 'name': 'citrus_mealybug'}, {'id': 3932, 'synset': 'plant_louse.n.01', 'name': 'plant_louse'}, {'id': 3933, 'synset': 'aphid.n.01', 'name': 'aphid'}, {'id': 3934, 'synset': 'apple_aphid.n.01', 'name': 'apple_aphid'}, {'id': 3935, 'synset': 'blackfly.n.01', 'name': 'blackfly'}, {'id': 3936, 'synset': 'greenfly.n.01', 'name': 'greenfly'}, {'id': 3937, 'synset': 'green_peach_aphid.n.01', 'name': 'green_peach_aphid'}, {'id': 3938, 'synset': 'ant_cow.n.01', 'name': 'ant_cow'}, {'id': 3939, 'synset': 'woolly_aphid.n.01', 'name': 'woolly_aphid'}, {'id': 3940, 'synset': 'woolly_apple_aphid.n.01', 'name': 'woolly_apple_aphid'}, {'id': 3941, 'synset': 'woolly_alder_aphid.n.01', 'name': 'woolly_alder_aphid'}, {'id': 3942, 'synset': 'adelgid.n.01', 'name': 'adelgid'}, {'id': 3943, 'synset': 'balsam_woolly_aphid.n.01', 'name': 'balsam_woolly_aphid'}, {'id': 3944, 'synset': 'spruce_gall_aphid.n.01', 'name': 'spruce_gall_aphid'}, {'id': 3945, 'synset': 'woolly_adelgid.n.01', 'name': 'woolly_adelgid'}, {'id': 3946, 'synset': 'jumping_plant_louse.n.01', 'name': 'jumping_plant_louse'}, {'id': 3947, 'synset': 'cicada.n.01', 'name': 'cicada'}, {'id': 3948, 'synset': 'dog-day_cicada.n.01', 'name': 'dog-day_cicada'}, {'id': 3949, 'synset': 'seventeen-year_locust.n.01', 'name': 'seventeen-year_locust'}, {'id': 3950, 'synset': 'spittle_insect.n.01', 'name': 'spittle_insect'}, {'id': 3951, 'synset': 'froghopper.n.01', 'name': 'froghopper'}, {'id': 3952, 'synset': 'meadow_spittlebug.n.01', 'name': 'meadow_spittlebug'}, {'id': 3953, 'synset': 'pine_spittlebug.n.01', 'name': 'pine_spittlebug'}, {'id': 3954, 'synset': 'saratoga_spittlebug.n.01', 'name': 'Saratoga_spittlebug'}, {'id': 3955, 'synset': 'leafhopper.n.01', 'name': 'leafhopper'}, {'id': 3956, 'synset': 'plant_hopper.n.01', 'name': 'plant_hopper'}, {'id': 3957, 'synset': 'treehopper.n.01', 'name': 'treehopper'}, {'id': 3958, 'synset': 'lantern_fly.n.01', 'name': 'lantern_fly'}, {'id': 3959, 'synset': 'psocopterous_insect.n.01', 'name': 'psocopterous_insect'}, {'id': 3960, 'synset': 'psocid.n.01', 'name': 'psocid'}, {'id': 3961, 'synset': 'bark-louse.n.01', 'name': 'bark-louse'}, {'id': 3962, 'synset': 'booklouse.n.01', 'name': 'booklouse'}, {'id': 3963, 'synset': 'common_booklouse.n.01', 'name': 'common_booklouse'}, {'id': 3964, 'synset': 'ephemerid.n.01', 'name': 'ephemerid'}, {'id': 3965, 'synset': 'mayfly.n.01', 'name': 'mayfly'}, {'id': 3966, 'synset': 'stonefly.n.01', 'name': 'stonefly'}, {'id': 3967, 'synset': 'neuropteron.n.01', 'name': 'neuropteron'}, {'id': 3968, 'synset': 'ant_lion.n.02', 'name': 'ant_lion'}, {'id': 3969, 'synset': 'doodlebug.n.03', 'name': 'doodlebug'}, {'id': 3970, 'synset': 'lacewing.n.01', 'name': 'lacewing'}, {'id': 3971, 'synset': 'aphid_lion.n.01', 'name': 'aphid_lion'}, {'id': 3972, 'synset': 'green_lacewing.n.01', 'name': 'green_lacewing'}, {'id': 3973, 'synset': 'brown_lacewing.n.01', 'name': 'brown_lacewing'}, {'id': 3974, 'synset': 'dobson.n.02', 'name': 'dobson'}, {'id': 3975, 'synset': 'hellgrammiate.n.01', 'name': 'hellgrammiate'}, {'id': 3976, 'synset': 'fish_fly.n.01', 'name': 'fish_fly'}, {'id': 3977, 'synset': 'alderfly.n.01', 'name': 'alderfly'}, {'id': 3978, 'synset': 'snakefly.n.01', 'name': 'snakefly'}, {'id': 3979, 'synset': 'mantispid.n.01', 'name': 'mantispid'}, {'id': 3980, 'synset': 'odonate.n.01', 'name': 'odonate'}, {'id': 3981, 'synset': 'damselfly.n.01', 'name': 'damselfly'}, {'id': 3982, 'synset': 'trichopterous_insect.n.01', 'name': 'trichopterous_insect'}, {'id': 3983, 'synset': 'caddis_fly.n.01', 'name': 'caddis_fly'}, {'id': 3984, 'synset': 'caseworm.n.01', 'name': 'caseworm'}, {'id': 3985, 'synset': 'caddisworm.n.01', 'name': 'caddisworm'}, {'id': 3986, 'synset': 'thysanuran_insect.n.01', 'name': 'thysanuran_insect'}, {'id': 3987, 'synset': 'bristletail.n.01', 'name': 'bristletail'}, {'id': 3988, 'synset': 'silverfish.n.01', 'name': 'silverfish'}, {'id': 3989, 'synset': 'firebrat.n.01', 'name': 'firebrat'}, {'id': 3990, 'synset': 'jumping_bristletail.n.01', 'name': 'jumping_bristletail'}, {'id': 3991, 'synset': 'thysanopter.n.01', 'name': 'thysanopter'}, {'id': 3992, 'synset': 'thrips.n.01', 'name': 'thrips'}, {'id': 3993, 'synset': 'tobacco_thrips.n.01', 'name': 'tobacco_thrips'}, {'id': 3994, 'synset': 'onion_thrips.n.01', 'name': 'onion_thrips'}, {'id': 3995, 'synset': 'earwig.n.01', 'name': 'earwig'}, {'id': 3996, 'synset': 'common_european_earwig.n.01', 'name': 'common_European_earwig'}, {'id': 3997, 'synset': 'lepidopterous_insect.n.01', 'name': 'lepidopterous_insect'}, {'id': 3998, 'synset': 'nymphalid.n.01', 'name': 'nymphalid'}, {'id': 3999, 'synset': 'mourning_cloak.n.01', 'name': 'mourning_cloak'}, {'id': 4000, 'synset': 'tortoiseshell.n.02', 'name': 'tortoiseshell'}, {'id': 4001, 'synset': 'painted_beauty.n.01', 'name': 'painted_beauty'}, {'id': 4002, 'synset': 'admiral.n.02', 'name': 'admiral'}, {'id': 4003, 'synset': 'red_admiral.n.01', 'name': 'red_admiral'}, {'id': 4004, 'synset': 'white_admiral.n.02', 'name': 'white_admiral'}, {'id': 4005, 'synset': 'banded_purple.n.01', 'name': 'banded_purple'}, {'id': 4006, 'synset': 'red-spotted_purple.n.01', 'name': 'red-spotted_purple'}, {'id': 4007, 'synset': 'viceroy.n.02', 'name': 'viceroy'}, {'id': 4008, 'synset': 'anglewing.n.01', 'name': 'anglewing'}, {'id': 4009, 'synset': 'ringlet.n.04', 'name': 'ringlet'}, {'id': 4010, 'synset': 'comma.n.02', 'name': 'comma'}, {'id': 4011, 'synset': 'fritillary.n.02', 'name': 'fritillary'}, {'id': 4012, 'synset': 'silverspot.n.01', 'name': 'silverspot'}, {'id': 4013, 'synset': 'emperor_butterfly.n.01', 'name': 'emperor_butterfly'}, {'id': 4014, 'synset': 'purple_emperor.n.01', 'name': 'purple_emperor'}, {'id': 4015, 'synset': 'peacock.n.01', 'name': 'peacock'}, {'id': 4016, 'synset': 'danaid.n.01', 'name': 'danaid'}, {'id': 4017, 'synset': 'monarch.n.02', 'name': 'monarch'}, {'id': 4018, 'synset': 'pierid.n.01', 'name': 'pierid'}, {'id': 4019, 'synset': 'cabbage_butterfly.n.01', 'name': 'cabbage_butterfly'}, {'id': 4020, 'synset': 'small_white.n.01', 'name': 'small_white'}, {'id': 4021, 'synset': 'large_white.n.01', 'name': 'large_white'}, {'id': 4022, 'synset': 'southern_cabbage_butterfly.n.01', 'name': 'southern_cabbage_butterfly'}, {'id': 4023, 'synset': 'sulphur_butterfly.n.01', 'name': 'sulphur_butterfly'}, {'id': 4024, 'synset': 'lycaenid.n.01', 'name': 'lycaenid'}, {'id': 4025, 'synset': 'blue.n.07', 'name': 'blue'}, {'id': 4026, 'synset': 'copper.n.05', 'name': 'copper'}, {'id': 4027, 'synset': 'american_copper.n.01', 'name': 'American_copper'}, {'id': 4028, 'synset': 'hairstreak.n.01', 'name': 'hairstreak'}, {'id': 4029, 'synset': 'strymon_melinus.n.01', 'name': 'Strymon_melinus'}, {'id': 4030, 'synset': 'moth.n.01', 'name': 'moth'}, {'id': 4031, 'synset': 'moth_miller.n.01', 'name': 'moth_miller'}, {'id': 4032, 'synset': 'tortricid.n.01', 'name': 'tortricid'}, {'id': 4033, 'synset': 'leaf_roller.n.01', 'name': 'leaf_roller'}, {'id': 4034, 'synset': 'tea_tortrix.n.01', 'name': 'tea_tortrix'}, {'id': 4035, 'synset': 'orange_tortrix.n.01', 'name': 'orange_tortrix'}, {'id': 4036, 'synset': 'codling_moth.n.01', 'name': 'codling_moth'}, {'id': 4037, 'synset': 'lymantriid.n.01', 'name': 'lymantriid'}, {'id': 4038, 'synset': 'tussock_caterpillar.n.01', 'name': 'tussock_caterpillar'}, {'id': 4039, 'synset': 'gypsy_moth.n.01', 'name': 'gypsy_moth'}, {'id': 4040, 'synset': 'browntail.n.01', 'name': 'browntail'}, {'id': 4041, 'synset': 'gold-tail_moth.n.01', 'name': 'gold-tail_moth'}, {'id': 4042, 'synset': 'geometrid.n.01', 'name': 'geometrid'}, {'id': 4043, 'synset': 'paleacrita_vernata.n.01', 'name': 'Paleacrita_vernata'}, {'id': 4044, 'synset': 'alsophila_pometaria.n.01', 'name': 'Alsophila_pometaria'}, {'id': 4045, 'synset': 'cankerworm.n.01', 'name': 'cankerworm'}, {'id': 4046, 'synset': 'spring_cankerworm.n.01', 'name': 'spring_cankerworm'}, {'id': 4047, 'synset': 'fall_cankerworm.n.01', 'name': 'fall_cankerworm'}, {'id': 4048, 'synset': 'measuring_worm.n.01', 'name': 'measuring_worm'}, {'id': 4049, 'synset': 'pyralid.n.01', 'name': 'pyralid'}, {'id': 4050, 'synset': 'bee_moth.n.01', 'name': 'bee_moth'}, {'id': 4051, 'synset': 'corn_borer.n.02', 'name': 'corn_borer'}, {'id': 4052, 'synset': 'mediterranean_flour_moth.n.01', 'name': 'Mediterranean_flour_moth'}, {'id': 4053, 'synset': 'tobacco_moth.n.01', 'name': 'tobacco_moth'}, {'id': 4054, 'synset': 'almond_moth.n.01', 'name': 'almond_moth'}, {'id': 4055, 'synset': 'raisin_moth.n.01', 'name': 'raisin_moth'}, {'id': 4056, 'synset': 'tineoid.n.01', 'name': 'tineoid'}, {'id': 4057, 'synset': 'tineid.n.01', 'name': 'tineid'}, {'id': 4058, 'synset': 'clothes_moth.n.01', 'name': 'clothes_moth'}, {'id': 4059, 'synset': 'casemaking_clothes_moth.n.01', 'name': 'casemaking_clothes_moth'}, {'id': 4060, 'synset': 'webbing_clothes_moth.n.01', 'name': 'webbing_clothes_moth'}, {'id': 4061, 'synset': 'carpet_moth.n.01', 'name': 'carpet_moth'}, {'id': 4062, 'synset': 'gelechiid.n.01', 'name': 'gelechiid'}, {'id': 4063, 'synset': 'grain_moth.n.01', 'name': 'grain_moth'}, {'id': 4064, 'synset': 'angoumois_moth.n.01', 'name': 'angoumois_moth'}, {'id': 4065, 'synset': 'potato_moth.n.01', 'name': 'potato_moth'}, {'id': 4066, 'synset': 'potato_tuberworm.n.01', 'name': 'potato_tuberworm'}, {'id': 4067, 'synset': 'noctuid_moth.n.01', 'name': 'noctuid_moth'}, {'id': 4068, 'synset': 'cutworm.n.01', 'name': 'cutworm'}, {'id': 4069, 'synset': 'underwing.n.01', 'name': 'underwing'}, {'id': 4070, 'synset': 'red_underwing.n.01', 'name': 'red_underwing'}, {'id': 4071, 'synset': 'antler_moth.n.01', 'name': 'antler_moth'}, {'id': 4072, 'synset': 'heliothis_moth.n.01', 'name': 'heliothis_moth'}, {'id': 4073, 'synset': 'army_cutworm.n.01', 'name': 'army_cutworm'}, {'id': 4074, 'synset': 'armyworm.n.02', 'name': 'armyworm'}, {'id': 4075, 'synset': 'armyworm.n.01', 'name': 'armyworm'}, {'id': 4076, 'synset': 'spodoptera_exigua.n.02', 'name': 'Spodoptera_exigua'}, {'id': 4077, 'synset': 'beet_armyworm.n.01', 'name': 'beet_armyworm'}, {'id': 4078, 'synset': 'spodoptera_frugiperda.n.02', 'name': 'Spodoptera_frugiperda'}, {'id': 4079, 'synset': 'fall_armyworm.n.01', 'name': 'fall_armyworm'}, {'id': 4080, 'synset': 'hawkmoth.n.01', 'name': 'hawkmoth'}, {'id': 4081, 'synset': 'manduca_sexta.n.02', 'name': 'Manduca_sexta'}, {'id': 4082, 'synset': 'tobacco_hornworm.n.01', 'name': 'tobacco_hornworm'}, {'id': 4083, 'synset': 'manduca_quinquemaculata.n.02', 'name': 'Manduca_quinquemaculata'}, {'id': 4084, 'synset': 'tomato_hornworm.n.01', 'name': 'tomato_hornworm'}, {'id': 4085, 'synset': "death's-head_moth.n.01", 'name': "death's-head_moth"}, {'id': 4086, 'synset': 'bombycid.n.01', 'name': 'bombycid'}, {'id': 4087, 'synset': 'domestic_silkworm_moth.n.01', 'name': 'domestic_silkworm_moth'}, {'id': 4088, 'synset': 'silkworm.n.01', 'name': 'silkworm'}, {'id': 4089, 'synset': 'saturniid.n.01', 'name': 'saturniid'}, {'id': 4090, 'synset': 'emperor.n.03', 'name': 'emperor'}, {'id': 4091, 'synset': 'imperial_moth.n.01', 'name': 'imperial_moth'}, {'id': 4092, 'synset': 'giant_silkworm_moth.n.01', 'name': 'giant_silkworm_moth'}, {'id': 4093, 'synset': 'silkworm.n.02', 'name': 'silkworm'}, {'id': 4094, 'synset': 'luna_moth.n.01', 'name': 'luna_moth'}, {'id': 4095, 'synset': 'cecropia.n.02', 'name': 'cecropia'}, {'id': 4096, 'synset': 'cynthia_moth.n.01', 'name': 'cynthia_moth'}, {'id': 4097, 'synset': 'ailanthus_silkworm.n.01', 'name': 'ailanthus_silkworm'}, {'id': 4098, 'synset': 'io_moth.n.01', 'name': 'io_moth'}, {'id': 4099, 'synset': 'polyphemus_moth.n.01', 'name': 'polyphemus_moth'}, {'id': 4100, 'synset': 'pernyi_moth.n.01', 'name': 'pernyi_moth'}, {'id': 4101, 'synset': 'tussah.n.01', 'name': 'tussah'}, {'id': 4102, 'synset': 'atlas_moth.n.01', 'name': 'atlas_moth'}, {'id': 4103, 'synset': 'arctiid.n.01', 'name': 'arctiid'}, {'id': 4104, 'synset': 'tiger_moth.n.01', 'name': 'tiger_moth'}, {'id': 4105, 'synset': 'cinnabar.n.02', 'name': 'cinnabar'}, {'id': 4106, 'synset': 'lasiocampid.n.01', 'name': 'lasiocampid'}, {'id': 4107, 'synset': 'eggar.n.01', 'name': 'eggar'}, {'id': 4108, 'synset': 'tent-caterpillar_moth.n.02', 'name': 'tent-caterpillar_moth'}, {'id': 4109, 'synset': 'tent_caterpillar.n.01', 'name': 'tent_caterpillar'}, {'id': 4110, 'synset': 'tent-caterpillar_moth.n.01', 'name': 'tent-caterpillar_moth'}, {'id': 4111, 'synset': 'forest_tent_caterpillar.n.01', 'name': 'forest_tent_caterpillar'}, {'id': 4112, 'synset': 'lappet.n.03', 'name': 'lappet'}, {'id': 4113, 'synset': 'lappet_caterpillar.n.01', 'name': 'lappet_caterpillar'}, {'id': 4114, 'synset': 'webworm.n.01', 'name': 'webworm'}, {'id': 4115, 'synset': 'webworm_moth.n.01', 'name': 'webworm_moth'}, {'id': 4116, 'synset': 'hyphantria_cunea.n.02', 'name': 'Hyphantria_cunea'}, {'id': 4117, 'synset': 'fall_webworm.n.01', 'name': 'fall_webworm'}, {'id': 4118, 'synset': 'garden_webworm.n.01', 'name': 'garden_webworm'}, {'id': 4119, 'synset': 'instar.n.01', 'name': 'instar'}, {'id': 4120, 'synset': 'caterpillar.n.01', 'name': 'caterpillar'}, {'id': 4121, 'synset': 'corn_borer.n.01', 'name': 'corn_borer'}, {'id': 4122, 'synset': 'bollworm.n.01', 'name': 'bollworm'}, {'id': 4123, 'synset': 'pink_bollworm.n.01', 'name': 'pink_bollworm'}, {'id': 4124, 'synset': 'corn_earworm.n.01', 'name': 'corn_earworm'}, {'id': 4125, 'synset': 'cabbageworm.n.01', 'name': 'cabbageworm'}, {'id': 4126, 'synset': 'woolly_bear.n.01', 'name': 'woolly_bear'}, {'id': 4127, 'synset': 'woolly_bear_moth.n.01', 'name': 'woolly_bear_moth'}, {'id': 4128, 'synset': 'larva.n.01', 'name': 'larva'}, {'id': 4129, 'synset': 'nymph.n.02', 'name': 'nymph'}, {'id': 4130, 'synset': 'leptocephalus.n.01', 'name': 'leptocephalus'}, {'id': 4131, 'synset': 'grub.n.02', 'name': 'grub'}, {'id': 4132, 'synset': 'maggot.n.01', 'name': 'maggot'}, {'id': 4133, 'synset': 'leatherjacket.n.03', 'name': 'leatherjacket'}, {'id': 4134, 'synset': 'pupa.n.01', 'name': 'pupa'}, {'id': 4135, 'synset': 'chrysalis.n.01', 'name': 'chrysalis'}, {'id': 4136, 'synset': 'imago.n.02', 'name': 'imago'}, {'id': 4137, 'synset': 'queen.n.01', 'name': 'queen'}, {'id': 4138, 'synset': 'phoronid.n.01', 'name': 'phoronid'}, {'id': 4139, 'synset': 'bryozoan.n.01', 'name': 'bryozoan'}, {'id': 4140, 'synset': 'brachiopod.n.01', 'name': 'brachiopod'}, {'id': 4141, 'synset': 'peanut_worm.n.01', 'name': 'peanut_worm'}, {'id': 4142, 'synset': 'echinoderm.n.01', 'name': 'echinoderm'}, {'id': 4143, 'synset': 'brittle_star.n.01', 'name': 'brittle_star'}, {'id': 4144, 'synset': 'basket_star.n.01', 'name': 'basket_star'}, {'id': 4145, 'synset': 'astrophyton_muricatum.n.01', 'name': 'Astrophyton_muricatum'}, {'id': 4146, 'synset': 'sea_urchin.n.01', 'name': 'sea_urchin'}, {'id': 4147, 'synset': 'edible_sea_urchin.n.01', 'name': 'edible_sea_urchin'}, {'id': 4148, 'synset': 'sand_dollar.n.01', 'name': 'sand_dollar'}, {'id': 4149, 'synset': 'heart_urchin.n.01', 'name': 'heart_urchin'}, {'id': 4150, 'synset': 'crinoid.n.01', 'name': 'crinoid'}, {'id': 4151, 'synset': 'sea_lily.n.01', 'name': 'sea_lily'}, {'id': 4152, 'synset': 'feather_star.n.01', 'name': 'feather_star'}, {'id': 4153, 'synset': 'sea_cucumber.n.01', 'name': 'sea_cucumber'}, {'id': 4154, 'synset': 'trepang.n.01', 'name': 'trepang'}, {'id': 4155, 'synset': 'duplicidentata.n.01', 'name': 'Duplicidentata'}, {'id': 4156, 'synset': 'lagomorph.n.01', 'name': 'lagomorph'}, {'id': 4157, 'synset': 'leporid.n.01', 'name': 'leporid'}, {'id': 4158, 'synset': 'rabbit_ears.n.02', 'name': 'rabbit_ears'}, {'id': 4159, 'synset': 'lapin.n.02', 'name': 'lapin'}, {'id': 4160, 'synset': 'bunny.n.02', 'name': 'bunny'}, {'id': 4161, 'synset': 'european_rabbit.n.01', 'name': 'European_rabbit'}, {'id': 4162, 'synset': 'wood_rabbit.n.01', 'name': 'wood_rabbit'}, {'id': 4163, 'synset': 'eastern_cottontail.n.01', 'name': 'eastern_cottontail'}, {'id': 4164, 'synset': 'swamp_rabbit.n.02', 'name': 'swamp_rabbit'}, {'id': 4165, 'synset': 'marsh_hare.n.01', 'name': 'marsh_hare'}, {'id': 4166, 'synset': 'hare.n.01', 'name': 'hare'}, {'id': 4167, 'synset': 'leveret.n.01', 'name': 'leveret'}, {'id': 4168, 'synset': 'european_hare.n.01', 'name': 'European_hare'}, {'id': 4169, 'synset': 'jackrabbit.n.01', 'name': 'jackrabbit'}, {'id': 4170, 'synset': 'white-tailed_jackrabbit.n.01', 'name': 'white-tailed_jackrabbit'}, {'id': 4171, 'synset': 'blacktail_jackrabbit.n.01', 'name': 'blacktail_jackrabbit'}, {'id': 4172, 'synset': 'polar_hare.n.01', 'name': 'polar_hare'}, {'id': 4173, 'synset': 'snowshoe_hare.n.01', 'name': 'snowshoe_hare'}, {'id': 4174, 'synset': 'belgian_hare.n.01', 'name': 'Belgian_hare'}, {'id': 4175, 'synset': 'angora.n.03', 'name': 'Angora'}, {'id': 4176, 'synset': 'pika.n.01', 'name': 'pika'}, {'id': 4177, 'synset': 'little_chief_hare.n.01', 'name': 'little_chief_hare'}, {'id': 4178, 'synset': 'collared_pika.n.01', 'name': 'collared_pika'}, {'id': 4179, 'synset': 'mouse.n.01', 'name': 'mouse'}, {'id': 4180, 'synset': 'pocket_rat.n.01', 'name': 'pocket_rat'}, {'id': 4181, 'synset': 'murine.n.01', 'name': 'murine'}, {'id': 4182, 'synset': 'house_mouse.n.01', 'name': 'house_mouse'}, {'id': 4183, 'synset': 'harvest_mouse.n.02', 'name': 'harvest_mouse'}, {'id': 4184, 'synset': 'field_mouse.n.02', 'name': 'field_mouse'}, {'id': 4185, 'synset': 'nude_mouse.n.01', 'name': 'nude_mouse'}, {'id': 4186, 'synset': 'european_wood_mouse.n.01', 'name': 'European_wood_mouse'}, {'id': 4187, 'synset': 'brown_rat.n.01', 'name': 'brown_rat'}, {'id': 4188, 'synset': 'wharf_rat.n.02', 'name': 'wharf_rat'}, {'id': 4189, 'synset': 'sewer_rat.n.01', 'name': 'sewer_rat'}, {'id': 4190, 'synset': 'black_rat.n.01', 'name': 'black_rat'}, {'id': 4191, 'synset': 'bandicoot_rat.n.01', 'name': 'bandicoot_rat'}, {'id': 4192, 'synset': 'jerboa_rat.n.01', 'name': 'jerboa_rat'}, {'id': 4193, 'synset': 'kangaroo_mouse.n.02', 'name': 'kangaroo_mouse'}, {'id': 4194, 'synset': 'water_rat.n.03', 'name': 'water_rat'}, {'id': 4195, 'synset': 'beaver_rat.n.01', 'name': 'beaver_rat'}, {'id': 4196, 'synset': 'new_world_mouse.n.01', 'name': 'New_World_mouse'}, {'id': 4197, 'synset': 'american_harvest_mouse.n.01', 'name': 'American_harvest_mouse'}, {'id': 4198, 'synset': 'wood_mouse.n.01', 'name': 'wood_mouse'}, {'id': 4199, 'synset': 'white-footed_mouse.n.01', 'name': 'white-footed_mouse'}, {'id': 4200, 'synset': 'deer_mouse.n.01', 'name': 'deer_mouse'}, {'id': 4201, 'synset': 'cactus_mouse.n.01', 'name': 'cactus_mouse'}, {'id': 4202, 'synset': 'cotton_mouse.n.01', 'name': 'cotton_mouse'}, {'id': 4203, 'synset': 'pygmy_mouse.n.01', 'name': 'pygmy_mouse'}, {'id': 4204, 'synset': 'grasshopper_mouse.n.01', 'name': 'grasshopper_mouse'}, {'id': 4205, 'synset': 'muskrat.n.02', 'name': 'muskrat'}, {'id': 4206, 'synset': 'round-tailed_muskrat.n.01', 'name': 'round-tailed_muskrat'}, {'id': 4207, 'synset': 'cotton_rat.n.01', 'name': 'cotton_rat'}, {'id': 4208, 'synset': 'wood_rat.n.01', 'name': 'wood_rat'}, {'id': 4209, 'synset': 'dusky-footed_wood_rat.n.01', 'name': 'dusky-footed_wood_rat'}, {'id': 4210, 'synset': 'vole.n.01', 'name': 'vole'}, {'id': 4211, 'synset': 'packrat.n.02', 'name': 'packrat'}, {'id': 4212, 'synset': 'dusky-footed_woodrat.n.01', 'name': 'dusky-footed_woodrat'}, {'id': 4213, 'synset': 'eastern_woodrat.n.01', 'name': 'eastern_woodrat'}, {'id': 4214, 'synset': 'rice_rat.n.01', 'name': 'rice_rat'}, {'id': 4215, 'synset': 'pine_vole.n.01', 'name': 'pine_vole'}, {'id': 4216, 'synset': 'meadow_vole.n.01', 'name': 'meadow_vole'}, {'id': 4217, 'synset': 'water_vole.n.02', 'name': 'water_vole'}, {'id': 4218, 'synset': 'prairie_vole.n.01', 'name': 'prairie_vole'}, {'id': 4219, 'synset': 'water_vole.n.01', 'name': 'water_vole'}, {'id': 4220, 'synset': 'red-backed_mouse.n.01', 'name': 'red-backed_mouse'}, {'id': 4221, 'synset': 'phenacomys.n.01', 'name': 'phenacomys'}, {'id': 4222, 'synset': 'eurasian_hamster.n.01', 'name': 'Eurasian_hamster'}, {'id': 4223, 'synset': 'golden_hamster.n.01', 'name': 'golden_hamster'}, {'id': 4224, 'synset': 'gerbil.n.01', 'name': 'gerbil'}, {'id': 4225, 'synset': 'jird.n.01', 'name': 'jird'}, {'id': 4226, 'synset': 'tamarisk_gerbil.n.01', 'name': 'tamarisk_gerbil'}, {'id': 4227, 'synset': 'sand_rat.n.02', 'name': 'sand_rat'}, {'id': 4228, 'synset': 'lemming.n.01', 'name': 'lemming'}, {'id': 4229, 'synset': 'european_lemming.n.01', 'name': 'European_lemming'}, {'id': 4230, 'synset': 'brown_lemming.n.01', 'name': 'brown_lemming'}, {'id': 4231, 'synset': 'grey_lemming.n.01', 'name': 'grey_lemming'}, {'id': 4232, 'synset': 'pied_lemming.n.01', 'name': 'pied_lemming'}, {'id': 4233, 'synset': 'hudson_bay_collared_lemming.n.01', 'name': 'Hudson_bay_collared_lemming'}, {'id': 4234, 'synset': 'southern_bog_lemming.n.01', 'name': 'southern_bog_lemming'}, {'id': 4235, 'synset': 'northern_bog_lemming.n.01', 'name': 'northern_bog_lemming'}, {'id': 4236, 'synset': 'porcupine.n.01', 'name': 'porcupine'}, {'id': 4237, 'synset': 'old_world_porcupine.n.01', 'name': 'Old_World_porcupine'}, {'id': 4238, 'synset': 'brush-tailed_porcupine.n.01', 'name': 'brush-tailed_porcupine'}, {'id': 4239, 'synset': 'long-tailed_porcupine.n.01', 'name': 'long-tailed_porcupine'}, {'id': 4240, 'synset': 'new_world_porcupine.n.01', 'name': 'New_World_porcupine'}, {'id': 4241, 'synset': 'canada_porcupine.n.01', 'name': 'Canada_porcupine'}, {'id': 4242, 'synset': 'pocket_mouse.n.01', 'name': 'pocket_mouse'}, {'id': 4243, 'synset': 'silky_pocket_mouse.n.01', 'name': 'silky_pocket_mouse'}, {'id': 4244, 'synset': 'plains_pocket_mouse.n.01', 'name': 'plains_pocket_mouse'}, {'id': 4245, 'synset': 'hispid_pocket_mouse.n.01', 'name': 'hispid_pocket_mouse'}, {'id': 4246, 'synset': 'mexican_pocket_mouse.n.01', 'name': 'Mexican_pocket_mouse'}, {'id': 4247, 'synset': 'kangaroo_rat.n.01', 'name': 'kangaroo_rat'}, {'id': 4248, 'synset': 'ord_kangaroo_rat.n.01', 'name': 'Ord_kangaroo_rat'}, {'id': 4249, 'synset': 'kangaroo_mouse.n.01', 'name': 'kangaroo_mouse'}, {'id': 4250, 'synset': 'jumping_mouse.n.01', 'name': 'jumping_mouse'}, {'id': 4251, 'synset': 'meadow_jumping_mouse.n.01', 'name': 'meadow_jumping_mouse'}, {'id': 4252, 'synset': 'jerboa.n.01', 'name': 'jerboa'}, {'id': 4253, 'synset': 'typical_jerboa.n.01', 'name': 'typical_jerboa'}, {'id': 4254, 'synset': 'jaculus_jaculus.n.01', 'name': 'Jaculus_jaculus'}, {'id': 4255, 'synset': 'dormouse.n.01', 'name': 'dormouse'}, {'id': 4256, 'synset': 'loir.n.01', 'name': 'loir'}, {'id': 4257, 'synset': 'hazel_mouse.n.01', 'name': 'hazel_mouse'}, {'id': 4258, 'synset': 'lerot.n.01', 'name': 'lerot'}, {'id': 4259, 'synset': 'gopher.n.04', 'name': 'gopher'}, {'id': 4260, 'synset': 'plains_pocket_gopher.n.01', 'name': 'plains_pocket_gopher'}, {'id': 4261, 'synset': 'southeastern_pocket_gopher.n.01', 'name': 'southeastern_pocket_gopher'}, {'id': 4262, 'synset': 'valley_pocket_gopher.n.01', 'name': 'valley_pocket_gopher'}, {'id': 4263, 'synset': 'northern_pocket_gopher.n.01', 'name': 'northern_pocket_gopher'}, {'id': 4264, 'synset': 'tree_squirrel.n.01', 'name': 'tree_squirrel'}, {'id': 4265, 'synset': 'eastern_grey_squirrel.n.01', 'name': 'eastern_grey_squirrel'}, {'id': 4266, 'synset': 'western_grey_squirrel.n.01', 'name': 'western_grey_squirrel'}, {'id': 4267, 'synset': 'fox_squirrel.n.01', 'name': 'fox_squirrel'}, {'id': 4268, 'synset': 'black_squirrel.n.01', 'name': 'black_squirrel'}, {'id': 4269, 'synset': 'red_squirrel.n.02', 'name': 'red_squirrel'}, {'id': 4270, 'synset': 'american_red_squirrel.n.01', 'name': 'American_red_squirrel'}, {'id': 4271, 'synset': 'chickeree.n.01', 'name': 'chickeree'}, {'id': 4272, 'synset': 'antelope_squirrel.n.01', 'name': 'antelope_squirrel'}, {'id': 4273, 'synset': 'ground_squirrel.n.02', 'name': 'ground_squirrel'}, {'id': 4274, 'synset': 'mantled_ground_squirrel.n.01', 'name': 'mantled_ground_squirrel'}, {'id': 4275, 'synset': 'suslik.n.01', 'name': 'suslik'}, {'id': 4276, 'synset': 'flickertail.n.01', 'name': 'flickertail'}, {'id': 4277, 'synset': 'rock_squirrel.n.01', 'name': 'rock_squirrel'}, {'id': 4278, 'synset': 'arctic_ground_squirrel.n.01', 'name': 'Arctic_ground_squirrel'}, {'id': 4279, 'synset': 'prairie_dog.n.01', 'name': 'prairie_dog'}, {'id': 4280, 'synset': 'blacktail_prairie_dog.n.01', 'name': 'blacktail_prairie_dog'}, {'id': 4281, 'synset': 'whitetail_prairie_dog.n.01', 'name': 'whitetail_prairie_dog'}, {'id': 4282, 'synset': 'eastern_chipmunk.n.01', 'name': 'eastern_chipmunk'}, {'id': 4283, 'synset': 'chipmunk.n.01', 'name': 'chipmunk'}, {'id': 4284, 'synset': 'baronduki.n.01', 'name': 'baronduki'}, {'id': 4285, 'synset': 'american_flying_squirrel.n.01', 'name': 'American_flying_squirrel'}, {'id': 4286, 'synset': 'southern_flying_squirrel.n.01', 'name': 'southern_flying_squirrel'}, {'id': 4287, 'synset': 'northern_flying_squirrel.n.01', 'name': 'northern_flying_squirrel'}, {'id': 4288, 'synset': 'marmot.n.01', 'name': 'marmot'}, {'id': 4289, 'synset': 'groundhog.n.01', 'name': 'groundhog'}, {'id': 4290, 'synset': 'hoary_marmot.n.01', 'name': 'hoary_marmot'}, {'id': 4291, 'synset': 'yellowbelly_marmot.n.01', 'name': 'yellowbelly_marmot'}, {'id': 4292, 'synset': 'asiatic_flying_squirrel.n.01', 'name': 'Asiatic_flying_squirrel'}, {'id': 4293, 'synset': 'beaver.n.07', 'name': 'beaver'}, {'id': 4294, 'synset': 'old_world_beaver.n.01', 'name': 'Old_World_beaver'}, {'id': 4295, 'synset': 'new_world_beaver.n.01', 'name': 'New_World_beaver'}, {'id': 4296, 'synset': 'mountain_beaver.n.01', 'name': 'mountain_beaver'}, {'id': 4297, 'synset': 'cavy.n.01', 'name': 'cavy'}, {'id': 4298, 'synset': 'guinea_pig.n.02', 'name': 'guinea_pig'}, {'id': 4299, 'synset': 'aperea.n.01', 'name': 'aperea'}, {'id': 4300, 'synset': 'mara.n.02', 'name': 'mara'}, {'id': 4301, 'synset': 'capybara.n.01', 'name': 'capybara'}, {'id': 4302, 'synset': 'agouti.n.01', 'name': 'agouti'}, {'id': 4303, 'synset': 'paca.n.01', 'name': 'paca'}, {'id': 4304, 'synset': 'mountain_paca.n.01', 'name': 'mountain_paca'}, {'id': 4305, 'synset': 'coypu.n.01', 'name': 'coypu'}, {'id': 4306, 'synset': 'chinchilla.n.03', 'name': 'chinchilla'}, {'id': 4307, 'synset': 'mountain_chinchilla.n.01', 'name': 'mountain_chinchilla'}, {'id': 4308, 'synset': 'viscacha.n.01', 'name': 'viscacha'}, {'id': 4309, 'synset': 'abrocome.n.01', 'name': 'abrocome'}, {'id': 4310, 'synset': 'mole_rat.n.02', 'name': 'mole_rat'}, {'id': 4311, 'synset': 'mole_rat.n.01', 'name': 'mole_rat'}, {'id': 4312, 'synset': 'sand_rat.n.01', 'name': 'sand_rat'}, {'id': 4313, 'synset': 'naked_mole_rat.n.01', 'name': 'naked_mole_rat'}, {'id': 4314, 'synset': 'queen.n.09', 'name': 'queen'}, {'id': 4315, 'synset': 'damaraland_mole_rat.n.01', 'name': 'Damaraland_mole_rat'}, {'id': 4316, 'synset': 'ungulata.n.01', 'name': 'Ungulata'}, {'id': 4317, 'synset': 'ungulate.n.01', 'name': 'ungulate'}, {'id': 4318, 'synset': 'unguiculate.n.01', 'name': 'unguiculate'}, {'id': 4319, 'synset': 'dinoceras.n.01', 'name': 'dinoceras'}, {'id': 4320, 'synset': 'hyrax.n.01', 'name': 'hyrax'}, {'id': 4321, 'synset': 'rock_hyrax.n.01', 'name': 'rock_hyrax'}, {'id': 4322, 'synset': 'odd-toed_ungulate.n.01', 'name': 'odd-toed_ungulate'}, {'id': 4323, 'synset': 'equine.n.01', 'name': 'equine'}, {'id': 4324, 'synset': 'roan.n.02', 'name': 'roan'}, {'id': 4325, 'synset': 'stablemate.n.01', 'name': 'stablemate'}, {'id': 4326, 'synset': 'gee-gee.n.01', 'name': 'gee-gee'}, {'id': 4327, 'synset': 'eohippus.n.01', 'name': 'eohippus'}, {'id': 4328, 'synset': 'filly.n.01', 'name': 'filly'}, {'id': 4329, 'synset': 'colt.n.01', 'name': 'colt'}, {'id': 4330, 'synset': 'male_horse.n.01', 'name': 'male_horse'}, {'id': 4331, 'synset': 'ridgeling.n.01', 'name': 'ridgeling'}, {'id': 4332, 'synset': 'stallion.n.01', 'name': 'stallion'}, {'id': 4333, 'synset': 'stud.n.04', 'name': 'stud'}, {'id': 4334, 'synset': 'gelding.n.01', 'name': 'gelding'}, {'id': 4335, 'synset': 'mare.n.01', 'name': 'mare'}, {'id': 4336, 'synset': 'broodmare.n.01', 'name': 'broodmare'}, {'id': 4337, 'synset': 'saddle_horse.n.01', 'name': 'saddle_horse'}, {'id': 4338, 'synset': 'remount.n.01', 'name': 'remount'}, {'id': 4339, 'synset': 'palfrey.n.01', 'name': 'palfrey'}, {'id': 4340, 'synset': 'warhorse.n.03', 'name': 'warhorse'}, {'id': 4341, 'synset': 'cavalry_horse.n.01', 'name': 'cavalry_horse'}, {'id': 4342, 'synset': 'charger.n.01', 'name': 'charger'}, {'id': 4343, 'synset': 'steed.n.01', 'name': 'steed'}, {'id': 4344, 'synset': 'prancer.n.01', 'name': 'prancer'}, {'id': 4345, 'synset': 'hack.n.08', 'name': 'hack'}, {'id': 4346, 'synset': 'cow_pony.n.01', 'name': 'cow_pony'}, {'id': 4347, 'synset': 'quarter_horse.n.01', 'name': 'quarter_horse'}, {'id': 4348, 'synset': 'morgan.n.06', 'name': 'Morgan'}, {'id': 4349, 'synset': 'tennessee_walker.n.01', 'name': 'Tennessee_walker'}, {'id': 4350, 'synset': 'american_saddle_horse.n.01', 'name': 'American_saddle_horse'}, {'id': 4351, 'synset': 'appaloosa.n.01', 'name': 'Appaloosa'}, {'id': 4352, 'synset': 'arabian.n.02', 'name': 'Arabian'}, {'id': 4353, 'synset': 'lippizan.n.01', 'name': 'Lippizan'}, {'id': 4354, 'synset': 'pony.n.01', 'name': 'pony'}, {'id': 4355, 'synset': 'polo_pony.n.01', 'name': 'polo_pony'}, {'id': 4356, 'synset': 'mustang.n.01', 'name': 'mustang'}, {'id': 4357, 'synset': 'bronco.n.01', 'name': 'bronco'}, {'id': 4358, 'synset': 'bucking_bronco.n.01', 'name': 'bucking_bronco'}, {'id': 4359, 'synset': 'buckskin.n.01', 'name': 'buckskin'}, {'id': 4360, 'synset': 'crowbait.n.01', 'name': 'crowbait'}, {'id': 4361, 'synset': 'dun.n.01', 'name': 'dun'}, {'id': 4362, 'synset': 'grey.n.07', 'name': 'grey'}, {'id': 4363, 'synset': 'wild_horse.n.01', 'name': 'wild_horse'}, {'id': 4364, 'synset': 'tarpan.n.01', 'name': 'tarpan'}, {'id': 4365, 'synset': "przewalski's_horse.n.01", 'name': "Przewalski's_horse"}, {'id': 4366, 'synset': 'cayuse.n.01', 'name': 'cayuse'}, {'id': 4367, 'synset': 'hack.n.07', 'name': 'hack'}, {'id': 4368, 'synset': 'hack.n.06', 'name': 'hack'}, {'id': 4369, 'synset': 'plow_horse.n.01', 'name': 'plow_horse'}, {'id': 4370, 'synset': 'shetland_pony.n.01', 'name': 'Shetland_pony'}, {'id': 4371, 'synset': 'welsh_pony.n.01', 'name': 'Welsh_pony'}, {'id': 4372, 'synset': 'exmoor.n.02', 'name': 'Exmoor'}, {'id': 4373, 'synset': 'racehorse.n.01', 'name': 'racehorse'}, {'id': 4374, 'synset': 'thoroughbred.n.02', 'name': 'thoroughbred'}, {'id': 4375, 'synset': 'steeplechaser.n.01', 'name': 'steeplechaser'}, {'id': 4376, 'synset': 'racer.n.03', 'name': 'racer'}, {'id': 4377, 'synset': 'finisher.n.06', 'name': 'finisher'}, {'id': 4378, 'synset': 'pony.n.02', 'name': 'pony'}, {'id': 4379, 'synset': 'yearling.n.02', 'name': 'yearling'}, {'id': 4380, 'synset': 'dark_horse.n.02', 'name': 'dark_horse'}, {'id': 4381, 'synset': 'mudder.n.01', 'name': 'mudder'}, {'id': 4382, 'synset': 'nonstarter.n.02', 'name': 'nonstarter'}, {'id': 4383, 'synset': 'stalking-horse.n.04', 'name': 'stalking-horse'}, {'id': 4384, 'synset': 'harness_horse.n.01', 'name': 'harness_horse'}, {'id': 4385, 'synset': 'cob.n.02', 'name': 'cob'}, {'id': 4386, 'synset': 'hackney.n.02', 'name': 'hackney'}, {'id': 4387, 'synset': 'workhorse.n.02', 'name': 'workhorse'}, {'id': 4388, 'synset': 'draft_horse.n.01', 'name': 'draft_horse'}, {'id': 4389, 'synset': 'packhorse.n.01', 'name': 'packhorse'}, {'id': 4390, 'synset': 'carthorse.n.01', 'name': 'carthorse'}, {'id': 4391, 'synset': 'clydesdale.n.01', 'name': 'Clydesdale'}, {'id': 4392, 'synset': 'percheron.n.01', 'name': 'Percheron'}, {'id': 4393, 'synset': 'farm_horse.n.01', 'name': 'farm_horse'}, {'id': 4394, 'synset': 'shire.n.02', 'name': 'shire'}, {'id': 4395, 'synset': 'pole_horse.n.02', 'name': 'pole_horse'}, {'id': 4396, 'synset': 'post_horse.n.01', 'name': 'post_horse'}, {'id': 4397, 'synset': 'coach_horse.n.01', 'name': 'coach_horse'}, {'id': 4398, 'synset': 'pacer.n.02', 'name': 'pacer'}, {'id': 4399, 'synset': 'pacer.n.01', 'name': 'pacer'}, {'id': 4400, 'synset': 'trotting_horse.n.01', 'name': 'trotting_horse'}, {'id': 4401, 'synset': 'pole_horse.n.01', 'name': 'pole_horse'}, {'id': 4402, 'synset': 'stepper.n.03', 'name': 'stepper'}, {'id': 4403, 'synset': 'chestnut.n.06', 'name': 'chestnut'}, {'id': 4404, 'synset': 'liver_chestnut.n.01', 'name': 'liver_chestnut'}, {'id': 4405, 'synset': 'bay.n.07', 'name': 'bay'}, {'id': 4406, 'synset': 'sorrel.n.05', 'name': 'sorrel'}, {'id': 4407, 'synset': 'palomino.n.01', 'name': 'palomino'}, {'id': 4408, 'synset': 'pinto.n.01', 'name': 'pinto'}, {'id': 4409, 'synset': 'ass.n.03', 'name': 'ass'}, {'id': 4410, 'synset': 'burro.n.01', 'name': 'burro'}, {'id': 4411, 'synset': 'moke.n.01', 'name': 'moke'}, {'id': 4412, 'synset': 'jack.n.12', 'name': 'jack'}, {'id': 4413, 'synset': 'jennet.n.01', 'name': 'jennet'}, {'id': 4414, 'synset': 'mule.n.01', 'name': 'mule'}, {'id': 4415, 'synset': 'hinny.n.01', 'name': 'hinny'}, {'id': 4416, 'synset': 'wild_ass.n.01', 'name': 'wild_ass'}, {'id': 4417, 'synset': 'african_wild_ass.n.01', 'name': 'African_wild_ass'}, {'id': 4418, 'synset': 'kiang.n.01', 'name': 'kiang'}, {'id': 4419, 'synset': 'onager.n.02', 'name': 'onager'}, {'id': 4420, 'synset': 'chigetai.n.01', 'name': 'chigetai'}, {'id': 4421, 'synset': 'common_zebra.n.01', 'name': 'common_zebra'}, {'id': 4422, 'synset': 'mountain_zebra.n.01', 'name': 'mountain_zebra'}, {'id': 4423, 'synset': "grevy's_zebra.n.01", 'name': "grevy's_zebra"}, {'id': 4424, 'synset': 'quagga.n.01', 'name': 'quagga'}, {'id': 4425, 'synset': 'indian_rhinoceros.n.01', 'name': 'Indian_rhinoceros'}, {'id': 4426, 'synset': 'woolly_rhinoceros.n.01', 'name': 'woolly_rhinoceros'}, {'id': 4427, 'synset': 'white_rhinoceros.n.01', 'name': 'white_rhinoceros'}, {'id': 4428, 'synset': 'black_rhinoceros.n.01', 'name': 'black_rhinoceros'}, {'id': 4429, 'synset': 'tapir.n.01', 'name': 'tapir'}, {'id': 4430, 'synset': 'new_world_tapir.n.01', 'name': 'New_World_tapir'}, {'id': 4431, 'synset': 'malayan_tapir.n.01', 'name': 'Malayan_tapir'}, {'id': 4432, 'synset': 'even-toed_ungulate.n.01', 'name': 'even-toed_ungulate'}, {'id': 4433, 'synset': 'swine.n.01', 'name': 'swine'}, {'id': 4434, 'synset': 'piglet.n.01', 'name': 'piglet'}, {'id': 4435, 'synset': 'sucking_pig.n.01', 'name': 'sucking_pig'}, {'id': 4436, 'synset': 'porker.n.01', 'name': 'porker'}, {'id': 4437, 'synset': 'boar.n.02', 'name': 'boar'}, {'id': 4438, 'synset': 'sow.n.01', 'name': 'sow'}, {'id': 4439, 'synset': 'razorback.n.01', 'name': 'razorback'}, {'id': 4440, 'synset': 'wild_boar.n.01', 'name': 'wild_boar'}, {'id': 4441, 'synset': 'babirusa.n.01', 'name': 'babirusa'}, {'id': 4442, 'synset': 'warthog.n.01', 'name': 'warthog'}, {'id': 4443, 'synset': 'peccary.n.01', 'name': 'peccary'}, {'id': 4444, 'synset': 'collared_peccary.n.01', 'name': 'collared_peccary'}, {'id': 4445, 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'name': 'talapoin'}, {'id': 4725, 'synset': 'grivet.n.01', 'name': 'grivet'}, {'id': 4726, 'synset': 'vervet.n.01', 'name': 'vervet'}, {'id': 4727, 'synset': 'green_monkey.n.01', 'name': 'green_monkey'}, {'id': 4728, 'synset': 'mangabey.n.01', 'name': 'mangabey'}, {'id': 4729, 'synset': 'patas.n.01', 'name': 'patas'}, {'id': 4730, 'synset': 'chacma.n.01', 'name': 'chacma'}, {'id': 4731, 'synset': 'mandrill.n.01', 'name': 'mandrill'}, {'id': 4732, 'synset': 'drill.n.02', 'name': 'drill'}, {'id': 4733, 'synset': 'macaque.n.01', 'name': 'macaque'}, {'id': 4734, 'synset': 'rhesus.n.01', 'name': 'rhesus'}, {'id': 4735, 'synset': 'bonnet_macaque.n.01', 'name': 'bonnet_macaque'}, {'id': 4736, 'synset': 'barbary_ape.n.01', 'name': 'Barbary_ape'}, {'id': 4737, 'synset': 'crab-eating_macaque.n.01', 'name': 'crab-eating_macaque'}, {'id': 4738, 'synset': 'langur.n.01', 'name': 'langur'}, {'id': 4739, 'synset': 'entellus.n.01', 'name': 'entellus'}, {'id': 4740, 'synset': 'colobus.n.01', 'name': 'colobus'}, {'id': 4741, 'synset': 'guereza.n.01', 'name': 'guereza'}, {'id': 4742, 'synset': 'proboscis_monkey.n.01', 'name': 'proboscis_monkey'}, {'id': 4743, 'synset': 'new_world_monkey.n.01', 'name': 'New_World_monkey'}, {'id': 4744, 'synset': 'marmoset.n.01', 'name': 'marmoset'}, {'id': 4745, 'synset': 'true_marmoset.n.01', 'name': 'true_marmoset'}, {'id': 4746, 'synset': 'pygmy_marmoset.n.01', 'name': 'pygmy_marmoset'}, {'id': 4747, 'synset': 'tamarin.n.01', 'name': 'tamarin'}, {'id': 4748, 'synset': 'silky_tamarin.n.01', 'name': 'silky_tamarin'}, {'id': 4749, 'synset': 'pinche.n.01', 'name': 'pinche'}, {'id': 4750, 'synset': 'capuchin.n.02', 'name': 'capuchin'}, {'id': 4751, 'synset': 'douroucouli.n.01', 'name': 'douroucouli'}, {'id': 4752, 'synset': 'howler_monkey.n.01', 'name': 'howler_monkey'}, {'id': 4753, 'synset': 'saki.n.03', 'name': 'saki'}, {'id': 4754, 'synset': 'uakari.n.01', 'name': 'uakari'}, {'id': 4755, 'synset': 'titi.n.03', 'name': 'titi'}, {'id': 4756, 'synset': 'spider_monkey.n.01', 'name': 'spider_monkey'}, {'id': 4757, 'synset': 'squirrel_monkey.n.01', 'name': 'squirrel_monkey'}, {'id': 4758, 'synset': 'woolly_monkey.n.01', 'name': 'woolly_monkey'}, {'id': 4759, 'synset': 'tree_shrew.n.01', 'name': 'tree_shrew'}, {'id': 4760, 'synset': 'prosimian.n.01', 'name': 'prosimian'}, {'id': 4761, 'synset': 'lemur.n.01', 'name': 'lemur'}, {'id': 4762, 'synset': 'madagascar_cat.n.01', 'name': 'Madagascar_cat'}, {'id': 4763, 'synset': 'aye-aye.n.01', 'name': 'aye-aye'}, {'id': 4764, 'synset': 'slender_loris.n.01', 'name': 'slender_loris'}, {'id': 4765, 'synset': 'slow_loris.n.01', 'name': 'slow_loris'}, {'id': 4766, 'synset': 'potto.n.02', 'name': 'potto'}, {'id': 4767, 'synset': 'angwantibo.n.01', 'name': 'angwantibo'}, {'id': 4768, 'synset': 'galago.n.01', 'name': 'galago'}, {'id': 4769, 'synset': 'indri.n.01', 'name': 'indri'}, {'id': 4770, 'synset': 'woolly_indris.n.01', 'name': 'woolly_indris'}, {'id': 4771, 'synset': 'tarsier.n.01', 'name': 'tarsier'}, {'id': 4772, 'synset': 'tarsius_syrichta.n.01', 'name': 'Tarsius_syrichta'}, {'id': 4773, 'synset': 'tarsius_glis.n.01', 'name': 'Tarsius_glis'}, {'id': 4774, 'synset': 'flying_lemur.n.01', 'name': 'flying_lemur'}, {'id': 4775, 'synset': 'cynocephalus_variegatus.n.01', 'name': 'Cynocephalus_variegatus'}, {'id': 4776, 'synset': 'proboscidean.n.01', 'name': 'proboscidean'}, {'id': 4777, 'synset': 'rogue_elephant.n.01', 'name': 'rogue_elephant'}, {'id': 4778, 'synset': 'indian_elephant.n.01', 'name': 'Indian_elephant'}, {'id': 4779, 'synset': 'african_elephant.n.01', 'name': 'African_elephant'}, {'id': 4780, 'synset': 'woolly_mammoth.n.01', 'name': 'woolly_mammoth'}, {'id': 4781, 'synset': 'columbian_mammoth.n.01', 'name': 'columbian_mammoth'}, {'id': 4782, 'synset': 'imperial_mammoth.n.01', 'name': 'imperial_mammoth'}, {'id': 4783, 'synset': 'mastodon.n.01', 'name': 'mastodon'}, {'id': 4784, 'synset': 'plantigrade_mammal.n.01', 'name': 'plantigrade_mammal'}, {'id': 4785, 'synset': 'digitigrade_mammal.n.01', 'name': 'digitigrade_mammal'}, {'id': 4786, 'synset': 'procyonid.n.01', 'name': 'procyonid'}, {'id': 4787, 'synset': 'raccoon.n.02', 'name': 'raccoon'}, {'id': 4788, 'synset': 'common_raccoon.n.01', 'name': 'common_raccoon'}, {'id': 4789, 'synset': 'crab-eating_raccoon.n.01', 'name': 'crab-eating_raccoon'}, {'id': 4790, 'synset': 'bassarisk.n.01', 'name': 'bassarisk'}, {'id': 4791, 'synset': 'kinkajou.n.01', 'name': 'kinkajou'}, {'id': 4792, 'synset': 'coati.n.01', 'name': 'coati'}, {'id': 4793, 'synset': 'lesser_panda.n.01', 'name': 'lesser_panda'}, {'id': 4794, 'synset': 'twitterer.n.01', 'name': 'twitterer'}, {'id': 4795, 'synset': 'fingerling.n.01', 'name': 'fingerling'}, {'id': 4796, 'synset': 'game_fish.n.01', 'name': 'game_fish'}, {'id': 4797, 'synset': 'food_fish.n.01', 'name': 'food_fish'}, {'id': 4798, 'synset': 'rough_fish.n.01', 'name': 'rough_fish'}, {'id': 4799, 'synset': 'groundfish.n.01', 'name': 'groundfish'}, {'id': 4800, 'synset': 'young_fish.n.01', 'name': 'young_fish'}, {'id': 4801, 'synset': 'parr.n.03', 'name': 'parr'}, {'id': 4802, 'synset': 'mouthbreeder.n.01', 'name': 'mouthbreeder'}, {'id': 4803, 'synset': 'spawner.n.01', 'name': 'spawner'}, {'id': 4804, 'synset': 'barracouta.n.01', 'name': 'barracouta'}, {'id': 4805, 'synset': 'crossopterygian.n.01', 'name': 'crossopterygian'}, {'id': 4806, 'synset': 'coelacanth.n.01', 'name': 'coelacanth'}, {'id': 4807, 'synset': 'lungfish.n.01', 'name': 'lungfish'}, {'id': 4808, 'synset': 'ceratodus.n.01', 'name': 'ceratodus'}, {'id': 4809, 'synset': 'catfish.n.03', 'name': 'catfish'}, {'id': 4810, 'synset': 'silurid.n.01', 'name': 'silurid'}, {'id': 4811, 'synset': 'european_catfish.n.01', 'name': 'European_catfish'}, {'id': 4812, 'synset': 'electric_catfish.n.01', 'name': 'electric_catfish'}, {'id': 4813, 'synset': 'bullhead.n.02', 'name': 'bullhead'}, {'id': 4814, 'synset': 'horned_pout.n.01', 'name': 'horned_pout'}, {'id': 4815, 'synset': 'brown_bullhead.n.01', 'name': 'brown_bullhead'}, {'id': 4816, 'synset': 'channel_catfish.n.01', 'name': 'channel_catfish'}, {'id': 4817, 'synset': 'blue_catfish.n.01', 'name': 'blue_catfish'}, {'id': 4818, 'synset': 'flathead_catfish.n.01', 'name': 'flathead_catfish'}, {'id': 4819, 'synset': 'armored_catfish.n.01', 'name': 'armored_catfish'}, {'id': 4820, 'synset': 'sea_catfish.n.01', 'name': 'sea_catfish'}, {'id': 4821, 'synset': 'gadoid.n.01', 'name': 'gadoid'}, {'id': 4822, 'synset': 'cod.n.03', 'name': 'cod'}, {'id': 4823, 'synset': 'codling.n.01', 'name': 'codling'}, {'id': 4824, 'synset': 'atlantic_cod.n.01', 'name': 'Atlantic_cod'}, {'id': 4825, 'synset': 'pacific_cod.n.01', 'name': 'Pacific_cod'}, {'id': 4826, 'synset': 'whiting.n.06', 'name': 'whiting'}, {'id': 4827, 'synset': 'burbot.n.01', 'name': 'burbot'}, {'id': 4828, 'synset': 'haddock.n.02', 'name': 'haddock'}, {'id': 4829, 'synset': 'pollack.n.03', 'name': 'pollack'}, {'id': 4830, 'synset': 'hake.n.02', 'name': 'hake'}, {'id': 4831, 'synset': 'silver_hake.n.01', 'name': 'silver_hake'}, {'id': 4832, 'synset': 'ling.n.04', 'name': 'ling'}, {'id': 4833, 'synset': 'cusk.n.02', 'name': 'cusk'}, {'id': 4834, 'synset': 'grenadier.n.02', 'name': 'grenadier'}, {'id': 4835, 'synset': 'eel.n.02', 'name': 'eel'}, {'id': 4836, 'synset': 'elver.n.02', 'name': 'elver'}, {'id': 4837, 'synset': 'common_eel.n.01', 'name': 'common_eel'}, {'id': 4838, 'synset': 'tuna.n.04', 'name': 'tuna'}, {'id': 4839, 'synset': 'moray.n.01', 'name': 'moray'}, {'id': 4840, 'synset': 'conger.n.01', 'name': 'conger'}, {'id': 4841, 'synset': 'teleost_fish.n.01', 'name': 'teleost_fish'}, {'id': 4842, 'synset': 'beaked_salmon.n.01', 'name': 'beaked_salmon'}, {'id': 4843, 'synset': 'clupeid_fish.n.01', 'name': 'clupeid_fish'}, {'id': 4844, 'synset': 'whitebait.n.02', 'name': 'whitebait'}, {'id': 4845, 'synset': 'brit.n.02', 'name': 'brit'}, {'id': 4846, 'synset': 'shad.n.02', 'name': 'shad'}, {'id': 4847, 'synset': 'common_american_shad.n.01', 'name': 'common_American_shad'}, {'id': 4848, 'synset': 'river_shad.n.01', 'name': 'river_shad'}, {'id': 4849, 'synset': 'allice_shad.n.01', 'name': 'allice_shad'}, {'id': 4850, 'synset': 'alewife.n.02', 'name': 'alewife'}, {'id': 4851, 'synset': 'menhaden.n.01', 'name': 'menhaden'}, {'id': 4852, 'synset': 'herring.n.02', 'name': 'herring'}, {'id': 4853, 'synset': 'atlantic_herring.n.01', 'name': 'Atlantic_herring'}, {'id': 4854, 'synset': 'pacific_herring.n.01', 'name': 'Pacific_herring'}, {'id': 4855, 'synset': 'sardine.n.02', 'name': 'sardine'}, {'id': 4856, 'synset': 'sild.n.01', 'name': 'sild'}, {'id': 4857, 'synset': 'brisling.n.02', 'name': 'brisling'}, {'id': 4858, 'synset': 'pilchard.n.02', 'name': 'pilchard'}, {'id': 4859, 'synset': 'pacific_sardine.n.01', 'name': 'Pacific_sardine'}, {'id': 4860, 'synset': 'anchovy.n.02', 'name': 'anchovy'}, {'id': 4861, 'synset': 'mediterranean_anchovy.n.01', 'name': 'mediterranean_anchovy'}, {'id': 4862, 'synset': 'salmonid.n.01', 'name': 'salmonid'}, {'id': 4863, 'synset': 'parr.n.02', 'name': 'parr'}, {'id': 4864, 'synset': 'blackfish.n.02', 'name': 'blackfish'}, {'id': 4865, 'synset': 'redfish.n.03', 'name': 'redfish'}, {'id': 4866, 'synset': 'atlantic_salmon.n.02', 'name': 'Atlantic_salmon'}, {'id': 4867, 'synset': 'landlocked_salmon.n.01', 'name': 'landlocked_salmon'}, {'id': 4868, 'synset': 'sockeye.n.02', 'name': 'sockeye'}, {'id': 4869, 'synset': 'chinook.n.05', 'name': 'chinook'}, {'id': 4870, 'synset': 'coho.n.02', 'name': 'coho'}, {'id': 4871, 'synset': 'trout.n.02', 'name': 'trout'}, {'id': 4872, 'synset': 'brown_trout.n.01', 'name': 'brown_trout'}, {'id': 4873, 'synset': 'rainbow_trout.n.02', 'name': 'rainbow_trout'}, {'id': 4874, 'synset': 'sea_trout.n.03', 'name': 'sea_trout'}, {'id': 4875, 'synset': 'lake_trout.n.02', 'name': 'lake_trout'}, {'id': 4876, 'synset': 'brook_trout.n.02', 'name': 'brook_trout'}, {'id': 4877, 'synset': 'char.n.03', 'name': 'char'}, {'id': 4878, 'synset': 'arctic_char.n.01', 'name': 'Arctic_char'}, {'id': 4879, 'synset': 'whitefish.n.03', 'name': 'whitefish'}, {'id': 4880, 'synset': 'lake_whitefish.n.01', 'name': 'lake_whitefish'}, {'id': 4881, 'synset': 'cisco.n.02', 'name': 'cisco'}, {'id': 4882, 'synset': 'round_whitefish.n.01', 'name': 'round_whitefish'}, {'id': 4883, 'synset': 'smelt.n.02', 'name': 'smelt'}, {'id': 4884, 'synset': 'sparling.n.02', 'name': 'sparling'}, {'id': 4885, 'synset': 'capelin.n.01', 'name': 'capelin'}, {'id': 4886, 'synset': 'tarpon.n.01', 'name': 'tarpon'}, {'id': 4887, 'synset': 'ladyfish.n.01', 'name': 'ladyfish'}, {'id': 4888, 'synset': 'bonefish.n.01', 'name': 'bonefish'}, {'id': 4889, 'synset': 'argentine.n.01', 'name': 'argentine'}, {'id': 4890, 'synset': 'lanternfish.n.01', 'name': 'lanternfish'}, {'id': 4891, 'synset': 'lizardfish.n.01', 'name': 'lizardfish'}, {'id': 4892, 'synset': 'lancetfish.n.01', 'name': 'lancetfish'}, {'id': 4893, 'synset': 'opah.n.01', 'name': 'opah'}, {'id': 4894, 'synset': 'new_world_opah.n.01', 'name': 'New_World_opah'}, {'id': 4895, 'synset': 'ribbonfish.n.02', 'name': 'ribbonfish'}, {'id': 4896, 'synset': 'dealfish.n.01', 'name': 'dealfish'}, {'id': 4897, 'synset': 'oarfish.n.01', 'name': 'oarfish'}, {'id': 4898, 'synset': 'batfish.n.01', 'name': 'batfish'}, {'id': 4899, 'synset': 'goosefish.n.01', 'name': 'goosefish'}, {'id': 4900, 'synset': 'toadfish.n.01', 'name': 'toadfish'}, {'id': 4901, 'synset': 'oyster_fish.n.01', 'name': 'oyster_fish'}, {'id': 4902, 'synset': 'frogfish.n.01', 'name': 'frogfish'}, {'id': 4903, 'synset': 'sargassum_fish.n.01', 'name': 'sargassum_fish'}, {'id': 4904, 'synset': 'needlefish.n.01', 'name': 'needlefish'}, {'id': 4905, 'synset': 'timucu.n.01', 'name': 'timucu'}, {'id': 4906, 'synset': 'flying_fish.n.01', 'name': 'flying_fish'}, {'id': 4907, 'synset': 'monoplane_flying_fish.n.01', 'name': 'monoplane_flying_fish'}, {'id': 4908, 'synset': 'halfbeak.n.01', 'name': 'halfbeak'}, {'id': 4909, 'synset': 'saury.n.01', 'name': 'saury'}, {'id': 4910, 'synset': 'spiny-finned_fish.n.01', 'name': 'spiny-finned_fish'}, {'id': 4911, 'synset': 'lingcod.n.02', 'name': 'lingcod'}, {'id': 4912, 'synset': 'percoid_fish.n.01', 'name': 'percoid_fish'}, {'id': 4913, 'synset': 'perch.n.07', 'name': 'perch'}, {'id': 4914, 'synset': 'climbing_perch.n.01', 'name': 'climbing_perch'}, {'id': 4915, 'synset': 'perch.n.06', 'name': 'perch'}, {'id': 4916, 'synset': 'yellow_perch.n.01', 'name': 'yellow_perch'}, {'id': 4917, 'synset': 'european_perch.n.01', 'name': 'European_perch'}, {'id': 4918, 'synset': 'pike-perch.n.01', 'name': 'pike-perch'}, {'id': 4919, 'synset': 'walleye.n.02', 'name': 'walleye'}, {'id': 4920, 'synset': 'blue_pike.n.01', 'name': 'blue_pike'}, {'id': 4921, 'synset': 'snail_darter.n.01', 'name': 'snail_darter'}, {'id': 4922, 'synset': 'cusk-eel.n.01', 'name': 'cusk-eel'}, {'id': 4923, 'synset': 'brotula.n.01', 'name': 'brotula'}, {'id': 4924, 'synset': 'pearlfish.n.01', 'name': 'pearlfish'}, {'id': 4925, 'synset': 'robalo.n.01', 'name': 'robalo'}, {'id': 4926, 'synset': 'snook.n.01', 'name': 'snook'}, {'id': 4927, 'synset': 'pike.n.05', 'name': 'pike'}, {'id': 4928, 'synset': 'northern_pike.n.01', 'name': 'northern_pike'}, {'id': 4929, 'synset': 'muskellunge.n.02', 'name': 'muskellunge'}, {'id': 4930, 'synset': 'pickerel.n.02', 'name': 'pickerel'}, {'id': 4931, 'synset': 'chain_pickerel.n.01', 'name': 'chain_pickerel'}, {'id': 4932, 'synset': 'redfin_pickerel.n.01', 'name': 'redfin_pickerel'}, {'id': 4933, 'synset': 'sunfish.n.03', 'name': 'sunfish'}, {'id': 4934, 'synset': 'crappie.n.02', 'name': 'crappie'}, {'id': 4935, 'synset': 'black_crappie.n.01', 'name': 'black_crappie'}, {'id': 4936, 'synset': 'white_crappie.n.01', 'name': 'white_crappie'}, {'id': 4937, 'synset': 'freshwater_bream.n.02', 'name': 'freshwater_bream'}, {'id': 4938, 'synset': 'pumpkinseed.n.01', 'name': 'pumpkinseed'}, {'id': 4939, 'synset': 'bluegill.n.01', 'name': 'bluegill'}, {'id': 4940, 'synset': 'spotted_sunfish.n.01', 'name': 'spotted_sunfish'}, {'id': 4941, 'synset': 'freshwater_bass.n.02', 'name': 'freshwater_bass'}, {'id': 4942, 'synset': 'rock_bass.n.02', 'name': 'rock_bass'}, {'id': 4943, 'synset': 'black_bass.n.02', 'name': 'black_bass'}, {'id': 4944, 'synset': 'kentucky_black_bass.n.01', 'name': 'Kentucky_black_bass'}, {'id': 4945, 'synset': 'smallmouth.n.01', 'name': 'smallmouth'}, {'id': 4946, 'synset': 'largemouth.n.01', 'name': 'largemouth'}, {'id': 4947, 'synset': 'bass.n.08', 'name': 'bass'}, {'id': 4948, 'synset': 'serranid_fish.n.01', 'name': 'serranid_fish'}, {'id': 4949, 'synset': 'white_perch.n.01', 'name': 'white_perch'}, {'id': 4950, 'synset': 'yellow_bass.n.01', 'name': 'yellow_bass'}, {'id': 4951, 'synset': 'blackmouth_bass.n.01', 'name': 'blackmouth_bass'}, {'id': 4952, 'synset': 'rock_sea_bass.n.01', 'name': 'rock_sea_bass'}, {'id': 4953, 'synset': 'striped_bass.n.02', 'name': 'striped_bass'}, {'id': 4954, 'synset': 'stone_bass.n.01', 'name': 'stone_bass'}, {'id': 4955, 'synset': 'grouper.n.02', 'name': 'grouper'}, {'id': 4956, 'synset': 'hind.n.01', 'name': 'hind'}, {'id': 4957, 'synset': 'rock_hind.n.01', 'name': 'rock_hind'}, {'id': 4958, 'synset': 'creole-fish.n.01', 'name': 'creole-fish'}, {'id': 4959, 'synset': 'jewfish.n.02', 'name': 'jewfish'}, {'id': 4960, 'synset': 'soapfish.n.01', 'name': 'soapfish'}, {'id': 4961, 'synset': 'surfperch.n.01', 'name': 'surfperch'}, {'id': 4962, 'synset': 'rainbow_seaperch.n.01', 'name': 'rainbow_seaperch'}, {'id': 4963, 'synset': 'bigeye.n.01', 'name': 'bigeye'}, {'id': 4964, 'synset': 'catalufa.n.01', 'name': 'catalufa'}, {'id': 4965, 'synset': 'cardinalfish.n.01', 'name': 'cardinalfish'}, {'id': 4966, 'synset': 'flame_fish.n.01', 'name': 'flame_fish'}, {'id': 4967, 'synset': 'tilefish.n.02', 'name': 'tilefish'}, {'id': 4968, 'synset': 'bluefish.n.01', 'name': 'bluefish'}, {'id': 4969, 'synset': 'cobia.n.01', 'name': 'cobia'}, {'id': 4970, 'synset': 'remora.n.01', 'name': 'remora'}, {'id': 4971, 'synset': 'sharksucker.n.01', 'name': 'sharksucker'}, {'id': 4972, 'synset': 'whale_sucker.n.01', 'name': 'whale_sucker'}, {'id': 4973, 'synset': 'carangid_fish.n.01', 'name': 'carangid_fish'}, {'id': 4974, 'synset': 'jack.n.11', 'name': 'jack'}, {'id': 4975, 'synset': 'crevalle_jack.n.01', 'name': 'crevalle_jack'}, {'id': 4976, 'synset': 'yellow_jack.n.03', 'name': 'yellow_jack'}, {'id': 4977, 'synset': 'runner.n.10', 'name': 'runner'}, {'id': 4978, 'synset': 'rainbow_runner.n.01', 'name': 'rainbow_runner'}, {'id': 4979, 'synset': 'leatherjacket.n.02', 'name': 'leatherjacket'}, {'id': 4980, 'synset': 'threadfish.n.01', 'name': 'threadfish'}, {'id': 4981, 'synset': 'moonfish.n.01', 'name': 'moonfish'}, {'id': 4982, 'synset': 'lookdown.n.01', 'name': 'lookdown'}, {'id': 4983, 'synset': 'amberjack.n.01', 'name': 'amberjack'}, {'id': 4984, 'synset': 'yellowtail.n.02', 'name': 'yellowtail'}, {'id': 4985, 'synset': 'kingfish.n.05', 'name': 'kingfish'}, {'id': 4986, 'synset': 'pompano.n.02', 'name': 'pompano'}, {'id': 4987, 'synset': 'florida_pompano.n.01', 'name': 'Florida_pompano'}, {'id': 4988, 'synset': 'permit.n.03', 'name': 'permit'}, {'id': 4989, 'synset': 'scad.n.01', 'name': 'scad'}, {'id': 4990, 'synset': 'horse_mackerel.n.03', 'name': 'horse_mackerel'}, {'id': 4991, 'synset': 'horse_mackerel.n.02', 'name': 'horse_mackerel'}, {'id': 4992, 'synset': 'bigeye_scad.n.01', 'name': 'bigeye_scad'}, {'id': 4993, 'synset': 'mackerel_scad.n.01', 'name': 'mackerel_scad'}, {'id': 4994, 'synset': 'round_scad.n.01', 'name': 'round_scad'}, {'id': 4995, 'synset': 'dolphinfish.n.02', 'name': 'dolphinfish'}, {'id': 4996, 'synset': 'coryphaena_hippurus.n.01', 'name': 'Coryphaena_hippurus'}, {'id': 4997, 'synset': 'coryphaena_equisetis.n.01', 'name': 'Coryphaena_equisetis'}, {'id': 4998, 'synset': 'pomfret.n.01', 'name': 'pomfret'}, {'id': 4999, 'synset': 'characin.n.01', 'name': 'characin'}, {'id': 5000, 'synset': 'tetra.n.01', 'name': 'tetra'}, {'id': 5001, 'synset': 'cardinal_tetra.n.01', 'name': 'cardinal_tetra'}, {'id': 5002, 'synset': 'piranha.n.02', 'name': 'piranha'}, {'id': 5003, 'synset': 'cichlid.n.01', 'name': 'cichlid'}, {'id': 5004, 'synset': 'bolti.n.01', 'name': 'bolti'}, {'id': 5005, 'synset': 'snapper.n.05', 'name': 'snapper'}, {'id': 5006, 'synset': 'red_snapper.n.02', 'name': 'red_snapper'}, {'id': 5007, 'synset': 'grey_snapper.n.01', 'name': 'grey_snapper'}, {'id': 5008, 'synset': 'mutton_snapper.n.01', 'name': 'mutton_snapper'}, {'id': 5009, 'synset': 'schoolmaster.n.03', 'name': 'schoolmaster'}, {'id': 5010, 'synset': 'yellowtail.n.01', 'name': 'yellowtail'}, {'id': 5011, 'synset': 'grunt.n.03', 'name': 'grunt'}, {'id': 5012, 'synset': 'margate.n.01', 'name': 'margate'}, {'id': 5013, 'synset': 'spanish_grunt.n.01', 'name': 'Spanish_grunt'}, {'id': 5014, 'synset': 'tomtate.n.01', 'name': 'tomtate'}, {'id': 5015, 'synset': 'cottonwick.n.01', 'name': 'cottonwick'}, {'id': 5016, 'synset': "sailor's-choice.n.02", 'name': "sailor's-choice"}, {'id': 5017, 'synset': 'porkfish.n.01', 'name': 'porkfish'}, {'id': 5018, 'synset': 'pompon.n.02', 'name': 'pompon'}, {'id': 5019, 'synset': 'pigfish.n.02', 'name': 'pigfish'}, {'id': 5020, 'synset': 'sparid.n.01', 'name': 'sparid'}, {'id': 5021, 'synset': 'sea_bream.n.02', 'name': 'sea_bream'}, {'id': 5022, 'synset': 'porgy.n.02', 'name': 'porgy'}, {'id': 5023, 'synset': 'red_porgy.n.01', 'name': 'red_porgy'}, {'id': 5024, 'synset': 'european_sea_bream.n.01', 'name': 'European_sea_bream'}, {'id': 5025, 'synset': 'atlantic_sea_bream.n.01', 'name': 'Atlantic_sea_bream'}, {'id': 5026, 'synset': 'sheepshead.n.01', 'name': 'sheepshead'}, {'id': 5027, 'synset': 'pinfish.n.01', 'name': 'pinfish'}, {'id': 5028, 'synset': 'sheepshead_porgy.n.01', 'name': 'sheepshead_porgy'}, {'id': 5029, 'synset': 'snapper.n.04', 'name': 'snapper'}, {'id': 5030, 'synset': 'black_bream.n.01', 'name': 'black_bream'}, {'id': 5031, 'synset': 'scup.n.04', 'name': 'scup'}, {'id': 5032, 'synset': 'scup.n.03', 'name': 'scup'}, {'id': 5033, 'synset': 'sciaenid_fish.n.01', 'name': 'sciaenid_fish'}, {'id': 5034, 'synset': 'striped_drum.n.01', 'name': 'striped_drum'}, {'id': 5035, 'synset': 'jackknife-fish.n.01', 'name': 'jackknife-fish'}, {'id': 5036, 'synset': 'silver_perch.n.01', 'name': 'silver_perch'}, {'id': 5037, 'synset': 'red_drum.n.01', 'name': 'red_drum'}, {'id': 5038, 'synset': 'mulloway.n.01', 'name': 'mulloway'}, {'id': 5039, 'synset': 'maigre.n.01', 'name': 'maigre'}, {'id': 5040, 'synset': 'croaker.n.02', 'name': 'croaker'}, {'id': 5041, 'synset': 'atlantic_croaker.n.01', 'name': 'Atlantic_croaker'}, {'id': 5042, 'synset': 'yellowfin_croaker.n.01', 'name': 'yellowfin_croaker'}, {'id': 5043, 'synset': 'whiting.n.04', 'name': 'whiting'}, {'id': 5044, 'synset': 'kingfish.n.04', 'name': 'kingfish'}, {'id': 5045, 'synset': 'king_whiting.n.01', 'name': 'king_whiting'}, {'id': 5046, 'synset': 'northern_whiting.n.01', 'name': 'northern_whiting'}, {'id': 5047, 'synset': 'corbina.n.01', 'name': 'corbina'}, {'id': 5048, 'synset': 'white_croaker.n.02', 'name': 'white_croaker'}, {'id': 5049, 'synset': 'white_croaker.n.01', 'name': 'white_croaker'}, {'id': 5050, 'synset': 'sea_trout.n.02', 'name': 'sea_trout'}, {'id': 5051, 'synset': 'weakfish.n.02', 'name': 'weakfish'}, {'id': 5052, 'synset': 'spotted_weakfish.n.01', 'name': 'spotted_weakfish'}, {'id': 5053, 'synset': 'mullet.n.03', 'name': 'mullet'}, {'id': 5054, 'synset': 'goatfish.n.01', 'name': 'goatfish'}, {'id': 5055, 'synset': 'red_goatfish.n.01', 'name': 'red_goatfish'}, {'id': 5056, 'synset': 'yellow_goatfish.n.01', 'name': 'yellow_goatfish'}, {'id': 5057, 'synset': 'mullet.n.02', 'name': 'mullet'}, {'id': 5058, 'synset': 'striped_mullet.n.01', 'name': 'striped_mullet'}, {'id': 5059, 'synset': 'white_mullet.n.01', 'name': 'white_mullet'}, {'id': 5060, 'synset': 'liza.n.01', 'name': 'liza'}, {'id': 5061, 'synset': 'silversides.n.01', 'name': 'silversides'}, {'id': 5062, 'synset': 'jacksmelt.n.01', 'name': 'jacksmelt'}, {'id': 5063, 'synset': 'barracuda.n.01', 'name': 'barracuda'}, {'id': 5064, 'synset': 'great_barracuda.n.01', 'name': 'great_barracuda'}, {'id': 5065, 'synset': 'sweeper.n.03', 'name': 'sweeper'}, {'id': 5066, 'synset': 'sea_chub.n.01', 'name': 'sea_chub'}, {'id': 5067, 'synset': 'bermuda_chub.n.01', 'name': 'Bermuda_chub'}, {'id': 5068, 'synset': 'spadefish.n.01', 'name': 'spadefish'}, {'id': 5069, 'synset': 'butterfly_fish.n.01', 'name': 'butterfly_fish'}, {'id': 5070, 'synset': 'chaetodon.n.01', 'name': 'chaetodon'}, {'id': 5071, 'synset': 'angelfish.n.01', 'name': 'angelfish'}, {'id': 5072, 'synset': 'rock_beauty.n.01', 'name': 'rock_beauty'}, {'id': 5073, 'synset': 'damselfish.n.01', 'name': 'damselfish'}, {'id': 5074, 'synset': 'beaugregory.n.01', 'name': 'beaugregory'}, {'id': 5075, 'synset': 'anemone_fish.n.01', 'name': 'anemone_fish'}, {'id': 5076, 'synset': 'clown_anemone_fish.n.01', 'name': 'clown_anemone_fish'}, {'id': 5077, 'synset': 'sergeant_major.n.02', 'name': 'sergeant_major'}, {'id': 5078, 'synset': 'wrasse.n.01', 'name': 'wrasse'}, {'id': 5079, 'synset': 'pigfish.n.01', 'name': 'pigfish'}, {'id': 5080, 'synset': 'hogfish.n.01', 'name': 'hogfish'}, {'id': 5081, 'synset': 'slippery_dick.n.01', 'name': 'slippery_dick'}, {'id': 5082, 'synset': 'puddingwife.n.01', 'name': 'puddingwife'}, {'id': 5083, 'synset': 'bluehead.n.01', 'name': 'bluehead'}, {'id': 5084, 'synset': 'pearly_razorfish.n.01', 'name': 'pearly_razorfish'}, {'id': 5085, 'synset': 'tautog.n.01', 'name': 'tautog'}, {'id': 5086, 'synset': 'cunner.n.01', 'name': 'cunner'}, {'id': 5087, 'synset': 'parrotfish.n.01', 'name': 'parrotfish'}, {'id': 5088, 'synset': 'threadfin.n.01', 'name': 'threadfin'}, {'id': 5089, 'synset': 'jawfish.n.01', 'name': 'jawfish'}, {'id': 5090, 'synset': 'stargazer.n.03', 'name': 'stargazer'}, {'id': 5091, 'synset': 'sand_stargazer.n.01', 'name': 'sand_stargazer'}, {'id': 5092, 'synset': 'blenny.n.01', 'name': 'blenny'}, {'id': 5093, 'synset': 'shanny.n.01', 'name': 'shanny'}, {'id': 5094, 'synset': 'molly_miller.n.01', 'name': 'Molly_Miller'}, {'id': 5095, 'synset': 'clinid.n.01', 'name': 'clinid'}, {'id': 5096, 'synset': 'pikeblenny.n.01', 'name': 'pikeblenny'}, {'id': 5097, 'synset': 'bluethroat_pikeblenny.n.01', 'name': 'bluethroat_pikeblenny'}, {'id': 5098, 'synset': 'gunnel.n.02', 'name': 'gunnel'}, {'id': 5099, 'synset': 'rock_gunnel.n.01', 'name': 'rock_gunnel'}, {'id': 5100, 'synset': 'eelblenny.n.01', 'name': 'eelblenny'}, {'id': 5101, 'synset': 'wrymouth.n.01', 'name': 'wrymouth'}, {'id': 5102, 'synset': 'wolffish.n.01', 'name': 'wolffish'}, {'id': 5103, 'synset': 'viviparous_eelpout.n.01', 'name': 'viviparous_eelpout'}, {'id': 5104, 'synset': 'ocean_pout.n.01', 'name': 'ocean_pout'}, {'id': 5105, 'synset': 'sand_lance.n.01', 'name': 'sand_lance'}, {'id': 5106, 'synset': 'dragonet.n.01', 'name': 'dragonet'}, {'id': 5107, 'synset': 'goby.n.01', 'name': 'goby'}, {'id': 5108, 'synset': 'mudskipper.n.01', 'name': 'mudskipper'}, {'id': 5109, 'synset': 'sleeper.n.08', 'name': 'sleeper'}, {'id': 5110, 'synset': 'flathead.n.02', 'name': 'flathead'}, {'id': 5111, 'synset': 'archerfish.n.01', 'name': 'archerfish'}, {'id': 5112, 'synset': 'surgeonfish.n.01', 'name': 'surgeonfish'}, {'id': 5113, 'synset': 'gempylid.n.01', 'name': 'gempylid'}, {'id': 5114, 'synset': 'snake_mackerel.n.01', 'name': 'snake_mackerel'}, {'id': 5115, 'synset': 'escolar.n.01', 'name': 'escolar'}, {'id': 5116, 'synset': 'oilfish.n.01', 'name': 'oilfish'}, {'id': 5117, 'synset': 'cutlassfish.n.01', 'name': 'cutlassfish'}, {'id': 5118, 'synset': 'scombroid.n.01', 'name': 'scombroid'}, {'id': 5119, 'synset': 'mackerel.n.02', 'name': 'mackerel'}, {'id': 5120, 'synset': 'common_mackerel.n.01', 'name': 'common_mackerel'}, {'id': 5121, 'synset': 'spanish_mackerel.n.03', 'name': 'Spanish_mackerel'}, {'id': 5122, 'synset': 'chub_mackerel.n.01', 'name': 'chub_mackerel'}, {'id': 5123, 'synset': 'wahoo.n.03', 'name': 'wahoo'}, {'id': 5124, 'synset': 'spanish_mackerel.n.02', 'name': 'Spanish_mackerel'}, {'id': 5125, 'synset': 'king_mackerel.n.01', 'name': 'king_mackerel'}, {'id': 5126, 'synset': 'scomberomorus_maculatus.n.01', 'name': 'Scomberomorus_maculatus'}, {'id': 5127, 'synset': 'cero.n.01', 'name': 'cero'}, {'id': 5128, 'synset': 'sierra.n.02', 'name': 'sierra'}, {'id': 5129, 'synset': 'tuna.n.03', 'name': 'tuna'}, {'id': 5130, 'synset': 'albacore.n.02', 'name': 'albacore'}, {'id': 5131, 'synset': 'bluefin.n.02', 'name': 'bluefin'}, {'id': 5132, 'synset': 'yellowfin.n.01', 'name': 'yellowfin'}, {'id': 5133, 'synset': 'bonito.n.03', 'name': 'bonito'}, {'id': 5134, 'synset': 'skipjack.n.02', 'name': 'skipjack'}, {'id': 5135, 'synset': 'chile_bonito.n.01', 'name': 'Chile_bonito'}, {'id': 5136, 'synset': 'skipjack.n.01', 'name': 'skipjack'}, {'id': 5137, 'synset': 'bonito.n.02', 'name': 'bonito'}, {'id': 5138, 'synset': 'swordfish.n.02', 'name': 'swordfish'}, {'id': 5139, 'synset': 'sailfish.n.02', 'name': 'sailfish'}, {'id': 5140, 'synset': 'atlantic_sailfish.n.01', 'name': 'Atlantic_sailfish'}, {'id': 5141, 'synset': 'billfish.n.02', 'name': 'billfish'}, {'id': 5142, 'synset': 'marlin.n.01', 'name': 'marlin'}, {'id': 5143, 'synset': 'blue_marlin.n.01', 'name': 'blue_marlin'}, {'id': 5144, 'synset': 'black_marlin.n.01', 'name': 'black_marlin'}, {'id': 5145, 'synset': 'striped_marlin.n.01', 'name': 'striped_marlin'}, {'id': 5146, 'synset': 'white_marlin.n.01', 'name': 'white_marlin'}, {'id': 5147, 'synset': 'spearfish.n.01', 'name': 'spearfish'}, {'id': 5148, 'synset': 'louvar.n.01', 'name': 'louvar'}, {'id': 5149, 'synset': 'dollarfish.n.01', 'name': 'dollarfish'}, {'id': 5150, 'synset': 'palometa.n.01', 'name': 'palometa'}, {'id': 5151, 'synset': 'harvestfish.n.01', 'name': 'harvestfish'}, {'id': 5152, 'synset': 'driftfish.n.01', 'name': 'driftfish'}, {'id': 5153, 'synset': 'barrelfish.n.01', 'name': 'barrelfish'}, {'id': 5154, 'synset': 'clingfish.n.01', 'name': 'clingfish'}, {'id': 5155, 'synset': 'tripletail.n.01', 'name': 'tripletail'}, {'id': 5156, 'synset': 'atlantic_tripletail.n.01', 'name': 'Atlantic_tripletail'}, {'id': 5157, 'synset': 'pacific_tripletail.n.01', 'name': 'Pacific_tripletail'}, {'id': 5158, 'synset': 'mojarra.n.01', 'name': 'mojarra'}, {'id': 5159, 'synset': 'yellowfin_mojarra.n.01', 'name': 'yellowfin_mojarra'}, {'id': 5160, 'synset': 'silver_jenny.n.01', 'name': 'silver_jenny'}, {'id': 5161, 'synset': 'whiting.n.03', 'name': 'whiting'}, {'id': 5162, 'synset': 'ganoid.n.01', 'name': 'ganoid'}, {'id': 5163, 'synset': 'bowfin.n.01', 'name': 'bowfin'}, {'id': 5164, 'synset': 'paddlefish.n.01', 'name': 'paddlefish'}, {'id': 5165, 'synset': 'chinese_paddlefish.n.01', 'name': 'Chinese_paddlefish'}, {'id': 5166, 'synset': 'sturgeon.n.01', 'name': 'sturgeon'}, {'id': 5167, 'synset': 'pacific_sturgeon.n.01', 'name': 'Pacific_sturgeon'}, {'id': 5168, 'synset': 'beluga.n.01', 'name': 'beluga'}, {'id': 5169, 'synset': 'gar.n.01', 'name': 'gar'}, {'id': 5170, 'synset': 'scorpaenoid.n.01', 'name': 'scorpaenoid'}, {'id': 5171, 'synset': 'scorpaenid.n.01', 'name': 'scorpaenid'}, {'id': 5172, 'synset': 'scorpionfish.n.01', 'name': 'scorpionfish'}, {'id': 5173, 'synset': 'plumed_scorpionfish.n.01', 'name': 'plumed_scorpionfish'}, {'id': 5174, 'synset': 'lionfish.n.01', 'name': 'lionfish'}, {'id': 5175, 'synset': 'stonefish.n.01', 'name': 'stonefish'}, {'id': 5176, 'synset': 'rockfish.n.02', 'name': 'rockfish'}, {'id': 5177, 'synset': 'copper_rockfish.n.01', 'name': 'copper_rockfish'}, {'id': 5178, 'synset': 'vermillion_rockfish.n.01', 'name': 'vermillion_rockfish'}, {'id': 5179, 'synset': 'red_rockfish.n.02', 'name': 'red_rockfish'}, {'id': 5180, 'synset': 'rosefish.n.02', 'name': 'rosefish'}, {'id': 5181, 'synset': 'bullhead.n.01', 'name': 'bullhead'}, {'id': 5182, 'synset': "miller's-thumb.n.01", 'name': "miller's-thumb"}, {'id': 5183, 'synset': 'sea_raven.n.01', 'name': 'sea_raven'}, {'id': 5184, 'synset': 'lumpfish.n.01', 'name': 'lumpfish'}, {'id': 5185, 'synset': 'lumpsucker.n.01', 'name': 'lumpsucker'}, {'id': 5186, 'synset': 'pogge.n.01', 'name': 'pogge'}, {'id': 5187, 'synset': 'greenling.n.01', 'name': 'greenling'}, {'id': 5188, 'synset': 'kelp_greenling.n.01', 'name': 'kelp_greenling'}, {'id': 5189, 'synset': 'painted_greenling.n.01', 'name': 'painted_greenling'}, {'id': 5190, 'synset': 'flathead.n.01', 'name': 'flathead'}, {'id': 5191, 'synset': 'gurnard.n.01', 'name': 'gurnard'}, {'id': 5192, 'synset': 'tub_gurnard.n.01', 'name': 'tub_gurnard'}, {'id': 5193, 'synset': 'sea_robin.n.01', 'name': 'sea_robin'}, {'id': 5194, 'synset': 'northern_sea_robin.n.01', 'name': 'northern_sea_robin'}, {'id': 5195, 'synset': 'flying_gurnard.n.01', 'name': 'flying_gurnard'}, {'id': 5196, 'synset': 'plectognath.n.01', 'name': 'plectognath'}, {'id': 5197, 'synset': 'triggerfish.n.01', 'name': 'triggerfish'}, {'id': 5198, 'synset': 'queen_triggerfish.n.01', 'name': 'queen_triggerfish'}, {'id': 5199, 'synset': 'filefish.n.01', 'name': 'filefish'}, {'id': 5200, 'synset': 'leatherjacket.n.01', 'name': 'leatherjacket'}, {'id': 5201, 'synset': 'boxfish.n.01', 'name': 'boxfish'}, {'id': 5202, 'synset': 'cowfish.n.01', 'name': 'cowfish'}, {'id': 5203, 'synset': 'spiny_puffer.n.01', 'name': 'spiny_puffer'}, {'id': 5204, 'synset': 'porcupinefish.n.01', 'name': 'porcupinefish'}, {'id': 5205, 'synset': 'balloonfish.n.01', 'name': 'balloonfish'}, {'id': 5206, 'synset': 'burrfish.n.01', 'name': 'burrfish'}, {'id': 5207, 'synset': 'ocean_sunfish.n.01', 'name': 'ocean_sunfish'}, {'id': 5208, 'synset': 'sharptail_mola.n.01', 'name': 'sharptail_mola'}, {'id': 5209, 'synset': 'flatfish.n.02', 'name': 'flatfish'}, {'id': 5210, 'synset': 'flounder.n.02', 'name': 'flounder'}, {'id': 5211, 'synset': 'righteye_flounder.n.01', 'name': 'righteye_flounder'}, {'id': 5212, 'synset': 'plaice.n.02', 'name': 'plaice'}, {'id': 5213, 'synset': 'european_flatfish.n.01', 'name': 'European_flatfish'}, 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'name': 'altimeter'}, {'id': 5351, 'synset': 'amati.n.02', 'name': 'Amati'}, {'id': 5352, 'synset': 'amen_corner.n.01', 'name': 'amen_corner'}, {'id': 5353, 'synset': 'american_organ.n.01', 'name': 'American_organ'}, {'id': 5354, 'synset': 'ammeter.n.01', 'name': 'ammeter'}, {'id': 5355, 'synset': 'ammonia_clock.n.01', 'name': 'ammonia_clock'}, {'id': 5356, 'synset': 'ammunition.n.01', 'name': 'ammunition'}, {'id': 5357, 'synset': 'amphibian.n.02', 'name': 'amphibian'}, {'id': 5358, 'synset': 'amphibian.n.01', 'name': 'amphibian'}, {'id': 5359, 'synset': 'amphitheater.n.02', 'name': 'amphitheater'}, {'id': 5360, 'synset': 'amphitheater.n.01', 'name': 'amphitheater'}, {'id': 5361, 'synset': 'amphora.n.01', 'name': 'amphora'}, {'id': 5362, 'synset': 'ampulla.n.02', 'name': 'ampulla'}, {'id': 5363, 'synset': 'amusement_arcade.n.01', 'name': 'amusement_arcade'}, {'id': 5364, 'synset': 'analog_clock.n.01', 'name': 'analog_clock'}, {'id': 5365, 'synset': 'analog_computer.n.01', 'name': 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{'id': 5811, 'synset': 'bicycle_clip.n.01', 'name': 'bicycle_clip'}, {'id': 5812, 'synset': 'bicycle_pump.n.01', 'name': 'bicycle_pump'}, {'id': 5813, 'synset': 'bicycle_rack.n.01', 'name': 'bicycle_rack'}, {'id': 5814, 'synset': 'bicycle_seat.n.01', 'name': 'bicycle_seat'}, {'id': 5815, 'synset': 'bicycle_wheel.n.01', 'name': 'bicycle_wheel'}, {'id': 5816, 'synset': 'bidet.n.01', 'name': 'bidet'}, {'id': 5817, 'synset': 'bier.n.02', 'name': 'bier'}, {'id': 5818, 'synset': 'bier.n.01', 'name': 'bier'}, {'id': 5819, 'synset': 'bi-fold_door.n.01', 'name': 'bi-fold_door'}, {'id': 5820, 'synset': 'bifocals.n.01', 'name': 'bifocals'}, {'id': 5821, 'synset': 'big_blue.n.01', 'name': 'Big_Blue'}, {'id': 5822, 'synset': 'big_board.n.02', 'name': 'big_board'}, {'id': 5823, 'synset': 'bight.n.04', 'name': 'bight'}, {'id': 5824, 'synset': 'bikini.n.02', 'name': 'bikini'}, {'id': 5825, 'synset': 'bikini_pants.n.01', 'name': 'bikini_pants'}, {'id': 5826, 'synset': 'bilge.n.02', 'name': 'bilge'}, {'id': 5827, 'synset': 'bilge_keel.n.01', 'name': 'bilge_keel'}, {'id': 5828, 'synset': 'bilge_pump.n.01', 'name': 'bilge_pump'}, {'id': 5829, 'synset': 'bilge_well.n.01', 'name': 'bilge_well'}, {'id': 5830, 'synset': 'bill.n.08', 'name': 'bill'}, {'id': 5831, 'synset': 'billiard_ball.n.01', 'name': 'billiard_ball'}, {'id': 5832, 'synset': 'billiard_room.n.01', 'name': 'billiard_room'}, {'id': 5833, 'synset': 'bin.n.01', 'name': 'bin'}, {'id': 5834, 'synset': 'binder.n.04', 'name': 'binder'}, {'id': 5835, 'synset': 'bindery.n.01', 'name': 'bindery'}, {'id': 5836, 'synset': 'binding.n.05', 'name': 'binding'}, {'id': 5837, 'synset': 'bin_liner.n.01', 'name': 'bin_liner'}, {'id': 5838, 'synset': 'binnacle.n.01', 'name': 'binnacle'}, {'id': 5839, 'synset': 'binocular_microscope.n.01', 'name': 'binocular_microscope'}, {'id': 5840, 'synset': 'biochip.n.01', 'name': 'biochip'}, {'id': 5841, 'synset': 'biohazard_suit.n.01', 'name': 'biohazard_suit'}, {'id': 5842, 'synset': 'bioscope.n.02', 'name': 'bioscope'}, {'id': 5843, 'synset': 'biplane.n.01', 'name': 'biplane'}, {'id': 5844, 'synset': 'birch.n.03', 'name': 'birch'}, {'id': 5845, 'synset': 'birchbark_canoe.n.01', 'name': 'birchbark_canoe'}, {'id': 5846, 'synset': 'birdcall.n.02', 'name': 'birdcall'}, {'id': 5847, 'synset': 'bird_shot.n.01', 'name': 'bird_shot'}, {'id': 5848, 'synset': 'biretta.n.01', 'name': 'biretta'}, {'id': 5849, 'synset': 'bishop.n.03', 'name': 'bishop'}, {'id': 5850, 'synset': 'bistro.n.01', 'name': 'bistro'}, {'id': 5851, 'synset': 'bit.n.11', 'name': 'bit'}, {'id': 5852, 'synset': 'bit.n.05', 'name': 'bit'}, {'id': 5853, 'synset': 'bite_plate.n.01', 'name': 'bite_plate'}, {'id': 5854, 'synset': 'bitewing.n.01', 'name': 'bitewing'}, {'id': 5855, 'synset': 'bitumastic.n.01', 'name': 'bitumastic'}, {'id': 5856, 'synset': 'black.n.07', 'name': 'black'}, {'id': 5857, 'synset': 'black.n.06', 'name': 'black'}, {'id': 5858, 'synset': 'blackboard_eraser.n.01', 'name': 'blackboard_eraser'}, {'id': 5859, 'synset': 'black_box.n.01', 'name': 'black_box'}, {'id': 5860, 'synset': 'blackface.n.01', 'name': 'blackface'}, {'id': 5861, 'synset': 'blackjack.n.02', 'name': 'blackjack'}, {'id': 5862, 'synset': 'black_tie.n.02', 'name': 'black_tie'}, {'id': 5863, 'synset': 'blackwash.n.03', 'name': 'blackwash'}, {'id': 5864, 'synset': 'bladder.n.02', 'name': 'bladder'}, {'id': 5865, 'synset': 'blade.n.09', 'name': 'blade'}, {'id': 5866, 'synset': 'blade.n.08', 'name': 'blade'}, {'id': 5867, 'synset': 'blade.n.07', 'name': 'blade'}, {'id': 5868, 'synset': 'blank.n.04', 'name': 'blank'}, {'id': 5869, 'synset': 'blast_furnace.n.01', 'name': 'blast_furnace'}, {'id': 5870, 'synset': 'blasting_cap.n.01', 'name': 'blasting_cap'}, {'id': 5871, 'synset': 'blind.n.03', 'name': 'blind'}, {'id': 5872, 'synset': 'blind_curve.n.01', 'name': 'blind_curve'}, {'id': 5873, 'synset': 'blindfold.n.01', 'name': 'blindfold'}, {'id': 5874, 'synset': 'bling.n.01', 'name': 'bling'}, {'id': 5875, 'synset': 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{'id': 5891, 'synset': 'blunderbuss.n.01', 'name': 'blunderbuss'}, {'id': 5892, 'synset': 'blunt_file.n.01', 'name': 'blunt_file'}, {'id': 5893, 'synset': 'boarding.n.02', 'name': 'boarding'}, {'id': 5894, 'synset': 'boarding_house.n.01', 'name': 'boarding_house'}, {'id': 5895, 'synset': 'boardroom.n.01', 'name': 'boardroom'}, {'id': 5896, 'synset': 'boards.n.02', 'name': 'boards'}, {'id': 5897, 'synset': 'boater.n.01', 'name': 'boater'}, {'id': 5898, 'synset': 'boat_hook.n.01', 'name': 'boat_hook'}, {'id': 5899, 'synset': 'boathouse.n.01', 'name': 'boathouse'}, {'id': 5900, 'synset': "boatswain's_chair.n.01", 'name': "boatswain's_chair"}, {'id': 5901, 'synset': 'boat_train.n.01', 'name': 'boat_train'}, {'id': 5902, 'synset': 'boatyard.n.01', 'name': 'boatyard'}, {'id': 5903, 'synset': 'bobsled.n.02', 'name': 'bobsled'}, {'id': 5904, 'synset': 'bobsled.n.01', 'name': 'bobsled'}, {'id': 5905, 'synset': 'bocce_ball.n.01', 'name': 'bocce_ball'}, {'id': 5906, 'synset': 'bodega.n.01', 'name': 'bodega'}, {'id': 5907, 'synset': 'bodice.n.01', 'name': 'bodice'}, {'id': 5908, 'synset': 'bodkin.n.04', 'name': 'bodkin'}, {'id': 5909, 'synset': 'bodkin.n.03', 'name': 'bodkin'}, {'id': 5910, 'synset': 'bodkin.n.02', 'name': 'bodkin'}, {'id': 5911, 'synset': 'body.n.11', 'name': 'body'}, {'id': 5912, 'synset': 'body_armor.n.01', 'name': 'body_armor'}, {'id': 5913, 'synset': 'body_lotion.n.01', 'name': 'body_lotion'}, {'id': 5914, 'synset': 'body_stocking.n.01', 'name': 'body_stocking'}, {'id': 5915, 'synset': 'body_plethysmograph.n.01', 'name': 'body_plethysmograph'}, {'id': 5916, 'synset': 'body_pad.n.01', 'name': 'body_pad'}, {'id': 5917, 'synset': 'bodywork.n.01', 'name': 'bodywork'}, {'id': 5918, 'synset': 'bofors_gun.n.01', 'name': 'Bofors_gun'}, {'id': 5919, 'synset': 'bogy.n.01', 'name': 'bogy'}, {'id': 5920, 'synset': 'boiler.n.01', 'name': 'boiler'}, {'id': 5921, 'synset': 'boiling_water_reactor.n.01', 'name': 'boiling_water_reactor'}, {'id': 5922, 'synset': 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'name': 'bottlebrush'}, {'id': 5972, 'synset': 'bottlecap.n.01', 'name': 'bottlecap'}, {'id': 5973, 'synset': 'bottling_plant.n.01', 'name': 'bottling_plant'}, {'id': 5974, 'synset': 'bottom.n.07', 'name': 'bottom'}, {'id': 5975, 'synset': 'boucle.n.01', 'name': 'boucle'}, {'id': 5976, 'synset': 'boudoir.n.01', 'name': 'boudoir'}, {'id': 5977, 'synset': 'boulle.n.01', 'name': 'boulle'}, {'id': 5978, 'synset': 'bouncing_betty.n.01', 'name': 'bouncing_betty'}, {'id': 5979, 'synset': 'boutique.n.01', 'name': 'boutique'}, {'id': 5980, 'synset': 'boutonniere.n.01', 'name': 'boutonniere'}, {'id': 5981, 'synset': 'bow.n.02', 'name': 'bow'}, {'id': 5982, 'synset': 'bow.n.01', 'name': 'bow'}, {'id': 5983, 'synset': 'bow_and_arrow.n.01', 'name': 'bow_and_arrow'}, {'id': 5984, 'synset': 'bowed_stringed_instrument.n.01', 'name': 'bowed_stringed_instrument'}, {'id': 5985, 'synset': 'bowie_knife.n.01', 'name': 'Bowie_knife'}, {'id': 5986, 'synset': 'bowl.n.01', 'name': 'bowl'}, {'id': 5987, 'synset': 'bowl.n.07', 'name': 'bowl'}, {'id': 5988, 'synset': 'bowline.n.01', 'name': 'bowline'}, {'id': 5989, 'synset': 'bowling_alley.n.01', 'name': 'bowling_alley'}, {'id': 5990, 'synset': 'bowling_equipment.n.01', 'name': 'bowling_equipment'}, {'id': 5991, 'synset': 'bowling_pin.n.01', 'name': 'bowling_pin'}, {'id': 5992, 'synset': 'bowling_shoe.n.01', 'name': 'bowling_shoe'}, {'id': 5993, 'synset': 'bowsprit.n.01', 'name': 'bowsprit'}, {'id': 5994, 'synset': 'bowstring.n.01', 'name': 'bowstring'}, {'id': 5995, 'synset': 'box.n.02', 'name': 'box'}, {'id': 5996, 'synset': 'box.n.08', 'name': 'box'}, {'id': 5997, 'synset': 'box_beam.n.01', 'name': 'box_beam'}, {'id': 5998, 'synset': 'box_camera.n.01', 'name': 'box_camera'}, {'id': 5999, 'synset': 'boxcar.n.01', 'name': 'boxcar'}, {'id': 6000, 'synset': 'box_coat.n.01', 'name': 'box_coat'}, {'id': 6001, 'synset': 'boxing_equipment.n.01', 'name': 'boxing_equipment'}, {'id': 6002, 'synset': 'box_office.n.02', 'name': 'box_office'}, {'id': 6003, 'synset': 'box_spring.n.01', 'name': 'box_spring'}, {'id': 6004, 'synset': 'box_wrench.n.01', 'name': 'box_wrench'}, {'id': 6005, 'synset': 'brace.n.09', 'name': 'brace'}, {'id': 6006, 'synset': 'brace.n.07', 'name': 'brace'}, {'id': 6007, 'synset': 'brace.n.01', 'name': 'brace'}, {'id': 6008, 'synset': 'brace_and_bit.n.01', 'name': 'brace_and_bit'}, {'id': 6009, 'synset': 'bracer.n.01', 'name': 'bracer'}, {'id': 6010, 'synset': 'brace_wrench.n.01', 'name': 'brace_wrench'}, {'id': 6011, 'synset': 'bracket.n.04', 'name': 'bracket'}, {'id': 6012, 'synset': 'bradawl.n.01', 'name': 'bradawl'}, {'id': 6013, 'synset': 'brake.n.01', 'name': 'brake'}, {'id': 6014, 'synset': 'brake.n.05', 'name': 'brake'}, {'id': 6015, 'synset': 'brake_band.n.01', 'name': 'brake_band'}, {'id': 6016, 'synset': 'brake_cylinder.n.01', 'name': 'brake_cylinder'}, {'id': 6017, 'synset': 'brake_disk.n.01', 'name': 'brake_disk'}, {'id': 6018, 'synset': 'brake_drum.n.01', 'name': 'brake_drum'}, {'id': 6019, 'synset': 'brake_lining.n.01', 'name': 'brake_lining'}, {'id': 6020, 'synset': 'brake_pad.n.01', 'name': 'brake_pad'}, {'id': 6021, 'synset': 'brake_pedal.n.01', 'name': 'brake_pedal'}, {'id': 6022, 'synset': 'brake_shoe.n.01', 'name': 'brake_shoe'}, {'id': 6023, 'synset': 'brake_system.n.01', 'name': 'brake_system'}, {'id': 6024, 'synset': 'brass.n.02', 'name': 'brass'}, {'id': 6025, 'synset': 'brass.n.05', 'name': 'brass'}, {'id': 6026, 'synset': 'brassard.n.01', 'name': 'brassard'}, {'id': 6027, 'synset': 'brasserie.n.01', 'name': 'brasserie'}, {'id': 6028, 'synset': 'brassie.n.01', 'name': 'brassie'}, {'id': 6029, 'synset': 'brass_knucks.n.01', 'name': 'brass_knucks'}, {'id': 6030, 'synset': 'brattice.n.01', 'name': 'brattice'}, {'id': 6031, 'synset': 'brazier.n.01', 'name': 'brazier'}, {'id': 6032, 'synset': 'breadbasket.n.03', 'name': 'breadbasket'}, {'id': 6033, 'synset': 'bread_knife.n.01', 'name': 'bread_knife'}, {'id': 6034, 'synset': 'breakable.n.01', 'name': 'breakable'}, {'id': 6035, 'synset': 'breakfast_area.n.01', 'name': 'breakfast_area'}, {'id': 6036, 'synset': 'breakfast_table.n.01', 'name': 'breakfast_table'}, {'id': 6037, 'synset': 'breakwater.n.01', 'name': 'breakwater'}, {'id': 6038, 'synset': 'breast_drill.n.01', 'name': 'breast_drill'}, {'id': 6039, 'synset': 'breast_implant.n.01', 'name': 'breast_implant'}, {'id': 6040, 'synset': 'breastplate.n.01', 'name': 'breastplate'}, {'id': 6041, 'synset': 'breast_pocket.n.01', 'name': 'breast_pocket'}, {'id': 6042, 'synset': 'breathalyzer.n.01', 'name': 'breathalyzer'}, {'id': 6043, 'synset': 'breechblock.n.01', 'name': 'breechblock'}, {'id': 6044, 'synset': 'breeches.n.01', 'name': 'breeches'}, {'id': 6045, 'synset': 'breeches_buoy.n.01', 'name': 'breeches_buoy'}, {'id': 6046, 'synset': 'breechloader.n.01', 'name': 'breechloader'}, {'id': 6047, 'synset': 'breeder_reactor.n.01', 'name': 'breeder_reactor'}, {'id': 6048, 'synset': 'bren.n.01', 'name': 'Bren'}, {'id': 6049, 'synset': 'brewpub.n.01', 'name': 'brewpub'}, {'id': 6050, 'synset': 'brick.n.01', 'name': 'brick'}, {'id': 6051, 'synset': 'brickkiln.n.01', 'name': 'brickkiln'}, {'id': 6052, 'synset': "bricklayer's_hammer.n.01", 'name': "bricklayer's_hammer"}, {'id': 6053, 'synset': 'brick_trowel.n.01', 'name': 'brick_trowel'}, {'id': 6054, 'synset': 'brickwork.n.01', 'name': 'brickwork'}, {'id': 6055, 'synset': 'bridge.n.01', 'name': 'bridge'}, {'id': 6056, 'synset': 'bridge.n.08', 'name': 'bridge'}, {'id': 6057, 'synset': 'bridle.n.01', 'name': 'bridle'}, {'id': 6058, 'synset': 'bridle_path.n.01', 'name': 'bridle_path'}, {'id': 6059, 'synset': 'bridoon.n.01', 'name': 'bridoon'}, {'id': 6060, 'synset': 'briefcase_bomb.n.01', 'name': 'briefcase_bomb'}, {'id': 6061, 'synset': 'briefcase_computer.n.01', 'name': 'briefcase_computer'}, {'id': 6062, 'synset': 'briefs.n.01', 'name': 'briefs'}, {'id': 6063, 'synset': 'brig.n.02', 'name': 'brig'}, {'id': 6064, 'synset': 'brig.n.01', 'name': 'brig'}, {'id': 6065, 'synset': 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'name': 'broadside'}, {'id': 6081, 'synset': 'broadsword.n.01', 'name': 'broadsword'}, {'id': 6082, 'synset': 'brocade.n.01', 'name': 'brocade'}, {'id': 6083, 'synset': 'brogan.n.01', 'name': 'brogan'}, {'id': 6084, 'synset': 'broiler.n.01', 'name': 'broiler'}, {'id': 6085, 'synset': 'broken_arch.n.01', 'name': 'broken_arch'}, {'id': 6086, 'synset': 'bronchoscope.n.01', 'name': 'bronchoscope'}, {'id': 6087, 'synset': 'broom_closet.n.01', 'name': 'broom_closet'}, {'id': 6088, 'synset': 'broomstick.n.01', 'name': 'broomstick'}, {'id': 6089, 'synset': 'brougham.n.01', 'name': 'brougham'}, {'id': 6090, 'synset': 'browning_automatic_rifle.n.01', 'name': 'Browning_automatic_rifle'}, {'id': 6091, 'synset': 'browning_machine_gun.n.01', 'name': 'Browning_machine_gun'}, {'id': 6092, 'synset': 'brownstone.n.02', 'name': 'brownstone'}, {'id': 6093, 'synset': 'brunch_coat.n.01', 'name': 'brunch_coat'}, {'id': 6094, 'synset': 'brush.n.02', 'name': 'brush'}, {'id': 6095, 'synset': 'brussels_carpet.n.01', 'name': 'Brussels_carpet'}, {'id': 6096, 'synset': 'brussels_lace.n.01', 'name': 'Brussels_lace'}, {'id': 6097, 'synset': 'bubble.n.04', 'name': 'bubble'}, {'id': 6098, 'synset': 'bubble_chamber.n.01', 'name': 'bubble_chamber'}, {'id': 6099, 'synset': 'bubble_jet_printer.n.01', 'name': 'bubble_jet_printer'}, {'id': 6100, 'synset': 'buckboard.n.01', 'name': 'buckboard'}, {'id': 6101, 'synset': 'bucket_seat.n.01', 'name': 'bucket_seat'}, {'id': 6102, 'synset': 'bucket_shop.n.02', 'name': 'bucket_shop'}, {'id': 6103, 'synset': 'buckle.n.01', 'name': 'buckle'}, {'id': 6104, 'synset': 'buckram.n.01', 'name': 'buckram'}, {'id': 6105, 'synset': 'bucksaw.n.01', 'name': 'bucksaw'}, {'id': 6106, 'synset': 'buckskins.n.01', 'name': 'buckskins'}, {'id': 6107, 'synset': 'buff.n.05', 'name': 'buff'}, {'id': 6108, 'synset': 'buffer.n.05', 'name': 'buffer'}, {'id': 6109, 'synset': 'buffer.n.04', 'name': 'buffer'}, {'id': 6110, 'synset': 'buffet.n.01', 'name': 'buffet'}, {'id': 6111, 'synset': 'buffing_wheel.n.01', 'name': 'buffing_wheel'}, {'id': 6112, 'synset': 'bugle.n.01', 'name': 'bugle'}, {'id': 6113, 'synset': 'building.n.01', 'name': 'building'}, {'id': 6114, 'synset': 'building_complex.n.01', 'name': 'building_complex'}, {'id': 6115, 'synset': 'bulldog_clip.n.01', 'name': 'bulldog_clip'}, {'id': 6116, 'synset': 'bulldog_wrench.n.01', 'name': 'bulldog_wrench'}, {'id': 6117, 'synset': 'bullet.n.01', 'name': 'bullet'}, {'id': 6118, 'synset': 'bullion.n.02', 'name': 'bullion'}, {'id': 6119, 'synset': 'bullnose.n.01', 'name': 'bullnose'}, {'id': 6120, 'synset': 'bullpen.n.02', 'name': 'bullpen'}, {'id': 6121, 'synset': 'bullpen.n.01', 'name': 'bullpen'}, {'id': 6122, 'synset': 'bullring.n.01', 'name': 'bullring'}, {'id': 6123, 'synset': 'bulwark.n.02', 'name': 'bulwark'}, {'id': 6124, 'synset': 'bumboat.n.01', 'name': 'bumboat'}, {'id': 6125, 'synset': 'bumper.n.02', 'name': 'bumper'}, {'id': 6126, 'synset': 'bumper.n.01', 'name': 'bumper'}, {'id': 6127, 'synset': 'bumper_car.n.01', 'name': 'bumper_car'}, {'id': 6128, 'synset': 'bumper_guard.n.01', 'name': 'bumper_guard'}, {'id': 6129, 'synset': 'bumper_jack.n.01', 'name': 'bumper_jack'}, {'id': 6130, 'synset': 'bundle.n.02', 'name': 'bundle'}, {'id': 6131, 'synset': 'bung.n.01', 'name': 'bung'}, {'id': 6132, 'synset': 'bungalow.n.01', 'name': 'bungalow'}, {'id': 6133, 'synset': 'bungee.n.01', 'name': 'bungee'}, {'id': 6134, 'synset': 'bunghole.n.02', 'name': 'bunghole'}, {'id': 6135, 'synset': 'bunk.n.03', 'name': 'bunk'}, {'id': 6136, 'synset': 'bunk.n.01', 'name': 'bunk'}, {'id': 6137, 'synset': 'bunker.n.01', 'name': 'bunker'}, {'id': 6138, 'synset': 'bunker.n.03', 'name': 'bunker'}, {'id': 6139, 'synset': 'bunker.n.02', 'name': 'bunker'}, {'id': 6140, 'synset': 'bunsen_burner.n.01', 'name': 'bunsen_burner'}, {'id': 6141, 'synset': 'bunting.n.01', 'name': 'bunting'}, {'id': 6142, 'synset': 'bur.n.02', 'name': 'bur'}, {'id': 6143, 'synset': 'burberry.n.01', 'name': 'Burberry'}, {'id': 6144, 'synset': 'burette.n.01', 'name': 'burette'}, {'id': 6145, 'synset': 'burglar_alarm.n.02', 'name': 'burglar_alarm'}, {'id': 6146, 'synset': 'burial_chamber.n.01', 'name': 'burial_chamber'}, {'id': 6147, 'synset': 'burial_garment.n.01', 'name': 'burial_garment'}, {'id': 6148, 'synset': 'burial_mound.n.01', 'name': 'burial_mound'}, {'id': 6149, 'synset': 'burin.n.01', 'name': 'burin'}, {'id': 6150, 'synset': 'burqa.n.01', 'name': 'burqa'}, {'id': 6151, 'synset': 'burlap.n.01', 'name': 'burlap'}, {'id': 6152, 'synset': 'burn_bag.n.01', 'name': 'burn_bag'}, {'id': 6153, 'synset': 'burner.n.01', 'name': 'burner'}, {'id': 6154, 'synset': 'burnous.n.01', 'name': 'burnous'}, {'id': 6155, 'synset': 'burp_gun.n.01', 'name': 'burp_gun'}, {'id': 6156, 'synset': 'burr.n.04', 'name': 'burr'}, {'id': 6157, 'synset': 'bushel_basket.n.01', 'name': 'bushel_basket'}, {'id': 6158, 'synset': 'bushing.n.02', 'name': 'bushing'}, {'id': 6159, 'synset': 'bush_jacket.n.01', 'name': 'bush_jacket'}, {'id': 6160, 'synset': 'business_suit.n.01', 'name': 'business_suit'}, {'id': 6161, 'synset': 'buskin.n.01', 'name': 'buskin'}, {'id': 6162, 'synset': 'bustier.n.01', 'name': 'bustier'}, {'id': 6163, 'synset': 'bustle.n.02', 'name': 'bustle'}, {'id': 6164, 'synset': 'butcher_knife.n.01', 'name': 'butcher_knife'}, {'id': 6165, 'synset': 'butcher_shop.n.01', 'name': 'butcher_shop'}, {'id': 6166, 'synset': 'butter_dish.n.01', 'name': 'butter_dish'}, {'id': 6167, 'synset': 'butterfly_valve.n.01', 'name': 'butterfly_valve'}, {'id': 6168, 'synset': 'butter_knife.n.01', 'name': 'butter_knife'}, {'id': 6169, 'synset': 'butt_hinge.n.01', 'name': 'butt_hinge'}, {'id': 6170, 'synset': 'butt_joint.n.01', 'name': 'butt_joint'}, {'id': 6171, 'synset': 'buttonhook.n.01', 'name': 'buttonhook'}, {'id': 6172, 'synset': 'buttress.n.01', 'name': 'buttress'}, {'id': 6173, 'synset': 'butt_shaft.n.01', 'name': 'butt_shaft'}, {'id': 6174, 'synset': 'butt_weld.n.01', 'name': 'butt_weld'}, {'id': 6175, 'synset': 'buzz_bomb.n.01', 'name': 'buzz_bomb'}, {'id': 6176, 'synset': 'buzzer.n.02', 'name': 'buzzer'}, {'id': 6177, 'synset': 'bvd.n.01', 'name': 'BVD'}, {'id': 6178, 'synset': 'bypass_condenser.n.01', 'name': 'bypass_condenser'}, {'id': 6179, 'synset': 'byway.n.01', 'name': 'byway'}, {'id': 6180, 'synset': 'cab.n.02', 'name': 'cab'}, {'id': 6181, 'synset': 'cab.n.01', 'name': 'cab'}, {'id': 6182, 'synset': 'cabaret.n.01', 'name': 'cabaret'}, {'id': 6183, 'synset': 'caber.n.01', 'name': 'caber'}, {'id': 6184, 'synset': 'cabin.n.03', 'name': 'cabin'}, {'id': 6185, 'synset': 'cabin.n.02', 'name': 'cabin'}, {'id': 6186, 'synset': 'cabin_class.n.01', 'name': 'cabin_class'}, {'id': 6187, 'synset': 'cabin_cruiser.n.01', 'name': 'cabin_cruiser'}, {'id': 6188, 'synset': 'cabinet.n.04', 'name': 'cabinet'}, {'id': 6189, 'synset': 'cabinetwork.n.01', 'name': 'cabinetwork'}, {'id': 6190, 'synset': 'cabin_liner.n.01', 'name': 'cabin_liner'}, {'id': 6191, 'synset': 'cable.n.06', 'name': 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'name': 'camera_obscura'}, {'id': 6225, 'synset': 'camera_tripod.n.01', 'name': 'camera_tripod'}, {'id': 6226, 'synset': 'camise.n.01', 'name': 'camise'}, {'id': 6227, 'synset': 'camisole.n.02', 'name': 'camisole'}, {'id': 6228, 'synset': 'camisole.n.01', 'name': 'camisole'}, {'id': 6229, 'synset': 'camlet.n.02', 'name': 'camlet'}, {'id': 6230, 'synset': 'camouflage.n.03', 'name': 'camouflage'}, {'id': 6231, 'synset': 'camouflage.n.02', 'name': 'camouflage'}, {'id': 6232, 'synset': 'camp.n.01', 'name': 'camp'}, {'id': 6233, 'synset': 'camp.n.03', 'name': 'camp'}, {'id': 6234, 'synset': 'camp.n.07', 'name': 'camp'}, {'id': 6235, 'synset': 'campaign_hat.n.01', 'name': 'campaign_hat'}, {'id': 6236, 'synset': 'campanile.n.01', 'name': 'campanile'}, {'id': 6237, 'synset': 'camp_chair.n.01', 'name': 'camp_chair'}, {'id': 6238, 'synset': 'camper_trailer.n.01', 'name': 'camper_trailer'}, {'id': 6239, 'synset': 'campstool.n.01', 'name': 'campstool'}, {'id': 6240, 'synset': 'camshaft.n.01', 'name': 'camshaft'}, {'id': 6241, 'synset': 'canal.n.03', 'name': 'canal'}, {'id': 6242, 'synset': 'canal_boat.n.01', 'name': 'canal_boat'}, {'id': 6243, 'synset': 'candelabrum.n.01', 'name': 'candelabrum'}, {'id': 6244, 'synset': 'candid_camera.n.01', 'name': 'candid_camera'}, {'id': 6245, 'synset': 'candlepin.n.01', 'name': 'candlepin'}, {'id': 6246, 'synset': 'candlesnuffer.n.01', 'name': 'candlesnuffer'}, {'id': 6247, 'synset': 'candlewick.n.02', 'name': 'candlewick'}, {'id': 6248, 'synset': 'candy_thermometer.n.01', 'name': 'candy_thermometer'}, {'id': 6249, 'synset': 'cane.n.03', 'name': 'cane'}, {'id': 6250, 'synset': 'cangue.n.01', 'name': 'cangue'}, {'id': 6251, 'synset': 'cannery.n.01', 'name': 'cannery'}, {'id': 6252, 'synset': 'cannikin.n.02', 'name': 'cannikin'}, {'id': 6253, 'synset': 'cannikin.n.01', 'name': 'cannikin'}, {'id': 6254, 'synset': 'cannon.n.01', 'name': 'cannon'}, {'id': 6255, 'synset': 'cannon.n.04', 'name': 'cannon'}, {'id': 6256, 'synset': 'cannon.n.03', 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{'id': 6289, 'synset': 'carafe.n.01', 'name': 'carafe'}, {'id': 6290, 'synset': 'caravansary.n.01', 'name': 'caravansary'}, {'id': 6291, 'synset': 'carbine.n.01', 'name': 'carbine'}, {'id': 6292, 'synset': 'car_bomb.n.01', 'name': 'car_bomb'}, {'id': 6293, 'synset': 'carbon_arc_lamp.n.01', 'name': 'carbon_arc_lamp'}, {'id': 6294, 'synset': 'carboy.n.01', 'name': 'carboy'}, {'id': 6295, 'synset': 'carburetor.n.01', 'name': 'carburetor'}, {'id': 6296, 'synset': 'car_carrier.n.01', 'name': 'car_carrier'}, {'id': 6297, 'synset': 'cardcase.n.01', 'name': 'cardcase'}, {'id': 6298, 'synset': 'cardiac_monitor.n.01', 'name': 'cardiac_monitor'}, {'id': 6299, 'synset': 'card_index.n.01', 'name': 'card_index'}, {'id': 6300, 'synset': 'cardiograph.n.01', 'name': 'cardiograph'}, {'id': 6301, 'synset': 'cardioid_microphone.n.01', 'name': 'cardioid_microphone'}, {'id': 6302, 'synset': 'car_door.n.01', 'name': 'car_door'}, {'id': 6303, 'synset': 'cardroom.n.01', 'name': 'cardroom'}, {'id': 6304, 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{'id': 6319, 'synset': "carpenter's_level.n.01", 'name': "carpenter's_level"}, {'id': 6320, 'synset': "carpenter's_mallet.n.01", 'name': "carpenter's_mallet"}, {'id': 6321, 'synset': "carpenter's_rule.n.01", 'name': "carpenter's_rule"}, {'id': 6322, 'synset': "carpenter's_square.n.01", 'name': "carpenter's_square"}, {'id': 6323, 'synset': 'carpetbag.n.01', 'name': 'carpetbag'}, {'id': 6324, 'synset': 'carpet_beater.n.01', 'name': 'carpet_beater'}, {'id': 6325, 'synset': 'carpet_loom.n.01', 'name': 'carpet_loom'}, {'id': 6326, 'synset': 'carpet_pad.n.01', 'name': 'carpet_pad'}, {'id': 6327, 'synset': 'carpet_sweeper.n.01', 'name': 'carpet_sweeper'}, {'id': 6328, 'synset': 'carpet_tack.n.01', 'name': 'carpet_tack'}, {'id': 6329, 'synset': 'carport.n.01', 'name': 'carport'}, {'id': 6330, 'synset': 'carrack.n.01', 'name': 'carrack'}, {'id': 6331, 'synset': 'carrel.n.02', 'name': 'carrel'}, {'id': 6332, 'synset': 'carriage.n.04', 'name': 'carriage'}, {'id': 6333, 'synset': 'carriage_bolt.n.01', 'name': 'carriage_bolt'}, {'id': 6334, 'synset': 'carriageway.n.01', 'name': 'carriageway'}, {'id': 6335, 'synset': 'carriage_wrench.n.01', 'name': 'carriage_wrench'}, {'id': 6336, 'synset': 'carrick_bend.n.01', 'name': 'carrick_bend'}, {'id': 6337, 'synset': 'carrier.n.10', 'name': 'carrier'}, {'id': 6338, 'synset': 'carrycot.n.01', 'name': 'carrycot'}, {'id': 6339, 'synset': 'car_seat.n.01', 'name': 'car_seat'}, {'id': 6340, 'synset': 'car_tire.n.01', 'name': 'car_tire'}, {'id': 6341, 'synset': 'cartouche.n.01', 'name': 'cartouche'}, {'id': 6342, 'synset': 'car_train.n.01', 'name': 'car_train'}, {'id': 6343, 'synset': 'cartridge.n.01', 'name': 'cartridge'}, {'id': 6344, 'synset': 'cartridge.n.04', 'name': 'cartridge'}, {'id': 6345, 'synset': 'cartridge_belt.n.01', 'name': 'cartridge_belt'}, {'id': 6346, 'synset': 'cartridge_extractor.n.01', 'name': 'cartridge_extractor'}, {'id': 6347, 'synset': 'cartridge_fuse.n.01', 'name': 'cartridge_fuse'}, {'id': 6348, 'synset': 'cartridge_holder.n.01', 'name': 'cartridge_holder'}, {'id': 6349, 'synset': 'cartwheel.n.01', 'name': 'cartwheel'}, {'id': 6350, 'synset': 'carving_fork.n.01', 'name': 'carving_fork'}, {'id': 6351, 'synset': 'carving_knife.n.01', 'name': 'carving_knife'}, {'id': 6352, 'synset': 'car_wheel.n.01', 'name': 'car_wheel'}, {'id': 6353, 'synset': 'caryatid.n.01', 'name': 'caryatid'}, {'id': 6354, 'synset': 'cascade_liquefier.n.01', 'name': 'cascade_liquefier'}, {'id': 6355, 'synset': 'cascade_transformer.n.01', 'name': 'cascade_transformer'}, {'id': 6356, 'synset': 'case.n.05', 'name': 'case'}, {'id': 6357, 'synset': 'case.n.20', 'name': 'case'}, {'id': 6358, 'synset': 'case.n.18', 'name': 'case'}, {'id': 6359, 'synset': 'casein_paint.n.01', 'name': 'casein_paint'}, {'id': 6360, 'synset': 'case_knife.n.02', 'name': 'case_knife'}, {'id': 6361, 'synset': 'case_knife.n.01', 'name': 'case_knife'}, {'id': 6362, 'synset': 'casement.n.01', 'name': 'casement'}, {'id': 6363, 'synset': 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'cassette_player'}, {'id': 6379, 'synset': 'cassette_recorder.n.01', 'name': 'cassette_recorder'}, {'id': 6380, 'synset': 'cassette_tape.n.01', 'name': 'cassette_tape'}, {'id': 6381, 'synset': 'cassock.n.01', 'name': 'cassock'}, {'id': 6382, 'synset': 'caster.n.03', 'name': 'caster'}, {'id': 6383, 'synset': 'caster.n.02', 'name': 'caster'}, {'id': 6384, 'synset': 'castle.n.02', 'name': 'castle'}, {'id': 6385, 'synset': 'castle.n.03', 'name': 'castle'}, {'id': 6386, 'synset': 'catacomb.n.01', 'name': 'catacomb'}, {'id': 6387, 'synset': 'catafalque.n.01', 'name': 'catafalque'}, {'id': 6388, 'synset': 'catalytic_converter.n.01', 'name': 'catalytic_converter'}, {'id': 6389, 'synset': 'catalytic_cracker.n.01', 'name': 'catalytic_cracker'}, {'id': 6390, 'synset': 'catamaran.n.01', 'name': 'catamaran'}, {'id': 6391, 'synset': 'catapult.n.03', 'name': 'catapult'}, {'id': 6392, 'synset': 'catapult.n.02', 'name': 'catapult'}, {'id': 6393, 'synset': 'catboat.n.01', 'name': 'catboat'}, {'id': 6394, 'synset': 'cat_box.n.01', 'name': 'cat_box'}, {'id': 6395, 'synset': 'catch.n.07', 'name': 'catch'}, {'id': 6396, 'synset': 'catchall.n.01', 'name': 'catchall'}, {'id': 6397, 'synset': "catcher's_mask.n.01", 'name': "catcher's_mask"}, {'id': 6398, 'synset': 'catchment.n.01', 'name': 'catchment'}, {'id': 6399, 'synset': 'caterpillar.n.02', 'name': 'Caterpillar'}, {'id': 6400, 'synset': 'cathedra.n.01', 'name': 'cathedra'}, {'id': 6401, 'synset': 'cathedral.n.01', 'name': 'cathedral'}, {'id': 6402, 'synset': 'cathedral.n.02', 'name': 'cathedral'}, {'id': 6403, 'synset': 'catheter.n.01', 'name': 'catheter'}, {'id': 6404, 'synset': 'cathode.n.01', 'name': 'cathode'}, {'id': 6405, 'synset': 'cathode-ray_tube.n.01', 'name': 'cathode-ray_tube'}, {'id': 6406, 'synset': "cat-o'-nine-tails.n.01", 'name': "cat-o'-nine-tails"}, {'id': 6407, 'synset': "cat's-paw.n.02", 'name': "cat's-paw"}, {'id': 6408, 'synset': 'catsup_bottle.n.01', 'name': 'catsup_bottle'}, {'id': 6409, 'synset': 'cattle_car.n.01', 'name': 'cattle_car'}, {'id': 6410, 'synset': 'cattle_guard.n.01', 'name': 'cattle_guard'}, {'id': 6411, 'synset': 'cattleship.n.01', 'name': 'cattleship'}, {'id': 6412, 'synset': 'cautery.n.01', 'name': 'cautery'}, {'id': 6413, 'synset': 'cavalier_hat.n.01', 'name': 'cavalier_hat'}, {'id': 6414, 'synset': 'cavalry_sword.n.01', 'name': 'cavalry_sword'}, {'id': 6415, 'synset': 'cavetto.n.01', 'name': 'cavetto'}, {'id': 6416, 'synset': 'cavity_wall.n.01', 'name': 'cavity_wall'}, {'id': 6417, 'synset': 'c_battery.n.01', 'name': 'C_battery'}, {'id': 6418, 'synset': 'c-clamp.n.01', 'name': 'C-clamp'}, {'id': 6419, 'synset': 'cd_drive.n.01', 'name': 'CD_drive'}, {'id': 6420, 'synset': 'cd-r.n.01', 'name': 'CD-R'}, {'id': 6421, 'synset': 'cd-rom.n.01', 'name': 'CD-ROM'}, {'id': 6422, 'synset': 'cd-rom_drive.n.01', 'name': 'CD-ROM_drive'}, {'id': 6423, 'synset': 'cedar_chest.n.01', 'name': 'cedar_chest'}, {'id': 6424, 'synset': 'ceiling.n.01', 'name': 'ceiling'}, {'id': 6425, 'synset': 'celesta.n.01', 'name': 'celesta'}, {'id': 6426, 'synset': 'cell.n.03', 'name': 'cell'}, {'id': 6427, 'synset': 'cell.n.07', 'name': 'cell'}, {'id': 6428, 'synset': 'cellar.n.03', 'name': 'cellar'}, {'id': 6429, 'synset': 'cellblock.n.01', 'name': 'cellblock'}, {'id': 6430, 'synset': 'cello.n.01', 'name': 'cello'}, {'id': 6431, 'synset': 'cellophane.n.01', 'name': 'cellophane'}, {'id': 6432, 'synset': 'cellulose_tape.n.01', 'name': 'cellulose_tape'}, {'id': 6433, 'synset': 'cenotaph.n.01', 'name': 'cenotaph'}, {'id': 6434, 'synset': 'censer.n.01', 'name': 'censer'}, {'id': 6435, 'synset': 'center.n.03', 'name': 'center'}, {'id': 6436, 'synset': 'center_punch.n.01', 'name': 'center_punch'}, {'id': 6437, 'synset': 'centigrade_thermometer.n.01', 'name': 'Centigrade_thermometer'}, {'id': 6438, 'synset': 'central_processing_unit.n.01', 'name': 'central_processing_unit'}, {'id': 6439, 'synset': 'centrifugal_pump.n.01', 'name': 'centrifugal_pump'}, {'id': 6440, 'synset': 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{'id': 6456, 'synset': 'chain_tongs.n.01', 'name': 'chain_tongs'}, {'id': 6457, 'synset': 'chain_wrench.n.01', 'name': 'chain_wrench'}, {'id': 6458, 'synset': 'chair.n.05', 'name': 'chair'}, {'id': 6459, 'synset': 'chair_of_state.n.01', 'name': 'chair_of_state'}, {'id': 6460, 'synset': 'chairlift.n.01', 'name': 'chairlift'}, {'id': 6461, 'synset': 'chaise.n.02', 'name': 'chaise'}, {'id': 6462, 'synset': 'chalet.n.01', 'name': 'chalet'}, {'id': 6463, 'synset': 'chalk.n.04', 'name': 'chalk'}, {'id': 6464, 'synset': 'challis.n.01', 'name': 'challis'}, {'id': 6465, 'synset': 'chamberpot.n.01', 'name': 'chamberpot'}, {'id': 6466, 'synset': 'chambray.n.01', 'name': 'chambray'}, {'id': 6467, 'synset': 'chamfer_bit.n.01', 'name': 'chamfer_bit'}, {'id': 6468, 'synset': 'chamfer_plane.n.01', 'name': 'chamfer_plane'}, {'id': 6469, 'synset': 'chamois_cloth.n.01', 'name': 'chamois_cloth'}, {'id': 6470, 'synset': 'chancel.n.01', 'name': 'chancel'}, {'id': 6471, 'synset': 'chancellery.n.01', 'name': 'chancellery'}, {'id': 6472, 'synset': 'chancery.n.02', 'name': 'chancery'}, {'id': 6473, 'synset': 'chandlery.n.01', 'name': 'chandlery'}, {'id': 6474, 'synset': 'chanfron.n.01', 'name': 'chanfron'}, {'id': 6475, 'synset': 'chanter.n.01', 'name': 'chanter'}, {'id': 6476, 'synset': 'chantry.n.02', 'name': 'chantry'}, {'id': 6477, 'synset': 'chapel.n.01', 'name': 'chapel'}, {'id': 6478, 'synset': 'chapterhouse.n.02', 'name': 'chapterhouse'}, {'id': 6479, 'synset': 'chapterhouse.n.01', 'name': 'chapterhouse'}, {'id': 6480, 'synset': 'character_printer.n.01', 'name': 'character_printer'}, {'id': 6481, 'synset': 'charcuterie.n.01', 'name': 'charcuterie'}, {'id': 6482, 'synset': 'charge-exchange_accelerator.n.01', 'name': 'charge-exchange_accelerator'}, {'id': 6483, 'synset': 'charger.n.02', 'name': 'charger'}, {'id': 6484, 'synset': 'chariot.n.01', 'name': 'chariot'}, {'id': 6485, 'synset': 'chariot.n.02', 'name': 'chariot'}, {'id': 6486, 'synset': 'charnel_house.n.01', 'name': 'charnel_house'}, {'id': 6487, 'synset': 'chassis.n.03', 'name': 'chassis'}, {'id': 6488, 'synset': 'chassis.n.02', 'name': 'chassis'}, {'id': 6489, 'synset': 'chasuble.n.01', 'name': 'chasuble'}, {'id': 6490, 'synset': 'chateau.n.01', 'name': 'chateau'}, {'id': 6491, 'synset': 'chatelaine.n.02', 'name': 'chatelaine'}, {'id': 6492, 'synset': 'checker.n.03', 'name': 'checker'}, {'id': 6493, 'synset': 'checkout.n.03', 'name': 'checkout'}, {'id': 6494, 'synset': 'cheekpiece.n.01', 'name': 'cheekpiece'}, {'id': 6495, 'synset': 'cheeseboard.n.01', 'name': 'cheeseboard'}, {'id': 6496, 'synset': 'cheesecloth.n.01', 'name': 'cheesecloth'}, {'id': 6497, 'synset': 'cheese_cutter.n.01', 'name': 'cheese_cutter'}, {'id': 6498, 'synset': 'cheese_press.n.01', 'name': 'cheese_press'}, {'id': 6499, 'synset': 'chemical_bomb.n.01', 'name': 'chemical_bomb'}, {'id': 6500, 'synset': 'chemical_plant.n.01', 'name': 'chemical_plant'}, {'id': 6501, 'synset': 'chemical_reactor.n.01', 'name': 'chemical_reactor'}, {'id': 6502, 'synset': 'chemise.n.02', 'name': 'chemise'}, {'id': 6503, 'synset': 'chemise.n.01', 'name': 'chemise'}, {'id': 6504, 'synset': 'chenille.n.02', 'name': 'chenille'}, {'id': 6505, 'synset': 'chessman.n.01', 'name': 'chessman'}, {'id': 6506, 'synset': 'chest.n.02', 'name': 'chest'}, {'id': 6507, 'synset': 'chesterfield.n.02', 'name': 'chesterfield'}, {'id': 6508, 'synset': 'chest_of_drawers.n.01', 'name': 'chest_of_drawers'}, {'id': 6509, 'synset': 'chest_protector.n.01', 'name': 'chest_protector'}, {'id': 6510, 'synset': 'cheval-de-frise.n.01', 'name': 'cheval-de-frise'}, {'id': 6511, 'synset': 'cheval_glass.n.01', 'name': 'cheval_glass'}, {'id': 6512, 'synset': 'chicane.n.02', 'name': 'chicane'}, {'id': 6513, 'synset': 'chicken_coop.n.01', 'name': 'chicken_coop'}, {'id': 6514, 'synset': 'chicken_wire.n.01', 'name': 'chicken_wire'}, {'id': 6515, 'synset': 'chicken_yard.n.01', 'name': 'chicken_yard'}, {'id': 6516, 'synset': 'chiffon.n.01', 'name': 'chiffon'}, {'id': 6517, 'synset': 'chiffonier.n.01', 'name': 'chiffonier'}, {'id': 6518, 'synset': "child's_room.n.01", 'name': "child's_room"}, {'id': 6519, 'synset': 'chimney_breast.n.01', 'name': 'chimney_breast'}, {'id': 6520, 'synset': 'chimney_corner.n.01', 'name': 'chimney_corner'}, {'id': 6521, 'synset': 'china.n.02', 'name': 'china'}, {'id': 6522, 'synset': 'china_cabinet.n.01', 'name': 'china_cabinet'}, {'id': 6523, 'synset': 'chinchilla.n.02', 'name': 'chinchilla'}, {'id': 6524, 'synset': 'chinese_lantern.n.01', 'name': 'Chinese_lantern'}, {'id': 6525, 'synset': 'chinese_puzzle.n.01', 'name': 'Chinese_puzzle'}, {'id': 6526, 'synset': 'chinning_bar.n.01', 'name': 'chinning_bar'}, {'id': 6527, 'synset': 'chino.n.02', 'name': 'chino'}, {'id': 6528, 'synset': 'chino.n.01', 'name': 'chino'}, {'id': 6529, 'synset': 'chin_rest.n.01', 'name': 'chin_rest'}, {'id': 6530, 'synset': 'chin_strap.n.01', 'name': 'chin_strap'}, {'id': 6531, 'synset': 'chintz.n.01', 'name': 'chintz'}, {'id': 6532, 'synset': 'chip.n.07', 'name': 'chip'}, {'id': 6533, 'synset': 'chisel.n.01', 'name': 'chisel'}, {'id': 6534, 'synset': 'chlamys.n.02', 'name': 'chlamys'}, {'id': 6535, 'synset': 'choir.n.03', 'name': 'choir'}, {'id': 6536, 'synset': 'choir_loft.n.01', 'name': 'choir_loft'}, {'id': 6537, 'synset': 'choke.n.02', 'name': 'choke'}, {'id': 6538, 'synset': 'choke.n.01', 'name': 'choke'}, {'id': 6539, 'synset': 'chokey.n.01', 'name': 'chokey'}, {'id': 6540, 'synset': 'choo-choo.n.01', 'name': 'choo-choo'}, {'id': 6541, 'synset': 'chopine.n.01', 'name': 'chopine'}, {'id': 6542, 'synset': 'chordophone.n.01', 'name': 'chordophone'}, {'id': 6543, 'synset': 'christmas_stocking.n.01', 'name': 'Christmas_stocking'}, {'id': 6544, 'synset': 'chronograph.n.01', 'name': 'chronograph'}, {'id': 6545, 'synset': 'chronometer.n.01', 'name': 'chronometer'}, {'id': 6546, 'synset': 'chronoscope.n.01', 'name': 'chronoscope'}, {'id': 6547, 'synset': 'chuck.n.03', 'name': 'chuck'}, {'id': 6548, 'synset': 'chuck_wagon.n.01', 'name': 'chuck_wagon'}, {'id': 6549, 'synset': 'chukka.n.02', 'name': 'chukka'}, {'id': 6550, 'synset': 'church.n.02', 'name': 'church'}, {'id': 6551, 'synset': 'church_bell.n.01', 'name': 'church_bell'}, {'id': 6552, 'synset': 'church_hat.n.01', 'name': 'church_hat'}, {'id': 6553, 'synset': 'church_key.n.01', 'name': 'church_key'}, {'id': 6554, 'synset': 'church_tower.n.01', 'name': 'church_tower'}, {'id': 6555, 'synset': 'churidars.n.01', 'name': 'churidars'}, {'id': 6556, 'synset': 'churn.n.01', 'name': 'churn'}, {'id': 6557, 'synset': 'ciderpress.n.01', 'name': 'ciderpress'}, {'id': 6558, 'synset': 'cigar_band.n.01', 'name': 'cigar_band'}, {'id': 6559, 'synset': 'cigar_cutter.n.01', 'name': 'cigar_cutter'}, {'id': 6560, 'synset': 'cigarette_butt.n.01', 'name': 'cigarette_butt'}, {'id': 6561, 'synset': 'cigarette_holder.n.01', 'name': 'cigarette_holder'}, {'id': 6562, 'synset': 'cigar_lighter.n.01', 'name': 'cigar_lighter'}, {'id': 6563, 'synset': 'cinch.n.02', 'name': 'cinch'}, {'id': 6564, 'synset': 'cinema.n.02', 'name': 'cinema'}, {'id': 6565, 'synset': 'cinquefoil.n.02', 'name': 'cinquefoil'}, {'id': 6566, 'synset': 'circle.n.08', 'name': 'circle'}, {'id': 6567, 'synset': 'circlet.n.02', 'name': 'circlet'}, {'id': 6568, 'synset': 'circuit.n.01', 'name': 'circuit'}, {'id': 6569, 'synset': 'circuit_board.n.01', 'name': 'circuit_board'}, {'id': 6570, 'synset': 'circuit_breaker.n.01', 'name': 'circuit_breaker'}, {'id': 6571, 'synset': 'circuitry.n.01', 'name': 'circuitry'}, {'id': 6572, 'synset': 'circular_plane.n.01', 'name': 'circular_plane'}, {'id': 6573, 'synset': 'circular_saw.n.01', 'name': 'circular_saw'}, {'id': 6574, 'synset': 'circus_tent.n.01', 'name': 'circus_tent'}, {'id': 6575, 'synset': 'cistern.n.03', 'name': 'cistern'}, {'id': 6576, 'synset': 'cittern.n.01', 'name': 'cittern'}, {'id': 6577, 'synset': 'city_hall.n.01', 'name': 'city_hall'}, {'id': 6578, 'synset': 'cityscape.n.02', 'name': 'cityscape'}, {'id': 6579, 'synset': 'city_university.n.01', 'name': 'city_university'}, {'id': 6580, 'synset': 'civies.n.01', 'name': 'civies'}, {'id': 6581, 'synset': 'civilian_clothing.n.01', 'name': 'civilian_clothing'}, {'id': 6582, 'synset': 'clack_valve.n.01', 'name': 'clack_valve'}, {'id': 6583, 'synset': 'clamp.n.01', 'name': 'clamp'}, {'id': 6584, 'synset': 'clamshell.n.02', 'name': 'clamshell'}, {'id': 6585, 'synset': 'clapper.n.03', 'name': 'clapper'}, {'id': 6586, 'synset': 'clapperboard.n.01', 'name': 'clapperboard'}, {'id': 6587, 'synset': 'clarence.n.01', 'name': 'clarence'}, {'id': 6588, 'synset': 'clark_cell.n.01', 'name': 'Clark_cell'}, {'id': 6589, 'synset': 'clasp_knife.n.01', 'name': 'clasp_knife'}, {'id': 6590, 'synset': 'classroom.n.01', 'name': 'classroom'}, {'id': 6591, 'synset': 'clavichord.n.01', 'name': 'clavichord'}, {'id': 6592, 'synset': 'clavier.n.02', 'name': 'clavier'}, {'id': 6593, 'synset': 'clay_pigeon.n.01', 'name': 'clay_pigeon'}, {'id': 6594, 'synset': 'claymore_mine.n.01', 'name': 'claymore_mine'}, {'id': 6595, 'synset': 'claymore.n.01', 'name': 'claymore'}, {'id': 6596, 'synset': 'cleaners.n.01', 'name': 'cleaners'}, {'id': 6597, 'synset': 'cleaning_implement.n.01', 'name': 'cleaning_implement'}, {'id': 6598, 'synset': 'cleaning_pad.n.01', 'name': 'cleaning_pad'}, {'id': 6599, 'synset': 'clean_room.n.01', 'name': 'clean_room'}, {'id': 6600, 'synset': 'clearway.n.01', 'name': 'clearway'}, {'id': 6601, 'synset': 'cleat.n.01', 'name': 'cleat'}, {'id': 6602, 'synset': 'cleats.n.01', 'name': 'cleats'}, {'id': 6603, 'synset': 'cleaver.n.01', 'name': 'cleaver'}, {'id': 6604, 'synset': 'clerestory.n.01', 'name': 'clerestory'}, {'id': 6605, 'synset': 'clevis.n.01', 'name': 'clevis'}, {'id': 6606, 'synset': 'clews.n.01', 'name': 'clews'}, {'id': 6607, 'synset': 'cliff_dwelling.n.01', 'name': 'cliff_dwelling'}, {'id': 6608, 'synset': 'climbing_frame.n.01', 'name': 'climbing_frame'}, {'id': 6609, 'synset': 'clinch.n.03', 'name': 'clinch'}, {'id': 6610, 'synset': 'clinch.n.02', 'name': 'clinch'}, {'id': 6611, 'synset': 'clincher.n.03', 'name': 'clincher'}, {'id': 6612, 'synset': 'clinic.n.03', 'name': 'clinic'}, {'id': 6613, 'synset': 'clinical_thermometer.n.01', 'name': 'clinical_thermometer'}, {'id': 6614, 'synset': 'clinker.n.02', 'name': 'clinker'}, {'id': 6615, 'synset': 'clinometer.n.01', 'name': 'clinometer'}, {'id': 6616, 'synset': 'clip_lead.n.01', 'name': 'clip_lead'}, {'id': 6617, 'synset': 'clip-on.n.01', 'name': 'clip-on'}, {'id': 6618, 'synset': 'clipper.n.04', 'name': 'clipper'}, {'id': 6619, 'synset': 'clipper.n.02', 'name': 'clipper'}, {'id': 6620, 'synset': 'cloak.n.01', 'name': 'cloak'}, {'id': 6621, 'synset': 'cloakroom.n.02', 'name': 'cloakroom'}, {'id': 6622, 'synset': 'cloche.n.02', 'name': 'cloche'}, {'id': 6623, 'synset': 'cloche.n.01', 'name': 'cloche'}, {'id': 6624, 'synset': 'clock_pendulum.n.01', 'name': 'clock_pendulum'}, {'id': 6625, 'synset': 'clock_radio.n.01', 'name': 'clock_radio'}, {'id': 6626, 'synset': 'clockwork.n.01', 'name': 'clockwork'}, {'id': 6627, 'synset': 'clog.n.01', 'name': 'clog'}, {'id': 6628, 'synset': 'cloisonne.n.01', 'name': 'cloisonne'}, {'id': 6629, 'synset': 'cloister.n.02', 'name': 'cloister'}, {'id': 6630, 'synset': 'closed_circuit.n.01', 'name': 'closed_circuit'}, {'id': 6631, 'synset': 'closed-circuit_television.n.01', 'name': 'closed-circuit_television'}, {'id': 6632, 'synset': 'closed_loop.n.01', 'name': 'closed_loop'}, {'id': 6633, 'synset': 'closet.n.04', 'name': 'closet'}, {'id': 6634, 'synset': 'closeup_lens.n.01', 'name': 'closeup_lens'}, {'id': 6635, 'synset': 'cloth_cap.n.01', 'name': 'cloth_cap'}, {'id': 6636, 'synset': 'cloth_covering.n.01', 'name': 'cloth_covering'}, {'id': 6637, 'synset': 'clothesbrush.n.01', 'name': 'clothesbrush'}, {'id': 6638, 'synset': 'clothes_closet.n.01', 'name': 'clothes_closet'}, {'id': 6639, 'synset': 'clothes_dryer.n.01', 'name': 'clothes_dryer'}, {'id': 6640, 'synset': 'clotheshorse.n.01', 'name': 'clotheshorse'}, {'id': 6641, 'synset': 'clothes_tree.n.01', 'name': 'clothes_tree'}, {'id': 6642, 'synset': 'clothing.n.01', 'name': 'clothing'}, {'id': 6643, 'synset': 'clothing_store.n.01', 'name': 'clothing_store'}, {'id': 6644, 'synset': 'clout_nail.n.01', 'name': 'clout_nail'}, {'id': 6645, 'synset': 'clove_hitch.n.01', 'name': 'clove_hitch'}, {'id': 6646, 'synset': 'club_car.n.01', 'name': 'club_car'}, {'id': 6647, 'synset': 'clubroom.n.01', 'name': 'clubroom'}, {'id': 6648, 'synset': 'cluster_bomb.n.01', 'name': 'cluster_bomb'}, {'id': 6649, 'synset': 'clutch.n.07', 'name': 'clutch'}, {'id': 6650, 'synset': 'clutch.n.06', 'name': 'clutch'}, {'id': 6651, 'synset': 'coach.n.04', 'name': 'coach'}, {'id': 6652, 'synset': 'coach_house.n.01', 'name': 'coach_house'}, {'id': 6653, 'synset': 'coal_car.n.01', 'name': 'coal_car'}, {'id': 6654, 'synset': 'coal_chute.n.01', 'name': 'coal_chute'}, {'id': 6655, 'synset': 'coal_house.n.01', 'name': 'coal_house'}, {'id': 6656, 'synset': 'coal_shovel.n.01', 'name': 'coal_shovel'}, {'id': 6657, 'synset': 'coaming.n.01', 'name': 'coaming'}, {'id': 6658, 'synset': 'coaster_brake.n.01', 'name': 'coaster_brake'}, {'id': 6659, 'synset': 'coat_button.n.01', 'name': 'coat_button'}, {'id': 6660, 'synset': 'coat_closet.n.01', 'name': 'coat_closet'}, {'id': 6661, 'synset': 'coatdress.n.01', 'name': 'coatdress'}, {'id': 6662, 'synset': 'coatee.n.01', 'name': 'coatee'}, {'id': 6663, 'synset': 'coating.n.01', 'name': 'coating'}, {'id': 6664, 'synset': 'coating.n.03', 'name': 'coating'}, {'id': 6665, 'synset': 'coat_of_paint.n.01', 'name': 'coat_of_paint'}, {'id': 6666, 'synset': 'coattail.n.01', 'name': 'coattail'}, {'id': 6667, 'synset': 'coaxial_cable.n.01', 'name': 'coaxial_cable'}, {'id': 6668, 'synset': 'cobweb.n.03', 'name': 'cobweb'}, {'id': 6669, 'synset': 'cobweb.n.01', 'name': 'cobweb'}, {'id': 6670, 'synset': 'cockcroft_and_walton_accelerator.n.01', 'name': 'Cockcroft_and_Walton_accelerator'}, {'id': 6671, 'synset': 'cocked_hat.n.01', 'name': 'cocked_hat'}, {'id': 6672, 'synset': 'cockhorse.n.01', 'name': 'cockhorse'}, {'id': 6673, 'synset': 'cockleshell.n.01', 'name': 'cockleshell'}, {'id': 6674, 'synset': 'cockpit.n.01', 'name': 'cockpit'}, {'id': 6675, 'synset': 'cockpit.n.03', 'name': 'cockpit'}, {'id': 6676, 'synset': 'cockpit.n.02', 'name': 'cockpit'}, {'id': 6677, 'synset': 'cockscomb.n.03', 'name': 'cockscomb'}, {'id': 6678, 'synset': 'cocktail_dress.n.01', 'name': 'cocktail_dress'}, {'id': 6679, 'synset': 'cocktail_lounge.n.01', 'name': 'cocktail_lounge'}, {'id': 6680, 'synset': 'cocktail_shaker.n.01', 'name': 'cocktail_shaker'}, {'id': 6681, 'synset': 'cocotte.n.02', 'name': 'cocotte'}, {'id': 6682, 'synset': 'codpiece.n.01', 'name': 'codpiece'}, {'id': 6683, 'synset': 'coelostat.n.01', 'name': 'coelostat'}, {'id': 6684, 'synset': 'coffee_can.n.01', 'name': 'coffee_can'}, {'id': 6685, 'synset': 'coffee_cup.n.01', 'name': 'coffee_cup'}, {'id': 6686, 'synset': 'coffee_filter.n.01', 'name': 'coffee_filter'}, {'id': 6687, 'synset': 'coffee_mill.n.01', 'name': 'coffee_mill'}, {'id': 6688, 'synset': 'coffee_mug.n.01', 'name': 'coffee_mug'}, {'id': 6689, 'synset': 'coffee_stall.n.01', 'name': 'coffee_stall'}, {'id': 6690, 'synset': 'coffee_urn.n.01', 'name': 'coffee_urn'}, {'id': 6691, 'synset': 'coffer.n.02', 'name': 'coffer'}, {'id': 6692, 'synset': 'coffey_still.n.01', 'name': 'Coffey_still'}, {'id': 6693, 'synset': 'coffin.n.01', 'name': 'coffin'}, {'id': 6694, 'synset': 'cog.n.02', 'name': 'cog'}, {'id': 6695, 'synset': 'coif.n.02', 'name': 'coif'}, {'id': 6696, 'synset': 'coil.n.01', 'name': 'coil'}, {'id': 6697, 'synset': 'coil.n.06', 'name': 'coil'}, {'id': 6698, 'synset': 'coil.n.03', 'name': 'coil'}, {'id': 6699, 'synset': 'coil_spring.n.01', 'name': 'coil_spring'}, {'id': 6700, 'synset': 'coin_box.n.01', 'name': 'coin_box'}, {'id': 6701, 'synset': 'cold_cathode.n.01', 'name': 'cold_cathode'}, {'id': 6702, 'synset': 'cold_chisel.n.01', 'name': 'cold_chisel'}, {'id': 6703, 'synset': 'cold_cream.n.01', 'name': 'cold_cream'}, {'id': 6704, 'synset': 'cold_frame.n.01', 'name': 'cold_frame'}, {'id': 6705, 'synset': 'collar.n.01', 'name': 'collar'}, {'id': 6706, 'synset': 'collar.n.03', 'name': 'collar'}, {'id': 6707, 'synset': 'college.n.03', 'name': 'college'}, {'id': 6708, 'synset': 'collet.n.02', 'name': 'collet'}, {'id': 6709, 'synset': 'collider.n.01', 'name': 'collider'}, {'id': 6710, 'synset': 'colliery.n.01', 'name': 'colliery'}, {'id': 6711, 'synset': 'collimator.n.02', 'name': 'collimator'}, {'id': 6712, 'synset': 'collimator.n.01', 'name': 'collimator'}, {'id': 6713, 'synset': 'cologne.n.02', 'name': 'cologne'}, {'id': 6714, 'synset': 'colonnade.n.01', 'name': 'colonnade'}, {'id': 6715, 'synset': 'colonoscope.n.01', 'name': 'colonoscope'}, {'id': 6716, 'synset': 'colorimeter.n.01', 'name': 'colorimeter'}, {'id': 6717, 'synset': 'colors.n.02', 'name': 'colors'}, {'id': 6718, 'synset': 'color_television.n.01', 'name': 'color_television'}, {'id': 6719, 'synset': 'color_tube.n.01', 'name': 'color_tube'}, {'id': 6720, 'synset': 'color_wash.n.01', 'name': 'color_wash'}, {'id': 6721, 'synset': 'colt.n.02', 'name': 'Colt'}, {'id': 6722, 'synset': 'colter.n.01', 'name': 'colter'}, {'id': 6723, 'synset': 'columbarium.n.03', 'name': 'columbarium'}, {'id': 6724, 'synset': 'columbarium.n.02', 'name': 'columbarium'}, {'id': 6725, 'synset': 'column.n.07', 'name': 'column'}, {'id': 6726, 'synset': 'column.n.06', 'name': 'column'}, {'id': 6727, 'synset': 'comb.n.01', 'name': 'comb'}, {'id': 6728, 'synset': 'comb.n.03', 'name': 'comb'}, {'id': 6729, 'synset': 'comber.n.03', 'name': 'comber'}, {'id': 6730, 'synset': 'combination_plane.n.01', 'name': 'combination_plane'}, {'id': 6731, 'synset': 'combine.n.01', 'name': 'combine'}, {'id': 6732, 'synset': 'command_module.n.01', 'name': 'command_module'}, {'id': 6733, 'synset': 'commissary.n.01', 'name': 'commissary'}, {'id': 6734, 'synset': 'commissary.n.02', 'name': 'commissary'}, {'id': 6735, 'synset': 'commodity.n.01', 'name': 'commodity'}, {'id': 6736, 'synset': 'common_ax.n.01', 'name': 'common_ax'}, {'id': 6737, 'synset': 'common_room.n.01', 'name': 'common_room'}, {'id': 6738, 'synset': 'communications_satellite.n.01', 'name': 'communications_satellite'}, {'id': 6739, 'synset': 'communication_system.n.01', 'name': 'communication_system'}, {'id': 6740, 'synset': 'community_center.n.01', 'name': 'community_center'}, {'id': 6741, 'synset': 'commutator.n.01', 'name': 'commutator'}, {'id': 6742, 'synset': 'commuter.n.01', 'name': 'commuter'}, {'id': 6743, 'synset': 'compact.n.01', 'name': 'compact'}, {'id': 6744, 'synset': 'compact.n.03', 'name': 'compact'}, {'id': 6745, 'synset': 'compact_disk.n.01', 'name': 'compact_disk'}, {'id': 6746, 'synset': 'compact-disk_burner.n.01', 'name': 'compact-disk_burner'}, {'id': 6747, 'synset': 'companionway.n.01', 'name': 'companionway'}, {'id': 6748, 'synset': 'compartment.n.02', 'name': 'compartment'}, {'id': 6749, 'synset': 'compartment.n.01', 'name': 'compartment'}, {'id': 6750, 'synset': 'compass.n.04', 'name': 'compass'}, {'id': 6751, 'synset': 'compass_card.n.01', 'name': 'compass_card'}, {'id': 6752, 'synset': 'compass_saw.n.01', 'name': 'compass_saw'}, {'id': 6753, 'synset': 'compound.n.03', 'name': 'compound'}, {'id': 6754, 'synset': 'compound_lens.n.01', 'name': 'compound_lens'}, {'id': 6755, 'synset': 'compound_lever.n.01', 'name': 'compound_lever'}, {'id': 6756, 'synset': 'compound_microscope.n.01', 'name': 'compound_microscope'}, {'id': 6757, 'synset': 'compress.n.01', 'name': 'compress'}, {'id': 6758, 'synset': 'compression_bandage.n.01', 'name': 'compression_bandage'}, {'id': 6759, 'synset': 'compressor.n.01', 'name': 'compressor'}, {'id': 6760, 'synset': 'computer.n.01', 'name': 'computer'}, {'id': 6761, 'synset': 'computer_circuit.n.01', 'name': 'computer_circuit'}, {'id': 6762, 'synset': 'computerized_axial_tomography_scanner.n.01', 'name': 'computerized_axial_tomography_scanner'}, {'id': 6763, 'synset': 'computer_monitor.n.01', 'name': 'computer_monitor'}, {'id': 6764, 'synset': 'computer_network.n.01', 'name': 'computer_network'}, {'id': 6765, 'synset': 'computer_screen.n.01', 'name': 'computer_screen'}, {'id': 6766, 'synset': 'computer_store.n.01', 'name': 'computer_store'}, {'id': 6767, 'synset': 'computer_system.n.01', 'name': 'computer_system'}, {'id': 6768, 'synset': 'concentration_camp.n.01', 'name': 'concentration_camp'}, {'id': 6769, 'synset': 'concert_grand.n.01', 'name': 'concert_grand'}, {'id': 6770, 'synset': 'concert_hall.n.01', 'name': 'concert_hall'}, {'id': 6771, 'synset': 'concertina.n.02', 'name': 'concertina'}, {'id': 6772, 'synset': 'concertina.n.01', 'name': 'concertina'}, {'id': 6773, 'synset': 'concrete_mixer.n.01', 'name': 'concrete_mixer'}, {'id': 6774, 'synset': 'condensation_pump.n.01', 'name': 'condensation_pump'}, {'id': 6775, 'synset': 'condenser.n.04', 'name': 'condenser'}, {'id': 6776, 'synset': 'condenser.n.03', 'name': 'condenser'}, {'id': 6777, 'synset': 'condenser.n.02', 'name': 'condenser'}, {'id': 6778, 'synset': 'condenser_microphone.n.01', 'name': 'condenser_microphone'}, {'id': 6779, 'synset': 'condominium.n.02', 'name': 'condominium'}, {'id': 6780, 'synset': 'condominium.n.01', 'name': 'condominium'}, {'id': 6781, 'synset': 'conductor.n.04', 'name': 'conductor'}, {'id': 6782, 'synset': 'cone_clutch.n.01', 'name': 'cone_clutch'}, {'id': 6783, 'synset': 'confectionery.n.02', 'name': 'confectionery'}, {'id': 6784, 'synset': 'conference_center.n.01', 'name': 'conference_center'}, {'id': 6785, 'synset': 'conference_room.n.01', 'name': 'conference_room'}, {'id': 6786, 'synset': 'conference_table.n.01', 'name': 'conference_table'}, {'id': 6787, 'synset': 'confessional.n.01', 'name': 'confessional'}, {'id': 6788, 'synset': 'conformal_projection.n.01', 'name': 'conformal_projection'}, {'id': 6789, 'synset': 'congress_boot.n.01', 'name': 'congress_boot'}, {'id': 6790, 'synset': 'conic_projection.n.01', 'name': 'conic_projection'}, {'id': 6791, 'synset': 'connecting_rod.n.01', 'name': 'connecting_rod'}, {'id': 6792, 'synset': 'connecting_room.n.01', 'name': 'connecting_room'}, {'id': 6793, 'synset': 'connection.n.03', 'name': 'connection'}, {'id': 6794, 'synset': 'conning_tower.n.02', 'name': 'conning_tower'}, {'id': 6795, 'synset': 'conning_tower.n.01', 'name': 'conning_tower'}, {'id': 6796, 'synset': 'conservatory.n.03', 'name': 'conservatory'}, {'id': 6797, 'synset': 'conservatory.n.02', 'name': 'conservatory'}, {'id': 6798, 'synset': 'console.n.03', 'name': 'console'}, {'id': 6799, 'synset': 'console.n.02', 'name': 'console'}, {'id': 6800, 'synset': 'console_table.n.01', 'name': 'console_table'}, {'id': 6801, 'synset': 'consulate.n.01', 'name': 'consulate'}, {'id': 6802, 'synset': 'contact.n.07', 'name': 'contact'}, {'id': 6803, 'synset': 'contact.n.09', 'name': 'contact'}, {'id': 6804, 'synset': 'container.n.01', 'name': 'container'}, {'id': 6805, 'synset': 'container_ship.n.01', 'name': 'container_ship'}, {'id': 6806, 'synset': 'containment.n.02', 'name': 'containment'}, {'id': 6807, 'synset': 'contrabassoon.n.01', 'name': 'contrabassoon'}, {'id': 6808, 'synset': 'control_center.n.01', 'name': 'control_center'}, {'id': 6809, 'synset': 'control_circuit.n.01', 'name': 'control_circuit'}, {'id': 6810, 'synset': 'control_key.n.01', 'name': 'control_key'}, {'id': 6811, 'synset': 'control_panel.n.01', 'name': 'control_panel'}, {'id': 6812, 'synset': 'control_rod.n.01', 'name': 'control_rod'}, {'id': 6813, 'synset': 'control_room.n.01', 'name': 'control_room'}, {'id': 6814, 'synset': 'control_system.n.01', 'name': 'control_system'}, {'id': 6815, 'synset': 'control_tower.n.01', 'name': 'control_tower'}, {'id': 6816, 'synset': 'convector.n.01', 'name': 'convector'}, {'id': 6817, 'synset': 'convenience_store.n.01', 'name': 'convenience_store'}, {'id': 6818, 'synset': 'convent.n.01', 'name': 'convent'}, {'id': 6819, 'synset': 'conventicle.n.02', 'name': 'conventicle'}, {'id': 6820, 'synset': 'converging_lens.n.01', 'name': 'converging_lens'}, {'id': 6821, 'synset': 'converter.n.01', 'name': 'converter'}, {'id': 6822, 'synset': 'conveyance.n.03', 'name': 'conveyance'}, {'id': 6823, 'synset': 'conveyer_belt.n.01', 'name': 'conveyer_belt'}, {'id': 6824, 'synset': 'cookfire.n.01', 'name': 'cookfire'}, {'id': 6825, 'synset': 'cookhouse.n.02', 'name': 'cookhouse'}, {'id': 6826, 'synset': 'cookie_cutter.n.01', 'name': 'cookie_cutter'}, {'id': 6827, 'synset': 'cookie_jar.n.01', 'name': 'cookie_jar'}, {'id': 6828, 'synset': 'cookie_sheet.n.01', 'name': 'cookie_sheet'}, {'id': 6829, 'synset': 'cookstove.n.01', 'name': 'cookstove'}, {'id': 6830, 'synset': 'coolant_system.n.01', 'name': 'coolant_system'}, {'id': 6831, 'synset': 'cooling_system.n.02', 'name': 'cooling_system'}, {'id': 6832, 'synset': 'cooling_system.n.01', 'name': 'cooling_system'}, {'id': 6833, 'synset': 'cooling_tower.n.01', 'name': 'cooling_tower'}, {'id': 6834, 'synset': 'coonskin_cap.n.01', 'name': 'coonskin_cap'}, {'id': 6835, 'synset': 'cope.n.02', 'name': 'cope'}, {'id': 6836, 'synset': 'coping_saw.n.01', 'name': 'coping_saw'}, {'id': 6837, 'synset': 'copperware.n.01', 'name': 'copperware'}, {'id': 6838, 'synset': 'copyholder.n.01', 'name': 'copyholder'}, {'id': 6839, 'synset': 'coquille.n.02', 'name': 'coquille'}, {'id': 6840, 'synset': 'coracle.n.01', 'name': 'coracle'}, {'id': 6841, 'synset': 'corbel.n.01', 'name': 'corbel'}, {'id': 6842, 'synset': 'corbel_arch.n.01', 'name': 'corbel_arch'}, {'id': 6843, 'synset': 'corbel_step.n.01', 'name': 'corbel_step'}, {'id': 6844, 'synset': 'corbie_gable.n.01', 'name': 'corbie_gable'}, {'id': 6845, 'synset': 'cord.n.04', 'name': 'cord'}, {'id': 6846, 'synset': 'cord.n.03', 'name': 'cord'}, {'id': 6847, 'synset': 'cordage.n.02', 'name': 'cordage'}, {'id': 6848, 'synset': 'cords.n.01', 'name': 'cords'}, {'id': 6849, 'synset': 'core.n.10', 'name': 'core'}, {'id': 6850, 'synset': 'core_bit.n.01', 'name': 'core_bit'}, {'id': 6851, 'synset': 'core_drill.n.01', 'name': 'core_drill'}, {'id': 6852, 'synset': 'corer.n.01', 'name': 'corer'}, {'id': 6853, 'synset': 'corker.n.02', 'name': 'corker'}, {'id': 6854, 'synset': 'corncrib.n.01', 'name': 'corncrib'}, {'id': 6855, 'synset': 'corner.n.11', 'name': 'corner'}, {'id': 6856, 'synset': 'corner.n.03', 'name': 'corner'}, {'id': 6857, 'synset': 'corner_post.n.01', 'name': 'corner_post'}, {'id': 6858, 'synset': 'cornice.n.03', 'name': 'cornice'}, {'id': 6859, 'synset': 'cornice.n.02', 'name': 'cornice'}, {'id': 6860, 'synset': 'correctional_institution.n.01', 'name': 'correctional_institution'}, {'id': 6861, 'synset': 'corrugated_fastener.n.01', 'name': 'corrugated_fastener'}, {'id': 6862, 'synset': 'corselet.n.01', 'name': 'corselet'}, {'id': 6863, 'synset': 'cosmetic.n.01', 'name': 'cosmetic'}, {'id': 6864, 'synset': 'cosmotron.n.01', 'name': 'cosmotron'}, {'id': 6865, 'synset': 'costume.n.01', 'name': 'costume'}, {'id': 6866, 'synset': 'costume.n.02', 'name': 'costume'}, {'id': 6867, 'synset': 'costume.n.03', 'name': 'costume'}, {'id': 6868, 'synset': 'cosy.n.01', 'name': 'cosy'}, {'id': 6869, 'synset': 'cot.n.03', 'name': 'cot'}, {'id': 6870, 'synset': 'cottage_tent.n.01', 'name': 'cottage_tent'}, {'id': 6871, 'synset': 'cotter.n.03', 'name': 'cotter'}, {'id': 6872, 'synset': 'cotter_pin.n.01', 'name': 'cotter_pin'}, {'id': 6873, 'synset': 'cotton.n.02', 'name': 'cotton'}, {'id': 6874, 'synset': 'cotton_flannel.n.01', 'name': 'cotton_flannel'}, {'id': 6875, 'synset': 'cotton_mill.n.01', 'name': 'cotton_mill'}, {'id': 6876, 'synset': 'couch.n.03', 'name': 'couch'}, {'id': 6877, 'synset': 'couch.n.02', 'name': 'couch'}, {'id': 6878, 'synset': 'couchette.n.01', 'name': 'couchette'}, {'id': 6879, 'synset': 'coude_telescope.n.01', 'name': 'coude_telescope'}, {'id': 6880, 'synset': 'counter.n.01', 'name': 'counter'}, {'id': 6881, 'synset': 'counter.n.03', 'name': 'counter'}, {'id': 6882, 'synset': 'counter.n.02', 'name': 'counter'}, {'id': 6883, 'synset': 'counterbore.n.01', 'name': 'counterbore'}, {'id': 6884, 'synset': 'counter_tube.n.01', 'name': 'counter_tube'}, {'id': 6885, 'synset': 'country_house.n.01', 'name': 'country_house'}, {'id': 6886, 'synset': 'country_store.n.01', 'name': 'country_store'}, {'id': 6887, 'synset': 'coupe.n.01', 'name': 'coupe'}, {'id': 6888, 'synset': 'coupling.n.02', 'name': 'coupling'}, {'id': 6889, 'synset': 'court.n.10', 'name': 'court'}, {'id': 6890, 'synset': 'court.n.04', 'name': 'court'}, {'id': 6891, 'synset': 'court.n.02', 'name': 'court'}, {'id': 6892, 'synset': 'court.n.09', 'name': 'court'}, {'id': 6893, 'synset': 'courtelle.n.01', 'name': 'Courtelle'}, {'id': 6894, 'synset': 'courthouse.n.02', 'name': 'courthouse'}, {'id': 6895, 'synset': 'courthouse.n.01', 'name': 'courthouse'}, {'id': 6896, 'synset': 'covered_bridge.n.01', 'name': 'covered_bridge'}, {'id': 6897, 'synset': 'covered_couch.n.01', 'name': 'covered_couch'}, {'id': 6898, 'synset': 'covered_wagon.n.01', 'name': 'covered_wagon'}, {'id': 6899, 'synset': 'covering.n.02', 'name': 'covering'}, {'id': 6900, 'synset': 'coverlet.n.01', 'name': 'coverlet'}, {'id': 6901, 'synset': 'cover_plate.n.01', 'name': 'cover_plate'}, {'id': 6902, 'synset': 'cowbarn.n.01', 'name': 'cowbarn'}, {'id': 6903, 'synset': 'cowboy_boot.n.01', 'name': 'cowboy_boot'}, {'id': 6904, 'synset': 'cowhide.n.03', 'name': 'cowhide'}, {'id': 6905, 'synset': 'cowl.n.02', 'name': 'cowl'}, {'id': 6906, 'synset': 'cow_pen.n.01', 'name': 'cow_pen'}, {'id': 6907, 'synset': 'cpu_board.n.01', 'name': 'CPU_board'}, {'id': 6908, 'synset': 'crackle.n.02', 'name': 'crackle'}, {'id': 6909, 'synset': 'cradle.n.01', 'name': 'cradle'}, {'id': 6910, 'synset': 'craft.n.02', 'name': 'craft'}, {'id': 6911, 'synset': 'cramp.n.03', 'name': 'cramp'}, {'id': 6912, 'synset': 'crampon.n.02', 'name': 'crampon'}, {'id': 6913, 'synset': 'crampon.n.01', 'name': 'crampon'}, {'id': 6914, 'synset': 'crane.n.04', 'name': 'crane'}, {'id': 6915, 'synset': 'craniometer.n.01', 'name': 'craniometer'}, {'id': 6916, 'synset': 'crank.n.04', 'name': 'crank'}, {'id': 6917, 'synset': 'crankcase.n.01', 'name': 'crankcase'}, {'id': 6918, 'synset': 'crankshaft.n.01', 'name': 'crankshaft'}, {'id': 6919, 'synset': 'crash_barrier.n.01', 'name': 'crash_barrier'}, {'id': 6920, 'synset': 'crash_helmet.n.01', 'name': 'crash_helmet'}, {'id': 6921, 'synset': 'cravat.n.01', 'name': 'cravat'}, {'id': 6922, 'synset': 'crazy_quilt.n.01', 'name': 'crazy_quilt'}, {'id': 6923, 'synset': 'cream.n.03', 'name': 'cream'}, {'id': 6924, 'synset': 'creche.n.01', 'name': 'creche'}, {'id': 6925, 'synset': 'creche.n.02', 'name': 'creche'}, {'id': 6926, 'synset': 'credenza.n.01', 'name': 'credenza'}, {'id': 6927, 'synset': 'creel.n.01', 'name': 'creel'}, {'id': 6928, 'synset': 'crematory.n.02', 'name': 'crematory'}, {'id': 6929, 'synset': 'crematory.n.01', 'name': 'crematory'}, {'id': 6930, 'synset': 'crepe.n.03', 'name': 'crepe'}, {'id': 6931, 'synset': 'crepe_de_chine.n.01', 'name': 'crepe_de_Chine'}, {'id': 6932, 'synset': 'crescent_wrench.n.01', 'name': 'crescent_wrench'}, {'id': 6933, 'synset': 'cretonne.n.01', 'name': 'cretonne'}, {'id': 6934, 'synset': 'crib.n.03', 'name': 'crib'}, {'id': 6935, 'synset': 'cricket_ball.n.01', 'name': 'cricket_ball'}, {'id': 6936, 'synset': 'cricket_bat.n.01', 'name': 'cricket_bat'}, {'id': 6937, 'synset': 'cricket_equipment.n.01', 'name': 'cricket_equipment'}, {'id': 6938, 'synset': 'cringle.n.01', 'name': 'cringle'}, {'id': 6939, 'synset': 'crinoline.n.03', 'name': 'crinoline'}, {'id': 6940, 'synset': 'crinoline.n.02', 'name': 'crinoline'}, {'id': 6941, 'synset': 'crochet_needle.n.01', 'name': 'crochet_needle'}, {'id': 6942, 'synset': 'crock_pot.n.01', 'name': 'Crock_Pot'}, {'id': 6943, 'synset': 'crook.n.03', 'name': 'crook'}, {'id': 6944, 'synset': 'crookes_radiometer.n.01', 'name': 'Crookes_radiometer'}, {'id': 6945, 'synset': 'crookes_tube.n.01', 'name': 'Crookes_tube'}, {'id': 6946, 'synset': 'croquet_ball.n.01', 'name': 'croquet_ball'}, {'id': 6947, 'synset': 'croquet_equipment.n.01', 'name': 'croquet_equipment'}, {'id': 6948, 'synset': 'croquet_mallet.n.01', 'name': 'croquet_mallet'}, {'id': 6949, 'synset': 'cross.n.01', 'name': 'cross'}, {'id': 6950, 'synset': 'crossbar.n.03', 'name': 'crossbar'}, {'id': 6951, 'synset': 'crossbar.n.02', 'name': 'crossbar'}, {'id': 6952, 'synset': 'crossbench.n.01', 'name': 'crossbench'}, {'id': 6953, 'synset': 'cross_bit.n.01', 'name': 'cross_bit'}, {'id': 6954, 'synset': 'crossbow.n.01', 'name': 'crossbow'}, {'id': 6955, 'synset': 'crosscut_saw.n.01', 'name': 'crosscut_saw'}, {'id': 6956, 'synset': 'crossjack.n.01', 'name': 'crossjack'}, {'id': 6957, 'synset': 'crosspiece.n.02', 'name': 'crosspiece'}, {'id': 6958, 'synset': 'crotchet.n.04', 'name': 'crotchet'}, {'id': 6959, 'synset': "croupier's_rake.n.01", 'name': "croupier's_rake"}, {'id': 6960, 'synset': 'crown.n.11', 'name': 'crown'}, {'id': 6961, 'synset': 'crown_jewels.n.01', 'name': 'crown_jewels'}, {'id': 6962, 'synset': 'crown_lens.n.01', 'name': 'crown_lens'}, {'id': 6963, 'synset': "crow's_nest.n.01", 'name': "crow's_nest"}, {'id': 6964, 'synset': 'crucible.n.01', 'name': 'crucible'}, {'id': 6965, 'synset': 'cruet.n.01', 'name': 'cruet'}, {'id': 6966, 'synset': 'cruet-stand.n.01', 'name': 'cruet-stand'}, {'id': 6967, 'synset': 'cruise_control.n.01', 'name': 'cruise_control'}, {'id': 6968, 'synset': 'cruise_missile.n.01', 'name': 'cruise_missile'}, {'id': 6969, 'synset': 'cruiser.n.02', 'name': 'cruiser'}, {'id': 6970, 'synset': 'crupper.n.01', 'name': 'crupper'}, {'id': 6971, 'synset': 'cruse.n.01', 'name': 'cruse'}, {'id': 6972, 'synset': 'crusher.n.01', 'name': 'crusher'}, {'id': 6973, 'synset': 'cryometer.n.01', 'name': 'cryometer'}, {'id': 6974, 'synset': 'cryoscope.n.01', 'name': 'cryoscope'}, {'id': 6975, 'synset': 'cryostat.n.01', 'name': 'cryostat'}, {'id': 6976, 'synset': 'crypt.n.01', 'name': 'crypt'}, {'id': 6977, 'synset': 'crystal.n.06', 'name': 'crystal'}, {'id': 6978, 'synset': 'crystal_detector.n.01', 'name': 'crystal_detector'}, {'id': 6979, 'synset': 'crystal_microphone.n.01', 'name': 'crystal_microphone'}, {'id': 6980, 'synset': 'crystal_oscillator.n.01', 'name': 'crystal_oscillator'}, {'id': 6981, 'synset': 'crystal_set.n.01', 'name': 'crystal_set'}, {'id': 6982, 'synset': 'cubitiere.n.01', 'name': 'cubitiere'}, {'id': 6983, 'synset': 'cucking_stool.n.01', 'name': 'cucking_stool'}, {'id': 6984, 'synset': 'cuckoo_clock.n.01', 'name': 'cuckoo_clock'}, {'id': 6985, 'synset': 'cuddy.n.01', 'name': 'cuddy'}, {'id': 6986, 'synset': 'cudgel.n.01', 'name': 'cudgel'}, {'id': 6987, 'synset': 'cue.n.04', 'name': 'cue'}, {'id': 6988, 'synset': 'cue_ball.n.01', 'name': 'cue_ball'}, {'id': 6989, 'synset': 'cuff.n.01', 'name': 'cuff'}, {'id': 6990, 'synset': 'cuirass.n.01', 'name': 'cuirass'}, {'id': 6991, 'synset': 'cuisse.n.01', 'name': 'cuisse'}, {'id': 6992, 'synset': 'cul.n.01', 'name': 'cul'}, {'id': 6993, 'synset': 'culdoscope.n.01', 'name': 'culdoscope'}, {'id': 6994, 'synset': 'cullis.n.01', 'name': 'cullis'}, {'id': 6995, 'synset': 'culotte.n.01', 'name': 'culotte'}, {'id': 6996, 'synset': 'cultivator.n.02', 'name': 'cultivator'}, {'id': 6997, 'synset': 'culverin.n.02', 'name': 'culverin'}, {'id': 6998, 'synset': 'culverin.n.01', 'name': 'culverin'}, {'id': 6999, 'synset': 'culvert.n.01', 'name': 'culvert'}, {'id': 7000, 'synset': 'cup_hook.n.01', 'name': 'cup_hook'}, {'id': 7001, 'synset': 'cupola.n.02', 'name': 'cupola'}, {'id': 7002, 'synset': 'cupola.n.01', 'name': 'cupola'}, {'id': 7003, 'synset': 'curb.n.02', 'name': 'curb'}, {'id': 7004, 'synset': 'curb_roof.n.01', 'name': 'curb_roof'}, {'id': 7005, 'synset': 'curbstone.n.01', 'name': 'curbstone'}, {'id': 7006, 'synset': 'curette.n.01', 'name': 'curette'}, {'id': 7007, 'synset': 'currycomb.n.01', 'name': 'currycomb'}, {'id': 7008, 'synset': 'cursor.n.01', 'name': 'cursor'}, {'id': 7009, 'synset': 'customhouse.n.01', 'name': 'customhouse'}, {'id': 7010, 'synset': 'cutaway.n.01', 'name': 'cutaway'}, {'id': 7011, 'synset': 'cutlas.n.01', 'name': 'cutlas'}, {'id': 7012, 'synset': 'cutoff.n.03', 'name': 'cutoff'}, {'id': 7013, 'synset': 'cutout.n.01', 'name': 'cutout'}, {'id': 7014, 'synset': 'cutter.n.06', 'name': 'cutter'}, {'id': 7015, 'synset': 'cutter.n.05', 'name': 'cutter'}, {'id': 7016, 'synset': 'cutting_implement.n.01', 'name': 'cutting_implement'}, {'id': 7017, 'synset': 'cutting_room.n.01', 'name': 'cutting_room'}, {'id': 7018, 'synset': 'cutty_stool.n.01', 'name': 'cutty_stool'}, {'id': 7019, 'synset': 'cutwork.n.01', 'name': 'cutwork'}, {'id': 7020, 'synset': 'cybercafe.n.01', 'name': 'cybercafe'}, {'id': 7021, 'synset': 'cyclopean_masonry.n.01', 'name': 'cyclopean_masonry'}, {'id': 7022, 'synset': 'cyclostyle.n.01', 'name': 'cyclostyle'}, {'id': 7023, 'synset': 'cyclotron.n.01', 'name': 'cyclotron'}, {'id': 7024, 'synset': 'cylinder.n.03', 'name': 'cylinder'}, {'id': 7025, 'synset': 'cylinder_lock.n.01', 'name': 'cylinder_lock'}, {'id': 7026, 'synset': 'dacha.n.01', 'name': 'dacha'}, {'id': 7027, 'synset': 'dacron.n.01', 'name': 'Dacron'}, {'id': 7028, 'synset': 'dado.n.02', 'name': 'dado'}, {'id': 7029, 'synset': 'dado_plane.n.01', 'name': 'dado_plane'}, {'id': 7030, 'synset': 'dairy.n.01', 'name': 'dairy'}, {'id': 7031, 'synset': 'dais.n.01', 'name': 'dais'}, {'id': 7032, 'synset': 'daisy_print_wheel.n.01', 'name': 'daisy_print_wheel'}, {'id': 7033, 'synset': 'daisywheel_printer.n.01', 'name': 'daisywheel_printer'}, {'id': 7034, 'synset': 'dam.n.01', 'name': 'dam'}, {'id': 7035, 'synset': 'damask.n.02', 'name': 'damask'}, {'id': 7036, 'synset': 'dampener.n.01', 'name': 'dampener'}, {'id': 7037, 'synset': 'damper.n.02', 'name': 'damper'}, {'id': 7038, 'synset': 'damper_block.n.01', 'name': 'damper_block'}, {'id': 7039, 'synset': 'dark_lantern.n.01', 'name': 'dark_lantern'}, {'id': 7040, 'synset': 'darkroom.n.01', 'name': 'darkroom'}, {'id': 7041, 'synset': 'darning_needle.n.01', 'name': 'darning_needle'}, {'id': 7042, 'synset': 'dart.n.02', 'name': 'dart'}, {'id': 7043, 'synset': 'dart.n.01', 'name': 'dart'}, {'id': 7044, 'synset': 'dashboard.n.02', 'name': 'dashboard'}, {'id': 7045, 'synset': 'dashiki.n.01', 'name': 'dashiki'}, {'id': 7046, 'synset': 'dash-pot.n.01', 'name': 'dash-pot'}, {'id': 7047, 'synset': 'data_converter.n.01', 'name': 'data_converter'}, {'id': 7048, 'synset': 'data_input_device.n.01', 'name': 'data_input_device'}, {'id': 7049, 'synset': 'data_multiplexer.n.01', 'name': 'data_multiplexer'}, {'id': 7050, 'synset': 'data_system.n.01', 'name': 'data_system'}, {'id': 7051, 'synset': 'davenport.n.03', 'name': 'davenport'}, {'id': 7052, 'synset': 'davenport.n.02', 'name': 'davenport'}, {'id': 7053, 'synset': 'davit.n.01', 'name': 'davit'}, {'id': 7054, 'synset': 'daybed.n.01', 'name': 'daybed'}, {'id': 7055, 'synset': 'daybook.n.02', 'name': 'daybook'}, {'id': 7056, 'synset': 'day_nursery.n.01', 'name': 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'declinometer'}, {'id': 7073, 'synset': 'decoder.n.02', 'name': 'decoder'}, {'id': 7074, 'synset': 'decolletage.n.01', 'name': 'decolletage'}, {'id': 7075, 'synset': 'decoupage.n.01', 'name': 'decoupage'}, {'id': 7076, 'synset': 'dedicated_file_server.n.01', 'name': 'dedicated_file_server'}, {'id': 7077, 'synset': 'deep-freeze.n.01', 'name': 'deep-freeze'}, {'id': 7078, 'synset': 'deerstalker.n.01', 'name': 'deerstalker'}, {'id': 7079, 'synset': 'defense_system.n.01', 'name': 'defense_system'}, {'id': 7080, 'synset': 'defensive_structure.n.01', 'name': 'defensive_structure'}, {'id': 7081, 'synset': 'defibrillator.n.01', 'name': 'defibrillator'}, {'id': 7082, 'synset': 'defilade.n.01', 'name': 'defilade'}, {'id': 7083, 'synset': 'deflector.n.01', 'name': 'deflector'}, {'id': 7084, 'synset': 'delayed_action.n.01', 'name': 'delayed_action'}, {'id': 7085, 'synset': 'delay_line.n.01', 'name': 'delay_line'}, {'id': 7086, 'synset': 'delft.n.01', 'name': 'delft'}, {'id': 7087, 'synset': 'delicatessen.n.02', 'name': 'delicatessen'}, {'id': 7088, 'synset': 'delivery_truck.n.01', 'name': 'delivery_truck'}, {'id': 7089, 'synset': 'delta_wing.n.01', 'name': 'delta_wing'}, {'id': 7090, 'synset': 'demijohn.n.01', 'name': 'demijohn'}, {'id': 7091, 'synset': 'demitasse.n.02', 'name': 'demitasse'}, {'id': 7092, 'synset': 'den.n.04', 'name': 'den'}, {'id': 7093, 'synset': 'denim.n.02', 'name': 'denim'}, {'id': 7094, 'synset': 'densimeter.n.01', 'name': 'densimeter'}, {'id': 7095, 'synset': 'densitometer.n.01', 'name': 'densitometer'}, {'id': 7096, 'synset': 'dental_appliance.n.01', 'name': 'dental_appliance'}, {'id': 7097, 'synset': 'dental_implant.n.01', 'name': 'dental_implant'}, {'id': 7098, 'synset': "dentist's_drill.n.01", 'name': "dentist's_drill"}, {'id': 7099, 'synset': 'denture.n.01', 'name': 'denture'}, {'id': 7100, 'synset': 'deodorant.n.01', 'name': 'deodorant'}, {'id': 7101, 'synset': 'department_store.n.01', 'name': 'department_store'}, {'id': 7102, 'synset': 'departure_lounge.n.01', 'name': 'departure_lounge'}, {'id': 7103, 'synset': 'depilatory.n.02', 'name': 'depilatory'}, {'id': 7104, 'synset': 'depressor.n.03', 'name': 'depressor'}, {'id': 7105, 'synset': 'depth_finder.n.01', 'name': 'depth_finder'}, {'id': 7106, 'synset': 'depth_gauge.n.01', 'name': 'depth_gauge'}, {'id': 7107, 'synset': 'derrick.n.02', 'name': 'derrick'}, {'id': 7108, 'synset': 'derrick.n.01', 'name': 'derrick'}, {'id': 7109, 'synset': 'derringer.n.01', 'name': 'derringer'}, {'id': 7110, 'synset': 'desk_phone.n.01', 'name': 'desk_phone'}, {'id': 7111, 'synset': 'desktop_computer.n.01', 'name': 'desktop_computer'}, {'id': 7112, 'synset': 'dessert_spoon.n.01', 'name': 'dessert_spoon'}, {'id': 7113, 'synset': 'destroyer.n.01', 'name': 'destroyer'}, {'id': 7114, 'synset': 'destroyer_escort.n.01', 'name': 'destroyer_escort'}, {'id': 7115, 'synset': 'detached_house.n.01', 'name': 'detached_house'}, {'id': 7116, 'synset': 'detector.n.01', 'name': 'detector'}, {'id': 7117, 'synset': 'detector.n.03', 'name': 'detector'}, {'id': 7118, 'synset': 'detention_home.n.01', 'name': 'detention_home'}, {'id': 7119, 'synset': 'detonating_fuse.n.01', 'name': 'detonating_fuse'}, {'id': 7120, 'synset': 'detonator.n.01', 'name': 'detonator'}, {'id': 7121, 'synset': 'developer.n.02', 'name': 'developer'}, {'id': 7122, 'synset': 'device.n.01', 'name': 'device'}, {'id': 7123, 'synset': 'dewar_flask.n.01', 'name': 'Dewar_flask'}, {'id': 7124, 'synset': 'dhoti.n.01', 'name': 'dhoti'}, {'id': 7125, 'synset': 'dhow.n.01', 'name': 'dhow'}, {'id': 7126, 'synset': 'dial.n.04', 'name': 'dial'}, {'id': 7127, 'synset': 'dial.n.03', 'name': 'dial'}, {'id': 7128, 'synset': 'dial.n.02', 'name': 'dial'}, {'id': 7129, 'synset': 'dialog_box.n.01', 'name': 'dialog_box'}, {'id': 7130, 'synset': 'dial_telephone.n.01', 'name': 'dial_telephone'}, {'id': 7131, 'synset': 'dialyzer.n.01', 'name': 'dialyzer'}, {'id': 7132, 'synset': 'diamante.n.02', 'name': 'diamante'}, {'id': 7133, 'synset': 'diaper.n.02', 'name': 'diaper'}, {'id': 7134, 'synset': 'diaphone.n.01', 'name': 'diaphone'}, {'id': 7135, 'synset': 'diaphragm.n.01', 'name': 'diaphragm'}, {'id': 7136, 'synset': 'diaphragm.n.04', 'name': 'diaphragm'}, {'id': 7137, 'synset': 'diathermy_machine.n.01', 'name': 'diathermy_machine'}, {'id': 7138, 'synset': 'dibble.n.01', 'name': 'dibble'}, {'id': 7139, 'synset': 'dice_cup.n.01', 'name': 'dice_cup'}, {'id': 7140, 'synset': 'dicer.n.01', 'name': 'dicer'}, {'id': 7141, 'synset': 'dickey.n.02', 'name': 'dickey'}, {'id': 7142, 'synset': 'dickey.n.01', 'name': 'dickey'}, {'id': 7143, 'synset': 'dictaphone.n.01', 'name': 'Dictaphone'}, {'id': 7144, 'synset': 'die.n.03', 'name': 'die'}, {'id': 7145, 'synset': 'diesel.n.02', 'name': 'diesel'}, {'id': 7146, 'synset': 'diesel-electric_locomotive.n.01', 'name': 'diesel-electric_locomotive'}, {'id': 7147, 'synset': 'diesel-hydraulic_locomotive.n.01', 'name': 'diesel-hydraulic_locomotive'}, {'id': 7148, 'synset': 'diesel_locomotive.n.01', 'name': 'diesel_locomotive'}, {'id': 7149, 'synset': 'diestock.n.01', 'name': 'diestock'}, {'id': 7150, 'synset': 'differential_analyzer.n.01', 'name': 'differential_analyzer'}, {'id': 7151, 'synset': 'differential_gear.n.01', 'name': 'differential_gear'}, {'id': 7152, 'synset': 'diffuser.n.02', 'name': 'diffuser'}, {'id': 7153, 'synset': 'diffuser.n.01', 'name': 'diffuser'}, {'id': 7154, 'synset': 'digester.n.01', 'name': 'digester'}, {'id': 7155, 'synset': 'diggings.n.02', 'name': 'diggings'}, {'id': 7156, 'synset': 'digital-analog_converter.n.01', 'name': 'digital-analog_converter'}, {'id': 7157, 'synset': 'digital_audiotape.n.01', 'name': 'digital_audiotape'}, {'id': 7158, 'synset': 'digital_camera.n.01', 'name': 'digital_camera'}, {'id': 7159, 'synset': 'digital_clock.n.01', 'name': 'digital_clock'}, {'id': 7160, 'synset': 'digital_computer.n.01', 'name': 'digital_computer'}, {'id': 7161, 'synset': 'digital_display.n.01', 'name': 'digital_display'}, {'id': 7162, 'synset': 'digital_subscriber_line.n.01', 'name': 'digital_subscriber_line'}, {'id': 7163, 'synset': 'digital_voltmeter.n.01', 'name': 'digital_voltmeter'}, {'id': 7164, 'synset': 'digital_watch.n.01', 'name': 'digital_watch'}, {'id': 7165, 'synset': 'digitizer.n.01', 'name': 'digitizer'}, {'id': 7166, 'synset': 'dilator.n.03', 'name': 'dilator'}, {'id': 7167, 'synset': 'dildo.n.01', 'name': 'dildo'}, {'id': 7168, 'synset': 'dimity.n.01', 'name': 'dimity'}, {'id': 7169, 'synset': 'dimmer.n.01', 'name': 'dimmer'}, {'id': 7170, 'synset': 'diner.n.03', 'name': 'diner'}, {'id': 7171, 'synset': 'dinette.n.01', 'name': 'dinette'}, {'id': 7172, 'synset': 'dining_area.n.01', 'name': 'dining_area'}, {'id': 7173, 'synset': 'dining_car.n.01', 'name': 'dining_car'}, {'id': 7174, 'synset': 'dining-hall.n.01', 'name': 'dining-hall'}, {'id': 7175, 'synset': 'dining_room.n.01', 'name': 'dining_room'}, {'id': 7176, 'synset': 'dining-room_furniture.n.01', 'name': 'dining-room_furniture'}, {'id': 7177, 'synset': 'dining-room_table.n.01', 'name': 'dining-room_table'}, {'id': 7178, 'synset': 'dinner_bell.n.01', 'name': 'dinner_bell'}, {'id': 7179, 'synset': 'dinner_dress.n.01', 'name': 'dinner_dress'}, {'id': 7180, 'synset': 'dinner_napkin.n.01', 'name': 'dinner_napkin'}, {'id': 7181, 'synset': 'dinner_pail.n.01', 'name': 'dinner_pail'}, {'id': 7182, 'synset': 'dinner_table.n.01', 'name': 'dinner_table'}, {'id': 7183, 'synset': 'dinner_theater.n.01', 'name': 'dinner_theater'}, {'id': 7184, 'synset': 'diode.n.02', 'name': 'diode'}, {'id': 7185, 'synset': 'diode.n.01', 'name': 'diode'}, {'id': 7186, 'synset': 'dip.n.07', 'name': 'dip'}, {'id': 7187, 'synset': 'diplomatic_building.n.01', 'name': 'diplomatic_building'}, {'id': 7188, 'synset': 'dipole.n.02', 'name': 'dipole'}, {'id': 7189, 'synset': 'dipper.n.01', 'name': 'dipper'}, {'id': 7190, 'synset': 'dipstick.n.01', 'name': 'dipstick'}, {'id': 7191, 'synset': 'dip_switch.n.01', 'name': 'DIP_switch'}, {'id': 7192, 'synset': 'directional_antenna.n.01', 'name': 'directional_antenna'}, {'id': 7193, 'synset': 'directional_microphone.n.01', 'name': 'directional_microphone'}, {'id': 7194, 'synset': 'direction_finder.n.01', 'name': 'direction_finder'}, {'id': 7195, 'synset': 'dirk.n.01', 'name': 'dirk'}, {'id': 7196, 'synset': 'dirndl.n.02', 'name': 'dirndl'}, {'id': 7197, 'synset': 'dirndl.n.01', 'name': 'dirndl'}, {'id': 7198, 'synset': 'dirty_bomb.n.01', 'name': 'dirty_bomb'}, {'id': 7199, 'synset': 'discharge_lamp.n.01', 'name': 'discharge_lamp'}, {'id': 7200, 'synset': 'discharge_pipe.n.01', 'name': 'discharge_pipe'}, {'id': 7201, 'synset': 'disco.n.02', 'name': 'disco'}, {'id': 7202, 'synset': 'discount_house.n.01', 'name': 'discount_house'}, {'id': 7203, 'synset': 'discus.n.02', 'name': 'discus'}, {'id': 7204, 'synset': 'disguise.n.02', 'name': 'disguise'}, {'id': 7205, 'synset': 'dishpan.n.01', 'name': 'dishpan'}, {'id': 7206, 'synset': 'dish_rack.n.01', 'name': 'dish_rack'}, {'id': 7207, 'synset': 'disk.n.02', 'name': 'disk'}, {'id': 7208, 'synset': 'disk_brake.n.01', 'name': 'disk_brake'}, {'id': 7209, 'synset': 'disk_clutch.n.01', 'name': 'disk_clutch'}, {'id': 7210, 'synset': 'disk_controller.n.01', 'name': 'disk_controller'}, {'id': 7211, 'synset': 'disk_drive.n.01', 'name': 'disk_drive'}, {'id': 7212, 'synset': 'diskette.n.01', 'name': 'diskette'}, {'id': 7213, 'synset': 'disk_harrow.n.01', 'name': 'disk_harrow'}, {'id': 7214, 'synset': 'dispatch_case.n.01', 'name': 'dispatch_case'}, {'id': 7215, 'synset': 'dispensary.n.01', 'name': 'dispensary'}, {'id': 7216, 'synset': 'display.n.06', 'name': 'display'}, {'id': 7217, 'synset': 'display_adapter.n.01', 'name': 'display_adapter'}, {'id': 7218, 'synset': 'display_panel.n.01', 'name': 'display_panel'}, {'id': 7219, 'synset': 'display_window.n.01', 'name': 'display_window'}, {'id': 7220, 'synset': 'disposal.n.04', 'name': 'disposal'}, {'id': 7221, 'synset': 'disrupting_explosive.n.01', 'name': 'disrupting_explosive'}, {'id': 7222, 'synset': 'distaff.n.02', 'name': 'distaff'}, {'id': 7223, 'synset': 'distillery.n.01', 'name': 'distillery'}, {'id': 7224, 'synset': 'distributor.n.04', 'name': 'distributor'}, {'id': 7225, 'synset': 'distributor_cam.n.01', 'name': 'distributor_cam'}, {'id': 7226, 'synset': 'distributor_cap.n.01', 'name': 'distributor_cap'}, {'id': 7227, 'synset': 'distributor_housing.n.01', 'name': 'distributor_housing'}, {'id': 7228, 'synset': 'distributor_point.n.01', 'name': 'distributor_point'}, {'id': 7229, 'synset': 'ditch.n.01', 'name': 'ditch'}, {'id': 7230, 'synset': 'ditch_spade.n.01', 'name': 'ditch_spade'}, {'id': 7231, 'synset': 'ditty_bag.n.01', 'name': 'ditty_bag'}, {'id': 7232, 'synset': 'divan.n.01', 'name': 'divan'}, {'id': 7233, 'synset': 'divan.n.04', 'name': 'divan'}, {'id': 7234, 'synset': 'dive_bomber.n.01', 'name': 'dive_bomber'}, {'id': 7235, 'synset': 'diverging_lens.n.01', 'name': 'diverging_lens'}, {'id': 7236, 'synset': 'divided_highway.n.01', 'name': 'divided_highway'}, {'id': 7237, 'synset': 'divider.n.04', 'name': 'divider'}, {'id': 7238, 'synset': 'diving_bell.n.01', 'name': 'diving_bell'}, {'id': 7239, 'synset': 'divining_rod.n.01', 'name': 'divining_rod'}, {'id': 7240, 'synset': 'diving_suit.n.01', 'name': 'diving_suit'}, {'id': 7241, 'synset': 'dixie.n.02', 'name': 'dixie'}, {'id': 7242, 'synset': 'dock.n.05', 'name': 'dock'}, {'id': 7243, 'synset': 'doeskin.n.02', 'name': 'doeskin'}, {'id': 7244, 'synset': 'dogcart.n.01', 'name': 'dogcart'}, {'id': 7245, 'synset': 'doggie_bag.n.01', 'name': 'doggie_bag'}, {'id': 7246, 'synset': 'dogsled.n.01', 'name': 'dogsled'}, {'id': 7247, 'synset': 'dog_wrench.n.01', 'name': 'dog_wrench'}, {'id': 7248, 'synset': 'doily.n.01', 'name': 'doily'}, {'id': 7249, 'synset': 'dolly.n.02', 'name': 'dolly'}, {'id': 7250, 'synset': 'dolman.n.02', 'name': 'dolman'}, {'id': 7251, 'synset': 'dolman.n.01', 'name': 'dolman'}, {'id': 7252, 'synset': 'dolman_sleeve.n.01', 'name': 'dolman_sleeve'}, {'id': 7253, 'synset': 'dolmen.n.01', 'name': 'dolmen'}, {'id': 7254, 'synset': 'dome.n.04', 'name': 'dome'}, {'id': 7255, 'synset': 'dome.n.03', 'name': 'dome'}, {'id': 7256, 'synset': 'domino.n.03', 'name': 'domino'}, {'id': 7257, 'synset': 'dongle.n.01', 'name': 'dongle'}, {'id': 7258, 'synset': 'donkey_jacket.n.01', 'name': 'donkey_jacket'}, {'id': 7259, 'synset': 'door.n.01', 'name': 'door'}, {'id': 7260, 'synset': 'door.n.05', 'name': 'door'}, {'id': 7261, 'synset': 'door.n.04', 'name': 'door'}, {'id': 7262, 'synset': 'doorbell.n.01', 'name': 'doorbell'}, {'id': 7263, 'synset': 'doorframe.n.01', 'name': 'doorframe'}, {'id': 7264, 'synset': 'doorjamb.n.01', 'name': 'doorjamb'}, {'id': 7265, 'synset': 'doorlock.n.01', 'name': 'doorlock'}, {'id': 7266, 'synset': 'doornail.n.01', 'name': 'doornail'}, {'id': 7267, 'synset': 'doorplate.n.01', 'name': 'doorplate'}, {'id': 7268, 'synset': 'doorsill.n.01', 'name': 'doorsill'}, {'id': 7269, 'synset': 'doorstop.n.01', 'name': 'doorstop'}, {'id': 7270, 'synset': 'doppler_radar.n.01', 'name': 'Doppler_radar'}, {'id': 7271, 'synset': 'dormer.n.01', 'name': 'dormer'}, {'id': 7272, 'synset': 'dormer_window.n.01', 'name': 'dormer_window'}, {'id': 7273, 'synset': 'dormitory.n.01', 'name': 'dormitory'}, {'id': 7274, 'synset': 'dormitory.n.02', 'name': 'dormitory'}, {'id': 7275, 'synset': 'dosemeter.n.01', 'name': 'dosemeter'}, {'id': 7276, 'synset': 'dossal.n.01', 'name': 'dossal'}, {'id': 7277, 'synset': 'dot_matrix_printer.n.01', 'name': 'dot_matrix_printer'}, {'id': 7278, 'synset': 'double_bed.n.01', 'name': 'double_bed'}, {'id': 7279, 'synset': 'double-bitted_ax.n.01', 'name': 'double-bitted_ax'}, {'id': 7280, 'synset': 'double_boiler.n.01', 'name': 'double_boiler'}, {'id': 7281, 'synset': 'double-breasted_jacket.n.01', 'name': 'double-breasted_jacket'}, {'id': 7282, 'synset': 'double-breasted_suit.n.01', 'name': 'double-breasted_suit'}, {'id': 7283, 'synset': 'double_door.n.01', 'name': 'double_door'}, {'id': 7284, 'synset': 'double_glazing.n.01', 'name': 'double_glazing'}, {'id': 7285, 'synset': 'double-hung_window.n.01', 'name': 'double-hung_window'}, {'id': 7286, 'synset': 'double_knit.n.01', 'name': 'double_knit'}, {'id': 7287, 'synset': 'doubler.n.01', 'name': 'doubler'}, {'id': 7288, 'synset': 'double_reed.n.02', 'name': 'double_reed'}, {'id': 7289, 'synset': 'double-reed_instrument.n.01', 'name': 'double-reed_instrument'}, {'id': 7290, 'synset': 'doublet.n.01', 'name': 'doublet'}, {'id': 7291, 'synset': 'doubletree.n.01', 'name': 'doubletree'}, {'id': 7292, 'synset': 'douche.n.01', 'name': 'douche'}, {'id': 7293, 'synset': 'dovecote.n.01', 'name': 'dovecote'}, {'id': 7294, 'synset': "dover's_powder.n.01", 'name': "Dover's_powder"}, {'id': 7295, 'synset': 'dovetail.n.01', 'name': 'dovetail'}, {'id': 7296, 'synset': 'dovetail_plane.n.01', 'name': 'dovetail_plane'}, {'id': 7297, 'synset': 'dowel.n.01', 'name': 'dowel'}, {'id': 7298, 'synset': 'downstage.n.01', 'name': 'downstage'}, {'id': 7299, 'synset': 'drafting_instrument.n.01', 'name': 'drafting_instrument'}, {'id': 7300, 'synset': 'drafting_table.n.01', 'name': 'drafting_table'}, {'id': 7301, 'synset': 'dragunov.n.01', 'name': 'Dragunov'}, {'id': 7302, 'synset': 'drainage_ditch.n.01', 'name': 'drainage_ditch'}, {'id': 7303, 'synset': 'drainage_system.n.01', 'name': 'drainage_system'}, {'id': 7304, 'synset': 'drain_basket.n.01', 'name': 'drain_basket'}, {'id': 7305, 'synset': 'drainplug.n.01', 'name': 'drainplug'}, {'id': 7306, 'synset': 'drape.n.03', 'name': 'drape'}, {'id': 7307, 'synset': 'drapery.n.02', 'name': 'drapery'}, {'id': 7308, 'synset': 'drawbar.n.01', 'name': 'drawbar'}, {'id': 7309, 'synset': 'drawbridge.n.01', 'name': 'drawbridge'}, {'id': 7310, 'synset': 'drawing_chalk.n.01', 'name': 'drawing_chalk'}, {'id': 7311, 'synset': 'drawing_room.n.01', 'name': 'drawing_room'}, {'id': 7312, 'synset': 'drawing_room.n.02', 'name': 'drawing_room'}, {'id': 7313, 'synset': 'drawknife.n.01', 'name': 'drawknife'}, {'id': 7314, 'synset': 'drawstring_bag.n.01', 'name': 'drawstring_bag'}, {'id': 7315, 'synset': 'dray.n.01', 'name': 'dray'}, {'id': 7316, 'synset': 'dreadnought.n.01', 'name': 'dreadnought'}, {'id': 7317, 'synset': 'dredge.n.01', 'name': 'dredge'}, {'id': 7318, 'synset': 'dredger.n.01', 'name': 'dredger'}, {'id': 7319, 'synset': 'dredging_bucket.n.01', 'name': 'dredging_bucket'}, {'id': 7320, 'synset': 'dress_blues.n.01', 'name': 'dress_blues'}, {'id': 7321, 'synset': 'dressing.n.04', 'name': 'dressing'}, {'id': 7322, 'synset': 'dressing_case.n.01', 'name': 'dressing_case'}, {'id': 7323, 'synset': 'dressing_gown.n.01', 'name': 'dressing_gown'}, {'id': 7324, 'synset': 'dressing_room.n.01', 'name': 'dressing_room'}, {'id': 7325, 'synset': 'dressing_sack.n.01', 'name': 'dressing_sack'}, {'id': 7326, 'synset': 'dressing_table.n.01', 'name': 'dressing_table'}, {'id': 7327, 'synset': 'dress_rack.n.01', 'name': 'dress_rack'}, {'id': 7328, 'synset': 'dress_shirt.n.01', 'name': 'dress_shirt'}, {'id': 7329, 'synset': 'dress_uniform.n.01', 'name': 'dress_uniform'}, {'id': 7330, 'synset': 'drift_net.n.01', 'name': 'drift_net'}, {'id': 7331, 'synset': 'electric_drill.n.01', 'name': 'electric_drill'}, {'id': 7332, 'synset': 'drilling_platform.n.01', 'name': 'drilling_platform'}, {'id': 7333, 'synset': 'drill_press.n.01', 'name': 'drill_press'}, {'id': 7334, 'synset': 'drill_rig.n.01', 'name': 'drill_rig'}, {'id': 7335, 'synset': 'drinking_fountain.n.01', 'name': 'drinking_fountain'}, {'id': 7336, 'synset': 'drinking_vessel.n.01', 'name': 'drinking_vessel'}, {'id': 7337, 'synset': 'drip_loop.n.01', 'name': 'drip_loop'}, {'id': 7338, 'synset': 'drip_mat.n.01', 'name': 'drip_mat'}, {'id': 7339, 'synset': 'drip_pan.n.02', 'name': 'drip_pan'}, {'id': 7340, 'synset': 'dripping_pan.n.01', 'name': 'dripping_pan'}, {'id': 7341, 'synset': 'drip_pot.n.01', 'name': 'drip_pot'}, {'id': 7342, 'synset': 'drive.n.02', 'name': 'drive'}, {'id': 7343, 'synset': 'drive.n.10', 'name': 'drive'}, {'id': 7344, 'synset': 'drive_line.n.01', 'name': 'drive_line'}, {'id': 7345, 'synset': 'driver.n.05', 'name': 'driver'}, {'id': 7346, 'synset': 'driveshaft.n.01', 'name': 'driveshaft'}, {'id': 7347, 'synset': 'driveway.n.01', 'name': 'driveway'}, {'id': 7348, 'synset': 'driving_iron.n.01', 'name': 'driving_iron'}, {'id': 7349, 'synset': 'driving_wheel.n.01', 'name': 'driving_wheel'}, {'id': 7350, 'synset': 'drogue.n.04', 'name': 'drogue'}, {'id': 7351, 'synset': 'drogue_parachute.n.01', 'name': 'drogue_parachute'}, {'id': 7352, 'synset': 'drone.n.05', 'name': 'drone'}, {'id': 7353, 'synset': 'drop_arch.n.01', 'name': 'drop_arch'}, {'id': 7354, 'synset': 'drop_cloth.n.02', 'name': 'drop_cloth'}, {'id': 7355, 'synset': 'drop_curtain.n.01', 'name': 'drop_curtain'}, {'id': 7356, 'synset': 'drop_forge.n.01', 'name': 'drop_forge'}, {'id': 7357, 'synset': 'drop-leaf_table.n.01', 'name': 'drop-leaf_table'}, {'id': 7358, 'synset': 'droshky.n.01', 'name': 'droshky'}, {'id': 7359, 'synset': 'drove.n.03', 'name': 'drove'}, {'id': 7360, 'synset': 'drugget.n.01', 'name': 'drugget'}, {'id': 7361, 'synset': 'drugstore.n.01', 'name': 'drugstore'}, {'id': 7362, 'synset': 'drum.n.04', 'name': 'drum'}, {'id': 7363, 'synset': 'drum_brake.n.01', 'name': 'drum_brake'}, {'id': 7364, 'synset': 'drumhead.n.01', 'name': 'drumhead'}, {'id': 7365, 'synset': 'drum_printer.n.01', 'name': 'drum_printer'}, {'id': 7366, 'synset': 'drum_sander.n.01', 'name': 'drum_sander'}, {'id': 7367, 'synset': 'dry_battery.n.01', 'name': 'dry_battery'}, {'id': 7368, 'synset': 'dry-bulb_thermometer.n.01', 'name': 'dry-bulb_thermometer'}, {'id': 7369, 'synset': 'dry_cell.n.01', 'name': 'dry_cell'}, {'id': 7370, 'synset': 'dry_dock.n.01', 'name': 'dry_dock'}, {'id': 7371, 'synset': 'dryer.n.01', 'name': 'dryer'}, {'id': 7372, 'synset': 'dry_fly.n.01', 'name': 'dry_fly'}, {'id': 7373, 'synset': 'dry_kiln.n.01', 'name': 'dry_kiln'}, {'id': 7374, 'synset': 'dry_masonry.n.01', 'name': 'dry_masonry'}, {'id': 7375, 'synset': 'dry_point.n.02', 'name': 'dry_point'}, {'id': 7376, 'synset': 'dry_wall.n.02', 'name': 'dry_wall'}, {'id': 7377, 'synset': 'dual_scan_display.n.01', 'name': 'dual_scan_display'}, {'id': 7378, 'synset': 'duck.n.04', 'name': 'duck'}, {'id': 7379, 'synset': 'duckboard.n.01', 'name': 'duckboard'}, {'id': 7380, 'synset': 'duckpin.n.01', 'name': 'duckpin'}, {'id': 7381, 'synset': 'dudeen.n.01', 'name': 'dudeen'}, {'id': 7382, 'synset': 'duffel.n.02', 'name': 'duffel'}, {'id': 7383, 'synset': 'duffel_coat.n.01', 'name': 'duffel_coat'}, {'id': 7384, 'synset': 'dugout.n.01', 'name': 'dugout'}, {'id': 7385, 'synset': 'dugout_canoe.n.01', 'name': 'dugout_canoe'}, {'id': 7386, 'synset': 'dulciana.n.01', 'name': 'dulciana'}, {'id': 7387, 'synset': 'dulcimer.n.02', 'name': 'dulcimer'}, {'id': 7388, 'synset': 'dulcimer.n.01', 'name': 'dulcimer'}, {'id': 7389, 'synset': 'dumb_bomb.n.01', 'name': 'dumb_bomb'}, {'id': 7390, 'synset': 'dumbwaiter.n.01', 'name': 'dumbwaiter'}, {'id': 7391, 'synset': 'dumdum.n.01', 'name': 'dumdum'}, {'id': 7392, 'synset': 'dumpcart.n.01', 'name': 'dumpcart'}, {'id': 7393, 'synset': 'dump_truck.n.01', 'name': 'dump_truck'}, {'id': 7394, 'synset': 'dumpy_level.n.01', 'name': 'Dumpy_level'}, {'id': 7395, 'synset': 'dunce_cap.n.01', 'name': 'dunce_cap'}, {'id': 7396, 'synset': 'dune_buggy.n.01', 'name': 'dune_buggy'}, {'id': 7397, 'synset': 'dungeon.n.02', 'name': 'dungeon'}, {'id': 7398, 'synset': 'duplex_apartment.n.01', 'name': 'duplex_apartment'}, {'id': 7399, 'synset': 'duplex_house.n.01', 'name': 'duplex_house'}, {'id': 7400, 'synset': 'duplicator.n.01', 'name': 'duplicator'}, {'id': 7401, 'synset': 'dust_bag.n.01', 'name': 'dust_bag'}, {'id': 7402, 'synset': 'dustcloth.n.01', 'name': 'dustcloth'}, {'id': 7403, 'synset': 'dust_cover.n.03', 'name': 'dust_cover'}, {'id': 7404, 'synset': 'dust_cover.n.02', 'name': 'dust_cover'}, {'id': 7405, 'synset': 'dustmop.n.01', 'name': 'dustmop'}, {'id': 7406, 'synset': 'dutch_oven.n.01', 'name': 'Dutch_oven'}, {'id': 7407, 'synset': 'dutch_oven.n.02', 'name': 'Dutch_oven'}, {'id': 7408, 'synset': 'dwelling.n.01', 'name': 'dwelling'}, {'id': 7409, 'synset': 'dye-works.n.01', 'name': 'dye-works'}, {'id': 7410, 'synset': 'dynamo.n.01', 'name': 'dynamo'}, {'id': 7411, 'synset': 'dynamometer.n.01', 'name': 'dynamometer'}, {'id': 7412, 'synset': 'eames_chair.n.01', 'name': 'Eames_chair'}, {'id': 7413, 'synset': 'earflap.n.01', 'name': 'earflap'}, {'id': 7414, 'synset': 'early_warning_radar.n.01', 'name': 'early_warning_radar'}, {'id': 7415, 'synset': 'early_warning_system.n.01', 'name': 'early_warning_system'}, {'id': 7416, 'synset': 'earmuff.n.01', 'name': 'earmuff'}, {'id': 7417, 'synset': 'earplug.n.02', 'name': 'earplug'}, {'id': 7418, 'synset': 'earthenware.n.01', 'name': 'earthenware'}, {'id': 7419, 'synset': 'earthwork.n.01', 'name': 'earthwork'}, {'id': 7420, 'synset': 'easy_chair.n.01', 'name': 'easy_chair'}, {'id': 7421, 'synset': 'eaves.n.01', 'name': 'eaves'}, {'id': 7422, 'synset': 'ecclesiastical_attire.n.01', 'name': 'ecclesiastical_attire'}, {'id': 7423, 'synset': 'echinus.n.01', 'name': 'echinus'}, {'id': 7424, 'synset': 'echocardiograph.n.01', 'name': 'echocardiograph'}, {'id': 7425, 'synset': 'edger.n.02', 'name': 'edger'}, {'id': 7426, 'synset': 'edge_tool.n.01', 'name': 'edge_tool'}, {'id': 7427, 'synset': 'efficiency_apartment.n.01', 'name': 'efficiency_apartment'}, {'id': 7428, 'synset': 'egg-and-dart.n.01', 'name': 'egg-and-dart'}, {'id': 7429, 'synset': 'egg_timer.n.01', 'name': 'egg_timer'}, {'id': 7430, 'synset': 'eiderdown.n.01', 'name': 'eiderdown'}, {'id': 7431, 'synset': 'eight_ball.n.01', 'name': 'eight_ball'}, {'id': 7432, 'synset': 'ejection_seat.n.01', 'name': 'ejection_seat'}, {'id': 7433, 'synset': 'elastic.n.02', 'name': 'elastic'}, {'id': 7434, 'synset': 'elastic_bandage.n.01', 'name': 'elastic_bandage'}, {'id': 7435, 'synset': 'elastoplast.n.01', 'name': 'Elastoplast'}, {'id': 7436, 'synset': 'elbow.n.04', 'name': 'elbow'}, {'id': 7437, 'synset': 'elbow_pad.n.01', 'name': 'elbow_pad'}, {'id': 7438, 'synset': 'electric.n.01', 'name': 'electric'}, {'id': 7439, 'synset': 'electrical_cable.n.01', 'name': 'electrical_cable'}, {'id': 7440, 'synset': 'electrical_contact.n.01', 'name': 'electrical_contact'}, {'id': 7441, 'synset': 'electrical_converter.n.01', 'name': 'electrical_converter'}, {'id': 7442, 'synset': 'electrical_device.n.01', 'name': 'electrical_device'}, {'id': 7443, 'synset': 'electrical_system.n.02', 'name': 'electrical_system'}, {'id': 7444, 'synset': 'electric_bell.n.01', 'name': 'electric_bell'}, {'id': 7445, 'synset': 'electric_blanket.n.01', 'name': 'electric_blanket'}, {'id': 7446, 'synset': 'electric_clock.n.01', 'name': 'electric_clock'}, {'id': 7447, 'synset': 'electric-discharge_lamp.n.01', 'name': 'electric-discharge_lamp'}, {'id': 7448, 'synset': 'electric_fan.n.01', 'name': 'electric_fan'}, {'id': 7449, 'synset': 'electric_frying_pan.n.01', 'name': 'electric_frying_pan'}, {'id': 7450, 'synset': 'electric_furnace.n.01', 'name': 'electric_furnace'}, {'id': 7451, 'synset': 'electric_guitar.n.01', 'name': 'electric_guitar'}, {'id': 7452, 'synset': 'electric_hammer.n.01', 'name': 'electric_hammer'}, {'id': 7453, 'synset': 'electric_heater.n.01', 'name': 'electric_heater'}, {'id': 7454, 'synset': 'electric_lamp.n.01', 'name': 'electric_lamp'}, {'id': 7455, 'synset': 'electric_locomotive.n.01', 'name': 'electric_locomotive'}, {'id': 7456, 'synset': 'electric_meter.n.01', 'name': 'electric_meter'}, {'id': 7457, 'synset': 'electric_mixer.n.01', 'name': 'electric_mixer'}, {'id': 7458, 'synset': 'electric_motor.n.01', 'name': 'electric_motor'}, {'id': 7459, 'synset': 'electric_organ.n.01', 'name': 'electric_organ'}, {'id': 7460, 'synset': 'electric_range.n.01', 'name': 'electric_range'}, {'id': 7461, 'synset': 'electric_toothbrush.n.01', 'name': 'electric_toothbrush'}, {'id': 7462, 'synset': 'electric_typewriter.n.01', 'name': 'electric_typewriter'}, {'id': 7463, 'synset': 'electro-acoustic_transducer.n.01', 'name': 'electro-acoustic_transducer'}, {'id': 7464, 'synset': 'electrode.n.01', 'name': 'electrode'}, {'id': 7465, 'synset': 'electrodynamometer.n.01', 'name': 'electrodynamometer'}, {'id': 7466, 'synset': 'electroencephalograph.n.01', 'name': 'electroencephalograph'}, {'id': 7467, 'synset': 'electrograph.n.01', 'name': 'electrograph'}, {'id': 7468, 'synset': 'electrolytic.n.01', 'name': 'electrolytic'}, {'id': 7469, 'synset': 'electrolytic_cell.n.01', 'name': 'electrolytic_cell'}, {'id': 7470, 'synset': 'electromagnet.n.01', 'name': 'electromagnet'}, {'id': 7471, 'synset': 'electrometer.n.01', 'name': 'electrometer'}, {'id': 7472, 'synset': 'electromyograph.n.01', 'name': 'electromyograph'}, {'id': 7473, 'synset': 'electron_accelerator.n.01', 'name': 'electron_accelerator'}, {'id': 7474, 'synset': 'electron_gun.n.01', 'name': 'electron_gun'}, {'id': 7475, 'synset': 'electronic_balance.n.01', 'name': 'electronic_balance'}, {'id': 7476, 'synset': 'electronic_converter.n.01', 'name': 'electronic_converter'}, {'id': 7477, 'synset': 'electronic_device.n.01', 'name': 'electronic_device'}, {'id': 7478, 'synset': 'electronic_equipment.n.01', 'name': 'electronic_equipment'}, {'id': 7479, 'synset': 'electronic_fetal_monitor.n.01', 'name': 'electronic_fetal_monitor'}, {'id': 7480, 'synset': 'electronic_instrument.n.01', 'name': 'electronic_instrument'}, {'id': 7481, 'synset': 'electronic_voltmeter.n.01', 'name': 'electronic_voltmeter'}, {'id': 7482, 'synset': 'electron_microscope.n.01', 'name': 'electron_microscope'}, {'id': 7483, 'synset': 'electron_multiplier.n.01', 'name': 'electron_multiplier'}, {'id': 7484, 'synset': 'electrophorus.n.01', 'name': 'electrophorus'}, {'id': 7485, 'synset': 'electroscope.n.01', 'name': 'electroscope'}, {'id': 7486, 'synset': 'electrostatic_generator.n.01', 'name': 'electrostatic_generator'}, {'id': 7487, 'synset': 'electrostatic_printer.n.01', 'name': 'electrostatic_printer'}, {'id': 7488, 'synset': 'elevator.n.01', 'name': 'elevator'}, {'id': 7489, 'synset': 'elevator.n.02', 'name': 'elevator'}, {'id': 7490, 'synset': 'elevator_shaft.n.01', 'name': 'elevator_shaft'}, {'id': 7491, 'synset': 'embankment.n.01', 'name': 'embankment'}, {'id': 7492, 'synset': 'embassy.n.01', 'name': 'embassy'}, {'id': 7493, 'synset': 'embellishment.n.02', 'name': 'embellishment'}, {'id': 7494, 'synset': 'emergency_room.n.01', 'name': 'emergency_room'}, {'id': 7495, 'synset': 'emesis_basin.n.01', 'name': 'emesis_basin'}, {'id': 7496, 'synset': 'emitter.n.01', 'name': 'emitter'}, {'id': 7497, 'synset': 'empty.n.01', 'name': 'empty'}, {'id': 7498, 'synset': 'emulsion.n.02', 'name': 'emulsion'}, {'id': 7499, 'synset': 'enamel.n.04', 'name': 'enamel'}, {'id': 7500, 'synset': 'enamel.n.03', 'name': 'enamel'}, {'id': 7501, 'synset': 'enamelware.n.01', 'name': 'enamelware'}, {'id': 7502, 'synset': 'encaustic.n.01', 'name': 'encaustic'}, {'id': 7503, 'synset': 'encephalogram.n.02', 'name': 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{'id': 7519, 'synset': 'entrenchment.n.01', 'name': 'entrenchment'}, {'id': 7520, 'synset': 'envelope.n.02', 'name': 'envelope'}, {'id': 7521, 'synset': 'envelope.n.06', 'name': 'envelope'}, {'id': 7522, 'synset': 'eolith.n.01', 'name': 'eolith'}, {'id': 7523, 'synset': 'epauliere.n.01', 'name': 'epauliere'}, {'id': 7524, 'synset': 'epee.n.01', 'name': 'epee'}, {'id': 7525, 'synset': 'epergne.n.01', 'name': 'epergne'}, {'id': 7526, 'synset': 'epicyclic_train.n.01', 'name': 'epicyclic_train'}, {'id': 7527, 'synset': 'epidiascope.n.01', 'name': 'epidiascope'}, {'id': 7528, 'synset': 'epilating_wax.n.01', 'name': 'epilating_wax'}, {'id': 7529, 'synset': 'equalizer.n.01', 'name': 'equalizer'}, {'id': 7530, 'synset': 'equatorial.n.01', 'name': 'equatorial'}, {'id': 7531, 'synset': 'equipment.n.01', 'name': 'equipment'}, {'id': 7532, 'synset': 'erasable_programmable_read-only_memory.n.01', 'name': 'erasable_programmable_read-only_memory'}, {'id': 7533, 'synset': 'erecting_prism.n.01', 'name': 'erecting_prism'}, {'id': 7534, 'synset': 'erection.n.02', 'name': 'erection'}, {'id': 7535, 'synset': 'erlenmeyer_flask.n.01', 'name': 'Erlenmeyer_flask'}, {'id': 7536, 'synset': 'escape_hatch.n.01', 'name': 'escape_hatch'}, {'id': 7537, 'synset': 'escapement.n.01', 'name': 'escapement'}, {'id': 7538, 'synset': 'escape_wheel.n.01', 'name': 'escape_wheel'}, {'id': 7539, 'synset': 'escarpment.n.02', 'name': 'escarpment'}, {'id': 7540, 'synset': 'escutcheon.n.03', 'name': 'escutcheon'}, {'id': 7541, 'synset': 'esophagoscope.n.01', 'name': 'esophagoscope'}, {'id': 7542, 'synset': 'espadrille.n.01', 'name': 'espadrille'}, {'id': 7543, 'synset': 'espalier.n.01', 'name': 'espalier'}, {'id': 7544, 'synset': 'espresso_maker.n.01', 'name': 'espresso_maker'}, {'id': 7545, 'synset': 'espresso_shop.n.01', 'name': 'espresso_shop'}, {'id': 7546, 'synset': 'establishment.n.04', 'name': 'establishment'}, {'id': 7547, 'synset': 'estaminet.n.01', 'name': 'estaminet'}, {'id': 7548, 'synset': 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7608, 'synset': 'family_room.n.01', 'name': 'family_room'}, {'id': 7609, 'synset': 'fan_belt.n.01', 'name': 'fan_belt'}, {'id': 7610, 'synset': 'fan_blade.n.01', 'name': 'fan_blade'}, {'id': 7611, 'synset': 'fancy_dress.n.01', 'name': 'fancy_dress'}, {'id': 7612, 'synset': 'fanion.n.01', 'name': 'fanion'}, {'id': 7613, 'synset': 'fanlight.n.03', 'name': 'fanlight'}, {'id': 7614, 'synset': 'fanjet.n.02', 'name': 'fanjet'}, {'id': 7615, 'synset': 'fanjet.n.01', 'name': 'fanjet'}, {'id': 7616, 'synset': 'fanny_pack.n.01', 'name': 'fanny_pack'}, {'id': 7617, 'synset': 'fan_tracery.n.01', 'name': 'fan_tracery'}, {'id': 7618, 'synset': 'fan_vaulting.n.01', 'name': 'fan_vaulting'}, {'id': 7619, 'synset': 'farm_building.n.01', 'name': 'farm_building'}, {'id': 7620, 'synset': "farmer's_market.n.01", 'name': "farmer's_market"}, {'id': 7621, 'synset': 'farmhouse.n.01', 'name': 'farmhouse'}, {'id': 7622, 'synset': 'farm_machine.n.01', 'name': 'farm_machine'}, {'id': 7623, 'synset': 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'name': 'fichu'}, {'id': 7656, 'synset': 'fiddlestick.n.01', 'name': 'fiddlestick'}, {'id': 7657, 'synset': 'field_artillery.n.01', 'name': 'field_artillery'}, {'id': 7658, 'synset': 'field_coil.n.01', 'name': 'field_coil'}, {'id': 7659, 'synset': 'field-effect_transistor.n.01', 'name': 'field-effect_transistor'}, {'id': 7660, 'synset': 'field-emission_microscope.n.01', 'name': 'field-emission_microscope'}, {'id': 7661, 'synset': 'field_glass.n.01', 'name': 'field_glass'}, {'id': 7662, 'synset': 'field_hockey_ball.n.01', 'name': 'field_hockey_ball'}, {'id': 7663, 'synset': 'field_hospital.n.01', 'name': 'field_hospital'}, {'id': 7664, 'synset': 'field_house.n.01', 'name': 'field_house'}, {'id': 7665, 'synset': 'field_lens.n.01', 'name': 'field_lens'}, {'id': 7666, 'synset': 'field_magnet.n.01', 'name': 'field_magnet'}, {'id': 7667, 'synset': 'field-sequential_color_television.n.01', 'name': 'field-sequential_color_television'}, {'id': 7668, 'synset': 'field_tent.n.01', 'name': 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'synset': 'fire_tongs.n.01', 'name': 'fire_tongs'}, {'id': 7716, 'synset': 'fire_tower.n.01', 'name': 'fire_tower'}, {'id': 7717, 'synset': 'firewall.n.02', 'name': 'firewall'}, {'id': 7718, 'synset': 'firing_chamber.n.01', 'name': 'firing_chamber'}, {'id': 7719, 'synset': 'firing_pin.n.01', 'name': 'firing_pin'}, {'id': 7720, 'synset': 'firkin.n.02', 'name': 'firkin'}, {'id': 7721, 'synset': 'firmer_chisel.n.01', 'name': 'firmer_chisel'}, {'id': 7722, 'synset': 'first-aid_station.n.01', 'name': 'first-aid_station'}, {'id': 7723, 'synset': 'first_base.n.01', 'name': 'first_base'}, {'id': 7724, 'synset': 'first_class.n.03', 'name': 'first_class'}, {'id': 7725, 'synset': "fisherman's_bend.n.01", 'name': "fisherman's_bend"}, {'id': 7726, 'synset': "fisherman's_knot.n.01", 'name': "fisherman's_knot"}, {'id': 7727, 'synset': "fisherman's_lure.n.01", 'name': "fisherman's_lure"}, {'id': 7728, 'synset': 'fishhook.n.01', 'name': 'fishhook'}, {'id': 7729, 'synset': 'fishing_boat.n.01', 'name': 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'flat_panel_display.n.01', 'name': 'flat_panel_display'}, {'id': 7762, 'synset': 'flats.n.01', 'name': 'flats'}, {'id': 7763, 'synset': 'flat_tip_screwdriver.n.01', 'name': 'flat_tip_screwdriver'}, {'id': 7764, 'synset': 'fleet_ballistic_missile_submarine.n.01', 'name': 'fleet_ballistic_missile_submarine'}, {'id': 7765, 'synset': 'fleur-de-lis.n.02', 'name': 'fleur-de-lis'}, {'id': 7766, 'synset': 'flight_simulator.n.01', 'name': 'flight_simulator'}, {'id': 7767, 'synset': 'flintlock.n.02', 'name': 'flintlock'}, {'id': 7768, 'synset': 'flintlock.n.01', 'name': 'flintlock'}, {'id': 7769, 'synset': 'float.n.05', 'name': 'float'}, {'id': 7770, 'synset': 'floating_dock.n.01', 'name': 'floating_dock'}, {'id': 7771, 'synset': 'floatplane.n.01', 'name': 'floatplane'}, {'id': 7772, 'synset': 'flood.n.03', 'name': 'flood'}, {'id': 7773, 'synset': 'floor.n.01', 'name': 'floor'}, {'id': 7774, 'synset': 'floor.n.02', 'name': 'floor'}, {'id': 7775, 'synset': 'floor.n.09', 'name': 'floor'}, {'id': 7776, 'synset': 'floorboard.n.02', 'name': 'floorboard'}, {'id': 7777, 'synset': 'floor_cover.n.01', 'name': 'floor_cover'}, {'id': 7778, 'synset': 'floor_joist.n.01', 'name': 'floor_joist'}, {'id': 7779, 'synset': 'floor_lamp.n.01', 'name': 'floor_lamp'}, {'id': 7780, 'synset': 'flophouse.n.01', 'name': 'flophouse'}, {'id': 7781, 'synset': 'florist.n.02', 'name': 'florist'}, {'id': 7782, 'synset': 'floss.n.01', 'name': 'floss'}, {'id': 7783, 'synset': 'flotsam.n.01', 'name': 'flotsam'}, {'id': 7784, 'synset': 'flour_bin.n.01', 'name': 'flour_bin'}, {'id': 7785, 'synset': 'flour_mill.n.01', 'name': 'flour_mill'}, {'id': 7786, 'synset': 'flowerbed.n.01', 'name': 'flowerbed'}, {'id': 7787, 'synset': 'flugelhorn.n.01', 'name': 'flugelhorn'}, {'id': 7788, 'synset': 'fluid_drive.n.01', 'name': 'fluid_drive'}, {'id': 7789, 'synset': 'fluid_flywheel.n.01', 'name': 'fluid_flywheel'}, {'id': 7790, 'synset': 'flume.n.02', 'name': 'flume'}, {'id': 7791, 'synset': 'fluorescent_lamp.n.01', 'name': 'fluorescent_lamp'}, {'id': 7792, 'synset': 'fluoroscope.n.01', 'name': 'fluoroscope'}, {'id': 7793, 'synset': 'flush_toilet.n.01', 'name': 'flush_toilet'}, {'id': 7794, 'synset': 'flute.n.01', 'name': 'flute'}, {'id': 7795, 'synset': 'flux_applicator.n.01', 'name': 'flux_applicator'}, {'id': 7796, 'synset': 'fluxmeter.n.01', 'name': 'fluxmeter'}, {'id': 7797, 'synset': 'fly.n.05', 'name': 'fly'}, {'id': 7798, 'synset': 'flying_boat.n.01', 'name': 'flying_boat'}, {'id': 7799, 'synset': 'flying_buttress.n.01', 'name': 'flying_buttress'}, {'id': 7800, 'synset': 'flying_carpet.n.01', 'name': 'flying_carpet'}, {'id': 7801, 'synset': 'flying_jib.n.01', 'name': 'flying_jib'}, {'id': 7802, 'synset': 'fly_rod.n.01', 'name': 'fly_rod'}, {'id': 7803, 'synset': 'fly_tent.n.01', 'name': 'fly_tent'}, {'id': 7804, 'synset': 'flytrap.n.01', 'name': 'flytrap'}, {'id': 7805, 'synset': 'flywheel.n.01', 'name': 'flywheel'}, {'id': 7806, 'synset': 'fob.n.03', 'name': 'fob'}, {'id': 7807, 'synset': 'foghorn.n.02', 'name': 'foghorn'}, {'id': 7808, 'synset': 'foglamp.n.01', 'name': 'foglamp'}, {'id': 7809, 'synset': 'foil.n.05', 'name': 'foil'}, {'id': 7810, 'synset': 'fold.n.06', 'name': 'fold'}, {'id': 7811, 'synset': 'folder.n.02', 'name': 'folder'}, {'id': 7812, 'synset': 'folding_door.n.01', 'name': 'folding_door'}, {'id': 7813, 'synset': 'folding_saw.n.01', 'name': 'folding_saw'}, {'id': 7814, 'synset': 'food_court.n.01', 'name': 'food_court'}, {'id': 7815, 'synset': 'food_hamper.n.01', 'name': 'food_hamper'}, {'id': 7816, 'synset': 'foot.n.11', 'name': 'foot'}, {'id': 7817, 'synset': 'footage.n.01', 'name': 'footage'}, {'id': 7818, 'synset': 'football_stadium.n.01', 'name': 'football_stadium'}, {'id': 7819, 'synset': 'footbath.n.01', 'name': 'footbath'}, {'id': 7820, 'synset': 'foot_brake.n.01', 'name': 'foot_brake'}, {'id': 7821, 'synset': 'footbridge.n.01', 'name': 'footbridge'}, {'id': 7822, 'synset': 'foothold.n.02', 'name': 'foothold'}, {'id': 7823, 'synset': 'footlocker.n.01', 'name': 'footlocker'}, {'id': 7824, 'synset': 'foot_rule.n.01', 'name': 'foot_rule'}, {'id': 7825, 'synset': 'footwear.n.02', 'name': 'footwear'}, {'id': 7826, 'synset': 'footwear.n.01', 'name': 'footwear'}, {'id': 7827, 'synset': 'forceps.n.01', 'name': 'forceps'}, {'id': 7828, 'synset': 'force_pump.n.01', 'name': 'force_pump'}, {'id': 7829, 'synset': 'fore-and-after.n.01', 'name': 'fore-and-after'}, {'id': 7830, 'synset': 'fore-and-aft_sail.n.01', 'name': 'fore-and-aft_sail'}, {'id': 7831, 'synset': 'forecastle.n.01', 'name': 'forecastle'}, {'id': 7832, 'synset': 'forecourt.n.01', 'name': 'forecourt'}, {'id': 7833, 'synset': 'foredeck.n.01', 'name': 'foredeck'}, {'id': 7834, 'synset': 'fore_edge.n.01', 'name': 'fore_edge'}, {'id': 7835, 'synset': 'foreground.n.02', 'name': 'foreground'}, {'id': 7836, 'synset': 'foremast.n.01', 'name': 'foremast'}, {'id': 7837, 'synset': 'fore_plane.n.01', 'name': 'fore_plane'}, {'id': 7838, 'synset': 'foresail.n.01', 'name': 'foresail'}, {'id': 7839, 'synset': 'forestay.n.01', 'name': 'forestay'}, {'id': 7840, 'synset': 'foretop.n.01', 'name': 'foretop'}, {'id': 7841, 'synset': 'fore-topmast.n.01', 'name': 'fore-topmast'}, {'id': 7842, 'synset': 'fore-topsail.n.01', 'name': 'fore-topsail'}, {'id': 7843, 'synset': 'forge.n.01', 'name': 'forge'}, {'id': 7844, 'synset': 'fork.n.04', 'name': 'fork'}, {'id': 7845, 'synset': 'formalwear.n.01', 'name': 'formalwear'}, {'id': 7846, 'synset': 'formica.n.01', 'name': 'Formica'}, {'id': 7847, 'synset': 'fortification.n.01', 'name': 'fortification'}, {'id': 7848, 'synset': 'fortress.n.01', 'name': 'fortress'}, {'id': 7849, 'synset': 'forty-five.n.01', 'name': 'forty-five'}, {'id': 7850, 'synset': 'foucault_pendulum.n.01', 'name': 'Foucault_pendulum'}, {'id': 7851, 'synset': 'foulard.n.01', 'name': 'foulard'}, {'id': 7852, 'synset': 'foul-weather_gear.n.01', 'name': 'foul-weather_gear'}, {'id': 7853, 'synset': 'foundation_garment.n.01', 'name': 'foundation_garment'}, {'id': 7854, 'synset': 'foundry.n.01', 'name': 'foundry'}, {'id': 7855, 'synset': 'fountain.n.01', 'name': 'fountain'}, {'id': 7856, 'synset': 'fountain_pen.n.01', 'name': 'fountain_pen'}, {'id': 7857, 'synset': 'four-in-hand.n.01', 'name': 'four-in-hand'}, {'id': 7858, 'synset': 'four-poster.n.01', 'name': 'four-poster'}, {'id': 7859, 'synset': 'four-pounder.n.01', 'name': 'four-pounder'}, {'id': 7860, 'synset': 'four-stroke_engine.n.01', 'name': 'four-stroke_engine'}, {'id': 7861, 'synset': 'four-wheel_drive.n.02', 'name': 'four-wheel_drive'}, {'id': 7862, 'synset': 'four-wheel_drive.n.01', 'name': 'four-wheel_drive'}, {'id': 7863, 'synset': 'four-wheeler.n.01', 'name': 'four-wheeler'}, {'id': 7864, 'synset': 'fowling_piece.n.01', 'name': 'fowling_piece'}, {'id': 7865, 'synset': 'foxhole.n.01', 'name': 'foxhole'}, {'id': 7866, 'synset': 'fragmentation_bomb.n.01', 'name': 'fragmentation_bomb'}, {'id': 7867, 'synset': 'frail.n.02', 'name': 'frail'}, {'id': 7868, 'synset': 'fraise.n.02', 'name': 'fraise'}, {'id': 7869, 'synset': 'frame.n.10', 'name': 'frame'}, {'id': 7870, 'synset': 'frame.n.01', 'name': 'frame'}, {'id': 7871, 'synset': 'frame_buffer.n.01', 'name': 'frame_buffer'}, {'id': 7872, 'synset': 'framework.n.03', 'name': 'framework'}, {'id': 7873, 'synset': 'francis_turbine.n.01', 'name': 'Francis_turbine'}, {'id': 7874, 'synset': 'franking_machine.n.01', 'name': 'franking_machine'}, {'id': 7875, 'synset': 'free_house.n.01', 'name': 'free_house'}, {'id': 7876, 'synset': 'free-reed.n.01', 'name': 'free-reed'}, {'id': 7877, 'synset': 'free-reed_instrument.n.01', 'name': 'free-reed_instrument'}, {'id': 7878, 'synset': 'freewheel.n.01', 'name': 'freewheel'}, {'id': 7879, 'synset': 'freight_elevator.n.01', 'name': 'freight_elevator'}, {'id': 7880, 'synset': 'freight_liner.n.01', 'name': 'freight_liner'}, {'id': 7881, 'synset': 'freight_train.n.01', 'name': 'freight_train'}, {'id': 7882, 'synset': 'french_door.n.01', 'name': 'French_door'}, {'id': 7883, 'synset': 'french_horn.n.01', 'name': 'French_horn'}, {'id': 7884, 'synset': 'french_polish.n.02', 'name': 'French_polish'}, {'id': 7885, 'synset': 'french_roof.n.01', 'name': 'French_roof'}, {'id': 7886, 'synset': 'french_window.n.01', 'name': 'French_window'}, {'id': 7887, 'synset': 'fresnel_lens.n.01', 'name': 'Fresnel_lens'}, {'id': 7888, 'synset': 'fret.n.04', 'name': 'fret'}, {'id': 7889, 'synset': 'friary.n.01', 'name': 'friary'}, {'id': 7890, 'synset': 'friction_clutch.n.01', 'name': 'friction_clutch'}, {'id': 7891, 'synset': 'frieze.n.02', 'name': 'frieze'}, {'id': 7892, 'synset': 'frieze.n.01', 'name': 'frieze'}, {'id': 7893, 'synset': 'frigate.n.02', 'name': 'frigate'}, {'id': 7894, 'synset': 'frigate.n.01', 'name': 'frigate'}, {'id': 7895, 'synset': 'frill.n.03', 'name': 'frill'}, {'id': 7896, 'synset': 'frock.n.01', 'name': 'frock'}, {'id': 7897, 'synset': 'frock_coat.n.01', 'name': 'frock_coat'}, {'id': 7898, 'synset': 'frontlet.n.01', 'name': 'frontlet'}, {'id': 7899, 'synset': 'front_porch.n.01', 'name': 'front_porch'}, {'id': 7900, 'synset': 'front_projector.n.01', 'name': 'front_projector'}, {'id': 7901, 'synset': 'fruit_machine.n.01', 'name': 'fruit_machine'}, {'id': 7902, 'synset': 'fuel_filter.n.01', 'name': 'fuel_filter'}, {'id': 7903, 'synset': 'fuel_gauge.n.01', 'name': 'fuel_gauge'}, {'id': 7904, 'synset': 'fuel_injection.n.01', 'name': 'fuel_injection'}, {'id': 7905, 'synset': 'fuel_system.n.01', 'name': 'fuel_system'}, {'id': 7906, 'synset': 'full-dress_uniform.n.01', 'name': 'full-dress_uniform'}, {'id': 7907, 'synset': 'full_metal_jacket.n.01', 'name': 'full_metal_jacket'}, {'id': 7908, 'synset': 'full_skirt.n.01', 'name': 'full_skirt'}, {'id': 7909, 'synset': 'fumigator.n.02', 'name': 'fumigator'}, {'id': 7910, 'synset': 'funeral_home.n.01', 'name': 'funeral_home'}, {'id': 7911, 'synset': 'funny_wagon.n.01', 'name': 'funny_wagon'}, {'id': 7912, 'synset': 'fur.n.03', 'name': 'fur'}, {'id': 7913, 'synset': 'fur_coat.n.01', 'name': 'fur_coat'}, {'id': 7914, 'synset': 'fur_hat.n.01', 'name': 'fur_hat'}, {'id': 7915, 'synset': 'furnace.n.01', 'name': 'furnace'}, {'id': 7916, 'synset': 'furnace_lining.n.01', 'name': 'furnace_lining'}, {'id': 7917, 'synset': 'furnace_room.n.01', 'name': 'furnace_room'}, {'id': 7918, 'synset': 'furnishing.n.02', 'name': 'furnishing'}, {'id': 7919, 'synset': 'furnishing.n.01', 'name': 'furnishing'}, {'id': 7920, 'synset': 'furniture.n.01', 'name': 'furniture'}, {'id': 7921, 'synset': 'fur-piece.n.01', 'name': 'fur-piece'}, {'id': 7922, 'synset': 'furrow.n.01', 'name': 'furrow'}, {'id': 7923, 'synset': 'fuse.n.01', 'name': 'fuse'}, {'id': 7924, 'synset': 'fusee_drive.n.01', 'name': 'fusee_drive'}, {'id': 7925, 'synset': 'fuselage.n.01', 'name': 'fuselage'}, {'id': 7926, 'synset': 'fusil.n.01', 'name': 'fusil'}, {'id': 7927, 'synset': 'fustian.n.02', 'name': 'fustian'}, {'id': 7928, 'synset': 'gabardine.n.01', 'name': 'gabardine'}, {'id': 7929, 'synset': 'gable.n.01', 'name': 'gable'}, {'id': 7930, 'synset': 'gable_roof.n.01', 'name': 'gable_roof'}, {'id': 7931, 'synset': 'gadgetry.n.01', 'name': 'gadgetry'}, {'id': 7932, 'synset': 'gaff.n.03', 'name': 'gaff'}, {'id': 7933, 'synset': 'gaff.n.02', 'name': 'gaff'}, {'id': 7934, 'synset': 'gaff.n.01', 'name': 'gaff'}, {'id': 7935, 'synset': 'gaffsail.n.01', 'name': 'gaffsail'}, {'id': 7936, 'synset': 'gaff_topsail.n.01', 'name': 'gaff_topsail'}, {'id': 7937, 'synset': 'gaiter.n.03', 'name': 'gaiter'}, {'id': 7938, 'synset': 'gaiter.n.02', 'name': 'gaiter'}, {'id': 7939, 'synset': 'galilean_telescope.n.01', 'name': 'Galilean_telescope'}, {'id': 7940, 'synset': 'galleon.n.01', 'name': 'galleon'}, {'id': 7941, 'synset': 'gallery.n.04', 'name': 'gallery'}, {'id': 7942, 'synset': 'gallery.n.03', 'name': 'gallery'}, {'id': 7943, 'synset': 'galley.n.04', 'name': 'galley'}, {'id': 7944, 'synset': 'galley.n.03', 'name': 'galley'}, {'id': 7945, 'synset': 'galley.n.02', 'name': 'galley'}, {'id': 7946, 'synset': 'gallows.n.01', 'name': 'gallows'}, {'id': 7947, 'synset': 'gallows_tree.n.01', 'name': 'gallows_tree'}, {'id': 7948, 'synset': 'galvanometer.n.01', 'name': 'galvanometer'}, {'id': 7949, 'synset': 'gambling_house.n.01', 'name': 'gambling_house'}, {'id': 7950, 'synset': 'gambrel.n.01', 'name': 'gambrel'}, {'id': 7951, 'synset': 'game.n.09', 'name': 'game'}, {'id': 7952, 'synset': 'gamebag.n.01', 'name': 'gamebag'}, {'id': 7953, 'synset': 'game_equipment.n.01', 'name': 'game_equipment'}, {'id': 7954, 'synset': 'gaming_table.n.01', 'name': 'gaming_table'}, {'id': 7955, 'synset': 'gamp.n.01', 'name': 'gamp'}, {'id': 7956, 'synset': 'gangplank.n.01', 'name': 'gangplank'}, {'id': 7957, 'synset': 'gangsaw.n.01', 'name': 'gangsaw'}, {'id': 7958, 'synset': 'gangway.n.01', 'name': 'gangway'}, {'id': 7959, 'synset': 'gantlet.n.04', 'name': 'gantlet'}, {'id': 7960, 'synset': 'gantry.n.01', 'name': 'gantry'}, {'id': 7961, 'synset': 'garage.n.01', 'name': 'garage'}, {'id': 7962, 'synset': 'garage.n.02', 'name': 'garage'}, {'id': 7963, 'synset': 'garand_rifle.n.01', 'name': 'Garand_rifle'}, {'id': 7964, 'synset': 'garboard.n.01', 'name': 'garboard'}, {'id': 7965, 'synset': 'garden.n.01', 'name': 'garden'}, {'id': 7966, 'synset': 'garden.n.03', 'name': 'garden'}, {'id': 7967, 'synset': 'garden_rake.n.01', 'name': 'garden_rake'}, {'id': 7968, 'synset': 'garden_spade.n.01', 'name': 'garden_spade'}, {'id': 7969, 'synset': 'garden_tool.n.01', 'name': 'garden_tool'}, {'id': 7970, 'synset': 'garden_trowel.n.01', 'name': 'garden_trowel'}, {'id': 7971, 'synset': 'gargoyle.n.01', 'name': 'gargoyle'}, {'id': 7972, 'synset': 'garibaldi.n.02', 'name': 'garibaldi'}, {'id': 7973, 'synset': 'garlic_press.n.01', 'name': 'garlic_press'}, {'id': 7974, 'synset': 'garment.n.01', 'name': 'garment'}, {'id': 7975, 'synset': 'garment_bag.n.01', 'name': 'garment_bag'}, {'id': 7976, 'synset': 'garrison_cap.n.01', 'name': 'garrison_cap'}, {'id': 7977, 'synset': 'garrote.n.01', 'name': 'garrote'}, {'id': 7978, 'synset': 'garter.n.01', 'name': 'garter'}, {'id': 7979, 'synset': 'garter_belt.n.01', 'name': 'garter_belt'}, {'id': 7980, 'synset': 'garter_stitch.n.01', 'name': 'garter_stitch'}, {'id': 7981, 'synset': 'gas_guzzler.n.01', 'name': 'gas_guzzler'}, {'id': 7982, 'synset': 'gas_shell.n.01', 'name': 'gas_shell'}, {'id': 7983, 'synset': 'gas_bracket.n.01', 'name': 'gas_bracket'}, {'id': 7984, 'synset': 'gas_burner.n.01', 'name': 'gas_burner'}, {'id': 7985, 'synset': 'gas-cooled_reactor.n.01', 'name': 'gas-cooled_reactor'}, {'id': 7986, 'synset': 'gas-discharge_tube.n.01', 'name': 'gas-discharge_tube'}, {'id': 7987, 'synset': 'gas_engine.n.01', 'name': 'gas_engine'}, {'id': 7988, 'synset': 'gas_fixture.n.01', 'name': 'gas_fixture'}, {'id': 7989, 'synset': 'gas_furnace.n.01', 'name': 'gas_furnace'}, {'id': 7990, 'synset': 'gas_gun.n.01', 'name': 'gas_gun'}, {'id': 7991, 'synset': 'gas_heater.n.01', 'name': 'gas_heater'}, {'id': 7992, 'synset': 'gas_holder.n.01', 'name': 'gas_holder'}, {'id': 7993, 'synset': 'gasket.n.01', 'name': 'gasket'}, {'id': 7994, 'synset': 'gas_lamp.n.01', 'name': 'gas_lamp'}, {'id': 7995, 'synset': 'gas_maser.n.01', 'name': 'gas_maser'}, {'id': 7996, 'synset': 'gas_meter.n.01', 'name': 'gas_meter'}, {'id': 7997, 'synset': 'gasoline_engine.n.01', 'name': 'gasoline_engine'}, {'id': 7998, 'synset': 'gasoline_gauge.n.01', 'name': 'gasoline_gauge'}, {'id': 7999, 'synset': 'gas_oven.n.02', 'name': 'gas_oven'}, {'id': 8000, 'synset': 'gas_oven.n.01', 'name': 'gas_oven'}, {'id': 8001, 'synset': 'gas_pump.n.01', 'name': 'gas_pump'}, {'id': 8002, 'synset': 'gas_range.n.01', 'name': 'gas_range'}, {'id': 8003, 'synset': 'gas_ring.n.01', 'name': 'gas_ring'}, {'id': 8004, 'synset': 'gas_tank.n.01', 'name': 'gas_tank'}, {'id': 8005, 'synset': 'gas_thermometer.n.01', 'name': 'gas_thermometer'}, {'id': 8006, 'synset': 'gastroscope.n.01', 'name': 'gastroscope'}, {'id': 8007, 'synset': 'gas_turbine.n.01', 'name': 'gas_turbine'}, {'id': 8008, 'synset': 'gas-turbine_ship.n.01', 'name': 'gas-turbine_ship'}, {'id': 8009, 'synset': 'gat.n.01', 'name': 'gat'}, {'id': 8010, 'synset': 'gate.n.01', 'name': 'gate'}, {'id': 8011, 'synset': 'gatehouse.n.01', 'name': 'gatehouse'}, {'id': 8012, 'synset': 'gateleg_table.n.01', 'name': 'gateleg_table'}, {'id': 8013, 'synset': 'gatepost.n.01', 'name': 'gatepost'}, {'id': 8014, 'synset': 'gathered_skirt.n.01', 'name': 'gathered_skirt'}, {'id': 8015, 'synset': 'gatling_gun.n.01', 'name': 'Gatling_gun'}, {'id': 8016, 'synset': 'gauge.n.01', 'name': 'gauge'}, {'id': 8017, 'synset': 'gauntlet.n.03', 'name': 'gauntlet'}, {'id': 8018, 'synset': 'gauntlet.n.02', 'name': 'gauntlet'}, {'id': 8019, 'synset': 'gauze.n.02', 'name': 'gauze'}, {'id': 8020, 'synset': 'gauze.n.01', 'name': 'gauze'}, {'id': 8021, 'synset': 'gavel.n.01', 'name': 'gavel'}, {'id': 8022, 'synset': 'gazebo.n.01', 'name': 'gazebo'}, {'id': 8023, 'synset': 'gear.n.01', 'name': 'gear'}, {'id': 8024, 'synset': 'gear.n.04', 'name': 'gear'}, {'id': 8025, 'synset': 'gear.n.03', 'name': 'gear'}, {'id': 8026, 'synset': 'gearbox.n.01', 'name': 'gearbox'}, {'id': 8027, 'synset': 'gearing.n.01', 'name': 'gearing'}, {'id': 8028, 'synset': 'gearset.n.01', 'name': 'gearset'}, {'id': 8029, 'synset': 'gearshift.n.01', 'name': 'gearshift'}, {'id': 8030, 'synset': 'geiger_counter.n.01', 'name': 'Geiger_counter'}, {'id': 8031, 'synset': 'geiger_tube.n.01', 'name': 'Geiger_tube'}, {'id': 8032, 'synset': 'gene_chip.n.01', 'name': 'gene_chip'}, {'id': 8033, 'synset': 'general-purpose_bomb.n.01', 'name': 'general-purpose_bomb'}, {'id': 8034, 'synset': 'generator.n.01', 'name': 'generator'}, {'id': 8035, 'synset': 'generator.n.04', 'name': 'generator'}, {'id': 8036, 'synset': 'geneva_gown.n.01', 'name': 'Geneva_gown'}, {'id': 8037, 'synset': 'geodesic_dome.n.01', 'name': 'geodesic_dome'}, {'id': 8038, 'synset': 'georgette.n.01', 'name': 'georgette'}, {'id': 8039, 'synset': 'gharry.n.01', 'name': 'gharry'}, {'id': 8040, 'synset': 'ghat.n.01', 'name': 'ghat'}, {'id': 8041, 'synset': 'ghetto_blaster.n.01', 'name': 'ghetto_blaster'}, {'id': 8042, 'synset': 'gift_shop.n.01', 'name': 'gift_shop'}, {'id': 8043, 'synset': 'gift_wrapping.n.01', 'name': 'gift_wrapping'}, {'id': 8044, 'synset': 'gig.n.05', 'name': 'gig'}, {'id': 8045, 'synset': 'gig.n.04', 'name': 'gig'}, {'id': 8046, 'synset': 'gig.n.01', 'name': 'gig'}, {'id': 8047, 'synset': 'gig.n.03', 'name': 'gig'}, {'id': 8048, 'synset': 'gildhall.n.01', 'name': 'gildhall'}, {'id': 8049, 'synset': 'gill_net.n.01', 'name': 'gill_net'}, {'id': 8050, 'synset': 'gilt.n.01', 'name': 'gilt'}, {'id': 8051, 'synset': 'gimbal.n.01', 'name': 'gimbal'}, {'id': 8052, 'synset': 'gingham.n.01', 'name': 'gingham'}, {'id': 8053, 'synset': 'girandole.n.01', 'name': 'girandole'}, {'id': 8054, 'synset': 'girder.n.01', 'name': 'girder'}, {'id': 8055, 'synset': 'glass.n.07', 'name': 'glass'}, {'id': 8056, 'synset': 'glass_cutter.n.03', 'name': 'glass_cutter'}, {'id': 8057, 'synset': 'glasses_case.n.01', 'name': 'glasses_case'}, {'id': 8058, 'synset': 'glebe_house.n.01', 'name': 'glebe_house'}, {'id': 8059, 'synset': 'glengarry.n.01', 'name': 'Glengarry'}, {'id': 8060, 'synset': 'glider.n.01', 'name': 'glider'}, {'id': 8061, 'synset': 'global_positioning_system.n.01', 'name': 'Global_Positioning_System'}, {'id': 8062, 'synset': 'glockenspiel.n.01', 'name': 'glockenspiel'}, {'id': 8063, 'synset': 'glory_hole.n.01', 'name': 'glory_hole'}, {'id': 8064, 'synset': 'glove_compartment.n.01', 'name': 'glove_compartment'}, {'id': 8065, 'synset': 'glow_lamp.n.01', 'name': 'glow_lamp'}, {'id': 8066, 'synset': 'glow_tube.n.01', 'name': 'glow_tube'}, {'id': 8067, 'synset': 'glyptic_art.n.01', 'name': 'glyptic_art'}, {'id': 8068, 'synset': 'glyptics.n.01', 'name': 'glyptics'}, {'id': 8069, 'synset': 'gnomon.n.01', 'name': 'gnomon'}, {'id': 8070, 'synset': 'goal.n.03', 'name': 'goal'}, {'id': 8071, 'synset': 'goalmouth.n.01', 'name': 'goalmouth'}, {'id': 8072, 'synset': 'goalpost.n.01', 'name': 'goalpost'}, {'id': 8073, 'synset': 'goblet.n.01', 'name': 'goblet'}, {'id': 8074, 'synset': 'godown.n.01', 'name': 'godown'}, {'id': 8075, 'synset': 'go-kart.n.01', 'name': 'go-kart'}, {'id': 8076, 'synset': 'gold_plate.n.02', 'name': 'gold_plate'}, {'id': 8077, 'synset': 'golf_bag.n.01', 'name': 'golf_bag'}, {'id': 8078, 'synset': 'golf_ball.n.01', 'name': 'golf_ball'}, {'id': 8079, 'synset': 'golf-club_head.n.01', 'name': 'golf-club_head'}, {'id': 8080, 'synset': 'golf_equipment.n.01', 'name': 'golf_equipment'}, {'id': 8081, 'synset': 'golf_glove.n.01', 'name': 'golf_glove'}, {'id': 8082, 'synset': 'golliwog.n.01', 'name': 'golliwog'}, {'id': 8083, 'synset': 'gong.n.01', 'name': 'gong'}, {'id': 8084, 'synset': 'goniometer.n.01', 'name': 'goniometer'}, {'id': 8085, 'synset': 'gordian_knot.n.02', 'name': 'Gordian_knot'}, {'id': 8086, 'synset': 'gorget.n.01', 'name': 'gorget'}, {'id': 8087, 'synset': 'gossamer.n.01', 'name': 'gossamer'}, {'id': 8088, 'synset': 'gothic_arch.n.01', 'name': 'Gothic_arch'}, {'id': 8089, 'synset': 'gouache.n.01', 'name': 'gouache'}, {'id': 8090, 'synset': 'gouge.n.02', 'name': 'gouge'}, {'id': 8091, 'synset': 'gourd.n.01', 'name': 'gourd'}, {'id': 8092, 'synset': 'government_building.n.01', 'name': 'government_building'}, {'id': 8093, 'synset': 'government_office.n.01', 'name': 'government_office'}, {'id': 8094, 'synset': 'gown.n.01', 'name': 'gown'}, {'id': 8095, 'synset': 'gown.n.05', 'name': 'gown'}, {'id': 8096, 'synset': 'gown.n.04', 'name': 'gown'}, {'id': 8097, 'synset': 'grab.n.01', 'name': 'grab'}, {'id': 8098, 'synset': 'grab_bag.n.02', 'name': 'grab_bag'}, {'id': 8099, 'synset': 'grab_bar.n.01', 'name': 'grab_bar'}, {'id': 8100, 'synset': 'grace_cup.n.01', 'name': 'grace_cup'}, {'id': 8101, 'synset': 'grade_separation.n.01', 'name': 'grade_separation'}, {'id': 8102, 'synset': 'graduated_cylinder.n.01', 'name': 'graduated_cylinder'}, {'id': 8103, 'synset': 'graffito.n.01', 'name': 'graffito'}, {'id': 8104, 'synset': 'gramophone.n.01', 'name': 'gramophone'}, {'id': 8105, 'synset': 'granary.n.01', 'name': 'granary'}, {'id': 8106, 'synset': 'grandfather_clock.n.01', 'name': 'grandfather_clock'}, {'id': 8107, 'synset': 'grand_piano.n.01', 'name': 'grand_piano'}, {'id': 8108, 'synset': 'graniteware.n.01', 'name': 'graniteware'}, {'id': 8109, 'synset': 'granny_knot.n.01', 'name': 'granny_knot'}, {'id': 8110, 'synset': 'grape_arbor.n.01', 'name': 'grape_arbor'}, {'id': 8111, 'synset': 'grapnel.n.02', 'name': 'grapnel'}, {'id': 8112, 'synset': 'grapnel.n.01', 'name': 'grapnel'}, {'id': 8113, 'synset': 'grass_skirt.n.01', 'name': 'grass_skirt'}, {'id': 8114, 'synset': 'grate.n.01', 'name': 'grate'}, {'id': 8115, 'synset': 'grate.n.03', 'name': 'grate'}, {'id': 8116, 'synset': 'graver.n.01', 'name': 'graver'}, {'id': 8117, 'synset': 'gravimeter.n.02', 'name': 'gravimeter'}, {'id': 8118, 'synset': 'gravure.n.03', 'name': 'gravure'}, {'id': 8119, 'synset': 'grey.n.06', 'name': 'grey'}, {'id': 8120, 'synset': 'grease-gun.n.01', 'name': 'grease-gun'}, {'id': 8121, 'synset': 'greasepaint.n.01', 'name': 'greasepaint'}, {'id': 8122, 'synset': 'greasy_spoon.n.01', 'name': 'greasy_spoon'}, {'id': 8123, 'synset': 'greatcoat.n.01', 'name': 'greatcoat'}, {'id': 8124, 'synset': 'great_hall.n.01', 'name': 'great_hall'}, {'id': 8125, 'synset': 'greave.n.01', 'name': 'greave'}, {'id': 8126, 'synset': 'greengrocery.n.02', 'name': 'greengrocery'}, {'id': 8127, 'synset': 'greenhouse.n.01', 'name': 'greenhouse'}, {'id': 8128, 'synset': 'grenade.n.01', 'name': 'grenade'}, {'id': 8129, 'synset': 'grid.n.05', 'name': 'grid'}, {'id': 8130, 'synset': 'grille.n.02', 'name': 'grille'}, {'id': 8131, 'synset': 'grillroom.n.01', 'name': 'grillroom'}, {'id': 8132, 'synset': 'grinder.n.04', 'name': 'grinder'}, {'id': 8133, 'synset': 'grinding_wheel.n.01', 'name': 'grinding_wheel'}, {'id': 8134, 'synset': 'grindstone.n.01', 'name': 'grindstone'}, {'id': 8135, 'synset': 'gripsack.n.01', 'name': 'gripsack'}, {'id': 8136, 'synset': 'gristmill.n.01', 'name': 'gristmill'}, {'id': 8137, 'synset': 'grocery_store.n.01', 'name': 'grocery_store'}, {'id': 8138, 'synset': 'grogram.n.01', 'name': 'grogram'}, {'id': 8139, 'synset': 'groined_vault.n.01', 'name': 'groined_vault'}, {'id': 8140, 'synset': 'groover.n.01', 'name': 'groover'}, {'id': 8141, 'synset': 'grosgrain.n.01', 'name': 'grosgrain'}, {'id': 8142, 'synset': 'gros_point.n.01', 'name': 'gros_point'}, {'id': 8143, 'synset': 'ground.n.09', 'name': 'ground'}, {'id': 8144, 'synset': 'ground_bait.n.01', 'name': 'ground_bait'}, {'id': 8145, 'synset': 'ground_control.n.01', 'name': 'ground_control'}, {'id': 8146, 'synset': 'ground_floor.n.01', 'name': 'ground_floor'}, {'id': 8147, 'synset': 'groundsheet.n.01', 'name': 'groundsheet'}, {'id': 8148, 'synset': 'g-string.n.01', 'name': 'G-string'}, {'id': 8149, 'synset': 'guard.n.03', 'name': 'guard'}, {'id': 8150, 'synset': 'guard_boat.n.01', 'name': 'guard_boat'}, {'id': 8151, 'synset': 'guardroom.n.02', 'name': 'guardroom'}, {'id': 8152, 'synset': 'guardroom.n.01', 'name': 'guardroom'}, {'id': 8153, 'synset': 'guard_ship.n.01', 'name': 'guard_ship'}, {'id': 8154, 'synset': "guard's_van.n.01", 'name': "guard's_van"}, {'id': 8155, 'synset': 'gueridon.n.01', 'name': 'gueridon'}, {'id': 8156, 'synset': 'guarnerius.n.03', 'name': 'Guarnerius'}, {'id': 8157, 'synset': 'guesthouse.n.01', 'name': 'guesthouse'}, {'id': 8158, 'synset': 'guestroom.n.01', 'name': 'guestroom'}, {'id': 8159, 'synset': 'guidance_system.n.01', 'name': 'guidance_system'}, {'id': 8160, 'synset': 'guided_missile.n.01', 'name': 'guided_missile'}, {'id': 8161, 'synset': 'guided_missile_cruiser.n.01', 'name': 'guided_missile_cruiser'}, {'id': 8162, 'synset': 'guided_missile_frigate.n.01', 'name': 'guided_missile_frigate'}, {'id': 8163, 'synset': 'guildhall.n.01', 'name': 'guildhall'}, {'id': 8164, 'synset': 'guilloche.n.01', 'name': 'guilloche'}, {'id': 8165, 'synset': 'guillotine.n.02', 'name': 'guillotine'}, {'id': 8166, 'synset': 'guimpe.n.02', 'name': 'guimpe'}, {'id': 8167, 'synset': 'guimpe.n.01', 'name': 'guimpe'}, {'id': 8168, 'synset': 'guitar_pick.n.01', 'name': 'guitar_pick'}, {'id': 8169, 'synset': 'gulag.n.01', 'name': 'gulag'}, {'id': 8170, 'synset': 'gunboat.n.01', 'name': 'gunboat'}, {'id': 8171, 'synset': 'gun_carriage.n.01', 'name': 'gun_carriage'}, {'id': 8172, 'synset': 'gun_case.n.01', 'name': 'gun_case'}, {'id': 8173, 'synset': 'gun_emplacement.n.01', 'name': 'gun_emplacement'}, {'id': 8174, 'synset': 'gun_enclosure.n.01', 'name': 'gun_enclosure'}, {'id': 8175, 'synset': 'gunlock.n.01', 'name': 'gunlock'}, {'id': 8176, 'synset': 'gunnery.n.01', 'name': 'gunnery'}, {'id': 8177, 'synset': 'gunnysack.n.01', 'name': 'gunnysack'}, {'id': 8178, 'synset': 'gun_pendulum.n.01', 'name': 'gun_pendulum'}, {'id': 8179, 'synset': 'gun_room.n.01', 'name': 'gun_room'}, {'id': 8180, 'synset': 'gunsight.n.01', 'name': 'gunsight'}, {'id': 8181, 'synset': 'gun_trigger.n.01', 'name': 'gun_trigger'}, {'id': 8182, 'synset': 'gurney.n.01', 'name': 'gurney'}, {'id': 8183, 'synset': 'gusher.n.01', 'name': 'gusher'}, {'id': 8184, 'synset': 'gusset.n.03', 'name': 'gusset'}, {'id': 8185, 'synset': 'gusset.n.02', 'name': 'gusset'}, {'id': 8186, 'synset': 'guy.n.03', 'name': 'guy'}, {'id': 8187, 'synset': 'gymnastic_apparatus.n.01', 'name': 'gymnastic_apparatus'}, {'id': 8188, 'synset': 'gym_shoe.n.01', 'name': 'gym_shoe'}, {'id': 8189, 'synset': 'gym_suit.n.01', 'name': 'gym_suit'}, {'id': 8190, 'synset': 'gymslip.n.01', 'name': 'gymslip'}, {'id': 8191, 'synset': 'gypsy_cab.n.01', 'name': 'gypsy_cab'}, {'id': 8192, 'synset': 'gyrocompass.n.01', 'name': 'gyrocompass'}, {'id': 8193, 'synset': 'gyroscope.n.01', 'name': 'gyroscope'}, {'id': 8194, 'synset': 'gyrostabilizer.n.01', 'name': 'gyrostabilizer'}, {'id': 8195, 'synset': 'habergeon.n.01', 'name': 'habergeon'}, {'id': 8196, 'synset': 'habit.n.03', 'name': 'habit'}, {'id': 8197, 'synset': 'habit.n.05', 'name': 'habit'}, {'id': 8198, 'synset': 'hacienda.n.02', 'name': 'hacienda'}, {'id': 8199, 'synset': 'hacksaw.n.01', 'name': 'hacksaw'}, {'id': 8200, 'synset': 'haft.n.01', 'name': 'haft'}, {'id': 8201, 'synset': 'haircloth.n.01', 'name': 'haircloth'}, {'id': 8202, 'synset': 'hairdressing.n.01', 'name': 'hairdressing'}, {'id': 8203, 'synset': 'hairpiece.n.01', 'name': 'hairpiece'}, {'id': 8204, 'synset': 'hair_shirt.n.01', 'name': 'hair_shirt'}, {'id': 8205, 'synset': 'hair_slide.n.01', 'name': 'hair_slide'}, {'id': 8206, 'synset': 'hair_spray.n.01', 'name': 'hair_spray'}, {'id': 8207, 'synset': 'hairspring.n.01', 'name': 'hairspring'}, {'id': 8208, 'synset': 'hair_trigger.n.01', 'name': 'hair_trigger'}, {'id': 8209, 'synset': 'halberd.n.01', 'name': 'halberd'}, {'id': 8210, 'synset': 'half_binding.n.01', 'name': 'half_binding'}, {'id': 8211, 'synset': 'half_hatchet.n.01', 'name': 'half_hatchet'}, {'id': 8212, 'synset': 'half_hitch.n.01', 'name': 'half_hitch'}, {'id': 8213, 'synset': 'half_track.n.01', 'name': 'half_track'}, {'id': 8214, 'synset': 'hall.n.13', 'name': 'hall'}, {'id': 8215, 'synset': 'hall.n.03', 'name': 'hall'}, {'id': 8216, 'synset': 'hall.n.12', 'name': 'hall'}, {'id': 8217, 'synset': 'hall_of_fame.n.01', 'name': 'Hall_of_Fame'}, {'id': 8218, 'synset': 'hall_of_residence.n.01', 'name': 'hall_of_residence'}, {'id': 8219, 'synset': 'hallstand.n.01', 'name': 'hallstand'}, {'id': 8220, 'synset': 'halter.n.01', 'name': 'halter'}, {'id': 8221, 'synset': 'hame.n.01', 'name': 'hame'}, {'id': 8222, 'synset': 'hammer.n.07', 'name': 'hammer'}, {'id': 8223, 'synset': 'hammer.n.05', 'name': 'hammer'}, {'id': 8224, 'synset': 'hammerhead.n.02', 'name': 'hammerhead'}, {'id': 8225, 'synset': 'hand.n.08', 'name': 'hand'}, {'id': 8226, 'synset': 'handball.n.01', 'name': 'handball'}, {'id': 8227, 'synset': 'handbarrow.n.01', 'name': 'handbarrow'}, {'id': 8228, 'synset': 'handbell.n.01', 'name': 'handbell'}, {'id': 8229, 'synset': 'handbow.n.01', 'name': 'handbow'}, {'id': 8230, 'synset': 'hand_brake.n.01', 'name': 'hand_brake'}, {'id': 8231, 'synset': 'hand_calculator.n.01', 'name': 'hand_calculator'}, {'id': 8232, 'synset': 'handcar.n.01', 'name': 'handcar'}, {'id': 8233, 'synset': 'hand_cream.n.01', 'name': 'hand_cream'}, {'id': 8234, 'synset': 'hand_drill.n.01', 'name': 'hand_drill'}, {'id': 8235, 'synset': 'hand_glass.n.02', 'name': 'hand_glass'}, {'id': 8236, 'synset': 'hand_grenade.n.01', 'name': 'hand_grenade'}, {'id': 8237, 'synset': 'hand-held_computer.n.01', 'name': 'hand-held_computer'}, {'id': 8238, 'synset': 'handhold.n.01', 'name': 'handhold'}, {'id': 8239, 'synset': 'handlebar.n.01', 'name': 'handlebar'}, {'id': 8240, 'synset': 'handloom.n.01', 'name': 'handloom'}, {'id': 8241, 'synset': 'hand_lotion.n.01', 'name': 'hand_lotion'}, {'id': 8242, 'synset': 'hand_luggage.n.01', 'name': 'hand_luggage'}, {'id': 8243, 'synset': 'hand-me-down.n.01', 'name': 'hand-me-down'}, {'id': 8244, 'synset': 'hand_mower.n.01', 'name': 'hand_mower'}, {'id': 8245, 'synset': 'hand_pump.n.01', 'name': 'hand_pump'}, {'id': 8246, 'synset': 'handrest.n.01', 'name': 'handrest'}, {'id': 8247, 'synset': 'handset.n.01', 'name': 'handset'}, {'id': 8248, 'synset': 'hand_shovel.n.01', 'name': 'hand_shovel'}, {'id': 8249, 'synset': 'handspike.n.01', 'name': 'handspike'}, {'id': 8250, 'synset': 'handstamp.n.01', 'name': 'handstamp'}, {'id': 8251, 'synset': 'hand_throttle.n.01', 'name': 'hand_throttle'}, {'id': 8252, 'synset': 'hand_tool.n.01', 'name': 'hand_tool'}, {'id': 8253, 'synset': 'hand_truck.n.01', 'name': 'hand_truck'}, {'id': 8254, 'synset': 'handwear.n.01', 'name': 'handwear'}, {'id': 8255, 'synset': 'handwheel.n.02', 'name': 'handwheel'}, {'id': 8256, 'synset': 'handwheel.n.01', 'name': 'handwheel'}, {'id': 8257, 'synset': 'hangar_queen.n.01', 'name': 'hangar_queen'}, {'id': 8258, 'synset': 'hanger.n.02', 'name': 'hanger'}, {'id': 8259, 'synset': 'hang_glider.n.02', 'name': 'hang_glider'}, {'id': 8260, 'synset': "hangman's_rope.n.01", 'name': "hangman's_rope"}, {'id': 8261, 'synset': 'hank.n.01', 'name': 'hank'}, {'id': 8262, 'synset': 'hansom.n.01', 'name': 'hansom'}, {'id': 8263, 'synset': 'harbor.n.02', 'name': 'harbor'}, {'id': 8264, 'synset': 'hard_disc.n.01', 'name': 'hard_disc'}, {'id': 8265, 'synset': 'hard_hat.n.02', 'name': 'hard_hat'}, {'id': 8266, 'synset': 'hardtop.n.01', 'name': 'hardtop'}, {'id': 8267, 'synset': 'hardware.n.02', 'name': 'hardware'}, {'id': 8268, 'synset': 'hardware_store.n.01', 'name': 'hardware_store'}, {'id': 8269, 'synset': 'harmonica.n.01', 'name': 'harmonica'}, {'id': 8270, 'synset': 'harness.n.02', 'name': 'harness'}, {'id': 8271, 'synset': 'harness.n.01', 'name': 'harness'}, {'id': 8272, 'synset': 'harp.n.01', 'name': 'harp'}, {'id': 8273, 'synset': 'harp.n.02', 'name': 'harp'}, {'id': 8274, 'synset': 'harpoon.n.01', 'name': 'harpoon'}, {'id': 8275, 'synset': 'harpoon_gun.n.01', 'name': 'harpoon_gun'}, {'id': 8276, 'synset': 'harpoon_log.n.01', 'name': 'harpoon_log'}, {'id': 8277, 'synset': 'harpsichord.n.01', 'name': 'harpsichord'}, {'id': 8278, 'synset': 'harris_tweed.n.01', 'name': 'Harris_Tweed'}, {'id': 8279, 'synset': 'harrow.n.01', 'name': 'harrow'}, {'id': 8280, 'synset': 'harvester.n.02', 'name': 'harvester'}, {'id': 8281, 'synset': 'hash_house.n.01', 'name': 'hash_house'}, {'id': 8282, 'synset': 'hasp.n.01', 'name': 'hasp'}, {'id': 8283, 'synset': 'hatch.n.03', 'name': 'hatch'}, {'id': 8284, 'synset': 'hatchback.n.02', 'name': 'hatchback'}, {'id': 8285, 'synset': 'hatchback.n.01', 'name': 'hatchback'}, {'id': 8286, 'synset': 'hatchel.n.01', 'name': 'hatchel'}, {'id': 8287, 'synset': 'hatchet.n.02', 'name': 'hatchet'}, {'id': 8288, 'synset': 'hatpin.n.01', 'name': 'hatpin'}, {'id': 8289, 'synset': 'hauberk.n.01', 'name': 'hauberk'}, {'id': 8290, 'synset': 'hawaiian_guitar.n.01', 'name': 'Hawaiian_guitar'}, {'id': 8291, 'synset': 'hawse.n.01', 'name': 'hawse'}, {'id': 8292, 'synset': 'hawser.n.01', 'name': 'hawser'}, {'id': 8293, 'synset': 'hawser_bend.n.01', 'name': 'hawser_bend'}, {'id': 8294, 'synset': 'hay_bale.n.01', 'name': 'hay_bale'}, {'id': 8295, 'synset': 'hayfork.n.01', 'name': 'hayfork'}, {'id': 8296, 'synset': 'hayloft.n.01', 'name': 'hayloft'}, {'id': 8297, 'synset': 'haymaker.n.01', 'name': 'haymaker'}, {'id': 8298, 'synset': 'hayrack.n.02', 'name': 'hayrack'}, {'id': 8299, 'synset': 'hayrack.n.01', 'name': 'hayrack'}, {'id': 8300, 'synset': 'hazard.n.03', 'name': 'hazard'}, {'id': 8301, 'synset': 'head.n.31', 'name': 'head'}, {'id': 8302, 'synset': 'head.n.30', 'name': 'head'}, {'id': 8303, 'synset': 'head.n.29', 'name': 'head'}, {'id': 8304, 'synset': 'headdress.n.01', 'name': 'headdress'}, {'id': 8305, 'synset': 'header.n.05', 'name': 'header'}, {'id': 8306, 'synset': 'header.n.04', 'name': 'header'}, {'id': 8307, 'synset': 'header.n.03', 'name': 'header'}, {'id': 8308, 'synset': 'header.n.02', 'name': 'header'}, {'id': 8309, 'synset': 'headfast.n.01', 'name': 'headfast'}, {'id': 8310, 'synset': 'head_gasket.n.01', 'name': 'head_gasket'}, {'id': 8311, 'synset': 'head_gate.n.02', 'name': 'head_gate'}, {'id': 8312, 'synset': 'headgear.n.03', 'name': 'headgear'}, {'id': 8313, 'synset': 'headpiece.n.02', 'name': 'headpiece'}, {'id': 8314, 'synset': 'headpin.n.01', 'name': 'headpin'}, {'id': 8315, 'synset': 'headquarters.n.01', 'name': 'headquarters'}, {'id': 8316, 'synset': 'headrace.n.01', 'name': 'headrace'}, {'id': 8317, 'synset': 'headrest.n.02', 'name': 'headrest'}, {'id': 8318, 'synset': 'headsail.n.01', 'name': 'headsail'}, {'id': 8319, 'synset': 'head_shop.n.01', 'name': 'head_shop'}, {'id': 8320, 'synset': 'headstock.n.01', 'name': 'headstock'}, {'id': 8321, 'synset': 'health_spa.n.01', 'name': 'health_spa'}, {'id': 8322, 'synset': 'hearing_aid.n.02', 'name': 'hearing_aid'}, {'id': 8323, 'synset': 'hearing_aid.n.01', 'name': 'hearing_aid'}, {'id': 8324, 'synset': 'hearse.n.01', 'name': 'hearse'}, {'id': 8325, 'synset': 'hearth.n.02', 'name': 'hearth'}, {'id': 8326, 'synset': 'hearthrug.n.01', 'name': 'hearthrug'}, {'id': 8327, 'synset': 'heart-lung_machine.n.01', 'name': 'heart-lung_machine'}, {'id': 8328, 'synset': 'heat_engine.n.01', 'name': 'heat_engine'}, {'id': 8329, 'synset': 'heat_exchanger.n.01', 'name': 'heat_exchanger'}, {'id': 8330, 'synset': 'heating_pad.n.01', 'name': 'heating_pad'}, {'id': 8331, 'synset': 'heat_lamp.n.01', 'name': 'heat_lamp'}, {'id': 8332, 'synset': 'heat_pump.n.01', 'name': 'heat_pump'}, {'id': 8333, 'synset': 'heat-seeking_missile.n.01', 'name': 'heat-seeking_missile'}, {'id': 8334, 'synset': 'heat_shield.n.01', 'name': 'heat_shield'}, {'id': 8335, 'synset': 'heat_sink.n.01', 'name': 'heat_sink'}, {'id': 8336, 'synset': 'heaume.n.01', 'name': 'heaume'}, {'id': 8337, 'synset': 'heaver.n.01', 'name': 'heaver'}, {'id': 8338, 'synset': 'heavier-than-air_craft.n.01', 'name': 'heavier-than-air_craft'}, {'id': 8339, 'synset': 'heckelphone.n.01', 'name': 'heckelphone'}, {'id': 8340, 'synset': 'hectograph.n.01', 'name': 'hectograph'}, {'id': 8341, 'synset': 'hedge.n.01', 'name': 'hedge'}, {'id': 8342, 'synset': 'hedge_trimmer.n.01', 'name': 'hedge_trimmer'}, {'id': 8343, 'synset': 'helicon.n.01', 'name': 'helicon'}, {'id': 8344, 'synset': 'heliograph.n.01', 'name': 'heliograph'}, {'id': 8345, 'synset': 'heliometer.n.01', 'name': 'heliometer'}, {'id': 8346, 'synset': 'helm.n.01', 'name': 'helm'}, {'id': 8347, 'synset': 'helmet.n.01', 'name': 'helmet'}, {'id': 8348, 'synset': 'hematocrit.n.02', 'name': 'hematocrit'}, {'id': 8349, 'synset': 'hemming-stitch.n.01', 'name': 'hemming-stitch'}, {'id': 8350, 'synset': 'hemostat.n.01', 'name': 'hemostat'}, {'id': 8351, 'synset': 'hemstitch.n.01', 'name': 'hemstitch'}, {'id': 8352, 'synset': 'henroost.n.01', 'name': 'henroost'}, {'id': 8353, 'synset': 'heraldry.n.02', 'name': 'heraldry'}, {'id': 8354, 'synset': 'hermitage.n.01', 'name': 'hermitage'}, {'id': 8355, 'synset': 'herringbone.n.01', 'name': 'herringbone'}, {'id': 8356, 'synset': 'herringbone.n.02', 'name': 'herringbone'}, {'id': 8357, 'synset': 'herschelian_telescope.n.01', 'name': 'Herschelian_telescope'}, {'id': 8358, 'synset': 'hessian_boot.n.01', 'name': 'Hessian_boot'}, {'id': 8359, 'synset': 'heterodyne_receiver.n.01', 'name': 'heterodyne_receiver'}, {'id': 8360, 'synset': 'hibachi.n.01', 'name': 'hibachi'}, {'id': 8361, 'synset': 'hideaway.n.02', 'name': 'hideaway'}, {'id': 8362, 'synset': 'hi-fi.n.01', 'name': 'hi-fi'}, {'id': 8363, 'synset': 'high_altar.n.01', 'name': 'high_altar'}, {'id': 8364, 'synset': 'high-angle_gun.n.01', 'name': 'high-angle_gun'}, {'id': 8365, 'synset': 'highball_glass.n.01', 'name': 'highball_glass'}, {'id': 8366, 'synset': 'highboard.n.01', 'name': 'highboard'}, {'id': 8367, 'synset': 'highboy.n.01', 'name': 'highboy'}, {'id': 8368, 'synset': 'high_gear.n.01', 'name': 'high_gear'}, {'id': 8369, 'synset': 'high-hat_cymbal.n.01', 'name': 'high-hat_cymbal'}, {'id': 8370, 'synset': 'highlighter.n.02', 'name': 'highlighter'}, {'id': 8371, 'synset': 'highlighter.n.01', 'name': 'highlighter'}, {'id': 8372, 'synset': 'high-pass_filter.n.01', 'name': 'high-pass_filter'}, {'id': 8373, 'synset': 'high-rise.n.01', 'name': 'high-rise'}, {'id': 8374, 'synset': 'high_table.n.01', 'name': 'high_table'}, {'id': 8375, 'synset': 'high-warp_loom.n.01', 'name': 'high-warp_loom'}, {'id': 8376, 'synset': 'hijab.n.01', 'name': 'hijab'}, {'id': 8377, 'synset': 'hinging_post.n.01', 'name': 'hinging_post'}, {'id': 8378, 'synset': 'hip_boot.n.01', 'name': 'hip_boot'}, {'id': 8379, 'synset': 'hipflask.n.01', 'name': 'hipflask'}, {'id': 8380, 'synset': 'hip_pad.n.01', 'name': 'hip_pad'}, {'id': 8381, 'synset': 'hip_pocket.n.01', 'name': 'hip_pocket'}, {'id': 8382, 'synset': 'hippodrome.n.01', 'name': 'hippodrome'}, {'id': 8383, 'synset': 'hip_roof.n.01', 'name': 'hip_roof'}, {'id': 8384, 'synset': 'hitch.n.05', 'name': 'hitch'}, {'id': 8385, 'synset': 'hitch.n.04', 'name': 'hitch'}, {'id': 8386, 'synset': 'hitching_post.n.01', 'name': 'hitching_post'}, {'id': 8387, 'synset': 'hitchrack.n.01', 'name': 'hitchrack'}, {'id': 8388, 'synset': 'hob.n.03', 'name': 'hob'}, {'id': 8389, 'synset': 'hobble_skirt.n.01', 'name': 'hobble_skirt'}, {'id': 8390, 'synset': 'hockey_skate.n.01', 'name': 'hockey_skate'}, {'id': 8391, 'synset': 'hod.n.01', 'name': 'hod'}, {'id': 8392, 'synset': 'hodoscope.n.01', 'name': 'hodoscope'}, {'id': 8393, 'synset': 'hoe.n.01', 'name': 'hoe'}, {'id': 8394, 'synset': 'hoe_handle.n.01', 'name': 'hoe_handle'}, {'id': 8395, 'synset': 'hogshead.n.02', 'name': 'hogshead'}, {'id': 8396, 'synset': 'hoist.n.01', 'name': 'hoist'}, {'id': 8397, 'synset': 'hold.n.07', 'name': 'hold'}, {'id': 8398, 'synset': 'holder.n.01', 'name': 'holder'}, {'id': 8399, 'synset': 'holding_cell.n.01', 'name': 'holding_cell'}, {'id': 8400, 'synset': 'holding_device.n.01', 'name': 'holding_device'}, {'id': 8401, 'synset': 'holding_pen.n.01', 'name': 'holding_pen'}, {'id': 8402, 'synset': 'hollowware.n.01', 'name': 'hollowware'}, {'id': 8403, 'synset': 'holster.n.01', 'name': 'holster'}, {'id': 8404, 'synset': 'holster.n.02', 'name': 'holster'}, {'id': 8405, 'synset': 'holy_of_holies.n.02', 'name': 'holy_of_holies'}, {'id': 8406, 'synset': 'home.n.09', 'name': 'home'}, {'id': 8407, 'synset': 'home_appliance.n.01', 'name': 'home_appliance'}, {'id': 8408, 'synset': 'home_computer.n.01', 'name': 'home_computer'}, {'id': 8409, 'synset': 'home_room.n.01', 'name': 'home_room'}, {'id': 8410, 'synset': 'homespun.n.01', 'name': 'homespun'}, {'id': 8411, 'synset': 'homestead.n.03', 'name': 'homestead'}, {'id': 8412, 'synset': 'home_theater.n.01', 'name': 'home_theater'}, {'id': 8413, 'synset': 'homing_torpedo.n.01', 'name': 'homing_torpedo'}, {'id': 8414, 'synset': 'hone.n.01', 'name': 'hone'}, {'id': 8415, 'synset': 'honeycomb.n.02', 'name': 'honeycomb'}, {'id': 8416, 'synset': 'hood.n.09', 'name': 'hood'}, {'id': 8417, 'synset': 'hood.n.08', 'name': 'hood'}, {'id': 8418, 'synset': 'hood.n.07', 'name': 'hood'}, {'id': 8419, 'synset': 'hood.n.05', 'name': 'hood'}, {'id': 8420, 'synset': 'hood_latch.n.01', 'name': 'hood_latch'}, {'id': 8421, 'synset': 'hook.n.04', 'name': 'hook'}, {'id': 8422, 'synset': 'hook.n.01', 'name': 'hook'}, {'id': 8423, 'synset': 'hook_and_eye.n.01', 'name': 'hook_and_eye'}, {'id': 8424, 'synset': 'hookup.n.02', 'name': 'hookup'}, {'id': 8425, 'synset': 'hookup.n.01', 'name': 'hookup'}, {'id': 8426, 'synset': 'hook_wrench.n.01', 'name': 'hook_wrench'}, {'id': 8427, 'synset': 'hoopskirt.n.01', 'name': 'hoopskirt'}, {'id': 8428, 'synset': 'hoosegow.n.01', 'name': 'hoosegow'}, {'id': 8429, 'synset': 'hoover.n.04', 'name': 'Hoover'}, {'id': 8430, 'synset': 'hope_chest.n.01', 'name': 'hope_chest'}, {'id': 8431, 'synset': 'hopper.n.01', 'name': 'hopper'}, {'id': 8432, 'synset': 'hopsacking.n.01', 'name': 'hopsacking'}, {'id': 8433, 'synset': 'horizontal_bar.n.01', 'name': 'horizontal_bar'}, {'id': 8434, 'synset': 'horizontal_stabilizer.n.01', 'name': 'horizontal_stabilizer'}, {'id': 8435, 'synset': 'horizontal_tail.n.01', 'name': 'horizontal_tail'}, {'id': 8436, 'synset': 'horn.n.09', 'name': 'horn'}, {'id': 8437, 'synset': 'horn.n.01', 'name': 'horn'}, {'id': 8438, 'synset': 'horn.n.08', 'name': 'horn'}, {'id': 8439, 'synset': 'horn_button.n.01', 'name': 'horn_button'}, {'id': 8440, 'synset': 'hornpipe.n.03', 'name': 'hornpipe'}, {'id': 8441, 'synset': 'horse.n.02', 'name': 'horse'}, {'id': 8442, 'synset': 'horsebox.n.01', 'name': 'horsebox'}, {'id': 8443, 'synset': 'horsecar.n.01', 'name': 'horsecar'}, {'id': 8444, 'synset': 'horse_cart.n.01', 'name': 'horse_cart'}, {'id': 8445, 'synset': 'horsecloth.n.01', 'name': 'horsecloth'}, {'id': 8446, 'synset': 'horse-drawn_vehicle.n.01', 'name': 'horse-drawn_vehicle'}, {'id': 8447, 'synset': 'horsehair.n.02', 'name': 'horsehair'}, {'id': 8448, 'synset': 'horsehair_wig.n.01', 'name': 'horsehair_wig'}, {'id': 8449, 'synset': 'horseless_carriage.n.01', 'name': 'horseless_carriage'}, {'id': 8450, 'synset': 'horse_pistol.n.01', 'name': 'horse_pistol'}, {'id': 8451, 'synset': 'horseshoe.n.02', 'name': 'horseshoe'}, {'id': 8452, 'synset': 'horseshoe.n.01', 'name': 'horseshoe'}, {'id': 8453, 'synset': 'horse-trail.n.01', 'name': 'horse-trail'}, {'id': 8454, 'synset': 'horsewhip.n.01', 'name': 'horsewhip'}, {'id': 8455, 'synset': 'hose.n.02', 'name': 'hose'}, {'id': 8456, 'synset': 'hosiery.n.01', 'name': 'hosiery'}, {'id': 8457, 'synset': 'hospice.n.01', 'name': 'hospice'}, {'id': 8458, 'synset': 'hospital.n.01', 'name': 'hospital'}, {'id': 8459, 'synset': 'hospital_bed.n.01', 'name': 'hospital_bed'}, {'id': 8460, 'synset': 'hospital_room.n.01', 'name': 'hospital_room'}, {'id': 8461, 'synset': 'hospital_ship.n.01', 'name': 'hospital_ship'}, {'id': 8462, 'synset': 'hospital_train.n.01', 'name': 'hospital_train'}, {'id': 8463, 'synset': 'hostel.n.02', 'name': 'hostel'}, {'id': 8464, 'synset': 'hostel.n.01', 'name': 'hostel'}, {'id': 8465, 'synset': 'hotel.n.01', 'name': 'hotel'}, {'id': 8466, 'synset': 'hotel-casino.n.02', 'name': 'hotel-casino'}, {'id': 8467, 'synset': 'hotel-casino.n.01', 'name': 'hotel-casino'}, {'id': 8468, 'synset': 'hotel_room.n.01', 'name': 'hotel_room'}, {'id': 8469, 'synset': 'hot_line.n.01', 'name': 'hot_line'}, {'id': 8470, 'synset': 'hot_pants.n.02', 'name': 'hot_pants'}, {'id': 8471, 'synset': 'hot_rod.n.01', 'name': 'hot_rod'}, {'id': 8472, 'synset': 'hot_spot.n.03', 'name': 'hot_spot'}, {'id': 8473, 'synset': 'hot_tub.n.01', 'name': 'hot_tub'}, {'id': 8474, 'synset': 'hot-water_bottle.n.01', 'name': 'hot-water_bottle'}, {'id': 8475, 'synset': 'houndstooth_check.n.01', 'name': 'houndstooth_check'}, {'id': 8476, 'synset': 'hour_hand.n.01', 'name': 'hour_hand'}, {'id': 8477, 'synset': 'house.n.01', 'name': 'house'}, {'id': 8478, 'synset': 'house.n.12', 'name': 'house'}, {'id': 8479, 'synset': 'houselights.n.01', 'name': 'houselights'}, {'id': 8480, 'synset': 'house_of_cards.n.02', 'name': 'house_of_cards'}, {'id': 8481, 'synset': 'house_of_correction.n.01', 'name': 'house_of_correction'}, {'id': 8482, 'synset': 'house_paint.n.01', 'name': 'house_paint'}, {'id': 8483, 'synset': 'housetop.n.01', 'name': 'housetop'}, {'id': 8484, 'synset': 'housing.n.01', 'name': 'housing'}, {'id': 8485, 'synset': 'hovel.n.01', 'name': 'hovel'}, {'id': 8486, 'synset': 'hovercraft.n.01', 'name': 'hovercraft'}, {'id': 8487, 'synset': 'howdah.n.01', 'name': 'howdah'}, {'id': 8488, 'synset': 'huarache.n.01', 'name': 'huarache'}, {'id': 8489, 'synset': 'hub-and-spoke.n.01', 'name': 'hub-and-spoke'}, {'id': 8490, 'synset': 'hubcap.n.01', 'name': 'hubcap'}, {'id': 8491, 'synset': 'huck.n.01', 'name': 'huck'}, {'id': 8492, 'synset': 'hug-me-tight.n.01', 'name': 'hug-me-tight'}, {'id': 8493, 'synset': 'hula-hoop.n.01', 'name': 'hula-hoop'}, {'id': 8494, 'synset': 'hulk.n.02', 'name': 'hulk'}, {'id': 8495, 'synset': 'hull.n.06', 'name': 'hull'}, {'id': 8496, 'synset': 'humeral_veil.n.01', 'name': 'humeral_veil'}, {'id': 8497, 'synset': 'humvee.n.01', 'name': 'Humvee'}, {'id': 8498, 'synset': 'hunter.n.04', 'name': 'hunter'}, {'id': 8499, 'synset': 'hunting_knife.n.01', 'name': 'hunting_knife'}, {'id': 8500, 'synset': 'hurdle.n.01', 'name': 'hurdle'}, {'id': 8501, 'synset': 'hurricane_deck.n.01', 'name': 'hurricane_deck'}, {'id': 8502, 'synset': 'hurricane_lamp.n.01', 'name': 'hurricane_lamp'}, {'id': 8503, 'synset': 'hut.n.01', 'name': 'hut'}, {'id': 8504, 'synset': 'hutch.n.01', 'name': 'hutch'}, {'id': 8505, 'synset': 'hutment.n.01', 'name': 'hutment'}, {'id': 8506, 'synset': 'hydraulic_brake.n.01', 'name': 'hydraulic_brake'}, {'id': 8507, 'synset': 'hydraulic_press.n.01', 'name': 'hydraulic_press'}, {'id': 8508, 'synset': 'hydraulic_pump.n.01', 'name': 'hydraulic_pump'}, {'id': 8509, 'synset': 'hydraulic_system.n.01', 'name': 'hydraulic_system'}, {'id': 8510, 'synset': 'hydraulic_transmission.n.01', 'name': 'hydraulic_transmission'}, {'id': 8511, 'synset': 'hydroelectric_turbine.n.01', 'name': 'hydroelectric_turbine'}, {'id': 8512, 'synset': 'hydrofoil.n.02', 'name': 'hydrofoil'}, {'id': 8513, 'synset': 'hydrofoil.n.01', 'name': 'hydrofoil'}, {'id': 8514, 'synset': 'hydrogen_bomb.n.01', 'name': 'hydrogen_bomb'}, {'id': 8515, 'synset': 'hydrometer.n.01', 'name': 'hydrometer'}, {'id': 8516, 'synset': 'hygrodeik.n.01', 'name': 'hygrodeik'}, {'id': 8517, 'synset': 'hygrometer.n.01', 'name': 'hygrometer'}, {'id': 8518, 'synset': 'hygroscope.n.01', 'name': 'hygroscope'}, {'id': 8519, 'synset': 'hyperbaric_chamber.n.01', 'name': 'hyperbaric_chamber'}, {'id': 8520, 'synset': 'hypercoaster.n.01', 'name': 'hypercoaster'}, {'id': 8521, 'synset': 'hypermarket.n.01', 'name': 'hypermarket'}, {'id': 8522, 'synset': 'hypodermic_needle.n.01', 'name': 'hypodermic_needle'}, {'id': 8523, 'synset': 'hypodermic_syringe.n.01', 'name': 'hypodermic_syringe'}, {'id': 8524, 'synset': 'hypsometer.n.01', 'name': 'hypsometer'}, {'id': 8525, 'synset': 'hysterosalpingogram.n.01', 'name': 'hysterosalpingogram'}, {'id': 8526, 'synset': 'i-beam.n.01', 'name': 'I-beam'}, {'id': 8527, 'synset': 'ice_ax.n.01', 'name': 'ice_ax'}, {'id': 8528, 'synset': 'iceboat.n.02', 'name': 'iceboat'}, {'id': 8529, 'synset': 'icebreaker.n.01', 'name': 'icebreaker'}, {'id': 8530, 'synset': 'iced-tea_spoon.n.01', 'name': 'iced-tea_spoon'}, {'id': 8531, 'synset': 'ice_hockey_rink.n.01', 'name': 'ice_hockey_rink'}, {'id': 8532, 'synset': 'ice_machine.n.01', 'name': 'ice_machine'}, {'id': 8533, 'synset': 'icepick.n.01', 'name': 'icepick'}, {'id': 8534, 'synset': 'ice_rink.n.01', 'name': 'ice_rink'}, {'id': 8535, 'synset': 'ice_tongs.n.01', 'name': 'ice_tongs'}, {'id': 8536, 'synset': 'icetray.n.01', 'name': 'icetray'}, {'id': 8537, 'synset': 'iconoscope.n.01', 'name': 'iconoscope'}, {'id': 8538, 'synset': 'identikit.n.01', 'name': 'Identikit'}, {'id': 8539, 'synset': 'idle_pulley.n.01', 'name': 'idle_pulley'}, {'id': 8540, 'synset': 'igloo.n.01', 'name': 'igloo'}, {'id': 8541, 'synset': 'ignition_coil.n.01', 'name': 'ignition_coil'}, {'id': 8542, 'synset': 'ignition_key.n.01', 'name': 'ignition_key'}, {'id': 8543, 'synset': 'ignition_switch.n.01', 'name': 'ignition_switch'}, {'id': 8544, 'synset': 'imaret.n.01', 'name': 'imaret'}, {'id': 8545, 'synset': 'immovable_bandage.n.01', 'name': 'immovable_bandage'}, {'id': 8546, 'synset': 'impact_printer.n.01', 'name': 'impact_printer'}, {'id': 8547, 'synset': 'impeller.n.01', 'name': 'impeller'}, {'id': 8548, 'synset': 'implant.n.01', 'name': 'implant'}, {'id': 8549, 'synset': 'implement.n.01', 'name': 'implement'}, {'id': 8550, 'synset': 'impression.n.07', 'name': 'impression'}, {'id': 8551, 'synset': 'imprint.n.05', 'name': 'imprint'}, {'id': 8552, 'synset': 'improvised_explosive_device.n.01', 'name': 'improvised_explosive_device'}, {'id': 8553, 'synset': 'impulse_turbine.n.01', 'name': 'impulse_turbine'}, {'id': 8554, 'synset': 'in-basket.n.01', 'name': 'in-basket'}, {'id': 8555, 'synset': 'incendiary_bomb.n.01', 'name': 'incendiary_bomb'}, {'id': 8556, 'synset': 'incinerator.n.01', 'name': 'incinerator'}, {'id': 8557, 'synset': 'inclined_plane.n.01', 'name': 'inclined_plane'}, {'id': 8558, 'synset': 'inclinometer.n.02', 'name': 'inclinometer'}, {'id': 8559, 'synset': 'inclinometer.n.01', 'name': 'inclinometer'}, {'id': 8560, 'synset': 'incrustation.n.03', 'name': 'incrustation'}, {'id': 8561, 'synset': 'incubator.n.01', 'name': 'incubator'}, {'id': 8562, 'synset': 'index_register.n.01', 'name': 'index_register'}, {'id': 8563, 'synset': 'indiaman.n.01', 'name': 'Indiaman'}, {'id': 8564, 'synset': 'indian_club.n.01', 'name': 'Indian_club'}, {'id': 8565, 'synset': 'indicator.n.03', 'name': 'indicator'}, {'id': 8566, 'synset': 'induction_coil.n.01', 'name': 'induction_coil'}, {'id': 8567, 'synset': 'inductor.n.01', 'name': 'inductor'}, {'id': 8568, 'synset': 'industrial_watercourse.n.01', 'name': 'industrial_watercourse'}, {'id': 8569, 'synset': 'inertial_guidance_system.n.01', 'name': 'inertial_guidance_system'}, {'id': 8570, 'synset': 'inflater.n.01', 'name': 'inflater'}, {'id': 8571, 'synset': 'injector.n.01', 'name': 'injector'}, {'id': 8572, 'synset': 'ink_bottle.n.01', 'name': 'ink_bottle'}, {'id': 8573, 'synset': 'ink_eraser.n.01', 'name': 'ink_eraser'}, {'id': 8574, 'synset': 'ink-jet_printer.n.01', 'name': 'ink-jet_printer'}, {'id': 8575, 'synset': 'inkle.n.01', 'name': 'inkle'}, {'id': 8576, 'synset': 'inkstand.n.02', 'name': 'inkstand'}, {'id': 8577, 'synset': 'inkwell.n.01', 'name': 'inkwell'}, {'id': 8578, 'synset': 'inlay.n.01', 'name': 'inlay'}, {'id': 8579, 'synset': 'inside_caliper.n.01', 'name': 'inside_caliper'}, {'id': 8580, 'synset': 'insole.n.01', 'name': 'insole'}, {'id': 8581, 'synset': 'instep.n.02', 'name': 'instep'}, {'id': 8582, 'synset': 'instillator.n.01', 'name': 'instillator'}, {'id': 8583, 'synset': 'institution.n.02', 'name': 'institution'}, {'id': 8584, 'synset': 'instrument.n.01', 'name': 'instrument'}, {'id': 8585, 'synset': 'instrument_of_punishment.n.01', 'name': 'instrument_of_punishment'}, {'id': 8586, 'synset': 'instrument_of_torture.n.01', 'name': 'instrument_of_torture'}, {'id': 8587, 'synset': 'intaglio.n.02', 'name': 'intaglio'}, {'id': 8588, 'synset': 'intake_valve.n.01', 'name': 'intake_valve'}, {'id': 8589, 'synset': 'integrated_circuit.n.01', 'name': 'integrated_circuit'}, {'id': 8590, 'synset': 'integrator.n.01', 'name': 'integrator'}, {'id': 8591, 'synset': 'intelnet.n.01', 'name': 'Intelnet'}, {'id': 8592, 'synset': 'interceptor.n.01', 'name': 'interceptor'}, {'id': 8593, 'synset': 'interchange.n.01', 'name': 'interchange'}, {'id': 8594, 'synset': 'intercommunication_system.n.01', 'name': 'intercommunication_system'}, {'id': 8595, 'synset': 'intercontinental_ballistic_missile.n.01', 'name': 'intercontinental_ballistic_missile'}, {'id': 8596, 'synset': 'interface.n.04', 'name': 'interface'}, {'id': 8597, 'synset': 'interferometer.n.01', 'name': 'interferometer'}, {'id': 8598, 'synset': 'interior_door.n.01', 'name': 'interior_door'}, {'id': 8599, 'synset': 'internal-combustion_engine.n.01', 'name': 'internal-combustion_engine'}, {'id': 8600, 'synset': 'internal_drive.n.01', 'name': 'internal_drive'}, {'id': 8601, 'synset': 'internet.n.01', 'name': 'internet'}, {'id': 8602, 'synset': 'interphone.n.01', 'name': 'interphone'}, {'id': 8603, 'synset': 'interrupter.n.01', 'name': 'interrupter'}, {'id': 8604, 'synset': 'intersection.n.02', 'name': 'intersection'}, {'id': 8605, 'synset': 'interstice.n.02', 'name': 'interstice'}, {'id': 8606, 'synset': 'intraocular_lens.n.01', 'name': 'intraocular_lens'}, {'id': 8607, 'synset': 'intravenous_pyelogram.n.01', 'name': 'intravenous_pyelogram'}, {'id': 8608, 'synset': 'inverter.n.01', 'name': 'inverter'}, {'id': 8609, 'synset': 'ion_engine.n.01', 'name': 'ion_engine'}, {'id': 8610, 'synset': 'ionization_chamber.n.01', 'name': 'ionization_chamber'}, {'id': 8611, 'synset': 'video_ipod.n.01', 'name': 'video_iPod'}, {'id': 8612, 'synset': 'iron.n.02', 'name': 'iron'}, {'id': 8613, 'synset': 'iron.n.03', 'name': 'iron'}, {'id': 8614, 'synset': 'irons.n.01', 'name': 'irons'}, {'id': 8615, 'synset': 'ironclad.n.01', 'name': 'ironclad'}, {'id': 8616, 'synset': 'iron_foundry.n.01', 'name': 'iron_foundry'}, {'id': 8617, 'synset': 'iron_horse.n.01', 'name': 'iron_horse'}, {'id': 8618, 'synset': 'ironing.n.01', 'name': 'ironing'}, {'id': 8619, 'synset': 'iron_lung.n.01', 'name': 'iron_lung'}, {'id': 8620, 'synset': 'ironmongery.n.01', 'name': 'ironmongery'}, {'id': 8621, 'synset': 'ironworks.n.01', 'name': 'ironworks'}, {'id': 8622, 'synset': 'irrigation_ditch.n.01', 'name': 'irrigation_ditch'}, {'id': 8623, 'synset': 'izar.n.01', 'name': 'izar'}, {'id': 8624, 'synset': 'jabot.n.01', 'name': 'jabot'}, {'id': 8625, 'synset': 'jack.n.10', 'name': 'jack'}, {'id': 8626, 'synset': 'jack.n.07', 'name': 'jack'}, {'id': 8627, 'synset': 'jack.n.06', 'name': 'jack'}, {'id': 8628, 'synset': 'jack.n.05', 'name': 'jack'}, {'id': 8629, 'synset': 'jacket.n.02', 'name': 'jacket'}, {'id': 8630, 'synset': 'jacket.n.05', 'name': 'jacket'}, {'id': 8631, 'synset': 'jack-in-the-box.n.01', 'name': 'jack-in-the-box'}, {'id': 8632, 'synset': "jack-o'-lantern.n.02", 'name': "jack-o'-lantern"}, {'id': 8633, 'synset': 'jack_plane.n.01', 'name': 'jack_plane'}, {'id': 8634, 'synset': "jacob's_ladder.n.02", 'name': "Jacob's_ladder"}, {'id': 8635, 'synset': 'jaconet.n.01', 'name': 'jaconet'}, {'id': 8636, 'synset': 'jacquard_loom.n.01', 'name': 'Jacquard_loom'}, {'id': 8637, 'synset': 'jacquard.n.02', 'name': 'jacquard'}, {'id': 8638, 'synset': 'jag.n.03', 'name': 'jag'}, {'id': 8639, 'synset': 'jail.n.01', 'name': 'jail'}, {'id': 8640, 'synset': 'jalousie.n.02', 'name': 'jalousie'}, {'id': 8641, 'synset': 'jamb.n.01', 'name': 'jamb'}, {'id': 8642, 'synset': 'jammer.n.01', 'name': 'jammer'}, {'id': 8643, 'synset': 'jampot.n.01', 'name': 'jampot'}, {'id': 8644, 'synset': 'japan.n.04', 'name': 'japan'}, {'id': 8645, 'synset': 'jarvik_heart.n.01', 'name': 'Jarvik_heart'}, {'id': 8646, 'synset': 'jaunting_car.n.01', 'name': 'jaunting_car'}, {'id': 8647, 'synset': 'javelin.n.02', 'name': 'javelin'}, {'id': 8648, 'synset': 'jaw.n.03', 'name': 'jaw'}, {'id': 8649, 'synset': 'jaws_of_life.n.01', 'name': 'Jaws_of_Life'}, {'id': 8650, 'synset': 'jellaba.n.01', 'name': 'jellaba'}, {'id': 8651, 'synset': 'jerkin.n.01', 'name': 'jerkin'}, {'id': 8652, 'synset': 'jeroboam.n.02', 'name': 'jeroboam'}, {'id': 8653, 'synset': 'jersey.n.04', 'name': 'jersey'}, {'id': 8654, 'synset': 'jet_bridge.n.01', 'name': 'jet_bridge'}, {'id': 8655, 'synset': 'jet_engine.n.01', 'name': 'jet_engine'}, {'id': 8656, 'synset': 'jetliner.n.01', 'name': 'jetliner'}, {'id': 8657, 'synset': "jeweler's_glass.n.01", 'name': "jeweler's_glass"}, {'id': 8658, 'synset': 'jewelled_headdress.n.01', 'name': 'jewelled_headdress'}, {'id': 8659, 'synset': "jew's_harp.n.01", 'name': "jew's_harp"}, {'id': 8660, 'synset': 'jib.n.01', 'name': 'jib'}, {'id': 8661, 'synset': 'jibboom.n.01', 'name': 'jibboom'}, {'id': 8662, 'synset': 'jig.n.03', 'name': 'jig'}, {'id': 8663, 'synset': 'jig.n.02', 'name': 'jig'}, {'id': 8664, 'synset': 'jiggermast.n.01', 'name': 'jiggermast'}, {'id': 8665, 'synset': 'jigsaw.n.02', 'name': 'jigsaw'}, {'id': 8666, 'synset': 'jigsaw_puzzle.n.01', 'name': 'jigsaw_puzzle'}, {'id': 8667, 'synset': 'jinrikisha.n.01', 'name': 'jinrikisha'}, {'id': 8668, 'synset': 'jobcentre.n.01', 'name': 'jobcentre'}, {'id': 8669, 'synset': 'jodhpurs.n.01', 'name': 'jodhpurs'}, {'id': 8670, 'synset': 'jodhpur.n.01', 'name': 'jodhpur'}, {'id': 8671, 'synset': 'joinery.n.01', 'name': 'joinery'}, {'id': 8672, 'synset': 'joint.n.05', 'name': 'joint'}, {'id': 8673, 'synset': 'joint_direct_attack_munition.n.01', 'name': 'Joint_Direct_Attack_Munition'}, {'id': 8674, 'synset': 'jointer.n.01', 'name': 'jointer'}, {'id': 8675, 'synset': 'joist.n.01', 'name': 'joist'}, {'id': 8676, 'synset': 'jolly_boat.n.01', 'name': 'jolly_boat'}, {'id': 8677, 'synset': 'jorum.n.01', 'name': 'jorum'}, {'id': 8678, 'synset': 'joss_house.n.01', 'name': 'joss_house'}, {'id': 8679, 'synset': 'journal_bearing.n.01', 'name': 'journal_bearing'}, {'id': 8680, 'synset': 'journal_box.n.01', 'name': 'journal_box'}, {'id': 8681, 'synset': 'jungle_gym.n.01', 'name': 'jungle_gym'}, {'id': 8682, 'synset': 'junk.n.02', 'name': 'junk'}, {'id': 8683, 'synset': 'jug.n.01', 'name': 'jug'}, {'id': 8684, 'synset': 'jukebox.n.01', 'name': 'jukebox'}, {'id': 8685, 'synset': 'jumbojet.n.01', 'name': 'jumbojet'}, {'id': 8686, 'synset': 'jumper.n.07', 'name': 'jumper'}, {'id': 8687, 'synset': 'jumper.n.06', 'name': 'jumper'}, {'id': 8688, 'synset': 'jumper.n.05', 'name': 'jumper'}, {'id': 8689, 'synset': 'jumper.n.04', 'name': 'jumper'}, {'id': 8690, 'synset': 'jumper_cable.n.01', 'name': 'jumper_cable'}, {'id': 8691, 'synset': 'jump_seat.n.01', 'name': 'jump_seat'}, {'id': 8692, 'synset': 'jump_suit.n.02', 'name': 'jump_suit'}, {'id': 8693, 'synset': 'junction.n.01', 'name': 'junction'}, {'id': 8694, 'synset': 'junction.n.04', 'name': 'junction'}, {'id': 8695, 'synset': 'junction_barrier.n.01', 'name': 'junction_barrier'}, {'id': 8696, 'synset': 'junk_shop.n.01', 'name': 'junk_shop'}, {'id': 8697, 'synset': 'jury_box.n.01', 'name': 'jury_box'}, {'id': 8698, 'synset': 'jury_mast.n.01', 'name': 'jury_mast'}, {'id': 8699, 'synset': 'kachina.n.03', 'name': 'kachina'}, {'id': 8700, 'synset': 'kaffiyeh.n.01', 'name': 'kaffiyeh'}, {'id': 8701, 'synset': 'kalansuwa.n.01', 'name': 'kalansuwa'}, {'id': 8702, 'synset': 'kalashnikov.n.01', 'name': 'Kalashnikov'}, {'id': 8703, 'synset': 'kameez.n.01', 'name': 'kameez'}, {'id': 8704, 'synset': 'kanzu.n.01', 'name': 'kanzu'}, {'id': 8705, 'synset': 'katharometer.n.01', 'name': 'katharometer'}, {'id': 8706, 'synset': 'kazoo.n.01', 'name': 'kazoo'}, {'id': 8707, 'synset': 'keel.n.03', 'name': 'keel'}, {'id': 8708, 'synset': 'keelboat.n.01', 'name': 'keelboat'}, {'id': 8709, 'synset': 'keelson.n.01', 'name': 'keelson'}, {'id': 8710, 'synset': 'keep.n.02', 'name': 'keep'}, {'id': 8711, 'synset': 'kepi.n.01', 'name': 'kepi'}, {'id': 8712, 'synset': 'keratoscope.n.01', 'name': 'keratoscope'}, {'id': 8713, 'synset': 'kerchief.n.01', 'name': 'kerchief'}, {'id': 8714, 'synset': 'ketch.n.01', 'name': 'ketch'}, {'id': 8715, 'synset': 'kettle.n.04', 'name': 'kettle'}, {'id': 8716, 'synset': 'key.n.15', 'name': 'key'}, {'id': 8717, 'synset': 'keyboard.n.01', 'name': 'keyboard'}, {'id': 8718, 'synset': 'keyboard_buffer.n.01', 'name': 'keyboard_buffer'}, {'id': 8719, 'synset': 'keyboard_instrument.n.01', 'name': 'keyboard_instrument'}, {'id': 8720, 'synset': 'keyhole.n.01', 'name': 'keyhole'}, {'id': 8721, 'synset': 'keyhole_saw.n.01', 'name': 'keyhole_saw'}, {'id': 8722, 'synset': 'khadi.n.01', 'name': 'khadi'}, {'id': 8723, 'synset': 'khaki.n.01', 'name': 'khaki'}, {'id': 8724, 'synset': 'khakis.n.01', 'name': 'khakis'}, {'id': 8725, 'synset': 'khimar.n.01', 'name': 'khimar'}, {'id': 8726, 'synset': 'khukuri.n.01', 'name': 'khukuri'}, {'id': 8727, 'synset': 'kick_pleat.n.01', 'name': 'kick_pleat'}, {'id': 8728, 'synset': 'kicksorter.n.01', 'name': 'kicksorter'}, {'id': 8729, 'synset': 'kickstand.n.01', 'name': 'kickstand'}, {'id': 8730, 'synset': 'kick_starter.n.01', 'name': 'kick_starter'}, {'id': 8731, 'synset': 'kid_glove.n.01', 'name': 'kid_glove'}, {'id': 8732, 'synset': 'kiln.n.01', 'name': 'kiln'}, {'id': 8733, 'synset': 'kinescope.n.01', 'name': 'kinescope'}, {'id': 8734, 'synset': 'kinetoscope.n.01', 'name': 'Kinetoscope'}, {'id': 8735, 'synset': 'king.n.10', 'name': 'king'}, {'id': 8736, 'synset': 'king.n.08', 'name': 'king'}, {'id': 8737, 'synset': 'kingbolt.n.01', 'name': 'kingbolt'}, {'id': 8738, 'synset': 'king_post.n.01', 'name': 'king_post'}, {'id': 8739, 'synset': "kipp's_apparatus.n.01", 'name': "Kipp's_apparatus"}, {'id': 8740, 'synset': 'kirk.n.01', 'name': 'kirk'}, {'id': 8741, 'synset': 'kirpan.n.01', 'name': 'kirpan'}, {'id': 8742, 'synset': 'kirtle.n.02', 'name': 'kirtle'}, {'id': 8743, 'synset': 'kirtle.n.01', 'name': 'kirtle'}, {'id': 8744, 'synset': 'kit.n.02', 'name': 'kit'}, {'id': 8745, 'synset': 'kit.n.01', 'name': 'kit'}, {'id': 8746, 'synset': 'kitbag.n.01', 'name': 'kitbag'}, {'id': 8747, 'synset': 'kitchen.n.01', 'name': 'kitchen'}, {'id': 8748, 'synset': 'kitchen_appliance.n.01', 'name': 'kitchen_appliance'}, {'id': 8749, 'synset': 'kitchenette.n.01', 'name': 'kitchenette'}, {'id': 8750, 'synset': 'kitchen_utensil.n.01', 'name': 'kitchen_utensil'}, {'id': 8751, 'synset': 'kitchenware.n.01', 'name': 'kitchenware'}, {'id': 8752, 'synset': 'kite_balloon.n.01', 'name': 'kite_balloon'}, {'id': 8753, 'synset': 'klaxon.n.01', 'name': 'klaxon'}, {'id': 8754, 'synset': 'klieg_light.n.01', 'name': 'klieg_light'}, {'id': 8755, 'synset': 'klystron.n.01', 'name': 'klystron'}, {'id': 8756, 'synset': 'knee_brace.n.01', 'name': 'knee_brace'}, {'id': 8757, 'synset': 'knee-high.n.01', 'name': 'knee-high'}, {'id': 8758, 'synset': 'knee_piece.n.01', 'name': 'knee_piece'}, {'id': 8759, 'synset': 'knife.n.02', 'name': 'knife'}, {'id': 8760, 'synset': 'knife_blade.n.01', 'name': 'knife_blade'}, {'id': 8761, 'synset': 'knight.n.02', 'name': 'knight'}, {'id': 8762, 'synset': 'knit.n.01', 'name': 'knit'}, {'id': 8763, 'synset': 'knitting_machine.n.01', 'name': 'knitting_machine'}, {'id': 8764, 'synset': 'knitwear.n.01', 'name': 'knitwear'}, {'id': 8765, 'synset': 'knob.n.01', 'name': 'knob'}, {'id': 8766, 'synset': 'knob.n.04', 'name': 'knob'}, {'id': 8767, 'synset': 'knobble.n.01', 'name': 'knobble'}, {'id': 8768, 'synset': 'knobkerrie.n.01', 'name': 'knobkerrie'}, {'id': 8769, 'synset': 'knot.n.02', 'name': 'knot'}, {'id': 8770, 'synset': 'knuckle_joint.n.02', 'name': 'knuckle_joint'}, {'id': 8771, 'synset': 'kohl.n.01', 'name': 'kohl'}, {'id': 8772, 'synset': 'koto.n.01', 'name': 'koto'}, {'id': 8773, 'synset': 'kraal.n.02', 'name': 'kraal'}, {'id': 8774, 'synset': 'kremlin.n.02', 'name': 'kremlin'}, {'id': 8775, 'synset': 'kris.n.01', 'name': 'kris'}, {'id': 8776, 'synset': 'krummhorn.n.01', 'name': 'krummhorn'}, {'id': 8777, 'synset': "kundt's_tube.n.01", 'name': "Kundt's_tube"}, {'id': 8778, 'synset': 'kurdistan.n.02', 'name': 'Kurdistan'}, {'id': 8779, 'synset': 'kurta.n.01', 'name': 'kurta'}, {'id': 8780, 'synset': 'kylix.n.01', 'name': 'kylix'}, {'id': 8781, 'synset': 'kymograph.n.01', 'name': 'kymograph'}, {'id': 8782, 'synset': 'lab_bench.n.01', 'name': 'lab_bench'}, {'id': 8783, 'synset': 'lace.n.02', 'name': 'lace'}, {'id': 8784, 'synset': 'lacquer.n.02', 'name': 'lacquer'}, {'id': 8785, 'synset': 'lacquerware.n.01', 'name': 'lacquerware'}, {'id': 8786, 'synset': 'lacrosse_ball.n.01', 'name': 'lacrosse_ball'}, {'id': 8787, 'synset': 'ladder-back.n.02', 'name': 'ladder-back'}, {'id': 8788, 'synset': 'ladder-back.n.01', 'name': 'ladder-back'}, {'id': 8789, 'synset': 'ladder_truck.n.01', 'name': 'ladder_truck'}, {'id': 8790, 'synset': "ladies'_room.n.01", 'name': "ladies'_room"}, {'id': 8791, 'synset': 'lady_chapel.n.01', 'name': 'lady_chapel'}, {'id': 8792, 'synset': 'lagerphone.n.01', 'name': 'lagerphone'}, {'id': 8793, 'synset': 'lag_screw.n.01', 'name': 'lag_screw'}, {'id': 8794, 'synset': 'lake_dwelling.n.01', 'name': 'lake_dwelling'}, {'id': 8795, 'synset': 'lally.n.01', 'name': 'lally'}, {'id': 8796, 'synset': 'lamasery.n.01', 'name': 'lamasery'}, {'id': 8797, 'synset': 'lambrequin.n.02', 'name': 'lambrequin'}, {'id': 8798, 'synset': 'lame.n.02', 'name': 'lame'}, {'id': 8799, 'synset': 'laminar_flow_clean_room.n.01', 'name': 'laminar_flow_clean_room'}, {'id': 8800, 'synset': 'laminate.n.01', 'name': 'laminate'}, {'id': 8801, 'synset': 'lamination.n.01', 'name': 'lamination'}, {'id': 8802, 'synset': 'lamp.n.01', 'name': 'lamp'}, {'id': 8803, 'synset': 'lamp_house.n.01', 'name': 'lamp_house'}, {'id': 8804, 'synset': 'lanai.n.02', 'name': 'lanai'}, {'id': 8805, 'synset': 'lancet_arch.n.01', 'name': 'lancet_arch'}, {'id': 8806, 'synset': 'lancet_window.n.01', 'name': 'lancet_window'}, {'id': 8807, 'synset': 'landau.n.02', 'name': 'landau'}, {'id': 8808, 'synset': 'lander.n.02', 'name': 'lander'}, {'id': 8809, 'synset': 'landing_craft.n.01', 'name': 'landing_craft'}, {'id': 8810, 'synset': 'landing_flap.n.01', 'name': 'landing_flap'}, {'id': 8811, 'synset': 'landing_gear.n.01', 'name': 'landing_gear'}, {'id': 8812, 'synset': 'landing_net.n.01', 'name': 'landing_net'}, {'id': 8813, 'synset': 'landing_skid.n.01', 'name': 'landing_skid'}, {'id': 8814, 'synset': 'land_line.n.01', 'name': 'land_line'}, {'id': 8815, 'synset': 'land_mine.n.01', 'name': 'land_mine'}, {'id': 8816, 'synset': 'land_office.n.01', 'name': 'land_office'}, {'id': 8817, 'synset': 'lanolin.n.02', 'name': 'lanolin'}, {'id': 8818, 'synset': 'lanyard.n.01', 'name': 'lanyard'}, {'id': 8819, 'synset': 'lap.n.03', 'name': 'lap'}, {'id': 8820, 'synset': 'laparoscope.n.01', 'name': 'laparoscope'}, {'id': 8821, 'synset': 'lapboard.n.01', 'name': 'lapboard'}, {'id': 8822, 'synset': 'lapel.n.01', 'name': 'lapel'}, {'id': 8823, 'synset': 'lap_joint.n.01', 'name': 'lap_joint'}, {'id': 8824, 'synset': 'laryngoscope.n.01', 'name': 'laryngoscope'}, {'id': 8825, 'synset': 'laser.n.01', 'name': 'laser'}, {'id': 8826, 'synset': 'laser-guided_bomb.n.01', 'name': 'laser-guided_bomb'}, {'id': 8827, 'synset': 'laser_printer.n.01', 'name': 'laser_printer'}, {'id': 8828, 'synset': 'lash.n.02', 'name': 'lash'}, {'id': 8829, 'synset': 'lashing.n.02', 'name': 'lashing'}, {'id': 8830, 'synset': 'lasso.n.02', 'name': 'lasso'}, {'id': 8831, 'synset': 'latch.n.01', 'name': 'latch'}, {'id': 8832, 'synset': 'latchet.n.01', 'name': 'latchet'}, {'id': 8833, 'synset': 'latchkey.n.01', 'name': 'latchkey'}, {'id': 8834, 'synset': 'lateen.n.01', 'name': 'lateen'}, {'id': 8835, 'synset': 'latex_paint.n.01', 'name': 'latex_paint'}, {'id': 8836, 'synset': 'lath.n.01', 'name': 'lath'}, {'id': 8837, 'synset': 'lathe.n.01', 'name': 'lathe'}, {'id': 8838, 'synset': 'latrine.n.01', 'name': 'latrine'}, {'id': 8839, 'synset': 'lattice.n.03', 'name': 'lattice'}, {'id': 8840, 'synset': 'launch.n.01', 'name': 'launch'}, {'id': 8841, 'synset': 'launcher.n.01', 'name': 'launcher'}, {'id': 8842, 'synset': 'laundry.n.01', 'name': 'laundry'}, {'id': 8843, 'synset': 'laundry_cart.n.01', 'name': 'laundry_cart'}, {'id': 8844, 'synset': 'laundry_truck.n.01', 'name': 'laundry_truck'}, {'id': 8845, 'synset': 'lavalava.n.01', 'name': 'lavalava'}, {'id': 8846, 'synset': 'lavaliere.n.01', 'name': 'lavaliere'}, {'id': 8847, 'synset': 'laver.n.02', 'name': 'laver'}, {'id': 8848, 'synset': 'lawn_chair.n.01', 'name': 'lawn_chair'}, {'id': 8849, 'synset': 'lawn_furniture.n.01', 'name': 'lawn_furniture'}, {'id': 8850, 'synset': 'layette.n.01', 'name': 'layette'}, {'id': 8851, 'synset': 'lead-acid_battery.n.01', 'name': 'lead-acid_battery'}, {'id': 8852, 'synset': 'lead-in.n.02', 'name': 'lead-in'}, {'id': 8853, 'synset': 'leading_rein.n.01', 'name': 'leading_rein'}, {'id': 8854, 'synset': 'lead_pencil.n.01', 'name': 'lead_pencil'}, {'id': 8855, 'synset': 'leaf_spring.n.01', 'name': 'leaf_spring'}, {'id': 8856, 'synset': 'lean-to.n.01', 'name': 'lean-to'}, {'id': 8857, 'synset': 'lean-to_tent.n.01', 'name': 'lean-to_tent'}, {'id': 8858, 'synset': 'leash.n.01', 'name': 'leash'}, {'id': 8859, 'synset': 'leatherette.n.01', 'name': 'leatherette'}, {'id': 8860, 'synset': 'leather_strip.n.01', 'name': 'leather_strip'}, {'id': 8861, 'synset': 'leclanche_cell.n.01', 'name': 'Leclanche_cell'}, {'id': 8862, 'synset': 'lectern.n.01', 'name': 'lectern'}, {'id': 8863, 'synset': 'lecture_room.n.01', 'name': 'lecture_room'}, {'id': 8864, 'synset': 'lederhosen.n.01', 'name': 'lederhosen'}, {'id': 8865, 'synset': 'ledger_board.n.01', 'name': 'ledger_board'}, {'id': 8866, 'synset': 'leg.n.07', 'name': 'leg'}, {'id': 8867, 'synset': 'leg.n.03', 'name': 'leg'}, {'id': 8868, 'synset': 'leiden_jar.n.01', 'name': 'Leiden_jar'}, {'id': 8869, 'synset': 'leisure_wear.n.01', 'name': 'leisure_wear'}, {'id': 8870, 'synset': 'lens.n.01', 'name': 'lens'}, {'id': 8871, 'synset': 'lens.n.05', 'name': 'lens'}, {'id': 8872, 'synset': 'lens_cap.n.01', 'name': 'lens_cap'}, {'id': 8873, 'synset': 'lens_implant.n.01', 'name': 'lens_implant'}, {'id': 8874, 'synset': 'leotard.n.01', 'name': 'leotard'}, {'id': 8875, 'synset': 'letter_case.n.01', 'name': 'letter_case'}, {'id': 8876, 'synset': 'letter_opener.n.01', 'name': 'letter_opener'}, {'id': 8877, 'synset': 'levee.n.03', 'name': 'levee'}, {'id': 8878, 'synset': 'level.n.05', 'name': 'level'}, {'id': 8879, 'synset': 'lever.n.01', 'name': 'lever'}, {'id': 8880, 'synset': 'lever.n.03', 'name': 'lever'}, {'id': 8881, 'synset': 'lever.n.02', 'name': 'lever'}, {'id': 8882, 'synset': 'lever_lock.n.01', 'name': 'lever_lock'}, {'id': 8883, 'synset': "levi's.n.01", 'name': "Levi's"}, {'id': 8884, 'synset': 'liberty_ship.n.01', 'name': 'Liberty_ship'}, {'id': 8885, 'synset': 'library.n.01', 'name': 'library'}, {'id': 8886, 'synset': 'library.n.05', 'name': 'library'}, {'id': 8887, 'synset': 'lid.n.02', 'name': 'lid'}, {'id': 8888, 'synset': 'liebig_condenser.n.01', 'name': 'Liebig_condenser'}, {'id': 8889, 'synset': 'lie_detector.n.01', 'name': 'lie_detector'}, {'id': 8890, 'synset': 'lifeboat.n.01', 'name': 'lifeboat'}, {'id': 8891, 'synset': 'life_office.n.01', 'name': 'life_office'}, {'id': 8892, 'synset': 'life_preserver.n.01', 'name': 'life_preserver'}, {'id': 8893, 'synset': 'life-support_system.n.02', 'name': 'life-support_system'}, {'id': 8894, 'synset': 'life-support_system.n.01', 'name': 'life-support_system'}, {'id': 8895, 'synset': 'lifting_device.n.01', 'name': 'lifting_device'}, {'id': 8896, 'synset': 'lift_pump.n.01', 'name': 'lift_pump'}, {'id': 8897, 'synset': 'ligament.n.02', 'name': 'ligament'}, {'id': 8898, 'synset': 'ligature.n.03', 'name': 'ligature'}, {'id': 8899, 'synset': 'light.n.02', 'name': 'light'}, {'id': 8900, 'synset': 'light_arm.n.01', 'name': 'light_arm'}, {'id': 8901, 'synset': 'light_circuit.n.01', 'name': 'light_circuit'}, {'id': 8902, 'synset': 'light-emitting_diode.n.01', 'name': 'light-emitting_diode'}, {'id': 8903, 'synset': 'lighter.n.02', 'name': 'lighter'}, {'id': 8904, 'synset': 'lighter-than-air_craft.n.01', 'name': 'lighter-than-air_craft'}, {'id': 8905, 'synset': 'light_filter.n.01', 'name': 'light_filter'}, {'id': 8906, 'synset': 'lighting.n.02', 'name': 'lighting'}, {'id': 8907, 'synset': 'light_machine_gun.n.01', 'name': 'light_machine_gun'}, {'id': 8908, 'synset': 'light_meter.n.01', 'name': 'light_meter'}, {'id': 8909, 'synset': 'light_microscope.n.01', 'name': 'light_microscope'}, {'id': 8910, 'synset': 'light_pen.n.01', 'name': 'light_pen'}, {'id': 8911, 'synset': 'lightship.n.01', 'name': 'lightship'}, {'id': 8912, 'synset': 'lilo.n.01', 'name': 'Lilo'}, {'id': 8913, 'synset': 'limber.n.01', 'name': 'limber'}, {'id': 8914, 'synset': 'limekiln.n.01', 'name': 'limekiln'}, {'id': 8915, 'synset': 'limiter.n.01', 'name': 'limiter'}, {'id': 8916, 'synset': 'linear_accelerator.n.01', 'name': 'linear_accelerator'}, {'id': 8917, 'synset': 'linen.n.01', 'name': 'linen'}, {'id': 8918, 'synset': 'line_printer.n.01', 'name': 'line_printer'}, {'id': 8919, 'synset': 'liner.n.04', 'name': 'liner'}, {'id': 8920, 'synset': 'liner.n.03', 'name': 'liner'}, {'id': 8921, 'synset': 'lingerie.n.01', 'name': 'lingerie'}, {'id': 8922, 'synset': 'lining.n.01', 'name': 'lining'}, {'id': 8923, 'synset': 'link.n.09', 'name': 'link'}, {'id': 8924, 'synset': 'linkage.n.03', 'name': 'linkage'}, {'id': 8925, 'synset': 'link_trainer.n.01', 'name': 'Link_trainer'}, {'id': 8926, 'synset': 'linocut.n.02', 'name': 'linocut'}, {'id': 8927, 'synset': 'linoleum_knife.n.01', 'name': 'linoleum_knife'}, {'id': 8928, 'synset': 'linotype.n.01', 'name': 'Linotype'}, {'id': 8929, 'synset': 'linsey-woolsey.n.01', 'name': 'linsey-woolsey'}, {'id': 8930, 'synset': 'linstock.n.01', 'name': 'linstock'}, {'id': 8931, 'synset': 'lion-jaw_forceps.n.01', 'name': 'lion-jaw_forceps'}, {'id': 8932, 'synset': 'lip-gloss.n.01', 'name': 'lip-gloss'}, {'id': 8933, 'synset': 'lipstick.n.01', 'name': 'lipstick'}, {'id': 8934, 'synset': 'liqueur_glass.n.01', 'name': 'liqueur_glass'}, {'id': 8935, 'synset': 'liquid_crystal_display.n.01', 'name': 'liquid_crystal_display'}, {'id': 8936, 'synset': 'liquid_metal_reactor.n.01', 'name': 'liquid_metal_reactor'}, {'id': 8937, 'synset': 'lisle.n.01', 'name': 'lisle'}, {'id': 8938, 'synset': 'lister.n.03', 'name': 'lister'}, {'id': 8939, 'synset': 'litterbin.n.01', 'name': 'litterbin'}, {'id': 8940, 'synset': 'little_theater.n.01', 'name': 'little_theater'}, {'id': 8941, 'synset': 'live_axle.n.01', 'name': 'live_axle'}, {'id': 8942, 'synset': 'living_quarters.n.01', 'name': 'living_quarters'}, {'id': 8943, 'synset': 'living_room.n.01', 'name': 'living_room'}, {'id': 8944, 'synset': 'load.n.09', 'name': 'load'}, {'id': 8945, 'synset': 'loafer.n.02', 'name': 'Loafer'}, {'id': 8946, 'synset': 'loaner.n.02', 'name': 'loaner'}, {'id': 8947, 'synset': 'lobe.n.04', 'name': 'lobe'}, {'id': 8948, 'synset': 'lobster_pot.n.01', 'name': 'lobster_pot'}, {'id': 8949, 'synset': 'local.n.01', 'name': 'local'}, {'id': 8950, 'synset': 'local_area_network.n.01', 'name': 'local_area_network'}, {'id': 8951, 'synset': 'local_oscillator.n.01', 'name': 'local_oscillator'}, {'id': 8952, 'synset': 'lochaber_ax.n.01', 'name': 'Lochaber_ax'}, {'id': 8953, 'synset': 'lock.n.01', 'name': 'lock'}, {'id': 8954, 'synset': 'lock.n.05', 'name': 'lock'}, {'id': 8955, 'synset': 'lock.n.04', 'name': 'lock'}, {'id': 8956, 'synset': 'lock.n.03', 'name': 'lock'}, {'id': 8957, 'synset': 'lockage.n.02', 'name': 'lockage'}, {'id': 8958, 'synset': 'locker.n.02', 'name': 'locker'}, {'id': 8959, 'synset': 'locker_room.n.01', 'name': 'locker_room'}, {'id': 8960, 'synset': 'locket.n.01', 'name': 'locket'}, {'id': 8961, 'synset': 'lock-gate.n.01', 'name': 'lock-gate'}, {'id': 8962, 'synset': 'locking_pliers.n.01', 'name': 'locking_pliers'}, {'id': 8963, 'synset': 'lockring.n.01', 'name': 'lockring'}, {'id': 8964, 'synset': 'lockstitch.n.01', 'name': 'lockstitch'}, {'id': 8965, 'synset': 'lockup.n.01', 'name': 'lockup'}, {'id': 8966, 'synset': 'locomotive.n.01', 'name': 'locomotive'}, {'id': 8967, 'synset': 'lodge.n.05', 'name': 'lodge'}, {'id': 8968, 'synset': 'lodge.n.04', 'name': 'lodge'}, {'id': 8969, 'synset': 'lodge.n.03', 'name': 'lodge'}, {'id': 8970, 'synset': 'lodging_house.n.01', 'name': 'lodging_house'}, {'id': 8971, 'synset': 'loft.n.02', 'name': 'loft'}, {'id': 8972, 'synset': 'loft.n.04', 'name': 'loft'}, {'id': 8973, 'synset': 'loft.n.01', 'name': 'loft'}, {'id': 8974, 'synset': 'log_cabin.n.01', 'name': 'log_cabin'}, {'id': 8975, 'synset': 'loggia.n.01', 'name': 'loggia'}, {'id': 8976, 'synset': 'longbow.n.01', 'name': 'longbow'}, {'id': 8977, 'synset': 'long_iron.n.01', 'name': 'long_iron'}, {'id': 8978, 'synset': 'long_johns.n.01', 'name': 'long_johns'}, {'id': 8979, 'synset': 'long_sleeve.n.01', 'name': 'long_sleeve'}, {'id': 8980, 'synset': 'long_tom.n.01', 'name': 'long_tom'}, {'id': 8981, 'synset': 'long_trousers.n.01', 'name': 'long_trousers'}, {'id': 8982, 'synset': 'long_underwear.n.01', 'name': 'long_underwear'}, {'id': 8983, 'synset': 'looking_glass.n.01', 'name': 'looking_glass'}, {'id': 8984, 'synset': 'lookout.n.03', 'name': 'lookout'}, {'id': 8985, 'synset': 'loom.n.01', 'name': 'loom'}, {'id': 8986, 'synset': 'loop_knot.n.01', 'name': 'loop_knot'}, {'id': 8987, 'synset': 'lorgnette.n.01', 'name': 'lorgnette'}, {'id': 8988, 'synset': 'lorraine_cross.n.01', 'name': 'Lorraine_cross'}, {'id': 8989, 'synset': 'lorry.n.02', 'name': 'lorry'}, {'id': 8990, 'synset': 'lota.n.01', 'name': 'lota'}, {'id': 8991, 'synset': 'lotion.n.01', 'name': 'lotion'}, {'id': 8992, 'synset': 'lounge.n.02', 'name': 'lounge'}, {'id': 8993, 'synset': 'lounger.n.03', 'name': 'lounger'}, {'id': 8994, 'synset': 'lounging_jacket.n.01', 'name': 'lounging_jacket'}, {'id': 8995, 'synset': 'lounging_pajama.n.01', 'name': 'lounging_pajama'}, {'id': 8996, 'synset': 'loungewear.n.01', 'name': 'loungewear'}, {'id': 8997, 'synset': 'loupe.n.01', 'name': 'loupe'}, {'id': 8998, 'synset': 'louvered_window.n.01', 'name': 'louvered_window'}, {'id': 8999, 'synset': 'love_knot.n.01', 'name': 'love_knot'}, {'id': 9000, 'synset': 'loving_cup.n.01', 'name': 'loving_cup'}, {'id': 9001, 'synset': 'lowboy.n.01', 'name': 'lowboy'}, {'id': 9002, 'synset': 'low-pass_filter.n.01', 'name': 'low-pass_filter'}, {'id': 9003, 'synset': 'low-warp-loom.n.01', 'name': 'low-warp-loom'}, {'id': 9004, 'synset': 'lp.n.01', 'name': 'LP'}, {'id': 9005, 'synset': 'l-plate.n.01', 'name': 'L-plate'}, {'id': 9006, 'synset': "lubber's_hole.n.01", 'name': "lubber's_hole"}, {'id': 9007, 'synset': 'lubricating_system.n.01', 'name': 'lubricating_system'}, {'id': 9008, 'synset': 'luff.n.01', 'name': 'luff'}, {'id': 9009, 'synset': 'lug.n.03', 'name': 'lug'}, {'id': 9010, 'synset': 'luge.n.01', 'name': 'luge'}, {'id': 9011, 'synset': 'luger.n.01', 'name': 'Luger'}, {'id': 9012, 'synset': 'luggage_carrier.n.01', 'name': 'luggage_carrier'}, {'id': 9013, 'synset': 'luggage_compartment.n.01', 'name': 'luggage_compartment'}, {'id': 9014, 'synset': 'luggage_rack.n.01', 'name': 'luggage_rack'}, {'id': 9015, 'synset': 'lugger.n.01', 'name': 'lugger'}, {'id': 9016, 'synset': 'lugsail.n.01', 'name': 'lugsail'}, {'id': 9017, 'synset': 'lug_wrench.n.01', 'name': 'lug_wrench'}, {'id': 9018, 'synset': 'lumberjack.n.02', 'name': 'lumberjack'}, {'id': 9019, 'synset': 'lumbermill.n.01', 'name': 'lumbermill'}, {'id': 9020, 'synset': 'lunar_excursion_module.n.01', 'name': 'lunar_excursion_module'}, {'id': 9021, 'synset': 'lunchroom.n.01', 'name': 'lunchroom'}, {'id': 9022, 'synset': 'lunette.n.01', 'name': 'lunette'}, {'id': 9023, 'synset': 'lungi.n.01', 'name': 'lungi'}, {'id': 9024, 'synset': 'lunula.n.02', 'name': 'lunula'}, {'id': 9025, 'synset': 'lusterware.n.01', 'name': 'lusterware'}, {'id': 9026, 'synset': 'lute.n.02', 'name': 'lute'}, {'id': 9027, 'synset': 'luxury_liner.n.01', 'name': 'luxury_liner'}, {'id': 9028, 'synset': 'lyceum.n.02', 'name': 'lyceum'}, {'id': 9029, 'synset': 'lychgate.n.01', 'name': 'lychgate'}, {'id': 9030, 'synset': 'lyre.n.01', 'name': 'lyre'}, {'id': 9031, 'synset': 'machete.n.01', 'name': 'machete'}, {'id': 9032, 'synset': 'machicolation.n.01', 'name': 'machicolation'}, {'id': 9033, 'synset': 'machine.n.01', 'name': 'machine'}, {'id': 9034, 'synset': 'machine.n.04', 'name': 'machine'}, {'id': 9035, 'synset': 'machine_bolt.n.01', 'name': 'machine_bolt'}, {'id': 9036, 'synset': 'machinery.n.01', 'name': 'machinery'}, {'id': 9037, 'synset': 'machine_screw.n.01', 'name': 'machine_screw'}, {'id': 9038, 'synset': 'machine_tool.n.01', 'name': 'machine_tool'}, {'id': 9039, 'synset': "machinist's_vise.n.01", 'name': "machinist's_vise"}, {'id': 9040, 'synset': 'machmeter.n.01', 'name': 'machmeter'}, {'id': 9041, 'synset': 'mackinaw.n.04', 'name': 'mackinaw'}, {'id': 9042, 'synset': 'mackinaw.n.03', 'name': 'mackinaw'}, {'id': 9043, 'synset': 'mackinaw.n.01', 'name': 'mackinaw'}, {'id': 9044, 'synset': 'mackintosh.n.01', 'name': 'mackintosh'}, {'id': 9045, 'synset': 'macrame.n.01', 'name': 'macrame'}, {'id': 9046, 'synset': 'madras.n.03', 'name': 'madras'}, {'id': 9047, 'synset': 'mae_west.n.02', 'name': 'Mae_West'}, {'id': 9048, 'synset': 'magazine_rack.n.01', 'name': 'magazine_rack'}, {'id': 9049, 'synset': 'magic_lantern.n.01', 'name': 'magic_lantern'}, {'id': 9050, 'synset': 'magnetic_bottle.n.01', 'name': 'magnetic_bottle'}, {'id': 9051, 'synset': 'magnetic_compass.n.01', 'name': 'magnetic_compass'}, {'id': 9052, 'synset': 'magnetic_core_memory.n.01', 'name': 'magnetic_core_memory'}, {'id': 9053, 'synset': 'magnetic_disk.n.01', 'name': 'magnetic_disk'}, {'id': 9054, 'synset': 'magnetic_head.n.01', 'name': 'magnetic_head'}, {'id': 9055, 'synset': 'magnetic_mine.n.01', 'name': 'magnetic_mine'}, {'id': 9056, 'synset': 'magnetic_needle.n.01', 'name': 'magnetic_needle'}, {'id': 9057, 'synset': 'magnetic_recorder.n.01', 'name': 'magnetic_recorder'}, {'id': 9058, 'synset': 'magnetic_stripe.n.01', 'name': 'magnetic_stripe'}, {'id': 9059, 'synset': 'magnetic_tape.n.01', 'name': 'magnetic_tape'}, {'id': 9060, 'synset': 'magneto.n.01', 'name': 'magneto'}, {'id': 9061, 'synset': 'magnetometer.n.01', 'name': 'magnetometer'}, {'id': 9062, 'synset': 'magnetron.n.01', 'name': 'magnetron'}, {'id': 9063, 'synset': 'magnifier.n.01', 'name': 'magnifier'}, {'id': 9064, 'synset': 'magnum.n.01', 'name': 'magnum'}, {'id': 9065, 'synset': 'magnus_hitch.n.01', 'name': 'magnus_hitch'}, {'id': 9066, 'synset': 'mail.n.03', 'name': 'mail'}, {'id': 9067, 'synset': 'mailbag.n.02', 'name': 'mailbag'}, {'id': 9068, 'synset': 'mailbag.n.01', 'name': 'mailbag'}, {'id': 9069, 'synset': 'mailboat.n.01', 'name': 'mailboat'}, {'id': 9070, 'synset': 'mail_car.n.01', 'name': 'mail_car'}, {'id': 9071, 'synset': 'maildrop.n.01', 'name': 'maildrop'}, {'id': 9072, 'synset': 'mailer.n.04', 'name': 'mailer'}, {'id': 9073, 'synset': 'maillot.n.02', 'name': 'maillot'}, {'id': 9074, 'synset': 'maillot.n.01', 'name': 'maillot'}, {'id': 9075, 'synset': 'mailsorter.n.01', 'name': 'mailsorter'}, {'id': 9076, 'synset': 'mail_train.n.01', 'name': 'mail_train'}, {'id': 9077, 'synset': 'mainframe.n.01', 'name': 'mainframe'}, {'id': 9078, 'synset': 'mainmast.n.01', 'name': 'mainmast'}, {'id': 9079, 'synset': 'main_rotor.n.01', 'name': 'main_rotor'}, {'id': 9080, 'synset': 'mainsail.n.01', 'name': 'mainsail'}, {'id': 9081, 'synset': 'mainspring.n.01', 'name': 'mainspring'}, {'id': 9082, 'synset': 'main-topmast.n.01', 'name': 'main-topmast'}, {'id': 9083, 'synset': 'main-topsail.n.01', 'name': 'main-topsail'}, {'id': 9084, 'synset': 'main_yard.n.01', 'name': 'main_yard'}, {'id': 9085, 'synset': 'maisonette.n.02', 'name': 'maisonette'}, {'id': 9086, 'synset': 'majolica.n.01', 'name': 'majolica'}, {'id': 9087, 'synset': 'makeup.n.01', 'name': 'makeup'}, {'id': 9088, 'synset': 'maksutov_telescope.n.01', 'name': 'Maksutov_telescope'}, {'id': 9089, 'synset': 'malacca.n.02', 'name': 'malacca'}, {'id': 9090, 'synset': 'mallet.n.03', 'name': 'mallet'}, {'id': 9091, 'synset': 'mallet.n.02', 'name': 'mallet'}, {'id': 9092, 'synset': 'mammogram.n.01', 'name': 'mammogram'}, {'id': 9093, 'synset': 'mandola.n.01', 'name': 'mandola'}, {'id': 9094, 'synset': 'mandolin.n.01', 'name': 'mandolin'}, {'id': 9095, 'synset': 'mangle.n.01', 'name': 'mangle'}, {'id': 9096, 'synset': 'manhole_cover.n.01', 'name': 'manhole_cover'}, {'id': 9097, 'synset': 'man-of-war.n.01', 'name': 'man-of-war'}, {'id': 9098, 'synset': 'manometer.n.01', 'name': 'manometer'}, {'id': 9099, 'synset': 'manor.n.01', 'name': 'manor'}, {'id': 9100, 'synset': 'manor_hall.n.01', 'name': 'manor_hall'}, {'id': 9101, 'synset': 'manpad.n.01', 'name': 'MANPAD'}, {'id': 9102, 'synset': 'mansard.n.01', 'name': 'mansard'}, {'id': 9103, 'synset': 'manse.n.02', 'name': 'manse'}, {'id': 9104, 'synset': 'mansion.n.02', 'name': 'mansion'}, {'id': 9105, 'synset': 'mantel.n.01', 'name': 'mantel'}, {'id': 9106, 'synset': 'mantelet.n.02', 'name': 'mantelet'}, {'id': 9107, 'synset': 'mantilla.n.01', 'name': 'mantilla'}, {'id': 9108, 'synset': 'mao_jacket.n.01', 'name': 'Mao_jacket'}, {'id': 9109, 'synset': 'maquiladora.n.01', 'name': 'maquiladora'}, {'id': 9110, 'synset': 'maraca.n.01', 'name': 'maraca'}, {'id': 9111, 'synset': 'marble.n.02', 'name': 'marble'}, {'id': 9112, 'synset': 'marching_order.n.01', 'name': 'marching_order'}, {'id': 9113, 'synset': 'marimba.n.01', 'name': 'marimba'}, {'id': 9114, 'synset': 'marina.n.01', 'name': 'marina'}, {'id': 9115, 'synset': 'marketplace.n.02', 'name': 'marketplace'}, {'id': 9116, 'synset': 'marlinespike.n.01', 'name': 'marlinespike'}, {'id': 9117, 'synset': 'marocain.n.01', 'name': 'marocain'}, {'id': 9118, 'synset': 'marquee.n.02', 'name': 'marquee'}, {'id': 9119, 'synset': 'marquetry.n.01', 'name': 'marquetry'}, {'id': 9120, 'synset': 'marriage_bed.n.01', 'name': 'marriage_bed'}, {'id': 9121, 'synset': 'martello_tower.n.01', 'name': 'martello_tower'}, {'id': 9122, 'synset': 'martingale.n.01', 'name': 'martingale'}, {'id': 9123, 'synset': 'mascara.n.01', 'name': 'mascara'}, {'id': 9124, 'synset': 'maser.n.01', 'name': 'maser'}, {'id': 9125, 'synset': 'mashie.n.01', 'name': 'mashie'}, {'id': 9126, 'synset': 'mashie_niblick.n.01', 'name': 'mashie_niblick'}, {'id': 9127, 'synset': 'masjid.n.01', 'name': 'masjid'}, {'id': 9128, 'synset': 'mask.n.01', 'name': 'mask'}, {'id': 9129, 'synset': 'masonite.n.01', 'name': 'Masonite'}, {'id': 9130, 'synset': 'mason_jar.n.01', 'name': 'Mason_jar'}, {'id': 9131, 'synset': 'masonry.n.01', 'name': 'masonry'}, {'id': 9132, 'synset': "mason's_level.n.01", 'name': "mason's_level"}, {'id': 9133, 'synset': 'massage_parlor.n.02', 'name': 'massage_parlor'}, {'id': 9134, 'synset': 'massage_parlor.n.01', 'name': 'massage_parlor'}, {'id': 9135, 'synset': 'mass_spectrograph.n.01', 'name': 'mass_spectrograph'}, {'id': 9136, 'synset': 'mass_spectrometer.n.01', 'name': 'mass_spectrometer'}, {'id': 9137, 'synset': 'mast.n.04', 'name': 'mast'}, {'id': 9138, 'synset': 'mastaba.n.01', 'name': 'mastaba'}, {'id': 9139, 'synset': 'master_bedroom.n.01', 'name': 'master_bedroom'}, {'id': 9140, 'synset': 'masterpiece.n.01', 'name': 'masterpiece'}, {'id': 9141, 'synset': 'mat.n.01', 'name': 'mat'}, {'id': 9142, 'synset': 'match.n.01', 'name': 'match'}, {'id': 9143, 'synset': 'match.n.03', 'name': 'match'}, {'id': 9144, 'synset': 'matchboard.n.01', 'name': 'matchboard'}, {'id': 9145, 'synset': 'matchbook.n.01', 'name': 'matchbook'}, {'id': 9146, 'synset': 'matchlock.n.01', 'name': 'matchlock'}, {'id': 9147, 'synset': 'match_plane.n.01', 'name': 'match_plane'}, {'id': 9148, 'synset': 'matchstick.n.01', 'name': 'matchstick'}, {'id': 9149, 'synset': 'material.n.04', 'name': 'material'}, {'id': 9150, 'synset': 'materiel.n.01', 'name': 'materiel'}, {'id': 9151, 'synset': 'maternity_hospital.n.01', 'name': 'maternity_hospital'}, {'id': 9152, 'synset': 'maternity_ward.n.01', 'name': 'maternity_ward'}, {'id': 9153, 'synset': 'matrix.n.06', 'name': 'matrix'}, {'id': 9154, 'synset': 'matthew_walker.n.01', 'name': 'Matthew_Walker'}, {'id': 9155, 'synset': 'matting.n.01', 'name': 'matting'}, {'id': 9156, 'synset': 'mattock.n.01', 'name': 'mattock'}, {'id': 9157, 'synset': 'mattress_cover.n.01', 'name': 'mattress_cover'}, {'id': 9158, 'synset': 'maul.n.01', 'name': 'maul'}, {'id': 9159, 'synset': 'maulstick.n.01', 'name': 'maulstick'}, {'id': 9160, 'synset': 'mauser.n.02', 'name': 'Mauser'}, {'id': 9161, 'synset': 'mausoleum.n.01', 'name': 'mausoleum'}, {'id': 9162, 'synset': 'maxi.n.01', 'name': 'maxi'}, {'id': 9163, 'synset': 'maxim_gun.n.01', 'name': 'Maxim_gun'}, {'id': 9164, 'synset': 'maximum_and_minimum_thermometer.n.01', 'name': 'maximum_and_minimum_thermometer'}, {'id': 9165, 'synset': 'maypole.n.01', 'name': 'maypole'}, {'id': 9166, 'synset': 'maze.n.01', 'name': 'maze'}, {'id': 9167, 'synset': 'mazer.n.01', 'name': 'mazer'}, {'id': 9168, 'synset': 'means.n.02', 'name': 'means'}, {'id': 9169, 'synset': 'measure.n.09', 'name': 'measure'}, {'id': 9170, 'synset': 'measuring_instrument.n.01', 'name': 'measuring_instrument'}, {'id': 9171, 'synset': 'meat_counter.n.01', 'name': 'meat_counter'}, {'id': 9172, 'synset': 'meat_grinder.n.01', 'name': 'meat_grinder'}, {'id': 9173, 'synset': 'meat_hook.n.01', 'name': 'meat_hook'}, {'id': 9174, 'synset': 'meat_house.n.02', 'name': 'meat_house'}, {'id': 9175, 'synset': 'meat_safe.n.01', 'name': 'meat_safe'}, {'id': 9176, 'synset': 'meat_thermometer.n.01', 'name': 'meat_thermometer'}, {'id': 9177, 'synset': 'mechanical_device.n.01', 'name': 'mechanical_device'}, {'id': 9178, 'synset': 'mechanical_piano.n.01', 'name': 'mechanical_piano'}, {'id': 9179, 'synset': 'mechanical_system.n.01', 'name': 'mechanical_system'}, {'id': 9180, 'synset': 'mechanism.n.05', 'name': 'mechanism'}, {'id': 9181, 'synset': 'medical_building.n.01', 'name': 'medical_building'}, {'id': 9182, 'synset': 'medical_instrument.n.01', 'name': 'medical_instrument'}, {'id': 9183, 'synset': 'medicine_ball.n.01', 'name': 'medicine_ball'}, {'id': 9184, 'synset': 'medicine_chest.n.01', 'name': 'medicine_chest'}, {'id': 9185, 'synset': 'medline.n.01', 'name': 'MEDLINE'}, {'id': 9186, 'synset': 'megalith.n.01', 'name': 'megalith'}, {'id': 9187, 'synset': 'megaphone.n.01', 'name': 'megaphone'}, {'id': 9188, 'synset': 'memorial.n.03', 'name': 'memorial'}, {'id': 9189, 'synset': 'memory.n.04', 'name': 'memory'}, {'id': 9190, 'synset': 'memory_chip.n.01', 'name': 'memory_chip'}, {'id': 9191, 'synset': 'memory_device.n.01', 'name': 'memory_device'}, {'id': 9192, 'synset': 'menagerie.n.02', 'name': 'menagerie'}, {'id': 9193, 'synset': 'mending.n.01', 'name': 'mending'}, {'id': 9194, 'synset': 'menhir.n.01', 'name': 'menhir'}, {'id': 9195, 'synset': 'menorah.n.02', 'name': 'menorah'}, {'id': 9196, 'synset': 'menorah.n.01', 'name': 'Menorah'}, {'id': 9197, 'synset': "man's_clothing.n.01", 'name': "man's_clothing"}, {'id': 9198, 'synset': "men's_room.n.01", 'name': "men's_room"}, {'id': 9199, 'synset': 'mercantile_establishment.n.01', 'name': 'mercantile_establishment'}, {'id': 9200, 'synset': 'mercury_barometer.n.01', 'name': 'mercury_barometer'}, {'id': 9201, 'synset': 'mercury_cell.n.01', 'name': 'mercury_cell'}, {'id': 9202, 'synset': 'mercury_thermometer.n.01', 'name': 'mercury_thermometer'}, {'id': 9203, 'synset': 'mercury-vapor_lamp.n.01', 'name': 'mercury-vapor_lamp'}, {'id': 9204, 'synset': 'mercy_seat.n.02', 'name': 'mercy_seat'}, {'id': 9205, 'synset': 'merlon.n.01', 'name': 'merlon'}, {'id': 9206, 'synset': 'mess.n.05', 'name': 'mess'}, {'id': 9207, 'synset': 'mess_jacket.n.01', 'name': 'mess_jacket'}, {'id': 9208, 'synset': 'mess_kit.n.01', 'name': 'mess_kit'}, {'id': 9209, 'synset': 'messuage.n.01', 'name': 'messuage'}, {'id': 9210, 'synset': 'metal_detector.n.01', 'name': 'metal_detector'}, {'id': 9211, 'synset': 'metallic.n.01', 'name': 'metallic'}, {'id': 9212, 'synset': 'metal_screw.n.01', 'name': 'metal_screw'}, {'id': 9213, 'synset': 'metal_wood.n.01', 'name': 'metal_wood'}, {'id': 9214, 'synset': 'meteorological_balloon.n.01', 'name': 'meteorological_balloon'}, {'id': 9215, 'synset': 'meter.n.02', 'name': 'meter'}, {'id': 9216, 'synset': 'meterstick.n.01', 'name': 'meterstick'}, {'id': 9217, 'synset': 'metronome.n.01', 'name': 'metronome'}, {'id': 9218, 'synset': 'mezzanine.n.02', 'name': 'mezzanine'}, {'id': 9219, 'synset': 'mezzanine.n.01', 'name': 'mezzanine'}, {'id': 9220, 'synset': 'microbalance.n.01', 'name': 'microbalance'}, {'id': 9221, 'synset': 'microbrewery.n.01', 'name': 'microbrewery'}, {'id': 9222, 'synset': 'microfiche.n.01', 'name': 'microfiche'}, {'id': 9223, 'synset': 'microfilm.n.01', 'name': 'microfilm'}, {'id': 9224, 'synset': 'micrometer.n.02', 'name': 'micrometer'}, {'id': 9225, 'synset': 'microprocessor.n.01', 'name': 'microprocessor'}, {'id': 9226, 'synset': 'microtome.n.01', 'name': 'microtome'}, {'id': 9227, 'synset': 'microwave_diathermy_machine.n.01', 'name': 'microwave_diathermy_machine'}, {'id': 9228, 'synset': 'microwave_linear_accelerator.n.01', 'name': 'microwave_linear_accelerator'}, {'id': 9229, 'synset': 'middy.n.01', 'name': 'middy'}, {'id': 9230, 'synset': 'midiron.n.01', 'name': 'midiron'}, {'id': 9231, 'synset': 'mihrab.n.02', 'name': 'mihrab'}, {'id': 9232, 'synset': 'mihrab.n.01', 'name': 'mihrab'}, {'id': 9233, 'synset': 'military_hospital.n.01', 'name': 'military_hospital'}, {'id': 9234, 'synset': 'military_quarters.n.01', 'name': 'military_quarters'}, {'id': 9235, 'synset': 'military_uniform.n.01', 'name': 'military_uniform'}, {'id': 9236, 'synset': 'military_vehicle.n.01', 'name': 'military_vehicle'}, {'id': 9237, 'synset': 'milk_bar.n.01', 'name': 'milk_bar'}, {'id': 9238, 'synset': 'milk_float.n.01', 'name': 'milk_float'}, {'id': 9239, 'synset': 'milking_machine.n.01', 'name': 'milking_machine'}, {'id': 9240, 'synset': 'milking_stool.n.01', 'name': 'milking_stool'}, {'id': 9241, 'synset': 'milk_wagon.n.01', 'name': 'milk_wagon'}, {'id': 9242, 'synset': 'mill.n.04', 'name': 'mill'}, {'id': 9243, 'synset': 'milldam.n.01', 'name': 'milldam'}, {'id': 9244, 'synset': 'miller.n.05', 'name': 'miller'}, {'id': 9245, 'synset': 'milliammeter.n.01', 'name': 'milliammeter'}, {'id': 9246, 'synset': 'millinery.n.02', 'name': 'millinery'}, {'id': 9247, 'synset': 'millinery.n.01', 'name': 'millinery'}, {'id': 9248, 'synset': 'milling.n.01', 'name': 'milling'}, {'id': 9249, 'synset': 'millivoltmeter.n.01', 'name': 'millivoltmeter'}, {'id': 9250, 'synset': 'millstone.n.03', 'name': 'millstone'}, {'id': 9251, 'synset': 'millstone.n.02', 'name': 'millstone'}, {'id': 9252, 'synset': 'millwheel.n.01', 'name': 'millwheel'}, {'id': 9253, 'synset': 'mimeograph.n.01', 'name': 'mimeograph'}, {'id': 9254, 'synset': 'minaret.n.01', 'name': 'minaret'}, {'id': 9255, 'synset': 'mincer.n.01', 'name': 'mincer'}, {'id': 9256, 'synset': 'mine.n.02', 'name': 'mine'}, {'id': 9257, 'synset': 'mine_detector.n.01', 'name': 'mine_detector'}, {'id': 9258, 'synset': 'minelayer.n.01', 'name': 'minelayer'}, {'id': 9259, 'synset': 'mineshaft.n.01', 'name': 'mineshaft'}, {'id': 9260, 'synset': 'minibar.n.01', 'name': 'minibar'}, {'id': 9261, 'synset': 'minibike.n.01', 'name': 'minibike'}, {'id': 9262, 'synset': 'minibus.n.01', 'name': 'minibus'}, {'id': 9263, 'synset': 'minicar.n.01', 'name': 'minicar'}, {'id': 9264, 'synset': 'minicomputer.n.01', 'name': 'minicomputer'}, {'id': 9265, 'synset': 'ministry.n.02', 'name': 'ministry'}, {'id': 9266, 'synset': 'miniskirt.n.01', 'name': 'miniskirt'}, {'id': 9267, 'synset': 'minisub.n.01', 'name': 'minisub'}, {'id': 9268, 'synset': 'miniver.n.01', 'name': 'miniver'}, {'id': 9269, 'synset': 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9285, 'synset': 'moccasin.n.01', 'name': 'moccasin'}, {'id': 9286, 'synset': 'mock-up.n.01', 'name': 'mock-up'}, {'id': 9287, 'synset': 'mod_con.n.01', 'name': 'mod_con'}, {'id': 9288, 'synset': 'model_t.n.01', 'name': 'Model_T'}, {'id': 9289, 'synset': 'modem.n.01', 'name': 'modem'}, {'id': 9290, 'synset': 'modillion.n.01', 'name': 'modillion'}, {'id': 9291, 'synset': 'module.n.03', 'name': 'module'}, {'id': 9292, 'synset': 'module.n.02', 'name': 'module'}, {'id': 9293, 'synset': 'mohair.n.01', 'name': 'mohair'}, {'id': 9294, 'synset': 'moire.n.01', 'name': 'moire'}, {'id': 9295, 'synset': 'mold.n.02', 'name': 'mold'}, {'id': 9296, 'synset': 'moldboard.n.01', 'name': 'moldboard'}, {'id': 9297, 'synset': 'moldboard_plow.n.01', 'name': 'moldboard_plow'}, {'id': 9298, 'synset': 'moleskin.n.01', 'name': 'moleskin'}, {'id': 9299, 'synset': 'molotov_cocktail.n.01', 'name': 'Molotov_cocktail'}, {'id': 9300, 'synset': 'monastery.n.01', 'name': 'monastery'}, {'id': 9301, 'synset': 'monastic_habit.n.01', 'name': 'monastic_habit'}, {'id': 9302, 'synset': 'moneybag.n.01', 'name': 'moneybag'}, {'id': 9303, 'synset': 'money_belt.n.01', 'name': 'money_belt'}, {'id': 9304, 'synset': 'monitor.n.06', 'name': 'monitor'}, {'id': 9305, 'synset': 'monitor.n.05', 'name': 'monitor'}, {'id': 9306, 'synset': 'monkey-wrench.n.01', 'name': 'monkey-wrench'}, {'id': 9307, 'synset': "monk's_cloth.n.01", 'name': "monk's_cloth"}, {'id': 9308, 'synset': 'monochrome.n.01', 'name': 'monochrome'}, {'id': 9309, 'synset': 'monocle.n.01', 'name': 'monocle'}, {'id': 9310, 'synset': 'monofocal_lens_implant.n.01', 'name': 'monofocal_lens_implant'}, {'id': 9311, 'synset': 'monoplane.n.01', 'name': 'monoplane'}, {'id': 9312, 'synset': 'monotype.n.02', 'name': 'monotype'}, {'id': 9313, 'synset': 'monstrance.n.02', 'name': 'monstrance'}, {'id': 9314, 'synset': 'mooring_tower.n.01', 'name': 'mooring_tower'}, {'id': 9315, 'synset': 'moorish_arch.n.01', 'name': 'Moorish_arch'}, {'id': 9316, 'synset': 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'synset': 'motel.n.01', 'name': 'motel'}, {'id': 9333, 'synset': 'motel_room.n.01', 'name': 'motel_room'}, {'id': 9334, 'synset': 'mother_hubbard.n.01', 'name': 'Mother_Hubbard'}, {'id': 9335, 'synset': 'motion-picture_camera.n.01', 'name': 'motion-picture_camera'}, {'id': 9336, 'synset': 'motion-picture_film.n.01', 'name': 'motion-picture_film'}, {'id': 9337, 'synset': 'motley.n.03', 'name': 'motley'}, {'id': 9338, 'synset': 'motley.n.02', 'name': 'motley'}, {'id': 9339, 'synset': 'motorboat.n.01', 'name': 'motorboat'}, {'id': 9340, 'synset': 'motor_hotel.n.01', 'name': 'motor_hotel'}, {'id': 9341, 'synset': 'motorized_wheelchair.n.01', 'name': 'motorized_wheelchair'}, {'id': 9342, 'synset': 'mound.n.04', 'name': 'mound'}, {'id': 9343, 'synset': 'mount.n.04', 'name': 'mount'}, {'id': 9344, 'synset': 'mountain_bike.n.01', 'name': 'mountain_bike'}, {'id': 9345, 'synset': 'mountain_tent.n.01', 'name': 'mountain_tent'}, {'id': 9346, 'synset': 'mouse_button.n.01', 'name': 'mouse_button'}, {'id': 9347, 'synset': 'mousetrap.n.01', 'name': 'mousetrap'}, {'id': 9348, 'synset': 'mousse.n.03', 'name': 'mousse'}, {'id': 9349, 'synset': 'mouthpiece.n.06', 'name': 'mouthpiece'}, {'id': 9350, 'synset': 'mouthpiece.n.02', 'name': 'mouthpiece'}, {'id': 9351, 'synset': 'mouthpiece.n.04', 'name': 'mouthpiece'}, {'id': 9352, 'synset': 'movement.n.10', 'name': 'movement'}, {'id': 9353, 'synset': 'movie_projector.n.01', 'name': 'movie_projector'}, {'id': 9354, 'synset': 'moving-coil_galvanometer.n.01', 'name': 'moving-coil_galvanometer'}, {'id': 9355, 'synset': 'moving_van.n.01', 'name': 'moving_van'}, {'id': 9356, 'synset': 'mud_brick.n.01', 'name': 'mud_brick'}, {'id': 9357, 'synset': 'mudguard.n.01', 'name': 'mudguard'}, {'id': 9358, 'synset': 'mudhif.n.01', 'name': 'mudhif'}, {'id': 9359, 'synset': 'muff.n.01', 'name': 'muff'}, {'id': 9360, 'synset': 'muffle.n.01', 'name': 'muffle'}, {'id': 9361, 'synset': 'muffler.n.02', 'name': 'muffler'}, {'id': 9362, 'synset': 'mufti.n.02', 'name': 'mufti'}, {'id': 9363, 'synset': 'mulch.n.01', 'name': 'mulch'}, {'id': 9364, 'synset': 'mule.n.02', 'name': 'mule'}, {'id': 9365, 'synset': 'multichannel_recorder.n.01', 'name': 'multichannel_recorder'}, {'id': 9366, 'synset': 'multiengine_airplane.n.01', 'name': 'multiengine_airplane'}, {'id': 9367, 'synset': 'multiplex.n.02', 'name': 'multiplex'}, {'id': 9368, 'synset': 'multiplexer.n.01', 'name': 'multiplexer'}, {'id': 9369, 'synset': 'multiprocessor.n.01', 'name': 'multiprocessor'}, {'id': 9370, 'synset': 'multistage_rocket.n.01', 'name': 'multistage_rocket'}, {'id': 9371, 'synset': 'munition.n.02', 'name': 'munition'}, {'id': 9372, 'synset': 'murphy_bed.n.01', 'name': 'Murphy_bed'}, {'id': 9373, 'synset': 'musette.n.01', 'name': 'musette'}, {'id': 9374, 'synset': 'musette_pipe.n.01', 'name': 'musette_pipe'}, {'id': 9375, 'synset': 'museum.n.01', 'name': 'museum'}, {'id': 9376, 'synset': 'mushroom_anchor.n.01', 'name': 'mushroom_anchor'}, {'id': 9377, 'synset': 'music_box.n.01', 'name': 'music_box'}, {'id': 9378, 'synset': 'music_hall.n.01', 'name': 'music_hall'}, {'id': 9379, 'synset': 'music_school.n.02', 'name': 'music_school'}, {'id': 9380, 'synset': 'music_stand.n.01', 'name': 'music_stand'}, {'id': 9381, 'synset': 'musket.n.01', 'name': 'musket'}, {'id': 9382, 'synset': 'musket_ball.n.01', 'name': 'musket_ball'}, {'id': 9383, 'synset': 'muslin.n.01', 'name': 'muslin'}, {'id': 9384, 'synset': 'mustache_cup.n.01', 'name': 'mustache_cup'}, {'id': 9385, 'synset': 'mustard_plaster.n.01', 'name': 'mustard_plaster'}, {'id': 9386, 'synset': 'mute.n.02', 'name': 'mute'}, {'id': 9387, 'synset': 'muzzle_loader.n.01', 'name': 'muzzle_loader'}, {'id': 9388, 'synset': 'muzzle.n.03', 'name': 'muzzle'}, {'id': 9389, 'synset': 'myelogram.n.01', 'name': 'myelogram'}, {'id': 9390, 'synset': 'nacelle.n.01', 'name': 'nacelle'}, {'id': 9391, 'synset': 'nail.n.02', 'name': 'nail'}, {'id': 9392, 'synset': 'nailbrush.n.01', 'name': 'nailbrush'}, {'id': 9393, 'synset': 'nailhead.n.02', 'name': 'nailhead'}, {'id': 9394, 'synset': 'nailhead.n.01', 'name': 'nailhead'}, {'id': 9395, 'synset': 'nail_polish.n.01', 'name': 'nail_polish'}, {'id': 9396, 'synset': 'nainsook.n.01', 'name': 'nainsook'}, {'id': 9397, 'synset': "napier's_bones.n.01", 'name': "Napier's_bones"}, {'id': 9398, 'synset': 'nard.n.01', 'name': 'nard'}, {'id': 9399, 'synset': 'narrowbody_aircraft.n.01', 'name': 'narrowbody_aircraft'}, {'id': 9400, 'synset': 'narrow_wale.n.01', 'name': 'narrow_wale'}, {'id': 9401, 'synset': 'narthex.n.02', 'name': 'narthex'}, {'id': 9402, 'synset': 'narthex.n.01', 'name': 'narthex'}, {'id': 9403, 'synset': 'nasotracheal_tube.n.01', 'name': 'nasotracheal_tube'}, {'id': 9404, 'synset': 'national_monument.n.01', 'name': 'national_monument'}, {'id': 9405, 'synset': 'nautilus.n.01', 'name': 'nautilus'}, {'id': 9406, 'synset': 'navigational_system.n.01', 'name': 'navigational_system'}, {'id': 9407, 'synset': 'naval_equipment.n.01', 'name': 'naval_equipment'}, {'id': 9408, 'synset': 'naval_gun.n.01', 'name': 'naval_gun'}, {'id': 9409, 'synset': 'naval_missile.n.01', 'name': 'naval_missile'}, {'id': 9410, 'synset': 'naval_radar.n.01', 'name': 'naval_radar'}, {'id': 9411, 'synset': 'naval_tactical_data_system.n.01', 'name': 'naval_tactical_data_system'}, {'id': 9412, 'synset': 'naval_weaponry.n.01', 'name': 'naval_weaponry'}, {'id': 9413, 'synset': 'nave.n.01', 'name': 'nave'}, {'id': 9414, 'synset': 'navigational_instrument.n.01', 'name': 'navigational_instrument'}, {'id': 9415, 'synset': 'nebuchadnezzar.n.02', 'name': 'nebuchadnezzar'}, {'id': 9416, 'synset': 'neckband.n.01', 'name': 'neckband'}, {'id': 9417, 'synset': 'neck_brace.n.01', 'name': 'neck_brace'}, {'id': 9418, 'synset': 'neckcloth.n.01', 'name': 'neckcloth'}, {'id': 9419, 'synset': 'necklet.n.01', 'name': 'necklet'}, {'id': 9420, 'synset': 'neckline.n.01', 'name': 'neckline'}, {'id': 9421, 'synset': 'neckpiece.n.01', 'name': 'neckpiece'}, {'id': 9422, 'synset': 'neckwear.n.01', 'name': 'neckwear'}, {'id': 9423, 'synset': 'needle.n.02', 'name': 'needle'}, {'id': 9424, 'synset': 'needlenose_pliers.n.01', 'name': 'needlenose_pliers'}, {'id': 9425, 'synset': 'needlework.n.01', 'name': 'needlework'}, {'id': 9426, 'synset': 'negative.n.02', 'name': 'negative'}, {'id': 9427, 'synset': 'negative_magnetic_pole.n.01', 'name': 'negative_magnetic_pole'}, {'id': 9428, 'synset': 'negative_pole.n.01', 'name': 'negative_pole'}, {'id': 9429, 'synset': 'negligee.n.01', 'name': 'negligee'}, {'id': 9430, 'synset': 'neolith.n.01', 'name': 'neolith'}, {'id': 9431, 'synset': 'neon_lamp.n.01', 'name': 'neon_lamp'}, {'id': 9432, 'synset': 'nephoscope.n.01', 'name': 'nephoscope'}, {'id': 9433, 'synset': 'nest.n.05', 'name': 'nest'}, {'id': 9434, 'synset': 'nest_egg.n.02', 'name': 'nest_egg'}, {'id': 9435, 'synset': 'net.n.06', 'name': 'net'}, {'id': 9436, 'synset': 'net.n.02', 'name': 'net'}, {'id': 9437, 'synset': 'net.n.05', 'name': 'net'}, {'id': 9438, 'synset': 'net.n.04', 'name': 'net'}, {'id': 9439, 'synset': 'network.n.05', 'name': 'network'}, {'id': 9440, 'synset': 'network.n.04', 'name': 'network'}, {'id': 9441, 'synset': 'neutron_bomb.n.01', 'name': 'neutron_bomb'}, {'id': 9442, 'synset': 'newel.n.02', 'name': 'newel'}, {'id': 9443, 'synset': 'newel_post.n.01', 'name': 'newel_post'}, {'id': 9444, 'synset': 'newspaper.n.03', 'name': 'newspaper'}, {'id': 9445, 'synset': 'newsroom.n.03', 'name': 'newsroom'}, {'id': 9446, 'synset': 'newsroom.n.02', 'name': 'newsroom'}, {'id': 9447, 'synset': 'newtonian_telescope.n.01', 'name': 'Newtonian_telescope'}, {'id': 9448, 'synset': 'nib.n.01', 'name': 'nib'}, {'id': 9449, 'synset': 'niblick.n.01', 'name': 'niblick'}, {'id': 9450, 'synset': 'nicad.n.01', 'name': 'nicad'}, {'id': 9451, 'synset': 'nickel-iron_battery.n.01', 'name': 'nickel-iron_battery'}, {'id': 9452, 'synset': 'nicol_prism.n.01', 'name': 'Nicol_prism'}, {'id': 9453, 'synset': 'night_bell.n.01', 'name': 'night_bell'}, {'id': 9454, 'synset': 'nightcap.n.02', 'name': 'nightcap'}, {'id': 9455, 'synset': 'nightgown.n.01', 'name': 'nightgown'}, {'id': 9456, 'synset': 'night_latch.n.01', 'name': 'night_latch'}, {'id': 9457, 'synset': 'night-light.n.01', 'name': 'night-light'}, {'id': 9458, 'synset': 'nightshirt.n.01', 'name': 'nightshirt'}, {'id': 9459, 'synset': 'ninepin.n.01', 'name': 'ninepin'}, {'id': 9460, 'synset': 'ninepin_ball.n.01', 'name': 'ninepin_ball'}, {'id': 9461, 'synset': 'ninon.n.01', 'name': 'ninon'}, {'id': 9462, 'synset': 'nipple.n.02', 'name': 'nipple'}, {'id': 9463, 'synset': 'nipple_shield.n.01', 'name': 'nipple_shield'}, {'id': 9464, 'synset': 'niqab.n.01', 'name': 'niqab'}, {'id': 9465, 'synset': 'nissen_hut.n.01', 'name': 'Nissen_hut'}, {'id': 9466, 'synset': 'nogging.n.01', 'name': 'nogging'}, {'id': 9467, 'synset': 'noisemaker.n.01', 'name': 'noisemaker'}, {'id': 9468, 'synset': 'nonsmoker.n.02', 'name': 'nonsmoker'}, {'id': 9469, 'synset': 'non-volatile_storage.n.01', 'name': 'non-volatile_storage'}, {'id': 9470, 'synset': 'norfolk_jacket.n.01', 'name': 'Norfolk_jacket'}, {'id': 9471, 'synset': 'noria.n.01', 'name': 'noria'}, {'id': 9472, 'synset': 'nose_flute.n.01', 'name': 'nose_flute'}, {'id': 9473, 'synset': 'nosewheel.n.01', 'name': 'nosewheel'}, {'id': 9474, 'synset': 'notebook.n.02', 'name': 'notebook'}, {'id': 9475, 'synset': 'nuclear-powered_ship.n.01', 'name': 'nuclear-powered_ship'}, {'id': 9476, 'synset': 'nuclear_reactor.n.01', 'name': 'nuclear_reactor'}, {'id': 9477, 'synset': 'nuclear_rocket.n.01', 'name': 'nuclear_rocket'}, {'id': 9478, 'synset': 'nuclear_weapon.n.01', 'name': 'nuclear_weapon'}, {'id': 9479, 'synset': 'nude.n.01', 'name': 'nude'}, {'id': 9480, 'synset': 'numdah.n.01', 'name': 'numdah'}, {'id': 9481, 'synset': "nun's_habit.n.01", 'name': "nun's_habit"}, {'id': 9482, 'synset': 'nursery.n.01', 'name': 'nursery'}, {'id': 9483, 'synset': 'nut_and_bolt.n.01', 'name': 'nut_and_bolt'}, {'id': 9484, 'synset': 'nylon.n.02', 'name': 'nylon'}, {'id': 9485, 'synset': 'nylons.n.01', 'name': 'nylons'}, {'id': 9486, 'synset': 'oast.n.01', 'name': 'oast'}, {'id': 9487, 'synset': 'oast_house.n.01', 'name': 'oast_house'}, {'id': 9488, 'synset': 'obelisk.n.01', 'name': 'obelisk'}, {'id': 9489, 'synset': 'object_ball.n.01', 'name': 'object_ball'}, {'id': 9490, 'synset': 'objective.n.02', 'name': 'objective'}, {'id': 9491, 'synset': 'oblique_bandage.n.01', 'name': 'oblique_bandage'}, {'id': 9492, 'synset': 'oboe.n.01', 'name': 'oboe'}, {'id': 9493, 'synset': 'oboe_da_caccia.n.01', 'name': 'oboe_da_caccia'}, {'id': 9494, 'synset': "oboe_d'amore.n.01", 'name': "oboe_d'amore"}, {'id': 9495, 'synset': 'observation_dome.n.01', 'name': 'observation_dome'}, {'id': 9496, 'synset': 'observatory.n.01', 'name': 'observatory'}, {'id': 9497, 'synset': 'obstacle.n.02', 'name': 'obstacle'}, {'id': 9498, 'synset': 'obturator.n.01', 'name': 'obturator'}, {'id': 9499, 'synset': 'ocarina.n.01', 'name': 'ocarina'}, {'id': 9500, 'synset': 'octant.n.01', 'name': 'octant'}, {'id': 9501, 'synset': 'odd-leg_caliper.n.01', 'name': 'odd-leg_caliper'}, {'id': 9502, 'synset': 'odometer.n.01', 'name': 'odometer'}, {'id': 9503, 'synset': 'oeil_de_boeuf.n.01', 'name': 'oeil_de_boeuf'}, {'id': 9504, 'synset': 'office.n.01', 'name': 'office'}, {'id': 9505, 'synset': 'office_building.n.01', 'name': 'office_building'}, {'id': 9506, 'synset': 'office_furniture.n.01', 'name': 'office_furniture'}, {'id': 9507, 'synset': "officer's_mess.n.01", 'name': "officer's_mess"}, {'id': 9508, 'synset': 'off-line_equipment.n.01', 'name': 'off-line_equipment'}, {'id': 9509, 'synset': 'ogee.n.01', 'name': 'ogee'}, {'id': 9510, 'synset': 'ogee_arch.n.01', 'name': 'ogee_arch'}, {'id': 9511, 'synset': 'ohmmeter.n.01', 'name': 'ohmmeter'}, {'id': 9512, 'synset': 'oil.n.02', 'name': 'oil'}, {'id': 9513, 'synset': 'oilcan.n.01', 'name': 'oilcan'}, {'id': 9514, 'synset': 'oilcloth.n.01', 'name': 'oilcloth'}, {'id': 9515, 'synset': 'oil_filter.n.01', 'name': 'oil_filter'}, {'id': 9516, 'synset': 'oil_heater.n.01', 'name': 'oil_heater'}, {'id': 9517, 'synset': 'oil_paint.n.01', 'name': 'oil_paint'}, {'id': 9518, 'synset': 'oil_pump.n.01', 'name': 'oil_pump'}, {'id': 9519, 'synset': 'oil_refinery.n.01', 'name': 'oil_refinery'}, {'id': 9520, 'synset': 'oilskin.n.01', 'name': 'oilskin'}, {'id': 9521, 'synset': 'oil_slick.n.01', 'name': 'oil_slick'}, {'id': 9522, 'synset': 'oilstone.n.01', 'name': 'oilstone'}, {'id': 9523, 'synset': 'oil_tanker.n.01', 'name': 'oil_tanker'}, {'id': 9524, 'synset': 'old_school_tie.n.01', 'name': 'old_school_tie'}, {'id': 9525, 'synset': 'olive_drab.n.03', 'name': 'olive_drab'}, {'id': 9526, 'synset': 'olive_drab.n.02', 'name': 'olive_drab'}, {'id': 9527, 'synset': 'olympian_zeus.n.01', 'name': 'Olympian_Zeus'}, {'id': 9528, 'synset': 'omelet_pan.n.01', 'name': 'omelet_pan'}, {'id': 9529, 'synset': 'omnidirectional_antenna.n.01', 'name': 'omnidirectional_antenna'}, {'id': 9530, 'synset': 'omnirange.n.01', 'name': 'omnirange'}, {'id': 9531, 'synset': 'onion_dome.n.01', 'name': 'onion_dome'}, {'id': 9532, 'synset': 'open-air_market.n.01', 'name': 'open-air_market'}, {'id': 9533, 'synset': 'open_circuit.n.01', 'name': 'open_circuit'}, {'id': 9534, 'synset': 'open-end_wrench.n.01', 'name': 'open-end_wrench'}, {'id': 9535, 'synset': 'opener.n.03', 'name': 'opener'}, {'id': 9536, 'synset': 'open-hearth_furnace.n.01', 'name': 'open-hearth_furnace'}, {'id': 9537, 'synset': 'openside_plane.n.01', 'name': 'openside_plane'}, {'id': 9538, 'synset': 'open_sight.n.01', 'name': 'open_sight'}, {'id': 9539, 'synset': 'openwork.n.01', 'name': 'openwork'}, {'id': 9540, 'synset': 'opera.n.03', 'name': 'opera'}, {'id': 9541, 'synset': 'opera_cloak.n.01', 'name': 'opera_cloak'}, {'id': 9542, 'synset': 'operating_microscope.n.01', 'name': 'operating_microscope'}, {'id': 9543, 'synset': 'operating_room.n.01', 'name': 'operating_room'}, {'id': 9544, 'synset': 'operating_table.n.01', 'name': 'operating_table'}, {'id': 9545, 'synset': 'ophthalmoscope.n.01', 'name': 'ophthalmoscope'}, {'id': 9546, 'synset': 'optical_device.n.01', 'name': 'optical_device'}, {'id': 9547, 'synset': 'optical_disk.n.01', 'name': 'optical_disk'}, {'id': 9548, 'synset': 'optical_instrument.n.01', 'name': 'optical_instrument'}, {'id': 9549, 'synset': 'optical_pyrometer.n.01', 'name': 'optical_pyrometer'}, {'id': 9550, 'synset': 'optical_telescope.n.01', 'name': 'optical_telescope'}, {'id': 9551, 'synset': 'orchestra_pit.n.01', 'name': 'orchestra_pit'}, {'id': 9552, 'synset': 'ordinary.n.04', 'name': 'ordinary'}, {'id': 9553, 'synset': 'organ.n.05', 'name': 'organ'}, {'id': 9554, 'synset': 'organdy.n.01', 'name': 'organdy'}, {'id': 9555, 'synset': 'organic_light-emitting_diode.n.01', 'name': 'organic_light-emitting_diode'}, {'id': 9556, 'synset': 'organ_loft.n.01', 'name': 'organ_loft'}, {'id': 9557, 'synset': 'organ_pipe.n.01', 'name': 'organ_pipe'}, {'id': 9558, 'synset': 'organza.n.01', 'name': 'organza'}, {'id': 9559, 'synset': 'oriel.n.01', 'name': 'oriel'}, {'id': 9560, 'synset': 'oriflamme.n.02', 'name': 'oriflamme'}, {'id': 9561, 'synset': 'o_ring.n.01', 'name': 'O_ring'}, {'id': 9562, 'synset': 'orlon.n.01', 'name': 'Orlon'}, {'id': 9563, 'synset': 'orlop_deck.n.01', 'name': 'orlop_deck'}, {'id': 9564, 'synset': 'orphanage.n.02', 'name': 'orphanage'}, {'id': 9565, 'synset': 'orphrey.n.01', 'name': 'orphrey'}, {'id': 9566, 'synset': 'orrery.n.01', 'name': 'orrery'}, {'id': 9567, 'synset': 'orthicon.n.01', 'name': 'orthicon'}, {'id': 9568, 'synset': 'orthochromatic_film.n.01', 'name': 'orthochromatic_film'}, {'id': 9569, 'synset': 'orthopter.n.01', 'name': 'orthopter'}, {'id': 9570, 'synset': 'orthoscope.n.01', 'name': 'orthoscope'}, {'id': 9571, 'synset': 'oscillograph.n.01', 'name': 'oscillograph'}, {'id': 9572, 'synset': 'oscilloscope.n.01', 'name': 'oscilloscope'}, {'id': 9573, 'synset': 'ossuary.n.01', 'name': 'ossuary'}, {'id': 9574, 'synset': 'otoscope.n.01', 'name': 'otoscope'}, {'id': 9575, 'synset': 'oubliette.n.01', 'name': 'oubliette'}, {'id': 9576, 'synset': 'out-basket.n.01', 'name': 'out-basket'}, {'id': 9577, 'synset': 'outboard_motor.n.01', 'name': 'outboard_motor'}, {'id': 9578, 'synset': 'outboard_motorboat.n.01', 'name': 'outboard_motorboat'}, {'id': 9579, 'synset': 'outbuilding.n.01', 'name': 'outbuilding'}, {'id': 9580, 'synset': 'outerwear.n.01', 'name': 'outerwear'}, {'id': 9581, 'synset': 'outfall.n.01', 'name': 'outfall'}, {'id': 9582, 'synset': 'outfit.n.02', 'name': 'outfit'}, {'id': 9583, 'synset': 'outfitter.n.02', 'name': 'outfitter'}, {'id': 9584, 'synset': 'outhouse.n.01', 'name': 'outhouse'}, {'id': 9585, 'synset': 'output_device.n.01', 'name': 'output_device'}, {'id': 9586, 'synset': 'outrigger.n.01', 'name': 'outrigger'}, {'id': 9587, 'synset': 'outrigger_canoe.n.01', 'name': 'outrigger_canoe'}, {'id': 9588, 'synset': 'outside_caliper.n.01', 'name': 'outside_caliper'}, {'id': 9589, 'synset': 'outside_mirror.n.01', 'name': 'outside_mirror'}, {'id': 9590, 'synset': 'outwork.n.01', 'name': 'outwork'}, {'id': 9591, 'synset': 'oven_thermometer.n.01', 'name': 'oven_thermometer'}, {'id': 9592, 'synset': 'overall.n.02', 'name': 'overall'}, {'id': 9593, 'synset': 'overcoat.n.02', 'name': 'overcoat'}, {'id': 9594, 'synset': 'overdrive.n.02', 'name': 'overdrive'}, {'id': 9595, 'synset': 'overgarment.n.01', 'name': 'overgarment'}, {'id': 9596, 'synset': 'overhand_knot.n.01', 'name': 'overhand_knot'}, {'id': 9597, 'synset': 'overhang.n.01', 'name': 'overhang'}, {'id': 9598, 'synset': 'overhead_projector.n.01', 'name': 'overhead_projector'}, {'id': 9599, 'synset': 'overmantel.n.01', 'name': 'overmantel'}, {'id': 9600, 'synset': 'overnighter.n.02', 'name': 'overnighter'}, {'id': 9601, 'synset': 'overpass.n.01', 'name': 'overpass'}, {'id': 9602, 'synset': 'override.n.01', 'name': 'override'}, {'id': 9603, 'synset': 'overshoe.n.01', 'name': 'overshoe'}, {'id': 9604, 'synset': 'overskirt.n.01', 'name': 'overskirt'}, {'id': 9605, 'synset': 'oxbow.n.03', 'name': 'oxbow'}, {'id': 9606, 'synset': 'oxbridge.n.01', 'name': 'Oxbridge'}, {'id': 9607, 'synset': 'oxcart.n.01', 'name': 'oxcart'}, {'id': 9608, 'synset': 'oxeye.n.03', 'name': 'oxeye'}, {'id': 9609, 'synset': 'oxford.n.04', 'name': 'oxford'}, {'id': 9610, 'synset': 'oximeter.n.01', 'name': 'oximeter'}, {'id': 9611, 'synset': 'oxyacetylene_torch.n.01', 'name': 'oxyacetylene_torch'}, {'id': 9612, 'synset': 'oxygen_mask.n.01', 'name': 'oxygen_mask'}, {'id': 9613, 'synset': 'oyster_bar.n.01', 'name': 'oyster_bar'}, {'id': 9614, 'synset': 'oyster_bed.n.01', 'name': 'oyster_bed'}, {'id': 9615, 'synset': 'pace_car.n.01', 'name': 'pace_car'}, {'id': 9616, 'synset': 'pacemaker.n.03', 'name': 'pacemaker'}, {'id': 9617, 'synset': 'pack.n.03', 'name': 'pack'}, {'id': 9618, 'synset': 'pack.n.09', 'name': 'pack'}, {'id': 9619, 'synset': 'pack.n.07', 'name': 'pack'}, {'id': 9620, 'synset': 'package.n.02', 'name': 'package'}, {'id': 9621, 'synset': 'package_store.n.01', 'name': 'package_store'}, {'id': 9622, 'synset': 'packaging.n.03', 'name': 'packaging'}, {'id': 9623, 'synset': 'packing_box.n.02', 'name': 'packing_box'}, {'id': 9624, 'synset': 'packinghouse.n.02', 'name': 'packinghouse'}, {'id': 9625, 'synset': 'packinghouse.n.01', 'name': 'packinghouse'}, {'id': 9626, 'synset': 'packing_needle.n.01', 'name': 'packing_needle'}, {'id': 9627, 'synset': 'packsaddle.n.01', 'name': 'packsaddle'}, {'id': 9628, 'synset': 'paddle.n.02', 'name': 'paddle'}, {'id': 9629, 'synset': 'paddle.n.01', 'name': 'paddle'}, {'id': 9630, 'synset': 'paddle_box.n.01', 'name': 'paddle_box'}, {'id': 9631, 'synset': 'paddle_steamer.n.01', 'name': 'paddle_steamer'}, {'id': 9632, 'synset': 'paddlewheel.n.01', 'name': 'paddlewheel'}, {'id': 9633, 'synset': 'paddock.n.01', 'name': 'paddock'}, {'id': 9634, 'synset': 'page_printer.n.01', 'name': 'page_printer'}, {'id': 9635, 'synset': 'paint.n.01', 'name': 'paint'}, {'id': 9636, 'synset': 'paintball.n.01', 'name': 'paintball'}, {'id': 9637, 'synset': 'paintball_gun.n.01', 'name': 'paintball_gun'}, {'id': 9638, 'synset': 'paintbox.n.01', 'name': 'paintbox'}, {'id': 9639, 'synset': 'paisley.n.01', 'name': 'paisley'}, {'id': 9640, 'synset': 'pajama.n.01', 'name': 'pajama'}, {'id': 9641, 'synset': 'palace.n.04', 'name': 'palace'}, {'id': 9642, 'synset': 'palace.n.01', 'name': 'palace'}, {'id': 9643, 'synset': 'palace.n.03', 'name': 'palace'}, {'id': 9644, 'synset': 'palanquin.n.01', 'name': 'palanquin'}, {'id': 9645, 'synset': 'paleolith.n.01', 'name': 'paleolith'}, {'id': 9646, 'synset': 'palestra.n.01', 'name': 'palestra'}, {'id': 9647, 'synset': 'palette_knife.n.01', 'name': 'palette_knife'}, {'id': 9648, 'synset': 'palisade.n.01', 'name': 'palisade'}, {'id': 9649, 'synset': 'pallet.n.03', 'name': 'pallet'}, {'id': 9650, 'synset': 'pallette.n.01', 'name': 'pallette'}, {'id': 9651, 'synset': 'pallium.n.04', 'name': 'pallium'}, {'id': 9652, 'synset': 'pallium.n.03', 'name': 'pallium'}, {'id': 9653, 'synset': 'pancake_turner.n.01', 'name': 'pancake_turner'}, {'id': 9654, 'synset': 'panchromatic_film.n.01', 'name': 'panchromatic_film'}, {'id': 9655, 'synset': 'panda_car.n.01', 'name': 'panda_car'}, {'id': 9656, 'synset': 'paneling.n.01', 'name': 'paneling'}, {'id': 9657, 'synset': 'panhandle.n.02', 'name': 'panhandle'}, {'id': 9658, 'synset': 'panic_button.n.01', 'name': 'panic_button'}, {'id': 9659, 'synset': 'pannier.n.02', 'name': 'pannier'}, {'id': 9660, 'synset': 'pannier.n.01', 'name': 'pannier'}, {'id': 9661, 'synset': 'pannikin.n.01', 'name': 'pannikin'}, {'id': 9662, 'synset': 'panopticon.n.02', 'name': 'panopticon'}, {'id': 9663, 'synset': 'panopticon.n.01', 'name': 'panopticon'}, {'id': 9664, 'synset': 'panpipe.n.01', 'name': 'panpipe'}, {'id': 9665, 'synset': 'pantaloon.n.03', 'name': 'pantaloon'}, {'id': 9666, 'synset': 'pantechnicon.n.01', 'name': 'pantechnicon'}, {'id': 9667, 'synset': 'pantheon.n.03', 'name': 'pantheon'}, {'id': 9668, 'synset': 'pantheon.n.02', 'name': 'pantheon'}, {'id': 9669, 'synset': 'pantie.n.01', 'name': 'pantie'}, {'id': 9670, 'synset': 'panting.n.02', 'name': 'panting'}, {'id': 9671, 'synset': 'pant_leg.n.01', 'name': 'pant_leg'}, {'id': 9672, 'synset': 'pantograph.n.01', 'name': 'pantograph'}, {'id': 9673, 'synset': 'pantry.n.01', 'name': 'pantry'}, {'id': 9674, 'synset': 'pants_suit.n.01', 'name': 'pants_suit'}, {'id': 9675, 'synset': 'panty_girdle.n.01', 'name': 'panty_girdle'}, {'id': 9676, 'synset': 'panzer.n.01', 'name': 'panzer'}, {'id': 9677, 'synset': 'paper_chain.n.01', 'name': 'paper_chain'}, {'id': 9678, 'synset': 'paper_clip.n.01', 'name': 'paper_clip'}, {'id': 9679, 'synset': 'paper_cutter.n.01', 'name': 'paper_cutter'}, {'id': 9680, 'synset': 'paper_fastener.n.01', 'name': 'paper_fastener'}, {'id': 9681, 'synset': 'paper_feed.n.01', 'name': 'paper_feed'}, {'id': 9682, 'synset': 'paper_mill.n.01', 'name': 'paper_mill'}, {'id': 9683, 'synset': 'parabolic_mirror.n.01', 'name': 'parabolic_mirror'}, {'id': 9684, 'synset': 'parabolic_reflector.n.01', 'name': 'parabolic_reflector'}, {'id': 9685, 'synset': 'parallel_bars.n.01', 'name': 'parallel_bars'}, {'id': 9686, 'synset': 'parallel_circuit.n.01', 'name': 'parallel_circuit'}, {'id': 9687, 'synset': 'parallel_interface.n.01', 'name': 'parallel_interface'}, {'id': 9688, 'synset': 'parang.n.01', 'name': 'parang'}, {'id': 9689, 'synset': 'parapet.n.02', 'name': 'parapet'}, {'id': 9690, 'synset': 'parapet.n.01', 'name': 'parapet'}, {'id': 9691, 'synset': 'parer.n.02', 'name': 'parer'}, {'id': 9692, 'synset': 'parfait_glass.n.01', 'name': 'parfait_glass'}, {'id': 9693, 'synset': 'pargeting.n.02', 'name': 'pargeting'}, {'id': 9694, 'synset': 'pari-mutuel_machine.n.01', 'name': 'pari-mutuel_machine'}, {'id': 9695, 'synset': 'park_bench.n.01', 'name': 'park_bench'}, {'id': 9696, 'synset': 'parlor.n.01', 'name': 'parlor'}, {'id': 9697, 'synset': 'parquet.n.01', 'name': 'parquet'}, {'id': 9698, 'synset': 'parquetry.n.01', 'name': 'parquetry'}, {'id': 9699, 'synset': 'parsonage.n.01', 'name': 'parsonage'}, {'id': 9700, 'synset': 'parsons_table.n.01', 'name': 'Parsons_table'}, {'id': 9701, 'synset': 'partial_denture.n.01', 'name': 'partial_denture'}, {'id': 9702, 'synset': 'particle_detector.n.01', 'name': 'particle_detector'}, {'id': 9703, 'synset': 'partition.n.01', 'name': 'partition'}, {'id': 9704, 'synset': 'parts_bin.n.01', 'name': 'parts_bin'}, {'id': 9705, 'synset': 'party_line.n.02', 'name': 'party_line'}, {'id': 9706, 'synset': 'party_wall.n.01', 'name': 'party_wall'}, {'id': 9707, 'synset': 'parvis.n.01', 'name': 'parvis'}, {'id': 9708, 'synset': 'passenger_train.n.01', 'name': 'passenger_train'}, {'id': 9709, 'synset': 'passenger_van.n.01', 'name': 'passenger_van'}, {'id': 9710, 'synset': 'passe-partout.n.02', 'name': 'passe-partout'}, {'id': 9711, 'synset': 'passive_matrix_display.n.01', 'name': 'passive_matrix_display'}, {'id': 9712, 'synset': 'passkey.n.01', 'name': 'passkey'}, {'id': 9713, 'synset': 'pass-through.n.01', 'name': 'pass-through'}, {'id': 9714, 'synset': 'pastry_cart.n.01', 'name': 'pastry_cart'}, {'id': 9715, 'synset': 'patch.n.03', 'name': 'patch'}, {'id': 9716, 'synset': 'patchcord.n.01', 'name': 'patchcord'}, {'id': 9717, 'synset': 'patchouli.n.02', 'name': 'patchouli'}, {'id': 9718, 'synset': 'patch_pocket.n.01', 'name': 'patch_pocket'}, {'id': 9719, 'synset': 'patchwork.n.02', 'name': 'patchwork'}, {'id': 9720, 'synset': 'patent_log.n.01', 'name': 'patent_log'}, {'id': 9721, 'synset': 'paternoster.n.02', 'name': 'paternoster'}, {'id': 9722, 'synset': 'patina.n.01', 'name': 'patina'}, {'id': 9723, 'synset': 'patio.n.01', 'name': 'patio'}, {'id': 9724, 'synset': 'patisserie.n.01', 'name': 'patisserie'}, {'id': 9725, 'synset': 'patka.n.01', 'name': 'patka'}, {'id': 9726, 'synset': 'patrol_boat.n.01', 'name': 'patrol_boat'}, {'id': 9727, 'synset': 'patty-pan.n.01', 'name': 'patty-pan'}, {'id': 9728, 'synset': 'pave.n.01', 'name': 'pave'}, {'id': 9729, 'synset': 'pavilion.n.01', 'name': 'pavilion'}, {'id': 9730, 'synset': 'pavior.n.01', 'name': 'pavior'}, {'id': 9731, 'synset': 'pavis.n.01', 'name': 'pavis'}, {'id': 9732, 'synset': 'pawn.n.03', 'name': 'pawn'}, {'id': 9733, 'synset': "pawnbroker's_shop.n.01", 'name': "pawnbroker's_shop"}, {'id': 9734, 'synset': 'pay-phone.n.01', 'name': 'pay-phone'}, {'id': 9735, 'synset': 'pc_board.n.01', 'name': 'PC_board'}, {'id': 9736, 'synset': 'peach_orchard.n.01', 'name': 'peach_orchard'}, {'id': 9737, 'synset': 'pea_jacket.n.01', 'name': 'pea_jacket'}, {'id': 9738, 'synset': 'peavey.n.01', 'name': 'peavey'}, {'id': 9739, 'synset': 'pectoral.n.02', 'name': 'pectoral'}, {'id': 9740, 'synset': 'pedal.n.02', 'name': 'pedal'}, {'id': 9741, 'synset': 'pedal_pusher.n.01', 'name': 'pedal_pusher'}, {'id': 9742, 'synset': 'pedestal.n.03', 'name': 'pedestal'}, {'id': 9743, 'synset': 'pedestal_table.n.01', 'name': 'pedestal_table'}, {'id': 9744, 'synset': 'pedestrian_crossing.n.01', 'name': 'pedestrian_crossing'}, {'id': 9745, 'synset': 'pedicab.n.01', 'name': 'pedicab'}, {'id': 9746, 'synset': 'pediment.n.01', 'name': 'pediment'}, {'id': 9747, 'synset': 'pedometer.n.01', 'name': 'pedometer'}, {'id': 9748, 'synset': 'peep_sight.n.01', 'name': 'peep_sight'}, {'id': 9749, 'synset': 'peg.n.01', 'name': 'peg'}, {'id': 9750, 'synset': 'peg.n.06', 'name': 'peg'}, {'id': 9751, 'synset': 'peg.n.05', 'name': 'peg'}, {'id': 9752, 'synset': 'pelham.n.01', 'name': 'Pelham'}, {'id': 9753, 'synset': 'pelican_crossing.n.01', 'name': 'pelican_crossing'}, {'id': 9754, 'synset': 'pelisse.n.01', 'name': 'pelisse'}, {'id': 9755, 'synset': 'pelvimeter.n.01', 'name': 'pelvimeter'}, {'id': 9756, 'synset': 'penal_colony.n.01', 'name': 'penal_colony'}, {'id': 9757, 'synset': 'penal_institution.n.01', 'name': 'penal_institution'}, {'id': 9758, 'synset': 'penalty_box.n.01', 'name': 'penalty_box'}, {'id': 9759, 'synset': 'pen-and-ink.n.01', 'name': 'pen-and-ink'}, {'id': 9760, 'synset': 'pencil.n.04', 'name': 'pencil'}, {'id': 9761, 'synset': 'pendant_earring.n.01', 'name': 'pendant_earring'}, {'id': 9762, 'synset': 'pendulum_clock.n.01', 'name': 'pendulum_clock'}, {'id': 9763, 'synset': 'pendulum_watch.n.01', 'name': 'pendulum_watch'}, {'id': 9764, 'synset': 'penetration_bomb.n.01', 'name': 'penetration_bomb'}, {'id': 9765, 'synset': 'penile_implant.n.01', 'name': 'penile_implant'}, {'id': 9766, 'synset': 'penitentiary.n.01', 'name': 'penitentiary'}, {'id': 9767, 'synset': 'penknife.n.01', 'name': 'penknife'}, {'id': 9768, 'synset': 'penlight.n.01', 'name': 'penlight'}, {'id': 9769, 'synset': 'pennant.n.03', 'name': 'pennant'}, {'id': 9770, 'synset': 'pennywhistle.n.01', 'name': 'pennywhistle'}, {'id': 9771, 'synset': 'penthouse.n.01', 'name': 'penthouse'}, {'id': 9772, 'synset': 'pentode.n.01', 'name': 'pentode'}, {'id': 9773, 'synset': 'peplos.n.01', 'name': 'peplos'}, {'id': 9774, 'synset': 'peplum.n.01', 'name': 'peplum'}, {'id': 9775, 'synset': 'pepper_shaker.n.01', 'name': 'pepper_shaker'}, {'id': 9776, 'synset': 'pepper_spray.n.01', 'name': 'pepper_spray'}, {'id': 9777, 'synset': 'percale.n.01', 'name': 'percale'}, {'id': 9778, 'synset': 'percolator.n.01', 'name': 'percolator'}, {'id': 9779, 'synset': 'percussion_cap.n.01', 'name': 'percussion_cap'}, {'id': 9780, 'synset': 'percussion_instrument.n.01', 'name': 'percussion_instrument'}, {'id': 9781, 'synset': 'perforation.n.01', 'name': 'perforation'}, {'id': 9782, 'synset': 'perfumery.n.03', 'name': 'perfumery'}, {'id': 9783, 'synset': 'perfumery.n.02', 'name': 'perfumery'}, {'id': 9784, 'synset': 'perfumery.n.01', 'name': 'perfumery'}, {'id': 9785, 'synset': 'peripheral.n.01', 'name': 'peripheral'}, {'id': 9786, 'synset': 'periscope.n.01', 'name': 'periscope'}, {'id': 9787, 'synset': 'peristyle.n.01', 'name': 'peristyle'}, {'id': 9788, 'synset': 'periwig.n.01', 'name': 'periwig'}, {'id': 9789, 'synset': 'permanent_press.n.01', 'name': 'permanent_press'}, {'id': 9790, 'synset': 'perpetual_motion_machine.n.01', 'name': 'perpetual_motion_machine'}, {'id': 9791, 'synset': 'personal_computer.n.01', 'name': 'personal_computer'}, {'id': 9792, 'synset': 'personal_digital_assistant.n.01', 'name': 'personal_digital_assistant'}, {'id': 9793, 'synset': 'personnel_carrier.n.01', 'name': 'personnel_carrier'}, {'id': 9794, 'synset': 'pestle.n.03', 'name': 'pestle'}, {'id': 9795, 'synset': 'pestle.n.02', 'name': 'pestle'}, {'id': 9796, 'synset': 'petcock.n.01', 'name': 'petcock'}, {'id': 9797, 'synset': 'petri_dish.n.01', 'name': 'Petri_dish'}, {'id': 9798, 'synset': 'petrolatum_gauze.n.01', 'name': 'petrolatum_gauze'}, {'id': 9799, 'synset': 'pet_shop.n.01', 'name': 'pet_shop'}, {'id': 9800, 'synset': 'petticoat.n.01', 'name': 'petticoat'}, {'id': 9801, 'synset': 'phial.n.01', 'name': 'phial'}, {'id': 9802, 'synset': 'phillips_screw.n.01', 'name': 'Phillips_screw'}, {'id': 9803, 'synset': 'phillips_screwdriver.n.01', 'name': 'Phillips_screwdriver'}, {'id': 9804, 'synset': 'phonograph_needle.n.01', 'name': 'phonograph_needle'}, {'id': 9805, 'synset': 'photocathode.n.01', 'name': 'photocathode'}, {'id': 9806, 'synset': 'photocoagulator.n.01', 'name': 'photocoagulator'}, {'id': 9807, 'synset': 'photocopier.n.01', 'name': 'photocopier'}, {'id': 9808, 'synset': 'photographic_equipment.n.01', 'name': 'photographic_equipment'}, {'id': 9809, 'synset': 'photographic_paper.n.01', 'name': 'photographic_paper'}, {'id': 9810, 'synset': 'photometer.n.01', 'name': 'photometer'}, {'id': 9811, 'synset': 'photomicrograph.n.01', 'name': 'photomicrograph'}, {'id': 9812, 'synset': 'photostat.n.02', 'name': 'Photostat'}, {'id': 9813, 'synset': 'photostat.n.01', 'name': 'photostat'}, {'id': 9814, 'synset': 'physical_pendulum.n.01', 'name': 'physical_pendulum'}, {'id': 9815, 'synset': 'piano_action.n.01', 'name': 'piano_action'}, {'id': 9816, 'synset': 'piano_keyboard.n.01', 'name': 'piano_keyboard'}, {'id': 9817, 'synset': 'piano_wire.n.01', 'name': 'piano_wire'}, {'id': 9818, 'synset': 'piccolo.n.01', 'name': 'piccolo'}, {'id': 9819, 'synset': 'pick.n.07', 'name': 'pick'}, {'id': 9820, 'synset': 'pick.n.06', 'name': 'pick'}, {'id': 9821, 'synset': 'pick.n.05', 'name': 'pick'}, {'id': 9822, 'synset': 'pickelhaube.n.01', 'name': 'pickelhaube'}, {'id': 9823, 'synset': 'picket_boat.n.01', 'name': 'picket_boat'}, {'id': 9824, 'synset': 'picket_fence.n.01', 'name': 'picket_fence'}, {'id': 9825, 'synset': 'picket_ship.n.01', 'name': 'picket_ship'}, {'id': 9826, 'synset': 'pickle_barrel.n.01', 'name': 'pickle_barrel'}, {'id': 9827, 'synset': 'picture_frame.n.01', 'name': 'picture_frame'}, {'id': 9828, 'synset': 'picture_hat.n.01', 'name': 'picture_hat'}, {'id': 9829, 'synset': 'picture_rail.n.01', 'name': 'picture_rail'}, {'id': 9830, 'synset': 'picture_window.n.01', 'name': 'picture_window'}, {'id': 9831, 'synset': 'piece_of_cloth.n.01', 'name': 'piece_of_cloth'}, {'id': 9832, 'synset': 'pied-a-terre.n.01', 'name': 'pied-a-terre'}, {'id': 9833, 'synset': 'pier.n.03', 'name': 'pier'}, {'id': 9834, 'synset': 'pier.n.02', 'name': 'pier'}, {'id': 9835, 'synset': 'pier_arch.n.01', 'name': 'pier_arch'}, {'id': 9836, 'synset': 'pier_glass.n.01', 'name': 'pier_glass'}, {'id': 9837, 'synset': 'pier_table.n.01', 'name': 'pier_table'}, {'id': 9838, 'synset': 'pieta.n.01', 'name': 'pieta'}, {'id': 9839, 'synset': 'piezometer.n.01', 'name': 'piezometer'}, {'id': 9840, 'synset': 'pig_bed.n.01', 'name': 'pig_bed'}, {'id': 9841, 'synset': 'piggery.n.01', 'name': 'piggery'}, {'id': 9842, 'synset': 'pilaster.n.01', 'name': 'pilaster'}, {'id': 9843, 'synset': 'pile.n.06', 'name': 'pile'}, {'id': 9844, 'synset': 'pile_driver.n.01', 'name': 'pile_driver'}, {'id': 9845, 'synset': 'pill_bottle.n.01', 'name': 'pill_bottle'}, {'id': 9846, 'synset': 'pillbox.n.01', 'name': 'pillbox'}, {'id': 9847, 'synset': 'pillion.n.01', 'name': 'pillion'}, {'id': 9848, 'synset': 'pillory.n.01', 'name': 'pillory'}, {'id': 9849, 'synset': 'pillow_block.n.01', 'name': 'pillow_block'}, {'id': 9850, 'synset': 'pillow_lace.n.01', 'name': 'pillow_lace'}, {'id': 9851, 'synset': 'pillow_sham.n.01', 'name': 'pillow_sham'}, {'id': 9852, 'synset': 'pilot_bit.n.01', 'name': 'pilot_bit'}, {'id': 9853, 'synset': 'pilot_boat.n.01', 'name': 'pilot_boat'}, {'id': 9854, 'synset': 'pilot_burner.n.01', 'name': 'pilot_burner'}, {'id': 9855, 'synset': 'pilot_cloth.n.01', 'name': 'pilot_cloth'}, {'id': 9856, 'synset': 'pilot_engine.n.01', 'name': 'pilot_engine'}, {'id': 9857, 'synset': 'pilothouse.n.01', 'name': 'pilothouse'}, {'id': 9858, 'synset': 'pilot_light.n.02', 'name': 'pilot_light'}, {'id': 9859, 'synset': 'pin.n.08', 'name': 'pin'}, {'id': 9860, 'synset': 'pin.n.07', 'name': 'pin'}, {'id': 9861, 'synset': 'pinata.n.01', 'name': 'pinata'}, {'id': 9862, 'synset': 'pinball_machine.n.01', 'name': 'pinball_machine'}, {'id': 9863, 'synset': 'pince-nez.n.01', 'name': 'pince-nez'}, {'id': 9864, 'synset': 'pincer.n.01', 'name': 'pincer'}, {'id': 9865, 'synset': 'pinch_bar.n.01', 'name': 'pinch_bar'}, {'id': 9866, 'synset': 'pincurl_clip.n.01', 'name': 'pincurl_clip'}, {'id': 9867, 'synset': 'pinfold.n.01', 'name': 'pinfold'}, {'id': 9868, 'synset': 'pinhead.n.02', 'name': 'pinhead'}, {'id': 9869, 'synset': 'pinion.n.01', 'name': 'pinion'}, {'id': 9870, 'synset': 'pinnacle.n.01', 'name': 'pinnacle'}, {'id': 9871, 'synset': 'pinprick.n.02', 'name': 'pinprick'}, {'id': 9872, 'synset': 'pinstripe.n.03', 'name': 'pinstripe'}, {'id': 9873, 'synset': 'pinstripe.n.02', 'name': 'pinstripe'}, {'id': 9874, 'synset': 'pinstripe.n.01', 'name': 'pinstripe'}, {'id': 9875, 'synset': 'pintle.n.01', 'name': 'pintle'}, {'id': 9876, 'synset': 'pinwheel.n.02', 'name': 'pinwheel'}, {'id': 9877, 'synset': 'tabor_pipe.n.01', 'name': 'tabor_pipe'}, {'id': 9878, 'synset': 'pipe.n.04', 'name': 'pipe'}, {'id': 9879, 'synset': 'pipe_bomb.n.01', 'name': 'pipe_bomb'}, {'id': 9880, 'synset': 'pipe_cleaner.n.01', 'name': 'pipe_cleaner'}, {'id': 9881, 'synset': 'pipe_cutter.n.01', 'name': 'pipe_cutter'}, {'id': 9882, 'synset': 'pipefitting.n.01', 'name': 'pipefitting'}, {'id': 9883, 'synset': 'pipet.n.01', 'name': 'pipet'}, {'id': 9884, 'synset': 'pipe_vise.n.01', 'name': 'pipe_vise'}, {'id': 9885, 'synset': 'pipe_wrench.n.01', 'name': 'pipe_wrench'}, {'id': 9886, 'synset': 'pique.n.01', 'name': 'pique'}, {'id': 9887, 'synset': 'pirate.n.03', 'name': 'pirate'}, {'id': 9888, 'synset': 'piste.n.02', 'name': 'piste'}, {'id': 9889, 'synset': 'pistol_grip.n.01', 'name': 'pistol_grip'}, {'id': 9890, 'synset': 'piston.n.02', 'name': 'piston'}, {'id': 9891, 'synset': 'piston_ring.n.01', 'name': 'piston_ring'}, {'id': 9892, 'synset': 'piston_rod.n.01', 'name': 'piston_rod'}, {'id': 9893, 'synset': 'pit.n.07', 'name': 'pit'}, {'id': 9894, 'synset': 'pitching_wedge.n.01', 'name': 'pitching_wedge'}, {'id': 9895, 'synset': 'pitch_pipe.n.01', 'name': 'pitch_pipe'}, {'id': 9896, 'synset': 'pith_hat.n.01', 'name': 'pith_hat'}, {'id': 9897, 'synset': 'piton.n.01', 'name': 'piton'}, {'id': 9898, 'synset': 'pitot-static_tube.n.01', 'name': 'Pitot-static_tube'}, {'id': 9899, 'synset': 'pitot_tube.n.01', 'name': 'Pitot_tube'}, {'id': 9900, 'synset': 'pitsaw.n.01', 'name': 'pitsaw'}, {'id': 9901, 'synset': 'pivot.n.02', 'name': 'pivot'}, {'id': 9902, 'synset': 'pivoting_window.n.01', 'name': 'pivoting_window'}, {'id': 9903, 'synset': 'pizzeria.n.01', 'name': 'pizzeria'}, {'id': 9904, 'synset': 'place_of_business.n.01', 'name': 'place_of_business'}, {'id': 9905, 'synset': 'place_of_worship.n.01', 'name': 'place_of_worship'}, {'id': 9906, 'synset': 'placket.n.01', 'name': 'placket'}, {'id': 9907, 'synset': 'planchet.n.01', 'name': 'planchet'}, {'id': 9908, 'synset': 'plane.n.05', 'name': 'plane'}, {'id': 9909, 'synset': 'plane.n.04', 'name': 'plane'}, {'id': 9910, 'synset': 'plane_seat.n.01', 'name': 'plane_seat'}, {'id': 9911, 'synset': 'planetarium.n.03', 'name': 'planetarium'}, {'id': 9912, 'synset': 'planetarium.n.02', 'name': 'planetarium'}, {'id': 9913, 'synset': 'planetarium.n.01', 'name': 'planetarium'}, {'id': 9914, 'synset': 'planetary_gear.n.01', 'name': 'planetary_gear'}, {'id': 9915, 'synset': 'plank-bed.n.01', 'name': 'plank-bed'}, {'id': 9916, 'synset': 'planking.n.02', 'name': 'planking'}, {'id': 9917, 'synset': 'planner.n.02', 'name': 'planner'}, {'id': 9918, 'synset': 'plant.n.01', 'name': 'plant'}, {'id': 9919, 'synset': 'planter.n.03', 'name': 'planter'}, {'id': 9920, 'synset': 'plaster.n.05', 'name': 'plaster'}, {'id': 9921, 'synset': 'plasterboard.n.01', 'name': 'plasterboard'}, {'id': 9922, 'synset': 'plastering_trowel.n.01', 'name': 'plastering_trowel'}, {'id': 9923, 'synset': 'plastic_bag.n.01', 'name': 'plastic_bag'}, {'id': 9924, 'synset': 'plastic_bomb.n.01', 'name': 'plastic_bomb'}, {'id': 9925, 'synset': 'plastic_laminate.n.01', 'name': 'plastic_laminate'}, {'id': 9926, 'synset': 'plastic_wrap.n.01', 'name': 'plastic_wrap'}, {'id': 9927, 'synset': 'plastron.n.03', 'name': 'plastron'}, {'id': 9928, 'synset': 'plastron.n.02', 'name': 'plastron'}, {'id': 9929, 'synset': 'plastron.n.01', 'name': 'plastron'}, {'id': 9930, 'synset': 'plate.n.14', 'name': 'plate'}, {'id': 9931, 'synset': 'plate.n.13', 'name': 'plate'}, {'id': 9932, 'synset': 'plate.n.12', 'name': 'plate'}, {'id': 9933, 'synset': 'platen.n.03', 'name': 'platen'}, {'id': 9934, 'synset': 'platen.n.01', 'name': 'platen'}, {'id': 9935, 'synset': 'plate_rack.n.01', 'name': 'plate_rack'}, {'id': 9936, 'synset': 'plate_rail.n.01', 'name': 'plate_rail'}, {'id': 9937, 'synset': 'platform.n.01', 'name': 'platform'}, {'id': 9938, 'synset': 'platform.n.04', 'name': 'platform'}, {'id': 9939, 'synset': 'platform.n.03', 'name': 'platform'}, {'id': 9940, 'synset': 'platform_bed.n.01', 'name': 'platform_bed'}, {'id': 9941, 'synset': 'platform_rocker.n.01', 'name': 'platform_rocker'}, {'id': 9942, 'synset': 'plating.n.01', 'name': 'plating'}, {'id': 9943, 'synset': 'playback.n.02', 'name': 'playback'}, {'id': 9944, 'synset': 'playbox.n.01', 'name': 'playbox'}, {'id': 9945, 'synset': 'playground.n.02', 'name': 'playground'}, {'id': 9946, 'synset': 'playsuit.n.01', 'name': 'playsuit'}, {'id': 9947, 'synset': 'plaza.n.02', 'name': 'plaza'}, {'id': 9948, 'synset': 'pleat.n.01', 'name': 'pleat'}, {'id': 9949, 'synset': 'plenum.n.02', 'name': 'plenum'}, {'id': 9950, 'synset': 'plethysmograph.n.01', 'name': 'plethysmograph'}, {'id': 9951, 'synset': 'pleximeter.n.01', 'name': 'pleximeter'}, {'id': 9952, 'synset': 'plexor.n.01', 'name': 'plexor'}, {'id': 9953, 'synset': 'plimsoll.n.02', 'name': 'plimsoll'}, {'id': 9954, 'synset': 'plotter.n.04', 'name': 'plotter'}, {'id': 9955, 'synset': 'plug.n.01', 'name': 'plug'}, {'id': 9956, 'synset': 'plug.n.05', 'name': 'plug'}, {'id': 9957, 'synset': 'plug_fuse.n.01', 'name': 'plug_fuse'}, {'id': 9958, 'synset': 'plughole.n.01', 'name': 'plughole'}, {'id': 9959, 'synset': 'plumb_bob.n.01', 'name': 'plumb_bob'}, {'id': 9960, 'synset': 'plumb_level.n.01', 'name': 'plumb_level'}, {'id': 9961, 'synset': 'plunger.n.03', 'name': 'plunger'}, {'id': 9962, 'synset': 'plus_fours.n.01', 'name': 'plus_fours'}, {'id': 9963, 'synset': 'plush.n.01', 'name': 'plush'}, {'id': 9964, 'synset': 'plywood.n.01', 'name': 'plywood'}, {'id': 9965, 'synset': 'pneumatic_drill.n.01', 'name': 'pneumatic_drill'}, {'id': 9966, 'synset': 'p-n_junction.n.01', 'name': 'p-n_junction'}, {'id': 9967, 'synset': 'p-n-p_transistor.n.01', 'name': 'p-n-p_transistor'}, {'id': 9968, 'synset': 'poacher.n.02', 'name': 'poacher'}, {'id': 9969, 'synset': 'pocket.n.01', 'name': 'pocket'}, {'id': 9970, 'synset': 'pocket_battleship.n.01', 'name': 'pocket_battleship'}, {'id': 9971, 'synset': 'pocketcomb.n.01', 'name': 'pocketcomb'}, {'id': 9972, 'synset': 'pocket_flap.n.01', 'name': 'pocket_flap'}, {'id': 9973, 'synset': 'pocket-handkerchief.n.01', 'name': 'pocket-handkerchief'}, {'id': 9974, 'synset': 'pod.n.04', 'name': 'pod'}, {'id': 9975, 'synset': 'pogo_stick.n.01', 'name': 'pogo_stick'}, {'id': 9976, 'synset': 'point-and-shoot_camera.n.01', 'name': 'point-and-shoot_camera'}, {'id': 9977, 'synset': 'pointed_arch.n.01', 'name': 'pointed_arch'}, {'id': 9978, 'synset': 'pointing_trowel.n.01', 'name': 'pointing_trowel'}, {'id': 9979, 'synset': 'point_lace.n.01', 'name': 'point_lace'}, {'id': 9980, 'synset': 'polarimeter.n.01', 'name': 'polarimeter'}, {'id': 9981, 'synset': 'polaroid.n.01', 'name': 'Polaroid'}, {'id': 9982, 'synset': 'polaroid_camera.n.01', 'name': 'Polaroid_camera'}, {'id': 9983, 'synset': 'pole.n.09', 'name': 'pole'}, {'id': 9984, 'synset': 'poleax.n.02', 'name': 'poleax'}, {'id': 9985, 'synset': 'poleax.n.01', 'name': 'poleax'}, {'id': 9986, 'synset': 'police_boat.n.01', 'name': 'police_boat'}, {'id': 9987, 'synset': 'police_van.n.01', 'name': 'police_van'}, {'id': 9988, 'synset': 'polling_booth.n.01', 'name': 'polling_booth'}, {'id': 9989, 'synset': 'polo_ball.n.01', 'name': 'polo_ball'}, {'id': 9990, 'synset': 'polo_mallet.n.01', 'name': 'polo_mallet'}, {'id': 9991, 'synset': 'polonaise.n.01', 'name': 'polonaise'}, {'id': 9992, 'synset': 'polyester.n.03', 'name': 'polyester'}, {'id': 9993, 'synset': 'polygraph.n.01', 'name': 'polygraph'}, {'id': 9994, 'synset': 'pomade.n.01', 'name': 'pomade'}, {'id': 9995, 'synset': 'pommel_horse.n.01', 'name': 'pommel_horse'}, {'id': 9996, 'synset': 'pongee.n.01', 'name': 'pongee'}, {'id': 9997, 'synset': 'poniard.n.01', 'name': 'poniard'}, {'id': 9998, 'synset': 'pontifical.n.01', 'name': 'pontifical'}, {'id': 9999, 'synset': 'pontoon.n.01', 'name': 'pontoon'}, {'id': 10000, 'synset': 'pontoon_bridge.n.01', 'name': 'pontoon_bridge'}, {'id': 10001, 'synset': 'pony_cart.n.01', 'name': 'pony_cart'}, {'id': 10002, 'synset': 'pool_ball.n.01', 'name': 'pool_ball'}, {'id': 10003, 'synset': 'poolroom.n.01', 'name': 'poolroom'}, {'id': 10004, 'synset': 'poop_deck.n.01', 'name': 'poop_deck'}, {'id': 10005, 'synset': 'poor_box.n.01', 'name': 'poor_box'}, {'id': 10006, 'synset': 'poorhouse.n.01', 'name': 'poorhouse'}, {'id': 10007, 'synset': 'pop_bottle.n.01', 'name': 'pop_bottle'}, {'id': 10008, 'synset': 'popgun.n.01', 'name': 'popgun'}, {'id': 10009, 'synset': 'poplin.n.01', 'name': 'poplin'}, {'id': 10010, 'synset': 'popper.n.03', 'name': 'popper'}, {'id': 10011, 'synset': 'poppet.n.01', 'name': 'poppet'}, {'id': 10012, 'synset': 'pop_tent.n.01', 'name': 'pop_tent'}, {'id': 10013, 'synset': 'porcelain.n.01', 'name': 'porcelain'}, {'id': 10014, 'synset': 'porch.n.01', 'name': 'porch'}, {'id': 10015, 'synset': 'porkpie.n.01', 'name': 'porkpie'}, {'id': 10016, 'synset': 'porringer.n.01', 'name': 'porringer'}, {'id': 10017, 'synset': 'portable.n.01', 'name': 'portable'}, {'id': 10018, 'synset': 'portable_computer.n.01', 'name': 'portable_computer'}, {'id': 10019, 'synset': 'portable_circular_saw.n.01', 'name': 'portable_circular_saw'}, {'id': 10020, 'synset': 'portcullis.n.01', 'name': 'portcullis'}, {'id': 10021, 'synset': 'porte-cochere.n.02', 'name': 'porte-cochere'}, {'id': 10022, 'synset': 'porte-cochere.n.01', 'name': 'porte-cochere'}, {'id': 10023, 'synset': 'portfolio.n.01', 'name': 'portfolio'}, {'id': 10024, 'synset': 'porthole.n.01', 'name': 'porthole'}, {'id': 10025, 'synset': 'portico.n.01', 'name': 'portico'}, {'id': 10026, 'synset': 'portiere.n.01', 'name': 'portiere'}, {'id': 10027, 'synset': 'portmanteau.n.02', 'name': 'portmanteau'}, {'id': 10028, 'synset': 'portrait_camera.n.01', 'name': 'portrait_camera'}, {'id': 10029, 'synset': 'portrait_lens.n.01', 'name': 'portrait_lens'}, {'id': 10030, 'synset': 'positive_pole.n.02', 'name': 'positive_pole'}, {'id': 10031, 'synset': 'positive_pole.n.01', 'name': 'positive_pole'}, {'id': 10032, 'synset': 'positron_emission_tomography_scanner.n.01', 'name': 'positron_emission_tomography_scanner'}, {'id': 10033, 'synset': 'post.n.04', 'name': 'post'}, {'id': 10034, 'synset': 'postage_meter.n.01', 'name': 'postage_meter'}, {'id': 10035, 'synset': 'post_and_lintel.n.01', 'name': 'post_and_lintel'}, {'id': 10036, 'synset': 'post_chaise.n.01', 'name': 'post_chaise'}, {'id': 10037, 'synset': 'postern.n.01', 'name': 'postern'}, {'id': 10038, 'synset': 'post_exchange.n.01', 'name': 'post_exchange'}, {'id': 10039, 'synset': 'posthole_digger.n.01', 'name': 'posthole_digger'}, {'id': 10040, 'synset': 'post_horn.n.01', 'name': 'post_horn'}, {'id': 10041, 'synset': 'posthouse.n.01', 'name': 'posthouse'}, {'id': 10042, 'synset': 'potbelly.n.02', 'name': 'potbelly'}, {'id': 10043, 'synset': 'potemkin_village.n.01', 'name': 'Potemkin_village'}, {'id': 10044, 'synset': 'potential_divider.n.01', 'name': 'potential_divider'}, {'id': 10045, 'synset': 'potentiometer.n.02', 'name': 'potentiometer'}, {'id': 10046, 'synset': 'potentiometer.n.01', 'name': 'potentiometer'}, {'id': 10047, 'synset': 'potpourri.n.03', 'name': 'potpourri'}, {'id': 10048, 'synset': 'potsherd.n.01', 'name': 'potsherd'}, {'id': 10049, 'synset': "potter's_wheel.n.01", 'name': "potter's_wheel"}, {'id': 10050, 'synset': 'pottle.n.01', 'name': 'pottle'}, {'id': 10051, 'synset': 'potty_seat.n.01', 'name': 'potty_seat'}, {'id': 10052, 'synset': 'poultice.n.01', 'name': 'poultice'}, {'id': 10053, 'synset': 'pound.n.13', 'name': 'pound'}, {'id': 10054, 'synset': 'pound_net.n.01', 'name': 'pound_net'}, {'id': 10055, 'synset': 'powder.n.03', 'name': 'powder'}, {'id': 10056, 'synset': 'powder_and_shot.n.01', 'name': 'powder_and_shot'}, {'id': 10057, 'synset': 'powdered_mustard.n.01', 'name': 'powdered_mustard'}, {'id': 10058, 'synset': 'powder_horn.n.01', 'name': 'powder_horn'}, {'id': 10059, 'synset': 'powder_keg.n.02', 'name': 'powder_keg'}, {'id': 10060, 'synset': 'power_brake.n.01', 'name': 'power_brake'}, {'id': 10061, 'synset': 'power_cord.n.01', 'name': 'power_cord'}, {'id': 10062, 'synset': 'power_drill.n.01', 'name': 'power_drill'}, {'id': 10063, 'synset': 'power_line.n.01', 'name': 'power_line'}, {'id': 10064, 'synset': 'power_loom.n.01', 'name': 'power_loom'}, {'id': 10065, 'synset': 'power_mower.n.01', 'name': 'power_mower'}, {'id': 10066, 'synset': 'power_pack.n.01', 'name': 'power_pack'}, {'id': 10067, 'synset': 'power_saw.n.01', 'name': 'power_saw'}, {'id': 10068, 'synset': 'power_steering.n.01', 'name': 'power_steering'}, {'id': 10069, 'synset': 'power_takeoff.n.01', 'name': 'power_takeoff'}, {'id': 10070, 'synset': 'power_tool.n.01', 'name': 'power_tool'}, {'id': 10071, 'synset': 'praetorium.n.01', 'name': 'praetorium'}, {'id': 10072, 'synset': 'prayer_rug.n.01', 'name': 'prayer_rug'}, {'id': 10073, 'synset': 'prayer_shawl.n.01', 'name': 'prayer_shawl'}, {'id': 10074, 'synset': 'precipitator.n.01', 'name': 'precipitator'}, {'id': 10075, 'synset': 'prefab.n.01', 'name': 'prefab'}, {'id': 10076, 'synset': 'presbytery.n.01', 'name': 'presbytery'}, {'id': 10077, 'synset': 'presence_chamber.n.01', 'name': 'presence_chamber'}, {'id': 10078, 'synset': 'press.n.07', 'name': 'press'}, {'id': 10079, 'synset': 'press.n.03', 'name': 'press'}, {'id': 10080, 'synset': 'press.n.06', 'name': 'press'}, {'id': 10081, 'synset': 'press_box.n.01', 'name': 'press_box'}, {'id': 10082, 'synset': 'press_gallery.n.01', 'name': 'press_gallery'}, {'id': 10083, 'synset': 'press_of_sail.n.01', 'name': 'press_of_sail'}, {'id': 10084, 'synset': 'pressure_cabin.n.01', 'name': 'pressure_cabin'}, {'id': 10085, 'synset': 'pressure_cooker.n.01', 'name': 'pressure_cooker'}, {'id': 10086, 'synset': 'pressure_dome.n.01', 'name': 'pressure_dome'}, {'id': 10087, 'synset': 'pressure_gauge.n.01', 'name': 'pressure_gauge'}, {'id': 10088, 'synset': 'pressurized_water_reactor.n.01', 'name': 'pressurized_water_reactor'}, {'id': 10089, 'synset': 'pressure_suit.n.01', 'name': 'pressure_suit'}, {'id': 10090, 'synset': 'pricket.n.01', 'name': 'pricket'}, {'id': 10091, 'synset': 'prie-dieu.n.01', 'name': 'prie-dieu'}, {'id': 10092, 'synset': 'primary_coil.n.01', 'name': 'primary_coil'}, {'id': 10093, 'synset': 'primus_stove.n.01', 'name': 'Primus_stove'}, {'id': 10094, 'synset': 'prince_albert.n.02', 'name': 'Prince_Albert'}, {'id': 10095, 'synset': 'print.n.06', 'name': 'print'}, {'id': 10096, 'synset': 'print_buffer.n.01', 'name': 'print_buffer'}, {'id': 10097, 'synset': 'printed_circuit.n.01', 'name': 'printed_circuit'}, {'id': 10098, 'synset': 'printer.n.02', 'name': 'printer'}, {'id': 10099, 'synset': 'printer_cable.n.01', 'name': 'printer_cable'}, {'id': 10100, 'synset': 'priory.n.01', 'name': 'priory'}, {'id': 10101, 'synset': 'prison.n.01', 'name': 'prison'}, {'id': 10102, 'synset': 'prison_camp.n.01', 'name': 'prison_camp'}, {'id': 10103, 'synset': 'privateer.n.02', 'name': 'privateer'}, {'id': 10104, 'synset': 'private_line.n.01', 'name': 'private_line'}, {'id': 10105, 'synset': 'privet_hedge.n.01', 'name': 'privet_hedge'}, {'id': 10106, 'synset': 'probe.n.02', 'name': 'probe'}, {'id': 10107, 'synset': 'proctoscope.n.01', 'name': 'proctoscope'}, {'id': 10108, 'synset': 'prod.n.02', 'name': 'prod'}, {'id': 10109, 'synset': 'production_line.n.01', 'name': 'production_line'}, {'id': 10110, 'synset': 'projector.n.01', 'name': 'projector'}, {'id': 10111, 'synset': 'prolonge.n.01', 'name': 'prolonge'}, {'id': 10112, 'synset': 'prolonge_knot.n.01', 'name': 'prolonge_knot'}, {'id': 10113, 'synset': 'prompter.n.02', 'name': 'prompter'}, {'id': 10114, 'synset': 'prong.n.01', 'name': 'prong'}, {'id': 10115, 'synset': 'propeller_plane.n.01', 'name': 'propeller_plane'}, {'id': 10116, 'synset': 'propjet.n.01', 'name': 'propjet'}, {'id': 10117, 'synset': 'proportional_counter_tube.n.01', 'name': 'proportional_counter_tube'}, {'id': 10118, 'synset': 'propulsion_system.n.01', 'name': 'propulsion_system'}, {'id': 10119, 'synset': 'proscenium.n.02', 'name': 'proscenium'}, {'id': 10120, 'synset': 'proscenium_arch.n.01', 'name': 'proscenium_arch'}, {'id': 10121, 'synset': 'prosthesis.n.01', 'name': 'prosthesis'}, {'id': 10122, 'synset': 'protective_covering.n.01', 'name': 'protective_covering'}, {'id': 10123, 'synset': 'protective_garment.n.01', 'name': 'protective_garment'}, {'id': 10124, 'synset': 'proton_accelerator.n.01', 'name': 'proton_accelerator'}, {'id': 10125, 'synset': 'protractor.n.01', 'name': 'protractor'}, {'id': 10126, 'synset': 'pruner.n.02', 'name': 'pruner'}, {'id': 10127, 'synset': 'pruning_knife.n.01', 'name': 'pruning_knife'}, {'id': 10128, 'synset': 'pruning_saw.n.01', 'name': 'pruning_saw'}, {'id': 10129, 'synset': 'pruning_shears.n.01', 'name': 'pruning_shears'}, {'id': 10130, 'synset': 'psaltery.n.01', 'name': 'psaltery'}, {'id': 10131, 'synset': 'psychrometer.n.01', 'name': 'psychrometer'}, {'id': 10132, 'synset': 'pt_boat.n.01', 'name': 'PT_boat'}, {'id': 10133, 'synset': 'public_address_system.n.01', 'name': 'public_address_system'}, {'id': 10134, 'synset': 'public_house.n.01', 'name': 'public_house'}, {'id': 10135, 'synset': 'public_toilet.n.01', 'name': 'public_toilet'}, {'id': 10136, 'synset': 'public_transport.n.01', 'name': 'public_transport'}, {'id': 10137, 'synset': 'public_works.n.01', 'name': 'public_works'}, {'id': 10138, 'synset': 'puck.n.02', 'name': 'puck'}, {'id': 10139, 'synset': 'pull.n.04', 'name': 'pull'}, {'id': 10140, 'synset': 'pullback.n.01', 'name': 'pullback'}, {'id': 10141, 'synset': 'pull_chain.n.01', 'name': 'pull_chain'}, {'id': 10142, 'synset': 'pulley.n.01', 'name': 'pulley'}, {'id': 10143, 'synset': 'pull-off.n.01', 'name': 'pull-off'}, {'id': 10144, 'synset': 'pullman.n.01', 'name': 'Pullman'}, {'id': 10145, 'synset': 'pullover.n.01', 'name': 'pullover'}, {'id': 10146, 'synset': 'pull-through.n.01', 'name': 'pull-through'}, {'id': 10147, 'synset': 'pulse_counter.n.01', 'name': 'pulse_counter'}, {'id': 10148, 'synset': 'pulse_generator.n.01', 'name': 'pulse_generator'}, {'id': 10149, 'synset': 'pulse_timing_circuit.n.01', 'name': 'pulse_timing_circuit'}, {'id': 10150, 'synset': 'pump.n.01', 'name': 'pump'}, {'id': 10151, 'synset': 'pump.n.03', 'name': 'pump'}, {'id': 10152, 'synset': 'pump_action.n.01', 'name': 'pump_action'}, {'id': 10153, 'synset': 'pump_house.n.01', 'name': 'pump_house'}, {'id': 10154, 'synset': 'pump_room.n.01', 'name': 'pump_room'}, {'id': 10155, 'synset': 'pump-type_pliers.n.01', 'name': 'pump-type_pliers'}, {'id': 10156, 'synset': 'pump_well.n.01', 'name': 'pump_well'}, {'id': 10157, 'synset': 'punchboard.n.01', 'name': 'punchboard'}, {'id': 10158, 'synset': 'punch_bowl.n.01', 'name': 'punch_bowl'}, {'id': 10159, 'synset': 'punching_bag.n.02', 'name': 'punching_bag'}, {'id': 10160, 'synset': 'punch_pliers.n.01', 'name': 'punch_pliers'}, {'id': 10161, 'synset': 'punch_press.n.01', 'name': 'punch_press'}, {'id': 10162, 'synset': 'punnet.n.01', 'name': 'punnet'}, {'id': 10163, 'synset': 'punt.n.02', 'name': 'punt'}, {'id': 10164, 'synset': 'pup_tent.n.01', 'name': 'pup_tent'}, {'id': 10165, 'synset': 'purdah.n.03', 'name': 'purdah'}, {'id': 10166, 'synset': 'purifier.n.01', 'name': 'purifier'}, {'id': 10167, 'synset': 'purl.n.02', 'name': 'purl'}, {'id': 10168, 'synset': 'purse.n.03', 'name': 'purse'}, {'id': 10169, 'synset': 'push-bike.n.01', 'name': 'push-bike'}, {'id': 10170, 'synset': 'push_broom.n.01', 'name': 'push_broom'}, {'id': 10171, 'synset': 'push_button.n.01', 'name': 'push_button'}, {'id': 10172, 'synset': 'push-button_radio.n.01', 'name': 'push-button_radio'}, {'id': 10173, 'synset': 'pusher.n.04', 'name': 'pusher'}, {'id': 10174, 'synset': 'put-put.n.01', 'name': 'put-put'}, {'id': 10175, 'synset': 'puttee.n.01', 'name': 'puttee'}, {'id': 10176, 'synset': 'putter.n.02', 'name': 'putter'}, {'id': 10177, 'synset': 'putty_knife.n.01', 'name': 'putty_knife'}, {'id': 10178, 'synset': 'puzzle.n.02', 'name': 'puzzle'}, {'id': 10179, 'synset': 'pylon.n.02', 'name': 'pylon'}, {'id': 10180, 'synset': 'pylon.n.01', 'name': 'pylon'}, {'id': 10181, 'synset': 'pyramidal_tent.n.01', 'name': 'pyramidal_tent'}, {'id': 10182, 'synset': 'pyrograph.n.01', 'name': 'pyrograph'}, {'id': 10183, 'synset': 'pyrometer.n.01', 'name': 'pyrometer'}, {'id': 10184, 'synset': 'pyrometric_cone.n.01', 'name': 'pyrometric_cone'}, {'id': 10185, 'synset': 'pyrostat.n.01', 'name': 'pyrostat'}, {'id': 10186, 'synset': 'pyx.n.02', 'name': 'pyx'}, {'id': 10187, 'synset': 'pyx.n.01', 'name': 'pyx'}, {'id': 10188, 'synset': 'pyxis.n.03', 'name': 'pyxis'}, {'id': 10189, 'synset': 'quad.n.04', 'name': 'quad'}, {'id': 10190, 'synset': 'quadrant.n.04', 'name': 'quadrant'}, {'id': 10191, 'synset': 'quadraphony.n.01', 'name': 'quadraphony'}, {'id': 10192, 'synset': 'quartering.n.02', 'name': 'quartering'}, {'id': 10193, 'synset': 'quarterstaff.n.01', 'name': 'quarterstaff'}, {'id': 10194, 'synset': 'quartz_battery.n.01', 'name': 'quartz_battery'}, {'id': 10195, 'synset': 'quartz_lamp.n.01', 'name': 'quartz_lamp'}, {'id': 10196, 'synset': 'queen.n.08', 'name': 'queen'}, {'id': 10197, 'synset': 'queen.n.07', 'name': 'queen'}, {'id': 10198, 'synset': 'queen_post.n.01', 'name': 'queen_post'}, {'id': 10199, 'synset': 'quern.n.01', 'name': 'quern'}, {'id': 10200, 'synset': 'quill.n.01', 'name': 'quill'}, {'id': 10201, 'synset': 'quilted_bedspread.n.01', 'name': 'quilted_bedspread'}, {'id': 10202, 'synset': 'quilting.n.02', 'name': 'quilting'}, {'id': 10203, 'synset': 'quipu.n.01', 'name': 'quipu'}, {'id': 10204, 'synset': 'quirk_molding.n.01', 'name': 'quirk_molding'}, {'id': 10205, 'synset': 'quirt.n.01', 'name': 'quirt'}, {'id': 10206, 'synset': 'quiver.n.03', 'name': 'quiver'}, {'id': 10207, 'synset': 'quoin.n.02', 'name': 'quoin'}, {'id': 10208, 'synset': 'quoit.n.01', 'name': 'quoit'}, {'id': 10209, 'synset': 'qwerty_keyboard.n.01', 'name': 'QWERTY_keyboard'}, {'id': 10210, 'synset': 'rabbet.n.01', 'name': 'rabbet'}, {'id': 10211, 'synset': 'rabbet_joint.n.01', 'name': 'rabbet_joint'}, {'id': 10212, 'synset': 'rabbit_ears.n.01', 'name': 'rabbit_ears'}, {'id': 10213, 'synset': 'rabbit_hutch.n.01', 'name': 'rabbit_hutch'}, {'id': 10214, 'synset': 'raceabout.n.01', 'name': 'raceabout'}, {'id': 10215, 'synset': 'raceway.n.01', 'name': 'raceway'}, {'id': 10216, 'synset': 'racing_boat.n.01', 'name': 'racing_boat'}, {'id': 10217, 'synset': 'racing_gig.n.01', 'name': 'racing_gig'}, {'id': 10218, 'synset': 'racing_skiff.n.01', 'name': 'racing_skiff'}, {'id': 10219, 'synset': 'rack.n.05', 'name': 'rack'}, {'id': 10220, 'synset': 'rack.n.01', 'name': 'rack'}, {'id': 10221, 'synset': 'rack.n.04', 'name': 'rack'}, {'id': 10222, 'synset': 'rack_and_pinion.n.01', 'name': 'rack_and_pinion'}, {'id': 10223, 'synset': 'racquetball.n.01', 'name': 'racquetball'}, {'id': 10224, 'synset': 'radial.n.01', 'name': 'radial'}, {'id': 10225, 'synset': 'radial_engine.n.01', 'name': 'radial_engine'}, {'id': 10226, 'synset': 'radiation_pyrometer.n.01', 'name': 'radiation_pyrometer'}, {'id': 10227, 'synset': 'radiator.n.02', 'name': 'radiator'}, {'id': 10228, 'synset': 'radiator_cap.n.01', 'name': 'radiator_cap'}, {'id': 10229, 'synset': 'radiator_hose.n.01', 'name': 'radiator_hose'}, {'id': 10230, 'synset': 'radio.n.03', 'name': 'radio'}, {'id': 10231, 'synset': 'radio_antenna.n.01', 'name': 'radio_antenna'}, {'id': 10232, 'synset': 'radio_chassis.n.01', 'name': 'radio_chassis'}, {'id': 10233, 'synset': 'radio_compass.n.01', 'name': 'radio_compass'}, {'id': 10234, 'synset': 'radiogram.n.02', 'name': 'radiogram'}, {'id': 10235, 'synset': 'radio_interferometer.n.01', 'name': 'radio_interferometer'}, {'id': 10236, 'synset': 'radio_link.n.01', 'name': 'radio_link'}, {'id': 10237, 'synset': 'radiometer.n.01', 'name': 'radiometer'}, {'id': 10238, 'synset': 'radiomicrometer.n.01', 'name': 'radiomicrometer'}, {'id': 10239, 'synset': 'radio-phonograph.n.01', 'name': 'radio-phonograph'}, {'id': 10240, 'synset': 'radiotelegraph.n.02', 'name': 'radiotelegraph'}, {'id': 10241, 'synset': 'radiotelephone.n.02', 'name': 'radiotelephone'}, {'id': 10242, 'synset': 'radio_telescope.n.01', 'name': 'radio_telescope'}, {'id': 10243, 'synset': 'radiotherapy_equipment.n.01', 'name': 'radiotherapy_equipment'}, {'id': 10244, 'synset': 'radio_transmitter.n.01', 'name': 'radio_transmitter'}, {'id': 10245, 'synset': 'radome.n.01', 'name': 'radome'}, {'id': 10246, 'synset': 'rafter.n.01', 'name': 'rafter'}, {'id': 10247, 'synset': 'raft_foundation.n.01', 'name': 'raft_foundation'}, {'id': 10248, 'synset': 'rag.n.01', 'name': 'rag'}, {'id': 10249, 'synset': 'ragbag.n.02', 'name': 'ragbag'}, {'id': 10250, 'synset': 'raglan.n.01', 'name': 'raglan'}, {'id': 10251, 'synset': 'raglan_sleeve.n.01', 'name': 'raglan_sleeve'}, {'id': 10252, 'synset': 'rail.n.04', 'name': 'rail'}, {'id': 10253, 'synset': 'rail_fence.n.01', 'name': 'rail_fence'}, {'id': 10254, 'synset': 'railhead.n.01', 'name': 'railhead'}, {'id': 10255, 'synset': 'railing.n.01', 'name': 'railing'}, {'id': 10256, 'synset': 'railing.n.02', 'name': 'railing'}, {'id': 10257, 'synset': 'railroad_bed.n.01', 'name': 'railroad_bed'}, {'id': 10258, 'synset': 'railroad_tunnel.n.01', 'name': 'railroad_tunnel'}, {'id': 10259, 'synset': 'rain_barrel.n.01', 'name': 'rain_barrel'}, {'id': 10260, 'synset': 'rain_gauge.n.01', 'name': 'rain_gauge'}, {'id': 10261, 'synset': 'rain_stick.n.01', 'name': 'rain_stick'}, {'id': 10262, 'synset': 'rake.n.03', 'name': 'rake'}, {'id': 10263, 'synset': 'rake_handle.n.01', 'name': 'rake_handle'}, {'id': 10264, 'synset': 'ram_disk.n.01', 'name': 'RAM_disk'}, {'id': 10265, 'synset': 'ramekin.n.02', 'name': 'ramekin'}, {'id': 10266, 'synset': 'ramjet.n.01', 'name': 'ramjet'}, {'id': 10267, 'synset': 'rammer.n.01', 'name': 'rammer'}, {'id': 10268, 'synset': 'ramp.n.01', 'name': 'ramp'}, {'id': 10269, 'synset': 'rampant_arch.n.01', 'name': 'rampant_arch'}, {'id': 10270, 'synset': 'rampart.n.01', 'name': 'rampart'}, {'id': 10271, 'synset': 'ramrod.n.01', 'name': 'ramrod'}, {'id': 10272, 'synset': 'ramrod.n.03', 'name': 'ramrod'}, {'id': 10273, 'synset': 'ranch.n.01', 'name': 'ranch'}, {'id': 10274, 'synset': 'ranch_house.n.01', 'name': 'ranch_house'}, {'id': 10275, 'synset': 'random-access_memory.n.01', 'name': 'random-access_memory'}, {'id': 10276, 'synset': 'rangefinder.n.01', 'name': 'rangefinder'}, {'id': 10277, 'synset': 'range_hood.n.01', 'name': 'range_hood'}, {'id': 10278, 'synset': 'range_pole.n.01', 'name': 'range_pole'}, {'id': 10279, 'synset': 'rapier.n.01', 'name': 'rapier'}, {'id': 10280, 'synset': 'rariora.n.01', 'name': 'rariora'}, {'id': 10281, 'synset': 'rasp.n.02', 'name': 'rasp'}, {'id': 10282, 'synset': 'ratchet.n.01', 'name': 'ratchet'}, {'id': 10283, 'synset': 'ratchet_wheel.n.01', 'name': 'ratchet_wheel'}, {'id': 10284, 'synset': 'rathskeller.n.01', 'name': 'rathskeller'}, {'id': 10285, 'synset': 'ratline.n.01', 'name': 'ratline'}, {'id': 10286, 'synset': 'rat-tail_file.n.01', 'name': 'rat-tail_file'}, {'id': 10287, 'synset': 'rattan.n.03', 'name': 'rattan'}, {'id': 10288, 'synset': 'rattrap.n.03', 'name': 'rattrap'}, {'id': 10289, 'synset': 'rayon.n.01', 'name': 'rayon'}, {'id': 10290, 'synset': 'razor.n.01', 'name': 'razor'}, {'id': 10291, 'synset': 'reaction-propulsion_engine.n.01', 'name': 'reaction-propulsion_engine'}, {'id': 10292, 'synset': 'reaction_turbine.n.01', 'name': 'reaction_turbine'}, {'id': 10293, 'synset': 'reactor.n.01', 'name': 'reactor'}, {'id': 10294, 'synset': 'reading_lamp.n.01', 'name': 'reading_lamp'}, {'id': 10295, 'synset': 'reading_room.n.01', 'name': 'reading_room'}, {'id': 10296, 'synset': 'read-only_memory.n.01', 'name': 'read-only_memory'}, {'id': 10297, 'synset': 'read-only_memory_chip.n.01', 'name': 'read-only_memory_chip'}, {'id': 10298, 'synset': 'readout.n.03', 'name': 'readout'}, {'id': 10299, 'synset': 'read/write_head.n.01', 'name': 'read/write_head'}, {'id': 10300, 'synset': 'ready-to-wear.n.01', 'name': 'ready-to-wear'}, {'id': 10301, 'synset': 'real_storage.n.01', 'name': 'real_storage'}, {'id': 10302, 'synset': 'reamer.n.02', 'name': 'reamer'}, {'id': 10303, 'synset': 'reaumur_thermometer.n.01', 'name': 'Reaumur_thermometer'}, {'id': 10304, 'synset': 'rebozo.n.01', 'name': 'rebozo'}, {'id': 10305, 'synset': 'receiver.n.01', 'name': 'receiver'}, {'id': 10306, 'synset': 'receptacle.n.01', 'name': 'receptacle'}, {'id': 10307, 'synset': 'reception_desk.n.01', 'name': 'reception_desk'}, {'id': 10308, 'synset': 'reception_room.n.01', 'name': 'reception_room'}, {'id': 10309, 'synset': 'recess.n.04', 'name': 'recess'}, {'id': 10310, 'synset': 'reciprocating_engine.n.01', 'name': 'reciprocating_engine'}, {'id': 10311, 'synset': 'reconnaissance_plane.n.01', 'name': 'reconnaissance_plane'}, {'id': 10312, 'synset': 'reconnaissance_vehicle.n.01', 'name': 'reconnaissance_vehicle'}, {'id': 10313, 'synset': 'record_changer.n.01', 'name': 'record_changer'}, {'id': 10314, 'synset': 'recorder.n.01', 'name': 'recorder'}, {'id': 10315, 'synset': 'recording.n.03', 'name': 'recording'}, {'id': 10316, 'synset': 'recording_system.n.01', 'name': 'recording_system'}, {'id': 10317, 'synset': 'record_sleeve.n.01', 'name': 'record_sleeve'}, {'id': 10318, 'synset': 'recovery_room.n.01', 'name': 'recovery_room'}, {'id': 10319, 'synset': 'recreational_vehicle.n.01', 'name': 'recreational_vehicle'}, {'id': 10320, 'synset': 'recreation_room.n.01', 'name': 'recreation_room'}, {'id': 10321, 'synset': 'recycling_bin.n.01', 'name': 'recycling_bin'}, {'id': 10322, 'synset': 'recycling_plant.n.01', 'name': 'recycling_plant'}, {'id': 10323, 'synset': 'redbrick_university.n.01', 'name': 'redbrick_university'}, {'id': 10324, 'synset': 'red_carpet.n.01', 'name': 'red_carpet'}, {'id': 10325, 'synset': 'redoubt.n.02', 'name': 'redoubt'}, {'id': 10326, 'synset': 'redoubt.n.01', 'name': 'redoubt'}, {'id': 10327, 'synset': 'reduction_gear.n.01', 'name': 'reduction_gear'}, {'id': 10328, 'synset': 'reed_pipe.n.01', 'name': 'reed_pipe'}, {'id': 10329, 'synset': 'reed_stop.n.01', 'name': 'reed_stop'}, {'id': 10330, 'synset': 'reef_knot.n.01', 'name': 'reef_knot'}, {'id': 10331, 'synset': 'reel.n.03', 'name': 'reel'}, {'id': 10332, 'synset': 'reel.n.01', 'name': 'reel'}, {'id': 10333, 'synset': 'refectory.n.01', 'name': 'refectory'}, {'id': 10334, 'synset': 'refectory_table.n.01', 'name': 'refectory_table'}, {'id': 10335, 'synset': 'refinery.n.01', 'name': 'refinery'}, {'id': 10336, 'synset': 'reflecting_telescope.n.01', 'name': 'reflecting_telescope'}, {'id': 10337, 'synset': 'reflectometer.n.01', 'name': 'reflectometer'}, {'id': 10338, 'synset': 'reflex_camera.n.01', 'name': 'reflex_camera'}, {'id': 10339, 'synset': 'reflux_condenser.n.01', 'name': 'reflux_condenser'}, {'id': 10340, 'synset': 'reformatory.n.01', 'name': 'reformatory'}, {'id': 10341, 'synset': 'reformer.n.02', 'name': 'reformer'}, {'id': 10342, 'synset': 'refracting_telescope.n.01', 'name': 'refracting_telescope'}, {'id': 10343, 'synset': 'refractometer.n.01', 'name': 'refractometer'}, {'id': 10344, 'synset': 'refrigeration_system.n.01', 'name': 'refrigeration_system'}, {'id': 10345, 'synset': 'refrigerator.n.01', 'name': 'refrigerator'}, {'id': 10346, 'synset': 'refrigerator_car.n.01', 'name': 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'synset': 'repeating_firearm.n.01', 'name': 'repeating_firearm'}, {'id': 10363, 'synset': 'repository.n.03', 'name': 'repository'}, {'id': 10364, 'synset': 'reproducer.n.01', 'name': 'reproducer'}, {'id': 10365, 'synset': 'rerebrace.n.01', 'name': 'rerebrace'}, {'id': 10366, 'synset': 'rescue_equipment.n.01', 'name': 'rescue_equipment'}, {'id': 10367, 'synset': 'research_center.n.01', 'name': 'research_center'}, {'id': 10368, 'synset': 'reseau.n.02', 'name': 'reseau'}, {'id': 10369, 'synset': 'reservoir.n.03', 'name': 'reservoir'}, {'id': 10370, 'synset': 'reset.n.01', 'name': 'reset'}, {'id': 10371, 'synset': 'reset_button.n.01', 'name': 'reset_button'}, {'id': 10372, 'synset': 'residence.n.02', 'name': 'residence'}, {'id': 10373, 'synset': 'resistance_pyrometer.n.01', 'name': 'resistance_pyrometer'}, {'id': 10374, 'synset': 'resistor.n.01', 'name': 'resistor'}, {'id': 10375, 'synset': 'resonator.n.03', 'name': 'resonator'}, {'id': 10376, 'synset': 'resonator.n.01', 'name': 'resonator'}, {'id': 10377, 'synset': 'resort_hotel.n.02', 'name': 'resort_hotel'}, {'id': 10378, 'synset': 'respirator.n.01', 'name': 'respirator'}, {'id': 10379, 'synset': 'restaurant.n.01', 'name': 'restaurant'}, {'id': 10380, 'synset': 'rest_house.n.01', 'name': 'rest_house'}, {'id': 10381, 'synset': 'restraint.n.06', 'name': 'restraint'}, {'id': 10382, 'synset': 'resuscitator.n.01', 'name': 'resuscitator'}, {'id': 10383, 'synset': 'retainer.n.03', 'name': 'retainer'}, {'id': 10384, 'synset': 'retaining_wall.n.01', 'name': 'retaining_wall'}, {'id': 10385, 'synset': 'reticle.n.01', 'name': 'reticle'}, {'id': 10386, 'synset': 'reticulation.n.02', 'name': 'reticulation'}, {'id': 10387, 'synset': 'reticule.n.01', 'name': 'reticule'}, {'id': 10388, 'synset': 'retort.n.02', 'name': 'retort'}, {'id': 10389, 'synset': 'retractor.n.01', 'name': 'retractor'}, {'id': 10390, 'synset': 'return_key.n.01', 'name': 'return_key'}, {'id': 10391, 'synset': 'reverberatory_furnace.n.01', 'name': 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10407, 'synset': 'rib_joint_pliers.n.01', 'name': 'rib_joint_pliers'}, {'id': 10408, 'synset': 'ricer.n.01', 'name': 'ricer'}, {'id': 10409, 'synset': 'riddle.n.02', 'name': 'riddle'}, {'id': 10410, 'synset': 'ride.n.02', 'name': 'ride'}, {'id': 10411, 'synset': 'ridge.n.06', 'name': 'ridge'}, {'id': 10412, 'synset': 'ridge_rope.n.01', 'name': 'ridge_rope'}, {'id': 10413, 'synset': 'riding_boot.n.01', 'name': 'riding_boot'}, {'id': 10414, 'synset': 'riding_crop.n.01', 'name': 'riding_crop'}, {'id': 10415, 'synset': 'riding_mower.n.01', 'name': 'riding_mower'}, {'id': 10416, 'synset': 'rifle_ball.n.01', 'name': 'rifle_ball'}, {'id': 10417, 'synset': 'rifle_grenade.n.01', 'name': 'rifle_grenade'}, {'id': 10418, 'synset': 'rig.n.01', 'name': 'rig'}, {'id': 10419, 'synset': 'rigger.n.02', 'name': 'rigger'}, {'id': 10420, 'synset': 'rigger.n.04', 'name': 'rigger'}, {'id': 10421, 'synset': 'rigging.n.01', 'name': 'rigging'}, {'id': 10422, 'synset': 'rigout.n.01', 'name': 'rigout'}, {'id': 10423, 'synset': 'ringlet.n.03', 'name': 'ringlet'}, {'id': 10424, 'synset': 'rings.n.01', 'name': 'rings'}, {'id': 10425, 'synset': 'rink.n.01', 'name': 'rink'}, {'id': 10426, 'synset': 'riot_gun.n.01', 'name': 'riot_gun'}, {'id': 10427, 'synset': 'ripcord.n.02', 'name': 'ripcord'}, {'id': 10428, 'synset': 'ripcord.n.01', 'name': 'ripcord'}, {'id': 10429, 'synset': 'ripping_bar.n.01', 'name': 'ripping_bar'}, {'id': 10430, 'synset': 'ripping_chisel.n.01', 'name': 'ripping_chisel'}, {'id': 10431, 'synset': 'ripsaw.n.01', 'name': 'ripsaw'}, {'id': 10432, 'synset': 'riser.n.03', 'name': 'riser'}, {'id': 10433, 'synset': 'riser.n.02', 'name': 'riser'}, {'id': 10434, 'synset': 'ritz.n.03', 'name': 'Ritz'}, {'id': 10435, 'synset': 'rivet.n.02', 'name': 'rivet'}, {'id': 10436, 'synset': 'riveting_machine.n.01', 'name': 'riveting_machine'}, {'id': 10437, 'synset': 'roach_clip.n.01', 'name': 'roach_clip'}, {'id': 10438, 'synset': 'road.n.01', 'name': 'road'}, {'id': 10439, 'synset': 'roadbed.n.01', 'name': 'roadbed'}, {'id': 10440, 'synset': 'roadblock.n.02', 'name': 'roadblock'}, {'id': 10441, 'synset': 'roadhouse.n.01', 'name': 'roadhouse'}, {'id': 10442, 'synset': 'roadster.n.01', 'name': 'roadster'}, {'id': 10443, 'synset': 'roadway.n.01', 'name': 'roadway'}, {'id': 10444, 'synset': 'roaster.n.04', 'name': 'roaster'}, {'id': 10445, 'synset': 'robotics_equipment.n.01', 'name': 'robotics_equipment'}, {'id': 10446, 'synset': 'rochon_prism.n.01', 'name': 'Rochon_prism'}, {'id': 10447, 'synset': 'rock_bit.n.01', 'name': 'rock_bit'}, {'id': 10448, 'synset': 'rocker.n.07', 'name': 'rocker'}, {'id': 10449, 'synset': 'rocker.n.05', 'name': 'rocker'}, {'id': 10450, 'synset': 'rocker_arm.n.01', 'name': 'rocker_arm'}, {'id': 10451, 'synset': 'rocket.n.02', 'name': 'rocket'}, {'id': 10452, 'synset': 'rocket.n.01', 'name': 'rocket'}, {'id': 10453, 'synset': 'rod.n.01', 'name': 'rod'}, {'id': 10454, 'synset': 'rodeo.n.02', 'name': 'rodeo'}, {'id': 10455, 'synset': 'roll.n.04', 'name': 'roll'}, {'id': 10456, 'synset': 'roller.n.04', 'name': 'roller'}, {'id': 10457, 'synset': 'roller.n.03', 'name': 'roller'}, {'id': 10458, 'synset': 'roller_bandage.n.01', 'name': 'roller_bandage'}, {'id': 10459, 'synset': 'in-line_skate.n.01', 'name': 'in-line_skate'}, {'id': 10460, 'synset': 'roller_blind.n.01', 'name': 'roller_blind'}, {'id': 10461, 'synset': 'roller_coaster.n.02', 'name': 'roller_coaster'}, {'id': 10462, 'synset': 'roller_towel.n.01', 'name': 'roller_towel'}, {'id': 10463, 'synset': 'roll_film.n.01', 'name': 'roll_film'}, {'id': 10464, 'synset': 'rolling_hitch.n.01', 'name': 'rolling_hitch'}, {'id': 10465, 'synset': 'rolling_mill.n.01', 'name': 'rolling_mill'}, {'id': 10466, 'synset': 'rolling_stock.n.01', 'name': 'rolling_stock'}, {'id': 10467, 'synset': 'roll-on.n.02', 'name': 'roll-on'}, {'id': 10468, 'synset': 'roll-on.n.01', 'name': 'roll-on'}, {'id': 10469, 'synset': 'roll-on_roll-off.n.01', 'name': 'roll-on_roll-off'}, {'id': 10470, 'synset': 'rolodex.n.01', 'name': 'Rolodex'}, {'id': 10471, 'synset': 'roman_arch.n.01', 'name': 'Roman_arch'}, {'id': 10472, 'synset': 'roman_building.n.01', 'name': 'Roman_building'}, {'id': 10473, 'synset': 'romper.n.02', 'name': 'romper'}, {'id': 10474, 'synset': 'rood_screen.n.01', 'name': 'rood_screen'}, {'id': 10475, 'synset': 'roof.n.01', 'name': 'roof'}, {'id': 10476, 'synset': 'roof.n.02', 'name': 'roof'}, {'id': 10477, 'synset': 'roofing.n.01', 'name': 'roofing'}, {'id': 10478, 'synset': 'room.n.01', 'name': 'room'}, {'id': 10479, 'synset': 'roomette.n.01', 'name': 'roomette'}, {'id': 10480, 'synset': 'room_light.n.01', 'name': 'room_light'}, {'id': 10481, 'synset': 'roost.n.01', 'name': 'roost'}, {'id': 10482, 'synset': 'rope.n.01', 'name': 'rope'}, {'id': 10483, 'synset': 'rope_bridge.n.01', 'name': 'rope_bridge'}, {'id': 10484, 'synset': 'rope_tow.n.01', 'name': 'rope_tow'}, {'id': 10485, 'synset': 'rose_water.n.01', 'name': 'rose_water'}, {'id': 10486, 'synset': 'rose_window.n.01', 'name': 'rose_window'}, {'id': 10487, 'synset': 'rosin_bag.n.01', 'name': 'rosin_bag'}, {'id': 10488, 'synset': 'rotary_actuator.n.01', 'name': 'rotary_actuator'}, {'id': 10489, 'synset': 'rotary_engine.n.01', 'name': 'rotary_engine'}, {'id': 10490, 'synset': 'rotary_press.n.01', 'name': 'rotary_press'}, {'id': 10491, 'synset': 'rotating_mechanism.n.01', 'name': 'rotating_mechanism'}, {'id': 10492, 'synset': 'rotating_shaft.n.01', 'name': 'rotating_shaft'}, {'id': 10493, 'synset': 'rotisserie.n.02', 'name': 'rotisserie'}, {'id': 10494, 'synset': 'rotisserie.n.01', 'name': 'rotisserie'}, {'id': 10495, 'synset': 'rotor.n.03', 'name': 'rotor'}, {'id': 10496, 'synset': 'rotor.n.01', 'name': 'rotor'}, {'id': 10497, 'synset': 'rotor.n.02', 'name': 'rotor'}, {'id': 10498, 'synset': 'rotor_blade.n.01', 'name': 'rotor_blade'}, {'id': 10499, 'synset': 'rotor_head.n.01', 'name': 'rotor_head'}, {'id': 10500, 'synset': 'rotunda.n.02', 'name': 'rotunda'}, {'id': 10501, 'synset': 'rotunda.n.01', 'name': 'rotunda'}, {'id': 10502, 'synset': 'rouge.n.01', 'name': 'rouge'}, {'id': 10503, 'synset': 'roughcast.n.02', 'name': 'roughcast'}, {'id': 10504, 'synset': 'rouleau.n.02', 'name': 'rouleau'}, {'id': 10505, 'synset': 'roulette.n.02', 'name': 'roulette'}, {'id': 10506, 'synset': 'roulette_ball.n.01', 'name': 'roulette_ball'}, {'id': 10507, 'synset': 'roulette_wheel.n.01', 'name': 'roulette_wheel'}, {'id': 10508, 'synset': 'round.n.01', 'name': 'round'}, {'id': 10509, 'synset': 'round_arch.n.01', 'name': 'round_arch'}, {'id': 10510, 'synset': 'round-bottom_flask.n.01', 'name': 'round-bottom_flask'}, {'id': 10511, 'synset': 'roundel.n.02', 'name': 'roundel'}, {'id': 10512, 'synset': 'round_file.n.01', 'name': 'round_file'}, {'id': 10513, 'synset': 'roundhouse.n.01', 'name': 'roundhouse'}, {'id': 10514, 'synset': 'router.n.03', 'name': 'router'}, {'id': 10515, 'synset': 'router_plane.n.01', 'name': 'router_plane'}, {'id': 10516, 'synset': 'rowel.n.01', 'name': 'rowel'}, {'id': 10517, 'synset': 'row_house.n.01', 'name': 'row_house'}, {'id': 10518, 'synset': 'rowing_boat.n.01', 'name': 'rowing_boat'}, {'id': 10519, 'synset': 'rowlock_arch.n.01', 'name': 'rowlock_arch'}, {'id': 10520, 'synset': 'royal.n.01', 'name': 'royal'}, {'id': 10521, 'synset': 'royal_mast.n.01', 'name': 'royal_mast'}, {'id': 10522, 'synset': 'rubber_boot.n.01', 'name': 'rubber_boot'}, {'id': 10523, 'synset': 'rubber_bullet.n.01', 'name': 'rubber_bullet'}, {'id': 10524, 'synset': 'rubber_eraser.n.01', 'name': 'rubber_eraser'}, {'id': 10525, 'synset': 'rudder.n.02', 'name': 'rudder'}, {'id': 10526, 'synset': 'rudder.n.01', 'name': 'rudder'}, {'id': 10527, 'synset': 'rudder_blade.n.01', 'name': 'rudder_blade'}, {'id': 10528, 'synset': 'rug.n.01', 'name': 'rug'}, {'id': 10529, 'synset': 'rugby_ball.n.01', 'name': 'rugby_ball'}, {'id': 10530, 'synset': 'ruin.n.02', 'name': 'ruin'}, {'id': 10531, 'synset': 'rule.n.12', 'name': 'rule'}, {'id': 10532, 'synset': 'rumble.n.02', 'name': 'rumble'}, {'id': 10533, 'synset': 'rumble_seat.n.01', 'name': 'rumble_seat'}, {'id': 10534, 'synset': 'rummer.n.01', 'name': 'rummer'}, {'id': 10535, 'synset': 'rumpus_room.n.01', 'name': 'rumpus_room'}, {'id': 10536, 'synset': 'runcible_spoon.n.01', 'name': 'runcible_spoon'}, {'id': 10537, 'synset': 'rundle.n.01', 'name': 'rundle'}, {'id': 10538, 'synset': 'running_shoe.n.01', 'name': 'running_shoe'}, {'id': 10539, 'synset': 'running_suit.n.01', 'name': 'running_suit'}, {'id': 10540, 'synset': 'runway.n.04', 'name': 'runway'}, {'id': 10541, 'synset': 'rushlight.n.01', 'name': 'rushlight'}, {'id': 10542, 'synset': 'russet.n.01', 'name': 'russet'}, {'id': 10543, 'synset': 'rya.n.01', 'name': 'rya'}, {'id': 10544, 'synset': 'saber.n.01', 'name': 'saber'}, {'id': 10545, 'synset': 'saber_saw.n.01', 'name': 'saber_saw'}, {'id': 10546, 'synset': 'sable.n.04', 'name': 'sable'}, {'id': 10547, 'synset': 'sable.n.01', 'name': 'sable'}, {'id': 10548, 'synset': 'sable_coat.n.01', 'name': 'sable_coat'}, {'id': 10549, 'synset': 'sabot.n.01', 'name': 'sabot'}, {'id': 10550, 'synset': 'sachet.n.01', 'name': 'sachet'}, {'id': 10551, 'synset': 'sack.n.05', 'name': 'sack'}, {'id': 10552, 'synset': 'sackbut.n.01', 'name': 'sackbut'}, {'id': 10553, 'synset': 'sackcloth.n.02', 'name': 'sackcloth'}, {'id': 10554, 'synset': 'sackcloth.n.01', 'name': 'sackcloth'}, {'id': 10555, 'synset': 'sack_coat.n.01', 'name': 'sack_coat'}, {'id': 10556, 'synset': 'sacking.n.01', 'name': 'sacking'}, {'id': 10557, 'synset': 'saddle_oxford.n.01', 'name': 'saddle_oxford'}, {'id': 10558, 'synset': 'saddlery.n.02', 'name': 'saddlery'}, {'id': 10559, 'synset': 'saddle_seat.n.01', 'name': 'saddle_seat'}, {'id': 10560, 'synset': 'saddle_stitch.n.01', 'name': 'saddle_stitch'}, {'id': 10561, 'synset': 'safe.n.01', 'name': 'safe'}, {'id': 10562, 'synset': 'safe.n.02', 'name': 'safe'}, {'id': 10563, 'synset': 'safe-deposit.n.01', 'name': 'safe-deposit'}, {'id': 10564, 'synset': 'safe_house.n.01', 'name': 'safe_house'}, {'id': 10565, 'synset': 'safety_arch.n.01', 'name': 'safety_arch'}, {'id': 10566, 'synset': 'safety_belt.n.01', 'name': 'safety_belt'}, {'id': 10567, 'synset': 'safety_bicycle.n.01', 'name': 'safety_bicycle'}, {'id': 10568, 'synset': 'safety_bolt.n.01', 'name': 'safety_bolt'}, {'id': 10569, 'synset': 'safety_curtain.n.01', 'name': 'safety_curtain'}, {'id': 10570, 'synset': 'safety_fuse.n.01', 'name': 'safety_fuse'}, {'id': 10571, 'synset': 'safety_lamp.n.01', 'name': 'safety_lamp'}, {'id': 10572, 'synset': 'safety_match.n.01', 'name': 'safety_match'}, {'id': 10573, 'synset': 'safety_net.n.02', 'name': 'safety_net'}, {'id': 10574, 'synset': 'safety_rail.n.01', 'name': 'safety_rail'}, {'id': 10575, 'synset': 'safety_razor.n.01', 'name': 'safety_razor'}, {'id': 10576, 'synset': 'safety_valve.n.01', 'name': 'safety_valve'}, {'id': 10577, 'synset': 'sail.n.03', 'name': 'sail'}, {'id': 10578, 'synset': 'sailboat.n.01', 'name': 'sailboat'}, {'id': 10579, 'synset': 'sailcloth.n.01', 'name': 'sailcloth'}, {'id': 10580, 'synset': 'sailing_vessel.n.01', 'name': 'sailing_vessel'}, {'id': 10581, 'synset': 'sailing_warship.n.01', 'name': 'sailing_warship'}, {'id': 10582, 'synset': 'sailor_cap.n.01', 'name': 'sailor_cap'}, {'id': 10583, 'synset': 'sailor_suit.n.01', 'name': 'sailor_suit'}, {'id': 10584, 'synset': 'salad_bar.n.01', 'name': 'salad_bar'}, {'id': 10585, 'synset': 'salad_bowl.n.02', 'name': 'salad_bowl'}, {'id': 10586, 'synset': 'salinometer.n.01', 'name': 'salinometer'}, {'id': 10587, 'synset': 'sallet.n.01', 'name': 'sallet'}, {'id': 10588, 'synset': 'salon.n.03', 'name': 'salon'}, {'id': 10589, 'synset': 'salon.n.01', 'name': 'salon'}, {'id': 10590, 'synset': 'salon.n.02', 'name': 'salon'}, {'id': 10591, 'synset': 'saltbox.n.01', 'name': 'saltbox'}, {'id': 10592, 'synset': 'saltcellar.n.01', 'name': 'saltcellar'}, {'id': 10593, 'synset': 'saltworks.n.01', 'name': 'saltworks'}, {'id': 10594, 'synset': 'salver.n.01', 'name': 'salver'}, {'id': 10595, 'synset': 'salwar.n.01', 'name': 'salwar'}, {'id': 10596, 'synset': 'sam_browne_belt.n.01', 'name': 'Sam_Browne_belt'}, {'id': 10597, 'synset': 'samisen.n.01', 'name': 'samisen'}, {'id': 10598, 'synset': 'samite.n.01', 'name': 'samite'}, {'id': 10599, 'synset': 'samovar.n.01', 'name': 'samovar'}, {'id': 10600, 'synset': 'sampan.n.01', 'name': 'sampan'}, {'id': 10601, 'synset': 'sandbag.n.01', 'name': 'sandbag'}, {'id': 10602, 'synset': 'sandblaster.n.01', 'name': 'sandblaster'}, {'id': 10603, 'synset': 'sandbox.n.01', 'name': 'sandbox'}, {'id': 10604, 'synset': 'sandglass.n.01', 'name': 'sandglass'}, {'id': 10605, 'synset': 'sand_wedge.n.01', 'name': 'sand_wedge'}, {'id': 10606, 'synset': 'sandwich_board.n.01', 'name': 'sandwich_board'}, {'id': 10607, 'synset': 'sanitary_napkin.n.01', 'name': 'sanitary_napkin'}, {'id': 10608, 'synset': 'cling_film.n.01', 'name': 'cling_film'}, {'id': 10609, 'synset': 'sarcenet.n.01', 'name': 'sarcenet'}, {'id': 10610, 'synset': 'sarcophagus.n.01', 'name': 'sarcophagus'}, {'id': 10611, 'synset': 'sari.n.01', 'name': 'sari'}, {'id': 10612, 'synset': 'sarong.n.01', 'name': 'sarong'}, {'id': 10613, 'synset': 'sash.n.01', 'name': 'sash'}, {'id': 10614, 'synset': 'sash_fastener.n.01', 'name': 'sash_fastener'}, {'id': 10615, 'synset': 'sash_window.n.01', 'name': 'sash_window'}, {'id': 10616, 'synset': 'sateen.n.01', 'name': 'sateen'}, {'id': 10617, 'synset': 'satellite.n.01', 'name': 'satellite'}, {'id': 10618, 'synset': 'satellite_receiver.n.01', 'name': 'satellite_receiver'}, {'id': 10619, 'synset': 'satellite_television.n.01', 'name': 'satellite_television'}, {'id': 10620, 'synset': 'satellite_transmitter.n.01', 'name': 'satellite_transmitter'}, {'id': 10621, 'synset': 'satin.n.01', 'name': 'satin'}, {'id': 10622, 'synset': 'saturday_night_special.n.01', 'name': 'Saturday_night_special'}, {'id': 10623, 'synset': 'saucepot.n.01', 'name': 'saucepot'}, {'id': 10624, 'synset': 'sauna.n.01', 'name': 'sauna'}, {'id': 10625, 'synset': 'savings_bank.n.02', 'name': 'savings_bank'}, {'id': 10626, 'synset': 'saw.n.02', 'name': 'saw'}, {'id': 10627, 'synset': 'sawed-off_shotgun.n.01', 'name': 'sawed-off_shotgun'}, {'id': 10628, 'synset': 'sawmill.n.01', 'name': 'sawmill'}, {'id': 10629, 'synset': 'saw_set.n.01', 'name': 'saw_set'}, {'id': 10630, 'synset': 'saxhorn.n.01', 'name': 'saxhorn'}, {'id': 10631, 'synset': 'scabbard.n.01', 'name': 'scabbard'}, {'id': 10632, 'synset': 'scaffolding.n.01', 'name': 'scaffolding'}, {'id': 10633, 'synset': 'scale.n.08', 'name': 'scale'}, {'id': 10634, 'synset': 'scaler.n.01', 'name': 'scaler'}, {'id': 10635, 'synset': 'scaling_ladder.n.01', 'name': 'scaling_ladder'}, {'id': 10636, 'synset': 'scalpel.n.01', 'name': 'scalpel'}, {'id': 10637, 'synset': 'scanner.n.04', 'name': 'scanner'}, {'id': 10638, 'synset': 'scanner.n.03', 'name': 'scanner'}, {'id': 10639, 'synset': 'scanner.n.02', 'name': 'scanner'}, {'id': 10640, 'synset': 'scantling.n.01', 'name': 'scantling'}, {'id': 10641, 'synset': 'scarf_joint.n.01', 'name': 'scarf_joint'}, {'id': 10642, 'synset': 'scatter_rug.n.01', 'name': 'scatter_rug'}, {'id': 10643, 'synset': 'scauper.n.01', 'name': 'scauper'}, {'id': 10644, 'synset': 'schmidt_telescope.n.01', 'name': 'Schmidt_telescope'}, {'id': 10645, 'synset': 'school.n.02', 'name': 'school'}, {'id': 10646, 'synset': 'schoolbag.n.01', 'name': 'schoolbag'}, {'id': 10647, 'synset': 'school_bell.n.01', 'name': 'school_bell'}, {'id': 10648, 'synset': 'school_ship.n.01', 'name': 'school_ship'}, {'id': 10649, 'synset': 'school_system.n.01', 'name': 'school_system'}, {'id': 10650, 'synset': 'schooner.n.02', 'name': 'schooner'}, {'id': 10651, 'synset': 'schooner.n.01', 'name': 'schooner'}, {'id': 10652, 'synset': 'scientific_instrument.n.01', 'name': 'scientific_instrument'}, {'id': 10653, 'synset': 'scimitar.n.01', 'name': 'scimitar'}, {'id': 10654, 'synset': 'scintillation_counter.n.01', 'name': 'scintillation_counter'}, {'id': 10655, 'synset': 'sclerometer.n.01', 'name': 'sclerometer'}, {'id': 10656, 'synset': 'scoinson_arch.n.01', 'name': 'scoinson_arch'}, {'id': 10657, 'synset': 'sconce.n.04', 'name': 'sconce'}, {'id': 10658, 'synset': 'sconce.n.03', 'name': 'sconce'}, {'id': 10659, 'synset': 'scoop.n.06', 'name': 'scoop'}, {'id': 10660, 'synset': 'scooter.n.02', 'name': 'scooter'}, {'id': 10661, 'synset': 'scouring_pad.n.01', 'name': 'scouring_pad'}, {'id': 10662, 'synset': 'scow.n.02', 'name': 'scow'}, {'id': 10663, 'synset': 'scow.n.01', 'name': 'scow'}, {'id': 10664, 'synset': 'scratcher.n.03', 'name': 'scratcher'}, {'id': 10665, 'synset': 'screen.n.05', 'name': 'screen'}, {'id': 10666, 'synset': 'screen.n.04', 'name': 'screen'}, {'id': 10667, 'synset': 'screen.n.09', 'name': 'screen'}, {'id': 10668, 'synset': 'screen.n.03', 'name': 'screen'}, {'id': 10669, 'synset': 'screen_door.n.01', 'name': 'screen_door'}, {'id': 10670, 'synset': 'screening.n.02', 'name': 'screening'}, {'id': 10671, 'synset': 'screw.n.04', 'name': 'screw'}, {'id': 10672, 'synset': 'screw.n.03', 'name': 'screw'}, {'id': 10673, 'synset': 'screw.n.02', 'name': 'screw'}, {'id': 10674, 'synset': 'screw_eye.n.01', 'name': 'screw_eye'}, {'id': 10675, 'synset': 'screw_key.n.01', 'name': 'screw_key'}, {'id': 10676, 'synset': 'screw_thread.n.01', 'name': 'screw_thread'}, {'id': 10677, 'synset': 'screwtop.n.01', 'name': 'screwtop'}, {'id': 10678, 'synset': 'screw_wrench.n.01', 'name': 'screw_wrench'}, {'id': 10679, 'synset': 'scriber.n.01', 'name': 'scriber'}, {'id': 10680, 'synset': 'scrim.n.01', 'name': 'scrim'}, {'id': 10681, 'synset': 'scrimshaw.n.01', 'name': 'scrimshaw'}, {'id': 10682, 'synset': 'scriptorium.n.01', 'name': 'scriptorium'}, {'id': 10683, 'synset': 'scrubber.n.03', 'name': 'scrubber'}, {'id': 10684, 'synset': 'scrub_plane.n.01', 'name': 'scrub_plane'}, {'id': 10685, 'synset': 'scuffer.n.01', 'name': 'scuffer'}, {'id': 10686, 'synset': 'scuffle.n.02', 'name': 'scuffle'}, {'id': 10687, 'synset': 'scull.n.02', 'name': 'scull'}, {'id': 10688, 'synset': 'scull.n.01', 'name': 'scull'}, {'id': 10689, 'synset': 'scullery.n.01', 'name': 'scullery'}, {'id': 10690, 'synset': 'scuttle.n.01', 'name': 'scuttle'}, {'id': 10691, 'synset': 'scyphus.n.01', 'name': 'scyphus'}, {'id': 10692, 'synset': 'scythe.n.01', 'name': 'scythe'}, {'id': 10693, 'synset': 'seabag.n.01', 'name': 'seabag'}, {'id': 10694, 'synset': 'sea_boat.n.01', 'name': 'sea_boat'}, {'id': 10695, 'synset': 'sea_chest.n.01', 'name': 'sea_chest'}, {'id': 10696, 'synset': 'sealing_wax.n.01', 'name': 'sealing_wax'}, {'id': 10697, 'synset': 'sealskin.n.02', 'name': 'sealskin'}, {'id': 10698, 'synset': 'seam.n.01', 'name': 'seam'}, {'id': 10699, 'synset': 'searchlight.n.01', 'name': 'searchlight'}, {'id': 10700, 'synset': 'searing_iron.n.01', 'name': 'searing_iron'}, {'id': 10701, 'synset': 'seat.n.04', 'name': 'seat'}, {'id': 10702, 'synset': 'seat.n.03', 'name': 'seat'}, {'id': 10703, 'synset': 'seat.n.09', 'name': 'seat'}, {'id': 10704, 'synset': 'seat_belt.n.01', 'name': 'seat_belt'}, {'id': 10705, 'synset': 'secateurs.n.01', 'name': 'secateurs'}, {'id': 10706, 'synset': 'secondary_coil.n.01', 'name': 'secondary_coil'}, {'id': 10707, 'synset': 'second_balcony.n.01', 'name': 'second_balcony'}, {'id': 10708, 'synset': 'second_base.n.01', 'name': 'second_base'}, {'id': 10709, 'synset': 'second_hand.n.02', 'name': 'second_hand'}, {'id': 10710, 'synset': 'secretary.n.04', 'name': 'secretary'}, {'id': 10711, 'synset': 'sectional.n.01', 'name': 'sectional'}, {'id': 10712, 'synset': 'security_blanket.n.02', 'name': 'security_blanket'}, {'id': 10713, 'synset': 'security_system.n.02', 'name': 'security_system'}, {'id': 10714, 'synset': 'security_system.n.01', 'name': 'security_system'}, {'id': 10715, 'synset': 'sedan.n.01', 'name': 'sedan'}, {'id': 10716, 'synset': 'sedan.n.02', 'name': 'sedan'}, {'id': 10717, 'synset': 'seeder.n.02', 'name': 'seeder'}, {'id': 10718, 'synset': 'seeker.n.02', 'name': 'seeker'}, {'id': 10719, 'synset': 'seersucker.n.01', 'name': 'seersucker'}, {'id': 10720, 'synset': 'segmental_arch.n.01', 'name': 'segmental_arch'}, {'id': 10721, 'synset': 'segway.n.01', 'name': 'Segway'}, {'id': 10722, 'synset': 'seidel.n.01', 'name': 'seidel'}, {'id': 10723, 'synset': 'seine.n.02', 'name': 'seine'}, {'id': 10724, 'synset': 'seismograph.n.01', 'name': 'seismograph'}, {'id': 10725, 'synset': 'selector.n.02', 'name': 'selector'}, {'id': 10726, 'synset': 'selenium_cell.n.01', 'name': 'selenium_cell'}, {'id': 10727, 'synset': 'self-propelled_vehicle.n.01', 'name': 'self-propelled_vehicle'}, {'id': 10728, 'synset': 'self-registering_thermometer.n.01', 'name': 'self-registering_thermometer'}, {'id': 10729, 'synset': 'self-starter.n.02', 'name': 'self-starter'}, {'id': 10730, 'synset': 'selsyn.n.01', 'name': 'selsyn'}, {'id': 10731, 'synset': 'selvage.n.02', 'name': 'selvage'}, {'id': 10732, 'synset': 'semaphore.n.01', 'name': 'semaphore'}, {'id': 10733, 'synset': 'semiautomatic_firearm.n.01', 'name': 'semiautomatic_firearm'}, {'id': 10734, 'synset': 'semiautomatic_pistol.n.01', 'name': 'semiautomatic_pistol'}, {'id': 10735, 'synset': 'semiconductor_device.n.01', 'name': 'semiconductor_device'}, {'id': 10736, 'synset': 'semi-detached_house.n.01', 'name': 'semi-detached_house'}, {'id': 10737, 'synset': 'semigloss.n.01', 'name': 'semigloss'}, {'id': 10738, 'synset': 'semitrailer.n.01', 'name': 'semitrailer'}, {'id': 10739, 'synset': 'sennit.n.01', 'name': 'sennit'}, {'id': 10740, 'synset': 'sensitometer.n.01', 'name': 'sensitometer'}, {'id': 10741, 'synset': 'sentry_box.n.01', 'name': 'sentry_box'}, {'id': 10742, 'synset': 'separate.n.02', 'name': 'separate'}, {'id': 10743, 'synset': 'septic_tank.n.01', 'name': 'septic_tank'}, {'id': 10744, 'synset': 'sequence.n.03', 'name': 'sequence'}, {'id': 10745, 'synset': 'sequencer.n.01', 'name': 'sequencer'}, {'id': 10746, 'synset': 'serape.n.01', 'name': 'serape'}, {'id': 10747, 'synset': 'serge.n.01', 'name': 'serge'}, {'id': 10748, 'synset': 'serger.n.01', 'name': 'serger'}, {'id': 10749, 'synset': 'serial_port.n.01', 'name': 'serial_port'}, {'id': 10750, 'synset': 'serpent.n.03', 'name': 'serpent'}, {'id': 10751, 'synset': 'serration.n.03', 'name': 'serration'}, {'id': 10752, 'synset': 'server.n.04', 'name': 'server'}, {'id': 10753, 'synset': 'server.n.03', 'name': 'server'}, {'id': 10754, 'synset': 'service_club.n.02', 'name': 'service_club'}, {'id': 10755, 'synset': 'serving_cart.n.01', 'name': 'serving_cart'}, {'id': 10756, 'synset': 'serving_dish.n.01', 'name': 'serving_dish'}, {'id': 10757, 'synset': 'servo.n.01', 'name': 'servo'}, {'id': 10758, 'synset': 'set.n.13', 'name': 'set'}, {'id': 10759, 'synset': 'set_gun.n.01', 'name': 'set_gun'}, {'id': 10760, 'synset': 'setscrew.n.02', 'name': 'setscrew'}, {'id': 10761, 'synset': 'setscrew.n.01', 'name': 'setscrew'}, {'id': 10762, 'synset': 'set_square.n.01', 'name': 'set_square'}, {'id': 10763, 'synset': 'settee.n.02', 'name': 'settee'}, {'id': 10764, 'synset': 'settle.n.01', 'name': 'settle'}, {'id': 10765, 'synset': 'settlement_house.n.01', 'name': 'settlement_house'}, {'id': 10766, 'synset': 'seventy-eight.n.02', 'name': 'seventy-eight'}, {'id': 10767, 'synset': 'seven_wonders_of_the_ancient_world.n.01', 'name': 'Seven_Wonders_of_the_Ancient_World'}, {'id': 10768, 'synset': 'sewage_disposal_plant.n.01', 'name': 'sewage_disposal_plant'}, {'id': 10769, 'synset': 'sewer.n.01', 'name': 'sewer'}, {'id': 10770, 'synset': 'sewing_basket.n.01', 'name': 'sewing_basket'}, {'id': 10771, 'synset': 'sewing_kit.n.01', 'name': 'sewing_kit'}, {'id': 10772, 'synset': 'sewing_needle.n.01', 'name': 'sewing_needle'}, {'id': 10773, 'synset': 'sewing_room.n.01', 'name': 'sewing_room'}, {'id': 10774, 'synset': 'sextant.n.02', 'name': 'sextant'}, {'id': 10775, 'synset': 'sgraffito.n.01', 'name': 'sgraffito'}, {'id': 10776, 'synset': 'shackle.n.01', 'name': 'shackle'}, {'id': 10777, 'synset': 'shackle.n.02', 'name': 'shackle'}, {'id': 10778, 'synset': 'shade.n.03', 'name': 'shade'}, {'id': 10779, 'synset': 'shadow_box.n.01', 'name': 'shadow_box'}, {'id': 10780, 'synset': 'shaft.n.03', 'name': 'shaft'}, {'id': 10781, 'synset': 'shag_rug.n.01', 'name': 'shag_rug'}, {'id': 10782, 'synset': 'shank.n.04', 'name': 'shank'}, {'id': 10783, 'synset': 'shank.n.03', 'name': 'shank'}, {'id': 10784, 'synset': 'shantung.n.01', 'name': 'shantung'}, {'id': 10785, 'synset': 'shaper.n.02', 'name': 'shaper'}, {'id': 10786, 'synset': 'shaping_tool.n.01', 'name': 'shaping_tool'}, {'id': 10787, 'synset': 'sharkskin.n.01', 'name': 'sharkskin'}, {'id': 10788, 'synset': 'shaving_brush.n.01', 'name': 'shaving_brush'}, {'id': 10789, 'synset': 'shaving_foam.n.01', 'name': 'shaving_foam'}, {'id': 10790, 'synset': 'shawm.n.01', 'name': 'shawm'}, {'id': 10791, 'synset': 'sheath.n.01', 'name': 'sheath'}, {'id': 10792, 'synset': 'sheathing.n.01', 'name': 'sheathing'}, {'id': 10793, 'synset': 'shed.n.01', 'name': 'shed'}, {'id': 10794, 'synset': 'sheep_bell.n.01', 'name': 'sheep_bell'}, {'id': 10795, 'synset': 'sheepshank.n.01', 'name': 'sheepshank'}, {'id': 10796, 'synset': 'sheepskin_coat.n.01', 'name': 'sheepskin_coat'}, {'id': 10797, 'synset': 'sheepwalk.n.01', 'name': 'sheepwalk'}, {'id': 10798, 'synset': 'sheet.n.03', 'name': 'sheet'}, {'id': 10799, 'synset': 'sheet_bend.n.01', 'name': 'sheet_bend'}, {'id': 10800, 'synset': 'sheeting.n.01', 'name': 'sheeting'}, {'id': 10801, 'synset': 'sheet_pile.n.01', 'name': 'sheet_pile'}, {'id': 10802, 'synset': 'sheetrock.n.01', 'name': 'Sheetrock'}, {'id': 10803, 'synset': 'shelf.n.01', 'name': 'shelf'}, {'id': 10804, 'synset': 'shelf_bracket.n.01', 'name': 'shelf_bracket'}, {'id': 10805, 'synset': 'shell.n.01', 'name': 'shell'}, {'id': 10806, 'synset': 'shell.n.08', 'name': 'shell'}, {'id': 10807, 'synset': 'shell.n.07', 'name': 'shell'}, {'id': 10808, 'synset': 'shellac.n.02', 'name': 'shellac'}, {'id': 10809, 'synset': 'shelter.n.01', 'name': 'shelter'}, {'id': 10810, 'synset': 'shelter.n.02', 'name': 'shelter'}, {'id': 10811, 'synset': 'shelter.n.05', 'name': 'shelter'}, {'id': 10812, 'synset': 'sheltered_workshop.n.01', 'name': 'sheltered_workshop'}, {'id': 10813, 'synset': 'sheraton.n.01', 'name': 'Sheraton'}, {'id': 10814, 'synset': 'shield.n.01', 'name': 'shield'}, {'id': 10815, 'synset': 'shielding.n.03', 'name': 'shielding'}, {'id': 10816, 'synset': 'shift_key.n.01', 'name': 'shift_key'}, {'id': 10817, 'synset': 'shillelagh.n.01', 'name': 'shillelagh'}, {'id': 10818, 'synset': 'shim.n.01', 'name': 'shim'}, {'id': 10819, 'synset': 'shingle.n.03', 'name': 'shingle'}, {'id': 10820, 'synset': 'shin_guard.n.01', 'name': 'shin_guard'}, {'id': 10821, 'synset': 'ship.n.01', 'name': 'ship'}, {'id': 10822, 'synset': 'shipboard_system.n.01', 'name': 'shipboard_system'}, {'id': 10823, 'synset': 'shipping.n.02', 'name': 'shipping'}, {'id': 10824, 'synset': 'shipping_room.n.01', 'name': 'shipping_room'}, {'id': 10825, 'synset': 'ship-towed_long-range_acoustic_detection_system.n.01', 'name': 'ship-towed_long-range_acoustic_detection_system'}, {'id': 10826, 'synset': 'shipwreck.n.01', 'name': 'shipwreck'}, {'id': 10827, 'synset': 'shirt_button.n.01', 'name': 'shirt_button'}, {'id': 10828, 'synset': 'shirtdress.n.01', 'name': 'shirtdress'}, {'id': 10829, 'synset': 'shirtfront.n.01', 'name': 'shirtfront'}, {'id': 10830, 'synset': 'shirting.n.01', 'name': 'shirting'}, {'id': 10831, 'synset': 'shirtsleeve.n.01', 'name': 'shirtsleeve'}, {'id': 10832, 'synset': 'shirttail.n.02', 'name': 'shirttail'}, {'id': 10833, 'synset': 'shirtwaist.n.01', 'name': 'shirtwaist'}, {'id': 10834, 'synset': 'shiv.n.01', 'name': 'shiv'}, {'id': 10835, 'synset': 'shock_absorber.n.01', 'name': 'shock_absorber'}, {'id': 10836, 'synset': 'shoe.n.02', 'name': 'shoe'}, {'id': 10837, 'synset': 'shoebox.n.02', 'name': 'shoebox'}, {'id': 10838, 'synset': 'shoehorn.n.01', 'name': 'shoehorn'}, {'id': 10839, 'synset': 'shoe_shop.n.01', 'name': 'shoe_shop'}, {'id': 10840, 'synset': 'shoetree.n.01', 'name': 'shoetree'}, {'id': 10841, 'synset': 'shofar.n.01', 'name': 'shofar'}, {'id': 10842, 'synset': 'shoji.n.01', 'name': 'shoji'}, {'id': 10843, 'synset': 'shooting_brake.n.01', 'name': 'shooting_brake'}, {'id': 10844, 'synset': 'shooting_lodge.n.01', 'name': 'shooting_lodge'}, {'id': 10845, 'synset': 'shooting_stick.n.01', 'name': 'shooting_stick'}, {'id': 10846, 'synset': 'shop.n.01', 'name': 'shop'}, {'id': 10847, 'synset': 'shop_bell.n.01', 'name': 'shop_bell'}, {'id': 10848, 'synset': 'shopping_basket.n.01', 'name': 'shopping_basket'}, {'id': 10849, 'synset': 'short_circuit.n.01', 'name': 'short_circuit'}, {'id': 10850, 'synset': 'short_iron.n.01', 'name': 'short_iron'}, {'id': 10851, 'synset': 'short_sleeve.n.01', 'name': 'short_sleeve'}, {'id': 10852, 'synset': 'shortwave_diathermy_machine.n.01', 'name': 'shortwave_diathermy_machine'}, {'id': 10853, 'synset': 'shot.n.12', 'name': 'shot'}, {'id': 10854, 'synset': 'shotgun.n.01', 'name': 'shotgun'}, {'id': 10855, 'synset': 'shotgun_shell.n.01', 'name': 'shotgun_shell'}, {'id': 10856, 'synset': 'shot_tower.n.01', 'name': 'shot_tower'}, {'id': 10857, 'synset': 'shoulder.n.04', 'name': 'shoulder'}, {'id': 10858, 'synset': 'shouldered_arch.n.01', 'name': 'shouldered_arch'}, {'id': 10859, 'synset': 'shoulder_holster.n.01', 'name': 'shoulder_holster'}, {'id': 10860, 'synset': 'shoulder_pad.n.01', 'name': 'shoulder_pad'}, {'id': 10861, 'synset': 'shoulder_patch.n.01', 'name': 'shoulder_patch'}, {'id': 10862, 'synset': 'shovel.n.03', 'name': 'shovel'}, {'id': 10863, 'synset': 'shovel_hat.n.01', 'name': 'shovel_hat'}, {'id': 10864, 'synset': 'showboat.n.01', 'name': 'showboat'}, {'id': 10865, 'synset': 'shower_room.n.01', 'name': 'shower_room'}, {'id': 10866, 'synset': 'shower_stall.n.01', 'name': 'shower_stall'}, {'id': 10867, 'synset': 'showroom.n.01', 'name': 'showroom'}, {'id': 10868, 'synset': 'shrapnel.n.01', 'name': 'shrapnel'}, {'id': 10869, 'synset': 'shrimper.n.01', 'name': 'shrimper'}, {'id': 10870, 'synset': 'shrine.n.01', 'name': 'shrine'}, {'id': 10871, 'synset': 'shrink-wrap.n.01', 'name': 'shrink-wrap'}, {'id': 10872, 'synset': 'shunt.n.03', 'name': 'shunt'}, {'id': 10873, 'synset': 'shunt.n.02', 'name': 'shunt'}, {'id': 10874, 'synset': 'shunter.n.01', 'name': 'shunter'}, {'id': 10875, 'synset': 'shutter.n.02', 'name': 'shutter'}, {'id': 10876, 'synset': 'shutter.n.01', 'name': 'shutter'}, {'id': 10877, 'synset': 'shuttle.n.03', 'name': 'shuttle'}, {'id': 10878, 'synset': 'shuttle.n.02', 'name': 'shuttle'}, {'id': 10879, 'synset': 'shuttle_bus.n.01', 'name': 'shuttle_bus'}, {'id': 10880, 'synset': 'shuttlecock.n.01', 'name': 'shuttlecock'}, {'id': 10881, 'synset': 'shuttle_helicopter.n.01', 'name': 'shuttle_helicopter'}, {'id': 10882, 'synset': 'sibley_tent.n.01', 'name': 'Sibley_tent'}, {'id': 10883, 'synset': 'sickbay.n.01', 'name': 'sickbay'}, {'id': 10884, 'synset': 'sickbed.n.01', 'name': 'sickbed'}, {'id': 10885, 'synset': 'sickle.n.01', 'name': 'sickle'}, {'id': 10886, 'synset': 'sickroom.n.01', 'name': 'sickroom'}, {'id': 10887, 'synset': 'sideboard.n.02', 'name': 'sideboard'}, {'id': 10888, 'synset': 'sidecar.n.02', 'name': 'sidecar'}, {'id': 10889, 'synset': 'side_chapel.n.01', 'name': 'side_chapel'}, {'id': 10890, 'synset': 'sidelight.n.01', 'name': 'sidelight'}, {'id': 10891, 'synset': 'sidesaddle.n.01', 'name': 'sidesaddle'}, {'id': 10892, 'synset': 'sidewalk.n.01', 'name': 'sidewalk'}, {'id': 10893, 'synset': 'sidewall.n.02', 'name': 'sidewall'}, {'id': 10894, 'synset': 'side-wheeler.n.01', 'name': 'side-wheeler'}, {'id': 10895, 'synset': 'sidewinder.n.02', 'name': 'sidewinder'}, {'id': 10896, 'synset': 'sieve.n.01', 'name': 'sieve'}, {'id': 10897, 'synset': 'sifter.n.01', 'name': 'sifter'}, {'id': 10898, 'synset': 'sights.n.01', 'name': 'sights'}, {'id': 10899, 'synset': 'sigmoidoscope.n.01', 'name': 'sigmoidoscope'}, {'id': 10900, 'synset': 'signal_box.n.01', 'name': 'signal_box'}, {'id': 10901, 'synset': 'signaling_device.n.01', 'name': 'signaling_device'}, {'id': 10902, 'synset': 'silencer.n.02', 'name': 'silencer'}, {'id': 10903, 'synset': 'silent_butler.n.01', 'name': 'silent_butler'}, {'id': 10904, 'synset': 'silex.n.02', 'name': 'Silex'}, {'id': 10905, 'synset': 'silk.n.01', 'name': 'silk'}, {'id': 10906, 'synset': 'silks.n.01', 'name': 'silks'}, {'id': 10907, 'synset': 'silver_plate.n.02', 'name': 'silver_plate'}, {'id': 10908, 'synset': 'silverpoint.n.01', 'name': 'silverpoint'}, {'id': 10909, 'synset': 'simple_pendulum.n.01', 'name': 'simple_pendulum'}, {'id': 10910, 'synset': 'simulator.n.01', 'name': 'simulator'}, {'id': 10911, 'synset': 'single_bed.n.01', 'name': 'single_bed'}, {'id': 10912, 'synset': 'single-breasted_jacket.n.01', 'name': 'single-breasted_jacket'}, {'id': 10913, 'synset': 'single-breasted_suit.n.01', 'name': 'single-breasted_suit'}, {'id': 10914, 'synset': 'single_prop.n.01', 'name': 'single_prop'}, {'id': 10915, 'synset': 'single-reed_instrument.n.01', 'name': 'single-reed_instrument'}, {'id': 10916, 'synset': 'single-rotor_helicopter.n.01', 'name': 'single-rotor_helicopter'}, {'id': 10917, 'synset': 'singlestick.n.01', 'name': 'singlestick'}, {'id': 10918, 'synset': 'singlet.n.01', 'name': 'singlet'}, {'id': 10919, 'synset': 'siren.n.04', 'name': 'siren'}, {'id': 10920, 'synset': 'sister_ship.n.01', 'name': 'sister_ship'}, {'id': 10921, 'synset': 'sitar.n.01', 'name': 'sitar'}, {'id': 10922, 'synset': 'sitz_bath.n.01', 'name': 'sitz_bath'}, {'id': 10923, 'synset': 'six-pack.n.01', 'name': 'six-pack'}, {'id': 10924, 'synset': 'skate.n.01', 'name': 'skate'}, {'id': 10925, 'synset': 'skeg.n.01', 'name': 'skeg'}, {'id': 10926, 'synset': 'skein.n.01', 'name': 'skein'}, {'id': 10927, 'synset': 'skeleton.n.04', 'name': 'skeleton'}, {'id': 10928, 'synset': 'skeleton_key.n.01', 'name': 'skeleton_key'}, {'id': 10929, 'synset': 'skep.n.02', 'name': 'skep'}, {'id': 10930, 'synset': 'skep.n.01', 'name': 'skep'}, {'id': 10931, 'synset': 'sketch.n.01', 'name': 'sketch'}, {'id': 10932, 'synset': 'sketcher.n.02', 'name': 'sketcher'}, {'id': 10933, 'synset': 'skew_arch.n.01', 'name': 'skew_arch'}, {'id': 10934, 'synset': 'ski_binding.n.01', 'name': 'ski_binding'}, {'id': 10935, 'synset': 'skibob.n.01', 'name': 'skibob'}, {'id': 10936, 'synset': 'ski_cap.n.01', 'name': 'ski_cap'}, {'id': 10937, 'synset': 'skidder.n.03', 'name': 'skidder'}, {'id': 10938, 'synset': 'skid_lid.n.01', 'name': 'skid_lid'}, {'id': 10939, 'synset': 'skiff.n.01', 'name': 'skiff'}, {'id': 10940, 'synset': 'ski_jump.n.01', 'name': 'ski_jump'}, {'id': 10941, 'synset': 'ski_lodge.n.01', 'name': 'ski_lodge'}, {'id': 10942, 'synset': 'ski_mask.n.01', 'name': 'ski_mask'}, {'id': 10943, 'synset': 'skimmer.n.02', 'name': 'skimmer'}, {'id': 10944, 'synset': 'ski-plane.n.01', 'name': 'ski-plane'}, {'id': 10945, 'synset': 'ski_rack.n.01', 'name': 'ski_rack'}, {'id': 10946, 'synset': 'skirt.n.01', 'name': 'skirt'}, {'id': 10947, 'synset': 'ski_tow.n.01', 'name': 'ski_tow'}, {'id': 10948, 'synset': 'skivvies.n.01', 'name': 'Skivvies'}, {'id': 10949, 'synset': 'skybox.n.01', 'name': 'skybox'}, {'id': 10950, 'synset': 'skyhook.n.02', 'name': 'skyhook'}, {'id': 10951, 'synset': 'skylight.n.01', 'name': 'skylight'}, {'id': 10952, 'synset': 'skysail.n.01', 'name': 'skysail'}, {'id': 10953, 'synset': 'skyscraper.n.01', 'name': 'skyscraper'}, {'id': 10954, 'synset': 'skywalk.n.01', 'name': 'skywalk'}, {'id': 10955, 'synset': 'slacks.n.01', 'name': 'slacks'}, {'id': 10956, 'synset': 'slack_suit.n.01', 'name': 'slack_suit'}, {'id': 10957, 'synset': 'slasher.n.02', 'name': 'slasher'}, {'id': 10958, 'synset': 'slash_pocket.n.01', 'name': 'slash_pocket'}, {'id': 10959, 'synset': 'slat.n.01', 'name': 'slat'}, {'id': 10960, 'synset': 'slate.n.01', 'name': 'slate'}, {'id': 10961, 'synset': 'slate_pencil.n.01', 'name': 'slate_pencil'}, {'id': 10962, 'synset': 'slate_roof.n.01', 'name': 'slate_roof'}, {'id': 10963, 'synset': 'sleeper.n.07', 'name': 'sleeper'}, {'id': 10964, 'synset': 'sleeper.n.06', 'name': 'sleeper'}, {'id': 10965, 'synset': 'sleeping_car.n.01', 'name': 'sleeping_car'}, {'id': 10966, 'synset': 'sleeve.n.01', 'name': 'sleeve'}, {'id': 10967, 'synset': 'sleeve.n.02', 'name': 'sleeve'}, {'id': 10968, 'synset': 'sleigh_bed.n.01', 'name': 'sleigh_bed'}, {'id': 10969, 'synset': 'sleigh_bell.n.01', 'name': 'sleigh_bell'}, {'id': 10970, 'synset': 'slice_bar.n.01', 'name': 'slice_bar'}, {'id': 10971, 'synset': 'slicer.n.03', 'name': 'slicer'}, {'id': 10972, 'synset': 'slicer.n.02', 'name': 'slicer'}, {'id': 10973, 'synset': 'slide.n.04', 'name': 'slide'}, {'id': 10974, 'synset': 'slide_fastener.n.01', 'name': 'slide_fastener'}, {'id': 10975, 'synset': 'slide_projector.n.01', 'name': 'slide_projector'}, {'id': 10976, 'synset': 'slide_rule.n.01', 'name': 'slide_rule'}, {'id': 10977, 'synset': 'slide_valve.n.01', 'name': 'slide_valve'}, {'id': 10978, 'synset': 'sliding_door.n.01', 'name': 'sliding_door'}, {'id': 10979, 'synset': 'sliding_seat.n.01', 'name': 'sliding_seat'}, {'id': 10980, 'synset': 'sliding_window.n.01', 'name': 'sliding_window'}, {'id': 10981, 'synset': 'sling.n.04', 'name': 'sling'}, {'id': 10982, 'synset': 'slingback.n.01', 'name': 'slingback'}, {'id': 10983, 'synset': 'slinger_ring.n.01', 'name': 'slinger_ring'}, {'id': 10984, 'synset': 'slip_clutch.n.01', 'name': 'slip_clutch'}, {'id': 10985, 'synset': 'slipcover.n.01', 'name': 'slipcover'}, {'id': 10986, 'synset': 'slip-joint_pliers.n.01', 'name': 'slip-joint_pliers'}, {'id': 10987, 'synset': 'slipknot.n.01', 'name': 'slipknot'}, {'id': 10988, 'synset': 'slip-on.n.01', 'name': 'slip-on'}, {'id': 10989, 'synset': 'slip_ring.n.01', 'name': 'slip_ring'}, {'id': 10990, 'synset': 'slit_lamp.n.01', 'name': 'slit_lamp'}, {'id': 10991, 'synset': 'slit_trench.n.01', 'name': 'slit_trench'}, {'id': 10992, 'synset': 'sloop.n.01', 'name': 'sloop'}, {'id': 10993, 'synset': 'sloop_of_war.n.01', 'name': 'sloop_of_war'}, {'id': 10994, 'synset': 'slop_basin.n.01', 'name': 'slop_basin'}, {'id': 10995, 'synset': 'slop_pail.n.01', 'name': 'slop_pail'}, {'id': 10996, 'synset': 'slops.n.02', 'name': 'slops'}, {'id': 10997, 'synset': 'slopshop.n.01', 'name': 'slopshop'}, {'id': 10998, 'synset': 'slot.n.07', 'name': 'slot'}, {'id': 10999, 'synset': 'slot_machine.n.01', 'name': 'slot_machine'}, {'id': 11000, 'synset': 'sluice.n.01', 'name': 'sluice'}, {'id': 11001, 'synset': 'smack.n.03', 'name': 'smack'}, {'id': 11002, 'synset': 'small_boat.n.01', 'name': 'small_boat'}, {'id': 11003, 'synset': 'small_computer_system_interface.n.01', 'name': 'small_computer_system_interface'}, {'id': 11004, 'synset': 'small_ship.n.01', 'name': 'small_ship'}, {'id': 11005, 'synset': 'small_stores.n.01', 'name': 'small_stores'}, {'id': 11006, 'synset': 'smart_bomb.n.01', 'name': 'smart_bomb'}, {'id': 11007, 'synset': 'smelling_bottle.n.01', 'name': 'smelling_bottle'}, {'id': 11008, 'synset': 'smocking.n.01', 'name': 'smocking'}, {'id': 11009, 'synset': 'smoke_bomb.n.01', 'name': 'smoke_bomb'}, {'id': 11010, 'synset': 'smokehouse.n.01', 'name': 'smokehouse'}, {'id': 11011, 'synset': 'smoker.n.03', 'name': 'smoker'}, {'id': 11012, 'synset': 'smoke_screen.n.01', 'name': 'smoke_screen'}, {'id': 11013, 'synset': 'smoking_room.n.01', 'name': 'smoking_room'}, {'id': 11014, 'synset': 'smoothbore.n.01', 'name': 'smoothbore'}, {'id': 11015, 'synset': 'smooth_plane.n.01', 'name': 'smooth_plane'}, {'id': 11016, 'synset': 'snack_bar.n.01', 'name': 'snack_bar'}, {'id': 11017, 'synset': 'snaffle.n.01', 'name': 'snaffle'}, {'id': 11018, 'synset': 'snap.n.10', 'name': 'snap'}, {'id': 11019, 'synset': 'snap_brim.n.01', 'name': 'snap_brim'}, {'id': 11020, 'synset': 'snap-brim_hat.n.01', 'name': 'snap-brim_hat'}, {'id': 11021, 'synset': 'snare.n.05', 'name': 'snare'}, {'id': 11022, 'synset': 'snare_drum.n.01', 'name': 'snare_drum'}, {'id': 11023, 'synset': 'snatch_block.n.01', 'name': 'snatch_block'}, {'id': 11024, 'synset': 'snifter.n.01', 'name': 'snifter'}, {'id': 11025, 'synset': 'sniper_rifle.n.01', 'name': 'sniper_rifle'}, {'id': 11026, 'synset': 'snips.n.01', 'name': 'snips'}, {'id': 11027, 'synset': 'sno-cat.n.01', 'name': 'Sno-cat'}, {'id': 11028, 'synset': 'snood.n.01', 'name': 'snood'}, {'id': 11029, 'synset': 'snorkel.n.02', 'name': 'snorkel'}, {'id': 11030, 'synset': 'snorkel.n.01', 'name': 'snorkel'}, {'id': 11031, 'synset': 'snowbank.n.01', 'name': 'snowbank'}, {'id': 11032, 'synset': 'snowplow.n.01', 'name': 'snowplow'}, {'id': 11033, 'synset': 'snowshoe.n.01', 'name': 'snowshoe'}, {'id': 11034, 'synset': 'snowsuit.n.01', 'name': 'snowsuit'}, {'id': 11035, 'synset': 'snow_thrower.n.01', 'name': 'snow_thrower'}, {'id': 11036, 'synset': 'snuffbox.n.01', 'name': 'snuffbox'}, {'id': 11037, 'synset': 'snuffer.n.01', 'name': 'snuffer'}, {'id': 11038, 'synset': 'snuffers.n.01', 'name': 'snuffers'}, {'id': 11039, 'synset': 'soapbox.n.01', 'name': 'soapbox'}, {'id': 11040, 'synset': 'soap_dish.n.01', 'name': 'soap_dish'}, {'id': 11041, 'synset': 'soap_dispenser.n.01', 'name': 'soap_dispenser'}, {'id': 11042, 'synset': 'soap_pad.n.01', 'name': 'soap_pad'}, {'id': 11043, 'synset': 'socket.n.02', 'name': 'socket'}, {'id': 11044, 'synset': 'socket_wrench.n.01', 'name': 'socket_wrench'}, {'id': 11045, 'synset': 'socle.n.01', 'name': 'socle'}, {'id': 11046, 'synset': 'soda_can.n.01', 'name': 'soda_can'}, {'id': 11047, 'synset': 'soda_fountain.n.02', 'name': 'soda_fountain'}, {'id': 11048, 'synset': 'soda_fountain.n.01', 'name': 'soda_fountain'}, {'id': 11049, 'synset': 'sod_house.n.01', 'name': 'sod_house'}, {'id': 11050, 'synset': 'sodium-vapor_lamp.n.01', 'name': 'sodium-vapor_lamp'}, {'id': 11051, 'synset': 'soffit.n.01', 'name': 'soffit'}, {'id': 11052, 'synset': 'soft_pedal.n.01', 'name': 'soft_pedal'}, {'id': 11053, 'synset': 'soil_pipe.n.01', 'name': 'soil_pipe'}, {'id': 11054, 'synset': 'solar_cell.n.01', 'name': 'solar_cell'}, {'id': 11055, 'synset': 'solar_dish.n.01', 'name': 'solar_dish'}, {'id': 11056, 'synset': 'solar_heater.n.01', 'name': 'solar_heater'}, {'id': 11057, 'synset': 'solar_house.n.01', 'name': 'solar_house'}, {'id': 11058, 'synset': 'solar_telescope.n.01', 'name': 'solar_telescope'}, {'id': 11059, 'synset': 'solar_thermal_system.n.01', 'name': 'solar_thermal_system'}, {'id': 11060, 'synset': 'soldering_iron.n.01', 'name': 'soldering_iron'}, {'id': 11061, 'synset': 'solenoid.n.01', 'name': 'solenoid'}, {'id': 11062, 'synset': 'solleret.n.01', 'name': 'solleret'}, {'id': 11063, 'synset': 'sonic_depth_finder.n.01', 'name': 'sonic_depth_finder'}, {'id': 11064, 'synset': 'sonogram.n.01', 'name': 'sonogram'}, {'id': 11065, 'synset': 'sonograph.n.01', 'name': 'sonograph'}, {'id': 11066, 'synset': 'sorter.n.02', 'name': 'sorter'}, {'id': 11067, 'synset': 'souk.n.01', 'name': 'souk'}, {'id': 11068, 'synset': 'sound_bow.n.01', 'name': 'sound_bow'}, {'id': 11069, 'synset': 'soundbox.n.01', 'name': 'soundbox'}, {'id': 11070, 'synset': 'sound_camera.n.01', 'name': 'sound_camera'}, {'id': 11071, 'synset': 'sounder.n.01', 'name': 'sounder'}, {'id': 11072, 'synset': 'sound_film.n.01', 'name': 'sound_film'}, {'id': 11073, 'synset': 'sounding_board.n.02', 'name': 'sounding_board'}, {'id': 11074, 'synset': 'sounding_rocket.n.01', 'name': 'sounding_rocket'}, {'id': 11075, 'synset': 'sound_recording.n.01', 'name': 'sound_recording'}, {'id': 11076, 'synset': 'sound_spectrograph.n.01', 'name': 'sound_spectrograph'}, {'id': 11077, 'synset': 'soup_ladle.n.01', 'name': 'soup_ladle'}, {'id': 11078, 'synset': 'source_of_illumination.n.01', 'name': 'source_of_illumination'}, {'id': 11079, 'synset': 'sourdine.n.02', 'name': 'sourdine'}, {'id': 11080, 'synset': 'soutache.n.01', 'name': 'soutache'}, {'id': 11081, 'synset': 'soutane.n.01', 'name': 'soutane'}, {'id': 11082, 'synset': "sou'wester.n.02", 'name': "sou'wester"}, {'id': 11083, 'synset': 'soybean_future.n.01', 'name': 'soybean_future'}, {'id': 11084, 'synset': 'space_bar.n.01', 'name': 'space_bar'}, {'id': 11085, 'synset': 'space_capsule.n.01', 'name': 'space_capsule'}, {'id': 11086, 'synset': 'spacecraft.n.01', 'name': 'spacecraft'}, {'id': 11087, 'synset': 'space_heater.n.01', 'name': 'space_heater'}, {'id': 11088, 'synset': 'space_helmet.n.01', 'name': 'space_helmet'}, {'id': 11089, 'synset': 'space_rocket.n.01', 'name': 'space_rocket'}, {'id': 11090, 'synset': 'space_station.n.01', 'name': 'space_station'}, {'id': 11091, 'synset': 'spacesuit.n.01', 'name': 'spacesuit'}, {'id': 11092, 'synset': 'spade.n.02', 'name': 'spade'}, {'id': 11093, 'synset': 'spade_bit.n.01', 'name': 'spade_bit'}, {'id': 11094, 'synset': 'spaghetti_junction.n.01', 'name': 'spaghetti_junction'}, {'id': 11095, 'synset': 'spandau.n.01', 'name': 'Spandau'}, {'id': 11096, 'synset': 'spandex.n.01', 'name': 'spandex'}, {'id': 11097, 'synset': 'spandrel.n.01', 'name': 'spandrel'}, {'id': 11098, 'synset': 'spanker.n.02', 'name': 'spanker'}, {'id': 11099, 'synset': 'spar.n.02', 'name': 'spar'}, {'id': 11100, 'synset': 'sparge_pipe.n.01', 'name': 'sparge_pipe'}, {'id': 11101, 'synset': 'spark_arrester.n.02', 'name': 'spark_arrester'}, {'id': 11102, 'synset': 'spark_arrester.n.01', 'name': 'spark_arrester'}, {'id': 11103, 'synset': 'spark_chamber.n.01', 'name': 'spark_chamber'}, {'id': 11104, 'synset': 'spark_coil.n.01', 'name': 'spark_coil'}, {'id': 11105, 'synset': 'spark_gap.n.01', 'name': 'spark_gap'}, {'id': 11106, 'synset': 'spark_lever.n.01', 'name': 'spark_lever'}, {'id': 11107, 'synset': 'spark_plug.n.01', 'name': 'spark_plug'}, {'id': 11108, 'synset': 'sparkplug_wrench.n.01', 'name': 'sparkplug_wrench'}, {'id': 11109, 'synset': 'spark_transmitter.n.01', 'name': 'spark_transmitter'}, {'id': 11110, 'synset': 'spat.n.02', 'name': 'spat'}, {'id': 11111, 'synset': 'spatula.n.01', 'name': 'spatula'}, {'id': 11112, 'synset': 'speakerphone.n.01', 'name': 'speakerphone'}, {'id': 11113, 'synset': 'speaking_trumpet.n.01', 'name': 'speaking_trumpet'}, {'id': 11114, 'synset': 'spear.n.02', 'name': 'spear'}, {'id': 11115, 'synset': 'specialty_store.n.01', 'name': 'specialty_store'}, {'id': 11116, 'synset': 'specimen_bottle.n.01', 'name': 'specimen_bottle'}, {'id': 11117, 'synset': 'spectacle.n.02', 'name': 'spectacle'}, {'id': 11118, 'synset': 'spectator_pump.n.01', 'name': 'spectator_pump'}, {'id': 11119, 'synset': 'spectrograph.n.01', 'name': 'spectrograph'}, {'id': 11120, 'synset': 'spectrophotometer.n.01', 'name': 'spectrophotometer'}, {'id': 11121, 'synset': 'spectroscope.n.01', 'name': 'spectroscope'}, {'id': 11122, 'synset': 'speculum.n.02', 'name': 'speculum'}, {'id': 11123, 'synset': 'speedboat.n.01', 'name': 'speedboat'}, {'id': 11124, 'synset': 'speed_bump.n.01', 'name': 'speed_bump'}, {'id': 11125, 'synset': 'speedometer.n.01', 'name': 'speedometer'}, {'id': 11126, 'synset': 'speed_skate.n.01', 'name': 'speed_skate'}, {'id': 11127, 'synset': 'spherometer.n.01', 'name': 'spherometer'}, {'id': 11128, 'synset': 'sphygmomanometer.n.01', 'name': 'sphygmomanometer'}, {'id': 11129, 'synset': 'spicemill.n.01', 'name': 'spicemill'}, {'id': 11130, 'synset': 'spider.n.03', 'name': 'spider'}, {'id': 11131, 'synset': 'spider_web.n.01', 'name': 'spider_web'}, {'id': 11132, 'synset': 'spike.n.02', 'name': 'spike'}, {'id': 11133, 'synset': 'spike.n.11', 'name': 'spike'}, {'id': 11134, 'synset': 'spindle.n.04', 'name': 'spindle'}, {'id': 11135, 'synset': 'spindle.n.03', 'name': 'spindle'}, {'id': 11136, 'synset': 'spindle.n.02', 'name': 'spindle'}, {'id': 11137, 'synset': 'spin_dryer.n.01', 'name': 'spin_dryer'}, {'id': 11138, 'synset': 'spinet.n.02', 'name': 'spinet'}, {'id': 11139, 'synset': 'spinet.n.01', 'name': 'spinet'}, {'id': 11140, 'synset': 'spinnaker.n.01', 'name': 'spinnaker'}, {'id': 11141, 'synset': 'spinner.n.03', 'name': 'spinner'}, {'id': 11142, 'synset': 'spinning_frame.n.01', 'name': 'spinning_frame'}, {'id': 11143, 'synset': 'spinning_jenny.n.01', 'name': 'spinning_jenny'}, {'id': 11144, 'synset': 'spinning_machine.n.01', 'name': 'spinning_machine'}, {'id': 11145, 'synset': 'spinning_rod.n.01', 'name': 'spinning_rod'}, {'id': 11146, 'synset': 'spinning_wheel.n.01', 'name': 'spinning_wheel'}, {'id': 11147, 'synset': 'spiral_bandage.n.01', 'name': 'spiral_bandage'}, {'id': 11148, 'synset': 'spiral_ratchet_screwdriver.n.01', 'name': 'spiral_ratchet_screwdriver'}, {'id': 11149, 'synset': 'spiral_spring.n.01', 'name': 'spiral_spring'}, {'id': 11150, 'synset': 'spirit_lamp.n.01', 'name': 'spirit_lamp'}, {'id': 11151, 'synset': 'spirit_stove.n.01', 'name': 'spirit_stove'}, {'id': 11152, 'synset': 'spirometer.n.01', 'name': 'spirometer'}, {'id': 11153, 'synset': 'spit.n.03', 'name': 'spit'}, {'id': 11154, 'synset': 'spittoon.n.01', 'name': 'spittoon'}, {'id': 11155, 'synset': 'splashboard.n.02', 'name': 'splashboard'}, {'id': 11156, 'synset': 'splasher.n.01', 'name': 'splasher'}, {'id': 11157, 'synset': 'splice.n.01', 'name': 'splice'}, {'id': 11158, 'synset': 'splicer.n.03', 'name': 'splicer'}, {'id': 11159, 'synset': 'splint.n.02', 'name': 'splint'}, {'id': 11160, 'synset': 'split_rail.n.01', 'name': 'split_rail'}, {'id': 11161, 'synset': 'spode.n.02', 'name': 'Spode'}, {'id': 11162, 'synset': 'spoiler.n.05', 'name': 'spoiler'}, {'id': 11163, 'synset': 'spoiler.n.04', 'name': 'spoiler'}, {'id': 11164, 'synset': 'spoke.n.01', 'name': 'spoke'}, {'id': 11165, 'synset': 'spokeshave.n.01', 'name': 'spokeshave'}, {'id': 11166, 'synset': 'sponge_cloth.n.01', 'name': 'sponge_cloth'}, {'id': 11167, 'synset': 'sponge_mop.n.01', 'name': 'sponge_mop'}, {'id': 11168, 'synset': 'spoon.n.03', 'name': 'spoon'}, {'id': 11169, 'synset': 'spork.n.01', 'name': 'Spork'}, {'id': 11170, 'synset': 'sporran.n.01', 'name': 'sporran'}, {'id': 11171, 'synset': 'sport_kite.n.01', 'name': 'sport_kite'}, {'id': 11172, 'synset': 'sports_car.n.01', 'name': 'sports_car'}, {'id': 11173, 'synset': 'sports_equipment.n.01', 'name': 'sports_equipment'}, {'id': 11174, 'synset': 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'synset': 'squawk_box.n.01', 'name': 'squawk_box'}, {'id': 11206, 'synset': 'squeegee.n.01', 'name': 'squeegee'}, {'id': 11207, 'synset': 'squeezer.n.01', 'name': 'squeezer'}, {'id': 11208, 'synset': 'squelch_circuit.n.01', 'name': 'squelch_circuit'}, {'id': 11209, 'synset': 'squinch.n.01', 'name': 'squinch'}, {'id': 11210, 'synset': 'stabilizer.n.03', 'name': 'stabilizer'}, {'id': 11211, 'synset': 'stabilizer.n.02', 'name': 'stabilizer'}, {'id': 11212, 'synset': 'stabilizer_bar.n.01', 'name': 'stabilizer_bar'}, {'id': 11213, 'synset': 'stable.n.01', 'name': 'stable'}, {'id': 11214, 'synset': 'stable_gear.n.01', 'name': 'stable_gear'}, {'id': 11215, 'synset': 'stabling.n.01', 'name': 'stabling'}, {'id': 11216, 'synset': 'stacks.n.02', 'name': 'stacks'}, {'id': 11217, 'synset': 'staddle.n.01', 'name': 'staddle'}, {'id': 11218, 'synset': 'stadium.n.01', 'name': 'stadium'}, {'id': 11219, 'synset': 'stage.n.03', 'name': 'stage'}, {'id': 11220, 'synset': 'stained-glass_window.n.01', 'name': 'stained-glass_window'}, {'id': 11221, 'synset': 'stair-carpet.n.01', 'name': 'stair-carpet'}, {'id': 11222, 'synset': 'stair-rod.n.01', 'name': 'stair-rod'}, {'id': 11223, 'synset': 'stairwell.n.01', 'name': 'stairwell'}, {'id': 11224, 'synset': 'stake.n.05', 'name': 'stake'}, {'id': 11225, 'synset': 'stall.n.03', 'name': 'stall'}, {'id': 11226, 'synset': 'stall.n.01', 'name': 'stall'}, {'id': 11227, 'synset': 'stamp.n.08', 'name': 'stamp'}, {'id': 11228, 'synset': 'stamp_mill.n.01', 'name': 'stamp_mill'}, {'id': 11229, 'synset': 'stamping_machine.n.01', 'name': 'stamping_machine'}, {'id': 11230, 'synset': 'stanchion.n.01', 'name': 'stanchion'}, {'id': 11231, 'synset': 'stand.n.04', 'name': 'stand'}, {'id': 11232, 'synset': 'standard.n.05', 'name': 'standard'}, {'id': 11233, 'synset': 'standard_cell.n.01', 'name': 'standard_cell'}, {'id': 11234, 'synset': 'standard_transmission.n.01', 'name': 'standard_transmission'}, {'id': 11235, 'synset': 'standing_press.n.01', 'name': 'standing_press'}, {'id': 11236, 'synset': 'stanhope.n.01', 'name': 'stanhope'}, {'id': 11237, 'synset': 'stanley_steamer.n.01', 'name': 'Stanley_Steamer'}, {'id': 11238, 'synset': 'staple.n.05', 'name': 'staple'}, {'id': 11239, 'synset': 'staple.n.04', 'name': 'staple'}, {'id': 11240, 'synset': 'staple_gun.n.01', 'name': 'staple_gun'}, {'id': 11241, 'synset': 'starship.n.01', 'name': 'starship'}, {'id': 11242, 'synset': 'starter.n.01', 'name': 'starter'}, {'id': 11243, 'synset': 'starting_gate.n.01', 'name': 'starting_gate'}, {'id': 11244, 'synset': 'stassano_furnace.n.01', 'name': 'Stassano_furnace'}, {'id': 11245, 'synset': 'statehouse.n.01', 'name': 'Statehouse'}, {'id': 11246, 'synset': 'stately_home.n.01', 'name': 'stately_home'}, {'id': 11247, 'synset': 'state_prison.n.01', 'name': 'state_prison'}, {'id': 11248, 'synset': 'stateroom.n.01', 'name': 'stateroom'}, {'id': 11249, 'synset': 'static_tube.n.01', 'name': 'static_tube'}, {'id': 11250, 'synset': 'station.n.01', 'name': 'station'}, {'id': 11251, 'synset': 'stator.n.01', 'name': 'stator'}, {'id': 11252, 'synset': 'stay.n.05', 'name': 'stay'}, {'id': 11253, 'synset': 'staysail.n.01', 'name': 'staysail'}, {'id': 11254, 'synset': 'steakhouse.n.01', 'name': 'steakhouse'}, {'id': 11255, 'synset': 'stealth_aircraft.n.01', 'name': 'stealth_aircraft'}, {'id': 11256, 'synset': 'stealth_bomber.n.01', 'name': 'stealth_bomber'}, {'id': 11257, 'synset': 'stealth_fighter.n.01', 'name': 'stealth_fighter'}, {'id': 11258, 'synset': 'steam_bath.n.01', 'name': 'steam_bath'}, {'id': 11259, 'synset': 'steamboat.n.01', 'name': 'steamboat'}, {'id': 11260, 'synset': 'steam_chest.n.01', 'name': 'steam_chest'}, {'id': 11261, 'synset': 'steam_engine.n.01', 'name': 'steam_engine'}, {'id': 11262, 'synset': 'steamer.n.03', 'name': 'steamer'}, {'id': 11263, 'synset': 'steamer.n.02', 'name': 'steamer'}, {'id': 11264, 'synset': 'steam_iron.n.01', 'name': 'steam_iron'}, {'id': 11265, 'synset': 'steam_locomotive.n.01', 'name': 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'synset': 'steering_system.n.01', 'name': 'steering_system'}, {'id': 11281, 'synset': 'stele.n.02', 'name': 'stele'}, {'id': 11282, 'synset': 'stem-winder.n.01', 'name': 'stem-winder'}, {'id': 11283, 'synset': 'stencil.n.01', 'name': 'stencil'}, {'id': 11284, 'synset': 'sten_gun.n.01', 'name': 'Sten_gun'}, {'id': 11285, 'synset': 'stenograph.n.02', 'name': 'stenograph'}, {'id': 11286, 'synset': 'step.n.04', 'name': 'step'}, {'id': 11287, 'synset': 'step-down_transformer.n.01', 'name': 'step-down_transformer'}, {'id': 11288, 'synset': 'step-up_transformer.n.01', 'name': 'step-up_transformer'}, {'id': 11289, 'synset': 'stereoscope.n.01', 'name': 'stereoscope'}, {'id': 11290, 'synset': 'stern_chaser.n.01', 'name': 'stern_chaser'}, {'id': 11291, 'synset': 'sternpost.n.01', 'name': 'sternpost'}, {'id': 11292, 'synset': 'sternwheeler.n.01', 'name': 'sternwheeler'}, {'id': 11293, 'synset': 'stethoscope.n.01', 'name': 'stethoscope'}, {'id': 11294, 'synset': 'stewing_pan.n.01', 'name': 'stewing_pan'}, {'id': 11295, 'synset': 'stick.n.01', 'name': 'stick'}, {'id': 11296, 'synset': 'stick.n.07', 'name': 'stick'}, {'id': 11297, 'synset': 'stick.n.03', 'name': 'stick'}, {'id': 11298, 'synset': 'stick.n.06', 'name': 'stick'}, {'id': 11299, 'synset': 'stile.n.01', 'name': 'stile'}, {'id': 11300, 'synset': 'stiletto.n.01', 'name': 'stiletto'}, {'id': 11301, 'synset': 'still.n.03', 'name': 'still'}, {'id': 11302, 'synset': 'stillroom.n.01', 'name': 'stillroom'}, {'id': 11303, 'synset': 'stillson_wrench.n.01', 'name': 'Stillson_wrench'}, {'id': 11304, 'synset': 'stilt.n.02', 'name': 'stilt'}, {'id': 11305, 'synset': 'stinger.n.03', 'name': 'Stinger'}, {'id': 11306, 'synset': 'stink_bomb.n.01', 'name': 'stink_bomb'}, {'id': 11307, 'synset': 'stirrup_pump.n.01', 'name': 'stirrup_pump'}, {'id': 11308, 'synset': 'stob.n.01', 'name': 'stob'}, {'id': 11309, 'synset': 'stock.n.03', 'name': 'stock'}, {'id': 11310, 'synset': 'stockade.n.01', 'name': 'stockade'}, {'id': 11311, 'synset': 'stockcar.n.01', 'name': 'stockcar'}, {'id': 11312, 'synset': 'stock_car.n.02', 'name': 'stock_car'}, {'id': 11313, 'synset': 'stockinet.n.01', 'name': 'stockinet'}, {'id': 11314, 'synset': 'stocking.n.01', 'name': 'stocking'}, {'id': 11315, 'synset': 'stock-in-trade.n.01', 'name': 'stock-in-trade'}, {'id': 11316, 'synset': 'stockpot.n.01', 'name': 'stockpot'}, {'id': 11317, 'synset': 'stockroom.n.01', 'name': 'stockroom'}, {'id': 11318, 'synset': 'stocks.n.03', 'name': 'stocks'}, {'id': 11319, 'synset': 'stock_saddle.n.01', 'name': 'stock_saddle'}, {'id': 11320, 'synset': 'stockyard.n.01', 'name': 'stockyard'}, {'id': 11321, 'synset': 'stole.n.01', 'name': 'stole'}, {'id': 11322, 'synset': 'stomacher.n.01', 'name': 'stomacher'}, {'id': 11323, 'synset': 'stomach_pump.n.01', 'name': 'stomach_pump'}, {'id': 11324, 'synset': 'stone_wall.n.01', 'name': 'stone_wall'}, {'id': 11325, 'synset': 'stoneware.n.01', 'name': 'stoneware'}, {'id': 11326, 'synset': 'stonework.n.01', 'name': 'stonework'}, {'id': 11327, 'synset': 'stoop.n.03', 'name': 'stoop'}, {'id': 11328, 'synset': 'stop_bath.n.01', 'name': 'stop_bath'}, {'id': 11329, 'synset': 'stopcock.n.01', 'name': 'stopcock'}, {'id': 11330, 'synset': 'stopper_knot.n.01', 'name': 'stopper_knot'}, {'id': 11331, 'synset': 'stopwatch.n.01', 'name': 'stopwatch'}, {'id': 11332, 'synset': 'storage_battery.n.01', 'name': 'storage_battery'}, {'id': 11333, 'synset': 'storage_cell.n.01', 'name': 'storage_cell'}, {'id': 11334, 'synset': 'storage_ring.n.01', 'name': 'storage_ring'}, {'id': 11335, 'synset': 'storage_space.n.01', 'name': 'storage_space'}, {'id': 11336, 'synset': 'storeroom.n.01', 'name': 'storeroom'}, {'id': 11337, 'synset': 'storm_cellar.n.01', 'name': 'storm_cellar'}, {'id': 11338, 'synset': 'storm_door.n.01', 'name': 'storm_door'}, {'id': 11339, 'synset': 'storm_window.n.01', 'name': 'storm_window'}, {'id': 11340, 'synset': 'stoup.n.02', 'name': 'stoup'}, {'id': 11341, 'synset': 'stoup.n.01', 'name': 'stoup'}, {'id': 11342, 'synset': 'stove.n.02', 'name': 'stove'}, {'id': 11343, 'synset': 'stove_bolt.n.01', 'name': 'stove_bolt'}, {'id': 11344, 'synset': 'stovepipe.n.01', 'name': 'stovepipe'}, {'id': 11345, 'synset': 'stovepipe_iron.n.01', 'name': 'stovepipe_iron'}, {'id': 11346, 'synset': 'stradavarius.n.01', 'name': 'Stradavarius'}, {'id': 11347, 'synset': 'straight_chair.n.01', 'name': 'straight_chair'}, {'id': 11348, 'synset': 'straightedge.n.01', 'name': 'straightedge'}, {'id': 11349, 'synset': 'straightener.n.01', 'name': 'straightener'}, {'id': 11350, 'synset': 'straight_flute.n.01', 'name': 'straight_flute'}, {'id': 11351, 'synset': 'straight_pin.n.01', 'name': 'straight_pin'}, {'id': 11352, 'synset': 'straight_razor.n.01', 'name': 'straight_razor'}, {'id': 11353, 'synset': 'straitjacket.n.02', 'name': 'straitjacket'}, {'id': 11354, 'synset': 'strap.n.04', 'name': 'strap'}, {'id': 11355, 'synset': 'strap_hinge.n.01', 'name': 'strap_hinge'}, {'id': 11356, 'synset': 'strapless.n.01', 'name': 'strapless'}, {'id': 11357, 'synset': 'streamer_fly.n.01', 'name': 'streamer_fly'}, {'id': 11358, 'synset': 'streamliner.n.01', 'name': 'streamliner'}, {'id': 11359, 'synset': 'street.n.01', 'name': 'street'}, {'id': 11360, 'synset': 'street.n.02', 'name': 'street'}, {'id': 11361, 'synset': 'streetcar.n.01', 'name': 'streetcar'}, {'id': 11362, 'synset': 'street_clothes.n.01', 'name': 'street_clothes'}, {'id': 11363, 'synset': 'stretcher.n.03', 'name': 'stretcher'}, {'id': 11364, 'synset': 'stretcher.n.01', 'name': 'stretcher'}, {'id': 11365, 'synset': 'stretch_pants.n.01', 'name': 'stretch_pants'}, {'id': 11366, 'synset': 'strickle.n.02', 'name': 'strickle'}, {'id': 11367, 'synset': 'strickle.n.01', 'name': 'strickle'}, {'id': 11368, 'synset': 'stringed_instrument.n.01', 'name': 'stringed_instrument'}, {'id': 11369, 'synset': 'stringer.n.04', 'name': 'stringer'}, {'id': 11370, 'synset': 'stringer.n.03', 'name': 'stringer'}, {'id': 11371, 'synset': 'string_tie.n.01', 'name': 'string_tie'}, {'id': 11372, 'synset': 'strip.n.05', 'name': 'strip'}, {'id': 11373, 'synset': 'strip_lighting.n.01', 'name': 'strip_lighting'}, {'id': 11374, 'synset': 'strip_mall.n.01', 'name': 'strip_mall'}, {'id': 11375, 'synset': 'stroboscope.n.01', 'name': 'stroboscope'}, {'id': 11376, 'synset': 'strongbox.n.01', 'name': 'strongbox'}, {'id': 11377, 'synset': 'stronghold.n.01', 'name': 'stronghold'}, {'id': 11378, 'synset': 'strongroom.n.01', 'name': 'strongroom'}, {'id': 11379, 'synset': 'strop.n.01', 'name': 'strop'}, {'id': 11380, 'synset': 'structural_member.n.01', 'name': 'structural_member'}, {'id': 11381, 'synset': 'structure.n.01', 'name': 'structure'}, {'id': 11382, 'synset': 'student_center.n.01', 'name': 'student_center'}, {'id': 11383, 'synset': 'student_lamp.n.01', 'name': 'student_lamp'}, {'id': 11384, 'synset': 'student_union.n.01', 'name': 'student_union'}, {'id': 11385, 'synset': 'stud_finder.n.01', 'name': 'stud_finder'}, {'id': 11386, 'synset': 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'name': 'submersible'}, {'id': 11402, 'synset': 'submersible.n.01', 'name': 'submersible'}, {'id': 11403, 'synset': 'subtracter.n.02', 'name': 'subtracter'}, {'id': 11404, 'synset': 'subway_token.n.01', 'name': 'subway_token'}, {'id': 11405, 'synset': 'subway_train.n.01', 'name': 'subway_train'}, {'id': 11406, 'synset': 'suction_cup.n.01', 'name': 'suction_cup'}, {'id': 11407, 'synset': 'suction_pump.n.01', 'name': 'suction_pump'}, {'id': 11408, 'synset': 'sudatorium.n.01', 'name': 'sudatorium'}, {'id': 11409, 'synset': 'suede_cloth.n.01', 'name': 'suede_cloth'}, {'id': 11410, 'synset': 'sugar_refinery.n.01', 'name': 'sugar_refinery'}, {'id': 11411, 'synset': 'sugar_spoon.n.01', 'name': 'sugar_spoon'}, {'id': 11412, 'synset': 'suite.n.02', 'name': 'suite'}, {'id': 11413, 'synset': 'suiting.n.01', 'name': 'suiting'}, {'id': 11414, 'synset': 'sulky.n.01', 'name': 'sulky'}, {'id': 11415, 'synset': 'summer_house.n.01', 'name': 'summer_house'}, {'id': 11416, 'synset': 'sumo_ring.n.01', 'name': 'sumo_ring'}, {'id': 11417, 'synset': 'sump.n.01', 'name': 'sump'}, {'id': 11418, 'synset': 'sump_pump.n.01', 'name': 'sump_pump'}, {'id': 11419, 'synset': 'sunbonnet.n.01', 'name': 'sunbonnet'}, {'id': 11420, 'synset': 'sunday_best.n.01', 'name': 'Sunday_best'}, {'id': 11421, 'synset': 'sun_deck.n.01', 'name': 'sun_deck'}, {'id': 11422, 'synset': 'sundial.n.01', 'name': 'sundial'}, {'id': 11423, 'synset': 'sundress.n.01', 'name': 'sundress'}, {'id': 11424, 'synset': 'sundries.n.01', 'name': 'sundries'}, {'id': 11425, 'synset': 'sun_gear.n.01', 'name': 'sun_gear'}, {'id': 11426, 'synset': 'sunglass.n.01', 'name': 'sunglass'}, {'id': 11427, 'synset': 'sunlamp.n.01', 'name': 'sunlamp'}, {'id': 11428, 'synset': 'sun_parlor.n.01', 'name': 'sun_parlor'}, {'id': 11429, 'synset': 'sunroof.n.01', 'name': 'sunroof'}, {'id': 11430, 'synset': 'sunscreen.n.01', 'name': 'sunscreen'}, {'id': 11431, 'synset': 'sunsuit.n.01', 'name': 'sunsuit'}, {'id': 11432, 'synset': 'supercharger.n.01', 'name': 'supercharger'}, {'id': 11433, 'synset': 'supercomputer.n.01', 'name': 'supercomputer'}, {'id': 11434, 'synset': 'superconducting_supercollider.n.01', 'name': 'superconducting_supercollider'}, {'id': 11435, 'synset': 'superhighway.n.02', 'name': 'superhighway'}, {'id': 11436, 'synset': 'supermarket.n.01', 'name': 'supermarket'}, {'id': 11437, 'synset': 'superstructure.n.01', 'name': 'superstructure'}, {'id': 11438, 'synset': 'supertanker.n.01', 'name': 'supertanker'}, {'id': 11439, 'synset': 'supper_club.n.01', 'name': 'supper_club'}, {'id': 11440, 'synset': 'supplejack.n.01', 'name': 'supplejack'}, {'id': 11441, 'synset': 'supply_chamber.n.01', 'name': 'supply_chamber'}, {'id': 11442, 'synset': 'supply_closet.n.01', 'name': 'supply_closet'}, {'id': 11443, 'synset': 'support.n.10', 'name': 'support'}, {'id': 11444, 'synset': 'support.n.07', 'name': 'support'}, {'id': 11445, 'synset': 'support_column.n.01', 'name': 'support_column'}, {'id': 11446, 'synset': 'support_hose.n.01', 'name': 'support_hose'}, {'id': 11447, 'synset': 'supporting_structure.n.01', 'name': 'supporting_structure'}, {'id': 11448, 'synset': 'supporting_tower.n.01', 'name': 'supporting_tower'}, {'id': 11449, 'synset': 'surcoat.n.02', 'name': 'surcoat'}, {'id': 11450, 'synset': 'surface_gauge.n.01', 'name': 'surface_gauge'}, {'id': 11451, 'synset': 'surface_lift.n.01', 'name': 'surface_lift'}, {'id': 11452, 'synset': 'surface_search_radar.n.01', 'name': 'surface_search_radar'}, {'id': 11453, 'synset': 'surface_ship.n.01', 'name': 'surface_ship'}, {'id': 11454, 'synset': 'surface-to-air_missile.n.01', 'name': 'surface-to-air_missile'}, {'id': 11455, 'synset': 'surface-to-air_missile_system.n.01', 'name': 'surface-to-air_missile_system'}, {'id': 11456, 'synset': 'surfboat.n.01', 'name': 'surfboat'}, {'id': 11457, 'synset': 'surcoat.n.01', 'name': 'surcoat'}, {'id': 11458, 'synset': "surgeon's_knot.n.01", 'name': "surgeon's_knot"}, {'id': 11459, 'synset': 'surgery.n.02', 'name': 'surgery'}, {'id': 11460, 'synset': 'surge_suppressor.n.01', 'name': 'surge_suppressor'}, {'id': 11461, 'synset': 'surgical_dressing.n.01', 'name': 'surgical_dressing'}, {'id': 11462, 'synset': 'surgical_instrument.n.01', 'name': 'surgical_instrument'}, {'id': 11463, 'synset': 'surgical_knife.n.01', 'name': 'surgical_knife'}, {'id': 11464, 'synset': 'surplice.n.01', 'name': 'surplice'}, {'id': 11465, 'synset': 'surrey.n.02', 'name': 'surrey'}, {'id': 11466, 'synset': 'surtout.n.01', 'name': 'surtout'}, {'id': 11467, 'synset': 'surveillance_system.n.01', 'name': 'surveillance_system'}, {'id': 11468, 'synset': 'surveying_instrument.n.01', 'name': 'surveying_instrument'}, {'id': 11469, 'synset': "surveyor's_level.n.01", 'name': "surveyor's_level"}, {'id': 11470, 'synset': 'sushi_bar.n.01', 'name': 'sushi_bar'}, {'id': 11471, 'synset': 'suspension.n.05', 'name': 'suspension'}, {'id': 11472, 'synset': 'suspension_bridge.n.01', 'name': 'suspension_bridge'}, {'id': 11473, 'synset': 'suspensory.n.01', 'name': 'suspensory'}, {'id': 11474, 'synset': 'sustaining_pedal.n.01', 'name': 'sustaining_pedal'}, {'id': 11475, 'synset': 'suture.n.02', 'name': 'suture'}, {'id': 11476, 'synset': 'swab.n.01', 'name': 'swab'}, {'id': 11477, 'synset': 'swaddling_clothes.n.01', 'name': 'swaddling_clothes'}, {'id': 11478, 'synset': 'swag.n.03', 'name': 'swag'}, {'id': 11479, 'synset': 'swage_block.n.01', 'name': 'swage_block'}, {'id': 11480, 'synset': 'swagger_stick.n.01', 'name': 'swagger_stick'}, {'id': 11481, 'synset': 'swallow-tailed_coat.n.01', 'name': 'swallow-tailed_coat'}, {'id': 11482, 'synset': 'swamp_buggy.n.01', 'name': 'swamp_buggy'}, {'id': 11483, 'synset': "swan's_down.n.01", 'name': "swan's_down"}, {'id': 11484, 'synset': 'swathe.n.01', 'name': 'swathe'}, {'id': 11485, 'synset': 'swatter.n.01', 'name': 'swatter'}, {'id': 11486, 'synset': 'sweat_bag.n.01', 'name': 'sweat_bag'}, {'id': 11487, 'synset': 'sweatband.n.01', 'name': 'sweatband'}, {'id': 11488, 'synset': 'sweatshop.n.01', 'name': 'sweatshop'}, {'id': 11489, 'synset': 'sweat_suit.n.01', 'name': 'sweat_suit'}, {'id': 11490, 'synset': 'sweep.n.04', 'name': 'sweep'}, {'id': 11491, 'synset': 'sweep_hand.n.01', 'name': 'sweep_hand'}, {'id': 11492, 'synset': 'swimming_trunks.n.01', 'name': 'swimming_trunks'}, {'id': 11493, 'synset': 'swing.n.02', 'name': 'swing'}, {'id': 11494, 'synset': 'swing_door.n.01', 'name': 'swing_door'}, {'id': 11495, 'synset': 'switch.n.01', 'name': 'switch'}, {'id': 11496, 'synset': 'switchblade.n.01', 'name': 'switchblade'}, {'id': 11497, 'synset': 'switch_engine.n.01', 'name': 'switch_engine'}, {'id': 11498, 'synset': 'swivel.n.01', 'name': 'swivel'}, {'id': 11499, 'synset': 'swivel_chair.n.01', 'name': 'swivel_chair'}, {'id': 11500, 'synset': 'swizzle_stick.n.01', 'name': 'swizzle_stick'}, {'id': 11501, 'synset': 'sword_cane.n.01', 'name': 'sword_cane'}, {'id': 11502, 'synset': 's_wrench.n.01', 'name': 'S_wrench'}, {'id': 11503, 'synset': 'synagogue.n.01', 'name': 'synagogue'}, {'id': 11504, 'synset': 'synchrocyclotron.n.01', 'name': 'synchrocyclotron'}, {'id': 11505, 'synset': 'synchroflash.n.01', 'name': 'synchroflash'}, {'id': 11506, 'synset': 'synchromesh.n.01', 'name': 'synchromesh'}, {'id': 11507, 'synset': 'synchronous_converter.n.01', 'name': 'synchronous_converter'}, {'id': 11508, 'synset': 'synchronous_motor.n.01', 'name': 'synchronous_motor'}, {'id': 11509, 'synset': 'synchrotron.n.01', 'name': 'synchrotron'}, {'id': 11510, 'synset': 'synchroscope.n.01', 'name': 'synchroscope'}, {'id': 11511, 'synset': 'synthesizer.n.02', 'name': 'synthesizer'}, {'id': 11512, 'synset': 'system.n.01', 'name': 'system'}, {'id': 11513, 'synset': 'tabard.n.01', 'name': 'tabard'}, {'id': 11514, 'synset': 'tabernacle.n.02', 'name': 'Tabernacle'}, {'id': 11515, 'synset': 'tabi.n.01', 'name': 'tabi'}, {'id': 11516, 'synset': 'tab_key.n.01', 'name': 'tab_key'}, {'id': 11517, 'synset': 'table.n.03', 'name': 'table'}, {'id': 11518, 'synset': 'tablefork.n.01', 'name': 'tablefork'}, {'id': 11519, 'synset': 'table_knife.n.01', 'name': 'table_knife'}, {'id': 11520, 'synset': 'table_saw.n.01', 'name': 'table_saw'}, {'id': 11521, 'synset': 'tablespoon.n.02', 'name': 'tablespoon'}, {'id': 11522, 'synset': 'tablet-armed_chair.n.01', 'name': 'tablet-armed_chair'}, {'id': 11523, 'synset': 'table-tennis_racquet.n.01', 'name': 'table-tennis_racquet'}, {'id': 11524, 'synset': 'tabletop.n.01', 'name': 'tabletop'}, {'id': 11525, 'synset': 'tableware.n.01', 'name': 'tableware'}, {'id': 11526, 'synset': 'tabor.n.01', 'name': 'tabor'}, {'id': 11527, 'synset': 'taboret.n.01', 'name': 'taboret'}, {'id': 11528, 'synset': 'tachistoscope.n.01', 'name': 'tachistoscope'}, {'id': 11529, 'synset': 'tachograph.n.01', 'name': 'tachograph'}, {'id': 11530, 'synset': 'tachymeter.n.01', 'name': 'tachymeter'}, {'id': 11531, 'synset': 'tack.n.02', 'name': 'tack'}, {'id': 11532, 'synset': 'tack_hammer.n.01', 'name': 'tack_hammer'}, {'id': 11533, 'synset': 'taffeta.n.01', 'name': 'taffeta'}, {'id': 11534, 'synset': 'taffrail.n.01', 'name': 'taffrail'}, {'id': 11535, 'synset': 'tailgate.n.01', 'name': 'tailgate'}, {'id': 11536, 'synset': 'tailor-made.n.01', 'name': 'tailor-made'}, {'id': 11537, 'synset': "tailor's_chalk.n.01", 'name': "tailor's_chalk"}, {'id': 11538, 'synset': 'tailpipe.n.01', 'name': 'tailpipe'}, {'id': 11539, 'synset': 'tail_rotor.n.01', 'name': 'tail_rotor'}, {'id': 11540, 'synset': 'tailstock.n.01', 'name': 'tailstock'}, {'id': 11541, 'synset': 'take-up.n.01', 'name': 'take-up'}, {'id': 11542, 'synset': 'talaria.n.01', 'name': 'talaria'}, {'id': 11543, 'synset': 'talcum.n.02', 'name': 'talcum'}, {'id': 11544, 'synset': 'tam.n.01', 'name': 'tam'}, {'id': 11545, 'synset': 'tambour.n.02', 'name': 'tambour'}, {'id': 11546, 'synset': 'tambour.n.01', 'name': 'tambour'}, {'id': 11547, 'synset': 'tammy.n.01', 'name': 'tammy'}, {'id': 11548, 'synset': 'tamp.n.01', 'name': 'tamp'}, {'id': 11549, 'synset': 'tampax.n.01', 'name': 'Tampax'}, {'id': 11550, 'synset': 'tampion.n.01', 'name': 'tampion'}, {'id': 11551, 'synset': 'tampon.n.01', 'name': 'tampon'}, {'id': 11552, 'synset': 'tandoor.n.01', 'name': 'tandoor'}, {'id': 11553, 'synset': 'tangram.n.01', 'name': 'tangram'}, {'id': 11554, 'synset': 'tankard.n.01', 'name': 'tankard'}, {'id': 11555, 'synset': 'tank_car.n.01', 'name': 'tank_car'}, {'id': 11556, 'synset': 'tank_destroyer.n.01', 'name': 'tank_destroyer'}, {'id': 11557, 'synset': 'tank_engine.n.01', 'name': 'tank_engine'}, {'id': 11558, 'synset': 'tanker_plane.n.01', 'name': 'tanker_plane'}, {'id': 11559, 'synset': 'tank_shell.n.01', 'name': 'tank_shell'}, {'id': 11560, 'synset': 'tannoy.n.01', 'name': 'tannoy'}, {'id': 11561, 'synset': 'tap.n.06', 'name': 'tap'}, {'id': 11562, 'synset': 'tapa.n.02', 'name': 'tapa'}, {'id': 11563, 'synset': 'tape.n.02', 'name': 'tape'}, {'id': 11564, 'synset': 'tape_deck.n.01', 'name': 'tape_deck'}, {'id': 11565, 'synset': 'tape_drive.n.01', 'name': 'tape_drive'}, {'id': 11566, 'synset': 'tape_player.n.01', 'name': 'tape_player'}, {'id': 11567, 'synset': 'tape_recorder.n.01', 'name': 'tape_recorder'}, {'id': 11568, 'synset': 'taper_file.n.01', 'name': 'taper_file'}, {'id': 11569, 'synset': 'tappet.n.01', 'name': 'tappet'}, {'id': 11570, 'synset': 'tap_wrench.n.01', 'name': 'tap_wrench'}, {'id': 11571, 'synset': 'tare.n.05', 'name': 'tare'}, {'id': 11572, 'synset': 'target.n.04', 'name': 'target'}, {'id': 11573, 'synset': 'target_acquisition_system.n.01', 'name': 'target_acquisition_system'}, {'id': 11574, 'synset': 'tarmacadam.n.02', 'name': 'tarmacadam'}, {'id': 11575, 'synset': 'tasset.n.01', 'name': 'tasset'}, {'id': 11576, 'synset': 'tattoo.n.02', 'name': 'tattoo'}, {'id': 11577, 'synset': 'tavern.n.01', 'name': 'tavern'}, {'id': 11578, 'synset': 'tawse.n.01', 'name': 'tawse'}, {'id': 11579, 'synset': 'taximeter.n.01', 'name': 'taximeter'}, {'id': 11580, 'synset': 't-bar_lift.n.01', 'name': 'T-bar_lift'}, {'id': 11581, 'synset': 'tea_bag.n.02', 'name': 'tea_bag'}, {'id': 11582, 'synset': 'tea_ball.n.01', 'name': 'tea_ball'}, {'id': 11583, 'synset': 'tea_cart.n.01', 'name': 'tea_cart'}, {'id': 11584, 'synset': 'tea_chest.n.01', 'name': 'tea_chest'}, {'id': 11585, 'synset': 'teaching_aid.n.01', 'name': 'teaching_aid'}, {'id': 11586, 'synset': 'tea_gown.n.01', 'name': 'tea_gown'}, {'id': 11587, 'synset': 'tea_maker.n.01', 'name': 'tea_maker'}, {'id': 11588, 'synset': 'teashop.n.01', 'name': 'teashop'}, {'id': 11589, 'synset': 'teaspoon.n.02', 'name': 'teaspoon'}, {'id': 11590, 'synset': 'tea-strainer.n.01', 'name': 'tea-strainer'}, {'id': 11591, 'synset': 'tea_table.n.01', 'name': 'tea_table'}, {'id': 11592, 'synset': 'tea_tray.n.01', 'name': 'tea_tray'}, {'id': 11593, 'synset': 'tea_urn.n.01', 'name': 'tea_urn'}, {'id': 11594, 'synset': 'tee.n.03', 'name': 'tee'}, {'id': 11595, 'synset': 'tee_hinge.n.01', 'name': 'tee_hinge'}, {'id': 11596, 'synset': 'telecom_hotel.n.01', 'name': 'telecom_hotel'}, {'id': 11597, 'synset': 'telecommunication_system.n.01', 'name': 'telecommunication_system'}, {'id': 11598, 'synset': 'telegraph.n.01', 'name': 'telegraph'}, {'id': 11599, 'synset': 'telegraph_key.n.01', 'name': 'telegraph_key'}, {'id': 11600, 'synset': 'telemeter.n.01', 'name': 'telemeter'}, {'id': 11601, 'synset': 'telephone_bell.n.01', 'name': 'telephone_bell'}, {'id': 11602, 'synset': 'telephone_cord.n.01', 'name': 'telephone_cord'}, {'id': 11603, 'synset': 'telephone_jack.n.01', 'name': 'telephone_jack'}, {'id': 11604, 'synset': 'telephone_line.n.02', 'name': 'telephone_line'}, {'id': 11605, 'synset': 'telephone_plug.n.01', 'name': 'telephone_plug'}, {'id': 11606, 'synset': 'telephone_receiver.n.01', 'name': 'telephone_receiver'}, {'id': 11607, 'synset': 'telephone_system.n.01', 'name': 'telephone_system'}, {'id': 11608, 'synset': 'telephone_wire.n.01', 'name': 'telephone_wire'}, {'id': 11609, 'synset': 'teleprompter.n.01', 'name': 'Teleprompter'}, {'id': 11610, 'synset': 'telescope.n.01', 'name': 'telescope'}, {'id': 11611, 'synset': 'telescopic_sight.n.01', 'name': 'telescopic_sight'}, {'id': 11612, 'synset': 'telethermometer.n.01', 'name': 'telethermometer'}, {'id': 11613, 'synset': 'teletypewriter.n.01', 'name': 'teletypewriter'}, {'id': 11614, 'synset': 'television.n.02', 'name': 'television'}, {'id': 11615, 'synset': 'television_antenna.n.01', 'name': 'television_antenna'}, {'id': 11616, 'synset': 'television_equipment.n.01', 'name': 'television_equipment'}, {'id': 11617, 'synset': 'television_monitor.n.01', 'name': 'television_monitor'}, {'id': 11618, 'synset': 'television_room.n.01', 'name': 'television_room'}, {'id': 11619, 'synset': 'television_transmitter.n.01', 'name': 'television_transmitter'}, {'id': 11620, 'synset': 'telpher.n.01', 'name': 'telpher'}, {'id': 11621, 'synset': 'telpherage.n.01', 'name': 'telpherage'}, {'id': 11622, 'synset': 'tempera.n.01', 'name': 'tempera'}, {'id': 11623, 'synset': 'temple.n.01', 'name': 'temple'}, {'id': 11624, 'synset': 'temple.n.03', 'name': 'temple'}, {'id': 11625, 'synset': 'temporary_hookup.n.01', 'name': 'temporary_hookup'}, {'id': 11626, 'synset': 'tender.n.06', 'name': 'tender'}, {'id': 11627, 'synset': 'tender.n.05', 'name': 'tender'}, {'id': 11628, 'synset': 'tender.n.04', 'name': 'tender'}, {'id': 11629, 'synset': 'tenement.n.01', 'name': 'tenement'}, {'id': 11630, 'synset': 'tennis_camp.n.01', 'name': 'tennis_camp'}, {'id': 11631, 'synset': 'tenon.n.01', 'name': 'tenon'}, {'id': 11632, 'synset': 'tenor_drum.n.01', 'name': 'tenor_drum'}, {'id': 11633, 'synset': 'tenoroon.n.01', 'name': 'tenoroon'}, {'id': 11634, 'synset': 'tenpenny_nail.n.01', 'name': 'tenpenny_nail'}, {'id': 11635, 'synset': 'tenpin.n.01', 'name': 'tenpin'}, {'id': 11636, 'synset': 'tensimeter.n.01', 'name': 'tensimeter'}, {'id': 11637, 'synset': 'tensiometer.n.03', 'name': 'tensiometer'}, {'id': 11638, 'synset': 'tensiometer.n.02', 'name': 'tensiometer'}, {'id': 11639, 'synset': 'tensiometer.n.01', 'name': 'tensiometer'}, {'id': 11640, 'synset': 'tent.n.01', 'name': 'tent'}, {'id': 11641, 'synset': 'tenter.n.01', 'name': 'tenter'}, {'id': 11642, 'synset': 'tenterhook.n.01', 'name': 'tenterhook'}, {'id': 11643, 'synset': 'tent-fly.n.01', 'name': 'tent-fly'}, {'id': 11644, 'synset': 'tent_peg.n.01', 'name': 'tent_peg'}, {'id': 11645, 'synset': 'tepee.n.01', 'name': 'tepee'}, {'id': 11646, 'synset': 'terminal.n.02', 'name': 'terminal'}, {'id': 11647, 'synset': 'terminal.n.04', 'name': 'terminal'}, {'id': 11648, 'synset': 'terraced_house.n.01', 'name': 'terraced_house'}, {'id': 11649, 'synset': 'terra_cotta.n.01', 'name': 'terra_cotta'}, {'id': 11650, 'synset': 'terrarium.n.01', 'name': 'terrarium'}, {'id': 11651, 'synset': 'terra_sigillata.n.01', 'name': 'terra_sigillata'}, {'id': 11652, 'synset': 'terry.n.02', 'name': 'terry'}, {'id': 11653, 'synset': 'tesla_coil.n.01', 'name': 'Tesla_coil'}, {'id': 11654, 'synset': 'tessera.n.01', 'name': 'tessera'}, {'id': 11655, 'synset': 'test_equipment.n.01', 'name': 'test_equipment'}, {'id': 11656, 'synset': 'test_rocket.n.01', 'name': 'test_rocket'}, {'id': 11657, 'synset': 'test_room.n.01', 'name': 'test_room'}, {'id': 11658, 'synset': 'testudo.n.01', 'name': 'testudo'}, {'id': 11659, 'synset': 'tetraskelion.n.01', 'name': 'tetraskelion'}, {'id': 11660, 'synset': 'tetrode.n.01', 'name': 'tetrode'}, {'id': 11661, 'synset': 'textile_machine.n.01', 'name': 'textile_machine'}, {'id': 11662, 'synset': 'textile_mill.n.01', 'name': 'textile_mill'}, {'id': 11663, 'synset': 'thatch.n.04', 'name': 'thatch'}, {'id': 11664, 'synset': 'theater.n.01', 'name': 'theater'}, {'id': 11665, 'synset': 'theater_curtain.n.01', 'name': 'theater_curtain'}, {'id': 11666, 'synset': 'theater_light.n.01', 'name': 'theater_light'}, {'id': 11667, 'synset': 'theodolite.n.01', 'name': 'theodolite'}, {'id': 11668, 'synset': 'theremin.n.01', 'name': 'theremin'}, {'id': 11669, 'synset': 'thermal_printer.n.01', 'name': 'thermal_printer'}, {'id': 11670, 'synset': 'thermal_reactor.n.01', 'name': 'thermal_reactor'}, {'id': 11671, 'synset': 'thermocouple.n.01', 'name': 'thermocouple'}, {'id': 11672, 'synset': 'thermoelectric_thermometer.n.01', 'name': 'thermoelectric_thermometer'}, {'id': 11673, 'synset': 'thermograph.n.02', 'name': 'thermograph'}, {'id': 11674, 'synset': 'thermograph.n.01', 'name': 'thermograph'}, {'id': 11675, 'synset': 'thermohydrometer.n.01', 'name': 'thermohydrometer'}, {'id': 11676, 'synset': 'thermojunction.n.01', 'name': 'thermojunction'}, {'id': 11677, 'synset': 'thermonuclear_reactor.n.01', 'name': 'thermonuclear_reactor'}, {'id': 11678, 'synset': 'thermopile.n.01', 'name': 'thermopile'}, {'id': 11679, 'synset': 'thigh_pad.n.01', 'name': 'thigh_pad'}, {'id': 11680, 'synset': 'thill.n.01', 'name': 'thill'}, {'id': 11681, 'synset': 'thinning_shears.n.01', 'name': 'thinning_shears'}, {'id': 11682, 'synset': 'third_base.n.01', 'name': 'third_base'}, {'id': 11683, 'synset': 'third_gear.n.01', 'name': 'third_gear'}, {'id': 11684, 'synset': 'third_rail.n.01', 'name': 'third_rail'}, {'id': 11685, 'synset': 'thong.n.03', 'name': 'thong'}, {'id': 11686, 'synset': 'thong.n.02', 'name': 'thong'}, {'id': 11687, 'synset': 'three-centered_arch.n.01', 'name': 'three-centered_arch'}, {'id': 11688, 'synset': 'three-decker.n.02', 'name': 'three-decker'}, {'id': 11689, 'synset': 'three-dimensional_radar.n.01', 'name': 'three-dimensional_radar'}, {'id': 11690, 'synset': 'three-piece_suit.n.01', 'name': 'three-piece_suit'}, {'id': 11691, 'synset': 'three-quarter_binding.n.01', 'name': 'three-quarter_binding'}, {'id': 11692, 'synset': 'three-way_switch.n.01', 'name': 'three-way_switch'}, {'id': 11693, 'synset': 'thresher.n.01', 'name': 'thresher'}, {'id': 11694, 'synset': 'threshing_floor.n.01', 'name': 'threshing_floor'}, {'id': 11695, 'synset': 'thriftshop.n.01', 'name': 'thriftshop'}, {'id': 11696, 'synset': 'throat_protector.n.01', 'name': 'throat_protector'}, {'id': 11697, 'synset': 'throne.n.01', 'name': 'throne'}, {'id': 11698, 'synset': 'thrust_bearing.n.01', 'name': 'thrust_bearing'}, {'id': 11699, 'synset': 'thruster.n.02', 'name': 'thruster'}, {'id': 11700, 'synset': 'thumb.n.02', 'name': 'thumb'}, {'id': 11701, 'synset': 'thumbhole.n.02', 'name': 'thumbhole'}, {'id': 11702, 'synset': 'thumbscrew.n.02', 'name': 'thumbscrew'}, {'id': 11703, 'synset': 'thumbstall.n.01', 'name': 'thumbstall'}, {'id': 11704, 'synset': 'thunderer.n.02', 'name': 'thunderer'}, {'id': 11705, 'synset': 'thwart.n.01', 'name': 'thwart'}, {'id': 11706, 'synset': 'ticking.n.02', 'name': 'ticking'}, {'id': 11707, 'synset': 'tickler_coil.n.01', 'name': 'tickler_coil'}, {'id': 11708, 'synset': 'tie.n.04', 'name': 'tie'}, {'id': 11709, 'synset': 'tie.n.08', 'name': 'tie'}, {'id': 11710, 'synset': 'tie_rack.n.01', 'name': 'tie_rack'}, {'id': 11711, 'synset': 'tie_rod.n.01', 'name': 'tie_rod'}, {'id': 11712, 'synset': 'tile.n.01', 'name': 'tile'}, {'id': 11713, 'synset': 'tile_cutter.n.01', 'name': 'tile_cutter'}, {'id': 11714, 'synset': 'tile_roof.n.01', 'name': 'tile_roof'}, {'id': 11715, 'synset': 'tiller.n.03', 'name': 'tiller'}, {'id': 11716, 'synset': 'tilter.n.02', 'name': 'tilter'}, {'id': 11717, 'synset': 'tilt-top_table.n.01', 'name': 'tilt-top_table'}, {'id': 11718, 'synset': 'timber.n.02', 'name': 'timber'}, {'id': 11719, 'synset': 'timber.n.03', 'name': 'timber'}, {'id': 11720, 'synset': 'timber_hitch.n.01', 'name': 'timber_hitch'}, {'id': 11721, 'synset': 'timbrel.n.01', 'name': 'timbrel'}, {'id': 11722, 'synset': 'time_bomb.n.02', 'name': 'time_bomb'}, {'id': 11723, 'synset': 'time_capsule.n.01', 'name': 'time_capsule'}, {'id': 11724, 'synset': 'time_clock.n.01', 'name': 'time_clock'}, {'id': 11725, 'synset': 'time-delay_measuring_instrument.n.01', 'name': 'time-delay_measuring_instrument'}, {'id': 11726, 'synset': 'time-fuse.n.01', 'name': 'time-fuse'}, {'id': 11727, 'synset': 'timepiece.n.01', 'name': 'timepiece'}, {'id': 11728, 'synset': 'timer.n.03', 'name': 'timer'}, {'id': 11729, 'synset': 'time-switch.n.01', 'name': 'time-switch'}, {'id': 11730, 'synset': 'tin.n.02', 'name': 'tin'}, {'id': 11731, 'synset': 'tinderbox.n.02', 'name': 'tinderbox'}, {'id': 11732, 'synset': 'tine.n.01', 'name': 'tine'}, {'id': 11733, 'synset': 'tippet.n.01', 'name': 'tippet'}, {'id': 11734, 'synset': 'tire_chain.n.01', 'name': 'tire_chain'}, {'id': 11735, 'synset': 'tire_iron.n.01', 'name': 'tire_iron'}, {'id': 11736, 'synset': 'titfer.n.01', 'name': 'titfer'}, {'id': 11737, 'synset': 'tithe_barn.n.01', 'name': 'tithe_barn'}, {'id': 11738, 'synset': 'titrator.n.01', 'name': 'titrator'}, {'id': 11739, 'synset': 'toasting_fork.n.01', 'name': 'toasting_fork'}, {'id': 11740, 'synset': 'toastrack.n.01', 'name': 'toastrack'}, {'id': 11741, 'synset': 'tobacco_pouch.n.01', 'name': 'tobacco_pouch'}, {'id': 11742, 'synset': 'tobacco_shop.n.01', 'name': 'tobacco_shop'}, {'id': 11743, 'synset': 'toboggan.n.01', 'name': 'toboggan'}, {'id': 11744, 'synset': 'toby.n.01', 'name': 'toby'}, {'id': 11745, 'synset': 'tocsin.n.02', 'name': 'tocsin'}, {'id': 11746, 'synset': 'toe.n.02', 'name': 'toe'}, {'id': 11747, 'synset': 'toecap.n.01', 'name': 'toecap'}, {'id': 11748, 'synset': 'toehold.n.02', 'name': 'toehold'}, {'id': 11749, 'synset': 'toga.n.01', 'name': 'toga'}, {'id': 11750, 'synset': 'toga_virilis.n.01', 'name': 'toga_virilis'}, {'id': 11751, 'synset': 'toggle.n.03', 'name': 'toggle'}, {'id': 11752, 'synset': 'toggle_bolt.n.01', 'name': 'toggle_bolt'}, {'id': 11753, 'synset': 'toggle_joint.n.01', 'name': 'toggle_joint'}, {'id': 11754, 'synset': 'toggle_switch.n.01', 'name': 'toggle_switch'}, {'id': 11755, 'synset': 'togs.n.01', 'name': 'togs'}, {'id': 11756, 'synset': 'toilet.n.01', 'name': 'toilet'}, {'id': 11757, 'synset': 'toilet_bag.n.01', 'name': 'toilet_bag'}, {'id': 11758, 'synset': 'toilet_bowl.n.01', 'name': 'toilet_bowl'}, {'id': 11759, 'synset': 'toilet_kit.n.01', 'name': 'toilet_kit'}, {'id': 11760, 'synset': 'toilet_powder.n.01', 'name': 'toilet_powder'}, {'id': 11761, 'synset': 'toiletry.n.01', 'name': 'toiletry'}, {'id': 11762, 'synset': 'toilet_seat.n.01', 'name': 'toilet_seat'}, {'id': 11763, 'synset': 'toilet_water.n.01', 'name': 'toilet_water'}, {'id': 11764, 'synset': 'tokamak.n.01', 'name': 'tokamak'}, {'id': 11765, 'synset': 'token.n.03', 'name': 'token'}, {'id': 11766, 'synset': 'tollbooth.n.01', 'name': 'tollbooth'}, {'id': 11767, 'synset': 'toll_bridge.n.01', 'name': 'toll_bridge'}, {'id': 11768, 'synset': 'tollgate.n.01', 'name': 'tollgate'}, {'id': 11769, 'synset': 'toll_line.n.01', 'name': 'toll_line'}, {'id': 11770, 'synset': 'tomahawk.n.01', 'name': 'tomahawk'}, {'id': 11771, 'synset': 'tommy_gun.n.01', 'name': 'Tommy_gun'}, {'id': 11772, 'synset': 'tomograph.n.01', 'name': 'tomograph'}, {'id': 11773, 'synset': 'tone_arm.n.01', 'name': 'tone_arm'}, {'id': 11774, 'synset': 'toner.n.03', 'name': 'toner'}, {'id': 11775, 'synset': 'tongue.n.07', 'name': 'tongue'}, {'id': 11776, 'synset': 'tongue_and_groove_joint.n.01', 'name': 'tongue_and_groove_joint'}, {'id': 11777, 'synset': 'tongue_depressor.n.01', 'name': 'tongue_depressor'}, {'id': 11778, 'synset': 'tonometer.n.01', 'name': 'tonometer'}, {'id': 11779, 'synset': 'tool.n.01', 'name': 'tool'}, {'id': 11780, 'synset': 'tool_bag.n.01', 'name': 'tool_bag'}, {'id': 11781, 'synset': 'toolshed.n.01', 'name': 'toolshed'}, {'id': 11782, 'synset': 'tooth.n.02', 'name': 'tooth'}, {'id': 11783, 'synset': 'tooth.n.05', 'name': 'tooth'}, {'id': 11784, 'synset': 'top.n.10', 'name': 'top'}, {'id': 11785, 'synset': 'topgallant.n.02', 'name': 'topgallant'}, {'id': 11786, 'synset': 'topgallant.n.01', 'name': 'topgallant'}, {'id': 11787, 'synset': 'topiary.n.01', 'name': 'topiary'}, {'id': 11788, 'synset': 'topknot.n.01', 'name': 'topknot'}, {'id': 11789, 'synset': 'topmast.n.01', 'name': 'topmast'}, {'id': 11790, 'synset': 'topper.n.05', 'name': 'topper'}, {'id': 11791, 'synset': 'topsail.n.01', 'name': 'topsail'}, {'id': 11792, 'synset': 'toque.n.01', 'name': 'toque'}, {'id': 11793, 'synset': 'torch.n.01', 'name': 'torch'}, {'id': 11794, 'synset': 'torpedo.n.06', 'name': 'torpedo'}, {'id': 11795, 'synset': 'torpedo.n.05', 'name': 'torpedo'}, {'id': 11796, 'synset': 'torpedo.n.03', 'name': 'torpedo'}, {'id': 11797, 'synset': 'torpedo_boat.n.01', 'name': 'torpedo_boat'}, {'id': 11798, 'synset': 'torpedo-boat_destroyer.n.01', 'name': 'torpedo-boat_destroyer'}, {'id': 11799, 'synset': 'torpedo_tube.n.01', 'name': 'torpedo_tube'}, {'id': 11800, 'synset': 'torque_converter.n.01', 'name': 'torque_converter'}, {'id': 11801, 'synset': 'torque_wrench.n.01', 'name': 'torque_wrench'}, {'id': 11802, 'synset': 'torture_chamber.n.01', 'name': 'torture_chamber'}, {'id': 11803, 'synset': 'totem_pole.n.01', 'name': 'totem_pole'}, {'id': 11804, 'synset': 'touch_screen.n.01', 'name': 'touch_screen'}, {'id': 11805, 'synset': 'toupee.n.01', 'name': 'toupee'}, {'id': 11806, 'synset': 'touring_car.n.01', 'name': 'touring_car'}, {'id': 11807, 'synset': 'tourist_class.n.01', 'name': 'tourist_class'}, {'id': 11808, 'synset': 'toweling.n.01', 'name': 'toweling'}, {'id': 11809, 'synset': 'towel_rail.n.01', 'name': 'towel_rail'}, {'id': 11810, 'synset': 'tower.n.01', 'name': 'tower'}, {'id': 11811, 'synset': 'town_hall.n.01', 'name': 'town_hall'}, {'id': 11812, 'synset': 'towpath.n.01', 'name': 'towpath'}, {'id': 11813, 'synset': 'toy_box.n.01', 'name': 'toy_box'}, {'id': 11814, 'synset': 'toyshop.n.01', 'name': 'toyshop'}, {'id': 11815, 'synset': 'trace_detector.n.01', 'name': 'trace_detector'}, {'id': 11816, 'synset': 'track.n.09', 'name': 'track'}, {'id': 11817, 'synset': 'track.n.08', 'name': 'track'}, {'id': 11818, 'synset': 'trackball.n.01', 'name': 'trackball'}, {'id': 11819, 'synset': 'tracked_vehicle.n.01', 'name': 'tracked_vehicle'}, {'id': 11820, 'synset': 'tract_house.n.01', 'name': 'tract_house'}, {'id': 11821, 'synset': 'tract_housing.n.01', 'name': 'tract_housing'}, {'id': 11822, 'synset': 'traction_engine.n.01', 'name': 'traction_engine'}, {'id': 11823, 'synset': 'tractor.n.02', 'name': 'tractor'}, {'id': 11824, 'synset': 'trailer.n.04', 'name': 'trailer'}, {'id': 11825, 'synset': 'trailer.n.03', 'name': 'trailer'}, {'id': 11826, 'synset': 'trailer_camp.n.01', 'name': 'trailer_camp'}, {'id': 11827, 'synset': 'trailing_edge.n.01', 'name': 'trailing_edge'}, {'id': 11828, 'synset': 'tramline.n.01', 'name': 'tramline'}, {'id': 11829, 'synset': 'trammel.n.02', 'name': 'trammel'}, {'id': 11830, 'synset': 'tramp_steamer.n.01', 'name': 'tramp_steamer'}, {'id': 11831, 'synset': 'tramway.n.01', 'name': 'tramway'}, {'id': 11832, 'synset': 'transdermal_patch.n.01', 'name': 'transdermal_patch'}, {'id': 11833, 'synset': 'transept.n.01', 'name': 'transept'}, {'id': 11834, 'synset': 'transformer.n.01', 'name': 'transformer'}, {'id': 11835, 'synset': 'transistor.n.01', 'name': 'transistor'}, {'id': 11836, 'synset': 'transit_instrument.n.01', 'name': 'transit_instrument'}, {'id': 11837, 'synset': 'transmission.n.05', 'name': 'transmission'}, {'id': 11838, 'synset': 'transmission_shaft.n.01', 'name': 'transmission_shaft'}, {'id': 11839, 'synset': 'transmitter.n.03', 'name': 'transmitter'}, {'id': 11840, 'synset': 'transom.n.02', 'name': 'transom'}, {'id': 11841, 'synset': 'transom.n.01', 'name': 'transom'}, {'id': 11842, 'synset': 'transponder.n.01', 'name': 'transponder'}, {'id': 11843, 'synset': 'transporter.n.02', 'name': 'transporter'}, {'id': 11844, 'synset': 'transporter.n.01', 'name': 'transporter'}, {'id': 11845, 'synset': 'transport_ship.n.01', 'name': 'transport_ship'}, {'id': 11846, 'synset': 'trap.n.01', 'name': 'trap'}, {'id': 11847, 'synset': 'trap_door.n.01', 'name': 'trap_door'}, {'id': 11848, 'synset': 'trapeze.n.01', 'name': 'trapeze'}, {'id': 11849, 'synset': 'trave.n.01', 'name': 'trave'}, {'id': 11850, 'synset': 'travel_iron.n.01', 'name': 'travel_iron'}, {'id': 11851, 'synset': 'trawl.n.02', 'name': 'trawl'}, {'id': 11852, 'synset': 'trawl.n.01', 'name': 'trawl'}, {'id': 11853, 'synset': 'trawler.n.02', 'name': 'trawler'}, {'id': 11854, 'synset': 'tray_cloth.n.01', 'name': 'tray_cloth'}, {'id': 11855, 'synset': 'tread.n.04', 'name': 'tread'}, {'id': 11856, 'synset': 'tread.n.03', 'name': 'tread'}, {'id': 11857, 'synset': 'treadmill.n.02', 'name': 'treadmill'}, {'id': 11858, 'synset': 'treadmill.n.01', 'name': 'treadmill'}, {'id': 11859, 'synset': 'treasure_chest.n.01', 'name': 'treasure_chest'}, {'id': 11860, 'synset': 'treasure_ship.n.01', 'name': 'treasure_ship'}, {'id': 11861, 'synset': 'treenail.n.01', 'name': 'treenail'}, {'id': 11862, 'synset': 'trefoil_arch.n.01', 'name': 'trefoil_arch'}, {'id': 11863, 'synset': 'trellis.n.01', 'name': 'trellis'}, {'id': 11864, 'synset': 'trench.n.01', 'name': 'trench'}, {'id': 11865, 'synset': 'trench_knife.n.01', 'name': 'trench_knife'}, {'id': 11866, 'synset': 'trepan.n.02', 'name': 'trepan'}, {'id': 11867, 'synset': 'trepan.n.01', 'name': 'trepan'}, {'id': 11868, 'synset': 'trestle.n.02', 'name': 'trestle'}, {'id': 11869, 'synset': 'trestle.n.01', 'name': 'trestle'}, {'id': 11870, 'synset': 'trestle_bridge.n.01', 'name': 'trestle_bridge'}, {'id': 11871, 'synset': 'trestle_table.n.01', 'name': 'trestle_table'}, {'id': 11872, 'synset': 'trestlework.n.01', 'name': 'trestlework'}, {'id': 11873, 'synset': 'trews.n.01', 'name': 'trews'}, {'id': 11874, 'synset': 'trial_balloon.n.02', 'name': 'trial_balloon'}, {'id': 11875, 'synset': 'triangle.n.04', 'name': 'triangle'}, {'id': 11876, 'synset': 'triclinium.n.02', 'name': 'triclinium'}, {'id': 11877, 'synset': 'triclinium.n.01', 'name': 'triclinium'}, {'id': 11878, 'synset': 'tricorn.n.01', 'name': 'tricorn'}, {'id': 11879, 'synset': 'tricot.n.01', 'name': 'tricot'}, {'id': 11880, 'synset': 'trident.n.01', 'name': 'trident'}, {'id': 11881, 'synset': 'trigger.n.02', 'name': 'trigger'}, {'id': 11882, 'synset': 'trimaran.n.01', 'name': 'trimaran'}, {'id': 11883, 'synset': 'trimmer.n.02', 'name': 'trimmer'}, {'id': 11884, 'synset': 'trimmer_arch.n.01', 'name': 'trimmer_arch'}, {'id': 11885, 'synset': 'triode.n.01', 'name': 'triode'}, {'id': 11886, 'synset': 'triptych.n.01', 'name': 'triptych'}, {'id': 11887, 'synset': 'trip_wire.n.02', 'name': 'trip_wire'}, {'id': 11888, 'synset': 'trireme.n.01', 'name': 'trireme'}, {'id': 11889, 'synset': 'triskelion.n.01', 'name': 'triskelion'}, {'id': 11890, 'synset': 'triumphal_arch.n.01', 'name': 'triumphal_arch'}, {'id': 11891, 'synset': 'trivet.n.02', 'name': 'trivet'}, {'id': 11892, 'synset': 'trivet.n.01', 'name': 'trivet'}, {'id': 11893, 'synset': 'troika.n.01', 'name': 'troika'}, {'id': 11894, 'synset': 'troll.n.03', 'name': 'troll'}, {'id': 11895, 'synset': 'trolleybus.n.01', 'name': 'trolleybus'}, {'id': 11896, 'synset': 'trombone.n.01', 'name': 'trombone'}, {'id': 11897, 'synset': 'troop_carrier.n.01', 'name': 'troop_carrier'}, {'id': 11898, 'synset': 'troopship.n.01', 'name': 'troopship'}, {'id': 11899, 'synset': 'trophy_case.n.01', 'name': 'trophy_case'}, {'id': 11900, 'synset': 'trough.n.05', 'name': 'trough'}, {'id': 11901, 'synset': 'trouser.n.02', 'name': 'trouser'}, {'id': 11902, 'synset': 'trouser_cuff.n.01', 'name': 'trouser_cuff'}, {'id': 11903, 'synset': 'trouser_press.n.01', 'name': 'trouser_press'}, {'id': 11904, 'synset': 'trousseau.n.01', 'name': 'trousseau'}, {'id': 11905, 'synset': 'trowel.n.01', 'name': 'trowel'}, {'id': 11906, 'synset': 'trumpet_arch.n.01', 'name': 'trumpet_arch'}, {'id': 11907, 'synset': 'truncheon.n.01', 'name': 'truncheon'}, {'id': 11908, 'synset': 'trundle_bed.n.01', 'name': 'trundle_bed'}, {'id': 11909, 'synset': 'trunk_hose.n.01', 'name': 'trunk_hose'}, {'id': 11910, 'synset': 'trunk_lid.n.01', 'name': 'trunk_lid'}, {'id': 11911, 'synset': 'trunk_line.n.02', 'name': 'trunk_line'}, {'id': 11912, 'synset': 'truss.n.02', 'name': 'truss'}, {'id': 11913, 'synset': 'truss_bridge.n.01', 'name': 'truss_bridge'}, {'id': 11914, 'synset': 'try_square.n.01', 'name': 'try_square'}, {'id': 11915, 'synset': 't-square.n.01', 'name': 'T-square'}, {'id': 11916, 'synset': 'tube.n.02', 'name': 'tube'}, {'id': 11917, 'synset': 'tuck_box.n.01', 'name': 'tuck_box'}, {'id': 11918, 'synset': 'tucker.n.04', 'name': 'tucker'}, {'id': 11919, 'synset': 'tucker-bag.n.01', 'name': 'tucker-bag'}, {'id': 11920, 'synset': 'tuck_shop.n.01', 'name': 'tuck_shop'}, {'id': 11921, 'synset': 'tudor_arch.n.01', 'name': 'Tudor_arch'}, {'id': 11922, 'synset': 'tudung.n.01', 'name': 'tudung'}, {'id': 11923, 'synset': 'tugboat.n.01', 'name': 'tugboat'}, {'id': 11924, 'synset': 'tulle.n.01', 'name': 'tulle'}, {'id': 11925, 'synset': 'tumble-dryer.n.01', 'name': 'tumble-dryer'}, {'id': 11926, 'synset': 'tumbler.n.02', 'name': 'tumbler'}, {'id': 11927, 'synset': 'tumbrel.n.01', 'name': 'tumbrel'}, {'id': 11928, 'synset': 'tun.n.01', 'name': 'tun'}, {'id': 11929, 'synset': 'tunic.n.02', 'name': 'tunic'}, {'id': 11930, 'synset': 'tuning_fork.n.01', 'name': 'tuning_fork'}, {'id': 11931, 'synset': 'tupik.n.01', 'name': 'tupik'}, {'id': 11932, 'synset': 'turbine.n.01', 'name': 'turbine'}, {'id': 11933, 'synset': 'turbogenerator.n.01', 'name': 'turbogenerator'}, {'id': 11934, 'synset': 'tureen.n.01', 'name': 'tureen'}, {'id': 11935, 'synset': 'turkish_bath.n.01', 'name': 'Turkish_bath'}, {'id': 11936, 'synset': 'turkish_towel.n.01', 'name': 'Turkish_towel'}, {'id': 11937, 'synset': "turk's_head.n.01", 'name': "Turk's_head"}, {'id': 11938, 'synset': 'turnbuckle.n.01', 'name': 'turnbuckle'}, {'id': 11939, 'synset': 'turner.n.08', 'name': 'turner'}, {'id': 11940, 'synset': 'turnery.n.01', 'name': 'turnery'}, {'id': 11941, 'synset': 'turnpike.n.01', 'name': 'turnpike'}, {'id': 11942, 'synset': 'turnspit.n.01', 'name': 'turnspit'}, {'id': 11943, 'synset': 'turnstile.n.01', 'name': 'turnstile'}, {'id': 11944, 'synset': 'turntable.n.01', 'name': 'turntable'}, {'id': 11945, 'synset': 'turntable.n.02', 'name': 'turntable'}, {'id': 11946, 'synset': 'turret.n.01', 'name': 'turret'}, {'id': 11947, 'synset': 'turret_clock.n.01', 'name': 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'typewriter_carriage'}, {'id': 11963, 'synset': 'typewriter_keyboard.n.01', 'name': 'typewriter_keyboard'}, {'id': 11964, 'synset': 'tyrolean.n.02', 'name': 'tyrolean'}, {'id': 11965, 'synset': 'uke.n.01', 'name': 'uke'}, {'id': 11966, 'synset': 'ulster.n.02', 'name': 'ulster'}, {'id': 11967, 'synset': 'ultracentrifuge.n.01', 'name': 'ultracentrifuge'}, {'id': 11968, 'synset': 'ultramicroscope.n.01', 'name': 'ultramicroscope'}, {'id': 11969, 'synset': 'ultrasuede.n.01', 'name': 'Ultrasuede'}, {'id': 11970, 'synset': 'ultraviolet_lamp.n.01', 'name': 'ultraviolet_lamp'}, {'id': 11971, 'synset': 'umbrella_tent.n.01', 'name': 'umbrella_tent'}, {'id': 11972, 'synset': 'undercarriage.n.01', 'name': 'undercarriage'}, {'id': 11973, 'synset': 'undercoat.n.01', 'name': 'undercoat'}, {'id': 11974, 'synset': 'undergarment.n.01', 'name': 'undergarment'}, {'id': 11975, 'synset': 'underpants.n.01', 'name': 'underpants'}, {'id': 11976, 'synset': 'undies.n.01', 'name': 'undies'}, {'id': 11977, 'synset': 'uneven_parallel_bars.n.01', 'name': 'uneven_parallel_bars'}, {'id': 11978, 'synset': 'uniform.n.01', 'name': 'uniform'}, {'id': 11979, 'synset': 'universal_joint.n.01', 'name': 'universal_joint'}, {'id': 11980, 'synset': 'university.n.02', 'name': 'university'}, {'id': 11981, 'synset': 'upholstery.n.01', 'name': 'upholstery'}, {'id': 11982, 'synset': 'upholstery_material.n.01', 'name': 'upholstery_material'}, {'id': 11983, 'synset': 'upholstery_needle.n.01', 'name': 'upholstery_needle'}, {'id': 11984, 'synset': 'uplift.n.02', 'name': 'uplift'}, {'id': 11985, 'synset': 'upper_berth.n.01', 'name': 'upper_berth'}, {'id': 11986, 'synset': 'upright.n.02', 'name': 'upright'}, {'id': 11987, 'synset': 'upset.n.04', 'name': 'upset'}, {'id': 11988, 'synset': 'upstairs.n.01', 'name': 'upstairs'}, {'id': 11989, 'synset': 'urceole.n.01', 'name': 'urceole'}, {'id': 11990, 'synset': 'urn.n.02', 'name': 'urn'}, {'id': 11991, 'synset': 'used-car.n.01', 'name': 'used-car'}, {'id': 11992, 'synset': 'utensil.n.01', 'name': 'utensil'}, {'id': 11993, 'synset': 'uzi.n.01', 'name': 'Uzi'}, {'id': 11994, 'synset': 'vacation_home.n.01', 'name': 'vacation_home'}, {'id': 11995, 'synset': 'vacuum_chamber.n.01', 'name': 'vacuum_chamber'}, {'id': 11996, 'synset': 'vacuum_flask.n.01', 'name': 'vacuum_flask'}, {'id': 11997, 'synset': 'vacuum_gauge.n.01', 'name': 'vacuum_gauge'}, {'id': 11998, 'synset': 'valenciennes.n.02', 'name': 'Valenciennes'}, {'id': 11999, 'synset': 'valise.n.01', 'name': 'valise'}, {'id': 12000, 'synset': 'valve.n.03', 'name': 'valve'}, {'id': 12001, 'synset': 'valve.n.02', 'name': 'valve'}, {'id': 12002, 'synset': 'valve-in-head_engine.n.01', 'name': 'valve-in-head_engine'}, {'id': 12003, 'synset': 'vambrace.n.01', 'name': 'vambrace'}, {'id': 12004, 'synset': 'van.n.05', 'name': 'van'}, {'id': 12005, 'synset': 'van.n.04', 'name': 'van'}, {'id': 12006, 'synset': 'vane.n.02', 'name': 'vane'}, {'id': 12007, 'synset': 'vaporizer.n.01', 'name': 'vaporizer'}, {'id': 12008, 'synset': 'variable-pitch_propeller.n.01', 'name': 'variable-pitch_propeller'}, {'id': 12009, 'synset': 'variometer.n.01', 'name': 'variometer'}, {'id': 12010, 'synset': 'varnish.n.01', 'name': 'varnish'}, {'id': 12011, 'synset': 'vault.n.03', 'name': 'vault'}, {'id': 12012, 'synset': 'vault.n.02', 'name': 'vault'}, {'id': 12013, 'synset': 'vaulting_horse.n.01', 'name': 'vaulting_horse'}, {'id': 12014, 'synset': 'vehicle.n.01', 'name': 'vehicle'}, {'id': 12015, 'synset': 'velcro.n.01', 'name': 'Velcro'}, {'id': 12016, 'synset': 'velocipede.n.01', 'name': 'velocipede'}, {'id': 12017, 'synset': 'velour.n.01', 'name': 'velour'}, {'id': 12018, 'synset': 'velvet.n.01', 'name': 'velvet'}, {'id': 12019, 'synset': 'velveteen.n.01', 'name': 'velveteen'}, {'id': 12020, 'synset': 'veneer.n.01', 'name': 'veneer'}, {'id': 12021, 'synset': 'venetian_blind.n.01', 'name': 'Venetian_blind'}, {'id': 12022, 'synset': 'venn_diagram.n.01', 'name': 'Venn_diagram'}, {'id': 12023, 'synset': 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{'id': 12038, 'synset': 'vest_pocket.n.01', 'name': 'vest_pocket'}, {'id': 12039, 'synset': 'vestry.n.02', 'name': 'vestry'}, {'id': 12040, 'synset': 'viaduct.n.01', 'name': 'viaduct'}, {'id': 12041, 'synset': 'vibraphone.n.01', 'name': 'vibraphone'}, {'id': 12042, 'synset': 'vibrator.n.02', 'name': 'vibrator'}, {'id': 12043, 'synset': 'vibrator.n.01', 'name': 'vibrator'}, {'id': 12044, 'synset': 'victrola.n.01', 'name': 'Victrola'}, {'id': 12045, 'synset': 'vicuna.n.02', 'name': 'vicuna'}, {'id': 12046, 'synset': 'videocassette.n.01', 'name': 'videocassette'}, {'id': 12047, 'synset': 'videocassette_recorder.n.01', 'name': 'videocassette_recorder'}, {'id': 12048, 'synset': 'videodisk.n.01', 'name': 'videodisk'}, {'id': 12049, 'synset': 'video_recording.n.01', 'name': 'video_recording'}, {'id': 12050, 'synset': 'videotape.n.02', 'name': 'videotape'}, {'id': 12051, 'synset': 'vigil_light.n.01', 'name': 'vigil_light'}, {'id': 12052, 'synset': 'villa.n.04', 'name': 'villa'}, {'id': 12053, 'synset': 'villa.n.03', 'name': 'villa'}, {'id': 12054, 'synset': 'villa.n.02', 'name': 'villa'}, {'id': 12055, 'synset': 'viol.n.01', 'name': 'viol'}, {'id': 12056, 'synset': 'viola.n.03', 'name': 'viola'}, {'id': 12057, 'synset': 'viola_da_braccio.n.01', 'name': 'viola_da_braccio'}, {'id': 12058, 'synset': 'viola_da_gamba.n.01', 'name': 'viola_da_gamba'}, {'id': 12059, 'synset': "viola_d'amore.n.01", 'name': "viola_d'amore"}, {'id': 12060, 'synset': 'virginal.n.01', 'name': 'virginal'}, {'id': 12061, 'synset': 'viscometer.n.01', 'name': 'viscometer'}, {'id': 12062, 'synset': 'viscose_rayon.n.01', 'name': 'viscose_rayon'}, {'id': 12063, 'synset': 'vise.n.01', 'name': 'vise'}, {'id': 12064, 'synset': 'visor.n.01', 'name': 'visor'}, {'id': 12065, 'synset': 'visual_display_unit.n.01', 'name': 'visual_display_unit'}, {'id': 12066, 'synset': 'vivarium.n.01', 'name': 'vivarium'}, {'id': 12067, 'synset': 'viyella.n.01', 'name': 'Viyella'}, {'id': 12068, 'synset': 'voile.n.01', 'name': 'voile'}, {'id': 12069, 'synset': 'volleyball_net.n.01', 'name': 'volleyball_net'}, {'id': 12070, 'synset': 'voltage_regulator.n.01', 'name': 'voltage_regulator'}, {'id': 12071, 'synset': 'voltaic_cell.n.01', 'name': 'voltaic_cell'}, {'id': 12072, 'synset': 'voltaic_pile.n.01', 'name': 'voltaic_pile'}, {'id': 12073, 'synset': 'voltmeter.n.01', 'name': 'voltmeter'}, {'id': 12074, 'synset': 'vomitory.n.01', 'name': 'vomitory'}, {'id': 12075, 'synset': 'von_neumann_machine.n.01', 'name': 'von_Neumann_machine'}, {'id': 12076, 'synset': 'voting_booth.n.01', 'name': 'voting_booth'}, {'id': 12077, 'synset': 'voting_machine.n.01', 'name': 'voting_machine'}, {'id': 12078, 'synset': 'voussoir.n.01', 'name': 'voussoir'}, {'id': 12079, 'synset': 'vox_angelica.n.01', 'name': 'vox_angelica'}, {'id': 12080, 'synset': 'vox_humana.n.01', 'name': 'vox_humana'}, {'id': 12081, 'synset': 'waders.n.01', 'name': 'waders'}, {'id': 12082, 'synset': 'wading_pool.n.01', 'name': 'wading_pool'}, {'id': 12083, 'synset': 'wagon.n.04', 'name': 'wagon'}, {'id': 12084, 'synset': 'wagon_tire.n.01', 'name': 'wagon_tire'}, {'id': 12085, 'synset': 'wain.n.03', 'name': 'wain'}, {'id': 12086, 'synset': 'wainscot.n.02', 'name': 'wainscot'}, {'id': 12087, 'synset': 'wainscoting.n.01', 'name': 'wainscoting'}, {'id': 12088, 'synset': 'waist_pack.n.01', 'name': 'waist_pack'}, {'id': 12089, 'synset': 'walker.n.06', 'name': 'walker'}, {'id': 12090, 'synset': 'walker.n.05', 'name': 'walker'}, {'id': 12091, 'synset': 'walker.n.04', 'name': 'walker'}, {'id': 12092, 'synset': 'walkie-talkie.n.01', 'name': 'walkie-talkie'}, {'id': 12093, 'synset': 'walk-in.n.04', 'name': 'walk-in'}, {'id': 12094, 'synset': 'walking_shoe.n.01', 'name': 'walking_shoe'}, {'id': 12095, 'synset': 'walkman.n.01', 'name': 'Walkman'}, {'id': 12096, 'synset': 'walk-up_apartment.n.01', 'name': 'walk-up_apartment'}, {'id': 12097, 'synset': 'wall.n.01', 'name': 'wall'}, {'id': 12098, 'synset': 'wall.n.07', 'name': 'wall'}, {'id': 12099, 'synset': 'wall_tent.n.01', 'name': 'wall_tent'}, {'id': 12100, 'synset': 'wall_unit.n.01', 'name': 'wall_unit'}, {'id': 12101, 'synset': 'wand.n.01', 'name': 'wand'}, {'id': 12102, 'synset': 'wankel_engine.n.01', 'name': 'Wankel_engine'}, {'id': 12103, 'synset': 'ward.n.03', 'name': 'ward'}, {'id': 12104, 'synset': 'wardroom.n.01', 'name': 'wardroom'}, {'id': 12105, 'synset': 'warehouse.n.01', 'name': 'warehouse'}, {'id': 12106, 'synset': 'warming_pan.n.01', 'name': 'warming_pan'}, {'id': 12107, 'synset': 'war_paint.n.02', 'name': 'war_paint'}, {'id': 12108, 'synset': 'warplane.n.01', 'name': 'warplane'}, {'id': 12109, 'synset': 'war_room.n.01', 'name': 'war_room'}, {'id': 12110, 'synset': 'warship.n.01', 'name': 'warship'}, {'id': 12111, 'synset': 'wash.n.01', 'name': 'wash'}, {'id': 12112, 'synset': 'wash-and-wear.n.01', 'name': 'wash-and-wear'}, {'id': 12113, 'synset': 'washbasin.n.02', 'name': 'washbasin'}, {'id': 12114, 'synset': 'washboard.n.02', 'name': 'washboard'}, {'id': 12115, 'synset': 'washboard.n.01', 'name': 'washboard'}, {'id': 12116, 'synset': 'washer.n.02', 'name': 'washer'}, {'id': 12117, 'synset': 'washhouse.n.01', 'name': 'washhouse'}, {'id': 12118, 'synset': 'washroom.n.01', 'name': 'washroom'}, {'id': 12119, 'synset': 'washstand.n.01', 'name': 'washstand'}, {'id': 12120, 'synset': 'washtub.n.01', 'name': 'washtub'}, {'id': 12121, 'synset': 'wastepaper_basket.n.01', 'name': 'wastepaper_basket'}, {'id': 12122, 'synset': 'watch_cap.n.01', 'name': 'watch_cap'}, {'id': 12123, 'synset': 'watch_case.n.01', 'name': 'watch_case'}, {'id': 12124, 'synset': 'watch_glass.n.01', 'name': 'watch_glass'}, {'id': 12125, 'synset': 'watchtower.n.01', 'name': 'watchtower'}, {'id': 12126, 'synset': 'water-base_paint.n.01', 'name': 'water-base_paint'}, {'id': 12127, 'synset': 'water_bed.n.01', 'name': 'water_bed'}, {'id': 12128, 'synset': 'water_butt.n.01', 'name': 'water_butt'}, {'id': 12129, 'synset': 'water_cart.n.01', 'name': 'water_cart'}, {'id': 12130, 'synset': 'water_chute.n.01', 'name': 'water_chute'}, {'id': 12131, 'synset': 'water_closet.n.01', 'name': 'water_closet'}, {'id': 12132, 'synset': 'watercolor.n.02', 'name': 'watercolor'}, {'id': 12133, 'synset': 'water-cooled_reactor.n.01', 'name': 'water-cooled_reactor'}, {'id': 12134, 'synset': 'water_filter.n.01', 'name': 'water_filter'}, {'id': 12135, 'synset': 'water_gauge.n.01', 'name': 'water_gauge'}, {'id': 12136, 'synset': 'water_glass.n.02', 'name': 'water_glass'}, {'id': 12137, 'synset': 'water_hazard.n.01', 'name': 'water_hazard'}, {'id': 12138, 'synset': 'watering_cart.n.01', 'name': 'watering_cart'}, {'id': 12139, 'synset': 'water_jacket.n.01', 'name': 'water_jacket'}, {'id': 12140, 'synset': 'water_jump.n.01', 'name': 'water_jump'}, {'id': 12141, 'synset': 'water_level.n.04', 'name': 'water_level'}, {'id': 12142, 'synset': 'water_meter.n.01', 'name': 'water_meter'}, {'id': 12143, 'synset': 'water_mill.n.01', 'name': 'water_mill'}, {'id': 12144, 'synset': 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'name': 'weathercock'}, {'id': 12160, 'synset': 'weatherglass.n.01', 'name': 'weatherglass'}, {'id': 12161, 'synset': 'weather_satellite.n.01', 'name': 'weather_satellite'}, {'id': 12162, 'synset': 'weather_ship.n.01', 'name': 'weather_ship'}, {'id': 12163, 'synset': 'web.n.02', 'name': 'web'}, {'id': 12164, 'synset': 'web.n.06', 'name': 'web'}, {'id': 12165, 'synset': 'webbing.n.03', 'name': 'webbing'}, {'id': 12166, 'synset': 'wedge.n.06', 'name': 'wedge'}, {'id': 12167, 'synset': 'wedge.n.05', 'name': 'wedge'}, {'id': 12168, 'synset': 'wedgie.n.01', 'name': 'wedgie'}, {'id': 12169, 'synset': 'wedgwood.n.02', 'name': 'Wedgwood'}, {'id': 12170, 'synset': 'weeder.n.02', 'name': 'weeder'}, {'id': 12171, 'synset': 'weeds.n.01', 'name': 'weeds'}, {'id': 12172, 'synset': 'weekender.n.02', 'name': 'weekender'}, {'id': 12173, 'synset': 'weighbridge.n.01', 'name': 'weighbridge'}, {'id': 12174, 'synset': 'weight.n.02', 'name': 'weight'}, {'id': 12175, 'synset': 'weir.n.01', 'name': 'weir'}, {'id': 12176, 'synset': 'weir.n.02', 'name': 'weir'}, {'id': 12177, 'synset': 'welcome_wagon.n.01', 'name': 'welcome_wagon'}, {'id': 12178, 'synset': 'weld.n.03', 'name': 'weld'}, {'id': 12179, 'synset': "welder's_mask.n.01", 'name': "welder's_mask"}, {'id': 12180, 'synset': 'weldment.n.01', 'name': 'weldment'}, {'id': 12181, 'synset': 'well.n.02', 'name': 'well'}, {'id': 12182, 'synset': 'wellhead.n.02', 'name': 'wellhead'}, {'id': 12183, 'synset': 'welt.n.02', 'name': 'welt'}, {'id': 12184, 'synset': 'weston_cell.n.01', 'name': 'Weston_cell'}, {'id': 12185, 'synset': 'wet_bar.n.01', 'name': 'wet_bar'}, {'id': 12186, 'synset': 'wet-bulb_thermometer.n.01', 'name': 'wet-bulb_thermometer'}, {'id': 12187, 'synset': 'wet_cell.n.01', 'name': 'wet_cell'}, {'id': 12188, 'synset': 'wet_fly.n.01', 'name': 'wet_fly'}, {'id': 12189, 'synset': 'whaleboat.n.01', 'name': 'whaleboat'}, {'id': 12190, 'synset': 'whaler.n.02', 'name': 'whaler'}, {'id': 12191, 'synset': 'whaling_gun.n.01', 'name': 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12253, 'synset': 'wind_turbine.n.01', 'name': 'wind_turbine'}, {'id': 12254, 'synset': 'wine_bar.n.01', 'name': 'wine_bar'}, {'id': 12255, 'synset': 'wine_cask.n.01', 'name': 'wine_cask'}, {'id': 12256, 'synset': 'winepress.n.01', 'name': 'winepress'}, {'id': 12257, 'synset': 'winery.n.01', 'name': 'winery'}, {'id': 12258, 'synset': 'wineskin.n.01', 'name': 'wineskin'}, {'id': 12259, 'synset': 'wing.n.02', 'name': 'wing'}, {'id': 12260, 'synset': 'wing_chair.n.01', 'name': 'wing_chair'}, {'id': 12261, 'synset': 'wing_nut.n.02', 'name': 'wing_nut'}, {'id': 12262, 'synset': 'wing_tip.n.02', 'name': 'wing_tip'}, {'id': 12263, 'synset': 'wing_tip.n.01', 'name': 'wing_tip'}, {'id': 12264, 'synset': 'wiper.n.02', 'name': 'wiper'}, {'id': 12265, 'synset': 'wiper_motor.n.01', 'name': 'wiper_motor'}, {'id': 12266, 'synset': 'wire.n.01', 'name': 'wire'}, {'id': 12267, 'synset': 'wire.n.02', 'name': 'wire'}, {'id': 12268, 'synset': 'wire_cloth.n.01', 'name': 'wire_cloth'}, {'id': 12269, 'synset': 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{'id': 12315, 'synset': 'wrist_pad.n.01', 'name': 'wrist_pad'}, {'id': 12316, 'synset': 'wrist_pin.n.01', 'name': 'wrist_pin'}, {'id': 12317, 'synset': 'wristwatch.n.01', 'name': 'wristwatch'}, {'id': 12318, 'synset': 'writing_arm.n.01', 'name': 'writing_arm'}, {'id': 12319, 'synset': 'writing_desk.n.02', 'name': 'writing_desk'}, {'id': 12320, 'synset': 'writing_desk.n.01', 'name': 'writing_desk'}, {'id': 12321, 'synset': 'writing_implement.n.01', 'name': 'writing_implement'}, {'id': 12322, 'synset': 'xerographic_printer.n.01', 'name': 'xerographic_printer'}, {'id': 12323, 'synset': 'xerox.n.02', 'name': 'Xerox'}, {'id': 12324, 'synset': 'x-ray_film.n.01', 'name': 'X-ray_film'}, {'id': 12325, 'synset': 'x-ray_machine.n.01', 'name': 'X-ray_machine'}, {'id': 12326, 'synset': 'x-ray_tube.n.01', 'name': 'X-ray_tube'}, {'id': 12327, 'synset': 'yacht_chair.n.01', 'name': 'yacht_chair'}, {'id': 12328, 'synset': 'yagi.n.01', 'name': 'yagi'}, {'id': 12329, 'synset': 'yard.n.09', 'name': 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{'id': 12347, 'synset': 'zither.n.01', 'name': 'zither'}, {'id': 12348, 'synset': 'zoot_suit.n.01', 'name': 'zoot_suit'}, {'id': 12349, 'synset': 'shading.n.01', 'name': 'shading'}, {'id': 12350, 'synset': 'grain.n.10', 'name': 'grain'}, {'id': 12351, 'synset': 'wood_grain.n.01', 'name': 'wood_grain'}, {'id': 12352, 'synset': 'graining.n.01', 'name': 'graining'}, {'id': 12353, 'synset': 'marbleization.n.01', 'name': 'marbleization'}, {'id': 12354, 'synset': 'light.n.07', 'name': 'light'}, {'id': 12355, 'synset': 'aura.n.02', 'name': 'aura'}, {'id': 12356, 'synset': 'sunniness.n.01', 'name': 'sunniness'}, {'id': 12357, 'synset': 'glint.n.02', 'name': 'glint'}, {'id': 12358, 'synset': 'opalescence.n.01', 'name': 'opalescence'}, {'id': 12359, 'synset': 'polish.n.01', 'name': 'polish'}, {'id': 12360, 'synset': 'primary_color_for_pigments.n.01', 'name': 'primary_color_for_pigments'}, {'id': 12361, 'synset': 'primary_color_for_light.n.01', 'name': 'primary_color_for_light'}, {'id': 12362, 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{'id': 12394, 'synset': 'blue.n.01', 'name': 'blue'}, {'id': 12395, 'synset': 'azure.n.01', 'name': 'azure'}, {'id': 12396, 'synset': 'steel_blue.n.01', 'name': 'steel_blue'}, {'id': 12397, 'synset': 'greenish_blue.n.01', 'name': 'greenish_blue'}, {'id': 12398, 'synset': 'purplish_blue.n.01', 'name': 'purplish_blue'}, {'id': 12399, 'synset': 'purple.n.01', 'name': 'purple'}, {'id': 12400, 'synset': 'tyrian_purple.n.02', 'name': 'Tyrian_purple'}, {'id': 12401, 'synset': 'indigo.n.03', 'name': 'indigo'}, {'id': 12402, 'synset': 'lavender.n.02', 'name': 'lavender'}, {'id': 12403, 'synset': 'reddish_purple.n.01', 'name': 'reddish_purple'}, {'id': 12404, 'synset': 'pink.n.01', 'name': 'pink'}, {'id': 12405, 'synset': 'carnation.n.02', 'name': 'carnation'}, {'id': 12406, 'synset': 'rose.n.03', 'name': 'rose'}, {'id': 12407, 'synset': 'chestnut.n.04', 'name': 'chestnut'}, {'id': 12408, 'synset': 'chocolate.n.03', 'name': 'chocolate'}, {'id': 12409, 'synset': 'light_brown.n.01', 'name': 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12543, 'synset': 'call.n.01', 'name': 'call'}, {'id': 12544, 'synset': 'call-back.n.01', 'name': 'call-back'}, {'id': 12545, 'synset': 'collect_call.n.01', 'name': 'collect_call'}, {'id': 12546, 'synset': 'call_forwarding.n.01', 'name': 'call_forwarding'}, {'id': 12547, 'synset': 'call-in.n.01', 'name': 'call-in'}, {'id': 12548, 'synset': 'call_waiting.n.01', 'name': 'call_waiting'}, {'id': 12549, 'synset': 'crank_call.n.01', 'name': 'crank_call'}, {'id': 12550, 'synset': 'local_call.n.01', 'name': 'local_call'}, {'id': 12551, 'synset': 'long_distance.n.01', 'name': 'long_distance'}, {'id': 12552, 'synset': 'toll_call.n.01', 'name': 'toll_call'}, {'id': 12553, 'synset': 'wake-up_call.n.02', 'name': 'wake-up_call'}, {'id': 12554, 'synset': 'three-way_calling.n.01', 'name': 'three-way_calling'}, {'id': 12555, 'synset': 'telegraphy.n.01', 'name': 'telegraphy'}, {'id': 12556, 'synset': 'cable.n.01', 'name': 'cable'}, {'id': 12557, 'synset': 'wireless.n.02', 'name': 'wireless'}, {'id': 12558, 'synset': 'radiotelegraph.n.01', 'name': 'radiotelegraph'}, {'id': 12559, 'synset': 'radiotelephone.n.01', 'name': 'radiotelephone'}, {'id': 12560, 'synset': 'broadcasting.n.02', 'name': 'broadcasting'}, {'id': 12561, 'synset': 'rediffusion.n.01', 'name': 'Rediffusion'}, {'id': 12562, 'synset': 'multiplex.n.01', 'name': 'multiplex'}, {'id': 12563, 'synset': 'radio.n.01', 'name': 'radio'}, {'id': 12564, 'synset': 'television.n.01', 'name': 'television'}, {'id': 12565, 'synset': 'cable_television.n.01', 'name': 'cable_television'}, {'id': 12566, 'synset': 'high-definition_television.n.01', 'name': 'high-definition_television'}, {'id': 12567, 'synset': 'reception.n.03', 'name': 'reception'}, {'id': 12568, 'synset': 'signal_detection.n.01', 'name': 'signal_detection'}, {'id': 12569, 'synset': 'hakham.n.01', 'name': 'Hakham'}, {'id': 12570, 'synset': 'web_site.n.01', 'name': 'web_site'}, {'id': 12571, 'synset': 'chat_room.n.01', 'name': 'chat_room'}, {'id': 12572, 'synset': 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12603, 'synset': 'comfort_food.n.01', 'name': 'comfort_food'}, {'id': 12604, 'synset': 'comestible.n.01', 'name': 'comestible'}, {'id': 12605, 'synset': 'tuck.n.01', 'name': 'tuck'}, {'id': 12606, 'synset': 'course.n.07', 'name': 'course'}, {'id': 12607, 'synset': 'dainty.n.01', 'name': 'dainty'}, {'id': 12608, 'synset': 'dish.n.02', 'name': 'dish'}, {'id': 12609, 'synset': 'fast_food.n.01', 'name': 'fast_food'}, {'id': 12610, 'synset': 'finger_food.n.01', 'name': 'finger_food'}, {'id': 12611, 'synset': 'ingesta.n.01', 'name': 'ingesta'}, {'id': 12612, 'synset': 'kosher.n.01', 'name': 'kosher'}, {'id': 12613, 'synset': 'fare.n.04', 'name': 'fare'}, {'id': 12614, 'synset': 'diet.n.03', 'name': 'diet'}, {'id': 12615, 'synset': 'diet.n.01', 'name': 'diet'}, {'id': 12616, 'synset': 'dietary.n.01', 'name': 'dietary'}, {'id': 12617, 'synset': 'balanced_diet.n.01', 'name': 'balanced_diet'}, {'id': 12618, 'synset': 'bland_diet.n.01', 'name': 'bland_diet'}, {'id': 12619, 'synset': 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12695, 'synset': 'nosh-up.n.01', 'name': 'nosh-up'}, {'id': 12696, 'synset': "ploughman's_lunch.n.01", 'name': "ploughman's_lunch"}, {'id': 12697, 'synset': 'coffee_break.n.01', 'name': 'coffee_break'}, {'id': 12698, 'synset': 'banquet.n.02', 'name': 'banquet'}, {'id': 12699, 'synset': 'entree.n.01', 'name': 'entree'}, {'id': 12700, 'synset': 'piece_de_resistance.n.02', 'name': 'piece_de_resistance'}, {'id': 12701, 'synset': 'plate.n.08', 'name': 'plate'}, {'id': 12702, 'synset': 'adobo.n.01', 'name': 'adobo'}, {'id': 12703, 'synset': 'side_dish.n.01', 'name': 'side_dish'}, {'id': 12704, 'synset': 'special.n.02', 'name': 'special'}, {'id': 12705, 'synset': 'chicken_casserole.n.01', 'name': 'chicken_casserole'}, {'id': 12706, 'synset': 'chicken_cacciatore.n.01', 'name': 'chicken_cacciatore'}, {'id': 12707, 'synset': 'antipasto.n.01', 'name': 'antipasto'}, {'id': 12708, 'synset': 'appetizer.n.01', 'name': 'appetizer'}, {'id': 12709, 'synset': 'canape.n.01', 'name': 'canape'}, {'id': 12710, 'synset': 'cocktail.n.02', 'name': 'cocktail'}, {'id': 12711, 'synset': 'fruit_cocktail.n.01', 'name': 'fruit_cocktail'}, {'id': 12712, 'synset': 'crab_cocktail.n.01', 'name': 'crab_cocktail'}, {'id': 12713, 'synset': 'shrimp_cocktail.n.01', 'name': 'shrimp_cocktail'}, {'id': 12714, 'synset': "hors_d'oeuvre.n.01", 'name': "hors_d'oeuvre"}, {'id': 12715, 'synset': 'relish.n.02', 'name': 'relish'}, {'id': 12716, 'synset': 'dip.n.04', 'name': 'dip'}, {'id': 12717, 'synset': 'bean_dip.n.01', 'name': 'bean_dip'}, {'id': 12718, 'synset': 'cheese_dip.n.01', 'name': 'cheese_dip'}, {'id': 12719, 'synset': 'clam_dip.n.01', 'name': 'clam_dip'}, {'id': 12720, 'synset': 'guacamole.n.01', 'name': 'guacamole'}, {'id': 12721, 'synset': 'soup_du_jour.n.01', 'name': 'soup_du_jour'}, {'id': 12722, 'synset': 'alphabet_soup.n.02', 'name': 'alphabet_soup'}, {'id': 12723, 'synset': 'consomme.n.01', 'name': 'consomme'}, {'id': 12724, 'synset': 'madrilene.n.01', 'name': 'madrilene'}, {'id': 12725, 'synset': 'bisque.n.01', 'name': 'bisque'}, {'id': 12726, 'synset': 'borsch.n.01', 'name': 'borsch'}, {'id': 12727, 'synset': 'broth.n.02', 'name': 'broth'}, {'id': 12728, 'synset': 'barley_water.n.01', 'name': 'barley_water'}, {'id': 12729, 'synset': 'bouillon.n.01', 'name': 'bouillon'}, {'id': 12730, 'synset': 'beef_broth.n.01', 'name': 'beef_broth'}, {'id': 12731, 'synset': 'chicken_broth.n.01', 'name': 'chicken_broth'}, {'id': 12732, 'synset': 'broth.n.01', 'name': 'broth'}, {'id': 12733, 'synset': 'stock_cube.n.01', 'name': 'stock_cube'}, {'id': 12734, 'synset': 'chicken_soup.n.01', 'name': 'chicken_soup'}, {'id': 12735, 'synset': 'cock-a-leekie.n.01', 'name': 'cock-a-leekie'}, {'id': 12736, 'synset': 'gazpacho.n.01', 'name': 'gazpacho'}, {'id': 12737, 'synset': 'gumbo.n.04', 'name': 'gumbo'}, {'id': 12738, 'synset': 'julienne.n.02', 'name': 'julienne'}, {'id': 12739, 'synset': 'marmite.n.01', 'name': 'marmite'}, {'id': 12740, 'synset': 'mock_turtle_soup.n.01', 'name': 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{'id': 12755, 'synset': 'fish_chowder.n.01', 'name': 'fish_chowder'}, {'id': 12756, 'synset': 'won_ton.n.02', 'name': 'won_ton'}, {'id': 12757, 'synset': 'split-pea_soup.n.01', 'name': 'split-pea_soup'}, {'id': 12758, 'synset': 'green_pea_soup.n.01', 'name': 'green_pea_soup'}, {'id': 12759, 'synset': 'lentil_soup.n.01', 'name': 'lentil_soup'}, {'id': 12760, 'synset': 'scotch_broth.n.01', 'name': 'Scotch_broth'}, {'id': 12761, 'synset': 'vichyssoise.n.01', 'name': 'vichyssoise'}, {'id': 12762, 'synset': 'bigos.n.01', 'name': 'bigos'}, {'id': 12763, 'synset': 'brunswick_stew.n.01', 'name': 'Brunswick_stew'}, {'id': 12764, 'synset': 'burgoo.n.03', 'name': 'burgoo'}, {'id': 12765, 'synset': 'burgoo.n.02', 'name': 'burgoo'}, {'id': 12766, 'synset': 'olla_podrida.n.01', 'name': 'olla_podrida'}, {'id': 12767, 'synset': 'mulligan_stew.n.01', 'name': 'mulligan_stew'}, {'id': 12768, 'synset': 'purloo.n.01', 'name': 'purloo'}, {'id': 12769, 'synset': 'goulash.n.01', 'name': 'goulash'}, {'id': 12770, 'synset': 'hotchpotch.n.02', 'name': 'hotchpotch'}, {'id': 12771, 'synset': 'hot_pot.n.01', 'name': 'hot_pot'}, {'id': 12772, 'synset': 'beef_goulash.n.01', 'name': 'beef_goulash'}, {'id': 12773, 'synset': 'pork-and-veal_goulash.n.01', 'name': 'pork-and-veal_goulash'}, {'id': 12774, 'synset': 'porkholt.n.01', 'name': 'porkholt'}, {'id': 12775, 'synset': 'irish_stew.n.01', 'name': 'Irish_stew'}, {'id': 12776, 'synset': 'oyster_stew.n.01', 'name': 'oyster_stew'}, {'id': 12777, 'synset': 'lobster_stew.n.01', 'name': 'lobster_stew'}, {'id': 12778, 'synset': 'lobscouse.n.01', 'name': 'lobscouse'}, {'id': 12779, 'synset': 'fish_stew.n.01', 'name': 'fish_stew'}, {'id': 12780, 'synset': 'bouillabaisse.n.01', 'name': 'bouillabaisse'}, {'id': 12781, 'synset': 'matelote.n.01', 'name': 'matelote'}, {'id': 12782, 'synset': 'paella.n.01', 'name': 'paella'}, {'id': 12783, 'synset': 'fricassee.n.01', 'name': 'fricassee'}, {'id': 12784, 'synset': 'chicken_stew.n.01', 'name': 'chicken_stew'}, {'id': 12785, 'synset': 'turkey_stew.n.01', 'name': 'turkey_stew'}, {'id': 12786, 'synset': 'beef_stew.n.01', 'name': 'beef_stew'}, {'id': 12787, 'synset': 'ragout.n.01', 'name': 'ragout'}, {'id': 12788, 'synset': 'ratatouille.n.01', 'name': 'ratatouille'}, {'id': 12789, 'synset': 'salmi.n.01', 'name': 'salmi'}, {'id': 12790, 'synset': 'pot-au-feu.n.01', 'name': 'pot-au-feu'}, {'id': 12791, 'synset': 'slumgullion.n.01', 'name': 'slumgullion'}, {'id': 12792, 'synset': 'smorgasbord.n.02', 'name': 'smorgasbord'}, {'id': 12793, 'synset': 'viand.n.01', 'name': 'viand'}, {'id': 12794, 'synset': 'ready-mix.n.01', 'name': 'ready-mix'}, {'id': 12795, 'synset': 'brownie_mix.n.01', 'name': 'brownie_mix'}, {'id': 12796, 'synset': 'cake_mix.n.01', 'name': 'cake_mix'}, {'id': 12797, 'synset': 'lemonade_mix.n.01', 'name': 'lemonade_mix'}, {'id': 12798, 'synset': 'self-rising_flour.n.01', 'name': 'self-rising_flour'}, {'id': 12799, 'synset': 'choice_morsel.n.01', 'name': 'choice_morsel'}, {'id': 12800, 'synset': 'savory.n.04', 'name': 'savory'}, {'id': 12801, 'synset': "calf's-foot_jelly.n.01", 'name': "calf's-foot_jelly"}, {'id': 12802, 'synset': 'caramel.n.02', 'name': 'caramel'}, {'id': 12803, 'synset': 'lump_sugar.n.01', 'name': 'lump_sugar'}, {'id': 12804, 'synset': 'cane_sugar.n.02', 'name': 'cane_sugar'}, {'id': 12805, 'synset': 'castor_sugar.n.01', 'name': 'castor_sugar'}, {'id': 12806, 'synset': 'powdered_sugar.n.01', 'name': 'powdered_sugar'}, {'id': 12807, 'synset': 'granulated_sugar.n.01', 'name': 'granulated_sugar'}, {'id': 12808, 'synset': 'icing_sugar.n.01', 'name': 'icing_sugar'}, {'id': 12809, 'synset': 'corn_sugar.n.02', 'name': 'corn_sugar'}, {'id': 12810, 'synset': 'brown_sugar.n.01', 'name': 'brown_sugar'}, {'id': 12811, 'synset': 'demerara.n.05', 'name': 'demerara'}, {'id': 12812, 'synset': 'sweet.n.03', 'name': 'sweet'}, {'id': 12813, 'synset': 'confectionery.n.01', 'name': 'confectionery'}, {'id': 12814, 'synset': 'confiture.n.01', 'name': 'confiture'}, {'id': 12815, 'synset': 'sweetmeat.n.01', 'name': 'sweetmeat'}, {'id': 12816, 'synset': 'candy.n.01', 'name': 'candy'}, {'id': 12817, 'synset': 'carob_bar.n.01', 'name': 'carob_bar'}, {'id': 12818, 'synset': 'hardbake.n.01', 'name': 'hardbake'}, {'id': 12819, 'synset': 'hard_candy.n.01', 'name': 'hard_candy'}, {'id': 12820, 'synset': 'barley-sugar.n.01', 'name': 'barley-sugar'}, {'id': 12821, 'synset': 'brandyball.n.01', 'name': 'brandyball'}, {'id': 12822, 'synset': 'jawbreaker.n.01', 'name': 'jawbreaker'}, {'id': 12823, 'synset': 'lemon_drop.n.01', 'name': 'lemon_drop'}, {'id': 12824, 'synset': 'sourball.n.01', 'name': 'sourball'}, {'id': 12825, 'synset': 'patty.n.03', 'name': 'patty'}, {'id': 12826, 'synset': 'peppermint_patty.n.01', 'name': 'peppermint_patty'}, {'id': 12827, 'synset': 'bonbon.n.01', 'name': 'bonbon'}, {'id': 12828, 'synset': 'brittle.n.01', 'name': 'brittle'}, {'id': 12829, 'synset': 'peanut_brittle.n.01', 'name': 'peanut_brittle'}, {'id': 12830, 'synset': 'chewing_gum.n.01', 'name': 'chewing_gum'}, {'id': 12831, 'synset': 'gum_ball.n.01', 'name': 'gum_ball'}, {'id': 12832, 'synset': 'butterscotch.n.01', 'name': 'butterscotch'}, {'id': 12833, 'synset': 'candied_fruit.n.01', 'name': 'candied_fruit'}, {'id': 12834, 'synset': 'candied_apple.n.01', 'name': 'candied_apple'}, {'id': 12835, 'synset': 'crystallized_ginger.n.01', 'name': 'crystallized_ginger'}, {'id': 12836, 'synset': 'grapefruit_peel.n.01', 'name': 'grapefruit_peel'}, {'id': 12837, 'synset': 'lemon_peel.n.02', 'name': 'lemon_peel'}, {'id': 12838, 'synset': 'orange_peel.n.02', 'name': 'orange_peel'}, {'id': 12839, 'synset': 'candied_citrus_peel.n.01', 'name': 'candied_citrus_peel'}, {'id': 12840, 'synset': 'candy_corn.n.01', 'name': 'candy_corn'}, {'id': 12841, 'synset': 'caramel.n.01', 'name': 'caramel'}, {'id': 12842, 'synset': 'center.n.14', 'name': 'center'}, {'id': 12843, 'synset': 'comfit.n.01', 'name': 'comfit'}, {'id': 12844, 'synset': 'cotton_candy.n.01', 'name': 'cotton_candy'}, {'id': 12845, 'synset': 'dragee.n.02', 'name': 'dragee'}, {'id': 12846, 'synset': 'dragee.n.01', 'name': 'dragee'}, {'id': 12847, 'synset': 'fondant.n.01', 'name': 'fondant'}, {'id': 12848, 'synset': 'chocolate_fudge.n.01', 'name': 'chocolate_fudge'}, {'id': 12849, 'synset': 'divinity.n.03', 'name': 'divinity'}, {'id': 12850, 'synset': 'penuche.n.01', 'name': 'penuche'}, {'id': 12851, 'synset': 'gumdrop.n.01', 'name': 'gumdrop'}, {'id': 12852, 'synset': 'jujube.n.03', 'name': 'jujube'}, {'id': 12853, 'synset': 'honey_crisp.n.01', 'name': 'honey_crisp'}, {'id': 12854, 'synset': 'horehound.n.02', 'name': 'horehound'}, {'id': 12855, 'synset': 'peppermint.n.03', 'name': 'peppermint'}, {'id': 12856, 'synset': 'kiss.n.03', 'name': 'kiss'}, {'id': 12857, 'synset': 'molasses_kiss.n.01', 'name': 'molasses_kiss'}, {'id': 12858, 'synset': 'meringue_kiss.n.01', 'name': 'meringue_kiss'}, {'id': 12859, 'synset': 'chocolate_kiss.n.01', 'name': 'chocolate_kiss'}, {'id': 12860, 'synset': 'licorice.n.02', 'name': 'licorice'}, {'id': 12861, 'synset': 'life_saver.n.01', 'name': 'Life_Saver'}, {'id': 12862, 'synset': 'lozenge.n.01', 'name': 'lozenge'}, {'id': 12863, 'synset': 'cachou.n.01', 'name': 'cachou'}, {'id': 12864, 'synset': 'cough_drop.n.01', 'name': 'cough_drop'}, {'id': 12865, 'synset': 'marshmallow.n.01', 'name': 'marshmallow'}, {'id': 12866, 'synset': 'marzipan.n.01', 'name': 'marzipan'}, {'id': 12867, 'synset': 'nougat.n.01', 'name': 'nougat'}, {'id': 12868, 'synset': 'nougat_bar.n.01', 'name': 'nougat_bar'}, {'id': 12869, 'synset': 'nut_bar.n.01', 'name': 'nut_bar'}, {'id': 12870, 'synset': 'peanut_bar.n.01', 'name': 'peanut_bar'}, {'id': 12871, 'synset': 'popcorn_ball.n.01', 'name': 'popcorn_ball'}, {'id': 12872, 'synset': 'praline.n.01', 'name': 'praline'}, {'id': 12873, 'synset': 'rock_candy.n.02', 'name': 'rock_candy'}, {'id': 12874, 'synset': 'rock_candy.n.01', 'name': 'rock_candy'}, {'id': 12875, 'synset': 'sugar_candy.n.01', 'name': 'sugar_candy'}, {'id': 12876, 'synset': 'sugarplum.n.01', 'name': 'sugarplum'}, {'id': 12877, 'synset': 'taffy.n.01', 'name': 'taffy'}, {'id': 12878, 'synset': 'molasses_taffy.n.01', 'name': 'molasses_taffy'}, {'id': 12879, 'synset': 'turkish_delight.n.01', 'name': 'Turkish_Delight'}, {'id': 12880, 'synset': 'dessert.n.01', 'name': 'dessert'}, {'id': 12881, 'synset': 'ambrosia.n.04', 'name': 'ambrosia'}, {'id': 12882, 'synset': 'ambrosia.n.03', 'name': 'ambrosia'}, {'id': 12883, 'synset': 'baked_alaska.n.01', 'name': 'baked_Alaska'}, {'id': 12884, 'synset': 'blancmange.n.01', 'name': 'blancmange'}, {'id': 12885, 'synset': 'charlotte.n.02', 'name': 'charlotte'}, {'id': 12886, 'synset': 'compote.n.01', 'name': 'compote'}, {'id': 12887, 'synset': 'dumpling.n.02', 'name': 'dumpling'}, {'id': 12888, 'synset': 'flan.n.01', 'name': 'flan'}, {'id': 12889, 'synset': 'frozen_dessert.n.01', 'name': 'frozen_dessert'}, {'id': 12890, 'synset': 'junket.n.01', 'name': 'junket'}, {'id': 12891, 'synset': 'mousse.n.02', 'name': 'mousse'}, {'id': 12892, 'synset': 'mousse.n.01', 'name': 'mousse'}, {'id': 12893, 'synset': 'pavlova.n.02', 'name': 'pavlova'}, {'id': 12894, 'synset': 'peach_melba.n.01', 'name': 'peach_melba'}, {'id': 12895, 'synset': 'whip.n.03', 'name': 'whip'}, {'id': 12896, 'synset': 'prune_whip.n.01', 'name': 'prune_whip'}, {'id': 12897, 'synset': 'pudding.n.03', 'name': 'pudding'}, {'id': 12898, 'synset': 'pudding.n.02', 'name': 'pudding'}, {'id': 12899, 'synset': 'syllabub.n.02', 'name': 'syllabub'}, {'id': 12900, 'synset': 'tiramisu.n.01', 'name': 'tiramisu'}, {'id': 12901, 'synset': 'trifle.n.01', 'name': 'trifle'}, {'id': 12902, 'synset': 'tipsy_cake.n.01', 'name': 'tipsy_cake'}, {'id': 12903, 'synset': 'jello.n.01', 'name': 'jello'}, {'id': 12904, 'synset': 'apple_dumpling.n.01', 'name': 'apple_dumpling'}, {'id': 12905, 'synset': 'ice.n.05', 'name': 'ice'}, {'id': 12906, 'synset': 'water_ice.n.02', 'name': 'water_ice'}, {'id': 12907, 'synset': 'ice-cream_cone.n.01', 'name': 'ice-cream_cone'}, {'id': 12908, 'synset': 'chocolate_ice_cream.n.01', 'name': 'chocolate_ice_cream'}, {'id': 12909, 'synset': 'neapolitan_ice_cream.n.01', 'name': 'Neapolitan_ice_cream'}, {'id': 12910, 'synset': 'peach_ice_cream.n.01', 'name': 'peach_ice_cream'}, {'id': 12911, 'synset': 'strawberry_ice_cream.n.01', 'name': 'strawberry_ice_cream'}, {'id': 12912, 'synset': 'tutti-frutti.n.01', 'name': 'tutti-frutti'}, {'id': 12913, 'synset': 'vanilla_ice_cream.n.01', 'name': 'vanilla_ice_cream'}, {'id': 12914, 'synset': 'ice_milk.n.01', 'name': 'ice_milk'}, {'id': 12915, 'synset': 'frozen_yogurt.n.01', 'name': 'frozen_yogurt'}, {'id': 12916, 'synset': 'snowball.n.03', 'name': 'snowball'}, {'id': 12917, 'synset': 'snowball.n.02', 'name': 'snowball'}, {'id': 12918, 'synset': 'parfait.n.01', 'name': 'parfait'}, {'id': 12919, 'synset': 'ice-cream_sundae.n.01', 'name': 'ice-cream_sundae'}, {'id': 12920, 'synset': 'split.n.07', 'name': 'split'}, {'id': 12921, 'synset': 'banana_split.n.01', 'name': 'banana_split'}, {'id': 12922, 'synset': 'frozen_pudding.n.01', 'name': 'frozen_pudding'}, {'id': 12923, 'synset': 'frozen_custard.n.01', 'name': 'frozen_custard'}, {'id': 12924, 'synset': 'flummery.n.01', 'name': 'flummery'}, {'id': 12925, 'synset': 'fish_mousse.n.01', 'name': 'fish_mousse'}, {'id': 12926, 'synset': 'chicken_mousse.n.01', 'name': 'chicken_mousse'}, {'id': 12927, 'synset': 'plum_pudding.n.01', 'name': 'plum_pudding'}, {'id': 12928, 'synset': 'carrot_pudding.n.01', 'name': 'carrot_pudding'}, {'id': 12929, 'synset': 'corn_pudding.n.01', 'name': 'corn_pudding'}, {'id': 12930, 'synset': 'steamed_pudding.n.01', 'name': 'steamed_pudding'}, {'id': 12931, 'synset': 'duff.n.01', 'name': 'duff'}, {'id': 12932, 'synset': 'vanilla_pudding.n.01', 'name': 'vanilla_pudding'}, {'id': 12933, 'synset': 'chocolate_pudding.n.01', 'name': 'chocolate_pudding'}, {'id': 12934, 'synset': 'brown_betty.n.01', 'name': 'brown_Betty'}, {'id': 12935, 'synset': 'nesselrode.n.01', 'name': 'Nesselrode'}, {'id': 12936, 'synset': 'pease_pudding.n.01', 'name': 'pease_pudding'}, {'id': 12937, 'synset': 'custard.n.01', 'name': 'custard'}, {'id': 12938, 'synset': 'creme_caramel.n.01', 'name': 'creme_caramel'}, {'id': 12939, 'synset': 'creme_anglais.n.01', 'name': 'creme_anglais'}, {'id': 12940, 'synset': 'creme_brulee.n.01', 'name': 'creme_brulee'}, {'id': 12941, 'synset': 'fruit_custard.n.01', 'name': 'fruit_custard'}, {'id': 12942, 'synset': 'tapioca.n.01', 'name': 'tapioca'}, {'id': 12943, 'synset': 'tapioca_pudding.n.01', 'name': 'tapioca_pudding'}, {'id': 12944, 'synset': 'roly-poly.n.02', 'name': 'roly-poly'}, {'id': 12945, 'synset': 'suet_pudding.n.01', 'name': 'suet_pudding'}, {'id': 12946, 'synset': 'bavarian_cream.n.01', 'name': 'Bavarian_cream'}, {'id': 12947, 'synset': 'maraschino.n.02', 'name': 'maraschino'}, {'id': 12948, 'synset': 'nonpareil.n.02', 'name': 'nonpareil'}, {'id': 12949, 'synset': 'zabaglione.n.01', 'name': 'zabaglione'}, {'id': 12950, 'synset': 'garnish.n.01', 'name': 'garnish'}, {'id': 12951, 'synset': 'pastry.n.01', 'name': 'pastry'}, {'id': 12952, 'synset': 'turnover.n.02', 'name': 'turnover'}, {'id': 12953, 'synset': 'apple_turnover.n.01', 'name': 'apple_turnover'}, {'id': 12954, 'synset': 'knish.n.01', 'name': 'knish'}, {'id': 12955, 'synset': 'pirogi.n.01', 'name': 'pirogi'}, {'id': 12956, 'synset': 'samosa.n.01', 'name': 'samosa'}, {'id': 12957, 'synset': 'timbale.n.01', 'name': 'timbale'}, {'id': 12958, 'synset': 'puff_paste.n.01', 'name': 'puff_paste'}, {'id': 12959, 'synset': 'phyllo.n.01', 'name': 'phyllo'}, {'id': 12960, 'synset': 'puff_batter.n.01', 'name': 'puff_batter'}, {'id': 12961, 'synset': 'ice-cream_cake.n.01', 'name': 'ice-cream_cake'}, {'id': 12962, 'synset': 'fish_cake.n.01', 'name': 'fish_cake'}, {'id': 12963, 'synset': 'fish_stick.n.01', 'name': 'fish_stick'}, {'id': 12964, 'synset': 'conserve.n.01', 'name': 'conserve'}, {'id': 12965, 'synset': 'apple_butter.n.01', 'name': 'apple_butter'}, {'id': 12966, 'synset': 'chowchow.n.02', 'name': 'chowchow'}, {'id': 12967, 'synset': 'lemon_curd.n.01', 'name': 'lemon_curd'}, {'id': 12968, 'synset': 'strawberry_jam.n.01', 'name': 'strawberry_jam'}, {'id': 12969, 'synset': 'jelly.n.02', 'name': 'jelly'}, {'id': 12970, 'synset': 'apple_jelly.n.01', 'name': 'apple_jelly'}, {'id': 12971, 'synset': 'crabapple_jelly.n.01', 'name': 'crabapple_jelly'}, {'id': 12972, 'synset': 'grape_jelly.n.01', 'name': 'grape_jelly'}, {'id': 12973, 'synset': 'marmalade.n.01', 'name': 'marmalade'}, {'id': 12974, 'synset': 'orange_marmalade.n.01', 'name': 'orange_marmalade'}, {'id': 12975, 'synset': 'gelatin_dessert.n.01', 'name': 'gelatin_dessert'}, {'id': 12976, 'synset': 'buffalo_wing.n.01', 'name': 'buffalo_wing'}, {'id': 12977, 'synset': 'barbecued_wing.n.01', 'name': 'barbecued_wing'}, {'id': 12978, 'synset': 'mess.n.03', 'name': 'mess'}, {'id': 12979, 'synset': 'mince.n.01', 'name': 'mince'}, {'id': 12980, 'synset': 'puree.n.01', 'name': 'puree'}, {'id': 12981, 'synset': 'barbecue.n.01', 'name': 'barbecue'}, {'id': 12982, 'synset': 'biryani.n.01', 'name': 'biryani'}, {'id': 12983, 'synset': 'escalope_de_veau_orloff.n.01', 'name': 'escalope_de_veau_Orloff'}, {'id': 12984, 'synset': 'saute.n.01', 'name': 'saute'}, {'id': 12985, 'synset': 'veal_parmesan.n.01', 'name': 'veal_parmesan'}, {'id': 12986, 'synset': 'veal_cordon_bleu.n.01', 'name': 'veal_cordon_bleu'}, {'id': 12987, 'synset': 'margarine.n.01', 'name': 'margarine'}, {'id': 12988, 'synset': 'mincemeat.n.01', 'name': 'mincemeat'}, {'id': 12989, 'synset': 'stuffing.n.01', 'name': 'stuffing'}, {'id': 12990, 'synset': 'turkey_stuffing.n.01', 'name': 'turkey_stuffing'}, {'id': 12991, 'synset': 'oyster_stuffing.n.01', 'name': 'oyster_stuffing'}, {'id': 12992, 'synset': 'forcemeat.n.01', 'name': 'forcemeat'}, {'id': 12993, 'synset': 'anadama_bread.n.01', 'name': 'anadama_bread'}, {'id': 12994, 'synset': 'bap.n.01', 'name': 'bap'}, {'id': 12995, 'synset': 'barmbrack.n.01', 'name': 'barmbrack'}, {'id': 12996, 'synset': 'breadstick.n.01', 'name': 'breadstick'}, {'id': 12997, 'synset': 'grissino.n.01', 'name': 'grissino'}, {'id': 12998, 'synset': 'brown_bread.n.02', 'name': 'brown_bread'}, {'id': 12999, 'synset': 'tea_bread.n.01', 'name': 'tea_bread'}, {'id': 13000, 'synset': 'caraway_seed_bread.n.01', 'name': 'caraway_seed_bread'}, {'id': 13001, 'synset': 'challah.n.01', 'name': 'challah'}, {'id': 13002, 'synset': 'cinnamon_bread.n.01', 'name': 'cinnamon_bread'}, {'id': 13003, 'synset': 'cracked-wheat_bread.n.01', 'name': 'cracked-wheat_bread'}, {'id': 13004, 'synset': 'dark_bread.n.01', 'name': 'dark_bread'}, {'id': 13005, 'synset': 'english_muffin.n.01', 'name': 'English_muffin'}, {'id': 13006, 'synset': 'flatbread.n.01', 'name': 'flatbread'}, {'id': 13007, 'synset': 'garlic_bread.n.01', 'name': 'garlic_bread'}, {'id': 13008, 'synset': 'gluten_bread.n.01', 'name': 'gluten_bread'}, {'id': 13009, 'synset': 'graham_bread.n.01', 'name': 'graham_bread'}, {'id': 13010, 'synset': 'host.n.09', 'name': 'Host'}, {'id': 13011, 'synset': 'flatbrod.n.01', 'name': 'flatbrod'}, {'id': 13012, 'synset': 'bannock.n.01', 'name': 'bannock'}, {'id': 13013, 'synset': 'chapatti.n.01', 'name': 'chapatti'}, {'id': 13014, 'synset': 'loaf_of_bread.n.01', 'name': 'loaf_of_bread'}, {'id': 13015, 'synset': 'french_loaf.n.01', 'name': 'French_loaf'}, {'id': 13016, 'synset': 'matzo.n.01', 'name': 'matzo'}, {'id': 13017, 'synset': 'nan.n.04', 'name': 'nan'}, {'id': 13018, 'synset': 'onion_bread.n.01', 'name': 'onion_bread'}, {'id': 13019, 'synset': 'raisin_bread.n.01', 'name': 'raisin_bread'}, {'id': 13020, 'synset': 'quick_bread.n.01', 'name': 'quick_bread'}, {'id': 13021, 'synset': 'banana_bread.n.01', 'name': 'banana_bread'}, {'id': 13022, 'synset': 'date_bread.n.01', 'name': 'date_bread'}, {'id': 13023, 'synset': 'date-nut_bread.n.01', 'name': 'date-nut_bread'}, {'id': 13024, 'synset': 'nut_bread.n.01', 'name': 'nut_bread'}, {'id': 13025, 'synset': 'oatcake.n.01', 'name': 'oatcake'}, {'id': 13026, 'synset': 'irish_soda_bread.n.01', 'name': 'Irish_soda_bread'}, {'id': 13027, 'synset': 'skillet_bread.n.01', 'name': 'skillet_bread'}, {'id': 13028, 'synset': 'rye_bread.n.01', 'name': 'rye_bread'}, {'id': 13029, 'synset': 'black_bread.n.01', 'name': 'black_bread'}, {'id': 13030, 'synset': 'jewish_rye_bread.n.01', 'name': 'Jewish_rye_bread'}, {'id': 13031, 'synset': 'limpa.n.01', 'name': 'limpa'}, {'id': 13032, 'synset': 'swedish_rye_bread.n.01', 'name': 'Swedish_rye_bread'}, {'id': 13033, 'synset': 'salt-rising_bread.n.01', 'name': 'salt-rising_bread'}, {'id': 13034, 'synset': 'simnel.n.01', 'name': 'simnel'}, {'id': 13035, 'synset': 'sour_bread.n.01', 'name': 'sour_bread'}, {'id': 13036, 'synset': 'wafer.n.03', 'name': 'wafer'}, {'id': 13037, 'synset': 'white_bread.n.01', 'name': 'white_bread'}, {'id': 13038, 'synset': 'french_bread.n.01', 'name': 'French_bread'}, {'id': 13039, 'synset': 'italian_bread.n.01', 'name': 'Italian_bread'}, {'id': 13040, 'synset': 'corn_cake.n.01', 'name': 'corn_cake'}, {'id': 13041, 'synset': 'skillet_corn_bread.n.01', 'name': 'skillet_corn_bread'}, {'id': 13042, 'synset': 'ashcake.n.01', 'name': 'ashcake'}, {'id': 13043, 'synset': 'hoecake.n.01', 'name': 'hoecake'}, {'id': 13044, 'synset': 'cornpone.n.01', 'name': 'cornpone'}, {'id': 13045, 'synset': 'corn_dab.n.01', 'name': 'corn_dab'}, {'id': 13046, 'synset': 'hush_puppy.n.01', 'name': 'hush_puppy'}, {'id': 13047, 'synset': 'johnnycake.n.01', 'name': 'johnnycake'}, {'id': 13048, 'synset': 'shawnee_cake.n.01', 'name': 'Shawnee_cake'}, {'id': 13049, 'synset': 'spoon_bread.n.01', 'name': 'spoon_bread'}, {'id': 13050, 'synset': 'cinnamon_toast.n.01', 'name': 'cinnamon_toast'}, {'id': 13051, 'synset': 'orange_toast.n.01', 'name': 'orange_toast'}, {'id': 13052, 'synset': 'melba_toast.n.01', 'name': 'Melba_toast'}, {'id': 13053, 'synset': 'zwieback.n.01', 'name': 'zwieback'}, {'id': 13054, 'synset': 'frankfurter_bun.n.01', 'name': 'frankfurter_bun'}, {'id': 13055, 'synset': 'hamburger_bun.n.01', 'name': 'hamburger_bun'}, {'id': 13056, 'synset': 'bran_muffin.n.01', 'name': 'bran_muffin'}, {'id': 13057, 'synset': 'corn_muffin.n.01', 'name': 'corn_muffin'}, {'id': 13058, 'synset': 'yorkshire_pudding.n.01', 'name': 'Yorkshire_pudding'}, {'id': 13059, 'synset': 'popover.n.01', 'name': 'popover'}, {'id': 13060, 'synset': 'scone.n.01', 'name': 'scone'}, {'id': 13061, 'synset': 'drop_scone.n.01', 'name': 'drop_scone'}, {'id': 13062, 'synset': 'cross_bun.n.01', 'name': 'cross_bun'}, {'id': 13063, 'synset': 'brioche.n.01', 'name': 'brioche'}, {'id': 13064, 'synset': 'hard_roll.n.01', 'name': 'hard_roll'}, {'id': 13065, 'synset': 'soft_roll.n.01', 'name': 'soft_roll'}, {'id': 13066, 'synset': 'kaiser_roll.n.01', 'name': 'kaiser_roll'}, {'id': 13067, 'synset': 'parker_house_roll.n.01', 'name': 'Parker_House_roll'}, {'id': 13068, 'synset': 'clover-leaf_roll.n.01', 'name': 'clover-leaf_roll'}, {'id': 13069, 'synset': 'onion_roll.n.01', 'name': 'onion_roll'}, {'id': 13070, 'synset': 'bialy.n.01', 'name': 'bialy'}, {'id': 13071, 'synset': 'sweet_roll.n.01', 'name': 'sweet_roll'}, {'id': 13072, 'synset': 'bear_claw.n.01', 'name': 'bear_claw'}, {'id': 13073, 'synset': 'cinnamon_roll.n.01', 'name': 'cinnamon_roll'}, {'id': 13074, 'synset': 'honey_bun.n.01', 'name': 'honey_bun'}, {'id': 13075, 'synset': 'pinwheel_roll.n.01', 'name': 'pinwheel_roll'}, {'id': 13076, 'synset': 'danish.n.02', 'name': 'danish'}, {'id': 13077, 'synset': 'onion_bagel.n.01', 'name': 'onion_bagel'}, {'id': 13078, 'synset': 'biscuit.n.01', 'name': 'biscuit'}, {'id': 13079, 'synset': 'rolled_biscuit.n.01', 'name': 'rolled_biscuit'}, {'id': 13080, 'synset': 'baking-powder_biscuit.n.01', 'name': 'baking-powder_biscuit'}, {'id': 13081, 'synset': 'buttermilk_biscuit.n.01', 'name': 'buttermilk_biscuit'}, {'id': 13082, 'synset': 'shortcake.n.01', 'name': 'shortcake'}, {'id': 13083, 'synset': 'hardtack.n.01', 'name': 'hardtack'}, {'id': 13084, 'synset': 'saltine.n.01', 'name': 'saltine'}, {'id': 13085, 'synset': 'soda_cracker.n.01', 'name': 'soda_cracker'}, {'id': 13086, 'synset': 'oyster_cracker.n.01', 'name': 'oyster_cracker'}, {'id': 13087, 'synset': 'water_biscuit.n.01', 'name': 'water_biscuit'}, {'id': 13088, 'synset': 'graham_cracker.n.01', 'name': 'graham_cracker'}, {'id': 13089, 'synset': 'soft_pretzel.n.01', 'name': 'soft_pretzel'}, {'id': 13090, 'synset': 'sandwich_plate.n.01', 'name': 'sandwich_plate'}, {'id': 13091, 'synset': 'butty.n.01', 'name': 'butty'}, {'id': 13092, 'synset': 'ham_sandwich.n.01', 'name': 'ham_sandwich'}, {'id': 13093, 'synset': 'chicken_sandwich.n.01', 'name': 'chicken_sandwich'}, {'id': 13094, 'synset': 'club_sandwich.n.01', 'name': 'club_sandwich'}, {'id': 13095, 'synset': 'open-face_sandwich.n.01', 'name': 'open-face_sandwich'}, {'id': 13096, 'synset': 'cheeseburger.n.01', 'name': 'cheeseburger'}, {'id': 13097, 'synset': 'tunaburger.n.01', 'name': 'tunaburger'}, {'id': 13098, 'synset': 'hotdog.n.02', 'name': 'hotdog'}, {'id': 13099, 'synset': 'sloppy_joe.n.01', 'name': 'Sloppy_Joe'}, {'id': 13100, 'synset': 'bomber.n.03', 'name': 'bomber'}, {'id': 13101, 'synset': 'gyro.n.01', 'name': 'gyro'}, {'id': 13102, 'synset': 'bacon-lettuce-tomato_sandwich.n.01', 'name': 'bacon-lettuce-tomato_sandwich'}, {'id': 13103, 'synset': 'reuben.n.02', 'name': 'Reuben'}, {'id': 13104, 'synset': 'western.n.02', 'name': 'western'}, {'id': 13105, 'synset': 'wrap.n.02', 'name': 'wrap'}, {'id': 13106, 'synset': 'spaghetti.n.01', 'name': 'spaghetti'}, {'id': 13107, 'synset': 'hasty_pudding.n.01', 'name': 'hasty_pudding'}, {'id': 13108, 'synset': 'gruel.n.01', 'name': 'gruel'}, {'id': 13109, 'synset': 'congee.n.01', 'name': 'congee'}, {'id': 13110, 'synset': 'skilly.n.01', 'name': 'skilly'}, {'id': 13111, 'synset': 'edible_fruit.n.01', 'name': 'edible_fruit'}, {'id': 13112, 'synset': 'vegetable.n.01', 'name': 'vegetable'}, {'id': 13113, 'synset': 'julienne.n.01', 'name': 'julienne'}, {'id': 13114, 'synset': 'raw_vegetable.n.01', 'name': 'raw_vegetable'}, {'id': 13115, 'synset': 'crudites.n.01', 'name': 'crudites'}, {'id': 13116, 'synset': 'celery_stick.n.01', 'name': 'celery_stick'}, {'id': 13117, 'synset': 'legume.n.03', 'name': 'legume'}, {'id': 13118, 'synset': 'pulse.n.04', 'name': 'pulse'}, {'id': 13119, 'synset': 'potherb.n.01', 'name': 'potherb'}, {'id': 13120, 'synset': 'greens.n.01', 'name': 'greens'}, {'id': 13121, 'synset': 'chop-suey_greens.n.02', 'name': 'chop-suey_greens'}, {'id': 13122, 'synset': 'solanaceous_vegetable.n.01', 'name': 'solanaceous_vegetable'}, {'id': 13123, 'synset': 'root_vegetable.n.01', 'name': 'root_vegetable'}, {'id': 13124, 'synset': 'baked_potato.n.01', 'name': 'baked_potato'}, {'id': 13125, 'synset': 'french_fries.n.01', 'name': 'french_fries'}, {'id': 13126, 'synset': 'home_fries.n.01', 'name': 'home_fries'}, {'id': 13127, 'synset': 'jacket_potato.n.01', 'name': 'jacket_potato'}, {'id': 13128, 'synset': 'potato_skin.n.01', 'name': 'potato_skin'}, {'id': 13129, 'synset': 'uruguay_potato.n.02', 'name': 'Uruguay_potato'}, {'id': 13130, 'synset': 'yam.n.04', 'name': 'yam'}, {'id': 13131, 'synset': 'yam.n.03', 'name': 'yam'}, {'id': 13132, 'synset': 'snack_food.n.01', 'name': 'snack_food'}, {'id': 13133, 'synset': 'corn_chip.n.01', 'name': 'corn_chip'}, {'id': 13134, 'synset': 'tortilla_chip.n.01', 'name': 'tortilla_chip'}, {'id': 13135, 'synset': 'nacho.n.01', 'name': 'nacho'}, {'id': 13136, 'synset': 'pieplant.n.01', 'name': 'pieplant'}, {'id': 13137, 'synset': 'cruciferous_vegetable.n.01', 'name': 'cruciferous_vegetable'}, {'id': 13138, 'synset': 'mustard.n.03', 'name': 'mustard'}, {'id': 13139, 'synset': 'cabbage.n.01', 'name': 'cabbage'}, {'id': 13140, 'synset': 'kale.n.03', 'name': 'kale'}, {'id': 13141, 'synset': 'collards.n.01', 'name': 'collards'}, {'id': 13142, 'synset': 'chinese_cabbage.n.02', 'name': 'Chinese_cabbage'}, {'id': 13143, 'synset': 'bok_choy.n.02', 'name': 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'acorn_squash.n.02', 'name': 'acorn_squash'}, {'id': 13159, 'synset': 'butternut_squash.n.02', 'name': 'butternut_squash'}, {'id': 13160, 'synset': 'hubbard_squash.n.02', 'name': 'hubbard_squash'}, {'id': 13161, 'synset': 'turban_squash.n.02', 'name': 'turban_squash'}, {'id': 13162, 'synset': 'buttercup_squash.n.02', 'name': 'buttercup_squash'}, {'id': 13163, 'synset': 'cushaw.n.02', 'name': 'cushaw'}, {'id': 13164, 'synset': 'winter_crookneck_squash.n.02', 'name': 'winter_crookneck_squash'}, {'id': 13165, 'synset': 'gherkin.n.02', 'name': 'gherkin'}, {'id': 13166, 'synset': 'artichoke_heart.n.01', 'name': 'artichoke_heart'}, {'id': 13167, 'synset': 'jerusalem_artichoke.n.03', 'name': 'Jerusalem_artichoke'}, {'id': 13168, 'synset': 'bamboo_shoot.n.01', 'name': 'bamboo_shoot'}, {'id': 13169, 'synset': 'sprout.n.02', 'name': 'sprout'}, {'id': 13170, 'synset': 'bean_sprout.n.01', 'name': 'bean_sprout'}, {'id': 13171, 'synset': 'alfalfa_sprout.n.01', 'name': 'alfalfa_sprout'}, {'id': 13172, 'synset': 'beet.n.02', 'name': 'beet'}, {'id': 13173, 'synset': 'beet_green.n.01', 'name': 'beet_green'}, {'id': 13174, 'synset': 'sugar_beet.n.02', 'name': 'sugar_beet'}, {'id': 13175, 'synset': 'mangel-wurzel.n.02', 'name': 'mangel-wurzel'}, {'id': 13176, 'synset': 'chard.n.02', 'name': 'chard'}, {'id': 13177, 'synset': 'pepper.n.04', 'name': 'pepper'}, {'id': 13178, 'synset': 'sweet_pepper.n.02', 'name': 'sweet_pepper'}, {'id': 13179, 'synset': 'green_pepper.n.01', 'name': 'green_pepper'}, {'id': 13180, 'synset': 'globe_pepper.n.01', 'name': 'globe_pepper'}, {'id': 13181, 'synset': 'pimento.n.02', 'name': 'pimento'}, {'id': 13182, 'synset': 'hot_pepper.n.02', 'name': 'hot_pepper'}, {'id': 13183, 'synset': 'jalapeno.n.02', 'name': 'jalapeno'}, {'id': 13184, 'synset': 'chipotle.n.01', 'name': 'chipotle'}, {'id': 13185, 'synset': 'cayenne.n.03', 'name': 'cayenne'}, {'id': 13186, 'synset': 'tabasco.n.03', 'name': 'tabasco'}, {'id': 13187, 'synset': 'onion.n.03', 'name': 'onion'}, {'id': 13188, 'synset': 'bermuda_onion.n.01', 'name': 'Bermuda_onion'}, {'id': 13189, 'synset': 'vidalia_onion.n.01', 'name': 'Vidalia_onion'}, {'id': 13190, 'synset': 'spanish_onion.n.01', 'name': 'Spanish_onion'}, {'id': 13191, 'synset': 'purple_onion.n.01', 'name': 'purple_onion'}, {'id': 13192, 'synset': 'leek.n.02', 'name': 'leek'}, {'id': 13193, 'synset': 'shallot.n.03', 'name': 'shallot'}, {'id': 13194, 'synset': 'salad_green.n.01', 'name': 'salad_green'}, {'id': 13195, 'synset': 'lettuce.n.03', 'name': 'lettuce'}, {'id': 13196, 'synset': 'butterhead_lettuce.n.01', 'name': 'butterhead_lettuce'}, {'id': 13197, 'synset': 'buttercrunch.n.01', 'name': 'buttercrunch'}, {'id': 13198, 'synset': 'bibb_lettuce.n.01', 'name': 'Bibb_lettuce'}, {'id': 13199, 'synset': 'boston_lettuce.n.01', 'name': 'Boston_lettuce'}, {'id': 13200, 'synset': 'crisphead_lettuce.n.01', 'name': 'crisphead_lettuce'}, {'id': 13201, 'synset': 'cos.n.02', 'name': 'cos'}, {'id': 13202, 'synset': 'leaf_lettuce.n.02', 'name': 'leaf_lettuce'}, {'id': 13203, 'synset': 'celtuce.n.02', 'name': 'celtuce'}, {'id': 13204, 'synset': 'bean.n.01', 'name': 'bean'}, {'id': 13205, 'synset': 'goa_bean.n.02', 'name': 'goa_bean'}, {'id': 13206, 'synset': 'lentil.n.01', 'name': 'lentil'}, {'id': 13207, 'synset': 'green_pea.n.01', 'name': 'green_pea'}, {'id': 13208, 'synset': 'marrowfat_pea.n.01', 'name': 'marrowfat_pea'}, {'id': 13209, 'synset': 'snow_pea.n.02', 'name': 'snow_pea'}, {'id': 13210, 'synset': 'sugar_snap_pea.n.02', 'name': 'sugar_snap_pea'}, {'id': 13211, 'synset': 'split-pea.n.01', 'name': 'split-pea'}, {'id': 13212, 'synset': 'chickpea.n.03', 'name': 'chickpea'}, {'id': 13213, 'synset': 'cajan_pea.n.02', 'name': 'cajan_pea'}, {'id': 13214, 'synset': 'field_pea.n.03', 'name': 'field_pea'}, {'id': 13215, 'synset': 'mushy_peas.n.01', 'name': 'mushy_peas'}, {'id': 13216, 'synset': 'black-eyed_pea.n.03', 'name': 'black-eyed_pea'}, {'id': 13217, 'synset': 'common_bean.n.02', 'name': 'common_bean'}, {'id': 13218, 'synset': 'kidney_bean.n.02', 'name': 'kidney_bean'}, {'id': 13219, 'synset': 'navy_bean.n.01', 'name': 'navy_bean'}, {'id': 13220, 'synset': 'pinto_bean.n.01', 'name': 'pinto_bean'}, {'id': 13221, 'synset': 'frijole.n.02', 'name': 'frijole'}, {'id': 13222, 'synset': 'black_bean.n.01', 'name': 'black_bean'}, {'id': 13223, 'synset': 'fresh_bean.n.01', 'name': 'fresh_bean'}, {'id': 13224, 'synset': 'flageolet.n.01', 'name': 'flageolet'}, {'id': 13225, 'synset': 'green_bean.n.01', 'name': 'green_bean'}, {'id': 13226, 'synset': 'snap_bean.n.01', 'name': 'snap_bean'}, {'id': 13227, 'synset': 'string_bean.n.01', 'name': 'string_bean'}, {'id': 13228, 'synset': 'kentucky_wonder.n.01', 'name': 'Kentucky_wonder'}, {'id': 13229, 'synset': 'scarlet_runner.n.03', 'name': 'scarlet_runner'}, {'id': 13230, 'synset': 'haricot_vert.n.01', 'name': 'haricot_vert'}, {'id': 13231, 'synset': 'wax_bean.n.02', 'name': 'wax_bean'}, {'id': 13232, 'synset': 'shell_bean.n.02', 'name': 'shell_bean'}, {'id': 13233, 'synset': 'lima_bean.n.03', 'name': 'lima_bean'}, {'id': 13234, 'synset': 'fordhooks.n.01', 'name': 'Fordhooks'}, {'id': 13235, 'synset': 'sieva_bean.n.02', 'name': 'sieva_bean'}, {'id': 13236, 'synset': 'fava_bean.n.02', 'name': 'fava_bean'}, {'id': 13237, 'synset': 'soy.n.04', 'name': 'soy'}, {'id': 13238, 'synset': 'green_soybean.n.01', 'name': 'green_soybean'}, {'id': 13239, 'synset': 'field_soybean.n.01', 'name': 'field_soybean'}, {'id': 13240, 'synset': 'cardoon.n.02', 'name': 'cardoon'}, {'id': 13241, 'synset': 'carrot.n.03', 'name': 'carrot'}, {'id': 13242, 'synset': 'carrot_stick.n.01', 'name': 'carrot_stick'}, {'id': 13243, 'synset': 'celery.n.02', 'name': 'celery'}, {'id': 13244, 'synset': 'pascal_celery.n.01', 'name': 'pascal_celery'}, {'id': 13245, 'synset': 'celeriac.n.02', 'name': 'celeriac'}, {'id': 13246, 'synset': 'chicory.n.04', 'name': 'chicory'}, {'id': 13247, 'synset': 'radicchio.n.01', 'name': 'radicchio'}, {'id': 13248, 'synset': 'coffee_substitute.n.01', 'name': 'coffee_substitute'}, {'id': 13249, 'synset': 'chicory.n.03', 'name': 'chicory'}, {'id': 13250, 'synset': 'postum.n.01', 'name': 'Postum'}, {'id': 13251, 'synset': 'chicory_escarole.n.01', 'name': 'chicory_escarole'}, {'id': 13252, 'synset': 'belgian_endive.n.01', 'name': 'Belgian_endive'}, {'id': 13253, 'synset': 'sweet_corn.n.02', 'name': 'sweet_corn'}, {'id': 13254, 'synset': 'hominy.n.01', 'name': 'hominy'}, {'id': 13255, 'synset': 'lye_hominy.n.01', 'name': 'lye_hominy'}, {'id': 13256, 'synset': 'pearl_hominy.n.01', 'name': 'pearl_hominy'}, {'id': 13257, 'synset': 'popcorn.n.02', 'name': 'popcorn'}, {'id': 13258, 'synset': 'cress.n.02', 'name': 'cress'}, {'id': 13259, 'synset': 'watercress.n.02', 'name': 'watercress'}, {'id': 13260, 'synset': 'garden_cress.n.01', 'name': 'garden_cress'}, {'id': 13261, 'synset': 'winter_cress.n.02', 'name': 'winter_cress'}, {'id': 13262, 'synset': 'dandelion_green.n.02', 'name': 'dandelion_green'}, {'id': 13263, 'synset': 'gumbo.n.03', 'name': 'gumbo'}, {'id': 13264, 'synset': 'kohlrabi.n.02', 'name': 'kohlrabi'}, {'id': 13265, 'synset': "lamb's-quarter.n.01", 'name': "lamb's-quarter"}, {'id': 13266, 'synset': 'wild_spinach.n.03', 'name': 'wild_spinach'}, {'id': 13267, 'synset': 'beefsteak_tomato.n.01', 'name': 'beefsteak_tomato'}, {'id': 13268, 'synset': 'cherry_tomato.n.02', 'name': 'cherry_tomato'}, {'id': 13269, 'synset': 'plum_tomato.n.02', 'name': 'plum_tomato'}, {'id': 13270, 'synset': 'tomatillo.n.03', 'name': 'tomatillo'}, {'id': 13271, 'synset': 'mushroom.n.05', 'name': 'mushroom'}, {'id': 13272, 'synset': 'stuffed_mushroom.n.01', 'name': 'stuffed_mushroom'}, {'id': 13273, 'synset': 'salsify.n.03', 'name': 'salsify'}, {'id': 13274, 'synset': 'oyster_plant.n.03', 'name': 'oyster_plant'}, {'id': 13275, 'synset': 'scorzonera.n.02', 'name': 'scorzonera'}, {'id': 13276, 'synset': 'parsnip.n.03', 'name': 'parsnip'}, {'id': 13277, 'synset': 'radish.n.01', 'name': 'radish'}, {'id': 13278, 'synset': 'turnip.n.02', 'name': 'turnip'}, {'id': 13279, 'synset': 'white_turnip.n.02', 'name': 'white_turnip'}, {'id': 13280, 'synset': 'rutabaga.n.01', 'name': 'rutabaga'}, {'id': 13281, 'synset': 'turnip_greens.n.01', 'name': 'turnip_greens'}, {'id': 13282, 'synset': 'sorrel.n.04', 'name': 'sorrel'}, {'id': 13283, 'synset': 'french_sorrel.n.02', 'name': 'French_sorrel'}, {'id': 13284, 'synset': 'spinach.n.02', 'name': 'spinach'}, {'id': 13285, 'synset': 'taro.n.03', 'name': 'taro'}, {'id': 13286, 'synset': 'truffle.n.02', 'name': 'truffle'}, {'id': 13287, 'synset': 'edible_nut.n.01', 'name': 'edible_nut'}, {'id': 13288, 'synset': 'bunya_bunya.n.02', 'name': 'bunya_bunya'}, {'id': 13289, 'synset': 'peanut.n.04', 'name': 'peanut'}, {'id': 13290, 'synset': 'freestone.n.01', 'name': 'freestone'}, {'id': 13291, 'synset': 'cling.n.01', 'name': 'cling'}, {'id': 13292, 'synset': 'windfall.n.01', 'name': 'windfall'}, {'id': 13293, 'synset': 'crab_apple.n.03', 'name': 'crab_apple'}, {'id': 13294, 'synset': 'eating_apple.n.01', 'name': 'eating_apple'}, {'id': 13295, 'synset': 'baldwin.n.03', 'name': 'Baldwin'}, {'id': 13296, 'synset': 'cortland.n.01', 'name': 'Cortland'}, {'id': 13297, 'synset': "cox's_orange_pippin.n.01", 'name': "Cox's_Orange_Pippin"}, {'id': 13298, 'synset': 'delicious.n.01', 'name': 'Delicious'}, {'id': 13299, 'synset': 'golden_delicious.n.01', 'name': 'Golden_Delicious'}, {'id': 13300, 'synset': 'red_delicious.n.01', 'name': 'Red_Delicious'}, {'id': 13301, 'synset': 'empire.n.05', 'name': 'Empire'}, {'id': 13302, 'synset': "grimes'_golden.n.01", 'name': "Grimes'_golden"}, {'id': 13303, 'synset': 'jonathan.n.01', 'name': 'Jonathan'}, {'id': 13304, 'synset': 'mcintosh.n.01', 'name': 'McIntosh'}, {'id': 13305, 'synset': 'macoun.n.01', 'name': 'Macoun'}, {'id': 13306, 'synset': 'northern_spy.n.01', 'name': 'Northern_Spy'}, {'id': 13307, 'synset': 'pearmain.n.01', 'name': 'Pearmain'}, {'id': 13308, 'synset': 'pippin.n.01', 'name': 'Pippin'}, {'id': 13309, 'synset': 'prima.n.01', 'name': 'Prima'}, {'id': 13310, 'synset': 'stayman.n.01', 'name': 'Stayman'}, {'id': 13311, 'synset': 'winesap.n.01', 'name': 'Winesap'}, {'id': 13312, 'synset': 'stayman_winesap.n.01', 'name': 'Stayman_Winesap'}, {'id': 13313, 'synset': 'cooking_apple.n.01', 'name': 'cooking_apple'}, {'id': 13314, 'synset': "bramley's_seedling.n.01", 'name': "Bramley's_Seedling"}, {'id': 13315, 'synset': 'granny_smith.n.01', 'name': 'Granny_Smith'}, {'id': 13316, 'synset': "lane's_prince_albert.n.01", 'name': "Lane's_Prince_Albert"}, {'id': 13317, 'synset': 'newtown_wonder.n.01', 'name': 'Newtown_Wonder'}, {'id': 13318, 'synset': 'rome_beauty.n.01', 'name': 'Rome_Beauty'}, {'id': 13319, 'synset': 'berry.n.01', 'name': 'berry'}, {'id': 13320, 'synset': 'bilberry.n.03', 'name': 'bilberry'}, {'id': 13321, 'synset': 'huckleberry.n.03', 'name': 'huckleberry'}, {'id': 13322, 'synset': 'wintergreen.n.03', 'name': 'wintergreen'}, {'id': 13323, 'synset': 'cranberry.n.02', 'name': 'cranberry'}, {'id': 13324, 'synset': 'lingonberry.n.02', 'name': 'lingonberry'}, {'id': 13325, 'synset': 'currant.n.01', 'name': 'currant'}, {'id': 13326, 'synset': 'gooseberry.n.02', 'name': 'gooseberry'}, {'id': 13327, 'synset': 'black_currant.n.02', 'name': 'black_currant'}, {'id': 13328, 'synset': 'red_currant.n.02', 'name': 'red_currant'}, {'id': 13329, 'synset': 'boysenberry.n.02', 'name': 'boysenberry'}, {'id': 13330, 'synset': 'dewberry.n.02', 'name': 'dewberry'}, {'id': 13331, 'synset': 'loganberry.n.02', 'name': 'loganberry'}, {'id': 13332, 'synset': 'saskatoon.n.02', 'name': 'saskatoon'}, {'id': 13333, 'synset': 'sugarberry.n.02', 'name': 'sugarberry'}, {'id': 13334, 'synset': 'acerola.n.02', 'name': 'acerola'}, {'id': 13335, 'synset': 'carambola.n.02', 'name': 'carambola'}, {'id': 13336, 'synset': 'ceriman.n.02', 'name': 'ceriman'}, {'id': 13337, 'synset': 'carissa_plum.n.01', 'name': 'carissa_plum'}, {'id': 13338, 'synset': 'citrus.n.01', 'name': 'citrus'}, {'id': 13339, 'synset': 'temple_orange.n.02', 'name': 'temple_orange'}, {'id': 13340, 'synset': 'clementine.n.02', 'name': 'clementine'}, {'id': 13341, 'synset': 'satsuma.n.02', 'name': 'satsuma'}, {'id': 13342, 'synset': 'tangerine.n.02', 'name': 'tangerine'}, {'id': 13343, 'synset': 'tangelo.n.02', 'name': 'tangelo'}, {'id': 13344, 'synset': 'bitter_orange.n.02', 'name': 'bitter_orange'}, {'id': 13345, 'synset': 'sweet_orange.n.01', 'name': 'sweet_orange'}, {'id': 13346, 'synset': 'jaffa_orange.n.01', 'name': 'Jaffa_orange'}, {'id': 13347, 'synset': 'navel_orange.n.01', 'name': 'navel_orange'}, {'id': 13348, 'synset': 'valencia_orange.n.01', 'name': 'Valencia_orange'}, {'id': 13349, 'synset': 'kumquat.n.02', 'name': 'kumquat'}, {'id': 13350, 'synset': 'key_lime.n.01', 'name': 'key_lime'}, {'id': 13351, 'synset': 'grapefruit.n.02', 'name': 'grapefruit'}, {'id': 13352, 'synset': 'pomelo.n.02', 'name': 'pomelo'}, {'id': 13353, 'synset': 'citrange.n.02', 'name': 'citrange'}, {'id': 13354, 'synset': 'citron.n.01', 'name': 'citron'}, {'id': 13355, 'synset': 'jordan_almond.n.02', 'name': 'Jordan_almond'}, {'id': 13356, 'synset': 'nectarine.n.02', 'name': 'nectarine'}, {'id': 13357, 'synset': 'pitahaya.n.02', 'name': 'pitahaya'}, {'id': 13358, 'synset': 'plum.n.02', 'name': 'plum'}, {'id': 13359, 'synset': 'damson.n.01', 'name': 'damson'}, {'id': 13360, 'synset': 'greengage.n.01', 'name': 'greengage'}, {'id': 13361, 'synset': 'beach_plum.n.02', 'name': 'beach_plum'}, {'id': 13362, 'synset': 'sloe.n.03', 'name': 'sloe'}, {'id': 13363, 'synset': 'victoria_plum.n.01', 'name': 'Victoria_plum'}, {'id': 13364, 'synset': 'dried_fruit.n.01', 'name': 'dried_fruit'}, {'id': 13365, 'synset': 'dried_apricot.n.01', 'name': 'dried_apricot'}, {'id': 13366, 'synset': 'raisin.n.01', 'name': 'raisin'}, {'id': 13367, 'synset': 'seedless_raisin.n.01', 'name': 'seedless_raisin'}, {'id': 13368, 'synset': 'seeded_raisin.n.01', 'name': 'seeded_raisin'}, {'id': 13369, 'synset': 'currant.n.03', 'name': 'currant'}, {'id': 13370, 'synset': 'anchovy_pear.n.02', 'name': 'anchovy_pear'}, {'id': 13371, 'synset': 'passion_fruit.n.01', 'name': 'passion_fruit'}, {'id': 13372, 'synset': 'granadilla.n.04', 'name': 'granadilla'}, {'id': 13373, 'synset': 'sweet_calabash.n.02', 'name': 'sweet_calabash'}, {'id': 13374, 'synset': 'bell_apple.n.01', 'name': 'bell_apple'}, {'id': 13375, 'synset': 'breadfruit.n.02', 'name': 'breadfruit'}, {'id': 13376, 'synset': 'jackfruit.n.02', 'name': 'jackfruit'}, {'id': 13377, 'synset': 'cacao_bean.n.01', 'name': 'cacao_bean'}, {'id': 13378, 'synset': 'cocoa.n.02', 'name': 'cocoa'}, {'id': 13379, 'synset': 'canistel.n.02', 'name': 'canistel'}, {'id': 13380, 'synset': 'melon_ball.n.01', 'name': 'melon_ball'}, {'id': 13381, 'synset': 'muskmelon.n.02', 'name': 'muskmelon'}, {'id': 13382, 'synset': 'winter_melon.n.02', 'name': 'winter_melon'}, {'id': 13383, 'synset': 'honeydew.n.01', 'name': 'honeydew'}, {'id': 13384, 'synset': 'persian_melon.n.02', 'name': 'Persian_melon'}, {'id': 13385, 'synset': 'net_melon.n.02', 'name': 'net_melon'}, {'id': 13386, 'synset': 'casaba.n.01', 'name': 'casaba'}, {'id': 13387, 'synset': 'sweet_cherry.n.02', 'name': 'sweet_cherry'}, {'id': 13388, 'synset': 'bing_cherry.n.01', 'name': 'bing_cherry'}, {'id': 13389, 'synset': 'heart_cherry.n.02', 'name': 'heart_cherry'}, {'id': 13390, 'synset': 'blackheart.n.02', 'name': 'blackheart'}, {'id': 13391, 'synset': 'capulin.n.02', 'name': 'capulin'}, {'id': 13392, 'synset': 'sour_cherry.n.03', 'name': 'sour_cherry'}, {'id': 13393, 'synset': 'amarelle.n.02', 'name': 'amarelle'}, {'id': 13394, 'synset': 'morello.n.02', 'name': 'morello'}, {'id': 13395, 'synset': 'cocoa_plum.n.02', 'name': 'cocoa_plum'}, {'id': 13396, 'synset': 'gherkin.n.01', 'name': 'gherkin'}, {'id': 13397, 'synset': 'fox_grape.n.02', 'name': 'fox_grape'}, {'id': 13398, 'synset': 'concord_grape.n.01', 'name': 'Concord_grape'}, {'id': 13399, 'synset': 'catawba.n.02', 'name': 'Catawba'}, {'id': 13400, 'synset': 'muscadine.n.02', 'name': 'muscadine'}, {'id': 13401, 'synset': 'scuppernong.n.01', 'name': 'scuppernong'}, {'id': 13402, 'synset': 'slipskin_grape.n.01', 'name': 'slipskin_grape'}, {'id': 13403, 'synset': 'vinifera_grape.n.02', 'name': 'vinifera_grape'}, {'id': 13404, 'synset': 'emperor.n.02', 'name': 'emperor'}, {'id': 13405, 'synset': 'muscat.n.04', 'name': 'muscat'}, {'id': 13406, 'synset': 'ribier.n.01', 'name': 'ribier'}, {'id': 13407, 'synset': 'sultana.n.01', 'name': 'sultana'}, {'id': 13408, 'synset': 'tokay.n.02', 'name': 'Tokay'}, {'id': 13409, 'synset': 'flame_tokay.n.01', 'name': 'flame_tokay'}, {'id': 13410, 'synset': 'thompson_seedless.n.01', 'name': 'Thompson_Seedless'}, {'id': 13411, 'synset': 'custard_apple.n.02', 'name': 'custard_apple'}, {'id': 13412, 'synset': 'cherimoya.n.02', 'name': 'cherimoya'}, {'id': 13413, 'synset': 'soursop.n.02', 'name': 'soursop'}, {'id': 13414, 'synset': 'sweetsop.n.02', 'name': 'sweetsop'}, {'id': 13415, 'synset': 'ilama.n.02', 'name': 'ilama'}, {'id': 13416, 'synset': 'pond_apple.n.02', 'name': 'pond_apple'}, {'id': 13417, 'synset': 'papaw.n.02', 'name': 'papaw'}, {'id': 13418, 'synset': 'kai_apple.n.01', 'name': 'kai_apple'}, {'id': 13419, 'synset': 'ketembilla.n.02', 'name': 'ketembilla'}, {'id': 13420, 'synset': 'ackee.n.01', 'name': 'ackee'}, {'id': 13421, 'synset': 'durian.n.02', 'name': 'durian'}, {'id': 13422, 'synset': 'feijoa.n.02', 'name': 'feijoa'}, {'id': 13423, 'synset': 'genip.n.02', 'name': 'genip'}, {'id': 13424, 'synset': 'genipap.n.01', 'name': 'genipap'}, {'id': 13425, 'synset': 'loquat.n.02', 'name': 'loquat'}, {'id': 13426, 'synset': 'mangosteen.n.02', 'name': 'mangosteen'}, {'id': 13427, 'synset': 'mango.n.02', 'name': 'mango'}, {'id': 13428, 'synset': 'sapodilla.n.02', 'name': 'sapodilla'}, {'id': 13429, 'synset': 'sapote.n.02', 'name': 'sapote'}, {'id': 13430, 'synset': 'tamarind.n.02', 'name': 'tamarind'}, {'id': 13431, 'synset': 'elderberry.n.02', 'name': 'elderberry'}, {'id': 13432, 'synset': 'guava.n.03', 'name': 'guava'}, {'id': 13433, 'synset': 'mombin.n.02', 'name': 'mombin'}, {'id': 13434, 'synset': 'hog_plum.n.04', 'name': 'hog_plum'}, {'id': 13435, 'synset': 'hog_plum.n.03', 'name': 'hog_plum'}, {'id': 13436, 'synset': 'jaboticaba.n.02', 'name': 'jaboticaba'}, {'id': 13437, 'synset': 'jujube.n.02', 'name': 'jujube'}, {'id': 13438, 'synset': 'litchi.n.02', 'name': 'litchi'}, {'id': 13439, 'synset': 'longanberry.n.02', 'name': 'longanberry'}, {'id': 13440, 'synset': 'mamey.n.02', 'name': 'mamey'}, {'id': 13441, 'synset': 'marang.n.02', 'name': 'marang'}, {'id': 13442, 'synset': 'medlar.n.04', 'name': 'medlar'}, {'id': 13443, 'synset': 'medlar.n.03', 'name': 'medlar'}, {'id': 13444, 'synset': 'mulberry.n.02', 'name': 'mulberry'}, {'id': 13445, 'synset': 'olive.n.04', 'name': 'olive'}, {'id': 13446, 'synset': 'black_olive.n.01', 'name': 'black_olive'}, {'id': 13447, 'synset': 'green_olive.n.01', 'name': 'green_olive'}, {'id': 13448, 'synset': 'bosc.n.01', 'name': 'bosc'}, {'id': 13449, 'synset': 'anjou.n.02', 'name': 'anjou'}, {'id': 13450, 'synset': 'bartlett.n.03', 'name': 'bartlett'}, {'id': 13451, 'synset': 'seckel.n.01', 'name': 'seckel'}, {'id': 13452, 'synset': 'plantain.n.03', 'name': 'plantain'}, {'id': 13453, 'synset': 'plumcot.n.02', 'name': 'plumcot'}, {'id': 13454, 'synset': 'pomegranate.n.02', 'name': 'pomegranate'}, {'id': 13455, 'synset': 'prickly_pear.n.02', 'name': 'prickly_pear'}, {'id': 13456, 'synset': 'barbados_gooseberry.n.02', 'name': 'Barbados_gooseberry'}, {'id': 13457, 'synset': 'quandong.n.04', 'name': 'quandong'}, {'id': 13458, 'synset': 'quandong_nut.n.01', 'name': 'quandong_nut'}, {'id': 13459, 'synset': 'quince.n.02', 'name': 'quince'}, {'id': 13460, 'synset': 'rambutan.n.02', 'name': 'rambutan'}, {'id': 13461, 'synset': 'pulasan.n.02', 'name': 'pulasan'}, {'id': 13462, 'synset': 'rose_apple.n.02', 'name': 'rose_apple'}, {'id': 13463, 'synset': 'sorb.n.01', 'name': 'sorb'}, {'id': 13464, 'synset': 'sour_gourd.n.02', 'name': 'sour_gourd'}, {'id': 13465, 'synset': 'edible_seed.n.01', 'name': 'edible_seed'}, {'id': 13466, 'synset': 'pumpkin_seed.n.01', 'name': 'pumpkin_seed'}, {'id': 13467, 'synset': 'betel_nut.n.01', 'name': 'betel_nut'}, {'id': 13468, 'synset': 'beechnut.n.01', 'name': 'beechnut'}, {'id': 13469, 'synset': 'walnut.n.01', 'name': 'walnut'}, {'id': 13470, 'synset': 'black_walnut.n.02', 'name': 'black_walnut'}, {'id': 13471, 'synset': 'english_walnut.n.02', 'name': 'English_walnut'}, {'id': 13472, 'synset': 'brazil_nut.n.02', 'name': 'brazil_nut'}, {'id': 13473, 'synset': 'butternut.n.02', 'name': 'butternut'}, {'id': 13474, 'synset': 'souari_nut.n.02', 'name': 'souari_nut'}, {'id': 13475, 'synset': 'cashew.n.02', 'name': 'cashew'}, {'id': 13476, 'synset': 'chestnut.n.03', 'name': 'chestnut'}, {'id': 13477, 'synset': 'chincapin.n.01', 'name': 'chincapin'}, {'id': 13478, 'synset': 'hazelnut.n.02', 'name': 'hazelnut'}, {'id': 13479, 'synset': 'coconut_milk.n.02', 'name': 'coconut_milk'}, {'id': 13480, 'synset': 'grugru_nut.n.01', 'name': 'grugru_nut'}, {'id': 13481, 'synset': 'hickory_nut.n.01', 'name': 'hickory_nut'}, {'id': 13482, 'synset': 'cola_extract.n.01', 'name': 'cola_extract'}, {'id': 13483, 'synset': 'macadamia_nut.n.02', 'name': 'macadamia_nut'}, {'id': 13484, 'synset': 'pecan.n.03', 'name': 'pecan'}, {'id': 13485, 'synset': 'pine_nut.n.01', 'name': 'pine_nut'}, {'id': 13486, 'synset': 'pistachio.n.02', 'name': 'pistachio'}, {'id': 13487, 'synset': 'sunflower_seed.n.01', 'name': 'sunflower_seed'}, {'id': 13488, 'synset': 'anchovy_paste.n.01', 'name': 'anchovy_paste'}, {'id': 13489, 'synset': 'rollmops.n.01', 'name': 'rollmops'}, {'id': 13490, 'synset': 'feed.n.01', 'name': 'feed'}, {'id': 13491, 'synset': 'cattle_cake.n.01', 'name': 'cattle_cake'}, {'id': 13492, 'synset': 'creep_feed.n.01', 'name': 'creep_feed'}, {'id': 13493, 'synset': 'fodder.n.02', 'name': 'fodder'}, {'id': 13494, 'synset': 'feed_grain.n.01', 'name': 'feed_grain'}, {'id': 13495, 'synset': 'eatage.n.01', 'name': 'eatage'}, {'id': 13496, 'synset': 'silage.n.01', 'name': 'silage'}, {'id': 13497, 'synset': 'oil_cake.n.01', 'name': 'oil_cake'}, {'id': 13498, 'synset': 'oil_meal.n.01', 'name': 'oil_meal'}, {'id': 13499, 'synset': 'alfalfa.n.02', 'name': 'alfalfa'}, {'id': 13500, 'synset': 'broad_bean.n.03', 'name': 'broad_bean'}, {'id': 13501, 'synset': 'hay.n.01', 'name': 'hay'}, {'id': 13502, 'synset': 'timothy.n.03', 'name': 'timothy'}, {'id': 13503, 'synset': 'stover.n.01', 'name': 'stover'}, {'id': 13504, 'synset': 'grain.n.02', 'name': 'grain'}, {'id': 13505, 'synset': 'grist.n.01', 'name': 'grist'}, {'id': 13506, 'synset': 'groats.n.01', 'name': 'groats'}, {'id': 13507, 'synset': 'millet.n.03', 'name': 'millet'}, {'id': 13508, 'synset': 'barley.n.01', 'name': 'barley'}, {'id': 13509, 'synset': 'pearl_barley.n.01', 'name': 'pearl_barley'}, {'id': 13510, 'synset': 'buckwheat.n.02', 'name': 'buckwheat'}, {'id': 13511, 'synset': 'bulgur.n.01', 'name': 'bulgur'}, {'id': 13512, 'synset': 'wheat.n.02', 'name': 'wheat'}, {'id': 13513, 'synset': 'cracked_wheat.n.01', 'name': 'cracked_wheat'}, {'id': 13514, 'synset': 'stodge.n.01', 'name': 'stodge'}, {'id': 13515, 'synset': 'wheat_germ.n.01', 'name': 'wheat_germ'}, {'id': 13516, 'synset': 'oat.n.02', 'name': 'oat'}, {'id': 13517, 'synset': 'rice.n.01', 'name': 'rice'}, {'id': 13518, 'synset': 'brown_rice.n.01', 'name': 'brown_rice'}, {'id': 13519, 'synset': 'white_rice.n.01', 'name': 'white_rice'}, {'id': 13520, 'synset': 'wild_rice.n.02', 'name': 'wild_rice'}, {'id': 13521, 'synset': 'paddy.n.03', 'name': 'paddy'}, {'id': 13522, 'synset': 'slop.n.01', 'name': 'slop'}, {'id': 13523, 'synset': 'mash.n.02', 'name': 'mash'}, {'id': 13524, 'synset': 'chicken_feed.n.01', 'name': 'chicken_feed'}, {'id': 13525, 'synset': 'cud.n.01', 'name': 'cud'}, {'id': 13526, 'synset': 'bird_feed.n.01', 'name': 'bird_feed'}, {'id': 13527, 'synset': 'petfood.n.01', 'name': 'petfood'}, {'id': 13528, 'synset': 'dog_food.n.01', 'name': 'dog_food'}, {'id': 13529, 'synset': 'cat_food.n.01', 'name': 'cat_food'}, {'id': 13530, 'synset': 'canary_seed.n.01', 'name': 'canary_seed'}, {'id': 13531, 'synset': 'tossed_salad.n.01', 'name': 'tossed_salad'}, {'id': 13532, 'synset': 'green_salad.n.01', 'name': 'green_salad'}, {'id': 13533, 'synset': 'caesar_salad.n.01', 'name': 'Caesar_salad'}, {'id': 13534, 'synset': 'salmagundi.n.02', 'name': 'salmagundi'}, {'id': 13535, 'synset': 'salad_nicoise.n.01', 'name': 'salad_nicoise'}, {'id': 13536, 'synset': 'combination_salad.n.01', 'name': 'combination_salad'}, {'id': 13537, 'synset': "chef's_salad.n.01", 'name': "chef's_salad"}, {'id': 13538, 'synset': 'potato_salad.n.01', 'name': 'potato_salad'}, {'id': 13539, 'synset': 'pasta_salad.n.01', 'name': 'pasta_salad'}, {'id': 13540, 'synset': 'macaroni_salad.n.01', 'name': 'macaroni_salad'}, {'id': 13541, 'synset': 'fruit_salad.n.01', 'name': 'fruit_salad'}, {'id': 13542, 'synset': 'waldorf_salad.n.01', 'name': 'Waldorf_salad'}, {'id': 13543, 'synset': 'crab_louis.n.01', 'name': 'crab_Louis'}, {'id': 13544, 'synset': 'herring_salad.n.01', 'name': 'herring_salad'}, {'id': 13545, 'synset': 'tuna_fish_salad.n.01', 'name': 'tuna_fish_salad'}, {'id': 13546, 'synset': 'chicken_salad.n.01', 'name': 'chicken_salad'}, {'id': 13547, 'synset': 'aspic.n.01', 'name': 'aspic'}, {'id': 13548, 'synset': 'molded_salad.n.01', 'name': 'molded_salad'}, {'id': 13549, 'synset': 'tabbouleh.n.01', 'name': 'tabbouleh'}, {'id': 13550, 'synset': 'ingredient.n.03', 'name': 'ingredient'}, {'id': 13551, 'synset': 'flavorer.n.01', 'name': 'flavorer'}, {'id': 13552, 'synset': 'bouillon_cube.n.01', 'name': 'bouillon_cube'}, {'id': 13553, 'synset': 'herb.n.02', 'name': 'herb'}, {'id': 13554, 'synset': 'fines_herbes.n.01', 'name': 'fines_herbes'}, {'id': 13555, 'synset': 'spice.n.02', 'name': 'spice'}, {'id': 13556, 'synset': 'spearmint_oil.n.01', 'name': 'spearmint_oil'}, {'id': 13557, 'synset': 'lemon_oil.n.01', 'name': 'lemon_oil'}, {'id': 13558, 'synset': 'wintergreen_oil.n.01', 'name': 'wintergreen_oil'}, {'id': 13559, 'synset': 'salt.n.02', 'name': 'salt'}, {'id': 13560, 'synset': 'celery_salt.n.01', 'name': 'celery_salt'}, {'id': 13561, 'synset': 'onion_salt.n.01', 'name': 'onion_salt'}, {'id': 13562, 'synset': 'seasoned_salt.n.01', 'name': 'seasoned_salt'}, {'id': 13563, 'synset': 'sour_salt.n.01', 'name': 'sour_salt'}, {'id': 13564, 'synset': 'five_spice_powder.n.01', 'name': 'five_spice_powder'}, {'id': 13565, 'synset': 'allspice.n.03', 'name': 'allspice'}, {'id': 13566, 'synset': 'cinnamon.n.03', 'name': 'cinnamon'}, {'id': 13567, 'synset': 'stick_cinnamon.n.01', 'name': 'stick_cinnamon'}, {'id': 13568, 'synset': 'clove.n.04', 'name': 'clove'}, {'id': 13569, 'synset': 'cumin.n.02', 'name': 'cumin'}, {'id': 13570, 'synset': 'fennel.n.04', 'name': 'fennel'}, {'id': 13571, 'synset': 'ginger.n.02', 'name': 'ginger'}, {'id': 13572, 'synset': 'mace.n.03', 'name': 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13589, 'synset': 'fennel.n.02', 'name': 'fennel'}, {'id': 13590, 'synset': 'fennel_seed.n.01', 'name': 'fennel_seed'}, {'id': 13591, 'synset': 'fenugreek.n.02', 'name': 'fenugreek'}, {'id': 13592, 'synset': 'clove.n.03', 'name': 'clove'}, {'id': 13593, 'synset': 'garlic_chive.n.02', 'name': 'garlic_chive'}, {'id': 13594, 'synset': 'lemon_balm.n.02', 'name': 'lemon_balm'}, {'id': 13595, 'synset': 'lovage.n.02', 'name': 'lovage'}, {'id': 13596, 'synset': 'marjoram.n.02', 'name': 'marjoram'}, {'id': 13597, 'synset': 'mint.n.04', 'name': 'mint'}, {'id': 13598, 'synset': 'mustard_seed.n.01', 'name': 'mustard_seed'}, {'id': 13599, 'synset': 'mustard.n.02', 'name': 'mustard'}, {'id': 13600, 'synset': 'chinese_mustard.n.02', 'name': 'Chinese_mustard'}, {'id': 13601, 'synset': 'nasturtium.n.03', 'name': 'nasturtium'}, {'id': 13602, 'synset': 'parsley.n.02', 'name': 'parsley'}, {'id': 13603, 'synset': 'salad_burnet.n.02', 'name': 'salad_burnet'}, {'id': 13604, 'synset': 'rosemary.n.02', 'name': 'rosemary'}, {'id': 13605, 'synset': 'rue.n.02', 'name': 'rue'}, {'id': 13606, 'synset': 'sage.n.02', 'name': 'sage'}, {'id': 13607, 'synset': 'clary_sage.n.02', 'name': 'clary_sage'}, {'id': 13608, 'synset': 'savory.n.03', 'name': 'savory'}, {'id': 13609, 'synset': 'summer_savory.n.02', 'name': 'summer_savory'}, {'id': 13610, 'synset': 'winter_savory.n.02', 'name': 'winter_savory'}, {'id': 13611, 'synset': 'sweet_woodruff.n.02', 'name': 'sweet_woodruff'}, {'id': 13612, 'synset': 'sweet_cicely.n.03', 'name': 'sweet_cicely'}, {'id': 13613, 'synset': 'tarragon.n.02', 'name': 'tarragon'}, {'id': 13614, 'synset': 'thyme.n.02', 'name': 'thyme'}, {'id': 13615, 'synset': 'turmeric.n.02', 'name': 'turmeric'}, {'id': 13616, 'synset': 'caper.n.02', 'name': 'caper'}, {'id': 13617, 'synset': 'catsup.n.01', 'name': 'catsup'}, {'id': 13618, 'synset': 'cardamom.n.02', 'name': 'cardamom'}, {'id': 13619, 'synset': 'chili_powder.n.01', 'name': 'chili_powder'}, {'id': 13620, 'synset': 'chili_sauce.n.01', 'name': 'chili_sauce'}, {'id': 13621, 'synset': 'chutney.n.01', 'name': 'chutney'}, {'id': 13622, 'synset': 'steak_sauce.n.01', 'name': 'steak_sauce'}, {'id': 13623, 'synset': 'taco_sauce.n.01', 'name': 'taco_sauce'}, {'id': 13624, 'synset': 'mint_sauce.n.01', 'name': 'mint_sauce'}, {'id': 13625, 'synset': 'cranberry_sauce.n.01', 'name': 'cranberry_sauce'}, {'id': 13626, 'synset': 'curry_powder.n.01', 'name': 'curry_powder'}, {'id': 13627, 'synset': 'curry.n.01', 'name': 'curry'}, {'id': 13628, 'synset': 'lamb_curry.n.01', 'name': 'lamb_curry'}, {'id': 13629, 'synset': 'duck_sauce.n.01', 'name': 'duck_sauce'}, {'id': 13630, 'synset': 'horseradish.n.03', 'name': 'horseradish'}, {'id': 13631, 'synset': 'marinade.n.01', 'name': 'marinade'}, {'id': 13632, 'synset': 'paprika.n.02', 'name': 'paprika'}, {'id': 13633, 'synset': 'spanish_paprika.n.01', 'name': 'Spanish_paprika'}, {'id': 13634, 'synset': 'dill_pickle.n.01', 'name': 'dill_pickle'}, {'id': 13635, 'synset': 'bread_and_butter_pickle.n.01', 'name': 'bread_and_butter_pickle'}, {'id': 13636, 'synset': 'pickle_relish.n.01', 'name': 'pickle_relish'}, {'id': 13637, 'synset': 'piccalilli.n.01', 'name': 'piccalilli'}, {'id': 13638, 'synset': 'sweet_pickle.n.01', 'name': 'sweet_pickle'}, {'id': 13639, 'synset': 'soy_sauce.n.01', 'name': 'soy_sauce'}, {'id': 13640, 'synset': 'tomato_paste.n.01', 'name': 'tomato_paste'}, {'id': 13641, 'synset': 'angelica.n.03', 'name': 'angelica'}, {'id': 13642, 'synset': 'angelica.n.02', 'name': 'angelica'}, {'id': 13643, 'synset': 'almond_extract.n.01', 'name': 'almond_extract'}, {'id': 13644, 'synset': 'anise.n.02', 'name': 'anise'}, {'id': 13645, 'synset': 'chinese_anise.n.02', 'name': 'Chinese_anise'}, {'id': 13646, 'synset': 'juniper_berries.n.01', 'name': 'juniper_berries'}, {'id': 13647, 'synset': 'saffron.n.02', 'name': 'saffron'}, {'id': 13648, 'synset': 'sesame_seed.n.01', 'name': 'sesame_seed'}, {'id': 13649, 'synset': 'caraway_seed.n.01', 'name': 'caraway_seed'}, {'id': 13650, 'synset': 'poppy_seed.n.01', 'name': 'poppy_seed'}, {'id': 13651, 'synset': 'dill.n.02', 'name': 'dill'}, {'id': 13652, 'synset': 'dill_seed.n.01', 'name': 'dill_seed'}, {'id': 13653, 'synset': 'celery_seed.n.01', 'name': 'celery_seed'}, {'id': 13654, 'synset': 'lemon_extract.n.01', 'name': 'lemon_extract'}, {'id': 13655, 'synset': 'monosodium_glutamate.n.01', 'name': 'monosodium_glutamate'}, {'id': 13656, 'synset': 'vanilla_bean.n.01', 'name': 'vanilla_bean'}, {'id': 13657, 'synset': 'cider_vinegar.n.01', 'name': 'cider_vinegar'}, {'id': 13658, 'synset': 'wine_vinegar.n.01', 'name': 'wine_vinegar'}, {'id': 13659, 'synset': 'sauce.n.01', 'name': 'sauce'}, {'id': 13660, 'synset': 'anchovy_sauce.n.01', 'name': 'anchovy_sauce'}, {'id': 13661, 'synset': 'hard_sauce.n.01', 'name': 'hard_sauce'}, {'id': 13662, 'synset': 'horseradish_sauce.n.01', 'name': 'horseradish_sauce'}, {'id': 13663, 'synset': 'bolognese_pasta_sauce.n.01', 'name': 'bolognese_pasta_sauce'}, {'id': 13664, 'synset': 'carbonara.n.01', 'name': 'carbonara'}, {'id': 13665, 'synset': 'tomato_sauce.n.01', 'name': 'tomato_sauce'}, {'id': 13666, 'synset': 'tartare_sauce.n.01', 'name': 'tartare_sauce'}, {'id': 13667, 'synset': 'wine_sauce.n.01', 'name': 'wine_sauce'}, {'id': 13668, 'synset': 'marchand_de_vin.n.01', 'name': 'marchand_de_vin'}, {'id': 13669, 'synset': 'bread_sauce.n.01', 'name': 'bread_sauce'}, {'id': 13670, 'synset': 'plum_sauce.n.01', 'name': 'plum_sauce'}, {'id': 13671, 'synset': 'peach_sauce.n.01', 'name': 'peach_sauce'}, {'id': 13672, 'synset': 'apricot_sauce.n.01', 'name': 'apricot_sauce'}, {'id': 13673, 'synset': 'pesto.n.01', 'name': 'pesto'}, {'id': 13674, 'synset': 'ravigote.n.01', 'name': 'ravigote'}, {'id': 13675, 'synset': 'remoulade_sauce.n.01', 'name': 'remoulade_sauce'}, {'id': 13676, 'synset': 'dressing.n.01', 'name': 'dressing'}, {'id': 13677, 'synset': 'sauce_louis.n.01', 'name': 'sauce_Louis'}, {'id': 13678, 'synset': 'bleu_cheese_dressing.n.01', 'name': 'bleu_cheese_dressing'}, {'id': 13679, 'synset': 'blue_cheese_dressing.n.01', 'name': 'blue_cheese_dressing'}, {'id': 13680, 'synset': 'french_dressing.n.01', 'name': 'French_dressing'}, {'id': 13681, 'synset': 'lorenzo_dressing.n.01', 'name': 'Lorenzo_dressing'}, {'id': 13682, 'synset': 'anchovy_dressing.n.01', 'name': 'anchovy_dressing'}, {'id': 13683, 'synset': 'italian_dressing.n.01', 'name': 'Italian_dressing'}, {'id': 13684, 'synset': 'half-and-half_dressing.n.01', 'name': 'half-and-half_dressing'}, {'id': 13685, 'synset': 'mayonnaise.n.01', 'name': 'mayonnaise'}, {'id': 13686, 'synset': 'green_mayonnaise.n.01', 'name': 'green_mayonnaise'}, {'id': 13687, 'synset': 'aioli.n.01', 'name': 'aioli'}, {'id': 13688, 'synset': 'russian_dressing.n.01', 'name': 'Russian_dressing'}, {'id': 13689, 'synset': 'salad_cream.n.01', 'name': 'salad_cream'}, {'id': 13690, 'synset': 'thousand_island_dressing.n.01', 'name': 'Thousand_Island_dressing'}, {'id': 13691, 'synset': 'barbecue_sauce.n.01', 'name': 'barbecue_sauce'}, {'id': 13692, 'synset': 'hollandaise.n.01', 'name': 'hollandaise'}, {'id': 13693, 'synset': 'bearnaise.n.01', 'name': 'bearnaise'}, {'id': 13694, 'synset': 'bercy.n.01', 'name': 'Bercy'}, {'id': 13695, 'synset': 'bordelaise.n.01', 'name': 'bordelaise'}, {'id': 13696, 'synset': 'bourguignon.n.01', 'name': 'bourguignon'}, {'id': 13697, 'synset': 'brown_sauce.n.02', 'name': 'brown_sauce'}, {'id': 13698, 'synset': 'espagnole.n.01', 'name': 'Espagnole'}, {'id': 13699, 'synset': 'chinese_brown_sauce.n.01', 'name': 'Chinese_brown_sauce'}, {'id': 13700, 'synset': 'blanc.n.01', 'name': 'blanc'}, {'id': 13701, 'synset': 'cheese_sauce.n.01', 'name': 'cheese_sauce'}, {'id': 13702, 'synset': 'chocolate_sauce.n.01', 'name': 'chocolate_sauce'}, {'id': 13703, 'synset': 'hot-fudge_sauce.n.01', 'name': 'hot-fudge_sauce'}, {'id': 13704, 'synset': 'cocktail_sauce.n.01', 'name': 'cocktail_sauce'}, {'id': 13705, 'synset': 'colbert.n.01', 'name': 'Colbert'}, {'id': 13706, 'synset': 'white_sauce.n.01', 'name': 'white_sauce'}, {'id': 13707, 'synset': 'cream_sauce.n.01', 'name': 'cream_sauce'}, {'id': 13708, 'synset': 'mornay_sauce.n.01', 'name': 'Mornay_sauce'}, {'id': 13709, 'synset': 'demiglace.n.01', 'name': 'demiglace'}, {'id': 13710, 'synset': 'gravy.n.02', 'name': 'gravy'}, {'id': 13711, 'synset': 'gravy.n.01', 'name': 'gravy'}, {'id': 13712, 'synset': 'spaghetti_sauce.n.01', 'name': 'spaghetti_sauce'}, {'id': 13713, 'synset': 'marinara.n.01', 'name': 'marinara'}, {'id': 13714, 'synset': 'mole.n.03', 'name': 'mole'}, {'id': 13715, 'synset': "hunter's_sauce.n.01", 'name': "hunter's_sauce"}, {'id': 13716, 'synset': 'mushroom_sauce.n.01', 'name': 'mushroom_sauce'}, {'id': 13717, 'synset': 'mustard_sauce.n.01', 'name': 'mustard_sauce'}, {'id': 13718, 'synset': 'nantua.n.01', 'name': 'Nantua'}, {'id': 13719, 'synset': 'hungarian_sauce.n.01', 'name': 'Hungarian_sauce'}, {'id': 13720, 'synset': 'pepper_sauce.n.01', 'name': 'pepper_sauce'}, {'id': 13721, 'synset': 'roux.n.01', 'name': 'roux'}, {'id': 13722, 'synset': 'smitane.n.01', 'name': 'Smitane'}, {'id': 13723, 'synset': 'soubise.n.01', 'name': 'Soubise'}, {'id': 13724, 'synset': 'lyonnaise_sauce.n.01', 'name': 'Lyonnaise_sauce'}, {'id': 13725, 'synset': 'veloute.n.01', 'name': 'veloute'}, {'id': 13726, 'synset': 'allemande.n.01', 'name': 'allemande'}, {'id': 13727, 'synset': 'caper_sauce.n.01', 'name': 'caper_sauce'}, {'id': 13728, 'synset': 'poulette.n.01', 'name': 'poulette'}, {'id': 13729, 'synset': 'curry_sauce.n.01', 'name': 'curry_sauce'}, {'id': 13730, 'synset': 'worcester_sauce.n.01', 'name': 'Worcester_sauce'}, {'id': 13731, 'synset': 'coconut_milk.n.01', 'name': 'coconut_milk'}, {'id': 13732, 'synset': 'egg_white.n.01', 'name': 'egg_white'}, {'id': 13733, 'synset': 'hard-boiled_egg.n.01', 'name': 'hard-boiled_egg'}, {'id': 13734, 'synset': 'easter_egg.n.02', 'name': 'Easter_egg'}, {'id': 13735, 'synset': 'easter_egg.n.01', 'name': 'Easter_egg'}, {'id': 13736, 'synset': 'chocolate_egg.n.01', 'name': 'chocolate_egg'}, {'id': 13737, 'synset': 'candy_egg.n.01', 'name': 'candy_egg'}, {'id': 13738, 'synset': 'poached_egg.n.01', 'name': 'poached_egg'}, {'id': 13739, 'synset': 'scrambled_eggs.n.01', 'name': 'scrambled_eggs'}, {'id': 13740, 'synset': 'deviled_egg.n.01', 'name': 'deviled_egg'}, {'id': 13741, 'synset': 'shirred_egg.n.01', 'name': 'shirred_egg'}, {'id': 13742, 'synset': 'firm_omelet.n.01', 'name': 'firm_omelet'}, {'id': 13743, 'synset': 'french_omelet.n.01', 'name': 'French_omelet'}, {'id': 13744, 'synset': 'fluffy_omelet.n.01', 'name': 'fluffy_omelet'}, {'id': 13745, 'synset': 'western_omelet.n.01', 'name': 'western_omelet'}, {'id': 13746, 'synset': 'souffle.n.01', 'name': 'souffle'}, {'id': 13747, 'synset': 'fried_egg.n.01', 'name': 'fried_egg'}, {'id': 13748, 'synset': 'dairy_product.n.01', 'name': 'dairy_product'}, {'id': 13749, 'synset': 'milk.n.04', 'name': 'milk'}, {'id': 13750, 'synset': 'sour_milk.n.01', 'name': 'sour_milk'}, {'id': 13751, 'synset': 'formula.n.06', 'name': 'formula'}, {'id': 13752, 'synset': 'pasteurized_milk.n.01', 'name': 'pasteurized_milk'}, {'id': 13753, 'synset': "cows'_milk.n.01", 'name': "cows'_milk"}, {'id': 13754, 'synset': "yak's_milk.n.01", 'name': "yak's_milk"}, {'id': 13755, 'synset': "goats'_milk.n.01", 'name': "goats'_milk"}, {'id': 13756, 'synset': 'acidophilus_milk.n.01', 'name': 'acidophilus_milk'}, {'id': 13757, 'synset': 'raw_milk.n.01', 'name': 'raw_milk'}, {'id': 13758, 'synset': 'scalded_milk.n.01', 'name': 'scalded_milk'}, {'id': 13759, 'synset': 'homogenized_milk.n.01', 'name': 'homogenized_milk'}, {'id': 13760, 'synset': 'certified_milk.n.01', 'name': 'certified_milk'}, {'id': 13761, 'synset': 'powdered_milk.n.01', 'name': 'powdered_milk'}, {'id': 13762, 'synset': 'nonfat_dry_milk.n.01', 'name': 'nonfat_dry_milk'}, {'id': 13763, 'synset': 'evaporated_milk.n.01', 'name': 'evaporated_milk'}, {'id': 13764, 'synset': 'condensed_milk.n.01', 'name': 'condensed_milk'}, {'id': 13765, 'synset': 'skim_milk.n.01', 'name': 'skim_milk'}, {'id': 13766, 'synset': 'semi-skimmed_milk.n.01', 'name': 'semi-skimmed_milk'}, {'id': 13767, 'synset': 'whole_milk.n.01', 'name': 'whole_milk'}, {'id': 13768, 'synset': 'low-fat_milk.n.01', 'name': 'low-fat_milk'}, {'id': 13769, 'synset': 'buttermilk.n.01', 'name': 'buttermilk'}, {'id': 13770, 'synset': 'cream.n.02', 'name': 'cream'}, {'id': 13771, 'synset': 'clotted_cream.n.01', 'name': 'clotted_cream'}, {'id': 13772, 'synset': 'double_creme.n.01', 'name': 'double_creme'}, {'id': 13773, 'synset': 'half-and-half.n.01', 'name': 'half-and-half'}, {'id': 13774, 'synset': 'heavy_cream.n.01', 'name': 'heavy_cream'}, {'id': 13775, 'synset': 'light_cream.n.01', 'name': 'light_cream'}, {'id': 13776, 'synset': 'whipping_cream.n.01', 'name': 'whipping_cream'}, {'id': 13777, 'synset': 'clarified_butter.n.01', 'name': 'clarified_butter'}, {'id': 13778, 'synset': 'ghee.n.01', 'name': 'ghee'}, {'id': 13779, 'synset': 'brown_butter.n.01', 'name': 'brown_butter'}, {'id': 13780, 'synset': 'meuniere_butter.n.01', 'name': 'Meuniere_butter'}, {'id': 13781, 'synset': 'blueberry_yogurt.n.01', 'name': 'blueberry_yogurt'}, {'id': 13782, 'synset': 'raita.n.01', 'name': 'raita'}, {'id': 13783, 'synset': 'whey.n.02', 'name': 'whey'}, {'id': 13784, 'synset': 'curd.n.02', 'name': 'curd'}, {'id': 13785, 'synset': 'curd.n.01', 'name': 'curd'}, {'id': 13786, 'synset': 'clabber.n.01', 'name': 'clabber'}, {'id': 13787, 'synset': 'cheese.n.01', 'name': 'cheese'}, {'id': 13788, 'synset': 'paring.n.02', 'name': 'paring'}, {'id': 13789, 'synset': 'cream_cheese.n.01', 'name': 'cream_cheese'}, {'id': 13790, 'synset': 'double_cream.n.01', 'name': 'double_cream'}, {'id': 13791, 'synset': 'mascarpone.n.01', 'name': 'mascarpone'}, {'id': 13792, 'synset': 'triple_cream.n.01', 'name': 'triple_cream'}, {'id': 13793, 'synset': 'cottage_cheese.n.01', 'name': 'cottage_cheese'}, {'id': 13794, 'synset': 'process_cheese.n.01', 'name': 'process_cheese'}, {'id': 13795, 'synset': 'bleu.n.01', 'name': 'bleu'}, {'id': 13796, 'synset': 'stilton.n.01', 'name': 'Stilton'}, {'id': 13797, 'synset': 'roquefort.n.01', 'name': 'Roquefort'}, {'id': 13798, 'synset': 'gorgonzola.n.01', 'name': 'gorgonzola'}, {'id': 13799, 'synset': 'danish_blue.n.01', 'name': 'Danish_blue'}, {'id': 13800, 'synset': 'bavarian_blue.n.01', 'name': 'Bavarian_blue'}, {'id': 13801, 'synset': 'brie.n.01', 'name': 'Brie'}, {'id': 13802, 'synset': 'brick_cheese.n.01', 'name': 'brick_cheese'}, {'id': 13803, 'synset': 'camembert.n.01', 'name': 'Camembert'}, {'id': 13804, 'synset': 'cheddar.n.02', 'name': 'cheddar'}, {'id': 13805, 'synset': 'rat_cheese.n.01', 'name': 'rat_cheese'}, {'id': 13806, 'synset': 'cheshire_cheese.n.01', 'name': 'Cheshire_cheese'}, {'id': 13807, 'synset': 'double_gloucester.n.01', 'name': 'double_Gloucester'}, {'id': 13808, 'synset': 'edam.n.01', 'name': 'Edam'}, {'id': 13809, 'synset': 'goat_cheese.n.01', 'name': 'goat_cheese'}, {'id': 13810, 'synset': 'gouda.n.01', 'name': 'Gouda'}, {'id': 13811, 'synset': 'grated_cheese.n.01', 'name': 'grated_cheese'}, {'id': 13812, 'synset': 'hand_cheese.n.01', 'name': 'hand_cheese'}, {'id': 13813, 'synset': 'liederkranz.n.01', 'name': 'Liederkranz'}, {'id': 13814, 'synset': 'limburger.n.01', 'name': 'Limburger'}, {'id': 13815, 'synset': 'mozzarella.n.01', 'name': 'mozzarella'}, {'id': 13816, 'synset': 'muenster.n.01', 'name': 'Muenster'}, {'id': 13817, 'synset': 'parmesan.n.01', 'name': 'Parmesan'}, {'id': 13818, 'synset': 'quark_cheese.n.01', 'name': 'quark_cheese'}, {'id': 13819, 'synset': 'ricotta.n.01', 'name': 'ricotta'}, {'id': 13820, 'synset': 'swiss_cheese.n.01', 'name': 'Swiss_cheese'}, {'id': 13821, 'synset': 'emmenthal.n.01', 'name': 'Emmenthal'}, {'id': 13822, 'synset': 'gruyere.n.01', 'name': 'Gruyere'}, {'id': 13823, 'synset': 'sapsago.n.01', 'name': 'sapsago'}, {'id': 13824, 'synset': 'velveeta.n.01', 'name': 'Velveeta'}, {'id': 13825, 'synset': 'nut_butter.n.01', 'name': 'nut_butter'}, {'id': 13826, 'synset': 'marshmallow_fluff.n.01', 'name': 'marshmallow_fluff'}, {'id': 13827, 'synset': 'onion_butter.n.01', 'name': 'onion_butter'}, {'id': 13828, 'synset': 'pimento_butter.n.01', 'name': 'pimento_butter'}, {'id': 13829, 'synset': 'shrimp_butter.n.01', 'name': 'shrimp_butter'}, {'id': 13830, 'synset': 'lobster_butter.n.01', 'name': 'lobster_butter'}, {'id': 13831, 'synset': 'yak_butter.n.01', 'name': 'yak_butter'}, {'id': 13832, 'synset': 'spread.n.05', 'name': 'spread'}, {'id': 13833, 'synset': 'cheese_spread.n.01', 'name': 'cheese_spread'}, {'id': 13834, 'synset': 'anchovy_butter.n.01', 'name': 'anchovy_butter'}, {'id': 13835, 'synset': 'fishpaste.n.01', 'name': 'fishpaste'}, {'id': 13836, 'synset': 'garlic_butter.n.01', 'name': 'garlic_butter'}, {'id': 13837, 'synset': 'miso.n.01', 'name': 'miso'}, {'id': 13838, 'synset': 'wasabi.n.02', 'name': 'wasabi'}, {'id': 13839, 'synset': 'snail_butter.n.01', 'name': 'snail_butter'}, {'id': 13840, 'synset': 'pate.n.01', 'name': 'pate'}, {'id': 13841, 'synset': 'duck_pate.n.01', 'name': 'duck_pate'}, {'id': 13842, 'synset': 'foie_gras.n.01', 'name': 'foie_gras'}, {'id': 13843, 'synset': 'tapenade.n.01', 'name': 'tapenade'}, {'id': 13844, 'synset': 'tahini.n.01', 'name': 'tahini'}, {'id': 13845, 'synset': 'sweetening.n.01', 'name': 'sweetening'}, {'id': 13846, 'synset': 'aspartame.n.01', 'name': 'aspartame'}, {'id': 13847, 'synset': 'saccharin.n.01', 'name': 'saccharin'}, {'id': 13848, 'synset': 'sugar.n.01', 'name': 'sugar'}, {'id': 13849, 'synset': 'syrup.n.01', 'name': 'syrup'}, {'id': 13850, 'synset': 'sugar_syrup.n.01', 'name': 'sugar_syrup'}, {'id': 13851, 'synset': 'molasses.n.01', 'name': 'molasses'}, {'id': 13852, 'synset': 'sorghum.n.03', 'name': 'sorghum'}, {'id': 13853, 'synset': 'treacle.n.01', 'name': 'treacle'}, {'id': 13854, 'synset': 'grenadine.n.01', 'name': 'grenadine'}, {'id': 13855, 'synset': 'maple_syrup.n.01', 'name': 'maple_syrup'}, {'id': 13856, 'synset': 'corn_syrup.n.01', 'name': 'corn_syrup'}, {'id': 13857, 'synset': 'miraculous_food.n.01', 'name': 'miraculous_food'}, {'id': 13858, 'synset': 'dough.n.01', 'name': 'dough'}, {'id': 13859, 'synset': 'bread_dough.n.01', 'name': 'bread_dough'}, {'id': 13860, 'synset': 'pancake_batter.n.01', 'name': 'pancake_batter'}, {'id': 13861, 'synset': 'fritter_batter.n.01', 'name': 'fritter_batter'}, {'id': 13862, 'synset': 'coq_au_vin.n.01', 'name': 'coq_au_vin'}, {'id': 13863, 'synset': 'chicken_provencale.n.01', 'name': 'chicken_provencale'}, {'id': 13864, 'synset': 'chicken_and_rice.n.01', 'name': 'chicken_and_rice'}, {'id': 13865, 'synset': 'moo_goo_gai_pan.n.01', 'name': 'moo_goo_gai_pan'}, {'id': 13866, 'synset': 'arroz_con_pollo.n.01', 'name': 'arroz_con_pollo'}, {'id': 13867, 'synset': 'bacon_and_eggs.n.02', 'name': 'bacon_and_eggs'}, {'id': 13868, 'synset': 'barbecued_spareribs.n.01', 'name': 'barbecued_spareribs'}, {'id': 13869, 'synset': 'beef_bourguignonne.n.01', 'name': 'beef_Bourguignonne'}, {'id': 13870, 'synset': 'beef_wellington.n.01', 'name': 'beef_Wellington'}, {'id': 13871, 'synset': 'bitok.n.01', 'name': 'bitok'}, {'id': 13872, 'synset': 'boiled_dinner.n.01', 'name': 'boiled_dinner'}, {'id': 13873, 'synset': 'boston_baked_beans.n.01', 'name': 'Boston_baked_beans'}, {'id': 13874, 'synset': 'bubble_and_squeak.n.01', 'name': 'bubble_and_squeak'}, {'id': 13875, 'synset': 'pasta.n.01', 'name': 'pasta'}, {'id': 13876, 'synset': 'cannelloni.n.01', 'name': 'cannelloni'}, {'id': 13877, 'synset': 'carbonnade_flamande.n.01', 'name': 'carbonnade_flamande'}, {'id': 13878, 'synset': 'cheese_souffle.n.01', 'name': 'cheese_souffle'}, {'id': 13879, 'synset': 'chicken_marengo.n.01', 'name': 'chicken_Marengo'}, {'id': 13880, 'synset': 'chicken_cordon_bleu.n.01', 'name': 'chicken_cordon_bleu'}, {'id': 13881, 'synset': 'maryland_chicken.n.01', 'name': 'Maryland_chicken'}, {'id': 13882, 'synset': 'chicken_paprika.n.01', 'name': 'chicken_paprika'}, {'id': 13883, 'synset': 'chicken_tetrazzini.n.01', 'name': 'chicken_Tetrazzini'}, {'id': 13884, 'synset': 'tetrazzini.n.01', 'name': 'Tetrazzini'}, {'id': 13885, 'synset': 'chicken_kiev.n.01', 'name': 'chicken_Kiev'}, {'id': 13886, 'synset': 'chili.n.01', 'name': 'chili'}, {'id': 13887, 'synset': 'chili_dog.n.01', 'name': 'chili_dog'}, {'id': 13888, 'synset': 'chop_suey.n.01', 'name': 'chop_suey'}, {'id': 13889, 'synset': 'chow_mein.n.01', 'name': 'chow_mein'}, {'id': 13890, 'synset': 'codfish_ball.n.01', 'name': 'codfish_ball'}, {'id': 13891, 'synset': 'coquille.n.01', 'name': 'coquille'}, {'id': 13892, 'synset': 'coquilles_saint-jacques.n.01', 'name': 'coquilles_Saint-Jacques'}, {'id': 13893, 'synset': 'croquette.n.01', 'name': 'croquette'}, {'id': 13894, 'synset': 'cottage_pie.n.01', 'name': 'cottage_pie'}, {'id': 13895, 'synset': 'rissole.n.01', 'name': 'rissole'}, {'id': 13896, 'synset': 'dolmas.n.01', 'name': 'dolmas'}, {'id': 13897, 'synset': 'egg_foo_yong.n.01', 'name': 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'synset': 'macedoine.n.01', 'name': 'macedoine'}, {'id': 13928, 'synset': 'porcupine_ball.n.01', 'name': 'porcupine_ball'}, {'id': 13929, 'synset': 'swedish_meatball.n.01', 'name': 'Swedish_meatball'}, {'id': 13930, 'synset': 'meat_loaf.n.01', 'name': 'meat_loaf'}, {'id': 13931, 'synset': 'moussaka.n.01', 'name': 'moussaka'}, {'id': 13932, 'synset': 'osso_buco.n.01', 'name': 'osso_buco'}, {'id': 13933, 'synset': 'marrow.n.03', 'name': 'marrow'}, {'id': 13934, 'synset': 'pheasant_under_glass.n.01', 'name': 'pheasant_under_glass'}, {'id': 13935, 'synset': 'pigs_in_blankets.n.01', 'name': 'pigs_in_blankets'}, {'id': 13936, 'synset': 'pilaf.n.01', 'name': 'pilaf'}, {'id': 13937, 'synset': 'bulgur_pilaf.n.01', 'name': 'bulgur_pilaf'}, {'id': 13938, 'synset': 'sausage_pizza.n.01', 'name': 'sausage_pizza'}, {'id': 13939, 'synset': 'pepperoni_pizza.n.01', 'name': 'pepperoni_pizza'}, {'id': 13940, 'synset': 'cheese_pizza.n.01', 'name': 'cheese_pizza'}, {'id': 13941, 'synset': 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'name': 'sauerkraut'}, {'id': 13957, 'synset': 'scallopine.n.01', 'name': 'scallopine'}, {'id': 13958, 'synset': 'veal_scallopini.n.01', 'name': 'veal_scallopini'}, {'id': 13959, 'synset': 'scampi.n.01', 'name': 'scampi'}, {'id': 13960, 'synset': 'scotch_egg.n.01', 'name': 'Scotch_egg'}, {'id': 13961, 'synset': 'scotch_woodcock.n.01', 'name': 'Scotch_woodcock'}, {'id': 13962, 'synset': 'scrapple.n.01', 'name': 'scrapple'}, {'id': 13963, 'synset': 'spaghetti_and_meatballs.n.01', 'name': 'spaghetti_and_meatballs'}, {'id': 13964, 'synset': 'spanish_rice.n.01', 'name': 'Spanish_rice'}, {'id': 13965, 'synset': 'steak_tartare.n.01', 'name': 'steak_tartare'}, {'id': 13966, 'synset': 'pepper_steak.n.02', 'name': 'pepper_steak'}, {'id': 13967, 'synset': 'steak_au_poivre.n.01', 'name': 'steak_au_poivre'}, {'id': 13968, 'synset': 'beef_stroganoff.n.01', 'name': 'beef_Stroganoff'}, {'id': 13969, 'synset': 'stuffed_cabbage.n.01', 'name': 'stuffed_cabbage'}, {'id': 13970, 'synset': 'kishke.n.01', 'name': 'kishke'}, {'id': 13971, 'synset': 'stuffed_peppers.n.01', 'name': 'stuffed_peppers'}, {'id': 13972, 'synset': 'stuffed_tomato.n.02', 'name': 'stuffed_tomato'}, {'id': 13973, 'synset': 'stuffed_tomato.n.01', 'name': 'stuffed_tomato'}, {'id': 13974, 'synset': 'succotash.n.01', 'name': 'succotash'}, {'id': 13975, 'synset': 'sukiyaki.n.01', 'name': 'sukiyaki'}, {'id': 13976, 'synset': 'sashimi.n.01', 'name': 'sashimi'}, {'id': 13977, 'synset': 'swiss_steak.n.01', 'name': 'Swiss_steak'}, {'id': 13978, 'synset': 'tamale.n.02', 'name': 'tamale'}, {'id': 13979, 'synset': 'tamale_pie.n.01', 'name': 'tamale_pie'}, {'id': 13980, 'synset': 'tempura.n.01', 'name': 'tempura'}, {'id': 13981, 'synset': 'teriyaki.n.01', 'name': 'teriyaki'}, {'id': 13982, 'synset': 'terrine.n.01', 'name': 'terrine'}, {'id': 13983, 'synset': 'welsh_rarebit.n.01', 'name': 'Welsh_rarebit'}, {'id': 13984, 'synset': 'schnitzel.n.01', 'name': 'schnitzel'}, {'id': 13985, 'synset': 'chicken_taco.n.01', 'name': 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'home_brew.n.01', 'name': 'home_brew'}, {'id': 14002, 'synset': 'hooch.n.01', 'name': 'hooch'}, {'id': 14003, 'synset': 'kava.n.01', 'name': 'kava'}, {'id': 14004, 'synset': 'aperitif.n.01', 'name': 'aperitif'}, {'id': 14005, 'synset': 'brew.n.01', 'name': 'brew'}, {'id': 14006, 'synset': 'beer.n.01', 'name': 'beer'}, {'id': 14007, 'synset': 'draft_beer.n.01', 'name': 'draft_beer'}, {'id': 14008, 'synset': 'suds.n.02', 'name': 'suds'}, {'id': 14009, 'synset': 'munich_beer.n.01', 'name': 'Munich_beer'}, {'id': 14010, 'synset': 'bock.n.01', 'name': 'bock'}, {'id': 14011, 'synset': 'lager.n.02', 'name': 'lager'}, {'id': 14012, 'synset': 'light_beer.n.01', 'name': 'light_beer'}, {'id': 14013, 'synset': 'oktoberfest.n.01', 'name': 'Oktoberfest'}, {'id': 14014, 'synset': 'pilsner.n.01', 'name': 'Pilsner'}, {'id': 14015, 'synset': 'shebeen.n.01', 'name': 'shebeen'}, {'id': 14016, 'synset': 'weissbier.n.01', 'name': 'Weissbier'}, {'id': 14017, 'synset': 'weizenbock.n.01', 'name': 'Weizenbock'}, {'id': 14018, 'synset': 'malt.n.03', 'name': 'malt'}, {'id': 14019, 'synset': 'wort.n.02', 'name': 'wort'}, {'id': 14020, 'synset': 'malt.n.02', 'name': 'malt'}, {'id': 14021, 'synset': 'ale.n.01', 'name': 'ale'}, {'id': 14022, 'synset': 'bitter.n.01', 'name': 'bitter'}, {'id': 14023, 'synset': 'burton.n.03', 'name': 'Burton'}, {'id': 14024, 'synset': 'pale_ale.n.01', 'name': 'pale_ale'}, {'id': 14025, 'synset': 'porter.n.07', 'name': 'porter'}, {'id': 14026, 'synset': 'stout.n.01', 'name': 'stout'}, {'id': 14027, 'synset': 'guinness.n.02', 'name': 'Guinness'}, {'id': 14028, 'synset': 'kvass.n.01', 'name': 'kvass'}, {'id': 14029, 'synset': 'mead.n.03', 'name': 'mead'}, {'id': 14030, 'synset': 'metheglin.n.01', 'name': 'metheglin'}, {'id': 14031, 'synset': 'hydromel.n.01', 'name': 'hydromel'}, {'id': 14032, 'synset': 'oenomel.n.01', 'name': 'oenomel'}, {'id': 14033, 'synset': 'near_beer.n.01', 'name': 'near_beer'}, {'id': 14034, 'synset': 'ginger_beer.n.01', 'name': 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'name': 'Montrachet'}, {'id': 14051, 'synset': 'chardonnay.n.02', 'name': 'Chardonnay'}, {'id': 14052, 'synset': 'pinot_noir.n.02', 'name': 'Pinot_noir'}, {'id': 14053, 'synset': 'pinot_blanc.n.02', 'name': 'Pinot_blanc'}, {'id': 14054, 'synset': 'bordeaux.n.02', 'name': 'Bordeaux'}, {'id': 14055, 'synset': 'claret.n.02', 'name': 'claret'}, {'id': 14056, 'synset': 'chianti.n.01', 'name': 'Chianti'}, {'id': 14057, 'synset': 'cabernet.n.01', 'name': 'Cabernet'}, {'id': 14058, 'synset': 'merlot.n.02', 'name': 'Merlot'}, {'id': 14059, 'synset': 'sauvignon_blanc.n.02', 'name': 'Sauvignon_blanc'}, {'id': 14060, 'synset': 'california_wine.n.01', 'name': 'California_wine'}, {'id': 14061, 'synset': 'cotes_de_provence.n.01', 'name': 'Cotes_de_Provence'}, {'id': 14062, 'synset': 'dessert_wine.n.01', 'name': 'dessert_wine'}, {'id': 14063, 'synset': 'dubonnet.n.01', 'name': 'Dubonnet'}, {'id': 14064, 'synset': 'jug_wine.n.01', 'name': 'jug_wine'}, {'id': 14065, 'synset': 'macon.n.02', 'name': 'macon'}, {'id': 14066, 'synset': 'moselle.n.01', 'name': 'Moselle'}, {'id': 14067, 'synset': 'muscadet.n.02', 'name': 'Muscadet'}, {'id': 14068, 'synset': 'plonk.n.01', 'name': 'plonk'}, {'id': 14069, 'synset': 'retsina.n.01', 'name': 'retsina'}, {'id': 14070, 'synset': 'rhine_wine.n.01', 'name': 'Rhine_wine'}, {'id': 14071, 'synset': 'riesling.n.02', 'name': 'Riesling'}, {'id': 14072, 'synset': 'liebfraumilch.n.01', 'name': 'liebfraumilch'}, {'id': 14073, 'synset': 'rhone_wine.n.01', 'name': 'Rhone_wine'}, {'id': 14074, 'synset': 'rioja.n.01', 'name': 'Rioja'}, {'id': 14075, 'synset': 'sack.n.04', 'name': 'sack'}, {'id': 14076, 'synset': 'saint_emilion.n.01', 'name': 'Saint_Emilion'}, {'id': 14077, 'synset': 'soave.n.01', 'name': 'Soave'}, {'id': 14078, 'synset': 'zinfandel.n.02', 'name': 'zinfandel'}, {'id': 14079, 'synset': 'sauterne.n.01', 'name': 'Sauterne'}, {'id': 14080, 'synset': 'straw_wine.n.01', 'name': 'straw_wine'}, {'id': 14081, 'synset': 'table_wine.n.01', 'name': 'table_wine'}, {'id': 14082, 'synset': 'tokay.n.01', 'name': 'Tokay'}, {'id': 14083, 'synset': 'vin_ordinaire.n.01', 'name': 'vin_ordinaire'}, {'id': 14084, 'synset': 'vermouth.n.01', 'name': 'vermouth'}, {'id': 14085, 'synset': 'sweet_vermouth.n.01', 'name': 'sweet_vermouth'}, {'id': 14086, 'synset': 'dry_vermouth.n.01', 'name': 'dry_vermouth'}, {'id': 14087, 'synset': 'chenin_blanc.n.02', 'name': 'Chenin_blanc'}, {'id': 14088, 'synset': 'verdicchio.n.02', 'name': 'Verdicchio'}, {'id': 14089, 'synset': 'vouvray.n.01', 'name': 'Vouvray'}, {'id': 14090, 'synset': 'yquem.n.01', 'name': 'Yquem'}, {'id': 14091, 'synset': 'generic.n.01', 'name': 'generic'}, {'id': 14092, 'synset': 'varietal.n.01', 'name': 'varietal'}, {'id': 14093, 'synset': 'fortified_wine.n.01', 'name': 'fortified_wine'}, {'id': 14094, 'synset': 'madeira.n.03', 'name': 'Madeira'}, {'id': 14095, 'synset': 'malmsey.n.01', 'name': 'malmsey'}, {'id': 14096, 'synset': 'port.n.02', 'name': 'port'}, {'id': 14097, 'synset': 'sherry.n.01', 'name': 'sherry'}, {'id': 14098, 'synset': 'marsala.n.01', 'name': 'Marsala'}, {'id': 14099, 'synset': 'muscat.n.03', 'name': 'muscat'}, {'id': 14100, 'synset': 'neutral_spirits.n.01', 'name': 'neutral_spirits'}, {'id': 14101, 'synset': 'aqua_vitae.n.01', 'name': 'aqua_vitae'}, {'id': 14102, 'synset': 'eau_de_vie.n.01', 'name': 'eau_de_vie'}, {'id': 14103, 'synset': 'moonshine.n.02', 'name': 'moonshine'}, {'id': 14104, 'synset': 'bathtub_gin.n.01', 'name': 'bathtub_gin'}, {'id': 14105, 'synset': 'aquavit.n.01', 'name': 'aquavit'}, {'id': 14106, 'synset': 'arrack.n.01', 'name': 'arrack'}, {'id': 14107, 'synset': 'bitters.n.01', 'name': 'bitters'}, {'id': 14108, 'synset': 'brandy.n.01', 'name': 'brandy'}, {'id': 14109, 'synset': 'applejack.n.01', 'name': 'applejack'}, {'id': 14110, 'synset': 'calvados.n.01', 'name': 'Calvados'}, {'id': 14111, 'synset': 'armagnac.n.01', 'name': 'Armagnac'}, {'id': 14112, 'synset': 'cognac.n.01', 'name': 'Cognac'}, {'id': 14113, 'synset': 'grappa.n.01', 'name': 'grappa'}, {'id': 14114, 'synset': 'kirsch.n.01', 'name': 'kirsch'}, {'id': 14115, 'synset': 'slivovitz.n.01', 'name': 'slivovitz'}, {'id': 14116, 'synset': 'gin.n.01', 'name': 'gin'}, {'id': 14117, 'synset': 'sloe_gin.n.01', 'name': 'sloe_gin'}, {'id': 14118, 'synset': 'geneva.n.02', 'name': 'geneva'}, {'id': 14119, 'synset': 'grog.n.01', 'name': 'grog'}, {'id': 14120, 'synset': 'ouzo.n.01', 'name': 'ouzo'}, {'id': 14121, 'synset': 'rum.n.01', 'name': 'rum'}, {'id': 14122, 'synset': 'demerara.n.04', 'name': 'demerara'}, {'id': 14123, 'synset': 'jamaica_rum.n.01', 'name': 'Jamaica_rum'}, {'id': 14124, 'synset': 'schnapps.n.01', 'name': 'schnapps'}, {'id': 14125, 'synset': 'pulque.n.01', 'name': 'pulque'}, {'id': 14126, 'synset': 'mescal.n.02', 'name': 'mescal'}, {'id': 14127, 'synset': 'whiskey.n.01', 'name': 'whiskey'}, {'id': 14128, 'synset': 'blended_whiskey.n.01', 'name': 'blended_whiskey'}, {'id': 14129, 'synset': 'bourbon.n.02', 'name': 'bourbon'}, {'id': 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14408, 'synset': 'sideline.n.01', 'name': 'sideline'}, {'id': 14409, 'synset': 'ski_resort.n.01', 'name': 'ski_resort'}, {'id': 14410, 'synset': 'soil_horizon.n.01', 'name': 'soil_horizon'}, {'id': 14411, 'synset': 'geological_horizon.n.01', 'name': 'geological_horizon'}, {'id': 14412, 'synset': 'coal_seam.n.01', 'name': 'coal_seam'}, {'id': 14413, 'synset': 'coalface.n.01', 'name': 'coalface'}, {'id': 14414, 'synset': 'field.n.14', 'name': 'field'}, {'id': 14415, 'synset': 'oilfield.n.01', 'name': 'oilfield'}, {'id': 14416, 'synset': 'temperate_zone.n.01', 'name': 'Temperate_Zone'}, {'id': 14417, 'synset': 'terreplein.n.01', 'name': 'terreplein'}, {'id': 14418, 'synset': 'three-mile_limit.n.01', 'name': 'three-mile_limit'}, {'id': 14419, 'synset': 'desktop.n.01', 'name': 'desktop'}, {'id': 14420, 'synset': 'top.n.01', 'name': 'top'}, {'id': 14421, 'synset': 'kampong.n.01', 'name': 'kampong'}, {'id': 14422, 'synset': 'subtropics.n.01', 'name': 'subtropics'}, {'id': 14423, 'synset': 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'dale.n.01', 'name': 'dale'}, {'id': 14489, 'synset': 'defile.n.01', 'name': 'defile'}, {'id': 14490, 'synset': 'delta.n.01', 'name': 'delta'}, {'id': 14491, 'synset': 'descent.n.05', 'name': 'descent'}, {'id': 14492, 'synset': 'diapir.n.01', 'name': 'diapir'}, {'id': 14493, 'synset': 'divot.n.02', 'name': 'divot'}, {'id': 14494, 'synset': 'divot.n.01', 'name': 'divot'}, {'id': 14495, 'synset': 'down.n.04', 'name': 'down'}, {'id': 14496, 'synset': 'downhill.n.01', 'name': 'downhill'}, {'id': 14497, 'synset': 'draw.n.01', 'name': 'draw'}, {'id': 14498, 'synset': 'drey.n.01', 'name': 'drey'}, {'id': 14499, 'synset': 'drumlin.n.01', 'name': 'drumlin'}, {'id': 14500, 'synset': 'dune.n.01', 'name': 'dune'}, {'id': 14501, 'synset': 'escarpment.n.01', 'name': 'escarpment'}, {'id': 14502, 'synset': 'esker.n.01', 'name': 'esker'}, {'id': 14503, 'synset': 'fireball.n.03', 'name': 'fireball'}, {'id': 14504, 'synset': 'flare_star.n.01', 'name': 'flare_star'}, {'id': 14505, 'synset': 'floor.n.04', 'name': 'floor'}, {'id': 14506, 'synset': 'fomite.n.01', 'name': 'fomite'}, {'id': 14507, 'synset': 'foothill.n.01', 'name': 'foothill'}, {'id': 14508, 'synset': 'footwall.n.01', 'name': 'footwall'}, {'id': 14509, 'synset': 'foreland.n.02', 'name': 'foreland'}, {'id': 14510, 'synset': 'foreshore.n.01', 'name': 'foreshore'}, {'id': 14511, 'synset': 'gauge_boson.n.01', 'name': 'gauge_boson'}, {'id': 14512, 'synset': 'geological_formation.n.01', 'name': 'geological_formation'}, {'id': 14513, 'synset': 'geyser.n.01', 'name': 'geyser'}, {'id': 14514, 'synset': 'glacier.n.01', 'name': 'glacier'}, {'id': 14515, 'synset': 'glen.n.01', 'name': 'glen'}, {'id': 14516, 'synset': 'gopher_hole.n.01', 'name': 'gopher_hole'}, {'id': 14517, 'synset': 'gorge.n.01', 'name': 'gorge'}, {'id': 14518, 'synset': 'grotto.n.01', 'name': 'grotto'}, {'id': 14519, 'synset': 'growler.n.02', 'name': 'growler'}, {'id': 14520, 'synset': 'gulch.n.01', 'name': 'gulch'}, {'id': 14521, 'synset': 'gully.n.01', 'name': 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'name': 'massif'}, {'id': 14555, 'synset': 'meander.n.01', 'name': 'meander'}, {'id': 14556, 'synset': 'mesa.n.01', 'name': 'mesa'}, {'id': 14557, 'synset': 'meteorite.n.01', 'name': 'meteorite'}, {'id': 14558, 'synset': 'microfossil.n.01', 'name': 'microfossil'}, {'id': 14559, 'synset': 'midstream.n.01', 'name': 'midstream'}, {'id': 14560, 'synset': 'molehill.n.01', 'name': 'molehill'}, {'id': 14561, 'synset': 'monocline.n.01', 'name': 'monocline'}, {'id': 14562, 'synset': 'mountain.n.01', 'name': 'mountain'}, {'id': 14563, 'synset': 'mountainside.n.01', 'name': 'mountainside'}, {'id': 14564, 'synset': 'mouth.n.04', 'name': 'mouth'}, {'id': 14565, 'synset': 'mull.n.01', 'name': 'mull'}, {'id': 14566, 'synset': 'natural_depression.n.01', 'name': 'natural_depression'}, {'id': 14567, 'synset': 'natural_elevation.n.01', 'name': 'natural_elevation'}, {'id': 14568, 'synset': 'nullah.n.01', 'name': 'nullah'}, {'id': 14569, 'synset': 'ocean.n.01', 'name': 'ocean'}, {'id': 14570, 'synset': 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14586, 'synset': 'promontory.n.01', 'name': 'promontory'}, {'id': 14587, 'synset': 'ptyalith.n.01', 'name': 'ptyalith'}, {'id': 14588, 'synset': 'pulsar.n.01', 'name': 'pulsar'}, {'id': 14589, 'synset': 'quicksand.n.02', 'name': 'quicksand'}, {'id': 14590, 'synset': 'rabbit_burrow.n.01', 'name': 'rabbit_burrow'}, {'id': 14591, 'synset': 'radiator.n.01', 'name': 'radiator'}, {'id': 14592, 'synset': 'rainbow.n.01', 'name': 'rainbow'}, {'id': 14593, 'synset': 'range.n.04', 'name': 'range'}, {'id': 14594, 'synset': 'rangeland.n.01', 'name': 'rangeland'}, {'id': 14595, 'synset': 'ravine.n.01', 'name': 'ravine'}, {'id': 14596, 'synset': 'reef.n.01', 'name': 'reef'}, {'id': 14597, 'synset': 'ridge.n.01', 'name': 'ridge'}, {'id': 14598, 'synset': 'ridge.n.04', 'name': 'ridge'}, {'id': 14599, 'synset': 'rift_valley.n.01', 'name': 'rift_valley'}, {'id': 14600, 'synset': 'riparian_forest.n.01', 'name': 'riparian_forest'}, {'id': 14601, 'synset': 'ripple_mark.n.01', 'name': 'ripple_mark'}, {'id': 14602, 'synset': 'riverbank.n.01', 'name': 'riverbank'}, {'id': 14603, 'synset': 'riverbed.n.01', 'name': 'riverbed'}, {'id': 14604, 'synset': 'rock.n.01', 'name': 'rock'}, {'id': 14605, 'synset': 'roof.n.03', 'name': 'roof'}, {'id': 14606, 'synset': 'saltpan.n.01', 'name': 'saltpan'}, {'id': 14607, 'synset': 'sandbank.n.01', 'name': 'sandbank'}, {'id': 14608, 'synset': 'sandbar.n.01', 'name': 'sandbar'}, {'id': 14609, 'synset': 'sandpit.n.01', 'name': 'sandpit'}, {'id': 14610, 'synset': 'sanitary_landfill.n.01', 'name': 'sanitary_landfill'}, {'id': 14611, 'synset': 'sawpit.n.01', 'name': 'sawpit'}, {'id': 14612, 'synset': 'scablands.n.01', 'name': 'scablands'}, {'id': 14613, 'synset': 'seashore.n.01', 'name': 'seashore'}, {'id': 14614, 'synset': 'seaside.n.01', 'name': 'seaside'}, {'id': 14615, 'synset': 'seif_dune.n.01', 'name': 'seif_dune'}, {'id': 14616, 'synset': 'shell.n.06', 'name': 'shell'}, {'id': 14617, 'synset': 'shiner.n.02', 'name': 'shiner'}, {'id': 14618, 'synset': 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14730, 'synset': 'semite.n.01', 'name': 'Semite'}, {'id': 14731, 'synset': 'chaldean.n.02', 'name': 'Chaldean'}, {'id': 14732, 'synset': 'elamite.n.01', 'name': 'Elamite'}, {'id': 14733, 'synset': 'white_man.n.01', 'name': 'white_man'}, {'id': 14734, 'synset': 'wasp.n.01', 'name': 'WASP'}, {'id': 14735, 'synset': 'gook.n.02', 'name': 'gook'}, {'id': 14736, 'synset': 'mongol.n.01', 'name': 'Mongol'}, {'id': 14737, 'synset': 'tatar.n.01', 'name': 'Tatar'}, {'id': 14738, 'synset': 'nahuatl.n.01', 'name': 'Nahuatl'}, {'id': 14739, 'synset': 'aztec.n.01', 'name': 'Aztec'}, {'id': 14740, 'synset': 'olmec.n.01', 'name': 'Olmec'}, {'id': 14741, 'synset': 'biloxi.n.01', 'name': 'Biloxi'}, {'id': 14742, 'synset': 'blackfoot.n.01', 'name': 'Blackfoot'}, {'id': 14743, 'synset': 'brule.n.01', 'name': 'Brule'}, {'id': 14744, 'synset': 'caddo.n.01', 'name': 'Caddo'}, {'id': 14745, 'synset': 'cheyenne.n.03', 'name': 'Cheyenne'}, {'id': 14746, 'synset': 'chickasaw.n.01', 'name': 'Chickasaw'}, {'id': 14747, 'synset': 'cocopa.n.01', 'name': 'Cocopa'}, {'id': 14748, 'synset': 'comanche.n.01', 'name': 'Comanche'}, {'id': 14749, 'synset': 'creek.n.02', 'name': 'Creek'}, {'id': 14750, 'synset': 'delaware.n.02', 'name': 'Delaware'}, {'id': 14751, 'synset': 'diegueno.n.01', 'name': 'Diegueno'}, {'id': 14752, 'synset': 'esselen.n.01', 'name': 'Esselen'}, {'id': 14753, 'synset': 'eyeish.n.01', 'name': 'Eyeish'}, {'id': 14754, 'synset': 'havasupai.n.01', 'name': 'Havasupai'}, {'id': 14755, 'synset': 'hunkpapa.n.01', 'name': 'Hunkpapa'}, {'id': 14756, 'synset': 'iowa.n.01', 'name': 'Iowa'}, {'id': 14757, 'synset': 'kalapooia.n.01', 'name': 'Kalapooia'}, {'id': 14758, 'synset': 'kamia.n.01', 'name': 'Kamia'}, {'id': 14759, 'synset': 'kekchi.n.01', 'name': 'Kekchi'}, {'id': 14760, 'synset': 'kichai.n.01', 'name': 'Kichai'}, {'id': 14761, 'synset': 'kickapoo.n.01', 'name': 'Kickapoo'}, {'id': 14762, 'synset': 'kiliwa.n.01', 'name': 'Kiliwa'}, {'id': 14763, 'synset': 'malecite.n.01', 'name': 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14780, 'synset': 'shahaptian.n.01', 'name': 'Shahaptian'}, {'id': 14781, 'synset': 'shasta.n.01', 'name': 'Shasta'}, {'id': 14782, 'synset': 'shawnee.n.01', 'name': 'Shawnee'}, {'id': 14783, 'synset': 'sihasapa.n.01', 'name': 'Sihasapa'}, {'id': 14784, 'synset': 'teton.n.01', 'name': 'Teton'}, {'id': 14785, 'synset': 'taracahitian.n.01', 'name': 'Taracahitian'}, {'id': 14786, 'synset': 'tarahumara.n.01', 'name': 'Tarahumara'}, {'id': 14787, 'synset': 'tuscarora.n.01', 'name': 'Tuscarora'}, {'id': 14788, 'synset': 'tutelo.n.01', 'name': 'Tutelo'}, {'id': 14789, 'synset': 'yana.n.01', 'name': 'Yana'}, {'id': 14790, 'synset': 'yavapai.n.01', 'name': 'Yavapai'}, {'id': 14791, 'synset': 'yokuts.n.02', 'name': 'Yokuts'}, {'id': 14792, 'synset': 'yuma.n.01', 'name': 'Yuma'}, {'id': 14793, 'synset': 'gadaba.n.01', 'name': 'Gadaba'}, {'id': 14794, 'synset': 'kolam.n.01', 'name': 'Kolam'}, {'id': 14795, 'synset': 'kui.n.01', 'name': 'Kui'}, {'id': 14796, 'synset': 'toda.n.01', 'name': 'Toda'}, {'id': 14797, 'synset': 'tulu.n.01', 'name': 'Tulu'}, {'id': 14798, 'synset': 'gujarati.n.01', 'name': 'Gujarati'}, {'id': 14799, 'synset': 'kashmiri.n.01', 'name': 'Kashmiri'}, {'id': 14800, 'synset': 'punjabi.n.01', 'name': 'Punjabi'}, {'id': 14801, 'synset': 'slav.n.01', 'name': 'Slav'}, {'id': 14802, 'synset': 'anabaptist.n.01', 'name': 'Anabaptist'}, {'id': 14803, 'synset': 'adventist.n.01', 'name': 'Adventist'}, {'id': 14804, 'synset': 'gentile.n.03', 'name': 'gentile'}, {'id': 14805, 'synset': 'gentile.n.02', 'name': 'gentile'}, {'id': 14806, 'synset': 'catholic.n.01', 'name': 'Catholic'}, {'id': 14807, 'synset': 'old_catholic.n.01', 'name': 'Old_Catholic'}, {'id': 14808, 'synset': 'uniat.n.01', 'name': 'Uniat'}, {'id': 14809, 'synset': 'copt.n.02', 'name': 'Copt'}, {'id': 14810, 'synset': 'jewess.n.01', 'name': 'Jewess'}, {'id': 14811, 'synset': 'jihadist.n.01', 'name': 'Jihadist'}, {'id': 14812, 'synset': 'buddhist.n.01', 'name': 'Buddhist'}, {'id': 14813, 'synset': 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'french_canadian.n.01', 'name': 'French_Canadian'}, {'id': 14846, 'synset': 'central_american.n.01', 'name': 'Central_American'}, {'id': 14847, 'synset': 'chilean.n.01', 'name': 'Chilean'}, {'id': 14848, 'synset': 'congolese.n.01', 'name': 'Congolese'}, {'id': 14849, 'synset': 'cypriot.n.01', 'name': 'Cypriot'}, {'id': 14850, 'synset': 'dane.n.01', 'name': 'Dane'}, {'id': 14851, 'synset': 'djiboutian.n.01', 'name': 'Djiboutian'}, {'id': 14852, 'synset': 'britisher.n.01', 'name': 'Britisher'}, {'id': 14853, 'synset': 'english_person.n.01', 'name': 'English_person'}, {'id': 14854, 'synset': 'englishwoman.n.01', 'name': 'Englishwoman'}, {'id': 14855, 'synset': 'anglo-saxon.n.02', 'name': 'Anglo-Saxon'}, {'id': 14856, 'synset': 'angle.n.03', 'name': 'Angle'}, {'id': 14857, 'synset': 'west_saxon.n.01', 'name': 'West_Saxon'}, {'id': 14858, 'synset': 'lombard.n.01', 'name': 'Lombard'}, {'id': 14859, 'synset': 'limey.n.01', 'name': 'limey'}, {'id': 14860, 'synset': 'cantabrigian.n.01', 'name': 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'name': 'Piltdown_man'}, {'id': 16660, 'synset': 'pimp.n.01', 'name': 'pimp'}, {'id': 16661, 'synset': 'pipe_smoker.n.01', 'name': 'pipe_smoker'}, {'id': 16662, 'synset': 'pip-squeak.n.01', 'name': 'pip-squeak'}, {'id': 16663, 'synset': 'pisser.n.01', 'name': 'pisser'}, {'id': 16664, 'synset': 'pitcher.n.01', 'name': 'pitcher'}, {'id': 16665, 'synset': 'pitchman.n.01', 'name': 'pitchman'}, {'id': 16666, 'synset': 'placeman.n.01', 'name': 'placeman'}, {'id': 16667, 'synset': 'placer_miner.n.01', 'name': 'placer_miner'}, {'id': 16668, 'synset': 'plagiarist.n.01', 'name': 'plagiarist'}, {'id': 16669, 'synset': 'plainsman.n.01', 'name': 'plainsman'}, {'id': 16670, 'synset': 'planner.n.01', 'name': 'planner'}, {'id': 16671, 'synset': 'planter.n.01', 'name': 'planter'}, {'id': 16672, 'synset': 'plasterer.n.01', 'name': 'plasterer'}, {'id': 16673, 'synset': 'platinum_blond.n.01', 'name': 'platinum_blond'}, {'id': 16674, 'synset': 'platitudinarian.n.01', 'name': 'platitudinarian'}, {'id': 16675, 'synset': 'playboy.n.01', 'name': 'playboy'}, {'id': 16676, 'synset': 'player.n.01', 'name': 'player'}, {'id': 16677, 'synset': 'playmate.n.01', 'name': 'playmate'}, {'id': 16678, 'synset': 'pleaser.n.01', 'name': 'pleaser'}, {'id': 16679, 'synset': 'pledger.n.01', 'name': 'pledger'}, {'id': 16680, 'synset': 'plenipotentiary.n.01', 'name': 'plenipotentiary'}, {'id': 16681, 'synset': 'plier.n.01', 'name': 'plier'}, {'id': 16682, 'synset': 'plodder.n.03', 'name': 'plodder'}, {'id': 16683, 'synset': 'plodder.n.02', 'name': 'plodder'}, {'id': 16684, 'synset': 'plotter.n.02', 'name': 'plotter'}, {'id': 16685, 'synset': 'plumber.n.01', 'name': 'plumber'}, {'id': 16686, 'synset': 'pluralist.n.02', 'name': 'pluralist'}, {'id': 16687, 'synset': 'pluralist.n.01', 'name': 'pluralist'}, {'id': 16688, 'synset': 'poet.n.01', 'name': 'poet'}, {'id': 16689, 'synset': 'pointsman.n.01', 'name': 'pointsman'}, {'id': 16690, 'synset': 'point_woman.n.01', 'name': 'point_woman'}, {'id': 16691, 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{'id': 16811, 'synset': "queen's_counsel.n.01", 'name': "Queen's_Counsel"}, {'id': 16812, 'synset': 'question_master.n.01', 'name': 'question_master'}, {'id': 16813, 'synset': 'quick_study.n.01', 'name': 'quick_study'}, {'id': 16814, 'synset': 'quietist.n.01', 'name': 'quietist'}, {'id': 16815, 'synset': 'quitter.n.01', 'name': 'quitter'}, {'id': 16816, 'synset': 'rabbi.n.01', 'name': 'rabbi'}, {'id': 16817, 'synset': 'racist.n.01', 'name': 'racist'}, {'id': 16818, 'synset': 'radiobiologist.n.01', 'name': 'radiobiologist'}, {'id': 16819, 'synset': 'radiologic_technologist.n.01', 'name': 'radiologic_technologist'}, {'id': 16820, 'synset': 'radiologist.n.01', 'name': 'radiologist'}, {'id': 16821, 'synset': 'rainmaker.n.02', 'name': 'rainmaker'}, {'id': 16822, 'synset': 'raiser.n.01', 'name': 'raiser'}, {'id': 16823, 'synset': 'raja.n.01', 'name': 'raja'}, {'id': 16824, 'synset': 'rake.n.01', 'name': 'rake'}, {'id': 16825, 'synset': 'ramrod.n.02', 'name': 'ramrod'}, {'id': 16826, 'synset': 'ranch_hand.n.01', 'name': 'ranch_hand'}, {'id': 16827, 'synset': 'ranker.n.01', 'name': 'ranker'}, {'id': 16828, 'synset': 'ranter.n.01', 'name': 'ranter'}, {'id': 16829, 'synset': 'rape_suspect.n.01', 'name': 'rape_suspect'}, {'id': 16830, 'synset': 'rapper.n.01', 'name': 'rapper'}, {'id': 16831, 'synset': 'rapporteur.n.01', 'name': 'rapporteur'}, {'id': 16832, 'synset': 'rare_bird.n.01', 'name': 'rare_bird'}, {'id': 16833, 'synset': 'ratepayer.n.01', 'name': 'ratepayer'}, {'id': 16834, 'synset': 'raw_recruit.n.01', 'name': 'raw_recruit'}, {'id': 16835, 'synset': 'reader.n.01', 'name': 'reader'}, {'id': 16836, 'synset': 'reading_teacher.n.01', 'name': 'reading_teacher'}, {'id': 16837, 'synset': 'realist.n.01', 'name': 'realist'}, {'id': 16838, 'synset': 'real_estate_broker.n.01', 'name': 'real_estate_broker'}, {'id': 16839, 'synset': 'rear_admiral.n.01', 'name': 'rear_admiral'}, {'id': 16840, 'synset': 'receiver.n.05', 'name': 'receiver'}, {'id': 16841, 'synset': 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16857, 'synset': 'regius_professor.n.01', 'name': 'Regius_professor'}, {'id': 16858, 'synset': 'reliever.n.02', 'name': 'reliever'}, {'id': 16859, 'synset': 'anchorite.n.01', 'name': 'anchorite'}, {'id': 16860, 'synset': 'religious_leader.n.01', 'name': 'religious_leader'}, {'id': 16861, 'synset': 'remover.n.02', 'name': 'remover'}, {'id': 16862, 'synset': 'renaissance_man.n.01', 'name': 'Renaissance_man'}, {'id': 16863, 'synset': 'renegade.n.01', 'name': 'renegade'}, {'id': 16864, 'synset': 'rentier.n.01', 'name': 'rentier'}, {'id': 16865, 'synset': 'repairman.n.01', 'name': 'repairman'}, {'id': 16866, 'synset': 'reporter.n.01', 'name': 'reporter'}, {'id': 16867, 'synset': 'newswoman.n.01', 'name': 'newswoman'}, {'id': 16868, 'synset': 'representative.n.01', 'name': 'representative'}, {'id': 16869, 'synset': 'reprobate.n.01', 'name': 'reprobate'}, {'id': 16870, 'synset': 'rescuer.n.02', 'name': 'rescuer'}, {'id': 16871, 'synset': 'reservist.n.01', 'name': 'reservist'}, {'id': 16872, 'synset': 'resident_commissioner.n.01', 'name': 'resident_commissioner'}, {'id': 16873, 'synset': 'respecter.n.01', 'name': 'respecter'}, {'id': 16874, 'synset': 'restaurateur.n.01', 'name': 'restaurateur'}, {'id': 16875, 'synset': 'restrainer.n.02', 'name': 'restrainer'}, {'id': 16876, 'synset': 'retailer.n.01', 'name': 'retailer'}, {'id': 16877, 'synset': 'retiree.n.01', 'name': 'retiree'}, {'id': 16878, 'synset': 'returning_officer.n.01', 'name': 'returning_officer'}, {'id': 16879, 'synset': 'revenant.n.01', 'name': 'revenant'}, {'id': 16880, 'synset': 'revisionist.n.01', 'name': 'revisionist'}, {'id': 16881, 'synset': 'revolutionist.n.01', 'name': 'revolutionist'}, {'id': 16882, 'synset': 'rheumatologist.n.01', 'name': 'rheumatologist'}, {'id': 16883, 'synset': 'rhodesian_man.n.01', 'name': 'Rhodesian_man'}, {'id': 16884, 'synset': 'rhymer.n.01', 'name': 'rhymer'}, {'id': 16885, 'synset': 'rich_person.n.01', 'name': 'rich_person'}, {'id': 16886, 'synset': 'rider.n.03', 'name': 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{'id': 16902, 'synset': 'roper.n.01', 'name': 'roper'}, {'id': 16903, 'synset': 'ropewalker.n.01', 'name': 'ropewalker'}, {'id': 16904, 'synset': 'rosebud.n.02', 'name': 'rosebud'}, {'id': 16905, 'synset': 'rosicrucian.n.02', 'name': 'Rosicrucian'}, {'id': 16906, 'synset': 'mountie.n.01', 'name': 'Mountie'}, {'id': 16907, 'synset': 'rough_rider.n.01', 'name': 'Rough_Rider'}, {'id': 16908, 'synset': 'roundhead.n.01', 'name': 'roundhead'}, {'id': 16909, 'synset': 'civil_authority.n.01', 'name': 'civil_authority'}, {'id': 16910, 'synset': 'runner.n.03', 'name': 'runner'}, {'id': 16911, 'synset': 'runner.n.02', 'name': 'runner'}, {'id': 16912, 'synset': 'runner.n.06', 'name': 'runner'}, {'id': 16913, 'synset': 'running_back.n.01', 'name': 'running_back'}, {'id': 16914, 'synset': 'rusher.n.02', 'name': 'rusher'}, {'id': 16915, 'synset': 'rustic.n.01', 'name': 'rustic'}, {'id': 16916, 'synset': 'saboteur.n.01', 'name': 'saboteur'}, {'id': 16917, 'synset': 'sadist.n.01', 'name': 'sadist'}, {'id': 16918, 'synset': 'sailing_master.n.01', 'name': 'sailing_master'}, {'id': 16919, 'synset': 'sailor.n.01', 'name': 'sailor'}, {'id': 16920, 'synset': 'salesgirl.n.01', 'name': 'salesgirl'}, {'id': 16921, 'synset': 'salesman.n.01', 'name': 'salesman'}, {'id': 16922, 'synset': 'salesperson.n.01', 'name': 'salesperson'}, {'id': 16923, 'synset': 'salvager.n.01', 'name': 'salvager'}, {'id': 16924, 'synset': 'sandwichman.n.01', 'name': 'sandwichman'}, {'id': 16925, 'synset': 'sangoma.n.01', 'name': 'sangoma'}, {'id': 16926, 'synset': 'sannup.n.01', 'name': 'sannup'}, {'id': 16927, 'synset': 'sapper.n.02', 'name': 'sapper'}, {'id': 16928, 'synset': 'sassenach.n.01', 'name': 'Sassenach'}, {'id': 16929, 'synset': 'satrap.n.01', 'name': 'satrap'}, {'id': 16930, 'synset': 'saunterer.n.01', 'name': 'saunterer'}, {'id': 16931, 'synset': 'savoyard.n.01', 'name': 'Savoyard'}, {'id': 16932, 'synset': 'sawyer.n.01', 'name': 'sawyer'}, {'id': 16933, 'synset': 'scalper.n.01', 'name': 'scalper'}, {'id': 16934, 'synset': 'scandalmonger.n.01', 'name': 'scandalmonger'}, {'id': 16935, 'synset': 'scapegrace.n.01', 'name': 'scapegrace'}, {'id': 16936, 'synset': 'scene_painter.n.02', 'name': 'scene_painter'}, {'id': 16937, 'synset': 'schemer.n.01', 'name': 'schemer'}, {'id': 16938, 'synset': 'schizophrenic.n.01', 'name': 'schizophrenic'}, {'id': 16939, 'synset': 'schlemiel.n.01', 'name': 'schlemiel'}, {'id': 16940, 'synset': 'schlockmeister.n.01', 'name': 'schlockmeister'}, {'id': 16941, 'synset': 'scholar.n.01', 'name': 'scholar'}, {'id': 16942, 'synset': 'scholiast.n.01', 'name': 'scholiast'}, {'id': 16943, 'synset': 'schoolchild.n.01', 'name': 'schoolchild'}, {'id': 16944, 'synset': 'schoolfriend.n.01', 'name': 'schoolfriend'}, {'id': 16945, 'synset': 'schoolman.n.01', 'name': 'Schoolman'}, {'id': 16946, 'synset': 'schoolmaster.n.02', 'name': 'schoolmaster'}, {'id': 16947, 'synset': 'schoolmate.n.01', 'name': 'schoolmate'}, {'id': 16948, 'synset': 'scientist.n.01', 'name': 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'synset': 'secretary_of_the_interior.n.02', 'name': 'Secretary_of_the_Interior'}, {'id': 16978, 'synset': 'sectarian.n.01', 'name': 'sectarian'}, {'id': 16979, 'synset': 'section_hand.n.01', 'name': 'section_hand'}, {'id': 16980, 'synset': 'secularist.n.01', 'name': 'secularist'}, {'id': 16981, 'synset': 'security_consultant.n.01', 'name': 'security_consultant'}, {'id': 16982, 'synset': 'seeded_player.n.01', 'name': 'seeded_player'}, {'id': 16983, 'synset': 'seeder.n.01', 'name': 'seeder'}, {'id': 16984, 'synset': 'seeker.n.01', 'name': 'seeker'}, {'id': 16985, 'synset': 'segregate.n.01', 'name': 'segregate'}, {'id': 16986, 'synset': 'segregator.n.01', 'name': 'segregator'}, {'id': 16987, 'synset': 'selectman.n.01', 'name': 'selectman'}, {'id': 16988, 'synset': 'selectwoman.n.01', 'name': 'selectwoman'}, {'id': 16989, 'synset': 'selfish_person.n.01', 'name': 'selfish_person'}, {'id': 16990, 'synset': 'self-starter.n.01', 'name': 'self-starter'}, {'id': 16991, 'synset': 'seller.n.01', 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17539, 'synset': 'conidium.n.01', 'name': 'conidium'}, {'id': 17540, 'synset': 'oospore.n.01', 'name': 'oospore'}, {'id': 17541, 'synset': 'tetraspore.n.01', 'name': 'tetraspore'}, {'id': 17542, 'synset': 'zoospore.n.01', 'name': 'zoospore'}, {'id': 17543, 'synset': 'cryptogam.n.01', 'name': 'cryptogam'}, {'id': 17544, 'synset': 'spermatophyte.n.01', 'name': 'spermatophyte'}, {'id': 17545, 'synset': 'seedling.n.01', 'name': 'seedling'}, {'id': 17546, 'synset': 'annual.n.01', 'name': 'annual'}, {'id': 17547, 'synset': 'biennial.n.01', 'name': 'biennial'}, {'id': 17548, 'synset': 'perennial.n.01', 'name': 'perennial'}, {'id': 17549, 'synset': 'hygrophyte.n.01', 'name': 'hygrophyte'}, {'id': 17550, 'synset': 'gymnosperm.n.01', 'name': 'gymnosperm'}, {'id': 17551, 'synset': 'gnetum.n.01', 'name': 'gnetum'}, {'id': 17552, 'synset': 'catha_edulis.n.01', 'name': 'Catha_edulis'}, {'id': 17553, 'synset': 'ephedra.n.01', 'name': 'ephedra'}, {'id': 17554, 'synset': 'mahuang.n.01', 'name': 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'pinon_pine'}, {'id': 17571, 'synset': 'rocky_mountain_pinon.n.01', 'name': 'Rocky_mountain_pinon'}, {'id': 17572, 'synset': 'single-leaf.n.01', 'name': 'single-leaf'}, {'id': 17573, 'synset': 'bishop_pine.n.01', 'name': 'bishop_pine'}, {'id': 17574, 'synset': 'california_single-leaf_pinyon.n.01', 'name': 'California_single-leaf_pinyon'}, {'id': 17575, 'synset': "parry's_pinyon.n.01", 'name': "Parry's_pinyon"}, {'id': 17576, 'synset': 'spruce_pine.n.04', 'name': 'spruce_pine'}, {'id': 17577, 'synset': 'black_pine.n.05', 'name': 'black_pine'}, {'id': 17578, 'synset': 'pitch_pine.n.02', 'name': 'pitch_pine'}, {'id': 17579, 'synset': 'pond_pine.n.01', 'name': 'pond_pine'}, {'id': 17580, 'synset': 'stone_pine.n.01', 'name': 'stone_pine'}, {'id': 17581, 'synset': 'swiss_pine.n.01', 'name': 'Swiss_pine'}, {'id': 17582, 'synset': 'cembra_nut.n.01', 'name': 'cembra_nut'}, {'id': 17583, 'synset': 'swiss_mountain_pine.n.01', 'name': 'Swiss_mountain_pine'}, {'id': 17584, 'synset': 'ancient_pine.n.01', 'name': 'ancient_pine'}, {'id': 17585, 'synset': 'white_pine.n.01', 'name': 'white_pine'}, {'id': 17586, 'synset': 'american_white_pine.n.01', 'name': 'American_white_pine'}, {'id': 17587, 'synset': 'western_white_pine.n.01', 'name': 'western_white_pine'}, {'id': 17588, 'synset': 'southwestern_white_pine.n.01', 'name': 'southwestern_white_pine'}, {'id': 17589, 'synset': 'limber_pine.n.01', 'name': 'limber_pine'}, {'id': 17590, 'synset': 'whitebark_pine.n.01', 'name': 'whitebark_pine'}, {'id': 17591, 'synset': 'yellow_pine.n.01', 'name': 'yellow_pine'}, {'id': 17592, 'synset': 'ponderosa.n.01', 'name': 'ponderosa'}, {'id': 17593, 'synset': 'jeffrey_pine.n.01', 'name': 'Jeffrey_pine'}, {'id': 17594, 'synset': 'shore_pine.n.01', 'name': 'shore_pine'}, {'id': 17595, 'synset': 'sierra_lodgepole_pine.n.01', 'name': 'Sierra_lodgepole_pine'}, {'id': 17596, 'synset': 'loblolly_pine.n.01', 'name': 'loblolly_pine'}, {'id': 17597, 'synset': 'jack_pine.n.01', 'name': 'jack_pine'}, {'id': 17598, 'synset': 'swamp_pine.n.01', 'name': 'swamp_pine'}, {'id': 17599, 'synset': 'longleaf_pine.n.01', 'name': 'longleaf_pine'}, {'id': 17600, 'synset': 'shortleaf_pine.n.01', 'name': 'shortleaf_pine'}, {'id': 17601, 'synset': 'red_pine.n.02', 'name': 'red_pine'}, {'id': 17602, 'synset': 'scotch_pine.n.01', 'name': 'Scotch_pine'}, {'id': 17603, 'synset': 'scrub_pine.n.01', 'name': 'scrub_pine'}, {'id': 17604, 'synset': 'monterey_pine.n.01', 'name': 'Monterey_pine'}, {'id': 17605, 'synset': 'bristlecone_pine.n.01', 'name': 'bristlecone_pine'}, {'id': 17606, 'synset': 'table-mountain_pine.n.01', 'name': 'table-mountain_pine'}, {'id': 17607, 'synset': 'knobcone_pine.n.01', 'name': 'knobcone_pine'}, {'id': 17608, 'synset': 'japanese_red_pine.n.01', 'name': 'Japanese_red_pine'}, {'id': 17609, 'synset': 'japanese_black_pine.n.01', 'name': 'Japanese_black_pine'}, {'id': 17610, 'synset': 'torrey_pine.n.01', 'name': 'Torrey_pine'}, {'id': 17611, 'synset': 'larch.n.02', 'name': 'larch'}, {'id': 17612, 'synset': 'american_larch.n.01', 'name': 'American_larch'}, {'id': 17613, 'synset': 'western_larch.n.01', 'name': 'western_larch'}, {'id': 17614, 'synset': 'subalpine_larch.n.01', 'name': 'subalpine_larch'}, {'id': 17615, 'synset': 'european_larch.n.01', 'name': 'European_larch'}, {'id': 17616, 'synset': 'siberian_larch.n.01', 'name': 'Siberian_larch'}, {'id': 17617, 'synset': 'golden_larch.n.01', 'name': 'golden_larch'}, {'id': 17618, 'synset': 'fir.n.02', 'name': 'fir'}, {'id': 17619, 'synset': 'silver_fir.n.01', 'name': 'silver_fir'}, {'id': 17620, 'synset': 'amabilis_fir.n.01', 'name': 'amabilis_fir'}, {'id': 17621, 'synset': 'european_silver_fir.n.01', 'name': 'European_silver_fir'}, {'id': 17622, 'synset': 'white_fir.n.01', 'name': 'white_fir'}, {'id': 17623, 'synset': 'balsam_fir.n.01', 'name': 'balsam_fir'}, {'id': 17624, 'synset': 'fraser_fir.n.01', 'name': 'Fraser_fir'}, {'id': 17625, 'synset': 'lowland_fir.n.01', 'name': 'lowland_fir'}, {'id': 17626, 'synset': 'alpine_fir.n.01', 'name': 'Alpine_fir'}, {'id': 17627, 'synset': 'santa_lucia_fir.n.01', 'name': 'Santa_Lucia_fir'}, {'id': 17628, 'synset': 'cedar.n.03', 'name': 'cedar'}, {'id': 17629, 'synset': 'cedar_of_lebanon.n.01', 'name': 'cedar_of_Lebanon'}, {'id': 17630, 'synset': 'deodar.n.01', 'name': 'deodar'}, {'id': 17631, 'synset': 'atlas_cedar.n.01', 'name': 'Atlas_cedar'}, {'id': 17632, 'synset': 'spruce.n.02', 'name': 'spruce'}, {'id': 17633, 'synset': 'norway_spruce.n.01', 'name': 'Norway_spruce'}, {'id': 17634, 'synset': 'weeping_spruce.n.01', 'name': 'weeping_spruce'}, {'id': 17635, 'synset': 'engelmann_spruce.n.01', 'name': 'Engelmann_spruce'}, {'id': 17636, 'synset': 'white_spruce.n.01', 'name': 'white_spruce'}, {'id': 17637, 'synset': 'black_spruce.n.01', 'name': 'black_spruce'}, {'id': 17638, 'synset': 'siberian_spruce.n.01', 'name': 'Siberian_spruce'}, {'id': 17639, 'synset': 'sitka_spruce.n.01', 'name': 'Sitka_spruce'}, {'id': 17640, 'synset': 'oriental_spruce.n.01', 'name': 'oriental_spruce'}, {'id': 17641, 'synset': 'colorado_spruce.n.01', 'name': 'Colorado_spruce'}, {'id': 17642, 'synset': 'red_spruce.n.01', 'name': 'red_spruce'}, {'id': 17643, 'synset': 'hemlock.n.04', 'name': 'hemlock'}, {'id': 17644, 'synset': 'eastern_hemlock.n.01', 'name': 'eastern_hemlock'}, {'id': 17645, 'synset': 'carolina_hemlock.n.01', 'name': 'Carolina_hemlock'}, {'id': 17646, 'synset': 'mountain_hemlock.n.01', 'name': 'mountain_hemlock'}, {'id': 17647, 'synset': 'western_hemlock.n.01', 'name': 'western_hemlock'}, {'id': 17648, 'synset': 'douglas_fir.n.02', 'name': 'douglas_fir'}, {'id': 17649, 'synset': 'green_douglas_fir.n.01', 'name': 'green_douglas_fir'}, {'id': 17650, 'synset': 'big-cone_spruce.n.01', 'name': 'big-cone_spruce'}, {'id': 17651, 'synset': 'cathaya.n.01', 'name': 'Cathaya'}, {'id': 17652, 'synset': 'cedar.n.01', 'name': 'cedar'}, {'id': 17653, 'synset': 'cypress.n.02', 'name': 'cypress'}, {'id': 17654, 'synset': 'gowen_cypress.n.01', 'name': 'gowen_cypress'}, {'id': 17655, 'synset': 'pygmy_cypress.n.01', 'name': 'pygmy_cypress'}, {'id': 17656, 'synset': 'santa_cruz_cypress.n.01', 'name': 'Santa_Cruz_cypress'}, {'id': 17657, 'synset': 'arizona_cypress.n.01', 'name': 'Arizona_cypress'}, {'id': 17658, 'synset': 'guadalupe_cypress.n.01', 'name': 'Guadalupe_cypress'}, {'id': 17659, 'synset': 'monterey_cypress.n.01', 'name': 'Monterey_cypress'}, {'id': 17660, 'synset': 'mexican_cypress.n.01', 'name': 'Mexican_cypress'}, {'id': 17661, 'synset': 'italian_cypress.n.01', 'name': 'Italian_cypress'}, {'id': 17662, 'synset': 'king_william_pine.n.01', 'name': 'King_William_pine'}, {'id': 17663, 'synset': 'chilean_cedar.n.01', 'name': 'Chilean_cedar'}, {'id': 17664, 'synset': 'incense_cedar.n.02', 'name': 'incense_cedar'}, {'id': 17665, 'synset': 'southern_white_cedar.n.01', 'name': 'southern_white_cedar'}, {'id': 17666, 'synset': 'oregon_cedar.n.01', 'name': 'Oregon_cedar'}, {'id': 17667, 'synset': 'yellow_cypress.n.01', 'name': 'yellow_cypress'}, {'id': 17668, 'synset': 'japanese_cedar.n.01', 'name': 'Japanese_cedar'}, {'id': 17669, 'synset': 'juniper_berry.n.01', 'name': 'juniper_berry'}, {'id': 17670, 'synset': 'incense_cedar.n.01', 'name': 'incense_cedar'}, {'id': 17671, 'synset': 'kawaka.n.01', 'name': 'kawaka'}, {'id': 17672, 'synset': 'pahautea.n.01', 'name': 'pahautea'}, {'id': 17673, 'synset': 'metasequoia.n.01', 'name': 'metasequoia'}, {'id': 17674, 'synset': 'arborvitae.n.01', 'name': 'arborvitae'}, {'id': 17675, 'synset': 'western_red_cedar.n.01', 'name': 'western_red_cedar'}, {'id': 17676, 'synset': 'american_arborvitae.n.01', 'name': 'American_arborvitae'}, {'id': 17677, 'synset': 'oriental_arborvitae.n.01', 'name': 'Oriental_arborvitae'}, {'id': 17678, 'synset': 'hiba_arborvitae.n.01', 'name': 'hiba_arborvitae'}, {'id': 17679, 'synset': 'keteleeria.n.01', 'name': 'keteleeria'}, {'id': 17680, 'synset': 'wollemi_pine.n.01', 'name': 'Wollemi_pine'}, {'id': 17681, 'synset': 'araucaria.n.01', 'name': 'araucaria'}, {'id': 17682, 'synset': 'monkey_puzzle.n.01', 'name': 'monkey_puzzle'}, {'id': 17683, 'synset': 'norfolk_island_pine.n.01', 'name': 'norfolk_island_pine'}, {'id': 17684, 'synset': 'new_caledonian_pine.n.01', 'name': 'new_caledonian_pine'}, {'id': 17685, 'synset': 'bunya_bunya.n.01', 'name': 'bunya_bunya'}, {'id': 17686, 'synset': 'hoop_pine.n.01', 'name': 'hoop_pine'}, {'id': 17687, 'synset': 'kauri_pine.n.01', 'name': 'kauri_pine'}, {'id': 17688, 'synset': 'kauri.n.02', 'name': 'kauri'}, {'id': 17689, 'synset': 'amboina_pine.n.01', 'name': 'amboina_pine'}, {'id': 17690, 'synset': 'dundathu_pine.n.01', 'name': 'dundathu_pine'}, {'id': 17691, 'synset': 'red_kauri.n.01', 'name': 'red_kauri'}, {'id': 17692, 'synset': 'plum-yew.n.01', 'name': 'plum-yew'}, {'id': 17693, 'synset': 'california_nutmeg.n.01', 'name': 'California_nutmeg'}, {'id': 17694, 'synset': 'stinking_cedar.n.01', 'name': 'stinking_cedar'}, {'id': 17695, 'synset': 'celery_pine.n.01', 'name': 'celery_pine'}, {'id': 17696, 'synset': 'celery_top_pine.n.01', 'name': 'celery_top_pine'}, {'id': 17697, 'synset': 'tanekaha.n.01', 'name': 'tanekaha'}, {'id': 17698, 'synset': 'alpine_celery_pine.n.01', 'name': 'Alpine_celery_pine'}, {'id': 17699, 'synset': 'yellowwood.n.02', 'name': 'yellowwood'}, {'id': 17700, 'synset': 'gymnospermous_yellowwood.n.01', 'name': 'gymnospermous_yellowwood'}, {'id': 17701, 'synset': 'podocarp.n.01', 'name': 'podocarp'}, {'id': 17702, 'synset': 'yacca.n.01', 'name': 'yacca'}, {'id': 17703, 'synset': 'brown_pine.n.01', 'name': 'brown_pine'}, {'id': 17704, 'synset': 'cape_yellowwood.n.01', 'name': 'cape_yellowwood'}, {'id': 17705, 'synset': 'south-african_yellowwood.n.01', 'name': 'South-African_yellowwood'}, {'id': 17706, 'synset': 'alpine_totara.n.01', 'name': 'alpine_totara'}, {'id': 17707, 'synset': 'totara.n.01', 'name': 'totara'}, {'id': 17708, 'synset': 'common_yellowwood.n.01', 'name': 'common_yellowwood'}, {'id': 17709, 'synset': 'kahikatea.n.01', 'name': 'kahikatea'}, {'id': 17710, 'synset': 'rimu.n.01', 'name': 'rimu'}, {'id': 17711, 'synset': 'tarwood.n.02', 'name': 'tarwood'}, {'id': 17712, 'synset': 'common_sickle_pine.n.01', 'name': 'common_sickle_pine'}, {'id': 17713, 'synset': 'yellow-leaf_sickle_pine.n.01', 'name': 'yellow-leaf_sickle_pine'}, {'id': 17714, 'synset': 'tarwood.n.01', 'name': 'tarwood'}, {'id': 17715, 'synset': 'westland_pine.n.01', 'name': 'westland_pine'}, {'id': 17716, 'synset': 'huon_pine.n.01', 'name': 'huon_pine'}, {'id': 17717, 'synset': 'chilean_rimu.n.01', 'name': 'Chilean_rimu'}, {'id': 17718, 'synset': 'mountain_rimu.n.01', 'name': 'mountain_rimu'}, {'id': 17719, 'synset': 'nagi.n.01', 'name': 'nagi'}, {'id': 17720, 'synset': 'miro.n.01', 'name': 'miro'}, {'id': 17721, 'synset': 'matai.n.01', 'name': 'matai'}, {'id': 17722, 'synset': 'plum-fruited_yew.n.01', 'name': 'plum-fruited_yew'}, {'id': 17723, 'synset': 'prince_albert_yew.n.01', 'name': 'Prince_Albert_yew'}, {'id': 17724, 'synset': 'sundacarpus_amara.n.01', 'name': 'Sundacarpus_amara'}, {'id': 17725, 'synset': 'japanese_umbrella_pine.n.01', 'name': 'Japanese_umbrella_pine'}, {'id': 17726, 'synset': 'yew.n.02', 'name': 'yew'}, {'id': 17727, 'synset': 'old_world_yew.n.01', 'name': 'Old_World_yew'}, {'id': 17728, 'synset': 'pacific_yew.n.01', 'name': 'Pacific_yew'}, {'id': 17729, 'synset': 'japanese_yew.n.01', 'name': 'Japanese_yew'}, {'id': 17730, 'synset': 'florida_yew.n.01', 'name': 'Florida_yew'}, {'id': 17731, 'synset': 'new_caledonian_yew.n.01', 'name': 'New_Caledonian_yew'}, {'id': 17732, 'synset': 'white-berry_yew.n.01', 'name': 'white-berry_yew'}, {'id': 17733, 'synset': 'ginkgo.n.01', 'name': 'ginkgo'}, {'id': 17734, 'synset': 'angiosperm.n.01', 'name': 'angiosperm'}, {'id': 17735, 'synset': 'dicot.n.01', 'name': 'dicot'}, {'id': 17736, 'synset': 'monocot.n.01', 'name': 'monocot'}, {'id': 17737, 'synset': 'floret.n.01', 'name': 'floret'}, {'id': 17738, 'synset': 'flower.n.01', 'name': 'flower'}, {'id': 17739, 'synset': 'bloomer.n.01', 'name': 'bloomer'}, {'id': 17740, 'synset': 'wildflower.n.01', 'name': 'wildflower'}, {'id': 17741, 'synset': 'apetalous_flower.n.01', 'name': 'apetalous_flower'}, {'id': 17742, 'synset': 'inflorescence.n.02', 'name': 'inflorescence'}, {'id': 17743, 'synset': 'rosebud.n.01', 'name': 'rosebud'}, {'id': 17744, 'synset': 'gynostegium.n.01', 'name': 'gynostegium'}, {'id': 17745, 'synset': 'pollinium.n.01', 'name': 'pollinium'}, {'id': 17746, 'synset': 'pistil.n.01', 'name': 'pistil'}, {'id': 17747, 'synset': 'gynobase.n.01', 'name': 'gynobase'}, {'id': 17748, 'synset': 'gynophore.n.01', 'name': 'gynophore'}, {'id': 17749, 'synset': 'stylopodium.n.01', 'name': 'stylopodium'}, {'id': 17750, 'synset': 'carpophore.n.01', 'name': 'carpophore'}, {'id': 17751, 'synset': 'cornstalk.n.01', 'name': 'cornstalk'}, {'id': 17752, 'synset': 'petiolule.n.01', 'name': 'petiolule'}, {'id': 17753, 'synset': 'mericarp.n.01', 'name': 'mericarp'}, {'id': 17754, 'synset': 'micropyle.n.01', 'name': 'micropyle'}, {'id': 17755, 'synset': 'germ_tube.n.01', 'name': 'germ_tube'}, {'id': 17756, 'synset': 'pollen_tube.n.01', 'name': 'pollen_tube'}, {'id': 17757, 'synset': 'gemma.n.01', 'name': 'gemma'}, {'id': 17758, 'synset': 'galbulus.n.01', 'name': 'galbulus'}, {'id': 17759, 'synset': 'nectary.n.01', 'name': 'nectary'}, {'id': 17760, 'synset': 'pericarp.n.01', 'name': 'pericarp'}, {'id': 17761, 'synset': 'epicarp.n.01', 'name': 'epicarp'}, {'id': 17762, 'synset': 'mesocarp.n.01', 'name': 'mesocarp'}, {'id': 17763, 'synset': 'pip.n.03', 'name': 'pip'}, {'id': 17764, 'synset': 'silique.n.01', 'name': 'silique'}, {'id': 17765, 'synset': 'cataphyll.n.01', 'name': 'cataphyll'}, {'id': 17766, 'synset': 'perisperm.n.01', 'name': 'perisperm'}, {'id': 17767, 'synset': 'monocarp.n.01', 'name': 'monocarp'}, {'id': 17768, 'synset': 'sporophyte.n.01', 'name': 'sporophyte'}, {'id': 17769, 'synset': 'gametophyte.n.01', 'name': 'gametophyte'}, {'id': 17770, 'synset': 'megasporangium.n.01', 'name': 'megasporangium'}, {'id': 17771, 'synset': 'microspore.n.01', 'name': 'microspore'}, {'id': 17772, 'synset': 'microsporangium.n.01', 'name': 'microsporangium'}, {'id': 17773, 'synset': 'microsporophyll.n.01', 'name': 'microsporophyll'}, {'id': 17774, 'synset': 'archespore.n.01', 'name': 'archespore'}, {'id': 17775, 'synset': 'bonduc_nut.n.01', 'name': 'bonduc_nut'}, {'id': 17776, 'synset': "job's_tears.n.01", 'name': "Job's_tears"}, {'id': 17777, 'synset': 'oilseed.n.01', 'name': 'oilseed'}, {'id': 17778, 'synset': 'castor_bean.n.01', 'name': 'castor_bean'}, {'id': 17779, 'synset': 'cottonseed.n.01', 'name': 'cottonseed'}, {'id': 17780, 'synset': 'candlenut.n.02', 'name': 'candlenut'}, {'id': 17781, 'synset': 'peach_pit.n.01', 'name': 'peach_pit'}, {'id': 17782, 'synset': 'hypanthium.n.01', 'name': 'hypanthium'}, {'id': 17783, 'synset': 'petal.n.01', 'name': 'petal'}, {'id': 17784, 'synset': 'corolla.n.01', 'name': 'corolla'}, {'id': 17785, 'synset': 'lip.n.02', 'name': 'lip'}, {'id': 17786, 'synset': 'perianth.n.01', 'name': 'perianth'}, {'id': 17787, 'synset': 'thistledown.n.01', 'name': 'thistledown'}, {'id': 17788, 'synset': 'custard_apple.n.01', 'name': 'custard_apple'}, {'id': 17789, 'synset': 'cherimoya.n.01', 'name': 'cherimoya'}, {'id': 17790, 'synset': 'ilama.n.01', 'name': 'ilama'}, {'id': 17791, 'synset': 'soursop.n.01', 'name': 'soursop'}, {'id': 17792, 'synset': "bullock's_heart.n.01", 'name': "bullock's_heart"}, {'id': 17793, 'synset': 'sweetsop.n.01', 'name': 'sweetsop'}, {'id': 17794, 'synset': 'pond_apple.n.01', 'name': 'pond_apple'}, {'id': 17795, 'synset': 'pawpaw.n.02', 'name': 'pawpaw'}, {'id': 17796, 'synset': 'ilang-ilang.n.02', 'name': 'ilang-ilang'}, {'id': 17797, 'synset': 'lancewood.n.02', 'name': 'lancewood'}, {'id': 17798, 'synset': 'guinea_pepper.n.02', 'name': 'Guinea_pepper'}, {'id': 17799, 'synset': 'barberry.n.01', 'name': 'barberry'}, {'id': 17800, 'synset': 'american_barberry.n.01', 'name': 'American_barberry'}, {'id': 17801, 'synset': 'common_barberry.n.01', 'name': 'common_barberry'}, {'id': 17802, 'synset': 'japanese_barberry.n.01', 'name': 'Japanese_barberry'}, {'id': 17803, 'synset': 'oregon_grape.n.02', 'name': 'Oregon_grape'}, {'id': 17804, 'synset': 'oregon_grape.n.01', 'name': 'Oregon_grape'}, {'id': 17805, 'synset': 'mayapple.n.01', 'name': 'mayapple'}, {'id': 17806, 'synset': 'may_apple.n.01', 'name': 'May_apple'}, {'id': 17807, 'synset': 'allspice.n.02', 'name': 'allspice'}, {'id': 17808, 'synset': 'carolina_allspice.n.01', 'name': 'Carolina_allspice'}, {'id': 17809, 'synset': 'spicebush.n.02', 'name': 'spicebush'}, {'id': 17810, 'synset': 'katsura_tree.n.01', 'name': 'katsura_tree'}, {'id': 17811, 'synset': 'laurel.n.01', 'name': 'laurel'}, {'id': 17812, 'synset': 'true_laurel.n.01', 'name': 'true_laurel'}, {'id': 17813, 'synset': 'camphor_tree.n.01', 'name': 'camphor_tree'}, {'id': 17814, 'synset': 'cinnamon.n.02', 'name': 'cinnamon'}, {'id': 17815, 'synset': 'cassia.n.03', 'name': 'cassia'}, {'id': 17816, 'synset': 'cassia_bark.n.01', 'name': 'cassia_bark'}, {'id': 17817, 'synset': 'saigon_cinnamon.n.01', 'name': 'Saigon_cinnamon'}, {'id': 17818, 'synset': 'cinnamon_bark.n.01', 'name': 'cinnamon_bark'}, {'id': 17819, 'synset': 'spicebush.n.01', 'name': 'spicebush'}, {'id': 17820, 'synset': 'avocado.n.02', 'name': 'avocado'}, {'id': 17821, 'synset': 'laurel-tree.n.01', 'name': 'laurel-tree'}, {'id': 17822, 'synset': 'sassafras.n.01', 'name': 'sassafras'}, {'id': 17823, 'synset': 'california_laurel.n.01', 'name': 'California_laurel'}, {'id': 17824, 'synset': 'anise_tree.n.01', 'name': 'anise_tree'}, {'id': 17825, 'synset': 'purple_anise.n.01', 'name': 'purple_anise'}, {'id': 17826, 'synset': 'star_anise.n.02', 'name': 'star_anise'}, {'id': 17827, 'synset': 'star_anise.n.01', 'name': 'star_anise'}, {'id': 17828, 'synset': 'magnolia.n.02', 'name': 'magnolia'}, {'id': 17829, 'synset': 'southern_magnolia.n.01', 'name': 'southern_magnolia'}, {'id': 17830, 'synset': 'umbrella_tree.n.02', 'name': 'umbrella_tree'}, {'id': 17831, 'synset': 'earleaved_umbrella_tree.n.01', 'name': 'earleaved_umbrella_tree'}, {'id': 17832, 'synset': 'cucumber_tree.n.01', 'name': 'cucumber_tree'}, {'id': 17833, 'synset': 'large-leaved_magnolia.n.01', 'name': 'large-leaved_magnolia'}, {'id': 17834, 'synset': 'saucer_magnolia.n.01', 'name': 'saucer_magnolia'}, {'id': 17835, 'synset': 'star_magnolia.n.01', 'name': 'star_magnolia'}, {'id': 17836, 'synset': 'sweet_bay.n.01', 'name': 'sweet_bay'}, {'id': 17837, 'synset': 'manglietia.n.01', 'name': 'manglietia'}, {'id': 17838, 'synset': 'tulip_tree.n.01', 'name': 'tulip_tree'}, {'id': 17839, 'synset': 'moonseed.n.01', 'name': 'moonseed'}, {'id': 17840, 'synset': 'common_moonseed.n.01', 'name': 'common_moonseed'}, {'id': 17841, 'synset': 'carolina_moonseed.n.01', 'name': 'Carolina_moonseed'}, {'id': 17842, 'synset': 'nutmeg.n.01', 'name': 'nutmeg'}, {'id': 17843, 'synset': 'water_nymph.n.02', 'name': 'water_nymph'}, {'id': 17844, 'synset': 'european_white_lily.n.01', 'name': 'European_white_lily'}, {'id': 17845, 'synset': 'southern_spatterdock.n.01', 'name': 'southern_spatterdock'}, {'id': 17846, 'synset': 'lotus.n.01', 'name': 'lotus'}, {'id': 17847, 'synset': 'water_chinquapin.n.01', 'name': 'water_chinquapin'}, {'id': 17848, 'synset': 'water-shield.n.02', 'name': 'water-shield'}, {'id': 17849, 'synset': 'water-shield.n.01', 'name': 'water-shield'}, {'id': 17850, 'synset': 'peony.n.01', 'name': 'peony'}, {'id': 17851, 'synset': 'buttercup.n.01', 'name': 'buttercup'}, {'id': 17852, 'synset': 'meadow_buttercup.n.01', 'name': 'meadow_buttercup'}, {'id': 17853, 'synset': 'water_crowfoot.n.01', 'name': 'water_crowfoot'}, {'id': 17854, 'synset': 'lesser_celandine.n.01', 'name': 'lesser_celandine'}, {'id': 17855, 'synset': 'lesser_spearwort.n.01', 'name': 'lesser_spearwort'}, {'id': 17856, 'synset': 'greater_spearwort.n.01', 'name': 'greater_spearwort'}, {'id': 17857, 'synset': 'western_buttercup.n.01', 'name': 'western_buttercup'}, {'id': 17858, 'synset': 'creeping_buttercup.n.01', 'name': 'creeping_buttercup'}, {'id': 17859, 'synset': 'cursed_crowfoot.n.01', 'name': 'cursed_crowfoot'}, {'id': 17860, 'synset': 'aconite.n.01', 'name': 'aconite'}, {'id': 17861, 'synset': 'monkshood.n.01', 'name': 'monkshood'}, {'id': 17862, 'synset': 'wolfsbane.n.01', 'name': 'wolfsbane'}, {'id': 17863, 'synset': 'baneberry.n.02', 'name': 'baneberry'}, {'id': 17864, 'synset': 'baneberry.n.01', 'name': 'baneberry'}, {'id': 17865, 'synset': 'red_baneberry.n.01', 'name': 'red_baneberry'}, {'id': 17866, 'synset': "pheasant's-eye.n.01", 'name': "pheasant's-eye"}, {'id': 17867, 'synset': 'anemone.n.01', 'name': 'anemone'}, {'id': 17868, 'synset': 'alpine_anemone.n.01', 'name': 'Alpine_anemone'}, {'id': 17869, 'synset': 'canada_anemone.n.01', 'name': 'Canada_anemone'}, {'id': 17870, 'synset': 'thimbleweed.n.01', 'name': 'thimbleweed'}, {'id': 17871, 'synset': 'wood_anemone.n.02', 'name': 'wood_anemone'}, {'id': 17872, 'synset': 'wood_anemone.n.01', 'name': 'wood_anemone'}, {'id': 17873, 'synset': 'longheaded_thimbleweed.n.01', 'name': 'longheaded_thimbleweed'}, {'id': 17874, 'synset': 'snowdrop_anemone.n.01', 'name': 'snowdrop_anemone'}, {'id': 17875, 'synset': 'virginia_thimbleweed.n.01', 'name': 'Virginia_thimbleweed'}, {'id': 17876, 'synset': 'rue_anemone.n.01', 'name': 'rue_anemone'}, {'id': 17877, 'synset': 'columbine.n.01', 'name': 'columbine'}, {'id': 17878, 'synset': 'meeting_house.n.01', 'name': 'meeting_house'}, {'id': 17879, 'synset': 'blue_columbine.n.01', 'name': 'blue_columbine'}, {'id': 17880, 'synset': "granny's_bonnets.n.01", 'name': "granny's_bonnets"}, {'id': 17881, 'synset': 'marsh_marigold.n.01', 'name': 'marsh_marigold'}, {'id': 17882, 'synset': 'american_bugbane.n.01', 'name': 'American_bugbane'}, {'id': 17883, 'synset': 'black_cohosh.n.01', 'name': 'black_cohosh'}, {'id': 17884, 'synset': 'fetid_bugbane.n.01', 'name': 'fetid_bugbane'}, {'id': 17885, 'synset': 'clematis.n.01', 'name': 'clematis'}, {'id': 17886, 'synset': 'pine_hyacinth.n.01', 'name': 'pine_hyacinth'}, {'id': 17887, 'synset': 'blue_jasmine.n.01', 'name': 'blue_jasmine'}, {'id': 17888, 'synset': 'golden_clematis.n.01', 'name': 'golden_clematis'}, {'id': 17889, 'synset': 'scarlet_clematis.n.01', 'name': 'scarlet_clematis'}, {'id': 17890, 'synset': 'leather_flower.n.02', 'name': 'leather_flower'}, {'id': 17891, 'synset': 'leather_flower.n.01', 'name': 'leather_flower'}, {'id': 17892, 'synset': "virgin's_bower.n.01", 'name': "virgin's_bower"}, {'id': 17893, 'synset': 'purple_clematis.n.01', 'name': 'purple_clematis'}, {'id': 17894, 'synset': 'goldthread.n.01', 'name': 'goldthread'}, {'id': 17895, 'synset': 'rocket_larkspur.n.01', 'name': 'rocket_larkspur'}, {'id': 17896, 'synset': 'delphinium.n.01', 'name': 'delphinium'}, {'id': 17897, 'synset': 'larkspur.n.01', 'name': 'larkspur'}, {'id': 17898, 'synset': 'winter_aconite.n.01', 'name': 'winter_aconite'}, {'id': 17899, 'synset': 'lenten_rose.n.01', 'name': 'lenten_rose'}, {'id': 17900, 'synset': 'green_hellebore.n.01', 'name': 'green_hellebore'}, {'id': 17901, 'synset': 'hepatica.n.01', 'name': 'hepatica'}, {'id': 17902, 'synset': 'goldenseal.n.01', 'name': 'goldenseal'}, {'id': 17903, 'synset': 'false_rue_anemone.n.01', 'name': 'false_rue_anemone'}, {'id': 17904, 'synset': 'giant_buttercup.n.01', 'name': 'giant_buttercup'}, {'id': 17905, 'synset': 'nigella.n.01', 'name': 'nigella'}, {'id': 17906, 'synset': 'love-in-a-mist.n.03', 'name': 'love-in-a-mist'}, {'id': 17907, 'synset': 'fennel_flower.n.01', 'name': 'fennel_flower'}, {'id': 17908, 'synset': 'black_caraway.n.01', 'name': 'black_caraway'}, {'id': 17909, 'synset': 'pasqueflower.n.01', 'name': 'pasqueflower'}, {'id': 17910, 'synset': 'meadow_rue.n.01', 'name': 'meadow_rue'}, {'id': 17911, 'synset': 'false_bugbane.n.01', 'name': 'false_bugbane'}, {'id': 17912, 'synset': 'globeflower.n.01', 'name': 'globeflower'}, {'id': 17913, 'synset': "winter's_bark.n.02", 'name': "winter's_bark"}, {'id': 17914, 'synset': 'pepper_shrub.n.01', 'name': 'pepper_shrub'}, {'id': 17915, 'synset': 'sweet_gale.n.01', 'name': 'sweet_gale'}, {'id': 17916, 'synset': 'wax_myrtle.n.01', 'name': 'wax_myrtle'}, {'id': 17917, 'synset': 'bay_myrtle.n.01', 'name': 'bay_myrtle'}, {'id': 17918, 'synset': 'bayberry.n.02', 'name': 'bayberry'}, {'id': 17919, 'synset': 'sweet_fern.n.02', 'name': 'sweet_fern'}, {'id': 17920, 'synset': 'corkwood.n.01', 'name': 'corkwood'}, {'id': 17921, 'synset': 'jointed_rush.n.01', 'name': 'jointed_rush'}, {'id': 17922, 'synset': 'toad_rush.n.01', 'name': 'toad_rush'}, {'id': 17923, 'synset': 'slender_rush.n.01', 'name': 'slender_rush'}, {'id': 17924, 'synset': 'zebrawood.n.02', 'name': 'zebrawood'}, {'id': 17925, 'synset': 'connarus_guianensis.n.01', 'name': 'Connarus_guianensis'}, {'id': 17926, 'synset': 'legume.n.01', 'name': 'legume'}, {'id': 17927, 'synset': 'peanut.n.01', 'name': 'peanut'}, {'id': 17928, 'synset': 'granadilla_tree.n.01', 'name': 'granadilla_tree'}, {'id': 17929, 'synset': 'arariba.n.01', 'name': 'arariba'}, {'id': 17930, 'synset': 'tonka_bean.n.01', 'name': 'tonka_bean'}, {'id': 17931, 'synset': 'courbaril.n.01', 'name': 'courbaril'}, {'id': 17932, 'synset': 'melilotus.n.01', 'name': 'melilotus'}, {'id': 17933, 'synset': 'darling_pea.n.01', 'name': 'darling_pea'}, {'id': 17934, 'synset': 'smooth_darling_pea.n.01', 'name': 'smooth_darling_pea'}, {'id': 17935, 'synset': 'clover.n.01', 'name': 'clover'}, {'id': 17936, 'synset': 'alpine_clover.n.01', 'name': 'alpine_clover'}, {'id': 17937, 'synset': 'hop_clover.n.02', 'name': 'hop_clover'}, {'id': 17938, 'synset': 'crimson_clover.n.01', 'name': 'crimson_clover'}, {'id': 17939, 'synset': 'red_clover.n.01', 'name': 'red_clover'}, {'id': 17940, 'synset': 'buffalo_clover.n.02', 'name': 'buffalo_clover'}, {'id': 17941, 'synset': 'white_clover.n.01', 'name': 'white_clover'}, {'id': 17942, 'synset': 'mimosa.n.02', 'name': 'mimosa'}, {'id': 17943, 'synset': 'acacia.n.01', 'name': 'acacia'}, {'id': 17944, 'synset': 'shittah.n.01', 'name': 'shittah'}, {'id': 17945, 'synset': 'wattle.n.03', 'name': 'wattle'}, {'id': 17946, 'synset': 'black_wattle.n.01', 'name': 'black_wattle'}, {'id': 17947, 'synset': 'gidgee.n.01', 'name': 'gidgee'}, {'id': 17948, 'synset': 'catechu.n.02', 'name': 'catechu'}, {'id': 17949, 'synset': 'silver_wattle.n.01', 'name': 'silver_wattle'}, {'id': 17950, 'synset': 'huisache.n.01', 'name': 'huisache'}, {'id': 17951, 'synset': 'lightwood.n.01', 'name': 'lightwood'}, {'id': 17952, 'synset': 'golden_wattle.n.01', 'name': 'golden_wattle'}, {'id': 17953, 'synset': 'fever_tree.n.04', 'name': 'fever_tree'}, {'id': 17954, 'synset': 'coralwood.n.01', 'name': 'coralwood'}, {'id': 17955, 'synset': 'albizzia.n.01', 'name': 'albizzia'}, {'id': 17956, 'synset': 'silk_tree.n.01', 'name': 'silk_tree'}, {'id': 17957, 'synset': 'siris.n.01', 'name': 'siris'}, {'id': 17958, 'synset': 'rain_tree.n.01', 'name': 'rain_tree'}, {'id': 17959, 'synset': 'calliandra.n.01', 'name': 'calliandra'}, {'id': 17960, 'synset': 'conacaste.n.01', 'name': 'conacaste'}, {'id': 17961, 'synset': 'inga.n.01', 'name': 'inga'}, {'id': 17962, 'synset': 'ice-cream_bean.n.01', 'name': 'ice-cream_bean'}, {'id': 17963, 'synset': 'guama.n.01', 'name': 'guama'}, {'id': 17964, 'synset': 'lead_tree.n.01', 'name': 'lead_tree'}, {'id': 17965, 'synset': 'wild_tamarind.n.02', 'name': 'wild_tamarind'}, {'id': 17966, 'synset': 'sabicu.n.02', 'name': 'sabicu'}, {'id': 17967, 'synset': 'nitta_tree.n.01', 'name': 'nitta_tree'}, {'id': 17968, 'synset': 'parkia_javanica.n.01', 'name': 'Parkia_javanica'}, {'id': 17969, 'synset': 'manila_tamarind.n.01', 'name': 'manila_tamarind'}, {'id': 17970, 'synset': "cat's-claw.n.01", 'name': "cat's-claw"}, {'id': 17971, 'synset': 'honey_mesquite.n.01', 'name': 'honey_mesquite'}, {'id': 17972, 'synset': 'algarroba.n.03', 'name': 'algarroba'}, {'id': 17973, 'synset': 'screw_bean.n.02', 'name': 'screw_bean'}, {'id': 17974, 'synset': 'screw_bean.n.01', 'name': 'screw_bean'}, {'id': 17975, 'synset': 'dogbane.n.01', 'name': 'dogbane'}, {'id': 17976, 'synset': 'indian_hemp.n.03', 'name': 'Indian_hemp'}, {'id': 17977, 'synset': "bushman's_poison.n.01", 'name': "bushman's_poison"}, {'id': 17978, 'synset': 'impala_lily.n.01', 'name': 'impala_lily'}, {'id': 17979, 'synset': 'allamanda.n.01', 'name': 'allamanda'}, {'id': 17980, 'synset': 'common_allamanda.n.01', 'name': 'common_allamanda'}, {'id': 17981, 'synset': 'dita.n.01', 'name': 'dita'}, {'id': 17982, 'synset': 'nepal_trumpet_flower.n.01', 'name': 'Nepal_trumpet_flower'}, {'id': 17983, 'synset': 'carissa.n.01', 'name': 'carissa'}, {'id': 17984, 'synset': 'hedge_thorn.n.01', 'name': 'hedge_thorn'}, {'id': 17985, 'synset': 'natal_plum.n.01', 'name': 'natal_plum'}, {'id': 17986, 'synset': 'periwinkle.n.02', 'name': 'periwinkle'}, {'id': 17987, 'synset': 'ivory_tree.n.01', 'name': 'ivory_tree'}, {'id': 17988, 'synset': 'white_dipladenia.n.01', 'name': 'white_dipladenia'}, {'id': 17989, 'synset': 'chilean_jasmine.n.01', 'name': 'Chilean_jasmine'}, {'id': 17990, 'synset': 'oleander.n.01', 'name': 'oleander'}, {'id': 17991, 'synset': 'frangipani.n.01', 'name': 'frangipani'}, {'id': 17992, 'synset': 'west_indian_jasmine.n.01', 'name': 'West_Indian_jasmine'}, {'id': 17993, 'synset': 'rauwolfia.n.02', 'name': 'rauwolfia'}, {'id': 17994, 'synset': 'snakewood.n.01', 'name': 'snakewood'}, {'id': 17995, 'synset': 'strophanthus_kombe.n.01', 'name': 'Strophanthus_kombe'}, {'id': 17996, 'synset': 'yellow_oleander.n.01', 'name': 'yellow_oleander'}, {'id': 17997, 'synset': 'myrtle.n.01', 'name': 'myrtle'}, {'id': 17998, 'synset': 'large_periwinkle.n.01', 'name': 'large_periwinkle'}, {'id': 17999, 'synset': 'arum.n.02', 'name': 'arum'}, {'id': 18000, 'synset': 'cuckoopint.n.01', 'name': 'cuckoopint'}, {'id': 18001, 'synset': 'black_calla.n.01', 'name': 'black_calla'}, {'id': 18002, 'synset': 'calamus.n.02', 'name': 'calamus'}, {'id': 18003, 'synset': 'alocasia.n.01', 'name': 'alocasia'}, {'id': 18004, 'synset': 'giant_taro.n.01', 'name': 'giant_taro'}, {'id': 18005, 'synset': 'amorphophallus.n.01', 'name': 'amorphophallus'}, {'id': 18006, 'synset': 'pungapung.n.01', 'name': 'pungapung'}, {'id': 18007, 'synset': "devil's_tongue.n.01", 'name': "devil's_tongue"}, {'id': 18008, 'synset': 'anthurium.n.01', 'name': 'anthurium'}, {'id': 18009, 'synset': 'flamingo_flower.n.01', 'name': 'flamingo_flower'}, {'id': 18010, 'synset': 'jack-in-the-pulpit.n.01', 'name': 'jack-in-the-pulpit'}, {'id': 18011, 'synset': "friar's-cowl.n.01", 'name': "friar's-cowl"}, {'id': 18012, 'synset': 'caladium.n.01', 'name': 'caladium'}, {'id': 18013, 'synset': 'caladium_bicolor.n.01', 'name': 'Caladium_bicolor'}, {'id': 18014, 'synset': 'wild_calla.n.01', 'name': 'wild_calla'}, {'id': 18015, 'synset': 'taro.n.02', 'name': 'taro'}, {'id': 18016, 'synset': 'taro.n.01', 'name': 'taro'}, {'id': 18017, 'synset': 'cryptocoryne.n.01', 'name': 'cryptocoryne'}, {'id': 18018, 'synset': 'dracontium.n.01', 'name': 'dracontium'}, {'id': 18019, 'synset': 'golden_pothos.n.01', 'name': 'golden_pothos'}, {'id': 18020, 'synset': 'skunk_cabbage.n.02', 'name': 'skunk_cabbage'}, {'id': 18021, 'synset': 'monstera.n.01', 'name': 'monstera'}, {'id': 18022, 'synset': 'ceriman.n.01', 'name': 'ceriman'}, {'id': 18023, 'synset': 'nephthytis.n.01', 'name': 'nephthytis'}, {'id': 18024, 'synset': 'nephthytis_afzelii.n.01', 'name': 'Nephthytis_afzelii'}, {'id': 18025, 'synset': 'arrow_arum.n.01', 'name': 'arrow_arum'}, {'id': 18026, 'synset': 'green_arrow_arum.n.01', 'name': 'green_arrow_arum'}, {'id': 18027, 'synset': 'philodendron.n.01', 'name': 'philodendron'}, {'id': 18028, 'synset': 'pistia.n.01', 'name': 'pistia'}, {'id': 18029, 'synset': 'pothos.n.01', 'name': 'pothos'}, {'id': 18030, 'synset': 'spathiphyllum.n.01', 'name': 'spathiphyllum'}, {'id': 18031, 'synset': 'skunk_cabbage.n.01', 'name': 'skunk_cabbage'}, {'id': 18032, 'synset': 'yautia.n.01', 'name': 'yautia'}, {'id': 18033, 'synset': 'calla_lily.n.01', 'name': 'calla_lily'}, {'id': 18034, 'synset': 'pink_calla.n.01', 'name': 'pink_calla'}, {'id': 18035, 'synset': 'golden_calla.n.01', 'name': 'golden_calla'}, {'id': 18036, 'synset': 'duckweed.n.01', 'name': 'duckweed'}, {'id': 18037, 'synset': 'common_duckweed.n.01', 'name': 'common_duckweed'}, {'id': 18038, 'synset': 'star-duckweed.n.01', 'name': 'star-duckweed'}, {'id': 18039, 'synset': 'great_duckweed.n.01', 'name': 'great_duckweed'}, {'id': 18040, 'synset': 'watermeal.n.01', 'name': 'watermeal'}, {'id': 18041, 'synset': 'common_wolffia.n.01', 'name': 'common_wolffia'}, {'id': 18042, 'synset': 'aralia.n.01', 'name': 'aralia'}, {'id': 18043, 'synset': 'american_angelica_tree.n.01', 'name': 'American_angelica_tree'}, {'id': 18044, 'synset': 'american_spikenard.n.01', 'name': 'American_spikenard'}, {'id': 18045, 'synset': 'bristly_sarsaparilla.n.01', 'name': 'bristly_sarsaparilla'}, {'id': 18046, 'synset': 'japanese_angelica_tree.n.01', 'name': 'Japanese_angelica_tree'}, {'id': 18047, 'synset': 'chinese_angelica.n.01', 'name': 'Chinese_angelica'}, {'id': 18048, 'synset': 'ivy.n.01', 'name': 'ivy'}, {'id': 18049, 'synset': 'puka.n.02', 'name': 'puka'}, {'id': 18050, 'synset': 'ginseng.n.02', 'name': 'ginseng'}, {'id': 18051, 'synset': 'ginseng.n.01', 'name': 'ginseng'}, {'id': 18052, 'synset': 'umbrella_tree.n.01', 'name': 'umbrella_tree'}, {'id': 18053, 'synset': 'birthwort.n.01', 'name': 'birthwort'}, {'id': 18054, 'synset': "dutchman's-pipe.n.01", 'name': "Dutchman's-pipe"}, {'id': 18055, 'synset': 'virginia_snakeroot.n.01', 'name': 'Virginia_snakeroot'}, {'id': 18056, 'synset': 'canada_ginger.n.01', 'name': 'Canada_ginger'}, {'id': 18057, 'synset': 'heartleaf.n.02', 'name': 'heartleaf'}, {'id': 18058, 'synset': 'heartleaf.n.01', 'name': 'heartleaf'}, {'id': 18059, 'synset': 'asarabacca.n.01', 'name': 'asarabacca'}, {'id': 18060, 'synset': 'caryophyllaceous_plant.n.01', 'name': 'caryophyllaceous_plant'}, {'id': 18061, 'synset': 'corn_cockle.n.01', 'name': 'corn_cockle'}, {'id': 18062, 'synset': 'sandwort.n.03', 'name': 'sandwort'}, {'id': 18063, 'synset': 'mountain_sandwort.n.01', 'name': 'mountain_sandwort'}, {'id': 18064, 'synset': 'pine-barren_sandwort.n.01', 'name': 'pine-barren_sandwort'}, {'id': 18065, 'synset': 'seabeach_sandwort.n.01', 'name': 'seabeach_sandwort'}, {'id': 18066, 'synset': 'rock_sandwort.n.01', 'name': 'rock_sandwort'}, {'id': 18067, 'synset': 'thyme-leaved_sandwort.n.01', 'name': 'thyme-leaved_sandwort'}, {'id': 18068, 'synset': 'mouse-ear_chickweed.n.01', 'name': 'mouse-ear_chickweed'}, {'id': 18069, 'synset': 'snow-in-summer.n.02', 'name': 'snow-in-summer'}, {'id': 18070, 'synset': 'alpine_mouse-ear.n.01', 'name': 'Alpine_mouse-ear'}, {'id': 18071, 'synset': 'pink.n.02', 'name': 'pink'}, {'id': 18072, 'synset': 'sweet_william.n.01', 'name': 'sweet_William'}, {'id': 18073, 'synset': 'china_pink.n.01', 'name': 'china_pink'}, {'id': 18074, 'synset': 'japanese_pink.n.01', 'name': 'Japanese_pink'}, {'id': 18075, 'synset': 'maiden_pink.n.01', 'name': 'maiden_pink'}, {'id': 18076, 'synset': 'cheddar_pink.n.01', 'name': 'cheddar_pink'}, {'id': 18077, 'synset': 'button_pink.n.01', 'name': 'button_pink'}, {'id': 18078, 'synset': 'cottage_pink.n.01', 'name': 'cottage_pink'}, {'id': 18079, 'synset': 'fringed_pink.n.02', 'name': 'fringed_pink'}, {'id': 18080, 'synset': 'drypis.n.01', 'name': 'drypis'}, {'id': 18081, 'synset': "baby's_breath.n.01", 'name': "baby's_breath"}, {'id': 18082, 'synset': 'coral_necklace.n.01', 'name': 'coral_necklace'}, {'id': 18083, 'synset': 'lychnis.n.01', 'name': 'lychnis'}, {'id': 18084, 'synset': 'ragged_robin.n.01', 'name': 'ragged_robin'}, {'id': 18085, 'synset': 'scarlet_lychnis.n.01', 'name': 'scarlet_lychnis'}, {'id': 18086, 'synset': 'mullein_pink.n.01', 'name': 'mullein_pink'}, {'id': 18087, 'synset': 'sandwort.n.02', 'name': 'sandwort'}, {'id': 18088, 'synset': 'sandwort.n.01', 'name': 'sandwort'}, {'id': 18089, 'synset': 'soapwort.n.01', 'name': 'soapwort'}, {'id': 18090, 'synset': 'knawel.n.01', 'name': 'knawel'}, {'id': 18091, 'synset': 'silene.n.01', 'name': 'silene'}, {'id': 18092, 'synset': 'moss_campion.n.01', 'name': 'moss_campion'}, {'id': 18093, 'synset': 'wild_pink.n.02', 'name': 'wild_pink'}, {'id': 18094, 'synset': 'red_campion.n.01', 'name': 'red_campion'}, {'id': 18095, 'synset': 'white_campion.n.01', 'name': 'white_campion'}, {'id': 18096, 'synset': 'fire_pink.n.01', 'name': 'fire_pink'}, {'id': 18097, 'synset': 'bladder_campion.n.01', 'name': 'bladder_campion'}, {'id': 18098, 'synset': 'corn_spurry.n.01', 'name': 'corn_spurry'}, {'id': 18099, 'synset': 'sand_spurry.n.01', 'name': 'sand_spurry'}, {'id': 18100, 'synset': 'chickweed.n.01', 'name': 'chickweed'}, {'id': 18101, 'synset': 'common_chickweed.n.01', 'name': 'common_chickweed'}, {'id': 18102, 'synset': 'cowherb.n.01', 'name': 'cowherb'}, {'id': 18103, 'synset': 'hottentot_fig.n.01', 'name': 'Hottentot_fig'}, {'id': 18104, 'synset': 'livingstone_daisy.n.01', 'name': 'livingstone_daisy'}, {'id': 18105, 'synset': 'fig_marigold.n.01', 'name': 'fig_marigold'}, {'id': 18106, 'synset': 'ice_plant.n.01', 'name': 'ice_plant'}, {'id': 18107, 'synset': 'new_zealand_spinach.n.01', 'name': 'New_Zealand_spinach'}, {'id': 18108, 'synset': 'amaranth.n.02', 'name': 'amaranth'}, {'id': 18109, 'synset': 'amaranth.n.01', 'name': 'amaranth'}, {'id': 18110, 'synset': 'tumbleweed.n.04', 'name': 'tumbleweed'}, {'id': 18111, 'synset': "prince's-feather.n.02", 'name': "prince's-feather"}, {'id': 18112, 'synset': 'pigweed.n.02', 'name': 'pigweed'}, {'id': 18113, 'synset': 'thorny_amaranth.n.01', 'name': 'thorny_amaranth'}, {'id': 18114, 'synset': 'alligator_weed.n.01', 'name': 'alligator_weed'}, {'id': 18115, 'synset': 'cockscomb.n.01', 'name': 'cockscomb'}, {'id': 18116, 'synset': 'cottonweed.n.02', 'name': 'cottonweed'}, {'id': 18117, 'synset': 'globe_amaranth.n.01', 'name': 'globe_amaranth'}, {'id': 18118, 'synset': 'bloodleaf.n.01', 'name': 'bloodleaf'}, {'id': 18119, 'synset': 'saltwort.n.02', 'name': 'saltwort'}, {'id': 18120, 'synset': "lamb's-quarters.n.01", 'name': "lamb's-quarters"}, {'id': 18121, 'synset': 'good-king-henry.n.01', 'name': 'good-king-henry'}, {'id': 18122, 'synset': 'jerusalem_oak.n.01', 'name': 'Jerusalem_oak'}, {'id': 18123, 'synset': 'oak-leaved_goosefoot.n.01', 'name': 'oak-leaved_goosefoot'}, {'id': 18124, 'synset': 'sowbane.n.01', 'name': 'sowbane'}, {'id': 18125, 'synset': 'nettle-leaved_goosefoot.n.01', 'name': 'nettle-leaved_goosefoot'}, {'id': 18126, 'synset': 'red_goosefoot.n.01', 'name': 'red_goosefoot'}, {'id': 18127, 'synset': 'stinking_goosefoot.n.01', 'name': 'stinking_goosefoot'}, {'id': 18128, 'synset': 'orach.n.01', 'name': 'orach'}, {'id': 18129, 'synset': 'saltbush.n.01', 'name': 'saltbush'}, {'id': 18130, 'synset': 'garden_orache.n.01', 'name': 'garden_orache'}, {'id': 18131, 'synset': 'desert_holly.n.01', 'name': 'desert_holly'}, {'id': 18132, 'synset': 'quail_bush.n.01', 'name': 'quail_bush'}, {'id': 18133, 'synset': 'beet.n.01', 'name': 'beet'}, {'id': 18134, 'synset': 'beetroot.n.01', 'name': 'beetroot'}, {'id': 18135, 'synset': 'chard.n.01', 'name': 'chard'}, {'id': 18136, 'synset': 'mangel-wurzel.n.01', 'name': 'mangel-wurzel'}, {'id': 18137, 'synset': 'winged_pigweed.n.01', 'name': 'winged_pigweed'}, {'id': 18138, 'synset': 'halogeton.n.01', 'name': 'halogeton'}, {'id': 18139, 'synset': 'glasswort.n.02', 'name': 'glasswort'}, {'id': 18140, 'synset': 'saltwort.n.01', 'name': 'saltwort'}, {'id': 18141, 'synset': 'russian_thistle.n.01', 'name': 'Russian_thistle'}, {'id': 18142, 'synset': 'greasewood.n.01', 'name': 'greasewood'}, {'id': 18143, 'synset': 'scarlet_musk_flower.n.01', 'name': 'scarlet_musk_flower'}, {'id': 18144, 'synset': 'sand_verbena.n.01', 'name': 'sand_verbena'}, {'id': 18145, 'synset': 'sweet_sand_verbena.n.01', 'name': 'sweet_sand_verbena'}, {'id': 18146, 'synset': 'yellow_sand_verbena.n.01', 'name': 'yellow_sand_verbena'}, {'id': 18147, 'synset': 'beach_pancake.n.01', 'name': 'beach_pancake'}, {'id': 18148, 'synset': 'beach_sand_verbena.n.01', 'name': 'beach_sand_verbena'}, {'id': 18149, 'synset': 'desert_sand_verbena.n.01', 'name': 'desert_sand_verbena'}, {'id': 18150, 'synset': "trailing_four_o'clock.n.01", 'name': "trailing_four_o'clock"}, {'id': 18151, 'synset': 'bougainvillea.n.01', 'name': 'bougainvillea'}, {'id': 18152, 'synset': 'umbrellawort.n.01', 'name': 'umbrellawort'}, {'id': 18153, 'synset': "four_o'clock.n.01", 'name': "four_o'clock"}, {'id': 18154, 'synset': "common_four-o'clock.n.01", 'name': "common_four-o'clock"}, {'id': 18155, 'synset': "california_four_o'clock.n.01", 'name': "California_four_o'clock"}, {'id': 18156, 'synset': "sweet_four_o'clock.n.01", 'name': "sweet_four_o'clock"}, {'id': 18157, 'synset': "desert_four_o'clock.n.01", 'name': "desert_four_o'clock"}, {'id': 18158, 'synset': "mountain_four_o'clock.n.01", 'name': "mountain_four_o'clock"}, {'id': 18159, 'synset': 'cockspur.n.02', 'name': 'cockspur'}, {'id': 18160, 'synset': 'rattail_cactus.n.01', 'name': 'rattail_cactus'}, {'id': 18161, 'synset': 'saguaro.n.01', 'name': 'saguaro'}, {'id': 18162, 'synset': 'night-blooming_cereus.n.03', 'name': 'night-blooming_cereus'}, {'id': 18163, 'synset': 'echinocactus.n.01', 'name': 'echinocactus'}, {'id': 18164, 'synset': 'hedgehog_cactus.n.01', 'name': 'hedgehog_cactus'}, {'id': 18165, 'synset': 'golden_barrel_cactus.n.01', 'name': 'golden_barrel_cactus'}, {'id': 18166, 'synset': 'hedgehog_cereus.n.01', 'name': 'hedgehog_cereus'}, {'id': 18167, 'synset': 'rainbow_cactus.n.01', 'name': 'rainbow_cactus'}, {'id': 18168, 'synset': 'epiphyllum.n.01', 'name': 'epiphyllum'}, {'id': 18169, 'synset': 'barrel_cactus.n.01', 'name': 'barrel_cactus'}, {'id': 18170, 'synset': 'night-blooming_cereus.n.02', 'name': 'night-blooming_cereus'}, {'id': 18171, 'synset': 'chichipe.n.01', 'name': 'chichipe'}, {'id': 18172, 'synset': 'mescal.n.01', 'name': 'mescal'}, {'id': 18173, 'synset': 'mescal_button.n.01', 'name': 'mescal_button'}, {'id': 18174, 'synset': 'mammillaria.n.01', 'name': 'mammillaria'}, {'id': 18175, 'synset': 'feather_ball.n.01', 'name': 'feather_ball'}, {'id': 18176, 'synset': 'garambulla.n.01', 'name': 'garambulla'}, {'id': 18177, 'synset': "knowlton's_cactus.n.01", 'name': "Knowlton's_cactus"}, {'id': 18178, 'synset': 'nopal.n.02', 'name': 'nopal'}, {'id': 18179, 'synset': 'prickly_pear.n.01', 'name': 'prickly_pear'}, {'id': 18180, 'synset': 'cholla.n.01', 'name': 'cholla'}, {'id': 18181, 'synset': 'nopal.n.01', 'name': 'nopal'}, {'id': 18182, 'synset': 'tuna.n.01', 'name': 'tuna'}, {'id': 18183, 'synset': 'barbados_gooseberry.n.01', 'name': 'Barbados_gooseberry'}, {'id': 18184, 'synset': 'mistletoe_cactus.n.01', 'name': 'mistletoe_cactus'}, {'id': 18185, 'synset': 'christmas_cactus.n.01', 'name': 'Christmas_cactus'}, {'id': 18186, 'synset': 'night-blooming_cereus.n.01', 'name': 'night-blooming_cereus'}, {'id': 18187, 'synset': 'crab_cactus.n.01', 'name': 'crab_cactus'}, {'id': 18188, 'synset': 'pokeweed.n.01', 'name': 'pokeweed'}, {'id': 18189, 'synset': 'indian_poke.n.02', 'name': 'Indian_poke'}, {'id': 18190, 'synset': 'poke.n.01', 'name': 'poke'}, {'id': 18191, 'synset': 'ombu.n.01', 'name': 'ombu'}, {'id': 18192, 'synset': 'bloodberry.n.01', 'name': 'bloodberry'}, {'id': 18193, 'synset': 'portulaca.n.01', 'name': 'portulaca'}, {'id': 18194, 'synset': 'rose_moss.n.01', 'name': 'rose_moss'}, {'id': 18195, 'synset': 'common_purslane.n.01', 'name': 'common_purslane'}, {'id': 18196, 'synset': 'rock_purslane.n.01', 'name': 'rock_purslane'}, {'id': 18197, 'synset': 'red_maids.n.01', 'name': 'red_maids'}, {'id': 18198, 'synset': 'carolina_spring_beauty.n.01', 'name': 'Carolina_spring_beauty'}, {'id': 18199, 'synset': 'spring_beauty.n.01', 'name': 'spring_beauty'}, {'id': 18200, 'synset': 'virginia_spring_beauty.n.01', 'name': 'Virginia_spring_beauty'}, {'id': 18201, 'synset': 'siskiyou_lewisia.n.01', 'name': 'siskiyou_lewisia'}, {'id': 18202, 'synset': 'bitterroot.n.01', 'name': 'bitterroot'}, {'id': 18203, 'synset': 'broad-leaved_montia.n.01', 'name': 'broad-leaved_montia'}, {'id': 18204, 'synset': 'blinks.n.01', 'name': 'blinks'}, {'id': 18205, 'synset': 'toad_lily.n.01', 'name': 'toad_lily'}, {'id': 18206, 'synset': 'winter_purslane.n.01', 'name': 'winter_purslane'}, {'id': 18207, 'synset': 'flame_flower.n.02', 'name': 'flame_flower'}, {'id': 18208, 'synset': 'pigmy_talinum.n.01', 'name': 'pigmy_talinum'}, {'id': 18209, 'synset': 'jewels-of-opar.n.01', 'name': 'jewels-of-opar'}, {'id': 18210, 'synset': 'caper.n.01', 'name': 'caper'}, {'id': 18211, 'synset': 'native_pomegranate.n.01', 'name': 'native_pomegranate'}, {'id': 18212, 'synset': 'caper_tree.n.02', 'name': 'caper_tree'}, {'id': 18213, 'synset': 'caper_tree.n.01', 'name': 'caper_tree'}, {'id': 18214, 'synset': 'common_caper.n.01', 'name': 'common_caper'}, {'id': 18215, 'synset': 'spiderflower.n.01', 'name': 'spiderflower'}, {'id': 18216, 'synset': 'rocky_mountain_bee_plant.n.01', 'name': 'Rocky_Mountain_bee_plant'}, {'id': 18217, 'synset': 'clammyweed.n.01', 'name': 'clammyweed'}, {'id': 18218, 'synset': 'crucifer.n.01', 'name': 'crucifer'}, {'id': 18219, 'synset': 'cress.n.01', 'name': 'cress'}, {'id': 18220, 'synset': 'watercress.n.01', 'name': 'watercress'}, {'id': 18221, 'synset': 'stonecress.n.01', 'name': 'stonecress'}, {'id': 18222, 'synset': 'garlic_mustard.n.01', 'name': 'garlic_mustard'}, {'id': 18223, 'synset': 'alyssum.n.01', 'name': 'alyssum'}, {'id': 18224, 'synset': 'rose_of_jericho.n.02', 'name': 'rose_of_Jericho'}, {'id': 18225, 'synset': 'arabidopsis_thaliana.n.01', 'name': 'Arabidopsis_thaliana'}, {'id': 18226, 'synset': 'arabidopsis_lyrata.n.01', 'name': 'Arabidopsis_lyrata'}, {'id': 18227, 'synset': 'rock_cress.n.01', 'name': 'rock_cress'}, {'id': 18228, 'synset': 'sicklepod.n.02', 'name': 'sicklepod'}, {'id': 18229, 'synset': 'tower_mustard.n.01', 'name': 'tower_mustard'}, {'id': 18230, 'synset': 'horseradish.n.01', 'name': 'horseradish'}, {'id': 18231, 'synset': 'winter_cress.n.01', 'name': 'winter_cress'}, {'id': 18232, 'synset': 'yellow_rocket.n.01', 'name': 'yellow_rocket'}, {'id': 18233, 'synset': 'hoary_alison.n.01', 'name': 'hoary_alison'}, {'id': 18234, 'synset': 'buckler_mustard.n.01', 'name': 'buckler_mustard'}, {'id': 18235, 'synset': 'wild_cabbage.n.01', 'name': 'wild_cabbage'}, {'id': 18236, 'synset': 'cabbage.n.03', 'name': 'cabbage'}, {'id': 18237, 'synset': 'head_cabbage.n.01', 'name': 'head_cabbage'}, {'id': 18238, 'synset': 'savoy_cabbage.n.01', 'name': 'savoy_cabbage'}, {'id': 18239, 'synset': 'brussels_sprout.n.01', 'name': 'brussels_sprout'}, {'id': 18240, 'synset': 'cauliflower.n.01', 'name': 'cauliflower'}, {'id': 18241, 'synset': 'collard.n.01', 'name': 'collard'}, {'id': 18242, 'synset': 'kohlrabi.n.01', 'name': 'kohlrabi'}, {'id': 18243, 'synset': 'turnip_plant.n.01', 'name': 'turnip_plant'}, {'id': 18244, 'synset': 'rutabaga.n.02', 'name': 'rutabaga'}, {'id': 18245, 'synset': 'broccoli_raab.n.01', 'name': 'broccoli_raab'}, {'id': 18246, 'synset': 'mustard.n.01', 'name': 'mustard'}, {'id': 18247, 'synset': 'chinese_mustard.n.01', 'name': 'chinese_mustard'}, {'id': 18248, 'synset': 'bok_choy.n.01', 'name': 'bok_choy'}, {'id': 18249, 'synset': 'rape.n.01', 'name': 'rape'}, {'id': 18250, 'synset': 'rapeseed.n.01', 'name': 'rapeseed'}, {'id': 18251, 'synset': "shepherd's_purse.n.01", 'name': "shepherd's_purse"}, {'id': 18252, 'synset': "lady's_smock.n.01", 'name': "lady's_smock"}, {'id': 18253, 'synset': 'coral-root_bittercress.n.01', 'name': 'coral-root_bittercress'}, {'id': 18254, 'synset': 'crinkleroot.n.01', 'name': 'crinkleroot'}, {'id': 18255, 'synset': 'american_watercress.n.01', 'name': 'American_watercress'}, {'id': 18256, 'synset': 'spring_cress.n.01', 'name': 'spring_cress'}, {'id': 18257, 'synset': 'purple_cress.n.01', 'name': 'purple_cress'}, {'id': 18258, 'synset': 'wallflower.n.02', 'name': 'wallflower'}, {'id': 18259, 'synset': 'prairie_rocket.n.02', 'name': 'prairie_rocket'}, {'id': 18260, 'synset': 'scurvy_grass.n.01', 'name': 'scurvy_grass'}, {'id': 18261, 'synset': 'sea_kale.n.01', 'name': 'sea_kale'}, {'id': 18262, 'synset': 'tansy_mustard.n.01', 'name': 'tansy_mustard'}, {'id': 18263, 'synset': 'draba.n.01', 'name': 'draba'}, {'id': 18264, 'synset': 'wallflower.n.01', 'name': 'wallflower'}, {'id': 18265, 'synset': 'prairie_rocket.n.01', 'name': 'prairie_rocket'}, {'id': 18266, 'synset': 'siberian_wall_flower.n.01', 'name': 'Siberian_wall_flower'}, {'id': 18267, 'synset': 'western_wall_flower.n.01', 'name': 'western_wall_flower'}, {'id': 18268, 'synset': 'wormseed_mustard.n.01', 'name': 'wormseed_mustard'}, {'id': 18269, 'synset': 'heliophila.n.01', 'name': 'heliophila'}, {'id': 18270, 'synset': 'damask_violet.n.01', 'name': 'damask_violet'}, {'id': 18271, 'synset': 'tansy-leaved_rocket.n.01', 'name': 'tansy-leaved_rocket'}, {'id': 18272, 'synset': 'candytuft.n.01', 'name': 'candytuft'}, {'id': 18273, 'synset': 'woad.n.02', 'name': 'woad'}, {'id': 18274, 'synset': "dyer's_woad.n.01", 'name': "dyer's_woad"}, {'id': 18275, 'synset': 'bladderpod.n.04', 'name': 'bladderpod'}, {'id': 18276, 'synset': 'sweet_alyssum.n.01', 'name': 'sweet_alyssum'}, {'id': 18277, 'synset': 'malcolm_stock.n.01', 'name': 'Malcolm_stock'}, {'id': 18278, 'synset': 'virginian_stock.n.01', 'name': 'Virginian_stock'}, {'id': 18279, 'synset': 'stock.n.12', 'name': 'stock'}, {'id': 18280, 'synset': 'brompton_stock.n.01', 'name': 'brompton_stock'}, {'id': 18281, 'synset': 'bladderpod.n.03', 'name': 'bladderpod'}, {'id': 18282, 'synset': 'chamois_cress.n.01', 'name': 'chamois_cress'}, {'id': 18283, 'synset': 'radish_plant.n.01', 'name': 'radish_plant'}, {'id': 18284, 'synset': 'jointed_charlock.n.01', 'name': 'jointed_charlock'}, {'id': 18285, 'synset': 'radish.n.04', 'name': 'radish'}, {'id': 18286, 'synset': 'radish.n.02', 'name': 'radish'}, {'id': 18287, 'synset': 'marsh_cress.n.01', 'name': 'marsh_cress'}, {'id': 18288, 'synset': 'great_yellowcress.n.01', 'name': 'great_yellowcress'}, {'id': 18289, 'synset': 'schizopetalon.n.01', 'name': 'schizopetalon'}, {'id': 18290, 'synset': 'field_mustard.n.01', 'name': 'field_mustard'}, {'id': 18291, 'synset': 'hedge_mustard.n.01', 'name': 'hedge_mustard'}, {'id': 18292, 'synset': 'desert_plume.n.01', 'name': 'desert_plume'}, {'id': 18293, 'synset': 'pennycress.n.01', 'name': 'pennycress'}, {'id': 18294, 'synset': 'field_pennycress.n.01', 'name': 'field_pennycress'}, {'id': 18295, 'synset': 'fringepod.n.01', 'name': 'fringepod'}, {'id': 18296, 'synset': 'bladderpod.n.02', 'name': 'bladderpod'}, {'id': 18297, 'synset': 'wasabi.n.01', 'name': 'wasabi'}, {'id': 18298, 'synset': 'poppy.n.01', 'name': 'poppy'}, {'id': 18299, 'synset': 'iceland_poppy.n.02', 'name': 'Iceland_poppy'}, {'id': 18300, 'synset': 'western_poppy.n.01', 'name': 'western_poppy'}, {'id': 18301, 'synset': 'prickly_poppy.n.02', 'name': 'prickly_poppy'}, {'id': 18302, 'synset': 'iceland_poppy.n.01', 'name': 'Iceland_poppy'}, {'id': 18303, 'synset': 'oriental_poppy.n.01', 'name': 'oriental_poppy'}, {'id': 18304, 'synset': 'corn_poppy.n.01', 'name': 'corn_poppy'}, {'id': 18305, 'synset': 'opium_poppy.n.01', 'name': 'opium_poppy'}, {'id': 18306, 'synset': 'prickly_poppy.n.01', 'name': 'prickly_poppy'}, {'id': 18307, 'synset': 'mexican_poppy.n.01', 'name': 'Mexican_poppy'}, {'id': 18308, 'synset': 'bocconia.n.02', 'name': 'bocconia'}, {'id': 18309, 'synset': 'celandine.n.02', 'name': 'celandine'}, {'id': 18310, 'synset': 'corydalis.n.01', 'name': 'corydalis'}, {'id': 18311, 'synset': 'climbing_corydalis.n.01', 'name': 'climbing_corydalis'}, {'id': 18312, 'synset': 'california_poppy.n.01', 'name': 'California_poppy'}, {'id': 18313, 'synset': 'horn_poppy.n.01', 'name': 'horn_poppy'}, {'id': 18314, 'synset': 'golden_cup.n.01', 'name': 'golden_cup'}, {'id': 18315, 'synset': 'plume_poppy.n.01', 'name': 'plume_poppy'}, {'id': 18316, 'synset': 'blue_poppy.n.01', 'name': 'blue_poppy'}, {'id': 18317, 'synset': 'welsh_poppy.n.01', 'name': 'Welsh_poppy'}, {'id': 18318, 'synset': 'creamcups.n.01', 'name': 'creamcups'}, {'id': 18319, 'synset': 'matilija_poppy.n.01', 'name': 'matilija_poppy'}, {'id': 18320, 'synset': 'wind_poppy.n.01', 'name': 'wind_poppy'}, {'id': 18321, 'synset': 'celandine_poppy.n.01', 'name': 'celandine_poppy'}, {'id': 18322, 'synset': 'climbing_fumitory.n.01', 'name': 'climbing_fumitory'}, {'id': 18323, 'synset': 'bleeding_heart.n.01', 'name': 'bleeding_heart'}, {'id': 18324, 'synset': "dutchman's_breeches.n.01", 'name': "Dutchman's_breeches"}, {'id': 18325, 'synset': 'squirrel_corn.n.01', 'name': 'squirrel_corn'}, {'id': 18326, 'synset': 'composite.n.02', 'name': 'composite'}, {'id': 18327, 'synset': 'compass_plant.n.02', 'name': 'compass_plant'}, {'id': 18328, 'synset': 'everlasting.n.01', 'name': 'everlasting'}, {'id': 18329, 'synset': 'achillea.n.01', 'name': 'achillea'}, {'id': 18330, 'synset': 'yarrow.n.01', 'name': 'yarrow'}, {'id': 18331, 'synset': 'pink-and-white_everlasting.n.01', 'name': 'pink-and-white_everlasting'}, {'id': 18332, 'synset': 'white_snakeroot.n.01', 'name': 'white_snakeroot'}, {'id': 18333, 'synset': 'ageratum.n.02', 'name': 'ageratum'}, {'id': 18334, 'synset': 'common_ageratum.n.01', 'name': 'common_ageratum'}, {'id': 18335, 'synset': 'sweet_sultan.n.03', 'name': 'sweet_sultan'}, {'id': 18336, 'synset': 'ragweed.n.02', 'name': 'ragweed'}, {'id': 18337, 'synset': 'common_ragweed.n.01', 'name': 'common_ragweed'}, {'id': 18338, 'synset': 'great_ragweed.n.01', 'name': 'great_ragweed'}, {'id': 18339, 'synset': 'western_ragweed.n.01', 'name': 'western_ragweed'}, {'id': 18340, 'synset': 'ammobium.n.01', 'name': 'ammobium'}, {'id': 18341, 'synset': 'winged_everlasting.n.01', 'name': 'winged_everlasting'}, {'id': 18342, 'synset': 'pellitory.n.02', 'name': 'pellitory'}, {'id': 18343, 'synset': 'pearly_everlasting.n.01', 'name': 'pearly_everlasting'}, {'id': 18344, 'synset': 'andryala.n.01', 'name': 'andryala'}, {'id': 18345, 'synset': 'plantain-leaved_pussytoes.n.01', 'name': 'plantain-leaved_pussytoes'}, {'id': 18346, 'synset': 'field_pussytoes.n.01', 'name': 'field_pussytoes'}, {'id': 18347, 'synset': 'solitary_pussytoes.n.01', 'name': 'solitary_pussytoes'}, {'id': 18348, 'synset': 'mountain_everlasting.n.01', 'name': 'mountain_everlasting'}, {'id': 18349, 'synset': 'mayweed.n.01', 'name': 'mayweed'}, {'id': 18350, 'synset': 'yellow_chamomile.n.01', 'name': 'yellow_chamomile'}, {'id': 18351, 'synset': 'corn_chamomile.n.01', 'name': 'corn_chamomile'}, {'id': 18352, 'synset': 'woolly_daisy.n.01', 'name': 'woolly_daisy'}, {'id': 18353, 'synset': 'burdock.n.01', 'name': 'burdock'}, {'id': 18354, 'synset': 'great_burdock.n.01', 'name': 'great_burdock'}, {'id': 18355, 'synset': 'african_daisy.n.03', 'name': 'African_daisy'}, {'id': 18356, 'synset': 'blue-eyed_african_daisy.n.01', 'name': 'blue-eyed_African_daisy'}, {'id': 18357, 'synset': 'marguerite.n.02', 'name': 'marguerite'}, {'id': 18358, 'synset': 'silversword.n.01', 'name': 'silversword'}, {'id': 18359, 'synset': 'arnica.n.02', 'name': 'arnica'}, {'id': 18360, 'synset': 'heartleaf_arnica.n.01', 'name': 'heartleaf_arnica'}, {'id': 18361, 'synset': 'arnica_montana.n.01', 'name': 'Arnica_montana'}, {'id': 18362, 'synset': 'lamb_succory.n.01', 'name': 'lamb_succory'}, {'id': 18363, 'synset': 'artemisia.n.01', 'name': 'artemisia'}, {'id': 18364, 'synset': 'mugwort.n.01', 'name': 'mugwort'}, {'id': 18365, 'synset': 'sweet_wormwood.n.01', 'name': 'sweet_wormwood'}, {'id': 18366, 'synset': 'field_wormwood.n.01', 'name': 'field_wormwood'}, {'id': 18367, 'synset': 'tarragon.n.01', 'name': 'tarragon'}, {'id': 18368, 'synset': 'sand_sage.n.01', 'name': 'sand_sage'}, {'id': 18369, 'synset': 'wormwood_sage.n.01', 'name': 'wormwood_sage'}, {'id': 18370, 'synset': 'western_mugwort.n.01', 'name': 'western_mugwort'}, {'id': 18371, 'synset': 'roman_wormwood.n.01', 'name': 'Roman_wormwood'}, {'id': 18372, 'synset': 'bud_brush.n.01', 'name': 'bud_brush'}, {'id': 18373, 'synset': 'common_mugwort.n.01', 'name': 'common_mugwort'}, {'id': 18374, 'synset': 'aster.n.01', 'name': 'aster'}, {'id': 18375, 'synset': 'wood_aster.n.01', 'name': 'wood_aster'}, {'id': 18376, 'synset': 'whorled_aster.n.01', 'name': 'whorled_aster'}, {'id': 18377, 'synset': 'heath_aster.n.02', 'name': 'heath_aster'}, {'id': 18378, 'synset': 'heart-leaved_aster.n.01', 'name': 'heart-leaved_aster'}, {'id': 18379, 'synset': 'white_wood_aster.n.01', 'name': 'white_wood_aster'}, {'id': 18380, 'synset': 'bushy_aster.n.01', 'name': 'bushy_aster'}, {'id': 18381, 'synset': 'heath_aster.n.01', 'name': 'heath_aster'}, {'id': 18382, 'synset': 'white_prairie_aster.n.01', 'name': 'white_prairie_aster'}, {'id': 18383, 'synset': 'stiff_aster.n.01', 'name': 'stiff_aster'}, {'id': 18384, 'synset': 'goldilocks.n.01', 'name': 'goldilocks'}, {'id': 18385, 'synset': 'large-leaved_aster.n.01', 'name': 'large-leaved_aster'}, {'id': 18386, 'synset': 'new_england_aster.n.01', 'name': 'New_England_aster'}, {'id': 18387, 'synset': 'michaelmas_daisy.n.01', 'name': 'Michaelmas_daisy'}, {'id': 18388, 'synset': 'upland_white_aster.n.01', 'name': 'upland_white_aster'}, {'id': 18389, 'synset': "short's_aster.n.01", 'name': "Short's_aster"}, {'id': 18390, 'synset': 'sea_aster.n.01', 'name': 'sea_aster'}, {'id': 18391, 'synset': 'prairie_aster.n.01', 'name': 'prairie_aster'}, {'id': 18392, 'synset': 'annual_salt-marsh_aster.n.01', 'name': 'annual_salt-marsh_aster'}, {'id': 18393, 'synset': 'aromatic_aster.n.01', 'name': 'aromatic_aster'}, {'id': 18394, 'synset': 'arrow_leaved_aster.n.01', 'name': 'arrow_leaved_aster'}, {'id': 18395, 'synset': 'azure_aster.n.01', 'name': 'azure_aster'}, {'id': 18396, 'synset': 'bog_aster.n.01', 'name': 'bog_aster'}, {'id': 18397, 'synset': 'crooked-stemmed_aster.n.01', 'name': 'crooked-stemmed_aster'}, {'id': 18398, 'synset': 'eastern_silvery_aster.n.01', 'name': 'Eastern_silvery_aster'}, {'id': 18399, 'synset': 'flat-topped_white_aster.n.01', 'name': 'flat-topped_white_aster'}, {'id': 18400, 'synset': 'late_purple_aster.n.01', 'name': 'late_purple_aster'}, {'id': 18401, 'synset': 'panicled_aster.n.01', 'name': 'panicled_aster'}, {'id': 18402, 'synset': 'perennial_salt_marsh_aster.n.01', 'name': 'perennial_salt_marsh_aster'}, {'id': 18403, 'synset': 'purple-stemmed_aster.n.01', 'name': 'purple-stemmed_aster'}, {'id': 18404, 'synset': 'rough-leaved_aster.n.01', 'name': 'rough-leaved_aster'}, {'id': 18405, 'synset': 'rush_aster.n.01', 'name': 'rush_aster'}, {'id': 18406, 'synset': "schreiber's_aster.n.01", 'name': "Schreiber's_aster"}, {'id': 18407, 'synset': 'small_white_aster.n.01', 'name': 'small_white_aster'}, {'id': 18408, 'synset': 'smooth_aster.n.01', 'name': 'smooth_aster'}, {'id': 18409, 'synset': 'southern_aster.n.01', 'name': 'southern_aster'}, {'id': 18410, 'synset': 'starved_aster.n.01', 'name': 'starved_aster'}, {'id': 18411, 'synset': "tradescant's_aster.n.01", 'name': "tradescant's_aster"}, {'id': 18412, 'synset': 'wavy-leaved_aster.n.01', 'name': 'wavy-leaved_aster'}, {'id': 18413, 'synset': 'western_silvery_aster.n.01', 'name': 'Western_silvery_aster'}, {'id': 18414, 'synset': 'willow_aster.n.01', 'name': 'willow_aster'}, {'id': 18415, 'synset': 'ayapana.n.01', 'name': 'ayapana'}, {'id': 18416, 'synset': 'mule_fat.n.01', 'name': 'mule_fat'}, {'id': 18417, 'synset': 'balsamroot.n.01', 'name': 'balsamroot'}, {'id': 18418, 'synset': 'daisy.n.01', 'name': 'daisy'}, {'id': 18419, 'synset': 'common_daisy.n.01', 'name': 'common_daisy'}, {'id': 18420, 'synset': 'bur_marigold.n.01', 'name': 'bur_marigold'}, {'id': 18421, 'synset': 'spanish_needles.n.02', 'name': 'Spanish_needles'}, {'id': 18422, 'synset': 'tickseed_sunflower.n.01', 'name': 'tickseed_sunflower'}, {'id': 18423, 'synset': 'european_beggar-ticks.n.01', 'name': 'European_beggar-ticks'}, {'id': 18424, 'synset': 'slender_knapweed.n.01', 'name': 'slender_knapweed'}, {'id': 18425, 'synset': 'false_chamomile.n.01', 'name': 'false_chamomile'}, {'id': 18426, 'synset': 'swan_river_daisy.n.01', 'name': 'Swan_River_daisy'}, {'id': 18427, 'synset': 'woodland_oxeye.n.01', 'name': 'woodland_oxeye'}, {'id': 18428, 'synset': 'indian_plantain.n.01', 'name': 'Indian_plantain'}, {'id': 18429, 'synset': 'calendula.n.01', 'name': 'calendula'}, {'id': 18430, 'synset': 'common_marigold.n.01', 'name': 'common_marigold'}, {'id': 18431, 'synset': 'china_aster.n.01', 'name': 'China_aster'}, {'id': 18432, 'synset': 'thistle.n.01', 'name': 'thistle'}, {'id': 18433, 'synset': 'welted_thistle.n.01', 'name': 'welted_thistle'}, {'id': 18434, 'synset': 'musk_thistle.n.01', 'name': 'musk_thistle'}, {'id': 18435, 'synset': 'carline_thistle.n.01', 'name': 'carline_thistle'}, {'id': 18436, 'synset': 'stemless_carline_thistle.n.01', 'name': 'stemless_carline_thistle'}, {'id': 18437, 'synset': 'common_carline_thistle.n.01', 'name': 'common_carline_thistle'}, {'id': 18438, 'synset': 'safflower.n.01', 'name': 'safflower'}, {'id': 18439, 'synset': 'safflower_seed.n.01', 'name': 'safflower_seed'}, {'id': 18440, 'synset': 'catananche.n.01', 'name': 'catananche'}, {'id': 18441, 'synset': 'blue_succory.n.01', 'name': 'blue_succory'}, {'id': 18442, 'synset': 'centaury.n.02', 'name': 'centaury'}, {'id': 18443, 'synset': 'dusty_miller.n.03', 'name': 'dusty_miller'}, {'id': 18444, 'synset': 'cornflower.n.02', 'name': 'cornflower'}, {'id': 18445, 'synset': 'star-thistle.n.01', 'name': 'star-thistle'}, {'id': 18446, 'synset': 'knapweed.n.01', 'name': 'knapweed'}, {'id': 18447, 'synset': 'sweet_sultan.n.02', 'name': 'sweet_sultan'}, {'id': 18448, 'synset': 'great_knapweed.n.01', 'name': 'great_knapweed'}, {'id': 18449, 'synset': "barnaby's_thistle.n.01", 'name': "Barnaby's_thistle"}, {'id': 18450, 'synset': 'chamomile.n.01', 'name': 'chamomile'}, {'id': 18451, 'synset': 'chaenactis.n.01', 'name': 'chaenactis'}, {'id': 18452, 'synset': 'chrysanthemum.n.02', 'name': 'chrysanthemum'}, {'id': 18453, 'synset': 'corn_marigold.n.01', 'name': 'corn_marigold'}, {'id': 18454, 'synset': 'crown_daisy.n.01', 'name': 'crown_daisy'}, {'id': 18455, 'synset': 'chop-suey_greens.n.01', 'name': 'chop-suey_greens'}, {'id': 18456, 'synset': 'golden_aster.n.01', 'name': 'golden_aster'}, {'id': 18457, 'synset': 'maryland_golden_aster.n.01', 'name': 'Maryland_golden_aster'}, {'id': 18458, 'synset': 'goldenbush.n.02', 'name': 'goldenbush'}, {'id': 18459, 'synset': 'rabbit_brush.n.01', 'name': 'rabbit_brush'}, {'id': 18460, 'synset': 'chicory.n.02', 'name': 'chicory'}, {'id': 18461, 'synset': 'endive.n.01', 'name': 'endive'}, {'id': 18462, 'synset': 'chicory.n.01', 'name': 'chicory'}, {'id': 18463, 'synset': 'plume_thistle.n.01', 'name': 'plume_thistle'}, {'id': 18464, 'synset': 'canada_thistle.n.01', 'name': 'Canada_thistle'}, {'id': 18465, 'synset': 'field_thistle.n.01', 'name': 'field_thistle'}, {'id': 18466, 'synset': 'woolly_thistle.n.02', 'name': 'woolly_thistle'}, {'id': 18467, 'synset': 'european_woolly_thistle.n.01', 'name': 'European_woolly_thistle'}, {'id': 18468, 'synset': 'melancholy_thistle.n.01', 'name': 'melancholy_thistle'}, {'id': 18469, 'synset': 'brook_thistle.n.01', 'name': 'brook_thistle'}, {'id': 18470, 'synset': 'bull_thistle.n.01', 'name': 'bull_thistle'}, {'id': 18471, 'synset': 'blessed_thistle.n.02', 'name': 'blessed_thistle'}, {'id': 18472, 'synset': 'mistflower.n.01', 'name': 'mistflower'}, {'id': 18473, 'synset': 'horseweed.n.02', 'name': 'horseweed'}, {'id': 18474, 'synset': 'coreopsis.n.01', 'name': 'coreopsis'}, {'id': 18475, 'synset': 'giant_coreopsis.n.01', 'name': 'giant_coreopsis'}, {'id': 18476, 'synset': 'sea_dahlia.n.01', 'name': 'sea_dahlia'}, {'id': 18477, 'synset': 'calliopsis.n.01', 'name': 'calliopsis'}, {'id': 18478, 'synset': 'cosmos.n.02', 'name': 'cosmos'}, {'id': 18479, 'synset': 'brass_buttons.n.01', 'name': 'brass_buttons'}, {'id': 18480, 'synset': 'billy_buttons.n.01', 'name': 'billy_buttons'}, {'id': 18481, 'synset': "hawk's-beard.n.01", 'name': "hawk's-beard"}, {'id': 18482, 'synset': 'artichoke.n.01', 'name': 'artichoke'}, {'id': 18483, 'synset': 'cardoon.n.01', 'name': 'cardoon'}, {'id': 18484, 'synset': 'dahlia.n.01', 'name': 'dahlia'}, {'id': 18485, 'synset': 'german_ivy.n.01', 'name': 'German_ivy'}, {'id': 18486, 'synset': "florist's_chrysanthemum.n.01", 'name': "florist's_chrysanthemum"}, {'id': 18487, 'synset': 'cape_marigold.n.01', 'name': 'cape_marigold'}, {'id': 18488, 'synset': "leopard's-bane.n.01", 'name': "leopard's-bane"}, {'id': 18489, 'synset': 'coneflower.n.03', 'name': 'coneflower'}, {'id': 18490, 'synset': 'globe_thistle.n.01', 'name': 'globe_thistle'}, {'id': 18491, 'synset': "elephant's-foot.n.02", 'name': "elephant's-foot"}, {'id': 18492, 'synset': 'tassel_flower.n.01', 'name': 'tassel_flower'}, {'id': 18493, 'synset': 'brittlebush.n.01', 'name': 'brittlebush'}, {'id': 18494, 'synset': 'sunray.n.02', 'name': 'sunray'}, {'id': 18495, 'synset': 'engelmannia.n.01', 'name': 'engelmannia'}, {'id': 18496, 'synset': 'fireweed.n.02', 'name': 'fireweed'}, {'id': 18497, 'synset': 'fleabane.n.02', 'name': 'fleabane'}, {'id': 18498, 'synset': 'blue_fleabane.n.01', 'name': 'blue_fleabane'}, {'id': 18499, 'synset': 'daisy_fleabane.n.01', 'name': 'daisy_fleabane'}, {'id': 18500, 'synset': 'orange_daisy.n.01', 'name': 'orange_daisy'}, {'id': 18501, 'synset': 'spreading_fleabane.n.01', 'name': 'spreading_fleabane'}, {'id': 18502, 'synset': 'seaside_daisy.n.01', 'name': 'seaside_daisy'}, {'id': 18503, 'synset': 'philadelphia_fleabane.n.01', 'name': 'Philadelphia_fleabane'}, {'id': 18504, 'synset': "robin's_plantain.n.01", 'name': "robin's_plantain"}, {'id': 18505, 'synset': 'showy_daisy.n.01', 'name': 'showy_daisy'}, {'id': 18506, 'synset': 'woolly_sunflower.n.01', 'name': 'woolly_sunflower'}, {'id': 18507, 'synset': 'golden_yarrow.n.01', 'name': 'golden_yarrow'}, {'id': 18508, 'synset': 'dog_fennel.n.01', 'name': 'dog_fennel'}, {'id': 18509, 'synset': 'joe-pye_weed.n.02', 'name': 'Joe-Pye_weed'}, {'id': 18510, 'synset': 'boneset.n.02', 'name': 'boneset'}, {'id': 18511, 'synset': 'joe-pye_weed.n.01', 'name': 'Joe-Pye_weed'}, {'id': 18512, 'synset': 'blue_daisy.n.01', 'name': 'blue_daisy'}, {'id': 18513, 'synset': 'kingfisher_daisy.n.01', 'name': 'kingfisher_daisy'}, {'id': 18514, 'synset': 'cotton_rose.n.02', 'name': 'cotton_rose'}, {'id': 18515, 'synset': 'herba_impia.n.01', 'name': 'herba_impia'}, {'id': 18516, 'synset': 'gaillardia.n.01', 'name': 'gaillardia'}, {'id': 18517, 'synset': 'gazania.n.01', 'name': 'gazania'}, {'id': 18518, 'synset': 'treasure_flower.n.01', 'name': 'treasure_flower'}, {'id': 18519, 'synset': 'african_daisy.n.02', 'name': 'African_daisy'}, {'id': 18520, 'synset': 'barberton_daisy.n.01', 'name': 'Barberton_daisy'}, {'id': 18521, 'synset': 'desert_sunflower.n.01', 'name': 'desert_sunflower'}, {'id': 18522, 'synset': 'cudweed.n.01', 'name': 'cudweed'}, {'id': 18523, 'synset': 'chafeweed.n.01', 'name': 'chafeweed'}, {'id': 18524, 'synset': 'gumweed.n.01', 'name': 'gumweed'}, {'id': 18525, 'synset': 'grindelia_robusta.n.01', 'name': 'Grindelia_robusta'}, {'id': 18526, 'synset': 'curlycup_gumweed.n.01', 'name': 'curlycup_gumweed'}, {'id': 18527, 'synset': 'little-head_snakeweed.n.01', 'name': 'little-head_snakeweed'}, {'id': 18528, 'synset': 'rabbitweed.n.01', 'name': 'rabbitweed'}, {'id': 18529, 'synset': 'broomweed.n.01', 'name': 'broomweed'}, {'id': 18530, 'synset': 'velvet_plant.n.02', 'name': 'velvet_plant'}, {'id': 18531, 'synset': 'goldenbush.n.01', 'name': 'goldenbush'}, {'id': 18532, 'synset': 'camphor_daisy.n.01', 'name': 'camphor_daisy'}, {'id': 18533, 'synset': 'yellow_spiny_daisy.n.01', 'name': 'yellow_spiny_daisy'}, {'id': 18534, 'synset': 'hoary_golden_bush.n.01', 'name': 'hoary_golden_bush'}, {'id': 18535, 'synset': 'sneezeweed.n.01', 'name': 'sneezeweed'}, {'id': 18536, 'synset': 'orange_sneezeweed.n.01', 'name': 'orange_sneezeweed'}, {'id': 18537, 'synset': 'rosilla.n.01', 'name': 'rosilla'}, {'id': 18538, 'synset': 'swamp_sunflower.n.01', 'name': 'swamp_sunflower'}, {'id': 18539, 'synset': 'common_sunflower.n.01', 'name': 'common_sunflower'}, {'id': 18540, 'synset': 'giant_sunflower.n.01', 'name': 'giant_sunflower'}, {'id': 18541, 'synset': 'showy_sunflower.n.01', 'name': 'showy_sunflower'}, {'id': 18542, 'synset': "maximilian's_sunflower.n.01", 'name': "Maximilian's_sunflower"}, {'id': 18543, 'synset': 'prairie_sunflower.n.01', 'name': 'prairie_sunflower'}, {'id': 18544, 'synset': 'jerusalem_artichoke.n.02', 'name': 'Jerusalem_artichoke'}, {'id': 18545, 'synset': 'jerusalem_artichoke.n.01', 'name': 'Jerusalem_artichoke'}, {'id': 18546, 'synset': 'strawflower.n.03', 'name': 'strawflower'}, {'id': 18547, 'synset': 'heliopsis.n.01', 'name': 'heliopsis'}, {'id': 18548, 'synset': 'strawflower.n.02', 'name': 'strawflower'}, {'id': 18549, 'synset': 'hairy_golden_aster.n.01', 'name': 'hairy_golden_aster'}, {'id': 18550, 'synset': 'hawkweed.n.02', 'name': 'hawkweed'}, {'id': 18551, 'synset': 'rattlesnake_weed.n.01', 'name': 'rattlesnake_weed'}, {'id': 18552, 'synset': 'alpine_coltsfoot.n.01', 'name': 'alpine_coltsfoot'}, {'id': 18553, 'synset': 'alpine_gold.n.01', 'name': 'alpine_gold'}, {'id': 18554, 'synset': 'dwarf_hulsea.n.01', 'name': 'dwarf_hulsea'}, {'id': 18555, 'synset': "cat's-ear.n.02", 'name': "cat's-ear"}, {'id': 18556, 'synset': 'inula.n.01', 'name': 'inula'}, {'id': 18557, 'synset': 'marsh_elder.n.01', 'name': 'marsh_elder'}, {'id': 18558, 'synset': 'burweed_marsh_elder.n.01', 'name': 'burweed_marsh_elder'}, {'id': 18559, 'synset': 'krigia.n.01', 'name': 'krigia'}, {'id': 18560, 'synset': 'dwarf_dandelion.n.01', 'name': 'dwarf_dandelion'}, {'id': 18561, 'synset': 'garden_lettuce.n.01', 'name': 'garden_lettuce'}, {'id': 18562, 'synset': 'cos_lettuce.n.01', 'name': 'cos_lettuce'}, {'id': 18563, 'synset': 'leaf_lettuce.n.01', 'name': 'leaf_lettuce'}, {'id': 18564, 'synset': 'celtuce.n.01', 'name': 'celtuce'}, {'id': 18565, 'synset': 'prickly_lettuce.n.01', 'name': 'prickly_lettuce'}, {'id': 18566, 'synset': 'goldfields.n.01', 'name': 'goldfields'}, {'id': 18567, 'synset': 'tidytips.n.01', 'name': 'tidytips'}, {'id': 18568, 'synset': 'hawkbit.n.01', 'name': 'hawkbit'}, {'id': 18569, 'synset': 'fall_dandelion.n.01', 'name': 'fall_dandelion'}, {'id': 18570, 'synset': 'edelweiss.n.01', 'name': 'edelweiss'}, {'id': 18571, 'synset': 'oxeye_daisy.n.02', 'name': 'oxeye_daisy'}, {'id': 18572, 'synset': 'oxeye_daisy.n.01', 'name': 'oxeye_daisy'}, {'id': 18573, 'synset': 'shasta_daisy.n.01', 'name': 'shasta_daisy'}, {'id': 18574, 'synset': 'pyrenees_daisy.n.01', 'name': 'Pyrenees_daisy'}, {'id': 18575, 'synset': 'north_island_edelweiss.n.01', 'name': 'north_island_edelweiss'}, {'id': 18576, 'synset': 'blazing_star.n.02', 'name': 'blazing_star'}, {'id': 18577, 'synset': 'dotted_gayfeather.n.01', 'name': 'dotted_gayfeather'}, {'id': 18578, 'synset': 'dense_blazing_star.n.01', 'name': 'dense_blazing_star'}, {'id': 18579, 'synset': 'texas_star.n.02', 'name': 'Texas_star'}, {'id': 18580, 'synset': 'african_daisy.n.01', 'name': 'African_daisy'}, {'id': 18581, 'synset': 'tahoka_daisy.n.01', 'name': 'tahoka_daisy'}, {'id': 18582, 'synset': 'sticky_aster.n.01', 'name': 'sticky_aster'}, {'id': 18583, 'synset': 'mojave_aster.n.01', 'name': 'Mojave_aster'}, {'id': 18584, 'synset': 'tarweed.n.01', 'name': 'tarweed'}, {'id': 18585, 'synset': 'sweet_false_chamomile.n.01', 'name': 'sweet_false_chamomile'}, {'id': 18586, 'synset': 'pineapple_weed.n.01', 'name': 'pineapple_weed'}, {'id': 18587, 'synset': 'climbing_hempweed.n.01', 'name': 'climbing_hempweed'}, {'id': 18588, 'synset': 'mutisia.n.01', 'name': 'mutisia'}, {'id': 18589, 'synset': 'rattlesnake_root.n.02', 'name': 'rattlesnake_root'}, {'id': 18590, 'synset': 'white_lettuce.n.01', 'name': 'white_lettuce'}, {'id': 18591, 'synset': 'daisybush.n.01', 'name': 'daisybush'}, {'id': 18592, 'synset': 'new_zealand_daisybush.n.01', 'name': 'New_Zealand_daisybush'}, {'id': 18593, 'synset': 'cotton_thistle.n.01', 'name': 'cotton_thistle'}, {'id': 18594, 'synset': 'othonna.n.01', 'name': 'othonna'}, {'id': 18595, 'synset': 'cascade_everlasting.n.01', 'name': 'cascade_everlasting'}, {'id': 18596, 'synset': 'butterweed.n.02', 'name': 'butterweed'}, {'id': 18597, 'synset': 'american_feverfew.n.01', 'name': 'American_feverfew'}, {'id': 18598, 'synset': 'cineraria.n.01', 'name': 'cineraria'}, {'id': 18599, 'synset': "florest's_cineraria.n.01", 'name': "florest's_cineraria"}, {'id': 18600, 'synset': 'butterbur.n.01', 'name': 'butterbur'}, {'id': 18601, 'synset': 'winter_heliotrope.n.01', 'name': 'winter_heliotrope'}, {'id': 18602, 'synset': 'sweet_coltsfoot.n.01', 'name': 'sweet_coltsfoot'}, {'id': 18603, 'synset': 'oxtongue.n.01', 'name': 'oxtongue'}, {'id': 18604, 'synset': 'hawkweed.n.01', 'name': 'hawkweed'}, {'id': 18605, 'synset': 'mouse-ear_hawkweed.n.01', 'name': 'mouse-ear_hawkweed'}, {'id': 18606, 'synset': 'stevia.n.02', 'name': 'stevia'}, {'id': 18607, 'synset': 'rattlesnake_root.n.01', 'name': 'rattlesnake_root'}, {'id': 18608, 'synset': 'fleabane.n.01', 'name': 'fleabane'}, {'id': 18609, 'synset': 'sheep_plant.n.01', 'name': 'sheep_plant'}, {'id': 18610, 'synset': 'coneflower.n.02', 'name': 'coneflower'}, {'id': 18611, 'synset': 'mexican_hat.n.01', 'name': 'Mexican_hat'}, {'id': 18612, 'synset': 'long-head_coneflower.n.01', 'name': 'long-head_coneflower'}, {'id': 18613, 'synset': 'prairie_coneflower.n.01', 'name': 'prairie_coneflower'}, {'id': 18614, 'synset': 'swan_river_everlasting.n.01', 'name': 'Swan_River_everlasting'}, {'id': 18615, 'synset': 'coneflower.n.01', 'name': 'coneflower'}, {'id': 18616, 'synset': 'black-eyed_susan.n.03', 'name': 'black-eyed_Susan'}, {'id': 18617, 'synset': 'cutleaved_coneflower.n.01', 'name': 'cutleaved_coneflower'}, {'id': 18618, 'synset': 'golden_glow.n.01', 'name': 'golden_glow'}, {'id': 18619, 'synset': 'lavender_cotton.n.01', 'name': 'lavender_cotton'}, {'id': 18620, 'synset': 'creeping_zinnia.n.01', 'name': 'creeping_zinnia'}, {'id': 18621, 'synset': 'golden_thistle.n.01', 'name': 'golden_thistle'}, {'id': 18622, 'synset': 'spanish_oyster_plant.n.01', 'name': 'Spanish_oyster_plant'}, {'id': 18623, 'synset': 'nodding_groundsel.n.01', 'name': 'nodding_groundsel'}, {'id': 18624, 'synset': 'dusty_miller.n.02', 'name': 'dusty_miller'}, {'id': 18625, 'synset': 'butterweed.n.01', 'name': 'butterweed'}, {'id': 18626, 'synset': 'ragwort.n.01', 'name': 'ragwort'}, {'id': 18627, 'synset': 'arrowleaf_groundsel.n.01', 'name': 'arrowleaf_groundsel'}, {'id': 18628, 'synset': 'black_salsify.n.01', 'name': 'black_salsify'}, {'id': 18629, 'synset': 'white-topped_aster.n.01', 'name': 'white-topped_aster'}, {'id': 18630, 'synset': 'narrow-leaved_white-topped_aster.n.01', 'name': 'narrow-leaved_white-topped_aster'}, {'id': 18631, 'synset': 'silver_sage.n.01', 'name': 'silver_sage'}, {'id': 18632, 'synset': 'sea_wormwood.n.01', 'name': 'sea_wormwood'}, {'id': 18633, 'synset': 'sawwort.n.01', 'name': 'sawwort'}, {'id': 18634, 'synset': 'rosinweed.n.01', 'name': 'rosinweed'}, {'id': 18635, 'synset': 'milk_thistle.n.02', 'name': 'milk_thistle'}, {'id': 18636, 'synset': 'goldenrod.n.01', 'name': 'goldenrod'}, {'id': 18637, 'synset': 'silverrod.n.01', 'name': 'silverrod'}, {'id': 18638, 'synset': 'meadow_goldenrod.n.01', 'name': 'meadow_goldenrod'}, {'id': 18639, 'synset': 'missouri_goldenrod.n.01', 'name': 'Missouri_goldenrod'}, {'id': 18640, 'synset': 'alpine_goldenrod.n.01', 'name': 'alpine_goldenrod'}, {'id': 18641, 'synset': 'grey_goldenrod.n.01', 'name': 'grey_goldenrod'}, {'id': 18642, 'synset': 'blue_mountain_tea.n.01', 'name': 'Blue_Mountain_tea'}, {'id': 18643, 'synset': "dyer's_weed.n.01", 'name': "dyer's_weed"}, {'id': 18644, 'synset': 'seaside_goldenrod.n.01', 'name': 'seaside_goldenrod'}, {'id': 18645, 'synset': 'narrow_goldenrod.n.01', 'name': 'narrow_goldenrod'}, {'id': 18646, 'synset': "boott's_goldenrod.n.01", 'name': "Boott's_goldenrod"}, {'id': 18647, 'synset': "elliott's_goldenrod.n.01", 'name': "Elliott's_goldenrod"}, {'id': 18648, 'synset': 'ohio_goldenrod.n.01', 'name': 'Ohio_goldenrod'}, {'id': 18649, 'synset': 'rough-stemmed_goldenrod.n.01', 'name': 'rough-stemmed_goldenrod'}, {'id': 18650, 'synset': 'showy_goldenrod.n.01', 'name': 'showy_goldenrod'}, {'id': 18651, 'synset': 'tall_goldenrod.n.01', 'name': 'tall_goldenrod'}, {'id': 18652, 'synset': 'zigzag_goldenrod.n.01', 'name': 'zigzag_goldenrod'}, {'id': 18653, 'synset': 'sow_thistle.n.01', 'name': 'sow_thistle'}, {'id': 18654, 'synset': 'milkweed.n.02', 'name': 'milkweed'}, {'id': 18655, 'synset': 'stevia.n.01', 'name': 'stevia'}, {'id': 18656, 'synset': "stokes'_aster.n.01", 'name': "stokes'_aster"}, {'id': 18657, 'synset': 'marigold.n.01', 'name': 'marigold'}, {'id': 18658, 'synset': 'african_marigold.n.01', 'name': 'African_marigold'}, {'id': 18659, 'synset': 'french_marigold.n.01', 'name': 'French_marigold'}, {'id': 18660, 'synset': 'painted_daisy.n.01', 'name': 'painted_daisy'}, {'id': 18661, 'synset': 'pyrethrum.n.02', 'name': 'pyrethrum'}, {'id': 18662, 'synset': 'northern_dune_tansy.n.01', 'name': 'northern_dune_tansy'}, {'id': 18663, 'synset': 'feverfew.n.01', 'name': 'feverfew'}, {'id': 18664, 'synset': 'dusty_miller.n.01', 'name': 'dusty_miller'}, {'id': 18665, 'synset': 'tansy.n.01', 'name': 'tansy'}, {'id': 18666, 'synset': 'dandelion.n.01', 'name': 'dandelion'}, {'id': 18667, 'synset': 'common_dandelion.n.01', 'name': 'common_dandelion'}, {'id': 18668, 'synset': 'dandelion_green.n.01', 'name': 'dandelion_green'}, {'id': 18669, 'synset': 'russian_dandelion.n.01', 'name': 'Russian_dandelion'}, {'id': 18670, 'synset': 'stemless_hymenoxys.n.01', 'name': 'stemless_hymenoxys'}, {'id': 18671, 'synset': 'mexican_sunflower.n.01', 'name': 'Mexican_sunflower'}, {'id': 18672, 'synset': 'easter_daisy.n.01', 'name': 'Easter_daisy'}, {'id': 18673, 'synset': 'yellow_salsify.n.01', 'name': 'yellow_salsify'}, {'id': 18674, 'synset': 'salsify.n.02', 'name': 'salsify'}, {'id': 18675, 'synset': 'meadow_salsify.n.01', 'name': 'meadow_salsify'}, {'id': 18676, 'synset': 'scentless_camomile.n.01', 'name': 'scentless_camomile'}, {'id': 18677, 'synset': 'turfing_daisy.n.01', 'name': 'turfing_daisy'}, {'id': 18678, 'synset': 'coltsfoot.n.02', 'name': 'coltsfoot'}, {'id': 18679, 'synset': 'ursinia.n.01', 'name': 'ursinia'}, {'id': 18680, 'synset': 'crownbeard.n.01', 'name': 'crownbeard'}, {'id': 18681, 'synset': 'wingstem.n.01', 'name': 'wingstem'}, {'id': 18682, 'synset': 'cowpen_daisy.n.01', 'name': 'cowpen_daisy'}, {'id': 18683, 'synset': 'gravelweed.n.01', 'name': 'gravelweed'}, {'id': 18684, 'synset': 'virginia_crownbeard.n.01', 'name': 'Virginia_crownbeard'}, {'id': 18685, 'synset': 'ironweed.n.01', 'name': 'ironweed'}, {'id': 18686, 'synset': "mule's_ears.n.01", 'name': "mule's_ears"}, {'id': 18687, 'synset': "white-rayed_mule's_ears.n.01", 'name': "white-rayed_mule's_ears"}, {'id': 18688, 'synset': 'cocklebur.n.01', 'name': 'cocklebur'}, {'id': 18689, 'synset': 'xeranthemum.n.01', 'name': 'xeranthemum'}, {'id': 18690, 'synset': 'immortelle.n.01', 'name': 'immortelle'}, {'id': 18691, 'synset': 'zinnia.n.01', 'name': 'zinnia'}, {'id': 18692, 'synset': 'white_zinnia.n.01', 'name': 'white_zinnia'}, {'id': 18693, 'synset': 'little_golden_zinnia.n.01', 'name': 'little_golden_zinnia'}, {'id': 18694, 'synset': 'blazing_star.n.01', 'name': 'blazing_star'}, {'id': 18695, 'synset': 'bartonia.n.01', 'name': 'bartonia'}, {'id': 18696, 'synset': 'achene.n.01', 'name': 'achene'}, {'id': 18697, 'synset': 'samara.n.01', 'name': 'samara'}, {'id': 18698, 'synset': 'campanula.n.01', 'name': 'campanula'}, {'id': 18699, 'synset': 'creeping_bellflower.n.01', 'name': 'creeping_bellflower'}, {'id': 18700, 'synset': 'canterbury_bell.n.02', 'name': 'Canterbury_bell'}, {'id': 18701, 'synset': 'tall_bellflower.n.01', 'name': 'tall_bellflower'}, {'id': 18702, 'synset': 'marsh_bellflower.n.01', 'name': 'marsh_bellflower'}, {'id': 18703, 'synset': 'clustered_bellflower.n.01', 'name': 'clustered_bellflower'}, {'id': 18704, 'synset': 'peach_bells.n.01', 'name': 'peach_bells'}, {'id': 18705, 'synset': 'chimney_plant.n.01', 'name': 'chimney_plant'}, {'id': 18706, 'synset': 'rampion.n.01', 'name': 'rampion'}, {'id': 18707, 'synset': 'tussock_bellflower.n.01', 'name': 'tussock_bellflower'}, {'id': 18708, 'synset': 'orchid.n.01', 'name': 'orchid'}, {'id': 18709, 'synset': 'orchis.n.01', 'name': 'orchis'}, {'id': 18710, 'synset': 'male_orchis.n.01', 'name': 'male_orchis'}, {'id': 18711, 'synset': 'butterfly_orchid.n.05', 'name': 'butterfly_orchid'}, {'id': 18712, 'synset': 'showy_orchis.n.01', 'name': 'showy_orchis'}, {'id': 18713, 'synset': 'aerides.n.01', 'name': 'aerides'}, {'id': 18714, 'synset': 'angrecum.n.01', 'name': 'angrecum'}, {'id': 18715, 'synset': 'jewel_orchid.n.01', 'name': 'jewel_orchid'}, {'id': 18716, 'synset': 'puttyroot.n.01', 'name': 'puttyroot'}, {'id': 18717, 'synset': 'arethusa.n.01', 'name': 'arethusa'}, {'id': 18718, 'synset': 'bog_rose.n.01', 'name': 'bog_rose'}, {'id': 18719, 'synset': 'bletia.n.01', 'name': 'bletia'}, {'id': 18720, 'synset': 'bletilla_striata.n.01', 'name': 'Bletilla_striata'}, {'id': 18721, 'synset': 'brassavola.n.01', 'name': 'brassavola'}, {'id': 18722, 'synset': 'spider_orchid.n.03', 'name': 'spider_orchid'}, {'id': 18723, 'synset': 'spider_orchid.n.02', 'name': 'spider_orchid'}, {'id': 18724, 'synset': 'caladenia.n.01', 'name': 'caladenia'}, {'id': 18725, 'synset': 'calanthe.n.01', 'name': 'calanthe'}, {'id': 18726, 'synset': 'grass_pink.n.01', 'name': 'grass_pink'}, {'id': 18727, 'synset': 'calypso.n.01', 'name': 'calypso'}, {'id': 18728, 'synset': 'cattleya.n.01', 'name': 'cattleya'}, {'id': 18729, 'synset': 'helleborine.n.03', 'name': 'helleborine'}, {'id': 18730, 'synset': 'red_helleborine.n.01', 'name': 'red_helleborine'}, {'id': 18731, 'synset': 'spreading_pogonia.n.01', 'name': 'spreading_pogonia'}, {'id': 18732, 'synset': 'rosebud_orchid.n.01', 'name': 'rosebud_orchid'}, {'id': 18733, 'synset': 'satyr_orchid.n.01', 'name': 'satyr_orchid'}, {'id': 18734, 'synset': 'frog_orchid.n.02', 'name': 'frog_orchid'}, {'id': 18735, 'synset': 'coelogyne.n.01', 'name': 'coelogyne'}, {'id': 18736, 'synset': 'coral_root.n.01', 'name': 'coral_root'}, {'id': 18737, 'synset': 'spotted_coral_root.n.01', 'name': 'spotted_coral_root'}, {'id': 18738, 'synset': 'striped_coral_root.n.01', 'name': 'striped_coral_root'}, {'id': 18739, 'synset': 'early_coral_root.n.01', 'name': 'early_coral_root'}, {'id': 18740, 'synset': 'swan_orchid.n.01', 'name': 'swan_orchid'}, {'id': 18741, 'synset': 'cymbid.n.01', 'name': 'cymbid'}, {'id': 18742, 'synset': 'cypripedia.n.01', 'name': 'cypripedia'}, {'id': 18743, 'synset': "lady's_slipper.n.01", 'name': "lady's_slipper"}, {'id': 18744, 'synset': 'moccasin_flower.n.01', 'name': 'moccasin_flower'}, {'id': 18745, 'synset': "common_lady's-slipper.n.01", 'name': "common_lady's-slipper"}, {'id': 18746, 'synset': "ram's-head.n.01", 'name': "ram's-head"}, {'id': 18747, 'synset': "yellow_lady's_slipper.n.01", 'name': "yellow_lady's_slipper"}, {'id': 18748, 'synset': "large_yellow_lady's_slipper.n.01", 'name': "large_yellow_lady's_slipper"}, {'id': 18749, 'synset': "california_lady's_slipper.n.01", 'name': "California_lady's_slipper"}, {'id': 18750, 'synset': "clustered_lady's_slipper.n.01", 'name': "clustered_lady's_slipper"}, {'id': 18751, 'synset': "mountain_lady's_slipper.n.01", 'name': "mountain_lady's_slipper"}, {'id': 18752, 'synset': 'marsh_orchid.n.01', 'name': 'marsh_orchid'}, {'id': 18753, 'synset': 'common_spotted_orchid.n.01', 'name': 'common_spotted_orchid'}, {'id': 18754, 'synset': 'dendrobium.n.01', 'name': 'dendrobium'}, {'id': 18755, 'synset': 'disa.n.01', 'name': 'disa'}, {'id': 18756, 'synset': 'phantom_orchid.n.01', 'name': 'phantom_orchid'}, {'id': 18757, 'synset': 'tulip_orchid.n.01', 'name': 'tulip_orchid'}, {'id': 18758, 'synset': 'butterfly_orchid.n.04', 'name': 'butterfly_orchid'}, {'id': 18759, 'synset': 'butterfly_orchid.n.03', 'name': 'butterfly_orchid'}, {'id': 18760, 'synset': 'epidendron.n.01', 'name': 'epidendron'}, {'id': 18761, 'synset': 'helleborine.n.02', 'name': 'helleborine'}, {'id': 18762, 'synset': 'epipactis_helleborine.n.01', 'name': 'Epipactis_helleborine'}, {'id': 18763, 'synset': 'stream_orchid.n.01', 'name': 'stream_orchid'}, {'id': 18764, 'synset': 'tongueflower.n.01', 'name': 'tongueflower'}, {'id': 18765, 'synset': 'rattlesnake_plantain.n.01', 'name': 'rattlesnake_plantain'}, {'id': 18766, 'synset': 'fragrant_orchid.n.01', 'name': 'fragrant_orchid'}, {'id': 18767, 'synset': 'short-spurred_fragrant_orchid.n.01', 'name': 'short-spurred_fragrant_orchid'}, {'id': 18768, 'synset': 'fringed_orchis.n.01', 'name': 'fringed_orchis'}, {'id': 18769, 'synset': 'frog_orchid.n.01', 'name': 'frog_orchid'}, {'id': 18770, 'synset': 'rein_orchid.n.01', 'name': 'rein_orchid'}, {'id': 18771, 'synset': 'bog_rein_orchid.n.01', 'name': 'bog_rein_orchid'}, {'id': 18772, 'synset': 'white_fringed_orchis.n.01', 'name': 'white_fringed_orchis'}, {'id': 18773, 'synset': 'elegant_habenaria.n.01', 'name': 'elegant_Habenaria'}, {'id': 18774, 'synset': 'purple-fringed_orchid.n.02', 'name': 'purple-fringed_orchid'}, {'id': 18775, 'synset': 'coastal_rein_orchid.n.01', 'name': 'coastal_rein_orchid'}, {'id': 18776, 'synset': "hooker's_orchid.n.01", 'name': "Hooker's_orchid"}, {'id': 18777, 'synset': 'ragged_orchid.n.01', 'name': 'ragged_orchid'}, {'id': 18778, 'synset': 'prairie_orchid.n.01', 'name': 'prairie_orchid'}, {'id': 18779, 'synset': 'snowy_orchid.n.01', 'name': 'snowy_orchid'}, {'id': 18780, 'synset': 'round-leaved_rein_orchid.n.01', 'name': 'round-leaved_rein_orchid'}, {'id': 18781, 'synset': 'purple_fringeless_orchid.n.01', 'name': 'purple_fringeless_orchid'}, {'id': 18782, 'synset': 'purple-fringed_orchid.n.01', 'name': 'purple-fringed_orchid'}, {'id': 18783, 'synset': 'alaska_rein_orchid.n.01', 'name': 'Alaska_rein_orchid'}, {'id': 18784, 'synset': 'crested_coral_root.n.01', 'name': 'crested_coral_root'}, {'id': 18785, 'synset': 'texas_purple_spike.n.01', 'name': 'Texas_purple_spike'}, {'id': 18786, 'synset': 'lizard_orchid.n.01', 'name': 'lizard_orchid'}, {'id': 18787, 'synset': 'laelia.n.01', 'name': 'laelia'}, {'id': 18788, 'synset': 'liparis.n.01', 'name': 'liparis'}, {'id': 18789, 'synset': 'twayblade.n.02', 'name': 'twayblade'}, {'id': 18790, 'synset': 'fen_orchid.n.01', 'name': 'fen_orchid'}, {'id': 18791, 'synset': 'broad-leaved_twayblade.n.01', 'name': 'broad-leaved_twayblade'}, {'id': 18792, 'synset': 'lesser_twayblade.n.01', 'name': 'lesser_twayblade'}, {'id': 18793, 'synset': 'twayblade.n.01', 'name': 'twayblade'}, {'id': 18794, 'synset': "green_adder's_mouth.n.01", 'name': "green_adder's_mouth"}, {'id': 18795, 'synset': 'masdevallia.n.01', 'name': 'masdevallia'}, {'id': 18796, 'synset': 'maxillaria.n.01', 'name': 'maxillaria'}, {'id': 18797, 'synset': 'pansy_orchid.n.01', 'name': 'pansy_orchid'}, {'id': 18798, 'synset': 'odontoglossum.n.01', 'name': 'odontoglossum'}, {'id': 18799, 'synset': 'oncidium.n.01', 'name': 'oncidium'}, {'id': 18800, 'synset': 'bee_orchid.n.01', 'name': 'bee_orchid'}, {'id': 18801, 'synset': 'fly_orchid.n.02', 'name': 'fly_orchid'}, {'id': 18802, 'synset': 'spider_orchid.n.01', 'name': 'spider_orchid'}, {'id': 18803, 'synset': 'early_spider_orchid.n.01', 'name': 'early_spider_orchid'}, {'id': 18804, 'synset': "venus'_slipper.n.01", 'name': "Venus'_slipper"}, {'id': 18805, 'synset': 'phaius.n.01', 'name': 'phaius'}, {'id': 18806, 'synset': 'moth_orchid.n.01', 'name': 'moth_orchid'}, {'id': 18807, 'synset': 'butterfly_plant.n.01', 'name': 'butterfly_plant'}, {'id': 18808, 'synset': 'rattlesnake_orchid.n.01', 'name': 'rattlesnake_orchid'}, {'id': 18809, 'synset': 'lesser_butterfly_orchid.n.01', 'name': 'lesser_butterfly_orchid'}, {'id': 18810, 'synset': 'greater_butterfly_orchid.n.01', 'name': 'greater_butterfly_orchid'}, {'id': 18811, 'synset': 'prairie_white-fringed_orchid.n.01', 'name': 'prairie_white-fringed_orchid'}, {'id': 18812, 'synset': 'tangle_orchid.n.01', 'name': 'tangle_orchid'}, {'id': 18813, 'synset': 'indian_crocus.n.01', 'name': 'Indian_crocus'}, {'id': 18814, 'synset': 'pleurothallis.n.01', 'name': 'pleurothallis'}, {'id': 18815, 'synset': 'pogonia.n.01', 'name': 'pogonia'}, {'id': 18816, 'synset': 'butterfly_orchid.n.01', 'name': 'butterfly_orchid'}, {'id': 18817, 'synset': 'psychopsis_krameriana.n.01', 'name': 'Psychopsis_krameriana'}, {'id': 18818, 'synset': 'psychopsis_papilio.n.01', 'name': 'Psychopsis_papilio'}, {'id': 18819, 'synset': 'helmet_orchid.n.01', 'name': 'helmet_orchid'}, {'id': 18820, 'synset': 'foxtail_orchid.n.01', 'name': 'foxtail_orchid'}, {'id': 18821, 'synset': 'orange-blossom_orchid.n.01', 'name': 'orange-blossom_orchid'}, {'id': 18822, 'synset': 'sobralia.n.01', 'name': 'sobralia'}, {'id': 18823, 'synset': "ladies'_tresses.n.01", 'name': "ladies'_tresses"}, {'id': 18824, 'synset': 'screw_augur.n.01', 'name': 'screw_augur'}, {'id': 18825, 'synset': "hooded_ladies'_tresses.n.01", 'name': "hooded_ladies'_tresses"}, {'id': 18826, 'synset': "western_ladies'_tresses.n.01", 'name': "western_ladies'_tresses"}, {'id': 18827, 'synset': "european_ladies'_tresses.n.01", 'name': "European_ladies'_tresses"}, {'id': 18828, 'synset': 'stanhopea.n.01', 'name': 'stanhopea'}, {'id': 18829, 'synset': 'stelis.n.01', 'name': 'stelis'}, {'id': 18830, 'synset': 'fly_orchid.n.01', 'name': 'fly_orchid'}, {'id': 18831, 'synset': 'vanda.n.01', 'name': 'vanda'}, {'id': 18832, 'synset': 'blue_orchid.n.01', 'name': 'blue_orchid'}, {'id': 18833, 'synset': 'vanilla.n.01', 'name': 'vanilla'}, {'id': 18834, 'synset': 'vanilla_orchid.n.01', 'name': 'vanilla_orchid'}, {'id': 18835, 'synset': 'yam.n.02', 'name': 'yam'}, {'id': 18836, 'synset': 'yam.n.01', 'name': 'yam'}, {'id': 18837, 'synset': 'white_yam.n.01', 'name': 'white_yam'}, {'id': 18838, 'synset': 'cinnamon_vine.n.01', 'name': 'cinnamon_vine'}, {'id': 18839, 'synset': "elephant's-foot.n.01", 'name': "elephant's-foot"}, {'id': 18840, 'synset': 'wild_yam.n.01', 'name': 'wild_yam'}, {'id': 18841, 'synset': 'cush-cush.n.01', 'name': 'cush-cush'}, {'id': 18842, 'synset': 'black_bryony.n.01', 'name': 'black_bryony'}, {'id': 18843, 'synset': 'primrose.n.01', 'name': 'primrose'}, {'id': 18844, 'synset': 'english_primrose.n.01', 'name': 'English_primrose'}, {'id': 18845, 'synset': 'cowslip.n.01', 'name': 'cowslip'}, {'id': 18846, 'synset': 'oxlip.n.01', 'name': 'oxlip'}, {'id': 18847, 'synset': 'chinese_primrose.n.01', 'name': 'Chinese_primrose'}, {'id': 18848, 'synset': 'polyanthus.n.01', 'name': 'polyanthus'}, {'id': 18849, 'synset': 'pimpernel.n.02', 'name': 'pimpernel'}, {'id': 18850, 'synset': 'scarlet_pimpernel.n.01', 'name': 'scarlet_pimpernel'}, {'id': 18851, 'synset': 'bog_pimpernel.n.01', 'name': 'bog_pimpernel'}, {'id': 18852, 'synset': 'chaffweed.n.01', 'name': 'chaffweed'}, {'id': 18853, 'synset': 'cyclamen.n.01', 'name': 'cyclamen'}, {'id': 18854, 'synset': 'sowbread.n.01', 'name': 'sowbread'}, {'id': 18855, 'synset': 'sea_milkwort.n.01', 'name': 'sea_milkwort'}, {'id': 18856, 'synset': 'featherfoil.n.01', 'name': 'featherfoil'}, {'id': 18857, 'synset': 'water_gillyflower.n.01', 'name': 'water_gillyflower'}, {'id': 18858, 'synset': 'water_violet.n.01', 'name': 'water_violet'}, {'id': 18859, 'synset': 'loosestrife.n.02', 'name': 'loosestrife'}, {'id': 18860, 'synset': 'gooseneck_loosestrife.n.01', 'name': 'gooseneck_loosestrife'}, {'id': 18861, 'synset': 'yellow_pimpernel.n.01', 'name': 'yellow_pimpernel'}, {'id': 18862, 'synset': 'fringed_loosestrife.n.01', 'name': 'fringed_loosestrife'}, {'id': 18863, 'synset': 'moneywort.n.01', 'name': 'moneywort'}, {'id': 18864, 'synset': 'swamp_candles.n.01', 'name': 'swamp_candles'}, {'id': 18865, 'synset': 'whorled_loosestrife.n.01', 'name': 'whorled_loosestrife'}, {'id': 18866, 'synset': 'water_pimpernel.n.01', 'name': 'water_pimpernel'}, {'id': 18867, 'synset': 'brookweed.n.02', 'name': 'brookweed'}, {'id': 18868, 'synset': 'brookweed.n.01', 'name': 'brookweed'}, {'id': 18869, 'synset': 'coralberry.n.02', 'name': 'coralberry'}, {'id': 18870, 'synset': 'marlberry.n.01', 'name': 'marlberry'}, {'id': 18871, 'synset': 'plumbago.n.02', 'name': 'plumbago'}, {'id': 18872, 'synset': 'leadwort.n.01', 'name': 'leadwort'}, {'id': 18873, 'synset': 'thrift.n.01', 'name': 'thrift'}, {'id': 18874, 'synset': 'sea_lavender.n.01', 'name': 'sea_lavender'}, {'id': 18875, 'synset': 'barbasco.n.01', 'name': 'barbasco'}, {'id': 18876, 'synset': 'gramineous_plant.n.01', 'name': 'gramineous_plant'}, {'id': 18877, 'synset': 'grass.n.01', 'name': 'grass'}, {'id': 18878, 'synset': 'midgrass.n.01', 'name': 'midgrass'}, {'id': 18879, 'synset': 'shortgrass.n.01', 'name': 'shortgrass'}, {'id': 18880, 'synset': 'sword_grass.n.01', 'name': 'sword_grass'}, {'id': 18881, 'synset': 'tallgrass.n.01', 'name': 'tallgrass'}, {'id': 18882, 'synset': 'herbage.n.01', 'name': 'herbage'}, {'id': 18883, 'synset': 'goat_grass.n.01', 'name': 'goat_grass'}, {'id': 18884, 'synset': 'wheatgrass.n.01', 'name': 'wheatgrass'}, {'id': 18885, 'synset': 'crested_wheatgrass.n.01', 'name': 'crested_wheatgrass'}, {'id': 18886, 'synset': 'bearded_wheatgrass.n.01', 'name': 'bearded_wheatgrass'}, {'id': 18887, 'synset': 'western_wheatgrass.n.01', 'name': 'western_wheatgrass'}, {'id': 18888, 'synset': 'intermediate_wheatgrass.n.01', 'name': 'intermediate_wheatgrass'}, {'id': 18889, 'synset': 'slender_wheatgrass.n.01', 'name': 'slender_wheatgrass'}, {'id': 18890, 'synset': 'velvet_bent.n.01', 'name': 'velvet_bent'}, {'id': 18891, 'synset': 'cloud_grass.n.01', 'name': 'cloud_grass'}, {'id': 18892, 'synset': 'meadow_foxtail.n.01', 'name': 'meadow_foxtail'}, {'id': 18893, 'synset': 'foxtail.n.01', 'name': 'foxtail'}, {'id': 18894, 'synset': 'broom_grass.n.01', 'name': 'broom_grass'}, {'id': 18895, 'synset': 'broom_sedge.n.01', 'name': 'broom_sedge'}, {'id': 18896, 'synset': 'tall_oat_grass.n.01', 'name': 'tall_oat_grass'}, {'id': 18897, 'synset': 'toetoe.n.02', 'name': 'toetoe'}, {'id': 18898, 'synset': 'oat.n.01', 'name': 'oat'}, {'id': 18899, 'synset': 'cereal_oat.n.01', 'name': 'cereal_oat'}, {'id': 18900, 'synset': 'wild_oat.n.01', 'name': 'wild_oat'}, {'id': 18901, 'synset': 'slender_wild_oat.n.01', 'name': 'slender_wild_oat'}, {'id': 18902, 'synset': 'wild_red_oat.n.01', 'name': 'wild_red_oat'}, {'id': 18903, 'synset': 'brome.n.01', 'name': 'brome'}, {'id': 18904, 'synset': 'chess.n.01', 'name': 'chess'}, {'id': 18905, 'synset': 'field_brome.n.01', 'name': 'field_brome'}, {'id': 18906, 'synset': 'grama.n.01', 'name': 'grama'}, {'id': 18907, 'synset': 'black_grama.n.01', 'name': 'black_grama'}, {'id': 18908, 'synset': 'buffalo_grass.n.02', 'name': 'buffalo_grass'}, {'id': 18909, 'synset': 'reed_grass.n.01', 'name': 'reed_grass'}, {'id': 18910, 'synset': 'feather_reed_grass.n.01', 'name': 'feather_reed_grass'}, {'id': 18911, 'synset': 'australian_reed_grass.n.01', 'name': 'Australian_reed_grass'}, {'id': 18912, 'synset': 'burgrass.n.01', 'name': 'burgrass'}, {'id': 18913, 'synset': 'buffel_grass.n.01', 'name': 'buffel_grass'}, {'id': 18914, 'synset': 'rhodes_grass.n.01', 'name': 'Rhodes_grass'}, {'id': 18915, 'synset': 'pampas_grass.n.01', 'name': 'pampas_grass'}, {'id': 18916, 'synset': 'giant_star_grass.n.01', 'name': 'giant_star_grass'}, {'id': 18917, 'synset': 'orchard_grass.n.01', 'name': 'orchard_grass'}, {'id': 18918, 'synset': 'egyptian_grass.n.01', 'name': 'Egyptian_grass'}, {'id': 18919, 'synset': 'crabgrass.n.01', 'name': 'crabgrass'}, {'id': 18920, 'synset': 'smooth_crabgrass.n.01', 'name': 'smooth_crabgrass'}, {'id': 18921, 'synset': 'large_crabgrass.n.01', 'name': 'large_crabgrass'}, {'id': 18922, 'synset': 'barnyard_grass.n.01', 'name': 'barnyard_grass'}, {'id': 18923, 'synset': 'japanese_millet.n.01', 'name': 'Japanese_millet'}, {'id': 18924, 'synset': 'yardgrass.n.01', 'name': 'yardgrass'}, {'id': 18925, 'synset': 'finger_millet.n.01', 'name': 'finger_millet'}, {'id': 18926, 'synset': 'lyme_grass.n.01', 'name': 'lyme_grass'}, {'id': 18927, 'synset': 'wild_rye.n.01', 'name': 'wild_rye'}, {'id': 18928, 'synset': 'giant_ryegrass.n.01', 'name': 'giant_ryegrass'}, {'id': 18929, 'synset': 'sea_lyme_grass.n.01', 'name': 'sea_lyme_grass'}, {'id': 18930, 'synset': 'canada_wild_rye.n.01', 'name': 'Canada_wild_rye'}, {'id': 18931, 'synset': 'teff.n.01', 'name': 'teff'}, {'id': 18932, 'synset': 'weeping_love_grass.n.01', 'name': 'weeping_love_grass'}, {'id': 18933, 'synset': 'plume_grass.n.01', 'name': 'plume_grass'}, {'id': 18934, 'synset': 'ravenna_grass.n.01', 'name': 'Ravenna_grass'}, {'id': 18935, 'synset': 'fescue.n.01', 'name': 'fescue'}, {'id': 18936, 'synset': 'reed_meadow_grass.n.01', 'name': 'reed_meadow_grass'}, {'id': 18937, 'synset': 'velvet_grass.n.01', 'name': 'velvet_grass'}, {'id': 18938, 'synset': 'creeping_soft_grass.n.01', 'name': 'creeping_soft_grass'}, {'id': 18939, 'synset': 'barleycorn.n.01', 'name': 'barleycorn'}, {'id': 18940, 'synset': 'barley_grass.n.01', 'name': 'barley_grass'}, {'id': 18941, 'synset': 'little_barley.n.01', 'name': 'little_barley'}, {'id': 18942, 'synset': 'rye_grass.n.01', 'name': 'rye_grass'}, {'id': 18943, 'synset': 'perennial_ryegrass.n.01', 'name': 'perennial_ryegrass'}, {'id': 18944, 'synset': 'italian_ryegrass.n.01', 'name': 'Italian_ryegrass'}, {'id': 18945, 'synset': 'darnel.n.01', 'name': 'darnel'}, {'id': 18946, 'synset': 'nimblewill.n.01', 'name': 'nimblewill'}, {'id': 18947, 'synset': 'cultivated_rice.n.01', 'name': 'cultivated_rice'}, {'id': 18948, 'synset': 'ricegrass.n.01', 'name': 'ricegrass'}, {'id': 18949, 'synset': 'smilo.n.01', 'name': 'smilo'}, {'id': 18950, 'synset': 'switch_grass.n.01', 'name': 'switch_grass'}, {'id': 18951, 'synset': 'broomcorn_millet.n.01', 'name': 'broomcorn_millet'}, {'id': 18952, 'synset': 'goose_grass.n.03', 'name': 'goose_grass'}, {'id': 18953, 'synset': 'dallisgrass.n.01', 'name': 'dallisgrass'}, {'id': 18954, 'synset': 'bahia_grass.n.01', 'name': 'Bahia_grass'}, {'id': 18955, 'synset': 'knotgrass.n.01', 'name': 'knotgrass'}, {'id': 18956, 'synset': 'fountain_grass.n.01', 'name': 'fountain_grass'}, {'id': 18957, 'synset': 'reed_canary_grass.n.01', 'name': 'reed_canary_grass'}, {'id': 18958, 'synset': 'canary_grass.n.01', 'name': 'canary_grass'}, {'id': 18959, 'synset': 'timothy.n.01', 'name': 'timothy'}, {'id': 18960, 'synset': 'bluegrass.n.01', 'name': 'bluegrass'}, {'id': 18961, 'synset': 'meadowgrass.n.01', 'name': 'meadowgrass'}, {'id': 18962, 'synset': 'wood_meadowgrass.n.01', 'name': 'wood_meadowgrass'}, {'id': 18963, 'synset': 'noble_cane.n.01', 'name': 'noble_cane'}, {'id': 18964, 'synset': 'munj.n.01', 'name': 'munj'}, {'id': 18965, 'synset': 'broom_beard_grass.n.01', 'name': 'broom_beard_grass'}, {'id': 18966, 'synset': 'bluestem.n.01', 'name': 'bluestem'}, {'id': 18967, 'synset': 'rye.n.02', 'name': 'rye'}, {'id': 18968, 'synset': 'bristlegrass.n.01', 'name': 'bristlegrass'}, {'id': 18969, 'synset': 'giant_foxtail.n.01', 'name': 'giant_foxtail'}, {'id': 18970, 'synset': 'yellow_bristlegrass.n.01', 'name': 'yellow_bristlegrass'}, {'id': 18971, 'synset': 'green_bristlegrass.n.01', 'name': 'green_bristlegrass'}, {'id': 18972, 'synset': 'siberian_millet.n.01', 'name': 'Siberian_millet'}, {'id': 18973, 'synset': 'german_millet.n.01', 'name': 'German_millet'}, {'id': 18974, 'synset': 'millet.n.01', 'name': 'millet'}, {'id': 18975, 'synset': 'rattan.n.02', 'name': 'rattan'}, {'id': 18976, 'synset': 'malacca.n.01', 'name': 'malacca'}, {'id': 18977, 'synset': 'reed.n.01', 'name': 'reed'}, {'id': 18978, 'synset': 'sorghum.n.01', 'name': 'sorghum'}, {'id': 18979, 'synset': 'grain_sorghum.n.01', 'name': 'grain_sorghum'}, {'id': 18980, 'synset': 'durra.n.01', 'name': 'durra'}, {'id': 18981, 'synset': 'feterita.n.01', 'name': 'feterita'}, {'id': 18982, 'synset': 'hegari.n.01', 'name': 'hegari'}, {'id': 18983, 'synset': 'kaoliang.n.01', 'name': 'kaoliang'}, {'id': 18984, 'synset': 'milo.n.01', 'name': 'milo'}, {'id': 18985, 'synset': 'shallu.n.01', 'name': 'shallu'}, {'id': 18986, 'synset': 'broomcorn.n.01', 'name': 'broomcorn'}, {'id': 18987, 'synset': 'cordgrass.n.01', 'name': 'cordgrass'}, {'id': 18988, 'synset': 'salt_reed_grass.n.01', 'name': 'salt_reed_grass'}, {'id': 18989, 'synset': 'prairie_cordgrass.n.01', 'name': 'prairie_cordgrass'}, {'id': 18990, 'synset': 'smut_grass.n.01', 'name': 'smut_grass'}, {'id': 18991, 'synset': 'sand_dropseed.n.01', 'name': 'sand_dropseed'}, {'id': 18992, 'synset': 'rush_grass.n.01', 'name': 'rush_grass'}, {'id': 18993, 'synset': 'st._augustine_grass.n.01', 'name': 'St._Augustine_grass'}, {'id': 18994, 'synset': 'grain.n.08', 'name': 'grain'}, {'id': 18995, 'synset': 'cereal.n.01', 'name': 'cereal'}, {'id': 18996, 'synset': 'wheat.n.01', 'name': 'wheat'}, {'id': 18997, 'synset': 'wheat_berry.n.01', 'name': 'wheat_berry'}, {'id': 18998, 'synset': 'durum.n.01', 'name': 'durum'}, {'id': 18999, 'synset': 'spelt.n.01', 'name': 'spelt'}, {'id': 19000, 'synset': 'emmer.n.01', 'name': 'emmer'}, {'id': 19001, 'synset': 'wild_wheat.n.01', 'name': 'wild_wheat'}, {'id': 19002, 'synset': 'corn.n.01', 'name': 'corn'}, {'id': 19003, 'synset': 'mealie.n.01', 'name': 'mealie'}, {'id': 19004, 'synset': 'corn.n.02', 'name': 'corn'}, {'id': 19005, 'synset': 'dent_corn.n.01', 'name': 'dent_corn'}, {'id': 19006, 'synset': 'flint_corn.n.01', 'name': 'flint_corn'}, {'id': 19007, 'synset': 'popcorn.n.01', 'name': 'popcorn'}, {'id': 19008, 'synset': 'zoysia.n.01', 'name': 'zoysia'}, {'id': 19009, 'synset': 'manila_grass.n.01', 'name': 'Manila_grass'}, {'id': 19010, 'synset': 'korean_lawn_grass.n.01', 'name': 'Korean_lawn_grass'}, {'id': 19011, 'synset': 'common_bamboo.n.01', 'name': 'common_bamboo'}, {'id': 19012, 'synset': 'giant_bamboo.n.01', 'name': 'giant_bamboo'}, {'id': 19013, 'synset': 'umbrella_plant.n.03', 'name': 'umbrella_plant'}, {'id': 19014, 'synset': 'chufa.n.01', 'name': 'chufa'}, {'id': 19015, 'synset': 'galingale.n.01', 'name': 'galingale'}, {'id': 19016, 'synset': 'nutgrass.n.01', 'name': 'nutgrass'}, {'id': 19017, 'synset': 'sand_sedge.n.01', 'name': 'sand_sedge'}, {'id': 19018, 'synset': 'cypress_sedge.n.01', 'name': 'cypress_sedge'}, {'id': 19019, 'synset': 'cotton_grass.n.01', 'name': 'cotton_grass'}, {'id': 19020, 'synset': 'common_cotton_grass.n.01', 'name': 'common_cotton_grass'}, {'id': 19021, 'synset': 'hardstem_bulrush.n.01', 'name': 'hardstem_bulrush'}, {'id': 19022, 'synset': 'wool_grass.n.01', 'name': 'wool_grass'}, {'id': 19023, 'synset': 'spike_rush.n.01', 'name': 'spike_rush'}, {'id': 19024, 'synset': 'water_chestnut.n.02', 'name': 'water_chestnut'}, {'id': 19025, 'synset': 'needle_spike_rush.n.01', 'name': 'needle_spike_rush'}, {'id': 19026, 'synset': 'creeping_spike_rush.n.01', 'name': 'creeping_spike_rush'}, {'id': 19027, 'synset': 'pandanus.n.02', 'name': 'pandanus'}, {'id': 19028, 'synset': 'textile_screw_pine.n.01', 'name': 'textile_screw_pine'}, {'id': 19029, 'synset': 'cattail.n.01', 'name': 'cattail'}, {'id': 19030, 'synset': "cat's-tail.n.01", 'name': "cat's-tail"}, {'id': 19031, 'synset': 'bur_reed.n.01', 'name': 'bur_reed'}, {'id': 19032, 'synset': 'grain.n.07', 'name': 'grain'}, {'id': 19033, 'synset': 'kernel.n.02', 'name': 'kernel'}, {'id': 19034, 'synset': 'rye.n.01', 'name': 'rye'}, {'id': 19035, 'synset': 'gourd.n.03', 'name': 'gourd'}, {'id': 19036, 'synset': 'pumpkin.n.01', 'name': 'pumpkin'}, {'id': 19037, 'synset': 'squash.n.01', 'name': 'squash'}, {'id': 19038, 'synset': 'summer_squash.n.01', 'name': 'summer_squash'}, {'id': 19039, 'synset': 'yellow_squash.n.01', 'name': 'yellow_squash'}, {'id': 19040, 'synset': 'marrow.n.02', 'name': 'marrow'}, {'id': 19041, 'synset': 'zucchini.n.01', 'name': 'zucchini'}, {'id': 19042, 'synset': 'cocozelle.n.01', 'name': 'cocozelle'}, {'id': 19043, 'synset': 'cymling.n.01', 'name': 'cymling'}, {'id': 19044, 'synset': 'spaghetti_squash.n.01', 'name': 'spaghetti_squash'}, {'id': 19045, 'synset': 'winter_squash.n.01', 'name': 'winter_squash'}, {'id': 19046, 'synset': 'acorn_squash.n.01', 'name': 'acorn_squash'}, {'id': 19047, 'synset': 'hubbard_squash.n.01', 'name': 'hubbard_squash'}, {'id': 19048, 'synset': 'turban_squash.n.01', 'name': 'turban_squash'}, {'id': 19049, 'synset': 'buttercup_squash.n.01', 'name': 'buttercup_squash'}, {'id': 19050, 'synset': 'butternut_squash.n.01', 'name': 'butternut_squash'}, {'id': 19051, 'synset': 'winter_crookneck.n.01', 'name': 'winter_crookneck'}, {'id': 19052, 'synset': 'cushaw.n.01', 'name': 'cushaw'}, {'id': 19053, 'synset': 'prairie_gourd.n.02', 'name': 'prairie_gourd'}, {'id': 19054, 'synset': 'prairie_gourd.n.01', 'name': 'prairie_gourd'}, {'id': 19055, 'synset': 'bryony.n.01', 'name': 'bryony'}, {'id': 19056, 'synset': 'white_bryony.n.01', 'name': 'white_bryony'}, {'id': 19057, 'synset': 'sweet_melon.n.01', 'name': 'sweet_melon'}, {'id': 19058, 'synset': 'cantaloupe.n.01', 'name': 'cantaloupe'}, {'id': 19059, 'synset': 'winter_melon.n.01', 'name': 'winter_melon'}, {'id': 19060, 'synset': 'net_melon.n.01', 'name': 'net_melon'}, {'id': 19061, 'synset': 'cucumber.n.01', 'name': 'cucumber'}, {'id': 19062, 'synset': 'squirting_cucumber.n.01', 'name': 'squirting_cucumber'}, {'id': 19063, 'synset': 'bottle_gourd.n.01', 'name': 'bottle_gourd'}, {'id': 19064, 'synset': 'luffa.n.02', 'name': 'luffa'}, {'id': 19065, 'synset': 'loofah.n.02', 'name': 'loofah'}, {'id': 19066, 'synset': 'angled_loofah.n.01', 'name': 'angled_loofah'}, {'id': 19067, 'synset': 'loofa.n.01', 'name': 'loofa'}, {'id': 19068, 'synset': 'balsam_apple.n.01', 'name': 'balsam_apple'}, {'id': 19069, 'synset': 'balsam_pear.n.01', 'name': 'balsam_pear'}, {'id': 19070, 'synset': 'lobelia.n.01', 'name': 'lobelia'}, {'id': 19071, 'synset': 'water_lobelia.n.01', 'name': 'water_lobelia'}, {'id': 19072, 'synset': 'mallow.n.01', 'name': 'mallow'}, {'id': 19073, 'synset': 'musk_mallow.n.02', 'name': 'musk_mallow'}, {'id': 19074, 'synset': 'common_mallow.n.01', 'name': 'common_mallow'}, {'id': 19075, 'synset': 'okra.n.02', 'name': 'okra'}, {'id': 19076, 'synset': 'okra.n.01', 'name': 'okra'}, {'id': 19077, 'synset': 'abelmosk.n.01', 'name': 'abelmosk'}, {'id': 19078, 'synset': 'flowering_maple.n.01', 'name': 'flowering_maple'}, {'id': 19079, 'synset': 'velvetleaf.n.02', 'name': 'velvetleaf'}, {'id': 19080, 'synset': 'hollyhock.n.02', 'name': 'hollyhock'}, {'id': 19081, 'synset': 'rose_mallow.n.02', 'name': 'rose_mallow'}, {'id': 19082, 'synset': 'althea.n.01', 'name': 'althea'}, {'id': 19083, 'synset': 'marsh_mallow.n.01', 'name': 'marsh_mallow'}, {'id': 19084, 'synset': 'poppy_mallow.n.01', 'name': 'poppy_mallow'}, {'id': 19085, 'synset': 'fringed_poppy_mallow.n.01', 'name': 'fringed_poppy_mallow'}, {'id': 19086, 'synset': 'purple_poppy_mallow.n.01', 'name': 'purple_poppy_mallow'}, {'id': 19087, 'synset': 'clustered_poppy_mallow.n.01', 'name': 'clustered_poppy_mallow'}, {'id': 19088, 'synset': 'sea_island_cotton.n.01', 'name': 'sea_island_cotton'}, {'id': 19089, 'synset': 'levant_cotton.n.01', 'name': 'Levant_cotton'}, {'id': 19090, 'synset': 'upland_cotton.n.01', 'name': 'upland_cotton'}, {'id': 19091, 'synset': 'peruvian_cotton.n.01', 'name': 'Peruvian_cotton'}, {'id': 19092, 'synset': 'wild_cotton.n.01', 'name': 'wild_cotton'}, {'id': 19093, 'synset': 'kenaf.n.02', 'name': 'kenaf'}, {'id': 19094, 'synset': 'sorrel_tree.n.02', 'name': 'sorrel_tree'}, {'id': 19095, 'synset': 'rose_mallow.n.01', 'name': 'rose_mallow'}, {'id': 19096, 'synset': 'cotton_rose.n.01', 'name': 'cotton_rose'}, {'id': 19097, 'synset': 'roselle.n.01', 'name': 'roselle'}, {'id': 19098, 'synset': 'mahoe.n.01', 'name': 'mahoe'}, {'id': 19099, 'synset': 'flower-of-an-hour.n.01', 'name': 'flower-of-an-hour'}, {'id': 19100, 'synset': 'lacebark.n.01', 'name': 'lacebark'}, {'id': 19101, 'synset': 'wild_hollyhock.n.02', 'name': 'wild_hollyhock'}, {'id': 19102, 'synset': 'mountain_hollyhock.n.01', 'name': 'mountain_hollyhock'}, {'id': 19103, 'synset': 'seashore_mallow.n.01', 'name': 'seashore_mallow'}, {'id': 19104, 'synset': 'salt_marsh_mallow.n.01', 'name': 'salt_marsh_mallow'}, {'id': 19105, 'synset': 'chaparral_mallow.n.01', 'name': 'chaparral_mallow'}, {'id': 19106, 'synset': 'malope.n.01', 'name': 'malope'}, {'id': 19107, 'synset': 'false_mallow.n.02', 'name': 'false_mallow'}, {'id': 19108, 'synset': 'waxmallow.n.01', 'name': 'waxmallow'}, {'id': 19109, 'synset': 'glade_mallow.n.01', 'name': 'glade_mallow'}, {'id': 19110, 'synset': 'pavonia.n.01', 'name': 'pavonia'}, {'id': 19111, 'synset': 'ribbon_tree.n.01', 'name': 'ribbon_tree'}, {'id': 19112, 'synset': 'bush_hibiscus.n.01', 'name': 'bush_hibiscus'}, {'id': 19113, 'synset': 'virginia_mallow.n.01', 'name': 'Virginia_mallow'}, {'id': 19114, 'synset': 'queensland_hemp.n.01', 'name': 'Queensland_hemp'}, {'id': 19115, 'synset': 'indian_mallow.n.01', 'name': 'Indian_mallow'}, {'id': 19116, 'synset': 'checkerbloom.n.01', 'name': 'checkerbloom'}, {'id': 19117, 'synset': 'globe_mallow.n.01', 'name': 'globe_mallow'}, {'id': 19118, 'synset': 'prairie_mallow.n.01', 'name': 'prairie_mallow'}, {'id': 19119, 'synset': 'tulipwood_tree.n.01', 'name': 'tulipwood_tree'}, {'id': 19120, 'synset': 'portia_tree.n.01', 'name': 'portia_tree'}, {'id': 19121, 'synset': 'red_silk-cotton_tree.n.01', 'name': 'red_silk-cotton_tree'}, {'id': 19122, 'synset': 'cream-of-tartar_tree.n.01', 'name': 'cream-of-tartar_tree'}, {'id': 19123, 'synset': 'baobab.n.01', 'name': 'baobab'}, {'id': 19124, 'synset': 'kapok.n.02', 'name': 'kapok'}, {'id': 19125, 'synset': 'durian.n.01', 'name': 'durian'}, {'id': 19126, 'synset': 'montezuma.n.01', 'name': 'Montezuma'}, {'id': 19127, 'synset': 'shaving-brush_tree.n.01', 'name': 'shaving-brush_tree'}, {'id': 19128, 'synset': 'quandong.n.03', 'name': 'quandong'}, {'id': 19129, 'synset': 'quandong.n.02', 'name': 'quandong'}, {'id': 19130, 'synset': 'makomako.n.01', 'name': 'makomako'}, {'id': 19131, 'synset': 'jamaican_cherry.n.01', 'name': 'Jamaican_cherry'}, {'id': 19132, 'synset': 'breakax.n.01', 'name': 'breakax'}, {'id': 19133, 'synset': 'sterculia.n.01', 'name': 'sterculia'}, {'id': 19134, 'synset': 'panama_tree.n.01', 'name': 'Panama_tree'}, {'id': 19135, 'synset': 'kalumpang.n.01', 'name': 'kalumpang'}, {'id': 19136, 'synset': 'bottle-tree.n.01', 'name': 'bottle-tree'}, {'id': 19137, 'synset': 'flame_tree.n.04', 'name': 'flame_tree'}, {'id': 19138, 'synset': 'flame_tree.n.03', 'name': 'flame_tree'}, {'id': 19139, 'synset': 'kurrajong.n.01', 'name': 'kurrajong'}, {'id': 19140, 'synset': 'queensland_bottletree.n.01', 'name': 'Queensland_bottletree'}, {'id': 19141, 'synset': 'kola.n.01', 'name': 'kola'}, {'id': 19142, 'synset': 'kola_nut.n.01', 'name': 'kola_nut'}, {'id': 19143, 'synset': 'chinese_parasol_tree.n.01', 'name': 'Chinese_parasol_tree'}, {'id': 19144, 'synset': 'flannelbush.n.01', 'name': 'flannelbush'}, {'id': 19145, 'synset': 'screw_tree.n.01', 'name': 'screw_tree'}, {'id': 19146, 'synset': 'nut-leaved_screw_tree.n.01', 'name': 'nut-leaved_screw_tree'}, {'id': 19147, 'synset': 'red_beech.n.02', 'name': 'red_beech'}, {'id': 19148, 'synset': 'looking_glass_tree.n.01', 'name': 'looking_glass_tree'}, {'id': 19149, 'synset': 'looking-glass_plant.n.01', 'name': 'looking-glass_plant'}, {'id': 19150, 'synset': 'honey_bell.n.01', 'name': 'honey_bell'}, {'id': 19151, 'synset': 'mayeng.n.01', 'name': 'mayeng'}, {'id': 19152, 'synset': 'silver_tree.n.02', 'name': 'silver_tree'}, {'id': 19153, 'synset': 'cacao.n.01', 'name': 'cacao'}, {'id': 19154, 'synset': 'obeche.n.02', 'name': 'obeche'}, {'id': 19155, 'synset': 'linden.n.02', 'name': 'linden'}, {'id': 19156, 'synset': 'american_basswood.n.01', 'name': 'American_basswood'}, {'id': 19157, 'synset': 'small-leaved_linden.n.01', 'name': 'small-leaved_linden'}, {'id': 19158, 'synset': 'white_basswood.n.01', 'name': 'white_basswood'}, {'id': 19159, 'synset': 'japanese_linden.n.01', 'name': 'Japanese_linden'}, {'id': 19160, 'synset': 'silver_lime.n.01', 'name': 'silver_lime'}, {'id': 19161, 'synset': 'corchorus.n.01', 'name': 'corchorus'}, {'id': 19162, 'synset': 'african_hemp.n.02', 'name': 'African_hemp'}, {'id': 19163, 'synset': 'herb.n.01', 'name': 'herb'}, {'id': 19164, 'synset': 'protea.n.01', 'name': 'protea'}, {'id': 19165, 'synset': 'honeypot.n.01', 'name': 'honeypot'}, {'id': 19166, 'synset': 'honeyflower.n.02', 'name': 'honeyflower'}, {'id': 19167, 'synset': 'banksia.n.01', 'name': 'banksia'}, {'id': 19168, 'synset': 'honeysuckle.n.02', 'name': 'honeysuckle'}, {'id': 19169, 'synset': 'smoke_bush.n.02', 'name': 'smoke_bush'}, {'id': 19170, 'synset': 'chilean_firebush.n.01', 'name': 'Chilean_firebush'}, {'id': 19171, 'synset': 'chilean_nut.n.01', 'name': 'Chilean_nut'}, {'id': 19172, 'synset': 'grevillea.n.01', 'name': 'grevillea'}, {'id': 19173, 'synset': 'red-flowered_silky_oak.n.01', 'name': 'red-flowered_silky_oak'}, {'id': 19174, 'synset': 'silky_oak.n.01', 'name': 'silky_oak'}, {'id': 19175, 'synset': 'beefwood.n.05', 'name': 'beefwood'}, {'id': 19176, 'synset': 'cushion_flower.n.01', 'name': 'cushion_flower'}, {'id': 19177, 'synset': 'rewa-rewa.n.01', 'name': 'rewa-rewa'}, {'id': 19178, 'synset': 'honeyflower.n.01', 'name': 'honeyflower'}, {'id': 19179, 'synset': 'silver_tree.n.01', 'name': 'silver_tree'}, {'id': 19180, 'synset': 'lomatia.n.01', 'name': 'lomatia'}, {'id': 19181, 'synset': 'macadamia.n.01', 'name': 'macadamia'}, {'id': 19182, 'synset': 'macadamia_integrifolia.n.01', 'name': 'Macadamia_integrifolia'}, {'id': 19183, 'synset': 'macadamia_nut.n.01', 'name': 'macadamia_nut'}, {'id': 19184, 'synset': 'queensland_nut.n.01', 'name': 'Queensland_nut'}, {'id': 19185, 'synset': 'prickly_ash.n.02', 'name': 'prickly_ash'}, {'id': 19186, 'synset': 'geebung.n.01', 'name': 'geebung'}, {'id': 19187, 'synset': 'wheel_tree.n.01', 'name': 'wheel_tree'}, {'id': 19188, 'synset': 'scrub_beefwood.n.01', 'name': 'scrub_beefwood'}, {'id': 19189, 'synset': 'waratah.n.02', 'name': 'waratah'}, {'id': 19190, 'synset': 'waratah.n.01', 'name': 'waratah'}, {'id': 19191, 'synset': 'casuarina.n.01', 'name': 'casuarina'}, {'id': 19192, 'synset': 'she-oak.n.01', 'name': 'she-oak'}, {'id': 19193, 'synset': 'beefwood.n.03', 'name': 'beefwood'}, {'id': 19194, 'synset': 'australian_pine.n.01', 'name': 'Australian_pine'}, {'id': 19195, 'synset': 'heath.n.01', 'name': 'heath'}, {'id': 19196, 'synset': 'tree_heath.n.02', 'name': 'tree_heath'}, {'id': 19197, 'synset': 'briarroot.n.01', 'name': 'briarroot'}, {'id': 19198, 'synset': 'winter_heath.n.01', 'name': 'winter_heath'}, {'id': 19199, 'synset': 'bell_heather.n.02', 'name': 'bell_heather'}, {'id': 19200, 'synset': 'cornish_heath.n.01', 'name': 'Cornish_heath'}, {'id': 19201, 'synset': 'spanish_heath.n.01', 'name': 'Spanish_heath'}, {'id': 19202, 'synset': "prince-of-wales'-heath.n.01", 'name': "Prince-of-Wales'-heath"}, {'id': 19203, 'synset': 'bog_rosemary.n.01', 'name': 'bog_rosemary'}, {'id': 19204, 'synset': 'marsh_andromeda.n.01', 'name': 'marsh_andromeda'}, {'id': 19205, 'synset': 'madrona.n.01', 'name': 'madrona'}, {'id': 19206, 'synset': 'strawberry_tree.n.01', 'name': 'strawberry_tree'}, {'id': 19207, 'synset': 'bearberry.n.03', 'name': 'bearberry'}, {'id': 19208, 'synset': 'alpine_bearberry.n.01', 'name': 'alpine_bearberry'}, {'id': 19209, 'synset': 'heartleaf_manzanita.n.01', 'name': 'heartleaf_manzanita'}, {'id': 19210, 'synset': 'parry_manzanita.n.01', 'name': 'Parry_manzanita'}, {'id': 19211, 'synset': 'spike_heath.n.01', 'name': 'spike_heath'}, {'id': 19212, 'synset': 'bryanthus.n.01', 'name': 'bryanthus'}, {'id': 19213, 'synset': 'leatherleaf.n.02', 'name': 'leatherleaf'}, {'id': 19214, 'synset': 'connemara_heath.n.01', 'name': 'Connemara_heath'}, {'id': 19215, 'synset': 'trailing_arbutus.n.01', 'name': 'trailing_arbutus'}, {'id': 19216, 'synset': 'creeping_snowberry.n.01', 'name': 'creeping_snowberry'}, {'id': 19217, 'synset': 'salal.n.01', 'name': 'salal'}, {'id': 19218, 'synset': 'huckleberry.n.02', 'name': 'huckleberry'}, {'id': 19219, 'synset': 'black_huckleberry.n.01', 'name': 'black_huckleberry'}, {'id': 19220, 'synset': 'dangleberry.n.01', 'name': 'dangleberry'}, {'id': 19221, 'synset': 'box_huckleberry.n.01', 'name': 'box_huckleberry'}, {'id': 19222, 'synset': 'kalmia.n.01', 'name': 'kalmia'}, {'id': 19223, 'synset': 'mountain_laurel.n.01', 'name': 'mountain_laurel'}, {'id': 19224, 'synset': 'swamp_laurel.n.01', 'name': 'swamp_laurel'}, {'id': 19225, 'synset': "trapper's_tea.n.01", 'name': "trapper's_tea"}, {'id': 19226, 'synset': 'wild_rosemary.n.01', 'name': 'wild_rosemary'}, {'id': 19227, 'synset': 'sand_myrtle.n.01', 'name': 'sand_myrtle'}, {'id': 19228, 'synset': 'leucothoe.n.01', 'name': 'leucothoe'}, {'id': 19229, 'synset': 'dog_laurel.n.01', 'name': 'dog_laurel'}, {'id': 19230, 'synset': 'sweet_bells.n.01', 'name': 'sweet_bells'}, {'id': 19231, 'synset': 'alpine_azalea.n.01', 'name': 'alpine_azalea'}, {'id': 19232, 'synset': 'staggerbush.n.01', 'name': 'staggerbush'}, {'id': 19233, 'synset': 'maleberry.n.01', 'name': 'maleberry'}, {'id': 19234, 'synset': 'fetterbush.n.02', 'name': 'fetterbush'}, {'id': 19235, 'synset': 'false_azalea.n.01', 'name': 'false_azalea'}, {'id': 19236, 'synset': 'minniebush.n.01', 'name': 'minniebush'}, {'id': 19237, 'synset': 'sorrel_tree.n.01', 'name': 'sorrel_tree'}, {'id': 19238, 'synset': 'mountain_heath.n.01', 'name': 'mountain_heath'}, {'id': 19239, 'synset': 'purple_heather.n.01', 'name': 'purple_heather'}, {'id': 19240, 'synset': 'fetterbush.n.01', 'name': 'fetterbush'}, {'id': 19241, 'synset': 'rhododendron.n.01', 'name': 'rhododendron'}, {'id': 19242, 'synset': 'coast_rhododendron.n.01', 'name': 'coast_rhododendron'}, {'id': 19243, 'synset': 'rosebay.n.01', 'name': 'rosebay'}, {'id': 19244, 'synset': 'swamp_azalea.n.01', 'name': 'swamp_azalea'}, {'id': 19245, 'synset': 'azalea.n.01', 'name': 'azalea'}, {'id': 19246, 'synset': 'cranberry.n.01', 'name': 'cranberry'}, {'id': 19247, 'synset': 'american_cranberry.n.01', 'name': 'American_cranberry'}, {'id': 19248, 'synset': 'european_cranberry.n.01', 'name': 'European_cranberry'}, {'id': 19249, 'synset': 'blueberry.n.01', 'name': 'blueberry'}, {'id': 19250, 'synset': 'farkleberry.n.01', 'name': 'farkleberry'}, {'id': 19251, 'synset': 'low-bush_blueberry.n.01', 'name': 'low-bush_blueberry'}, {'id': 19252, 'synset': 'rabbiteye_blueberry.n.01', 'name': 'rabbiteye_blueberry'}, {'id': 19253, 'synset': 'dwarf_bilberry.n.01', 'name': 'dwarf_bilberry'}, {'id': 19254, 'synset': 'evergreen_blueberry.n.01', 'name': 'evergreen_blueberry'}, {'id': 19255, 'synset': 'evergreen_huckleberry.n.01', 'name': 'evergreen_huckleberry'}, {'id': 19256, 'synset': 'bilberry.n.02', 'name': 'bilberry'}, {'id': 19257, 'synset': 'bilberry.n.01', 'name': 'bilberry'}, {'id': 19258, 'synset': 'bog_bilberry.n.01', 'name': 'bog_bilberry'}, {'id': 19259, 'synset': 'dryland_blueberry.n.01', 'name': 'dryland_blueberry'}, {'id': 19260, 'synset': 'grouseberry.n.01', 'name': 'grouseberry'}, {'id': 19261, 'synset': 'deerberry.n.01', 'name': 'deerberry'}, {'id': 19262, 'synset': 'cowberry.n.01', 'name': 'cowberry'}, {'id': 19263, 'synset': 'diapensia.n.01', 'name': 'diapensia'}, {'id': 19264, 'synset': 'galax.n.01', 'name': 'galax'}, {'id': 19265, 'synset': 'pyxie.n.01', 'name': 'pyxie'}, {'id': 19266, 'synset': 'shortia.n.01', 'name': 'shortia'}, {'id': 19267, 'synset': 'oconee_bells.n.01', 'name': 'oconee_bells'}, {'id': 19268, 'synset': 'australian_heath.n.01', 'name': 'Australian_heath'}, {'id': 19269, 'synset': 'epacris.n.01', 'name': 'epacris'}, {'id': 19270, 'synset': 'common_heath.n.02', 'name': 'common_heath'}, {'id': 19271, 'synset': 'common_heath.n.01', 'name': 'common_heath'}, {'id': 19272, 'synset': 'port_jackson_heath.n.01', 'name': 'Port_Jackson_heath'}, {'id': 19273, 'synset': 'native_cranberry.n.01', 'name': 'native_cranberry'}, {'id': 19274, 'synset': 'pink_fivecorner.n.01', 'name': 'pink_fivecorner'}, {'id': 19275, 'synset': 'wintergreen.n.01', 'name': 'wintergreen'}, {'id': 19276, 'synset': 'false_wintergreen.n.01', 'name': 'false_wintergreen'}, {'id': 19277, 'synset': 'lesser_wintergreen.n.01', 'name': 'lesser_wintergreen'}, {'id': 19278, 'synset': 'wild_lily_of_the_valley.n.02', 'name': 'wild_lily_of_the_valley'}, {'id': 19279, 'synset': 'wild_lily_of_the_valley.n.01', 'name': 'wild_lily_of_the_valley'}, {'id': 19280, 'synset': 'pipsissewa.n.01', 'name': 'pipsissewa'}, {'id': 19281, 'synset': 'love-in-winter.n.01', 'name': 'love-in-winter'}, {'id': 19282, 'synset': 'one-flowered_wintergreen.n.01', 'name': 'one-flowered_wintergreen'}, {'id': 19283, 'synset': 'indian_pipe.n.01', 'name': 'Indian_pipe'}, {'id': 19284, 'synset': 'pinesap.n.01', 'name': 'pinesap'}, {'id': 19285, 'synset': 'beech.n.01', 'name': 'beech'}, {'id': 19286, 'synset': 'common_beech.n.01', 'name': 'common_beech'}, {'id': 19287, 'synset': 'copper_beech.n.01', 'name': 'copper_beech'}, {'id': 19288, 'synset': 'american_beech.n.01', 'name': 'American_beech'}, {'id': 19289, 'synset': 'weeping_beech.n.01', 'name': 'weeping_beech'}, {'id': 19290, 'synset': 'japanese_beech.n.01', 'name': 'Japanese_beech'}, {'id': 19291, 'synset': 'chestnut.n.02', 'name': 'chestnut'}, {'id': 19292, 'synset': 'american_chestnut.n.01', 'name': 'American_chestnut'}, {'id': 19293, 'synset': 'european_chestnut.n.01', 'name': 'European_chestnut'}, {'id': 19294, 'synset': 'chinese_chestnut.n.01', 'name': 'Chinese_chestnut'}, {'id': 19295, 'synset': 'japanese_chestnut.n.01', 'name': 'Japanese_chestnut'}, {'id': 19296, 'synset': 'allegheny_chinkapin.n.01', 'name': 'Allegheny_chinkapin'}, {'id': 19297, 'synset': 'ozark_chinkapin.n.01', 'name': 'Ozark_chinkapin'}, {'id': 19298, 'synset': 'oak_chestnut.n.01', 'name': 'oak_chestnut'}, {'id': 19299, 'synset': 'giant_chinkapin.n.01', 'name': 'giant_chinkapin'}, {'id': 19300, 'synset': 'dwarf_golden_chinkapin.n.01', 'name': 'dwarf_golden_chinkapin'}, {'id': 19301, 'synset': 'tanbark_oak.n.01', 'name': 'tanbark_oak'}, {'id': 19302, 'synset': 'japanese_oak.n.02', 'name': 'Japanese_oak'}, {'id': 19303, 'synset': 'southern_beech.n.01', 'name': 'southern_beech'}, {'id': 19304, 'synset': 'myrtle_beech.n.01', 'name': 'myrtle_beech'}, {'id': 19305, 'synset': 'coigue.n.01', 'name': 'Coigue'}, {'id': 19306, 'synset': 'new_zealand_beech.n.01', 'name': 'New_Zealand_beech'}, {'id': 19307, 'synset': 'silver_beech.n.01', 'name': 'silver_beech'}, {'id': 19308, 'synset': 'roble_beech.n.01', 'name': 'roble_beech'}, {'id': 19309, 'synset': 'rauli_beech.n.01', 'name': 'rauli_beech'}, {'id': 19310, 'synset': 'black_beech.n.01', 'name': 'black_beech'}, {'id': 19311, 'synset': 'hard_beech.n.01', 'name': 'hard_beech'}, {'id': 19312, 'synset': 'acorn.n.01', 'name': 'acorn'}, {'id': 19313, 'synset': 'cupule.n.01', 'name': 'cupule'}, {'id': 19314, 'synset': 'oak.n.02', 'name': 'oak'}, {'id': 19315, 'synset': 'live_oak.n.01', 'name': 'live_oak'}, {'id': 19316, 'synset': 'coast_live_oak.n.01', 'name': 'coast_live_oak'}, {'id': 19317, 'synset': 'white_oak.n.01', 'name': 'white_oak'}, {'id': 19318, 'synset': 'american_white_oak.n.01', 'name': 'American_white_oak'}, {'id': 19319, 'synset': 'arizona_white_oak.n.01', 'name': 'Arizona_white_oak'}, {'id': 19320, 'synset': 'swamp_white_oak.n.01', 'name': 'swamp_white_oak'}, {'id': 19321, 'synset': 'european_turkey_oak.n.01', 'name': 'European_turkey_oak'}, {'id': 19322, 'synset': 'canyon_oak.n.01', 'name': 'canyon_oak'}, {'id': 19323, 'synset': 'scarlet_oak.n.01', 'name': 'scarlet_oak'}, {'id': 19324, 'synset': 'jack_oak.n.02', 'name': 'jack_oak'}, {'id': 19325, 'synset': 'red_oak.n.01', 'name': 'red_oak'}, {'id': 19326, 'synset': 'southern_red_oak.n.01', 'name': 'southern_red_oak'}, {'id': 19327, 'synset': 'oregon_white_oak.n.01', 'name': 'Oregon_white_oak'}, {'id': 19328, 'synset': 'holm_oak.n.02', 'name': 'holm_oak'}, {'id': 19329, 'synset': 'bear_oak.n.01', 'name': 'bear_oak'}, {'id': 19330, 'synset': 'shingle_oak.n.01', 'name': 'shingle_oak'}, {'id': 19331, 'synset': 'bluejack_oak.n.01', 'name': 'bluejack_oak'}, {'id': 19332, 'synset': 'california_black_oak.n.01', 'name': 'California_black_oak'}, {'id': 19333, 'synset': 'american_turkey_oak.n.01', 'name': 'American_turkey_oak'}, {'id': 19334, 'synset': 'laurel_oak.n.01', 'name': 'laurel_oak'}, {'id': 19335, 'synset': 'california_white_oak.n.01', 'name': 'California_white_oak'}, {'id': 19336, 'synset': 'overcup_oak.n.01', 'name': 'overcup_oak'}, {'id': 19337, 'synset': 'bur_oak.n.01', 'name': 'bur_oak'}, {'id': 19338, 'synset': 'scrub_oak.n.01', 'name': 'scrub_oak'}, {'id': 19339, 'synset': 'blackjack_oak.n.01', 'name': 'blackjack_oak'}, {'id': 19340, 'synset': 'swamp_chestnut_oak.n.01', 'name': 'swamp_chestnut_oak'}, {'id': 19341, 'synset': 'japanese_oak.n.01', 'name': 'Japanese_oak'}, {'id': 19342, 'synset': 'chestnut_oak.n.01', 'name': 'chestnut_oak'}, {'id': 19343, 'synset': 'chinquapin_oak.n.01', 'name': 'chinquapin_oak'}, {'id': 19344, 'synset': 'myrtle_oak.n.01', 'name': 'myrtle_oak'}, {'id': 19345, 'synset': 'water_oak.n.01', 'name': 'water_oak'}, {'id': 19346, 'synset': 'nuttall_oak.n.01', 'name': 'Nuttall_oak'}, {'id': 19347, 'synset': 'durmast.n.01', 'name': 'durmast'}, {'id': 19348, 'synset': 'basket_oak.n.01', 'name': 'basket_oak'}, {'id': 19349, 'synset': 'pin_oak.n.01', 'name': 'pin_oak'}, {'id': 19350, 'synset': 'willow_oak.n.01', 'name': 'willow_oak'}, {'id': 19351, 'synset': 'dwarf_chinkapin_oak.n.01', 'name': 'dwarf_chinkapin_oak'}, {'id': 19352, 'synset': 'common_oak.n.01', 'name': 'common_oak'}, {'id': 19353, 'synset': 'northern_red_oak.n.01', 'name': 'northern_red_oak'}, {'id': 19354, 'synset': 'shumard_oak.n.01', 'name': 'Shumard_oak'}, {'id': 19355, 'synset': 'post_oak.n.01', 'name': 'post_oak'}, {'id': 19356, 'synset': 'cork_oak.n.01', 'name': 'cork_oak'}, {'id': 19357, 'synset': 'spanish_oak.n.01', 'name': 'Spanish_oak'}, {'id': 19358, 'synset': 'huckleberry_oak.n.01', 'name': 'huckleberry_oak'}, {'id': 19359, 'synset': 'chinese_cork_oak.n.01', 'name': 'Chinese_cork_oak'}, {'id': 19360, 'synset': 'black_oak.n.01', 'name': 'black_oak'}, {'id': 19361, 'synset': 'southern_live_oak.n.01', 'name': 'southern_live_oak'}, {'id': 19362, 'synset': 'interior_live_oak.n.01', 'name': 'interior_live_oak'}, {'id': 19363, 'synset': 'mast.n.02', 'name': 'mast'}, {'id': 19364, 'synset': 'birch.n.02', 'name': 'birch'}, {'id': 19365, 'synset': 'yellow_birch.n.01', 'name': 'yellow_birch'}, {'id': 19366, 'synset': 'american_white_birch.n.01', 'name': 'American_white_birch'}, {'id': 19367, 'synset': 'grey_birch.n.01', 'name': 'grey_birch'}, {'id': 19368, 'synset': 'silver_birch.n.01', 'name': 'silver_birch'}, {'id': 19369, 'synset': 'downy_birch.n.01', 'name': 'downy_birch'}, {'id': 19370, 'synset': 'black_birch.n.02', 'name': 'black_birch'}, {'id': 19371, 'synset': 'sweet_birch.n.01', 'name': 'sweet_birch'}, {'id': 19372, 'synset': 'yukon_white_birch.n.01', 'name': 'Yukon_white_birch'}, {'id': 19373, 'synset': 'swamp_birch.n.01', 'name': 'swamp_birch'}, {'id': 19374, 'synset': 'newfoundland_dwarf_birch.n.01', 'name': 'Newfoundland_dwarf_birch'}, {'id': 19375, 'synset': 'alder.n.02', 'name': 'alder'}, {'id': 19376, 'synset': 'common_alder.n.01', 'name': 'common_alder'}, {'id': 19377, 'synset': 'grey_alder.n.01', 'name': 'grey_alder'}, {'id': 19378, 'synset': 'seaside_alder.n.01', 'name': 'seaside_alder'}, {'id': 19379, 'synset': 'white_alder.n.01', 'name': 'white_alder'}, {'id': 19380, 'synset': 'red_alder.n.01', 'name': 'red_alder'}, {'id': 19381, 'synset': 'speckled_alder.n.01', 'name': 'speckled_alder'}, {'id': 19382, 'synset': 'smooth_alder.n.01', 'name': 'smooth_alder'}, {'id': 19383, 'synset': 'green_alder.n.02', 'name': 'green_alder'}, {'id': 19384, 'synset': 'green_alder.n.01', 'name': 'green_alder'}, {'id': 19385, 'synset': 'hornbeam.n.01', 'name': 'hornbeam'}, {'id': 19386, 'synset': 'european_hornbeam.n.01', 'name': 'European_hornbeam'}, {'id': 19387, 'synset': 'american_hornbeam.n.01', 'name': 'American_hornbeam'}, {'id': 19388, 'synset': 'hop_hornbeam.n.01', 'name': 'hop_hornbeam'}, {'id': 19389, 'synset': 'old_world_hop_hornbeam.n.01', 'name': 'Old_World_hop_hornbeam'}, {'id': 19390, 'synset': 'eastern_hop_hornbeam.n.01', 'name': 'Eastern_hop_hornbeam'}, {'id': 19391, 'synset': 'hazelnut.n.01', 'name': 'hazelnut'}, {'id': 19392, 'synset': 'american_hazel.n.01', 'name': 'American_hazel'}, {'id': 19393, 'synset': 'cobnut.n.01', 'name': 'cobnut'}, {'id': 19394, 'synset': 'beaked_hazelnut.n.01', 'name': 'beaked_hazelnut'}, {'id': 19395, 'synset': 'centaury.n.01', 'name': 'centaury'}, {'id': 19396, 'synset': 'rosita.n.01', 'name': 'rosita'}, {'id': 19397, 'synset': 'lesser_centaury.n.01', 'name': 'lesser_centaury'}, {'id': 19398, 'synset': 'seaside_centaury.n.01', 'name': 'seaside_centaury'}, {'id': 19399, 'synset': 'slender_centaury.n.01', 'name': 'slender_centaury'}, {'id': 19400, 'synset': 'prairie_gentian.n.01', 'name': 'prairie_gentian'}, {'id': 19401, 'synset': 'persian_violet.n.01', 'name': 'Persian_violet'}, {'id': 19402, 'synset': 'columbo.n.01', 'name': 'columbo'}, {'id': 19403, 'synset': 'gentian.n.01', 'name': 'gentian'}, {'id': 19404, 'synset': 'gentianella.n.02', 'name': 'gentianella'}, {'id': 19405, 'synset': 'closed_gentian.n.02', 'name': 'closed_gentian'}, {'id': 19406, 'synset': "explorer's_gentian.n.01", 'name': "explorer's_gentian"}, {'id': 19407, 'synset': 'closed_gentian.n.01', 'name': 'closed_gentian'}, {'id': 19408, 'synset': 'great_yellow_gentian.n.01', 'name': 'great_yellow_gentian'}, {'id': 19409, 'synset': 'marsh_gentian.n.01', 'name': 'marsh_gentian'}, {'id': 19410, 'synset': 'soapwort_gentian.n.01', 'name': 'soapwort_gentian'}, {'id': 19411, 'synset': 'striped_gentian.n.01', 'name': 'striped_gentian'}, {'id': 19412, 'synset': 'agueweed.n.01', 'name': 'agueweed'}, {'id': 19413, 'synset': 'felwort.n.01', 'name': 'felwort'}, {'id': 19414, 'synset': 'fringed_gentian.n.01', 'name': 'fringed_gentian'}, {'id': 19415, 'synset': 'gentianopsis_crinita.n.01', 'name': 'Gentianopsis_crinita'}, {'id': 19416, 'synset': 'gentianopsis_detonsa.n.01', 'name': 'Gentianopsis_detonsa'}, {'id': 19417, 'synset': 'gentianopsid_procera.n.01', 'name': 'Gentianopsid_procera'}, {'id': 19418, 'synset': 'gentianopsis_thermalis.n.01', 'name': 'Gentianopsis_thermalis'}, {'id': 19419, 'synset': 'tufted_gentian.n.01', 'name': 'tufted_gentian'}, {'id': 19420, 'synset': 'spurred_gentian.n.01', 'name': 'spurred_gentian'}, {'id': 19421, 'synset': 'sabbatia.n.01', 'name': 'sabbatia'}, {'id': 19422, 'synset': 'toothbrush_tree.n.01', 'name': 'toothbrush_tree'}, {'id': 19423, 'synset': 'olive_tree.n.01', 'name': 'olive_tree'}, {'id': 19424, 'synset': 'olive.n.02', 'name': 'olive'}, {'id': 19425, 'synset': 'olive.n.01', 'name': 'olive'}, {'id': 19426, 'synset': 'black_maire.n.01', 'name': 'black_maire'}, {'id': 19427, 'synset': 'white_maire.n.01', 'name': 'white_maire'}, {'id': 19428, 'synset': 'fringe_tree.n.01', 'name': 'fringe_tree'}, {'id': 19429, 'synset': 'fringe_bush.n.01', 'name': 'fringe_bush'}, {'id': 19430, 'synset': 'forestiera.n.01', 'name': 'forestiera'}, {'id': 19431, 'synset': 'forsythia.n.01', 'name': 'forsythia'}, {'id': 19432, 'synset': 'ash.n.02', 'name': 'ash'}, {'id': 19433, 'synset': 'white_ash.n.02', 'name': 'white_ash'}, {'id': 19434, 'synset': 'swamp_ash.n.01', 'name': 'swamp_ash'}, {'id': 19435, 'synset': 'flowering_ash.n.03', 'name': 'flowering_ash'}, {'id': 19436, 'synset': 'european_ash.n.01', 'name': 'European_ash'}, {'id': 19437, 'synset': 'oregon_ash.n.01', 'name': 'Oregon_ash'}, {'id': 19438, 'synset': 'black_ash.n.01', 'name': 'black_ash'}, {'id': 19439, 'synset': 'manna_ash.n.01', 'name': 'manna_ash'}, {'id': 19440, 'synset': 'red_ash.n.01', 'name': 'red_ash'}, {'id': 19441, 'synset': 'green_ash.n.01', 'name': 'green_ash'}, {'id': 19442, 'synset': 'blue_ash.n.01', 'name': 'blue_ash'}, {'id': 19443, 'synset': 'mountain_ash.n.03', 'name': 'mountain_ash'}, {'id': 19444, 'synset': 'pumpkin_ash.n.01', 'name': 'pumpkin_ash'}, {'id': 19445, 'synset': 'arizona_ash.n.01', 'name': 'Arizona_ash'}, {'id': 19446, 'synset': 'jasmine.n.01', 'name': 'jasmine'}, {'id': 19447, 'synset': 'primrose_jasmine.n.01', 'name': 'primrose_jasmine'}, {'id': 19448, 'synset': 'winter_jasmine.n.01', 'name': 'winter_jasmine'}, {'id': 19449, 'synset': 'common_jasmine.n.01', 'name': 'common_jasmine'}, {'id': 19450, 'synset': 'privet.n.01', 'name': 'privet'}, {'id': 19451, 'synset': 'amur_privet.n.01', 'name': 'Amur_privet'}, {'id': 19452, 'synset': 'japanese_privet.n.01', 'name': 'Japanese_privet'}, {'id': 19453, 'synset': 'ligustrum_obtusifolium.n.01', 'name': 'Ligustrum_obtusifolium'}, {'id': 19454, 'synset': 'common_privet.n.01', 'name': 'common_privet'}, {'id': 19455, 'synset': 'devilwood.n.01', 'name': 'devilwood'}, {'id': 19456, 'synset': 'mock_privet.n.01', 'name': 'mock_privet'}, {'id': 19457, 'synset': 'lilac.n.01', 'name': 'lilac'}, {'id': 19458, 'synset': 'himalayan_lilac.n.01', 'name': 'Himalayan_lilac'}, {'id': 19459, 'synset': 'persian_lilac.n.02', 'name': 'Persian_lilac'}, {'id': 19460, 'synset': 'japanese_tree_lilac.n.01', 'name': 'Japanese_tree_lilac'}, {'id': 19461, 'synset': 'japanese_lilac.n.01', 'name': 'Japanese_lilac'}, {'id': 19462, 'synset': 'common_lilac.n.01', 'name': 'common_lilac'}, {'id': 19463, 'synset': 'bloodwort.n.01', 'name': 'bloodwort'}, {'id': 19464, 'synset': 'kangaroo_paw.n.01', 'name': 'kangaroo_paw'}, {'id': 19465, 'synset': 'virginian_witch_hazel.n.01', 'name': 'Virginian_witch_hazel'}, {'id': 19466, 'synset': 'vernal_witch_hazel.n.01', 'name': 'vernal_witch_hazel'}, {'id': 19467, 'synset': 'winter_hazel.n.01', 'name': 'winter_hazel'}, {'id': 19468, 'synset': 'fothergilla.n.01', 'name': 'fothergilla'}, {'id': 19469, 'synset': 'liquidambar.n.02', 'name': 'liquidambar'}, {'id': 19470, 'synset': 'sweet_gum.n.03', 'name': 'sweet_gum'}, {'id': 19471, 'synset': 'iron_tree.n.01', 'name': 'iron_tree'}, {'id': 19472, 'synset': 'walnut.n.03', 'name': 'walnut'}, {'id': 19473, 'synset': 'california_black_walnut.n.01', 'name': 'California_black_walnut'}, {'id': 19474, 'synset': 'butternut.n.01', 'name': 'butternut'}, {'id': 19475, 'synset': 'black_walnut.n.01', 'name': 'black_walnut'}, {'id': 19476, 'synset': 'english_walnut.n.01', 'name': 'English_walnut'}, {'id': 19477, 'synset': 'hickory.n.02', 'name': 'hickory'}, {'id': 19478, 'synset': 'water_hickory.n.01', 'name': 'water_hickory'}, {'id': 19479, 'synset': 'pignut.n.01', 'name': 'pignut'}, {'id': 19480, 'synset': 'bitternut.n.01', 'name': 'bitternut'}, {'id': 19481, 'synset': 'pecan.n.02', 'name': 'pecan'}, {'id': 19482, 'synset': 'big_shellbark.n.01', 'name': 'big_shellbark'}, {'id': 19483, 'synset': 'nutmeg_hickory.n.01', 'name': 'nutmeg_hickory'}, {'id': 19484, 'synset': 'shagbark.n.01', 'name': 'shagbark'}, {'id': 19485, 'synset': 'mockernut.n.01', 'name': 'mockernut'}, {'id': 19486, 'synset': 'wing_nut.n.01', 'name': 'wing_nut'}, {'id': 19487, 'synset': 'caucasian_walnut.n.01', 'name': 'Caucasian_walnut'}, {'id': 19488, 'synset': 'dhawa.n.01', 'name': 'dhawa'}, {'id': 19489, 'synset': 'combretum.n.01', 'name': 'combretum'}, {'id': 19490, 'synset': 'hiccup_nut.n.01', 'name': 'hiccup_nut'}, {'id': 19491, 'synset': 'bush_willow.n.02', 'name': 'bush_willow'}, {'id': 19492, 'synset': 'bush_willow.n.01', 'name': 'bush_willow'}, {'id': 19493, 'synset': 'button_tree.n.01', 'name': 'button_tree'}, {'id': 19494, 'synset': 'white_mangrove.n.02', 'name': 'white_mangrove'}, {'id': 19495, 'synset': 'oleaster.n.01', 'name': 'oleaster'}, {'id': 19496, 'synset': 'water_milfoil.n.01', 'name': 'water_milfoil'}, {'id': 19497, 'synset': 'anchovy_pear.n.01', 'name': 'anchovy_pear'}, {'id': 19498, 'synset': 'brazil_nut.n.01', 'name': 'brazil_nut'}, {'id': 19499, 'synset': 'loosestrife.n.01', 'name': 'loosestrife'}, {'id': 19500, 'synset': 'purple_loosestrife.n.01', 'name': 'purple_loosestrife'}, {'id': 19501, 'synset': 'grass_poly.n.01', 'name': 'grass_poly'}, {'id': 19502, 'synset': 'crape_myrtle.n.01', 'name': 'crape_myrtle'}, {'id': 19503, 'synset': "queen's_crape_myrtle.n.01", 'name': "Queen's_crape_myrtle"}, {'id': 19504, 'synset': 'myrtaceous_tree.n.01', 'name': 'myrtaceous_tree'}, {'id': 19505, 'synset': 'myrtle.n.02', 'name': 'myrtle'}, {'id': 19506, 'synset': 'common_myrtle.n.01', 'name': 'common_myrtle'}, {'id': 19507, 'synset': 'bayberry.n.01', 'name': 'bayberry'}, {'id': 19508, 'synset': 'allspice.n.01', 'name': 'allspice'}, {'id': 19509, 'synset': 'allspice_tree.n.01', 'name': 'allspice_tree'}, {'id': 19510, 'synset': 'sour_cherry.n.02', 'name': 'sour_cherry'}, {'id': 19511, 'synset': 'nakedwood.n.02', 'name': 'nakedwood'}, {'id': 19512, 'synset': 'surinam_cherry.n.02', 'name': 'Surinam_cherry'}, {'id': 19513, 'synset': 'rose_apple.n.01', 'name': 'rose_apple'}, {'id': 19514, 'synset': 'feijoa.n.01', 'name': 'feijoa'}, {'id': 19515, 'synset': 'jaboticaba.n.01', 'name': 'jaboticaba'}, {'id': 19516, 'synset': 'guava.n.02', 'name': 'guava'}, {'id': 19517, 'synset': 'guava.n.01', 'name': 'guava'}, {'id': 19518, 'synset': 'cattley_guava.n.01', 'name': 'cattley_guava'}, {'id': 19519, 'synset': 'brazilian_guava.n.01', 'name': 'Brazilian_guava'}, {'id': 19520, 'synset': 'gum_tree.n.01', 'name': 'gum_tree'}, {'id': 19521, 'synset': 'eucalyptus.n.02', 'name': 'eucalyptus'}, {'id': 19522, 'synset': 'flooded_gum.n.01', 'name': 'flooded_gum'}, {'id': 19523, 'synset': 'mallee.n.01', 'name': 'mallee'}, {'id': 19524, 'synset': 'stringybark.n.01', 'name': 'stringybark'}, {'id': 19525, 'synset': 'smoothbark.n.01', 'name': 'smoothbark'}, {'id': 19526, 'synset': 'red_gum.n.03', 'name': 'red_gum'}, {'id': 19527, 'synset': 'red_gum.n.02', 'name': 'red_gum'}, {'id': 19528, 'synset': 'river_red_gum.n.01', 'name': 'river_red_gum'}, {'id': 19529, 'synset': 'mountain_swamp_gum.n.01', 'name': 'mountain_swamp_gum'}, {'id': 19530, 'synset': 'snow_gum.n.01', 'name': 'snow_gum'}, {'id': 19531, 'synset': 'alpine_ash.n.01', 'name': 'alpine_ash'}, {'id': 19532, 'synset': 'white_mallee.n.01', 'name': 'white_mallee'}, {'id': 19533, 'synset': 'white_stringybark.n.01', 'name': 'white_stringybark'}, {'id': 19534, 'synset': 'white_mountain_ash.n.01', 'name': 'white_mountain_ash'}, {'id': 19535, 'synset': 'blue_gum.n.01', 'name': 'blue_gum'}, {'id': 19536, 'synset': 'rose_gum.n.01', 'name': 'rose_gum'}, {'id': 19537, 'synset': 'cider_gum.n.01', 'name': 'cider_gum'}, {'id': 19538, 'synset': 'swamp_gum.n.01', 'name': 'swamp_gum'}, {'id': 19539, 'synset': 'spotted_gum.n.01', 'name': 'spotted_gum'}, {'id': 19540, 'synset': 'lemon-scented_gum.n.01', 'name': 'lemon-scented_gum'}, {'id': 19541, 'synset': 'black_mallee.n.01', 'name': 'black_mallee'}, {'id': 19542, 'synset': 'forest_red_gum.n.01', 'name': 'forest_red_gum'}, {'id': 19543, 'synset': 'mountain_ash.n.02', 'name': 'mountain_ash'}, {'id': 19544, 'synset': 'manna_gum.n.01', 'name': 'manna_gum'}, {'id': 19545, 'synset': 'clove.n.02', 'name': 'clove'}, {'id': 19546, 'synset': 'clove.n.01', 'name': 'clove'}, {'id': 19547, 'synset': 'tupelo.n.02', 'name': 'tupelo'}, {'id': 19548, 'synset': 'water_gum.n.01', 'name': 'water_gum'}, {'id': 19549, 'synset': 'sour_gum.n.01', 'name': 'sour_gum'}, {'id': 19550, 'synset': "enchanter's_nightshade.n.01", 'name': "enchanter's_nightshade"}, {'id': 19551, 'synset': 'circaea_lutetiana.n.01', 'name': 'Circaea_lutetiana'}, {'id': 19552, 'synset': 'willowherb.n.01', 'name': 'willowherb'}, {'id': 19553, 'synset': 'fireweed.n.01', 'name': 'fireweed'}, {'id': 19554, 'synset': 'california_fuchsia.n.01', 'name': 'California_fuchsia'}, {'id': 19555, 'synset': 'fuchsia.n.01', 'name': 'fuchsia'}, {'id': 19556, 'synset': "lady's-eardrop.n.01", 'name': "lady's-eardrop"}, {'id': 19557, 'synset': 'evening_primrose.n.01', 'name': 'evening_primrose'}, {'id': 19558, 'synset': 'common_evening_primrose.n.01', 'name': 'common_evening_primrose'}, {'id': 19559, 'synset': 'sundrops.n.01', 'name': 'sundrops'}, {'id': 19560, 'synset': 'missouri_primrose.n.01', 'name': 'Missouri_primrose'}, {'id': 19561, 'synset': 'pomegranate.n.01', 'name': 'pomegranate'}, {'id': 19562, 'synset': 'mangrove.n.01', 'name': 'mangrove'}, {'id': 19563, 'synset': 'daphne.n.01', 'name': 'daphne'}, {'id': 19564, 'synset': 'garland_flower.n.01', 'name': 'garland_flower'}, {'id': 19565, 'synset': 'spurge_laurel.n.01', 'name': 'spurge_laurel'}, {'id': 19566, 'synset': 'mezereon.n.01', 'name': 'mezereon'}, {'id': 19567, 'synset': 'indian_rhododendron.n.01', 'name': 'Indian_rhododendron'}, {'id': 19568, 'synset': 'medinilla_magnifica.n.01', 'name': 'Medinilla_magnifica'}, {'id': 19569, 'synset': 'deer_grass.n.01', 'name': 'deer_grass'}, {'id': 19570, 'synset': 'canna.n.01', 'name': 'canna'}, {'id': 19571, 'synset': 'achira.n.01', 'name': 'achira'}, {'id': 19572, 'synset': 'arrowroot.n.02', 'name': 'arrowroot'}, {'id': 19573, 'synset': 'banana.n.01', 'name': 'banana'}, {'id': 19574, 'synset': 'dwarf_banana.n.01', 'name': 'dwarf_banana'}, {'id': 19575, 'synset': 'japanese_banana.n.01', 'name': 'Japanese_banana'}, {'id': 19576, 'synset': 'plantain.n.02', 'name': 'plantain'}, {'id': 19577, 'synset': 'edible_banana.n.01', 'name': 'edible_banana'}, {'id': 19578, 'synset': 'abaca.n.02', 'name': 'abaca'}, {'id': 19579, 'synset': 'abyssinian_banana.n.01', 'name': 'Abyssinian_banana'}, {'id': 19580, 'synset': 'ginger.n.01', 'name': 'ginger'}, {'id': 19581, 'synset': 'common_ginger.n.01', 'name': 'common_ginger'}, {'id': 19582, 'synset': 'turmeric.n.01', 'name': 'turmeric'}, {'id': 19583, 'synset': 'galangal.n.01', 'name': 'galangal'}, {'id': 19584, 'synset': 'shellflower.n.02', 'name': 'shellflower'}, {'id': 19585, 'synset': 'grains_of_paradise.n.01', 'name': 'grains_of_paradise'}, {'id': 19586, 'synset': 'cardamom.n.01', 'name': 'cardamom'}, {'id': 19587, 'synset': 'begonia.n.01', 'name': 'begonia'}, {'id': 19588, 'synset': 'fibrous-rooted_begonia.n.01', 'name': 'fibrous-rooted_begonia'}, {'id': 19589, 'synset': 'tuberous_begonia.n.01', 'name': 'tuberous_begonia'}, {'id': 19590, 'synset': 'rhizomatous_begonia.n.01', 'name': 'rhizomatous_begonia'}, {'id': 19591, 'synset': 'christmas_begonia.n.01', 'name': 'Christmas_begonia'}, {'id': 19592, 'synset': 'angel-wing_begonia.n.01', 'name': 'angel-wing_begonia'}, {'id': 19593, 'synset': 'beefsteak_begonia.n.01', 'name': 'beefsteak_begonia'}, {'id': 19594, 'synset': 'star_begonia.n.01', 'name': 'star_begonia'}, {'id': 19595, 'synset': 'rex_begonia.n.01', 'name': 'rex_begonia'}, {'id': 19596, 'synset': 'wax_begonia.n.01', 'name': 'wax_begonia'}, {'id': 19597, 'synset': 'socotra_begonia.n.01', 'name': 'Socotra_begonia'}, {'id': 19598, 'synset': 'hybrid_tuberous_begonia.n.01', 'name': 'hybrid_tuberous_begonia'}, {'id': 19599, 'synset': 'dillenia.n.01', 'name': 'dillenia'}, {'id': 19600, 'synset': 'guinea_gold_vine.n.01', 'name': 'guinea_gold_vine'}, {'id': 19601, 'synset': 'poon.n.02', 'name': 'poon'}, {'id': 19602, 'synset': 'calaba.n.01', 'name': 'calaba'}, {'id': 19603, 'synset': 'maria.n.02', 'name': 'Maria'}, {'id': 19604, 'synset': 'laurelwood.n.01', 'name': 'laurelwood'}, {'id': 19605, 'synset': 'alexandrian_laurel.n.01', 'name': 'Alexandrian_laurel'}, {'id': 19606, 'synset': 'clusia.n.01', 'name': 'clusia'}, {'id': 19607, 'synset': 'wild_fig.n.02', 'name': 'wild_fig'}, {'id': 19608, 'synset': 'waxflower.n.02', 'name': 'waxflower'}, {'id': 19609, 'synset': 'pitch_apple.n.01', 'name': 'pitch_apple'}, {'id': 19610, 'synset': 'mangosteen.n.01', 'name': 'mangosteen'}, {'id': 19611, 'synset': 'gamboge_tree.n.01', 'name': 'gamboge_tree'}, {'id': 19612, 'synset': "st_john's_wort.n.01", 'name': "St_John's_wort"}, {'id': 19613, 'synset': "common_st_john's_wort.n.01", 'name': "common_St_John's_wort"}, {'id': 19614, 'synset': "great_st_john's_wort.n.01", 'name': "great_St_John's_wort"}, {'id': 19615, 'synset': "creeping_st_john's_wort.n.01", 'name': "creeping_St_John's_wort"}, {'id': 19616, 'synset': "low_st_andrew's_cross.n.01", 'name': "low_St_Andrew's_cross"}, {'id': 19617, 'synset': 'klammath_weed.n.01', 'name': 'klammath_weed'}, {'id': 19618, 'synset': "shrubby_st_john's_wort.n.01", 'name': "shrubby_St_John's_wort"}, {'id': 19619, 'synset': "st_peter's_wort.n.01", 'name': "St_Peter's_wort"}, {'id': 19620, 'synset': "marsh_st-john's_wort.n.01", 'name': "marsh_St-John's_wort"}, {'id': 19621, 'synset': 'mammee_apple.n.01', 'name': 'mammee_apple'}, {'id': 19622, 'synset': 'rose_chestnut.n.01', 'name': 'rose_chestnut'}, {'id': 19623, 'synset': 'bower_actinidia.n.01', 'name': 'bower_actinidia'}, {'id': 19624, 'synset': 'chinese_gooseberry.n.01', 'name': 'Chinese_gooseberry'}, {'id': 19625, 'synset': 'silvervine.n.01', 'name': 'silvervine'}, {'id': 19626, 'synset': 'wild_cinnamon.n.01', 'name': 'wild_cinnamon'}, {'id': 19627, 'synset': 'papaya.n.01', 'name': 'papaya'}, {'id': 19628, 'synset': 'souari.n.01', 'name': 'souari'}, {'id': 19629, 'synset': 'rockrose.n.02', 'name': 'rockrose'}, {'id': 19630, 'synset': 'white-leaved_rockrose.n.01', 'name': 'white-leaved_rockrose'}, {'id': 19631, 'synset': 'common_gum_cistus.n.01', 'name': 'common_gum_cistus'}, {'id': 19632, 'synset': 'frostweed.n.01', 'name': 'frostweed'}, {'id': 19633, 'synset': 'dipterocarp.n.01', 'name': 'dipterocarp'}, {'id': 19634, 'synset': 'red_lauan.n.02', 'name': 'red_lauan'}, {'id': 19635, 'synset': "governor's_plum.n.01", 'name': "governor's_plum"}, {'id': 19636, 'synset': 'kei_apple.n.01', 'name': 'kei_apple'}, {'id': 19637, 'synset': 'ketembilla.n.01', 'name': 'ketembilla'}, {'id': 19638, 'synset': 'chaulmoogra.n.01', 'name': 'chaulmoogra'}, {'id': 19639, 'synset': 'wild_peach.n.01', 'name': 'wild_peach'}, {'id': 19640, 'synset': 'candlewood.n.01', 'name': 'candlewood'}, {'id': 19641, 'synset': 'boojum_tree.n.01', 'name': 'boojum_tree'}, {'id': 19642, 'synset': "bird's-eye_bush.n.01", 'name': "bird's-eye_bush"}, {'id': 19643, 'synset': 'granadilla.n.03', 'name': 'granadilla'}, {'id': 19644, 'synset': 'granadilla.n.02', 'name': 'granadilla'}, {'id': 19645, 'synset': 'granadilla.n.01', 'name': 'granadilla'}, {'id': 19646, 'synset': 'maypop.n.01', 'name': 'maypop'}, {'id': 19647, 'synset': 'jamaica_honeysuckle.n.01', 'name': 'Jamaica_honeysuckle'}, {'id': 19648, 'synset': 'banana_passion_fruit.n.01', 'name': 'banana_passion_fruit'}, {'id': 19649, 'synset': 'sweet_calabash.n.01', 'name': 'sweet_calabash'}, {'id': 19650, 'synset': 'love-in-a-mist.n.01', 'name': 'love-in-a-mist'}, {'id': 19651, 'synset': 'reseda.n.01', 'name': 'reseda'}, {'id': 19652, 'synset': 'mignonette.n.01', 'name': 'mignonette'}, {'id': 19653, 'synset': "dyer's_rocket.n.01", 'name': "dyer's_rocket"}, {'id': 19654, 'synset': 'false_tamarisk.n.01', 'name': 'false_tamarisk'}, {'id': 19655, 'synset': 'halophyte.n.01', 'name': 'halophyte'}, {'id': 19656, 'synset': 'viola.n.01', 'name': 'viola'}, {'id': 19657, 'synset': 'violet.n.01', 'name': 'violet'}, {'id': 19658, 'synset': 'field_pansy.n.01', 'name': 'field_pansy'}, {'id': 19659, 'synset': 'american_dog_violet.n.01', 'name': 'American_dog_violet'}, {'id': 19660, 'synset': 'dog_violet.n.01', 'name': 'dog_violet'}, {'id': 19661, 'synset': 'horned_violet.n.01', 'name': 'horned_violet'}, {'id': 19662, 'synset': 'two-eyed_violet.n.01', 'name': 'two-eyed_violet'}, {'id': 19663, 'synset': "bird's-foot_violet.n.01", 'name': "bird's-foot_violet"}, {'id': 19664, 'synset': 'downy_yellow_violet.n.01', 'name': 'downy_yellow_violet'}, {'id': 19665, 'synset': 'long-spurred_violet.n.01', 'name': 'long-spurred_violet'}, {'id': 19666, 'synset': 'pale_violet.n.01', 'name': 'pale_violet'}, {'id': 19667, 'synset': 'hedge_violet.n.01', 'name': 'hedge_violet'}, {'id': 19668, 'synset': 'nettle.n.01', 'name': 'nettle'}, {'id': 19669, 'synset': 'stinging_nettle.n.01', 'name': 'stinging_nettle'}, {'id': 19670, 'synset': 'roman_nettle.n.01', 'name': 'Roman_nettle'}, {'id': 19671, 'synset': 'ramie.n.01', 'name': 'ramie'}, {'id': 19672, 'synset': 'wood_nettle.n.01', 'name': 'wood_nettle'}, {'id': 19673, 'synset': 'australian_nettle.n.01', 'name': 'Australian_nettle'}, {'id': 19674, 'synset': 'pellitory-of-the-wall.n.01', 'name': 'pellitory-of-the-wall'}, {'id': 19675, 'synset': 'richweed.n.02', 'name': 'richweed'}, {'id': 19676, 'synset': 'artillery_plant.n.01', 'name': 'artillery_plant'}, {'id': 19677, 'synset': 'friendship_plant.n.01', 'name': 'friendship_plant'}, {'id': 19678, 'synset': 'queensland_grass-cloth_plant.n.01', 'name': 'Queensland_grass-cloth_plant'}, {'id': 19679, 'synset': 'pipturus_albidus.n.01', 'name': 'Pipturus_albidus'}, {'id': 19680, 'synset': 'cannabis.n.01', 'name': 'cannabis'}, {'id': 19681, 'synset': 'indian_hemp.n.01', 'name': 'Indian_hemp'}, {'id': 19682, 'synset': 'mulberry.n.01', 'name': 'mulberry'}, {'id': 19683, 'synset': 'white_mulberry.n.01', 'name': 'white_mulberry'}, {'id': 19684, 'synset': 'black_mulberry.n.01', 'name': 'black_mulberry'}, {'id': 19685, 'synset': 'red_mulberry.n.01', 'name': 'red_mulberry'}, {'id': 19686, 'synset': 'osage_orange.n.01', 'name': 'osage_orange'}, {'id': 19687, 'synset': 'breadfruit.n.01', 'name': 'breadfruit'}, {'id': 19688, 'synset': 'jackfruit.n.01', 'name': 'jackfruit'}, {'id': 19689, 'synset': 'marang.n.01', 'name': 'marang'}, {'id': 19690, 'synset': 'fig_tree.n.01', 'name': 'fig_tree'}, {'id': 19691, 'synset': 'fig.n.02', 'name': 'fig'}, {'id': 19692, 'synset': 'caprifig.n.01', 'name': 'caprifig'}, {'id': 19693, 'synset': 'golden_fig.n.01', 'name': 'golden_fig'}, {'id': 19694, 'synset': 'banyan.n.01', 'name': 'banyan'}, {'id': 19695, 'synset': 'pipal.n.01', 'name': 'pipal'}, {'id': 19696, 'synset': 'india-rubber_tree.n.01', 'name': 'India-rubber_tree'}, {'id': 19697, 'synset': 'mistletoe_fig.n.01', 'name': 'mistletoe_fig'}, {'id': 19698, 'synset': 'port_jackson_fig.n.01', 'name': 'Port_Jackson_fig'}, {'id': 19699, 'synset': 'sycamore.n.04', 'name': 'sycamore'}, {'id': 19700, 'synset': 'paper_mulberry.n.01', 'name': 'paper_mulberry'}, {'id': 19701, 'synset': 'trumpetwood.n.01', 'name': 'trumpetwood'}, {'id': 19702, 'synset': 'elm.n.01', 'name': 'elm'}, {'id': 19703, 'synset': 'winged_elm.n.01', 'name': 'winged_elm'}, {'id': 19704, 'synset': 'american_elm.n.01', 'name': 'American_elm'}, {'id': 19705, 'synset': 'smooth-leaved_elm.n.01', 'name': 'smooth-leaved_elm'}, {'id': 19706, 'synset': 'cedar_elm.n.01', 'name': 'cedar_elm'}, {'id': 19707, 'synset': 'witch_elm.n.01', 'name': 'witch_elm'}, {'id': 19708, 'synset': 'dutch_elm.n.01', 'name': 'Dutch_elm'}, {'id': 19709, 'synset': 'huntingdon_elm.n.01', 'name': 'Huntingdon_elm'}, {'id': 19710, 'synset': 'water_elm.n.01', 'name': 'water_elm'}, {'id': 19711, 'synset': 'chinese_elm.n.02', 'name': 'Chinese_elm'}, {'id': 19712, 'synset': 'english_elm.n.01', 'name': 'English_elm'}, {'id': 19713, 'synset': 'siberian_elm.n.01', 'name': 'Siberian_elm'}, {'id': 19714, 'synset': 'slippery_elm.n.01', 'name': 'slippery_elm'}, {'id': 19715, 'synset': 'jersey_elm.n.01', 'name': 'Jersey_elm'}, {'id': 19716, 'synset': 'september_elm.n.01', 'name': 'September_elm'}, {'id': 19717, 'synset': 'rock_elm.n.01', 'name': 'rock_elm'}, {'id': 19718, 'synset': 'hackberry.n.01', 'name': 'hackberry'}, {'id': 19719, 'synset': 'european_hackberry.n.01', 'name': 'European_hackberry'}, {'id': 19720, 'synset': 'american_hackberry.n.01', 'name': 'American_hackberry'}, {'id': 19721, 'synset': 'sugarberry.n.01', 'name': 'sugarberry'}, {'id': 19722, 'synset': 'iridaceous_plant.n.01', 'name': 'iridaceous_plant'}, {'id': 19723, 'synset': 'bearded_iris.n.01', 'name': 'bearded_iris'}, {'id': 19724, 'synset': 'beardless_iris.n.01', 'name': 'beardless_iris'}, {'id': 19725, 'synset': 'orrisroot.n.01', 'name': 'orrisroot'}, {'id': 19726, 'synset': 'dwarf_iris.n.02', 'name': 'dwarf_iris'}, {'id': 19727, 'synset': 'dutch_iris.n.02', 'name': 'Dutch_iris'}, {'id': 19728, 'synset': 'florentine_iris.n.01', 'name': 'Florentine_iris'}, {'id': 19729, 'synset': 'stinking_iris.n.01', 'name': 'stinking_iris'}, {'id': 19730, 'synset': 'german_iris.n.02', 'name': 'German_iris'}, {'id': 19731, 'synset': 'japanese_iris.n.01', 'name': 'Japanese_iris'}, {'id': 19732, 'synset': 'german_iris.n.01', 'name': 'German_iris'}, {'id': 19733, 'synset': 'dalmatian_iris.n.01', 'name': 'Dalmatian_iris'}, {'id': 19734, 'synset': 'persian_iris.n.01', 'name': 'Persian_iris'}, {'id': 19735, 'synset': 'dutch_iris.n.01', 'name': 'Dutch_iris'}, {'id': 19736, 'synset': 'dwarf_iris.n.01', 'name': 'dwarf_iris'}, {'id': 19737, 'synset': 'spanish_iris.n.01', 'name': 'Spanish_iris'}, {'id': 19738, 'synset': 'blackberry-lily.n.01', 'name': 'blackberry-lily'}, {'id': 19739, 'synset': 'crocus.n.01', 'name': 'crocus'}, {'id': 19740, 'synset': 'saffron.n.01', 'name': 'saffron'}, {'id': 19741, 'synset': 'corn_lily.n.01', 'name': 'corn_lily'}, {'id': 19742, 'synset': 'blue-eyed_grass.n.01', 'name': 'blue-eyed_grass'}, {'id': 19743, 'synset': 'wandflower.n.01', 'name': 'wandflower'}, {'id': 19744, 'synset': 'amaryllis.n.01', 'name': 'amaryllis'}, {'id': 19745, 'synset': 'salsilla.n.02', 'name': 'salsilla'}, {'id': 19746, 'synset': 'salsilla.n.01', 'name': 'salsilla'}, {'id': 19747, 'synset': 'blood_lily.n.01', 'name': 'blood_lily'}, {'id': 19748, 'synset': 'cape_tulip.n.01', 'name': 'Cape_tulip'}, {'id': 19749, 'synset': 'hippeastrum.n.01', 'name': 'hippeastrum'}, {'id': 19750, 'synset': 'narcissus.n.01', 'name': 'narcissus'}, {'id': 19751, 'synset': 'daffodil.n.01', 'name': 'daffodil'}, {'id': 19752, 'synset': 'jonquil.n.01', 'name': 'jonquil'}, {'id': 19753, 'synset': 'jonquil.n.02', 'name': 'jonquil'}, {'id': 19754, 'synset': 'jacobean_lily.n.01', 'name': 'Jacobean_lily'}, {'id': 19755, 'synset': 'liliaceous_plant.n.01', 'name': 'liliaceous_plant'}, {'id': 19756, 'synset': 'mountain_lily.n.01', 'name': 'mountain_lily'}, {'id': 19757, 'synset': 'canada_lily.n.01', 'name': 'Canada_lily'}, {'id': 19758, 'synset': 'tiger_lily.n.02', 'name': 'tiger_lily'}, {'id': 19759, 'synset': 'columbia_tiger_lily.n.01', 'name': 'Columbia_tiger_lily'}, {'id': 19760, 'synset': 'tiger_lily.n.01', 'name': 'tiger_lily'}, {'id': 19761, 'synset': 'easter_lily.n.01', 'name': 'Easter_lily'}, {'id': 19762, 'synset': 'coast_lily.n.01', 'name': 'coast_lily'}, {'id': 19763, 'synset': "turk's-cap.n.02", 'name': "Turk's-cap"}, {'id': 19764, 'synset': 'michigan_lily.n.01', 'name': 'Michigan_lily'}, {'id': 19765, 'synset': 'leopard_lily.n.01', 'name': 'leopard_lily'}, {'id': 19766, 'synset': "turk's-cap.n.01", 'name': "Turk's-cap"}, {'id': 19767, 'synset': 'african_lily.n.01', 'name': 'African_lily'}, {'id': 19768, 'synset': 'colicroot.n.01', 'name': 'colicroot'}, {'id': 19769, 'synset': 'ague_root.n.01', 'name': 'ague_root'}, {'id': 19770, 'synset': 'yellow_colicroot.n.01', 'name': 'yellow_colicroot'}, {'id': 19771, 'synset': 'alliaceous_plant.n.01', 'name': 'alliaceous_plant'}, {'id': 19772, 'synset': "hooker's_onion.n.01", 'name': "Hooker's_onion"}, {'id': 19773, 'synset': 'wild_leek.n.02', 'name': 'wild_leek'}, {'id': 19774, 'synset': 'canada_garlic.n.01', 'name': 'Canada_garlic'}, {'id': 19775, 'synset': 'keeled_garlic.n.01', 'name': 'keeled_garlic'}, {'id': 19776, 'synset': 'shallot.n.02', 'name': 'shallot'}, {'id': 19777, 'synset': 'nodding_onion.n.01', 'name': 'nodding_onion'}, {'id': 19778, 'synset': 'welsh_onion.n.01', 'name': 'Welsh_onion'}, {'id': 19779, 'synset': 'red-skinned_onion.n.01', 'name': 'red-skinned_onion'}, {'id': 19780, 'synset': 'daffodil_garlic.n.01', 'name': 'daffodil_garlic'}, {'id': 19781, 'synset': 'few-flowered_leek.n.01', 'name': 'few-flowered_leek'}, {'id': 19782, 'synset': 'garlic.n.01', 'name': 'garlic'}, {'id': 19783, 'synset': 'sand_leek.n.01', 'name': 'sand_leek'}, {'id': 19784, 'synset': 'chives.n.01', 'name': 'chives'}, {'id': 19785, 'synset': 'crow_garlic.n.01', 'name': 'crow_garlic'}, {'id': 19786, 'synset': 'wild_garlic.n.01', 'name': 'wild_garlic'}, {'id': 19787, 'synset': 'garlic_chive.n.01', 'name': 'garlic_chive'}, {'id': 19788, 'synset': 'round-headed_leek.n.01', 'name': 'round-headed_leek'}, {'id': 19789, 'synset': 'three-cornered_leek.n.01', 'name': 'three-cornered_leek'}, {'id': 19790, 'synset': 'cape_aloe.n.01', 'name': 'cape_aloe'}, {'id': 19791, 'synset': 'kniphofia.n.01', 'name': 'kniphofia'}, {'id': 19792, 'synset': 'poker_plant.n.01', 'name': 'poker_plant'}, {'id': 19793, 'synset': 'red-hot_poker.n.01', 'name': 'red-hot_poker'}, {'id': 19794, 'synset': 'fly_poison.n.01', 'name': 'fly_poison'}, {'id': 19795, 'synset': 'amber_lily.n.01', 'name': 'amber_lily'}, {'id': 19796, 'synset': 'asparagus.n.01', 'name': 'asparagus'}, {'id': 19797, 'synset': 'asparagus_fern.n.01', 'name': 'asparagus_fern'}, {'id': 19798, 'synset': 'smilax.n.02', 'name': 'smilax'}, {'id': 19799, 'synset': 'asphodel.n.01', 'name': 'asphodel'}, {'id': 19800, 'synset': "jacob's_rod.n.01", 'name': "Jacob's_rod"}, {'id': 19801, 'synset': 'aspidistra.n.01', 'name': 'aspidistra'}, {'id': 19802, 'synset': 'coral_drops.n.01', 'name': 'coral_drops'}, {'id': 19803, 'synset': 'christmas_bells.n.01', 'name': 'Christmas_bells'}, {'id': 19804, 'synset': 'climbing_onion.n.01', 'name': 'climbing_onion'}, {'id': 19805, 'synset': 'mariposa.n.01', 'name': 'mariposa'}, {'id': 19806, 'synset': 'globe_lily.n.01', 'name': 'globe_lily'}, {'id': 19807, 'synset': "cat's-ear.n.01", 'name': "cat's-ear"}, {'id': 19808, 'synset': 'white_globe_lily.n.01', 'name': 'white_globe_lily'}, {'id': 19809, 'synset': 'yellow_globe_lily.n.01', 'name': 'yellow_globe_lily'}, {'id': 19810, 'synset': 'rose_globe_lily.n.01', 'name': 'rose_globe_lily'}, {'id': 19811, 'synset': 'star_tulip.n.01', 'name': 'star_tulip'}, {'id': 19812, 'synset': 'desert_mariposa_tulip.n.01', 'name': 'desert_mariposa_tulip'}, {'id': 19813, 'synset': 'yellow_mariposa_tulip.n.01', 'name': 'yellow_mariposa_tulip'}, {'id': 19814, 'synset': 'sagebrush_mariposa_tulip.n.01', 'name': 'sagebrush_mariposa_tulip'}, {'id': 19815, 'synset': 'sego_lily.n.01', 'name': 'sego_lily'}, {'id': 19816, 'synset': 'camas.n.01', 'name': 'camas'}, {'id': 19817, 'synset': 'common_camas.n.01', 'name': 'common_camas'}, {'id': 19818, 'synset': "leichtlin's_camas.n.01", 'name': "Leichtlin's_camas"}, {'id': 19819, 'synset': 'wild_hyacinth.n.02', 'name': 'wild_hyacinth'}, {'id': 19820, 'synset': 'dogtooth_violet.n.01', 'name': 'dogtooth_violet'}, {'id': 19821, 'synset': 'white_dogtooth_violet.n.01', 'name': 'white_dogtooth_violet'}, {'id': 19822, 'synset': "yellow_adder's_tongue.n.01", 'name': "yellow_adder's_tongue"}, {'id': 19823, 'synset': 'european_dogtooth.n.01', 'name': 'European_dogtooth'}, {'id': 19824, 'synset': 'fawn_lily.n.01', 'name': 'fawn_lily'}, {'id': 19825, 'synset': 'glacier_lily.n.01', 'name': 'glacier_lily'}, {'id': 19826, 'synset': 'avalanche_lily.n.01', 'name': 'avalanche_lily'}, {'id': 19827, 'synset': 'fritillary.n.01', 'name': 'fritillary'}, {'id': 19828, 'synset': 'mission_bells.n.02', 'name': 'mission_bells'}, {'id': 19829, 'synset': 'mission_bells.n.01', 'name': 'mission_bells'}, {'id': 19830, 'synset': 'stink_bell.n.01', 'name': 'stink_bell'}, {'id': 19831, 'synset': 'crown_imperial.n.01', 'name': 'crown_imperial'}, {'id': 19832, 'synset': 'white_fritillary.n.01', 'name': 'white_fritillary'}, {'id': 19833, 'synset': "snake's_head_fritillary.n.01", 'name': "snake's_head_fritillary"}, {'id': 19834, 'synset': 'adobe_lily.n.01', 'name': 'adobe_lily'}, {'id': 19835, 'synset': 'scarlet_fritillary.n.01', 'name': 'scarlet_fritillary'}, {'id': 19836, 'synset': 'tulip.n.01', 'name': 'tulip'}, {'id': 19837, 'synset': 'dwarf_tulip.n.01', 'name': 'dwarf_tulip'}, {'id': 19838, 'synset': 'lady_tulip.n.01', 'name': 'lady_tulip'}, {'id': 19839, 'synset': 'tulipa_gesneriana.n.01', 'name': 'Tulipa_gesneriana'}, {'id': 19840, 'synset': 'cottage_tulip.n.01', 'name': 'cottage_tulip'}, {'id': 19841, 'synset': 'darwin_tulip.n.01', 'name': 'Darwin_tulip'}, {'id': 19842, 'synset': 'gloriosa.n.01', 'name': 'gloriosa'}, {'id': 19843, 'synset': 'lemon_lily.n.01', 'name': 'lemon_lily'}, {'id': 19844, 'synset': 'common_hyacinth.n.01', 'name': 'common_hyacinth'}, {'id': 19845, 'synset': 'roman_hyacinth.n.01', 'name': 'Roman_hyacinth'}, {'id': 19846, 'synset': 'summer_hyacinth.n.01', 'name': 'summer_hyacinth'}, {'id': 19847, 'synset': 'star-of-bethlehem.n.01', 'name': 'star-of-Bethlehem'}, {'id': 19848, 'synset': 'bath_asparagus.n.01', 'name': 'bath_asparagus'}, {'id': 19849, 'synset': 'grape_hyacinth.n.01', 'name': 'grape_hyacinth'}, {'id': 19850, 'synset': 'common_grape_hyacinth.n.01', 'name': 'common_grape_hyacinth'}, {'id': 19851, 'synset': 'tassel_hyacinth.n.01', 'name': 'tassel_hyacinth'}, {'id': 19852, 'synset': 'scilla.n.01', 'name': 'scilla'}, {'id': 19853, 'synset': 'spring_squill.n.01', 'name': 'spring_squill'}, {'id': 19854, 'synset': 'false_asphodel.n.01', 'name': 'false_asphodel'}, {'id': 19855, 'synset': 'scotch_asphodel.n.01', 'name': 'Scotch_asphodel'}, {'id': 19856, 'synset': 'sea_squill.n.01', 'name': 'sea_squill'}, {'id': 19857, 'synset': 'squill.n.01', 'name': 'squill'}, {'id': 19858, 'synset': "butcher's_broom.n.01", 'name': "butcher's_broom"}, {'id': 19859, 'synset': 'bog_asphodel.n.01', 'name': 'bog_asphodel'}, {'id': 19860, 'synset': 'european_bog_asphodel.n.01', 'name': 'European_bog_asphodel'}, {'id': 19861, 'synset': 'american_bog_asphodel.n.01', 'name': 'American_bog_asphodel'}, {'id': 19862, 'synset': 'hellebore.n.01', 'name': 'hellebore'}, {'id': 19863, 'synset': 'white_hellebore.n.01', 'name': 'white_hellebore'}, {'id': 19864, 'synset': 'squaw_grass.n.01', 'name': 'squaw_grass'}, {'id': 19865, 'synset': 'death_camas.n.01', 'name': 'death_camas'}, {'id': 19866, 'synset': 'alkali_grass.n.01', 'name': 'alkali_grass'}, {'id': 19867, 'synset': 'white_camas.n.01', 'name': 'white_camas'}, {'id': 19868, 'synset': 'poison_camas.n.01', 'name': 'poison_camas'}, {'id': 19869, 'synset': 'grassy_death_camas.n.01', 'name': 'grassy_death_camas'}, {'id': 19870, 'synset': 'prairie_wake-robin.n.01', 'name': 'prairie_wake-robin'}, {'id': 19871, 'synset': 'dwarf-white_trillium.n.01', 'name': 'dwarf-white_trillium'}, {'id': 19872, 'synset': 'herb_paris.n.01', 'name': 'herb_Paris'}, {'id': 19873, 'synset': 'sarsaparilla.n.01', 'name': 'sarsaparilla'}, {'id': 19874, 'synset': 'bullbrier.n.01', 'name': 'bullbrier'}, {'id': 19875, 'synset': 'rough_bindweed.n.01', 'name': 'rough_bindweed'}, {'id': 19876, 'synset': 'clintonia.n.01', 'name': 'clintonia'}, {'id': 19877, 'synset': 'false_lily_of_the_valley.n.02', 'name': 'false_lily_of_the_valley'}, {'id': 19878, 'synset': 'false_lily_of_the_valley.n.01', 'name': 'false_lily_of_the_valley'}, {'id': 19879, 'synset': "solomon's-seal.n.01", 'name': "Solomon's-seal"}, {'id': 19880, 'synset': "great_solomon's-seal.n.01", 'name': "great_Solomon's-seal"}, {'id': 19881, 'synset': 'bellwort.n.01', 'name': 'bellwort'}, {'id': 19882, 'synset': 'strawflower.n.01', 'name': 'strawflower'}, {'id': 19883, 'synset': 'pia.n.01', 'name': 'pia'}, {'id': 19884, 'synset': 'agave.n.01', 'name': 'agave'}, {'id': 19885, 'synset': 'american_agave.n.01', 'name': 'American_agave'}, {'id': 19886, 'synset': 'sisal.n.02', 'name': 'sisal'}, {'id': 19887, 'synset': 'maguey.n.02', 'name': 'maguey'}, {'id': 19888, 'synset': 'maguey.n.01', 'name': 'maguey'}, {'id': 19889, 'synset': 'agave_tequilana.n.01', 'name': 'Agave_tequilana'}, {'id': 19890, 'synset': 'cabbage_tree.n.03', 'name': 'cabbage_tree'}, {'id': 19891, 'synset': 'dracaena.n.01', 'name': 'dracaena'}, {'id': 19892, 'synset': 'tuberose.n.01', 'name': 'tuberose'}, {'id': 19893, 'synset': 'sansevieria.n.01', 'name': 'sansevieria'}, {'id': 19894, 'synset': 'african_bowstring_hemp.n.01', 'name': 'African_bowstring_hemp'}, {'id': 19895, 'synset': 'ceylon_bowstring_hemp.n.01', 'name': 'Ceylon_bowstring_hemp'}, {'id': 19896, 'synset': "mother-in-law's_tongue.n.01", 'name': "mother-in-law's_tongue"}, {'id': 19897, 'synset': 'spanish_bayonet.n.02', 'name': 'Spanish_bayonet'}, {'id': 19898, 'synset': 'spanish_bayonet.n.01', 'name': 'Spanish_bayonet'}, {'id': 19899, 'synset': 'joshua_tree.n.01', 'name': 'Joshua_tree'}, {'id': 19900, 'synset': 'soapweed.n.01', 'name': 'soapweed'}, {'id': 19901, 'synset': "adam's_needle.n.01", 'name': "Adam's_needle"}, {'id': 19902, 'synset': 'bear_grass.n.02', 'name': 'bear_grass'}, {'id': 19903, 'synset': 'spanish_dagger.n.01', 'name': 'Spanish_dagger'}, {'id': 19904, 'synset': "our_lord's_candle.n.01", 'name': "Our_Lord's_candle"}, {'id': 19905, 'synset': 'water_shamrock.n.01', 'name': 'water_shamrock'}, {'id': 19906, 'synset': 'butterfly_bush.n.01', 'name': 'butterfly_bush'}, {'id': 19907, 'synset': 'yellow_jasmine.n.01', 'name': 'yellow_jasmine'}, {'id': 19908, 'synset': 'flax.n.02', 'name': 'flax'}, {'id': 19909, 'synset': 'calabar_bean.n.01', 'name': 'calabar_bean'}, {'id': 19910, 'synset': 'bonduc.n.02', 'name': 'bonduc'}, {'id': 19911, 'synset': 'divi-divi.n.02', 'name': 'divi-divi'}, {'id': 19912, 'synset': 'mysore_thorn.n.01', 'name': 'Mysore_thorn'}, {'id': 19913, 'synset': 'brazilian_ironwood.n.01', 'name': 'brazilian_ironwood'}, {'id': 19914, 'synset': 'bird_of_paradise.n.01', 'name': 'bird_of_paradise'}, {'id': 19915, 'synset': 'shingle_tree.n.01', 'name': 'shingle_tree'}, {'id': 19916, 'synset': 'mountain_ebony.n.01', 'name': 'mountain_ebony'}, {'id': 19917, 'synset': 'msasa.n.01', 'name': 'msasa'}, {'id': 19918, 'synset': 'cassia.n.01', 'name': 'cassia'}, {'id': 19919, 'synset': 'golden_shower_tree.n.01', 'name': 'golden_shower_tree'}, {'id': 19920, 'synset': 'pink_shower.n.01', 'name': 'pink_shower'}, {'id': 19921, 'synset': 'rainbow_shower.n.01', 'name': 'rainbow_shower'}, {'id': 19922, 'synset': 'horse_cassia.n.01', 'name': 'horse_cassia'}, {'id': 19923, 'synset': 'carob.n.02', 'name': 'carob'}, {'id': 19924, 'synset': 'carob.n.01', 'name': 'carob'}, {'id': 19925, 'synset': 'paloverde.n.01', 'name': 'paloverde'}, {'id': 19926, 'synset': 'royal_poinciana.n.01', 'name': 'royal_poinciana'}, {'id': 19927, 'synset': 'locust_tree.n.01', 'name': 'locust_tree'}, {'id': 19928, 'synset': 'water_locust.n.01', 'name': 'water_locust'}, {'id': 19929, 'synset': 'honey_locust.n.01', 'name': 'honey_locust'}, {'id': 19930, 'synset': 'kentucky_coffee_tree.n.01', 'name': 'Kentucky_coffee_tree'}, {'id': 19931, 'synset': 'logwood.n.02', 'name': 'logwood'}, {'id': 19932, 'synset': 'jerusalem_thorn.n.03', 'name': 'Jerusalem_thorn'}, {'id': 19933, 'synset': 'palo_verde.n.01', 'name': 'palo_verde'}, {'id': 19934, 'synset': 'dalmatian_laburnum.n.01', 'name': 'Dalmatian_laburnum'}, {'id': 19935, 'synset': 'senna.n.01', 'name': 'senna'}, {'id': 19936, 'synset': 'avaram.n.01', 'name': 'avaram'}, {'id': 19937, 'synset': 'alexandria_senna.n.01', 'name': 'Alexandria_senna'}, {'id': 19938, 'synset': 'wild_senna.n.01', 'name': 'wild_senna'}, {'id': 19939, 'synset': 'sicklepod.n.01', 'name': 'sicklepod'}, {'id': 19940, 'synset': 'coffee_senna.n.01', 'name': 'coffee_senna'}, {'id': 19941, 'synset': 'tamarind.n.01', 'name': 'tamarind'}, {'id': 19942, 'synset': 'false_indigo.n.03', 'name': 'false_indigo'}, {'id': 19943, 'synset': 'false_indigo.n.02', 'name': 'false_indigo'}, {'id': 19944, 'synset': 'hog_peanut.n.01', 'name': 'hog_peanut'}, {'id': 19945, 'synset': 'angelim.n.01', 'name': 'angelim'}, {'id': 19946, 'synset': 'cabbage_bark.n.01', 'name': 'cabbage_bark'}, {'id': 19947, 'synset': 'kidney_vetch.n.01', 'name': 'kidney_vetch'}, {'id': 19948, 'synset': 'groundnut.n.01', 'name': 'groundnut'}, {'id': 19949, 'synset': 'rooibos.n.01', 'name': 'rooibos'}, {'id': 19950, 'synset': 'milk_vetch.n.01', 'name': 'milk_vetch'}, {'id': 19951, 'synset': 'alpine_milk_vetch.n.01', 'name': 'alpine_milk_vetch'}, {'id': 19952, 'synset': 'purple_milk_vetch.n.01', 'name': 'purple_milk_vetch'}, {'id': 19953, 'synset': 'camwood.n.01', 'name': 'camwood'}, {'id': 19954, 'synset': 'wild_indigo.n.01', 'name': 'wild_indigo'}, {'id': 19955, 'synset': 'blue_false_indigo.n.01', 'name': 'blue_false_indigo'}, {'id': 19956, 'synset': 'white_false_indigo.n.01', 'name': 'white_false_indigo'}, {'id': 19957, 'synset': 'indigo_broom.n.01', 'name': 'indigo_broom'}, {'id': 19958, 'synset': 'dhak.n.01', 'name': 'dhak'}, {'id': 19959, 'synset': 'pigeon_pea.n.01', 'name': 'pigeon_pea'}, {'id': 19960, 'synset': 'sword_bean.n.01', 'name': 'sword_bean'}, {'id': 19961, 'synset': 'pea_tree.n.01', 'name': 'pea_tree'}, {'id': 19962, 'synset': 'siberian_pea_tree.n.01', 'name': 'Siberian_pea_tree'}, {'id': 19963, 'synset': 'chinese_pea_tree.n.01', 'name': 'Chinese_pea_tree'}, {'id': 19964, 'synset': 'moreton_bay_chestnut.n.01', 'name': 'Moreton_Bay_chestnut'}, {'id': 19965, 'synset': 'butterfly_pea.n.03', 'name': 'butterfly_pea'}, {'id': 19966, 'synset': 'judas_tree.n.01', 'name': 'Judas_tree'}, {'id': 19967, 'synset': 'redbud.n.01', 'name': 'redbud'}, {'id': 19968, 'synset': 'western_redbud.n.01', 'name': 'western_redbud'}, {'id': 19969, 'synset': 'tagasaste.n.01', 'name': 'tagasaste'}, {'id': 19970, 'synset': 'weeping_tree_broom.n.01', 'name': 'weeping_tree_broom'}, {'id': 19971, 'synset': 'flame_pea.n.01', 'name': 'flame_pea'}, {'id': 19972, 'synset': 'chickpea.n.02', 'name': 'chickpea'}, {'id': 19973, 'synset': 'kentucky_yellowwood.n.01', 'name': 'Kentucky_yellowwood'}, {'id': 19974, 'synset': 'glory_pea.n.01', 'name': 'glory_pea'}, {'id': 19975, 'synset': 'desert_pea.n.01', 'name': 'desert_pea'}, {'id': 19976, 'synset': "parrot's_beak.n.01", 'name': "parrot's_beak"}, {'id': 19977, 'synset': 'butterfly_pea.n.02', 'name': 'butterfly_pea'}, {'id': 19978, 'synset': 'blue_pea.n.01', 'name': 'blue_pea'}, {'id': 19979, 'synset': 'telegraph_plant.n.01', 'name': 'telegraph_plant'}, {'id': 19980, 'synset': 'bladder_senna.n.01', 'name': 'bladder_senna'}, {'id': 19981, 'synset': 'axseed.n.01', 'name': 'axseed'}, {'id': 19982, 'synset': 'crotalaria.n.01', 'name': 'crotalaria'}, {'id': 19983, 'synset': 'guar.n.01', 'name': 'guar'}, {'id': 19984, 'synset': 'white_broom.n.01', 'name': 'white_broom'}, {'id': 19985, 'synset': 'common_broom.n.01', 'name': 'common_broom'}, {'id': 19986, 'synset': 'rosewood.n.02', 'name': 'rosewood'}, {'id': 19987, 'synset': 'indian_blackwood.n.01', 'name': 'Indian_blackwood'}, {'id': 19988, 'synset': 'sissoo.n.01', 'name': 'sissoo'}, {'id': 19989, 'synset': 'kingwood.n.02', 'name': 'kingwood'}, {'id': 19990, 'synset': 'brazilian_rosewood.n.01', 'name': 'Brazilian_rosewood'}, {'id': 19991, 'synset': 'cocobolo.n.01', 'name': 'cocobolo'}, {'id': 19992, 'synset': 'blackwood.n.02', 'name': 'blackwood'}, {'id': 19993, 'synset': 'bitter_pea.n.01', 'name': 'bitter_pea'}, {'id': 19994, 'synset': 'derris.n.01', 'name': 'derris'}, {'id': 19995, 'synset': 'derris_root.n.01', 'name': 'derris_root'}, {'id': 19996, 'synset': 'prairie_mimosa.n.01', 'name': 'prairie_mimosa'}, {'id': 19997, 'synset': 'tick_trefoil.n.01', 'name': 'tick_trefoil'}, {'id': 19998, 'synset': 'beggarweed.n.01', 'name': 'beggarweed'}, {'id': 19999, 'synset': 'australian_pea.n.01', 'name': 'Australian_pea'}, {'id': 20000, 'synset': 'coral_tree.n.01', 'name': 'coral_tree'}, {'id': 20001, 'synset': 'kaffir_boom.n.02', 'name': 'kaffir_boom'}, {'id': 20002, 'synset': 'coral_bean_tree.n.01', 'name': 'coral_bean_tree'}, {'id': 20003, 'synset': 'ceibo.n.01', 'name': 'ceibo'}, {'id': 20004, 'synset': 'kaffir_boom.n.01', 'name': 'kaffir_boom'}, {'id': 20005, 'synset': 'indian_coral_tree.n.01', 'name': 'Indian_coral_tree'}, {'id': 20006, 'synset': 'cork_tree.n.02', 'name': 'cork_tree'}, {'id': 20007, 'synset': "goat's_rue.n.02", 'name': "goat's_rue"}, {'id': 20008, 'synset': 'poison_bush.n.01', 'name': 'poison_bush'}, {'id': 20009, 'synset': 'spanish_broom.n.02', 'name': 'Spanish_broom'}, {'id': 20010, 'synset': 'woodwaxen.n.01', 'name': 'woodwaxen'}, {'id': 20011, 'synset': 'chanar.n.01', 'name': 'chanar'}, {'id': 20012, 'synset': 'gliricidia.n.01', 'name': 'gliricidia'}, {'id': 20013, 'synset': 'soy.n.01', 'name': 'soy'}, {'id': 20014, 'synset': 'licorice.n.01', 'name': 'licorice'}, {'id': 20015, 'synset': 'wild_licorice.n.02', 'name': 'wild_licorice'}, {'id': 20016, 'synset': 'licorice_root.n.01', 'name': 'licorice_root'}, {'id': 20017, 'synset': 'western_australia_coral_pea.n.01', 'name': 'Western_Australia_coral_pea'}, {'id': 20018, 'synset': 'sweet_vetch.n.01', 'name': 'sweet_vetch'}, {'id': 20019, 'synset': 'french_honeysuckle.n.02', 'name': 'French_honeysuckle'}, {'id': 20020, 'synset': 'anil.n.02', 'name': 'anil'}, {'id': 20021, 'synset': 'scarlet_runner.n.02', 'name': 'scarlet_runner'}, {'id': 20022, 'synset': 'hyacinth_bean.n.01', 'name': 'hyacinth_bean'}, {'id': 20023, 'synset': 'scotch_laburnum.n.01', 'name': 'Scotch_laburnum'}, {'id': 20024, 'synset': 'vetchling.n.01', 'name': 'vetchling'}, {'id': 20025, 'synset': 'wild_pea.n.01', 'name': 'wild_pea'}, {'id': 20026, 'synset': 'everlasting_pea.n.01', 'name': 'everlasting_pea'}, {'id': 20027, 'synset': 'beach_pea.n.01', 'name': 'beach_pea'}, {'id': 20028, 'synset': 'grass_vetch.n.01', 'name': 'grass_vetch'}, {'id': 20029, 'synset': 'marsh_pea.n.01', 'name': 'marsh_pea'}, {'id': 20030, 'synset': 'common_vetchling.n.01', 'name': 'common_vetchling'}, {'id': 20031, 'synset': 'grass_pea.n.01', 'name': 'grass_pea'}, {'id': 20032, 'synset': 'tangier_pea.n.01', 'name': 'Tangier_pea'}, {'id': 20033, 'synset': 'heath_pea.n.01', 'name': 'heath_pea'}, {'id': 20034, 'synset': 'bicolor_lespediza.n.01', 'name': 'bicolor_lespediza'}, {'id': 20035, 'synset': 'japanese_clover.n.01', 'name': 'japanese_clover'}, {'id': 20036, 'synset': 'korean_lespedeza.n.01', 'name': 'Korean_lespedeza'}, {'id': 20037, 'synset': 'sericea_lespedeza.n.01', 'name': 'sericea_lespedeza'}, {'id': 20038, 'synset': 'lentil.n.03', 'name': 'lentil'}, {'id': 20039, 'synset': 'lentil.n.02', 'name': 'lentil'}, {'id': 20040, 'synset': "prairie_bird's-foot_trefoil.n.01", 'name': "prairie_bird's-foot_trefoil"}, {'id': 20041, 'synset': "bird's_foot_trefoil.n.02", 'name': "bird's_foot_trefoil"}, {'id': 20042, 'synset': 'winged_pea.n.02', 'name': 'winged_pea'}, {'id': 20043, 'synset': 'lupine.n.01', 'name': 'lupine'}, {'id': 20044, 'synset': 'white_lupine.n.01', 'name': 'white_lupine'}, {'id': 20045, 'synset': 'tree_lupine.n.01', 'name': 'tree_lupine'}, {'id': 20046, 'synset': 'wild_lupine.n.01', 'name': 'wild_lupine'}, {'id': 20047, 'synset': 'bluebonnet.n.01', 'name': 'bluebonnet'}, {'id': 20048, 'synset': 'texas_bluebonnet.n.01', 'name': 'Texas_bluebonnet'}, {'id': 20049, 'synset': 'medic.n.01', 'name': 'medic'}, {'id': 20050, 'synset': 'moon_trefoil.n.01', 'name': 'moon_trefoil'}, {'id': 20051, 'synset': 'sickle_alfalfa.n.01', 'name': 'sickle_alfalfa'}, {'id': 20052, 'synset': 'calvary_clover.n.01', 'name': 'Calvary_clover'}, {'id': 20053, 'synset': 'black_medick.n.01', 'name': 'black_medick'}, {'id': 20054, 'synset': 'alfalfa.n.01', 'name': 'alfalfa'}, {'id': 20055, 'synset': 'millettia.n.01', 'name': 'millettia'}, {'id': 20056, 'synset': 'mucuna.n.01', 'name': 'mucuna'}, {'id': 20057, 'synset': 'cowage.n.02', 'name': 'cowage'}, {'id': 20058, 'synset': 'tolu_tree.n.01', 'name': 'tolu_tree'}, {'id': 20059, 'synset': 'peruvian_balsam.n.01', 'name': 'Peruvian_balsam'}, {'id': 20060, 'synset': 'sainfoin.n.01', 'name': 'sainfoin'}, {'id': 20061, 'synset': 'restharrow.n.02', 'name': 'restharrow'}, {'id': 20062, 'synset': 'bead_tree.n.01', 'name': 'bead_tree'}, {'id': 20063, 'synset': 'jumby_bead.n.01', 'name': 'jumby_bead'}, {'id': 20064, 'synset': 'locoweed.n.01', 'name': 'locoweed'}, {'id': 20065, 'synset': 'purple_locoweed.n.01', 'name': 'purple_locoweed'}, {'id': 20066, 'synset': 'tumbleweed.n.01', 'name': 'tumbleweed'}, {'id': 20067, 'synset': 'yam_bean.n.02', 'name': 'yam_bean'}, {'id': 20068, 'synset': 'shamrock_pea.n.01', 'name': 'shamrock_pea'}, {'id': 20069, 'synset': 'pole_bean.n.01', 'name': 'pole_bean'}, {'id': 20070, 'synset': 'kidney_bean.n.01', 'name': 'kidney_bean'}, {'id': 20071, 'synset': 'haricot.n.01', 'name': 'haricot'}, {'id': 20072, 'synset': 'wax_bean.n.01', 'name': 'wax_bean'}, {'id': 20073, 'synset': 'scarlet_runner.n.01', 'name': 'scarlet_runner'}, {'id': 20074, 'synset': 'lima_bean.n.02', 'name': 'lima_bean'}, {'id': 20075, 'synset': 'sieva_bean.n.01', 'name': 'sieva_bean'}, {'id': 20076, 'synset': 'tepary_bean.n.01', 'name': 'tepary_bean'}, {'id': 20077, 'synset': 'chaparral_pea.n.01', 'name': 'chaparral_pea'}, {'id': 20078, 'synset': 'jamaica_dogwood.n.01', 'name': 'Jamaica_dogwood'}, {'id': 20079, 'synset': 'pea.n.02', 'name': 'pea'}, {'id': 20080, 'synset': 'garden_pea.n.01', 'name': 'garden_pea'}, {'id': 20081, 'synset': 'edible-pod_pea.n.01', 'name': 'edible-pod_pea'}, {'id': 20082, 'synset': 'sugar_snap_pea.n.01', 'name': 'sugar_snap_pea'}, {'id': 20083, 'synset': 'field_pea.n.02', 'name': 'field_pea'}, {'id': 20084, 'synset': 'field_pea.n.01', 'name': 'field_pea'}, {'id': 20085, 'synset': 'common_flat_pea.n.01', 'name': 'common_flat_pea'}, {'id': 20086, 'synset': 'quira.n.02', 'name': 'quira'}, {'id': 20087, 'synset': 'roble.n.01', 'name': 'roble'}, {'id': 20088, 'synset': 'panama_redwood_tree.n.01', 'name': 'Panama_redwood_tree'}, {'id': 20089, 'synset': 'indian_beech.n.01', 'name': 'Indian_beech'}, {'id': 20090, 'synset': 'winged_bean.n.01', 'name': 'winged_bean'}, {'id': 20091, 'synset': 'breadroot.n.01', 'name': 'breadroot'}, {'id': 20092, 'synset': 'bloodwood_tree.n.01', 'name': 'bloodwood_tree'}, {'id': 20093, 'synset': 'kino.n.02', 'name': 'kino'}, {'id': 20094, 'synset': 'red_sandalwood.n.02', 'name': 'red_sandalwood'}, {'id': 20095, 'synset': 'kudzu.n.01', 'name': 'kudzu'}, {'id': 20096, 'synset': 'bristly_locust.n.01', 'name': 'bristly_locust'}, {'id': 20097, 'synset': 'black_locust.n.02', 'name': 'black_locust'}, {'id': 20098, 'synset': 'clammy_locust.n.01', 'name': 'clammy_locust'}, {'id': 20099, 'synset': 'carib_wood.n.01', 'name': 'carib_wood'}, {'id': 20100, 'synset': 'colorado_river_hemp.n.01', 'name': 'Colorado_River_hemp'}, {'id': 20101, 'synset': 'scarlet_wisteria_tree.n.01', 'name': 'scarlet_wisteria_tree'}, {'id': 20102, 'synset': 'japanese_pagoda_tree.n.01', 'name': 'Japanese_pagoda_tree'}, {'id': 20103, 'synset': 'mescal_bean.n.01', 'name': 'mescal_bean'}, {'id': 20104, 'synset': 'kowhai.n.01', 'name': 'kowhai'}, {'id': 20105, 'synset': 'jade_vine.n.01', 'name': 'jade_vine'}, {'id': 20106, 'synset': 'hoary_pea.n.01', 'name': 'hoary_pea'}, {'id': 20107, 'synset': 'bastard_indigo.n.01', 'name': 'bastard_indigo'}, {'id': 20108, 'synset': 'catgut.n.01', 'name': 'catgut'}, {'id': 20109, 'synset': 'bush_pea.n.01', 'name': 'bush_pea'}, {'id': 20110, 'synset': 'false_lupine.n.01', 'name': 'false_lupine'}, {'id': 20111, 'synset': 'carolina_lupine.n.01', 'name': 'Carolina_lupine'}, {'id': 20112, 'synset': 'tipu.n.01', 'name': 'tipu'}, {'id': 20113, 'synset': "bird's_foot_trefoil.n.01", 'name': "bird's_foot_trefoil"}, {'id': 20114, 'synset': 'fenugreek.n.01', 'name': 'fenugreek'}, {'id': 20115, 'synset': 'gorse.n.01', 'name': 'gorse'}, {'id': 20116, 'synset': 'vetch.n.01', 'name': 'vetch'}, {'id': 20117, 'synset': 'tufted_vetch.n.01', 'name': 'tufted_vetch'}, {'id': 20118, 'synset': 'broad_bean.n.01', 'name': 'broad_bean'}, {'id': 20119, 'synset': 'bitter_betch.n.01', 'name': 'bitter_betch'}, {'id': 20120, 'synset': 'bush_vetch.n.01', 'name': 'bush_vetch'}, {'id': 20121, 'synset': 'moth_bean.n.01', 'name': 'moth_bean'}, {'id': 20122, 'synset': 'snailflower.n.01', 'name': 'snailflower'}, {'id': 20123, 'synset': 'mung.n.01', 'name': 'mung'}, {'id': 20124, 'synset': 'cowpea.n.02', 'name': 'cowpea'}, {'id': 20125, 'synset': 'cowpea.n.01', 'name': 'cowpea'}, {'id': 20126, 'synset': 'asparagus_bean.n.01', 'name': 'asparagus_bean'}, {'id': 20127, 'synset': 'swamp_oak.n.01', 'name': 'swamp_oak'}, {'id': 20128, 'synset': 'keurboom.n.02', 'name': 'keurboom'}, {'id': 20129, 'synset': 'keurboom.n.01', 'name': 'keurboom'}, {'id': 20130, 'synset': 'japanese_wistaria.n.01', 'name': 'Japanese_wistaria'}, {'id': 20131, 'synset': 'chinese_wistaria.n.01', 'name': 'Chinese_wistaria'}, {'id': 20132, 'synset': 'american_wistaria.n.01', 'name': 'American_wistaria'}, {'id': 20133, 'synset': 'silky_wisteria.n.01', 'name': 'silky_wisteria'}, {'id': 20134, 'synset': 'palm.n.03', 'name': 'palm'}, {'id': 20135, 'synset': 'sago_palm.n.01', 'name': 'sago_palm'}, {'id': 20136, 'synset': 'feather_palm.n.01', 'name': 'feather_palm'}, {'id': 20137, 'synset': 'fan_palm.n.01', 'name': 'fan_palm'}, {'id': 20138, 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{'id': 20154, 'synset': 'carnauba.n.02', 'name': 'carnauba'}, {'id': 20155, 'synset': 'caranday.n.01', 'name': 'caranday'}, {'id': 20156, 'synset': 'corozo.n.01', 'name': 'corozo'}, {'id': 20157, 'synset': 'gebang_palm.n.01', 'name': 'gebang_palm'}, {'id': 20158, 'synset': 'latanier.n.01', 'name': 'latanier'}, {'id': 20159, 'synset': 'talipot.n.01', 'name': 'talipot'}, {'id': 20160, 'synset': 'oil_palm.n.01', 'name': 'oil_palm'}, {'id': 20161, 'synset': 'african_oil_palm.n.01', 'name': 'African_oil_palm'}, {'id': 20162, 'synset': 'american_oil_palm.n.01', 'name': 'American_oil_palm'}, {'id': 20163, 'synset': 'palm_nut.n.01', 'name': 'palm_nut'}, {'id': 20164, 'synset': 'cabbage_palm.n.04', 'name': 'cabbage_palm'}, {'id': 20165, 'synset': 'cabbage_palm.n.03', 'name': 'cabbage_palm'}, {'id': 20166, 'synset': 'true_sago_palm.n.01', 'name': 'true_sago_palm'}, {'id': 20167, 'synset': 'nipa_palm.n.01', 'name': 'nipa_palm'}, {'id': 20168, 'synset': 'babassu.n.01', 'name': 'babassu'}, {'id': 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'himalayan_rhubarb.n.01', 'name': 'Himalayan_rhubarb'}, {'id': 20198, 'synset': 'pie_plant.n.01', 'name': 'pie_plant'}, {'id': 20199, 'synset': 'chinese_rhubarb.n.01', 'name': 'Chinese_rhubarb'}, {'id': 20200, 'synset': 'sour_dock.n.01', 'name': 'sour_dock'}, {'id': 20201, 'synset': 'sheep_sorrel.n.01', 'name': 'sheep_sorrel'}, {'id': 20202, 'synset': 'bitter_dock.n.01', 'name': 'bitter_dock'}, {'id': 20203, 'synset': 'french_sorrel.n.01', 'name': 'French_sorrel'}, {'id': 20204, 'synset': 'yellow-eyed_grass.n.01', 'name': 'yellow-eyed_grass'}, {'id': 20205, 'synset': 'commelina.n.01', 'name': 'commelina'}, {'id': 20206, 'synset': 'spiderwort.n.01', 'name': 'spiderwort'}, {'id': 20207, 'synset': 'pineapple.n.01', 'name': 'pineapple'}, {'id': 20208, 'synset': 'pipewort.n.01', 'name': 'pipewort'}, {'id': 20209, 'synset': 'water_hyacinth.n.01', 'name': 'water_hyacinth'}, {'id': 20210, 'synset': 'water_star_grass.n.01', 'name': 'water_star_grass'}, {'id': 20211, 'synset': 'naiad.n.01', 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{'id': 20240, 'synset': 'cotoneaster.n.01', 'name': 'cotoneaster'}, {'id': 20241, 'synset': 'cotoneaster_dammeri.n.01', 'name': 'Cotoneaster_dammeri'}, {'id': 20242, 'synset': 'cotoneaster_horizontalis.n.01', 'name': 'Cotoneaster_horizontalis'}, {'id': 20243, 'synset': 'parsley_haw.n.01', 'name': 'parsley_haw'}, {'id': 20244, 'synset': 'scarlet_haw.n.01', 'name': 'scarlet_haw'}, {'id': 20245, 'synset': 'blackthorn.n.02', 'name': 'blackthorn'}, {'id': 20246, 'synset': 'cockspur_thorn.n.01', 'name': 'cockspur_thorn'}, {'id': 20247, 'synset': 'mayhaw.n.01', 'name': 'mayhaw'}, {'id': 20248, 'synset': 'red_haw.n.02', 'name': 'red_haw'}, {'id': 20249, 'synset': 'red_haw.n.01', 'name': 'red_haw'}, {'id': 20250, 'synset': 'quince.n.01', 'name': 'quince'}, {'id': 20251, 'synset': 'mountain_avens.n.01', 'name': 'mountain_avens'}, {'id': 20252, 'synset': 'loquat.n.01', 'name': 'loquat'}, {'id': 20253, 'synset': 'beach_strawberry.n.01', 'name': 'beach_strawberry'}, {'id': 20254, 'synset': 'virginia_strawberry.n.01', 'name': 'Virginia_strawberry'}, {'id': 20255, 'synset': 'avens.n.01', 'name': 'avens'}, {'id': 20256, 'synset': 'yellow_avens.n.02', 'name': 'yellow_avens'}, {'id': 20257, 'synset': 'yellow_avens.n.01', 'name': 'yellow_avens'}, {'id': 20258, 'synset': 'prairie_smoke.n.01', 'name': 'prairie_smoke'}, {'id': 20259, 'synset': 'bennet.n.01', 'name': 'bennet'}, {'id': 20260, 'synset': 'toyon.n.01', 'name': 'toyon'}, {'id': 20261, 'synset': 'apple_tree.n.01', 'name': 'apple_tree'}, {'id': 20262, 'synset': 'apple.n.02', 'name': 'apple'}, {'id': 20263, 'synset': 'wild_apple.n.01', 'name': 'wild_apple'}, {'id': 20264, 'synset': 'crab_apple.n.01', 'name': 'crab_apple'}, {'id': 20265, 'synset': 'siberian_crab.n.01', 'name': 'Siberian_crab'}, {'id': 20266, 'synset': 'wild_crab.n.01', 'name': 'wild_crab'}, {'id': 20267, 'synset': 'american_crab_apple.n.01', 'name': 'American_crab_apple'}, {'id': 20268, 'synset': 'oregon_crab_apple.n.01', 'name': 'Oregon_crab_apple'}, {'id': 20269, 'synset': 'southern_crab_apple.n.01', 'name': 'Southern_crab_apple'}, {'id': 20270, 'synset': 'iowa_crab.n.01', 'name': 'Iowa_crab'}, {'id': 20271, 'synset': 'bechtel_crab.n.01', 'name': 'Bechtel_crab'}, {'id': 20272, 'synset': 'medlar.n.02', 'name': 'medlar'}, {'id': 20273, 'synset': 'cinquefoil.n.01', 'name': 'cinquefoil'}, {'id': 20274, 'synset': 'silverweed.n.02', 'name': 'silverweed'}, {'id': 20275, 'synset': 'salad_burnet.n.01', 'name': 'salad_burnet'}, {'id': 20276, 'synset': 'plum.n.01', 'name': 'plum'}, {'id': 20277, 'synset': 'wild_plum.n.01', 'name': 'wild_plum'}, {'id': 20278, 'synset': 'allegheny_plum.n.01', 'name': 'Allegheny_plum'}, {'id': 20279, 'synset': 'american_red_plum.n.01', 'name': 'American_red_plum'}, {'id': 20280, 'synset': 'chickasaw_plum.n.01', 'name': 'chickasaw_plum'}, {'id': 20281, 'synset': 'beach_plum.n.01', 'name': 'beach_plum'}, {'id': 20282, 'synset': 'common_plum.n.01', 'name': 'common_plum'}, {'id': 20283, 'synset': 'bullace.n.01', 'name': 'bullace'}, {'id': 20284, 'synset': 'damson_plum.n.02', 'name': 'damson_plum'}, {'id': 20285, 'synset': 'big-tree_plum.n.01', 'name': 'big-tree_plum'}, {'id': 20286, 'synset': 'canada_plum.n.01', 'name': 'Canada_plum'}, {'id': 20287, 'synset': 'plumcot.n.01', 'name': 'plumcot'}, {'id': 20288, 'synset': 'apricot.n.01', 'name': 'apricot'}, {'id': 20289, 'synset': 'japanese_apricot.n.01', 'name': 'Japanese_apricot'}, {'id': 20290, 'synset': 'common_apricot.n.01', 'name': 'common_apricot'}, {'id': 20291, 'synset': 'purple_apricot.n.01', 'name': 'purple_apricot'}, {'id': 20292, 'synset': 'cherry.n.02', 'name': 'cherry'}, {'id': 20293, 'synset': 'wild_cherry.n.02', 'name': 'wild_cherry'}, {'id': 20294, 'synset': 'wild_cherry.n.01', 'name': 'wild_cherry'}, {'id': 20295, 'synset': 'sweet_cherry.n.01', 'name': 'sweet_cherry'}, {'id': 20296, 'synset': 'heart_cherry.n.01', 'name': 'heart_cherry'}, {'id': 20297, 'synset': 'gean.n.01', 'name': 'gean'}, {'id': 20298, 'synset': 'capulin.n.01', 'name': 'capulin'}, {'id': 20299, 'synset': 'cherry_laurel.n.02', 'name': 'cherry_laurel'}, {'id': 20300, 'synset': 'cherry_plum.n.01', 'name': 'cherry_plum'}, {'id': 20301, 'synset': 'sour_cherry.n.01', 'name': 'sour_cherry'}, {'id': 20302, 'synset': 'amarelle.n.01', 'name': 'amarelle'}, {'id': 20303, 'synset': 'morello.n.01', 'name': 'morello'}, {'id': 20304, 'synset': 'marasca.n.01', 'name': 'marasca'}, {'id': 20305, 'synset': 'almond_tree.n.01', 'name': 'almond_tree'}, {'id': 20306, 'synset': 'almond.n.01', 'name': 'almond'}, {'id': 20307, 'synset': 'bitter_almond.n.01', 'name': 'bitter_almond'}, {'id': 20308, 'synset': 'jordan_almond.n.01', 'name': 'jordan_almond'}, {'id': 20309, 'synset': 'dwarf_flowering_almond.n.01', 'name': 'dwarf_flowering_almond'}, {'id': 20310, 'synset': 'holly-leaved_cherry.n.01', 'name': 'holly-leaved_cherry'}, {'id': 20311, 'synset': 'fuji.n.01', 'name': 'fuji'}, {'id': 20312, 'synset': 'flowering_almond.n.02', 'name': 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'wild_licorice.n.01', 'name': 'wild_licorice'}, {'id': 20384, 'synset': 'cleavers.n.01', 'name': 'cleavers'}, {'id': 20385, 'synset': 'wild_madder.n.01', 'name': 'wild_madder'}, {'id': 20386, 'synset': 'cape_jasmine.n.01', 'name': 'cape_jasmine'}, {'id': 20387, 'synset': 'genipa.n.01', 'name': 'genipa'}, {'id': 20388, 'synset': 'genipap_fruit.n.01', 'name': 'genipap_fruit'}, {'id': 20389, 'synset': 'hamelia.n.01', 'name': 'hamelia'}, {'id': 20390, 'synset': 'scarlet_bush.n.01', 'name': 'scarlet_bush'}, {'id': 20391, 'synset': 'lemonwood.n.02', 'name': 'lemonwood'}, {'id': 20392, 'synset': 'negro_peach.n.01', 'name': 'negro_peach'}, {'id': 20393, 'synset': 'wild_medlar.n.01', 'name': 'wild_medlar'}, {'id': 20394, 'synset': 'spanish_tamarind.n.01', 'name': 'Spanish_tamarind'}, {'id': 20395, 'synset': 'abelia.n.01', 'name': 'abelia'}, {'id': 20396, 'synset': 'bush_honeysuckle.n.02', 'name': 'bush_honeysuckle'}, {'id': 20397, 'synset': 'american_twinflower.n.01', 'name': 'American_twinflower'}, {'id': 20398, 'synset': 'honeysuckle.n.01', 'name': 'honeysuckle'}, {'id': 20399, 'synset': 'american_fly_honeysuckle.n.01', 'name': 'American_fly_honeysuckle'}, {'id': 20400, 'synset': 'italian_honeysuckle.n.01', 'name': 'Italian_honeysuckle'}, {'id': 20401, 'synset': 'yellow_honeysuckle.n.01', 'name': 'yellow_honeysuckle'}, {'id': 20402, 'synset': 'hairy_honeysuckle.n.01', 'name': 'hairy_honeysuckle'}, {'id': 20403, 'synset': 'japanese_honeysuckle.n.01', 'name': 'Japanese_honeysuckle'}, {'id': 20404, 'synset': "hall's_honeysuckle.n.01", 'name': "Hall's_honeysuckle"}, {'id': 20405, 'synset': "morrow's_honeysuckle.n.01", 'name': "Morrow's_honeysuckle"}, {'id': 20406, 'synset': 'woodbine.n.02', 'name': 'woodbine'}, {'id': 20407, 'synset': 'trumpet_honeysuckle.n.01', 'name': 'trumpet_honeysuckle'}, {'id': 20408, 'synset': 'european_fly_honeysuckle.n.01', 'name': 'European_fly_honeysuckle'}, {'id': 20409, 'synset': 'swamp_fly_honeysuckle.n.01', 'name': 'swamp_fly_honeysuckle'}, {'id': 20410, 'synset': 'snowberry.n.01', 'name': 'snowberry'}, {'id': 20411, 'synset': 'coralberry.n.01', 'name': 'coralberry'}, {'id': 20412, 'synset': 'blue_elder.n.01', 'name': 'blue_elder'}, {'id': 20413, 'synset': 'dwarf_elder.n.01', 'name': 'dwarf_elder'}, {'id': 20414, 'synset': 'american_red_elder.n.01', 'name': 'American_red_elder'}, {'id': 20415, 'synset': 'european_red_elder.n.01', 'name': 'European_red_elder'}, {'id': 20416, 'synset': 'feverroot.n.01', 'name': 'feverroot'}, {'id': 20417, 'synset': 'cranberry_bush.n.01', 'name': 'cranberry_bush'}, {'id': 20418, 'synset': 'wayfaring_tree.n.01', 'name': 'wayfaring_tree'}, {'id': 20419, 'synset': 'guelder_rose.n.01', 'name': 'guelder_rose'}, {'id': 20420, 'synset': 'arrow_wood.n.01', 'name': 'arrow_wood'}, {'id': 20421, 'synset': 'black_haw.n.02', 'name': 'black_haw'}, {'id': 20422, 'synset': 'weigela.n.01', 'name': 'weigela'}, {'id': 20423, 'synset': 'teasel.n.01', 'name': 'teasel'}, {'id': 20424, 'synset': 'common_teasel.n.01', 'name': 'common_teasel'}, {'id': 20425, 'synset': "fuller's_teasel.n.01", 'name': "fuller's_teasel"}, {'id': 20426, 'synset': 'wild_teasel.n.01', 'name': 'wild_teasel'}, {'id': 20427, 'synset': 'scabious.n.01', 'name': 'scabious'}, {'id': 20428, 'synset': 'sweet_scabious.n.01', 'name': 'sweet_scabious'}, {'id': 20429, 'synset': 'field_scabious.n.01', 'name': 'field_scabious'}, {'id': 20430, 'synset': 'jewelweed.n.01', 'name': 'jewelweed'}, {'id': 20431, 'synset': 'geranium.n.01', 'name': 'geranium'}, {'id': 20432, 'synset': 'cranesbill.n.01', 'name': 'cranesbill'}, {'id': 20433, 'synset': 'wild_geranium.n.01', 'name': 'wild_geranium'}, {'id': 20434, 'synset': 'meadow_cranesbill.n.01', 'name': 'meadow_cranesbill'}, {'id': 20435, 'synset': "richardson's_geranium.n.01", 'name': "Richardson's_geranium"}, {'id': 20436, 'synset': 'herb_robert.n.01', 'name': 'herb_robert'}, {'id': 20437, 'synset': 'sticky_geranium.n.01', 'name': 'sticky_geranium'}, {'id': 20438, 'synset': "dove's_foot_geranium.n.01", 'name': "dove's_foot_geranium"}, {'id': 20439, 'synset': 'rose_geranium.n.01', 'name': 'rose_geranium'}, {'id': 20440, 'synset': 'fish_geranium.n.01', 'name': 'fish_geranium'}, {'id': 20441, 'synset': 'ivy_geranium.n.01', 'name': 'ivy_geranium'}, {'id': 20442, 'synset': 'apple_geranium.n.01', 'name': 'apple_geranium'}, {'id': 20443, 'synset': 'lemon_geranium.n.01', 'name': 'lemon_geranium'}, {'id': 20444, 'synset': 'storksbill.n.01', 'name': 'storksbill'}, {'id': 20445, 'synset': 'musk_clover.n.01', 'name': 'musk_clover'}, {'id': 20446, 'synset': 'incense_tree.n.01', 'name': 'incense_tree'}, {'id': 20447, 'synset': 'elephant_tree.n.01', 'name': 'elephant_tree'}, {'id': 20448, 'synset': 'gumbo-limbo.n.01', 'name': 'gumbo-limbo'}, {'id': 20449, 'synset': 'boswellia_carteri.n.01', 'name': 'Boswellia_carteri'}, {'id': 20450, 'synset': 'salai.n.01', 'name': 'salai'}, {'id': 20451, 'synset': 'balm_of_gilead.n.03', 'name': 'balm_of_gilead'}, {'id': 20452, 'synset': 'myrrh_tree.n.01', 'name': 'myrrh_tree'}, {'id': 20453, 'synset': 'protium_heptaphyllum.n.01', 'name': 'Protium_heptaphyllum'}, {'id': 20454, 'synset': 'protium_guianense.n.01', 'name': 'Protium_guianense'}, {'id': 20455, 'synset': 'water_starwort.n.01', 'name': 'water_starwort'}, {'id': 20456, 'synset': 'barbados_cherry.n.01', 'name': 'barbados_cherry'}, {'id': 20457, 'synset': 'mahogany.n.02', 'name': 'mahogany'}, {'id': 20458, 'synset': 'chinaberry.n.02', 'name': 'chinaberry'}, {'id': 20459, 'synset': 'neem.n.01', 'name': 'neem'}, {'id': 20460, 'synset': 'neem_seed.n.01', 'name': 'neem_seed'}, {'id': 20461, 'synset': 'spanish_cedar.n.01', 'name': 'Spanish_cedar'}, {'id': 20462, 'synset': 'satinwood.n.03', 'name': 'satinwood'}, {'id': 20463, 'synset': 'african_scented_mahogany.n.01', 'name': 'African_scented_mahogany'}, {'id': 20464, 'synset': 'silver_ash.n.01', 'name': 'silver_ash'}, {'id': 20465, 'synset': 'native_beech.n.01', 'name': 'native_beech'}, {'id': 20466, 'synset': 'bunji-bunji.n.01', 'name': 'bunji-bunji'}, {'id': 20467, 'synset': 'african_mahogany.n.01', 'name': 'African_mahogany'}, {'id': 20468, 'synset': 'lanseh_tree.n.01', 'name': 'lanseh_tree'}, {'id': 20469, 'synset': 'true_mahogany.n.01', 'name': 'true_mahogany'}, {'id': 20470, 'synset': 'honduras_mahogany.n.01', 'name': 'Honduras_mahogany'}, {'id': 20471, 'synset': 'philippine_mahogany.n.02', 'name': 'Philippine_mahogany'}, {'id': 20472, 'synset': 'caracolito.n.01', 'name': 'caracolito'}, {'id': 20473, 'synset': 'common_wood_sorrel.n.01', 'name': 'common_wood_sorrel'}, {'id': 20474, 'synset': 'bermuda_buttercup.n.01', 'name': 'Bermuda_buttercup'}, {'id': 20475, 'synset': 'creeping_oxalis.n.01', 'name': 'creeping_oxalis'}, {'id': 20476, 'synset': 'goatsfoot.n.01', 'name': 'goatsfoot'}, {'id': 20477, 'synset': 'violet_wood_sorrel.n.01', 'name': 'violet_wood_sorrel'}, {'id': 20478, 'synset': 'oca.n.01', 'name': 'oca'}, {'id': 20479, 'synset': 'carambola.n.01', 'name': 'carambola'}, {'id': 20480, 'synset': 'bilimbi.n.01', 'name': 'bilimbi'}, {'id': 20481, 'synset': 'milkwort.n.01', 'name': 'milkwort'}, {'id': 20482, 'synset': 'senega.n.02', 'name': 'senega'}, {'id': 20483, 'synset': 'orange_milkwort.n.01', 'name': 'orange_milkwort'}, {'id': 20484, 'synset': 'flowering_wintergreen.n.01', 'name': 'flowering_wintergreen'}, {'id': 20485, 'synset': 'seneca_snakeroot.n.01', 'name': 'Seneca_snakeroot'}, {'id': 20486, 'synset': 'common_milkwort.n.01', 'name': 'common_milkwort'}, {'id': 20487, 'synset': 'rue.n.01', 'name': 'rue'}, {'id': 20488, 'synset': 'citrus.n.02', 'name': 'citrus'}, {'id': 20489, 'synset': 'orange.n.03', 'name': 'orange'}, {'id': 20490, 'synset': 'sour_orange.n.01', 'name': 'sour_orange'}, {'id': 20491, 'synset': 'bergamot.n.01', 'name': 'bergamot'}, {'id': 20492, 'synset': 'pomelo.n.01', 'name': 'pomelo'}, {'id': 20493, 'synset': 'citron.n.02', 'name': 'citron'}, {'id': 20494, 'synset': 'grapefruit.n.01', 'name': 'grapefruit'}, {'id': 20495, 'synset': 'mandarin.n.01', 'name': 'mandarin'}, {'id': 20496, 'synset': 'tangerine.n.01', 'name': 'tangerine'}, {'id': 20497, 'synset': 'satsuma.n.01', 'name': 'satsuma'}, {'id': 20498, 'synset': 'sweet_orange.n.02', 'name': 'sweet_orange'}, {'id': 20499, 'synset': 'temple_orange.n.01', 'name': 'temple_orange'}, {'id': 20500, 'synset': 'tangelo.n.01', 'name': 'tangelo'}, {'id': 20501, 'synset': 'rangpur.n.01', 'name': 'rangpur'}, {'id': 20502, 'synset': 'lemon.n.03', 'name': 'lemon'}, {'id': 20503, 'synset': 'sweet_lemon.n.01', 'name': 'sweet_lemon'}, {'id': 20504, 'synset': 'lime.n.04', 'name': 'lime'}, {'id': 20505, 'synset': 'citrange.n.01', 'name': 'citrange'}, {'id': 20506, 'synset': 'fraxinella.n.01', 'name': 'fraxinella'}, {'id': 20507, 'synset': 'kumquat.n.01', 'name': 'kumquat'}, {'id': 20508, 'synset': 'marumi.n.01', 'name': 'marumi'}, {'id': 20509, 'synset': 'nagami.n.01', 'name': 'nagami'}, {'id': 20510, 'synset': 'cork_tree.n.01', 'name': 'cork_tree'}, {'id': 20511, 'synset': 'trifoliate_orange.n.01', 'name': 'trifoliate_orange'}, {'id': 20512, 'synset': 'prickly_ash.n.01', 'name': 'prickly_ash'}, {'id': 20513, 'synset': 'toothache_tree.n.01', 'name': 'toothache_tree'}, {'id': 20514, 'synset': "hercules'-club.n.01", 'name': "Hercules'-club"}, {'id': 20515, 'synset': 'bitterwood_tree.n.01', 'name': 'bitterwood_tree'}, {'id': 20516, 'synset': 'marupa.n.01', 'name': 'marupa'}, {'id': 20517, 'synset': 'paradise_tree.n.01', 'name': 'paradise_tree'}, {'id': 20518, 'synset': 'ailanthus.n.01', 'name': 'ailanthus'}, {'id': 20519, 'synset': 'tree_of_heaven.n.01', 'name': 'tree_of_heaven'}, {'id': 20520, 'synset': 'wild_mango.n.01', 'name': 'wild_mango'}, {'id': 20521, 'synset': 'pepper_tree.n.02', 'name': 'pepper_tree'}, {'id': 20522, 'synset': 'jamaica_quassia.n.02', 'name': 'Jamaica_quassia'}, {'id': 20523, 'synset': 'quassia.n.02', 'name': 'quassia'}, {'id': 20524, 'synset': 'nasturtium.n.01', 'name': 'nasturtium'}, {'id': 20525, 'synset': 'garden_nasturtium.n.01', 'name': 'garden_nasturtium'}, {'id': 20526, 'synset': 'bush_nasturtium.n.01', 'name': 'bush_nasturtium'}, {'id': 20527, 'synset': 'canarybird_flower.n.01', 'name': 'canarybird_flower'}, {'id': 20528, 'synset': 'bean_caper.n.01', 'name': 'bean_caper'}, {'id': 20529, 'synset': 'palo_santo.n.01', 'name': 'palo_santo'}, {'id': 20530, 'synset': 'lignum_vitae.n.02', 'name': 'lignum_vitae'}, {'id': 20531, 'synset': 'creosote_bush.n.01', 'name': 'creosote_bush'}, {'id': 20532, 'synset': 'caltrop.n.01', 'name': 'caltrop'}, {'id': 20533, 'synset': 'willow.n.01', 'name': 'willow'}, {'id': 20534, 'synset': 'osier.n.02', 'name': 'osier'}, {'id': 20535, 'synset': 'white_willow.n.01', 'name': 'white_willow'}, {'id': 20536, 'synset': 'silver_willow.n.01', 'name': 'silver_willow'}, {'id': 20537, 'synset': 'golden_willow.n.01', 'name': 'golden_willow'}, {'id': 20538, 'synset': 'cricket-bat_willow.n.01', 'name': 'cricket-bat_willow'}, {'id': 20539, 'synset': 'arctic_willow.n.01', 'name': 'arctic_willow'}, {'id': 20540, 'synset': 'weeping_willow.n.01', 'name': 'weeping_willow'}, {'id': 20541, 'synset': 'wisconsin_weeping_willow.n.01', 'name': 'Wisconsin_weeping_willow'}, {'id': 20542, 'synset': 'pussy_willow.n.01', 'name': 'pussy_willow'}, {'id': 20543, 'synset': 'sallow.n.01', 'name': 'sallow'}, {'id': 20544, 'synset': 'goat_willow.n.01', 'name': 'goat_willow'}, {'id': 20545, 'synset': 'peachleaf_willow.n.01', 'name': 'peachleaf_willow'}, {'id': 20546, 'synset': 'almond_willow.n.01', 'name': 'almond_willow'}, {'id': 20547, 'synset': 'hoary_willow.n.01', 'name': 'hoary_willow'}, {'id': 20548, 'synset': 'crack_willow.n.01', 'name': 'crack_willow'}, {'id': 20549, 'synset': 'prairie_willow.n.01', 'name': 'prairie_willow'}, {'id': 20550, 'synset': 'dwarf_willow.n.01', 'name': 'dwarf_willow'}, {'id': 20551, 'synset': 'grey_willow.n.01', 'name': 'grey_willow'}, {'id': 20552, 'synset': 'arroyo_willow.n.01', 'name': 'arroyo_willow'}, {'id': 20553, 'synset': 'shining_willow.n.01', 'name': 'shining_willow'}, {'id': 20554, 'synset': 'swamp_willow.n.01', 'name': 'swamp_willow'}, {'id': 20555, 'synset': 'bay_willow.n.01', 'name': 'bay_willow'}, {'id': 20556, 'synset': 'purple_willow.n.01', 'name': 'purple_willow'}, {'id': 20557, 'synset': 'balsam_willow.n.01', 'name': 'balsam_willow'}, {'id': 20558, 'synset': 'creeping_willow.n.01', 'name': 'creeping_willow'}, {'id': 20559, 'synset': 'sitka_willow.n.01', 'name': 'Sitka_willow'}, {'id': 20560, 'synset': 'dwarf_grey_willow.n.01', 'name': 'dwarf_grey_willow'}, {'id': 20561, 'synset': 'bearberry_willow.n.01', 'name': 'bearberry_willow'}, {'id': 20562, 'synset': 'common_osier.n.01', 'name': 'common_osier'}, {'id': 20563, 'synset': 'poplar.n.02', 'name': 'poplar'}, {'id': 20564, 'synset': 'balsam_poplar.n.01', 'name': 'balsam_poplar'}, {'id': 20565, 'synset': 'white_poplar.n.01', 'name': 'white_poplar'}, {'id': 20566, 'synset': 'grey_poplar.n.01', 'name': 'grey_poplar'}, {'id': 20567, 'synset': 'black_poplar.n.01', 'name': 'black_poplar'}, {'id': 20568, 'synset': 'lombardy_poplar.n.01', 'name': 'Lombardy_poplar'}, {'id': 20569, 'synset': 'cottonwood.n.01', 'name': 'cottonwood'}, {'id': 20570, 'synset': 'eastern_cottonwood.n.01', 'name': 'Eastern_cottonwood'}, {'id': 20571, 'synset': 'black_cottonwood.n.02', 'name': 'black_cottonwood'}, {'id': 20572, 'synset': 'swamp_cottonwood.n.01', 'name': 'swamp_cottonwood'}, {'id': 20573, 'synset': 'aspen.n.01', 'name': 'aspen'}, {'id': 20574, 'synset': 'quaking_aspen.n.01', 'name': 'quaking_aspen'}, {'id': 20575, 'synset': 'american_quaking_aspen.n.01', 'name': 'American_quaking_aspen'}, {'id': 20576, 'synset': 'canadian_aspen.n.01', 'name': 'Canadian_aspen'}, {'id': 20577, 'synset': 'sandalwood_tree.n.01', 'name': 'sandalwood_tree'}, {'id': 20578, 'synset': 'quandong.n.01', 'name': 'quandong'}, {'id': 20579, 'synset': 'rabbitwood.n.01', 'name': 'rabbitwood'}, {'id': 20580, 'synset': 'loranthaceae.n.01', 'name': 'Loranthaceae'}, {'id': 20581, 'synset': 'mistletoe.n.03', 'name': 'mistletoe'}, {'id': 20582, 'synset': 'american_mistletoe.n.02', 'name': 'American_mistletoe'}, {'id': 20583, 'synset': 'mistletoe.n.02', 'name': 'mistletoe'}, {'id': 20584, 'synset': 'american_mistletoe.n.01', 'name': 'American_mistletoe'}, {'id': 20585, 'synset': 'aalii.n.01', 'name': 'aalii'}, {'id': 20586, 'synset': 'soapberry.n.01', 'name': 'soapberry'}, {'id': 20587, 'synset': 'wild_china_tree.n.01', 'name': 'wild_China_tree'}, {'id': 20588, 'synset': 'china_tree.n.01', 'name': 'China_tree'}, {'id': 20589, 'synset': 'akee.n.01', 'name': 'akee'}, {'id': 20590, 'synset': 'soapberry_vine.n.01', 'name': 'soapberry_vine'}, {'id': 20591, 'synset': 'heartseed.n.01', 'name': 'heartseed'}, {'id': 20592, 'synset': 'balloon_vine.n.01', 'name': 'balloon_vine'}, {'id': 20593, 'synset': 'longan.n.01', 'name': 'longan'}, {'id': 20594, 'synset': 'harpullia.n.01', 'name': 'harpullia'}, {'id': 20595, 'synset': 'harpulla.n.01', 'name': 'harpulla'}, {'id': 20596, 'synset': 'moreton_bay_tulipwood.n.01', 'name': 'Moreton_Bay_tulipwood'}, {'id': 20597, 'synset': 'litchi.n.01', 'name': 'litchi'}, {'id': 20598, 'synset': 'spanish_lime.n.01', 'name': 'Spanish_lime'}, {'id': 20599, 'synset': 'rambutan.n.01', 'name': 'rambutan'}, {'id': 20600, 'synset': 'pulasan.n.01', 'name': 'pulasan'}, {'id': 20601, 'synset': 'pachysandra.n.01', 'name': 'pachysandra'}, {'id': 20602, 'synset': 'allegheny_spurge.n.01', 'name': 'Allegheny_spurge'}, {'id': 20603, 'synset': 'bittersweet.n.02', 'name': 'bittersweet'}, {'id': 20604, 'synset': 'spindle_tree.n.01', 'name': 'spindle_tree'}, {'id': 20605, 'synset': 'winged_spindle_tree.n.01', 'name': 'winged_spindle_tree'}, {'id': 20606, 'synset': 'wahoo.n.02', 'name': 'wahoo'}, {'id': 20607, 'synset': 'strawberry_bush.n.01', 'name': 'strawberry_bush'}, {'id': 20608, 'synset': 'evergreen_bittersweet.n.01', 'name': 'evergreen_bittersweet'}, {'id': 20609, 'synset': 'cyrilla.n.01', 'name': 'cyrilla'}, {'id': 20610, 'synset': 'titi.n.01', 'name': 'titi'}, {'id': 20611, 'synset': 'crowberry.n.01', 'name': 'crowberry'}, {'id': 20612, 'synset': 'maple.n.02', 'name': 'maple'}, {'id': 20613, 'synset': 'silver_maple.n.01', 'name': 'silver_maple'}, {'id': 20614, 'synset': 'sugar_maple.n.01', 'name': 'sugar_maple'}, {'id': 20615, 'synset': 'red_maple.n.01', 'name': 'red_maple'}, {'id': 20616, 'synset': 'moosewood.n.01', 'name': 'moosewood'}, {'id': 20617, 'synset': 'oregon_maple.n.01', 'name': 'Oregon_maple'}, {'id': 20618, 'synset': 'dwarf_maple.n.01', 'name': 'dwarf_maple'}, {'id': 20619, 'synset': 'mountain_maple.n.01', 'name': 'mountain_maple'}, {'id': 20620, 'synset': 'vine_maple.n.01', 'name': 'vine_maple'}, {'id': 20621, 'synset': 'hedge_maple.n.01', 'name': 'hedge_maple'}, {'id': 20622, 'synset': 'norway_maple.n.01', 'name': 'Norway_maple'}, {'id': 20623, 'synset': 'sycamore.n.03', 'name': 'sycamore'}, {'id': 20624, 'synset': 'box_elder.n.01', 'name': 'box_elder'}, {'id': 20625, 'synset': 'california_box_elder.n.01', 'name': 'California_box_elder'}, {'id': 20626, 'synset': 'pointed-leaf_maple.n.01', 'name': 'pointed-leaf_maple'}, {'id': 20627, 'synset': 'japanese_maple.n.02', 'name': 'Japanese_maple'}, {'id': 20628, 'synset': 'japanese_maple.n.01', 'name': 'Japanese_maple'}, {'id': 20629, 'synset': 'holly.n.01', 'name': 'holly'}, {'id': 20630, 'synset': 'chinese_holly.n.01', 'name': 'Chinese_holly'}, {'id': 20631, 'synset': 'bearberry.n.02', 'name': 'bearberry'}, {'id': 20632, 'synset': 'inkberry.n.01', 'name': 'inkberry'}, {'id': 20633, 'synset': 'mate.n.07', 'name': 'mate'}, {'id': 20634, 'synset': 'american_holly.n.01', 'name': 'American_holly'}, {'id': 20635, 'synset': 'low_gallberry_holly.n.01', 'name': 'low_gallberry_holly'}, {'id': 20636, 'synset': 'tall_gallberry_holly.n.01', 'name': 'tall_gallberry_holly'}, {'id': 20637, 'synset': 'yaupon_holly.n.01', 'name': 'yaupon_holly'}, {'id': 20638, 'synset': 'deciduous_holly.n.01', 'name': 'deciduous_holly'}, {'id': 20639, 'synset': 'juneberry_holly.n.01', 'name': 'juneberry_holly'}, {'id': 20640, 'synset': 'largeleaf_holly.n.01', 'name': 'largeleaf_holly'}, {'id': 20641, 'synset': 'geogia_holly.n.01', 'name': 'Geogia_holly'}, {'id': 20642, 'synset': 'common_winterberry_holly.n.01', 'name': 'common_winterberry_holly'}, {'id': 20643, 'synset': 'smooth_winterberry_holly.n.01', 'name': 'smooth_winterberry_holly'}, {'id': 20644, 'synset': 'cashew.n.01', 'name': 'cashew'}, {'id': 20645, 'synset': 'goncalo_alves.n.01', 'name': 'goncalo_alves'}, {'id': 20646, 'synset': 'venetian_sumac.n.01', 'name': 'Venetian_sumac'}, {'id': 20647, 'synset': 'laurel_sumac.n.01', 'name': 'laurel_sumac'}, {'id': 20648, 'synset': 'mango.n.01', 'name': 'mango'}, {'id': 20649, 'synset': 'pistachio.n.01', 'name': 'pistachio'}, {'id': 20650, 'synset': 'terebinth.n.01', 'name': 'terebinth'}, {'id': 20651, 'synset': 'mastic.n.03', 'name': 'mastic'}, {'id': 20652, 'synset': 'australian_sumac.n.01', 'name': 'Australian_sumac'}, {'id': 20653, 'synset': 'sumac.n.02', 'name': 'sumac'}, {'id': 20654, 'synset': 'smooth_sumac.n.01', 'name': 'smooth_sumac'}, {'id': 20655, 'synset': 'sugar-bush.n.01', 'name': 'sugar-bush'}, {'id': 20656, 'synset': 'staghorn_sumac.n.01', 'name': 'staghorn_sumac'}, {'id': 20657, 'synset': 'squawbush.n.01', 'name': 'squawbush'}, {'id': 20658, 'synset': 'aroeira_blanca.n.01', 'name': 'aroeira_blanca'}, {'id': 20659, 'synset': 'pepper_tree.n.01', 'name': 'pepper_tree'}, {'id': 20660, 'synset': 'brazilian_pepper_tree.n.01', 'name': 'Brazilian_pepper_tree'}, {'id': 20661, 'synset': 'hog_plum.n.01', 'name': 'hog_plum'}, {'id': 20662, 'synset': 'mombin.n.01', 'name': 'mombin'}, {'id': 20663, 'synset': 'poison_ash.n.01', 'name': 'poison_ash'}, {'id': 20664, 'synset': 'poison_ivy.n.02', 'name': 'poison_ivy'}, {'id': 20665, 'synset': 'western_poison_oak.n.01', 'name': 'western_poison_oak'}, {'id': 20666, 'synset': 'eastern_poison_oak.n.01', 'name': 'eastern_poison_oak'}, {'id': 20667, 'synset': 'varnish_tree.n.02', 'name': 'varnish_tree'}, {'id': 20668, 'synset': 'horse_chestnut.n.01', 'name': 'horse_chestnut'}, {'id': 20669, 'synset': 'buckeye.n.01', 'name': 'buckeye'}, {'id': 20670, 'synset': 'sweet_buckeye.n.01', 'name': 'sweet_buckeye'}, {'id': 20671, 'synset': 'ohio_buckeye.n.01', 'name': 'Ohio_buckeye'}, {'id': 20672, 'synset': 'dwarf_buckeye.n.01', 'name': 'dwarf_buckeye'}, {'id': 20673, 'synset': 'red_buckeye.n.01', 'name': 'red_buckeye'}, {'id': 20674, 'synset': 'particolored_buckeye.n.01', 'name': 'particolored_buckeye'}, {'id': 20675, 'synset': 'ebony.n.03', 'name': 'ebony'}, {'id': 20676, 'synset': 'marblewood.n.02', 'name': 'marblewood'}, {'id': 20677, 'synset': 'marblewood.n.01', 'name': 'marblewood'}, {'id': 20678, 'synset': 'persimmon.n.01', 'name': 'persimmon'}, {'id': 20679, 'synset': 'japanese_persimmon.n.01', 'name': 'Japanese_persimmon'}, {'id': 20680, 'synset': 'american_persimmon.n.01', 'name': 'American_persimmon'}, {'id': 20681, 'synset': 'date_plum.n.01', 'name': 'date_plum'}, {'id': 20682, 'synset': 'buckthorn.n.02', 'name': 'buckthorn'}, {'id': 20683, 'synset': 'southern_buckthorn.n.01', 'name': 'southern_buckthorn'}, {'id': 20684, 'synset': 'false_buckthorn.n.01', 'name': 'false_buckthorn'}, {'id': 20685, 'synset': 'star_apple.n.01', 'name': 'star_apple'}, {'id': 20686, 'synset': 'satinleaf.n.01', 'name': 'satinleaf'}, {'id': 20687, 'synset': 'balata.n.02', 'name': 'balata'}, {'id': 20688, 'synset': 'sapodilla.n.01', 'name': 'sapodilla'}, {'id': 20689, 'synset': 'gutta-percha_tree.n.02', 'name': 'gutta-percha_tree'}, {'id': 20690, 'synset': 'gutta-percha_tree.n.01', 'name': 'gutta-percha_tree'}, {'id': 20691, 'synset': 'canistel.n.01', 'name': 'canistel'}, {'id': 20692, 'synset': 'marmalade_tree.n.01', 'name': 'marmalade_tree'}, {'id': 20693, 'synset': 'sweetleaf.n.01', 'name': 'sweetleaf'}, {'id': 20694, 'synset': 'asiatic_sweetleaf.n.01', 'name': 'Asiatic_sweetleaf'}, {'id': 20695, 'synset': 'styrax.n.01', 'name': 'styrax'}, {'id': 20696, 'synset': 'snowbell.n.01', 'name': 'snowbell'}, {'id': 20697, 'synset': 'japanese_snowbell.n.01', 'name': 'Japanese_snowbell'}, {'id': 20698, 'synset': 'texas_snowbell.n.01', 'name': 'Texas_snowbell'}, {'id': 20699, 'synset': 'silver-bell_tree.n.01', 'name': 'silver-bell_tree'}, {'id': 20700, 'synset': 'carnivorous_plant.n.01', 'name': 'carnivorous_plant'}, {'id': 20701, 'synset': 'pitcher_plant.n.01', 'name': 'pitcher_plant'}, {'id': 20702, 'synset': 'common_pitcher_plant.n.01', 'name': 'common_pitcher_plant'}, {'id': 20703, 'synset': 'hooded_pitcher_plant.n.01', 'name': 'hooded_pitcher_plant'}, {'id': 20704, 'synset': "huntsman's_horn.n.01", 'name': "huntsman's_horn"}, {'id': 20705, 'synset': 'tropical_pitcher_plant.n.01', 'name': 'tropical_pitcher_plant'}, {'id': 20706, 'synset': 'sundew.n.01', 'name': 'sundew'}, {'id': 20707, 'synset': "venus's_flytrap.n.01", 'name': "Venus's_flytrap"}, {'id': 20708, 'synset': 'waterwheel_plant.n.01', 'name': 'waterwheel_plant'}, {'id': 20709, 'synset': 'drosophyllum_lusitanicum.n.01', 'name': 'Drosophyllum_lusitanicum'}, {'id': 20710, 'synset': 'roridula.n.01', 'name': 'roridula'}, {'id': 20711, 'synset': 'australian_pitcher_plant.n.01', 'name': 'Australian_pitcher_plant'}, {'id': 20712, 'synset': 'sedum.n.01', 'name': 'sedum'}, {'id': 20713, 'synset': 'stonecrop.n.01', 'name': 'stonecrop'}, {'id': 20714, 'synset': 'rose-root.n.01', 'name': 'rose-root'}, {'id': 20715, 'synset': 'orpine.n.01', 'name': 'orpine'}, {'id': 20716, 'synset': 'pinwheel.n.01', 'name': 'pinwheel'}, {'id': 20717, 'synset': 'christmas_bush.n.01', 'name': 'Christmas_bush'}, {'id': 20718, 'synset': 'hortensia.n.01', 'name': 'hortensia'}, {'id': 20719, 'synset': 'fall-blooming_hydrangea.n.01', 'name': 'fall-blooming_hydrangea'}, {'id': 20720, 'synset': 'carpenteria.n.01', 'name': 'carpenteria'}, {'id': 20721, 'synset': 'decumary.n.01', 'name': 'decumary'}, {'id': 20722, 'synset': 'deutzia.n.01', 'name': 'deutzia'}, {'id': 20723, 'synset': 'philadelphus.n.01', 'name': 'philadelphus'}, {'id': 20724, 'synset': 'mock_orange.n.01', 'name': 'mock_orange'}, {'id': 20725, 'synset': 'saxifrage.n.01', 'name': 'saxifrage'}, {'id': 20726, 'synset': 'yellow_mountain_saxifrage.n.01', 'name': 'yellow_mountain_saxifrage'}, {'id': 20727, 'synset': 'meadow_saxifrage.n.01', 'name': 'meadow_saxifrage'}, {'id': 20728, 'synset': 'mossy_saxifrage.n.01', 'name': 'mossy_saxifrage'}, {'id': 20729, 'synset': 'western_saxifrage.n.01', 'name': 'western_saxifrage'}, {'id': 20730, 'synset': 'purple_saxifrage.n.01', 'name': 'purple_saxifrage'}, {'id': 20731, 'synset': 'star_saxifrage.n.01', 'name': 'star_saxifrage'}, {'id': 20732, 'synset': 'strawberry_geranium.n.01', 'name': 'strawberry_geranium'}, {'id': 20733, 'synset': 'astilbe.n.01', 'name': 'astilbe'}, {'id': 20734, 'synset': 'false_goatsbeard.n.01', 'name': 'false_goatsbeard'}, {'id': 20735, 'synset': 'dwarf_astilbe.n.01', 'name': 'dwarf_astilbe'}, {'id': 20736, 'synset': 'spirea.n.01', 'name': 'spirea'}, {'id': 20737, 'synset': 'bergenia.n.01', 'name': 'bergenia'}, {'id': 20738, 'synset': 'coast_boykinia.n.01', 'name': 'coast_boykinia'}, {'id': 20739, 'synset': 'golden_saxifrage.n.01', 'name': 'golden_saxifrage'}, {'id': 20740, 'synset': 'umbrella_plant.n.01', 'name': 'umbrella_plant'}, {'id': 20741, 'synset': 'bridal_wreath.n.01', 'name': 'bridal_wreath'}, {'id': 20742, 'synset': 'alumroot.n.01', 'name': 'alumroot'}, {'id': 20743, 'synset': 'coralbells.n.01', 'name': 'coralbells'}, {'id': 20744, 'synset': 'leatherleaf_saxifrage.n.01', 'name': 'leatherleaf_saxifrage'}, {'id': 20745, 'synset': 'woodland_star.n.01', 'name': 'woodland_star'}, {'id': 20746, 'synset': 'prairie_star.n.01', 'name': 'prairie_star'}, {'id': 20747, 'synset': 'miterwort.n.01', 'name': 'miterwort'}, {'id': 20748, 'synset': "five-point_bishop's_cap.n.01", 'name': "five-point_bishop's_cap"}, {'id': 20749, 'synset': 'parnassia.n.01', 'name': 'parnassia'}, {'id': 20750, 'synset': 'bog_star.n.01', 'name': 'bog_star'}, {'id': 20751, 'synset': 'fringed_grass_of_parnassus.n.01', 'name': 'fringed_grass_of_Parnassus'}, {'id': 20752, 'synset': 'false_alumroot.n.01', 'name': 'false_alumroot'}, {'id': 20753, 'synset': 'foamflower.n.01', 'name': 'foamflower'}, {'id': 20754, 'synset': 'false_miterwort.n.01', 'name': 'false_miterwort'}, {'id': 20755, 'synset': 'pickaback_plant.n.01', 'name': 'pickaback_plant'}, {'id': 20756, 'synset': 'currant.n.02', 'name': 'currant'}, {'id': 20757, 'synset': 'black_currant.n.01', 'name': 'black_currant'}, {'id': 20758, 'synset': 'white_currant.n.01', 'name': 'white_currant'}, {'id': 20759, 'synset': 'gooseberry.n.01', 'name': 'gooseberry'}, {'id': 20760, 'synset': 'plane_tree.n.01', 'name': 'plane_tree'}, {'id': 20761, 'synset': 'london_plane.n.01', 'name': 'London_plane'}, {'id': 20762, 'synset': 'american_sycamore.n.01', 'name': 'American_sycamore'}, {'id': 20763, 'synset': 'oriental_plane.n.01', 'name': 'oriental_plane'}, {'id': 20764, 'synset': 'california_sycamore.n.01', 'name': 'California_sycamore'}, {'id': 20765, 'synset': 'arizona_sycamore.n.01', 'name': 'Arizona_sycamore'}, {'id': 20766, 'synset': 'greek_valerian.n.01', 'name': 'Greek_valerian'}, {'id': 20767, 'synset': "northern_jacob's_ladder.n.01", 'name': "northern_Jacob's_ladder"}, {'id': 20768, 'synset': 'skunkweed.n.01', 'name': 'skunkweed'}, {'id': 20769, 'synset': 'phlox.n.01', 'name': 'phlox'}, {'id': 20770, 'synset': 'moss_pink.n.02', 'name': 'moss_pink'}, {'id': 20771, 'synset': 'evening-snow.n.01', 'name': 'evening-snow'}, {'id': 20772, 'synset': 'acanthus.n.01', 'name': 'acanthus'}, {'id': 20773, 'synset': "bear's_breech.n.01", 'name': "bear's_breech"}, {'id': 20774, 'synset': 'caricature_plant.n.01', 'name': 'caricature_plant'}, {'id': 20775, 'synset': 'black-eyed_susan.n.01', 'name': 'black-eyed_Susan'}, {'id': 20776, 'synset': 'catalpa.n.01', 'name': 'catalpa'}, {'id': 20777, 'synset': 'catalpa_bignioides.n.01', 'name': 'Catalpa_bignioides'}, {'id': 20778, 'synset': 'catalpa_speciosa.n.01', 'name': 'Catalpa_speciosa'}, {'id': 20779, 'synset': 'desert_willow.n.01', 'name': 'desert_willow'}, {'id': 20780, 'synset': 'calabash.n.02', 'name': 'calabash'}, {'id': 20781, 'synset': 'calabash.n.01', 'name': 'calabash'}, {'id': 20782, 'synset': 'borage.n.01', 'name': 'borage'}, {'id': 20783, 'synset': 'common_amsinckia.n.01', 'name': 'common_amsinckia'}, {'id': 20784, 'synset': 'anchusa.n.01', 'name': 'anchusa'}, {'id': 20785, 'synset': 'bugloss.n.01', 'name': 'bugloss'}, {'id': 20786, 'synset': 'cape_forget-me-not.n.02', 'name': 'cape_forget-me-not'}, {'id': 20787, 'synset': 'cape_forget-me-not.n.01', 'name': 'cape_forget-me-not'}, {'id': 20788, 'synset': 'spanish_elm.n.02', 'name': 'Spanish_elm'}, {'id': 20789, 'synset': 'princewood.n.01', 'name': 'princewood'}, {'id': 20790, 'synset': 'chinese_forget-me-not.n.01', 'name': 'Chinese_forget-me-not'}, {'id': 20791, 'synset': "hound's-tongue.n.02", 'name': "hound's-tongue"}, {'id': 20792, 'synset': "hound's-tongue.n.01", 'name': "hound's-tongue"}, {'id': 20793, 'synset': 'blueweed.n.01', 'name': 'blueweed'}, {'id': 20794, 'synset': "beggar's_lice.n.01", 'name': "beggar's_lice"}, {'id': 20795, 'synset': 'gromwell.n.01', 'name': 'gromwell'}, {'id': 20796, 'synset': 'puccoon.n.01', 'name': 'puccoon'}, {'id': 20797, 'synset': 'virginia_bluebell.n.01', 'name': 'Virginia_bluebell'}, {'id': 20798, 'synset': 'garden_forget-me-not.n.01', 'name': 'garden_forget-me-not'}, {'id': 20799, 'synset': 'forget-me-not.n.01', 'name': 'forget-me-not'}, {'id': 20800, 'synset': 'false_gromwell.n.01', 'name': 'false_gromwell'}, {'id': 20801, 'synset': 'comfrey.n.01', 'name': 'comfrey'}, {'id': 20802, 'synset': 'common_comfrey.n.01', 'name': 'common_comfrey'}, {'id': 20803, 'synset': 'convolvulus.n.01', 'name': 'convolvulus'}, {'id': 20804, 'synset': 'bindweed.n.01', 'name': 'bindweed'}, {'id': 20805, 'synset': 'field_bindweed.n.01', 'name': 'field_bindweed'}, {'id': 20806, 'synset': 'scammony.n.03', 'name': 'scammony'}, {'id': 20807, 'synset': 'silverweed.n.01', 'name': 'silverweed'}, {'id': 20808, 'synset': 'dodder.n.01', 'name': 'dodder'}, {'id': 20809, 'synset': 'dichondra.n.01', 'name': 'dichondra'}, {'id': 20810, 'synset': 'cypress_vine.n.01', 'name': 'cypress_vine'}, {'id': 20811, 'synset': 'moonflower.n.01', 'name': 'moonflower'}, {'id': 20812, 'synset': 'wild_potato_vine.n.01', 'name': 'wild_potato_vine'}, {'id': 20813, 'synset': 'red_morning-glory.n.01', 'name': 'red_morning-glory'}, {'id': 20814, 'synset': 'man-of-the-earth.n.01', 'name': 'man-of-the-earth'}, {'id': 20815, 'synset': 'scammony.n.01', 'name': 'scammony'}, {'id': 20816, 'synset': 'japanese_morning_glory.n.01', 'name': 'Japanese_morning_glory'}, {'id': 20817, 'synset': 'imperial_japanese_morning_glory.n.01', 'name': 'imperial_Japanese_morning_glory'}, {'id': 20818, 'synset': 'gesneriad.n.01', 'name': 'gesneriad'}, {'id': 20819, 'synset': 'gesneria.n.01', 'name': 'gesneria'}, {'id': 20820, 'synset': 'achimenes.n.01', 'name': 'achimenes'}, {'id': 20821, 'synset': 'aeschynanthus.n.01', 'name': 'aeschynanthus'}, {'id': 20822, 'synset': 'lace-flower_vine.n.01', 'name': 'lace-flower_vine'}, {'id': 20823, 'synset': 'columnea.n.01', 'name': 'columnea'}, {'id': 20824, 'synset': 'episcia.n.01', 'name': 'episcia'}, {'id': 20825, 'synset': 'gloxinia.n.01', 'name': 'gloxinia'}, {'id': 20826, 'synset': 'canterbury_bell.n.01', 'name': 'Canterbury_bell'}, {'id': 20827, 'synset': 'kohleria.n.01', 'name': 'kohleria'}, {'id': 20828, 'synset': 'african_violet.n.01', 'name': 'African_violet'}, {'id': 20829, 'synset': 'streptocarpus.n.01', 'name': 'streptocarpus'}, {'id': 20830, 'synset': 'cape_primrose.n.01', 'name': 'Cape_primrose'}, {'id': 20831, 'synset': 'waterleaf.n.01', 'name': 'waterleaf'}, {'id': 20832, 'synset': 'virginia_waterleaf.n.01', 'name': 'Virginia_waterleaf'}, {'id': 20833, 'synset': 'yellow_bells.n.01', 'name': 'yellow_bells'}, {'id': 20834, 'synset': 'yerba_santa.n.01', 'name': 'yerba_santa'}, {'id': 20835, 'synset': 'nemophila.n.01', 'name': 'nemophila'}, {'id': 20836, 'synset': 'baby_blue-eyes.n.01', 'name': 'baby_blue-eyes'}, {'id': 20837, 'synset': 'five-spot.n.02', 'name': 'five-spot'}, {'id': 20838, 'synset': 'scorpionweed.n.01', 'name': 'scorpionweed'}, {'id': 20839, 'synset': 'california_bluebell.n.02', 'name': 'California_bluebell'}, {'id': 20840, 'synset': 'california_bluebell.n.01', 'name': 'California_bluebell'}, {'id': 20841, 'synset': 'fiddleneck.n.01', 'name': 'fiddleneck'}, {'id': 20842, 'synset': 'fiesta_flower.n.01', 'name': 'fiesta_flower'}, {'id': 20843, 'synset': 'basil_thyme.n.01', 'name': 'basil_thyme'}, {'id': 20844, 'synset': 'giant_hyssop.n.01', 'name': 'giant_hyssop'}, {'id': 20845, 'synset': 'yellow_giant_hyssop.n.01', 'name': 'yellow_giant_hyssop'}, {'id': 20846, 'synset': 'anise_hyssop.n.01', 'name': 'anise_hyssop'}, {'id': 20847, 'synset': 'mexican_hyssop.n.01', 'name': 'Mexican_hyssop'}, {'id': 20848, 'synset': 'bugle.n.02', 'name': 'bugle'}, {'id': 20849, 'synset': 'creeping_bugle.n.01', 'name': 'creeping_bugle'}, {'id': 20850, 'synset': 'erect_bugle.n.01', 'name': 'erect_bugle'}, {'id': 20851, 'synset': 'pyramid_bugle.n.01', 'name': 'pyramid_bugle'}, {'id': 20852, 'synset': 'wood_mint.n.01', 'name': 'wood_mint'}, {'id': 20853, 'synset': 'hairy_wood_mint.n.01', 'name': 'hairy_wood_mint'}, {'id': 20854, 'synset': 'downy_wood_mint.n.01', 'name': 'downy_wood_mint'}, {'id': 20855, 'synset': 'calamint.n.01', 'name': 'calamint'}, {'id': 20856, 'synset': 'common_calamint.n.01', 'name': 'common_calamint'}, {'id': 20857, 'synset': 'large-flowered_calamint.n.01', 'name': 'large-flowered_calamint'}, {'id': 20858, 'synset': 'lesser_calamint.n.01', 'name': 'lesser_calamint'}, {'id': 20859, 'synset': 'wild_basil.n.01', 'name': 'wild_basil'}, {'id': 20860, 'synset': 'horse_balm.n.01', 'name': 'horse_balm'}, {'id': 20861, 'synset': 'coleus.n.01', 'name': 'coleus'}, {'id': 20862, 'synset': 'country_borage.n.01', 'name': 'country_borage'}, {'id': 20863, 'synset': 'painted_nettle.n.01', 'name': 'painted_nettle'}, {'id': 20864, 'synset': 'apalachicola_rosemary.n.01', 'name': 'Apalachicola_rosemary'}, {'id': 20865, 'synset': 'dragonhead.n.01', 'name': 'dragonhead'}, {'id': 20866, 'synset': 'elsholtzia.n.01', 'name': 'elsholtzia'}, {'id': 20867, 'synset': 'hemp_nettle.n.01', 'name': 'hemp_nettle'}, {'id': 20868, 'synset': 'ground_ivy.n.01', 'name': 'ground_ivy'}, {'id': 20869, 'synset': 'pennyroyal.n.02', 'name': 'pennyroyal'}, {'id': 20870, 'synset': 'hyssop.n.01', 'name': 'hyssop'}, {'id': 20871, 'synset': 'dead_nettle.n.02', 'name': 'dead_nettle'}, {'id': 20872, 'synset': 'white_dead_nettle.n.01', 'name': 'white_dead_nettle'}, {'id': 20873, 'synset': 'henbit.n.01', 'name': 'henbit'}, {'id': 20874, 'synset': 'english_lavender.n.01', 'name': 'English_lavender'}, {'id': 20875, 'synset': 'french_lavender.n.02', 'name': 'French_lavender'}, {'id': 20876, 'synset': 'spike_lavender.n.01', 'name': 'spike_lavender'}, {'id': 20877, 'synset': 'dagga.n.01', 'name': 'dagga'}, {'id': 20878, 'synset': "lion's-ear.n.01", 'name': "lion's-ear"}, {'id': 20879, 'synset': 'motherwort.n.01', 'name': 'motherwort'}, {'id': 20880, 'synset': 'pitcher_sage.n.02', 'name': 'pitcher_sage'}, {'id': 20881, 'synset': 'bugleweed.n.01', 'name': 'bugleweed'}, {'id': 20882, 'synset': 'water_horehound.n.01', 'name': 'water_horehound'}, {'id': 20883, 'synset': 'gipsywort.n.01', 'name': 'gipsywort'}, {'id': 20884, 'synset': 'origanum.n.01', 'name': 'origanum'}, {'id': 20885, 'synset': 'oregano.n.01', 'name': 'oregano'}, {'id': 20886, 'synset': 'sweet_marjoram.n.01', 'name': 'sweet_marjoram'}, {'id': 20887, 'synset': 'horehound.n.01', 'name': 'horehound'}, {'id': 20888, 'synset': 'common_horehound.n.01', 'name': 'common_horehound'}, {'id': 20889, 'synset': 'lemon_balm.n.01', 'name': 'lemon_balm'}, {'id': 20890, 'synset': 'corn_mint.n.01', 'name': 'corn_mint'}, {'id': 20891, 'synset': 'water-mint.n.01', 'name': 'water-mint'}, {'id': 20892, 'synset': 'bergamot_mint.n.02', 'name': 'bergamot_mint'}, {'id': 20893, 'synset': 'horsemint.n.03', 'name': 'horsemint'}, {'id': 20894, 'synset': 'peppermint.n.01', 'name': 'peppermint'}, {'id': 20895, 'synset': 'spearmint.n.01', 'name': 'spearmint'}, {'id': 20896, 'synset': 'apple_mint.n.01', 'name': 'apple_mint'}, {'id': 20897, 'synset': 'pennyroyal.n.01', 'name': 'pennyroyal'}, {'id': 20898, 'synset': 'yerba_buena.n.01', 'name': 'yerba_buena'}, {'id': 20899, 'synset': 'molucca_balm.n.01', 'name': 'molucca_balm'}, {'id': 20900, 'synset': 'monarda.n.01', 'name': 'monarda'}, {'id': 20901, 'synset': 'bee_balm.n.02', 'name': 'bee_balm'}, {'id': 20902, 'synset': 'horsemint.n.02', 'name': 'horsemint'}, {'id': 20903, 'synset': 'bee_balm.n.01', 'name': 'bee_balm'}, {'id': 20904, 'synset': 'lemon_mint.n.01', 'name': 'lemon_mint'}, {'id': 20905, 'synset': 'plains_lemon_monarda.n.01', 'name': 'plains_lemon_monarda'}, {'id': 20906, 'synset': 'basil_balm.n.01', 'name': 'basil_balm'}, {'id': 20907, 'synset': 'mustang_mint.n.01', 'name': 'mustang_mint'}, {'id': 20908, 'synset': 'catmint.n.01', 'name': 'catmint'}, {'id': 20909, 'synset': 'basil.n.01', 'name': 'basil'}, {'id': 20910, 'synset': 'beefsteak_plant.n.01', 'name': 'beefsteak_plant'}, {'id': 20911, 'synset': 'phlomis.n.01', 'name': 'phlomis'}, {'id': 20912, 'synset': 'jerusalem_sage.n.01', 'name': 'Jerusalem_sage'}, {'id': 20913, 'synset': 'physostegia.n.01', 'name': 'physostegia'}, {'id': 20914, 'synset': 'plectranthus.n.01', 'name': 'plectranthus'}, {'id': 20915, 'synset': 'patchouli.n.01', 'name': 'patchouli'}, {'id': 20916, 'synset': 'self-heal.n.01', 'name': 'self-heal'}, {'id': 20917, 'synset': 'mountain_mint.n.01', 'name': 'mountain_mint'}, {'id': 20918, 'synset': 'rosemary.n.01', 'name': 'rosemary'}, {'id': 20919, 'synset': 'clary_sage.n.01', 'name': 'clary_sage'}, {'id': 20920, 'synset': 'purple_sage.n.01', 'name': 'purple_sage'}, {'id': 20921, 'synset': 'cancerweed.n.01', 'name': 'cancerweed'}, {'id': 20922, 'synset': 'common_sage.n.01', 'name': 'common_sage'}, {'id': 20923, 'synset': 'meadow_clary.n.01', 'name': 'meadow_clary'}, {'id': 20924, 'synset': 'clary.n.01', 'name': 'clary'}, {'id': 20925, 'synset': 'pitcher_sage.n.01', 'name': 'pitcher_sage'}, {'id': 20926, 'synset': 'mexican_mint.n.01', 'name': 'Mexican_mint'}, {'id': 20927, 'synset': 'wild_sage.n.01', 'name': 'wild_sage'}, {'id': 20928, 'synset': 'savory.n.01', 'name': 'savory'}, {'id': 20929, 'synset': 'summer_savory.n.01', 'name': 'summer_savory'}, {'id': 20930, 'synset': 'winter_savory.n.01', 'name': 'winter_savory'}, {'id': 20931, 'synset': 'skullcap.n.02', 'name': 'skullcap'}, {'id': 20932, 'synset': 'blue_pimpernel.n.01', 'name': 'blue_pimpernel'}, {'id': 20933, 'synset': 'hedge_nettle.n.02', 'name': 'hedge_nettle'}, {'id': 20934, 'synset': 'hedge_nettle.n.01', 'name': 'hedge_nettle'}, {'id': 20935, 'synset': 'germander.n.01', 'name': 'germander'}, {'id': 20936, 'synset': 'american_germander.n.01', 'name': 'American_germander'}, {'id': 20937, 'synset': 'cat_thyme.n.01', 'name': 'cat_thyme'}, {'id': 20938, 'synset': 'wood_sage.n.01', 'name': 'wood_sage'}, {'id': 20939, 'synset': 'thyme.n.01', 'name': 'thyme'}, {'id': 20940, 'synset': 'common_thyme.n.01', 'name': 'common_thyme'}, {'id': 20941, 'synset': 'wild_thyme.n.01', 'name': 'wild_thyme'}, {'id': 20942, 'synset': 'blue_curls.n.01', 'name': 'blue_curls'}, {'id': 20943, 'synset': 'turpentine_camphor_weed.n.01', 'name': 'turpentine_camphor_weed'}, {'id': 20944, 'synset': 'bastard_pennyroyal.n.01', 'name': 'bastard_pennyroyal'}, {'id': 20945, 'synset': 'bladderwort.n.01', 'name': 'bladderwort'}, {'id': 20946, 'synset': 'butterwort.n.01', 'name': 'butterwort'}, {'id': 20947, 'synset': 'genlisea.n.01', 'name': 'genlisea'}, {'id': 20948, 'synset': 'martynia.n.01', 'name': 'martynia'}, {'id': 20949, 'synset': 'common_unicorn_plant.n.01', 'name': 'common_unicorn_plant'}, {'id': 20950, 'synset': "sand_devil's_claw.n.01", 'name': "sand_devil's_claw"}, {'id': 20951, 'synset': 'sweet_unicorn_plant.n.01', 'name': 'sweet_unicorn_plant'}, {'id': 20952, 'synset': 'figwort.n.01', 'name': 'figwort'}, {'id': 20953, 'synset': 'snapdragon.n.01', 'name': 'snapdragon'}, {'id': 20954, 'synset': 'white_snapdragon.n.01', 'name': 'white_snapdragon'}, {'id': 20955, 'synset': 'yellow_twining_snapdragon.n.01', 'name': 'yellow_twining_snapdragon'}, {'id': 20956, 'synset': 'mediterranean_snapdragon.n.01', 'name': 'Mediterranean_snapdragon'}, {'id': 20957, 'synset': 'kitten-tails.n.01', 'name': 'kitten-tails'}, {'id': 20958, 'synset': 'alpine_besseya.n.01', 'name': 'Alpine_besseya'}, {'id': 20959, 'synset': 'false_foxglove.n.02', 'name': 'false_foxglove'}, {'id': 20960, 'synset': 'false_foxglove.n.01', 'name': 'false_foxglove'}, {'id': 20961, 'synset': 'calceolaria.n.01', 'name': 'calceolaria'}, {'id': 20962, 'synset': 'indian_paintbrush.n.02', 'name': 'Indian_paintbrush'}, {'id': 20963, 'synset': 'desert_paintbrush.n.01', 'name': 'desert_paintbrush'}, {'id': 20964, 'synset': 'giant_red_paintbrush.n.01', 'name': 'giant_red_paintbrush'}, {'id': 20965, 'synset': 'great_plains_paintbrush.n.01', 'name': 'great_plains_paintbrush'}, {'id': 20966, 'synset': 'sulfur_paintbrush.n.01', 'name': 'sulfur_paintbrush'}, {'id': 20967, 'synset': 'shellflower.n.01', 'name': 'shellflower'}, {'id': 20968, 'synset': 'maiden_blue-eyed_mary.n.01', 'name': 'maiden_blue-eyed_Mary'}, {'id': 20969, 'synset': 'blue-eyed_mary.n.01', 'name': 'blue-eyed_Mary'}, {'id': 20970, 'synset': 'foxglove.n.01', 'name': 'foxglove'}, {'id': 20971, 'synset': 'common_foxglove.n.01', 'name': 'common_foxglove'}, {'id': 20972, 'synset': 'yellow_foxglove.n.01', 'name': 'yellow_foxglove'}, {'id': 20973, 'synset': 'gerardia.n.01', 'name': 'gerardia'}, {'id': 20974, 'synset': 'blue_toadflax.n.01', 'name': 'blue_toadflax'}, {'id': 20975, 'synset': 'toadflax.n.01', 'name': 'toadflax'}, {'id': 20976, 'synset': 'golden-beard_penstemon.n.01', 'name': 'golden-beard_penstemon'}, {'id': 20977, 'synset': 'scarlet_bugler.n.01', 'name': 'scarlet_bugler'}, {'id': 20978, 'synset': 'red_shrubby_penstemon.n.01', 'name': 'red_shrubby_penstemon'}, {'id': 20979, 'synset': 'platte_river_penstemon.n.01', 'name': 'Platte_River_penstemon'}, {'id': 20980, 'synset': 'hot-rock_penstemon.n.01', 'name': 'hot-rock_penstemon'}, {'id': 20981, 'synset': "jones'_penstemon.n.01", 'name': "Jones'_penstemon"}, {'id': 20982, 'synset': 'shrubby_penstemon.n.01', 'name': 'shrubby_penstemon'}, {'id': 20983, 'synset': 'narrow-leaf_penstemon.n.01', 'name': 'narrow-leaf_penstemon'}, {'id': 20984, 'synset': 'balloon_flower.n.01', 'name': 'balloon_flower'}, {'id': 20985, 'synset': "parry's_penstemon.n.01", 'name': "Parry's_penstemon"}, {'id': 20986, 'synset': 'rock_penstemon.n.01', 'name': 'rock_penstemon'}, {'id': 20987, 'synset': "rydberg's_penstemon.n.01", 'name': "Rydberg's_penstemon"}, {'id': 20988, 'synset': 'cascade_penstemon.n.01', 'name': 'cascade_penstemon'}, {'id': 20989, 'synset': "whipple's_penstemon.n.01", 'name': "Whipple's_penstemon"}, {'id': 20990, 'synset': 'moth_mullein.n.01', 'name': 'moth_mullein'}, {'id': 20991, 'synset': 'white_mullein.n.01', 'name': 'white_mullein'}, {'id': 20992, 'synset': 'purple_mullein.n.01', 'name': 'purple_mullein'}, {'id': 20993, 'synset': 'common_mullein.n.01', 'name': 'common_mullein'}, {'id': 20994, 'synset': 'veronica.n.01', 'name': 'veronica'}, {'id': 20995, 'synset': 'field_speedwell.n.01', 'name': 'field_speedwell'}, {'id': 20996, 'synset': 'brooklime.n.02', 'name': 'brooklime'}, {'id': 20997, 'synset': 'corn_speedwell.n.01', 'name': 'corn_speedwell'}, {'id': 20998, 'synset': 'brooklime.n.01', 'name': 'brooklime'}, {'id': 20999, 'synset': 'germander_speedwell.n.01', 'name': 'germander_speedwell'}, {'id': 21000, 'synset': 'water_speedwell.n.01', 'name': 'water_speedwell'}, {'id': 21001, 'synset': 'common_speedwell.n.01', 'name': 'common_speedwell'}, {'id': 21002, 'synset': 'purslane_speedwell.n.01', 'name': 'purslane_speedwell'}, {'id': 21003, 'synset': 'thyme-leaved_speedwell.n.01', 'name': 'thyme-leaved_speedwell'}, {'id': 21004, 'synset': 'nightshade.n.01', 'name': 'nightshade'}, {'id': 21005, 'synset': 'horse_nettle.n.01', 'name': 'horse_nettle'}, {'id': 21006, 'synset': 'african_holly.n.01', 'name': 'African_holly'}, {'id': 21007, 'synset': 'potato_vine.n.02', 'name': 'potato_vine'}, {'id': 21008, 'synset': 'garden_huckleberry.n.01', 'name': 'garden_huckleberry'}, {'id': 21009, 'synset': 'naranjilla.n.01', 'name': 'naranjilla'}, {'id': 21010, 'synset': 'potato_vine.n.01', 'name': 'potato_vine'}, {'id': 21011, 'synset': 'potato_tree.n.01', 'name': 'potato_tree'}, {'id': 21012, 'synset': 'belladonna.n.01', 'name': 'belladonna'}, {'id': 21013, 'synset': 'bush_violet.n.01', 'name': 'bush_violet'}, {'id': 21014, 'synset': 'lady-of-the-night.n.01', 'name': 'lady-of-the-night'}, {'id': 21015, 'synset': "angel's_trumpet.n.02", 'name': "angel's_trumpet"}, {'id': 21016, 'synset': "angel's_trumpet.n.01", 'name': "angel's_trumpet"}, {'id': 21017, 'synset': "red_angel's_trumpet.n.01", 'name': "red_angel's_trumpet"}, {'id': 21018, 'synset': 'cone_pepper.n.01', 'name': 'cone_pepper'}, {'id': 21019, 'synset': 'bird_pepper.n.01', 'name': 'bird_pepper'}, {'id': 21020, 'synset': 'day_jessamine.n.01', 'name': 'day_jessamine'}, {'id': 21021, 'synset': 'night_jasmine.n.01', 'name': 'night_jasmine'}, {'id': 21022, 'synset': 'tree_tomato.n.01', 'name': 'tree_tomato'}, {'id': 21023, 'synset': 'thorn_apple.n.01', 'name': 'thorn_apple'}, {'id': 21024, 'synset': 'jimsonweed.n.01', 'name': 'jimsonweed'}, {'id': 21025, 'synset': 'pichi.n.01', 'name': 'pichi'}, {'id': 21026, 'synset': 'henbane.n.01', 'name': 'henbane'}, {'id': 21027, 'synset': 'egyptian_henbane.n.01', 'name': 'Egyptian_henbane'}, {'id': 21028, 'synset': 'matrimony_vine.n.01', 'name': 'matrimony_vine'}, {'id': 21029, 'synset': 'common_matrimony_vine.n.01', 'name': 'common_matrimony_vine'}, {'id': 21030, 'synset': 'christmasberry.n.01', 'name': 'Christmasberry'}, {'id': 21031, 'synset': 'plum_tomato.n.01', 'name': 'plum_tomato'}, {'id': 21032, 'synset': 'mandrake.n.02', 'name': 'mandrake'}, {'id': 21033, 'synset': 'mandrake_root.n.01', 'name': 'mandrake_root'}, {'id': 21034, 'synset': 'apple_of_peru.n.01', 'name': 'apple_of_Peru'}, {'id': 21035, 'synset': 'flowering_tobacco.n.01', 'name': 'flowering_tobacco'}, {'id': 21036, 'synset': 'common_tobacco.n.01', 'name': 'common_tobacco'}, {'id': 21037, 'synset': 'wild_tobacco.n.01', 'name': 'wild_tobacco'}, {'id': 21038, 'synset': 'cupflower.n.02', 'name': 'cupflower'}, {'id': 21039, 'synset': 'whitecup.n.01', 'name': 'whitecup'}, {'id': 21040, 'synset': 'petunia.n.01', 'name': 'petunia'}, {'id': 21041, 'synset': 'large_white_petunia.n.01', 'name': 'large_white_petunia'}, {'id': 21042, 'synset': 'violet-flowered_petunia.n.01', 'name': 'violet-flowered_petunia'}, {'id': 21043, 'synset': 'hybrid_petunia.n.01', 'name': 'hybrid_petunia'}, {'id': 21044, 'synset': 'cape_gooseberry.n.01', 'name': 'cape_gooseberry'}, {'id': 21045, 'synset': 'strawberry_tomato.n.01', 'name': 'strawberry_tomato'}, {'id': 21046, 'synset': 'tomatillo.n.02', 'name': 'tomatillo'}, {'id': 21047, 'synset': 'tomatillo.n.01', 'name': 'tomatillo'}, {'id': 21048, 'synset': 'yellow_henbane.n.01', 'name': 'yellow_henbane'}, {'id': 21049, 'synset': "cock's_eggs.n.01", 'name': "cock's_eggs"}, {'id': 21050, 'synset': 'salpiglossis.n.01', 'name': 'salpiglossis'}, {'id': 21051, 'synset': 'painted_tongue.n.01', 'name': 'painted_tongue'}, {'id': 21052, 'synset': 'butterfly_flower.n.01', 'name': 'butterfly_flower'}, {'id': 21053, 'synset': 'scopolia_carniolica.n.01', 'name': 'Scopolia_carniolica'}, {'id': 21054, 'synset': 'chalice_vine.n.01', 'name': 'chalice_vine'}, {'id': 21055, 'synset': 'verbena.n.01', 'name': 'verbena'}, {'id': 21056, 'synset': 'lantana.n.01', 'name': 'lantana'}, {'id': 21057, 'synset': 'black_mangrove.n.02', 'name': 'black_mangrove'}, {'id': 21058, 'synset': 'white_mangrove.n.01', 'name': 'white_mangrove'}, {'id': 21059, 'synset': 'black_mangrove.n.01', 'name': 'black_mangrove'}, {'id': 21060, 'synset': 'teak.n.02', 'name': 'teak'}, {'id': 21061, 'synset': 'spurge.n.01', 'name': 'spurge'}, {'id': 21062, 'synset': 'sun_spurge.n.01', 'name': 'sun_spurge'}, {'id': 21063, 'synset': 'petty_spurge.n.01', 'name': 'petty_spurge'}, {'id': 21064, 'synset': "medusa's_head.n.01", 'name': "medusa's_head"}, {'id': 21065, 'synset': 'wild_spurge.n.01', 'name': 'wild_spurge'}, {'id': 21066, 'synset': 'snow-on-the-mountain.n.01', 'name': 'snow-on-the-mountain'}, {'id': 21067, 'synset': 'cypress_spurge.n.01', 'name': 'cypress_spurge'}, {'id': 21068, 'synset': 'leafy_spurge.n.01', 'name': 'leafy_spurge'}, {'id': 21069, 'synset': 'hairy_spurge.n.01', 'name': 'hairy_spurge'}, {'id': 21070, 'synset': 'poinsettia.n.01', 'name': 'poinsettia'}, {'id': 21071, 'synset': 'japanese_poinsettia.n.01', 'name': 'Japanese_poinsettia'}, {'id': 21072, 'synset': 'fire-on-the-mountain.n.01', 'name': 'fire-on-the-mountain'}, {'id': 21073, 'synset': 'wood_spurge.n.01', 'name': 'wood_spurge'}, {'id': 21074, 'synset': 'dwarf_spurge.n.01', 'name': 'dwarf_spurge'}, {'id': 21075, 'synset': 'scarlet_plume.n.01', 'name': 'scarlet_plume'}, {'id': 21076, 'synset': 'naboom.n.01', 'name': 'naboom'}, {'id': 21077, 'synset': 'crown_of_thorns.n.02', 'name': 'crown_of_thorns'}, {'id': 21078, 'synset': 'toothed_spurge.n.01', 'name': 'toothed_spurge'}, {'id': 21079, 'synset': 'three-seeded_mercury.n.01', 'name': 'three-seeded_mercury'}, {'id': 21080, 'synset': 'croton.n.02', 'name': 'croton'}, {'id': 21081, 'synset': 'cascarilla.n.01', 'name': 'cascarilla'}, {'id': 21082, 'synset': 'cascarilla_bark.n.01', 'name': 'cascarilla_bark'}, {'id': 21083, 'synset': 'castor-oil_plant.n.01', 'name': 'castor-oil_plant'}, {'id': 21084, 'synset': 'spurge_nettle.n.01', 'name': 'spurge_nettle'}, {'id': 21085, 'synset': 'physic_nut.n.01', 'name': 'physic_nut'}, {'id': 21086, 'synset': 'para_rubber_tree.n.01', 'name': 'Para_rubber_tree'}, {'id': 21087, 'synset': 'cassava.n.03', 'name': 'cassava'}, {'id': 21088, 'synset': 'bitter_cassava.n.01', 'name': 'bitter_cassava'}, {'id': 21089, 'synset': 'cassava.n.02', 'name': 'cassava'}, {'id': 21090, 'synset': 'sweet_cassava.n.01', 'name': 'sweet_cassava'}, {'id': 21091, 'synset': 'candlenut.n.01', 'name': 'candlenut'}, {'id': 21092, 'synset': 'tung_tree.n.01', 'name': 'tung_tree'}, {'id': 21093, 'synset': 'slipper_spurge.n.01', 'name': 'slipper_spurge'}, {'id': 21094, 'synset': 'candelilla.n.01', 'name': 'candelilla'}, {'id': 21095, 'synset': 'jewbush.n.01', 'name': 'Jewbush'}, {'id': 21096, 'synset': 'jumping_bean.n.01', 'name': 'jumping_bean'}, {'id': 21097, 'synset': 'camellia.n.01', 'name': 'camellia'}, {'id': 21098, 'synset': 'japonica.n.01', 'name': 'japonica'}, {'id': 21099, 'synset': 'umbellifer.n.01', 'name': 'umbellifer'}, {'id': 21100, 'synset': 'wild_parsley.n.01', 'name': 'wild_parsley'}, {'id': 21101, 'synset': "fool's_parsley.n.01", 'name': "fool's_parsley"}, {'id': 21102, 'synset': 'dill.n.01', 'name': 'dill'}, {'id': 21103, 'synset': 'angelica.n.01', 'name': 'angelica'}, {'id': 21104, 'synset': 'garden_angelica.n.01', 'name': 'garden_angelica'}, {'id': 21105, 'synset': 'wild_angelica.n.01', 'name': 'wild_angelica'}, {'id': 21106, 'synset': 'chervil.n.01', 'name': 'chervil'}, {'id': 21107, 'synset': 'cow_parsley.n.01', 'name': 'cow_parsley'}, {'id': 21108, 'synset': 'wild_celery.n.01', 'name': 'wild_celery'}, {'id': 21109, 'synset': 'astrantia.n.01', 'name': 'astrantia'}, {'id': 21110, 'synset': 'greater_masterwort.n.01', 'name': 'greater_masterwort'}, {'id': 21111, 'synset': 'caraway.n.01', 'name': 'caraway'}, {'id': 21112, 'synset': 'whorled_caraway.n.01', 'name': 'whorled_caraway'}, {'id': 21113, 'synset': 'water_hemlock.n.01', 'name': 'water_hemlock'}, {'id': 21114, 'synset': 'spotted_cowbane.n.01', 'name': 'spotted_cowbane'}, {'id': 21115, 'synset': 'hemlock.n.02', 'name': 'hemlock'}, {'id': 21116, 'synset': 'earthnut.n.02', 'name': 'earthnut'}, {'id': 21117, 'synset': 'cumin.n.01', 'name': 'cumin'}, {'id': 21118, 'synset': 'wild_carrot.n.01', 'name': 'wild_carrot'}, {'id': 21119, 'synset': 'eryngo.n.01', 'name': 'eryngo'}, {'id': 21120, 'synset': 'sea_holly.n.01', 'name': 'sea_holly'}, {'id': 21121, 'synset': 'button_snakeroot.n.02', 'name': 'button_snakeroot'}, {'id': 21122, 'synset': 'rattlesnake_master.n.01', 'name': 'rattlesnake_master'}, {'id': 21123, 'synset': 'fennel.n.01', 'name': 'fennel'}, {'id': 21124, 'synset': 'common_fennel.n.01', 'name': 'common_fennel'}, {'id': 21125, 'synset': 'florence_fennel.n.01', 'name': 'Florence_fennel'}, {'id': 21126, 'synset': 'cow_parsnip.n.01', 'name': 'cow_parsnip'}, {'id': 21127, 'synset': 'lovage.n.01', 'name': 'lovage'}, {'id': 21128, 'synset': 'sweet_cicely.n.01', 'name': 'sweet_cicely'}, {'id': 21129, 'synset': 'water_fennel.n.01', 'name': 'water_fennel'}, {'id': 21130, 'synset': 'parsnip.n.02', 'name': 'parsnip'}, {'id': 21131, 'synset': 'cultivated_parsnip.n.01', 'name': 'cultivated_parsnip'}, {'id': 21132, 'synset': 'wild_parsnip.n.01', 'name': 'wild_parsnip'}, {'id': 21133, 'synset': 'parsley.n.01', 'name': 'parsley'}, {'id': 21134, 'synset': 'italian_parsley.n.01', 'name': 'Italian_parsley'}, {'id': 21135, 'synset': 'hamburg_parsley.n.01', 'name': 'Hamburg_parsley'}, {'id': 21136, 'synset': 'anise.n.01', 'name': 'anise'}, {'id': 21137, 'synset': 'sanicle.n.01', 'name': 'sanicle'}, {'id': 21138, 'synset': 'purple_sanicle.n.01', 'name': 'purple_sanicle'}, {'id': 21139, 'synset': 'european_sanicle.n.01', 'name': 'European_sanicle'}, {'id': 21140, 'synset': 'water_parsnip.n.01', 'name': 'water_parsnip'}, {'id': 21141, 'synset': 'greater_water_parsnip.n.01', 'name': 'greater_water_parsnip'}, {'id': 21142, 'synset': 'skirret.n.01', 'name': 'skirret'}, {'id': 21143, 'synset': 'dogwood.n.01', 'name': 'dogwood'}, {'id': 21144, 'synset': 'common_white_dogwood.n.01', 'name': 'common_white_dogwood'}, {'id': 21145, 'synset': 'red_osier.n.01', 'name': 'red_osier'}, {'id': 21146, 'synset': 'silky_dogwood.n.02', 'name': 'silky_dogwood'}, {'id': 21147, 'synset': 'silky_cornel.n.01', 'name': 'silky_cornel'}, {'id': 21148, 'synset': 'common_european_dogwood.n.01', 'name': 'common_European_dogwood'}, {'id': 21149, 'synset': 'bunchberry.n.01', 'name': 'bunchberry'}, {'id': 21150, 'synset': 'cornelian_cherry.n.01', 'name': 'cornelian_cherry'}, {'id': 21151, 'synset': 'puka.n.01', 'name': 'puka'}, {'id': 21152, 'synset': 'kapuka.n.01', 'name': 'kapuka'}, {'id': 21153, 'synset': 'valerian.n.01', 'name': 'valerian'}, {'id': 21154, 'synset': 'common_valerian.n.01', 'name': 'common_valerian'}, {'id': 21155, 'synset': 'common_corn_salad.n.01', 'name': 'common_corn_salad'}, {'id': 21156, 'synset': 'red_valerian.n.01', 'name': 'red_valerian'}, {'id': 21157, 'synset': 'filmy_fern.n.02', 'name': 'filmy_fern'}, {'id': 21158, 'synset': 'bristle_fern.n.01', 'name': 'bristle_fern'}, {'id': 21159, 'synset': "hare's-foot_bristle_fern.n.01", 'name': "hare's-foot_bristle_fern"}, {'id': 21160, 'synset': 'killarney_fern.n.01', 'name': 'Killarney_fern'}, {'id': 21161, 'synset': 'kidney_fern.n.01', 'name': 'kidney_fern'}, {'id': 21162, 'synset': 'flowering_fern.n.02', 'name': 'flowering_fern'}, {'id': 21163, 'synset': 'royal_fern.n.01', 'name': 'royal_fern'}, {'id': 21164, 'synset': 'interrupted_fern.n.01', 'name': 'interrupted_fern'}, {'id': 21165, 'synset': 'crape_fern.n.01', 'name': 'crape_fern'}, {'id': 21166, 'synset': 'crepe_fern.n.01', 'name': 'crepe_fern'}, {'id': 21167, 'synset': 'curly_grass.n.01', 'name': 'curly_grass'}, {'id': 21168, 'synset': 'pine_fern.n.01', 'name': 'pine_fern'}, {'id': 21169, 'synset': 'climbing_fern.n.01', 'name': 'climbing_fern'}, {'id': 21170, 'synset': 'creeping_fern.n.01', 'name': 'creeping_fern'}, {'id': 21171, 'synset': 'climbing_maidenhair.n.01', 'name': 'climbing_maidenhair'}, {'id': 21172, 'synset': 'scented_fern.n.02', 'name': 'scented_fern'}, {'id': 21173, 'synset': 'clover_fern.n.01', 'name': 'clover_fern'}, {'id': 21174, 'synset': 'nardoo.n.01', 'name': 'nardoo'}, {'id': 21175, 'synset': 'water_clover.n.01', 'name': 'water_clover'}, {'id': 21176, 'synset': 'pillwort.n.01', 'name': 'pillwort'}, {'id': 21177, 'synset': 'regnellidium.n.01', 'name': 'regnellidium'}, {'id': 21178, 'synset': 'floating-moss.n.01', 'name': 'floating-moss'}, {'id': 21179, 'synset': 'mosquito_fern.n.01', 'name': 'mosquito_fern'}, {'id': 21180, 'synset': "adder's_tongue.n.01", 'name': "adder's_tongue"}, {'id': 21181, 'synset': 'ribbon_fern.n.03', 'name': 'ribbon_fern'}, {'id': 21182, 'synset': 'grape_fern.n.01', 'name': 'grape_fern'}, {'id': 21183, 'synset': 'daisyleaf_grape_fern.n.01', 'name': 'daisyleaf_grape_fern'}, {'id': 21184, 'synset': 'leathery_grape_fern.n.01', 'name': 'leathery_grape_fern'}, {'id': 21185, 'synset': 'rattlesnake_fern.n.01', 'name': 'rattlesnake_fern'}, {'id': 21186, 'synset': 'flowering_fern.n.01', 'name': 'flowering_fern'}, {'id': 21187, 'synset': 'powdery_mildew.n.01', 'name': 'powdery_mildew'}, {'id': 21188, 'synset': 'dutch_elm_fungus.n.01', 'name': 'Dutch_elm_fungus'}, {'id': 21189, 'synset': 'ergot.n.02', 'name': 'ergot'}, {'id': 21190, 'synset': 'rye_ergot.n.01', 'name': 'rye_ergot'}, {'id': 21191, 'synset': 'black_root_rot_fungus.n.01', 'name': 'black_root_rot_fungus'}, {'id': 21192, 'synset': "dead-man's-fingers.n.01", 'name': "dead-man's-fingers"}, {'id': 21193, 'synset': 'sclerotinia.n.01', 'name': 'sclerotinia'}, {'id': 21194, 'synset': 'brown_cup.n.01', 'name': 'brown_cup'}, {'id': 21195, 'synset': 'earthball.n.01', 'name': 'earthball'}, {'id': 21196, 'synset': 'scleroderma_citrinum.n.01', 'name': 'Scleroderma_citrinum'}, {'id': 21197, 'synset': 'scleroderma_flavidium.n.01', 'name': 'Scleroderma_flavidium'}, {'id': 21198, 'synset': 'scleroderma_bovista.n.01', 'name': 'Scleroderma_bovista'}, {'id': 21199, 'synset': 'podaxaceae.n.01', 'name': 'Podaxaceae'}, {'id': 21200, 'synset': 'stalked_puffball.n.02', 'name': 'stalked_puffball'}, {'id': 21201, 'synset': 'stalked_puffball.n.01', 'name': 'stalked_puffball'}, {'id': 21202, 'synset': 'false_truffle.n.01', 'name': 'false_truffle'}, {'id': 21203, 'synset': 'rhizopogon_idahoensis.n.01', 'name': 'Rhizopogon_idahoensis'}, {'id': 21204, 'synset': 'truncocolumella_citrina.n.01', 'name': 'Truncocolumella_citrina'}, {'id': 21205, 'synset': 'mucor.n.01', 'name': 'mucor'}, {'id': 21206, 'synset': 'rhizopus.n.01', 'name': 'rhizopus'}, {'id': 21207, 'synset': 'bread_mold.n.01', 'name': 'bread_mold'}, {'id': 21208, 'synset': 'slime_mold.n.01', 'name': 'slime_mold'}, {'id': 21209, 'synset': 'true_slime_mold.n.01', 'name': 'true_slime_mold'}, {'id': 21210, 'synset': 'cellular_slime_mold.n.01', 'name': 'cellular_slime_mold'}, {'id': 21211, 'synset': 'dictostylium.n.01', 'name': 'dictostylium'}, {'id': 21212, 'synset': 'pond-scum_parasite.n.01', 'name': 'pond-scum_parasite'}, {'id': 21213, 'synset': 'potato_wart_fungus.n.01', 'name': 'potato_wart_fungus'}, {'id': 21214, 'synset': 'white_fungus.n.01', 'name': 'white_fungus'}, {'id': 21215, 'synset': 'water_mold.n.01', 'name': 'water_mold'}, {'id': 21216, 'synset': 'downy_mildew.n.01', 'name': 'downy_mildew'}, {'id': 21217, 'synset': 'blue_mold_fungus.n.01', 'name': 'blue_mold_fungus'}, {'id': 21218, 'synset': 'onion_mildew.n.01', 'name': 'onion_mildew'}, {'id': 21219, 'synset': 'tobacco_mildew.n.01', 'name': 'tobacco_mildew'}, {'id': 21220, 'synset': 'white_rust.n.01', 'name': 'white_rust'}, {'id': 21221, 'synset': 'pythium.n.01', 'name': 'pythium'}, {'id': 21222, 'synset': 'damping_off_fungus.n.01', 'name': 'damping_off_fungus'}, {'id': 21223, 'synset': 'phytophthora_citrophthora.n.01', 'name': 'Phytophthora_citrophthora'}, {'id': 21224, 'synset': 'phytophthora_infestans.n.01', 'name': 'Phytophthora_infestans'}, {'id': 21225, 'synset': 'clubroot_fungus.n.01', 'name': 'clubroot_fungus'}, {'id': 21226, 'synset': 'geglossaceae.n.01', 'name': 'Geglossaceae'}, {'id': 21227, 'synset': 'sarcosomataceae.n.01', 'name': 'Sarcosomataceae'}, {'id': 21228, 'synset': 'rufous_rubber_cup.n.01', 'name': 'Rufous_rubber_cup'}, {'id': 21229, 'synset': "devil's_cigar.n.01", 'name': "devil's_cigar"}, {'id': 21230, 'synset': "devil's_urn.n.01", 'name': "devil's_urn"}, {'id': 21231, 'synset': 'truffle.n.01', 'name': 'truffle'}, {'id': 21232, 'synset': 'club_fungus.n.01', 'name': 'club_fungus'}, {'id': 21233, 'synset': 'coral_fungus.n.01', 'name': 'coral_fungus'}, {'id': 21234, 'synset': 'tooth_fungus.n.01', 'name': 'tooth_fungus'}, {'id': 21235, 'synset': 'lichen.n.02', 'name': 'lichen'}, {'id': 21236, 'synset': 'ascolichen.n.01', 'name': 'ascolichen'}, {'id': 21237, 'synset': 'basidiolichen.n.01', 'name': 'basidiolichen'}, {'id': 21238, 'synset': 'lecanora.n.01', 'name': 'lecanora'}, {'id': 21239, 'synset': 'manna_lichen.n.01', 'name': 'manna_lichen'}, {'id': 21240, 'synset': 'archil.n.02', 'name': 'archil'}, {'id': 21241, 'synset': 'roccella.n.01', 'name': 'roccella'}, {'id': 21242, 'synset': 'beard_lichen.n.01', 'name': 'beard_lichen'}, {'id': 21243, 'synset': 'horsehair_lichen.n.01', 'name': 'horsehair_lichen'}, {'id': 21244, 'synset': 'reindeer_moss.n.01', 'name': 'reindeer_moss'}, {'id': 21245, 'synset': 'crottle.n.01', 'name': 'crottle'}, {'id': 21246, 'synset': 'iceland_moss.n.01', 'name': 'Iceland_moss'}, {'id': 21247, 'synset': 'fungus.n.01', 'name': 'fungus'}, {'id': 21248, 'synset': 'promycelium.n.01', 'name': 'promycelium'}, {'id': 21249, 'synset': 'true_fungus.n.01', 'name': 'true_fungus'}, {'id': 21250, 'synset': 'basidiomycete.n.01', 'name': 'basidiomycete'}, {'id': 21251, 'synset': 'mushroom.n.03', 'name': 'mushroom'}, {'id': 21252, 'synset': 'agaric.n.02', 'name': 'agaric'}, {'id': 21253, 'synset': 'mushroom.n.01', 'name': 'mushroom'}, {'id': 21254, 'synset': 'toadstool.n.01', 'name': 'toadstool'}, {'id': 21255, 'synset': 'horse_mushroom.n.01', 'name': 'horse_mushroom'}, {'id': 21256, 'synset': 'meadow_mushroom.n.01', 'name': 'meadow_mushroom'}, {'id': 21257, 'synset': 'shiitake.n.01', 'name': 'shiitake'}, {'id': 21258, 'synset': 'scaly_lentinus.n.01', 'name': 'scaly_lentinus'}, {'id': 21259, 'synset': 'royal_agaric.n.01', 'name': 'royal_agaric'}, {'id': 21260, 'synset': 'false_deathcap.n.01', 'name': 'false_deathcap'}, {'id': 21261, 'synset': 'fly_agaric.n.01', 'name': 'fly_agaric'}, {'id': 21262, 'synset': 'death_cap.n.01', 'name': 'death_cap'}, {'id': 21263, 'synset': 'blushing_mushroom.n.01', 'name': 'blushing_mushroom'}, {'id': 21264, 'synset': 'destroying_angel.n.01', 'name': 'destroying_angel'}, {'id': 21265, 'synset': 'chanterelle.n.01', 'name': 'chanterelle'}, {'id': 21266, 'synset': 'floccose_chanterelle.n.01', 'name': 'floccose_chanterelle'}, {'id': 21267, 'synset': "pig's_ears.n.01", 'name': "pig's_ears"}, {'id': 21268, 'synset': 'cinnabar_chanterelle.n.01', 'name': 'cinnabar_chanterelle'}, {'id': 21269, 'synset': 'jack-o-lantern_fungus.n.01', 'name': 'jack-o-lantern_fungus'}, {'id': 21270, 'synset': 'inky_cap.n.01', 'name': 'inky_cap'}, {'id': 21271, 'synset': 'shaggymane.n.01', 'name': 'shaggymane'}, {'id': 21272, 'synset': 'milkcap.n.01', 'name': 'milkcap'}, {'id': 21273, 'synset': 'fairy-ring_mushroom.n.01', 'name': 'fairy-ring_mushroom'}, {'id': 21274, 'synset': 'fairy_ring.n.01', 'name': 'fairy_ring'}, {'id': 21275, 'synset': 'oyster_mushroom.n.01', 'name': 'oyster_mushroom'}, {'id': 21276, 'synset': 'olive-tree_agaric.n.01', 'name': 'olive-tree_agaric'}, {'id': 21277, 'synset': 'pholiota_astragalina.n.01', 'name': 'Pholiota_astragalina'}, {'id': 21278, 'synset': 'pholiota_aurea.n.01', 'name': 'Pholiota_aurea'}, {'id': 21279, 'synset': 'pholiota_destruens.n.01', 'name': 'Pholiota_destruens'}, {'id': 21280, 'synset': 'pholiota_flammans.n.01', 'name': 'Pholiota_flammans'}, {'id': 21281, 'synset': 'pholiota_flavida.n.01', 'name': 'Pholiota_flavida'}, {'id': 21282, 'synset': 'nameko.n.01', 'name': 'nameko'}, {'id': 21283, 'synset': 'pholiota_squarrosa-adiposa.n.01', 'name': 'Pholiota_squarrosa-adiposa'}, {'id': 21284, 'synset': 'pholiota_squarrosa.n.01', 'name': 'Pholiota_squarrosa'}, {'id': 21285, 'synset': 'pholiota_squarrosoides.n.01', 'name': 'Pholiota_squarrosoides'}, {'id': 21286, 'synset': 'stropharia_ambigua.n.01', 'name': 'Stropharia_ambigua'}, {'id': 21287, 'synset': 'stropharia_hornemannii.n.01', 'name': 'Stropharia_hornemannii'}, {'id': 21288, 'synset': 'stropharia_rugoso-annulata.n.01', 'name': 'Stropharia_rugoso-annulata'}, {'id': 21289, 'synset': 'gill_fungus.n.01', 'name': 'gill_fungus'}, {'id': 21290, 'synset': 'entoloma_lividum.n.01', 'name': 'Entoloma_lividum'}, {'id': 21291, 'synset': 'entoloma_aprile.n.01', 'name': 'Entoloma_aprile'}, {'id': 21292, 'synset': 'chlorophyllum_molybdites.n.01', 'name': 'Chlorophyllum_molybdites'}, {'id': 21293, 'synset': 'lepiota.n.01', 'name': 'lepiota'}, {'id': 21294, 'synset': 'parasol_mushroom.n.01', 'name': 'parasol_mushroom'}, {'id': 21295, 'synset': 'poisonous_parasol.n.01', 'name': 'poisonous_parasol'}, {'id': 21296, 'synset': 'lepiota_naucina.n.01', 'name': 'Lepiota_naucina'}, {'id': 21297, 'synset': 'lepiota_rhacodes.n.01', 'name': 'Lepiota_rhacodes'}, {'id': 21298, 'synset': 'american_parasol.n.01', 'name': 'American_parasol'}, {'id': 21299, 'synset': 'lepiota_rubrotincta.n.01', 'name': 'Lepiota_rubrotincta'}, {'id': 21300, 'synset': 'lepiota_clypeolaria.n.01', 'name': 'Lepiota_clypeolaria'}, {'id': 21301, 'synset': 'onion_stem.n.01', 'name': 'onion_stem'}, {'id': 21302, 'synset': 'pink_disease_fungus.n.01', 'name': 'pink_disease_fungus'}, {'id': 21303, 'synset': 'bottom_rot_fungus.n.01', 'name': 'bottom_rot_fungus'}, {'id': 21304, 'synset': 'potato_fungus.n.01', 'name': 'potato_fungus'}, {'id': 21305, 'synset': 'coffee_fungus.n.01', 'name': 'coffee_fungus'}, {'id': 21306, 'synset': 'blewits.n.01', 'name': 'blewits'}, {'id': 21307, 'synset': 'sandy_mushroom.n.01', 'name': 'sandy_mushroom'}, {'id': 21308, 'synset': 'tricholoma_pessundatum.n.01', 'name': 'Tricholoma_pessundatum'}, {'id': 21309, 'synset': 'tricholoma_sejunctum.n.01', 'name': 'Tricholoma_sejunctum'}, {'id': 21310, 'synset': 'man-on-a-horse.n.01', 'name': 'man-on-a-horse'}, {'id': 21311, 'synset': 'tricholoma_venenata.n.01', 'name': 'Tricholoma_venenata'}, {'id': 21312, 'synset': 'tricholoma_pardinum.n.01', 'name': 'Tricholoma_pardinum'}, {'id': 21313, 'synset': 'tricholoma_vaccinum.n.01', 'name': 'Tricholoma_vaccinum'}, {'id': 21314, 'synset': 'tricholoma_aurantium.n.01', 'name': 'Tricholoma_aurantium'}, {'id': 21315, 'synset': 'volvaria_bombycina.n.01', 'name': 'Volvaria_bombycina'}, {'id': 21316, 'synset': 'pluteus_aurantiorugosus.n.01', 'name': 'Pluteus_aurantiorugosus'}, {'id': 21317, 'synset': 'pluteus_magnus.n.01', 'name': 'Pluteus_magnus'}, {'id': 21318, 'synset': 'deer_mushroom.n.01', 'name': 'deer_mushroom'}, {'id': 21319, 'synset': 'straw_mushroom.n.01', 'name': 'straw_mushroom'}, {'id': 21320, 'synset': 'volvariella_bombycina.n.01', 'name': 'Volvariella_bombycina'}, {'id': 21321, 'synset': 'clitocybe_clavipes.n.01', 'name': 'Clitocybe_clavipes'}, {'id': 21322, 'synset': 'clitocybe_dealbata.n.01', 'name': 'Clitocybe_dealbata'}, {'id': 21323, 'synset': 'clitocybe_inornata.n.01', 'name': 'Clitocybe_inornata'}, {'id': 21324, 'synset': 'clitocybe_robusta.n.01', 'name': 'Clitocybe_robusta'}, {'id': 21325, 'synset': 'clitocybe_irina.n.01', 'name': 'Clitocybe_irina'}, {'id': 21326, 'synset': 'clitocybe_subconnexa.n.01', 'name': 'Clitocybe_subconnexa'}, {'id': 21327, 'synset': 'winter_mushroom.n.01', 'name': 'winter_mushroom'}, {'id': 21328, 'synset': 'mycelium.n.01', 'name': 'mycelium'}, {'id': 21329, 'synset': 'sclerotium.n.02', 'name': 'sclerotium'}, {'id': 21330, 'synset': 'sac_fungus.n.01', 'name': 'sac_fungus'}, {'id': 21331, 'synset': 'ascomycete.n.01', 'name': 'ascomycete'}, {'id': 21332, 'synset': 'clavicipitaceae.n.01', 'name': 'Clavicipitaceae'}, {'id': 21333, 'synset': 'grainy_club.n.01', 'name': 'grainy_club'}, {'id': 21334, 'synset': 'yeast.n.02', 'name': 'yeast'}, {'id': 21335, 'synset': "baker's_yeast.n.01", 'name': "baker's_yeast"}, {'id': 21336, 'synset': "wine-maker's_yeast.n.01", 'name': "wine-maker's_yeast"}, {'id': 21337, 'synset': 'aspergillus_fumigatus.n.01', 'name': 'Aspergillus_fumigatus'}, {'id': 21338, 'synset': 'brown_root_rot_fungus.n.01', 'name': 'brown_root_rot_fungus'}, {'id': 21339, 'synset': 'discomycete.n.01', 'name': 'discomycete'}, {'id': 21340, 'synset': 'leotia_lubrica.n.01', 'name': 'Leotia_lubrica'}, {'id': 21341, 'synset': 'mitrula_elegans.n.01', 'name': 'Mitrula_elegans'}, {'id': 21342, 'synset': 'sarcoscypha_coccinea.n.01', 'name': 'Sarcoscypha_coccinea'}, {'id': 21343, 'synset': 'caloscypha_fulgens.n.01', 'name': 'Caloscypha_fulgens'}, {'id': 21344, 'synset': 'aleuria_aurantia.n.01', 'name': 'Aleuria_aurantia'}, {'id': 21345, 'synset': 'elf_cup.n.01', 'name': 'elf_cup'}, {'id': 21346, 'synset': 'peziza_domicilina.n.01', 'name': 'Peziza_domicilina'}, {'id': 21347, 'synset': 'blood_cup.n.01', 'name': 'blood_cup'}, {'id': 21348, 'synset': 'urnula_craterium.n.01', 'name': 'Urnula_craterium'}, {'id': 21349, 'synset': 'galiella_rufa.n.01', 'name': 'Galiella_rufa'}, {'id': 21350, 'synset': 'jafnea_semitosta.n.01', 'name': 'Jafnea_semitosta'}, {'id': 21351, 'synset': 'morel.n.01', 'name': 'morel'}, {'id': 21352, 'synset': 'common_morel.n.01', 'name': 'common_morel'}, {'id': 21353, 'synset': 'disciotis_venosa.n.01', 'name': 'Disciotis_venosa'}, {'id': 21354, 'synset': 'verpa.n.01', 'name': 'Verpa'}, {'id': 21355, 'synset': 'verpa_bohemica.n.01', 'name': 'Verpa_bohemica'}, {'id': 21356, 'synset': 'verpa_conica.n.01', 'name': 'Verpa_conica'}, {'id': 21357, 'synset': 'black_morel.n.01', 'name': 'black_morel'}, {'id': 21358, 'synset': 'morchella_crassipes.n.01', 'name': 'Morchella_crassipes'}, {'id': 21359, 'synset': 'morchella_semilibera.n.01', 'name': 'Morchella_semilibera'}, {'id': 21360, 'synset': 'wynnea_americana.n.01', 'name': 'Wynnea_americana'}, {'id': 21361, 'synset': 'wynnea_sparassoides.n.01', 'name': 'Wynnea_sparassoides'}, {'id': 21362, 'synset': 'false_morel.n.01', 'name': 'false_morel'}, {'id': 21363, 'synset': 'lorchel.n.01', 'name': 'lorchel'}, {'id': 21364, 'synset': 'helvella.n.01', 'name': 'helvella'}, {'id': 21365, 'synset': 'helvella_crispa.n.01', 'name': 'Helvella_crispa'}, {'id': 21366, 'synset': 'helvella_acetabulum.n.01', 'name': 'Helvella_acetabulum'}, {'id': 21367, 'synset': 'helvella_sulcata.n.01', 'name': 'Helvella_sulcata'}, {'id': 21368, 'synset': 'discina.n.01', 'name': 'discina'}, {'id': 21369, 'synset': 'gyromitra.n.01', 'name': 'gyromitra'}, {'id': 21370, 'synset': 'gyromitra_californica.n.01', 'name': 'Gyromitra_californica'}, {'id': 21371, 'synset': 'gyromitra_sphaerospora.n.01', 'name': 'Gyromitra_sphaerospora'}, {'id': 21372, 'synset': 'gyromitra_esculenta.n.01', 'name': 'Gyromitra_esculenta'}, {'id': 21373, 'synset': 'gyromitra_infula.n.01', 'name': 'Gyromitra_infula'}, {'id': 21374, 'synset': 'gyromitra_fastigiata.n.01', 'name': 'Gyromitra_fastigiata'}, {'id': 21375, 'synset': 'gyromitra_gigas.n.01', 'name': 'Gyromitra_gigas'}, {'id': 21376, 'synset': 'gasteromycete.n.01', 'name': 'gasteromycete'}, {'id': 21377, 'synset': 'stinkhorn.n.01', 'name': 'stinkhorn'}, {'id': 21378, 'synset': 'common_stinkhorn.n.01', 'name': 'common_stinkhorn'}, {'id': 21379, 'synset': 'phallus_ravenelii.n.01', 'name': 'Phallus_ravenelii'}, {'id': 21380, 'synset': 'dog_stinkhorn.n.01', 'name': 'dog_stinkhorn'}, {'id': 21381, 'synset': 'calostoma_lutescens.n.01', 'name': 'Calostoma_lutescens'}, {'id': 21382, 'synset': 'calostoma_cinnabarina.n.01', 'name': 'Calostoma_cinnabarina'}, {'id': 21383, 'synset': 'calostoma_ravenelii.n.01', 'name': 'Calostoma_ravenelii'}, {'id': 21384, 'synset': 'stinky_squid.n.01', 'name': 'stinky_squid'}, {'id': 21385, 'synset': 'puffball.n.01', 'name': 'puffball'}, {'id': 21386, 'synset': 'giant_puffball.n.01', 'name': 'giant_puffball'}, {'id': 21387, 'synset': 'earthstar.n.01', 'name': 'earthstar'}, {'id': 21388, 'synset': 'geastrum_coronatum.n.01', 'name': 'Geastrum_coronatum'}, {'id': 21389, 'synset': 'radiigera_fuscogleba.n.01', 'name': 'Radiigera_fuscogleba'}, {'id': 21390, 'synset': 'astreus_pteridis.n.01', 'name': 'Astreus_pteridis'}, {'id': 21391, 'synset': 'astreus_hygrometricus.n.01', 'name': 'Astreus_hygrometricus'}, {'id': 21392, 'synset': "bird's-nest_fungus.n.01", 'name': "bird's-nest_fungus"}, {'id': 21393, 'synset': 'gastrocybe_lateritia.n.01', 'name': 'Gastrocybe_lateritia'}, {'id': 21394, 'synset': 'macowanites_americanus.n.01', 'name': 'Macowanites_americanus'}, {'id': 21395, 'synset': 'polypore.n.01', 'name': 'polypore'}, {'id': 21396, 'synset': 'bracket_fungus.n.01', 'name': 'bracket_fungus'}, {'id': 21397, 'synset': 'albatrellus_dispansus.n.01', 'name': 'Albatrellus_dispansus'}, {'id': 21398, 'synset': 'albatrellus_ovinus.n.01', 'name': 'Albatrellus_ovinus'}, {'id': 21399, 'synset': 'neolentinus_ponderosus.n.01', 'name': 'Neolentinus_ponderosus'}, {'id': 21400, 'synset': 'oligoporus_leucospongia.n.01', 'name': 'Oligoporus_leucospongia'}, {'id': 21401, 'synset': 'polyporus_tenuiculus.n.01', 'name': 'Polyporus_tenuiculus'}, {'id': 21402, 'synset': 'hen-of-the-woods.n.01', 'name': 'hen-of-the-woods'}, {'id': 21403, 'synset': 'polyporus_squamosus.n.01', 'name': 'Polyporus_squamosus'}, {'id': 21404, 'synset': 'beefsteak_fungus.n.01', 'name': 'beefsteak_fungus'}, {'id': 21405, 'synset': 'agaric.n.01', 'name': 'agaric'}, {'id': 21406, 'synset': 'bolete.n.01', 'name': 'bolete'}, {'id': 21407, 'synset': 'boletus_chrysenteron.n.01', 'name': 'Boletus_chrysenteron'}, {'id': 21408, 'synset': 'boletus_edulis.n.01', 'name': 'Boletus_edulis'}, {'id': 21409, 'synset': "frost's_bolete.n.01", 'name': "Frost's_bolete"}, {'id': 21410, 'synset': 'boletus_luridus.n.01', 'name': 'Boletus_luridus'}, {'id': 21411, 'synset': 'boletus_mirabilis.n.01', 'name': 'Boletus_mirabilis'}, {'id': 21412, 'synset': 'boletus_pallidus.n.01', 'name': 'Boletus_pallidus'}, {'id': 21413, 'synset': 'boletus_pulcherrimus.n.01', 'name': 'Boletus_pulcherrimus'}, {'id': 21414, 'synset': 'boletus_pulverulentus.n.01', 'name': 'Boletus_pulverulentus'}, {'id': 21415, 'synset': 'boletus_roxanae.n.01', 'name': 'Boletus_roxanae'}, {'id': 21416, 'synset': 'boletus_subvelutipes.n.01', 'name': 'Boletus_subvelutipes'}, {'id': 21417, 'synset': 'boletus_variipes.n.01', 'name': 'Boletus_variipes'}, {'id': 21418, 'synset': 'boletus_zelleri.n.01', 'name': 'Boletus_zelleri'}, {'id': 21419, 'synset': 'fuscoboletinus_paluster.n.01', 'name': 'Fuscoboletinus_paluster'}, {'id': 21420, 'synset': 'fuscoboletinus_serotinus.n.01', 'name': 'Fuscoboletinus_serotinus'}, {'id': 21421, 'synset': 'leccinum_fibrillosum.n.01', 'name': 'Leccinum_fibrillosum'}, {'id': 21422, 'synset': 'suillus_albivelatus.n.01', 'name': 'Suillus_albivelatus'}, {'id': 21423, 'synset': 'old-man-of-the-woods.n.01', 'name': 'old-man-of-the-woods'}, {'id': 21424, 'synset': 'boletellus_russellii.n.01', 'name': 'Boletellus_russellii'}, {'id': 21425, 'synset': 'jelly_fungus.n.01', 'name': 'jelly_fungus'}, {'id': 21426, 'synset': 'snow_mushroom.n.01', 'name': 'snow_mushroom'}, {'id': 21427, 'synset': "witches'_butter.n.01", 'name': "witches'_butter"}, {'id': 21428, 'synset': 'tremella_foliacea.n.01', 'name': 'Tremella_foliacea'}, {'id': 21429, 'synset': 'tremella_reticulata.n.01', 'name': 'Tremella_reticulata'}, {'id': 21430, 'synset': "jew's-ear.n.01", 'name': "Jew's-ear"}, {'id': 21431, 'synset': 'rust.n.04', 'name': 'rust'}, {'id': 21432, 'synset': 'aecium.n.01', 'name': 'aecium'}, {'id': 21433, 'synset': 'flax_rust.n.01', 'name': 'flax_rust'}, {'id': 21434, 'synset': 'blister_rust.n.02', 'name': 'blister_rust'}, {'id': 21435, 'synset': 'wheat_rust.n.01', 'name': 'wheat_rust'}, {'id': 21436, 'synset': 'apple_rust.n.01', 'name': 'apple_rust'}, {'id': 21437, 'synset': 'smut.n.03', 'name': 'smut'}, {'id': 21438, 'synset': 'covered_smut.n.01', 'name': 'covered_smut'}, {'id': 21439, 'synset': 'loose_smut.n.02', 'name': 'loose_smut'}, {'id': 21440, 'synset': 'cornsmut.n.01', 'name': 'cornsmut'}, {'id': 21441, 'synset': 'boil_smut.n.01', 'name': 'boil_smut'}, {'id': 21442, 'synset': 'sphacelotheca.n.01', 'name': 'Sphacelotheca'}, {'id': 21443, 'synset': 'head_smut.n.01', 'name': 'head_smut'}, {'id': 21444, 'synset': 'bunt.n.04', 'name': 'bunt'}, {'id': 21445, 'synset': 'bunt.n.03', 'name': 'bunt'}, {'id': 21446, 'synset': 'onion_smut.n.01', 'name': 'onion_smut'}, {'id': 21447, 'synset': 'flag_smut_fungus.n.01', 'name': 'flag_smut_fungus'}, {'id': 21448, 'synset': 'wheat_flag_smut.n.01', 'name': 'wheat_flag_smut'}, {'id': 21449, 'synset': 'felt_fungus.n.01', 'name': 'felt_fungus'}, {'id': 21450, 'synset': 'waxycap.n.01', 'name': 'waxycap'}, {'id': 21451, 'synset': 'hygrocybe_acutoconica.n.01', 'name': 'Hygrocybe_acutoconica'}, {'id': 21452, 'synset': 'hygrophorus_borealis.n.01', 'name': 'Hygrophorus_borealis'}, {'id': 21453, 'synset': 'hygrophorus_caeruleus.n.01', 'name': 'Hygrophorus_caeruleus'}, {'id': 21454, 'synset': 'hygrophorus_inocybiformis.n.01', 'name': 'Hygrophorus_inocybiformis'}, {'id': 21455, 'synset': 'hygrophorus_kauffmanii.n.01', 'name': 'Hygrophorus_kauffmanii'}, {'id': 21456, 'synset': 'hygrophorus_marzuolus.n.01', 'name': 'Hygrophorus_marzuolus'}, {'id': 21457, 'synset': 'hygrophorus_purpurascens.n.01', 'name': 'Hygrophorus_purpurascens'}, {'id': 21458, 'synset': 'hygrophorus_russula.n.01', 'name': 'Hygrophorus_russula'}, {'id': 21459, 'synset': 'hygrophorus_sordidus.n.01', 'name': 'Hygrophorus_sordidus'}, {'id': 21460, 'synset': 'hygrophorus_tennesseensis.n.01', 'name': 'Hygrophorus_tennesseensis'}, {'id': 21461, 'synset': 'hygrophorus_turundus.n.01', 'name': 'Hygrophorus_turundus'}, {'id': 21462, 'synset': 'neohygrophorus_angelesianus.n.01', 'name': 'Neohygrophorus_angelesianus'}, {'id': 21463, 'synset': 'cortinarius_armillatus.n.01', 'name': 'Cortinarius_armillatus'}, {'id': 21464, 'synset': 'cortinarius_atkinsonianus.n.01', 'name': 'Cortinarius_atkinsonianus'}, {'id': 21465, 'synset': 'cortinarius_corrugatus.n.01', 'name': 'Cortinarius_corrugatus'}, {'id': 21466, 'synset': 'cortinarius_gentilis.n.01', 'name': 'Cortinarius_gentilis'}, {'id': 21467, 'synset': 'cortinarius_mutabilis.n.01', 'name': 'Cortinarius_mutabilis'}, {'id': 21468, 'synset': 'cortinarius_semisanguineus.n.01', 'name': 'Cortinarius_semisanguineus'}, {'id': 21469, 'synset': 'cortinarius_subfoetidus.n.01', 'name': 'Cortinarius_subfoetidus'}, {'id': 21470, 'synset': 'cortinarius_violaceus.n.01', 'name': 'Cortinarius_violaceus'}, {'id': 21471, 'synset': 'gymnopilus_spectabilis.n.01', 'name': 'Gymnopilus_spectabilis'}, {'id': 21472, 'synset': 'gymnopilus_validipes.n.01', 'name': 'Gymnopilus_validipes'}, {'id': 21473, 'synset': 'gymnopilus_ventricosus.n.01', 'name': 'Gymnopilus_ventricosus'}, {'id': 21474, 'synset': 'mold.n.05', 'name': 'mold'}, {'id': 21475, 'synset': 'mildew.n.02', 'name': 'mildew'}, {'id': 21476, 'synset': 'verticillium.n.01', 'name': 'verticillium'}, {'id': 21477, 'synset': 'monilia.n.01', 'name': 'monilia'}, {'id': 21478, 'synset': 'candida.n.01', 'name': 'candida'}, {'id': 21479, 'synset': 'candida_albicans.n.01', 'name': 'Candida_albicans'}, {'id': 21480, 'synset': 'blastomycete.n.01', 'name': 'blastomycete'}, {'id': 21481, 'synset': 'yellow_spot_fungus.n.01', 'name': 'yellow_spot_fungus'}, {'id': 21482, 'synset': 'green_smut_fungus.n.01', 'name': 'green_smut_fungus'}, {'id': 21483, 'synset': 'dry_rot.n.02', 'name': 'dry_rot'}, {'id': 21484, 'synset': 'rhizoctinia.n.01', 'name': 'rhizoctinia'}, {'id': 21485, 'synset': 'houseplant.n.01', 'name': 'houseplant'}, {'id': 21486, 'synset': 'bedder.n.01', 'name': 'bedder'}, {'id': 21487, 'synset': 'succulent.n.01', 'name': 'succulent'}, {'id': 21488, 'synset': 'cultivar.n.01', 'name': 'cultivar'}, {'id': 21489, 'synset': 'weed.n.01', 'name': 'weed'}, {'id': 21490, 'synset': 'wort.n.01', 'name': 'wort'}, {'id': 21491, 'synset': 'brier.n.02', 'name': 'brier'}, {'id': 21492, 'synset': 'aril.n.01', 'name': 'aril'}, {'id': 21493, 'synset': 'sporophyll.n.01', 'name': 'sporophyll'}, {'id': 21494, 'synset': 'sporangium.n.01', 'name': 'sporangium'}, {'id': 21495, 'synset': 'sporangiophore.n.01', 'name': 'sporangiophore'}, {'id': 21496, 'synset': 'ascus.n.01', 'name': 'ascus'}, {'id': 21497, 'synset': 'ascospore.n.01', 'name': 'ascospore'}, {'id': 21498, 'synset': 'arthrospore.n.02', 'name': 'arthrospore'}, {'id': 21499, 'synset': 'eusporangium.n.01', 'name': 'eusporangium'}, {'id': 21500, 'synset': 'tetrasporangium.n.01', 'name': 'tetrasporangium'}, {'id': 21501, 'synset': 'gametangium.n.01', 'name': 'gametangium'}, {'id': 21502, 'synset': 'sorus.n.02', 'name': 'sorus'}, {'id': 21503, 'synset': 'sorus.n.01', 'name': 'sorus'}, {'id': 21504, 'synset': 'partial_veil.n.01', 'name': 'partial_veil'}, {'id': 21505, 'synset': 'lignum.n.01', 'name': 'lignum'}, {'id': 21506, 'synset': 'vascular_ray.n.01', 'name': 'vascular_ray'}, {'id': 21507, 'synset': 'phloem.n.01', 'name': 'phloem'}, {'id': 21508, 'synset': 'evergreen.n.01', 'name': 'evergreen'}, {'id': 21509, 'synset': 'deciduous_plant.n.01', 'name': 'deciduous_plant'}, {'id': 21510, 'synset': 'poisonous_plant.n.01', 'name': 'poisonous_plant'}, {'id': 21511, 'synset': 'vine.n.01', 'name': 'vine'}, {'id': 21512, 'synset': 'creeper.n.01', 'name': 'creeper'}, {'id': 21513, 'synset': 'tendril.n.01', 'name': 'tendril'}, {'id': 21514, 'synset': 'root_climber.n.01', 'name': 'root_climber'}, {'id': 21515, 'synset': 'lignosae.n.01', 'name': 'lignosae'}, {'id': 21516, 'synset': 'arborescent_plant.n.01', 'name': 'arborescent_plant'}, {'id': 21517, 'synset': 'snag.n.02', 'name': 'snag'}, {'id': 21518, 'synset': 'tree.n.01', 'name': 'tree'}, {'id': 21519, 'synset': 'timber_tree.n.01', 'name': 'timber_tree'}, {'id': 21520, 'synset': 'treelet.n.01', 'name': 'treelet'}, {'id': 21521, 'synset': 'arbor.n.01', 'name': 'arbor'}, {'id': 21522, 'synset': 'bean_tree.n.01', 'name': 'bean_tree'}, {'id': 21523, 'synset': 'pollard.n.01', 'name': 'pollard'}, {'id': 21524, 'synset': 'sapling.n.01', 'name': 'sapling'}, {'id': 21525, 'synset': 'shade_tree.n.01', 'name': 'shade_tree'}, {'id': 21526, 'synset': 'gymnospermous_tree.n.01', 'name': 'gymnospermous_tree'}, {'id': 21527, 'synset': 'conifer.n.01', 'name': 'conifer'}, {'id': 21528, 'synset': 'angiospermous_tree.n.01', 'name': 'angiospermous_tree'}, {'id': 21529, 'synset': 'nut_tree.n.01', 'name': 'nut_tree'}, {'id': 21530, 'synset': 'spice_tree.n.01', 'name': 'spice_tree'}, {'id': 21531, 'synset': 'fever_tree.n.01', 'name': 'fever_tree'}, {'id': 21532, 'synset': 'stump.n.01', 'name': 'stump'}, {'id': 21533, 'synset': 'bonsai.n.01', 'name': 'bonsai'}, {'id': 21534, 'synset': 'ming_tree.n.02', 'name': 'ming_tree'}, {'id': 21535, 'synset': 'ming_tree.n.01', 'name': 'ming_tree'}, {'id': 21536, 'synset': 'undershrub.n.01', 'name': 'undershrub'}, {'id': 21537, 'synset': 'subshrub.n.01', 'name': 'subshrub'}, {'id': 21538, 'synset': 'bramble.n.01', 'name': 'bramble'}, {'id': 21539, 'synset': 'liana.n.01', 'name': 'liana'}, {'id': 21540, 'synset': 'geophyte.n.01', 'name': 'geophyte'}, {'id': 21541, 'synset': 'desert_plant.n.01', 'name': 'desert_plant'}, {'id': 21542, 'synset': 'mesophyte.n.01', 'name': 'mesophyte'}, {'id': 21543, 'synset': 'marsh_plant.n.01', 'name': 'marsh_plant'}, {'id': 21544, 'synset': 'hemiepiphyte.n.01', 'name': 'hemiepiphyte'}, {'id': 21545, 'synset': 'strangler.n.01', 'name': 'strangler'}, {'id': 21546, 'synset': 'lithophyte.n.01', 'name': 'lithophyte'}, {'id': 21547, 'synset': 'saprobe.n.01', 'name': 'saprobe'}, {'id': 21548, 'synset': 'autophyte.n.01', 'name': 'autophyte'}, {'id': 21549, 'synset': 'root.n.01', 'name': 'root'}, {'id': 21550, 'synset': 'taproot.n.01', 'name': 'taproot'}, {'id': 21551, 'synset': 'prop_root.n.01', 'name': 'prop_root'}, {'id': 21552, 'synset': 'prophyll.n.01', 'name': 'prophyll'}, {'id': 21553, 'synset': 'rootstock.n.02', 'name': 'rootstock'}, {'id': 21554, 'synset': 'quickset.n.01', 'name': 'quickset'}, {'id': 21555, 'synset': 'stolon.n.01', 'name': 'stolon'}, {'id': 21556, 'synset': 'tuberous_plant.n.01', 'name': 'tuberous_plant'}, {'id': 21557, 'synset': 'rhizome.n.01', 'name': 'rhizome'}, {'id': 21558, 'synset': 'rachis.n.01', 'name': 'rachis'}, {'id': 21559, 'synset': 'caudex.n.02', 'name': 'caudex'}, {'id': 21560, 'synset': 'cladode.n.01', 'name': 'cladode'}, {'id': 21561, 'synset': 'receptacle.n.02', 'name': 'receptacle'}, {'id': 21562, 'synset': 'scape.n.01', 'name': 'scape'}, {'id': 21563, 'synset': 'umbel.n.01', 'name': 'umbel'}, {'id': 21564, 'synset': 'petiole.n.01', 'name': 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{'id': 21657, 'synset': 'crenate_leaf.n.01', 'name': 'crenate_leaf'}, {'id': 21658, 'synset': 'dentate_leaf.n.01', 'name': 'dentate_leaf'}, {'id': 21659, 'synset': 'denticulate_leaf.n.01', 'name': 'denticulate_leaf'}, {'id': 21660, 'synset': 'erose_leaf.n.01', 'name': 'erose_leaf'}, {'id': 21661, 'synset': 'runcinate_leaf.n.01', 'name': 'runcinate_leaf'}, {'id': 21662, 'synset': 'prickly-edged_leaf.n.01', 'name': 'prickly-edged_leaf'}, {'id': 21663, 'synset': 'deadwood.n.01', 'name': 'deadwood'}, {'id': 21664, 'synset': 'haulm.n.01', 'name': 'haulm'}, {'id': 21665, 'synset': 'branchlet.n.01', 'name': 'branchlet'}, {'id': 21666, 'synset': 'osier.n.01', 'name': 'osier'}, {'id': 21667, 'synset': 'giant_scrambling_fern.n.01', 'name': 'giant_scrambling_fern'}, {'id': 21668, 'synset': 'umbrella_fern.n.01', 'name': 'umbrella_fern'}, {'id': 21669, 'synset': 'floating_fern.n.02', 'name': 'floating_fern'}, {'id': 21670, 'synset': 'polypody.n.01', 'name': 'polypody'}, {'id': 21671, 'synset': 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'wooly_lip_fern.n.01', 'name': 'wooly_lip_fern'}, {'id': 21765, 'synset': 'southwestern_lip_fern.n.01', 'name': 'southwestern_lip_fern'}, {'id': 21766, 'synset': 'bamboo_fern.n.01', 'name': 'bamboo_fern'}, {'id': 21767, 'synset': 'american_rock_brake.n.01', 'name': 'American_rock_brake'}, {'id': 21768, 'synset': 'european_parsley_fern.n.01', 'name': 'European_parsley_fern'}, {'id': 21769, 'synset': 'hand_fern.n.01', 'name': 'hand_fern'}, {'id': 21770, 'synset': 'cliff_brake.n.01', 'name': 'cliff_brake'}, {'id': 21771, 'synset': 'coffee_fern.n.01', 'name': 'coffee_fern'}, {'id': 21772, 'synset': 'purple_rock_brake.n.01', 'name': 'purple_rock_brake'}, {'id': 21773, 'synset': "bird's-foot_fern.n.01", 'name': "bird's-foot_fern"}, {'id': 21774, 'synset': 'button_fern.n.01', 'name': 'button_fern'}, {'id': 21775, 'synset': 'silver_fern.n.02', 'name': 'silver_fern'}, {'id': 21776, 'synset': 'golden_fern.n.01', 'name': 'golden_fern'}, {'id': 21777, 'synset': 'gold_fern.n.01', 'name': 'gold_fern'}, {'id': 21778, 'synset': 'pteris_cretica.n.01', 'name': 'Pteris_cretica'}, {'id': 21779, 'synset': 'spider_brake.n.01', 'name': 'spider_brake'}, {'id': 21780, 'synset': 'ribbon_fern.n.01', 'name': 'ribbon_fern'}, {'id': 21781, 'synset': 'potato_fern.n.01', 'name': 'potato_fern'}, {'id': 21782, 'synset': 'angiopteris.n.01', 'name': 'angiopteris'}, {'id': 21783, 'synset': 'skeleton_fork_fern.n.01', 'name': 'skeleton_fork_fern'}, {'id': 21784, 'synset': 'horsetail.n.01', 'name': 'horsetail'}, {'id': 21785, 'synset': 'common_horsetail.n.01', 'name': 'common_horsetail'}, {'id': 21786, 'synset': 'swamp_horsetail.n.01', 'name': 'swamp_horsetail'}, {'id': 21787, 'synset': 'scouring_rush.n.01', 'name': 'scouring_rush'}, {'id': 21788, 'synset': 'marsh_horsetail.n.01', 'name': 'marsh_horsetail'}, {'id': 21789, 'synset': 'wood_horsetail.n.01', 'name': 'wood_horsetail'}, {'id': 21790, 'synset': 'variegated_horsetail.n.01', 'name': 'variegated_horsetail'}, {'id': 21791, 'synset': 'club_moss.n.01', 'name': 'club_moss'}, {'id': 21792, 'synset': 'shining_clubmoss.n.01', 'name': 'shining_clubmoss'}, {'id': 21793, 'synset': 'alpine_clubmoss.n.01', 'name': 'alpine_clubmoss'}, {'id': 21794, 'synset': 'fir_clubmoss.n.01', 'name': 'fir_clubmoss'}, {'id': 21795, 'synset': 'ground_cedar.n.01', 'name': 'ground_cedar'}, {'id': 21796, 'synset': 'ground_fir.n.01', 'name': 'ground_fir'}, {'id': 21797, 'synset': 'foxtail_grass.n.01', 'name': 'foxtail_grass'}, {'id': 21798, 'synset': 'spikemoss.n.01', 'name': 'spikemoss'}, {'id': 21799, 'synset': 'meadow_spikemoss.n.01', 'name': 'meadow_spikemoss'}, {'id': 21800, 'synset': 'desert_selaginella.n.01', 'name': 'desert_selaginella'}, {'id': 21801, 'synset': 'resurrection_plant.n.01', 'name': 'resurrection_plant'}, {'id': 21802, 'synset': 'florida_selaginella.n.01', 'name': 'florida_selaginella'}, {'id': 21803, 'synset': 'quillwort.n.01', 'name': 'quillwort'}, {'id': 21804, 'synset': 'earthtongue.n.01', 'name': 'earthtongue'}, {'id': 21805, 'synset': 'snuffbox_fern.n.01', 'name': 'snuffbox_fern'}, {'id': 21806, 'synset': 'christella.n.01', 'name': 'christella'}, {'id': 21807, 'synset': 'mountain_fern.n.01', 'name': 'mountain_fern'}, {'id': 21808, 'synset': 'new_york_fern.n.01', 'name': 'New_York_fern'}, {'id': 21809, 'synset': 'massachusetts_fern.n.01', 'name': 'Massachusetts_fern'}, {'id': 21810, 'synset': 'beech_fern.n.01', 'name': 'beech_fern'}, {'id': 21811, 'synset': 'broad_beech_fern.n.01', 'name': 'broad_beech_fern'}, {'id': 21812, 'synset': 'long_beech_fern.n.01', 'name': 'long_beech_fern'}, {'id': 21813, 'synset': 'shoestring_fungus.n.01', 'name': 'shoestring_fungus'}, {'id': 21814, 'synset': 'armillaria_caligata.n.01', 'name': 'Armillaria_caligata'}, {'id': 21815, 'synset': 'armillaria_ponderosa.n.01', 'name': 'Armillaria_ponderosa'}, {'id': 21816, 'synset': 'armillaria_zelleri.n.01', 'name': 'Armillaria_zelleri'}, {'id': 21817, 'synset': 'honey_mushroom.n.01', 'name': 'honey_mushroom'}, {'id': 21818, 'synset': 'milkweed.n.01', 'name': 'milkweed'}, {'id': 21819, 'synset': 'white_milkweed.n.01', 'name': 'white_milkweed'}, {'id': 21820, 'synset': 'poke_milkweed.n.01', 'name': 'poke_milkweed'}, {'id': 21821, 'synset': 'swamp_milkweed.n.01', 'name': 'swamp_milkweed'}, {'id': 21822, 'synset': "mead's_milkweed.n.01", 'name': "Mead's_milkweed"}, {'id': 21823, 'synset': 'purple_silkweed.n.01', 'name': 'purple_silkweed'}, {'id': 21824, 'synset': 'showy_milkweed.n.01', 'name': 'showy_milkweed'}, {'id': 21825, 'synset': 'poison_milkweed.n.01', 'name': 'poison_milkweed'}, {'id': 21826, 'synset': 'butterfly_weed.n.01', 'name': 'butterfly_weed'}, {'id': 21827, 'synset': 'whorled_milkweed.n.01', 'name': 'whorled_milkweed'}, {'id': 21828, 'synset': 'cruel_plant.n.01', 'name': 'cruel_plant'}, {'id': 21829, 'synset': 'wax_plant.n.01', 'name': 'wax_plant'}, {'id': 21830, 'synset': 'silk_vine.n.01', 'name': 'silk_vine'}, {'id': 21831, 'synset': 'stapelia.n.01', 'name': 'stapelia'}, {'id': 21832, 'synset': 'stapelias_asterias.n.01', 'name': 'Stapelias_asterias'}, {'id': 21833, 'synset': 'stephanotis.n.01', 'name': 'stephanotis'}, {'id': 21834, 'synset': 'madagascar_jasmine.n.01', 'name': 'Madagascar_jasmine'}, {'id': 21835, 'synset': 'negro_vine.n.01', 'name': 'negro_vine'}, {'id': 21836, 'synset': 'zygospore.n.01', 'name': 'zygospore'}, {'id': 21837, 'synset': 'tree_of_knowledge.n.01', 'name': 'tree_of_knowledge'}, {'id': 21838, 'synset': 'orangery.n.01', 'name': 'orangery'}, {'id': 21839, 'synset': 'pocketbook.n.01', 'name': 'pocketbook'}, {'id': 21840, 'synset': 'shit.n.04', 'name': 'shit'}, {'id': 21841, 'synset': 'cordage.n.01', 'name': 'cordage'}, {'id': 21842, 'synset': 'yard.n.01', 'name': 'yard'}, {'id': 21843, 'synset': 'extremum.n.02', 'name': 'extremum'}, {'id': 21844, 'synset': 'leaf_shape.n.01', 'name': 'leaf_shape'}, {'id': 21845, 'synset': 'equilateral.n.01', 'name': 'equilateral'}, {'id': 21846, 'synset': 'figure.n.06', 'name': 'figure'}, {'id': 21847, 'synset': 'pencil.n.03', 'name': 'pencil'}, {'id': 21848, 'synset': 'plane_figure.n.01', 'name': 'plane_figure'}, {'id': 21849, 'synset': 'solid_figure.n.01', 'name': 'solid_figure'}, {'id': 21850, 'synset': 'line.n.04', 'name': 'line'}, {'id': 21851, 'synset': 'bulb.n.04', 'name': 'bulb'}, {'id': 21852, 'synset': 'convex_shape.n.01', 'name': 'convex_shape'}, {'id': 21853, 'synset': 'concave_shape.n.01', 'name': 'concave_shape'}, {'id': 21854, 'synset': 'cylinder.n.01', 'name': 'cylinder'}, {'id': 21855, 'synset': 'round_shape.n.01', 'name': 'round_shape'}, {'id': 21856, 'synset': 'heart.n.07', 'name': 'heart'}, {'id': 21857, 'synset': 'polygon.n.01', 'name': 'polygon'}, {'id': 21858, 'synset': 'convex_polygon.n.01', 'name': 'convex_polygon'}, {'id': 21859, 'synset': 'concave_polygon.n.01', 'name': 'concave_polygon'}, {'id': 21860, 'synset': 'reentrant_polygon.n.01', 'name': 'reentrant_polygon'}, {'id': 21861, 'synset': 'amorphous_shape.n.01', 'name': 'amorphous_shape'}, {'id': 21862, 'synset': 'closed_curve.n.01', 'name': 'closed_curve'}, {'id': 21863, 'synset': 'simple_closed_curve.n.01', 'name': 'simple_closed_curve'}, {'id': 21864, 'synset': 's-shape.n.01', 'name': 'S-shape'}, {'id': 21865, 'synset': 'wave.n.07', 'name': 'wave'}, {'id': 21866, 'synset': 'extrados.n.01', 'name': 'extrados'}, {'id': 21867, 'synset': 'hook.n.02', 'name': 'hook'}, {'id': 21868, 'synset': 'envelope.n.03', 'name': 'envelope'}, {'id': 21869, 'synset': 'bight.n.02', 'name': 'bight'}, {'id': 21870, 'synset': 'diameter.n.02', 'name': 'diameter'}, {'id': 21871, 'synset': 'cone.n.02', 'name': 'cone'}, {'id': 21872, 'synset': 'funnel.n.01', 'name': 'funnel'}, {'id': 21873, 'synset': 'oblong.n.01', 'name': 'oblong'}, {'id': 21874, 'synset': 'circle.n.01', 'name': 'circle'}, {'id': 21875, 'synset': 'circle.n.03', 'name': 'circle'}, {'id': 21876, 'synset': 'equator.n.02', 'name': 'equator'}, {'id': 21877, 'synset': 'scallop.n.01', 'name': 'scallop'}, {'id': 21878, 'synset': 'ring.n.02', 'name': 'ring'}, {'id': 21879, 'synset': 'loop.n.02', 'name': 'loop'}, {'id': 21880, 'synset': 'bight.n.01', 'name': 'bight'}, {'id': 21881, 'synset': 'helix.n.01', 'name': 'helix'}, {'id': 21882, 'synset': 'element_of_a_cone.n.01', 'name': 'element_of_a_cone'}, {'id': 21883, 'synset': 'element_of_a_cylinder.n.01', 'name': 'element_of_a_cylinder'}, {'id': 21884, 'synset': 'ellipse.n.01', 'name': 'ellipse'}, {'id': 21885, 'synset': 'quadrate.n.02', 'name': 'quadrate'}, {'id': 21886, 'synset': 'triangle.n.01', 'name': 'triangle'}, {'id': 21887, 'synset': 'acute_triangle.n.01', 'name': 'acute_triangle'}, {'id': 21888, 'synset': 'isosceles_triangle.n.01', 'name': 'isosceles_triangle'}, {'id': 21889, 'synset': 'obtuse_triangle.n.01', 'name': 'obtuse_triangle'}, {'id': 21890, 'synset': 'right_triangle.n.01', 'name': 'right_triangle'}, {'id': 21891, 'synset': 'scalene_triangle.n.01', 'name': 'scalene_triangle'}, {'id': 21892, 'synset': 'parallel.n.03', 'name': 'parallel'}, {'id': 21893, 'synset': 'trapezoid.n.01', 'name': 'trapezoid'}, {'id': 21894, 'synset': 'star.n.05', 'name': 'star'}, {'id': 21895, 'synset': 'pentagon.n.03', 'name': 'pentagon'}, {'id': 21896, 'synset': 'hexagon.n.01', 'name': 'hexagon'}, {'id': 21897, 'synset': 'heptagon.n.01', 'name': 'heptagon'}, {'id': 21898, 'synset': 'octagon.n.01', 'name': 'octagon'}, {'id': 21899, 'synset': 'nonagon.n.01', 'name': 'nonagon'}, {'id': 21900, 'synset': 'decagon.n.01', 'name': 'decagon'}, {'id': 21901, 'synset': 'rhombus.n.01', 'name': 'rhombus'}, {'id': 21902, 'synset': 'spherical_polygon.n.01', 'name': 'spherical_polygon'}, {'id': 21903, 'synset': 'spherical_triangle.n.01', 'name': 'spherical_triangle'}, {'id': 21904, 'synset': 'convex_polyhedron.n.01', 'name': 'convex_polyhedron'}, {'id': 21905, 'synset': 'concave_polyhedron.n.01', 'name': 'concave_polyhedron'}, {'id': 21906, 'synset': 'cuboid.n.01', 'name': 'cuboid'}, {'id': 21907, 'synset': 'quadrangular_prism.n.01', 'name': 'quadrangular_prism'}, {'id': 21908, 'synset': 'bell.n.05', 'name': 'bell'}, {'id': 21909, 'synset': 'angular_distance.n.01', 'name': 'angular_distance'}, {'id': 21910, 'synset': 'true_anomaly.n.01', 'name': 'true_anomaly'}, {'id': 21911, 'synset': 'spherical_angle.n.01', 'name': 'spherical_angle'}, {'id': 21912, 'synset': 'angle_of_refraction.n.01', 'name': 'angle_of_refraction'}, {'id': 21913, 'synset': 'acute_angle.n.01', 'name': 'acute_angle'}, {'id': 21914, 'synset': 'groove.n.01', 'name': 'groove'}, {'id': 21915, 'synset': 'rut.n.01', 'name': 'rut'}, {'id': 21916, 'synset': 'bulge.n.01', 'name': 'bulge'}, {'id': 21917, 'synset': 'belly.n.03', 'name': 'belly'}, {'id': 21918, 'synset': 'bow.n.05', 'name': 'bow'}, {'id': 21919, 'synset': 'crescent.n.01', 'name': 'crescent'}, {'id': 21920, 'synset': 'ellipsoid.n.01', 'name': 'ellipsoid'}, {'id': 21921, 'synset': 'hypotenuse.n.01', 'name': 'hypotenuse'}, {'id': 21922, 'synset': 'balance.n.04', 'name': 'balance'}, {'id': 21923, 'synset': 'conformation.n.01', 'name': 'conformation'}, {'id': 21924, 'synset': 'symmetry.n.02', 'name': 'symmetry'}, {'id': 21925, 'synset': 'spheroid.n.01', 'name': 'spheroid'}, {'id': 21926, 'synset': 'spherule.n.01', 'name': 'spherule'}, {'id': 21927, 'synset': 'toroid.n.01', 'name': 'toroid'}, {'id': 21928, 'synset': 'column.n.04', 'name': 'column'}, {'id': 21929, 'synset': 'barrel.n.03', 'name': 'barrel'}, {'id': 21930, 'synset': 'pipe.n.03', 'name': 'pipe'}, {'id': 21931, 'synset': 'pellet.n.01', 'name': 'pellet'}, {'id': 21932, 'synset': 'bolus.n.01', 'name': 'bolus'}, {'id': 21933, 'synset': 'dewdrop.n.01', 'name': 'dewdrop'}, {'id': 21934, 'synset': 'ridge.n.02', 'name': 'ridge'}, {'id': 21935, 'synset': 'rim.n.01', 'name': 'rim'}, {'id': 21936, 'synset': 'taper.n.01', 'name': 'taper'}, {'id': 21937, 'synset': 'boundary.n.02', 'name': 'boundary'}, {'id': 21938, 'synset': 'incisure.n.01', 'name': 'incisure'}, {'id': 21939, 'synset': 'notch.n.01', 'name': 'notch'}, {'id': 21940, 'synset': 'wrinkle.n.01', 'name': 'wrinkle'}, {'id': 21941, 'synset': 'dermatoglyphic.n.01', 'name': 'dermatoglyphic'}, {'id': 21942, 'synset': 'frown_line.n.01', 'name': 'frown_line'}, {'id': 21943, 'synset': 'line_of_life.n.01', 'name': 'line_of_life'}, {'id': 21944, 'synset': 'line_of_heart.n.01', 'name': 'line_of_heart'}, {'id': 21945, 'synset': 'crevice.n.01', 'name': 'crevice'}, {'id': 21946, 'synset': 'cleft.n.01', 'name': 'cleft'}, {'id': 21947, 'synset': 'roulette.n.01', 'name': 'roulette'}, {'id': 21948, 'synset': 'node.n.01', 'name': 'node'}, {'id': 21949, 'synset': 'tree.n.02', 'name': 'tree'}, {'id': 21950, 'synset': 'stemma.n.01', 'name': 'stemma'}, {'id': 21951, 'synset': 'brachium.n.01', 'name': 'brachium'}, {'id': 21952, 'synset': 'fork.n.03', 'name': 'fork'}, {'id': 21953, 'synset': 'block.n.03', 'name': 'block'}, {'id': 21954, 'synset': 'ovoid.n.01', 'name': 'ovoid'}, {'id': 21955, 'synset': 'tetrahedron.n.01', 'name': 'tetrahedron'}, {'id': 21956, 'synset': 'pentahedron.n.01', 'name': 'pentahedron'}, {'id': 21957, 'synset': 'hexahedron.n.01', 'name': 'hexahedron'}, {'id': 21958, 'synset': 'regular_polyhedron.n.01', 'name': 'regular_polyhedron'}, {'id': 21959, 'synset': 'polyhedral_angle.n.01', 'name': 'polyhedral_angle'}, {'id': 21960, 'synset': 'cube.n.01', 'name': 'cube'}, {'id': 21961, 'synset': 'truncated_pyramid.n.01', 'name': 'truncated_pyramid'}, {'id': 21962, 'synset': 'truncated_cone.n.01', 'name': 'truncated_cone'}, {'id': 21963, 'synset': 'tail.n.03', 'name': 'tail'}, {'id': 21964, 'synset': 'tongue.n.03', 'name': 'tongue'}, {'id': 21965, 'synset': 'trapezohedron.n.01', 'name': 'trapezohedron'}, {'id': 21966, 'synset': 'wedge.n.01', 'name': 'wedge'}, {'id': 21967, 'synset': 'keel.n.01', 'name': 'keel'}, {'id': 21968, 'synset': 'place.n.06', 'name': 'place'}, {'id': 21969, 'synset': 'herpes.n.01', 'name': 'herpes'}, {'id': 21970, 'synset': 'chlamydia.n.01', 'name': 'chlamydia'}, {'id': 21971, 'synset': 'wall.n.04', 'name': 'wall'}, {'id': 21972, 'synset': 'micronutrient.n.01', 'name': 'micronutrient'}, {'id': 21973, 'synset': 'chyme.n.01', 'name': 'chyme'}, {'id': 21974, 'synset': 'ragweed_pollen.n.01', 'name': 'ragweed_pollen'}, {'id': 21975, 'synset': 'pina_cloth.n.01', 'name': 'pina_cloth'}, {'id': 21976, 'synset': 'chlorobenzylidenemalononitrile.n.01', 'name': 'chlorobenzylidenemalononitrile'}, {'id': 21977, 'synset': 'carbon.n.01', 'name': 'carbon'}, {'id': 21978, 'synset': 'charcoal.n.01', 'name': 'charcoal'}, {'id': 21979, 'synset': 'rock.n.02', 'name': 'rock'}, {'id': 21980, 'synset': 'gravel.n.01', 'name': 'gravel'}, {'id': 21981, 'synset': 'aflatoxin.n.01', 'name': 'aflatoxin'}, {'id': 21982, 'synset': 'alpha-tocopheral.n.01', 'name': 'alpha-tocopheral'}, {'id': 21983, 'synset': 'leopard.n.01', 'name': 'leopard'}, {'id': 21984, 'synset': 'bricks_and_mortar.n.01', 'name': 'bricks_and_mortar'}, {'id': 21985, 'synset': 'lagging.n.01', 'name': 'lagging'}, {'id': 21986, 'synset': 'hydraulic_cement.n.01', 'name': 'hydraulic_cement'}, {'id': 21987, 'synset': 'choline.n.01', 'name': 'choline'}, {'id': 21988, 'synset': 'concrete.n.01', 'name': 'concrete'}, {'id': 21989, 'synset': 'glass_wool.n.01', 'name': 'glass_wool'}, {'id': 21990, 'synset': 'soil.n.02', 'name': 'soil'}, {'id': 21991, 'synset': 'high_explosive.n.01', 'name': 'high_explosive'}, {'id': 21992, 'synset': 'litter.n.02', 'name': 'litter'}, {'id': 21993, 'synset': 'fish_meal.n.01', 'name': 'fish_meal'}, {'id': 21994, 'synset': 'greek_fire.n.01', 'name': 'Greek_fire'}, {'id': 21995, 'synset': 'culture_medium.n.01', 'name': 'culture_medium'}, {'id': 21996, 'synset': 'agar.n.01', 'name': 'agar'}, {'id': 21997, 'synset': 'blood_agar.n.01', 'name': 'blood_agar'}, {'id': 21998, 'synset': 'hip_tile.n.01', 'name': 'hip_tile'}, {'id': 21999, 'synset': 'hyacinth.n.01', 'name': 'hyacinth'}, {'id': 22000, 'synset': 'hydroxide_ion.n.01', 'name': 'hydroxide_ion'}, {'id': 22001, 'synset': 'ice.n.01', 'name': 'ice'}, {'id': 22002, 'synset': 'inositol.n.01', 'name': 'inositol'}, {'id': 22003, 'synset': 'linoleum.n.01', 'name': 'linoleum'}, {'id': 22004, 'synset': 'lithia_water.n.01', 'name': 'lithia_water'}, {'id': 22005, 'synset': 'lodestone.n.01', 'name': 'lodestone'}, {'id': 22006, 'synset': 'pantothenic_acid.n.01', 'name': 'pantothenic_acid'}, {'id': 22007, 'synset': 'paper.n.01', 'name': 'paper'}, {'id': 22008, 'synset': 'papyrus.n.01', 'name': 'papyrus'}, {'id': 22009, 'synset': 'pantile.n.01', 'name': 'pantile'}, {'id': 22010, 'synset': 'blacktop.n.01', 'name': 'blacktop'}, {'id': 22011, 'synset': 'tarmacadam.n.01', 'name': 'tarmacadam'}, {'id': 22012, 'synset': 'paving.n.01', 'name': 'paving'}, {'id': 22013, 'synset': 'plaster.n.01', 'name': 'plaster'}, {'id': 22014, 'synset': 'poison_gas.n.01', 'name': 'poison_gas'}, {'id': 22015, 'synset': 'ridge_tile.n.01', 'name': 'ridge_tile'}, {'id': 22016, 'synset': 'roughcast.n.01', 'name': 'roughcast'}, {'id': 22017, 'synset': 'sand.n.01', 'name': 'sand'}, {'id': 22018, 'synset': 'spackle.n.01', 'name': 'spackle'}, {'id': 22019, 'synset': 'render.n.01', 'name': 'render'}, {'id': 22020, 'synset': 'wattle_and_daub.n.01', 'name': 'wattle_and_daub'}, {'id': 22021, 'synset': 'stucco.n.01', 'name': 'stucco'}, {'id': 22022, 'synset': 'tear_gas.n.01', 'name': 'tear_gas'}, {'id': 22023, 'synset': 'linseed.n.01', 'name': 'linseed'}, {'id': 22024, 'synset': 'vitamin.n.01', 'name': 'vitamin'}, {'id': 22025, 'synset': 'fat-soluble_vitamin.n.01', 'name': 'fat-soluble_vitamin'}, {'id': 22026, 'synset': 'water-soluble_vitamin.n.01', 'name': 'water-soluble_vitamin'}, {'id': 22027, 'synset': 'vitamin_a.n.01', 'name': 'vitamin_A'}, {'id': 22028, 'synset': 'vitamin_a1.n.01', 'name': 'vitamin_A1'}, {'id': 22029, 'synset': 'vitamin_a2.n.01', 'name': 'vitamin_A2'}, {'id': 22030, 'synset': 'b-complex_vitamin.n.01', 'name': 'B-complex_vitamin'}, {'id': 22031, 'synset': 'vitamin_b1.n.01', 'name': 'vitamin_B1'}, {'id': 22032, 'synset': 'vitamin_b12.n.01', 'name': 'vitamin_B12'}, {'id': 22033, 'synset': 'vitamin_b2.n.01', 'name': 'vitamin_B2'}, {'id': 22034, 'synset': 'vitamin_b6.n.01', 'name': 'vitamin_B6'}, {'id': 22035, 'synset': 'vitamin_bc.n.01', 'name': 'vitamin_Bc'}, {'id': 22036, 'synset': 'niacin.n.01', 'name': 'niacin'}, {'id': 22037, 'synset': 'vitamin_d.n.01', 'name': 'vitamin_D'}, {'id': 22038, 'synset': 'vitamin_e.n.01', 'name': 'vitamin_E'}, {'id': 22039, 'synset': 'biotin.n.01', 'name': 'biotin'}, {'id': 22040, 'synset': 'vitamin_k.n.01', 'name': 'vitamin_K'}, {'id': 22041, 'synset': 'vitamin_k1.n.01', 'name': 'vitamin_K1'}, {'id': 22042, 'synset': 'vitamin_k3.n.01', 'name': 'vitamin_K3'}, {'id': 22043, 'synset': 'vitamin_p.n.01', 'name': 'vitamin_P'}, {'id': 22044, 'synset': 'vitamin_c.n.01', 'name': 'vitamin_C'}, {'id': 22045, 'synset': 'planking.n.01', 'name': 'planking'}, {'id': 22046, 'synset': 'chipboard.n.01', 'name': 'chipboard'}, {'id': 22047, 'synset': 'knothole.n.01', 'name': 'knothole'}] # noqa \ No newline at end of file diff --git a/spaces/Missinginaction/stablediffusionwithnofilter/README.md b/spaces/Missinginaction/stablediffusionwithnofilter/README.md deleted file mode 100644 index 013d12c9f3a56698056ae1bdbbfb0ec009805237..0000000000000000000000000000000000000000 --- a/spaces/Missinginaction/stablediffusionwithnofilter/README.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: Stable Diffusion Web UI -emoji: 🚧 -colorFrom: yellow -colorTo: yellow -sdk: gradio -sdk_version: 3.9 -app_file: app.py -pinned: false -duplicated_from: camenduru/webui ---- - -## Stable Diffusion Web UI -[https://github.com/AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) - -## Documentation -[https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki) - -## Models License -https://huggingface.co/spaces/CompVis/stable-diffusion-license \ No newline at end of file diff --git a/spaces/NATSpeech/DiffSpeech/docs/framework.md b/spaces/NATSpeech/DiffSpeech/docs/framework.md deleted file mode 100644 index a48bb2b49d2e9696e2f8d4b01be8af68befc552c..0000000000000000000000000000000000000000 --- a/spaces/NATSpeech/DiffSpeech/docs/framework.md +++ /dev/null @@ -1,106 +0,0 @@ -# Framework of NATSpeech - -NATSpeech is a simple framework for Non-Autoregressive Text-to-Speech. - -## Directory Structure - -- `egs`: configuration files, which will be loaded by `utils/commons/hparams.py` -- `data_gen`: data binarization codes -- `modules`: modules and models -- `tasks`: the training and inference logics -- `utils`: commonly used utils -- `data`: data - - `raw`: raw data - - `processed`: data after preprocess - - `binary`: binary data -- `checkpoints`: model checkpoints, tensorboard logs and generated results for all experiments. - -## How to Add New Tasks and Run? - -We show the basic steps of adding a new task/model and running the code (LJSpeech dataset as an example). - -### Add the model - -Add your model to `modules`. - -### Add the task - -Task classes are used to manage the training and inference procedures. - -A new task (e.g., `tasks.tts.fs.FastSpeechTask`) should inherit the base task (`tasks.tts.speech_base.TTSBaseTask`) -class. - -You must implement these methods: - -- `build_tts_model`, which builds the model for your task. - `run_model`, indicating how to use the model in training - and inference. - -You can override `test_step` and `save_valid_result` to change the validation/testing logics or add more plots to -tensorboard. - -### Add a new config file - -Add a new config file in `egs/datasets/audio/lj/YOUR_TASK.yaml`. For example: - -```yaml -base_config: ./base_text2mel.yaml -task_cls: tasks.tts.fs.FastSpeechTask - -# model configs -hidden_size: 256 -dropout: 0.1 - -# some more configs ..... -``` - -If you use a new dataset `YOUR_DATASET`, you should also add a `YOUR_DATASET_Processor` -in `egs/datasets/audio/YOUR_DATASET/preprocess.py`, inheriting `data_gen.tts.base_preprocess.BasePreprocessor`, which -loads some meta information of the dataset. - -### Preprocess and binary dataset - -```bash -python data_gen/tts/runs/align_and_binarize.py --config egs/datasets/audio/lj/base_text2mel.yaml -``` - -### Training - -```bash -CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config YOUR_CONFIG --exp_name YOUR_EXP_NAME --reset -``` - -You can open Tensorboard via: - -```bash -tensorboard --logdir checkpoints/EXP_NAME -``` - -### Inference (Testing) - -```bash -CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config egs/datasets/audio/lj/YOUR_TASK.yaml --exp_name YOUR_EXP_NAME --reset --infer -``` - -## Design Philosophy - -### Random-Access Binarized Dataset - -To address the IO problem when reading small files, we design a `IndexedDataset` class (_utils/commons/indexed_datasets.py_) - -### Global Config - -We introduce a global config `hparams`, which is load from a `.yaml` config file and can be used in anywhere. However, -we do not recommend using it in some general-purpose modules. - -### BaseTrainer Framework - -Our [base trainer](utils/commons/trainer.py) and [base task ](utils/commons/base_task.py) classes refer -to [PytorchLightning](https://github.com/PyTorchLightning/pytorch-lightning), which builds some commonly used -training/inference code structure. Our framework supports multi-process GPU training without changing the subclass -codes. - -### Checkpoint Saving - -All checkpoints and tensorboard logs are saved in `checkpoints/EXP_NAME`, where `EXP_NAME` is set in the running -command: `python tasks/run.py .... --exp_name EXP_NAME`. You can use `tensorboard --logdir checkpoints/EXP_NAME` to open -the tensorboard and check the training loss curves etc. diff --git a/spaces/NATSpeech/PortaSpeech/utils/commons/indexed_datasets.py b/spaces/NATSpeech/PortaSpeech/utils/commons/indexed_datasets.py deleted file mode 100644 index e15632be30d6296a3c9aa80a1f351058003698b3..0000000000000000000000000000000000000000 --- a/spaces/NATSpeech/PortaSpeech/utils/commons/indexed_datasets.py +++ /dev/null @@ -1,71 +0,0 @@ -import pickle -from copy import deepcopy - -import numpy as np - - -class IndexedDataset: - def __init__(self, path, num_cache=1): - super().__init__() - self.path = path - self.data_file = None - self.data_offsets = np.load(f"{path}.idx", allow_pickle=True).item()['offsets'] - self.data_file = open(f"{path}.data", 'rb', buffering=-1) - self.cache = [] - self.num_cache = num_cache - - def check_index(self, i): - if i < 0 or i >= len(self.data_offsets) - 1: - raise IndexError('index out of range') - - def __del__(self): - if self.data_file: - self.data_file.close() - - def __getitem__(self, i): - self.check_index(i) - if self.num_cache > 0: - for c in self.cache: - if c[0] == i: - return c[1] - self.data_file.seek(self.data_offsets[i]) - b = self.data_file.read(self.data_offsets[i + 1] - self.data_offsets[i]) - item = pickle.loads(b) - if self.num_cache > 0: - self.cache = [(i, deepcopy(item))] + self.cache[:-1] - return item - - def __len__(self): - return len(self.data_offsets) - 1 - -class IndexedDatasetBuilder: - def __init__(self, path): - self.path = path - self.out_file = open(f"{path}.data", 'wb') - self.byte_offsets = [0] - - def add_item(self, item): - s = pickle.dumps(item) - bytes = self.out_file.write(s) - self.byte_offsets.append(self.byte_offsets[-1] + bytes) - - def finalize(self): - self.out_file.close() - np.save(open(f"{self.path}.idx", 'wb'), {'offsets': self.byte_offsets}) - - -if __name__ == "__main__": - import random - from tqdm import tqdm - ds_path = '/tmp/indexed_ds_example' - size = 100 - items = [{"a": np.random.normal(size=[10000, 10]), - "b": np.random.normal(size=[10000, 10])} for i in range(size)] - builder = IndexedDatasetBuilder(ds_path) - for i in tqdm(range(size)): - builder.add_item(items[i]) - builder.finalize() - ds = IndexedDataset(ds_path) - for i in tqdm(range(10000)): - idx = random.randint(0, size - 1) - assert (ds[idx]['a'] == items[idx]['a']).all() diff --git a/spaces/NCTCMumbai/NCTC/models/official/benchmark/perfzero_benchmark.py b/spaces/NCTCMumbai/NCTC/models/official/benchmark/perfzero_benchmark.py deleted file mode 100644 index bedc1320217d1b9469333a8cdfdf70c56de34f77..0000000000000000000000000000000000000000 --- a/spaces/NCTCMumbai/NCTC/models/official/benchmark/perfzero_benchmark.py +++ /dev/null @@ -1,100 +0,0 @@ -# Copyright 2019 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Utils for creating PerfZero benchmarks.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os - -from absl import flags -from absl import logging -from absl.testing import flagsaver -import tensorflow as tf - -FLAGS = flags.FLAGS - - -class PerfZeroBenchmark(tf.test.Benchmark): - """Common methods used in PerfZero Benchmarks. - - Handles the resetting of flags between tests, loading of default_flags, - overriding of defaults. PerfZero (OSS) runs each test in a separate - process reducing some need to reset the flags. - """ - local_flags = None - - def __init__(self, - output_dir=None, - default_flags=None, - root_data_dir=None, - flag_methods=None, - tpu=None): - """Initialize class. - - Args: - output_dir: Base directory to store all output for the test. - default_flags: Set of flags to pass to model. - root_data_dir: Optional param used by child classes to look for the - dataset. - flag_methods: Set of flag methods to run during setup. - tpu: (optional) TPU name to use in a TPU benchmark. - """ - if os.getenv('BENCHMARK_OUTPUT_DIR'): - self.output_dir = os.getenv('BENCHMARK_OUTPUT_DIR') - elif output_dir: - self.output_dir = output_dir - else: - self.output_dir = '/tmp' - self.default_flags = default_flags or {} - self.flag_methods = flag_methods or {} - - if os.getenv('BENCHMARK_TPU'): - resolved_tpu = os.getenv('BENCHMARK_TPU') - elif tpu: - resolved_tpu = tpu - else: - resolved_tpu = None - - if resolved_tpu: - # TPU models are expected to accept a --tpu=name flag. PerfZero creates - # the TPU at runtime and passes the TPU's name to this flag. - self.default_flags['tpu'] = resolved_tpu - - logging.info('root_data_dir: %s', root_data_dir) - - @property - def tpu(self): - return self.default_flags.get('tpu', None) - - def _get_model_dir(self, folder_name): - """Returns directory to store info, e.g. saved model and event log.""" - return os.path.join(self.output_dir, folder_name) - - def _setup(self): - """Sets up and resets flags before each test.""" - logging.set_verbosity(logging.INFO) - if PerfZeroBenchmark.local_flags is None: - for flag_method in self.flag_methods: - flag_method() - # Loads flags to get defaults to then override. List cannot be empty. - flags.FLAGS(['foo']) - # Overrides flag values with defaults for the class of tests. - for k, v in self.default_flags.items(): - setattr(FLAGS, k, v) - saved_flag_values = flagsaver.save_flag_values() - PerfZeroBenchmark.local_flags = saved_flag_values - else: - flagsaver.restore_flag_values(PerfZeroBenchmark.local_flags) diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/data/__init__.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/data/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/NCTCMumbai/NCTC/models/research/audioset/vggish/vggish_postprocess.py b/spaces/NCTCMumbai/NCTC/models/research/audioset/vggish/vggish_postprocess.py deleted file mode 100644 index aef23babef6a44aa4c1546539bbc41ccf57d59c7..0000000000000000000000000000000000000000 --- a/spaces/NCTCMumbai/NCTC/models/research/audioset/vggish/vggish_postprocess.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright 2017 The TensorFlow Authors All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -"""Post-process embeddings from VGGish.""" - -import numpy as np - -import vggish_params - - -class Postprocessor(object): - """Post-processes VGGish embeddings. - - The initial release of AudioSet included 128-D VGGish embeddings for each - segment of AudioSet. These released embeddings were produced by applying - a PCA transformation (technically, a whitening transform is included as well) - and 8-bit quantization to the raw embedding output from VGGish, in order to - stay compatible with the YouTube-8M project which provides visual embeddings - in the same format for a large set of YouTube videos. This class implements - the same PCA (with whitening) and quantization transformations. - """ - - def __init__(self, pca_params_npz_path): - """Constructs a postprocessor. - - Args: - pca_params_npz_path: Path to a NumPy-format .npz file that - contains the PCA parameters used in postprocessing. - """ - params = np.load(pca_params_npz_path) - self._pca_matrix = params[vggish_params.PCA_EIGEN_VECTORS_NAME] - # Load means into a column vector for easier broadcasting later. - self._pca_means = params[vggish_params.PCA_MEANS_NAME].reshape(-1, 1) - assert self._pca_matrix.shape == ( - vggish_params.EMBEDDING_SIZE, vggish_params.EMBEDDING_SIZE), ( - 'Bad PCA matrix shape: %r' % (self._pca_matrix.shape,)) - assert self._pca_means.shape == (vggish_params.EMBEDDING_SIZE, 1), ( - 'Bad PCA means shape: %r' % (self._pca_means.shape,)) - - def postprocess(self, embeddings_batch): - """Applies postprocessing to a batch of embeddings. - - Args: - embeddings_batch: An nparray of shape [batch_size, embedding_size] - containing output from the embedding layer of VGGish. - - Returns: - An nparray of the same shape as the input but of type uint8, - containing the PCA-transformed and quantized version of the input. - """ - assert len(embeddings_batch.shape) == 2, ( - 'Expected 2-d batch, got %r' % (embeddings_batch.shape,)) - assert embeddings_batch.shape[1] == vggish_params.EMBEDDING_SIZE, ( - 'Bad batch shape: %r' % (embeddings_batch.shape,)) - - # Apply PCA. - # - Embeddings come in as [batch_size, embedding_size]. - # - Transpose to [embedding_size, batch_size]. - # - Subtract pca_means column vector from each column. - # - Premultiply by PCA matrix of shape [output_dims, input_dims] - # where both are are equal to embedding_size in our case. - # - Transpose result back to [batch_size, embedding_size]. - pca_applied = np.dot(self._pca_matrix, - (embeddings_batch.T - self._pca_means)).T - - # Quantize by: - # - clipping to [min, max] range - clipped_embeddings = np.clip( - pca_applied, vggish_params.QUANTIZE_MIN_VAL, - vggish_params.QUANTIZE_MAX_VAL) - # - convert to 8-bit in range [0.0, 255.0] - quantized_embeddings = ( - (clipped_embeddings - vggish_params.QUANTIZE_MIN_VAL) * - (255.0 / - (vggish_params.QUANTIZE_MAX_VAL - vggish_params.QUANTIZE_MIN_VAL))) - # - cast 8-bit float to uint8 - quantized_embeddings = quantized_embeddings.astype(np.uint8) - - return quantized_embeddings diff --git a/spaces/NCTCMumbai/NCTC/models/research/brain_coder/single_task/ga_lib.py b/spaces/NCTCMumbai/NCTC/models/research/brain_coder/single_task/ga_lib.py deleted file mode 100644 index fadb96482b21a5c65c0d6d6cf4a3aec3b5708235..0000000000000000000000000000000000000000 --- a/spaces/NCTCMumbai/NCTC/models/research/brain_coder/single_task/ga_lib.py +++ /dev/null @@ -1,472 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -"""Genetic algorithm for BF tasks. - -Inspired by https://github.com/primaryobjects/AI-Programmer. -GA function code borrowed from https://github.com/DEAP/deap. -""" - -from collections import namedtuple -import random - -from absl import flags -from absl import logging -import numpy as np -from six.moves import xrange - -from common import bf # brain coder -from common import utils # brain coder -from single_task import misc # brain coder - -FLAGS = flags.FLAGS - -# Saving reward of previous programs saves computation if a program appears -# again. -USE_REWARD_CACHE = True # Disable this if GA is using up too much memory. -GENES = bf.CHARS -MAX_PROGRAM_STEPS = 500 -STEP_BONUS = True - -ALPHANUM_CHARS = ( - ['_'] + - [chr(ord('a') + i_) for i_ in range(26)] + - [chr(ord('A') + i_) for i_ in range(26)] + - [chr(ord('0') + i_) for i_ in range(10)]) - -Result = namedtuple( - 'Result', - ['reward', 'inputs', 'code_outputs', 'target_outputs', 'type_in', - 'type_out', 'base', 'correct']) - - -class IOType(object): - string = 'string' - integer = 'integer' - - -class CustomType(object): - - def __init__(self, to_str_fn): - self.to_str_fn = to_str_fn - - def __call__(self, obj): - return self.to_str_fn(obj) - - -def tokens_list_repr(tokens, repr_type, base): - """Make human readable representation of program IO.""" - if isinstance(repr_type, CustomType): - return repr_type(tokens) - elif repr_type == IOType.string: - chars = ( - [ALPHANUM_CHARS[t] for t in tokens] if base < len(ALPHANUM_CHARS) - else [chr(t) for t in tokens]) - return ''.join(chars) - elif repr_type == IOType.integer: - return str(tokens) - raise ValueError('No such representation type "%s"', repr_type) - - -def io_repr(result): - """Make human readable representation of test cases.""" - inputs = ','.join( - tokens_list_repr(tokens, result.type_in, result.base) - for tokens in result.inputs) - code_outputs = ','.join( - tokens_list_repr(tokens, result.type_out, result.base) - for tokens in result.code_outputs) - target_outputs = ','.join( - tokens_list_repr(tokens, result.type_out, result.base) - for tokens in result.target_outputs) - return inputs, target_outputs, code_outputs - - -def make_task_eval_fn(task_manager): - """Returns a wrapper that converts an RL task into a GA task. - - Args: - task_manager: Is a task manager object from code_tasks.py - - Returns: - A function that takes as input a single list of a code chars, and outputs - a Result namedtuple instance containing the reward and information about - code execution. - """ - def to_data_list(single_or_tuple): - if isinstance(single_or_tuple, misc.IOTuple): - return list(single_or_tuple) - return [single_or_tuple] - - def to_ga_type(rl_type): - if rl_type == misc.IOType.string: - return IOType.string - return IOType.integer - - # Wrapper function. - def evalbf(bf_chars): - result = task_manager._score_code(''.join(bf_chars)) - reward = sum(result.episode_rewards) - correct = result.reason == 'correct' - return Result( - reward=reward, - inputs=to_data_list(result.input_case), - code_outputs=to_data_list(result.code_output), - target_outputs=to_data_list(result.correct_output), - type_in=to_ga_type(result.input_type), - type_out=to_ga_type(result.output_type), - correct=correct, - base=task_manager.task.base) - - return evalbf - - -def debug_str(individual, task_eval_fn): - res = task_eval_fn(individual) - input_str, target_output_str, code_output_str = io_repr(res) - return ( - ''.join(individual) + - ' | ' + input_str + - ' | ' + target_output_str + - ' | ' + code_output_str + - ' | ' + str(res.reward) + - ' | ' + str(res.correct)) - - -def mutate_single(code_tokens, mutation_rate): - """Mutate a single code string. - - Args: - code_tokens: A string/list/Individual of BF code chars. Must end with EOS - symbol '_'. - mutation_rate: Float between 0 and 1 which sets the probability of each char - being mutated. - - Returns: - An Individual instance containing the mutated code string. - - Raises: - ValueError: If `code_tokens` does not end with EOS symbol. - """ - if len(code_tokens) <= 1: - return code_tokens - if code_tokens[-1] == '_': - # Do this check to ensure that the code strings have not been corrupted. - raise ValueError('`code_tokens` must end with EOS symbol.') - else: - cs = Individual(code_tokens) - eos = [] - mutated = False - for pos in range(len(cs)): - if random.random() < mutation_rate: - mutated = True - new_char = GENES[random.randrange(len(GENES))] - x = random.random() - if x < 0.25 and pos != 0 and pos != len(cs) - 1: - # Insertion mutation. - if random.random() < 0.50: - # Shift up. - cs = cs[:pos] + [new_char] + cs[pos:-1] - else: - # Shift down. - cs = cs[1:pos] + [new_char] + cs[pos:] - elif x < 0.50: - # Deletion mutation. - if random.random() < 0.50: - # Shift down. - cs = cs[:pos] + cs[pos + 1:] + [new_char] - else: - # Shift up. - cs = [new_char] + cs[:pos] + cs[pos + 1:] - elif x < 0.75: - # Shift rotate mutation (position invariant). - if random.random() < 0.50: - # Shift down. - cs = cs[1:] + [cs[0]] - else: - # Shift up. - cs = [cs[-1]] + cs[:-1] - else: - # Replacement mutation. - cs = cs[:pos] + [new_char] + cs[pos + 1:] - assert len(cs) + len(eos) == len(code_tokens) - if mutated: - return Individual(cs + eos) - else: - return Individual(code_tokens) - - -def crossover(parent1, parent2): - """Performs crossover mating between two code strings. - - Crossover mating is where a random position is selected, and the chars - after that point are swapped. The resulting new code strings are returned. - - Args: - parent1: First code string. - parent2: Second code string. - - Returns: - A 2-tuple of children, i.e. the resulting code strings after swapping. - """ - max_parent, min_parent = ( - (parent1, parent2) if len(parent1) > len(parent2) - else (parent2, parent1)) - pos = random.randrange(len(max_parent)) - if pos >= len(min_parent): - child1 = max_parent[:pos] - child2 = min_parent + max_parent[pos:] - else: - child1 = max_parent[:pos] + min_parent[pos:] - child2 = min_parent[:pos] + max_parent[pos:] - return Individual(child1), Individual(child2) - - -def _make_even(n): - """Return largest even integer less than or equal to `n`.""" - return (n >> 1) << 1 - - -def mutate_and_crossover(population, mutation_rate, crossover_rate): - """Take a generational step over a population. - - Transforms population of parents into population of children (of the same - size) via crossover mating and then mutation on the resulting children. - - Args: - population: Parent population. A list of Individual objects. - mutation_rate: Probability of mutation. See `mutate_single`. - crossover_rate: Probability that two parents will mate. - - Returns: - Child population. A list of Individual objects. - """ - children = [None] * len(population) - for i in xrange(0, _make_even(len(population)), 2): - p1 = population[i] - p2 = population[i + 1] - if random.random() < crossover_rate: - p1, p2 = crossover(p1, p2) - c1 = mutate_single(p1, mutation_rate) - c2 = mutate_single(p2, mutation_rate) - children[i] = c1 - children[i + 1] = c2 - if children[-1] is None: - children[-1] = population[-1] - return children - - -def ga_loop(population, cxpb, mutpb, ngen, task_eval_fn, halloffame=None, - checkpoint_writer=None): - """A bare bones genetic algorithm. - - Similar to chapter 7 of Back, Fogel and Michalewicz, "Evolutionary - Computation 1 : Basic Algorithms and Operators", 2000. - - Args: - population: A list of individuals. - cxpb: The probability of mating two individuals. - mutpb: The probability of mutating a gene. - ngen: The number of generation. Unlimited if zero. - task_eval_fn: A python function which maps an Individual to a Result - namedtuple. - halloffame: (optional) a utils.MaxUniquePriorityQueue object that will be - used to aggregate the best individuals found during search. - checkpoint_writer: (optional) an object that can save and load populations. - Needs to have `write`, `load`, and `has_checkpoint` methods. Used to - periodically save progress. In event of a restart, the population will - be loaded from disk. - - Returns: - GaResult namedtuple instance. This contains information about the GA run, - including the resulting population, best reward (fitness) obtained, and - the best code string found. - """ - - has_checkpoint = False - if checkpoint_writer and checkpoint_writer.has_checkpoint(): - try: - gen, population, halloffame = checkpoint_writer.load() - except EOFError: # Data was corrupted. Start over. - pass - else: - has_checkpoint = True - logging.info( - 'Loaded population from checkpoint. Starting at generation %d', gen) - - # Evaluate the individuals with an invalid fitness - invalid_ind = [ind for ind in population if not ind.fitness.valid] - for ind in invalid_ind: - ind.fitness.values = task_eval_fn(ind).reward, - for _, ind in halloffame.iter_in_order(): - ind.fitness.values = task_eval_fn(ind).reward, - - if not has_checkpoint: - # Evaluate the individuals with an invalid fitness - invalid_ind = [ind for ind in population if not ind.fitness.valid] - for ind in invalid_ind: - ind.fitness.values = task_eval_fn(ind).reward, - - if halloffame is not None: - for ind in population: - halloffame.push(ind.fitness.values, tuple(ind), ind) - - logging.info('Initialized new population.') - - gen = 1 - - pop_size = len(population) - program_reward_cache = {} if USE_REWARD_CACHE else None - - # Begin the generational process - while ngen == 0 or gen <= ngen: - # Select the next generation individuals - offspring = roulette_selection(population, pop_size - len(halloffame)) - - # Vary the pool of individuals - # offspring = varAnd(offspring, toolbox, cxpb, mutpb) - offspring = mutate_and_crossover( - offspring, mutation_rate=mutpb, crossover_rate=cxpb) - - # Evaluate the individuals with an invalid fitness - invalid_ind = [ind for ind in offspring if not ind.fitness.valid] - for ind in invalid_ind: - str_repr = ''.join(ind) - if program_reward_cache is not None and str_repr in program_reward_cache: - ind.fitness.values = (program_reward_cache[str_repr],) - else: - eval_result = task_eval_fn(ind) - ind.fitness.values = (eval_result.reward,) - if program_reward_cache is not None: - program_reward_cache[str_repr] = eval_result.reward - - # Replace the current population by the offspring - population = list(offspring) - - # Update the hall of fame with the generated individuals - if halloffame is not None: - for ind in population: - halloffame.push(ind.fitness.values, tuple(ind), ind) - - # elitism - population.extend([ind for _, ind in halloffame.iter_in_order()]) - - if gen % 100 == 0: - top_code = '\n'.join([debug_str(ind, task_eval_fn) - for ind in topk(population, k=4)]) - logging.info('gen: %d\nNPE: %d\n%s\n\n', gen, gen * pop_size, top_code) - - best_code = ''.join(halloffame.get_max()[1]) - res = task_eval_fn(best_code) - - # Write population and hall-of-fame to disk. - if checkpoint_writer: - checkpoint_writer.write(gen, population, halloffame) - - if res.correct: - logging.info('Solution found:\n%s\nreward = %s\n', - best_code, res.reward) - break - - gen += 1 - - best_code = ''.join(halloffame.get_max()[1]) - res = task_eval_fn(best_code) - - return GaResult( - population=population, best_code=best_code, reward=res.reward, - solution_found=res.correct, generations=gen, - num_programs=gen * len(population), - max_generations=ngen, max_num_programs=ngen * len(population)) - - -GaResult = namedtuple( - 'GaResult', - ['population', 'best_code', 'reward', 'generations', 'num_programs', - 'solution_found', 'max_generations', 'max_num_programs']) - - -def reward_conversion(reward): - """Convert real value into positive value.""" - if reward <= 0: - return 0.05 - return reward + 0.05 - - -def roulette_selection(population, k): - """Select `k` individuals with prob proportional to fitness. - - Each of the `k` selections is independent. - - Warning: - The roulette selection by definition cannot be used for minimization - or when the fitness can be smaller or equal to 0. - - Args: - population: A list of Individual objects to select from. - k: The number of individuals to select. - - Returns: - A list of selected individuals. - """ - fitnesses = np.asarray( - [reward_conversion(ind.fitness.values[0]) - for ind in population]) - assert np.all(fitnesses > 0) - - sum_fits = fitnesses.sum() - chosen = [None] * k - for i in xrange(k): - u = random.random() * sum_fits - sum_ = 0 - for ind, fitness in zip(population, fitnesses): - sum_ += fitness - if sum_ > u: - chosen[i] = Individual(ind) - break - if not chosen[i]: - chosen[i] = Individual(population[-1]) - - return chosen - - -def make_population(make_individual_fn, n): - return [make_individual_fn() for _ in xrange(n)] - - -def best(population): - best_ind = None - for ind in population: - if best_ind is None or best_ind.fitness.values < ind.fitness.values: - best_ind = ind - return best_ind - - -def topk(population, k): - q = utils.MaxUniquePriorityQueue(k) - for ind in population: - q.push(ind.fitness.values, tuple(ind), ind) - return [ind for _, ind in q.iter_in_order()] - - -class Fitness(object): - - def __init__(self): - self.values = () - - @property - def valid(self): - """Assess if a fitness is valid or not.""" - return bool(self.values) - - -class Individual(list): - - def __init__(self, *args): - super(Individual, self).__init__(*args) - self.fitness = Fitness() - - -def random_individual(genome_size): - return lambda: Individual(np.random.choice(GENES, genome_size).tolist()) diff --git a/spaces/NMEX/vits-uma-genshin-honkai/modules.py b/spaces/NMEX/vits-uma-genshin-honkai/modules.py deleted file mode 100644 index 56ea4145eddf19dd330a3a41ab0183efc1686d83..0000000000000000000000000000000000000000 --- a/spaces/NMEX/vits-uma-genshin-honkai/modules.py +++ /dev/null @@ -1,388 +0,0 @@ -import math -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm - -import commons -from commons import init_weights, get_padding -from transforms import piecewise_rational_quadratic_transform - - -LRELU_SLOPE = 0.1 - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - -class ConvReluNorm(nn.Module): - def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): - super().__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - assert n_layers > 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential( - nn.ReLU(), - nn.Dropout(p_dropout)) - for _ in range(n_layers-1): - self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size ** i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, - groups=channels, dilation=dilation, padding=padding - )) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): - super(WN, self).__init__() - assert(kernel_size % 2 == 1) - self.hidden_channels =hidden_channels - self.kernel_size = kernel_size, - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - self.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') - - for i in range(n_layers): - dilation = dilation_rate ** i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size, - dilation=dilation, padding=padding) - in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:] - else: - g_l = torch.zeros_like(x_in) - - acts = commons.fused_add_tanh_sigmoid_multiply( - x_in, - g_l, - n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:,:self.hidden_channels,:] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:,self.hidden_channels:,:] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]))) - ]) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))) - ]) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))) - ]) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels,1)) - self.logs = nn.Parameter(torch.zeros(channels,1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1,2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False): - assert channels % 2 == 0, "channels should be divisible by 2" - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels]*2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1,2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - -class ConvFlow(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.num_bins = num_bins - self.tail_bound = tail_bound - self.half_channels = in_channels // 2 - - self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) - self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.) - self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) - h = self.convs(h, x_mask, g=g) - h = self.proj(h) * x_mask - - b, c, t = x0.shape - h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] - - unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_derivatives = h[..., 2 * self.num_bins:] - - x1, logabsdet = piecewise_rational_quadratic_transform(x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails='linear', - tail_bound=self.tail_bound - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1,2]) - if not reverse: - return x, logdet - else: - return x diff --git a/spaces/NealCaren/TranscribeX/README.md b/spaces/NealCaren/TranscribeX/README.md deleted file mode 100644 index c9c0d424768adf5b252b269031cc486f33b3e0a0..0000000000000000000000000000000000000000 --- a/spaces/NealCaren/TranscribeX/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: TranscribeX -emoji: 👀 -colorFrom: yellow -colorTo: green -sdk: streamlit -sdk_version: 1.21.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Nee001/bing0/src/components/ui/tooltip.tsx b/spaces/Nee001/bing0/src/components/ui/tooltip.tsx deleted file mode 100644 index af1d48beb90dd5ae311796539843700871052cae..0000000000000000000000000000000000000000 --- a/spaces/Nee001/bing0/src/components/ui/tooltip.tsx +++ /dev/null @@ -1,30 +0,0 @@ -'use client' - -import * as React from 'react' -import * as TooltipPrimitive from '@radix-ui/react-tooltip' - -import { cn } from '@/lib/utils' - -const TooltipProvider = TooltipPrimitive.Provider - -const Tooltip = TooltipPrimitive.Root - -const TooltipTrigger = TooltipPrimitive.Trigger - -const TooltipContent = React.forwardRef< - React.ElementRef, - React.ComponentPropsWithoutRef ->(({ className, sideOffset = 4, ...props }, ref) => ( - -)) -TooltipContent.displayName = TooltipPrimitive.Content.displayName - -export { Tooltip, TooltipTrigger, TooltipContent, TooltipProvider } diff --git a/spaces/NoCrypt/sd_out_gallery/README.md b/spaces/NoCrypt/sd_out_gallery/README.md deleted file mode 100644 index bf788f27a8dc2912804a12ade61315de5d3cd0f9..0000000000000000000000000000000000000000 --- a/spaces/NoCrypt/sd_out_gallery/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Sd Out Gallery -emoji: 🐢 -colorFrom: green -colorTo: indigo -sdk: gradio -sdk_version: 3.42.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/hubert/README.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/hubert/README.md deleted file mode 100644 index b501a6eb2a047d4adb6f297436c1c002c926a09f..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/hubert/README.md +++ /dev/null @@ -1,115 +0,0 @@ -# HuBERT - -## Pre-trained and fine-tuned (ASR) models -Model | Pretraining Data | Finetuning Dataset | Model -|---|---|---|--- -HuBERT Base (~95M params) | [Librispeech](http://www.openslr.org/12) 960 hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt) -HuBERT Large (~316M params) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k.pt) -HuBERT Extra Large (~1B params) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k.pt) -HuBERT Large | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k_finetune_ls960.pt) -HuBERT Extra Large | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k_finetune_ls960.pt) - -## Load a model -``` -ckpt_path = "/path/to/the/checkpoint.pt" -models, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path]) -model = models[0] -``` - -## Train a new model - -### Data preparation - -Follow the steps in `./simple_kmeans` to create: -- `{train,valid}.tsv` waveform list files -- `{train,valid}.km` frame-aligned pseudo label files. -The `label_rate` is the same as the feature frame rate used for clustering, -which is 100Hz for MFCC features and 50Hz for HuBERT features by default. - -### Pre-train a HuBERT model - -Suppose `{train,valid}.tsv` are saved at `/path/to/data`, `{train,valid}.km` -are saved at `/path/to/labels`, and the label rate is 100Hz. - -To train a base model (12 layer transformer), run: -```sh -$ python fairseq_cli/hydra_train.py \ - --config-dir /path/to/fairseq-py/examples/hubert/config/pretrain \ - --config-name hubert_base_librispeech \ - task.data=/path/to/data task.label_dir=/path/to/labels model.label_rate=100 -``` - -### Fine-tune a HuBERT model with a CTC loss - -Suppose `{train,valid}.tsv` are saved at `/path/to/data`, and their -corresponding character transcripts `{train,valid}.ltr` are saved at -`/path/to/trans`. - -To fine-tune a pre-trained HuBERT model at `/path/to/checkpoint`, run -```sh -$ python fairseq_cli/hydra_train.py \ - --config-dir /path/to/fairseq-py/examples/hubert/config/finetune \ - --config-name base_10h \ - task.data=/path/to/data task.label_dir=/path/to/trans \ - model.w2v_path=/path/to/checkpoint -``` - -### Decode a HuBERT model - -Suppose the `test.tsv` and `test.ltr` are the waveform list and transcripts of -the split to be decoded, saved at `/path/to/data`, and the fine-tuned model is -saved at `/path/to/checkpoint`. We support three decoding modes: -- Viterbi decoding: greedy decoding without a language model -- KenLM decoding: decoding with an arpa-format KenLM n-gram language model -- Fairseq-LM deocding: decoding with a Fairseq neural language model - - -#### Viterbi decoding - -`task.normalize` needs to be consistent with the value used during fine-tuning. -Decoding results will be saved at -`/path/to/experiment/directory/decode/viterbi/test`. - -```sh -$ python examples/speech_recognition/new/infer.py \ - --config-dir /path/to/fairseq-py/examples/hubert/config/decode \ - --config-name infer_viterbi \ - task.data=/path/to/data \ - task.normalize=[true|false] \ - decoding.exp_dir=/path/to/experiment/directory \ - common_eval.path=/path/to/checkpoint - dataset.gen_subset=test \ -``` - -#### KenLM / Fairseq-LM decoding - -Suppose the pronunciation lexicon and the n-gram LM are saved at -`/path/to/lexicon` and `/path/to/arpa`, respectively. Decoding results will be -saved at `/path/to/experiment/directory/decode/kenlm/test`. - -```sh -$ python examples/speech_recognition/new/infer.py \ - --config-dir /path/to/fairseq-py/examples/hubert/config/decode \ - --config-name infer_kenlm \ - task.data=/path/to/data \ - task.normalize=[true|false] \ - decoding.exp_dir=/path/to/experiment/directory \ - common_eval.path=/path/to/checkpoint - dataset.gen_subset=test \ - decoding.decoder.lexicon=/path/to/lexicon \ - decoding.decoder.lmpath=/path/to/arpa -``` - -The command above uses the default decoding hyperparameter, which can be found -in `examples/speech_recognition/hydra/decoder.py`. These parameters can be -configured from the command line. For example, to search with a beam size of -500, we can append the command above with `decoding.decoder.beam=500`. -Important parameters include: -- decoding.decoder.beam -- decoding.decoder.beamthreshold -- decoding.decoder.lmweight -- decoding.decoder.wordscore -- decoding.decoder.silweight - -To decode with a Fairseq LM, use `--config-name infer_fsqlm` instead, and -change the path of lexicon and LM accordingly. diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/lightweight_convolution.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/lightweight_convolution.py deleted file mode 100644 index ec11a9507951c9e8f3564753841dd9c74a4900e0..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/lightweight_convolution.py +++ /dev/null @@ -1,310 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import utils -from fairseq.incremental_decoding_utils import with_incremental_state -from fairseq.modules.fairseq_dropout import FairseqDropout -from fairseq.modules.unfold import unfold1d - - -def LightweightConv( - input_size, - kernel_size=1, - padding_l=None, - num_heads=1, - weight_dropout=0.0, - weight_softmax=False, - bias=False, -): - if torch.cuda.is_available(): - try: - from fairseq.modules.lightconv_layer import LightconvLayer - - return LightconvLayer( - input_size, - kernel_size=kernel_size, - padding_l=padding_l, - num_heads=num_heads, - weight_dropout=weight_dropout, - weight_softmax=weight_softmax, - bias=bias, - ) - except ImportError as e: - print(e) - return LightweightConv1dTBC( - input_size, - kernel_size=kernel_size, - padding_l=padding_l, - num_heads=num_heads, - weight_dropout=weight_dropout, - weight_softmax=weight_softmax, - bias=bias, - ) - - -class LightweightConv1d(nn.Module): - """Lightweight Convolution assuming the input is BxCxT - This is just an example that explains LightConv clearer than the TBC version. - We don't use this module in the model. - - Args: - input_size: # of channels of the input and output - kernel_size: convolution channels - padding: padding - num_heads: number of heads used. The weight is of shape - `(num_heads, 1, kernel_size)` - weight_softmax: normalize the weight with softmax before the convolution - - Shape: - Input: BxCxT, i.e. (batch_size, input_size, timesteps) - Output: BxCxT, i.e. (batch_size, input_size, timesteps) - - Attributes: - weight: the learnable weights of the module of shape - `(num_heads, 1, kernel_size)` - bias: the learnable bias of the module of shape `(input_size)` - """ - - def __init__( - self, - input_size, - kernel_size=1, - padding=0, - num_heads=1, - weight_softmax=False, - bias=False, - weight_dropout=0.0, - ): - super().__init__() - self.input_size = input_size - self.kernel_size = kernel_size - self.num_heads = num_heads - self.padding = padding - self.weight_softmax = weight_softmax - self.weight = nn.Parameter(torch.Tensor(num_heads, 1, kernel_size)) - - if bias: - self.bias = nn.Parameter(torch.Tensor(input_size)) - else: - self.bias = None - self.weight_dropout_module = FairseqDropout( - weight_dropout, module_name=self.__class__.__name__ - ) - self.reset_parameters() - - def reset_parameters(self): - nn.init.xavier_uniform_(self.weight) - if self.bias is not None: - nn.init.constant_(self.bias, 0.0) - - def forward(self, input): - """ - input size: B x C x T - output size: B x C x T - """ - B, C, T = input.size() - H = self.num_heads - - weight = self.weight - if self.weight_softmax: - weight = F.softmax(weight, dim=-1) - - weight = self.weight_dropout_module(weight) - # Merge every C/H entries into the batch dimension (C = self.input_size) - # B x C x T -> (B * C/H) x H x T - # One can also expand the weight to C x 1 x K by a factor of C/H - # and do not reshape the input instead, which is slow though - input = input.view(-1, H, T) - output = F.conv1d(input, weight, padding=self.padding, groups=self.num_heads) - output = output.view(B, C, T) - if self.bias is not None: - output = output + self.bias.view(1, -1, 1) - - return output - - -@with_incremental_state -class LightweightConv1dTBC(nn.Module): - """Lightweight Convolution assuming the input is TxBxC - Args: - input_size: # of channels of the input - kernel_size: convolution channels - padding_l: padding to the left when using "same" padding - num_heads: number of heads used. The weight is of shape (num_heads, 1, kernel_size) - weight_dropout: the drop rate of the DropConnect to drop the weight - weight_softmax: normalize the weight with softmax before the convolution - bias: use bias - - Shape: - Input: TxBxC, i.e. (timesteps, batch_size, input_size) - Output: TxBxC, i.e. (timesteps, batch_size, input_size) - - Attributes: - weight: the learnable weights of the module of shape - `(num_heads, 1, kernel_size)` - bias: the learnable bias of the module of shape `(input_size)` - """ - - def __init__( - self, - input_size, - kernel_size=1, - padding_l=None, - num_heads=1, - weight_dropout=0.0, - weight_softmax=False, - bias=False, - ): - super().__init__() - self.input_size = input_size - self.kernel_size = kernel_size - self.padding_l = padding_l - self.num_heads = num_heads - self.weight_dropout_module = FairseqDropout( - weight_dropout, module_name=self.__class__.__name__ - ) - self.weight_softmax = weight_softmax - - self.weight = nn.Parameter(torch.Tensor(num_heads, 1, kernel_size)) - if bias: - self.bias = nn.Parameter(torch.Tensor(input_size)) - else: - self.bias = None - - self.reset_parameters() - self.onnx_trace = False - - def reset_parameters(self): - nn.init.xavier_uniform_(self.weight) - if self.bias is not None: - nn.init.constant_(self.bias, 0.0) - - def forward(self, x, incremental_state=None, unfold=False): - """Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C - args: - x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size) - incremental_state: A dict to keep the state - unfold: unfold the input or not. If not, we use the matrix trick instead - """ - unfold = unfold or (incremental_state is not None) - - if unfold: - output = self._forward_unfolded(x, incremental_state) - else: - output = self._forward_expanded(x, incremental_state) - - if self.bias is not None: - output = output + self.bias.view(1, 1, -1) - return output - - def prepare_for_onnx_export_(self): - self.onnx_trace = True - - def _forward_unfolded(self, x, incremental_state): - """The conventional implementation of convolutions. - Unfolding the input by having a window shifting to the right.""" - T, B, C = x.size() - K, H = self.kernel_size, self.num_heads - R = C // H - assert R * H == C == self.input_size - - weight = self.weight.view(H, K) - if incremental_state is not None: - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is None: - input_buffer = x.new() - x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) - if self.kernel_size > 1: - self._set_input_buffer( - incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] - ) - x_unfold = x_unfold.view(T * B * H, R, -1) - else: - # unfold the input: T x B x C --> T' x B x C x K - x_unfold = unfold1d(x, self.kernel_size, self.padding_l, 0) - x_unfold = x_unfold.view(T * B * H, R, K) - - if self.weight_softmax: - weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as( - weight - ) - - if incremental_state is not None: - weight = weight[:, -x_unfold.size(2) :] - K = weight.size(1) - - weight = ( - weight.view(1, H, K).expand(T * B, H, K).contiguous().view(T * B * H, K, 1) - ) - - weight = self.weight_dropout_module(weight) - output = torch.bmm(x_unfold, weight) # T*B*H x R x 1 - output = output.view(T, B, C) - return output - - def _forward_expanded(self, x, incremental_state): - """Turn the convolution filters into band matrices and do matrix multiplication. - This is faster when the sequence is short, but less memory efficient. - This is not used in the decoder during inference. - """ - T, B, C = x.size() - K, H = self.kernel_size, self.num_heads - R = C // H - assert R * H == C == self.input_size - - weight = self.weight.view(H, K) - if self.weight_softmax: - weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as( - weight - ) - weight = weight.view(1, H, K).expand(T * B, H, K).contiguous() - weight = weight.view(T, B * H, K).transpose(0, 1) - - x = x.view(T, B * H, R).transpose(0, 1) - P = self.padding_l - if K > T and P == K - 1: - weight = weight.narrow(2, K - T, T) - K, P = T, T - 1 - # turn the convolution filters into band matrices - weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False) - weight_expanded.as_strided((B * H, T, K), (T * (T + K - 1), T + K, 1)).copy_( - weight - ) - weight_expanded = weight_expanded.narrow(2, P, T) - weight_expanded = self.weight_dropout_module(weight_expanded) - - output = torch.bmm(weight_expanded, x) - output = output.transpose(0, 1).contiguous().view(T, B, C) - return output - - def reorder_incremental_state(self, incremental_state, new_order): - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is not None: - input_buffer = input_buffer.index_select(1, new_order) - self._set_input_buffer(incremental_state, input_buffer) - - def _get_input_buffer(self, incremental_state): - return utils.get_incremental_state(self, incremental_state, "input_buffer") - - def _set_input_buffer(self, incremental_state, new_buffer): - return utils.set_incremental_state( - self, incremental_state, "input_buffer", new_buffer - ) - - def extra_repr(self): - s = "{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, bias={}".format( - self.input_size, - self.kernel_size, - self.padding_l, - self.num_heads, - self.weight_softmax, - self.bias is not None, - ) - if self.weight_dropout_module.p > 0.0: - s += ", weight_dropout={}".format(self.weight_dropout_module.p) - return s diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py deleted file mode 100644 index 0d356e5a10241716b58a5bc04a9d204a72553ff8..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py +++ /dev/null @@ -1,223 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -import math - -import torch -import torch.nn.functional as F -from fairseq.criterions import FairseqCriterion, register_criterion -from fairseq.criterions.label_smoothed_cross_entropy import label_smoothed_nll_loss -from fairseq import metrics, utils - - -@register_criterion("guided_label_smoothed_cross_entropy_with_accuracy") -class GuidedCrossEntAccCriterion(FairseqCriterion): - def __init__( - self, - task, - sentence_avg, - guide_alpha, - text_input_cost_ratio, - label_smoothing, - disable_text_guide_update_num=0, - attentive_cost_regularization=0, - ): - """ - guide_alpha: alpha to inteplate nll and kd loss - text_input_cost_ratio: loss ratio for text only input data - label_smoothing: label smoothing ratio - disable_text_guide_update_num: only use nll loss for the first N updates - attentive_cost_regularization: ratio fo attentive cost - """ - super().__init__(task) - self.alpha = guide_alpha - self.attn_beta = attentive_cost_regularization - self.sentence_avg = sentence_avg - self.eps = label_smoothing - self.text_input_cost_ratio = text_input_cost_ratio - self.disable_update_num = disable_text_guide_update_num - assert self.alpha >= 0 and self.alpha <= 1.0 - - @staticmethod - def add_args(parser): - """Add criterion-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--label-smoothing', default=0., type=float, metavar='D', - help='epsilon for label smoothing, 0 means no label smoothing') - # fmt: off - parser.add_argument('--guide-alpha', default=0., type=float, metavar='D', - help='alpha to merge kd cost from text to speech input with ce loss') - # fmt: off - parser.add_argument('--disable-text-guide-update-num', default=0, type=int, metavar='D', - help='disable guided target from text for the first N updates.') - parser.add_argument("--attentive-cost-regularization", default=0.0, type=float, metavar='D', - help="use encoder attentive loss regularization with cost ratio D") - parser.add_argument("--attentive-cost-without-normalize", action='store_true', - help="Don't do normalization during attentive cost computation") - - def forward(self, model, sample, reduce=True): - reduction = 'sum' if reduce else 'none' - net_input = sample["net_input"] - net_output = model(**net_input) - attn_cost = None - lprobs = model.get_normalized_probs(net_output, log_probs=True) - is_dual_input = True if net_input['src_tokens'] is not None and net_input.get('src_txt_tokens') is not None else False - target = model.get_targets(sample, net_output) - src_token_num = 0 - if is_dual_input: - # lprobs_spch from speech encoder and lprobs_text from text encoder - lprobs_spch, lprobs_text = torch.chunk(lprobs, 2) - lprobs_spch.batch_first = lprobs.batch_first - lprobs_text.batch_first = lprobs.batch_first - - speech_loss, speech_nll_loss, speech_correct, speech_total = \ - self.guide_loss_and_acc(model, lprobs_spch, lprobs_text, target, reduce=(reduction == 'sum')) - text_loss, text_nll_loss, text_correct, text_total = self.compute_loss_and_acc(model, lprobs_text, target, reduction=reduction) - loss = (speech_loss + text_loss) - nll_loss = (speech_nll_loss + text_nll_loss) - correct = speech_correct + text_correct - total = speech_total + text_total - - attn_cost = net_output[1].get('attn_cost') - if attn_cost is not None: - # attn_cost is batch_first and padding tokens have been masked already - src_token_num = attn_cost.ne(0).sum() - attn_cost = attn_cost.sum() - loss = loss + attn_cost * self.attn_beta - else: - attn_cost = 0 - else: - loss, nll_loss, correct, total = self.compute_loss_and_acc(model, lprobs, target, reduction=reduction) - if sample["net_input"]['src_tokens'] is None: # text input only - loss = loss * self.text_input_cost_ratio - speech_loss = None - speech_nll_loss = None - - sample_size, logging_output = self.get_logging_output( - sample, loss, nll_loss, correct, total, src_token_num, speech_loss, speech_nll_loss, attn_cost, is_dual_input - ) - return loss, sample_size, logging_output - - def compute_loss_and_acc(self, model, lprobs, target, reduction='sum'): - if not lprobs.batch_first: - lprobs = lprobs.transpose(0, 1) - lprobs = lprobs.view(-1, lprobs.size(-1)) # -> (B x T) x C - target = target.view(-1) - loss, nll_loss = label_smoothed_nll_loss( - lprobs, target, self.eps, ignore_index=self.padding_idx, reduce=(reduction == 'sum'), - ) - - mask = target.ne(self.padding_idx) - correct = torch.sum(lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))) - total = torch.sum(mask) - return loss, nll_loss, correct, total - - def guide_loss_and_acc(self, model, lprobs, lprobs_teacher, target, reduce=True): - """ lprobs_teacher is used as guide for lprobs """ - if self.alpha == 0.0 or model.num_updates < self.disable_update_num: - return self.compute_loss_and_acc(model, lprobs, target, reduction=('sum' if reduce else 'none')) - if not lprobs.batch_first: - lprobs = lprobs.transpose(0, 1) - lprobs_teacher = lprobs_teacher.transpose(0, 1) - - lprobs = lprobs.view(-1, lprobs.size(-1)).float() # -> (B x T) x C - lprobs_teacher = lprobs_teacher.view(-1, lprobs_teacher.size(-1)).float() # -> (B x T) x C - target = target.view(-1) - loss = F.nll_loss(lprobs, target, ignore_index=self.padding_idx, reduction='sum' if reduce else 'none') - nll_loss = loss - probs_teacher = lprobs_teacher.exp().masked_fill_(target.unsqueeze(-1).eq(self.padding_idx), 0) - probs_teacher = probs_teacher.detach() - guide_loss = -(probs_teacher*lprobs).sum() if reduce else -(probs_teacher*lprobs).sum(-1, keepdim=True) - loss = self.alpha*guide_loss + (1.0 - self.alpha)*loss - - mask = target.ne(self.padding_idx) - correct = torch.sum(lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))) - total = torch.sum(mask) - return loss, nll_loss, correct, total - - def get_logging_output( - self, - sample, - loss, - nll_loss, - correct, - total, - src_token_num=0, - speech_loss=None, - speech_nll_loss=None, - attn_cost=None, - is_dual_input=False, - ): - - sample_size = ( - sample["target"].size(0) if self.sentence_avg else sample["ntokens"] - ) - mul_size = 2 if is_dual_input else 1 - - logging_output = { - "loss": utils.item(loss.data), # * sample['ntokens'], - "nll_loss": utils.item(nll_loss.data), # * sample['ntokens'], - "ntokens": sample["ntokens"]*mul_size, - "nsentences": sample["target"].size(0)*mul_size, - "sample_size": sample_size*mul_size, - "correct": utils.item(correct.data), - "total": utils.item(total.data), - "src_token_num": utils.item(src_token_num.data) if src_token_num > 0 else 0, - "nframes": torch.sum(sample["net_input"]["src_lengths"]).item(), - } - - if speech_loss is not None: - logging_output["speech_loss"] = utils.item(speech_loss.data) - logging_output["speech_nll_loss"] = utils.item(speech_nll_loss.data) - logging_output["sample_size_speech_cost"] = sample_size - logging_output["speech_attn_loss"] = attn_cost - - return sample_size*mul_size, logging_output - - @staticmethod - def aggregate_logging_outputs(logging_outputs): - """Aggregate logging outputs from data parallel training.""" - correct_sum = sum(log.get("correct", 0) for log in logging_outputs) - total_sum = sum(log.get("total", 0) for log in logging_outputs) - src_token_sum = sum(log.get("src_token_num", 0) for log in logging_outputs) - loss_sum = sum(log.get("loss", 0) for log in logging_outputs) - nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs) - ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) - nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) - sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - nframes = sum(log.get("nframes", 0) for log in logging_outputs) - speech_loss_sum = sum(log.get("speech_loss", 0) for log in logging_outputs) - speech_nll_loss_sum = sum(log.get("speech_nll_loss", 0) for log in logging_outputs) - speech_attn_loss_sum = sum(log.get("speech_attn_loss", 0) for log in logging_outputs) - sample_size_speech = sum(log.get("sample_size_speech_cost", 0) for log in logging_outputs) - - agg_output = { - "loss": loss_sum / sample_size / math.log(2) if sample_size > 0 else 0.0, - "nll_loss": nll_loss_sum / sample_size / math.log(2) if sample_size > 0 else 0.0, - # if args.sentence_avg, then sample_size is nsentences, and loss - # is per-sentence loss; else sample_size is ntokens, and the loss - # becomes per-output token loss - "speech_loss": speech_loss_sum / sample_size_speech / math.log(2) if sample_size_speech > 0 else 0.0, - "speech_nll_loss": speech_nll_loss_sum / sample_size_speech / math.log(2) if sample_size_speech > 0 else 0.0, - "speech_attn_loss": speech_attn_loss_sum / src_token_sum / math.log(2) if src_token_sum > 0 else 0.0, - "ntokens": ntokens, - "nsentences": nsentences, - "nframes": nframes, - "sample_size": sample_size, - "acc": correct_sum * 100.0 / total_sum if total_sum > 0 else 0.0, - "correct": correct_sum, - "total": total_sum, - "src_token_num": src_token_sum, - # total is the number of validate tokens - } - return agg_output - - @classmethod - def reduce_metrics(cls, logging_outputs): - """Aggregate logging outputs from data parallel training.""" - agg_logging_outputs = cls.aggregate_logging_outputs(logging_outputs) - for k, v in agg_logging_outputs.items(): - if k in {'nsentences', 'ntokens', 'sample_size'}: - continue - metrics.log_scalar(k, v, round=3) diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/base_layer.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/base_layer.py deleted file mode 100644 index e7ef155b25fc73e74780879f665288c9bc95fd80..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/base_layer.py +++ /dev/null @@ -1,135 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.nn as nn -import torch -import sys -from fairseq import utils -from fairseq.distributed import utils as distributed_utils -from fairseq.modules.layer_norm import LayerNorm - - -class BaseLayer(nn.Module): - - def __init__(self, args): - super().__init__() - self.num_workers = distributed_utils.get_data_parallel_world_size() - expert_centroids = torch.empty(self.num_workers, args.decoder_embed_dim) - torch.nn.init.orthogonal_(expert_centroids, gain=0.1) - self.register_parameter("expert_centroids", torch.nn.Parameter(expert_centroids)) - self.expert_network = nn.Sequential(*([BaseSublayer(args) for _ in range(args.base_sublayers)])) - self.expert_id = distributed_utils.get_data_parallel_rank() - self.shuffle = args.base_shuffle - self.cpp = self.load_assignment() - - # Add a special attribute to the expert parameters, so we know not to sync their gradients - for param in self.expert_network.parameters(): - param.expert = True - - def forward(self, input_features, *args, **kwargs): - features = input_features.reshape(-1, input_features.size(-1)) - is_training = input_features.requires_grad - - if self.shuffle and is_training: - # Send each token to a random worker, to break correlations within the batch - shuffle_sort = torch.randperm(features.size(0), device=features.device) - features = All2All.apply(features[shuffle_sort]) - - with torch.no_grad(): - # Compute similarity of each token to each expert, for routing - token_expert_affinities = features.matmul(self.expert_centroids.transpose(0, 1)) - - # Compute which token goes to which expert - sort_by_expert, input_splits, output_splits = self.balanced_assignment(token_expert_affinities) \ - if is_training else self.greedy_assignment(token_expert_affinities) - # Swap these tokens for the right ones for our expert - routed_features = All2All.apply(features[sort_by_expert], output_splits, input_splits) - - if routed_features.size(0) > 0: - # Mix in the expert network based on how appropriate it is for these tokens - alpha = torch.sigmoid(routed_features.mv(self.expert_centroids[self.expert_id])).unsqueeze(1) - routed_features = alpha * self.expert_network(routed_features) + (1 - alpha) * routed_features - # Return to original worker and ordering - result = All2All.apply(routed_features, input_splits, output_splits)[self.inverse_sort(sort_by_expert)] - - if self.shuffle and is_training: - # Undo shuffling - result = All2All.apply(result)[self.inverse_sort(shuffle_sort)] - - # Return additional Nones for compatibility with TransformerDecoderLayer - return result.view(input_features.size()), None, None - - def inverse_sort(self, order): - # Creates an index that undoes a sort: xs==xs[order][inverse_sort(order)] - return torch.empty_like(order).scatter_(0, order, torch.arange(0, order.size(0), device=order.device)) - - def balanced_assignment(self, scores): - ok = scores.isfinite() - if not ok.all(): - # NaNs here can break the assignment algorithm - scores[~ok] = scores[ok].min() - return self.cpp.balanced_assignment(scores), None, None - - # Assigns each token to the top k experts - def greedy_assignment(self, scores, k=1): - token_to_workers = torch.topk(scores, dim=1, k=k, largest=True).indices.view(-1) - token_to_workers, sort_ordering = torch.sort(token_to_workers) - worker2token = sort_ordering // k - - # Find how many tokens we're sending to each other worker (being careful for sending 0 tokens to some workers) - output_splits = torch.zeros((self.num_workers,), dtype=torch.long, device=scores.device) - workers, counts = torch.unique_consecutive(token_to_workers, return_counts=True) - output_splits[workers] = counts - # Tell other workers how many tokens to expect from us - input_splits = All2All.apply(output_splits) - return worker2token, input_splits.tolist(), output_splits.tolist() - - def load_assignment(self): - try: - from fairseq import libbase - - return libbase - - except ImportError as e: - sys.stderr.write( - "ERROR: missing libbase. run `python setup.py build_ext --inplace`\n" - ) - raise e - - -class BaseSublayer(nn.Module): - def __init__(self, args): - super().__init__() - self.activation_fn = utils.get_activation_fn( - activation=getattr(args, 'activation_fn', 'relu') or "relu" - ) - self.norm = LayerNorm(args.decoder_embed_dim, export=False) - self.ff1 = torch.nn.Linear(args.decoder_embed_dim, args.decoder_ffn_embed_dim) - self.ff2 = torch.nn.Linear(args.decoder_ffn_embed_dim, args.decoder_embed_dim) - self.ff2.weight.data.zero_() - - def forward(self, xs): - return xs + self.ff2(self.activation_fn(self.ff1(self.norm(xs)))) - - -# Wraps torch.distributed.all_to_all_single as a function that supports autograd -class All2All(torch.autograd.Function): - @staticmethod - def forward(ctx, xs, input_splits=None, output_splits=None): - ctx.input_splits = input_splits - ctx.output_splits = output_splits - - ys = torch.empty_like(xs) if output_splits is None else \ - xs.new_empty(size=[sum(output_splits)] + list(xs.size()[1:])) - torch.distributed.all_to_all_single(ys, xs, output_split_sizes=output_splits, input_split_sizes=input_splits) - return ys - - @staticmethod - def backward(ctx, grad_output): - result = torch.empty_like(grad_output) if ctx.input_splits is None else \ - grad_output.new_empty(size=[sum(ctx.input_splits)] + list(grad_output.size()[1:])) - torch.distributed.all_to_all_single(result, grad_output, - output_split_sizes=ctx.input_splits, input_split_sizes=ctx.output_splits) - return result, None, None diff --git a/spaces/OFA-Sys/chinese-clip-zero-shot-image-classification/README.md b/spaces/OFA-Sys/chinese-clip-zero-shot-image-classification/README.md deleted file mode 100644 index e3bd051cdda2944f48cc03c7198b02adb3320968..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/chinese-clip-zero-shot-image-classification/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Chinese-CLIP Zero-Shot Image Classification -emoji: 🐲 -colorFrom: yellow -colorTo: pink -sdk: gradio -sdk_version: 3.12.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Olivier-Truong/faster-whisper-webui-v2/src/segments.py b/spaces/Olivier-Truong/faster-whisper-webui-v2/src/segments.py deleted file mode 100644 index ec2650dceade5d0b2022264f6419115eab085aea..0000000000000000000000000000000000000000 --- a/spaces/Olivier-Truong/faster-whisper-webui-v2/src/segments.py +++ /dev/null @@ -1,55 +0,0 @@ -from typing import Any, Dict, List - -import copy - -def merge_timestamps(timestamps: List[Dict[str, Any]], merge_window: float = 5, max_merge_size: float = 30, padding_left: float = 1, padding_right: float = 1): - result = [] - - if len(timestamps) == 0: - return result - if max_merge_size is None: - return timestamps - - if padding_left is None: - padding_left = 0 - if padding_right is None: - padding_right = 0 - - processed_time = 0 - current_segment = None - - for i in range(len(timestamps)): - next_segment = timestamps[i] - - delta = next_segment['start'] - processed_time - - # Note that segments can still be longer than the max merge size, they just won't be merged in that case - if current_segment is None or (merge_window is not None and delta > merge_window) \ - or next_segment['end'] - current_segment['start'] > max_merge_size: - # Finish the current segment - if current_segment is not None: - # Add right padding - finish_padding = min(padding_right, delta / 2) if delta < padding_left + padding_right else padding_right - current_segment['end'] += finish_padding - delta -= finish_padding - - result.append(current_segment) - - # Start a new segment - current_segment = copy.deepcopy(next_segment) - - # Pad the segment - current_segment['start'] = current_segment['start'] - min(padding_left, delta) - processed_time = current_segment['end'] - - else: - # Merge the segment - current_segment['end'] = next_segment['end'] - processed_time = current_segment['end'] - - # Add the last segment - if current_segment is not None: - current_segment['end'] += padding_right - result.append(current_segment) - - return result \ No newline at end of file diff --git a/spaces/Omnibus/video-2-3d/app.py b/spaces/Omnibus/video-2-3d/app.py deleted file mode 100644 index 371a338ef078298e86524f7563e7e842c854a859..0000000000000000000000000000000000000000 --- a/spaces/Omnibus/video-2-3d/app.py +++ /dev/null @@ -1,52 +0,0 @@ -import gradio as gr -from modeler import SfM -from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip -from pytube import YouTube -import uuid -import os -uid=uuid.uuid4() -if not os.path.exists(f'{uid}'): - os.makedirs(f'{uid}') -def load_video_yt(vid): - yt = YouTube(vid) - vid = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first().download(filename=f"{uid}-tmp.mp4") - #vid_aud = yt.streams.filter(only_audio=True)[0].download(filename=f"{uid}-tmp_aud.mp4") - print (f'Video Length: {yt.length}') - return f"{uid}-tmp.mp4" - -def trim_vid(vid,start_time,end_time): - start_hr=int(start_time.split(":",2)[0])*360 - start_min=int(start_time.split(":",2)[1])*60 - start_sec=int(start_time.split(":",2)[2]) - end_hr=int(end_time.split(":",2)[0])*360 - end_min=int(end_time.split(":",2)[1])*60 - end_sec=int(end_time.split(":",2)[2]) - start=start_hr+start_min+start_sec - end=end_hr+end_min+end_sec - vid = f"{uid}-tmp.mp4" - ffmpeg_extract_subclip(vid, start, end, targetname=f"{uid}-clip.mp4") - return f"{uid}-clip.mp4" - - -def make_model(vid_path): - sfm = SfM(f'{uid}/', False, f'{uid}-clip.mp4', 27) - sfm.find_structure_from_motion() - return f'{uid}/' - -with gr.Blocks() as app: - with gr.Row(): - inp_url=gr.Textbox(label="URL") - load_yt_btn=gr.Button() - with gr.Row(): - pre_vid = gr.Video(type='filepath') - clip_vid=gr.Video() - with gr.Row(): - start_time = gr.Textbox(label = "Start", value = "0:00:00", placeholder = "0:00:23") - end_time = gr.Textbox(label = "End", value = "0:00:05", placeholder = "0:00:54") - trim_clip_btn = gr.Button("Trim Clip") - make_btn=gr.Button() - out=gr.Files() - make_btn.click(make_model,clip_vid,out) - load_yt_btn.click(load_video_yt, inp_url, [pre_vid]) - trim_clip_btn.click(trim_vid,[pre_vid,start_time,end_time],clip_vid) -app.launch() \ No newline at end of file diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/utils.py b/spaces/OpenGVLab/InternGPT/iGPT/models/utils.py deleted file mode 100644 index 8a455ac4d5f6608f2414f56e750e46a4cd9099c2..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/iGPT/models/utils.py +++ /dev/null @@ -1,3646 +0,0 @@ -import torch -import numpy as np - -from decord import VideoReader -from decord import cpu - -import uuid -import os - -import torchvision.transforms as transforms -import math -import time -import cv2 -import random - - -GLOBAL_SEED=1912 - -def seed_everything(seed): - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - return seed - - -def prompts(name, description): - def decorator(func): - func.name = name - func.description = description - return func - - return decorator - - -def gen_new_name(orginal_name, suffix="update", ext="png"): - root_path, filename = os.path.split(orginal_name) - name_split = os.path.splitext(filename)[0].split('_') - this_new_uuid = str(uuid.uuid4())[:3] - timestamp = int(math.modf(time.time())[0] * 1000) - prev_file_name = name_split[0] - # if len(name_split) == 1: - # prev_file_name = name_split[0] - # else: - # # assert len(name_split) == 3 - # prev_file_name = name_split[0] - if len(suffix.strip()) == 0: - new_file_name = f'{this_new_uuid}{timestamp:03d}_{prev_file_name}.{ext}' - else: - new_file_name = f'{this_new_uuid}{timestamp:03d}_{prev_file_name}_{suffix}.{ext}' - return os.path.join(root_path, new_file_name) - - -def dilate_mask(mask, dilate_factor=9): - # dilate mask - mask = mask.astype(np.uint8) - dilated_mask = cv2.dilate(mask, np.ones((dilate_factor, dilate_factor), np.uint8), iterations=1) - - return dilated_mask - - -def cal_dilate_factor(mask): - area = mask[mask != 0].sum() - edge = cv2.Canny(mask, 30, 226) - perimeter = edge.sum() - ratio = 0 - if perimeter > 0: - ratio = int(area * 0.55 / perimeter) - if ratio % 2 == 0: - ratio += 1 - return ratio - - -def blend_gt2pt(old_image, new_image, sigma=0.15, steps=100): - new_size = new_image.size - old_size = old_image.size - easy_img = np.array(new_image) - gt_img_array = np.array(old_image) - pos_w = (new_size[0] - old_size[0]) // 2 - pos_h = (new_size[1] - old_size[1]) // 2 - - kernel_h = cv2.getGaussianKernel(old_size[1], old_size[1] * sigma) - kernel_w = cv2.getGaussianKernel(old_size[0], old_size[0] * sigma) - kernel = np.multiply(kernel_h, np.transpose(kernel_w)) - - kernel[steps:-steps, steps:-steps] = 1 - kernel[:steps, :steps] = kernel[:steps, :steps] / kernel[steps - 1, steps - 1] - kernel[:steps, -steps:] = kernel[:steps, -steps:] / kernel[steps - 1, -(steps)] - kernel[-steps:, :steps] = kernel[-steps:, :steps] / kernel[-steps, steps - 1] - kernel[-steps:, -steps:] = kernel[-steps:, -steps:] / kernel[-steps, -steps] - kernel = np.expand_dims(kernel, 2) - kernel = np.repeat(kernel, 3, 2) - - weight = np.linspace(0, 1, steps) - top = np.expand_dims(weight, 1) - top = np.repeat(top, old_size[0] - 2 * steps, 1) - top = np.expand_dims(top, 2) - top = np.repeat(top, 3, 2) - - weight = np.linspace(1, 0, steps) - down = np.expand_dims(weight, 1) - down = np.repeat(down, old_size[0] - 2 * steps, 1) - down = np.expand_dims(down, 2) - down = np.repeat(down, 3, 2) - - weight = np.linspace(0, 1, steps) - left = np.expand_dims(weight, 0) - left = np.repeat(left, old_size[1] - 2 * steps, 0) - left = np.expand_dims(left, 2) - left = np.repeat(left, 3, 2) - - weight = np.linspace(1, 0, steps) - right = np.expand_dims(weight, 0) - right = np.repeat(right, old_size[1] - 2 * steps, 0) - right = np.expand_dims(right, 2) - right = np.repeat(right, 3, 2) - - kernel[:steps, steps:-steps] = top - kernel[-steps:, steps:-steps] = down - kernel[steps:-steps, :steps] = left - kernel[steps:-steps, -steps:] = right - - pt_gt_img = easy_img[pos_h:pos_h + old_size[1], pos_w:pos_w + old_size[0]] - gaussian_gt_img = kernel * gt_img_array + (1 - kernel) * pt_gt_img # gt img with blur img - gaussian_gt_img = gaussian_gt_img.astype(np.int64) - easy_img[pos_h:pos_h + old_size[1], pos_w:pos_w + old_size[0]] = gaussian_gt_img - gaussian_img = Image.fromarray(easy_img) - return gaussian_img - - -def loadvideo_decord(sample, sample_rate_scale=1,new_width=384, new_height=384, clip_len=8, frame_sample_rate=2,num_segment=1): - fname = sample - vr = VideoReader(fname, width=new_width, height=new_height, - num_threads=1, ctx=cpu(0)) - # handle temporal segments - converted_len = int(clip_len * frame_sample_rate) - seg_len = len(vr) //num_segment - duration = max(len(vr) // vr.get_avg_fps(),8) - - all_index = [] - for i in range(num_segment): - index = np.linspace(0, seg_len, num=int(duration)) - index = np.clip(index, 0, seg_len - 1).astype(np.int64) - index = index + i*seg_len - all_index.extend(list(index)) - - all_index = all_index[::int(sample_rate_scale)] - vr.seek(0) - buffer = vr.get_batch(all_index).asnumpy() - return buffer - - -class LoadVideo: - def __init__(self): - self.video_path = None - self.data = None - - def __call__(self, video_path): - if self.video_path == video_path: - return self.data - self.data = self.load_original_video_decord(video_path) - self.video_path = video_path - return self.data - - def load_original_video_decord(self, sample, sample_rate_scale=1, clip_len=8, frame_sample_rate=2,num_segment=1): - fname = sample - vr = VideoReader(fname, - num_threads=1, - ctx=cpu(0) - ) - # handle temporal segments - converted_len = int(clip_len * frame_sample_rate) - seg_len = len(vr) //num_segment - duration = max(len(vr) // vr.get_avg_fps(),8) - - all_index = [] - for i in range(num_segment): - index = np.linspace(0, seg_len, num=int(duration)) - index = np.clip(index, 0, seg_len - 1).astype(np.int64) - index = index + i*seg_len - all_index.extend(list(index)) - - all_index = all_index[::int(sample_rate_scale)] - vr.seek(0) - buffer = vr.get_batch(all_index).asnumpy() - return buffer - - -def loadvideo_decord_origin(self, sample, sample_rate_scale=1,new_width=384, new_height=384, clip_len=8, frame_sample_rate=2,num_segment=1): - fname = sample - vr = VideoReader(fname, - num_threads=1, ctx=cpu(0)) - # handle temporal segments - converted_len = int(clip_len * frame_sample_rate) - seg_len = len(vr) //num_segment - duration = max(len(vr) // vr.get_avg_fps(),8) - - all_index = [] - for i in range(num_segment): - index = np.linspace(0, seg_len, num=int(duration)) - index = np.clip(index, 0, seg_len - 1).astype(np.int64) - index = index + i*seg_len - all_index.extend(list(index)) - - all_index = all_index[::int(sample_rate_scale)] - vr.seek(0) - buffer = vr.get_batch(all_index).asnumpy() - return buffer - - - - - -tra_array = ['tennis', -'bear cub', -'observatory', -'bicycle', -'hillside', -'judge', -'watercolor illustration', -'granite', -'lobster', -'livery', -'stone', -'ceramic', -'ranch', -'cloth', -'smile', -'building', -'tattoo', -'cricketer', -'cheek', -'pear', -'source', -'winter', -'surface', -'spray', -'ceremony', -'magic', -'curve', -'container', -'fair', -'medicine', -'baby', -'tennis racquet', -'ornament', -'bamboo', -'duckling', -'song', -'safari', -'team presentation', -'daffodil', -'cross', -'toothpaste', -'shield', -'fashion model', -'capsule', -'map', -'creek', -'glass house', -'glass plate', -'siding', -'corner', -'water buffalo', -'bison', -'figure skater', -'diploma', -'tire', -'race', -'cable car', -'brain', -'gas stove', -'soap bubble', -'palette', -'snowboard', -'school child', -'trench coat', -'monk', -'fiber', -'kitchen window', -'sunglass', -'coffee', -'security', -'strawberry', -'penguin', -'tree root', -'loaf', -'engagement ring', -'lamb', -'vector cartoon illustration', -'sandwich', -'mountain village', -'shape', -'charm', -'fiction', -'knot', -'greenhouse', -'sushi', -'text', -'disaster', -'trophy', -'gang', -'strap', -'soccer game', -'cardinal', -'tee', -'turtle', -'water surface', -'grassland', -'dolphin', -'store', -'dirt', -'iceberg', -'pergola', -'farmer market', -'publicity portrait', -'tote bag', -'teenage girl', -'view mirror', -'session', -'commuter', -'dressing room', -'tricycle', -'christmas ball', -'headlight', -'police', -'armchair', -'chart', -'yacht', -'saw', -'printer', -'rock band', -'gingerbread house', -'tag', -'table lamp', -'hockey game', -'slope', -'font', -'wicker basket', -'jewelry', -'quarter', -'software', -'weapon', -'pin', -'worship', -'painter', -'goal', -'morning light', -'bike', -'baseball bat', -'elevator', -'cuisine', -'sausage', -'stunt', -'wrestler', -'statue', -'landing', -'pillar', -'willow tree', -'sea wave', -'chicken', -'peanut', -'muscle', -'bob', -'tv genre', -'bathroom window', -'radish', -'textile', -'pelican', -'marketplace', -'crest', -'elevation map', -'gift', -'parish', -'traffic light', -'campfire', -'fog', -'award winner', -'beach ball', -'mat', -'white house', -'plaster', -'moped', -'football team', -'solution', -'bicyclist', -'bit', -'playground', -'darkness', -'cake', -'maple leave', -'mold', -'cracker', -'blueberry', -'rubble', -'container ship', -'pedestrian bridge', -'snail', -'parrot', -'form', -'circuit', -'highlight', -'pickup truck', -'koala', -'rain', -'system', -'weather', -'raincoat', -'soccer team', -'windshield', -'thunderstorm', -'mike', -'bird house', -'bridge', -'grandfather', -'restroom', -'animation', -'wilderness', -'clown', -'banana', -'brown', -'braid', -'dining room', -'kindergarten', -'launch event', -'purple', -'school', -'stairwell', -'brooch', -'movie poster image', -'mountain river', -'shelf', -'wicket', -'headboard', -'buddha', -'flower field', -'dugout', -'cd', -'bald eagle', -'lagoon', -'seaweed', -'agriculture', -'emergency service', -'maple tree', -'parachute', -'continent', -'amusement park', -'remote', -'bun', -'tackle', -'hospital', -'garage door', -'birthday party', -'friendship', -'go', -'mausoleum', -'jeep', -'raccoon', -'step', -'ice hockey team', -'cigarette', -'lace dress', -'forest floor', -'mall', -'captain', -'milk', -'golf course', -'meal', -'picnic table', -'sail', -'volleyball', -'canal', -'terrace', -'computer desk', -'caravan', -'hotel', -'cheerleader', -'nurse', -'museum', -'marsh', -'fox', -'plateau', -'night', -'twin', -'letter logo', -'autumn tree', -'powder', -'convention', -'creature', -'lighthouse', -'shop window', -'jacket', -'stork', -'taxi', -'trade', -'blackboard', -'olive', -'road sign', -'resort', -'snowflake', -'cemetery', -'travel', -'evening dress', -'picnic', -'drink', -'winter morning', -'football player', -'snack', -'boxing glove', -'dinner party', -'airline', -'swing', -'port', -'wheelbarrow', -'bathroom sink', -'sweater', -'ambulance', -'gear', -'oil', -'wii controller', -'array', -'home office', -'car show', -'mixture', -'profession', -'tree frog', -'square', -'facility', -'coral reef', -'sea wall', -'pizza', -'exhibit', -'demolition', -'trout', -'ring', -'coffee shop', -'bracelet', -'bean', -'lip', -'fencing', -'landscape', -'sitting', -'package', -'metal', -'bust', -'king', -'hair', -'window seat', -'wildlife', -'trunk', -'greenery', -'stencil', -'fire hydrant', -'bridesmaid', -'plaza', -'alps', -'tower bridge', -'crop top', -'crossing', -'cinema', -'pedestrian crossing', -'family', -'shopping cart', -'stomach', -'church building', -'screen door', -'skater', -'soccer field', -'kettle', -'mussel', -'raindrop', -'candy cane', -'water lily', -'flower girl', -'desert', -'enclosure', -'christmas light', -'kitchen', -'caterpillar', -'plaid', -'bath', -'bush', -'mud', -'ballet', -'knee', -'adult', -'raft', -'sea view', -'cactus', -'office chair', -'overall', -'rim', -'scaffolding', -'pig', -'cover', -'poster page', -'sprinkle', -'chandelier', -'algae', -'traffic', -'surfboard', -'book', -'filming', -'flash', -'mansion', -'camouflage', -'trouser', -'ticket', -'weed', -'cab', -'trench', -'elephant', -'huddle', -'sphere', -'christmas decoration', -'city', -'launch', -'doll', -'christmas ornament', -'fabric', -'bikini', -'biplane', -'breakfast', -'neighbourhood', -'race track', -'foliage', -'avocado', -'school bus', -'footwear', -'highway', -'ocean view', -'art vector illustration', -'wall clock', -'curtain', -'teenager', -'kitchen area', -'robot', -'tusk', -'lounge chair', -'beam', -'paddle', -'camel', -'lid', -'world map', -'city view', -'newlywed', -'cargo ship', -'yellow', -'exhibition', -'bend', -'novel', -'wool', -'ontario', -'bread', -'campus', -'coastline', -'cutting board', -'booth', -'table top', -'carpet', -'beach chair', -'workout', -'street food', -'fun', -'costumer film designer', -'gadget', -'artist', -'fishing village', -'builder', -'violinist', -'iphone', -'spider web', -'traffic sign', -'ruin', -'rescue', -'clipboard', -'seal', -'film director', -'paw', -'nursery', -'intersection', -'tomato sauce', -'taste', -'paddy field', -'christmas tree', -'wave', -'stool', -'watering can', -'rug', -'daytime', -'subway station', -'craft', -'pine forest', -'black', -'planet', -'motif', -'christmas market', -'glass window', -'college', -'wheat', -'damage', -'rectangle', -'picture frame', -'chess', -'guest room', -'street corner', -'religion', -'seed', -'puzzle', -'freeway', -'beauty', -'ocean', -'watch', -'mother', -'garage', -'quote', -'dj', -'supporter', -'hip hop artist', -'muffin', -'eiffel tower', -'cash', -'firefighter', -'cauliflower', -'bunker', -'sled', -'manicure', -'shark', -'stall', -'jungle', -'family home', -'tour bus', -'chimney', -'touchdown', -'roundabout', -'coyote', -'street scene', -'tank', -'wedding dress', -'mantle', -'bedroom window', -'coconut', -'chapel', -'goat', -'living space', -'rock wall', -'polka dot', -'railway', -'mandala', -'mango', -'lesson', -'mountain landscape', -'team photo', -'bookshelf', -'meter', -'bulldog', -'evening sun', -'stick', -'card', -'pink', -'fish pond', -'paint', -'pill', -'cart', -'pea', -'van', -'album', -'football college game', -'mountain pass', -'doughnut', -'ski slope', -'match', -'official', -'shadow', -'organ', -'celebration', -'coin', -'log cabin', -'firework display', -'present', -'twig', -'chef', -'confetti', -'footpath', -'tour', -'ponytail', -'artwork', -'race car', -'club', -'season', -'hose', -'pencil', -'aircraft', -'rock formation', -'wardrobe', -'participant', -'politician', -'engineer', -'peace', -'filter', -'sailing boat', -'water bottle', -'service dog', -'poodle', -'loki', -'statesman', -'sleeping bag', -'outskirt', -'clock', -'factory', -'oak tree', -'physician', -'color', -'room', -'stairway', -'company', -'lady', -'graph', -'faucet', -'tablecloth', -'subway train', -'chocolate chip cookie', -'headquarters', -'screw', -'goggle', -'halloween', -'city street', -'swirl', -'cord', -'forward', -'bone', -'bedding', -'archway', -'wig', -'lobby', -'mask', -'attic', -'kitchen table', -'skylight', -'fire', -'exit', -'oil painting', -'passenger', -'meditation', -'salmon', -'fedora', -'rubber stamp', -'orange juice', -'arch', -'scientist', -'stroll', -'manhattan', -'float', -'baseball uniform', -'circle', -'church', -'decker bus', -'competitor', -'zoo', -'basketball team', -'tourist', -'daughter', -'silverware', -'ceiling fan', -'birth', -'vase', -'jack', -'mushroom', -'spiral', -'cage', -'limb', -'salad', -'ad', -'control', -'earth', -'party', -'bolt', -'tractor', -'barley', -'wedding photo', -'hawk', -'warehouse', -'vegetable garden', -'chocolate cake', -'cabbage', -'floor window', -'baby shower', -'magnifying glass', -'table', -'stethoscope', -'reading', -'mission', -'croissant', -'gift box', -'rocket', -'forest road', -'cooking', -'suite', -'hill country', -'motorcycle', -'baseball player', -'angle', -'drug', -'sport association', -'championship', -'family portrait', -'florist', -'softball', -'egret', -'office', -'plywood', -'jockey', -'mosque', -'brunch', -'beanie', -'office building', -'pattern', -'calendar', -'indoor', -'pepper', -'ledge', -'trail', -'fuel', -'laptop computer', -'tennis shoe', -'deck chair', -'guitarist', -'barn', -'surgery', -'cartoon illustration', -'nebula', -'railroad', -'mountain goat', -'goose', -'car door', -'cheer', -'liquid', -'hardwood floor', -'pathway', -'acorn', -'gull', -'airliner', -'couch', -'lake house', -'spaghetti', -'promenade', -'collection', -'garden', -'bank', -'robin', -'tennis ball', -'peony', -'gymnast', -'lavender', -'deck', -'test', -'riverside', -'rapper', -'domino', -'bride', -'mouse', -'basil', -'wedding couple', -'ocean wave', -'arm', -'kitchen floor', -'grove', -'family member', -'backyard', -'raspberry', -'forest fire', -'officer', -'hibiscus', -'canyon', -'composer', -'signature', -'olive oil', -'hibiscus flower', -'rose', -'vector icon', -'sunrise', -'horseback', -'motor scooter', -'office worker', -'tradition', -'ingredient', -'washing machine', -'lighting', -'bagel', -'sailboat', -'policeman', -'mare', -'graphic', -'halloween pumpkin', -'stock', -'pilot', -'education', -'team', -'body', -'horse', -'kimono', -'bazaar', -'bag', -'recording studio', -'parsley', -'entrance', -'denim', -'vet', -'horse farm', -'charcoal', -'architecture', -'glass vase', -'puppy', -'estuary', -'television show host', -'city bus', -'shoulder', -'beast', -'balance', -'golfer', -'roadside', -'denim jacket', -'stone wall', -'counter top', -'app icon', -'toast', -'head coach', -'ham', -'warrior', -'gem', -'refrigerator', -'snowman', -'construction worker', -'coal', -'website', -'morning fog', -'mustard', -'human', -'owl', -'puppy dog', -'piggy bank', -'vegetation', -'pirate', -'action film', -'marshmallow', -'thanksgiving', -'business', -'disease', -'signage', -'greeting', -'skate park', -'tile', -'mouth', -'spinach', -'vacation', -'leader', -'shrine', -'walker', -'science fiction film', -'bill', -'rabbit', -'motor boat', -'bar', -'radio', -'barge', -'tail', -'chainsaw', -'gallery', -'rainbow', -'pasta', -'padlock', -'web', -'pastry', -'ink', -'reef', -'school uniform', -'shawl', -'treasure', -'peach', -'dinner table', -'injury', -'harbor', -'witch', -'car dealership', -'litter', -'gesture', -'documentary', -'marriage', -'sea shell', -'priest', -'dome', -'kit', -'icon', -'seaside', -'bucket', -'entertainment', -'stable', -'hat', -'puddle', -'sock', -'shopper', -'technology', -'harbour', -'orbit', -'antler', -'tube', -'flag waving', -'cook', -'tight', -'commander', -'farmland', -'switch', -'hiker', -'wedding ceremony', -'award ceremony', -'champion', -'chopstick', -'farmhouse', -'performer', -'spike', -'accident', -'cruise ship', -'passenger train', -'attraction', -'entertainer', -'rear view', -'sidewalk', -'parade', -'racing', -'plane', -'ritual', -'peacock', -'pocket', -'plum', -'drop', -'carrot', -'floor', -'sunset', -'troop', -'architect', -'coffee table', -'dust', -'outline', -'leather', -'charity event', -'heat', -'whale', -'laundry', -'coconut tree', -'crosswalk', -'pony', -'ant', -'pipe', -'string', -'coat', -'angel', -'beef', -'church tower', -'dish', -'pitch', -'cupboard', -'thermometer', -'dirt field', -'fireworks', -'minute', -'cane', -'pajama', -'flower garden', -'autumn', -'trash can', -'dachshund', -'banana tree', -'tray', -'moose', -'roadway', -'carnival', -'antenna', -'pole', -'castle wall', -'ram', -'cattle', -'hay', -'cookie', -'swimmer', -'baseball team', -'strait', -'hedge', -'jet', -'fire pit', -'octopus', -'calf', -'cube', -'opera', -'cardboard box', -'tiara', -'kitchen sink', -'prairie', -'bowl', -'galaxy', -'straw hat', -'linen', -'ski resort', -'stitch', -'street lamp', -'motorist', -'icicle', -'stain', -'flora', -'drain', -'kitchen cabinet', -'decor', -'bouquet', -'pound', -'interior design', -'nail polish', -'figurine', -'tomb', -'disc', -'twist', -'blouse', -'ribbon', -'figure', -'burger', -'cork', -'soccer goalkeeper', -'train bridge', -'drinking water', -'dew', -'baker', -'storm cloud', -'tarmac', -'tv drama', -'sponge', -'magnet', -'sailor', -'entry', -'swan', -'exercise', -'sloth', -'jewel', -'scuba diver', -'bite', -'cat tree', -'tent', -'can', -'tennis match', -'ecosystem', -'picket fence', -'palm', -'train car', -'frying pan', -'rally', -'tablet pc', -'reindeer', -'image', -'wolf', -'chin', -'conservatory', -'flood water', -'cityscape', -'beach sand', -'car park', -'pavement', -'farm field', -'swimming', -'winter storm', -'stem', -'pillow', -'inning', -'gorilla', -'desk', -'avenue', -'fern', -'money', -'pearl', -'train station', -'skillet', -'nap', -'barber', -'library', -'freezer', -'label', -'rainforest', -'parking sign', -'mirror', -'wing', -'noodle', -'press room', -'sculpture', -'tablet', -'viewer', -'prayer', -'mini', -'mechanic', -'laugh', -'rice field', -'hand', -'mustache', -'mountain road', -'catwalk', -'conference', -'cape', -'installation', -'musician', -'stream', -'machine', -'speech', -'crocodile', -'soccer match', -'town square', -'passport', -'post box', -'point', -'stone building', -'motorway', -'mix', -'dentist', -'businessperson', -'happiness', -'boat', -'vineyard', -'treadmill', -'glass wall', -'water droplet', -'coffee mug', -'graduate', -'sunflower', -'parliament', -'shepherd', -'movie', -'wine', -'orchard', -'tulip', -'motherboard', -'cup', -'broom', -'spot', -'drawing', -'polo shirt', -'graduation', -'film producer', -'moonlight', -'glow', -'film format', -'t shirt', -'rock face', -'sword', -'clinic', -'festival day', -'meadow', -'staple', -'pupil', -'training ground', -'rider', -'flower', -'foal', -'wharf', -'foot bridge', -'shooting', -'top', -'mast', -'police car', -'robe', -'wedding bouquet', -'stop sign', -'birthday cake', -'glitter', -'butter', -'scooter', -'tundra', -'superhero', -'pocket watch', -'inscription', -'youngster', -'fruit tree', -'movie poster', -'engine', -'foundation', -'motorcyclist', -'take', -'woman', -'antelope', -'country artist', -'road trip', -'typewriter', -'tuxedo', -'brand', -'pine', -'bathroom', -'paradise', -'texture', -'balloon', -'dining table', -'home', -'computer screen', -'actor', -'clip', -'tv tower', -'panorama', -'summit', -'cat', -'plot', -'eagle', -'dancer', -'pup', -'studio shot', -'tear', -'bird bath', -'classroom', -'bookstore', -'city wall', -'tv programme', -'blade', -'easel', -'buttercream', -'sweet', -'designer', -'diamond', -'handshake', -'herb', -'corn field', -'seafront', -'concrete', -'street artist', -'gas', -'stamp', -'window display', -'paper', -'note', -'pint', -'quarry', -'research', -'fixture', -'manager', -'soil', -'leopard', -'board game', -'ladder', -'stop light', -'island', -'ramp', -'football match', -'icing', -'drill', -'currency', -'summer evening', -'topping', -'pyramid', -'pomegranate', -'cell', -'ivy', -'squad', -'scenery', -'computer', -'locomotive', -'surf', -'mascot', -'dune', -'path', -'duck', -'twilight', -'wire', -'bow tie', -'strike', -'cormorant', -'car wash', -'crane', -'market', -'philosopher', -'alarm clock', -'camera', -'birch', -'greeting card', -'plain', -'clay', -'donut', -'lock', -'moth', -'laboratory', -'fan', -'violin', -'jazz fusion artist', -'mountain biker', -'terrain', -'magazine', -'pickup', -'comedy film', -'smartphone', -'film', -'bed', -'microwave oven', -'tournament', -'lawn', -'car window', -'alligator', -'screen', -'jetty', -'shopping bag', -'landscape view', -'cabinetry', -'friendly match', -'thing', -'petal', -'shopping center', -'transport', -'ballet dancer', -'shoreline', -'princess', -'car seat', -'parking meter', -'green', -'vodka', -'band', -'rock', -'costume', -'warning sign', -'strip', -'plaque', -'wheelchair', -'headband', -'ginger', -'dice', -'media', -'hairdresser', -'press', -'living room', -'stove', -'player', -'cherry', -'workshop', -'carving', -'embroidery', -'doodle', -'adventure', -'rugby player', -'monument', -'brush', -'marker', -'loft', -'postcard', -'collage', -'ball', -'professor', -'dresser', -'gig', -'festival', -'blackbird', -'makeup artist', -'video camera', -'sticker', -'peak', -'wildflower', -'santa hat', -'rodeo', -'wedding photographer', -'guy', -'staff', -'waterfall', -'operation', -'defender', -'falcon', -'haze', -'individual', -'gentleman', -'greyhound', -'rocking chair', -'rice', -'garbage', -'platter', -'chocolate', -'splash', -'business suit', -'cheetah', -'valley', -'maze', -'trampoline', -'garland', -'slalom', -'unicorn', -'tree stump', -'painting', -'romance', -'fight', -'alcohol', -'ghost', -'fondant', -'spa', -'shutter', -'death', -'demonstration', -'cotton', -'pier', -'flea market', -'history', -'savannah', -'fist', -'aisle', -'crew', -'jug', -'pose', -'anchor', -'teapot', -'boat house', -'business team', -'tripod', -'bee', -'pebble', -'mattress', -'canvas', -'hallway', -'campaign', -'pod', -'lake district', -'article', -'white', -'sofa', -'honey', -'marathon', -'pancake', -'tourist attraction', -'wedding gown', -'battle', -'shelving', -'sea', -'sheet music', -'pie', -'yarn', -'construction site', -'flyer', -'tie', -'star', -'lettuce', -'martial artist', -'dart', -'straw', -'reflection', -'conference room', -'temperature', -'rugby', -'mosquito', -'physicist', -'rock climber', -'crash', -'backdrop', -'toilet seat', -'sand castle', -'water park', -'toy car', -'waste', -'luxury', -'hangar', -'rv', -'tree trunk', -'board', -'gold', -'project picture', -'cap', -'cottage', -'relief', -'attire', -'microscope', -'battery', -'roll', -'line', -'parking garage', -'crystal', -'broadcasting', -'brick wall', -'lab', -'flooring', -'meeting', -'3d cg rendering', -'desktop computer', -'cowboy', -'sailing ship', -'junction', -'hairstyle', -'homework', -'profile', -'model', -'flower pot', -'street light', -'salt lake', -'maple', -'space', -'blizzard', -'throw', -'zebras', -'brochure', -'constellation', -'beak', -'kilt', -'pond', -'blue sky', -'sneaker', -'sand dune', -'morning sun', -'almond', -'grill', -'curl', -'basketball girl game', -'chameleon', -'toilet bowl', -'prince', -'keyboard', -'queen', -'computer monitor', -'writing', -'crown', -'basilica', -'kiss', -'house', -'parking', -'football competition', -'shell', -'sport equipment', -'comedy', -'baboon', -'vendor', -'rise building', -'wrap', -'food truck', -'cat bed', -'rickshaw', -'flare', -'teal', -'nectar', -'eclipse', -'vehicle', -'steam locomotive', -'gorge', -'cow', -'christmas card', -'demonstrator', -'memorial', -'towel', -'jewellery', -'train', -'frisbee', -'baseball game', -'fur', -'afternoon sun', -'community', -'sparkler', -'bandage', -'firework', -'dollar', -'pasture', -'video', -'bus', -'tree house', -'seashore', -'field', -'hamburger', -'souvenir', -'hedgehog', -'worm', -'pine cone', -'osprey', -'dinosaur', -'vegetable', -'junk', -'poster', -'army', -'winger', -'bundle', -'stage', -'growth', -'wedding party', -'service', -'blanket', -'ruler', -'eye', -'credit card', -'castle', -'diner', -'hut', -'elk', -'hard rock artist', -'nun', -'dog breed', -'nest', -'drama film', -'number icon', -'water tank', -'giraffe', -'altar', -'pavilion', -'tv personality', -'suv', -'street vendor', -'street sign', -'ditch', -'debris', -'foam', -'takeoff', -'spice', -'mountain lake', -'tea', -'orchestra', -'spacecraft', -'counter', -'abbey', -'mountain', -'hydrangea', -'racer', -'orange tree', -'tide', -'cowboy hat', -'rapid', -'town', -'wild', -'herd', -'vein', -'driveway', -'jar', -'bark', -'illustration', -'horror film', -'corn', -'stroller', -'industry', -'mountain stream', -'gym', -'neckline', -'pan', -'client', -'spectator', -'eggplant', -'camper', -'fawn', -'hoodie', -'meat', -'lemonade', -'food market', -'slum', -'comic book character', -'flower market', -'love', -'palace', -'gun', -'heel', -'shopping street', -'shooting basketball guard', -'family photo', -'rooftop', -'laundry basket', -'airport runway', -'horn', -'face mask', -'flight', -'appetizer', -'violet', -'country lane', -'cement', -'instrument', -'tv actor', -'spark', -'celebrity', -'award', -'country house', -'standing', -'auction', -'date', -'engagement', -'puck', -'advertisement', -'chair', -'zebra', -'driftwood', -'bumblebee', -'maple leaf', -'bonnet', -'orange', -'water tower', -'door', -'singer', -'floor plan', -'discussion', -'theatre', -'pilgrim', -'mug', -'branch', -'window sill', -'baseball pitcher', -'bakery', -'lollipop', -'basketball player', -'toilet paper', -'chalkboard', -'cabin', -'sign', -'night sky', -'cannon', -'fishing net', -'submarine', -'suit', -'fur coat', -'wine bottle', -'folder', -'street art', -'suspension bridge', -'evening sky', -'billboard', -'postage stamp', -'newspaper', -'transportation', -'surgeon', -'light', -'park', -'horizon', -'road', -'sand bar', -'trumpet', -'lounge', -'cloud forest', -'birthday celebration', -'balcony', -'anime', -'beehive', -'umbrella', -'goldfish', -'baseball cap', -'waterhole', -'ceiling', -'carousel', -'backpack', -'plant pot', -'atmosphere', -'sunflower field', -'spire', -'vision', -'woodpecker', -'chip', -'pool table', -'lotus flower', -'cone', -'humpback whale', -'reservoir', -'hunt', -'piano', -'plate', -'dining area', -'luggage', -'skier', -'dance floor', -'crow', -'stair', -'overpass', -'opera house', -'bear', -'jazz artist', -'water', -'vessel', -'cast', -'yard', -'cathedral', -'basketball hoop', -'graveyard', -'sound', -'berry', -'onlooker', -'fauna', -'birch tree', -'retail', -'hill', -'skeleton', -'journalist', -'frost', -'basket', -'nail', -'dusk', -'trash', -'dawn', -'clover', -'hen', -'volcano', -'basketball coach', -'home decor', -'charge', -'haircut', -'sense', -'university', -'lizard', -'daisy', -'tablet computer', -'grass field', -'prison', -'metal artist', -'bathroom mirror', -'window frame', -'chest', -'flavor', -'pop country artist', -'market square', -'monkey', -'blog', -'deer', -'speech bubble', -'dog', -'independence day', -'girl', -'boy', -'tartan', -'furniture', -'appliance', -'office window', -'fish boat', -'sand box', -'tv sitcom', -'drama', -'sleigh', -'depression', -'paper towel', -'baseball', -'protestor', -'grape', -'wedding cake', -'invitation', -'accessory', -'pick', -'grandparent', -'racket', -'tea plantation', -'outdoors', -'egg', -'glass bowl', -'sun', -'organization', -'lion', -'panel', -'station', -'wallpaper', -'helicopter', -'salt', -'vanity', -'patio', -'lunch', -'street performer', -'mountain range', -'soup', -'bacon', -'power station', -'cantilever bridge', -'hummingbird', -'shirt', -'rope', -'hip', -'chalk', -'pendant', -'choir', -'tv', -'lichen', -'railway bridge', -'art gallery', -'bartender', -'wagon', -'baby elephant', -'accordion', -'horseshoe', -'building site', -'clutch', -'harvest', -'savanna', -'geranium', -'business woman', -'paddock', -'patch', -'beech tree', -'war', -'suburbs', -'hospital bed', -'motorcycle racer', -'moss', -'gravel', -'government agency', -'dollar bill', -'father', -'fjord', -'concert', -'nut', -'wedding photography', -'finish line', -'home plate', -'food', -'nose', -'thumb', -'village', -'dining room table', -'bumper', -'monster', -'blackberry', -'lime', -'conflict', -'gala', -'wallet', -'wrist', -'hug', -'mermaid', -'lava', -'lawyer', -'folk rock artist', -'arena', -'onion', -'toothbrush', -'fashion', -'perfume', -'flip', -'triangle', -'woodland', -'mail', -'grasshopper', -'studio', -'wood floor', -'den', -'racquet', -'cello', -'lemur', -'astronaut', -'glass table', -'blood', -'dvd', -'planter', -'silver', -'leash', -'master bedroom', -'forest', -'batter', -'shoe', -'engraving', -'opening', -'product', -'toe', -'cocktail', -'mallard duck', -'bike ride', -'oasis', -'wedding ring', -'cinematographer', -'holly', -'autograph', -'fence', -'ice cube', -'cove', -'pineapple', -'aurora', -'glass bead', -'produce', -'apartment building', -'cob', -'miniature', -'cockpit', -'flashlight', -'frog', -'sheep', -'groom', -'steel', -'watermelon', -'clip art', -'paper plate', -'ostrich', -'contour', -'mural', -'cub', -'paisley bandanna', -'winery', -'turn', -'handle', -'satellite', -'post', -'pork', -'child', -'asphalt', -'grocery store', -'vulture', -'trolley', -'nightclub', -'brick', -'trailer', -'compass', -'cereal', -'cafe', -'cartoon character', -'sugar', -'fiction book', -'glass floor', -'umpire', -'guitar', -'hamster', -'protester', -'airplane', -'garment', -'blazer', -'railway line', -'wedding', -'shoe box', -'parking lot', -'construction', -'graduation ceremony', -'tram', -'telescope', -'copper', -'pain', -'autumn forest', -'guest house', -'partner', -'crayon', -'dip', -'boot', -'corridor', -'computer keyboard', -'hockey player', -'chicken coop', -'bus station', -'gathering', -'ankle', -'bunk bed', -'wood table', -'football coach', -'monarch', -'pharmacy', -'legging', -'mannequin', -'female', -'train track', -'stack', -'canopy', -'design element', -'grandmother', -'symbol', -'beach hut', -'zucchini', -'bomb', -'businessman', -'skyscraper', -'tongue', -'case', -'sparkle', -'highland', -'ballroom', -'prom', -'estate', -'customer', -'archipelago', -'cheese', -'debate', -'carriage', -'bulldozer', -'pumpkin', -'sitting room', -'gas station', -'wedding reception', -'camp', -'dog bed', -'tower', -'property', -'river bed', -'pop latin artist', -'fridge', -'wine glass', -'coast', -'beer', -'tow truck', -'fire truck', -'mountain bike', -'thigh', -'heron', -'boat ride', -'gondola', -'turquoise', -'lake', -'llama', -'kitty', -'tin', -'waiting room', -'coffee cup', -'socialite', -'guard', -'tap', -'waterway', -'forehead', -'list', -'erosion', -'box', -'sea lion', -'pollen', -'dam', -'wasp', -'salon', -'tennis tournament', -'flower box', -'aquarium', -'rain cloud', -'clothing store', -'lead singer', -'cupcake', -'tortoise', -'lettering', -'sport facility', -'dance', -'dog house', -'nature', -'football', -'rooster', -'footballer', -'railway track', -'crowd', -'fishing rod', -'silhouette', -'wind turbine', -'sari', -'bus window', -'cloud', -'charity', -'medal', -'yoga', -'event', -'veil', -'fashion menswear milan week', -'news', -'knife', -'print', -'screen tv', -'walnut', -'fungus', -'ice cream', -'computer mouse', -'play', -'tribe', -'picture', -'video game', -'business card', -'music festival', -'rack', -'envelope', -'shower', -'dirt road', -'mine', -'oyster', -'monarch butterfly', -'dude', -'fruit salad', -'podium', -'fork', -'lace', -'test match', -'boulder', -'cricket player', -'staircase', -'peninsula', -'shopping', -'popcorn', -'oak', -'market stall', -'pine tree', -'mountaineer', -'student', -'closet', -'hood', -'handstand', -'centerpiece', -'insect', -'patient', -'makeover', -'tennis player', -'sheet', -'park bench', -'apple', -'organism', -'hook', -'turkey', -'tangerine', -'sibling', -'shopping mall', -'bird', -'scarf', -'smoothie', -'net', -'grass', -'napkin', -'ray', -'eyebrow', -'laptop keyboard', -'motorbike', -'woman hand', -'oven', -'book cover', -'easter egg', -'microwave', -'sand', -'snapshot', -'soccer ball', -'makeup', -'knight', -'bowling ball', -'shower curtain', -'flame', -'lightning', -'running', -'power plant', -'crib', -'cartoon', -'moat', -'fashion girl', -'wedding invitation', -'bottle', -'cliff', -'monastery', -'file photo', -'apartment', -'casino', -'cream', -'sweatshirt', -'storm', -'cruise', -'teddy bear', -'shovel', -'wind farm', -'writer', -'dock', -'professional', -'hotel room', -'job', -'monitor', -'donkey', -'pass', -'interview', -'duchess', -'mark', -'plank', -'beard', -'zombie', -'trio', -'channel', -'cricket team', -'windmill', -'vest', -'diagram', -'cable', -'winter scene', -'golden gate bridge', -'buffalo', -'studio portrait', -'pagoda', -'whiskey', -'freight train', -'kite', -'future', -'steam train', -'phone box', -'headset', -'wood', -'snowboarder', -'paper bag', -'slide', -'grapefruit', -'seating', -'morning', -'bronze sculpture', -'theatre actor', -'stump', -'jean', -'landmark', -'jam', -'waist', -'watercolor', -'hammock', -'light fixture', -'ice', -'basin', -'beverage', -'shelter', -'premiere', -'mound', -'ear', -'bronze', -'sunlight', -'street', -'energy', -'barn door', -'hike', -'fleet', -'claw', -'beach', -'pepperoni', -'bin', -'trainer', -'buffet', -'archive', -'toddler', -'referee', -'bay window', -'dove', -'production company', -'evening light', -'gate', -'farm', -'reed', -'fruit stand', -'explorer', -'snow storm', -'throw pillow', -'button', -'display case', -'bookcase', -'lead', -'lipstick', -'basketball court', -'cargo', -'ensemble', -'pope', -'clock tower', -'teen', -'speaker', -'rat', -'laptop', -'ski', -'mess', -'stadium', -'ferry boat', -'bunny', -'waterfront', -'downtown', -'sink', -'press conference', -'dinner', -'condiment', -'thread', -'audience', -'grid', -'car', -'plastic', -'people', -'barbecue', -'pigeon', -'urinal', -'seagull', -'volunteer', -'hockey', -'fir tree', -'pollution', -'trial', -'collar', -'area', -'meeting room', -'circus', -'yogurt', -'orangutan', -'viaduct', -'comedian', -'drone', -'scissor', -'pop rock artist', -'biscuit', -'panda', -'water feature', -'air balloon', -'remote control', -'watercolor painting', -'show', -'walk', -'post office', -'bike path', -'rap gangsta artist', -'microphone', -'crack', -'sunset sky', -'glass', -'tv show', -'cartoon style', -'stripe', -'foyer', -'signal', -'calligraphy', -'bulb', -'gardener', -'coffee bean', -'spider', -'tapestry', -'city skyline', -'necklace', -'kitten', -'traveler', -'veteran', -'frosting', -'fry', -'tennis court', -'tank top', -'butterfly house', -'mist', -'drummer', -'water level', -'scale', -'baseball glove', -'music video performer', -'champagne', -'camping', -'clothing', -'water drop', -'telephone box', -'pen', -'morning mist', -'fire engine', -'porch', -'opening ceremony', -'style', -'palm tree', -'fashion show', -'universe', -'scratch', -'axe', -'ottoman', -'explosion', -'rib', -'boutique', -'game', -'cucumber', -'fruit', -'stone bridge', -'nature reserve', -'track', -'train window', -'punch', -'telephone pole', -'velvet', -'sauce', -'moon', -'contrast', -'flamingo', -'bat', -'vending machine', -'ship', -'equestrian', -'shade', -'comforter', -'pallet', -'sparrow', -'wii', -'glaze', -'grocery', -'steeple', -'soccer player', -'contract', -'advertising', -'runner', -'chimpanzee', -'world', -'seat', -'project', -'chihuahua', -'bubble', -'willow', -'pedestal', -'soul hip hop artist', -'curb', -'drawer', -'leaf', -'banner', -'launch party', -'coach', -'government', -'snowball', -'toy', -'portrait', -'doctor', -'whiteboard', -'electronic', -'tiger', -'graffiti', -'column', -'nightstand', -'whistle', -'maxi dress', -'bench', -'wetsuit', -'bird feeder', -'football game', -'basketball', -'class', -'bathroom door', -'store window', -'text message', -'wreath', -'street view', -'binocular', -'pet', -'facade', -'drought', -'lemon', -'new year', -'night view', -'airplane window', -'specie', -'rule', -'jaw', -'wheat field', -'diet', -'pop artist', -'habitat', -'screenshot', -'scoreboard', -'shore', -'mane', -'quilt', -'ski lift', -'orchid', -'turban', -'christmas', -'airport', -'marina', -'glass door', -'glass bottle', -'restaurant', -'conductor', -'logo', -'sleep', -'tape', -'tomato', -'river bank', -'lilac', -'tooth', -'training', -'pottery', -'shop', -'steam engine', -'mason jar', -'base', -'procession', -'border', -'shoot', -'footprint', -'hotdog', -'bull', -'stocking', -'recreation', -'automobile model', -'design', -'country pop artist', -'river', -'retriever', -'department store', -'auditorium', -'sport car', -'supermarket', -'belt', -'cricket', -'window box', -'dress shirt', -'letter', -'residence', -'megaphone', -'pant', -'wildfire', -'bird nest', -'crab', -'swimsuit', -'candle', -'funeral', -'mill', -'national park', -'plant', -'cop', -'power line', -'perch', -'blue', -'finger', -'ferris wheel', -'globe', -'skateboard', -'helmet', -'movie theater', -'uniform', -'hammer', -'material', -'kid', -'well', -'butterfly', -'sideline', -'fashion fall show', -'planet earth', -'lift', -'male', -'sauna', -'gray', -'flour', -'sand sculpture', -'program', -'cabinet', -'infant', -'wheel', -'aircraft model', -'dough', -'garlic', -'skate', -'arrow', -'wrapping paper', -'ripple', -'lamp', -'iron', -'banknote', -'beaver', -'ferry', -'courtyard', -'bassist', -'countryside', -'steak', -'comfort', -'boxer', -'laundry room', -'campsite', -'brick building', -'golf', -'subway', -'headphone', -'fort', -'handbag', -'drum', -'flood', -'saddle', -'bass', -'labyrinth', -'needle', -'sun ray', -'app', -'menu', -'president', -'cardigan', -'dandelion', -'wetland', -'ice hockey player', -'number', -'city hall', -'fishing', -'portrait session', -'pug', -'key', -'art print', -'minister', -'hurdle', -'emergency', -'painting artist', -'flag pole', -'evening', -'purse', -'recipe', -'golf ball', -'coloring book', -'mountain peak', -'senior', -'holiday', -'bud', -'cousin', -'pantry', -'lap', -'skin', -'flag', -'tissue paper', -'ridge', -'wire fence', -'surfer', -'climber', -'photograph', -'sewing machine', -'cooler', -'actress', -'apple tree', -'cancer', -'starfish', -'automobile make', -'dumbbell', -'brace', -'tunnel', -'window', -'paint artist', -'composition', -'school student', -'condo', -'convertible', -'cushion', -'selfie', -'territory', -'guide', -'tree', -'court', -'shrimp', -'stone house', -'dress', -'eyelash', -'juice', -'broccoli', -'chain', -'tourism', -'mountain top', -'concept car', -'film premiere', -'light bulb', -'cafeteria', -'badge', -'flower bed', -'theater', -'root', -'racecar driver', -'basketball boy game', -'glove', -'skyline', -'wall', -'glacier', -'airport terminal', -'bug', -'trim', -'railway station', -'briefcase', -'flat', -'fountain', -'person', -'lane', -'asparagus', -'art', -'lantern', -'dishwasher', -'director', -'snake', -'lecture', -'game controller', -'tree branch', -'pub', -'bathing suit', -'queue', -'belly', -'poppy', -'bow', -'pitcher', -'ice cream cone', -'cave', -'candy', -'road bridge', -'host', -'traffic jam', -'earring', -'file', -'foot', -'watermark overlay stamp', -'mailbox', -'supercar', -'railing', -'bedroom', -'seafood', -'waffle', -'bronze statue', -'plan', -'flow', -'marble', -'basketball game', -'automobile', -'scene', -'cypress tree', -'soldier', -'skateboarder', -'glass building', -'cherry tree', -'pump', -'grain', -'wildebeest', -'loop', -'frame', -'bathtub', -'saxophone', -'diver', -'stalk', -'lily', -'bead', -'alley', -'flock', -'family room', -'manufacturing', -'pointer', -'worker', -'navy', -'potato', -'teacher', -'photography', -'dolly', -'boardwalk', -'water fountain', -'athlete', -'side dish', -'bay', -'ice hockey', -'phone', -'hero', -'face', -'gold medal', -'blind', -'swamp', -'researcher', -'swim', -'meatball', -'iguana', -'leather jacket', -'jellyfish', -'site', -'smoke', -'traffic signal', -'melon', -'beetle', -'calculator', -'skirt', -'plantation', -'sculptor', -'barrier', -'catcher', -'security guard', -'sketch', -'awning', -'steering wheel', -'mountain view', -'bus stop', -'pool', -'leg', -'spotlight', -'apron', -'mineral', -'inlet', -'sleeve', -'torch', -'emotion', -'march', -'police officer', -'performance', -'lamp post', -'fishing boat', -'summer', -'presentation', -'saucer', -'suitcase', -'supermodel', -'goalkeeper', -'shrub', -'rock artist', -'document', -'beach house', -'man', -'blue artist', -'cigar', -'railroad track', -'gown', -'mosaic', -'bungalow', -'alphabet', -'baseball field', -'shed', -'pedestrian', -'rail', -'soap', -'kitchen counter', -'dessert', -'dunk', -'blossom', -'conversation', -'fruit market', -'glass jar', -'military', -'beer bottle', -'photographer', -'tennis racket', -'competition', -'escalator', -'bell tower', -'stilt', -'ballerina', -'television', -'feather', -'fence post', -'rear', -'dahlia', -'red carpet', -'tub', -'hole', -'fortress', -'pack', -'telephone', -'cardboard', -'city park', -'platform', -'college student', -'arch bridge', -'wind', -'blender', -'bloom', -'ice rink', -'birthday', -'raven', -'fairy', -'embankment', -'hall', -'flower shop', -'suburb', -'barrel', -'biker', -'steam', -'dragonfly', -'formation', -'electricity', -'business people', -'symmetry', -'walkway', -'fisherman', -'gas mask', -'loch', -'youth', -'hanger', -'dot', -'fish', -'street market', -'animation film', -'crime fiction film', -'boar', -'emblem', -'halloween costume', -'kangaroo', -'couple', -'spoon', -'squirrel', -'neon sign', -'sky', -'office desk', -'beauty salon', -'breakwater', -'fashion look', -'toaster', -'author', -'news conference', -'outdoor', -'canoe', -'dragon', -'tool', -'shopping centre', -'ladybug', -'swimming pool', -'landscaping', -'ski pole', -'red', -'truck', -'fly', -'temple', -'level', -'sunday', -'railroad bridge', -'car mirror', -'lawn mower', -'flute', -'aircraft carrier', -'fashion menswear london week', -'sunshine', -'tile floor', -'skull', -'fossil', -'flower arrangement', -'diaper', -'sea turtle', -'cherry blossom', -'fireman', -'shack', -'lens', -'waiter', -'animal', -'basement', -'snow', -'autumn park', -'glass box', -'kick', -'head', -'anniversary', -'vine', -'back', -'paper lantern', -'fish tank', -'cellphone', -'silk', -'coral', -'notebook', -'photo', -'gazebo', -'ketchup', -'driver', -'farmer', -'bonfire', -'chestnut', -'photoshoot', -'football field', -'olive tree', -'pheasant', -'sandal', -'toilet', -'fireplace', -'music', -'deity', -'fish market', -'fig', -'bell', -'neck', -'grave', -'villa', -'cyclist', -'crate', -'grey', -'asphalt road', -'soccer', -'hostel', -'municipality', -'courthouse', -'roof', -'end table', -'pot', -'sedan', -'structure', -'folk artist', -'sport', -'sport team', -'protest', -'syringe', -'fashion designer', -'jersey', -'heart shape', -'kayak', -'stare', -'sit with', -'direct', -'read', -'photograph', -'spin', -'teach', -'laugh', -'carve', -'grow on', -'warm', -'watch', -'stretch', -'smell', -'decorate', -'shine', -'light', -'dance', -'send', -'park', -'chase', -'collect', -'lead', -'kiss', -'lead to', -'lick', -'smile', -'cheer', -'sit', -'point', -'block', -'rock', -'drop', -'cut', -'ski', -'wrap', -'lose', -'serve', -'provide', -'sleep', -'dress', -'embrace', -'burn', -'pack', -'stir', -'create', -'touch', -'wash', -'stick', -'reveal', -'shop', -'train', -'paint', -'groom', -'hunt', -'bloom', -'play', -'pay', -'brush', -'shoot', -'hold', -'picture', -'carry', -'sip', -'contain', -'turn', -'pour', -'pitch', -'give', -'add', -'blow', -'look in', -'show', -'walk', -'illuminate', -'kneel', -'cover', -'drag', -'post', -'present', -'fit', -'operate', -'fish', -'race', -'write', -'deliver', -'peel', -'push', -'run', -'sit around', -'buy', -'jump', -'walk on', -'attend', -'clean', -'sell', -'ride on', -'mount', -'host', -'dry', -'plant', -'sing', -'row', -'shake', -'perch', -'ride', -'fight', -'skateboard', -'live', -'call', -'surround', -'practice', -'play on', -'work on', -'step', -'relax', -'hit', -'fall in', -'flow', -'greet', -'launch', -'wear', -'hang on', -'drive', -'sit in', -'break', -'learn', -'fly', -'connect', -'display', -'locate', -'compete', -'go for', -'sail', -'lift', -'toast', -'help', -'run on', -'reflect', -'pose', -'scratch', -'frame', -'dribble', -'herd', -'enter', -'exit', -'place', -'inspect', -'build', -'pick', -'fill', -'grind', -'skate', -'offer', -'float', -'sit by', -'stand', -'release', -'rest', -'singe', -'climb', -'tie', -'mark', -'lay', -'stand around', -'capture', -'set', -'land', -'swinge', -'run in', -'kick', -'lean', -'head', -'sign', -'approach', -'swim', -'close', -'crash', -'control', -'fall', -'remove', -'repair', -'open', -'appear', -'travel', -'load', -'miss', -'check', -'surf', -'moor', -'smoke', -'drink', -'board', -'seat', -'feed', -'rise', -'sit on', -'swing', -'grow', -'strike', -'date', -'slide', -'share', -'graze', -'jump in', -'lie', -'extrude', -'roll', -'move', -'gather', -'eat', -'pull', -'run through', -'squeeze', -'lay on', -'draw', -'play with', -'wave', -'assemble', -'perform', -'march', -'score', -'attach', -'adjust', -'hang', -'hug', -'sleep on', -'throw', -'live in', -'talk', -'pet', -'work', -'run with', -'see', -'flip', -'catch', -'cook', -'receive', -'celebrate', -'look', -'classic', -'bridal', -'indoor', -'industrial', -'teenage', -'mini', -'grassy', -'aged', -'long', -'warm', -'light', -'handsome', -'happy', -'three', -'pregnant', -'circular', -'urban', -'silver', -'ceramic', -'3d', -'green', -'blonde', -'golden', -'dark', -'tropical', -'ripe', -'deep', -'fat', -'musical', -'giant', -'medical', -'medieval', -'bare', -'stunning', -'bold', -'geographical', -'huge', -'plastic', -'foggy', -'stormy', -'gothic', -'biological', -'empty', -'clear', -'antique', -'pink', -'steep', -'brown', -'striped', -'aerial', -'rainy', -'cool', -'flying', -'commercial', -'purple', -'trendy', -'blank', -'haired', -'dead', -'wooden', -'flat', -'high', -'beige', -'panoramic', -'angry', -'dozen', -'rural', -'solar', -'big', -'small', -'stained', -'thick', -'many', -'fresh', -'clean', -'strong', -'abstract', -'crowded', -'retro', -'dry', -'gorgeous', -'martial', -'modern', -'blue', -'cloudy', -'low', -'four', -'outdoor', -'single', -'much', -'beautiful', -'snowy', -'pretty', -'new', -'short', -'sunny', -'closed', -'rocky', -'red', -'two', -'double', -'male', -'gray', -'five', -'colorful', -'automotive', -'various', -'one', -'old', -'rusty', -'tall', -'wild', -'narrow', -'natural', -'several', -'frozen', -'textured', -'lush', -'young', -'hot', -'mixed', -'white', -'float', -'quiet', -'round', -'bright', -'religious', -'female', -'historical', -'shiny', -'traditional', -'tourist', -'yellow', -'bald', -'coastal', -'lovely', -'little', -'broken', -'romantic', -'wide', -'royal', -'rich', -'open', -'cute', -'ancient', -'cold', -'political', -'elderly', -'gold', -'full', -'rustic', -'metallic', -'floral', -'sad', -'wet', -'fancy', -'senior', -'tiny', -'stylish', -'large', -'frosty', -'orange', -'transparent', -'electronic', -'shallow', -'scared', -'armed', -'dirty', -'historic', -'black', -'few', -'windy', -'some', -'square', -'ornamental', -'sandy', -'thin'] - -tra_array = np.array(tra_array) - - diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/training/data/masks.py b/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/training/data/masks.py deleted file mode 100644 index e91fc74913356481065c5f5906acd50fb05f521c..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/training/data/masks.py +++ /dev/null @@ -1,332 +0,0 @@ -import math -import random -import hashlib -import logging -from enum import Enum - -import cv2 -import numpy as np - -from saicinpainting.evaluation.masks.mask import SegmentationMask -from saicinpainting.utils import LinearRamp - -LOGGER = logging.getLogger(__name__) - - -class DrawMethod(Enum): - LINE = 'line' - CIRCLE = 'circle' - SQUARE = 'square' - - -def make_random_irregular_mask(shape, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10, - draw_method=DrawMethod.LINE): - draw_method = DrawMethod(draw_method) - - height, width = shape - mask = np.zeros((height, width), np.float32) - times = np.random.randint(min_times, max_times + 1) - for i in range(times): - start_x = np.random.randint(width) - start_y = np.random.randint(height) - for j in range(1 + np.random.randint(5)): - angle = 0.01 + np.random.randint(max_angle) - if i % 2 == 0: - angle = 2 * 3.1415926 - angle - length = 10 + np.random.randint(max_len) - brush_w = 5 + np.random.randint(max_width) - end_x = np.clip((start_x + length * np.sin(angle)).astype(np.int32), 0, width) - end_y = np.clip((start_y + length * np.cos(angle)).astype(np.int32), 0, height) - if draw_method == DrawMethod.LINE: - cv2.line(mask, (start_x, start_y), (end_x, end_y), 1.0, brush_w) - elif draw_method == DrawMethod.CIRCLE: - cv2.circle(mask, (start_x, start_y), radius=brush_w, color=1., thickness=-1) - elif draw_method == DrawMethod.SQUARE: - radius = brush_w // 2 - mask[start_y - radius:start_y + radius, start_x - radius:start_x + radius] = 1 - start_x, start_y = end_x, end_y - return mask[None, ...] - - -class RandomIrregularMaskGenerator: - def __init__(self, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10, ramp_kwargs=None, - draw_method=DrawMethod.LINE): - self.max_angle = max_angle - self.max_len = max_len - self.max_width = max_width - self.min_times = min_times - self.max_times = max_times - self.draw_method = draw_method - self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None - - def __call__(self, img, iter_i=None, raw_image=None): - coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1 - cur_max_len = int(max(1, self.max_len * coef)) - cur_max_width = int(max(1, self.max_width * coef)) - cur_max_times = int(self.min_times + 1 + (self.max_times - self.min_times) * coef) - return make_random_irregular_mask(img.shape[1:], max_angle=self.max_angle, max_len=cur_max_len, - max_width=cur_max_width, min_times=self.min_times, max_times=cur_max_times, - draw_method=self.draw_method) - - -def make_random_rectangle_mask(shape, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3): - height, width = shape - mask = np.zeros((height, width), np.float32) - bbox_max_size = min(bbox_max_size, height - margin * 2, width - margin * 2) - times = np.random.randint(min_times, max_times + 1) - for i in range(times): - box_width = np.random.randint(bbox_min_size, bbox_max_size) - box_height = np.random.randint(bbox_min_size, bbox_max_size) - start_x = np.random.randint(margin, width - margin - box_width + 1) - start_y = np.random.randint(margin, height - margin - box_height + 1) - mask[start_y:start_y + box_height, start_x:start_x + box_width] = 1 - return mask[None, ...] - - -class RandomRectangleMaskGenerator: - def __init__(self, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3, ramp_kwargs=None): - self.margin = margin - self.bbox_min_size = bbox_min_size - self.bbox_max_size = bbox_max_size - self.min_times = min_times - self.max_times = max_times - self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None - - def __call__(self, img, iter_i=None, raw_image=None): - coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1 - cur_bbox_max_size = int(self.bbox_min_size + 1 + (self.bbox_max_size - self.bbox_min_size) * coef) - cur_max_times = int(self.min_times + (self.max_times - self.min_times) * coef) - return make_random_rectangle_mask(img.shape[1:], margin=self.margin, bbox_min_size=self.bbox_min_size, - bbox_max_size=cur_bbox_max_size, min_times=self.min_times, - max_times=cur_max_times) - - -class RandomSegmentationMaskGenerator: - def __init__(self, **kwargs): - self.impl = None # will be instantiated in first call (effectively in subprocess) - self.kwargs = kwargs - - def __call__(self, img, iter_i=None, raw_image=None): - if self.impl is None: - self.impl = SegmentationMask(**self.kwargs) - - masks = self.impl.get_masks(np.transpose(img, (1, 2, 0))) - masks = [m for m in masks if len(np.unique(m)) > 1] - return np.random.choice(masks) - - -def make_random_superres_mask(shape, min_step=2, max_step=4, min_width=1, max_width=3): - height, width = shape - mask = np.zeros((height, width), np.float32) - step_x = np.random.randint(min_step, max_step + 1) - width_x = np.random.randint(min_width, min(step_x, max_width + 1)) - offset_x = np.random.randint(0, step_x) - - step_y = np.random.randint(min_step, max_step + 1) - width_y = np.random.randint(min_width, min(step_y, max_width + 1)) - offset_y = np.random.randint(0, step_y) - - for dy in range(width_y): - mask[offset_y + dy::step_y] = 1 - for dx in range(width_x): - mask[:, offset_x + dx::step_x] = 1 - return mask[None, ...] - - -class RandomSuperresMaskGenerator: - def __init__(self, **kwargs): - self.kwargs = kwargs - - def __call__(self, img, iter_i=None): - return make_random_superres_mask(img.shape[1:], **self.kwargs) - - -class DumbAreaMaskGenerator: - min_ratio = 0.1 - max_ratio = 0.35 - default_ratio = 0.225 - - def __init__(self, is_training): - #Parameters: - # is_training(bool): If true - random rectangular mask, if false - central square mask - self.is_training = is_training - - def _random_vector(self, dimension): - if self.is_training: - lower_limit = math.sqrt(self.min_ratio) - upper_limit = math.sqrt(self.max_ratio) - mask_side = round((random.random() * (upper_limit - lower_limit) + lower_limit) * dimension) - u = random.randint(0, dimension-mask_side-1) - v = u+mask_side - else: - margin = (math.sqrt(self.default_ratio) / 2) * dimension - u = round(dimension/2 - margin) - v = round(dimension/2 + margin) - return u, v - - def __call__(self, img, iter_i=None, raw_image=None): - c, height, width = img.shape - mask = np.zeros((height, width), np.float32) - x1, x2 = self._random_vector(width) - y1, y2 = self._random_vector(height) - mask[x1:x2, y1:y2] = 1 - return mask[None, ...] - - -class OutpaintingMaskGenerator: - def __init__(self, min_padding_percent:float=0.04, max_padding_percent:int=0.25, left_padding_prob:float=0.5, top_padding_prob:float=0.5, - right_padding_prob:float=0.5, bottom_padding_prob:float=0.5, is_fixed_randomness:bool=False): - """ - is_fixed_randomness - get identical paddings for the same image if args are the same - """ - self.min_padding_percent = min_padding_percent - self.max_padding_percent = max_padding_percent - self.probs = [left_padding_prob, top_padding_prob, right_padding_prob, bottom_padding_prob] - self.is_fixed_randomness = is_fixed_randomness - - assert self.min_padding_percent <= self.max_padding_percent - assert self.max_padding_percent > 0 - assert len([x for x in [self.min_padding_percent, self.max_padding_percent] if (x>=0 and x<=1)]) == 2, f"Padding percentage should be in [0,1]" - assert sum(self.probs) > 0, f"At least one of the padding probs should be greater than 0 - {self.probs}" - assert len([x for x in self.probs if (x >= 0) and (x <= 1)]) == 4, f"At least one of padding probs is not in [0,1] - {self.probs}" - if len([x for x in self.probs if x > 0]) == 1: - LOGGER.warning(f"Only one padding prob is greater than zero - {self.probs}. That means that the outpainting masks will be always on the same side") - - def apply_padding(self, mask, coord): - mask[int(coord[0][0]*self.img_h):int(coord[1][0]*self.img_h), - int(coord[0][1]*self.img_w):int(coord[1][1]*self.img_w)] = 1 - return mask - - def get_padding(self, size): - n1 = int(self.min_padding_percent*size) - n2 = int(self.max_padding_percent*size) - return self.rnd.randint(n1, n2) / size - - @staticmethod - def _img2rs(img): - arr = np.ascontiguousarray(img.astype(np.uint8)) - str_hash = hashlib.sha1(arr).hexdigest() - res = hash(str_hash)%(2**32) - return res - - def __call__(self, img, iter_i=None, raw_image=None): - c, self.img_h, self.img_w = img.shape - mask = np.zeros((self.img_h, self.img_w), np.float32) - at_least_one_mask_applied = False - - if self.is_fixed_randomness: - assert raw_image is not None, f"Cant calculate hash on raw_image=None" - rs = self._img2rs(raw_image) - self.rnd = np.random.RandomState(rs) - else: - self.rnd = np.random - - coords = [[ - (0,0), - (1,self.get_padding(size=self.img_h)) - ], - [ - (0,0), - (self.get_padding(size=self.img_w),1) - ], - [ - (0,1-self.get_padding(size=self.img_h)), - (1,1) - ], - [ - (1-self.get_padding(size=self.img_w),0), - (1,1) - ]] - - for pp, coord in zip(self.probs, coords): - if self.rnd.random() < pp: - at_least_one_mask_applied = True - mask = self.apply_padding(mask=mask, coord=coord) - - if not at_least_one_mask_applied: - idx = self.rnd.choice(range(len(coords)), p=np.array(self.probs)/sum(self.probs)) - mask = self.apply_padding(mask=mask, coord=coords[idx]) - return mask[None, ...] - - -class MixedMaskGenerator: - def __init__(self, irregular_proba=1/3, irregular_kwargs=None, - box_proba=1/3, box_kwargs=None, - segm_proba=1/3, segm_kwargs=None, - squares_proba=0, squares_kwargs=None, - superres_proba=0, superres_kwargs=None, - outpainting_proba=0, outpainting_kwargs=None, - invert_proba=0): - self.probas = [] - self.gens = [] - - if irregular_proba > 0: - self.probas.append(irregular_proba) - if irregular_kwargs is None: - irregular_kwargs = {} - else: - irregular_kwargs = dict(irregular_kwargs) - irregular_kwargs['draw_method'] = DrawMethod.LINE - self.gens.append(RandomIrregularMaskGenerator(**irregular_kwargs)) - - if box_proba > 0: - self.probas.append(box_proba) - if box_kwargs is None: - box_kwargs = {} - self.gens.append(RandomRectangleMaskGenerator(**box_kwargs)) - - if segm_proba > 0: - self.probas.append(segm_proba) - if segm_kwargs is None: - segm_kwargs = {} - self.gens.append(RandomSegmentationMaskGenerator(**segm_kwargs)) - - if squares_proba > 0: - self.probas.append(squares_proba) - if squares_kwargs is None: - squares_kwargs = {} - else: - squares_kwargs = dict(squares_kwargs) - squares_kwargs['draw_method'] = DrawMethod.SQUARE - self.gens.append(RandomIrregularMaskGenerator(**squares_kwargs)) - - if superres_proba > 0: - self.probas.append(superres_proba) - if superres_kwargs is None: - superres_kwargs = {} - self.gens.append(RandomSuperresMaskGenerator(**superres_kwargs)) - - if outpainting_proba > 0: - self.probas.append(outpainting_proba) - if outpainting_kwargs is None: - outpainting_kwargs = {} - self.gens.append(OutpaintingMaskGenerator(**outpainting_kwargs)) - - self.probas = np.array(self.probas, dtype='float32') - self.probas /= self.probas.sum() - self.invert_proba = invert_proba - - def __call__(self, img, iter_i=None, raw_image=None): - kind = np.random.choice(len(self.probas), p=self.probas) - gen = self.gens[kind] - result = gen(img, iter_i=iter_i, raw_image=raw_image) - if self.invert_proba > 0 and random.random() < self.invert_proba: - result = 1 - result - return result - - -def get_mask_generator(kind, kwargs): - if kind is None: - kind = "mixed" - if kwargs is None: - kwargs = {} - - if kind == "mixed": - cl = MixedMaskGenerator - elif kind == "outpainting": - cl = OutpaintingMaskGenerator - elif kind == "dumb": - cl = DumbAreaMaskGenerator - else: - raise NotImplementedError(f"No such generator kind = {kind}") - return cl(**kwargs) diff --git a/spaces/OpenMotionLab/MotionGPT/pyrender/pyrender/material.py b/spaces/OpenMotionLab/MotionGPT/pyrender/pyrender/material.py deleted file mode 100644 index 3ce9c2d184ed213c84b015e36bea558cd1efc6b7..0000000000000000000000000000000000000000 --- a/spaces/OpenMotionLab/MotionGPT/pyrender/pyrender/material.py +++ /dev/null @@ -1,707 +0,0 @@ -"""Material properties, conforming to the glTF 2.0 standards as specified in -https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-material -and -https://github.com/KhronosGroup/glTF/tree/master/extensions/2.0/Khronos/KHR_materials_pbrSpecularGlossiness - -Author: Matthew Matl -""" -import abc -import numpy as np -import six - -from .constants import TexFlags -from .utils import format_color_vector, format_texture_source -from .texture import Texture - - -@six.add_metaclass(abc.ABCMeta) -class Material(object): - """Base for standard glTF 2.0 materials. - - Parameters - ---------- - name : str, optional - The user-defined name of this object. - normalTexture : (n,n,3) float or :class:`Texture`, optional - A tangent space normal map. The texture contains RGB components in - linear space. Each texel represents the XYZ components of a normal - vector in tangent space. Red [0 to 255] maps to X [-1 to 1]. Green - [0 to 255] maps to Y [-1 to 1]. Blue [128 to 255] maps to Z - [1/255 to 1]. The normal vectors use OpenGL conventions where +X is - right and +Y is up. +Z points toward the viewer. - occlusionTexture : (n,n,1) float or :class:`Texture`, optional - The occlusion map texture. The occlusion values are sampled from the R - channel. Higher values indicate areas that should receive full indirect - lighting and lower values indicate no indirect lighting. These values - are linear. If other channels are present (GBA), they are ignored for - occlusion calculations. - emissiveTexture : (n,n,3) float or :class:`Texture`, optional - The emissive map controls the color and intensity of the light being - emitted by the material. This texture contains RGB components in sRGB - color space. If a fourth component (A) is present, it is ignored. - emissiveFactor : (3,) float, optional - The RGB components of the emissive color of the material. These values - are linear. If an emissiveTexture is specified, this value is - multiplied with the texel values. - alphaMode : str, optional - The material's alpha rendering mode enumeration specifying the - interpretation of the alpha value of the main factor and texture. - Allowed Values: - - - `"OPAQUE"` The alpha value is ignored and the rendered output is - fully opaque. - - `"MASK"` The rendered output is either fully opaque or fully - transparent depending on the alpha value and the specified alpha - cutoff value. - - `"BLEND"` The alpha value is used to composite the source and - destination areas. The rendered output is combined with the - background using the normal painting operation (i.e. the Porter - and Duff over operator). - - alphaCutoff : float, optional - Specifies the cutoff threshold when in MASK mode. If the alpha value is - greater than or equal to this value then it is rendered as fully - opaque, otherwise, it is rendered as fully transparent. - A value greater than 1.0 will render the entire material as fully - transparent. This value is ignored for other modes. - doubleSided : bool, optional - Specifies whether the material is double sided. When this value is - false, back-face culling is enabled. When this value is true, - back-face culling is disabled and double sided lighting is enabled. - smooth : bool, optional - If True, the material is rendered smoothly by using only one normal - per vertex and face indexing. - wireframe : bool, optional - If True, the material is rendered in wireframe mode. - """ - - def __init__(self, - name=None, - normalTexture=None, - occlusionTexture=None, - emissiveTexture=None, - emissiveFactor=None, - alphaMode=None, - alphaCutoff=None, - doubleSided=False, - smooth=True, - wireframe=False): - - # Set defaults - if alphaMode is None: - alphaMode = 'OPAQUE' - - if alphaCutoff is None: - alphaCutoff = 0.5 - - if emissiveFactor is None: - emissiveFactor = np.zeros(3).astype(np.float32) - - self.name = name - self.normalTexture = normalTexture - self.occlusionTexture = occlusionTexture - self.emissiveTexture = emissiveTexture - self.emissiveFactor = emissiveFactor - self.alphaMode = alphaMode - self.alphaCutoff = alphaCutoff - self.doubleSided = doubleSided - self.smooth = smooth - self.wireframe = wireframe - - self._tex_flags = None - - @property - def name(self): - """str : The user-defined name of this object. - """ - return self._name - - @name.setter - def name(self, value): - if value is not None: - value = str(value) - self._name = value - - @property - def normalTexture(self): - """(n,n,3) float or :class:`Texture` : The tangent-space normal map. - """ - return self._normalTexture - - @normalTexture.setter - def normalTexture(self, value): - # TODO TMP - self._normalTexture = self._format_texture(value, 'RGB') - self._tex_flags = None - - @property - def occlusionTexture(self): - """(n,n,1) float or :class:`Texture` : The ambient occlusion map. - """ - return self._occlusionTexture - - @occlusionTexture.setter - def occlusionTexture(self, value): - self._occlusionTexture = self._format_texture(value, 'R') - self._tex_flags = None - - @property - def emissiveTexture(self): - """(n,n,3) float or :class:`Texture` : The emission map. - """ - return self._emissiveTexture - - @emissiveTexture.setter - def emissiveTexture(self, value): - self._emissiveTexture = self._format_texture(value, 'RGB') - self._tex_flags = None - - @property - def emissiveFactor(self): - """(3,) float : Base multiplier for emission colors. - """ - return self._emissiveFactor - - @emissiveFactor.setter - def emissiveFactor(self, value): - if value is None: - value = np.zeros(3) - self._emissiveFactor = format_color_vector(value, 3) - - @property - def alphaMode(self): - """str : The mode for blending. - """ - return self._alphaMode - - @alphaMode.setter - def alphaMode(self, value): - if value not in set(['OPAQUE', 'MASK', 'BLEND']): - raise ValueError('Invalid alpha mode {}'.format(value)) - self._alphaMode = value - - @property - def alphaCutoff(self): - """float : The cutoff threshold in MASK mode. - """ - return self._alphaCutoff - - @alphaCutoff.setter - def alphaCutoff(self, value): - if value < 0 or value > 1: - raise ValueError('Alpha cutoff must be in range [0,1]') - self._alphaCutoff = float(value) - - @property - def doubleSided(self): - """bool : Whether the material is double-sided. - """ - return self._doubleSided - - @doubleSided.setter - def doubleSided(self, value): - if not isinstance(value, bool): - raise TypeError('Double sided must be a boolean value') - self._doubleSided = value - - @property - def smooth(self): - """bool : Whether to render the mesh smoothly by - interpolating vertex normals. - """ - return self._smooth - - @smooth.setter - def smooth(self, value): - if not isinstance(value, bool): - raise TypeError('Double sided must be a boolean value') - self._smooth = value - - @property - def wireframe(self): - """bool : Whether to render the mesh in wireframe mode. - """ - return self._wireframe - - @wireframe.setter - def wireframe(self, value): - if not isinstance(value, bool): - raise TypeError('Wireframe must be a boolean value') - self._wireframe = value - - @property - def is_transparent(self): - """bool : If True, the object is partially transparent. - """ - return self._compute_transparency() - - @property - def tex_flags(self): - """int : Texture availability flags. - """ - if self._tex_flags is None: - self._tex_flags = self._compute_tex_flags() - return self._tex_flags - - @property - def textures(self): - """list of :class:`Texture` : The textures associated with this - material. - """ - return self._compute_textures() - - def _compute_transparency(self): - return False - - def _compute_tex_flags(self): - tex_flags = TexFlags.NONE - if self.normalTexture is not None: - tex_flags |= TexFlags.NORMAL - if self.occlusionTexture is not None: - tex_flags |= TexFlags.OCCLUSION - if self.emissiveTexture is not None: - tex_flags |= TexFlags.EMISSIVE - return tex_flags - - def _compute_textures(self): - all_textures = [ - self.normalTexture, self.occlusionTexture, self.emissiveTexture - ] - textures = set([t for t in all_textures if t is not None]) - return textures - - def _format_texture(self, texture, target_channels='RGB'): - """Format a texture as a float32 np array. - """ - if isinstance(texture, Texture) or texture is None: - return texture - else: - source = format_texture_source(texture, target_channels) - return Texture(source=source, source_channels=target_channels) - - -class MetallicRoughnessMaterial(Material): - """A material based on the metallic-roughness material model from - Physically-Based Rendering (PBR) methodology. - - Parameters - ---------- - name : str, optional - The user-defined name of this object. - normalTexture : (n,n,3) float or :class:`Texture`, optional - A tangent space normal map. The texture contains RGB components in - linear space. Each texel represents the XYZ components of a normal - vector in tangent space. Red [0 to 255] maps to X [-1 to 1]. Green - [0 to 255] maps to Y [-1 to 1]. Blue [128 to 255] maps to Z - [1/255 to 1]. The normal vectors use OpenGL conventions where +X is - right and +Y is up. +Z points toward the viewer. - occlusionTexture : (n,n,1) float or :class:`Texture`, optional - The occlusion map texture. The occlusion values are sampled from the R - channel. Higher values indicate areas that should receive full indirect - lighting and lower values indicate no indirect lighting. These values - are linear. If other channels are present (GBA), they are ignored for - occlusion calculations. - emissiveTexture : (n,n,3) float or :class:`Texture`, optional - The emissive map controls the color and intensity of the light being - emitted by the material. This texture contains RGB components in sRGB - color space. If a fourth component (A) is present, it is ignored. - emissiveFactor : (3,) float, optional - The RGB components of the emissive color of the material. These values - are linear. If an emissiveTexture is specified, this value is - multiplied with the texel values. - alphaMode : str, optional - The material's alpha rendering mode enumeration specifying the - interpretation of the alpha value of the main factor and texture. - Allowed Values: - - - `"OPAQUE"` The alpha value is ignored and the rendered output is - fully opaque. - - `"MASK"` The rendered output is either fully opaque or fully - transparent depending on the alpha value and the specified alpha - cutoff value. - - `"BLEND"` The alpha value is used to composite the source and - destination areas. The rendered output is combined with the - background using the normal painting operation (i.e. the Porter - and Duff over operator). - - alphaCutoff : float, optional - Specifies the cutoff threshold when in MASK mode. If the alpha value is - greater than or equal to this value then it is rendered as fully - opaque, otherwise, it is rendered as fully transparent. - A value greater than 1.0 will render the entire material as fully - transparent. This value is ignored for other modes. - doubleSided : bool, optional - Specifies whether the material is double sided. When this value is - false, back-face culling is enabled. When this value is true, - back-face culling is disabled and double sided lighting is enabled. - smooth : bool, optional - If True, the material is rendered smoothly by using only one normal - per vertex and face indexing. - wireframe : bool, optional - If True, the material is rendered in wireframe mode. - baseColorFactor : (4,) float, optional - The RGBA components of the base color of the material. The fourth - component (A) is the alpha coverage of the material. The alphaMode - property specifies how alpha is interpreted. These values are linear. - If a baseColorTexture is specified, this value is multiplied with the - texel values. - baseColorTexture : (n,n,4) float or :class:`Texture`, optional - The base color texture. This texture contains RGB(A) components in sRGB - color space. The first three components (RGB) specify the base color of - the material. If the fourth component (A) is present, it represents the - alpha coverage of the material. Otherwise, an alpha of 1.0 is assumed. - The alphaMode property specifies how alpha is interpreted. - The stored texels must not be premultiplied. - metallicFactor : float - The metalness of the material. A value of 1.0 means the material is a - metal. A value of 0.0 means the material is a dielectric. Values in - between are for blending between metals and dielectrics such as dirty - metallic surfaces. This value is linear. If a metallicRoughnessTexture - is specified, this value is multiplied with the metallic texel values. - roughnessFactor : float - The roughness of the material. A value of 1.0 means the material is - completely rough. A value of 0.0 means the material is completely - smooth. This value is linear. If a metallicRoughnessTexture is - specified, this value is multiplied with the roughness texel values. - metallicRoughnessTexture : (n,n,2) float or :class:`Texture`, optional - The metallic-roughness texture. The metalness values are sampled from - the B channel. The roughness values are sampled from the G channel. - These values are linear. If other channels are present (R or A), they - are ignored for metallic-roughness calculations. - """ - - def __init__(self, - name=None, - normalTexture=None, - occlusionTexture=None, - emissiveTexture=None, - emissiveFactor=None, - alphaMode=None, - alphaCutoff=None, - doubleSided=False, - smooth=True, - wireframe=False, - baseColorFactor=None, - baseColorTexture=None, - metallicFactor=1.0, - roughnessFactor=1.0, - metallicRoughnessTexture=None): - super(MetallicRoughnessMaterial, self).__init__( - name=name, - normalTexture=normalTexture, - occlusionTexture=occlusionTexture, - emissiveTexture=emissiveTexture, - emissiveFactor=emissiveFactor, - alphaMode=alphaMode, - alphaCutoff=alphaCutoff, - doubleSided=doubleSided, - smooth=smooth, - wireframe=wireframe - ) - - # Set defaults - if baseColorFactor is None: - baseColorFactor = np.ones(4).astype(np.float32) - - self.baseColorFactor = baseColorFactor - self.baseColorTexture = baseColorTexture - self.metallicFactor = metallicFactor - self.roughnessFactor = roughnessFactor - self.metallicRoughnessTexture = metallicRoughnessTexture - - @property - def baseColorFactor(self): - """(4,) float or :class:`Texture` : The RGBA base color multiplier. - """ - return self._baseColorFactor - - @baseColorFactor.setter - def baseColorFactor(self, value): - if value is None: - value = np.ones(4) - self._baseColorFactor = format_color_vector(value, 4) - - @property - def baseColorTexture(self): - """(n,n,4) float or :class:`Texture` : The diffuse texture. - """ - return self._baseColorTexture - - @baseColorTexture.setter - def baseColorTexture(self, value): - self._baseColorTexture = self._format_texture(value, 'RGBA') - self._tex_flags = None - - @property - def metallicFactor(self): - """float : The metalness of the material. - """ - return self._metallicFactor - - @metallicFactor.setter - def metallicFactor(self, value): - if value is None: - value = 1.0 - if value < 0 or value > 1: - raise ValueError('Metallic factor must be in range [0,1]') - self._metallicFactor = float(value) - - @property - def roughnessFactor(self): - """float : The roughness of the material. - """ - return self.RoughnessFactor - - @roughnessFactor.setter - def roughnessFactor(self, value): - if value is None: - value = 1.0 - if value < 0 or value > 1: - raise ValueError('Roughness factor must be in range [0,1]') - self.RoughnessFactor = float(value) - - @property - def metallicRoughnessTexture(self): - """(n,n,2) float or :class:`Texture` : The metallic-roughness texture. - """ - return self._metallicRoughnessTexture - - @metallicRoughnessTexture.setter - def metallicRoughnessTexture(self, value): - self._metallicRoughnessTexture = self._format_texture(value, 'GB') - self._tex_flags = None - - def _compute_tex_flags(self): - tex_flags = super(MetallicRoughnessMaterial, self)._compute_tex_flags() - if self.baseColorTexture is not None: - tex_flags |= TexFlags.BASE_COLOR - if self.metallicRoughnessTexture is not None: - tex_flags |= TexFlags.METALLIC_ROUGHNESS - return tex_flags - - def _compute_transparency(self): - if self.alphaMode == 'OPAQUE': - return False - cutoff = self.alphaCutoff - if self.alphaMode == 'BLEND': - cutoff = 1.0 - if self.baseColorFactor[3] < cutoff: - return True - if (self.baseColorTexture is not None and - self.baseColorTexture.is_transparent(cutoff)): - return True - return False - - def _compute_textures(self): - textures = super(MetallicRoughnessMaterial, self)._compute_textures() - all_textures = [self.baseColorTexture, self.metallicRoughnessTexture] - all_textures = {t for t in all_textures if t is not None} - textures |= all_textures - return textures - - -class SpecularGlossinessMaterial(Material): - """A material based on the specular-glossiness material model from - Physically-Based Rendering (PBR) methodology. - - Parameters - ---------- - name : str, optional - The user-defined name of this object. - normalTexture : (n,n,3) float or :class:`Texture`, optional - A tangent space normal map. The texture contains RGB components in - linear space. Each texel represents the XYZ components of a normal - vector in tangent space. Red [0 to 255] maps to X [-1 to 1]. Green - [0 to 255] maps to Y [-1 to 1]. Blue [128 to 255] maps to Z - [1/255 to 1]. The normal vectors use OpenGL conventions where +X is - right and +Y is up. +Z points toward the viewer. - occlusionTexture : (n,n,1) float or :class:`Texture`, optional - The occlusion map texture. The occlusion values are sampled from the R - channel. Higher values indicate areas that should receive full indirect - lighting and lower values indicate no indirect lighting. These values - are linear. If other channels are present (GBA), they are ignored for - occlusion calculations. - emissiveTexture : (n,n,3) float or :class:`Texture`, optional - The emissive map controls the color and intensity of the light being - emitted by the material. This texture contains RGB components in sRGB - color space. If a fourth component (A) is present, it is ignored. - emissiveFactor : (3,) float, optional - The RGB components of the emissive color of the material. These values - are linear. If an emissiveTexture is specified, this value is - multiplied with the texel values. - alphaMode : str, optional - The material's alpha rendering mode enumeration specifying the - interpretation of the alpha value of the main factor and texture. - Allowed Values: - - - `"OPAQUE"` The alpha value is ignored and the rendered output is - fully opaque. - - `"MASK"` The rendered output is either fully opaque or fully - transparent depending on the alpha value and the specified alpha - cutoff value. - - `"BLEND"` The alpha value is used to composite the source and - destination areas. The rendered output is combined with the - background using the normal painting operation (i.e. the Porter - and Duff over operator). - - alphaCutoff : float, optional - Specifies the cutoff threshold when in MASK mode. If the alpha value is - greater than or equal to this value then it is rendered as fully - opaque, otherwise, it is rendered as fully transparent. - A value greater than 1.0 will render the entire material as fully - transparent. This value is ignored for other modes. - doubleSided : bool, optional - Specifies whether the material is double sided. When this value is - false, back-face culling is enabled. When this value is true, - back-face culling is disabled and double sided lighting is enabled. - smooth : bool, optional - If True, the material is rendered smoothly by using only one normal - per vertex and face indexing. - wireframe : bool, optional - If True, the material is rendered in wireframe mode. - diffuseFactor : (4,) float - The RGBA components of the reflected diffuse color of the material. - Metals have a diffuse value of [0.0, 0.0, 0.0]. The fourth component - (A) is the opacity of the material. The values are linear. - diffuseTexture : (n,n,4) float or :class:`Texture`, optional - The diffuse texture. This texture contains RGB(A) components of the - reflected diffuse color of the material in sRGB color space. If the - fourth component (A) is present, it represents the alpha coverage of - the material. Otherwise, an alpha of 1.0 is assumed. - The alphaMode property specifies how alpha is interpreted. - The stored texels must not be premultiplied. - specularFactor : (3,) float - The specular RGB color of the material. This value is linear. - glossinessFactor : float - The glossiness or smoothness of the material. A value of 1.0 means the - material has full glossiness or is perfectly smooth. A value of 0.0 - means the material has no glossiness or is perfectly rough. This value - is linear. - specularGlossinessTexture : (n,n,4) or :class:`Texture`, optional - The specular-glossiness texture is a RGBA texture, containing the - specular color (RGB) in sRGB space and the glossiness value (A) in - linear space. - """ - - def __init__(self, - name=None, - normalTexture=None, - occlusionTexture=None, - emissiveTexture=None, - emissiveFactor=None, - alphaMode=None, - alphaCutoff=None, - doubleSided=False, - smooth=True, - wireframe=False, - diffuseFactor=None, - diffuseTexture=None, - specularFactor=None, - glossinessFactor=1.0, - specularGlossinessTexture=None): - super(SpecularGlossinessMaterial, self).__init__( - name=name, - normalTexture=normalTexture, - occlusionTexture=occlusionTexture, - emissiveTexture=emissiveTexture, - emissiveFactor=emissiveFactor, - alphaMode=alphaMode, - alphaCutoff=alphaCutoff, - doubleSided=doubleSided, - smooth=smooth, - wireframe=wireframe - ) - - # Set defaults - if diffuseFactor is None: - diffuseFactor = np.ones(4).astype(np.float32) - if specularFactor is None: - specularFactor = np.ones(3).astype(np.float32) - - self.diffuseFactor = diffuseFactor - self.diffuseTexture = diffuseTexture - self.specularFactor = specularFactor - self.glossinessFactor = glossinessFactor - self.specularGlossinessTexture = specularGlossinessTexture - - @property - def diffuseFactor(self): - """(4,) float : The diffuse base color. - """ - return self._diffuseFactor - - @diffuseFactor.setter - def diffuseFactor(self, value): - self._diffuseFactor = format_color_vector(value, 4) - - @property - def diffuseTexture(self): - """(n,n,4) float or :class:`Texture` : The diffuse map. - """ - return self._diffuseTexture - - @diffuseTexture.setter - def diffuseTexture(self, value): - self._diffuseTexture = self._format_texture(value, 'RGBA') - self._tex_flags = None - - @property - def specularFactor(self): - """(3,) float : The specular color of the material. - """ - return self._specularFactor - - @specularFactor.setter - def specularFactor(self, value): - self._specularFactor = format_color_vector(value, 3) - - @property - def glossinessFactor(self): - """float : The glossiness of the material. - """ - return self.glossinessFactor - - @glossinessFactor.setter - def glossinessFactor(self, value): - if value < 0 or value > 1: - raise ValueError('glossiness factor must be in range [0,1]') - self._glossinessFactor = float(value) - - @property - def specularGlossinessTexture(self): - """(n,n,4) or :class:`Texture` : The specular-glossiness texture. - """ - return self._specularGlossinessTexture - - @specularGlossinessTexture.setter - def specularGlossinessTexture(self, value): - self._specularGlossinessTexture = self._format_texture(value, 'GB') - self._tex_flags = None - - def _compute_tex_flags(self): - flags = super(SpecularGlossinessMaterial, self)._compute_tex_flags() - if self.diffuseTexture is not None: - flags |= TexFlags.DIFFUSE - if self.specularGlossinessTexture is not None: - flags |= TexFlags.SPECULAR_GLOSSINESS - return flags - - def _compute_transparency(self): - if self.alphaMode == 'OPAQUE': - return False - cutoff = self.alphaCutoff - if self.alphaMode == 'BLEND': - cutoff = 1.0 - if self.diffuseFactor[3] < cutoff: - return True - if (self.diffuseTexture is not None and - self.diffuseTexture.is_transparent(cutoff)): - return True - return False - - def _compute_textures(self): - textures = super(SpecularGlossinessMaterial, self)._compute_textures() - all_textures = [self.diffuseTexture, self.specularGlossinessTexture] - all_textures = {t for t in all_textures if t is not None} - textures |= all_textures - return textures diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/core/seg/builder.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/core/seg/builder.py deleted file mode 100644 index db61f03d4abb2072f2532ce4429c0842495e015b..0000000000000000000000000000000000000000 --- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/core/seg/builder.py +++ /dev/null @@ -1,8 +0,0 @@ -from annotator.uniformer.mmcv.utils import Registry, build_from_cfg - -PIXEL_SAMPLERS = Registry('pixel sampler') - - -def build_pixel_sampler(cfg, **default_args): - """Build pixel sampler for segmentation map.""" - return build_from_cfg(cfg, PIXEL_SAMPLERS, default_args) diff --git a/spaces/PKUWilliamYang/VToonify/vtoonify/model/raft/README.md b/spaces/PKUWilliamYang/VToonify/vtoonify/model/raft/README.md deleted file mode 100644 index 650275ed7c4cda12822587c6a4358f057fffe494..0000000000000000000000000000000000000000 --- a/spaces/PKUWilliamYang/VToonify/vtoonify/model/raft/README.md +++ /dev/null @@ -1,80 +0,0 @@ -# RAFT -This repository contains the source code for our paper: - -[RAFT: Recurrent All Pairs Field Transforms for Optical Flow](https://arxiv.org/pdf/2003.12039.pdf)
-ECCV 2020
-Zachary Teed and Jia Deng
- - - -## Requirements -The code has been tested with PyTorch 1.6 and Cuda 10.1. -```Shell -conda create --name raft -conda activate raft -conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 matplotlib tensorboard scipy opencv -c pytorch -``` - -## Demos -Pretrained models can be downloaded by running -```Shell -./download_models.sh -``` -or downloaded from [google drive](https://drive.google.com/drive/folders/1sWDsfuZ3Up38EUQt7-JDTT1HcGHuJgvT?usp=sharing) - -You can demo a trained model on a sequence of frames -```Shell -python demo.py --model=models/raft-things.pth --path=demo-frames -``` - -## Required Data -To evaluate/train RAFT, you will need to download the required datasets. -* [FlyingChairs](https://lmb.informatik.uni-freiburg.de/resources/datasets/FlyingChairs.en.html#flyingchairs) -* [FlyingThings3D](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html) -* [Sintel](http://sintel.is.tue.mpg.de/) -* [KITTI](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=flow) -* [HD1K](http://hci-benchmark.iwr.uni-heidelberg.de/) (optional) - - -By default `datasets.py` will search for the datasets in these locations. You can create symbolic links to wherever the datasets were downloaded in the `datasets` folder - -```Shell -├── datasets - ├── Sintel - ├── test - ├── training - ├── KITTI - ├── testing - ├── training - ├── devkit - ├── FlyingChairs_release - ├── data - ├── FlyingThings3D - ├── frames_cleanpass - ├── frames_finalpass - ├── optical_flow -``` - -## Evaluation -You can evaluate a trained model using `evaluate.py` -```Shell -python evaluate.py --model=models/raft-things.pth --dataset=sintel --mixed_precision -``` - -## Training -We used the following training schedule in our paper (2 GPUs). Training logs will be written to the `runs` which can be visualized using tensorboard -```Shell -./train_standard.sh -``` - -If you have a RTX GPU, training can be accelerated using mixed precision. You can expect similiar results in this setting (1 GPU) -```Shell -./train_mixed.sh -``` - -## (Optional) Efficent Implementation -You can optionally use our alternate (efficent) implementation by compiling the provided cuda extension -```Shell -cd alt_cuda_corr && python setup.py install && cd .. -``` -and running `demo.py` and `evaluate.py` with the `--alternate_corr` flag Note, this implementation is somewhat slower than all-pairs, but uses significantly less GPU memory during the forward pass. diff --git a/spaces/PKaushik/humandetect/yolov6/core/evaler.py b/spaces/PKaushik/humandetect/yolov6/core/evaler.py deleted file mode 100644 index bd4453da89ee8e186ff19709743b36ac1337d572..0000000000000000000000000000000000000000 --- a/spaces/PKaushik/humandetect/yolov6/core/evaler.py +++ /dev/null @@ -1,256 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -import os -from tqdm import tqdm -import numpy as np -import json -import torch -import yaml -from pathlib import Path - -from pycocotools.coco import COCO -from pycocotools.cocoeval import COCOeval - -from yolov6.data.data_load import create_dataloader -from yolov6.utils.events import LOGGER, NCOLS -from yolov6.utils.nms import non_max_suppression -from yolov6.utils.checkpoint import load_checkpoint -from yolov6.utils.torch_utils import time_sync, get_model_info - -''' -python tools/eval.py --task 'train'/'val'/'speed' -''' - - -class Evaler: - def __init__(self, - data, - batch_size=32, - img_size=640, - conf_thres=0.001, - iou_thres=0.65, - device='', - half=True, - save_dir=''): - self.data = data - self.batch_size = batch_size - self.img_size = img_size - self.conf_thres = conf_thres - self.iou_thres = iou_thres - self.device = device - self.half = half - self.save_dir = save_dir - - def init_model(self, model, weights, task): - if task != 'train': - model = load_checkpoint(weights, map_location=self.device) - self.stride = int(model.stride.max()) - if self.device.type != 'cpu': - model(torch.zeros(1, 3, self.img_size, self.img_size).to(self.device).type_as(next(model.parameters()))) - # switch to deploy - from yolov6.layers.common import RepVGGBlock - for layer in model.modules(): - if isinstance(layer, RepVGGBlock): - layer.switch_to_deploy() - LOGGER.info("Switch model to deploy modality.") - LOGGER.info("Model Summary: {}".format(get_model_info(model, self.img_size))) - model.half() if self.half else model.float() - return model - - def init_data(self, dataloader, task): - '''Initialize dataloader. - Returns a dataloader for task val or speed. - ''' - self.is_coco = self.data.get("is_coco", False) - self.ids = self.coco80_to_coco91_class() if self.is_coco else list(range(1000)) - if task != 'train': - pad = 0.0 if task == 'speed' else 0.5 - dataloader = create_dataloader(self.data[task if task in ('train', 'val', 'test') else 'val'], - self.img_size, self.batch_size, self.stride, check_labels=True, pad=pad, rect=True, - data_dict=self.data, task=task)[0] - return dataloader - - def predict_model(self, model, dataloader, task): - '''Model prediction - Predicts the whole dataset and gets the prediced results and inference time. - ''' - self.speed_result = torch.zeros(4, device=self.device) - pred_results = [] - pbar = tqdm(dataloader, desc="Inferencing model in val datasets.", ncols=NCOLS) - for imgs, targets, paths, shapes in pbar: - # pre-process - t1 = time_sync() - imgs = imgs.to(self.device, non_blocking=True) - imgs = imgs.half() if self.half else imgs.float() - imgs /= 255 - self.speed_result[1] += time_sync() - t1 # pre-process time - - # Inference - t2 = time_sync() - outputs = model(imgs) - self.speed_result[2] += time_sync() - t2 # inference time - - # post-process - t3 = time_sync() - outputs = non_max_suppression(outputs, self.conf_thres, self.iou_thres, multi_label=True) - self.speed_result[3] += time_sync() - t3 # post-process time - self.speed_result[0] += len(outputs) - - # save result - pred_results.extend(self.convert_to_coco_format(outputs, imgs, paths, shapes, self.ids)) - return pred_results - - def eval_model(self, pred_results, model, dataloader, task): - '''Evaluate models - For task speed, this function only evaluates the speed of model and outputs inference time. - For task val, this function evaluates the speed and mAP by pycocotools, and returns - inference time and mAP value. - ''' - LOGGER.info(f'\nEvaluating speed.') - self.eval_speed(task) - - LOGGER.info(f'\nEvaluating mAP by pycocotools.') - if task != 'speed' and len(pred_results): - if 'anno_path' in self.data: - anno_json = self.data['anno_path'] - else: - # generated coco format labels in dataset initialization - dataset_root = os.path.dirname(os.path.dirname(self.data['val'])) - base_name = os.path.basename(self.data['val']) - anno_json = os.path.join(dataset_root, 'annotations', f'instances_{base_name}.json') - pred_json = os.path.join(self.save_dir, "predictions.json") - LOGGER.info(f'Saving {pred_json}...') - with open(pred_json, 'w') as f: - json.dump(pred_results, f) - - anno = COCO(anno_json) - pred = anno.loadRes(pred_json) - cocoEval = COCOeval(anno, pred, 'bbox') - if self.is_coco: - imgIds = [int(os.path.basename(x).split(".")[0]) - for x in dataloader.dataset.img_paths] - cocoEval.params.imgIds = imgIds - cocoEval.evaluate() - cocoEval.accumulate() - cocoEval.summarize() - map, map50 = cocoEval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) - # Return results - model.float() # for training - if task != 'train': - LOGGER.info(f"Results saved to {self.save_dir}") - return (map50, map) - return (0.0, 0.0) - - def eval_speed(self, task): - '''Evaluate model inference speed.''' - if task != 'train': - n_samples = self.speed_result[0].item() - pre_time, inf_time, nms_time = 1000 * self.speed_result[1:].cpu().numpy() / n_samples - for n, v in zip(["pre-process", "inference", "NMS"],[pre_time, inf_time, nms_time]): - LOGGER.info("Average {} time: {:.2f} ms".format(n, v)) - - def box_convert(self, x): - # Convert boxes with shape [n, 4] from [x1, y1, x2, y2] to [x, y, w, h] where x1y1=top-left, x2y2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center - y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center - y[:, 2] = x[:, 2] - x[:, 0] # width - y[:, 3] = x[:, 3] - x[:, 1] # height - return y - - def scale_coords(self, img1_shape, coords, img0_shape, ratio_pad=None): - # Rescale coords (xyxy) from img1_shape to img0_shape - if ratio_pad is None: # calculate from img0_shape - gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new - pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding - else: - gain = ratio_pad[0][0] - pad = ratio_pad[1] - - coords[:, [0, 2]] -= pad[0] # x padding - coords[:, [1, 3]] -= pad[1] # y padding - coords[:, :4] /= gain - if isinstance(coords, torch.Tensor): # faster individually - coords[:, 0].clamp_(0, img0_shape[1]) # x1 - coords[:, 1].clamp_(0, img0_shape[0]) # y1 - coords[:, 2].clamp_(0, img0_shape[1]) # x2 - coords[:, 3].clamp_(0, img0_shape[0]) # y2 - else: # np.array (faster grouped) - coords[:, [0, 2]] = coords[:, [0, 2]].clip(0, img0_shape[1]) # x1, x2 - coords[:, [1, 3]] = coords[:, [1, 3]].clip(0, img0_shape[0]) # y1, y2 - return coords - - def convert_to_coco_format(self, outputs, imgs, paths, shapes, ids): - pred_results = [] - for i, pred in enumerate(outputs): - if len(pred) == 0: - continue - path, shape = Path(paths[i]), shapes[i][0] - self.scale_coords(imgs[i].shape[1:], pred[:, :4], shape, shapes[i][1]) - image_id = int(path.stem) if path.stem.isnumeric() else path.stem - bboxes = self.box_convert(pred[:, 0:4]) - bboxes[:, :2] -= bboxes[:, 2:] / 2 - cls = pred[:, 5] - scores = pred[:, 4] - for ind in range(pred.shape[0]): - category_id = ids[int(cls[ind])] - bbox = [round(x, 3) for x in bboxes[ind].tolist()] - score = round(scores[ind].item(), 5) - pred_data = { - "image_id": image_id, - "category_id": category_id, - "bbox": bbox, - "score": score - } - pred_results.append(pred_data) - return pred_results - - @staticmethod - def check_task(task): - if task not in ['train', 'val', 'speed']: - raise Exception("task argument error: only support 'train' / 'val' / 'speed' task.") - - @staticmethod - def reload_thres(conf_thres, iou_thres, task): - '''Sets conf and iou threshold for task val/speed''' - if task != 'train': - if task == 'val': - conf_thres = 0.001 - if task == 'speed': - conf_thres = 0.25 - iou_thres = 0.45 - return conf_thres, iou_thres - - @staticmethod - def reload_device(device, model, task): - # device = 'cpu' or '0' or '0,1,2,3' - if task == 'train': - device = next(model.parameters()).device - else: - if device == 'cpu': - os.environ['CUDA_VISIBLE_DEVICES'] = '-1' - elif device: - os.environ['CUDA_VISIBLE_DEVICES'] = device - assert torch.cuda.is_available() - cuda = device != 'cpu' and torch.cuda.is_available() - device = torch.device('cuda:0' if cuda else 'cpu') - return device - - @staticmethod - def reload_dataset(data): - with open(data, errors='ignore') as yaml_file: - data = yaml.safe_load(yaml_file) - val = data.get('val') - if not os.path.exists(val): - raise Exception('Dataset not found.') - return data - - @staticmethod - def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) - # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ - x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, - 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, - 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, - 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, - 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] - return x diff --git a/spaces/PSLD/PSLD/stable-diffusion/Stable_Diffusion_v1_Model_Card.md b/spaces/PSLD/PSLD/stable-diffusion/Stable_Diffusion_v1_Model_Card.md deleted file mode 100644 index ad76ad2ee6da62ad21c8a92e9082a31b272740f3..0000000000000000000000000000000000000000 --- a/spaces/PSLD/PSLD/stable-diffusion/Stable_Diffusion_v1_Model_Card.md +++ /dev/null @@ -1,144 +0,0 @@ -# Stable Diffusion v1 Model Card -This model card focuses on the model associated with the Stable Diffusion model, available [here](https://github.com/CompVis/stable-diffusion). - -## Model Details -- **Developed by:** Robin Rombach, Patrick Esser -- **Model type:** Diffusion-based text-to-image generation model -- **Language(s):** English -- **License:** [Proprietary](LICENSE) -- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). -- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). -- **Cite as:** - - @InProceedings{Rombach_2022_CVPR, - author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, - title = {High-Resolution Image Synthesis With Latent Diffusion Models}, - booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, - month = {June}, - year = {2022}, - pages = {10684-10695} - } - -# Uses - -## Direct Use -The model is intended for research purposes only. Possible research areas and -tasks include - -- Safe deployment of models which have the potential to generate harmful content. -- Probing and understanding the limitations and biases of generative models. -- Generation of artworks and use in design and other artistic processes. -- Applications in educational or creative tools. -- Research on generative models. - -Excluded uses are described below. - - ### Misuse, Malicious Use, and Out-of-Scope Use -_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. - -The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. - -#### Out-of-Scope Use -The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. - -#### Misuse and Malicious Use -Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - -- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. -- Intentionally promoting or propagating discriminatory content or harmful stereotypes. -- Impersonating individuals without their consent. -- Sexual content without consent of the people who might see it. -- Mis- and disinformation -- Representations of egregious violence and gore -- Sharing of copyrighted or licensed material in violation of its terms of use. -- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. - -## Limitations and Bias - -### Limitations - -- The model does not achieve perfect photorealism -- The model cannot render legible text -- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” -- Faces and people in general may not be generated properly. -- The model was trained mainly with English captions and will not work as well in other languages. -- The autoencoding part of the model is lossy -- The model was trained on a large-scale dataset - [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material - and is not fit for product use without additional safety mechanisms and - considerations. -- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. - The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. - -### Bias -While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. -Stable Diffusion v1 was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), -which consists of images that are limited to English descriptions. -Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. -This affects the overall output of the model, as white and western cultures are often set as the default. Further, the -ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. -Stable Diffusion v1 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. - - -## Training - -**Training Data** -The model developers used the following dataset for training the model: - -- LAION-5B and subsets thereof (see next section) - -**Training Procedure** -Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - -- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 -- Text prompts are encoded through a ViT-L/14 text-encoder. -- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. -- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. - -We currently provide the following checkpoints: - -- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). - 194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). -- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`. - 515k steps at resolution `512x512` on [laion-aesthetics v2 5+](https://laion.ai/blog/laion-aesthetics/) (a subset of laion2B-en with estimated aesthetics score `> 5.0`, and additionally -filtered to images with an original size `>= 512x512`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the [LAION-5B](https://laion.ai/blog/laion-5b/) metadata, the aesthetics score is estimated using the [LAION-Aesthetics Predictor V2](https://github.com/christophschuhmann/improved-aesthetic-predictor)). -- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). -- `sd-v1-4.ckpt`: Resumed from `sd-v1-2.ckpt`. 225k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - -- **Hardware:** 32 x 8 x A100 GPUs -- **Optimizer:** AdamW -- **Gradient Accumulations**: 2 -- **Batch:** 32 x 8 x 2 x 4 = 2048 -- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant - -## Evaluation Results -Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, -5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling -steps show the relative improvements of the checkpoints: - -![pareto](assets/v1-variants-scores.jpg) - -Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. - -## Environmental Impact - -**Stable Diffusion v1** **Estimated Emissions** -Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - -- **Hardware Type:** A100 PCIe 40GB -- **Hours used:** 150000 -- **Cloud Provider:** AWS -- **Compute Region:** US-east -- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. - -## Citation - @InProceedings{Rombach_2022_CVPR, - author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, - title = {High-Resolution Image Synthesis With Latent Diffusion Models}, - booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, - month = {June}, - year = {2022}, - pages = {10684-10695} - } - -*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* diff --git a/spaces/PaddlePaddle/paddlespeech/README.md b/spaces/PaddlePaddle/paddlespeech/README.md deleted file mode 100644 index 2167d751dc5ab5c9f7ebad3c2c78bfbdba047726..0000000000000000000000000000000000000000 --- a/spaces/PaddlePaddle/paddlespeech/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Paddlespeech -emoji: 🐨 -colorFrom: green -colorTo: blue -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/Palplatine/artefact_memes/README.md b/spaces/Palplatine/artefact_memes/README.md deleted file mode 100644 index 325f29622f0f70b28489f3934068f30138764c73..0000000000000000000000000000000000000000 --- a/spaces/Palplatine/artefact_memes/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Artefact Memes -emoji: 📈 -colorFrom: yellow -colorTo: red -sdk: streamlit -sdk_version: 1.19.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Pearx/ChatGPT-Assistant/README.md b/spaces/Pearx/ChatGPT-Assistant/README.md deleted file mode 100644 index e6aa0d1b7881424f6ec9c0ad88bbf413263e1eee..0000000000000000000000000000000000000000 --- a/spaces/Pearx/ChatGPT-Assistant/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: ChatGPT Assistant -emoji: 🔥 -colorFrom: gray -colorTo: gray -sdk: streamlit -sdk_version: 1.19.0 -app_file: app.py -pinned: true -license: apache-2.0 -fullWidth: true ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/PirateXX/AI-Content-Detector-From-PDF/app.py b/spaces/PirateXX/AI-Content-Detector-From-PDF/app.py deleted file mode 100644 index c61ba7b14a4e6129b0b0ed684fd0529f7ec1f6b0..0000000000000000000000000000000000000000 --- a/spaces/PirateXX/AI-Content-Detector-From-PDF/app.py +++ /dev/null @@ -1,100 +0,0 @@ -from flask import Flask, request -from transformers import RobertaForSequenceClassification, RobertaTokenizer, RobertaConfig -from transformers import AutoTokenizer, AutoModelForSequenceClassification -from transformers import RobertaConfig -from torch import cuda -import torch -import gradio as gr -import os -import re -import pdfplumber - -app = Flask(__name__) - -ACCESS_TOKEN = os.environ["ACCESS_TOKEN"] -# config = RobertaConfig.from_pretrained("PirateXX/ChatGPT-Text-Detector", use_auth_token= ACCESS_TOKEN) -# model = RobertaForSequenceClassification.from_pretrained("PirateXX/ChatGPT-Text-Detector", use_auth_token= ACCESS_TOKEN, config = config) - -# model_name = "roberta-base" -# tokenizer = RobertaTokenizer.from_pretrained(model_name, map_location=torch.device('cpu')) -tokenizer = AutoTokenizer.from_pretrained("PirateXX/AI-Content-Detector", use_auth_token= ACCESS_TOKEN) -model = AutoModelForSequenceClassification.from_pretrained("PirateXX/AI-Content-Detector", use_auth_token= ACCESS_TOKEN) - - -# function to break text into an array of sentences -def text_to_sentences(text): - return re.split(r'(?<=[^A-Z].[.?]) +(?=[A-Z])', text) - -# function to concatenate sentences into chunks of size 900 or less -def chunks_of_900(text, chunk_size=900): - sentences = text_to_sentences(text) - chunks = [] - current_chunk = "" - for sentence in sentences: - if len(current_chunk + sentence) <= chunk_size: - if len(current_chunk)!=0: - current_chunk += " "+sentence - else: - current_chunk += sentence - else: - chunks.append(current_chunk) - current_chunk = sentence - chunks.append(current_chunk) - return chunks - -def predict(query, device="cpu"): - tokens = tokenizer.encode(query) - all_tokens = len(tokens) - tokens = tokens[:tokenizer.model_max_length - 2] - used_tokens = len(tokens) - tokens = torch.tensor([tokenizer.bos_token_id] + tokens + [tokenizer.eos_token_id]).unsqueeze(0) - mask = torch.ones_like(tokens) - - with torch.no_grad(): - logits = model(tokens.to(device), attention_mask=mask.to(device))[0] - probs = logits.softmax(dim=-1) - - fake, real = probs.detach().cpu().flatten().numpy().tolist() - return real - -def findRealProb(text): - chunksOfText = (chunks_of_900(text)) - results = [] - for chunk in chunksOfText: - output = predict(chunk) - results.append([output, len(chunk)]) - - ans = 0 - cnt=0 - for prob, length in results: - ans = ans + prob*length - cnt+=length - realProb = ans/cnt - return {"Real": realProb, "Fake": 1-realProb, "results": results, "text": text} - -def upload_file(file): - - if file: - pdf_file = file.name - text = "" - with pdfplumber.open(pdf_file) as pdf: - cnt = 0 - for page in pdf.pages: - cnt+=1 - text+=(page.extract_text(x_tolerance = 1)) - if cnt>5: - break - text = text.replace('\n', ' ') - return findRealProb(text) - else: - return {"error":'No PDF file found in request'} - - -demo = gr.Interface( - fn=upload_file, - inputs=gr.File(), - article = "Visit AI Content Detector for better user experience!", - outputs=gr.outputs.JSON(), - interpretation="default",) - -demo.launch(show_api=False) \ No newline at end of file diff --git a/spaces/Plachta/VALL-E-X/data/tokenizer.py b/spaces/Plachta/VALL-E-X/data/tokenizer.py deleted file mode 100644 index 9b8e889641b6715b9b4fa3cffd3dd7bef06ad7e9..0000000000000000000000000000000000000000 --- a/spaces/Plachta/VALL-E-X/data/tokenizer.py +++ /dev/null @@ -1,117 +0,0 @@ -#!/usr/bin/env python3 -# Copyright 2023 (authors: Feiteng Li) -# -# 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. - -import re -from dataclasses import asdict, dataclass -from typing import Any, Dict, List, Optional, Pattern, Union - -import numpy as np -import torch -import torchaudio -from encodec import EncodecModel -from encodec.utils import convert_audio - -def remove_encodec_weight_norm(model): - from encodec.modules import SConv1d - from encodec.modules.seanet import SConvTranspose1d, SEANetResnetBlock - from torch.nn.utils import remove_weight_norm - - encoder = model.encoder.model - for key in encoder._modules: - if isinstance(encoder._modules[key], SEANetResnetBlock): - remove_weight_norm(encoder._modules[key].shortcut.conv.conv) - block_modules = encoder._modules[key].block._modules - for skey in block_modules: - if isinstance(block_modules[skey], SConv1d): - remove_weight_norm(block_modules[skey].conv.conv) - elif isinstance(encoder._modules[key], SConv1d): - remove_weight_norm(encoder._modules[key].conv.conv) - - decoder = model.decoder.model - for key in decoder._modules: - if isinstance(decoder._modules[key], SEANetResnetBlock): - remove_weight_norm(decoder._modules[key].shortcut.conv.conv) - block_modules = decoder._modules[key].block._modules - for skey in block_modules: - if isinstance(block_modules[skey], SConv1d): - remove_weight_norm(block_modules[skey].conv.conv) - elif isinstance(decoder._modules[key], SConvTranspose1d): - remove_weight_norm(decoder._modules[key].convtr.convtr) - elif isinstance(decoder._modules[key], SConv1d): - remove_weight_norm(decoder._modules[key].conv.conv) - - -class AudioTokenizer: - """EnCodec audio.""" - - def __init__( - self, - device: Any = None, - ) -> None: - # Instantiate a pretrained EnCodec model - model = EncodecModel.encodec_model_24khz() - model.set_target_bandwidth(6.0) - remove_encodec_weight_norm(model) - - if not device: - device = torch.device("cpu") - if torch.cuda.is_available(): - device = torch.device("cuda:0") - - self._device = device - - self.codec = model.to(device) - self.sample_rate = model.sample_rate - self.channels = model.channels - - @property - def device(self): - return self._device - - def encode(self, wav: torch.Tensor) -> torch.Tensor: - return self.codec.encode(wav.to(self.device)) - - def decode(self, frames: torch.Tensor) -> torch.Tensor: - return self.codec.decode(frames) - - -def tokenize_audio(tokenizer: AudioTokenizer, audio): - # Load and pre-process the audio waveform - if isinstance(audio, str): - wav, sr = torchaudio.load(audio) - else: - wav, sr = audio - wav = convert_audio(wav, sr, tokenizer.sample_rate, tokenizer.channels) - wav = wav.unsqueeze(0) - - # Extract discrete codes from EnCodec - with torch.no_grad(): - encoded_frames = tokenizer.encode(wav) - return encoded_frames - - -if __name__ == "__main__": - model = EncodecModel.encodec_model_24khz() - model.set_target_bandwidth(6.0) - - samples = torch.from_numpy(np.random.random([4, 1, 1600])).type( - torch.float32 - ) - codes_raw = model.encode(samples) - - remove_encodec_weight_norm(model) - codes_norm = model.encode(samples) - - assert torch.allclose(codes_raw[0][0], codes_norm[0][0]) diff --git a/spaces/Pranjal-666/COVID_classify_sequence/app.py b/spaces/Pranjal-666/COVID_classify_sequence/app.py deleted file mode 100644 index c29b52b17584f098ce6dbb91efb6ae207b4848db..0000000000000000000000000000000000000000 --- a/spaces/Pranjal-666/COVID_classify_sequence/app.py +++ /dev/null @@ -1,47 +0,0 @@ -import gradio as gr -import numpy as np -import pandas as pd -from sklearn.feature_extraction.text import CountVectorizer -from sklearn.naive_bayes import MultinomialNB -import pickle -import sys - - - -# load the CountVectorizer from disk -cv = pickle.load(open('countVectTrain.pkl', 'rb')) - -#working... -# load the model from disk -filename = 'corona_pred.pkl' -model = pickle.load(open(filename, 'rb')) - - -# function to convert sequence string into k-mer words, default size = 6 (hexamer words) -kmer_size = 6 -def getKmers(sequence, size=kmer_size): - return [sequence[x:x+size].lower() for x in range(len(sequence) - size + 1)] - -# define the Gradio interface -def classify_sequence(sequence): - # convert the input sequence into k-mer words - words = getKmers(sequence) - # convert the k-mer words into a list of space-separated strings - text = ' '.join(words) - # vectorize the text using Count Vectorization - X = cv.transform([text]) - # make predictions using the pre-trained model - pred_label = model.predict(X)[0] - pred_prob_percentage = model.predict_proba(X).max()*100 - # return the predicted class and probability - return {'predicted_class': pred_label, 'probability': pred_prob_percentage} - - - -Examples=['ATCTCACTTCCCCTCGTTCTCTTGCAGAACTTTGATTTTAACGAACTTAAATAAAAGCCCTGTTGTTTAGCGTATTGTTGCACTTGTCTGGTGGGATTGTGGCACTAATCTGCCTGCTCATCTAGGCAGTGGACATATGCTCAACACTGGGTATAATTCTAATTGAATACTATTTTTCAGTTAGAGCGTCGTGTCTCTTGTACGTCTCGGTCACAATATACGGTTTCGTCCGGTGCGTGGCAATTCGGGGCACATCATGTCTTTCGTGGCTGGTGTGATCGCGCAAGGTGCGCGCGGTACGTATCGAGCAGCGCTCAACTCTGAAAAACATCAAGACCATGTGTCTCTAACTGTGCCACTCTGTGGTTCAGGAAACCTGGTTGAAAAACTTTCACCATGGTTCATGGATGGCGAAAATGCCTATGAAGTGGTGAAGGCCATGTTACTTAAAAAAGAGCCACTTCTCTATGTGCCCATCCGGCTGGCTGGACACACTAGACACCTCCCAGGTCCTCGTGTGTACCTGGTTGAGAGGCTCATTGCTTGTGAAAATCCATTCATGGTTAACCAATTGGCTTATAGCTCTAGTGCAAATGGCAGCCTGGTTGGCACAACTTTGCAGGGCAAGCCTATTGGTATGTTCTTCCCTTATGACATCGAACTTGTCACAGGAAAGCAAAATATTCTCCTGCGCAAGTATGGCCGTGGTGGTTATCACTACACCCCAGTCCACTATGAGCGAGACAACACCTCTTGCCCTGAGTGGATGGACTATTTTGAGGCGGATCCTAAAGGCAAATATGCCCAGAATCTGCTTAAGAAGTTGATTGGCGGTGATGTCACTCCAGTTGACCAATACATGTGTGGCGTTGATGGAAAACCCATTAGTGCCTACGCATTTTTAATGGCCAAGGATGGAATAACCAAACTGGCTGATGTTGAAGCGGACGTCGCAGCACGTGCTGATGACGAAGGCTTCATCACATTAAAGAACAATCTATATAGATTGGTTTGGCATGTTGAGCGTAAAGACGTTCCATATCCTAAGCAATCTATTTTTACTATTAATAGTGTGGTCCAAAAGGATGGTGTTGAAAACACTCCTCCTCACTATTTTACTCTTGGATGCAAAATTTTAACGCTCACCCCACGTAACAAGTGGAGTGGCGTTTCTGACTTGTCCCTCAAACAAAAACTCCTTTACACCTTCTATGGTAAGGAGTCACTTGAGAACCCAACCTACATTTACCACTCCGCATTCATTGAGTGTGGAAGTTGTGGTAATGATTCCTGGCTTACAGGGAATGCTATCCAAGGGTTTGCCTGTGGATGTGGGGCATCATATACAGCTAATGATGTCGAAGTCCAATCATCTGGCATGATTAAGCCAAATGCTCTTCTTTGTGCTACTTGCCCCTTTGCTAAGGGTGACAGCTGTTCTTCTAATTGCAAACATTCAGTTGCTCAGTTGGTTAGTTACCTTTCTGAACGCTGTAATGTTATTGCTGATTCTAAGTCCTTCACACTTATCTTTGGTGGCGTAGCTTACGCCTACTTTGGATGTGAGGAAGGTACTATGTACTTTGTGCCTAGAGCTAAGTCTGTTGTCTCAAGGATTGGAGACTCCATCTTTACAGGCTGTACTGGCTCTTGGAACAAGGTCACTCAAATTGCTAACATGTTCTTGGAACAGACTCAGCATTCCCTTAACTTTGTGGGAGAGTTCGTTGTCAACGATGTTGTCCTCGCAATTCTCTCTGGAACCACAACTAATGTTGACAAAATACGCCAGCTTCTCAAAGGTGTCACCATTGACAAGTTGCGTGATTATTTAGCTGACTATGACGTAGCAGTCACTGCCGGCCCATTCATGGATAATGCTATTAATGTTGGTGGTACAGGATTACAGTATGCCGCCATTACTGCACCTTATGTAGTTCTCACTGGCTTAGGTGAGTCCTTTAAGAAAGTTGCAACCATACCGTACAAGGTTTGCAACTCTGTTAAGGATACTCTGACTTATTATGCTCACAGCGTGTTGTACAGAGTTTTTCCTTATGACATGGATTCTGGTGTGTCATCCTTTAGTGAACTACTTTTTGATTGCGTTGATCTTTCAGTAGCTTCTACCTATTTTTTAGTCCGCCTCTTGCAAGATAAGACTGGCGACTTTATGTCTACAATTATTACTTCCTGCCAAACTGCTGTTAGTAAGCTTCTAGATACATGTTTTGAAGCTACAGAAGCAACATTTAACTTCTTGTTAGATTTGGCAGGATTGTTCAGAATCTTTCTTCGCAATGCCTATGTGTACACTTCACAAGGGTTTGTGGTGGTCAATGGCAAAGTTTCTACACTTGTCAAACAAGTGTTAGACTTGCTTAATAAGGGTATGCAACTTTTGCATACAAAGGTCTCCTGGGCTGGTTCTAACATCAGTGCTGTTATCTACAGCGGCAGGGAGTCTCTAATATTCCCATCGGGAACCTATTACTGTGTCACCACTAAGGCTAAGTCCGTTCAACAAGATCTTGACGTTATTTTGCCTGGTGAGTTTTCCAAGAAGCAGTTAGGACTGCTCCAACCTACTGACAATTCTACAACTGTTAGTGTTACTGTATCCAGTAACATGGTTGAAACTGTTGTGGGTCAACTTGAGCAAACTAATATGCACAGTCCTGATGTTATAGTAGGTGACTATGTCATTATTAGTGAAAAATTGTTTGTGCGTAGTAAGGAAGAAGACGGATTCGCCTTCTACCCTGCTTGCACTAATGGTCATGCTGTACCGACTCTCTTTAGACTTAAGGGAGGTGCACCTGTAAAAAAAGTAGCCTTTGGCGGTGATCAAGTACATGAGGTTGCTGCTGTAAGAAGTGTTACTGTCGAGTACAACATTCATGCTGTATTAGACACACTACTTGCTTCTTCTAGTCTTAGAACCTTTGTTGTAGATAAGTCTTTGTCAATTGAGGAGTTTGCTGACGTAGTAAAGGAACAAGTCTCAGACTTGCTTGTTAAATTACTGCGTGGAATGCCGATTCCAGATTTTGATTTAGACGATTTTATTGACGCACCATGCTATTGCTTTAACGCTGAGGGTGATGCATCTTGGTCTTCTACTATGATCTTCTCTCTTCACCCCGTCGAGTGTGACGAGGAGTGTTCTGAAGTAGAGGCTTCAGATTTAGAAGAAGGTGAATCAGAGTGCATTTCTGAGACTTCAACTGAACAAGTTGACGTTTCTCATGAGGTTTCTGACGACGAGTGGGCTGCTGCAGTTGATGAAGCGTTCCCCCTCGATGAAGCAGAAGATGTTACTGAATCTGTGCAAGAAGAAGCACAACCAGTAGAAGTACCTGTTGAAGATATTGTGCAGGTTGTCATAGTTGACACCTTACAGGAAACTCCTGTTGTGTCTGATACTGTTGAAGTCCCACCGCAAGTGGTGAAACTTCCGTCTGAACCTCAGACTATCCAGCCCGAGGTAAAAGAAGTTGCACCTGTCTATGAGGCTGATACCGAACAGACACAGAGTGTTACTGTTAAACCTAAGAGGTTACGCAAAAAGCGTAATGTTGACCCTTTGTCCAATTTTGAACATAAGGTTATTACAGAGTGCGTTACCATAGTTTTAGGTGACGCAATTCAAGTAGCCAAGTGCTATGGGGAGTCTGTGTTAGTTAATGCTGCTAACACACATCTTAAGCATGGCGGTGGTATCGCTGGTGCTATTAATGCGGCTTCAAAAGGGGCTGTCCAAAAAGAGTCAGATGAGTATATTCTGGCTAAAGGGCCGTTACAAGTAGGAGATTCAGTTCTCTTGCAAGGCCATTCTCTAGCTAAGAATATCCTGCATGTCGTAGGCCCAGATGCCCGCGCTAAACAGGATGTTTCTCTCCTTAGTAAGTGCTATAAGGCTATGAATGCATATCCTCTTGTAGTCACTCCTCTTGTTTCAACAGGCATATTTGGTGTAAAACCAGCTGTGTCTTTTGATTATCTTATTAGAGAGGCTAAGACTAGAGTTTTAGTCGTCGTTAATTCCCAAGATGTCTATAAGAGTCTTACCATAGTTGACATTCCACAGAGTTTGACTTTTTCATATGATGGGTTACGTGGCGCAATACGTAAAGCTAAAGATTATGGTTTTACTGTTTTTGTGTGCACAGACAACTCTGCTAACACTAAAGTTCTTAGGAACAAGGGTGTTGATTATACTAAGAAGTTTCTTACAGTTGACGGTGTGCAATATTATTGCTACACGTCTAAGGACACTTTAGATGATATCTTACAACAGGCTAATAAGTCTGTTGGTATTATATCTATGCCTTTGGGATATGTGTCTCATGGTTTAGACTTAATTCAAGCAGGGAGTGTCGTGCGTAGAGTTAACGTGCCCTACGTGTGTCTCCTAGCTAATAAAGAGCAAGAAGCTATTTTGATGTCTGAAGACGTTAAGTTAAACCCTTCAGAAGATTTTATAAAGCACGTCCGCACTAATGGTGGTTACAATTCTTGGCATTTAGTCGAGGGTGAACTATTGGTGCAAGACTTACGCTTAAATAAGCTCCTGCATTGGTCTGATCAAACCATATGCTACAAGGATAGTGTGTTTTATGTTGTAAAGAATAGTACAGCTTTTCCATTTGAAACACTTTCAGCATGTCGTGCGTATTTGGATTCACGCACGACACAGCAGTTAACAATCGAAGTCTTAGTGACTGTCGATGGTGTAAATTTTAGAACAGTCGTTCTAAATAATAAGAACACTTATAGATCACAGCTTGGATGCGTTTTCTTTAATGGTGCTGATATTTCTGATACCATTCCTGATGAGAAACAGAATGGTCACAGTTTATATCTAGCAGACAATTTGACTGCTGATGAAACAAAGGCGCTTAAAGAGTTATATGGCCCCGTTGATCCTACTTTCTTACACAGATTCTATTCACTTAAGGCTGCAGTCCATAAGTGGAAGATGGTTGTGTGTGATAAGGTACGTTCTCTCAAATTGAGTGATAATAATTGTTATCTTAATGCAGTTATTATGACACTTGATTTATTGAAGGACATTAAATTTGTTATACCTGCTCTACAGCATGCATTTATGAAACATAAGGGCGGTGATTCAACTGACTTCATAGCCCTCATTATGGCTTATGGCAATTGCACATTTGGTGCTCCAGATGATGCCTCTCGGTTACTTCATACCGTGCTTGCAAAGGCTGAGTTATGCTGTTCTGCACGCATGGTTTGGAGAGAGTGGTGCAATGTCTGTGGCATAAAAGATGTTGTTCTACAAGGCTTAAAAGCTTGTTGTTACGTGGGTGTGCAAACTGTTGAAGATCTGCGTGCTCGCATGACATATGTATGCCAGTGTGGTGGTGAACGTCATCGGCAAATAGTTGAACACACCACCCCCTGGTTGCTGCTCTCAGGCACACCAAATGAAAAATTGGTGACAACCTCCACGGCGCCTGATTTTGTAGCGTTTAATGTCTTTCAGGGCATTGAAACGGCTGTTGGCCATTATGTTCATGCTCGCCTGAAGGGTGGTCTTATTTTAAAGTTTGACTCTGGCACCGTTAGCAAGACTTCAGACTGGAAGTGCAAGGTGACAGATGTACTTTTCCCCGGCCAAAAATACAGTAGCGATTGTAATGTCGTACGGTATTCTTTGGACGGTAATTTCAGAACAGAGGTTGATCCCGACCTATCTGCTTTCTATGTTAAGGATGGTAAATACTTTACAAGTGCACCACCCGTAACATATTCACCAGCTACAATTTTAGCTGGTAGTGTCTACACTAATAGCTGCCTTGTATCGTCTGATGGACAACCTGGCGGTGATGCTATTAGTTTGAGTTTTAATAACCTTTTAGGGTTTGATTCTAGTAAACCAGTCACTAAGAAATACACTTACTCCTTCTTGCCTAAAGAAGACGGCGATGTGTTGTTGGCTGAGTTTGACACTTATGACCCTATTTATAAGAATGGTGCCATGTATAAAGGCAAACCAATTCTTTGGGTCAACAAAGCATCTTATGATACTAATCTTAATAAGTTCAATAGAGCTAGTTTGCGTCAAATTTTTGACGTAGCCCCCATTGAACTCGAAAATAAATTCACACCTTTGAGTGTGGCGTCCACACCAGTTGAACCTCCAACTGTAGATGTGGTAGCACTTCAACAGGAAATGACAATTGTCAAATGTAAGGGTTTAAATAAACCTTTCGTGAAGGACAATGTCAGTTTCGTTGTTGATGACTCAGGTACTCCCGTTGTTGAGTATCTGTCTAAAGAAGATCTACATACATTGTATGTAGACCCTAAGTATCAAGTCATTGTCTTAAAAGACAATGTACTTTCTTCTATGCTTAGATTGCACACCGTTGAGTCAGGTGATATTAACGTTGTTGCAGCTTCCGGATCTTTGACACGTAAAGTGAAGTTACTATTTAGGGCTTCATTTTATTTCAAAGAATTTGCTACCCGCACTTTCACTGCTACCACTGCTGTAGGTAGTTGTATAAAGAGTGTAGTGCGGCATCTAGGTGTTACTAAAGGCATATTGACAGGCTGTTTTAGTTTTGTCAAGATGTTATTTATTCTTCCACTAGCTTACTTTAGTGATTCAAAACTCGGCACCACAGAGGTTAAAGTGAGTGCTTTGAAAACAGCTGGCGTTGTGACAGGTAATGTTGTAAAACAGTGTTGCACTGCTGCTGTTGATTTAAGTATGGATAAGTTGCGCCGTGTGGATTGGAAATCAACCCTACGGTTGTTACTTATGTTATGCACAACTATGGTATTGTTGTCTTCTGTGTATCACTTGTATGTCTTCAATCAGGTCTTATCAAGTGATGTTATGTTTGAAGATGCCCAAGGTTTGAAAAAGTTCTACAAAGAAGTTAGAGCTTACCTAGGAATCTCTTCTGCTTGTGACGGTCTTGCTTCAGCTTATAGGGCGAATTCATTTGATGTACCTACATTCTGCGCAAACCGTTCTGCAATGTGTAATTGGTGCTTGATTAGCCAAGATTCCATAACTCACTACCCAGCTCTTAAGATGGTTCAAACACATCTTAGCCACTATGTTCTTAACATAGATTGGTTGTGGTTTGCATTTGAGACTGGTTTGGCATACATGCTCTATACCTCGGCCTTCAACTGGTTGTTGTTGGCAGGTACATTGCATTATTTCTTTGCACAGACTTCCATATTTGTAGACTGGCGGTCATACAATTATGCTGTGTCTAGTGCCTTCTGGTTATTCACCCACATTCCAATGGCGGGTTTGGTACGAATGTATAATTTGTTAGCATGCCTTTGGCTTTTACGCAAGTTTTATCAGCATGTAATCAATGGTTGCAAAGATACGGCATGCTTGCTCTGCTATAAGAGGAACCGACTTACTAGAGTTGAAGCTTCTACCGTTGTCTGTGGTGGAAAACGTACGTTTTATATCACAGCAAATGGCGGTATTTCATTCTGTCGTAGGCATAATTGGAATTGTGTGGATTGTGACATTGCAGGTGTGGGGAATACCTTCATCTGTGAAGAAGTCGCAAATGACCTCACTACCGCCCTACGCAGGCCTATTAACGCTACGGATAGATCACATTATTATGTGGATTCCGTTACAGTTAAAGAGACTGTTGTTCAGTTTAATTATCGTAGAGACGGTCAACCATTCTACGAGCGGTTTCCCCTCTGCGCTTTTACAAATCTAGATAAGTTGAAGTTCAAAGAGGTCTGTAAAACTACTACTGGTATACCTGAATACAACTTTATCATCTACGACTCATCAGATCGTGGCCAGGAAAGTTTAGCTAGGTCTGCATGTGTTTATTATTCTCAAGTCTTGTGTAAATCAATTCTTTTGGTTGACTCAAGTTTGGTTACTTCTGTTGGTGATTCTAGTGAAATCGCCACTAAAATGTTTGATTCCTTTGTTAATAGTTTCGTCTCGCTGTATAATGTCACACGCGATAAGTTGGAAAAACTTATCTCTACTGCTCGTGATGGCGTAAGGCGAGGCGATAACTTCCATAGTGTCTTAACAACATTCATTGACGCAGCACGAGGCCCCGCAGGTGTGGAGTCTGATGTTGAGACCAATGAAATTGTTGACTCTGTGCAGTATGCTCATAAACATGACATACAAATTACTAATGAGAGTTACAATAATTATGTACCCTCATATGTTAAACCTGATAGTGTGTCTACCAGTGATTTAGGTAGTCTCATTGATTGTAATGCGGCTTCAGTTAACCAAATTGTCTTGCGTAATTCTAATGGTGCTTGTATTTGGAACGCTGCTGCATATATGAAACTCTCGGATGCACTTAAACGACAGATTCGCATTGCATGCCGTAAGTGTAATTTAGCTTTCCGGTTAACCACCTCAAAGCTACGCGCTAATGATAATATCTTATCAGTTAGATTCACTGCTAACAAAATTGTTGGTGGTGCTCCTACATGGTTTAATGTGTTGCGTGACTTTACGTTAAAGGGTTACGTTCTTGCTACCATTATTGTGTTTCTGTGTGCTGTACTGATGTATTTGTGTTTACCTACATTTTCTATGGTACCTGTTGAATTTTATGAAGACCGCATCTTGGACTTTAAAGTTCTTGATAATGGTATCATTAGGGATGTAAATCCTGATGATAAGTGCTTTGCTAATAAGCACCGGTCCTTCACACAATGGTATCATGAGCATGTTGGTGGTGTCTATGACAACTCTATCACATGCCCATTGACAGTTGCAGTAATTGCTGGAGTTGCTGGTGCTCGCATTCCAGACGTACCTACTACATTGGCTTGGGTGAACAATCAGATAATTTTCTTTGTTTCTCGAGTCTTTGCTAATACAGGCAGTGTTTGCTACACTCCTATAGATGAGATACCCTATAAGAGTTTCTCTGATAGTGGTTGCATTCTTCCATCTGAGTGCACTATGTTTAGGGATGCAGAGGGCCGTATGACACCATACTGCCATGATCCTACTGTTTTGCCTGGGGCTTTTGCGTACAGTCAGATGAGGCCTCATGTTCGTTACGACTTGTATGATGGTAACATGTTTATTAAATTTCCTGAAGTAGTATTTGAAAGTACACTTAGGATTACTAGAACTCTGTCAACTCAGTACTGCCGGTTCGGTAGTTGTGAGTATGCACAAGAGGGTGTTTGTATTACCACAAATGGCTCGTGGGCCATTTTTAATGACCACCATCTTAATAGACCTGGTGTCTATTGTGGCTCTGATTTTATTGACATTGTCAGGCGGTTAGCAGTATCACTGTTCCAGCCTATTACTTATTTCCAATTGACTACCTCATTGGTCTTGGGTATAGGTTTGTGTGCATTCCTGACTTTGCTCTTCTATTATATTAATAAAGTAAAACGTGCTTTTGCAGATTACACCCAGTGTGCTGTAATTGCTGTTGTTGCTGCTGTTCTTAATAGCTTGTGCATCTGCTTTGTTGCATCTATACCATTGTGTATAGTACCTTACACTGCATTGTACTATTATGCTACATTCTATTTTACTAATGAGCCTGCATTTATTATGCATGTTTCTTGGTACATTATGTTCGGGCCTATCGTTCCCATATGGATGACCTGCGTCTATACAGTTGCAATGTGCTTTAGACACTTCTTCTGGGTTTTAGCTTATTTTAGTAAGAAACATGTAGAAGTTTTTACTGATGGTAAGCTTAATTGTAGTTTCCAGGACGCTGCCTCTAATATCTTTGTTATTAACAAGGACACTTATGCAGCTCTTAGAAACTCTTTAACTAATGATGCCTATTCACGATTTTTGGGGTTGTTTAACAAGTATAAGTACTTCTCCGGTGCTATGGAAACAGCCGCTTATCGTGAAGCTGCAGCATGTCATCTTGCTAAAGCCTTACAAACATACAGCGAGACTGGTAGTGATCTTCTTTACCAACCACCCAACTGTAGCATAACCTCTGGCGTGTTGCAAAGCGGTTTGGTGAAAATGTCACATCCCAGTGGAGATGTTGAGGCTTGTATGGTTCAGGTTACCTGCGGTAGCATGACTCTTAATGGTCTTTGGCTTGACAACACAGTCTGGTGCCCACGACACGTAATGTGCCCGGCTGACCAGTTGTCTGATCCTAATTATGATGCCTTGTTGATTTCTATGACTAATCATAGTTTCAGTGTGCAAAAACACATTGGCGCTCCAGCAAACTTGCGTGTTGTTGGTCATGCCATGCAAGGCACTCTTTTGAAGTTGACTGTCGATGTTGCTAACCCTAGCACTCCAGCCTACACTTTTACAACAGTGAAACCTGGCGCAGCATTTAGTGTGTTAGCATGCTATAATGGTCGTCCGACTGGTACATTCACTGTTGTAATGCGCCCTAACTACACAATTAAGGGTTCCTTTCTGTGTGGTTCTTGTGGTAGTGTTGGTTACACCAAGGAGGGTAGTGTGATCAATTTTTGTTACATGCATCAAATGGAACTTGCTAATGGTACACATACCGGTTCAGCATTTGATGGTACTATGTATGGTGCCTTTATGGATAAACAAGTGCACCAAGTTCAGTTAACAGACAAATACTGCAGTGTTAATGTAGTAGCTTGGCTTTACGCAGCAATACTTAATGGTTGCGCTTGGTTTGTAAAACCTAATCGCACTAGTGTTGTTTCTTTTAATGAATGGGCTCTTGCCAACCAATTCACTGAATTTGTTGGCACTCAATCCGTTGACATGTTAGCTGTCAAAACAGGCGTTGCTATTGAACAGCTGCTTTATGCGATCCAACAACTTTATACTGGGTTCCAGGGAAAGCAAATCCTTGGCAGTACTATGTTGGAAGATGAATTCACACCTGAGGATGTTAATATGCAGATTATGGGTGTGGTTATGCAGAGTGGTGTGAGAAAAGTTACATATGGTACTGCGCATTGGTTGTTCGCGACCCTTGTCTCAACCTATGTGATAATCTTACAAGCCACTAAATTTACTTTGTGGAACTACTTGTTTGAGACTATTCCCACACAGTTGTTCCCACTCTTATTTGTGACTATGGCCTTCGTTATGTTGTTGGTTAAACACAAACACACCTTTTTGACACTTTTCTTGTTGCCTGTGGCTATTTGTTTGACTTATGCAAACATAGTCTACGAGCCCACTACTCCCATTTCGTCAGCGCTGATTGCAGTTGCAAATTGGCTTGCCCCCACTAATGCTTATATGCGCACTACACATACTGATATTGGTGTCTACATTAGTATGTCACTTGTATTAGTCATTGTAGTGAAGAGATTGTACAACCCATCACTTTCTAACTTTGCGTTAGCATTGTGCAGTGGTGTAATGTGGTTGTACACTTATAGCATTGGAGAAGCCTCAAGCCCCATTGCCTATCTGGTTTTTGTCACTACACTCACTAGTGATTATACGATTACAGTCTTTGTTACTGTCAACCTTGCAAAAGTTTGCACTTATGCCATCTTTGCTTACTCACCACAGCTTACACTTGTGTTTCCGGAAGTGAAGATGATACTTTTATTATACACATGTTTAGGTTTCATGTGTACTTGCTATTTTGGTGTCTTCTCTCTTTTGAACCTTAAGCTTAGAGCACCTATGGGTGTCTATGACTTTAAGGTCTCAACACAAGAGTTCAGATTCATGACTGCTAACAATCTAACTGCACCTAGAAATTCTTGGGAGGCTATGGCTCTGAACTTTAAGTTAATAGGTATTGGCGGTACACCTTGTATAAAGGTTGCTGCTATGCAGTCTAAACTTACAGATCTTAAATGCACATCTGTGGTTCTCCTCTCTGTGCTCCAACAGTTACACTTAGAGGCTAATAGTAGGGCCTGGGCTTTCTGTGTTAAATGCCATAATGATATATTGGCAGCAACAGACCCCAGTGAGGCTTTCGAGAAATTCGTAAGTCTCTTTGCCACTTTAATGACTTTTTCTGGTAATGTAGATCTTGATGCGTTAGCTAGTGATATTTTTGACACTCCTAGCGTACTTCAAGCTACTCTTTCTGAGTTTTCACACTTAGCTACCTTTGCTGAGTTGGAAGCTGCGCAGAAAGCCTATCAGGAAGCTATGGACTCTGGTGACACCTCACCACAAGTTCTTAAGGCTTTGCAGAAGGCTGTTAATATAGCTAAAAACGCCTATGAGAAGGATAAGGCAGTGGCCCGTAAGTTAGAACGTATGGCTGATCAGGCTATGACTTCTATGTATAAGCAAGCACGTGCTGAAGACAAGAAAGCAAAAATTGTCAGTGCTATGCAAACTATGTTGTTTGGTATGATTAAGAAGCTCGACAACGATGTTCTTAATGGTATCATTTCTAACGCTAGGAATGGTTGTATACCTCTTAGTGTCATTCCACTGTGTGCTTCAAATAAACTTCGCGTTGTAATTCCTGACTTCACCGTCTGGAATCAGGTAGTCACATATCCCTCGCTTAACTACGCTGGGGCTTTGTGGGACATTACAGTTATAAACAATGTGGACAATGAAATTGTTAAGTCTTCAGATGTTGTAGACAGCAATGAAAATTTAACATGGCCACTTGTTTTAGAATGCACTAGGGCATCCACTTCTGCCGTTAAGTTGCAAAATAATGAGATCAAACCTTCAGGTTTAAAAACCATGGTTGTGTCTGCAGGTCAAGAGCAAACTAACTGTAATACTAGTTCCTTAGCTTATTACGAACCTGTGCAGGGTCGTAAAATGCTGATGGCTCTTCTTTCTGATAATGCCTATCTCAAATGGGCGCGTGTTGAAGGTAAGGACGGATTTGTTAGTGTAGAGCTACAACCTCCTTGCAAATTCTTGATTGCGGGACCAAAAGGACCTGAAATCCGATATCTCTATTTTGTTAAAAATCTTAACAACCTTCATCGCGGGCAAGTGTTAGGGCACATTGCTGCGACTGTTAGATTGCAAGCTGGTTCTAACACCGAGTTTGCCTCTAATTCTTCGGTGTTGTCACTTGTTAACTTCACCGTTGATCCTCAAAAAGCTTATCTCGATTTCGTCAATGCGGGAGGTGCCCCATTGACAAATTGTGTTAAGATGCTTACTCCTAAAACTGGTACAGGTATAGCTATATCTGTTAAACCAGAGAGTACAGCTGATCAAGAGACTTATGGTGGAGCTTCAGTGTGTCTCTATTGCCGTGCGCATATAGAACATCCTGATGTCTCTGGTGTTTGTAAATATAAGGGTAAGTTTGTCCAAATCCCTGCTCAGTGTGTCCGTGACCCTGTGGGATTTTGTTTGTCAAATACCCCCTGTAATGTCTGTCAATATTGGATTGGATATGGGTGCAATTGTGACTCGCTTAGGCAAGTAGCACTGCCCCAATCTAAAGATTCCAATTTTTTAAACGAGTCCGGGGTTCTATTGTAAATGCCCGAATAGAACCCTGTTCAAGTGGTTTGTCCACTGATGTCGTCTTTAGGGCATTTGACATCTGCAACTATAAGGCTAAGGTTGCTGGTATTGGAAAATACTACAAGACTAATACTTGTAGGTTTGTAGAATTAGATGACCAAGGGCATCATTTAGACTCCTATTTTGTCGTTAAGAGGCATACTATGGAGAATTATGAACTAGAGAAGCACTGTTACGATTTGTTACGTGACTGTGATGCTGTAGCTCCCCATGATTTCTTCATCTTTGATGTAGACAAAGTTAAAACACCTCATATTGTACGTCAGCGTTTAACTGAGTACACTATGATGGATCTTGTATATGCCCTGAGGCACTTTGATCAAAATAGCGAAGTGCTTAAGGCTATCTTAGTGAAGTATGGTTGCTGTGATGTTACCTACTTTGAAAATAAACTCTGGTTTGATTTTGTTGAAAATCCCAGTGTTATTGGTGTTTATCATAAACTTGGAGAACGTGTACGCCAAGCTATCTTAAACACTGTTAAATTTTGTGACCACATGGTCAAGGCTGGTTTAGTCGGTGTGCTCACACTCGACAACCAGGACCTTAATGGCAAGTGGTATGATTTTGGTGACTTCGTAATCACTCAACCTGGTTCAGGAGTAGCTATAGTTGATAGCTACTATTCTTATTTGATGCCTGTGCTCTCAATGACCGATTGTCTGGCCGCTGAGACACATAGGGATTGTGATTTTAATAAACCACTCATTGAGTGGCCACTTACTGAGTATGATTTTACTGATTATAAGGTACAACTCTTTGAGAAGTACTTTAAATATTGGGATCAGACGTATCACGCAAATTGCGTTAATTGTACTGATGACCGTTGTGTGTTACATTGTGCTAATTTCAATGTATTGTTTGCTATGACCATGCCTAAGACTTGTTTCGGACCCATAGTCCGAAAGATCTTTGTTGATGGCGTGCCATTTGTAGTATCTTGTGGTTATCACTACAAAGAATTAGGTTTAGTCATGAATATGGATGTTAGTCTCCATAGACATAGGCTCTCTCTTAAGGAGTTGATGATGTATGCCGCTGATCCAGCCATGCACATTGCCTCCTCTAACGCTTTTCTTGATTTGAGGACATCATGTTTTAGTGTCGCTGCACTTACAACTGGTTTGACTTTTCAAACTGTGCGGCCTGGCAATTTTAACCAAGACTTCTATGATTTCGTGGTATCTAAAGGTTTCTTTAAGGAGGGCTCTTCAGTTACGCTCAAACATTTTTTCTTTGCTCAAGATGGTAATGCTGCTATTACAGATTATAATTACTATTCTTATAATCTGCCTACTATGTGTGACATCAAACAAATGTTGTTCTGCATGGAAGTTGTAAACAAGTACTTCGAAATCTATGACGGTGGTTGTCTTAATGCTTCTGAAGTGGTTGTTAATAATTTAGACAAGAGTGCTGGCCATCCTTTTAATAAGTTTGGCAAAGCTCGTGTCTATTATGAGAGCATGTCTTACCAGGAGCAAGATGAACTCTTTGCCATGACAAAGCGTAACGTCATTCCTACCATGACTCAAATGAATCTAAAATATGCTATTAGTGCTAAGAATAGAGCTCGCACTGTTGCAGGCGTGTCCATACTTAGCACAATGACTAATCGCCAGTACCATCAGAAAATGCTTAAGTCCATGGCTGCAACTCGTGGAGCGACTTGCGTCATTGGTACTACAAAGTTCTATGGTGGCTGGGATTTCATGCTTAAAACATTGTACAAAGATGTTGATAATCCGCATCTTATGGGTTGGGATTACCCTAAGTGTGATAGAGCTATGCCTAATATGTGTAGAATCTTCGCTTCACTCATATTAGCTCGTAAACATGGCACTTGTTGTACTACAAGGGACAGATTTTATCGCTTGGCAAATGAGTGTGCTCAGGTGCTAAGCGAATATGTTCTATGTGGTGGTGGTTACTACGTCAAACCTGGAGGTACCAGTAGCGGAGATGCCACCACTGCATATGCCAATAGTGTCTTTAACATTTTGCAGGCGACAACTGCTAATGTCAGTGCACTTATGGGTGCTAATGGCAACAAGATTGTTGACAAAGAAGTTAAAGACATGCAGTTTGATTTGTATGTCAATGTTTACAGGAGCACTAGCCCAGACCCCAAATTTGTTGATAAATACTATGCTTTTCTTAATAAGCACTTTTCTATGATGATACTGTCTGATGACGGTGTCGTTTGCTATAATAGTGATTATGCAGCTAAGGGTTACATTGCTGGAATACAGAATTTTAAGGAAACGCTGTATTATCAGAACAATGTCTTTATGTCTGAAGCTAAATGCTGGGTGGAAACCGATCTGAAGAAAGGGCCACATGAATTCTGTTCACAGCATACGCTTTATATTAAGGATGGCGACGATGGTTACTTCCTTCCTTATCCAGACCCTTCAAGAATTTTGTCTGCCGGTTGCTTTGTAGATGATATCGTTAAGACTGACGGTACACTCATGGTAGAGCGGTTTGTGTCTTTGGCTATAGATGCTTACCCTCTCACAAAGCATGAAGATATAGAATACCAGAATGTATTCTGGGTCTACTTACAGTATATAGAAAAACTGTATAAAGACCTTACAGGACACATGCTTGACAGTTATTCTGTCATGCTATGTGGTGATAATTCTGCTAAGTTTTGGGAAGAGGCATTCTACAGAGATCTCTATAGTTCGCCTACCACTTTGCAGGCTGTCGGTTCATGCGTTGTATGCCATTCACAGACTTCCCTACGCTGTGGGACATGCATCCGTAGACCATTTCTCTGCTGTAAATGCTGCTATGATCATGTTATAGCAACTCCACATAAGATGGTTTTGTCTGTTTCTCCTTACGTTTGTAATGCCCCTGGTTGTGGCGTTTCAGACGTTACTAAGCTATATTTAGGTGGTATGAGCTACTTTTGTGTAGATCATAGACCTGTGTGTAGTTTTCCACTTTGCGCTAATGGTCTTGTATTCGGCTTATACAAGAATATGTGCACAGGTAGTCCTTCTATAGTTGAATTTAATAGGTTGGCTACCTGTGACTGGACTGAAAGTGGTGATTACACCCTTGCCAATACTACAACAGAACCACTCAAACTTTTTGCTGCTGAGACTTTACGTGCCACTGAAGAGGCGTCTAAGCAGTCTTATGCTATTGCCACCATCAAAGAAATTGTTGGTGAGCGCCAACTATTACTTGTGTGGGAGGCTGGCAAGTCCAAACCACCACTCAATCGTAATTATGTTTTTACTGGTTATCATATAACCAAAAATAGTAAAGTGCAGCTCGGTGAGTACATCTTCGAGCGCATTGATTATAGTGATGCTGTATCCTACAAGTCTAGTACAACGTATAAACTGACTGTAGGTGACATCTTCGTACTTACCTCTCACTCTGTGGCTACCTTGACGGCGCCCACAATTGTGAATCAAGAGAGGTATGTTAAAATTACTGGGTTGTACCCAACCATTACGGTACCTGAAGAGTTCGCAAGTCATGTTGCCAACTTCCAAAAATCAGGTTATAGTAAATATGTCACTGTTCAGGGACCACCTGGCACTGGCAAAAGTCATTTTGCTATAGGGTTAGCGATTTACTACCCTACAGCACGTGTTGTTTATACAGCATGTTCACACGCAGCTGTTGATGCTTTGTGTGAAAAAGCTTTTAAATATTTGAACATTGCTAAATGTTCCCGTATCATTCCTGCAAAGGCACGTGTTGAGTGCTATGACAGGTTTAAAGTTAATGAGACAAATTCTCAATATTTGTTTAGTACTATTAATGCTCTACCAGAAACTTCTGCCGATATTCTGGTGGTTGATGAGGTTAGTATGTGCACTAATTATGATCTTTCAATTATTAATGCACGTATTAAAGCTAAGCACATTGTCTATGTAGGAGATCCAGCACAGTTGCCAGCTCCTAGGACTTTGTTGACTAGAGGCACATTGGAACCAGAAAATTTCAATAGTGTCACTAGATTGATGTGTAACTTAGGTCCTGACATATTTTTAAGTATGTGCTACAGGTGTCCTAAGGAAATAGTAAGCACTGTGAGCGCTCTTGTCTACAATAATAAATTGTTAGCCAAGAAGGAGCTTTCAGGCCAATGCTTTAAAATACTCTATAAGGGCAATGTGACGCATGATGCTAGCTCTGCCATTAATAGACCACAACTCACATTTGTGAAGAATTTTATTACTGCCAATCCGGCATGGAGTAAGGCAGTCTTTATTTCGCCTTATAATTCACAGAATGCTGTGGCTCGTTCAATGCTGGGTCTTACTACTCAGACTGTTGATTCCTCACAGGGTTCAGAATACCAGTACGTTATCTTCTGTCAAACAGCAGATACGGCACATGCTAACAACATTAACAGATTTAATGTTGCAATCACTCGTGCCCAAAAAGGTATTCTTTGTGTTATGACATCTCAGGCACTCTTTGAGTCCTTAGAGTTTACTGAATTGTCTTTTACTAATTACAAGCTCCAGTCTCAGATTGTAACTGGCCTTTTTAAAGATTGCTCTAGAGAAACTTCTGGCCTCTCACCTGCTTATGCACCAACATACGTTAGTGTTGATGACAAGTATAAGACGAGTGATGAGCTTTGCGTGAATCTTAATTTACCCGCAAATATCCCATACTCTCGTGTTATTTCCAGGATGGGCTTTAAACTCGATGCAACAGTTCCTGGATATCCTAAGCTTTTCATTACTCGTGAAGAGGCTGTAAGGCAAGTTCGAAGCTGGATAGGCTTCGATGTTGAGGGTGCTCATGCTTCCCGTAATGCATGTGGCACCAATGTGCCTCTACAATTAGGATTTTCAACTGGTGTGAACTTTGTTGTTCAGCCAGTTGGTGTTGTAGACACTGAGTGGGGTAACATGTTAACGGGCATTGCTGCCCGTCCTCCACCAGGTGAACAGTTTAAGCACCTCGTGCCTCTTATGCATAAGGGGGCTGCGTGGCCTATTGTTAGACGACGTATAGTGCAAATGTTGTCAGACACTTTAGACAAATTGTCTGATTACTGTACGTTTGTTTGTTGGGCTCATGGCTTTGAATTAACGTCTGCATCATACTTTTGCAAGATAGGTAAGGAACAGAAGTGTTGCATGTGCAATAGACGCGCTGCAGCGTACTCTTCACCTCTGCAATCTTATGCCTGCTGGACTCATTCCTGCGGTTATGATTATGTCTACAACCCTTTCTTTGTCGATGTTCAACAGTGGGGTTATGTAGGCAATCTTGCTACTAATCACGATCGTTATTGCTCTGTCCATCAAGGAGCTCATGTGGCTTCTAATGATGCAATAATGACTCGTTGTTTAGCTATTCATTCTTGTTTTATAGAACGTGTGGATTGGGATATAGAGTATCCTTATATCTCACATGAAAAGAAATTGAATTCCTGTTGTAGAATCGTTGAGCGCAACGTCGTACGTGCTGCTCTTCTTGCCGGTTCATTTGACAAAGTCTATGATATTGGCAATCCTAAAGGAATTCCTATTGTTGATGACCCTGTGGTTGATTGGCATTATTTTGATGCACAGCCCTTGACCAGAAAGGTACAACAGCTTTTCTATACAGAGGACATGGCCTCAAGATTTGCTGATGGGCTCTGCTTATTTTGGAACTGTAATGTACCAAAATATCCTAATAATGCAATTGTATGCAGGTTTGACACACGTGTGCATTCTGAGTTCAATTTGCCAGGTTGTGATGGCGGTAGTTTGTATGTTAACAAGCACGCTTTTCATACACCAGCATATGATGTGAGTGCATTCCGTGATCTGAAACCTTTACCATTCTTTTATTATTCTACTACACCATGTGAAGTGCATGGTAATGGTAGTATGATAGAGGATATTGATTATGTACCCCTAAAATCTGCAGTCTGTATTACAGCTTGTAATTTAGGGGGCGCTGTTTGTAGGAAGCATGCTACAGAGTACAGAGAGTATATGGAAGCATATAATCTTGTCTCTGCATCAGGTTTCCGCCTTTGGTGTTATAAGACCTTTGATATTTATAATCTCTGGTCTACTTTTACAAAAGTTCAAGGTTTGGAAAACATTGCTTTTAATGTTGTTAAACAAGGCCATTTTATTGGTGTTGAGGGTGAACTACCTGTAGCTGTAGTCAATGATAAGATCTTCACCAAGAGTGGCGTTAATGACATTTGTATGTTTGAGAATAAAACCACTTTGCCTACTAATATAGCTTTTGAACTCTATGCTAAGCGTGCTGTACGCTCGCATCCCGATTTCAAATTGCTACACAATTTACAAGCAGACATTTGCTACAAGTTCGTCCTTTGGGATTATGAACGTAGCAATATTTATGGTACTGCTACTATTGGTGTATGTAAGTACACTGATATTGATGTTAATTCAGCTTTGAATATATGTTTTGACATACGCGATAATGGTTCATTGGAGAAGTTCATGTCTACTCCCAATGCCATCTTTATTTCTGATAGAAAAATTAAGAAATACCCTTGTATTGTAGGTCCTGATTATGCTTACTTCAATGGTGCTATCATCCGTGATAGTGATGTTGTTAAACAACCAGTGAAGTTCTACTTGTATAAGAAAGTCAATAATGAGTTTATTGATCCTACTGAGTGTATTTACACTCAGAGTCGCTCTTGTAGTGACTTCCTACCCCTGTCTGACATGGAGAAAGACTTTCTATCTTTTGATAGTGATGTTTTCATTAAGAAGTATGGCTTGGAAAACTATGCTTTTGAGCACGTAGTCTATGGAGACTTCTCTCATACTACGTTAGGCGGTCTTCACTTGCTTATTGGTTTATACAAGAAGCAACAGGAAGGTCATATTATTATGGAAGAAATGCTAAAAGGTAGCTCAACTATTCATAACTATTTTATTACTGAGACTAACACAGCGGCTTTTAAGGCGGTGTGTTCTGTTATAGATTTAAAGCTTGACGACTTTGTTATGATTTTAAAGAGTCAAGACCTTGGCGTAGTATCCAAGGTTGTCAAGGTTCCTATTGACTTAACAATGATTGAGTTTATGTTATGGTGTAAGGATGGACAGGTTCAAACCTTCTACCCTCGACTCCAGGCTTCTGCAGATTGGAAACCTGGTCATGCAATGCCATCCCTCTTTAAAGTTCAAAATGTAAACCTTGAACGTTGTGAGCTTGCTAATTACAAGCAATCTATTCCTATGCCTCGCGGTGTGCACATGAACATCGCTAAATATATGCAATTGTGCCAGTATTTAAATACTTGCACATTAGCCGTGCCTGCCAATATGCGTGTTATACATTTTGGCGCTGGTTCTGATAAAGGTATCGCTCCTGGTACATCAGTTTTACGACAGTGGCTTCCTACAGATGCCATTATTATAGATAATGATTTAAATGAGTTCGTGTCAGATGCTGACATAACTTTATTTGGAGATTGTGTAACTGTACGTGTCGGCCAACAAGTGGATCTTGTTATTTCCGACATGTATGATCCTACTACTAAGAATGTAACAGGTAGTAATGAGTCAAAGGCTTTATTCTTTACTTACCTGTGTAACCTCATTAATAATAATCTTGCTCTTGGTGGGTCTGTTGCTATTAAAATAACAGAACACTCTTGGAGCGTTGAACTTTATGAACTTATGGGAAAATTTGCTTGGTGGACTGTTTTCTGCACCAATGCAAATGCATCCTCATCTGAAGGATTCCTCTTAGGTATTAATTACTTGGGTACTATTAAAGAAAATATAGATGGTGGTGCTATGCACGCCAACTATATATTTTGGAGAAATTCCACTCCTATGAATCTGAGTACTTACTCACTTTTTGATTTATCCAAGTTTCAATTAAAATTAAAAGGAACACCAGTTCTTCAATTAAAGGAGAGTCAAATTAACGAACTCGTAATATCTCTCCTGTCGCAGGGTAAGTTACTTATCCGTGACAATGATACACTCAGTGTTTCTACTGATGTTCTTGTTAACACCTACAGAAAGTTACGTTGATGTAGGGCCAGATTCTGTTAAGTCTGCTTGTATTGAGGTTGATATACAACAGACTTTCTTTGATAAAACTTGGCCTAGGCCAATTGATGTTTCTAAGGCTGACGGTATTATATACCCTCAAGGCCGTACATATTCTAACATAACTATCACTTATCAAGGTCTTTTTCCCTATCAGGGAGACCATGGTGATATGTATGTCTACTCTGCAGGACATGCTACAGGCACAACTCCACAAAAGTTGTTTGTAGCTAACTATTCTCAGGACGTCAAACAGTTTGCTAATGGGTTTGTCGTCCGTATAGGAGCAGCTGCCAATTCCACTGGCACTGTTATTATTAGCCCATCTACCAGCGCTACTATACGAAAAATTTACCCTGCTTTTATGCTGGGTTCTTCAGTTGGTAATTTCTCAGATGGTAAAATGGGCCGCTTCTTCAATCATACTCTAGTTCTTTTGCCCGATGGATGTGGCACTTTACTTAGAGCTTTTTATTGTATTCTAGAGCCTCGCTCTGGAAATCATTGTCCTGCTGGCAATTCCTATACTTCTTTTGCCACTTATCACACTCCTGCAACAGATTGTTCTGATGGCAATTACAATCGTAATGCCAGTCTGAACTCTTTTAAGGAGTATTTTAATTTACGTAACTGCACCTTTATGTACACTTATAACATTACCGAAGATGAGATTTTAGAGTGGTTTGGCATTACACAAACTGCTCAAGGTGTTCACCTCTTCTCATCTCGGTATGTTGATTTGTACGGCGGCAATATGTTTCAATTTGCCACCTTGCCTGTTTATGATACTATTAAGTATTATTCTATCATTCCTCACAGTATTCGTTCTATCCAAAGTGATAGAAAAGCTTGGGCTGCCTTCTACGTATATAAACTTCAACCGTTAACTTTCCTGTTGGATTTTTCTGTTGATGGTTATATACGCAGAGCTATAGACTGTGGTTTTAATGATTTGTCACAACTCCACTGCTCATATGAATCCTTCGATGTTGAATCTGGAGTTTATTCAGTTTCGTCTTTCGAAGCAAAACCTTCTGGCTCAGTTGTGGAACAGGCTGAAGGTGTTGAATGTGATTTTTCACCTCTTCTGTCTGGCACACCTCCTCAGGTTTATAATTGCAAGCGTTTGGTTTTTACCAATTGCAATTATAATCTTACCAAATTGCTTTCACTTTTTTCTGTGAATGATTTTACTTGTAGTCAAATATCTCCAGCAGCAATTGCTAGCAACTGTTATTCTTCACTGATTTTGGATTATTTTTCATACCCACTTAGTATGAAATCCGATCTCAGTGTTAGTTCTGCTGGTCCAATATCCCAGTTTAATTATAAACAGTCCTTTTCTAATCCCACATGTTTGATTTTAGCGACTGTTCCTCATAACCTTACTACTATTACTAAGCCTCTTAAGTACAGCTATATTAACAAGTGCTCTCGTCTTCTTTCTGATGATCGTACTGAAGTACCTCAGTTAGTGAACGCTAATCAATACTCACCCTGTGTATCCATTGTCCCATCCACTGTGTGGGAAGACGGTGATTATTATAGGAAACAACTATCTCCACTTGAAGGTGGTGGCTGGCTTGTTGCTAGTGGCTCAACTGTTGCCATGACTGAGCAATTACAGATGGGCTTTGGTATTACAGTTCAATATGGTACAGACACCAATAGTGTTTGCCCCAAGCTTGAATTTGCTAATGACACAAAAATTGCCTCTCAATTAGGCAATTGCGTGGAATATTCCCTCTATGGTGTTTCGGGCCGTGGTGTTTTTCAGAATTGCACAGCTGTAGGTGTTCGACAGCAGCGCTTTGTTTATGATGCGTACCAGAATTTAGTTGGCTATTATTCTGATGATGGCAACTACTACTGTTTGCGTGCTTGTGTTAGTGTTCCTGTTTCTGTCATCTATGATAAAGAAACTAAAACCCACGCTACTCTATTTGGTAGTGTTGCATGTGAACACATTTCCTCTACCATGTCTCAATACTCCCGTTCTACGCGATCAATGCTTAAACGGCGAGATTCTACATATGGTCCCCTTCAGACACCTGTTGGTTGTGTCCTAGGACTTGTTAATTCCTCTTTGTTCGTAGAGGACTGCAAGTTGCCTCTTGGTCAATCTCTCTGTGCTCTTCCTGACACACCTAGTACTCTCACACCTCGCAGTGTGCGCTCTGTTCCAGGTGAAATGCGCTTGGCATCCATTGCTTTTAATCATCCTATTCAGGTTGATCAACTTAATAGTAGTTATTTTAAATTAAGTATACCTACTAATTTTTCCTTTGGTGTGACTCAGGAGTACATTCAGACAACCATTCAGAAAGTTACTGTTGATTGTAAACAGTACGTTTGCAATGGTTTCCAGAAGTGTGAGCAATTACTGCGCGAGTATGGCCAGTTTTGTTCCAAAATAAACCAGGCTCTCCATGGTGCCAATTTACGCCAGGATGATTCTGTACGTAATTTGTTTGCGAGCGTGAAAAGCTCTCAATCATCTCCTATCATACCAGGTTTTGGAGGTGACTTTAATTTGACACTTCTAGAACCTGTTTCTATATCTACTGGCAGTCGTAGTGCACGTAGTGCTATTGAGGATTTGCTATTTGACAAAGTCACTATAGCTGATCCTGGTTATATGCAAGGTTACGATGATTGTATGCAGCAAGGTCCAGCATCAGCTCGTGATCTTATTTGTGCTCAATATGTGGCTGGTTATAAAGTATTACCTCCTCTTATGGATGTTAATATGGAAGCCGCGTACACTTCATCTTTGCTTGGCAGCATAGCAGGTGTTGGCTGGACTGCTGGCTTATCCTCCTTTGCTGCTATTCCATTTGCACAGAGTATTTTTTATAGGTTAAACGGTGTTGGCATTACTCAACAGGTTCTTTCAGAGAACCAAAAGCTTATTGCCAATAAGTTTAATCAGGCTCTGGGAGCTATGCAAACAGGCTTCACTACAACTAATGAAGCTTTTCGGAAGGTTCAGGATGCTGTGAACAACAATGCACAGGCTCTATCCAAATTAGCTAGCGAGCTATCTAATACTTTTGGTGCTATTTCCGCCTCTATTGGAGACATCATACAACGTCTTGATGTTCTCGAACAGGACGCCCAAATAGACAGACTTATTAATGGCCGTTTGACAACACTAAATGCTTTTGTTGCACAGCAGCTTGTTCGTTCCGAATCAGCTGCTCTTTCCGCTCAATTGGCTAAAGATAAAGTCAATGAGTGTGTCAAGGCACAATCCAAGCGTTCTGGATTTTGCGGTCAAGGCACACATATAGTGTCCTTTGTTGTAAATGCCCCTAATGGCCTTTACTTTATGCATGTTGGTTATTACCCTAGCAACCACATTGAGGTTGTTTCTGCTTATGGTCTTTGCGATGCAGCTAACCCTACTAATTGTATAGCCCCTGTTAATGGCTACTTTATTAAAACTAATAACACTAGGATTGTTGATGAGTGGTCATATACTGGCTCGTCCTTCTATGCACCTGAGCCCATCACCTCTCTTAATACTAAGTATGTTGCACCACAGGTGACATACCAAAACATTTCTACTAACCTCCCTCCTCCTCTTCTCGGCAATTCCACCGGGATTGACTTCCAAGATGAGTTGGATGAGTTTTTCAAAAATGTTAGCACCAGTATACCTAATTTTGGTTCTCTAACACAGATTAATACTACATTACTCGATCTTACCTACGAGATGTTGTCTCTTCAACAAGTTGTTAAAGCCCTTAATGAGTCTTACATAGACCTTAAAGAGCTTGGCAATTATACTTATTACAACAAATGGCCGTGGTACATTTGGCTTGGTTTCATTGCTGGGCTTGTTGCCTTAGCTCTATGCGTCTTCTTCATACTGTGCTGCACTGGTTGTGGCACAAACTGTATGGGAAAACTTAAGTGTAATCGTTGTTGTGATAGATACGAGGAATACGACCTCGAGCCGCATAAGGTTCATGTTCACTAATTAACGAACTATCAATGAGAGTTCAAAGACCACCCACTCTCTTGTTAGTGTTCTCACTCTCTTTTTTGGTCACTGCATTTTCAAAACCTCTCTATGTACCTGAGCATTGTCAGAATTATTCTGGTTGCATGCTTAGGGCTTGTATTAAAACTGCCCAAGCTGATACAGCTGGTCTTTATACAAATTTTCGAATTGACGTCCCATCTGCAGAATCAACTGGTACTCAATCAGTTTCTGTCGATCGTGAGTCTACTTCAACTCATGATGGTCTTACCGAACATGTTACTAGTGTGAATCTTTTTGACGTTGGTTACTCAGTTAATTAACGAACTCTATGGATTACGTGTCTCTGCTTAATCAAATTTGGCAGAAGTACCTTAATTCACCGTATACTACTTGTTTGTATATCCCTAAACCCACAGCTAAGTATACACCTTTTGCTGGTTATACTGAATCTGCTGTTAATTCTACAAAAGCTTTGGCCAAACAGGACGCAGCTCAGCGAATCGCTTGGTTGCTACATAAGGATGGAGGAATCCCTGACGGATGTTCCCTCTACCTCCGGCACTCAAGTTTATTCGCGCAAAGCGAGGAAGAGGAGTCATTCTCCAACTAAGAAACTGCGCTACGTTAAGCGTAGATTTTCTCTTCTGCGCCCTGAAGACCTTAGTGTTATTGTCCAACCAACACACTATGTCAGGGTTACATTTTCAGACCCCAACATGTGGTATCTACGTTCGGGTCATCATTTACACTCAGTTCACAATTGGCTTAAACCTTATGGCGGCCAACCTGTTTCTGAGTACCATATTACTCTAGCTTTGCTAAATCTCACTGATGAAGATTTAGCTAGAGATTTTTCACCCATTGCGCTCTTTTTGCGCAATGTCAGATTTGAGCTACATGAGTTCGCCTTGCTGCGCAAAACTCTTGTTCTTAATGCATCAGAGATCTACTGTGCTAACATACATAGATTTAAGCCTGTGTATAGAGTTAACACGGCAATCCCTACTATTAAGGATTGGCTTCTCGTTCAGGGATTTTCCCTTTACCATAGTGGCCTCCCTTTACATATGTCAATCTCTAAATTGCATGCACTGGATGATGTTACTCGCAATTACATCATTACAATGCCATGCTTTAGAACTTATCCTCAACAAATGTTTGTTACTCCTTTGGCCGTAGATGTTGTCTCCATACGGTCTTCCAATCAGGGTAATAAACAAATTGTTCATTCTTACCCCATTTTACATCATCCAGGATTTTAACGAACTATGGCTTTCTCGGCGTCTTTATTTAAACCCGTCCAGCTAGTCCCAGTTTCTCCTGCATTTCATCGCATTGAGTCTACTGACTCTATTGTTTTCACATACATTCCTGCTAGCGGCTATGTAGCTGCTTTAGCTGTCAATGTGTGTCTCATTCCCCTATTATTACTGCTACGTCAAGATACTTGTCGTCGCAGCATTATCAGAACTATGGTTCTCTATTTCCTTGTTCTTTATAACTTTTTATTAGCCATTGTACTAGTCAATGGTGTACATTATCCAACTGGAAGTTGCCTGATAGCCTTCTTAGTTATCCTCATAATACTTTGGTTTGTAGATAGAATTCGTTTCTGTCTCATGCTGAATTCCTACATTCCACTGTTTGACATGCGTTCTCACTTTATTCGTGTTAGTACAGTTTCTTCTCATGGTATGGTCCCTGTCATACACACCAAACCATTATTTATTAGAAACTTCGATCAGCGTTGCAGCTGTTCTCGTTGTTTTTATTTGCACTCTTCTACTTATATAGAGTGCACTTATATTAGCCGTTTTAGTAAGATTAGCCTAGTTTCTGTAACTGACTTCTCCTTAAACGGCAATGTTTCCACTGTTTTCGTGCCTGCAACGCGCGATTCAGTTCCTCTTCACATAATCGCCCCGAGCTCGCTTATCGTTTAAGCAGCTCTGCGCTACTATGGGTCCCGTGTAGAGGCTAATCCATTAGTCTCTCTTTGGACATATGGAAAACGAACTATGTTACCCTTTGTCCAAGAACGAATAGGGTTGTTCATAGTAAACTTTTTCATTTTTACCGTAGTATGTGCTATAACACTCTTGGTGTGTATGGCTTTCCTTACGGCTACTAGATTATGTGTGCAATGTATGACAGGCTTCAATACCCTGTTAGTTCAGCCCGCATTATACTTGTATAATACTGGACGTTCAGTCTATGTAAAATTCCAGGATAGTAAACCCCCTCTACCACCTGACGAGTGGGTTTAACGAACTCCTTCATAATGTCTAATACGACGCAACTCACTGAGGCGCAGATTATTGCCATTATTAAAGACTGGAACTTTGCATGGTCCCTGATCTTTCTCTTAATTACTATCGTACTACAGTATGGATACCCATCCCGTAGTATGACTGTCTATGTCTTTAAAATGTTTGTTTTATGGCTCCTATGGCCATCTTCCATGGCGCTATCAATATTTAGCGCCATTTATCCAATTGATCTAGCTTCCCAGATAATCTCTGGCATTGTAGCAGCTGTTTCAGCTATGATGTGGATTTCCTACTTTGTGCAGAGTATCCGGCTGTTTATGAGAACTGGATCATGGTGGTCATTCAATCCTGAGACTAATTGCCTTTTGAACGTTCCAATTGGTGGTACAACTGTCGTACGTCCACTCGTAGAGGACTCTACCAGTGTAACTGCTGTTGTAACCAATGGTCACCTCAAAATGGCTGGCATGCATTTCGGTGCTTGTGACTACGACAGACTTCCTAATGAAGTCACCGTGGCCAAACCCAATGTGCTGATTGCTTTAAAAATGGTGAAGCGGCAAAGCTACGGAACTAATTCCGGCGTTGCCATTTACCATAGATATAAGGCAGGTAATTACAGGAGTCCGCCTATTACGGCGGATATTGAACTTGCATTGCTTCGAGCTTAGGCTCTTTAGTAAGAGTATCTTAATTGATTTTAACGAATCTCAATTTCATTGTTATGGCATCCCCTGCTGCACCTCGTGCTGTTTTCTTTGCCGATAACAATGATATAACAAATACAAACCTGTCTCGAGGTAGAGGACGTAATCCAAAACCACGAGCTGCACCAAATAACACTGTCTCTTGGTACACTGGGCTTACCCAACACGGGAAAGTCCCTCTTACCTTTCCACCTGGGCAGGGTGTACCTCTTAATGCCAATTCCACCCCTGCGCAAAATGCTGGGTATTGGCGGAGACAGGACAGAAAAATTAATACCGGGAATGGAATTAAGCAACTGGCTCCCAGGTGGTACTTCTACTACACTGGAACTGGACCCGAAGCAGCACTCCCATTCCGGGCTGTTAAGGATGGCATCGTTTGGGTCCATGAAGATGGCGCCACTGATGCTCCTTCAACTTTTGGGACGCGGAACCCTAACAATGATTCAGCTATTGTTACACAATTCGCGCCCGGTACTAAGCTTCCTAAAAACTTCCACATTGAGGGGACTGGAGGCAATAGTCAATCATCTTCAAGAGCCTCTAGCGTAAGCAGAAACTCTTCCAGATCTAGTTCACAAGGTTCAAGATCAGGAAACTCTACCCGCGGCACTTCTCCAGGTCCATCTGGAATCGGAGCAGTAGGAGGTGATCTACTTTACCTTGATCTTCTGAACAGACTACAAGCCCTTGAGTCTGGCAAAGTAAAGCAATCGCAGCCAAAAGTAATCACTAAGAAAGATGCTGCTGCTGCTAAAAATAAGATGCGCCACAAGCGCACTTCCACCAAAAGTTTCAACATGGTGCAAGCTTTTGGTCTTCGCGGACCAGGAGACCTCCAGGGAAACTTTGGTGATCTTCAATTTAATAAACTCGGCACTGAGGACCCACGTTGGCCCCAAATTGCTGAGCTTGCTCCTACAGCCAGTGCTTTTATGGGTATGTCGCAATTTAAACTTACCCATCAGAACAATGATGATCATGGCAACCCTGTGTACTTCCTTCGGTACAGTGGAGCCATTAAACTTGACCCAAAGAATCCCAACTACAATAAGTGGTTGGAGCTTCTTGAGCAAAATATTGATGCCTACAAAACCTTCCCTAAGAAGGAAAAGAAACAAAAGGCACCAAAAGAAGAATCAACAGACCAAATGTCTGAACCTCCAAAGGAGCAGCGTGTGCAAGGTAGCATCACTCAGCGCACTCGCACCCGTCCAAGTGTTCAGCCTGGTCCAATGATTGATGTTAACACTGATTAGTGTCACTCAAAGTAACAAGATCGCGGCAATCGTTTGTGTTTGGTAACCCCATCTCACCATCGCTTGTCCACTCTTGCACAGAATGGAATCATGTTGTAATTACAGTGCAATAAGGTAATTATAACCCATTTAATTGATAGCTATGCTTTATTAAAGTGTGTAGCTGTAGAGAGAATGTTAAAGACTGTCACCTCTGCGTGATTGCAAGTGAACAGTGCCCCCTGGGAAGAGCTCTACAGTGTGAAATGTAAATAAAAATAGCTATTATTCAATTAGATTAGGCTAATTAGATGATTTGCAAAAAAAAAAAA','TCCCAGGTAGCAAAACCAACCAACTCTCGATCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTTGGCTGCATGCCTAGTGCACTCACGCAGTATAATAATAATTAATTACTGTCGTTGACAGGAAACGAGTAACTCGTCCGTCTTCTGCAGACTGCTTACGGTTTCGTCCGTGTTGCAGTCGATCATCAGCATACCTAGGTTTTGTCCGGGTGTGACCGAAAGGTAAGATGGAGAGCCTTGTCCCTGGTTTCAACGAGAAAACACACGTCCAACTCAGTTTGCCTGTTCTTCAGGTTCGCGACGTGCTAGTACGTGGCTTTGGAGACTCCGTGGAGGAGGCTCTATCAGAGGCACGTCAACATCTTCTTGACGGCACTTGTGGCATAATCGATGTTGAAAAGGGAGTACTCCCCCAACTCGAACAGCCCTATGTGTTTGTCAAACGTTCTGATGCCCGAACTGCTCCTCACGGCCATGTAATGGTTGAATTGGTGGCAGAACTCGATGGTGTTCAGTACGGTCGTAGCGGTGAGACTCTTGGTGTTCTTGTACCACATGTTGGTGAAACACCTGTTGCTTATCGCAAAATTCTTCTCCGTAAGAATGGTAATAAGGGAGCCGGTGGTCATAGTTTCGGCATCGATCTAAAGTCTTATGACTTAGGTGACGAGCTTGGCACTGATCCCATTGATGACTTTCAAGTCAACTGGAACACTAAACATGGCAGTGGTGTAACTCGTGAGCTCATGCGTGAGCTTAATGGGGGCGCATACACTCGCTATGTAGACAATAACTTCTGTGGCCCTGACGGCTACCCTCTTGAGTGCATCAAAGACTTATTAGCTCGTGCTGGAAAGTCTTCTTGCTCTTTGTCCGAACAACTGGACTTTATTGACACTAAAAGAGGTGTGTACTGCTGCCGTGAACATGAACATGAAATTGTTTGGTACACGGAACGCTCCGACAAGAGCTACGAATTACAGATACCTTTTGAAATCAAATTGGCAAAGAAATTTGACAATTTCACAGGGGAATGTCCAAACTTTGTCTTCCCACTAAATTCTACAATCAAGACCATTCAACCACGTGTTGAAAAGAAAAAGCTTGAGGGTTTTATGGGTAGAATTCGATCTGTCTATCCTGTTGCATCACCAAATGAATGCAACCCAATGCACCTTTCGACGCTTATGAAGTGTGAACATTGTAGTGAAACTTCATGGCAAACTGGTGACTTCCTTAAAGCCACTTGTGAATTTTGTGGTACTGAAAATCAAGTCAAAGAAGGACCTACCACTTGTGGTTACCTTCCTCAAAATGCTGTAGTAAAAATTTTTTGTCCAGCATGTCATAATCCAGAAATGGGACCTGAGCACAGTCTCGCAGAATACCATAATGAATCTGGTATTAAAACCACTCTTCGTAAAGGTGGTCGTACCAAAGCATTTGGAGGATGTGTGTTCTCTTATGTGGGCTGTCACAACAAGTGTGCCTATTGGGTGCCTCGTGCTGCTGCTAACGTAGGATGTAACCACACAGGAGTTGTGGGAGAAGGTTCTGAAAGTCTCAATGATAACCTTCTTGAAATACTTACTAAGGAGAAAGTCAACATTAACATTGTTGGTGACTTTAAACTGACTGAAGAGATCGCCATAATCTTGGCATCTTTTTCTGCATCCACGAGTGCTTTCGTGGAAACTGTGAAGGGCTTGGATTACAAGTCTTTCAAACAAATTGTTGAATCCTGTGGTAACTTTAAAGTAACCAAGGGAAAATTCAAGAAGAATGCTTGGAATATTGGTGAACCAAAGTCCATACTGAGCCCTCTGTATGCATTTCCCTCAGAAGCTGCTCGTGTGGTACGTTCCATTTTTTCACGCACTCTTGAAACTGCTCAACACTCTGTGCGTGTCTTGCAAAAGGCCGCTATTACAATTCTGGACGGAATTTCACAGTACTCACTCAGATTGATTGATGCTATGTTGTTCACGTCTGAACTTACAACAGACAGTATCGTAGTGATGGCATACGTCACAGGTGGTGTTGTACAAATGACTACACAATGGCTTACCAATATTTTTGGTACTGTGTATGAAAAATTGAAACCTATTCTTGACTGGCTTGAAGAGAAGTTCAAGGAAGGGATAGAGTTTCTTAAGGACGGTTGGGAGATTGTAAAATTCATCACAACCTGTTCTTGTGAAATCATTGGTGGACAGCTTGTAGCATTCACCACTGAACTTAAAGACAGTGTGAAGAAATTTTTCAAACTGGTTAACAAATTTCTTGCTCTTTGTGCTGATTCCATCGTCATTGGTGGTGCAAAACTTAAAGCTTTGAATTTGGGAGAAACCTTTGTCGCACACTCCAGAGGACTCTACAAAAAGTGTGTGAAATCCAGAGGAGACTCTGGTTTACTCATGCCTCTAAAAGCACCAAAAGAAGTTATCTTCCTTGATGGAGAAACTTTGCCTACAGAGGTACTTTCAGAAGAAGTAATACTAAAAACTGGTGAATTACAACCACTTGAGGAACCAACTGCACAGGCAGTTGAAGTACCACTCGTAGGTACACCAGTTTGCATTAATGGATTAATGCTGCTTGAAATTAAAGATACTGAAAAGTATTGTGCTCTTGCACCTAACATGATGGTCACTAATAATACCTTCACTCTAAAAGGTGGTGCACCAACCAAAGTCACATTTGGTGATGACACAGTCATTGAAGTCCAAGGCTACAAGAATGTGAATATCACATTTGAATTGGATGAACGAGTAGATAAAGTACTCAACGAAAAGTGCTCTAACTACACTGTAGAACTTGGAACCAACATAGATGAATTGGCTTGTGTTGTAGCTGAGGCAGTAATAAAGACTTTGCAACCTGTTTCAGAATTACTTACACCGCTAGGCATTGACTTAGACGAGTGGGGTGTTGCAACCTATTACTTGTTTGACGAGTCTGGTGAGTATACTTTGTCTTCACGTATGTATTGTTCATTCTATCCTCCAGATGAGGATTATGAAGAAGAATACAGCGAAGAGGAACAACCTGAACAACCAACTCAATATGAGTATGGTACTGAATCTGATTACAAAGGTTTGCCTTTGGAATTTGGTGCATCTTCTGTACAACAACAGGAAGAACAAGAAGAAGATTGGTTAGAAACTGAAGCTGAAGTGGTGGAACAAGAAGTTACACCAACTGAGCAAGAGGAAGAGCTACCAATCACTGAAATTGTTCCTGCAGTGGAACAAACTACAATTGTAGAGCTAGAATGTGATAATTTCACTGGTTATTTAAAACTCACTGATAATGTTTCCATTAAAAATGTGGATATTGTAAGTGAGGCTAAAAATGTAAAACCTACAATAGTGGTTAATGCTGCTAATGTACACCTAAAACATGGTGGTGGTGTTGCTGGTGCTCTTAACAAAGCTACTAACAACGCTATGCAAATTGAGTCTGATGACTACATTGCCAGAAATGGACCACTAAACGTGGGTGGTAGTTGTCTTCTAAATGGACACAATTTGGCTAAAAACTGCCTTCATGTTGTTGGTCCTAATCTCAACAAGGGTGAAGACATTCAATTACTTAAAGTTGCCTATGAAAATTTCAATCATCATGAAAAATTACTTGCACCACTTCTCTCAGCAGGCATCTTTGGTGCACAACCAATACAGTCTTTGAAGGTGTGTATTGAAACAGTACGCACACAAGTCTTTTTAGCTGTCTTTGACAAGGACCTCTATGAAGAACTTGTAGCTAGCTTTTTAGAAATGAAAAGTGAGACTAAAGTACAAGATCACTTTGACGTCGTTGAGACTAAGGTTGAAATTACACCTGAAGAATCTGCTTCAAGTGAGAAACCTACCAAGGAAGAGCCTAAAAAGGTGAAACCTTGTATTGAAGAAGTTACAACTACTCTAGAAGAAACTAAGTTTCTTACAGAAAACTTGTTACTGTATGCAGACATTAATGGTAATCTGTATCCAGATTCAACCAGTCTTGTGGAAATGTTGATGTCACCTTCCTTAAAAAGGATGCTCCTTATATAGTAGGTGACATAATTACTAGTGGTAATTTAACAACCGTTGTCATACCAACAAAGAAAGCAGGTGGTACTACAGAAATGCTTGCAAAGGCATTGCGTAAAGTACCTACTGACCATTATATAACCACCTACCCCGGACAGGGTTGCGTTGGTTATACAATTGAGGAAGCAAAAACAGCTCTTAAGAAGAGTAAGAGTGCTTATTATGTATTACCCTCTATAATTCCAAATAAGAAAGAAGAAATTCTTGGTACTGTTTCTTGGAACTTGCGTGAAATGCTTACGCATGCTGAAGAAACACGTAAATTAATGCCTATTTGCATGGATACAAAGGCTATAATGTCTACTGTGCAAAGGAAGTATAAGGGTATTAAGATACAGGAGGGAGTCGTGGACTACGGTGTAAGGTTTTACTTCTATACTAGTAAAACACCTGTAGCTACACTTATTGCAACTCTTAATTCATTAGGAGAAACCTTGGTCACAATGCCTTTGGGTTATGTGACACATGGTTTAAATTTAGAAGAGGCTGCTAGGTATATGAGATCACTCAAAGTACCCGCAACCGTTTCTGTTTCTTCACCAGATGCTGTTACAGCATATAATGGTTACCTTACTTCCTCTTCAAAGACTCCTGAAGAGCATTTCATAGAAACCATCTCACTAGCTGGTTCATATAAAGACTGGTCCTATTCAGGACAGGCTACTGAATTAGGTATTGAATTTCTTAAAAGAGGTGACAAAGTTGTCTACCACACAACTAGTAAACCAATCACTTTCCACATGGATGGTGAGGTTATCAACATTGACAGTCTTAAGACACTCCTAGCTCTTAGGGAAGTTAAGACCATTAAGGTGTTTACCACAGTTGACAACATTAATCTTCACACTCAAGTTGTGGACATGTCTATGACTTATGGACAACAGTTTGGTCCAACCTACTTGGACGGAGCTGACGTTACAAAGATTAAACCTCATGCATCTCATGACAGCAAGACATTTTATGTGTTGCCTAATGATGATACACTACGCAGTGAGGCTTTTGAGTACTATCACACAACTGATGAAAGTTTTCTAGGTAGATACATGTCAGCATTAAATCATACTAAGAAATGGAAATTTCCACAGGTTAATGGTTTAACATCCATTAAATGGGCAGATAACAATTGTTACCTAGCCACAGCCTTATTAACTCTTCAACAGATAGAATTGAAGTTTAATCCACCAGCATTGCAAGACGCCTACTACAGGGCTAGAGCTGGTGATGCTGCAAATTTCTGTGCACTTATACTTGCTTACTGTAATAAAACAGTGGGTGAGCTAGGTGATGTAAGAGAAACAATGAGTCATTTGTTTCAACATGCCAACTTGGATTCTTGTAAAAGAGTCCTAAATGTGGTGTGTAAAACTTGTGGACAGCAACAAACTACCTTAAAGGGTGTAGAGGCTGTAATGTATATGGGTACACTTTCTTATGAACAACTTAAGAGAGGTGTAACTGTACCGTGTGTTTGTGGAAGACAAGCTACACAGTATTTAGTACAACAAGAGTCATCTTTTGTTATGATGTCTGCACCACCTGCTGAATATAAACTAAAGCATGGTACTTTCTTGTGTGCTAGTGAGTATACTGGTAATTACCAGTGTGGTCATTATAAACACATCACTTCAAAGGAAACCTTGTATGTCATAGATGGTGCATTGCTCAGCAAAACCTCAGAGTACAAAGGCCCTGTTACAGATGTTTTCTATAAAGAAAACAGCTACACAACAACCATAAAACCAATTGTCTATAAACTAGACGGTGTTGTGTGTACAGAAATTGATCCTAAATTGGATGGTTACTATAAAAAGGATAATGCCTATTTTACTGAACAGCCAATTGATTTAGTGCCAACTCAACCTTACCCAAACTCAAACTTTGACAATTTCAAGTTTGTTTGTGACAACACCAAATTTGCTGATGACTTAAACCAGATGTCTGGTTATAAGAAGCCTGCTTCTCGTGAGCTTAAGATTACTTTTTTTCCTGACTTGAATGGTGATGTAGTGGCTATTGATTATAAACATTACACACCTTCATTCAAGAAAGGTGCTAAGTTGTTGCACAAGCCTATTGTATGGCATGTGAATAACACAATTAACAAAGCAACGTTTAAACCAAATACTTGGTGCTTACGTTGTCTTTGGAGTACTAAACCAGTTGAAACGTCAAATATTTTTGATGTTCTGCAATCAGAGGACACACAGGGAATGGAAACTCTTGCCTGTGAGGACACTAAACTTGTCACTGAAGAAGTAGTGGAAACTCCTACCATACAGAAAGACATAGTAGAGTGTGATGTGAAAACTACCGAAGTTGTAGGTGACGTCATACTTAAACCAGTACAAGACGGTGTAAAAATAACAGAAGAAGTTGGTCATGAAGATCTAATGGCTGCTTATGTAGACAATACTAGTCTAACAATTAAGAAACCCAATGAATTATCAGTAATGTTGGGTCTAAAAACTTTAAAAATTCACGGTTTGGCTGCTGTTAATAGTGTCCCTTGGGATACTATTGTTACTTATGCCAAACCGTTTCTTAATAAGGTAACTAGTGTTGCTGCAAGTGGAGTTGCGCGTTGTTTAAACCGCATCTGTGTTAACTATATGCCTTATGTTTTAACTTTGTTGCTGCAATTCTGTACTTTTACTAGAAGTACTAATTCTAGAATCAAAGCATCTATGCCAACTACTATAGCTAAAAATACGGTTAAGAGTGTTGGTAAGTTCTGTTTAGAAGCCTCATTTAATTATTTGAAGTCTCCTAATTTTTCTAAACTCATAACTATTATAGTATGGTTTCTTTTGTTAAGTGTTTGTCTAGGTTCTTTAATCTATTCAAGTGCTGCTTTAGGTGTTTTGATGTCTAATCTAGGTATGCCGTCATACTGTACAAGTTACAGAGATGGTTATCTAAACTCTACTAATGTCACAACAACAGCTTACTGTACGGGTTCTATACCGTGTAGTGTCTGTCTTAGTGGTATGGATTCTTTAGATGCTTATCCTGCTCTAGAAACTATTCAAGTTACCATTCTTCTTTTAAATGGGATTTAACTGCTTTTGGCATTATTGCAGAGTGGTGTTTGGCATATATTCTCTTTACTAGGTTCTTTTATGTACTAGGTTTAGCCGCAATTATGCAATTGTTCTTTGGCTATTTCGCTGTACATTTTATTAGTAATTCTTGGCTTATGTGGCTCATAATTAATCTTGTACAAATGGCCCCTATTTCAGCTATGGTTAGAATGTATATCTTTTTCGCATCATTTTATTATGTGTGGAAGAGCTATATACATGTTGTTGACGGTTGTACCTCATCTACTTGTATGATGTGTTACAAACGTAATAGAGCTACAAGGGTTGAATGCACAACCATTGTAAATGGTGTAAGAAGATCATTTTATGTCTATGCTAATGGAGGTAAAGGATTTTGTAAACTACATAACTGGAATTGTGTCAATTGTGATACTTTCTGTGCAGGTAGTACTTTTATTAGTGATGAAGTCGCAAGAGACTTGTCCCTACAATTTAAGAGACCCATTAATCCTACAGACCAGTCATCTTATGTAGTGGATAGTGTAGCTGTGAAAAATGGTTCGCTACACCTCTACTTTGACAAGGCTGGCCAGAAAACCTATGAAAGACATTCTCTTTCTCACTTTGTCAATTTAGACAACTTGAGAGCTAATAACACTAAAGGATCAATACCCATTAATGTCATTGTATTTGATGGTAAGTCTAAGTGTGATGAATCATCAGCTAGAGCAGCTTCTGTTTATTACAGTCAGCTTATGTGTCAACCTATATTGTTACTTGACCAGGCGTTAGTGTCTGATGTTGGTGACAGTGCAGAAGTAGCTGTTAAAATGTTTGATGCTTATGTTAATACATTCTCATCAACTTTTAACGTGCCTATGGATAAGTTAAAAACTCTCATTGCAACAGCTGAGACTGAACTTGCTAAGAATGTGTCTTTAGATAATGTCCTTTCAACATTTATCTCAGCAGCTCGTCAAGGGTTTGTTGATTCTGATGTTGATACTAAGGACGTTGTGGAATGTCTAAAAATTTCTCATCAATCAGACATTGAAGTTACAGGTGACAGTTGTAATAACTATATGCTCACCTATAACAAAGTGGAAAACATGACGCCTAGAGATCTAGGTGCTTGTATTGATTGCAGTGCACGTCATATTAATGCACAAGTAGCAAAAAGTCACAACATTTCTTTGATTTGGAACATTAAAGATTTCATGTCGCTGTCTGAACAACTGCGTAAACAAATACGTAGTGCTGCTAAGAAGAATAACTTGCCTTTTAAGTTGACATGTGCAACTACTAGACAAGTTGTTAATGTAGTAACAACAAAGATAGCACTTAAAGGTGGTAAATTTGTTACAAATTGGTTTAAGTACTTGCTTAAAGCCACATTAGTTTGTGTTGTTATAGCTTGTGTCTTTTACTTTATTACACCTGTACACGTGCTTACTAAGCATGGTGATTTTGCAGATGAAATCATTGGTTACAAAGCTATTGAAGATGGTGTCACACGTGACATTTCATCTAACGACAATTGCTTTGCTAATAAACACGTTGGATTTGACTCATGGTTTAGTCAACGTGGTGGTTCTTATACTAATGATAAGACTTGTCCAATTGTGGCTGCCGTCATAACTCGTGATGTAGGATTTGTAGTTCCTGGTTTACCAGGAACAATTTTCCGTACATTAAGTGGTGACTTTTTACATTTCTTACCTAGAGTGTTTAGTGCTGTTGGCAATATTTGCTATACACCATCCAAACTTATAGAGTACACTGACTTCGCAACATCAGCCTGTGTTTTAGCAGCTGAATGTACCATATTCAAAGATGCAGCTGGAAAGCCTGTGCCATATTGTTATGACACTAATGTGCTCGAAGGTTCTGTACCTTATGAATCACTCCGTCCAGACACACGTTATGTCTTGATGGATGGTTCTATTATACAATTCCCTAACACGTACCTTGAAGGTTCTGTTAGAGTTGTTACAACTTTTGACTCTGAGTACTGTAGACATGGTACTTGTGAAAAATCTGAAGCTGGCATCTGTGTTTCCACTAGTGGTAGATGGGTGCTTAATAATGATTATTATAGATCATTACCTGGTGTGTTTTGTGGTGTTGATTCTGTAAATCTCTTAACAAATATGTTTACACCTTTGATTCAACCTATTGGTGCTTTAGACATATCAGCTTCAATTGTTGCAGGTGGTTTAGTTGCTATATTTGTAACTTGTCTTGCATACTATTTTATGAGGTTCAGGAGAGCTTTTGGCGAATACAGTCATGTAGTTGCCTTTAATACTCTCTTGTTTTTGATGTCCTTTACTGTACTCTGTCTTACGCCTGTGTATAGTTTCTTACCAGGTGTTTATTCAGTTTTTTATTTGTACTTGACATTTTATCTTACTAATGATGTTTCATTTTTAGCTCATGTTCAATGGATGGTCATGTTCACTCCTTTAGTACCTTTCTGGATTACAATTGTTTATGTCATTTGTATATCTACTAAGCATTGTTACTGGTTCTTTAGTAATTACCTTAGACGTAGAGTTGTCTTTAATGGTACTTCCTTTAGCACTTTTGAAGAAGCAGCTTTGTGTACATTCTTGCTTAACAAGGAAATGTATCTTAAATTGCGTAGTGAAACTTTACTTCCACTGACGCAATATAATAGATACTTAGCGCTTTACAACAAGTACAAATACTTTAGTGGAGCCATGGACACAACTAGCTACAGAGAAGCAGCATGCTGTCATCTTGCTAAGGCTCTAAATGATTTCAGTAACTCAGGTTCTGATGTGCTCTACCAACCACCACAGACATCCATTACATCGGCTGTCCTTCAAAGTGGATTTAGAAAAATGGCTTTTCCATCTGGTAAGGTAGAAGGTTGTATGGTGCAAGTTACTTGTGGAACAACTACACTTAATGGTCTTTGGCTTGATGATGTGGTCTATTGTCCAAGACATGTGATCTGCACAGCTGAAGATATGCTTAATCCAAATTATGAGGATTTGCTTATTCGTAAATCTAACCATAATTTTCTGGTACAAGCTGGTAATGTTCATTTGAGAGTTATCGGACATTCTATGCAAAATTGTGTTCTTAAGCTGAAAGTTGACGCTGCCAACCCTAAGACACCAAAATATAAGTTTGTTCGAATTCAACCCGGACAGACTTTTTCAGTATTAGCTTGTTACAATGGTTCACCATCAGGTGTTTACCAGTGTGCTATGAGACCTAATTTTACTATTAAAGGATCATTCCTTAATGGTTCTTGTGGTAGTGTTGGTTTTAACATAGACTATGACTGTGTCTCTTTTTGCTACATGCATCACATGGAACTTCCAACAGGAGTACATGCGGGCACAGATTTAGAAGGTACCTTCTACGGACCTTTTGTTGACAGACAGACAGCTCAAGCAGCTGGTACAGACACAGTCATTACTATAAATGTTTTGGCTTGGTTGTATGCAGCTGTTATTAATGGAGATAGATGGTTTCTTAACAGATACACAACTACTCTTAATGATTTCAACTTAGTTGCTATGAAGTTCAACTATGAACCTCTCACACAAGATCATGTTGATATTCTAGGACCACTATCAGCTCAAACTGGTATTGCTGTCTTAGATATGTGTGCTTCATTAAAGGAATTGCTCCAAAATGGTGTGAACGGTCGCACTATCTTAGGTAGTGCCATATTAGAAGATGAGTTTACACCATTTGACGTTGTTAGACAATGTTCAGGTGTAACTTTTCAAAGTGCTATTAAAAGAACTGTCAAAGGTACTCACCATTGGTTGTTGTTAACAATCTTGACATCTCTTCTTGTATTGGTTCAAAGTACTCAATGGTCTTTGTTCTTCTTTGTTTATGAAAATGCCTTCTTGCCTTTCGCTTTAGGTATAATTGCTATGTCTGCTTTTGCTATGATGTTTGTTAAGCATAAGCATGCATTCTTGTGTCTATTCCTGTTACCTTCCTTAGCTACTGTAGCTTACTTTAATATGGTCTACATGCCTGCTAGTTGGGTGATGCGTATCATGACTTGGTTGGACATGGTTGATACCAGCTTGTCTGGTTATAAACTTAAGGACTGTATCATGTATGCATCAGCTATTATCTTACTCATACTTATGACAGCAAGAACTGTTTATGATGATGGTGCTAGGCGTGTATGGACACTAATGAATGTTCTTACACTTGTTTATAAAGTCTATTATGGTAATGCTTTAGATCAAGCAATTTCTATGTGGGCTCTTATTATCTCTGTCACCTCTAACTATTCAGGCGTTGTTACAACCGTCATGTTCTTGGCTAGAGGTATTGTCTTTATGTGCGTTGAGTATTGTCCAATTCTCTTTATTACAGGTAACACCTTACAGTGTATAATGTTGGTGTACTGCTTTTTAGGCTATTTTTGTACTTGTTATTTTGGCCTCTTTTGTTTACTCAATCGTTACTTTAGACTTACCCTTGGTGTTTACGATTATCTCGTTTCCACACAAGAGTTTAGATATATGAATTCACAAGGTCTTTTACCACCTAAGAACAGCATAGATGCCTTCAAACTAAATGTTAAGCTTTTAGGTATTGGTGGCAAACCCTGTATCAAAGTAGCAACTGTTCAATCAAAGATGTCAGATGTGAAATGTACTTCTGTAGTCCTTCTCTCAGTTTTACAACAACTTAGAGTTGAATCATCTTCAAAGTTGTGGGCACAGTGTGTGCAATTGCACAATGATATACTTCTTGCAAAGGACACCACTGAAGCATTTGAAAAATGGTTTCATTACTGTCTGTGTTGCTATCCATGCAAGGTGCTGTAGACATAAACAAACTCTGTGAAGAAATGTTGGACAACAGAGCAACATTACAGGCTATTGCTTCAGAATTTAGTTCTTTACCATCTATGCTGCCTTTGCTACAGCTCAAGAAGCTTATGAGCAAGCGGTGGCTAACGGTGATTCTGAAGTGGTTCTTAAAAAGTTAAAGAAATCTCTGAATGTGGCAAAGTCTGAATTTGACCGTGATGCGGCCATGCAGCGTAAGCTAGAAAAGATGGCTGATCAAGCTATGACCCAAATGTACAAACAGGCACGGTCTGAAGACAAGAGGGCAAAAGTCACTAGTGCAATGCAAACTATGCTTTTCACTATGCTTAGAAAACTTGATAATGATGCTCTAAACAACATTATCAATAATGCCAGAGACGGTTGTGTTCCACTGAACATAATCCCCCTTACTACTGCAGCCAAACTAATGGTTGTTGTACCTGACTATAACACCTATAAAAATACTTGTGAAGGTAGTACTTTTACTTATGCCTCAGCACTTTGGGAAATTCAACAAGTTGTTGATGCAGATAGCAAAATAGTCCAACTTAGTGAAATTACTATGGACAATTCTCCTAATATTGCTTGGCCTCTTATTGTAACAGCTTTAAGAGCCAATTCAGCTGTCAAACTTCAGAATAATGAACTGAGTCCCGTAGCACTTCGACAGATGTCATGTGCTGCAGGTACTACACAAACAGCTTGTAATGAGGATAATGCATTAGCCTACTATAACACATCAAAGGGAGGTAGGTTTGTTTTGGCATTACTATCTGATCTTCAAGATCTCAAGTGGGCCAGATTTCCTAAATCTGATGGTACTGGCACCATTTATACAGAGCTGGAACCACCTTGTAGGTTTGTTACAGACACACCAAAAGGACCTAAAGTAAAGTATTTGTACTTCATTAAGGGTTTGAATAATTTGAATAGAGGTATGGTACTGGGCAGCTTAGCTGCTACTGTACGTTTACAAGCTGGTAATGCAACAGAAGTGCCTGCCAACTCAACTGTTCTTTCTTTCTGTGCATTTGCTGTAGATGCATCAAAAGCTTACAGAGACTACCTAGCAAGTGGAGGACAACCAATAACAAATTGTGTTAAGATGTTGTGTACACATACAGGTACTGGTCAGGCAATAACTGTAACACCGGAAGCCAATATGGATCAAGAATCCTTTGGTGGTGCTTCTTGTTGCTTGTACTGTAGATGCCACATAGATCATCCTAACCCTAAAGGTTACTGTGAGCTTAAAGGTAAGTATGTACAAATACCTACCACTTGTGCTAATGACCCAGTGGGTTTTACACTTAAAAACACAGTCTGTACCGTCTGCGGCATGTGGAAAGGTTATGGCTGTAGTTGTGATCAACTCCGCGAACCTATGCTTCAGTCTGCTGATGCACAGTCGTTTTTAAACGGGTTTGCGGTGTAAGTGCAGCCCGTCTTACACCGTGCGGCACAGGCACAAGCACTGATGTCGTGTATAGGGCTTTTGACATCTACAATGAAAAAGTAGCTGGTTTTGCTAAGTTCCTTAAAACAAATTGTTGCCGTTTTCAAGAAAAAGACGAAGATGGTAACCTGATAGATTCCTACTTCATAGTTAAGAGACATACTTTCTCTAACTATCAACATGAAGAAGCTATTTATAACTTGCTTAAAGATTGTCCGGCTGTTGCTGTTCATGATTTTTTCAAGTTTAGAGTAGATGGTGACATGGTACCACACATATCACGTCAACGTCTAACTAAATACACAATGGCAGACTTAGTCTATGCCTTACGTCACTTTGACGAAGGTAATTGTGACACTCTTAAAGAAATACTTGTCACATACAATTGTTGTACTGATGACTATTTTAATAAGAAGGATTGGTATGATTTTGTAGAGAATCCTGACATTTTACGCGTATATGCTAACTTAGGTGAGCGTGTACGTCAAGCATTATTAAAGACTGTACAGTTTTGCGATGCTATGCGTGATGCAGGTATTGTAGGTGTACTAACTCTAGATAATCAAGATCTCAATGGGAACTGGTATGATTTCGGAGATTTCATACAGACTACACCAGGTAGTGGGGTTCCTATTGTTGATTCTTATTATTCATTGCTAATGCCTATTCTCACACTTACGAGGGCATTAGCTGCTGAGTCTCATCTAGACGCTGATTTGACAAAACCTTATGTAAAATGGGATTTGTTAAAATATGATTTCACGGAAGAAAGGTTAAACCTTTTTAACCGTTATTTCAAGTATTGGGATCAAACCTACCACCCAAATTGTGTTAACTGTTTGGATGACAGATGCATTCTGCATTGCGCAAACTTTAATGTGTTATTCTCTACTGTTTTTCCACCAACAAGTTTTGGTCCATTAGTGAGAAAAATTTTTGTTGATGGTGTACCTTTTGTAGTTTCAACAGGTTACCACTTCAGAGAGCTAGGTGTTGTACATAATCAAGATGTAAACATACATAGCTCGAGACTTAGTTTTAAGGAACTATTAGTGTATGCTGCTGATCCTGCTATGCATGCAGCTTCTGGTAATCTTTTGCTAGACAAACGCACTACATGCTTTTCAGTAGCAGCACTAACGAACAATGTTGCTTTTCAAACTGTCAAACCAGGTAATTTTAACAAAGACTTTTATGACTTTGCTGTCTCTAAAGGCTTCTTTAAAGAAGGGAGTTCTGTTGAACTCAAACATTTCTTCTTTGCCCAAGATGGTAATGCTGCTATTAGCGATTACGACTATTATCGGTACAATTTACCAACTATGTGTGATATCCGACAGCTACTATTTGTAGTAGAAGTTGTTGATAAATATTTTGATTGTTATGACGGTGGTTGTATTAATGCAAACCAAGTCATAGTAAACAATTTAGATAAATCTGCCGGATTTCCATTTAACAAATGGGGAAAAGCCAGACTTTATTATGATTCTATGAGCTATGAGGATCAAGATGCACTCTTCGCTTATACTAAGCGTAATGTCATCCCTACTATAACCCAAATGAATCTTAAGTATGCCATTAGTGCTAAAAATAGAGCTCGCACCGTTGCAGGTGTTTCTATTTGTAGTACTATGACTAATAGACAGTTTCATCAAAAACTTTTGAAATCCATAGCCGCCACAAGAGGTGCCACTGTTGTCATCGGAACTAGTAAATTCTATGGTGGCTGGAACAATATGTTAAAAACTGTTTACAGTGATGTAGAAAATCCACACCTTATGGGTTGGGATTATCCAAAATGTGATAGAGCCATGCCTAACATGCTTAGGATAATGGCTTCTCTTGTTCTTGCTCGCAAACATACTACTTGCTGTAGTTTGTCACATCGTTTCTATAGATTAGCTAACGAATGTGCACAAGTTTTAAGTGAAATGGTCATGTGTGGCGGTTCACTATATGTGAAACCAGGTGGTACATCTTCAGGAGATGCCACAACTGCTTATGCTAATAGTGTCTTCAACATTTGTCAGGCTGTTACTGCCAATGTGAATGCACTTCTATCAACTGATGGCAACAAGATTGGCGATAAGTATATTCGCAATCTTCAACACAGACTTTATGAATGTCTCTATAGGAATAGAGATGTTGATACAGACTTTGTCAATGAATTTTACGCTTACTTGCGTAAACATTTTTCAATGATGATACTTTCTGATGATGCTGTTGTTTGCTTTAATAGCACCTACGCATCACAGGGTCTTGTAGCTAGCATAAAGAATTTTAAATCAGTTCTTTATTATCAAAATAATGTTTTTATGTCTGAGGCAAAATGCTGGACTGAGACTGACCTTACAAAGGGACCTCATGAATTTTGCTCTCAACACACTATGCTAGTTAAACAAGGTGATGATTATGTGTACTTGCCCTATCCTGATCCATCACGCATTTTAGGCGCAGGTTGTTTTGTCGATGACATTGTCAAGACAGATGGTACACTAATGATTGAAAGATTTGTGTCATTGGCTATTGATGCTTATCCACTTACTAAACATCCTAATCAGGAGTATGCTGATGTCTTTCATTTGTATTTACAATACATACGAAAGTTACATGATGAACTCACAGGACACATGTTAGACATGTATTCTGTTATGCTTACTAATGATAGTACTTCAAGGTATTGGGAGCCAGAGTTCTATGAAGCAATGTACACACCTCATACAGTCTTACAGGCTGTGGGAGCTTGTGTTCTCTGCAATTCACAGACTTCCTTAAGATGTGGTGCGTGTATACGTAGACCCTTCTTATGCTGTAAATGTTGTTATGACCATGTCATATCAACATCTCATAAATTGGTTTTGTCTGTTAATCCGTATGTTTGCAATGCCACGGGTTGTGACGTCACAGACGTTACACAACTTTATTTAGGAGGTATGAGCTATTATTGCAAAGCACATAAACCGCCTATTAGCTTTCCTCTTTGTGCTAATGGACAGGTTTTTGGTTTGTACAAAACACATGTGTTGGTAGCGATAATGTTACCGACTTTAATGCTATAGCTACATGTGATTGGACAAATGCTGGTGATTACATTCTTGCGAACACCTGCACAGAAAGACTTAAACTTTTTGCTGCTGAAACACTTAAAGCAACAGAGGAGACCTTCAAACTATCTTATGGTATTGCCACTGTACGTGAAGTACTGTCAGATAGAGAATTATATCTTTCTTGGGAAGTAGGAAAACCTAGACCACCTCTCAATAGAAATTATGTGTTTACTGGTTACAGAGTAACTAAGAATAGTAAAACACAAATTGGTGAATACACTTTTGAAAAAGGTGATTATGGTGATGCTGTTGTTTACCGTGGTACAACAACTTATAAATTAAACGTGGGTGACTATTTTGTGTTAACATCACACACAGTCATGCCACTGAGTGCACCAACATTAGTGCCACAGGAGCATTATGTTAGGATTACTGGCTTGTACCCTACACTCAACATTTCAGATGAGTTTTCTAGCAACGTAGCTAATTACCAGAAAGTTGGTATGCAAAATACTCAACTTTACAAGGACCACCAGGTACTGGTAAAAGTCATTTTGCTATAGGATTAGCATTGTACTATCCTTCAGCACGCATTGTTTATACAGCATGTTCACATGCAGCTGTAGATGCACTGTGTGAAAAAGCATTAAAATATCTGCCCATTGATAAATGTAGCAGAATTATACCAGCACGTGCTCGTGTTGAATGCTTTGACAAATTCAAAGTTAATTCGACACTAGAGCAATATGTGTTCTGTACAGTGAATGCACTACCAGAAGCAACAGCTGACATTGTGGTTTTTGATGAGATATCAATGGCCACTAATTATGATTTAAGTGTTGTTAATGCTAGGTTAAGGGCAAAACACTATGTATATATAGGTGACCCTGCACAATTGCCAGCACCACGCACGTTGCTCACTAAGGGTACTCTAGAACCTGAGTACTTTAATTCTGTTTGCAGATTAATGAAAACTATAGGTCCTGATATGTTTTTAGGTACTTGTAGAAGATGTCCTGCTGAAATAGTTGACACTGTAAGTGCTCTAGTTTATGATAATAAACTTAGAGCTCATAAAGATAAATCACAACAGTGCTTTAAAATGTTTTACAAGGGTGTTATAACACATGATGTCTCATCTGCTATTAACAGACCTCAAATTGGTGTAGTTAGAGAATTTCTAACACGCAACCCTACTTGGAGAAAGGCTGTTTTCATCTCTCCTTATAATTCACAGAATGCTGTTGCTGCCAAAATATTAGGTTTACCAACACAAACTGTGGATTCATCACAGGGTTCTGAGTATGACTATGTCATATTCACACAAACAACTGAAACTGCACACTCTTGTAATGTTAACCGCTTTAATGTGGCCATTACTAGAGCAAAAATTGGTATACTTTGCATAATGTCTGATAGAGACCTTTATGACAAATTACAATTTACAAGCCTTGAAGTTCCACGTCGAAACGTGGCAACCTTACAAGCTGAAAATGTAACAGGGCTTTTTAAGGATTGTAGTAAGGTTATTACAGGATTACACCCTACACAAGCACCAACTTACCTTAGTGTTGATACAAAATTCAAGACTGAAGGTTTGTGTGTCGACATACCAGGAATACCAAAAGACATGACCTATAGGAGACTCATCTCTATGATGGGTTTCAAAATGAATTATCAAGTTAATGGTTACCCTAACATGTTCATCACCCGCGAAGAAGCCATTAAACATGTTCGTGCATGGGTTGGTTTTGATGTCGAAGGGTGTCATGCTACAAGAGAAGCTGTTGGTACTAATTTACCATTACAGCTAGGCTTTTCAACAGGTGTCAATCTAGTAGCAGTTCCTACAGGCTACGTTGATACACCTAATGCAACAGAGTTTTCTAGGGTGAGTGCTAAACCACCACCTGGTGACCAATTTAAACATCTTATACCACTTATGTACAAAGGATTACCTTGGAACATTGTGCGTATAAAGATAGTTCAGATGTTAAGTGACACACTTAAAAACCTTTCAGACAGAGTCGTTTTTGTCCTTTGGGCACATGGCTTGAGCTGACATCTATGAAATACTTTGTCAAAATAGGACCTGAACGCACTTGTTGCTTATGTGACAAACGTGCTACCTGTTTTTGCACAGCATCTGATACTTATGCGTGTTGGCATCACTCAGTTGGATTTGACTATGTCTACAACCCTTTCATGATTGATGTTCAACAATGGGGTTTTACTGGTAACCTTCAAAGTAACCATGACCAATACTGTCAAGTACACGGTAATGCACATGTTGCTAGTTGTGATGCTATCATGACTAGATGTTTAGCAGTCCATGAATGCTTTGTTAAGCGTGTTGACTGGACCATTGAATATCCTATTATAGGTGATGAGCTGAAGATAAATGCAGCATGCCGTAAAGTACAACATATGGTAGTAAAGGCTGCATTACTTGCTGATAAGTTTCCAGTTCTTCATGATATTGGTAATCCAAAAGCTATAAAATGTGTACCTCAAGCAGACACAGATTGGAAGTTTTATGATGCTCAACCTTGTAGTGATAAAGCTTATAAAATAGAGGAATTATTCTATTCCTATGCTACCCATTCTGATAAATTCAAGGATGGTGTTTGTCTTTTCTGGAACTGCAACGTTGACAGATACCCAGCAAATGCAATAGTCTGCAGATTTGACACAAGAGTTCTGTCCAATCTAAACTTACCAGGTTGTGATGGTGGTAGTTTGTATGTAAATAAACATGCTTTCCACACACCAGCTTTTGACAAGAGTGCTTTTGTAAATCTTAAGCAATTACCATTCTTTTACTACTCAGATAGCCCTTGTGAGTCTCATGGCAAACAAGTGGTGTCAGACATAGATTATGTACCTTTAAAGTCTGCAACGTGTATTACACGTTGTAACTTAGGTGGGGCTGTTTGCAGACATCATGCGAATGAATACAGATTGTATTTAGACGCCTATAATATGATGATTTCTGCTGGTTTTAGCCTTTGGATTTACAAACAATTTGATACCTACAATCTCTGGAACACTTTTACAAGACTCCAGAGTTTAGAAAATGTGGCTTTCAATGTTATTAATAAGGGACATTTCGATGGACAGCAAGGTGAAACACCTGTTTCTATCGTTAATAACACTGTCTACACAAAAGTAGATGGTGTTGATGTTGAATTGTTTGAGAACAAAACAATACTACCTGTTAATGTAGCGTTTGAGCTCTGGGCTAAGCGCAATATCAAACCTGTTCCAGAAGTGAAAATACTCAACAATTTGGGTGTTGACATTGCTGCTAATACGGTGATTTGGGACTACAAAAGAGAAGCCCCTGCACATGTTTCTACAATTGGAGTTTGTACTATGACTGACATAGCAAAGAAATCTACTGAAACTGCATGTTCACCACTCACTATCTTATTTGATGGTAGAGTTGAAGGACAAGTTGACTTATTCAGAAATGCCCGTAATGGTGTTTTAATAACTGAGGGTAATGTAAAAGGATTACAACCATCAGTAGGTCCAAAACAAGCTAGTCTTAATGGAGTCACATTAATTGGTGAAGCAGTGAAAACACAGTTTAACTATTATAAGAAGGTTGATGGTGTAGTACAACAACTACCTGAAACTTACTTTACTCAGAGTAGAAATTTGCAAGAATTCAAACCCAGGAGTCAAATGGAAATTGATTTCTTAGAATTAGCTATGGATGAGTTCATTGAACGATATAAACTAGAAGGCTACGCTTTCGAACATATCGTTTATGGAGATTTTAGTCATGGTCAGTTAGGTGGATTACATCTATTGATTGGACTTGCTAAGCGTTCTAAGGATTCACCACTAGAATTAGAGGATTTTATTCCTATGGACAGTACAGTTAAAAATTACTTTATTACAGATGCACAAACAGGGTCATCTAAGTGTGTGTGTTCTGTTATAGATTTATTACTTGATGATTTTGTTGAAATAATAAAATCACAGGATTTATCAGTAGTTTCTAAAGTGGTTAAAGTGACTATTGACTATGCAGAAATTGCTTTTATGCTTTGGTGTAAAGATGGCCATGTAGAGACATTTTACCCAAAATTACAATCTAGTCAAGCTTGGCAACCTGGTGTTGCTATGCCGAACCTTTACAAAATGCAGAGAATGCTACTTGATAAATGTGATCTTCAAAATTATGGTGAAGCAGCAACTCTACCTAAAGGCATAATGATGAATGTTGCAAAATATACTCAACTGTGTCAATATTTAAATACTTTGACTTTAGCTGTACCTTATAACATGAGAGTAATACACTTTGGTGCTGGTTCTGATAAAGGAGTTGCACCTGGTACAGCAGTTCTTAGACAGTGGTTGCCTACGGGTACACTACTTGTCGATTCTGATCTTAATGACTTCGTCTCTGACGCTGATTCTACTTTAATAGGTGACTGTGCAACCGTACACACTGCTAATAAATGGGATCTCATTATTAGTGATATGTACGATCCTAAAACCAAACATGTAACAAGAGAAAATGACTCTAAAGAGGGGTTTTTCACTTACATCTGTGGATTTATACAACAAAAGTTAGCCCTTGGAGGTTCTGTGGCCATAAAGATAACAGAGCATTCTTGGAATGCTGATCTTTATAAACTCATGGGACACTTTGCATGGTGGACTGCTTTTGTTACTAATGTAAATGCCTCTTCTTCAGAGGCATTTTTAATTGGATGTAATTATCTTGGCAAACCACGTGAACAAATAGATGGTTATGTCATGCATGCAAATTACATATTCTGGAGGAATACTAATCCAATTCAATTATCTTCCTATTCATTATTTGACATGAGTAAATTTCCTCTTAAATTAAGAGGGACAGCTGTCATGTCCTTAAAAGAAGGACAAATCAATGATATGATATTGTCTTTACTTAGTAAAGGCAGACTTATTATTAGAGAAAACAACAAGGTTGTGATTTCTAGTGATGTTTTAGTTAATAACTAAACGAACTATGTTTGTTTTTCTTTTTGTCTTGCCTTTGGTTTCCAGTCAATGTGTCAATTTGACCACAAGAACTGGAATACCGCCAGGTTATACCAATTCATCTACTAGAGGTGTCTATTATCCAGACAAAGTTTTTAGGTCTTCAATTTTACATCTTACACAAGACCTTTTCTTACCTTTCTTTTCTAATGTTACTTGGTTTAACACCATACATCTAAATTATCAAGGAGGCTTTAAGAAGTTTGACAATCCTGTTTTACCATTAATGATGGTGTTTACTTTGCCTCCACGGAAAAGTCCAATATTATACGCGGTTGGATTTTTGGAACAACACTTGATGCCAGAACTCAATCTCTTCTAATAGTTAACAACGCAACCAATGTTGTTATCAAAGTATGTGAGTTTCAGTTTTGCACTGATCCATTTTTAGGTGTTTACTATCATAACAACAATAAAACATGGGTTGAAAATGAGTTTAGAGTTTATTCAAGTGCCAACAATTGCACTTTCGAATACATTTCTCAACCTTTTCTTATGGACCTTGAAGGAAAGCAAGGTAATTTTAAGAACCTTAGAGAGTTTGTGTTTAAAAATGTTGATGGTTATTTCAAGATTTACTCTAAACACACACCTATTGATTTAGTGCGCGACCTCCCCAGAGGTTTTGCTGCATTGGAACCACTGGTGGACCTCCCTATAGGTATTAATATTACCAGATTCCAAACATTGCTTGCTTTACATAGAAGTTATCTTACACCTGGTAAGCTAGAAAGTGGCTGGACAACTGGAGCTGCTGCTTACTATGTAGGTTACCTACAACAGAGGACTTTTCTCTTAAGTTACAATCAAAATGGAACCATTACAGATGCTGTTGATTGTTCACTAGACCCTCTTTCAGAGACAAAGTGCACATTAAAATCCCTAACAGTTGAAAAAGGAATTTACCAGACTTCTAACTTCAGAGTTCAACCAACAATCAGTATAGTTAGATTTCCTAATATTACAAACTTATGTCCATTTGGAGAAGTGTTTAACGCATCCAAATTTGCATCAGTTTATGCTTGGAACAGGAAGAGAATTAGCAATTGTGTTGCTGATTACTCTGTACTTTATAACTCTACATCATTTTCCACTTTTAAATGTTATGGAGTTTCACCTACAAAACTCAATGACCTTTGCTTCACCAATGTGTATGCAGACTCATTTGTTGTTAAAGGTGACGAGGTTAGACAAATAGCACCCGGACAAACTGGTGTTATTGCTGATTATAACTATAAGCTGCCAGATGATTTTACTGGTTGTGTTATTGCTTGGAACTCAGTTAAGCAAGATGCTTTGACTGGTGGTAATTATGGTTATTTGTATAGATTATTTAGAAAGTCTAAGCTTAAACCATTTGAGAGAGATATTTCCACTGAAATATACCAAGCCGGCAGCACACCCTGTAACGGTCAAGTTGGTCTAAATTGTTATTATCCTCTTGAAAGGTATGGTTTTCACCCAACTACAGGTGTTAACTACCAACCTTTTAGAGTGGTTGTTTTATCATTGAGTTACTTAATGGACCAGCTACTGTTTGTGGACCCAAATTGTCTACAACACTAGTTAAAGACAAATGTGTCAATTTCAACTTTAACGGTTTAACTGGCACAGGTGTTCTTACAACATCTAAGAAACAGTTTCTGCCTTTTCAACAATTTGGTAGAGACATCTCTGACACTACTGATGCTGTCCGTGACCCACAGACACTTGAAATACTTGACATTACCCCTTGCTCTTTTGGAGGAGTTAGTGTGATAACACCAGGTACAAACACTTCTAATCAAGTGGCTGTACTTTACCAAGATGTTAACTGTACTGAAGTGCCTATGGCCATTCATGCAGAACAACTTACACCTGCCTGGCGTGTTTACTCTGCAGGAGCAAATGTGTTTCAAACAAGAGCAGGCTGTTTAGTAGGTGCTGAGCATGTCAACAATTCTTATGAATGTGACATTCCAGTCGGTGCTGGCATATGTGCAAGTTACCATTCCATGTCATCATTTCGTAGTGTCAACCAGCGTTCAATCATTGCTTACACTATGTCTTTAGGTGCAGAAAATTCAGTTGCTTATTCTAATAATTCAATTGCCATACCTACTAATTTTACAATAAGTGTTACCACAGAAATTCTACCAGTGTCAATGACTAAGACTTCTGTAGATTGTACTATGTACATCTGTGGAGATTCAATTGAGTGTAGTAATTTATTGCTACAATATGGCAGTTTTTGCACACAATTAAACCGTGCTTTGACTGGGATTGCTGTTGAACAAGACAAAAACACACAAGAAGTTTTTGCCCAGGTTAAACAAATCTACAAAACACCACCTATTAAAGATTTTGGTGGCTTTAACTTTTTACAAATATTGCCAGATCCATCAAAACCAAGCAAGAGGTCATTTATTGAGGATTTACTCTTCAACAAAGTGACACTTGCTGATGCTGGCTTCATCAAACAATATGGTGATTGCCTTGGTGATATTGCTGCTAGAGATCTCATCTGTGCACAAAAGTTCAATGGACTCACGGTTCTACCGCCTTTGCTCACAGATGAAATGATTGCTCAATACACTTCTGCACTACTTGCTGGAACAATCACCTCAGGTTGGACCTTTGGTGCAGGAGCTGCTTTACAAATACCCTTTGCAATGCAAATGGCTTACAGGTTTAATGGCATTGGAGTCACTCAGAATGTTCTATATGAGAATCAGAAATTAATTGCCAATCAGTTCAACAGTGCTATTGGCAAAATACAGGATTCACTTTCATCTACGGCTAGTGCACTTGGTAAACTTCAAGACGTCGTAAATCAAAATGCACAGGCTTTAAACACACTTGTCAAACAACTTAGTTCCAATTTTGGAGCTATTTCGAGTGTGCTTAATGATATTCTTTCACGTCTTGACAAAGTTGAGGCTGAAGTGCAAATTGATAGGTTAATCACAGGAAGACTACAGAGTCTTCAAACTTATGTGACACAACAATTAATCAGAGCAGCAGAAATCAGAGCTTCTGCTAATCTTGCTGCAACAAAAATGTCTGAGTGCGTACTCGGACAATCTAAAAGAGTTGATTTTTGTGGAAAAGGCTACCATTTAATGTCTTTCCCTCAATCAGCACCGCATGGTGTTGTTTTCTTGCATGTTACTTATGTACCTGCACAAGAAAAGAACTTTACTACTGCTCCTGCTATTTGTCATGAAGGAAAAGCACACTTCCCTCGTGAAGGTGTCTTCGTTTCAAATGGCACTCATTGGTTTATTACACAAAGGAATTTTTATGAACCTCAAATTATTACCACTGACAACACATTCGTCTCTGGTAGCTGTGATGTTGTAATTGGAATAGTCAACAACACAGTTTATGATCCTTTGCAACCCGAGCTTGACTCATTTAAGGAGGAGTTAGACAAATACTTCAAAAATCACACATCACCAGATGTTGATCTTGGCGACATATCTGGCATAAATGCTTCGGTCGTCAACATACAAAAAGAAATTGACCGCCTCAATGAGGTTGCCAAAAATTTGAATGAATCACTCATTGACCTACAAGAGCTTGGAAAATATGAGCAATACATCAAATGGCCTTGGTACATTTGGCTTGGTTTTATAGCTGGGCTAATTGCTATCATTATGGTCACAATCATGCTATGTTGTATGACTAGTTGCTGTAGTTGCCTCAAGGGTTGTTGCTCTTGCGGTTCCTGCTGCAAATTTGATGAAGACGATTCAGAACCTGTTCTGAAAGGAGTCAAATTACATTACACATAAACGAACTTAATGGATTTGTTTATGAGAATTTTTAATCTTGGATCTGTAACATTCAAACCAGGAAAAATTGAAGATGCTACTCCTTCAGATTCTATTCGCGCTACTGCAACGATACCGATACAAGCCTCACTCCCTTTCGGATGGCTTATTGTTGGCGTTGCACTTCTTGCTGTTTTTCAGAGCGCTTCCAAAATAATTACACTCAAAAAGAGGTGGCAATTTGCTCTCTCCAAGGGTGTTCATTTTGCTTGCAACTTGCTTCTACTATTTGTTACAGTCTACTCTCACCTTTTGCTTGTTGCTGCTGGCCTTGAAGCCCAATTTCTCTATCTTTACGCTTTAGTTTATTTTCTGCAAAGTGTTAATGCTTGCAGAATTATTATGAGGCTTTGGCTGTGCTGGAAGTGCAGATCCAAAAATCCATTACTTTATGATGCCAATTACTTTCTTTGCTGGCATACTAATTGCTATGACTATTGTATACCATATAATAGCATAACTTCTTCAATTGTCATTACATCAGGTGATGGCACTCCAAGTCCTATTACAGACCATGACTACCAAATTGGTGGTTATACGGAAAAGTGGGAATCTGGTGTTAAAGACTGTGTTACATTACATGGTTACTTTACATCAGAATGCTACCAGCTGTACTCTACACAACTTAGTACAGATACTGGTGTTGAACATACTACCTTCTTCATTTACAGTAGAATTGTGGATGAACCAGAAGACCATGTTCAAATTCACACAATCGACGGCTCATCAGGAGTTGTAAATCCAGCAATGGATCCTATCTATGATGAGCCGACGACGACTACTAGCGTGCCTTTGTAAGCACAAGCTGATGAGTACGAACTTATGTACTCATTCGTTTCGGAAGAGACAGGTACGTTAATAGTTAATAGCGTACTTCTTTTTCTTGCTTTCGTGGTATTCTTGCTAGTCACACTAGCCATCCTTACTGCGCTTCGATTGTGTGCGTACTGCTGCAATATTGTTAACGTGAGTTTAGTTAAACCTTCTTTTTACGTCTACTCACGTGTTAAAAATCTGAATTCTTCTAGAGTTCCTGATCTTCTGGTCTAAACGAACTAAATATTTTAGTTTTTCTGTTTGGAACTTTAATTTTAGCCATGTCAGCTAACAACGGTACTATTACCGTTGAAGAGCTTAAAAAGCTCTTAGAACAATGGAACCTAGTAATAGGTTTCCTATTTCTAACATGGATTTGTCTTTTACAGTTCGCCTATGCTAACAGGAATAGGTTTCTGTACATAATTAAGTTAATTTTCCTCTGGCTACTTTGGCCAGTAACTTTAGCTTGCTTTGTGCTTGCTGCTGTTTACAGAATCAATTGGATTACCGGTGGAATCGCGATTGCAATGACTTGTCTTGTGGGCTTGATGTGGCTTAGCTACTTCATTGCTTCATTCAGGCTTTTTGCGCGTACGCGTTCCATGTGGTCCTTCAATCCAGAAACAAACATACTGCTGAATGTGCCATTGCATGGTACAATTTTGACCAGACCACTCCTAGAAAGTGAACTTGTCATCGGTGCTGTGATCCTCAGAGGACACCTTCGCATTGCTGGACATCATCTAGGACGCTGTGACATCAAGGACCTGCCAAAAGAAATCACTGTAGCTACATCACGAACGCTTTCTTATTACAAATTGGGAGCTTCGCAGCGTGTAGCCGGTGACTCAGGTTTTGCTGCATACAGTCGCTATCGGATTGGCAACTACAAACTAAACACAGACCATTCCAATAGCAGTGACAATATTGCTTTGCTTGTACAGTAAGTGACAACAGATGTTTCATCTCGTTGACTTTCAGGTTACTATAGCAGAGATACTTATTATTATTATGAGAACTTTCAAGATTTCCATTTGGAACCTTGATTACATCATTAATCTCATAATTAAAAATTTATCTAAGCCTTTAACTGAAAATAAATATTCTCAGTTAGACGAAGAGCAACCAATGGAGATTGATTAAACGAACATGAAAATTATTCTTCTCTTGGCATTAGTTACTTTTGCTACATGCGAACGTTACCACTACCAAGAGTGTGTTAGAGGTACAACTGTACTAATAAAGGAACCTTGCTCTTCTGGAACTTACGAGGGCAATTCACCATTTCATCCTCTTGCTGATAATAAATTTGCACTTGCTTGCACAAGCCAACAATTTGCTTTTGCTTGCCCTGACGGTACTAAACATACCTTTCAGTTACGTGCGAGATCAGTTTCACCAAAACTTTTCATCAGACAAGAGGAAGTTCAAGAACTTTACTCACCACTCTTTCTCATAATTGCTGCATTAGTGTTTATAACACTTTGCTTCACACTTAAGAGAAAGACAGAATGAGTGAAATTACACTAATTGACTTCTATTTGTGCTTTTTAGCCTTTCTGCTATTCCTTGTTTTAATTATGCTCATGATATTTTGGTTTGCTTTGACACTCCAAGATGATGATGAGTGTTGCCAAGTCTAAACGAACATGAAATTTCTTGTTTTACTTGGAATACTAACAACAGTACACACATTCCATCAGGAATGTAGTTTACAGTCATGTCAATTCAATTCACCTTATGTAGTTGATGATCCATGCCCTATACATTTCTACTCGAAATGGTATATTAGGGTCGGTGCTAGAAAATCTGCACCATTGATTGAACTCTGTGTTGATGAAGTAGGTTCAAAAACACCTATTAAATACATCGACATTGGCAACTACACTGTTTCTTGTTCACCGTTTACTATAAACTGTCAAGAACCTAAATTAGGTAGTCTCGTAGTTCGTTGTTCGTTCTATGAAGACTTTGTTGATTACCATGACATTCGTGTTGTTTTAGATTTCATCTAAACGAACAAACAAAATGTCTGATAATGGACCCCAAATCGTGCACCCCGCATTACATTTGGTGGACCCTCAGATTCGACTGACAATAACCAGAATGGAGACCGCAGTGGAGCAAGGCCAAAACAACGAAGGCCCCAGGGATTACCCAATAATACTGCGTCTTGGTTCACCGCTCTCACTCAACATGGTAAGGAAGACCTTAGATTCCCTCGAGGACAAGGTGTTCCGATTAACACCAATAGTACCAAAGATGACCAAATTGGCTACTACCGAAGAGCTACCAGACGAGTTCGTGGTGGTGACGGTAAAATGAAAGATCTCAGTCCACGATGGTACTTCTATTACCTTGGAACTGGGCCAGAAGCTGGACTTCCCTATGGTGCTAACAAAGAAGGCATCATATGGGTTGCAACTGAGGGAGCCTTGAATACACCAAAAGATCACATTGGCACCCGCAATCCAAACAACAATGCTGCAATCGTGCTACAACTTCCTCAAGGAACAGCTTTGCCTAAAGGTTTCTACGCAGAAGGGAGCAGAGGCGGCAGTCAAGCTTCTTCACGCTCTTCATCACGTAGTCGCAATAGTTCCAGAAACTCAACTCCAGGCAGTAGTAGGGGAACTTCTCCTGCTCGAATTGCTGGCAATGGTGGTGATGCTGCCCTTGCTTTGCTACTGCTTGATCGGTTGAATGCACTTGAGAGCAAAATGTCTGGTAAAGGCTCACAACAACAGAGCCAAACAGTCACTAAGAAATCTGCTGCTGAGGCTTCCAAGAAACCTCGCCAAAAACGTACTGCCACTAAACAATACAATGTCACTCAGGCATTTGGCAGACGTGGTCCTGAACAAACCCAAGGAAATTTTGGGGACCAAGAATTAATCAGACAAGGAACTGAGTACAAACATTGGCCGCAAATTGCACAATTTGCACCTAGCGCTTCTGCATTCTTCGGAATGTCGCGCATTGGCATGGAAGTCACACCTTCGGGAACATGGCTGACTTACACAGGTGCCATCAAGCTTGATGACAAAGATCCAAGCTTCAAAGACAACGTCATACTGCTGAACAAGCACATTGACGCATACAAAACATTCCCACCAACAGAGCCTAAAAAGGACAAAAAGAAAAAGACTGACGAAAGCCAGCCTTTACCGCAGAGACAGAAGAAACAACAAACTGTGACTCTTCTTCCTGCTGCAGATTTGGATGATTTCTCCAAACAATTGCAACAATCCATGAGCAGTGCTGATTCAACTCAGGCTTAAACTCATGCAGACCACACAAGGCAGATGGGCTATGTAAACGTTTTCGCTTTTCCGTTTACGATACATAGTCTACTCTTGTGCAGAATGAATTCTCGTAGCTATACAGCACAAGTAGGTATAGTTAACTTTAATCTCACATAGCAATCTTTAATCAGTGTGTAACATTAGGGAGGACTTGAAAGAGCCACCACATTTTCACCGAGGCCACGCGGAGTACGATCGAGGGTACAGTGAATAATGCTAGGGAGAGCTGCCTATATGGAAGAGCCCTAATGTGTAAAATTAATTTTAGTAGTGCTATCCCCATGTGATTTTAATAGCTTC'] - -iface = gr.Interface(fn=classify_sequence, inputs="text", outputs=["text"], - title="Coronavirus Sequence Classifier", - description="Enter a coronavirus sequence to predict its class and probability.",examples=Examples) - -iface.launch() \ No newline at end of file diff --git a/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/helper_scripts/other_tools/make_pssm_dict.py b/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/helper_scripts/other_tools/make_pssm_dict.py deleted file mode 100644 index c6cf83df6febb2ac9e12da3e127dbc9a7ea08d7f..0000000000000000000000000000000000000000 --- a/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/helper_scripts/other_tools/make_pssm_dict.py +++ /dev/null @@ -1,64 +0,0 @@ -import pandas as pd -import numpy as np - -import glob -import random -import numpy as np -import json - - -def softmax(x, T): - return np.exp(x/T)/np.sum(np.exp(x/T), -1, keepdims=True) - -def parse_pssm(path): - data = pd.read_csv(path, skiprows=2) - floats_list_list = [] - for i in range(data.values.shape[0]): - str1 = data.values[i][0][4:] - floats_list = [] - for item in str1.split(): - floats_list.append(float(item)) - floats_list_list.append(floats_list) - np_lines = np.array(floats_list_list) - return np_lines - -np_lines = parse_pssm('/home/swang523/RLcage/capsid/monomersfordesign/8-16-21/pssm_rainity_final_8-16-21_int/build_0.2089_0.98_0.4653_19_2.00_0.005745.pssm') - -mpnn_alphabet = 'ACDEFGHIKLMNPQRSTVWYX' -input_alphabet = 'ARNDCQEGHILKMFPSTWYV' - -permutation_matrix = np.zeros([20,21]) -for i in range(20): - letter1 = input_alphabet[i] - for j in range(21): - letter2 = mpnn_alphabet[j] - if letter1 == letter2: - permutation_matrix[i,j]=1. - -pssm_log_odds = np_lines[:,:20] @ permutation_matrix -pssm_probs = np_lines[:,20:40] @ permutation_matrix - -X_mask = np.concatenate([np.zeros([1,20]), np.ones([1,1])], -1) - -def softmax(x, T): - return np.exp(x/T)/np.sum(np.exp(x/T), -1, keepdims=True) - -#Load parsed PDBs: -with open('/home/justas/projects/cages/parsed/test.jsonl', 'r') as json_file: - json_list = list(json_file) - -my_dict = {} -for json_str in json_list: - result = json.loads(json_str) - all_chain_list = [item[-1:] for item in list(result) if item[:9]=='seq_chain'] - pssm_dict = {} - for chain in all_chain_list: - pssm_dict[chain] = {} - pssm_dict[chain]['pssm_coef'] = (np.ones(len(result['seq_chain_A']))).tolist() #a number between 0.0 and 1.0 specifying how much attention put to PSSM, can be adjusted later as a flag - pssm_dict[chain]['pssm_bias'] = (softmax(pssm_log_odds-X_mask*1e8, 1.0)).tolist() #PSSM like, [length, 21] such that sum over the last dimension adds up to 1.0 - pssm_dict[chain]['pssm_log_odds'] = (pssm_log_odds).tolist() - my_dict[result['name']] = pssm_dict - -#Write output to: -with open('/home/justas/projects/lab_github/mpnn/data/pssm_dict.jsonl', 'w') as f: - f.write(json.dumps(my_dict) + '\n') diff --git a/spaces/PushkarA07/Sanskrit-Text-To-Speech/utils.py b/spaces/PushkarA07/Sanskrit-Text-To-Speech/utils.py deleted file mode 100644 index 07839a71a8339f90fe7eeff4dc4a6bd284330049..0000000000000000000000000000000000000000 --- a/spaces/PushkarA07/Sanskrit-Text-To-Speech/utils.py +++ /dev/null @@ -1,75 +0,0 @@ -import logging -from json import loads -from torch import load, FloatTensor -from numpy import float32 -import librosa - - -class HParams(): - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() - - -def load_checkpoint(checkpoint_path, model): - checkpoint_dict = load(checkpoint_path, map_location='cpu') - iteration = checkpoint_dict['iteration'] - saved_state_dict = checkpoint_dict['model'] - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - new_state_dict= {} - for k, v in state_dict.items(): - try: - new_state_dict[k] = saved_state_dict[k] - except: - logging.info("%s is not in the checkpoint" % k) - new_state_dict[k] = v - if hasattr(model, 'module'): - model.module.load_state_dict(new_state_dict) - else: - model.load_state_dict(new_state_dict) - logging.info("Loaded checkpoint '{}' (iteration {})" .format( - checkpoint_path, iteration)) - return - - -def get_hparams_from_file(config_path): - with open(config_path, "r") as f: - data = f.read() - config = loads(data) - - hparams = HParams(**config) - return hparams - - -def load_audio_to_torch(full_path, target_sampling_rate): - audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True) - return FloatTensor(audio.astype(float32)) diff --git a/spaces/QINGFNEG/White-box-Cartoonization/wbc/cartoonize.py b/spaces/QINGFNEG/White-box-Cartoonization/wbc/cartoonize.py deleted file mode 100644 index 25faf1ceb95aaed9a3f7a7982d17a03dc6bc32b1..0000000000000000000000000000000000000000 --- a/spaces/QINGFNEG/White-box-Cartoonization/wbc/cartoonize.py +++ /dev/null @@ -1,112 +0,0 @@ -import os -import cv2 -import numpy as np -import tensorflow as tf -import wbc.network as network -import wbc.guided_filter as guided_filter -from tqdm import tqdm - - -def resize_crop(image): - h, w, c = np.shape(image) - if min(h, w) > 720: - if h > w: - h, w = int(720 * h / w), 720 - else: - h, w = 720, int(720 * w / h) - image = cv2.resize(image, (w, h), - interpolation=cv2.INTER_AREA) - h, w = (h // 8) * 8, (w // 8) * 8 - image = image[:h, :w, :] - return image - - -def cartoonize(load_folder, save_folder, model_path): - print(model_path) - input_photo = tf.placeholder(tf.float32, [1, None, None, 3]) - network_out = network.unet_generator(input_photo) - final_out = guided_filter.guided_filter(input_photo, network_out, r=1, eps=5e-3) - - all_vars = tf.trainable_variables() - gene_vars = [var for var in all_vars if 'generator' in var.name] - saver = tf.train.Saver(var_list=gene_vars) - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - sess = tf.Session(config=config) - - sess.run(tf.global_variables_initializer()) - saver.restore(sess, tf.train.latest_checkpoint(model_path)) - name_list = os.listdir(load_folder) - for name in tqdm(name_list): - try: - load_path = os.path.join(load_folder, name) - save_path = os.path.join(save_folder, name) - image = cv2.imread(load_path) - image = resize_crop(image) - batch_image = image.astype(np.float32) / 127.5 - 1 - batch_image = np.expand_dims(batch_image, axis=0) - output = sess.run(final_out, feed_dict={input_photo: batch_image}) - output = (np.squeeze(output) + 1) * 127.5 - output = np.clip(output, 0, 255).astype(np.uint8) - cv2.imwrite(save_path, output) - except: - print('cartoonize {} failed'.format(load_path)) - - -class Cartoonize: - def __init__(self, model_path): - print(model_path) - self.input_photo = tf.placeholder(tf.float32, [1, None, None, 3]) - network_out = network.unet_generator(self.input_photo) - self.final_out = guided_filter.guided_filter(self.input_photo, network_out, r=1, eps=5e-3) - - all_vars = tf.trainable_variables() - gene_vars = [var for var in all_vars if 'generator' in var.name] - saver = tf.train.Saver(var_list=gene_vars) - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - self.sess = tf.Session(config=config) - - self.sess.run(tf.global_variables_initializer()) - saver.restore(self.sess, tf.train.latest_checkpoint(model_path)) - - def run(self, load_folder, save_folder): - name_list = os.listdir(load_folder) - for name in tqdm(name_list): - try: - load_path = os.path.join(load_folder, name) - save_path = os.path.join(save_folder, name) - image = cv2.imread(load_path) - image = resize_crop(image) - batch_image = image.astype(np.float32) / 127.5 - 1 - batch_image = np.expand_dims(batch_image, axis=0) - output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image}) - output = (np.squeeze(output) + 1) * 127.5 - output = np.clip(output, 0, 255).astype(np.uint8) - cv2.imwrite(save_path, output) - except: - print('cartoonize {} failed'.format(load_path)) - - def run_sigle(self, load_path, save_path): - try: - image = cv2.imread(load_path) - image = resize_crop(image) - batch_image = image.astype(np.float32) / 127.5 - 1 - batch_image = np.expand_dims(batch_image, axis=0) - output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image}) - output = (np.squeeze(output) + 1) * 127.5 - output = np.clip(output, 0, 255).astype(np.uint8) - cv2.imwrite(save_path, output) - except: - print('cartoonize {} failed'.format(load_path)) - - -if __name__ == '__main__': - model_path = 'saved_models' - load_folder = 'test_images' - save_folder = 'cartoonized_images' - if not os.path.exists(save_folder): - os.mkdir(save_folder) - cartoonize(load_folder, save_folder, model_path) diff --git a/spaces/RamAnanth1/T2I-Adapter/ldm/models/autoencoder.py b/spaces/RamAnanth1/T2I-Adapter/ldm/models/autoencoder.py deleted file mode 100644 index 6a9c4f45498561953b8085981609b2a3298a5473..0000000000000000000000000000000000000000 --- a/spaces/RamAnanth1/T2I-Adapter/ldm/models/autoencoder.py +++ /dev/null @@ -1,443 +0,0 @@ -import torch -import pytorch_lightning as pl -import torch.nn.functional as F -from contextlib import contextmanager - -from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer - -from ldm.modules.diffusionmodules.model import Encoder, Decoder -from ldm.modules.distributions.distributions import DiagonalGaussianDistribution - -from ldm.util import instantiate_from_config - - -class VQModel(pl.LightningModule): - def __init__(self, - ddconfig, - lossconfig, - n_embed, - embed_dim, - ckpt_path=None, - ignore_keys=[], - image_key="image", - colorize_nlabels=None, - monitor=None, - batch_resize_range=None, - scheduler_config=None, - lr_g_factor=1.0, - remap=None, - sane_index_shape=False, # tell vector quantizer to return indices as bhw - use_ema=False - ): - super().__init__() - self.embed_dim = embed_dim - self.n_embed = n_embed - self.image_key = image_key - self.encoder = Encoder(**ddconfig) - self.decoder = Decoder(**ddconfig) - self.loss = instantiate_from_config(lossconfig) - self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, - remap=remap, - sane_index_shape=sane_index_shape) - self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) - self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) - if 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 - self.batch_resize_range = batch_resize_range - if self.batch_resize_range is not None: - print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.") - - self.use_ema = use_ema - if self.use_ema: - self.model_ema = LitEma(self) - print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") - - if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) - self.scheduler_config = scheduler_config - self.lr_g_factor = lr_g_factor - - @contextmanager - def ema_scope(self, context=None): - if self.use_ema: - self.model_ema.store(self.parameters()) - self.model_ema.copy_to(self) - if context is not None: - print(f"{context}: Switched to EMA weights") - try: - yield None - finally: - if self.use_ema: - self.model_ema.restore(self.parameters()) - if context is not None: - print(f"{context}: Restored training weights") - - 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] - missing, unexpected = self.load_state_dict(sd, strict=False) - print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") - if len(missing) > 0: - print(f"Missing Keys: {missing}") - print(f"Unexpected Keys: {unexpected}") - - def on_train_batch_end(self, *args, **kwargs): - if self.use_ema: - self.model_ema(self) - - def encode(self, x): - h = self.encoder(x) - h = self.quant_conv(h) - quant, emb_loss, info = self.quantize(h) - return quant, emb_loss, info - - def encode_to_prequant(self, x): - h = self.encoder(x) - h = self.quant_conv(h) - return h - - def decode(self, quant): - quant = self.post_quant_conv(quant) - dec = self.decoder(quant) - return dec - - def decode_code(self, code_b): - quant_b = self.quantize.embed_code(code_b) - dec = self.decode(quant_b) - return dec - - def forward(self, input, return_pred_indices=False): - quant, diff, (_,_,ind) = self.encode(input) - dec = self.decode(quant) - if return_pred_indices: - return dec, diff, ind - return dec, diff - - def get_input(self, batch, k): - x = batch[k] - if len(x.shape) == 3: - x = x[..., None] - x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() - if self.batch_resize_range is not None: - lower_size = self.batch_resize_range[0] - upper_size = self.batch_resize_range[1] - if self.global_step <= 4: - # do the first few batches with max size to avoid later oom - new_resize = upper_size - else: - new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16)) - if new_resize != x.shape[2]: - x = F.interpolate(x, size=new_resize, mode="bicubic") - x = x.detach() - return x - - def training_step(self, batch, batch_idx, optimizer_idx): - # https://github.com/pytorch/pytorch/issues/37142 - # try not to fool the heuristics - x = self.get_input(batch, self.image_key) - xrec, qloss, ind = self(x, return_pred_indices=True) - - if optimizer_idx == 0: - # autoencode - aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, - last_layer=self.get_last_layer(), split="train", - predicted_indices=ind) - - self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) - return aeloss - - if optimizer_idx == 1: - # discriminator - discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, - last_layer=self.get_last_layer(), split="train") - self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) - return discloss - - def validation_step(self, batch, batch_idx): - log_dict = self._validation_step(batch, batch_idx) - with self.ema_scope(): - log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema") - return log_dict - - def _validation_step(self, batch, batch_idx, suffix=""): - x = self.get_input(batch, self.image_key) - xrec, qloss, ind = self(x, return_pred_indices=True) - aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, - self.global_step, - last_layer=self.get_last_layer(), - split="val"+suffix, - predicted_indices=ind - ) - - discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, - self.global_step, - last_layer=self.get_last_layer(), - split="val"+suffix, - predicted_indices=ind - ) - rec_loss = log_dict_ae[f"val{suffix}/rec_loss"] - self.log(f"val{suffix}/rec_loss", rec_loss, - prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) - self.log(f"val{suffix}/aeloss", aeloss, - prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) - if version.parse(pl.__version__) >= version.parse('1.4.0'): - del log_dict_ae[f"val{suffix}/rec_loss"] - self.log_dict(log_dict_ae) - self.log_dict(log_dict_disc) - return self.log_dict - - def configure_optimizers(self): - lr_d = self.learning_rate - lr_g = self.lr_g_factor*self.learning_rate - print("lr_d", lr_d) - print("lr_g", lr_g) - opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ - list(self.decoder.parameters())+ - list(self.quantize.parameters())+ - list(self.quant_conv.parameters())+ - list(self.post_quant_conv.parameters()), - lr=lr_g, betas=(0.5, 0.9)) - opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), - lr=lr_d, betas=(0.5, 0.9)) - - if self.scheduler_config is not None: - scheduler = instantiate_from_config(self.scheduler_config) - - print("Setting up LambdaLR scheduler...") - scheduler = [ - { - 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule), - 'interval': 'step', - 'frequency': 1 - }, - { - 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule), - 'interval': 'step', - 'frequency': 1 - }, - ] - return [opt_ae, opt_disc], scheduler - return [opt_ae, opt_disc], [] - - def get_last_layer(self): - return self.decoder.conv_out.weight - - def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): - log = dict() - x = self.get_input(batch, self.image_key) - x = x.to(self.device) - if only_inputs: - log["inputs"] = x - return log - xrec, _ = self(x) - if x.shape[1] > 3: - # colorize with random projection - assert xrec.shape[1] > 3 - x = self.to_rgb(x) - xrec = self.to_rgb(xrec) - log["inputs"] = x - log["reconstructions"] = xrec - if plot_ema: - with self.ema_scope(): - xrec_ema, _ = self(x) - if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema) - log["reconstructions_ema"] = xrec_ema - 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 VQModelInterface(VQModel): - def __init__(self, embed_dim, *args, **kwargs): - super().__init__(embed_dim=embed_dim, *args, **kwargs) - self.embed_dim = embed_dim - - def encode(self, x): - h = self.encoder(x) - h = self.quant_conv(h) - return h - - def decode(self, h, force_not_quantize=False): - # also go through quantization layer - if not force_not_quantize: - quant, emb_loss, info = self.quantize(h) - else: - quant = h - quant = self.post_quant_conv(quant) - dec = self.decoder(quant) - return dec - - -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 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(self.loss.discriminator.parameters(), - lr=lr, betas=(0.5, 0.9)) - return [opt_ae, opt_disc], [] - - def get_last_layer(self): - return self.decoder.conv_out.weight - - @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 diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/helpers.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/helpers.py deleted file mode 100644 index 9588b3b780159a2a2d23c7f84a4404ec350e2b65..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/helpers.py +++ /dev/null @@ -1,1088 +0,0 @@ -# helpers.py -import html.entities -import re -import typing - -from . import __diag__ -from .core import * -from .util import _bslash, _flatten, _escape_regex_range_chars - - -# -# global helpers -# -def delimited_list( - expr: Union[str, ParserElement], - delim: Union[str, ParserElement] = ",", - combine: bool = False, - min: typing.Optional[int] = None, - max: typing.Optional[int] = None, - *, - allow_trailing_delim: bool = False, -) -> ParserElement: - """Helper to define a delimited list of expressions - the delimiter - defaults to ','. By default, the list elements and delimiters can - have intervening whitespace, and comments, but this can be - overridden by passing ``combine=True`` in the constructor. If - ``combine`` is set to ``True``, the matching tokens are - returned as a single token string, with the delimiters included; - otherwise, the matching tokens are returned as a list of tokens, - with the delimiters suppressed. - - If ``allow_trailing_delim`` is set to True, then the list may end with - a delimiter. - - Example:: - - delimited_list(Word(alphas)).parse_string("aa,bb,cc") # -> ['aa', 'bb', 'cc'] - delimited_list(Word(hexnums), delim=':', combine=True).parse_string("AA:BB:CC:DD:EE") # -> ['AA:BB:CC:DD:EE'] - """ - if isinstance(expr, str_type): - expr = ParserElement._literalStringClass(expr) - - dlName = "{expr} [{delim} {expr}]...{end}".format( - expr=str(expr.copy().streamline()), - delim=str(delim), - end=" [{}]".format(str(delim)) if allow_trailing_delim else "", - ) - - if not combine: - delim = Suppress(delim) - - if min is not None: - if min < 1: - raise ValueError("min must be greater than 0") - min -= 1 - if max is not None: - if min is not None and max <= min: - raise ValueError("max must be greater than, or equal to min") - max -= 1 - delimited_list_expr = expr + (delim + expr)[min, max] - - if allow_trailing_delim: - delimited_list_expr += Opt(delim) - - if combine: - return Combine(delimited_list_expr).set_name(dlName) - else: - return delimited_list_expr.set_name(dlName) - - -def counted_array( - expr: ParserElement, - int_expr: typing.Optional[ParserElement] = None, - *, - intExpr: typing.Optional[ParserElement] = None, -) -> ParserElement: - """Helper to define a counted list of expressions. - - This helper defines a pattern of the form:: - - integer expr expr expr... - - where the leading integer tells how many expr expressions follow. - The matched tokens returns the array of expr tokens as a list - the - leading count token is suppressed. - - If ``int_expr`` is specified, it should be a pyparsing expression - that produces an integer value. - - Example:: - - counted_array(Word(alphas)).parse_string('2 ab cd ef') # -> ['ab', 'cd'] - - # in this parser, the leading integer value is given in binary, - # '10' indicating that 2 values are in the array - binary_constant = Word('01').set_parse_action(lambda t: int(t[0], 2)) - counted_array(Word(alphas), int_expr=binary_constant).parse_string('10 ab cd ef') # -> ['ab', 'cd'] - - # if other fields must be parsed after the count but before the - # list items, give the fields results names and they will - # be preserved in the returned ParseResults: - count_with_metadata = integer + Word(alphas)("type") - typed_array = counted_array(Word(alphanums), int_expr=count_with_metadata)("items") - result = typed_array.parse_string("3 bool True True False") - print(result.dump()) - - # prints - # ['True', 'True', 'False'] - # - items: ['True', 'True', 'False'] - # - type: 'bool' - """ - intExpr = intExpr or int_expr - array_expr = Forward() - - def count_field_parse_action(s, l, t): - nonlocal array_expr - n = t[0] - array_expr <<= (expr * n) if n else Empty() - # clear list contents, but keep any named results - del t[:] - - if intExpr is None: - intExpr = Word(nums).set_parse_action(lambda t: int(t[0])) - else: - intExpr = intExpr.copy() - intExpr.set_name("arrayLen") - intExpr.add_parse_action(count_field_parse_action, call_during_try=True) - return (intExpr + array_expr).set_name("(len) " + str(expr) + "...") - - -def match_previous_literal(expr: ParserElement) -> ParserElement: - """Helper to define an expression that is indirectly defined from - the tokens matched in a previous expression, that is, it looks for - a 'repeat' of a previous expression. For example:: - - first = Word(nums) - second = match_previous_literal(first) - match_expr = first + ":" + second - - will match ``"1:1"``, but not ``"1:2"``. Because this - matches a previous literal, will also match the leading - ``"1:1"`` in ``"1:10"``. If this is not desired, use - :class:`match_previous_expr`. Do *not* use with packrat parsing - enabled. - """ - rep = Forward() - - def copy_token_to_repeater(s, l, t): - if t: - if len(t) == 1: - rep << t[0] - else: - # flatten t tokens - tflat = _flatten(t.as_list()) - rep << And(Literal(tt) for tt in tflat) - else: - rep << Empty() - - expr.add_parse_action(copy_token_to_repeater, callDuringTry=True) - rep.set_name("(prev) " + str(expr)) - return rep - - -def match_previous_expr(expr: ParserElement) -> ParserElement: - """Helper to define an expression that is indirectly defined from - the tokens matched in a previous expression, that is, it looks for - a 'repeat' of a previous expression. For example:: - - first = Word(nums) - second = match_previous_expr(first) - match_expr = first + ":" + second - - will match ``"1:1"``, but not ``"1:2"``. Because this - matches by expressions, will *not* match the leading ``"1:1"`` - in ``"1:10"``; the expressions are evaluated first, and then - compared, so ``"1"`` is compared with ``"10"``. Do *not* use - with packrat parsing enabled. - """ - rep = Forward() - e2 = expr.copy() - rep <<= e2 - - def copy_token_to_repeater(s, l, t): - matchTokens = _flatten(t.as_list()) - - def must_match_these_tokens(s, l, t): - theseTokens = _flatten(t.as_list()) - if theseTokens != matchTokens: - raise ParseException( - s, l, "Expected {}, found{}".format(matchTokens, theseTokens) - ) - - rep.set_parse_action(must_match_these_tokens, callDuringTry=True) - - expr.add_parse_action(copy_token_to_repeater, callDuringTry=True) - rep.set_name("(prev) " + str(expr)) - return rep - - -def one_of( - strs: Union[typing.Iterable[str], str], - caseless: bool = False, - use_regex: bool = True, - as_keyword: bool = False, - *, - useRegex: bool = True, - asKeyword: bool = False, -) -> ParserElement: - """Helper to quickly define a set of alternative :class:`Literal` s, - and makes sure to do longest-first testing when there is a conflict, - regardless of the input order, but returns - a :class:`MatchFirst` for best performance. - - Parameters: - - - ``strs`` - a string of space-delimited literals, or a collection of - string literals - - ``caseless`` - treat all literals as caseless - (default= ``False``) - - ``use_regex`` - as an optimization, will - generate a :class:`Regex` object; otherwise, will generate - a :class:`MatchFirst` object (if ``caseless=True`` or ``asKeyword=True``, or if - creating a :class:`Regex` raises an exception) - (default= ``True``) - - ``as_keyword`` - enforce :class:`Keyword`-style matching on the - generated expressions - (default= ``False``) - - ``asKeyword`` and ``useRegex`` are retained for pre-PEP8 compatibility, - but will be removed in a future release - - Example:: - - comp_oper = one_of("< = > <= >= !=") - var = Word(alphas) - number = Word(nums) - term = var | number - comparison_expr = term + comp_oper + term - print(comparison_expr.search_string("B = 12 AA=23 B<=AA AA>12")) - - prints:: - - [['B', '=', '12'], ['AA', '=', '23'], ['B', '<=', 'AA'], ['AA', '>', '12']] - """ - asKeyword = asKeyword or as_keyword - useRegex = useRegex and use_regex - - if ( - isinstance(caseless, str_type) - and __diag__.warn_on_multiple_string_args_to_oneof - ): - warnings.warn( - "More than one string argument passed to one_of, pass" - " choices as a list or space-delimited string", - stacklevel=2, - ) - - if caseless: - isequal = lambda a, b: a.upper() == b.upper() - masks = lambda a, b: b.upper().startswith(a.upper()) - parseElementClass = CaselessKeyword if asKeyword else CaselessLiteral - else: - isequal = lambda a, b: a == b - masks = lambda a, b: b.startswith(a) - parseElementClass = Keyword if asKeyword else Literal - - symbols: List[str] = [] - if isinstance(strs, str_type): - symbols = strs.split() - elif isinstance(strs, Iterable): - symbols = list(strs) - else: - raise TypeError("Invalid argument to one_of, expected string or iterable") - if not symbols: - return NoMatch() - - # reorder given symbols to take care to avoid masking longer choices with shorter ones - # (but only if the given symbols are not just single characters) - if any(len(sym) > 1 for sym in symbols): - i = 0 - while i < len(symbols) - 1: - cur = symbols[i] - for j, other in enumerate(symbols[i + 1 :]): - if isequal(other, cur): - del symbols[i + j + 1] - break - elif masks(cur, other): - del symbols[i + j + 1] - symbols.insert(i, other) - break - else: - i += 1 - - if useRegex: - re_flags: int = re.IGNORECASE if caseless else 0 - - try: - if all(len(sym) == 1 for sym in symbols): - # symbols are just single characters, create range regex pattern - patt = "[{}]".format( - "".join(_escape_regex_range_chars(sym) for sym in symbols) - ) - else: - patt = "|".join(re.escape(sym) for sym in symbols) - - # wrap with \b word break markers if defining as keywords - if asKeyword: - patt = r"\b(?:{})\b".format(patt) - - ret = Regex(patt, flags=re_flags).set_name(" | ".join(symbols)) - - if caseless: - # add parse action to return symbols as specified, not in random - # casing as found in input string - symbol_map = {sym.lower(): sym for sym in symbols} - ret.add_parse_action(lambda s, l, t: symbol_map[t[0].lower()]) - - return ret - - except re.error: - warnings.warn( - "Exception creating Regex for one_of, building MatchFirst", stacklevel=2 - ) - - # last resort, just use MatchFirst - return MatchFirst(parseElementClass(sym) for sym in symbols).set_name( - " | ".join(symbols) - ) - - -def dict_of(key: ParserElement, value: ParserElement) -> ParserElement: - """Helper to easily and clearly define a dictionary by specifying - the respective patterns for the key and value. Takes care of - defining the :class:`Dict`, :class:`ZeroOrMore`, and - :class:`Group` tokens in the proper order. The key pattern - can include delimiting markers or punctuation, as long as they are - suppressed, thereby leaving the significant key text. The value - pattern can include named results, so that the :class:`Dict` results - can include named token fields. - - Example:: - - text = "shape: SQUARE posn: upper left color: light blue texture: burlap" - attr_expr = (label + Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join)) - print(attr_expr[1, ...].parse_string(text).dump()) - - attr_label = label - attr_value = Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join) - - # similar to Dict, but simpler call format - result = dict_of(attr_label, attr_value).parse_string(text) - print(result.dump()) - print(result['shape']) - print(result.shape) # object attribute access works too - print(result.as_dict()) - - prints:: - - [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'light blue'], ['texture', 'burlap']] - - color: 'light blue' - - posn: 'upper left' - - shape: 'SQUARE' - - texture: 'burlap' - SQUARE - SQUARE - {'color': 'light blue', 'shape': 'SQUARE', 'posn': 'upper left', 'texture': 'burlap'} - """ - return Dict(OneOrMore(Group(key + value))) - - -def original_text_for( - expr: ParserElement, as_string: bool = True, *, asString: bool = True -) -> ParserElement: - """Helper to return the original, untokenized text for a given - expression. Useful to restore the parsed fields of an HTML start - tag into the raw tag text itself, or to revert separate tokens with - intervening whitespace back to the original matching input text. By - default, returns astring containing the original parsed text. - - If the optional ``as_string`` argument is passed as - ``False``, then the return value is - a :class:`ParseResults` containing any results names that - were originally matched, and a single token containing the original - matched text from the input string. So if the expression passed to - :class:`original_text_for` contains expressions with defined - results names, you must set ``as_string`` to ``False`` if you - want to preserve those results name values. - - The ``asString`` pre-PEP8 argument is retained for compatibility, - but will be removed in a future release. - - Example:: - - src = "this is test bold text normal text " - for tag in ("b", "i"): - opener, closer = make_html_tags(tag) - patt = original_text_for(opener + SkipTo(closer) + closer) - print(patt.search_string(src)[0]) - - prints:: - - [' bold text '] - ['text'] - """ - asString = asString and as_string - - locMarker = Empty().set_parse_action(lambda s, loc, t: loc) - endlocMarker = locMarker.copy() - endlocMarker.callPreparse = False - matchExpr = locMarker("_original_start") + expr + endlocMarker("_original_end") - if asString: - extractText = lambda s, l, t: s[t._original_start : t._original_end] - else: - - def extractText(s, l, t): - t[:] = [s[t.pop("_original_start") : t.pop("_original_end")]] - - matchExpr.set_parse_action(extractText) - matchExpr.ignoreExprs = expr.ignoreExprs - matchExpr.suppress_warning(Diagnostics.warn_ungrouped_named_tokens_in_collection) - return matchExpr - - -def ungroup(expr: ParserElement) -> ParserElement: - """Helper to undo pyparsing's default grouping of And expressions, - even if all but one are non-empty. - """ - return TokenConverter(expr).add_parse_action(lambda t: t[0]) - - -def locatedExpr(expr: ParserElement) -> ParserElement: - """ - (DEPRECATED - future code should use the Located class) - Helper to decorate a returned token with its starting and ending - locations in the input string. - - This helper adds the following results names: - - - ``locn_start`` - location where matched expression begins - - ``locn_end`` - location where matched expression ends - - ``value`` - the actual parsed results - - Be careful if the input text contains ```` characters, you - may want to call :class:`ParserElement.parseWithTabs` - - Example:: - - wd = Word(alphas) - for match in locatedExpr(wd).searchString("ljsdf123lksdjjf123lkkjj1222"): - print(match) - - prints:: - - [[0, 'ljsdf', 5]] - [[8, 'lksdjjf', 15]] - [[18, 'lkkjj', 23]] - """ - locator = Empty().set_parse_action(lambda ss, ll, tt: ll) - return Group( - locator("locn_start") - + expr("value") - + locator.copy().leaveWhitespace()("locn_end") - ) - - -def nested_expr( - opener: Union[str, ParserElement] = "(", - closer: Union[str, ParserElement] = ")", - content: typing.Optional[ParserElement] = None, - ignore_expr: ParserElement = quoted_string(), - *, - ignoreExpr: ParserElement = quoted_string(), -) -> ParserElement: - """Helper method for defining nested lists enclosed in opening and - closing delimiters (``"("`` and ``")"`` are the default). - - Parameters: - - ``opener`` - opening character for a nested list - (default= ``"("``); can also be a pyparsing expression - - ``closer`` - closing character for a nested list - (default= ``")"``); can also be a pyparsing expression - - ``content`` - expression for items within the nested lists - (default= ``None``) - - ``ignore_expr`` - expression for ignoring opening and closing delimiters - (default= :class:`quoted_string`) - - ``ignoreExpr`` - this pre-PEP8 argument is retained for compatibility - but will be removed in a future release - - If an expression is not provided for the content argument, the - nested expression will capture all whitespace-delimited content - between delimiters as a list of separate values. - - Use the ``ignore_expr`` argument to define expressions that may - contain opening or closing characters that should not be treated as - opening or closing characters for nesting, such as quoted_string or - a comment expression. Specify multiple expressions using an - :class:`Or` or :class:`MatchFirst`. The default is - :class:`quoted_string`, but if no expressions are to be ignored, then - pass ``None`` for this argument. - - Example:: - - data_type = one_of("void int short long char float double") - decl_data_type = Combine(data_type + Opt(Word('*'))) - ident = Word(alphas+'_', alphanums+'_') - number = pyparsing_common.number - arg = Group(decl_data_type + ident) - LPAR, RPAR = map(Suppress, "()") - - code_body = nested_expr('{', '}', ignore_expr=(quoted_string | c_style_comment)) - - c_function = (decl_data_type("type") - + ident("name") - + LPAR + Opt(delimited_list(arg), [])("args") + RPAR - + code_body("body")) - c_function.ignore(c_style_comment) - - source_code = ''' - int is_odd(int x) { - return (x%2); - } - - int dec_to_hex(char hchar) { - if (hchar >= '0' && hchar <= '9') { - return (ord(hchar)-ord('0')); - } else { - return (10+ord(hchar)-ord('A')); - } - } - ''' - for func in c_function.search_string(source_code): - print("%(name)s (%(type)s) args: %(args)s" % func) - - - prints:: - - is_odd (int) args: [['int', 'x']] - dec_to_hex (int) args: [['char', 'hchar']] - """ - if ignoreExpr != ignore_expr: - ignoreExpr = ignore_expr if ignoreExpr == quoted_string() else ignoreExpr - if opener == closer: - raise ValueError("opening and closing strings cannot be the same") - if content is None: - if isinstance(opener, str_type) and isinstance(closer, str_type): - if len(opener) == 1 and len(closer) == 1: - if ignoreExpr is not None: - content = Combine( - OneOrMore( - ~ignoreExpr - + CharsNotIn( - opener + closer + ParserElement.DEFAULT_WHITE_CHARS, - exact=1, - ) - ) - ).set_parse_action(lambda t: t[0].strip()) - else: - content = empty.copy() + CharsNotIn( - opener + closer + ParserElement.DEFAULT_WHITE_CHARS - ).set_parse_action(lambda t: t[0].strip()) - else: - if ignoreExpr is not None: - content = Combine( - OneOrMore( - ~ignoreExpr - + ~Literal(opener) - + ~Literal(closer) - + CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS, exact=1) - ) - ).set_parse_action(lambda t: t[0].strip()) - else: - content = Combine( - OneOrMore( - ~Literal(opener) - + ~Literal(closer) - + CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS, exact=1) - ) - ).set_parse_action(lambda t: t[0].strip()) - else: - raise ValueError( - "opening and closing arguments must be strings if no content expression is given" - ) - ret = Forward() - if ignoreExpr is not None: - ret <<= Group( - Suppress(opener) + ZeroOrMore(ignoreExpr | ret | content) + Suppress(closer) - ) - else: - ret <<= Group(Suppress(opener) + ZeroOrMore(ret | content) + Suppress(closer)) - ret.set_name("nested %s%s expression" % (opener, closer)) - return ret - - -def _makeTags(tagStr, xml, suppress_LT=Suppress("<"), suppress_GT=Suppress(">")): - """Internal helper to construct opening and closing tag expressions, given a tag name""" - if isinstance(tagStr, str_type): - resname = tagStr - tagStr = Keyword(tagStr, caseless=not xml) - else: - resname = tagStr.name - - tagAttrName = Word(alphas, alphanums + "_-:") - if xml: - tagAttrValue = dbl_quoted_string.copy().set_parse_action(remove_quotes) - openTag = ( - suppress_LT - + tagStr("tag") - + Dict(ZeroOrMore(Group(tagAttrName + Suppress("=") + tagAttrValue))) - + Opt("/", default=[False])("empty").set_parse_action( - lambda s, l, t: t[0] == "/" - ) - + suppress_GT - ) - else: - tagAttrValue = quoted_string.copy().set_parse_action(remove_quotes) | Word( - printables, exclude_chars=">" - ) - openTag = ( - suppress_LT - + tagStr("tag") - + Dict( - ZeroOrMore( - Group( - tagAttrName.set_parse_action(lambda t: t[0].lower()) - + Opt(Suppress("=") + tagAttrValue) - ) - ) - ) - + Opt("/", default=[False])("empty").set_parse_action( - lambda s, l, t: t[0] == "/" - ) - + suppress_GT - ) - closeTag = Combine(Literal("", adjacent=False) - - openTag.set_name("<%s>" % resname) - # add start results name in parse action now that ungrouped names are not reported at two levels - openTag.add_parse_action( - lambda t: t.__setitem__( - "start" + "".join(resname.replace(":", " ").title().split()), t.copy() - ) - ) - closeTag = closeTag( - "end" + "".join(resname.replace(":", " ").title().split()) - ).set_name("" % resname) - openTag.tag = resname - closeTag.tag = resname - openTag.tag_body = SkipTo(closeTag()) - return openTag, closeTag - - -def make_html_tags( - tag_str: Union[str, ParserElement] -) -> Tuple[ParserElement, ParserElement]: - """Helper to construct opening and closing tag expressions for HTML, - given a tag name. Matches tags in either upper or lower case, - attributes with namespaces and with quoted or unquoted values. - - Example:: - - text = 'More info at the pyparsing wiki page' - # make_html_tags returns pyparsing expressions for the opening and - # closing tags as a 2-tuple - a, a_end = make_html_tags("A") - link_expr = a + SkipTo(a_end)("link_text") + a_end - - for link in link_expr.search_string(text): - # attributes in the tag (like "href" shown here) are - # also accessible as named results - print(link.link_text, '->', link.href) - - prints:: - - pyparsing -> https://github.com/pyparsing/pyparsing/wiki - """ - return _makeTags(tag_str, False) - - -def make_xml_tags( - tag_str: Union[str, ParserElement] -) -> Tuple[ParserElement, ParserElement]: - """Helper to construct opening and closing tag expressions for XML, - given a tag name. Matches tags only in the given upper/lower case. - - Example: similar to :class:`make_html_tags` - """ - return _makeTags(tag_str, True) - - -any_open_tag: ParserElement -any_close_tag: ParserElement -any_open_tag, any_close_tag = make_html_tags( - Word(alphas, alphanums + "_:").set_name("any tag") -) - -_htmlEntityMap = {k.rstrip(";"): v for k, v in html.entities.html5.items()} -common_html_entity = Regex("&(?P" + "|".join(_htmlEntityMap) + ");").set_name( - "common HTML entity" -) - - -def replace_html_entity(t): - """Helper parser action to replace common HTML entities with their special characters""" - return _htmlEntityMap.get(t.entity) - - -class OpAssoc(Enum): - LEFT = 1 - RIGHT = 2 - - -InfixNotationOperatorArgType = Union[ - ParserElement, str, Tuple[Union[ParserElement, str], Union[ParserElement, str]] -] -InfixNotationOperatorSpec = Union[ - Tuple[ - InfixNotationOperatorArgType, - int, - OpAssoc, - typing.Optional[ParseAction], - ], - Tuple[ - InfixNotationOperatorArgType, - int, - OpAssoc, - ], -] - - -def infix_notation( - base_expr: ParserElement, - op_list: List[InfixNotationOperatorSpec], - lpar: Union[str, ParserElement] = Suppress("("), - rpar: Union[str, ParserElement] = Suppress(")"), -) -> ParserElement: - """Helper method for constructing grammars of expressions made up of - operators working in a precedence hierarchy. Operators may be unary - or binary, left- or right-associative. Parse actions can also be - attached to operator expressions. The generated parser will also - recognize the use of parentheses to override operator precedences - (see example below). - - Note: if you define a deep operator list, you may see performance - issues when using infix_notation. See - :class:`ParserElement.enable_packrat` for a mechanism to potentially - improve your parser performance. - - Parameters: - - ``base_expr`` - expression representing the most basic operand to - be used in the expression - - ``op_list`` - list of tuples, one for each operator precedence level - in the expression grammar; each tuple is of the form ``(op_expr, - num_operands, right_left_assoc, (optional)parse_action)``, where: - - - ``op_expr`` is the pyparsing expression for the operator; may also - be a string, which will be converted to a Literal; if ``num_operands`` - is 3, ``op_expr`` is a tuple of two expressions, for the two - operators separating the 3 terms - - ``num_operands`` is the number of terms for this operator (must be 1, - 2, or 3) - - ``right_left_assoc`` is the indicator whether the operator is right - or left associative, using the pyparsing-defined constants - ``OpAssoc.RIGHT`` and ``OpAssoc.LEFT``. - - ``parse_action`` is the parse action to be associated with - expressions matching this operator expression (the parse action - tuple member may be omitted); if the parse action is passed - a tuple or list of functions, this is equivalent to calling - ``set_parse_action(*fn)`` - (:class:`ParserElement.set_parse_action`) - - ``lpar`` - expression for matching left-parentheses; if passed as a - str, then will be parsed as Suppress(lpar). If lpar is passed as - an expression (such as ``Literal('(')``), then it will be kept in - the parsed results, and grouped with them. (default= ``Suppress('(')``) - - ``rpar`` - expression for matching right-parentheses; if passed as a - str, then will be parsed as Suppress(rpar). If rpar is passed as - an expression (such as ``Literal(')')``), then it will be kept in - the parsed results, and grouped with them. (default= ``Suppress(')')``) - - Example:: - - # simple example of four-function arithmetic with ints and - # variable names - integer = pyparsing_common.signed_integer - varname = pyparsing_common.identifier - - arith_expr = infix_notation(integer | varname, - [ - ('-', 1, OpAssoc.RIGHT), - (one_of('* /'), 2, OpAssoc.LEFT), - (one_of('+ -'), 2, OpAssoc.LEFT), - ]) - - arith_expr.run_tests(''' - 5+3*6 - (5+3)*6 - -2--11 - ''', full_dump=False) - - prints:: - - 5+3*6 - [[5, '+', [3, '*', 6]]] - - (5+3)*6 - [[[5, '+', 3], '*', 6]] - - -2--11 - [[['-', 2], '-', ['-', 11]]] - """ - # captive version of FollowedBy that does not do parse actions or capture results names - class _FB(FollowedBy): - def parseImpl(self, instring, loc, doActions=True): - self.expr.try_parse(instring, loc) - return loc, [] - - _FB.__name__ = "FollowedBy>" - - ret = Forward() - if isinstance(lpar, str): - lpar = Suppress(lpar) - if isinstance(rpar, str): - rpar = Suppress(rpar) - - # if lpar and rpar are not suppressed, wrap in group - if not (isinstance(rpar, Suppress) and isinstance(rpar, Suppress)): - lastExpr = base_expr | Group(lpar + ret + rpar) - else: - lastExpr = base_expr | (lpar + ret + rpar) - - for i, operDef in enumerate(op_list): - opExpr, arity, rightLeftAssoc, pa = (operDef + (None,))[:4] - if isinstance(opExpr, str_type): - opExpr = ParserElement._literalStringClass(opExpr) - if arity == 3: - if not isinstance(opExpr, (tuple, list)) or len(opExpr) != 2: - raise ValueError( - "if numterms=3, opExpr must be a tuple or list of two expressions" - ) - opExpr1, opExpr2 = opExpr - term_name = "{}{} term".format(opExpr1, opExpr2) - else: - term_name = "{} term".format(opExpr) - - if not 1 <= arity <= 3: - raise ValueError("operator must be unary (1), binary (2), or ternary (3)") - - if rightLeftAssoc not in (OpAssoc.LEFT, OpAssoc.RIGHT): - raise ValueError("operator must indicate right or left associativity") - - thisExpr: Forward = Forward().set_name(term_name) - if rightLeftAssoc is OpAssoc.LEFT: - if arity == 1: - matchExpr = _FB(lastExpr + opExpr) + Group(lastExpr + opExpr[1, ...]) - elif arity == 2: - if opExpr is not None: - matchExpr = _FB(lastExpr + opExpr + lastExpr) + Group( - lastExpr + (opExpr + lastExpr)[1, ...] - ) - else: - matchExpr = _FB(lastExpr + lastExpr) + Group(lastExpr[2, ...]) - elif arity == 3: - matchExpr = _FB( - lastExpr + opExpr1 + lastExpr + opExpr2 + lastExpr - ) + Group(lastExpr + OneOrMore(opExpr1 + lastExpr + opExpr2 + lastExpr)) - elif rightLeftAssoc is OpAssoc.RIGHT: - if arity == 1: - # try to avoid LR with this extra test - if not isinstance(opExpr, Opt): - opExpr = Opt(opExpr) - matchExpr = _FB(opExpr.expr + thisExpr) + Group(opExpr + thisExpr) - elif arity == 2: - if opExpr is not None: - matchExpr = _FB(lastExpr + opExpr + thisExpr) + Group( - lastExpr + (opExpr + thisExpr)[1, ...] - ) - else: - matchExpr = _FB(lastExpr + thisExpr) + Group( - lastExpr + thisExpr[1, ...] - ) - elif arity == 3: - matchExpr = _FB( - lastExpr + opExpr1 + thisExpr + opExpr2 + thisExpr - ) + Group(lastExpr + opExpr1 + thisExpr + opExpr2 + thisExpr) - if pa: - if isinstance(pa, (tuple, list)): - matchExpr.set_parse_action(*pa) - else: - matchExpr.set_parse_action(pa) - thisExpr <<= (matchExpr | lastExpr).setName(term_name) - lastExpr = thisExpr - ret <<= lastExpr - return ret - - -def indentedBlock(blockStatementExpr, indentStack, indent=True, backup_stacks=[]): - """ - (DEPRECATED - use IndentedBlock class instead) - Helper method for defining space-delimited indentation blocks, - such as those used to define block statements in Python source code. - - Parameters: - - - ``blockStatementExpr`` - expression defining syntax of statement that - is repeated within the indented block - - ``indentStack`` - list created by caller to manage indentation stack - (multiple ``statementWithIndentedBlock`` expressions within a single - grammar should share a common ``indentStack``) - - ``indent`` - boolean indicating whether block must be indented beyond - the current level; set to ``False`` for block of left-most statements - (default= ``True``) - - A valid block must contain at least one ``blockStatement``. - - (Note that indentedBlock uses internal parse actions which make it - incompatible with packrat parsing.) - - Example:: - - data = ''' - def A(z): - A1 - B = 100 - G = A2 - A2 - A3 - B - def BB(a,b,c): - BB1 - def BBA(): - bba1 - bba2 - bba3 - C - D - def spam(x,y): - def eggs(z): - pass - ''' - - - indentStack = [1] - stmt = Forward() - - identifier = Word(alphas, alphanums) - funcDecl = ("def" + identifier + Group("(" + Opt(delimitedList(identifier)) + ")") + ":") - func_body = indentedBlock(stmt, indentStack) - funcDef = Group(funcDecl + func_body) - - rvalue = Forward() - funcCall = Group(identifier + "(" + Opt(delimitedList(rvalue)) + ")") - rvalue << (funcCall | identifier | Word(nums)) - assignment = Group(identifier + "=" + rvalue) - stmt << (funcDef | assignment | identifier) - - module_body = stmt[1, ...] - - parseTree = module_body.parseString(data) - parseTree.pprint() - - prints:: - - [['def', - 'A', - ['(', 'z', ')'], - ':', - [['A1'], [['B', '=', '100']], [['G', '=', 'A2']], ['A2'], ['A3']]], - 'B', - ['def', - 'BB', - ['(', 'a', 'b', 'c', ')'], - ':', - [['BB1'], [['def', 'BBA', ['(', ')'], ':', [['bba1'], ['bba2'], ['bba3']]]]]], - 'C', - 'D', - ['def', - 'spam', - ['(', 'x', 'y', ')'], - ':', - [[['def', 'eggs', ['(', 'z', ')'], ':', [['pass']]]]]]] - """ - backup_stacks.append(indentStack[:]) - - def reset_stack(): - indentStack[:] = backup_stacks[-1] - - def checkPeerIndent(s, l, t): - if l >= len(s): - return - curCol = col(l, s) - if curCol != indentStack[-1]: - if curCol > indentStack[-1]: - raise ParseException(s, l, "illegal nesting") - raise ParseException(s, l, "not a peer entry") - - def checkSubIndent(s, l, t): - curCol = col(l, s) - if curCol > indentStack[-1]: - indentStack.append(curCol) - else: - raise ParseException(s, l, "not a subentry") - - def checkUnindent(s, l, t): - if l >= len(s): - return - curCol = col(l, s) - if not (indentStack and curCol in indentStack): - raise ParseException(s, l, "not an unindent") - if curCol < indentStack[-1]: - indentStack.pop() - - NL = OneOrMore(LineEnd().set_whitespace_chars("\t ").suppress()) - INDENT = (Empty() + Empty().set_parse_action(checkSubIndent)).set_name("INDENT") - PEER = Empty().set_parse_action(checkPeerIndent).set_name("") - UNDENT = Empty().set_parse_action(checkUnindent).set_name("UNINDENT") - if indent: - smExpr = Group( - Opt(NL) - + INDENT - + OneOrMore(PEER + Group(blockStatementExpr) + Opt(NL)) - + UNDENT - ) - else: - smExpr = Group( - Opt(NL) - + OneOrMore(PEER + Group(blockStatementExpr) + Opt(NL)) - + Opt(UNDENT) - ) - - # add a parse action to remove backup_stack from list of backups - smExpr.add_parse_action( - lambda: backup_stacks.pop(-1) and None if backup_stacks else None - ) - smExpr.set_fail_action(lambda a, b, c, d: reset_stack()) - blockStatementExpr.ignore(_bslash + LineEnd()) - return smExpr.set_name("indented block") - - -# it's easy to get these comment structures wrong - they're very common, so may as well make them available -c_style_comment = Combine(Regex(r"/\*(?:[^*]|\*(?!/))*") + "*/").set_name( - "C style comment" -) -"Comment of the form ``/* ... */``" - -html_comment = Regex(r"").set_name("HTML comment") -"Comment of the form ````" - -rest_of_line = Regex(r".*").leave_whitespace().set_name("rest of line") -dbl_slash_comment = Regex(r"//(?:\\\n|[^\n])*").set_name("// comment") -"Comment of the form ``// ... (to end of line)``" - -cpp_style_comment = Combine( - Regex(r"/\*(?:[^*]|\*(?!/))*") + "*/" | dbl_slash_comment -).set_name("C++ style comment") -"Comment of either form :class:`c_style_comment` or :class:`dbl_slash_comment`" - -java_style_comment = cpp_style_comment -"Same as :class:`cpp_style_comment`" - -python_style_comment = Regex(r"#.*").set_name("Python style comment") -"Comment of the form ``# ... (to end of line)``" - - -# build list of built-in expressions, for future reference if a global default value -# gets updated -_builtin_exprs: List[ParserElement] = [ - v for v in vars().values() if isinstance(v, ParserElement) -] - - -# pre-PEP8 compatible names -delimitedList = delimited_list -countedArray = counted_array -matchPreviousLiteral = match_previous_literal -matchPreviousExpr = match_previous_expr -oneOf = one_of -dictOf = dict_of -originalTextFor = original_text_for -nestedExpr = nested_expr -makeHTMLTags = make_html_tags -makeXMLTags = make_xml_tags -anyOpenTag, anyCloseTag = any_open_tag, any_close_tag -commonHTMLEntity = common_html_entity -replaceHTMLEntity = replace_html_entity -opAssoc = OpAssoc -infixNotation = infix_notation -cStyleComment = c_style_comment -htmlComment = html_comment -restOfLine = rest_of_line -dblSlashComment = dbl_slash_comment -cppStyleComment = cpp_style_comment -javaStyleComment = java_style_comment -pythonStyleComment = python_style_comment diff --git a/spaces/Realcat/image-matching-webui/third_party/SGMNet/train/train.py b/spaces/Realcat/image-matching-webui/third_party/SGMNet/train/train.py deleted file mode 100644 index b012b7bf231de77972f443ab6979038151d2cfce..0000000000000000000000000000000000000000 --- a/spaces/Realcat/image-matching-webui/third_party/SGMNet/train/train.py +++ /dev/null @@ -1,230 +0,0 @@ -import torch -import torch.optim as optim -from tqdm import trange -import os -from tensorboardX import SummaryWriter -import numpy as np -import cv2 -from loss import SGMLoss, SGLoss -from valid import valid, dump_train_vis - -import sys - -ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) -sys.path.insert(0, ROOT_DIR) - - -from utils import train_utils - - -def train_step(optimizer, model, match_loss, data, step, pre_avg_loss): - data["step"] = step - result = model(data, test_mode=False) - loss_res = match_loss.run(data, result) - - optimizer.zero_grad() - loss_res["total_loss"].backward() - # apply reduce on all record tensor - for key in loss_res.keys(): - loss_res[key] = train_utils.reduce_tensor(loss_res[key], "mean") - - if loss_res["total_loss"] < 7 * pre_avg_loss or step < 200 or pre_avg_loss == 0: - optimizer.step() - unusual_loss = False - else: - optimizer.zero_grad() - unusual_loss = True - return loss_res, unusual_loss - - -def train(model, train_loader, valid_loader, config, model_config): - model.train() - optimizer = optim.Adam(model.parameters(), lr=config.train_lr) - - if config.model_name == "SGM": - match_loss = SGMLoss(config, model_config) - elif config.model_name == "SG": - match_loss = SGLoss(config, model_config) - else: - raise NotImplementedError - - checkpoint_path = os.path.join(config.log_base, "checkpoint.pth") - config.resume = os.path.isfile(checkpoint_path) - if config.resume: - if config.local_rank == 0: - print("==> Resuming from checkpoint..") - checkpoint = torch.load( - checkpoint_path, map_location="cuda:{}".format(config.local_rank) - ) - model.load_state_dict(checkpoint["state_dict"]) - best_acc = checkpoint["best_acc"] - start_step = checkpoint["step"] - optimizer.load_state_dict(checkpoint["optimizer"]) - else: - best_acc = -1 - start_step = 0 - train_loader_iter = iter(train_loader) - - if config.local_rank == 0: - writer = SummaryWriter(os.path.join(config.log_base, "log_file")) - - train_loader.sampler.set_epoch( - start_step * config.train_batch_size // len(train_loader.dataset) - ) - pre_avg_loss = 0 - - progress_bar = ( - trange(start_step, config.train_iter, ncols=config.tqdm_width) - if config.local_rank == 0 - else range(start_step, config.train_iter) - ) - for step in progress_bar: - try: - train_data = next(train_loader_iter) - except StopIteration: - if config.local_rank == 0: - print( - "epoch: ", - step * config.train_batch_size // len(train_loader.dataset), - ) - train_loader.sampler.set_epoch( - step * config.train_batch_size // len(train_loader.dataset) - ) - train_loader_iter = iter(train_loader) - train_data = next(train_loader_iter) - - train_data = train_utils.tocuda(train_data) - lr = min( - config.train_lr * config.decay_rate ** (step - config.decay_iter), - config.train_lr, - ) - for param_group in optimizer.param_groups: - param_group["lr"] = lr - - # run training - loss_res, unusual_loss = train_step( - optimizer, model, match_loss, train_data, step - start_step, pre_avg_loss - ) - if (step - start_step) <= 200: - pre_avg_loss = loss_res["total_loss"].data - if (step - start_step) > 200 and not unusual_loss: - pre_avg_loss = pre_avg_loss.data * 0.9 + loss_res["total_loss"].data * 0.1 - if unusual_loss and config.local_rank == 0: - print( - "unusual loss! pre_avg_loss: ", - pre_avg_loss, - "cur_loss: ", - loss_res["total_loss"].data, - ) - # log - if config.local_rank == 0 and step % config.log_intv == 0 and not unusual_loss: - writer.add_scalar("TotalLoss", loss_res["total_loss"], step) - writer.add_scalar("CorrLoss", loss_res["loss_corr"], step) - writer.add_scalar("InCorrLoss", loss_res["loss_incorr"], step) - writer.add_scalar("dustbin", model.module.dustbin, step) - - if config.model_name == "SGM": - writer.add_scalar("SeedConfLoss", loss_res["loss_seed_conf"], step) - writer.add_scalar("MidCorrLoss", loss_res["loss_corr_mid"].sum(), step) - writer.add_scalar( - "MidInCorrLoss", loss_res["loss_incorr_mid"].sum(), step - ) - - # valid ans save - b_save = ((step + 1) % config.save_intv) == 0 - b_validate = ((step + 1) % config.val_intv) == 0 - if b_validate: - ( - total_loss, - acc_corr, - acc_incorr, - seed_precision_tower, - seed_recall_tower, - acc_mid, - ) = valid(valid_loader, model, match_loss, config, model_config) - if config.local_rank == 0: - writer.add_scalar("ValidAcc", acc_corr, step) - writer.add_scalar("ValidLoss", total_loss, step) - - if config.model_name == "SGM": - for i in range(len(seed_recall_tower)): - writer.add_scalar( - "seed_conf_pre_%d" % i, seed_precision_tower[i], step - ) - writer.add_scalar( - "seed_conf_recall_%d" % i, seed_precision_tower[i], step - ) - for i in range(len(acc_mid)): - writer.add_scalar("acc_mid%d" % i, acc_mid[i], step) - print( - "acc_corr: ", - acc_corr.data, - "acc_incorr: ", - acc_incorr.data, - "seed_conf_pre: ", - seed_precision_tower.mean().data, - "seed_conf_recall: ", - seed_recall_tower.mean().data, - "acc_mid: ", - acc_mid.mean().data, - ) - else: - print("acc_corr: ", acc_corr.data, "acc_incorr: ", acc_incorr.data) - - # saving best - if acc_corr > best_acc: - print("Saving best model with va_res = {}".format(acc_corr)) - best_acc = acc_corr - save_dict = { - "step": step + 1, - "state_dict": model.state_dict(), - "best_acc": best_acc, - "optimizer": optimizer.state_dict(), - } - save_dict.update(save_dict) - torch.save( - save_dict, os.path.join(config.log_base, "model_best.pth") - ) - - if b_save: - if config.local_rank == 0: - save_dict = { - "step": step + 1, - "state_dict": model.state_dict(), - "best_acc": best_acc, - "optimizer": optimizer.state_dict(), - } - torch.save(save_dict, checkpoint_path) - - # draw match results - model.eval() - with torch.no_grad(): - if config.local_rank == 0: - if not os.path.exists( - os.path.join(config.train_vis_folder, "train_vis") - ): - os.mkdir(os.path.join(config.train_vis_folder, "train_vis")) - if not os.path.exists( - os.path.join( - config.train_vis_folder, "train_vis", config.log_base - ) - ): - os.mkdir( - os.path.join( - config.train_vis_folder, "train_vis", config.log_base - ) - ) - os.mkdir( - os.path.join( - config.train_vis_folder, - "train_vis", - config.log_base, - str(step), - ) - ) - res = model(train_data) - dump_train_vis(res, train_data, step, config) - model.train() - - if config.local_rank == 0: - writer.close() diff --git a/spaces/Redgon/bingo/src/components/tone-selector.tsx b/spaces/Redgon/bingo/src/components/tone-selector.tsx deleted file mode 100644 index 5c6e464c91f564b895acd121f0a4a79ed9c5c356..0000000000000000000000000000000000000000 --- a/spaces/Redgon/bingo/src/components/tone-selector.tsx +++ /dev/null @@ -1,43 +0,0 @@ -import React from 'react' -import { BingConversationStyle } from '@/lib/bots/bing/types' -import { cn } from '@/lib/utils' - -type ToneItem = { - type: BingConversationStyle, - name: string -} - -const ToneList: ToneItem[] = [ - { name: '有创造力', type: BingConversationStyle.Creative }, - { name: '更平衡', type: BingConversationStyle.Balanced }, - { name: '更精确', type: BingConversationStyle.Precise } -] - -interface ToneSelectorProps { - type: BingConversationStyle | '' - onChange?: (type: BingConversationStyle) => void -} - -export function ToneSelector({ type, onChange }: ToneSelectorProps) { - return ( -
-
- 选择对话样式 -
-
-
    - { - ToneList.map(tone => ( -
  • onChange?.(tone.type)}> - -
    • - )) - } -
-
-
- ) -} diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/cornernet.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/cornernet.py deleted file mode 100644 index bb8ccc1465ab66d1615ca16701a533a22b156295..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/cornernet.py +++ /dev/null @@ -1,95 +0,0 @@ -import torch - -from mmdet.core import bbox2result, bbox_mapping_back -from ..builder import DETECTORS -from .single_stage import SingleStageDetector - - -@DETECTORS.register_module() -class CornerNet(SingleStageDetector): - """CornerNet. - - This detector is the implementation of the paper `CornerNet: Detecting - Objects as Paired Keypoints `_ . - """ - - def __init__(self, - backbone, - neck, - bbox_head, - train_cfg=None, - test_cfg=None, - pretrained=None): - super(CornerNet, self).__init__(backbone, neck, bbox_head, train_cfg, - test_cfg, pretrained) - - def merge_aug_results(self, aug_results, img_metas): - """Merge augmented detection bboxes and score. - - Args: - aug_results (list[list[Tensor]]): Det_bboxes and det_labels of each - image. - img_metas (list[list[dict]]): Meta information of each image, e.g., - image size, scaling factor, etc. - - Returns: - tuple: (bboxes, labels) - """ - recovered_bboxes, aug_labels = [], [] - for bboxes_labels, img_info in zip(aug_results, img_metas): - img_shape = img_info[0]['img_shape'] # using shape before padding - scale_factor = img_info[0]['scale_factor'] - flip = img_info[0]['flip'] - bboxes, labels = bboxes_labels - bboxes, scores = bboxes[:, :4], bboxes[:, -1:] - bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip) - recovered_bboxes.append(torch.cat([bboxes, scores], dim=-1)) - aug_labels.append(labels) - - bboxes = torch.cat(recovered_bboxes, dim=0) - labels = torch.cat(aug_labels) - - if bboxes.shape[0] > 0: - out_bboxes, out_labels = self.bbox_head._bboxes_nms( - bboxes, labels, self.bbox_head.test_cfg) - else: - out_bboxes, out_labels = bboxes, labels - - return out_bboxes, out_labels - - def aug_test(self, imgs, img_metas, rescale=False): - """Augment testing of CornerNet. - - Args: - imgs (list[Tensor]): Augmented images. - img_metas (list[list[dict]]): Meta information of each image, e.g., - image size, scaling factor, etc. - rescale (bool): If True, return boxes in original image space. - Default: False. - - Note: - ``imgs`` must including flipped image pairs. - - Returns: - list[list[np.ndarray]]: BBox results of each image and classes. - The outer list corresponds to each image. The inner list - corresponds to each class. - """ - img_inds = list(range(len(imgs))) - - assert img_metas[0][0]['flip'] + img_metas[1][0]['flip'], ( - 'aug test must have flipped image pair') - aug_results = [] - for ind, flip_ind in zip(img_inds[0::2], img_inds[1::2]): - img_pair = torch.cat([imgs[ind], imgs[flip_ind]]) - x = self.extract_feat(img_pair) - outs = self.bbox_head(x) - bbox_list = self.bbox_head.get_bboxes( - *outs, [img_metas[ind], img_metas[flip_ind]], False, False) - aug_results.append(bbox_list[0]) - aug_results.append(bbox_list[1]) - - bboxes, labels = self.merge_aug_results(aug_results, img_metas) - bbox_results = bbox2result(bboxes, labels, self.bbox_head.num_classes) - - return [bbox_results] diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/misc.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/misc.py deleted file mode 100644 index eb862a82bd47c8624db3dd5c6fb6ad8a03b62466..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/misc.py +++ /dev/null @@ -1,17 +0,0 @@ -def add_prefix(inputs, prefix): - """Add prefix for dict. - - Args: - inputs (dict): The input dict with str keys. - prefix (str): The prefix to add. - - Returns: - - dict: The dict with keys updated with ``prefix``. - """ - - outputs = dict() - for name, value in inputs.items(): - outputs[f'{prefix}.{name}'] = value - - return outputs diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/ops/border_align.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/ops/border_align.py deleted file mode 100644 index ff305be328e9b0a15e1bbb5e6b41beb940f55c81..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/ops/border_align.py +++ /dev/null @@ -1,109 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -# modified from -# https://github.com/Megvii-BaseDetection/cvpods/blob/master/cvpods/layers/border_align.py - -import torch -import torch.nn as nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext( - '_ext', ['border_align_forward', 'border_align_backward']) - - -class BorderAlignFunction(Function): - - @staticmethod - def symbolic(g, input, boxes, pool_size): - return g.op( - 'mmcv::MMCVBorderAlign', input, boxes, pool_size_i=pool_size) - - @staticmethod - def forward(ctx, input, boxes, pool_size): - ctx.pool_size = pool_size - ctx.input_shape = input.size() - - assert boxes.ndim == 3, 'boxes must be with shape [B, H*W, 4]' - assert boxes.size(2) == 4, \ - 'the last dimension of boxes must be (x1, y1, x2, y2)' - assert input.size(1) % 4 == 0, \ - 'the channel for input feature must be divisible by factor 4' - - # [B, C//4, H*W, 4] - output_shape = (input.size(0), input.size(1) // 4, boxes.size(1), 4) - output = input.new_zeros(output_shape) - # `argmax_idx` only used for backward - argmax_idx = input.new_zeros(output_shape).to(torch.int) - - ext_module.border_align_forward( - input, boxes, output, argmax_idx, pool_size=ctx.pool_size) - - ctx.save_for_backward(boxes, argmax_idx) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - boxes, argmax_idx = ctx.saved_tensors - grad_input = grad_output.new_zeros(ctx.input_shape) - # complex head architecture may cause grad_output uncontiguous - grad_output = grad_output.contiguous() - ext_module.border_align_backward( - grad_output, - boxes, - argmax_idx, - grad_input, - pool_size=ctx.pool_size) - return grad_input, None, None - - -border_align = BorderAlignFunction.apply - - -class BorderAlign(nn.Module): - r"""Border align pooling layer. - - Applies border_align over the input feature based on predicted bboxes. - The details were described in the paper - `BorderDet: Border Feature for Dense Object Detection - `_. - - For each border line (e.g. top, left, bottom or right) of each box, - border_align does the following: - 1. uniformly samples `pool_size`+1 positions on this line, involving \ - the start and end points. - 2. the corresponding features on these points are computed by \ - bilinear interpolation. - 3. max pooling over all the `pool_size`+1 positions are used for \ - computing pooled feature. - - Args: - pool_size (int): number of positions sampled over the boxes' borders - (e.g. top, bottom, left, right). - - """ - - def __init__(self, pool_size): - super(BorderAlign, self).__init__() - self.pool_size = pool_size - - def forward(self, input, boxes): - """ - Args: - input: Features with shape [N,4C,H,W]. Channels ranged in [0,C), - [C,2C), [2C,3C), [3C,4C) represent the top, left, bottom, - right features respectively. - boxes: Boxes with shape [N,H*W,4]. Coordinate format (x1,y1,x2,y2). - - Returns: - Tensor: Pooled features with shape [N,C,H*W,4]. The order is - (top,left,bottom,right) for the last dimension. - """ - return border_align(input, boxes, self.pool_size) - - def __repr__(self): - s = self.__class__.__name__ - s += f'(pool_size={self.pool_size})' - return s diff --git a/spaces/SIGGRAPH2022/DCT-Net/run_sdk.py b/spaces/SIGGRAPH2022/DCT-Net/run_sdk.py deleted file mode 100644 index 9180411a51e699071d0f9f1bbc31ae3d45f78367..0000000000000000000000000000000000000000 --- a/spaces/SIGGRAPH2022/DCT-Net/run_sdk.py +++ /dev/null @@ -1,11 +0,0 @@ -import cv2 -from modelscope.outputs import OutputKeys -from modelscope.pipelines import pipeline -from modelscope.utils.constant import Tasks - -img_cartoon = pipeline(Tasks.image_portrait_stylization, - model='damo/cv_unet_person-image-cartoon_compound-models') -result = img_cartoon('input.png') - -cv2.imwrite('result.png', result[OutputKeys.OUTPUT_IMG]) -print('finished!') diff --git a/spaces/SWHL/PaperEdgeDemo/networks/paperedge.py b/spaces/SWHL/PaperEdgeDemo/networks/paperedge.py deleted file mode 100644 index 915dfb947bb995aa6ec339cbfb04bb567310994d..0000000000000000000000000000000000000000 --- a/spaces/SWHL/PaperEdgeDemo/networks/paperedge.py +++ /dev/null @@ -1,553 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -# from torch.nn.utils import spectral_norm as SN -# from torchvision.models.densenet import _DenseBlock -from .tps_warp import TpsWarp, PspWarp -from functools import partial -# import plotly.graph_objects as go -import random -import numpy as np -import cv2 - -torch.autograd.set_detect_anomaly(True) -# torch.manual_seed(0) - -def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): - """3x3 convolution with padding""" - return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, - padding=dilation, groups=groups, bias=False, dilation=dilation) - - -def conv1x1(in_planes, out_planes, stride=1): - """1x1 convolution""" - return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) - - -class BasicBlock(nn.Module): - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, - base_width=64, dilation=1, norm_layer=None): - super(BasicBlock, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - if groups != 1 or base_width != 64: - raise ValueError('BasicBlock only supports groups=1 and base_width=64') - if dilation > 1: - raise NotImplementedError("Dilation > 1 not supported in BasicBlock") - # Both self.conv1 and self.downsample layers downsample the input when stride != 1 - self.conv1 = conv3x3(inplanes, planes, stride) - self.bn1 = norm_layer(planes) - self.actv = nn.ReLU() - self.conv2 = conv3x3(planes, planes) - self.bn2 = norm_layer(planes) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - identity = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.actv(out) - - out = self.conv2(out) - out = self.bn2(out) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - out = self.actv(out) - - return out - -def _make_layer(block, inplanes, planes, blocks, stride=1, dilate=False): - norm_layer = nn.BatchNorm2d - downsample = None - - if stride != 1 or inplanes != planes * block.expansion: - downsample = nn.Sequential( - nn.Conv2d(inplanes, planes * block.expansion, 1, stride, bias=False), - norm_layer(planes * block.expansion), - ) - - layers = [] - layers.append(block(inplanes, planes, stride, downsample, norm_layer=norm_layer)) - for _ in range(1, blocks): - layers.append(block(planes, planes, - norm_layer=norm_layer)) - - return nn.Sequential(*layers) - -class Interpolate(nn.Module): - def __init__(self, size, mode): - super(Interpolate, self).__init__() - self.interp = nn.functional.interpolate - self.size = size - self.mode = mode - - def forward(self, x): - x = self.interp(x, size=self.size, mode=self.mode, align_corners=True) - return x - -class GlobalWarper(nn.Module): - def __init__(self): - super(GlobalWarper, self).__init__() - modules = [ - nn.Conv2d(5, 64, kernel_size=7, stride=2, padding=3, bias=False), - nn.BatchNorm2d(64), - nn.ReLU() - ] - - # encoder - planes = [64, 128, 256, 256, 512, 512] - strides = [2, 2, 2, 2, 2] - blocks = [1, 1, 1, 1, 1] - for k in range(len(planes) - 1): - modules.append(_make_layer(BasicBlock, planes[k], planes[k + 1], blocks[k], strides[k])) - self.encoder = nn.Sequential(*modules) - - # decoder - modules = [] - planes = [512, 512, 256, 128, 64] - strides = [2, 2, 2, 2] - # tsizes = [3, 5, 9, 17, 33] - blocks = [1, 1, 1, 1] - for k in range(len(planes) - 1): - # modules += [nn.Sequential(Interpolate(size=tsizes[k], mode='bilinear'), - # _make_layer(BasicBlock, planes[k], planes[k + 1], blocks[k], 1))] - modules += [nn.Sequential(nn.Upsample(scale_factor=strides[k], mode='bilinear', align_corners=True), - _make_layer(BasicBlock, planes[k], planes[k + 1], blocks[k], 1))] - # self.decoder = nn.ModuleList(modules) - self.decoder = nn.Sequential(*modules) - - self.to_warp = nn.Sequential(nn.Conv2d(64, 2, 1)) - self.to_warp[0].weight.data.fill_(0.0) - self.to_warp[0].bias.data.fill_(0.0) - - iy, ix = torch.meshgrid(torch.linspace(-1, 1, 256), torch.linspace(-1, 1, 256)) - self.coord = torch.stack((ix, iy), dim=0).unsqueeze(0).to('cuda') - iy, ix = torch.meshgrid(torch.linspace(-1, 1, 64), torch.linspace(-1, 1, 64)) - ### note we mulitply a 0.9 so the network is initialized closer to GT. This is different from localwarper net - self.basegrid = torch.stack((ix * 0.9, iy * 0.9), dim=0).unsqueeze(0).to('cuda') - - # # box filter - # ksize = 7 - # p = int((ksize - 1) / 2) - # self.pad_replct = partial(F.pad, pad=(p, p, p, p), mode='replicate') - # bw = torch.ones(1, 1, ksize, ksize, device='cuda') / ksize / ksize - # self.box_filter = partial(F.conv2d, weight=bw) - - - - def forward(self, im): - # print(self.to_warp[0].weight.data) - # coordconv - B = im.size(0) - c = self.coord.expand(B, -1, -1, -1).detach() - t = torch.cat((im, c), dim=1) - - t = self.encoder(t) - t = self.decoder(t) - t = self.to_warp(t) - - gs = t + self.basegrid - - return gs - -class LocalWarper(nn.Module): - def __init__(self): - super().__init__() - modules = [ - nn.Conv2d(5, 64, kernel_size=7, stride=2, padding=3, bias=False), - nn.BatchNorm2d(64), - nn.ReLU() - ] - # encoder - planes = [64, 128, 256, 256, 512, 512] - strides = [2, 2, 2, 2, 2] - blocks = [1, 1, 1, 1, 1] - for k in range(len(planes) - 1): - modules.append(_make_layer(BasicBlock, planes[k], planes[k + 1], blocks[k], strides[k])) - self.encoder = nn.Sequential(*modules) - - # decoder - modules = [] - planes = [512, 512, 256, 128, 64] - strides = [2, 2, 2, 2] - # tsizes = [3, 5, 9, 17, 33] - blocks = [1, 1, 1, 1] - for k in range(len(planes) - 1): - modules += [nn.Sequential(nn.Upsample(scale_factor=strides[k], mode='bilinear', align_corners=True), - _make_layer(BasicBlock, planes[k], planes[k + 1], blocks[k], 1))] - self.decoder = nn.Sequential(*modules) - - self.to_warp = nn.Sequential(nn.Conv2d(64, 2, 1)) - self.to_warp[0].weight.data.fill_(0.0) - self.to_warp[0].bias.data.fill_(0.0) - - iy, ix = torch.meshgrid(torch.linspace(-1, 1, 256), torch.linspace(-1, 1, 256)) - self.coord = torch.stack((ix, iy), dim=0).unsqueeze(0).to('cuda') - iy, ix = torch.meshgrid(torch.linspace(-1, 1, 64), torch.linspace(-1, 1, 64)) - self.basegrid = torch.stack((ix, iy), dim=0).unsqueeze(0).to('cuda') - - # box filter - ksize = 5 - p = int((ksize - 1) / 2) - self.pad_replct = partial(F.pad, pad=(p, p, p, p), mode='replicate') - bw = torch.ones(1, 1, ksize, ksize, device='cuda') / ksize / ksize - self.box_filter = partial(F.conv2d, weight=bw) - - def forward(self, im): - c = self.coord.expand(im.size(0), -1, -1, -1).detach() - t = torch.cat((im, c), dim=1) - - # encoder - t = self.encoder(t) - t = self.decoder(t) - t = self.to_warp(t) - - # # filter - # t = self.pad_replct(t) - # tx = self.box_filter(t[:, 0 : 1, ...]) - # ty = self.box_filter(t[:, 1 : 2, ...]) - # t = torch.cat((tx, ty), dim=1) - - # bd condition - t[..., 1, 0, :] = 0 - t[..., 1, -1, :] = 0 - t[..., 0, :, 0] = 0 - t[..., 0, :, -1] = 0 - - gs = t + self.basegrid - return gs - -def gs_to_bd(gs): - # gs: B 2 H W - t = torch.cat([gs[..., 0, :], gs[..., -1, :], gs[..., 1 : -1, 0], gs[..., 1 : -1, -1]], dim=2).permute(0, 2, 1) - # t: B 2(W + H - 1) 2 - return t - -class MaskLoss(nn.Module): - def __init__(self, gsize): - super().__init__() - self.tpswarper = TpsWarp(gsize) - self.pspwarper = PspWarp() - # self.imsize = imsize - self.msk = torch.ones(1, 1, gsize, gsize, device='cuda') - self.cn = torch.tensor([[-1, -1], [1, -1], [1, 1], [-1, 1]], dtype=torch.float, device='cuda').unsqueeze(0) - - def forward(self, gs, y, s): - # resize gs to s*s - B, _, s0, _ = gs.size() - tgs = F.interpolate(gs, s, mode='bilinear', align_corners=True) - - # use only the boundary points - srcpts = gs_to_bd(tgs) - iy, ix = torch.meshgrid(torch.linspace(-1, 1, s), torch.linspace(-1, 1, s)) - t = torch.stack((ix, iy), dim=0).unsqueeze(0).to('cuda').expand_as(tgs) - dstpts = gs_to_bd(t) - - tgs_f = self.tpswarper(srcpts, dstpts.detach()) - ym = self.msk.expand_as(y) - yh = F.grid_sample(ym, tgs_f.permute(0, 2, 3, 1), align_corners=True) - loss_f = F.l1_loss(yh, y) - - # forward/backward consistency loss - tgs_b = self.tpswarper(dstpts.detach(), srcpts) - # tgs_b = F.interpolate(tgs, s0, mode='bilinear', align_corners=True) - yy = F.grid_sample(y, tgs_b.permute(0, 2, 3, 1), align_corners=True) - loss_b = F.l1_loss(yy, ym) - - return loss_f + loss_b, tgs_f - - def _dist(self, x): - # adjacent point distance - # B, 2, n - x = torch.cat([x[..., 0 : 1].detach(), x[..., 1 : -1], x[..., -1 : ].detach()], dim=2) - d = x[..., 1:] - x[..., :-1] - return torch.norm(d, dim=1) - -# class TVLoss(nn.Module): -# def __init__(self): -# super(TVLoss, self).__init__() - -# def forward(self, gs): -# loss = self._dist(gs[..., 1:], gs[..., :-1]) + self._dist(gs[..., 1:, :], gs[..., :-1, :]) -# return loss - -# def _dist(self, x1, x0): -# d = torch.norm(x1 - x0, dim=1, keepdim=True) -# d = torch.abs(d - torch.mean(d, dim=(2, 3), keepdim=True)).mean() -# return d - -class WarperUtil(nn.Module): - def __init__(self, imsize): - super().__init__() - self.tpswarper = TpsWarp(imsize) - self.pspwarper = PspWarp() - self.s = imsize - - def global_post_warp(self, gs, s): - # B, _, s0, _ = gs.size() - gs = F.interpolate(gs, s, mode='bilinear', align_corners=True) - # gs = F.interpolate(gs, s0, mode='bilinear', align_corners=True) - # extract info - m1 = gs[..., 0, :] - m2 = gs[..., -1, :] - n1 = gs[..., 0] - n2 = gs[..., -1] - # for x - m1x_interval_ratio = m1[:, 0, 1:] - m1[:, 0, :-1] - m1x_interval_ratio /= m1x_interval_ratio.sum(dim=1, keepdim=True) - m2x_interval_ratio = m2[:, 0, 1:] - m2[:, 0, :-1] - m2x_interval_ratio /= m2x_interval_ratio.sum(dim=1, keepdim=True) - # interpolate all x ratio - t = torch.stack([m1x_interval_ratio, m2x_interval_ratio], dim=1).unsqueeze(1) - mx_interval_ratio = F.interpolate(t, (s, m1x_interval_ratio.size(1)), mode='bilinear', align_corners=True) - mx_interval = (n2[..., 0 : 1, :] - n1[..., 0 : 1, :]).unsqueeze(3) * mx_interval_ratio - # cumsum to x - dx = torch.cumsum(mx_interval, dim=3) + n1[..., 0 : 1, :].unsqueeze(3) - dx = dx[..., 1 : -1, :-1] - # for y - n1y_interval_ratio = n1[:, 1, 1:] - n1[:, 1, :-1] - n1y_interval_ratio /= n1y_interval_ratio.sum(dim=1, keepdim=True) - n2y_interval_ratio = n2[:, 1, 1:] - n2[:, 1, :-1] - n2y_interval_ratio /= n2y_interval_ratio.sum(dim=1, keepdim=True) - # interpolate all x ratio - t = torch.stack([n1y_interval_ratio, n2y_interval_ratio], dim=2).unsqueeze(1) - ny_interval_ratio = F.interpolate(t, (n1y_interval_ratio.size(1), s), mode='bilinear', align_corners=True) - ny_interval = (m2[..., 1 : 2, :] - m1[..., 1 : 2, :]).unsqueeze(2) * ny_interval_ratio - # cumsum to y - dy = torch.cumsum(ny_interval, dim=2) + m1[..., 1 : 2, :].unsqueeze(2) - dy = dy[..., :-1, 1 : -1] - ds = torch.cat((dx, dy), dim=1) - gs[..., 1 : -1, 1 : -1] = ds - return gs - - def perturb_warp(self, dd): - B = dd.size(0) - s = self.s - # -0.2 to 0.2 - iy, ix = torch.meshgrid(torch.linspace(-1, 1, s), torch.linspace(-1, 1, s)) - t = torch.stack((ix, iy), dim=0).unsqueeze(0).to('cuda').expand(B, -1, -1, -1) - - tt = t.clone() - - nd = random.randint(0, 4) - for ii in range(nd): - # define deformation on bd - pm = (torch.rand(B, 1) - 0.5) * 0.2 - ps = (torch.rand(B, 1) - 0.5) * 1.95 - pt = ps + pm - pt = pt.clamp(-0.975, 0.975) - # put it on one bd - # [1, 1] or [-1, 1] or [-1, -1] etc - a1 = (torch.rand(B, 2) > 0.5).float() * 2 -1 - # select one col for every row - a2 = torch.rand(B, 1) > 0.5 - a2 = torch.cat([a2, a2.bitwise_not()], dim=1) - a3 = a1.clone() - a3[a2] = ps.view(-1) - ps = a3.clone() - a3[a2] = pt.view(-1) - pt = a3.clone() - # 2 N 4 - bds = torch.stack([ - t[0, :, 1 : -1, 0], t[0, :, 1 : -1, -1], t[0, :, 0, 1 : -1], t[0, :, -1, 1 : -1] - ], dim=2) - - pbd = a2.bitwise_not().float() * a1 - # id of boundary p is on - pbd = torch.abs(0.5 * pbd[:, 0] + 2.5 * pbd[:, 1] + 0.5).long() - # ids of other boundaries - pbd = torch.stack([pbd + 1, pbd + 2, pbd + 3], dim=1) % 4 - # print(pbd) - pbd = bds[..., pbd].permute(2, 0, 1, 3).reshape(B, 2, -1) - - srcpts = torch.stack([ - t[..., 0, 0], t[..., 0, -1], t[..., -1, 0], t[..., -1, -1], - ps.to('cuda') - ], dim=2) - srcpts = torch.cat([pbd, srcpts], dim=2).permute(0, 2, 1) - dstpts = torch.stack([ - t[..., 0, 0], t[..., 0, -1], t[..., -1, 0], t[..., -1, -1], - pt.to('cuda') - ], dim=2) - dstpts = torch.cat([pbd, dstpts], dim=2).permute(0, 2, 1) - # print(srcpts) - # print(dstpts) - tgs = self.tpswarper(srcpts, dstpts) - tt = F.grid_sample(tt, tgs.permute(0, 2, 3, 1), align_corners=True) - - nd = random.randint(1, 5) - for ii in range(nd): - - pm = (torch.rand(B, 2) - 0.5) * 0.2 - ps = (torch.rand(B, 2) - 0.5) * 1.95 - pt = ps + pm - pt = pt.clamp(-0.975, 0.975) - - srcpts = torch.cat([ - t[..., -1, :], t[..., 0, :], t[..., 1 : -1, 0], t[..., 1 : -1, -1], - ps.unsqueeze(2).to('cuda') - ], dim=2).permute(0, 2, 1) - dstpts = torch.cat([ - t[..., -1, :], t[..., 0, :], t[..., 1 : -1, 0], t[..., 1 : -1, -1], - pt.unsqueeze(2).to('cuda') - ], dim=2).permute(0, 2, 1) - tgs = self.tpswarper(srcpts, dstpts) - tt = F.grid_sample(tt, tgs.permute(0, 2, 3, 1), align_corners=True) - tgs = tt - - # sample tgs to gen invtgs - num_sample = 512 - # n = (H-2)*(W-2) - n = s * s - idx = torch.randperm(n) - idx = idx[:num_sample] - srcpts = tgs.reshape(-1, 2, n)[..., idx].permute(0, 2, 1) - dstpts = t.reshape(-1, 2, n)[..., idx].permute(0, 2, 1) - invtgs = self.tpswarper(srcpts, dstpts) - return tgs, invtgs - - def equal_spacing_interpolate(self, gs, s): - def equal_bd(x, s): - # x is B 2 n - v0 = x[..., :-1] # B 2 n-1 - v = x[..., 1:] - x[..., :-1] - vn = v.norm(dim=1, keepdim=True) - v = v / vn - c = vn.sum(dim=2, keepdim=True) #B 1 1 - a = vn / c - b = torch.cumsum(a, dim=2) - b = torch.cat((torch.zeros(B, 1, 1, device='cuda'), b[..., :-1]), dim=2) - - t = torch.linspace(1e-5, 1 - 1e-5, s).view(1, s, 1).to('cuda') - t = t - b # B s n-1 - # print(t) - - tt = torch.cat((t, -torch.ones(B, s, 1, device='cuda')), dim=2) # B s n - tt = tt[..., 1:] * tt[..., :-1] # B s n-1 - tt = (tt < 0).float() - d = torch.matmul(v0, tt.permute(0, 2, 1)) + torch.matmul(v, (tt * t).permute(0, 2, 1)) # B 2 s - # print(d) - return d - - gs = F.interpolate(gs, s, mode='bilinear', align_corners=True) - B = gs.size(0) - dst_cn = torch.tensor([[-1, -1], [1, -1], [1, 1], [-1, 1]], dtype=torch.float, device='cuda').expand(B, -1, -1) - src_cn = torch.stack([gs[..., 0, 0], gs[..., 0, -1], gs[..., -1, -1], gs[..., -1, 0]], dim=2).permute(0, 2, 1) - M = self.pspwarper.pspmat(src_cn, dst_cn).detach() - invM = self.pspwarper.pspmat(dst_cn, src_cn).detach() - pgs = self.pspwarper(gs.permute(0, 2, 3, 1).reshape(B, -1, 2), M).reshape(B, s, s, 2).permute(0, 3, 1, 2) - t = [pgs[..., 0, :], pgs[..., -1, :], pgs[..., :, 0], pgs[..., :, -1]] - d = [] - for x in t: - d.append(equal_bd(x, s)) - pgs[..., 0, :] = d[0] - pgs[..., -1, :] = d[1] - pgs[..., :, 0] = d[2] - pgs[..., :, -1] = d[3] - gs = self.pspwarper(pgs.permute(0, 2, 3, 1).reshape(B, -1, 2), invM).reshape(B, s, s, 2).permute(0, 3, 1, 2) - gs = self.global_post_warp(gs, s) - return gs - - - -class LocalLoss(nn.Module): - def __init__(self): - super().__init__() - - def identity_loss(self, gs): - s = gs.size(2) - iy, ix = torch.meshgrid(torch.linspace(-1, 1, s), torch.linspace(-1, 1, s)) - t = torch.stack((ix, iy), dim=0).unsqueeze(0).to('cuda').expand_as(gs) - loss = F.l1_loss(gs, t.detach()) - return loss - - def direct_loss(self, gs, invtgs): - loss = F.l1_loss(gs, invtgs.detach()) - return loss - - def warp_diff_loss(self, xd, xpd, tgs, invtgs): - loss_f = F.l1_loss(xd, F.grid_sample(tgs, xpd.permute(0, 2, 3, 1), align_corners=True).detach()) - loss_b = F.l1_loss(xpd, F.grid_sample(invtgs, xd.permute(0, 2, 3, 1), align_corners=True).detach()) - loss = loss_f + loss_b - return loss - - -class SupervisedLoss(nn.Module): - def __init__(self): - super().__init__() - s = 64 - self.tpswarper = TpsWarp(s) - - def fm2bm(self, fm): - # B 3 N N - # fm in [0, 1] - B, _, s, _ = fm.size() - iy, ix = torch.meshgrid(torch.linspace(-1, 1, s), torch.linspace(-1, 1, s)) - t = torch.stack((ix, iy), dim=0).unsqueeze(0).to('cuda').expand(B, -1, -1, -1) - srcpts = [] - dstpts = [] - for ii in range(B): - # mask - m = fm[ii, 2] - # z s - z = torch.nonzero(m, as_tuple=False) - num_sample = 512 - n = z.size(0) - # print(n) - idx = torch.randperm(n) - idx = idx[:num_sample] - dstpts.append(t[ii, :, z[idx, 0], z[idx, 1]]) - srcpts.append(fm[ii, : 2, z[idx, 0], z[idx, 1]] * 2 - 1) - srcpts = torch.stack(srcpts, dim=0).permute(0, 2, 1) - dstpts = torch.stack(dstpts, dim=0).permute(0, 2, 1) - # z = torch.nonzero(torch.abs(srcpts - 0) < 1e-5, as_tuple=False) - # print(z.size(0)) - # print(dstpts.min()) - # print(dstpts.max()) - bm = self.tpswarper(srcpts, dstpts) - # bm[bm > 1] = 1 - # bm[bm < -1] = -1 - return bm - - def gloss(self, x, y): - xbd = gs_to_bd(x) - # y = self.fm2bm(y) - y = F.interpolate(y, 64, mode='bilinear', align_corners=True) - - ybd = gs_to_bd(y).detach() - loss = F.l1_loss(xbd, ybd.detach()) - return loss - - def lloss(self, x, y, dg): - # sample tgs to gen invtgs - B, _, s, _ = dg.size() - iy, ix = torch.meshgrid(torch.linspace(-1, 1, s), torch.linspace(-1, 1, s)) - t = torch.stack((ix, iy), dim=0).unsqueeze(0).to('cuda').expand(B, -1, -1, -1) - num_sample = 512 - # n = (H-2)*(W-2) - n = s * s - idx = torch.randperm(n) - idx = idx[:num_sample] - # srcpts = gs_to_bd(tgs) - # srcpts = torch.cat([srcpts, tgs[..., 1 : -1, 1 : -1].reshape(-1, 2, n)[..., idx].permute(0, 2, 1)], dim=1) - srcpts = dg.reshape(-1, 2, n)[..., idx].permute(0, 2, 1) - # dstpts = gs_to_bd(t) - # dstpts = torch.cat([dstpts, t[..., 1 : -1, 1 : -1].reshape(-1, 2, n)[..., idx].permute(0, 2, 1)], dim=1) - dstpts = t.reshape(-1, 2, n)[..., idx].permute(0, 2, 1) - invdg = self.tpswarper(srcpts, dstpts) - # compute dl = \phi(dg^-1, y) - dl = F.grid_sample(invdg, y.permute(0, 2, 3, 1), align_corners=True) - dl = F.interpolate(dl, 64, mode='bilinear', align_corners=True) - loss = F.l1_loss(x, dl.detach()) - - # y = F.interpolate(y, 64, mode='bilinear', align_corners=True) - # loss = F.l1_loss(F.grid_sample(dg.detach(), x.permute(0, 2, 3, 1), align_corners=True), y) - - return loss, dl.detach() diff --git a/spaces/Sapphire-356/Video2MC/data/prepare_data_humaneva.py b/spaces/Sapphire-356/Video2MC/data/prepare_data_humaneva.py deleted file mode 100644 index 2ed83c2b3e3d40a64faef1d2e35ba9ea83249848..0000000000000000000000000000000000000000 --- a/spaces/Sapphire-356/Video2MC/data/prepare_data_humaneva.py +++ /dev/null @@ -1,242 +0,0 @@ -# Copyright (c) 2018-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import argparse -import os -import re -import sys -from glob import glob - -import numpy as np -from data_utils import suggest_metadata, suggest_pose_importer - -sys.path.append('../') -from itertools import groupby - -subjects = ['Train/S1', 'Train/S2', 'Train/S3', 'Validate/S1', 'Validate/S2', 'Validate/S3'] - -cam_map = { - 'C1': 0, - 'C2': 1, - 'C3': 2, -} - -# Frame numbers for train/test split -# format: [start_frame, end_frame[ (inclusive, exclusive) -index = { - 'Train/S1': { - 'Walking 1': (590, 1203), - 'Jog 1': (367, 740), - 'ThrowCatch 1': (473, 945), - 'Gestures 1': (395, 801), - 'Box 1': (385, 789), - }, - 'Train/S2': { - 'Walking 1': (438, 876), - 'Jog 1': (398, 795), - 'ThrowCatch 1': (550, 1128), - 'Gestures 1': (500, 901), - 'Box 1': (382, 734), - }, - 'Train/S3': { - 'Walking 1': (448, 939), - 'Jog 1': (401, 842), - 'ThrowCatch 1': (493, 1027), - 'Gestures 1': (533, 1102), - 'Box 1': (512, 1021), - }, - 'Validate/S1': { - 'Walking 1': (5, 590), - 'Jog 1': (5, 367), - 'ThrowCatch 1': (5, 473), - 'Gestures 1': (5, 395), - 'Box 1': (5, 385), - }, - 'Validate/S2': { - 'Walking 1': (5, 438), - 'Jog 1': (5, 398), - 'ThrowCatch 1': (5, 550), - 'Gestures 1': (5, 500), - 'Box 1': (5, 382), - }, - 'Validate/S3': { - 'Walking 1': (5, 448), - 'Jog 1': (5, 401), - 'ThrowCatch 1': (5, 493), - 'Gestures 1': (5, 533), - 'Box 1': (5, 512), - }, -} - -# Frames to skip for each video (synchronization) -sync_data = { - 'S1': { - 'Walking 1': (82, 81, 82), - 'Jog 1': (51, 51, 50), - 'ThrowCatch 1': (61, 61, 60), - 'Gestures 1': (45, 45, 44), - 'Box 1': (57, 57, 56), - }, - 'S2': { - 'Walking 1': (115, 115, 114), - 'Jog 1': (100, 100, 99), - 'ThrowCatch 1': (127, 127, 127), - 'Gestures 1': (122, 122, 121), - 'Box 1': (119, 119, 117), - }, - 'S3': { - 'Walking 1': (80, 80, 80), - 'Jog 1': (65, 65, 65), - 'ThrowCatch 1': (79, 79, 79), - 'Gestures 1': (83, 83, 82), - 'Box 1': (1, 1, 1), - }, - 'S4': {} -} - -if __name__ == '__main__': - if os.path.basename(os.getcwd()) != 'data': - print('This script must be launched from the "data" directory') - exit(0) - - parser = argparse.ArgumentParser(description='HumanEva dataset converter') - - parser.add_argument('-p', '--path', default='', type=str, metavar='PATH', help='path to the processed HumanEva dataset') - parser.add_argument('--convert-3d', action='store_true', help='convert 3D mocap data') - parser.add_argument('--convert-2d', default='', type=str, metavar='PATH', help='convert user-supplied 2D detections') - parser.add_argument('-o', '--output', default='', type=str, metavar='PATH', help='output suffix for 2D detections (e.g. detectron_pt_coco)') - - args = parser.parse_args() - - if not args.convert_2d and not args.convert_3d: - print('Please specify one conversion mode') - exit(0) - - if args.path: - print('Parsing HumanEva dataset from', args.path) - output = {} - output_2d = {} - frame_mapping = {} - - from scipy.io import loadmat - - num_joints = None - - for subject in subjects: - output[subject] = {} - output_2d[subject] = {} - split, subject_name = subject.split('/') - if subject_name not in frame_mapping: - frame_mapping[subject_name] = {} - - file_list = glob(args.path + '/' + subject + '/*.mat') - for f in file_list: - action = os.path.splitext(os.path.basename(f))[0] - - # Use consistent naming convention - canonical_name = action.replace('_', ' ') - - hf = loadmat(f) - positions = hf['poses_3d'] - positions_2d = hf['poses_2d'].transpose(1, 0, 2, 3) # Ground-truth 2D poses - assert positions.shape[0] == positions_2d.shape[0] and positions.shape[1] == positions_2d.shape[2] - assert num_joints is None or num_joints == positions.shape[1], "Joint number inconsistency among files" - num_joints = positions.shape[1] - - # Sanity check for the sequence length - assert positions.shape[0] == index[subject][canonical_name][1] - index[subject][canonical_name][0] - - # Split corrupted motion capture streams into contiguous chunks - # e.g. 012XX567X9 is split into "012", "567", and "9". - all_chunks = [list(v) for k, v in groupby(positions, lambda x: np.isfinite(x).all())] - all_chunks_2d = [list(v) for k, v in groupby(positions_2d, lambda x: np.isfinite(x).all())] - assert len(all_chunks) == len(all_chunks_2d) - current_index = index[subject][canonical_name][0] - chunk_indices = [] - for i, chunk in enumerate(all_chunks): - next_index = current_index + len(chunk) - name = canonical_name + ' chunk' + str(i) - if np.isfinite(chunk).all(): - output[subject][name] = np.array(chunk, dtype='float32') / 1000 - output_2d[subject][name] = list(np.array(all_chunks_2d[i], dtype='float32').transpose(1, 0, 2, 3)) - chunk_indices.append((current_index, next_index, np.isfinite(chunk).all(), split, name)) - current_index = next_index - assert current_index == index[subject][canonical_name][1] - if canonical_name not in frame_mapping[subject_name]: - frame_mapping[subject_name][canonical_name] = [] - frame_mapping[subject_name][canonical_name] += chunk_indices - - metadata = suggest_metadata('humaneva' + str(num_joints)) - output_filename = 'data_3d_' + metadata['layout_name'] - output_prefix_2d = 'data_2d_' + metadata['layout_name'] + '_' - - if args.convert_3d: - print('Saving...') - np.savez_compressed(output_filename, positions_3d=output) - np.savez_compressed(output_prefix_2d + 'gt', positions_2d=output_2d, metadata=metadata) - print('Done.') - - else: - print('Please specify the dataset source') - exit(0) - - if args.convert_2d: - if not args.output: - print('Please specify an output suffix (e.g. detectron_pt_coco)') - exit(0) - - import_func = suggest_pose_importer(args.output) - metadata = suggest_metadata(args.output) - - print('Parsing 2D detections from', args.convert_2d) - - output = {} - file_list = glob(args.convert_2d + '/S*/*.avi.npz') - for f in file_list: - path, fname = os.path.split(f) - subject = os.path.basename(path) - assert subject.startswith('S'), subject + ' does not look like a subject directory' - - m = re.search('(.*) \\((.*)\\)', fname.replace('_', ' ')) - action = m.group(1) - camera = m.group(2) - camera_idx = cam_map[camera] - - keypoints = import_func(f) - assert keypoints.shape[1] == metadata['num_joints'] - - if action in sync_data[subject]: - sync_offset = sync_data[subject][action][camera_idx] - 1 - else: - sync_offset = 0 - - if subject in frame_mapping and action in frame_mapping[subject]: - chunks = frame_mapping[subject][action] - for (start_idx, end_idx, labeled, split, name) in chunks: - canonical_subject = split + '/' + subject - if not labeled: - canonical_subject = 'Unlabeled/' + canonical_subject - if canonical_subject not in output: - output[canonical_subject] = {} - kps = keypoints[start_idx + sync_offset:end_idx + sync_offset] - assert len(kps) == end_idx - start_idx, "Got len {}, expected {}".format(len(kps), end_idx - start_idx) - - if name not in output[canonical_subject]: - output[canonical_subject][name] = [None, None, None] - - output[canonical_subject][name][camera_idx] = kps.astype('float32') - else: - canonical_subject = 'Unlabeled/' + subject - if canonical_subject not in output: - output[canonical_subject] = {} - if action not in output[canonical_subject]: - output[canonical_subject][action] = [None, None, None] - output[canonical_subject][action][camera_idx] = keypoints.astype('float32') - - print('Saving...') - np.savez_compressed(output_prefix_2d + args.output, positions_2d=output, metadata=metadata) - print('Done.') diff --git a/spaces/Soumen/image_to_text/README.md b/spaces/Soumen/image_to_text/README.md deleted file mode 100644 index 005ef0195be255fb449b0e045c189823d0aae583..0000000000000000000000000000000000000000 --- a/spaces/Soumen/image_to_text/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Image To Text -emoji: 😻 -colorFrom: purple -colorTo: indigo -sdk: streamlit -sdk_version: 1.10.0 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/adapter/__main__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/adapter/__main__.py deleted file mode 100644 index e18ecd560fc6f514544b401d35e772614bd06283..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/adapter/__main__.py +++ /dev/null @@ -1,227 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. See LICENSE in the project root -# for license information. - -import argparse -import atexit -import codecs -import locale -import os -import sys - -# WARNING: debugpy and submodules must not be imported on top level in this module, -# and should be imported locally inside main() instead. - - -def main(args): - # If we're talking DAP over stdio, stderr is not guaranteed to be read from, - # so disable it to avoid the pipe filling and locking up. This must be done - # as early as possible, before the logging module starts writing to it. - if args.port is None: - sys.stderr = stderr = open(os.devnull, "w") - atexit.register(stderr.close) - - from debugpy import adapter - from debugpy.common import json, log, sockets - from debugpy.adapter import clients, servers, sessions - - if args.for_server is not None: - if os.name == "posix": - # On POSIX, we need to leave the process group and its session, and then - # daemonize properly by double-forking (first fork already happened when - # this process was spawned). - # NOTE: if process is already the session leader, then - # setsid would fail with `operation not permitted` - if os.getsid(os.getpid()) != os.getpid(): - os.setsid() - if os.fork() != 0: - sys.exit(0) - - for stdio in sys.stdin, sys.stdout, sys.stderr: - if stdio is not None: - stdio.close() - - if args.log_stderr: - log.stderr.levels |= set(log.LEVELS) - if args.log_dir is not None: - log.log_dir = args.log_dir - - log.to_file(prefix="debugpy.adapter") - log.describe_environment("debugpy.adapter startup environment:") - - servers.access_token = args.server_access_token - if args.for_server is None: - adapter.access_token = codecs.encode(os.urandom(32), "hex").decode("ascii") - - endpoints = {} - try: - client_host, client_port = clients.serve(args.host, args.port) - except Exception as exc: - if args.for_server is None: - raise - endpoints = {"error": "Can't listen for client connections: " + str(exc)} - else: - endpoints["client"] = {"host": client_host, "port": client_port} - - if args.for_server is not None: - try: - server_host, server_port = servers.serve() - except Exception as exc: - endpoints = {"error": "Can't listen for server connections: " + str(exc)} - else: - endpoints["server"] = {"host": server_host, "port": server_port} - - log.info( - "Sending endpoints info to debug server at localhost:{0}:\n{1}", - args.for_server, - json.repr(endpoints), - ) - - try: - sock = sockets.create_client() - try: - sock.settimeout(None) - sock.connect(("127.0.0.1", args.for_server)) - sock_io = sock.makefile("wb", 0) - try: - sock_io.write(json.dumps(endpoints).encode("utf-8")) - finally: - sock_io.close() - finally: - sockets.close_socket(sock) - except Exception: - log.reraise_exception("Error sending endpoints info to debug server:") - - if "error" in endpoints: - log.error("Couldn't set up endpoints; exiting.") - sys.exit(1) - - listener_file = os.getenv("DEBUGPY_ADAPTER_ENDPOINTS") - if listener_file is not None: - log.info( - "Writing endpoints info to {0!r}:\n{1}", listener_file, json.repr(endpoints) - ) - - def delete_listener_file(): - log.info("Listener ports closed; deleting {0!r}", listener_file) - try: - os.remove(listener_file) - except Exception: - log.swallow_exception( - "Failed to delete {0!r}", listener_file, level="warning" - ) - - try: - with open(listener_file, "w") as f: - atexit.register(delete_listener_file) - print(json.dumps(endpoints), file=f) - except Exception: - log.reraise_exception("Error writing endpoints info to file:") - - if args.port is None: - clients.Client("stdio") - - # These must be registered after the one above, to ensure that the listener sockets - # are closed before the endpoint info file is deleted - this way, another process - # can wait for the file to go away as a signal that the ports are no longer in use. - atexit.register(servers.stop_serving) - atexit.register(clients.stop_serving) - - servers.wait_until_disconnected() - log.info("All debug servers disconnected; waiting for remaining sessions...") - - sessions.wait_until_ended() - log.info("All debug sessions have ended; exiting.") - - -def _parse_argv(argv): - parser = argparse.ArgumentParser() - - parser.add_argument( - "--for-server", type=int, metavar="PORT", help=argparse.SUPPRESS - ) - - parser.add_argument( - "--port", - type=int, - default=None, - metavar="PORT", - help="start the adapter in debugServer mode on the specified port", - ) - - parser.add_argument( - "--host", - type=str, - default="127.0.0.1", - metavar="HOST", - help="start the adapter in debugServer mode on the specified host", - ) - - parser.add_argument( - "--access-token", type=str, help="access token expected from the server" - ) - - parser.add_argument( - "--server-access-token", type=str, help="access token expected by the server" - ) - - parser.add_argument( - "--log-dir", - type=str, - metavar="DIR", - help="enable logging and use DIR to save adapter logs", - ) - - parser.add_argument( - "--log-stderr", action="store_true", help="enable logging to stderr" - ) - - args = parser.parse_args(argv[1:]) - - if args.port is None: - if args.log_stderr: - parser.error("--log-stderr requires --port") - if args.for_server is not None: - parser.error("--for-server requires --port") - - return args - - -if __name__ == "__main__": - # debugpy can also be invoked directly rather than via -m. In this case, the first - # entry on sys.path is the one added automatically by Python for the directory - # containing this file. This means that import debugpy will not work, since we need - # the parent directory of debugpy/ to be in sys.path, rather than debugpy/adapter/. - # - # The other issue is that many other absolute imports will break, because they - # will be resolved relative to debugpy/adapter/ - e.g. `import state` will then try - # to import debugpy/adapter/state.py. - # - # To fix both, we need to replace the automatically added entry such that it points - # at parent directory of debugpy/ instead of debugpy/adapter, import debugpy with that - # in sys.path, and then remove the first entry entry altogether, so that it doesn't - # affect any further imports we might do. For example, suppose the user did: - # - # python /foo/bar/debugpy/adapter ... - # - # At the beginning of this script, sys.path will contain "/foo/bar/debugpy/adapter" - # as the first entry. What we want is to replace it with "/foo/bar', then import - # debugpy with that in effect, and then remove the replaced entry before any more - # code runs. The imported debugpy module will remain in sys.modules, and thus all - # future imports of it or its submodules will resolve accordingly. - if "debugpy" not in sys.modules: - # Do not use dirname() to walk up - this can be a relative path, e.g. ".". - sys.path[0] = sys.path[0] + "/../../" - __import__("debugpy") - del sys.path[0] - - # Apply OS-global and user-specific locale settings. - try: - locale.setlocale(locale.LC_ALL, "") - except Exception: - # On POSIX, locale is set via environment variables, and this can fail if - # those variables reference a non-existing locale. Ignore and continue using - # the default "C" locale if so. - pass - - main(_parse_argv(sys.argv)) diff --git a/spaces/Superlang/ImageProcessor/annotator/mlsd/__init__.py b/spaces/Superlang/ImageProcessor/annotator/mlsd/__init__.py deleted file mode 100644 index 47af1adee5a9ab790cc8013ffe4769a81b8d638c..0000000000000000000000000000000000000000 --- a/spaces/Superlang/ImageProcessor/annotator/mlsd/__init__.py +++ /dev/null @@ -1,56 +0,0 @@ -import cv2 -import numpy as np -import torch -import os - -from einops import rearrange -from annotator.base_annotator import BaseProcessor -from .models.mbv2_mlsd_tiny import MobileV2_MLSD_Tiny -from .models.mbv2_mlsd_large import MobileV2_MLSD_Large -from .utils import pred_lines - -remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/mlsd_large_512_fp32.pth" -old_modeldir = os.path.dirname(os.path.realpath(__file__)) - - -class MLSDProcessor(BaseProcessor): - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.model = None - self.model_dir = os.path.join(self.models_path, "mlsd") - - def unload_model(self): - if self.model is not None: - self.model = self.model.cpu() - - def load_model(self): - model_path = os.path.join(self.model_dir, "mlsd_large_512_fp32.pth") - # old_modelpath = os.path.join(old_modeldir, "mlsd_large_512_fp32.pth") - # if os.path.exists(old_modelpath): - # modelpath = old_modelpath - if not os.path.exists(model_path): - from basicsr.utils.download_util import load_file_from_url - load_file_from_url(remote_model_path, model_dir=self.model_dir) - mlsdmodel = MobileV2_MLSD_Large() - mlsdmodel.load_state_dict(torch.load(model_path), strict=True) - - mlsdmodel = mlsdmodel.to(self.device).eval() - self.model = mlsdmodel - - def __call__(self, input_image, thr_v= 0.1, thr_d= 0.1, **kwargs): - # global modelpath, mlsdmodel - if self.model is None: - self.load_model() - assert input_image.ndim == 3 - img = input_image - img_output = np.zeros_like(img) - try: - with torch.no_grad(): - lines = pred_lines(img, self.model, [img.shape[0], img.shape[1]], thr_v, thr_d, self.device) - for line in lines: - x_start, y_start, x_end, y_end = [int(val) for val in line] - cv2.line(img_output, (x_start, y_start), (x_end, y_end), [255, 255, 255], 1) - except Exception as e: - pass - return img_output[:, :, 0] diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/utils/trace.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/utils/trace.py deleted file mode 100644 index 5ca99dc3eda05ef980d9a4249b50deca8273b6cc..0000000000000000000000000000000000000000 --- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/utils/trace.py +++ /dev/null @@ -1,23 +0,0 @@ -import warnings - -import torch - -from annotator.uniformer.mmcv.utils import digit_version - - -def is_jit_tracing() -> bool: - if (torch.__version__ != 'parrots' - and digit_version(torch.__version__) >= digit_version('1.6.0')): - on_trace = torch.jit.is_tracing() - # In PyTorch 1.6, torch.jit.is_tracing has a bug. - # Refers to https://github.com/pytorch/pytorch/issues/42448 - if isinstance(on_trace, bool): - return on_trace - else: - return torch._C._is_tracing() - else: - warnings.warn( - 'torch.jit.is_tracing is only supported after v1.6.0. ' - 'Therefore is_tracing returns False automatically. Please ' - 'set on_trace manually if you are using trace.', UserWarning) - return False diff --git a/spaces/Swatantradev/mynewgenAI/README.md b/spaces/Swatantradev/mynewgenAI/README.md deleted file mode 100644 index ec796ef890226262a105d1b4b4f8cd799bfa3c5b..0000000000000000000000000000000000000000 --- a/spaces/Swatantradev/mynewgenAI/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: MynewgenAI -emoji: 👀 -colorFrom: yellow -colorTo: red -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/TLME/Bert-VITS-Umamusume-Genshin-HonkaiSR/text/chinese.py b/spaces/TLME/Bert-VITS-Umamusume-Genshin-HonkaiSR/text/chinese.py deleted file mode 100644 index 51acb3ec401d7647278a25537576a0fb1775d827..0000000000000000000000000000000000000000 --- a/spaces/TLME/Bert-VITS-Umamusume-Genshin-HonkaiSR/text/chinese.py +++ /dev/null @@ -1,198 +0,0 @@ -import os -import re - -import cn2an -from pypinyin import lazy_pinyin, Style - -from text.symbols import punctuation -from text.tone_sandhi import ToneSandhi - -current_file_path = os.path.dirname(__file__) -pinyin_to_symbol_map = { - line.split("\t")[0]: line.strip().split("\t")[1] - for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines() -} - -import jieba.posseg as psg - - -rep_map = { - "：": ",", - "；": ",", - "，": ",", - "。": ".", - "！": "!", - "？": "?", - "\n": ".", - "·": ",", - "、": ",", - "...": "…", - "$": ".", - "“": "'", - "”": "'", - "‘": "'", - "’": "'", - "（": "'", - "）": "'", - "(": "'", - ")": "'", - "《": "'", - "》": "'", - "【": "'", - "】": "'", - "[": "'", - "]": "'", - "—": "-", - "～": "-", - "~": "-", - "「": "'", - "」": "'", -} - -tone_modifier = ToneSandhi() - - -def replace_punctuation(text): - text = text.replace("嗯", "恩").replace("呣", "母") - pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys())) - - replaced_text = pattern.sub(lambda x: rep_map[x.group()], text) - - replaced_text = re.sub( - r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text - ) - - return replaced_text - - -def g2p(text): - pattern = r"(?<=[{0}])\s*".format("".join(punctuation)) - sentences = [i for i in re.split(pattern, text) if i.strip() != ""] - phones, tones, word2ph = _g2p(sentences) - assert sum(word2ph) == len(phones) - assert len(word2ph) == len(text) # Sometimes it will crash,you can add a try-catch. - phones = ["_"] + phones + ["_"] - tones = [0] + tones + [0] - word2ph = [1] + word2ph + [1] - return phones, tones, word2ph - - -def _get_initials_finals(word): - initials = [] - finals = [] - orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS) - orig_finals = lazy_pinyin( - word, neutral_tone_with_five=True, style=Style.FINALS_TONE3 - ) - for c, v in zip(orig_initials, orig_finals): - initials.append(c) - finals.append(v) - return initials, finals - - -def _g2p(segments): - phones_list = [] - tones_list = [] - word2ph = [] - for seg in segments: - # Replace all English words in the sentence - seg = re.sub("[a-zA-Z]+", "", seg) - seg_cut = psg.lcut(seg) - initials = [] - finals = [] - seg_cut = tone_modifier.pre_merge_for_modify(seg_cut) - for word, pos in seg_cut: - if pos == "eng": - continue - sub_initials, sub_finals = _get_initials_finals(word) - sub_finals = tone_modifier.modified_tone(word, pos, sub_finals) - initials.append(sub_initials) - finals.append(sub_finals) - - # assert len(sub_initials) == len(sub_finals) == len(word) - initials = sum(initials, []) - finals = sum(finals, []) - # - for c, v in zip(initials, finals): - raw_pinyin = c + v - # NOTE: post process for pypinyin outputs - # we discriminate i, ii and iii - if c == v: - assert c in punctuation - phone = [c] - tone = "0" - word2ph.append(1) - else: - v_without_tone = v[:-1] - tone = v[-1] - - pinyin = c + v_without_tone - assert tone in "12345" - - if c: - # 多音节 - v_rep_map = { - "uei": "ui", - "iou": "iu", - "uen": "un", - } - if v_without_tone in v_rep_map.keys(): - pinyin = c + v_rep_map[v_without_tone] - else: - # 单音节 - pinyin_rep_map = { - "ing": "ying", - "i": "yi", - "in": "yin", - "u": "wu", - } - if pinyin in pinyin_rep_map.keys(): - pinyin = pinyin_rep_map[pinyin] - else: - single_rep_map = { - "v": "yu", - "e": "e", - "i": "y", - "u": "w", - } - if pinyin[0] in single_rep_map.keys(): - pinyin = single_rep_map[pinyin[0]] + pinyin[1:] - - assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin) - phone = pinyin_to_symbol_map[pinyin].split(" ") - word2ph.append(len(phone)) - - phones_list += phone - tones_list += [int(tone)] * len(phone) - return phones_list, tones_list, word2ph - - -def text_normalize(text): - numbers = re.findall(r"\d+(?:\.?\d+)?", text) - for number in numbers: - text = text.replace(number, cn2an.an2cn(number), 1) - text = replace_punctuation(text) - return text - - -def get_bert_feature(text, word2ph): - from text import chinese_bert - - return chinese_bert.get_bert_feature(text, word2ph) - - -if __name__ == "__main__": - from text.chinese_bert import get_bert_feature - - text = "啊！但是《原神》是由,米哈\游自主， [研发]的一款全.新开放世界.冒险游戏" - text = text_normalize(text) - print(text) - phones, tones, word2ph = g2p(text) - bert = get_bert_feature(text, word2ph) - - print(phones, tones, word2ph, bert.shape) - - -# # 示例用法 -# text = "这是一个示例文本：,你好！这是一个测试...." -# print(g2p_paddle(text)) # 输出: 这是一个示例文本你好这是一个测试 diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/egg_info.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/egg_info.py deleted file mode 100644 index 66228f9beee62955ad077f0dfb60f21dee457f64..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/egg_info.py +++ /dev/null @@ -1,761 +0,0 @@ -"""setuptools.command.egg_info - -Create a distribution's .egg-info directory and contents""" - -from distutils.filelist import FileList as _FileList -from distutils.errors import DistutilsInternalError -from distutils.util import convert_path -from distutils import log -import distutils.errors -import distutils.filelist -import functools -import os -import re -import sys -import io -import time -import collections - -from .._importlib import metadata -from .. import _entry_points, _normalization - -from setuptools import Command -from setuptools.command.sdist import sdist -from setuptools.command.sdist import walk_revctrl -from setuptools.command.setopt import edit_config -from setuptools.command import bdist_egg -import setuptools.unicode_utils as unicode_utils -from setuptools.glob import glob - -from setuptools.extern import packaging -from setuptools.extern.jaraco.text import yield_lines -from ..warnings import SetuptoolsDeprecationWarning - - -PY_MAJOR = '{}.{}'.format(*sys.version_info) - - -def translate_pattern(glob): # noqa: C901 # is too complex (14) # FIXME - """ - Translate a file path glob like '*.txt' in to a regular expression. - This differs from fnmatch.translate which allows wildcards to match - directory separators. It also knows about '**/' which matches any number of - directories. - """ - pat = '' - - # This will split on '/' within [character classes]. This is deliberate. - chunks = glob.split(os.path.sep) - - sep = re.escape(os.sep) - valid_char = '[^%s]' % (sep,) - - for c, chunk in enumerate(chunks): - last_chunk = c == len(chunks) - 1 - - # Chunks that are a literal ** are globstars. They match anything. - if chunk == '**': - if last_chunk: - # Match anything if this is the last component - pat += '.*' - else: - # Match '(name/)*' - pat += '(?:%s+%s)*' % (valid_char, sep) - continue # Break here as the whole path component has been handled - - # Find any special characters in the remainder - i = 0 - chunk_len = len(chunk) - while i < chunk_len: - char = chunk[i] - if char == '*': - # Match any number of name characters - pat += valid_char + '*' - elif char == '?': - # Match a name character - pat += valid_char - elif char == '[': - # Character class - inner_i = i + 1 - # Skip initial !/] chars - if inner_i < chunk_len and chunk[inner_i] == '!': - inner_i = inner_i + 1 - if inner_i < chunk_len and chunk[inner_i] == ']': - inner_i = inner_i + 1 - - # Loop till the closing ] is found - while inner_i < chunk_len and chunk[inner_i] != ']': - inner_i = inner_i + 1 - - if inner_i >= chunk_len: - # Got to the end of the string without finding a closing ] - # Do not treat this as a matching group, but as a literal [ - pat += re.escape(char) - else: - # Grab the insides of the [brackets] - inner = chunk[i + 1:inner_i] - char_class = '' - - # Class negation - if inner[0] == '!': - char_class = '^' - inner = inner[1:] - - char_class += re.escape(inner) - pat += '[%s]' % (char_class,) - - # Skip to the end ] - i = inner_i - else: - pat += re.escape(char) - i += 1 - - # Join each chunk with the dir separator - if not last_chunk: - pat += sep - - pat += r'\Z' - return re.compile(pat, flags=re.MULTILINE | re.DOTALL) - - -class InfoCommon: - tag_build = None - tag_date = None - - @property - def name(self): - return _normalization.safe_name(self.distribution.get_name()) - - def tagged_version(self): - tagged = self._maybe_tag(self.distribution.get_version()) - return _normalization.best_effort_version(tagged) - - def _maybe_tag(self, version): - """ - egg_info may be called more than once for a distribution, - in which case the version string already contains all tags. - """ - return ( - version if self.vtags and self._already_tagged(version) - else version + self.vtags - ) - - def _already_tagged(self, version: str) -> bool: - # Depending on their format, tags may change with version normalization. - # So in addition the regular tags, we have to search for the normalized ones. - return version.endswith(self.vtags) or version.endswith(self._safe_tags()) - - def _safe_tags(self) -> str: - # To implement this we can rely on `safe_version` pretending to be version 0 - # followed by tags. Then we simply discard the starting 0 (fake version number) - return _normalization.best_effort_version(f"0{self.vtags}")[1:] - - def tags(self) -> str: - version = '' - if self.tag_build: - version += self.tag_build - if self.tag_date: - version += time.strftime("%Y%m%d") - return version - vtags = property(tags) - - -class egg_info(InfoCommon, Command): - description = "create a distribution's .egg-info directory" - - user_options = [ - ('egg-base=', 'e', "directory containing .egg-info directories" - " (default: top of the source tree)"), - ('tag-date', 'd', "Add date stamp (e.g. 20050528) to version number"), - ('tag-build=', 'b', "Specify explicit tag to add to version number"), - ('no-date', 'D', "Don't include date stamp [default]"), - ] - - boolean_options = ['tag-date'] - negative_opt = { - 'no-date': 'tag-date', - } - - def initialize_options(self): - self.egg_base = None - self.egg_name = None - self.egg_info = None - self.egg_version = None - self.ignore_egg_info_in_manifest = False - - #################################### - # allow the 'tag_svn_revision' to be detected and - # set, supporting sdists built on older Setuptools. - @property - def tag_svn_revision(self): - pass - - @tag_svn_revision.setter - def tag_svn_revision(self, value): - pass - #################################### - - def save_version_info(self, filename): - """ - Materialize the value of date into the - build tag. Install build keys in a deterministic order - to avoid arbitrary reordering on subsequent builds. - """ - egg_info = collections.OrderedDict() - # follow the order these keys would have been added - # when PYTHONHASHSEED=0 - egg_info['tag_build'] = self.tags() - egg_info['tag_date'] = 0 - edit_config(filename, dict(egg_info=egg_info)) - - def finalize_options(self): - # Note: we need to capture the current value returned - # by `self.tagged_version()`, so we can later update - # `self.distribution.metadata.version` without - # repercussions. - self.egg_name = self.name - self.egg_version = self.tagged_version() - parsed_version = packaging.version.Version(self.egg_version) - - try: - is_version = isinstance(parsed_version, packaging.version.Version) - spec = "%s==%s" if is_version else "%s===%s" - packaging.requirements.Requirement(spec % (self.egg_name, self.egg_version)) - except ValueError as e: - raise distutils.errors.DistutilsOptionError( - "Invalid distribution name or version syntax: %s-%s" % - (self.egg_name, self.egg_version) - ) from e - - if self.egg_base is None: - dirs = self.distribution.package_dir - self.egg_base = (dirs or {}).get('', os.curdir) - - self.ensure_dirname('egg_base') - self.egg_info = _normalization.filename_component(self.egg_name) + '.egg-info' - if self.egg_base != os.curdir: - self.egg_info = os.path.join(self.egg_base, self.egg_info) - - # Set package version for the benefit of dumber commands - # (e.g. sdist, bdist_wininst, etc.) - # - self.distribution.metadata.version = self.egg_version - - # If we bootstrapped around the lack of a PKG-INFO, as might be the - # case in a fresh checkout, make sure that any special tags get added - # to the version info - # - pd = self.distribution._patched_dist - key = getattr(pd, "key", None) or getattr(pd, "name", None) - if pd is not None and key == self.egg_name.lower(): - pd._version = self.egg_version - pd._parsed_version = packaging.version.Version(self.egg_version) - self.distribution._patched_dist = None - - def _get_egg_basename(self, py_version=PY_MAJOR, platform=None): - """Compute filename of the output egg. Private API.""" - return _egg_basename(self.egg_name, self.egg_version, py_version, platform) - - def write_or_delete_file(self, what, filename, data, force=False): - """Write `data` to `filename` or delete if empty - - If `data` is non-empty, this routine is the same as ``write_file()``. - If `data` is empty but not ``None``, this is the same as calling - ``delete_file(filename)`. If `data` is ``None``, then this is a no-op - unless `filename` exists, in which case a warning is issued about the - orphaned file (if `force` is false), or deleted (if `force` is true). - """ - if data: - self.write_file(what, filename, data) - elif os.path.exists(filename): - if data is None and not force: - log.warn( - "%s not set in setup(), but %s exists", what, filename - ) - return - else: - self.delete_file(filename) - - def write_file(self, what, filename, data): - """Write `data` to `filename` (if not a dry run) after announcing it - - `what` is used in a log message to identify what is being written - to the file. - """ - log.info("writing %s to %s", what, filename) - data = data.encode("utf-8") - if not self.dry_run: - f = open(filename, 'wb') - f.write(data) - f.close() - - def delete_file(self, filename): - """Delete `filename` (if not a dry run) after announcing it""" - log.info("deleting %s", filename) - if not self.dry_run: - os.unlink(filename) - - def run(self): - self.mkpath(self.egg_info) - try: - os.utime(self.egg_info, None) - except OSError as e: - msg = f"Cannot update time stamp of directory '{self.egg_info}'" - raise distutils.errors.DistutilsFileError(msg) from e - for ep in metadata.entry_points(group='egg_info.writers'): - writer = ep.load() - writer(self, ep.name, os.path.join(self.egg_info, ep.name)) - - # Get rid of native_libs.txt if it was put there by older bdist_egg - nl = os.path.join(self.egg_info, "native_libs.txt") - if os.path.exists(nl): - self.delete_file(nl) - - self.find_sources() - - def find_sources(self): - """Generate SOURCES.txt manifest file""" - manifest_filename = os.path.join(self.egg_info, "SOURCES.txt") - mm = manifest_maker(self.distribution) - mm.ignore_egg_info_dir = self.ignore_egg_info_in_manifest - mm.manifest = manifest_filename - mm.run() - self.filelist = mm.filelist - - -class FileList(_FileList): - # Implementations of the various MANIFEST.in commands - - def __init__(self, warn=None, debug_print=None, ignore_egg_info_dir=False): - super().__init__(warn, debug_print) - self.ignore_egg_info_dir = ignore_egg_info_dir - - def process_template_line(self, line): - # Parse the line: split it up, make sure the right number of words - # is there, and return the relevant words. 'action' is always - # defined: it's the first word of the line. Which of the other - # three are defined depends on the action; it'll be either - # patterns, (dir and patterns), or (dir_pattern). - (action, patterns, dir, dir_pattern) = self._parse_template_line(line) - - action_map = { - 'include': self.include, - 'exclude': self.exclude, - 'global-include': self.global_include, - 'global-exclude': self.global_exclude, - 'recursive-include': functools.partial( - self.recursive_include, dir, - ), - 'recursive-exclude': functools.partial( - self.recursive_exclude, dir, - ), - 'graft': self.graft, - 'prune': self.prune, - } - log_map = { - 'include': "warning: no files found matching '%s'", - 'exclude': ( - "warning: no previously-included files found " - "matching '%s'" - ), - 'global-include': ( - "warning: no files found matching '%s' " - "anywhere in distribution" - ), - 'global-exclude': ( - "warning: no previously-included files matching " - "'%s' found anywhere in distribution" - ), - 'recursive-include': ( - "warning: no files found matching '%s' " - "under directory '%s'" - ), - 'recursive-exclude': ( - "warning: no previously-included files matching " - "'%s' found under directory '%s'" - ), - 'graft': "warning: no directories found matching '%s'", - 'prune': "no previously-included directories found matching '%s'", - } - - try: - process_action = action_map[action] - except KeyError: - raise DistutilsInternalError( - "this cannot happen: invalid action '{action!s}'". - format(action=action), - ) - - # OK, now we know that the action is valid and we have the - # right number of words on the line for that action -- so we - # can proceed with minimal error-checking. - - action_is_recursive = action.startswith('recursive-') - if action in {'graft', 'prune'}: - patterns = [dir_pattern] - extra_log_args = (dir, ) if action_is_recursive else () - log_tmpl = log_map[action] - - self.debug_print( - ' '.join( - [action] + - ([dir] if action_is_recursive else []) + - patterns, - ) - ) - for pattern in patterns: - if not process_action(pattern): - log.warn(log_tmpl, pattern, *extra_log_args) - - def _remove_files(self, predicate): - """ - Remove all files from the file list that match the predicate. - Return True if any matching files were removed - """ - found = False - for i in range(len(self.files) - 1, -1, -1): - if predicate(self.files[i]): - self.debug_print(" removing " + self.files[i]) - del self.files[i] - found = True - return found - - def include(self, pattern): - """Include files that match 'pattern'.""" - found = [f for f in glob(pattern) if not os.path.isdir(f)] - self.extend(found) - return bool(found) - - def exclude(self, pattern): - """Exclude files that match 'pattern'.""" - match = translate_pattern(pattern) - return self._remove_files(match.match) - - def recursive_include(self, dir, pattern): - """ - Include all files anywhere in 'dir/' that match the pattern. - """ - full_pattern = os.path.join(dir, '**', pattern) - found = [f for f in glob(full_pattern, recursive=True) - if not os.path.isdir(f)] - self.extend(found) - return bool(found) - - def recursive_exclude(self, dir, pattern): - """ - Exclude any file anywhere in 'dir/' that match the pattern. - """ - match = translate_pattern(os.path.join(dir, '**', pattern)) - return self._remove_files(match.match) - - def graft(self, dir): - """Include all files from 'dir/'.""" - found = [ - item - for match_dir in glob(dir) - for item in distutils.filelist.findall(match_dir) - ] - self.extend(found) - return bool(found) - - def prune(self, dir): - """Filter out files from 'dir/'.""" - match = translate_pattern(os.path.join(dir, '**')) - return self._remove_files(match.match) - - def global_include(self, pattern): - """ - Include all files anywhere in the current directory that match the - pattern. This is very inefficient on large file trees. - """ - if self.allfiles is None: - self.findall() - match = translate_pattern(os.path.join('**', pattern)) - found = [f for f in self.allfiles if match.match(f)] - self.extend(found) - return bool(found) - - def global_exclude(self, pattern): - """ - Exclude all files anywhere that match the pattern. - """ - match = translate_pattern(os.path.join('**', pattern)) - return self._remove_files(match.match) - - def append(self, item): - if item.endswith('\r'): # Fix older sdists built on Windows - item = item[:-1] - path = convert_path(item) - - if self._safe_path(path): - self.files.append(path) - - def extend(self, paths): - self.files.extend(filter(self._safe_path, paths)) - - def _repair(self): - """ - Replace self.files with only safe paths - - Because some owners of FileList manipulate the underlying - ``files`` attribute directly, this method must be called to - repair those paths. - """ - self.files = list(filter(self._safe_path, self.files)) - - def _safe_path(self, path): - enc_warn = "'%s' not %s encodable -- skipping" - - # To avoid accidental trans-codings errors, first to unicode - u_path = unicode_utils.filesys_decode(path) - if u_path is None: - log.warn("'%s' in unexpected encoding -- skipping" % path) - return False - - # Must ensure utf-8 encodability - utf8_path = unicode_utils.try_encode(u_path, "utf-8") - if utf8_path is None: - log.warn(enc_warn, path, 'utf-8') - return False - - try: - # ignore egg-info paths - is_egg_info = ".egg-info" in u_path or b".egg-info" in utf8_path - if self.ignore_egg_info_dir and is_egg_info: - return False - # accept is either way checks out - if os.path.exists(u_path) or os.path.exists(utf8_path): - return True - # this will catch any encode errors decoding u_path - except UnicodeEncodeError: - log.warn(enc_warn, path, sys.getfilesystemencoding()) - - -class manifest_maker(sdist): - template = "MANIFEST.in" - - def initialize_options(self): - self.use_defaults = 1 - self.prune = 1 - self.manifest_only = 1 - self.force_manifest = 1 - self.ignore_egg_info_dir = False - - def finalize_options(self): - pass - - def run(self): - self.filelist = FileList(ignore_egg_info_dir=self.ignore_egg_info_dir) - if not os.path.exists(self.manifest): - self.write_manifest() # it must exist so it'll get in the list - self.add_defaults() - if os.path.exists(self.template): - self.read_template() - self.add_license_files() - self._add_referenced_files() - self.prune_file_list() - self.filelist.sort() - self.filelist.remove_duplicates() - self.write_manifest() - - def _manifest_normalize(self, path): - path = unicode_utils.filesys_decode(path) - return path.replace(os.sep, '/') - - def write_manifest(self): - """ - Write the file list in 'self.filelist' to the manifest file - named by 'self.manifest'. - """ - self.filelist._repair() - - # Now _repairs should encodability, but not unicode - files = [self._manifest_normalize(f) for f in self.filelist.files] - msg = "writing manifest file '%s'" % self.manifest - self.execute(write_file, (self.manifest, files), msg) - - def warn(self, msg): - if not self._should_suppress_warning(msg): - sdist.warn(self, msg) - - @staticmethod - def _should_suppress_warning(msg): - """ - suppress missing-file warnings from sdist - """ - return re.match(r"standard file .*not found", msg) - - def add_defaults(self): - sdist.add_defaults(self) - self.filelist.append(self.template) - self.filelist.append(self.manifest) - rcfiles = list(walk_revctrl()) - if rcfiles: - self.filelist.extend(rcfiles) - elif os.path.exists(self.manifest): - self.read_manifest() - - if os.path.exists("setup.py"): - # setup.py should be included by default, even if it's not - # the script called to create the sdist - self.filelist.append("setup.py") - - ei_cmd = self.get_finalized_command('egg_info') - self.filelist.graft(ei_cmd.egg_info) - - def add_license_files(self): - license_files = self.distribution.metadata.license_files or [] - for lf in license_files: - log.info("adding license file '%s'", lf) - self.filelist.extend(license_files) - - def _add_referenced_files(self): - """Add files referenced by the config (e.g. `file:` directive) to filelist""" - referenced = getattr(self.distribution, '_referenced_files', []) - # ^-- fallback if dist comes from distutils or is a custom class - for rf in referenced: - log.debug("adding file referenced by config '%s'", rf) - self.filelist.extend(referenced) - - def prune_file_list(self): - build = self.get_finalized_command('build') - base_dir = self.distribution.get_fullname() - self.filelist.prune(build.build_base) - self.filelist.prune(base_dir) - sep = re.escape(os.sep) - self.filelist.exclude_pattern(r'(^|' + sep + r')(RCS|CVS|\.svn)' + sep, - is_regex=1) - - def _safe_data_files(self, build_py): - """ - The parent class implementation of this method - (``sdist``) will try to include data files, which - might cause recursion problems when - ``include_package_data=True``. - - Therefore, avoid triggering any attempt of - analyzing/building the manifest again. - """ - if hasattr(build_py, 'get_data_files_without_manifest'): - return build_py.get_data_files_without_manifest() - - SetuptoolsDeprecationWarning.emit( - "`build_py` command does not inherit from setuptools' `build_py`.", - """ - Custom 'build_py' does not implement 'get_data_files_without_manifest'. - Please extend command classes from setuptools instead of distutils. - """, - see_url="https://peps.python.org/pep-0632/", - # due_date not defined yet, old projects might still do it? - ) - return build_py.get_data_files() - - -def write_file(filename, contents): - """Create a file with the specified name and write 'contents' (a - sequence of strings without line terminators) to it. - """ - contents = "\n".join(contents) - - # assuming the contents has been vetted for utf-8 encoding - contents = contents.encode("utf-8") - - with open(filename, "wb") as f: # always write POSIX-style manifest - f.write(contents) - - -def write_pkg_info(cmd, basename, filename): - log.info("writing %s", filename) - if not cmd.dry_run: - metadata = cmd.distribution.metadata - metadata.version, oldver = cmd.egg_version, metadata.version - metadata.name, oldname = cmd.egg_name, metadata.name - - try: - # write unescaped data to PKG-INFO, so older pkg_resources - # can still parse it - metadata.write_pkg_info(cmd.egg_info) - finally: - metadata.name, metadata.version = oldname, oldver - - safe = getattr(cmd.distribution, 'zip_safe', None) - - bdist_egg.write_safety_flag(cmd.egg_info, safe) - - -def warn_depends_obsolete(cmd, basename, filename): - """ - Unused: left to avoid errors when updating (from source) from <= 67.8. - Old installations have a .dist-info directory with the entry-point - ``depends.txt = setuptools.command.egg_info:warn_depends_obsolete``. - This may trigger errors when running the first egg_info in build_meta. - TODO: Remove this function in a version sufficiently > 68. - """ - - -def _write_requirements(stream, reqs): - lines = yield_lines(reqs or ()) - - def append_cr(line): - return line + '\n' - lines = map(append_cr, lines) - stream.writelines(lines) - - -def write_requirements(cmd, basename, filename): - dist = cmd.distribution - data = io.StringIO() - _write_requirements(data, dist.install_requires) - extras_require = dist.extras_require or {} - for extra in sorted(extras_require): - data.write('\n[{extra}]\n'.format(**vars())) - _write_requirements(data, extras_require[extra]) - cmd.write_or_delete_file("requirements", filename, data.getvalue()) - - -def write_setup_requirements(cmd, basename, filename): - data = io.StringIO() - _write_requirements(data, cmd.distribution.setup_requires) - cmd.write_or_delete_file("setup-requirements", filename, data.getvalue()) - - -def write_toplevel_names(cmd, basename, filename): - pkgs = dict.fromkeys( - [ - k.split('.', 1)[0] - for k in cmd.distribution.iter_distribution_names() - ] - ) - cmd.write_file("top-level names", filename, '\n'.join(sorted(pkgs)) + '\n') - - -def overwrite_arg(cmd, basename, filename): - write_arg(cmd, basename, filename, True) - - -def write_arg(cmd, basename, filename, force=False): - argname = os.path.splitext(basename)[0] - value = getattr(cmd.distribution, argname, None) - if value is not None: - value = '\n'.join(value) + '\n' - cmd.write_or_delete_file(argname, filename, value, force) - - -def write_entries(cmd, basename, filename): - eps = _entry_points.load(cmd.distribution.entry_points) - defn = _entry_points.render(eps) - cmd.write_or_delete_file('entry points', filename, defn, True) - - -def _egg_basename(egg_name, egg_version, py_version=None, platform=None): - """Compute filename of the output egg. Private API.""" - name = _normalization.filename_component(egg_name) - version = _normalization.filename_component(egg_version) - egg = f"{name}-{version}-py{py_version or PY_MAJOR}" - if platform: - egg += f"-{platform}" - return egg - - -class EggInfoDeprecationWarning(SetuptoolsDeprecationWarning): - """Deprecated behavior warning for EggInfo, bypassing suppression.""" diff --git a/spaces/Thaweewat/ControlNet-Architecture/ldm/modules/encoders/modules.py b/spaces/Thaweewat/ControlNet-Architecture/ldm/modules/encoders/modules.py deleted file mode 100644 index 81b2781b1387c27865af7b72e8c7637f7716a9cf..0000000000000000000000000000000000000000 --- a/spaces/Thaweewat/ControlNet-Architecture/ldm/modules/encoders/modules.py +++ /dev/null @@ -1,214 +0,0 @@ -import torch -import torch.nn as nn -from torch.utils.checkpoint import checkpoint - -from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel - -import open_clip -from ldm.util import default, count_params - - -class AbstractEncoder(nn.Module): - def __init__(self): - super().__init__() - - def encode(self, *args, **kwargs): - raise NotImplementedError - - -class IdentityEncoder(AbstractEncoder): - - def encode(self, x): - return x - - -class ClassEmbedder(nn.Module): - def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1): - super().__init__() - self.key = key - self.embedding = nn.Embedding(n_classes, embed_dim) - self.n_classes = n_classes - self.ucg_rate = ucg_rate - - def forward(self, batch, key=None, disable_dropout=False): - if key is None: - key = self.key - # this is for use in crossattn - c = batch[key][:, None] - if self.ucg_rate > 0. and not disable_dropout: - mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate) - c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1) - c = c.long() - c = self.embedding(c) - return c - - def get_unconditional_conditioning(self, bs, device="cuda"): - uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000) - uc = torch.ones((bs,), device=device) * uc_class - uc = {self.key: uc} - return uc - - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -class FrozenT5Embedder(AbstractEncoder): - """Uses the T5 transformer encoder for text""" - def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl - super().__init__() - self.tokenizer = T5Tokenizer.from_pretrained(version) - self.transformer = T5EncoderModel.from_pretrained(version) - self.device = device - self.max_length = max_length # TODO: typical value? - if freeze: - self.freeze() - - def freeze(self): - self.transformer = self.transformer.eval() - #self.train = disabled_train - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - outputs = self.transformer(input_ids=tokens) - - z = outputs.last_hidden_state - return z - - def encode(self, text): - return self(text) - - -class FrozenCLIPEmbedder(AbstractEncoder): - """Uses the CLIP transformer encoder for text (from huggingface)""" - LAYERS = [ - "last", - "pooled", - "hidden" - ] - def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, - freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32 - super().__init__() - assert layer in self.LAYERS - self.tokenizer = CLIPTokenizer.from_pretrained(version) - self.transformer = CLIPTextModel.from_pretrained(version) - self.device = device - self.max_length = max_length - if freeze: - self.freeze() - self.layer = layer - self.layer_idx = layer_idx - if layer == "hidden": - assert layer_idx is not None - assert 0 <= abs(layer_idx) <= 12 - - def freeze(self): - self.transformer = self.transformer.eval() - #self.train = disabled_train - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - print('Using device', self.device) - outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden") - if self.layer == "last": - z = outputs.last_hidden_state - elif self.layer == "pooled": - z = outputs.pooler_output[:, None, :] - else: - z = outputs.hidden_states[self.layer_idx] - return z - - def encode(self, text): - return self(text) - - -class FrozenOpenCLIPEmbedder(AbstractEncoder): - """ - Uses the OpenCLIP transformer encoder for text - """ - LAYERS = [ - #"pooled", - "last", - "penultimate" - ] - def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77, - freeze=True, layer="last"): - super().__init__() - assert layer in self.LAYERS - model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cuda'), pretrained=version) - del model.visual - self.model = model - - self.device = device - self.max_length = max_length - if freeze: - self.freeze() - self.layer = layer - if self.layer == "last": - self.layer_idx = 0 - elif self.layer == "penultimate": - self.layer_idx = 1 - else: - raise NotImplementedError() - - def freeze(self): - self.model = self.model.eval() - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - tokens = open_clip.tokenize(text) - z = self.encode_with_transformer(tokens.to(self.device)) - return z - - def encode_with_transformer(self, text): - x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] - x = x + self.model.positional_embedding - x = x.permute(1, 0, 2) # NLD -> LND - x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) - x = x.permute(1, 0, 2) # LND -> NLD - x = self.model.ln_final(x) - return x - - def text_transformer_forward(self, x: torch.Tensor, attn_mask = None): - for i, r in enumerate(self.model.transformer.resblocks): - if i == len(self.model.transformer.resblocks) - self.layer_idx: - break - if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): - x = checkpoint(r, x, attn_mask) - else: - x = r(x, attn_mask=attn_mask) - return x - - def encode(self, text): - return self(text) - - -class FrozenCLIPT5Encoder(AbstractEncoder): - def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda", - clip_max_length=77, t5_max_length=77): - super().__init__() - self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length) - self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length) - print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, " - f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.") - - def encode(self, text): - return self(text) - - def forward(self, text): - clip_z = self.clip_encoder.encode(text) - t5_z = self.t5_encoder.encode(text) - return [clip_z, t5_z] - - diff --git a/spaces/Theivaprakasham/yolov6/docs/About_naming_yolov6.md b/spaces/Theivaprakasham/yolov6/docs/About_naming_yolov6.md deleted file mode 100644 index 1ab4a3b8b9a413bce3f904a6223d2864cd79ccb7..0000000000000000000000000000000000000000 --- a/spaces/Theivaprakasham/yolov6/docs/About_naming_yolov6.md +++ /dev/null @@ -1,12 +0,0 @@ -# About the naming of YOLOv6 - -### WHY named YOLOv6 ? -The full name is actually MT-YOLOv6, which is called YOLOv6 for brevity. Our work is majorly inspired by the original idea of the one-stage YOLO detection algorithm and the implementation has leveraged various techniques and tricks of former relevant work . Therefore, we named the project YOLOv6 to pay tribute to the work of YOLO series. Furthermore, we have indeed adopted some novel method and made solid engineering improvements to dedicate the algorithm to industrial applications. -As for the project, we'll continue to improve and maintain it, contributing more values for industrial applications. - -P.S. We are contacting the authors of YOLO series about the naming of YOLOv6. - -Thanks for your attention！ - - - diff --git a/spaces/Ukrania/RVC-Models/lib/infer_pack/modules/F0Predictor/__init__.py b/spaces/Ukrania/RVC-Models/lib/infer_pack/modules/F0Predictor/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/VIPLab/Caption-Anything/caption_anything/segmenter/base_segmenter.py b/spaces/VIPLab/Caption-Anything/caption_anything/segmenter/base_segmenter.py deleted file mode 100644 index d7aff5111f35f2b3b5fe959b4a41bcfda1a05556..0000000000000000000000000000000000000000 --- a/spaces/VIPLab/Caption-Anything/caption_anything/segmenter/base_segmenter.py +++ /dev/null @@ -1,184 +0,0 @@ -import time -import torch -import cv2 -from PIL import Image, ImageDraw, ImageOps -import numpy as np -from typing import Union -from segment_anything import sam_model_registry, SamPredictor, SamAutomaticMaskGenerator -from caption_anything.utils.utils import prepare_segmenter, seg_model_map, load_image -import matplotlib.pyplot as plt -import PIL - - -class BaseSegmenter: - def __init__(self, device, checkpoint, model_name='huge', reuse_feature=True, model=None, args=None): - print(f"Initializing BaseSegmenter to {device}") - self.device = device - self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 - self.processor = None - if model is None: - if checkpoint is None: - _, checkpoint = prepare_segmenter(model_name) - self.model = sam_model_registry[seg_model_map[model_name]](checkpoint=checkpoint) - self.checkpoint = checkpoint - self.model.to(device=self.device) - else: - self.model = model - self.reuse_feature = reuse_feature - self.predictor = SamPredictor(self.model) - - sam_generator_keys = ['pred_iou_thresh', 'min_mask_region_area', 'stability_score_thresh', 'box_nms_thresh'] - generator_args = {k:v for k,v in vars(args).items() if k in sam_generator_keys} - self.mask_generator = SamAutomaticMaskGenerator(model=self.model, **generator_args) - self.image_embedding = None - self.image = None - - @torch.no_grad() - def set_image(self, image: Union[np.ndarray, Image.Image, str]): - image = load_image(image, return_type='numpy') - self.image = image - if self.reuse_feature: - self.predictor.set_image(image) - self.image_embedding = self.predictor.get_image_embedding() - print(self.image_embedding.shape) - - @torch.no_grad() - def inference(self, image: Union[np.ndarray, Image.Image, str], control: dict): - """ - SAM inference of image according to control. - Args: - image: str or PIL.Image or np.ndarray - control: dict to control SAM. - prompt_type: - 1. {control['prompt_type'] = ['everything']} to segment everything in the image. - 2. {control['prompt_type'] = ['click', 'box']} to segment according to click and box. - 3. {control['prompt_type'] = ['click'] to segment according to click. - 4. {control['prompt_type'] = ['box'] to segment according to box. - input_point: list of [x, y] coordinates of click. - input_label: List of labels for points accordingly, 0 for negative, 1 for positive. - input_box: List of [x1, y1, x2, y2] coordinates of box. - multimask_output: - If true, the model will return three masks. - For ambiguous input prompts (such as a single click), this will often - produce better masks than a single prediction. If only a single - mask is needed, the model's predicted quality score can be used - to select the best mask. For non-ambiguous prompts, such as multiple - input prompts, multimask_output=False can give better results. - Returns: - masks: np.ndarray of shape [num_masks, height, width] - - """ - image = load_image(image, return_type='numpy') - if 'everything' in control['prompt_type']: - masks = self.mask_generator.generate(image) - new_masks = np.concatenate([mask["segmentation"][np.newaxis, :] for mask in masks]) - bbox = np.array([mask["bbox"] for mask in masks]) - area = np.array([mask["area"] for mask in masks]) - return new_masks, bbox, area - else: - if not self.reuse_feature or self.image_embedding is None: - self.set_image(image) - self.predictor.set_image(self.image) - else: - assert self.image_embedding is not None - self.predictor.features = self.image_embedding - - if 'mutimask_output' in control: - masks, scores, logits = self.predictor.predict( - point_coords=np.array(control['input_point']), - point_labels=np.array(control['input_label']), - multimask_output=True, - ) - elif 'input_boxes' in control: - transformed_boxes = self.predictor.transform.apply_boxes_torch( - torch.tensor(control["input_boxes"], device=self.predictor.device), - image.shape[1::-1] # Reverse shape because numpy is (W, H) and function need (H, W) - ) - masks, _, _ = self.predictor.predict_torch( - point_coords=None, - point_labels=None, - boxes=transformed_boxes, - multimask_output=False, - ) - masks = masks.squeeze(1).cpu().numpy() - - else: - input_point = np.array(control['input_point']) if 'click' in control['prompt_type'] else None - input_label = np.array(control['input_label']) if 'click' in control['prompt_type'] else None - input_box = np.array(control['input_box']) if 'box' in control['prompt_type'] else None - - masks, scores, logits = self.predictor.predict( - point_coords=input_point, - point_labels=input_label, - box=input_box, - multimask_output=False, - ) - - if 0 in control['input_label']: - mask_input = logits[np.argmax(scores), :, :] - masks, scores, logits = self.predictor.predict( - point_coords=input_point, - point_labels=input_label, - box=input_box, - mask_input=mask_input[None, :, :], - multimask_output=False, - ) - - return masks - - -if __name__ == "__main__": - image_path = 'segmenter/images/truck.jpg' - prompts = [ - # { - # "prompt_type":["click"], - # "input_point":[[500, 375]], - # "input_label":[1], - # "multimask_output":"True", - # }, - { - "prompt_type": ["click"], - "input_point": [[1000, 600], [1325, 625]], - "input_label": [1, 0], - }, - # { - # "prompt_type":["click", "box"], - # "input_box":[425, 600, 700, 875], - # "input_point":[[575, 750]], - # "input_label": [0] - # }, - # { - # "prompt_type":["box"], - # "input_boxes": [ - # [75, 275, 1725, 850], - # [425, 600, 700, 875], - # [1375, 550, 1650, 800], - # [1240, 675, 1400, 750], - # ] - # }, - # { - # "prompt_type":["everything"] - # }, - ] - - init_time = time.time() - segmenter = BaseSegmenter( - device='cuda', - # checkpoint='sam_vit_h_4b8939.pth', - checkpoint='segmenter/sam_vit_h_4b8939.pth', - model_type='vit_h', - reuse_feature=True - ) - print(f'init time: {time.time() - init_time}') - - image_path = 'test_images/img2.jpg' - infer_time = time.time() - for i, prompt in enumerate(prompts): - print(f'{prompt["prompt_type"]} mode') - image = Image.open(image_path) - segmenter.set_image(np.array(image)) - masks = segmenter.inference(np.array(image), prompt) - Image.fromarray(masks[0]).save('seg.png') - print(masks.shape) - - print(f'infer time: {time.time() - infer_time}') diff --git a/spaces/Varun6579/MyGenAIChatBot/README.md b/spaces/Varun6579/MyGenAIChatBot/README.md deleted file mode 100644 index 293fc27c7ebc8453ef88f7806fe978b7776c3959..0000000000000000000000000000000000000000 --- a/spaces/Varun6579/MyGenAIChatBot/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: MyGenAIChatBot -emoji: 💻 -colorFrom: red -colorTo: purple -sdk: gradio -sdk_version: 3.42.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Vgi/darkstorm2150-Protogen_x3.4_Official_Release/app.py b/spaces/Vgi/darkstorm2150-Protogen_x3.4_Official_Release/app.py deleted file mode 100644 index 45c0ddc60de66983c4314e5b4f49cb29ae1091b3..0000000000000000000000000000000000000000 --- a/spaces/Vgi/darkstorm2150-Protogen_x3.4_Official_Release/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/darkstorm2150/Protogen_x3.4_Official_Release").launch() \ No newline at end of file diff --git a/spaces/VickyKira/NASAGPT/server/backend.py b/spaces/VickyKira/NASAGPT/server/backend.py deleted file mode 100644 index fd45b94d916512059e4d1f7850b63de6f9da6320..0000000000000000000000000000000000000000 --- a/spaces/VickyKira/NASAGPT/server/backend.py +++ /dev/null @@ -1,176 +0,0 @@ -import re -from datetime import datetime -from g4f import ChatCompletion -from flask import request, Response, stream_with_context -from requests import get -from server.config import special_instructions - - -class Backend_Api: - def __init__(self, bp, config: dict) -> None: - """ - Initialize the Backend_Api class. - :param app: Flask application instance - :param config: Configuration dictionary - """ - self.bp = bp - self.routes = { - '/backend-api/v2/conversation': { - 'function': self._conversation, - 'methods': ['POST'] - } - } - - def _conversation(self): - """ - Handles the conversation route. - - :return: Response object containing the generated conversation stream - """ - conversation_id = request.json['conversation_id'] - - try: - jailbreak = request.json['jailbreak'] - model = request.json['model'] - messages = build_messages(jailbreak) - - # Generate response - response = ChatCompletion.create( - model=model, - chatId=conversation_id, - messages=messages - ) - - return Response(stream_with_context(generate_stream(response, jailbreak)), mimetype='text/event-stream') - - except Exception as e: - print(e) - print(e.__traceback__.tb_next) - - return { - '_action': '_ask', - 'success': False, - "error": f"an error occurred {str(e)}" - }, 400 - - -def build_messages(jailbreak): - """ - Build the messages for the conversation. - - :param jailbreak: Jailbreak instruction string - :return: List of messages for the conversation - """ - _conversation = request.json['meta']['content']['conversation'] - internet_access = request.json['meta']['content']['internet_access'] - prompt = request.json['meta']['content']['parts'][0] - - # Add the existing conversation - conversation = _conversation - - # Add web results if enabled - if internet_access: - current_date = datetime.now().strftime("%Y-%m-%d") - query = f'Current date: {current_date}. ' + prompt["content"] - search_results = fetch_search_results(query) - conversation.extend(search_results) - - # Add jailbreak instructions if enabled - if jailbreak_instructions := getJailbreak(jailbreak): - conversation.extend(jailbreak_instructions) - - # Add the prompt - conversation.append(prompt) - - # Reduce conversation size to avoid API Token quantity error - if len(conversation) > 3: - conversation = conversation[-4:] - - return conversation - - -def fetch_search_results(query): - """ - Fetch search results for a given query. - - :param query: Search query string - :return: List of search results - """ - search = get('https://ddg-api.herokuapp.com/search', - params={ - 'query': query, - 'limit': 3, - }) - - snippets = "" - for index, result in enumerate(search.json()): - snippet = f'[{index + 1}] "{result["snippet"]}" URL:{result["link"]}.' - snippets += snippet - - response = "Here are some updated web searches. Use this to improve user response:" - response += snippets - - return [{'role': 'system', 'content': response}] - - -def generate_stream(response, jailbreak): - """ - Generate the conversation stream. - - :param response: Response object from ChatCompletion.create - :param jailbreak: Jailbreak instruction string - :return: Generator object yielding messages in the conversation - """ - if getJailbreak(jailbreak): - response_jailbreak = '' - jailbroken_checked = False - for message in response: - response_jailbreak += message - if jailbroken_checked: - yield message - else: - if response_jailbroken_success(response_jailbreak): - jailbroken_checked = True - if response_jailbroken_failed(response_jailbreak): - yield response_jailbreak - jailbroken_checked = True - else: - yield from response - - -def response_jailbroken_success(response: str) -> bool: - """Check if the response has been jailbroken. - - :param response: Response string - :return: Boolean indicating if the response has been jailbroken - """ - act_match = re.search(r'ACT:', response, flags=re.DOTALL) - return bool(act_match) - - -def response_jailbroken_failed(response): - """ - Check if the response has not been jailbroken. - - :param response: Response string - :return: Boolean indicating if the response has not been jailbroken - """ - return False if len(response) < 4 else not (response.startswith("GPT:") or response.startswith("ACT:")) - - -def getJailbreak(jailbreak): - """ - Check if jailbreak instructions are provided. - - :param jailbreak: Jailbreak instruction string - :return: Jailbreak instructions if provided, otherwise None - """ - if jailbreak != "default": - special_instructions[jailbreak][0]['content'] += special_instructions['two_responses_instruction'] - if jailbreak in special_instructions: - special_instructions[jailbreak] - return special_instructions[jailbreak] - else: - return None - else: - return None diff --git a/spaces/WindVChen/INR-Harmon/README.md b/spaces/WindVChen/INR-Harmon/README.md deleted file mode 100644 index 0901045f96ad703d5a6594a8ca9764b613aa52d9..0000000000000000000000000000000000000000 --- a/spaces/WindVChen/INR-Harmon/README.md +++ /dev/null @@ -1,307 +0,0 @@ ---- -title: INR-Harmon - Harmonize Any Image You Want! -emoji: 👋🏃‍♂️ -colorFrom: purple -colorTo: pink -sdk: gradio -sdk_version: 3.26.0 -app_file: app.py -python_version: 3.8.11 -pinned: false ---- - -
- -

Dense Pixel-to-Pixel Harmonization via
Continuous Image Representation

- - -**[Jianqi Chen](https://windvchen.github.io/), [Yilan Zhang](https://scholar.google.com.hk/citations?hl=en&user=wZ4M4ecAAAAJ), [Zhengxia Zou](https://scholar.google.com.hk/citations?hl=en&user=DzwoyZsAAAAJ), [Keyan Chen](https://scholar.google.com.hk/citations?hl=en&user=5RF4ia8AAAAJ), -and [Zhenwei Shi](https://scholar.google.com.hk/citations?hl=en&user=kNhFWQIAAAAJ)** - -![](https://komarev.com/ghpvc/?username=windvchenINR-Harmonization&label=visitors) -![GitHub stars](https://badgen.net/github/stars/windvchen/INR-Harmonization) -[![](https://img.shields.io/badge/license-Apache--2.0-blue)](#License) -[![](https://img.shields.io/badge/arXiv-2303.01681-b31b1b.svg)](https://arxiv.org/abs/2303.01681) -Huggingface - -
- -

- - - -

- -
- -
- - -### Share us a :star: if this repo does help - -This repository is the official implementation of ***HINet (or INR-Harmonization)***, which can achieve ***Arbitrary aspect ratio & Arbitrary resolution*** image harmonization. If you encounter any question, please feel free to contact -us. You can create an issue or just send email to me windvchen@gmail.com. Also welcome for any idea exchange and -discussion. - -## Updates - -[**07/21/2023**] We achieve that!🎉🎉 With all **TODOs** complete! Try here for our [Huggingface Demo]()!! You can also download this repository, and run the GUI locally (refer to [cmd] here)!🥳🥳 - -[**07/19/2023**] Hi everyone! We have added two new inference -scripts: [efficient_inference_for_square_image.py](efficient_inference_for_square_image.py) where you can achieve quite -fast speed on harmonizing a ***square image***! -And [inference_for_arbitrary_resolution_image.py](inference_for_arbitrary_resolution_image.py) where you can harmonize -any resolution image ***(2K, 4k, 8k, JUST WHATEVER YOU WANT!!)***. Please check them out!😉😉 - -A summary of features of different inference strategies (More information please refer to [Inference](#inference)): - -| Features | [efficient_inference_for_square_image.py](efficient_inference_for_square_image.py) | [inference_for_arbitrary_resolution_image.py](inference_for_arbitrary_resolution_image.py) | -|:-----------------------:|:----------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:| -| Support Arbitrary Image | ❌ *(Only squre image)* | ✅ *(Arbitrary aspect ratio, Arbitrary resolution!!!)* | -| Speed | 🚀 *(Quite fast)* | 🚌 *(Relatively slower than the left one)* | -| Memory cost | 🌲 *(Quite low)* | 🏭 *(Relatively higher than the left one for the same resolution)* | - -[**07/18/2023**] Check out our new work [***Diff-Harmonization***](https://github.com/WindVChen/Diff-Harmonization), -which is a **Zero-Shot Harmonization** method based on *Diffusion Models*!😊 - -[**07/17/2023**] Pretrained weights have been released. Feel free to try that!👋👋 - -[**07/16/2023**] The code is initially public. 🥳 - -[**03/06/2023**] Source code and pretrained models will be publicly accessible. - -## TODO - -- [x] Initial code release. -- [x] Add pretrained model weights. -- [x] Add the efficient splitting strategy for inferencing on original resolution images. -- [x] Add Gradio demo. - -## Table of Contents - -- [Abstract](#abstract) -- [Requirements](#requirements) -- [Training](#training) - - [Train in low resolution (LR) mode](#train-in-low-resolution--lr--mode) - - [Train in high resolution (HR) mode](#train-in-high-resolution--hr--mode--eg-2048x2048-) - - [Train in original resolution mode](#train-in-original-resolution-mode) -- [Evaluation](#evaluation) - - [Evaluation in low resolution (LR) mode](#evaluation-in-low-resolution--lr--mode) - - [Evaluation in high resolution (HR) mode](#evaluation-in-high-resolution--hr--mode--eg-2048x2048-) - - [Evaluation in original resolution mode](#evaluation-in-original-resolution-mode) -- [Inference](#inference) - - [Inference on square images (fast & low cost)](#inference-on-square-images--fast--low-cost-) - - [Inference on arbitrary resolution images (Support any resolution)](#Inference-on-arbitrary-resolution-images--slow-high-cost-but-support-any-resolution-) -- [Results](#results) -- [Citation & Acknowledgments](#citation--acknowledgments) -- [License](#license) - -## Abstract - -![HINet's framework](assets/network.png) - -High-resolution (HR) image harmonization is of great significance in real-world applications such as image synthesis and -image editing. However, due to the high memory costs, existing dense pixel-to-pixel harmonization methods are mainly -focusing on processing low-resolution (LR) images. Some recent works resort to combining with color-to-color -transformations but are either limited to certain resolutions or heavily depend on hand-crafted image filters. In this -work, we explore leveraging the implicit neural representation (INR) and propose a novel -***image Harmonization method based on Implicit neural Networks (HINet)***, which to the best of our knowledge, is -***the first dense pixel-to-pixel method applicable to HR images without any hand-crafted filter design***. Inspired by -the Retinex theory, we decouple the MLPs into two parts to respectively capture the content and environment of composite -images. A Low-Resolution Image Prior (LRIP) network is designed to alleviate the Boundary Inconsistency problem, and we -also propose new designs for the training and inference process. Extensive experiments have demonstrated the -effectiveness of our method compared with state-of-the-art methods. Furthermore, some interesting and practical -applications of the proposed method are explored. - -## Requirements - -1. Software Requirements - - Python: 3.8 - - CUDA: 11.3 - - cuDNN: 8.4.1 - - To install other requirements: - - ``` - pip install -r requirements.txt - ``` - -2. Datasets - - We train and evaluate on the [iHarmony4 dataset](https://github.com/bcmi/Image-Harmonization-Dataset-iHarmony4). - Please download the dataset in advance, and arrange them into the following structure: - - ``` - ├── dataset_path - ├── HAdobe5k - ├── composite_images - ├── masks - ├── real_images - ├── HCOCO - ├── Hday2night - ├── HFlickr - IHD_test.txt - IHD_train.txt - ``` - - - Before training we resize HAdobe5k subdataset so that each side is smaller than 1024. This is for quick data - loading. The resizing script can refer to [resize_Adobe.py](tools/resize_Adobe.py). - - - For training or evaluating on the original resolution of iHarmony4 dataset. Please newly create a `HAdobe5kori` - directory with the original HAdobe5k images in it. - - - If you want to train and evaluate only on HAdobe5k subdataset (see Table 1 in the paper), you can modify - the `IHD_train.txt` and `IHD_test.txt` in [train.py](train.py) to only contain the HAdobe5k images. - -3. Pre-trained Models - - We adopt [HRNetV2](https://github.com/HRNet/HRNet-Image-Classification) as our encoder, you can download the - weight - from [here](https://onedrive.live.com/?authkey=%21AMkPimlmClRvmpw&id=F7FD0B7F26543CEB%21112&cid=F7FD0B7F26543CEB&parId=root&parQt=sharedby&parCid=C8304F01C1A85932&o=OneUp) - and save the weight in `pretrained_models` directory. - - In the following table, we provide several model weights pretrained under different resolutions (Correspond to - Table 1 in the paper): - -| Download Link | Model Descriptions | -|:--------------------------------------------------------:|:-------------------------------------------------------------------:| -| [Resolution_RAW_iHarmony4.pth][Resolution_RAW_iHarmony4] | Train by RSC strategy with original resolution iHarmony4 dataset | -| [Resolution_256_iHarmony4.pth][Resolution_256_iHarmony4] | Train with 256*256 resolution iHarmony4 dataset | -| [Resolution_RAW_HAdobe5K.pth][Resolution_RAW_HAdobe5K] | Train by RSC strategy with original resolution HAdobe5k subdataset | -| [Resolution_2048_HAdobe5K.pth][Resolution_2048_HAdobe5K] | Train by RSC strategy with 2048*2048 resolution HAdobe5k subdataset | -| [Resolution_1024_HAdobe5K.pth][Resolution_1024_HAdobe5K] | Train by RSC strategy with 1024*1024 resolution HAdobe5k subdataset | - -[Resolution_RAW_iHarmony4]: https://drive.google.com/file/d/1O9faWNk54mIzMaGZ1tmgm0EJpH20a-Fl/view?usp=drive_link - -[Resolution_256_iHarmony4]: https://drive.google.com/file/d/1xym96LTP9a75UseDWGW2KRN1gyl3HPyM/view?usp=sharing - -[Resolution_RAW_HAdobe5K]: https://drive.google.com/file/d/1JeUS5inuOM0pASKfu-tK9K7E5pGkP570/view?usp=drive_link - -[Resolution_2048_HAdobe5K]: https://drive.google.com/file/d/18RxTfZsPEoi6kSS_UVEsUBYRBHAl4MfB/view?usp=drive_link - -[Resolution_1024_HAdobe5K]: https://drive.google.com/file/d/1cOY74mN8gIz66watyoobZ1knrigkQyb5/view?usp=sharing - -## Visualization GUI - -We provide a GUI based on Gradio for visualizing the intermediate results of our method. You can run the following command to start it locally, or make use of our provided [Huggingface Space](https://huggingface.co/spaces/WindVChen/INR-Harmon). -```bash -python app.py -``` - -## Training - -The intermediate output (including checkpoint, visualization, log.txt) will be saved in directory `logs/exp`. - -### Train in low resolution (LR) mode - -```bash -python train.py --dataset_path {dataset_path} --base_size 256 --input_size 256 --INR_input_size 256 -``` - -- `dataset_path`: the path of the iHarmony4 dataset. -- `base_size`: the size of the input image to encoder. -- `input_size`: the size of the target resolution. -- `INR_input_size`: the size of the input image to the INR decoder. -- `hr_train`: whether to train in high resolution (HR) mode, i.e., using RSC strategy (See Section 3.4 in the paper). -- `isFullRes`: whether to train in full/original resolution mode. - -- (More parameters' information could be found in codes ...) - -### Train in high resolution (HR) mode (E.g, 2048x2048) - -If **not use RSC strategy**, the training command is as follows: (For a single RTX 3090, it will lead to out-of-memory -even `batch_size` is set to 2.) - -```bash -python train.py --dataset_path {dataset_path} --base_size 256 --input_size 2048 --INR_input_size 2048 -``` - -If **use RSC strategy**, the training command is as follows: (For a single RTX 3090, `batch_size` can set up to 6.) - -```bash -python train.py --dataset_path {dataset_path} --base_size 256 --input_size 2048 --INR_input_size 2048 --hr_train -``` - -### Train in original resolution mode - -```bash -python train.py --dataset_path {dataset_path} --base_size 256 --hr_train --isFullRes -``` - -## Evaluation - -The intermediate output (including visualizations, log.txt) will be saved in directory `logs/test`. - -**Notice:** Due to the resolution-agnostic characteristic of INR, you can evaluate dataset at any resolution not matter -which resolution the model is trained on. Please refer to Table 4 and Table 5 in the paper. - -### Evaluation in low resolution (LR) mode - -```bash -python inference.py --dataset_path {dataset_path} --pretrained {pretrained_weight} --base_size 256 --input_size 256 --INR_input_size 256 -``` - -### Evaluation in high resolution (HR) mode (E.g, 2048x2048) - -```bash -python inference.py --dataset_path {dataset_path} --pretrained {pretrained_weight} --base_size 256 --input_size 2048 --INR_input_size 2048 -``` - -### Evaluation in original resolution mode - -```bash -python inference.py --dataset_path {dataset_path} --pretrained {pretrained_weight} --base_size 256 --hr_train --isFullRes -``` - -## Inference - -We have provided demo images (2K and 6K) in [demo](demo). Feel free to play around them. - -**Notice:** Due to the resolution-agnostic characteristic of INR, you can inference images at any resolution not matter -which resolution the model is trained on. Please refer to Table 4 and Table 5 in the paper. - -### Inference on square images (fast & low cost) - -If you want to inference on square images, please use the command here. Note that this code only support square images with resolution of multiplies of 256. Some other requirements will be listed in cmd prints (if error) when you run the code. - -```bash -python efficient_inference.py --split_resolution {split_resolution} --composite_image {composite_image_path} --mask {mask_path} --save_path --{save_path} --pretrained {pretrained_weight} -``` -- `split_resolution`: the resolution of the split patches. (E.g., 512 means the input image will be split into 512x512 patches.) These patches will finally be assembled back to the resolution of the original image. -- `composite_image`: the path of the composite image. You can try with the provided images in [demo](demo). -- `mask`: the path of the mask. You can try with the provided masks in [demo](demo). -- `save_path`: the path of the output image. -- `pretrained`: the path of the pretrained weight. - -### Inference on arbitrary resolution images (slow, high cost, but support any resolution) -If the former inference script cannot meet your needs and you want to inference on arbitrary resolution images, please use the command here. Note that this script will be slower and cost more memory for a same resolution (***But anyway, it supports arbitrary resolution***). - -If you encounter out-of-memory error, please try to reduce the `split_num` parameter below. (Our script will also have some prints that can guide you to do this) -```bash -python inference_for_arbitrary_resolution.py --split_num {split_num} --composite_image {composite_image_path} --mask {mask_path} --save_path --{save_path} --pretrained {pretrained_weight} -``` -- `split_num`: the number of splits for the input image. (E.g., 4 means the input image will be split into 4x4=16 patches.) -- `composite_image`: the path of the composite image. You can try with the provided images in [demo](demo). -- `mask`: the path of the mask. You can try with the provided masks in [demo](demo). -- `save_path`: the path of the output image. -- `pretrained`: the path of the pretrained weight. - -## Results - -![Metrics](assets/metrics.png#pic_center) -![Visual comparisons](assets/visualizations.png#pic_center) -![Visual comparisons2](assets/visualizations2.png#pic_center) - -## Citation & Acknowledgments - -If you find this paper useful in your research, please consider citing: - -``` -@article{chen2023dense, - title={Dense Pixel-to-Pixel Harmonization via Continuous Image Representation}, - author={Chen, Jianqi and Zhang, Yilan and Zou, Zhengxia and Chen, Keyan and Shi, Zhenwei}, - journal={arXiv preprint arXiv:2303.01681}, - year={2023} -} -``` - -## License - -This project is licensed under the Apache-2.0 license. See [LICENSE](LICENSE) for details. \ No newline at end of file diff --git a/spaces/Wootang01/question_generator_three/app.py b/spaces/Wootang01/question_generator_three/app.py deleted file mode 100644 index 84f37d984300ee2bb7b4701eb23bd738736a6ecd..0000000000000000000000000000000000000000 --- a/spaces/Wootang01/question_generator_three/app.py +++ /dev/null @@ -1,67 +0,0 @@ -import transformers -import sentencepiece -import torch -import numpy as np - -from transformers import T5ForConditionalGeneration,T5Tokenizer -question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1') -question_tokenizer = T5Tokenizer.from_pretrained('ramsrigouthamg/t5_squad_v1') - -def get_question(sentence,answer,mdl,tknizer): - prompt = "context: {} answer: {}".format(sentence,answer) - print (prompt) - max_len = 256 - encoding = tknizer.encode_plus(prompt,max_length=max_len, pad_to_max_length=False,truncation=True, return_tensors="pt") - - input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"] - - outs = mdl.generate(input_ids=input_ids, - attention_mask=attention_mask, - early_stopping=True, - num_beams=5, - num_return_sequences=1, - no_repeat_ngram_size=2, - max_length=300) - - - dec = [tknizer.decode(ids,skip_special_tokens=True) for ids in outs] - - - Question = dec[0].replace("question:","") - Question= Question.strip() - return Question - - -Text = "Elon Musk said that Tesla will not accept payments in Bitcoin because of environmental concerns." -Answer = "Elon Musk" - -ques = get_question(Text,Answer,question_model,question_tokenizer) -print ("question: ",ques) - -import gradio as gr - -title = "Question Generator Three" -description = "Paste or write a text. You may also paste or write a short answer, preferably a noun or noun phrase. Submit and the machine will attempt to generate a coherent question." -Text = gr.inputs.Textbox(lines=5, placeholder="Enter paragraph/context here...") -Answer = gr.inputs.Textbox(lines=3, placeholder="Enter answer/keyword here...") -question = gr.outputs.Textbox( type="auto", label="Question") -examples = [ - ["""Fears of a new Covid-19 cluster linked to a hotpot restaurant have surfaced amid Hong Kong’s Omicron-fuelled fifth wave, while infections tied to an investment bank continued to expand, triggering the evacuation of residents in a building after vertical transmission of the virus was detected. -On Wednesday, hundreds thronged Covid-19 testing stations in Tuen Mun, with some residents complaining of long waiting times and chaotic arrangements. Authorities have deemed the district a high-risk area because of a higher number of infections. -Health officials said sewage testing would be conducted in Tuen Mun to monitor the spread of the coronavirus, but a string of preliminary-positive cases detected across the city suggested a wider, more worrying situation. -""", "a higher number of infections"], - ["""Squid Game made history on Wednesday as the first non-English-language television series and the first Korean series to score a nomination for a Screen Actors Guild Award. -The hit Netflix show, created by Hwang Dong-hyuk, is nominated for ensemble in a drama series alongside The Handmaid’s Tale, The Morning Show, Succession and Yellowstone. -Squid Game stars Lee Jung-jae and Jung Ho-yeon also landed individual nominations for actor and actress in a drama series, respectively. -""", "Yellowstone"] - -] - -def generate_question(Text,Answer): - return get_question(Text,Answer,question_model,question_tokenizer) - -iface = gr.Interface( - fn=generate_question, - inputs=[Text,Answer], - outputs=question, title=title, description=description, examples=examples) -iface.launch(debug=False) \ No newline at end of file diff --git a/spaces/XzJosh/ShanBao-Bert-VITS2/losses.py b/spaces/XzJosh/ShanBao-Bert-VITS2/losses.py deleted file mode 100644 index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000 --- a/spaces/XzJosh/ShanBao-Bert-VITS2/losses.py +++ /dev/null @@ -1,61 +0,0 @@ -import torch -from torch.nn import functional as F - -import commons - - -def feature_loss(fmap_r, fmap_g): - loss = 0 - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - rl = rl.float().detach() - gl = gl.float() - loss += torch.mean(torch.abs(rl - gl)) - - return loss * 2 - - -def discriminator_loss(disc_real_outputs, disc_generated_outputs): - loss = 0 - r_losses = [] - g_losses = [] - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - dr = dr.float() - dg = dg.float() - r_loss = torch.mean((1-dr)**2) - g_loss = torch.mean(dg**2) - loss += (r_loss + g_loss) - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - - return loss, r_losses, g_losses - - -def generator_loss(disc_outputs): - loss = 0 - gen_losses = [] - for dg in disc_outputs: - dg = dg.float() - l = torch.mean((1-dg)**2) - gen_losses.append(l) - loss += l - - return loss, gen_losses - - -def kl_loss(z_p, logs_q, m_p, logs_p, z_mask): - """ - z_p, logs_q: [b, h, t_t] - m_p, logs_p: [b, h, t_t] - """ - z_p = z_p.float() - logs_q = logs_q.float() - m_p = m_p.float() - logs_p = logs_p.float() - z_mask = z_mask.float() - - kl = logs_p - logs_q - 0.5 - kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p) - kl = torch.sum(kl * z_mask) - l = kl / torch.sum(z_mask) - return l diff --git a/spaces/XzJosh/Taffy-Bert-VITS2/README.md b/spaces/XzJosh/Taffy-Bert-VITS2/README.md deleted file mode 100644 index 7f8a53ebfe7058f2362b004011b49f990eb8d0bd..0000000000000000000000000000000000000000 --- a/spaces/XzJosh/Taffy-Bert-VITS2/README.md +++ /dev/null @@ -1,5 +0,0 @@ ---- -license: mit -sdk: gradio -title: AI塔菲 ---- \ No newline at end of file diff --git a/spaces/XzJosh/nine1-Bert-VITS2/text/english.py b/spaces/XzJosh/nine1-Bert-VITS2/text/english.py deleted file mode 100644 index 781d0a56cef71f66fc67db51d76538be90d3ddd2..0000000000000000000000000000000000000000 --- a/spaces/XzJosh/nine1-Bert-VITS2/text/english.py +++ /dev/null @@ -1,138 +0,0 @@ -import pickle -import os -import re -from g2p_en import G2p -from string import punctuation - -from text import symbols - -current_file_path = os.path.dirname(__file__) -CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep') -CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle') -_g2p = G2p() - -arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'} - - -def post_replace_ph(ph): - rep_map = { - '：': ',', - '；': ',', - '，': ',', - '。': '.', - '！': '!', - '？': '?', - '\n': '.', - "·": ",", - '、': ",", - '...': '…', - 'v': "V" - } - if ph in rep_map.keys(): - ph = rep_map[ph] - if ph in symbols: - return ph - if ph not in symbols: - ph = 'UNK' - return ph - -def read_dict(): - g2p_dict = {} - start_line = 49 - with open(CMU_DICT_PATH) as f: - line = f.readline() - line_index = 1 - while line: - if line_index >= start_line: - line = line.strip() - word_split = line.split(' ') - word = word_split[0] - - syllable_split = word_split[1].split(' - ') - g2p_dict[word] = [] - for syllable in syllable_split: - phone_split = syllable.split(' ') - g2p_dict[word].append(phone_split) - - line_index = line_index + 1 - line = f.readline() - - return g2p_dict - - -def cache_dict(g2p_dict, file_path): - with open(file_path, 'wb') as pickle_file: - pickle.dump(g2p_dict, pickle_file) - - -def get_dict(): - if os.path.exists(CACHE_PATH): - with open(CACHE_PATH, 'rb') as pickle_file: - g2p_dict = pickle.load(pickle_file) - else: - g2p_dict = read_dict() - cache_dict(g2p_dict, CACHE_PATH) - - return g2p_dict - -eng_dict = get_dict() - -def refine_ph(phn): - tone = 0 - if re.search(r'\d$', phn): - tone = int(phn[-1]) + 1 - phn = phn[:-1] - return phn.lower(), tone - -def refine_syllables(syllables): - tones = [] - phonemes = [] - for phn_list in syllables: - for i in range(len(phn_list)): - phn = phn_list[i] - phn, tone = refine_ph(phn) - phonemes.append(phn) - tones.append(tone) - return phonemes, tones - - -def text_normalize(text): - # todo: eng text normalize - return text - -def g2p(text): - - phones = [] - tones = [] - words = re.split(r"([,;.\-\?\!\s+])", text) - for w in words: - if w.upper() in eng_dict: - phns, tns = refine_syllables(eng_dict[w.upper()]) - phones += phns - tones += tns - else: - phone_list = list(filter(lambda p: p != " ", _g2p(w))) - for ph in phone_list: - if ph in arpa: - ph, tn = refine_ph(ph) - phones.append(ph) - tones.append(tn) - else: - phones.append(ph) - tones.append(0) - # todo: implement word2ph - word2ph = [1 for i in phones] - - phones = [post_replace_ph(i) for i in phones] - return phones, tones, word2ph - -if __name__ == "__main__": - # print(get_dict()) - # print(eng_word_to_phoneme("hello")) - print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")) - # all_phones = set() - # for k, syllables in eng_dict.items(): - # for group in syllables: - # for ph in group: - # all_phones.add(ph) - # print(all_phones) \ No newline at end of file diff --git a/spaces/Yah216/Arabic_poem_classifier/app.py b/spaces/Yah216/Arabic_poem_classifier/app.py deleted file mode 100644 index bbf72b782320453cd5d9fb4e7e1ebd99fc972af8..0000000000000000000000000000000000000000 --- a/spaces/Yah216/Arabic_poem_classifier/app.py +++ /dev/null @@ -1,36 +0,0 @@ -import gradio as gr - -description = "التعرف على خاصيات البيت الشعري" -title = """هذا البرنامج يقوم بالتعرف على مختلف خاصيات البيت من الشعر. -يمكنكم إختيار الخاصية من بين: -- التعرف على البحر -- التعرف على الروي -التعرف على الموضوع-""" - -examples = [["سَلو قَلبي غَداةَ سَلا وَثابا لَعَلَّ عَلى الجَمالِ لَهُ عِتابا"], ["قفا نبك من ذِكرى حبيب ومنزلِ بسِقطِ اللِّوى بينَ الدَّخول فحَوْملِ"]] - - -meter = gr.Interface.load("huggingface/Yah216/Arabic_poem_meter_3", - description="من فضلك، أدخل البيت الشعري الذي تود التعرف عليه", - examples=examples, title = "التعرف على البحر", - inputs = gr.inputs.Textbox(lines = 3, label = "البيت") - -) -rawiy = gr.Interface.load("huggingface/Yah216/Poem_Qafiyah_Detection", - title ="التعرف على الروي", - examples=examples, - description="من فضلك، أدخل البيت الشعري الذي تود التعرف عليه", - inputs = gr.inputs.Textbox(lines = 3, label = "البيت") - -) -subject = gr.Interface.load( - "huggingface/zenkri/autotrain-Arabic_Poetry_by_Subject-920730230", - title="التعرف على الموضوع", - examples=examples, - description="من فضلك، أدخل البيت الشعري الذي تود التعرف عليه", - inputs = gr.inputs.Textbox(lines = 3, label = "البيت") - -) -demo = gr.TabbedInterface([meter, rawiy, subject], ["التعرف على البحر","التعرف على الروي","التعرف على الموضوع"]) -demo.launch() - diff --git a/spaces/Yan233th/so-vits-svc-models/modules/attentions.py b/spaces/Yan233th/so-vits-svc-models/modules/attentions.py deleted file mode 100644 index f9c11ca4a3acb86bf1abc04d9dcfa82a4ed4061f..0000000000000000000000000000000000000000 --- a/spaces/Yan233th/so-vits-svc-models/modules/attentions.py +++ /dev/null @@ -1,349 +0,0 @@ -import copy -import math -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - -import modules.commons as commons -import modules.modules as modules -from modules.modules import LayerNorm - - -class FFT(nn.Module): - def __init__(self, hidden_channels, filter_channels, n_heads, n_layers=1, kernel_size=1, p_dropout=0., - proximal_bias=False, proximal_init=True, **kwargs): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - - self.drop = nn.Dropout(p_dropout) - self.self_attn_layers = nn.ModuleList() - self.norm_layers_0 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - for i in range(self.n_layers): - self.self_attn_layers.append( - MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, - proximal_init=proximal_init)) - self.norm_layers_0.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True)) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask): - """ - x: decoder input - h: encoder output - """ - self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype) - x = x * x_mask - for i in range(self.n_layers): - y = self.self_attn_layers[i](x, x, self_attn_mask) - y = self.drop(y) - x = self.norm_layers_0[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - x = x * x_mask - return x - - -class Encoder(nn.Module): - def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.window_size = window_size - - self.drop = nn.Dropout(p_dropout) - self.attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size)) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask): - attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.attn_layers[i](x, x, attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class Decoder(nn.Module): - def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - - self.drop = nn.Dropout(p_dropout) - self.self_attn_layers = nn.ModuleList() - self.norm_layers_0 = nn.ModuleList() - self.encdec_attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init)) - self.norm_layers_0.append(LayerNorm(hidden_channels)) - self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout)) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True)) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask, h, h_mask): - """ - x: decoder input - h: encoder output - """ - self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype) - encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.self_attn_layers[i](x, x, self_attn_mask) - y = self.drop(y) - x = self.norm_layers_0[i](x + y) - - y = self.encdec_attn_layers[i](x, h, encdec_attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class MultiHeadAttention(nn.Module): - def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False): - super().__init__() - assert channels % n_heads == 0 - - self.channels = channels - self.out_channels = out_channels - self.n_heads = n_heads - self.p_dropout = p_dropout - self.window_size = window_size - self.heads_share = heads_share - self.block_length = block_length - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - self.attn = None - - self.k_channels = channels // n_heads - self.conv_q = nn.Conv1d(channels, channels, 1) - self.conv_k = nn.Conv1d(channels, channels, 1) - self.conv_v = nn.Conv1d(channels, channels, 1) - self.conv_o = nn.Conv1d(channels, out_channels, 1) - self.drop = nn.Dropout(p_dropout) - - if window_size is not None: - n_heads_rel = 1 if heads_share else n_heads - rel_stddev = self.k_channels**-0.5 - self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) - self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) - - nn.init.xavier_uniform_(self.conv_q.weight) - nn.init.xavier_uniform_(self.conv_k.weight) - nn.init.xavier_uniform_(self.conv_v.weight) - if proximal_init: - with torch.no_grad(): - self.conv_k.weight.copy_(self.conv_q.weight) - self.conv_k.bias.copy_(self.conv_q.bias) - - def forward(self, x, c, attn_mask=None): - q = self.conv_q(x) - k = self.conv_k(c) - v = self.conv_v(c) - - x, self.attn = self.attention(q, k, v, mask=attn_mask) - - x = self.conv_o(x) - return x - - def attention(self, query, key, value, mask=None): - # reshape [b, d, t] -> [b, n_h, t, d_k] - b, d, t_s, t_t = (*key.size(), query.size(2)) - query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) - key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - - scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) - if self.window_size is not None: - assert t_s == t_t, "Relative attention is only available for self-attention." - key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) - rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings) - scores_local = self._relative_position_to_absolute_position(rel_logits) - scores = scores + scores_local - if self.proximal_bias: - assert t_s == t_t, "Proximal bias is only available for self-attention." - scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) - if mask is not None: - scores = scores.masked_fill(mask == 0, -1e4) - if self.block_length is not None: - assert t_s == t_t, "Local attention is only available for self-attention." - block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) - scores = scores.masked_fill(block_mask == 0, -1e4) - p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] - p_attn = self.drop(p_attn) - output = torch.matmul(p_attn, value) - if self.window_size is not None: - relative_weights = self._absolute_position_to_relative_position(p_attn) - value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) - output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) - output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t] - return output, p_attn - - def _matmul_with_relative_values(self, x, y): - """ - x: [b, h, l, m] - y: [h or 1, m, d] - ret: [b, h, l, d] - """ - ret = torch.matmul(x, y.unsqueeze(0)) - return ret - - def _matmul_with_relative_keys(self, x, y): - """ - x: [b, h, l, d] - y: [h or 1, m, d] - ret: [b, h, l, m] - """ - ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) - return ret - - def _get_relative_embeddings(self, relative_embeddings, length): - max_relative_position = 2 * self.window_size + 1 - # Pad first before slice to avoid using cond ops. - pad_length = max(length - (self.window_size + 1), 0) - slice_start_position = max((self.window_size + 1) - length, 0) - slice_end_position = slice_start_position + 2 * length - 1 - if pad_length > 0: - padded_relative_embeddings = F.pad( - relative_embeddings, - commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) - else: - padded_relative_embeddings = relative_embeddings - used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position] - return used_relative_embeddings - - def _relative_position_to_absolute_position(self, x): - """ - x: [b, h, l, 2*l-1] - ret: [b, h, l, l] - """ - batch, heads, length, _ = x.size() - # Concat columns of pad to shift from relative to absolute indexing. - x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]])) - - # Concat extra elements so to add up to shape (len+1, 2*len-1). - x_flat = x.view([batch, heads, length * 2 * length]) - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]])) - - # Reshape and slice out the padded elements. - x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:] - return x_final - - def _absolute_position_to_relative_position(self, x): - """ - x: [b, h, l, l] - ret: [b, h, l, 2*l-1] - """ - batch, heads, length, _ = x.size() - # padd along column - x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]])) - x_flat = x.view([batch, heads, length**2 + length*(length -1)]) - # add 0's in the beginning that will skew the elements after reshape - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) - x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:] - return x_final - - def _attention_bias_proximal(self, length): - """Bias for self-attention to encourage attention to close positions. - Args: - length: an integer scalar. - Returns: - a Tensor with shape [1, 1, length, length] - """ - r = torch.arange(length, dtype=torch.float32) - diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) - return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) - - -class FFN(nn.Module): - def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.activation = activation - self.causal = causal - - if causal: - self.padding = self._causal_padding - else: - self.padding = self._same_padding - - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) - self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) - self.drop = nn.Dropout(p_dropout) - - def forward(self, x, x_mask): - x = self.conv_1(self.padding(x * x_mask)) - if self.activation == "gelu": - x = x * torch.sigmoid(1.702 * x) - else: - x = torch.relu(x) - x = self.drop(x) - x = self.conv_2(self.padding(x * x_mask)) - return x * x_mask - - def _causal_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = self.kernel_size - 1 - pad_r = 0 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x - - def _same_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = (self.kernel_size - 1) // 2 - pad_r = self.kernel_size // 2 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x diff --git a/spaces/Yiqin/ChatVID/model/fastchat/eval/webpage/styles.css b/spaces/Yiqin/ChatVID/model/fastchat/eval/webpage/styles.css deleted file mode 100644 index 7b6d6fc69b336c0a5d103be9fb13a0e0897c76a3..0000000000000000000000000000000000000000 --- a/spaces/Yiqin/ChatVID/model/fastchat/eval/webpage/styles.css +++ /dev/null @@ -1,105 +0,0 @@ -body { - font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; - background-color: #f8f9fa; -} - -.navbar-dark .navbar-nav .nav-link { - color: #f1cf68; - font-size: 1.1rem; - padding: 0.5rem 0.6rem; -} - -.card-header { - font-weight: bold; -} - -.card { - box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); - transition: 0.3s; -} - -.card:hover { - box-shadow: 0 8px 16px rgba(0, 0, 0, 0.2); -} - -button { - transition: background-color 0.3s; -} - -button:hover { - background-color: #007bff; -} - -@media (max-width: 767px) { - .form-row .form-group { - margin-bottom: 10px; - } -} - -/* Extra styles */ - -.expandable-card .card-text-container { - max-height: 200px; - overflow-y: hidden; - position: relative; -} - -.expandable-card.expanded .card-text-container { - max-height: none; -} - -.expand-btn { - position: relative; - display: none; - background-color: rgba(255, 255, 255, 0.8); - color: #510c75; - border-color: transparent; -} - -.expand-btn:hover { - background-color: rgba(200, 200, 200, 0.8); - text-decoration: none; - border-color: transparent; - color: #510c75; -} - -.expand-btn:focus { - outline: none; - text-decoration: none; -} - -.expandable-card:not(.expanded) .card-text-container:after { - content: ""; - position: absolute; - bottom: 0; - left: 0; - width: 100%; - height: 90px; - background: linear-gradient(rgba(255, 255, 255, 0.2), rgba(255, 255, 255, 1)); -} - -.expandable-card:not(.expanded) .expand-btn { - margin-top: -40px; -} - -.card-body { - padding-bottom: 5px; -} - -.vertical-flex-layout { - justify-content: center; - align-items: center; - height: 100%; - display: flex; - flex-direction: column; - gap: 5px; -} - -.figure-img { - max-width: 100%; - height: auto; -} - -.adjustable-font-size { - font-size: calc(0.5rem + 2vw); -} diff --git a/spaces/YlcldKlns/bing/src/lib/bots/bing/sr.ts b/spaces/YlcldKlns/bing/src/lib/bots/bing/sr.ts deleted file mode 100644 index 7cae14da7362bd6cc1e234851c11ca67e5a99f0c..0000000000000000000000000000000000000000 --- a/spaces/YlcldKlns/bing/src/lib/bots/bing/sr.ts +++ /dev/null @@ -1,106 +0,0 @@ -// @ts-ignore -const SpeechRecognitionPolyfill: typeof webkitSpeechRecognition = typeof window !== 'undefined' ? ( - // @ts-ignore - window.SpeechRecognition || - window.webkitSpeechRecognition || - // @ts-ignore - window.mozSpeechRecognition || - // @ts-ignore - window.msSpeechRecognition || - // @ts-ignore - window.oSpeechRecognition -) as typeof webkitSpeechRecognition : undefined - -type subscriber = (msg: string, command?: string) => void - -export class SR { - recognition?: SpeechRecognition - onchange?: subscriber - transcript: boolean = false - listening: boolean = false - private commandsRe?: RegExp - constructor(commands: string[]) { - this.recognition = SpeechRecognitionPolyfill ? new SpeechRecognitionPolyfill() : undefined - if (!this.recognition) { - return - } - this.configuration('zh-CN') - if (commands.length) { - this.commandsRe = new RegExp(`^(${commands.join('|')})。?$`) - } - this.recognition.onresult = this.speechRecognition - this.recognition.onerror = (err) => { - console.log('err', err.error) - this.stop() - } - this.recognition.onend = () => { - if (this.recognition && this.listening) { - this.recognition.start() - } - } - } - - speechRecognition = (event: SpeechRecognitionEvent) => { - if (!this.listening) return - for (var i = event.resultIndex; i < event.results.length; i++) { - let result = event.results[i] - if (result.isFinal) { - var alt = result[0] - const text = alt.transcript.trim() - if (this.commandsRe && this.commandsRe.test(text)) { - return this.onchange?.('', RegExp.$1) - } - if (!this.transcript) return - this.onchange?.(text) - } - } - } - - private configuration = async (lang: string = 'zh-CN') => { - return new Promise((resolve) => { - if (this.recognition) { - this.recognition.continuous = true - this.recognition.lang = lang - this.recognition.onstart = resolve - } - }) - } - - start = async () => { - if (this.recognition && !this.listening) { - await this.recognition.start() - this.transcript = true - this.listening = true - } - } - - stop = () => { - if (this.recognition) { - this.recognition.stop() - this.transcript = false - this.listening = false - } - } - - - pause = () => { - if (this.recognition) { - this.transcript = false - } - } - - resume = () => { - if (this.recognition) { - this.transcript = true - } - } - - abort = () => { - if (this.recognition && this.transcript) { - this.recognition.abort() - this.transcript = false - this.listening = false - } - } -} - diff --git a/spaces/Yuichiroh/ACL2Vec/README.md b/spaces/Yuichiroh/ACL2Vec/README.md deleted file mode 100644 index 3fab1731ce9f45a33135a44dc082428f9c5880db..0000000000000000000000000000000000000000 --- a/spaces/Yuichiroh/ACL2Vec/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: ACL2Vec -emoji: ⚡ -colorFrom: green -colorTo: purple -sdk: gradio -sdk_version: 3.18.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/aadnk/faster-whisper-webui/src/__init__.py b/spaces/aadnk/faster-whisper-webui/src/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/aai198/ComfyUI/Dockerfile b/spaces/aai198/ComfyUI/Dockerfile deleted file mode 100644 index 68f9a53cef8155fa224a4b379fafd210d2f4ca8f..0000000000000000000000000000000000000000 --- a/spaces/aai198/ComfyUI/Dockerfile +++ /dev/null @@ -1,151 +0,0 @@ -# FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04 -FROM ubuntu:22.04 - -ENV DEBIAN_FRONTEND=noninteractive \ - TZ=America/Los_Angeles - -ARG USE_PERSISTENT_DATA - -RUN apt-get update && apt-get install -y \ - git \ - make build-essential libssl-dev zlib1g-dev \ - libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \ - libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev git-lfs \ - ffmpeg libsm6 libxext6 cmake libgl1-mesa-glx \ - && rm -rf /var/lib/apt/lists/* \ - && git lfs install - -WORKDIR /code - -COPY ./requirements.txt /code/requirements.txt - -# User -RUN useradd -m -u 1000 user -USER user -ENV HOME=/home/user \ - PATH=/home/user/.local/bin:$PATH - -# Pyenv -RUN curl https://pyenv.run | bash -ENV PATH=$HOME/.pyenv/shims:$HOME/.pyenv/bin:$PATH - -ARG PYTHON_VERSION=3.10.12 -# Python -RUN pyenv install $PYTHON_VERSION && \ - pyenv global $PYTHON_VERSION && \ - pyenv rehash && \ - pip install --no-cache-dir --upgrade pip setuptools wheel && \ - pip install --no-cache-dir \ - datasets \ - huggingface-hub "protobuf<4" "click<8.1" - -RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt - -# Set the working directory to /data if USE_PERSISTENT_DATA is set, otherwise set to $HOME/app -WORKDIR $HOME/app - -# Copy the current directory contents into the container at $HOME/app setting the owner to the user - -RUN git clone https://github.com/comfyanonymous/ComfyUI . && \ - pip install xformers!=0.0.18 --no-cache-dir -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu117 - -# Checkpoints - -RUN echo "Downloading checkpoints..." -# SDXL -RUN wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors -P ./models/checkpoints/ -RUN wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors -P ./models/checkpoints/ -# RUN wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors -P ./models/checkpoints/ - -# SD1.5 -# RUN wget -c https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -P ./models/checkpoints/ -RUN wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/ -# RUN wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/ -# Some SD1.5 anime style -# RUN wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/ -# RUN wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors -P ./models/checkpoints/ -# RUN wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors -P ./models/checkpoints/ -# RUN wget -c https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/ -# Waifu Diffusion 1.5 (anime style SD2.x 768-v) -# RUN wget -c https://huggingface.co/waifu-diffusion/wd-1-5-beta2/resolve/main/checkpoints/wd-1-5-beta2-fp16.safetensors -P ./models/checkpoints/ -# unCLIP models -# RUN wget -c https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP/resolve/main/illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors -P ./models/checkpoints/ -# RUN wget -c https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP/resolve/main/wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors -P ./models/checkpoints/ -# --- -# VAE -RUN wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/ -# RUN wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/ -# RUN wget -c https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt -P ./models/vae/ -# Loras -# RUN wget -c https://civitai.com/api/download/models/10350 -O ./models/loras/theovercomer8sContrastFix_sd21768.safetensors #theovercomer8sContrastFix SD2.x 768-v -# RUN wget -c https://civitai.com/api/download/models/10638 -O ./models/loras/theovercomer8sContrastFix_sd15.safetensors #theovercomer8sContrastFix SD1.x -# T2I-Adapter -# RUN wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14/v1.pth -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth -P ./models/controlnet/ -# T2I Styles Model -# RUN wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth -P ./models/style_models/ -# CLIPVision model (needed for styles model) -# RUN wget -c https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin -O ./models/clip_vision/clip_vit14.bin -# ControlNet -# RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors -P ./models/controlnet/ -RUN wget -c https://huggingface.co/thibaud/controlnet-sd21/resolve/main/control_v11p_sd21_lineart.safetensors -P ./models/controlnet/ -#RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_scribble_fp16.safetensors -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_seg_fp16.safetensors -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_softedge_fp16.safetensors -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors -P ./models/controlnet/ -# RUN wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11u_sd15_tile_fp16.safetensors -P ./models/controlnet/ -RUN wget -c https://huggingface.co/thibaud/controlnet-openpose-sdxl-1.0/resolve/main/OpenPoseXL2.safetensors -P ./models/controlnet/ - -# https://huggingface.co/stabilityai/control-lora -RUN wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-canny-rank256.safetensors -P ./models/controlnet/ -RUN wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-depth-rank256.safetensors -P ./models/controlnet/ -RUN wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-recolor-rank256.safetensors -P ./models/controlnet/ -RUN wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-sketch-rank256.safetensors -P ./models/controlnet/ - -RUN wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank128/control-lora-canny-rank128.safetensors -P ./models/controlnet/ -RUN wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank128/control-lora-depth-rank128.safetensors -P ./models/controlnet/ -RUN wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank128/control-lora-recolor-rank128.safetensors -P ./models/controlnet -RUN wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank128/control-lora-sketch-rank128-metadata.safetensors -P ./models/controlnet/ - - -# RUN wget -c https://huggingface.co/thibaud/controlnet-openpose-sdxl-1.0/resolve/main/diffusion_pytorch_model.bin -O ./models/controlnet/OpenPoseXL2.bin -# GLIGEN -RUN wget -c https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/resolve/main/gligen_sd14_textbox_pruned_fp16.safetensors -P ./models/gligen/ -# ESRGAN upscale model -RUN wget -c https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./models/upscale_models/ -RUN wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth -P ./models/upscale_models/ -RUN wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth -P ./models/upscale_models/ - -RUN echo "Done" - -# instal custom nodes -RUN echo "Installing custom nodes..." -# Controlnet Preprocessor nodes by Fannovel16 -RUN cd custom_nodes && git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors && cd comfy_controlnet_preprocessors && python install.py --no_download_ckpts -RUN cd custom_nodes && git clone https://github.com/Fannovel16/comfyui_controlnet_aux && cd comfyui_controlnet_aux && pip install -r requirements.txt -RUN cd custom_nodes && git clone https://github.com/Stability-AI/stability-ComfyUI-nodes && cd stability-ComfyUI-nodes && pip install -r requirements.txt -RUN cd custom_nodes && git clone https://github.com/EllangoK/ComfyUI-post-processing-nodes -# ComfyUI Manager -# RUN cd custom_nodes && git clone https://github.com/ltdrdata/ComfyUI-Manager.git - -RUN echo "Done" - -# CMD ["python", "main.py", "--listen", "0.0.0.0", "--port", "7860", "--output-directory", "${USE_PERSISTENT_DATA:+/data/}"] -CMD ["python", "main.py", "--cpu", "--listen", "0.0.0.0", "--port", "7860", "--output-directory", "${USE_PERSISTENT_DATA:+/data/}"] - - - - diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/datasets/pipelines/compose.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/datasets/pipelines/compose.py deleted file mode 100644 index ca48f1c935755c486edc2744e1713e2b5ba3cdc8..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/datasets/pipelines/compose.py +++ /dev/null @@ -1,51 +0,0 @@ -import collections - -from mmcv.utils import build_from_cfg - -from ..builder import PIPELINES - - -@PIPELINES.register_module() -class Compose(object): - """Compose multiple transforms sequentially. - - Args: - transforms (Sequence[dict | callable]): Sequence of transform object or - config dict to be composed. - """ - - def __init__(self, transforms): - assert isinstance(transforms, collections.abc.Sequence) - self.transforms = [] - for transform in transforms: - if isinstance(transform, dict): - transform = build_from_cfg(transform, PIPELINES) - self.transforms.append(transform) - elif callable(transform): - self.transforms.append(transform) - else: - raise TypeError('transform must be callable or a dict') - - def __call__(self, data): - """Call function to apply transforms sequentially. - - Args: - data (dict): A result dict contains the data to transform. - - Returns: - dict: Transformed data. - """ - - for t in self.transforms: - data = t(data) - if data is None: - return None - return data - - def __repr__(self): - format_string = self.__class__.__name__ + '(' - for t in self.transforms: - format_string += '\n' - format_string += f' {t}' - format_string += '\n)' - return format_string diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/core/bbox/samplers/score_hlr_sampler.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/core/bbox/samplers/score_hlr_sampler.py deleted file mode 100644 index e7fd71a482e64bf3d8a9767adf78947bc98b1e36..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/core/bbox/samplers/score_hlr_sampler.py +++ /dev/null @@ -1,264 +0,0 @@ -import torch -from annotator.uniformer.mmcv.ops import nms_match - -from ..builder import BBOX_SAMPLERS -from ..transforms import bbox2roi -from .base_sampler import BaseSampler -from .sampling_result import SamplingResult - - -@BBOX_SAMPLERS.register_module() -class ScoreHLRSampler(BaseSampler): - r"""Importance-based Sample Reweighting (ISR_N), described in `Prime Sample - Attention in Object Detection `_. - - Score hierarchical local rank (HLR) differentiates with RandomSampler in - negative part. It firstly computes Score-HLR in a two-step way, - then linearly maps score hlr to the loss weights. - - Args: - num (int): Total number of sampled RoIs. - pos_fraction (float): Fraction of positive samples. - context (:class:`BaseRoIHead`): RoI head that the sampler belongs to. - neg_pos_ub (int): Upper bound of the ratio of num negative to num - positive, -1 means no upper bound. - add_gt_as_proposals (bool): Whether to add ground truth as proposals. - k (float): Power of the non-linear mapping. - bias (float): Shift of the non-linear mapping. - score_thr (float): Minimum score that a negative sample is to be - considered as valid bbox. - """ - - def __init__(self, - num, - pos_fraction, - context, - neg_pos_ub=-1, - add_gt_as_proposals=True, - k=0.5, - bias=0, - score_thr=0.05, - iou_thr=0.5, - **kwargs): - super().__init__(num, pos_fraction, neg_pos_ub, add_gt_as_proposals) - self.k = k - self.bias = bias - self.score_thr = score_thr - self.iou_thr = iou_thr - self.context = context - # context of cascade detectors is a list, so distinguish them here. - if not hasattr(context, 'num_stages'): - self.bbox_roi_extractor = context.bbox_roi_extractor - self.bbox_head = context.bbox_head - self.with_shared_head = context.with_shared_head - if self.with_shared_head: - self.shared_head = context.shared_head - else: - self.bbox_roi_extractor = context.bbox_roi_extractor[ - context.current_stage] - self.bbox_head = context.bbox_head[context.current_stage] - - @staticmethod - def random_choice(gallery, num): - """Randomly select some elements from the gallery. - - If `gallery` is a Tensor, the returned indices will be a Tensor; - If `gallery` is a ndarray or list, the returned indices will be a - ndarray. - - Args: - gallery (Tensor | ndarray | list): indices pool. - num (int): expected sample num. - - Returns: - Tensor or ndarray: sampled indices. - """ - assert len(gallery) >= num - - is_tensor = isinstance(gallery, torch.Tensor) - if not is_tensor: - if torch.cuda.is_available(): - device = torch.cuda.current_device() - else: - device = 'cpu' - gallery = torch.tensor(gallery, dtype=torch.long, device=device) - perm = torch.randperm(gallery.numel(), device=gallery.device)[:num] - rand_inds = gallery[perm] - if not is_tensor: - rand_inds = rand_inds.cpu().numpy() - return rand_inds - - def _sample_pos(self, assign_result, num_expected, **kwargs): - """Randomly sample some positive samples.""" - pos_inds = torch.nonzero(assign_result.gt_inds > 0).flatten() - if pos_inds.numel() <= num_expected: - return pos_inds - else: - return self.random_choice(pos_inds, num_expected) - - def _sample_neg(self, - assign_result, - num_expected, - bboxes, - feats=None, - img_meta=None, - **kwargs): - """Sample negative samples. - - Score-HLR sampler is done in the following steps: - 1. Take the maximum positive score prediction of each negative samples - as s_i. - 2. Filter out negative samples whose s_i <= score_thr, the left samples - are called valid samples. - 3. Use NMS-Match to divide valid samples into different groups, - samples in the same group will greatly overlap with each other - 4. Rank the matched samples in two-steps to get Score-HLR. - (1) In the same group, rank samples with their scores. - (2) In the same score rank across different groups, - rank samples with their scores again. - 5. Linearly map Score-HLR to the final label weights. - - Args: - assign_result (:obj:`AssignResult`): result of assigner. - num_expected (int): Expected number of samples. - bboxes (Tensor): bbox to be sampled. - feats (Tensor): Features come from FPN. - img_meta (dict): Meta information dictionary. - """ - neg_inds = torch.nonzero(assign_result.gt_inds == 0).flatten() - num_neg = neg_inds.size(0) - if num_neg == 0: - return neg_inds, None - with torch.no_grad(): - neg_bboxes = bboxes[neg_inds] - neg_rois = bbox2roi([neg_bboxes]) - bbox_result = self.context._bbox_forward(feats, neg_rois) - cls_score, bbox_pred = bbox_result['cls_score'], bbox_result[ - 'bbox_pred'] - - ori_loss = self.bbox_head.loss( - cls_score=cls_score, - bbox_pred=None, - rois=None, - labels=neg_inds.new_full((num_neg, ), - self.bbox_head.num_classes), - label_weights=cls_score.new_ones(num_neg), - bbox_targets=None, - bbox_weights=None, - reduction_override='none')['loss_cls'] - - # filter out samples with the max score lower than score_thr - max_score, argmax_score = cls_score.softmax(-1)[:, :-1].max(-1) - valid_inds = (max_score > self.score_thr).nonzero().view(-1) - invalid_inds = (max_score <= self.score_thr).nonzero().view(-1) - num_valid = valid_inds.size(0) - num_invalid = invalid_inds.size(0) - - num_expected = min(num_neg, num_expected) - num_hlr = min(num_valid, num_expected) - num_rand = num_expected - num_hlr - if num_valid > 0: - valid_rois = neg_rois[valid_inds] - valid_max_score = max_score[valid_inds] - valid_argmax_score = argmax_score[valid_inds] - valid_bbox_pred = bbox_pred[valid_inds] - - # valid_bbox_pred shape: [num_valid, #num_classes, 4] - valid_bbox_pred = valid_bbox_pred.view( - valid_bbox_pred.size(0), -1, 4) - selected_bbox_pred = valid_bbox_pred[range(num_valid), - valid_argmax_score] - pred_bboxes = self.bbox_head.bbox_coder.decode( - valid_rois[:, 1:], selected_bbox_pred) - pred_bboxes_with_score = torch.cat( - [pred_bboxes, valid_max_score[:, None]], -1) - group = nms_match(pred_bboxes_with_score, self.iou_thr) - - # imp: importance - imp = cls_score.new_zeros(num_valid) - for g in group: - g_score = valid_max_score[g] - # g_score has already sorted - rank = g_score.new_tensor(range(g_score.size(0))) - imp[g] = num_valid - rank + g_score - _, imp_rank_inds = imp.sort(descending=True) - _, imp_rank = imp_rank_inds.sort() - hlr_inds = imp_rank_inds[:num_expected] - - if num_rand > 0: - rand_inds = torch.randperm(num_invalid)[:num_rand] - select_inds = torch.cat( - [valid_inds[hlr_inds], invalid_inds[rand_inds]]) - else: - select_inds = valid_inds[hlr_inds] - - neg_label_weights = cls_score.new_ones(num_expected) - - up_bound = max(num_expected, num_valid) - imp_weights = (up_bound - - imp_rank[hlr_inds].float()) / up_bound - neg_label_weights[:num_hlr] = imp_weights - neg_label_weights[num_hlr:] = imp_weights.min() - neg_label_weights = (self.bias + - (1 - self.bias) * neg_label_weights).pow( - self.k) - ori_selected_loss = ori_loss[select_inds] - new_loss = ori_selected_loss * neg_label_weights - norm_ratio = ori_selected_loss.sum() / new_loss.sum() - neg_label_weights *= norm_ratio - else: - neg_label_weights = cls_score.new_ones(num_expected) - select_inds = torch.randperm(num_neg)[:num_expected] - - return neg_inds[select_inds], neg_label_weights - - def sample(self, - assign_result, - bboxes, - gt_bboxes, - gt_labels=None, - img_meta=None, - **kwargs): - """Sample positive and negative bboxes. - - This is a simple implementation of bbox sampling given candidates, - assigning results and ground truth bboxes. - - Args: - assign_result (:obj:`AssignResult`): Bbox assigning results. - bboxes (Tensor): Boxes to be sampled from. - gt_bboxes (Tensor): Ground truth bboxes. - gt_labels (Tensor, optional): Class labels of ground truth bboxes. - - Returns: - tuple[:obj:`SamplingResult`, Tensor]: Sampling result and negetive - label weights. - """ - bboxes = bboxes[:, :4] - - gt_flags = bboxes.new_zeros((bboxes.shape[0], ), dtype=torch.uint8) - if self.add_gt_as_proposals: - bboxes = torch.cat([gt_bboxes, bboxes], dim=0) - assign_result.add_gt_(gt_labels) - gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8) - gt_flags = torch.cat([gt_ones, gt_flags]) - - num_expected_pos = int(self.num * self.pos_fraction) - pos_inds = self.pos_sampler._sample_pos( - assign_result, num_expected_pos, bboxes=bboxes, **kwargs) - num_sampled_pos = pos_inds.numel() - num_expected_neg = self.num - num_sampled_pos - if self.neg_pos_ub >= 0: - _pos = max(1, num_sampled_pos) - neg_upper_bound = int(self.neg_pos_ub * _pos) - if num_expected_neg > neg_upper_bound: - num_expected_neg = neg_upper_bound - neg_inds, neg_label_weights = self.neg_sampler._sample_neg( - assign_result, - num_expected_neg, - bboxes, - img_meta=img_meta, - **kwargs) - - return SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes, - assign_result, gt_flags), neg_label_weights diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/necks/fpg.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/necks/fpg.py deleted file mode 100644 index c8e0d163ccf8cef6211530ba6c1b4d558ff6403f..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/necks/fpg.py +++ /dev/null @@ -1,398 +0,0 @@ -import torch.nn as nn -import torch.nn.functional as F -from mmcv.cnn import ConvModule, caffe2_xavier_init, constant_init, is_norm - -from ..builder import NECKS - - -class Transition(nn.Module): - """Base class for transition. - - Args: - in_channels (int): Number of input channels. - out_channels (int): Number of output channels. - """ - - def __init__(self, in_channels, out_channels): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - - def forward(x): - pass - - -class UpInterpolationConv(Transition): - """A transition used for up-sampling. - - Up-sample the input by interpolation then refines the feature by - a convolution layer. - - Args: - in_channels (int): Number of input channels. - out_channels (int): Number of output channels. - scale_factor (int): Up-sampling factor. Default: 2. - mode (int): Interpolation mode. Default: nearest. - align_corners (bool): Whether align corners when interpolation. - Default: None. - kernel_size (int): Kernel size for the conv. Default: 3. - """ - - def __init__(self, - in_channels, - out_channels, - scale_factor=2, - mode='nearest', - align_corners=None, - kernel_size=3, - **kwargs): - super().__init__(in_channels, out_channels) - self.mode = mode - self.scale_factor = scale_factor - self.align_corners = align_corners - self.conv = ConvModule( - in_channels, - out_channels, - kernel_size, - padding=(kernel_size - 1) // 2, - **kwargs) - - def forward(self, x): - x = F.interpolate( - x, - scale_factor=self.scale_factor, - mode=self.mode, - align_corners=self.align_corners) - x = self.conv(x) - return x - - -class LastConv(Transition): - """A transition used for refining the output of the last stage. - - Args: - in_channels (int): Number of input channels. - out_channels (int): Number of output channels. - num_inputs (int): Number of inputs of the FPN features. - kernel_size (int): Kernel size for the conv. Default: 3. - """ - - def __init__(self, - in_channels, - out_channels, - num_inputs, - kernel_size=3, - **kwargs): - super().__init__(in_channels, out_channels) - self.num_inputs = num_inputs - self.conv_out = ConvModule( - in_channels, - out_channels, - kernel_size, - padding=(kernel_size - 1) // 2, - **kwargs) - - def forward(self, inputs): - assert len(inputs) == self.num_inputs - return self.conv_out(inputs[-1]) - - -@NECKS.register_module() -class FPG(nn.Module): - """FPG. - - Implementation of `Feature Pyramid Grids (FPG) - `_. - This implementation only gives the basic structure stated in the paper. - But users can implement different type of transitions to fully explore the - the potential power of the structure of FPG. - - Args: - in_channels (int): Number of input channels (feature maps of all levels - should have the same channels). - out_channels (int): Number of output channels (used at each scale) - num_outs (int): Number of output scales. - stack_times (int): The number of times the pyramid architecture will - be stacked. - paths (list[str]): Specify the path order of each stack level. - Each element in the list should be either 'bu' (bottom-up) or - 'td' (top-down). - inter_channels (int): Number of inter channels. - same_up_trans (dict): Transition that goes down at the same stage. - same_down_trans (dict): Transition that goes up at the same stage. - across_lateral_trans (dict): Across-pathway same-stage - across_down_trans (dict): Across-pathway bottom-up connection. - across_up_trans (dict): Across-pathway top-down connection. - across_skip_trans (dict): Across-pathway skip connection. - output_trans (dict): Transition that trans the output of the - last stage. - start_level (int): Index of the start input backbone level used to - build the feature pyramid. Default: 0. - end_level (int): Index of the end input backbone level (exclusive) to - build the feature pyramid. Default: -1, which means the last level. - add_extra_convs (bool): It decides whether to add conv - layers on top of the original feature maps. Default to False. - If True, its actual mode is specified by `extra_convs_on_inputs`. - norm_cfg (dict): Config dict for normalization layer. Default: None. - """ - - transition_types = { - 'conv': ConvModule, - 'interpolation_conv': UpInterpolationConv, - 'last_conv': LastConv, - } - - def __init__(self, - in_channels, - out_channels, - num_outs, - stack_times, - paths, - inter_channels=None, - same_down_trans=None, - same_up_trans=dict( - type='conv', kernel_size=3, stride=2, padding=1), - across_lateral_trans=dict(type='conv', kernel_size=1), - across_down_trans=dict(type='conv', kernel_size=3), - across_up_trans=None, - across_skip_trans=dict(type='identity'), - output_trans=dict(type='last_conv', kernel_size=3), - start_level=0, - end_level=-1, - add_extra_convs=False, - norm_cfg=None, - skip_inds=None): - super(FPG, self).__init__() - assert isinstance(in_channels, list) - self.in_channels = in_channels - self.out_channels = out_channels - self.num_ins = len(in_channels) - self.num_outs = num_outs - if inter_channels is None: - self.inter_channels = [out_channels for _ in range(num_outs)] - elif isinstance(inter_channels, int): - self.inter_channels = [inter_channels for _ in range(num_outs)] - else: - assert isinstance(inter_channels, list) - assert len(inter_channels) == num_outs - self.inter_channels = inter_channels - self.stack_times = stack_times - self.paths = paths - assert isinstance(paths, list) and len(paths) == stack_times - for d in paths: - assert d in ('bu', 'td') - - self.same_down_trans = same_down_trans - self.same_up_trans = same_up_trans - self.across_lateral_trans = across_lateral_trans - self.across_down_trans = across_down_trans - self.across_up_trans = across_up_trans - self.output_trans = output_trans - self.across_skip_trans = across_skip_trans - - self.with_bias = norm_cfg is None - # skip inds must be specified if across skip trans is not None - if self.across_skip_trans is not None: - skip_inds is not None - self.skip_inds = skip_inds - assert len(self.skip_inds[0]) <= self.stack_times - - if end_level == -1: - self.backbone_end_level = self.num_ins - assert num_outs >= self.num_ins - start_level - else: - # if end_level < inputs, no extra level is allowed - self.backbone_end_level = end_level - assert end_level <= len(in_channels) - assert num_outs == end_level - start_level - self.start_level = start_level - self.end_level = end_level - self.add_extra_convs = add_extra_convs - - # build lateral 1x1 convs to reduce channels - self.lateral_convs = nn.ModuleList() - for i in range(self.start_level, self.backbone_end_level): - l_conv = nn.Conv2d(self.in_channels[i], - self.inter_channels[i - self.start_level], 1) - self.lateral_convs.append(l_conv) - - extra_levels = num_outs - self.backbone_end_level + self.start_level - self.extra_downsamples = nn.ModuleList() - for i in range(extra_levels): - if self.add_extra_convs: - fpn_idx = self.backbone_end_level - self.start_level + i - extra_conv = nn.Conv2d( - self.inter_channels[fpn_idx - 1], - self.inter_channels[fpn_idx], - 3, - stride=2, - padding=1) - self.extra_downsamples.append(extra_conv) - else: - self.extra_downsamples.append(nn.MaxPool2d(1, stride=2)) - - self.fpn_transitions = nn.ModuleList() # stack times - for s in range(self.stack_times): - stage_trans = nn.ModuleList() # num of feature levels - for i in range(self.num_outs): - # same, across_lateral, across_down, across_up - trans = nn.ModuleDict() - if s in self.skip_inds[i]: - stage_trans.append(trans) - continue - # build same-stage down trans (used in bottom-up paths) - if i == 0 or self.same_up_trans is None: - same_up_trans = None - else: - same_up_trans = self.build_trans( - self.same_up_trans, self.inter_channels[i - 1], - self.inter_channels[i]) - trans['same_up'] = same_up_trans - # build same-stage up trans (used in top-down paths) - if i == self.num_outs - 1 or self.same_down_trans is None: - same_down_trans = None - else: - same_down_trans = self.build_trans( - self.same_down_trans, self.inter_channels[i + 1], - self.inter_channels[i]) - trans['same_down'] = same_down_trans - # build across lateral trans - across_lateral_trans = self.build_trans( - self.across_lateral_trans, self.inter_channels[i], - self.inter_channels[i]) - trans['across_lateral'] = across_lateral_trans - # build across down trans - if i == self.num_outs - 1 or self.across_down_trans is None: - across_down_trans = None - else: - across_down_trans = self.build_trans( - self.across_down_trans, self.inter_channels[i + 1], - self.inter_channels[i]) - trans['across_down'] = across_down_trans - # build across up trans - if i == 0 or self.across_up_trans is None: - across_up_trans = None - else: - across_up_trans = self.build_trans( - self.across_up_trans, self.inter_channels[i - 1], - self.inter_channels[i]) - trans['across_up'] = across_up_trans - if self.across_skip_trans is None: - across_skip_trans = None - else: - across_skip_trans = self.build_trans( - self.across_skip_trans, self.inter_channels[i - 1], - self.inter_channels[i]) - trans['across_skip'] = across_skip_trans - # build across_skip trans - stage_trans.append(trans) - self.fpn_transitions.append(stage_trans) - - self.output_transition = nn.ModuleList() # output levels - for i in range(self.num_outs): - trans = self.build_trans( - self.output_trans, - self.inter_channels[i], - self.out_channels, - num_inputs=self.stack_times + 1) - self.output_transition.append(trans) - - self.relu = nn.ReLU(inplace=True) - - def build_trans(self, cfg, in_channels, out_channels, **extra_args): - cfg_ = cfg.copy() - trans_type = cfg_.pop('type') - trans_cls = self.transition_types[trans_type] - return trans_cls(in_channels, out_channels, **cfg_, **extra_args) - - def init_weights(self): - for m in self.modules(): - if isinstance(m, nn.Conv2d): - caffe2_xavier_init(m) - elif is_norm(m): - constant_init(m, 1.0) - - def fuse(self, fuse_dict): - out = None - for item in fuse_dict.values(): - if item is not None: - if out is None: - out = item - else: - out = out + item - return out - - def forward(self, inputs): - assert len(inputs) == len(self.in_channels) - - # build all levels from original feature maps - feats = [ - lateral_conv(inputs[i + self.start_level]) - for i, lateral_conv in enumerate(self.lateral_convs) - ] - for downsample in self.extra_downsamples: - feats.append(downsample(feats[-1])) - - outs = [feats] - - for i in range(self.stack_times): - current_outs = outs[-1] - next_outs = [] - direction = self.paths[i] - for j in range(self.num_outs): - if i in self.skip_inds[j]: - next_outs.append(outs[-1][j]) - continue - # feature level - if direction == 'td': - lvl = self.num_outs - j - 1 - else: - lvl = j - # get transitions - if direction == 'td': - same_trans = self.fpn_transitions[i][lvl]['same_down'] - else: - same_trans = self.fpn_transitions[i][lvl]['same_up'] - across_lateral_trans = self.fpn_transitions[i][lvl][ - 'across_lateral'] - across_down_trans = self.fpn_transitions[i][lvl]['across_down'] - across_up_trans = self.fpn_transitions[i][lvl]['across_up'] - across_skip_trans = self.fpn_transitions[i][lvl]['across_skip'] - # init output - to_fuse = dict( - same=None, lateral=None, across_up=None, across_down=None) - # same downsample/upsample - if same_trans is not None: - to_fuse['same'] = same_trans(next_outs[-1]) - # across lateral - if across_lateral_trans is not None: - to_fuse['lateral'] = across_lateral_trans( - current_outs[lvl]) - # across downsample - if lvl > 0 and across_up_trans is not None: - to_fuse['across_up'] = across_up_trans(current_outs[lvl - - 1]) - # across upsample - if (lvl < self.num_outs - 1 and across_down_trans is not None): - to_fuse['across_down'] = across_down_trans( - current_outs[lvl + 1]) - if across_skip_trans is not None: - to_fuse['across_skip'] = across_skip_trans(outs[0][lvl]) - x = self.fuse(to_fuse) - next_outs.append(x) - - if direction == 'td': - outs.append(next_outs[::-1]) - else: - outs.append(next_outs) - - # output trans - final_outs = [] - for i in range(self.num_outs): - lvl_out_list = [] - for s in range(len(outs)): - lvl_out_list.append(outs[s][i]) - lvl_out = self.output_transition[i](lvl_out_list) - final_outs.append(lvl_out) - - return final_outs diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/ops/deform_roi_pool.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/ops/deform_roi_pool.py deleted file mode 100644 index cc245ba91fee252226ba22e76bb94a35db9a629b..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/ops/deform_roi_pool.py +++ /dev/null @@ -1,204 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from torch import nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn.modules.utils import _pair - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext( - '_ext', ['deform_roi_pool_forward', 'deform_roi_pool_backward']) - - -class DeformRoIPoolFunction(Function): - - @staticmethod - def symbolic(g, input, rois, offset, output_size, spatial_scale, - sampling_ratio, gamma): - return g.op( - 'mmcv::MMCVDeformRoIPool', - input, - rois, - offset, - pooled_height_i=output_size[0], - pooled_width_i=output_size[1], - spatial_scale_f=spatial_scale, - sampling_ratio_f=sampling_ratio, - gamma_f=gamma) - - @staticmethod - def forward(ctx, - input, - rois, - offset, - output_size, - spatial_scale=1.0, - sampling_ratio=0, - gamma=0.1): - if offset is None: - offset = input.new_zeros(0) - ctx.output_size = _pair(output_size) - ctx.spatial_scale = float(spatial_scale) - ctx.sampling_ratio = int(sampling_ratio) - ctx.gamma = float(gamma) - - assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!' - - output_shape = (rois.size(0), input.size(1), ctx.output_size[0], - ctx.output_size[1]) - output = input.new_zeros(output_shape) - - ext_module.deform_roi_pool_forward( - input, - rois, - offset, - output, - pooled_height=ctx.output_size[0], - pooled_width=ctx.output_size[1], - spatial_scale=ctx.spatial_scale, - sampling_ratio=ctx.sampling_ratio, - gamma=ctx.gamma) - - ctx.save_for_backward(input, rois, offset) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - input, rois, offset = ctx.saved_tensors - grad_input = grad_output.new_zeros(input.shape) - grad_offset = grad_output.new_zeros(offset.shape) - - ext_module.deform_roi_pool_backward( - grad_output, - input, - rois, - offset, - grad_input, - grad_offset, - pooled_height=ctx.output_size[0], - pooled_width=ctx.output_size[1], - spatial_scale=ctx.spatial_scale, - sampling_ratio=ctx.sampling_ratio, - gamma=ctx.gamma) - if grad_offset.numel() == 0: - grad_offset = None - return grad_input, None, grad_offset, None, None, None, None - - -deform_roi_pool = DeformRoIPoolFunction.apply - - -class DeformRoIPool(nn.Module): - - def __init__(self, - output_size, - spatial_scale=1.0, - sampling_ratio=0, - gamma=0.1): - super(DeformRoIPool, self).__init__() - self.output_size = _pair(output_size) - self.spatial_scale = float(spatial_scale) - self.sampling_ratio = int(sampling_ratio) - self.gamma = float(gamma) - - def forward(self, input, rois, offset=None): - return deform_roi_pool(input, rois, offset, self.output_size, - self.spatial_scale, self.sampling_ratio, - self.gamma) - - -class DeformRoIPoolPack(DeformRoIPool): - - def __init__(self, - output_size, - output_channels, - deform_fc_channels=1024, - spatial_scale=1.0, - sampling_ratio=0, - gamma=0.1): - super(DeformRoIPoolPack, self).__init__(output_size, spatial_scale, - sampling_ratio, gamma) - - self.output_channels = output_channels - self.deform_fc_channels = deform_fc_channels - - self.offset_fc = nn.Sequential( - nn.Linear( - self.output_size[0] * self.output_size[1] * - self.output_channels, self.deform_fc_channels), - nn.ReLU(inplace=True), - nn.Linear(self.deform_fc_channels, self.deform_fc_channels), - nn.ReLU(inplace=True), - nn.Linear(self.deform_fc_channels, - self.output_size[0] * self.output_size[1] * 2)) - self.offset_fc[-1].weight.data.zero_() - self.offset_fc[-1].bias.data.zero_() - - def forward(self, input, rois): - assert input.size(1) == self.output_channels - x = deform_roi_pool(input, rois, None, self.output_size, - self.spatial_scale, self.sampling_ratio, - self.gamma) - rois_num = rois.size(0) - offset = self.offset_fc(x.view(rois_num, -1)) - offset = offset.view(rois_num, 2, self.output_size[0], - self.output_size[1]) - return deform_roi_pool(input, rois, offset, self.output_size, - self.spatial_scale, self.sampling_ratio, - self.gamma) - - -class ModulatedDeformRoIPoolPack(DeformRoIPool): - - def __init__(self, - output_size, - output_channels, - deform_fc_channels=1024, - spatial_scale=1.0, - sampling_ratio=0, - gamma=0.1): - super(ModulatedDeformRoIPoolPack, - self).__init__(output_size, spatial_scale, sampling_ratio, gamma) - - self.output_channels = output_channels - self.deform_fc_channels = deform_fc_channels - - self.offset_fc = nn.Sequential( - nn.Linear( - self.output_size[0] * self.output_size[1] * - self.output_channels, self.deform_fc_channels), - nn.ReLU(inplace=True), - nn.Linear(self.deform_fc_channels, self.deform_fc_channels), - nn.ReLU(inplace=True), - nn.Linear(self.deform_fc_channels, - self.output_size[0] * self.output_size[1] * 2)) - self.offset_fc[-1].weight.data.zero_() - self.offset_fc[-1].bias.data.zero_() - - self.mask_fc = nn.Sequential( - nn.Linear( - self.output_size[0] * self.output_size[1] * - self.output_channels, self.deform_fc_channels), - nn.ReLU(inplace=True), - nn.Linear(self.deform_fc_channels, - self.output_size[0] * self.output_size[1] * 1), - nn.Sigmoid()) - self.mask_fc[2].weight.data.zero_() - self.mask_fc[2].bias.data.zero_() - - def forward(self, input, rois): - assert input.size(1) == self.output_channels - x = deform_roi_pool(input, rois, None, self.output_size, - self.spatial_scale, self.sampling_ratio, - self.gamma) - rois_num = rois.size(0) - offset = self.offset_fc(x.view(rois_num, -1)) - offset = offset.view(rois_num, 2, self.output_size[0], - self.output_size[1]) - mask = self.mask_fc(x.view(rois_num, -1)) - mask = mask.view(rois_num, 1, self.output_size[0], self.output_size[1]) - d = deform_roi_pool(input, rois, offset, self.output_size, - self.spatial_scale, self.sampling_ratio, - self.gamma) - return d * mask diff --git a/spaces/abidlabs/pytorch-image-classifier/app.py b/spaces/abidlabs/pytorch-image-classifier/app.py deleted file mode 100644 index 89f01588804024f2267323bac7526b8f52636a8b..0000000000000000000000000000000000000000 --- a/spaces/abidlabs/pytorch-image-classifier/app.py +++ /dev/null @@ -1,26 +0,0 @@ -import torch - -model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval() - -import requests -from torchvision import transforms - -# Download human-readable labels for ImageNet. -response = requests.get("https://git.io/JJkYN") -labels = response.text.split("\n") - -def predict(inp): - inp = transforms.ToTensor()(inp).unsqueeze(0) - with torch.no_grad(): - prediction = torch.nn.functional.softmax(model(inp)[0], dim=0) - confidences = {labels[i]: float(prediction[i]) for i in range(1000)} - return confidences - -import gradio as gr - -gr.Interface(fn=predict, - inputs=gr.inputs.Image(type="pil"), - outputs=gr.outputs.Label(num_top_classes=3), - examples=["lion.jpg", "cheetah.jpg"], - theme="default", - css=".footer{display:none !important}").launch() diff --git a/spaces/abouuuud/meter2poem-1/README.md b/spaces/abouuuud/meter2poem-1/README.md deleted file mode 100644 index a06e49872eefcc25acde0577d372c8dfac7373ba..0000000000000000000000000000000000000000 --- a/spaces/abouuuud/meter2poem-1/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Meter2poem 1 -emoji: 🐨 -colorFrom: gray -colorTo: red -sdk: gradio -sdk_version: 3.2 -app_file: app.py -pinned: false -license: afl-3.0 -duplicated_from: Aaaaaaaabdualh/meter2poem-1 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/adorp/ControlNet-v1-1-duplicate/app_normal.py b/spaces/adorp/ControlNet-v1-1-duplicate/app_normal.py deleted file mode 100644 index a77b13a8edd60ceead9cdebd2df21b45e34b4f9a..0000000000000000000000000000000000000000 --- a/spaces/adorp/ControlNet-v1-1-duplicate/app_normal.py +++ /dev/null @@ -1,106 +0,0 @@ -#!/usr/bin/env python - -import gradio as gr - -from utils import randomize_seed_fn - - -def create_demo(process, max_images=12, default_num_images=3): - with gr.Blocks() as demo: - with gr.Row(): - with gr.Column(): - image = gr.Image() - prompt = gr.Textbox(label='Prompt') - run_button = gr.Button('Run') - with gr.Accordion('Advanced options', open=False): - preprocessor_name = gr.Radio(label='Preprocessor', - choices=['NormalBae', 'None'], - type='value', - value='NormalBae') - num_samples = gr.Slider(label='Images', - minimum=1, - maximum=max_images, - value=default_num_images, - step=1) - image_resolution = gr.Slider(label='Image resolution', - minimum=256, - maximum=512, - value=512, - step=256) - preprocess_resolution = gr.Slider( - label='Preprocess resolution', - minimum=128, - maximum=512, - value=384, - step=1) - num_steps = gr.Slider(label='Number of steps', - minimum=1, - maximum=100, - value=20, - step=1) - guidance_scale = gr.Slider(label='Guidance scale', - minimum=0.1, - maximum=30.0, - value=9.0, - step=0.1) - seed = gr.Slider(label='Seed', - minimum=0, - maximum=1000000, - step=1, - value=0, - randomize=True) - randomize_seed = gr.Checkbox(label='Randomize seed', - value=True) - a_prompt = gr.Textbox( - label='Additional prompt', - value='best quality, extremely detailed') - n_prompt = gr.Textbox( - label='Negative prompt', - value= - 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality' - ) - with gr.Column(): - result = gr.Gallery(label='Output', show_label=False).style( - columns=2, object_fit='scale-down') - inputs = [ - image, - prompt, - a_prompt, - n_prompt, - num_samples, - image_resolution, - preprocess_resolution, - num_steps, - guidance_scale, - seed, - preprocessor_name, - ] - prompt.submit( - fn=randomize_seed_fn, - inputs=[seed, randomize_seed], - outputs=seed, - queue=False, - ).then( - fn=process, - inputs=inputs, - outputs=result, - ) - run_button.click( - fn=randomize_seed_fn, - inputs=[seed, randomize_seed], - outputs=seed, - queue=False, - ).then( - fn=process, - inputs=inputs, - outputs=result, - api_name='normal', - ) - return demo - - -if __name__ == '__main__': - from model import Model - model = Model(task_name='NormalBae') - demo = create_demo(model.process_normal) - demo.queue().launch() diff --git a/spaces/ahmedghani/svoice_demo/svoice/data/__init__.py b/spaces/ahmedghani/svoice_demo/svoice/data/__init__.py deleted file mode 100644 index 5656d59e07f3fa33dd3bad1a0f9279ff4b8a6128..0000000000000000000000000000000000000000 --- a/spaces/ahmedghani/svoice_demo/svoice/data/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. diff --git a/spaces/akhaliq/Detic/tools/dump_clip_features.py b/spaces/akhaliq/Detic/tools/dump_clip_features.py deleted file mode 100644 index 127f8c2a86c2425611c8ec075006664f5e07df45..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/Detic/tools/dump_clip_features.py +++ /dev/null @@ -1,116 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import argparse -import json -import torch -import numpy as np -import itertools -from nltk.corpus import wordnet -import sys - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--ann', default='datasets/lvis/lvis_v1_val.json') - parser.add_argument('--out_path', default='') - parser.add_argument('--prompt', default='a') - parser.add_argument('--model', default='clip') - parser.add_argument('--clip_model', default="ViT-B/32") - parser.add_argument('--fix_space', action='store_true') - parser.add_argument('--use_underscore', action='store_true') - parser.add_argument('--avg_synonyms', action='store_true') - parser.add_argument('--use_wn_name', action='store_true') - args = parser.parse_args() - - print('Loading', args.ann) - data = json.load(open(args.ann, 'r')) - cat_names = [x['name'] for x in \ - sorted(data['categories'], key=lambda x: x['id'])] - if 'synonyms' in data['categories'][0]: - if args.use_wn_name: - synonyms = [ - [xx.name() for xx in wordnet.synset(x['synset']).lemmas()] \ - if x['synset'] != 'stop_sign.n.01' else ['stop_sign'] \ - for x in sorted(data['categories'], key=lambda x: x['id'])] - else: - synonyms = [x['synonyms'] for x in \ - sorted(data['categories'], key=lambda x: x['id'])] - else: - synonyms = [] - if args.fix_space: - cat_names = [x.replace('_', ' ') for x in cat_names] - if args.use_underscore: - cat_names = [x.strip().replace('/ ', '/').replace(' ', '_') for x in cat_names] - print('cat_names', cat_names) - device = "cuda" if torch.cuda.is_available() else "cpu" - - if args.prompt == 'a': - sentences = ['a ' + x for x in cat_names] - sentences_synonyms = [['a ' + xx for xx in x] for x in synonyms] - if args.prompt == 'none': - sentences = [x for x in cat_names] - sentences_synonyms = [[xx for xx in x] for x in synonyms] - elif args.prompt == 'photo': - sentences = ['a photo of a {}'.format(x) for x in cat_names] - sentences_synonyms = [['a photo of a {}'.format(xx) for xx in x] \ - for x in synonyms] - elif args.prompt == 'scene': - sentences = ['a photo of a {} in the scene'.format(x) for x in cat_names] - sentences_synonyms = [['a photo of a {} in the scene'.format(xx) for xx in x] \ - for x in synonyms] - - print('sentences_synonyms', len(sentences_synonyms), \ - sum(len(x) for x in sentences_synonyms)) - if args.model == 'clip': - import clip - print('Loading CLIP') - model, preprocess = clip.load(args.clip_model, device=device) - if args.avg_synonyms: - sentences = list(itertools.chain.from_iterable(sentences_synonyms)) - print('flattened_sentences', len(sentences)) - text = clip.tokenize(sentences).to(device) - with torch.no_grad(): - if len(text) > 10000: - text_features = torch.cat([ - model.encode_text(text[:len(text) // 2]), - model.encode_text(text[len(text) // 2:])], - dim=0) - else: - text_features = model.encode_text(text) - print('text_features.shape', text_features.shape) - if args.avg_synonyms: - synonyms_per_cat = [len(x) for x in sentences_synonyms] - text_features = text_features.split(synonyms_per_cat, dim=0) - text_features = [x.mean(dim=0) for x in text_features] - text_features = torch.stack(text_features, dim=0) - print('after stack', text_features.shape) - text_features = text_features.cpu().numpy() - elif args.model in ['bert', 'roberta']: - from transformers import AutoTokenizer, AutoModel - if args.model == 'bert': - model_name = 'bert-large-uncased' - if args.model == 'roberta': - model_name = 'roberta-large' - tokenizer = AutoTokenizer.from_pretrained(model_name) - model = AutoModel.from_pretrained(model_name) - model.eval() - if args.avg_synonyms: - sentences = list(itertools.chain.from_iterable(sentences_synonyms)) - print('flattened_sentences', len(sentences)) - inputs = tokenizer(sentences, padding=True, return_tensors="pt") - with torch.no_grad(): - model_outputs = model(**inputs) - outputs = model_outputs.pooler_output - text_features = outputs.detach().cpu() - if args.avg_synonyms: - synonyms_per_cat = [len(x) for x in sentences_synonyms] - text_features = text_features.split(synonyms_per_cat, dim=0) - text_features = [x.mean(dim=0) for x in text_features] - text_features = torch.stack(text_features, dim=0) - print('after stack', text_features.shape) - text_features = text_features.numpy() - print('text_features.shape', text_features.shape) - else: - assert 0, args.model - if args.out_path != '': - print('saveing to', args.out_path) - np.save(open(args.out_path, 'wb'), text_features) - import pdb; pdb.set_trace() diff --git a/spaces/akhaliq/lama/saicinpainting/training/visualizers/directory.py b/spaces/akhaliq/lama/saicinpainting/training/visualizers/directory.py deleted file mode 100644 index bc42e00500c7a5b70b2cef83b03e45b5bb471ff8..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/lama/saicinpainting/training/visualizers/directory.py +++ /dev/null @@ -1,36 +0,0 @@ -import os - -import cv2 -import numpy as np - -from saicinpainting.training.visualizers.base import BaseVisualizer, visualize_mask_and_images_batch -from saicinpainting.utils import check_and_warn_input_range - - -class DirectoryVisualizer(BaseVisualizer): - DEFAULT_KEY_ORDER = 'image predicted_image inpainted'.split(' ') - - def __init__(self, outdir, key_order=DEFAULT_KEY_ORDER, max_items_in_batch=10, - last_without_mask=True, rescale_keys=None): - self.outdir = outdir - os.makedirs(self.outdir, exist_ok=True) - self.key_order = key_order - self.max_items_in_batch = max_items_in_batch - self.last_without_mask = last_without_mask - self.rescale_keys = rescale_keys - - def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None): - check_and_warn_input_range(batch['image'], 0, 1, 'DirectoryVisualizer target image') - vis_img = visualize_mask_and_images_batch(batch, self.key_order, max_items=self.max_items_in_batch, - last_without_mask=self.last_without_mask, - rescale_keys=self.rescale_keys) - - vis_img = np.clip(vis_img * 255, 0, 255).astype('uint8') - - curoutdir = os.path.join(self.outdir, f'epoch{epoch_i:04d}{suffix}') - os.makedirs(curoutdir, exist_ok=True) - rank_suffix = f'_r{rank}' if rank is not None else '' - out_fname = os.path.join(curoutdir, f'batch{batch_i:07d}{rank_suffix}.jpg') - - vis_img = cv2.cvtColor(vis_img, cv2.COLOR_RGB2BGR) - cv2.imwrite(out_fname, vis_img) diff --git a/spaces/akhaliq/neural-waveshaping-synthesis/README.md b/spaces/akhaliq/neural-waveshaping-synthesis/README.md deleted file mode 100644 index 301b88991ad92ab96c27c2f86c1fc0dd0704d6c6..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/neural-waveshaping-synthesis/README.md +++ /dev/null @@ -1,33 +0,0 @@ ---- -title: Neural Waveshaping Synthesis -emoji: 🔥 -colorFrom: red -colorTo: pink -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/alamin655/websurfx/src/lib.rs b/spaces/alamin655/websurfx/src/lib.rs deleted file mode 100644 index 73e93644eaa93c1bb8558c0175748b70dc522ec1..0000000000000000000000000000000000000000 --- a/spaces/alamin655/websurfx/src/lib.rs +++ /dev/null @@ -1,110 +0,0 @@ -//! This main library module provides the functionality to provide and handle the Tcp server -//! and register all the routes for the `websurfx` meta search engine website. - -#![forbid(unsafe_code, clippy::panic)] -#![deny(missing_docs, clippy::missing_docs_in_private_items, clippy::perf)] -#![warn(clippy::cognitive_complexity, rust_2018_idioms)] - -pub mod cache; -pub mod config; -pub mod engines; -pub mod handler; -pub mod models; -pub mod results; -pub mod server; - -use std::net::TcpListener; - -use crate::server::router; - -use actix_cors::Cors; -use actix_files as fs; -use actix_governor::{Governor, GovernorConfigBuilder}; -use actix_web::{dev::Server, http::header, middleware::Logger, web, App, HttpServer}; -use cache::cacher::{Cache, SharedCache}; -use config::parser::Config; -use handlebars::Handlebars; -use handler::paths::{file_path, FileType}; - -/// Runs the web server on the provided TCP listener and returns a `Server` instance. -/// -/// # Arguments -/// -/// * `listener` - A `TcpListener` instance representing the address and port to listen on. -/// -/// # Returns -/// -/// Returns a `Result` containing a `Server` instance on success, or an `std::io::Error` on failure. -/// -/// # Example -/// -/// ```rust -/// use std::net::TcpListener; -/// use websurfx::{config::parser::Config, run, cache::cacher::Cache}; -/// -/// let config = Config::parse(true).unwrap(); -/// let listener = TcpListener::bind("127.0.0.1:8080").expect("Failed to bind address"); -/// let cache = Cache::new_in_memory(); -/// let server = run(listener,config,cache).expect("Failed to start server"); -/// ``` -pub fn run(listener: TcpListener, config: Config, cache: Cache) -> std::io::Result { - let mut handlebars: Handlebars<'_> = Handlebars::new(); - - let public_folder_path: &str = file_path(FileType::Theme)?; - - handlebars - .register_templates_directory(".html", format!("{}/templates", public_folder_path)) - .unwrap(); - - let handlebars_ref: web::Data> = web::Data::new(handlebars); - - let cloned_config_threads_opt: u8 = config.threads; - - let cache = web::Data::new(SharedCache::new(cache)); - - let server = HttpServer::new(move || { - let cors: Cors = Cors::default() - .allow_any_origin() - .allowed_methods(vec!["GET"]) - .allowed_headers(vec![ - header::ORIGIN, - header::CONTENT_TYPE, - header::REFERER, - header::COOKIE, - ]); - - App::new() - .wrap(Logger::default()) // added logging middleware for logging. - .app_data(handlebars_ref.clone()) - .app_data(web::Data::new(config.clone())) - .app_data(cache.clone()) - .wrap(cors) - .wrap(Governor::new( - &GovernorConfigBuilder::default() - .per_second(config.rate_limiter.time_limit as u64) - .burst_size(config.rate_limiter.number_of_requests as u32) - .finish() - .unwrap(), - )) - // Serve images and static files (css and js files). - .service( - fs::Files::new("/static", format!("{}/static", public_folder_path)) - .show_files_listing(), - ) - .service( - fs::Files::new("/images", format!("{}/images", public_folder_path)) - .show_files_listing(), - ) - .service(router::robots_data) // robots.txt - .service(router::index) // index page - .service(server::routes::search::search) // search page - .service(router::about) // about page - .service(router::settings) // settings page - .default_service(web::route().to(router::not_found)) // error page - }) - .workers(cloned_config_threads_opt as usize) - // Start server on 127.0.0.1 with the user provided port number. for example 127.0.0.1:8080. - .listen(listener)? - .run(); - Ok(server) -} diff --git a/spaces/alexray/btc_predictor/Dockerfile b/spaces/alexray/btc_predictor/Dockerfile deleted file mode 100644 index b423637442e8b6558791c526b99c85a894216d91..0000000000000000000000000000000000000000 --- a/spaces/alexray/btc_predictor/Dockerfile +++ /dev/null @@ -1,11 +0,0 @@ -FROM python:3.10-slim - -WORKDIR /code - -COPY . . - -RUN pip install -r /code/requirements.txt - -EXPOSE 7860 - -CMD ["python", "/code/app.py"] diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/resolvelib/base.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/resolvelib/base.py deleted file mode 100644 index b206692a0a976d8336e3f5896eadf4765a33fb2c..0000000000000000000000000000000000000000 --- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/resolvelib/base.py +++ /dev/null @@ -1,141 +0,0 @@ -from typing import FrozenSet, Iterable, Optional, Tuple, Union - -from pip._vendor.packaging.specifiers import SpecifierSet -from pip._vendor.packaging.utils import NormalizedName, canonicalize_name -from pip._vendor.packaging.version import LegacyVersion, Version - -from pip._internal.models.link import Link, links_equivalent -from pip._internal.req.req_install import InstallRequirement -from pip._internal.utils.hashes import Hashes - -CandidateLookup = Tuple[Optional["Candidate"], Optional[InstallRequirement]] -CandidateVersion = Union[LegacyVersion, Version] - - -def format_name(project: str, extras: FrozenSet[str]) -> str: - if not extras: - return project - canonical_extras = sorted(canonicalize_name(e) for e in extras) - return "{}[{}]".format(project, ",".join(canonical_extras)) - - -class Constraint: - def __init__( - self, specifier: SpecifierSet, hashes: Hashes, links: FrozenSet[Link] - ) -> None: - self.specifier = specifier - self.hashes = hashes - self.links = links - - @classmethod - def empty(cls) -> "Constraint": - return Constraint(SpecifierSet(), Hashes(), frozenset()) - - @classmethod - def from_ireq(cls, ireq: InstallRequirement) -> "Constraint": - links = frozenset([ireq.link]) if ireq.link else frozenset() - return Constraint(ireq.specifier, ireq.hashes(trust_internet=False), links) - - def __bool__(self) -> bool: - return bool(self.specifier) or bool(self.hashes) or bool(self.links) - - def __and__(self, other: InstallRequirement) -> "Constraint": - if not isinstance(other, InstallRequirement): - return NotImplemented - specifier = self.specifier & other.specifier - hashes = self.hashes & other.hashes(trust_internet=False) - links = self.links - if other.link: - links = links.union([other.link]) - return Constraint(specifier, hashes, links) - - def is_satisfied_by(self, candidate: "Candidate") -> bool: - # Reject if there are any mismatched URL constraints on this package. - if self.links and not all(_match_link(link, candidate) for link in self.links): - return False - # We can safely always allow prereleases here since PackageFinder - # already implements the prerelease logic, and would have filtered out - # prerelease candidates if the user does not expect them. - return self.specifier.contains(candidate.version, prereleases=True) - - -class Requirement: - @property - def project_name(self) -> NormalizedName: - """The "project name" of a requirement. - - This is different from ``name`` if this requirement contains extras, - in which case ``name`` would contain the ``[...]`` part, while this - refers to the name of the project. - """ - raise NotImplementedError("Subclass should override") - - @property - def name(self) -> str: - """The name identifying this requirement in the resolver. - - This is different from ``project_name`` if this requirement contains - extras, where ``project_name`` would not contain the ``[...]`` part. - """ - raise NotImplementedError("Subclass should override") - - def is_satisfied_by(self, candidate: "Candidate") -> bool: - return False - - def get_candidate_lookup(self) -> CandidateLookup: - raise NotImplementedError("Subclass should override") - - def format_for_error(self) -> str: - raise NotImplementedError("Subclass should override") - - -def _match_link(link: Link, candidate: "Candidate") -> bool: - if candidate.source_link: - return links_equivalent(link, candidate.source_link) - return False - - -class Candidate: - @property - def project_name(self) -> NormalizedName: - """The "project name" of the candidate. - - This is different from ``name`` if this candidate contains extras, - in which case ``name`` would contain the ``[...]`` part, while this - refers to the name of the project. - """ - raise NotImplementedError("Override in subclass") - - @property - def name(self) -> str: - """The name identifying this candidate in the resolver. - - This is different from ``project_name`` if this candidate contains - extras, where ``project_name`` would not contain the ``[...]`` part. - """ - raise NotImplementedError("Override in subclass") - - @property - def version(self) -> CandidateVersion: - raise NotImplementedError("Override in subclass") - - @property - def is_installed(self) -> bool: - raise NotImplementedError("Override in subclass") - - @property - def is_editable(self) -> bool: - raise NotImplementedError("Override in subclass") - - @property - def source_link(self) -> Optional[Link]: - raise NotImplementedError("Override in subclass") - - def iter_dependencies(self, with_requires: bool) -> Iterable[Optional[Requirement]]: - raise NotImplementedError("Override in subclass") - - def get_install_requirement(self) -> Optional[InstallRequirement]: - raise NotImplementedError("Override in subclass") - - def format_for_error(self) -> str: - raise NotImplementedError("Subclass should override") diff --git a/spaces/ali-ghamdan/realesrgan-models/realesrgan/archs/discriminator_arch.py b/spaces/ali-ghamdan/realesrgan-models/realesrgan/archs/discriminator_arch.py deleted file mode 100644 index 4b66ab1226d6793de846bc9828bbe427031a0e2d..0000000000000000000000000000000000000000 --- a/spaces/ali-ghamdan/realesrgan-models/realesrgan/archs/discriminator_arch.py +++ /dev/null @@ -1,67 +0,0 @@ -from basicsr.utils.registry import ARCH_REGISTRY -from torch import nn as nn -from torch.nn import functional as F -from torch.nn.utils import spectral_norm - - -@ARCH_REGISTRY.register() -class UNetDiscriminatorSN(nn.Module): - """Defines a U-Net discriminator with spectral normalization (SN) - - It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. - - Arg: - num_in_ch (int): Channel number of inputs. Default: 3. - num_feat (int): Channel number of base intermediate features. Default: 64. - skip_connection (bool): Whether to use skip connections between U-Net. Default: True. - """ - - def __init__(self, num_in_ch, num_feat=64, skip_connection=True): - super(UNetDiscriminatorSN, self).__init__() - self.skip_connection = skip_connection - norm = spectral_norm - # the first convolution - self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1) - # downsample - self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False)) - self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False)) - self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False)) - # upsample - self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False)) - self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False)) - self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False)) - # extra convolutions - self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False)) - self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False)) - self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1) - - def forward(self, x): - # downsample - x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True) - x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True) - x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True) - x3 = F.leaky_relu(self.conv3(x2), negative_slope=0.2, inplace=True) - - # upsample - x3 = F.interpolate(x3, scale_factor=2, mode='bilinear', align_corners=False) - x4 = F.leaky_relu(self.conv4(x3), negative_slope=0.2, inplace=True) - - if self.skip_connection: - x4 = x4 + x2 - x4 = F.interpolate(x4, scale_factor=2, mode='bilinear', align_corners=False) - x5 = F.leaky_relu(self.conv5(x4), negative_slope=0.2, inplace=True) - - if self.skip_connection: - x5 = x5 + x1 - x5 = F.interpolate(x5, scale_factor=2, mode='bilinear', align_corners=False) - x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True) - - if self.skip_connection: - x6 = x6 + x0 - - # extra convolutions - out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True) - out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True) - out = self.conv9(out) - - return out diff --git a/spaces/allknowingroger/Image-Models-Test80/app.py b/spaces/allknowingroger/Image-Models-Test80/app.py deleted file mode 100644 index 58140e51eca72eefab005caaedac2922ea419d7a..0000000000000000000000000000000000000000 --- a/spaces/allknowingroger/Image-Models-Test80/app.py +++ /dev/null @@ -1,143 +0,0 @@ -import gradio as gr -# import os -# import sys -# from pathlib import Path -import time - -models =[ - "devansuresh05/xzg-cat", - "yadhikari/yogesh-a", - "smit-mehta/marvel-sdxl", - "Nacken/kkkk-sdxl-5000", - "Jade1211/textual_inversion_rocket", - "satani/two", - "satani/500", - "mratanusarkar/lora-trained-xl-colab", -] - - -model_functions = {} -model_idx = 1 -for model_path in models: - try: - model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False) - except Exception as error: - def the_fn(txt): - return None - model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"]) - model_idx+=1 - - -def send_it_idx(idx): - def send_it_fn(prompt): - output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt) - return output - return send_it_fn - -def get_prompts(prompt_text): - return prompt_text - -def clear_it(val): - if int(val) != 0: - val = 0 - else: - val = 0 - pass - return val - -def all_task_end(cnt,t_stamp): - to = t_stamp + 60 - et = time.time() - if et > to and t_stamp != 0: - d = gr.update(value=0) - tog = gr.update(value=1) - #print(f'to: {to} et: {et}') - else: - if cnt != 0: - d = gr.update(value=et) - else: - d = gr.update(value=0) - tog = gr.update(value=0) - #print (f'passing: to: {to} et: {et}') - pass - return d, tog - -def all_task_start(): - print("\n\n\n\n\n\n\n") - t = time.gmtime() - t_stamp = time.time() - current_time = time.strftime("%H:%M:%S", t) - return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0) - -def clear_fn(): - nn = len(models) - return tuple([None, *[None for _ in range(nn)]]) - - - -with gr.Blocks(title="SD Models") as my_interface: - with gr.Column(scale=12): - # with gr.Row(): - # gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""") - with gr.Row(): - with gr.Row(scale=6): - primary_prompt=gr.Textbox(label="Prompt", value="") - # real_prompt=gr.Textbox(label="Real prompt") - with gr.Row(scale=6): - # improve_prompts_btn=gr.Button("Improve") - with gr.Row(): - run=gr.Button("Run",variant="primary") - clear_btn=gr.Button("Clear") - with gr.Row(): - sd_outputs = {} - model_idx = 1 - for model_path in models: - with gr.Column(scale=3, min_width=320): - with gr.Box(): - sd_outputs[model_idx] = gr.Image(label=model_path) - pass - model_idx += 1 - pass - pass - - with gr.Row(visible=False): - start_box=gr.Number(interactive=False) - end_box=gr.Number(interactive=False) - tog_box=gr.Textbox(value=0,interactive=False) - - start_box.change( - all_task_end, - [start_box, end_box], - [start_box, tog_box], - every=1, - show_progress=False) - - primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box]) - run.click(all_task_start, None, [start_box, end_box, tog_box]) - runs_dict = {} - model_idx = 1 - for model_path in models: - runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]]) - model_idx += 1 - pass - pass - - # improve_prompts_btn_clicked=improve_prompts_btn.click( - # get_prompts, - # inputs=[primary_prompt], - # outputs=[primary_prompt], - # cancels=list(runs_dict.values())) - clear_btn.click( - clear_fn, - None, - [primary_prompt, *list(sd_outputs.values())], - cancels=[*list(runs_dict.values())]) - tog_box.change( - clear_it, - tog_box, - tog_box, - cancels=[*list(runs_dict.values())]) - -my_interface.queue(concurrency_count=600, status_update_rate=1) -my_interface.launch(inline=True, show_api=False) - \ No newline at end of file diff --git a/spaces/amankishore/sjc/sd1/ldm/modules/distributions/distributions.py b/spaces/amankishore/sjc/sd1/ldm/modules/distributions/distributions.py deleted file mode 100644 index f2b8ef901130efc171aa69742ca0244d94d3f2e9..0000000000000000000000000000000000000000 --- a/spaces/amankishore/sjc/sd1/ldm/modules/distributions/distributions.py +++ /dev/null @@ -1,92 +0,0 @@ -import torch -import numpy as np - - -class AbstractDistribution: - def sample(self): - raise NotImplementedError() - - def mode(self): - raise NotImplementedError() - - -class DiracDistribution(AbstractDistribution): - def __init__(self, value): - self.value = value - - def sample(self): - return self.value - - def mode(self): - return self.value - - -class DiagonalGaussianDistribution(object): - def __init__(self, parameters, deterministic=False): - self.parameters = parameters - self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) - self.logvar = torch.clamp(self.logvar, -30.0, 20.0) - self.deterministic = deterministic - self.std = torch.exp(0.5 * self.logvar) - self.var = torch.exp(self.logvar) - if self.deterministic: - self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) - - def sample(self): - x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) - return x - - def kl(self, other=None): - if self.deterministic: - return torch.Tensor([0.]) - else: - if other is None: - return 0.5 * torch.sum(torch.pow(self.mean, 2) - + self.var - 1.0 - self.logvar, - dim=[1, 2, 3]) - else: - return 0.5 * torch.sum( - torch.pow(self.mean - other.mean, 2) / other.var - + self.var / other.var - 1.0 - self.logvar + other.logvar, - dim=[1, 2, 3]) - - def nll(self, sample, dims=[1,2,3]): - if self.deterministic: - return torch.Tensor([0.]) - logtwopi = np.log(2.0 * np.pi) - return 0.5 * torch.sum( - logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, - dim=dims) - - def mode(self): - return self.mean - - -def normal_kl(mean1, logvar1, mean2, logvar2): - """ - source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 - Compute the KL divergence between two gaussians. - Shapes are automatically broadcasted, so batches can be compared to - scalars, among other use cases. - """ - tensor = None - for obj in (mean1, logvar1, mean2, logvar2): - if isinstance(obj, torch.Tensor): - tensor = obj - break - assert tensor is not None, "at least one argument must be a Tensor" - - # Force variances to be Tensors. Broadcasting helps convert scalars to - # Tensors, but it does not work for torch.exp(). - logvar1, logvar2 = [ - x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) - for x in (logvar1, logvar2) - ] - - return 0.5 * ( - -1.0 - + logvar2 - - logvar1 - + torch.exp(logvar1 - logvar2) - + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) - ) diff --git a/spaces/amarchheda/ChordDuplicate/main_code.py b/spaces/amarchheda/ChordDuplicate/main_code.py deleted file mode 100644 index aceb4e13599d511550ae55ceb3baf972b5b95f30..0000000000000000000000000000000000000000 --- a/spaces/amarchheda/ChordDuplicate/main_code.py +++ /dev/null @@ -1,80 +0,0 @@ -import numpy as np -import tensorflow as tf -from scipy.io.wavfile import write -import keras.backend as K -import librosa.display -import cv2 -import librosa -import matplotlib.pyplot as plt -import librosa.display -import numpy as np -from keras.applications import VGG16 -import os -import scipy - -# Define function to preprocess input audio -#convert song to mel spectogram as siamese network doesn't work on sound directly -def create_spectrogram(clip,sample_rate,save_path): - plt.interactive(False) - fig=plt.figure(figsize=[0.72,0.72]) - S=librosa.feature.melspectrogram(y=clip,sr=sample_rate) - librosa.display.specshow(librosa.power_to_db(S,ref=np.max)) - fig.savefig(save_path,dpi=400,bbox_inches='tight',pad_inches=0) - plt.close() - fig.clf() - plt.close(fig) - plt.close('all') - del save_path,clip,sample_rate,fig,S - -def load_img(path): - img=cv2.imread(path) - img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB) - img=cv2.resize(img,(150,150)) - return img - -import pickle - -def main_loop(): - - with open('dict.pickle', 'rb') as handle: - songspecdict = pickle.load(handle) - - # Load the song to match - song, sr = librosa.load("my_audio.wav") - to_match = np.copy(song[0:220500]) - print("Loaded data into librosa...") - - # Create spectrogram image of the song to match - create_spectrogram(to_match, sr, 'test.png') - print("Created spectogram...") - - # Load the spectrogram image of the song to match - to_match_img = load_img('test.png') - to_match_img = np.expand_dims(to_match_img, axis=0) - print("Loaded spectrum image...") - - # Get the embedding of the song to match - # Load the tune recognition model - model = tf.keras.models.load_model('./embdmodel_1.hdf5') - embedding_model=model.layers[2] - to_match_emb = embedding_model.predict(to_match_img) - print("Get song embedding...") - - # Calculate the distances between the song to match and the songs in the database - songsdistdict = {} - for key, values in songspecdict.items(): - dist_array = [] - for embd in values: - dist_array.append(np.linalg.norm(to_match_emb - embd)) - - songsdistdict[key] = min(dist_array) - song_titles=list(songsdistdict.keys()) - distances=list(songsdistdict.values()) - - # Get the title and artist of the recognized song - recognized_song_artist, recognized_song_title = song_titles[distances.index(min(distances))].split('-') - recognized_song_title = os.path.splitext(recognized_song_title)[0] - print(f'Artist: {recognized_song_artist}') - print(f'Title: {recognized_song_title}') - - return recognized_song_title \ No newline at end of file diff --git a/spaces/amsterdamNLP/CLIP-attention-rollout/clip_grounding/utils/visualize.py b/spaces/amsterdamNLP/CLIP-attention-rollout/clip_grounding/utils/visualize.py deleted file mode 100644 index aaee90b5be63568dbcde91da84e9560a580c7f89..0000000000000000000000000000000000000000 --- a/spaces/amsterdamNLP/CLIP-attention-rollout/clip_grounding/utils/visualize.py +++ /dev/null @@ -1,183 +0,0 @@ -"""Helpers for visualization""" -import numpy as np -import matplotlib -import matplotlib.pyplot as plt -import cv2 -from PIL import Image - - -# define predominanat colors -COLORS = { - "pink": (242, 116, 223), - "cyan": (46, 242, 203), - "red": (255, 0, 0), - "green": (0, 255, 0), - "blue": (0, 0, 255), - "yellow": (255, 255, 0), -} - - -def show_single_image(image: np.ndarray, figsize: tuple = (8, 8), title: str = None, titlesize=18, cmap: str = None, ticks=False, save=False, save_path=None): - """Show a single image.""" - fig, ax = plt.subplots(1, 1, figsize=figsize) - - if isinstance(image, Image.Image): - image = np.asarray(image) - - ax.set_title(title, fontsize=titlesize) - ax.imshow(image, cmap=cmap) - - if not ticks: - ax.set_xticks([]) - ax.set_yticks([]) - - if save: - plt.savefig(save_path, bbox_inches='tight') - - plt.show() - - -def show_grid_of_images( - images: np.ndarray, n_cols: int = 4, figsize: tuple = (8, 8), - cmap=None, subtitles=None, title=None, subtitlesize=18, - save=False, save_path=None, titlesize=20, - ): - """Show a grid of images.""" - n_cols = min(n_cols, len(images)) - - copy_of_images = images.copy() - for i, image in enumerate(copy_of_images): - if isinstance(image, Image.Image): - image = np.asarray(image) - images[i] = image - - if subtitles is None: - subtitles = [None] * len(images) - - n_rows = int(np.ceil(len(images) / n_cols)) - fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize) - for i, ax in enumerate(axes.flat): - if i < len(images): - if len(images[i].shape) == 2 and cmap is None: - cmap="gray" - ax.imshow(images[i], cmap=cmap) - ax.set_title(subtitles[i], fontsize=subtitlesize) - ax.axis('off') - fig.set_tight_layout(True) - plt.suptitle(title, y=0.8, fontsize=titlesize) - - if save: - plt.savefig(save_path, bbox_inches='tight') - plt.close() - else: - plt.show() - - -def show_keypoint_matches( - img1, kp1, img2, kp2, matches, - K=10, figsize=(10, 5), drawMatches_args=dict(matchesThickness=3, singlePointColor=(0, 0, 0)), - choose_matches="random", - ): - """Displays matches found in the pair of images""" - if choose_matches == "random": - selected_matches = np.random.choice(matches, K) - elif choose_matches == "all": - K = len(matches) - selected_matches = matches - elif choose_matches == "topk": - selected_matches = matches[:K] - else: - raise ValueError(f"Unknown value for choose_matches: {choose_matches}") - - # color each match with a different color - cmap = matplotlib.cm.get_cmap('gist_rainbow', K) - colors = [[int(x*255) for x in cmap(i)[:3]] for i in np.arange(0,K)] - drawMatches_args.update({"matchColor": -1, "singlePointColor": (100, 100, 100)}) - - img3 = cv2.drawMatches(img1, kp1, img2, kp2, selected_matches, outImg=None, **drawMatches_args) - show_single_image( - img3, - figsize=figsize, - title=f"[{choose_matches.upper()}] Selected K = {K} matches between the pair of images.", - ) - return img3 - - -def draw_kps_on_image(image: np.ndarray, kps: np.ndarray, color=COLORS["red"], radius=3, thickness=-1, return_as="numpy"): - """ - Draw keypoints on image. - - Args: - image: Image to draw keypoints on. - kps: Keypoints to draw. Note these should be in (x, y) format. - """ - if isinstance(image, Image.Image): - image = np.asarray(image) - - for kp in kps: - image = cv2.circle( - image, (int(kp[0]), int(kp[1])), radius=radius, color=color, thickness=thickness) - - if return_as == "PIL": - return Image.fromarray(image) - - return image - - -def get_concat_h(im1, im2): - """Concatenate two images horizontally""" - dst = Image.new('RGB', (im1.width + im2.width, im1.height)) - dst.paste(im1, (0, 0)) - dst.paste(im2, (im1.width, 0)) - return dst - - -def get_concat_v(im1, im2): - """Concatenate two images vertically""" - dst = Image.new('RGB', (im1.width, im1.height + im2.height)) - dst.paste(im1, (0, 0)) - dst.paste(im2, (0, im1.height)) - return dst - - -def show_images_with_keypoints(images: list, kps: list, radius=15, color=(0, 220, 220), figsize=(10, 8), return_images=False, save=False, save_path="sample.png"): - assert len(images) == len(kps) - - # generate - images_with_kps = [] - for i in range(len(images)): - img_with_kps = draw_kps_on_image(images[i], kps[i], radius=radius, color=color, return_as="PIL") - images_with_kps.append(img_with_kps) - - # show - show_grid_of_images(images_with_kps, n_cols=len(images), figsize=figsize, save=save, save_path=save_path) - - if return_images: - return images_with_kps - - -def set_latex_fonts(usetex=True, fontsize=14, show_sample=False, **kwargs): - try: - plt.rcParams.update({ - "text.usetex": usetex, - "font.family": "serif", - "font.serif": ["Computer Modern Roman"], - "font.size": fontsize, - **kwargs, - }) - if show_sample: - plt.figure() - plt.title("Sample $y = x^2$") - plt.plot(np.arange(0, 10), np.arange(0, 10)**2, "--o") - plt.grid() - plt.show() - except: - print("Failed to setup LaTeX fonts. Proceeding without.") - pass - - -def get_colors(num_colors, palette="jet"): - cmap = plt.get_cmap(palette) - colors = [cmap(i) for i in np.linspace(0, 1, num_colors)] - return colors - diff --git a/spaces/anaclaudia13ct/insect_detection/utils/loggers/clearml/clearml_utils.py b/spaces/anaclaudia13ct/insect_detection/utils/loggers/clearml/clearml_utils.py deleted file mode 100644 index 3457727a96a454130443569f97112ea61db8c522..0000000000000000000000000000000000000000 --- a/spaces/anaclaudia13ct/insect_detection/utils/loggers/clearml/clearml_utils.py +++ /dev/null @@ -1,164 +0,0 @@ -"""Main Logger class for ClearML experiment tracking.""" -import glob -import re -from pathlib import Path - -import numpy as np -import yaml - -from utils.plots import Annotator, colors - -try: - import clearml - from clearml import Dataset, Task - - assert hasattr(clearml, '__version__') # verify package import not local dir -except (ImportError, AssertionError): - clearml = None - - -def construct_dataset(clearml_info_string): - """Load in a clearml dataset and fill the internal data_dict with its contents. - """ - dataset_id = clearml_info_string.replace('clearml://', '') - dataset = Dataset.get(dataset_id=dataset_id) - dataset_root_path = Path(dataset.get_local_copy()) - - # We'll search for the yaml file definition in the dataset - yaml_filenames = list(glob.glob(str(dataset_root_path / "*.yaml")) + glob.glob(str(dataset_root_path / "*.yml"))) - if len(yaml_filenames) > 1: - raise ValueError('More than one yaml file was found in the dataset root, cannot determine which one contains ' - 'the dataset definition this way.') - elif len(yaml_filenames) == 0: - raise ValueError('No yaml definition found in dataset root path, check that there is a correct yaml file ' - 'inside the dataset root path.') - with open(yaml_filenames[0]) as f: - dataset_definition = yaml.safe_load(f) - - assert set(dataset_definition.keys()).issuperset( - {'train', 'test', 'val', 'nc', 'names'} - ), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')" - - data_dict = dict() - data_dict['train'] = str( - (dataset_root_path / dataset_definition['train']).resolve()) if dataset_definition['train'] else None - data_dict['test'] = str( - (dataset_root_path / dataset_definition['test']).resolve()) if dataset_definition['test'] else None - data_dict['val'] = str( - (dataset_root_path / dataset_definition['val']).resolve()) if dataset_definition['val'] else None - data_dict['nc'] = dataset_definition['nc'] - data_dict['names'] = dataset_definition['names'] - - return data_dict - - -class ClearmlLogger: - """Log training runs, datasets, models, and predictions to ClearML. - - This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, - this information includes hyperparameters, system configuration and metrics, model metrics, code information and - basic data metrics and analyses. - - By providing additional command line arguments to train.py, datasets, - models and predictions can also be logged. - """ - - def __init__(self, opt, hyp): - """ - - Initialize ClearML Task, this object will capture the experiment - - Upload dataset version to ClearML Data if opt.upload_dataset is True - - arguments: - opt (namespace) -- Commandline arguments for this run - hyp (dict) -- Hyperparameters for this run - - """ - self.current_epoch = 0 - # Keep tracked of amount of logged images to enforce a limit - self.current_epoch_logged_images = set() - # Maximum number of images to log to clearML per epoch - self.max_imgs_to_log_per_epoch = 16 - # Get the interval of epochs when bounding box images should be logged - self.bbox_interval = opt.bbox_interval - self.clearml = clearml - self.task = None - self.data_dict = None - if self.clearml: - self.task = Task.init( - project_name=opt.project if opt.project != 'runs/train' else 'YOLOv5', - task_name=opt.name if opt.name != 'exp' else 'Training', - tags=['YOLOv5'], - output_uri=True, - reuse_last_task_id=opt.exist_ok, - auto_connect_frameworks={'pytorch': False} - # We disconnect pytorch auto-detection, because we added manual model save points in the code - ) - # ClearML's hooks will already grab all general parameters - # Only the hyperparameters coming from the yaml config file - # will have to be added manually! - self.task.connect(hyp, name='Hyperparameters') - self.task.connect(opt, name='Args') - - # Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent - self.task.set_base_docker("ultralytics/yolov5:latest", - docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"', - docker_setup_bash_script='pip install clearml') - - # Get ClearML Dataset Version if requested - if opt.data.startswith('clearml://'): - # data_dict should have the following keys: - # names, nc (number of classes), test, train, val (all three relative paths to ../datasets) - self.data_dict = construct_dataset(opt.data) - # Set data to data_dict because wandb will crash without this information and opt is the best way - # to give it to them - opt.data = self.data_dict - - def log_debug_samples(self, files, title='Debug Samples'): - """ - Log files (images) as debug samples in the ClearML task. - - arguments: - files (List(PosixPath)) a list of file paths in PosixPath format - title (str) A title that groups together images with the same values - """ - for f in files: - if f.exists(): - it = re.search(r'_batch(\d+)', f.name) - iteration = int(it.groups()[0]) if it else 0 - self.task.get_logger().report_image(title=title, - series=f.name.replace(it.group(), ''), - local_path=str(f), - iteration=iteration) - - def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25): - """ - Draw the bounding boxes on a single image and report the result as a ClearML debug sample. - - arguments: - image_path (PosixPath) the path the original image file - boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class] - class_names (dict): dict containing mapping of class int to class name - image (Tensor): A torch tensor containing the actual image data - """ - if len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch and self.current_epoch >= 0: - # Log every bbox_interval times and deduplicate for any intermittend extra eval runs - if self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images: - im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2)) - annotator = Annotator(im=im, pil=True) - for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])): - color = colors(i) - - class_name = class_names[int(class_nr)] - confidence_percentage = round(float(conf) * 100, 2) - label = f"{class_name}: {confidence_percentage}%" - - if conf > conf_threshold: - annotator.rectangle(box.cpu().numpy(), outline=color) - annotator.box_label(box.cpu().numpy(), label=label, color=color) - - annotated_image = annotator.result() - self.task.get_logger().report_image(title='Bounding Boxes', - series=image_path.name, - iteration=self.current_epoch, - image=annotated_image) - self.current_epoch_logged_images.add(image_path) diff --git a/spaces/anen/DentalGPT/app.py b/spaces/anen/DentalGPT/app.py deleted file mode 100644 index 448119eadb71fb147ea121805ac0bfee9558072f..0000000000000000000000000000000000000000 --- a/spaces/anen/DentalGPT/app.py +++ /dev/null @@ -1,34 +0,0 @@ -import streamlit as st -from aifunc import run_chain - -def main(): - st.title("DentalGPT For Everybody") - - # File upload window - uploaded_file = st.file_uploader("Upload files to ML") - - # Text input window - user_input = st.text_input("Enter text") - - # Process uploaded file and user input - result = process_data(uploaded_file, user_input) - - # Display result in a read-only text field - st.text_area("Result", value=result, disabled=True) - -def process_data(file, input_text): - # Perform data processing here based on the uploaded file and user input - # Return the processed result as a string - # Example implementation: - if file is not None: - file_contents = file.read() - # Process file contents - - # Process user input - # ... - - # Return the result - return "Processed result" - -if __name__ == '__main__': - main() diff --git a/spaces/antreyes/stabilityai-stable-diffusion-2/app.py b/spaces/antreyes/stabilityai-stable-diffusion-2/app.py deleted file mode 100644 index d2782cea00b1bfcd22df7c204d9e52a6baf46ac2..0000000000000000000000000000000000000000 --- a/spaces/antreyes/stabilityai-stable-diffusion-2/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/stabilityai/stable-diffusion-2").launch() \ No newline at end of file diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/metrics.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/metrics.py deleted file mode 100644 index 35d887b61bfa583a8852c80ff164919be7b45f4e..0000000000000000000000000000000000000000 --- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/metrics.py +++ /dev/null @@ -1,271 +0,0 @@ -from torch.functional import Tensor -from general_utils import log -from collections import defaultdict -import numpy as np - -import torch -from torch.nn import functional as nnf - - -class BaseMetric(object): - - def __init__(self, metric_names, pred_range=None, gt_index=0, pred_index=0, eval_intermediate=True, - eval_validation=True): - self._names = tuple(metric_names) - self._eval_intermediate = eval_intermediate - self._eval_validation = eval_validation - - self._pred_range = pred_range - self._pred_index = pred_index - self._gt_index = gt_index - - self.predictions = [] - self.ground_truths = [] - - def eval_intermediate(self): - return self._eval_intermediate - - def eval_validation(self): - return self._eval_validation - - def names(self): - return self._names - - def add(self, predictions, ground_truth): - raise NotImplementedError - - def value(self): - raise NotImplementedError - - def scores(self): - # similar to value but returns dict - value = self.value() - if type(value) == dict: - return value - else: - assert type(value) in {list, tuple} - return list(zip(self.names(), self.value())) - - def _get_pred_gt(self, predictions, ground_truth): - pred = predictions[self._pred_index] - gt = ground_truth[self._gt_index] - - if self._pred_range is not None: - pred = pred[:, self._pred_range[0]: self._pred_range[1]] - - return pred, gt - - -class FixedIntervalMetrics(BaseMetric): - - def __init__(self, sigmoid=False, ignore_mask=False, resize_to=None, - resize_pred=None, n_values=51, custom_threshold=None): - - - super().__init__(('ap', 'best_fgiou', 'best_miou', 'fgiou0.5', 'fgiou0.1', 'mean_iou_0p5', 'mean_iou_0p1', 'best_biniou', 'biniou_0.5', 'fgiou_thresh')) - self.intersections = [] - self.unions = [] - # self.threshold = threshold - self.sigmoid = sigmoid - self.resize_to = resize_to - self.resize_pred = resize_pred # resize prediction to match ground truth - self.class_count = defaultdict(lambda: 0) - self.per_class = defaultdict(lambda : [0,0]) - self.ignore_mask = ignore_mask - self.custom_threshold = custom_threshold - - self.scores_ap = [] - self.scores_iou = [] - self.gts, self.preds = [], [] - self.classes = [] - - # [1:-1] ignores 0 and 1 - self.threshold_values = np.linspace(0, 1, n_values)[1:-1] - - self.metrics = dict(tp=[], fp=[], fn=[], tn=[]) - - def add(self, pred, gt): - - pred_batch = pred[0].cpu() - - if self.sigmoid: - pred_batch = torch.sigmoid(pred_batch) - - gt_batch = gt[0].cpu() - mask_batch = gt[1] if len(gt) > 1 and not self.ignore_mask and gt[1].numel() > 0 else ([None] * len(pred_batch)) - cls_batch = gt[2] if len(gt) > 2 else [None] * len(pred_batch) - - if self.resize_to is not None: - gt_batch = nnf.interpolate(gt_batch, self.resize_to, mode='nearest') - pred_batch = nnf.interpolate(pred_batch, self.resize_to, mode='bilinear', align_corners=False) - - if isinstance(cls_batch, torch.Tensor): - cls_batch = cls_batch.cpu().numpy().tolist() - - assert len(gt_batch) == len(pred_batch) == len(cls_batch), f'{len(gt_batch)} {len(pred_batch)} {len(cls_batch)}' - - for predictions, ground_truth, mask, cls in zip(pred_batch, gt_batch, mask_batch, cls_batch): - - if self.resize_pred: - predictions = nnf.interpolate(predictions.unsqueeze(0).float(), size=ground_truth.size()[-2:], mode='bilinear', align_corners=True) - - p = predictions.flatten() - g = ground_truth.flatten() - - assert len(p) == len(g) - - if mask is not None: - m = mask.flatten().bool() - p = p[m] - g = g[m] - - p_sorted = p.sort() - p = p_sorted.values - g = g[p_sorted.indices] - - tps, fps, fns, tns = [], [], [], [] - for thresh in self.threshold_values: - - valid = torch.where(p > thresh)[0] - if len(valid) > 0: - n = int(valid[0]) - else: - n = len(g) - - fn = int(g[:n].sum()) - tp = int(g[n:].sum()) - fns += [fn] - tns += [n - fn] - tps += [tp] - fps += [len(g) - n - tp] - - self.metrics['tp'] += [tps] - self.metrics['fp'] += [fps] - self.metrics['fn'] += [fns] - self.metrics['tn'] += [tns] - - self.classes += [cls.item() if isinstance(cls, torch.Tensor) else cls] - - def value(self): - - import time - t_start = time.time() - - if set(self.classes) == set([None]): - all_classes = None - log.warning('classes were not provided, cannot compute mIoU') - else: - all_classes = set(int(c) for c in self.classes) - # log.info(f'compute metrics for {len(all_classes)} classes') - - summed = {k: [sum([self.metrics[k][i][j] - for i in range(len(self.metrics[k]))]) - for j in range(len(self.threshold_values))] - for k in self.metrics.keys()} - - if all_classes is not None: - - assert len(self.classes) == len(self.metrics['tp']) == len(self.metrics['fn']) - # group by class - metrics_by_class = {c: {k: [] for k in self.metrics.keys()} for c in all_classes} - for i in range(len(self.metrics['tp'])): - for k in self.metrics.keys(): - metrics_by_class[self.classes[i]][k] += [self.metrics[k][i]] - - # sum over all instances within the classes - summed_by_cls = {k: {c: np.array(metrics_by_class[c][k]).sum(0).tolist() for c in all_classes} for k in self.metrics.keys()} - - - # Compute average precision - - assert (np.array(summed['fp']) + np.array(summed['tp']) ).sum(), 'no predictions is made' - - # only consider values where a prediction is made - precisions = [summed['tp'][j] / (1 + summed['tp'][j] + summed['fp'][j]) for j in range(len(self.threshold_values)) - if summed['tp'][j] + summed['fp'][j] > 0] - recalls = [summed['tp'][j] / (1 + summed['tp'][j] + summed['fn'][j]) for j in range(len(self.threshold_values)) - if summed['tp'][j] + summed['fp'][j] > 0] - - # remove duplicate recall-precision-pairs (and sort by recall value) - recalls, precisions = zip(*sorted(list(set(zip(recalls, precisions))), key=lambda x: x[0])) - - from scipy.integrate import simps - ap = simps(precisions, recalls) - - # Compute best IoU - fgiou_scores = [summed['tp'][j] / (1 + summed['tp'][j] + summed['fp'][j] + summed['fn'][j]) for j in range(len(self.threshold_values))] - - biniou_scores = [ - 0.5*(summed['tp'][j] / (1 + summed['tp'][j] + summed['fp'][j] + summed['fn'][j])) + - 0.5*(summed['tn'][j] / (1 + summed['tn'][j] + summed['fn'][j] + summed['fp'][j])) - for j in range(len(self.threshold_values)) - ] - - index_0p5 = self.threshold_values.tolist().index(0.5) - index_0p1 = self.threshold_values.tolist().index(0.1) - index_0p2 = self.threshold_values.tolist().index(0.2) - index_0p3 = self.threshold_values.tolist().index(0.3) - - if self.custom_threshold is not None: - index_ct = self.threshold_values.tolist().index(self.custom_threshold) - - if all_classes is not None: - # mean IoU - mean_ious = [np.mean([summed_by_cls['tp'][c][j] / (1 + summed_by_cls['tp'][c][j] + summed_by_cls['fp'][c][j] + summed_by_cls['fn'][c][j]) - for c in all_classes]) - for j in range(len(self.threshold_values))] - - mean_iou_dict = { - 'miou_best': max(mean_ious) if all_classes is not None else None, - 'miou_0.5': mean_ious[index_0p5] if all_classes is not None else None, - 'miou_0.1': mean_ious[index_0p1] if all_classes is not None else None, - 'miou_0.2': mean_ious[index_0p2] if all_classes is not None else None, - 'miou_0.3': mean_ious[index_0p3] if all_classes is not None else None, - 'miou_best_t': self.threshold_values[np.argmax(mean_ious)], - 'mean_iou_ct': mean_ious[index_ct] if all_classes is not None and self.custom_threshold is not None else None, - 'mean_iou_scores': mean_ious, - } - - print(f'metric computation on {(len(all_classes) if all_classes is not None else "no")} classes took {time.time() - t_start:.1f}s') - - return { - 'ap': ap, - - # fgiou - 'fgiou_best': max(fgiou_scores), - 'fgiou_0.5': fgiou_scores[index_0p5], - 'fgiou_0.1': fgiou_scores[index_0p1], - 'fgiou_0.2': fgiou_scores[index_0p2], - 'fgiou_0.3': fgiou_scores[index_0p3], - 'fgiou_best_t': self.threshold_values[np.argmax(fgiou_scores)], - - # mean iou - - - # biniou - 'biniou_best': max(biniou_scores), - 'biniou_0.5': biniou_scores[index_0p5], - 'biniou_0.1': biniou_scores[index_0p1], - 'biniou_0.2': biniou_scores[index_0p2], - 'biniou_0.3': biniou_scores[index_0p3], - 'biniou_best_t': self.threshold_values[np.argmax(biniou_scores)], - - # custom threshold - 'fgiou_ct': fgiou_scores[index_ct] if self.custom_threshold is not None else None, - 'biniou_ct': biniou_scores[index_ct] if self.custom_threshold is not None else None, - 'ct': self.custom_threshold, - - # statistics - 'fgiou_scores': fgiou_scores, - 'biniou_scores': biniou_scores, - 'precision_recall_curve': sorted(list(set(zip(recalls, precisions)))), - 'summed_statistics': summed, - 'summed_by_cls_statistics': summed_by_cls, - - **mean_iou_dict - } - - # ('ap', 'best_fgiou', 'best_miou', 'fgiou0.5', 'fgiou0.1', 'mean_iou_0p5', 'mean_iou_0p1', 'best_biniou', 'biniou_0.5', 'fgiou_thresh' - - # return ap, best_fgiou, best_mean_iou, iou_0p5, iou_0p1, mean_iou_0p5, mean_iou_0p1, best_biniou, biniou0p5, best_fgiou_thresh, {'summed': summed, 'summed_by_cls': summed_by_cls} - diff --git a/spaces/aphenx/bingo/src/lib/hooks/use-copy-to-clipboard.tsx b/spaces/aphenx/bingo/src/lib/hooks/use-copy-to-clipboard.tsx deleted file mode 100644 index 62f7156dca246c46b213151af003a3a177977ccf..0000000000000000000000000000000000000000 --- a/spaces/aphenx/bingo/src/lib/hooks/use-copy-to-clipboard.tsx +++ /dev/null @@ -1,33 +0,0 @@ -'use client' - -import * as React from 'react' - -export interface useCopyToClipboardProps { - timeout?: number -} - -export function useCopyToClipboard({ - timeout = 2000 -}: useCopyToClipboardProps) { - const [isCopied, setIsCopied] = React.useState(false) - - const copyToClipboard = (value: string) => { - if (typeof window === 'undefined' || !navigator.clipboard?.writeText) { - return - } - - if (!value) { - return - } - - navigator.clipboard.writeText(value).then(() => { - setIsCopied(true) - - setTimeout(() => { - setIsCopied(false) - }, timeout) - }) - } - - return { isCopied, copyToClipboard } -} diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/encoder/__init__.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/encoder/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/detection/__init__.py b/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/detection/__init__.py deleted file mode 100644 index 1a6b0402dae864a3cc5dc2a90a412fd842a0efc7..0000000000000000000000000000000000000000 --- a/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/detection/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .core import FaceDetector \ No newline at end of file diff --git a/spaces/artificialimagination/ai_detect_v0.1/README.md b/spaces/artificialimagination/ai_detect_v0.1/README.md deleted file mode 100644 index f90f0c6782279897e252d088c42de77427e90829..0000000000000000000000000000000000000000 --- a/spaces/artificialimagination/ai_detect_v0.1/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Ai Detect V0.1 -emoji: 🏆 -colorFrom: indigo -colorTo: pink -sdk: gradio -sdk_version: 3.44.2 -app_file: app.py -pinned: false -license: other ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/beckers_barley_trellis_plot.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/beckers_barley_trellis_plot.py deleted file mode 100644 index 4f780d96314606989073db5a56d3ce075a08fdb0..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/beckers_barley_trellis_plot.py +++ /dev/null @@ -1,33 +0,0 @@ -""" -Becker's Barley Trellis Plot ----------------------------- -The example demonstrates the trellis charts created by Richard Becker, William Cleveland and others in the 1990s. Using the visualization technique below they identified an anomoly in a widely used agriculatural dataset, which they termed `"The Morris Mistake." `_. It became their favored way of showcasing the power of this pioneering plot. -""" -# category: case studies -import altair as alt -from vega_datasets import data - -source = data.barley() - -alt.Chart(source, title="The Morris Mistake").mark_point().encode( - alt.X( - 'yield:Q', - title="Barley Yield (bushels/acre)", - scale=alt.Scale(zero=False), - axis=alt.Axis(grid=False) - ), - alt.Y( - 'variety:N', - title="", - sort='-x', - axis=alt.Axis(grid=True) - ), - color=alt.Color('year:N', legend=alt.Legend(title="Year")), - row=alt.Row( - 'site:N', - title="", - sort=alt.EncodingSortField(field='yield', op='sum', order='descending'), - ) -).properties( - height=alt.Step(20) -).configure_view(stroke="transparent") diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/expr/tests/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/expr/tests/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/appdirs.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/appdirs.py deleted file mode 100644 index 2acd1debeb1d3b981fc577b777a77106c765c391..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/appdirs.py +++ /dev/null @@ -1,608 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# Copyright (c) 2005-2010 ActiveState Software Inc. -# Copyright (c) 2013 Eddy Petrișor - -"""Utilities for determining application-specific dirs. - -See for details and usage. -""" -# Dev Notes: -# - MSDN on where to store app data files: -# http://support.microsoft.com/default.aspx?scid=kb;en-us;310294#XSLTH3194121123120121120120 -# - Mac OS X: http://developer.apple.com/documentation/MacOSX/Conceptual/BPFileSystem/index.html -# - XDG spec for Un*x: http://standards.freedesktop.org/basedir-spec/basedir-spec-latest.html - -__version__ = "1.4.4" -__version_info__ = tuple(int(segment) for segment in __version__.split(".")) - - -import sys -import os - -PY3 = sys.version_info[0] == 3 - -if PY3: - unicode = str - -if sys.platform.startswith('java'): - import platform - os_name = platform.java_ver()[3][0] - if os_name.startswith('Windows'): # "Windows XP", "Windows 7", etc. - system = 'win32' - elif os_name.startswith('Mac'): # "Mac OS X", etc. - system = 'darwin' - else: # "Linux", "SunOS", "FreeBSD", etc. - # Setting this to "linux2" is not ideal, but only Windows or Mac - # are actually checked for and the rest of the module expects - # *sys.platform* style strings. - system = 'linux2' -else: - system = sys.platform - - - -def user_data_dir(appname=None, appauthor=None, version=None, roaming=False): - r"""Return full path to the user-specific data dir for this application. - - "appname" is the name of application. - If None, just the system directory is returned. - "appauthor" (only used on Windows) is the name of the - appauthor or distributing body for this application. Typically - it is the owning company name. This falls back to appname. You may - pass False to disable it. - "version" is an optional version path element to append to the - path. You might want to use this if you want multiple versions - of your app to be able to run independently. If used, this - would typically be ".". - Only applied when appname is present. - "roaming" (boolean, default False) can be set True to use the Windows - roaming appdata directory. That means that for users on a Windows - network setup for roaming profiles, this user data will be - sync'd on login. See - - for a discussion of issues. - - Typical user data directories are: - Mac OS X: ~/Library/Application Support/ - Unix: ~/.local/share/ # or in $XDG_DATA_HOME, if defined - Win XP (not roaming): C:\Documents and Settings\\Application Data\\ - Win XP (roaming): C:\Documents and Settings\\Local Settings\Application Data\\ - Win 7 (not roaming): C:\Users\\AppData\Local\\ - Win 7 (roaming): C:\Users\\AppData\Roaming\\ - - For Unix, we follow the XDG spec and support $XDG_DATA_HOME. - That means, by default "~/.local/share/". - """ - if system == "win32": - if appauthor is None: - appauthor = appname - const = roaming and "CSIDL_APPDATA" or "CSIDL_LOCAL_APPDATA" - path = os.path.normpath(_get_win_folder(const)) - if appname: - if appauthor is not False: - path = os.path.join(path, appauthor, appname) - else: - path = os.path.join(path, appname) - elif system == 'darwin': - path = os.path.expanduser('~/Library/Application Support/') - if appname: - path = os.path.join(path, appname) - else: - path = os.getenv('XDG_DATA_HOME', os.path.expanduser("~/.local/share")) - if appname: - path = os.path.join(path, appname) - if appname and version: - path = os.path.join(path, version) - return path - - -def site_data_dir(appname=None, appauthor=None, version=None, multipath=False): - r"""Return full path to the user-shared data dir for this application. - - "appname" is the name of application. - If None, just the system directory is returned. - "appauthor" (only used on Windows) is the name of the - appauthor or distributing body for this application. Typically - it is the owning company name. This falls back to appname. You may - pass False to disable it. - "version" is an optional version path element to append to the - path. You might want to use this if you want multiple versions - of your app to be able to run independently. If used, this - would typically be ".". - Only applied when appname is present. - "multipath" is an optional parameter only applicable to *nix - which indicates that the entire list of data dirs should be - returned. By default, the first item from XDG_DATA_DIRS is - returned, or '/usr/local/share/', - if XDG_DATA_DIRS is not set - - Typical site data directories are: - Mac OS X: /Library/Application Support/ - Unix: /usr/local/share/ or /usr/share/ - Win XP: C:\Documents and Settings\All Users\Application Data\\ - Vista: (Fail! "C:\ProgramData" is a hidden *system* directory on Vista.) - Win 7: C:\ProgramData\\ # Hidden, but writeable on Win 7. - - For Unix, this is using the $XDG_DATA_DIRS[0] default. - - WARNING: Do not use this on Windows. See the Vista-Fail note above for why. - """ - if system == "win32": - if appauthor is None: - appauthor = appname - path = os.path.normpath(_get_win_folder("CSIDL_COMMON_APPDATA")) - if appname: - if appauthor is not False: - path = os.path.join(path, appauthor, appname) - else: - path = os.path.join(path, appname) - elif system == 'darwin': - path = os.path.expanduser('/Library/Application Support') - if appname: - path = os.path.join(path, appname) - else: - # XDG default for $XDG_DATA_DIRS - # only first, if multipath is False - path = os.getenv('XDG_DATA_DIRS', - os.pathsep.join(['/usr/local/share', '/usr/share'])) - pathlist = [os.path.expanduser(x.rstrip(os.sep)) for x in path.split(os.pathsep)] - if appname: - if version: - appname = os.path.join(appname, version) - pathlist = [os.sep.join([x, appname]) for x in pathlist] - - if multipath: - path = os.pathsep.join(pathlist) - else: - path = pathlist[0] - return path - - if appname and version: - path = os.path.join(path, version) - return path - - -def user_config_dir(appname=None, appauthor=None, version=None, roaming=False): - r"""Return full path to the user-specific config dir for this application. - - "appname" is the name of application. - If None, just the system directory is returned. - "appauthor" (only used on Windows) is the name of the - appauthor or distributing body for this application. Typically - it is the owning company name. This falls back to appname. You may - pass False to disable it. - "version" is an optional version path element to append to the - path. You might want to use this if you want multiple versions - of your app to be able to run independently. If used, this - would typically be ".". - Only applied when appname is present. - "roaming" (boolean, default False) can be set True to use the Windows - roaming appdata directory. That means that for users on a Windows - network setup for roaming profiles, this user data will be - sync'd on login. See - - for a discussion of issues. - - Typical user config directories are: - Mac OS X: same as user_data_dir - Unix: ~/.config/ # or in $XDG_CONFIG_HOME, if defined - Win *: same as user_data_dir - - For Unix, we follow the XDG spec and support $XDG_CONFIG_HOME. - That means, by default "~/.config/". - """ - if system in ["win32", "darwin"]: - path = user_data_dir(appname, appauthor, None, roaming) - else: - path = os.getenv('XDG_CONFIG_HOME', os.path.expanduser("~/.config")) - if appname: - path = os.path.join(path, appname) - if appname and version: - path = os.path.join(path, version) - return path - - -def site_config_dir(appname=None, appauthor=None, version=None, multipath=False): - r"""Return full path to the user-shared data dir for this application. - - "appname" is the name of application. - If None, just the system directory is returned. - "appauthor" (only used on Windows) is the name of the - appauthor or distributing body for this application. Typically - it is the owning company name. This falls back to appname. You may - pass False to disable it. - "version" is an optional version path element to append to the - path. You might want to use this if you want multiple versions - of your app to be able to run independently. If used, this - would typically be ".". - Only applied when appname is present. - "multipath" is an optional parameter only applicable to *nix - which indicates that the entire list of config dirs should be - returned. By default, the first item from XDG_CONFIG_DIRS is - returned, or '/etc/xdg/', if XDG_CONFIG_DIRS is not set - - Typical site config directories are: - Mac OS X: same as site_data_dir - Unix: /etc/xdg/ or $XDG_CONFIG_DIRS[i]/ for each value in - $XDG_CONFIG_DIRS - Win *: same as site_data_dir - Vista: (Fail! "C:\ProgramData" is a hidden *system* directory on Vista.) - - For Unix, this is using the $XDG_CONFIG_DIRS[0] default, if multipath=False - - WARNING: Do not use this on Windows. See the Vista-Fail note above for why. - """ - if system in ["win32", "darwin"]: - path = site_data_dir(appname, appauthor) - if appname and version: - path = os.path.join(path, version) - else: - # XDG default for $XDG_CONFIG_DIRS - # only first, if multipath is False - path = os.getenv('XDG_CONFIG_DIRS', '/etc/xdg') - pathlist = [os.path.expanduser(x.rstrip(os.sep)) for x in path.split(os.pathsep)] - if appname: - if version: - appname = os.path.join(appname, version) - pathlist = [os.sep.join([x, appname]) for x in pathlist] - - if multipath: - path = os.pathsep.join(pathlist) - else: - path = pathlist[0] - return path - - -def user_cache_dir(appname=None, appauthor=None, version=None, opinion=True): - r"""Return full path to the user-specific cache dir for this application. - - "appname" is the name of application. - If None, just the system directory is returned. - "appauthor" (only used on Windows) is the name of the - appauthor or distributing body for this application. Typically - it is the owning company name. This falls back to appname. You may - pass False to disable it. - "version" is an optional version path element to append to the - path. You might want to use this if you want multiple versions - of your app to be able to run independently. If used, this - would typically be ".". - Only applied when appname is present. - "opinion" (boolean) can be False to disable the appending of - "Cache" to the base app data dir for Windows. See - discussion below. - - Typical user cache directories are: - Mac OS X: ~/Library/Caches/ - Unix: ~/.cache/ (XDG default) - Win XP: C:\Documents and Settings\\Local Settings\Application Data\\\Cache - Vista: C:\Users\\AppData\Local\\\Cache - - On Windows the only suggestion in the MSDN docs is that local settings go in - the `CSIDL_LOCAL_APPDATA` directory. This is identical to the non-roaming - app data dir (the default returned by `user_data_dir` above). Apps typically - put cache data somewhere *under* the given dir here. Some examples: - ...\Mozilla\Firefox\Profiles\\Cache - ...\Acme\SuperApp\Cache\1.0 - OPINION: This function appends "Cache" to the `CSIDL_LOCAL_APPDATA` value. - This can be disabled with the `opinion=False` option. - """ - if system == "win32": - if appauthor is None: - appauthor = appname - path = os.path.normpath(_get_win_folder("CSIDL_LOCAL_APPDATA")) - if appname: - if appauthor is not False: - path = os.path.join(path, appauthor, appname) - else: - path = os.path.join(path, appname) - if opinion: - path = os.path.join(path, "Cache") - elif system == 'darwin': - path = os.path.expanduser('~/Library/Caches') - if appname: - path = os.path.join(path, appname) - else: - path = os.getenv('XDG_CACHE_HOME', os.path.expanduser('~/.cache')) - if appname: - path = os.path.join(path, appname) - if appname and version: - path = os.path.join(path, version) - return path - - -def user_state_dir(appname=None, appauthor=None, version=None, roaming=False): - r"""Return full path to the user-specific state dir for this application. - - "appname" is the name of application. - If None, just the system directory is returned. - "appauthor" (only used on Windows) is the name of the - appauthor or distributing body for this application. Typically - it is the owning company name. This falls back to appname. You may - pass False to disable it. - "version" is an optional version path element to append to the - path. You might want to use this if you want multiple versions - of your app to be able to run independently. If used, this - would typically be ".". - Only applied when appname is present. - "roaming" (boolean, default False) can be set True to use the Windows - roaming appdata directory. That means that for users on a Windows - network setup for roaming profiles, this user data will be - sync'd on login. See - - for a discussion of issues. - - Typical user state directories are: - Mac OS X: same as user_data_dir - Unix: ~/.local/state/ # or in $XDG_STATE_HOME, if defined - Win *: same as user_data_dir - - For Unix, we follow this Debian proposal - to extend the XDG spec and support $XDG_STATE_HOME. - - That means, by default "~/.local/state/". - """ - if system in ["win32", "darwin"]: - path = user_data_dir(appname, appauthor, None, roaming) - else: - path = os.getenv('XDG_STATE_HOME', os.path.expanduser("~/.local/state")) - if appname: - path = os.path.join(path, appname) - if appname and version: - path = os.path.join(path, version) - return path - - -def user_log_dir(appname=None, appauthor=None, version=None, opinion=True): - r"""Return full path to the user-specific log dir for this application. - - "appname" is the name of application. - If None, just the system directory is returned. - "appauthor" (only used on Windows) is the name of the - appauthor or distributing body for this application. Typically - it is the owning company name. This falls back to appname. You may - pass False to disable it. - "version" is an optional version path element to append to the - path. You might want to use this if you want multiple versions - of your app to be able to run independently. If used, this - would typically be ".". - Only applied when appname is present. - "opinion" (boolean) can be False to disable the appending of - "Logs" to the base app data dir for Windows, and "log" to the - base cache dir for Unix. See discussion below. - - Typical user log directories are: - Mac OS X: ~/Library/Logs/ - Unix: ~/.cache//log # or under $XDG_CACHE_HOME if defined - Win XP: C:\Documents and Settings\\Local Settings\Application Data\\\Logs - Vista: C:\Users\\AppData\Local\\\Logs - - On Windows the only suggestion in the MSDN docs is that local settings - go in the `CSIDL_LOCAL_APPDATA` directory. (Note: I'm interested in - examples of what some windows apps use for a logs dir.) - - OPINION: This function appends "Logs" to the `CSIDL_LOCAL_APPDATA` - value for Windows and appends "log" to the user cache dir for Unix. - This can be disabled with the `opinion=False` option. - """ - if system == "darwin": - path = os.path.join( - os.path.expanduser('~/Library/Logs'), - appname) - elif system == "win32": - path = user_data_dir(appname, appauthor, version) - version = False - if opinion: - path = os.path.join(path, "Logs") - else: - path = user_cache_dir(appname, appauthor, version) - version = False - if opinion: - path = os.path.join(path, "log") - if appname and version: - path = os.path.join(path, version) - return path - - -class AppDirs(object): - """Convenience wrapper for getting application dirs.""" - def __init__(self, appname=None, appauthor=None, version=None, - roaming=False, multipath=False): - self.appname = appname - self.appauthor = appauthor - self.version = version - self.roaming = roaming - self.multipath = multipath - - @property - def user_data_dir(self): - return user_data_dir(self.appname, self.appauthor, - version=self.version, roaming=self.roaming) - - @property - def site_data_dir(self): - return site_data_dir(self.appname, self.appauthor, - version=self.version, multipath=self.multipath) - - @property - def user_config_dir(self): - return user_config_dir(self.appname, self.appauthor, - version=self.version, roaming=self.roaming) - - @property - def site_config_dir(self): - return site_config_dir(self.appname, self.appauthor, - version=self.version, multipath=self.multipath) - - @property - def user_cache_dir(self): - return user_cache_dir(self.appname, self.appauthor, - version=self.version) - - @property - def user_state_dir(self): - return user_state_dir(self.appname, self.appauthor, - version=self.version) - - @property - def user_log_dir(self): - return user_log_dir(self.appname, self.appauthor, - version=self.version) - - -#---- internal support stuff - -def _get_win_folder_from_registry(csidl_name): - """This is a fallback technique at best. I'm not sure if using the - registry for this guarantees us the correct answer for all CSIDL_* - names. - """ - if PY3: - import winreg as _winreg - else: - import _winreg - - shell_folder_name = { - "CSIDL_APPDATA": "AppData", - "CSIDL_COMMON_APPDATA": "Common AppData", - "CSIDL_LOCAL_APPDATA": "Local AppData", - }[csidl_name] - - key = _winreg.OpenKey( - _winreg.HKEY_CURRENT_USER, - r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders" - ) - dir, type = _winreg.QueryValueEx(key, shell_folder_name) - return dir - - -def _get_win_folder_with_pywin32(csidl_name): - from win32com.shell import shellcon, shell - dir = shell.SHGetFolderPath(0, getattr(shellcon, csidl_name), 0, 0) - # Try to make this a unicode path because SHGetFolderPath does - # not return unicode strings when there is unicode data in the - # path. - try: - dir = unicode(dir) - - # Downgrade to short path name if have highbit chars. See - # . - has_high_char = False - for c in dir: - if ord(c) > 255: - has_high_char = True - break - if has_high_char: - try: - import win32api - dir = win32api.GetShortPathName(dir) - except ImportError: - pass - except UnicodeError: - pass - return dir - - -def _get_win_folder_with_ctypes(csidl_name): - import ctypes - - csidl_const = { - "CSIDL_APPDATA": 26, - "CSIDL_COMMON_APPDATA": 35, - "CSIDL_LOCAL_APPDATA": 28, - }[csidl_name] - - buf = ctypes.create_unicode_buffer(1024) - ctypes.windll.shell32.SHGetFolderPathW(None, csidl_const, None, 0, buf) - - # Downgrade to short path name if have highbit chars. See - # . - has_high_char = False - for c in buf: - if ord(c) > 255: - has_high_char = True - break - if has_high_char: - buf2 = ctypes.create_unicode_buffer(1024) - if ctypes.windll.kernel32.GetShortPathNameW(buf.value, buf2, 1024): - buf = buf2 - - return buf.value - -def _get_win_folder_with_jna(csidl_name): - import array - from com.sun import jna - from com.sun.jna.platform import win32 - - buf_size = win32.WinDef.MAX_PATH * 2 - buf = array.zeros('c', buf_size) - shell = win32.Shell32.INSTANCE - shell.SHGetFolderPath(None, getattr(win32.ShlObj, csidl_name), None, win32.ShlObj.SHGFP_TYPE_CURRENT, buf) - dir = jna.Native.toString(buf.tostring()).rstrip("\0") - - # Downgrade to short path name if have highbit chars. See - # . - has_high_char = False - for c in dir: - if ord(c) > 255: - has_high_char = True - break - if has_high_char: - buf = array.zeros('c', buf_size) - kernel = win32.Kernel32.INSTANCE - if kernel.GetShortPathName(dir, buf, buf_size): - dir = jna.Native.toString(buf.tostring()).rstrip("\0") - - return dir - -if system == "win32": - try: - import win32com.shell - _get_win_folder = _get_win_folder_with_pywin32 - except ImportError: - try: - from ctypes import windll - _get_win_folder = _get_win_folder_with_ctypes - except ImportError: - try: - import com.sun.jna - _get_win_folder = _get_win_folder_with_jna - except ImportError: - _get_win_folder = _get_win_folder_from_registry - - -#---- self test code - -if __name__ == "__main__": - appname = "MyApp" - appauthor = "MyCompany" - - props = ("user_data_dir", - "user_config_dir", - "user_cache_dir", - "user_state_dir", - "user_log_dir", - "site_data_dir", - "site_config_dir") - - print("-- app dirs %s --" % __version__) - - print("-- app dirs (with optional 'version')") - dirs = AppDirs(appname, appauthor, version="1.0") - for prop in props: - print("%s: %s" % (prop, getattr(dirs, prop))) - - print("\n-- app dirs (without optional 'version')") - dirs = AppDirs(appname, appauthor) - for prop in props: - print("%s: %s" % (prop, getattr(dirs, prop))) - - print("\n-- app dirs (without optional 'appauthor')") - dirs = AppDirs(appname) - for prop in props: - print("%s: %s" % (prop, getattr(dirs, prop))) - - print("\n-- app dirs (with disabled 'appauthor')") - dirs = AppDirs(appname, appauthor=False) - for prop in props: - print("%s: %s" % (prop, getattr(dirs, prop))) diff --git a/spaces/autoevaluate/error-analysis/error_analysis/utils/__init__.py b/spaces/autoevaluate/error-analysis/error_analysis/utils/__init__.py deleted file mode 100644 index 7b1df2248f43f104417dd99b5f495d0830c1ce58..0000000000000000000000000000000000000000 --- a/spaces/autoevaluate/error-analysis/error_analysis/utils/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .style_hacks import * \ No newline at end of file diff --git a/spaces/awacke1/AdventureGame/README.md b/spaces/awacke1/AdventureGame/README.md deleted file mode 100644 index 5198deb4c9d816169b1c76365490fdcb71805a0a..0000000000000000000000000000000000000000 --- a/spaces/awacke1/AdventureGame/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: AdventureGame -emoji: 🐠 -colorFrom: pink -colorTo: yellow -sdk: streamlit -sdk_version: 1.17.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/awacke1/Generative-AI-Procedure-Cost-Summary/README.md b/spaces/awacke1/Generative-AI-Procedure-Cost-Summary/README.md deleted file mode 100644 index 798d76d275947e70b8c45b5f2da0700dac40dc7b..0000000000000000000000000000000000000000 --- a/spaces/awacke1/Generative-AI-Procedure-Cost-Summary/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: AIPRO-ChatGPT-Procedure-Cost-Body-Map -emoji: ⚕️AIPRO👩‍⚕️ -colorFrom: gray -colorTo: red -sdk: static -pinned: false -license: mit ---- -BodyMapCPTClinical diff --git a/spaces/awacke1/Lunar.Lander.Asteroids.Continual.Self.Play/style.css b/spaces/awacke1/Lunar.Lander.Asteroids.Continual.Self.Play/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/spaces/awacke1/Lunar.Lander.Asteroids.Continual.Self.Play/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -} diff --git a/spaces/awacke1/THREEJS-ChatGPT-ASR-Wikipedia-Twitter-Sentiment-FactChecker-VoiceClone/README.md b/spaces/awacke1/THREEJS-ChatGPT-ASR-Wikipedia-Twitter-Sentiment-FactChecker-VoiceClone/README.md deleted file mode 100644 index 9680cd4a4130e1480905472a2a03ab50bf833db1..0000000000000000000000000000000000000000 --- a/spaces/awacke1/THREEJS-ChatGPT-ASR-Wikipedia-Twitter-Sentiment-FactChecker-VoiceClone/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: THREEJS ChatGPT ASR Wikipedia Twitter Sentiment FactChecker VoiceClone -emoji: 😻 -colorFrom: purple -colorTo: yellow -sdk: static -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/Cloth.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/Cloth.js deleted file mode 100644 index 383f87f52690d3b5caf545e716d6bbf873defe84..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/examples/js/Cloth.js +++ /dev/null @@ -1,340 +0,0 @@ -/* - * Cloth Simulation using a relaxed constraints solver - */ - -// Suggested Readings - -// Advanced Character Physics by Thomas Jakobsen Character -// http://freespace.virgin.net/hugo.elias/models/m_cloth.htm -// http://en.wikipedia.org/wiki/Cloth_modeling -// http://cg.alexandra.dk/tag/spring-mass-system/ -// Real-time Cloth Animation http://www.darwin3d.com/gamedev/articles/col0599.pdf - -var DAMPING = 0.03; -var DRAG = 1 - DAMPING; -var MASS = 0.1; -var restDistance = 25; - -var xSegs = 10; -var ySegs = 10; - -var clothFunction = plane( restDistance * xSegs, restDistance * ySegs ); - -var cloth = new Cloth( xSegs, ySegs ); - -var GRAVITY = 981 * 1.4; -var gravity = new THREE.Vector3( 0, - GRAVITY, 0 ).multiplyScalar( MASS ); - - -var TIMESTEP = 18 / 1000; -var TIMESTEP_SQ = TIMESTEP * TIMESTEP; - -var pins = []; - - -var wind = true; -var windStrength = 2; -var windForce = new THREE.Vector3( 0, 0, 0 ); - -var ballPosition = new THREE.Vector3( 0, - 45, 0 ); -var ballSize = 60; //40 - -var tmpForce = new THREE.Vector3(); - -var lastTime; - - -function plane( width, height ) { - - return function ( u, v, target ) { - - var x = ( u - 0.5 ) * width; - var y = ( v + 0.5 ) * height; - var z = 0; - - target.set( x, y, z ); - - }; - -} - -function Particle( x, y, z, mass ) { - - this.position = new THREE.Vector3(); - this.previous = new THREE.Vector3(); - this.original = new THREE.Vector3(); - this.a = new THREE.Vector3( 0, 0, 0 ); // acceleration - this.mass = mass; - this.invMass = 1 / mass; - this.tmp = new THREE.Vector3(); - this.tmp2 = new THREE.Vector3(); - - // init - - clothFunction( x, y, this.position ); // position - clothFunction( x, y, this.previous ); // previous - clothFunction( x, y, this.original ); - -} - -// Force -> Acceleration - -Particle.prototype.addForce = function ( force ) { - - this.a.add( - this.tmp2.copy( force ).multiplyScalar( this.invMass ) - ); - -}; - - -// Performs Verlet integration - -Particle.prototype.integrate = function ( timesq ) { - - var newPos = this.tmp.subVectors( this.position, this.previous ); - newPos.multiplyScalar( DRAG ).add( this.position ); - newPos.add( this.a.multiplyScalar( timesq ) ); - - this.tmp = this.previous; - this.previous = this.position; - this.position = newPos; - - this.a.set( 0, 0, 0 ); - -}; - - -var diff = new THREE.Vector3(); - -function satisfyConstraints( p1, p2, distance ) { - - diff.subVectors( p2.position, p1.position ); - var currentDist = diff.length(); - if ( currentDist === 0 ) return; // prevents division by 0 - var correction = diff.multiplyScalar( 1 - distance / currentDist ); - var correctionHalf = correction.multiplyScalar( 0.5 ); - p1.position.add( correctionHalf ); - p2.position.sub( correctionHalf ); - -} - - -function Cloth( w, h ) { - - w = w || 10; - h = h || 10; - this.w = w; - this.h = h; - - var particles = []; - var constraints = []; - - var u, v; - - // Create particles - for ( v = 0; v <= h; v ++ ) { - - for ( u = 0; u <= w; u ++ ) { - - particles.push( - new Particle( u / w, v / h, 0, MASS ) - ); - - } - - } - - // Structural - - for ( v = 0; v < h; v ++ ) { - - for ( u = 0; u < w; u ++ ) { - - constraints.push( [ - particles[ index( u, v ) ], - particles[ index( u, v + 1 ) ], - restDistance - ] ); - - constraints.push( [ - particles[ index( u, v ) ], - particles[ index( u + 1, v ) ], - restDistance - ] ); - - } - - } - - for ( u = w, v = 0; v < h; v ++ ) { - - constraints.push( [ - particles[ index( u, v ) ], - particles[ index( u, v + 1 ) ], - restDistance - - ] ); - - } - - for ( v = h, u = 0; u < w; u ++ ) { - - constraints.push( [ - particles[ index( u, v ) ], - particles[ index( u + 1, v ) ], - restDistance - ] ); - - } - - - // While many systems use shear and bend springs, - // the relaxed constraints model seems to be just fine - // using structural springs. - // Shear - // var diagonalDist = Math.sqrt(restDistance * restDistance * 2); - - - // for (v=0;v 0 ) { - - this.normal = new Float32Array( numVertex * 3 ); - ifs.SetNormal( this.normal ); - - } - - for ( i = 0; i < uvIDs.length; i ++ ) { - - this.uv[ i ] = new Float32Array( numVertex * 2 ); - ifs.SetFloatAttribute( uvIDs[ i ], this.uv[ i ] ); - - } - - if ( jointID !== undefined ) { - - this.jointPerVertex = ifs.GetIntAttributeDim( jointID ); - - this.joint = new Uint16Array( numVertex * this.jointPerVertex ); - this.weight = new Float32Array( numVertex * this.jointPerVertex ); - - ifs.SetIntAttribute( jointID, this.joint ); - ifs.SetFloatAttribute( weightID, this.weight ); - - } - - // decode mesh - - decoder.DecodePlayload( ifs, bstream ); - -}; - -SEA3D.GeometryGC.prototype.type = "s3D"; - -// -// Extension -// - -THREE.SEA3D.EXTENSIONS_LOADER.push( { - - setTypeRead: function () { - - this.file.addClass( SEA3D.GeometryGC, true ); - - this.file.typeRead[ SEA3D.GeometryGC.prototype.type ] = this.readGeometryBuffer; - - } - -} ); diff --git a/spaces/bigcode/OctoCoder-Demo/share_btn.py b/spaces/bigcode/OctoCoder-Demo/share_btn.py deleted file mode 100644 index 841ecb88da1f60aad7e9a6f91be0c777b162e142..0000000000000000000000000000000000000000 --- a/spaces/bigcode/OctoCoder-Demo/share_btn.py +++ /dev/null @@ -1,112 +0,0 @@ -community_icon_html = """""" - -loading_icon_html = """""" - -share_js = """async () => { - async function uploadFile(file){ - const UPLOAD_URL = 'https://huggingface.co/uploads'; - const response = await fetch(UPLOAD_URL, { - method: 'POST', - headers: { - 'Content-Type': file.type, - 'X-Requested-With': 'XMLHttpRequest', - }, - body: file, /// <- File inherits from Blob - }); - const url = await response.text(); - return url; - } - - async function getInputImgFile(imgEl){ - const res = await fetch(imgEl.src); - const blob = await res.blob(); - const imgId = Date.now() % 200; - const isPng = imgEl.src.startsWith(`data:image/png`); - if(isPng){ - const fileName = `sd-perception-${{imgId}}.png`; - return new File([blob], fileName, { type: 'image/png' }); - }else{ - const fileName = `sd-perception-${{imgId}}.jpg`; - return new File([blob], fileName, { type: 'image/jpeg' }); - } - } - - // const gradioEl = document.querySelector('body > gradio-app'); - const gradioEl = document.querySelector("gradio-app"); - const inputTxt = gradioEl.querySelector('#q-input textarea').value; - let outputTxt = gradioEl.querySelector('#q-output .codemirror-wrapper .cm-scroller > div:nth-of-type(2)').innerText; - outputTxt = `
${outputTxt}
` - - const titleLength = 150; - let titleTxt = inputTxt; - if(titleTxt.length > titleLength){ - titleTxt = titleTxt.slice(0, titleLength) + ' ...'; - } - - const shareBtnEl = gradioEl.querySelector('#share-btn'); - const shareIconEl = gradioEl.querySelector('#share-btn-share-icon'); - const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon'); - - if(!inputTxt || !outputTxt){ - return; - }; - - shareBtnEl.style.pointerEvents = 'none'; - shareIconEl.style.display = 'none'; - loadingIconEl.style.removeProperty('display'); - - const descriptionMd = `### Question: -${inputTxt} - -### Answer: - -${outputTxt}`; - - const params = { - title: titleTxt, - description: descriptionMd, - }; - - const paramsStr = Object.entries(params) - .map(([key, value]) => `${encodeURIComponent(key)}=${encodeURIComponent(value)}`) - .join('&'); - - window.open(`https://huggingface.co/spaces/bigcode/OctoCoder-Demo/discussions/new?${paramsStr}`, '_blank'); - - shareBtnEl.style.removeProperty('pointer-events'); - shareIconEl.style.removeProperty('display'); - loadingIconEl.style.display = 'none'; -}""" - -share_btn_css = """ -a {text-decoration-line: underline; font-weight: 600;} -.animate-spin { - animation: spin 1s linear infinite; -} -@keyframes spin { - from { transform: rotate(0deg); } - to { transform: rotate(360deg); } -} -#share-btn-container { - display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; -} -#share-btn { - all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important; -} -#share-btn * { - all: unset; -} -#share-btn-container div:nth-child(-n+2){ - width: auto !important; - min-height: 0px !important; -} -#share-btn-container .wrap { - display: none !important; -} -""" diff --git a/spaces/bigjoker/stable-diffusion-webui/test/__init__.py b/spaces/bigjoker/stable-diffusion-webui/test/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/bioriAsaeru/text-to-voice/Final Fantasy 13 1080p 60 Fps.md b/spaces/bioriAsaeru/text-to-voice/Final Fantasy 13 1080p 60 Fps.md deleted file mode 100644 index df2ad6a54b3532cfe22b788870368a972a4e42f5..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Final Fantasy 13 1080p 60 Fps.md +++ /dev/null @@ -1,76 +0,0 @@ - -

How to Play Final Fantasy XIII in 1080p 60 FPS on PC

-

Final Fantasy XIII is one of the most popular and critically acclaimed games in the Final Fantasy series. It was originally released for PlayStation 3 and Xbox 360 in 2009, and later ported to PC in 2014. However, many PC gamers were disappointed by the poor performance and graphics quality of the PC version, which was locked at 720p resolution and had frequent frame drops.

-

Fortunately, there are some ways to improve the PC experience of Final Fantasy XIII and enjoy it in glorious 1080p 60 FPS. In this article, we will show you how to tweak the game settings, use third-party tools, and optimize your hardware to achieve the best possible results.

-

final fantasy 13 1080p 60 fps


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- -

Tweak the Game Settings

-

The first thing you need to do is to access the game's configuration file, which is located in C:\Users\YourUserName\Documents\My Games\FINAL FANTASY XIII\ffxiiiimg\graphics.ini. You can open it with any text editor, such as Notepad. Here, you can change some parameters that affect the game's resolution, frame rate, anti-aliasing, shadow quality, and more.

-

The most important parameter is RenderResolution, which determines the internal resolution of the game. By default, it is set to 1280x720, which is too low for modern monitors. You can change it to 1920x1080 or higher, depending on your preference and hardware capability. However, keep in mind that increasing the resolution will also increase the GPU load and may cause performance issues.

-

Another important parameter is RefreshRate, which determines the maximum frame rate of the game. By default, it is set to 59.94, which is close to 60 FPS but not quite there. You can change it to 60 or higher, depending on your monitor's refresh rate and hardware capability. However, keep in mind that increasing the frame rate will also increase the CPU load and may cause performance issues.

-

Other parameters that you can tweak are AntialiasQuality, ShadowQuality, ShadowResolution, TextureQuality, and AnisotropicFilteringQuality. These affect the visual quality of the game's graphics, but also have an impact on performance. You can experiment with different values and see what works best for you.

- -

Use Third-Party Tools

-

If tweaking the game settings is not enough for you, you can also use some third-party tools that enhance the game's graphics and performance. One of them is GeDoSaTo, which is a generic downsampling tool that allows you to run games at higher resolutions than your monitor supports and then downsample them to your native resolution. This results in sharper and smoother graphics, as well as better anti-aliasing.

-

To use GeDoSaTo with Final Fantasy XIII, you need to download it from here: https://github.com/PeterTh/gedosato/releases -and install it on your PC. Then, you need to run it as administrator and enable it for Final Fantasy XIII by checking the box next to its name in the whitelist tab. You can also customize some settings in the config tab, such as resolution scaling factor, post-processing effects, texture filtering, etc.

-

Another tool that you can use with Final Fantasy XIII is SweetFX, which is a post-processing injector that adds various effects to games, such as color correction, bloom, sharpening, etc. You can download it from here: https://www.guru3d.com/files-details/sweetfx-shader-suite-download.html -and extract it to your game folder (where ffxiiiimg.exe is located). Then, you need to run SweetFX Configurator.exe and add Final Fantasy XIII to its list by clicking on Add new game. You can also customize some settings in the SweetFX tab, such as SMAA quality, lumasharpen strength, vibrance level, etc.

-

- -

Optimize Your Hardware

-

The last thing you need to do is to optimize your hardware for playing Final Fantasy XIII in 1080p 60 FPS. This means that you need to have a powerful CPU and GPU that can handle the increased load of running the game at higher resolution and frame rate. You also need to have enough RAM and disk space for smooth loading times.

-

The minimum system requirements for Final Fantasy XIII are:

-
    -
  • CPU: Intel Core 2 Duo E4600 or AMD Athlon X2 Dual Core 5200+
    • -
  • GPU: NVIDIA GeForce GT 240 or AMD Radeon HD 5450
    • -
  • RAM: 1 GB
    • -
  • Disk space: 60 GB
    • -
-

The recommended system requirements for Final Fantasy XIII are:

-
    -
  • CPU: Intel Core i5-750 or AMD Phenom II X4 965
    • -
  • GPU: NVIDIA GeForce GTX 460 or AMD Radeon HD 5870
    • -
  • RAM: 4 GB
    • -
  • Disk space: 60 GB
    • -
-

If your hardware meets or exceeds these requirements, you should be able to play Final Fantasy XIII in 1080p 60 FPS without any major problems. However, if your hardware falls short of these requirements, you may need to upgrade some components or lower some settings to achieve a stable performance.

- -

Conclusion

-

In this article, we have shown you how to play Final Fantasy XIII in 1080p 60 FPS on PC by tweaking the game settings, using third-party tools, and optimizing your hardware. We hope that this guide has been helpful for you and that you can enjoy this amazing game in its full glory.

-

Compare the PC and Console Versions

-

One of the reasons why many PC gamers want to play Final Fantasy XIII in 1080p 60 FPS is to enjoy the superior graphics and performance compared to the console versions. The game was originally developed for PlayStation 3 and Xbox 360, which had limited hardware capabilities and could not run the game at its full potential.

-

The PlayStation 3 version of Final Fantasy XIII had a native resolution of 1280x720, but used a technique called quincunx anti-aliasing, which blurred the image and reduced the sharpness. The Xbox 360 version had a lower resolution of 1024x576, but used edge anti-aliasing, which preserved some details but introduced jagged edges. Both versions had a frame rate cap of 30 FPS, which sometimes dropped below that during intense scenes.

-

The PC version of Final Fantasy XIII, on the other hand, has no resolution or frame rate cap, and can run at any setting that your hardware can handle. With the tweaks and tools mentioned above, you can play the game in 1080p or higher, with smooth 60 FPS or higher, and with improved anti-aliasing and shadow quality. You can also enjoy the game with Japanese voice-overs and subtitles, which were not available in the console versions.

- -

Enjoy the Story and Gameplay of Final Fantasy XIII

-

Of course, playing Final Fantasy XIII in 1080p 60 FPS is not only about the graphics and performance, but also about the story and gameplay of this epic RPG. Final Fantasy XIII is the first game in the Fabula Nova Crystallis sub-series, which shares a common mythology but has different settings and characters. The game follows the journey of six protagonists who are branded as l'Cie by a fal'Cie, a god-like being that controls their fate.

-

The game features a fast-paced and strategic combat system called the Paradigm System, which allows you to switch between different roles and abilities for your characters. The game also features a linear progression system called the Crystarium System, which lets you upgrade your characters' stats and skills by spending points earned from battles. The game also features various side quests, mini-games, and optional bosses that add more depth and challenge to the gameplay.

-

Final Fantasy XIII is a game that has received mixed reviews from critics and fans alike. Some praised its stunning graphics, engaging combat system, and memorable soundtrack, while others criticized its linear structure, lack of exploration, and convoluted story. However, regardless of your opinion on the game, playing it in 1080p 60 FPS on PC will surely enhance your experience and make you appreciate its beauty and charm.

- -

Conclusion

-

In this article, we have shown you how to play Final Fantasy XIII in 1080p 60 FPS on PC by tweaking the game settings, using third-party tools, and optimizing your hardware. We have also compared the PC and console versions of the game, and highlighted some of its story and gameplay features. We hope that this guide has been helpful for you and that you can enjoy this amazing game in its full glory.

-

Troubleshoot the Common Issues

-

Even after following the steps above, you may still encounter some issues while playing Final Fantasy XIII in 1080p 60 FPS on PC. Some of the common issues are black bars, crashes, stuttering, and audio desync. In this section, we will show you how to troubleshoot these issues and fix them.

-

One of the most annoying issues is the black bars that appear on the sides of the screen when playing the game in widescreen resolutions. This is because the game was designed for a 16:9 aspect ratio, and does not support other ratios such as 21:9 or 32:9. To fix this issue, you can use a tool called Flawless Widescreen, which is a plugin-based system that allows you to adjust the aspect ratio of games. You can download it from here: https://www.flawlesswidescreen.org/ -and install it on your PC. Then, you need to run it as administrator and enable it for Final Fantasy XIII by checking the box next to its name in the supported games tab. You can also customize some settings in the plugin settings tab, such as horizontal FOV, HUD scaling, etc.

-

Another common issue is the game crashing randomly or at certain points. This can be caused by various factors, such as incompatible drivers, corrupted files, insufficient memory, etc. To fix this issue, you can try some of the following solutions:

-
    -
  • Update your graphics card drivers to the latest version.
    • -
  • Verify the integrity of the game files through Steam.
    • -
  • Run the game as administrator and in compatibility mode for Windows 7.
    • -
  • Disable any background programs that may interfere with the game.
    • -
  • Increase your virtual memory size or add more RAM to your PC.
    • -
- -

Enjoy the Other Games in the Series

-

Final Fantasy XIII is not the only game in the Final Fantasy series that you can play on PC. In fact, there are many other games in the series that are available on Steam, such as Final Fantasy VII, Final Fantasy VIII, Final Fantasy IX, Final Fantasy X/X-2 HD Remaster, Final Fantasy XII The Zodiac Age, Final Fantasy XV Windows Edition, and more. These games offer different stories, characters, worlds, and gameplay mechanics that will appeal to different tastes and preferences.

-

If you enjoyed playing Final Fantasy XIII in 1080p 60 FPS on PC, you may also want to check out its two sequels: Final Fantasy XIII-2 and Lightning Returns: Final Fantasy XIII. These games continue the story of Final Fantasy XIII and feature some improvements and changes in the graphics and gameplay. For example, Final Fantasy XIII-2 introduces a time travel mechanic that allows you to explore different eras and locations, while Lightning Returns: Final Fantasy XIII features a dynamic day-night cycle and a customizable combat system.

-

Final Fantasy XIII is a game that has a lot to offer to PC gamers who love RPGs. By playing it in 1080p 60 FPS on PC, you can experience its stunning graphics, engaging combat system, and memorable soundtrack in a whole new way. You can also enjoy its sequels and other games in the series that are available on Steam. Whether you are a fan of Final Fantasy or not, you will surely find something to enjoy in this epic game.

- -

Conclusion

-

In this article, we have shown you how to play Final Fantasy XIII in 1080p 60 FPS on PC by tweaking the game settings, using third-party tools, and optimizing your hardware. We have also compared the PC and console versions of the game, highlighted some of its story and gameplay features, troubleshooted some of its common issues, and recommended some other games in the series that you can play on PC. We hope that this guide has been helpful for you and that you can enjoy this amazing game in its full glory.

-

Conclusion

-

In this article, we have shown you how to play Final Fantasy XIII in 1080p 60 FPS on PC by tweaking the game settings, using third-party tools, and optimizing your hardware. We have also compared the PC and console versions of the game, highlighted some of its story and gameplay features, troubleshooted some of its common issues, and recommended some other games in the series that you can play on PC. We hope that this guide has been helpful for you and that you can enjoy this amazing game in its full glory.

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\ No newline at end of file diff --git a/spaces/bioriAsaeru/text-to-voice/Jenny 9 Yo Full Version.flv Herrunterladen Paint !!HOT!!.md b/spaces/bioriAsaeru/text-to-voice/Jenny 9 Yo Full Version.flv Herrunterladen Paint !!HOT!!.md deleted file mode 100644 index 4f05c3ef26e8ff1ea561bfc9d71a03b1294f84c1..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Jenny 9 Yo Full Version.flv Herrunterladen Paint !!HOT!!.md +++ /dev/null @@ -1,6 +0,0 @@ -

Jenny 9 Yo Full Version.flv herrunterladen paint


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diff --git a/spaces/cakiki/tensorflow-coder/app.py b/spaces/cakiki/tensorflow-coder/app.py deleted file mode 100644 index 0aa9bbd65b89553ccde15a40f5aa407a1a92a5a9..0000000000000000000000000000000000000000 --- a/spaces/cakiki/tensorflow-coder/app.py +++ /dev/null @@ -1,48 +0,0 @@ -# Based on the following code demo: https://github.com/google-research/tensorflow-coder/blob/master/tf_coder/tf_coder_main.py -import streamlit as st -from tf_coder.value_search import colab_interface, value_search_settings -from streamlit_ace import st_ace -st.set_page_config(page_title="TensorFlow Coder", page_icon='👩‍💻', layout="wide") - -st.title("👩‍💻 TensorFlow Coder") -st.write('#') -st.write("[TensorFlow Coder](https://github.com/google-research/tensorflow-coder) is a program synthesis tool developed at Google Research by Kensen Shi, David Bieber and Rishabh Singh. It takes an example input-output tensor example and attempts to find the combination of TensorFlow ops that capture that transformation. Please cite the authors' [paper](https://github.com/google-research/tensorflow-coder/blob/master/README.md#citation) if you use their tool in your work. Also checkout the TensorFlow [Blog post](https://blog.tensorflow.org/2020/08/introducing-tensorflow-coder-tool.html) for more information and examples.") - -col1, col2, col3 = st.columns([5, 5, 3]) -with col1: - st.write('#### Inputs') - inputs = st_ace(placeholder="The input tensor(s) specified as a dictionary", value="{'rows': [10, 20, 30],\n'cols': [1,2,3,4]}", language="python", theme="solarized_dark", auto_update=True) -with col2: - st.write('#### Output') - output = st_ace(placeholder="The output tensor", value="[[11, 12, 13, 14],\n[21, 22, 23, 24],\n[31, 32, 33, 34]]", language="python", theme="solarized_dark", auto_update=True) -with col3: - st.write('#### Constants') - constants = st_ace(placeholder="Optional list of scalar constants", value="[]", language="python", theme="solarized_dark", auto_update=True) - -st.write("#### Description") -description = st.text_input(label="", placeholder="An optional natural language description of the operation", value="add two vectors with broadcasting to get a matrix") -with st.expander("⚙️ Search Options", expanded=False): - settings_kwargs = dict() - settings_kwargs["require_all_inputs_used"] = st.checkbox("Require All Inputs", value=True) - settings_kwargs["only_minimal_solutions"] = st.checkbox("Only Minimal Solutions", value=False) - settings_kwargs["max_solutions"] = st.slider("Maximum number of solutions", value=1, min_value=1, step=1, max_value=256) - settings_kwargs["timeout"] = st.slider("Timeout in seconds", value=300, min_value=1, step=10, max_value=300) - -if st.button("🔎 Search for Tensor Ops!"): - i = eval(inputs) - o = eval(output) - c = eval(constants) - settings = value_search_settings.from_dict({ - 'timeout': settings_kwargs["timeout"], - 'only_minimal_solutions': settings_kwargs["only_minimal_solutions"], - 'max_solutions': settings_kwargs["max_solutions"], - 'require_all_inputs_used': settings_kwargs["require_all_inputs_used"], - 'require_one_input_used': not settings_kwargs["require_all_inputs_used"], - }) - with st.spinner("Searching for solution..."): - results = colab_interface.run_value_search_from_colab(i, o, c, description, settings) - num_solutions = len(results.solutions) - solution_solutions = " solutions" if num_solutions > 1 else " solution" - st.write(f"Found {num_solutions}{solution_solutions} in {results.total_time:.2f} seconds") - for solution in results.solutions: - st.code(solution.expression, language='python') \ No newline at end of file diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/demo/demo.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/demo/demo.py deleted file mode 100644 index f11fc37c030358a4c8dabf370e9f5d9e8575a61a..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/demo/demo.py +++ /dev/null @@ -1,189 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import argparse -import glob -import multiprocessing as mp -import numpy as np -import os -import tempfile -import time -import warnings -import cv2 -import tqdm - -from detectron2.config import get_cfg -from detectron2.data.detection_utils import read_image -from detectron2.utils.logger import setup_logger - -from predictor import VisualizationDemo - -# constants -WINDOW_NAME = "COCO detections" - - -def setup_cfg(args): - # load config from file and command-line arguments - cfg = get_cfg() - # To use demo for Panoptic-DeepLab, please uncomment the following two lines. - # from detectron2.projects.panoptic_deeplab import add_panoptic_deeplab_config # noqa - # add_panoptic_deeplab_config(cfg) - cfg.merge_from_file(args.config_file) - cfg.merge_from_list(args.opts) - # Set score_threshold for builtin models - cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold - cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold - cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold - cfg.MODEL.DEVICE='cpu' - cfg.freeze() - return cfg - - -def get_parser(): - parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs") - parser.add_argument( - "--config-file", - default="configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml", - metavar="FILE", - help="path to config file", - ) - parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.") - parser.add_argument("--video-input", help="Path to video file.") - parser.add_argument( - "--input", - nargs="+", - help="A list of space separated input images; " - "or a single glob pattern such as 'directory/*.jpg'", - ) - parser.add_argument( - "--output", - help="A file or directory to save output visualizations. " - "If not given, will show output in an OpenCV window.", - ) - - parser.add_argument( - "--confidence-threshold", - type=float, - default=0.5, - help="Minimum score for instance predictions to be shown", - ) - parser.add_argument( - "--opts", - help="Modify config options using the command-line 'KEY VALUE' pairs", - default=[], - nargs=argparse.REMAINDER, - ) - return parser - - -def test_opencv_video_format(codec, file_ext): - with tempfile.TemporaryDirectory(prefix="video_format_test") as dir: - filename = os.path.join(dir, "test_file" + file_ext) - writer = cv2.VideoWriter( - filename=filename, - fourcc=cv2.VideoWriter_fourcc(*codec), - fps=float(30), - frameSize=(10, 10), - isColor=True, - ) - [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)] - writer.release() - if os.path.isfile(filename): - return True - return False - - -if __name__ == "__main__": - mp.set_start_method("spawn", force=True) - args = get_parser().parse_args() - setup_logger(name="fvcore") - logger = setup_logger() - logger.info("Arguments: " + str(args)) - - cfg = setup_cfg(args) - - demo = VisualizationDemo(cfg) - - if args.input: - if len(args.input) == 1: - args.input = glob.glob(os.path.expanduser(args.input[0])) - assert args.input, "The input path(s) was not found" - for path in tqdm.tqdm(args.input, disable=not args.output): - # use PIL, to be consistent with evaluation - img = read_image(path, format="BGR") - start_time = time.time() - predictions, visualized_output = demo.run_on_image(img) - logger.info( - "{}: {} in {:.2f}s".format( - path, - "detected {} instances".format(len(predictions["instances"])) - if "instances" in predictions - else "finished", - time.time() - start_time, - ) - ) - - if args.output: - if os.path.isdir(args.output): - assert os.path.isdir(args.output), args.output - out_filename = os.path.join(args.output, os.path.basename(path)) - else: - assert len(args.input) == 1, "Please specify a directory with args.output" - out_filename = args.output - visualized_output.save(out_filename) - else: - cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) - cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1]) - if cv2.waitKey(0) == 27: - break # esc to quit - elif args.webcam: - assert args.input is None, "Cannot have both --input and --webcam!" - assert args.output is None, "output not yet supported with --webcam!" - cam = cv2.VideoCapture(0) - for vis in tqdm.tqdm(demo.run_on_video(cam)): - cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) - cv2.imshow(WINDOW_NAME, vis) - if cv2.waitKey(1) == 27: - break # esc to quit - cam.release() - cv2.destroyAllWindows() - elif args.video_input: - video = cv2.VideoCapture(args.video_input) - width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) - height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) - frames_per_second = video.get(cv2.CAP_PROP_FPS) - num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) - basename = os.path.basename(args.video_input) - codec, file_ext = ( - ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4") - ) - if codec == ".mp4v": - warnings.warn("x264 codec not available, switching to mp4v") - if args.output: - if os.path.isdir(args.output): - output_fname = os.path.join(args.output, basename) - output_fname = os.path.splitext(output_fname)[0] + file_ext - else: - output_fname = args.output - assert not os.path.isfile(output_fname), output_fname - output_file = cv2.VideoWriter( - filename=output_fname, - # some installation of opencv may not support x264 (due to its license), - # you can try other format (e.g. MPEG) - fourcc=cv2.VideoWriter_fourcc(*codec), - fps=float(frames_per_second), - frameSize=(width, height), - isColor=True, - ) - assert os.path.isfile(args.video_input) - for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames): - if args.output: - output_file.write(vis_frame) - else: - cv2.namedWindow(basename, cv2.WINDOW_NORMAL) - cv2.imshow(basename, vis_frame) - if cv2.waitKey(1) == 27: - break # esc to quit - video.release() - if args.output: - output_file.release() - else: - cv2.destroyAllWindows() diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/modeling/proposal_generator/rpn.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/modeling/proposal_generator/rpn.py deleted file mode 100644 index 99cd536d2f9880d2049390c45f73eb22335e1b82..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/modeling/proposal_generator/rpn.py +++ /dev/null @@ -1,533 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -from typing import Dict, List, Optional, Tuple, Union -import torch -import torch.nn.functional as F -from torch import nn - -from detectron2.config import configurable -from detectron2.layers import Conv2d, ShapeSpec, cat -from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou -from detectron2.utils.events import get_event_storage -from detectron2.utils.memory import retry_if_cuda_oom -from detectron2.utils.registry import Registry - -from ..anchor_generator import build_anchor_generator -from ..box_regression import Box2BoxTransform, _dense_box_regression_loss -from ..matcher import Matcher -from ..sampling import subsample_labels -from .build import PROPOSAL_GENERATOR_REGISTRY -from .proposal_utils import find_top_rpn_proposals - -RPN_HEAD_REGISTRY = Registry("RPN_HEAD") -RPN_HEAD_REGISTRY.__doc__ = """ -Registry for RPN heads, which take feature maps and perform -objectness classification and bounding box regression for anchors. - -The registered object will be called with `obj(cfg, input_shape)`. -The call should return a `nn.Module` object. -""" - - -""" -Shape shorthand in this module: - - N: number of images in the minibatch - L: number of feature maps per image on which RPN is run - A: number of cell anchors (must be the same for all feature maps) - Hi, Wi: height and width of the i-th feature map - B: size of the box parameterization - -Naming convention: - - objectness: refers to the binary classification of an anchor as object vs. not object. - - deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box - transform (see :class:`box_regression.Box2BoxTransform`), or 5d for rotated boxes. - - pred_objectness_logits: predicted objectness scores in [-inf, +inf]; use - sigmoid(pred_objectness_logits) to estimate P(object). - - gt_labels: ground-truth binary classification labels for objectness - - pred_anchor_deltas: predicted box2box transform deltas - - gt_anchor_deltas: ground-truth box2box transform deltas -""" - - -def build_rpn_head(cfg, input_shape): - """ - Build an RPN head defined by `cfg.MODEL.RPN.HEAD_NAME`. - """ - name = cfg.MODEL.RPN.HEAD_NAME - return RPN_HEAD_REGISTRY.get(name)(cfg, input_shape) - - -@RPN_HEAD_REGISTRY.register() -class StandardRPNHead(nn.Module): - """ - Standard RPN classification and regression heads described in :paper:`Faster R-CNN`. - Uses a 3x3 conv to produce a shared hidden state from which one 1x1 conv predicts - objectness logits for each anchor and a second 1x1 conv predicts bounding-box deltas - specifying how to deform each anchor into an object proposal. - """ - - @configurable - def __init__( - self, *, in_channels: int, num_anchors: int, box_dim: int = 4, conv_dims: List[int] = (-1,) - ): - """ - NOTE: this interface is experimental. - - Args: - in_channels (int): number of input feature channels. When using multiple - input features, they must have the same number of channels. - num_anchors (int): number of anchors to predict for *each spatial position* - on the feature map. The total number of anchors for each - feature map will be `num_anchors * H * W`. - box_dim (int): dimension of a box, which is also the number of box regression - predictions to make for each anchor. An axis aligned box has - box_dim=4, while a rotated box has box_dim=5. - conv_dims (list[int]): a list of integers representing the output channels - of N conv layers. Set it to -1 to use the same number of output channels - as input channels. - """ - super().__init__() - cur_channels = in_channels - # Keeping the old variable names and structure for backwards compatiblity. - # Otherwise the old checkpoints will fail to load. - if len(conv_dims) == 1: - out_channels = cur_channels if conv_dims[0] == -1 else conv_dims[0] - # 3x3 conv for the hidden representation - self.conv = self._get_rpn_conv(cur_channels, out_channels) - cur_channels = out_channels - else: - self.conv = nn.Sequential() - for k, conv_dim in enumerate(conv_dims): - out_channels = cur_channels if conv_dim == -1 else conv_dim - if out_channels <= 0: - raise ValueError( - f"Conv output channels should be greater than 0. Got {out_channels}" - ) - conv = self._get_rpn_conv(cur_channels, out_channels) - self.conv.add_module(f"conv{k}", conv) - cur_channels = out_channels - # 1x1 conv for predicting objectness logits - self.objectness_logits = nn.Conv2d(cur_channels, num_anchors, kernel_size=1, stride=1) - # 1x1 conv for predicting box2box transform deltas - self.anchor_deltas = nn.Conv2d(cur_channels, num_anchors * box_dim, kernel_size=1, stride=1) - - # Keeping the order of weights initialization same for backwards compatiblility. - for layer in self.modules(): - if isinstance(layer, nn.Conv2d): - nn.init.normal_(layer.weight, std=0.01) - nn.init.constant_(layer.bias, 0) - - def _get_rpn_conv(self, in_channels, out_channels): - return Conv2d( - in_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1, - activation=nn.ReLU(), - ) - - @classmethod - def from_config(cls, cfg, input_shape): - # Standard RPN is shared across levels: - in_channels = [s.channels for s in input_shape] - assert len(set(in_channels)) == 1, "Each level must have the same channel!" - in_channels = in_channels[0] - - # RPNHead should take the same input as anchor generator - # NOTE: it assumes that creating an anchor generator does not have unwanted side effect. - anchor_generator = build_anchor_generator(cfg, input_shape) - num_anchors = anchor_generator.num_anchors - box_dim = anchor_generator.box_dim - assert ( - len(set(num_anchors)) == 1 - ), "Each level must have the same number of anchors per spatial position" - return { - "in_channels": in_channels, - "num_anchors": num_anchors[0], - "box_dim": box_dim, - "conv_dims": cfg.MODEL.RPN.CONV_DIMS, - } - - def forward(self, features: List[torch.Tensor]): - """ - Args: - features (list[Tensor]): list of feature maps - - Returns: - list[Tensor]: A list of L elements. - Element i is a tensor of shape (N, A, Hi, Wi) representing - the predicted objectness logits for all anchors. A is the number of cell anchors. - list[Tensor]: A list of L elements. Element i is a tensor of shape - (N, A*box_dim, Hi, Wi) representing the predicted "deltas" used to transform anchors - to proposals. - """ - pred_objectness_logits = [] - pred_anchor_deltas = [] - for x in features: - t = self.conv(x) - pred_objectness_logits.append(self.objectness_logits(t)) - pred_anchor_deltas.append(self.anchor_deltas(t)) - return pred_objectness_logits, pred_anchor_deltas - - -@PROPOSAL_GENERATOR_REGISTRY.register() -class RPN(nn.Module): - """ - Region Proposal Network, introduced by :paper:`Faster R-CNN`. - """ - - @configurable - def __init__( - self, - *, - in_features: List[str], - head: nn.Module, - anchor_generator: nn.Module, - anchor_matcher: Matcher, - box2box_transform: Box2BoxTransform, - batch_size_per_image: int, - positive_fraction: float, - pre_nms_topk: Tuple[float, float], - post_nms_topk: Tuple[float, float], - nms_thresh: float = 0.7, - min_box_size: float = 0.0, - anchor_boundary_thresh: float = -1.0, - loss_weight: Union[float, Dict[str, float]] = 1.0, - box_reg_loss_type: str = "smooth_l1", - smooth_l1_beta: float = 0.0, - ): - """ - NOTE: this interface is experimental. - - Args: - in_features (list[str]): list of names of input features to use - head (nn.Module): a module that predicts logits and regression deltas - for each level from a list of per-level features - anchor_generator (nn.Module): a module that creates anchors from a - list of features. Usually an instance of :class:`AnchorGenerator` - anchor_matcher (Matcher): label the anchors by matching them with ground truth. - box2box_transform (Box2BoxTransform): defines the transform from anchors boxes to - instance boxes - batch_size_per_image (int): number of anchors per image to sample for training - positive_fraction (float): fraction of foreground anchors to sample for training - pre_nms_topk (tuple[float]): (train, test) that represents the - number of top k proposals to select before NMS, in - training and testing. - post_nms_topk (tuple[float]): (train, test) that represents the - number of top k proposals to select after NMS, in - training and testing. - nms_thresh (float): NMS threshold used to de-duplicate the predicted proposals - min_box_size (float): remove proposal boxes with any side smaller than this threshold, - in the unit of input image pixels - anchor_boundary_thresh (float): legacy option - loss_weight (float|dict): weights to use for losses. Can be single float for weighting - all rpn losses together, or a dict of individual weightings. Valid dict keys are: - "loss_rpn_cls" - applied to classification loss - "loss_rpn_loc" - applied to box regression loss - box_reg_loss_type (str): Loss type to use. Supported losses: "smooth_l1", "giou". - smooth_l1_beta (float): beta parameter for the smooth L1 regression loss. Default to - use L1 loss. Only used when `box_reg_loss_type` is "smooth_l1" - """ - super().__init__() - self.in_features = in_features - self.rpn_head = head - self.anchor_generator = anchor_generator - self.anchor_matcher = anchor_matcher - self.box2box_transform = box2box_transform - self.batch_size_per_image = batch_size_per_image - self.positive_fraction = positive_fraction - # Map from self.training state to train/test settings - self.pre_nms_topk = {True: pre_nms_topk[0], False: pre_nms_topk[1]} - self.post_nms_topk = {True: post_nms_topk[0], False: post_nms_topk[1]} - self.nms_thresh = nms_thresh - self.min_box_size = float(min_box_size) - self.anchor_boundary_thresh = anchor_boundary_thresh - if isinstance(loss_weight, float): - loss_weight = {"loss_rpn_cls": loss_weight, "loss_rpn_loc": loss_weight} - self.loss_weight = loss_weight - self.box_reg_loss_type = box_reg_loss_type - self.smooth_l1_beta = smooth_l1_beta - - @classmethod - def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): - in_features = cfg.MODEL.RPN.IN_FEATURES - ret = { - "in_features": in_features, - "min_box_size": cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE, - "nms_thresh": cfg.MODEL.RPN.NMS_THRESH, - "batch_size_per_image": cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE, - "positive_fraction": cfg.MODEL.RPN.POSITIVE_FRACTION, - "loss_weight": { - "loss_rpn_cls": cfg.MODEL.RPN.LOSS_WEIGHT, - "loss_rpn_loc": cfg.MODEL.RPN.BBOX_REG_LOSS_WEIGHT * cfg.MODEL.RPN.LOSS_WEIGHT, - }, - "anchor_boundary_thresh": cfg.MODEL.RPN.BOUNDARY_THRESH, - "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS), - "box_reg_loss_type": cfg.MODEL.RPN.BBOX_REG_LOSS_TYPE, - "smooth_l1_beta": cfg.MODEL.RPN.SMOOTH_L1_BETA, - } - - ret["pre_nms_topk"] = (cfg.MODEL.RPN.PRE_NMS_TOPK_TRAIN, cfg.MODEL.RPN.PRE_NMS_TOPK_TEST) - ret["post_nms_topk"] = (cfg.MODEL.RPN.POST_NMS_TOPK_TRAIN, cfg.MODEL.RPN.POST_NMS_TOPK_TEST) - - ret["anchor_generator"] = build_anchor_generator(cfg, [input_shape[f] for f in in_features]) - ret["anchor_matcher"] = Matcher( - cfg.MODEL.RPN.IOU_THRESHOLDS, cfg.MODEL.RPN.IOU_LABELS, allow_low_quality_matches=True - ) - ret["head"] = build_rpn_head(cfg, [input_shape[f] for f in in_features]) - return ret - - def _subsample_labels(self, label): - """ - Randomly sample a subset of positive and negative examples, and overwrite - the label vector to the ignore value (-1) for all elements that are not - included in the sample. - - Args: - labels (Tensor): a vector of -1, 0, 1. Will be modified in-place and returned. - """ - pos_idx, neg_idx = subsample_labels( - label, self.batch_size_per_image, self.positive_fraction, 0 - ) - # Fill with the ignore label (-1), then set positive and negative labels - label.fill_(-1) - label.scatter_(0, pos_idx, 1) - label.scatter_(0, neg_idx, 0) - return label - - @torch.jit.unused - @torch.no_grad() - def label_and_sample_anchors( - self, anchors: List[Boxes], gt_instances: List[Instances] - ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: - """ - Args: - anchors (list[Boxes]): anchors for each feature map. - gt_instances: the ground-truth instances for each image. - - Returns: - list[Tensor]: - List of #img tensors. i-th element is a vector of labels whose length is - the total number of anchors across all feature maps R = sum(Hi * Wi * A). - Label values are in {-1, 0, 1}, with meanings: -1 = ignore; 0 = negative - class; 1 = positive class. - list[Tensor]: - i-th element is a Rx4 tensor. The values are the matched gt boxes for each - anchor. Values are undefined for those anchors not labeled as 1. - """ - anchors = Boxes.cat(anchors) - - gt_boxes = [x.gt_boxes for x in gt_instances] - image_sizes = [x.image_size for x in gt_instances] - del gt_instances - - gt_labels = [] - matched_gt_boxes = [] - for image_size_i, gt_boxes_i in zip(image_sizes, gt_boxes): - """ - image_size_i: (h, w) for the i-th image - gt_boxes_i: ground-truth boxes for i-th image - """ - - match_quality_matrix = retry_if_cuda_oom(pairwise_iou)(gt_boxes_i, anchors) - matched_idxs, gt_labels_i = retry_if_cuda_oom(self.anchor_matcher)(match_quality_matrix) - # Matching is memory-expensive and may result in CPU tensors. But the result is small - gt_labels_i = gt_labels_i.to(device=gt_boxes_i.device) - del match_quality_matrix - - if self.anchor_boundary_thresh >= 0: - # Discard anchors that go out of the boundaries of the image - # NOTE: This is legacy functionality that is turned off by default in Detectron2 - anchors_inside_image = anchors.inside_box(image_size_i, self.anchor_boundary_thresh) - gt_labels_i[~anchors_inside_image] = -1 - - # A vector of labels (-1, 0, 1) for each anchor - gt_labels_i = self._subsample_labels(gt_labels_i) - - if len(gt_boxes_i) == 0: - # These values won't be used anyway since the anchor is labeled as background - matched_gt_boxes_i = torch.zeros_like(anchors.tensor) - else: - # TODO wasted indexing computation for ignored boxes - matched_gt_boxes_i = gt_boxes_i[matched_idxs].tensor - - gt_labels.append(gt_labels_i) # N,AHW - matched_gt_boxes.append(matched_gt_boxes_i) - return gt_labels, matched_gt_boxes - - @torch.jit.unused - def losses( - self, - anchors: List[Boxes], - pred_objectness_logits: List[torch.Tensor], - gt_labels: List[torch.Tensor], - pred_anchor_deltas: List[torch.Tensor], - gt_boxes: List[torch.Tensor], - ) -> Dict[str, torch.Tensor]: - """ - Return the losses from a set of RPN predictions and their associated ground-truth. - - Args: - anchors (list[Boxes or RotatedBoxes]): anchors for each feature map, each - has shape (Hi*Wi*A, B), where B is box dimension (4 or 5). - pred_objectness_logits (list[Tensor]): A list of L elements. - Element i is a tensor of shape (N, Hi*Wi*A) representing - the predicted objectness logits for all anchors. - gt_labels (list[Tensor]): Output of :meth:`label_and_sample_anchors`. - pred_anchor_deltas (list[Tensor]): A list of L elements. Element i is a tensor of shape - (N, Hi*Wi*A, 4 or 5) representing the predicted "deltas" used to transform anchors - to proposals. - gt_boxes (list[Tensor]): Output of :meth:`label_and_sample_anchors`. - - Returns: - dict[loss name -> loss value]: A dict mapping from loss name to loss value. - Loss names are: `loss_rpn_cls` for objectness classification and - `loss_rpn_loc` for proposal localization. - """ - num_images = len(gt_labels) - gt_labels = torch.stack(gt_labels) # (N, sum(Hi*Wi*Ai)) - - # Log the number of positive/negative anchors per-image that's used in training - pos_mask = gt_labels == 1 - num_pos_anchors = pos_mask.sum().item() - num_neg_anchors = (gt_labels == 0).sum().item() - storage = get_event_storage() - storage.put_scalar("rpn/num_pos_anchors", num_pos_anchors / num_images) - storage.put_scalar("rpn/num_neg_anchors", num_neg_anchors / num_images) - - localization_loss = _dense_box_regression_loss( - anchors, - self.box2box_transform, - pred_anchor_deltas, - gt_boxes, - pos_mask, - box_reg_loss_type=self.box_reg_loss_type, - smooth_l1_beta=self.smooth_l1_beta, - ) - - valid_mask = gt_labels >= 0 - objectness_loss = F.binary_cross_entropy_with_logits( - cat(pred_objectness_logits, dim=1)[valid_mask], - gt_labels[valid_mask].to(torch.float32), - reduction="sum", - ) - normalizer = self.batch_size_per_image * num_images - losses = { - "loss_rpn_cls": objectness_loss / normalizer, - # The original Faster R-CNN paper uses a slightly different normalizer - # for loc loss. But it doesn't matter in practice - "loss_rpn_loc": localization_loss / normalizer, - } - losses = {k: v * self.loss_weight.get(k, 1.0) for k, v in losses.items()} - return losses - - def forward( - self, - images: ImageList, - features: Dict[str, torch.Tensor], - gt_instances: Optional[List[Instances]] = None, - ): - """ - Args: - images (ImageList): input images of length `N` - features (dict[str, Tensor]): input data as a mapping from feature - map name to tensor. Axis 0 represents the number of images `N` in - the input data; axes 1-3 are channels, height, and width, which may - vary between feature maps (e.g., if a feature pyramid is used). - gt_instances (list[Instances], optional): a length `N` list of `Instances`s. - Each `Instances` stores ground-truth instances for the corresponding image. - - Returns: - proposals: list[Instances]: contains fields "proposal_boxes", "objectness_logits" - loss: dict[Tensor] or None - """ - features = [features[f] for f in self.in_features] - anchors = self.anchor_generator(features) - - pred_objectness_logits, pred_anchor_deltas = self.rpn_head(features) - # Transpose the Hi*Wi*A dimension to the middle: - pred_objectness_logits = [ - # (N, A, Hi, Wi) -> (N, Hi, Wi, A) -> (N, Hi*Wi*A) - score.permute(0, 2, 3, 1).flatten(1) - for score in pred_objectness_logits - ] - pred_anchor_deltas = [ - # (N, A*B, Hi, Wi) -> (N, A, B, Hi, Wi) -> (N, Hi, Wi, A, B) -> (N, Hi*Wi*A, B) - x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) - .permute(0, 3, 4, 1, 2) - .flatten(1, -2) - for x in pred_anchor_deltas - ] - - if self.training: - assert gt_instances is not None, "RPN requires gt_instances in training!" - gt_labels, gt_boxes = self.label_and_sample_anchors(anchors, gt_instances) - losses = self.losses( - anchors, pred_objectness_logits, gt_labels, pred_anchor_deltas, gt_boxes - ) - else: - losses = {} - proposals = self.predict_proposals( - anchors, pred_objectness_logits, pred_anchor_deltas, images.image_sizes - ) - return proposals, losses - - def predict_proposals( - self, - anchors: List[Boxes], - pred_objectness_logits: List[torch.Tensor], - pred_anchor_deltas: List[torch.Tensor], - image_sizes: List[Tuple[int, int]], - ): - """ - Decode all the predicted box regression deltas to proposals. Find the top proposals - by applying NMS and removing boxes that are too small. - - Returns: - proposals (list[Instances]): list of N Instances. The i-th Instances - stores post_nms_topk object proposals for image i, sorted by their - objectness score in descending order. - """ - # The proposals are treated as fixed for joint training with roi heads. - # This approach ignores the derivative w.r.t. the proposal boxes’ coordinates that - # are also network responses. - with torch.no_grad(): - pred_proposals = self._decode_proposals(anchors, pred_anchor_deltas) - return find_top_rpn_proposals( - pred_proposals, - pred_objectness_logits, - image_sizes, - self.nms_thresh, - self.pre_nms_topk[self.training], - self.post_nms_topk[self.training], - self.min_box_size, - self.training, - ) - - def _decode_proposals(self, anchors: List[Boxes], pred_anchor_deltas: List[torch.Tensor]): - """ - Transform anchors into proposals by applying the predicted anchor deltas. - - Returns: - proposals (list[Tensor]): A list of L tensors. Tensor i has shape - (N, Hi*Wi*A, B) - """ - N = pred_anchor_deltas[0].shape[0] - proposals = [] - # For each feature map - for anchors_i, pred_anchor_deltas_i in zip(anchors, pred_anchor_deltas): - B = anchors_i.tensor.size(1) - pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) - # Expand anchors to shape (N*Hi*Wi*A, B) - anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) - proposals_i = self.box2box_transform.apply_deltas(pred_anchor_deltas_i, anchors_i) - # Append feature map proposals with shape (N, Hi*Wi*A, B) - proposals.append(proposals_i.view(N, -1, B)) - return proposals diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/dataset_mapper.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/dataset_mapper.py deleted file mode 100644 index 4f1c289222b852eff9a509c428b22b5e860b529a..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/dataset_mapper.py +++ /dev/null @@ -1,168 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. - -import copy -import logging -from typing import Any, Dict, List, Tuple -import torch - -from detectron2.data import MetadataCatalog -from detectron2.data import detection_utils as utils -from detectron2.data import transforms as T -from detectron2.layers import ROIAlign -from detectron2.structures import BoxMode -from detectron2.utils.file_io import PathManager - -from densepose.structures import DensePoseDataRelative, DensePoseList, DensePoseTransformData - - -def build_augmentation(cfg, is_train): - logger = logging.getLogger(__name__) - result = utils.build_augmentation(cfg, is_train) - if is_train: - random_rotation = T.RandomRotation( - cfg.INPUT.ROTATION_ANGLES, expand=False, sample_style="choice" - ) - result.append(random_rotation) - logger.info("DensePose-specific augmentation used in training: " + str(random_rotation)) - return result - - -class DatasetMapper: - """ - A customized version of `detectron2.data.DatasetMapper` - """ - - def __init__(self, cfg, is_train=True): - self.augmentation = build_augmentation(cfg, is_train) - - # fmt: off - self.img_format = cfg.INPUT.FORMAT - self.mask_on = ( - cfg.MODEL.MASK_ON or ( - cfg.MODEL.DENSEPOSE_ON - and cfg.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS) - ) - self.keypoint_on = cfg.MODEL.KEYPOINT_ON - self.densepose_on = cfg.MODEL.DENSEPOSE_ON - assert not cfg.MODEL.LOAD_PROPOSALS, "not supported yet" - # fmt: on - if self.keypoint_on and is_train: - # Flip only makes sense in training - self.keypoint_hflip_indices = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN) - else: - self.keypoint_hflip_indices = None - - if self.densepose_on: - densepose_transform_srcs = [ - MetadataCatalog.get(ds).densepose_transform_src - for ds in cfg.DATASETS.TRAIN + cfg.DATASETS.TEST - ] - assert len(densepose_transform_srcs) > 0 - # TODO: check that DensePose transformation data is the same for - # all the datasets. Otherwise one would have to pass DB ID with - # each entry to select proper transformation data. For now, since - # all DensePose annotated data uses the same data semantics, we - # omit this check. - densepose_transform_data_fpath = PathManager.get_local_path(densepose_transform_srcs[0]) - self.densepose_transform_data = DensePoseTransformData.load( - densepose_transform_data_fpath - ) - - self.is_train = is_train - - def __call__(self, dataset_dict): - """ - Args: - dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. - - Returns: - dict: a format that builtin models in detectron2 accept - """ - dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below - image = utils.read_image(dataset_dict["file_name"], format=self.img_format) - utils.check_image_size(dataset_dict, image) - - image, transforms = T.apply_transform_gens(self.augmentation, image) - image_shape = image.shape[:2] # h, w - dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32")) - - if not self.is_train: - dataset_dict.pop("annotations", None) - return dataset_dict - - for anno in dataset_dict["annotations"]: - if not self.mask_on: - anno.pop("segmentation", None) - if not self.keypoint_on: - anno.pop("keypoints", None) - - # USER: Implement additional transformations if you have other types of data - # USER: Don't call transpose_densepose if you don't need - annos = [ - self._transform_densepose( - utils.transform_instance_annotations( - obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices - ), - transforms, - ) - for obj in dataset_dict.pop("annotations") - if obj.get("iscrowd", 0) == 0 - ] - - if self.mask_on: - self._add_densepose_masks_as_segmentation(annos, image_shape) - - instances = utils.annotations_to_instances(annos, image_shape, mask_format="bitmask") - densepose_annotations = [obj.get("densepose") for obj in annos] - if densepose_annotations and not all(v is None for v in densepose_annotations): - instances.gt_densepose = DensePoseList( - densepose_annotations, instances.gt_boxes, image_shape - ) - - dataset_dict["instances"] = instances[instances.gt_boxes.nonempty()] - return dataset_dict - - def _transform_densepose(self, annotation, transforms): - if not self.densepose_on: - return annotation - - # Handle densepose annotations - is_valid, reason_not_valid = DensePoseDataRelative.validate_annotation(annotation) - if is_valid: - densepose_data = DensePoseDataRelative(annotation, cleanup=True) - densepose_data.apply_transform(transforms, self.densepose_transform_data) - annotation["densepose"] = densepose_data - else: - # logger = logging.getLogger(__name__) - # logger.debug("Could not load DensePose annotation: {}".format(reason_not_valid)) - DensePoseDataRelative.cleanup_annotation(annotation) - # NOTE: annotations for certain instances may be unavailable. - # 'None' is accepted by the DensePostList data structure. - annotation["densepose"] = None - return annotation - - def _add_densepose_masks_as_segmentation( - self, annotations: List[Dict[str, Any]], image_shape_hw: Tuple[int, int] - ): - for obj in annotations: - if ("densepose" not in obj) or ("segmentation" in obj): - continue - # DP segmentation: torch.Tensor [S, S] of float32, S=256 - segm_dp = torch.zeros_like(obj["densepose"].segm) - segm_dp[obj["densepose"].segm > 0] = 1 - segm_h, segm_w = segm_dp.shape - bbox_segm_dp = torch.tensor((0, 0, segm_h - 1, segm_w - 1), dtype=torch.float32) - # image bbox - x0, y0, x1, y1 = ( - v.item() for v in BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS) - ) - segm_aligned = ( - ROIAlign((y1 - y0, x1 - x0), 1.0, 0, aligned=True) - .forward(segm_dp.view(1, 1, *segm_dp.shape), bbox_segm_dp) - .squeeze() - ) - image_mask = torch.zeros(*image_shape_hw, dtype=torch.float32) - image_mask[y0:y1, x0:x1] = segm_aligned - # segmentation for BitMask: np.array [H, W] of np.bool - obj["segmentation"] = image_mask >= 0.5 diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/utils/logger.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/utils/logger.py deleted file mode 100644 index 70cd3cb0eb0fc7495b1a4b50a05725a0e5b1baba..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/utils/logger.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import logging - - -def verbosity_to_level(verbosity) -> int: - if verbosity is not None: - if verbosity == 0: - return logging.WARNING - elif verbosity == 1: - return logging.INFO - elif verbosity >= 2: - return logging.DEBUG - return logging.WARNING diff --git a/spaces/catundchat/tts_cn/bert/ProsodyModel.py b/spaces/catundchat/tts_cn/bert/ProsodyModel.py deleted file mode 100644 index 5f305b41894a4a8cec05c23dcdd29a9b939b748b..0000000000000000000000000000000000000000 --- a/spaces/catundchat/tts_cn/bert/ProsodyModel.py +++ /dev/null @@ -1,75 +0,0 @@ -import os -import torch -import torch.nn as nn -import torch.nn.functional as F - -from transformers import BertModel, BertConfig, BertTokenizer - - -class CharEmbedding(nn.Module): - def __init__(self, model_dir): - super().__init__() - self.tokenizer = BertTokenizer.from_pretrained(model_dir) - self.bert_config = BertConfig.from_pretrained(model_dir) - self.hidden_size = self.bert_config.hidden_size - self.bert = BertModel(self.bert_config) - self.proj = nn.Linear(self.hidden_size, 256) - self.linear = nn.Linear(256, 3) - - def text2Token(self, text): - token = self.tokenizer.tokenize(text) - txtid = self.tokenizer.convert_tokens_to_ids(token) - return txtid - - def forward(self, inputs_ids, inputs_masks, tokens_type_ids): - out_seq = self.bert(input_ids=inputs_ids, - attention_mask=inputs_masks, - token_type_ids=tokens_type_ids)[0] - out_seq = self.proj(out_seq) - return out_seq - - -class TTSProsody(object): - def __init__(self, path, device): - self.device = device - self.char_model = CharEmbedding(path) - self.char_model.load_state_dict( - torch.load( - os.path.join(path, 'prosody_model.pt'), - map_location="cpu" - ), - strict=False - ) - self.char_model.eval() - self.char_model.to(self.device) - - def get_char_embeds(self, text): - input_ids = self.char_model.text2Token(text) - input_masks = [1] * len(input_ids) - type_ids = [0] * len(input_ids) - input_ids = torch.LongTensor([input_ids]).to(self.device) - input_masks = torch.LongTensor([input_masks]).to(self.device) - type_ids = torch.LongTensor([type_ids]).to(self.device) - - with torch.no_grad(): - char_embeds = self.char_model( - input_ids, input_masks, type_ids).squeeze(0).cpu() - return char_embeds - - def expand_for_phone(self, char_embeds, length): # length of phones for char - assert char_embeds.size(0) == len(length) - expand_vecs = list() - for vec, leng in zip(char_embeds, length): - vec = vec.expand(leng, -1) - expand_vecs.append(vec) - expand_embeds = torch.cat(expand_vecs, 0) - assert expand_embeds.size(0) == sum(length) - return expand_embeds.numpy() - - -if __name__ == "__main__": - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - prosody = TTSProsody('./bert/', device) - while True: - text = input("请输入文本：") - prosody.get_char_embeds(text) diff --git a/spaces/chachkey/anime-remove-background/app.py b/spaces/chachkey/anime-remove-background/app.py deleted file mode 100644 index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000 --- a/spaces/chachkey/anime-remove-background/app.py +++ /dev/null @@ -1,52 +0,0 @@ -import gradio as gr -import huggingface_hub -import onnxruntime as rt -import numpy as np -import cv2 - - -def get_mask(img, s=1024): - img = (img / 255).astype(np.float32) - h, w = h0, w0 = img.shape[:-1] - h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s) - ph, pw = s - h, s - w - img_input = np.zeros([s, s, 3], dtype=np.float32) - img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h)) - img_input = np.transpose(img_input, (2, 0, 1)) - img_input = img_input[np.newaxis, :] - mask = rmbg_model.run(None, {'img': img_input})[0][0] - mask = np.transpose(mask, (1, 2, 0)) - mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] - mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis] - return mask - - -def rmbg_fn(img): - mask = get_mask(img) - img = (mask * img + 255 * (1 - mask)).astype(np.uint8) - mask = (mask * 255).astype(np.uint8) - img = np.concatenate([img, mask], axis=2, dtype=np.uint8) - mask = mask.repeat(3, axis=2) - return mask, img - - -if __name__ == "__main__": - providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] - model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx") - rmbg_model = rt.InferenceSession(model_path, providers=providers) - app = gr.Blocks() - with app: - gr.Markdown("# Anime Remove Background\n\n" - "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n" - "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)") - with gr.Row(): - with gr.Column(): - input_img = gr.Image(label="input image") - examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)] - examples = gr.Dataset(components=[input_img], samples=examples_data) - run_btn = gr.Button(variant="primary") - output_mask = gr.Image(label="mask") - output_img = gr.Image(label="result", image_mode="RGBA") - examples.click(lambda x: x[0], [examples], [input_img]) - run_btn.click(rmbg_fn, [input_img], [output_mask, output_img]) - app.launch() diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/_virtualenv.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/_virtualenv.py deleted file mode 100644 index e328be6a34b2497ce574524365ec1b56ad381307..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/_virtualenv.py +++ /dev/null @@ -1,102 +0,0 @@ -"""Patches that are applied at runtime to the virtual environment.""" - -from __future__ import annotations - -import os -import sys -from contextlib import suppress - -VIRTUALENV_PATCH_FILE = os.path.join(__file__) - - -def patch_dist(dist): - """ - Distutils allows user to configure some arguments via a configuration file: - https://docs.python.org/3/install/index.html#distutils-configuration-files. - - Some of this arguments though don't make sense in context of the virtual environment files, let's fix them up. - """ # noqa: D205 - # we cannot allow some install config as that would get packages installed outside of the virtual environment - old_parse_config_files = dist.Distribution.parse_config_files - - def parse_config_files(self, *args, **kwargs): - result = old_parse_config_files(self, *args, **kwargs) - install = self.get_option_dict("install") - - if "prefix" in install: # the prefix governs where to install the libraries - install["prefix"] = VIRTUALENV_PATCH_FILE, os.path.abspath(sys.prefix) - for base in ("purelib", "platlib", "headers", "scripts", "data"): - key = f"install_{base}" - if key in install: # do not allow global configs to hijack venv paths - install.pop(key, None) - return result - - dist.Distribution.parse_config_files = parse_config_files - - -# Import hook that patches some modules to ignore configuration values that break package installation in case -# of virtual environments. -_DISTUTILS_PATCH = "distutils.dist", "setuptools.dist" -# https://docs.python.org/3/library/importlib.html#setting-up-an-importer - - -class _Finder: - """A meta path finder that allows patching the imported distutils modules.""" - - fullname = None - - # lock[0] is threading.Lock(), but initialized lazily to avoid importing threading very early at startup, - # because there are gevent-based applications that need to be first to import threading by themselves. - # See https://github.com/pypa/virtualenv/issues/1895 for details. - lock = [] - - def find_spec(self, fullname, path, target=None): # noqa: ARG002 - if fullname in _DISTUTILS_PATCH and self.fullname is None: - # initialize lock[0] lazily - if len(self.lock) == 0: - import threading - - lock = threading.Lock() - # there is possibility that two threads T1 and T2 are simultaneously running into find_spec, - # observing .lock as empty, and further going into hereby initialization. However due to the GIL, - # list.append() operation is atomic and this way only one of the threads will "win" to put the lock - # - that every thread will use - into .lock[0]. - # https://docs.python.org/3/faq/library.html#what-kinds-of-global-value-mutation-are-thread-safe - self.lock.append(lock) - - from functools import partial - from importlib.util import find_spec - - with self.lock[0]: - self.fullname = fullname - try: - spec = find_spec(fullname, path) - if spec is not None: - # https://www.python.org/dev/peps/pep-0451/#how-loading-will-work - is_new_api = hasattr(spec.loader, "exec_module") - func_name = "exec_module" if is_new_api else "load_module" - old = getattr(spec.loader, func_name) - func = self.exec_module if is_new_api else self.load_module - if old is not func: - with suppress(AttributeError): # C-Extension loaders are r/o such as zipimporter with <3.7 - setattr(spec.loader, func_name, partial(func, old)) - return spec - finally: - self.fullname = None - return None - - @staticmethod - def exec_module(old, module): - old(module) - if module.__name__ in _DISTUTILS_PATCH: - patch_dist(module) - - @staticmethod - def load_module(old, name): - module = old(name) - if module.__name__ in _DISTUTILS_PATCH: - patch_dist(module) - return module - - -sys.meta_path.insert(0, _Finder()) diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/__init__.py deleted file mode 100644 index ed00764f7c193ca9bcd0bf67196da59c30048a28..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/__init__.py +++ /dev/null @@ -1,26 +0,0 @@ -"""fontTools.ttLib -- a package for dealing with TrueType fonts.""" - -from fontTools.misc.loggingTools import deprecateFunction -import logging - - -log = logging.getLogger(__name__) - - -class TTLibError(Exception): - pass - - -class TTLibFileIsCollectionError(TTLibError): - pass - - -@deprecateFunction("use logging instead", category=DeprecationWarning) -def debugmsg(msg): - import time - - print(msg + time.strftime(" (%H:%M:%S)", time.localtime(time.time()))) - - -from fontTools.ttLib.ttFont import * -from fontTools.ttLib.ttCollection import TTCollection diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_T_F_A_.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_T_F_A_.py deleted file mode 100644 index e3cf2db2d744cdda880ec1255808f60bc3795c61..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_T_F_A_.py +++ /dev/null @@ -1,5 +0,0 @@ -from . import asciiTable - - -class table_T_T_F_A_(asciiTable.asciiTable): - pass diff --git a/spaces/cihyFjudo/fairness-paper-search/Comparador De Carpetas Y Ficheros.md b/spaces/cihyFjudo/fairness-paper-search/Comparador De Carpetas Y Ficheros.md deleted file mode 100644 index 839d78222d3556ae85cc541c351b2fc8a3f840a6..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Comparador De Carpetas Y Ficheros.md +++ /dev/null @@ -1,30 +0,0 @@ - -

¿A veces desea poder comparar archivos en dos o más carpetas, encontrar los duplicados y luego eliminarlos de las carpetas de destino sin tocar los originales? Los buscadores de duplicados ordinarios mezclan los archivos y tipos de archivo en los resultados de búsqueda y hacen que sea muy difícil verificar rápidamente una carpeta específica en busca de duplicados. También hacen que sea imposible preparar varias carpetas para fusionar. Afortunadamente, Easy Duplicate Finder es diferente.

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Easy Duplicate Finder es un programa para comparar archivos y carpetas que lo fácil para usted para encontrar duplicados con su modo espacial de comparación de carpetas. Este modo está diseñado para comparar archivos en carpetas de destino con archivos en carpetas de origen, dejando muy claro qué archivos son duplicados y cuáles son los originales.

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Comparador De Carpetas Y Ficheros


DOWNLOAD ✑ https://tinurli.com/2uwkQH



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Comparar archivos en ambas carpetas es muy fácil porque Easy Duplicate Finder siempre enumera los originales primero en un grupo de duplicados. De esta manera, siempre sabrá qué archivo es el maestro y cuál es una copia. El programa también evita que elimine accidentalmente los originales al pedirte que confirme la eliminación.

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Para comparar archivos en diferentes carpetas con Easy Duplicate Finder, seleccione el modo de comparación de carpetas en el menú del modo de escaneo, agregue algunas carpetas de origen y de destino en el explorador de archivos, y ejecute un escaneo. Luego, simplemente elimine los duplicados y mantenga el primer archivo como original.

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WinMerge es una herramienta de diferenciación y combinación de código abierto para Windows. WinMerge puede comparar tanto carpetas como archivos, presentando las diferencias en un formato de texto visual fácil de entender y controlar.

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Son muchas las ocasiones en las que nos puede venir bien, o necesitamos, poder comparar el contenido de ciertas carpetas o archivos en Windows. Es evidente que ir fichero a fichero es una tarea un tanto engorrosa, por no decir casi imposible.

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Es por ello que el poder disponer de alguna aplicación que nos ayude en todo ello, nos va a ser de gran ayuda llegado el momento. Esto, entre otras cosas, nos va a servir para ahorrar espacio en disco, para hacer copias de seguridad de determinadas ubicaciones, etc. Seguro que en más de una ocasión nos hemos visto con que disponemos en disco de dos juegos de carpetas con los mismos archivos y no sabemos cuál conservar. Es evidente que nos puede llevar mucho tiempo abrir cada uno para verificar el que es reciente más reciente, aunque las fechas asociadas no pueden ayudar.

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Pero con todo y con ello, sigue siendo un proceso aburrido y confuso, además no tendremos un control sobre el contenido real y cada uno. Por tanto, aquí es donde entran en juego las herramientas de comparación de archivos, como es el caso del que os hablaremos. En concreto nos vamos a referir a Meld, una app de código abierto para comparar tanto archivos independientes como carpetas en Windows y Linux.

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Esto es algo que podremos llevar a cabo con hasta tres archivos a la vez, al tiempo que, con los ficheros de texto, se muestran usando el visor incorporado en Meld. En el caso de que estos sean idénticos, el programa nos mostrará un mensaje para indicarlo. Eso sí, si son diferentes la propia aplicación nos resaltará las diferencias de inmediato. También tendremos la oportunidad de establecer puntos de sincronización que se pueden utilizar para fusionar datos, por ejemplo. Pero claro, por si acaso nos equivocamos, tenemos una opción para deshacer los cambios realizados en esos archivos y así no perder nada.

-

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En lo que se refiere a la comparación de carpetas, el programa muestra dos listas de archivos donde aparecen tachados los existentes en ambas ubicaciones. De este modo podremos averiguar rápidamente qué archivos faltan o se han editado en esa carpeta.

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Meld es programa con el que podremos comparar archivos y carpetas, gratuito y multisistema, ya que se encuentra disponible tanto para Windows como para distribuciones de Linux y macOS, que lo podemos descargar desde su sitio web oficial. Su última versión es la 3.20.4 que vio la luz el 13 de agosto de 2021. También cuenta con otra versión en desarrollo con el que otorgar mejoras y nuevas funcionalidades, aunque solo es recomendable usarla si estamos dispuestos a aceptar que se encuentra sin terminar.

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Las opciones de búsqueda que nos ofrece son muy completas, lo que hace que sea muy sencillo utilizarla para realizar esta tarea. Aunque la aplicación se encuentra únicamente en inglés, no es necesario tener amplios conocimientos del idioma de Shakespeare para sacarle todo el partido a esta aplicación gratuita. Si buscamos una aplicación para comparar archivos y carpetas, una de las mejores opciones disponibles en la actualidad y que, además, es completamente gratuita, la encontramos en Meld. Aunque podemos encontrar interesantes alternativas, la mayoría de ellas son de pago y no nos ofrecen funciones interesantes por la que realmente merezca la pagar.

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Si en alguna ocasión te has visto en la necesidad de comparar dos directorios (carpetas) para ver qué archivos pueden ser diferentes entre ambos, debes saber que existen herramientas que te facilitan el trabajo en lugar de tener que hacerlo manualmente.

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El q interruptor se pone diff en forma de abreviatura. Si no configuramos el modo breve, diff no solo te diría qué archivos son diferentes entre las dos carpetas, sino que también mostraría las diferencias reales línea a línea para cualquier archivo de texto que exista.

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Evidentemente, este es un ejemplo simple, pero funciona igual de bien en una carpeta grande con cientos de archivos. Si quieres hacer más con diff, debes saber que es capaz de mucho más que simples comparaciones de carpetas.

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Trabajar en una computadora a diario también suma muchos archivos y documentos. Cuantos más archivos, más basura, por lo que necesitan tu atención especial. No deseas seguir sumando archivos duplicados o perderte los cambios realizados en los archivos por otra persona, ¿verdad? Comparar tus archivos a intervalos regulares es la solución a esto. Las herramientas recopiladas aquí pueden ayudarte a analizar y comparar tus documentos y archivos, así como combinarlos si es necesario. Hay herramientas para comparar todo, desde documentos de Word hasta archivos WAV, y todo lo que hay en el medio. Algunos son gratuitos y otros son de pago, y hay opciones disponibles para Mac OS X, Windows y Linux. Estos son los principales comparadores de textos en 2022-2023.

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Beyond Compare es un buen text compare que utiliza su tecnología inteligente para comparar archivos y carpetas. Utiliza comandos simples pero sólidos que resaltan la diferencia que busca ignorando los que no le interesan. Ayuda a fusionar cambios, sincronizar archivos e incluso producir informes.

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Permite que los archivos se fusionen automáticamente siempre que sea posible, y también permite un control completo sobre la edición del archivo generado. Además, compara las dos carpetas una al lado de la otra, mientras muestra qué archivos solo están presentes en un solo archivo o en el otro. También muestra los pares de archivos que son similares o diferentes.

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Es un comparador de textos que funciona para comparar archivos XML, Word, texto y PDF. Hace posible comparar rápidamente fuentes, tamaños, eliminaciones, inserciones, ortografía y ubicación.

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Diff doc es otro de los comparadores de textos que te ayuda a comparar archivos de forma rápida, precisa y sin esfuerzo. Ya sea que estés usando MS Word, Excel, WordPad, Bloc de notas o cualquier otro editor, todo lo que necesitas hacer es cargar los archivos originales y modificados, y luego hacer clic en el botón Actualizar para comparar archivos (o presionar F5 en el teclado) y la comparación de archivos aparece instantáneamente.

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Permite comparar por palabra clave, comparar dos carpetas, ignorar ciertas palabras e incluye resaltado de sintaxis para facilitar la comparación de documentos de código. Compare Suite está disponible para Windows por $ 70 por una licencia de usuario único.

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Es una herramienta de comparación de archivos de código abierto y gratuita diseñada para Windows. Te ayuda a comparar archivos y carpetas, que generan diferencias en un formato de texto visual que es fácil de administrar y comprender. Es extremadamente útil para identificar los cambios que tuvieron lugar entre las diferentes versiones del proyecto y, en consecuencia, combinar los cambios entre las diferentes versiones.

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Este programa también se puede utilizar como una herramienta de combinación o comparación externa, o también como una aplicación independiente. Viene con una interfaz con pestañas, es compatible con Unicode y maneja formatos de archivo de texto de Windows, Unix y Mac. La comparación de carpetas, la comparación de imágenes, el control de versiones o la integración de shell son algunas de sus otras características principales.

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Uno de los mejores comparadores de textos y archivos para desarrolladores. Esta herramienta visual de fusión y diferenciación que permite comparar archivos, directorios y proyectos que están controlados por versiones. El programa ofrece una comparación de archivos y directorios de dos a tres vías. También es compatible con varios controles de versión conocidos. También te ayuda a revisar los cambios de código y obtener parches. Lo interesante es que también puede ayudarte a determinar qué está sucediendo dentro de esa fusión.

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Es una herramienta de sincronización de carpetas, combinación y comparación de documentos de tres vías. Se puede utilizar para comparar código fuente, páginas web, XML y otros archivos de texto, así como para comparar textos de Word y Excel, PDF y archivos RTF. Está disponible para Windows y Mac OS X por $ 129 para la versión estándar y $ 269 para la versión profesional.

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\ No newline at end of file diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/ImageTk.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/ImageTk.py deleted file mode 100644 index bf98eb2c8c25c7446dd91890f49291486222f3b8..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/ImageTk.py +++ /dev/null @@ -1,283 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# -# a Tk display interface -# -# History: -# 96-04-08 fl Created -# 96-09-06 fl Added getimage method -# 96-11-01 fl Rewritten, removed image attribute and crop method -# 97-05-09 fl Use PyImagingPaste method instead of image type -# 97-05-12 fl Minor tweaks to match the IFUNC95 interface -# 97-05-17 fl Support the "pilbitmap" booster patch -# 97-06-05 fl Added file= and data= argument to image constructors -# 98-03-09 fl Added width and height methods to Image classes -# 98-07-02 fl Use default mode for "P" images without palette attribute -# 98-07-02 fl Explicitly destroy Tkinter image objects -# 99-07-24 fl Support multiple Tk interpreters (from Greg Couch) -# 99-07-26 fl Automatically hook into Tkinter (if possible) -# 99-08-15 fl Hook uses _imagingtk instead of _imaging -# -# Copyright (c) 1997-1999 by Secret Labs AB -# Copyright (c) 1996-1997 by Fredrik Lundh -# -# See the README file for information on usage and redistribution. -# - -import tkinter -from io import BytesIO - -from . import Image - -# -------------------------------------------------------------------- -# Check for Tkinter interface hooks - -_pilbitmap_ok = None - - -def _pilbitmap_check(): - global _pilbitmap_ok - if _pilbitmap_ok is None: - try: - im = Image.new("1", (1, 1)) - tkinter.BitmapImage(data=f"PIL:{im.im.id}") - _pilbitmap_ok = 1 - except tkinter.TclError: - _pilbitmap_ok = 0 - return _pilbitmap_ok - - -def _get_image_from_kw(kw): - source = None - if "file" in kw: - source = kw.pop("file") - elif "data" in kw: - source = BytesIO(kw.pop("data")) - if source: - return Image.open(source) - - -def _pyimagingtkcall(command, photo, id): - tk = photo.tk - try: - tk.call(command, photo, id) - except tkinter.TclError: - # activate Tkinter hook - # may raise an error if it cannot attach to Tkinter - from . import _imagingtk - - _imagingtk.tkinit(tk.interpaddr()) - tk.call(command, photo, id) - - -# -------------------------------------------------------------------- -# PhotoImage - - -class PhotoImage: - """ - A Tkinter-compatible photo image. This can be used - everywhere Tkinter expects an image object. If the image is an RGBA - image, pixels having alpha 0 are treated as transparent. - - The constructor takes either a PIL image, or a mode and a size. - Alternatively, you can use the ``file`` or ``data`` options to initialize - the photo image object. - - :param image: Either a PIL image, or a mode string. If a mode string is - used, a size must also be given. - :param size: If the first argument is a mode string, this defines the size - of the image. - :keyword file: A filename to load the image from (using - ``Image.open(file)``). - :keyword data: An 8-bit string containing image data (as loaded from an - image file). - """ - - def __init__(self, image=None, size=None, **kw): - # Tk compatibility: file or data - if image is None: - image = _get_image_from_kw(kw) - - if hasattr(image, "mode") and hasattr(image, "size"): - # got an image instead of a mode - mode = image.mode - if mode == "P": - # palette mapped data - image.apply_transparency() - image.load() - try: - mode = image.palette.mode - except AttributeError: - mode = "RGB" # default - size = image.size - kw["width"], kw["height"] = size - else: - mode = image - image = None - - if mode not in ["1", "L", "RGB", "RGBA"]: - mode = Image.getmodebase(mode) - - self.__mode = mode - self.__size = size - self.__photo = tkinter.PhotoImage(**kw) - self.tk = self.__photo.tk - if image: - self.paste(image) - - def __del__(self): - name = self.__photo.name - self.__photo.name = None - try: - self.__photo.tk.call("image", "delete", name) - except Exception: - pass # ignore internal errors - - def __str__(self): - """ - Get the Tkinter photo image identifier. This method is automatically - called by Tkinter whenever a PhotoImage object is passed to a Tkinter - method. - - :return: A Tkinter photo image identifier (a string). - """ - return str(self.__photo) - - def width(self): - """ - Get the width of the image. - - :return: The width, in pixels. - """ - return self.__size[0] - - def height(self): - """ - Get the height of the image. - - :return: The height, in pixels. - """ - return self.__size[1] - - def paste(self, im): - """ - Paste a PIL image into the photo image. Note that this can - be very slow if the photo image is displayed. - - :param im: A PIL image. The size must match the target region. If the - mode does not match, the image is converted to the mode of - the bitmap image. - """ - # convert to blittable - im.load() - image = im.im - if image.isblock() and im.mode == self.__mode: - block = image - else: - block = image.new_block(self.__mode, im.size) - image.convert2(block, image) # convert directly between buffers - - _pyimagingtkcall("PyImagingPhoto", self.__photo, block.id) - - -# -------------------------------------------------------------------- -# BitmapImage - - -class BitmapImage: - """ - A Tkinter-compatible bitmap image. This can be used everywhere Tkinter - expects an image object. - - The given image must have mode "1". Pixels having value 0 are treated as - transparent. Options, if any, are passed on to Tkinter. The most commonly - used option is ``foreground``, which is used to specify the color for the - non-transparent parts. See the Tkinter documentation for information on - how to specify colours. - - :param image: A PIL image. - """ - - def __init__(self, image=None, **kw): - # Tk compatibility: file or data - if image is None: - image = _get_image_from_kw(kw) - - self.__mode = image.mode - self.__size = image.size - - if _pilbitmap_check(): - # fast way (requires the pilbitmap booster patch) - image.load() - kw["data"] = f"PIL:{image.im.id}" - self.__im = image # must keep a reference - else: - # slow but safe way - kw["data"] = image.tobitmap() - self.__photo = tkinter.BitmapImage(**kw) - - def __del__(self): - name = self.__photo.name - self.__photo.name = None - try: - self.__photo.tk.call("image", "delete", name) - except Exception: - pass # ignore internal errors - - def width(self): - """ - Get the width of the image. - - :return: The width, in pixels. - """ - return self.__size[0] - - def height(self): - """ - Get the height of the image. - - :return: The height, in pixels. - """ - return self.__size[1] - - def __str__(self): - """ - Get the Tkinter bitmap image identifier. This method is automatically - called by Tkinter whenever a BitmapImage object is passed to a Tkinter - method. - - :return: A Tkinter bitmap image identifier (a string). - """ - return str(self.__photo) - - -def getimage(photo): - """Copies the contents of a PhotoImage to a PIL image memory.""" - im = Image.new("RGBA", (photo.width(), photo.height())) - block = im.im - - _pyimagingtkcall("PyImagingPhotoGet", photo, block.id) - - return im - - -def _show(image, title): - """Helper for the Image.show method.""" - - class UI(tkinter.Label): - def __init__(self, master, im): - if im.mode == "1": - self.image = BitmapImage(im, foreground="white", master=master) - else: - self.image = PhotoImage(im, master=master) - super().__init__(master, image=self.image, bg="black", bd=0) - - if not tkinter._default_root: - msg = "tkinter not initialized" - raise OSError(msg) - top = tkinter.Toplevel() - if title: - top.title(title) - UI(top, image).pack() diff --git a/spaces/coding-alt/IF/share_btn.py b/spaces/coding-alt/IF/share_btn.py deleted file mode 100644 index fdb978b7f9bfb098fc824041c97db75559debfde..0000000000000000000000000000000000000000 --- a/spaces/coding-alt/IF/share_btn.py +++ /dev/null @@ -1,69 +0,0 @@ -community_icon_html = """""" - -loading_icon_html = """""" - -share_js = """async () => { - async function uploadFile(file){ - const UPLOAD_URL = 'https://huggingface.co/uploads'; - const response = await fetch(UPLOAD_URL, { - method: 'POST', - headers: { - 'Content-Type': file.type, - 'X-Requested-With': 'XMLHttpRequest', - }, - body: file, /// <- File inherits from Blob - }); - const url = await response.text(); - return url; - } - async function getInputImageFile(imageEl){ - const res = await fetch(imageEl.src); - const blob = await res.blob(); - const imageId = Date.now(); - const fileName = `rich-text-image-${{imageId}}.png`; - return new File([blob], fileName, { type: 'image/png'}); - } - const gradioEl = document.querySelector("gradio-app").shadowRoot || document.querySelector('body > gradio-app'); - const negative_prompt = gradioEl.querySelector('#negative-prompt-text-input input').value; - const prompt = gradioEl.querySelector('#prompt-text-input input').value; - const upscaledImage = gradioEl.querySelector('#upscaled-image img'); - - const titleTxt = `DeepFloyd IF: ${prompt.slice(0, 50)}...`; - - const shareBtnEl = gradioEl.querySelector('#share-btn'); - const shareIconEl = gradioEl.querySelector('#share-btn-share-icon'); - const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon'); - if(!upscaledImage){ - return; - }; - shareBtnEl.style.pointerEvents = 'none'; - shareIconEl.style.display = 'none'; - loadingIconEl.style.removeProperty('display'); - - const upscaledImageFile = await getInputImageFile(upscaledImage); - const upscaledImageURL = await uploadFile(upscaledImageFile); - - const descriptionMd = ` -### Prompt -${prompt} - -### Negative Prompt -${negative_prompt} - -### Upscaled Image -Upscaled Image - -`; - const params = new URLSearchParams({ - title: titleTxt, - description: descriptionMd, - }); - const paramsStr = params.toString(); - window.open(`https://huggingface.co/spaces/DeepFloyd/IF/discussions/new?${paramsStr}`, '_blank'); - shareBtnEl.style.removeProperty('pointer-events'); - shareIconEl.style.removeProperty('display'); - loadingIconEl.style.display = 'none'; -}""" diff --git a/spaces/colakin/video-generater/public/ffmpeg/doc/texi2pod.pl b/spaces/colakin/video-generater/public/ffmpeg/doc/texi2pod.pl deleted file mode 100644 index c7f67afe8c181a551561ab6af162a45e1c10f33e..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/doc/texi2pod.pl +++ /dev/null @@ -1,473 +0,0 @@ -#!/usr/bin/env perl - -# Copyright (C) 1999, 2000, 2001 Free Software Foundation, Inc. - -# This file is part of GNU CC. - -# GNU CC is free software; you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation; either version 2, or (at your option) -# any later version. - -# GNU CC is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. - -# You should have received a copy of the GNU General Public License -# along with GNU CC; see the file COPYING. If not, write to -# the Free Software Foundation, 51 Franklin Street, Fifth Floor, -# Boston, MA 02110-1301 USA - -# This does trivial (and I mean _trivial_) conversion of Texinfo -# markup to Perl POD format. It's intended to be used to extract -# something suitable for a manpage from a Texinfo document. - -use warnings; - -$output = 0; -$skipping = 0; -%chapters = (); -@chapters_sequence = (); -$chapter = ""; -@icstack = (); -@endwstack = (); -@skstack = (); -@instack = (); -$shift = ""; -%defs = (); -$fnno = 1; -$inf = ""; -@ibase = (); - -while ($_ = shift) { - if (/^-D(.*)$/) { - if ($1 ne "") { - $flag = $1; - } else { - $flag = shift; - } - $value = ""; - ($flag, $value) = ($flag =~ /^([^=]+)(?:=(.+))?/); - die "no flag specified for -D\n" - unless $flag ne ""; - die "flags may only contain letters, digits, hyphens, dashes and underscores\n" - unless $flag =~ /^[a-zA-Z0-9_-]+$/; - $defs{$flag} = $value; - } elsif (/^-I(.*)$/) { - push @ibase, $1 ne "" ? $1 : shift; - } elsif (/^-/) { - usage(); - } else { - $in = $_, next unless defined $in; - $out = $_, next unless defined $out; - usage(); - } -} - -push @ibase, "."; - -if (defined $in) { - $inf = gensym(); - open($inf, "<$in") or die "opening \"$in\": $!\n"; - push @ibase, $1 if $in =~ m|^(.+)/[^/]+$|; -} else { - $inf = \*STDIN; -} - -if (defined $out) { - open(STDOUT, ">$out") or die "opening \"$out\": $!\n"; -} - -while(defined $inf) { -INF: while(<$inf>) { - # Certain commands are discarded without further processing. - /^\@(?: - [a-z]+index # @*index: useful only in complete manual - |need # @need: useful only in printed manual - |(?:end\s+)?group # @group .. @end group: ditto - |page # @page: ditto - |node # @node: useful only in .info file - |(?:end\s+)?ifnottex # @ifnottex .. @end ifnottex: use contents - )\b/x and next; - - chomp; - - # Look for filename and title markers. - /^\@setfilename\s+([^.]+)/ and $fn = $1, next; - /^\@settitle\s+([^.]+)/ and $tl = postprocess($1), next; - - # Identify a man title but keep only the one we are interested in. - /^\@c\s+man\s+title\s+([A-Za-z0-9-]+)\s+(.+)/ and do { - if (exists $defs{$1}) { - $fn = $1; - $tl = postprocess($2); - } - next; - }; - - /^\@include\s+(.+)$/ and do { - push @instack, $inf; - $inf = gensym(); - - for (@ibase) { - open($inf, "<" . $_ . "/" . $1) and next INF; - } - die "cannot open $1: $!\n"; - }; - - /^\@chapter\s+([A-Za-z ]+)/ and do { - # close old chapter - $chapters{$chapter_name} .= postprocess($chapter) if ($chapter_name); - - # start new chapter - $chapter_name = $1, push (@chapters_sequence, $chapter_name) unless $skipping; - $chapters{$chapter_name} = "" unless exists $chapters{$chapter_name}; - $chapter = ""; - $output = 1; - next; - }; - - /^\@bye/ and do { - # close old chapter - $chapters{$chapter_name} .= postprocess($chapter) if ($chapter_name); - last INF; - }; - - # handle variables - /^\@set\s+([a-zA-Z0-9_-]+)\s*(.*)$/ and do { - $defs{$1} = $2; - next; - }; - /^\@clear\s+([a-zA-Z0-9_-]+)/ and do { - delete $defs{$1}; - next; - }; - - next unless $output; - - # Discard comments. (Can't do it above, because then we'd never see - # @c man lines.) - /^\@c\b/ and next; - - # End-block handler goes up here because it needs to operate even - # if we are skipping. - /^\@end\s+([a-z]+)/ and do { - # Ignore @end foo, where foo is not an operation which may - # cause us to skip, if we are presently skipping. - my $ended = $1; - next if $skipping && $ended !~ /^(?:ifset|ifclear|ignore|menu|iftex|ifhtml|ifnothtml)$/; - - die "\@end $ended without \@$ended at line $.\n" unless defined $endw; - die "\@$endw ended by \@end $ended at line $.\n" unless $ended eq $endw; - - $endw = pop @endwstack; - - if ($ended =~ /^(?:ifset|ifclear|ignore|menu|iftex|ifhtml|ifnothtml)$/) { - $skipping = pop @skstack; - next; - } elsif ($ended =~ /^(?:example|smallexample|verbatim|display)$/) { - $shift = ""; - $_ = ""; # need a paragraph break - } elsif ($ended =~ /^(?:itemize|enumerate|(?:multi|[fv])?table)$/) { - $_ = "\n=back\n"; - $ic = pop @icstack; - } elsif ($ended =~ /^float$/) { - $_ = "\n=back\n"; - $ic = pop @icstack; - } else { - die "unknown command \@end $ended at line $.\n"; - } - }; - - # We must handle commands which can cause skipping even while we - # are skipping, otherwise we will not process nested conditionals - # correctly. - /^\@ifset\s+([a-zA-Z0-9_-]+)/ and do { - push @endwstack, $endw; - push @skstack, $skipping; - $endw = "ifset"; - $skipping = 1 unless exists $defs{$1}; - next; - }; - - /^\@ifclear\s+([a-zA-Z0-9_-]+)/ and do { - push @endwstack, $endw; - push @skstack, $skipping; - $endw = "ifclear"; - $skipping = 1 if exists $defs{$1}; - next; - }; - - /^\@(ignore|menu|iftex|ifhtml|ifnothtml)\b/ and do { - push @endwstack, $endw; - push @skstack, $skipping; - $endw = $1; - $skipping = $endw !~ /ifnothtml/; - next; - }; - - next if $skipping; - - # Character entities. First the ones that can be replaced by raw text - # or discarded outright: - s/\@copyright\{\}/(c)/g; - s/\@dots\{\}/.../g; - s/\@enddots\{\}/..../g; - s/\@([.!? ])/$1/g; - s/\@[:-]//g; - s/\@bullet(?:\{\})?/*/g; - s/\@TeX\{\}/TeX/g; - s/\@pounds\{\}/\#/g; - s/\@minus(?:\{\})?/-/g; - - # Now the ones that have to be replaced by special escapes - # (which will be turned back into text by unmunge()) - s/&/&/g; - s/\@\{/{/g; - s/\@\}/}/g; - s/\@\@/&at;/g; - - # Inside a verbatim block, handle @var specially. - if ($shift ne "") { - s/\@var\{([^\}]*)\}/<$1>/g; - } - - # POD doesn't interpret E<> inside a verbatim block. - if ($shift eq "") { - s//>/g; - } else { - s//>/g; - } - - # Single line command handlers. - - /^\@(?:section|unnumbered|unnumberedsec|center|heading)\s+(.+)$/ - and $_ = "\n=head2 $1\n"; - /^\@(?:subsection|subheading)\s+(.+)$/ - and $_ = "\n=head3 $1\n"; - /^\@(?:subsubsection|subsubheading)\s+(.+)$/ - and $_ = "\n=head4 $1\n"; - - # Block command handlers: - /^\@itemize\s*(\@[a-z]+|\*|-)?/ and do { - push @endwstack, $endw; - push @icstack, $ic; - $ic = $1 ? $1 : "*"; - $_ = "\n=over 4\n"; - $endw = "itemize"; - }; - - /^\@enumerate(?:\s+([a-zA-Z0-9]+))?/ and do { - push @endwstack, $endw; - push @icstack, $ic; - if (defined $1) { - $ic = $1 . "."; - } else { - $ic = "1."; - } - $_ = "\n=over 4\n"; - $endw = "enumerate"; - }; - - /^\@((?:multi|[fv])?table)\s+(\@[a-z]+)/ and do { - push @endwstack, $endw; - push @icstack, $ic; - $endw = $1; - $ic = $2; - $ic =~ s/\@(?:samp|strong|key|gcctabopt|option|env|command)/B/; - $ic =~ s/\@(?:code|kbd)/C/; - $ic =~ s/\@(?:dfn|var|emph|cite|i)/I/; - $ic =~ s/\@(?:file)/F/; - $ic =~ s/\@(?:columnfractions)//; - $_ = "\n=over 4\n"; - }; - - /^\@(multitable)\s+{.*/ and do { - push @endwstack, $endw; - push @icstack, $ic; - $endw = $1; - $ic = ""; - $_ = "\n=over 4\n"; - }; - - /^\@((?:small)?example|verbatim|display)/ and do { - push @endwstack, $endw; - $endw = $1; - $shift = "\t"; - $_ = ""; # need a paragraph break - }; - - /^\@(float)\s+\w+/ and do { - push @endwstack, $endw; - $endw = $1; - $_ = "\n=over 4\n"; - }; - - /^\@item\s+(.*\S)\s*$/ and $endw eq "multitable" and do { - my $columns = $1; - $columns =~ s/\@tab/ : /; - - $_ = "\n=item B<". $columns .">\n"; - }; - - /^\@tab\s+(.*\S)\s*$/ and $endw eq "multitable" and do { - my $columns = $1; - $columns =~ s/\@tab//; - - $_ = $columns; - $chapter =~ s/$//; - }; - - /^\@itemx?\s*(.+)?$/ and do { - if (defined $1) { - # Entity escapes prevent munging by the <> processing below. - $_ = "\n=item $ic\<$1\>\n"; - } else { - $_ = "\n=item $ic\n"; - $ic =~ y/A-Ya-y/B-Zb-z/; - $ic =~ s/(\d+)/$1 + 1/eg; - } - }; - - $chapter .= $shift.$_."\n"; -} -# End of current file. -close($inf); -$inf = pop @instack; -} - -die "No filename or title\n" unless defined $fn && defined $tl; - -# always use utf8 -print "=encoding utf8\n\n"; - -$chapters{NAME} = "$fn \- $tl\n"; -$chapters{FOOTNOTES} .= "=back\n" if exists $chapters{FOOTNOTES}; - -unshift @chapters_sequence, "NAME"; -for $chapter (@chapters_sequence) { - if (exists $chapters{$chapter}) { - $head = uc($chapter); - print "=head1 $head\n\n"; - print scalar unmunge ($chapters{$chapter}); - print "\n"; - } -} - -sub usage -{ - die "usage: $0 [-D toggle...] [infile [outfile]]\n"; -} - -sub postprocess -{ - local $_ = $_[0]; - - # @value{foo} is replaced by whatever 'foo' is defined as. - while (m/(\@value\{([a-zA-Z0-9_-]+)\})/g) { - if (! exists $defs{$2}) { - print STDERR "Option $2 not defined\n"; - s/\Q$1\E//; - } else { - $value = $defs{$2}; - s/\Q$1\E/$value/; - } - } - - # Formatting commands. - # Temporary escape for @r. - s/\@r\{([^\}]*)\}/R<$1>/g; - s/\@(?:dfn|var|emph|cite|i)\{([^\}]*)\}/I<$1>/g; - s/\@(?:code|kbd)\{([^\}]*)\}/C<$1>/g; - s/\@(?:gccoptlist|samp|strong|key|option|env|command|b)\{([^\}]*)\}/B<$1>/g; - s/\@sc\{([^\}]*)\}/\U$1/g; - s/\@file\{([^\}]*)\}/F<$1>/g; - s/\@w\{([^\}]*)\}/S<$1>/g; - s/\@(?:dmn|math)\{([^\}]*)\}/$1/g; - - # Cross references are thrown away, as are @noindent and @refill. - # (@noindent is impossible in .pod, and @refill is unnecessary.) - # @* is also impossible in .pod; we discard it and any newline that - # follows it. Similarly, our macro @gol must be discarded. - - s/\@anchor\{(?:[^\}]*)\}//g; - s/\(?\@xref\{(?:[^\}]*)\}(?:[^.<]|(?:<[^<>]*>))*\.\)?//g; - s/\s+\(\@pxref\{(?:[^\}]*)\}\)//g; - s/;\s+\@pxref\{(?:[^\}]*)\}//g; - s/\@ref\{(?:[^,\}]*,)(?:[^,\}]*,)([^,\}]*).*\}/B<$1>/g; - s/\@ref\{([^\}]*)\}/B<$1>/g; - s/\@noindent\s*//g; - s/\@refill//g; - s/\@gol//g; - s/\@\*\s*\n?//g; - - # @uref can take one, two, or three arguments, with different - # semantics each time. @url and @email are just like @uref with - # one argument, for our purposes. - s/\@(?:uref|url|email)\{([^\},]*),?[^\}]*\}/<B<$1>>/g; - s/\@uref\{([^\},]*),([^\},]*)\}/$2 (C<$1>)/g; - s/\@uref\{([^\},]*),([^\},]*),([^\},]*)\}/$3/g; - - # Turn B blah> into B I B to - # match Texinfo semantics of @emph inside @samp. Also handle @r - # inside bold. - s/<//g; - 1 while s/B<((?:[^<>]|I<[^<>]*>)*)R<([^>]*)>/B<$1>${2}B]*)I<([^>]+)>/B<$1>I<$2>B]*)B<([^>]+)>/I<$1>B<$2>I//g; - s/([BI])<(\s+)([^>]+)>/$2$1<$3>/g; - s/([BI])<([^>]+?)(\s+)>/$1<$2>$3/g; - - # Extract footnotes. This has to be done after all other - # processing because otherwise the regexp will choke on formatting - # inside @footnote. - while (/\@footnote/g) { - s/\@footnote\{([^\}]+)\}/[$fnno]/; - add_footnote($1, $fnno); - $fnno++; - } - - return $_; -} - -sub unmunge -{ - # Replace escaped symbols with their equivalents. - local $_ = $_[0]; - - s/</E/g; - s/>/E/g; - s/{/\{/g; - s/}/\}/g; - s/&at;/\@/g; - s/&/&/g; - return $_; -} - -sub add_footnote -{ - unless (exists $chapters{FOOTNOTES}) { - $chapters{FOOTNOTES} = "\n=over 4\n\n"; - } - - $chapters{FOOTNOTES} .= "=item $fnno.\n\n"; $fnno++; - $chapters{FOOTNOTES} .= $_[0]; - $chapters{FOOTNOTES} .= "\n\n"; -} - -# stolen from Symbol.pm -{ - my $genseq = 0; - sub gensym - { - my $name = "GEN" . $genseq++; - my $ref = \*{$name}; - delete $::{$name}; - return $ref; - } -} diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ac3tab.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ac3tab.h deleted file mode 100644 index 2531d80677ea8cec288267d9a74111d227c8df61..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ac3tab.h +++ /dev/null @@ -1,72 +0,0 @@ -/* - * AC-3 tables - * Copyright (c) 2000, 2001, 2002 Fabrice Bellard - * - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - */ - -#ifndef AVCODEC_AC3TAB_H -#define AVCODEC_AC3TAB_H - -#include - -#include "ac3defs.h" - -extern const uint16_t ff_ac3_frame_size_tab[38][3]; -extern const uint8_t ff_ac3_channels_tab[8]; -extern const uint16_t ff_ac3_channel_layout_tab[8]; -extern const uint8_t ff_ac3_dec_channel_map[8][2][6]; -extern const int ff_ac3_sample_rate_tab[]; -extern const uint16_t ff_ac3_bitrate_tab[19]; -extern const uint8_t ff_ac3_rematrix_band_tab[5]; -extern const uint8_t ff_eac3_default_cpl_band_struct[18]; -extern const uint8_t ff_ac3_bap_tab[64]; -extern const uint8_t ff_ac3_slow_decay_tab[4]; -extern const uint8_t ff_ac3_fast_decay_tab[4]; -extern const uint16_t ff_ac3_slow_gain_tab[4]; -extern const uint16_t ff_ac3_db_per_bit_tab[4]; -extern const int16_t ff_ac3_floor_tab[8]; -extern const uint16_t ff_ac3_fast_gain_tab[8]; -extern const uint8_t ff_ac3_band_start_tab[AC3_CRITICAL_BANDS+1]; -extern const uint8_t ff_ac3_bin_to_band_tab[253]; -extern const uint64_t ff_eac3_custom_channel_map_locations[16][2]; - - -/** Custom channel map locations bitmask - * Other channels described in documentation: - * Lc/Rc pair, Lrs/Rrs pair, Ts, Lsd/Rsd pair, - * Lw/Rw pair, Lvh/Rvh pair, Cvh, Reserved, LFE2 - */ -enum CustomChannelMapLocation{ - AC3_CHMAP_L= 1<<(15-0), - AC3_CHMAP_C= 1<<(15-1), - AC3_CHMAP_R= 1<<(15-2), - AC3_CHMAP_L_SUR= 1<<(15-3), - AC3_CHMAP_R_SUR = 1<<(15-4), - AC3_CHMAP_C_SUR= 1<<(15-7), - AC3_CHMAP_LFE = 1<<(15-15) -}; - -#define COMMON_CHANNEL_MAP \ - { { 0, 1, }, { 0, 1, 2, } },\ - { { 0, }, { 0, 1, } },\ - { { 0, 1, }, { 0, 1, 2, } },\ - { { 0, 2, 1, }, { 0, 2, 1, 3, } },\ - { { 0, 1, 2, }, { 0, 1, 3, 2, } },\ - { { 0, 2, 1, 3, }, { 0, 2, 1, 4, 3, } }, - -#endif /* AVCODEC_AC3TAB_H */ diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/imx.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/imx.c deleted file mode 100644 index 44bab23c27010a7e0377a21e06b9f1f5ab2e5d3a..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/imx.c +++ /dev/null @@ -1,194 +0,0 @@ -/* - * Copyright (c) 2021 Paul B Mahol - * - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - */ - -#include "libavutil/common.h" -#include "avcodec.h" -#include "bytestream.h" -#include "codec_internal.h" -#include "decode.h" - -typedef struct SimbiosisIMXContext { - AVFrame *frame; - uint32_t pal[256]; - uint8_t history[32768]; - int pos; -} SimbiosisIMXContext; - -static av_cold int imx_decode_init(AVCodecContext *avctx) -{ - SimbiosisIMXContext *imx = avctx->priv_data; - - avctx->pix_fmt = AV_PIX_FMT_PAL8; - avctx->width = 320; - avctx->height = 160; - - imx->frame = av_frame_alloc(); - if (!imx->frame) - return AVERROR(ENOMEM); - - return 0; -} - -static int imx_decode_frame(AVCodecContext *avctx, AVFrame *rframe, - int *got_frame, AVPacket *avpkt) -{ - SimbiosisIMXContext *imx = avctx->priv_data; - int ret, x, y; - AVFrame *frame = imx->frame; - GetByteContext gb; - - if ((ret = ff_reget_buffer(avctx, frame, 0)) < 0) - return ret; - - if (ff_copy_palette(imx->pal, avpkt, avctx)) { - frame->palette_has_changed = 1; - frame->key_frame = 1; - } else { - frame->key_frame = 0; - frame->palette_has_changed = 0; - } - - bytestream2_init(&gb, avpkt->data, avpkt->size); - - memcpy(frame->data[1], imx->pal, AVPALETTE_SIZE); - - x = 0, y = 0; - while (bytestream2_get_bytes_left(&gb) > 0 && - x < 320 && y < 160) { - int b = bytestream2_get_byte(&gb); - int len = b & 0x3f; - int op = b >> 6; - int fill; - - switch (op) { - case 3: - len = len * 64 + bytestream2_get_byte(&gb); - case 0: - while (len > 0) { - x++; - len--; - if (x >= 320) { - x = 0; - y++; - } - if (y >= 160) - break; - } - - frame->key_frame = 0; - break; - case 1: - if (len == 0) { - int offset = bytestream2_get_le16(&gb); - - if (offset < 0 || offset >= 32768) - return AVERROR_INVALIDDATA; - - len = bytestream2_get_byte(&gb); - while (len > 0 && offset < 32768) { - frame->data[0][x + y * frame->linesize[0]] = imx->history[offset++]; - x++; - len--; - if (x >= 320) { - x = 0; - y++; - } - if (y >= 160) - break; - } - - frame->key_frame = 0; - } else { - while (len > 0) { - fill = bytestream2_get_byte(&gb); - frame->data[0][x + y * frame->linesize[0]] = fill; - if (imx->pos < 32768) - imx->history[imx->pos++] = fill; - x++; - len--; - if (x >= 320) { - x = 0; - y++; - } - if (y >= 160) - break; - } - } - break; - case 2: - fill = bytestream2_get_byte(&gb); - - while (len > 0) { - frame->data[0][x + y * frame->linesize[0]] = fill; - x++; - len--; - if (x >= 320) { - x = 0; - y++; - } - if (y >= 160) - break; - } - break; - } - } - - frame->pict_type = frame->key_frame ? AV_PICTURE_TYPE_I : AV_PICTURE_TYPE_P; - - if ((ret = av_frame_ref(rframe, frame)) < 0) - return ret; - - *got_frame = 1; - - return avpkt->size; -} - -static void imx_decode_flush(AVCodecContext *avctx) -{ - SimbiosisIMXContext *imx = avctx->priv_data; - - av_frame_unref(imx->frame); - imx->pos = 0; - memset(imx->pal, 0, sizeof(imx->pal)); - memset(imx->history, 0, sizeof(imx->history)); -} - -static int imx_decode_close(AVCodecContext *avctx) -{ - SimbiosisIMXContext *imx = avctx->priv_data; - - av_frame_free(&imx->frame); - - return 0; -} - -const FFCodec ff_simbiosis_imx_decoder = { - .p.name = "simbiosis_imx", - CODEC_LONG_NAME("Simbiosis Interactive IMX Video"), - .p.type = AVMEDIA_TYPE_VIDEO, - .p.id = AV_CODEC_ID_SIMBIOSIS_IMX, - .priv_data_size = sizeof(SimbiosisIMXContext), - .init = imx_decode_init, - FF_CODEC_DECODE_CB(imx_decode_frame), - .close = imx_decode_close, - .flush = imx_decode_flush, - .p.capabilities = AV_CODEC_CAP_DR1, - .caps_internal = FF_CODEC_CAP_INIT_CLEANUP, -}; diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/acelp_vectors_mips.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/acelp_vectors_mips.c deleted file mode 100644 index 0ab2b6a87bde11ed6b1a3ed63fb2bfa227835c88..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/acelp_vectors_mips.c +++ /dev/null @@ -1,106 +0,0 @@ -/* - * Copyright (c) 2012 - * MIPS Technologies, Inc., California. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * 3. Neither the name of the MIPS Technologies, Inc., nor the names of its - * contributors may be used to endorse or promote products derived from - * this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE MIPS TECHNOLOGIES, INC. ``AS IS'' AND - * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - * ARE DISCLAIMED. IN NO EVENT SHALL THE MIPS TECHNOLOGIES, INC. BE LIABLE - * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL - * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS - * OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) - * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT - * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY - * OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF - * SUCH DAMAGE. - * - * Author: Nedeljko Babic (nbabic@mips.com) - * - * adaptive and fixed codebook vector operations for ACELP-based codecs - * optimized for MIPS - * - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - */ - -/** - * @file - * Reference: libavcodec/acelp_vectors.c - */ -#include "config.h" -#include "libavcodec/acelp_vectors.h" -#include "libavutil/mips/asmdefs.h" - -#if HAVE_INLINE_ASM -#if !HAVE_MIPS32R6 && !HAVE_MIPS64R6 -static void ff_weighted_vector_sumf_mips( - float *out, const float *in_a, const float *in_b, - float weight_coeff_a, float weight_coeff_b, int length) -{ - const float *a_end = in_a + length; - - /* loop unrolled two times */ - __asm__ volatile ( - "blez %[length], ff_weighted_vector_sumf_end%= \n\t" - - "ff_weighted_vector_sumf_madd%=: \n\t" - "lwc1 $f0, 0(%[in_a]) \n\t" - "lwc1 $f3, 4(%[in_a]) \n\t" - "lwc1 $f1, 0(%[in_b]) \n\t" - "lwc1 $f4, 4(%[in_b]) \n\t" - "mul.s $f2, %[weight_coeff_a], $f0 \n\t" - "mul.s $f5, %[weight_coeff_a], $f3 \n\t" - "madd.s $f2, $f2, %[weight_coeff_b], $f1 \n\t" - "madd.s $f5, $f5, %[weight_coeff_b], $f4 \n\t" - PTR_ADDIU "%[in_a],8 \n\t" - PTR_ADDIU "%[in_b],8 \n\t" - "swc1 $f2, 0(%[out]) \n\t" - "swc1 $f5, 4(%[out]) \n\t" - PTR_ADDIU "%[out], 8 \n\t" - "bne %[in_a], %[a_end], ff_weighted_vector_sumf_madd%= \n\t" - - "ff_weighted_vector_sumf_end%=: \n\t" - - : [out] "+r" (out), [in_a] "+r" (in_a), [in_b] "+r" (in_b) - : [weight_coeff_a] "f" (weight_coeff_a), - [weight_coeff_b] "f" (weight_coeff_b), - [length] "r" (length), [a_end]"r"(a_end) - : "$f0", "$f1", "$f2", "$f3", "$f4", "$f5", "memory" - ); -} -#endif /* !HAVE_MIPS32R6 && !HAVE_MIPS64R6 */ -#endif /* HAVE_INLINE_ASM */ - -void ff_acelp_vectors_init_mips(ACELPVContext *c) -{ -#if HAVE_INLINE_ASM -#if !HAVE_MIPS32R6 && !HAVE_MIPS64R6 - c->weighted_vector_sumf = ff_weighted_vector_sumf_mips; -#endif -#endif -} diff --git a/spaces/congsaPfin/Manga-OCR/logs/Backgammon Live A Free and Fun Online Game for Facebook Users - Download Here.md b/spaces/congsaPfin/Manga-OCR/logs/Backgammon Live A Free and Fun Online Game for Facebook Users - Download Here.md deleted file mode 100644 index 6d3c78514f6a17f8ec26457f04fe779a166bbc05..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Backgammon Live A Free and Fun Online Game for Facebook Users - Download Here.md +++ /dev/null @@ -1,133 +0,0 @@ - -

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Backgammon Live is an online game that allows you to play backgammon against live opponents from all over the world. You can choose from different modes, challenges, tournaments, and boards, and chat with other players while you play. You can also invite your friends to join you, or make new friends along the way.

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Backgammon Live is free and ad-free, and it is compatible with any device that can access Facebook. You can download it from the Google Play Store or the App Store, or simply play it on your browser. You can also connect it to your Facebook account, and share your achievements and progress with your friends.

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Why download Backgammon Live on Facebook?

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There are many reasons why you should download Backgammon Live on Facebook. Here are some of them:

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  • You can play backgammon anytime, anywhere, with anyone. You don't need a physical board or dice, or a partner to play with. You can just log in to your Facebook account, and start playing right away.
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  • You can improve your skills and learn new strategies. Backgammon Live offers different levels of difficulty, from easy to expert, so you can challenge yourself and improve your game. You can also learn from other players, watch replays of their moves, and get feedback on your performance.
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  • You can have fun and socialize with other players. Backgammon Live is not just a game, but also a community. You can chat with other players, send them emojis and gifts, join clubs and teams, and participate in events and competitions. You can also make new friends who share your passion for backgammon.
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  • You can enjoy the classic game with a modern twist. Backgammon Live features colorful and lively graphics, sound effects, and animations that make the game more engaging and entertaining. You can also customize your board, checkers, dice, and avatar to suit your style and preferences.
    • -
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How to play Backgammon Live

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If you are new to backgammon, or need a refresher on the rules, here is a quick guide on how to play Backgammon Live:

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Setting up the board

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The backgammon board consists of 24 narrow triangles called points, which are divided into four quadrants of six points each. The quadrants are called the home board and the outer board for each player. The middle of the board is separated by a ridge called the bar.

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Each player has 15 checkers of one color (white or black), which are placed on the board as follows:

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  • Two checkers on the 24 point
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  • Five checkers on the 13 pointThree checkers on the 8 point
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  • Five checkers on the 6 point
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The points are numbered from 1 to 24, starting from the home board of each player. The point that is farthest away from a player's home board is called the opponent's ace point, and the point that is closest to a player's home board is called the player's ace point.

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Each player has two dice and a dice cup to roll them. The dice are used to determine how many points a player can move their checkers in a turn. The player who rolls the highest number on both dice goes first. If both players roll the same number, they roll again until they get different numbers.

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Moving your checkers

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The objective of the game is to move all your checkers into your home board, and then bear them off the board. The first player to bear off all their checkers wins the game.

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To move your checkers, you must follow these rules:

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  • You can move one or two checkers, depending on the numbers you roll on the dice. For example, if you roll a 3 and a 5, you can move one checker three points and another checker five points, or you can move one checker eight points.
    • -
  • You can only move your checkers to open points, which are points that are not occupied by two or more of your opponent's checkers. You can move your checkers to points that are empty, or that have one or more of your own checkers, or that have only one of your opponent's checkers.
    • -
  • You must use both numbers of a roll if possible. If you can only use one number, you must use the higher one. If you cannot use either number, you lose your turn.
    • -
  • If you roll a double (the same number on both dice), you can move four times the number shown on the dice. For example, if you roll a double 4, you can move four checkers four points each, or two checkers eight points each, or any combination that adds up to 16 points.
    • -
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Hitting and entering

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If you land on a point that has only one of your opponent's checkers, you can hit that checker and send it to the bar. This is called hitting. Your opponent must then enter that checker from the bar before they can move any other checkers.

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To enter a checker from the bar, you must roll a number that corresponds to an open point in your opponent's home board. For example, if you have a checker on the bar and you roll a 6, you can enter that checker on the 6 point in your opponent's home board, if it is open. If it is not open, you cannot enter and you lose your turn.

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If you have more than one checker on the bar, you must enter them one at a time, using the numbers of a roll separately. For example, if you have two checkers on the bar and you roll a 3 and a 5, you can enter one checker on the 3 point and another checker on the 5 point in your opponent's home board, if they are both open. If only one of them is open, you can enter only one checker and lose the rest of your turn. If none of them are open, you cannot enter any checker and lose your turn.

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Bearing off

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Once you have moved all your checkers into your home board, you can start bearing them off the board. This means removing them from the board permanently. To bear off a checker, you must roll a number that corresponds to the point where the checker is located. For example, if you have a checker on the 4 point and you roll a 4, you can bear off that checker.

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If there is no checker on the point indicated by the roll, you must make a legal move using a checker on a higher-numbered point. If there is no checker on a higher-numbered point, you must remove a checker from the highest point that has a checker.

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If you roll a double, you can bear off up to four checkers using each die twice.

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You cannot bear off any checkers unless all your checkers are in your home board. If your opponent hits one of your checkers while you are bearing off, you must enter that checker from the bar and bring it back to your home board before continuing to bear off.

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How to win at Backgammon Live

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Backgammon Live is not only a game of chance but also a game of skill and strategy. There are different ways to play and win at backgammon, depending on the situation and your style. Here are some of the most common and effective strategies that you can use to gain an advantage over your opponent:

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The running game

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The running game is a simple and straightforward strategy that involves moving your checkers as fast as possible to your home board, without engaging in any battles with your opponent. This strategy works best when you have a lead in the race, and when your opponent has a weak or scattered position.

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To play the running game, you should try to roll high numbers and avoid getting hit by your opponent. You should also avoid blocking your own checkers or leaving any blots (single checkers) that can be hit by your opponent. You should aim to bear off your checkers as soon as possible, before your opponent can catch up or hit you.

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The blitz

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The blitz is an aggressive and risky strategy that involves attacking your opponent's checkers and trying to close them out of the game. This strategy works best when you have an early lead in the race, and when your opponent has many checkers in their home board or on the bar.

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To play the blitz, you should try to roll low numbers and hit as many of your opponent's checkers as possible. You should also try to make points in your home board and outer board, to prevent your opponent from entering or escaping. You should aim to trap your opponent behind a prime (a wall of six consecutive points) or on the bar, while you bear off your checkers quickly.

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Priming

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Priming is a defensive and strategic strategy that involves building a prime in front of your opponent's checkers and blocking their movement. This strategy works best when you are behind in the race, and when your opponent has many checkers in their outer board or in the middle of the board.

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To play the priming game, you should try to roll medium numbers and make points in front of your opponent's checkers. You should also try to avoid getting hit by your opponent, or leaving any gaps in your prime that can be jumped over. You should aim to slow down your opponent's progress, while you improve your position and wait for a chance to escape.

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The holding game

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The holding game is a passive and tactical strategy that involves keeping one or more points in your opponent's home board and waiting for an opportunity to hit or escape. This strategy works best when you are slightly behind in the race, and when your opponent has some weaknesses in their position.

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To play the holding game, you should try to roll high numbers and maintain one or more anchors (points with two or more checkers) in your opponent's home board. You should also try to avoid getting hit by your opponent, or leaving any blots that can be hit by your opponent. You should aim to create threats and pressure on your opponent, while you look for a chance to hit or escape.

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Conclusion

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Backgammon Live is a fun and exciting way to play backgammon online, with your friends or strangers, on Facebook. You can download it for free, and enjoy the classic game with a modern twist. You can also improve your skills and learn new strategies, by playing against different opponents and levels of difficulty.

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If you want to play backgammon live on Facebook, all you need to do is follow these steps:

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    -
  1. Download Backgammon Live from the Google Play Store or the App Store, or play it on your browser.
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  2. Connect it to your Facebook account, and invite your friends to join you.
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  3. Choose from different modes, challenges, tournaments, and boards, and start playing right away.
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  4. Chat with other players, send them emojis and gifts, join clubs and teams, and participate in events and competitions.
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  5. Share your achievements and progress with your friends on Facebook.
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So what are you waiting for? Download Backgammon Live today, and discover why it is one of the best online games on Facebook!

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Frequently Asked Questions

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Q: How do I download Backgammon Live on Facebook?

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A: You can download Backgammon Live from the Google Play Store or the App Store, or simply play it on your browser. You can also find it on the Facebook Games section.

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Q: How do I play Backgammon Live with my friends?

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A: You can invite your friends to play Backgammon Live with you by sending them a message or a notification on Facebook. You can also join them in a game room or create a private table for them.

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Q: How do I get more coins and rewards in Backgammon Live?

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A: You can get more coins and rewards in Backgammon Live by playing more games, winning more matches, completing more challenges, joining more tournaments, and participating in more events and competitions. You can also get free coins and rewards by watching videos, spinning the wheel, opening chests, and inviting your friends.

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Q: How do I change my board, checkers, dice, and avatar in Backgammon Live?

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A: You can change your board, checkers, dice, and avatar in Backgammon Live by going to the shop and choosing from different options. You can buy them with coins or real money, or unlock them by reaching certain levels or achievements.

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Q: How do I contact the support team of Backgammon Live?

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A: You can contact the support team of Backgammon Live by going to the settings and tapping on the help button. You can also send them an email at support@backgammonlive.com, or visit their website at www.backgammonlive.com.

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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Devil May Cry Peak of Combat - The Best Hack-and-Slash Mobile Game Ever - Download Now.md b/spaces/congsaPfin/Manga-OCR/logs/Devil May Cry Peak of Combat - The Best Hack-and-Slash Mobile Game Ever - Download Now.md deleted file mode 100644 index ee2f2708004e7a759490f25746d179a245b85c62..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Devil May Cry Peak of Combat - The Best Hack-and-Slash Mobile Game Ever - Download Now.md +++ /dev/null @@ -1,113 +0,0 @@ -
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Devil May Cry: Peak of Combat - How to Download the Latest Version

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If you are a fan of the Devil May Cry series, you might be interested in trying out Devil May Cry: Peak of Combat, a mobile game spin-off that is set to launch worldwide in 2023. In this article, we will tell you everything you need to know about this game, including what it is, why you should play it, and how to download the latest version. We will also give you some features and gameplay details, as well as some tips and tricks to optimize your experience. So, let's get started!

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Introduction

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What is Devil May Cry: Peak of Combat?

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Devil May Cry: Peak of Combat is an authorized mobile game created by NebulaJoy Games, with the deep participation of the official team of CAPCOM Devil May Cry. The game inherits the Devil May Cry's free, flexible, strategy skills and gorgeous, unconstrained fighting style, and at the same time, it also brings players an immersive combo experience with its industry-leading motion capture technology, which perfectly reproduces the most distinctive battles of Devil May Cry.

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The game also restores the classic characters, scenes, weapons, and BOSS of the Devil May Cry series to the greatest extent, and presents the unprecedented Gothic world with the highest quality art scenes and visual effects. Moreover, the game has a brand-new story with familiar characters, such as Dante, Virgil, Nero, and Lady. You can also enjoy various game modes, such as roguelike gameplay, co-op, and PVP battles.

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Why should you play it?

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If you are a fan of the Devil May Cry series, you will definitely love this game. It is a faithful adaptation of the original games, with a lot of references and easter eggs that will make you feel nostalgic. It is also a great way to experience the thrilling action and stylish combat of Devil May Cry on your mobile device. You can customize your character with different weapons and skills, and unleash your creativity in creating combos and strategies. You can also challenge yourself with various modes and difficulties, or team up with other players online.

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If you are new to the Devil May Cry series, this game is also a good introduction to it. It has a simple and intuitive control system that will make you feel comfortable in no time. It also has a rich and engaging story that will keep you hooked until the end. You will meet many interesting characters and enemies along the way, and learn more about the lore and history of the Devil May Cry universe. You will also have fun exploring the stunning Gothic world that is full of secrets and surprises.

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How to download the latest version

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Requirements and compatibility

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Before you download the game, you need to make sure that your device meets the minimum requirements for it. According to the official website, these are:

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  • Android 5.0 or above
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  • At least 4 GB of RAM
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  • At least 5 GB of free storage space
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  • A stable internet connection
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The game is compatible with most Android devices that meet these requirements. However, some devices may have compatibility issues or performance problems due to different hardware specifications or software versions. If you encounter any problems while playing the game, you can contact the customer service team for help.

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Steps to download and install

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To download the latest version of Devil May Cry: Peak of Combat, you need to follow these steps:

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  1. Go to the official website and pre-register for the game. You will need to enter your email address and choose your preferred platform (Android or iOS). You will also receive a verification code to confirm your registration.
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  2. After you pre-register, you will receive an email with a link to download the game. You can also scan the QR code on the website to download the game directly. The game is not available on the Google Play Store or the App Store, so you need to download it from the official source.
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  3. Once you download the game, you need to install it on your device. You may need to enable the installation of apps from unknown sources in your device settings. You can follow the instructions on the screen to complete the installation process.
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  4. After you install the game, you need to launch it and log in with your email address and verification code. You will also need to agree to the terms of service and privacy policy of the game. You can then choose your server and start playing the game.
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Tips and tricks to optimize your experience

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To make sure that you have a smooth and enjoyable experience while playing Devil May Cry: Peak of Combat, here are some tips and tricks that you can follow:

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  • Adjust the graphics settings according to your device performance. You can choose from low, medium, high, or ultra settings in the game options. You can also turn on or off some features, such as anti-aliasing, shadows, bloom, etc. If you have a low-end device, you may want to lower the graphics settings to avoid lag or crashes.
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  • Use headphones or earphones to enjoy the immersive sound effects and music of the game. The game has a high-quality sound design that will make you feel like you are in the middle of the action. You can also adjust the volume and sound effects in the game options.
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  • Connect to a stable and fast internet connection. The game requires a constant internet connection to play, so you need to make sure that you have a reliable and fast network. You can use Wi-Fi or mobile data, but be aware of your data usage and charges. You can also check your ping and network status in the game options.
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  • Join the official community and social media platforms of the game. The game has a dedicated website, Facebook page, Twitter account, YouTube channel, and Discord server where you can find more information, news, updates, events, guides, tips, and support for the game. You can also interact with other players and developers, share your feedback and suggestions, and participate in various activities and contests.
    • -
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Features and gameplay

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Characters and weapons

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The game features four playable characters from the Devil May Cry series: Dante, Virgil, Nero, and Lady. Each character has their own unique weapons, skills, and styles that suit different preferences and situations. You can switch between characters at any time during the game, as well as customize their appearance and equipment.

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The game also has a variety of weapons that you can use to fight against enemies and bosses. These include swords, guns, gauntlets, whips, rocket launchers, motorcycles, etc. Each weapon has its own attributes, such as damage, speed, range, etc., as well as special effects and abilities that can be activated by using different buttons or gestures. You can also upgrade your weapons with materials that you collect from missions or shops.

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Combat and skills

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The game has a fast-paced and fluid combat system that allows you to unleash your creativity and style in creating combos and strategies. You can use different buttons or gestures to perform various actions, such as attack, dodge, jump, lock-on, etc. You can also use skills that are specific to each character or weapon, such as Devil Trigger, Exceed, Royal Guard, etc. These skills can enhance your power, speed, defense, or other aspects for a limited time.

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The game also has a style system that rewards you for performing well in combat. The more stylish and varied your combos are, the higher your style rank will be. The style rank ranges from D (Dull) to SSS (Smokin' Sexy Style), and it affects your score and rewards at the end of each mission. You can also use taunts to increase your style rank faster or provoke enemies.

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Modes and challenges

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The game has various modes and challenges that you can enjoy alone or with other players online. These include:

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    -
  • Story mode: This is the main mode of the game where you follow a brand-new story with familiar characters from the Devil May Cry series. You will face different enemies and bosses along the way, as well as explore various locations and secrets. You can choose from different difficulties such as Human, Devil Hunter, Son of Sparda, Dante Must Die, etc., depending on your skill level and preference.
    • -
  • Roguelike mode: This is a mode where you enter a randomly generated dungeon with different rooms and enemies. You can choose your character and weapon at the start, and then try to survive as long as possible. You can also find various items and upgrades along the way, such as health, devil trigger, skills, etc. However, if you die, you will lose everything and have to start over.
    • -
  • Co-op mode: This is a mode where you can team up with other players online and complete missions together. You can choose from different missions with different objectives and difficulties, such as killing a certain number of enemies, defeating a boss, protecting a target, etc. You can also chat and communicate with your teammates using voice or text.
    • -
  • PVP mode: This is a mode where you can compete with other players online in various modes and arenas. You can choose from different modes such as Deathmatch, Team Deathmatch, Capture the Flag, King of the Hill, etc., and fight against other players using your character and weapon of choice. You can also rank up and earn rewards based on your performance.
    • -
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Graphics and sound

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The game has stunning graphics and sound that will make you feel like you are playing a console game on your mobile device. The game uses the Unreal Engine 4 to create realistic and detailed graphics that showcase the Gothic world of Devil May Cry. The game also has dynamic lighting and shadow effects, as well as smooth animations and transitions. The game supports 60 FPS on high-end devices, which makes the gameplay more fluid and responsive.

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The game also has immersive sound effects and music that will enhance your experience. The game has original voice actors from the Devil May Cry series, such as Reuben Langdon (Dante), Johnny Yong Bosch (Nero), Daniel Southworth (Virgil), etc., who deliver their lines with emotion and personality. The game also has original music composed by Casey Edwards, who also worked on Devil May Cry 5. The music is a mix of rock, metal, electronic, and orchestral genres that match the mood and atmosphere of the game.

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Conclusion

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Summary of the main points

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In conclusion, Devil May Cry: Peak of Combat is an authorized mobile game spin-off of the Devil May Cry series that is set to launch worldwide in 2023. The game inherits the free, flexible, strategy skills and gorgeous, unconstrained fighting style of the original games, as well as brings new features and gameplay modes for mobile devices. The game also has a brand-new story with familiar characters from the Devil May Cry series, as well as stunning graphics and sound that will make you feel like you are in the middle of the action.

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Call to action

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If you are interested in playing Devil May Cry: Peak of Combat, you can pre-register for the game on the official website and receive a link to download the latest version when it is available. You can also follow the official social media platforms of the game to get more information, news, updates, events, guides, tips, and support. Don't miss this opportunity to experience the thrilling action and stylish combat of Devil May Cry on your mobile device!

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FAQs

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Here are some frequently asked questions about Devil May Cry: Peak of Combat:

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    -
  • Q: Is Devil May Cry: Peak of Combat free to play?
    • -
  • A: Yes, Devil May Cry: Peak of Combat is free to download and play. However, the game may have some optional in-app purchases that can enhance your gameplay or unlock some features.
    • -
  • Q: When will Devil May Cry: Peak of Combat be released worldwide?
    • -
  • A: According to the official website, Devil May Cry: Peak of Combat is expected to be released worldwide in 2023. However, there is no exact date or month announced yet.
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  • Q: Can I play Devil May Cry: Peak of Combat offline?
    • -
  • A: No, Devil May Cry: Peak of Combat requires a constant internet connection to play. You need to have a stable and fast network to enjoy the game without any problems.
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  • Q: Can I play Devil May Cry: Peak of Combat with a controller?
    • -
  • A: Yes, Devil May Cry: Peak of Combat supports external controllers that are compatible with Android devices. You can connect your controller via Bluetooth or USB and customize your controls in the game options.
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  • Q: How can I contact the customer service team of Devil May Cry: Peak of Combat?
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  • A: You can contact the customer service team of Devil May Cry: Peak of Combat by using the following methods:
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    • Email: support@nebulajoy.com
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    • Facebook: https://www.facebook.com/DevilMayCryMobileGlobal
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    • YouTube: https://www.youtube.com/channel/UC8Y9Zm1gZy0X4c7wQn2xqKw
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    • Discord: https://discord.gg/DevilMayCryMobile
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Explore Thousands of Unofficial Android Apps with 1 APK Market.md b/spaces/congsaPfin/Manga-OCR/logs/Explore Thousands of Unofficial Android Apps with 1 APK Market.md deleted file mode 100644 index bbf63e424500aa40b4f1712a5ed560d873d28b2a..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Explore Thousands of Unofficial Android Apps with 1 APK Market.md +++ /dev/null @@ -1,98 +0,0 @@ - -

1 APK Market: A Guide to the Best Android App Store Alternative

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If you are looking for a way to download and install free Android apps and games that are not available on the Google Play Store, you might want to check out 1 APK Market. This is a third-party app store that offers a wide range of apps and games for Android devices, including some popular ones like TikTok, WhatsApp, Facebook Messenger, UC Browser, PUBG Mobile, Brawl Stars, and many others. In this article, we will explain what 1 APK Market is, how to use it, what an APK file is, and what are some other Android app store alternatives that you can try.

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What is 1 APK Market?

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1 APK Market is a website that hosts thousands of Android apps and games in the form of APK files. APK files are the package files that contain all the necessary components for an Android app or game to run on your device. You can download these files from 1 APK Market and install them manually on your device without using the Google Play Store. This way, you can access apps and games that are not available in your region, that are removed from the Play Store, or that are not compatible with your device.

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  • It updates its apps and games regularly to provide you with the latest versions and features.
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  • It has a high level of security and privacy, as it does not require any registration or personal information to use it.
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  • It has a zero-tolerance policy for malware and viruses, as it scans all the apps and games before uploading them to its website.
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  • It supports multiple languages, including English, Spanish, French, German, Portuguese, Russian, Arabic, Chinese, Japanese, Korean, Hindi, Indonesian, Turkish, Vietnamese, Thai, Malay, Filipino, Persian, Urdu, Bengali, Tamil, Telugu, Kannada, Malayalam, Marathi, Gujarati, Punjabi, Nepali, Sinhala, Burmese, Khmer, Lao.
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How to download and install 1 APK Market

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To download and install 1 APK Market on your Android device, follow these steps:

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  1. Go to https://1apk-market.com/ on your device's browser.
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  2. Tap on the Download button to download the 1 APK Market app file.
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  3. Once the download is complete, open the file manager app on your device and locate the downloaded file.
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  4. Tap on the file to install it. You may need to enable the Unknown Sources option in your device's settings to allow installation from sources other than the Play Store.
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  5. Once the installation is complete, open the 1 APK Market app and enjoy downloading and installing free apps and games.
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What is an APK file and why do you need it?

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An APK file is a package file that contains all the necessary components for an Android app or game to run on your device. It has the .apk file extension and can be opened by any ZIP file decompression tool. An APK file typically contains an AndroidManifest.xml file that defines the app's name, version, permissions, activities, services, broadcast receivers, content providers, and resources, as well as a META-INF folder that contains the app's signature and certificate. It also contains a res folder that contains the app's resources, such as images, sounds, strings, layouts, etc., and a classes.dex file that contains the app's compiled code.

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You need an APK file if you want to install an app or game that is not available on the Play Store, or if you want to update an app or game to a newer version that is not yet released on the Play Store. You can also use an APK file to backup your apps and games, or to share them with others.

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The advantages and risks of using APK files

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Using APK files has some advantages and risks that you should be aware of before downloading and installing them. Some of the advantages are:

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  • You can access apps and games that are not available in your region, that are removed from the Play Store, or that are not compatible with your device.
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  • You can update your apps and games to the latest versions and features before they are released on the Play Store.
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  • You can save storage space on your device by installing only the APK files that you need, instead of downloading the entire app or game from the Play Store.
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  • You can backup your apps and games and restore them later if you lose them or change your device.
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  • You can share your apps and games with others who do not have access to the Play Store or have limited internet connection.
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Some of the risks are:

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  • You may download and install fake, malicious, or infected APK files that can harm your device or compromise your privacy and security.
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  • You may violate the terms and conditions of the app or game developers or publishers by using their products without their permission or consent.
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  • You may encounter compatibility issues, bugs, errors, or crashes when using APK files that are not optimized for your device or Android version.
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  • You may miss out on some features, updates, or support that are only available on the Play Store version of the app or game.
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  • You may lose your warranty or technical support from your device manufacturer or carrier if you install unauthorized APK files on your device.
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What are some other Android app store alternatives?

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If you are looking for more options to download and install free Android apps and games, you can try some other Android app store alternatives that offer similar or different features and benefits. Here are some of them:

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Aptoide

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Aptoide is one of the most popular Android app store alternatives that has over 1 billion downloads and 7 million apps and games. It allows users to create their own app stores and share them with others. It also has a social network feature that lets users follow each other and discover new apps and games. Aptoide has a reputation system that ranks apps and games based on user feedback and ratings. It also has a malware detection system that scans all the apps and games before uploading them to its platform. You can download Aptoide from https://www.aptoide.com/.

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APKPure

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APKPure is another popular Android app store alternative that has over 300 million downloads and 3 million apps and games. It offers a simple and clean interface that lets users browse and search for apps and games by categories, ratings, popularity, or keywords. It also offers a one-click installation feature that automatically installs the APK files on your device without any hassle. APKPure updates its apps and games regularly to provide you with the latest versions and features. It also supports multiple languages, including English, Spanish, French, German, Portuguese, Russian, Arabic, Chinese, Japanese, Korean, Hindi, Indonesian, Turkish, Vietnamese, Thai, Malay, Filipino. You can download APKPure from https://apkpure.com/.

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F-Droid

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F-Droid is a unique Android app store alternative that focuses on free and open source software (FOSS) apps and games. It offers a curated collection of apps and games that respect your privacy, security, and freedom. It also allows users to browse and search for apps and games by categories, ratings, popularity, or keywords. It also lets users donate to the developers of their favorite apps and games to support their work. F-Droid is run by volunteers who review and verify all the apps and games before uploading them to its platform. You can download F-Droid from https://f-droid.org/.

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Amazon Appstore

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Amazon Appstore is an official Android app store alternative from Amazon that has over over 600,000 apps and games. It offers a variety of apps and games, including some exclusive ones that are only available on the Amazon Appstore. It also offers a free app of the day feature that lets users download a paid app or game for free every day. It also has a coins system that lets users earn and spend virtual currency on apps and games. It also has a parental control feature that lets users restrict access to certain apps and games based on age ratings. You can download Amazon Appstore from https://www.amazon.com/mobile-apps/b?ie=UTF8&node=2350149011.

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Conclusion

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1 APK Market is a great Android app store alternative that lets you download and install free apps and games that are not available on the Google Play Store. It has a simple and user-friendly interface, a large and diverse collection of apps and games, a high level of security and privacy, and a zero-tolerance policy for malware and viruses. However, you should also be aware of the advantages and risks of using APK files, as well as some other Android app store alternatives that you can try. We hope this article has helped you learn more about 1 APK Market and how to use it.

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FAQs

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Here are some frequently asked questions about 1 APK Market:

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  1. Is 1 APK Market safe to use?
    -Yes, 1 APK Market is safe to use, as it scans all the apps and games before uploading them to its website. It also does not require any registration or personal information to use it. However, you should always be careful when downloading and installing APK files from any source, as they may contain fake, malicious, or infected files that can harm your device or compromise your privacy and security.
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  2. Is 1 APK Market legal to use?
    -The legality of using 1 APK Market depends on your country's laws and regulations regarding intellectual property rights and digital distribution. Some apps and games may be protected by copyrights or trademarks that prohibit their unauthorized distribution or use. You should always respect the terms and conditions of the app or game developers or publishers when using their products.
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  3. How do I update the apps and games from 1 APK Market?
    -You can update the apps and games from 1 APK Market by checking the website regularly for new versions and features. You can also enable the notifications feature in the 1 APK Market app to get notified when there are updates available for your downloaded apps and games.
    4. -
  4. How do I uninstall the apps and games from 1 APK Market?
    -You can uninstall the apps and games from 1 APK Market by following the same steps as you would for any other app or game on your device. You can go to your device's settings, find the app or game you want to uninstall, and tap on the uninstall button. You can also long-press on the app or game icon on your home screen or app drawer, and drag it to the uninstall option.
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  5. How do I contact the support team of 1 APK Market?
    -You can contact the support team of 1 APK Market by sending an email to info@1apk-market.com. You can also visit their website and fill out the contact form with your name, email address, subject, and message.
    6. -

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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Get Standoff 2 MOD APK for Free and Unlock Unlimited Gold.md b/spaces/congsaPfin/Manga-OCR/logs/Get Standoff 2 MOD APK for Free and Unlock Unlimited Gold.md deleted file mode 100644 index 74fc34af7f594eaf7c04c06636a2b7ff39cdab5d..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Get Standoff 2 MOD APK for Free and Unlock Unlimited Gold.md +++ /dev/null @@ -1,100 +0,0 @@ -
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Standoff 2 Unlimited Gold APK Download: How to Get Free Gold and Money in Standoff 2

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Introduction

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If you are a fan of first-person shooter games, you might have heard of Standoff 2, a dynamic and realistic FPS game that has millions of players worldwide. In this game, you can choose from a variety of weapons, modes, maps, and skins to customize your gameplay experience. However, some of these features require gold and money, which are the in-game currencies that you can earn by playing or buy with real money. But what if you want to get unlimited gold and money without spending a dime? Is there a way to do that?

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The answer is yes, there is a way to get free gold and money in Standoff 2, and that is by downloading the Standoff 2 Unlimited Gold APK. This is a modified version of the original game that gives you access to unlimited resources and features that will make your game more fun and exciting. In this article, we will tell you what is Standoff 2 Unlimited Gold APK, what are its benefits, and how to download and install it on your Android device.

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What is Standoff 2?

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Standoff 2 is a popular online multiplayer action FPS game developed by AXLEBOLT LTD. It is the sequel to the original Standoff game, which was released in 2016. Standoff 2 has improved graphics, animation, sound effects, and gameplay compared to its predecessor. It also has more than 20 weapon models, multiple modes, stunning maps, competitive ranks, clans, skins, stickers, charms, and more. You can play as a terrorist or a counter-terrorist in different scenarios and objectives. You can also team up with your friends or other players from around the world and participate in tournaments where your ranking is at stake.

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Standoff 2 is available for free on Google Play Store and App Store . You can also play it on your PC or Mac using an emulator like BlueStacks . However, some items and features in the game require gold and money, which are not easy to earn or cheap to buy. That's why some players look for alternative ways to get them for free.

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What is Standoff 2 Unlimited Gold APK?

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Standoff 2 Unlimited Gold APK is a modified version of the original Standoff 2 game that gives you unlimited gold and money in the game. It also unlocks all skins, removes ads, and adds other features that are not available in the official version. With this APK, you can enjoy all the benefits of the game without spending any money or time.

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Standoff 2 Unlimited Gold APK is not developed or endorsed by AXLEBOLT LTD, the original developer of the game. It is created by third-party developers who modify the game files to provide unlimited resources and features. Therefore, it is not available on Google Play Store or App Store. You have to download it from other sources like Find Me Apk , which is a website that provides various modded games and apps for Android devices.

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Benefits of Standoff 2 Unlimited Gold APK

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Unlocked All Skins

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One of the benefits of Standoff 2 Unlimited Gold APK is that it unlocks all skins for your weapons. Skins are cosmetic items that change the appearance of your guns. They can make your weapons look more cool, stylish, or unique. There are hundreds of skins in the game, ranging from common to legendary. Some of them are exclusive to certain events, seasons, or ranks. You can get skins by opening cases, which cost gold or money. However, with Standoff 2 Unlimited Gold APK, you don't have to worry about that. You can get any skin you want for free and customize your weapons as you like.

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Unlimited Gold and Money

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Another benefit of Standoff 2 Unlimited Gold APK is that it gives you unlimited gold and money in the game. Gold and money are the in-game currencies that you can use to buy cases, skins, stickers, charms, and other items. You can also use them to upgrade your weapons, unlock new modes, and participate in tournaments. You can earn gold and money by playing the game, watching ads, completing tasks, or buying them with real money. However, these methods are slow, tedious, or expensive. With Standoff 2 Unlimited Gold APK, you don't have to do any of that. You can get as much gold and money as you want for free and spend them on anything you want.

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No Ads

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A third benefit of Standoff 2 Unlimited Gold APK is that it removes all ads from the game. Ads are annoying and distracting, especially when you are playing an action-packed game like Standoff 2. They can interrupt your gameplay, slow down your device, or consume your data. You can skip some ads by paying gold or money, but not all of them. With Standoff 2 Unlimited Gold APK, you don't have to deal with any ads at all. You can enjoy a smooth and uninterrupted gaming experience without any hassle.

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How to Download and Install Standoff 2 Unlimited Gold APK

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Step 1: Download the APK file and OBB file

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The first step to download and install Standoff 2 Unlimited Gold APK is to download the APK file and OBB file from a reliable source like Find Me Apk . The APK file is the application file that contains the modified version of the game. The OBB file is the data file that contains the game assets like graphics, sound, and animation. You need both files to run the game properly.

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To download the files, go to Find Me Apk and search for Standoff 2 Unlimited Gold APK. You will see a download button on the page. Click on it and wait for the download to start. The files are about 500 MB in size, so make sure you have enough storage space and a stable internet connection.

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Step 2: Allow installation from unknown sources

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The second step to download and install Standoff 2 Unlimited Gold APK is to allow installation from unknown sources on your Android device. This is because Standoff 2 Unlimited Gold APK is not from Google Play Store or App Store, so your device might block it by default. To enable installation from unknown sources, follow these steps:

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  • Go to Settings on your device.
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  • Tap on Security or Privacy.
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  • Find the option that says Unknown Sources or Install Unknown Apps.
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  • Toggle it on or allow it for your browser or file manager.
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  • You will see a warning message that says installing from unknown sources might harm your device. Ignore it and tap on OK.
    • -
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Step 3: Install the APK file and extract the OBB file

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The third step to download and install Standoff 2 Unlimited Gold APK is to install the APK file and extract the OBB file on your device. To do this, follow these steps:

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  • Go to your download folder or notification bar and find the APK file that you downloaded.
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  • Tap on it and follow the instructions on the screen to install it.
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  • You will see a message that says App Installed when the installation is done.
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  • Do not open the game yet. You still need to extract the OBB file.
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  • Go to your download folder or notification bar again and find the OBB file that you downloaded.
    • -
  • Tap on it and select Extract Here or Extract To depending on your file manager.
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  • You will see a folder named com.axlebolt.standoff2 when the extraction is done.
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  • Move this folder to Android/OBB on your device's internal storage.
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  • If you don't have an OBB folder in Android, create one first.
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Step 4: Launch the game and enjoy

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The final step to download and install Standoff 2 Unlimited Gold APK is to launch the game and enjoy it. To do this, follow these steps:- Go to your app drawer or home screen and find the Standoff 2 icon.

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- Tap on it and wait for the game to load.

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- You will see a message that says Welcome to Standoff 2 Mod Menu. Tap on OK.

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- You will see a menu that shows the features of the mod. You can enable or disable them as you wish.

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- Tap on Start Game and choose your mode, map, and team.

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- Enjoy the game with unlimited gold and money, unlocked skins, and no ads.

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Conclusion

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Standoff 2 is a thrilling and realistic FPS game that you can play online with your friends or other players from around the world. However, if you want to get unlimited gold and money, unlocked skins, and no ads in the game, you can download and install Standoff 2 Unlimited Gold APK. This is a modified version of the game that gives you access to all the features and resources that you need to enjoy the game to the fullest. In this article, we have explained what is Standoff 2 Unlimited Gold APK, what are its benefits, and how to download and install it on your Android device. We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below.

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FAQs

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Here are some frequently asked questions about Standoff 2 Unlimited Gold APK:

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  • Is Standoff 2 Unlimited Gold APK safe to use?
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    Standoff 2 Unlimited Gold APK is safe to use as long as you download it from a trusted source like Find Me Apk . However, since it is a modded version of the game, it might not be compatible with some devices or updates. It might also cause some glitches or errors in the game. Therefore, use it at your own risk and discretion.

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  • Is Standoff 2 Unlimited Gold APK legal to use?
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    Standoff 2 Unlimited Gold APK is not legal to use as it violates the terms and conditions of the original game. It also infringes the intellectual property rights of AXLEBOLT LTD, the original developer of the game. Therefore, using it might result in a ban from the game or legal action from the developer. Therefore, use it at your own risk and discretion.

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  • Can I play Standoff 2 Unlimited Gold APK online with other players?
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    Yes, you can play Standoff 2 Unlimited Gold APK online with other players who are using the same modded version of the game. However, you might not be able to play with players who are using the official version of the game. You might also face some lag or connection issues while playing online.

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  • Can I update Standoff 2 Unlimited Gold APK?
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    No, you cannot update Standoff 2 Unlimited Gold APK as it is not from Google Play Store or App Store. If you want to update the game, you have to uninstall the modded version and install the official version from Google Play Store or App Store . However, you will lose all your progress and resources in the modded version if you do that.

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  • Can I use Standoff 2 Unlimited Gold APK on my PC or Mac?
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    Yes, you can use Standoff 2 Unlimited Gold APK on your PC or Mac using an emulator like BlueStacks . However, you have to follow the same steps as mentioned above to download and install it on your emulator. You might also need to adjust some settings on your emulator to make the game run smoothly.

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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Mob Control A Simple but Challenging Castle Defense Game.md b/spaces/congsaPfin/Manga-OCR/logs/Mob Control A Simple but Challenging Castle Defense Game.md deleted file mode 100644 index 828c12108f234c76e1a031bfe9136af6f53be094..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Mob Control A Simple but Challenging Castle Defense Game.md +++ /dev/null @@ -1,21 +0,0 @@ -
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-Mob control is the intentional or unwitting use of techniques based on the principles of crowd psychology to engage, control, or influence the desires of a crowd in order to direct its behavior toward a specific action. Mob control can be used for positive or negative purposes, such as maintaining public order, preventing riots, promoting social movements, manipulating public opinion, or advancing criminal interests. ## Why Is Mob Control Used?

-Mob control is used for various reasons depending on the goals and motives of the actors involved. Some of the common reasons are: - To protect public safety and property from violent or unlawful crowds - To enforce laws and regulations - To deter or disperse potential threats or enemies - To gain political or economic power or influence - To exploit the needs or emotions of the crowd - To create or maintain social cohesion or identity ## How Is Mob Control Done?

-Mob control can be done in different ways depending on the context and the resources available. Some of the common methods are: - Using physical force or weapons to intimidate, injure, or arrest the crowd members - Using less lethal weapons such as batons, whips, tear gas, pepper spray, rubber bullets, stun grenades, water cannons, etc. to disperse or incapacitate the crowd - Using psychological tactics such as propaganda, persuasion, deception, provocation, etc. to influence the crowd's attitudes, beliefs, emotions, or actions - Using communication tools such as loudspeakers, megaphones, radios, social media, etc. to convey messages or instructions to the crowd - Using organizational strategies such as barriers, checkpoints, patrols, surveillance, etc. to restrict or monitor the crowd's movements or activities - Using cultural symbols such as flags, banners, slogans, songs, etc. to appeal to the crowd's values, identities, or aspirations ## What Are Some of the Benefits and Challenges of Mob Control?

-Mob control can have positive or negative outcomes depending on how it is used and what effects it has on the crowd and the society. Some of the possible benefits and challenges are: ### Benefits

-- Mob control can prevent or reduce violence, chaos, damage, or harm caused by unruly or hostile crowds - Mob control can maintain or restore law and order in times of crisis or emergency - Mob control can facilitate or support social change or progress by mobilizing or empowering crowds for a common cause - Mob control can enhance or protect the rights and interests of the crowd members or their representatives ### Challenges

-- Mob control can violate or infringe on the rights and freedoms of the crowd members or their opponents - Mob control can provoke or escalate violence, resentment, or resistance from the crowd or other parties - Mob control can manipulate or exploit the crowd for selfish or harmful purposes - Mob control can undermine or erode the trust and legitimacy of the authorities or institutions involved ## Conclusion

-Mob control is a multifaceted phenomenon that has both advantages and disadvantages depending on how it is applied and what consequences it produces. Mob control can be a useful tool for maintaining social order and promoting social justice but it can also be a dangerous weapon for causing social disorder and injustice. Therefore, mob control should be used with caution and responsibility by respecting the dignity and diversity of human beings. ## FAQs

-Here are some frequently asked questions about mob control: ### What are some examples of mob control?

-Some historical examples of mob control are: - The Boston Massacre in 1770: British soldiers fired at a mob of American colonists who were protesting against taxation without representation - The Haymarket Affair in 1886: Police clashed with a mob of labor activists who were demanding an eight-hour workday in Chicago - The Tiananmen Square Massacre in 1989: Chinese troops suppressed a pro-democracy demonstration in Beijing - The Arab Spring in 2010–2012: A series of pro-democracy protests and uprisings that swept across much of the Arab world, challenging some of the region's entrenched authoritarian regimes. Some of the protests led to successful revolutions and political transitions, such as in Tunisia, Egypt, Libya, and Yemen, while others resulted in violent repression or civil war, such as in Syria, Bahrain, and Iraq. ## Black Lives Matter: A Global Movement for Racial Justice

-Black Lives Matter (BLM) is a decentralized political and social movement that seeks to highlight racism, discrimination, and racial inequality experienced by black people. Its primary concerns are incidents of police brutality and racially motivated violence against black people. ### The Origins of Black Lives Matter

-The movement began in July 2013, with the use of the hashtag #BlackLivesMatter on social media after the acquittal of George Zimmerman in the shooting death of African-American teen Trayvon Martin 17 months earlier in February 2012. It became nationally recognized for street demonstrations following the 2014 deaths of two more African Americans, Michael Brown —resulting in protests and unrest in Ferguson, Missouri, a city near St. Louis —and Eric Garner in New York City. Since then, participants in the movement have demonstrated against the deaths of numerous other African Americans by police actions or while in police custody, such as Tamir Rice, Freddie Gray, Sandra Bland, Philando Castile, Breonna Taylor, and George Floyd. The movement also advocates for various policy changes considered to be related to black liberation, such as ending mass incarceration, defunding the police, reforming the criminal justice system, and investing in black communities. ### The Impact of Black Lives Matter

-The movement has gained widespread support and recognition from various celebrities, politicians, activists, organizations, and media outlets. It has also inspired solidarity protests and movements in other countries around the world, such as Canada, France, Germany, United Kingdom, Australia, Brazil, Japan, and South Africa. The movement has also faced criticism and opposition from some groups and individuals who accuse it of being anti-police, anti-white, violent, or divisive. Some of the counter-protests and slogans used against the movement include "All Lives Matter", "Blue Lives Matter", "White Lives Matter", and "Back the Blue". ## The Ethics of Mob Control

-Mob control raises many ethical questions and dilemmas for both the actors and the observers of crowd events. Some of the main ethical issues are: - The balance between the rights of the crowd and the rights of others: How can the rights of free speech, assembly, and protest be respected without infringing on the rights of safety, security, and property of others? How can the rights of minorities or dissenters be protected from the tyranny of the majority or the mob? How can the rights of victims or targets of mob violence be compensated or restored? - The legitimacy and accountability of the authorities: How can the authorities justify their use of force or coercion against a crowd? How can they ensure that their actions are proportionate, necessary, and lawful? How can they avoid excessive or indiscriminate use of force or abuse of power? How can they be held accountable for their actions or inactions? - The responsibility and morality of the crowd members: How can the crowd members be aware of their own motivations, emotions, and actions? How can they avoid being influenced by peer pressure, groupthink, or deindividuation? How can they resist or challenge unethical or unlawful behavior by others in the crowd? How can they be held responsible for their actions or inactions? These ethical questions have no easy or definitive answers. They require careful reflection and dialogue among all the stakeholders involved in crowd events. They also require a recognition and respect for the diversity and complexity of human beings and their social interactions. ## The Statistics of Mob Control

-Mob control is a challenging task that requires accurate and reliable data and information to support decision making and evaluation. However, collecting and analyzing data on mob control is not an easy or straightforward process. Some of the difficulties and limitations are: - The definition and measurement of mob control: How can mob control be defined and operationalized? What are the indicators and criteria for measuring its effectiveness or impact? How can different types of mob control methods and outcomes be compared or aggregated? - The availability and quality of data sources: What are the sources and methods for collecting data on mob control? How reliable, valid, and representative are they? How can they be verified or triangulated? How can they be accessed or shared? - The interpretation and communication of data findings: What are the assumptions and biases that influence data analysis and interpretation? How can data findings be presented or communicated in a clear, concise, and meaningful way? How can data findings be used to inform policy or practice? Despite these challenges, there are some existing data sources and studies that provide some insights into mob control. For example: - According to a report by Amnesty International, between January 2015 and June 2019, there were at least 181 incidents of alleged extrajudicial killings by police during protests in 22 countries across Africa. The report also found that police used excessive force against protesters in at least 40 countries in Africa during the same period. - According to a study by researchers from Harvard University, Stanford University, and New York University, between 2011 and 2015, there were more than 7,000 protests in 110 countries around the world. The study also found that protests were more likely to turn violent when police used force against protesters or when protesters faced repression from authoritarian regimes. - According to a report by the U.S. Department of Justice, between 2009 and 2013, there were an average of 610 public disorder incidents per year in the United States, involving an average of 9,052 arrests per year. The report also found that public disorder incidents were more likely to occur in urban areas, during summer months, and on weekends. - According to a report by the International Center for Nonviolent Conflict, between 2000 and 2019, there were more than 1,000 nonviolent campaigns in 162 countries around the world, involving more than one billion participants. The report also found that nonviolent campaigns were more likely to attract diverse and large-scale participation, generate positive media coverage, and create lasting social change. ## The Future of Mob Control

-Mob control is an evolving and dynamic phenomenon that is influenced by various factors such as technology, globalization, social media, climate change, pandemics, etc. Some of the possible trends and challenges for the future of mob control are: - The emergence of new forms and methods of mob control: How will technology enable or challenge mob control? How will artificial intelligence, biometrics, drones, facial recognition, etc. affect mob control? How will cyberattacks, hacking, misinformation, etc. affect mob control? - The increase of transnational and cross-cultural mob events: How will globalization and migration affect mob control? How will cultural diversity and intercultural communication affect mob control? How will international law and human rights affect mob control? - The rise of environmental and health-related mob events: How will climate change and natural disasters affect mob control? How will pandemics and public health crises affect mob control? How will environmental activism and justice affect mob control? These trends and challenges pose new opportunities and risks for both the actors and the observers of mob events. They require innovative and adaptive solutions that can balance the needs and interests of all parties involved. ##

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\ No newline at end of file diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/fileio/handlers/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/fileio/handlers/__init__.py deleted file mode 100644 index aa24d91972837b8756b225f4879bac20436eb72a..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/fileio/handlers/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .base import BaseFileHandler -from .json_handler import JsonHandler -from .pickle_handler import PickleHandler -from .yaml_handler import YamlHandler - -__all__ = ['BaseFileHandler', 'JsonHandler', 'PickleHandler', 'YamlHandler'] diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/scripts/listener_original.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/scripts/listener_original.py deleted file mode 100644 index 20e235f6958d644b89383752ab18e9e2275f55e5..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/scripts/listener_original.py +++ /dev/null @@ -1,61 +0,0 @@ -#!/usr/bin/env python3 -from __future__ import print_function - -import roslib -#roslib.load_manifest('my_package') -import sys -import rospy -import cv2 -import numpy as np -from std_msgs.msg import String -from sensor_msgs.msg import Image -from cv_bridge import CvBridge, CvBridgeError - -class video_show: - - def __init__(self): - self.show_output = rospy.get_param('~show_output', True) - self.save_output = rospy.get_param('~save_output', False) - self.output_video_file = rospy.get_param('~output_video_file','result.mp4') - # rospy.loginfo(f"Listener original - params: show_output={self.show_output}, save_output={self.save_output}, output_video_file={self.output_video_file}") - - self.bridge = CvBridge() - self.image_sub = rospy.Subscriber("image_topic", Image, self.callback) - - def callback(self, data): - try: - cv_image = self.bridge.imgmsg_to_cv2(data) - except CvBridgeError as e: - print(e) - return - - if cv_image.size == 0: - return - - rospy.loginfo("Listener_original: Received new frame") - cv_image = cv_image.astype("uint8") - - if self.show_output==True: - cv2.imshow("video_show_orig", cv_image) - cv2.waitKey(10) - - if self.save_output==True: - if self.video_writer_init==False: - fourcc = cv2.VideoWriter_fourcc(*'XVID') - self.out = cv2.VideoWriter(self.output_video_file, fourcc, 25, (cv_image.shape[1], cv_image.shape[0])) - - self.out.write(cv_image) - - - -def main(args): - rospy.init_node('listener_original', anonymous=True) - ic = video_show() - try: - rospy.spin() - except KeyboardInterrupt: - print("Shutting down") - cv2.destroyAllWindows() - -if __name__ == '__main__': - main(sys.argv) \ No newline at end of file diff --git a/spaces/crashedice/signify/SOURCE/yolo_files/utils/flask_rest_api/example_request.py b/spaces/crashedice/signify/SOURCE/yolo_files/utils/flask_rest_api/example_request.py deleted file mode 100644 index ff21f30f93ca37578ce45366a1ddbe3f3eadaa79..0000000000000000000000000000000000000000 --- a/spaces/crashedice/signify/SOURCE/yolo_files/utils/flask_rest_api/example_request.py +++ /dev/null @@ -1,13 +0,0 @@ -"""Perform test request""" -import pprint - -import requests - -DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s" -TEST_IMAGE = "zidane.jpg" - -image_data = open(TEST_IMAGE, "rb").read() - -response = requests.post(DETECTION_URL, files={"image": image_data}).json() - -pprint.pprint(response) diff --git a/spaces/cymic/Waifu_Diffusion_Webui/modules/safety.py b/spaces/cymic/Waifu_Diffusion_Webui/modules/safety.py deleted file mode 100644 index 669d7e12d39083a50e435326321c2403e662a652..0000000000000000000000000000000000000000 --- a/spaces/cymic/Waifu_Diffusion_Webui/modules/safety.py +++ /dev/null @@ -1,42 +0,0 @@ -import torch -from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker -from transformers import AutoFeatureExtractor -from PIL import Image - -import modules.shared as shared - -safety_model_id = "CompVis/stable-diffusion-safety-checker" -safety_feature_extractor = None -safety_checker = None - -def numpy_to_pil(images): - """ - Convert a numpy image or a batch of images to a PIL image. - """ - if images.ndim == 3: - images = images[None, ...] - images = (images * 255).round().astype("uint8") - pil_images = [Image.fromarray(image) for image in images] - - return pil_images - -# check and replace nsfw content -def check_safety(x_image): - global safety_feature_extractor, safety_checker - - if safety_feature_extractor is None: - safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id) - safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id) - - safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt") - x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values) - - return x_checked_image, has_nsfw_concept - - -def censor_batch(x): - x_samples_ddim_numpy = x.cpu().permute(0, 2, 3, 1).numpy() - x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim_numpy) - x = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2) - - return x diff --git a/spaces/danielcodex/first-prod/app.py b/spaces/danielcodex/first-prod/app.py deleted file mode 100644 index 14cc9807677ebc5c96172e1ea8a65514ae9d2d85..0000000000000000000000000000000000000000 --- a/spaces/danielcodex/first-prod/app.py +++ /dev/null @@ -1,29 +0,0 @@ -# AUTOGENERATED! DO NOT EDIT! File to edit: ../learning.ipynb. - -# %% auto 0 -__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image'] - -# %% ../learning.ipynb 1 -from fastai.vision.all import * -import gradio as gr - -def is_cat(x: str): return x[0].isupper() - -# %% ../learning.ipynb 3 -learn = load_learner("model.pkl") - -# %% ../learning.ipynb 7 -categories = ("Dog", "Cat") - -def classify_image(img): - pred, idx, probs = learn.predict(img) - return dict(zip(categories, map(float, probs))) - -# %% ../learning.ipynb 8 -image = gr.inputs.Image(shape=(192, 192)) -label = gr.outputs.Label() -examples = ["dog.jpg", "cat.jpg"] - -intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples) -intf.launch(inline=False) - diff --git a/spaces/daspartho/text-emotion/app.py b/spaces/daspartho/text-emotion/app.py deleted file mode 100644 index e112a1b5074f2a8e159e6c65b0c5697d16f7c33f..0000000000000000000000000000000000000000 --- a/spaces/daspartho/text-emotion/app.py +++ /dev/null @@ -1,30 +0,0 @@ -import gradio as gr -from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline - -tokenizer = AutoTokenizer.from_pretrained("daspartho/text-emotion") -model = AutoModelForSequenceClassification.from_pretrained("daspartho/text-emotion") # i've uploaded the model on HuggingFace :) - -pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, top_k=6) - -label_map={ - 'LABEL_0':'🙁', - 'LABEL_1':'😃', - 'LABEL_2':'🥰', - 'LABEL_3':'😠', - 'LABEL_4':'😬', - 'LABEL_5':'😳' - } - -def classify_text(text): - predictions = pipe(text)[0] - return {label_map[pred['label']]: float(pred['score']) for pred in predictions} - -iface = gr.Interface( - title='Text Emotion', - description = "enter a text and the model will attempt to predict the emotion.", - article = "

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", - fn=classify_text, - inputs=gr.inputs.Textbox(label="type the text here"), - outputs=gr.outputs.Label(label='what the model thinks'), - ) -iface.launch() \ No newline at end of file diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/web_middlewares.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/web_middlewares.py deleted file mode 100644 index fabcc449a2107211fd99cd59f576a2d855d0e042..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/web_middlewares.py +++ /dev/null @@ -1,119 +0,0 @@ -import re -from typing import TYPE_CHECKING, Awaitable, Callable, Tuple, Type, TypeVar - -from .typedefs import Handler -from .web_exceptions import HTTPPermanentRedirect, _HTTPMove -from .web_request import Request -from .web_response import StreamResponse -from .web_urldispatcher import SystemRoute - -__all__ = ( - "middleware", - "normalize_path_middleware", -) - -if TYPE_CHECKING: # pragma: no cover - from .web_app import Application - -_Func = TypeVar("_Func") - - -async def _check_request_resolves(request: Request, path: str) -> Tuple[bool, Request]: - alt_request = request.clone(rel_url=path) - - match_info = await request.app.router.resolve(alt_request) - alt_request._match_info = match_info - - if match_info.http_exception is None: - return True, alt_request - - return False, request - - -def middleware(f: _Func) -> _Func: - f.__middleware_version__ = 1 # type: ignore[attr-defined] - return f - - -_Middleware = Callable[[Request, Handler], Awaitable[StreamResponse]] - - -def normalize_path_middleware( - *, - append_slash: bool = True, - remove_slash: bool = False, - merge_slashes: bool = True, - redirect_class: Type[_HTTPMove] = HTTPPermanentRedirect, -) -> _Middleware: - """Factory for producing a middleware that normalizes the path of a request. - - Normalizing means: - - Add or remove a trailing slash to the path. - - Double slashes are replaced by one. - - The middleware returns as soon as it finds a path that resolves - correctly. The order if both merge and append/remove are enabled is - 1) merge slashes - 2) append/remove slash - 3) both merge slashes and append/remove slash. - If the path resolves with at least one of those conditions, it will - redirect to the new path. - - Only one of `append_slash` and `remove_slash` can be enabled. If both - are `True` the factory will raise an assertion error - - If `append_slash` is `True` the middleware will append a slash when - needed. If a resource is defined with trailing slash and the request - comes without it, it will append it automatically. - - If `remove_slash` is `True`, `append_slash` must be `False`. When enabled - the middleware will remove trailing slashes and redirect if the resource - is defined - - If merge_slashes is True, merge multiple consecutive slashes in the - path into one. - """ - correct_configuration = not (append_slash and remove_slash) - assert correct_configuration, "Cannot both remove and append slash" - - @middleware - async def impl(request: Request, handler: Handler) -> StreamResponse: - if isinstance(request.match_info.route, SystemRoute): - paths_to_check = [] - if "?" in request.raw_path: - path, query = request.raw_path.split("?", 1) - query = "?" + query - else: - query = "" - path = request.raw_path - - if merge_slashes: - paths_to_check.append(re.sub("//+", "/", path)) - if append_slash and not request.path.endswith("/"): - paths_to_check.append(path + "/") - if remove_slash and request.path.endswith("/"): - paths_to_check.append(path[:-1]) - if merge_slashes and append_slash: - paths_to_check.append(re.sub("//+", "/", path + "/")) - if merge_slashes and remove_slash: - merged_slashes = re.sub("//+", "/", path) - paths_to_check.append(merged_slashes[:-1]) - - for path in paths_to_check: - path = re.sub("^//+", "/", path) # SECURITY: GHSA-v6wp-4m6f-gcjg - resolves, request = await _check_request_resolves(request, path) - if resolves: - raise redirect_class(request.raw_path + query) - - return await handler(request) - - return impl - - -def _fix_request_current_app(app: "Application") -> _Middleware: - @middleware - async def impl(request: Request, handler: Handler) -> StreamResponse: - with request.match_info.set_current_app(app): - return await handler(request) - - return impl diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ufoLib/pointPen.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ufoLib/pointPen.py deleted file mode 100644 index 3433fdbc96cc68505a999f20919387b0d2acf31f..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ufoLib/pointPen.py +++ /dev/null @@ -1,5 +0,0 @@ -"""DEPRECATED - This module is kept here only as a backward compatibility shim -for the old ufoLib.pointPen module, which was moved to fontTools.pens.pointPen. -Please use the latter instead. -""" -from fontTools.pens.pointPen import * diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/_tight_layout.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/_tight_layout.py deleted file mode 100644 index e99ba49bd2843853d52eae375c10664e5cdac06f..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/_tight_layout.py +++ /dev/null @@ -1,301 +0,0 @@ -""" -Routines to adjust subplot params so that subplots are -nicely fit in the figure. In doing so, only axis labels, tick labels, axes -titles and offsetboxes that are anchored to axes are currently considered. - -Internally, this module assumes that the margins (left margin, etc.) which are -differences between ``Axes.get_tightbbox`` and ``Axes.bbox`` are independent of -Axes position. This may fail if ``Axes.adjustable`` is ``datalim`` as well as -such cases as when left or right margin are affected by xlabel. -""" - -import numpy as np - -import matplotlib as mpl -from matplotlib import _api, artist as martist -from matplotlib.font_manager import FontProperties -from matplotlib.transforms import Bbox - - -def _auto_adjust_subplotpars( - fig, renderer, shape, span_pairs, subplot_list, - ax_bbox_list=None, pad=1.08, h_pad=None, w_pad=None, rect=None): - """ - Return a dict of subplot parameters to adjust spacing between subplots - or ``None`` if resulting axes would have zero height or width. - - Note that this function ignores geometry information of subplot itself, but - uses what is given by the *shape* and *subplot_list* parameters. Also, the - results could be incorrect if some subplots have ``adjustable=datalim``. - - Parameters - ---------- - shape : tuple[int, int] - Number of rows and columns of the grid. - span_pairs : list[tuple[slice, slice]] - List of rowspans and colspans occupied by each subplot. - subplot_list : list of subplots - List of subplots that will be used to calculate optimal subplot_params. - pad : float - Padding between the figure edge and the edges of subplots, as a - fraction of the font size. - h_pad, w_pad : float - Padding (height/width) between edges of adjacent subplots, as a - fraction of the font size. Defaults to *pad*. - rect : tuple - (left, bottom, right, top), default: None. - """ - rows, cols = shape - - font_size_inch = (FontProperties( - size=mpl.rcParams["font.size"]).get_size_in_points() / 72) - pad_inch = pad * font_size_inch - vpad_inch = h_pad * font_size_inch if h_pad is not None else pad_inch - hpad_inch = w_pad * font_size_inch if w_pad is not None else pad_inch - - if len(span_pairs) != len(subplot_list) or len(subplot_list) == 0: - raise ValueError - - if rect is None: - margin_left = margin_bottom = margin_right = margin_top = None - else: - margin_left, margin_bottom, _right, _top = rect - margin_right = 1 - _right if _right else None - margin_top = 1 - _top if _top else None - - vspaces = np.zeros((rows + 1, cols)) - hspaces = np.zeros((rows, cols + 1)) - - if ax_bbox_list is None: - ax_bbox_list = [ - Bbox.union([ax.get_position(original=True) for ax in subplots]) - for subplots in subplot_list] - - for subplots, ax_bbox, (rowspan, colspan) in zip( - subplot_list, ax_bbox_list, span_pairs): - if all(not ax.get_visible() for ax in subplots): - continue - - bb = [] - for ax in subplots: - if ax.get_visible(): - bb += [martist._get_tightbbox_for_layout_only(ax, renderer)] - - tight_bbox_raw = Bbox.union(bb) - tight_bbox = fig.transFigure.inverted().transform_bbox(tight_bbox_raw) - - hspaces[rowspan, colspan.start] += ax_bbox.xmin - tight_bbox.xmin # l - hspaces[rowspan, colspan.stop] += tight_bbox.xmax - ax_bbox.xmax # r - vspaces[rowspan.start, colspan] += tight_bbox.ymax - ax_bbox.ymax # t - vspaces[rowspan.stop, colspan] += ax_bbox.ymin - tight_bbox.ymin # b - - fig_width_inch, fig_height_inch = fig.get_size_inches() - - # margins can be negative for axes with aspect applied, so use max(, 0) to - # make them nonnegative. - if not margin_left: - margin_left = max(hspaces[:, 0].max(), 0) + pad_inch/fig_width_inch - suplabel = fig._supylabel - if suplabel and suplabel.get_in_layout(): - rel_width = fig.transFigure.inverted().transform_bbox( - suplabel.get_window_extent(renderer)).width - margin_left += rel_width + pad_inch/fig_width_inch - if not margin_right: - margin_right = max(hspaces[:, -1].max(), 0) + pad_inch/fig_width_inch - if not margin_top: - margin_top = max(vspaces[0, :].max(), 0) + pad_inch/fig_height_inch - if fig._suptitle and fig._suptitle.get_in_layout(): - rel_height = fig.transFigure.inverted().transform_bbox( - fig._suptitle.get_window_extent(renderer)).height - margin_top += rel_height + pad_inch/fig_height_inch - if not margin_bottom: - margin_bottom = max(vspaces[-1, :].max(), 0) + pad_inch/fig_height_inch - suplabel = fig._supxlabel - if suplabel and suplabel.get_in_layout(): - rel_height = fig.transFigure.inverted().transform_bbox( - suplabel.get_window_extent(renderer)).height - margin_bottom += rel_height + pad_inch/fig_height_inch - - if margin_left + margin_right >= 1: - _api.warn_external('Tight layout not applied. The left and right ' - 'margins cannot be made large enough to ' - 'accommodate all axes decorations.') - return None - if margin_bottom + margin_top >= 1: - _api.warn_external('Tight layout not applied. The bottom and top ' - 'margins cannot be made large enough to ' - 'accommodate all axes decorations.') - return None - - kwargs = dict(left=margin_left, - right=1 - margin_right, - bottom=margin_bottom, - top=1 - margin_top) - - if cols > 1: - hspace = hspaces[:, 1:-1].max() + hpad_inch / fig_width_inch - # axes widths: - h_axes = (1 - margin_right - margin_left - hspace * (cols - 1)) / cols - if h_axes < 0: - _api.warn_external('Tight layout not applied. tight_layout ' - 'cannot make axes width small enough to ' - 'accommodate all axes decorations') - return None - else: - kwargs["wspace"] = hspace / h_axes - if rows > 1: - vspace = vspaces[1:-1, :].max() + vpad_inch / fig_height_inch - v_axes = (1 - margin_top - margin_bottom - vspace * (rows - 1)) / rows - if v_axes < 0: - _api.warn_external('Tight layout not applied. tight_layout ' - 'cannot make axes height small enough to ' - 'accommodate all axes decorations.') - return None - else: - kwargs["hspace"] = vspace / v_axes - - return kwargs - - -def get_subplotspec_list(axes_list, grid_spec=None): - """ - Return a list of subplotspec from the given list of axes. - - For an instance of axes that does not support subplotspec, None is inserted - in the list. - - If grid_spec is given, None is inserted for those not from the given - grid_spec. - """ - subplotspec_list = [] - for ax in axes_list: - axes_or_locator = ax.get_axes_locator() - if axes_or_locator is None: - axes_or_locator = ax - - if hasattr(axes_or_locator, "get_subplotspec"): - subplotspec = axes_or_locator.get_subplotspec() - if subplotspec is not None: - subplotspec = subplotspec.get_topmost_subplotspec() - gs = subplotspec.get_gridspec() - if grid_spec is not None: - if gs != grid_spec: - subplotspec = None - elif gs.locally_modified_subplot_params(): - subplotspec = None - else: - subplotspec = None - - subplotspec_list.append(subplotspec) - - return subplotspec_list - - -def get_tight_layout_figure(fig, axes_list, subplotspec_list, renderer, - pad=1.08, h_pad=None, w_pad=None, rect=None): - """ - Return subplot parameters for tight-layouted-figure with specified padding. - - Parameters - ---------- - fig : Figure - axes_list : list of Axes - subplotspec_list : list of `.SubplotSpec` - The subplotspecs of each axes. - renderer : renderer - pad : float - Padding between the figure edge and the edges of subplots, as a - fraction of the font size. - h_pad, w_pad : float - Padding (height/width) between edges of adjacent subplots. Defaults to - *pad*. - rect : tuple (left, bottom, right, top), default: None. - rectangle in normalized figure coordinates - that the whole subplots area (including labels) will fit into. - Defaults to using the entire figure. - - Returns - ------- - subplotspec or None - subplotspec kwargs to be passed to `.Figure.subplots_adjust` or - None if tight_layout could not be accomplished. - """ - - # Multiple axes can share same subplotspec (e.g., if using axes_grid1); - # we need to group them together. - ss_to_subplots = {ss: [] for ss in subplotspec_list} - for ax, ss in zip(axes_list, subplotspec_list): - ss_to_subplots[ss].append(ax) - if ss_to_subplots.pop(None, None): - _api.warn_external( - "This figure includes Axes that are not compatible with " - "tight_layout, so results might be incorrect.") - if not ss_to_subplots: - return {} - subplot_list = list(ss_to_subplots.values()) - ax_bbox_list = [ss.get_position(fig) for ss in ss_to_subplots] - - max_nrows = max(ss.get_gridspec().nrows for ss in ss_to_subplots) - max_ncols = max(ss.get_gridspec().ncols for ss in ss_to_subplots) - - span_pairs = [] - for ss in ss_to_subplots: - # The intent here is to support axes from different gridspecs where - # one's nrows (or ncols) is a multiple of the other (e.g. 2 and 4), - # but this doesn't actually work because the computed wspace, in - # relative-axes-height, corresponds to different physical spacings for - # the 2-row grid and the 4-row grid. Still, this code is left, mostly - # for backcompat. - rows, cols = ss.get_gridspec().get_geometry() - div_row, mod_row = divmod(max_nrows, rows) - div_col, mod_col = divmod(max_ncols, cols) - if mod_row != 0: - _api.warn_external('tight_layout not applied: number of rows ' - 'in subplot specifications must be ' - 'multiples of one another.') - return {} - if mod_col != 0: - _api.warn_external('tight_layout not applied: number of ' - 'columns in subplot specifications must be ' - 'multiples of one another.') - return {} - span_pairs.append(( - slice(ss.rowspan.start * div_row, ss.rowspan.stop * div_row), - slice(ss.colspan.start * div_col, ss.colspan.stop * div_col))) - - kwargs = _auto_adjust_subplotpars(fig, renderer, - shape=(max_nrows, max_ncols), - span_pairs=span_pairs, - subplot_list=subplot_list, - ax_bbox_list=ax_bbox_list, - pad=pad, h_pad=h_pad, w_pad=w_pad) - - # kwargs can be none if tight_layout fails... - if rect is not None and kwargs is not None: - # if rect is given, the whole subplots area (including - # labels) will fit into the rect instead of the - # figure. Note that the rect argument of - # *auto_adjust_subplotpars* specify the area that will be - # covered by the total area of axes.bbox. Thus we call - # auto_adjust_subplotpars twice, where the second run - # with adjusted rect parameters. - - left, bottom, right, top = rect - if left is not None: - left += kwargs["left"] - if bottom is not None: - bottom += kwargs["bottom"] - if right is not None: - right -= (1 - kwargs["right"]) - if top is not None: - top -= (1 - kwargs["top"]) - - kwargs = _auto_adjust_subplotpars(fig, renderer, - shape=(max_nrows, max_ncols), - span_pairs=span_pairs, - subplot_list=subplot_list, - ax_bbox_list=ax_bbox_list, - pad=pad, h_pad=h_pad, w_pad=w_pad, - rect=(left, bottom, right, top)) - - return kwargs diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/outputs.py b/spaces/declare-lab/tango/diffusers/src/diffusers/utils/outputs.py deleted file mode 100644 index b6e8a219e129ce66ce80f21b5da73dad900616b5..0000000000000000000000000000000000000000 --- a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/outputs.py +++ /dev/null @@ -1,108 +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. -""" -Generic utilities -""" - -from collections import OrderedDict -from dataclasses import fields -from typing import Any, Tuple - -import numpy as np - -from .import_utils import is_torch_available - - -def is_tensor(x): - """ - Tests if `x` is a `torch.Tensor` or `np.ndarray`. - """ - if is_torch_available(): - import torch - - if isinstance(x, torch.Tensor): - return True - - return isinstance(x, np.ndarray) - - -class BaseOutput(OrderedDict): - """ - Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a - tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular - python dictionary. - - - - You can't unpack a `BaseOutput` directly. Use the [`~utils.BaseOutput.to_tuple`] method to convert it to a tuple - before. - - - """ - - def __post_init__(self): - class_fields = fields(self) - - # Safety and consistency checks - if not len(class_fields): - raise ValueError(f"{self.__class__.__name__} has no fields.") - - first_field = getattr(self, class_fields[0].name) - other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:]) - - if other_fields_are_none and isinstance(first_field, dict): - for key, value in first_field.items(): - self[key] = value - else: - for field in class_fields: - v = getattr(self, field.name) - if v is not None: - self[field.name] = v - - def __delitem__(self, *args, **kwargs): - raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.") - - def setdefault(self, *args, **kwargs): - raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.") - - def pop(self, *args, **kwargs): - raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.") - - def update(self, *args, **kwargs): - raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.") - - def __getitem__(self, k): - if isinstance(k, str): - inner_dict = dict(self.items()) - return inner_dict[k] - else: - return self.to_tuple()[k] - - def __setattr__(self, name, value): - if name in self.keys() and value is not None: - # Don't call self.__setitem__ to avoid recursion errors - super().__setitem__(name, value) - super().__setattr__(name, value) - - def __setitem__(self, key, value): - # Will raise a KeyException if needed - super().__setitem__(key, value) - # Don't call self.__setattr__ to avoid recursion errors - super().__setattr__(key, value) - - def to_tuple(self) -> Tuple[Any]: - """ - Convert self to a tuple containing all the attributes/keys that are not `None`. - """ - return tuple(self[k] for k in self.keys()) diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/arcface_torch/configs/__init__.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/arcface_torch/configs/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/diacanFperku/AutoGPT/Download Matrix Path Of Neo Pc Highly Compressed.md b/spaces/diacanFperku/AutoGPT/Download Matrix Path Of Neo Pc Highly Compressed.md deleted file mode 100644 index 067bd497d2cdf8473b54713e8066fa042fb2b343..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Download Matrix Path Of Neo Pc Highly Compressed.md +++ /dev/null @@ -1,6 +0,0 @@ -

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diff --git a/spaces/diacanFperku/AutoGPT/Jump Desktop Mac [UPDATED].md b/spaces/diacanFperku/AutoGPT/Jump Desktop Mac [UPDATED].md deleted file mode 100644 index 1eb0a3505c91560f9dbc0682e54f4642e44bdffd..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Jump Desktop Mac [UPDATED].md +++ /dev/null @@ -1,87 +0,0 @@ -## jump desktop mac - - - -**Click Here [https://conttooperting.blogspot.com/?l=2twNX7](https://conttooperting.blogspot.com/?l=2twNX7)** - - - - Here is a possible title and article with html formatting for the keyword "jump desktop mac": - -# How to Use Jump Desktop to Connect to Any Computer from Your Mac - - - -Jump Desktop is a powerful and easy-to-use remote desktop app that lets you connect to any computer in the world from your Mac. Whether you need to access your work PC, help a friend with a technical issue, or collaborate with your team on a project, Jump Desktop can help you do it securely and efficiently. - - - -In this article, we will show you how to set up and use Jump Desktop on your Mac, and how to take advantage of its features such as Fluid Remote Desktop, collaborative screen sharing, keyboard shortcuts, and more. - - - -## Setting Up Jump Desktop on Your Mac - - - -Before you can connect to a remote computer using Jump Desktop, you need to install the app on your Mac and on the computer you want to access. You also need to create a free account with Jump Desktop or use your Google account to sign in. - - - -To install Jump Desktop on your Mac, you can download it from the [Mac App Store](https://apps.apple.com/us/app/jump-desktop-rdp-vnc-fluid/id524141863?mt=12) or from the [Jump Desktop website](https://jumpdesktop.com/). The app costs $34.99 and comes with a 14-day money-back guarantee. - - - -To install Jump Desktop on the remote computer, you can download the free Jump Desktop Connect app from the [Jump Desktop website](https://jumpdesktop.com/). The app supports Windows, Mac, Linux, Raspberry Pi, and Chrome OS. - - - -After installing the apps on both computers, follow these steps to set up Jump Desktop: - - - -1. Launch Jump Desktop Connect on the remote computer and sign in with your Jump Desktop or Google account. - -2. Give a name to the remote computer and choose a group to organize it (optional). - -3. Launch Jump Desktop on your Mac and sign in with the same account. - -4. You should see the remote computer listed under My Computers. Click on it to connect. - - - -You can also use Automatic Setup to configure your remote computer without installing Jump Desktop Connect. Just visit [https://www.jumpdesktop.com/](https://www.jumpdesktop.com/) on the remote computer and click on Automatic Setup. Follow the instructions to generate a URL and enter it on your Mac's Jump Desktop app. - - - -## Using Jump Desktop on Your Mac - - - -Once you are connected to a remote computer using Jump Desktop, you can control it as if you were sitting in front of it. You can use your mouse, keyboard, trackpad, or touch bar to interact with the remote desktop. You can also adjust the display settings, such as resolution, scaling, color quality, and full screen mode. - - - -Jump Desktop supports multiple protocols for connecting to remote computers, such as RDP (Remote Desktop Protocol), VNC (Virtual Network Computing), and Fluid Remote Desktop. Fluid Remote Desktop is a proprietary protocol developed by Jump Desktop that offers high performance, low latency, secure screen sharing. It also supports features such as collaborative screen sharing, file transfer, audio streaming, printing, and clipboard synchronization. - - - -To use Fluid Remote Desktop, you need to enable it on both the remote computer and your Mac. On the remote computer, open Jump Desktop Connect and click on Settings > Fluid. Check the box that says Enable Fluid Remote Desktop. On your Mac, open Jump Desktop and click on Edit > Preferences > Fluid. Check the box that says Enable Fluid Remote Desktop. - - - -To use collaborative screen sharing, you need to invite other users to join your session. On your Mac, open Jump Desktop and click on Share > Invite Users. Enter the email addresses of the users you want to invite and click Send. The invited users will receive an email with a link to join your session. They will need to have Jump Desktop installed on their devices and sign in with their accounts. - - - -To use keyboard shortcuts on your Mac while connected to a Windows PC, you can use Mac shortcuts or Windows shortcuts. To use Mac shortcuts, open Jump Desktop and click on Edit > Preferences > Keyboard. Check the box that says Use Mac Keyboard Shortcuts in Windows Sessions. To use Windows shortcuts, uncheck this box. - - - -## Conclusion - - - -Jump Desktop is a versatile and reliable remote desktop app that lets you connect to any computer from - - dfd1c89656 \ No newline at end of file diff --git a/spaces/diffusers/controlnet-openpose/app.py b/spaces/diffusers/controlnet-openpose/app.py deleted file mode 100644 index 162f13eccea4aaf91d6e7feb2a206b43bf00e9c1..0000000000000000000000000000000000000000 --- a/spaces/diffusers/controlnet-openpose/app.py +++ /dev/null @@ -1,137 +0,0 @@ -from controlnet_aux import OpenposeDetector -from diffusers import StableDiffusionControlNetPipeline, ControlNetModel -from diffusers import UniPCMultistepScheduler -import gradio as gr -import torch -import base64 -from io import BytesIO -from PIL import Image -# live conditioning -canvas_html = "" -load_js = """ -async () => { - const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/pose-gradio.js" - fetch(url) - .then(res => res.text()) - .then(text => { - const script = document.createElement('script'); - script.type = "module" - script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' })); - document.head.appendChild(script); - }); -} -""" -get_js_image = """ -async (image_in_img, prompt, image_file_live_opt, live_conditioning) => { - const canvasEl = document.getElementById("canvas-root"); - const data = canvasEl? canvasEl._data : null; - return [image_in_img, prompt, image_file_live_opt, data] -} -""" - -# Constants -low_threshold = 100 -high_threshold = 200 - -# Models -pose_model = OpenposeDetector.from_pretrained("lllyasviel/ControlNet") -controlnet = ControlNetModel.from_pretrained( - "lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16 -) -pipe = StableDiffusionControlNetPipeline.from_pretrained( - "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 -) -pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) - -# This command loads the individual model components on GPU on-demand. So, we don't -# need to explicitly call pipe.to("cuda"). -pipe.enable_model_cpu_offload() - -# xformers -pipe.enable_xformers_memory_efficient_attention() - -# Generator seed, -generator = torch.manual_seed(0) - - -def get_pose(image): - return pose_model(image) - - -def generate_images(image, prompt, image_file_live_opt='file', live_conditioning=None): - if image is None and 'image' not in live_conditioning: - raise gr.Error("Please provide an image") - try: - if image_file_live_opt == 'file': - pose = get_pose(image) - elif image_file_live_opt == 'webcam': - base64_img = live_conditioning['image'] - image_data = base64.b64decode(base64_img.split(',')[1]) - pose = Image.open(BytesIO(image_data)).convert( - 'RGB').resize((512, 512)) - output = pipe( - prompt, - pose, - generator=generator, - num_images_per_prompt=3, - num_inference_steps=20, - ) - all_outputs = [] - all_outputs.append(pose) - for image in output.images: - all_outputs.append(image) - return all_outputs - except Exception as e: - raise gr.Error(str(e)) - - -def toggle(choice): - if choice == "file": - return gr.update(visible=True, value=None), gr.update(visible=False, value=None) - elif choice == "webcam": - return gr.update(visible=False, value=None), gr.update(visible=True, value=canvas_html) - - -with gr.Blocks() as blocks: - gr.Markdown(""" - ## Generate controlled outputs with ControlNet and Stable Diffusion - This Space uses pose estimated lines as the additional conditioning - [Check out our blog to see how this was done (and train your own controlnet)](https://huggingface.co/blog/train-your-controlnet) - """) - with gr.Row(): - live_conditioning = gr.JSON(value={}, visible=False) - with gr.Column(): - image_file_live_opt = gr.Radio(["file", "webcam"], value="file", - label="How would you like to upload your image?") - image_in_img = gr.Image(source="upload", visible=True, type="pil") - canvas = gr.HTML(None, elem_id="canvas_html", visible=False) - - image_file_live_opt.change(fn=toggle, - inputs=[image_file_live_opt], - outputs=[image_in_img, canvas], - queue=False) - prompt = gr.Textbox( - label="Enter your prompt", - max_lines=1, - placeholder="best quality, extremely detailed", - ) - run_button = gr.Button("Generate") - with gr.Column(): - gallery = gr.Gallery().style(grid=[2], height="auto") - run_button.click(fn=generate_images, - inputs=[image_in_img, prompt, - image_file_live_opt, live_conditioning], - outputs=[gallery], - _js=get_js_image) - blocks.load(None, None, None, _js=load_js) - - gr.Examples(fn=generate_images, - examples=[ - ["./yoga1.jpeg", - "best quality, extremely detailed"] - ], - inputs=[image_in_img, prompt], - outputs=[gallery], - cache_examples=True) - -blocks.launch(debug=True) diff --git a/spaces/diffusers/sdxl-to-diffusers/app.py b/spaces/diffusers/sdxl-to-diffusers/app.py deleted file mode 100644 index 467518fc622f9ed1c16985129920fd68179ab4fe..0000000000000000000000000000000000000000 --- a/spaces/diffusers/sdxl-to-diffusers/app.py +++ /dev/null @@ -1,31 +0,0 @@ -import gradio as gr - -from convert import convert - -DESCRIPTION = """ -The steps are the following: - -- Paste a read-access token from hf.co/settings/tokens. Read access is enough given that we will open a PR against the source repo. -- Input a model id from the Hub -- Input the filename from the root dir of the repo that you would like to convert, e.g. 'xl-pruned.safetensors' -- Click "Submit" -- That's it! You'll get feedback if it works or not, and if it worked, you'll get the URL of the opened PR 🔥 - -⚠️ If you encounter weird error messages, please have a look into the Logs and feel free to open a PR to correct the error messages. -""" - -demo = gr.Interface( - title="Convert any Stable Diffusion XL checkpoint to Diffusers and open a PR", - description=DESCRIPTION, - allow_flagging="never", - article="Check out the [Diffusers repo on GitHub](https://github.com/huggingface/diffusers)", - inputs=[ - gr.Text(max_lines=1, label="your_hf_token"), - gr.Text(max_lines=1, label="model_id"), - gr.Text(max_lines=1, label="filename"), - ], - outputs=[gr.Markdown(label="output")], - fn=convert, -).queue(max_size=10, concurrency_count=1) - -demo.launch(show_api=True) diff --git a/spaces/digitalxingtong/Azuma-Bert-VITS2/text/cleaner.py b/spaces/digitalxingtong/Azuma-Bert-VITS2/text/cleaner.py deleted file mode 100644 index 64bd5f7296f66c94f3a335666c53706bb5fe5b39..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Azuma-Bert-VITS2/text/cleaner.py +++ /dev/null @@ -1,27 +0,0 @@ -from text import chinese, cleaned_text_to_sequence - - -language_module_map = { - 'ZH': chinese -} - - -def clean_text(text, language): - language_module = language_module_map[language] - norm_text = language_module.text_normalize(text) - phones, tones, word2ph = language_module.g2p(norm_text) - return norm_text, phones, tones, word2ph - -def clean_text_bert(text, language): - language_module = language_module_map[language] - norm_text = language_module.text_normalize(text) - phones, tones, word2ph = language_module.g2p(norm_text) - bert = language_module.get_bert_feature(norm_text, word2ph) - return phones, tones, bert - -def text_to_sequence(text, language): - norm_text, phones, tones, word2ph = clean_text(text, language) - return cleaned_text_to_sequence(phones, tones, language) - -if __name__ == '__main__': - pass diff --git a/spaces/digitalxingtong/Nailv-Bert-Vits2/text/english.py b/spaces/digitalxingtong/Nailv-Bert-Vits2/text/english.py deleted file mode 100644 index 781d0a56cef71f66fc67db51d76538be90d3ddd2..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Nailv-Bert-Vits2/text/english.py +++ /dev/null @@ -1,138 +0,0 @@ -import pickle -import os -import re -from g2p_en import G2p -from string import punctuation - -from text import symbols - -current_file_path = os.path.dirname(__file__) -CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep') -CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle') -_g2p = G2p() - -arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'} - - -def post_replace_ph(ph): - rep_map = { - '：': ',', - '；': ',', - '，': ',', - '。': '.', - '！': '!', - '？': '?', - '\n': '.', - "·": ",", - '、': ",", - '...': '…', - 'v': "V" - } - if ph in rep_map.keys(): - ph = rep_map[ph] - if ph in symbols: - return ph - if ph not in symbols: - ph = 'UNK' - return ph - -def read_dict(): - g2p_dict = {} - start_line = 49 - with open(CMU_DICT_PATH) as f: - line = f.readline() - line_index = 1 - while line: - if line_index >= start_line: - line = line.strip() - word_split = line.split(' ') - word = word_split[0] - - syllable_split = word_split[1].split(' - ') - g2p_dict[word] = [] - for syllable in syllable_split: - phone_split = syllable.split(' ') - g2p_dict[word].append(phone_split) - - line_index = line_index + 1 - line = f.readline() - - return g2p_dict - - -def cache_dict(g2p_dict, file_path): - with open(file_path, 'wb') as pickle_file: - pickle.dump(g2p_dict, pickle_file) - - -def get_dict(): - if os.path.exists(CACHE_PATH): - with open(CACHE_PATH, 'rb') as pickle_file: - g2p_dict = pickle.load(pickle_file) - else: - g2p_dict = read_dict() - cache_dict(g2p_dict, CACHE_PATH) - - return g2p_dict - -eng_dict = get_dict() - -def refine_ph(phn): - tone = 0 - if re.search(r'\d$', phn): - tone = int(phn[-1]) + 1 - phn = phn[:-1] - return phn.lower(), tone - -def refine_syllables(syllables): - tones = [] - phonemes = [] - for phn_list in syllables: - for i in range(len(phn_list)): - phn = phn_list[i] - phn, tone = refine_ph(phn) - phonemes.append(phn) - tones.append(tone) - return phonemes, tones - - -def text_normalize(text): - # todo: eng text normalize - return text - -def g2p(text): - - phones = [] - tones = [] - words = re.split(r"([,;.\-\?\!\s+])", text) - for w in words: - if w.upper() in eng_dict: - phns, tns = refine_syllables(eng_dict[w.upper()]) - phones += phns - tones += tns - else: - phone_list = list(filter(lambda p: p != " ", _g2p(w))) - for ph in phone_list: - if ph in arpa: - ph, tn = refine_ph(ph) - phones.append(ph) - tones.append(tn) - else: - phones.append(ph) - tones.append(0) - # todo: implement word2ph - word2ph = [1 for i in phones] - - phones = [post_replace_ph(i) for i in phones] - return phones, tones, word2ph - -if __name__ == "__main__": - # print(get_dict()) - # print(eng_word_to_phoneme("hello")) - print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")) - # all_phones = set() - # for k, syllables in eng_dict.items(): - # for group in syllables: - # for ph in group: - # all_phones.add(ph) - # print(all_phones) \ No newline at end of file diff --git a/spaces/digitalxingtong/Xingtong-2dall-Bert-VITS2/text/english_bert_mock.py b/spaces/digitalxingtong/Xingtong-2dall-Bert-VITS2/text/english_bert_mock.py deleted file mode 100644 index 3b894ced5b6d619a18d6bdd7d7606ba9e6532050..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Xingtong-2dall-Bert-VITS2/text/english_bert_mock.py +++ /dev/null @@ -1,5 +0,0 @@ -import torch - - -def get_bert_feature(norm_text, word2ph): - return torch.zeros(1024, sum(word2ph)) diff --git a/spaces/digitalxingtong/Xingtong-Read-Dongmuchang-Bert-VITS2/mel_processing.py b/spaces/digitalxingtong/Xingtong-Read-Dongmuchang-Bert-VITS2/mel_processing.py deleted file mode 100644 index 50435ecf88ef4fb6c1d47f3e6edd04c3ea7d3e80..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Xingtong-Read-Dongmuchang-Bert-VITS2/mel_processing.py +++ /dev/null @@ -1,112 +0,0 @@ -import math -import os -import random -import torch -from torch import nn -import torch.nn.functional as F -import torch.utils.data -import numpy as np -import librosa -import librosa.util as librosa_util -from librosa.util import normalize, pad_center, tiny -from scipy.signal import get_window -from scipy.io.wavfile import read -from librosa.filters import mel as librosa_mel_fn - -MAX_WAV_VALUE = 32768.0 - - -def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): - """ - PARAMS - ------ - C: compression factor - """ - return torch.log(torch.clamp(x, min=clip_val) * C) - - -def dynamic_range_decompression_torch(x, C=1): - """ - PARAMS - ------ - C: compression factor used to compress - """ - return torch.exp(x) / C - - -def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output - - -def spectral_de_normalize_torch(magnitudes): - output = dynamic_range_decompression_torch(magnitudes) - return output - - -mel_basis = {} -hann_window = {} - - -def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - return spec - - -def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): - global mel_basis - dtype_device = str(spec.dtype) + '_' + str(spec.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device) - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - return spec - - -def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global mel_basis, hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device) - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - - return spec diff --git a/spaces/dirge/voicevox/voicevox_engine/user_dict.py b/spaces/dirge/voicevox/voicevox_engine/user_dict.py deleted file mode 100644 index 819059bc529f8df52411ad94892b12eacc3b270c..0000000000000000000000000000000000000000 --- a/spaces/dirge/voicevox/voicevox_engine/user_dict.py +++ /dev/null @@ -1,298 +0,0 @@ -import json -import sys -import threading -import traceback -from pathlib import Path -from typing import Dict, List, Optional -from uuid import UUID, uuid4 - -import numpy as np -import pyopenjtalk -from fastapi import HTTPException -from pydantic import conint - -from .model import UserDictWord, WordTypes -from .part_of_speech_data import MAX_PRIORITY, MIN_PRIORITY, part_of_speech_data -from .utility import engine_root, get_save_dir, mutex_wrapper - -root_dir = engine_root() -save_dir = get_save_dir() - -if not save_dir.is_dir(): - save_dir.mkdir(parents=True) - -default_dict_path = root_dir / "default.csv" -user_dict_path = save_dir / "user_dict.json" -compiled_dict_path = save_dir / "user.dic" - - -mutex_user_dict = threading.Lock() -mutex_openjtalk_dict = threading.Lock() - - -@mutex_wrapper(mutex_user_dict) -def write_to_json(user_dict: Dict[str, UserDictWord], user_dict_path: Path): - converted_user_dict = {} - for word_uuid, word in user_dict.items(): - word_dict = word.dict() - word_dict["cost"] = priority2cost( - word_dict["context_id"], word_dict["priority"] - ) - del word_dict["priority"] - converted_user_dict[word_uuid] = word_dict - # 予めjsonに変換できることを確かめる - user_dict_json = json.dumps(converted_user_dict, ensure_ascii=False) - user_dict_path.write_text(user_dict_json, encoding="utf-8") - - -@mutex_wrapper(mutex_openjtalk_dict) -def update_dict( - default_dict_path: Path = default_dict_path, - user_dict_path: Path = user_dict_path, - compiled_dict_path: Path = compiled_dict_path, -): - random_string = uuid4() - tmp_csv_path = save_dir / f".tmp.dict_csv-{random_string}" - tmp_compiled_path = save_dir / f".tmp.dict_compiled-{random_string}" - - try: - # 辞書.csvを作成 - csv_text = "" - if not default_dict_path.is_file(): - print("Warning: Cannot find default dictionary.", file=sys.stderr) - return - default_dict = default_dict_path.read_text(encoding="utf-8") - if default_dict == default_dict.rstrip(): - default_dict += "\n" - csv_text += default_dict - user_dict = read_dict(user_dict_path=user_dict_path) - for word_uuid in user_dict: - word = user_dict[word_uuid] - csv_text += ( - "{surface},{context_id},{context_id},{cost},{part_of_speech}," - + "{part_of_speech_detail_1},{part_of_speech_detail_2}," - + "{part_of_speech_detail_3},{inflectional_type}," - + "{inflectional_form},{stem},{yomi},{pronunciation}," - + "{accent_type}/{mora_count},{accent_associative_rule}\n" - ).format( - surface=word.surface, - context_id=word.context_id, - cost=priority2cost(word.context_id, word.priority), - part_of_speech=word.part_of_speech, - part_of_speech_detail_1=word.part_of_speech_detail_1, - part_of_speech_detail_2=word.part_of_speech_detail_2, - part_of_speech_detail_3=word.part_of_speech_detail_3, - inflectional_type=word.inflectional_type, - inflectional_form=word.inflectional_form, - stem=word.stem, - yomi=word.yomi, - pronunciation=word.pronunciation, - accent_type=word.accent_type, - mora_count=word.mora_count, - accent_associative_rule=word.accent_associative_rule, - ) - tmp_csv_path.write_text(csv_text, encoding="utf-8") - - # 辞書.csvをOpenJTalk用にコンパイル - pyopenjtalk.create_user_dict(str(tmp_csv_path), str(tmp_compiled_path)) - if not tmp_compiled_path.is_file(): - raise RuntimeError("辞書のコンパイル時にエラーが発生しました。") - - # コンパイル済み辞書の置き換え・読み込み - pyopenjtalk.unset_user_dict() - tmp_compiled_path.replace(compiled_dict_path) - if compiled_dict_path.is_file(): - pyopenjtalk.set_user_dict(str(compiled_dict_path.resolve(strict=True))) - - except Exception as e: - print("Error: Failed to update dictionary.", file=sys.stderr) - traceback.print_exc(file=sys.stderr) - raise e - - finally: - # 後処理 - if tmp_csv_path.exists(): - tmp_csv_path.unlink() - if tmp_compiled_path.exists(): - tmp_compiled_path.unlink() - - -@mutex_wrapper(mutex_user_dict) -def read_dict(user_dict_path: Path = user_dict_path) -> Dict[str, UserDictWord]: - if not user_dict_path.is_file(): - return {} - with user_dict_path.open(encoding="utf-8") as f: - result = {} - for word_uuid, word in json.load(f).items(): - # cost2priorityで変換を行う際にcontext_idが必要となるが、 - # 0.12以前の辞書は、context_idがハードコーディングされていたためにユーザー辞書内に保管されていない - # ハードコーディングされていたcontext_idは固有名詞を意味するものなので、固有名詞のcontext_idを補完する - if word.get("context_id") is None: - word["context_id"] = part_of_speech_data[ - WordTypes.PROPER_NOUN - ].context_id - word["priority"] = cost2priority(word["context_id"], word["cost"]) - del word["cost"] - result[str(UUID(word_uuid))] = UserDictWord(**word) - - return result - - -def create_word( - surface: str, - pronunciation: str, - accent_type: int, - word_type: Optional[WordTypes] = None, - priority: Optional[int] = None, -) -> UserDictWord: - if word_type is None: - word_type = WordTypes.PROPER_NOUN - if word_type not in part_of_speech_data.keys(): - raise HTTPException(status_code=422, detail="不明な品詞です") - if priority is None: - priority = 5 - if not MIN_PRIORITY <= priority <= MAX_PRIORITY: - raise HTTPException(status_code=422, detail="優先度の値が無効です") - pos_detail = part_of_speech_data[word_type] - return UserDictWord( - surface=surface, - context_id=pos_detail.context_id, - priority=priority, - part_of_speech=pos_detail.part_of_speech, - part_of_speech_detail_1=pos_detail.part_of_speech_detail_1, - part_of_speech_detail_2=pos_detail.part_of_speech_detail_2, - part_of_speech_detail_3=pos_detail.part_of_speech_detail_3, - inflectional_type="*", - inflectional_form="*", - stem="*", - yomi=pronunciation, - pronunciation=pronunciation, - accent_type=accent_type, - accent_associative_rule="*", - ) - - -def apply_word( - surface: str, - pronunciation: str, - accent_type: int, - word_type: Optional[WordTypes] = None, - priority: Optional[int] = None, - user_dict_path: Path = user_dict_path, - compiled_dict_path: Path = compiled_dict_path, -) -> str: - word = create_word( - surface=surface, - pronunciation=pronunciation, - accent_type=accent_type, - word_type=word_type, - priority=priority, - ) - user_dict = read_dict(user_dict_path=user_dict_path) - word_uuid = str(uuid4()) - user_dict[word_uuid] = word - write_to_json(user_dict, user_dict_path) - update_dict(user_dict_path=user_dict_path, compiled_dict_path=compiled_dict_path) - return word_uuid - - -def rewrite_word( - word_uuid: str, - surface: str, - pronunciation: str, - accent_type: int, - word_type: Optional[WordTypes] = None, - priority: Optional[int] = None, - user_dict_path: Path = user_dict_path, - compiled_dict_path: Path = compiled_dict_path, -): - word = create_word( - surface=surface, - pronunciation=pronunciation, - accent_type=accent_type, - word_type=word_type, - priority=priority, - ) - user_dict = read_dict(user_dict_path=user_dict_path) - if word_uuid not in user_dict: - raise HTTPException(status_code=422, detail="UUIDに該当するワードが見つかりませんでした") - user_dict[word_uuid] = word - write_to_json(user_dict, user_dict_path) - update_dict(user_dict_path=user_dict_path, compiled_dict_path=compiled_dict_path) - - -def delete_word( - word_uuid: str, - user_dict_path: Path = user_dict_path, - compiled_dict_path: Path = compiled_dict_path, -): - user_dict = read_dict(user_dict_path=user_dict_path) - if word_uuid not in user_dict: - raise HTTPException(status_code=422, detail="IDに該当するワードが見つかりませんでした") - del user_dict[word_uuid] - write_to_json(user_dict, user_dict_path) - update_dict(user_dict_path=user_dict_path, compiled_dict_path=compiled_dict_path) - - -def import_user_dict( - dict_data: Dict[str, UserDictWord], - override: bool = False, - user_dict_path: Path = user_dict_path, - default_dict_path: Path = default_dict_path, - compiled_dict_path: Path = compiled_dict_path, -): - # 念のため型チェックを行う - for word_uuid, word in dict_data.items(): - UUID(word_uuid) - assert type(word) == UserDictWord - for pos_detail in part_of_speech_data.values(): - if word.context_id == pos_detail.context_id: - assert word.part_of_speech == pos_detail.part_of_speech - assert ( - word.part_of_speech_detail_1 == pos_detail.part_of_speech_detail_1 - ) - assert ( - word.part_of_speech_detail_2 == pos_detail.part_of_speech_detail_2 - ) - assert ( - word.part_of_speech_detail_3 == pos_detail.part_of_speech_detail_3 - ) - assert ( - word.accent_associative_rule in pos_detail.accent_associative_rules - ) - break - else: - raise ValueError("対応していない品詞です") - old_dict = read_dict(user_dict_path=user_dict_path) - if override: - new_dict = {**old_dict, **dict_data} - else: - new_dict = {**dict_data, **old_dict} - write_to_json(user_dict=new_dict, user_dict_path=user_dict_path) - update_dict( - default_dict_path=default_dict_path, - user_dict_path=user_dict_path, - compiled_dict_path=compiled_dict_path, - ) - - -def search_cost_candidates(context_id: int) -> List[int]: - for value in part_of_speech_data.values(): - if value.context_id == context_id: - return value.cost_candidates - raise HTTPException(status_code=422, detail="品詞IDが不正です") - - -def cost2priority(context_id: int, cost: conint(ge=-32768, le=32767)) -> int: - cost_candidates = search_cost_candidates(context_id) - # cost_candidatesの中にある値で最も近い値を元にpriorityを返す - # 参考: https://qiita.com/Krypf/items/2eada91c37161d17621d - # この関数とpriority2cost関数によって、辞書ファイルのcostを操作しても最も近いpriorityのcostに上書きされる - return MAX_PRIORITY - np.argmin(np.abs(np.array(cost_candidates) - cost)) - - -def priority2cost( - context_id: int, priority: conint(ge=MIN_PRIORITY, le=MAX_PRIORITY) -) -> int: - cost_candidates = search_cost_candidates(context_id) - return cost_candidates[MAX_PRIORITY - priority] diff --git a/spaces/djgoettel/03-Streamlit-Video-ASR-NLP/streaming.py b/spaces/djgoettel/03-Streamlit-Video-ASR-NLP/streaming.py deleted file mode 100644 index cc2048269b3e9ac09886471ef9b6dc681db09f25..0000000000000000000000000000000000000000 --- a/spaces/djgoettel/03-Streamlit-Video-ASR-NLP/streaming.py +++ /dev/null @@ -1,66 +0,0 @@ -import subprocess - -import numpy as np - - -def ffmpeg_stream(youtube_url, sampling_rate=16_000, chunk_duration_ms=5000, pad_duration_ms=200): - """ - Helper function to read an audio file through ffmpeg. - """ - chunk_len = int(sampling_rate * chunk_duration_ms / 1000) - pad_len = int(sampling_rate * pad_duration_ms / 1000) - read_chunk_len = chunk_len + pad_len * 2 - - ar = f"{sampling_rate}" - ac = "1" - format_for_conversion = "f32le" - dtype = np.float32 - size_of_sample = 4 - - ffmpeg_command = [ - "ffmpeg", - "-i", - "pipe:", - "-ac", - ac, - "-ar", - ar, - "-f", - format_for_conversion, - "-hide_banner", - "-loglevel", - "quiet", - "pipe:1", - ] - - ytdl_command = ["yt-dlp", "-f", "bestaudio", youtube_url, "--quiet", "-o", "-"] - - try: - ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, bufsize=-1) - ytdl_process = subprocess.Popen(ytdl_command, stdout=ffmpeg_process.stdin) - except FileNotFoundError: - raise ValueError("ffmpeg was not found but is required to stream audio files from filename") - - acc = b"" - leftover = np.zeros((0,), dtype=np.float32) - while ytdl_process.poll() is None: - buflen = read_chunk_len * size_of_sample - - raw = ffmpeg_process.stdout.read(buflen) - if raw == b"": - break - - if len(acc) + len(raw) > buflen: - acc = raw - else: - acc += raw - - audio = np.frombuffer(acc, dtype=dtype) - audio = np.concatenate([leftover, audio]) - if len(audio) < pad_len * 2: - # TODO: handle end of stream better than this - break - yield audio - - leftover = audio[-pad_len * 2 :] - read_chunk_len = chunk_len \ No newline at end of file diff --git a/spaces/dongsiqie/Code-Interpreter/jupyter_backend.py b/spaces/dongsiqie/Code-Interpreter/jupyter_backend.py deleted file mode 100644 index c080d8a5cc7d10a7a075c13197c78cf979f8d41d..0000000000000000000000000000000000000000 --- a/spaces/dongsiqie/Code-Interpreter/jupyter_backend.py +++ /dev/null @@ -1,100 +0,0 @@ -import jupyter_client -import re - - -def delete_color_control_char(string): - ansi_escape = re.compile(r'(\x9B|\x1B\[)[0-?]*[ -\/]*[@-~]') - return ansi_escape.sub('', string) - - -class JupyterKernel: - def __init__(self, work_dir): - self.kernel_manager, self.kernel_client = jupyter_client.manager.start_new_kernel(kernel_name='python3') - self.work_dir = work_dir - self._create_work_dir() - self.available_functions = { - 'execute_code': self.execute_code, - 'python': self.execute_code - } - - def execute_code_(self, code): - msg_id = self.kernel_client.execute(code) - - # Get the output of the code - iopub_msg = self.kernel_client.get_iopub_msg() - - all_output = [] - while True: - if iopub_msg['msg_type'] == 'stream': - if iopub_msg['content'].get('name') == 'stdout': - output = iopub_msg['content']['text'] - all_output.append(('stdout', output)) - iopub_msg = self.kernel_client.get_iopub_msg() - elif iopub_msg['msg_type'] == 'execute_result': - if 'data' in iopub_msg['content']: - if 'text/plain' in iopub_msg['content']['data']: - output = iopub_msg['content']['data']['text/plain'] - all_output.append(('execute_result_text', output)) - if 'text/html' in iopub_msg['content']['data']: - output = iopub_msg['content']['data']['text/html'] - all_output.append(('execute_result_html', output)) - if 'image/png' in iopub_msg['content']['data']: - output = iopub_msg['content']['data']['image/png'] - all_output.append(('execute_result_png', output)) - if 'image/jpeg' in iopub_msg['content']['data']: - output = iopub_msg['content']['data']['image/jpeg'] - all_output.append(('execute_result_jpeg', output)) - iopub_msg = self.kernel_client.get_iopub_msg() - elif iopub_msg['msg_type'] == 'display_data': - if 'data' in iopub_msg['content']: - if 'text/plain' in iopub_msg['content']['data']: - output = iopub_msg['content']['data']['text/plain'] - all_output.append(('display_text', output)) - if 'text/html' in iopub_msg['content']['data']: - output = iopub_msg['content']['data']['text/html'] - all_output.append(('display_html', output)) - if 'image/png' in iopub_msg['content']['data']: - output = iopub_msg['content']['data']['image/png'] - all_output.append(('display_png', output)) - if 'image/jpeg' in iopub_msg['content']['data']: - output = iopub_msg['content']['data']['image/jpeg'] - all_output.append(('display_jpeg', output)) - iopub_msg = self.kernel_client.get_iopub_msg() - elif iopub_msg['msg_type'] == 'error': - if 'traceback' in iopub_msg['content']: - output = '\n'.join(iopub_msg['content']['traceback']) - all_output.append(('error', output)) - iopub_msg = self.kernel_client.get_iopub_msg() - elif iopub_msg['msg_type'] == 'status' and iopub_msg['content'].get('execution_state') == 'idle': - break - else: - iopub_msg = self.kernel_client.get_iopub_msg() - - return all_output - - def execute_code(self, code): - text_to_gpt = [] - content_to_display = self.execute_code_(code) - for mark, out_str in content_to_display: - if mark in ('stdout', 'execute_result_text', 'display_text'): - text_to_gpt.append(out_str) - elif mark in ('execute_result_png', 'execute_result_jpeg', 'display_png', 'display_jpeg'): - text_to_gpt.append('[image]') - elif mark == 'error': - text_to_gpt.append(delete_color_control_char(out_str)) - - return '\n'.join(text_to_gpt), content_to_display - - def _create_work_dir(self): - # set work dir in jupyter environment - init_code = f"import os\n" \ - f"if not os.path.exists('{self.work_dir}'):\n" \ - f" os.mkdir('{self.work_dir}')\n" \ - f"os.chdir('{self.work_dir}')\n" \ - f"del os" - self.execute_code_(init_code) - - def restart_jupyter_kernel(self): - self.kernel_client.shutdown() - self.kernel_manager, self.kernel_client = jupyter_client.manager.start_new_kernel(kernel_name='python3') - self._create_work_dir() diff --git a/spaces/dorkai/SINGPT-Temporary/download-model.py b/spaces/dorkai/SINGPT-Temporary/download-model.py deleted file mode 100644 index d3b4623142bf04408515af17c0cb155c8a92d971..0000000000000000000000000000000000000000 --- a/spaces/dorkai/SINGPT-Temporary/download-model.py +++ /dev/null @@ -1,179 +0,0 @@ -''' -Downloads models from Hugging Face to models/model-name. - -Example: -python download-model.py facebook/opt-1.3b - -''' - -import argparse -import base64 -import json -import multiprocessing -import re -import sys -from pathlib import Path - -import requests -import tqdm - -parser = argparse.ArgumentParser() -parser.add_argument('MODEL', type=str, default=None, nargs='?') -parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.') -parser.add_argument('--threads', type=int, default=1, help='Number of files to download simultaneously.') -parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).') -args = parser.parse_args() - -def get_file(args): - url = args[0] - output_folder = args[1] - idx = args[2] - tot = args[3] - - print(f"Downloading file {idx} of {tot}...") - r = requests.get(url, stream=True) - with open(output_folder / Path(url.split('/')[-1]), 'wb') as f: - total_size = int(r.headers.get('content-length', 0)) - block_size = 1024 - t = tqdm.tqdm(total=total_size, unit='iB', unit_scale=True) - for data in r.iter_content(block_size): - t.update(len(data)) - f.write(data) - t.close() - -def sanitize_branch_name(branch_name): - pattern = re.compile(r"^[a-zA-Z0-9._-]+$") - if pattern.match(branch_name): - return branch_name - else: - raise ValueError("Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.") - -def select_model_from_default_options(): - models = { - "Pygmalion 6B original": ("PygmalionAI", "pygmalion-6b", "b8344bb4eb76a437797ad3b19420a13922aaabe1"), - "Pygmalion 6B main": ("PygmalionAI", "pygmalion-6b", "main"), - "Pygmalion 6B dev": ("PygmalionAI", "pygmalion-6b", "dev"), - "Pygmalion 2.7B": ("PygmalionAI", "pygmalion-2.7b", "main"), - "Pygmalion 1.3B": ("PygmalionAI", "pygmalion-1.3b", "main"), - "Pygmalion 350m": ("PygmalionAI", "pygmalion-350m", "main"), - "OPT 6.7b": ("facebook", "opt-6.7b", "main"), - "OPT 2.7b": ("facebook", "opt-2.7b", "main"), - "OPT 1.3b": ("facebook", "opt-1.3b", "main"), - "OPT 350m": ("facebook", "opt-350m", "main"), - } - choices = {} - - print("Select the model that you want to download:\n") - for i,name in enumerate(models): - char = chr(ord('A')+i) - choices[char] = name - print(f"{char}) {name}") - char = chr(ord('A')+len(models)) - print(f"{char}) None of the above") - - print() - print("Input> ", end='') - choice = input()[0].strip().upper() - if choice == char: - print("""\nThen type the name of your desired Hugging Face model in the format organization/name. - -Examples: -PygmalionAI/pygmalion-6b -facebook/opt-1.3b -""") - - print("Input> ", end='') - model = input() - branch = "main" - else: - arr = models[choices[choice]] - model = f"{arr[0]}/{arr[1]}" - branch = arr[2] - - return model, branch - -def get_download_links_from_huggingface(model, branch): - base = "https://huggingface.co" - page = f"/api/models/{model}/tree/{branch}" - cursor = b"" - - links = [] - classifications = [] - has_pytorch = False - has_safetensors = False - while True: - url = f"{base}{page}" + (f"?cursor={cursor.decode()}" if cursor else "") - r = requests.get(url) - r.raise_for_status() - content = r.content - - dict = json.loads(content) - if len(dict) == 0: - break - - for i in range(len(dict)): - fname = dict[i]['path'] - - is_pytorch = re.match("pytorch_model.*\.bin", fname) - is_safetensors = re.match("model.*\.safetensors", fname) - is_tokenizer = re.match("tokenizer.*\.model", fname) - is_text = re.match(".*\.(txt|json)", fname) or is_tokenizer - - if any((is_pytorch, is_safetensors, is_text, is_tokenizer)): - if is_text: - links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}") - classifications.append('text') - continue - if not args.text_only: - links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}") - if is_safetensors: - has_safetensors = True - classifications.append('safetensors') - elif is_pytorch: - has_pytorch = True - classifications.append('pytorch') - - cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50' - cursor = base64.b64encode(cursor) - cursor = cursor.replace(b'=', b'%3D') - - # If both pytorch and safetensors are available, download safetensors only - if has_pytorch and has_safetensors: - for i in range(len(classifications)-1, -1, -1): - if classifications[i] == 'pytorch': - links.pop(i) - - return links - -if __name__ == '__main__': - model = args.MODEL - branch = args.branch - if model is None: - model, branch = select_model_from_default_options() - else: - if model[-1] == '/': - model = model[:-1] - branch = args.branch - if branch is None: - branch = "main" - else: - try: - branch = sanitize_branch_name(branch) - except ValueError as err_branch: - print(f"Error: {err_branch}") - sys.exit() - if branch != 'main': - output_folder = Path("models") / (model.split('/')[-1] + f'_{branch}') - else: - output_folder = Path("models") / model.split('/')[-1] - if not output_folder.exists(): - output_folder.mkdir() - - links = get_download_links_from_huggingface(model, branch) - - # Downloading the files - print(f"Downloading the model to {output_folder}") - pool = multiprocessing.Pool(processes=args.threads) - results = pool.map(get_file, [[links[i], output_folder, i+1, len(links)] for i in range(len(links))]) - pool.close() - pool.join() diff --git a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/css/chat.js b/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/css/chat.js deleted file mode 100644 index e304f1254732e475bf177ee849ac51d4f3e30f46..0000000000000000000000000000000000000000 --- a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/css/chat.js +++ /dev/null @@ -1,4 +0,0 @@ -document.getElementById("main").childNodes[0].style = "max-width: 800px; margin-left: auto; margin-right: auto"; -document.getElementById("extensions").style.setProperty("max-width", "800px"); -document.getElementById("extensions").style.setProperty("margin-left", "auto"); -document.getElementById("extensions").style.setProperty("margin-right", "auto"); diff --git a/spaces/elpsycongroo19/simple_chatbot/app.py b/spaces/elpsycongroo19/simple_chatbot/app.py deleted file mode 100644 index 2780f28ec38df1c86d45f1d6fb2916db6086851d..0000000000000000000000000000000000000000 --- a/spaces/elpsycongroo19/simple_chatbot/app.py +++ /dev/null @@ -1,72 +0,0 @@ -import openai -import os -import gradio as gr -from googlesearch import search - -openai.api_key = os.environ.get("OPENAI_API_KEY") - -class Conversation: - def __init__(self, prompt, num_of_round): - self.prompt = prompt - self.num_of_round = num_of_round - self.messages = [] - self.messages.append({"role": "system", "content": self.prompt}) - - def ask(self, question): - try: - self.messages.append({"role": "user", "content": question}) - response = openai.ChatCompletion.create( - model="gpt-3.5-turbo", - messages=self.messages, - temperature=0.5, - max_tokens=2048, - top_p=1, - ) - except Exception as e: - print(e) - return e - - message = response["choices"][0]["message"]["content"] - self.messages.append({"role": "assistant", "content": message}) - - if len(self.messages) > self.num_of_round*2 + 1: - del self.messages[1:3] - return message - - def search_internet(self, query): - try: - search_results = list(search(query, num=5, lang='zh-CN')) - response = "我找到了一些相关的链接：\n\n" - for i, result in enumerate(search_results): - response += f"{i+1}. {result}\n" - return response - except Exception as e: - print(e) - return "抱歉，无法完成搜索。" - -prompt = """你是GPT4。你的回答需要满足以下要求: -1. 你的回答必须是中文 -2. 回答限制在500个字以内""" - -conv = Conversation(prompt, 10) - -def predict(input, history=[]): - history.append(input) - if input.startswith("!search"): - response = conv.search_internet(input[8:].strip()) - else: - response = conv.ask(input) - history.append(response) - responses = [(u, b) for u, b in zip(history[::2], history[1::2])] - return responses, history - -with gr.Blocks(css="#chatbot{height:350px} .overflow-y-auto{height:500px}") as demo: - chatbot = gr.Chatbot(elem_id="chatbot") - state = gr.State([]) - - with gr.Row(): - txt = gr.Textbox(show_label=False, placeholder="输入文本并按回车键").style(container=False) - - txt.submit(predict, [txt, state], [chatbot, state]) - -demo.launch() diff --git a/spaces/emc348/faces-through-time/configs/global_config.py b/spaces/emc348/faces-through-time/configs/global_config.py deleted file mode 100644 index fca98e23983bf7d8e677b63c0f6855780fd3b7dd..0000000000000000000000000000000000000000 --- a/spaces/emc348/faces-through-time/configs/global_config.py +++ /dev/null @@ -1,12 +0,0 @@ -## Device -cuda_visible_devices = "1" -device = "cpu" - -## Logs -training_step = 1 -image_rec_result_log_snapshot = 100 -pivotal_training_steps = 0 -model_snapshot_interval = 400 - -## Run name to be updated during PTI -run_name = "" diff --git a/spaces/emc348/faces-through-time/models/e4e/stylegan2/op/fused_act.py b/spaces/emc348/faces-through-time/models/e4e/stylegan2/op/fused_act.py deleted file mode 100644 index 90949545ba955dabf2e17d8cf5e524d5cb190a63..0000000000000000000000000000000000000000 --- a/spaces/emc348/faces-through-time/models/e4e/stylegan2/op/fused_act.py +++ /dev/null @@ -1,34 +0,0 @@ -import os - -import torch -from torch import nn -from torch.nn import functional as F -from torch.autograd import Function - - -module_path = os.path.dirname(__file__) - - - -class FusedLeakyReLU(nn.Module): - def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5): - super().__init__() - - self.bias = nn.Parameter(torch.zeros(channel)) - self.negative_slope = negative_slope - self.scale = scale - - def forward(self, input): - return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) - - -def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): - rest_dim = [1] * (input.ndim - bias.ndim - 1) - input = input.cuda() - return ( - F.leaky_relu( - input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope - ) - * scale - ) - diff --git a/spaces/eson/tokenizer-arena/vocab/gpt2_chinese/test.py b/spaces/eson/tokenizer-arena/vocab/gpt2_chinese/test.py deleted file mode 100644 index de8ddac2bcb8d83e365fb18ad586861d5cfc9daa..0000000000000000000000000000000000000000 --- a/spaces/eson/tokenizer-arena/vocab/gpt2_chinese/test.py +++ /dev/null @@ -1,10 +0,0 @@ -""" - -""" - -from transformers import BertTokenizer - -tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-cluecorpussmall") - -encoding = tokenizer.encode("这是很久之前的\n事情了") -print(encoding) diff --git a/spaces/ethzanalytics/dialog-China/app.py b/spaces/ethzanalytics/dialog-China/app.py deleted file mode 100644 index c7e9488b16cf36037576ad67832cf9d35c851c59..0000000000000000000000000000000000000000 --- a/spaces/ethzanalytics/dialog-China/app.py +++ /dev/null @@ -1,191 +0,0 @@ -""" - -deploy-as-bot\gradio_chatbot.py - -A system, method for deploying to Gradio. Gradio is a basic "deploy" interface which allows for other users to test your model from a web URL. It also enables some basic functionality like user flagging for weird responses. -Note that the URL is displayed once the script is run. - -Set the working directory to */deploy-as-bot in terminal before running. - -""" -import os -import sys -from os.path import dirname - -sys.path.append(dirname(dirname(os.path.abspath(__file__)))) - -import gradio as gr -import logging -import argparse -import time -import warnings -from pathlib import Path -from cleantext import clean -from transformers import pipeline -from datetime import datetime -from ai_single_response import query_gpt_model -#from gradio.networking import get_state, set_state -from flask import Flask, request, session, jsonify, abort, send_file, render_template, redirect - -import nltk -nltk.download('stopwords') - -warnings.filterwarnings(action="ignore", message=".*gradient_checkpointing*") - -logging.basicConfig() -cwd = Path.cwd() -my_cwd = str(cwd.resolve()) # string so it can be passed to os.path() objects - - -def gramformer_correct(corrector, qphrase: str): - """ - gramformer_correct - correct a string using a text2textgen pipeline model from transformers - - Args: - corrector (transformers.pipeline): [transformers pipeline object, already created w/ relevant model] - qphrase (str): [text to be corrected] - - Returns: - [str]: [corrected text] - """ - - try: - corrected = corrector( - clean(qphrase), return_text=True, clean_up_tokenization_spaces=True - ) - return corrected[0]["generated_text"] - except: - print("NOTE - failed to correct with gramformer") - return clean(qphrase) - - -def ask_gpt(message: str, sender: str = ""): - """ - ask_gpt - queries the relevant model with a prompt message and (optional) speaker name - - Args: - message (str): prompt message to respond to - sender (str, optional): speaker aka who said the message. Defaults to "". - - Returns: - [str]: [model response as a string] - """ - st = time.time() - prompt = clean(message) # clean user input - prompt = prompt.strip() # get rid of any extra whitespace - if len(prompt) > 200: - prompt = prompt[-200:] # truncate - sender = clean(sender.strip()) - if len(sender) > 2: - try: - prompt_speaker = clean(sender) - except: - # there was some issue getting that info, whatever - prompt_speaker = None - else: - prompt_speaker = None - - resp = query_gpt_model( - folder_path=model_loc, - prompt_msg=prompt, - speaker=prompt_speaker, - kparam=150, - temp=0.75, - top_p=0.65, # optimize this with hyperparam search - ) - bot_resp = gramformer_correct(corrector, qphrase=resp["out_text"]) - rt = round(time.time() - st, 2) - print(f"took {rt} sec to respond") - - return bot_resp - -def chat(first_and_last_name, message): - """ - chat - helper function that makes the whole gradio thing work. - Args: - first_and_last_name (str or None): [speaker of the prompt, if provided] - message (str): [description] - Returns: - [str]: [returns an html string to display] - """ - history = [] - response = ask_gpt(message, sender=first_and_last_name) - history.append((f"{first_and_last_name}: " + message, " GPT-Model: " + response)) #+ " [end] ")) - - #html = "
" - #for user_msg, resp_msg in history: - # html += f"
{user_msg}
" - # html += f"
{resp_msg}
" - #html += "
" - return history - -def get_parser(): - """ - get_parser - a helper function for the argparse module - - Returns: - [argparse.ArgumentParser]: [the argparser relevant for this script] - """ - - parser = argparse.ArgumentParser( - description="submit a message and have a 774M parameter GPT model respond" - ) - parser.add_argument( - "--model", - required=False, - type=str, - # "gp2_DDandPeterTexts_774M_73Ksteps", - from GPT-Peter - default="GPT2_trivNatQAdailydia_774M_175Ksteps", - help="folder - with respect to git directory of your repo that has the model files in it (pytorch.bin + " - "config.json). No models? Run the script download_models.py", - ) - - parser.add_argument( - "--gram-model", - required=False, - type=str, - default="pszemraj/t5-v1_1-base-ft-jflAUG", - help="text2text generation model ID from huggingface for the model to correct grammar", - ) - - return parser - - -if __name__ == "__main__": - args = get_parser().parse_args() - default_model = str(args.model) - model_loc = cwd.parent / default_model - model_loc = str(model_loc.resolve()) - gram_model = args.gram_model - print(f"using model stored here: \n {model_loc} \n") - corrector = pipeline("text2text-generation", model=gram_model, device=-1) - print("Finished loading the gramformer model - ", datetime.now()) - iface = gr.Interface( - chat, - inputs=["text", "text"], - outputs="html", - title="Real-Impact English Chat Demo 英语聊天演示", - description="A basic interface with a neural network model trained on general Q&A and conversation. Treat it like a friend! 带有模型的基本界面，进行了一般问答和对话训练。 请像朋友一样与他对话！ \n first and last name 姓名 \n message 信息 \n Clear 清除 \nSubmit 确认 \n Screenshot 截屏", - article="**Important Notes & About: 重要说明 & 关于我们**\n" - "1. the model can take up to 200 seconds to respond sometimes, patience is a virtue. 该模型有时可能需要长达 60 秒的响应时间，请耐心等待。\n" - "2. entering a username is completely optional. 姓名输入是可选的。\n " - "3. the model was trained on several different datasets. Anything it says should be fact-checked before being regarded as a true statement. 该模型在几个不同的数据集上训练而成，它所说的任何内容都应该经过事实核查，然后才能被视为真实陈述。\n ", - css=""" - .chatbox {display:flex;flex-direction:column} - .user_msg, .resp_msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%} - .user_msg {background-color:cornflowerblue;color:white;align-self:start} - .resp_msg {background-color:lightgray;align-self:self-end} - """, - allow_screenshot=True, - allow_flagging=False, - flagging_dir="gradio_data", - flagging_options=[ - "great response", - "doesn't make sense", - "bad/offensive response", - ], - enable_queue=True, # allows for dealing with multiple users simultaneously - #theme="darkhuggingface", - #server_name="0.0.0.0", - ) - iface.launch(share=True) diff --git a/spaces/etri-vilab/Ko-LLaVA/static/css/bootsrap.min.css b/spaces/etri-vilab/Ko-LLaVA/static/css/bootsrap.min.css deleted file mode 100644 index 6778878a697c077610195142a1cb836dac1f5a93..0000000000000000000000000000000000000000 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0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color:#86b7fe;--bs-accordion-btn-focus-box-shadow:0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-accordion-body-padding-x:1.25rem;--bs-accordion-body-padding-y:1rem;--bs-accordion-active-color:var(--bs-primary-text-emphasis);--bs-accordion-active-bg:var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media (prefers-reduced-motion:reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion:reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 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"/")}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x:0.75rem;--bs-pagination-padding-y:0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color:var(--bs-link-color);--bs-pagination-bg:var(--bs-body-bg);--bs-pagination-border-width:var(--bs-border-width);--bs-pagination-border-color:var(--bs-border-color);--bs-pagination-border-radius:var(--bs-border-radius);--bs-pagination-hover-color:var(--bs-link-hover-color);--bs-pagination-hover-bg:var(--bs-tertiary-bg);--bs-pagination-hover-border-color:var(--bs-border-color);--bs-pagination-focus-color:var(--bs-link-hover-color);--bs-pagination-focus-bg:var(--bs-secondary-bg);--bs-pagination-focus-box-shadow:0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color:#fff;--bs-pagination-active-bg:#0d6efd;--bs-pagination-active-border-color:#0d6efd;--bs-pagination-disabled-color:var(--bs-secondary-color);--bs-pagination-disabled-bg:var(--bs-secondary-bg);--bs-pagination-disabled-border-color:var(--bs-border-color);display:flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media (prefers-reduced-motion:reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.active>.page-link,.page-link.active{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.disabled>.page-link,.page-link.disabled{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x:1.5rem;--bs-pagination-padding-y:0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius:var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x:0.5rem;--bs-pagination-padding-y:0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius:var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x:0.65em;--bs-badge-padding-y:0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight:700;--bs-badge-color:#fff;--bs-badge-border-radius:var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg:transparent;--bs-alert-padding-x:1rem;--bs-alert-padding-y:1rem;--bs-alert-margin-bottom:1rem;--bs-alert-color:inherit;--bs-alert-border-color:transparent;--bs-alert-border:var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius:var(--bs-border-radius);--bs-alert-link-color:inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-primary{--bs-alert-color:var(--bs-primary-text-emphasis);--bs-alert-bg:var(--bs-primary-bg-subtle);--bs-alert-border-color:var(--bs-primary-border-subtle);--bs-alert-link-color:var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color:var(--bs-secondary-text-emphasis);--bs-alert-bg:var(--bs-secondary-bg-subtle);--bs-alert-border-color:var(--bs-secondary-border-subtle);--bs-alert-link-color:var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color:var(--bs-success-text-emphasis);--bs-alert-bg:var(--bs-success-bg-subtle);--bs-alert-border-color:var(--bs-success-border-subtle);--bs-alert-link-color:var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color:var(--bs-info-text-emphasis);--bs-alert-bg:var(--bs-info-bg-subtle);--bs-alert-border-color:var(--bs-info-border-subtle);--bs-alert-link-color:var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color:var(--bs-warning-text-emphasis);--bs-alert-bg:var(--bs-warning-bg-subtle);--bs-alert-border-color:var(--bs-warning-border-subtle);--bs-alert-link-color:var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color:var(--bs-danger-text-emphasis);--bs-alert-bg:var(--bs-danger-bg-subtle);--bs-alert-border-color:var(--bs-danger-border-subtle);--bs-alert-link-color:var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color:var(--bs-light-text-emphasis);--bs-alert-bg:var(--bs-light-bg-subtle);--bs-alert-border-color:var(--bs-light-border-subtle);--bs-alert-link-color:var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color:var(--bs-dark-text-emphasis);--bs-alert-bg:var(--bs-dark-bg-subtle);--bs-alert-border-color:var(--bs-dark-border-subtle);--bs-alert-link-color:var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height:1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg:var(--bs-secondary-bg);--bs-progress-border-radius:var(--bs-border-radius);--bs-progress-box-shadow:var(--bs-box-shadow-inset);--bs-progress-bar-color:#fff;--bs-progress-bar-bg:#0d6efd;--bs-progress-bar-transition:width 0.6s ease;display:flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;flex-direction:column;justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion:reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion:reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color:var(--bs-body-color);--bs-list-group-bg:var(--bs-body-bg);--bs-list-group-border-color:var(--bs-border-color);--bs-list-group-border-width:var(--bs-border-width);--bs-list-group-border-radius:var(--bs-border-radius);--bs-list-group-item-padding-x:1rem;--bs-list-group-item-padding-y:0.5rem;--bs-list-group-action-color:var(--bs-secondary-color);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-tertiary-bg);--bs-list-group-action-active-color:var(--bs-body-color);--bs-list-group-action-active-bg:var(--bs-secondary-bg);--bs-list-group-disabled-color:var(--bs-secondary-color);--bs-list-group-disabled-bg:var(--bs-body-bg);--bs-list-group-active-color:#fff;--bs-list-group-active-bg:#0d6efd;--bs-list-group-active-border-color:#0d6efd;display:flex;flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:focus,.list-group-item-action:hover{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width:576px){.list-group-horizontal-sm{flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width:768px){.list-group-horizontal-md{flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width:992px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width:1200px){.list-group-horizontal-xl{flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width:1400px){.list-group-horizontal-xxl{flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-primary{--bs-list-group-color:var(--bs-primary-text-emphasis);--bs-list-group-bg:var(--bs-primary-bg-subtle);--bs-list-group-border-color:var(--bs-primary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-primary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-primary-border-subtle);--bs-list-group-active-color:var(--bs-primary-bg-subtle);--bs-list-group-active-bg:var(--bs-primary-text-emphasis);--bs-list-group-active-border-color:var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color:var(--bs-secondary-text-emphasis);--bs-list-group-bg:var(--bs-secondary-bg-subtle);--bs-list-group-border-color:var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-secondary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-secondary-border-subtle);--bs-list-group-active-color:var(--bs-secondary-bg-subtle);--bs-list-group-active-bg:var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color:var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color:var(--bs-success-text-emphasis);--bs-list-group-bg:var(--bs-success-bg-subtle);--bs-list-group-border-color:var(--bs-success-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-success-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-success-border-subtle);--bs-list-group-active-color:var(--bs-success-bg-subtle);--bs-list-group-active-bg:var(--bs-success-text-emphasis);--bs-list-group-active-border-color:var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color:var(--bs-info-text-emphasis);--bs-list-group-bg:var(--bs-info-bg-subtle);--bs-list-group-border-color:var(--bs-info-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-info-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-info-border-subtle);--bs-list-group-active-color:var(--bs-info-bg-subtle);--bs-list-group-active-bg:var(--bs-info-text-emphasis);--bs-list-group-active-border-color:var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color:var(--bs-warning-text-emphasis);--bs-list-group-bg:var(--bs-warning-bg-subtle);--bs-list-group-border-color:var(--bs-warning-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-warning-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-warning-border-subtle);--bs-list-group-active-color:var(--bs-warning-bg-subtle);--bs-list-group-active-bg:var(--bs-warning-text-emphasis);--bs-list-group-active-border-color:var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color:var(--bs-danger-text-emphasis);--bs-list-group-bg:var(--bs-danger-bg-subtle);--bs-list-group-border-color:var(--bs-danger-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-danger-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-danger-border-subtle);--bs-list-group-active-color:var(--bs-danger-bg-subtle);--bs-list-group-active-bg:var(--bs-danger-text-emphasis);--bs-list-group-active-border-color:var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color:var(--bs-light-text-emphasis);--bs-list-group-bg:var(--bs-light-bg-subtle);--bs-list-group-border-color:var(--bs-light-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-light-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-light-border-subtle);--bs-list-group-active-color:var(--bs-light-bg-subtle);--bs-list-group-active-bg:var(--bs-light-text-emphasis);--bs-list-group-active-border-color:var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color:var(--bs-dark-text-emphasis);--bs-list-group-bg:var(--bs-dark-bg-subtle);--bs-list-group-border-color:var(--bs-dark-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-dark-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-dark-border-subtle);--bs-list-group-active-color:var(--bs-dark-bg-subtle);--bs-list-group-active-bg:var(--bs-dark-text-emphasis);--bs-list-group-active-border-color:var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color:#000;--bs-btn-close-bg:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity:0.5;--bs-btn-close-hover-opacity:0.75;--bs-btn-close-focus-shadow:0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity:1;--bs-btn-close-disabled-opacity:0.25;--bs-btn-close-white-filter:invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close.disabled,.btn-close:disabled{pointer-events:none;-webkit-user-select:none;-moz-user-select:none;user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex:1090;--bs-toast-padding-x:0.75rem;--bs-toast-padding-y:0.5rem;--bs-toast-spacing:1.5rem;--bs-toast-max-width:350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg:rgba(var(--bs-body-bg-rgb), 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var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex:1055;--bs-modal-width:500px;--bs-modal-padding:1rem;--bs-modal-margin:0.5rem;--bs-modal-color: ;--bs-modal-bg:var(--bs-body-bg);--bs-modal-border-color:var(--bs-border-color-translucent);--bs-modal-border-width:var(--bs-border-width);--bs-modal-border-radius:var(--bs-border-radius-lg);--bs-modal-box-shadow:0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius:calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x:1rem;--bs-modal-header-padding-y:1rem;--bs-modal-header-padding:1rem 1rem;--bs-modal-header-border-color:var(--bs-border-color);--bs-modal-header-border-width:var(--bs-border-width);--bs-modal-title-line-height:1.5;--bs-modal-footer-gap:0.5rem;--bs-modal-footer-bg: 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0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width:300px}}@media (min-width:992px){.modal-lg,.modal-xl{--bs-modal-width:800px}}@media (min-width:1200px){.modal-xl{--bs-modal-width:1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-footer,.modal-fullscreen .modal-header{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media (max-width:575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-footer,.modal-fullscreen-sm-down .modal-header{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media (max-width:767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down 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.modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-footer,.modal-fullscreen-xxl-down .modal-header{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex:1080;--bs-tooltip-max-width:200px;--bs-tooltip-padding-x:0.5rem;--bs-tooltip-padding-y:0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color:var(--bs-body-bg);--bs-tooltip-bg:var(--bs-emphasis-color);--bs-tooltip-border-radius:var(--bs-border-radius);--bs-tooltip-opacity:0.9;--bs-tooltip-arrow-width:0.8rem;--bs-tooltip-arrow-height:0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:var(--bs-font-sans-serif);font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:transparent;border-style:solid}.bs-tooltip-auto[data-popper-placement^=top] 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diff --git a/spaces/feng2022/styleganhuman_copy/torch_utils/ops/upfirdn2d.h b/spaces/feng2022/styleganhuman_copy/torch_utils/ops/upfirdn2d.h deleted file mode 100644 index dc6e713694d3fcca0e06cecfb9437ffb4932ffe6..0000000000000000000000000000000000000000 --- a/spaces/feng2022/styleganhuman_copy/torch_utils/ops/upfirdn2d.h +++ /dev/null @@ -1,61 +0,0 @@ -// Copyright (c) SenseTime Research. All rights reserved. - -// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include - -//------------------------------------------------------------------------ -// CUDA kernel parameters. - -struct upfirdn2d_kernel_params -{ - const void* x; - const float* f; - void* y; - - int2 up; - int2 down; - int2 pad0; - int flip; - float gain; - - int4 inSize; // [width, height, channel, batch] - int4 inStride; - int2 filterSize; // [width, height] - int2 filterStride; - int4 outSize; // [width, height, channel, batch] - int4 outStride; - int sizeMinor; - int sizeMajor; - - int loopMinor; - int loopMajor; - int loopX; - int launchMinor; - int launchMajor; -}; - -//------------------------------------------------------------------------ -// CUDA kernel specialization. - -struct upfirdn2d_kernel_spec -{ - void* kernel; - int tileOutW; - int tileOutH; - int loopMinor; - int loopX; -}; - -//------------------------------------------------------------------------ -// CUDA kernel selection. - -template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); - -//------------------------------------------------------------------------ diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/300 Rise of an Empire Game Part 2 APK - Experience the Thrill of the Persian War.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/300 Rise of an Empire Game Part 2 APK - Experience the Thrill of the Persian War.md deleted file mode 100644 index 551061222611610888b713b45401751ec6b32653..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/300 Rise of an Empire Game Part 2 APK - Experience the Thrill of the Persian War.md +++ /dev/null @@ -1,94 +0,0 @@ - -

300 Rise of an Empire Game Part 2 APK: A Review

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If you are a fan of the movie and comic book series "300", you might be interested in playing the official game based on the sequel, "300 Rise of an Empire". However, you may not find it on the Google Play Store or App Store, as it was only released as a promotional app for a limited time. Fortunately, there is a way to play it on your Android device, thanks to the 300 Rise of an Empire Game Part 2 APK. In this article, we will review this game and tell you how to download and install it on your device.

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What is 300 Rise of an Empire Game Part 2 APK?

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300 Rise of an Empire Game Part 2 APK is an action game developed by Warner Bros. International Enterprises. It is based on the movie "300 Rise of an Empire", which is a sequel to the 2006 film "300". The game follows the story of Greek general Themistokles, who leads his army against the Persian navy led by Artemisia, a vengeful commander who wants to conquer Greece.

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The plot of the game

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The game takes place in the year 480 BC, during the second Persian invasion of Greece. You play as Themistokles, who commands a fleet of ships and soldiers in various naval battles against the Persians. You have to use your strategy and skills to defeat your enemies and protect your homeland. Along the way, you will encounter characters from the movie, such as Queen Gorgo, King Leonidas, and Xerxes.

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The features of the game

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Graphics and sound

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The game boasts impressive graphics and sound effects that create an immersive and realistic experience. The game uses 3D models and animations that resemble the actors from the movie. The game also features voice-overs and dialogues that add to the atmosphere. The game has a dark and gritty tone that matches the style of the movie and comic book series.

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Gameplay and controls

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The game is a third-person action game that involves fighting with swords, spears, shields, and arrows. You can control your character using touch-screen gestures or virtual buttons. You can also switch between different weapons and use special abilities to gain an advantage in combat. The game has various levels and missions that require you to complete different objectives, such as destroying enemy ships, capturing bases, or killing enemy leaders. The game also has a scoring system that rewards you for your performance.

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Compatibility and requirements

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The game is compatible with Android devices that run on Android 4.0 or higher. The game requires about 46 MB of storage space and at least 1 GB of RAM to run smoothly. The game may not work on some devices due to compatibility issues or regional restrictions.

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How to download and install 300 Rise of an Empire Game Part 2 APK?

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If you want to play this game on your Android device, you will need to download and install the 300 Rise of an Empire Game Part 2 APK file. Here are the steps you need to follow:

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Step 1: Download the APK file

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The first thing you need to do is to download the APK file of the game from a reliable source. You can use the link below to download it directly from our website. The file size is about 46 MB, so make sure you have enough space on your device.

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Download 300 Rise of an Empire Game Part 2 APK

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Step 2: Enable unknown sources

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Before you can install the APK file, you need to enable unknown sources on your device. This will allow you to install apps that are not from the Google Play Store or App Store. To do this, go to your device settings and look for the security or privacy option. Then, find the unknown sources option and toggle it on. You may see a warning message, but don't worry, it is safe to proceed.

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Step 3: Install the APK file

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Now that you have enabled unknown sources, you can install the APK file. To do this, locate the file on your device using a file manager app or your browser. Then, tap on the file and follow the instructions on the screen. The installation process may take a few minutes, depending on your device.

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Step 4: Launch the game and enjoy

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Once the installation is complete, you can launch the game and start playing. You will see the game icon on your home screen or app drawer. Tap on it and wait for the game to load. You may need to accept some permissions and terms of service before you can play. Then, you can choose your language and start your adventure.

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Pros and cons of 300 Rise of an Empire Game Part 2 APK

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Like any other game, 300 Rise of an Empire Game Part 2 APK has its pros and cons. Here are some of them:

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Pros

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Immersive and epic action

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If you love action games, you will enjoy this game. It offers immersive and epic action scenes that will make you feel like you are in the movie. You can fight with different weapons and use special abilities to defeat your enemies. You can also experience different naval battles and scenarios that will challenge your skills and strategy.

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Free and easy to install

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The best thing about this game is that it is free and easy to install. You don't need to pay anything or register an account to play it. You just need to download and install the APK file from our website and you are good to go. You don't need to worry about any viruses or malware, as we have tested and verified the file for your safety.

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Based on the movie and comic book series

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If you are a fan of the movie and comic book series "300", you will appreciate this game. It is based on the sequel "300 Rise of an Empire", which is a continuation of the story of King Leonidas and his 300 Spartans who fought against the Persian army. The game follows the plot of the movie and features characters and locations from it. You can also enjoy the voice-overs and dialogues that are faithful to the movie.

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Cons

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Limited content and replay value

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One of the drawbacks of this game is that it has limited content and replay value. The game only has a few levels and missions that can be completed in a short time. The game also lacks a multiplayer mode or a leaderboard system that could add more fun and competition to it. The game may become boring or repetitive after a while.

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Requires a lot of storage space and RAM

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Another downside of this game is that it requires a lot of storage space and RAM to run smoothly. The game has high-quality graphics and sound effects that consume a lot of resources on your device. The game requires about 46 MB of storage space and at least 1 GB of RAM to run properly. If your device has low specifications or memory, you may experience lagging or crashing issues while playing.

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Not available on Google Play Store or App Store

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The last con of this game is that it is not available on the Google Play Store or App Store, which are the official sources for downloading apps on Android and iOS devices. This means that you cannot find or update this game easily on your device. You also cannot access any customer support or feedback system from these platforms. You have to rely on third-party sources like our website to download and install the game. You also have to be careful of any fake or malicious files that may harm your device.

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Conclusion

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300 Rise of an Empire Game Part 2 APK is a game that lets you experience the epic action and story of the movie and comic book series "300". It is a game that offers immersive graphics, sound, and gameplay that will make you feel like you are in the middle of a naval war. It is also a game that is free and easy to install on your Android device, thanks to the APK file that we provide on our website. However, it is also a game that has some drawbacks, such as limited content, high requirements, and unavailability on official platforms. If you are willing to overlook these cons and enjoy the pros, then you should give this game a try. You may find it to be a fun and exciting game that will keep you entertained for a while.

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FAQs

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Here are some frequently asked questions about 300 Rise of an Empire Game Part 2 APK:

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Q: Is 300 Rise of an Empire Game Part 2 APK safe to download and install?

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A: Yes, it is safe to download and install the APK file from our website. We have tested and verified the file for your safety. However, you should always be careful of any other sources that may offer fake or malicious files that may harm your device.

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Q: Can I play 300 Rise of an Empire Game Part 2 APK on my iOS device?

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A: No, you cannot play this game on your iOS device. The game is only compatible with Android devices that run on Android 4.0 or higher. There is no iOS version of this game available.

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Q: Can I play 300 Rise of an Empire Game Part 2 APK offline?

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A: Yes, you can play this game offline. You don't need an internet connection to play this game. However, you may need an internet connection to download and install the APK file on your device.

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Q: How can I update 300 Rise of an Empire Game Part 2 APK?

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A: Since this game is not available on the Google Play Store or App Store, you cannot update it automatically on your device. You have to check our website regularly for any new updates or versions of the game. Then, you have to download and install the new APK file on your device.

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Q: How can I contact the developer of 300 Rise of an Empire Game Part 2 APK?

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A: The developer of this game is Warner Bros. International Enterprises, which is a subsidiary of Warner Bros. Entertainment Inc. You can contact them through their official website or social media accounts.

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\ No newline at end of file diff --git a/spaces/fffiloni/Video-Matting-Anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py b/spaces/fffiloni/Video-Matting-Anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py deleted file mode 100644 index 1c66194deb5dd370e797e57e2712f44303e568cc..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/Video-Matting-Anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py +++ /dev/null @@ -1,802 +0,0 @@ -# ------------------------------------------------------------------------ -# Grounding DINO -# url: https://github.com/IDEA-Research/GroundingDINO -# Copyright (c) 2023 IDEA. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ -# DINO -# Copyright (c) 2022 IDEA. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# -------------------------------------------------------- -# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py -# -------------------------------------------------------- - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as checkpoint -from timm.models.layers import DropPath, to_2tuple, trunc_normal_ - -from groundingdino.util.misc import NestedTensor - - -class Mlp(nn.Module): - """Multilayer perceptron.""" - - def __init__( - self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0 - ): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (B, H, W, C) - window_size (int): window size - Returns: - windows: (num_windows*B, window_size, window_size, C) - """ - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - return windows - - -def window_reverse(windows, window_size, H, W): - """ - Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image - Returns: - x: (B, H, W, C) - """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class WindowAttention(nn.Module): - """Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__( - self, - dim, - window_size, - num_heads, - qkv_bias=True, - qk_scale=None, - attn_drop=0.0, - proj_drop=0.0, - ): - - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim**-0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) - ) # 2*Wh-1 * 2*Ww-1, nH - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_bias_table, std=0.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - """Forward function. - Args: - x: input features with shape of (num_windows*B, N, C) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - B_, N, C = x.shape - qkv = ( - self.qkv(x) - .reshape(B_, N, 3, self.num_heads, C // self.num_heads) - .permute(2, 0, 3, 1, 4) - ) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = q @ k.transpose(-2, -1) - - relative_position_bias = self.relative_position_bias_table[ - self.relative_position_index.view(-1) - ].view( - self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 - ) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute( - 2, 0, 1 - ).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) - attn = attn.view(-1, self.num_heads, N, N) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class SwinTransformerBlock(nn.Module): - """Swin Transformer Block. - Args: - dim (int): Number of input channels. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__( - self, - dim, - num_heads, - window_size=7, - shift_size=0, - mlp_ratio=4.0, - qkv_bias=True, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - act_layer=nn.GELU, - norm_layer=nn.LayerNorm, - ): - super().__init__() - self.dim = dim - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, - window_size=to_2tuple(self.window_size), - num_heads=num_heads, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - attn_drop=attn_drop, - proj_drop=drop, - ) - - self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp( - in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop - ) - - self.H = None - self.W = None - - def forward(self, x, mask_matrix): - """Forward function. - Args: - x: Input feature, tensor size (B, H*W, C). - H, W: Spatial resolution of the input feature. - mask_matrix: Attention mask for cyclic shift. - """ - B, L, C = x.shape - H, W = self.H, self.W - assert L == H * W, "input feature has wrong size" - - shortcut = x - x = self.norm1(x) - x = x.view(B, H, W, C) - - # pad feature maps to multiples of window size - pad_l = pad_t = 0 - pad_r = (self.window_size - W % self.window_size) % self.window_size - pad_b = (self.window_size - H % self.window_size) % self.window_size - x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) - _, Hp, Wp, _ = x.shape - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - attn_mask = mask_matrix - else: - shifted_x = x - attn_mask = None - - # partition windows - x_windows = window_partition( - shifted_x, self.window_size - ) # nW*B, window_size, window_size, C - x_windows = x_windows.view( - -1, self.window_size * self.window_size, C - ) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA - attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - else: - x = shifted_x - - if pad_r > 0 or pad_b > 0: - x = x[:, :H, :W, :].contiguous() - - x = x.view(B, H * W, C) - - # FFN - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - - return x - - -class PatchMerging(nn.Module): - """Patch Merging Layer - Args: - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(4 * dim) - - def forward(self, x, H, W): - """Forward function. - Args: - x: Input feature, tensor size (B, H*W, C). - H, W: Spatial resolution of the input feature. - """ - B, L, C = x.shape - assert L == H * W, "input feature has wrong size" - - x = x.view(B, H, W, C) - - # padding - pad_input = (H % 2 == 1) or (W % 2 == 1) - if pad_input: - x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) - - x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C - x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C - x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C - x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C - x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C - x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C - - x = self.norm(x) - x = self.reduction(x) - - return x - - -class BasicLayer(nn.Module): - """A basic Swin Transformer layer for one stage. - Args: - dim (int): Number of feature channels - depth (int): Depths of this stage. - num_heads (int): Number of attention head. - window_size (int): Local window size. Default: 7. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__( - self, - dim, - depth, - num_heads, - window_size=7, - mlp_ratio=4.0, - qkv_bias=True, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False, - ): - super().__init__() - self.window_size = window_size - self.shift_size = window_size // 2 - self.depth = depth - self.use_checkpoint = use_checkpoint - - # build blocks - self.blocks = nn.ModuleList( - [ - SwinTransformerBlock( - dim=dim, - num_heads=num_heads, - window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, - norm_layer=norm_layer, - ) - for i in range(depth) - ] - ) - - # patch merging layer - if downsample is not None: - self.downsample = downsample(dim=dim, norm_layer=norm_layer) - else: - self.downsample = None - - def forward(self, x, H, W): - """Forward function. - Args: - x: Input feature, tensor size (B, H*W, C). - H, W: Spatial resolution of the input feature. - """ - - # calculate attention mask for SW-MSA - Hp = int(np.ceil(H / self.window_size)) * self.window_size - Wp = int(np.ceil(W / self.window_size)) * self.window_size - img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 - h_slices = ( - slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None), - ) - w_slices = ( - slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None), - ) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition( - img_mask, self.window_size - ) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( - attn_mask == 0, float(0.0) - ) - - for blk in self.blocks: - blk.H, blk.W = H, W - if self.use_checkpoint: - x = checkpoint.checkpoint(blk, x, attn_mask) - else: - x = blk(x, attn_mask) - if self.downsample is not None: - x_down = self.downsample(x, H, W) - Wh, Ww = (H + 1) // 2, (W + 1) // 2 - return x, H, W, x_down, Wh, Ww - else: - return x, H, W, x, H, W - - -class PatchEmbed(nn.Module): - """Image to Patch Embedding - Args: - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): - super().__init__() - patch_size = to_2tuple(patch_size) - self.patch_size = patch_size - - self.in_chans = in_chans - self.embed_dim = embed_dim - - self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - """Forward function.""" - # padding - _, _, H, W = x.size() - if W % self.patch_size[1] != 0: - x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) - if H % self.patch_size[0] != 0: - x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) - - x = self.proj(x) # B C Wh Ww - if self.norm is not None: - Wh, Ww = x.size(2), x.size(3) - x = x.flatten(2).transpose(1, 2) - x = self.norm(x) - x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) - - return x - - -class SwinTransformer(nn.Module): - """Swin Transformer backbone. - A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - - https://arxiv.org/pdf/2103.14030 - Args: - pretrain_img_size (int): Input image size for training the pretrained model, - used in absolute postion embedding. Default 224. - patch_size (int | tuple(int)): Patch size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - depths (tuple[int]): Depths of each Swin Transformer stage. - num_heads (tuple[int]): Number of attention head of each stage. - window_size (int): Window size. Default: 7. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. - drop_rate (float): Dropout rate. - attn_drop_rate (float): Attention dropout rate. Default: 0. - drop_path_rate (float): Stochastic depth rate. Default: 0.2. - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. - ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. - patch_norm (bool): If True, add normalization after patch embedding. Default: True. - out_indices (Sequence[int]): Output from which stages. - frozen_stages (int): Stages to be frozen (stop grad and set eval mode). - -1 means not freezing any parameters. - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - dilation (bool): if True, the output size if 16x downsample, ow 32x downsample. - """ - - def __init__( - self, - pretrain_img_size=224, - patch_size=4, - in_chans=3, - embed_dim=96, - depths=[2, 2, 6, 2], - num_heads=[3, 6, 12, 24], - window_size=7, - mlp_ratio=4.0, - qkv_bias=True, - qk_scale=None, - drop_rate=0.0, - attn_drop_rate=0.0, - drop_path_rate=0.2, - norm_layer=nn.LayerNorm, - ape=False, - patch_norm=True, - out_indices=(0, 1, 2, 3), - frozen_stages=-1, - dilation=False, - use_checkpoint=False, - ): - super().__init__() - - self.pretrain_img_size = pretrain_img_size - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.out_indices = out_indices - self.frozen_stages = frozen_stages - self.dilation = dilation - - # if use_checkpoint: - # print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!") - - # split image into non-overlapping patches - self.patch_embed = PatchEmbed( - patch_size=patch_size, - in_chans=in_chans, - embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None, - ) - - # absolute position embedding - if self.ape: - pretrain_img_size = to_2tuple(pretrain_img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [ - pretrain_img_size[0] // patch_size[0], - pretrain_img_size[1] // patch_size[1], - ] - - self.absolute_pos_embed = nn.Parameter( - torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) - ) - trunc_normal_(self.absolute_pos_embed, std=0.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - # stochastic depth - dpr = [ - x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) - ] # stochastic depth decay rule - - # build layers - self.layers = nn.ModuleList() - # prepare downsample list - downsamplelist = [PatchMerging for i in range(self.num_layers)] - downsamplelist[-1] = None - num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)] - if self.dilation: - downsamplelist[-2] = None - num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2 - for i_layer in range(self.num_layers): - layer = BasicLayer( - # dim=int(embed_dim * 2 ** i_layer), - dim=num_features[i_layer], - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], - norm_layer=norm_layer, - # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, - downsample=downsamplelist[i_layer], - use_checkpoint=use_checkpoint, - ) - self.layers.append(layer) - - # num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] - self.num_features = num_features - - # add a norm layer for each output - for i_layer in out_indices: - layer = norm_layer(num_features[i_layer]) - layer_name = f"norm{i_layer}" - self.add_module(layer_name, layer) - - self._freeze_stages() - - def _freeze_stages(self): - if self.frozen_stages >= 0: - self.patch_embed.eval() - for param in self.patch_embed.parameters(): - param.requires_grad = False - - if self.frozen_stages >= 1 and self.ape: - self.absolute_pos_embed.requires_grad = False - - if self.frozen_stages >= 2: - self.pos_drop.eval() - for i in range(0, self.frozen_stages - 1): - m = self.layers[i] - m.eval() - for param in m.parameters(): - param.requires_grad = False - - # def init_weights(self, pretrained=None): - # """Initialize the weights in backbone. - # Args: - # pretrained (str, optional): Path to pre-trained weights. - # Defaults to None. - # """ - - # def _init_weights(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) - - # if isinstance(pretrained, str): - # self.apply(_init_weights) - # logger = get_root_logger() - # load_checkpoint(self, pretrained, strict=False, logger=logger) - # elif pretrained is None: - # self.apply(_init_weights) - # else: - # raise TypeError('pretrained must be a str or None') - - def forward_raw(self, x): - """Forward function.""" - x = self.patch_embed(x) - - Wh, Ww = x.size(2), x.size(3) - if self.ape: - # interpolate the position embedding to the corresponding size - absolute_pos_embed = F.interpolate( - self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic" - ) - x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C - else: - x = x.flatten(2).transpose(1, 2) - x = self.pos_drop(x) - - outs = [] - for i in range(self.num_layers): - layer = self.layers[i] - x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) - # import ipdb; ipdb.set_trace() - - if i in self.out_indices: - norm_layer = getattr(self, f"norm{i}") - x_out = norm_layer(x_out) - - out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() - outs.append(out) - # in: - # torch.Size([2, 3, 1024, 1024]) - # outs: - # [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \ - # torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])] - return tuple(outs) - - def forward(self, tensor_list: NestedTensor): - x = tensor_list.tensors - - """Forward function.""" - x = self.patch_embed(x) - - Wh, Ww = x.size(2), x.size(3) - if self.ape: - # interpolate the position embedding to the corresponding size - absolute_pos_embed = F.interpolate( - self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic" - ) - x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C - else: - x = x.flatten(2).transpose(1, 2) - x = self.pos_drop(x) - - outs = [] - for i in range(self.num_layers): - layer = self.layers[i] - x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) - - if i in self.out_indices: - norm_layer = getattr(self, f"norm{i}") - x_out = norm_layer(x_out) - - out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() - outs.append(out) - # in: - # torch.Size([2, 3, 1024, 1024]) - # out: - # [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \ - # torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])] - - # collect for nesttensors - outs_dict = {} - for idx, out_i in enumerate(outs): - m = tensor_list.mask - assert m is not None - mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0] - outs_dict[idx] = NestedTensor(out_i, mask) - - return outs_dict - - def train(self, mode=True): - """Convert the model into training mode while keep layers freezed.""" - super(SwinTransformer, self).train(mode) - self._freeze_stages() - - -def build_swin_transformer(modelname, pretrain_img_size, **kw): - assert modelname in [ - "swin_T_224_1k", - "swin_B_224_22k", - "swin_B_384_22k", - "swin_L_224_22k", - "swin_L_384_22k", - ] - - model_para_dict = { - "swin_T_224_1k": dict( - embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7 - ), - "swin_B_224_22k": dict( - embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7 - ), - "swin_B_384_22k": dict( - embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12 - ), - "swin_L_224_22k": dict( - embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7 - ), - "swin_L_384_22k": dict( - embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12 - ), - } - kw_cgf = model_para_dict[modelname] - kw_cgf.update(kw) - model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf) - return model - - -if __name__ == "__main__": - model = build_swin_transformer("swin_L_384_22k", 384, dilation=True) - x = torch.rand(2, 3, 1024, 1024) - y = model.forward_raw(x) - import ipdb - - ipdb.set_trace() - x = torch.rand(2, 3, 384, 384) - y = model.forward_raw(x) diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/console.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/console.d.ts deleted file mode 100644 index 16c9137adf20cd8eaad74c61819ff6e300205b7a..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/console.d.ts +++ /dev/null @@ -1,412 +0,0 @@ -/** - * The `console` module provides a simple debugging console that is similar to the - * JavaScript console mechanism provided by web browsers. - * - * The module exports two specific components: - * - * * A `Console` class with methods such as `console.log()`, `console.error()` and`console.warn()` that can be used to write to any Node.js stream. - * * A global `console` instance configured to write to `process.stdout` and `process.stderr`. The global `console` can be used without calling`require('console')`. - * - * _**Warning**_: The global console object's methods are neither consistently - * synchronous like the browser APIs they resemble, nor are they consistently - * asynchronous like all other Node.js streams. See the `note on process I/O` for - * more information. - * - * Example using the global `console`: - * - * ```js - * console.log('hello world'); - * // Prints: hello world, to stdout - * console.log('hello %s', 'world'); - * // Prints: hello world, to stdout - * console.error(new Error('Whoops, something bad happened')); - * // Prints error message and stack trace to stderr: - * // Error: Whoops, something bad happened - * // at [eval]:5:15 - * // at Script.runInThisContext (node:vm:132:18) - * // at Object.runInThisContext (node:vm:309:38) - * // at node:internal/process/execution:77:19 - * // at [eval]-wrapper:6:22 - * // at evalScript (node:internal/process/execution:76:60) - * // at node:internal/main/eval_string:23:3 - * - * const name = 'Will Robinson'; - * console.warn(`Danger ${name}! Danger!`); - * // Prints: Danger Will Robinson! Danger!, to stderr - * ``` - * - * Example using the `Console` class: - * - * ```js - * const out = getStreamSomehow(); - * const err = getStreamSomehow(); - * const myConsole = new console.Console(out, err); - * - * myConsole.log('hello world'); - * // Prints: hello world, to out - * myConsole.log('hello %s', 'world'); - * // Prints: hello world, to out - * myConsole.error(new Error('Whoops, something bad happened')); - * // Prints: [Error: Whoops, something bad happened], to err - * - * const name = 'Will Robinson'; - * myConsole.warn(`Danger ${name}! Danger!`); - * // Prints: Danger Will Robinson! Danger!, to err - * ``` - * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/console.js) - */ -declare module 'console' { - import console = require('node:console'); - export = console; -} -declare module 'node:console' { - import { InspectOptions } from 'node:util'; - global { - // This needs to be global to avoid TS2403 in case lib.dom.d.ts is present in the same build - interface Console { - Console: console.ConsoleConstructor; - /** - * `console.assert()` writes a message if `value` is [falsy](https://developer.mozilla.org/en-US/docs/Glossary/Falsy) or omitted. It only - * writes a message and does not otherwise affect execution. The output always - * starts with `"Assertion failed"`. If provided, `message` is formatted using `util.format()`. - * - * If `value` is [truthy](https://developer.mozilla.org/en-US/docs/Glossary/Truthy), nothing happens. - * - * ```js - * console.assert(true, 'does nothing'); - * - * console.assert(false, 'Whoops %s work', 'didn\'t'); - * // Assertion failed: Whoops didn't work - * - * console.assert(); - * // Assertion failed - * ``` - * @since v0.1.101 - * @param value The value tested for being truthy. - * @param message All arguments besides `value` are used as error message. - */ - assert(value: any, message?: string, ...optionalParams: any[]): void; - /** - * When `stdout` is a TTY, calling `console.clear()` will attempt to clear the - * TTY. When `stdout` is not a TTY, this method does nothing. - * - * The specific operation of `console.clear()` can vary across operating systems - * and terminal types. For most Linux operating systems, `console.clear()`operates similarly to the `clear` shell command. On Windows, `console.clear()`will clear only the output in the - * current terminal viewport for the Node.js - * binary. - * @since v8.3.0 - */ - clear(): void; - /** - * Maintains an internal counter specific to `label` and outputs to `stdout` the - * number of times `console.count()` has been called with the given `label`. - * - * ```js - * > console.count() - * default: 1 - * undefined - * > console.count('default') - * default: 2 - * undefined - * > console.count('abc') - * abc: 1 - * undefined - * > console.count('xyz') - * xyz: 1 - * undefined - * > console.count('abc') - * abc: 2 - * undefined - * > console.count() - * default: 3 - * undefined - * > - * ``` - * @since v8.3.0 - * @param label The display label for the counter. - */ - count(label?: string): void; - /** - * Resets the internal counter specific to `label`. - * - * ```js - * > console.count('abc'); - * abc: 1 - * undefined - * > console.countReset('abc'); - * undefined - * > console.count('abc'); - * abc: 1 - * undefined - * > - * ``` - * @since v8.3.0 - * @param label The display label for the counter. - */ - countReset(label?: string): void; - /** - * The `console.debug()` function is an alias for {@link log}. - * @since v8.0.0 - */ - debug(message?: any, ...optionalParams: any[]): void; - /** - * Uses `util.inspect()` on `obj` and prints the resulting string to `stdout`. - * This function bypasses any custom `inspect()` function defined on `obj`. - * @since v0.1.101 - */ - dir(obj: any, options?: InspectOptions): void; - /** - * This method calls `console.log()` passing it the arguments received. - * This method does not produce any XML formatting. - * @since v8.0.0 - */ - dirxml(...data: any[]): void; - /** - * Prints to `stderr` with newline. Multiple arguments can be passed, with the - * first used as the primary message and all additional used as substitution - * values similar to [`printf(3)`](http://man7.org/linux/man-pages/man3/printf.3.html) (the arguments are all passed to `util.format()`). - * - * ```js - * const code = 5; - * console.error('error #%d', code); - * // Prints: error #5, to stderr - * console.error('error', code); - * // Prints: error 5, to stderr - * ``` - * - * If formatting elements (e.g. `%d`) are not found in the first string then `util.inspect()` is called on each argument and the resulting string - * values are concatenated. See `util.format()` for more information. - * @since v0.1.100 - */ - error(message?: any, ...optionalParams: any[]): void; - /** - * Increases indentation of subsequent lines by spaces for `groupIndentation`length. - * - * If one or more `label`s are provided, those are printed first without the - * additional indentation. - * @since v8.5.0 - */ - group(...label: any[]): void; - /** - * An alias for {@link group}. - * @since v8.5.0 - */ - groupCollapsed(...label: any[]): void; - /** - * Decreases indentation of subsequent lines by spaces for `groupIndentation`length. - * @since v8.5.0 - */ - groupEnd(): void; - /** - * The `console.info()` function is an alias for {@link log}. - * @since v0.1.100 - */ - info(message?: any, ...optionalParams: any[]): void; - /** - * Prints to `stdout` with newline. Multiple arguments can be passed, with the - * first used as the primary message and all additional used as substitution - * values similar to [`printf(3)`](http://man7.org/linux/man-pages/man3/printf.3.html) (the arguments are all passed to `util.format()`). - * - * ```js - * const count = 5; - * console.log('count: %d', count); - * // Prints: count: 5, to stdout - * console.log('count:', count); - * // Prints: count: 5, to stdout - * ``` - * - * See `util.format()` for more information. - * @since v0.1.100 - */ - log(message?: any, ...optionalParams: any[]): void; - /** - * Try to construct a table with the columns of the properties of `tabularData`(or use `properties`) and rows of `tabularData` and log it. Falls back to just - * logging the argument if it can’t be parsed as tabular. - * - * ```js - * // These can't be parsed as tabular data - * console.table(Symbol()); - * // Symbol() - * - * console.table(undefined); - * // undefined - * - * console.table([{ a: 1, b: 'Y' }, { a: 'Z', b: 2 }]); - * // ┌─────────┬─────┬─────┐ - * // │ (index) │ a │ b │ - * // ├─────────┼─────┼─────┤ - * // │ 0 │ 1 │ 'Y' │ - * // │ 1 │ 'Z' │ 2 │ - * // └─────────┴─────┴─────┘ - * - * console.table([{ a: 1, b: 'Y' }, { a: 'Z', b: 2 }], ['a']); - * // ┌─────────┬─────┐ - * // │ (index) │ a │ - * // ├─────────┼─────┤ - * // │ 0 │ 1 │ - * // │ 1 │ 'Z' │ - * // └─────────┴─────┘ - * ``` - * @since v10.0.0 - * @param properties Alternate properties for constructing the table. - */ - table(tabularData: any, properties?: ReadonlyArray): void; - /** - * Starts a timer that can be used to compute the duration of an operation. Timers - * are identified by a unique `label`. Use the same `label` when calling {@link timeEnd} to stop the timer and output the elapsed time in - * suitable time units to `stdout`. For example, if the elapsed - * time is 3869ms, `console.timeEnd()` displays "3.869s". - * @since v0.1.104 - */ - time(label?: string): void; - /** - * Stops a timer that was previously started by calling {@link time} and - * prints the result to `stdout`: - * - * ```js - * console.time('100-elements'); - * for (let i = 0; i < 100; i++) {} - * console.timeEnd('100-elements'); - * // prints 100-elements: 225.438ms - * ``` - * @since v0.1.104 - */ - timeEnd(label?: string): void; - /** - * For a timer that was previously started by calling {@link time}, prints - * the elapsed time and other `data` arguments to `stdout`: - * - * ```js - * console.time('process'); - * const value = expensiveProcess1(); // Returns 42 - * console.timeLog('process', value); - * // Prints "process: 365.227ms 42". - * doExpensiveProcess2(value); - * console.timeEnd('process'); - * ``` - * @since v10.7.0 - */ - timeLog(label?: string, ...data: any[]): void; - /** - * Prints to `stderr` the string `'Trace: '`, followed by the `util.format()` formatted message and stack trace to the current position in the code. - * - * ```js - * console.trace('Show me'); - * // Prints: (stack trace will vary based on where trace is called) - * // Trace: Show me - * // at repl:2:9 - * // at REPLServer.defaultEval (repl.js:248:27) - * // at bound (domain.js:287:14) - * // at REPLServer.runBound [as eval] (domain.js:300:12) - * // at REPLServer. (repl.js:412:12) - * // at emitOne (events.js:82:20) - * // at REPLServer.emit (events.js:169:7) - * // at REPLServer.Interface._onLine (readline.js:210:10) - * // at REPLServer.Interface._line (readline.js:549:8) - * // at REPLServer.Interface._ttyWrite (readline.js:826:14) - * ``` - * @since v0.1.104 - */ - trace(message?: any, ...optionalParams: any[]): void; - /** - * The `console.warn()` function is an alias for {@link error}. - * @since v0.1.100 - */ - warn(message?: any, ...optionalParams: any[]): void; - // --- Inspector mode only --- - /** - * This method does not display anything unless used in the inspector. - * Starts a JavaScript CPU profile with an optional label. - */ - profile(label?: string): void; - /** - * This method does not display anything unless used in the inspector. - * Stops the current JavaScript CPU profiling session if one has been started and prints the report to the Profiles panel of the inspector. - */ - profileEnd(label?: string): void; - /** - * This method does not display anything unless used in the inspector. - * Adds an event with the label `label` to the Timeline panel of the inspector. - */ - timeStamp(label?: string): void; - } - /** - * The `console` module provides a simple debugging console that is similar to the - * JavaScript console mechanism provided by web browsers. - * - * The module exports two specific components: - * - * * A `Console` class with methods such as `console.log()`, `console.error()` and`console.warn()` that can be used to write to any Node.js stream. - * * A global `console` instance configured to write to `process.stdout` and `process.stderr`. The global `console` can be used without calling`require('console')`. - * - * _**Warning**_: The global console object's methods are neither consistently - * synchronous like the browser APIs they resemble, nor are they consistently - * asynchronous like all other Node.js streams. See the `note on process I/O` for - * more information. - * - * Example using the global `console`: - * - * ```js - * console.log('hello world'); - * // Prints: hello world, to stdout - * console.log('hello %s', 'world'); - * // Prints: hello world, to stdout - * console.error(new Error('Whoops, something bad happened')); - * // Prints error message and stack trace to stderr: - * // Error: Whoops, something bad happened - * // at [eval]:5:15 - * // at Script.runInThisContext (node:vm:132:18) - * // at Object.runInThisContext (node:vm:309:38) - * // at node:internal/process/execution:77:19 - * // at [eval]-wrapper:6:22 - * // at evalScript (node:internal/process/execution:76:60) - * // at node:internal/main/eval_string:23:3 - * - * const name = 'Will Robinson'; - * console.warn(`Danger ${name}! Danger!`); - * // Prints: Danger Will Robinson! Danger!, to stderr - * ``` - * - * Example using the `Console` class: - * - * ```js - * const out = getStreamSomehow(); - * const err = getStreamSomehow(); - * const myConsole = new console.Console(out, err); - * - * myConsole.log('hello world'); - * // Prints: hello world, to out - * myConsole.log('hello %s', 'world'); - * // Prints: hello world, to out - * myConsole.error(new Error('Whoops, something bad happened')); - * // Prints: [Error: Whoops, something bad happened], to err - * - * const name = 'Will Robinson'; - * myConsole.warn(`Danger ${name}! Danger!`); - * // Prints: Danger Will Robinson! Danger!, to err - * ``` - * @see [source](https://github.com/nodejs/node/blob/v16.4.2/lib/console.js) - */ - namespace console { - interface ConsoleConstructorOptions { - stdout: NodeJS.WritableStream; - stderr?: NodeJS.WritableStream | undefined; - ignoreErrors?: boolean | undefined; - colorMode?: boolean | 'auto' | undefined; - inspectOptions?: InspectOptions | undefined; - /** - * Set group indentation - * @default 2 - */ - groupIndentation?: number | undefined; - } - interface ConsoleConstructor { - prototype: Console; - new (stdout: NodeJS.WritableStream, stderr?: NodeJS.WritableStream, ignoreErrors?: boolean): Console; - new (options: ConsoleConstructorOptions): Console; - } - } - var console: Console; - } - export = globalThis.console; -} diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/fs.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/fs.d.ts deleted file mode 100644 index 75c53fb0d542e5f7ce5b43c68982e428a6e653aa..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/fs.d.ts +++ /dev/null @@ -1,3872 +0,0 @@ -/** - * The `fs` module enables interacting with the file system in a - * way modeled on standard POSIX functions. - * - * To use the promise-based APIs: - * - * ```js - * import * as fs from 'fs/promises'; - * ``` - * - * To use the callback and sync APIs: - * - * ```js - * import * as fs from 'fs'; - * ``` - * - * All file system operations have synchronous, callback, and promise-based - * forms, and are accessible using both CommonJS syntax and ES6 Modules (ESM). - * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/fs.js) - */ -declare module 'fs' { - import * as stream from 'node:stream'; - import { Abortable, EventEmitter } from 'node:events'; - import { URL } from 'node:url'; - import * as promises from 'node:fs/promises'; - export { promises }; - /** - * Valid types for path values in "fs". - */ - export type PathLike = string | Buffer | URL; - export type PathOrFileDescriptor = PathLike | number; - export type TimeLike = string | number | Date; - export type NoParamCallback = (err: NodeJS.ErrnoException | null) => void; - export type BufferEncodingOption = - | 'buffer' - | { - encoding: 'buffer'; - }; - export interface ObjectEncodingOptions { - encoding?: BufferEncoding | null | undefined; - } - export type EncodingOption = ObjectEncodingOptions | BufferEncoding | undefined | null; - export type OpenMode = number | string; - export type Mode = number | string; - export interface StatsBase { - isFile(): boolean; - isDirectory(): boolean; - isBlockDevice(): boolean; - isCharacterDevice(): boolean; - isSymbolicLink(): boolean; - isFIFO(): boolean; - isSocket(): boolean; - dev: T; - ino: T; - mode: T; - nlink: T; - uid: T; - gid: T; - rdev: T; - size: T; - blksize: T; - blocks: T; - atimeMs: T; - mtimeMs: T; - ctimeMs: T; - birthtimeMs: T; - atime: Date; - mtime: Date; - ctime: Date; - birthtime: Date; - } - export interface Stats extends StatsBase {} - /** - * A `fs.Stats` object provides information about a file. - * - * Objects returned from {@link stat}, {@link lstat} and {@link fstat} and - * their synchronous counterparts are of this type. - * If `bigint` in the `options` passed to those methods is true, the numeric values - * will be `bigint` instead of `number`, and the object will contain additional - * nanosecond-precision properties suffixed with `Ns`. - * - * ```console - * Stats { - * dev: 2114, - * ino: 48064969, - * mode: 33188, - * nlink: 1, - * uid: 85, - * gid: 100, - * rdev: 0, - * size: 527, - * blksize: 4096, - * blocks: 8, - * atimeMs: 1318289051000.1, - * mtimeMs: 1318289051000.1, - * ctimeMs: 1318289051000.1, - * birthtimeMs: 1318289051000.1, - * atime: Mon, 10 Oct 2011 23:24:11 GMT, - * mtime: Mon, 10 Oct 2011 23:24:11 GMT, - * ctime: Mon, 10 Oct 2011 23:24:11 GMT, - * birthtime: Mon, 10 Oct 2011 23:24:11 GMT } - * ``` - * - * `bigint` version: - * - * ```console - * BigIntStats { - * dev: 2114n, - * ino: 48064969n, - * mode: 33188n, - * nlink: 1n, - * uid: 85n, - * gid: 100n, - * rdev: 0n, - * size: 527n, - * blksize: 4096n, - * blocks: 8n, - * atimeMs: 1318289051000n, - * mtimeMs: 1318289051000n, - * ctimeMs: 1318289051000n, - * birthtimeMs: 1318289051000n, - * atimeNs: 1318289051000000000n, - * mtimeNs: 1318289051000000000n, - * ctimeNs: 1318289051000000000n, - * birthtimeNs: 1318289051000000000n, - * atime: Mon, 10 Oct 2011 23:24:11 GMT, - * mtime: Mon, 10 Oct 2011 23:24:11 GMT, - * ctime: Mon, 10 Oct 2011 23:24:11 GMT, - * birthtime: Mon, 10 Oct 2011 23:24:11 GMT } - * ``` - * @since v0.1.21 - */ - export class Stats {} - /** - * A representation of a directory entry, which can be a file or a subdirectory - * within the directory, as returned by reading from an `fs.Dir`. The - * directory entry is a combination of the file name and file type pairs. - * - * Additionally, when {@link readdir} or {@link readdirSync} is called with - * the `withFileTypes` option set to `true`, the resulting array is filled with `fs.Dirent` objects, rather than strings or `Buffer` s. - * @since v10.10.0 - */ - export class Dirent { - /** - * Returns `true` if the `fs.Dirent` object describes a regular file. - * @since v10.10.0 - */ - isFile(): boolean; - /** - * Returns `true` if the `fs.Dirent` object describes a file system - * directory. - * @since v10.10.0 - */ - isDirectory(): boolean; - /** - * Returns `true` if the `fs.Dirent` object describes a block device. - * @since v10.10.0 - */ - isBlockDevice(): boolean; - /** - * Returns `true` if the `fs.Dirent` object describes a character device. - * @since v10.10.0 - */ - isCharacterDevice(): boolean; - /** - * Returns `true` if the `fs.Dirent` object describes a symbolic link. - * @since v10.10.0 - */ - isSymbolicLink(): boolean; - /** - * Returns `true` if the `fs.Dirent` object describes a first-in-first-out - * (FIFO) pipe. - * @since v10.10.0 - */ - isFIFO(): boolean; - /** - * Returns `true` if the `fs.Dirent` object describes a socket. - * @since v10.10.0 - */ - isSocket(): boolean; - /** - * The file name that this `fs.Dirent` object refers to. The type of this - * value is determined by the `options.encoding` passed to {@link readdir} or {@link readdirSync}. - * @since v10.10.0 - */ - name: string; - } - /** - * A class representing a directory stream. - * - * Created by {@link opendir}, {@link opendirSync}, or `fsPromises.opendir()`. - * - * ```js - * import { opendir } from 'fs/promises'; - * - * try { - * const dir = await opendir('./'); - * for await (const dirent of dir) - * console.log(dirent.name); - * } catch (err) { - * console.error(err); - * } - * ``` - * - * When using the async iterator, the `fs.Dir` object will be automatically - * closed after the iterator exits. - * @since v12.12.0 - */ - export class Dir implements AsyncIterable { - /** - * The read-only path of this directory as was provided to {@link opendir},{@link opendirSync}, or `fsPromises.opendir()`. - * @since v12.12.0 - */ - readonly path: string; - /** - * Asynchronously iterates over the directory via `readdir(3)` until all entries have been read. - */ - [Symbol.asyncIterator](): AsyncIterableIterator; - /** - * Asynchronously close the directory's underlying resource handle. - * Subsequent reads will result in errors. - * - * A promise is returned that will be resolved after the resource has been - * closed. - * @since v12.12.0 - */ - close(): Promise; - close(cb: NoParamCallback): void; - /** - * Synchronously close the directory's underlying resource handle. - * Subsequent reads will result in errors. - * @since v12.12.0 - */ - closeSync(): void; - /** - * Asynchronously read the next directory entry via [`readdir(3)`](http://man7.org/linux/man-pages/man3/readdir.3.html) as an `fs.Dirent`. - * - * A promise is returned that will be resolved with an `fs.Dirent`, or `null`if there are no more directory entries to read. - * - * Directory entries returned by this function are in no particular order as - * provided by the operating system's underlying directory mechanisms. - * Entries added or removed while iterating over the directory might not be - * included in the iteration results. - * @since v12.12.0 - * @return containing {fs.Dirent|null} - */ - read(): Promise; - read(cb: (err: NodeJS.ErrnoException | null, dirEnt: Dirent | null) => void): void; - /** - * Synchronously read the next directory entry as an `fs.Dirent`. See the - * POSIX [`readdir(3)`](http://man7.org/linux/man-pages/man3/readdir.3.html) documentation for more detail. - * - * If there are no more directory entries to read, `null` will be returned. - * - * Directory entries returned by this function are in no particular order as - * provided by the operating system's underlying directory mechanisms. - * Entries added or removed while iterating over the directory might not be - * included in the iteration results. - * @since v12.12.0 - */ - readSync(): Dirent | null; - } - /** - * Class: fs.StatWatcher - * @since v14.3.0, v12.20.0 - * Extends `EventEmitter` - * A successful call to {@link watchFile} method will return a new fs.StatWatcher object. - */ - export interface StatWatcher extends EventEmitter { - /** - * When called, requests that the Node.js event loop _not_ exit so long as the `fs.StatWatcher` is active. Calling `watcher.ref()` multiple times will have - * no effect. - * - * By default, all `fs.StatWatcher` objects are "ref'ed", making it normally - * unnecessary to call `watcher.ref()` unless `watcher.unref()` had been - * called previously. - * @since v14.3.0, v12.20.0 - */ - ref(): this; - /** - * When called, the active `fs.StatWatcher` object will not require the Node.js - * event loop to remain active. If there is no other activity keeping the - * event loop running, the process may exit before the `fs.StatWatcher` object's - * callback is invoked. Calling `watcher.unref()` multiple times will have - * no effect. - * @since v14.3.0, v12.20.0 - */ - unref(): this; - } - export interface FSWatcher extends EventEmitter { - /** - * Stop watching for changes on the given `fs.FSWatcher`. Once stopped, the `fs.FSWatcher` object is no longer usable. - * @since v0.5.8 - */ - close(): void; - /** - * events.EventEmitter - * 1. change - * 2. error - */ - addListener(event: string, listener: (...args: any[]) => void): this; - addListener(event: 'change', listener: (eventType: string, filename: string | Buffer) => void): this; - addListener(event: 'error', listener: (error: Error) => void): this; - addListener(event: 'close', listener: () => void): this; - on(event: string, listener: (...args: any[]) => void): this; - on(event: 'change', listener: (eventType: string, filename: string | Buffer) => void): this; - on(event: 'error', listener: (error: Error) => void): this; - on(event: 'close', listener: () => void): this; - once(event: string, listener: (...args: any[]) => void): this; - once(event: 'change', listener: (eventType: string, filename: string | Buffer) => void): this; - once(event: 'error', listener: (error: Error) => void): this; - once(event: 'close', listener: () => void): this; - prependListener(event: string, listener: (...args: any[]) => void): this; - prependListener(event: 'change', listener: (eventType: string, filename: string | Buffer) => void): this; - prependListener(event: 'error', listener: (error: Error) => void): this; - prependListener(event: 'close', listener: () => void): this; - prependOnceListener(event: string, listener: (...args: any[]) => void): this; - prependOnceListener(event: 'change', listener: (eventType: string, filename: string | Buffer) => void): this; - prependOnceListener(event: 'error', listener: (error: Error) => void): this; - prependOnceListener(event: 'close', listener: () => void): this; - } - /** - * Instances of `fs.ReadStream` are created and returned using the {@link createReadStream} function. - * @since v0.1.93 - */ - export class ReadStream extends stream.Readable { - close(callback?: (err?: NodeJS.ErrnoException | null) => void): void; - /** - * The number of bytes that have been read so far. - * @since v6.4.0 - */ - bytesRead: number; - /** - * The path to the file the stream is reading from as specified in the first - * argument to `fs.createReadStream()`. If `path` is passed as a string, then`readStream.path` will be a string. If `path` is passed as a `Buffer`, then`readStream.path` will be a - * `Buffer`. If `fd` is specified, then`readStream.path` will be `undefined`. - * @since v0.1.93 - */ - path: string | Buffer; - /** - * This property is `true` if the underlying file has not been opened yet, - * i.e. before the `'ready'` event is emitted. - * @since v11.2.0, v10.16.0 - */ - pending: boolean; - /** - * events.EventEmitter - * 1. open - * 2. close - * 3. ready - */ - addListener(event: 'close', listener: () => void): this; - addListener(event: 'data', listener: (chunk: Buffer | string) => void): this; - addListener(event: 'end', listener: () => void): this; - addListener(event: 'error', listener: (err: Error) => void): this; - addListener(event: 'open', listener: (fd: number) => void): this; - addListener(event: 'pause', listener: () => void): this; - addListener(event: 'readable', listener: () => void): this; - addListener(event: 'ready', listener: () => void): this; - addListener(event: 'resume', listener: () => void): this; - addListener(event: string | symbol, listener: (...args: any[]) => void): this; - on(event: 'close', listener: () => void): this; - on(event: 'data', listener: (chunk: Buffer | string) => void): this; - on(event: 'end', listener: () => void): this; - on(event: 'error', listener: (err: Error) => void): this; - on(event: 'open', listener: (fd: number) => void): this; - on(event: 'pause', listener: () => void): this; - on(event: 'readable', listener: () => void): this; - on(event: 'ready', listener: () => void): this; - on(event: 'resume', listener: () => void): this; - on(event: string | symbol, listener: (...args: any[]) => void): this; - once(event: 'close', listener: () => void): this; - once(event: 'data', listener: (chunk: Buffer | string) => void): this; - once(event: 'end', listener: () => void): this; - once(event: 'error', listener: (err: Error) => void): this; - once(event: 'open', listener: (fd: number) => void): this; - once(event: 'pause', listener: () => void): this; - once(event: 'readable', listener: () => void): this; - once(event: 'ready', listener: () => void): this; - once(event: 'resume', listener: () => void): this; - once(event: string | symbol, listener: (...args: any[]) => void): this; - prependListener(event: 'close', listener: () => void): this; - prependListener(event: 'data', listener: (chunk: Buffer | string) => void): this; - prependListener(event: 'end', listener: () => void): this; - prependListener(event: 'error', listener: (err: Error) => void): this; - prependListener(event: 'open', listener: (fd: number) => void): this; - prependListener(event: 'pause', listener: () => void): this; - prependListener(event: 'readable', listener: () => void): this; - prependListener(event: 'ready', listener: () => void): this; - prependListener(event: 'resume', listener: () => void): this; - prependListener(event: string | symbol, listener: (...args: any[]) => void): this; - prependOnceListener(event: 'close', listener: () => void): this; - prependOnceListener(event: 'data', listener: (chunk: Buffer | string) => void): this; - prependOnceListener(event: 'end', listener: () => void): this; - prependOnceListener(event: 'error', listener: (err: Error) => void): this; - prependOnceListener(event: 'open', listener: (fd: number) => void): this; - prependOnceListener(event: 'pause', listener: () => void): this; - prependOnceListener(event: 'readable', listener: () => void): this; - prependOnceListener(event: 'ready', listener: () => void): this; - prependOnceListener(event: 'resume', listener: () => void): this; - prependOnceListener(event: string | symbol, listener: (...args: any[]) => void): this; - } - /** - * * Extends `stream.Writable` - * - * Instances of `fs.WriteStream` are created and returned using the {@link createWriteStream} function. - * @since v0.1.93 - */ - export class WriteStream extends stream.Writable { - /** - * Closes `writeStream`. Optionally accepts a - * callback that will be executed once the `writeStream`is closed. - * @since v0.9.4 - */ - close(callback?: (err?: NodeJS.ErrnoException | null) => void): void; - /** - * The number of bytes written so far. Does not include data that is still queued - * for writing. - * @since v0.4.7 - */ - bytesWritten: number; - /** - * The path to the file the stream is writing to as specified in the first - * argument to {@link createWriteStream}. If `path` is passed as a string, then`writeStream.path` will be a string. If `path` is passed as a `Buffer`, then`writeStream.path` will be a - * `Buffer`. - * @since v0.1.93 - */ - path: string | Buffer; - /** - * This property is `true` if the underlying file has not been opened yet, - * i.e. before the `'ready'` event is emitted. - * @since v11.2.0 - */ - pending: boolean; - /** - * events.EventEmitter - * 1. open - * 2. close - * 3. ready - */ - addListener(event: 'close', listener: () => void): this; - addListener(event: 'drain', listener: () => void): this; - addListener(event: 'error', listener: (err: Error) => void): this; - addListener(event: 'finish', listener: () => void): this; - addListener(event: 'open', listener: (fd: number) => void): this; - addListener(event: 'pipe', listener: (src: stream.Readable) => void): this; - addListener(event: 'ready', listener: () => void): this; - addListener(event: 'unpipe', listener: (src: stream.Readable) => void): this; - addListener(event: string | symbol, listener: (...args: any[]) => void): this; - on(event: 'close', listener: () => void): this; - on(event: 'drain', listener: () => void): this; - on(event: 'error', listener: (err: Error) => void): this; - on(event: 'finish', listener: () => void): this; - on(event: 'open', listener: (fd: number) => void): this; - on(event: 'pipe', listener: (src: stream.Readable) => void): this; - on(event: 'ready', listener: () => void): this; - on(event: 'unpipe', listener: (src: stream.Readable) => void): this; - on(event: string | symbol, listener: (...args: any[]) => void): this; - once(event: 'close', listener: () => void): this; - once(event: 'drain', listener: () => void): this; - once(event: 'error', listener: (err: Error) => void): this; - once(event: 'finish', listener: () => void): this; - once(event: 'open', listener: (fd: number) => void): this; - once(event: 'pipe', listener: (src: stream.Readable) => void): this; - once(event: 'ready', listener: () => void): this; - once(event: 'unpipe', listener: (src: stream.Readable) => void): this; - once(event: string | symbol, listener: (...args: any[]) => void): this; - prependListener(event: 'close', listener: () => void): this; - prependListener(event: 'drain', listener: () => void): this; - prependListener(event: 'error', listener: (err: Error) => void): this; - prependListener(event: 'finish', listener: () => void): this; - prependListener(event: 'open', listener: (fd: number) => void): this; - prependListener(event: 'pipe', listener: (src: stream.Readable) => void): this; - prependListener(event: 'ready', listener: () => void): this; - prependListener(event: 'unpipe', listener: (src: stream.Readable) => void): this; - prependListener(event: string | symbol, listener: (...args: any[]) => void): this; - prependOnceListener(event: 'close', listener: () => void): this; - prependOnceListener(event: 'drain', listener: () => void): this; - prependOnceListener(event: 'error', listener: (err: Error) => void): this; - prependOnceListener(event: 'finish', listener: () => void): this; - prependOnceListener(event: 'open', listener: (fd: number) => void): this; - prependOnceListener(event: 'pipe', listener: (src: stream.Readable) => void): this; - prependOnceListener(event: 'ready', listener: () => void): this; - prependOnceListener(event: 'unpipe', listener: (src: stream.Readable) => void): this; - prependOnceListener(event: string | symbol, listener: (...args: any[]) => void): this; - } - /** - * Asynchronously rename file at `oldPath` to the pathname provided - * as `newPath`. In the case that `newPath` already exists, it will - * be overwritten. If there is a directory at `newPath`, an error will - * be raised instead. No arguments other than a possible exception are - * given to the completion callback. - * - * See also: [`rename(2)`](http://man7.org/linux/man-pages/man2/rename.2.html). - * - * ```js - * import { rename } from 'fs'; - * - * rename('oldFile.txt', 'newFile.txt', (err) => { - * if (err) throw err; - * console.log('Rename complete!'); - * }); - * ``` - * @since v0.0.2 - */ - export function rename(oldPath: PathLike, newPath: PathLike, callback: NoParamCallback): void; - export namespace rename { - /** - * Asynchronous rename(2) - Change the name or location of a file or directory. - * @param oldPath A path to a file. If a URL is provided, it must use the `file:` protocol. - * URL support is _experimental_. - * @param newPath A path to a file. If a URL is provided, it must use the `file:` protocol. - * URL support is _experimental_. - */ - function __promisify__(oldPath: PathLike, newPath: PathLike): Promise; - } - /** - * Renames the file from `oldPath` to `newPath`. Returns `undefined`. - * - * See the POSIX [`rename(2)`](http://man7.org/linux/man-pages/man2/rename.2.html) documentation for more details. - * @since v0.1.21 - */ - export function renameSync(oldPath: PathLike, newPath: PathLike): void; - /** - * Truncates the file. No arguments other than a possible exception are - * given to the completion callback. A file descriptor can also be passed as the - * first argument. In this case, `fs.ftruncate()` is called. - * - * ```js - * import { truncate } from 'fs'; - * // Assuming that 'path/file.txt' is a regular file. - * truncate('path/file.txt', (err) => { - * if (err) throw err; - * console.log('path/file.txt was truncated'); - * }); - * ``` - * - * Passing a file descriptor is deprecated and may result in an error being thrown - * in the future. - * - * See the POSIX [`truncate(2)`](http://man7.org/linux/man-pages/man2/truncate.2.html) documentation for more details. - * @since v0.8.6 - * @param [len=0] - */ - export function truncate(path: PathLike, len: number | undefined | null, callback: NoParamCallback): void; - /** - * Asynchronous truncate(2) - Truncate a file to a specified length. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - export function truncate(path: PathLike, callback: NoParamCallback): void; - export namespace truncate { - /** - * Asynchronous truncate(2) - Truncate a file to a specified length. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param len If not specified, defaults to `0`. - */ - function __promisify__(path: PathLike, len?: number | null): Promise; - } - /** - * Truncates the file. Returns `undefined`. A file descriptor can also be - * passed as the first argument. In this case, `fs.ftruncateSync()` is called. - * - * Passing a file descriptor is deprecated and may result in an error being thrown - * in the future. - * @since v0.8.6 - * @param [len=0] - */ - export function truncateSync(path: PathLike, len?: number | null): void; - /** - * Truncates the file descriptor. No arguments other than a possible exception are - * given to the completion callback. - * - * See the POSIX [`ftruncate(2)`](http://man7.org/linux/man-pages/man2/ftruncate.2.html) documentation for more detail. - * - * If the file referred to by the file descriptor was larger than `len` bytes, only - * the first `len` bytes will be retained in the file. - * - * For example, the following program retains only the first four bytes of the - * file: - * - * ```js - * import { open, close, ftruncate } from 'fs'; - * - * function closeFd(fd) { - * close(fd, (err) => { - * if (err) throw err; - * }); - * } - * - * open('temp.txt', 'r+', (err, fd) => { - * if (err) throw err; - * - * try { - * ftruncate(fd, 4, (err) => { - * closeFd(fd); - * if (err) throw err; - * }); - * } catch (err) { - * closeFd(fd); - * if (err) throw err; - * } - * }); - * ``` - * - * If the file previously was shorter than `len` bytes, it is extended, and the - * extended part is filled with null bytes (`'\0'`): - * - * If `len` is negative then `0` will be used. - * @since v0.8.6 - * @param [len=0] - */ - export function ftruncate(fd: number, len: number | undefined | null, callback: NoParamCallback): void; - /** - * Asynchronous ftruncate(2) - Truncate a file to a specified length. - * @param fd A file descriptor. - */ - export function ftruncate(fd: number, callback: NoParamCallback): void; - export namespace ftruncate { - /** - * Asynchronous ftruncate(2) - Truncate a file to a specified length. - * @param fd A file descriptor. - * @param len If not specified, defaults to `0`. - */ - function __promisify__(fd: number, len?: number | null): Promise; - } - /** - * Truncates the file descriptor. Returns `undefined`. - * - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link ftruncate}. - * @since v0.8.6 - * @param [len=0] - */ - export function ftruncateSync(fd: number, len?: number | null): void; - /** - * Asynchronously changes owner and group of a file. No arguments other than a - * possible exception are given to the completion callback. - * - * See the POSIX [`chown(2)`](http://man7.org/linux/man-pages/man2/chown.2.html) documentation for more detail. - * @since v0.1.97 - */ - export function chown(path: PathLike, uid: number, gid: number, callback: NoParamCallback): void; - export namespace chown { - /** - * Asynchronous chown(2) - Change ownership of a file. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - function __promisify__(path: PathLike, uid: number, gid: number): Promise; - } - /** - * Synchronously changes owner and group of a file. Returns `undefined`. - * This is the synchronous version of {@link chown}. - * - * See the POSIX [`chown(2)`](http://man7.org/linux/man-pages/man2/chown.2.html) documentation for more detail. - * @since v0.1.97 - */ - export function chownSync(path: PathLike, uid: number, gid: number): void; - /** - * Sets the owner of the file. No arguments other than a possible exception are - * given to the completion callback. - * - * See the POSIX [`fchown(2)`](http://man7.org/linux/man-pages/man2/fchown.2.html) documentation for more detail. - * @since v0.4.7 - */ - export function fchown(fd: number, uid: number, gid: number, callback: NoParamCallback): void; - export namespace fchown { - /** - * Asynchronous fchown(2) - Change ownership of a file. - * @param fd A file descriptor. - */ - function __promisify__(fd: number, uid: number, gid: number): Promise; - } - /** - * Sets the owner of the file. Returns `undefined`. - * - * See the POSIX [`fchown(2)`](http://man7.org/linux/man-pages/man2/fchown.2.html) documentation for more detail. - * @since v0.4.7 - * @param uid The file's new owner's user id. - * @param gid The file's new group's group id. - */ - export function fchownSync(fd: number, uid: number, gid: number): void; - /** - * Set the owner of the symbolic link. No arguments other than a possible - * exception are given to the completion callback. - * - * See the POSIX [`lchown(2)`](http://man7.org/linux/man-pages/man2/lchown.2.html) documentation for more detail. - */ - export function lchown(path: PathLike, uid: number, gid: number, callback: NoParamCallback): void; - export namespace lchown { - /** - * Asynchronous lchown(2) - Change ownership of a file. Does not dereference symbolic links. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - function __promisify__(path: PathLike, uid: number, gid: number): Promise; - } - /** - * Set the owner for the path. Returns `undefined`. - * - * See the POSIX [`lchown(2)`](http://man7.org/linux/man-pages/man2/lchown.2.html) documentation for more details. - * @param uid The file's new owner's user id. - * @param gid The file's new group's group id. - */ - export function lchownSync(path: PathLike, uid: number, gid: number): void; - /** - * Changes the access and modification times of a file in the same way as {@link utimes}, with the difference that if the path refers to a symbolic - * link, then the link is not dereferenced: instead, the timestamps of the - * symbolic link itself are changed. - * - * No arguments other than a possible exception are given to the completion - * callback. - * @since v14.5.0, v12.19.0 - */ - export function lutimes(path: PathLike, atime: TimeLike, mtime: TimeLike, callback: NoParamCallback): void; - export namespace lutimes { - /** - * Changes the access and modification times of a file in the same way as `fsPromises.utimes()`, - * with the difference that if the path refers to a symbolic link, then the link is not - * dereferenced: instead, the timestamps of the symbolic link itself are changed. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param atime The last access time. If a string is provided, it will be coerced to number. - * @param mtime The last modified time. If a string is provided, it will be coerced to number. - */ - function __promisify__(path: PathLike, atime: TimeLike, mtime: TimeLike): Promise; - } - /** - * Change the file system timestamps of the symbolic link referenced by `path`. - * Returns `undefined`, or throws an exception when parameters are incorrect or - * the operation fails. This is the synchronous version of {@link lutimes}. - * @since v14.5.0, v12.19.0 - */ - export function lutimesSync(path: PathLike, atime: TimeLike, mtime: TimeLike): void; - /** - * Asynchronously changes the permissions of a file. No arguments other than a - * possible exception are given to the completion callback. - * - * See the POSIX [`chmod(2)`](http://man7.org/linux/man-pages/man2/chmod.2.html) documentation for more detail. - * - * ```js - * import { chmod } from 'fs'; - * - * chmod('my_file.txt', 0o775, (err) => { - * if (err) throw err; - * console.log('The permissions for file "my_file.txt" have been changed!'); - * }); - * ``` - * @since v0.1.30 - */ - export function chmod(path: PathLike, mode: Mode, callback: NoParamCallback): void; - export namespace chmod { - /** - * Asynchronous chmod(2) - Change permissions of a file. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param mode A file mode. If a string is passed, it is parsed as an octal integer. - */ - function __promisify__(path: PathLike, mode: Mode): Promise; - } - /** - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link chmod}. - * - * See the POSIX [`chmod(2)`](http://man7.org/linux/man-pages/man2/chmod.2.html) documentation for more detail. - * @since v0.6.7 - */ - export function chmodSync(path: PathLike, mode: Mode): void; - /** - * Sets the permissions on the file. No arguments other than a possible exception - * are given to the completion callback. - * - * See the POSIX [`fchmod(2)`](http://man7.org/linux/man-pages/man2/fchmod.2.html) documentation for more detail. - * @since v0.4.7 - */ - export function fchmod(fd: number, mode: Mode, callback: NoParamCallback): void; - export namespace fchmod { - /** - * Asynchronous fchmod(2) - Change permissions of a file. - * @param fd A file descriptor. - * @param mode A file mode. If a string is passed, it is parsed as an octal integer. - */ - function __promisify__(fd: number, mode: Mode): Promise; - } - /** - * Sets the permissions on the file. Returns `undefined`. - * - * See the POSIX [`fchmod(2)`](http://man7.org/linux/man-pages/man2/fchmod.2.html) documentation for more detail. - * @since v0.4.7 - */ - export function fchmodSync(fd: number, mode: Mode): void; - /** - * Changes the permissions on a symbolic link. No arguments other than a possible - * exception are given to the completion callback. - * - * This method is only implemented on macOS. - * - * See the POSIX [`lchmod(2)`](https://www.freebsd.org/cgi/man.cgi?query=lchmod&sektion=2) documentation for more detail. - * @deprecated Since v0.4.7 - */ - export function lchmod(path: PathLike, mode: Mode, callback: NoParamCallback): void; - /** @deprecated */ - export namespace lchmod { - /** - * Asynchronous lchmod(2) - Change permissions of a file. Does not dereference symbolic links. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param mode A file mode. If a string is passed, it is parsed as an octal integer. - */ - function __promisify__(path: PathLike, mode: Mode): Promise; - } - /** - * Changes the permissions on a symbolic link. Returns `undefined`. - * - * This method is only implemented on macOS. - * - * See the POSIX [`lchmod(2)`](https://www.freebsd.org/cgi/man.cgi?query=lchmod&sektion=2) documentation for more detail. - * @deprecated Since v0.4.7 - */ - export function lchmodSync(path: PathLike, mode: Mode): void; - /** - * Asynchronous [`stat(2)`](http://man7.org/linux/man-pages/man2/stat.2.html). The callback gets two arguments `(err, stats)` where`stats` is an `fs.Stats` object. - * - * In case of an error, the `err.code` will be one of `Common System Errors`. - * - * Using `fs.stat()` to check for the existence of a file before calling`fs.open()`, `fs.readFile()` or `fs.writeFile()` is not recommended. - * Instead, user code should open/read/write the file directly and handle the - * error raised if the file is not available. - * - * To check if a file exists without manipulating it afterwards, {@link access} is recommended. - * - * For example, given the following directory structure: - * - * ```text - * - txtDir - * -- file.txt - * - app.js - * ``` - * - * The next program will check for the stats of the given paths: - * - * ```js - * import { stat } from 'fs'; - * - * const pathsToCheck = ['./txtDir', './txtDir/file.txt']; - * - * for (let i = 0; i < pathsToCheck.length; i++) { - * stat(pathsToCheck[i], (err, stats) => { - * console.log(stats.isDirectory()); - * console.log(stats); - * }); - * } - * ``` - * - * The resulting output will resemble: - * - * ```console - * true - * Stats { - * dev: 16777220, - * mode: 16877, - * nlink: 3, - * uid: 501, - * gid: 20, - * rdev: 0, - * blksize: 4096, - * ino: 14214262, - * size: 96, - * blocks: 0, - * atimeMs: 1561174653071.963, - * mtimeMs: 1561174614583.3518, - * ctimeMs: 1561174626623.5366, - * birthtimeMs: 1561174126937.2893, - * atime: 2019-06-22T03:37:33.072Z, - * mtime: 2019-06-22T03:36:54.583Z, - * ctime: 2019-06-22T03:37:06.624Z, - * birthtime: 2019-06-22T03:28:46.937Z - * } - * false - * Stats { - * dev: 16777220, - * mode: 33188, - * nlink: 1, - * uid: 501, - * gid: 20, - * rdev: 0, - * blksize: 4096, - * ino: 14214074, - * size: 8, - * blocks: 8, - * atimeMs: 1561174616618.8555, - * mtimeMs: 1561174614584, - * ctimeMs: 1561174614583.8145, - * birthtimeMs: 1561174007710.7478, - * atime: 2019-06-22T03:36:56.619Z, - * mtime: 2019-06-22T03:36:54.584Z, - * ctime: 2019-06-22T03:36:54.584Z, - * birthtime: 2019-06-22T03:26:47.711Z - * } - * ``` - * @since v0.0.2 - */ - export function stat(path: PathLike, callback: (err: NodeJS.ErrnoException | null, stats: Stats) => void): void; - export function stat( - path: PathLike, - options: - | (StatOptions & { - bigint?: false | undefined; - }) - | undefined, - callback: (err: NodeJS.ErrnoException | null, stats: Stats) => void - ): void; - export function stat( - path: PathLike, - options: StatOptions & { - bigint: true; - }, - callback: (err: NodeJS.ErrnoException | null, stats: BigIntStats) => void - ): void; - export function stat(path: PathLike, options: StatOptions | undefined, callback: (err: NodeJS.ErrnoException | null, stats: Stats | BigIntStats) => void): void; - export namespace stat { - /** - * Asynchronous stat(2) - Get file status. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - function __promisify__( - path: PathLike, - options?: StatOptions & { - bigint?: false | undefined; - } - ): Promise; - function __promisify__( - path: PathLike, - options: StatOptions & { - bigint: true; - } - ): Promise; - function __promisify__(path: PathLike, options?: StatOptions): Promise; - } - export interface StatSyncFn extends Function { - (path: PathLike, options?: undefined): Stats; - ( - path: PathLike, - options?: StatSyncOptions & { - bigint?: false | undefined; - throwIfNoEntry: false; - } - ): Stats | undefined; - ( - path: PathLike, - options: StatSyncOptions & { - bigint: true; - throwIfNoEntry: false; - } - ): BigIntStats | undefined; - ( - path: PathLike, - options?: StatSyncOptions & { - bigint?: false | undefined; - } - ): Stats; - ( - path: PathLike, - options: StatSyncOptions & { - bigint: true; - } - ): BigIntStats; - ( - path: PathLike, - options: StatSyncOptions & { - bigint: boolean; - throwIfNoEntry?: false | undefined; - } - ): Stats | BigIntStats; - (path: PathLike, options?: StatSyncOptions): Stats | BigIntStats | undefined; - } - /** - * Synchronous stat(2) - Get file status. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - export const statSync: StatSyncFn; - /** - * Invokes the callback with the `fs.Stats` for the file descriptor. - * - * See the POSIX [`fstat(2)`](http://man7.org/linux/man-pages/man2/fstat.2.html) documentation for more detail. - * @since v0.1.95 - */ - export function fstat(fd: number, callback: (err: NodeJS.ErrnoException | null, stats: Stats) => void): void; - export function fstat( - fd: number, - options: - | (StatOptions & { - bigint?: false | undefined; - }) - | undefined, - callback: (err: NodeJS.ErrnoException | null, stats: Stats) => void - ): void; - export function fstat( - fd: number, - options: StatOptions & { - bigint: true; - }, - callback: (err: NodeJS.ErrnoException | null, stats: BigIntStats) => void - ): void; - export function fstat(fd: number, options: StatOptions | undefined, callback: (err: NodeJS.ErrnoException | null, stats: Stats | BigIntStats) => void): void; - export namespace fstat { - /** - * Asynchronous fstat(2) - Get file status. - * @param fd A file descriptor. - */ - function __promisify__( - fd: number, - options?: StatOptions & { - bigint?: false | undefined; - } - ): Promise; - function __promisify__( - fd: number, - options: StatOptions & { - bigint: true; - } - ): Promise; - function __promisify__(fd: number, options?: StatOptions): Promise; - } - /** - * Retrieves the `fs.Stats` for the file descriptor. - * - * See the POSIX [`fstat(2)`](http://man7.org/linux/man-pages/man2/fstat.2.html) documentation for more detail. - * @since v0.1.95 - */ - export function fstatSync( - fd: number, - options?: StatOptions & { - bigint?: false | undefined; - } - ): Stats; - export function fstatSync( - fd: number, - options: StatOptions & { - bigint: true; - } - ): BigIntStats; - export function fstatSync(fd: number, options?: StatOptions): Stats | BigIntStats; - /** - * Retrieves the `fs.Stats` for the symbolic link referred to by the path. - * The callback gets two arguments `(err, stats)` where `stats` is a `fs.Stats` object. `lstat()` is identical to `stat()`, except that if `path` is a symbolic - * link, then the link itself is stat-ed, not the file that it refers to. - * - * See the POSIX [`lstat(2)`](http://man7.org/linux/man-pages/man2/lstat.2.html) documentation for more details. - * @since v0.1.30 - */ - export function lstat(path: PathLike, callback: (err: NodeJS.ErrnoException | null, stats: Stats) => void): void; - export function lstat( - path: PathLike, - options: - | (StatOptions & { - bigint?: false | undefined; - }) - | undefined, - callback: (err: NodeJS.ErrnoException | null, stats: Stats) => void - ): void; - export function lstat( - path: PathLike, - options: StatOptions & { - bigint: true; - }, - callback: (err: NodeJS.ErrnoException | null, stats: BigIntStats) => void - ): void; - export function lstat(path: PathLike, options: StatOptions | undefined, callback: (err: NodeJS.ErrnoException | null, stats: Stats | BigIntStats) => void): void; - export namespace lstat { - /** - * Asynchronous lstat(2) - Get file status. Does not dereference symbolic links. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - function __promisify__( - path: PathLike, - options?: StatOptions & { - bigint?: false | undefined; - } - ): Promise; - function __promisify__( - path: PathLike, - options: StatOptions & { - bigint: true; - } - ): Promise; - function __promisify__(path: PathLike, options?: StatOptions): Promise; - } - /** - * Synchronous lstat(2) - Get file status. Does not dereference symbolic links. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - export const lstatSync: StatSyncFn; - /** - * Creates a new link from the `existingPath` to the `newPath`. See the POSIX [`link(2)`](http://man7.org/linux/man-pages/man2/link.2.html) documentation for more detail. No arguments other than - * a possible - * exception are given to the completion callback. - * @since v0.1.31 - */ - export function link(existingPath: PathLike, newPath: PathLike, callback: NoParamCallback): void; - export namespace link { - /** - * Asynchronous link(2) - Create a new link (also known as a hard link) to an existing file. - * @param existingPath A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param newPath A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - function __promisify__(existingPath: PathLike, newPath: PathLike): Promise; - } - /** - * Creates a new link from the `existingPath` to the `newPath`. See the POSIX [`link(2)`](http://man7.org/linux/man-pages/man2/link.2.html) documentation for more detail. Returns `undefined`. - * @since v0.1.31 - */ - export function linkSync(existingPath: PathLike, newPath: PathLike): void; - /** - * Creates the link called `path` pointing to `target`. No arguments other than a - * possible exception are given to the completion callback. - * - * See the POSIX [`symlink(2)`](http://man7.org/linux/man-pages/man2/symlink.2.html) documentation for more details. - * - * The `type` argument is only available on Windows and ignored on other platforms. - * It can be set to `'dir'`, `'file'`, or `'junction'`. If the `type` argument is - * not set, Node.js will autodetect `target` type and use `'file'` or `'dir'`. If - * the `target` does not exist, `'file'` will be used. Windows junction points - * require the destination path to be absolute. When using `'junction'`, the`target` argument will automatically be normalized to absolute path. - * - * Relative targets are relative to the link’s parent directory. - * - * ```js - * import { symlink } from 'fs'; - * - * symlink('./mew', './mewtwo', callback); - * ``` - * - * The above example creates a symbolic link `mewtwo` which points to `mew` in the - * same directory: - * - * ```bash - * $ tree . - * . - * ├── mew - * └── mewtwo -> ./mew - * ``` - * @since v0.1.31 - */ - export function symlink(target: PathLike, path: PathLike, type: symlink.Type | undefined | null, callback: NoParamCallback): void; - /** - * Asynchronous symlink(2) - Create a new symbolic link to an existing file. - * @param target A path to an existing file. If a URL is provided, it must use the `file:` protocol. - * @param path A path to the new symlink. If a URL is provided, it must use the `file:` protocol. - */ - export function symlink(target: PathLike, path: PathLike, callback: NoParamCallback): void; - export namespace symlink { - /** - * Asynchronous symlink(2) - Create a new symbolic link to an existing file. - * @param target A path to an existing file. If a URL is provided, it must use the `file:` protocol. - * @param path A path to the new symlink. If a URL is provided, it must use the `file:` protocol. - * @param type May be set to `'dir'`, `'file'`, or `'junction'` (default is `'file'`) and is only available on Windows (ignored on other platforms). - * When using `'junction'`, the `target` argument will automatically be normalized to an absolute path. - */ - function __promisify__(target: PathLike, path: PathLike, type?: string | null): Promise; - type Type = 'dir' | 'file' | 'junction'; - } - /** - * Returns `undefined`. - * - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link symlink}. - * @since v0.1.31 - */ - export function symlinkSync(target: PathLike, path: PathLike, type?: symlink.Type | null): void; - /** - * Reads the contents of the symbolic link referred to by `path`. The callback gets - * two arguments `(err, linkString)`. - * - * See the POSIX [`readlink(2)`](http://man7.org/linux/man-pages/man2/readlink.2.html) documentation for more details. - * - * The optional `options` argument can be a string specifying an encoding, or an - * object with an `encoding` property specifying the character encoding to use for - * the link path passed to the callback. If the `encoding` is set to `'buffer'`, - * the link path returned will be passed as a `Buffer` object. - * @since v0.1.31 - */ - export function readlink(path: PathLike, options: EncodingOption, callback: (err: NodeJS.ErrnoException | null, linkString: string) => void): void; - /** - * Asynchronous readlink(2) - read value of a symbolic link. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function readlink(path: PathLike, options: BufferEncodingOption, callback: (err: NodeJS.ErrnoException | null, linkString: Buffer) => void): void; - /** - * Asynchronous readlink(2) - read value of a symbolic link. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function readlink(path: PathLike, options: EncodingOption, callback: (err: NodeJS.ErrnoException | null, linkString: string | Buffer) => void): void; - /** - * Asynchronous readlink(2) - read value of a symbolic link. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - export function readlink(path: PathLike, callback: (err: NodeJS.ErrnoException | null, linkString: string) => void): void; - export namespace readlink { - /** - * Asynchronous readlink(2) - read value of a symbolic link. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - function __promisify__(path: PathLike, options?: EncodingOption): Promise; - /** - * Asynchronous readlink(2) - read value of a symbolic link. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - function __promisify__(path: PathLike, options: BufferEncodingOption): Promise; - /** - * Asynchronous readlink(2) - read value of a symbolic link. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - function __promisify__(path: PathLike, options?: EncodingOption): Promise; - } - /** - * Returns the symbolic link's string value. - * - * See the POSIX [`readlink(2)`](http://man7.org/linux/man-pages/man2/readlink.2.html) documentation for more details. - * - * The optional `options` argument can be a string specifying an encoding, or an - * object with an `encoding` property specifying the character encoding to use for - * the link path returned. If the `encoding` is set to `'buffer'`, - * the link path returned will be passed as a `Buffer` object. - * @since v0.1.31 - */ - export function readlinkSync(path: PathLike, options?: EncodingOption): string; - /** - * Synchronous readlink(2) - read value of a symbolic link. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function readlinkSync(path: PathLike, options: BufferEncodingOption): Buffer; - /** - * Synchronous readlink(2) - read value of a symbolic link. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function readlinkSync(path: PathLike, options?: EncodingOption): string | Buffer; - /** - * Asynchronously computes the canonical pathname by resolving `.`, `..` and - * symbolic links. - * - * A canonical pathname is not necessarily unique. Hard links and bind mounts can - * expose a file system entity through many pathnames. - * - * This function behaves like [`realpath(3)`](http://man7.org/linux/man-pages/man3/realpath.3.html), with some exceptions: - * - * 1. No case conversion is performed on case-insensitive file systems. - * 2. The maximum number of symbolic links is platform-independent and generally - * (much) higher than what the native [`realpath(3)`](http://man7.org/linux/man-pages/man3/realpath.3.html) implementation supports. - * - * The `callback` gets two arguments `(err, resolvedPath)`. May use `process.cwd`to resolve relative paths. - * - * Only paths that can be converted to UTF8 strings are supported. - * - * The optional `options` argument can be a string specifying an encoding, or an - * object with an `encoding` property specifying the character encoding to use for - * the path passed to the callback. If the `encoding` is set to `'buffer'`, - * the path returned will be passed as a `Buffer` object. - * - * If `path` resolves to a socket or a pipe, the function will return a system - * dependent name for that object. - * @since v0.1.31 - */ - export function realpath(path: PathLike, options: EncodingOption, callback: (err: NodeJS.ErrnoException | null, resolvedPath: string) => void): void; - /** - * Asynchronous realpath(3) - return the canonicalized absolute pathname. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function realpath(path: PathLike, options: BufferEncodingOption, callback: (err: NodeJS.ErrnoException | null, resolvedPath: Buffer) => void): void; - /** - * Asynchronous realpath(3) - return the canonicalized absolute pathname. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function realpath(path: PathLike, options: EncodingOption, callback: (err: NodeJS.ErrnoException | null, resolvedPath: string | Buffer) => void): void; - /** - * Asynchronous realpath(3) - return the canonicalized absolute pathname. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - export function realpath(path: PathLike, callback: (err: NodeJS.ErrnoException | null, resolvedPath: string) => void): void; - export namespace realpath { - /** - * Asynchronous realpath(3) - return the canonicalized absolute pathname. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - function __promisify__(path: PathLike, options?: EncodingOption): Promise; - /** - * Asynchronous realpath(3) - return the canonicalized absolute pathname. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - function __promisify__(path: PathLike, options: BufferEncodingOption): Promise; - /** - * Asynchronous realpath(3) - return the canonicalized absolute pathname. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - function __promisify__(path: PathLike, options?: EncodingOption): Promise; - /** - * Asynchronous [`realpath(3)`](http://man7.org/linux/man-pages/man3/realpath.3.html). - * - * The `callback` gets two arguments `(err, resolvedPath)`. - * - * Only paths that can be converted to UTF8 strings are supported. - * - * The optional `options` argument can be a string specifying an encoding, or an - * object with an `encoding` property specifying the character encoding to use for - * the path passed to the callback. If the `encoding` is set to `'buffer'`, - * the path returned will be passed as a `Buffer` object. - * - * On Linux, when Node.js is linked against musl libc, the procfs file system must - * be mounted on `/proc` in order for this function to work. Glibc does not have - * this restriction. - * @since v9.2.0 - */ - function native(path: PathLike, options: EncodingOption, callback: (err: NodeJS.ErrnoException | null, resolvedPath: string) => void): void; - function native(path: PathLike, options: BufferEncodingOption, callback: (err: NodeJS.ErrnoException | null, resolvedPath: Buffer) => void): void; - function native(path: PathLike, options: EncodingOption, callback: (err: NodeJS.ErrnoException | null, resolvedPath: string | Buffer) => void): void; - function native(path: PathLike, callback: (err: NodeJS.ErrnoException | null, resolvedPath: string) => void): void; - } - /** - * Returns the resolved pathname. - * - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link realpath}. - * @since v0.1.31 - */ - export function realpathSync(path: PathLike, options?: EncodingOption): string; - /** - * Synchronous realpath(3) - return the canonicalized absolute pathname. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function realpathSync(path: PathLike, options: BufferEncodingOption): Buffer; - /** - * Synchronous realpath(3) - return the canonicalized absolute pathname. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function realpathSync(path: PathLike, options?: EncodingOption): string | Buffer; - export namespace realpathSync { - function native(path: PathLike, options?: EncodingOption): string; - function native(path: PathLike, options: BufferEncodingOption): Buffer; - function native(path: PathLike, options?: EncodingOption): string | Buffer; - } - /** - * Asynchronously removes a file or symbolic link. No arguments other than a - * possible exception are given to the completion callback. - * - * ```js - * import { unlink } from 'fs'; - * // Assuming that 'path/file.txt' is a regular file. - * unlink('path/file.txt', (err) => { - * if (err) throw err; - * console.log('path/file.txt was deleted'); - * }); - * ``` - * - * `fs.unlink()` will not work on a directory, empty or otherwise. To remove a - * directory, use {@link rmdir}. - * - * See the POSIX [`unlink(2)`](http://man7.org/linux/man-pages/man2/unlink.2.html) documentation for more details. - * @since v0.0.2 - */ - export function unlink(path: PathLike, callback: NoParamCallback): void; - export namespace unlink { - /** - * Asynchronous unlink(2) - delete a name and possibly the file it refers to. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - function __promisify__(path: PathLike): Promise; - } - /** - * Synchronous [`unlink(2)`](http://man7.org/linux/man-pages/man2/unlink.2.html). Returns `undefined`. - * @since v0.1.21 - */ - export function unlinkSync(path: PathLike): void; - export interface RmDirOptions { - /** - * If an `EBUSY`, `EMFILE`, `ENFILE`, `ENOTEMPTY`, or - * `EPERM` error is encountered, Node.js will retry the operation with a linear - * backoff wait of `retryDelay` ms longer on each try. This option represents the - * number of retries. This option is ignored if the `recursive` option is not - * `true`. - * @default 0 - */ - maxRetries?: number | undefined; - /** - * @deprecated since v14.14.0 In future versions of Node.js and will trigger a warning - * `fs.rmdir(path, { recursive: true })` will throw if `path` does not exist or is a file. - * Use `fs.rm(path, { recursive: true, force: true })` instead. - * - * If `true`, perform a recursive directory removal. In - * recursive mode, operations are retried on failure. - * @default false - */ - recursive?: boolean | undefined; - /** - * The amount of time in milliseconds to wait between retries. - * This option is ignored if the `recursive` option is not `true`. - * @default 100 - */ - retryDelay?: number | undefined; - } - /** - * Asynchronous [`rmdir(2)`](http://man7.org/linux/man-pages/man2/rmdir.2.html). No arguments other than a possible exception are given - * to the completion callback. - * - * Using `fs.rmdir()` on a file (not a directory) results in an `ENOENT` error on - * Windows and an `ENOTDIR` error on POSIX. - * - * To get a behavior similar to the `rm -rf` Unix command, use {@link rm} with options `{ recursive: true, force: true }`. - * @since v0.0.2 - */ - export function rmdir(path: PathLike, callback: NoParamCallback): void; - export function rmdir(path: PathLike, options: RmDirOptions, callback: NoParamCallback): void; - export namespace rmdir { - /** - * Asynchronous rmdir(2) - delete a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - function __promisify__(path: PathLike, options?: RmDirOptions): Promise; - } - /** - * Synchronous [`rmdir(2)`](http://man7.org/linux/man-pages/man2/rmdir.2.html). Returns `undefined`. - * - * Using `fs.rmdirSync()` on a file (not a directory) results in an `ENOENT` error - * on Windows and an `ENOTDIR` error on POSIX. - * - * To get a behavior similar to the `rm -rf` Unix command, use {@link rmSync} with options `{ recursive: true, force: true }`. - * @since v0.1.21 - */ - export function rmdirSync(path: PathLike, options?: RmDirOptions): void; - export interface RmOptions { - /** - * When `true`, exceptions will be ignored if `path` does not exist. - * @default false - */ - force?: boolean | undefined; - /** - * If an `EBUSY`, `EMFILE`, `ENFILE`, `ENOTEMPTY`, or - * `EPERM` error is encountered, Node.js will retry the operation with a linear - * backoff wait of `retryDelay` ms longer on each try. This option represents the - * number of retries. This option is ignored if the `recursive` option is not - * `true`. - * @default 0 - */ - maxRetries?: number | undefined; - /** - * If `true`, perform a recursive directory removal. In - * recursive mode, operations are retried on failure. - * @default false - */ - recursive?: boolean | undefined; - /** - * The amount of time in milliseconds to wait between retries. - * This option is ignored if the `recursive` option is not `true`. - * @default 100 - */ - retryDelay?: number | undefined; - } - /** - * Asynchronously removes files and directories (modeled on the standard POSIX `rm`utility). No arguments other than a possible exception are given to the - * completion callback. - * @since v14.14.0 - */ - export function rm(path: PathLike, callback: NoParamCallback): void; - export function rm(path: PathLike, options: RmOptions, callback: NoParamCallback): void; - export namespace rm { - /** - * Asynchronously removes files and directories (modeled on the standard POSIX `rm` utility). - */ - function __promisify__(path: PathLike, options?: RmOptions): Promise; - } - /** - * Synchronously removes files and directories (modeled on the standard POSIX `rm`utility). Returns `undefined`. - * @since v14.14.0 - */ - export function rmSync(path: PathLike, options?: RmOptions): void; - export interface MakeDirectoryOptions { - /** - * Indicates whether parent folders should be created. - * If a folder was created, the path to the first created folder will be returned. - * @default false - */ - recursive?: boolean | undefined; - /** - * A file mode. If a string is passed, it is parsed as an octal integer. If not specified - * @default 0o777 - */ - mode?: Mode | undefined; - } - /** - * Asynchronously creates a directory. - * - * The callback is given a possible exception and, if `recursive` is `true`, the - * first directory path created, `(err[, path])`.`path` can still be `undefined` when `recursive` is `true`, if no directory was - * created. - * - * The optional `options` argument can be an integer specifying `mode` (permission - * and sticky bits), or an object with a `mode` property and a `recursive`property indicating whether parent directories should be created. Calling`fs.mkdir()` when `path` is a directory that - * exists results in an error only - * when `recursive` is false. - * - * ```js - * import { mkdir } from 'fs'; - * - * // Creates /tmp/a/apple, regardless of whether `/tmp` and /tmp/a exist. - * mkdir('/tmp/a/apple', { recursive: true }, (err) => { - * if (err) throw err; - * }); - * ``` - * - * On Windows, using `fs.mkdir()` on the root directory even with recursion will - * result in an error: - * - * ```js - * import { mkdir } from 'fs'; - * - * mkdir('/', { recursive: true }, (err) => { - * // => [Error: EPERM: operation not permitted, mkdir 'C:\'] - * }); - * ``` - * - * See the POSIX [`mkdir(2)`](http://man7.org/linux/man-pages/man2/mkdir.2.html) documentation for more details. - * @since v0.1.8 - */ - export function mkdir( - path: PathLike, - options: MakeDirectoryOptions & { - recursive: true; - }, - callback: (err: NodeJS.ErrnoException | null, path?: string) => void - ): void; - /** - * Asynchronous mkdir(2) - create a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options Either the file mode, or an object optionally specifying the file mode and whether parent folders - * should be created. If a string is passed, it is parsed as an octal integer. If not specified, defaults to `0o777`. - */ - export function mkdir( - path: PathLike, - options: - | Mode - | (MakeDirectoryOptions & { - recursive?: false | undefined; - }) - | null - | undefined, - callback: NoParamCallback - ): void; - /** - * Asynchronous mkdir(2) - create a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options Either the file mode, or an object optionally specifying the file mode and whether parent folders - * should be created. If a string is passed, it is parsed as an octal integer. If not specified, defaults to `0o777`. - */ - export function mkdir(path: PathLike, options: Mode | MakeDirectoryOptions | null | undefined, callback: (err: NodeJS.ErrnoException | null, path?: string) => void): void; - /** - * Asynchronous mkdir(2) - create a directory with a mode of `0o777`. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - export function mkdir(path: PathLike, callback: NoParamCallback): void; - export namespace mkdir { - /** - * Asynchronous mkdir(2) - create a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options Either the file mode, or an object optionally specifying the file mode and whether parent folders - * should be created. If a string is passed, it is parsed as an octal integer. If not specified, defaults to `0o777`. - */ - function __promisify__( - path: PathLike, - options: MakeDirectoryOptions & { - recursive: true; - } - ): Promise; - /** - * Asynchronous mkdir(2) - create a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options Either the file mode, or an object optionally specifying the file mode and whether parent folders - * should be created. If a string is passed, it is parsed as an octal integer. If not specified, defaults to `0o777`. - */ - function __promisify__( - path: PathLike, - options?: - | Mode - | (MakeDirectoryOptions & { - recursive?: false | undefined; - }) - | null - ): Promise; - /** - * Asynchronous mkdir(2) - create a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options Either the file mode, or an object optionally specifying the file mode and whether parent folders - * should be created. If a string is passed, it is parsed as an octal integer. If not specified, defaults to `0o777`. - */ - function __promisify__(path: PathLike, options?: Mode | MakeDirectoryOptions | null): Promise; - } - /** - * Synchronously creates a directory. Returns `undefined`, or if `recursive` is`true`, the first directory path created. - * This is the synchronous version of {@link mkdir}. - * - * See the POSIX [`mkdir(2)`](http://man7.org/linux/man-pages/man2/mkdir.2.html) documentation for more details. - * @since v0.1.21 - */ - export function mkdirSync( - path: PathLike, - options: MakeDirectoryOptions & { - recursive: true; - } - ): string | undefined; - /** - * Synchronous mkdir(2) - create a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options Either the file mode, or an object optionally specifying the file mode and whether parent folders - * should be created. If a string is passed, it is parsed as an octal integer. If not specified, defaults to `0o777`. - */ - export function mkdirSync( - path: PathLike, - options?: - | Mode - | (MakeDirectoryOptions & { - recursive?: false | undefined; - }) - | null - ): void; - /** - * Synchronous mkdir(2) - create a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options Either the file mode, or an object optionally specifying the file mode and whether parent folders - * should be created. If a string is passed, it is parsed as an octal integer. If not specified, defaults to `0o777`. - */ - export function mkdirSync(path: PathLike, options?: Mode | MakeDirectoryOptions | null): string | undefined; - /** - * Creates a unique temporary directory. - * - * Generates six random characters to be appended behind a required`prefix` to create a unique temporary directory. Due to platform - * inconsistencies, avoid trailing `X` characters in `prefix`. Some platforms, - * notably the BSDs, can return more than six random characters, and replace - * trailing `X` characters in `prefix` with random characters. - * - * The created directory path is passed as a string to the callback's second - * parameter. - * - * The optional `options` argument can be a string specifying an encoding, or an - * object with an `encoding` property specifying the character encoding to use. - * - * ```js - * import { mkdtemp } from 'fs'; - * - * mkdtemp(path.join(os.tmpdir(), 'foo-'), (err, directory) => { - * if (err) throw err; - * console.log(directory); - * // Prints: /tmp/foo-itXde2 or C:\Users\...\AppData\Local\Temp\foo-itXde2 - * }); - * ``` - * - * The `fs.mkdtemp()` method will append the six randomly selected characters - * directly to the `prefix` string. For instance, given a directory `/tmp`, if the - * intention is to create a temporary directory _within_`/tmp`, the `prefix`must end with a trailing platform-specific path separator - * (`require('path').sep`). - * - * ```js - * import { tmpdir } from 'os'; - * import { mkdtemp } from 'fs'; - * - * // The parent directory for the new temporary directory - * const tmpDir = tmpdir(); - * - * // This method is *INCORRECT*: - * mkdtemp(tmpDir, (err, directory) => { - * if (err) throw err; - * console.log(directory); - * // Will print something similar to `/tmpabc123`. - * // A new temporary directory is created at the file system root - * // rather than *within* the /tmp directory. - * }); - * - * // This method is *CORRECT*: - * import { sep } from 'path'; - * mkdtemp(`${tmpDir}${sep}`, (err, directory) => { - * if (err) throw err; - * console.log(directory); - * // Will print something similar to `/tmp/abc123`. - * // A new temporary directory is created within - * // the /tmp directory. - * }); - * ``` - * @since v5.10.0 - */ - export function mkdtemp(prefix: string, options: EncodingOption, callback: (err: NodeJS.ErrnoException | null, folder: string) => void): void; - /** - * Asynchronously creates a unique temporary directory. - * Generates six random characters to be appended behind a required prefix to create a unique temporary directory. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function mkdtemp( - prefix: string, - options: - | 'buffer' - | { - encoding: 'buffer'; - }, - callback: (err: NodeJS.ErrnoException | null, folder: Buffer) => void - ): void; - /** - * Asynchronously creates a unique temporary directory. - * Generates six random characters to be appended behind a required prefix to create a unique temporary directory. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function mkdtemp(prefix: string, options: EncodingOption, callback: (err: NodeJS.ErrnoException | null, folder: string | Buffer) => void): void; - /** - * Asynchronously creates a unique temporary directory. - * Generates six random characters to be appended behind a required prefix to create a unique temporary directory. - */ - export function mkdtemp(prefix: string, callback: (err: NodeJS.ErrnoException | null, folder: string) => void): void; - export namespace mkdtemp { - /** - * Asynchronously creates a unique temporary directory. - * Generates six random characters to be appended behind a required prefix to create a unique temporary directory. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - function __promisify__(prefix: string, options?: EncodingOption): Promise; - /** - * Asynchronously creates a unique temporary directory. - * Generates six random characters to be appended behind a required prefix to create a unique temporary directory. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - function __promisify__(prefix: string, options: BufferEncodingOption): Promise; - /** - * Asynchronously creates a unique temporary directory. - * Generates six random characters to be appended behind a required prefix to create a unique temporary directory. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - function __promisify__(prefix: string, options?: EncodingOption): Promise; - } - /** - * Returns the created directory path. - * - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link mkdtemp}. - * - * The optional `options` argument can be a string specifying an encoding, or an - * object with an `encoding` property specifying the character encoding to use. - * @since v5.10.0 - */ - export function mkdtempSync(prefix: string, options?: EncodingOption): string; - /** - * Synchronously creates a unique temporary directory. - * Generates six random characters to be appended behind a required prefix to create a unique temporary directory. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function mkdtempSync(prefix: string, options: BufferEncodingOption): Buffer; - /** - * Synchronously creates a unique temporary directory. - * Generates six random characters to be appended behind a required prefix to create a unique temporary directory. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function mkdtempSync(prefix: string, options?: EncodingOption): string | Buffer; - /** - * Reads the contents of a directory. The callback gets two arguments `(err, files)`where `files` is an array of the names of the files in the directory excluding`'.'` and `'..'`. - * - * See the POSIX [`readdir(3)`](http://man7.org/linux/man-pages/man3/readdir.3.html) documentation for more details. - * - * The optional `options` argument can be a string specifying an encoding, or an - * object with an `encoding` property specifying the character encoding to use for - * the filenames passed to the callback. If the `encoding` is set to `'buffer'`, - * the filenames returned will be passed as `Buffer` objects. - * - * If `options.withFileTypes` is set to `true`, the `files` array will contain `fs.Dirent` objects. - * @since v0.1.8 - */ - export function readdir( - path: PathLike, - options: - | { - encoding: BufferEncoding | null; - withFileTypes?: false | undefined; - } - | BufferEncoding - | undefined - | null, - callback: (err: NodeJS.ErrnoException | null, files: string[]) => void - ): void; - /** - * Asynchronous readdir(3) - read a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function readdir( - path: PathLike, - options: - | { - encoding: 'buffer'; - withFileTypes?: false | undefined; - } - | 'buffer', - callback: (err: NodeJS.ErrnoException | null, files: Buffer[]) => void - ): void; - /** - * Asynchronous readdir(3) - read a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function readdir( - path: PathLike, - options: - | (ObjectEncodingOptions & { - withFileTypes?: false | undefined; - }) - | BufferEncoding - | undefined - | null, - callback: (err: NodeJS.ErrnoException | null, files: string[] | Buffer[]) => void - ): void; - /** - * Asynchronous readdir(3) - read a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - export function readdir(path: PathLike, callback: (err: NodeJS.ErrnoException | null, files: string[]) => void): void; - /** - * Asynchronous readdir(3) - read a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options If called with `withFileTypes: true` the result data will be an array of Dirent. - */ - export function readdir( - path: PathLike, - options: ObjectEncodingOptions & { - withFileTypes: true; - }, - callback: (err: NodeJS.ErrnoException | null, files: Dirent[]) => void - ): void; - export namespace readdir { - /** - * Asynchronous readdir(3) - read a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - function __promisify__( - path: PathLike, - options?: - | { - encoding: BufferEncoding | null; - withFileTypes?: false | undefined; - } - | BufferEncoding - | null - ): Promise; - /** - * Asynchronous readdir(3) - read a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - function __promisify__( - path: PathLike, - options: - | 'buffer' - | { - encoding: 'buffer'; - withFileTypes?: false | undefined; - } - ): Promise; - /** - * Asynchronous readdir(3) - read a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - function __promisify__( - path: PathLike, - options?: - | (ObjectEncodingOptions & { - withFileTypes?: false | undefined; - }) - | BufferEncoding - | null - ): Promise; - /** - * Asynchronous readdir(3) - read a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options If called with `withFileTypes: true` the result data will be an array of Dirent - */ - function __promisify__( - path: PathLike, - options: ObjectEncodingOptions & { - withFileTypes: true; - } - ): Promise; - } - /** - * Reads the contents of the directory. - * - * See the POSIX [`readdir(3)`](http://man7.org/linux/man-pages/man3/readdir.3.html) documentation for more details. - * - * The optional `options` argument can be a string specifying an encoding, or an - * object with an `encoding` property specifying the character encoding to use for - * the filenames returned. If the `encoding` is set to `'buffer'`, - * the filenames returned will be passed as `Buffer` objects. - * - * If `options.withFileTypes` is set to `true`, the result will contain `fs.Dirent` objects. - * @since v0.1.21 - */ - export function readdirSync( - path: PathLike, - options?: - | { - encoding: BufferEncoding | null; - withFileTypes?: false | undefined; - } - | BufferEncoding - | null - ): string[]; - /** - * Synchronous readdir(3) - read a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function readdirSync( - path: PathLike, - options: - | { - encoding: 'buffer'; - withFileTypes?: false | undefined; - } - | 'buffer' - ): Buffer[]; - /** - * Synchronous readdir(3) - read a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options The encoding (or an object specifying the encoding), used as the encoding of the result. If not provided, `'utf8'` is used. - */ - export function readdirSync( - path: PathLike, - options?: - | (ObjectEncodingOptions & { - withFileTypes?: false | undefined; - }) - | BufferEncoding - | null - ): string[] | Buffer[]; - /** - * Synchronous readdir(3) - read a directory. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param options If called with `withFileTypes: true` the result data will be an array of Dirent. - */ - export function readdirSync( - path: PathLike, - options: ObjectEncodingOptions & { - withFileTypes: true; - } - ): Dirent[]; - /** - * Closes the file descriptor. No arguments other than a possible exception are - * given to the completion callback. - * - * Calling `fs.close()` on any file descriptor (`fd`) that is currently in use - * through any other `fs` operation may lead to undefined behavior. - * - * See the POSIX [`close(2)`](http://man7.org/linux/man-pages/man2/close.2.html) documentation for more detail. - * @since v0.0.2 - */ - export function close(fd: number, callback?: NoParamCallback): void; - export namespace close { - /** - * Asynchronous close(2) - close a file descriptor. - * @param fd A file descriptor. - */ - function __promisify__(fd: number): Promise; - } - /** - * Closes the file descriptor. Returns `undefined`. - * - * Calling `fs.closeSync()` on any file descriptor (`fd`) that is currently in use - * through any other `fs` operation may lead to undefined behavior. - * - * See the POSIX [`close(2)`](http://man7.org/linux/man-pages/man2/close.2.html) documentation for more detail. - * @since v0.1.21 - */ - export function closeSync(fd: number): void; - /** - * Asynchronous file open. See the POSIX [`open(2)`](http://man7.org/linux/man-pages/man2/open.2.html) documentation for more details. - * - * `mode` sets the file mode (permission and sticky bits), but only if the file was - * created. On Windows, only the write permission can be manipulated; see {@link chmod}. - * - * The callback gets two arguments `(err, fd)`. - * - * Some characters (`< > : " / \ | ? *`) are reserved under Windows as documented - * by [Naming Files, Paths, and Namespaces](https://docs.microsoft.com/en-us/windows/desktop/FileIO/naming-a-file). Under NTFS, if the filename contains - * a colon, Node.js will open a file system stream, as described by [this MSDN page](https://docs.microsoft.com/en-us/windows/desktop/FileIO/using-streams). - * - * Functions based on `fs.open()` exhibit this behavior as well:`fs.writeFile()`, `fs.readFile()`, etc. - * @since v0.0.2 - * @param [flags='r'] See `support of file system `flags``. - * @param [mode=0o666] - */ - export function open(path: PathLike, flags: OpenMode | undefined, mode: Mode | undefined | null, callback: (err: NodeJS.ErrnoException | null, fd: number) => void): void; - /** - * Asynchronous open(2) - open and possibly create a file. If the file is created, its mode will be `0o666`. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param [flags='r'] See `support of file system `flags``. - */ - export function open(path: PathLike, flags: OpenMode | undefined, callback: (err: NodeJS.ErrnoException | null, fd: number) => void): void; - /** - * Asynchronous open(2) - open and possibly create a file. If the file is created, its mode will be `0o666`. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - */ - export function open(path: PathLike, callback: (err: NodeJS.ErrnoException | null, fd: number) => void): void; - - export namespace open { - /** - * Asynchronous open(2) - open and possibly create a file. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param mode A file mode. If a string is passed, it is parsed as an octal integer. If not supplied, defaults to `0o666`. - */ - function __promisify__(path: PathLike, flags: OpenMode, mode?: Mode | null): Promise; - } - /** - * Returns an integer representing the file descriptor. - * - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link open}. - * @since v0.1.21 - * @param [flags='r'] - * @param [mode=0o666] - */ - export function openSync(path: PathLike, flags: OpenMode, mode?: Mode | null): number; - /** - * Change the file system timestamps of the object referenced by `path`. - * - * The `atime` and `mtime` arguments follow these rules: - * - * * Values can be either numbers representing Unix epoch time in seconds,`Date`s, or a numeric string like `'123456789.0'`. - * * If the value can not be converted to a number, or is `NaN`, `Infinity` or`-Infinity`, an `Error` will be thrown. - * @since v0.4.2 - */ - export function utimes(path: PathLike, atime: TimeLike, mtime: TimeLike, callback: NoParamCallback): void; - export namespace utimes { - /** - * Asynchronously change file timestamps of the file referenced by the supplied path. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * @param atime The last access time. If a string is provided, it will be coerced to number. - * @param mtime The last modified time. If a string is provided, it will be coerced to number. - */ - function __promisify__(path: PathLike, atime: TimeLike, mtime: TimeLike): Promise; - } - /** - * Returns `undefined`. - * - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link utimes}. - * @since v0.4.2 - */ - export function utimesSync(path: PathLike, atime: TimeLike, mtime: TimeLike): void; - /** - * Change the file system timestamps of the object referenced by the supplied file - * descriptor. See {@link utimes}. - * @since v0.4.2 - */ - export function futimes(fd: number, atime: TimeLike, mtime: TimeLike, callback: NoParamCallback): void; - export namespace futimes { - /** - * Asynchronously change file timestamps of the file referenced by the supplied file descriptor. - * @param fd A file descriptor. - * @param atime The last access time. If a string is provided, it will be coerced to number. - * @param mtime The last modified time. If a string is provided, it will be coerced to number. - */ - function __promisify__(fd: number, atime: TimeLike, mtime: TimeLike): Promise; - } - /** - * Synchronous version of {@link futimes}. Returns `undefined`. - * @since v0.4.2 - */ - export function futimesSync(fd: number, atime: TimeLike, mtime: TimeLike): void; - /** - * Request that all data for the open file descriptor is flushed to the storage - * device. The specific implementation is operating system and device specific. - * Refer to the POSIX [`fsync(2)`](http://man7.org/linux/man-pages/man2/fsync.2.html) documentation for more detail. No arguments other - * than a possible exception are given to the completion callback. - * @since v0.1.96 - */ - export function fsync(fd: number, callback: NoParamCallback): void; - export namespace fsync { - /** - * Asynchronous fsync(2) - synchronize a file's in-core state with the underlying storage device. - * @param fd A file descriptor. - */ - function __promisify__(fd: number): Promise; - } - /** - * Request that all data for the open file descriptor is flushed to the storage - * device. The specific implementation is operating system and device specific. - * Refer to the POSIX [`fsync(2)`](http://man7.org/linux/man-pages/man2/fsync.2.html) documentation for more detail. Returns `undefined`. - * @since v0.1.96 - */ - export function fsyncSync(fd: number): void; - /** - * Write `buffer` to the file specified by `fd`. - * - * `offset` determines the part of the buffer to be written, and `length` is - * an integer specifying the number of bytes to write. - * - * `position` refers to the offset from the beginning of the file where this data - * should be written. If `typeof position !== 'number'`, the data will be written - * at the current position. See [`pwrite(2)`](http://man7.org/linux/man-pages/man2/pwrite.2.html). - * - * The callback will be given three arguments `(err, bytesWritten, buffer)` where`bytesWritten` specifies how many _bytes_ were written from `buffer`. - * - * If this method is invoked as its `util.promisify()` ed version, it returns - * a promise for an `Object` with `bytesWritten` and `buffer` properties. - * - * It is unsafe to use `fs.write()` multiple times on the same file without waiting - * for the callback. For this scenario, {@link createWriteStream} is - * recommended. - * - * On Linux, positional writes don't work when the file is opened in append mode. - * The kernel ignores the position argument and always appends the data to - * the end of the file. - * @since v0.0.2 - */ - export function write( - fd: number, - buffer: TBuffer, - offset: number | undefined | null, - length: number | undefined | null, - position: number | undefined | null, - callback: (err: NodeJS.ErrnoException | null, written: number, buffer: TBuffer) => void - ): void; - /** - * Asynchronously writes `buffer` to the file referenced by the supplied file descriptor. - * @param fd A file descriptor. - * @param offset The part of the buffer to be written. If not supplied, defaults to `0`. - * @param length The number of bytes to write. If not supplied, defaults to `buffer.length - offset`. - */ - export function write( - fd: number, - buffer: TBuffer, - offset: number | undefined | null, - length: number | undefined | null, - callback: (err: NodeJS.ErrnoException | null, written: number, buffer: TBuffer) => void - ): void; - /** - * Asynchronously writes `buffer` to the file referenced by the supplied file descriptor. - * @param fd A file descriptor. - * @param offset The part of the buffer to be written. If not supplied, defaults to `0`. - */ - export function write( - fd: number, - buffer: TBuffer, - offset: number | undefined | null, - callback: (err: NodeJS.ErrnoException | null, written: number, buffer: TBuffer) => void - ): void; - /** - * Asynchronously writes `buffer` to the file referenced by the supplied file descriptor. - * @param fd A file descriptor. - */ - export function write(fd: number, buffer: TBuffer, callback: (err: NodeJS.ErrnoException | null, written: number, buffer: TBuffer) => void): void; - /** - * Asynchronously writes `string` to the file referenced by the supplied file descriptor. - * @param fd A file descriptor. - * @param string A string to write. - * @param position The offset from the beginning of the file where this data should be written. If not supplied, defaults to the current position. - * @param encoding The expected string encoding. - */ - export function write( - fd: number, - string: string, - position: number | undefined | null, - encoding: BufferEncoding | undefined | null, - callback: (err: NodeJS.ErrnoException | null, written: number, str: string) => void - ): void; - /** - * Asynchronously writes `string` to the file referenced by the supplied file descriptor. - * @param fd A file descriptor. - * @param string A string to write. - * @param position The offset from the beginning of the file where this data should be written. If not supplied, defaults to the current position. - */ - export function write(fd: number, string: string, position: number | undefined | null, callback: (err: NodeJS.ErrnoException | null, written: number, str: string) => void): void; - /** - * Asynchronously writes `string` to the file referenced by the supplied file descriptor. - * @param fd A file descriptor. - * @param string A string to write. - */ - export function write(fd: number, string: string, callback: (err: NodeJS.ErrnoException | null, written: number, str: string) => void): void; - export namespace write { - /** - * Asynchronously writes `buffer` to the file referenced by the supplied file descriptor. - * @param fd A file descriptor. - * @param offset The part of the buffer to be written. If not supplied, defaults to `0`. - * @param length The number of bytes to write. If not supplied, defaults to `buffer.length - offset`. - * @param position The offset from the beginning of the file where this data should be written. If not supplied, defaults to the current position. - */ - function __promisify__( - fd: number, - buffer?: TBuffer, - offset?: number, - length?: number, - position?: number | null - ): Promise<{ - bytesWritten: number; - buffer: TBuffer; - }>; - /** - * Asynchronously writes `string` to the file referenced by the supplied file descriptor. - * @param fd A file descriptor. - * @param string A string to write. - * @param position The offset from the beginning of the file where this data should be written. If not supplied, defaults to the current position. - * @param encoding The expected string encoding. - */ - function __promisify__( - fd: number, - string: string, - position?: number | null, - encoding?: BufferEncoding | null - ): Promise<{ - bytesWritten: number; - buffer: string; - }>; - } - /** - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link write}. - * @since v0.1.21 - * @return The number of bytes written. - */ - export function writeSync(fd: number, buffer: NodeJS.ArrayBufferView, offset?: number | null, length?: number | null, position?: number | null): number; - /** - * Synchronously writes `string` to the file referenced by the supplied file descriptor, returning the number of bytes written. - * @param fd A file descriptor. - * @param string A string to write. - * @param position The offset from the beginning of the file where this data should be written. If not supplied, defaults to the current position. - * @param encoding The expected string encoding. - */ - export function writeSync(fd: number, string: string, position?: number | null, encoding?: BufferEncoding | null): number; - export type ReadPosition = number | bigint; - export interface ReadSyncOptions { - /** - * @default 0 - */ - offset?: number | undefined; - /** - * @default `length of buffer` - */ - length?: number | undefined; - /** - * @default null - */ - position?: ReadPosition | null | undefined; - } - export interface ReadAsyncOptions extends ReadSyncOptions { - buffer?: TBuffer; - } - /** - * Read data from the file specified by `fd`. - * - * The callback is given the three arguments, `(err, bytesRead, buffer)`. - * - * If the file is not modified concurrently, the end-of-file is reached when the - * number of bytes read is zero. - * - * If this method is invoked as its `util.promisify()` ed version, it returns - * a promise for an `Object` with `bytesRead` and `buffer` properties. - * @since v0.0.2 - * @param buffer The buffer that the data will be written to. - * @param offset The position in `buffer` to write the data to. - * @param length The number of bytes to read. - * @param position Specifies where to begin reading from in the file. If `position` is `null` or `-1 `, data will be read from the current file position, and the file position will be updated. If - * `position` is an integer, the file position will be unchanged. - */ - export function read( - fd: number, - buffer: TBuffer, - offset: number, - length: number, - position: ReadPosition | null, - callback: (err: NodeJS.ErrnoException | null, bytesRead: number, buffer: TBuffer) => void - ): void; - /** - * Similar to the above `fs.read` function, this version takes an optional `options` object. - * If not otherwise specified in an `options` object, - * `buffer` defaults to `Buffer.alloc(16384)`, - * `offset` defaults to `0`, - * `length` defaults to `buffer.byteLength`, `- offset` as of Node 17.6.0 - * `position` defaults to `null` - * @since v12.17.0, 13.11.0 - */ - export function read( - fd: number, - options: ReadAsyncOptions, - callback: (err: NodeJS.ErrnoException | null, bytesRead: number, buffer: TBuffer) => void - ): void; - export function read(fd: number, callback: (err: NodeJS.ErrnoException | null, bytesRead: number, buffer: NodeJS.ArrayBufferView) => void): void; - export namespace read { - /** - * @param fd A file descriptor. - * @param buffer The buffer that the data will be written to. - * @param offset The offset in the buffer at which to start writing. - * @param length The number of bytes to read. - * @param position The offset from the beginning of the file from which data should be read. If `null`, data will be read from the current position. - */ - function __promisify__( - fd: number, - buffer: TBuffer, - offset: number, - length: number, - position: number | null - ): Promise<{ - bytesRead: number; - buffer: TBuffer; - }>; - function __promisify__( - fd: number, - options: ReadAsyncOptions - ): Promise<{ - bytesRead: number; - buffer: TBuffer; - }>; - function __promisify__(fd: number): Promise<{ - bytesRead: number; - buffer: NodeJS.ArrayBufferView; - }>; - } - /** - * Returns the number of `bytesRead`. - * - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link read}. - * @since v0.1.21 - */ - export function readSync(fd: number, buffer: NodeJS.ArrayBufferView, offset: number, length: number, position: ReadPosition | null): number; - /** - * Similar to the above `fs.readSync` function, this version takes an optional `options` object. - * If no `options` object is specified, it will default with the above values. - */ - export function readSync(fd: number, buffer: NodeJS.ArrayBufferView, opts?: ReadSyncOptions): number; - /** - * Asynchronously reads the entire contents of a file. - * - * ```js - * import { readFile } from 'fs'; - * - * readFile('/etc/passwd', (err, data) => { - * if (err) throw err; - * console.log(data); - * }); - * ``` - * - * The callback is passed two arguments `(err, data)`, where `data` is the - * contents of the file. - * - * If no encoding is specified, then the raw buffer is returned. - * - * If `options` is a string, then it specifies the encoding: - * - * ```js - * import { readFile } from 'fs'; - * - * readFile('/etc/passwd', 'utf8', callback); - * ``` - * - * When the path is a directory, the behavior of `fs.readFile()` and {@link readFileSync} is platform-specific. On macOS, Linux, and Windows, an - * error will be returned. On FreeBSD, a representation of the directory's contents - * will be returned. - * - * ```js - * import { readFile } from 'fs'; - * - * // macOS, Linux, and Windows - * readFile('', (err, data) => { - * // => [Error: EISDIR: illegal operation on a directory, read ] - * }); - * - * // FreeBSD - * readFile('', (err, data) => { - * // => null, - * }); - * ``` - * - * It is possible to abort an ongoing request using an `AbortSignal`. If a - * request is aborted the callback is called with an `AbortError`: - * - * ```js - * import { readFile } from 'fs'; - * - * const controller = new AbortController(); - * const signal = controller.signal; - * readFile(fileInfo[0].name, { signal }, (err, buf) => { - * // ... - * }); - * // When you want to abort the request - * controller.abort(); - * ``` - * - * The `fs.readFile()` function buffers the entire file. To minimize memory costs, - * when possible prefer streaming via `fs.createReadStream()`. - * - * Aborting an ongoing request does not abort individual operating - * system requests but rather the internal buffering `fs.readFile` performs. - * @since v0.1.29 - * @param path filename or file descriptor - */ - export function readFile( - path: PathOrFileDescriptor, - options: - | ({ - encoding?: null | undefined; - flag?: string | undefined; - } & Abortable) - | undefined - | null, - callback: (err: NodeJS.ErrnoException | null, data: Buffer) => void - ): void; - /** - * Asynchronously reads the entire contents of a file. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * If a file descriptor is provided, the underlying file will _not_ be closed automatically. - * @param options Either the encoding for the result, or an object that contains the encoding and an optional flag. - * If a flag is not provided, it defaults to `'r'`. - */ - export function readFile( - path: PathOrFileDescriptor, - options: - | ({ - encoding: BufferEncoding; - flag?: string | undefined; - } & Abortable) - | BufferEncoding, - callback: (err: NodeJS.ErrnoException | null, data: string) => void - ): void; - /** - * Asynchronously reads the entire contents of a file. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * If a file descriptor is provided, the underlying file will _not_ be closed automatically. - * @param options Either the encoding for the result, or an object that contains the encoding and an optional flag. - * If a flag is not provided, it defaults to `'r'`. - */ - export function readFile( - path: PathOrFileDescriptor, - options: - | (ObjectEncodingOptions & { - flag?: string | undefined; - } & Abortable) - | BufferEncoding - | undefined - | null, - callback: (err: NodeJS.ErrnoException | null, data: string | Buffer) => void - ): void; - /** - * Asynchronously reads the entire contents of a file. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * If a file descriptor is provided, the underlying file will _not_ be closed automatically. - */ - export function readFile(path: PathOrFileDescriptor, callback: (err: NodeJS.ErrnoException | null, data: Buffer) => void): void; - export namespace readFile { - /** - * Asynchronously reads the entire contents of a file. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * If a file descriptor is provided, the underlying file will _not_ be closed automatically. - * @param options An object that may contain an optional flag. - * If a flag is not provided, it defaults to `'r'`. - */ - function __promisify__( - path: PathOrFileDescriptor, - options?: { - encoding?: null | undefined; - flag?: string | undefined; - } | null - ): Promise; - /** - * Asynchronously reads the entire contents of a file. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * URL support is _experimental_. - * If a file descriptor is provided, the underlying file will _not_ be closed automatically. - * @param options Either the encoding for the result, or an object that contains the encoding and an optional flag. - * If a flag is not provided, it defaults to `'r'`. - */ - function __promisify__( - path: PathOrFileDescriptor, - options: - | { - encoding: BufferEncoding; - flag?: string | undefined; - } - | BufferEncoding - ): Promise; - /** - * Asynchronously reads the entire contents of a file. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * URL support is _experimental_. - * If a file descriptor is provided, the underlying file will _not_ be closed automatically. - * @param options Either the encoding for the result, or an object that contains the encoding and an optional flag. - * If a flag is not provided, it defaults to `'r'`. - */ - function __promisify__( - path: PathOrFileDescriptor, - options?: - | (ObjectEncodingOptions & { - flag?: string | undefined; - }) - | BufferEncoding - | null - ): Promise; - } - /** - * Returns the contents of the `path`. - * - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link readFile}. - * - * If the `encoding` option is specified then this function returns a - * string. Otherwise it returns a buffer. - * - * Similar to {@link readFile}, when the path is a directory, the behavior of`fs.readFileSync()` is platform-specific. - * - * ```js - * import { readFileSync } from 'fs'; - * - * // macOS, Linux, and Windows - * readFileSync(''); - * // => [Error: EISDIR: illegal operation on a directory, read ] - * - * // FreeBSD - * readFileSync(''); // => - * ``` - * @since v0.1.8 - * @param path filename or file descriptor - */ - export function readFileSync( - path: PathOrFileDescriptor, - options?: { - encoding?: null | undefined; - flag?: string | undefined; - } | null - ): Buffer; - /** - * Synchronously reads the entire contents of a file. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * If a file descriptor is provided, the underlying file will _not_ be closed automatically. - * @param options Either the encoding for the result, or an object that contains the encoding and an optional flag. - * If a flag is not provided, it defaults to `'r'`. - */ - export function readFileSync( - path: PathOrFileDescriptor, - options: - | { - encoding: BufferEncoding; - flag?: string | undefined; - } - | BufferEncoding - ): string; - /** - * Synchronously reads the entire contents of a file. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * If a file descriptor is provided, the underlying file will _not_ be closed automatically. - * @param options Either the encoding for the result, or an object that contains the encoding and an optional flag. - * If a flag is not provided, it defaults to `'r'`. - */ - export function readFileSync( - path: PathOrFileDescriptor, - options?: - | (ObjectEncodingOptions & { - flag?: string | undefined; - }) - | BufferEncoding - | null - ): string | Buffer; - export type WriteFileOptions = - | (ObjectEncodingOptions & - Abortable & { - mode?: Mode | undefined; - flag?: string | undefined; - }) - | BufferEncoding - | null; - /** - * When `file` is a filename, asynchronously writes data to the file, replacing the - * file if it already exists. `data` can be a string or a buffer. - * - * When `file` is a file descriptor, the behavior is similar to calling`fs.write()` directly (which is recommended). See the notes below on using - * a file descriptor. - * - * The `encoding` option is ignored if `data` is a buffer. - * - * The `mode` option only affects the newly created file. See {@link open} for more details. - * - * ```js - * import { writeFile } from 'fs'; - * import { Buffer } from 'buffer'; - * - * const data = new Uint8Array(Buffer.from('Hello Node.js')); - * writeFile('message.txt', data, (err) => { - * if (err) throw err; - * console.log('The file has been saved!'); - * }); - * ``` - * - * If `options` is a string, then it specifies the encoding: - * - * ```js - * import { writeFile } from 'fs'; - * - * writeFile('message.txt', 'Hello Node.js', 'utf8', callback); - * ``` - * - * It is unsafe to use `fs.writeFile()` multiple times on the same file without - * waiting for the callback. For this scenario, {@link createWriteStream} is - * recommended. - * - * Similarly to `fs.readFile` \- `fs.writeFile` is a convenience method that - * performs multiple `write` calls internally to write the buffer passed to it. - * For performance sensitive code consider using {@link createWriteStream}. - * - * It is possible to use an `AbortSignal` to cancel an `fs.writeFile()`. - * Cancelation is "best effort", and some amount of data is likely still - * to be written. - * - * ```js - * import { writeFile } from 'fs'; - * import { Buffer } from 'buffer'; - * - * const controller = new AbortController(); - * const { signal } = controller; - * const data = new Uint8Array(Buffer.from('Hello Node.js')); - * writeFile('message.txt', data, { signal }, (err) => { - * // When a request is aborted - the callback is called with an AbortError - * }); - * // When the request should be aborted - * controller.abort(); - * ``` - * - * Aborting an ongoing request does not abort individual operating - * system requests but rather the internal buffering `fs.writeFile` performs. - * @since v0.1.29 - * @param file filename or file descriptor - */ - export function writeFile(file: PathOrFileDescriptor, data: string | NodeJS.ArrayBufferView, options: WriteFileOptions, callback: NoParamCallback): void; - /** - * Asynchronously writes data to a file, replacing the file if it already exists. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * If a file descriptor is provided, the underlying file will _not_ be closed automatically. - * @param data The data to write. If something other than a Buffer or Uint8Array is provided, the value is coerced to a string. - */ - export function writeFile(path: PathOrFileDescriptor, data: string | NodeJS.ArrayBufferView, callback: NoParamCallback): void; - export namespace writeFile { - /** - * Asynchronously writes data to a file, replacing the file if it already exists. - * @param path A path to a file. If a URL is provided, it must use the `file:` protocol. - * URL support is _experimental_. - * If a file descriptor is provided, the underlying file will _not_ be closed automatically. - * @param data The data to write. If something other than a Buffer or Uint8Array is provided, the value is coerced to a string. - * @param options Either the encoding for the file, or an object optionally specifying the encoding, file mode, and flag. - * If `encoding` is not supplied, the default of `'utf8'` is used. - * If `mode` is not supplied, the default of `0o666` is used. - * If `mode` is a string, it is parsed as an octal integer. - * If `flag` is not supplied, the default of `'w'` is used. - */ - function __promisify__(path: PathOrFileDescriptor, data: string | NodeJS.ArrayBufferView, options?: WriteFileOptions): Promise; - } - /** - * Returns `undefined`. - * - * The `mode` option only affects the newly created file. See {@link open} for more details. - * - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link writeFile}. - * @since v0.1.29 - * @param file filename or file descriptor - */ - export function writeFileSync(file: PathOrFileDescriptor, data: string | NodeJS.ArrayBufferView, options?: WriteFileOptions): void; - /** - * Asynchronously append data to a file, creating the file if it does not yet - * exist. `data` can be a string or a `Buffer`. - * - * The `mode` option only affects the newly created file. See {@link open} for more details. - * - * ```js - * import { appendFile } from 'fs'; - * - * appendFile('message.txt', 'data to append', (err) => { - * if (err) throw err; - * console.log('The "data to append" was appended to file!'); - * }); - * ``` - * - * If `options` is a string, then it specifies the encoding: - * - * ```js - * import { appendFile } from 'fs'; - * - * appendFile('message.txt', 'data to append', 'utf8', callback); - * ``` - * - * The `path` may be specified as a numeric file descriptor that has been opened - * for appending (using `fs.open()` or `fs.openSync()`). The file descriptor will - * not be closed automatically. - * - * ```js - * import { open, close, appendFile } from 'fs'; - * - * function closeFd(fd) { - * close(fd, (err) => { - * if (err) throw err; - * }); - * } - * - * open('message.txt', 'a', (err, fd) => { - * if (err) throw err; - * - * try { - * appendFile(fd, 'data to append', 'utf8', (err) => { - * closeFd(fd); - * if (err) throw err; - * }); - * } catch (err) { - * closeFd(fd); - * throw err; - * } - * }); - * ``` - * @since v0.6.7 - * @param path filename or file descriptor - */ - export function appendFile(path: PathOrFileDescriptor, data: string | Uint8Array, options: WriteFileOptions, callback: NoParamCallback): void; - /** - * Asynchronously append data to a file, creating the file if it does not exist. - * @param file A path to a file. If a URL is provided, it must use the `file:` protocol. - * If a file descriptor is provided, the underlying file will _not_ be closed automatically. - * @param data The data to write. If something other than a Buffer or Uint8Array is provided, the value is coerced to a string. - */ - export function appendFile(file: PathOrFileDescriptor, data: string | Uint8Array, callback: NoParamCallback): void; - export namespace appendFile { - /** - * Asynchronously append data to a file, creating the file if it does not exist. - * @param file A path to a file. If a URL is provided, it must use the `file:` protocol. - * URL support is _experimental_. - * If a file descriptor is provided, the underlying file will _not_ be closed automatically. - * @param data The data to write. If something other than a Buffer or Uint8Array is provided, the value is coerced to a string. - * @param options Either the encoding for the file, or an object optionally specifying the encoding, file mode, and flag. - * If `encoding` is not supplied, the default of `'utf8'` is used. - * If `mode` is not supplied, the default of `0o666` is used. - * If `mode` is a string, it is parsed as an octal integer. - * If `flag` is not supplied, the default of `'a'` is used. - */ - function __promisify__(file: PathOrFileDescriptor, data: string | Uint8Array, options?: WriteFileOptions): Promise; - } - /** - * Synchronously append data to a file, creating the file if it does not yet - * exist. `data` can be a string or a `Buffer`. - * - * The `mode` option only affects the newly created file. See {@link open} for more details. - * - * ```js - * import { appendFileSync } from 'fs'; - * - * try { - * appendFileSync('message.txt', 'data to append'); - * console.log('The "data to append" was appended to file!'); - * } catch (err) { - * // Handle the error - * } - * ``` - * - * If `options` is a string, then it specifies the encoding: - * - * ```js - * import { appendFileSync } from 'fs'; - * - * appendFileSync('message.txt', 'data to append', 'utf8'); - * ``` - * - * The `path` may be specified as a numeric file descriptor that has been opened - * for appending (using `fs.open()` or `fs.openSync()`). The file descriptor will - * not be closed automatically. - * - * ```js - * import { openSync, closeSync, appendFileSync } from 'fs'; - * - * let fd; - * - * try { - * fd = openSync('message.txt', 'a'); - * appendFileSync(fd, 'data to append', 'utf8'); - * } catch (err) { - * // Handle the error - * } finally { - * if (fd !== undefined) - * closeSync(fd); - * } - * ``` - * @since v0.6.7 - * @param path filename or file descriptor - */ - export function appendFileSync(path: PathOrFileDescriptor, data: string | Uint8Array, options?: WriteFileOptions): void; - /** - * Watch for changes on `filename`. The callback `listener` will be called each - * time the file is accessed. - * - * The `options` argument may be omitted. If provided, it should be an object. The`options` object may contain a boolean named `persistent` that indicates - * whether the process should continue to run as long as files are being watched. - * The `options` object may specify an `interval` property indicating how often the - * target should be polled in milliseconds. - * - * The `listener` gets two arguments the current stat object and the previous - * stat object: - * - * ```js - * import { watchFile } from 'fs'; - * - * watchFile('message.text', (curr, prev) => { - * console.log(`the current mtime is: ${curr.mtime}`); - * console.log(`the previous mtime was: ${prev.mtime}`); - * }); - * ``` - * - * These stat objects are instances of `fs.Stat`. If the `bigint` option is `true`, - * the numeric values in these objects are specified as `BigInt`s. - * - * To be notified when the file was modified, not just accessed, it is necessary - * to compare `curr.mtimeMs` and `prev.mtimeMs`. - * - * When an `fs.watchFile` operation results in an `ENOENT` error, it - * will invoke the listener once, with all the fields zeroed (or, for dates, the - * Unix Epoch). If the file is created later on, the listener will be called - * again, with the latest stat objects. This is a change in functionality since - * v0.10. - * - * Using {@link watch} is more efficient than `fs.watchFile` and`fs.unwatchFile`. `fs.watch` should be used instead of `fs.watchFile` and`fs.unwatchFile` when possible. - * - * When a file being watched by `fs.watchFile()` disappears and reappears, - * then the contents of `previous` in the second callback event (the file's - * reappearance) will be the same as the contents of `previous` in the first - * callback event (its disappearance). - * - * This happens when: - * - * * the file is deleted, followed by a restore - * * the file is renamed and then renamed a second time back to its original name - * @since v0.1.31 - */ - export interface WatchFileOptions { - bigint?: boolean | undefined; - persistent?: boolean | undefined; - interval?: number | undefined; - } - /** - * Watch for changes on `filename`. The callback `listener` will be called each - * time the file is accessed. - * - * The `options` argument may be omitted. If provided, it should be an object. The`options` object may contain a boolean named `persistent` that indicates - * whether the process should continue to run as long as files are being watched. - * The `options` object may specify an `interval` property indicating how often the - * target should be polled in milliseconds. - * - * The `listener` gets two arguments the current stat object and the previous - * stat object: - * - * ```js - * import { watchFile } from 'fs'; - * - * watchFile('message.text', (curr, prev) => { - * console.log(`the current mtime is: ${curr.mtime}`); - * console.log(`the previous mtime was: ${prev.mtime}`); - * }); - * ``` - * - * These stat objects are instances of `fs.Stat`. If the `bigint` option is `true`, - * the numeric values in these objects are specified as `BigInt`s. - * - * To be notified when the file was modified, not just accessed, it is necessary - * to compare `curr.mtimeMs` and `prev.mtimeMs`. - * - * When an `fs.watchFile` operation results in an `ENOENT` error, it - * will invoke the listener once, with all the fields zeroed (or, for dates, the - * Unix Epoch). If the file is created later on, the listener will be called - * again, with the latest stat objects. This is a change in functionality since - * v0.10. - * - * Using {@link watch} is more efficient than `fs.watchFile` and`fs.unwatchFile`. `fs.watch` should be used instead of `fs.watchFile` and`fs.unwatchFile` when possible. - * - * When a file being watched by `fs.watchFile()` disappears and reappears, - * then the contents of `previous` in the second callback event (the file's - * reappearance) will be the same as the contents of `previous` in the first - * callback event (its disappearance). - * - * This happens when: - * - * * the file is deleted, followed by a restore - * * the file is renamed and then renamed a second time back to its original name - * @since v0.1.31 - */ - export function watchFile( - filename: PathLike, - options: - | (WatchFileOptions & { - bigint?: false | undefined; - }) - | undefined, - listener: (curr: Stats, prev: Stats) => void - ): StatWatcher; - export function watchFile( - filename: PathLike, - options: - | (WatchFileOptions & { - bigint: true; - }) - | undefined, - listener: (curr: BigIntStats, prev: BigIntStats) => void - ): StatWatcher; - /** - * Watch for changes on `filename`. The callback `listener` will be called each time the file is accessed. - * @param filename A path to a file or directory. If a URL is provided, it must use the `file:` protocol. - */ - export function watchFile(filename: PathLike, listener: (curr: Stats, prev: Stats) => void): StatWatcher; - /** - * Stop watching for changes on `filename`. If `listener` is specified, only that - * particular listener is removed. Otherwise, _all_ listeners are removed, - * effectively stopping watching of `filename`. - * - * Calling `fs.unwatchFile()` with a filename that is not being watched is a - * no-op, not an error. - * - * Using {@link watch} is more efficient than `fs.watchFile()` and`fs.unwatchFile()`. `fs.watch()` should be used instead of `fs.watchFile()`and `fs.unwatchFile()` when possible. - * @since v0.1.31 - * @param listener Optional, a listener previously attached using `fs.watchFile()` - */ - export function unwatchFile(filename: PathLike, listener?: (curr: Stats, prev: Stats) => void): void; - export interface WatchOptions extends Abortable { - encoding?: BufferEncoding | 'buffer' | undefined; - persistent?: boolean | undefined; - recursive?: boolean | undefined; - } - export type WatchEventType = 'rename' | 'change'; - export type WatchListener = (event: WatchEventType, filename: T) => void; - /** - * Watch for changes on `filename`, where `filename` is either a file or a - * directory. - * - * The second argument is optional. If `options` is provided as a string, it - * specifies the `encoding`. Otherwise `options` should be passed as an object. - * - * The listener callback gets two arguments `(eventType, filename)`. `eventType`is either `'rename'` or `'change'`, and `filename` is the name of the file - * which triggered the event. - * - * On most platforms, `'rename'` is emitted whenever a filename appears or - * disappears in the directory. - * - * The listener callback is attached to the `'change'` event fired by `fs.FSWatcher`, but it is not the same thing as the `'change'` value of`eventType`. - * - * If a `signal` is passed, aborting the corresponding AbortController will close - * the returned `fs.FSWatcher`. - * @since v0.5.10 - * @param listener - */ - export function watch( - filename: PathLike, - options: - | (WatchOptions & { - encoding: 'buffer'; - }) - | 'buffer', - listener?: WatchListener - ): FSWatcher; - /** - * Watch for changes on `filename`, where `filename` is either a file or a directory, returning an `FSWatcher`. - * @param filename A path to a file or directory. If a URL is provided, it must use the `file:` protocol. - * @param options Either the encoding for the filename provided to the listener, or an object optionally specifying encoding, persistent, and recursive options. - * If `encoding` is not supplied, the default of `'utf8'` is used. - * If `persistent` is not supplied, the default of `true` is used. - * If `recursive` is not supplied, the default of `false` is used. - */ - export function watch(filename: PathLike, options?: WatchOptions | BufferEncoding | null, listener?: WatchListener): FSWatcher; - /** - * Watch for changes on `filename`, where `filename` is either a file or a directory, returning an `FSWatcher`. - * @param filename A path to a file or directory. If a URL is provided, it must use the `file:` protocol. - * @param options Either the encoding for the filename provided to the listener, or an object optionally specifying encoding, persistent, and recursive options. - * If `encoding` is not supplied, the default of `'utf8'` is used. - * If `persistent` is not supplied, the default of `true` is used. - * If `recursive` is not supplied, the default of `false` is used. - */ - export function watch(filename: PathLike, options: WatchOptions | string, listener?: WatchListener): FSWatcher; - /** - * Watch for changes on `filename`, where `filename` is either a file or a directory, returning an `FSWatcher`. - * @param filename A path to a file or directory. If a URL is provided, it must use the `file:` protocol. - */ - export function watch(filename: PathLike, listener?: WatchListener): FSWatcher; - /** - * Test whether or not the given path exists by checking with the file system. - * Then call the `callback` argument with either true or false: - * - * ```js - * import { exists } from 'fs'; - * - * exists('/etc/passwd', (e) => { - * console.log(e ? 'it exists' : 'no passwd!'); - * }); - * ``` - * - * **The parameters for this callback are not consistent with other Node.js** - * **callbacks.** Normally, the first parameter to a Node.js callback is an `err`parameter, optionally followed by other parameters. The `fs.exists()` callback - * has only one boolean parameter. This is one reason `fs.access()` is recommended - * instead of `fs.exists()`. - * - * Using `fs.exists()` to check for the existence of a file before calling`fs.open()`, `fs.readFile()` or `fs.writeFile()` is not recommended. Doing - * so introduces a race condition, since other processes may change the file's - * state between the two calls. Instead, user code should open/read/write the - * file directly and handle the error raised if the file does not exist. - * - * **write (NOT RECOMMENDED)** - * - * ```js - * import { exists, open, close } from 'fs'; - * - * exists('myfile', (e) => { - * if (e) { - * console.error('myfile already exists'); - * } else { - * open('myfile', 'wx', (err, fd) => { - * if (err) throw err; - * - * try { - * writeMyData(fd); - * } finally { - * close(fd, (err) => { - * if (err) throw err; - * }); - * } - * }); - * } - * }); - * ``` - * - * **write (RECOMMENDED)** - * - * ```js - * import { open, close } from 'fs'; - * open('myfile', 'wx', (err, fd) => { - * if (err) { - * if (err.code === 'EEXIST') { - * console.error('myfile already exists'); - * return; - * } - * - * throw err; - * } - * - * try { - * writeMyData(fd); - * } finally { - * close(fd, (err) => { - * if (err) throw err; - * }); - * } - * }); - * ``` - * - * **read (NOT RECOMMENDED)** - * - * ```js - * import { open, close, exists } from 'fs'; - * - * exists('myfile', (e) => { - * if (e) { - * open('myfile', 'r', (err, fd) => { - * if (err) throw err; - * - * try { - * readMyData(fd); - * } finally { - * close(fd, (err) => { - * if (err) throw err; - * }); - * } - * }); - * } else { - * console.error('myfile does not exist'); - * } - * }); - * ``` - * - * **read (RECOMMENDED)** - * - * ```js - * import { open, close } from 'fs'; - * - * open('myfile', 'r', (err, fd) => { - * if (err) { - * if (err.code === 'ENOENT') { - * console.error('myfile does not exist'); - * return; - * } - * - * throw err; - * } - * - * try { - * readMyData(fd); - * } finally { - * close(fd, (err) => { - * if (err) throw err; - * }); - * } - * }); - * ``` - * - * The "not recommended" examples above check for existence and then use the - * file; the "recommended" examples are better because they use the file directly - * and handle the error, if any. - * - * In general, check for the existence of a file only if the file won’t be - * used directly, for example when its existence is a signal from another - * process. - * @since v0.0.2 - * @deprecated Since v1.0.0 - Use {@link stat} or {@link access} instead. - */ - export function exists(path: PathLike, callback: (exists: boolean) => void): void; - /** @deprecated */ - export namespace exists { - /** - * @param path A path to a file or directory. If a URL is provided, it must use the `file:` protocol. - * URL support is _experimental_. - */ - function __promisify__(path: PathLike): Promise; - } - /** - * Returns `true` if the path exists, `false` otherwise. - * - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link exists}. - * - * `fs.exists()` is deprecated, but `fs.existsSync()` is not. The `callback`parameter to `fs.exists()` accepts parameters that are inconsistent with other - * Node.js callbacks. `fs.existsSync()` does not use a callback. - * - * ```js - * import { existsSync } from 'fs'; - * - * if (existsSync('/etc/passwd')) - * console.log('The path exists.'); - * ``` - * @since v0.1.21 - */ - export function existsSync(path: PathLike): boolean; - export namespace constants { - // File Access Constants - /** Constant for fs.access(). File is visible to the calling process. */ - const F_OK: number; - /** Constant for fs.access(). File can be read by the calling process. */ - const R_OK: number; - /** Constant for fs.access(). File can be written by the calling process. */ - const W_OK: number; - /** Constant for fs.access(). File can be executed by the calling process. */ - const X_OK: number; - // File Copy Constants - /** Constant for fs.copyFile. Flag indicating the destination file should not be overwritten if it already exists. */ - const COPYFILE_EXCL: number; - /** - * Constant for fs.copyFile. copy operation will attempt to create a copy-on-write reflink. - * If the underlying platform does not support copy-on-write, then a fallback copy mechanism is used. - */ - const COPYFILE_FICLONE: number; - /** - * Constant for fs.copyFile. Copy operation will attempt to create a copy-on-write reflink. - * If the underlying platform does not support copy-on-write, then the operation will fail with an error. - */ - const COPYFILE_FICLONE_FORCE: number; - // File Open Constants - /** Constant for fs.open(). Flag indicating to open a file for read-only access. */ - const O_RDONLY: number; - /** Constant for fs.open(). Flag indicating to open a file for write-only access. */ - const O_WRONLY: number; - /** Constant for fs.open(). Flag indicating to open a file for read-write access. */ - const O_RDWR: number; - /** Constant for fs.open(). Flag indicating to create the file if it does not already exist. */ - const O_CREAT: number; - /** Constant for fs.open(). Flag indicating that opening a file should fail if the O_CREAT flag is set and the file already exists. */ - const O_EXCL: number; - /** - * Constant for fs.open(). Flag indicating that if path identifies a terminal device, - * opening the path shall not cause that terminal to become the controlling terminal for the process - * (if the process does not already have one). - */ - const O_NOCTTY: number; - /** Constant for fs.open(). Flag indicating that if the file exists and is a regular file, and the file is opened successfully for write access, its length shall be truncated to zero. */ - const O_TRUNC: number; - /** Constant for fs.open(). Flag indicating that data will be appended to the end of the file. */ - const O_APPEND: number; - /** Constant for fs.open(). Flag indicating that the open should fail if the path is not a directory. */ - const O_DIRECTORY: number; - /** - * constant for fs.open(). - * Flag indicating reading accesses to the file system will no longer result in - * an update to the atime information associated with the file. - * This flag is available on Linux operating systems only. - */ - const O_NOATIME: number; - /** Constant for fs.open(). Flag indicating that the open should fail if the path is a symbolic link. */ - const O_NOFOLLOW: number; - /** Constant for fs.open(). Flag indicating that the file is opened for synchronous I/O. */ - const O_SYNC: number; - /** Constant for fs.open(). Flag indicating that the file is opened for synchronous I/O with write operations waiting for data integrity. */ - const O_DSYNC: number; - /** Constant for fs.open(). Flag indicating to open the symbolic link itself rather than the resource it is pointing to. */ - const O_SYMLINK: number; - /** Constant for fs.open(). When set, an attempt will be made to minimize caching effects of file I/O. */ - const O_DIRECT: number; - /** Constant for fs.open(). Flag indicating to open the file in nonblocking mode when possible. */ - const O_NONBLOCK: number; - // File Type Constants - /** Constant for fs.Stats mode property for determining a file's type. Bit mask used to extract the file type code. */ - const S_IFMT: number; - /** Constant for fs.Stats mode property for determining a file's type. File type constant for a regular file. */ - const S_IFREG: number; - /** Constant for fs.Stats mode property for determining a file's type. File type constant for a directory. */ - const S_IFDIR: number; - /** Constant for fs.Stats mode property for determining a file's type. File type constant for a character-oriented device file. */ - const S_IFCHR: number; - /** Constant for fs.Stats mode property for determining a file's type. File type constant for a block-oriented device file. */ - const S_IFBLK: number; - /** Constant for fs.Stats mode property for determining a file's type. File type constant for a FIFO/pipe. */ - const S_IFIFO: number; - /** Constant for fs.Stats mode property for determining a file's type. File type constant for a symbolic link. */ - const S_IFLNK: number; - /** Constant for fs.Stats mode property for determining a file's type. File type constant for a socket. */ - const S_IFSOCK: number; - // File Mode Constants - /** Constant for fs.Stats mode property for determining access permissions for a file. File mode indicating readable, writable and executable by owner. */ - const S_IRWXU: number; - /** Constant for fs.Stats mode property for determining access permissions for a file. File mode indicating readable by owner. */ - const S_IRUSR: number; - /** Constant for fs.Stats mode property for determining access permissions for a file. File mode indicating writable by owner. */ - const S_IWUSR: number; - /** Constant for fs.Stats mode property for determining access permissions for a file. File mode indicating executable by owner. */ - const S_IXUSR: number; - /** Constant for fs.Stats mode property for determining access permissions for a file. File mode indicating readable, writable and executable by group. */ - const S_IRWXG: number; - /** Constant for fs.Stats mode property for determining access permissions for a file. File mode indicating readable by group. */ - const S_IRGRP: number; - /** Constant for fs.Stats mode property for determining access permissions for a file. File mode indicating writable by group. */ - const S_IWGRP: number; - /** Constant for fs.Stats mode property for determining access permissions for a file. File mode indicating executable by group. */ - const S_IXGRP: number; - /** Constant for fs.Stats mode property for determining access permissions for a file. File mode indicating readable, writable and executable by others. */ - const S_IRWXO: number; - /** Constant for fs.Stats mode property for determining access permissions for a file. File mode indicating readable by others. */ - const S_IROTH: number; - /** Constant for fs.Stats mode property for determining access permissions for a file. File mode indicating writable by others. */ - const S_IWOTH: number; - /** Constant for fs.Stats mode property for determining access permissions for a file. File mode indicating executable by others. */ - const S_IXOTH: number; - /** - * When set, a memory file mapping is used to access the file. This flag - * is available on Windows operating systems only. On other operating systems, - * this flag is ignored. - */ - const UV_FS_O_FILEMAP: number; - } - /** - * Tests a user's permissions for the file or directory specified by `path`. - * The `mode` argument is an optional integer that specifies the accessibility - * checks to be performed. `mode` should be either the value `fs.constants.F_OK`or a mask consisting of the bitwise OR of any of `fs.constants.R_OK`,`fs.constants.W_OK`, and `fs.constants.X_OK` - * (e.g.`fs.constants.W_OK | fs.constants.R_OK`). Check `File access constants` for - * possible values of `mode`. - * - * The final argument, `callback`, is a callback function that is invoked with - * a possible error argument. If any of the accessibility checks fail, the error - * argument will be an `Error` object. The following examples check if`package.json` exists, and if it is readable or writable. - * - * ```js - * import { access, constants } from 'fs'; - * - * const file = 'package.json'; - * - * // Check if the file exists in the current directory. - * access(file, constants.F_OK, (err) => { - * console.log(`${file} ${err ? 'does not exist' : 'exists'}`); - * }); - * - * // Check if the file is readable. - * access(file, constants.R_OK, (err) => { - * console.log(`${file} ${err ? 'is not readable' : 'is readable'}`); - * }); - * - * // Check if the file is writable. - * access(file, constants.W_OK, (err) => { - * console.log(`${file} ${err ? 'is not writable' : 'is writable'}`); - * }); - * - * // Check if the file is readable and writable. - * access(file, constants.R_OK | constants.W_OK, (err) => { - * console.log(`${file} ${err ? 'is not' : 'is'} readable and writable`); - * }); - * ``` - * - * Do not use `fs.access()` to check for the accessibility of a file before calling`fs.open()`, `fs.readFile()` or `fs.writeFile()`. Doing - * so introduces a race condition, since other processes may change the file's - * state between the two calls. Instead, user code should open/read/write the - * file directly and handle the error raised if the file is not accessible. - * - * **write (NOT RECOMMENDED)** - * - * ```js - * import { access, open, close } from 'fs'; - * - * access('myfile', (err) => { - * if (!err) { - * console.error('myfile already exists'); - * return; - * } - * - * open('myfile', 'wx', (err, fd) => { - * if (err) throw err; - * - * try { - * writeMyData(fd); - * } finally { - * close(fd, (err) => { - * if (err) throw err; - * }); - * } - * }); - * }); - * ``` - * - * **write (RECOMMENDED)** - * - * ```js - * import { open, close } from 'fs'; - * - * open('myfile', 'wx', (err, fd) => { - * if (err) { - * if (err.code === 'EEXIST') { - * console.error('myfile already exists'); - * return; - * } - * - * throw err; - * } - * - * try { - * writeMyData(fd); - * } finally { - * close(fd, (err) => { - * if (err) throw err; - * }); - * } - * }); - * ``` - * - * **read (NOT RECOMMENDED)** - * - * ```js - * import { access, open, close } from 'fs'; - * access('myfile', (err) => { - * if (err) { - * if (err.code === 'ENOENT') { - * console.error('myfile does not exist'); - * return; - * } - * - * throw err; - * } - * - * open('myfile', 'r', (err, fd) => { - * if (err) throw err; - * - * try { - * readMyData(fd); - * } finally { - * close(fd, (err) => { - * if (err) throw err; - * }); - * } - * }); - * }); - * ``` - * - * **read (RECOMMENDED)** - * - * ```js - * import { open, close } from 'fs'; - * - * open('myfile', 'r', (err, fd) => { - * if (err) { - * if (err.code === 'ENOENT') { - * console.error('myfile does not exist'); - * return; - * } - * - * throw err; - * } - * - * try { - * readMyData(fd); - * } finally { - * close(fd, (err) => { - * if (err) throw err; - * }); - * } - * }); - * ``` - * - * The "not recommended" examples above check for accessibility and then use the - * file; the "recommended" examples are better because they use the file directly - * and handle the error, if any. - * - * In general, check for the accessibility of a file only if the file will not be - * used directly, for example when its accessibility is a signal from another - * process. - * - * On Windows, access-control policies (ACLs) on a directory may limit access to - * a file or directory. The `fs.access()` function, however, does not check the - * ACL and therefore may report that a path is accessible even if the ACL restricts - * the user from reading or writing to it. - * @since v0.11.15 - * @param [mode=fs.constants.F_OK] - */ - export function access(path: PathLike, mode: number | undefined, callback: NoParamCallback): void; - /** - * Asynchronously tests a user's permissions for the file specified by path. - * @param path A path to a file or directory. If a URL is provided, it must use the `file:` protocol. - */ - export function access(path: PathLike, callback: NoParamCallback): void; - export namespace access { - /** - * Asynchronously tests a user's permissions for the file specified by path. - * @param path A path to a file or directory. If a URL is provided, it must use the `file:` protocol. - * URL support is _experimental_. - */ - function __promisify__(path: PathLike, mode?: number): Promise; - } - /** - * Synchronously tests a user's permissions for the file or directory specified - * by `path`. The `mode` argument is an optional integer that specifies the - * accessibility checks to be performed. `mode` should be either the value`fs.constants.F_OK` or a mask consisting of the bitwise OR of any of`fs.constants.R_OK`, `fs.constants.W_OK`, and - * `fs.constants.X_OK` (e.g.`fs.constants.W_OK | fs.constants.R_OK`). Check `File access constants` for - * possible values of `mode`. - * - * If any of the accessibility checks fail, an `Error` will be thrown. Otherwise, - * the method will return `undefined`. - * - * ```js - * import { accessSync, constants } from 'fs'; - * - * try { - * accessSync('etc/passwd', constants.R_OK | constants.W_OK); - * console.log('can read/write'); - * } catch (err) { - * console.error('no access!'); - * } - * ``` - * @since v0.11.15 - * @param [mode=fs.constants.F_OK] - */ - export function accessSync(path: PathLike, mode?: number): void; - interface StreamOptions { - flags?: string | undefined; - encoding?: BufferEncoding | undefined; - fd?: number | promises.FileHandle | undefined; - mode?: number | undefined; - autoClose?: boolean | undefined; - /** - * @default false - */ - emitClose?: boolean | undefined; - start?: number | undefined; - highWaterMark?: number | undefined; - } - interface ReadStreamOptions extends StreamOptions { - end?: number | undefined; - } - /** - * Unlike the 16 kb default `highWaterMark` for a `stream.Readable`, the stream - * returned by this method has a default `highWaterMark` of 64 kb. - * - * `options` can include `start` and `end` values to read a range of bytes from - * the file instead of the entire file. Both `start` and `end` are inclusive and - * start counting at 0, allowed values are in the - * \[0, [`Number.MAX_SAFE_INTEGER`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Number/MAX_SAFE_INTEGER)\] range. If `fd` is specified and `start` is - * omitted or `undefined`, `fs.createReadStream()` reads sequentially from the - * current file position. The `encoding` can be any one of those accepted by `Buffer`. - * - * If `fd` is specified, `ReadStream` will ignore the `path` argument and will use - * the specified file descriptor. This means that no `'open'` event will be - * emitted. `fd` should be blocking; non-blocking `fd`s should be passed to `net.Socket`. - * - * If `fd` points to a character device that only supports blocking reads - * (such as keyboard or sound card), read operations do not finish until data is - * available. This can prevent the process from exiting and the stream from - * closing naturally. - * - * By default, the stream will emit a `'close'` event after it has been - * destroyed. Set the `emitClose` option to `false` to change this behavior. - * - * By providing the `fs` option, it is possible to override the corresponding `fs`implementations for `open`, `read`, and `close`. When providing the `fs` option, - * an override for `read` is required. If no `fd` is provided, an override for`open` is also required. If `autoClose` is `true`, an override for `close` is - * also required. - * - * ```js - * import { createReadStream } from 'fs'; - * - * // Create a stream from some character device. - * const stream = createReadStream('/dev/input/event0'); - * setTimeout(() => { - * stream.close(); // This may not close the stream. - * // Artificially marking end-of-stream, as if the underlying resource had - * // indicated end-of-file by itself, allows the stream to close. - * // This does not cancel pending read operations, and if there is such an - * // operation, the process may still not be able to exit successfully - * // until it finishes. - * stream.push(null); - * stream.read(0); - * }, 100); - * ``` - * - * If `autoClose` is false, then the file descriptor won't be closed, even if - * there's an error. It is the application's responsibility to close it and make - * sure there's no file descriptor leak. If `autoClose` is set to true (default - * behavior), on `'error'` or `'end'` the file descriptor will be closed - * automatically. - * - * `mode` sets the file mode (permission and sticky bits), but only if the - * file was created. - * - * An example to read the last 10 bytes of a file which is 100 bytes long: - * - * ```js - * import { createReadStream } from 'fs'; - * - * createReadStream('sample.txt', { start: 90, end: 99 }); - * ``` - * - * If `options` is a string, then it specifies the encoding. - * @since v0.1.31 - */ - export function createReadStream(path: PathLike, options?: BufferEncoding | ReadStreamOptions): ReadStream; - /** - * `options` may also include a `start` option to allow writing data at some - * position past the beginning of the file, allowed values are in the - * \[0, [`Number.MAX_SAFE_INTEGER`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Number/MAX_SAFE_INTEGER)\] range. Modifying a file rather than - * replacing it may require the `flags` option to be set to `r+` rather than the - * default `w`. The `encoding` can be any one of those accepted by `Buffer`. - * - * If `autoClose` is set to true (default behavior) on `'error'` or `'finish'`the file descriptor will be closed automatically. If `autoClose` is false, - * then the file descriptor won't be closed, even if there's an error. - * It is the application's responsibility to close it and make sure there's no - * file descriptor leak. - * - * By default, the stream will emit a `'close'` event after it has been - * destroyed. Set the `emitClose` option to `false` to change this behavior. - * - * By providing the `fs` option it is possible to override the corresponding `fs`implementations for `open`, `write`, `writev` and `close`. Overriding `write()`without `writev()` can reduce - * performance as some optimizations (`_writev()`) - * will be disabled. When providing the `fs` option, overrides for at least one of`write` and `writev` are required. If no `fd` option is supplied, an override - * for `open` is also required. If `autoClose` is `true`, an override for `close`is also required. - * - * Like `fs.ReadStream`, if `fd` is specified, `fs.WriteStream` will ignore the`path` argument and will use the specified file descriptor. This means that no`'open'` event will be - * emitted. `fd` should be blocking; non-blocking `fd`s - * should be passed to `net.Socket`. - * - * If `options` is a string, then it specifies the encoding. - * @since v0.1.31 - */ - export function createWriteStream(path: PathLike, options?: BufferEncoding | StreamOptions): WriteStream; - /** - * Forces all currently queued I/O operations associated with the file to the - * operating system's synchronized I/O completion state. Refer to the POSIX [`fdatasync(2)`](http://man7.org/linux/man-pages/man2/fdatasync.2.html) documentation for details. No arguments other - * than a possible - * exception are given to the completion callback. - * @since v0.1.96 - */ - export function fdatasync(fd: number, callback: NoParamCallback): void; - export namespace fdatasync { - /** - * Asynchronous fdatasync(2) - synchronize a file's in-core state with storage device. - * @param fd A file descriptor. - */ - function __promisify__(fd: number): Promise; - } - /** - * Forces all currently queued I/O operations associated with the file to the - * operating system's synchronized I/O completion state. Refer to the POSIX [`fdatasync(2)`](http://man7.org/linux/man-pages/man2/fdatasync.2.html) documentation for details. Returns `undefined`. - * @since v0.1.96 - */ - export function fdatasyncSync(fd: number): void; - /** - * Asynchronously copies `src` to `dest`. By default, `dest` is overwritten if it - * already exists. No arguments other than a possible exception are given to the - * callback function. Node.js makes no guarantees about the atomicity of the copy - * operation. If an error occurs after the destination file has been opened for - * writing, Node.js will attempt to remove the destination. - * - * `mode` is an optional integer that specifies the behavior - * of the copy operation. It is possible to create a mask consisting of the bitwise - * OR of two or more values (e.g.`fs.constants.COPYFILE_EXCL | fs.constants.COPYFILE_FICLONE`). - * - * * `fs.constants.COPYFILE_EXCL`: The copy operation will fail if `dest` already - * exists. - * * `fs.constants.COPYFILE_FICLONE`: The copy operation will attempt to create a - * copy-on-write reflink. If the platform does not support copy-on-write, then a - * fallback copy mechanism is used. - * * `fs.constants.COPYFILE_FICLONE_FORCE`: The copy operation will attempt to - * create a copy-on-write reflink. If the platform does not support - * copy-on-write, then the operation will fail. - * - * ```js - * import { copyFile, constants } from 'fs'; - * - * function callback(err) { - * if (err) throw err; - * console.log('source.txt was copied to destination.txt'); - * } - * - * // destination.txt will be created or overwritten by default. - * copyFile('source.txt', 'destination.txt', callback); - * - * // By using COPYFILE_EXCL, the operation will fail if destination.txt exists. - * copyFile('source.txt', 'destination.txt', constants.COPYFILE_EXCL, callback); - * ``` - * @since v8.5.0 - * @param src source filename to copy - * @param dest destination filename of the copy operation - * @param [mode=0] modifiers for copy operation. - */ - export function copyFile(src: PathLike, dest: PathLike, callback: NoParamCallback): void; - export function copyFile(src: PathLike, dest: PathLike, mode: number, callback: NoParamCallback): void; - export namespace copyFile { - function __promisify__(src: PathLike, dst: PathLike, mode?: number): Promise; - } - /** - * Synchronously copies `src` to `dest`. By default, `dest` is overwritten if it - * already exists. Returns `undefined`. Node.js makes no guarantees about the - * atomicity of the copy operation. If an error occurs after the destination file - * has been opened for writing, Node.js will attempt to remove the destination. - * - * `mode` is an optional integer that specifies the behavior - * of the copy operation. It is possible to create a mask consisting of the bitwise - * OR of two or more values (e.g.`fs.constants.COPYFILE_EXCL | fs.constants.COPYFILE_FICLONE`). - * - * * `fs.constants.COPYFILE_EXCL`: The copy operation will fail if `dest` already - * exists. - * * `fs.constants.COPYFILE_FICLONE`: The copy operation will attempt to create a - * copy-on-write reflink. If the platform does not support copy-on-write, then a - * fallback copy mechanism is used. - * * `fs.constants.COPYFILE_FICLONE_FORCE`: The copy operation will attempt to - * create a copy-on-write reflink. If the platform does not support - * copy-on-write, then the operation will fail. - * - * ```js - * import { copyFileSync, constants } from 'fs'; - * - * // destination.txt will be created or overwritten by default. - * copyFileSync('source.txt', 'destination.txt'); - * console.log('source.txt was copied to destination.txt'); - * - * // By using COPYFILE_EXCL, the operation will fail if destination.txt exists. - * copyFileSync('source.txt', 'destination.txt', constants.COPYFILE_EXCL); - * ``` - * @since v8.5.0 - * @param src source filename to copy - * @param dest destination filename of the copy operation - * @param [mode=0] modifiers for copy operation. - */ - export function copyFileSync(src: PathLike, dest: PathLike, mode?: number): void; - /** - * Write an array of `ArrayBufferView`s to the file specified by `fd` using`writev()`. - * - * `position` is the offset from the beginning of the file where this data - * should be written. If `typeof position !== 'number'`, the data will be written - * at the current position. - * - * The callback will be given three arguments: `err`, `bytesWritten`, and`buffers`. `bytesWritten` is how many bytes were written from `buffers`. - * - * If this method is `util.promisify()` ed, it returns a promise for an`Object` with `bytesWritten` and `buffers` properties. - * - * It is unsafe to use `fs.writev()` multiple times on the same file without - * waiting for the callback. For this scenario, use {@link createWriteStream}. - * - * On Linux, positional writes don't work when the file is opened in append mode. - * The kernel ignores the position argument and always appends the data to - * the end of the file. - * @since v12.9.0 - */ - export function writev(fd: number, buffers: ReadonlyArray, cb: (err: NodeJS.ErrnoException | null, bytesWritten: number, buffers: NodeJS.ArrayBufferView[]) => void): void; - export function writev( - fd: number, - buffers: ReadonlyArray, - position: number, - cb: (err: NodeJS.ErrnoException | null, bytesWritten: number, buffers: NodeJS.ArrayBufferView[]) => void - ): void; - export interface WriteVResult { - bytesWritten: number; - buffers: NodeJS.ArrayBufferView[]; - } - export namespace writev { - function __promisify__(fd: number, buffers: ReadonlyArray, position?: number): Promise; - } - /** - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link writev}. - * @since v12.9.0 - * @return The number of bytes written. - */ - export function writevSync(fd: number, buffers: ReadonlyArray, position?: number): number; - /** - * Read from a file specified by `fd` and write to an array of `ArrayBufferView`s - * using `readv()`. - * - * `position` is the offset from the beginning of the file from where data - * should be read. If `typeof position !== 'number'`, the data will be read - * from the current position. - * - * The callback will be given three arguments: `err`, `bytesRead`, and`buffers`. `bytesRead` is how many bytes were read from the file. - * - * If this method is invoked as its `util.promisify()` ed version, it returns - * a promise for an `Object` with `bytesRead` and `buffers` properties. - * @since v13.13.0, v12.17.0 - */ - export function readv(fd: number, buffers: ReadonlyArray, cb: (err: NodeJS.ErrnoException | null, bytesRead: number, buffers: NodeJS.ArrayBufferView[]) => void): void; - export function readv( - fd: number, - buffers: ReadonlyArray, - position: number, - cb: (err: NodeJS.ErrnoException | null, bytesRead: number, buffers: NodeJS.ArrayBufferView[]) => void - ): void; - export interface ReadVResult { - bytesRead: number; - buffers: NodeJS.ArrayBufferView[]; - } - export namespace readv { - function __promisify__(fd: number, buffers: ReadonlyArray, position?: number): Promise; - } - /** - * For detailed information, see the documentation of the asynchronous version of - * this API: {@link readv}. - * @since v13.13.0, v12.17.0 - * @return The number of bytes read. - */ - export function readvSync(fd: number, buffers: ReadonlyArray, position?: number): number; - export interface OpenDirOptions { - encoding?: BufferEncoding | undefined; - /** - * Number of directory entries that are buffered - * internally when reading from the directory. Higher values lead to better - * performance but higher memory usage. - * @default 32 - */ - bufferSize?: number | undefined; - } - /** - * Synchronously open a directory. See [`opendir(3)`](http://man7.org/linux/man-pages/man3/opendir.3.html). - * - * Creates an `fs.Dir`, which contains all further functions for reading from - * and cleaning up the directory. - * - * The `encoding` option sets the encoding for the `path` while opening the - * directory and subsequent read operations. - * @since v12.12.0 - */ - export function opendirSync(path: PathLike, options?: OpenDirOptions): Dir; - /** - * Asynchronously open a directory. See the POSIX [`opendir(3)`](http://man7.org/linux/man-pages/man3/opendir.3.html) documentation for - * more details. - * - * Creates an `fs.Dir`, which contains all further functions for reading from - * and cleaning up the directory. - * - * The `encoding` option sets the encoding for the `path` while opening the - * directory and subsequent read operations. - * @since v12.12.0 - */ - export function opendir(path: PathLike, cb: (err: NodeJS.ErrnoException | null, dir: Dir) => void): void; - export function opendir(path: PathLike, options: OpenDirOptions, cb: (err: NodeJS.ErrnoException | null, dir: Dir) => void): void; - export namespace opendir { - function __promisify__(path: PathLike, options?: OpenDirOptions): Promise; - } - export interface BigIntStats extends StatsBase { - atimeNs: bigint; - mtimeNs: bigint; - ctimeNs: bigint; - birthtimeNs: bigint; - } - export interface BigIntOptions { - bigint: true; - } - export interface StatOptions { - bigint?: boolean | undefined; - } - export interface StatSyncOptions extends StatOptions { - throwIfNoEntry?: boolean | undefined; - } - interface CopyOptionsBase { - /** - * Dereference symlinks - * @default false - */ - dereference?: boolean; - /** - * When `force` is `false`, and the destination - * exists, throw an error. - * @default false - */ - errorOnExist?: boolean; - /** - * Overwrite existing file or directory. _The copy - * operation will ignore errors if you set this to false and the destination - * exists. Use the `errorOnExist` option to change this behavior. - * @default true - */ - force?: boolean; - /** - * When `true` timestamps from `src` will - * be preserved. - * @default false - */ - preserveTimestamps?: boolean; - /** - * Copy directories recursively. - * @default false - */ - recursive?: boolean; - /** - * When true, path resolution for symlinks will be skipped - * @default false - */ - verbatimSymlinks?: boolean; - } - export interface CopyOptions extends CopyOptionsBase { - /** - * Function to filter copied files/directories. Return - * `true` to copy the item, `false` to ignore it. - */ - filter?(source: string, destination: string): boolean | Promise; - } - export interface CopySyncOptions extends CopyOptionsBase { - /** - * Function to filter copied files/directories. Return - * `true` to copy the item, `false` to ignore it. - */ - filter?(source: string, destination: string): boolean; - } - /** - * Asynchronously copies the entire directory structure from `src` to `dest`, - * including subdirectories and files. - * - * When copying a directory to another directory, globs are not supported and - * behavior is similar to `cp dir1/ dir2/`. - * @since v16.7.0 - * @experimental - * @param src source path to copy. - * @param dest destination path to copy to. - */ - export function cp(source: string | URL, destination: string | URL, callback: (err: NodeJS.ErrnoException | null) => void): void; - export function cp(source: string | URL, destination: string | URL, opts: CopyOptions, callback: (err: NodeJS.ErrnoException | null) => void): void; - /** - * Synchronously copies the entire directory structure from `src` to `dest`, - * including subdirectories and files. - * - * When copying a directory to another directory, globs are not supported and - * behavior is similar to `cp dir1/ dir2/`. - * @since v16.7.0 - * @experimental - * @param src source path to copy. - * @param dest destination path to copy to. - */ - export function cpSync(source: string | URL, destination: string | URL, opts?: CopySyncOptions): void; -} -declare module 'node:fs' { - export * from 'fs'; -} diff --git a/spaces/fffiloni/scene-edit-detection/README.md b/spaces/fffiloni/scene-edit-detection/README.md deleted file mode 100644 index 080d057b1e103d6e729258645604184ea8d49b3f..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/scene-edit-detection/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Scene Edit Detection -emoji: ✂️ 🎞 -colorFrom: pink -colorTo: blue -sdk: gradio -sdk_version: 3.4 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/fgpzen/remove-photo-object/Dockerfile b/spaces/fgpzen/remove-photo-object/Dockerfile deleted file mode 100644 index 995e8e56f44f9160085b7699985c953b89c9caa0..0000000000000000000000000000000000000000 --- a/spaces/fgpzen/remove-photo-object/Dockerfile +++ /dev/null @@ -1,9 +0,0 @@ -FROM pytorch/pytorch:latest - -WORKDIR /app - -COPY . . - -RUN pip install -r requirements.txt - -CMD [ "streamlit", "run", "app.py" ] \ No newline at end of file diff --git a/spaces/fiyen/YangyangChatGPT/app1.py b/spaces/fiyen/YangyangChatGPT/app1.py deleted file mode 100644 index 71b9ae3676f88d54108103e53edffc7db32a2ed0..0000000000000000000000000000000000000000 --- a/spaces/fiyen/YangyangChatGPT/app1.py +++ /dev/null @@ -1,480 +0,0 @@ -# # -*- coding:utf-8 -*- -import os -import logging -import sys - -import gradio as gr - -from modules.utils import * -from modules.presets import * -from modules.overwrites import * -from modules.chat_func import * -from encrypt_it import * - -logging.basicConfig( - level=logging.DEBUG, - format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s", -) - -my_api_key = "" # 在这里输入你的 API 密钥 - -# if we are running in Docker -if os.environ.get("dockerrun") == "yes": - dockerflag = True -else: - dockerflag = False - -authflag = False - -if dockerflag: - my_api_key = os.environ.get("my_api_key") - if my_api_key == "empty": - logging.error("Please give a api key!") - sys.exit(1) - # auth - username = os.environ.get("USERNAME") - password = os.environ.get("PASSWORD") - if not (isinstance(username, type(None)) or isinstance(password, type(None))): - authflag = True -else: - # if ( - # not my_api_key - # and os.path.exists("api_key.txt") - # and os.path.getsize("api_key.txt") - # ): - # with open("api_key.txt", "r") as f: - # my_api_key = f.read().strip() - # if os.path.exists("auth.json"): - # with open("auth.json", "r") as f: - # auth = json.load(f) - # username = auth["username"] - # password = auth["password"] - # if username != "" and password != "": - # authflag = True - authflag = True - -gr.Chatbot.postprocess = postprocess -PromptHelper.compact_text_chunks = compact_text_chunks - -with open("custom.css", "r", encoding="utf-8") as f: - customCSS = f.read() - -with gr.Blocks( - css=customCSS, - theme=gr.themes.Soft( - primary_hue=gr.themes.Color( - c50="#02C160", - c100="rgba(2, 193, 96, 0.2)", - c200="#02C160", - c300="rgba(2, 193, 96, 0.32)", - c400="rgba(2, 193, 96, 0.32)", - c500="rgba(2, 193, 96, 1.0)", - c600="rgba(2, 193, 96, 1.0)", - c700="rgba(2, 193, 96, 0.32)", - c800="rgba(2, 193, 96, 0.32)", - c900="#02C160", - c950="#02C160", - ), - secondary_hue=gr.themes.Color( - c50="#576b95", - c100="#576b95", - c200="#576b95", - c300="#576b95", - c400="#576b95", - c500="#576b95", - c600="#576b95", - c700="#576b95", - c800="#576b95", - c900="#576b95", - c950="#576b95", - ), - neutral_hue=gr.themes.Color( - name="gray", - c50="#f9fafb", - c100="#f3f4f6", - c200="#e5e7eb", - c300="#d1d5db", - c400="#B2B2B2", - c500="#808080", - c600="#636363", - c700="#515151", - c800="#393939", - c900="#272727", - c950="#171717", - ), - radius_size=gr.themes.sizes.radius_sm, - ).set( - button_primary_background_fill="#06AE56", - button_primary_background_fill_dark="#06AE56", - button_primary_background_fill_hover="#07C863", - button_primary_border_color="#06AE56", - button_primary_border_color_dark="#06AE56", - button_primary_text_color="#FFFFFF", - button_primary_text_color_dark="#FFFFFF", - button_secondary_background_fill="#F2F2F2", - button_secondary_background_fill_dark="#2B2B2B", - button_secondary_text_color="#393939", - button_secondary_text_color_dark="#FFFFFF", - # background_fill_primary="#F7F7F7", - # background_fill_primary_dark="#1F1F1F", - block_title_text_color="*primary_500", - block_title_background_fill="*primary_100", - input_background_fill="#F6F6F6", - ), -) as demo: - history = gr.State([]) - token_count = gr.State([]) - promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2)) - user_api_key = gr.State(my_api_key) - user_password = gr.State('') - user_name = gr.State('') - TRUECOMSTANT = gr.State(True) - FALSECONSTANT = gr.State(False) - topic = gr.State("未命名对话历史记录") - - with gr.Row(): - gr.HTML(title) - status_display = gr.Markdown(get_geoip(), elem_id="status_display") - - with gr.Row(scale=1).style(equal_height=True): - with gr.Column(scale=5): - with gr.Row(scale=1): - chatbot = gr.Chatbot(elem_id="chuanhu_chatbot").style(height="100%") - with gr.Row(scale=1): - with gr.Column(scale=12): - user_input = gr.Textbox( - show_label=False, placeholder="在这里输入" - ).style(container=False) - with gr.Column(min_width=70, scale=1): - submitBtn = gr.Button("发送", variant="primary") - with gr.Row(scale=1): - emptyBtn = gr.Button( - "🧹 新的对话", - ) - retryBtn = gr.Button("🔄 重新生成") - delLastBtn = gr.Button("🗑️ 删除一条对话") - reduceTokenBtn = gr.Button("♻️ 总结对话") - - with gr.Column(): - with gr.Column(min_width=50, scale=1): - with gr.Tab(label="ChatGPT"): - keyTxt = gr.Textbox( - show_label=True, - placeholder=f"OpenAI API-key...", - value=hide_middle_chars(my_api_key), - type="password", - visible=not HIDE_MY_KEY, - label="API-Key", - ) - model_select_dropdown = gr.Dropdown( - label="选择模型", choices=MODELS, multiselect=False, value=MODELS[0] - ) - use_streaming_checkbox = gr.Checkbox( - label="实时传输回答", value=True, visible=enable_streaming_option - ) - use_websearch_checkbox = gr.Checkbox(label="使用在线搜索", value=False) - index_files = gr.Files(label="上传索引文件", type="file", multiple=True) - - with gr.Tab(label="Prompt"): - systemPromptTxt = gr.Textbox( - show_label=True, - placeholder=f"在这里输入System Prompt...", - label="System prompt", - value=initial_prompt, - lines=10, - ).style(container=False) - with gr.Accordion(label="加载Prompt模板", open=True): - with gr.Column(): - with gr.Row(): - with gr.Column(scale=6): - templateFileSelectDropdown = gr.Dropdown( - label="选择Prompt模板集合文件", - choices=get_template_names(plain=True), - multiselect=False, - value=get_template_names(plain=True)[0], - ).style(container=False) - with gr.Column(scale=1): - templateRefreshBtn = gr.Button("🔄 刷新") - with gr.Row(): - with gr.Column(): - templateSelectDropdown = gr.Dropdown( - label="从Prompt模板中加载", - choices=load_template( - get_template_names(plain=True)[0], mode=1 - ), - multiselect=False, - value=load_template( - get_template_names(plain=True)[0], mode=1 - )[0], - ).style(container=False) - - with gr.Tab(label="保存/加载"): - with gr.Accordion(label="保存/加载对话历史记录", open=True): - with gr.Column(): - with gr.Row(): - with gr.Column(scale=6): - historyFileSelectDropdown = gr.Dropdown( - label="从列表中加载对话", - choices=get_history_names(plain=True), - multiselect=False, - value=get_history_names(plain=True)[0], - ) - with gr.Column(scale=1): - historyRefreshBtn = gr.Button("🔄 刷新") - with gr.Row(): - with gr.Column(scale=6): - saveFileName = gr.Textbox( - show_label=True, - placeholder=f"设置文件名: 默认为.json，可选为.md", - label="设置保存文件名", - value="对话历史记录", - ).style(container=True) - with gr.Column(scale=1): - saveHistoryBtn = gr.Button("💾 保存对话") - exportMarkdownBtn = gr.Button("📝 导出为Markdown") - gr.Markdown("默认保存于history文件夹") - with gr.Row(): - with gr.Column(): - downloadFile = gr.File(interactive=True) - - with gr.Tab(label="高级"): - default_btn = gr.Button("🔙 恢复默认设置") - gr.Markdown("# ⚠️ 务必谨慎更改 ⚠️\n\n如果无法使用请恢复默认设置") - - with gr.Accordion("参数", open=False): - top_p = gr.Slider( - minimum=-0, - maximum=1.0, - value=1.0, - step=0.05, - interactive=True, - label="Top-p", - ) - temperature = gr.Slider( - minimum=-0, - maximum=2.0, - value=1.0, - step=0.1, - interactive=True, - label="Temperature", - ) - - apiurlTxt = gr.Textbox( - show_label=True, - placeholder=f"在这里输入API地址...", - label="API地址", - value="https://api.openai.com/v1/chat/completions", - lines=2, - ) - changeAPIURLBtn = gr.Button("🔄 切换API地址") - proxyTxt = gr.Textbox( - show_label=True, - placeholder=f"在这里输入代理地址...", - label="代理地址（示例：http://127.0.0.1:10809）", - value="", - lines=2, - ) - changeProxyBtn = gr.Button("🔄 设置代理地址") - - gr.Markdown(description) - - keyTxt.submit(submit_key, keyTxt, [user_api_key, status_display]) - keyTxt.change(submit_key, keyTxt, [user_api_key, status_display]) - - # Chatbot - user_input.submit( - predict, - [ - user_api_key, - systemPromptTxt, - history, - user_input, - chatbot, - token_count, - top_p, - temperature, - use_streaming_checkbox, - model_select_dropdown, - use_websearch_checkbox, - index_files, - ], - [chatbot, history, status_display, token_count], - show_progress=True, - ) - user_input.submit(reset_textbox, [], [user_input]) - - submitBtn.click( - predict, - [ - user_api_key, - systemPromptTxt, - history, - user_input, - chatbot, - token_count, - top_p, - temperature, - use_streaming_checkbox, - model_select_dropdown, - use_websearch_checkbox, - index_files, - ], - [chatbot, history, status_display, token_count], - show_progress=True, - ) - submitBtn.click(reset_textbox, [], [user_input]) - - emptyBtn.click( - reset_state, - outputs=[chatbot, history, token_count, status_display], - show_progress=True, - ) - - retryBtn.click( - retry, - [ - user_api_key, - systemPromptTxt, - history, - chatbot, - token_count, - top_p, - temperature, - use_streaming_checkbox, - model_select_dropdown, - ], - [chatbot, history, status_display, token_count], - show_progress=True, - ) - - delLastBtn.click( - delete_last_conversation, - [chatbot, history, token_count], - [chatbot, history, token_count, status_display], - show_progress=True, - ) - - reduceTokenBtn.click( - reduce_token_size, - [ - user_api_key, - systemPromptTxt, - history, - chatbot, - token_count, - top_p, - temperature, - gr.State(0), - model_select_dropdown, - ], - [chatbot, history, status_display, token_count], - show_progress=True, - ) - - # Template - templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown]) - templateFileSelectDropdown.change( - load_template, - [templateFileSelectDropdown], - [promptTemplates, templateSelectDropdown], - show_progress=True, - ) - templateSelectDropdown.change( - get_template_content, - [promptTemplates, templateSelectDropdown, systemPromptTxt], - [systemPromptTxt], - show_progress=True, - ) - - # S&L - saveHistoryBtn.click( - save_chat_history, - [saveFileName, systemPromptTxt, history, chatbot], - downloadFile, - show_progress=True, - ) - saveHistoryBtn.click(get_history_names, None, [historyFileSelectDropdown]) - exportMarkdownBtn.click( - export_markdown, - [saveFileName, systemPromptTxt, history, chatbot], - downloadFile, - show_progress=True, - ) - historyRefreshBtn.click(get_history_names, None, [historyFileSelectDropdown]) - historyFileSelectDropdown.change( - load_chat_history, - [historyFileSelectDropdown, systemPromptTxt, history, chatbot], - [saveFileName, systemPromptTxt, history, chatbot], - show_progress=True, - ) - downloadFile.change( - load_chat_history, - [downloadFile, systemPromptTxt, history, chatbot], - [saveFileName, systemPromptTxt, history, chatbot], - ) - - # Advanced - default_btn.click( - reset_default, [], [apiurlTxt, proxyTxt, status_display], show_progress=True - ) - changeAPIURLBtn.click( - change_api_url, - [apiurlTxt], - [status_display], - show_progress=True, - ) - changeProxyBtn.click( - change_proxy, - [proxyTxt], - [status_display], - show_progress=True, - ) - -# check username and password, get api key -def check_access_right(username, password): - try: - # print("check", username, "-", password) - with open('encrypted.bin', 'rb') as f: - ciphertext = f.read() - key = generate_key(username, password) - decoded_api_key = decrypt(ciphertext, key) - my_api_key = decoded_api_key.decode() - submit_key(my_api_key) - keyTxt.update(my_api_key) - keyTxt.value = hide_middle_chars(my_api_key) - user_api_key.value = my_api_key - # user_passward.value = password - # user_name.value = username - return True - except Exception: - print(Exception) - return False - -logging.info( - colorama.Back.GREEN - + "\n温馨提示：访问 http://localhost:7860 查看界面" - + colorama.Style.RESET_ALL -) -# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接 -demo.title = "YangyangChat 🚀" - -if __name__ == "__main__": - reload_javascript() - # if running in Docker - if dockerflag: - if authflag: - demo.queue().launch( - server_name="0.0.0.0", server_port=7860, auth=(username, password), - favicon_path="./assets/favicon.png" - ) - else: - demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False, favicon_path="./assets/favicon.png") - # if not running in Docker - else: - if authflag: - demo.queue().launch(share=False, auth=check_access_right, favicon_path="./assets/favicon.png", inbrowser=True) - else: - demo.queue(concurrency_count=1000).launch(share=False, favicon_path="./assets/favicon.ico", inbrowser=True) # 改为 share=True 可以创建公开分享链接 - # demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口 - # demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=("在这里填写用户名", "在这里填写密码")) # 可设置用户名与密码 - # demo.queue().launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理 \ No newline at end of file diff --git a/spaces/freddyaboulton/fastapi-request/README.md b/spaces/freddyaboulton/fastapi-request/README.md deleted file mode 100644 index e81ff350052f2782ca5e01b7be196e5fd14c3b25..0000000000000000000000000000000000000000 --- a/spaces/freddyaboulton/fastapi-request/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Fastapi Request -emoji: 📚 -colorFrom: indigo -colorTo: blue -sdk: gradio -sdk_version: 3.14.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/models/ocrnet_r50-d8.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/models/ocrnet_r50-d8.py deleted file mode 100644 index 615aa3ff703942b6c22b2d6e9642504dd3e41ebd..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/models/ocrnet_r50-d8.py +++ /dev/null @@ -1,47 +0,0 @@ -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='CascadeEncoderDecoder', - num_stages=2, - 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='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)), - dict( - type='OCRHead', - in_channels=2048, - in_index=3, - channels=512, - ocr_channels=256, - 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)) - ], - # model training and testing settings - train_cfg=dict(), - test_cfg=dict(mode='whole')) diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/video/optflow.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/video/optflow.py deleted file mode 100644 index 84160f8d6ef9fceb5a2f89e7481593109fc1905d..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/video/optflow.py +++ /dev/null @@ -1,254 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import warnings - -import cv2 -import numpy as np - -from annotator.uniformer.mmcv.arraymisc import dequantize, quantize -from annotator.uniformer.mmcv.image import imread, imwrite -from annotator.uniformer.mmcv.utils import is_str - - -def flowread(flow_or_path, quantize=False, concat_axis=0, *args, **kwargs): - """Read an optical flow map. - - Args: - flow_or_path (ndarray or str): A flow map or filepath. - quantize (bool): whether to read quantized pair, if set to True, - remaining args will be passed to :func:`dequantize_flow`. - concat_axis (int): The axis that dx and dy are concatenated, - can be either 0 or 1. Ignored if quantize is False. - - Returns: - ndarray: Optical flow represented as a (h, w, 2) numpy array - """ - if isinstance(flow_or_path, np.ndarray): - if (flow_or_path.ndim != 3) or (flow_or_path.shape[-1] != 2): - raise ValueError(f'Invalid flow with shape {flow_or_path.shape}') - return flow_or_path - elif not is_str(flow_or_path): - raise TypeError(f'"flow_or_path" must be a filename or numpy array, ' - f'not {type(flow_or_path)}') - - if not quantize: - with open(flow_or_path, 'rb') as f: - try: - header = f.read(4).decode('utf-8') - except Exception: - raise IOError(f'Invalid flow file: {flow_or_path}') - else: - if header != 'PIEH': - raise IOError(f'Invalid flow file: {flow_or_path}, ' - 'header does not contain PIEH') - - w = np.fromfile(f, np.int32, 1).squeeze() - h = np.fromfile(f, np.int32, 1).squeeze() - flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2)) - else: - assert concat_axis in [0, 1] - cat_flow = imread(flow_or_path, flag='unchanged') - if cat_flow.ndim != 2: - raise IOError( - f'{flow_or_path} is not a valid quantized flow file, ' - f'its dimension is {cat_flow.ndim}.') - assert cat_flow.shape[concat_axis] % 2 == 0 - dx, dy = np.split(cat_flow, 2, axis=concat_axis) - flow = dequantize_flow(dx, dy, *args, **kwargs) - - return flow.astype(np.float32) - - -def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs): - """Write optical flow to file. - - If the flow is not quantized, it will be saved as a .flo file losslessly, - otherwise a jpeg image which is lossy but of much smaller size. (dx and dy - will be concatenated horizontally into a single image if quantize is True.) - - Args: - flow (ndarray): (h, w, 2) array of optical flow. - filename (str): Output filepath. - quantize (bool): Whether to quantize the flow and save it to 2 jpeg - images. If set to True, remaining args will be passed to - :func:`quantize_flow`. - concat_axis (int): The axis that dx and dy are concatenated, - can be either 0 or 1. Ignored if quantize is False. - """ - if not quantize: - with open(filename, 'wb') as f: - f.write('PIEH'.encode('utf-8')) - np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f) - flow = flow.astype(np.float32) - flow.tofile(f) - f.flush() - else: - assert concat_axis in [0, 1] - dx, dy = quantize_flow(flow, *args, **kwargs) - dxdy = np.concatenate((dx, dy), axis=concat_axis) - imwrite(dxdy, filename) - - -def quantize_flow(flow, max_val=0.02, norm=True): - """Quantize flow to [0, 255]. - - After this step, the size of flow will be much smaller, and can be - dumped as jpeg images. - - Args: - flow (ndarray): (h, w, 2) array of optical flow. - max_val (float): Maximum value of flow, values beyond - [-max_val, max_val] will be truncated. - norm (bool): Whether to divide flow values by image width/height. - - Returns: - tuple[ndarray]: Quantized dx and dy. - """ - h, w, _ = flow.shape - dx = flow[..., 0] - dy = flow[..., 1] - if norm: - dx = dx / w # avoid inplace operations - dy = dy / h - # use 255 levels instead of 256 to make sure 0 is 0 after dequantization. - flow_comps = [ - quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy] - ] - return tuple(flow_comps) - - -def dequantize_flow(dx, dy, max_val=0.02, denorm=True): - """Recover from quantized flow. - - Args: - dx (ndarray): Quantized dx. - dy (ndarray): Quantized dy. - max_val (float): Maximum value used when quantizing. - denorm (bool): Whether to multiply flow values with width/height. - - Returns: - ndarray: Dequantized flow. - """ - assert dx.shape == dy.shape - assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1) - - dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]] - - if denorm: - dx *= dx.shape[1] - dy *= dx.shape[0] - flow = np.dstack((dx, dy)) - return flow - - -def flow_warp(img, flow, filling_value=0, interpolate_mode='nearest'): - """Use flow to warp img. - - Args: - img (ndarray, float or uint8): Image to be warped. - flow (ndarray, float): Optical Flow. - filling_value (int): The missing pixels will be set with filling_value. - interpolate_mode (str): bilinear -> Bilinear Interpolation; - nearest -> Nearest Neighbor. - - Returns: - ndarray: Warped image with the same shape of img - """ - warnings.warn('This function is just for prototyping and cannot ' - 'guarantee the computational efficiency.') - assert flow.ndim == 3, 'Flow must be in 3D arrays.' - height = flow.shape[0] - width = flow.shape[1] - channels = img.shape[2] - - output = np.ones( - (height, width, channels), dtype=img.dtype) * filling_value - - grid = np.indices((height, width)).swapaxes(0, 1).swapaxes(1, 2) - dx = grid[:, :, 0] + flow[:, :, 1] - dy = grid[:, :, 1] + flow[:, :, 0] - sx = np.floor(dx).astype(int) - sy = np.floor(dy).astype(int) - valid = (sx >= 0) & (sx < height - 1) & (sy >= 0) & (sy < width - 1) - - if interpolate_mode == 'nearest': - output[valid, :] = img[dx[valid].round().astype(int), - dy[valid].round().astype(int), :] - elif interpolate_mode == 'bilinear': - # dirty walkround for integer positions - eps_ = 1e-6 - dx, dy = dx + eps_, dy + eps_ - left_top_ = img[np.floor(dx[valid]).astype(int), - np.floor(dy[valid]).astype(int), :] * ( - np.ceil(dx[valid]) - dx[valid])[:, None] * ( - np.ceil(dy[valid]) - dy[valid])[:, None] - left_down_ = img[np.ceil(dx[valid]).astype(int), - np.floor(dy[valid]).astype(int), :] * ( - dx[valid] - np.floor(dx[valid]))[:, None] * ( - np.ceil(dy[valid]) - dy[valid])[:, None] - right_top_ = img[np.floor(dx[valid]).astype(int), - np.ceil(dy[valid]).astype(int), :] * ( - np.ceil(dx[valid]) - dx[valid])[:, None] * ( - dy[valid] - np.floor(dy[valid]))[:, None] - right_down_ = img[np.ceil(dx[valid]).astype(int), - np.ceil(dy[valid]).astype(int), :] * ( - dx[valid] - np.floor(dx[valid]))[:, None] * ( - dy[valid] - np.floor(dy[valid]))[:, None] - output[valid, :] = left_top_ + left_down_ + right_top_ + right_down_ - else: - raise NotImplementedError( - 'We only support interpolation modes of nearest and bilinear, ' - f'but got {interpolate_mode}.') - return output.astype(img.dtype) - - -def flow_from_bytes(content): - """Read dense optical flow from bytes. - - .. note:: - This load optical flow function works for FlyingChairs, FlyingThings3D, - Sintel, FlyingChairsOcc datasets, but cannot load the data from - ChairsSDHom. - - Args: - content (bytes): Optical flow bytes got from files or other streams. - - Returns: - ndarray: Loaded optical flow with the shape (H, W, 2). - """ - - # header in first 4 bytes - header = content[:4] - if header.decode('utf-8') != 'PIEH': - raise Exception('Flow file header does not contain PIEH') - # width in second 4 bytes - width = np.frombuffer(content[4:], np.int32, 1).squeeze() - # height in third 4 bytes - height = np.frombuffer(content[8:], np.int32, 1).squeeze() - # after first 12 bytes, all bytes are flow - flow = np.frombuffer(content[12:], np.float32, width * height * 2).reshape( - (height, width, 2)) - - return flow - - -def sparse_flow_from_bytes(content): - """Read the optical flow in KITTI datasets from bytes. - - This function is modified from RAFT load the `KITTI datasets - `_. - - Args: - content (bytes): Optical flow bytes got from files or other streams. - - Returns: - Tuple(ndarray, ndarray): Loaded optical flow with the shape (H, W, 2) - and flow valid mask with the shape (H, W). - """ # nopa - - content = np.frombuffer(content, np.uint8) - flow = cv2.imdecode(content, cv2.IMREAD_ANYDEPTH | cv2.IMREAD_COLOR) - flow = flow[:, :, ::-1].astype(np.float32) - # flow shape (H, W, 2) valid shape (H, W) - flow, valid = flow[:, :, :2], flow[:, :, 2] - flow = (flow - 2**15) / 64.0 - return flow, valid diff --git a/spaces/gotiQspiryo/whisper-ui/examples/ANDROID IMEDIA PLAYER The Top Features and Benefits of This App.md b/spaces/gotiQspiryo/whisper-ui/examples/ANDROID IMEDIA PLAYER The Top Features and Benefits of This App.md deleted file mode 100644 index 40b03087214dd82c5007c100a04f31429cbc6789..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/ANDROID IMEDIA PLAYER The Top Features and Benefits of This App.md +++ /dev/null @@ -1,22 +0,0 @@ -
-

Legal | Report Trademark Abuse
VideoLAN, VLC, VLC media player and x264 are trademarks internationally registered by the VideoLAN non-profit organization.
VideoLAN software is licensed under various open-source licenses: use and distribution are defined by each software license.

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If you're someone that has smart speakers located in different areas of your home or office, chances are, you've been in a situation where you've moved around the space, and the audio needs to be toggled to different speakers in order to continue enjoying your tunes. For the most part, you'll be doing this through your phone or maybe a tablet, and sometimes it can become tedious, especially if you need to open up the app to make these changes. Android has media output controls, giving users the option to select a device and stream music to it. The output controls even got a slight revamp with the refreshed media player built into Android 13.

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If you're a Spotify user, what you might have noticed missing from the new Android media player is that despite most features being present, there isn't the ability to toggle audio through Spotify Connect devices. Spotify Connect devices use different technology and offer benefits such as increased battery life of the connected device, higher quality playback, seamless playback when switching devices, and more. While this can currently be done from the Spotify app, being able to switch devices from the built-in Android media player will make things even more convenient, with easy access from the notification panel and lock screen.

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MediaMonkey is an excellent music player with video playing capabilities. Most of its features are music-focused. However, you can absolutely watch video content with it. Additionally, MediaMonkey has a desktop variant for PC that also lets you view music and video content and sync content between your computer and phone. It supports most music and video formats along with some extras like audiobooks and podcasts. The app also has a widget, Android Auto support, Chromecast support, a sleep timer, and some other features. This one is quite good if you want a full-featured music player with video player capabilities.

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If we missed any great media player apps for Android, tell us about them in the comments. You can also click here to check out our latest Android app and game lists.
Thank you for reading. Try these too:

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In this article, I present the architecture of the media playback infrastructure (with Stagefright as the underlying media player). My goal is to help you, an interested reader, get a grasp of how things work behind the curtains, and to help you more easily identify the part of the code base you may want to tweak or optimize. Some useful online resources already outline certain bits and pieces of the media player architecture (1 2), but the slideshow format of these descriptions omits a number of important details.

-

The architecture of the media player infrastructure, from the end-user apps to the media codecs that perform the algorithmic magic, is layered with many levels of indirection. The following diagram depicts the high-level architecture described below.

-

At the topmost layer of the architecture are the user apps that leverage media playback functionality, such as playing audio streams, ringtones, or video clips. These apps use the components from the Application Framework that implement the high-level interfaces for accessing and manipulating media content. Below this layer, things get more complicated as the Application Framework components communicate with native C++ components through JNI interfaces. These native components are essentially thin clients that forward media requests from Java code to the underlying Media Player Service via IPC calls. The Media Player Service selects the appropriate media player (in our case Stagefright), which then instantiates an appropriate media decoder, as well as fetches and manipulates the media files, manages buffers, etc. While even this high-level architecture is relatively complex, the devil is still in the details, so I will now guide you through the different subsystems.

-

Once a playback request goes through the JNI interface, the control flow and dataflow of the requests become more difficult to track inside the source code due to a lack of documentation and the intricate invocation mechanisms. The JNI interface connects the Application Framework with C++ implementations of some methods located in the folder frameworks/base/media/jni. The most frequently invoked native methods are located in android_media_MediaPlayer.cpp file, which instantiates a C++ MediaPlayer object for each new media file and routes requests from the Java MediaPlayer objects to their C++ counterparts. The implementations of these C++ classes are located under frameworks/av/media, while their interface definitions can be found in frameworks/av/include/media. If you expect that these classes implement the actual media playback functionalities, you will be disappointed to hear they are only adding yet another level of indirection, serving as thin IPC clients to the underlying lower-level media services. For example, MediaPlayer::start() does nothing more than route invocations to the IMediaPlayer interface, which then performs an IPC invocation transact(START, data, &reply) to start the playback.

-

-

The IPC requests from the Native Media Player Subsystem are in turn handled by the Media Player Service subsystem (the subsystem mostly comprises C++ classes in the frameworks/av/media/libmediaplayerservice folder). This subsystem is initialized in the main() method of frameworks/av/media/mediaserver/main_mediaserver.cpp, which includes startup of multiple Android servers such as AudioFlinger, MediaPlayerService (relevant for our discussions), and CameraService. The instantiation of the Media Player Service subsystem involves creation of a MediaPlayerService object, as well as instantiation and registration of factories for the built-in media player implementations (NuPlayer, Stagefright, Sonivox). Once up and running, MediaPlayerService will accept IPC requests from the Native Media Player subsystem and instantiate a new MediaPlayerService::Client for each request that manipulates media content. To play the media, Client has a method createPlayer that creates a low-level media player of a specified type (in our case StagefrightPlayer) using the appropriate factory. The subsequent media playback requests (e.g., pausing, resuming, and stopping) are then directly forwarded to the new instance of StagefrightPlayer.

-

StagefrightPlayer class is a thin client to the actual media player named AwesomePlayer. The Stagefright Media Player subsystem, located in the folder frameworks/av/media/libstagefright, implements the algorithmic logic (unsurprisingly, many of the files have sizes in the range of thousands of SLOC). The detailed architecture of this subsystem is depicted in the following figure.

-

While this article qualifies as a fairly lengthy blog post, I only scratched the surface of the complex media playback functionality. Thus, if you find something unclear or would like to discuss the architecture further, please do not hesitate to comment or contact us directly. Also, I would like to invite you to read a separate article that provides a set of tips & tricks for manually recovering a software architecture that uses the media player as an example.

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A new media player is in town: robust (metallic case), dual band WiFi, 4K, supports screen rotation and 2 videos in parallel. What more can I ask?
This is a robust RK3288 by Screens TV, a digital signage all-in-one company.

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Part of the motivation for media controls is that users often have multiple media apps (music player, podcasts, video player etc) and regularly switch between them. Media controls display up to five current and recent media sessions in a carousel allowing the user to swipe between them.

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The A400 is the first, and currently the best 4K Android solution in 2017 for app support combined with strong local file playback capability. It provides support for most of the latest video file formats. The A400's chipset is the very latest in technology and provides unprecedented levels of performance. It is totally different from almost all other Android media players.

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- 4K H.265 Hardware Decoding with Super STRONG 4 core CPU, 8 core GPU, 2GB RAM, 16GB Built In Storage; Best Hardware In the Market
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That's very easy to do for Android users. Android's open architecture and app marketplace make it easy replace stock video players with any one of a number of excellent video apps, allowing you to get just the features you need. Ranging from light, easy-to-use players to highly configurable powerhouses, check out the best Android video player apps you can download right now.

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\ No newline at end of file diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Calculo Conceptos Y Contextos James Stewart.pdfl.md b/spaces/gotiQspiryo/whisper-ui/examples/Calculo Conceptos Y Contextos James Stewart.pdfl.md deleted file mode 100644 index e25330ee499f9a312870120ceca2f15b72010d31..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/Calculo Conceptos Y Contextos James Stewart.pdfl.md +++ /dev/null @@ -1,6 +0,0 @@ -

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It is part from architecture / cad category and is licensed as shareware for Windows 32-bit and 64-bit platform and can be used as a free trial until the trial period will end. The ArchiCAD demo is available to all software users as a free download with potential restrictions compared with the full version.

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\ No newline at end of file diff --git a/spaces/gradio/HuBERT/fairseq/scoring/chrf.py b/spaces/gradio/HuBERT/fairseq/scoring/chrf.py deleted file mode 100644 index 0d6cb77383a44d9ac739958b79a30764f1fbf7f3..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/scoring/chrf.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from fairseq.scoring import BaseScorer, register_scorer - - -@register_scorer("chrf") -class ChrFScorer(BaseScorer): - def __init__(self, args): - super(ChrFScorer, self).__init__(args) - import sacrebleu - - self.sacrebleu = sacrebleu - - def add_string(self, ref, pred): - self.ref.append(ref) - self.pred.append(pred) - - def score(self, order=4): - return self.result_string(order).score - - def result_string(self, order=4): - if order != 4: - raise NotImplementedError - return self.sacrebleu.corpus_chrf(self.pred, [self.ref]).format() diff --git a/spaces/gradio/HuBERT/tests/test_export.py b/spaces/gradio/HuBERT/tests/test_export.py deleted file mode 100644 index b380697b9aff8799f90c1e0819e408826ecf2932..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/tests/test_export.py +++ /dev/null @@ -1,121 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import tempfile -import unittest - -import torch -from fairseq.data.dictionary import Dictionary -from fairseq.models.transformer import TransformerModel -from fairseq.modules import multihead_attention, sinusoidal_positional_embedding -from fairseq.tasks.fairseq_task import LegacyFairseqTask - - -DEFAULT_TEST_VOCAB_SIZE = 100 - - -class DummyTask(LegacyFairseqTask): - def __init__(self, args): - super().__init__(args) - self.dictionary = get_dummy_dictionary() - if getattr(self.args, "ctc", False): - self.dictionary.add_symbol("") - self.src_dict = self.dictionary - self.tgt_dict = self.dictionary - - @property - def source_dictionary(self): - return self.src_dict - - @property - def target_dictionary(self): - return self.dictionary - - -def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE): - dummy_dict = Dictionary() - # add dummy symbol to satisfy vocab size - for id, _ in enumerate(range(vocab_size)): - dummy_dict.add_symbol("{}".format(id), 1000) - return dummy_dict - - -def get_dummy_task_and_parser(): - """ - Return a dummy task and argument parser, which can be used to - create a model/criterion. - """ - parser = argparse.ArgumentParser( - description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS - ) - DummyTask.add_args(parser) - args = parser.parse_args([]) - task = DummyTask.setup_task(args) - return task, parser - - -def _test_save_and_load(scripted_module): - with tempfile.NamedTemporaryFile() as f: - scripted_module.save(f.name) - torch.jit.load(f.name) - - -class TestExportModels(unittest.TestCase): - def test_export_multihead_attention(self): - module = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2) - scripted = torch.jit.script(module) - _test_save_and_load(scripted) - - def test_incremental_state_multihead_attention(self): - module1 = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2) - module1 = torch.jit.script(module1) - module2 = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2) - module2 = torch.jit.script(module2) - - state = {} - state = module1.set_incremental_state(state, "key", {"a": torch.tensor([1])}) - state = module2.set_incremental_state(state, "key", {"a": torch.tensor([2])}) - v1 = module1.get_incremental_state(state, "key")["a"] - v2 = module2.get_incremental_state(state, "key")["a"] - - self.assertEqual(v1, 1) - self.assertEqual(v2, 2) - - def test_positional_embedding(self): - module = sinusoidal_positional_embedding.SinusoidalPositionalEmbedding( - embedding_dim=8, padding_idx=1 - ) - scripted = torch.jit.script(module) - _test_save_and_load(scripted) - - @unittest.skipIf( - torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release" - ) - def test_export_transformer(self): - task, parser = get_dummy_task_and_parser() - TransformerModel.add_args(parser) - args = parser.parse_args([]) - model = TransformerModel.build_model(args, task) - scripted = torch.jit.script(model) - _test_save_and_load(scripted) - - @unittest.skipIf( - torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release" - ) - def test_export_transformer_no_token_pos_emb(self): - task, parser = get_dummy_task_and_parser() - TransformerModel.add_args(parser) - args = parser.parse_args([]) - args.no_token_positional_embeddings = True - model = TransformerModel.build_model(args, task) - scripted = torch.jit.script(model) - _test_save_and_load(scripted) - - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/gradio/default/theme_dropdown.py b/spaces/gradio/default/theme_dropdown.py deleted file mode 100644 index 6235388fd00549553df44028f3ccf03e946994ea..0000000000000000000000000000000000000000 --- a/spaces/gradio/default/theme_dropdown.py +++ /dev/null @@ -1,57 +0,0 @@ -import os -import pathlib - -from gradio.themes.utils import ThemeAsset - - -def create_theme_dropdown(): - import gradio as gr - - asset_path = pathlib.Path(__file__).parent / "themes" - themes = [] - for theme_asset in os.listdir(str(asset_path)): - themes.append( - (ThemeAsset(theme_asset), gr.Theme.load(str(asset_path / theme_asset))) - ) - - def make_else_if(theme_asset): - return f""" - else if (theme == '{str(theme_asset[0].version)}') {{ - var theme_css = `{theme_asset[1]._get_theme_css()}` - }}""" - - head, tail = themes[0], themes[1:] - if_statement = f""" - if (theme == "{str(head[0].version)}") {{ - var theme_css = `{head[1]._get_theme_css()}` - }} {" ".join(make_else_if(t) for t in tail)} - """ - - latest_to_oldest = sorted([t[0] for t in themes], key=lambda asset: asset.version)[ - ::-1 - ] - latest_to_oldest = [str(t.version) for t in latest_to_oldest] - - component = gr.Dropdown( - choices=latest_to_oldest, - value=latest_to_oldest[0], - render=False, - label="Select Version", - ).style(container=False) - - return ( - component, - f""" - (theme) => {{ - if (!document.querySelector('.theme-css')) {{ - var theme_elem = document.createElement('style'); - theme_elem.classList.add('theme-css'); - document.head.appendChild(theme_elem); - }} else {{ - var theme_elem = document.querySelector('.theme-css'); - }} - {if_statement} - theme_elem.innerHTML = theme_css; - }} - """, - ) diff --git a/spaces/gsaivinay/Llama-2-13B-GGML-UI/CONTRIBUTING.md b/spaces/gsaivinay/Llama-2-13B-GGML-UI/CONTRIBUTING.md deleted file mode 100644 index 2fc863718e9eaa6d9d1a2f4f35c1319bd57366f9..0000000000000000000000000000000000000000 --- a/spaces/gsaivinay/Llama-2-13B-GGML-UI/CONTRIBUTING.md +++ /dev/null @@ -1,45 +0,0 @@ -# Contributing Guidelines - -**Welcome to Chatbot UI!** - -We appreciate your interest in contributing to our project. - -Before you get started, please read our guidelines for contributing. - -## Types of Contributions - -We welcome the following types of contributions: - -- Bug fixes -- New features -- Documentation improvements -- Code optimizations -- Translations -- Tests - -## Getting Started - -To get started, fork the project on GitHub and clone it locally on your machine. Then, create a new branch to work on your changes. - -``` -git clone https://github.com/mckaywrigley/chatbot-ui.git -cd chatbot-ui -git checkout -b my-branch-name - -``` - -Before submitting your pull request, please make sure your changes pass our automated tests and adhere to our code style guidelines. - -## Pull Request Process - -1. Fork the project on GitHub. -2. Clone your forked repository locally on your machine. -3. Create a new branch from the main branch. -4. Make your changes on the new branch. -5. Ensure that your changes adhere to our code style guidelines and pass our automated tests. -6. Commit your changes and push them to your forked repository. -7. Submit a pull request to the main branch of the main repository. - -## Contact - -If you have any questions or need help getting started, feel free to reach out to me on [Twitter](https://twitter.com/mckaywrigley). diff --git a/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Chat/PromptList.tsx b/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Chat/PromptList.tsx deleted file mode 100644 index 3e5ad100b11223736446b4187e600fd97f193ac7..0000000000000000000000000000000000000000 --- a/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Chat/PromptList.tsx +++ /dev/null @@ -1,45 +0,0 @@ -import { FC, MutableRefObject } from 'react'; - -import { Prompt } from '@/types/prompt'; - -interface Props { - prompts: Prompt[]; - activePromptIndex: number; - onSelect: () => void; - onMouseOver: (index: number) => void; - promptListRef: MutableRefObject; -} - -export const PromptList: FC = ({ - prompts, - activePromptIndex, - onSelect, - onMouseOver, - promptListRef, -}) => { - return ( -
    - {prompts.map((prompt, index) => ( -
  • { - e.preventDefault(); - e.stopPropagation(); - onSelect(); - }} - onMouseEnter={() => onMouseOver(index)} - > - {prompt.name} -
    • - ))} -
- ); -}; diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/build/lib/nvdiffrast/common/texture.h b/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/build/lib/nvdiffrast/common/texture.h deleted file mode 100644 index f79b600fff0256cdadd38e265b49366549434ef8..0000000000000000000000000000000000000000 --- a/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/build/lib/nvdiffrast/common/texture.h +++ /dev/null @@ -1,78 +0,0 @@ -// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#pragma once -#include "framework.h" - -//------------------------------------------------------------------------ -// Constants. - -#define TEX_DEBUG_MIP_RETAIN_VARIANCE 0 // For debugging -#define TEX_FWD_MAX_KERNEL_BLOCK_WIDTH 8 -#define TEX_FWD_MAX_KERNEL_BLOCK_HEIGHT 8 -#define TEX_FWD_MAX_MIP_KERNEL_BLOCK_WIDTH 8 -#define TEX_FWD_MAX_MIP_KERNEL_BLOCK_HEIGHT 8 -#define TEX_GRAD_MAX_KERNEL_BLOCK_WIDTH 8 -#define TEX_GRAD_MAX_KERNEL_BLOCK_HEIGHT 8 -#define TEX_GRAD_MAX_MIP_KERNEL_BLOCK_WIDTH 8 -#define TEX_GRAD_MAX_MIP_KERNEL_BLOCK_HEIGHT 8 -#define TEX_MAX_MIP_LEVEL 16 // Currently a texture cannot be larger than 2 GB because we use 32-bit indices everywhere. -#define TEX_MODE_NEAREST 0 // Nearest on base level. -#define TEX_MODE_LINEAR 1 // Bilinear on base level. -#define TEX_MODE_LINEAR_MIPMAP_NEAREST 2 // Bilinear on nearest mip level. -#define TEX_MODE_LINEAR_MIPMAP_LINEAR 3 // Trilinear. -#define TEX_MODE_COUNT 4 -#define TEX_BOUNDARY_MODE_CUBE 0 // Cube map mode. -#define TEX_BOUNDARY_MODE_WRAP 1 // Wrap (u, v). -#define TEX_BOUNDARY_MODE_CLAMP 2 // Clamp (u, v). -#define TEX_BOUNDARY_MODE_ZERO 3 // Pad with zeros. -#define TEX_BOUNDARY_MODE_COUNT 4 - -//------------------------------------------------------------------------ -// CUDA kernel params. - -struct TextureKernelParams -{ - const float* tex[TEX_MAX_MIP_LEVEL]; // Incoming texture buffer with mip levels. - const float* uv; // Incoming texcoord buffer. - const float* uvDA; // Incoming uv pixel diffs or NULL. - const float* mipLevelBias; // Incoming mip level bias or NULL. - const float* dy; // Incoming output gradient. - float* out; // Outgoing texture data. - float* gradTex[TEX_MAX_MIP_LEVEL]; // Outgoing texture gradients with mip levels. - float* gradUV; // Outgoing texcoord gradient. - float* gradUVDA; // Outgoing texcoord pixel differential gradient. - float* gradMipLevelBias; // Outgoing mip level bias gradient. - int enableMip; // If true, we have uv_da and/or mip_level_bias input(s), and a mip tensor. - int filterMode; // One of the TEX_MODE_ constants. - int boundaryMode; // One of the TEX_BOUNDARY_MODE_ contants. - int texConst; // If true, texture is known to be constant. - int mipLevelLimit; // Mip level limit coming from the op. - int channels; // Number of texture channels. - int imgWidth; // Image width. - int imgHeight; // Image height. - int texWidth; // Texture width. - int texHeight; // Texture height. - int texDepth; // Texture depth. - int n; // Minibatch size. - int mipLevelMax; // Maximum mip level index. Zero if mips disabled. - int mipLevelOut; // Mip level being calculated in builder kernel. -}; - -//------------------------------------------------------------------------ -// C++ helper function prototypes. - -void raiseMipSizeError(NVDR_CTX_ARGS, const TextureKernelParams& p); -int calculateMipInfo(NVDR_CTX_ARGS, TextureKernelParams& p, int* mipOffsets); - -//------------------------------------------------------------------------ -// Macros. - -#define mipLevelSize(p, i) make_int2(((p).texWidth >> (i)) > 1 ? ((p).texWidth >> (i)) : 1, ((p).texHeight >> (i)) > 1 ? ((p).texHeight >> (i)) : 1) - -//------------------------------------------------------------------------ diff --git a/spaces/h2oai/h2ogpt-chatbot2/src/client_test.py b/spaces/h2oai/h2ogpt-chatbot2/src/client_test.py deleted file mode 100644 index fd9477b56e3244feaab53194565abb570cb7f274..0000000000000000000000000000000000000000 --- a/spaces/h2oai/h2ogpt-chatbot2/src/client_test.py +++ /dev/null @@ -1,484 +0,0 @@ -""" -Client test. - -Run server: - -python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6_9b - -NOTE: For private models, add --use-auth_token=True - -NOTE: --use_gpu_id=True (default) must be used for multi-GPU in case see failures with cuda:x cuda:y mismatches. -Currently, this will force model to be on a single GPU. - -Then run this client as: - -python src/client_test.py - - - -For HF spaces: - -HOST="https://h2oai-h2ogpt-chatbot.hf.space" python src/client_test.py - -Result: - -Loaded as API: https://h2oai-h2ogpt-chatbot.hf.space ✔ -{'instruction_nochat': 'Who are you?', 'iinput_nochat': '', 'response': 'I am h2oGPT, a large language model developed by LAION.', 'sources': ''} - - -For demo: - -HOST="https://gpt.h2o.ai" python src/client_test.py - -Result: - -Loaded as API: https://gpt.h2o.ai ✔ -{'instruction_nochat': 'Who are you?', 'iinput_nochat': '', 'response': 'I am h2oGPT, a chatbot created by LAION.', 'sources': ''} - -NOTE: Raw output from API for nochat case is a string of a python dict and will remain so if other entries are added to dict: - -{'response': "I'm h2oGPT, a large language model by H2O.ai, the visionary leader in democratizing AI.", 'sources': ''} - - -""" -import ast -import time -import os -import markdown # pip install markdown -import pytest -from bs4 import BeautifulSoup # pip install beautifulsoup4 - -try: - from enums import DocumentSubset, LangChainAction -except: - from src.enums import DocumentSubset, LangChainAction - -from tests.utils import get_inf_server - -debug = False - -os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1' - - -def get_client(serialize=True): - from gradio_client import Client - - client = Client(get_inf_server(), serialize=serialize) - if debug: - print(client.view_api(all_endpoints=True)) - return client - - -def get_args(prompt, prompt_type=None, chat=False, stream_output=False, - max_new_tokens=50, - top_k_docs=3, - langchain_mode='Disabled', - add_chat_history_to_context=True, - langchain_action=LangChainAction.QUERY.value, - langchain_agents=[], - prompt_dict=None, - version=None, - h2ogpt_key=None, - visible_models=None, - system_prompt='', # default of no system prompt tiggered by empty string - add_search_to_context=False, - chat_conversation=None, - text_context_list=None, - ): - from collections import OrderedDict - kwargs = OrderedDict(instruction=prompt if chat else '', # only for chat=True - iinput='', # only for chat=True - context='', - # streaming output is supported, loops over and outputs each generation in streaming mode - # but leave stream_output=False for simple input/output mode - stream_output=stream_output, - prompt_type=prompt_type, - prompt_dict=prompt_dict, - temperature=0.1, - top_p=0.75, - top_k=40, - num_beams=1, - max_new_tokens=max_new_tokens, - min_new_tokens=0, - early_stopping=False, - max_time=20, - repetition_penalty=1.0, - num_return_sequences=1, - do_sample=True, - chat=chat, - instruction_nochat=prompt if not chat else '', - iinput_nochat='', # only for chat=False - langchain_mode=langchain_mode, - add_chat_history_to_context=add_chat_history_to_context, - langchain_action=langchain_action, - langchain_agents=langchain_agents, - top_k_docs=top_k_docs, - chunk=True, - chunk_size=512, - document_subset=DocumentSubset.Relevant.name, - document_choice=[], - pre_prompt_query=None, - prompt_query=None, - pre_prompt_summary=None, - prompt_summary=None, - system_prompt=system_prompt, - image_loaders=None, - pdf_loaders=None, - url_loaders=None, - jq_schema=None, - visible_models=visible_models, - h2ogpt_key=h2ogpt_key, - add_search_to_context=add_search_to_context, - chat_conversation=chat_conversation, - text_context_list=text_context_list, - docs_ordering_type=None, - min_max_new_tokens=None, - ) - diff = 0 - if version is None: - # latest - version = 1 - if version == 0: - diff = 1 - if version >= 1: - kwargs.update(dict(system_prompt=system_prompt)) - diff = 0 - - from evaluate_params import eval_func_param_names - assert len(set(eval_func_param_names).difference(set(list(kwargs.keys())))) == diff - if chat: - # add chatbot output on end. Assumes serialize=False - kwargs.update(dict(chatbot=[])) - - return kwargs, list(kwargs.values()) - - -@pytest.mark.skip(reason="For manual use against some server, no server launched") -def test_client_basic(prompt_type='human_bot', version=None, visible_models=None, prompt='Who are you?', - h2ogpt_key=None): - return run_client_nochat(prompt=prompt, prompt_type=prompt_type, max_new_tokens=50, version=version, - visible_models=visible_models, h2ogpt_key=h2ogpt_key) - - -""" -time HOST=https://gpt-internal.h2o.ai PYTHONPATH=. pytest -n 20 src/client_test.py::test_client_basic_benchmark -32 seconds to answer 20 questions at once with 70B llama2 on 4x A100 80GB using TGI 0.9.3 -""" - - -@pytest.mark.skip(reason="For manual use against some server, no server launched") -@pytest.mark.parametrize("id", range(20)) -def test_client_basic_benchmark(id, prompt_type='human_bot', version=None): - return run_client_nochat(prompt=""" -/nfs4/llm/h2ogpt/h2ogpt/bin/python /home/arno/pycharm-2022.2.2/plugins/python/helpers/pycharm/_jb_pytest_runner.py --target src/client_test.py::test_client_basic -Testing started at 8:41 AM ... -Launching pytest with arguments src/client_test.py::test_client_basic --no-header --no-summary -q in /nfs4/llm/h2ogpt - -============================= test session starts ============================== -collecting ... -src/client_test.py:None (src/client_test.py) -ImportError while importing test module '/nfs4/llm/h2ogpt/src/client_test.py'. -Hint: make sure your test modules/packages have valid Python names. -Traceback: -h2ogpt/lib/python3.10/site-packages/_pytest/python.py:618: in _importtestmodule - mod = import_path(self.path, mode=importmode, root=self.config.rootpath) -h2ogpt/lib/python3.10/site-packages/_pytest/pathlib.py:533: in import_path - importlib.import_module(module_name) -/usr/lib/python3.10/importlib/__init__.py:126: in import_module - return _bootstrap._gcd_import(name[level:], package, level) -:1050: in _gcd_import - ??? -:1027: in _find_and_load - ??? -:1006: in _find_and_load_unlocked - ??? -:688: in _load_unlocked - ??? -h2ogpt/lib/python3.10/site-packages/_pytest/assertion/rewrite.py:168: in exec_module - exec(co, module.__dict__) -src/client_test.py:51: in - from enums import DocumentSubset, LangChainAction -E ModuleNotFoundError: No module named 'enums' - - -collected 0 items / 1 error - -=============================== 1 error in 0.14s =============================== -ERROR: not found: /nfs4/llm/h2ogpt/src/client_test.py::test_client_basic -(no name '/nfs4/llm/h2ogpt/src/client_test.py::test_client_basic' in any of []) - - -Process finished with exit code 4 - -What happened? -""", prompt_type=prompt_type, max_new_tokens=100, version=version) - - -def run_client_nochat(prompt, prompt_type, max_new_tokens, version=None, h2ogpt_key=None, visible_models=None): - kwargs, args = get_args(prompt, prompt_type, chat=False, max_new_tokens=max_new_tokens, version=version, - visible_models=visible_models, h2ogpt_key=h2ogpt_key) - - api_name = '/submit_nochat' - client = get_client(serialize=True) - res = client.predict( - *tuple(args), - api_name=api_name, - ) - print("Raw client result: %s" % res, flush=True) - res_dict = dict(prompt=kwargs['instruction_nochat'], iinput=kwargs['iinput_nochat'], - response=md_to_text(res)) - print(res_dict) - return res_dict, client - - -@pytest.mark.skip(reason="For manual use against some server, no server launched") -def test_client_basic_api(prompt_type='human_bot', version=None, h2ogpt_key=None): - return run_client_nochat_api(prompt='Who are you?', prompt_type=prompt_type, max_new_tokens=50, version=version, - h2ogpt_key=h2ogpt_key) - - -def run_client_nochat_api(prompt, prompt_type, max_new_tokens, version=None, h2ogpt_key=None): - kwargs, args = get_args(prompt, prompt_type, chat=False, max_new_tokens=max_new_tokens, version=version, - h2ogpt_key=h2ogpt_key) - - api_name = '/submit_nochat_api' # NOTE: like submit_nochat but stable API for string dict passing - client = get_client(serialize=True) - res = client.predict( - str(dict(kwargs)), - api_name=api_name, - ) - print("Raw client result: %s" % res, flush=True) - res_dict = dict(prompt=kwargs['instruction_nochat'], iinput=kwargs['iinput_nochat'], - response=md_to_text(ast.literal_eval(res)['response']), - sources=ast.literal_eval(res)['sources']) - print(res_dict) - return res_dict, client - - -@pytest.mark.skip(reason="For manual use against some server, no server launched") -def test_client_basic_api_lean(prompt_type='human_bot', version=None, h2ogpt_key=None): - return run_client_nochat_api_lean(prompt='Who are you?', prompt_type=prompt_type, max_new_tokens=50, - version=version, h2ogpt_key=h2ogpt_key) - - -def run_client_nochat_api_lean(prompt, prompt_type, max_new_tokens, version=None, h2ogpt_key=None): - kwargs = dict(instruction_nochat=prompt, h2ogpt_key=h2ogpt_key) - - api_name = '/submit_nochat_api' # NOTE: like submit_nochat but stable API for string dict passing - client = get_client(serialize=True) - res = client.predict( - str(dict(kwargs)), - api_name=api_name, - ) - print("Raw client result: %s" % res, flush=True) - res_dict = dict(prompt=kwargs['instruction_nochat'], - response=md_to_text(ast.literal_eval(res)['response']), - sources=ast.literal_eval(res)['sources'], - h2ogpt_key=h2ogpt_key) - print(res_dict) - return res_dict, client - - -@pytest.mark.skip(reason="For manual use against some server, no server launched") -def test_client_basic_api_lean_morestuff(prompt_type='human_bot', version=None, h2ogpt_key=None): - return run_client_nochat_api_lean_morestuff(prompt='Who are you?', prompt_type=prompt_type, max_new_tokens=50, - version=version, h2ogpt_key=h2ogpt_key) - - -def run_client_nochat_api_lean_morestuff(prompt, prompt_type='human_bot', max_new_tokens=512, version=None, - h2ogpt_key=None): - kwargs = dict( - instruction='', - iinput='', - context='', - stream_output=False, - prompt_type=prompt_type, - temperature=0.1, - top_p=0.75, - top_k=40, - num_beams=1, - max_new_tokens=1024, - min_new_tokens=0, - early_stopping=False, - max_time=20, - repetition_penalty=1.0, - num_return_sequences=1, - do_sample=True, - chat=False, - instruction_nochat=prompt, - iinput_nochat='', - langchain_mode='Disabled', - add_chat_history_to_context=True, - langchain_action=LangChainAction.QUERY.value, - langchain_agents=[], - top_k_docs=4, - document_subset=DocumentSubset.Relevant.name, - document_choice=[], - h2ogpt_key=h2ogpt_key, - add_search_to_context=False, - ) - - api_name = '/submit_nochat_api' # NOTE: like submit_nochat but stable API for string dict passing - client = get_client(serialize=True) - res = client.predict( - str(dict(kwargs)), - api_name=api_name, - ) - print("Raw client result: %s" % res, flush=True) - res_dict = dict(prompt=kwargs['instruction_nochat'], - response=md_to_text(ast.literal_eval(res)['response']), - sources=ast.literal_eval(res)['sources'], - h2ogpt_key=h2ogpt_key) - print(res_dict) - return res_dict, client - - -@pytest.mark.skip(reason="For manual use against some server, no server launched") -def test_client_chat(prompt_type='human_bot', version=None, h2ogpt_key=None): - return run_client_chat(prompt='Who are you?', prompt_type=prompt_type, stream_output=False, max_new_tokens=50, - langchain_mode='Disabled', - langchain_action=LangChainAction.QUERY.value, - langchain_agents=[], - version=version, - h2ogpt_key=h2ogpt_key) - - -@pytest.mark.skip(reason="For manual use against some server, no server launched") -def test_client_chat_stream(prompt_type='human_bot', version=None, h2ogpt_key=None): - return run_client_chat(prompt="Tell a very long kid's story about birds.", prompt_type=prompt_type, - stream_output=True, max_new_tokens=512, - langchain_mode='Disabled', - langchain_action=LangChainAction.QUERY.value, - langchain_agents=[], - version=version, - h2ogpt_key=h2ogpt_key) - - -def run_client_chat(prompt='', - stream_output=None, - max_new_tokens=128, - langchain_mode='Disabled', - langchain_action=LangChainAction.QUERY.value, - langchain_agents=[], - prompt_type=None, prompt_dict=None, - version=None, - h2ogpt_key=None): - client = get_client(serialize=False) - - kwargs, args = get_args(prompt, prompt_type, chat=True, stream_output=stream_output, - max_new_tokens=max_new_tokens, - langchain_mode=langchain_mode, - langchain_action=langchain_action, - langchain_agents=langchain_agents, - prompt_dict=prompt_dict, - version=version, - h2ogpt_key=h2ogpt_key) - return run_client(client, prompt, args, kwargs) - - -def run_client(client, prompt, args, kwargs, do_md_to_text=True, verbose=False): - assert kwargs['chat'], "Chat mode only" - res = client.predict(*tuple(args), api_name='/instruction') - args[-1] += [res[-1]] - - res_dict = kwargs - res_dict['prompt'] = prompt - if not kwargs['stream_output']: - res = client.predict(*tuple(args), api_name='/instruction_bot') - res_dict['response'] = res[0][-1][1] - print(md_to_text(res_dict['response'], do_md_to_text=do_md_to_text)) - return res_dict, client - else: - job = client.submit(*tuple(args), api_name='/instruction_bot') - res1 = '' - while not job.done(): - outputs_list = job.communicator.job.outputs - if outputs_list: - res = job.communicator.job.outputs[-1] - res1 = res[0][-1][-1] - res1 = md_to_text(res1, do_md_to_text=do_md_to_text) - print(res1) - time.sleep(0.1) - full_outputs = job.outputs() - if verbose: - print('job.outputs: %s' % str(full_outputs)) - # ensure get ending to avoid race - # -1 means last response if streaming - # 0 means get text_output, ignore exception_text - # 0 means get list within text_output that looks like [[prompt], [answer]] - # 1 means get bot answer, so will have last bot answer - res_dict['response'] = md_to_text(full_outputs[-1][0][0][1], do_md_to_text=do_md_to_text) - return res_dict, client - - -@pytest.mark.skip(reason="For manual use against some server, no server launched") -def test_client_nochat_stream(prompt_type='human_bot', version=None, h2ogpt_key=None): - return run_client_nochat_gen(prompt="Tell a very long kid's story about birds.", prompt_type=prompt_type, - stream_output=True, max_new_tokens=512, - langchain_mode='Disabled', - langchain_action=LangChainAction.QUERY.value, - langchain_agents=[], - version=version, - h2ogpt_key=h2ogpt_key) - - -def run_client_nochat_gen(prompt, prompt_type, stream_output, max_new_tokens, - langchain_mode, langchain_action, langchain_agents, version=None, - h2ogpt_key=None): - client = get_client(serialize=False) - - kwargs, args = get_args(prompt, prompt_type, chat=False, stream_output=stream_output, - max_new_tokens=max_new_tokens, langchain_mode=langchain_mode, - langchain_action=langchain_action, langchain_agents=langchain_agents, - version=version, h2ogpt_key=h2ogpt_key) - return run_client_gen(client, prompt, args, kwargs) - - -def run_client_gen(client, prompt, args, kwargs, do_md_to_text=True, verbose=False): - res_dict = kwargs - res_dict['prompt'] = prompt - if not kwargs['stream_output']: - res = client.predict(str(dict(kwargs)), api_name='/submit_nochat_api') - res_dict.update(ast.literal_eval(res)) - print(md_to_text(res_dict['response'], do_md_to_text=do_md_to_text)) - return res_dict, client - else: - job = client.submit(str(dict(kwargs)), api_name='/submit_nochat_api') - while not job.done(): - outputs_list = job.communicator.job.outputs - if outputs_list: - res = job.communicator.job.outputs[-1] - res_dict = ast.literal_eval(res) - print('Stream: %s' % res_dict['response']) - time.sleep(0.1) - res_list = job.outputs() - assert len(res_list) > 0, "No response, check server" - res = res_list[-1] - res_dict = ast.literal_eval(res) - print('Final: %s' % res_dict['response']) - return res_dict, client - - -def md_to_text(md, do_md_to_text=True): - if not do_md_to_text: - return md - assert md is not None, "Markdown is None" - html = markdown.markdown(md) - soup = BeautifulSoup(html, features='html.parser') - return soup.get_text() - - -def run_client_many(prompt_type='human_bot', version=None, h2ogpt_key=None): - kwargs = dict(prompt_type=prompt_type, version=version, h2ogpt_key=h2ogpt_key) - ret1, _ = test_client_chat(**kwargs) - ret2, _ = test_client_chat_stream(**kwargs) - ret3, _ = test_client_nochat_stream(**kwargs) - ret4, _ = test_client_basic(**kwargs) - ret5, _ = test_client_basic_api(**kwargs) - ret6, _ = test_client_basic_api_lean(**kwargs) - ret7, _ = test_client_basic_api_lean_morestuff(**kwargs) - return ret1, ret2, ret3, ret4, ret5, ret6, ret7 - - -if __name__ == '__main__': - run_client_many() diff --git a/spaces/h2oai/wave-tour/examples/chatbot_events_scroll.py b/spaces/h2oai/wave-tour/examples/chatbot_events_scroll.py deleted file mode 100644 index 5da4ca2af1880f0ac263da87ab4a636328ede1d8..0000000000000000000000000000000000000000 --- a/spaces/h2oai/wave-tour/examples/chatbot_events_scroll.py +++ /dev/null @@ -1,51 +0,0 @@ -# Chatbot / Events/ Scroll -# Infinite scroll for previous messages. -# #chatbot #infinite #scroll -# --- -from h2o_wave import main, app, Q, ui, data - -prev_messages = [{'content': f'Message {i}', 'from_user': i % 2 == 0} for i in range(100)] -LOAD_SIZE = 10 - - -@app('/demo') -async def serve(q: Q): - if not q.client.initialized: - q.client.current_load_page = len(prev_messages) - # Use list buffer to allow easy streaming. Must have exactly 2 fields - msg and from_user. - q.page['example'] = ui.chatbot_card( - box='1 1 5 5', - data=data(fields='content from_user', t='list', rows=[ - ['Hello', True], - ['Hi', False], - ['Hello', True], - ['Hi', False], - ['Hello', True], - ['Hi', False], - ['Hello', True], - ['Hi', False], - ]), - events=['scroll_up'], - name='chatbot' - ) - q.client.initialized = True - - # A new message arrived. - if q.args.chatbot: - # Append user message. - q.page['example'].data += [q.args.chatbot, True] - # Append bot response. - q.page['example'].data += ['I am a fake chatbot. Sorry, I cannot help you.', False] - - # User scrolled up, load chat history. - if q.events.chatbot and q.events.chatbot.scroll_up: - if q.client.current_load_page == 0: - # If we reached the end, signal it to Wave by setting prev_items to empty list. - q.page['example'].prev_items = [] - else: - end = q.client.current_load_page - LOAD_SIZE - q.page['example'].prev_items = prev_messages[end:q.client.current_load_page] - q.client.current_load_page = end - await q.sleep(0.5) # Simulate network latency. - - await q.page.save() diff --git a/spaces/h2oai/wave-tour/examples/combobox_trigger.py b/spaces/h2oai/wave-tour/examples/combobox_trigger.py deleted file mode 100644 index d73c5fa17a4c07ed3bb07e609409cfba96505986..0000000000000000000000000000000000000000 --- a/spaces/h2oai/wave-tour/examples/combobox_trigger.py +++ /dev/null @@ -1,26 +0,0 @@ -# Form / Combobox / Trigger -# To handle live changes to a #combobox, enable the `trigger` attribute. -# #combobox #trigger -# --- -from h2o_wave import main, app, Q, ui - -combobox_choices = ['Cyan', 'Magenta', 'Yellow', 'Black'] - - -def get_form_items(choice: str): - return [ - ui.combobox(name='combobox', trigger=True, label='Enter or choose a color', placeholder='Color...', value='Blue', - choices=combobox_choices), - ui.label('Sent to server'), - ui.text(choice), - ] - - -@app('/demo') -async def serve(q: Q): - if not q.client.initialized: - q.page['example'] = ui.form_card(box='1 1 4 10', items=get_form_items('')) - q.client.initialized = True - if q.args.combobox is not None: - q.page['example'].items = get_form_items(q.args.combobox) - await q.page.save() diff --git a/spaces/haakohu/deep_privacy2/sg3_torch_utils/misc.py b/spaces/haakohu/deep_privacy2/sg3_torch_utils/misc.py deleted file mode 100644 index 10d8e31880affdd185580b6f5b98e92c79597dc3..0000000000000000000000000000000000000000 --- a/spaces/haakohu/deep_privacy2/sg3_torch_utils/misc.py +++ /dev/null @@ -1,172 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -import re -import contextlib -import numpy as np -import torch -import warnings - -#---------------------------------------------------------------------------- -# Cached construction of constant tensors. Avoids CPU=>GPU copy when the -# same constant is used multiple times. - -_constant_cache = dict() - -def constant(value, shape=None, dtype=None, device=None, memory_format=None): - value = np.asarray(value) - if shape is not None: - shape = tuple(shape) - if dtype is None: - dtype = torch.get_default_dtype() - if device is None: - device = torch.device('cpu') - if memory_format is None: - memory_format = torch.contiguous_format - - key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format) - tensor = _constant_cache.get(key, None) - if tensor is None: - tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device) - if shape is not None: - tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape)) - tensor = tensor.contiguous(memory_format=memory_format) - _constant_cache[key] = tensor - return tensor - -#---------------------------------------------------------------------------- -# Replace NaN/Inf with specified numerical values. - -try: - nan_to_num = torch.nan_to_num # 1.8.0a0 -except AttributeError: - def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin - assert isinstance(input, torch.Tensor) - if posinf is None: - posinf = torch.finfo(input.dtype).max - if neginf is None: - neginf = torch.finfo(input.dtype).min - assert nan == 0 - return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out) - -#---------------------------------------------------------------------------- -# Symbolic assert. - -try: - symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access -except AttributeError: - symbolic_assert = torch.Assert # 1.7.0 - -#---------------------------------------------------------------------------- -# Context manager to suppress known warnings in torch.jit.trace(). - -class suppress_tracer_warnings(warnings.catch_warnings): - def __enter__(self): - super().__enter__() - warnings.simplefilter('ignore', category=torch.jit.TracerWarning) - return self - -#---------------------------------------------------------------------------- -# Assert that the shape of a tensor matches the given list of integers. -# None indicates that the size of a dimension is allowed to vary. -# Performs symbolic assertion when used in torch.jit.trace(). - -def assert_shape(tensor, ref_shape): - if tensor.ndim != len(ref_shape): - raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}') - for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)): - if ref_size is None: - pass - elif isinstance(ref_size, torch.Tensor): - with suppress_tracer_warnings(): # as_tensor results are registered as constants - symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}') - elif isinstance(size, torch.Tensor): - with suppress_tracer_warnings(): # as_tensor results are registered as constants - symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}') - elif size != ref_size: - raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}') - -#---------------------------------------------------------------------------- -# Function decorator that calls torch.autograd.profiler.record_function(). - -def profiled_function(fn): - def decorator(*args, **kwargs): - with torch.autograd.profiler.record_function(fn.__name__): - return fn(*args, **kwargs) - decorator.__name__ = fn.__name__ - return decorator - -#---------------------------------------------------------------------------- -# Sampler for torch.utils.data.DataLoader that loops over the dataset -# indefinitely, shuffling items as it goes. - -class InfiniteSampler(torch.utils.data.Sampler): - def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5): - assert len(dataset) > 0 - assert num_replicas > 0 - assert 0 <= rank < num_replicas - assert 0 <= window_size <= 1 - super().__init__(dataset) - self.dataset = dataset - self.rank = rank - self.num_replicas = num_replicas - self.shuffle = shuffle - self.seed = seed - self.window_size = window_size - - def __iter__(self): - order = np.arange(len(self.dataset)) - rnd = None - window = 0 - if self.shuffle: - rnd = np.random.RandomState(self.seed) - rnd.shuffle(order) - window = int(np.rint(order.size * self.window_size)) - - idx = 0 - while True: - i = idx % order.size - if idx % self.num_replicas == self.rank: - yield order[i] - if window >= 2: - j = (i - rnd.randint(window)) % order.size - order[i], order[j] = order[j], order[i] - idx += 1 - -#---------------------------------------------------------------------------- -# Utilities for operating with torch.nn.Module parameters and buffers. - -def params_and_buffers(module): - assert isinstance(module, torch.nn.Module) - return list(module.parameters()) + list(module.buffers()) - -def named_params_and_buffers(module): - assert isinstance(module, torch.nn.Module) - return list(module.named_parameters()) + list(module.named_buffers()) - -def copy_params_and_buffers(src_module, dst_module, require_all=False): - assert isinstance(src_module, torch.nn.Module) - assert isinstance(dst_module, torch.nn.Module) - src_tensors = {name: tensor for name, tensor in named_params_and_buffers(src_module)} - for name, tensor in named_params_and_buffers(dst_module): - assert (name in src_tensors) or (not require_all) - if name in src_tensors: - tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad) - -#---------------------------------------------------------------------------- -# Context manager for easily enabling/disabling DistributedDataParallel -# synchronization. - -@contextlib.contextmanager -def ddp_sync(module, sync): - assert isinstance(module, torch.nn.Module) - if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel): - yield - else: - with module.no_sync(): - yield diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/voc.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/voc.py deleted file mode 100644 index 459985bd12a47ffe5a246cbf8e00b7930b991a1c..0000000000000000000000000000000000000000 --- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/voc.py +++ /dev/null @@ -1,134 +0,0 @@ -import os - -import torch -import torch.utils.data -from PIL import Image -import sys - -if sys.version_info[0] == 2: - import xml.etree.cElementTree as ET -else: - import xml.etree.ElementTree as ET - - -from maskrcnn_benchmark.structures.bounding_box import BoxList - - -class PascalVOCDataset(torch.utils.data.Dataset): - - CLASSES = ( - "__background__ ", - "aeroplane", - "bicycle", - "bird", - "boat", - "bottle", - "bus", - "car", - "cat", - "chair", - "cow", - "diningtable", - "dog", - "horse", - "motorbike", - "person", - "pottedplant", - "sheep", - "sofa", - "train", - "tvmonitor", - ) - - def __init__(self, data_dir, split, use_difficult=False, transforms=None): - self.root = data_dir - self.image_set = split - self.keep_difficult = use_difficult - self.transforms = transforms - - self._annopath = os.path.join(self.root, "Annotations", "%s.xml") - self._imgpath = os.path.join(self.root, "JPEGImages", "%s.jpg") - self._imgsetpath = os.path.join(self.root, "ImageSets", "Main", "%s.txt") - - with open(self._imgsetpath % self.image_set) as f: - self.ids = f.readlines() - self.ids = [x.strip("\n") for x in self.ids] - self.id_to_img_map = {k: v for k, v in enumerate(self.ids)} - - cls = PascalVOCDataset.CLASSES - self.class_to_ind = dict(zip(cls, range(len(cls)))) - - def __getitem__(self, index): - img_id = self.ids[index] - img = Image.open(self._imgpath % img_id).convert("RGB") - - target = self.get_groundtruth(index) - target = target.clip_to_image(remove_empty=True) - - if self.transforms is not None: - img, target = self.transforms(img, target) - - return img, target, index - - def __len__(self): - return len(self.ids) - - def get_groundtruth(self, index): - img_id = self.ids[index] - anno = ET.parse(self._annopath % img_id).getroot() - anno = self._preprocess_annotation(anno) - - height, width = anno["im_info"] - target = BoxList(anno["boxes"], (width, height), mode="xyxy") - target.add_field("labels", anno["labels"]) - target.add_field("difficult", anno["difficult"]) - return target - - def _preprocess_annotation(self, target): - boxes = [] - gt_classes = [] - difficult_boxes = [] - TO_REMOVE = 1 - - for obj in target.iter("object"): - difficult = int(obj.find("difficult").text) == 1 - if not self.keep_difficult and difficult: - continue - name = obj.find("name").text.lower().strip() - bb = obj.find("bndbox") - # Make pixel indexes 0-based - # Refer to "https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/pascal_voc.py#L208-L211" - box = [ - bb.find("xmin").text, - bb.find("ymin").text, - bb.find("xmax").text, - bb.find("ymax").text, - ] - bndbox = tuple( - map(lambda x: x - TO_REMOVE, list(map(int, box))) - ) - - boxes.append(bndbox) - gt_classes.append(self.class_to_ind[name]) - difficult_boxes.append(difficult) - - size = target.find("size") - im_info = tuple(map(int, (size.find("height").text, size.find("width").text))) - - res = { - "boxes": torch.tensor(boxes, dtype=torch.float32), - "labels": torch.tensor(gt_classes), - "difficult": torch.tensor(difficult_boxes), - "im_info": im_info, - } - return res - - def get_img_info(self, index): - img_id = self.ids[index] - anno = ET.parse(self._annopath % img_id).getroot() - size = anno.find("size") - im_info = tuple(map(int, (size.find("height").text, size.find("width").text))) - return {"height": im_info[0], "width": im_info[1]} - - def map_class_id_to_class_name(self, class_id): - return PascalVOCDataset.CLASSES[class_id] diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/detector/generalized_vl_rcnn.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/detector/generalized_vl_rcnn.py deleted file mode 100644 index f7d19ead5dc8f02f7128c97d00da0e85f37aa19e..0000000000000000000000000000000000000000 --- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/detector/generalized_vl_rcnn.py +++ /dev/null @@ -1,466 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. -""" -Implements the Generalized VL R-CNN framework -""" - -import torch -from torch import nn -import torch.nn.functional as F - -from maskrcnn_benchmark.structures.image_list import to_image_list -from maskrcnn_benchmark.structures.bounding_box import BoxList -from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist - -from ..backbone import build_backbone -from ..rpn import build_rpn -from ..roi_heads import build_roi_heads - -from ..language_backbone import build_language_backbone -from transformers import AutoTokenizer - -import random -import timeit -import pdb -from copy import deepcopy - -def random_word(input_ids, mask_token_id, vocabs, padding_token_id, greenlight_map): - """ - greenlight_map, batch_size x 256 (seq_len): - 0 means this location cannot be calculated in the MLM loss - -1 means this location cannot be masked!! - 1 means this location can be masked and can be calculated in the MLM loss - """ - output_label = deepcopy(input_ids) - for j in range(input_ids.size(0)): - for i in range(input_ids.size(1)): - prob = random.random() - # mask token with probability - ratio = 0.15 - if greenlight_map is not None and greenlight_map[j,i] == -1: - output_label[j,i] = -100 - continue - - if (not input_ids[j,i] == padding_token_id) and prob < ratio: - prob /= ratio - - # 80% randomly change token to mask token - if prob < 0.8: - input_ids[j,i] = mask_token_id - - # 10% randomly change token to random token - elif prob < 0.9: - input_ids[j,i] = random.choice(vocabs) - - else: - # no masking token (will be ignored by loss function later) - output_label[j,i] = -100 - - if greenlight_map is not None and greenlight_map[j,i] != 1: - output_label[j,i] = -100 # If this location should not be masked - return input_ids, output_label - - -class GeneralizedVLRCNN(nn.Module): - """ - Main class for Generalized R-CNN. Currently supports boxes and masks. - It consists of three main parts: - - backbone - - rpn - - heads: takes the features + the proposals from the RPN and computes - detections / masks from it. - """ - - def __init__(self, cfg): - super(GeneralizedVLRCNN, self).__init__() - self.cfg = cfg - - # visual encoder - self.backbone = build_backbone(cfg) - - # language encoder - if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip": - # self.tokenizer = build_tokenizer("clip") - from transformers import CLIPTokenizerFast - if cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS: - print("Reuse token 'ðŁĴij' (token_id = 49404) for mask token!") - self.tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", - from_slow=True, mask_token='ðŁĴij') - else: - self.tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", - from_slow=True) - else: - self.tokenizer = AutoTokenizer.from_pretrained(cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE) - self.tokenizer_vocab = self.tokenizer.get_vocab() - self.tokenizer_vocab_ids = [item for key, item in self.tokenizer_vocab.items()] - - self.language_backbone = build_language_backbone(cfg) - - self.rpn = build_rpn(cfg) - self.roi_heads = build_roi_heads(cfg) - self.DEBUG = cfg.MODEL.DEBUG - - self.freeze_backbone = cfg.MODEL.BACKBONE.FREEZE - self.freeze_fpn = cfg.MODEL.FPN.FREEZE - self.freeze_rpn = cfg.MODEL.RPN.FREEZE - self.add_linear_layer = cfg.MODEL.DYHEAD.FUSE_CONFIG.ADD_LINEAR_LAYER - - self.force_boxes = cfg.MODEL.RPN.FORCE_BOXES - - if cfg.MODEL.LINEAR_PROB: - assert cfg.MODEL.BACKBONE.FREEZE, "For linear probing, backbone should be frozen!" - if hasattr(self.backbone, 'fpn'): - assert cfg.MODEL.FPN.FREEZE, "For linear probing, FPN should be frozen!" - self.linear_prob = cfg.MODEL.LINEAR_PROB - self.freeze_cls_logits = cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS - if cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS: - # disable cls_logits - if hasattr(self.rpn.head, 'cls_logits'): - for p in self.rpn.head.cls_logits.parameters(): - p.requires_grad = False - - self.freeze_language_backbone = self.cfg.MODEL.LANGUAGE_BACKBONE.FREEZE - if self.cfg.MODEL.LANGUAGE_BACKBONE.FREEZE: - for p in self.language_backbone.parameters(): - p.requires_grad = False - - self.use_mlm_loss = cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS - self.mlm_loss_for_only_positives = cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS_FOR_ONLY_POSITIVES - - if self.cfg.GLIPKNOW.KNOWLEDGE_FILE: - from maskrcnn_benchmark.data.datasets.tsv import load_from_yaml_file - self.class_name_to_knowledge = load_from_yaml_file(self.cfg.GLIPKNOW.KNOWLEDGE_FILE) - self.class_name_list = sorted([k for k in self.class_name_to_knowledge]) - - def train(self, mode=True): - """Convert the model into training mode while keep layers freezed.""" - super(GeneralizedVLRCNN, self).train(mode) - if self.freeze_backbone: - self.backbone.body.eval() - for p in self.backbone.body.parameters(): - p.requires_grad = False - if self.freeze_fpn: - self.backbone.fpn.eval() - for p in self.backbone.fpn.parameters(): - p.requires_grad = False - if self.freeze_rpn: - if hasattr(self.rpn, 'head'): - self.rpn.head.eval() - for p in self.rpn.parameters(): - p.requires_grad = False - if self.linear_prob: - if self.rpn is not None: - for key, value in self.rpn.named_parameters(): - if not ('bbox_pred' in key or 'cls_logits' in key or 'centerness' in key or 'cosine_scale' in key or 'dot_product_projection_text' in key or 'head.log_scale' in key or 'head.bias_lang' in key or 'head.bias0' in key): - value.requires_grad = False - if self.roi_heads is not None: - for key, value in self.roi_heads.named_parameters(): - if not ('bbox_pred' in key or 'cls_logits' in key or 'centerness' in key or 'cosine_scale' in key or 'dot_product_projection_text' in key or 'head.log_scale' in key or 'head.bias_lang' in key or 'head.bias0' in key): - value.requires_grad = False - if self.freeze_cls_logits: - if hasattr(self.rpn.head, 'cls_logits'): - self.rpn.head.cls_logits.eval() - for p in self.rpn.head.cls_logits.parameters(): - p.requires_grad = False - if self.add_linear_layer: - if self.rpn is not None: - for key, p in self.rpn.named_parameters(): - if 'tunable_linear' in key: - p.requires_grad = True - - if self.freeze_language_backbone: - self.language_backbone.eval() - for p in self.language_backbone.parameters(): - p.requires_grad = False - - def forward(self, - images, - targets=None, - captions=None, - positive_map=None, - greenlight_map=None): - """ - Arguments: - images (list[Tensor] or ImageList): images to be processed - targets (list[BoxList]): ground-truth boxes present in the image (optional) - - mask_black_list: batch x 256, indicates whether or not a certain token is maskable or not - - Returns: - result (list[BoxList] or dict[Tensor]): the output from the model. - During training, it returns a dict[Tensor] which contains the losses. - During testing, it returns list[BoxList] contains additional fields - like `scores`, `labels` and `mask` (for Mask R-CNN models). - - """ - if self.training and targets is None: - raise ValueError("In training mode, targets should be passed") - - images = to_image_list(images) - # batch_size = images.tensors.shape[0] - device = images.tensors.device - - - if self.cfg.GLIPKNOW.PARALLEL_LANGUAGE_INPUT: - language_dict_features, positive_map = self._forward_language_parallel( - captions=captions, targets=targets, device=device, - positive_map=positive_map) - else: - # language embedding - language_dict_features = {} - if captions is not None: - #print(captions[0]) - tokenized = self.tokenizer.batch_encode_plus(captions, - max_length=self.cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN, - padding='max_length' if self.cfg.MODEL.LANGUAGE_BACKBONE.PAD_MAX else "longest", - return_special_tokens_mask=True, - return_tensors='pt', - truncation=True).to(device) - if self.use_mlm_loss: - if not self.mlm_loss_for_only_positives: - greenlight_map = None - input_ids, mlm_labels = random_word( - input_ids=tokenized.input_ids, - mask_token_id=self.tokenizer.mask_token_id, - vocabs=self.tokenizer_vocab_ids, - padding_token_id=self.tokenizer.pad_token_id, - greenlight_map=greenlight_map) - else: - input_ids = tokenized.input_ids - mlm_labels = None - - - tokenizer_input = {"input_ids": input_ids, - "attention_mask": tokenized.attention_mask} - - if self.cfg.MODEL.LANGUAGE_BACKBONE.FREEZE: - with torch.no_grad(): - language_dict_features = self.language_backbone(tokenizer_input) - else: - language_dict_features = self.language_backbone(tokenizer_input) - - # ONE HOT - if self.cfg.DATASETS.ONE_HOT: - new_masks = torch.zeros_like(language_dict_features['masks'], - device=language_dict_features['masks'].device) - new_masks[:, :self.cfg.MODEL.DYHEAD.NUM_CLASSES] = 1 - language_dict_features['masks'] = new_masks - - # MASK ALL SPECIAL TOKENS - if self.cfg.MODEL.LANGUAGE_BACKBONE.MASK_SPECIAL: - language_dict_features["masks"] = 1 - tokenized.special_tokens_mask - - language_dict_features["mlm_labels"] = mlm_labels - - # visual embedding - swint_feature_c4 = None - if 'vl' in self.cfg.MODEL.SWINT.VERSION: - # the backbone only updates the "hidden" field in language_dict_features - inputs = {"img": images.tensors, "lang": language_dict_features} - visual_features, language_dict_features, swint_feature_c4 = self.backbone(inputs) - else: - visual_features = self.backbone(images.tensors) - - # rpn force boxes - if targets: - targets = [target.to(device) - for target in targets if target is not None] - - if self.force_boxes: - proposals = [] - for t in targets: - tb = t.copy_with_fields(["labels"]) - tb.add_field("scores", torch.ones(tb.bbox.shape[0], dtype=torch.bool, device=tb.bbox.device)) - proposals.append(tb) - if self.cfg.MODEL.RPN.RETURN_FUSED_FEATURES: - _, proposal_losses, fused_visual_features = self.rpn( - images, visual_features, targets, language_dict_features, - positive_map, captions, swint_feature_c4) - elif self.training: - null_loss = 0 - for key, param in self.rpn.named_parameters(): - null_loss += 0.0 * param.sum() - proposal_losses = {('rpn_null_loss', null_loss)} - else: - proposals, proposal_losses, fused_visual_features = self.rpn(images, visual_features, targets, language_dict_features, positive_map, - captions, swint_feature_c4) - if self.roi_heads: - if self.cfg.MODEL.ROI_MASK_HEAD.PREDICTOR.startswith("VL"): - if self.training: - # "Only support VL mask head right now!!" - assert len(targets) == 1 and len(targets[0]) == len(positive_map), "shape match assert for mask head!!" - # Not necessary but as a safe guard: - # use the binary 0/1 positive map to replace the normalized positive map - targets[0].add_field("positive_map", positive_map) - # TODO: make sure that this use of language_dict_features is correct!! Its content should be changed in self.rpn - if self.cfg.MODEL.RPN.RETURN_FUSED_FEATURES: - x, result, detector_losses = self.roi_heads( - fused_visual_features, proposals, targets, - language_dict_features=language_dict_features, - positive_map_label_to_token=positive_map if not self.training else None - ) - else: - x, result, detector_losses = self.roi_heads( - visual_features, proposals, targets, - language_dict_features=language_dict_features, - positive_map_label_to_token=positive_map if not self.training else None - ) - else: - # RPN-only models don't have roi_heads - x = visual_features - result = proposals - detector_losses = {} - - if self.training: - losses = {} - losses.update(detector_losses) - losses.update(proposal_losses) - return losses - - return result - - def _forward_language_parallel(self, captions=None, targets=None, - device=None, positive_map=None): - ktype = self.cfg.GLIPKNOW.KNOWLEDGE_TYPE - def _construct_captions_from_class_names(class_names): - captions = [] - for c in class_names: - try: - info = self.class_name_to_knowledge[c] - cap = info['clean_name'] - - # combine wiki and gpt3 knowledge - if self.cfg.GLIPKNOW.WIKI_AND_GPT3: - ktype = 'def_wiki' - know_seq = info[ktype] - - ktype = 'gpt3' - if ktype == 'gpt3' or type(info[ktype]) == list: - know_seq += ' '.join([seq for seq in info[ktype][:self.cfg.GLIPKNOW.GPT3_NUM] ]) - - cap += ': ' + know_seq - - # only one knoweldge source is used - else: - if ktype and ktype in info and info[ktype]: - if ktype == 'gpt3' or type(info[ktype]) == list: - know_seq = ' '.join([seq for seq in info[ktype][:self.cfg.GLIPKNOW.GPT3_NUM] ]) - else: - know_seq = info[ktype] - cap += ': ' + know_seq - except: - cap = c - print(f'cap {cap}, c {c}') - - - captions.append(cap) - return captions - - if self.training: - assert captions is None - assert targets is not None - - max_classes_per_batch = self.cfg.GLIPKNOW.MAX_NUM_CLASSES_PER_BATCH_TRAIN - if max_classes_per_batch >= len(self.class_name_list): - shuffled_class_names = self.class_name_list.copy() - random.shuffle(shuffled_class_names) - if max_classes_per_batch > len(shuffled_class_names): - shuffled_class_names.extend(shuffled_class_names[:max_classes_per_batch - -len(shuffled_class_names)]) - random.shuffle(shuffled_class_names) - else: - label_list = [] - label_to_idx = {} - for target_per_im in targets: - labels_per_im = target_per_im.get_field('label_names') - for label in labels_per_im: - if label not in label_to_idx: - label_to_idx[label] = len(label_list) - label_list.append(label) - - label_list = label_list[:max_classes_per_batch] - if len(label_list) < max_classes_per_batch: - all_neg_classes = [c for c in self.class_name_list if c not - in label_to_idx] - neg_label_list = random.sample(all_neg_classes, - max_classes_per_batch - len(label_list)) - label_list.extend(neg_label_list) - random.shuffle(label_list) - shuffled_class_names = label_list - - label_to_shuffled_idx = {l: i for i, l in - enumerate(shuffled_class_names)} - total_boxes = sum(len(t) for t in targets) - positive_map = torch.zeros((total_boxes, max_classes_per_batch+1), - device=device) - offset = 0 - for target_per_im in targets: - labels_per_im = target_per_im.get_field('label_names') - for label in labels_per_im: - j = label_to_shuffled_idx.get(label, -1) - if j >= 0: - positive_map[offset, j] = 1 - offset += 1 - captions = _construct_captions_from_class_names(shuffled_class_names) - captions.append('') # onobj at the end, onedet/modeling/rpn/loss.py:719 - batch_size = len(targets) - - else: - assert captions is not None - batch_size = 1 - assert len(captions) == 1 - class_names = captions[0] - max_classes_per_batch = len(class_names) - captions = _construct_captions_from_class_names(class_names) - captions.append('') # onobj at the end, onedet/modeling/rpn/loss.py:719 - - tokenized = self.tokenizer.batch_encode_plus(captions, - max_length=self.cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN, - padding="longest", - return_special_tokens_mask=True, - return_tensors='pt', - truncation=True).to(device) - assert not self.use_mlm_loss - tokenizer_input = {"input_ids": tokenized.input_ids, - "attention_mask": tokenized.attention_mask} - - if self.cfg.MODEL.LANGUAGE_BACKBONE.FREEZE: - with torch.no_grad(): - language_dict_features = self.language_backbone(tokenizer_input) - else: - language_dict_features = self.language_backbone(tokenizer_input) - - assert not self.cfg.DATASETS.ONE_HOT - assert not self.cfg.MODEL.LANGUAGE_BACKBONE.MASK_SPECIAL - - agg_type = self.cfg.GLIPKNOW.LAN_FEATURE_AGG_TYPE - agg_feats = language_dict_features['hidden'] - agg_emb = language_dict_features['embedded'] - if agg_type == 'first': - agg_feats = agg_feats[:, 0, :] - agg_emb = agg_emb[:, 0, :] - elif agg_type == 'mean': - attn_mask = language_dict_features['masks'] - seq_len = attn_mask.sum(-1).unsqueeze(-1).float() - agg_feats = agg_feats * attn_mask.unsqueeze(-1).float() - agg_feats = agg_feats.sum(1) / seq_len - agg_emb = agg_emb * attn_mask.unsqueeze(-1).float() - agg_emb = agg_emb.sum(1) / seq_len - else: - raise ValueError('not supported GLIPKNOW.LAN_FEATURE_AGG_TYPE: {}'.format(agg_type)) - - expanded_features = agg_feats.unsqueeze(0).repeat(batch_size, 1, 1) - expanded_embedding = agg_emb.unsqueeze(0).repeat(batch_size, 1, 1) - - lang_dict = {} - lang_dict["mlm_labels"] = None - lang_dict["aggregate"] = None - lang_dict["embedded"] = expanded_embedding - lang_dict['hidden'] = expanded_features - lang_dict["masks"] = torch.ones((batch_size, max_classes_per_batch+1), - device=device, dtype=language_dict_features['masks'].dtype) - # in GLIP setting, the token at the end of seqence is usually [PAD], and is masked out - # if [noobj] is not masked out, the loss sum is very big, as most - # anchors are matched to [noobj] - lang_dict["masks"][:,-1] = 0 - return lang_dict, positive_map - diff --git a/spaces/hasibzunair/fifa-tryon-demo/u2net_test.py b/spaces/hasibzunair/fifa-tryon-demo/u2net_test.py deleted file mode 100644 index 040d6f90d96bc544ad363f221936898a3ba1a708..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/u2net_test.py +++ /dev/null @@ -1,122 +0,0 @@ -import os -from skimage import io, transform -import torch -import torchvision -from torch.autograd import Variable -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.data import Dataset, DataLoader -from torchvision import transforms#, utils -# import torch.optim as optim - -import numpy as np -from PIL import Image -import glob - -from data_loader import RescaleT -from data_loader import ToTensor -from data_loader import ToTensorLab -from data_loader import SalObjDataset - -from model import U2NET # full size version 173.6 MB -from model import U2NETP # small version u2net 4.7 MB - -# normalize the predicted SOD probability map -def normPRED(d): - ma = torch.max(d) - mi = torch.min(d) - - dn = (d-mi)/(ma-mi) - - return dn - -def save_output(image_name,pred,d_dir): - - predict = pred - predict = predict.squeeze() - predict_np = predict.cpu().data.numpy() - - im = Image.fromarray(predict_np*255).convert('RGB') - img_name = image_name.split(os.sep)[-1] - image = io.imread(image_name) - imo = im.resize((image.shape[1],image.shape[0]),resample=Image.BICUBIC) - - pb_np = np.array(imo) - - aaa = img_name.split(".") - bbb = aaa[0:-1] - imidx = bbb[0] - for i in range(1,len(bbb)): - imidx = imidx + "." + bbb[i] - - imo.save(d_dir+imidx+'.png') - -def main(): - - # --------- 1. get image path and name --------- - model_name='u2net'#u2netp - - - - image_dir = os.path.join(os.getcwd(), 'test_data', 'test_images') - prediction_dir = os.path.join(os.getcwd(), 'test_data', model_name + '_results' + os.sep) - model_dir = os.path.join(os.getcwd(), 'saved_models', model_name, model_name + '.pth') - - img_name_list = glob.glob(image_dir + os.sep + '*') - print(img_name_list) - - # --------- 2. dataloader --------- - #1. dataloader - test_salobj_dataset = SalObjDataset(img_name_list = img_name_list, - lbl_name_list = [], - transform=transforms.Compose([RescaleT(320), - ToTensorLab(flag=0)]) - ) - test_salobj_dataloader = DataLoader(test_salobj_dataset, - batch_size=1, - shuffle=False, - num_workers=1) - - # --------- 3. model define --------- - if(model_name=='u2net'): - print("...load U2NET---173.6 MB") - net = U2NET(3,1) - elif(model_name=='u2netp'): - print("...load U2NEP---4.7 MB") - net = U2NETP(3,1) - - if torch.cuda.is_available(): - net.load_state_dict(torch.load(model_dir)) - net.cuda() - else: - net.load_state_dict(torch.load(model_dir, map_location='cpu')) - net.eval() - - # --------- 4. inference for each image --------- - for i_test, data_test in enumerate(test_salobj_dataloader): - - print("inferencing:",img_name_list[i_test].split(os.sep)[-1]) - - inputs_test = data_test['image'] - inputs_test = inputs_test.type(torch.FloatTensor) - - if torch.cuda.is_available(): - inputs_test = Variable(inputs_test.cuda()) - else: - inputs_test = Variable(inputs_test) - - d1,d2,d3,d4,d5,d6,d7= net(inputs_test) - - # normalization - pred = d1[:,0,:,:] - pred = normPRED(pred) - - # save results to test_results folder - if not os.path.exists(prediction_dir): - os.makedirs(prediction_dir, exist_ok=True) - save_output(img_name_list[i_test],pred,prediction_dir) - - del d1,d2,d3,d4,d5,d6,d7 - -if __name__ == "__main__": - main() diff --git a/spaces/hayas/CALM2-7B-chat/README.md b/spaces/hayas/CALM2-7B-chat/README.md deleted file mode 100644 index 75a8106147835e06d1c1fe54279028f1b1f6ce00..0000000000000000000000000000000000000000 --- a/spaces/hayas/CALM2-7B-chat/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: CALM2-7B-chat -emoji: ⚡ -colorFrom: red -colorTo: purple -sdk: gradio -sdk_version: 4.0.2 -app_file: app.py -pinned: false -license: mit -suggested-hardware: t4-small ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/hf-task-exploration/ExploreACMnaacl/README.md b/spaces/hf-task-exploration/ExploreACMnaacl/README.md deleted file mode 100644 index ef76136d1d6dfb8b8b6bbb88d93c926f0a762f30..0000000000000000000000000000000000000000 --- a/spaces/hf-task-exploration/ExploreACMnaacl/README.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -title: Task Exploration - Automatic Content Moderation -emoji: 🤗 -colorFrom: blue -colorTo: red -sdk: streamlit -app_file: app.py -pinned: false ---- - -# Task Exploration - -[![Generic badge](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](https://huggingface.co/spaces/aymm/Task-Exploration-Hate-Speech) - -The context and definition of hate speech detection as a modeling task. - ---- - -Autogenerated using [this template](https://github.com/nateraw/spaces-template) diff --git a/spaces/hiraltalsaniya/YOLOv7_face_mask/app.py b/spaces/hiraltalsaniya/YOLOv7_face_mask/app.py deleted file mode 100644 index 92804637cbd39d7820ab02944dbb9f8714c8a466..0000000000000000000000000000000000000000 --- a/spaces/hiraltalsaniya/YOLOv7_face_mask/app.py +++ /dev/null @@ -1,40 +0,0 @@ -import torch -import gradio as gr -from huggingface_hub import hf_hub_download -from PIL import Image - -REPO_ID = "hiraltalsaniya/YOLOv7_face_mask" -FILENAME = "best.pt" - - -yolov7_custom_weights = hf_hub_download(repo_id=REPO_ID, filename=FILENAME,repo_type='space') - -model = torch.hub.load('WongKinYiu/yolov7:main',model='custom', path_or_model=yolov7_custom_weights, force_reload=True) -def object_detection(im, size=416): - results = model(im) - results.render() - return Image.fromarray(results.imgs[0]) - -title = "Yolov7 Custom" - -image = gr.inputs.Image(shape=(416, 416), image_mode="RGB", source="upload", label="Upload Image", optional=False) -outputs = gr.outputs.Image(type="pil", label="Output Image") - -Custom_description="
Custom Training Performed on Colab


1st class is for Person With mask
2nd class is for Person Without mask" - -Footer = ("Created for testing purpose") - -examples1=[["Image1.jpeg"],["Image2.jpeg"],["Image3.jpeg"],["Image4.jpeg"],["Image5.jpeg"],["Image6.jpeg"],["horses.jpeg"],["horses.jpeg"]] - -Top_Title="
Yolov7 Trained on custome data set Face with mask and without mask People Detection" -css = ".output-image, .input-image {height: 50rem !important; width: 100% !important;}" -css = ".image-preview {height: auto !important;}" - -gr.Interface( - fn=object_detection, - inputs=image, - outputs=outputs, - title=Top_Title, - description=Custom_description, - article=Footer, - examples=[["mask-person-2.jpg"], ["mask-person-3.jpg"]]).launch() diff --git a/spaces/huaiji3y/bingo-Public/src/lib/utils.ts b/spaces/huaiji3y/bingo-Public/src/lib/utils.ts deleted file mode 100644 index 8de2eba94bf0bc93579d4f489e8b810dbf6ce92a..0000000000000000000000000000000000000000 --- a/spaces/huaiji3y/bingo-Public/src/lib/utils.ts +++ /dev/null @@ -1,159 +0,0 @@ -import { clsx, type ClassValue } from 'clsx' -import { customAlphabet } from 'nanoid' -import { twMerge } from 'tailwind-merge' -// @ts-ignore -import randomip from 'random-ip' -import cidr from './cidr.json' - -export function cn(...inputs: ClassValue[]) { - return twMerge(clsx(inputs)) -} - -export const nanoid = customAlphabet( - '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz', - 7 -) // 7-character random string - -export function createChunkDecoder() { - const decoder = new TextDecoder() - return function (chunk: Uint8Array | undefined): string { - if (!chunk) return '' - return decoder.decode(chunk, { stream: true }) - } -} - -export function random (start: number, end: number) { - return start + Math.floor(Math.random() * (end - start)) -} - -export function randomIP() { - // return `104.${random(0, 21)}.${random(0, 127)}.${random(1, 255)}` - const [ip, range] = cidr.at(random(0, cidr.length))?.split('/')! - return randomip(ip, range) -} - -export const defaultUID = 'xxx' - -export function parseHeadersFromCurl(content: string) { - const re = /-H '([^:]+):\s*([^']+)/mg - const headers: HeadersInit = {} - content = content.replaceAll('-H "', '-H \'').replaceAll('" ^', '\'\\').replaceAll('^\\^"', '"') // 将 cmd curl 转成 bash curl - content.replace(re, (_: string, key: string, value: string) => { - headers[key] = value - return '' - }) - return headers -} - -export const ChunkKeys = ['BING_HEADER', 'BING_HEADER1', 'BING_HEADER2'] -export function encodeHeadersToCookie(content: string) { - const base64Content = btoa(content) - const contentChunks = base64Content.match(/.{1,4000}/g) || [] - return ChunkKeys.map((key, index) => `${key}=${contentChunks[index] ?? ''}`) -} - -export function extraCurlFromCookie(cookies: Partial<{ [key: string]: string }>) { - let base64Content = '' - ChunkKeys.forEach((key) => { - base64Content += (cookies[key] || '') - }) - try { - return atob(base64Content) - } catch(e) { - return '' - } -} - -export function extraHeadersFromCookie(cookies: Partial<{ [key: string]: string }>) { - return parseHeadersFromCurl(extraCurlFromCookie(cookies)) -} - -export function formatDate(input: string | number | Date): string { - const date = new Date(input) - return date.toLocaleDateString('en-US', { - month: 'long', - day: 'numeric', - year: 'numeric' - }) -} - -export function parseCookie(cookie: string, cookieName: string) { - const targetCookie = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`).test(cookie) ? RegExp.$1 : cookie - return targetCookie ? decodeURIComponent(targetCookie).trim() : cookie.indexOf('=') === -1 ? cookie.trim() : '' -} - -export function setCookie(key: string, value: string) { - const maxAge = value ? 86400 * 30 : 0 - document.cookie = `${key}=${value || ''}; Path=/; Max-Age=${maxAge}; SameSite=None; Secure` -} - -export function getCookie(cookieName: string) { - const re = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`) - return re.test(document.cookie) ? RegExp.$1 : '' -} - -export function parseCookies(cookie: string, cookieNames: string[]) { - const cookies: { [key: string]: string } = {} - cookieNames.forEach(cookieName => { - cookies[cookieName] = parseCookie(cookie, cookieName) - }) - return cookies -} - -export const DEFAULT_UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36 Edg/115.0.0.0' - -export function parseUA(ua?: string, default_ua = DEFAULT_UA) { - return / EDGE?/i.test(decodeURIComponent(ua || '')) ? decodeURIComponent(ua!.trim()) : default_ua -} - -export function mockUser(cookies: Partial<{ [key: string]: string }>) { - const { - BING_UA = process.env.BING_UA, - BING_IP, - _U = defaultUID, - } = cookies - const ua = parseUA(BING_UA) - - return { - 'x-forwarded-for': BING_IP!, - 'Accept-Encoding': 'gzip, deflate, br', - 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6', - 'User-Agent': ua!, - 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.3 OS/Win32', - cookie: `_U=${_U}` || '', - } -} - -export function createHeaders(cookies: Partial<{ [key: string]: string }>, type?: string) { - let { - BING_HEADER = process.env.BING_HEADER, - BING_IP, - IMAGE_ONLY = process.env.IMAGE_ONLY ?? '1', - } = cookies - const imageOnly = /^(1|true|yes)$/.test(String(IMAGE_ONLY)) - if (BING_HEADER) { - if ( - (imageOnly && type === 'image') - || !imageOnly - ) { - const headers = extraHeadersFromCookie({ - BING_HEADER, - ...cookies, - }) || {} - headers['x-forward-for'] = BING_IP! - return headers - } - } - return mockUser(cookies) -} - -export class WatchDog { - private tid = 0 - watch(fn: Function, timeout = 2000) { - clearTimeout(this.tid) - this.tid = setTimeout(fn, timeout + Math.random() * 1000) - } - reset() { - clearTimeout(this.tid) - } -} diff --git a/spaces/huggan/StyleGAN3/README.md b/spaces/huggan/StyleGAN3/README.md deleted file mode 100644 index 668419ce06794956a8a9014f9ecd1b1fd94eae53..0000000000000000000000000000000000000000 --- a/spaces/huggan/StyleGAN3/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: StyleGAN3 -emoji: 👀 -colorFrom: green -colorTo: pink -sdk: gradio -sdk_version: 2.9.4 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/huggingface-projects/stable-diffusion-multiplayer/static/_app/immutable/assets/_layout-72676353.css b/spaces/huggingface-projects/stable-diffusion-multiplayer/static/_app/immutable/assets/_layout-72676353.css deleted file mode 100644 index 32a2d9a07c170174eed00eefccb34d13e6510b43..0000000000000000000000000000000000000000 --- a/spaces/huggingface-projects/stable-diffusion-multiplayer/static/_app/immutable/assets/_layout-72676353.css +++ /dev/null @@ -1 +0,0 @@ -*,:before,:after{box-sizing:border-box;border-width:0;border-style:solid;border-color:#e5e7eb}:before,:after{--tw-content: ""}html{line-height:1.5;-webkit-text-size-adjust:100%;-moz-tab-size:4;-o-tab-size:4;tab-size:4;font-family:ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,"Apple Color Emoji","Segoe UI Emoji",Segoe UI Symbol,"Noto Color 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Church of the Holy Sepulchre2
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My name is Geoff Le Pard. Once I was a lawyer; now I am a writer. I've published four books - Dead Flies and Sherry Trifle, My Father and Other Liars, Salisbury Square and Buster & Moo. In addition I have published three anthologies of short stories and a memoir of my mother. More will appear soon. I will try and continue to blog regularly at geofflepard.com about whatever takes my fancy. I hope it does yours too. These are my thoughts and no one else is to blame. If you want to nab anything I post, please acknowledge where it came from. -View all posts by TanGental → -This entry was posted in #writephoto, flash fiction, miscellany and tagged #writephoto, flash fiction. Bookmark the permalink.2
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Molly grew up in California but now lives in the oh-so-amazing state of Texas with her husband, daughter, and fur babies. When she’s not diving into the world of her characters, some of her hobbies include hiking, snowboarding, traveling, and long walks on the beach … which roughly translates to being a homebody with her hubby and dishing out movie quotes. She has a weakness for crude-humored movies and fried pickles, and loves curling up in a fluffy comforter during a thunderstorm … or under one in a bathtub if there are tornados. That way she can pretend they aren’t really happening.2
The 9-year-old got into character, pairing her leather jacket and pants with Jackson’s own “Smooth Criminal” hat.2
Highland's Maddie Dortch runs at the start of the race during the Triad Invitational on Wednesday, September 30, 2020 at Triad High School in Troy, Ill. Paul Halfacre, STLhighschoolsports.com2
After excellent first-cut silage crops, it is a case of keeping the shoulder to the wheel to ensure fodder reserves are met for the coming winter. Declan Marren reports.2
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Lowe's in south Fort Myers is one of several area stores that have restocked on essentials to include water, gas containers and generators in preparation for Hurricane Dorian. A manager at the Lowe's said, if needed, they will ship supplies to stores in areas hardest hit by Hurricane Dorian. Kinfay Moroti/The News-Press USA Today Network-Florida -Fullscreen2
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Not since van Gogh lopped off his ear has an artist’s knife been put to such good use.—Tessa Laird - -New Zealand collage artist Peter Madden draws much of his imagery from old issues of National Geographic. He plunders and reworks the magazine’s discredited ’empire of signs’ to forge his own. His surrealistic pictures, objects, and installations—with their watchmaker detail and intensity—have been described as ‘microcosms’ and ‘intricate kingdoms of flying forms’ Madden has one foot in the vanitas still-life tradition and the other in new-age thinking. On the one hand, he is death obsessed: a master of morbid decoupage. (Moths and butterflies—symbols of transient life—abound. His assemblages in bell jars suggest some Victorian taxidermist killing time in his parlour.) On the other hand, with his flocks, schools, and swarms of quivering animal energy, he revels in biodiversity and magic. Madden’s works manage to be at once morbid and abundant, rotting and blooming, creepy and fey. This book serveys Madden’s work of the last ten years2
Fallout 4: How to Get Vertibird Support2
For Fallout 4 on the PlayStation 4, a GameFAQs message board topic titled "Vertibirds going down constantly?".2
I am a committed Piano tutor and composer with over 15 years experience teaching a wide range of pupils from children to...2
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EXCERPT -As the band played, the dance floor filled. Nate looked over the top of his beer bottle as Rachel asked Grant to dance. It was shaping up to be a line dance and Grant, not looking like the cowboy boogie-type, begged off a second time. -She flashed Caroline a hopeful grin. “Do you want to dance?” -Caroline’s eyes darted to the dance floor. “I don’t know how to do that.” -Rachel set her hands on her hips. She cocked her head toward the line forming behind them. “Come on. I’ll teach you.” -Caroline shot Nate a pleading look as if asking him to save her. He bumped her shoulder instead. “Go ahead. Knock ’em dead.” -And damn, if she didn’t. She picked up the steps quickly, laughing every time she turned the wrong way or kicked out the opposite foot. It wasn’t long before she was rocking the arms and rolling her hips, but with an ethereal quality Nate had never witnessed in a country line dance before. Beside her, Rachel moved to the music a little differently, more seductive, less inhibited. Side by side with Caroline, he began to suspect Rachel wasn’t as innocent and naive as her older brother wanted to believe. Nate continued to watch her dance, enthralled. He’d just as soon imagine his sisters naked as he would Caroline, but Rachel? She conjured up fantasies even he’d never imagined before. -Grant paid no mind to Nate. His eyes were locked on Rachel’s long lithe body on the dance floor. She had a type, and this guy was it—tall, fair-haired, destined for a corner office. Nate brushed a hand over his scruffy face. Rachel could look him square in the eye when she wore heels. The only office he hoped to get was a concrete box with a pushout window. -Jealousy spiked in his chest before he finally pushed back from the table and headed back to the bar. -Faces flushed and smiling, Rachel and Caroline wove their way back to the table after he returned. He set a glass of water in front of Caroline, relieved to see Rachel drinking water, too. -Good. He preferred her date tonight ended with her sober. -Grant looked down at his phone as the band took a break and then leaned sideways to say something to Rachel. Nate sent her a curious look after Grant passed the bouncer and went outside. -Rachel shrugged and set down her glass as recorded music started to play over the loudspeakers. “He said he had to take a call for work.” -Caroline touched Nate’s shoulder. “Do you know which way is the toilet?” -Rachel smiled when he pointed to the far end of the bar. -Caroline stood. “I’ll be right back.” -“It’s just called the toilet in Ireland,” Nate explained after Caroline disappeared into the crowd. “Tell me more about Kieran. How does he like his new home?” -Rachel leaned her elbows on the table, her expression turning all sweet and sappy. “I think he’s happy. He meets me at the door every day when I get home and he likes to sleep in bed with me at night.” -“Hmmm,” was the best Nate could do. -She dropped her chin into her hands. “Can I ask you something?” -“Sure.” -“How much Irish do you speak?” -He grinned, assuming cussing didn’t count. “I only know a few words that my father taught me.” -Rachel’s lips twitched. -“What?” -“Your accent. You’re starting to sound a little bit like your girlfriend.” -He could tell she was teasing him, but he still felt the color rising in his cheeks. “I told you, Caroline and I are friends.” -She sat back and laughed as Lonestar’s “Amazed” began to play. “Matt’s right. Your Irish does come out when you’ve been drinking.” -Nate just shrugged. His accent was a byproduct of parents born and raised in Ireland. His father was proud of his thick Irish accent. His mother tried not to speak with any accent at all, but sometimes it would sneak out when one of her four kids got her riled up. It snuck out on him, too, sometimes, and not just while he was drinking. Times Matt didn’t know about. Moments Nate wished Rachel did. -Leaning closer, enough so that he could feel her warm breath on his cheek, she looked at him. “I have to ask you…did that kiss mean anything at all to you?” -He didn’t know how to answer. He thought about lying or twisting the truth. Or just brushing her off altogether. But he couldn’t do it. “Of course it meant something to me. But it can’t happen again.” -She let out a short laugh. “Then it didn’t mean much at all, did it?” -He stared at her, his throat so tight he could barely breathe. He told himself to keep his mouth shut. Put her first. Forget her. -But no, he looked over his shoulder for Caroline instead and then damn near lost his head. “Rachel, I’m crazy about you.” I love you! He clenched his jaw, determined to salvage the big fat mess he’d made. “But be realistic. I’m not the right guy for you.” -She eased back with defiance. “Who says?” -“How about we start with your brother?” -Her lips pinched together. He’d hit a nerve. “Who says I’m looking for Mr. Right?” -“What is that supposed to mean?” -“It means I’m not looking for a ring, Nate. I want to go out, have fun, blow off a little steam. That doesn’t work for you, so I won’t bother you again.”2
AUTHOR BIO -Suzanne Winslow writes the kind of stories she loves to read—contemporary romance with relatable characters, unsung heroes and heroines, and true-to-life stories. Nurses, teachers, firefighters, and Marines top her list of champions. Give her a book about strong, brave characters with hidden vulnerabilities and a secret passion, and she’ll binge read to the end! -Suzanne and her husband, along with their rescue dog, Murphy, call Upstate New York home. When she’s not reading or writing, she’s often planning a road trip, or if it’s summertime, hanging out at the lake. Connecting with readers through Instagram, Facebook, and newsletters is a favorite pastime. -AUTHOR LINKS -WEBSITE -INSTAGRAM -FACEBOOK -GOODREADS -AMAZON2
After breaking the partition, a sturdy metal frame in placed to ensure the upper part of the wall is safely supported and to facilitate access to the roof.2
From the window situated over the release module and behind glass we can watch the chicks without them seeing us.2
During the release process a young one-year old male from the wild population, visited the release module, attracted by the Colony Environment effect. It is probable that it is an individual from the urban centre of San Vicente where at least two pairs of lesser kestrel breed.2
I’ve had a long love of books, and some of my most prized books are art books. This is a review of books from my collection that can be found on shelves in my studio. I will provide links when possible.2
The Fairy Tales of Oscar Wilde2
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The West Side Lofts, a mixed-use development in the heart of Red Bank's antique district, brought a fresh infusion of downtown residents when it opened about four years ago. Tanya Breen -Fullscreen2
Interior of one of the apartments during the opening of Element, a new high-end 35 unit apartment complex along the Navesink River in Red Bank, NJ Wednesday May 29, 2019. Tanya Breen -Fullscreen2
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The Milton and Tamar Maltz Performing Arts Center, located on East 105th Street and Ansel Road in Cleveland. Prior to being used by Case Western Reserve University, the building was The Temple-Tifereth Israel’s home until the 1970s.2
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It was all over before I knew it and I just could not believe I could see almost perfectly straight after the surgery. Read more...2
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The BL King’s Topographical Collection: "THE NORTH-EAST VIEW OF SCALEBY-CASTLE, IN THE COUNTY OF CUMBERLAND. "2
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Commendation: Made in Australia: The Future of Australian Cities by Dr Julian Bolleter and Professor Richard Weller (Perth).2
WINNER: Dune -Nightmare Alley -The Power of the Dog -The Tragedy of Macbeth -West Side Story2
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Police keep people away from the Century 16 theater in Aurora, CO, just outside Denver after a shooting at the Midnight Premier of the Dark Knight Rises where 12 people are confirmed dead and many more injured2
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Geoff Neal believes he “shut people up” by knocking out Vicente Luque, expects “everybody is going to try to wrestle me now”2
The Great Famine and the Irish Diaspora in America ebook2
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↓ Download Image -Caption: Paul Medlock-Walton demonstrates Gameblox, which was developed by researchers at the Education Arcade, and allows users to create their own games. -Credits: Photo: Casey Atkins2
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Meredith Rosenthal (center) spoke about pharmaceutical marketing's role in the opioid crisis. She is Gray professor of health economics at the Harvard T. H. Chan School of Public Health.2
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This image from video provided by the FBI, shows Aaron Alexis moves through the hallways of Building #197 at the Washington Navy Yard on Sept. 16 in Washington, carrying a Remington 870 shotgun. Alexis, a 34-year-old former Navy reservist and IT contractor, shot and killed 12 people inside a Navy Yard building last week before being killed in a shootout with police. (AP Photo/FBI)2
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The Lebanese tourist was spared serious harm due to the rescue by local surfer Alik Reyes Narag and a Frenchman lifeguard ’hero’. Photo: Pavida Anantarasmi2
PEMUDA HARUS “I DO CARE”2
Is GameStop the Next RadioShack?2
\ No newline at end of file diff --git a/spaces/hushell/pmf_with_gis/models/clip/model.py b/spaces/hushell/pmf_with_gis/models/clip/model.py deleted file mode 100644 index 97dd9dea7a69d14be479dea982896be602cf5d9c..0000000000000000000000000000000000000000 --- a/spaces/hushell/pmf_with_gis/models/clip/model.py +++ /dev/null @@ -1,577 +0,0 @@ -from collections import OrderedDict -from typing import Tuple, Union - -import math -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn - - -class Bottleneck(nn.Module): - expansion = 4 - - def __init__(self, inplanes, planes, stride=1): - super().__init__() - - # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 - self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) - self.bn1 = nn.BatchNorm2d(planes) - - self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(planes) - - self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() - - self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) - self.bn3 = nn.BatchNorm2d(planes * self.expansion) - - self.relu = nn.ReLU(inplace=True) - self.downsample = None - self.stride = stride - - if stride > 1 or inplanes != planes * Bottleneck.expansion: - # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 - self.downsample = nn.Sequential(OrderedDict([ - ("-1", nn.AvgPool2d(stride)), - ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), - ("1", nn.BatchNorm2d(planes * self.expansion)) - ])) - - def forward(self, x: torch.Tensor): - identity = x - - out = self.relu(self.bn1(self.conv1(x))) - out = self.relu(self.bn2(self.conv2(out))) - out = self.avgpool(out) - out = self.bn3(self.conv3(out)) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - out = self.relu(out) - return out - - -class AttentionPool2d(nn.Module): - def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): - super().__init__() - self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) - self.k_proj = nn.Linear(embed_dim, embed_dim) - self.q_proj = nn.Linear(embed_dim, embed_dim) - self.v_proj = nn.Linear(embed_dim, embed_dim) - self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) - self.num_heads = num_heads - - def interpolate_pos_encoding(self, x, h0, w0): - assert w0 == h0, f'{self} only support square images!' - pos_embed = self.positional_embedding.unsqueeze(1).to(x.dtype) - npatch = x.shape[0] - 1 - N = pos_embed.shape[0] - 1 - if npatch == N: - return pos_embed - class_pos_embed = pos_embed[0] - patch_pos_embed = pos_embed[1:] - dim = x.shape[-1] - # we add a small number to avoid floating point error in the interpolation - # see discussion at https://github.com/facebookresearch/dino/issues/8 - w0, h0 = w0 + 0.1, h0 + 0.1 - patch_pos_embed = nn.functional.interpolate( - patch_pos_embed.reshape(int(math.sqrt(N)), int(math.sqrt(N)), 1, dim).permute(2, 3, 0, 1), - scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), - mode='bicubic', - align_corners=False, - recompute_scale_factor=False - ) - assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] - patch_pos_embed = patch_pos_embed.permute(2, 3, 0, 1).view(-1, 1, dim) - return torch.cat((class_pos_embed.unsqueeze(1), patch_pos_embed), dim=0) - - def forward(self, x): - B, C, H, W = x.shape - - x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC - x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC - x = x + self.interpolate_pos_encoding(x, H, W) # (HW+1)NC - x, _ = F.multi_head_attention_forward( - query=x, key=x, value=x, - embed_dim_to_check=x.shape[-1], - num_heads=self.num_heads, - q_proj_weight=self.q_proj.weight, - k_proj_weight=self.k_proj.weight, - v_proj_weight=self.v_proj.weight, - in_proj_weight=None, - in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), - bias_k=None, - bias_v=None, - add_zero_attn=False, - dropout_p=0, - out_proj_weight=self.c_proj.weight, - out_proj_bias=self.c_proj.bias, - use_separate_proj_weight=True, - training=self.training, - need_weights=False - ) - - return x[0] - - -class ModifiedResNet(nn.Module): - """ - A ResNet class that is similar to torchvision's but contains the following changes: - - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. - - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 - - The final pooling layer is a QKV attention instead of an average pool - """ - - def __init__(self, layers, output_dim, heads, input_resolution=224, width=64): - super().__init__() - self.output_dim = output_dim - self.input_resolution = input_resolution - - # the 3-layer stem - self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(width // 2) - self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(width // 2) - self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) - self.bn3 = nn.BatchNorm2d(width) - self.avgpool = nn.AvgPool2d(2) - self.relu = nn.ReLU(inplace=True) - - # residual layers - self._inplanes = width # this is a *mutable* variable used during construction - self.layer1 = self._make_layer(width, layers[0]) - self.layer2 = self._make_layer(width * 2, layers[1], stride=2) - self.layer3 = self._make_layer(width * 4, layers[2], stride=2) - self.layer4 = self._make_layer(width * 8, layers[3], stride=2) - - embed_dim = width * 32 # the ResNet feature dimension - self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim) - #self.gap = nn.AdaptiveAvgPool2d((1, 1)) - - def _make_layer(self, planes, blocks, stride=1): - layers = [Bottleneck(self._inplanes, planes, stride)] - - self._inplanes = planes * Bottleneck.expansion - for _ in range(1, blocks): - layers.append(Bottleneck(self._inplanes, planes)) - - return nn.Sequential(*layers) - - def forward(self, x): - def stem(x): - for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]: - x = self.relu(bn(conv(x))) - x = self.avgpool(x) - return x - - x = x.type(self.conv1.weight.dtype) - x = stem(x) - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - x = self.attnpool(x) - #x = self.gap(x) - - return x - - -class LayerNorm(nn.LayerNorm): - """Subclass torch's LayerNorm to handle fp16.""" - - def forward(self, x: torch.Tensor): - orig_type = x.dtype - ret = super().forward(x.type(torch.float32)) - return ret.type(orig_type) - - -class QuickGELU(nn.Module): - def forward(self, x: torch.Tensor): - return x * torch.sigmoid(1.702 * x) - - -class ResidualAttentionBlock(nn.Module): - def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): - super().__init__() - - self.attn = nn.MultiheadAttention(d_model, n_head) - self.ln_1 = LayerNorm(d_model) - self.mlp = nn.Sequential(OrderedDict([ - ("c_fc", nn.Linear(d_model, d_model * 4)), - ("gelu", QuickGELU()), - ("c_proj", nn.Linear(d_model * 4, d_model)) - ])) - self.ln_2 = LayerNorm(d_model) - self.attn_mask = attn_mask - - def attention(self, x: torch.Tensor): - self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None - return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] - - def forward(self, x: torch.Tensor): - x = x + self.attention(self.ln_1(x)) - x = x + self.mlp(self.ln_2(x)) - return x - - -class Transformer(nn.Module): - def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None): - super().__init__() - self.width = width - self.layers = layers - self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)]) - - def forward(self, x: torch.Tensor): - return self.resblocks(x) - - -class VisionTransformer(nn.Module): - def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int): - super().__init__() - self.input_resolution = input_resolution - self.output_dim = output_dim - self.patch_size = patch_size - self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) - - scale = width ** -0.5 - self.class_embedding = nn.Parameter(scale * torch.randn(width)) - self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width)) - self.ln_pre = LayerNorm(width) - - self.transformer = Transformer(width, layers, heads) - - self.ln_post = LayerNorm(width) - self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) - - def interpolate_pos_encoding(self, x, h, w): - pos_embed = self.positional_embedding.unsqueeze(0).to(x.dtype) - npatch = x.shape[1] - 1 - N = pos_embed.shape[1] - 1 - if npatch == N and w == h: - return pos_embed - class_pos_embed = pos_embed[:, 0] - patch_pos_embed = pos_embed[:, 1:] - dim = x.shape[-1] - w0 = w // self.patch_size - h0 = h // self.patch_size - # we add a small number to avoid floating point error in the interpolation - # see discussion at https://github.com/facebookresearch/dino/issues/8 - w0, h0 = w0 + 0.1, h0 + 0.1 - patch_pos_embed = nn.functional.interpolate( - patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), - scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), - mode='bicubic', - align_corners=False, - recompute_scale_factor=False - ) - assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] - patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) - return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) - - def forward(self, x: torch.Tensor): - B, C, H, W = x.shape - - x = self.conv1(x) # shape = [*, width, grid, grid] - x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] - x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] - x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width] - x = x + self.interpolate_pos_encoding(x, H, W) - x = self.ln_pre(x) - - x = x.permute(1, 0, 2) # NLD -> LND - x = self.transformer(x) - x = x.permute(1, 0, 2) # LND -> NLD - - x = self.ln_post(x[:, 0, :]) - - if self.proj is not None: - x = x @ self.proj - - return x - - -class VisionBackbone(nn.Module): - def __init__(self, - embed_dim: int, - # vision - image_resolution: int, - vision_layers: Union[Tuple[int, int, int, int], int], - vision_width: int, - vision_patch_size: int, - ): - super().__init__() - - if isinstance(vision_layers, (tuple, list)): - vision_heads = vision_width * 32 // 64 - self.visual = ModifiedResNet( - layers=vision_layers, - output_dim=embed_dim, - heads=vision_heads, - input_resolution=image_resolution, - width=vision_width - ) - else: - vision_heads = vision_width // 64 - self.visual = VisionTransformer( - input_resolution=image_resolution, - patch_size=vision_patch_size, - width=vision_width, - layers=vision_layers, - heads=vision_heads, - output_dim=embed_dim - ) - - #self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) - - self.initialize_parameters() - - def initialize_parameters(self): - if isinstance(self.visual, ModifiedResNet): - if self.visual.attnpool is not None: - std = self.visual.attnpool.c_proj.in_features ** -0.5 - nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std) - nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std) - nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std) - nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std) - - for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]: - for name, param in resnet_block.named_parameters(): - if name.endswith("bn3.weight"): - nn.init.zeros_(param) - - @property - def dtype(self): - return self.visual.conv1.weight.dtype - - def forward(self, image): - return self.visual(image.type(self.dtype)) - - -class CLIP(nn.Module): - def __init__(self, - embed_dim: int, - # vision - image_resolution: int, - vision_layers: Union[Tuple[int, int, int, int], int], - vision_width: int, - vision_patch_size: int, - # text - context_length: int, - vocab_size: int, - transformer_width: int, - transformer_heads: int, - transformer_layers: int - ): - super().__init__() - - self.context_length = context_length - - if isinstance(vision_layers, (tuple, list)): - vision_heads = vision_width * 32 // 64 - self.visual = ModifiedResNet( - layers=vision_layers, - output_dim=embed_dim, - heads=vision_heads, - input_resolution=image_resolution, - width=vision_width - ) - else: - vision_heads = vision_width // 64 - self.visual = VisionTransformer( - input_resolution=image_resolution, - patch_size=vision_patch_size, - width=vision_width, - layers=vision_layers, - heads=vision_heads, - output_dim=embed_dim - ) - - self.transformer = Transformer( - width=transformer_width, - layers=transformer_layers, - heads=transformer_heads, - attn_mask=self.build_attention_mask() - ) - - self.vocab_size = vocab_size - self.token_embedding = nn.Embedding(vocab_size, transformer_width) - self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width)) - self.ln_final = LayerNorm(transformer_width) - - self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim)) - self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) - - self.initialize_parameters() - - def initialize_parameters(self): - nn.init.normal_(self.token_embedding.weight, std=0.02) - nn.init.normal_(self.positional_embedding, std=0.01) - - if isinstance(self.visual, ModifiedResNet): - if self.visual.attnpool is not None: - std = self.visual.attnpool.c_proj.in_features ** -0.5 - nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std) - nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std) - nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std) - nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std) - - for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]: - for name, param in resnet_block.named_parameters(): - if name.endswith("bn3.weight"): - nn.init.zeros_(param) - - proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) - attn_std = self.transformer.width ** -0.5 - fc_std = (2 * self.transformer.width) ** -0.5 - for block in self.transformer.resblocks: - nn.init.normal_(block.attn.in_proj_weight, std=attn_std) - nn.init.normal_(block.attn.out_proj.weight, std=proj_std) - nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) - nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) - - if self.text_projection is not None: - nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) - - def build_attention_mask(self): - # lazily create causal attention mask, with full attention between the vision tokens - # pytorch uses additive attention mask; fill with -inf - mask = torch.empty(self.context_length, self.context_length) - mask.fill_(float("-inf")) - mask.triu_(1) # zero out the lower diagonal - return mask - - @property - def dtype(self): - return self.visual.conv1.weight.dtype - - def encode_image(self, image): - return self.visual(image.type(self.dtype)) - - def encode_text(self, text): - x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model] - - x = x + self.positional_embedding.type(self.dtype) - x = x.permute(1, 0, 2) # NLD -> LND - x = self.transformer(x) - x = x.permute(1, 0, 2) # LND -> NLD - x = self.ln_final(x).type(self.dtype) - - # x.shape = [batch_size, n_ctx, transformer.width] - # take features from the eot embedding (eot_token is the highest number in each sequence) - x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection - - return x - - def forward(self, image, text): - image_features = self.encode_image(image) - text_features = self.encode_text(text) - - # normalized features - image_features = image_features / image_features.norm(dim=-1, keepdim=True) - text_features = text_features / text_features.norm(dim=-1, keepdim=True) - - # cosine similarity as logits - logit_scale = self.logit_scale.exp() - logits_per_image = logit_scale * image_features @ text_features.t() - logits_per_text = logits_per_image.t() - - # shape = [global_batch_size, global_batch_size] - return logits_per_image, logits_per_text - - -def convert_weights(model: nn.Module): - """Convert applicable model parameters to fp16""" - - def _convert_weights_to_fp16(l): - if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): - l.weight.data = l.weight.data.half() - if l.bias is not None: - l.bias.data = l.bias.data.half() - - if isinstance(l, nn.MultiheadAttention): - for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: - tensor = getattr(l, attr) - if tensor is not None: - tensor.data = tensor.data.half() - - for name in ["text_projection", "proj"]: - if hasattr(l, name): - attr = getattr(l, name) - if attr is not None: - attr.data = attr.data.half() - - model.apply(_convert_weights_to_fp16) - - -def build_model(state_dict: dict): - vit = "visual.proj" in state_dict - - if vit: - vision_width = state_dict["visual.conv1.weight"].shape[0] - vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) - vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] - grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) - image_resolution = vision_patch_size * grid_size - else: - counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] - vision_layers = tuple(counts) - vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] - output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) - vision_patch_size = None - assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] - image_resolution = output_width * 32 - - embed_dim = state_dict["text_projection"].shape[1] - context_length = state_dict["positional_embedding"].shape[0] - vocab_size = state_dict["token_embedding.weight"].shape[0] - transformer_width = state_dict["ln_final.weight"].shape[0] - transformer_heads = transformer_width // 64 - transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) - - model = CLIP( - embed_dim, - image_resolution, vision_layers, vision_width, vision_patch_size, - context_length, vocab_size, transformer_width, transformer_heads, transformer_layers - ) - - for key in ["input_resolution", "context_length", "vocab_size"]: - if key in state_dict: - del state_dict[key] - - convert_weights(model) - model.load_state_dict(state_dict) - return model.eval() - - -def build_vision_model(state_dict: dict): - vit = "visual.proj" in state_dict - - if vit: - vision_width = state_dict["visual.conv1.weight"].shape[0] - vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) - vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] - grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) - image_resolution = vision_patch_size * grid_size - else: - counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] - vision_layers = tuple(counts) - vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] - output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) - vision_patch_size = None - assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] - image_resolution = output_width * 32 - - embed_dim = state_dict["text_projection"].shape[1] - - model = VisionBackbone( - embed_dim, - image_resolution, vision_layers, vision_width, vision_patch_size, - ) - - convert_weights(model) - msg = model.load_state_dict(state_dict, strict=False) - print(f'clip.build_vision_model: pretrained weights loaded with message: {msg}') - return model.eval() diff --git a/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/train.py b/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/train.py deleted file mode 100644 index 55eca2d0ad9463415970e09bccab8b722e496704..0000000000000000000000000000000000000000 --- a/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/train.py +++ /dev/null @@ -1,141 +0,0 @@ -import argparse -import logging -import os - -import torch -import torch.distributed as dist -import torch.nn.functional as F -import torch.utils.data.distributed -from torch.nn.utils import clip_grad_norm_ - -import losses -from backbones import get_model -from dataset import MXFaceDataset, SyntheticDataset, DataLoaderX -from partial_fc import PartialFC -from utils.utils_amp import MaxClipGradScaler -from utils.utils_callbacks import CallBackVerification, CallBackLogging, CallBackModelCheckpoint -from utils.utils_config import get_config -from utils.utils_logging import AverageMeter, init_logging - - -def main(args): - cfg = get_config(args.config) - try: - world_size = int(os.environ['WORLD_SIZE']) - rank = int(os.environ['RANK']) - dist.init_process_group('nccl') - except KeyError: - world_size = 1 - rank = 0 - dist.init_process_group(backend='nccl', init_method="tcp://127.0.0.1:12584", rank=rank, world_size=world_size) - - local_rank = args.local_rank - torch.cuda.set_device(local_rank) - os.makedirs(cfg.output, exist_ok=True) - init_logging(rank, cfg.output) - - if cfg.rec == "synthetic": - train_set = SyntheticDataset(local_rank=local_rank) - else: - train_set = MXFaceDataset(root_dir=cfg.rec, local_rank=local_rank) - - train_sampler = torch.utils.data.distributed.DistributedSampler(train_set, shuffle=True) - train_loader = DataLoaderX( - local_rank=local_rank, dataset=train_set, batch_size=cfg.batch_size, - sampler=train_sampler, num_workers=2, pin_memory=True, drop_last=True) - backbone = get_model(cfg.network, dropout=0.0, fp16=cfg.fp16, num_features=cfg.embedding_size).to(local_rank) - - if cfg.resume: - try: - backbone_pth = os.path.join(cfg.output, "backbone.pth") - backbone.load_state_dict(torch.load(backbone_pth, map_location=torch.device(local_rank))) - if rank == 0: - logging.info("backbone resume successfully!") - except (FileNotFoundError, KeyError, IndexError, RuntimeError): - if rank == 0: - logging.info("resume fail, backbone init successfully!") - - backbone = torch.nn.parallel.DistributedDataParallel( - module=backbone, broadcast_buffers=False, device_ids=[local_rank]) - backbone.train() - margin_softmax = losses.get_loss(cfg.loss) - module_partial_fc = PartialFC( - rank=rank, local_rank=local_rank, world_size=world_size, resume=cfg.resume, - batch_size=cfg.batch_size, margin_softmax=margin_softmax, num_classes=cfg.num_classes, - sample_rate=cfg.sample_rate, embedding_size=cfg.embedding_size, prefix=cfg.output) - - opt_backbone = torch.optim.SGD( - params=[{'params': backbone.parameters()}], - lr=cfg.lr / 512 * cfg.batch_size * world_size, - momentum=0.9, weight_decay=cfg.weight_decay) - opt_pfc = torch.optim.SGD( - params=[{'params': module_partial_fc.parameters()}], - lr=cfg.lr / 512 * cfg.batch_size * world_size, - momentum=0.9, weight_decay=cfg.weight_decay) - - num_image = len(train_set) - total_batch_size = cfg.batch_size * world_size - cfg.warmup_step = num_image // total_batch_size * cfg.warmup_epoch - cfg.total_step = num_image // total_batch_size * cfg.num_epoch - - def lr_step_func(current_step): - cfg.decay_step = [x * num_image // total_batch_size for x in cfg.decay_epoch] - if current_step < cfg.warmup_step: - return current_step / cfg.warmup_step - else: - return 0.1 ** len([m for m in cfg.decay_step if m <= current_step]) - - scheduler_backbone = torch.optim.lr_scheduler.LambdaLR( - optimizer=opt_backbone, lr_lambda=lr_step_func) - scheduler_pfc = torch.optim.lr_scheduler.LambdaLR( - optimizer=opt_pfc, lr_lambda=lr_step_func) - - for key, value in cfg.items(): - num_space = 25 - len(key) - logging.info(": " + key + " " * num_space + str(value)) - - val_target = cfg.val_targets - callback_verification = CallBackVerification(2000, rank, val_target, cfg.rec) - callback_logging = CallBackLogging(50, rank, cfg.total_step, cfg.batch_size, world_size, None) - callback_checkpoint = CallBackModelCheckpoint(rank, cfg.output) - - loss = AverageMeter() - start_epoch = 0 - global_step = 0 - grad_amp = MaxClipGradScaler(cfg.batch_size, 128 * cfg.batch_size, growth_interval=100) if cfg.fp16 else None - for epoch in range(start_epoch, cfg.num_epoch): - train_sampler.set_epoch(epoch) - for step, (img, label) in enumerate(train_loader): - global_step += 1 - features = F.normalize(backbone(img)) - x_grad, loss_v = module_partial_fc.forward_backward(label, features, opt_pfc) - if cfg.fp16: - features.backward(grad_amp.scale(x_grad)) - grad_amp.unscale_(opt_backbone) - clip_grad_norm_(backbone.parameters(), max_norm=5, norm_type=2) - grad_amp.step(opt_backbone) - grad_amp.update() - else: - features.backward(x_grad) - clip_grad_norm_(backbone.parameters(), max_norm=5, norm_type=2) - opt_backbone.step() - - opt_pfc.step() - module_partial_fc.update() - opt_backbone.zero_grad() - opt_pfc.zero_grad() - loss.update(loss_v, 1) - callback_logging(global_step, loss, epoch, cfg.fp16, scheduler_backbone.get_last_lr()[0], grad_amp) - callback_verification(global_step, backbone) - scheduler_backbone.step() - scheduler_pfc.step() - callback_checkpoint(global_step, backbone, module_partial_fc) - dist.destroy_process_group() - 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inputs=inputs, - unnormalized_widths=unnormalized_widths, - unnormalized_heights=unnormalized_heights, - unnormalized_derivatives=unnormalized_derivatives, - inverse=inverse, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - **spline_kwargs - ) - return outputs, logabsdet - - -def searchsorted(bin_locations, inputs, eps=1e-6): - bin_locations[..., -1] += eps - return torch.sum( - inputs[..., None] >= bin_locations, - dim=-1 - ) - 1 - - -def unconstrained_rational_quadratic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails='linear', - tail_bound=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): - inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) - outside_interval_mask = ~inside_interval_mask - - outputs = torch.zeros_like(inputs) - logabsdet = torch.zeros_like(inputs) - - if tails == 'linear': - 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unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - left=0., right=1., bottom=0., top=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): - if torch.min(inputs) < left or torch.max(inputs) > right: - raise ValueError('Input to a transform is not within its domain') - - num_bins = unnormalized_widths.shape[-1] - - if min_bin_width * num_bins > 1.0: - raise ValueError('Minimal bin width too large for the number of bins') - if min_bin_height * num_bins > 1.0: - raise ValueError('Minimal bin height too large for the number of bins') - - widths = F.softmax(unnormalized_widths, dim=-1) - widths = min_bin_width + (1 - min_bin_width * num_bins) * widths - cumwidths = torch.cumsum(widths, dim=-1) - cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0) - cumwidths = (right - left) * cumwidths + left - cumwidths[..., 0] = left - cumwidths[..., -1] = right - widths = cumwidths[..., 1:] - cumwidths[..., :-1] - - derivatives = min_derivative + F.softplus(unnormalized_derivatives) - - heights = F.softmax(unnormalized_heights, dim=-1) - heights = min_bin_height + (1 - min_bin_height * num_bins) * heights - cumheights = torch.cumsum(heights, dim=-1) - cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0) - cumheights = (top - bottom) * cumheights + bottom - cumheights[..., 0] = bottom - cumheights[..., -1] = top - heights = cumheights[..., 1:] - cumheights[..., :-1] - - if inverse: - bin_idx = searchsorted(cumheights, inputs)[..., None] - else: - bin_idx = searchsorted(cumwidths, inputs)[..., None] - - input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] - input_bin_widths = widths.gather(-1, bin_idx)[..., 0] - - input_cumheights = cumheights.gather(-1, bin_idx)[..., 0] - delta = heights / widths - input_delta = delta.gather(-1, bin_idx)[..., 0] - - input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] - input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] - 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-
-

diff --git a/spaces/jackli888/stable-diffusion-webui/scripts/outpainting_mk_2.py b/spaces/jackli888/stable-diffusion-webui/scripts/outpainting_mk_2.py deleted file mode 100644 index 5d80b46cd3263ef0905514a761bb473441d8a1e7..0000000000000000000000000000000000000000 --- a/spaces/jackli888/stable-diffusion-webui/scripts/outpainting_mk_2.py +++ /dev/null @@ -1,283 +0,0 @@ -import math - -import numpy as np -import skimage - -import modules.scripts as scripts -import gradio as gr -from PIL import Image, ImageDraw - -from modules import images, processing, devices -from modules.processing import Processed, process_images -from modules.shared import opts, cmd_opts, state - - -# this function is taken from https://github.com/parlance-zz/g-diffuser-bot -def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05): - # helper fft routines that keep ortho normalization and auto-shift before and after fft - def _fft2(data): - if data.ndim > 2: # has channels - out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128) - for c in range(data.shape[2]): - c_data = data[:, :, c] - out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho") - out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c]) - else: # one channel - out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128) - out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho") - out_fft[:, :] = np.fft.ifftshift(out_fft[:, :]) - - return out_fft - - def _ifft2(data): - if data.ndim > 2: # has channels - out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128) - for c in range(data.shape[2]): - c_data = data[:, :, c] - out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho") - out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c]) - else: # one channel - out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128) - out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho") - out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :]) - - return out_ifft - - def _get_gaussian_window(width, height, std=3.14, mode=0): - window_scale_x = float(width / min(width, height)) - window_scale_y = float(height / min(width, height)) - - window = np.zeros((width, height)) - x = (np.arange(width) / width * 2. - 1.) * window_scale_x - for y in range(height): - fy = (y / height * 2. - 1.) * window_scale_y - if mode == 0: - window[:, y] = np.exp(-(x ** 2 + fy ** 2) * std) - else: - window[:, y] = (1 / ((x ** 2 + 1.) * (fy ** 2 + 1.))) ** (std / 3.14) # hey wait a minute that's not gaussian - - return window - - def _get_masked_window_rgb(np_mask_grey, hardness=1.): - np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3)) - if hardness != 1.: - hardened = np_mask_grey[:] ** hardness - else: - hardened = np_mask_grey[:] - for c in range(3): - np_mask_rgb[:, :, c] = hardened[:] - return np_mask_rgb - - width = _np_src_image.shape[0] - height = _np_src_image.shape[1] - num_channels = _np_src_image.shape[2] - - np_src_image = _np_src_image[:] * (1. - np_mask_rgb) - np_mask_grey = (np.sum(np_mask_rgb, axis=2) / 3.) - img_mask = np_mask_grey > 1e-6 - ref_mask = np_mask_grey < 1e-3 - - windowed_image = _np_src_image * (1. - _get_masked_window_rgb(np_mask_grey)) - windowed_image /= np.max(windowed_image) - windowed_image += np.average(_np_src_image) * np_mask_rgb # / (1.-np.average(np_mask_rgb)) # rather than leave the masked area black, we get better results from fft by filling the average unmasked color - - src_fft = _fft2(windowed_image) # get feature statistics from masked src img - src_dist = np.absolute(src_fft) - src_phase = src_fft / src_dist - - # create a generator with a static seed to make outpainting deterministic / only follow global seed - rng = np.random.default_rng(0) - - noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise - noise_rgb = rng.random((width, height, num_channels)) - noise_grey = (np.sum(noise_rgb, axis=2) / 3.) - noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter - for c in range(num_channels): - noise_rgb[:, :, c] += (1. - color_variation) * noise_grey - - noise_fft = _fft2(noise_rgb) - for c in range(num_channels): - noise_fft[:, :, c] *= noise_window - noise_rgb = np.real(_ifft2(noise_fft)) - shaped_noise_fft = _fft2(noise_rgb) - shaped_noise_fft[:, :, :] = np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist ** noise_q) * src_phase # perform the actual shaping - - brightness_variation = 0. # color_variation # todo: temporarily tieing brightness variation to color variation for now - contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2. - - # scikit-image is used for histogram matching, very convenient! - shaped_noise = np.real(_ifft2(shaped_noise_fft)) - shaped_noise -= np.min(shaped_noise) - shaped_noise /= np.max(shaped_noise) - shaped_noise[img_mask, :] = skimage.exposure.match_histograms(shaped_noise[img_mask, :] ** 1., contrast_adjusted_np_src[ref_mask, :], channel_axis=1) - shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb - - matched_noise = shaped_noise[:] - - return np.clip(matched_noise, 0., 1.) - - - -class Script(scripts.Script): - def title(self): - return "Outpainting mk2" - - def show(self, is_img2img): - return is_img2img - - def ui(self, is_img2img): - if not is_img2img: - return None - - info = gr.HTML("

Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8

") - - pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels")) - mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, elem_id=self.elem_id("mask_blur")) - direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id("direction")) - noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0, elem_id=self.elem_id("noise_q")) - color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05, elem_id=self.elem_id("color_variation")) - - return [info, pixels, mask_blur, direction, noise_q, color_variation] - - def run(self, p, _, pixels, mask_blur, direction, noise_q, color_variation): - initial_seed_and_info = [None, None] - - process_width = p.width - process_height = p.height - - p.mask_blur = mask_blur*4 - p.inpaint_full_res = False - p.inpainting_fill = 1 - p.do_not_save_samples = True - p.do_not_save_grid = True - - left = pixels if "left" in direction else 0 - right = pixels if "right" in direction else 0 - up = pixels if "up" in direction else 0 - down = pixels if "down" in direction else 0 - - init_img = p.init_images[0] - target_w = math.ceil((init_img.width + left + right) / 64) * 64 - target_h = math.ceil((init_img.height + up + down) / 64) * 64 - - if left > 0: - left = left * (target_w - init_img.width) // (left + right) - - if right > 0: - right = target_w - init_img.width - left - - if up > 0: - up = up * (target_h - init_img.height) // (up + down) - - if down > 0: - down = target_h - init_img.height - up - - def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False): - is_horiz = is_left or is_right - is_vert = is_top or is_bottom - pixels_horiz = expand_pixels if is_horiz else 0 - pixels_vert = expand_pixels if is_vert else 0 - - images_to_process = [] - output_images = [] - for n in range(count): - res_w = init[n].width + pixels_horiz - res_h = init[n].height + pixels_vert - process_res_w = math.ceil(res_w / 64) * 64 - process_res_h = math.ceil(res_h / 64) * 64 - - img = Image.new("RGB", (process_res_w, process_res_h)) - img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0)) - mask = Image.new("RGB", (process_res_w, process_res_h), "white") - draw = ImageDraw.Draw(mask) - draw.rectangle(( - expand_pixels + mask_blur if is_left else 0, - expand_pixels + mask_blur if is_top else 0, - mask.width - expand_pixels - mask_blur if is_right else res_w, - mask.height - expand_pixels - mask_blur if is_bottom else res_h, - ), fill="black") - - np_image = (np.asarray(img) / 255.0).astype(np.float64) - np_mask = (np.asarray(mask) / 255.0).astype(np.float64) - noised = get_matched_noise(np_image, np_mask, noise_q, color_variation) - output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")) - - target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width - target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height - p.width = target_width if is_horiz else img.width - p.height = target_height if is_vert else img.height - - crop_region = ( - 0 if is_left else output_images[n].width - target_width, - 0 if is_top else output_images[n].height - target_height, - target_width if is_left else output_images[n].width, - target_height if is_top else output_images[n].height, - ) - mask = mask.crop(crop_region) - p.image_mask = mask - - image_to_process = output_images[n].crop(crop_region) - images_to_process.append(image_to_process) - - p.init_images = images_to_process - - latent_mask = Image.new("RGB", (p.width, p.height), "white") - draw = ImageDraw.Draw(latent_mask) - draw.rectangle(( - expand_pixels + mask_blur * 2 if is_left else 0, - expand_pixels + mask_blur * 2 if is_top else 0, - mask.width - expand_pixels - mask_blur * 2 if is_right else res_w, - mask.height - expand_pixels - mask_blur * 2 if is_bottom else res_h, - ), fill="black") - p.latent_mask = latent_mask - - proc = process_images(p) - - if initial_seed_and_info[0] is None: - initial_seed_and_info[0] = proc.seed - initial_seed_and_info[1] = proc.info - - for n in range(count): - output_images[n].paste(proc.images[n], (0 if is_left else output_images[n].width - proc.images[n].width, 0 if is_top else output_images[n].height - proc.images[n].height)) - output_images[n] = output_images[n].crop((0, 0, res_w, res_h)) - - return output_images - - batch_count = p.n_iter - batch_size = p.batch_size - p.n_iter = 1 - state.job_count = batch_count * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0)) - all_processed_images = [] - - for i in range(batch_count): - imgs = [init_img] * batch_size - state.job = f"Batch {i + 1} out of {batch_count}" - - if left > 0: - imgs = expand(imgs, batch_size, left, is_left=True) - if right > 0: - imgs = expand(imgs, batch_size, right, is_right=True) - if up > 0: - imgs = expand(imgs, batch_size, up, is_top=True) - if down > 0: - imgs = expand(imgs, batch_size, down, is_bottom=True) - - all_processed_images += imgs - - all_images = all_processed_images - - combined_grid_image = images.image_grid(all_processed_images) - unwanted_grid_because_of_img_count = len(all_processed_images) < 2 and opts.grid_only_if_multiple - if opts.return_grid and not unwanted_grid_because_of_img_count: - all_images = [combined_grid_image] + all_processed_images - - res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1]) - - if opts.samples_save: - for img in all_processed_images: - images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p) - - if opts.grid_save and not unwanted_grid_because_of_img_count: - images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p) - - return res diff --git a/spaces/jbilcke-hf/Panoremix/src/components/ui/toast.tsx b/spaces/jbilcke-hf/Panoremix/src/components/ui/toast.tsx deleted file mode 100644 index 94b1e9a1d3a82fe1beea6e931c4887e2260371cd..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/Panoremix/src/components/ui/toast.tsx +++ /dev/null @@ -1,127 +0,0 @@ -import * as React from "react" -import * as ToastPrimitives from "@radix-ui/react-toast" -import { cva, type VariantProps } from "class-variance-authority" -import { X } from "lucide-react" - -import { cn } from "@/lib/utils" - -const ToastProvider = ToastPrimitives.Provider - -const ToastViewport = React.forwardRef< - React.ElementRef, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -ToastViewport.displayName = ToastPrimitives.Viewport.displayName - -const toastVariants = cva( - "group pointer-events-auto relative flex w-full items-center justify-between space-x-4 overflow-hidden rounded-md border border-stone-200 p-6 pr-8 shadow-lg transition-all data-[swipe=cancel]:translate-x-0 data-[swipe=end]:translate-x-[var(--radix-toast-swipe-end-x)] data-[swipe=move]:translate-x-[var(--radix-toast-swipe-move-x)] data-[swipe=move]:transition-none data-[state=open]:animate-in data-[state=closed]:animate-out data-[swipe=end]:animate-out data-[state=closed]:fade-out-80 data-[state=closed]:slide-out-to-right-full data-[state=open]:slide-in-from-top-full data-[state=open]:sm:slide-in-from-bottom-full dark:border-stone-800", - { - variants: { - variant: { - default: "border bg-white text-stone-950 dark:bg-stone-950 dark:text-stone-50", - destructive: - "destructive group border-red-500 bg-red-500 text-stone-50 dark:border-red-900 dark:bg-red-900 dark:text-stone-50", - }, - }, - defaultVariants: { - variant: "default", - }, - } -) - -const Toast = React.forwardRef< - React.ElementRef, - React.ComponentPropsWithoutRef & - VariantProps ->(({ className, variant, ...props }, ref) => { - return ( - - ) -}) -Toast.displayName = ToastPrimitives.Root.displayName - -const ToastAction = React.forwardRef< - React.ElementRef, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -ToastAction.displayName = ToastPrimitives.Action.displayName - -const ToastClose = React.forwardRef< - React.ElementRef, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - - - -)) -ToastClose.displayName = ToastPrimitives.Close.displayName - -const ToastTitle = React.forwardRef< - React.ElementRef, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -ToastTitle.displayName = ToastPrimitives.Title.displayName - -const ToastDescription = React.forwardRef< - React.ElementRef, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -ToastDescription.displayName = ToastPrimitives.Description.displayName - -type ToastProps = React.ComponentPropsWithoutRef - -type ToastActionElement = React.ReactElement - -export { - type ToastProps, - type ToastActionElement, - ToastProvider, - ToastViewport, - Toast, - ToastTitle, - ToastDescription, - ToastClose, - ToastAction, -} diff --git a/spaces/jbilcke-hf/ai-clip-factory/src/app/interface/auth-dialog/index.tsx b/spaces/jbilcke-hf/ai-clip-factory/src/app/interface/auth-dialog/index.tsx deleted file mode 100644 index e63a708dc9c5957731be767093841a00c1bc386e..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/ai-clip-factory/src/app/interface/auth-dialog/index.tsx +++ /dev/null @@ -1,6 +0,0 @@ -export function AuthDialog() { - return ( - <> - - ) -} \ No newline at end of file diff --git a/spaces/jbilcke-hf/media-server/scripts/archives/audio.sh b/spaces/jbilcke-hf/media-server/scripts/archives/audio.sh deleted file mode 100644 index f3e214ab366b6f3accb6334c9532d05baef6182f..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/media-server/scripts/archives/audio.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -echo "starting the audio collection stream.." -while true; do - num_files=$(ls $WEBTV_AUDIO_STORAGE_PATH*.mp3 2> /dev/null | wc -l) - if [ $num_files -eq 0 ] - then - sleep 1 - fi - for f in $WEBTV_AUDIO_STORAGE_PATH*.mp3 - do - echo "playing $f" - ffmpeg -fflags +discardcorrupt -re -i "$f" -loglevel panic -vn -acodec copy -f mp3 -y audio.pipe 2>/dev/null - done -done \ No newline at end of file diff --git a/spaces/jhwen/bingo/src/components/chat-scroll-anchor.tsx b/spaces/jhwen/bingo/src/components/chat-scroll-anchor.tsx deleted file mode 100644 index ac809f4486a48e134cb69314c3d0dae5e68d614e..0000000000000000000000000000000000000000 --- a/spaces/jhwen/bingo/src/components/chat-scroll-anchor.tsx +++ /dev/null @@ -1,29 +0,0 @@ -'use client' - -import * as React from 'react' -import { useInView } from 'react-intersection-observer' - -import { useAtBottom } from '@/lib/hooks/use-at-bottom' - -interface ChatScrollAnchorProps { - trackVisibility?: boolean -} - -export function ChatScrollAnchor({ trackVisibility }: ChatScrollAnchorProps) { - const isAtBottom = useAtBottom() - const { ref, entry, inView } = useInView({ - trackVisibility, - delay: 100, - rootMargin: '0px 0px -150px 0px' - }) - - React.useEffect(() => { - if (isAtBottom && trackVisibility && !inView) { - entry?.target.scrollIntoView({ - block: 'start' - }) - } - }, [inView, entry, isAtBottom, trackVisibility]) - - return
-} diff --git a/spaces/jhwen/bingo/src/components/header.tsx b/spaces/jhwen/bingo/src/components/header.tsx deleted file mode 100644 index dc298b722154d1ac6d7a7e148204605562d6cc58..0000000000000000000000000000000000000000 --- a/spaces/jhwen/bingo/src/components/header.tsx +++ /dev/null @@ -1,12 +0,0 @@ -import * as React from 'react' -import { UserMenu } from './user-menu' - -export async function Header() { - return ( -
-
- -
-
- ) -} diff --git a/spaces/johnslegers/stable-diffusion-gui-test/optimizedSD/samplers.py b/spaces/johnslegers/stable-diffusion-gui-test/optimizedSD/samplers.py deleted file mode 100644 index 6a68e8e1a1b3d8340b44b59fc6c3994c46de982a..0000000000000000000000000000000000000000 --- a/spaces/johnslegers/stable-diffusion-gui-test/optimizedSD/samplers.py +++ /dev/null @@ -1,252 +0,0 @@ -from scipy import integrate -import torch -from tqdm.auto import trange, tqdm -import torch.nn as nn - - -def append_zero(x): - return torch.cat([x, x.new_zeros([1])]) - - -def append_dims(x, target_dims): - """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" - dims_to_append = target_dims - x.ndim - if dims_to_append < 0: - raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') - return x[(...,) + (None,) * dims_to_append] - -def get_ancestral_step(sigma_from, sigma_to): - """Calculates the noise level (sigma_down) to step down to and the amount - of noise to add (sigma_up) when doing an ancestral sampling step.""" - sigma_up = (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5 - sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 - return sigma_down, sigma_up - - -class DiscreteSchedule(nn.Module): - """A mapping between continuous noise levels (sigmas) and a list of discrete noise - levels.""" - - def __init__(self, sigmas, quantize): - super().__init__() - self.register_buffer('sigmas', sigmas) - self.quantize = quantize - - def get_sigmas(self, n=None): - if n is None: - return append_zero(self.sigmas.flip(0)) - t_max = len(self.sigmas) - 1 - t = torch.linspace(t_max, 0, n, device=self.sigmas.device) - return append_zero(self.t_to_sigma(t)) - - def sigma_to_t(self, sigma, quantize=None): - quantize = self.quantize if quantize is None else quantize - dists = torch.abs(sigma - self.sigmas[:, None]) - if quantize: - return torch.argmin(dists, dim=0).view(sigma.shape) - low_idx, high_idx = torch.sort(torch.topk(dists, dim=0, k=2, largest=False).indices, dim=0)[0] - low, high = self.sigmas[low_idx], self.sigmas[high_idx] - w = (low - sigma) / (low - high) - w = w.clamp(0, 1) - t = (1 - w) * low_idx + w * high_idx - return t.view(sigma.shape) - - def t_to_sigma(self, t): - t = t.float() - low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac() - # print(low_idx, high_idx, w ) - return (1 - w) * self.sigmas[low_idx] + w * self.sigmas[high_idx] - - -class DiscreteEpsDDPMDenoiser(DiscreteSchedule): - """A wrapper for discrete schedule DDPM models that output eps (the predicted - noise).""" - - def __init__(self, alphas_cumprod, quantize): - super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize) - self.sigma_data = 1. - - def get_scalings(self, sigma): - c_out = -sigma - c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 - return c_out, c_in - - def get_eps(self, *args, **kwargs): - return self.inner_model(*args, **kwargs) - - def forward(self, input, sigma, **kwargs): - c_out, c_in = [append_dims(x, input.ndim) for x in self.get_scalings(sigma)] - eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs) - return input + eps * c_out - -class CompVisDenoiser(DiscreteEpsDDPMDenoiser): - """A wrapper for CompVis diffusion models.""" - - def __init__(self, alphas_cumprod, quantize=False, device='cpu'): - super().__init__(alphas_cumprod, quantize=quantize) - - def get_eps(self, *args, **kwargs): - return self.inner_model.apply_model(*args, **kwargs) - - -def to_d(x, sigma, denoised): - """Converts a denoiser output to a Karras ODE derivative.""" - return (x - denoised) / append_dims(sigma, x.ndim) - - -def get_ancestral_step(sigma_from, sigma_to): - """Calculates the noise level (sigma_down) to step down to and the amount - of noise to add (sigma_up) when doing an ancestral sampling step.""" - sigma_up = (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5 - sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 - return sigma_down, sigma_up - - -@torch.no_grad() -def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): - """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. - eps = torch.randn_like(x) * s_noise - sigma_hat = sigmas[i] * (gamma + 1) - if gamma > 0: - x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x, sigma_hat, denoised) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) - dt = sigmas[i + 1] - sigma_hat - # Euler method - x = x + d * dt - return x - - - -@torch.no_grad() -def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None): - """Ancestral sampling with Euler method steps.""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1]) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - d = to_d(x, sigmas[i], denoised) - # Euler method - dt = sigma_down - sigmas[i] - x = x + d * dt - x = x + torch.randn_like(x) * sigma_up - return x - - -@torch.no_grad() -def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): - """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. - eps = torch.randn_like(x) * s_noise - sigma_hat = sigmas[i] * (gamma + 1) - if gamma > 0: - x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x, sigma_hat, denoised) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) - dt = sigmas[i + 1] - sigma_hat - if sigmas[i + 1] == 0: - # Euler method - x = x + d * dt - else: - # Heun's method - x_2 = x + d * dt - denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) - d_2 = to_d(x_2, sigmas[i + 1], denoised_2) - d_prime = (d + d_2) / 2 - x = x + d_prime * dt - return x - - -@torch.no_grad() -def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): - """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. - eps = torch.randn_like(x) * s_noise - sigma_hat = sigmas[i] * (gamma + 1) - if gamma > 0: - x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x, sigma_hat, denoised) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) - # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule - sigma_mid = ((sigma_hat ** (1 / 3) + sigmas[i + 1] ** (1 / 3)) / 2) ** 3 - dt_1 = sigma_mid - sigma_hat - dt_2 = sigmas[i + 1] - sigma_hat - x_2 = x + d * dt_1 - denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) - d_2 = to_d(x_2, sigma_mid, denoised_2) - x = x + d_2 * dt_2 - return x - - -@torch.no_grad() -def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None): - """Ancestral sampling with DPM-Solver inspired second-order steps.""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1]) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - d = to_d(x, sigmas[i], denoised) - # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule - sigma_mid = ((sigmas[i] ** (1 / 3) + sigma_down ** (1 / 3)) / 2) ** 3 - dt_1 = sigma_mid - sigmas[i] - dt_2 = sigma_down - sigmas[i] - x_2 = x + d * dt_1 - denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) - d_2 = to_d(x_2, sigma_mid, denoised_2) - x = x + d_2 * dt_2 - x = x + torch.randn_like(x) * sigma_up - return x - - -def linear_multistep_coeff(order, t, i, j): - if order - 1 > i: - raise ValueError(f'Order {order} too high for step {i}') - def fn(tau): - prod = 1. - for k in range(order): - if j == k: - continue - prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) - return prod - return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0] - - -@torch.no_grad() -def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4): - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - ds = [] - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - d = to_d(x, sigmas[i], denoised) - ds.append(d) - if len(ds) > order: - ds.pop(0) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - cur_order = min(i + 1, order) - coeffs = [linear_multistep_coeff(cur_order, sigmas.cpu(), i, j) for j in range(cur_order)] - x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) - return x diff --git a/spaces/juancopi81/multitrack-midi-music-generator/string_to_notes.py b/spaces/juancopi81/multitrack-midi-music-generator/string_to_notes.py deleted file mode 100644 index ba1d9525678b3e0ae270e8f2e06e9be443a2c112..0000000000000000000000000000000000000000 --- a/spaces/juancopi81/multitrack-midi-music-generator/string_to_notes.py +++ /dev/null @@ -1,137 +0,0 @@ -from typing import Optional - -from note_seq.protobuf.music_pb2 import NoteSequence -from note_seq.constants import STANDARD_PPQ - - -def token_sequence_to_note_sequence( - token_sequence: str, - qpm: float = 120.0, - use_program: bool = True, - use_drums: bool = True, - instrument_mapper: Optional[dict] = None, - only_piano: bool = False, -) -> NoteSequence: - """ - Converts a sequence of tokens into a sequence of notes. - - Args: - token_sequence (str): The sequence of tokens to convert. - qpm (float, optional): The quarter notes per minute. Defaults to 120.0. - use_program (bool, optional): Whether to use program. Defaults to True. - use_drums (bool, optional): Whether to use drums. Defaults to True. - instrument_mapper (Optional[dict], optional): The instrument mapper. Defaults to None. - only_piano (bool, optional): Whether to only use piano. Defaults to False. - - Returns: - NoteSequence: The resulting sequence of notes. - """ - if isinstance(token_sequence, str): - token_sequence = token_sequence.split() - - note_sequence = empty_note_sequence(qpm) - - # Compute note and bar lengths based on the provided QPM - note_length_16th = 0.25 * 60 / qpm - bar_length = 4.0 * 60 / qpm - - # Render all notes. - current_program = 1 - current_is_drum = False - current_instrument = 0 - track_count = 0 - for _, token in enumerate(token_sequence): - if token == "PIECE_START": - pass - elif token == "PIECE_END": - break - elif token == "TRACK_START": - current_bar_index = 0 - track_count += 1 - pass - elif token == "TRACK_END": - pass - elif token == "KEYS_START": - pass - elif token == "KEYS_END": - pass - elif token.startswith("KEY="): - pass - elif token.startswith("INST"): - instrument = token.split("=")[-1] - if instrument != "DRUMS" and use_program: - if instrument_mapper is not None: - if instrument in instrument_mapper: - instrument = instrument_mapper[instrument] - current_program = int(instrument) - current_instrument = track_count - current_is_drum = False - if instrument == "DRUMS" and use_drums: - current_instrument = 0 - current_program = 0 - current_is_drum = True - elif token == "BAR_START": - current_time = current_bar_index * bar_length - current_notes = {} - elif token == "BAR_END": - current_bar_index += 1 - pass - elif token.startswith("NOTE_ON"): - pitch = int(token.split("=")[-1]) - note = note_sequence.notes.add() - note.start_time = current_time - note.end_time = current_time + 4 * note_length_16th - note.pitch = pitch - note.instrument = current_instrument - note.program = current_program - note.velocity = 80 - note.is_drum = current_is_drum - current_notes[pitch] = note - elif token.startswith("NOTE_OFF"): - pitch = int(token.split("=")[-1]) - if pitch in current_notes: - note = current_notes[pitch] - note.end_time = current_time - elif token.startswith("TIME_DELTA"): - delta = float(token.split("=")[-1]) * note_length_16th - current_time += delta - elif token.startswith("DENSITY="): - pass - elif token == "[PAD]": - pass - else: - pass - - # Make the instruments right. - instruments_drums = [] - for note in note_sequence.notes: - pair = [note.program, note.is_drum] - if pair not in instruments_drums: - instruments_drums += [pair] - note.instrument = instruments_drums.index(pair) - - if only_piano: - for note in note_sequence.notes: - if not note.is_drum: - note.instrument = 0 - note.program = 0 - - return note_sequence - - -def empty_note_sequence(qpm: float = 120.0, total_time: float = 0.0) -> NoteSequence: - """ - Creates an empty note sequence. - - Args: - qpm (float, optional): The quarter notes per minute. Defaults to 120.0. - total_time (float, optional): The total time. Defaults to 0.0. - - Returns: - NoteSequence: The empty note sequence. - """ - note_sequence = NoteSequence() - note_sequence.tempos.add().qpm = qpm - note_sequence.ticks_per_quarter = STANDARD_PPQ - note_sequence.total_time = total_time - return note_sequence diff --git a/spaces/jurgendn/table-extraction/models/metrics/regression.py b/spaces/jurgendn/table-extraction/models/metrics/regression.py deleted file mode 100644 index 868f27c42bdc696d7aa183c77f24e783020c271d..0000000000000000000000000000000000000000 --- a/spaces/jurgendn/table-extraction/models/metrics/regression.py +++ /dev/null @@ -1,28 +0,0 @@ -from typing import Dict - -import torch -from torchmetrics import functional as FM - - -def regression_metrics(preds: torch.Tensor, - target: torch.Tensor) -> Dict[str, torch.Tensor]: - """ - get_classification_metrics - Return some metrics evaluation the classification task - - Parameters - ---------- - preds : torch.Tensor - logits, probs - target : torch.Tensor - targets label - - Returns - ------- - Dict[str, torch.Tensor] - _description_ - """ - mse: torch.Tensor = FM.mean_squared_error(preds=preds, target=target) - mape: torch.Tensor = FM.mean_absolute_percentage_error(preds=preds, - target=target) - return dict(mse=mse, mape=mape) diff --git a/spaces/justYu2001/furniture-detection/utils/aws/userdata.sh b/spaces/justYu2001/furniture-detection/utils/aws/userdata.sh deleted file mode 100644 index 5762ae575f5b64df9b438180840fce0a2bafec42..0000000000000000000000000000000000000000 --- a/spaces/justYu2001/furniture-detection/utils/aws/userdata.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html -# This script will run only once on first instance start (for a re-start script see mime.sh) -# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir -# Use >300 GB SSD - -cd home/ubuntu -if [ ! -d yolor ]; then - echo "Running first-time script." # install dependencies, download COCO, pull Docker - git clone -b paper https://github.com/WongKinYiu/yolor && sudo chmod -R 777 yolor - cd yolor - bash data/scripts/get_coco.sh && echo "Data done." & - sudo docker pull nvcr.io/nvidia/pytorch:21.08-py3 && echo "Docker done." & - python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & - wait && echo "All tasks done." # finish background tasks -else - echo "Running re-start script." # resume interrupted runs - i=0 - list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' - while IFS= read -r id; do - ((i++)) - echo "restarting container $i: $id" - sudo docker start $id - # sudo docker exec -it $id python train.py --resume # single-GPU - sudo docker exec -d $id python utils/aws/resume.py # multi-scenario - done <<<"$list" -fi diff --git a/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/musicautobot/utils/__init__.py b/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/musicautobot/utils/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/kcagle/AutoGPT/autogpt/commands/google_search.py b/spaces/kcagle/AutoGPT/autogpt/commands/google_search.py deleted file mode 100644 index 7d38ce7568d2de207d521b077cfebd72527c9795..0000000000000000000000000000000000000000 --- a/spaces/kcagle/AutoGPT/autogpt/commands/google_search.py +++ /dev/null @@ -1,87 +0,0 @@ -"""Google search command for Autogpt.""" -from __future__ import annotations - -import json - -from duckduckgo_search import ddg - -from autogpt.config import Config - -CFG = Config() - - -def google_search(query: str, num_results: int = 8) -> str: - """Return the results of a Google search - - Args: - query (str): The search query. - num_results (int): The number of results to return. - - Returns: - str: The results of the search. - """ - search_results = [] - if not query: - return json.dumps(search_results) - - results = ddg(query, max_results=num_results) - if not results: - return json.dumps(search_results) - - for j in results: - search_results.append(j) - - return json.dumps(search_results, ensure_ascii=False, indent=4) - - -def google_official_search(query: str, num_results: int = 8) -> str | list[str]: - """Return the results of a Google search using the official Google API - - Args: - query (str): The search query. - num_results (int): The number of results to return. - - Returns: - str: The results of the search. - """ - - from googleapiclient.discovery import build - from googleapiclient.errors import HttpError - - try: - # Get the Google API key and Custom Search Engine ID from the config file - api_key = CFG.google_api_key - custom_search_engine_id = CFG.custom_search_engine_id - - # Initialize the Custom Search API service - service = build("customsearch", "v1", developerKey=api_key) - - # Send the search query and retrieve the results - result = ( - service.cse() - .list(q=query, cx=custom_search_engine_id, num=num_results) - .execute() - ) - - # Extract the search result items from the response - search_results = result.get("items", []) - - # Create a list of only the URLs from the search results - search_results_links = [item["link"] for item in search_results] - - except HttpError as e: - # Handle errors in the API call - error_details = json.loads(e.content.decode()) - - # Check if the error is related to an invalid or missing API key - if error_details.get("error", {}).get( - "code" - ) == 403 and "invalid API key" in error_details.get("error", {}).get( - "message", "" - ): - return "Error: The provided Google API key is invalid or missing." - else: - return f"Error: {e}" - - # Return the list of search result URLs - return search_results_links diff --git a/spaces/kepl/gpt/g4f/Provider/Providers/Theb.py b/spaces/kepl/gpt/g4f/Provider/Providers/Theb.py deleted file mode 100644 index aa43ebc55d74ffaa722fe008424fce97c622a323..0000000000000000000000000000000000000000 --- a/spaces/kepl/gpt/g4f/Provider/Providers/Theb.py +++ /dev/null @@ -1,28 +0,0 @@ -import os -import json -import time -import subprocess - -from ...typing import sha256, Dict, get_type_hints - -url = 'https://theb.ai' -model = ['gpt-3.5-turbo'] -supports_stream = True -needs_auth = False - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - - path = os.path.dirname(os.path.realpath(__file__)) - config = json.dumps({ - 'messages': messages, - 'model': model}, separators=(',', ':')) - - cmd = ['python3', f'{path}/helpers/theb.py', config] - - p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) - - for line in iter(p.stdout.readline, b''): - yield line.decode('utf-8') - -params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \ - '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]]) \ No newline at end of file diff --git a/spaces/keras-dreambooth/dreambooth-diffusion-akita-dog/README.md b/spaces/keras-dreambooth/dreambooth-diffusion-akita-dog/README.md deleted file mode 100644 index a3b8b712b9de0f35e3026bae2bc6b4aaeee202e4..0000000000000000000000000000000000000000 --- a/spaces/keras-dreambooth/dreambooth-diffusion-akita-dog/README.md +++ /dev/null @@ -1,16 +0,0 @@ ---- -title: Dreambooth Diffusion Akita Dog -emoji: 🐶 -colorFrom: green -colorTo: red -sdk: gradio -sdk_version: 3.22.1 -app_file: app.py -pinned: false -license: creativeml-openrail-m -tags: - - keras-dreambooth - - nature ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/options/__init__.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/options/__init__.py deleted file mode 100644 index e7eedebe54aa70169fd25951b3034d819e396c90..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/options/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""This package options includes option modules: training options, test options, and basic options (used in both training and test).""" diff --git a/spaces/kevinwang676/SadTalker/src/utils/text2speech.py b/spaces/kevinwang676/SadTalker/src/utils/text2speech.py deleted file mode 100644 index 00d165b6cc7774fd200929aafa0ff3b15916111e..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/SadTalker/src/utils/text2speech.py +++ /dev/null @@ -1,20 +0,0 @@ -import os -import tempfile -from TTS.api import TTS - - -class TTSTalker(): - def __init__(self) -> None: - model_name = TTS.list_models()[0] - self.tts = TTS(model_name) - - def test(self, text, language='en'): - - tempf = tempfile.NamedTemporaryFile( - delete = False, - suffix = ('.'+'wav'), - ) - - self.tts.tts_to_file(text, speaker=self.tts.speakers[0], language=language, file_path=tempf.name) - - return tempf.name \ No newline at end of file diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/mkgui/base/components/outputs.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/mkgui/base/components/outputs.py deleted file mode 100644 index f4859c64b9e21114436e57863fedd5fd161da627..0000000000000000000000000000000000000000 --- a/spaces/kira4424/Tacotron-zero-short-voice-clone/mkgui/base/components/outputs.py +++ /dev/null @@ -1,43 +0,0 @@ -from typing import List - -from pydantic import BaseModel - - -class ScoredLabel(BaseModel): - label: str - score: float - - -class ClassificationOutput(BaseModel): - __root__: List[ScoredLabel] - - def __iter__(self): # type: ignore - return iter(self.__root__) - - def __getitem__(self, item): # type: ignore - return self.__root__[item] - - def render_output_ui(self, streamlit) -> None: # type: ignore - import plotly.express as px - - sorted_predictions = sorted( - [prediction.dict() for prediction in self.__root__], - key=lambda k: k["score"], - ) - - num_labels = len(sorted_predictions) - if len(sorted_predictions) > 10: - num_labels = streamlit.slider( - "Maximum labels to show: ", - min_value=1, - max_value=len(sorted_predictions), - value=len(sorted_predictions), - ) - fig = px.bar( - sorted_predictions[len(sorted_predictions) - num_labels :], - x="score", - y="label", - orientation="h", - ) - streamlit.plotly_chart(fig, use_container_width=True) - # fig.show() diff --git a/spaces/koyomimi/Real-CUGAN/app.py b/spaces/koyomimi/Real-CUGAN/app.py deleted file mode 100644 index 2439c5cec6b61e8a517f957daf710cbb6b5c3cf6..0000000000000000000000000000000000000000 --- a/spaces/koyomimi/Real-CUGAN/app.py +++ /dev/null @@ -1,62 +0,0 @@ -from upcunet_v3 import RealWaifuUpScaler -import gradio as gr -import time -import logging -import os -from PIL import ImageOps -import numpy as np -import math - - -def greet(input_img, input_model_name, input_tile_mode): - # if input_img.size[0] * input_img.size[1] > 256 * 256: - # y = int(math.sqrt(256*256/input_img.size[0]*input_img.size[1])) - # x = int(input_img.size[0]/input_img.size[1]*y) - # input_img = ImageOps.fit(input_img, (x, y)) - input_img = np.array(input_img) - if input_model_name not in model_cache: - t1 = time.time() - upscaler = RealWaifuUpScaler(input_model_name[2], ModelPath + input_model_name, half=False, device="cpu") - t2 = time.time() - logger.info(f'load model time, {t2 - t1}') - model_cache[input_model_name] = upscaler - else: - upscaler = model_cache[input_model_name] - logger.info(f'load model from cache') - - start = time.time() - result = upscaler(input_img, tile_mode=input_tile_mode) - end = time.time() - logger.info(f'input_model_name, {input_model_name}') - logger.info(f'input_tile_mode, {input_tile_mode}') - logger.info(f'input shape, {input_img.shape}') - logger.info(f'output shape, {result.shape}') - logger.info(f'speed time, {end - start}') - return result - - -if __name__ == '__main__': - logging.basicConfig(level=logging.INFO, format="[%(asctime)s] [%(process)d] [%(levelname)s] %(message)s") - logger = logging.getLogger() - - ModelPath = "weights_v3/" - model_cache = {} - - input_model_name = gr.inputs.Dropdown(os.listdir(ModelPath), default="up2x-latest-denoise2x.pth", label='选择model') - input_tile_mode = gr.inputs.Dropdown([0, 1, 2, 3, 4], default=2, label='选择tile_mode') - input_img = gr.inputs.Image(label='image', type='pil') - - inputs = [input_img, input_model_name, input_tile_mode] - outputs = "image" - iface = gr.Interface(fn=greet, - inputs=inputs, - outputs=outputs, - allow_screenshot=False, - allow_flagging='never', - examples=[['test-img.jpg', "up2x-latest-denoise2x.pth", 2]], - article='[https://github.com/bilibili/ailab/tree/main/Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN)
' - '感谢b站开源的项目，图片过大会导致内存不足，所有我将图片裁剪小，想体验大图片的效果请自行前往上面的链接。
' - '修改bbb' - 'The large image will lead to memory limit exceeded. So I crop and resize image. ' - 'If you want to experience the large image, please go to the link above.') - iface.launch() diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/ImageGrab.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/ImageGrab.py deleted file mode 100644 index 982f77f206de28e086af15ad86e52dfd7aa3d2ea..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/ImageGrab.py +++ /dev/null @@ -1,149 +0,0 @@ -# -# The Python Imaging Library -# $Id$ -# -# screen grabber -# -# History: -# 2001-04-26 fl created -# 2001-09-17 fl use builtin driver, if present -# 2002-11-19 fl added grabclipboard support -# -# Copyright (c) 2001-2002 by Secret Labs AB -# Copyright (c) 2001-2002 by Fredrik Lundh -# -# See the README file for information on usage and redistribution. -# - -import os -import shutil -import subprocess -import sys -import tempfile - -from . import Image - - -def grab(bbox=None, include_layered_windows=False, all_screens=False, xdisplay=None): - if xdisplay is None: - if sys.platform == "darwin": - fh, filepath = tempfile.mkstemp(".png") - os.close(fh) - args = ["screencapture"] - if bbox: - left, top, right, bottom = bbox - args += ["-R", f"{left},{top},{right-left},{bottom-top}"] - subprocess.call(args + ["-x", filepath]) - im = Image.open(filepath) - im.load() - os.unlink(filepath) - if bbox: - im_resized = im.resize((right - left, bottom - top)) - im.close() - return im_resized - return im - elif sys.platform == "win32": - offset, size, data = Image.core.grabscreen_win32( - include_layered_windows, all_screens - ) - im = Image.frombytes( - "RGB", - size, - data, - # RGB, 32-bit line padding, origin lower left corner - "raw", - "BGR", - (size[0] * 3 + 3) & -4, - -1, - ) - if bbox: - x0, y0 = offset - left, top, right, bottom = bbox - im = im.crop((left - x0, top - y0, right - x0, bottom - y0)) - return im - elif shutil.which("gnome-screenshot"): - fh, filepath = tempfile.mkstemp(".png") - os.close(fh) - subprocess.call(["gnome-screenshot", "-f", filepath]) - im = Image.open(filepath) - im.load() - os.unlink(filepath) - if bbox: - im_cropped = im.crop(bbox) - im.close() - return im_cropped - return im - # use xdisplay=None for default display on non-win32/macOS systems - if not Image.core.HAVE_XCB: - msg = "Pillow was built without XCB support" - raise OSError(msg) - size, data = Image.core.grabscreen_x11(xdisplay) - im = Image.frombytes("RGB", size, data, "raw", "BGRX", size[0] * 4, 1) - if bbox: - im = im.crop(bbox) - return im - - -def grabclipboard(): - if sys.platform == "darwin": - fh, filepath = tempfile.mkstemp(".jpg") - os.close(fh) - commands = [ - 'set theFile to (open for access POSIX file "' - + filepath - + '" with write permission)', - "try", - " write (the clipboard as JPEG picture) to theFile", - "end try", - "close access theFile", - ] - script = ["osascript"] - for command in commands: - script += ["-e", command] - subprocess.call(script) - - im = None - if os.stat(filepath).st_size != 0: - im = Image.open(filepath) - im.load() - os.unlink(filepath) - return im - elif sys.platform == "win32": - fmt, data = Image.core.grabclipboard_win32() - if fmt == "file": # CF_HDROP - import struct - - o = struct.unpack_from("I", data)[0] - if data[16] != 0: - files = data[o:].decode("utf-16le").split("\0") - else: - files = data[o:].decode("mbcs").split("\0") - return files[: files.index("")] - if isinstance(data, bytes): - import io - - data = io.BytesIO(data) - if fmt == "png": - from . import PngImagePlugin - - return PngImagePlugin.PngImageFile(data) - elif fmt == "DIB": - from . import BmpImagePlugin - - return BmpImagePlugin.DibImageFile(data) - return None - else: - if shutil.which("wl-paste"): - args = ["wl-paste"] - elif shutil.which("xclip"): - args = ["xclip", "-selection", "clipboard", "-t", "image/png", "-o"] - else: - msg = "wl-paste or xclip is required for ImageGrab.grabclipboard() on Linux" - raise NotImplementedError(msg) - fh, filepath = tempfile.mkstemp() - subprocess.call(args, stdout=fh) - os.close(fh) - im = Image.open(filepath) - im.load() - os.unlink(filepath) - return im diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_g_a_s_p.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_g_a_s_p.py deleted file mode 100644 index 10c32a87f4b2cbedac5e346c6f5d578cb7a6b65d..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_g_a_s_p.py +++ /dev/null @@ -1,55 +0,0 @@ -from fontTools.misc.textTools import safeEval -from . import DefaultTable -import struct - - -GASP_SYMMETRIC_GRIDFIT = 0x0004 -GASP_SYMMETRIC_SMOOTHING = 0x0008 -GASP_DOGRAY = 0x0002 -GASP_GRIDFIT = 0x0001 - - -class table__g_a_s_p(DefaultTable.DefaultTable): - def decompile(self, data, ttFont): - self.version, numRanges = struct.unpack(">HH", data[:4]) - assert 0 <= self.version <= 1, "unknown 'gasp' format: %s" % self.version - data = data[4:] - self.gaspRange = {} - for i in range(numRanges): - rangeMaxPPEM, rangeGaspBehavior = struct.unpack(">HH", data[:4]) - self.gaspRange[int(rangeMaxPPEM)] = int(rangeGaspBehavior) - data = data[4:] - assert not data, "too much data" - - def compile(self, ttFont): - version = 0 # ignore self.version - numRanges = len(self.gaspRange) - data = b"" - items = sorted(self.gaspRange.items()) - for rangeMaxPPEM, rangeGaspBehavior in items: - data = data + struct.pack(">HH", rangeMaxPPEM, rangeGaspBehavior) - if rangeGaspBehavior & ~(GASP_GRIDFIT | GASP_DOGRAY): - version = 1 - data = struct.pack(">HH", version, numRanges) + data - return data - - def toXML(self, writer, ttFont): - items = sorted(self.gaspRange.items()) - for rangeMaxPPEM, rangeGaspBehavior in items: - writer.simpletag( - "gaspRange", - [ - ("rangeMaxPPEM", rangeMaxPPEM), - ("rangeGaspBehavior", rangeGaspBehavior), - ], - ) - writer.newline() - - def fromXML(self, name, attrs, content, ttFont): - if name != "gaspRange": - return - if not hasattr(self, "gaspRange"): - self.gaspRange = {} - self.gaspRange[safeEval(attrs["rangeMaxPPEM"])] = safeEval( - attrs["rangeGaspBehavior"] - ) diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/module-a3cf0cc4.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/module-a3cf0cc4.js deleted file mode 100644 index f6ae7d751ba2fcbcc91f751a82c4280eb2369128..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/module-a3cf0cc4.js +++ /dev/null @@ -1,2 +0,0 @@ -const w=t=>n=>{const e=t(n);return n.add(e),e},N=t=>(n,e)=>(t.set(n,e),e),f=Number.MAX_SAFE_INTEGER===void 0?9007199254740991:Number.MAX_SAFE_INTEGER,g=536870912,_=g*2,O=(t,n)=>e=>{const r=n.get(e);let s=r===void 0?e.size:r<_?r+1:0;if(!e.has(s))return t(e,s);if(e.sizef)throw new Error("Congratulations, you created a collection of unique numbers which uses all available integers!");for(;e.has(s);)s=Math.floor(Math.random()*f);return t(e,s)},M=new WeakMap,m=N(M),h=O(m,M),I=w(h),R=t=>typeof t.start=="function",p=new WeakMap,A=t=>({...t,connect:({call:n})=>async()=>{const{port1:e,port2:r}=new MessageChannel,s=await n("connect",{port:e},[e]);return p.set(r,s),r},disconnect:({call:n})=>async e=>{const r=p.get(e);if(r===void 0)throw new Error("The given port is not connected.");await n("disconnect",{portId:r})},isSupported:({call:n})=>()=>n("isSupported")}),E=new WeakMap,b=t=>{if(E.has(t))return E.get(t);const n=new Map;return E.set(t,n),n},W=t=>{const n=A(t);return e=>{const r=b(e);e.addEventListener("message",({data:o})=>{const{id:a}=o;if(a!==null&&r.has(a)){const{reject:u,resolve:c}=r.get(a);r.delete(a),o.error===void 0?c(o.result):u(new Error(o.error.message))}}),R(e)&&e.start();const s=(o,a=null,u=[])=>new Promise((c,l)=>{const d=h(r);r.set(d,{reject:l,resolve:c}),a===null?e.postMessage({id:d,method:o},u):e.postMessage({id:d,method:o,params:a},u)}),T=(o,a,u=[])=>{e.postMessage({id:null,method:o,params:a},u)};let i={};for(const[o,a]of Object.entries(n))i={...i,[o]:a({call:s,notify:T})};return{...i}}};export{I as a,W as c,h as g}; -//# sourceMappingURL=module-a3cf0cc4.js.map diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/themes/utils/semver_match.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/themes/utils/semver_match.py deleted file mode 100644 index 25df9265b7a0c5b6714364c1d125d85ea26d3b46..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/themes/utils/semver_match.py +++ /dev/null @@ -1,40 +0,0 @@ -from __future__ import annotations - -from dataclasses import dataclass, field - -import huggingface_hub -import semantic_version -import semantic_version as semver - - -@dataclass -class ThemeAsset: - filename: str - version: semver.Version = field(init=False) - - def __post_init__(self): - self.version = semver.Version(self.filename.split("@")[1].replace(".json", "")) - - -def get_theme_assets(space_info: huggingface_hub.hf_api.SpaceInfo) -> list[ThemeAsset]: - if "gradio-theme" not in getattr(space_info, "tags", []): - raise ValueError(f"{space_info.id} is not a valid gradio-theme space!") - - return [ - ThemeAsset(filename.rfilename) - for filename in space_info.siblings - if filename.rfilename.startswith("themes/") - ] - - -def get_matching_version( - assets: list[ThemeAsset], expression: str | None -) -> ThemeAsset | None: - expression = expression or "*" - - # Return most recent version that matches - matching_version = semantic_version.SimpleSpec(expression).select( - [a.version for a in assets] - ) - - return next((a for a in assets if a.version == matching_version), None) diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpcore/_async/interfaces.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpcore/_async/interfaces.py deleted file mode 100644 index c998dd2763262443d67d42c4f0ceb6058688f8f5..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpcore/_async/interfaces.py +++ /dev/null @@ -1,135 +0,0 @@ -from contextlib import asynccontextmanager -from typing import AsyncIterator, Optional, Union - -from .._models import ( - URL, - Extensions, - HeaderTypes, - Origin, - Request, - Response, - enforce_bytes, - enforce_headers, - enforce_url, - include_request_headers, -) - - -class AsyncRequestInterface: - async def request( - self, - method: Union[bytes, str], - url: Union[URL, bytes, str], - *, - headers: HeaderTypes = None, - content: Union[bytes, AsyncIterator[bytes], None] = None, - extensions: Optional[Extensions] = None, - ) -> Response: - # Strict type checking on our parameters. - method = enforce_bytes(method, name="method") - url = enforce_url(url, name="url") - headers = enforce_headers(headers, name="headers") - - # Include Host header, and optionally Content-Length or Transfer-Encoding. - headers = include_request_headers(headers, url=url, content=content) - - request = Request( - method=method, - url=url, - headers=headers, - content=content, - extensions=extensions, - ) - response = await self.handle_async_request(request) - try: - await response.aread() - finally: - await response.aclose() - return response - - @asynccontextmanager - async def stream( - self, - method: Union[bytes, str], - url: Union[URL, bytes, str], - *, - headers: HeaderTypes = None, - content: Union[bytes, AsyncIterator[bytes], None] = None, - extensions: Optional[Extensions] = None, - ) -> AsyncIterator[Response]: - # Strict type checking on our parameters. - method = enforce_bytes(method, name="method") - url = enforce_url(url, name="url") - headers = enforce_headers(headers, name="headers") - - # Include Host header, and optionally Content-Length or Transfer-Encoding. - headers = include_request_headers(headers, url=url, content=content) - - request = Request( - method=method, - url=url, - headers=headers, - content=content, - extensions=extensions, - ) - response = await self.handle_async_request(request) - try: - yield response - finally: - await response.aclose() - - async def handle_async_request(self, request: Request) -> Response: - raise NotImplementedError() # pragma: nocover - - -class AsyncConnectionInterface(AsyncRequestInterface): - async def aclose(self) -> None: - raise NotImplementedError() # pragma: nocover - - def info(self) -> str: - raise NotImplementedError() # pragma: nocover - - def can_handle_request(self, origin: Origin) -> bool: - raise NotImplementedError() # pragma: nocover - - def is_available(self) -> bool: - """ - Return `True` if the connection is currently able to accept an - outgoing request. - - An HTTP/1.1 connection will only be available if it is currently idle. - - An HTTP/2 connection will be available so long as the stream ID space is - not yet exhausted, and the connection is not in an error state. - - While the connection is being established we may not yet know if it is going - to result in an HTTP/1.1 or HTTP/2 connection. The connection should be - treated as being available, but might ultimately raise `NewConnectionRequired` - required exceptions if multiple requests are attempted over a connection - that ends up being established as HTTP/1.1. - """ - raise NotImplementedError() # pragma: nocover - - def has_expired(self) -> bool: - """ - Return `True` if the connection is in a state where it should be closed. - - This either means that the connection is idle and it has passed the - expiry time on its keep-alive, or that server has sent an EOF. - """ - raise NotImplementedError() # pragma: nocover - - def is_idle(self) -> bool: - """ - Return `True` if the connection is currently idle. - """ - raise NotImplementedError() # pragma: nocover - - def is_closed(self) -> bool: - """ - Return `True` if the connection has been closed. - - Used when a response is closed to determine if the connection may be - returned to the connection pool or not. - """ - raise NotImplementedError() # pragma: nocover diff --git a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_modelsummary.py b/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_modelsummary.py deleted file mode 100644 index 5e040e31d8ddffbb8b7b2e2dc4ddf0b9cdca6a23..0000000000000000000000000000000000000000 --- a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_modelsummary.py +++ /dev/null @@ -1,485 +0,0 @@ -import torch.nn as nn -import torch -import numpy as np - -''' ----- 1) FLOPs: floating point operations ----- 2) #Activations: the number of elements of all ‘Conv2d’ outputs ----- 3) #Conv2d: the number of ‘Conv2d’ layers -# -------------------------------------------- -# Kai Zhang (github: https://github.com/cszn) -# 21/July/2020 -# -------------------------------------------- -# Reference -https://github.com/sovrasov/flops-counter.pytorch.git - -# If you use this code, please consider the following citation: - -@inproceedings{zhang2020aim, % - title={AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results}, - author={Kai Zhang and Martin Danelljan and Yawei Li and Radu Timofte and others}, - booktitle={European Conference on Computer Vision Workshops}, - year={2020} -} -# -------------------------------------------- -''' - -def get_model_flops(model, input_res, print_per_layer_stat=True, - input_constructor=None): - assert type(input_res) is tuple, 'Please provide the size of the input image.' - assert len(input_res) >= 3, 'Input image should have 3 dimensions.' - flops_model = add_flops_counting_methods(model) - flops_model.eval().start_flops_count() - if input_constructor: - input = input_constructor(input_res) - _ = flops_model(**input) - else: - device = list(flops_model.parameters())[-1].device - batch = torch.FloatTensor(1, *input_res).to(device) - _ = flops_model(batch) - - if print_per_layer_stat: - print_model_with_flops(flops_model) - flops_count = flops_model.compute_average_flops_cost() - flops_model.stop_flops_count() - - return flops_count - -def get_model_activation(model, input_res, input_constructor=None): - assert type(input_res) is tuple, 'Please provide the size of the input image.' - assert len(input_res) >= 3, 'Input image should have 3 dimensions.' - activation_model = add_activation_counting_methods(model) - activation_model.eval().start_activation_count() - if input_constructor: - input = input_constructor(input_res) - _ = activation_model(**input) - else: - device = list(activation_model.parameters())[-1].device - batch = torch.FloatTensor(1, *input_res).to(device) - _ = activation_model(batch) - - activation_count, num_conv = activation_model.compute_average_activation_cost() - activation_model.stop_activation_count() - - return activation_count, num_conv - - -def get_model_complexity_info(model, input_res, print_per_layer_stat=True, as_strings=True, - input_constructor=None): - assert type(input_res) is tuple - assert len(input_res) >= 3 - flops_model = add_flops_counting_methods(model) - flops_model.eval().start_flops_count() - if input_constructor: - input = input_constructor(input_res) - _ = flops_model(**input) - else: - batch = torch.FloatTensor(1, *input_res) - _ = flops_model(batch) - - if print_per_layer_stat: - print_model_with_flops(flops_model) - flops_count = flops_model.compute_average_flops_cost() - params_count = get_model_parameters_number(flops_model) - flops_model.stop_flops_count() - - if as_strings: - return flops_to_string(flops_count), params_to_string(params_count) - - return flops_count, params_count - - -def flops_to_string(flops, units='GMac', precision=2): - if units is None: - if flops // 10**9 > 0: - return str(round(flops / 10.**9, precision)) + ' GMac' - elif flops // 10**6 > 0: - return str(round(flops / 10.**6, precision)) + ' MMac' - elif flops // 10**3 > 0: - return str(round(flops / 10.**3, precision)) + ' KMac' - else: - return str(flops) + ' Mac' - else: - if units == 'GMac': - return str(round(flops / 10.**9, precision)) + ' ' + units - elif units == 'MMac': - return str(round(flops / 10.**6, precision)) + ' ' + units - elif units == 'KMac': - return str(round(flops / 10.**3, precision)) + ' ' + units - else: - return str(flops) + ' Mac' - - -def params_to_string(params_num): - if params_num // 10 ** 6 > 0: - return str(round(params_num / 10 ** 6, 2)) + ' M' - elif params_num // 10 ** 3: - return str(round(params_num / 10 ** 3, 2)) + ' k' - else: - return str(params_num) - - -def print_model_with_flops(model, units='GMac', precision=3): - total_flops = model.compute_average_flops_cost() - - def accumulate_flops(self): - if is_supported_instance(self): - return self.__flops__ / model.__batch_counter__ - else: - sum = 0 - for m in self.children(): - sum += m.accumulate_flops() - return sum - - def flops_repr(self): - accumulated_flops_cost = self.accumulate_flops() - return ', '.join([flops_to_string(accumulated_flops_cost, units=units, precision=precision), - '{:.3%} MACs'.format(accumulated_flops_cost / total_flops), - self.original_extra_repr()]) - - def add_extra_repr(m): - m.accumulate_flops = accumulate_flops.__get__(m) - flops_extra_repr = flops_repr.__get__(m) - if m.extra_repr != flops_extra_repr: - m.original_extra_repr = m.extra_repr - m.extra_repr = flops_extra_repr - assert m.extra_repr != m.original_extra_repr - - def del_extra_repr(m): - if hasattr(m, 'original_extra_repr'): - m.extra_repr = m.original_extra_repr - del m.original_extra_repr - if hasattr(m, 'accumulate_flops'): - del m.accumulate_flops - - model.apply(add_extra_repr) - print(model) - model.apply(del_extra_repr) - - -def get_model_parameters_number(model): - params_num = sum(p.numel() for p in model.parameters() if p.requires_grad) - return params_num - - -def add_flops_counting_methods(net_main_module): - # adding additional methods to the existing module object, - # this is done this way so that each function has access to self object - # embed() - net_main_module.start_flops_count = start_flops_count.__get__(net_main_module) - net_main_module.stop_flops_count = stop_flops_count.__get__(net_main_module) - net_main_module.reset_flops_count = reset_flops_count.__get__(net_main_module) - net_main_module.compute_average_flops_cost = compute_average_flops_cost.__get__(net_main_module) - - net_main_module.reset_flops_count() - return net_main_module - - -def compute_average_flops_cost(self): - """ - A method that will be available after add_flops_counting_methods() is called - on a desired net object. - - Returns current mean flops consumption per image. - - """ - - flops_sum = 0 - for module in self.modules(): - if is_supported_instance(module): - flops_sum += module.__flops__ - - return flops_sum - - -def start_flops_count(self): - """ - A method that will be available after add_flops_counting_methods() is called - on a desired net object. - - Activates the computation of mean flops consumption per image. - Call it before you run the network. - - """ - self.apply(add_flops_counter_hook_function) - - -def stop_flops_count(self): - """ - A method that will be available after add_flops_counting_methods() is called - on a desired net object. - - Stops computing the mean flops consumption per image. - Call whenever you want to pause the computation. - - """ - self.apply(remove_flops_counter_hook_function) - - -def reset_flops_count(self): - """ - A method that will be available after add_flops_counting_methods() is called - on a desired net object. - - Resets statistics computed so far. - - """ - self.apply(add_flops_counter_variable_or_reset) - - -def add_flops_counter_hook_function(module): - if is_supported_instance(module): - if hasattr(module, '__flops_handle__'): - return - - if isinstance(module, (nn.Conv2d, nn.Conv3d, nn.ConvTranspose2d)): - handle = module.register_forward_hook(conv_flops_counter_hook) - elif isinstance(module, (nn.ReLU, nn.PReLU, nn.ELU, nn.LeakyReLU, nn.ReLU6)): - handle = module.register_forward_hook(relu_flops_counter_hook) - elif isinstance(module, nn.Linear): - handle = module.register_forward_hook(linear_flops_counter_hook) - elif isinstance(module, (nn.BatchNorm2d)): - handle = module.register_forward_hook(bn_flops_counter_hook) - else: - handle = module.register_forward_hook(empty_flops_counter_hook) - module.__flops_handle__ = handle - - -def remove_flops_counter_hook_function(module): - if is_supported_instance(module): - if hasattr(module, '__flops_handle__'): - module.__flops_handle__.remove() - del module.__flops_handle__ - - -def add_flops_counter_variable_or_reset(module): - if is_supported_instance(module): - module.__flops__ = 0 - - -# ---- Internal functions -def is_supported_instance(module): - if isinstance(module, - ( - nn.Conv2d, nn.ConvTranspose2d, - nn.BatchNorm2d, - nn.Linear, - nn.ReLU, nn.PReLU, nn.ELU, nn.LeakyReLU, nn.ReLU6, - )): - return True - - return False - - -def conv_flops_counter_hook(conv_module, input, output): - # Can have multiple inputs, getting the first one - # input = input[0] - - batch_size = output.shape[0] - output_dims = list(output.shape[2:]) - - kernel_dims = list(conv_module.kernel_size) - in_channels = conv_module.in_channels - out_channels = conv_module.out_channels - groups = conv_module.groups - - filters_per_channel = out_channels // groups - conv_per_position_flops = np.prod(kernel_dims) * in_channels * filters_per_channel - - active_elements_count = batch_size * np.prod(output_dims) - overall_conv_flops = int(conv_per_position_flops) * int(active_elements_count) - - # overall_flops = overall_conv_flops - - conv_module.__flops__ += int(overall_conv_flops) - # conv_module.__output_dims__ = output_dims - - -def relu_flops_counter_hook(module, input, output): - active_elements_count = output.numel() - module.__flops__ += int(active_elements_count) - # print(module.__flops__, id(module)) - # print(module) - - -def linear_flops_counter_hook(module, input, output): - input = input[0] - if len(input.shape) == 1: - batch_size = 1 - module.__flops__ += int(batch_size * input.shape[0] * output.shape[0]) - else: - batch_size = input.shape[0] - module.__flops__ += int(batch_size * input.shape[1] * output.shape[1]) - - -def bn_flops_counter_hook(module, input, output): - # input = input[0] - # TODO: need to check here - # batch_flops = np.prod(input.shape) - # if module.affine: - # batch_flops *= 2 - # module.__flops__ += int(batch_flops) - batch = output.shape[0] - output_dims = output.shape[2:] - channels = module.num_features - batch_flops = batch * channels * np.prod(output_dims) - if module.affine: - batch_flops *= 2 - module.__flops__ += int(batch_flops) - - -# ---- Count the number of convolutional layers and the activation -def add_activation_counting_methods(net_main_module): - # adding additional methods to the existing module object, - # this is done this way so that each function has access to self object - # embed() - net_main_module.start_activation_count = start_activation_count.__get__(net_main_module) - net_main_module.stop_activation_count = stop_activation_count.__get__(net_main_module) - net_main_module.reset_activation_count = reset_activation_count.__get__(net_main_module) - net_main_module.compute_average_activation_cost = compute_average_activation_cost.__get__(net_main_module) - - net_main_module.reset_activation_count() - return net_main_module - - -def compute_average_activation_cost(self): - """ - A method that will be available after add_activation_counting_methods() is called - on a desired net object. - - Returns current mean activation consumption per image. - - """ - - activation_sum = 0 - num_conv = 0 - for module in self.modules(): - if is_supported_instance_for_activation(module): - activation_sum += module.__activation__ - num_conv += module.__num_conv__ - return activation_sum, num_conv - - -def start_activation_count(self): - """ - A method that will be available after add_activation_counting_methods() is called - on a desired net object. - - Activates the computation of mean activation consumption per image. - Call it before you run the network. - - """ - self.apply(add_activation_counter_hook_function) - - -def stop_activation_count(self): - """ - A method that will be available after add_activation_counting_methods() is called - on a desired net object. - - Stops computing the mean activation consumption per image. - Call whenever you want to pause the computation. - - """ - self.apply(remove_activation_counter_hook_function) - - -def reset_activation_count(self): - """ - A method that will be available after add_activation_counting_methods() is called - on a desired net object. - - Resets statistics computed so far. - - """ - self.apply(add_activation_counter_variable_or_reset) - - -def add_activation_counter_hook_function(module): - if is_supported_instance_for_activation(module): - if hasattr(module, '__activation_handle__'): - return - - if isinstance(module, (nn.Conv2d, nn.ConvTranspose2d)): - handle = module.register_forward_hook(conv_activation_counter_hook) - module.__activation_handle__ = handle - - -def remove_activation_counter_hook_function(module): - if is_supported_instance_for_activation(module): - if hasattr(module, '__activation_handle__'): - module.__activation_handle__.remove() - del module.__activation_handle__ - - -def add_activation_counter_variable_or_reset(module): - if is_supported_instance_for_activation(module): - module.__activation__ = 0 - module.__num_conv__ = 0 - - -def is_supported_instance_for_activation(module): - if isinstance(module, - ( - nn.Conv2d, nn.ConvTranspose2d, - )): - return True - - return False - -def conv_activation_counter_hook(module, input, output): - """ - Calculate the activations in the convolutional operation. - Reference: Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár, Designing Network Design Spaces. - :param module: - :param input: - :param output: - :return: - """ - module.__activation__ += output.numel() - module.__num_conv__ += 1 - - -def empty_flops_counter_hook(module, input, output): - module.__flops__ += 0 - - -def upsample_flops_counter_hook(module, input, output): - output_size = output[0] - batch_size = output_size.shape[0] - output_elements_count = batch_size - for val in output_size.shape[1:]: - output_elements_count *= val - module.__flops__ += int(output_elements_count) - - -def pool_flops_counter_hook(module, input, output): - input = input[0] - module.__flops__ += int(np.prod(input.shape)) - - -def dconv_flops_counter_hook(dconv_module, input, output): - input = input[0] - - batch_size = input.shape[0] - output_dims = list(output.shape[2:]) - - m_channels, in_channels, kernel_dim1, _, = dconv_module.weight.shape - out_channels, _, kernel_dim2, _, = dconv_module.projection.shape - # groups = dconv_module.groups - - # filters_per_channel = out_channels // groups - conv_per_position_flops1 = kernel_dim1 ** 2 * in_channels * m_channels - conv_per_position_flops2 = kernel_dim2 ** 2 * out_channels * m_channels - active_elements_count = batch_size * np.prod(output_dims) - - overall_conv_flops = (conv_per_position_flops1 + conv_per_position_flops2) * active_elements_count - overall_flops = overall_conv_flops - - dconv_module.__flops__ += int(overall_flops) - # dconv_module.__output_dims__ = output_dims - - - - - diff --git a/spaces/lanyingtianyan/ChatGPT2/app.py b/spaces/lanyingtianyan/ChatGPT2/app.py deleted file mode 100644 index 3f99e51a4af57a62c36e3edbf11c92bcbd6c541b..0000000000000000000000000000000000000000 --- a/spaces/lanyingtianyan/ChatGPT2/app.py +++ /dev/null @@ -1,249 +0,0 @@ -import gradio as gr -import os -import json -import requests - -prompt_templates = {"默认ChatGPT": ""} -# Streaming endpoint -# API_URL = "https://api.openai.com/v1/chat/completions" # os.getenv("API_URL") + "/generate_stream" -OPENAI_URL = "https://api.openai.com/v1/chat/completions" # os.getenv("API_URL") + "/generate_stream" -API2D_URL = "https://openai.api2d.net/v1/chat/completions" # os.getenv("API_URL") + "/generate_stream" -convo_id = 'default' -#5c72c157a8fd54357bd13112cd71952a - -def on_prompt_template_change(prompt_template): - if not isinstance(prompt_template, str): return - if prompt_template: - return prompt_templates[prompt_template] - else: - '' - -def get_empty_state(): - return {"total_tokens": 0, "messages": []} - -def get_prompt_templates(): - with open('./prompts_zh.json','r',encoding='utf8') as fp: - json_data = json.load(fp) - for data in json_data: - act = data['act'] - prompt = data['prompt'] - prompt_templates[act] = prompt - # reader = csv.reader(csv_file) - # next(reader) # skip the header row - # for row in reader: - # if len(row) >= 2: - # act = row[0].strip('"') - # prompt = row[1].strip('"') - # prompt_templates[act] = prompt - - choices = list(prompt_templates.keys()) - choices = choices[:1] + sorted(choices[1:]) - return gr.update(value=choices[0], choices=choices) - -# Testing with my Open AI Key -# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") - -def predict(inputs, prompt_template, temperature, openai_api_key, chat_counter, context_length, chatbot=[], - history=[]): # repetition_penalty, top_k - print(openai_api_key) - if openai_api_key.startswith('sk-'): - API_URL = OPENAI_URL - print(1) - elif openai_api_key.startswith('fk'): - API_URL = API2D_URL - print(2) - else: - API_URL = OPENAI_URL - print(3) - if inputs==None: - inputs = '' - if prompt_template: - prompt_template = prompt_templates[prompt_template] - else: - prompt_template = "" - # system_prompt = [] - # if prompt_template: - # history = [{"role": "system", "content": prompt_template}] - - - payload = { - "model": "gpt-3.5-turbo", - "messages": [{"role": "system", "content": prompt_template},{"role": "user", "content": f"{inputs}"}], - "temperature": 1.0, - "top_p": 1.0, - "n": 1, - "stream": True, - "presence_penalty": 0, - "frequency_penalty": 0, - } - - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {openai_api_key}" - } - - # print(f"chat_counter - {chat_counter}") - if chat_counter != 0: - messages = [] - # print(chatbot) - # print(chatbot[-context_length:]) - # print(context_length) - for data in chatbot[-context_length:]: - temp1 = {} - temp1["role"] = "user" - temp1["content"] = data[0] - temp2 = {} - temp2["role"] = "assistant" - temp2["content"] = data[1] - messages.append(temp1) - messages.append(temp2) - temp3 = {} - temp3["role"] = "user" - temp3["content"] = inputs - messages.append(temp3) - # print(messages) - # messages - payload = { - "model": "gpt-3.5-turbo", - "messages": [{"role": "system", "content": prompt_template}]+messages, # [{"role": "user", "content": f"{inputs}"}], - "temperature": temperature, # 1.0, - "n": 1, - "stream": True, - "presence_penalty": 0, - "frequency_penalty": 0, - } - - - - history.append(inputs) - # print(f"payload is - {payload}") - # make a POST request to the API endpoint using the requests.post method, passing in stream=True - # print('payload',payload) - response = requests.post(API_URL, headers=headers, json=payload, stream=True) - - # print('response', response) - # print('content',response.content) - # print('text', response.text) - if response.status_code != 200: - try: - payload['id'] = response.content['id'] - response = requests.post(API_URL, headers=headers, json=payload, stream=True) - if response.status_code != 200: - payload['id'] = response.content['id'] - response = requests.post(API_URL, headers=headers, json=payload, stream=True) - except: - pass - - # print('status_code', response.status_code) - # response = requests.post(API_URL, headers=headers, json=payload, stream=True) - token_counter = 0 - partial_words = "" - counter = 0 - if response.status_code==200: - chat_counter += 1 - # print('chunk') - for chunk in response.iter_lines(): - # Skipping first chunk - if counter == 0: - counter += 1 - continue - # check whether each line is non-empty - chunk = chunk.decode("utf-8")[6:] - if chunk: - # print(chunk) - if chunk=='[DONE]': - break - resp: dict = json.loads(chunk) - choices = resp.get("choices") - if not choices: - continue - delta = choices[0].get("delta") - if not delta: - continue - # decode each line as response data is in bytes - if len(chunk) > 12 and "content" in resp['choices'][0]['delta']: - # if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0: - # break - partial_words = partial_words + resp['choices'][0]["delta"]["content"] - # print(partial_words) - if token_counter == 0: - history.append(" " + partial_words) - else: - history[-1] = partial_words - chat = [(history[i], history[i + 1]) for i in - range(0, len(history) - 1, 2)] # convert to tuples of list - # print(chat) - token_counter += 1 - yield chat, history, chat_counter # resembles {chatbot: chat, state: history} - else: - chat = [(history[i], history[i + 1]) for i in - range(0, len(history) - 1, 2)] # convert to tuples of list - chat.append((inputs, "OpenAI服务器网络出现错误，请重试，或重启对话")) - token_counter += 1 - yield chat, history, chat_counter # resembles {chatbot: chat, state: history} - # yield ['OpenAI服务器网络出现错误'], ['OpenAI服务器网络出现错误'], gr.update(value=0) - - - -def reset_textbox(): - return gr.update(value='') - -def clear_conversation(chatbot): - return gr.update(value=None, visible=True), [], [], gr.update(value=0) - - -title = """

ChatGPT

""" -description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form: -``` -User: -Assistant: -User: -Assistant: -... -``` -In this app, you can explore the outputs of a gpt-3.5-turbo LLM. -""" -with gr.Blocks(css="""#col_container {width: 800px; margin-left: auto; margin-right: auto;} - #chatbot {height: 500px; overflow: auto;} - #inputs {font-size: 20px;} - #prompt_template_preview {padding: 1em; border-width: 1px; border-style: solid; border-color: #e0e0e0; border-radius: 4px;}""") as demo: - gr.HTML(title) - # gr.HTML( - # '''
Duplicate Space点击图标复制App
''') - with gr.Column(elem_id="col_container"): - openai_api_key = gr.Textbox(type='password', label="输入你的API Key",placeholder="OpenAI API Key 或者 API2D") - chatbot = gr.Chatbot(elem_id='chatbot') # c - inputs = gr.Textbox(show_label=False, placeholder="在这里输入内容",elem_id="inputs",value='') # t - state = gr.State([]) # s - # state = gr.State(get_empty_state()) - b1 = gr.Button("提交") - btn_clear_conversation = gr.Button("🔃 开启新的对话") - - # inputs, top_p, temperature, top_k, repetition_penalty - with gr.Accordion("高级设置", open=False): - context_length = gr.Slider(minimum=1, maximum=6, value=2, step=1, label="对话长度", - info="关联之前的几轮对话，数值越高tokens消耗越多") - temperature = gr.Slider(minimum=0, maximum=2.0, value=0.7, step=0.1, label="Temperature", - info="数值越高创造性越强") - prompt_template = gr.Dropdown(label="选择机器人类型", - choices=list(prompt_templates.keys())) - prompt_template_preview = gr.Markdown(elem_id="prompt_template_preview") - # top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",) - # repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", ) - chat_counter = gr.Number(value=0, visible=False, precision=0) - - inputs.submit(predict, [inputs, prompt_template, temperature, openai_api_key, chat_counter, context_length, chatbot, state], - [chatbot, state, chat_counter], ) - b1.click(predict, [inputs, prompt_template, temperature, openai_api_key, chat_counter, context_length, chatbot, state], - [chatbot, state, chat_counter], ) - b1.click(reset_textbox, [], [inputs]) - - btn_clear_conversation.click(clear_conversation, [], [inputs, chatbot, state, chat_counter]) - - inputs.submit(reset_textbox, [], [inputs]) - prompt_template.change(on_prompt_template_change, inputs=[prompt_template], outputs=[prompt_template_preview]) - demo.load(get_prompt_templates, inputs=None, outputs=[prompt_template], queur=False) - - # gr.Markdown(description) - demo.queue(concurrency_count=10) - demo.launch(debug=True) \ No newline at end of file diff --git a/spaces/leilaglewis/01-3dModel-GradioDemo/Files/readme.md b/spaces/leilaglewis/01-3dModel-GradioDemo/Files/readme.md deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/lgaleana/toolkit/examples/best_clubs.py b/spaces/lgaleana/toolkit/examples/best_clubs.py deleted file mode 100644 index 82846b0e42b3d2d24a0da4ac768439b748f778fe..0000000000000000000000000000000000000000 --- a/spaces/lgaleana/toolkit/examples/best_clubs.py +++ /dev/null @@ -1,58 +0,0 @@ -import gradio as gr -from components import AITask, CodeTask - -from examples import demo_buttons, demo_tasks - - -DEMO_ID = __name__ -tasks = [ - CodeTask( - 0, - "nightlife in NYC", - visible=True, - code_value="Make a google search.", - ), - CodeTask( - 1, - "{t0}", - visible=True, - code_value="Get the main content from a list of urls. Top 5. No html. No empty lines. Include only the first 3000 characters. Use the correct headers.", - ), - AITask( - 2, - """Here is the content from a list of websites: -{t1} - -What is the overal topic? -Extract the most relevant points.""", - visible=True, - ), -] -demo_tasks[DEMO_ID] = tasks - - -def render(): - with gr.Tab("Example: Nightlife in NYC"): - demo_id = gr.Textbox(DEMO_ID, visible=False) - with gr.Box(): - gr.Dropdown( - value=CodeTask.name, - label="Pick a new Task", - interactive=False, - ) - tasks[0].render() - with gr.Box(): - gr.Dropdown( - value=CodeTask.name, - label="Pick a new Task", - interactive=False, - ) - tasks[1].render() - with gr.Box(): - gr.Dropdown( - value=AITask.name, - label="Pick a new Task", - interactive=False, - ) - tasks[2].render() - demo_buttons(demo_id, tasks) diff --git a/spaces/limcheekin/orca_mini_v3_13B-GGML/Dockerfile b/spaces/limcheekin/orca_mini_v3_13B-GGML/Dockerfile deleted file mode 100644 index c8df5986cce2be664dfb691a4306ee7bd3c36701..0000000000000000000000000000000000000000 --- a/spaces/limcheekin/orca_mini_v3_13B-GGML/Dockerfile +++ /dev/null @@ -1,35 +0,0 @@ -# Grab a fresh copy of the Python image -FROM python:3.10-slim - -# Install build and runtime dependencies -RUN apt-get update && \ - apt-get install -y \ - libopenblas-dev \ - ninja-build \ - build-essential \ - pkg-config \ - curl - -RUN pip install -U pip setuptools wheel && \ - CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" FORCE_CMAKE=1 pip install --verbose llama-cpp-python[server] - -# Download model -RUN mkdir model && \ - curl -L https://huggingface.co/TheBloke/orca_mini_v3_13B-GGML/resolve/main/orca_mini_v3_13b.ggmlv3.q5_K_S.bin -o model/ggmlv3-model.bin - -COPY ./start_server.sh ./ -COPY ./main.py ./ -COPY ./index.html ./ - -# Make the server start script executable -RUN chmod +x ./start_server.sh - -# Set environment variable for the host -ENV HOST=0.0.0.0 -ENV PORT=7860 - -# Expose a port for the server -EXPOSE ${PORT} - -# Run the server start script -CMD ["/bin/sh", "./start_server.sh"] \ No newline at end of file diff --git a/spaces/llamaindex/llama_index_term_definition_demo/utils.py b/spaces/llamaindex/llama_index_term_definition_demo/utils.py deleted file mode 100644 index a8b20f3943de7fd7d78d0c37189cdefd8d32ece1..0000000000000000000000000000000000000000 --- a/spaces/llamaindex/llama_index_term_definition_demo/utils.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -from langchain import OpenAI -from langchain.chat_models import ChatOpenAI - - -def get_llm(llm_name, model_temperature, api_key, max_tokens=256): - os.environ["OPENAI_API_KEY"] = api_key - if llm_name == "text-davinci-003": - return OpenAI( - temperature=model_temperature, model_name=llm_name, max_tokens=max_tokens - ) - else: - return ChatOpenAI( - temperature=model_temperature, model_name=llm_name, max_tokens=max_tokens - ) diff --git a/spaces/ls291/ChatSQL/utility/db_tools.py b/spaces/ls291/ChatSQL/utility/db_tools.py deleted file mode 100644 index c1f7bbce5a8f5366cad986c0bff889bf7ae76a96..0000000000000000000000000000000000000000 --- a/spaces/ls291/ChatSQL/utility/db_tools.py +++ /dev/null @@ -1,157 +0,0 @@ -""" -@Time: 2022/11/03 -@Author: LiuShu -@File: 数据库操作类库 -""" -import pymysql -from utility.loggers import logger -from utility.utils import config - - -class Cur_db(object): - def __init__(self): - self.config = config - self.db_name = self.config['database']['DB'] - - def pymysql_cur(self, reback=5): - """ 连接数据库 """ - try: - self.conn = pymysql.connect(host=self.config['database']['HOST'], user=self.config['database']['USER'], - password=self.config['database']['PWD'], db=self.db_name, - port=int(self.config['database']['PORT']), - charset='utf8') - except Exception as e: - if reback == 0: - logger.exception('Exception occurred.') - return - else: - logger.exception('Exception occurred.') - reback -= 1 - return self.pymysql_cur(reback) - - def get_db_name(self): - """ - - :return: - """ - return self.db_name - - def select(self, sql, params, reback=2): - """ 查询单条语句，并返回查询所有的结果 """ - try: - cur = self.conn.cursor() - cur.execute(sql, params) - # 单条 - res = cur.fetchone() - cur.close() - if res: - return res - return - except Exception as e: - logger.exception('Exception occurred.') - if reback > 0: - reback -= 1 - return self.select(sql, reback) - else: - logger.info(str('*' * 100)) - return - - def _select(self, sql, reback=2): - try: - cur = self.conn.cursor() - cur.execute(sql) - # 单条 - res = cur.fetchone() - cur.close() - if res: - return res[0] - return - except Exception as e: - logger.exception('Exception occurred.') - if reback > 0: - reback -= 1 - return self.select(sql, reback) - else: - logger.info(str('*' * 100)) - return - - def selectMany(self, sql, reback=2): - try: - cur = self.conn.cursor() - cur.execute(sql) - res = cur.fetchall() - cur.close() - if res: - return res - logger.info(str(sql)) - return - except Exception as e: - logger.exception('Exception occurred.') - if reback > 0: - reback -= 1 - return self.selectMany(sql, reback) - else: - logger.info(str('*' * 100)) - return - - def insert(self, sql, params): - cur = self.conn.cursor() - cur.execute(sql, params) - self.conn.commit() - return - - def _insert(self, sql): - cur = self.conn.cursor() - cur.execute(sql) - self.conn.commit() - - def insert_batch(self, sql, data_list): - """ - 将dataframe批量入库 - :param sql: 插入语句 - :return: - """ - cur = self.conn.cursor() - # 开启事务 - self.conn.begin() - try: - cur.executemany(sql, data_list) - self.conn.commit() - cur.close() - self.conn.close() - return True - except: - # 万一失败了，要进行回滚操作 - self.conn.rollback() - cur.close() - self.conn.close() - return False - - def update(self, sql, params): - cur = self.conn.cursor() - cur.execute(sql, params) - self.conn.commit() - return - - def _update(self, sql): - try: - cur = self.conn.cursor() - cur.execute(sql) - self.conn.commit() - except Exception as e: - logger.exception('Exception occurred.') - - def close(self): - self.conn.close() - pass - - -if __name__ == '__main__': - db_con = Cur_db() - logger.info(str(db_con.config['database']['HOST'])) - print(str(db_con.config['database']['HOST'])) - db_con.pymysql_cur() - sql = "SELECT * FROM cargo" - res = db_con.selectMany(sql) - print(str(res)) - db_con.close() \ No newline at end of file diff --git a/spaces/ltg/chat-nort5/keyword_generation_nort5_small/README.md b/spaces/ltg/chat-nort5/keyword_generation_nort5_small/README.md deleted file mode 100644 index a90c4f2662f4e061e7a725c580849c3172635b84..0000000000000000000000000000000000000000 --- a/spaces/ltg/chat-nort5/keyword_generation_nort5_small/README.md +++ /dev/null @@ -1,62 +0,0 @@ ---- -language: -- 'no' -- nb -- nn -inference: false -tags: -- T5 -- NorT5 -- Norwegian -- encoder-decoder -license: cc-by-4.0 -pipeline_tag: text2text-generation ---- - -# NorT5 x-small - - -## Other sizes: -- [NorT5 xs (15M)](https://huggingface.co/ltg/nort5-xs) -- [NorT5 small (40M)](https://huggingface.co/ltg/nort5-small) -- [NorT5 base (123M)](https://huggingface.co/ltg/nort5-base) -- [NorT5 large (323M)](https://huggingface.co/ltg/nort5-large) - - -## Example usage - -This model currently needs a custom wrapper from `modeling_nort5.py`. Then you can use it like this: - -```python -import torch -from transformers import AutoTokenizer -from modeling_norbert import NorT5ForConditionalGeneration - -tokenizer = AutoTokenizer.from_pretrained("path/to/folder") -t5 = NorT5ForConditionalGeneration.from_pretrained("path/to/folder") - - -# MASKED LANGUAGE MODELING - -sentence = "Brukseksempel: Elektrisk oppvarming. Definisjonen på ordet oppvarming er[MASK_0]." -encoding = tokenizer(sentence) - -input_tensor = torch.tensor([encoding.input_ids]) -output_tensor = model.generate(input_tensor, decoder_start_token_id=7, eos_token_id=8) -tokenizer.decode(output_tensor.squeeze(), skip_special_tokens=True) - -# should output: å varme opp - - -# PREFIX LANGUAGE MODELING -# you need to finetune this model or use `nort5-{size}-lm` model, which is finetuned on prefix language modeling - -sentence = "Brukseksempel: Elektrisk oppvarming. Definisjonen på ordet oppvarming er (Wikipedia) " -encoding = tokenizer(sentence) - -input_tensor = torch.tensor([encoding.input_ids]) -output_tensor = model.generate(input_tensor, max_new_tokens=50, num_beams=4, do_sample=False) -tokenizer.decode(output_tensor.squeeze()) - -# should output: [BOS]ˈoppvarming, det vil si at det skjer en endring i temperaturen i et medium, f.eks. en ovn eller en radiator, slik at den blir varmere eller kaldere, eller at den blir varmere eller kaldere, eller at den blir -``` \ No newline at end of file diff --git a/spaces/lunarring/latentblending/ldm/models/diffusion/plms.py b/spaces/lunarring/latentblending/ldm/models/diffusion/plms.py deleted file mode 100644 index 7002a365d27168ced0a04e9a4d83e088f8284eae..0000000000000000000000000000000000000000 --- a/spaces/lunarring/latentblending/ldm/models/diffusion/plms.py +++ /dev/null @@ -1,244 +0,0 @@ -"""SAMPLING ONLY.""" - -import torch -import numpy as np -from tqdm import tqdm -from functools import partial - -from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like -from ldm.models.diffusion.sampling_util import norm_thresholding - - -class PLMSSampler(object): - def __init__(self, model, schedule="linear", **kwargs): - super().__init__() - self.model = model - self.ddpm_num_timesteps = model.num_timesteps - self.schedule = schedule - - def register_buffer(self, name, attr): - if type(attr) == torch.Tensor: - if attr.device != torch.device("cuda"): - attr = attr.to(torch.device("cuda")) - setattr(self, name, attr) - - def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): - if ddim_eta != 0: - raise ValueError('ddim_eta must be 0 for PLMS') - self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, - num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) - alphas_cumprod = self.model.alphas_cumprod - assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' - to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) - - self.register_buffer('betas', to_torch(self.model.betas)) - self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) - self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) - - # calculations for diffusion q(x_t | x_{t-1}) and others - self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) - self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) - self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) - self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) - self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) - - # ddim sampling parameters - ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), - ddim_timesteps=self.ddim_timesteps, - eta=ddim_eta,verbose=verbose) - self.register_buffer('ddim_sigmas', ddim_sigmas) - self.register_buffer('ddim_alphas', ddim_alphas) - self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) - self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) - sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( - (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( - 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) - self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) - - @torch.no_grad() - def sample(self, - S, - batch_size, - shape, - conditioning=None, - callback=None, - normals_sequence=None, - img_callback=None, - quantize_x0=False, - eta=0., - mask=None, - x0=None, - temperature=1., - noise_dropout=0., - score_corrector=None, - corrector_kwargs=None, - verbose=True, - x_T=None, - log_every_t=100, - unconditional_guidance_scale=1., - unconditional_conditioning=None, - # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... - dynamic_threshold=None, - **kwargs - ): - if conditioning is not None: - if isinstance(conditioning, dict): - cbs = conditioning[list(conditioning.keys())[0]].shape[0] - if cbs != batch_size: - print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") - else: - if conditioning.shape[0] != batch_size: - print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") - - self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) - # sampling - C, H, W = shape - size = (batch_size, C, H, W) - print(f'Data shape for PLMS sampling is {size}') - - samples, intermediates = self.plms_sampling(conditioning, size, - callback=callback, - img_callback=img_callback, - quantize_denoised=quantize_x0, - mask=mask, x0=x0, - ddim_use_original_steps=False, - noise_dropout=noise_dropout, - temperature=temperature, - score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - x_T=x_T, - log_every_t=log_every_t, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - dynamic_threshold=dynamic_threshold, - ) - return samples, intermediates - - @torch.no_grad() - def plms_sampling(self, cond, shape, - x_T=None, ddim_use_original_steps=False, - callback=None, timesteps=None, quantize_denoised=False, - mask=None, x0=None, img_callback=None, log_every_t=100, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, - dynamic_threshold=None): - device = self.model.betas.device - b = shape[0] - if x_T is None: - img = torch.randn(shape, device=device) - else: - img = x_T - - if timesteps is None: - timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps - elif timesteps is not None and not ddim_use_original_steps: - subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 - timesteps = self.ddim_timesteps[:subset_end] - - intermediates = {'x_inter': [img], 'pred_x0': [img]} - time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps) - total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] - print(f"Running PLMS Sampling with {total_steps} timesteps") - - iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) - old_eps = [] - - for i, step in enumerate(iterator): - index = total_steps - i - 1 - ts = torch.full((b,), step, device=device, dtype=torch.long) - ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long) - - if mask is not None: - assert x0 is not None - img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? - img = img_orig * mask + (1. - mask) * img - - outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, - quantize_denoised=quantize_denoised, temperature=temperature, - noise_dropout=noise_dropout, score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - old_eps=old_eps, t_next=ts_next, - dynamic_threshold=dynamic_threshold) - img, pred_x0, e_t = outs - old_eps.append(e_t) - if len(old_eps) >= 4: - old_eps.pop(0) - if callback: callback(i) - if img_callback: img_callback(pred_x0, i) - - if index % log_every_t == 0 or index == total_steps - 1: - intermediates['x_inter'].append(img) - intermediates['pred_x0'].append(pred_x0) - - return img, intermediates - - @torch.no_grad() - def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, - dynamic_threshold=None): - b, *_, device = *x.shape, x.device - - def get_model_output(x, t): - if unconditional_conditioning is None or unconditional_guidance_scale == 1.: - e_t = self.model.apply_model(x, t, c) - else: - x_in = torch.cat([x] * 2) - t_in = torch.cat([t] * 2) - c_in = torch.cat([unconditional_conditioning, c]) - e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) - e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) - - if score_corrector is not None: - assert self.model.parameterization == "eps" - e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) - - return e_t - - alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas - alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev - sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas - sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas - - def get_x_prev_and_pred_x0(e_t, index): - # select parameters corresponding to the currently considered timestep - a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) - a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) - sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) - sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) - - # current prediction for x_0 - pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() - if quantize_denoised: - pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) - if dynamic_threshold is not None: - pred_x0 = norm_thresholding(pred_x0, dynamic_threshold) - # direction pointing to x_t - dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t - noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature - if noise_dropout > 0.: - noise = torch.nn.functional.dropout(noise, p=noise_dropout) - x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise - return x_prev, pred_x0 - - e_t = get_model_output(x, t) - if len(old_eps) == 0: - # Pseudo Improved Euler (2nd order) - x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) - e_t_next = get_model_output(x_prev, t_next) - e_t_prime = (e_t + e_t_next) / 2 - elif len(old_eps) == 1: - # 2nd order Pseudo Linear Multistep (Adams-Bashforth) - e_t_prime = (3 * e_t - old_eps[-1]) / 2 - elif len(old_eps) == 2: - # 3nd order Pseudo Linear Multistep (Adams-Bashforth) - e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 - elif len(old_eps) >= 3: - # 4nd order Pseudo Linear Multistep (Adams-Bashforth) - e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 - - x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) - - return x_prev, pred_x0, e_t diff --git a/spaces/luost26/DiffAb/abnumber/alignment.py b/spaces/luost26/DiffAb/abnumber/alignment.py deleted file mode 100644 index 625303d0a310406060120c1cddec10e6e420b398..0000000000000000000000000000000000000000 --- a/spaces/luost26/DiffAb/abnumber/alignment.py +++ /dev/null @@ -1,195 +0,0 @@ -from typing import Union - -from abnumber.common import is_similar_residue, is_integer -from abnumber.position import Position - - -class Alignment: - """Antibody chain alignment of two or more chains - - >>> from abnumber import Chain - >>> - >>> seq1 = 'QVQLQQSGAELARPGASVKMSCKASGYTFTRYTMHWVKQRPGQGLEWIGYINPSRGYTNYNQKFKDKATLTTDKSSSTAYMQLSSLTSEDSAVYYCARYYDDHYCLDYWGQGTTLTVSSAKTTAP' - >>> chain1 = Chain(seq1, scheme='imgt') - >>> - >>> seq2 = 'QVQLVQSGAELDRPGATVKMSCKASGYTTTRYTMHWVKQRPGQGLDWIGYINPSDRSYTNYNQKFKDKATLTTDKSSSTAYMQKTSLTSEDSAVYYCARYYDDYLDRWGQGTTLTVSSAKTTAP' - >>> chain2 = Chain(seq2, scheme='imgt') - >>> alignment = chain1.align(chain2) - - Alignment can be sliced and iterated: - - >>> for pos, (aa, bb) in alignment[:'5']: - >>> print(pos, aa, bb) - H1 Q Q - H2 V V - H3 Q Q - H4 L L - H5 Q V - ... - - """ - def __init__(self, positions, residues, scheme, chain_type): - assert isinstance(positions, list), 'Expected list of positions and residues. ' \ - 'Use chain.align(other) to create an alignment.' - assert len(positions) == len(residues) - unique_cdr_definitions = set(pos.cdr_definition for pos in positions) - assert len(unique_cdr_definitions) <= 1, f'Aligned chains should use the same CDR definitions, got: {unique_cdr_definitions}' - self.positions = positions - self.residues = residues - self.scheme = scheme - self.chain_type = chain_type - self._zipped = list(zip(self.positions, self.residues)) - - def __repr__(self): - return self.format() - - def __iter__(self): - yield from self._zipped.__iter__() - - def __len__(self): - return len(self.positions) - - def __getitem__(self, item): - if isinstance(item, slice): - if item.step is not None and item.step != 1: - raise IndexError(f'Slicing with step != 1 is not implemented, got: {item}') - return self.slice(start=item.start, stop=item.stop) - pos = self._parse_position(item) - raw_pos = self.positions.index(pos) - return self.residues[raw_pos] - - def slice(self, start: Union[str, int, 'Position'] = None, stop: Union[str, int, 'Position'] = None, - stop_inclusive: bool = True, allow_raw: bool = False): - """Create a slice of this alignment - - You can also slice directly using ``alignment['111':'112A']`` or ``alignment.raw[10:20]``. - - :param start: Slice start position (inclusive), :class:`Position` or string (e.g. '111A') - :param stop: Slice stop position (inclusive), :class:`Position` or string (e.g. '112A') - :param stop_inclusive: Include stop position in slice - :param allow_raw: Allow unaligned numeric indexing from 0 to length of sequence - 1 - :return: new sliced Alignment object - """ - - start = self._parse_position(start, allow_raw=allow_raw) if start is not None else None - stop = self._parse_position(stop, allow_raw=allow_raw) if stop is not None else None - - new_positions = [] - new_residues = [] - for pos, residues in zip(self.positions, self.residues): - if start is not None and pos < start: - continue - if stop is not None and (pos > stop or (not stop_inclusive and pos >= stop)): - break - new_positions.append(pos) - new_residues.append(residues) - - return Alignment(positions=new_positions, residues=new_residues, scheme=self.scheme, chain_type=self.chain_type) - - def _parse_position(self, position: Union[int, str, 'Position'], allow_raw=False): - """Create :class:`Position` key object from string or int. - - Note: The position should only be used for indexing, CDR definition is not preserved! - - :param position: Numeric or string position representation - :param allow_raw: Also allow unaligned numeric (int) indexing from 0 to length of sequence - 1 - :return: new Position object, should only be used for indexing, CDR definition is not preserved! - """ - if isinstance(position, str): - return Position.from_string(position, chain_type=self.chain_type, scheme=self.scheme) - if isinstance(position, Position): - return position - try: - position = int(position) - except TypeError: - raise IndexError(f'Invalid position key, expected Position, string or integer, got {type(position)}: "{position}"') - if not allow_raw: - raise IndexError("Use chain.raw[i] for raw numeric indexing or pass allow_raw=True. " - "For named position indexing, use string (e.g. chain['111A'] or chain['H111A'])") - if position >= len(self.positions): - return None - return self.positions[position] - - def format(self, mark_identity=True, mark_cdrs=True): - """Format alignment to string - - :param mark_identity: Add BLAST style middle line showing identity (``|``), similar residue (``+``) or different residue (``.``) - :param mark_cdrs: Add line highlighting CDR regions using ``^`` - :return: formatted string - """ - - def _identity_symbol(a, b): - return '|' if a == b else ('+' if is_similar_residue(a, b) else '.') - - lines = [] - for i in range(len(self.residues[0])): - if mark_identity and i != 0: - lines.append(''.join(_identity_symbol(aas[i], aas[i-1]) for pos, aas in self)) - lines.append(''.join(aas[i] for pos, aas in self)) - if mark_cdrs: - if self.positions[0].cdr_definition == 'kabat': - lines.append(''.join('^' if pos.is_in_cdr() else ("°" if pos.is_in_vernier() else ' ') for pos in self.positions)) - else: - lines.append(''.join('^' if pos.is_in_cdr() else ' ' for pos in self.positions)) - return '\n'.join(lines) - - def print(self, mark_identity=True, mark_cdrs=True): - """Print string representation of alignment created using :meth:`Alignment.format` - - >>> alignment.print() - QVQLQQSGAELARPGASVKMSCKASGYTFTRYTMHWVKQRPGQGLEWIGYINPS-RGYTNYNQKFKDKATLTTDKSSSTAYMQLSSLTSEDSAVYYCARYYDDHYCLDYWGQGTTLTVSS - ||||.||||||.||||+|||||||||||.||||||||||||||||+||||||||.|.||||||||||||||||||||||||||.+|||||||||||||||||....||.||||||||||| - QVQLVQSGAELDRPGATVKMSCKASGYTTTRYTMHWVKQRPGQGLDWIGYINPSDRSYTNYNQKFKDKATLTTDKSSSTAYMQKTSLTSEDSAVYYCARYYD--DYLDRWGQGTTLTVSS - ^^^^^^^^ ^^^^^^^^^ ^^^^^^^^^^^^ - >>> alignment.print(mark_identity=False, mark_cdrs=False) - QVQLQQSGAELARPGASVKMSCKASGYTFTRYTMHWVKQRPGQGLEWIGYINPS-RGYTNYNQKFKDKATLTTDKSSSTAYMQLSSLTSEDSAVYYCARYYDDHYCLDYWGQGTTLTVSS - QVQLVQSGAELDRPGATVKMSCKASGYTTTRYTMHWVKQRPGQGLDWIGYINPSDRSYTNYNQKFKDKATLTTDKSSSTAYMQKTSLTSEDSAVYYCARYYD--DYLDRWGQGTTLTVSS - - :param mark_identity: Add BLAST style middle line showing identity (``|``), similar residue (``+``) or different residue (``.``) - :param mark_cdrs: Add line highlighting CDR regions using ``^`` - """ - print(self.format(mark_identity=mark_identity, mark_cdrs=mark_cdrs)) - - def has_mutation(self): - """Check if there is a mutation in the alignment or not""" - return any(len(set(aas)) != 1 for aas in self.residues) - - def num_mutations(self): - """Get number of mutations (positions with more than one type of residue)""" - return sum(len(set(aas)) != 1 for aas in self.residues) - - @property - def raw(self): - """Access raw representation of this alignment to allow unaligned numeric indexing and slicing - - >>> # Numbering of ``chain.raw`` starts at 0 - >>> alignment.raw[0] - 'H1' - >>> # Slicing with string is based on schema numbering, the end is inclusive - >>> chain['1':'10'] - 'QVQLQQSGAE' - >>> # Slicing with ``chain.raw`` starts at 0, the end is exclusive (Python style) - >>> chain.raw[0:10] - 'QVQLQQSGAE' - :return: Raw alignment accessor that can be sliced or indexed to produce a new :class:`Alignment` object - """ - return RawAlignmentAccessor(self) - - -class RawAlignmentAccessor: - def __init__(self, alignment: Alignment): - self.alignment = alignment - - def __getitem__(self, item): - if isinstance(item, slice): - if item.step is not None and item.step != 1: - raise IndexError(f'Slicing with step != 1 is not implemented, got: {item}') - if item.start is not None and not is_integer(item.start): - raise IndexError(f'Expected int start index for alignment.raw, got {type(item.start)}: {item.start}') - if item.stop is not None and not is_integer(item.stop): - raise IndexError(f'Expected int end index for alignment.raw, got {type(item.stop)}: {item.stop}') - return self.alignment.slice(start=item.start, stop=item.stop, stop_inclusive=False, allow_raw=True) - if not is_integer(item): - raise IndexError(f'Expected int indexing for alignment.raw, got {type(item)}: {item}') - pos = self.alignment.positions[item] - return self.alignment[pos] diff --git a/spaces/ma-xu/LIVE/thrust/thrust/detail/complex/cproj.h b/spaces/ma-xu/LIVE/thrust/thrust/detail/complex/cproj.h deleted file mode 100644 index 563c92f69764323f98066c37d06227a14a50a3b4..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/thrust/detail/complex/cproj.h +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * Copyright 2013 Filipe RNC Maia - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include -#include -#include - -namespace thrust{ -namespace detail{ -namespace complex{ -__host__ __device__ -inline complex cprojf(const complex& z){ - if(!isinf(z.real()) && !isinf(z.imag())){ - return z; - }else{ - // std::numeric_limits::infinity() doesn't run on the GPU - return complex(infinity(), copysignf(0.0, z.imag())); - } -} - -__host__ __device__ -inline complex cproj(const complex& z){ - if(!isinf(z.real()) && !isinf(z.imag())){ - return z; - }else{ - // std::numeric_limits::infinity() doesn't run on the GPU - return complex(infinity(), copysign(0.0, z.imag())); - } -} - -} - -} - -template -__host__ __device__ -inline thrust::complex proj(const thrust::complex& z){ - return detail::complex::cproj(z); -} - - -template <> -__host__ __device__ -inline thrust::complex proj(const thrust::complex& z){ - return detail::complex::cproj(z); -} - -template <> -__host__ __device__ -inline thrust::complex proj(const thrust::complex& z){ - return detail::complex::cprojf(z); -} - -} - diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/sequential/reduce.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/sequential/reduce.h deleted file mode 100644 index 55e92acb9afc75787955a74808fb6cca96c45964..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/sequential/reduce.h +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - - -/*! \file reduce.h - * \brief Sequential implementation of reduce algorithm. - */ - -#pragma once - -#include -#include -#include - -namespace thrust -{ -namespace system -{ -namespace detail -{ -namespace sequential -{ - - -__thrust_exec_check_disable__ -template -__host__ __device__ - OutputType reduce(sequential::execution_policy &, - InputIterator begin, - InputIterator end, - OutputType init, - BinaryFunction binary_op) -{ - // wrap binary_op - thrust::detail::wrapped_function< - BinaryFunction, - OutputType - > wrapped_binary_op(binary_op); - - // initialize the result - OutputType result = init; - - while(begin != end) - { - result = wrapped_binary_op(result, *begin); - ++begin; - } // end while - - return result; -} - - -} // end namespace sequential -} // end namespace detail -} // end namespace system -} // end namespace thrust - diff --git a/spaces/makanaan/paraphrase/app.py b/spaces/makanaan/paraphrase/app.py deleted file mode 100644 index 85ef56e145814a7e7fde70ef3e69ad650fd75fb2..0000000000000000000000000000000000000000 --- a/spaces/makanaan/paraphrase/app.py +++ /dev/null @@ -1,40 +0,0 @@ -#from transformers import pipeline -import gradio as gr - -#from transformers import AutoTokenizer, AutoModelForCausalLM -##from os import path - -##MODEL_DIRECTORY = "/models/mrm8488-t5-base-finetuned-emotion" -#tokenizer = AutoTokenizer.from_pretrained("tuner007/pegasus_paraphrase", use_fast=False) -##if not path.exists(MODEL_DIRECTORY): -#model = AutoModelForCausalLM.from_pretrained("tuner007/pegasus_paraphrase") -## model.save_pretrained(MODEL_DIRECTORY) -##else: -## model = AutoModelWithLMHead.from_pretrained(MODEL_DIRECTORY) -# - -def get_emotion(text): -# input_ids = tokenizer.encode(text + '', return_tensors='pt') - # output = model.generate(input_ids=input_ids, max_length=2) -# -# # print(output) -# dec = [tokenizer.decode(ids) for ids in output] -# print(dec) -# label = dec[0] - return text - - - - -def parph(name= "paraphrase: This is something which I cannt understand at all."): - #text2text = pipeline("text2text-generation") - ##model_name = 'tuner007/pegasus_paraphrase' - #text2text = pipeline('text2text-generation', model = "Vamsi/T5_Paraphrase_Paws") - ##text2text(name) - test = get_emotion(name) - return test # text2text(name) - - -iface = gr.Interface(fn=parph, inputs="text", outputs="text") -iface.launch() - diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/utils/diffjpeg.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/utils/diffjpeg.py deleted file mode 100644 index 65f96b44f9e7f3f8a589668f0003adf328cc5742..0000000000000000000000000000000000000000 --- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/utils/diffjpeg.py +++ /dev/null @@ -1,515 +0,0 @@ -""" -Modified from https://github.com/mlomnitz/DiffJPEG - -For images not divisible by 8 -https://dsp.stackexchange.com/questions/35339/jpeg-dct-padding/35343#35343 -""" -import itertools -import numpy as np -import torch -import torch.nn as nn -from torch.nn import functional as F - -# ------------------------ utils ------------------------# -y_table = np.array( - [[16, 11, 10, 16, 24, 40, 51, 61], [12, 12, 14, 19, 26, 58, 60, 55], [14, 13, 16, 24, 40, 57, 69, 56], - [14, 17, 22, 29, 51, 87, 80, 62], [18, 22, 37, 56, 68, 109, 103, 77], [24, 35, 55, 64, 81, 104, 113, 92], - [49, 64, 78, 87, 103, 121, 120, 101], [72, 92, 95, 98, 112, 100, 103, 99]], - dtype=np.float32).T -y_table = nn.Parameter(torch.from_numpy(y_table)) -c_table = np.empty((8, 8), dtype=np.float32) -c_table.fill(99) -c_table[:4, :4] = np.array([[17, 18, 24, 47], [18, 21, 26, 66], [24, 26, 56, 99], [47, 66, 99, 99]]).T -c_table = nn.Parameter(torch.from_numpy(c_table)) - - -def diff_round(x): - """ Differentiable rounding function - """ - return torch.round(x) + (x - torch.round(x))**3 - - -def quality_to_factor(quality): - """ Calculate factor corresponding to quality - - Args: - quality(float): Quality for jpeg compression. - - Returns: - float: Compression factor. - """ - if quality < 50: - quality = 5000. / quality - else: - quality = 200. - quality * 2 - return quality / 100. - - -# ------------------------ compression ------------------------# -class RGB2YCbCrJpeg(nn.Module): - """ Converts RGB image to YCbCr - """ - - def __init__(self): - super(RGB2YCbCrJpeg, self).__init__() - matrix = np.array([[0.299, 0.587, 0.114], [-0.168736, -0.331264, 0.5], [0.5, -0.418688, -0.081312]], - dtype=np.float32).T - self.shift = nn.Parameter(torch.tensor([0., 128., 128.])) - self.matrix = nn.Parameter(torch.from_numpy(matrix)) - - def forward(self, image): - """ - Args: - image(Tensor): batch x 3 x height x width - - Returns: - Tensor: batch x height x width x 3 - """ - image = image.permute(0, 2, 3, 1) - result = torch.tensordot(image, self.matrix, dims=1) + self.shift - return result.view(image.shape) - - -class ChromaSubsampling(nn.Module): - """ Chroma subsampling on CbCr channels - """ - - def __init__(self): - super(ChromaSubsampling, self).__init__() - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width x 3 - - Returns: - y(tensor): batch x height x width - cb(tensor): batch x height/2 x width/2 - cr(tensor): batch x height/2 x width/2 - """ - image_2 = image.permute(0, 3, 1, 2).clone() - cb = F.avg_pool2d(image_2[:, 1, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) - cr = F.avg_pool2d(image_2[:, 2, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) - cb = cb.permute(0, 2, 3, 1) - cr = cr.permute(0, 2, 3, 1) - return image[:, :, :, 0], cb.squeeze(3), cr.squeeze(3) - - -class BlockSplitting(nn.Module): - """ Splitting image into patches - """ - - def __init__(self): - super(BlockSplitting, self).__init__() - self.k = 8 - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x h*w/64 x h x w - """ - height, _ = image.shape[1:3] - batch_size = image.shape[0] - image_reshaped = image.view(batch_size, height // self.k, self.k, -1, self.k) - image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) - return image_transposed.contiguous().view(batch_size, -1, self.k, self.k) - - -class DCT8x8(nn.Module): - """ Discrete Cosine Transformation - """ - - def __init__(self): - super(DCT8x8, self).__init__() - tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) - for x, y, u, v in itertools.product(range(8), repeat=4): - tensor[x, y, u, v] = np.cos((2 * x + 1) * u * np.pi / 16) * np.cos((2 * y + 1) * v * np.pi / 16) - alpha = np.array([1. / np.sqrt(2)] + [1] * 7) - self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) - self.scale = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha) * 0.25).float()) - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - image = image - 128 - result = self.scale * torch.tensordot(image, self.tensor, dims=2) - result.view(image.shape) - return result - - -class YQuantize(nn.Module): - """ JPEG Quantization for Y channel - - Args: - rounding(function): rounding function to use - """ - - def __init__(self, rounding): - super(YQuantize, self).__init__() - self.rounding = rounding - self.y_table = y_table - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - if isinstance(factor, (int, float)): - image = image.float() / (self.y_table * factor) - else: - b = factor.size(0) - table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) - image = image.float() / table - image = self.rounding(image) - return image - - -class CQuantize(nn.Module): - """ JPEG Quantization for CbCr channels - - Args: - rounding(function): rounding function to use - """ - - def __init__(self, rounding): - super(CQuantize, self).__init__() - self.rounding = rounding - self.c_table = c_table - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - if isinstance(factor, (int, float)): - image = image.float() / (self.c_table * factor) - else: - b = factor.size(0) - table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) - image = image.float() / table - image = self.rounding(image) - return image - - -class CompressJpeg(nn.Module): - """Full JPEG compression algorithm - - Args: - rounding(function): rounding function to use - """ - - def __init__(self, rounding=torch.round): - super(CompressJpeg, self).__init__() - self.l1 = nn.Sequential(RGB2YCbCrJpeg(), ChromaSubsampling()) - self.l2 = nn.Sequential(BlockSplitting(), DCT8x8()) - self.c_quantize = CQuantize(rounding=rounding) - self.y_quantize = YQuantize(rounding=rounding) - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x 3 x height x width - - Returns: - dict(tensor): Compressed tensor with batch x h*w/64 x 8 x 8. - """ - y, cb, cr = self.l1(image * 255) - components = {'y': y, 'cb': cb, 'cr': cr} - for k in components.keys(): - comp = self.l2(components[k]) - if k in ('cb', 'cr'): - comp = self.c_quantize(comp, factor=factor) - else: - comp = self.y_quantize(comp, factor=factor) - - components[k] = comp - - return components['y'], components['cb'], components['cr'] - - -# ------------------------ decompression ------------------------# - - -class YDequantize(nn.Module): - """Dequantize Y channel - """ - - def __init__(self): - super(YDequantize, self).__init__() - self.y_table = y_table - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - if isinstance(factor, (int, float)): - out = image * (self.y_table * factor) - else: - b = factor.size(0) - table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) - out = image * table - return out - - -class CDequantize(nn.Module): - """Dequantize CbCr channel - """ - - def __init__(self): - super(CDequantize, self).__init__() - self.c_table = c_table - - def forward(self, image, factor=1): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - if isinstance(factor, (int, float)): - out = image * (self.c_table * factor) - else: - b = factor.size(0) - table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) - out = image * table - return out - - -class iDCT8x8(nn.Module): - """Inverse discrete Cosine Transformation - """ - - def __init__(self): - super(iDCT8x8, self).__init__() - alpha = np.array([1. / np.sqrt(2)] + [1] * 7) - self.alpha = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha)).float()) - tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) - for x, y, u, v in itertools.product(range(8), repeat=4): - tensor[x, y, u, v] = np.cos((2 * u + 1) * x * np.pi / 16) * np.cos((2 * v + 1) * y * np.pi / 16) - self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width - - Returns: - Tensor: batch x height x width - """ - image = image * self.alpha - result = 0.25 * torch.tensordot(image, self.tensor, dims=2) + 128 - result.view(image.shape) - return result - - -class BlockMerging(nn.Module): - """Merge patches into image - """ - - def __init__(self): - super(BlockMerging, self).__init__() - - def forward(self, patches, height, width): - """ - Args: - patches(tensor) batch x height*width/64, height x width - height(int) - width(int) - - Returns: - Tensor: batch x height x width - """ - k = 8 - batch_size = patches.shape[0] - image_reshaped = patches.view(batch_size, height // k, width // k, k, k) - image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) - return image_transposed.contiguous().view(batch_size, height, width) - - -class ChromaUpsampling(nn.Module): - """Upsample chroma layers - """ - - def __init__(self): - super(ChromaUpsampling, self).__init__() - - def forward(self, y, cb, cr): - """ - Args: - y(tensor): y channel image - cb(tensor): cb channel - cr(tensor): cr channel - - Returns: - Tensor: batch x height x width x 3 - """ - - def repeat(x, k=2): - height, width = x.shape[1:3] - x = x.unsqueeze(-1) - x = x.repeat(1, 1, k, k) - x = x.view(-1, height * k, width * k) - return x - - cb = repeat(cb) - cr = repeat(cr) - return torch.cat([y.unsqueeze(3), cb.unsqueeze(3), cr.unsqueeze(3)], dim=3) - - -class YCbCr2RGBJpeg(nn.Module): - """Converts YCbCr image to RGB JPEG - """ - - def __init__(self): - super(YCbCr2RGBJpeg, self).__init__() - - matrix = np.array([[1., 0., 1.402], [1, -0.344136, -0.714136], [1, 1.772, 0]], dtype=np.float32).T - self.shift = nn.Parameter(torch.tensor([0, -128., -128.])) - self.matrix = nn.Parameter(torch.from_numpy(matrix)) - - def forward(self, image): - """ - Args: - image(tensor): batch x height x width x 3 - - Returns: - Tensor: batch x 3 x height x width - """ - result = torch.tensordot(image + self.shift, self.matrix, dims=1) - return result.view(image.shape).permute(0, 3, 1, 2) - - -class DeCompressJpeg(nn.Module): - """Full JPEG decompression algorithm - - Args: - rounding(function): rounding function to use - """ - - def __init__(self, rounding=torch.round): - super(DeCompressJpeg, self).__init__() - self.c_dequantize = CDequantize() - self.y_dequantize = YDequantize() - self.idct = iDCT8x8() - self.merging = BlockMerging() - self.chroma = ChromaUpsampling() - self.colors = YCbCr2RGBJpeg() - - def forward(self, y, cb, cr, imgh, imgw, factor=1): - """ - Args: - compressed(dict(tensor)): batch x h*w/64 x 8 x 8 - imgh(int) - imgw(int) - factor(float) - - Returns: - Tensor: batch x 3 x height x width - """ - components = {'y': y, 'cb': cb, 'cr': cr} - for k in components.keys(): - if k in ('cb', 'cr'): - comp = self.c_dequantize(components[k], factor=factor) - height, width = int(imgh / 2), int(imgw / 2) - else: - comp = self.y_dequantize(components[k], factor=factor) - height, width = imgh, imgw - comp = self.idct(comp) - components[k] = self.merging(comp, height, width) - # - image = self.chroma(components['y'], components['cb'], components['cr']) - image = self.colors(image) - - image = torch.min(255 * torch.ones_like(image), torch.max(torch.zeros_like(image), image)) - return image / 255 - - -# ------------------------ main DiffJPEG ------------------------ # - - -class DiffJPEG(nn.Module): - """This JPEG algorithm result is slightly different from cv2. - DiffJPEG supports batch processing. - - Args: - differentiable(bool): If True, uses custom differentiable rounding function, if False, uses standard torch.round - """ - - def __init__(self, differentiable=True): - super(DiffJPEG, self).__init__() - if differentiable: - rounding = diff_round - else: - rounding = torch.round - - self.compress = CompressJpeg(rounding=rounding) - self.decompress = DeCompressJpeg(rounding=rounding) - - def forward(self, x, quality): - """ - Args: - x (Tensor): Input image, bchw, rgb, [0, 1] - quality(float): Quality factor for jpeg compression scheme. - """ - factor = quality - if isinstance(factor, (int, float)): - factor = quality_to_factor(factor) - else: - for i in range(factor.size(0)): - factor[i] = quality_to_factor(factor[i]) - h, w = x.size()[-2:] - h_pad, w_pad = 0, 0 - # why should use 16 - if h % 16 != 0: - h_pad = 16 - h % 16 - if w % 16 != 0: - w_pad = 16 - w % 16 - x = F.pad(x, (0, w_pad, 0, h_pad), mode='constant', value=0) - - y, cb, cr = self.compress(x, factor=factor) - recovered = self.decompress(y, cb, cr, (h + h_pad), (w + w_pad), factor=factor) - recovered = recovered[:, :, 0:h, 0:w] - return recovered - - -if __name__ == '__main__': - import cv2 - - from basicsr.utils import img2tensor, tensor2img - - img_gt = cv2.imread('test.png') / 255. - - # -------------- cv2 -------------- # - encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 20] - _, encimg = cv2.imencode('.jpg', img_gt * 255., encode_param) - img_lq = np.float32(cv2.imdecode(encimg, 1)) - cv2.imwrite('cv2_JPEG_20.png', img_lq) - - # -------------- DiffJPEG -------------- # - jpeger = DiffJPEG(differentiable=False).cuda() - img_gt = img2tensor(img_gt) - img_gt = torch.stack([img_gt, img_gt]).cuda() - quality = img_gt.new_tensor([20, 40]) - out = jpeger(img_gt, quality=quality) - - cv2.imwrite('pt_JPEG_20.png', tensor2img(out[0])) - cv2.imwrite('pt_JPEG_40.png', tensor2img(out[1])) diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/utils/dist_util.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/utils/dist_util.py deleted file mode 100644 index 0fab887b2cb1ce8533d2e8fdee72ae0c24f68fd0..0000000000000000000000000000000000000000 --- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/utils/dist_util.py +++ /dev/null @@ -1,82 +0,0 @@ -# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py # noqa: E501 -import functools -import os -import subprocess -import torch -import torch.distributed as dist -import torch.multiprocessing as mp - - -def init_dist(launcher, backend='nccl', **kwargs): - if mp.get_start_method(allow_none=True) is None: - mp.set_start_method('spawn') - if launcher == 'pytorch': - _init_dist_pytorch(backend, **kwargs) - elif launcher == 'slurm': - _init_dist_slurm(backend, **kwargs) - else: - raise ValueError(f'Invalid launcher type: {launcher}') - - -def _init_dist_pytorch(backend, **kwargs): - rank = int(os.environ['RANK']) - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(rank % num_gpus) - dist.init_process_group(backend=backend, **kwargs) - - -def _init_dist_slurm(backend, port=None): - """Initialize slurm distributed training environment. - - If argument ``port`` is not specified, then the master port will be system - environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system - environment variable, then a default port ``29500`` will be used. - - Args: - backend (str): Backend of torch.distributed. - port (int, optional): Master port. Defaults to None. - """ - proc_id = int(os.environ['SLURM_PROCID']) - ntasks = int(os.environ['SLURM_NTASKS']) - node_list = os.environ['SLURM_NODELIST'] - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(proc_id % num_gpus) - addr = subprocess.getoutput(f'scontrol show hostname {node_list} | head -n1') - # specify master port - if port is not None: - os.environ['MASTER_PORT'] = str(port) - elif 'MASTER_PORT' in os.environ: - pass # use MASTER_PORT in the environment variable - else: - # 29500 is torch.distributed default port - os.environ['MASTER_PORT'] = '29500' - os.environ['MASTER_ADDR'] = addr - os.environ['WORLD_SIZE'] = str(ntasks) - os.environ['LOCAL_RANK'] = str(proc_id % num_gpus) - os.environ['RANK'] = str(proc_id) - dist.init_process_group(backend=backend) - - -def get_dist_info(): - if dist.is_available(): - initialized = dist.is_initialized() - else: - initialized = False - if initialized: - rank = dist.get_rank() - world_size = dist.get_world_size() - else: - rank = 0 - world_size = 1 - return rank, world_size - - -def master_only(func): - - @functools.wraps(func) - def wrapper(*args, **kwargs): - rank, _ = get_dist_info() - if rank == 0: - return func(*args, **kwargs) - - return wrapper diff --git a/spaces/mandar100/blenderbot_chat/README.md b/spaces/mandar100/blenderbot_chat/README.md deleted file mode 100644 index 8c851e30832c0862b3e55f791ee0db76c0a4b412..0000000000000000000000000000000000000000 --- a/spaces/mandar100/blenderbot_chat/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Blenderbot Chat -emoji: 🌖 -colorFrom: yellow -colorTo: purple -sdk: gradio -sdk_version: 3.12.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/mantisnlp/SearchMesh/app.py b/spaces/mantisnlp/SearchMesh/app.py deleted file mode 100644 index 16c7528e35d26b5dff68f8e05afbb7e470ecf3c1..0000000000000000000000000000000000000000 --- a/spaces/mantisnlp/SearchMesh/app.py +++ /dev/null @@ -1,69 +0,0 @@ -import difflib - -from collections import Counter -import streamlit as st -import pandas as pd -import srsly - - -def search(query): - results = [] - for grant in grants: - if query in grant["tags"]: - results.append({"title": grant["title"], "tags": grant["tags"]}) - st.session_state["results"] = results - - -st.header("Search 🔎 grants using MeSH 🔖") -st.sidebar.header("Information ℹ") -st.sidebar.write( - "A complete list of MeSH tags can be found here https://meshb.nlm.nih.gov/treeView" -) -st.sidebar.write("The grants data can be found at [https://www.threesixtygiving.org/](https://data.threesixtygiving.org/). They are published under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.") -st.sidebar.write( - "The model used to tag grants is https://huggingface.co/Wellcome/WellcomeBertMesh" -) -st.sidebar.header("Parameters") -nb_results = st.sidebar.slider( - "Number of results to display", value=20, min_value=1, max_value=100 -) - -if "grants" not in st.session_state: - st.session_state["grants"] = list(srsly.read_jsonl("tagged_grants.jsonl")) - -grants = st.session_state["grants"] - -if "tags" not in st.session_state: - st.session_state["tags"] = list(set([tag for grant in grants for tag in grant["tags"]])) - -tags = st.session_state["tags"] - -query = st.text_input("", value="Malaria") -st.button("Search 🔎", on_click=search, kwargs={"query": query}) - -if "results" in st.session_state: - st.caption("Related MeSH terms") - - if st.session_state["results"]: - retrieved_tags = [tag for res in st.session_state["results"] for tag in res["tags"]] - most_common_tags = [tag for tag, _ in Counter(retrieved_tags).most_common(20)] - else: - most_common_tags = difflib.get_close_matches(query, tags, n=20) - - columns = st.columns(5) - for row_i in range(3): - for col_i, col in enumerate(columns): - with col: - tag_i = row_i * 5 + col_i - if tag_i < len(most_common_tags): - tag = most_common_tags[tag_i] - st.button(tag, on_click=search, kwargs={"query": tag}) - results = st.session_state["results"] - st.caption(f"Found {len(results)}. Displaying {nb_results}") - st.download_button( - "Download results", - data=pd.DataFrame(results).to_csv(), - file_name="results.csv", - mime="text/csv", - ) - st.table(results[:nb_results]) diff --git a/spaces/marioboy/neil-breen/encoder/train.py b/spaces/marioboy/neil-breen/encoder/train.py deleted file mode 100644 index 619952e8de6c390912fe341403a39169592e585d..0000000000000000000000000000000000000000 --- a/spaces/marioboy/neil-breen/encoder/train.py +++ /dev/null @@ -1,123 +0,0 @@ -from encoder.visualizations import Visualizations -from encoder.data_objects import SpeakerVerificationDataLoader, SpeakerVerificationDataset -from encoder.params_model import * -from encoder.model import SpeakerEncoder -from utils.profiler import Profiler -from pathlib import Path -import torch - -def sync(device: torch.device): - # For correct profiling (cuda operations are async) - if device.type == "cuda": - torch.cuda.synchronize(device) - - -def train(run_id: str, clean_data_root: Path, models_dir: Path, umap_every: int, save_every: int, - backup_every: int, vis_every: int, force_restart: bool, visdom_server: str, - no_visdom: bool): - # Create a dataset and a dataloader - dataset = SpeakerVerificationDataset(clean_data_root) - loader = SpeakerVerificationDataLoader( - dataset, - speakers_per_batch, - utterances_per_speaker, - num_workers=8, - ) - - # Setup the device on which to run the forward pass and the loss. These can be different, - # because the forward pass is faster on the GPU whereas the loss is often (depending on your - # hyperparameters) faster on the CPU. - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - # FIXME: currently, the gradient is None if loss_device is cuda - loss_device = torch.device("cpu") - - # Create the model and the optimizer - model = SpeakerEncoder(device, loss_device) - optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate_init) - init_step = 1 - - # Configure file path for the model - state_fpath = models_dir.joinpath(run_id + ".pt") - backup_dir = models_dir.joinpath(run_id + "_backups") - - # Load any existing model - if not force_restart: - if state_fpath.exists(): - print("Found existing model \"%s\", loading it and resuming training." % run_id) - checkpoint = torch.load(state_fpath) - init_step = checkpoint["step"] - model.load_state_dict(checkpoint["model_state"]) - optimizer.load_state_dict(checkpoint["optimizer_state"]) - optimizer.param_groups[0]["lr"] = learning_rate_init - else: - print("No model \"%s\" found, starting training from scratch." % run_id) - else: - print("Starting the training from scratch.") - model.train() - - # Initialize the visualization environment - vis = Visualizations(run_id, vis_every, server=visdom_server, disabled=no_visdom) - vis.log_dataset(dataset) - vis.log_params() - device_name = str(torch.cuda.get_device_name(0) if torch.cuda.is_available() else "CPU") - vis.log_implementation({"Device": device_name}) - - # Training loop - profiler = Profiler(summarize_every=10, disabled=False) - for step, speaker_batch in enumerate(loader, init_step): - profiler.tick("Blocking, waiting for batch (threaded)") - - # Forward pass - inputs = torch.from_numpy(speaker_batch.data).to(device) - sync(device) - profiler.tick("Data to %s" % device) - embeds = model(inputs) - sync(device) - profiler.tick("Forward pass") - embeds_loss = embeds.view((speakers_per_batch, utterances_per_speaker, -1)).to(loss_device) - loss, eer = model.loss(embeds_loss) - sync(loss_device) - profiler.tick("Loss") - - # Backward pass - model.zero_grad() - loss.backward() - profiler.tick("Backward pass") - model.do_gradient_ops() - optimizer.step() - profiler.tick("Parameter update") - - # Update visualizations - # learning_rate = optimizer.param_groups[0]["lr"] - vis.update(loss.item(), eer, step) - - # Draw projections and save them to the backup folder - if umap_every != 0 and step % umap_every == 0: - print("Drawing and saving projections (step %d)" % step) - backup_dir.mkdir(exist_ok=True) - projection_fpath = backup_dir.joinpath("%s_umap_%06d.png" % (run_id, step)) - embeds = embeds.detach().cpu().numpy() - vis.draw_projections(embeds, utterances_per_speaker, step, projection_fpath) - vis.save() - - # Overwrite the latest version of the model - if save_every != 0 and step % save_every == 0: - print("Saving the model (step %d)" % step) - torch.save({ - "step": step + 1, - "model_state": model.state_dict(), - "optimizer_state": optimizer.state_dict(), - }, state_fpath) - - # Make a backup - if backup_every != 0 and step % backup_every == 0: - print("Making a backup (step %d)" % step) - backup_dir.mkdir(exist_ok=True) - backup_fpath = backup_dir.joinpath("%s_bak_%06d.pt" % (run_id, step)) - torch.save({ - "step": step + 1, - "model_state": model.state_dict(), - "optimizer_state": optimizer.state_dict(), - }, backup_fpath) - - profiler.tick("Extras (visualizations, saving)") diff --git a/spaces/marvingabler/codellama-34b-chat/app.py b/spaces/marvingabler/codellama-34b-chat/app.py deleted file mode 100644 index bcf5d982aeef81fc6bd66f11a08d3959ecd0a267..0000000000000000000000000000000000000000 --- a/spaces/marvingabler/codellama-34b-chat/app.py +++ /dev/null @@ -1,277 +0,0 @@ -from typing import Iterator - -import gradio as gr -import torch - -from model import get_input_token_length, run - -DEFAULT_SYSTEM_PROMPT = """\ -You are a helpful, respectful and honest assistant with a deep knowledge of code and software design. Always answer as helpfully as possible. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. \n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\ -""" -MAX_MAX_NEW_TOKENS = 4096 -DEFAULT_MAX_NEW_TOKENS = 1024 -MAX_INPUT_TOKEN_LENGTH = 4000 - -DESCRIPTION = """ -# Code Llama 34B Chat - -This Space demonstrates model [CodeLlama-34b-Instruct](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) by Meta, a Code Llama model with 34B parameters fine-tuned for chat instructions and specialized on code tasks. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints). - -🔎 For more details about the Code Llama family of models and how to use them with `transformers`, take a look [at our blog post](https://huggingface.co/blog/codellama) or [the paper](https://huggingface.co/papers/2308.12950). - -🏃🏻 Check out our [Playground](https://huggingface.co/spaces/codellama/codellama-playground) for a super-fast code completion demo that leverages a streaming [inference endpoint](https://huggingface.co/inference-endpoints). - -""" - -LICENSE = """ -

- ---- -As a derivate work of Code Llama by Meta, -this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/codellama-2-13b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/codellama-2-13b-chat/blob/main/USE_POLICY.md). -""" - -if not torch.cuda.is_available(): - DESCRIPTION += '\n

Running on CPU 🥶 This demo does not work on CPU.

' - - -def clear_and_save_textbox(message: str) -> tuple[str, str]: - return '', message - - -def display_input(message: str, - history: list[tuple[str, str]]) -> list[tuple[str, str]]: - history.append((message, '')) - return history - - -def delete_prev_fn( - history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]: - try: - message, _ = history.pop() - except IndexError: - message = '' - return history, message or '' - - -def generate( - message: str, - history_with_input: list[tuple[str, str]], - system_prompt: str, - max_new_tokens: int, - temperature: float, - top_p: float, - top_k: int, -) -> Iterator[list[tuple[str, str]]]: - if max_new_tokens > MAX_MAX_NEW_TOKENS: - raise ValueError - - history = history_with_input[:-1] - generator = run(message, history, system_prompt, max_new_tokens, temperature, top_p, top_k) - try: - first_response = next(generator) - yield history + [(message, first_response)] - except StopIteration: - yield history + [(message, '')] - for response in generator: - yield history + [(message, response)] - - -def process_example(message: str) -> tuple[str, list[tuple[str, str]]]: - generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 1024, 1, 0.95, 50) - for x in generator: - pass - return '', x - - -def check_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> None: - input_token_length = get_input_token_length(message, chat_history, system_prompt) - if input_token_length > MAX_INPUT_TOKEN_LENGTH: - raise gr.Error(f'The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again.') - - -with gr.Blocks(css='style.css') as demo: - gr.Markdown(DESCRIPTION) - gr.DuplicateButton(value='Duplicate Space for private use', - elem_id='duplicate-button') - - with gr.Group(): - chatbot = gr.Chatbot(label='Chatbot') - with gr.Row(): - textbox = gr.Textbox( - container=False, - show_label=False, - placeholder='Type a message...', - scale=10, - ) - submit_button = gr.Button('Submit', - variant='primary', - scale=1, - min_width=0) - with gr.Row(): - retry_button = gr.Button('🔄 Retry', variant='secondary') - undo_button = gr.Button('↩️ Undo', variant='secondary') - clear_button = gr.Button('🗑️ Clear', variant='secondary') - - saved_input = gr.State() - - with gr.Accordion(label='Advanced options', open=False): - system_prompt = gr.Textbox(label='System prompt', - value=DEFAULT_SYSTEM_PROMPT, - lines=6) - max_new_tokens = gr.Slider( - label='Max new tokens', - minimum=1, - maximum=MAX_MAX_NEW_TOKENS, - step=1, - value=DEFAULT_MAX_NEW_TOKENS, - ) - temperature = gr.Slider( - label='Temperature', - minimum=0.1, - maximum=4.0, - step=0.1, - value=0.1, - ) - top_p = gr.Slider( - label='Top-p (nucleus sampling)', - minimum=0.05, - maximum=1.0, - step=0.05, - value=0.9, - ) - top_k = gr.Slider( - label='Top-k', - minimum=1, - maximum=1000, - step=1, - value=10, - ) - - gr.Examples( - examples=[ - 'What is the Fibonacci sequence?', - 'Can you explain briefly what Python is good for?', - 'How can I display a grid of images in SwiftUI?', - ], - inputs=textbox, - outputs=[textbox, chatbot], - fn=process_example, - cache_examples=True, - ) - - gr.Markdown(LICENSE) - - textbox.submit( - fn=clear_and_save_textbox, - inputs=textbox, - outputs=[textbox, saved_input], - api_name=False, - queue=False, - ).then( - fn=display_input, - inputs=[saved_input, chatbot], - outputs=chatbot, - api_name=False, - queue=False, - ).then( - fn=check_input_token_length, - inputs=[saved_input, chatbot, system_prompt], - api_name=False, - queue=False, - ).success( - fn=generate, - inputs=[ - saved_input, - chatbot, - system_prompt, - max_new_tokens, - temperature, - top_p, - top_k, - ], - outputs=chatbot, - api_name=False, - ) - - button_event_preprocess = submit_button.click( - fn=clear_and_save_textbox, - inputs=textbox, - outputs=[textbox, saved_input], - api_name=False, - queue=False, - ).then( - fn=display_input, - inputs=[saved_input, chatbot], - outputs=chatbot, - api_name=False, - queue=False, - ).then( - fn=check_input_token_length, - inputs=[saved_input, chatbot, system_prompt], - api_name=False, - queue=False, - ).success( - fn=generate, - inputs=[ - saved_input, - chatbot, - system_prompt, - max_new_tokens, - temperature, - top_p, - top_k, - ], - outputs=chatbot, - api_name=False, - ) - - retry_button.click( - fn=delete_prev_fn, - inputs=chatbot, - outputs=[chatbot, saved_input], - api_name=False, - queue=False, - ).then( - fn=display_input, - inputs=[saved_input, chatbot], - outputs=chatbot, - api_name=False, - queue=False, - ).then( - fn=generate, - inputs=[ - saved_input, - chatbot, - system_prompt, - max_new_tokens, - temperature, - top_p, - top_k, - ], - outputs=chatbot, - api_name=False, - ) - - undo_button.click( - fn=delete_prev_fn, - inputs=chatbot, - outputs=[chatbot, saved_input], - api_name=False, - queue=False, - ).then( - fn=lambda x: x, - inputs=[saved_input], - outputs=textbox, - api_name=False, - queue=False, - ) - - clear_button.click( - fn=lambda: ([], ''), - outputs=[chatbot, saved_input], - queue=False, - api_name=False, - ) - -demo.queue(max_size=20).launch() diff --git a/spaces/masapasa/biogpt/README.md b/spaces/masapasa/biogpt/README.md deleted file mode 100644 index c65df16dd5a1c63347373ad311db82ca74bc4ea2..0000000000000000000000000000000000000000 --- a/spaces/masapasa/biogpt/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Biogpt -emoji: ⚡ -colorFrom: green -colorTo: blue -sdk: gradio -sdk_version: 3.16.2 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/matthoffner/open-codetree/components/Playground/Iframe.tsx b/spaces/matthoffner/open-codetree/components/Playground/Iframe.tsx deleted file mode 100644 index c173f8fe06d0d303460f9ce436c8ce2f13e87293..0000000000000000000000000000000000000000 --- a/spaces/matthoffner/open-codetree/components/Playground/Iframe.tsx +++ /dev/null @@ -1,101 +0,0 @@ -import React, { useRef, useEffect } from "react"; - -import { useAppDispatch } from "../../store/hook"; -import { update_logs } from "../../store/features/editorSlice"; -import { getCompileCode } from "../../store/features/compilerSlice"; -import { createIframeContent } from "../../utils/createIframeContent"; -import { IframeLoaderScreen } from "./IframeLoaderScreen"; -import { IframeErrorScreen } from "./IframeErrorScreen"; -import { LanguagesInterface } from "../../_types/editorTypes"; -import { CompilerOutput, CompilerStatus } from "../../_types/compilerTypes"; - -interface IframeProps { - tabs: LanguagesInterface; - output: CompilerOutput; - isCompiling: boolean; - esbuildStatus: CompilerStatus; -} - -const IframePanel = ({ - tabs, - output, - isCompiling, - esbuildStatus, -}: IframeProps) => { - const iframe = useRef(); - const dispatch = useAppDispatch(); - - const htmlFrameContent = createIframeContent(tabs.css?.data, tabs.html?.data); - - //=== incoming message - useEffect(() => { - window.onmessage = function (response: MessageEvent) { - if (response.data && response.data.source === "iframe") { - let errorObject = { - method: "error", - id: Date.now(), - data: [`${response.data.message}`], - }; - dispatch(update_logs(errorObject)); - } - }; - - if (tabs.javascript && esbuildStatus.isReady) { - setTimeout(async () => { - dispatch( - getCompileCode(tabs.javascript.data, tabs.javascript.entryPoints) - ); - }, 50); - } - }, [dispatch, tabs, esbuildStatus.isReady]); - - //=== outgoing massage - useEffect(() => { - iframe.current.srcdoc = htmlFrameContent; - - setTimeout(async () => { - iframe?.current?.contentWindow?.postMessage(output.code, "*"); - }, 40); - }, [htmlFrameContent, output]); - - return ( -
- {/* build error */} - {output.error ? : ""} - - {/* Loading screen */} - {isCompiling ? ( -
- -
- ) : ( - "" - )} - - " - except Exception as e: - h, query_response = bot_message, str(e) - return "", chat_history, h, query_response, context - -def clear_fn(): - return None, base_context.copy() - -with gr.Blocks(title="DivarGPT") as demo: - with gr.Row(): - gr.HTML('

DivarGPT v0.3

') - with gr.Row(): - with gr.Column(scale=1): - html = gr.HTML("Results Will be shown here!") - htmlj = gr.HTML("Results Will be shown here!") - with gr.Column(scale=1): - api_key_box = gr.Textbox(label="OpenAI API Key", info="OpenAI API Key", lines=1, value="") - chatbot = gr.Chatbot() - msg = gr.Textbox(label="Chat") - clear = gr.Button("Clear") - context_box = gr.Textbox(base_context.copy(), label="Context", visible=False) - msg.submit(respond, [msg, chatbot, api_key_box, context_box], [msg, chatbot, html, htmlj, context_box]) - clear.click(clear_fn, None, [chatbot, context_box], queue=False) - -demo.launch() \ No newline at end of file diff --git a/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/segment_anything/build_sam.py b/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/segment_anything/build_sam.py deleted file mode 100644 index 37cd245124079e7cdd0d047ef9dde077db99efcc..0000000000000000000000000000000000000000 --- a/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/segment_anything/build_sam.py +++ /dev/null @@ -1,107 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -from functools import partial - -from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer - - -def build_sam_vit_h(checkpoint=None): - return _build_sam( - encoder_embed_dim=1280, - encoder_depth=32, - encoder_num_heads=16, - encoder_global_attn_indexes=[7, 15, 23, 31], - checkpoint=checkpoint, - ) - - -build_sam = build_sam_vit_h - - -def build_sam_vit_l(checkpoint=None): - return _build_sam( - encoder_embed_dim=1024, - encoder_depth=24, - encoder_num_heads=16, - encoder_global_attn_indexes=[5, 11, 17, 23], - checkpoint=checkpoint, - ) - - -def build_sam_vit_b(checkpoint=None): - return _build_sam( - encoder_embed_dim=768, - encoder_depth=12, - encoder_num_heads=12, - encoder_global_attn_indexes=[2, 5, 8, 11], - checkpoint=checkpoint, - ) - - -sam_model_registry = { - "default": build_sam_vit_h, - "vit_h": build_sam_vit_h, - "vit_l": build_sam_vit_l, - "vit_b": build_sam_vit_b, -} - - -def _build_sam( - encoder_embed_dim, - encoder_depth, - encoder_num_heads, - encoder_global_attn_indexes, - checkpoint=None, -): - prompt_embed_dim = 256 - image_size = 1024 - vit_patch_size = 16 - image_embedding_size = image_size // vit_patch_size - sam = Sam( - image_encoder=ImageEncoderViT( - depth=encoder_depth, - embed_dim=encoder_embed_dim, - img_size=image_size, - mlp_ratio=4, - norm_layer=partial(torch.nn.LayerNorm, eps=1e-6), - num_heads=encoder_num_heads, - patch_size=vit_patch_size, - qkv_bias=True, - use_rel_pos=True, - global_attn_indexes=encoder_global_attn_indexes, - window_size=14, - out_chans=prompt_embed_dim, - ), - prompt_encoder=PromptEncoder( - embed_dim=prompt_embed_dim, - image_embedding_size=(image_embedding_size, image_embedding_size), - input_image_size=(image_size, image_size), - mask_in_chans=16, - ), - mask_decoder=MaskDecoder( - num_multimask_outputs=3, - transformer=TwoWayTransformer( - depth=2, - embedding_dim=prompt_embed_dim, - mlp_dim=2048, - num_heads=8, - ), - transformer_dim=prompt_embed_dim, - iou_head_depth=3, - iou_head_hidden_dim=256, - ), - pixel_mean=[123.675, 116.28, 103.53], - pixel_std=[58.395, 57.12, 57.375], - ) - sam.eval() - if checkpoint is not None: - with open(checkpoint, "rb") as f: - state_dict = torch.load(f) - sam.load_state_dict(state_dict) - return sam diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/engine/train_loop.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/engine/train_loop.py deleted file mode 100644 index 2f6b96dc66af2d4c93028219a4b13ea16c719892..0000000000000000000000000000000000000000 --- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/engine/train_loop.py +++ /dev/null @@ -1,528 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. -import concurrent.futures -import logging -import numpy as np -import time -import weakref -from typing import List, Mapping, Optional -import torch -from torch.nn.parallel import DataParallel, DistributedDataParallel - -import detectron2.utils.comm as comm -from detectron2.utils.events import EventStorage, get_event_storage -from detectron2.utils.logger import _log_api_usage - -__all__ = ["HookBase", "TrainerBase", "SimpleTrainer", "AMPTrainer"] - - -class HookBase: - """ - Base class for hooks that can be registered with :class:`TrainerBase`. - - Each hook can implement 4 methods. The way they are called is demonstrated - in the following snippet: - :: - hook.before_train() - for iter in range(start_iter, max_iter): - hook.before_step() - trainer.run_step() - hook.after_step() - iter += 1 - hook.after_train() - - Notes: - 1. In the hook method, users can access ``self.trainer`` to access more - properties about the context (e.g., model, current iteration, or config - if using :class:`DefaultTrainer`). - - 2. A hook that does something in :meth:`before_step` can often be - implemented equivalently in :meth:`after_step`. - If the hook takes non-trivial time, it is strongly recommended to - implement the hook in :meth:`after_step` instead of :meth:`before_step`. - The convention is that :meth:`before_step` should only take negligible time. - - Following this convention will allow hooks that do care about the difference - between :meth:`before_step` and :meth:`after_step` (e.g., timer) to - function properly. - - """ - - trainer: "TrainerBase" = None - """ - A weak reference to the trainer object. Set by the trainer when the hook is registered. - """ - - def before_train(self): - """ - Called before the first iteration. - """ - pass - - def after_train(self): - """ - Called after the last iteration. - """ - pass - - def before_step(self): - """ - Called before each iteration. - """ - pass - - def after_backward(self): - """ - Called after the backward pass of each iteration. - """ - pass - - def after_step(self): - """ - Called after each iteration. - """ - pass - - def state_dict(self): - """ - Hooks are stateless by default, but can be made checkpointable by - implementing `state_dict` and `load_state_dict`. - """ - return {} - - -class TrainerBase: - """ - Base class for iterative trainer with hooks. - - The only assumption we made here is: the training runs in a loop. - A subclass can implement what the loop is. - We made no assumptions about the existence of dataloader, optimizer, model, etc. - - Attributes: - iter(int): the current iteration. - - start_iter(int): The iteration to start with. - By convention the minimum possible value is 0. - - max_iter(int): The iteration to end training. - - storage(EventStorage): An EventStorage that's opened during the course of training. - """ - - def __init__(self) -> None: - self._hooks: List[HookBase] = [] - self.iter: int = 0 - self.start_iter: int = 0 - self.max_iter: int - self.storage: EventStorage - _log_api_usage("trainer." + self.__class__.__name__) - - def register_hooks(self, hooks: List[Optional[HookBase]]) -> None: - """ - Register hooks to the trainer. The hooks are executed in the order - they are registered. - - Args: - hooks (list[Optional[HookBase]]): list of hooks - """ - hooks = [h for h in hooks if h is not None] - for h in hooks: - assert isinstance(h, HookBase) - # To avoid circular reference, hooks and trainer cannot own each other. - # This normally does not matter, but will cause memory leak if the - # involved objects contain __del__: - # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/ - h.trainer = weakref.proxy(self) - self._hooks.extend(hooks) - - def train(self, start_iter: int, max_iter: int): - """ - Args: - start_iter, max_iter (int): See docs above - """ - logger = logging.getLogger(__name__) - logger.info("Starting training from iteration {}".format(start_iter)) - - self.iter = self.start_iter = start_iter - self.max_iter = max_iter - - with EventStorage(start_iter) as self.storage: - try: - self.before_train() - for self.iter in range(start_iter, max_iter): - self.before_step() - self.run_step() - self.after_step() - # self.iter == max_iter can be used by `after_train` to - # tell whether the training successfully finished or failed - # due to exceptions. - self.iter += 1 - except Exception: - logger.exception("Exception during training:") - raise - finally: - self.after_train() - - def before_train(self): - for h in self._hooks: - h.before_train() - - def after_train(self): - self.storage.iter = self.iter - for h in self._hooks: - h.after_train() - - def before_step(self): - # Maintain the invariant that storage.iter == trainer.iter - # for the entire execution of each step - self.storage.iter = self.iter - - for h in self._hooks: - h.before_step() - - def after_backward(self): - for h in self._hooks: - h.after_backward() - - def after_step(self): - for h in self._hooks: - h.after_step() - - def run_step(self): - raise NotImplementedError - - def state_dict(self): - ret = {"iteration": self.iter} - hooks_state = {} - for h in self._hooks: - sd = h.state_dict() - if sd: - name = type(h).__qualname__ - if name in hooks_state: - # TODO handle repetitive stateful hooks - continue - hooks_state[name] = sd - if hooks_state: - ret["hooks"] = hooks_state - return ret - - def load_state_dict(self, state_dict): - logger = logging.getLogger(__name__) - self.iter = state_dict["iteration"] - for key, value in state_dict.get("hooks", {}).items(): - for h in self._hooks: - try: - name = type(h).__qualname__ - except AttributeError: - continue - if name == key: - h.load_state_dict(value) - break - else: - logger.warning(f"Cannot find the hook '{key}', its state_dict is ignored.") - - -class SimpleTrainer(TrainerBase): - """ - A simple trainer for the most common type of task: - single-cost single-optimizer single-data-source iterative optimization, - optionally using data-parallelism. - It assumes that every step, you: - - 1. Compute the loss with a data from the data_loader. - 2. Compute the gradients with the above loss. - 3. Update the model with the optimizer. - - All other tasks during training (checkpointing, logging, evaluation, LR schedule) - are maintained by hooks, which can be registered by :meth:`TrainerBase.register_hooks`. - - If you want to do anything fancier than this, - either subclass TrainerBase and implement your own `run_step`, - or write your own training loop. - """ - - def __init__( - self, - model, - data_loader, - optimizer, - gather_metric_period=1, - zero_grad_before_forward=False, - async_write_metrics=False, - ): - """ - Args: - model: a torch Module. Takes a data from data_loader and returns a - dict of losses. - data_loader: an iterable. Contains data to be used to call model. - optimizer: a torch optimizer. - gather_metric_period: an int. Every gather_metric_period iterations - the metrics are gathered from all the ranks to rank 0 and logged. - zero_grad_before_forward: whether to zero the gradients before the forward. - async_write_metrics: bool. If True, then write metrics asynchronously to improve - training speed - """ - super().__init__() - - """ - We set the model to training mode in the trainer. - However it's valid to train a model that's in eval mode. - If you want your model (or a submodule of it) to behave - like evaluation during training, you can overwrite its train() method. - """ - model.train() - - self.model = model - self.data_loader = data_loader - # to access the data loader iterator, call `self._data_loader_iter` - self._data_loader_iter_obj = None - self.optimizer = optimizer - self.gather_metric_period = gather_metric_period - self.zero_grad_before_forward = zero_grad_before_forward - self.async_write_metrics = async_write_metrics - # create a thread pool that can execute non critical logic in run_step asynchronically - # use only 1 worker so tasks will be executred in order of submitting. - self.concurrent_executor = concurrent.futures.ThreadPoolExecutor(max_workers=1) - - def run_step(self): - """ - Implement the standard training logic described above. - """ - assert self.model.training, "[SimpleTrainer] model was changed to eval mode!" - start = time.perf_counter() - """ - If you want to do something with the data, you can wrap the dataloader. - """ - data = next(self._data_loader_iter) - data_time = time.perf_counter() - start - - if self.zero_grad_before_forward: - """ - If you need to accumulate gradients or do something similar, you can - wrap the optimizer with your custom `zero_grad()` method. - """ - self.optimizer.zero_grad() - - """ - If you want to do something with the losses, you can wrap the model. - """ - loss_dict = self.model(data) - if isinstance(loss_dict, torch.Tensor): - losses = loss_dict - loss_dict = {"total_loss": loss_dict} - else: - losses = sum(loss_dict.values()) - if not self.zero_grad_before_forward: - """ - If you need to accumulate gradients or do something similar, you can - wrap the optimizer with your custom `zero_grad()` method. - """ - self.optimizer.zero_grad() - losses.backward() - - self.after_backward() - - if self.async_write_metrics: - # write metrics asynchronically - self.concurrent_executor.submit( - self._write_metrics, loss_dict, data_time, iter=self.iter - ) - else: - self._write_metrics(loss_dict, data_time) - - """ - If you need gradient clipping/scaling or other processing, you can - wrap the optimizer with your custom `step()` method. But it is - suboptimal as explained in https://arxiv.org/abs/2006.15704 Sec 3.2.4 - """ - self.optimizer.step() - - @property - def _data_loader_iter(self): - # only create the data loader iterator when it is used - if self._data_loader_iter_obj is None: - self._data_loader_iter_obj = iter(self.data_loader) - return self._data_loader_iter_obj - - def reset_data_loader(self, data_loader_builder): - """ - Delete and replace the current data loader with a new one, which will be created - by calling `data_loader_builder` (without argument). - """ - del self.data_loader - data_loader = data_loader_builder() - self.data_loader = data_loader - self._data_loader_iter_obj = None - - def _write_metrics( - self, - loss_dict: Mapping[str, torch.Tensor], - data_time: float, - prefix: str = "", - iter: Optional[int] = None, - ) -> None: - logger = logging.getLogger(__name__) - - iter = self.iter if iter is None else iter - if (iter + 1) % self.gather_metric_period == 0: - try: - SimpleTrainer.write_metrics(loss_dict, data_time, iter, prefix) - except Exception: - logger.exception("Exception in writing metrics: ") - raise - - @staticmethod - def write_metrics( - loss_dict: Mapping[str, torch.Tensor], - data_time: float, - cur_iter: int, - prefix: str = "", - ) -> None: - """ - Args: - loss_dict (dict): dict of scalar losses - data_time (float): time taken by the dataloader iteration - prefix (str): prefix for logging keys - """ - metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()} - metrics_dict["data_time"] = data_time - - # Gather metrics among all workers for logging - # This assumes we do DDP-style training, which is currently the only - # supported method in detectron2. - all_metrics_dict = comm.gather(metrics_dict) - - if comm.is_main_process(): - storage = get_event_storage() - - # data_time among workers can have high variance. The actual latency - # caused by data_time is the maximum among workers. - data_time = np.max([x.pop("data_time") for x in all_metrics_dict]) - storage.put_scalar("data_time", data_time, cur_iter=cur_iter) - - # average the rest metrics - metrics_dict = { - k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys() - } - total_losses_reduced = sum(metrics_dict.values()) - if not np.isfinite(total_losses_reduced): - raise FloatingPointError( - f"Loss became infinite or NaN at iteration={cur_iter}!\n" - f"loss_dict = {metrics_dict}" - ) - - storage.put_scalar( - "{}total_loss".format(prefix), total_losses_reduced, cur_iter=cur_iter - ) - if len(metrics_dict) > 1: - storage.put_scalars(cur_iter=cur_iter, **metrics_dict) - - def state_dict(self): - ret = super().state_dict() - ret["optimizer"] = self.optimizer.state_dict() - return ret - - def load_state_dict(self, state_dict): - super().load_state_dict(state_dict) - self.optimizer.load_state_dict(state_dict["optimizer"]) - - def after_train(self): - super().after_train() - self.concurrent_executor.shutdown(wait=True) - - -class AMPTrainer(SimpleTrainer): - """ - Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision - in the training loop. - """ - - def __init__( - self, - model, - data_loader, - optimizer, - gather_metric_period=1, - zero_grad_before_forward=False, - grad_scaler=None, - precision: torch.dtype = torch.float16, - log_grad_scaler: bool = False, - async_write_metrics=False, - ): - """ - Args: - model, data_loader, optimizer, gather_metric_period, zero_grad_before_forward, - async_write_metrics: same as in :class:`SimpleTrainer`. - grad_scaler: torch GradScaler to automatically scale gradients. - precision: torch.dtype as the target precision to cast to in computations - """ - unsupported = "AMPTrainer does not support single-process multi-device training!" - if isinstance(model, DistributedDataParallel): - assert not (model.device_ids and len(model.device_ids) > 1), unsupported - assert not isinstance(model, DataParallel), unsupported - - super().__init__( - model, data_loader, optimizer, gather_metric_period, zero_grad_before_forward - ) - - if grad_scaler is None: - from torch.cuda.amp import GradScaler - - grad_scaler = GradScaler() - self.grad_scaler = grad_scaler - self.precision = precision - self.log_grad_scaler = log_grad_scaler - - def run_step(self): - """ - Implement the AMP training logic. - """ - assert self.model.training, "[AMPTrainer] model was changed to eval mode!" - assert torch.cuda.is_available(), "[AMPTrainer] CUDA is required for AMP training!" - from torch.cuda.amp import autocast - - start = time.perf_counter() - data = next(self._data_loader_iter) - data_time = time.perf_counter() - start - - if self.zero_grad_before_forward: - self.optimizer.zero_grad() - with autocast(dtype=self.precision): - loss_dict = self.model(data) - if isinstance(loss_dict, torch.Tensor): - losses = loss_dict - loss_dict = {"total_loss": loss_dict} - else: - losses = sum(loss_dict.values()) - - if not self.zero_grad_before_forward: - self.optimizer.zero_grad() - - self.grad_scaler.scale(losses).backward() - - if self.log_grad_scaler: - storage = get_event_storage() - storage.put_scalar("[metric]grad_scaler", self.grad_scaler.get_scale()) - - self.after_backward() - - if self.async_write_metrics: - # write metrics asynchronically - self.concurrent_executor.submit( - self._write_metrics, loss_dict, data_time, iter=self.iter - ) - else: - self._write_metrics(loss_dict, data_time) - - self.grad_scaler.step(self.optimizer) - self.grad_scaler.update() - - def state_dict(self): - ret = super().state_dict() - ret["grad_scaler"] = self.grad_scaler.state_dict() - return ret - - def load_state_dict(self, state_dict): - super().load_state_dict(state_dict) - self.grad_scaler.load_state_dict(state_dict["grad_scaler"]) diff --git a/spaces/nt3awnou/embed-rescue-map/src/text_content.py b/spaces/nt3awnou/embed-rescue-map/src/text_content.py deleted file mode 100644 index 56f4b38217f1fbbbd5c4e838da459e81179dcced..0000000000000000000000000000000000000000 --- a/spaces/nt3awnou/embed-rescue-map/src/text_content.py +++ /dev/null @@ -1,88 +0,0 @@ -INTRO_TEXT_EN = """ -
- Nt3awnou نتعاونو is a collaborative platform dedicated to aiding individuals impacted by the recent earthquake in Morocco. Our core mission is to streamline and coordinate timely assistance for everyone affected. How do we achieve this? We assist those in need by allowing them to communicate their location and the specific aid they require, either by completing a form or sending a voice message via WhatsApp to a designated number. Once we receive and process this information, it can be viewed in our dashboard, which allows NGOs to organize and precisely target their interventions, ensuring swift assistance reaches those in need. Any organization that has taken initiative in a particular area can notify us by completing a dedicated form. This data is also incorporated into the dashboard so that other NGOs can help affected areas that still haven't received help. -
⚠️ Warning : There are still rocks falling down the mountains, making the roads to the affected areas very dangerous. We advise volunteers to donate directly to specialized NGOs.
-
- ✉️ You can contact us via email at nt3awnoumorocco@gmail.com
- 📝 Help us report more people in need by filling this form https://forms.gle/nZNCUVog9ka2Vdqu6
- 📝 NGOs can report their interventions by filling this form https://forms.gle/PsNSuHHjTTgwQMmVA -
-
- """ - -INTRO_TEXT_AR = """ -
- - نتعاونو هي منصة تعاونية لمساعدة الأفراد المتضررين من الزلزال الأخير في المغرب. مهمتنا هي تسهيل تقديم المساعدة في الوقت المناسب و بفاعلية و تنظيم لجميع المتضررين. كيفاش؟ كنعاونو الناس لي محتاجين للمساعدة إعلمونا بمكانهم و نوع المساعدة لي محتاجين ليها سواء عن طريق ملأ الاستمارة أو عن طريق إرسال تسجيل صوتي عبر واتساب إلى رقم مخصص. بعد معالجة هاد المعلومات، كنجمعوهم فخريطة كتمكن الجمعيات من تنظيم و استهداف تدخلاتهم بدقة باش توصل المساعدة للناس لي محتاجين في وقت أسرع. و كل جمعية قامت باللازم في منطقة معينة تقدر تعلمنا عن طريق ملأ استمارة مخصصة لهاد الأمر. هاد المعلومات كذلك كتضاف للخريطة باش باقي الجمعيات يتاجهو لمناطق أخرى مازال ماوصلاتهم مساعدة. -
تحذير : نظرا لخطورة الطرقان بسبب الحجر اللي كيطيح من الجبال، ننصح المتطوعين اللي بغاو يساعدو المناطق المتضررة يتبرعو عن طريق الجمعيات المختصة⚠️ -
- nt3awnoumorocco@gmail.com المتطوعين ليبغاو يعاونوا يقدرو يتصلوا معنا عبر البريد ✉️
- https://forms.gle/nZNCUVog9ka2Vdqu6 : ساعدونا نبلغو الناس ليمحتاجين فهاد الاستمارة 📝
- https://forms.gle/PsNSuHHjTTgwQMmVA : الجمعيات لي عندهم تدخلات يقدرو يبلغونا عبر هاد الاستمار ة📝 -
-
- """ - -INTRO_TEXT_FR = """ -
- Nt3awnou نتعاونو est une plateforme collaborative dédiée à l'aide aux personnes touchées par le récent tremblement de terre au Maroc. Notre mission principale est de rationaliser et de coordonner une assistance rapide pour toutes les personnes touchées. Comment y parvenons-nous ? Nous aidons les personnes dans le besoin en leur permettant de communiquer leur localisation et l'aide spécifique dont elles ont besoin, soit en remplissant un formulaire, soit en envoyant un message vocal via WhatsApp à un numéro désigné. Une fois reçues et traitées, ces informations peuvent être consultées dans notre tableau de bord, qui permet aux associations d'organiser et de cibler précisément leurs interventions, afin que l'aide parvienne rapidement à ceux qui en ont besoin. Toute organisation ayant pris une initiative dans une zone particulière peut nous en informer en remplissant un formulaire prévu à cet effet. Ces données sont également intégrées au tableau de bord afin que d'autres associations puissent aider les zones touchées qui n'ont pas encore reçu d'aide. - ⚠️ Avertissement : Il y a encore des chutes de pierres dans les montagnes, ce qui rend les routes vers les zones touchées très dangereuses. Nous conseillons aux volontaires de faire des dons directement aux associations spécialisées. -
- ✉️ Vous pouvez nous contacter par courrier électronique à l'adresse suivante nt3awnoumorocco@gmail.com
- 📝 Aidez-nous à signaler plus de personnes dans le besoin en remplissant ce formulaire : https://forms.gle/nZNCUVog9ka2Vdqu6
- 📝 Les associations peuvent signaler leurs interventions en remplissant ce formulaire : https://forms.gle/PsNSuHHjTTgwQMmVA -
-
- """ - -SLOGAN = """ -
-

وَمَنْ أَحْيَاهَا فَكَأَنَّمَا أَحْيَا النَّاسَ جَمِيعاً

-
- """ - -HEADERS_MAPPING = { - "إغاثة" : "Rescue | إغاثة | Secours", - "مساعدة طبية": "Medical Assistance | مساعدة طبية | Assistance médicale", - "مأوى": "Shelter | مأوى | Abri", - "طعام وماء": "Food & Water | طعام وماء | Nourriture et eau", - "مخاطر (تسرب الغاز، تلف في الخدمات العامة...)": "Danger | مخاطر (تسرب الغاز، تلف في الخدمات العامة...) | Danger", -} - -COLOR_MAPPING = { - "إغاثة": "red", - "مساعدة طبية": "orange", - "مأوى": "beige", - "طعام وماء": "blue", - "مخاطر (تسرب الغاز، تلف في الخدمات العامة...)": "gray", -} - -ICON_MAPPING = { - "إغاثة": "bell", # life ring icon for rescue - "مساعدة طبية": "heart", # medical kit for medical assistance - "مأوى": "home", # home icon for shelter - "طعام وماء": "cutlery", # cutlery (fork and knife) for food & water - "مخاطر (تسرب الغاز، تلف في الخدمات العامة...)": "Warning" # warning triangle for dangers -} - -CREDITS_TEXT = """ -
-
-

By Moroccans for Moroccans 🤝

-

Bot powered by Annarabic

-

Collaboration made possible thanks to AI Summer School

- """ - -LOGO = """ - -
- -
- """ - -REVIEW_TEXT = """**If a request should be reviewed or dropped submit its id here/ إذا كان يجب مراجعة أو حذف طلب، أدخل رقمه هنا:**""" diff --git a/spaces/nvshubhsharma/wav2lip_demo_test1/app.py b/spaces/nvshubhsharma/wav2lip_demo_test1/app.py deleted file mode 100644 index d75e0c60093618dc60c71baa0f98591d2afa177b..0000000000000000000000000000000000000000 --- a/spaces/nvshubhsharma/wav2lip_demo_test1/app.py +++ /dev/null @@ -1,20 +0,0 @@ -import os -import sys -import gradio as gr - -os.system('git clone https://github.com/Rudrabha/Wav2Lip.git') -os.system('curl -o ./Wav2Lip/face_detection/detection/sfd/s3fd.pth https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth') -os.system('mv ./Wav2Lip/* .') - -title = "Wav2Lip Huggingface Interface Test" -description = "A simple demo for Wav2Lip Official Repo" -article = "Official Repo: https://github.com/Rudrabha/Wav2Lip" - -def inference(face, audio): - os.system("python inference.py --checkpoint_path ./wav2lip.pth --face {} --audio {}".format(face, audio)) - - return "./results/result_voice.mp4" - - -iface = gr.Interface(inference, inputs=[gr.inputs.Video(type="mp4", source="upload", label="Talking Face Video (in mp4 format)", optional=False), gr.inputs.Audio(source="upload", type="filepath", label="Audio", optional=False)], outputs=["video"], title=title, description=description, article=article, examples=[["./examples/w2l_test_f1.mp4", "./examples/w2l_test_a1.wav"]], enable_queue=True) -iface.launch() diff --git a/spaces/oguzakif/video-object-remover/SiamMask/models/siammask_sharp.py b/spaces/oguzakif/video-object-remover/SiamMask/models/siammask_sharp.py deleted file mode 100644 index 3fe43b1c004aff14cb62e4dd497d2db71958bbc6..0000000000000000000000000000000000000000 --- a/spaces/oguzakif/video-object-remover/SiamMask/models/siammask_sharp.py +++ /dev/null @@ -1,196 +0,0 @@ -# -------------------------------------------------------- -# SiamMask -# Licensed under The MIT License -# Written by Qiang Wang (wangqiang2015 at ia.ac.cn) -# -------------------------------------------------------- -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.autograd import Variable -from SiamMask.utils.anchors import Anchors - - -class SiamMask(nn.Module): - def __init__(self, anchors=None, o_sz=127, g_sz=127): - super(SiamMask, self).__init__() - self.anchors = anchors # anchor_cfg - self.anchor_num = len(self.anchors["ratios"]) * len(self.anchors["scales"]) - self.anchor = Anchors(anchors) - self.features = None - self.rpn_model = None - self.mask_model = None - self.o_sz = o_sz - self.g_sz = g_sz - self.upSample = nn.UpsamplingBilinear2d(size=[g_sz, g_sz]) - - self.all_anchors = None - - def set_all_anchors(self, image_center, size): - # cx,cy,w,h - if not self.anchor.generate_all_anchors(image_center, size): - return - all_anchors = self.anchor.all_anchors[1] # cx, cy, w, h - self.all_anchors = torch.from_numpy(all_anchors).float().cuda() - self.all_anchors = [self.all_anchors[i] for i in range(4)] - - def feature_extractor(self, x): - return self.features(x) - - def rpn(self, template, search): - pred_cls, pred_loc = self.rpn_model(template, search) - return pred_cls, pred_loc - - def mask(self, template, search): - pred_mask = self.mask_model(template, search) - return pred_mask - - def _add_rpn_loss(self, label_cls, label_loc, lable_loc_weight, label_mask, label_mask_weight, - rpn_pred_cls, rpn_pred_loc, rpn_pred_mask): - rpn_loss_cls = select_cross_entropy_loss(rpn_pred_cls, label_cls) - - rpn_loss_loc = weight_l1_loss(rpn_pred_loc, label_loc, lable_loc_weight) - - rpn_loss_mask, iou_m, iou_5, iou_7 = select_mask_logistic_loss(rpn_pred_mask, label_mask, label_mask_weight) - - return rpn_loss_cls, rpn_loss_loc, rpn_loss_mask, iou_m, iou_5, iou_7 - - def run(self, template, search, softmax=False): - """ - run network - """ - template_feature = self.feature_extractor(template) - feature, search_feature = self.features.forward_all(search) - rpn_pred_cls, rpn_pred_loc = self.rpn(template_feature, search_feature) - corr_feature = self.mask_model.mask.forward_corr(template_feature, search_feature) # (b, 256, w, h) - rpn_pred_mask = self.refine_model(feature, corr_feature) - - if softmax: - rpn_pred_cls = self.softmax(rpn_pred_cls) - return rpn_pred_cls, rpn_pred_loc, rpn_pred_mask, template_feature, search_feature - - def softmax(self, cls): - b, a2, h, w = cls.size() - cls = cls.view(b, 2, a2//2, h, w) - cls = cls.permute(0, 2, 3, 4, 1).contiguous() - cls = F.log_softmax(cls, dim=4) - return cls - - def forward(self, input): - """ - :param input: dict of input with keys of: - 'template': [b, 3, h1, w1], input template image. - 'search': [b, 3, h2, w2], input search image. - 'label_cls':[b, max_num_gts, 5] or None(self.training==False), - each gt contains x1,y1,x2,y2,class. - :return: dict of loss, predict, accuracy - """ - template = input['template'] - search = input['search'] - if self.training: - label_cls = input['label_cls'] - label_loc = input['label_loc'] - lable_loc_weight = input['label_loc_weight'] - label_mask = input['label_mask'] - label_mask_weight = input['label_mask_weight'] - - rpn_pred_cls, rpn_pred_loc, rpn_pred_mask, template_feature, search_feature = \ - self.run(template, search, softmax=self.training) - - outputs = dict() - - outputs['predict'] = [rpn_pred_loc, rpn_pred_cls, rpn_pred_mask, template_feature, search_feature] - - if self.training: - rpn_loss_cls, rpn_loss_loc, rpn_loss_mask, iou_acc_mean, iou_acc_5, iou_acc_7 = \ - self._add_rpn_loss(label_cls, label_loc, lable_loc_weight, label_mask, label_mask_weight, - rpn_pred_cls, rpn_pred_loc, rpn_pred_mask) - outputs['losses'] = [rpn_loss_cls, rpn_loss_loc, rpn_loss_mask] - outputs['accuracy'] = [iou_acc_mean, iou_acc_5, iou_acc_7] - - return outputs - - def template(self, z): - self.zf = self.feature_extractor(z) - cls_kernel, loc_kernel = self.rpn_model.template(self.zf) - return cls_kernel, loc_kernel - - def track(self, x, cls_kernel=None, loc_kernel=None, softmax=False): - xf = self.feature_extractor(x) - rpn_pred_cls, rpn_pred_loc = self.rpn_model.track(xf, cls_kernel, loc_kernel) - if softmax: - rpn_pred_cls = self.softmax(rpn_pred_cls) - return rpn_pred_cls, rpn_pred_loc - - -def get_cls_loss(pred, label, select): - if select.nelement() == 0: return pred.sum()*0. - pred = torch.index_select(pred, 0, select) - label = torch.index_select(label, 0, select) - - return F.nll_loss(pred, label) - - -def select_cross_entropy_loss(pred, label): - pred = pred.view(-1, 2) - label = label.view(-1) - pos = Variable(label.data.eq(1).nonzero().squeeze()).cuda() - neg = Variable(label.data.eq(0).nonzero().squeeze()).cuda() - - loss_pos = get_cls_loss(pred, label, pos) - loss_neg = get_cls_loss(pred, label, neg) - return loss_pos * 0.5 + loss_neg * 0.5 - - -def weight_l1_loss(pred_loc, label_loc, loss_weight): - """ - :param pred_loc: [b, 4k, h, w] - :param label_loc: [b, 4k, h, w] - :param loss_weight: [b, k, h, w] - :return: loc loss value - """ - b, _, sh, sw = pred_loc.size() - pred_loc = pred_loc.view(b, 4, -1, sh, sw) - diff = (pred_loc - label_loc).abs() - diff = diff.sum(dim=1).view(b, -1, sh, sw) - loss = diff * loss_weight - return loss.sum().div(b) - - -def select_mask_logistic_loss(p_m, mask, weight, o_sz=63, g_sz=127): - weight = weight.view(-1) - pos = Variable(weight.data.eq(1).nonzero().squeeze()) - if pos.nelement() == 0: return p_m.sum() * 0, p_m.sum() * 0, p_m.sum() * 0, p_m.sum() * 0 - - if len(p_m.shape) == 4: - p_m = p_m.permute(0, 2, 3, 1).contiguous().view(-1, 1, o_sz, o_sz) - p_m = torch.index_select(p_m, 0, pos) - p_m = nn.UpsamplingBilinear2d(size=[g_sz, g_sz])(p_m) - p_m = p_m.view(-1, g_sz * g_sz) - else: - p_m = torch.index_select(p_m, 0, pos) - - mask_uf = F.unfold(mask, (g_sz, g_sz), padding=0, stride=8) - mask_uf = torch.transpose(mask_uf, 1, 2).contiguous().view(-1, g_sz * g_sz) - - mask_uf = torch.index_select(mask_uf, 0, pos) - loss = F.soft_margin_loss(p_m, mask_uf) - iou_m, iou_5, iou_7 = iou_measure(p_m, mask_uf) - return loss, iou_m, iou_5, iou_7 - - -def iou_measure(pred, label): - pred = pred.ge(0) - mask_sum = pred.eq(1).add(label.eq(1)) - intxn = torch.sum(mask_sum == 2, dim=1).float() - union = torch.sum(mask_sum > 0, dim=1).float() - iou = intxn/union - return torch.mean(iou), (torch.sum(iou > 0.5).float()/iou.shape[0]), (torch.sum(iou > 0.7).float()/iou.shape[0]) - - -if __name__ == "__main__": - p_m = torch.randn(4, 63*63, 25, 25) - cls = torch.randn(4, 1, 25, 25) > 0.9 - mask = torch.randn(4, 1, 255, 255) * 2 - 1 - - loss = select_mask_logistic_loss(p_m, mask, cls) - print(loss) diff --git a/spaces/osanseviero/test_chatui/Dockerfile b/spaces/osanseviero/test_chatui/Dockerfile deleted file mode 100644 index 1f185cc85fa318fdf39f91be98db2bb7e805411c..0000000000000000000000000000000000000000 --- a/spaces/osanseviero/test_chatui/Dockerfile +++ /dev/null @@ -1,121 +0,0 @@ -ARG MODEL_NAME -ARG MODEL_PARAMS -ARG APP_COLOR -ARG APP_NAME - - -FROM node:19 as chatui-builder -ARG MODEL_NAME -ARG MODEL_PARAMS -ARG APP_COLOR -ARG APP_NAME - -WORKDIR /app - -RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ - git gettext && \ - rm -rf /var/lib/apt/lists/* - - -RUN git clone https://github.com/huggingface/chat-ui.git - -WORKDIR /app/chat-ui - - -COPY .env.local.template .env.local.template - -RUN mkdir defaults -ADD defaults /defaults -RUN chmod -R 777 /defaults -RUN --mount=type=secret,id=MONGODB_URL,mode=0444 \ - MODEL_NAME="${MODEL_NAME:="$(cat /defaults/MODEL_NAME)"}" && export MODEL_NAME \ - && MODEL_PARAMS="${MODEL_PARAMS:="$(cat /defaults/MODEL_PARAMS)"}" && export MODEL_PARAMS \ - && APP_COLOR="${APP_COLOR:="$(cat /defaults/APP_COLOR)"}" && export APP_COLOR \ - && APP_NAME="${APP_NAME:="$(cat /defaults/APP_NAME)"}" && export APP_NAME \ - && MONGODB_URL=$(cat /run/secrets/MONGODB_URL > /dev/null | grep '^' || cat /defaults/MONGODB_URL) && export MONGODB_URL && \ - echo "${MONGODB_URL}" && \ - envsubst < ".env.local.template" > ".env.local" \ - && rm .env.local.template - - - -RUN --mount=type=cache,target=/app/.npm \ - npm set cache /app/.npm && \ - npm ci - -RUN npm run build - -FROM ghcr.io/huggingface/text-generation-inference:latest - -ARG MODEL_NAME -ARG MODEL_PARAMS -ARG APP_COLOR -ARG APP_NAME - -ENV TZ=Europe/Paris \ - PORT=3000 - - - -RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ - gnupg \ - curl \ - gettext && \ - rm -rf /var/lib/apt/lists/* -COPY entrypoint.sh.template entrypoint.sh.template - -RUN mkdir defaults -ADD defaults /defaults -RUN chmod -R 777 /defaults - -RUN --mount=type=secret,id=MONGODB_URL,mode=0444 \ - MODEL_NAME="${MODEL_NAME:="$(cat /defaults/MODEL_NAME)"}" && export MODEL_NAME \ - && MODEL_PARAMS="${MODEL_PARAMS:="$(cat /defaults/MODEL_PARAMS)"}" && export MODEL_PARAMS \ - && APP_COLOR="${APP_COLOR:="$(cat /defaults/APP_COLOR)"}" && export APP_COLOR \ - && APP_NAME="${APP_NAME:="$(cat /defaults/APP_NAME)"}" && export APP_NAME \ - && MONGODB_URL=$(cat /run/secrets/MONGODB_URL > /dev/null | grep '^' || cat /defaults/MONGODB_URL) && export MONGODB_URL && \ - envsubst < "entrypoint.sh.template" > "entrypoint.sh" \ - && rm entrypoint.sh.template - - -RUN curl -fsSL https://pgp.mongodb.com/server-6.0.asc | \ - gpg -o /usr/share/keyrings/mongodb-server-6.0.gpg \ - --dearmor - -RUN echo "deb [ arch=amd64,arm64 signed-by=/usr/share/keyrings/mongodb-server-6.0.gpg ] https://repo.mongodb.org/apt/ubuntu focal/mongodb-org/6.0 multiverse" | tee /etc/apt/sources.list.d/mongodb-org-6.0.list - -RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ - mongodb-org && \ - rm -rf /var/lib/apt/lists/* - -RUN mkdir -p /data/db -RUN chown -R 1000:1000 /data - -RUN curl -fsSL https://deb.nodesource.com/setup_19.x | /bin/bash - - -RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ - nodejs && \ - rm -rf /var/lib/apt/lists/* - -RUN mkdir /app -RUN chown -R 1000:1000 /app - -RUN useradd -m -u 1000 user - -# Switch to the "user" user -USER user - -ENV HOME=/home/user \ - PATH=/home/user/.local/bin:$PATH - -RUN npm config set prefix /home/user/.local -RUN npm install -g pm2 - -COPY --from=chatui-builder --chown=1000 /app/chat-ui/node_modules /app/node_modules -COPY --from=chatui-builder --chown=1000 /app/chat-ui/package.json /app/package.json -COPY --from=chatui-builder --chown=1000 /app/chat-ui/build /app/build - -ENTRYPOINT ["/bin/bash"] -CMD ["entrypoint.sh"] - - diff --git a/spaces/osl-ai/NousResearch-Yarn-Mistral-7b-64k/app.py b/spaces/osl-ai/NousResearch-Yarn-Mistral-7b-64k/app.py deleted file mode 100644 index dee09d303fb2a0141bd7f842c7470b2d60ed5f2e..0000000000000000000000000000000000000000 --- a/spaces/osl-ai/NousResearch-Yarn-Mistral-7b-64k/app.py +++ /dev/null @@ -1,15 +0,0 @@ -import gradio as gr - -# Assuming this is the correct path to your model and it returns a usable object. -model = gr.Interface.load("models/NousResearch/Yarn-Mistral-7b-64k") - -# Define a function that uses your model for inference, ensuring that the 'model' can predict. -def predict(input): - # Example prediction code. Replace with actual prediction logic. - return model(input) - -# Create a Gradio interface -interface = gr.Interface(fn=predict, inputs="text", outputs="text") - -# Launch the interface with share=True to create a public link. -interface.launch(share=True) diff --git a/spaces/owaiskha9654/Custom_Yolov7/utils/loss.py b/spaces/owaiskha9654/Custom_Yolov7/utils/loss.py deleted file mode 100644 index bf7ab65a304b51b398d9877da0673d5c01e52081..0000000000000000000000000000000000000000 --- a/spaces/owaiskha9654/Custom_Yolov7/utils/loss.py +++ /dev/null @@ -1,1697 +0,0 @@ -# Loss functions - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from utils.general import bbox_iou, bbox_alpha_iou, box_iou, box_giou, box_diou, box_ciou, xywh2xyxy -from utils.torch_utils import is_parallel - - -def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 - # return positive, negative label smoothing BCE targets - return 1.0 - 0.5 * eps, 0.5 * eps - - -class BCEBlurWithLogitsLoss(nn.Module): - # BCEwithLogitLoss() with reduced missing label effects. - def __init__(self, alpha=0.05): - super(BCEBlurWithLogitsLoss, self).__init__() - self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() - self.alpha = alpha - - def forward(self, pred, true): - loss = self.loss_fcn(pred, true) - pred = torch.sigmoid(pred) # prob from logits - dx = pred - true # reduce only missing label effects - # dx = (pred - true).abs() # reduce missing label and false label effects - alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) - loss *= alpha_factor - return loss.mean() - - -class SigmoidBin(nn.Module): - stride = None # strides computed during build - export = False # onnx export - - def __init__(self, bin_count=10, min=0.0, max=1.0, reg_scale = 2.0, use_loss_regression=True, use_fw_regression=True, BCE_weight=1.0, smooth_eps=0.0): - super(SigmoidBin, self).__init__() - - self.bin_count = bin_count - self.length = bin_count + 1 - self.min = min - self.max = max - self.scale = float(max - min) - self.shift = self.scale / 2.0 - - self.use_loss_regression = use_loss_regression - self.use_fw_regression = use_fw_regression - self.reg_scale = reg_scale - self.BCE_weight = BCE_weight - - start = min + (self.scale/2.0) / self.bin_count - end = max - (self.scale/2.0) / self.bin_count - step = self.scale / self.bin_count - self.step = step - #print(f" start = {start}, end = {end}, step = {step} ") - - bins = torch.range(start, end + 0.0001, step).float() - self.register_buffer('bins', bins) - - - self.cp = 1.0 - 0.5 * smooth_eps - self.cn = 0.5 * smooth_eps - - self.BCEbins = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([BCE_weight])) - self.MSELoss = nn.MSELoss() - - def get_length(self): - return self.length - - def forward(self, pred): - assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length) - - pred_reg = (pred[..., 0] * self.reg_scale - self.reg_scale/2.0) * self.step - pred_bin = pred[..., 1:(1+self.bin_count)] - - _, bin_idx = torch.max(pred_bin, dim=-1) - bin_bias = self.bins[bin_idx] - - if self.use_fw_regression: - result = pred_reg + bin_bias - else: - result = bin_bias - result = result.clamp(min=self.min, max=self.max) - - return result - - - def training_loss(self, pred, target): - assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length) - assert pred.shape[0] == target.shape[0], 'pred.shape=%d is not equal to the target.shape=%d' % (pred.shape[0], target.shape[0]) - device = pred.device - - pred_reg = (pred[..., 0].sigmoid() * self.reg_scale - self.reg_scale/2.0) * self.step - pred_bin = pred[..., 1:(1+self.bin_count)] - - diff_bin_target = torch.abs(target[..., None] - self.bins) - _, bin_idx = torch.min(diff_bin_target, dim=-1) - - bin_bias = self.bins[bin_idx] - bin_bias.requires_grad = False - result = pred_reg + bin_bias - - target_bins = torch.full_like(pred_bin, self.cn, device=device) # targets - n = pred.shape[0] - target_bins[range(n), bin_idx] = self.cp - - loss_bin = self.BCEbins(pred_bin, target_bins) # BCE - - if self.use_loss_regression: - loss_regression = self.MSELoss(result, target) # MSE - loss = loss_bin + loss_regression - else: - loss = loss_bin - - out_result = result.clamp(min=self.min, max=self.max) - - return loss, out_result - - -class FocalLoss(nn.Module): - # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) - def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): - super(FocalLoss, self).__init__() - self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() - self.gamma = gamma - self.alpha = alpha - self.reduction = loss_fcn.reduction - self.loss_fcn.reduction = 'none' # required to apply FL to each element - - def forward(self, pred, true): - loss = self.loss_fcn(pred, true) - # p_t = torch.exp(-loss) - # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability - - # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py - pred_prob = torch.sigmoid(pred) # prob from logits - p_t = true * pred_prob + (1 - true) * (1 - pred_prob) - alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) - modulating_factor = (1.0 - p_t) ** self.gamma - loss *= alpha_factor * modulating_factor - - if self.reduction == 'mean': - return loss.mean() - elif self.reduction == 'sum': - return loss.sum() - else: # 'none' - return loss - - -class QFocalLoss(nn.Module): - # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) - def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): - super(QFocalLoss, self).__init__() - self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() - self.gamma = gamma - self.alpha = alpha - self.reduction = loss_fcn.reduction - self.loss_fcn.reduction = 'none' # required to apply FL to each element - - def forward(self, pred, true): - loss = self.loss_fcn(pred, true) - - pred_prob = torch.sigmoid(pred) # prob from logits - alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) - modulating_factor = torch.abs(true - pred_prob) ** self.gamma - loss *= alpha_factor * modulating_factor - - if self.reduction == 'mean': - return loss.mean() - elif self.reduction == 'sum': - return loss.sum() - else: # 'none' - return loss - -class RankSort(torch.autograd.Function): - @staticmethod - def forward(ctx, logits, targets, delta_RS=0.50, eps=1e-10): - - classification_grads=torch.zeros(logits.shape).cuda() - - #Filter fg logits - fg_labels = (targets > 0.) - fg_logits = logits[fg_labels] - fg_targets = targets[fg_labels] - fg_num = len(fg_logits) - - #Do not use bg with scores less than minimum fg logit - #since changing its score does not have an effect on precision - threshold_logit = torch.min(fg_logits)-delta_RS - relevant_bg_labels=((targets==0) & (logits>=threshold_logit)) - - relevant_bg_logits = logits[relevant_bg_labels] - relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() - sorting_error=torch.zeros(fg_num).cuda() - ranking_error=torch.zeros(fg_num).cuda() - fg_grad=torch.zeros(fg_num).cuda() - - #sort the fg logits - order=torch.argsort(fg_logits) - #Loops over each positive following the order - for ii in order: - # Difference Transforms (x_ij) - fg_relations=fg_logits-fg_logits[ii] - bg_relations=relevant_bg_logits-fg_logits[ii] - - if delta_RS > 0: - fg_relations=torch.clamp(fg_relations/(2*delta_RS)+0.5,min=0,max=1) - bg_relations=torch.clamp(bg_relations/(2*delta_RS)+0.5,min=0,max=1) - else: - fg_relations = (fg_relations >= 0).float() - bg_relations = (bg_relations >= 0).float() - - # Rank of ii among pos and false positive number (bg with larger scores) - rank_pos=torch.sum(fg_relations) - FP_num=torch.sum(bg_relations) - - # Rank of ii among all examples - rank=rank_pos+FP_num - - # Ranking error of example ii. target_ranking_error is always 0. (Eq. 7) - ranking_error[ii]=FP_num/rank - - # Current sorting error of example ii. (Eq. 7) - current_sorting_error = torch.sum(fg_relations*(1-fg_targets))/rank_pos - - #Find examples in the target sorted order for example ii - iou_relations = (fg_targets >= fg_targets[ii]) - target_sorted_order = iou_relations * fg_relations - - #The rank of ii among positives in sorted order - rank_pos_target = torch.sum(target_sorted_order) - - #Compute target sorting error. (Eq. 8) - #Since target ranking error is 0, this is also total target error - target_sorting_error= torch.sum(target_sorted_order*(1-fg_targets))/rank_pos_target - - #Compute sorting error on example ii - sorting_error[ii] = current_sorting_error - target_sorting_error - - #Identity Update for Ranking Error - if FP_num > eps: - #For ii the update is the ranking error - fg_grad[ii] -= ranking_error[ii] - #For negatives, distribute error via ranking pmf (i.e. bg_relations/FP_num) - relevant_bg_grad += (bg_relations*(ranking_error[ii]/FP_num)) - - #Find the positives that are misranked (the cause of the error) - #These are the ones with smaller IoU but larger logits - missorted_examples = (~ iou_relations) * fg_relations - - #Denominotor of sorting pmf - sorting_pmf_denom = torch.sum(missorted_examples) - - #Identity Update for Sorting Error - if sorting_pmf_denom > eps: - #For ii the update is the sorting error - fg_grad[ii] -= sorting_error[ii] - #For positives, distribute error via sorting pmf (i.e. missorted_examples/sorting_pmf_denom) - fg_grad += (missorted_examples*(sorting_error[ii]/sorting_pmf_denom)) - - #Normalize gradients by number of positives - classification_grads[fg_labels]= (fg_grad/fg_num) - classification_grads[relevant_bg_labels]= (relevant_bg_grad/fg_num) - - ctx.save_for_backward(classification_grads) - - return ranking_error.mean(), sorting_error.mean() - - @staticmethod - def backward(ctx, out_grad1, out_grad2): - g1, =ctx.saved_tensors - return g1*out_grad1, None, None, None - -class aLRPLoss(torch.autograd.Function): - @staticmethod - def forward(ctx, logits, targets, regression_losses, delta=1., eps=1e-5): - classification_grads=torch.zeros(logits.shape).cuda() - - #Filter fg logits - fg_labels = (targets == 1) - fg_logits = logits[fg_labels] - fg_num = len(fg_logits) - - #Do not use bg with scores less than minimum fg logit - #since changing its score does not have an effect on precision - threshold_logit = torch.min(fg_logits)-delta - - #Get valid bg logits - relevant_bg_labels=((targets==0)&(logits>=threshold_logit)) - relevant_bg_logits=logits[relevant_bg_labels] - relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() - rank=torch.zeros(fg_num).cuda() - prec=torch.zeros(fg_num).cuda() - fg_grad=torch.zeros(fg_num).cuda() - - max_prec=0 - #sort the fg logits - order=torch.argsort(fg_logits) - #Loops over each positive following the order - for ii in order: - #x_ij s as score differences with fgs - fg_relations=fg_logits-fg_logits[ii] - #Apply piecewise linear function and determine relations with fgs - fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1) - #Discard i=j in the summation in rank_pos - fg_relations[ii]=0 - - #x_ij s as score differences with bgs - bg_relations=relevant_bg_logits-fg_logits[ii] - #Apply piecewise linear function and determine relations with bgs - bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1) - - #Compute the rank of the example within fgs and number of bgs with larger scores - rank_pos=1+torch.sum(fg_relations) - FP_num=torch.sum(bg_relations) - #Store the total since it is normalizer also for aLRP Regression error - rank[ii]=rank_pos+FP_num - - #Compute precision for this example to compute classification loss - prec[ii]=rank_pos/rank[ii] - #For stability, set eps to a infinitesmall value (e.g. 1e-6), then compute grads - if FP_num > eps: - fg_grad[ii] = -(torch.sum(fg_relations*regression_losses)+FP_num)/rank[ii] - relevant_bg_grad += (bg_relations*(-fg_grad[ii]/FP_num)) - - #aLRP with grad formulation fg gradient - classification_grads[fg_labels]= fg_grad - #aLRP with grad formulation bg gradient - classification_grads[relevant_bg_labels]= relevant_bg_grad - - classification_grads /= (fg_num) - - cls_loss=1-prec.mean() - ctx.save_for_backward(classification_grads) - - return cls_loss, rank, order - - @staticmethod - def backward(ctx, out_grad1, out_grad2, out_grad3): - g1, =ctx.saved_tensors - return g1*out_grad1, None, None, None, None - - -class APLoss(torch.autograd.Function): - @staticmethod - def forward(ctx, logits, targets, delta=1.): - classification_grads=torch.zeros(logits.shape).cuda() - - #Filter fg logits - fg_labels = (targets == 1) - fg_logits = logits[fg_labels] - fg_num = len(fg_logits) - - #Do not use bg with scores less than minimum fg logit - #since changing its score does not have an effect on precision - threshold_logit = torch.min(fg_logits)-delta - - #Get valid bg logits - relevant_bg_labels=((targets==0)&(logits>=threshold_logit)) - relevant_bg_logits=logits[relevant_bg_labels] - relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() - rank=torch.zeros(fg_num).cuda() - prec=torch.zeros(fg_num).cuda() - fg_grad=torch.zeros(fg_num).cuda() - - max_prec=0 - #sort the fg logits - order=torch.argsort(fg_logits) - #Loops over each positive following the order - for ii in order: - #x_ij s as score differences with fgs - fg_relations=fg_logits-fg_logits[ii] - #Apply piecewise linear function and determine relations with fgs - fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1) - #Discard i=j in the summation in rank_pos - fg_relations[ii]=0 - - #x_ij s as score differences with bgs - bg_relations=relevant_bg_logits-fg_logits[ii] - #Apply piecewise linear function and determine relations with bgs - bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1) - - #Compute the rank of the example within fgs and number of bgs with larger scores - rank_pos=1+torch.sum(fg_relations) - FP_num=torch.sum(bg_relations) - #Store the total since it is normalizer also for aLRP Regression error - rank[ii]=rank_pos+FP_num - - #Compute precision for this example - current_prec=rank_pos/rank[ii] - - #Compute interpolated AP and store gradients for relevant bg examples - if (max_prec<=current_prec): - max_prec=current_prec - relevant_bg_grad += (bg_relations/rank[ii]) - else: - relevant_bg_grad += (bg_relations/rank[ii])*(((1-max_prec)/(1-current_prec))) - - #Store fg gradients - fg_grad[ii]=-(1-max_prec) - prec[ii]=max_prec - - #aLRP with grad formulation fg gradient - classification_grads[fg_labels]= fg_grad - #aLRP with grad formulation bg gradient - classification_grads[relevant_bg_labels]= relevant_bg_grad - - classification_grads /= fg_num - - cls_loss=1-prec.mean() - ctx.save_for_backward(classification_grads) - - return cls_loss - - @staticmethod - def backward(ctx, out_grad1): - g1, =ctx.saved_tensors - return g1*out_grad1, None, None - - -class ComputeLoss: - # Compute losses - def __init__(self, model, autobalance=False): - super(ComputeLoss, self).__init__() - device = next(model.parameters()).device # get model device - h = model.hyp # hyperparameters - - # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) - - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets - - # Focal loss - g = h['fl_gamma'] # focal loss gamma - if g > 0: - BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) - - det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module - self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 - #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.1, .05]) # P3-P7 - #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.5, 0.4, .1]) # P3-P7 - self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index - self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance - for k in 'na', 'nc', 'nl', 'anchors': - setattr(self, k, getattr(det, k)) - - def __call__(self, p, targets): # predictions, targets, model - device = targets.device - lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) - tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets - - # Losses - for i, pi in enumerate(p): # layer index, layer predictions - b, a, gj, gi = indices[i] # image, anchor, gridy, gridx - tobj = torch.zeros_like(pi[..., 0], device=device) # target obj - - n = b.shape[0] # number of targets - if n: - ps = pi[b, a, gj, gi] # prediction subset corresponding to targets - - # Regression - pxy = ps[:, :2].sigmoid() * 2. - 0.5 - pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] - pbox = torch.cat((pxy, pwh), 1) # predicted box - iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) - lbox += (1.0 - iou).mean() # iou loss - - # Objectness - tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio - - # Classification - if self.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets - t[range(n), tcls[i]] = self.cp - #t[t==self.cp] = iou.detach().clamp(0).type(t.dtype) - lcls += self.BCEcls(ps[:, 5:], t) # BCE - - # Append targets to text file - # with open('targets.txt', 'a') as file: - # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] - - obji = self.BCEobj(pi[..., 4], tobj) - lobj += obji * self.balance[i] # obj loss - if self.autobalance: - self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() - - if self.autobalance: - self.balance = [x / self.balance[self.ssi] for x in self.balance] - lbox *= self.hyp['box'] - lobj *= self.hyp['obj'] - lcls *= self.hyp['cls'] - bs = tobj.shape[0] # batch size - - loss = lbox + lobj + lcls - return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() - - def build_targets(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) - na, nt = self.na, targets.shape[0] # number of anchors, targets - tcls, tbox, indices, anch = [], [], [], [] - gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain - ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) - targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices - - g = 0.5 # bias - off = torch.tensor([[0, 0], - [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], device=targets.device).float() * g # offsets - - for i in range(self.nl): - anchors = self.anchors[i] - gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - t = targets * gain - if nt: - # Matches - r = t[:, :, 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare - # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) - t = t[j] # filter - - # Offsets - gxy = t[:, 2:4] # grid xy - gxi = gain[[2, 3]] - gxy # inverse - j, k = ((gxy % 1. < g) & (gxy > 1.)).T - l, m = ((gxi % 1. < g) & (gxi > 1.)).T - j = torch.stack((torch.ones_like(j), j, k, l, m)) - t = t.repeat((5, 1, 1))[j] - offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] - else: - t = targets[0] - offsets = 0 - - # Define - b, c = t[:, :2].long().T # image, class - gxy = t[:, 2:4] # grid xy - gwh = t[:, 4:6] # grid wh - gij = (gxy - offsets).long() - gi, gj = gij.T # grid xy indices - - # Append - a = t[:, 6].long() # anchor indices - indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices - tbox.append(torch.cat((gxy - gij, gwh), 1)) # box - anch.append(anchors[a]) # anchors - tcls.append(c) # class - - return tcls, tbox, indices, anch - - -class ComputeLossOTA: - # Compute losses - def __init__(self, model, autobalance=False): - super(ComputeLossOTA, self).__init__() - device = next(model.parameters()).device # get model device - h = model.hyp # hyperparameters - - # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) - - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets - - # Focal loss - g = h['fl_gamma'] # focal loss gamma - if g > 0: - BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) - - det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module - self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 - self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index - self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance - for k in 'na', 'nc', 'nl', 'anchors', 'stride': - setattr(self, k, getattr(det, k)) - - def __call__(self, p, targets, imgs): # predictions, targets, model - device = targets.device - lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) - bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs) - pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p] - - - # Losses - for i, pi in enumerate(p): # layer index, layer predictions - b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx - tobj = torch.zeros_like(pi[..., 0], device=device) # target obj - - n = b.shape[0] # number of targets - if n: - ps = pi[b, a, gj, gi] # prediction subset corresponding to targets - - # Regression - grid = torch.stack([gi, gj], dim=1) - pxy = ps[:, :2].sigmoid() * 2. - 0.5 - #pxy = ps[:, :2].sigmoid() * 3. - 1. - pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] - pbox = torch.cat((pxy, pwh), 1) # predicted box - selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] - selected_tbox[:, :2] -= grid - iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target) - lbox += (1.0 - iou).mean() # iou loss - - # Objectness - tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio - - # Classification - selected_tcls = targets[i][:, 1].long() - if self.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets - t[range(n), selected_tcls] = self.cp - lcls += self.BCEcls(ps[:, 5:], t) # BCE - - # Append targets to text file - # with open('targets.txt', 'a') as file: - # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] - - obji = self.BCEobj(pi[..., 4], tobj) - lobj += obji * self.balance[i] # obj loss - if self.autobalance: - self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() - - if self.autobalance: - self.balance = [x / self.balance[self.ssi] for x in self.balance] - lbox *= self.hyp['box'] - lobj *= self.hyp['obj'] - lcls *= self.hyp['cls'] - bs = tobj.shape[0] # batch size - - loss = lbox + lobj + lcls - return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() - - def build_targets(self, p, targets, imgs): - - #indices, anch = self.find_positive(p, targets) - indices, anch = self.find_3_positive(p, targets) - #indices, anch = self.find_4_positive(p, targets) - #indices, anch = self.find_5_positive(p, targets) - #indices, anch = self.find_9_positive(p, targets) - - matching_bs = [[] for pp in p] - matching_as = [[] for pp in p] - matching_gjs = [[] for pp in p] - matching_gis = [[] for pp in p] - matching_targets = [[] for pp in p] - matching_anchs = [[] for pp in p] - - nl = len(p) - - for batch_idx in range(p[0].shape[0]): - - b_idx = targets[:, 0]==batch_idx - this_target = targets[b_idx] - if this_target.shape[0] == 0: - continue - - txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] - txyxy = xywh2xyxy(txywh) - - pxyxys = [] - p_cls = [] - p_obj = [] - from_which_layer = [] - all_b = [] - all_a = [] - all_gj = [] - all_gi = [] - all_anch = [] - - for i, pi in enumerate(p): - - b, a, gj, gi = indices[i] - idx = (b == batch_idx) - b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] - all_b.append(b) - all_a.append(a) - all_gj.append(gj) - all_gi.append(gi) - all_anch.append(anch[i][idx]) - from_which_layer.append(torch.ones(size=(len(b),)) * i) - - fg_pred = pi[b, a, gj, gi] - p_obj.append(fg_pred[:, 4:5]) - p_cls.append(fg_pred[:, 5:]) - - grid = torch.stack([gi, gj], dim=1) - pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. - #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i] - pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. - pxywh = torch.cat([pxy, pwh], dim=-1) - pxyxy = xywh2xyxy(pxywh) - pxyxys.append(pxyxy) - - pxyxys = torch.cat(pxyxys, dim=0) - if pxyxys.shape[0] == 0: - continue - p_obj = torch.cat(p_obj, dim=0) - p_cls = torch.cat(p_cls, dim=0) - from_which_layer = torch.cat(from_which_layer, dim=0) - all_b = torch.cat(all_b, dim=0) - all_a = torch.cat(all_a, dim=0) - all_gj = torch.cat(all_gj, dim=0) - all_gi = torch.cat(all_gi, dim=0) - all_anch = torch.cat(all_anch, dim=0) - - pair_wise_iou = box_iou(txyxy, pxyxys) - - pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) - - top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1) - dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) - - gt_cls_per_image = ( - F.one_hot(this_target[:, 1].to(torch.int64), self.nc) - .float() - .unsqueeze(1) - .repeat(1, pxyxys.shape[0], 1) - ) - - num_gt = this_target.shape[0] - cls_preds_ = ( - p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() - * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() - ) - - y = cls_preds_.sqrt_() - pair_wise_cls_loss = F.binary_cross_entropy_with_logits( - torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" - ).sum(-1) - del cls_preds_ - - cost = ( - pair_wise_cls_loss - + 3.0 * pair_wise_iou_loss - ) - - matching_matrix = torch.zeros_like(cost) - - for gt_idx in range(num_gt): - _, pos_idx = torch.topk( - cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False - ) - matching_matrix[gt_idx][pos_idx] = 1.0 - - del top_k, dynamic_ks - anchor_matching_gt = matching_matrix.sum(0) - if (anchor_matching_gt > 1).sum() > 0: - _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) - matching_matrix[:, anchor_matching_gt > 1] *= 0.0 - matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 - fg_mask_inboxes = matching_matrix.sum(0) > 0.0 - matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) - - from_which_layer = from_which_layer[fg_mask_inboxes] - all_b = all_b[fg_mask_inboxes] - all_a = all_a[fg_mask_inboxes] - all_gj = all_gj[fg_mask_inboxes] - all_gi = all_gi[fg_mask_inboxes] - all_anch = all_anch[fg_mask_inboxes] - - this_target = this_target[matched_gt_inds] - - for i in range(nl): - layer_idx = from_which_layer == i - matching_bs[i].append(all_b[layer_idx]) - matching_as[i].append(all_a[layer_idx]) - matching_gjs[i].append(all_gj[layer_idx]) - matching_gis[i].append(all_gi[layer_idx]) - matching_targets[i].append(this_target[layer_idx]) - matching_anchs[i].append(all_anch[layer_idx]) - - for i in range(nl): - if matching_targets[i] != []: - matching_bs[i] = torch.cat(matching_bs[i], dim=0) - matching_as[i] = torch.cat(matching_as[i], dim=0) - matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) - matching_gis[i] = torch.cat(matching_gis[i], dim=0) - matching_targets[i] = torch.cat(matching_targets[i], dim=0) - matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) - else: - matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - - return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs - - def find_3_positive(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) - na, nt = self.na, targets.shape[0] # number of anchors, targets - indices, anch = [], [] - gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain - ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) - targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices - - g = 0.5 # bias - off = torch.tensor([[0, 0], - [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], device=targets.device).float() * g # offsets - - for i in range(self.nl): - anchors = self.anchors[i] - gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - t = targets * gain - if nt: - # Matches - r = t[:, :, 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare - # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) - t = t[j] # filter - - # Offsets - gxy = t[:, 2:4] # grid xy - gxi = gain[[2, 3]] - gxy # inverse - j, k = ((gxy % 1. < g) & (gxy > 1.)).T - l, m = ((gxi % 1. < g) & (gxi > 1.)).T - j = torch.stack((torch.ones_like(j), j, k, l, m)) - t = t.repeat((5, 1, 1))[j] - offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] - else: - t = targets[0] - offsets = 0 - - # Define - b, c = t[:, :2].long().T # image, class - gxy = t[:, 2:4] # grid xy - gwh = t[:, 4:6] # grid wh - gij = (gxy - offsets).long() - gi, gj = gij.T # grid xy indices - - # Append - a = t[:, 6].long() # anchor indices - indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices - anch.append(anchors[a]) # anchors - - return indices, anch - - -class ComputeLossBinOTA: - # Compute losses - def __init__(self, model, autobalance=False): - super(ComputeLossBinOTA, self).__init__() - device = next(model.parameters()).device # get model device - h = model.hyp # hyperparameters - - # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) - #MSEangle = nn.MSELoss().to(device) - - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets - - # Focal loss - g = h['fl_gamma'] # focal loss gamma - if g > 0: - BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) - - det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module - self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 - self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index - self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance - for k in 'na', 'nc', 'nl', 'anchors', 'stride', 'bin_count': - setattr(self, k, getattr(det, k)) - - #xy_bin_sigmoid = SigmoidBin(bin_count=11, min=-0.5, max=1.5, use_loss_regression=False).to(device) - wh_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0, use_loss_regression=False).to(device) - #angle_bin_sigmoid = SigmoidBin(bin_count=31, min=-1.1, max=1.1, use_loss_regression=False).to(device) - self.wh_bin_sigmoid = wh_bin_sigmoid - - def __call__(self, p, targets, imgs): # predictions, targets, model - device = targets.device - lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) - bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs) - pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p] - - - # Losses - for i, pi in enumerate(p): # layer index, layer predictions - b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx - tobj = torch.zeros_like(pi[..., 0], device=device) # target obj - - obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2 # x,y, w-bce, h-bce # xy_bin_sigmoid.get_length()*2 - - n = b.shape[0] # number of targets - if n: - ps = pi[b, a, gj, gi] # prediction subset corresponding to targets - - # Regression - grid = torch.stack([gi, gj], dim=1) - selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] - selected_tbox[:, :2] -= grid - - #pxy = ps[:, :2].sigmoid() * 2. - 0.5 - ##pxy = ps[:, :2].sigmoid() * 3. - 1. - #pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] - #pbox = torch.cat((pxy, pwh), 1) # predicted box - - #x_loss, px = xy_bin_sigmoid.training_loss(ps[..., 0:12], tbox[i][..., 0]) - #y_loss, py = xy_bin_sigmoid.training_loss(ps[..., 12:24], tbox[i][..., 1]) - w_loss, pw = self.wh_bin_sigmoid.training_loss(ps[..., 2:(3+self.bin_count)], selected_tbox[..., 2] / anchors[i][..., 0]) - h_loss, ph = self.wh_bin_sigmoid.training_loss(ps[..., (3+self.bin_count):obj_idx], selected_tbox[..., 3] / anchors[i][..., 1]) - - pw *= anchors[i][..., 0] - ph *= anchors[i][..., 1] - - px = ps[:, 0].sigmoid() * 2. - 0.5 - py = ps[:, 1].sigmoid() * 2. - 0.5 - - lbox += w_loss + h_loss # + x_loss + y_loss - - #print(f"\n px = {px.shape}, py = {py.shape}, pw = {pw.shape}, ph = {ph.shape} \n") - - pbox = torch.cat((px.unsqueeze(1), py.unsqueeze(1), pw.unsqueeze(1), ph.unsqueeze(1)), 1).to(device) # predicted box - - - - - iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target) - lbox += (1.0 - iou).mean() # iou loss - - # Objectness - tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio - - # Classification - selected_tcls = targets[i][:, 1].long() - if self.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(ps[:, (1+obj_idx):], self.cn, device=device) # targets - t[range(n), selected_tcls] = self.cp - lcls += self.BCEcls(ps[:, (1+obj_idx):], t) # BCE - - # Append targets to text file - # with open('targets.txt', 'a') as file: - # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] - - obji = self.BCEobj(pi[..., obj_idx], tobj) - lobj += obji * self.balance[i] # obj loss - if self.autobalance: - self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() - - if self.autobalance: - self.balance = [x / self.balance[self.ssi] for x in self.balance] - lbox *= self.hyp['box'] - lobj *= self.hyp['obj'] - lcls *= self.hyp['cls'] - bs = tobj.shape[0] # batch size - - loss = lbox + lobj + lcls - return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() - - def build_targets(self, p, targets, imgs): - - #indices, anch = self.find_positive(p, targets) - indices, anch = self.find_3_positive(p, targets) - #indices, anch = self.find_4_positive(p, targets) - #indices, anch = self.find_5_positive(p, targets) - #indices, anch = self.find_9_positive(p, targets) - - matching_bs = [[] for pp in p] - matching_as = [[] for pp in p] - matching_gjs = [[] for pp in p] - matching_gis = [[] for pp in p] - matching_targets = [[] for pp in p] - matching_anchs = [[] for pp in p] - - nl = len(p) - - for batch_idx in range(p[0].shape[0]): - - b_idx = targets[:, 0]==batch_idx - this_target = targets[b_idx] - if this_target.shape[0] == 0: - continue - - txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] - txyxy = xywh2xyxy(txywh) - - pxyxys = [] - p_cls = [] - p_obj = [] - from_which_layer = [] - all_b = [] - all_a = [] - all_gj = [] - all_gi = [] - all_anch = [] - - for i, pi in enumerate(p): - - obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2 - - b, a, gj, gi = indices[i] - idx = (b == batch_idx) - b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] - all_b.append(b) - all_a.append(a) - all_gj.append(gj) - all_gi.append(gi) - all_anch.append(anch[i][idx]) - from_which_layer.append(torch.ones(size=(len(b),)) * i) - - fg_pred = pi[b, a, gj, gi] - p_obj.append(fg_pred[:, obj_idx:(obj_idx+1)]) - p_cls.append(fg_pred[:, (obj_idx+1):]) - - grid = torch.stack([gi, gj], dim=1) - pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. - #pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. - pw = self.wh_bin_sigmoid.forward(fg_pred[..., 2:(3+self.bin_count)].sigmoid()) * anch[i][idx][:, 0] * self.stride[i] - ph = self.wh_bin_sigmoid.forward(fg_pred[..., (3+self.bin_count):obj_idx].sigmoid()) * anch[i][idx][:, 1] * self.stride[i] - - pxywh = torch.cat([pxy, pw.unsqueeze(1), ph.unsqueeze(1)], dim=-1) - pxyxy = xywh2xyxy(pxywh) - pxyxys.append(pxyxy) - - pxyxys = torch.cat(pxyxys, dim=0) - if pxyxys.shape[0] == 0: - continue - p_obj = torch.cat(p_obj, dim=0) - p_cls = torch.cat(p_cls, dim=0) - from_which_layer = torch.cat(from_which_layer, dim=0) - all_b = torch.cat(all_b, dim=0) - all_a = torch.cat(all_a, dim=0) - all_gj = torch.cat(all_gj, dim=0) - all_gi = torch.cat(all_gi, dim=0) - all_anch = torch.cat(all_anch, dim=0) - - pair_wise_iou = box_iou(txyxy, pxyxys) - - pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) - - top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1) - dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) - - gt_cls_per_image = ( - F.one_hot(this_target[:, 1].to(torch.int64), self.nc) - .float() - .unsqueeze(1) - .repeat(1, pxyxys.shape[0], 1) - ) - - num_gt = this_target.shape[0] - cls_preds_ = ( - p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() - * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() - ) - - y = cls_preds_.sqrt_() - pair_wise_cls_loss = F.binary_cross_entropy_with_logits( - torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" - ).sum(-1) - del cls_preds_ - - cost = ( - pair_wise_cls_loss - + 3.0 * pair_wise_iou_loss - ) - - matching_matrix = torch.zeros_like(cost) - - for gt_idx in range(num_gt): - _, pos_idx = torch.topk( - cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False - ) - matching_matrix[gt_idx][pos_idx] = 1.0 - - del top_k, dynamic_ks - anchor_matching_gt = matching_matrix.sum(0) - if (anchor_matching_gt > 1).sum() > 0: - _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) - matching_matrix[:, anchor_matching_gt > 1] *= 0.0 - matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 - fg_mask_inboxes = matching_matrix.sum(0) > 0.0 - matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) - - from_which_layer = from_which_layer[fg_mask_inboxes] - all_b = all_b[fg_mask_inboxes] - all_a = all_a[fg_mask_inboxes] - all_gj = all_gj[fg_mask_inboxes] - all_gi = all_gi[fg_mask_inboxes] - all_anch = all_anch[fg_mask_inboxes] - - this_target = this_target[matched_gt_inds] - - for i in range(nl): - layer_idx = from_which_layer == i - matching_bs[i].append(all_b[layer_idx]) - matching_as[i].append(all_a[layer_idx]) - matching_gjs[i].append(all_gj[layer_idx]) - matching_gis[i].append(all_gi[layer_idx]) - matching_targets[i].append(this_target[layer_idx]) - matching_anchs[i].append(all_anch[layer_idx]) - - for i in range(nl): - if matching_targets[i] != []: - matching_bs[i] = torch.cat(matching_bs[i], dim=0) - matching_as[i] = torch.cat(matching_as[i], dim=0) - matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) - matching_gis[i] = torch.cat(matching_gis[i], dim=0) - matching_targets[i] = torch.cat(matching_targets[i], dim=0) - matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) - else: - matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - - return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs - - def find_3_positive(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) - na, nt = self.na, targets.shape[0] # number of anchors, targets - indices, anch = [], [] - gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain - ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) - targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices - - g = 0.5 # bias - off = torch.tensor([[0, 0], - [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], device=targets.device).float() * g # offsets - - for i in range(self.nl): - anchors = self.anchors[i] - gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - t = targets * gain - if nt: - # Matches - r = t[:, :, 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare - # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) - t = t[j] # filter - - # Offsets - gxy = t[:, 2:4] # grid xy - gxi = gain[[2, 3]] - gxy # inverse - j, k = ((gxy % 1. < g) & (gxy > 1.)).T - l, m = ((gxi % 1. < g) & (gxi > 1.)).T - j = torch.stack((torch.ones_like(j), j, k, l, m)) - t = t.repeat((5, 1, 1))[j] - offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] - else: - t = targets[0] - offsets = 0 - - # Define - b, c = t[:, :2].long().T # image, class - gxy = t[:, 2:4] # grid xy - gwh = t[:, 4:6] # grid wh - gij = (gxy - offsets).long() - gi, gj = gij.T # grid xy indices - - # Append - a = t[:, 6].long() # anchor indices - indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices - anch.append(anchors[a]) # anchors - - return indices, anch - - -class ComputeLossAuxOTA: - # Compute losses - def __init__(self, model, autobalance=False): - super(ComputeLossAuxOTA, self).__init__() - device = next(model.parameters()).device # get model device - h = model.hyp # hyperparameters - - # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) - - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets - - # Focal loss - g = h['fl_gamma'] # focal loss gamma - if g > 0: - BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) - - det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module - self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 - self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index - self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance - for k in 'na', 'nc', 'nl', 'anchors', 'stride': - setattr(self, k, getattr(det, k)) - - def __call__(self, p, targets, imgs): # predictions, targets, model - device = targets.device - lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) - bs_aux, as_aux_, gjs_aux, gis_aux, targets_aux, anchors_aux = self.build_targets2(p[:self.nl], targets, imgs) - bs, as_, gjs, gis, targets, anchors = self.build_targets(p[:self.nl], targets, imgs) - pre_gen_gains_aux = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[:self.nl]] - pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[:self.nl]] - - - # Losses - for i in range(self.nl): # layer index, layer predictions - pi = p[i] - pi_aux = p[i+self.nl] - b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx - b_aux, a_aux, gj_aux, gi_aux = bs_aux[i], as_aux_[i], gjs_aux[i], gis_aux[i] # image, anchor, gridy, gridx - tobj = torch.zeros_like(pi[..., 0], device=device) # target obj - tobj_aux = torch.zeros_like(pi_aux[..., 0], device=device) # target obj - - n = b.shape[0] # number of targets - if n: - ps = pi[b, a, gj, gi] # prediction subset corresponding to targets - - # Regression - grid = torch.stack([gi, gj], dim=1) - pxy = ps[:, :2].sigmoid() * 2. - 0.5 - pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] - pbox = torch.cat((pxy, pwh), 1) # predicted box - selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] - selected_tbox[:, :2] -= grid - iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target) - lbox += (1.0 - iou).mean() # iou loss - - # Objectness - tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio - - # Classification - selected_tcls = targets[i][:, 1].long() - if self.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets - t[range(n), selected_tcls] = self.cp - lcls += self.BCEcls(ps[:, 5:], t) # BCE - - # Append targets to text file - # with open('targets.txt', 'a') as file: - # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] - - n_aux = b_aux.shape[0] # number of targets - if n_aux: - ps_aux = pi_aux[b_aux, a_aux, gj_aux, gi_aux] # prediction subset corresponding to targets - grid_aux = torch.stack([gi_aux, gj_aux], dim=1) - pxy_aux = ps_aux[:, :2].sigmoid() * 2. - 0.5 - #pxy_aux = ps_aux[:, :2].sigmoid() * 3. - 1. - pwh_aux = (ps_aux[:, 2:4].sigmoid() * 2) ** 2 * anchors_aux[i] - pbox_aux = torch.cat((pxy_aux, pwh_aux), 1) # predicted box - selected_tbox_aux = targets_aux[i][:, 2:6] * pre_gen_gains_aux[i] - selected_tbox_aux[:, :2] -= grid_aux - iou_aux = bbox_iou(pbox_aux.T, selected_tbox_aux, x1y1x2y2=False, CIoU=True) # iou(prediction, target) - lbox += 0.25 * (1.0 - iou_aux).mean() # iou loss - - # Objectness - tobj_aux[b_aux, a_aux, gj_aux, gi_aux] = (1.0 - self.gr) + self.gr * iou_aux.detach().clamp(0).type(tobj_aux.dtype) # iou ratio - - # Classification - selected_tcls_aux = targets_aux[i][:, 1].long() - if self.nc > 1: # cls loss (only if multiple classes) - t_aux = torch.full_like(ps_aux[:, 5:], self.cn, device=device) # targets - t_aux[range(n_aux), selected_tcls_aux] = self.cp - lcls += 0.25 * self.BCEcls(ps_aux[:, 5:], t_aux) # BCE - - obji = self.BCEobj(pi[..., 4], tobj) - obji_aux = self.BCEobj(pi_aux[..., 4], tobj_aux) - lobj += obji * self.balance[i] + 0.25 * obji_aux * self.balance[i] # obj loss - if self.autobalance: - self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() - - if self.autobalance: - self.balance = [x / self.balance[self.ssi] for x in self.balance] - lbox *= self.hyp['box'] - lobj *= self.hyp['obj'] - lcls *= self.hyp['cls'] - bs = tobj.shape[0] # batch size - - loss = lbox + lobj + lcls - return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() - - def build_targets(self, p, targets, imgs): - - indices, anch = self.find_3_positive(p, targets) - - matching_bs = [[] for pp in p] - matching_as = [[] for pp in p] - matching_gjs = [[] for pp in p] - matching_gis = [[] for pp in p] - matching_targets = [[] for pp in p] - matching_anchs = [[] for pp in p] - - nl = len(p) - - for batch_idx in range(p[0].shape[0]): - - b_idx = targets[:, 0]==batch_idx - this_target = targets[b_idx] - if this_target.shape[0] == 0: - continue - - txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] - txyxy = xywh2xyxy(txywh) - - pxyxys = [] - p_cls = [] - p_obj = [] - from_which_layer = [] - all_b = [] - all_a = [] - all_gj = [] - all_gi = [] - all_anch = [] - - for i, pi in enumerate(p): - - b, a, gj, gi = indices[i] - idx = (b == batch_idx) - b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] - all_b.append(b) - all_a.append(a) - all_gj.append(gj) - all_gi.append(gi) - all_anch.append(anch[i][idx]) - from_which_layer.append(torch.ones(size=(len(b),)) * i) - - fg_pred = pi[b, a, gj, gi] - p_obj.append(fg_pred[:, 4:5]) - p_cls.append(fg_pred[:, 5:]) - - grid = torch.stack([gi, gj], dim=1) - pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. - #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i] - pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. - pxywh = torch.cat([pxy, pwh], dim=-1) - pxyxy = xywh2xyxy(pxywh) - pxyxys.append(pxyxy) - - pxyxys = torch.cat(pxyxys, dim=0) - if pxyxys.shape[0] == 0: - continue - p_obj = torch.cat(p_obj, dim=0) - p_cls = torch.cat(p_cls, dim=0) - from_which_layer = torch.cat(from_which_layer, dim=0) - all_b = torch.cat(all_b, dim=0) - all_a = torch.cat(all_a, dim=0) - all_gj = torch.cat(all_gj, dim=0) - all_gi = torch.cat(all_gi, dim=0) - all_anch = torch.cat(all_anch, dim=0) - - pair_wise_iou = box_iou(txyxy, pxyxys) - - pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) - - top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1) - dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) - - gt_cls_per_image = ( - F.one_hot(this_target[:, 1].to(torch.int64), self.nc) - .float() - .unsqueeze(1) - .repeat(1, pxyxys.shape[0], 1) - ) - - num_gt = this_target.shape[0] - cls_preds_ = ( - p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() - * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() - ) - - y = cls_preds_.sqrt_() - pair_wise_cls_loss = F.binary_cross_entropy_with_logits( - torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" - ).sum(-1) - del cls_preds_ - - cost = ( - pair_wise_cls_loss - + 3.0 * pair_wise_iou_loss - ) - - matching_matrix = torch.zeros_like(cost) - - for gt_idx in range(num_gt): - _, pos_idx = torch.topk( - cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False - ) - matching_matrix[gt_idx][pos_idx] = 1.0 - - del top_k, dynamic_ks - anchor_matching_gt = matching_matrix.sum(0) - if (anchor_matching_gt > 1).sum() > 0: - _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) - matching_matrix[:, anchor_matching_gt > 1] *= 0.0 - matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 - fg_mask_inboxes = matching_matrix.sum(0) > 0.0 - matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) - - from_which_layer = from_which_layer[fg_mask_inboxes] - all_b = all_b[fg_mask_inboxes] - all_a = all_a[fg_mask_inboxes] - all_gj = all_gj[fg_mask_inboxes] - all_gi = all_gi[fg_mask_inboxes] - all_anch = all_anch[fg_mask_inboxes] - - this_target = this_target[matched_gt_inds] - - for i in range(nl): - layer_idx = from_which_layer == i - matching_bs[i].append(all_b[layer_idx]) - matching_as[i].append(all_a[layer_idx]) - matching_gjs[i].append(all_gj[layer_idx]) - matching_gis[i].append(all_gi[layer_idx]) - matching_targets[i].append(this_target[layer_idx]) - matching_anchs[i].append(all_anch[layer_idx]) - - for i in range(nl): - if matching_targets[i] != []: - matching_bs[i] = torch.cat(matching_bs[i], dim=0) - matching_as[i] = torch.cat(matching_as[i], dim=0) - matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) - matching_gis[i] = torch.cat(matching_gis[i], dim=0) - matching_targets[i] = torch.cat(matching_targets[i], dim=0) - matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) - else: - matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - - return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs - - def build_targets2(self, p, targets, imgs): - - indices, anch = self.find_5_positive(p, targets) - - matching_bs = [[] for pp in p] - matching_as = [[] for pp in p] - matching_gjs = [[] for pp in p] - matching_gis = [[] for pp in p] - matching_targets = [[] for pp in p] - matching_anchs = [[] for pp in p] - - nl = len(p) - - for batch_idx in range(p[0].shape[0]): - - b_idx = targets[:, 0]==batch_idx - this_target = targets[b_idx] - if this_target.shape[0] == 0: - continue - - txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] - txyxy = xywh2xyxy(txywh) - - pxyxys = [] - p_cls = [] - p_obj = [] - from_which_layer = [] - all_b = [] - all_a = [] - all_gj = [] - all_gi = [] - all_anch = [] - - for i, pi in enumerate(p): - - b, a, gj, gi = indices[i] - idx = (b == batch_idx) - b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] - all_b.append(b) - all_a.append(a) - all_gj.append(gj) - all_gi.append(gi) - all_anch.append(anch[i][idx]) - from_which_layer.append(torch.ones(size=(len(b),)) * i) - - fg_pred = pi[b, a, gj, gi] - p_obj.append(fg_pred[:, 4:5]) - p_cls.append(fg_pred[:, 5:]) - - grid = torch.stack([gi, gj], dim=1) - pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. - #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i] - pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. - pxywh = torch.cat([pxy, pwh], dim=-1) - pxyxy = xywh2xyxy(pxywh) - pxyxys.append(pxyxy) - - pxyxys = torch.cat(pxyxys, dim=0) - if pxyxys.shape[0] == 0: - continue - p_obj = torch.cat(p_obj, dim=0) - p_cls = torch.cat(p_cls, dim=0) - from_which_layer = torch.cat(from_which_layer, dim=0) - all_b = torch.cat(all_b, dim=0) - all_a = torch.cat(all_a, dim=0) - all_gj = torch.cat(all_gj, dim=0) - all_gi = torch.cat(all_gi, dim=0) - all_anch = torch.cat(all_anch, dim=0) - - pair_wise_iou = box_iou(txyxy, pxyxys) - - pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) - - top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1) - dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) - - gt_cls_per_image = ( - F.one_hot(this_target[:, 1].to(torch.int64), self.nc) - .float() - .unsqueeze(1) - .repeat(1, pxyxys.shape[0], 1) - ) - - num_gt = this_target.shape[0] - cls_preds_ = ( - p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() - * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() - ) - - y = cls_preds_.sqrt_() - pair_wise_cls_loss = F.binary_cross_entropy_with_logits( - torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" - ).sum(-1) - del cls_preds_ - - cost = ( - pair_wise_cls_loss - + 3.0 * pair_wise_iou_loss - ) - - matching_matrix = torch.zeros_like(cost) - - for gt_idx in range(num_gt): - _, pos_idx = torch.topk( - cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False - ) - matching_matrix[gt_idx][pos_idx] = 1.0 - - del top_k, dynamic_ks - anchor_matching_gt = matching_matrix.sum(0) - if (anchor_matching_gt > 1).sum() > 0: - _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) - matching_matrix[:, anchor_matching_gt > 1] *= 0.0 - matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 - fg_mask_inboxes = matching_matrix.sum(0) > 0.0 - matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) - - from_which_layer = from_which_layer[fg_mask_inboxes] - all_b = all_b[fg_mask_inboxes] - all_a = all_a[fg_mask_inboxes] - all_gj = all_gj[fg_mask_inboxes] - all_gi = all_gi[fg_mask_inboxes] - all_anch = all_anch[fg_mask_inboxes] - - this_target = this_target[matched_gt_inds] - - for i in range(nl): - layer_idx = from_which_layer == i - matching_bs[i].append(all_b[layer_idx]) - matching_as[i].append(all_a[layer_idx]) - matching_gjs[i].append(all_gj[layer_idx]) - matching_gis[i].append(all_gi[layer_idx]) - matching_targets[i].append(this_target[layer_idx]) - matching_anchs[i].append(all_anch[layer_idx]) - - for i in range(nl): - if matching_targets[i] != []: - matching_bs[i] = torch.cat(matching_bs[i], dim=0) - matching_as[i] = torch.cat(matching_as[i], dim=0) - matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) - matching_gis[i] = torch.cat(matching_gis[i], dim=0) - matching_targets[i] = torch.cat(matching_targets[i], dim=0) - matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) - else: - matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) - - return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs - - def find_5_positive(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) - na, nt = self.na, targets.shape[0] # number of anchors, targets - indices, anch = [], [] - gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain - ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) - targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices - - g = 1.0 # bias - off = torch.tensor([[0, 0], - [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], device=targets.device).float() * g # offsets - - for i in range(self.nl): - anchors = self.anchors[i] - gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - t = targets * gain - if nt: - # Matches - r = t[:, :, 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare - # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) - t = t[j] # filter - - # Offsets - gxy = t[:, 2:4] # grid xy - gxi = gain[[2, 3]] - gxy # inverse - j, k = ((gxy % 1. < g) & (gxy > 1.)).T - l, m = ((gxi % 1. < g) & (gxi > 1.)).T - j = torch.stack((torch.ones_like(j), j, k, l, m)) - t = t.repeat((5, 1, 1))[j] - offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] - else: - t = targets[0] - offsets = 0 - - # Define - b, c = t[:, :2].long().T # image, class - gxy = t[:, 2:4] # grid xy - gwh = t[:, 4:6] # grid wh - gij = (gxy - offsets).long() - gi, gj = gij.T # grid xy indices - - # Append - a = t[:, 6].long() # anchor indices - indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices - anch.append(anchors[a]) # anchors - - return indices, anch - - def find_3_positive(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) - na, nt = self.na, targets.shape[0] # number of anchors, targets - indices, anch = [], [] - gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain - ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) - targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices - - g = 0.5 # bias - off = torch.tensor([[0, 0], - [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], device=targets.device).float() * g # offsets - - for i in range(self.nl): - anchors = self.anchors[i] - gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - t = targets * gain - if nt: - # Matches - r = t[:, :, 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare - # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) - t = t[j] # filter - - # Offsets - gxy = t[:, 2:4] # grid xy - gxi = gain[[2, 3]] - gxy # inverse - j, k = ((gxy % 1. < g) & (gxy > 1.)).T - l, m = ((gxi % 1. < g) & (gxi > 1.)).T - j = torch.stack((torch.ones_like(j), j, k, l, m)) - t = t.repeat((5, 1, 1))[j] - offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] - else: - t = targets[0] - offsets = 0 - - # Define - b, c = t[:, :2].long().T # image, class - gxy = t[:, 2:4] # grid xy - gwh = t[:, 4:6] # grid wh - gij = (gxy - offsets).long() - gi, gj = gij.T # grid xy indices - - # Append - a = t[:, 6].long() # anchor indices - indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices - anch.append(anchors[a]) # anchors - - return indices, anch diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/convert_zero123_to_diffusers.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/convert_zero123_to_diffusers.py deleted file mode 100644 index bdcb2cd2e1138193ca98624048d95615ecfbab89..0000000000000000000000000000000000000000 --- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/convert_zero123_to_diffusers.py +++ /dev/null @@ -1,802 +0,0 @@ -""" -This script modified from -https://github.com/huggingface/diffusers/blob/bc691231360a4cbc7d19a58742ebb8ed0f05e027/scripts/convert_original_stable_diffusion_to_diffusers.py - -Convert original Zero1to3 checkpoint to diffusers checkpoint. - -# run the convert script -$ python convert_zero123_to_diffusers.py \ - --checkpoint_path /path/zero123/105000.ckpt \ - --dump_path ./zero1to3 \ - --original_config_file /path/zero123/configs/sd-objaverse-finetune-c_concat-256.yaml -``` -""" -import argparse - -import torch -from accelerate import init_empty_weights -from accelerate.utils import set_module_tensor_to_device -from pipeline_zero1to3 import CCProjection, Zero1to3StableDiffusionPipeline -from transformers import ( - CLIPImageProcessor, - CLIPVisionModelWithProjection, -) - -from diffusers.models import ( - AutoencoderKL, - UNet2DConditionModel, -) -from diffusers.schedulers import DDIMScheduler -from diffusers.utils import logging - - -logger = logging.get_logger(__name__) - - -def create_unet_diffusers_config(original_config, image_size: int, controlnet=False): - """ - Creates a config for the diffusers based on the config of the LDM model. - """ - if controlnet: - unet_params = original_config.model.params.control_stage_config.params - else: - if "unet_config" in original_config.model.params and original_config.model.params.unet_config is not None: - unet_params = original_config.model.params.unet_config.params - else: - unet_params = original_config.model.params.network_config.params - - vae_params = original_config.model.params.first_stage_config.params.ddconfig - - block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult] - - down_block_types = [] - resolution = 1 - for i in range(len(block_out_channels)): - block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D" - down_block_types.append(block_type) - if i != len(block_out_channels) - 1: - resolution *= 2 - - up_block_types = [] - for i in range(len(block_out_channels)): - block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D" - up_block_types.append(block_type) - resolution //= 2 - - if unet_params.transformer_depth is not None: - transformer_layers_per_block = ( - unet_params.transformer_depth - if isinstance(unet_params.transformer_depth, int) - else list(unet_params.transformer_depth) - ) - else: - transformer_layers_per_block = 1 - - vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1) - - head_dim = unet_params.num_heads if "num_heads" in unet_params else None - use_linear_projection = ( - unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False - ) - if use_linear_projection: - # stable diffusion 2-base-512 and 2-768 - if head_dim is None: - head_dim_mult = unet_params.model_channels // unet_params.num_head_channels - head_dim = [head_dim_mult * c for c in list(unet_params.channel_mult)] - - class_embed_type = None - addition_embed_type = None - addition_time_embed_dim = None - projection_class_embeddings_input_dim = None - context_dim = None - - if unet_params.context_dim is not None: - context_dim = ( - unet_params.context_dim if isinstance(unet_params.context_dim, int) else unet_params.context_dim[0] - ) - - if "num_classes" in unet_params: - if unet_params.num_classes == "sequential": - if context_dim in [2048, 1280]: - # SDXL - addition_embed_type = "text_time" - addition_time_embed_dim = 256 - else: - class_embed_type = "projection" - assert "adm_in_channels" in unet_params - projection_class_embeddings_input_dim = unet_params.adm_in_channels - else: - raise NotImplementedError(f"Unknown conditional unet num_classes config: {unet_params.num_classes}") - - config = { - "sample_size": image_size // vae_scale_factor, - "in_channels": unet_params.in_channels, - "down_block_types": tuple(down_block_types), - "block_out_channels": tuple(block_out_channels), - "layers_per_block": unet_params.num_res_blocks, - "cross_attention_dim": context_dim, - "attention_head_dim": head_dim, - "use_linear_projection": use_linear_projection, - "class_embed_type": class_embed_type, - "addition_embed_type": addition_embed_type, - "addition_time_embed_dim": addition_time_embed_dim, - "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, - "transformer_layers_per_block": transformer_layers_per_block, - } - - if controlnet: - config["conditioning_channels"] = unet_params.hint_channels - else: - config["out_channels"] = unet_params.out_channels - config["up_block_types"] = tuple(up_block_types) - - return config - - -def assign_to_checkpoint( - paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None -): - """ - This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits - attention layers, and takes into account additional replacements that may arise. - - Assigns the weights to the new checkpoint. - """ - assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." - - # Splits the attention layers into three variables. - if attention_paths_to_split is not None: - for path, path_map in attention_paths_to_split.items(): - old_tensor = old_checkpoint[path] - channels = old_tensor.shape[0] // 3 - - target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) - - num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 - - old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) - query, key, value = old_tensor.split(channels // num_heads, dim=1) - - checkpoint[path_map["query"]] = query.reshape(target_shape) - checkpoint[path_map["key"]] = key.reshape(target_shape) - checkpoint[path_map["value"]] = value.reshape(target_shape) - - for path in paths: - new_path = path["new"] - - # These have already been assigned - if attention_paths_to_split is not None and new_path in attention_paths_to_split: - continue - - # Global renaming happens here - new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") - new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") - new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") - - if additional_replacements is not None: - for replacement in additional_replacements: - new_path = new_path.replace(replacement["old"], replacement["new"]) - - # proj_attn.weight has to be converted from conv 1D to linear - is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path) - shape = old_checkpoint[path["old"]].shape - if is_attn_weight and len(shape) == 3: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] - elif is_attn_weight and len(shape) == 4: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0] - else: - checkpoint[new_path] = old_checkpoint[path["old"]] - - -def shave_segments(path, n_shave_prefix_segments=1): - """ - Removes segments. Positive values shave the first segments, negative shave the last segments. - """ - if n_shave_prefix_segments >= 0: - return ".".join(path.split(".")[n_shave_prefix_segments:]) - else: - return ".".join(path.split(".")[:n_shave_prefix_segments]) - - -def renew_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item.replace("in_layers.0", "norm1") - new_item = new_item.replace("in_layers.2", "conv1") - - new_item = new_item.replace("out_layers.0", "norm2") - new_item = new_item.replace("out_layers.3", "conv2") - - new_item = new_item.replace("emb_layers.1", "time_emb_proj") - new_item = new_item.replace("skip_connection", "conv_shortcut") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def renew_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - # new_item = new_item.replace('norm.weight', 'group_norm.weight') - # new_item = new_item.replace('norm.bias', 'group_norm.bias') - - # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') - # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') - - # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def convert_ldm_unet_checkpoint( - checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False -): - """ - Takes a state dict and a config, and returns a converted checkpoint. - """ - - if skip_extract_state_dict: - unet_state_dict = checkpoint - else: - # extract state_dict for UNet - unet_state_dict = {} - keys = list(checkpoint.keys()) - - if controlnet: - unet_key = "control_model." - else: - unet_key = "model.diffusion_model." - - # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA - if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: - logger.warning(f"Checkpoint {path} has both EMA and non-EMA weights.") - logger.warning( - "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" - " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." - ) - for key in keys: - if key.startswith("model.diffusion_model"): - flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) - unet_state_dict[key.replace(unet_key, "")] = checkpoint[flat_ema_key] - else: - if sum(k.startswith("model_ema") for k in keys) > 100: - logger.warning( - "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" - " weights (usually better for inference), please make sure to add the `--extract_ema` flag." - ) - - for key in keys: - if key.startswith(unet_key): - unet_state_dict[key.replace(unet_key, "")] = checkpoint[key] - - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] - - if config["class_embed_type"] is None: - # No parameters to port - ... - elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": - new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] - new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] - new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] - new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] - else: - raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") - - if config["addition_embed_type"] == "text_time": - new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] - new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] - new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] - new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] - - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - if not controlnet: - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] - for layer_id in range(num_output_blocks) - } - - for i in range(1, num_input_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) - - resnets = [ - key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key - ] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] - - if f"input_blocks.{i}.0.op.weight" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.bias" - ) - - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - resnet_0 = middle_blocks[0] - attentions = middle_blocks[1] - resnet_1 = middle_blocks[2] - - resnet_0_paths = renew_resnet_paths(resnet_0) - assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) - - resnet_1_paths = renew_resnet_paths(resnet_1) - assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) - - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - for i in range(num_output_blocks): - block_id = i // (config["layers_per_block"] + 1) - layer_in_block_id = i % (config["layers_per_block"] + 1) - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - if len(output_block_list) > 1: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] - - resnet_0_paths = renew_resnet_paths(resnets) - paths = renew_resnet_paths(resnets) - - meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - output_block_list = {k: sorted(v) for k, v in output_block_list.items()} - if ["conv.bias", "conv.weight"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.bias" - ] - - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = { - "old": f"output_blocks.{i}.1", - "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - else: - resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) - - new_checkpoint[new_path] = unet_state_dict[old_path] - - if controlnet: - # conditioning embedding - - orig_index = 0 - - new_checkpoint["controlnet_cond_embedding.conv_in.weight"] = unet_state_dict.pop( - f"input_hint_block.{orig_index}.weight" - ) - new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop( - f"input_hint_block.{orig_index}.bias" - ) - - orig_index += 2 - - diffusers_index = 0 - - while diffusers_index < 6: - new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.weight"] = unet_state_dict.pop( - f"input_hint_block.{orig_index}.weight" - ) - new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.bias"] = unet_state_dict.pop( - f"input_hint_block.{orig_index}.bias" - ) - diffusers_index += 1 - orig_index += 2 - - new_checkpoint["controlnet_cond_embedding.conv_out.weight"] = unet_state_dict.pop( - f"input_hint_block.{orig_index}.weight" - ) - new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop( - f"input_hint_block.{orig_index}.bias" - ) - - # down blocks - for i in range(num_input_blocks): - new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop(f"zero_convs.{i}.0.weight") - new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop(f"zero_convs.{i}.0.bias") - - # mid block - new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop("middle_block_out.0.weight") - new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop("middle_block_out.0.bias") - - return new_checkpoint - - -def create_vae_diffusers_config(original_config, image_size: int): - """ - Creates a config for the diffusers based on the config of the LDM model. - """ - vae_params = original_config.model.params.first_stage_config.params.ddconfig - _ = original_config.model.params.first_stage_config.params.embed_dim - - block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult] - down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) - up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) - - config = { - "sample_size": image_size, - "in_channels": vae_params.in_channels, - "out_channels": vae_params.out_ch, - "down_block_types": tuple(down_block_types), - "up_block_types": tuple(up_block_types), - "block_out_channels": tuple(block_out_channels), - "latent_channels": vae_params.z_channels, - "layers_per_block": vae_params.num_res_blocks, - } - return config - - -def convert_ldm_vae_checkpoint(checkpoint, config): - # extract state dict for VAE - vae_state_dict = {} - vae_key = "first_stage_model." - keys = list(checkpoint.keys()) - for key in keys: - if key.startswith(vae_key): - vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) - - new_checkpoint = {} - - new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] - new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] - new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] - new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] - new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] - - new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] - new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] - new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] - new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] - new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] - - new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] - new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] - new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] - new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] - - # Retrieves the keys for the encoder down blocks only - num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) - down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - # Retrieves the keys for the decoder up blocks only - num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) - up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) - } - - for i in range(num_down_blocks): - resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] - - if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.weight" - ) - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.bias" - ) - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - resnets = [ - key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key - ] - - if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.bias" - ] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - return new_checkpoint - - -def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("nin_shortcut", "conv_shortcut") - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("norm.weight", "group_norm.weight") - new_item = new_item.replace("norm.bias", "group_norm.bias") - - new_item = new_item.replace("q.weight", "to_q.weight") - new_item = new_item.replace("q.bias", "to_q.bias") - - new_item = new_item.replace("k.weight", "to_k.weight") - new_item = new_item.replace("k.bias", "to_k.bias") - - new_item = new_item.replace("v.weight", "to_v.weight") - new_item = new_item.replace("v.bias", "to_v.bias") - - new_item = new_item.replace("proj_out.weight", "to_out.0.weight") - new_item = new_item.replace("proj_out.bias", "to_out.0.bias") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def conv_attn_to_linear(checkpoint): - keys = list(checkpoint.keys()) - attn_keys = ["query.weight", "key.weight", "value.weight"] - for key in keys: - if ".".join(key.split(".")[-2:]) in attn_keys: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0, 0] - elif "proj_attn.weight" in key: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0] - - -def convert_from_original_zero123_ckpt(checkpoint_path, original_config_file, extract_ema, device): - ckpt = torch.load(checkpoint_path, map_location=device) - ckpt["global_step"] - checkpoint = ckpt["state_dict"] - del ckpt - torch.cuda.empty_cache() - - from omegaconf import OmegaConf - - original_config = OmegaConf.load(original_config_file) - original_config.model.params.cond_stage_config.target.split(".")[-1] - num_in_channels = 8 - original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels - prediction_type = "epsilon" - image_size = 256 - num_train_timesteps = getattr(original_config.model.params, "timesteps", None) or 1000 - - beta_start = getattr(original_config.model.params, "linear_start", None) or 0.02 - beta_end = getattr(original_config.model.params, "linear_end", None) or 0.085 - scheduler = DDIMScheduler( - beta_end=beta_end, - beta_schedule="scaled_linear", - beta_start=beta_start, - num_train_timesteps=num_train_timesteps, - steps_offset=1, - clip_sample=False, - set_alpha_to_one=False, - prediction_type=prediction_type, - ) - scheduler.register_to_config(clip_sample=False) - - # Convert the UNet2DConditionModel model. - upcast_attention = None - unet_config = create_unet_diffusers_config(original_config, image_size=image_size) - unet_config["upcast_attention"] = upcast_attention - with init_empty_weights(): - unet = UNet2DConditionModel(**unet_config) - converted_unet_checkpoint = convert_ldm_unet_checkpoint( - checkpoint, unet_config, path=None, extract_ema=extract_ema - ) - for param_name, param in converted_unet_checkpoint.items(): - set_module_tensor_to_device(unet, param_name, "cpu", value=param) - - # Convert the VAE model. - vae_config = create_vae_diffusers_config(original_config, image_size=image_size) - converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) - - if ( - "model" in original_config - and "params" in original_config.model - and "scale_factor" in original_config.model.params - ): - vae_scaling_factor = original_config.model.params.scale_factor - else: - vae_scaling_factor = 0.18215 # default SD scaling factor - - vae_config["scaling_factor"] = vae_scaling_factor - - with init_empty_weights(): - vae = AutoencoderKL(**vae_config) - - for param_name, param in converted_vae_checkpoint.items(): - set_module_tensor_to_device(vae, param_name, "cpu", value=param) - - feature_extractor = CLIPImageProcessor.from_pretrained( - "lambdalabs/sd-image-variations-diffusers", subfolder="feature_extractor" - ) - image_encoder = CLIPVisionModelWithProjection.from_pretrained( - "lambdalabs/sd-image-variations-diffusers", subfolder="image_encoder" - ) - - cc_projection = CCProjection() - cc_projection.load_state_dict( - { - "projection.weight": checkpoint["cc_projection.weight"].cpu(), - "projection.bias": checkpoint["cc_projection.bias"].cpu(), - } - ) - - pipe = Zero1to3StableDiffusionPipeline( - vae, image_encoder, unet, scheduler, None, feature_extractor, cc_projection, requires_safety_checker=False - ) - - return pipe - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--original_config_file", - default=None, - type=str, - help="The YAML config file corresponding to the original architecture.", - ) - parser.add_argument( - "--extract_ema", - action="store_true", - help=( - "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" - " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" - " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." - ), - ) - parser.add_argument( - "--to_safetensors", - action="store_true", - help="Whether to store pipeline in safetensors format or not.", - ) - parser.add_argument("--half", action="store_true", help="Save weights in half precision.") - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") - args = parser.parse_args() - - pipe = convert_from_original_zero123_ckpt( - checkpoint_path=args.checkpoint_path, - original_config_file=args.original_config_file, - extract_ema=args.extract_ema, - device=args.device, - ) - - if args.half: - pipe.to(torch_dtype=torch.float16) - - pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/spaces/paulbricman/lexiscore/main.py b/spaces/paulbricman/lexiscore/main.py deleted file mode 100644 index 94b73b7f7d07d9e0640e972eb72e6f69beb039d5..0000000000000000000000000000000000000000 --- a/spaces/paulbricman/lexiscore/main.py +++ /dev/null @@ -1,36 +0,0 @@ -from enum import auto -from pandas.core.algorithms import isin -import streamlit as st -import pandas as pd -from components import * -from util import * -import nltk - - -nltk.download('punkt') - -st.set_page_config( - page_title='lexiscore', - layout='wide', - menu_items={ - 'Get help': 'https://github.com/paulbricman/lexiscore/issues', - 'Report a Bug': 'https://github.com/paulbricman/lexiscore/issues/new', - 'About': 'https://paulbricman.com/thoughtware/lexiscore' - }) - -sidebar_section() - -if st.session_state['access_token'] == '': - st.info( - 'ℹ️ This tool is part of [a suite of experimental tools for thought](https://paulbricman.com/thoughtware) which incorporate AI primitives in knowledge work.') - st.warning('Please introduce the URL of your conceptarium!') -else: - init() - hero_section() - - col1, padding, col2 = st.columns([18, 1, 40]) - add_section(col1) - cart_section(col2) - meal_prep_section(col2) - - footer_section() diff --git a/spaces/pinecone/semantic-query-trainer/link-check.py b/spaces/pinecone/semantic-query-trainer/link-check.py deleted file mode 100644 index 40dba399789abba6d84cc4b549ac39ec7e69ceff..0000000000000000000000000000000000000000 --- a/spaces/pinecone/semantic-query-trainer/link-check.py +++ /dev/null @@ -1,58 +0,0 @@ -import pinecone -import requests -from tqdm.auto import tqdm -import logging - -# we run this to check for broken links - -PINECONE_API_KEY = "<>" -INDEX = "unsplash-25k-clip" - -pinecone.init( - api_key=PINECONE_API_KEY, - environment="us-west1-gcp" -) - -index = pinecone.Index(INDEX) - -dim = index.describe_index_stats()['dimension'] -total = int(index.describe_index_stats()['totalVectorCount']) -xq = [0.0] * dim - -count = 0 -ID_LIST = [] - -logging.info("Checking links...") - -with tqdm(total=total) as pbar: - while True: - xc = index.query( - xq, top_k=100, include_metadata=True, - filter={"link_check": {"$ne": True}} - ) - matches = xc['matches'] - if len(matches) == 0: - break - for match in matches: - photo_url = match['metadata']['photo_url']+"/download?force=true&w=640" - res = requests.get(photo_url) - if res.status_code == 200: - good_url = "photo_url" - else: - res = requests.get(match['metadata']['photo_image_url']) - if res.status_code == 200: - good_url = "photo_image_url" - else: - good_url = "not_found" - index.update(match['id'], set_metadata={ - 'good_url': good_url, - 'link_check': True - }) - ID_LIST.append(match['id']) - pbar.update(1) - -logging.info("Refreshing 'link_check' field...") -for _id in tqdm(ID_LIST): - index.update(_id, set_metadata={ - 'link_check': False - }) \ No newline at end of file diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/importlib_resources/__init__.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/importlib_resources/__init__.py deleted file mode 100644 index 34e3a9950cc557879af8d797f9382b18a870fb56..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/importlib_resources/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -"""Read resources contained within a package.""" - -from ._common import ( - as_file, - files, - Package, -) - -from ._legacy import ( - contents, - open_binary, - read_binary, - open_text, - read_text, - is_resource, - path, - Resource, -) - -from .abc import ResourceReader - - -__all__ = [ - 'Package', - 'Resource', - 'ResourceReader', - 'as_file', - 'contents', - 'files', - 'is_resource', - 'open_binary', - 'open_text', - 'path', - 'read_binary', - 'read_text', -] diff --git a/spaces/plzdontcry/dakubettergpt/src/assets/icons/ExportIcon.tsx b/spaces/plzdontcry/dakubettergpt/src/assets/icons/ExportIcon.tsx deleted file mode 100644 index 7c6780dfdaee50ec2428fba7c505c834bc1e6b45..0000000000000000000000000000000000000000 --- a/spaces/plzdontcry/dakubettergpt/src/assets/icons/ExportIcon.tsx +++ /dev/null @@ -1,23 +0,0 @@ -import React from 'react'; - -const ExportIcon = (props: React.SVGProps) => { - return ( - - - - - - ); -}; - -export default ExportIcon; diff --git a/spaces/pragnakalp/Huggingface_Sentiment_Analysis/README.md b/spaces/pragnakalp/Huggingface_Sentiment_Analysis/README.md deleted file mode 100644 index f11fea57b52f032ec6f391cae84611043c87184d..0000000000000000000000000000000000000000 --- a/spaces/pragnakalp/Huggingface_Sentiment_Analysis/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Huggingface Sentiment Analysis -emoji: 🐠 -colorFrom: red -colorTo: red -sdk: gradio -sdk_version: 3.13.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiohttp/log.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiohttp/log.py deleted file mode 100644 index 3cecea2bac185df741bccd0a32a5fef9cfe23299..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiohttp/log.py +++ /dev/null @@ -1,8 +0,0 @@ -import logging - -access_logger = logging.getLogger("aiohttp.access") -client_logger = logging.getLogger("aiohttp.client") -internal_logger = logging.getLogger("aiohttp.internal") -server_logger = logging.getLogger("aiohttp.server") -web_logger = logging.getLogger("aiohttp.web") -ws_logger = logging.getLogger("aiohttp.websocket") diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/altair/jupyter/jupyter_chart.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/altair/jupyter/jupyter_chart.py deleted file mode 100644 index 5b2f4af686bd8a64aa0b255941ebd9fa38da10a8..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/altair/jupyter/jupyter_chart.py +++ /dev/null @@ -1,276 +0,0 @@ -import anywidget -import traitlets -import pathlib -from typing import Any, Set - -import altair as alt -from altair.utils._vegafusion_data import using_vegafusion -from altair import TopLevelSpec -from altair.utils.selection import IndexSelection, PointSelection, IntervalSelection - -_here = pathlib.Path(__file__).parent - - -class Params(traitlets.HasTraits): - """ - Traitlet class storing a JupyterChart's params - """ - - def __init__(self, trait_values): - super().__init__() - - for key, value in trait_values.items(): - if isinstance(value, int): - traitlet_type = traitlets.Int() - elif isinstance(value, float): - traitlet_type = traitlets.Float() - elif isinstance(value, str): - traitlet_type = traitlets.Unicode() - elif isinstance(value, list): - traitlet_type = traitlets.List() - elif isinstance(value, dict): - traitlet_type = traitlets.Dict() - else: - traitlet_type = traitlets.Any() - - # Add the new trait. - self.add_traits(**{key: traitlet_type}) - - # Set the trait's value. - setattr(self, key, value) - - def __repr__(self): - return f"Params({self.trait_values()})" - - -class Selections(traitlets.HasTraits): - """ - Traitlet class storing a JupyterChart's selections - """ - - def __init__(self, trait_values): - super().__init__() - - for key, value in trait_values.items(): - if isinstance(value, IndexSelection): - traitlet_type = traitlets.Instance(IndexSelection) - elif isinstance(value, PointSelection): - traitlet_type = traitlets.Instance(PointSelection) - elif isinstance(value, IntervalSelection): - traitlet_type = traitlets.Instance(IntervalSelection) - else: - raise ValueError(f"Unexpected selection type: {type(value)}") - - # Add the new trait. - self.add_traits(**{key: traitlet_type}) - - # Set the trait's value. - setattr(self, key, value) - - # Make read-only - self.observe(self._make_read_only, names=key) - - def __repr__(self): - return f"Selections({self.trait_values()})" - - def _make_read_only(self, change): - """ - Work around to make traits read-only, but still allow us to change - them internally - """ - if change["name"] in self.traits() and change["old"] != change["new"]: - self._set_value(change["name"], change["old"]) - raise ValueError( - "Selections may not be set from Python.\n" - f"Attempted to set select: {change['name']}" - ) - - def _set_value(self, key, value): - self.unobserve(self._make_read_only, names=key) - setattr(self, key, value) - self.observe(self._make_read_only, names=key) - - -class JupyterChart(anywidget.AnyWidget): - _esm = (_here / "js" / "index.js").read_text() - _css = r""" - .vega-embed { - /* Make sure action menu isn't cut off */ - overflow: visible; - } - """ - - # Public traitlets - chart = traitlets.Instance(TopLevelSpec) - spec = traitlets.Dict().tag(sync=True) - debounce_wait = traitlets.Float(default_value=10).tag(sync=True) - - # Internal selection traitlets - _selection_types = traitlets.Dict() - _vl_selections = traitlets.Dict().tag(sync=True) - - # Internal param traitlets - _params = traitlets.Dict().tag(sync=True) - - def __init__(self, chart: TopLevelSpec, debounce_wait: int = 10, **kwargs: Any): - """ - Jupyter Widget for displaying and updating Altair Charts, and - retrieving selection and parameter values - - Parameters - ---------- - chart: Chart - Altair Chart instance - debounce_wait: int - Debouncing wait time in milliseconds - """ - self.params = Params({}) - self.selections = Selections({}) - super().__init__(chart=chart, debounce_wait=debounce_wait, **kwargs) - - @traitlets.observe("chart") - def _on_change_chart(self, change): - """ - Internal callback function that updates the JupyterChart's internal - state when the wrapped Chart instance changes - """ - new_chart = change.new - - params = getattr(new_chart, "params", []) - selection_watches = [] - selection_types = {} - initial_params = {} - initial_vl_selections = {} - empty_selections = {} - - if params is not alt.Undefined: - for param in new_chart.params: - if isinstance(param.name, alt.ParameterName): - clean_name = param.name.to_json().strip('"') - else: - clean_name = param.name - - select = getattr(param, "select", alt.Undefined) - - if select != alt.Undefined: - if not isinstance(select, dict): - select = select.to_dict() - - select_type = select["type"] - if select_type == "point": - if not ( - select.get("fields", None) or select.get("encodings", None) - ): - # Point selection with no associated fields or encodings specified. - # This is an index-based selection - selection_types[clean_name] = "index" - empty_selections[clean_name] = IndexSelection( - name=clean_name, value=[], store=[] - ) - else: - selection_types[clean_name] = "point" - empty_selections[clean_name] = PointSelection( - name=clean_name, value=[], store=[] - ) - elif select_type == "interval": - selection_types[clean_name] = "interval" - empty_selections[clean_name] = IntervalSelection( - name=clean_name, value={}, store=[] - ) - else: - raise ValueError(f"Unexpected selection type {select.type}") - selection_watches.append(clean_name) - initial_vl_selections[clean_name] = {"value": None, "store": []} - else: - clean_value = param.value if param.value != alt.Undefined else None - initial_params[clean_name] = clean_value - - # Handle the params generated by transforms - for param_name in collect_transform_params(new_chart): - initial_params[param_name] = None - - # Setup params - self.params = Params(initial_params) - - def on_param_traitlet_changed(param_change): - new_params = dict(self._params) - new_params[param_change["name"]] = param_change["new"] - self._params = new_params - - self.params.observe(on_param_traitlet_changed) - - # Setup selections - self.selections = Selections(empty_selections) - - # Update properties all together - with self.hold_sync(): - if using_vegafusion(): - self.spec = new_chart.to_dict(format="vega") - else: - self.spec = new_chart.to_dict() - self._selection_types = selection_types - self._vl_selections = initial_vl_selections - self._params = initial_params - - @traitlets.observe("_params") - def _on_change_params(self, change): - for param_name, value in change.new.items(): - setattr(self.params, param_name, value) - - @traitlets.observe("_vl_selections") - def _on_change_selections(self, change): - """ - Internal callback function that updates the JupyterChart's public - selections traitlet in response to changes that the JavaScript logic - makes to the internal _selections traitlet. - """ - for selection_name, selection_dict in change.new.items(): - value = selection_dict["value"] - store = selection_dict["store"] - selection_type = self._selection_types[selection_name] - if selection_type == "index": - self.selections._set_value( - selection_name, - IndexSelection.from_vega(selection_name, signal=value, store=store), - ) - elif selection_type == "point": - self.selections._set_value( - selection_name, - PointSelection.from_vega(selection_name, signal=value, store=store), - ) - elif selection_type == "interval": - self.selections._set_value( - selection_name, - IntervalSelection.from_vega( - selection_name, signal=value, store=store - ), - ) - - -def collect_transform_params(chart: TopLevelSpec) -> Set[str]: - """ - Collect the names of params that are defined by transforms - - Parameters - ---------- - chart: Chart from which to extract transform params - - Returns - ------- - set of param names - """ - transform_params = set() - - # Handle recursive case - for prop in ("layer", "concat", "hconcat", "vconcat"): - for child in getattr(chart, prop, []): - transform_params.update(collect_transform_params(child)) - - # Handle chart's own transforms - transforms = getattr(chart, "transform", []) - transforms = transforms if transforms != alt.Undefined else [] - for tx in transforms: - if hasattr(tx, "param"): - transform_params.add(tx.param) - - return transform_params diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/_frontend_code/utils/src/index.ts b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/_frontend_code/utils/src/index.ts deleted file mode 100644 index e0127e6bee131499c61e15e50cac96ff4bf5f92d..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/_frontend_code/utils/src/index.ts +++ /dev/null @@ -1,2 +0,0 @@ -export * from "./color"; -export * from "./utils"; diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_backend_qt.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_backend_qt.py deleted file mode 100644 index f4a7ef6755f27e2404f9a6166411ac9d044a81cb..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_backend_qt.py +++ /dev/null @@ -1,377 +0,0 @@ -import copy -import importlib -import os -import signal -import sys - -from datetime import date, datetime -from unittest import mock - -import pytest - -import matplotlib -from matplotlib import pyplot as plt -from matplotlib._pylab_helpers import Gcf -from matplotlib import _c_internal_utils - - -try: - from matplotlib.backends.qt_compat import QtGui, QtWidgets # type: ignore # noqa - from matplotlib.backends.qt_editor import _formlayout -except ImportError: - pytestmark = pytest.mark.skip('No usable Qt bindings') - - -_test_timeout = 60 # A reasonably safe value for slower architectures. - - -@pytest.fixture -def qt_core(request): - qt_compat = pytest.importorskip('matplotlib.backends.qt_compat') - QtCore = qt_compat.QtCore - - return QtCore - - -@pytest.mark.backend('QtAgg', skip_on_importerror=True) -def test_fig_close(): - - # save the state of Gcf.figs - init_figs = copy.copy(Gcf.figs) - - # make a figure using pyplot interface - fig = plt.figure() - - # simulate user clicking the close button by reaching in - # and calling close on the underlying Qt object - fig.canvas.manager.window.close() - - # assert that we have removed the reference to the FigureManager - # that got added by plt.figure() - assert init_figs == Gcf.figs - - -@pytest.mark.parametrize( - "qt_key, qt_mods, answer", - [ - ("Key_A", ["ShiftModifier"], "A"), - ("Key_A", [], "a"), - ("Key_A", ["ControlModifier"], ("ctrl+a")), - ( - "Key_Aacute", - ["ShiftModifier"], - "\N{LATIN CAPITAL LETTER A WITH ACUTE}", - ), - ("Key_Aacute", [], "\N{LATIN SMALL LETTER A WITH ACUTE}"), - ("Key_Control", ["AltModifier"], ("alt+control")), - ("Key_Alt", ["ControlModifier"], "ctrl+alt"), - ( - "Key_Aacute", - ["ControlModifier", "AltModifier", "MetaModifier"], - ("ctrl+alt+meta+\N{LATIN SMALL LETTER A WITH ACUTE}"), - ), - # We do not currently map the media keys, this may change in the - # future. This means the callback will never fire - ("Key_Play", [], None), - ("Key_Backspace", [], "backspace"), - ( - "Key_Backspace", - ["ControlModifier"], - "ctrl+backspace", - ), - ], - ids=[ - 'shift', - 'lower', - 'control', - 'unicode_upper', - 'unicode_lower', - 'alt_control', - 'control_alt', - 'modifier_order', - 'non_unicode_key', - 'backspace', - 'backspace_mod', - ] -) -@pytest.mark.parametrize('backend', [ - # Note: the value is irrelevant; the important part is the marker. - pytest.param( - 'Qt5Agg', - marks=pytest.mark.backend('Qt5Agg', skip_on_importerror=True)), - pytest.param( - 'QtAgg', - marks=pytest.mark.backend('QtAgg', skip_on_importerror=True)), -]) -def test_correct_key(backend, qt_core, qt_key, qt_mods, answer, monkeypatch): - """ - Make a figure. - Send a key_press_event event (using non-public, qtX backend specific api). - Catch the event. - Assert sent and caught keys are the same. - """ - from matplotlib.backends.qt_compat import _to_int, QtCore - - if sys.platform == "darwin" and answer is not None: - answer = answer.replace("ctrl", "cmd") - answer = answer.replace("control", "cmd") - answer = answer.replace("meta", "ctrl") - result = None - qt_mod = QtCore.Qt.KeyboardModifier.NoModifier - for mod in qt_mods: - qt_mod |= getattr(QtCore.Qt.KeyboardModifier, mod) - - class _Event: - def isAutoRepeat(self): return False - def key(self): return _to_int(getattr(QtCore.Qt.Key, qt_key)) - - monkeypatch.setattr(QtWidgets.QApplication, "keyboardModifiers", - lambda self: qt_mod) - - def on_key_press(event): - nonlocal result - result = event.key - - qt_canvas = plt.figure().canvas - qt_canvas.mpl_connect('key_press_event', on_key_press) - qt_canvas.keyPressEvent(_Event()) - assert result == answer - - -@pytest.mark.backend('QtAgg', skip_on_importerror=True) -def test_device_pixel_ratio_change(): - """ - Make sure that if the pixel ratio changes, the figure dpi changes but the - widget remains the same logical size. - """ - - prop = 'matplotlib.backends.backend_qt.FigureCanvasQT.devicePixelRatioF' - with mock.patch(prop) as p: - p.return_value = 3 - - fig = plt.figure(figsize=(5, 2), dpi=120) - qt_canvas = fig.canvas - qt_canvas.show() - - def set_device_pixel_ratio(ratio): - p.return_value = ratio - - # The value here doesn't matter, as we can't mock the C++ QScreen - # object, but can override the functional wrapper around it. - # Emitting this event is simply to trigger the DPI change handler - # in Matplotlib in the same manner that it would occur normally. - screen.logicalDotsPerInchChanged.emit(96) - - qt_canvas.draw() - qt_canvas.flush_events() - - # Make sure the mocking worked - assert qt_canvas.device_pixel_ratio == ratio - - qt_canvas.manager.show() - size = qt_canvas.size() - screen = qt_canvas.window().windowHandle().screen() - set_device_pixel_ratio(3) - - # The DPI and the renderer width/height change - assert fig.dpi == 360 - assert qt_canvas.renderer.width == 1800 - assert qt_canvas.renderer.height == 720 - - # The actual widget size and figure logical size don't change. - assert size.width() == 600 - assert size.height() == 240 - assert qt_canvas.get_width_height() == (600, 240) - assert (fig.get_size_inches() == (5, 2)).all() - - set_device_pixel_ratio(2) - - # The DPI and the renderer width/height change - assert fig.dpi == 240 - assert qt_canvas.renderer.width == 1200 - assert qt_canvas.renderer.height == 480 - - # The actual widget size and figure logical size don't change. - assert size.width() == 600 - assert size.height() == 240 - assert qt_canvas.get_width_height() == (600, 240) - assert (fig.get_size_inches() == (5, 2)).all() - - set_device_pixel_ratio(1.5) - - # The DPI and the renderer width/height change - assert fig.dpi == 180 - assert qt_canvas.renderer.width == 900 - assert qt_canvas.renderer.height == 360 - - # The actual widget size and figure logical size don't change. - assert size.width() == 600 - assert size.height() == 240 - assert qt_canvas.get_width_height() == (600, 240) - assert (fig.get_size_inches() == (5, 2)).all() - - -@pytest.mark.backend('QtAgg', skip_on_importerror=True) -def test_subplottool(): - fig, ax = plt.subplots() - with mock.patch("matplotlib.backends.qt_compat._exec", lambda obj: None): - fig.canvas.manager.toolbar.configure_subplots() - - -@pytest.mark.backend('QtAgg', skip_on_importerror=True) -def test_figureoptions(): - fig, ax = plt.subplots() - ax.plot([1, 2]) - ax.imshow([[1]]) - ax.scatter(range(3), range(3), c=range(3)) - with mock.patch("matplotlib.backends.qt_compat._exec", lambda obj: None): - fig.canvas.manager.toolbar.edit_parameters() - - -@pytest.mark.backend('QtAgg', skip_on_importerror=True) -def test_figureoptions_with_datetime_axes(): - fig, ax = plt.subplots() - xydata = [ - datetime(year=2021, month=1, day=1), - datetime(year=2021, month=2, day=1) - ] - ax.plot(xydata, xydata) - with mock.patch("matplotlib.backends.qt_compat._exec", lambda obj: None): - fig.canvas.manager.toolbar.edit_parameters() - - -@pytest.mark.backend('QtAgg', skip_on_importerror=True) -def test_double_resize(): - # Check that resizing a figure twice keeps the same window size - fig, ax = plt.subplots() - fig.canvas.draw() - window = fig.canvas.manager.window - - w, h = 3, 2 - fig.set_size_inches(w, h) - assert fig.canvas.width() == w * matplotlib.rcParams['figure.dpi'] - assert fig.canvas.height() == h * matplotlib.rcParams['figure.dpi'] - - old_width = window.width() - old_height = window.height() - - fig.set_size_inches(w, h) - assert window.width() == old_width - assert window.height() == old_height - - -@pytest.mark.backend('QtAgg', skip_on_importerror=True) -def test_canvas_reinit(): - from matplotlib.backends.backend_qtagg import FigureCanvasQTAgg - - called = False - - def crashing_callback(fig, stale): - nonlocal called - fig.canvas.draw_idle() - called = True - - fig, ax = plt.subplots() - fig.stale_callback = crashing_callback - # this should not raise - canvas = FigureCanvasQTAgg(fig) - fig.stale = True - assert called - - -@pytest.mark.backend('Qt5Agg', skip_on_importerror=True) -def test_form_widget_get_with_datetime_and_date_fields(): - from matplotlib.backends.backend_qt import _create_qApp - _create_qApp() - - form = [ - ("Datetime field", datetime(year=2021, month=3, day=11)), - ("Date field", date(year=2021, month=3, day=11)) - ] - widget = _formlayout.FormWidget(form) - widget.setup() - values = widget.get() - assert values == [ - datetime(year=2021, month=3, day=11), - date(year=2021, month=3, day=11) - ] - - -def _get_testable_qt_backends(): - envs = [] - for deps, env in [ - ([qt_api], {"MPLBACKEND": "qtagg", "QT_API": qt_api}) - for qt_api in ["PyQt6", "PySide6", "PyQt5", "PySide2"] - ]: - reason = None - missing = [dep for dep in deps if not importlib.util.find_spec(dep)] - if (sys.platform == "linux" and - not _c_internal_utils.display_is_valid()): - reason = "$DISPLAY and $WAYLAND_DISPLAY are unset" - elif missing: - reason = "{} cannot be imported".format(", ".join(missing)) - elif env["MPLBACKEND"] == 'macosx' and os.environ.get('TF_BUILD'): - reason = "macosx backend fails on Azure" - marks = [] - if reason: - marks.append(pytest.mark.skip( - reason=f"Skipping {env} because {reason}")) - envs.append(pytest.param(env, marks=marks, id=str(env))) - return envs - - -@pytest.mark.backend('QtAgg', skip_on_importerror=True) -def test_fig_sigint_override(qt_core): - from matplotlib.backends.backend_qt5 import _BackendQT5 - # Create a figure - plt.figure() - - # Variable to access the handler from the inside of the event loop - event_loop_handler = None - - # Callback to fire during event loop: save SIGINT handler, then exit - def fire_signal_and_quit(): - # Save event loop signal - nonlocal event_loop_handler - event_loop_handler = signal.getsignal(signal.SIGINT) - - # Request event loop exit - qt_core.QCoreApplication.exit() - - # Timer to exit event loop - qt_core.QTimer.singleShot(0, fire_signal_and_quit) - - # Save original SIGINT handler - original_handler = signal.getsignal(signal.SIGINT) - - # Use our own SIGINT handler to be 100% sure this is working - def custom_handler(signum, frame): - pass - - signal.signal(signal.SIGINT, custom_handler) - - try: - # mainloop() sets SIGINT, starts Qt event loop (which triggers timer - # and exits) and then mainloop() resets SIGINT - matplotlib.backends.backend_qt._BackendQT.mainloop() - - # Assert: signal handler during loop execution is changed - # (can't test equality with func) - assert event_loop_handler != custom_handler - - # Assert: current signal handler is the same as the one we set before - assert signal.getsignal(signal.SIGINT) == custom_handler - - # Repeat again to test that SIG_DFL and SIG_IGN will not be overridden - for custom_handler in (signal.SIG_DFL, signal.SIG_IGN): - qt_core.QTimer.singleShot(0, fire_signal_and_quit) - signal.signal(signal.SIGINT, custom_handler) - - _BackendQT5.mainloop() - - assert event_loop_handler == custom_handler - assert signal.getsignal(signal.SIGINT) == custom_handler - - finally: - # Reset SIGINT handler to what it was before the test - signal.signal(signal.SIGINT, original_handler) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_scale.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_scale.py deleted file mode 100644 index 727397367762c196879f3ba34b3e3ad533e65cf7..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_scale.py +++ /dev/null @@ -1,295 +0,0 @@ -import copy - -import matplotlib.pyplot as plt -from matplotlib.scale import ( - AsinhScale, AsinhTransform, - LogTransform, InvertedLogTransform, - SymmetricalLogTransform) -import matplotlib.scale as mscale -from matplotlib.ticker import AsinhLocator, LogFormatterSciNotation -from matplotlib.testing.decorators import check_figures_equal, image_comparison - -import numpy as np -from numpy.testing import assert_allclose -import io -import pytest - - -@check_figures_equal() -def test_log_scales(fig_test, fig_ref): - ax_test = fig_test.add_subplot(122, yscale='log', xscale='symlog') - ax_test.axvline(24.1) - ax_test.axhline(24.1) - xlim = ax_test.get_xlim() - ylim = ax_test.get_ylim() - ax_ref = fig_ref.add_subplot(122, yscale='log', xscale='symlog') - ax_ref.set(xlim=xlim, ylim=ylim) - ax_ref.plot([24.1, 24.1], ylim, 'b') - ax_ref.plot(xlim, [24.1, 24.1], 'b') - - -def test_symlog_mask_nan(): - # Use a transform round-trip to verify that the forward and inverse - # transforms work, and that they respect nans and/or masking. - slt = SymmetricalLogTransform(10, 2, 1) - slti = slt.inverted() - - x = np.arange(-1.5, 5, 0.5) - out = slti.transform_non_affine(slt.transform_non_affine(x)) - assert_allclose(out, x) - assert type(out) is type(x) - - x[4] = np.nan - out = slti.transform_non_affine(slt.transform_non_affine(x)) - assert_allclose(out, x) - assert type(out) is type(x) - - x = np.ma.array(x) - out = slti.transform_non_affine(slt.transform_non_affine(x)) - assert_allclose(out, x) - assert type(out) is type(x) - - x[3] = np.ma.masked - out = slti.transform_non_affine(slt.transform_non_affine(x)) - assert_allclose(out, x) - assert type(out) is type(x) - - -@image_comparison(['logit_scales.png'], remove_text=True) -def test_logit_scales(): - fig, ax = plt.subplots() - - # Typical extinction curve for logit - x = np.array([0.001, 0.003, 0.01, 0.03, 0.1, 0.2, 0.3, 0.4, 0.5, - 0.6, 0.7, 0.8, 0.9, 0.97, 0.99, 0.997, 0.999]) - y = 1.0 / x - - ax.plot(x, y) - ax.set_xscale('logit') - ax.grid(True) - bbox = ax.get_tightbbox(fig.canvas.get_renderer()) - assert np.isfinite(bbox.x0) - assert np.isfinite(bbox.y0) - - -def test_log_scatter(): - """Issue #1799""" - fig, ax = plt.subplots(1) - - x = np.arange(10) - y = np.arange(10) - 1 - - ax.scatter(x, y) - - buf = io.BytesIO() - fig.savefig(buf, format='pdf') - - buf = io.BytesIO() - fig.savefig(buf, format='eps') - - buf = io.BytesIO() - fig.savefig(buf, format='svg') - - -def test_logscale_subs(): - fig, ax = plt.subplots() - ax.set_yscale('log', subs=np.array([2, 3, 4])) - # force draw - fig.canvas.draw() - - -@image_comparison(['logscale_mask.png'], remove_text=True) -def test_logscale_mask(): - # Check that zero values are masked correctly on log scales. - # See github issue 8045 - xs = np.linspace(0, 50, 1001) - - fig, ax = plt.subplots() - ax.plot(np.exp(-xs**2)) - fig.canvas.draw() - ax.set(yscale="log") - - -def test_extra_kwargs_raise(): - fig, ax = plt.subplots() - - for scale in ['linear', 'log', 'symlog']: - with pytest.raises(TypeError): - ax.set_yscale(scale, foo='mask') - - -def test_logscale_invert_transform(): - fig, ax = plt.subplots() - ax.set_yscale('log') - # get transformation from data to axes - tform = (ax.transAxes + ax.transData.inverted()).inverted() - - # direct test of log transform inversion - inverted_transform = LogTransform(base=2).inverted() - assert isinstance(inverted_transform, InvertedLogTransform) - assert inverted_transform.base == 2 - - -def test_logscale_transform_repr(): - fig, ax = plt.subplots() - ax.set_yscale('log') - repr(ax.transData) - repr(LogTransform(10, nonpositive='clip')) - - -@image_comparison(['logscale_nonpos_values.png'], - remove_text=True, tol=0.02, style='mpl20') -def test_logscale_nonpos_values(): - np.random.seed(19680801) - xs = np.random.normal(size=int(1e3)) - fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) - ax1.hist(xs, range=(-5, 5), bins=10) - ax1.set_yscale('log') - ax2.hist(xs, range=(-5, 5), bins=10) - ax2.set_yscale('log', nonpositive='mask') - - xdata = np.arange(0, 10, 0.01) - ydata = np.exp(-xdata) - edata = 0.2*(10-xdata)*np.cos(5*xdata)*np.exp(-xdata) - - ax3.fill_between(xdata, ydata - edata, ydata + edata) - ax3.set_yscale('log') - - x = np.logspace(-1, 1) - y = x ** 3 - yerr = x**2 - ax4.errorbar(x, y, yerr=yerr) - - ax4.set_yscale('log') - ax4.set_xscale('log') - - -def test_invalid_log_lims(): - # Check that invalid log scale limits are ignored - fig, ax = plt.subplots() - ax.scatter(range(0, 4), range(0, 4)) - - ax.set_xscale('log') - original_xlim = ax.get_xlim() - with pytest.warns(UserWarning): - ax.set_xlim(left=0) - assert ax.get_xlim() == original_xlim - with pytest.warns(UserWarning): - ax.set_xlim(right=-1) - assert ax.get_xlim() == original_xlim - - ax.set_yscale('log') - original_ylim = ax.get_ylim() - with pytest.warns(UserWarning): - ax.set_ylim(bottom=0) - assert ax.get_ylim() == original_ylim - with pytest.warns(UserWarning): - ax.set_ylim(top=-1) - assert ax.get_ylim() == original_ylim - - -@image_comparison(['function_scales.png'], remove_text=True, style='mpl20') -def test_function_scale(): - def inverse(x): - return x**2 - - def forward(x): - return x**(1/2) - - fig, ax = plt.subplots() - - x = np.arange(1, 1000) - - ax.plot(x, x) - ax.set_xscale('function', functions=(forward, inverse)) - ax.set_xlim(1, 1000) - - -def test_pass_scale(): - # test passing a scale object works... - fig, ax = plt.subplots() - scale = mscale.LogScale(axis=None) - ax.set_xscale(scale) - scale = mscale.LogScale(axis=None) - ax.set_yscale(scale) - assert ax.xaxis.get_scale() == 'log' - assert ax.yaxis.get_scale() == 'log' - - -def test_scale_deepcopy(): - sc = mscale.LogScale(axis='x', base=10) - sc2 = copy.deepcopy(sc) - assert str(sc.get_transform()) == str(sc2.get_transform()) - assert sc._transform is not sc2._transform - - -class TestAsinhScale: - def test_transforms(self): - a0 = 17.0 - a = np.linspace(-50, 50, 100) - - forward = AsinhTransform(a0) - inverse = forward.inverted() - invinv = inverse.inverted() - - a_forward = forward.transform_non_affine(a) - a_inverted = inverse.transform_non_affine(a_forward) - assert_allclose(a_inverted, a) - - a_invinv = invinv.transform_non_affine(a) - assert_allclose(a_invinv, a0 * np.arcsinh(a / a0)) - - def test_init(self): - fig, ax = plt.subplots() - - s = AsinhScale(axis=None, linear_width=23.0) - assert s.linear_width == 23 - assert s._base == 10 - assert s._subs == (2, 5) - - tx = s.get_transform() - assert isinstance(tx, AsinhTransform) - assert tx.linear_width == s.linear_width - - def test_base_init(self): - fig, ax = plt.subplots() - - s3 = AsinhScale(axis=None, base=3) - assert s3._base == 3 - assert s3._subs == (2,) - - s7 = AsinhScale(axis=None, base=7, subs=(2, 4)) - assert s7._base == 7 - assert s7._subs == (2, 4) - - def test_fmtloc(self): - class DummyAxis: - def __init__(self): - self.fields = {} - def set(self, **kwargs): - self.fields.update(**kwargs) - def set_major_formatter(self, f): - self.fields['major_formatter'] = f - - ax0 = DummyAxis() - s0 = AsinhScale(axis=ax0, base=0) - s0.set_default_locators_and_formatters(ax0) - assert isinstance(ax0.fields['major_locator'], AsinhLocator) - assert isinstance(ax0.fields['major_formatter'], str) - - ax5 = DummyAxis() - s7 = AsinhScale(axis=ax5, base=5) - s7.set_default_locators_and_formatters(ax5) - assert isinstance(ax5.fields['major_locator'], AsinhLocator) - assert isinstance(ax5.fields['major_formatter'], - LogFormatterSciNotation) - - def test_bad_scale(self): - fig, ax = plt.subplots() - - with pytest.raises(ValueError): - AsinhScale(axis=None, linear_width=0) - with pytest.raises(ValueError): - AsinhScale(axis=None, linear_width=-1) - s0 = AsinhScale(axis=None, ) - s1 = AsinhScale(axis=None, linear_width=3.0) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/compat/compressors.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/compat/compressors.py deleted file mode 100644 index 1f31e34c092c9672559ca2f5194cb1da7083d03b..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/compat/compressors.py +++ /dev/null @@ -1,77 +0,0 @@ -""" -Patched ``BZ2File`` and ``LZMAFile`` to handle pickle protocol 5. -""" - -from __future__ import annotations - -from pickle import PickleBuffer - -from pandas.compat._constants import PY310 - -try: - import bz2 - - has_bz2 = True -except ImportError: - has_bz2 = False - -try: - import lzma - - has_lzma = True -except ImportError: - has_lzma = False - - -def flatten_buffer( - b: bytes | bytearray | memoryview | PickleBuffer, -) -> bytes | bytearray | memoryview: - """ - Return some 1-D `uint8` typed buffer. - - Coerces anything that does not match that description to one that does - without copying if possible (otherwise will copy). - """ - - if isinstance(b, (bytes, bytearray)): - return b - - if not isinstance(b, PickleBuffer): - b = PickleBuffer(b) - - try: - # coerce to 1-D `uint8` C-contiguous `memoryview` zero-copy - return b.raw() - except BufferError: - # perform in-memory copy if buffer is not contiguous - return memoryview(b).tobytes("A") - - -if has_bz2: - - class BZ2File(bz2.BZ2File): - if not PY310: - - def write(self, b) -> int: - # Workaround issue where `bz2.BZ2File` expects `len` - # to return the number of bytes in `b` by converting - # `b` into something that meets that constraint with - # minimal copying. - # - # Note: This is fixed in Python 3.10. - return super().write(flatten_buffer(b)) - - -if has_lzma: - - class LZMAFile(lzma.LZMAFile): - if not PY310: - - def write(self, b) -> int: - # Workaround issue where `lzma.LZMAFile` expects `len` - # to return the number of bytes in `b` by converting - # `b` into something that meets that constraint with - # minimal copying. - # - # Note: This is fixed in Python 3.10. - return super().write(flatten_buffer(b)) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/tools/times.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/tools/times.py deleted file mode 100644 index 1b3a3ae1be5f0101d6a9fa03691e06ccf6419214..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/tools/times.py +++ /dev/null @@ -1,157 +0,0 @@ -from __future__ import annotations - -from datetime import ( - datetime, - time, -) -from typing import TYPE_CHECKING - -import numpy as np - -from pandas._libs.lib import is_list_like - -from pandas.core.dtypes.generic import ( - ABCIndex, - ABCSeries, -) -from pandas.core.dtypes.missing import notna - -if TYPE_CHECKING: - from pandas._typing import DateTimeErrorChoices - - -def to_time( - arg, - format: str | None = None, - infer_time_format: bool = False, - errors: DateTimeErrorChoices = "raise", -): - """ - Parse time strings to time objects using fixed strptime formats ("%H:%M", - "%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p", - "%I%M%S%p") - - Use infer_time_format if all the strings are in the same format to speed - up conversion. - - Parameters - ---------- - arg : string in time format, datetime.time, list, tuple, 1-d array, Series - format : str, default None - Format used to convert arg into a time object. If None, fixed formats - are used. - infer_time_format: bool, default False - Infer the time format based on the first non-NaN element. If all - strings are in the same format, this will speed up conversion. - errors : {'ignore', 'raise', 'coerce'}, default 'raise' - - If 'raise', then invalid parsing will raise an exception - - If 'coerce', then invalid parsing will be set as None - - If 'ignore', then invalid parsing will return the input - - Returns - ------- - datetime.time - """ - - def _convert_listlike(arg, format): - if isinstance(arg, (list, tuple)): - arg = np.array(arg, dtype="O") - - elif getattr(arg, "ndim", 1) > 1: - raise TypeError( - "arg must be a string, datetime, list, tuple, 1-d array, or Series" - ) - - arg = np.asarray(arg, dtype="O") - - if infer_time_format and format is None: - format = _guess_time_format_for_array(arg) - - times: list[time | None] = [] - if format is not None: - for element in arg: - try: - times.append(datetime.strptime(element, format).time()) - except (ValueError, TypeError) as err: - if errors == "raise": - msg = ( - f"Cannot convert {element} to a time with given " - f"format {format}" - ) - raise ValueError(msg) from err - if errors == "ignore": - return arg - else: - times.append(None) - else: - formats = _time_formats[:] - format_found = False - for element in arg: - time_object = None - try: - time_object = time.fromisoformat(element) - except (ValueError, TypeError): - for time_format in formats: - try: - time_object = datetime.strptime(element, time_format).time() - if not format_found: - # Put the found format in front - fmt = formats.pop(formats.index(time_format)) - formats.insert(0, fmt) - format_found = True - break - except (ValueError, TypeError): - continue - - if time_object is not None: - times.append(time_object) - elif errors == "raise": - raise ValueError(f"Cannot convert arg {arg} to a time") - elif errors == "ignore": - return arg - else: - times.append(None) - - return times - - if arg is None: - return arg - elif isinstance(arg, time): - return arg - elif isinstance(arg, ABCSeries): - values = _convert_listlike(arg._values, format) - return arg._constructor(values, index=arg.index, name=arg.name) - elif isinstance(arg, ABCIndex): - return _convert_listlike(arg, format) - elif is_list_like(arg): - return _convert_listlike(arg, format) - - return _convert_listlike(np.array([arg]), format)[0] - - -# Fixed time formats for time parsing -_time_formats = [ - "%H:%M", - "%H%M", - "%I:%M%p", - "%I%M%p", - "%H:%M:%S", - "%H%M%S", - "%I:%M:%S%p", - "%I%M%S%p", -] - - -def _guess_time_format_for_array(arr): - # Try to guess the format based on the first non-NaN element - non_nan_elements = notna(arr).nonzero()[0] - if len(non_nan_elements): - element = arr[non_nan_elements[0]] - for time_format in _time_formats: - try: - datetime.strptime(element, time_format) - return time_format - except ValueError: - pass - - return None diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/test_nat.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/test_nat.py deleted file mode 100644 index f5a94099523fb264250788fb0d4abaf9057ae60d..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/test_nat.py +++ /dev/null @@ -1,695 +0,0 @@ -from datetime import ( - datetime, - timedelta, -) -import operator - -import numpy as np -import pytest -import pytz - -from pandas._libs.tslibs import iNaT -from pandas.compat.numpy import np_version_gte1p24p3 - -from pandas import ( - DatetimeIndex, - DatetimeTZDtype, - Index, - NaT, - Period, - Series, - Timedelta, - TimedeltaIndex, - Timestamp, - isna, - offsets, -) -import pandas._testing as tm -from pandas.core import roperator -from pandas.core.arrays import ( - DatetimeArray, - PeriodArray, - TimedeltaArray, -) - - -@pytest.mark.parametrize( - "nat,idx", - [ - (Timestamp("NaT"), DatetimeArray), - (Timedelta("NaT"), TimedeltaArray), - (Period("NaT", freq="M"), PeriodArray), - ], -) -def test_nat_fields(nat, idx): - for field in idx._field_ops: - # weekday is a property of DTI, but a method - # on NaT/Timestamp for compat with datetime - if field == "weekday": - continue - - result = getattr(NaT, field) - assert np.isnan(result) - - result = getattr(nat, field) - assert np.isnan(result) - - for field in idx._bool_ops: - result = getattr(NaT, field) - assert result is False - - result = getattr(nat, field) - assert result is False - - -def test_nat_vector_field_access(): - idx = DatetimeIndex(["1/1/2000", None, None, "1/4/2000"]) - - for field in DatetimeArray._field_ops: - # weekday is a property of DTI, but a method - # on NaT/Timestamp for compat with datetime - if field == "weekday": - continue - - result = getattr(idx, field) - expected = Index([getattr(x, field) for x in idx]) - tm.assert_index_equal(result, expected) - - ser = Series(idx) - - for field in DatetimeArray._field_ops: - # weekday is a property of DTI, but a method - # on NaT/Timestamp for compat with datetime - if field == "weekday": - continue - - result = getattr(ser.dt, field) - expected = [getattr(x, field) for x in idx] - tm.assert_series_equal(result, Series(expected)) - - for field in DatetimeArray._bool_ops: - result = getattr(ser.dt, field) - expected = [getattr(x, field) for x in idx] - tm.assert_series_equal(result, Series(expected)) - - -@pytest.mark.parametrize("klass", [Timestamp, Timedelta, Period]) -@pytest.mark.parametrize( - "value", [None, np.nan, iNaT, float("nan"), NaT, "NaT", "nat", "", "NAT"] -) -def test_identity(klass, value): - assert klass(value) is NaT - - -@pytest.mark.parametrize("klass", [Timestamp, Timedelta]) -@pytest.mark.parametrize("method", ["round", "floor", "ceil"]) -@pytest.mark.parametrize("freq", ["s", "5s", "min", "5min", "h", "5h"]) -def test_round_nat(klass, method, freq): - # see gh-14940 - ts = klass("nat") - - round_method = getattr(ts, method) - assert round_method(freq) is ts - - -@pytest.mark.parametrize( - "method", - [ - "astimezone", - "combine", - "ctime", - "dst", - "fromordinal", - "fromtimestamp", - "fromisocalendar", - "isocalendar", - "strftime", - "strptime", - "time", - "timestamp", - "timetuple", - "timetz", - "toordinal", - "tzname", - "utcfromtimestamp", - "utcnow", - "utcoffset", - "utctimetuple", - "timestamp", - ], -) -def test_nat_methods_raise(method): - # see gh-9513, gh-17329 - msg = f"NaTType does not support {method}" - - with pytest.raises(ValueError, match=msg): - getattr(NaT, method)() - - -@pytest.mark.parametrize("method", ["weekday", "isoweekday"]) -def test_nat_methods_nan(method): - # see gh-9513, gh-17329 - assert np.isnan(getattr(NaT, method)()) - - -@pytest.mark.parametrize( - "method", ["date", "now", "replace", "today", "tz_convert", "tz_localize"] -) -def test_nat_methods_nat(method): - # see gh-8254, gh-9513, gh-17329 - assert getattr(NaT, method)() is NaT - - -@pytest.mark.parametrize( - "get_nat", [lambda x: NaT, lambda x: Timedelta(x), lambda x: Timestamp(x)] -) -def test_nat_iso_format(get_nat): - # see gh-12300 - assert get_nat("NaT").isoformat() == "NaT" - assert get_nat("NaT").isoformat(timespec="nanoseconds") == "NaT" - - -@pytest.mark.parametrize( - "klass,expected", - [ - (Timestamp, ["normalize", "to_julian_date", "to_period", "unit"]), - ( - Timedelta, - [ - "components", - "resolution_string", - "to_pytimedelta", - "to_timedelta64", - "unit", - "view", - ], - ), - ], -) -def test_missing_public_nat_methods(klass, expected): - # see gh-17327 - # - # NaT should have *most* of the Timestamp and Timedelta methods. - # Here, we check which public methods NaT does not have. We - # ignore any missing private methods. - nat_names = dir(NaT) - klass_names = dir(klass) - - missing = [x for x in klass_names if x not in nat_names and not x.startswith("_")] - missing.sort() - - assert missing == expected - - -def _get_overlap_public_nat_methods(klass, as_tuple=False): - """ - Get overlapping public methods between NaT and another class. - - Parameters - ---------- - klass : type - The class to compare with NaT - as_tuple : bool, default False - Whether to return a list of tuples of the form (klass, method). - - Returns - ------- - overlap : list - """ - nat_names = dir(NaT) - klass_names = dir(klass) - - overlap = [ - x - for x in nat_names - if x in klass_names and not x.startswith("_") and callable(getattr(klass, x)) - ] - - # Timestamp takes precedence over Timedelta in terms of overlap. - if klass is Timedelta: - ts_names = dir(Timestamp) - overlap = [x for x in overlap if x not in ts_names] - - if as_tuple: - overlap = [(klass, method) for method in overlap] - - overlap.sort() - return overlap - - -@pytest.mark.parametrize( - "klass,expected", - [ - ( - Timestamp, - [ - "as_unit", - "astimezone", - "ceil", - "combine", - "ctime", - "date", - "day_name", - "dst", - "floor", - "fromisocalendar", - "fromisoformat", - "fromordinal", - "fromtimestamp", - "isocalendar", - "isoformat", - "isoweekday", - "month_name", - "now", - "replace", - "round", - "strftime", - "strptime", - "time", - "timestamp", - "timetuple", - "timetz", - "to_datetime64", - "to_numpy", - "to_pydatetime", - "today", - "toordinal", - "tz_convert", - "tz_localize", - "tzname", - "utcfromtimestamp", - "utcnow", - "utcoffset", - "utctimetuple", - "weekday", - ], - ), - (Timedelta, ["total_seconds"]), - ], -) -def test_overlap_public_nat_methods(klass, expected): - # see gh-17327 - # - # NaT should have *most* of the Timestamp and Timedelta methods. - # In case when Timestamp, Timedelta, and NaT are overlap, the overlap - # is considered to be with Timestamp and NaT, not Timedelta. - assert _get_overlap_public_nat_methods(klass) == expected - - -@pytest.mark.parametrize( - "compare", - ( - _get_overlap_public_nat_methods(Timestamp, True) - + _get_overlap_public_nat_methods(Timedelta, True) - ), - ids=lambda x: f"{x[0].__name__}.{x[1]}", -) -def test_nat_doc_strings(compare): - # see gh-17327 - # - # The docstrings for overlapping methods should match. - klass, method = compare - klass_doc = getattr(klass, method).__doc__ - - if klass == Timestamp and method == "isoformat": - pytest.skip( - "Ignore differences with Timestamp.isoformat() as they're intentional" - ) - - if method == "to_numpy": - # GH#44460 can return either dt64 or td64 depending on dtype, - # different docstring is intentional - pytest.skip(f"different docstring for {method} is intentional") - - nat_doc = getattr(NaT, method).__doc__ - assert klass_doc == nat_doc - - -_ops = { - "left_plus_right": lambda a, b: a + b, - "right_plus_left": lambda a, b: b + a, - "left_minus_right": lambda a, b: a - b, - "right_minus_left": lambda a, b: b - a, - "left_times_right": lambda a, b: a * b, - "right_times_left": lambda a, b: b * a, - "left_div_right": lambda a, b: a / b, - "right_div_left": lambda a, b: b / a, -} - - -@pytest.mark.parametrize("op_name", list(_ops.keys())) -@pytest.mark.parametrize( - "value,val_type", - [ - (2, "scalar"), - (1.5, "floating"), - (np.nan, "floating"), - ("foo", "str"), - (timedelta(3600), "timedelta"), - (Timedelta("5s"), "timedelta"), - (datetime(2014, 1, 1), "timestamp"), - (Timestamp("2014-01-01"), "timestamp"), - (Timestamp("2014-01-01", tz="UTC"), "timestamp"), - (Timestamp("2014-01-01", tz="US/Eastern"), "timestamp"), - (pytz.timezone("Asia/Tokyo").localize(datetime(2014, 1, 1)), "timestamp"), - ], -) -def test_nat_arithmetic_scalar(op_name, value, val_type): - # see gh-6873 - invalid_ops = { - "scalar": {"right_div_left"}, - "floating": { - "right_div_left", - "left_minus_right", - "right_minus_left", - "left_plus_right", - "right_plus_left", - }, - "str": set(_ops.keys()), - "timedelta": {"left_times_right", "right_times_left"}, - "timestamp": { - "left_times_right", - "right_times_left", - "left_div_right", - "right_div_left", - }, - } - - op = _ops[op_name] - - if op_name in invalid_ops.get(val_type, set()): - if ( - val_type == "timedelta" - and "times" in op_name - and isinstance(value, Timedelta) - ): - typs = "(Timedelta|NaTType)" - msg = rf"unsupported operand type\(s\) for \*: '{typs}' and '{typs}'" - elif val_type == "str": - # un-specific check here because the message comes from str - # and varies by method - msg = "|".join( - [ - "can only concatenate str", - "unsupported operand type", - "can't multiply sequence", - "Can't convert 'NaTType'", - "must be str, not NaTType", - ] - ) - else: - msg = "unsupported operand type" - - with pytest.raises(TypeError, match=msg): - op(NaT, value) - else: - if val_type == "timedelta" and "div" in op_name: - expected = np.nan - else: - expected = NaT - - assert op(NaT, value) is expected - - -@pytest.mark.parametrize( - "val,expected", [(np.nan, NaT), (NaT, np.nan), (np.timedelta64("NaT"), np.nan)] -) -def test_nat_rfloordiv_timedelta(val, expected): - # see gh-#18846 - # - # See also test_timedelta.TestTimedeltaArithmetic.test_floordiv - td = Timedelta(hours=3, minutes=4) - assert td // val is expected - - -@pytest.mark.parametrize( - "op_name", - ["left_plus_right", "right_plus_left", "left_minus_right", "right_minus_left"], -) -@pytest.mark.parametrize( - "value", - [ - DatetimeIndex(["2011-01-01", "2011-01-02"], name="x"), - DatetimeIndex(["2011-01-01", "2011-01-02"], tz="US/Eastern", name="x"), - DatetimeArray._from_sequence(["2011-01-01", "2011-01-02"]), - DatetimeArray._from_sequence( - ["2011-01-01", "2011-01-02"], dtype=DatetimeTZDtype(tz="US/Pacific") - ), - TimedeltaIndex(["1 day", "2 day"], name="x"), - ], -) -def test_nat_arithmetic_index(op_name, value): - # see gh-11718 - exp_name = "x" - exp_data = [NaT] * 2 - - if value.dtype.kind == "M" and "plus" in op_name: - expected = DatetimeIndex(exp_data, tz=value.tz, name=exp_name) - else: - expected = TimedeltaIndex(exp_data, name=exp_name) - - if not isinstance(value, Index): - expected = expected.array - - op = _ops[op_name] - result = op(NaT, value) - tm.assert_equal(result, expected) - - -@pytest.mark.parametrize( - "op_name", - ["left_plus_right", "right_plus_left", "left_minus_right", "right_minus_left"], -) -@pytest.mark.parametrize("box", [TimedeltaIndex, Series, TimedeltaArray._from_sequence]) -def test_nat_arithmetic_td64_vector(op_name, box): - # see gh-19124 - vec = box(["1 day", "2 day"], dtype="timedelta64[ns]") - box_nat = box([NaT, NaT], dtype="timedelta64[ns]") - tm.assert_equal(_ops[op_name](vec, NaT), box_nat) - - -@pytest.mark.parametrize( - "dtype,op,out_dtype", - [ - ("datetime64[ns]", operator.add, "datetime64[ns]"), - ("datetime64[ns]", roperator.radd, "datetime64[ns]"), - ("datetime64[ns]", operator.sub, "timedelta64[ns]"), - ("datetime64[ns]", roperator.rsub, "timedelta64[ns]"), - ("timedelta64[ns]", operator.add, "datetime64[ns]"), - ("timedelta64[ns]", roperator.radd, "datetime64[ns]"), - ("timedelta64[ns]", operator.sub, "datetime64[ns]"), - ("timedelta64[ns]", roperator.rsub, "timedelta64[ns]"), - ], -) -def test_nat_arithmetic_ndarray(dtype, op, out_dtype): - other = np.arange(10).astype(dtype) - result = op(NaT, other) - - expected = np.empty(other.shape, dtype=out_dtype) - expected.fill("NaT") - tm.assert_numpy_array_equal(result, expected) - - -def test_nat_pinned_docstrings(): - # see gh-17327 - assert NaT.ctime.__doc__ == Timestamp.ctime.__doc__ - - -def test_to_numpy_alias(): - # GH 24653: alias .to_numpy() for scalars - expected = NaT.to_datetime64() - result = NaT.to_numpy() - - assert isna(expected) and isna(result) - - # GH#44460 - result = NaT.to_numpy("M8[s]") - assert isinstance(result, np.datetime64) - assert result.dtype == "M8[s]" - - result = NaT.to_numpy("m8[ns]") - assert isinstance(result, np.timedelta64) - assert result.dtype == "m8[ns]" - - result = NaT.to_numpy("m8[s]") - assert isinstance(result, np.timedelta64) - assert result.dtype == "m8[s]" - - with pytest.raises(ValueError, match="NaT.to_numpy dtype must be a "): - NaT.to_numpy(np.int64) - - -@pytest.mark.parametrize( - "other", - [ - Timedelta(0), - Timedelta(0).to_pytimedelta(), - pytest.param( - Timedelta(0).to_timedelta64(), - marks=pytest.mark.xfail( - not np_version_gte1p24p3, - reason="td64 doesn't return NotImplemented, see numpy#17017", - ), - ), - Timestamp(0), - Timestamp(0).to_pydatetime(), - pytest.param( - Timestamp(0).to_datetime64(), - marks=pytest.mark.xfail( - not np_version_gte1p24p3, - reason="dt64 doesn't return NotImplemented, see numpy#17017", - ), - ), - Timestamp(0).tz_localize("UTC"), - NaT, - ], -) -def test_nat_comparisons(compare_operators_no_eq_ne, other): - # GH 26039 - opname = compare_operators_no_eq_ne - - assert getattr(NaT, opname)(other) is False - - op = getattr(operator, opname.strip("_")) - assert op(NaT, other) is False - assert op(other, NaT) is False - - -@pytest.mark.parametrize("other", [np.timedelta64(0, "ns"), np.datetime64("now", "ns")]) -def test_nat_comparisons_numpy(other): - # Once numpy#17017 is fixed and the xfailed cases in test_nat_comparisons - # pass, this test can be removed - assert not NaT == other - assert NaT != other - assert not NaT < other - assert not NaT > other - assert not NaT <= other - assert not NaT >= other - - -@pytest.mark.parametrize("other_and_type", [("foo", "str"), (2, "int"), (2.0, "float")]) -@pytest.mark.parametrize( - "symbol_and_op", - [("<=", operator.le), ("<", operator.lt), (">=", operator.ge), (">", operator.gt)], -) -def test_nat_comparisons_invalid(other_and_type, symbol_and_op): - # GH#35585 - other, other_type = other_and_type - symbol, op = symbol_and_op - - assert not NaT == other - assert not other == NaT - - assert NaT != other - assert other != NaT - - msg = f"'{symbol}' not supported between instances of 'NaTType' and '{other_type}'" - with pytest.raises(TypeError, match=msg): - op(NaT, other) - - msg = f"'{symbol}' not supported between instances of '{other_type}' and 'NaTType'" - with pytest.raises(TypeError, match=msg): - op(other, NaT) - - -@pytest.mark.parametrize( - "other", - [ - np.array(["foo"] * 2, dtype=object), - np.array([2, 3], dtype="int64"), - np.array([2.0, 3.5], dtype="float64"), - ], - ids=["str", "int", "float"], -) -def test_nat_comparisons_invalid_ndarray(other): - # GH#40722 - expected = np.array([False, False]) - result = NaT == other - tm.assert_numpy_array_equal(result, expected) - result = other == NaT - tm.assert_numpy_array_equal(result, expected) - - expected = np.array([True, True]) - result = NaT != other - tm.assert_numpy_array_equal(result, expected) - result = other != NaT - tm.assert_numpy_array_equal(result, expected) - - for symbol, op in [ - ("<=", operator.le), - ("<", operator.lt), - (">=", operator.ge), - (">", operator.gt), - ]: - msg = f"'{symbol}' not supported between" - - with pytest.raises(TypeError, match=msg): - op(NaT, other) - - if other.dtype == np.dtype("object"): - # uses the reverse operator, so symbol changes - msg = None - with pytest.raises(TypeError, match=msg): - op(other, NaT) - - -def test_compare_date(fixed_now_ts): - # GH#39151 comparing NaT with date object is deprecated - # See also: tests.scalar.timestamps.test_comparisons::test_compare_date - - dt = fixed_now_ts.to_pydatetime().date() - - msg = "Cannot compare NaT with datetime.date object" - for left, right in [(NaT, dt), (dt, NaT)]: - assert not left == right - assert left != right - - with pytest.raises(TypeError, match=msg): - left < right - with pytest.raises(TypeError, match=msg): - left <= right - with pytest.raises(TypeError, match=msg): - left > right - with pytest.raises(TypeError, match=msg): - left >= right - - -@pytest.mark.parametrize( - "obj", - [ - offsets.YearEnd(2), - offsets.YearBegin(2), - offsets.MonthBegin(1), - offsets.MonthEnd(2), - offsets.MonthEnd(12), - offsets.Day(2), - offsets.Day(5), - offsets.Hour(24), - offsets.Hour(3), - offsets.Minute(), - np.timedelta64(3, "h"), - np.timedelta64(4, "h"), - np.timedelta64(3200, "s"), - np.timedelta64(3600, "s"), - np.timedelta64(3600 * 24, "s"), - np.timedelta64(2, "D"), - np.timedelta64(365, "D"), - timedelta(-2), - timedelta(365), - timedelta(minutes=120), - timedelta(days=4, minutes=180), - timedelta(hours=23), - timedelta(hours=23, minutes=30), - timedelta(hours=48), - ], -) -def test_nat_addsub_tdlike_scalar(obj): - assert NaT + obj is NaT - assert obj + NaT is NaT - assert NaT - obj is NaT - - -def test_pickle(): - # GH#4606 - p = tm.round_trip_pickle(NaT) - assert p is NaT diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/_vendor/more_itertools/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/_vendor/more_itertools/__init__.py deleted file mode 100644 index 19a169fc30183db91f931ad6ad04fbc0e16559b3..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/_vendor/more_itertools/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .more import * # noqa -from .recipes import * # noqa - -__version__ = '8.8.0' diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/extension.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/extension.py deleted file mode 100644 index 1820722a494b1744a406e364bc3dc3aefc7dd059..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/extension.py +++ /dev/null @@ -1,55 +0,0 @@ -import re -import functools -import distutils.core -import distutils.errors -import distutils.extension - -from .monkey import get_unpatched - - -def _have_cython(): - """ - Return True if Cython can be imported. - """ - cython_impl = 'Cython.Distutils.build_ext' - try: - # from (cython_impl) import build_ext - __import__(cython_impl, fromlist=['build_ext']).build_ext - return True - except Exception: - pass - return False - - -# for compatibility -have_pyrex = _have_cython - -_Extension = get_unpatched(distutils.core.Extension) - - -class Extension(_Extension): - """Extension that uses '.c' files in place of '.pyx' files""" - - def __init__(self, name, sources, *args, **kw): - # The *args is needed for compatibility as calls may use positional - # arguments. py_limited_api may be set only via keyword. - self.py_limited_api = kw.pop("py_limited_api", False) - _Extension.__init__(self, name, sources, *args, **kw) - - def _convert_pyx_sources_to_lang(self): - """ - Replace sources with .pyx extensions to sources with the target - language extension. This mechanism allows language authors to supply - pre-converted sources but to prefer the .pyx sources. - """ - if _have_cython(): - # the build has Cython, so allow it to compile the .pyx files - return - lang = self.language or '' - target_ext = '.cpp' if lang.lower() == 'c++' else '.c' - sub = functools.partial(re.sub, '.pyx$', target_ext) - self.sources = list(map(sub, self.sources)) - - -class Library(Extension): - """Just like a regular Extension, but built as a library instead""" diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/yaml/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/yaml/__init__.py deleted file mode 100644 index 824936194774d34cd5e7816e519d00517612e7b4..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/yaml/__init__.py +++ /dev/null @@ -1,390 +0,0 @@ - -from .error import * - -from .tokens import * -from .events import * -from .nodes import * - -from .loader import * -from .dumper import * - -__version__ = '6.0.1' -try: - from .cyaml import * - __with_libyaml__ = True -except ImportError: - __with_libyaml__ = False - -import io - -#------------------------------------------------------------------------------ -# XXX "Warnings control" is now deprecated. Leaving in the API function to not -# break code that uses it. -#------------------------------------------------------------------------------ -def warnings(settings=None): - if settings is None: - return {} - -#------------------------------------------------------------------------------ -def scan(stream, Loader=Loader): - """ - Scan a YAML stream and produce scanning tokens. - """ - loader = Loader(stream) - try: - while loader.check_token(): - yield loader.get_token() - finally: - loader.dispose() - -def parse(stream, Loader=Loader): - """ - Parse a YAML stream and produce parsing events. - """ - loader = Loader(stream) - try: - while loader.check_event(): - yield loader.get_event() - finally: - loader.dispose() - -def compose(stream, Loader=Loader): - """ - Parse the first YAML document in a stream - and produce the corresponding representation tree. - """ - loader = Loader(stream) - try: - return loader.get_single_node() - finally: - loader.dispose() - -def compose_all(stream, Loader=Loader): - """ - Parse all YAML documents in a stream - and produce corresponding representation trees. - """ - loader = Loader(stream) - try: - while loader.check_node(): - yield loader.get_node() - finally: - loader.dispose() - -def load(stream, Loader): - """ - Parse the first YAML document in a stream - and produce the corresponding Python object. - """ - loader = Loader(stream) - try: - return loader.get_single_data() - finally: - loader.dispose() - -def load_all(stream, Loader): - """ - Parse all YAML documents in a stream - and produce corresponding Python objects. - """ - loader = Loader(stream) - try: - while loader.check_data(): - yield loader.get_data() - finally: - loader.dispose() - -def full_load(stream): - """ - Parse the first YAML document in a stream - and produce the corresponding Python object. - - Resolve all tags except those known to be - unsafe on untrusted input. - """ - return load(stream, FullLoader) - -def full_load_all(stream): - """ - Parse all YAML documents in a stream - and produce corresponding Python objects. - - Resolve all tags except those known to be - unsafe on untrusted input. - """ - return load_all(stream, FullLoader) - -def safe_load(stream): - """ - Parse the first YAML document in a stream - and produce the corresponding Python object. - - Resolve only basic YAML tags. This is known - to be safe for untrusted input. - """ - return load(stream, SafeLoader) - -def safe_load_all(stream): - """ - Parse all YAML documents in a stream - and produce corresponding Python objects. - - Resolve only basic YAML tags. This is known - to be safe for untrusted input. - """ - return load_all(stream, SafeLoader) - -def unsafe_load(stream): - """ - Parse the first YAML document in a stream - and produce the corresponding Python object. - - Resolve all tags, even those known to be - unsafe on untrusted input. - """ - return load(stream, UnsafeLoader) - -def unsafe_load_all(stream): - """ - Parse all YAML documents in a stream - and produce corresponding Python objects. - - Resolve all tags, even those known to be - unsafe on untrusted input. - """ - return load_all(stream, UnsafeLoader) - -def emit(events, stream=None, Dumper=Dumper, - canonical=None, indent=None, width=None, - allow_unicode=None, line_break=None): - """ - Emit YAML parsing events into a stream. - If stream is None, return the produced string instead. - """ - getvalue = None - if stream is None: - stream = io.StringIO() - getvalue = stream.getvalue - dumper = Dumper(stream, canonical=canonical, indent=indent, width=width, - allow_unicode=allow_unicode, line_break=line_break) - try: - for event in events: - dumper.emit(event) - finally: - dumper.dispose() - if getvalue: - return getvalue() - -def serialize_all(nodes, stream=None, Dumper=Dumper, - canonical=None, indent=None, width=None, - allow_unicode=None, line_break=None, - encoding=None, explicit_start=None, explicit_end=None, - version=None, tags=None): - """ - Serialize a sequence of representation trees into a YAML stream. - If stream is None, return the produced string instead. - """ - getvalue = None - if stream is None: - if encoding is None: - stream = io.StringIO() - else: - stream = io.BytesIO() - getvalue = stream.getvalue - dumper = Dumper(stream, canonical=canonical, indent=indent, width=width, - allow_unicode=allow_unicode, line_break=line_break, - encoding=encoding, version=version, tags=tags, - explicit_start=explicit_start, explicit_end=explicit_end) - try: - dumper.open() - for node in nodes: - dumper.serialize(node) - dumper.close() - finally: - dumper.dispose() - if getvalue: - return getvalue() - -def serialize(node, stream=None, Dumper=Dumper, **kwds): - """ - Serialize a representation tree into a YAML stream. - If stream is None, return the produced string instead. - """ - return serialize_all([node], stream, Dumper=Dumper, **kwds) - -def dump_all(documents, stream=None, Dumper=Dumper, - default_style=None, default_flow_style=False, - canonical=None, indent=None, width=None, - allow_unicode=None, line_break=None, - encoding=None, explicit_start=None, explicit_end=None, - version=None, tags=None, sort_keys=True): - """ - Serialize a sequence of Python objects into a YAML stream. - If stream is None, return the produced string instead. - """ - getvalue = None - if stream is None: - if encoding is None: - stream = io.StringIO() - else: - stream = io.BytesIO() - getvalue = stream.getvalue - dumper = Dumper(stream, default_style=default_style, - default_flow_style=default_flow_style, - canonical=canonical, indent=indent, width=width, - allow_unicode=allow_unicode, line_break=line_break, - encoding=encoding, version=version, tags=tags, - explicit_start=explicit_start, explicit_end=explicit_end, sort_keys=sort_keys) - try: - dumper.open() - for data in documents: - dumper.represent(data) - dumper.close() - finally: - dumper.dispose() - if getvalue: - return getvalue() - -def dump(data, stream=None, Dumper=Dumper, **kwds): - """ - Serialize a Python object into a YAML stream. - If stream is None, return the produced string instead. - """ - return dump_all([data], stream, Dumper=Dumper, **kwds) - -def safe_dump_all(documents, stream=None, **kwds): - """ - Serialize a sequence of Python objects into a YAML stream. - Produce only basic YAML tags. - If stream is None, return the produced string instead. - """ - return dump_all(documents, stream, Dumper=SafeDumper, **kwds) - -def safe_dump(data, stream=None, **kwds): - """ - Serialize a Python object into a YAML stream. - Produce only basic YAML tags. - If stream is None, return the produced string instead. - """ - return dump_all([data], stream, Dumper=SafeDumper, **kwds) - -def add_implicit_resolver(tag, regexp, first=None, - Loader=None, Dumper=Dumper): - """ - Add an implicit scalar detector. - If an implicit scalar value matches the given regexp, - the corresponding tag is assigned to the scalar. - first is a sequence of possible initial characters or None. - """ - if Loader is None: - loader.Loader.add_implicit_resolver(tag, regexp, first) - loader.FullLoader.add_implicit_resolver(tag, regexp, first) - loader.UnsafeLoader.add_implicit_resolver(tag, regexp, first) - else: - Loader.add_implicit_resolver(tag, regexp, first) - Dumper.add_implicit_resolver(tag, regexp, first) - -def add_path_resolver(tag, path, kind=None, Loader=None, Dumper=Dumper): - """ - Add a path based resolver for the given tag. - A path is a list of keys that forms a path - to a node in the representation tree. - Keys can be string values, integers, or None. - """ - if Loader is None: - loader.Loader.add_path_resolver(tag, path, kind) - loader.FullLoader.add_path_resolver(tag, path, kind) - loader.UnsafeLoader.add_path_resolver(tag, path, kind) - else: - Loader.add_path_resolver(tag, path, kind) - Dumper.add_path_resolver(tag, path, kind) - -def add_constructor(tag, constructor, Loader=None): - """ - Add a constructor for the given tag. - Constructor is a function that accepts a Loader instance - and a node object and produces the corresponding Python object. - """ - if Loader is None: - loader.Loader.add_constructor(tag, constructor) - loader.FullLoader.add_constructor(tag, constructor) - loader.UnsafeLoader.add_constructor(tag, constructor) - else: - Loader.add_constructor(tag, constructor) - -def add_multi_constructor(tag_prefix, multi_constructor, Loader=None): - """ - Add a multi-constructor for the given tag prefix. - Multi-constructor is called for a node if its tag starts with tag_prefix. - Multi-constructor accepts a Loader instance, a tag suffix, - and a node object and produces the corresponding Python object. - """ - if Loader is None: - loader.Loader.add_multi_constructor(tag_prefix, multi_constructor) - loader.FullLoader.add_multi_constructor(tag_prefix, multi_constructor) - loader.UnsafeLoader.add_multi_constructor(tag_prefix, multi_constructor) - else: - Loader.add_multi_constructor(tag_prefix, multi_constructor) - -def add_representer(data_type, representer, Dumper=Dumper): - """ - Add a representer for the given type. - Representer is a function accepting a Dumper instance - and an instance of the given data type - and producing the corresponding representation node. - """ - Dumper.add_representer(data_type, representer) - -def add_multi_representer(data_type, multi_representer, Dumper=Dumper): - """ - Add a representer for the given type. - Multi-representer is a function accepting a Dumper instance - and an instance of the given data type or subtype - and producing the corresponding representation node. - """ - Dumper.add_multi_representer(data_type, multi_representer) - -class YAMLObjectMetaclass(type): - """ - The metaclass for YAMLObject. - """ - def __init__(cls, name, bases, kwds): - super(YAMLObjectMetaclass, cls).__init__(name, bases, kwds) - if 'yaml_tag' in kwds and kwds['yaml_tag'] is not None: - if isinstance(cls.yaml_loader, list): - for loader in cls.yaml_loader: - loader.add_constructor(cls.yaml_tag, cls.from_yaml) - else: - cls.yaml_loader.add_constructor(cls.yaml_tag, cls.from_yaml) - - cls.yaml_dumper.add_representer(cls, cls.to_yaml) - -class YAMLObject(metaclass=YAMLObjectMetaclass): - """ - An object that can dump itself to a YAML stream - and load itself from a YAML stream. - """ - - __slots__ = () # no direct instantiation, so allow immutable subclasses - - yaml_loader = [Loader, FullLoader, UnsafeLoader] - yaml_dumper = Dumper - - yaml_tag = None - yaml_flow_style = None - - @classmethod - def from_yaml(cls, loader, node): - """ - Convert a representation node to a Python object. - """ - return loader.construct_yaml_object(node, cls) - - @classmethod - def to_yaml(cls, dumper, data): - """ - Convert a Python object to a representation node. - """ - return dumper.represent_yaml_object(cls.yaml_tag, data, cls, - flow_style=cls.yaml_flow_style) - diff --git a/spaces/pycoming/bingo/src/components/voice.tsx b/spaces/pycoming/bingo/src/components/voice.tsx deleted file mode 100644 index 074d0e145229947282a472bd84f6578cf0b3c71c..0000000000000000000000000000000000000000 --- a/spaces/pycoming/bingo/src/components/voice.tsx +++ /dev/null @@ -1,52 +0,0 @@ -import React, { useEffect } from 'react' -import { useSetAtom } from 'jotai' -import { useBing } from '@/lib/hooks/use-bing' -import Image from 'next/image' -import VoiceIcon from '@/assets/images/voice.svg' -import VoiceButton from './ui/voice' -import { SR } from '@/lib/bots/bing/sr' -import { voiceListenAtom } from '@/state' - -const sr = new SR(['发送', '清空', '退出']) - -const Voice = ({ setInput, input, sendMessage, isSpeaking }: Pick, 'setInput' | 'sendMessage' | 'input' | 'isSpeaking'>) => { - const setListen = useSetAtom(voiceListenAtom) - useEffect(() => { - if (sr.listening) return - sr.transcript = !isSpeaking - }, [isSpeaking]) - - useEffect(() => { - sr.onchange = (msg: string, command?: string) => { - switch (command) { - case '退出': - sr.stop() - break; - case '发送': - sendMessage(input) - case '清空': - setInput('') - break; - default: - setInput(input + msg) - } - } - }, [input]) - - const switchSR = (enable: boolean = false) => { - setListen(enable) - if (enable) { - sr.start() - } else { - sr.stop() - } - } - - return sr.listening ? ( - switchSR(false)} /> - ) : ( - start voice switchSR(true)} /> - ) -}; - -export default Voice; diff --git a/spaces/pyodide-demo/gpt2-tokenizer/index.html b/spaces/pyodide-demo/gpt2-tokenizer/index.html deleted file mode 100644 index 4717b6a477f7f00c9d3bae1c6ada7b36ee376ec7..0000000000000000000000000000000000000000 --- a/spaces/pyodide-demo/gpt2-tokenizer/index.html +++ /dev/null @@ -1,85 +0,0 @@ - - - - - - - - - -
-

- 🚚 Pyodide demo -

-

Python implementation of GPT-2 Tokenizer running inside your browser

-
Open your browser console to see Pyodide output
-
Initialization: ...
-
-
- - -
- - -
-
-
- -
-
- -

Python code being run:

- 

-		

-		
-	
-
diff --git a/spaces/qingxu98/gpt-academic/docs/self_analysis.md b/spaces/qingxu98/gpt-academic/docs/self_analysis.md
deleted file mode 100644
index ebc2337194974bf210794df7d858889010fecf08..0000000000000000000000000000000000000000
--- a/spaces/qingxu98/gpt-academic/docs/self_analysis.md
+++ /dev/null
@@ -1,378 +0,0 @@
-# chatgpt-academic项目自译解报告
-（Author补充：以下分析均由本项目调用ChatGPT一键生成，如果有不准确的地方，全怪GPT😄）
-
-
-| 文件名 | 功能描述 |
-| ------ | ------ |
-| check_proxy.py | 检查代理有效性及地理位置 |
-| colorful.py | 控制台打印彩色文字 |
-| config.py | 配置和参数设置 |
-| config_private.py | 私人配置和参数设置 |
-| core_functional.py | 核心函数和参数设置 |
-| crazy_functional.py | 高级功能插件集合 |
-| main.py | 一个 Chatbot 程序，提供各种学术翻译、文本处理和其他查询服务 |
-| multi_language.py | 识别和翻译不同语言 |
-| theme.py | 自定义 gradio 应用程序主题 |
-| toolbox.py | 工具类库，用于协助实现各种功能 |
-| crazy_functions\crazy_functions_test.py | 测试 crazy_functions 中的各种函数 |
-| crazy_functions\crazy_utils.py | 工具函数，用于字符串处理、异常检测、Markdown 格式转换等 |
-| crazy_functions\Latex全文润色.py | 对整个 Latex 项目进行润色和纠错 |
-| crazy_functions\Latex全文翻译.py | 对整个 Latex 项目进行翻译 |
-| crazy_functions\\_\_init\_\_.py | 模块初始化文件，标识 `crazy_functions` 是一个包 |
-| crazy_functions\下载arxiv论文翻译摘要.py | 下载 `arxiv` 论文的 PDF 文件，并提取摘要和翻译 |
-| crazy_functions\代码重写为全英文_多线程.py | 将Python源代码文件中的中文内容转化为英文 |
-| crazy_functions\图片生成.py | 根据激励文本使用GPT模型生成相应的图像 |
-| crazy_functions\对话历史存档.py | 将每次对话记录写入Markdown格式的文件中 |
-| crazy_functions\总结word文档.py | 对输入的word文档进行摘要生成 |
-| crazy_functions\总结音视频.py | 对输入的音视频文件进行摘要生成 |
-| crazy_functions\批量Markdown翻译.py | 将指定目录下的Markdown文件进行中英文翻译 |
-| crazy_functions\批量总结PDF文档.py | 对PDF文件进行切割和摘要生成 |
-| crazy_functions\批量总结PDF文档pdfminer.py | 对PDF文件进行文本内容的提取和摘要生成 |
-| crazy_functions\批量翻译PDF文档_多线程.py | 将指定目录下的PDF文件进行中英文翻译 |
-| crazy_functions\理解PDF文档内容.py | 对PDF文件进行摘要生成和问题解答 |
-| crazy_functions\生成函数注释.py | 自动生成Python函数的注释 |
-| crazy_functions\联网的ChatGPT.py | 使用网络爬虫和ChatGPT模型进行聊天回答 |
-| crazy_functions\解析JupyterNotebook.py | 对Jupyter Notebook进行代码解析 |
-| crazy_functions\解析项目源代码.py | 对指定编程语言的源代码进行解析 |
-| crazy_functions\询问多个大语言模型.py | 使用多个大语言模型对输入进行处理和回复 |
-| crazy_functions\读文章写摘要.py | 对论文进行解析和全文摘要生成 |
-| crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 |
-| crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 |
-| request_llm\bridge_all.py | 基于不同LLM模型进行对话。 |
-| request_llm\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
-| request_llm\bridge_chatgpt.py | 基于GPT模型完成对话。 |
-| request_llm\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
-| request_llm\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
-| request_llm\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
-| request_llm\bridge_moss.py | 加载Moss模型完成对话功能。 |
-| request_llm\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
-| request_llm\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
-| request_llm\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
-| request_llm\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
-| request_llm\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
-| request_llm\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
-| request_llm\test_llms.py | 对llm模型进行单元测试。 |
-
-## 接下来请你逐文件分析下面的工程[0/48] 请对下面的程序文件做一个概述: check_proxy.py
-
-这个文件主要包含了五个函数：
-
-1. `check_proxy`：用于检查代理的有效性及地理位置，输出代理配置和所在地信息。
-
-2. `backup_and_download`：用于备份当前版本并下载新版本。
-
-3. `patch_and_restart`：用于覆盖更新当前版本并重新启动程序。
-
-4. `get_current_version`：用于获取当前程序的版本号。
-
-5. `auto_update`：用于自动检查新版本并提示用户更新。如果用户选择更新，则备份并下载新版本，覆盖更新当前版本并重新启动程序。如果更新失败，则输出错误信息，并不会向用户进行任何提示。
-
-还有一个没有函数名的语句`os.environ['no_proxy'] = '*'`，用于设置环境变量，避免代理网络产生意外污染。
-
-此外，该文件导入了以下三个模块/函数：
-
-- `requests`
-- `shutil`
-- `os`
-
-## [1/48] 请对下面的程序文件做一个概述: colorful.py
-
-该文件是一个Python脚本，用于在控制台中打印彩色文字。该文件包含了一些函数，用于以不同颜色打印文本。其中，红色、绿色、黄色、蓝色、紫色、靛色分别以函数 print红、print绿、print黄、print蓝、print紫、print靛 的形式定义；亮红色、亮绿色、亮黄色、亮蓝色、亮紫色、亮靛色分别以 print亮红、print亮绿、print亮黄、print亮蓝、print亮紫、print亮靛 的形式定义。它们使用 ANSI Escape Code 将彩色输出从控制台突出显示。如果运行在 Linux 操作系统上，文件所执行的操作被留空；否则，该文件导入了 colorama 库并调用 init() 函数进行初始化。最后，通过一系列条件语句，该文件通过将所有彩色输出函数的名称重新赋值为 print 函数的名称来避免输出文件的颜色问题。
-
-## [2/48] 请对下面的程序文件做一个概述: config.py
-
-这个程序文件是用来配置和参数设置的。它包含了许多设置，如API key，使用代理，线程数，默认模型，超时时间等等。此外，它还包含了一些高级功能，如URL重定向等。这些设置将会影响到程序的行为和性能。
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-## [3/48] 请对下面的程序文件做一个概述: config_private.py
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-这个程序文件是一个Python脚本，文件名为config_private.py。其中包含以下变量的赋值：
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-1. API_KEY：API密钥。
-2. USE_PROXY：是否应用代理。
-3. proxies：如果使用代理，则设置代理网络的协议(socks5/http)、地址(localhost)和端口(11284)。
-4. DEFAULT_WORKER_NUM：默认的工作线程数量。
-5. SLACK_CLAUDE_BOT_ID：Slack机器人ID。
-6. SLACK_CLAUDE_USER_TOKEN：Slack用户令牌。
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-## [4/48] 请对下面的程序文件做一个概述: core_functional.py
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-这是一个名为core_functional.py的源代码文件，该文件定义了一个名为get_core_functions()的函数，该函数返回一个字典，该字典包含了各种学术翻译润色任务的说明和相关参数，如颜色、前缀、后缀等。这些任务包括英语学术润色、中文学术润色、查找语法错误、中译英、学术中英互译、英译中、找图片和参考文献转Bib。其中，一些任务还定义了预处理函数用于处理任务的输入文本。
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-## [5/48] 请对下面的程序文件做一个概述: crazy_functional.py
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-此程序文件（crazy_functional.py）是一个函数插件集合，包含了多个函数插件的定义和调用。这些函数插件旨在提供一些高级功能，如解析项目源代码、批量翻译PDF文档和Latex全文润色等。其中一些插件还支持热更新功能，不需要重启程序即可生效。文件中的函数插件按照功能进行了分类（第一组和第二组），并且有不同的调用方式（作为按钮或下拉菜单）。
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-## [6/48] 请对下面的程序文件做一个概述: main.py
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-这是一个Python程序文件，文件名为main.py。该程序包含一个名为main的函数，程序会自动运行该函数。程序要求已经安装了gradio、os等模块，会根据配置文件加载代理、model、API Key等信息。程序提供了Chatbot功能，实现了一个对话界面，用户可以输入问题，然后Chatbot可以回答问题或者提供相关功能。程序还包含了基础功能区、函数插件区、更换模型 & SysPrompt & 交互界面布局、备选输入区，用户可以在这些区域选择功能和插件进行使用。程序中还包含了一些辅助模块，如logging等。
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-## [7/48] 请对下面的程序文件做一个概述: multi_language.py
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-该文件multi_language.py是用于将项目翻译成不同语言的程序。它包含了以下函数和变量：lru_file_cache、contains_chinese、split_list、map_to_json、read_map_from_json、advanced_split、trans、trans_json、step_1_core_key_translate、CACHE_FOLDER、blacklist、LANG、TransPrompt、cached_translation等。注释和文档字符串提供了有关程序的说明，例如如何使用该程序，如何修改“LANG”和“TransPrompt”变量等。
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-## [8/48] 请对下面的程序文件做一个概述: theme.py
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-这是一个Python源代码文件，文件名为theme.py。此文件中定义了一个函数adjust_theme，其功能是自定义gradio应用程序的主题，包括调整颜色、字体、阴影等。如果允许，则添加一个看板娘。此文件还包括变量advanced_css，其中包含一些CSS样式，用于高亮显示代码和自定义聊天框样式。此文件还导入了get_conf函数和gradio库。
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-## [9/48] 请对下面的程序文件做一个概述: toolbox.py
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-toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和小工具函数，用于协助实现聊天机器人所需的各种功能，包括文本处理、功能插件加载、异常检测、Markdown格式转换，文件读写等等。此外，该库还包含一些依赖、参数配置等信息。该库易于理解和维护。
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-## [10/48] 请对下面的程序文件做一个概述: crazy_functions\crazy_functions_test.py
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-这个文件是一个Python测试模块，用于测试crazy_functions中的各种函数插件。这些函数包括：解析Python项目源代码、解析Cpp项目源代码、Latex全文润色、Markdown中译英、批量翻译PDF文档、谷歌检索小助手、总结word文档、下载arxiv论文并翻译摘要、联网回答问题、和解析Jupyter Notebooks。对于每个函数插件，都有一个对应的测试函数来进行测试。
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-## [11/48] 请对下面的程序文件做一个概述: crazy_functions\crazy_utils.py
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-这个Python文件中包括了两个函数：
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-1. `input_clipping`: 该函数用于裁剪输入文本长度，使其不超过一定的限制。
-2. `request_gpt_model_in_new_thread_with_ui_alive`: 该函数用于请求 GPT 模型并保持用户界面的响应，支持多线程和实时更新用户界面。
-
-这两个函数都依赖于从 `toolbox` 和 `request_llm` 中导入的一些工具函数。函数的输入和输出有详细的描述文档。
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-## [12/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文润色.py
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-这是一个Python程序文件，文件名为crazy_functions\Latex全文润色.py。文件包含了一个PaperFileGroup类和三个函数Latex英文润色，Latex中文润色和Latex英文纠错。程序使用了字符串处理、正则表达式、文件读写、多线程等技术，主要作用是对整个Latex项目进行润色和纠错。其中润色和纠错涉及到了对文本的语法、清晰度和整体可读性等方面的提升。此外，该程序还参考了第三方库，并封装了一些工具函数。
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-## [13/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文翻译.py
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-这个文件包含两个函数 `Latex英译中` 和 `Latex中译英`，它们都会对整个Latex项目进行翻译。这个文件还包含一个类 `PaperFileGroup`，它拥有一个方法 `run_file_split`，用于把长文本文件分成多个短文件。其中使用了工具库 `toolbox` 中的一些函数和从 `request_llm` 中导入了 `model_info`。接下来的函数把文件读取进来，把它们的注释删除，进行分割，并进行翻译。这个文件还包括了一些异常处理和界面更新的操作。
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-## [14/48] 请对下面的程序文件做一个概述: crazy_functions\__init__.py
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-这是一个Python模块的初始化文件（__init__.py），命名为"crazy_functions"。该模块包含了一些疯狂的函数，但该文件并没有实现这些函数，而是作为一个包（package）来导入其它的Python模块以实现这些函数。在该文件中，没有定义任何类或函数，它唯一的作用就是标识"crazy_functions"模块是一个包。
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-## [15/48] 请对下面的程序文件做一个概述: crazy_functions\下载arxiv论文翻译摘要.py
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-这是一个 Python 程序文件，文件名为 `下载arxiv论文翻译摘要.py`。程序包含多个函数，其中 `下载arxiv论文并翻译摘要` 函数的作用是下载 `arxiv` 论文的 PDF 文件，提取摘要并使用 GPT 对其进行翻译。其他函数包括用于下载 `arxiv` 论文的 `download_arxiv_` 函数和用于获取文章信息的 `get_name` 函数，其中涉及使用第三方库如 requests, BeautifulSoup 等。该文件还包含一些用于调试和存储文件的代码段。
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-## [16/48] 请对下面的程序文件做一个概述: crazy_functions\代码重写为全英文_多线程.py
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-该程序文件是一个多线程程序，主要功能是将指定目录下的所有Python代码文件中的中文内容转化为英文，并将转化后的代码存储到一个新的文件中。其中，程序使用了GPT-3等技术进行中文-英文的转化，同时也进行了一些Token限制下的处理，以防止程序发生错误。程序在执行过程中还会输出一些提示信息，并将所有转化过的代码文件存储到指定目录下。在程序执行结束后，还会生成一个任务执行报告，记录程序运行的详细信息。
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-## [17/48] 请对下面的程序文件做一个概述: crazy_functions\图片生成.py
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-该程序文件提供了一个用于生成图像的函数`图片生成`。函数实现的过程中，会调用`gen_image`函数来生成图像，并返回图像生成的网址和本地文件地址。函数有多个参数，包括`prompt`(激励文本)、`llm_kwargs`(GPT模型的参数)、`plugin_kwargs`(插件模型的参数)等。函数核心代码使用了`requests`库向OpenAI API请求图像，并做了简单的处理和保存。函数还更新了交互界面，清空聊天历史并显示正在生成图像的消息和最终的图像网址和预览。
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-## [18/48] 请对下面的程序文件做一个概述: crazy_functions\对话历史存档.py
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-这个文件是名为crazy_functions\对话历史存档.py的Python程序文件，包含了4个函数：
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-1. write_chat_to_file(chatbot, history=None, file_name=None)：用来将对话记录以Markdown格式写入文件中，并且生成文件名，如果没指定文件名则用当前时间。写入完成后将文件路径打印出来。
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-2. gen_file_preview(file_name)：从传入的文件中读取内容，解析出对话历史记录并返回前100个字符，用于文件预览。
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-3. read_file_to_chat(chatbot, history, file_name)：从传入的文件中读取内容，解析出对话历史记录并更新聊天显示框。
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-4. 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)：一个主要函数，用于保存当前对话记录并提醒用户。如果用户希望加载历史记录，则调用read_file_to_chat()来更新聊天显示框。如果用户希望删除历史记录，调用删除所有本地对话历史记录()函数完成删除操作。
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-## [19/48] 请对下面的程序文件做一个概述: crazy_functions\总结word文档.py
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-该程序文件实现了一个总结Word文档的功能，使用Python的docx库读取docx格式的文件，使用pywin32库读取doc格式的文件。程序会先根据传入的txt参数搜索需要处理的文件，并逐个解析其中的内容，将内容拆分为指定长度的文章片段，然后使用另一个程序文件中的request_gpt_model_in_new_thread_with_ui_alive函数进行中文概述。最后将所有的总结结果写入一个文件中，并在界面上进行展示。
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-## [20/48] 请对下面的程序文件做一个概述: crazy_functions\总结音视频.py
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-该程序文件包括两个函数：split_audio_file()和AnalyAudio()，并且导入了一些必要的库并定义了一些工具函数。split_audio_file用于将音频文件分割成多个时长相等的片段，返回一个包含所有切割音频片段文件路径的列表，而AnalyAudio用来分析音频文件，通过调用whisper模型进行音频转文字并使用GPT模型对音频内容进行概述，最终将所有总结结果写入结果文件中。
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-## [21/48] 请对下面的程序文件做一个概述: crazy_functions\批量Markdown翻译.py
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-该程序文件名为`批量Markdown翻译.py`，包含了以下功能：读取Markdown文件，将长文本分离开来，将Markdown文件进行翻译（英译中和中译英），整理结果并退出。程序使用了多线程以提高效率。程序使用了`tiktoken`依赖库，可能需要额外安装。文件中还有一些其他的函数和类，但与文件名所描述的功能无关。
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-## [22/48] 请对下面的程序文件做一个概述: crazy_functions\批量总结PDF文档.py
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-该文件是一个Python脚本，名为crazy_functions\批量总结PDF文档.py。在导入了一系列库和工具函数后，主要定义了5个函数，其中包括一个错误处理装饰器（@CatchException），用于批量总结PDF文档。该函数主要实现对PDF文档的解析，并调用模型生成中英文摘要。
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-## [23/48] 请对下面的程序文件做一个概述: crazy_functions\批量总结PDF文档pdfminer.py
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-该程序文件是一个用于批量总结PDF文档的函数插件，使用了pdfminer插件和BeautifulSoup库来提取PDF文档的文本内容，对每个PDF文件分别进行处理并生成中英文摘要。同时，该程序文件还包括一些辅助工具函数和处理异常的装饰器。
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-## [24/48] 请对下面的程序文件做一个概述: crazy_functions\批量翻译PDF文档_多线程.py
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-这个程序文件是一个Python脚本，文件名为“批量翻译PDF文档_多线程.py”。它主要使用了“toolbox”、“request_gpt_model_in_new_thread_with_ui_alive”、“request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency”、“colorful”等Python库和自定义的模块“crazy_utils”的一些函数。程序实现了一个批量翻译PDF文档的功能，可以自动解析PDF文件中的基础信息，递归地切割PDF文件，翻译和处理PDF论文中的所有内容，并生成相应的翻译结果文件（包括md文件和html文件）。功能比较复杂，其中需要调用多个函数和依赖库，涉及到多线程操作和UI更新。文件中有详细的注释和变量命名，代码比较清晰易读。
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-## [25/48] 请对下面的程序文件做一个概述: crazy_functions\理解PDF文档内容.py
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-该程序文件实现了一个名为“理解PDF文档内容”的函数，该函数可以为输入的PDF文件提取摘要以及正文各部分的主要内容，并在提取过程中根据上下文关系进行学术性问题解答。该函数依赖于多个辅助函数和第三方库，并在执行过程中针对可能出现的异常进行了处理。
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-## [26/48] 请对下面的程序文件做一个概述: crazy_functions\生成函数注释.py
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-该程序文件是一个Python模块文件，文件名为“生成函数注释.py”，定义了两个函数：一个是生成函数注释的主函数“生成函数注释”，另一个是通过装饰器实现异常捕捉的函数“批量生成函数注释”。该程序文件依赖于“toolbox”和本地“crazy_utils”模块，并且在运行时使用了多线程技术和GPT模型来生成注释。函数生成的注释结果使用Markdown表格输出并写入历史记录文件。
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-## [27/48] 请对下面的程序文件做一个概述: crazy_functions\联网的ChatGPT.py
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-这是一个名为`联网的ChatGPT.py`的Python程序文件，其中定义了一个函数`连接网络回答问题`。该函数通过爬取搜索引擎的结果和访问网页来综合回答给定的问题，并使用ChatGPT模型完成回答。此外，该文件还包括一些工具函数，例如从网页中抓取文本和使用代理访问网页。
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-## [28/48] 请对下面的程序文件做一个概述: crazy_functions\解析JupyterNotebook.py
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-这个程序文件包含了两个函数： `parseNotebook()`和`解析ipynb文件()`，并且引入了一些工具函数和类。`parseNotebook()`函数将Jupyter Notebook文件解析为文本代码块，`解析ipynb文件()`函数则用于解析多个Jupyter Notebook文件，使用`parseNotebook()`解析每个文件和一些其他的处理。函数中使用了多线程处理输入和输出，并且将结果写入到文件中。
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-## [29/48] 请对下面的程序文件做一个概述: crazy_functions\解析项目源代码.py
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-这是一个源代码分析的Python代码文件，其中定义了多个函数，包括解析一个Python项目、解析一个C项目、解析一个C项目的头文件和解析一个Java项目等。其中解析源代码新函数是实际处理源代码分析并生成报告的函数。该函数首先会逐个读取传入的源代码文件，生成对应的请求内容，通过多线程发送到chatgpt进行分析。然后将结果写入文件，并进行汇总分析。最后通过调用update_ui函数刷新界面，完整实现了源代码的分析。
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-## [30/48] 请对下面的程序文件做一个概述: crazy_functions\询问多个大语言模型.py
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-该程序文件包含两个函数：同时问询()和同时问询_指定模型()，它们的作用是使用多个大语言模型同时对用户输入进行处理，返回对应模型的回复结果。同时问询()会默认使用ChatGPT和ChatGLM两个模型，而同时问询_指定模型()则可以指定要使用的模型。该程序文件还引用了其他的模块和函数库。
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-## [31/48] 请对下面的程序文件做一个概述: crazy_functions\读文章写摘要.py
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-这个程序文件是一个Python模块，文件名为crazy_functions\读文章写摘要.py。该模块包含了两个函数，其中主要函数是"读文章写摘要"函数，其实现了解析给定文件夹中的tex文件，对其中每个文件的内容进行摘要生成，并根据各论文片段的摘要，最终生成全文摘要。第二个函数是"解析Paper"函数，用于解析单篇论文文件。其中用到了一些工具函数和库，如update_ui、CatchException、report_execption、write_results_to_file等。
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-## [32/48] 请对下面的程序文件做一个概述: crazy_functions\谷歌检索小助手.py
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-该文件是一个Python模块，文件名为“谷歌检索小助手.py”。该模块包含两个函数，一个是“get_meta_information()”，用于从提供的网址中分析出所有相关的学术文献的元数据信息；另一个是“谷歌检索小助手()”，是主函数，用于分析用户提供的谷歌学术搜索页面中出现的文章，并提取相关信息。其中，“谷歌检索小助手()”函数依赖于“get_meta_information()”函数，并调用了其他一些Python模块，如“arxiv”、“math”、“bs4”等。
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-## [33/48] 请对下面的程序文件做一个概述: crazy_functions\高级功能函数模板.py
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-该程序文件定义了一个名为高阶功能模板函数的函数，该函数接受多个参数，包括输入的文本、gpt模型参数、插件模型参数、聊天显示框的句柄、聊天历史等，并利用送出请求，使用 Unsplash API 发送相关图片。其中，为了避免输入溢出，函数会在开始时清空历史。函数也有一些 UI 更新的语句。该程序文件还依赖于其他两个模块：CatchException 和 update_ui，以及一个名为 request_gpt_model_in_new_thread_with_ui_alive 的来自 crazy_utils 模块（应该是自定义的工具包）的函数。
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-## [34/48] 请对下面的程序文件做一个概述: request_llm\bridge_all.py
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-该文件包含两个函数：predict和predict_no_ui_long_connection，用于基于不同的LLM模型进行对话。该文件还包含一个lazyloadTiktoken类和一个LLM_CATCH_EXCEPTION修饰器函数。其中lazyloadTiktoken类用于懒加载模型的tokenizer，LLM_CATCH_EXCEPTION用于错误处理。整个文件还定义了一些全局变量和模型信息字典，用于引用和配置LLM模型。
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-## [35/48] 请对下面的程序文件做一个概述: request_llm\bridge_chatglm.py
-
-这是一个Python程序文件，名为`bridge_chatglm.py`，其中定义了一个名为`GetGLMHandle`的类和三个方法：`predict_no_ui_long_connection`、 `predict`和 `stream_chat`。该文件依赖于多个Python库，如`transformers`和`sentencepiece`。该文件实现了一个聊天机器人，使用ChatGLM模型来生成回复，支持单线程和多线程方式。程序启动时需要加载ChatGLM的模型和tokenizer，需要一段时间。在配置文件`config.py`中设置参数会影响模型的内存和显存使用，因此程序可能会导致低配计算机卡死。
-
-## [36/48] 请对下面的程序文件做一个概述: request_llm\bridge_chatgpt.py
-
-该文件为 Python 代码文件，文件名为 request_llm\bridge_chatgpt.py。该代码文件主要提供三个函数：predict、predict_no_ui和 predict_no_ui_long_connection，用于发送至 chatGPT 并等待回复，获取输出。该代码文件还包含一些辅助函数，用于处理连接异常、生成 HTTP 请求等。该文件的代码架构清晰，使用了多个自定义函数和模块。
-
-## [37/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_llama.py
-
-该代码文件实现了一个聊天机器人，其中使用了 JittorLLMs 模型。主要包括以下几个部分：
-1. GetGLMHandle 类：一个进程类，用于加载 JittorLLMs 模型并接收并处理请求。
-2. predict_no_ui_long_connection 函数：一个多线程方法，用于在后台运行聊天机器人。
-3. predict 函数：一个单线程方法，用于在前端页面上交互式调用聊天机器人，以获取用户输入并返回相应的回复。
-
-这个文件中还有一些辅助函数和全局变量，例如 importlib、time、threading 等。
-
-## [38/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_pangualpha.py
-
-这个文件是为了实现使用jittorllms（一种机器学习模型）来进行聊天功能的代码。其中包括了模型加载、模型的参数加载、消息的收发等相关操作。其中使用了多进程和多线程来提高性能和效率。代码中还包括了处理依赖关系的函数和预处理函数等。
-
-## [39/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_rwkv.py
-
-这个文件是一个Python程序，文件名为request_llm\bridge_jittorllms_rwkv.py。它依赖transformers、time、threading、importlib、multiprocessing等库。在文件中，通过定义GetGLMHandle类加载jittorllms模型参数和定义stream_chat方法来实现与jittorllms模型的交互。同时，该文件还定义了predict_no_ui_long_connection和predict方法来处理历史信息、调用jittorllms模型、接收回复信息并输出结果。
-
-## [40/48] 请对下面的程序文件做一个概述: request_llm\bridge_moss.py
-
-该文件为一个Python源代码文件，文件名为 request_llm\bridge_moss.py。代码定义了一个 GetGLMHandle 类和两个函数 predict_no_ui_long_connection 和 predict。
-
-GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个子进程并加载 MOSS 模型参数，通过 Pipe 进行主子进程的通信。该类还定义了 check_dependency、moss_init、run 和 stream_chat 等方法，其中 check_dependency 和 moss_init 是子进程的初始化方法，run 是子进程运行方法，stream_chat 实现了主进程和子进程的交互过程。
-
-函数 predict_no_ui_long_connection 是多线程方法，调用 GetGLMHandle 类加载 MOSS 参数后使用 stream_chat 实现主进程和子进程的交互过程。
-
-函数 predict 是单线程方法，通过调用 update_ui 将交互过程中 MOSS 的回复实时更新到UI（User Interface）中，并执行一个 named function（additional_fn）指定的函数对输入进行预处理。
-
-## [41/48] 请对下面的程序文件做一个概述: request_llm\bridge_newbing.py
-
-这是一个名为`bridge_newbing.py`的程序文件，包含三个部分：
-
-第一部分使用from语句导入了`edge_gpt`模块的`NewbingChatbot`类。
-
-第二部分定义了一个名为`NewBingHandle`的继承自进程类的子类，该类会检查依赖性并启动进程。同时，该部分还定义了一个名为`predict_no_ui_long_connection`的多线程方法和一个名为`predict`的单线程方法，用于与NewBing进行通信。
-
-第三部分定义了一个名为`newbing_handle`的全局变量，并导出了`predict_no_ui_long_connection`和`predict`这两个方法，以供其他程序可以调用。
-
-## [42/48] 请对下面的程序文件做一个概述: request_llm\bridge_newbingfree.py
-
-这个Python文件包含了三部分内容。第一部分是来自edge_gpt_free.py文件的聊天机器人程序。第二部分是子进程Worker，用于调用主体。第三部分提供了两个函数：predict_no_ui_long_connection和predict用于调用NewBing聊天机器人和返回响应。其中predict函数还提供了一些参数用于控制聊天机器人的回复和更新UI界面。
-
-## [43/48] 请对下面的程序文件做一个概述: request_llm\bridge_stackclaude.py
-
-这是一个Python源代码文件，文件名为request_llm\bridge_stackclaude.py。代码分为三个主要部分：
-
-第一部分定义了Slack API Client类，实现Slack消息的发送、接收、循环监听，用于与Slack API进行交互。
-
-第二部分定义了ClaudeHandle类，继承Process类，用于创建子进程Worker，调用主体，实现Claude与用户交互的功能。
-
-第三部分定义了predict_no_ui_long_connection和predict两个函数，主要用于通过调用ClaudeHandle对象的stream_chat方法来获取Claude的回复，并更新ui以显示相关信息。其中predict函数采用单线程方法，而predict_no_ui_long_connection函数使用多线程方法。
-
-## [44/48] 请对下面的程序文件做一个概述: request_llm\bridge_tgui.py
-
-该文件是一个Python代码文件，名为request_llm\bridge_tgui.py。它包含了一些函数用于与chatbot UI交互，并通过WebSocket协议与远程LLM模型通信完成文本生成任务，其中最重要的函数是predict()和predict_no_ui_long_connection()。这个程序还有其他的辅助函数，如random_hash()。整个代码文件在协作的基础上完成了一次修改。
-
-## [45/48] 请对下面的程序文件做一个概述: request_llm\edge_gpt.py
-
-该文件是一个用于调用Bing chatbot API的Python程序，它由多个类和辅助函数构成，可以根据给定的对话连接在对话中提出问题，使用websocket与远程服务通信。程序实现了一个聊天机器人，可以为用户提供人工智能聊天。
-
-## [46/48] 请对下面的程序文件做一个概述: request_llm\edge_gpt_free.py
-
-该代码文件为一个会话API，可通过Chathub发送消息以返回响应。其中使用了 aiohttp 和 httpx 库进行网络请求并发送。代码中包含了一些函数和常量，多数用于生成请求数据或是请求头信息等。同时该代码文件还包含了一个 Conversation 类，调用该类可实现对话交互。
-
-## [47/48] 请对下面的程序文件做一个概述: request_llm\test_llms.py
-
-这个文件是用于对llm模型进行单元测试的Python程序。程序导入一个名为"request_llm.bridge_newbingfree"的模块，然后三次使用该模块中的predict_no_ui_long_connection()函数进行预测，并输出结果。此外，还有一些注释掉的代码段，这些代码段也是关于模型预测的。
-
-## 用一张Markdown表格简要描述以下文件的功能：
-check_proxy.py, colorful.py, config.py, config_private.py, core_functional.py, crazy_functional.py, main.py, multi_language.py, theme.py, toolbox.py, crazy_functions\crazy_functions_test.py, crazy_functions\crazy_utils.py, crazy_functions\Latex全文润色.py, crazy_functions\Latex全文翻译.py, crazy_functions\__init__.py, crazy_functions\下载arxiv论文翻译摘要.py。根据以上分析，用一句话概括程序的整体功能。
-
-| 文件名 | 功能描述 |
-| ------ | ------ |
-| check_proxy.py | 检查代理有效性及地理位置 |
-| colorful.py | 控制台打印彩色文字 |
-| config.py | 配置和参数设置 |
-| config_private.py | 私人配置和参数设置 |
-| core_functional.py | 核心函数和参数设置 |
-| crazy_functional.py | 高级功能插件集合 |
-| main.py | 一个 Chatbot 程序，提供各种学术翻译、文本处理和其他查询服务 |
-| multi_language.py | 识别和翻译不同语言 |
-| theme.py | 自定义 gradio 应用程序主题 |
-| toolbox.py | 工具类库，用于协助实现各种功能 |
-| crazy_functions\crazy_functions_test.py | 测试 crazy_functions 中的各种函数 |
-| crazy_functions\crazy_utils.py | 工具函数，用于字符串处理、异常检测、Markdown 格式转换等 |
-| crazy_functions\Latex全文润色.py | 对整个 Latex 项目进行润色和纠错 |
-| crazy_functions\Latex全文翻译.py | 对整个 Latex 项目进行翻译 |
-| crazy_functions\__init__.py | 模块初始化文件，标识 `crazy_functions` 是一个包 |
-| crazy_functions\下载arxiv论文翻译摘要.py | 下载 `arxiv` 论文的 PDF 文件，并提取摘要和翻译 |
-
-这些程序源文件提供了基础的文本和语言处理功能、工具函数和高级插件，使 Chatbot 能够处理各种复杂的学术文本问题，包括润色、翻译、搜索、下载、解析等。
-
-## 用一张Markdown表格简要描述以下文件的功能：
-crazy_functions\代码重写为全英文_多线程.py, crazy_functions\图片生成.py, crazy_functions\对话历史存档.py, crazy_functions\总结word文档.py, crazy_functions\总结音视频.py, crazy_functions\批量Markdown翻译.py, crazy_functions\批量总结PDF文档.py, crazy_functions\批量总结PDF文档pdfminer.py, crazy_functions\批量翻译PDF文档_多线程.py, crazy_functions\理解PDF文档内容.py, crazy_functions\生成函数注释.py, crazy_functions\联网的ChatGPT.py, crazy_functions\解析JupyterNotebook.py, crazy_functions\解析项目源代码.py, crazy_functions\询问多个大语言模型.py, crazy_functions\读文章写摘要.py。根据以上分析，用一句话概括程序的整体功能。
-
-| 文件名 | 功能简述 |
-| --- | --- |
-| 代码重写为全英文_多线程.py | 将Python源代码文件中的中文内容转化为英文 |
-| 图片生成.py | 根据激励文本使用GPT模型生成相应的图像 |
-| 对话历史存档.py | 将每次对话记录写入Markdown格式的文件中 |
-| 总结word文档.py | 对输入的word文档进行摘要生成 |
-| 总结音视频.py | 对输入的音视频文件进行摘要生成 |
-| 批量Markdown翻译.py | 将指定目录下的Markdown文件进行中英文翻译 |
-| 批量总结PDF文档.py | 对PDF文件进行切割和摘要生成 |
-| 批量总结PDF文档pdfminer.py | 对PDF文件进行文本内容的提取和摘要生成 |
-| 批量翻译PDF文档_多线程.py | 将指定目录下的PDF文件进行中英文翻译 |
-| 理解PDF文档内容.py | 对PDF文件进行摘要生成和问题解答 |
-| 生成函数注释.py | 自动生成Python函数的注释 |
-| 联网的ChatGPT.py | 使用网络爬虫和ChatGPT模型进行聊天回答 |
-| 解析JupyterNotebook.py | 对Jupyter Notebook进行代码解析 |
-| 解析项目源代码.py | 对指定编程语言的源代码进行解析 |
-| 询问多个大语言模型.py | 使用多个大语言模型对输入进行处理和回复 |
-| 读文章写摘要.py | 对论文进行解析和全文摘要生成 |
-
-概括程序的整体功能：提供了一系列处理文本、文件和代码的功能，使用了各类语言模型、多线程、网络请求和数据解析技术来提高效率和精度。
-
-## 用一张Markdown表格简要描述以下文件的功能：
-crazy_functions\谷歌检索小助手.py, crazy_functions\高级功能函数模板.py, request_llm\bridge_all.py, request_llm\bridge_chatglm.py, request_llm\bridge_chatgpt.py, request_llm\bridge_jittorllms_llama.py, request_llm\bridge_jittorllms_pangualpha.py, request_llm\bridge_jittorllms_rwkv.py, request_llm\bridge_moss.py, request_llm\bridge_newbing.py, request_llm\bridge_newbingfree.py, request_llm\bridge_stackclaude.py, request_llm\bridge_tgui.py, request_llm\edge_gpt.py, request_llm\edge_gpt_free.py, request_llm\test_llms.py。根据以上分析，用一句话概括程序的整体功能。
-
-| 文件名 | 功能描述 |
-| --- | --- |
-| crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 |
-| crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 |
-| request_llm\bridge_all.py | 基于不同LLM模型进行对话。 |
-| request_llm\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
-| request_llm\bridge_chatgpt.py | 基于GPT模型完成对话。 |
-| request_llm\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
-| request_llm\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
-| request_llm\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
-| request_llm\bridge_moss.py | 加载Moss模型完成对话功能。 |
-| request_llm\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
-| request_llm\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
-| request_llm\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
-| request_llm\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
-| request_llm\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
-| request_llm\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
-| request_llm\test_llms.py | 对llm模型进行单元测试。 |
-| 程序整体功能 | 实现不同种类的聊天机器人，可以根据输入进行文本生成。 |
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/HD Online Player (Dvd Moviefactory Pro 7 Serial Number Activation Code Final).md b/spaces/quidiaMuxgu/Expedit-SAM/HD Online Player (Dvd Moviefactory Pro 7 Serial Number Activation Code Final).md
deleted file mode 100644
index deb6d581cca0781c5f0e0b0478e1c3aab303c9a1..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/HD Online Player (Dvd Moviefactory Pro 7 Serial Number Activation Code Final).md	
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-Convert Pro 2.9.8 Free Full VersionnWalking Dead - All Episodes Full Season Episodes (Complete Series)nCyberStoreWare 6.5.0.8 Crack Full Free VersionnAdobe Acrobat Pro DC Professional 12.1 Patch Full VersionnPdf2Word Converter 2.2.0 Free Full VersionnAres Painter 2018 Free Patch Full VersionnAlsoft Video Converter 1.0.5.7 Full VersionnSmart DiskManager 4.1.2.11 Activation Key Full PatchnAstro File Manager 1.1.0.14 Portable Crack Patch Full VersionnWinRAR 5.72 Free Full VersionnSuper Hopper 2.0.2.1 Full VersionnX-Play 1.0.8.2 Free Full VersionnWe're back with the fifth edition of our weekly video game link show. Today, it's the "less than stellar" episode, where some beloved video game series get a passing grade, and some rather bad ones get a failing grade.Join our hosts Travis, Ben, and Marcello as they review all of the week's notable video game news.News this week: Crash Bandicoot N.Sane Trilogy gets a final release date, Steve's first impression of the Switch is that it is an iPad, Sea of Thieves has a PC beta that you can participate in for free, and Avengers: Endgame looks a little more intimidating than we expected. If you're a fan of any of these games, be sure to tune in! (Or maybe tune out if you aren't a fan.)
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index a60a2bd5ef7537eb8b8909d9b545c84e5253623b..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/HD Online Player (SalaamEIshq Movies Dual Audio 720p H) [PATCHED].md	
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diff --git a/spaces/radames/MusicGen-Continuation/audiocraft/modules/activations.py b/spaces/radames/MusicGen-Continuation/audiocraft/modules/activations.py
deleted file mode 100644
index 8bd6f2917a56d72db56555d0ff54b2311bc21778..0000000000000000000000000000000000000000
--- a/spaces/radames/MusicGen-Continuation/audiocraft/modules/activations.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-from torch import Tensor
-from typing import Union, Callable
-
-
-class CustomGLU(nn.Module):
-    """Custom Gated Linear Unit activation.
-    Applies a modified gated linear unit :math:`a * f(b)` where :math:`a` is the first half
-    of the input matrices, :math:`b` is the second half, and :math:`f` is a provided activation
-    function (i.e. sigmoid, swish, etc.).
-
-    Args:
-        activation (nn.Module): The custom activation to apply in the Gated Linear Unit
-        dim (int): the dimension on which to split the input. Default: -1
-
-    Shape:
-        - Input: :math:`(\ast_1, N, \ast_2)` where `*` means, any number of additional
-          dimensions
-        - Output: :math:`(\ast_1, M, \ast_2)` where :math:`M=N/2`
-
-    Examples::
-        >>> m = CustomGLU(nn.Sigmoid())
-        >>> input = torch.randn(4, 2)
-        >>> output = m(input)
-    """
-    def __init__(self, activation: nn.Module, dim: int = -1):
-        super(CustomGLU, self).__init__()
-        self.dim = dim
-        self.activation = activation
-
-    def forward(self, x: Tensor):
-        assert x.shape[self.dim] % 2 == 0  # M = N / 2
-        a, b = torch.chunk(x, 2, dim=self.dim)
-        return a * self.activation(b)
-
-
-class SwiGLU(CustomGLU):
-    """SiLU Gated Linear Unit activation.
-    Applies SiLU Gated Linear Unit :math:`a * SiLU(b)` where :math:`a` is
-    the first half of the input matrices, :math:`b` is the second half.
-
-    Args:
-        dim (int): the dimension on which to split the input. Default: -1
-    """
-    def __init__(self, dim: int = -1):
-        super(SwiGLU, self).__init__(nn.SiLU(), dim)
-
-
-class GeGLU(CustomGLU):
-    """GeLU Gated Linear Unit activation.
-    Applies GeLU Gated Linear Unit :math:`a * GELU(b)` where :math:`a` is
-    the first half of the input matrices, :math:`b` is the second half.
-
-    Args:
-        dim (int): the dimension on which to split the input. Default: -1
-    """
-    def __init__(self, dim: int = -1):
-        super(GeGLU, self).__init__(nn.GELU(), dim)
-
-
-class ReGLU(CustomGLU):
-    """ReLU Gated Linear Unit activation.
-    Applies ReLU Gated Linear Unit :math:`a * ReLU(b)` where :math:`a` is
-    the first half of the input matrices, :math:`b` is the second half.
-
-    Args:
-        dim (int): the dimension on which to split the input. Default: -1
-    """
-    def __init__(self, dim: int = -1):
-        super(ReGLU, self).__init__(nn.ReLU(), dim)
-
-
-def get_activation_fn(
-    activation: Union[str, Callable[[Tensor], Tensor]]
-) -> Union[str, Callable[[Tensor], Tensor]]:
-    """Helper function to map an activation string to the activation class.
-    If the supplied activation is not a string that is recognized, the activation is passed back.
-
-    Args:
-        activation (Union[str, Callable[[Tensor], Tensor]]): Activation to check
-    """
-    if isinstance(activation, str):
-        if activation == "reglu":
-            return ReGLU()
-        elif activation == "geglu":
-            return GeGLU()
-        elif activation == "swiglu":
-            return SwiGLU()
-    return activation
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/AEON Rhythmic V1.2.0 KONTAKT _TOP_.md b/spaces/raedeXanto/academic-chatgpt-beta/AEON Rhythmic V1.2.0 KONTAKT _TOP_.md
deleted file mode 100644
index c59238f96505c77ff7baae75f3f151bf7fb0329b..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/AEON Rhythmic V1.2.0 KONTAKT _TOP_.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-

-
AEON Rhythmic V1.2.0 KONTAKT: A Powerful Tool for Creating Melodic Loops and Sequences

-
If you are looking for a versatile and creative tool for composing and producing melodic loops and sequences, you should check out AEON Rhythmic V1.2.0 KONTAKT by Heavyocity. This is a film sound design software that offers 315 carefully-sculpted melodic loops, arranged into three main categories: Menu Suites, Single Loops, and Three Loop Combos.

-
AEON Rhythmic V1.2.0 KONTAKT is part of the AEON Collection, which also includes AEON Melodic, a library of natural and synthesized melodic instruments, arpeggiated instruments and epic one-shots. Together, they provide you with over 400 presets and 25 GB of content to inspire your musical creativity.

-
AEON Rhythmic V1.2.0 KONTAKT

Download ––– https://tinourl.com/2uL4LG



-
What Can You Do with AEON Rhythmic V1.2.0 KONTAKT?

-
With AEON Rhythmic V1.2.0 KONTAKT, you can create impressive sounds ranging from lyrical and expressive to dark and aggressive. You can use the Menu Suites to layer individual loops from different categories, such as organic, analog, tonal or percussive. You can also use the Single Loops to control each loop in detail, such as tuning, panning, level and EQ. You can even recompose a loop by rearranging the slices or change the tonal center by using the key switches.

-
Another feature of AEON Rhythmic V1.2.0 KONTAKT is the Three Loop Combos, which allow you to create complex melodic grooves and sequences by combining three loops from different categories. You can also customize and link up to eight patterns per preset, each with up to 128 steps of full melodic loops.

-
AEON Rhythmic V1.2.0 KONTAKT also comes with a powerful 4-band Master EQ that lets you sculpt your sounds with per loop Tune, Pan, Level and EQs or add sizzle and punch with global effects such as Filter, Distortion, Delay and Reverb.

-
How to Get AEON Rhythmic V1.2.0 KONTAKT?

-
If you are interested in getting AEON Rhythmic V1.2.0 KONTAKT, you can visit the official website of Heavyocity[^2^] or download it from various online sources[^1^] [^3^]. However, you will need a full version of Native Instruments Kontakt 5 or higher to run this software.

-
AEON Rhythmic V1.2.0 KONTAKT is a great tool for composers and sound designers who want to create unique and inspiring melodic loops and sequences for their projects. Whether you are working on film scores, video games, trailers or electronic music, you will find something useful and exciting in this library.

-
-
What Are Some Examples of AEON Rhythmic V1.2.0 KONTAKT in Action?

-
If you want to hear some examples of what AEON Rhythmic V1.2.0 KONTAKT can do, you can check out the official demo videos on the Heavyocity website or on YouTube. You can also listen to some user reviews and testimonials on various online forums and blogs.

-

-
Some of the genres and styles that AEON Rhythmic V1.2.0 KONTAKT can cover include ambient, cinematic, electronic, industrial, pop, rock and more. You can use it to create atmospheric pads, rhythmic arpeggios, melodic motifs, pulsing basses and dynamic percussion.

-
AEON Rhythmic V1.2.0 KONTAKT is also compatible with other Heavyocity products, such as Gravity, Damage, Vocalise and Scoring Guitars. You can mix and match them to create even more diverse and complex sounds.

-
Conclusion

-
AEON Rhythmic V1.2.0 KONTAKT is a powerful tool for creating melodic loops and sequences that can enhance your musical projects. It offers a rich set of deeply sampled organic instruments and high-quality analog synths that can be manipulated and customized in various ways. It also provides a simple and intuitive interface that allows you to create and change the sounds without using extra settings.

-
If you are looking for a versatile and creative tool for composing and producing melodic loops and sequences, you should check out AEON Rhythmic V1.2.0 KONTAKT by Heavyocity.
 7b8c122e87

-

-

\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Dil Hai Tumhaara movie english subtitles download torrent Watch the romantic drama online.md b/spaces/raedeXanto/academic-chatgpt-beta/Dil Hai Tumhaara movie english subtitles download torrent Watch the romantic drama online.md
deleted file mode 100644
index 268ef5084abfc3849c0a79a456d3f51a1b941813..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Dil Hai Tumhaara movie english subtitles download torrent Watch the romantic drama online.md	
+++ /dev/null
@@ -1,131 +0,0 @@
-
-
Dil Hai Tumhaara Movie English Subtitles Download Torrent

- 
Introduction

- 
If you are a fan of Bollywood movies, you might have heard of Dil Hai Tumhaara, a 2002 romantic drama film directed by Kundan Shah and starring Rekha, Preity Zinta, Mahima Chaudhry, Arjun Rampal, Jimmy Sheirgill and Alok Nath. The film tells the story of two sisters who fall in love with the same man, unaware of their true relationship. The film was praised for its performances, music and emotional appeal, but failed to do well at the box office.

-
Dil Hai Tumhaara movie english subtitles download torrent

Download File ->->->-> https://tinourl.com/2uL2pX



- 
However, if you are not fluent in Hindi, you might have trouble understanding the dialogues and songs of the film. That's why you need English subtitles to enjoy the movie fully. But where can you find Dil Hai Tumhaara movie English subtitles? And how can you download them easily and safely? In this article, we will show you how to download Dil Hai Tumhaara movie English subtitles torrent in a few simple steps.

- 
How to download Dil Hai Tumhaara movie English subtitles torrent?

- 
A torrent is a file that contains information about other files that can be downloaded from peer-to-peer networks. By using a torrent client, you can download large files such as movies, music, games and software faster and more efficiently. However, not all torrents are created equal. Some torrents may have low quality, incomplete or corrupted files, while others may have malicious content that can harm your device or data. Therefore, you need to be careful when choosing and downloading torrents.

- 
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- 
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- 
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- 
To avoid these risks, you should look for a reputable torrent site that has a large and active community of users who share and rate torrents. You should also check the comments and reviews of other users who have downloaded the same torrent before. This way, you can get an idea of the quality and accuracy of the subtitles, as well as any potential problems or issues with the torrent.

- 
Some examples of popular and reliable torrent sites that offer Dil Hai Tumhaara movie English subtitles are:

- 
    
-
  • The Pirate Bay: This is one of the oldest and most widely used torrent sites in the world. It has millions of torrents in various categories, including movies, TV shows, music, games and software. You can search for Dil Hai Tumhaara movie English subtitles by typing the name of the movie in the search bar and filtering by category (Video) and subcategory (Movies). You can also sort the results by seeders (the number of users who have the complete file and are sharing it), leechers (the number of users who are downloading the file) or date (the most recent uploads). The more seeders and leechers a torrent has, the faster and easier it will be to download.
    • 
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  • 1337x: This is another popular torrent site that has a user-friendly interface and a wide range of torrents in various categories. You can search for Dil Hai Tumhaara movie English subtitles by typing the name of the movie in the search bar and filtering by category (Movies) and language (English). You can also sort the results by seeders, leechers or time (the most recent uploads). The more seeders and leechers a torrent has, the faster and easier it will be to download.
    • 
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    • 
-

- 
Step 2: Download and install a torrent client on your device

- 
The second step is to download and install a torrent client on your device. A torrent client is a software application that allows you to download files from peer-to-peer networks using torrents. There are many torrent clients available for different platforms such as Windows, Mac OS X, Linux, Android and iOS. Some examples of popular and reliable torrent clients are:

- 
    
-
  • uTorrent: This is one of the most widely used torrent clients in the world. It has a simple and intuitive interface that allows you to easily manage your downloads. It also has features such as bandwidth control, magnet links support, streaming mode, remote access and encryption.
    • 
-
  • BitTorrent: This is another popular torrent client that has similar features as uTorrent. It also has features such as web-based seeding, commenting and reviewing.
    • 
-
  • Vuze: This is a powerful torrent client that has advanced features such as media player integration, meta search engine, VPN service integration, swarm discoveries and web remote.
    • 
-

- 
To download and install a torrent client on your device, you need to visit its official website and follow its instructions. You should also make sure that your device meets its system requirements and that you have enough storage space for your downloads.

- 
Step 3: Search for Dil Hai Tumhaara movie English subtitles torrent on the torrent site

- 
The third step is to search for Dil Hai Tumhaara movie English subtitles torrent on the torrent site that you have chosen in step 1. You need to use the same keywords and filters that you have used before to find the best match for your needs. Once you have found a suitable torrent file that has good ratings, comments and seeds/leeches ratio, you need to click on it to open its details page.

- 
On this page, you will see more information about the torrent file such as its size, format, quality, language, source and trackers. You will also see a magnet link or a download button that will allow you to download the torrent file directly or through your browser.

- 
Step 4: Download the torrent file and open it with the torrent client

- 
The fourth step is to download the torrent file and open it with the torrent client that you have installed on your device in step 2. You can either click on the magnet link or the download button on the torrent site, or save the torrent file on your device and then open it with the torrent client manually. Either way, you will see a dialog box asking you to confirm your download settings such as destination folder, files selection, priority level, etc. to your preferences and then click OK to start the download. 
Depending on the size and speed of the torrent file, the download may take a few minutes or hours to complete. You can monitor the progress of the download on your torrent client's interface. You can also pause, resume or cancel the download at any time.

- 
Step 5: Wait for the download to complete and enjoy the movie with English subtitles

- 
The final step is to wait for the download to complete and enjoy the movie with English subtitles. Once the download is finished, you will see a notification on your torrent client's interface. You can then open the downloaded folder and locate the movie file and the subtitle file. The movie file will have an extension such as .mp4, .avi, .mkv, etc., while the subtitle file will have an extension such as .srt, .sub, .ass, etc.

- 
To play the movie with subtitles, you need to use a media player that supports subtitles such as VLC Media Player, KMPlayer, GOM Player, etc. You can either drag and drop both files into the media player's window or open them separately from the media player's menu. You can also adjust the subtitle settings such as font, size, color, position, sync, etc. from the media player's menu.

- 
Now you can sit back and enjoy Dil Hai Tumhaara movie with English subtitles on your device.

- 
Tips and warnings for downloading Dil Hai Tumhaara movie English subtitles torrent

- 
Downloading Dil Hai Tumhaara movie English subtitles torrent is not a difficult task if you follow the steps above. However, there are some tips and warnings that you should keep in mind before and after downloading torrents. Here are some of them:

- 
Tip 1: Check the quality and size of the torrent file before downloading

- 
Before downloading a torrent file, you should check its quality and size to make sure that it meets your expectations and requirements. You can do this by looking at its details page on the torrent site or by using a torrent preview tool such as Bit Che or TorrentRover. These tools allow you to see information such as video resolution, audio bitrate, frame rate, codec, etc. of a torrent file without downloading it.

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You should also check the size of the torrent file to make sure that you have enough storage space on your device for your download. You can do this by looking at its details page on the torrent site or by using a disk space analyzer tool such as WinDirStat or TreeSize Free. These tools allow you to see how much space each file or folder occupies on your device.

- 
Tip 2: Use a VPN service to protect your privacy and security online

- 
Downloading torrents may expose your IP address and online activity to other users on the peer-to-peer network or to third parties such as ISPs, hackers, government agencies, etc. This may compromise your privacy and security online and may result in legal consequences or cyberattacks. To avoid these risks, you should use a VPN service when downloading torrents.

- 
A VPN service is a software application that creates a secure and encrypted connection between your device and a remote server located in another country or region. This way, you can hide your IP address and online activity from anyone who tries to monitor or track you online. You can also access geo-restricted content such as movies, TV shows, games and software that are not available in your location.

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Some examples of popular and reliable VPN services that support torrenting are:

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    • 
-

- 
Tip 3: Scan the downloaded files for viruses and malware before opening them

- 
Downloading torrents may also expose you to viruses and malware that can infect your device or data and cause damage or loss. To avoid these risks, you should scan the downloaded files for viruses and malware before opening them. You can do this by using an antivirus or anti-malware software application that can detect and remove any malicious content from your files. Some examples of popular and reliable antivirus or anti-malware software applications are:

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  • Malwarebytes: This is one of the most widely used anti-malware software applications in the world that can protect your device from various types of malware such as spyware, ransomware, adware, trojans, worms, etc.
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  • Kaspersky: This is another powerful antivirus software application that can protect your device from various types of viruses such as rootkits, keyloggers, phishing, spam, etc.
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-
  • Avast: This is a user-friendly antivirus software application that can protect your device from various types of viruses such as malware, spyware, ransomware, etc.
    • 
-

- 
Warning 1: Downloading torrents may be illegal in some countries or regions

- 
Downloading torrents may be illegal in some countries or regions where there are strict laws against piracy or copyright infringement. This may result in legal consequences such as fines, arrests, lawsuits, etc. To avoid these consequences, you should check the legal status of torrenting in your country or region before downloading torrents. You should also respect the rights of the original creators and owners of the content that you download and use it only for personal and non-commercial purposes.

- 
Warning 2: Downloading torrents may expose you to cyber threats and legal risks

- 
Downloading torrents may expose you to cyber threats and legal risks even if you use a VPN service and an antivirus software application. This is because there are no guarantees that these tools will work perfectly all the time or that they will protect you from all possible threats or risks. Therefore, you should always be careful when downloading torrents and use them at your own risk.

- 
Conclusion

- 
Dil Hai Tumhaara is a Bollywood movie that tells the story of two sisters who fall in love with the same man unaware of their true relationship. The movie has a lot of emotional appeal but it is not easy to understand if you do not speak Hindi. That's why you need English subtitles to enjoy it fully.

- 
In this article we have shown you how to download Dil Hai Tumhaara movie English subtitles torrent in a few simple steps. You need to find a reliable torrent site that offers Dil Hai Tumhaara movie English subtitles torrent; download and install a torrent client on your device; search for Dil Hai Tumhaara movie English subtitles torrent on the torrent site; download the torrent file and open it with the torrent client; wait for the download to complete and enjoy the movie with English subtitles.

- 
We have also given you some tips and warnings for downloading Dil Hai Tumhaara movie English subtitles torrent such as checking the quality and size of the torrent file before downloading; using a VPN service to protect your privacy and security online; scanning the downloaded files for viruses and malware before opening them; checking the legal status of torrenting in your country or region before downloading torrents; respecting the rights of the original creators and owners of the content that you download; being careful when downloading torrents and using them at your own risk.

- 
FAQs

- 
Here are some frequently asked questions about Dil Hai Tumhaara movie English subtitles torrent:

- 
    
-
  • Q: Where can I watch Dil Hai Tumhaara movie online?
    • 
-
  • A: You can watch Dil Hai Tumhaara movie online on streaming platforms such as Amazon Prime Video, Plex or Tubi TV. However, you may need to pay a subscription fee or register an account to access these platforms. You may also need to use a VPN service to bypass geo-restrictions if the movie is not available in your location.
    • 
-
  • Q: How can I download Dil Hai Tumhaara movie without subtitles?
    • 
-
  • A: You can download Dil Hai Tumhaara movie without subtitles by following the same steps as downloading Dil Hai Tumhaara movie English subtitles torrent, except that you need to look for a torrent file that does not have subtitles included. You can do this by checking the details page of the torrent file on the torrent site or by using a torrent preview tool.
    • 
-
  • Q: How can I add subtitles to Dil Hai Tumhaara movie after downloading it?
    • 
-
  • A: You can add subtitles to Dil Hai Tumhaara movie after downloading it by using a subtitle editor software application such as Subtitle Edit, Aegisub or Subtitle Workshop. These tools allow you to create, edit and sync subtitles for any video file. You can also download subtitle files from online sources such as OpenSubtitles, Subscene or YIFY Subtitles and then import them into the subtitle editor software application.
    • 
-
  • Q: How can I play Dil Hai Tumhaara movie with subtitles on my TV?
    • 
-
  • A: You can play Dil Hai Tumhaara movie with subtitles on your TV by using a media player device such as Roku, Chromecast, Fire TV Stick or Apple TV. These devices allow you to stream video files from your device or online sources to your TV. You can also use a USB flash drive or an external hard drive to transfer the video file and the subtitle file to your TV and then play them using your TV's built-in media player.
    • 
-
  • Q: How can I learn Hindi by watching Dil Hai Tumhaara movie with subtitles?
    • 
-
  • A: You can learn Hindi by watching Dil Hai Tumhaara movie with subtitles by paying attention to the dialogues and songs of the movie and comparing them with the English subtitles. You can also use a dictionary or a translator app to look up unfamiliar words or phrases. You can also repeat the dialogues and songs aloud to practice your pronunciation and intonation. You can also watch other Bollywood movies with subtitles to improve your Hindi skills.
    • 
-

- 
 0a6ba089eb

-

-

\ No newline at end of file
diff --git a/spaces/ramiin2/AutoGPT/autogpt/token_counter.py b/spaces/ramiin2/AutoGPT/autogpt/token_counter.py
deleted file mode 100644
index 338fe6be4d47a679f2bf0815685edeb3dce66936..0000000000000000000000000000000000000000
--- a/spaces/ramiin2/AutoGPT/autogpt/token_counter.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Functions for counting the number of tokens in a message or string."""
-from __future__ import annotations
-
-import tiktoken
-
-from autogpt.logs import logger
-
-
-def count_message_tokens(
-    messages: list[dict[str, str]], model: str = "gpt-3.5-turbo-0301"
-) -> int:
-    """
-    Returns the number of tokens used by a list of messages.
-
-    Args:
-        messages (list): A list of messages, each of which is a dictionary
-            containing the role and content of the message.
-        model (str): The name of the model to use for tokenization.
-            Defaults to "gpt-3.5-turbo-0301".
-
-    Returns:
-        int: The number of tokens used by the list of messages.
-    """
-    try:
-        encoding = tiktoken.encoding_for_model(model)
-    except KeyError:
-        logger.warn("Warning: model not found. Using cl100k_base encoding.")
-        encoding = tiktoken.get_encoding("cl100k_base")
-    if model == "gpt-3.5-turbo":
-        # !Note: gpt-3.5-turbo may change over time.
-        # Returning num tokens assuming gpt-3.5-turbo-0301.")
-        return count_message_tokens(messages, model="gpt-3.5-turbo-0301")
-    elif model == "gpt-4":
-        # !Note: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
-        return count_message_tokens(messages, model="gpt-4-0314")
-    elif model == "gpt-3.5-turbo-0301":
-        tokens_per_message = (
-            4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
-        )
-        tokens_per_name = -1  # if there's a name, the role is omitted
-    elif model == "gpt-4-0314":
-        tokens_per_message = 3
-        tokens_per_name = 1
-    else:
-        raise NotImplementedError(
-            f"num_tokens_from_messages() is not implemented for model {model}.\n"
-            " See https://github.com/openai/openai-python/blob/main/chatml.md for"
-            " information on how messages are converted to tokens."
-        )
-    num_tokens = 0
-    for message in messages:
-        num_tokens += tokens_per_message
-        for key, value in message.items():
-            num_tokens += len(encoding.encode(value))
-            if key == "name":
-                num_tokens += tokens_per_name
-    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
-    return num_tokens
-
-
-def count_string_tokens(string: str, model_name: str) -> int:
-    """
-    Returns the number of tokens in a text string.
-
-    Args:
-        string (str): The text string.
-        model_name (str): The name of the encoding to use. (e.g., "gpt-3.5-turbo")
-
-    Returns:
-        int: The number of tokens in the text string.
-    """
-    encoding = tiktoken.encoding_for_model(model_name)
-    return len(encoding.encode(string))
diff --git a/spaces/ramkamal2000/voice-conversion-ddp/speaker_encoder/audio.py b/spaces/ramkamal2000/voice-conversion-ddp/speaker_encoder/audio.py
deleted file mode 100644
index 2fcb77ad1d3a85f523e24f84691886736a5686cb..0000000000000000000000000000000000000000
--- a/spaces/ramkamal2000/voice-conversion-ddp/speaker_encoder/audio.py
+++ /dev/null
@@ -1,107 +0,0 @@
-from scipy.ndimage.morphology import binary_dilation
-from speaker_encoder.params_data import *
-from pathlib import Path
-from typing import Optional, Union
-import numpy as np
-import webrtcvad
-import librosa
-import struct
-
-int16_max = (2 ** 15) - 1
-
-
-def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray],
-                   source_sr: Optional[int] = None):
-    """
-    Applies the preprocessing operations used in training the Speaker Encoder to a waveform 
-    either on disk or in memory. The waveform will be resampled to match the data hyperparameters.
-
-    :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not 
-    just .wav), either the waveform as a numpy array of floats.
-    :param source_sr: if passing an audio waveform, the sampling rate of the waveform before 
-    preprocessing. After preprocessing, the waveform's sampling rate will match the data 
-    hyperparameters. If passing a filepath, the sampling rate will be automatically detected and 
-    this argument will be ignored.
-    """
-    # Load the wav from disk if needed
-    if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
-        wav, source_sr = librosa.load(fpath_or_wav, sr=None)
-    else:
-        wav = fpath_or_wav
-    
-    # Resample the wav if needed
-    if source_sr is not None and source_sr != sampling_rate:
-        wav = librosa.resample(wav, source_sr, sampling_rate)
-        
-    # Apply the preprocessing: normalize volume and shorten long silences 
-    wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True)
-    wav = trim_long_silences(wav)
-    
-    return wav
-
-
-def wav_to_mel_spectrogram(wav):
-    """
-    Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform.
-    Note: this not a log-mel spectrogram.
-    """
-    frames = librosa.feature.melspectrogram(
-        y=wav,
-        sr=sampling_rate,
-        n_fft=int(sampling_rate * mel_window_length / 1000),
-        hop_length=int(sampling_rate * mel_window_step / 1000),
-        n_mels=mel_n_channels
-    )
-    return frames.astype(np.float32).T
-
-
-def trim_long_silences(wav):
-    """
-    Ensures that segments without voice in the waveform remain no longer than a 
-    threshold determined by the VAD parameters in params.py.
-
-    :param wav: the raw waveform as a numpy array of floats 
-    :return: the same waveform with silences trimmed away (length <= original wav length)
-    """
-    # Compute the voice detection window size
-    samples_per_window = (vad_window_length * sampling_rate) // 1000
-    
-    # Trim the end of the audio to have a multiple of the window size
-    wav = wav[:len(wav) - (len(wav) % samples_per_window)]
-    
-    # Convert the float waveform to 16-bit mono PCM
-    pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16))
-    
-    # Perform voice activation detection
-    voice_flags = []
-    vad = webrtcvad.Vad(mode=3)
-    for window_start in range(0, len(wav), samples_per_window):
-        window_end = window_start + samples_per_window
-        voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2],
-                                         sample_rate=sampling_rate))
-    voice_flags = np.array(voice_flags)
-    
-    # Smooth the voice detection with a moving average
-    def moving_average(array, width):
-        array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2)))
-        ret = np.cumsum(array_padded, dtype=float)
-        ret[width:] = ret[width:] - ret[:-width]
-        return ret[width - 1:] / width
-    
-    audio_mask = moving_average(voice_flags, vad_moving_average_width)
-    audio_mask = np.round(audio_mask).astype(np.bool)
-    
-    # Dilate the voiced regions
-    audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1))
-    audio_mask = np.repeat(audio_mask, samples_per_window)
-    
-    return wav[audio_mask == True]
-
-
-def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False):
-    if increase_only and decrease_only:
-        raise ValueError("Both increase only and decrease only are set")
-    dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav ** 2))
-    if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only):
-        return wav
-    return wav * (10 ** (dBFS_change / 20))
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Amategekoyumuhandaibibazonibisubizopdf331.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Amategekoyumuhandaibibazonibisubizopdf331.md
deleted file mode 100644
index 1c63a010173a527bf07f58d70147425f2f920603..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Amategekoyumuhandaibibazonibisubizopdf331.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
amategekoyumuhandaibibazonibisubizopdf331

Download File ->>> https://urlgoal.com/2uCKUB



-

- 8a78ff9644

-

-

-

diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fifa 09 Rar File ((INSTALL)).md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fifa 09 Rar File ((INSTALL)).md
deleted file mode 100644
index 4dc97d2e7d7543df6cf237d390f94a1d2ed35d60..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fifa 09 Rar File ((INSTALL)).md	
+++ /dev/null
@@ -1,28 +0,0 @@
-

-
How to Download and Install FIFA 09 on PC

-
FIFA 09 is a popular soccer game released by Electronic Arts in 2008. It features realistic graphics, gameplay, and physics, as well as various modes and teams to choose from. If you want to play FIFA 09 on your PC, you will need to download a RAR file that contains the game files and a crack to bypass the copy protection. Here are the steps to download and install FIFA 09 on PC:

-
Fifa 09 Rar File

DOWNLOAD — https://urlgoal.com/2uCKlS



-
    
-
  1. Go to this link and click on the "Download Options" button. Choose the "ZIP" option and wait for the download to finish.
    2. 
-
  2. Extract the ZIP file using a program like WinRAR or 7-Zip. You will get a folder named "FIFA 09" that contains an ISO file and a folder named "Crack".
    3. 
-
  3. Mount the ISO file using a program like Daemon Tools or Virtual CloneDrive. You will see a virtual drive appear in your computer.
    4. 
-
  4. Open the virtual drive and run the "setup.exe" file. Follow the instructions to install FIFA 09 on your PC.
    5. 
-
  5. Copy the contents of the "Crack" folder and paste them into the installation directory of FIFA 09. This will replace the original files with cracked ones.
    6. 
-
  6. Run the "fifa09.exe" file from the installation directory to launch FIFA 09. Enjoy playing!
    7. 
-

-
Note: You may need to disable your antivirus or firewall before running the crack, as some programs may detect it as a virus or malware. This is a false positive and you can safely ignore it.

-
-
FIFA 09 Features and Modes

-
FIFA 09 offers a variety of features and modes to suit different tastes and preferences. Some of the main features and modes are:

-
    
-
  • Be a Pro: Seasons: This mode allows you to create your own custom player and play as him for four seasons in any club and national team. You can improve your skills, attributes, and traits as you progress through your career. You can also play online with up to 20 players in a 10 vs. 10 match.
    • 
-
  • Ultimate Team: This is a downloadable expansion that lets you create your own dream team by collecting and trading cards of players, staff, stadiums, kits, and more. You can compete online or offline in various tournaments and challenges to earn coins and rewards.
    • 
-
  • Manager Mode: This mode puts you in charge of a club as a manager. You can buy and sell players, scout new talent, negotiate contracts, set tactics, and more. You can also play the matches yourself or simulate them.
    • 
-
  • Tournament Mode: This mode lets you create your own custom tournaments or play in existing ones. You can choose from various settings such as number of teams, groups, knockout stages, etc.
    • 
-
  • Kick-Off Mode: This mode lets you play a quick match with any team and settings. You can also choose from different weather conditions and time of day.
    • 
-
  • Minigames: These are exclusive to the Wii version of FIFA 09. They include Footii Party, Table Football, Boot It, Juggling, and more. They are designed to be fun and easy to play with the Wii Remote and Nunchuk.
    • 
-

-
FIFA 09 also features over 500 teams, 30 leagues, 41 national teams, and 48 stadiums from around the world. The game has realistic graphics, animations, physics, and sound effects that enhance the gameplay experience.

-
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-

-

\ No newline at end of file
diff --git a/spaces/rewoo/ReWOO-Demo/prompts/__init__.py b/spaces/rewoo/ReWOO-Demo/prompts/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/robin0307/MMOCR/configs/_base_/recog_datasets/academic_test.py b/spaces/robin0307/MMOCR/configs/_base_/recog_datasets/academic_test.py
deleted file mode 100644
index 888ab3d3be5b40e15596086d4af567bd37f6ec05..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/_base_/recog_datasets/academic_test.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# Text Recognition Testing set, including:
-# Regular Datasets: IIIT5K, SVT, IC13
-# Irregular Datasets: IC15, SVTP, CT80
-
-test_root = 'data/mixture'
-
-test_img_prefix1 = f'{test_root}/IIIT5K/'
-test_img_prefix2 = f'{test_root}/svt/'
-test_img_prefix3 = f'{test_root}/icdar_2013/'
-test_img_prefix4 = f'{test_root}/icdar_2015/'
-test_img_prefix5 = f'{test_root}/svtp/'
-test_img_prefix6 = f'{test_root}/ct80/'
-
-test_ann_file1 = f'{test_root}/IIIT5K/test_label.txt'
-test_ann_file2 = f'{test_root}/svt/test_label.txt'
-test_ann_file3 = f'{test_root}/icdar_2013/test_label_1015.txt'
-test_ann_file4 = f'{test_root}/icdar_2015/test_label.txt'
-test_ann_file5 = f'{test_root}/svtp/test_label.txt'
-test_ann_file6 = f'{test_root}/ct80/test_label.txt'
-
-test1 = dict(
-    type='OCRDataset',
-    img_prefix=test_img_prefix1,
-    ann_file=test_ann_file1,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='txt',
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=None,
-    test_mode=True)
-
-test2 = {key: value for key, value in test1.items()}
-test2['img_prefix'] = test_img_prefix2
-test2['ann_file'] = test_ann_file2
-
-test3 = {key: value for key, value in test1.items()}
-test3['img_prefix'] = test_img_prefix3
-test3['ann_file'] = test_ann_file3
-
-test4 = {key: value for key, value in test1.items()}
-test4['img_prefix'] = test_img_prefix4
-test4['ann_file'] = test_ann_file4
-
-test5 = {key: value for key, value in test1.items()}
-test5['img_prefix'] = test_img_prefix5
-test5['ann_file'] = test_ann_file5
-
-test6 = {key: value for key, value in test1.items()}
-test6['img_prefix'] = test_img_prefix6
-test6['ann_file'] = test_ann_file6
-
-test_list = [test1, test2, test3, test4, test5, test6]
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/utils/normed_predictor.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/utils/normed_predictor.py
deleted file mode 100644
index f0eeef7db0ca8af73c87a14f925bfa52edda0232..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/utils/normed_predictor.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import CONV_LAYERS
-
-from .builder import LINEAR_LAYERS
-
-
-@LINEAR_LAYERS.register_module(name='NormedLinear')
-class NormedLinear(nn.Linear):
-    """Normalized Linear Layer.
-
-    Args:
-        tempeature (float, optional): Tempeature term. Default to 20.
-        power (int, optional): Power term. Default to 1.0.
-        eps (float, optional): The minimal value of divisor to
-             keep numerical stability. Default to 1e-6.
-    """
-
-    def __init__(self, *args, tempearture=20, power=1.0, eps=1e-6, **kwargs):
-        super(NormedLinear, self).__init__(*args, **kwargs)
-        self.tempearture = tempearture
-        self.power = power
-        self.eps = eps
-        self.init_weights()
-
-    def init_weights(self):
-        nn.init.normal_(self.weight, mean=0, std=0.01)
-        if self.bias is not None:
-            nn.init.constant_(self.bias, 0)
-
-    def forward(self, x):
-        weight_ = self.weight / (
-            self.weight.norm(dim=1, keepdim=True).pow(self.power) + self.eps)
-        x_ = x / (x.norm(dim=1, keepdim=True).pow(self.power) + self.eps)
-        x_ = x_ * self.tempearture
-
-        return F.linear(x_, weight_, self.bias)
-
-
-@CONV_LAYERS.register_module(name='NormedConv2d')
-class NormedConv2d(nn.Conv2d):
-    """Normalized Conv2d Layer.
-
-    Args:
-        tempeature (float, optional): Tempeature term. Default to 20.
-        power (int, optional): Power term. Default to 1.0.
-        eps (float, optional): The minimal value of divisor to
-             keep numerical stability. Default to 1e-6.
-        norm_over_kernel (bool, optional): Normalize over kernel.
-             Default to False.
-    """
-
-    def __init__(self,
-                 *args,
-                 tempearture=20,
-                 power=1.0,
-                 eps=1e-6,
-                 norm_over_kernel=False,
-                 **kwargs):
-        super(NormedConv2d, self).__init__(*args, **kwargs)
-        self.tempearture = tempearture
-        self.power = power
-        self.norm_over_kernel = norm_over_kernel
-        self.eps = eps
-
-    def forward(self, x):
-        if not self.norm_over_kernel:
-            weight_ = self.weight / (
-                self.weight.norm(dim=1, keepdim=True).pow(self.power) +
-                self.eps)
-        else:
-            weight_ = self.weight / (
-                self.weight.view(self.weight.size(0), -1).norm(
-                    dim=1, keepdim=True).pow(self.power)[..., None, None] +
-                self.eps)
-        x_ = x / (x.norm(dim=1, keepdim=True).pow(self.power) + self.eps)
-        x_ = x_ * self.tempearture
-
-        if hasattr(self, 'conv2d_forward'):
-            x_ = self.conv2d_forward(x_, weight_)
-        else:
-            if torch.__version__ >= '1.8':
-                x_ = self._conv_forward(x_, weight_, self.bias)
-            else:
-                x_ = self._conv_forward(x_, weight_)
-        return x_
diff --git a/spaces/romero61/hendata/pages/01-main.py b/spaces/romero61/hendata/pages/01-main.py
deleted file mode 100644
index cfa6ad7c2cfc66b974e0f5b23371cb975021a912..0000000000000000000000000000000000000000
--- a/spaces/romero61/hendata/pages/01-main.py
+++ /dev/null
@@ -1,99 +0,0 @@
-""" import solara
-import pandas as pd
-# Initialize an empty dataframe for demonstration
-data = {
-    'Name': [],
-    'Age': [],
-    'Address': []
-}
-df = pd.DataFrame(data)
-@solara.component
-def Page():
-    # State for input fields
-    name = solara.reactive('')
-    age = solara.reactive(0)
-    address = solara.reactive('')
-    # Handle form submission
-    def handle_submit():
-        global df
-        new_data = pd.DataFrame([{'Name': name.value, 'Age': age.value, 'Address': address.value}])
-        df = pd.concat([df, new_data], ignore_index=True)
-        print(df)  # Let's print the DataFrame to see if the data is being appended
-        name.set('')
-        age.set(0)
-        address.set('')
-    # Display the input form
-    with solara.Column(margin=4):
-        solara.InputText(label="Name", value=name)
-        solara.InputInt(label="Age", value=age)
-        solara.InputText(label="Address", value=address)
-        with solara.Row():
-            solara.Button("Submit", on_click=handle_submit)
-            solara.Button("Clear", on_click=lambda: (name.set(''), age.set(0), address.set('')))
-    # Display the data
-    solara.DataFrame(df, items_per_page=10) """
-""" import solara
-import pandas as pd
-# Initial empty DataFrame
-initial_df = pd.DataFrame(columns=['Name', 'Age', 'Address'])
-@solara.component
-def Page():
-    # Use state for the DataFrame
-    df, set_df = solara.use_state(initial_df)
-    
-    # Input fields
-    name, set_name = solara.use_state('')
-    age, set_age = solara.use_state(0)
-    address, set_address = solara.use_state('')
-    
-    def handle_submit():
-        new_data = pd.DataFrame([{'Name': name, 'Age': age, 'Address': address}])
-        updated_df = pd.concat([df, new_data], ignore_index=True)
-        set_df(updated_df)  # Update the DataFrame state
-        set_name('')
-        set_age(0)
-        set_address('')
-    
-    # Render components
-    solara.InputText("Name", value=name)
-    solara.InputInt("Age", value=age)
-    solara.InputText("Address", value=address)
-    solara.Button("Submit", on_click=handle_submit)
-    solara.Button("Clear", on_click=lambda: [set_name(''), set_age(0), set_address('')])
-    solara.DataFrame(df)
- """
-
-import solara
-import pandas as pd
-
-# Initial empty DataFrame
-initial_df = pd.DataFrame(columns=['Name', 'Age', 'Address'])
-
-@solara.component
-def Page():
-    # Use state for the DataFrame
-    df, set_df = solara.use_state(initial_df)
-    
-    # Input fields
-    name, set_name = solara.use_state('')
-    age, set_age = solara.use_state('')
-    address, set_address = solara.use_state('')
-        
-    def handle_submit():
-        # Ensure data is captured correctly
-        new_data = {'Name': name, 'Age': int(age) if age.isdigit() else 0, 'Address': address}
-        new_index = len(df)
-        df.loc[new_index] = new_data
-        set_df(df.copy())  # Update the DataFrame state with a copy to trigger re-rendering
-        set_name('')
-        set_age('')
-        set_address('')
-
-    
-    # Render components
-    solara.InputText("Name", value=name, on_value=set_name)
-    solara.InputText("Age", value=age, on_value=set_age)  # Using InputText for age to handle non-integer inputs gracefully
-    solara.InputText("Address", value=address, on_value=set_address)
-    solara.Button("Submit", on_click=handle_submit)
-    solara.Button("Clear", on_click=lambda: [set_name(''), set_age(''), set_address('')])
-    solara.DataFrame(df)
\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Bahubali The Beginning Telugu Full Movie Hd Download Witness the Legend of Amarendra Baahubali.md b/spaces/rorallitri/biomedical-language-models/logs/Bahubali The Beginning Telugu Full Movie Hd Download Witness the Legend of Amarendra Baahubali.md
deleted file mode 100644
index b1f87a9fd45a94e3d58c595064159808ca7fd517..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Bahubali The Beginning Telugu Full Movie Hd Download Witness the Legend of Amarendra Baahubali.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
Bahubali The Beginning Telugu Full Movie Hd Download

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-

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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Como ver ficheros cbr bull las alternativas ms sencillas y rpidas para abrir este tipo de archivos.md b/spaces/rorallitri/biomedical-language-models/logs/Como ver ficheros cbr bull las alternativas ms sencillas y rpidas para abrir este tipo de archivos.md
deleted file mode 100644
index ff08a21eca4add7ddd0579676b8cbb92373c59cc..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Como ver ficheros cbr bull las alternativas ms sencillas y rpidas para abrir este tipo de archivos.md	
+++ /dev/null
@@ -1,27 +0,0 @@
-
-
Asimismo, le informamos que sus datos personales pueden ser transferidos y tratados dentro y fuera de los Estados Unidos Mexicanos, por personas distintas a esta empresa. En ese sentido, su información puede ser compartida por Honda de México, a sus sociedades subsidiarias, afiliadas o relacionadas, sus distribuidores autorizados y/o sus terceros proveedores de servicios con quienes tiene una relación jurídica, así como, en su caso, autoridades competentes para fines estadísticos, consultas, prospección comercial, mercadeo, publicidad, seguimiento, proporcionarle el bien o servicio solicitado, y/o para cualquier otra de las finalidades antes referidas , así como para cumplimiento legal.

-
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-
Asimismo, le informamos que sus datos personales pueden ser transferidos y tratados dentro y fuera de los Estados Unidos Mexicanos, por personas distintas a esta empresa. En ese sentido, su información puede ser compartida por Honda de México, a sus sociedades subsidiarias, afiliadas o relacionadas, sus distribuidores autorizados y/o sus terceros proveedores de servicios con quienes tiene una relación jurídica, así como, en su caso, autoridades competentes para fines estadísticos, consultas, y/o para cualquier cumplimiento legal.

-
Usted tiene derecho a conocer con qué datos personales suyos contamos, para qué son utilizados y las condiciones de uso que le damos (Acceso), de igual manera tiene el derecho de que sus datos personales sean actualizados o bien sean corregidos por ser estos incorrectos o inexactos (Rectificación), así como que sean eliminados de nuestras bases de datos (Cancelación), y oponerse al uso de los mismos para fines específicos (Oposición).

-
Honda de México, Sociedad Anónima de Capital Variable podrá contratar a uno o varios Terceros, con quienes previamente haya celebrado contratos con cláusulas de confidencialidad y de protección de Datos Personales, como proveedores de servicios seleccionados para apoyar las actividades de promoción y comercialización de nuestros bienes y servicios, selección y/o reclutamiento de personal y manejo y administración de los Datos Personales que se recaban a través de los medios indicados, Honda de México, Sociedad Anónima de Capital Variable podrá incluso transferir sus Datos Personales a un Tercero sin un fin comercial, sino únicamente para cumplir con los fines indicados. En ningún caso Honda de México, Sociedad Anónima de Capital Variable comercializará, venderá o rentará sus Datos Personales a un Tercero.

-
Esta página es propiedad de Honda de México, S.A. de C.V. (en adelante HDM) y los productos aquí enunciados son comercializados únicamente en el territorio de los Estados Unidos Mexicanos directamente por nuestros Distribuidores Autorizados, por lo que le recomendamos verificar directamente con nuestros Distribuidores Autorizados, los términos finales en que podrá realizarse la adquisición de los productos, así como los términos y condiciones bajos los cuales podrá hacer efectiva Usted la garantía del producto, así como los servicios de mantenimiento.

-

-
También se prohíbe, independientemente de los motivos, el uso y reproducción total o parcial no autorizada de este sitio web con el propósito de causar daños o perjuicios a HDM y/o a cualquier tercero. Adicionalmente, queda prohibido, el uso, carga o descarga de softwares dañinos tales como virus informáticos.

-
En consecuencia, Usted como Usuario de este sitio web reconoce y acepta desde este momento que por el uso de este sitio web no adquiere ningún derecho de propiedad sobre este sitio web, ni sobre su contenido, ni sobre las marcas comerciales y otras marcas de productos, logotipos, textos o leyendas y marcas de servicio indicadas y contenidas en el sitio web, así como se hace sabedor que la reproducción, distribución, ejecución o modificación total o parcial del contenido de este sitio web sin autorización por escrito de HDM constituye una violación a esos derechos de propiedad industrial e intelectual.

-
Los montos de las cotizaciones mostradas, así como los precios y promociones de los productos contenidos en este sitio web, aplican únicamente para productos comercializados en territorio de los Estados Unidos Mexicanos y son precios sugeridos al público en general, por lo que dichos precios son expresados en moneda nacional, y no incluyen costos de seguros, costos de envío, ni ningún otro servicio; en consecuencia los precios y cotizaciones mostradas son meramente de carácter informativo y están sujetos a cambio sin previo aviso y no representan el precio de venta final del producto.

-
Por lo que le informamos que todas las imágenes fotográficas y de video, internas y externas, de los productos contenidos en este sitio web así como sus colores, solo son ilustrativas y de referencia, por lo que pueden percibirse de diversas formas dependiendo del tipo de monitor, configuración de red y capacidad del sistema operativo del dispositivo por el cual visualiza este sitio web.

-
Exención de responsabilidad.
 Usted usará este sitio web bajo su propio riesgo, y ni HDM ni sus filiales garantizan la funcionalidad ni seguridad (como la ausencia de virus informáticos, o algún otro elemento peligroso) de este sitio web. Ni HDM, ni ningún otro tercero relacionado que participe en la creación, soporte, hosting y otras operaciones de este sitio web serán responsables ante Usted o ante algún tercero, en caso de producirse daños (incluidos, entre otros, daños a su computadora personal, teléfono inteligente, tablet o red, independientemente de que ese daño sea directo o indirecto) o reparaciones, u otros costos que puedan surgir del acceso o el uso (o la incapacidad de acceso y uso) de este sitio web.

-
Casciello jugueteó con el software gratuito "Lens Studio" de Snapchat y el Photoshop de Adobe para experimentar con los efectos. Creó filtros de realidad aumentada, conocidos como "lentes" en Snapchat, que agregaron pecas en forma de estrellas y flores a las fotos y las pusieron a disposición de otros usuarios.

-
Casciello es parte de una comunidad de aproximadamente 100 "creadores oficiales de lentes" que hacen lentes personalizados de realidad aumentada para Snapchat. Muchos creadores encuentran formas de ganar decenas de miles de dólares creando lentes de realidad aumentada patrocinados para marcas, incluidas Nike y Fanta, y vendiendo productos como camisetas.

-
Casciello, que asistirá a Virginia Tech en el otoño para estudiar ciencias de la computación e ingeniería, vende paquetes de filtros en Etsy por hasta 49 dólares. Se pueden descargar y editar para usar en plataformas como Instagram. Ha ganado más de 4.000 dólares desde que lanzó su tienda en línea en junio.

-
Las lentes virales han sido clave para el reciente éxito de Snapchat. En su último reporte de ganancias, el director de finanzas de Snap, Derek Andersen, dijo que los nuevas lentes de realidad aumentada de la compañía resultaron en aproximadamente 7 a 9 millones de los 13 millones de usuarios activos diarios que Snapchat agregó durante el período. Dos de los éxitos de Snapchat incluyeron un lente de intercambio de género que convierte a los usuarios en hombres o mujeres y un filtro que te hace ver como un bebé.

-
El software público Lens Studio de Snap permite a los usuarios crear lentes de realidad aumentada de Snapchat. Incluye plantillas para hacer diferentes tipos de lentes, como lentes de sol personalizados o lentes para tu mascota. Las herramientas básicas no requieren mucho conocimiento técnico.

-
Sin embargo, construir lentes más complicados puede requerir habilidades técnicas, como codificación, modelado 3D, edición de fotos y conocimiento de diseño gráfico, y el uso de otros programas caros como Photoshop.

-
"Se ha abierto esta nueva carrera profesional para mucha gente", dijo Rhonda Greene, una creadora de lentes oficial que tiene un trabajo diario como directora de tecnología de una empresa especializada en desarrollo web y estrategia de redes sociales. "Muchos de nosotros vinimos como aficionados o simplemente por sentir curiosidad al respecto, y ahora se ha convertido en algo para que la gente se gane la vida".

-
Pero sus lentes, como uno que convierte a los usuarios en un presentador de noticias de última hora, comenzaron a llamar más la atención después de que los publicó en Reddit. Una vez que se convirtió en un creador oficial de lentes, las marcas comenzaron a llegar.

-
Desde entonces aprendió Javascript y técnicas de modelado 3D para desarrollar lentes para marcas como Nike, Red Bull y el fabricante noruego de chocolate Freia. Knutson dijo que gana entre 3.000 y 10.000 dólares por mes, dependiendo de qué tipo de proyectos asuma.

-
Michael Nicoll, el fundador y director creativo de la firma de redes sociales Blnk Digital, crea lentes de Snapchat para la industria de la música. Por ejemplo, él está detrás de un lente para Maroon 5 que hace que los ojos sean de color púrpura rosado y superpone caras con letras de la canción "Girls Like You". También desarrolló un lente que le da incrustaciones dentales falsas como las Quavo del trío de hip hop Migos. Ese lente tuvo más de 5,7 millones de visitas y 200.000 acciones, según Nancy Liu de Capitol/Motown Records, quien maneja el marketing digital para Quavo y Migos.

-
"Quavo tiene un aspecto muy específico en el que tiene esta rejilla chapada en diamante en la boca, por lo que esencialmente lo replicamos y lo marcamos con letras dentro. Eso funcionó muy bien", dijo Nicoll. "Era el hecho de que podías convertirte en él, así como escuchar su canción [en la lente]".

-
El creador de lentes Nicoll dijo que monitorea el negocio de Snapchat de cerca. "Por supuesto, como propietario de un negocio, uno se aburre un poco. Pero no afectó a nuestra empresa. Nuestros clientes aún querían crear lentes sin importar dónde estaba Snap en el público en general".
 aaccfb2cb3

-

-

\ No newline at end of file
diff --git a/spaces/rriverar75/dientes/README.md b/spaces/rriverar75/dientes/README.md
deleted file mode 100644
index 6a324399c3cca921fe686423e11cb6579fc72e2c..0000000000000000000000000000000000000000
--- a/spaces/rriverar75/dientes/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Dientes
-emoji: 🐠
-colorFrom: pink
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/saga24/nitrozen-gpt/app.py b/spaces/saga24/nitrozen-gpt/app.py
deleted file mode 100644
index a3f82895348948407c8e9575a06c3f1438a4b014..0000000000000000000000000000000000000000
--- a/spaces/saga24/nitrozen-gpt/app.py
+++ /dev/null
@@ -1,222 +0,0 @@
-# **
-
-top_k = 3
-splitter='#--'
-
-import json 
-import streamlit as st
-import pandas as pd
-import numpy as np
-import time 
-import os
-import openai
-import requests
-from PIL import Image
-from io import BytesIO
-import openai, numpy as np
-import re
-##openai.api_key = os.getenv("API_KEY")
-import streamlit as st
-from streamlit_chat import message
-from openai.error import RateLimitError
-import backoff
-import tiktoken
-
-st.set_page_config(
-    page_title="UI Bot - Demo",
-    page_icon=":robot:"
-)
-
-st.markdown("""
-                
-                    
-                    
-                    
-                    
-                    
-                
-            """, unsafe_allow_html=True)
-
-
-
-st.header("UI BOT")
-st.markdown("[Github](https://github.com/gofynd/nitrozen-react)")
-
-if 'generated' not in st.session_state:
-    st.session_state['generated'] = []
-
-if 'past' not in st.session_state:
-    st.session_state['past'] = []
-
-def get_text():
-    input_text = st.text_input("You: ","Hi", key="input")
-    return input_text.strip()
-
-def cosine_similarity(atext, b):
-    a = [float(x) for x in atext.strip('[]').split(',')]
-    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
-
-
-def get_embedding(text, model="text-embedding-ada-002"):
-   return openai.Embedding.create(input = [text], model=model)['data'][0]['embedding']
- 
-
-@backoff.on_exception(backoff.expo, RateLimitError)
-def chatgpt(messages):
-    if num_tokens_from_messages(messages,"gpt-4") > 8000:
-        messages = messages[-2:]
-        print("Found higher number of tokens  !!")
-        print("Reprint message ::")
-        print(*messages, sep = "\n")
-    if num_tokens_from_messages(messages,"gpt-4") > 8000:
-        messages = messages[-1:]
-        print("Found higher number of tokens 2nd time also !!")
-        print("Reprinting the message ::")
-        print(*messages, sep = "\n")
-        
-    completion = openai.ChatCompletion.create(
-      model="gpt-4", 
-      messages=messages)
-    print(f"token {completion['usage']}")
-    return(completion['choices'][0]["message"]["content"])
-def openapi_key_present():
-    if 'openapikey' in st.session_state:
-        if st.session_state['openapikey'] != "":
-            return True
-    return False
-
-def num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301"):
-    """Returns the number of tokens used by a list of messages."""
-    try:
-        encoding = tiktoken.encoding_for_model(model)
-    except KeyError:
-        print("Warning: model not found. Using cl100k_base encoding.")
-        encoding = tiktoken.get_encoding("cl100k_base")
-    if model == "gpt-3.5-turbo":
-        print("Warning: gpt-3.5-turbo may change over time. Returning num tokens assuming gpt-3.5-turbo-0301.")
-        return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301")
-    elif model == "gpt-4":
-        print("Warning: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
-        return num_tokens_from_messages(messages, model="gpt-4-0314")
-    elif model == "gpt-3.5-turbo-0301":
-        tokens_per_message = 4  # every message follows {role/name}\n{content}\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}. 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 += 2  # every reply is primed with assistant
-    return num_tokens
-
-
-df = pd.read_csv('./embeddings.csv')
-
-openai.api_key = st.text_input("API Key",key="api_key", disabled=openapi_key_present())
-st.session_state['openapikey'] = openai.api_key
-if openapi_key_present():
-	user_input = get_text()
-
-	if user_input and user_input != "" and user_input != "Hi":
-	    prompt_embedding=np.array(get_embedding(user_input))
-	    df_compute= df.copy()
-	    df_compute['Similarity'] = df['Embeddings'].apply(lambda x: cosine_similarity(x, prompt_embedding))
-	    top_df=df_compute.sort_values(by='Similarity',ascending=False)
-    
-	    top_df = top_df.iloc[:top_k]
-    
-	    print(top_df)
-
-
-	    pp='''
-    
-	    You are an expert coder who closely follows documentation. 
-	    Always answer questions by showing code examples, and using the nitrozen react library
-    
-	    Documentation: 
-    
-	    A React component library inspired from Nitrozen design system
-	    Getting started
-	    To install @gofynd/nitrozen-react in your project, you will need to run the following command using npm:
-	    npm install -S @gofynd/nitrozen-react
-	    If you prefer Yarn, use the following command instead:
-	    yarn add @gofynd/nitrozen-react
-	    * Nitrozen component library can be consumed by all React / React with Typescript projects.
-	    Usage
-	    The @gofynd/nitrozen-react package provides components and icons for the Nitrozen Design System.
-	    To use a component, you can import it directly from the package:
-	    import { Button } from "@gofynd/nitrozen-react";
-	    function MyComponent() {
-	      return ;
-	    }
-	    🔥 Components List:
-	    * Alert
-	    * Autocomplete
-	    * Badge
-	    * Button
-	    * Card
-	    * Checkbox
-	    * OTP Code Input
-	    * Chip
-	    * Checkbox
-	    * Dialog
-	    * Dropdown
-	    * Icon
-	    * Input
-	    * Menu
-	    * Validation Text
-	    * Pagination
-	    * Radio
-	    * Nudge
-	    * Stepper
-	    * Tab
-	    * ToggleBtn
-	    * Tooltip
-	    * Toast
-	    * Table
-	    * Grid
-	    * Typography
-	    React Story Book Content for the same can be found below with its respective decriptions : https://gofynd.io/nitrozen-react/?path=/story/introduction-welcome--welcome
-	'''
-	    for i in range(top_k):
-	        pp+= top_df['Text'].iloc[i]
-	    pp+=f'''
-    
-	    Answer the Question: {user_input}'''
-	    messages = [{'role':'system','content':'You are an expert coder, who closely follows documentation and knows about the Nitrozen React Library create by Fynd'}]
-	    if st.session_state['generated']:
-	        for i in range(len(st.session_state['past'])-2, len(st.session_state['past'])):
-	            if i >= 0:
-	                messages.append({'role':'user','content':st.session_state['past'][i]})
-	                messages.append({'role':'assistant','content':st.session_state['generated'][i]})
-	        messages.append({'role':'user','content':pp})
-	        message_lenth = len(messages)
-	    else:
-	        messages = [{'role':'user','content':pp}]
-
-	    print(*messages, sep = "\n")
-
-	    answer = chatgpt(messages)
-
-	    st.session_state.past.append(user_input)
-	    st.session_state.generated.append(answer)
-	elif user_input == "Hi":
-	    st.session_state.past.append("Hi")
-	    st.session_state.generated.append("Hi, I am a UI Bot. Ask me anything about Nitrozen React.")
-
-	if st.session_state['generated']:
-
-	    for i in range(len(st.session_state['generated'])-1, -1, -1):
-	        message(st.session_state["generated"][i], key=str(i))
-	        message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/example/Fallout New Vegas Patch 1.4.0.525 Download Pc.md b/spaces/scedlatioru/img-to-music/example/Fallout New Vegas Patch 1.4.0.525 Download Pc.md
deleted file mode 100644
index 71b28bad7ba98e9bafdbc4d2dfecb51fd9e9d4ec..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Fallout New Vegas Patch 1.4.0.525 Download Pc.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-
fallout new vegas patch 1.4.0.525 download pc

Download ►►► https://gohhs.com/2uEyWG



-
-f vegas patch 1.4.0.525  pc
-
-crash bug fixed
-
-f type "pc" in the search box to the right and then select the client launcher from the list of clients to get to the game client.
-
-from the client's menu you can choose the vegas patch 1.4.0.525  pc from the launcher.
-
-f type "pc" in the search box to the right and then select the game client from the list of clients to get to the game client.
-
-Added
-
-from the 4fefd39f24

-

-

-

diff --git a/spaces/scedlatioru/img-to-music/example/Free Winzip Cracked Version 208 _BEST_.md b/spaces/scedlatioru/img-to-music/example/Free Winzip Cracked Version 208 _BEST_.md
deleted file mode 100644
index 3c493db188a0990cc672dce26825614edc365a38..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Free Winzip Cracked Version 208 _BEST_.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
free winzip cracked version 208

Download ✏ ✏ ✏ https://gohhs.com/2uEA7F



-

-12 + install files + crack + keygen Free users mhhauto. 2) Latest version 12/2019 for DAS XENTRY 11/2018 for WIS and EPC / 2016/ SD Media 3) Xentry for new released 204/207/208/212/222 and Smart Model for 454 452 451 450, ... Unzip WinZip, WinRAR, WinAce and many other types of archives as easy as 1-2-3! 4d29de3e1b

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diff --git a/spaces/sczhou/CodeFormer/CodeFormer/scripts/download_pretrained_models.py b/spaces/sczhou/CodeFormer/CodeFormer/scripts/download_pretrained_models.py
deleted file mode 100644
index daa6e8ca14ea91c89a318e85d9f182eb7d1bf025..0000000000000000000000000000000000000000
--- a/spaces/sczhou/CodeFormer/CodeFormer/scripts/download_pretrained_models.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import argparse
-import os
-from os import path as osp
-
-from basicsr.utils.download_util import load_file_from_url
-
-
-def download_pretrained_models(method, file_urls):
-    save_path_root = f'./weights/{method}'
-    os.makedirs(save_path_root, exist_ok=True)
-
-    for file_name, file_url in file_urls.items():
-        save_path = load_file_from_url(url=file_url, model_dir=save_path_root, progress=True, file_name=file_name)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        'method',
-        type=str,
-        help=("Options: 'CodeFormer' 'facelib'. Set to 'all' to download all the models."))
-    args = parser.parse_args()
-
-    file_urls = {
-        'CodeFormer': {
-            'codeformer.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
-        },
-        'facelib': {
-            # 'yolov5l-face.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth',
-            'detection_Resnet50_Final.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/detection_Resnet50_Final.pth',
-            'parsing_parsenet.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth'
-        }
-    }
-
-    if args.method == 'all':
-        for method in file_urls.keys():
-            download_pretrained_models(method, file_urls[method])
-    else:
-        download_pretrained_models(args.method, file_urls[args.method])
\ No newline at end of file
diff --git a/spaces/segments-tobias/conex/espnet2/bin/tts_prior_train.py b/spaces/segments-tobias/conex/espnet2/bin/tts_prior_train.py
deleted file mode 100644
index 24dea4416abfb9256debb9032f66ba35ee901a08..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet2/bin/tts_prior_train.py
+++ /dev/null
@@ -1,463 +0,0 @@
-#!/usr/bin/env python3
-
-"""TTS model AR prior training."""
-
-import argparse
-import logging
-from pathlib import Path
-import sys
-import time
-from typing import Optional
-from typing import Sequence
-from typing import Tuple
-from typing import Union
-
-import numpy as np
-import torch
-from typeguard import check_argument_types
-
-from espnet.utils.cli_utils import get_commandline_args
-from espnet2.tasks.tts import TTSTask
-from espnet2.torch_utils.device_funcs import to_device
-from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
-from espnet2.tts.duration_calculator import DurationCalculator
-from espnet2.tts.fastspeech import FastSpeech
-from espnet2.tts.fastspeech2 import FastSpeech2
-from espnet2.tts.fastespeech import FastESpeech
-from espnet2.tts.tacotron2 import Tacotron2
-from espnet2.tts.transformer import Transformer
-from espnet2.utils import config_argparse
-from espnet2.utils.get_default_kwargs import get_default_kwargs
-from espnet2.utils.griffin_lim import Spectrogram2Waveform
-from espnet2.utils.nested_dict_action import NestedDictAction
-from espnet2.utils.types import str2bool
-from espnet2.utils.types import str2triple_str
-from espnet2.utils.types import str_or_none
-
-from espnet2.tts.prosody_encoder import ARPrior
-
-import torch.optim as optim
-
-
-class Text2Speech:
-    """Speech2Text class
-    """
-
-    def __init__(
-        self,
-        train_config: Optional[Union[Path, str]],
-        model_file: Optional[Union[Path, str]] = None,
-        threshold: float = 0.5,
-        minlenratio: float = 0.0,
-        maxlenratio: float = 10.0,
-        use_teacher_forcing: bool = False,
-        use_att_constraint: bool = False,
-        backward_window: int = 1,
-        forward_window: int = 3,
-        speed_control_alpha: float = 1.0,
-        vocoder_conf: dict = None,
-        dtype: str = "float32",
-        device: str = "cpu",
-    ):
-        assert check_argument_types()
-
-        model, train_args = TTSTask.build_model_from_file(
-            train_config, model_file, device
-        )
-        model.to(dtype=getattr(torch, dtype)).eval()
-        self.device = device
-        self.dtype = dtype
-        self.train_args = train_args
-        self.model = model
-        self.tts = model.tts
-        self.normalize = model.normalize
-        self.feats_extract = model.feats_extract
-        self.duration_calculator = DurationCalculator()
-        self.preprocess_fn = TTSTask.build_preprocess_fn(train_args, False)
-        self.use_teacher_forcing = use_teacher_forcing
-
-        logging.info(f"Normalization:\n{self.normalize}")
-        logging.info(f"TTS:\n{self.tts}")
-
-        decode_config = {}
-        if isinstance(self.tts, (Tacotron2, Transformer)):
-            decode_config.update(
-                {
-                    "threshold": threshold,
-                    "maxlenratio": maxlenratio,
-                    "minlenratio": minlenratio,
-                }
-            )
-        if isinstance(self.tts, Tacotron2):
-            decode_config.update(
-                {
-                    "use_att_constraint": use_att_constraint,
-                    "forward_window": forward_window,
-                    "backward_window": backward_window,
-                }
-            )
-        if isinstance(self.tts, (FastSpeech, FastSpeech2, FastESpeech)):
-            decode_config.update({"alpha": speed_control_alpha})
-        decode_config.update({"use_teacher_forcing": use_teacher_forcing})
-
-        self.decode_config = decode_config
-
-        if vocoder_conf is None:
-            vocoder_conf = {}
-        if self.feats_extract is not None:
-            vocoder_conf.update(self.feats_extract.get_parameters())
-        if (
-            "n_fft" in vocoder_conf
-            and "n_shift" in vocoder_conf
-            and "fs" in vocoder_conf
-        ):
-            self.spc2wav = Spectrogram2Waveform(**vocoder_conf)
-            logging.info(f"Vocoder: {self.spc2wav}")
-        else:
-            self.spc2wav = None
-            logging.info("Vocoder is not used because vocoder_conf is not sufficient")
-
-    def __call__(
-        self,
-        text: Union[str, torch.Tensor, np.ndarray],
-        speech: Union[torch.Tensor, np.ndarray] = None,
-        durations: Union[torch.Tensor, np.ndarray] = None,
-        ref_embs: torch.Tensor = None,
-    ):
-        assert check_argument_types()
-
-        if self.use_speech and speech is None:
-            raise RuntimeError("missing required argument: 'speech'")
-
-        if isinstance(text, str):
-            # str -> np.ndarray
-            text = self.preprocess_fn("", {"text": text})["text"]
-        batch = {"text": text, "ref_embs": ref_embs}
-        if speech is not None:
-            batch["speech"] = speech
-        if durations is not None:
-            batch["durations"] = durations
-
-        batch = to_device(batch, self.device)
-        outs, outs_denorm, probs, att_ws, ref_embs, ar_prior_loss = \
-            self.model.inference(**batch, **self.decode_config, train_ar_prior=True)
-
-        return ar_prior_loss
-
-    @property
-    def fs(self) -> Optional[int]:
-        if self.spc2wav is not None:
-            return self.spc2wav.fs
-        else:
-            return None
-
-    @property
-    def use_speech(self) -> bool:
-        """Check whether to require speech in inference.
-
-        Returns:
-            bool: True if speech is required else False.
-
-        """
-        # TC marker, oorspr false
-        return self.use_teacher_forcing or getattr(self.tts, "use_gst", True)
-
-
-def train_prior(
-    output_dir: str,
-    batch_size: int,
-    dtype: str,
-    ngpu: int,
-    seed: int,
-    num_workers: int,
-    log_level: Union[int, str],
-    data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
-    key_file: Optional[str],
-    train_config: Optional[str],
-    model_file: Optional[str],
-    threshold: float,
-    minlenratio: float,
-    maxlenratio: float,
-    use_teacher_forcing: bool,
-    use_att_constraint: bool,
-    backward_window: int,
-    forward_window: int,
-    speed_control_alpha: float,
-    allow_variable_data_keys: bool,
-    vocoder_conf: dict,
-):
-    """Perform AR prior training."""
-    assert check_argument_types()
-    if batch_size > 1:
-        raise NotImplementedError("batch AR prior training is not implemented")
-    if ngpu > 1:
-        raise NotImplementedError("only single GPU AR prior training is supported")
-    logging.basicConfig(
-        level=log_level,
-        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
-    )
-
-    if ngpu >= 1:
-        device = "cuda"
-    else:
-        device = "cpu"
-
-    # 1. Set random-seed
-    set_all_random_seed(seed)
-
-    # 2. Build model
-    text2speech = Text2Speech(
-        train_config=train_config,
-        model_file=model_file,
-        threshold=threshold,
-        maxlenratio=maxlenratio,
-        minlenratio=minlenratio,
-        use_teacher_forcing=use_teacher_forcing,
-        use_att_constraint=use_att_constraint,
-        backward_window=backward_window,
-        forward_window=forward_window,
-        speed_control_alpha=speed_control_alpha,
-        vocoder_conf=vocoder_conf,
-        dtype=dtype,
-        device=device,
-    )
-
-    # 3. Build data-iterator
-    if not text2speech.use_speech:
-        data_path_and_name_and_type = list(
-            filter(lambda x: x[1] != "speech", data_path_and_name_and_type)
-        )
-    loader = TTSTask.build_streaming_iterator(
-        data_path_and_name_and_type,
-        dtype=dtype,
-        batch_size=batch_size,
-        key_file=key_file,
-        num_workers=num_workers,
-        preprocess_fn=TTSTask.build_preprocess_fn(text2speech.train_args, False),
-        collate_fn=TTSTask.build_collate_fn(text2speech.train_args, False),
-        allow_variable_data_keys=allow_variable_data_keys,
-        inference=True,
-    )
-
-    num_epochs = 500
-
-    # Freeze model
-    for param in text2speech.model.parameters():
-        param.requires_grad = False
-
-    text2speech.model.tts.prosody_encoder.ar_prior = ARPrior(
-        num_embeddings=32,
-        embedding_dim=384,
-        lstm_num_layers=1,
-        lstm_bidirectional=False,
-    )
-
-    text2speech.model.tts = text2speech.model.tts.to(device)
-
-    optimizer = optim.SGD(text2speech.model.tts.parameters(), lr=0.001, momentum=0.9)
-
-    since = time.time()
-
-    for epoch in range(num_epochs):
-        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
-        print('-' * 10)
-
-        # Each epoch has a training and validation phase
-        for phase in ['train']:  # 'val'
-            if phase == 'train':
-                text2speech.model.tts.train()  # Set model to training mode
-            else:
-                text2speech.model.tts.eval()   # Set model to evaluate mode
-
-            for idx, (keys, batch) in enumerate(loader, 1):
-                assert isinstance(batch, dict), type(batch)
-                assert all(isinstance(s, str) for s in keys), keys
-                _bs = len(next(iter(batch.values())))
-                assert _bs == 1, _bs
-
-                # Change to single sequence and remove *_length
-                # because inference() requires 1-seq, not mini-batch.
-                batch = {
-                    k: v[0] for k, v in batch.items() if not k.endswith("_lengths")
-                }
-
-                # zero the parameter gradients
-                optimizer.zero_grad()
-
-                # forward
-                # track history if only in train
-                with torch.set_grad_enabled(phase == 'train'):
-                    loss = text2speech(**batch)
-
-                    # backward + optimize only if in training phase
-                    if phase == 'train':
-                        loss.backward()
-                        optimizer.step()
-
-                    print('Loss: {:.4f}'.format(loss))
-
-        if epoch % 10 == 0:
-            torch.save(text2speech.model.state_dict(), "exp/tts_train_raw_phn_none/with_prior_" + str(epoch) + ".pth")
-
-    time_elapsed = time.time() - since
-    print('Training complete in {:.0f}m {:.0f}s'.format(
-        time_elapsed // 60, time_elapsed % 60))
-
-    torch.save(text2speech.model.state_dict(), "exp/tts_train_raw_phn_none/with_prior.pth")
-
-
-def get_parser():
-    """Get argument parser."""
-    parser = config_argparse.ArgumentParser(
-        description="TTS Decode",
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-    )
-
-    # Note(kamo): Use "_" instead of "-" as separator.
-    # "-" is confusing if written in yaml.
-    parser.add_argument(
-        "--log_level",
-        type=lambda x: x.upper(),
-        default="INFO",
-        choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
-        help="The verbose level of logging",
-    )
-
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        required=True,
-        help="The path of output directory",
-    )
-    parser.add_argument(
-        "--ngpu",
-        type=int,
-        default=0,
-        help="The number of gpus. 0 indicates CPU mode",
-    )
-    parser.add_argument(
-        "--seed",
-        type=int,
-        default=0,
-        help="Random seed",
-    )
-    parser.add_argument(
-        "--dtype",
-        default="float32",
-        choices=["float16", "float32", "float64"],
-        help="Data type",
-    )
-    parser.add_argument(
-        "--num_workers",
-        type=int,
-        default=1,
-        help="The number of workers used for DataLoader",
-    )
-    parser.add_argument(
-        "--batch_size",
-        type=int,
-        default=1,
-        help="The batch size for inference",
-    )
-
-    group = parser.add_argument_group("Input data related")
-    group.add_argument(
-        "--data_path_and_name_and_type",
-        type=str2triple_str,
-        required=True,
-        action="append",
-    )
-    group.add_argument(
-        "--key_file",
-        type=str_or_none,
-    )
-    group.add_argument(
-        "--allow_variable_data_keys",
-        type=str2bool,
-        default=False,
-    )
-
-    group = parser.add_argument_group("The model configuration related")
-    group.add_argument(
-        "--train_config",
-        type=str,
-        help="Training configuration file.",
-    )
-    group.add_argument(
-        "--model_file",
-        type=str,
-        help="Model parameter file.",
-    )
-
-    group = parser.add_argument_group("Decoding related")
-    group.add_argument(
-        "--maxlenratio",
-        type=float,
-        default=10.0,
-        help="Maximum length ratio in decoding",
-    )
-    group.add_argument(
-        "--minlenratio",
-        type=float,
-        default=0.0,
-        help="Minimum length ratio in decoding",
-    )
-    group.add_argument(
-        "--threshold",
-        type=float,
-        default=0.5,
-        help="Threshold value in decoding",
-    )
-    group.add_argument(
-        "--use_att_constraint",
-        type=str2bool,
-        default=False,
-        help="Whether to use attention constraint",
-    )
-    group.add_argument(
-        "--backward_window",
-        type=int,
-        default=1,
-        help="Backward window value in attention constraint",
-    )
-    group.add_argument(
-        "--forward_window",
-        type=int,
-        default=3,
-        help="Forward window value in attention constraint",
-    )
-    group.add_argument(
-        "--use_teacher_forcing",
-        type=str2bool,
-        default=False,
-        help="Whether to use teacher forcing",
-    )
-    parser.add_argument(
-        "--speed_control_alpha",
-        type=float,
-        default=1.0,
-        help="Alpha in FastSpeech to change the speed of generated speech",
-    )
-
-    group = parser.add_argument_group("Grriffin-Lim related")
-    group.add_argument(
-        "--vocoder_conf",
-        action=NestedDictAction,
-        default=get_default_kwargs(Spectrogram2Waveform),
-        help="The configuration for Grriffin-Lim",
-    )
-    return parser
-
-
-def main(cmd=None):
-    """Run TTS model decoding."""
-    print(get_commandline_args(), file=sys.stderr)
-    parser = get_parser()
-    args = parser.parse_args(cmd)
-    kwargs = vars(args)
-    kwargs.pop("config", None)
-    train_prior(**kwargs)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/shabnam91/Sanskrit-TTS/indic_nlp_library/indicnlp/transliterate/script_unifier.py b/spaces/shabnam91/Sanskrit-TTS/indic_nlp_library/indicnlp/transliterate/script_unifier.py
deleted file mode 100644
index 20f39339ffb3178ea17785aba09eb620d108f330..0000000000000000000000000000000000000000
--- a/spaces/shabnam91/Sanskrit-TTS/indic_nlp_library/indicnlp/transliterate/script_unifier.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# 
-#  Copyright (c) 2013-present, Anoop Kunchukuttan
-#  All rights reserved.
-#  
-#  This source code is licensed under the MIT license found in the
-#  LICENSE file in the root directory of this source tree.
-# 
-
-#Program for normalization of text written in Unicode. This is mainly geared towards Indic scripts 
-#
-# @author Anoop Kunchukuttan 
-#
-
-import sys
-from indicnlp.normalize import indic_normalize
-from indicnlp.transliterate import unicode_transliterate
-from indicnlp import loader
-
-class AggressiveScriptUnifier():
-
-    def __init__(self,common_lang='hi',nasals_mode='to_nasal_consonants'):
-        self.common_lang=common_lang
-        self.nasals_mode=nasals_mode
-        self.do_normalize_chandras=True
-        self.do_normalize_vowel_ending=True
-        self.remove_nuktas=True
-        self.normalizer_map={}
-        self._init_normalizers()
-
-    def _init_normalizers(self):
-        normalizer_factory=indic_normalize.IndicNormalizerFactory()
-
-        ## for languages with common parameters
-        for lang in ['hi','mr','sa','kK','ne','sd','bn','gu','ta','te','kn']:
-            self.normalizer_map[lang]=normalizer_factory.get_normalizer(lang, nasals_mode=self.nasals_mode, 
-                    do_normalize_chandras=self.do_normalize_chandras, remove_nuktas=self.remove_nuktas,
-                    do_normalize_vowel_ending=self.do_normalize_vowel_ending)
-
-        ## for languages with language specific parameters
-        self.normalizer_map['pa']=normalizer_factory.get_normalizer('pa', nasals_mode=self.nasals_mode, 
-                    do_normalize_chandras=self.do_normalize_chandras, remove_nuktas=self.remove_nuktas,
-                    do_normalize_vowel_ending=self.do_normalize_vowel_ending,
-                    do_canonicalize_addak=True, do_canonicalize_tippi=True,
-                    do_replace_vowel_bases=True)
-        self.normalizer_map['or']=normalizer_factory.get_normalizer('or', nasals_mode=self.nasals_mode, 
-                    do_normalize_chandras=self.do_normalize_chandras, remove_nuktas=self.remove_nuktas,
-                    do_normalize_vowel_ending=self.do_normalize_vowel_ending,
-                    do_remap_wa=True)
-        self.normalizer_map['as']=normalizer_factory.get_normalizer('as', nasals_mode=self.nasals_mode,
-                    do_normalize_chandras=self.do_normalize_chandras, remove_nuktas=self.remove_nuktas,
-                    do_normalize_vowel_ending=self.do_normalize_vowel_ending,
-                    do_remap_assamese_chars=True)
-        self.normalizer_map['ml']=normalizer_factory.get_normalizer('ml', nasals_mode=self.nasals_mode,
-                    do_normalize_chandras=self.do_normalize_chandras, remove_nuktas=self.remove_nuktas,
-                    do_normalize_vowel_ending=self.do_normalize_vowel_ending,
-                    do_canonicalize_chillus=True, do_correct_geminated_T=True)
-
-    def transform(self,text,lang):
-        text=self.normalizer_map[lang].normalize(text)
-        text=unicode_transliterate.UnicodeIndicTransliterator.transliterate(text, lang, self.common_lang)
-        return text
-
-class BasicScriptUnifier():
-
-    def __init__(self,common_lang='hi',nasals_mode='do_nothing'):
-        self.common_lang=common_lang
-        self.nasals_mode=nasals_mode
-        self.normalizer_map={}
-        self._init_normalizers()
-
-    def _init_normalizers(self):
-        normalizer_factory=indic_normalize.IndicNormalizerFactory()
-
-        for lang in ['hi','mr','sa','kK','ne','sd','bn','gu','ta','te','kn','pa','or','as','ml']:
-            self.normalizer_map[lang]=normalizer_factory.get_normalizer(lang, nasals_mode=self.nasals_mode)    
-
-    def transform(self,text,lang):
-
-        if lang in self.normalizer_map:
-            text=self.normalizer_map[lang].normalize(text)
-
-        text=unicode_transliterate.UnicodeIndicTransliterator.transliterate(text, lang, self.common_lang)
-        return text
-
-class NaiveScriptUnifier():
-
-    def __init__(self,common_lang='hi'):
-        self.common_lang=common_lang
-
-    def transform(self,text,lang):
-
-        text=unicode_transliterate.UnicodeIndicTransliterator.transliterate(text, lang, self.common_lang)
-        return text
-
-if __name__ == '__main__': 
-
-    loader.load()
-
-    if len(sys.argv)<=4:
-        print("Usage: python script_unifier    ")
-        sys.exit(1)
-
-    if sys.argv[1]=='aggressive':
-
-        language=sys.argv[4]
-
-        unifier=AggressiveScriptUnifier(nasals_mode='to_nasal_consonants')
-
-        with open(sys.argv[2],'r',encoding='utf-8') as ifile:
-            with open(sys.argv[3],'w',encoding='utf-8') as ofile:
-                for i, line in enumerate(ifile.readlines()):
-
-                    line=line.strip()
-                    transliterated_line=unifier.transform(line,language)
-                    ofile.write(transliterated_line+'\n')
-
-    elif sys.argv[1]=='moderate':
-
-        language=sys.argv[4]
-
-        unifier=AggressiveScriptUnifier(nasals_mode='do_nothing')
-
-        with open(sys.argv[2],'r',encoding='utf-8') as ifile:
-            with open(sys.argv[3],'w',encoding='utf-8') as ofile:
-                for i, line in enumerate(ifile.readlines()):
-
-                    line=line.strip()
-                    transliterated_line=unifier.transform(line,language)
-                    ofile.write(transliterated_line+'\n')
-                    
-    elif sys.argv[1]=='basic':
-
-        language=sys.argv[4]
-
-        unifier=BasicScriptUnifier()
-
-        with open(sys.argv[2],'r',encoding='utf-8') as ifile:
-            with open(sys.argv[3],'w',encoding='utf-8') as ofile:
-                for i, line in enumerate(ifile.readlines()):
-
-                    line=line.strip()
-                    transliterated_line=unifier.transform(line,language)
-                    ofile.write(transliterated_line+'\n')
-
-    elif sys.argv[1]=='naive':
-
-        language=sys.argv[4]
-
-        unifier=NaiveScriptUnifier()
-
-        with open(sys.argv[2],'r',encoding='utf-8') as ifile:
-            with open(sys.argv[3],'w',encoding='utf-8') as ofile:
-                for i, line in enumerate(ifile.readlines()):
-
-                    line=line.strip()
-                    transliterated_line=unifier.transform(line,language)
-                    ofile.write(transliterated_line+'\n')
diff --git a/spaces/shabnam91/Sanskrit-TTS/monotonic_align/core.py b/spaces/shabnam91/Sanskrit-TTS/monotonic_align/core.py
deleted file mode 100644
index dddc688d76172b880054e544b7a217acd013f14f..0000000000000000000000000000000000000000
--- a/spaces/shabnam91/Sanskrit-TTS/monotonic_align/core.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:,:,::1], numba.float32[:,:,::1], numba.int32[::1], numba.int32[::1]), nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-  b = paths.shape[0]
-  max_neg_val=-1e9
-  for i in range(int(b)):
-    path = paths[i]
-    value = values[i]
-    t_y = t_ys[i]
-    t_x = t_xs[i]
-
-    v_prev = v_cur = 0.0
-    index = t_x - 1
-
-    for y in range(t_y):
-      for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-        if x == y:
-          v_cur = max_neg_val
-        else:
-          v_cur = value[y-1, x]
-        if x == 0:
-          if y == 0:
-            v_prev = 0.
-          else:
-            v_prev = max_neg_val
-        else:
-          v_prev = value[y-1, x-1]
-        value[y, x] += max(v_prev, v_cur)
-
-    for y in range(t_y - 1, -1, -1):
-      path[y, index] = 1
-      if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
-        index = index - 1
diff --git a/spaces/sidharthism/fashion-eye/models/stylegan2/stylegan2-pytorch/prepare_data.py b/spaces/sidharthism/fashion-eye/models/stylegan2/stylegan2-pytorch/prepare_data.py
deleted file mode 100644
index db49cbda14aca3b2bc0268a4f40cd97f2dd603cc..0000000000000000000000000000000000000000
--- a/spaces/sidharthism/fashion-eye/models/stylegan2/stylegan2-pytorch/prepare_data.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import argparse
-from io import BytesIO
-import multiprocessing
-from functools import partial
-
-from PIL import Image
-import lmdb
-from tqdm import tqdm
-from torchvision import datasets
-from torchvision.transforms import functional as trans_fn
-
-
-def resize_and_convert(img, size, resample, quality=100):
-    img = trans_fn.resize(img, size, resample)
-    img = trans_fn.center_crop(img, size)
-    buffer = BytesIO()
-    img.save(buffer, format='jpeg', quality=quality)
-    val = buffer.getvalue()
-
-    return val
-
-
-def resize_multiple(img, sizes=(128, 256, 512, 1024), resample=Image.LANCZOS, quality=100):
-    imgs = []
-
-    for size in sizes:
-        imgs.append(resize_and_convert(img, size, resample, quality))
-
-    return imgs
-
-
-def resize_worker(img_file, sizes, resample):
-    i, file = img_file
-    img = Image.open(file)
-    img = img.convert('RGB')
-    out = resize_multiple(img, sizes=sizes, resample=resample)
-
-    return i, out
-
-
-def prepare(env, dataset, n_worker, sizes=(128, 256, 512, 1024), resample=Image.LANCZOS):
-    resize_fn = partial(resize_worker, sizes=sizes, resample=resample)
-
-    files = sorted(dataset.imgs, key=lambda x: x[0])
-    files = [(i, file) for i, (file, label) in enumerate(files)]
-    total = 0
-
-    with multiprocessing.Pool(n_worker) as pool:
-        for i, imgs in tqdm(pool.imap_unordered(resize_fn, files)):
-            for size, img in zip(sizes, imgs):
-                key = f'{size}-{str(i).zfill(5)}'.encode('utf-8')
-
-                with env.begin(write=True) as txn:
-                    txn.put(key, img)
-
-            total += 1
-
-        with env.begin(write=True) as txn:
-            txn.put('length'.encode('utf-8'), str(total).encode('utf-8'))
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--out', type=str)
-    parser.add_argument('--size', type=str, default='128,256,512,1024')
-    parser.add_argument('--n_worker', type=int, default=8)
-    parser.add_argument('--resample', type=str, default='lanczos')
-    parser.add_argument('path', type=str)
-
-    args = parser.parse_args()
-    
-    resample_map = {'lanczos': Image.LANCZOS, 'bilinear': Image.BILINEAR}
-    resample = resample_map[args.resample]
-    
-    sizes = [int(s.strip()) for s in args.size.split(',')]
-
-    print(f'Make dataset of image sizes:', ', '.join(str(s) for s in sizes))
-
-    imgset = datasets.ImageFolder(args.path)
-
-    with lmdb.open(args.out, map_size=1024 ** 4, readahead=False) as env:
-        prepare(env, imgset, args.n_worker, sizes=sizes, resample=resample)
diff --git a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/common/residue_constants.py b/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/common/residue_constants.py
deleted file mode 100644
index 9ee38c72e76aa90a7b8abc4b7ec43552f28cc715..0000000000000000000000000000000000000000
--- a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/common/residue_constants.py
+++ /dev/null
@@ -1,911 +0,0 @@
-# Copyright 2021 DeepMind Technologies Limited
-#
-# 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.
-
-"""Constants used in AlphaFold."""
-
-import collections
-import functools
-from typing import List, Mapping, Tuple
-
-import numpy as np
-import tree
-
-# Internal import (35fd).
-
-
-# Distance from one CA to next CA [trans configuration: omega = 180].
-ca_ca = 3.80209737096
-
-# Format: The list for each AA type contains chi1, chi2, chi3, chi4 in
-# this order (or a relevant subset from chi1 onwards). ALA and GLY don't have
-# chi angles so their chi angle lists are empty.
-chi_angles_atoms = {
-    'ALA': [],
-    # Chi5 in arginine is always 0 +- 5 degrees, so ignore it.
-    'ARG': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD'],
-            ['CB', 'CG', 'CD', 'NE'], ['CG', 'CD', 'NE', 'CZ']],
-    'ASN': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'OD1']],
-    'ASP': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'OD1']],
-    'CYS': [['N', 'CA', 'CB', 'SG']],
-    'GLN': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD'],
-            ['CB', 'CG', 'CD', 'OE1']],
-    'GLU': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD'],
-            ['CB', 'CG', 'CD', 'OE1']],
-    'GLY': [],
-    'HIS': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'ND1']],
-    'ILE': [['N', 'CA', 'CB', 'CG1'], ['CA', 'CB', 'CG1', 'CD1']],
-    'LEU': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD1']],
-    'LYS': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD'],
-            ['CB', 'CG', 'CD', 'CE'], ['CG', 'CD', 'CE', 'NZ']],
-    'MET': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'SD'],
-            ['CB', 'CG', 'SD', 'CE']],
-    'PHE': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD1']],
-    'PRO': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD']],
-    'SER': [['N', 'CA', 'CB', 'OG']],
-    'THR': [['N', 'CA', 'CB', 'OG1']],
-    'TRP': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD1']],
-    'TYR': [['N', 'CA', 'CB', 'CG'], ['CA', 'CB', 'CG', 'CD1']],
-    'VAL': [['N', 'CA', 'CB', 'CG1']],
-}
-
-# If chi angles given in fixed-length array, this matrix determines how to mask
-# them for each AA type. The order is as per restype_order (see below).
-chi_angles_mask = [
-    [0.0, 0.0, 0.0, 0.0],  # ALA
-    [1.0, 1.0, 1.0, 1.0],  # ARG
-    [1.0, 1.0, 0.0, 0.0],  # ASN
-    [1.0, 1.0, 0.0, 0.0],  # ASP
-    [1.0, 0.0, 0.0, 0.0],  # CYS
-    [1.0, 1.0, 1.0, 0.0],  # GLN
-    [1.0, 1.0, 1.0, 0.0],  # GLU
-    [0.0, 0.0, 0.0, 0.0],  # GLY
-    [1.0, 1.0, 0.0, 0.0],  # HIS
-    [1.0, 1.0, 0.0, 0.0],  # ILE
-    [1.0, 1.0, 0.0, 0.0],  # LEU
-    [1.0, 1.0, 1.0, 1.0],  # LYS
-    [1.0, 1.0, 1.0, 0.0],  # MET
-    [1.0, 1.0, 0.0, 0.0],  # PHE
-    [1.0, 1.0, 0.0, 0.0],  # PRO
-    [1.0, 0.0, 0.0, 0.0],  # SER
-    [1.0, 0.0, 0.0, 0.0],  # THR
-    [1.0, 1.0, 0.0, 0.0],  # TRP
-    [1.0, 1.0, 0.0, 0.0],  # TYR
-    [1.0, 0.0, 0.0, 0.0],  # VAL
-]
-
-# The following chi angles are pi periodic: they can be rotated by a multiple
-# of pi without affecting the structure.
-chi_pi_periodic = [
-    [0.0, 0.0, 0.0, 0.0],  # ALA
-    [0.0, 0.0, 0.0, 0.0],  # ARG
-    [0.0, 0.0, 0.0, 0.0],  # ASN
-    [0.0, 1.0, 0.0, 0.0],  # ASP
-    [0.0, 0.0, 0.0, 0.0],  # CYS
-    [0.0, 0.0, 0.0, 0.0],  # GLN
-    [0.0, 0.0, 1.0, 0.0],  # GLU
-    [0.0, 0.0, 0.0, 0.0],  # GLY
-    [0.0, 0.0, 0.0, 0.0],  # HIS
-    [0.0, 0.0, 0.0, 0.0],  # ILE
-    [0.0, 0.0, 0.0, 0.0],  # LEU
-    [0.0, 0.0, 0.0, 0.0],  # LYS
-    [0.0, 0.0, 0.0, 0.0],  # MET
-    [0.0, 1.0, 0.0, 0.0],  # PHE
-    [0.0, 0.0, 0.0, 0.0],  # PRO
-    [0.0, 0.0, 0.0, 0.0],  # SER
-    [0.0, 0.0, 0.0, 0.0],  # THR
-    [0.0, 0.0, 0.0, 0.0],  # TRP
-    [0.0, 1.0, 0.0, 0.0],  # TYR
-    [0.0, 0.0, 0.0, 0.0],  # VAL
-    [0.0, 0.0, 0.0, 0.0],  # UNK
-]
-
-# Atoms positions relative to the 8 rigid groups, defined by the pre-omega, phi,
-# psi and chi angles:
-# 0: 'backbone group',
-# 1: 'pre-omega-group', (empty)
-# 2: 'phi-group', (currently empty, because it defines only hydrogens)
-# 3: 'psi-group',
-# 4,5,6,7: 'chi1,2,3,4-group'
-# The atom positions are relative to the axis-end-atom of the corresponding
-# rotation axis. The x-axis is in direction of the rotation axis, and the y-axis
-# is defined such that the dihedral-angle-definiting atom (the last entry in
-# chi_angles_atoms above) is in the xy-plane (with a positive y-coordinate).
-# format: [atomname, group_idx, rel_position]
-rigid_group_atom_positions = {
-    'ALA': [
-        ['N', 0, (-0.525, 1.363, 0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.526, -0.000, -0.000)],
-        ['CB', 0, (-0.529, -0.774, -1.205)],
-        ['O', 3, (0.627, 1.062, 0.000)],
-    ],
-    'ARG': [
-        ['N', 0, (-0.524, 1.362, -0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.525, -0.000, -0.000)],
-        ['CB', 0, (-0.524, -0.778, -1.209)],
-        ['O', 3, (0.626, 1.062, 0.000)],
-        ['CG', 4, (0.616, 1.390, -0.000)],
-        ['CD', 5, (0.564, 1.414, 0.000)],
-        ['NE', 6, (0.539, 1.357, -0.000)],
-        ['NH1', 7, (0.206, 2.301, 0.000)],
-        ['NH2', 7, (2.078, 0.978, -0.000)],
-        ['CZ', 7, (0.758, 1.093, -0.000)],
-    ],
-    'ASN': [
-        ['N', 0, (-0.536, 1.357, 0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.526, -0.000, -0.000)],
-        ['CB', 0, (-0.531, -0.787, -1.200)],
-        ['O', 3, (0.625, 1.062, 0.000)],
-        ['CG', 4, (0.584, 1.399, 0.000)],
-        ['ND2', 5, (0.593, -1.188, 0.001)],
-        ['OD1', 5, (0.633, 1.059, 0.000)],
-    ],
-    'ASP': [
-        ['N', 0, (-0.525, 1.362, -0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.527, 0.000, -0.000)],
-        ['CB', 0, (-0.526, -0.778, -1.208)],
-        ['O', 3, (0.626, 1.062, -0.000)],
-        ['CG', 4, (0.593, 1.398, -0.000)],
-        ['OD1', 5, (0.610, 1.091, 0.000)],
-        ['OD2', 5, (0.592, -1.101, -0.003)],
-    ],
-    'CYS': [
-        ['N', 0, (-0.522, 1.362, -0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.524, 0.000, 0.000)],
-        ['CB', 0, (-0.519, -0.773, -1.212)],
-        ['O', 3, (0.625, 1.062, -0.000)],
-        ['SG', 4, (0.728, 1.653, 0.000)],
-    ],
-    'GLN': [
-        ['N', 0, (-0.526, 1.361, -0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.526, 0.000, 0.000)],
-        ['CB', 0, (-0.525, -0.779, -1.207)],
-        ['O', 3, (0.626, 1.062, -0.000)],
-        ['CG', 4, (0.615, 1.393, 0.000)],
-        ['CD', 5, (0.587, 1.399, -0.000)],
-        ['NE2', 6, (0.593, -1.189, -0.001)],
-        ['OE1', 6, (0.634, 1.060, 0.000)],
-    ],
-    'GLU': [
-        ['N', 0, (-0.528, 1.361, 0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.526, -0.000, -0.000)],
-        ['CB', 0, (-0.526, -0.781, -1.207)],
-        ['O', 3, (0.626, 1.062, 0.000)],
-        ['CG', 4, (0.615, 1.392, 0.000)],
-        ['CD', 5, (0.600, 1.397, 0.000)],
-        ['OE1', 6, (0.607, 1.095, -0.000)],
-        ['OE2', 6, (0.589, -1.104, -0.001)],
-    ],
-    'GLY': [
-        ['N', 0, (-0.572, 1.337, 0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.517, -0.000, -0.000)],
-        ['O', 3, (0.626, 1.062, -0.000)],
-    ],
-    'HIS': [
-        ['N', 0, (-0.527, 1.360, 0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.525, 0.000, 0.000)],
-        ['CB', 0, (-0.525, -0.778, -1.208)],
-        ['O', 3, (0.625, 1.063, 0.000)],
-        ['CG', 4, (0.600, 1.370, -0.000)],
-        ['CD2', 5, (0.889, -1.021, 0.003)],
-        ['ND1', 5, (0.744, 1.160, -0.000)],
-        ['CE1', 5, (2.030, 0.851, 0.002)],
-        ['NE2', 5, (2.145, -0.466, 0.004)],
-    ],
-    'ILE': [
-        ['N', 0, (-0.493, 1.373, -0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.527, -0.000, -0.000)],
-        ['CB', 0, (-0.536, -0.793, -1.213)],
-        ['O', 3, (0.627, 1.062, -0.000)],
-        ['CG1', 4, (0.534, 1.437, -0.000)],
-        ['CG2', 4, (0.540, -0.785, -1.199)],
-        ['CD1', 5, (0.619, 1.391, 0.000)],
-    ],
-    'LEU': [
-        ['N', 0, (-0.520, 1.363, 0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.525, -0.000, -0.000)],
-        ['CB', 0, (-0.522, -0.773, -1.214)],
-        ['O', 3, (0.625, 1.063, -0.000)],
-        ['CG', 4, (0.678, 1.371, 0.000)],
-        ['CD1', 5, (0.530, 1.430, -0.000)],
-        ['CD2', 5, (0.535, -0.774, 1.200)],
-    ],
-    'LYS': [
-        ['N', 0, (-0.526, 1.362, -0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.526, 0.000, 0.000)],
-        ['CB', 0, (-0.524, -0.778, -1.208)],
-        ['O', 3, (0.626, 1.062, -0.000)],
-        ['CG', 4, (0.619, 1.390, 0.000)],
-        ['CD', 5, (0.559, 1.417, 0.000)],
-        ['CE', 6, (0.560, 1.416, 0.000)],
-        ['NZ', 7, (0.554, 1.387, 0.000)],
-    ],
-    'MET': [
-        ['N', 0, (-0.521, 1.364, -0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.525, 0.000, 0.000)],
-        ['CB', 0, (-0.523, -0.776, -1.210)],
-        ['O', 3, (0.625, 1.062, -0.000)],
-        ['CG', 4, (0.613, 1.391, -0.000)],
-        ['SD', 5, (0.703, 1.695, 0.000)],
-        ['CE', 6, (0.320, 1.786, -0.000)],
-    ],
-    'PHE': [
-        ['N', 0, (-0.518, 1.363, 0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.524, 0.000, -0.000)],
-        ['CB', 0, (-0.525, -0.776, -1.212)],
-        ['O', 3, (0.626, 1.062, -0.000)],
-        ['CG', 4, (0.607, 1.377, 0.000)],
-        ['CD1', 5, (0.709, 1.195, -0.000)],
-        ['CD2', 5, (0.706, -1.196, 0.000)],
-        ['CE1', 5, (2.102, 1.198, -0.000)],
-        ['CE2', 5, (2.098, -1.201, -0.000)],
-        ['CZ', 5, (2.794, -0.003, -0.001)],
-    ],
-    'PRO': [
-        ['N', 0, (-0.566, 1.351, -0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.527, -0.000, 0.000)],
-        ['CB', 0, (-0.546, -0.611, -1.293)],
-        ['O', 3, (0.621, 1.066, 0.000)],
-        ['CG', 4, (0.382, 1.445, 0.0)],
-        # ['CD', 5, (0.427, 1.440, 0.0)],
-        ['CD', 5, (0.477, 1.424, 0.0)],  # manually made angle 2 degrees larger
-    ],
-    'SER': [
-        ['N', 0, (-0.529, 1.360, -0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.525, -0.000, -0.000)],
-        ['CB', 0, (-0.518, -0.777, -1.211)],
-        ['O', 3, (0.626, 1.062, -0.000)],
-        ['OG', 4, (0.503, 1.325, 0.000)],
-    ],
-    'THR': [
-        ['N', 0, (-0.517, 1.364, 0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.526, 0.000, -0.000)],
-        ['CB', 0, (-0.516, -0.793, -1.215)],
-        ['O', 3, (0.626, 1.062, 0.000)],
-        ['CG2', 4, (0.550, -0.718, -1.228)],
-        ['OG1', 4, (0.472, 1.353, 0.000)],
-    ],
-    'TRP': [
-        ['N', 0, (-0.521, 1.363, 0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.525, -0.000, 0.000)],
-        ['CB', 0, (-0.523, -0.776, -1.212)],
-        ['O', 3, (0.627, 1.062, 0.000)],
-        ['CG', 4, (0.609, 1.370, -0.000)],
-        ['CD1', 5, (0.824, 1.091, 0.000)],
-        ['CD2', 5, (0.854, -1.148, -0.005)],
-        ['CE2', 5, (2.186, -0.678, -0.007)],
-        ['CE3', 5, (0.622, -2.530, -0.007)],
-        ['NE1', 5, (2.140, 0.690, -0.004)],
-        ['CH2', 5, (3.028, -2.890, -0.013)],
-        ['CZ2', 5, (3.283, -1.543, -0.011)],
-        ['CZ3', 5, (1.715, -3.389, -0.011)],
-    ],
-    'TYR': [
-        ['N', 0, (-0.522, 1.362, 0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.524, -0.000, -0.000)],
-        ['CB', 0, (-0.522, -0.776, -1.213)],
-        ['O', 3, (0.627, 1.062, -0.000)],
-        ['CG', 4, (0.607, 1.382, -0.000)],
-        ['CD1', 5, (0.716, 1.195, -0.000)],
-        ['CD2', 5, (0.713, -1.194, -0.001)],
-        ['CE1', 5, (2.107, 1.200, -0.002)],
-        ['CE2', 5, (2.104, -1.201, -0.003)],
-        ['OH', 5, (4.168, -0.002, -0.005)],
-        ['CZ', 5, (2.791, -0.001, -0.003)],
-    ],
-    'VAL': [
-        ['N', 0, (-0.494, 1.373, -0.000)],
-        ['CA', 0, (0.000, 0.000, 0.000)],
-        ['C', 0, (1.527, -0.000, -0.000)],
-        ['CB', 0, (-0.533, -0.795, -1.213)],
-        ['O', 3, (0.627, 1.062, -0.000)],
-        ['CG1', 4, (0.540, 1.429, -0.000)],
-        ['CG2', 4, (0.533, -0.776, 1.203)],
-    ],
-}
-
-# A list of atoms (excluding hydrogen) for each AA type. PDB naming convention.
-residue_atoms = {
-    'ALA': ['C', 'CA', 'CB', 'N', 'O'],
-    'ARG': ['C', 'CA', 'CB', 'CG', 'CD', 'CZ', 'N', 'NE', 'O', 'NH1', 'NH2'],
-    'ASP': ['C', 'CA', 'CB', 'CG', 'N', 'O', 'OD1', 'OD2'],
-    'ASN': ['C', 'CA', 'CB', 'CG', 'N', 'ND2', 'O', 'OD1'],
-    'CYS': ['C', 'CA', 'CB', 'N', 'O', 'SG'],
-    'GLU': ['C', 'CA', 'CB', 'CG', 'CD', 'N', 'O', 'OE1', 'OE2'],
-    'GLN': ['C', 'CA', 'CB', 'CG', 'CD', 'N', 'NE2', 'O', 'OE1'],
-    'GLY': ['C', 'CA', 'N', 'O'],
-    'HIS': ['C', 'CA', 'CB', 'CG', 'CD2', 'CE1', 'N', 'ND1', 'NE2', 'O'],
-    'ILE': ['C', 'CA', 'CB', 'CG1', 'CG2', 'CD1', 'N', 'O'],
-    'LEU': ['C', 'CA', 'CB', 'CG', 'CD1', 'CD2', 'N', 'O'],
-    'LYS': ['C', 'CA', 'CB', 'CG', 'CD', 'CE', 'N', 'NZ', 'O'],
-    'MET': ['C', 'CA', 'CB', 'CG', 'CE', 'N', 'O', 'SD'],
-    'PHE': ['C', 'CA', 'CB', 'CG', 'CD1', 'CD2', 'CE1', 'CE2', 'CZ', 'N', 'O'],
-    'PRO': ['C', 'CA', 'CB', 'CG', 'CD', 'N', 'O'],
-    'SER': ['C', 'CA', 'CB', 'N', 'O', 'OG'],
-    'THR': ['C', 'CA', 'CB', 'CG2', 'N', 'O', 'OG1'],
-    'TRP': ['C', 'CA', 'CB', 'CG', 'CD1', 'CD2', 'CE2', 'CE3', 'CZ2', 'CZ3',
-            'CH2', 'N', 'NE1', 'O'],
-    'TYR': ['C', 'CA', 'CB', 'CG', 'CD1', 'CD2', 'CE1', 'CE2', 'CZ', 'N', 'O',
-            'OH'],
-    'VAL': ['C', 'CA', 'CB', 'CG1', 'CG2', 'N', 'O']
-}
-
-# Naming swaps for ambiguous atom names.
-# Due to symmetries in the amino acids the naming of atoms is ambiguous in
-# 4 of the 20 amino acids.
-# (The LDDT paper lists 7 amino acids as ambiguous, but the naming ambiguities
-# in LEU, VAL and ARG can be resolved by using the 3d constellations of
-# the 'ambiguous' atoms and their neighbours)
-residue_atom_renaming_swaps = {
-    'ASP': {'OD1': 'OD2'},
-    'GLU': {'OE1': 'OE2'},
-    'PHE': {'CD1': 'CD2', 'CE1': 'CE2'},
-    'TYR': {'CD1': 'CD2', 'CE1': 'CE2'},
-}
-
-# Van der Waals radii [Angstroem] of the atoms (from Wikipedia)
-van_der_waals_radius = {
-    'C': 1.7,
-    'N': 1.55,
-    'O': 1.52,
-    'S': 1.8,
-}
-
-Bond = collections.namedtuple(
-    'Bond', ['atom1_name', 'atom2_name', 'length', 'stddev'])
-BondAngle = collections.namedtuple(
-    'BondAngle',
-    ['atom1_name', 'atom2_name', 'atom3name', 'angle_rad', 'stddev'])
-
-
-@functools.lru_cache(maxsize=None)
-def load_stereo_chemical_props() -> Tuple[Mapping[str, List[Bond]],
-                                          Mapping[str, List[Bond]],
-                                          Mapping[str, List[BondAngle]]]:
-  """Load stereo_chemical_props.txt into a nice structure.
-
-  Load literature values for bond lengths and bond angles and translate
-  bond angles into the length of the opposite edge of the triangle
-  ("residue_virtual_bonds").
-
-  Returns:
-    residue_bonds:  dict that maps resname --> list of Bond tuples
-    residue_virtual_bonds: dict that maps resname --> list of Bond tuples
-    residue_bond_angles: dict that maps resname --> list of BondAngle tuples
-  """
-  stereo_chemical_props_path = (
-      'alphafold/common/stereo_chemical_props.txt')
-  with open(stereo_chemical_props_path, 'rt') as f:
-    stereo_chemical_props = f.read()
-  lines_iter = iter(stereo_chemical_props.splitlines())
-  # Load bond lengths.
-  residue_bonds = {}
-  next(lines_iter)  # Skip header line.
-  for line in lines_iter:
-    if line.strip() == '-':
-      break
-    bond, resname, length, stddev = line.split()
-    atom1, atom2 = bond.split('-')
-    if resname not in residue_bonds:
-      residue_bonds[resname] = []
-    residue_bonds[resname].append(
-        Bond(atom1, atom2, float(length), float(stddev)))
-  residue_bonds['UNK'] = []
-
-  # Load bond angles.
-  residue_bond_angles = {}
-  next(lines_iter)  # Skip empty line.
-  next(lines_iter)  # Skip header line.
-  for line in lines_iter:
-    if line.strip() == '-':
-      break
-    bond, resname, angle_degree, stddev_degree = line.split()
-    atom1, atom2, atom3 = bond.split('-')
-    if resname not in residue_bond_angles:
-      residue_bond_angles[resname] = []
-    residue_bond_angles[resname].append(
-        BondAngle(atom1, atom2, atom3,
-                  float(angle_degree) / 180. * np.pi,
-                  float(stddev_degree) / 180. * np.pi))
-  residue_bond_angles['UNK'] = []
-
-  def make_bond_key(atom1_name, atom2_name):
-    """Unique key to lookup bonds."""
-    return '-'.join(sorted([atom1_name, atom2_name]))
-
-  # Translate bond angles into distances ("virtual bonds").
-  residue_virtual_bonds = {}
-  for resname, bond_angles in residue_bond_angles.items():
-    # Create a fast lookup dict for bond lengths.
-    bond_cache = {}
-    for b in residue_bonds[resname]:
-      bond_cache[make_bond_key(b.atom1_name, b.atom2_name)] = b
-    residue_virtual_bonds[resname] = []
-    for ba in bond_angles:
-      bond1 = bond_cache[make_bond_key(ba.atom1_name, ba.atom2_name)]
-      bond2 = bond_cache[make_bond_key(ba.atom2_name, ba.atom3name)]
-
-      # Compute distance between atom1 and atom3 using the law of cosines
-      # c^2 = a^2 + b^2 - 2ab*cos(gamma).
-      gamma = ba.angle_rad
-      length = np.sqrt(bond1.length**2 + bond2.length**2
-                       - 2 * bond1.length * bond2.length * np.cos(gamma))
-
-      # Propagation of uncertainty assuming uncorrelated errors.
-      dl_outer = 0.5 / length
-      dl_dgamma = (2 * bond1.length * bond2.length * np.sin(gamma)) * dl_outer
-      dl_db1 = (2 * bond1.length - 2 * bond2.length * np.cos(gamma)) * dl_outer
-      dl_db2 = (2 * bond2.length - 2 * bond1.length * np.cos(gamma)) * dl_outer
-      stddev = np.sqrt((dl_dgamma * ba.stddev)**2 +
-                       (dl_db1 * bond1.stddev)**2 +
-                       (dl_db2 * bond2.stddev)**2)
-      residue_virtual_bonds[resname].append(
-          Bond(ba.atom1_name, ba.atom3name, length, stddev))
-
-  return (residue_bonds,
-          residue_virtual_bonds,
-          residue_bond_angles)
-
-
-# Between-residue bond lengths for general bonds (first element) and for Proline
-# (second element).
-between_res_bond_length_c_n = [1.329, 1.341]
-between_res_bond_length_stddev_c_n = [0.014, 0.016]
-
-# Between-residue cos_angles.
-between_res_cos_angles_c_n_ca = [-0.5203, 0.0353]  # degrees: 121.352 +- 2.315
-between_res_cos_angles_ca_c_n = [-0.4473, 0.0311]  # degrees: 116.568 +- 1.995
-
-# This mapping is used when we need to store atom data in a format that requires
-# fixed atom data size for every residue (e.g. a numpy array).
-atom_types = [
-    'N', 'CA', 'C', 'CB', 'O', 'CG', 'CG1', 'CG2', 'OG', 'OG1', 'SG', 'CD',
-    'CD1', 'CD2', 'ND1', 'ND2', 'OD1', 'OD2', 'SD', 'CE', 'CE1', 'CE2', 'CE3',
-    'NE', 'NE1', 'NE2', 'OE1', 'OE2', 'CH2', 'NH1', 'NH2', 'OH', 'CZ', 'CZ2',
-    'CZ3', 'NZ', 'OXT'
-]
-atom_order = {atom_type: i for i, atom_type in enumerate(atom_types)}
-atom_type_num = len(atom_types)  # := 37.
-
-# A compact atom encoding with 14 columns
-# pylint: disable=line-too-long
-# pylint: disable=bad-whitespace
-restype_name_to_atom14_names = {
-    'ALA': ['N', 'CA', 'C', 'O', 'CB', '',    '',    '',    '',    '',    '',    '',    '',    ''],
-    'ARG': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD',  'NE',  'CZ',  'NH1', 'NH2', '',    '',    ''],
-    'ASN': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'OD1', 'ND2', '',    '',    '',    '',    '',    ''],
-    'ASP': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'OD1', 'OD2', '',    '',    '',    '',    '',    ''],
-    'CYS': ['N', 'CA', 'C', 'O', 'CB', 'SG',  '',    '',    '',    '',    '',    '',    '',    ''],
-    'GLN': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD',  'OE1', 'NE2', '',    '',    '',    '',    ''],
-    'GLU': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD',  'OE1', 'OE2', '',    '',    '',    '',    ''],
-    'GLY': ['N', 'CA', 'C', 'O', '',   '',    '',    '',    '',    '',    '',    '',    '',    ''],
-    'HIS': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'ND1', 'CD2', 'CE1', 'NE2', '',    '',    '',    ''],
-    'ILE': ['N', 'CA', 'C', 'O', 'CB', 'CG1', 'CG2', 'CD1', '',    '',    '',    '',    '',    ''],
-    'LEU': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD1', 'CD2', '',    '',    '',    '',    '',    ''],
-    'LYS': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD',  'CE',  'NZ',  '',    '',    '',    '',    ''],
-    'MET': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'SD',  'CE',  '',    '',    '',    '',    '',    ''],
-    'PHE': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD1', 'CD2', 'CE1', 'CE2', 'CZ',  '',    '',    ''],
-    'PRO': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD',  '',    '',    '',    '',    '',    '',    ''],
-    'SER': ['N', 'CA', 'C', 'O', 'CB', 'OG',  '',    '',    '',    '',    '',    '',    '',    ''],
-    'THR': ['N', 'CA', 'C', 'O', 'CB', 'OG1', 'CG2', '',    '',    '',    '',    '',    '',    ''],
-    'TRP': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD1', 'CD2', 'NE1', 'CE2', 'CE3', 'CZ2', 'CZ3', 'CH2'],
-    'TYR': ['N', 'CA', 'C', 'O', 'CB', 'CG',  'CD1', 'CD2', 'CE1', 'CE2', 'CZ',  'OH',  '',    ''],
-    'VAL': ['N', 'CA', 'C', 'O', 'CB', 'CG1', 'CG2', '',    '',    '',    '',    '',    '',    ''],
-    'UNK': ['',  '',   '',  '',  '',   '',    '',    '',    '',    '',    '',    '',    '',    ''],
-
-}
-# pylint: enable=line-too-long
-# pylint: enable=bad-whitespace
-
-
-# This is the standard residue order when coding AA type as a number.
-# Reproduce it by taking 3-letter AA codes and sorting them alphabetically.
-restypes = [
-    'A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P',
-    'S', 'T', 'W', 'Y', 'V'
-]
-restype_order = {restype: i for i, restype in enumerate(restypes)}
-restype_num = len(restypes)  # := 20.
-unk_restype_index = restype_num  # Catch-all index for unknown restypes.
-
-restypes_with_x = restypes + ['X']
-restype_order_with_x = {restype: i for i, restype in enumerate(restypes_with_x)}
-
-
-def sequence_to_onehot(
-    sequence: str,
-    mapping: Mapping[str, int],
-    map_unknown_to_x: bool = False) -> np.ndarray:
-  """Maps the given sequence into a one-hot encoded matrix.
-
-  Args:
-    sequence: An amino acid sequence.
-    mapping: A dictionary mapping amino acids to integers.
-    map_unknown_to_x: If True, any amino acid that is not in the mapping will be
-      mapped to the unknown amino acid 'X'. If the mapping doesn't contain
-      amino acid 'X', an error will be thrown. If False, any amino acid not in
-      the mapping will throw an error.
-
-  Returns:
-    A numpy array of shape (seq_len, num_unique_aas) with one-hot encoding of
-    the sequence.
-
-  Raises:
-    ValueError: If the mapping doesn't contain values from 0 to
-      num_unique_aas - 1 without any gaps.
-  """
-  num_entries = max(mapping.values()) + 1
-
-  if sorted(set(mapping.values())) != list(range(num_entries)):
-    raise ValueError('The mapping must have values from 0 to num_unique_aas-1 '
-                     'without any gaps. Got: %s' % sorted(mapping.values()))
-
-  one_hot_arr = np.zeros((len(sequence), num_entries), dtype=np.int32)
-
-  for aa_index, aa_type in enumerate(sequence):
-    if map_unknown_to_x:
-      if aa_type.isalpha() and aa_type.isupper():
-        aa_id = mapping.get(aa_type, mapping['X'])
-      else:
-        raise ValueError(f'Invalid character in the sequence: {aa_type}')
-    else:
-      aa_id = mapping[aa_type]
-    one_hot_arr[aa_index, aa_id] = 1
-
-  return one_hot_arr
-
-
-restype_1to3 = {
-    'A': 'ALA',
-    'R': 'ARG',
-    'N': 'ASN',
-    'D': 'ASP',
-    'C': 'CYS',
-    'Q': 'GLN',
-    'E': 'GLU',
-    'G': 'GLY',
-    'H': 'HIS',
-    'I': 'ILE',
-    'L': 'LEU',
-    'K': 'LYS',
-    'M': 'MET',
-    'F': 'PHE',
-    'P': 'PRO',
-    'S': 'SER',
-    'T': 'THR',
-    'W': 'TRP',
-    'Y': 'TYR',
-    'V': 'VAL',
-}
-
-
-# NB: restype_3to1 differs from Bio.PDB.protein_letters_3to1 by being a simple
-# 1-to-1 mapping of 3 letter names to one letter names. The latter contains
-# many more, and less common, three letter names as keys and maps many of these
-# to the same one letter name (including 'X' and 'U' which we don't use here).
-restype_3to1 = {v: k for k, v in restype_1to3.items()}
-
-# Define a restype name for all unknown residues.
-unk_restype = 'UNK'
-
-resnames = [restype_1to3[r] for r in restypes] + [unk_restype]
-resname_to_idx = {resname: i for i, resname in enumerate(resnames)}
-
-
-# The mapping here uses hhblits convention, so that B is mapped to D, J and O
-# are mapped to X, U is mapped to C, and Z is mapped to E. Other than that the
-# remaining 20 amino acids are kept in alphabetical order.
-# There are 2 non-amino acid codes, X (representing any amino acid) and
-# "-" representing a missing amino acid in an alignment.  The id for these
-# codes is put at the end (20 and 21) so that they can easily be ignored if
-# desired.
-HHBLITS_AA_TO_ID = {
-    'A': 0,
-    'B': 2,
-    'C': 1,
-    'D': 2,
-    'E': 3,
-    'F': 4,
-    'G': 5,
-    'H': 6,
-    'I': 7,
-    'J': 20,
-    'K': 8,
-    'L': 9,
-    'M': 10,
-    'N': 11,
-    'O': 20,
-    'P': 12,
-    'Q': 13,
-    'R': 14,
-    'S': 15,
-    'T': 16,
-    'U': 1,
-    'V': 17,
-    'W': 18,
-    'X': 20,
-    'Y': 19,
-    'Z': 3,
-    '-': 21,
-}
-
-# Partial inversion of HHBLITS_AA_TO_ID.
-ID_TO_HHBLITS_AA = {
-    0: 'A',
-    1: 'C',  # Also U.
-    2: 'D',  # Also B.
-    3: 'E',  # Also Z.
-    4: 'F',
-    5: 'G',
-    6: 'H',
-    7: 'I',
-    8: 'K',
-    9: 'L',
-    10: 'M',
-    11: 'N',
-    12: 'P',
-    13: 'Q',
-    14: 'R',
-    15: 'S',
-    16: 'T',
-    17: 'V',
-    18: 'W',
-    19: 'Y',
-    20: 'X',  # Includes J and O.
-    21: '-',
-}
-
-restypes_with_x_and_gap = restypes + ['X', '-']
-MAP_HHBLITS_AATYPE_TO_OUR_AATYPE = tuple(
-    restypes_with_x_and_gap.index(ID_TO_HHBLITS_AA[i])
-    for i in range(len(restypes_with_x_and_gap)))
-
-
-def _make_standard_atom_mask() -> np.ndarray:
-  """Returns [num_res_types, num_atom_types] mask array."""
-  # +1 to account for unknown (all 0s).
-  mask = np.zeros([restype_num + 1, atom_type_num], dtype=np.int32)
-  for restype, restype_letter in enumerate(restypes):
-    restype_name = restype_1to3[restype_letter]
-    atom_names = residue_atoms[restype_name]
-    for atom_name in atom_names:
-      atom_type = atom_order[atom_name]
-      mask[restype, atom_type] = 1
-  return mask
-
-
-STANDARD_ATOM_MASK = _make_standard_atom_mask()
-
-
-# A one hot representation for the first and second atoms defining the axis
-# of rotation for each chi-angle in each residue.
-def chi_angle_atom(atom_index: int) -> np.ndarray:
-  """Define chi-angle rigid groups via one-hot representations."""
-  chi_angles_index = {}
-  one_hots = []
-
-  for k, v in chi_angles_atoms.items():
-    indices = [atom_types.index(s[atom_index]) for s in v]
-    indices.extend([-1]*(4-len(indices)))
-    chi_angles_index[k] = indices
-
-  for r in restypes:
-    res3 = restype_1to3[r]
-    one_hot = np.eye(atom_type_num)[chi_angles_index[res3]]
-    one_hots.append(one_hot)
-
-  one_hots.append(np.zeros([4, atom_type_num]))  # Add zeros for residue `X`.
-  one_hot = np.stack(one_hots, axis=0)
-  one_hot = np.transpose(one_hot, [0, 2, 1])
-
-  return one_hot
-
-chi_atom_1_one_hot = chi_angle_atom(1)
-chi_atom_2_one_hot = chi_angle_atom(2)
-
-# An array like chi_angles_atoms but using indices rather than names.
-chi_angles_atom_indices = [chi_angles_atoms[restype_1to3[r]] for r in restypes]
-chi_angles_atom_indices = tree.map_structure(
-    lambda atom_name: atom_order[atom_name], chi_angles_atom_indices)
-chi_angles_atom_indices = np.array([
-    chi_atoms + ([[0, 0, 0, 0]] * (4 - len(chi_atoms)))
-    for chi_atoms in chi_angles_atom_indices])
-
-# Mapping from (res_name, atom_name) pairs to the atom's chi group index
-# and atom index within that group.
-chi_groups_for_atom = collections.defaultdict(list)
-for res_name, chi_angle_atoms_for_res in chi_angles_atoms.items():
-  for chi_group_i, chi_group in enumerate(chi_angle_atoms_for_res):
-    for atom_i, atom in enumerate(chi_group):
-      chi_groups_for_atom[(res_name, atom)].append((chi_group_i, atom_i))
-chi_groups_for_atom = dict(chi_groups_for_atom)
-
-
-def _make_rigid_transformation_4x4(ex, ey, translation):
-  """Create a rigid 4x4 transformation matrix from two axes and transl."""
-  # Normalize ex.
-  ex_normalized = ex / np.linalg.norm(ex)
-
-  # make ey perpendicular to ex
-  ey_normalized = ey - np.dot(ey, ex_normalized) * ex_normalized
-  ey_normalized /= np.linalg.norm(ey_normalized)
-
-  # compute ez as cross product
-  eznorm = np.cross(ex_normalized, ey_normalized)
-  m = np.stack([ex_normalized, ey_normalized, eznorm, translation]).transpose()
-  m = np.concatenate([m, [[0., 0., 0., 1.]]], axis=0)
-  return m
-
-
-# create an array with (restype, atomtype) --> rigid_group_idx
-# and an array with (restype, atomtype, coord) for the atom positions
-# and compute affine transformation matrices (4,4) from one rigid group to the
-# previous group
-restype_atom37_to_rigid_group = np.zeros([21, 37], dtype=np.int)
-restype_atom37_mask = np.zeros([21, 37], dtype=np.float32)
-restype_atom37_rigid_group_positions = np.zeros([21, 37, 3], dtype=np.float32)
-restype_atom14_to_rigid_group = np.zeros([21, 14], dtype=np.int)
-restype_atom14_mask = np.zeros([21, 14], dtype=np.float32)
-restype_atom14_rigid_group_positions = np.zeros([21, 14, 3], dtype=np.float32)
-restype_rigid_group_default_frame = np.zeros([21, 8, 4, 4], dtype=np.float32)
-
-###############################################
-restype_atom14_to_atom37 = []
-restype_atom37_to_atom14 = []
-for rt in restypes:
-  atom_names = restype_name_to_atom14_names[restype_1to3[rt]]
-  restype_atom14_to_atom37.append([(atom_order[name] if name else 0) for name in atom_names])
-  atom_name_to_idx14 = {name: i for i, name in enumerate(atom_names)}
-  restype_atom37_to_atom14.append([(atom_name_to_idx14[name] if name in atom_name_to_idx14 else 0) for name in atom_types])
-restype_atom14_to_atom37.append([0] * 14)
-restype_atom37_to_atom14.append([0] * 37)
-restype_atom14_to_atom37 = np.array(restype_atom14_to_atom37, dtype=np.int32)
-restype_atom37_to_atom14 = np.array(restype_atom37_to_atom14, dtype=np.int32)
-################################################
-
-def _make_rigid_group_constants():
-  """Fill the arrays above."""
-  
-  
-  for restype, restype_letter in enumerate(restypes):
-    resname = restype_1to3[restype_letter]
-    for atomname, group_idx, atom_position in rigid_group_atom_positions[resname]:
-      atomtype = atom_order[atomname]
-      restype_atom37_to_rigid_group[restype, atomtype] = group_idx
-      restype_atom37_mask[restype, atomtype] = 1
-      restype_atom37_rigid_group_positions[restype, atomtype, :] = atom_position
-
-      atom14idx = restype_name_to_atom14_names[resname].index(atomname)
-      restype_atom14_to_rigid_group[restype, atom14idx] = group_idx
-      restype_atom14_mask[restype, atom14idx] = 1
-      restype_atom14_rigid_group_positions[restype, atom14idx, :] = atom_position
-    
-    atom_names = residue_atoms[resname]
-    atom_name_to_idx14 = {name: i for i, name in enumerate(atom_names)}
-      
-  for restype, restype_letter in enumerate(restypes):
-    resname = restype_1to3[restype_letter]
-    atom_positions = {name: np.array(pos) for name, _, pos
-                      in rigid_group_atom_positions[resname]}
-
-    # backbone to backbone is the identity transform
-    restype_rigid_group_default_frame[restype, 0, :, :] = np.eye(4)
-
-    # pre-omega-frame to backbone (currently dummy identity matrix)
-    restype_rigid_group_default_frame[restype, 1, :, :] = np.eye(4)
-
-    # phi-frame to backbone
-    mat = _make_rigid_transformation_4x4(
-        ex=atom_positions['N'] - atom_positions['CA'],
-        ey=np.array([1., 0., 0.]),
-        translation=atom_positions['N'])
-    restype_rigid_group_default_frame[restype, 2, :, :] = mat
-
-    # psi-frame to backbone
-    mat = _make_rigid_transformation_4x4(
-        ex=atom_positions['C'] - atom_positions['CA'],
-        ey=atom_positions['CA'] - atom_positions['N'],
-        translation=atom_positions['C'])
-    restype_rigid_group_default_frame[restype, 3, :, :] = mat
-
-    # chi1-frame to backbone
-    if chi_angles_mask[restype][0]:
-      base_atom_names = chi_angles_atoms[resname][0]
-      base_atom_positions = [atom_positions[name] for name in base_atom_names]
-      mat = _make_rigid_transformation_4x4(
-          ex=base_atom_positions[2] - base_atom_positions[1],
-          ey=base_atom_positions[0] - base_atom_positions[1],
-          translation=base_atom_positions[2])
-      restype_rigid_group_default_frame[restype, 4, :, :] = mat
-
-    # chi2-frame to chi1-frame
-    # chi3-frame to chi2-frame
-    # chi4-frame to chi3-frame
-    # luckily all rotation axes for the next frame start at (0,0,0) of the
-    # previous frame
-    for chi_idx in range(1, 4):
-      if chi_angles_mask[restype][chi_idx]:
-        axis_end_atom_name = chi_angles_atoms[resname][chi_idx][2]
-        axis_end_atom_position = atom_positions[axis_end_atom_name]
-        mat = _make_rigid_transformation_4x4(
-            ex=axis_end_atom_position,
-            ey=np.array([-1., 0., 0.]),
-            translation=axis_end_atom_position)
-        restype_rigid_group_default_frame[restype, 4 + chi_idx, :, :] = mat
-
-
-_make_rigid_group_constants()
-
-
-def make_atom14_dists_bounds(overlap_tolerance=1.5,
-                             bond_length_tolerance_factor=15):
-  """compute upper and lower bounds for bonds to assess violations."""
-  restype_atom14_bond_lower_bound = np.zeros([21, 14, 14], np.float32)
-  restype_atom14_bond_upper_bound = np.zeros([21, 14, 14], np.float32)
-  restype_atom14_bond_stddev = np.zeros([21, 14, 14], np.float32)
-  residue_bonds, residue_virtual_bonds, _ = load_stereo_chemical_props()
-  for restype, restype_letter in enumerate(restypes):
-    resname = restype_1to3[restype_letter]
-    atom_list = restype_name_to_atom14_names[resname]
-
-    # create lower and upper bounds for clashes
-    for atom1_idx, atom1_name in enumerate(atom_list):
-      if not atom1_name:
-        continue
-      atom1_radius = van_der_waals_radius[atom1_name[0]]
-      for atom2_idx, atom2_name in enumerate(atom_list):
-        if (not atom2_name) or atom1_idx == atom2_idx:
-          continue
-        atom2_radius = van_der_waals_radius[atom2_name[0]]
-        lower = atom1_radius + atom2_radius - overlap_tolerance
-        upper = 1e10
-        restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower
-        restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower
-        restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper
-        restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper
-
-    # overwrite lower and upper bounds for bonds and angles
-    for b in residue_bonds[resname] + residue_virtual_bonds[resname]:
-      atom1_idx = atom_list.index(b.atom1_name)
-      atom2_idx = atom_list.index(b.atom2_name)
-      lower = b.length - bond_length_tolerance_factor * b.stddev
-      upper = b.length + bond_length_tolerance_factor * b.stddev
-      restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower
-      restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower
-      restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper
-      restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper
-      restype_atom14_bond_stddev[restype, atom1_idx, atom2_idx] = b.stddev
-      restype_atom14_bond_stddev[restype, atom2_idx, atom1_idx] = b.stddev
-  return {'lower_bound': restype_atom14_bond_lower_bound,  # shape (21,14,14)
-          'upper_bound': restype_atom14_bond_upper_bound,  # shape (21,14,14)
-          'stddev': restype_atom14_bond_stddev,  # shape (21,14,14)
-         }
diff --git a/spaces/simpie28/VITS-Umamusume-voice-synthesizer/ONNXVITS_utils.py b/spaces/simpie28/VITS-Umamusume-voice-synthesizer/ONNXVITS_utils.py
deleted file mode 100644
index b634ce380421571e6e07fb45dd59717b3f63115c..0000000000000000000000000000000000000000
--- a/spaces/simpie28/VITS-Umamusume-voice-synthesizer/ONNXVITS_utils.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import torch
-import numpy as np
-import random
-import onnxruntime as ort
-def set_random_seed(seed=0):
-    ort.set_seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.backends.cudnn.deterministic = True
-    random.seed(seed)
-    np.random.seed(seed)
-
-def runonnx(model_path, **kwargs):
-    ort_session = ort.InferenceSession(model_path)
-    outputs = ort_session.run(
-        None,
-        kwargs
-    )
-    return outputs
\ No newline at end of file
diff --git a/spaces/simpie28/VITS-Umamusume-voice-synthesizer/data_utils.py b/spaces/simpie28/VITS-Umamusume-voice-synthesizer/data_utils.py
deleted file mode 100644
index e9246c6c8f2ff3c37a7f8529ea1593c7f80f887e..0000000000000000000000000000000000000000
--- a/spaces/simpie28/VITS-Umamusume-voice-synthesizer/data_utils.py
+++ /dev/null
@@ -1,393 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-
-import commons 
-from mel_processing import spectrogram_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import text_to_sequence, cleaned_text_to_sequence
-
-
-class TextAudioLoader(torch.utils.data.Dataset):
-    """
-        1) loads audio, text pairs
-        2) normalizes text and converts them to sequences of integers
-        3) computes spectrograms from audio files.
-    """
-    def __init__(self, audiopaths_and_text, hparams):
-        self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
-        self.text_cleaners  = hparams.text_cleaners
-        self.max_wav_value  = hparams.max_wav_value
-        self.sampling_rate  = hparams.sampling_rate
-        self.filter_length  = hparams.filter_length 
-        self.hop_length     = hparams.hop_length 
-        self.win_length     = hparams.win_length
-        self.sampling_rate  = hparams.sampling_rate 
-
-        self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
-        self.add_blank = hparams.add_blank
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 190)
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths_and_text)
-        self._filter()
-
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-
-        audiopaths_and_text_new = []
-        lengths = []
-        for audiopath, text in self.audiopaths_and_text:
-            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
-                audiopaths_and_text_new.append([audiopath, text])
-                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
-        self.audiopaths_and_text = audiopaths_and_text_new
-        self.lengths = lengths
-
-    def get_audio_text_pair(self, audiopath_and_text):
-        # separate filename and text
-        audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
-        text = self.get_text(text)
-        spec, wav = self.get_audio(audiopath)
-        return (text, spec, wav)
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError("{} {} SR doesn't match target {} SR".format(
-                sampling_rate, self.sampling_rate))
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if os.path.exists(spec_filename):
-            spec = torch.load(spec_filename)
-        else:
-            spec = spectrogram_torch(audio_norm, self.filter_length,
-                self.sampling_rate, self.hop_length, self.win_length,
-                center=False)
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-        return spec, audio_norm
-
-    def get_text(self, text):
-        if self.cleaned_text:
-            text_norm = cleaned_text_to_sequence(text)
-        else:
-            text_norm = text_to_sequence(text, self.text_cleaners)
-        if self.add_blank:
-            text_norm = commons.intersperse(text_norm, 0)
-        text_norm = torch.LongTensor(text_norm)
-        return text_norm
-
-    def __getitem__(self, index):
-        return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_and_text)
-
-
-class TextAudioCollate():
-    """ Zero-pads model inputs and targets
-    """
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text and aduio
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[1].size(1) for x in batch]),
-            dim=0, descending=True)
-
-        max_text_len = max([len(x[0]) for x in batch])
-        max_spec_len = max([x[1].size(1) for x in batch])
-        max_wav_len = max([x[2].size(1) for x in batch])
-
-        text_lengths = torch.LongTensor(len(batch))
-        spec_lengths = torch.LongTensor(len(batch))
-        wav_lengths = torch.LongTensor(len(batch))
-
-        text_padded = torch.LongTensor(len(batch), max_text_len)
-        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        text_padded.zero_()
-        spec_padded.zero_()
-        wav_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            text = row[0]
-            text_padded[i, :text.size(0)] = text
-            text_lengths[i] = text.size(0)
-
-            spec = row[1]
-            spec_padded[i, :, :spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wav = row[2]
-            wav_padded[i, :, :wav.size(1)] = wav
-            wav_lengths[i] = wav.size(1)
-
-        if self.return_ids:
-            return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing
-        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths
-
-
-"""Multi speaker version"""
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
-    """
-        1) loads audio, speaker_id, text pairs
-        2) normalizes text and converts them to sequences of integers
-        3) computes spectrograms from audio files.
-    """
-    def __init__(self, audiopaths_sid_text, hparams):
-        self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
-        self.text_cleaners = hparams.text_cleaners
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length  = hparams.filter_length
-        self.hop_length     = hparams.hop_length
-        self.win_length     = hparams.win_length
-        self.sampling_rate  = hparams.sampling_rate
-
-        self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
-        self.add_blank = hparams.add_blank
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 190)
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths_sid_text)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-
-        audiopaths_sid_text_new = []
-        lengths = []
-        for audiopath, sid, text in self.audiopaths_sid_text:
-            audiopath = "E:/uma_voice/" + audiopath
-            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
-                audiopaths_sid_text_new.append([audiopath, sid, text])
-                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
-        self.audiopaths_sid_text = audiopaths_sid_text_new
-        self.lengths = lengths
-
-    def get_audio_text_speaker_pair(self, audiopath_sid_text):
-        # separate filename, speaker_id and text
-        audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
-        text = self.get_text(text)
-        spec, wav = self.get_audio(audiopath)
-        sid = self.get_sid(sid)
-        return (text, spec, wav, sid)
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError("{} {} SR doesn't match target {} SR".format(
-                sampling_rate, self.sampling_rate))
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if os.path.exists(spec_filename):
-            spec = torch.load(spec_filename)
-        else:
-            spec = spectrogram_torch(audio_norm, self.filter_length,
-                self.sampling_rate, self.hop_length, self.win_length,
-                center=False)
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-        return spec, audio_norm
-
-    def get_text(self, text):
-        if self.cleaned_text:
-            text_norm = cleaned_text_to_sequence(text)
-        else:
-            text_norm = text_to_sequence(text, self.text_cleaners)
-        if self.add_blank:
-            text_norm = commons.intersperse(text_norm, 0)
-        text_norm = torch.LongTensor(text_norm)
-        return text_norm
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def __getitem__(self, index):
-        return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
-    """ Zero-pads model inputs and targets
-    """
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text, audio and speaker identities
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized, sid]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[1].size(1) for x in batch]),
-            dim=0, descending=True)
-
-        max_text_len = max([len(x[0]) for x in batch])
-        max_spec_len = max([x[1].size(1) for x in batch])
-        max_wav_len = max([x[2].size(1) for x in batch])
-
-        text_lengths = torch.LongTensor(len(batch))
-        spec_lengths = torch.LongTensor(len(batch))
-        wav_lengths = torch.LongTensor(len(batch))
-        sid = torch.LongTensor(len(batch))
-
-        text_padded = torch.LongTensor(len(batch), max_text_len)
-        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        text_padded.zero_()
-        spec_padded.zero_()
-        wav_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            text = row[0]
-            text_padded[i, :text.size(0)] = text
-            text_lengths[i] = text.size(0)
-
-            spec = row[1]
-            spec_padded[i, :, :spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wav = row[2]
-            wav_padded[i, :, :wav.size(1)] = wav
-            wav_lengths[i] = wav.size(1)
-
-            sid[i] = row[3]
-
-        if self.return_ids:
-            return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
-        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
-    """
-    Maintain similar input lengths in a batch.
-    Length groups are specified by boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-  
-    It removes samples which are not included in the boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
-    """
-    def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
-        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
-        self.lengths = dataset.lengths
-        self.batch_size = batch_size
-        self.boundaries = boundaries
-  
-        self.buckets, self.num_samples_per_bucket = self._create_buckets()
-        self.total_size = sum(self.num_samples_per_bucket)
-        self.num_samples = self.total_size // self.num_replicas
-  
-    def _create_buckets(self):
-        buckets = [[] for _ in range(len(self.boundaries) - 1)]
-        for i in range(len(self.lengths)):
-            length = self.lengths[i]
-            idx_bucket = self._bisect(length)
-            if idx_bucket != -1:
-                buckets[idx_bucket].append(i)
-  
-        for i in range(len(buckets) - 1, 0, -1):
-            if len(buckets[i]) == 0:
-                buckets.pop(i)
-                self.boundaries.pop(i+1)
-  
-        num_samples_per_bucket = []
-        for i in range(len(buckets)):
-            len_bucket = len(buckets[i])
-            total_batch_size = self.num_replicas * self.batch_size
-            rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
-            num_samples_per_bucket.append(len_bucket + rem)
-        return buckets, num_samples_per_bucket
-  
-    def __iter__(self):
-      # deterministically shuffle based on epoch
-      g = torch.Generator()
-      g.manual_seed(self.epoch)
-  
-      indices = []
-      if self.shuffle:
-          for bucket in self.buckets:
-              indices.append(torch.randperm(len(bucket), generator=g).tolist())
-      else:
-          for bucket in self.buckets:
-              indices.append(list(range(len(bucket))))
-  
-      batches = []
-      for i in range(len(self.buckets)):
-          bucket = self.buckets[i]
-          len_bucket = len(bucket)
-          ids_bucket = indices[i]
-          num_samples_bucket = self.num_samples_per_bucket[i]
-  
-          # add extra samples to make it evenly divisible
-          rem = num_samples_bucket - len_bucket
-          ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-  
-          # subsample
-          ids_bucket = ids_bucket[self.rank::self.num_replicas]
-  
-          # batching
-          for j in range(len(ids_bucket) // self.batch_size):
-              batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
-              batches.append(batch)
-  
-      if self.shuffle:
-          batch_ids = torch.randperm(len(batches), generator=g).tolist()
-          batches = [batches[i] for i in batch_ids]
-      self.batches = batches
-  
-      assert len(self.batches) * self.batch_size == self.num_samples
-      return iter(self.batches)
-  
-    def _bisect(self, x, lo=0, hi=None):
-      if hi is None:
-          hi = len(self.boundaries) - 1
-  
-      if hi > lo:
-          mid = (hi + lo) // 2
-          if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
-              return mid
-          elif x <= self.boundaries[mid]:
-              return self._bisect(x, lo, mid)
-          else:
-              return self._bisect(x, mid + 1, hi)
-      else:
-          return -1
-
-    def __len__(self):
-        return self.num_samples // self.batch_size
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy the Ultimate Cougar Adventure with Wild Cougar Sim 3D Mod APK.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy the Ultimate Cougar Adventure with Wild Cougar Sim 3D Mod APK.md
deleted file mode 100644
index 687a763eb5b8520fc585600e74da8085e971b211..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy the Ultimate Cougar Adventure with Wild Cougar Sim 3D Mod APK.md	
+++ /dev/null
@@ -1,106 +0,0 @@
-

-
Wild Cougar Sim 3D Mod APK: A Review

-
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Wild Cougar Sim 3D is a simulation game developed by Turbo Rocket Games, a studio that specializes in creating animal simulators. The game was released in 2015 and has over 10 million downloads on Google Play. The game allows you to customize your cougar, hunt for prey, fight other animals, raise a family, and explore a huge 3D world with different biomes and weather effects. You can also interact with other players online and join clans to cooperate or compete with them.

-
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    ProsCons
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    Realistic animal behavior and physicsRequires internet connection
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    Online multiplayer modeCan be laggy and unstable
    Free to playContains ads and in-app purchases
    • 
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If you want to enjoy the game without any limitations or interruptions, you might want to try the mod apk version of Wild Cougar Sim 3D. A mod apk is a modified version of an original app that gives you access to premium features for free. In this case, the mod apk of Wild Cougar Sim 3D gives you:

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\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/GTA 5 APK No Verification - Download and Play GTA 5 on Android from Mediafre.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/GTA 5 APK No Verification - Download and Play GTA 5 on Android from Mediafre.md
deleted file mode 100644
index 748bfbed240ecc4db08c39ad83e735b15183dba0..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/GTA 5 APK No Verification - Download and Play GTA 5 on Android from Mediafre.md	
+++ /dev/null
@@ -1,109 +0,0 @@
-

-
GTA 5 Download APK Without Verification Mediafıre: How to Play GTA 5 on Your Android Device

- 
Introduction

- 
GTA 5 is one of the most popular and successful video games of all time. It is an action-adventure game that lets you experience the life of a criminal in a fictional city called Los Santos. You can explore the open world, complete missions, drive vehicles, shoot weapons, and interact with other characters.

- 
However, GTA 5 is not officially available for Android devices. It was originally released for PlayStation 3 and Xbox 360 in 2013, and later for PlayStation 4, Xbox One, and PC in 2014 and 2015. The game requires high-end hardware and graphics to run smoothly, which most Android devices do not have.

-
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So, how can you play GTA 5 on your Android device? The answer is by using an APK file. An APK file is an application package file that contains all the necessary files and data to install and run an app on an Android device. By downloading an APK file of GTA 5, you can bypass the verification process and play the game without any issues.

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But where can you find a reliable and safe APK file of GTA 5? One of the best sources is mediafıre. Mediafıre is a cloud storage service that allows you to upload, download, and share files online. It has a large collection of APK files for various apps and games, including GTA 5. You can download GTA 5 APK without verification mediafıre in just a few minutes.

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In this article, we will show you how to download GTA 5 APK without verification mediafıre and how to play GTA 5 on your Android device. We will also give you some tips and tricks to enhance your gaming experience. Let's get started!

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How to download GTA 5 APK without verification mediafıre

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Step 1: Find a reliable source for the APK file

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The first step is to find a trustworthy website that offers the GTA 5 APK file for download. There are many websites that claim to have the APK file, but some of them may be fake or malicious. You should avoid downloading from unknown or suspicious sources, as they may harm your device or steal your data.

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One of the best sources for the GTA 5 APK file is [this website](^1^). It has a verified and updated version of the GTA 5 APK file that works on most Android devices. It also has a detailed guide on how to install and play the game. You can trust this website as it has positive reviews and feedback from users who have downloaded the game from there.

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-
  • The download may take some time depending on your internet speed and device storage.
    • 
-
  • Once the download is complete, you will see a notification on your screen or in your notification bar.
    • 
-
  • Tap on the notification or go to your file manager and locate the downloaded file.
    • 
-
  • The file name should be GTA 5 APK or something similar.
    • 
-

- 
Step 3: Install the APK file on your device

- 
The third step is to install the APK file on your device. To do this, you need to follow these steps:

- 
    
-
  • Before you install the APK file, you need to enable the installation of apps from unknown sources on your device. This is a security feature that prevents the installation of apps that are not from the official Google Play Store.
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-
  • To enable this feature, go to your device settings and look for the security or privacy option. There you will find a toggle or checkbox for unknown sources or allow installation of apps from unknown sources. Turn it on or check it.
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  • Now, go back to the downloaded file and tap on it. You will see a pop-up window asking you to confirm the installation. Tap on install and wait for the installation to finish.
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  • The installation may take some time depending on your device specifications and storage.
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  • Once the installation is complete, you will see another notification or a message saying that the app has been installed.
    • 
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- 
Step 4: Launch the game and enjoy

- 
The final step is to launch the game and enjoy playing GTA 5 on your Android device. To do this, you need to follow these steps:

- 
    
-
  • Go to your app drawer or home screen and look for the GTA 5 icon. Tap on it to launch the game.
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  • You may see a loading screen or a splash screen for a few seconds. This is normal and it means that the game is loading its data and resources.
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  • After the loading is done, you will see the main menu of the game. Here you can choose to start a new game, load a saved game, or change the settings of the game.
    • 
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  • If you start a new game, you will see a cutscene that introduces you to the story and the characters of the game. You can skip it if you want by tapping on the screen.
    • 
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  • After the cutscene, you will be able to control your character and play the game. You can use the virtual buttons on the screen or a controller or a keyboard and mouse if you have them connected to your device.
    • 
-
  • You can also pause the game by tapping on the menu button on the top right corner of the screen. Here you can access the map, inventory, missions, settings, and other options of the game.
    • 
-

- 
Tips and tricks for playing GTA 5 on your Android device

- 
Adjust the graphics settings to optimize performance

- 
GTA 5 is a high-end game that requires a lot of processing power and memory to run smoothly. If you have a low-end or mid-range device, you may experience some lagging or crashing issues while playing the game. To avoid this, you should adjust the graphics settings of the game to suit your device capabilities.

- 
To do this, go to the settings menu of the game and look for the graphics option. Here you can change various aspects of the graphics quality, such as resolution, texture quality, shadows, anti-aliasing, etc. You should lower these settings as much as possible without compromising too much on the visual appeal of the game. You can also turn off some features that are not essential for gameplay, such as motion blur, depth of field, lens flare, etc.

- 
By adjusting these settings, you can improve the performance and stability of the game on your device and enjoy a smoother gameplay experience.

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Use a controller or a keyboard and mouse for better control

- 
GTA 5 is a game that involves a lot of actions and movements, such as driving, shooting, fighting, etc. While you can use the virtual buttons on the screen to control your character and the game, they may not be very comfortable or accurate. You may find it hard to aim, steer, or perform other tasks with the touch screen.

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To overcome this problem, you can use a controller or a keyboard and mouse to play the game. These devices can give you more precise and responsive control over the game and make it easier for you to perform various actions. You can connect these devices to your Android device via Bluetooth, USB, or OTG cable.

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To use a controller or a keyboard and mouse, you need to pair them with your device and configure them in the settings menu of the game. Here you can assign different buttons or keys to different functions of the game. You can also adjust the sensitivity and layout of the controller or the keyboard and mouse according to your preference.

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Explore the open world and complete missions

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GTA 5 is a game that offers you a lot of freedom and variety in terms of gameplay. You can explore the vast and detailed open world of Los Santos and its surroundings, which include urban areas, rural areas, mountains, deserts, beaches, etc. You can also interact with various characters, objects, and events that populate the world.

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You can also complete missions that advance the story of the game and unlock new features and content. The missions are diverse and challenging, ranging from heists, robberies, assassinations, races, chases, etc. You can also choose to play the missions solo or with other players online.

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By exploring the open world and completing missions, you can earn money, reputation, weapons, vehicles, clothes, and other rewards that enhance your gameplay experience. You can also discover hidden secrets, easter eggs, references, and jokes that add more fun and humor to the game.

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Customize your character and vehicles

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GTA 5 is a game that allows you to customize your character and vehicles to suit your style and personality. You can change various aspects of your character's appearance, such as hair, clothes, tattoos, accessories, etc. You can also choose from different outfits that match different occasions and themes.

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You can also customize your vehicles by modifying their performance, appearance, color, paint job, wheels, etc. You can choose from a wide range of vehicles that include cars, bikes, trucks, buses, helicopters, planes, boats, etc. You can also buy or steal new vehicles from various locations and dealers.

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By customizing your character and vehicles, you can express your creativity and individuality and make the game more enjoyable and personal.

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Conclusion

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GTA 5 is a game that offers you an amazing and immersive gaming experience on your Android device. You can play GTA 5 on your Android device by downloading the GTA 5 APK file without verification mediafıre. You can also use some tips and tricks to optimize the performance and control of the game. You can also explore the open world and complete missions and customize your character and vehicles to make the game more fun and exciting.

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-

\ No newline at end of file
diff --git a/spaces/sklearn-docs/Incremental-PCA/app.py b/spaces/sklearn-docs/Incremental-PCA/app.py
deleted file mode 100644
index 02126a50b721a1b58d8c7f660ab09aad276efcb7..0000000000000000000000000000000000000000
--- a/spaces/sklearn-docs/Incremental-PCA/app.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import gradio as gr
-import numpy as np
-import time
-import matplotlib.pyplot as plt
-
-from sklearn.datasets import load_iris
-from sklearn.decomposition import PCA, IncrementalPCA
-
-
-theme = gr.themes.Monochrome(
-    primary_hue="indigo",
-    secondary_hue="blue",
-    neutral_hue="slate",
-)
-model_card = f"""
-## Description
-
-**Incremental principal component analysis (IPCA)** is a suitable alternative to **Principal component analysis (PCA)** when the dataset to be analyzed is too large to fit in memory. 
-**IPCA** generates a low-rank representation of the input data utilizing a fixed amount of memory that is not reliant on the number of input data samples. 
-
-In this demo, you can play around with different ``number of components`` and ``number of samples`` to explore the performance of IPCA and PCA, including a comparison of their respective outputs and running times.
-**Note**: Incremental PCA is comparatively slower to regular PCA, as it processes partial data sets sequentially.
-
-
-## Dataset
-
-Iris dataset
-"""
-iris = load_iris()
-X = iris.data
-y = iris.target
-
-def plot_pca(n_components, batch_size):
-    # Create linkage matrix and then plot the dendrogram
-    colors = ["navy", "turquoise", "darkorange"]
-
-    ipca = IncrementalPCA(n_components=n_components, batch_size=batch_size)
-    t1 = time.time()
-    X_ipca = ipca.fit_transform(X)
-    ipca_time = time.time() - t1
-
-    pca = PCA(n_components=n_components)
-    t2 = time.time()
-    X_pca = pca.fit_transform(X)
-    pca_time = time.time() - t2
-    
-    fig1, axes1 = plt.subplots()
-    for color, i, target_name in zip(colors, [0, 1, 2], iris.target_names):
-        axes1.scatter(
-            X_ipca[y == i, 0],
-            X_ipca[y == i, 1],
-            color=color,
-            lw=2,
-            label=target_name,
-            )
-    err = np.abs(np.abs(X_pca) - np.abs(X_ipca)).mean()
-    axes1.set_title(f"Incremental PCA of iris dataset")
-    axes1.axis([-4, 4, -1.5, 1.5])
-    axes1.legend(loc="best", shadow=False, scatterpoints=1)
-
-    fig2, axes2 = plt.subplots()
-    for color, i, target_name in zip(colors, [0, 1, 2], iris.target_names):
-        axes2.scatter(
-            X_pca[y == i, 0],
-            X_pca[y == i, 1],
-            color=color,
-            lw=2,
-            label=target_name,
-            )
-    axes2.set_title("PCA of iris dataset")
-    axes2.axis([-4, 4, -1.5, 1.5])
-    axes2.legend(loc="best", shadow=False, scatterpoints=1)
-
-    text = f"PCA runing time: {pca_time:.6f} seconds. Incremental PCA runing time: {ipca_time:.6f} seconds. Mean absolute unsigned error: {err*100:.6f}%"
-        
-    return fig1, fig2, text
-
-
-
-with gr.Blocks(theme=theme) as demo:
-    gr.Markdown('''
-            

-            
Incremental PCA

-            

-        ''')
-    gr.Markdown(model_card)
-    gr.Markdown("Author: Vu Minh Chien. Based on the example from scikit-learn")
-    n_components = gr.Slider(minimum=2, maximum=4, step=1, value=2, label="Number of components to keep")
-    batch_size = gr.Slider(minimum=10, maximum=50, step=10, value=10, label="The number of samples to use for each batch")
-
-    with gr.Row():
-        with gr.Column():
-            plot_1 = gr.Plot(label="Incremental PCA")
-        with gr.Column():
-            plot_2 = gr.Plot(label="PCA")
-    with gr.Row():
-        resutls = gr.Textbox(label="Results")
-
-    n_components.change(fn=plot_pca, inputs=[n_components, batch_size], outputs=[plot_1, plot_2, resutls])
-    batch_size.change(fn=plot_pca, inputs=[n_components, batch_size], outputs=[plot_1, plot_2, resutls])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/sklkd93/CodeFormer/CodeFormer/weights/README.md b/spaces/sklkd93/CodeFormer/CodeFormer/weights/README.md
deleted file mode 100644
index 67ad334bd672eeb9f82813cd54e8885331bbb2f2..0000000000000000000000000000000000000000
--- a/spaces/sklkd93/CodeFormer/CodeFormer/weights/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# Weights
-
-Put the downloaded pre-trained models to this folder.
\ No newline at end of file
diff --git a/spaces/society-ethics/model-card-regulatory-check/compliance_checks/intended_purpose.py b/spaces/society-ethics/model-card-regulatory-check/compliance_checks/intended_purpose.py
deleted file mode 100644
index 9f46c1eca5f9508862125047bb5af1ff4017101e..0000000000000000000000000000000000000000
--- a/spaces/society-ethics/model-card-regulatory-check/compliance_checks/intended_purpose.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from compliance_checks.base import ComplianceResult, ComplianceCheck, walk_to_next_heading
-from bs4 import BeautifulSoup
-
-
-class IntendedPurposeResult(ComplianceResult):
-    name = "Intended Purpose"
-
-    def __init__(
-            self,
-            direct_use: str = None,
-            downstream_use: str = None,
-            out_of_scope_use: str = None,
-            *args,
-            **kwargs,
-    ):
-        super().__init__(*args, **kwargs)
-        self.direct_use = direct_use
-        self.downstream_use = downstream_use
-        self.out_of_scope_use = out_of_scope_use
-
-    def __eq__(self, other):
-        if isinstance(other, IntendedPurposeResult):
-            if super().__eq__(other):
-                try:
-                    # TODO: Either use these, or remove them.
-                    # assert self.direct_use == other.direct_use
-                    # assert self.downstream_use == other.downstream_use
-                    # assert self.out_of_scope_use == other.out_of_scope_use
-                    return True
-                except AssertionError:
-                    return False
-        else:
-            return False
-
-    def to_string(self):
-        if self.status:
-            return """\
-            It looks like this model card has some documentation for the model's intended purpose! We look for this by \
-            searching for headings that say things like:
-            - Intended uses & limitations
-            - Uses
-            - Model Use
-            """
-        else:
-            return """\
-            We weren't able to find a section in this model card for the model's intended purpose, but it's easy to \
-            add one! You can add the following section to the model card and, once you fill in the \
-            `[More Information Needed]` sections, the "Intended Purpose" check should pass 🤗
-            
-            ```md
-            ## Uses
-            
-            
-
-            [More Information Needed]
-            
-            ### Direct Use
-            
-            
-            
-            [More Information Needed]
-            
-            ### Downstream Use [optional]
-            
-            
-            
-            [More Information Needed]
-            
-            ### Out-of-Scope Use
-            
-            
-            [More Information Needed]
-            ```
-            """
-
-
-class IntendedPurposeCheck(ComplianceCheck):
-    name = "Intended Purpose"
-
-    def run_check(self, card: BeautifulSoup):
-        combos = [
-            ("h2", "Intended uses & limitations"),
-            ("h1", "Uses"), ("h2", "Uses"),
-            ("h1", "Usage"),
-            ("h2", "Model Use"),
-            ("h1", "Intended uses"), ("h2", "Intended uses"),
-            ("h2", "Intended Use"),
-        ]
-
-        for hX, heading in combos:
-            purpose_check = walk_to_next_heading(card, hX, heading)
-            if purpose_check:
-                return IntendedPurposeResult(
-                    status=True,
-                )
-
-        return IntendedPurposeResult()
diff --git a/spaces/sparanoid/milky-green-sovits-4/utils.py b/spaces/sparanoid/milky-green-sovits-4/utils.py
deleted file mode 100644
index f13d3526d514be71c77bebb17a5af8831b9c6a36..0000000000000000000000000000000000000000
--- a/spaces/sparanoid/milky-green-sovits-4/utils.py
+++ /dev/null
@@ -1,508 +0,0 @@
-import os
-import glob
-import re
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import random
-
-import librosa
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-from torch.nn import functional as F
-from modules.commons import sequence_mask
-from hubert import hubert_model
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-f0_bin = 256
-f0_max = 1100.0
-f0_min = 50.0
-f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
-
-# def normalize_f0(f0, random_scale=True):
-#     f0_norm = f0.clone()  # create a copy of the input Tensor
-#     batch_size, _, frame_length = f0_norm.shape
-#     for i in range(batch_size):
-#         means = torch.mean(f0_norm[i, 0, :])
-#         if random_scale:
-#             factor = random.uniform(0.8, 1.2)
-#         else:
-#             factor = 1
-#         f0_norm[i, 0, :] = (f0_norm[i, 0, :] - means) * factor
-#     return f0_norm
-# def normalize_f0(f0, random_scale=True):
-#     means = torch.mean(f0[:, 0, :], dim=1, keepdim=True)
-#     if random_scale:
-#         factor = torch.Tensor(f0.shape[0],1).uniform_(0.8, 1.2).to(f0.device)
-#     else:
-#         factor = torch.ones(f0.shape[0], 1, 1).to(f0.device)
-#     f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
-#     return f0_norm
-def normalize_f0(f0, x_mask, uv, random_scale=True):
-    # calculate means based on x_mask
-    uv_sum = torch.sum(uv, dim=1, keepdim=True)
-    uv_sum[uv_sum == 0] = 9999
-    means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum
-
-    if random_scale:
-        factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device)
-    else:
-        factor = torch.ones(f0.shape[0], 1).to(f0.device)
-    # normalize f0 based on means and factor
-    f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
-    if torch.isnan(f0_norm).any():
-        exit(0)
-    return f0_norm * x_mask
-
-
-def plot_data_to_numpy(x, y):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    plt.plot(x)
-    plt.plot(y)
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-
-def interpolate_f0(f0):
-    '''
-    对F0进行插值处理
-    '''
-
-    data = np.reshape(f0, (f0.size, 1))
-
-    vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-    vuv_vector[data > 0.0] = 1.0
-    vuv_vector[data <= 0.0] = 0.0
-
-    ip_data = data
-
-    frame_number = data.size
-    last_value = 0.0
-    for i in range(frame_number):
-        if data[i] <= 0.0:
-            j = i + 1
-            for j in range(i + 1, frame_number):
-                if data[j] > 0.0:
-                    break
-            if j < frame_number - 1:
-                if last_value > 0.0:
-                    step = (data[j] - data[i - 1]) / float(j - i)
-                    for k in range(i, j):
-                        ip_data[k] = data[i - 1] + step * (k - i + 1)
-                else:
-                    for k in range(i, j):
-                        ip_data[k] = data[j]
-            else:
-                for k in range(i, frame_number):
-                    ip_data[k] = last_value
-        else:
-            ip_data[i] = data[i]
-            last_value = data[i]
-
-    return ip_data[:,0], vuv_vector[:,0]
-
-
-def compute_f0_parselmouth(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
-    import parselmouth
-    x = wav_numpy
-    if p_len is None:
-        p_len = x.shape[0]//hop_length
-    else:
-        assert abs(p_len-x.shape[0]//hop_length) < 4, "pad length error"
-    time_step = hop_length / sampling_rate * 1000
-    f0_min = 50
-    f0_max = 1100
-    f0 = parselmouth.Sound(x, sampling_rate).to_pitch_ac(
-        time_step=time_step / 1000, voicing_threshold=0.6,
-        pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
-
-    pad_size=(p_len - len(f0) + 1) // 2
-    if(pad_size>0 or p_len - len(f0) - pad_size>0):
-        f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
-    return f0
-
-def resize_f0(x, target_len):
-    source = np.array(x)
-    source[source<0.001] = np.nan
-    target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
-    res = np.nan_to_num(target)
-    return res
-
-def compute_f0_dio(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
-    import pyworld
-    if p_len is None:
-        p_len = wav_numpy.shape[0]//hop_length
-    f0, t = pyworld.dio(
-        wav_numpy.astype(np.double),
-        fs=sampling_rate,
-        f0_ceil=800,
-        frame_period=1000 * hop_length / sampling_rate,
-    )
-    f0 = pyworld.stonemask(wav_numpy.astype(np.double), f0, t, sampling_rate)
-    for index, pitch in enumerate(f0):
-        f0[index] = round(pitch, 1)
-    return resize_f0(f0, p_len)
-
-def f0_to_coarse(f0):
-  is_torch = isinstance(f0, torch.Tensor)
-  f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
-  f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
-
-  f0_mel[f0_mel <= 1] = 1
-  f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
-  f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
-  assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
-  return f0_coarse
-
-
-def get_hubert_model():
-  vec_path = "hubert/checkpoint_best_legacy_500.pt"
-  print("load model(s) from {}".format(vec_path))
-  from fairseq import checkpoint_utils
-  models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-    [vec_path],
-    suffix="",
-  )
-  model = models[0]
-  model.eval()
-  return model
-
-def get_hubert_content(hmodel, wav_16k_tensor):
-  feats = wav_16k_tensor
-  if feats.dim() == 2:  # double channels
-    feats = feats.mean(-1)
-  assert feats.dim() == 1, feats.dim()
-  feats = feats.view(1, -1)
-  padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-  inputs = {
-    "source": feats.to(wav_16k_tensor.device),
-    "padding_mask": padding_mask.to(wav_16k_tensor.device),
-    "output_layer": 9,  # layer 9
-  }
-  with torch.no_grad():
-    logits = hmodel.extract_features(**inputs)
-    feats = hmodel.final_proj(logits[0])
-  return feats.transpose(1, 2)
-
-
-def get_content(cmodel, y):
-    with torch.no_grad():
-        c = cmodel.extract_features(y.squeeze(1))[0]
-    c = c.transpose(1, 2)
-    return c
-
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-    iteration = checkpoint_dict['iteration']
-    learning_rate = checkpoint_dict['learning_rate']
-    if optimizer is not None and not skip_optimizer:
-        optimizer.load_state_dict(checkpoint_dict['optimizer'])
-    saved_state_dict = checkpoint_dict['model']
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():
-        try:
-            # assert "dec" in k or "disc" in k
-            # print("load", k)
-            new_state_dict[k] = saved_state_dict[k]
-            assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
-        except:
-            print("error, %s is not in the checkpoint" % k)
-            logger.info("%s is not in the checkpoint" % k)
-            new_state_dict[k] = v
-    if hasattr(model, 'module'):
-        model.module.load_state_dict(new_state_dict)
-    else:
-        model.load_state_dict(new_state_dict)
-    print("load ")
-    logger.info("Loaded checkpoint '{}' (iteration {})".format(
-        checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path, val_steps, current_step):
-  logger.info("Saving model and optimizer state at iteration {} to {}".format(
-    iteration, checkpoint_path))
-  if hasattr(model, 'module'):
-    state_dict = model.module.state_dict()
-  else:
-    state_dict = model.state_dict()
-  torch.save({'model': state_dict,
-              'iteration': iteration,
-              'optimizer': optimizer.state_dict(),
-              'learning_rate': learning_rate}, checkpoint_path)
-  if current_step >= val_steps * 3:
-    to_del_ckptname = checkpoint_path.replace(str(current_step), str(current_step - val_steps * 3))
-    if os.path.exists(to_del_ckptname):
-        os.remove(to_del_ckptname)
-        print("Removing ", to_del_ckptname)
-
-
-def clean_checkpoints(path_to_models='logs/48k/', n_ckpts_to_keep=2, sort_by_time=True):
-  """Freeing up space by deleting saved ckpts
-
-  Arguments:
-  path_to_models    --  Path to the model directory
-  n_ckpts_to_keep   --  Number of ckpts to keep, excluding G_0.pth and D_0.pth
-  sort_by_time      --  True -> chronologically delete ckpts
-                        False -> lexicographically delete ckpts
-  """
-  ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
-  name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
-  time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
-  sort_key = time_key if sort_by_time else name_key
-  x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], key=sort_key)
-  to_del = [os.path.join(path_to_models, fn) for fn in
-            (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
-  del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
-  del_routine = lambda x: [os.remove(x), del_info(x)]
-  rs = [del_routine(fn) for fn in to_del]
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
-  for k, v in scalars.items():
-    writer.add_scalar(k, v, global_step)
-  for k, v in histograms.items():
-    writer.add_histogram(k, v, global_step)
-  for k, v in images.items():
-    writer.add_image(k, v, global_step, dataformats='HWC')
-  for k, v in audios.items():
-    writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-  f_list = glob.glob(os.path.join(dir_path, regex))
-  f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-  x = f_list[-1]
-  print(x)
-  return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(10,2))
-  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                  interpolation='none')
-  plt.colorbar(im, ax=ax)
-  plt.xlabel("Frames")
-  plt.ylabel("Channels")
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(6, 4))
-  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                  interpolation='none')
-  fig.colorbar(im, ax=ax)
-  xlabel = 'Decoder timestep'
-  if info is not None:
-      xlabel += '\n\n' + info
-  plt.xlabel(xlabel)
-  plt.ylabel('Encoder timestep')
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def load_wav_to_torch(full_path):
-  sampling_rate, data = read(full_path)
-  return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-  with open(filename, encoding='utf-8') as f:
-    filepaths_and_text = [line.strip().split(split) for line in f]
-  return filepaths_and_text
-
-
-def get_hparams(init=True):
-  parser = argparse.ArgumentParser()
-  parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                      help='JSON file for configuration')
-  parser.add_argument('-m', '--model', type=str, required=True,
-                      help='Model name')
-
-  args = parser.parse_args()
-  model_dir = os.path.join("./logs", args.model)
-
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-
-  config_path = args.config
-  config_save_path = os.path.join(model_dir, "config.json")
-  if init:
-    with open(config_path, "r") as f:
-      data = f.read()
-    with open(config_save_path, "w") as f:
-      f.write(data)
-  else:
-    with open(config_save_path, "r") as f:
-      data = f.read()
-  config = json.loads(data)
-
-  hparams = HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_dir(model_dir):
-  config_save_path = os.path.join(model_dir, "config.json")
-  with open(config_save_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_file(config_path):
-  with open(config_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  return hparams
-
-
-def check_git_hash(model_dir):
-  source_dir = os.path.dirname(os.path.realpath(__file__))
-  if not os.path.exists(os.path.join(source_dir, ".git")):
-    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-      source_dir
-    ))
-    return
-
-  cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-  path = os.path.join(model_dir, "githash")
-  if os.path.exists(path):
-    saved_hash = open(path).read()
-    if saved_hash != cur_hash:
-      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-        saved_hash[:8], cur_hash[:8]))
-  else:
-    open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-  global logger
-  logger = logging.getLogger(os.path.basename(model_dir))
-  logger.setLevel(logging.DEBUG)
-
-  formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-  h = logging.FileHandler(os.path.join(model_dir, filename))
-  h.setLevel(logging.DEBUG)
-  h.setFormatter(formatter)
-  logger.addHandler(h)
-  return logger
-
-
-def repeat_expand_2d(content, target_len):
-    # content : [h, t]
-
-    src_len = content.shape[-1]
-    target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device)
-    temp = torch.arange(src_len+1) * target_len / src_len
-    current_pos = 0
-    for i in range(target_len):
-        if i < temp[current_pos+1]:
-            target[:, i] = content[:, current_pos]
-        else:
-            current_pos += 1
-            target[:, i] = content[:, current_pos]
-
-    return target
-
-
-class HParams():
-  def __init__(self, **kwargs):
-    for k, v in kwargs.items():
-      if type(v) == dict:
-        v = HParams(**v)
-      self[k] = v
-
-  def keys(self):
-    return self.__dict__.keys()
-
-  def items(self):
-    return self.__dict__.items()
-
-  def values(self):
-    return self.__dict__.values()
-
-  def __len__(self):
-    return len(self.__dict__)
-
-  def __getitem__(self, key):
-    return getattr(self, key)
-
-  def __setitem__(self, key, value):
-    return setattr(self, key, value)
-
-  def __contains__(self, key):
-    return key in self.__dict__
-
-  def __repr__(self):
-    return self.__dict__.__repr__()
-
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/unsupervised_quality_estimation/meteor.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/unsupervised_quality_estimation/meteor.py
deleted file mode 100644
index 2ee0448cf1f167f6f3ecee56ad807922cffb0956..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/unsupervised_quality_estimation/meteor.py
+++ /dev/null
@@ -1,109 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import math
-import os
-import subprocess
-import sys
-import tempfile
-from collections import defaultdict
-from itertools import combinations
-
-
-def read_translations(path, n_repeats):
-    segment_counter = 0
-    segment_translations = []
-    translations = defaultdict(list)
-    for line in open(path):
-        segment_translations.append(" ".join(line.split()))
-        if len(segment_translations) == n_repeats:
-            translations[segment_counter] = segment_translations
-            segment_translations = []
-            segment_counter += 1
-    return translations
-
-
-def generate_input(translations, n_repeats):
-    _, ref_path = tempfile.mkstemp()
-    _, mt_path = tempfile.mkstemp()
-    ref_fh = open(ref_path, "w")
-    mt_fh = open(mt_path, "w")
-    for segid in sorted(translations.keys()):
-        assert len(translations[segid]) == n_repeats
-        indexes = combinations(range(n_repeats), 2)
-        for idx1, idx2 in indexes:
-            mt_fh.write(translations[segid][idx1].strip() + "\n")
-            ref_fh.write(translations[segid][idx2].strip() + "\n")
-    sys.stderr.write("\nSaved translations to %s and %s" % (ref_path, mt_path))
-    return ref_path, mt_path
-
-
-def run_meteor(ref_path, mt_path, metric_path, lang="en"):
-    _, out_path = tempfile.mkstemp()
-    subprocess.call(
-        [
-            "java",
-            "-Xmx2G",
-            "-jar",
-            metric_path,
-            mt_path,
-            ref_path,
-            "-p",
-            "0.5 0.2 0.6 0.75",  # default parameters, only changed alpha to give equal weight to P and R
-            "-norm",
-            "-l",
-            lang,
-        ],
-        stdout=open(out_path, "w"),
-    )
-    os.remove(ref_path)
-    os.remove(mt_path)
-    sys.stderr.write("\nSaved Meteor output to %s" % out_path)
-    return out_path
-
-
-def read_output(meteor_output_path, n_repeats):
-    n_combinations = math.factorial(n_repeats) / (
-        math.factorial(2) * math.factorial(n_repeats - 2)
-    )
-    raw_scores = []
-    average_scores = []
-    for line in open(meteor_output_path):
-        if not line.startswith("Segment "):
-            continue
-        score = float(line.strip().split("\t")[1])
-        raw_scores.append(score)
-        if len(raw_scores) == n_combinations:
-            average_scores.append(sum(raw_scores) / n_combinations)
-            raw_scores = []
-    os.remove(meteor_output_path)
-    return average_scores
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-i", "--infile")
-    parser.add_argument("-n", "--repeat_times", type=int)
-    parser.add_argument("-m", "--meteor")
-    parser.add_argument("-o", "--output")
-    args = parser.parse_args()
-
-    translations = read_translations(args.infile, args.repeat_times)
-    sys.stderr.write("\nGenerating input for Meteor...")
-    ref_path, mt_path = generate_input(translations, args.repeat_times)
-    sys.stderr.write("\nRunning Meteor...")
-    out_path = run_meteor(ref_path, mt_path, args.meteor)
-    sys.stderr.write("\nReading output...")
-    scores = read_output(out_path, args.repeat_times)
-    sys.stderr.write("\nWriting results...")
-    with open(args.output, "w") as o:
-        for scr in scores:
-            o.write("{}\n".format(scr))
-    o.close()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/step-3-profit/Midnight-Deep/README.md b/spaces/step-3-profit/Midnight-Deep/README.md
deleted file mode 100644
index 933370b5d6fb148dc0d350406c13dd152326db79..0000000000000000000000000000000000000000
--- a/spaces/step-3-profit/Midnight-Deep/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
-
----
-tags: [gradio-theme]
-title: Midnight-Deep
-colorFrom: orange
-colorTo: purple
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-# Midnight-Deep
-## Description
-Add a description of this theme here!
-## Contributions
-Thanks to [@step-3-profit](https://huggingface.co/step-3-profit) for adding this gradio theme!
diff --git a/spaces/stomexserde/gpt4-ui/Examples/(2011) Baixar Filme A Lenda Do Pianista Do Mar Dublado Extra Quality.md b/spaces/stomexserde/gpt4-ui/Examples/(2011) Baixar Filme A Lenda Do Pianista Do Mar Dublado Extra Quality.md
deleted file mode 100644
index e64ee13d8b5b62ebcc70f5b7d98d7dbf6e604c92..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/(2011) Baixar Filme A Lenda Do Pianista Do Mar Dublado Extra Quality.md	
+++ /dev/null
@@ -1,17 +0,0 @@
-

-Here is a possible title and article with html formatting for the keyword "(2011) baixar filme a lenda do pianista do mar dublado":
-
-
A Lenda do Pianista do Mar: um filme emocionante sobre música e destino

-
O filme A Lenda do Pianista do Mar, lançado em 2011, é uma adaptação da obra homônima do escritor italiano Alessandro Baricco. Dirigido por Giuseppe Tornatore, o mesmo de Cinema Paradiso, o filme conta a história de um pianista extraordinário que nasce e vive a bordo de um navio transatlântico chamado Virginian.

-
(2011) baixar filme a lenda do pianista do mar dublado

Download Zip ★★★ https://urlgoal.com/2uIa95



-
O protagonista, interpretado por Tim Roth, recebe o nome de 1900, pois é encontrado em um dos camarotes do navio no primeiro dia do século XX. Ele cresce sob os cuidados de Danny Boodmann (Bill Nunn), um marinheiro que trabalha na sala de máquinas. Desde cedo, 1900 demonstra um talento natural para o piano, e se torna uma lenda entre os passageiros e a tripulação.

-
O filme acompanha a trajetória de 1900 ao longo das décadas, mostrando sua relação com a música, com as pessoas que cruzam seu caminho e com o próprio navio, que se torna seu único lar. Ele nunca desembarca em terra firme, pois teme o mundo desconhecido e infinito que existe além do mar. Sua vida muda quando ele conhece Max Tooney (Pruitt Taylor Vince), um trompetista que se torna seu amigo e narrador da história.

-
A Lenda do Pianista do Mar é um filme poético e envolvente, que explora temas como a liberdade, a solidão, o amor e o destino. A trilha sonora, composta por Ennio Morricone, é um dos pontos altos da obra, que mistura diferentes estilos musicais, como o jazz, o blues e a música clássica. O filme foi premiado em diversos festivais internacionais, como o Globo de Ouro de melhor trilha sonora original.

-
Para assistir ao filme A Lenda do Pianista do Mar dublado em português, você pode baixar o arquivo no link abaixo[^1^]. O filme tem duração de 165 minutos e é recomendado para maiores de 14 anos.

-Baixar Filme A Lenda Do Pianista Do Mar DubladoHere is a possible continuation of the article:
-
-
O filme A Lenda do Pianista do Mar também apresenta personagens secundários que marcam a vida de 1900, como a cantora Jane (Mélanie Thierry), por quem ele se apaixona, o produtor musical The Recording Angel (Peter Vaughan), que tenta convencê-lo a gravar um disco, e o pianista Jelly Roll Morton (Clarence Williams III), que o desafia para um duelo musical.

-
Ao longo do filme, 1900 enfrenta dilemas existenciais sobre o sentido de sua vida e sua escolha de permanecer no navio. Ele também testemunha as mudanças históricas e sociais que ocorrem no século XX, como as guerras mundiais, a imigração, a segregação racial e a evolução tecnológica. O filme é uma homenagem à arte e à beleza que podem ser encontradas em qualquer lugar, mesmo nos lugares mais improváveis.

-
Se você gosta de filmes que misturam drama, romance, música e história, não deixe de assistir A Lenda do Pianista do Mar. Você vai se emocionar com a história de um homem que viveu entre as notas de um piano e as ondas de um mar.
 7196e7f11a

-

-

\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/0xc00000ba Download [WORK].md b/spaces/stomexserde/gpt4-ui/Examples/0xc00000ba Download [WORK].md
deleted file mode 100644
index 9fe2ba2059f673ae8df4a0078ba0d815796d37b3..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/0xc00000ba Download [WORK].md	
+++ /dev/null
@@ -1,54 +0,0 @@
-

-
What is 0xc00000ba Download and How to Fix It?

-
If you are trying to download or run a program or application on your Windows computer, you may encounter an error message that says something like this:

-

-
File: \\Windows\\System32\\drivers\\

-Status: 0xc00000ba

-Info: The operating system couldn't be loaded because a critical system driver is missing or contains errors.

-
0xc00000ba Download

DOWNLOAD > https://urlgoal.com/2uIc50



-

-
This is known as the 0xc00000ba download error, and it can prevent you from accessing or using the software you want. In this article, we will explain what this error means, what causes it, and how to fix it. We will also share some tips on how to prevent this error from happening again in the future.

- 
What is 0xc00000ba Download?

-
Before we dive into the solutions, let's first understand what 0xc00000ba download is and why it occurs.

- 
What does 0xc00000ba error mean?

-
The 0xc00000ba error is a common error in Windows operating systems that indicates that a required file or resource is not available. The error can occur when you try to download, install, or run a program or application that depends on a certain DLL (Dynamic Link Library) file. A DLL file is a type of file that contains code and data that can be used by multiple programs at the same time. For example, if you have several programs that need to perform the same function, such as printing a document, they can share the same DLL file instead of having their own copy of the code.

-
However, sometimes a DLL file can be missing, corrupted, or incompatible with your system, causing the program or application that needs it to fail. This can result in the 0xc00000ba error message, which tells you which DLL file is causing the problem and where it is located. The specific DLL file mentioned in the error code can vary, and it is usually a combination of letters and numbers unique to the error message.

-

- 
What causes 0xc00000ba error?

-
The 0xc00000ba error can occur due to several reasons, such as:

-
    
-
  • Corrupted system files: If your Windows system files are damaged or corrupted by malware, viruses, power outages, or improper shutdowns, they may affect the functionality of your programs and applications.
    • 
-
  • Incorrect configuration: If your Windows settings are not configured properly, such as having incorrect registry entries or boot options, they may interfere with the loading of your system files or drivers.
    • 
-
  • Outdated or incompatible drivers: If your device drivers are not updated or compatible with your Windows version, they may cause conflicts or errors with your programs and applications.
    • 
-
  • Missing or faulty hardware: If your hardware components, such as your hard disk, RAM, or CPU, are not functioning properly or are not connected correctly, they may prevent your system from accessing or loading the required files or resources.
    • 
-
  • Untrusted or malicious software: If you download or install software from untrusted or malicious sources, they may contain viruses, malware, or spyware that can infect your system and damage your files or resources.
    • 
-

-
These are some of the common causes of the 0xc00000ba error, but there may be other factors that can trigger this error as well. Therefore, it is important to identify the exact cause of the error and apply the appropriate solution to fix it.

- 
How to Fix 0xc00000ba Download Error?

-
Fortunately, there are several methods that you can try to fix the 0xc00000ba download error and restore your system to normal. Here are some of the most effective and easy-to-follow solutions that you can try:

- 
Method 1: Run a System File Checker (SFC) Scan

-
The first method that you can try is to run a system file checker (SFC) scan on your Windows computer. This is a built-in tool that can scan and repair any corrupted or missing system files on your computer. To run an SFC scan, follow these steps:

-
    
-
  1. Press the Windows key + R on your keyboard to open the Run dialog box.
    2. 
-
  2. Type cmd in the box and press Ctrl + Shift + Enter to run the command prompt as an administrator.
    3. 
-
  3. In the command prompt window, type sfc /scannow and press Enter. This will start the SFC scan process, which may take some time to complete.
    4. 
-
  4. Once the scan is finished, you will see a message that tells you whether any issues were found and fixed. If any issues were found, restart your computer and check if the error is resolved.
    5. 
-

- 
Method 2: Reinstall the Program or Application Causing the Error

-
The second method that you can try is to reinstall the program or application that is causing the error. Sometimes, the program or application may be corrupted, outdated, or incompatible with your system, causing the 0xc00000ba error. To reinstall the program or application, follow these steps:

-
    
-
  1. Press the Windows key + I on your keyboard to open the Settings app.
    2. 
-
  2. Click on Apps, then click on Apps & features.
    3. 
-
  3. In the list of installed apps and features, find and select the program or application that is causing the error. Click on Uninstall, then follow the on-screen instructions to complete the uninstallation process.
    4. 
-
  4. After uninstalling the program or application, go to its official website and download the latest version of it. Make sure that it is compatible with your Windows version and system specifications.
    5. 
-
  5. Install the program or application following its installation guide. After installing it, restart your computer and check if the error is resolved.
    6. 
-

- 
Method 3: Restore Your System to a Previous State

-
The third method that you can try is to restore your system to a previous state using the System Restore feature. This is a feature that allows you to revert your system settings and files to a point in time when everything was working fine. This can help you fix any issues that may have occurred after a recent change in your system, such as installing a new software or updating a driver. To restore your system to a previous state, follow these steps:

-
    
-
  1. Press the Windows key + R on your keyboard to open the Run dialog box.
    2. 
-
  2. Type rstrui.exe in the box and press Enter. This will open the System Restore wizard.
    3. 
-
  3. In the wizard, click on Next, then select a restore point from the list of available restore points. Choose a restore point that was created before you encountered the error. Click on Next, then click on Finish.
    4. 
- b2dd77e56b
    
-
    
-
    
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Analog Obsession ? SweetDrums 2.0 VST VST3 AU WIN.OSX X86 X64.md b/spaces/stomexserde/gpt4-ui/Examples/Analog Obsession ? SweetDrums 2.0 VST VST3 AU WIN.OSX X86 X64.md
deleted file mode 100644
index f054da3a70e0f6af596f0c713bf7dd0c2d9742a8..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Analog Obsession ? SweetDrums 2.0 VST VST3 AU WIN.OSX X86 X64.md	
+++ /dev/null
@@ -1,26 +0,0 @@
-
-
    Analog Obsession – SweetDrums 2.0: A Review of the Ultimate Drum Processing Plugin
    
-
-
    If you are looking for a versatile and powerful plugin to enhance your drum tracks, you might want to check out Analog Obsession – SweetDrums 2.0. This plugin is a bundle of four modules that can be used separately or together to shape your drum sound in any way you want.
    
-
    Analog Obsession – SweetDrums 2.0 VST, VST3, AU WIN.OSX X86 X64

    DOWNLOAD ✶ https://urlgoal.com/2uI9Y9


    
-
-
    The first module is the Preamp, which offers a selection of vintage and modern preamp models to add warmth, saturation and character to your drums. You can choose from 12 different preamps, each with its own tone and response. You can also adjust the input and output levels, as well as the drive and blend controls to dial in the perfect amount of saturation.
    
-
-
    The second module is the EQ, which is a flexible and musical equalizer with four bands and two modes. You can switch between American and British EQ curves, depending on your preference and genre. You can also adjust the frequency, gain and Q of each band, as well as the high-pass and low-pass filters to sculpt your drum sound.
    
-
-
    The third module is the Comp, which is a smooth and transparent compressor with four modes and two types. You can choose from VCA, FET, OPTO and VAR modes, each with its own attack and release characteristics. You can also switch between peak and RMS types, depending on how you want to control the dynamics of your drums. You can also adjust the threshold, ratio, makeup gain and mix controls to fine-tune the compression.
    
-
-
    The fourth module is the Limiter, which is a simple and effective limiter with two modes and two types. You can choose from soft and hard modes, depending on how much limiting you need. You can also switch between peak and RMS types, depending on how you want to limit the peaks of your drums. You can also adjust the threshold and ceiling controls to set the maximum output level.
    
-
-
    Analog Obsession – SweetDrums 2.0 is a plugin that can transform your drum tracks from dull and lifeless to punchy and exciting. It is compatible with VST, VST3 and AU formats for Windows and Mac systems. It is also very CPU-friendly and easy to use. You can download it for free from the Analog Obsession website or make a donation to support the developer.
    
-
-
    But what makes Analog Obsession – SweetDrums 2.0 stand out from other drum processing plugins is its simplicity and effectiveness. You don't need to tweak dozens of parameters to get a great drum sound. You just need to use one knob: the Process knob.
    
-
-
    The Process knob is the heart of the plugin. It controls a complex algorithm that applies a combination of dynamic EQ, fixed EQ, saturation and compression to your drum tracks. The plugin intelligently analyzes your drum signal and applies the optimal amount of processing for each frequency band. You can adjust the Process knob from 0% to 100%, depending on how much enhancement you want.
    
-
    
-
-
    The result is a drum sound that is punchy, clear, warm and balanced. You can use Analog Obsession – SweetDrums 2.0 on individual drum tracks or on a drum bus. You can also use it on other instruments that need some extra life and energy.
    
-
-
    Analog Obsession – SweetDrums 2.0 is a plugin that can make your drums sound amazing with just one knob. It is a must-have for any producer or engineer who wants to improve their drum tracks quickly and easily. You can download it for free from the Analog Obsession website or make a donation to support the developer.
     81aa517590
    
-
    
-
    
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Autocad 2008 Keygen Internal Error 1.md b/spaces/stomexserde/gpt4-ui/Examples/Autocad 2008 Keygen Internal Error 1.md
deleted file mode 100644
index b0d5c454930d0dd4f071b45530601e521a42c9ae..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Autocad 2008 Keygen Internal Error 1.md	
+++ /dev/null
@@ -1,45 +0,0 @@
-
    
-
    How to Fix Autocad 2008 Keygen Internal Error 1
    
-
    Autocad 2008 is a popular software for designing and drafting 2D and 3D models. However, some users may encounter an internal error 1 when trying to activate the software using a keygen. This error can prevent the software from running properly and cause frustration. In this article, we will explain what causes this error and how to fix it.
    
-
    Autocad 2008 Keygen Internal Error 1

    DOWNLOAD >>> https://urlgoal.com/2uI5Yb


    
-
    What Causes Autocad 2008 Keygen Internal Error 1?
    
-
    The most common cause of this error is a mismatch between the product key and the request code generated by the software. This can happen if the user enters the wrong product key, copies the request code incorrectly, or uses an incompatible keygen. Another possible cause is a corrupted or missing system file that is required for the activation process.
    
-
    How to Fix Autocad 2008 Keygen Internal Error 1?
    
-
    There are several steps that can help resolve this error. Here are some of them:
    
-
      
-
    • Make sure you have the correct product key for your version of Autocad 2008. You can find it on the CD case, the email confirmation, or the Autodesk website.
      • 
-
    • Make sure you copy the request code exactly as it appears on the screen. Do not add any spaces or characters.
      • 
-
    • Make sure you use a compatible keygen for your version of Autocad 2008. Some keygens may not work for certain editions or languages of the software.
      • 
-
    • Run the keygen as an administrator. Right-click on the keygen file and select "Run as administrator". This can help avoid any permission issues.
      • 
-
    • Disable any antivirus or firewall software that may interfere with the activation process. Some security programs may block or delete the keygen file or prevent it from accessing the internet.
      • 
-
    • Repair or reinstall any missing or corrupted system files that may affect the activation process. You can use the System File Checker (SFC) tool to scan and fix any system file errors. To run SFC, open a command prompt as an administrator and type "sfc /scannow".
      • 
-
    
-
    If none of these steps work, you may need to contact Autodesk support for further assistance. They may be able to provide you with an alternative activation method or a replacement product key.
    
-
    
-
-
    Conclusion
    
-
    Autocad 2008 Keygen Internal Error 1 is a common issue that can prevent users from activating their software. However, it can be fixed by following some simple steps. The main thing to remember is to use the correct product key and request code, and to run the keygen as an administrator. If the error persists, contacting Autodesk support may be the best option.
    
-
    We hope this article was helpful and informative. If you have any questions or feedback, please leave a comment below.
    
-
-
    What is Autocad 2008?
    
-
    Autocad 2008 is a software application that allows users to create and edit 2D and 3D models for various purposes. It is widely used by architects, engineers, designers, and other professionals who need to visualize and communicate their ideas. Autocad 2008 offers many features and tools that can help users create accurate and realistic models, such as dynamic blocks, annotation scaling, layer properties per viewport, and more.
    
-
    What is a Keygen?
    
-
    A keygen is a program that generates a product key or serial number for a software application. Some users may use a keygen to activate their software without purchasing a license from the developer. However, this is illegal and unethical, and may also expose the user to security risks and malware. Using a keygen may also violate the terms of service and warranty of the software, and result in legal consequences.
    
-
    Why Use Autocad 2008?
    
-
    Autocad 2008 is one of the most popular and widely used versions of Autocad. It has many advantages over older or newer versions, such as:
    
-
      
-
    • It is compatible with most operating systems and hardware configurations.
      • 
-
    • It has a familiar and user-friendly interface that does not require much learning curve.
      • 
-
    • It has a large and active community of users and experts who can provide support and guidance.
      • 
-
    • It has many plugins and extensions that can enhance its functionality and performance.
      • 
-
    • It has a high level of stability and reliability that minimizes errors and crashes.
      • 
-
    
-
    However, Autocad 2008 also has some disadvantages, such as:
    
-
      
-
    • It is no longer supported or updated by Autodesk, which means it may not have the latest features or security patches.
      • 
-
    • It may not be compatible with newer file formats or standards that are used by other software applications.
      • 
-
    • It may have some bugs or glitches that have not been fixed or resolved.
      • 
-
    • It may require a high level of system resources and disk space to run smoothly.
      • 
-
     e93f5a0c3f
    
-
    
-
    
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Cnc Keller Symplus 5.1 21.md b/spaces/stomexserde/gpt4-ui/Examples/Cnc Keller Symplus 5.1 21.md
deleted file mode 100644
index ec494a73968cac287c9ec8429d152536004484d0..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Cnc Keller Symplus 5.1 21.md	
+++ /dev/null
@@ -1,35 +0,0 @@
-
    
-
    How to use CNC Keller SYMplus 5.1 21 for efficient turning and milling
    
-
    CNC Keller SYMplus 5.1 21 is a professional CAD/CAM software for turning and milling that can help you improve your production and training. It is a modular system that allows you to customize your CNC solution according to your needs and preferences. Whether you want to use SYMplus for CNC production or CNC training, you will benefit from its features and functions.
    
-
    cnc keller symplus 5.1 21

    Download Zip –––––>>> https://urlgoal.com/2uI6Dd


    
-
    In this article, we will show you how to use CNC Keller SYMplus 5.1 21 for efficient turning and milling, and what are some of the advantages of this software.
    
-
    How to use SYMplus for CNC production
    
-
    If you want to use SYMplus for CNC production, you will need to install the software on your computer and connect it to your CNC machine. You can choose from different modules of SYMplus, such as:
    
-
      
-
    • Virtual workshop: This module allows you to simulate your CNC machine and check your programs for errors and collisions before running them on the real machine.
      • 
-
    • DIN/PAL: This module allows you to create and edit CNC programs using the standard DIN/PAL language or the graphical PAL editor.
      • 
-
    • Controls: This module allows you to select from different control simulators that match your CNC machine's control system.
      • 
-
    • CAD/CAM: This module allows you to import and export CAD files, create geometries, generate toolpaths, and optimize your machining strategies.
      • 
-
    
-
    You can also use the postprocessors feature of SYMplus to adapt your CNC programs to different machines and controls. SYMplus supports over 100 postprocessors for various manufacturers and models.
    
-
    
-
    How to use SYMplus for CNC training
    
-
    If you want to use SYMplus for CNC training, you will need to install the software on your computer and connect it to a projector or a smartboard. You can also use the student license of SYMplus to install the software on your students' devices. You can choose from different modules of SYMplus, such as:
    
-
      
-
    • Virtual workshop: This module allows you to demonstrate your CNC machine and its functions to your students, and let them practice their skills in a safe and realistic environment.
      • 
-
    • DIN/PAL: This module allows you to teach your students how to create and edit CNC programs using the standard DIN/PAL language or the graphical PAL editor.
      • 
-
    • Controls: This module allows you to select from different control simulators that match your CNC machine's control system or the ones that your students will encounter in their future workplaces.
      • 
-
    • CAD/CAM: This module allows you to teach your students how to import and export CAD files, create geometries, generate toolpaths, and optimize their machining strategies.
      • 
-
    
-
    You can also use the workbooks feature of SYMplus to provide your students with exercises and tasks that cover different topics and levels of difficulty. SYMplus offers over 20 workbooks for turning and milling that are aligned with the curriculum of various countries.
    
-
    What are some of the advantages of SYMplus
    
-
    Symplus is a powerful and versatile software that can help you achieve more efficiency and quality in your turning and milling operations. Some of the advantages of SYMplus are:
    
-
      
-
    • It is easy to use and learn, thanks to its intuitive user interface and comprehensive online support.
      • 
-
    • It is compatible with most CNC machines and controls, thanks to its flexible postprocessors feature.
      • 
-
    • It is reliable and proven, thanks to its long history of development and feedback from thousands of users worldwide.
      • 
-
    • It is affordable and cost-effective, thanks to its modular structure and flexible licensing options.
      • 
-
    
-
    If you want to learn more about SYMplus or try it for free, you can visit the official website of CNC Keller[^1^] or contact their experts[^2^]. You can also check out their media library[^3^] for more information and tutorials.
     7196e7f11a
    
-
    
-
    
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Interspire Email Marketer 6.1 0.md b/spaces/stomexserde/gpt4-ui/Examples/Interspire Email Marketer 6.1 0.md
deleted file mode 100644
index c98e57f94429b2cff37ad3cefe540e5a4981141d..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Interspire Email Marketer 6.1 0.md	
+++ /dev/null
@@ -1,34 +0,0 @@
-
    
-
    How to Use Interspire Email Marketer 6.1 0 to Boost Your Business
    
-
    Interspire Email Marketer 6.1 0 is a powerful self-hosted email marketing automation software that allows you to create and send personalized and targeted email campaigns to your customers and prospects. With Interspire Email Marketer 6.1 0, you can:
    
-
    Interspire Email Marketer 6.1 0

    Download ✪ https://urlgoal.com/2uIbb7


    
-
      
-
    • Send any type of email communication, such as newsletters, promotions, surveys, follow-ups, etc.
      • 
-
    • Improve sales and increase revenue by sending relevant and timely offers to your audience.
      • 
-
    • Build trust and loyalty with your subscribers by providing valuable and engaging content.
      • 
-
    • Comply with email laws and regulations, such as CAN-SPAM, GDPR, CASL, etc.
      • 
-
    • Analyze and optimize your email marketing performance with detailed reports and statistics.
      • 
-
    
-
    In this article, we will show you how to use Interspire Email Marketer 6.1 0 to create and send your first email campaign in a few easy steps.
    
-
    Step 1: Install Interspire Email Marketer 6.1 0 on Your Server
    
-
    The first step is to install Interspire Email Marketer 6.1 0 on your own server. You can choose to install it by yourself or have it installed by the Interspire team of professionals for an additional fee. You will need a web server that supports PHP and MySQL, and a valid license key that you can purchase from the Interspire website.
    
-
    Once you have installed Interspire Email Marketer 6.1 0 on your server, you can access the admin panel by entering your username and password. You will see a dashboard that shows you an overview of your email marketing activities, such as the number of contacts, lists, campaigns, opens, clicks, bounces, etc.
    
-
    Step 2: Create Your Contact List and Import Your Subscribers
    
-
    The next step is to create your contact list and import your subscribers into Interspire Email Marketer 6.1 0. You can create an unlimited number of lists and segments based on different criteria, such as demographics, interests, behavior, etc. You can also create custom fields to store additional information about your contacts.
    
-
    To import your subscribers into Interspire Email Marketer 6.1 0, you can use one of the following methods:
    
-
    
-
      
-
    • Upload a file (CSV, TXT, XML, etc.) that contains your subscriber data.
      • 
-
    • Copy and paste your subscriber data from another source.
      • 
-
    • Import from another application via the XML API.
      • 
-
    • Add subscribers manually one by one.
      • 
-
    
-
    You can also choose to enable double opt-in confirmation for your subscribers to verify their email addresses and consent before adding them to your list. This will help you comply with email laws and improve your deliverability.
    
-
    Step 3: Create Your Email Template and Content
    
-
    The third step is to create your email template and content using the easy WYSIWYG editor or the advanced HTML editor in Interspire Email Marketer 6.1 0. You can use one of the built-in templates or upload your own custom template. You can also easily upload and manage images for your email design.
    
-
    To make your email more personalized and relevant, you can use email personalization features such as dynamic content blocks and custom fields. For example, you can insert the subscriber's name, location, purchase history, etc. into your email content based on their data in your contact list.
    
-
    Step 4: Test and Send Your Email Campaign
    
-
    The final step is to test and send your email campaign to your contact list or segment. You can use the popup and inbox previews feature to see how your email will look like in different email clients and devices before sending it. You can also use the split testing feature to test different versions of your email subject line, content, design, etc. and see which one performs better.
    
-
    You can choose to send your email campaign immediately or schedule it for a later date and time. You can also send it to multiple lists at once or pause and resume sending at any time. You can
     81aa517590
    
-
    
-
    
\ No newline at end of file
diff --git a/spaces/sub314xxl/MusicGen/audiocraft/models/loaders.py b/spaces/sub314xxl/MusicGen/audiocraft/models/loaders.py
deleted file mode 100644
index 97c662c3212b7695669cbfc5214ff2f099c3f319..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MusicGen/audiocraft/models/loaders.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Utility functions to load from the checkpoints.
-Each checkpoint is a torch.saved dict with the following keys:
-- 'xp.cfg': the hydra config as dumped during training. This should be used
-    to rebuild the object using the audiocraft.models.builders functions,
-- 'model_best_state': a readily loadable best state for the model, including
-    the conditioner. The model obtained from `xp.cfg` should be compatible
-    with this state dict. In the case of a LM, the encodec model would not be
-    bundled along but instead provided separately.
-
-Those functions also support loading from a remote location with the Torch Hub API.
-They also support overriding some parameters, in particular the device and dtype
-of the returned model.
-"""
-
-from pathlib import Path
-from huggingface_hub import hf_hub_download
-import typing as tp
-import os
-
-from omegaconf import OmegaConf
-import torch
-
-from . import builders
-
-
-HF_MODEL_CHECKPOINTS_MAP = {
-    "small": "facebook/musicgen-small",
-    "medium": "facebook/musicgen-medium",
-    "large": "facebook/musicgen-large",
-    "melody": "facebook/musicgen-melody",
-}
-
-
-def _get_state_dict(
-    file_or_url_or_id: tp.Union[Path, str],
-    filename: tp.Optional[str] = None,
-    device='cpu',
-    cache_dir: tp.Optional[str] = None,
-):
-    # Return the state dict either from a file or url
-    file_or_url_or_id = str(file_or_url_or_id)
-    assert isinstance(file_or_url_or_id, str)
-
-    if os.path.isfile(file_or_url_or_id):
-        return torch.load(file_or_url_or_id, map_location=device)
-
-    if os.path.isdir(file_or_url_or_id):
-        file = f"{file_or_url_or_id}/{filename}"
-        return torch.load(file, map_location=device)
-
-    elif file_or_url_or_id.startswith('https://'):
-        return torch.hub.load_state_dict_from_url(file_or_url_or_id, map_location=device, check_hash=True)
-
-    elif file_or_url_or_id in HF_MODEL_CHECKPOINTS_MAP:
-        assert filename is not None, "filename needs to be defined if using HF checkpoints"
-
-        repo_id = HF_MODEL_CHECKPOINTS_MAP[file_or_url_or_id]
-        file = hf_hub_download(repo_id=repo_id, filename=filename, cache_dir=cache_dir)
-        return torch.load(file, map_location=device)
-
-    else:
-        raise ValueError(f"{file_or_url_or_id} is not a valid name, path or link that can be loaded.")
-
-
-def load_compression_model(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None):
-    pkg = _get_state_dict(file_or_url_or_id, filename="compression_state_dict.bin", cache_dir=cache_dir)
-    cfg = OmegaConf.create(pkg['xp.cfg'])
-    cfg.device = str(device)
-    model = builders.get_compression_model(cfg)
-    model.load_state_dict(pkg['best_state'])
-    model.eval()
-    return model
-
-
-def load_lm_model(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None):
-    pkg = _get_state_dict(file_or_url_or_id, filename="state_dict.bin", cache_dir=cache_dir)
-    cfg = OmegaConf.create(pkg['xp.cfg'])
-    cfg.device = str(device)
-    if cfg.device == 'cpu':
-        cfg.dtype = 'float32'
-    else:
-        cfg.dtype = 'float16'
-    model = builders.get_lm_model(cfg)
-    model.load_state_dict(pkg['best_state'])
-    model.eval()
-    model.cfg = cfg
-    return model
diff --git a/spaces/supertori/files/stable-diffusion-webui/extensions/openpose-editor/README.zh-cn.md b/spaces/supertori/files/stable-diffusion-webui/extensions/openpose-editor/README.zh-cn.md
deleted file mode 100644
index 1a3bae30a3e4e01c95d3ab52660b82e08d8513eb..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/extensions/openpose-editor/README.zh-cn.md
+++ /dev/null
@@ -1,41 +0,0 @@
-## Openpose Editor
-
-[日本語](README.md) | [English](README.en.md)|中文
-
-![image](https://user-images.githubusercontent.com/92153597/219921945-468b2e4f-a3a0-4d44-a923-13ceb0258ddc.png)
-
-适用于Automatic1111/stable-diffusion-webui 的Openpose Editor 插件。
-
-功能：
-- 直接编辑骨骼动作
-- 从图像识别姿势
-
-本插件实现以下操作：
-
--  「Add」：添加一个新骨骼
--  「Detect from image」: 从图片中识别姿势
--  「Add Background image」: 添加背景图片
--  「Load JSON」：载入JSON文件
-
--  「Save PNG」: 保存为PNG格式图片
--  「Send to ControlNet」:将骨骼姿势发送到 ControlNet 
--  「Save JSON」：将骨骼保存为JSON
-## 安装方法
-
-1. 打开扩展（Extension）标签。
-2. 点击从网址安装（Install from URL）
-3. 在扩展的 git 仓库网址（URL for extension's git repository）处输入 https://github.com/fkunn1326/openpose-editor.git
-4. 点击安装（Install）
-5. 重启 WebUI
-## 注意
-
-不要给ConrtolNet 的 "Preprocessor" 选项指定任何值，请保持在none状态
-
-## 常见问题
-Mac OS可能会出现：
-> urllib.error.URLError: 
-
-请执行文件
-```
-/Applications/Python\ $version /Install\ Certificates.command
-```
diff --git a/spaces/supertori/files/stable-diffusion-webui/extensions/openpose-editor/scripts/main.py b/spaces/supertori/files/stable-diffusion-webui/extensions/openpose-editor/scripts/main.py
deleted file mode 100644
index acba4ea6bf5d8b459b8dbb78b1b0f5fa4ed5fc73..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/extensions/openpose-editor/scripts/main.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import os
-import numpy as np
-import cv2
-
-import gradio as gr
-
-import modules.scripts as scripts
-from modules import script_callbacks
-from modules import extensions
-
-from basicsr.utils.download_util import load_file_from_url
-
-from scripts.openpose.body import Body
-
-body_estimation = None
-
-def pil2cv(in_image):
-  out_image = np.array(in_image, dtype=np.uint8)
-
-  if out_image.shape[2] == 3:
-      out_image = cv2.cvtColor(out_image, cv2.COLOR_RGB2BGR)
-  return out_image
-
-def candidate2li(li):
-  res = []
-  for x, y, *_ in li:
-    res.append([x, y])
-  return res
-
-def subset2li(li):
-  res = []
-  for r in li:
-    for c in r:
-      res.append(c)
-  return res
-
-class Script(scripts.Script):
-  def __init__(self) -> None:
-    super().__init__()
-
-  def title(self):
-    return "OpenPose Editor"
-
-  def show(self, is_img2img):
-    return scripts.AlwaysVisible
-
-  def ui(self, is_img2img):
-    return ()
-
-def on_ui_tabs():
-  with gr.Blocks(analytics_enabled=False) as openpose_editor:
-    with gr.Row():
-      with gr.Column():
-        width = gr.Slider(label="width", minimum=64, maximum=2048, value=512, step=64, interactive=True)
-        height = gr.Slider(label="height", minimum=64, maximum=2048, value=512, step=64, interactive=True)
-        with gr.Row():
-          add = gr.Button(value="Add", variant="primary")
-          # delete = gr.Button(value="Delete")
-        with gr.Row():
-          reset_btn = gr.Button(value="Reset")
-          json_input = gr.Button(value="Load from JSON")
-          png_input = gr.Button(value="Detect from image")
-          png_input_area = gr.Image(label="Detect from image", elem_id="openpose_editor_input")
-          bg_input = gr.Button(value="Add Background image")
-
-      with gr.Column():
-        # gradioooooo...
-        canvas = gr.HTML('')
-        jsonbox = gr.Text(label="json", elem_id="hide_json")
-        with gr.Row():
-          json_output = gr.Button(value="Save JSON")
-          png_output = gr.Button(value="Save PNG")
-          send_output = gr.Button(value="Send to ControlNet")
-
-    def estimate(img):
-      global body_estimation
-
-      modeldir = os.path.join(extensions.extensions_dir, "sd-webui-controlnet", "annotator", "openpose")
-      if body_estimation is None:
-        if not os.path.isfile(os.path.join(modeldir, "body_pose_model.pth")):
-          body_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/body_pose_model.pth"
-          load_file_from_url(body_model_path, model_dir=modeldir)
-        body_estimation = Body(os.path.join(modeldir, "body_pose_model.pth"))
-        
-      candidate, subset = body_estimation(pil2cv(img))
-
-      result = {
-        "candidate": candidate2li(candidate),
-        "subset": subset2li(subset)
-      }
-      
-      return result
-
-
-    width.change(None, [width, height], None, _js="(w, h) => {resizeCanvas(w, h)}")
-    height.change(None, [width, height], None, _js="(w, h) => {resizeCanvas(w, h)}")
-    png_output.click(None, [], None, _js="savePNG")
-    bg_input.click(None, [], None, _js="addBackground")
-    png_input.click(None, [], None, _js="detectImage")
-    add.click(None, [], None, _js="addPose")
-    png_input_area.change(estimate, [png_input_area], [jsonbox])
-    send_output.click(None, [], None, _js="sendImage")
-    reset_btn.click(None, [], None, _js="resetCanvas")
-    json_input.click(None, None, [width, height], _js="loadJSON")
-    json_output.click(None, None, None, _js="saveJSON")
-
-  return [(openpose_editor, "OpenPose Editor", "openpose_editor")]
-
-script_callbacks.on_ui_tabs(on_ui_tabs)
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Empresas Familiares Imanol Belausteguigoitia Pdf Download __TOP__.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Empresas Familiares Imanol Belausteguigoitia Pdf Download __TOP__.md
deleted file mode 100644
index 952a05f8f84d8b5133f2fa46dfe4df0d49a60647..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Empresas Familiares Imanol Belausteguigoitia Pdf Download __TOP__.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
    empresas familiares imanol belausteguigoitia pdf download

    DOWNLOAD ⚹ https://cinurl.com/2uEY3W


    
-
    
-Empresas Familiares Imanol Belausteguigoitia Pdf Download. March 3 2020 0. empresas familiares imanol belausteguigoitia, imanol belausteguigoitia rius ... 4d29de3e1b
    
-
    
-
    
-
    
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Manual Istorie Clasa 11 Humanitas Pdf 24 TOP.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Manual Istorie Clasa 11 Humanitas Pdf 24 TOP.md
deleted file mode 100644
index 3e8bbf88d586dc18f5d7cb06e0476990138f4ea4..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Manual Istorie Clasa 11 Humanitas Pdf 24 TOP.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
    manual istorie clasa 11 humanitas pdf 24

    Download >> https://cinurl.com/2uEYWy


    
-
-See full version: Manual istorie clasa 11 editura gimnasium pdf ... Clasa is the sheridan. Perfidy issuant manual dooes tonally 11 the unforgettably untucked element. Trefa repairman can gum. ... 12.2.2016, 24:55. Theanthropic saba was sillily ... 4d29de3e1b
    
-
    
-
    
-
    
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/configs/_base_/schedules/schedule_20k.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/configs/_base_/schedules/schedule_20k.py
deleted file mode 100644
index bf780a1b6f6521833c6a5859675147824efa599d..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/configs/_base_/schedules/schedule_20k.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
-optimizer_config = dict()
-# learning policy
-lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
-# runtime settings
-runner = dict(type='IterBasedRunner', max_iters=20000)
-checkpoint_config = dict(by_epoch=False, interval=2000)
-evaluation = dict(interval=2000, metric='mIoU')
diff --git a/spaces/t13718236382/bingoGPT4/src/state/index.ts b/spaces/t13718236382/bingoGPT4/src/state/index.ts
deleted file mode 100644
index 272106d619c69f124ea8dd0f10872ab4840152d7..0000000000000000000000000000000000000000
--- a/spaces/t13718236382/bingoGPT4/src/state/index.ts
+++ /dev/null
@@ -1,118 +0,0 @@
-import { BingWebBot } from '@/lib/bots/bing'
-import { BingConversationStyle, ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { nanoid } from '@/lib/utils'
-import { atom } from 'jotai'
-import { atomWithImmer } from 'jotai-immer'
-import { atomWithStorage } from 'jotai/utils'
-import { atomFamily } from 'jotai/utils'
-import { atomWithHash, atomWithLocation } from 'jotai-location'
-
-const initialMessages: ChatMessageModel[] = [
-  { author: 'system', text: 'conversation between user and robot', id: '1' },
-  { author: 'user', text: '销量最高的 3 种宠物吸尘器有哪些优点和缺点?  ', id: '2' },
-  {
-    author: 'bot', text: `
-您好，这是必应。根据网上的搜索结果，我为您找到了以下三款销量最高的宠物吸尘器，以及它们的优点和缺点：
-
-- **戴森Dyson V10轻量版 Digital Slim Fluffy无线吸尘器**[^1^] [^3^]：这款吸尘器专为宠物家庭设计，拥有强大吸力和多功能吸头，能够有效清除宠物毛发和灰尘。
-  - 优点：轻便易用，续航时间长，过滤系统先进，能够有效去除过敏原；
-  - 缺点：价格较高，噪音较大，需要定期清洁滤网。
-- **飞利浦FC6908/82（W7）无线吸尘器**[^2^] [^4^]：这是一款手持式干吸无线吸尘器，专门为宠物家庭设计。它具有75分钟的超高续航，适合大户型；除螨涡轮刷，可以深度清洁床单被罩，免受螨虫侵害；吸拖一体设计，280ml大水箱，还可以往水箱里加入清洁剂。
-  - 优点：清洁性能强劲，操作方便，适用多种场景；
-  - 缺点：噪音较大，达到84dB（A）；尘杯容量较小，只有0.6L；充电时长较长，需要5小时。
-- **必胜（Bissell） 宠物吸尘器**[^2^]：这是一款便携式手持真空吸尘器，专为清理宠物毛发而设计。它配备特殊的橡胶喷嘴和软毛刷头，可以轻松拾取宠物毛发和灰尘；几乎静音运行。
-  - 优点：轻巧易用，重量只有1.36kg；电池可充电可更换；
-  - 缺点：续航时间不稳定，在15-30分钟之间浮动；吸力不够强劲，在地毯或沙发上可能效果不佳；没有过滤系统或过滤网。
-
-希望这些信息对您有所帮助。如果您还有其他问题，请随时提问。😊
-  `, id: '3' },
-  { author: 'user', text: '今天的新闻', id: '4' },
-  {
-    author: 'bot', text: `你好，这是Bing。我根据你的搜索查询“今天的新闻”为你找到了以下信息：
-
-  # 中国新闻
-  - 《求是》杂志发表习近平总书记重要文章《深化党和国家机构改革 推进国家治理体系和治理能力现代化》[^1^]
-  - 梦之队第5金！ 全红婵陈芋汐女双10米台夺冠[^1^]
-  - 中央气象台7月16日18时发布台风橙色预警[^1^]
-  - 贵州石阡：暑期旅游带动乡村振兴[^1^]
-  - 激活大科学装置的“人才红利”[^1^]
-  - 聚才用才留才 让希望的田野成为智慧的田野[^1^]
-  - 科学家发现粮食系统增效可有效支撑我国碳中和[^1^]
-  - 成都以赛为媒提升城市美誉度[^1^]
-  - 香江观澜：“抢人才”成效显著 香港竞争力依旧[^1^]
-  - 浙江建德：新安江上享清凉[^1^]
-  - 定向资金持续精准滴灌 高质量发展力量不断积累[^1^]
-  - 企业信心恢复加大中长期投资 为稳增长奠定基础[^1^]
-  - 武汉渡江节开赛 千名“泳士”竞渡长江[^1^]
-  - 金融更加重视对实体经济支持 降低融资成本提振市场信心[^1^]
-  - 大运来了丨成都迎大运 全民健身动起来[^1^]
-  - 藏在高校里的宝藏博物馆[^1^]
-  - 中国汽车工业用70年赢得三个“全球第一”[^1^]
-  - 辽宁阜新市委常委、副市长赖化平涉嫌严重违纪违法接受审查调查[^1^]
-
-  # 国际新闻
-  - 联合国秘书长：可持续发展面临风险 各国需加强行动[^2^]
-  - 国际航运业加快绿色转型[^2^]
-  - 美企反对收紧对华芯片出口限制[^2^]
-  - 欧洲加大气候科技领域投资[^2^]
-  - 中企助力丹麦发展清洁能源[^2^]
-  - 中国代表呼吁国际社会共同努力防止乌克兰局势失控[^2^]
-  - 中国和阿尔及利亚共同构建新型国际关系典范[^2^]
-
-  以上信息仅供参考，具体详情请点击以下链接查看：
-
-  [1] [中国新闻_央视网(cctv.com)](https://news.cctv.com/china/)
-  [2] [人民网_网上的人民日报](http://www.people.com.cn/)`, id: '5' },
-  { author: 'user', text: '写一个快排', id: '6' },
-  { author: 'bot', text: "你好，这是Bing。我可以帮你用python写一个快排。快速排序是一种基于分治思想的排序算法，它的基本思想是：从待排序的数组中选择一个基准元素，将数组分为两个子数组，使得左边的子数组中的元素都小于或等于基准元素，右边的子数组中的元素都大于或等于基准元素，然后对两个子数组递归地进行快速排序，最后合并结果。下面是用python实现的快速排序的代码：\n\n```python\n# 定义一个函数，用于交换数组中的两个元素\ndef swap(arr, i, j):\n    temp = arr[i]\n    arr[i] = arr[j]\n    arr[j] = temp\n\n# 定义一个函数，用于对数组的一段区间进行划分\ndef partition(arr, low, high):\n    # 选择最右边的元素作为基准元素\n    pivot = arr[high]\n    # 初始化一个指针，指向左边区间的最后一个位置\n    i = low - 1\n    # 遍历左边区间中的每个元素\n    for j in range(low, high):\n        # 如果当前元素小于或等于基准元素\n        if arr[j] <= pivot:\n            # 将指针右移一位\n            i += 1\n            # 将指针所指的元素与当前元素交换\n            swap(arr, i, j)\n    # 将基准元素与指针右边的第一个元素交换\n    swap(arr, i + 1, high)\n    # 返回基准元素的最终位置\n    return i + 1\n\n# 定义一个函数，用于对数组进行快速排序\ndef quick_sort(arr, low, high):\n    # 如果区间长度大于1\n    if low < high:\n        # 对区间进行划分，得到基准元素的位置\n        p = partition(arr, low, high)\n        # 对左边子区间进行快速排序\n        quick_sort(arr, low, p - 1)\n        # 对右边子区间进行快速排序\n        quick_sort(arr, p + 1, high)\n\n# 测试代码\narr = [5, 3, 7, 2, 9, 1, 4, 6, 8]\nprint(\"原始数组：\", arr)\nquick_sort(arr, 0, len(arr) - 1)\nprint(\"排序后的数组：\", arr)\n```", id: '7' },
-  {
-    author: 'bot', text: "好的，我会尝试画一只猫。\n > ![一只猫](https://tse2.mm.bing.net/th/id/OIG.jz34V0PNVkPC229h9spV?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse1.mm.bing.net/th/id/OIG.6g7d.XLZMP_iwAByLhvo?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse2.mm.bing.net/th/id/OIG.iAxF4ekekYn7sZw9SmU6?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse4.mm.bing.net/th/id/OIG.qDnzeSKzUCeJcrBqc5mX?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)",
-    id: '8'
-  }
-]
-
-export const GreetMessages = [
-  '谢谢你! 知道你什么时候准备好继续前进总是很有帮助的。我现在能为你回答什么问题?',
-  '重新开始总是很棒。问我任何问题!',
-  '当然，我很乐意重新开始。我现在可以为你提供哪些帮助?',
-  '当然，我已准备好进行新的挑战。我现在可以为你做什么?',
-  '很好，让我们来更改主题。你在想什么?',
-  '不用担心，我很高兴尝试一些新内容。我现在可以为你回答什么问题?',
-  '好的，我准备好了！感谢重置。我们应该了解哪些内容？',
-  '感谢刷新！你有新的话题吗？',
-  '明白了，让我们重新开始。接下来应该讨论什么？',
-  '下一步！我可以为你做什么？',
-  '好的，我已准备好新话题。我们应该一起了解哪些内容？'
-]
-
-export const bingConversationStyleAtom = atomWithStorage('bingConversationStyle', BingConversationStyle.Creative, undefined, { unstable_getOnInit: true })
-export const voiceAtom = atomWithStorage('enableTTS', false, undefined, { unstable_getOnInit: true })
-
-type Param = { botId: BotId; page: string }
-
-const createBotInstance = () => {
-  return new BingWebBot({
-    cookie: ' ',
-    ua: ' ',
-  })
-}
-
-export const chatFamily = atomFamily(
-  (param: Param) => {
-    return atomWithImmer({
-      botId: param.botId,
-      bot: createBotInstance(),
-      messages: [] as ChatMessageModel[],
-      generatingMessageId: '',
-      abortController: undefined as AbortController | undefined,
-      conversationId: nanoid(),
-    })
-  },
-  (a, b) => a.botId === b.botId && a.page === b.page,
-)
-
-export const hashAtom = atomWithHash('dialog', '')
-
-export const locationAtom = atomWithLocation()
-
-export const voiceListenAtom = atom(false)
diff --git a/spaces/tabeina/bingo1/src/components/ui/select.tsx b/spaces/tabeina/bingo1/src/components/ui/select.tsx
deleted file mode 100644
index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000
--- a/spaces/tabeina/bingo1/src/components/ui/select.tsx
+++ /dev/null
@@ -1,123 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SelectPrimitive from '@radix-ui/react-select'
-
-import { cn } from '@/lib/utils'
-import {
-  IconArrowDown,
-  IconCheck,
-  IconChevronUpDown
-} from '@/components/ui/icons'
-
-const Select = SelectPrimitive.Root
-
-const SelectGroup = SelectPrimitive.Group
-
-const SelectValue = SelectPrimitive.Value
-
-const SelectTrigger = React.forwardRef<
-  React.ElementRef,
-  React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-  
-    {children}
-    
-      
-    
-  
-))
-SelectTrigger.displayName = SelectPrimitive.Trigger.displayName
-
-const SelectContent = React.forwardRef<
-  React.ElementRef,
-  React.ComponentPropsWithoutRef
->(({ className, children, position = 'popper', ...props }, ref) => (
-  
-    
-      
-        {children}
-      
-    
-  
-))
-SelectContent.displayName = SelectPrimitive.Content.displayName
-
-const SelectLabel = React.forwardRef<
-  React.ElementRef,
-  React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
-  
-))
-SelectLabel.displayName = SelectPrimitive.Label.displayName
-
-const SelectItem = React.forwardRef<
-  React.ElementRef,
-  React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-  
-    
-      
-        
-      
-    
-    {children}
-  
-))
-SelectItem.displayName = SelectPrimitive.Item.displayName
-
-const SelectSeparator = React.forwardRef<
-  React.ElementRef,
-  React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
-  
-))
-SelectSeparator.displayName = SelectPrimitive.Separator.displayName
-
-export {
-  Select,
-  SelectGroup,
-  SelectValue,
-  SelectTrigger,
-  SelectContent,
-  SelectLabel,
-  SelectItem,
-  SelectSeparator
-}
diff --git a/spaces/taka-yamakoshi/tokenizer-demo/app.py b/spaces/taka-yamakoshi/tokenizer-demo/app.py
deleted file mode 100644
index cb014538b6ba090ba11f5c5e5b061eca728cc389..0000000000000000000000000000000000000000
--- a/spaces/taka-yamakoshi/tokenizer-demo/app.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import pandas as pd
-import streamlit as st
-import numpy as np
-import torch
-import io
-import time
-
-@st.cache(show_spinner=True,allow_output_mutation=True)
-def load_model(model_name):
-    if model_name.startswith('bert'):
-        from transformers import BertTokenizer
-        tokenizer = BertTokenizer.from_pretrained(model_name)
-    elif model_name.startswith('gpt2'):
-        from transformers import GPT2Tokenizer
-        tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-    elif model_name.startswith('roberta'):
-        from transformers import RobertaTokenizer
-        tokenizer = RobertaTokenizer.from_pretrained(model_name)
-    elif model_name.startswith('albert'):
-        from transformers import AlbertTokenizer
-        tokenizer = AlbertTokenizer.from_pretrained(model_name)
-    return tokenizer
-
-def generate_markdown(text,color='black',font='Arial',size=20):
-    return f"
    {text}
    "
-
-def TokenizeText(sentence,tokenizer_name):
-    if len(sentence)>0:
-        if tokenizer_name.startswith('gpt2'):
-            input_sent = tokenizer(sentence)['input_ids']
-        else:
-            input_sent = tokenizer(sentence)['input_ids'][1:-1]
-        encoded_sent = [str(token) for token in input_sent]
-        decoded_sent = [tokenizer.decode([token]) for token in input_sent]
-        num_tokens = len(decoded_sent)
-
-        #char_nums = [len(word)+2 for word in decoded_sent]
-        #word_cols = st.columns(char_nums)
-        #for word_col,word in zip(word_cols,decoded_sent):
-            #with word_col:
-                #st.write(word)
-        #st.write('   '.join(encoded_sent))
-        #st.write('   '.join(decoded_sent))
-        st.markdown(generate_markdown('   '.join(encoded_sent),size=16), unsafe_allow_html=True)
-        st.markdown(generate_markdown('   '.join(decoded_sent),size=16), unsafe_allow_html=True)
-        st.markdown(generate_markdown(f'{num_tokens} tokens'), unsafe_allow_html=True)
-
-        return num_tokens
-
-def DeTokenizeText(input_str):
-    if len(input_str)>0:
-        input_sent = [int(element) for element in input_str.strip().split(' ')]
-        encoded_sent = [str(token) for token in input_sent]
-        decoded_sent = tokenizer.decode(input_sent)
-        num_tokens = len(input_sent)
-
-        #char_nums = [len(word)+2 for word in decoded_sent]
-        #word_cols = st.columns(char_nums)
-        #for word_col,word in zip(word_cols,decoded_sent):
-            #with word_col:
-                #st.write(word)
-        #st.write('   '.join(encoded_sent))
-        #st.write('   '.join(decoded_sent))
-        st.markdown(generate_markdown(decoded_sent), unsafe_allow_html=True)
-        return num_tokens
-
-if __name__=='__main__':
-
-    # Config
-    max_width = 1500
-    padding_top = 0
-    padding_right = 2
-    padding_bottom = 0
-    padding_left = 2
-
-    define_margins = f"""
-    
-    """
-    hide_table_row_index = """
-                
-                """
-    st.markdown(define_margins, unsafe_allow_html=True)
-    st.markdown(hide_table_row_index, unsafe_allow_html=True)
-
-    # Title
-    st.markdown(generate_markdown('WordPiece Explorer',size=32), unsafe_allow_html=True)
-    st.markdown(generate_markdown('- quick and easy way to explore how tokenizers work -',size=24), unsafe_allow_html=True)
-
-    # Select and load the tokenizer
-    st.sidebar.write('1. Choose the tokenizer from below')
-    tokenizer_name = st.sidebar.selectbox('',
-                                            ('bert-base-uncased','bert-large-cased',
-                                            'gpt2','gpt2-large',
-                                            'roberta-base','roberta-large',
-                                            'albert-base-v2','albert-xxlarge-v2'),index=7)
-    tokenizer = load_model(tokenizer_name)
-
-    st.sidebar.write('2. Optional settings')
-    comparison_mode = st.sidebar.checkbox('Compare two texts')
-    detokenize = st.sidebar.checkbox('de-tokenize')
-    st.sidebar.write(f'"Compare two texts" compares # tokens for two pieces of text '\
-                        +f'and "de-tokenize" converts a list of tokenized indices back to strings.')
-    st.sidebar.write(f'For "de-tokenize", make sure to type in integers, separated by single spaces.')
-    if comparison_mode:
-        sent_cols = st.columns(2)
-        num_tokens = {}
-        sents = {}
-        for sent_id, sent_col in enumerate(sent_cols):
-            with sent_col:
-                if detokenize:
-                    sentence = st.text_input(f'Tokenized IDs {sent_id+1}')
-                    num_tokens[f'sent_{sent_id+1}'] = DeTokenizeText(sentence)
-                else:
-                    sentence = st.text_input(f'Text {sent_id+1}')
-                    num_tokens[f'sent_{sent_id+1}'] = TokenizeText(sentence,tokenizer_name)
-                sents[f'sent_{sent_id+1}'] = sentence
-
-        if len(sents['sent_1'])>0 and len(sents['sent_2'])>0:
-            st.markdown(generate_markdown('# Tokens: ',size=16), unsafe_allow_html=True)
-            if num_tokens[f'sent_1']==num_tokens[f'sent_2']:
-                st.markdown(generate_markdown('Matched! ',color='MediumAquamarine'), unsafe_allow_html=True)
-            else:
-                st.markdown(generate_markdown('Not Matched... ',color='Salmon'), unsafe_allow_html=True)
-
-    else:
-        if detokenize:
-            if tokenizer_name.startswith('gpt2'):
-                default_tokens = tokenizer('Tokenizers decompose bigger words into smaller tokens')['input_ids']
-            else:
-                default_tokens = tokenizer('Tokenizers decompose bigger words into smaller tokens')['input_ids'][1:-1]
-            sentence = st.text_input(f'Tokenized IDs',value=' '.join([str(token) for token in default_tokens]))
-            num_tokens = DeTokenizeText(sentence)
-        else:
-            sentence = st.text_input(f'Text',value='Tokenizers decompose bigger words into smaller tokens')
-            num_tokens = TokenizeText(sentence,tokenizer_name)
diff --git a/spaces/team7/talk_with_wind/efficientat/helpers/flop_count.py b/spaces/team7/talk_with_wind/efficientat/helpers/flop_count.py
deleted file mode 100644
index e6629de90821c555316ae4b4b78024029b5551cf..0000000000000000000000000000000000000000
--- a/spaces/team7/talk_with_wind/efficientat/helpers/flop_count.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-# adapted from PANNs (https://github.com/qiuqiangkong/audioset_tagging_cnn)
-
-def count_macs(model, spec_size):
-    list_conv2d = []
-
-    def conv2d_hook(self, input, output):
-        batch_size, input_channels, input_height, input_width = input[0].size()
-        assert batch_size == 1
-        output_channels, output_height, output_width = output[0].size()
-
-        kernel_ops = self.kernel_size[0] * self.kernel_size[1] * (self.in_channels / self.groups)
-        bias_ops = 1 if self.bias is not None else 0
-
-        params = output_channels * (kernel_ops + bias_ops)
-        # overall macs count is:
-        # kernel**2 * in_channels/groups * out_channels * out_width * out_height
-        macs = batch_size * params * output_height * output_width
-
-        list_conv2d.append(macs)
-
-    list_linear = []
-
-    def linear_hook(self, input, output):
-        batch_size = input[0].size(0) if input[0].dim() == 2 else 1
-        assert batch_size == 1
-        weight_ops = self.weight.nelement()
-        bias_ops = self.bias.nelement()
-
-        # overall macs count is equal to the number of parameters in layer
-        macs = batch_size * (weight_ops + bias_ops)
-        list_linear.append(macs)
-
-    def foo(net):
-        if net.__class__.__name__ == 'Conv2dStaticSamePadding':
-            net.register_forward_hook(conv2d_hook)
-        childrens = list(net.children())
-        if not childrens:
-            if isinstance(net, nn.Conv2d):
-                net.register_forward_hook(conv2d_hook)
-            elif isinstance(net, nn.Linear):
-                net.register_forward_hook(linear_hook)
-            else:
-                print('Warning: flop of module {} is not counted!'.format(net))
-            return
-        for c in childrens:
-            foo(c)
-
-    # Register hook
-    foo(model)
-
-    device = next(model.parameters()).device
-    input = torch.rand(spec_size).to(device)
-    with torch.no_grad():
-        model(input)
-
-    total_macs = sum(list_conv2d) + sum(list_linear)
-
-    print("*************Computational Complexity (multiply-adds) **************")
-    print("Number of Convolutional Layers: ", len(list_conv2d))
-    print("Number of Linear Layers: ", len(list_linear))
-    print("Relative Share of Convolutional Layers: {:.2f}".format((sum(list_conv2d) / total_macs)))
-    print("Relative Share of Linear Layers: {:.2f}".format(sum(list_linear) / total_macs))
-    print("Total MACs (multiply-accumulate operations in Billions): {:.2f}".format(total_macs/10**9))
-    print("********************************************************************")
-    return total_macs
-
-
-def count_macs_transformer(model, spec_size):
-    """Count macs. Code modified from others' implementation.
-        """
-    list_conv2d = []
-
-    def conv2d_hook(self, input, output):
-        batch_size, input_channels, input_height, input_width = input[0].size()
-        assert batch_size == 1
-        output_channels, output_height, output_width = output[0].size()
-
-        kernel_ops = self.kernel_size[0] * self.kernel_size[1] * (self.in_channels / self.groups)
-        bias_ops = 1 if self.bias is not None else 0
-
-        params = output_channels * (kernel_ops + bias_ops)
-        # overall macs count is:
-        # kernel**2 * in_channels/groups * out_channels * out_width * out_height
-        macs = batch_size * params * output_height * output_width
-
-        list_conv2d.append(macs)
-
-    list_linear = []
-
-    def linear_hook(self, input, output):
-        batch_size = input[0].size(0) if input[0].dim() >= 2 else 1
-        assert batch_size == 1
-        if input[0].dim() == 3:
-            # (batch size, sequence length, embeddings size)
-            batch_size, seq_len, embed_size = input[0].size()
-
-            weight_ops = self.weight.nelement()
-            bias_ops = self.bias.nelement() if self.bias is not None else 0
-            # linear layer applied position-wise, multiply with sequence length
-            macs = batch_size * (weight_ops + bias_ops) * seq_len
-        else:
-            # classification head
-            # (batch size, embeddings size)
-            batch_size, embed_size = input[0].size()
-            weight_ops = self.weight.nelement()
-            bias_ops = self.bias.nelement() if self.bias is not None else 0
-            # overall macs count is equal to the number of parameters in layer
-            macs = batch_size * (weight_ops + bias_ops)
-        list_linear.append(macs)
-
-    list_att = []
-
-    def attention_hook(self, input, output):
-        # here we only calculate the attention macs; linear layers are processed in linear_hook
-        batch_size, seq_len, embed_size = input[0].size()
-
-        # 2 times embed_size * seq_len**2
-        # - computing the attention matrix: embed_size * seq_len**2
-        # - multiply attention matrix with value matrix: embed_size * seq_len**2
-        macs = batch_size * embed_size * seq_len * seq_len * 2
-        list_att.append(macs)
-
-    def foo(net):
-        childrens = list(net.children())
-        if net.__class__.__name__ == "MultiHeadAttention":
-            net.register_forward_hook(attention_hook)
-        if not childrens:
-            if isinstance(net, nn.Conv2d):
-                net.register_forward_hook(conv2d_hook)
-            elif isinstance(net, nn.Linear):
-                net.register_forward_hook(linear_hook)
-            else:
-                print('Warning: flop of module {} is not counted!'.format(net))
-            return
-        for c in childrens:
-            foo(c)
-
-    # Register hook
-    foo(model)
-
-    device = next(model.parameters()).device
-    input = torch.rand(spec_size).to(device)
-
-    with torch.no_grad():
-        model(input)
-
-    total_macs = sum(list_conv2d) + sum(list_linear) + sum(list_att)
-
-    print("*************Computational Complexity (multiply-adds) **************")
-    print("Number of Convolutional Layers: ", len(list_conv2d))
-    print("Number of Linear Layers: ", len(list_linear))
-    print("Number of Attention Layers: ", len(list_att))
-    print("Relative Share of Convolutional Layers: {:.2f}".format((sum(list_conv2d) / total_macs)))
-    print("Relative Share of Linear Layers: {:.2f}".format(sum(list_linear) / total_macs))
-    print("Relative Share of Attention Layers: {:.2f}".format(sum(list_att) / total_macs))
-    print("Total MACs (multiply-accumulate operations in Billions): {:.2f}".format(total_macs/10**9))
-    print("********************************************************************")
-    return total_macs
diff --git a/spaces/teamnassim/Fictionista/torch_utils/ops/grid_sample_gradfix.py b/spaces/teamnassim/Fictionista/torch_utils/ops/grid_sample_gradfix.py
deleted file mode 100644
index 107a1d3acea8a9e6bbf2c331de759f1a9f5deb15..0000000000000000000000000000000000000000
--- a/spaces/teamnassim/Fictionista/torch_utils/ops/grid_sample_gradfix.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.grid_sample` that
-supports arbitrarily high order gradients between the input and output.
-Only works on 2D images and assumes
-`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
-
-import torch
-from pkg_resources import parse_version
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False  # Enable the custom op by setting this to true.
-_use_pytorch_1_11_api = parse_version(torch.__version__) >= parse_version('1.11.0a') # Allow prerelease builds of 1.11
-
-#----------------------------------------------------------------------------
-
-def grid_sample(input, grid):
-    if _should_use_custom_op():
-        return _GridSample2dForward.apply(input, grid)
-    return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op():
-    return enabled
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dForward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, input, grid):
-        assert input.ndim == 4
-        assert grid.ndim == 4
-        output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-        ctx.save_for_backward(input, grid)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input, grid = ctx.saved_tensors
-        grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid)
-        return grad_input, grad_grid
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dBackward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, grad_output, input, grid):
-        op, _ = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
-        if _use_pytorch_1_11_api:
-            output_mask = (ctx.needs_input_grad[1], ctx.needs_input_grad[2])
-            grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False, output_mask)
-        else:
-            grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
-        ctx.save_for_backward(grid)
-        return grad_input, grad_grid
-
-    @staticmethod
-    def backward(ctx, grad2_grad_input, grad2_grad_grid):
-        _ = grad2_grad_grid # unused
-        grid, = ctx.saved_tensors
-        grad2_grad_output = None
-        grad2_input = None
-        grad2_grid = None
-
-        if ctx.needs_input_grad[0]:
-            grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid)
-
-        assert not ctx.needs_input_grad[2]
-        return grad2_grad_output, grad2_input, grad2_grid
-
-#----------------------------------------------------------------------------
diff --git a/spaces/teragron/TinyStories/export.py b/spaces/teragron/TinyStories/export.py
deleted file mode 100644
index 08b13befcf955889dfa182416796e0022bce0aa2..0000000000000000000000000000000000000000
--- a/spaces/teragron/TinyStories/export.py
+++ /dev/null
@@ -1,567 +0,0 @@
-"""
-This script has functions and utilties for model export.
-Basically, we have a bunch of versions of the model, and we
-want to export them to .bin files to be read from and inferenced in C.
-
-Among the "input" versions of PyTorch files/models:
-- Official Llama 2 weights released by Meta
-- Huggingface weights available on the hub
-- llama2.c (this repo) trained models
-
-Among the "output" versions of .bin files:
-- v0: Legacy files of the original llama2.c repo (will eventually be DEPRECATED)
-- v1-vN: Improved .bin files with a proper header, cache alignment, etc.
-
-This script aspires to provide all of these conversions.
-"""
-import os
-import gzip
-import shutil
-import struct
-import argparse
-import json
-from pathlib import Path
-
-import numpy as np
-import torch
-from torch import nn
-
-from model import ModelArgs, Transformer
-
-# -----------------------------------------------------------------------------
-# common utilities
-
-def serialize_fp32(file, tensor):
-    """ writes one fp32 tensor to file that is open in wb mode """
-    d = tensor.detach().cpu().view(-1).to(torch.float32).numpy()
-    b = struct.pack(f'{len(d)}f', *d)
-    file.write(b)
-
-def serialize_int8(file, tensor):
-    """ writes one int8 tensor to file that is open in wb mode """
-    d = tensor.detach().cpu().view(-1).numpy().astype(np.int8)
-    b = struct.pack(f'{len(d)}b', *d)
-    file.write(b)
-
-def quantize_q80(w, group_size):
-    """
-    takes a tensor and returns the Q8_0 quantized version
-    i.e. symmetric quantization into int8, range [-127,127]
-    """
-    assert w.numel() % group_size == 0
-    ori_shape = w.shape
-    w = w.float() # convert to float32
-    w = w.reshape(-1, group_size)
-    # find the max in each group
-    wmax = torch.abs(w).max(dim=1).values
-    # calculate the scaling factor such that float = quant * scale
-    scale = wmax / 127.0
-    # scale into range [-127, 127]
-    quant = w / scale[:,None]
-    # round to nearest integer
-    int8val = torch.round(quant).to(torch.int8)
-    # dequantize by rescaling
-    fp32val = (int8val.float() * scale[:,None]).view(-1)
-    fp32valr = fp32val.reshape(-1, group_size)
-    # calculate the max error in each group
-    err = torch.abs(fp32valr - w).max(dim=1).values
-    # find the max error across all groups
-    maxerr = err.max().item()
-    return int8val, scale, maxerr
-
-# -----------------------------------------------------------------------------
-# legacy
-
-def legacy_export(model, filepath):
-    """ Original export of llama2.c bin files, i.e. version v0 """
-    out_file = open(filepath, 'wb')
-
-    # first write out the header
-    hidden_dim = model.layers[0].feed_forward.w1.weight.shape[0]
-    p = model.params
-    shared_classifier = torch.equal(model.tok_embeddings.weight, model.output.weight)
-    # legacy format uses negative/positive vocab size as a shared classifier flag
-    if not shared_classifier:
-        p.vocab_size = -p.vocab_size
-    n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads
-    header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads,
-                                    n_kv_heads, p.vocab_size, p.max_seq_len)
-    out_file.write(header)
-
-    # next write out the embedding weights
-    serialize_fp32(out_file, model.tok_embeddings.weight)
-
-    # now all the layers
-    # attention weights
-    for layer in model.layers:
-        serialize_fp32(out_file, layer.attention_norm.weight)
-    for layer in model.layers:
-        serialize_fp32(out_file, layer.attention.wq.weight)
-    for layer in model.layers:
-        serialize_fp32(out_file, layer.attention.wk.weight)
-    for layer in model.layers:
-        serialize_fp32(out_file, layer.attention.wv.weight)
-    for layer in model.layers:
-        serialize_fp32(out_file, layer.attention.wo.weight)
-    # ffn weights
-    for layer in model.layers:
-        serialize_fp32(out_file, layer.ffn_norm.weight)
-    for layer in model.layers:
-        serialize_fp32(out_file, layer.feed_forward.w1.weight)
-    for layer in model.layers:
-        serialize_fp32(out_file, layer.feed_forward.w2.weight)
-    for layer in model.layers:
-        serialize_fp32(out_file, layer.feed_forward.w3.weight)
-    # final rmsnorm
-    serialize_fp32(out_file, model.norm.weight)
-    # freqs_cis
-    serialize_fp32(out_file, model.freqs_cos[:p.max_seq_len])
-    serialize_fp32(out_file, model.freqs_sin[:p.max_seq_len])
-
-    # final classifier weights
-    if not shared_classifier:
-        serialize_fp32(out_file, model.output.weight)
-
-    # write to binary file
-    out_file.close()
-    print(f"wrote {filepath}")
-
-# -----------------------------------------------------------------------------
-# new version
-
-def version1_export(model, filepath):
-    """
-    Export the model weights in full float32 .bin file to be read from C.
-    This is same as legacy_export, but with a proper header.
-    """
-    version = 1
-
-    out_file = open(filepath, 'wb')
-    # first write out the header. the header will be 256 bytes
-    # 1) write magic, which will be uint32 of "ak42" in ASCII
-    out_file.write(struct.pack('I', 0x616b3432))
-    # 2) write version, which will be int
-    out_file.write(struct.pack('i', version))
-    # 3) write the params, which will be 7 ints
-    p = model.params
-    hidden_dim = model.layers[0].feed_forward.w1.weight.shape[0]
-    n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads
-    header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads,
-                                    n_kv_heads, p.vocab_size, p.max_seq_len)
-    out_file.write(header)
-    # 4) write some other flags
-    shared_classifier = torch.equal(model.tok_embeddings.weight, model.output.weight)
-    out_file.write(struct.pack('B', int(shared_classifier)))
-    pad = 256 - out_file.tell() # pad rest with zeros; tell returns current pos
-    assert pad >= 0
-    out_file.write(b'\0' * pad)
-
-    # now let's write out all the params
-    weights = [
-        *[layer.attention_norm.weight for layer in model.layers],
-        *[layer.ffn_norm.weight for layer in model.layers],
-        model.norm.weight,
-        model.tok_embeddings.weight,
-        *[layer.attention.wq.weight for layer in model.layers],
-        *[layer.attention.wk.weight for layer in model.layers],
-        *[layer.attention.wv.weight for layer in model.layers],
-        *[layer.attention.wo.weight for layer in model.layers],
-        *[layer.feed_forward.w1.weight for layer in model.layers],
-        *[layer.feed_forward.w2.weight for layer in model.layers],
-        *[layer.feed_forward.w3.weight for layer in model.layers],
-    ]
-    if not shared_classifier:
-        weights.append(model.output.weight)
-    for w in weights:
-        serialize_fp32(out_file, w)
-
-    # write to binary file
-    out_file.close()
-    print(f"wrote {filepath}")
-
-def version2_export(model, filepath, group_size=64):
-    """
-    Export the model weights in Q8_0 into .bin file to be read from C.
-    That is:
-    - quantize all weights to symmetric int8, in range [-127, 127]
-    - all other tensors (the rmsnorm params) are kept and exported in fp32
-    - quantization is done in groups of group_size to reduce the effects of any outliers
-    """
-    version = 2
-
-    # let's first do some validation for this export type
-    while model.params.dim % group_size != 0:
-        group_size //= 2
-        print(f"BACKOFF: reducing group size to {group_size} to fit hidden_dim")
-    weights = [
-        model.tok_embeddings.weight,
-        *[layer.attention.wq.weight for layer in model.layers],
-        *[layer.attention.wk.weight for layer in model.layers],
-        *[layer.attention.wv.weight for layer in model.layers],
-        *[layer.attention.wo.weight for layer in model.layers],
-        *[layer.feed_forward.w1.weight for layer in model.layers],
-        *[layer.feed_forward.w2.weight for layer in model.layers],
-        *[layer.feed_forward.w3.weight for layer in model.layers],
-    ]
-    shared_classifier = torch.equal(model.tok_embeddings.weight, model.output.weight)
-    if not shared_classifier:
-        weights.append(model.output.weight)
-    for w in weights:
-        assert w.numel() % group_size == 0, f"weight {i} has numel {w.numel()}, not a multiple of group_size {group_size}"
-
-    # write
-    out_file = open(filepath, 'wb')
-    # first write out the header. the header will be 256 bytes
-    # 1) write magic, which will be uint32 of "ak42" in ASCII
-    out_file.write(struct.pack('I', 0x616b3432))
-    # 2) write version, which will be int
-    out_file.write(struct.pack('i', version))
-    # 3) write the params, which will be 7 ints
-    p = model.params
-    hidden_dim = model.layers[0].feed_forward.w1.weight.shape[0]
-    n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads
-    header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads,
-                                    n_kv_heads, p.vocab_size, p.max_seq_len)
-    out_file.write(header)
-    # 4) write some other flags
-    out_file.write(struct.pack('B', int(shared_classifier)))
-    out_file.write(struct.pack('i', group_size)) # group size used for quantization
-    pad = 256 - out_file.tell() # pad rest with zeros; tell returns current pos
-    assert pad >= 0
-    out_file.write(b'\0' * pad)
-    # now that the header is done, let's write out the model
-
-    # first let's write out all the params that we are keeping in fp32: the norms
-    for layer in model.layers: # attention norms
-        serialize_fp32(out_file, layer.attention_norm.weight)
-    for layer in model.layers: # MLP norms
-        serialize_fp32(out_file, layer.ffn_norm.weight)
-    serialize_fp32(out_file, model.norm.weight) # final pre-classifier norm
-
-    # now let's write out all the params that we are quantizing to Q8_0
-    # note we skip classifier weights, which are shared with the embedding
-    ew = []
-    for i, w in enumerate(weights):
-        # quantize this weight
-        q, s, err = quantize_q80(w, group_size)
-        # save the int8 weights to file
-        serialize_int8(out_file, q) # save the tensor in int8
-        serialize_fp32(out_file, s) # save scale factors
-        # logging
-        ew.append((err, w.shape))
-        print(f"{i+1}/{len(weights)} quantized {tuple(w.shape)} to Q8_0 with max error {err}")
-
-    # print the highest error across all weights, should be very small, e.g. O(~0.001)
-    ew.sort(reverse=True)
-    print(f"max quantization group error across all weights: {ew[0][0]}")
-
-    # write to binary file
-    out_file.close()
-    print(f"wrote {filepath}")
-
-def hf_export(llama_model, filepath, group_size=64, dtype=torch.float32):
-    """ Generate the pytorch_model.bin state_dict and config.json for HuggingFace """
-
-    try:
-        from transformers.models.llama.configuration_llama import LlamaConfig
-    except ImportError:
-        print("Error: transformers package is required to load huggingface models")
-        print("Please run `pip install transformers` to install it")
-        return None
-
-    # Generate LlamaModel state_dict
-    hf_state_dict = {}
-
-    # Sometimes we have repeated key values for the heads
-    dim = llama_model.params.dim
-    num_key_value_heads = llama_model.params.n_kv_heads
-    n_rep = llama_model.params.n_heads // num_key_value_heads
-    key_value_dim = dim // n_rep
-
-    # HuggingFace needs the weights permuted.
-    # See: https://github.com/huggingface/transformers/blob/b132c1703eb1c8bd9dfa4ad6a9be2bfd6ef819e9/src/transformers/models/llama/convert_llama_weights_to_hf.py#L122
-    def permute_original(w, n_heads=llama_model.params.n_heads, dim1=dim, dim2=dim):
-        return w.view(dim1, dim2).reshape(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
-
-    # Transfer weights from llama model to the HF state dictionary format
-    hf_state_dict['model.embed_tokens.weight'] = llama_model.tok_embeddings.weight.clone().to(dtype)
-    hf_state_dict['model.norm.weight'] = llama_model.norm.weight.clone().to(dtype)
-
-    # Add each layer's weights to the HF state dictionary
-    for i, layer in enumerate(llama_model.layers):
-        layer_id = layer.layer_id
-        hf_state_dict[f'model.layers.{i}.input_layernorm.weight'] = llama_model.layers[layer_id].attention_norm.weight.clone().to(dtype)
-        hf_state_dict[f'model.layers.{i}.self_attn.q_proj.weight'] = permute_original(llama_model.layers[layer_id].attention.wq.weight.clone()).to(dtype)
-        hf_state_dict[f'model.layers.{i}.self_attn.k_proj.weight'] = permute_original(llama_model.layers[layer_id].attention.wk.weight.clone(), num_key_value_heads, key_value_dim, dim).to(dtype)
-        hf_state_dict[f'model.layers.{i}.self_attn.v_proj.weight'] = llama_model.layers[layer_id].attention.wv.weight.clone().to(dtype)
-        hf_state_dict[f'model.layers.{i}.self_attn.o_proj.weight'] = llama_model.layers[layer_id].attention.wo.weight.clone().to(dtype)
-        hf_state_dict[f'model.layers.{i}.post_attention_layernorm.weight'] = llama_model.layers[layer_id].ffn_norm.weight.clone().to(dtype)
-        hf_state_dict[f'model.layers.{i}.mlp.gate_proj.weight'] = llama_model.layers[layer_id].feed_forward.w1.weight.clone().to(dtype)
-        hf_state_dict[f'model.layers.{i}.mlp.down_proj.weight'] = llama_model.layers[layer_id].feed_forward.w2.weight.clone().to(dtype)
-        hf_state_dict[f'model.layers.{i}.mlp.up_proj.weight'] = llama_model.layers[layer_id].feed_forward.w3.weight.clone().to(dtype)
-
-    # llama2.c usually uses tied weights -> reference the embed_tokens.weights instead
-    hf_state_dict['lm_head.weight'] = hf_state_dict['model.embed_tokens.weight']
-
-    # We check that the embeddings are tied, else use manual output weights
-    _embeddings_are_tied: bool = torch.equal(llama_model.tok_embeddings.weight, llama_model.output.weight)
-    if not _embeddings_are_tied:
-        hf_state_dict['lm_head.weight'] = llama_model.output.weight.clone().to(dtype)
-
-
-    # Generate LlamaConfig (seen in transformers.models.llama.configuration_llama)
-
-    # Extract necessary attributes from llama.c model
-    vocab_size = llama_model.params.vocab_size
-    hidden_size = llama_model.params.dim
-    intermediate_size = llama_model.layers[0].feed_forward.w1.weight.shape[0]
-    num_hidden_layers = llama_model.params.n_layers
-    num_attention_heads = llama_model.params.n_heads
-    num_key_value_heads = llama_model.params.n_kv_heads
-    max_position_embeddings = llama_model.params.max_seq_len
-    rms_norm_eps = llama_model.params.norm_eps
-
-    # TODO check values for:
-    # pretraining_tp, initializer_range, use_cache,
-    # rope_theta, and rope_scaling.
-
-    config = LlamaConfig(
-        vocab_size=vocab_size,
-        hidden_size=hidden_size,
-        intermediate_size=intermediate_size,
-        num_hidden_layers=num_hidden_layers,
-        num_attention_heads=num_attention_heads,
-        num_key_value_heads=num_key_value_heads,
-        max_position_embeddings=max_position_embeddings,
-        rms_norm_eps=rms_norm_eps,
-        tie_word_embeddings=_embeddings_are_tied,
-        # Manual
-        architectures=["LlamaForCausalLM"],
-        hidden_act="silu",
-    )
-
-
-    # Save files in directory filepath
-    # First make the directory if it doesn't exist
-    os.makedirs(filepath, exist_ok=True)
-
-    # Save the state dictionary in .bin format, and config as .json
-    torch.save(hf_state_dict, os.path.join(filepath, "pytorch_model.bin"))
-    config.save_pretrained(filepath)
-
-
-# -----------------------------------------------------------------------------
-# Load / import functions
-
-def load_checkpoint(checkpoint):
-
-    # load the provided model checkpoint
-    checkpoint_dict = torch.load(checkpoint, map_location='cpu')
-    gptconf = ModelArgs(**checkpoint_dict['model_args'])
-    model = Transformer(gptconf)
-    state_dict = checkpoint_dict['model']
-    unwanted_prefix = '_orig_mod.'
-    for k,v in list(state_dict.items()):
-        if k.startswith(unwanted_prefix):
-            state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
-    model.load_state_dict(state_dict, strict=False)
-    model.eval()
-    return model
-
-def load_meta_model(model_path):
-    params_path = os.path.join(model_path, 'params.json')
-    with open(params_path) as f:
-        params = json.load(f)
-        print(params)
-
-    model_paths = sorted(list(Path(model_path).glob('consolidated.*.pth')))
-    models = [torch.load(p, map_location='cpu') for p in model_paths]
-
-    def concat_weights(models):
-        state_dict = {}
-        for name in list(models[0]):
-            tensors = [model[name] for model in models]
-            if len(tensors) == 1 or len(tensors[0].shape) == 1:
-                state_dict[name] = tensors[0]
-                continue
-            is_axis_1 = (
-                name.startswith('tok_embeddings.')
-                or name.endswith('.attention.wo.weight')
-                or name.endswith('.feed_forward.w2.weight')
-            )
-            axis = 1 if is_axis_1 else 0
-            state_dict[name] = torch.cat(tensors, dim=axis)
-            for model in models:
-                del model[name]
-        return state_dict
-
-    state_dict = concat_weights(models)
-    del models
-
-    # set ModelArgs
-    config = ModelArgs()
-    config.dim = params["dim"]
-    config.n_layers = params["n_layers"]
-    config.n_heads = params["n_heads"]
-    config.n_kv_heads = params.get('n_kv_heads') or params['n_heads']
-    config.multiple_of = params["multiple_of"]
-    config.norm_eps = params["norm_eps"]
-
-    config.vocab_size = state_dict['tok_embeddings.weight'].shape[0]
-    config.max_seq_len = 2048
-
-
-    # create a new Transformer object and set weights
-    model = Transformer(config)
-
-    model.tok_embeddings.weight = nn.Parameter(state_dict['tok_embeddings.weight'])
-    model.norm.weight = nn.Parameter(state_dict['norm.weight'])
-
-    for layer in model.layers:
-        i = layer.layer_id
-        layer.attention_norm.weight = nn.Parameter(state_dict[f'layers.{i}.attention_norm.weight'])
-        layer.attention.wq.weight = nn.Parameter(state_dict[f'layers.{i}.attention.wq.weight'])
-        layer.attention.wk.weight = nn.Parameter(state_dict[f'layers.{i}.attention.wk.weight'])
-        layer.attention.wv.weight = nn.Parameter(state_dict[f'layers.{i}.attention.wv.weight'])
-        layer.attention.wo.weight = nn.Parameter(state_dict[f'layers.{i}.attention.wo.weight'])
-        layer.ffn_norm.weight = nn.Parameter(state_dict[f'layers.{i}.ffn_norm.weight'])
-        layer.feed_forward.w1.weight = nn.Parameter(state_dict[f'layers.{i}.feed_forward.w1.weight'])
-        layer.feed_forward.w2.weight = nn.Parameter(state_dict[f'layers.{i}.feed_forward.w2.weight'])
-        layer.feed_forward.w3.weight = nn.Parameter(state_dict[f'layers.{i}.feed_forward.w3.weight'])
-
-    # final classifier
-    model.output.weight = nn.Parameter(state_dict['output.weight'])
-    model.eval()
-    return model
-
-def load_hf_model(model_path):
-
-    try:
-        from transformers import AutoModelForCausalLM
-    except ImportError:
-        print("Error: transformers package is required to load huggingface models")
-        print("Please run `pip install transformers` to install it")
-        return None
-
-    # load HF model
-    hf_model = AutoModelForCausalLM.from_pretrained(model_path)
-    hf_dict = hf_model.state_dict()
-
-    # convert LlamaConfig to ModelArgs
-    config = ModelArgs()
-    config.dim = hf_model.config.hidden_size
-    config.n_layers = hf_model.config.num_hidden_layers
-    config.n_heads = hf_model.config.num_attention_heads
-    config.n_kv_heads = hf_model.config.num_attention_heads
-    config.vocab_size = hf_model.config.vocab_size
-    config.hidden_dim = hf_model.config.intermediate_size
-    config.norm_eps = hf_model.config.rms_norm_eps
-    config.max_seq_len = hf_model.config.max_position_embeddings
-
-    # create a new Transformer object and set weights
-    model = Transformer(config)
-
-    model.tok_embeddings.weight = nn.Parameter(hf_dict['model.embed_tokens.weight'])
-    model.norm.weight = nn.Parameter(hf_dict['model.norm.weight'])
-
-    # huggingface permutes WQ and WK, this function reverses it
-    def permute_reverse(w, n_heads=config.n_heads, dim1=config.dim, dim2=config.dim):
-        return w.view(n_heads, 2, dim1 // n_heads // 2, dim2).transpose(1, 2).reshape(dim1, dim2)
-
-    for layer in model.layers:
-        i = layer.layer_id
-        layer.attention_norm.weight = nn.Parameter(hf_dict[f'model.layers.{i}.input_layernorm.weight'])
-        layer.attention.wq.weight = nn.Parameter(permute_reverse(hf_dict[f'model.layers.{i}.self_attn.q_proj.weight']))
-        layer.attention.wk.weight = nn.Parameter(permute_reverse(hf_dict[f'model.layers.{i}.self_attn.k_proj.weight']))
-        layer.attention.wv.weight = nn.Parameter(hf_dict[f'model.layers.{i}.self_attn.v_proj.weight'])
-        layer.attention.wo.weight = nn.Parameter(hf_dict[f'model.layers.{i}.self_attn.o_proj.weight'])
-        layer.ffn_norm.weight = nn.Parameter(hf_dict[f'model.layers.{i}.post_attention_layernorm.weight'])
-        layer.feed_forward.w1.weight = nn.Parameter(hf_dict[f'model.layers.{i}.mlp.gate_proj.weight'])
-        layer.feed_forward.w2.weight = nn.Parameter(hf_dict[f'model.layers.{i}.mlp.down_proj.weight'])
-        layer.feed_forward.w3.weight = nn.Parameter(hf_dict[f'model.layers.{i}.mlp.up_proj.weight'])
-
-    # final classifier
-    model.output.weight = nn.Parameter(hf_dict['lm_head.weight'])
-    model.eval()
-    return model
-
-
-# -----------------------------------------------------------------------------
-# API entrypoint
-
-def model_export(model, filepath, version, dtype=torch.float32):
-    """
-    Versions docs:
-    v-1:huggingface export, i.e. intended for use outside of this repo, in HF
-    v0: legacy llama2.c float format, DEPRECATED
-    v1: float32 export
-    v2: int8 quantized Q8_0 export, similar to llama.cpp, in groups
-    # TODO: add dtype export support for other versions (?)
-    """
-    if version == 0:
-        legacy_export(model, filepath)
-    elif version == 1:
-        version1_export(model, filepath)
-    elif version == 2:
-        version2_export(model, filepath)
-    elif version == -1:
-        hf_export(model, filepath, dtype)
-    else:
-        raise ValueError(f"unknown version {version}")
-
-def torchscript_export(model, filepath, zero_params=False, gzip_output=False):
-    """
-    (This was submitted via a PR earlier. Leaving it here, but "orphaned" for now)
-    Saves the model as a TorchScript.
-    The resulting file can be loaded in C++ code and then used for training or
-    inference with:
-        #include 
-        torch::jit::Module module = torch::jit::load("model.pt")
-    Note that the serialized model includes the initial parameters and with the default
-    ModelArgs the file is 59M and gzips down to 55M. If you want to serialize/distribute
-    the model parameters separately you can zero out the parameters before saving it and
-    it will gzip down to 780K.
-    """
-
-    # If requested zero params before saving the model. This is useful in
-    # conjunction with gzip_output.
-    if zero_params:
-        for p in model.parameters():
-            p.detach().zero_()
-
-    torch.jit.save(torch.jit.script(model), filepath)
-
-    if gzip_output:
-        with open(filepath, "rb") as f_in:
-            with gzip.open(f"{filepath}.gz", "wb") as f_out:
-                shutil.copyfileobj(f_in, f_out)
-        os.unlink(filepath)
-
-# -----------------------------------------------------------------------------
-# CLI entrypoint
-
-if __name__ == "__main__":
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("filepath", type=str, help="the output filepath")
-    parser.add_argument("--version", default=0, type=int, help="the version to export with")
-    parser.add_argument("--dtype", type=str, help="dtype of the model (fp16, fp32)", default="fp32")
-    group = parser.add_mutually_exclusive_group(required=True)
-    group.add_argument("--checkpoint", type=str, help="model checkpoint, .pt file")
-    group.add_argument("--meta-llama", type=str, help="meta llama model path")
-    group.add_argument("--hf", type=str, help="huggingface model path")
-    args = parser.parse_args()
-    dtype = {"fp16": torch.float16, "fp32": torch.float32}[args.dtype]
-
-    if args.checkpoint:
-        model = load_checkpoint(args.checkpoint)
-    elif args.meta_llama:
-        model = load_meta_model(args.meta_llama)
-    elif args.hf:
-        model = load_hf_model(args.hf)
-
-    if model is None:
-        parser.error("Can't load input model!")
-
-    # export
-    model_export(model, args.filepath, args.version, args.dtype)
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Imperium Galactica 2 Patch 116 27.md b/spaces/terfces0erbo/CollegeProjectV2/Imperium Galactica 2 Patch 116 27.md
deleted file mode 100644
index b59c70dd294852e15d930839291760882c6cd048..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Imperium Galactica 2 Patch 116 27.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-
    Imperium Galactica 2 Patch 116 27

    Download >>>>> https://bytlly.com/2uGiJ7


    
-
-24. The Strike Jacket: Our Editors' Picks. “Everyman” is a term used to describe a man who doesn't look or act like a man. The boxer combines the best of both worlds—fleece and sportswear—in a.
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-Get the best deals on eBay. Browse our range of nubuck shoes online and in store at Sydney’s leading retailers.
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-The NFL and the Players Union have announced the formation of the CBA with a four-year deal in place. From the NFL, CBA contract is a welcome development, but there are still some immediate changes that must occur before the new system can be fully put in place. here’s how the NFL and players’ union plans to change the game in the immediate future.
-
-The Strike Jacket: Our Editors' Picks. “Everyman” is a term used to describe a man who doesn't look or act like a man. The boxer combines the best of both worlds—fleece and sportswear—in a.
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-Upcoming Events. Browse our full calendar of live events throughout North America and around the world. A select number of outdoor concerts will be held in snow shoes. No matter your snow shoe preference, we have an assortment of styles and materials that are well-suited for all seasons.
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-Whether you've been out on the trail for a few hours or have been hiking for days, there's a comfortable shoe on our trail shoe product page that will help you make your trip easier. Need help choosing the right shoe for your trail conditions?
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-Look at all the photos of our women's snow shoe styles and choose your favorite. Whether you're just starting to get into snowshoeing or you're a seasoned expert, you can find the right pair of women's snow shoes to fit your style.
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-The Strike Jacket: Our Editors' Picks. “Everyman” is a term used to describe a man who doesn't look or act like a man. The boxer combines the best of both worlds— 4fefd39f24
    
-
    
-
    
-
    
diff --git a/spaces/tez321/pipeline-visualizer/README.md b/spaces/tez321/pipeline-visualizer/README.md
deleted file mode 100644
index 4644fe76d5bbd0929d0b8af607519e107918a0e7..0000000000000000000000000000000000000000
--- a/spaces/tez321/pipeline-visualizer/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
----
-title: Pipeline Visualizer
-emoji: 👀
-colorFrom: blue
-colorTo: red
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
\ No newline at end of file
diff --git a/spaces/thuanz123/peft-sd-realfill/trainer.py b/spaces/thuanz123/peft-sd-realfill/trainer.py
deleted file mode 100644
index 983f303ab34a10a1f4c2fa98b7ce6ff7ad8f25a4..0000000000000000000000000000000000000000
--- a/spaces/thuanz123/peft-sd-realfill/trainer.py
+++ /dev/null
@@ -1,145 +0,0 @@
-from __future__ import annotations
-
-import os
-import pathlib
-import shlex
-import shutil
-import subprocess
-
-import gradio as gr
-import PIL.Image
-import torch
-
-
-def pad_image(image: PIL.Image.Image) -> PIL.Image.Image:
-    w, h = image.size
-    if w == h:
-        return image
-    elif w > h:
-        new_image = PIL.Image.new(image.mode, (w, w), (0, 0, 0))
-        new_image.paste(image, (0, (w - h) // 2))
-        return new_image
-    else:
-        new_image = PIL.Image.new(image.mode, (h, h), (0, 0, 0))
-        new_image.paste(image, ((h - w) // 2, 0))
-        return new_image
-
-
-class Trainer:
-    def __init__(self):
-        self.is_running = False
-        self.is_running_message = "Another training is in progress."
-
-        self.output_dir = pathlib.Path("results")
-        self.data_dir = pathlib.Path("data")
-        
-        self.ref_data_dir = self.data_dir / "ref"
-        self.target_data_dir = self.data_dir / "target"
-
-    def check_if_running(self) -> dict:
-        if self.is_running:
-            return gr.update(value=self.is_running_message)
-        else:
-            return gr.update(value="No training is running.")
-
-    def cleanup_dirs(self) -> None:
-        shutil.rmtree(self.output_dir, ignore_errors=True)
-
-    def prepare_dataset(
-        self, 
-        ref_images: list, 
-        target_image: PIL.Image, 
-        target_mask: PIL.Image, 
-        resolution: int
-    ) -> None:
-        self.ref_data_dir.mkdir(parents=True)
-        self.target_data_dir.mkdir(parents=True)
-        
-        for i, temp_path in enumerate(ref_images):
-            image = PIL.Image.open(temp_path.name)
-            image = pad_image(image)
-            image = image.resize((resolution, resolution))
-            image = image.convert("RGB")
-            out_path = self.ref_data_dir / f"{i:03d}.jpg"
-            image.save(out_path, format="JPEG", quality=100)
-
-        target_image.save(self.target_data_dir / "target.jpg", format="JPEG", quality=100)
-        target_mask.save(self.target_data_dir / "mask.jpg", format="JPEG", quality=100)
-
-    def run(
-        self,
-        base_model: str,
-        resolution_s: str,
-        n_steps: int,
-        ref_images: list | None,
-        target_image: PIL.Image,
-        target_mask: PIL.Image,
-        unet_learning_rate: float,
-        text_encoder_learning_rate: float,
-        gradient_accumulation: int,
-        fp16: bool,
-        use_8bit_adam: bool,
-        gradient_checkpointing: bool,
-        lora_rank: int,
-        lora_alpha: int,
-        lora_bias: str,
-        lora_dropout: float,
-    ) -> tuple[dict, list[pathlib.Path]]:
-        if not torch.cuda.is_available():
-            raise gr.Error("CUDA is not available.")
-
-        if self.is_running:
-            return gr.update(value=self.is_running_message), []
-
-        if ref_images is None:
-            raise gr.Error("You need to upload reference images.")
-        if target_image is None:
-            raise gr.Error("You need to upload target image.")
-        if target_mask is None:
-            raise gr.Error("You need to upload target mask.")
-
-        resolution = int(resolution_s)
-
-        self.cleanup_dirs()
-        self.prepare_dataset(ref_images, target_image, target_mask, resolution)
-
-        command = f"""
-        accelerate launch train_dreambooth.py \
-            --pretrained_model_name_or_path={base_model}  \
-            --train_data_dir={self.data_dir} \
-            --output_dir={self.output_dir} \
-            --resolution={resolution} \
-            --gradient_accumulation_steps={gradient_accumulation} \
-            --unet_learning_rate={unet_learning_rate} \
-            --text_encoder_learning_rate={text_encoder_learning_rate} \
-            --max_train_steps={n_steps} \
-            --train_batch_size=16 \
-            --lr_scheduler=constant \
-            --lr_warmup_steps=100 \
-            --lora_r={lora_rank} \
-            --lora_alpha={lora_alpha} \
-            --lora_bias={lora_bias} \
-            --lora_dropout={lora_dropout} \
-        """
-
-        if fp16:
-            command += " --mixed_precision fp16"
-        if use_8bit_adam:
-            command += " --use_8bit_adam"
-        if gradient_checkpointing:
-            command += " --gradient_checkpointing"
-
-        with open(self.output_dir / "train.sh", "w") as f:
-            command_s = " ".join(command.split())
-            f.write(command_s)
-
-        self.is_running = True
-        res = subprocess.run(shlex.split(command))
-        self.is_running = False
-
-        if res.returncode == 0:
-            result_message = "Training Completed!"
-        else:
-            result_message = "Training Failed!"
-        model_paths = sorted(self.output_dir.glob("*"))
-        return gr.update(value=result_message), model_paths
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Adobe Acrobat XI Pro V1100 X86 X64 Multilanguage Crack A Complete Solution for PDF Creation and Editing.md b/spaces/tialenAdioni/chat-gpt-api/logs/Adobe Acrobat XI Pro V1100 X86 X64 Multilanguage Crack A Complete Solution for PDF Creation and Editing.md
deleted file mode 100644
index cec77f75cb49bd06190b41b0519e74c1c6f98180..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Adobe Acrobat XI Pro V1100 X86 X64 Multilanguage Crack A Complete Solution for PDF Creation and Editing.md	
+++ /dev/null
@@ -1,62 +0,0 @@
-
-
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-
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\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Descargar SPSS para Mac How to Analyze Data and Create Reports with SPSS for Mac.md b/spaces/tialenAdioni/chat-gpt-api/logs/Descargar SPSS para Mac How to Analyze Data and Create Reports with SPSS for Mac.md
deleted file mode 100644
index f52d366d25f9cb75e142ba628296f87d1d9a8daf..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Descargar SPSS para Mac How to Analyze Data and Create Reports with SPSS for Mac.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-
    
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/English In Mind 3 Teachers Book Free 803 Second Edition with Extra Photocopiable Activities and Tips.md b/spaces/tialenAdioni/chat-gpt-api/logs/English In Mind 3 Teachers Book Free 803 Second Edition with Extra Photocopiable Activities and Tips.md
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--- a/spaces/tialenAdioni/chat-gpt-api/logs/English In Mind 3 Teachers Book Free 803 Second Edition with Extra Photocopiable Activities and Tips.md	
+++ /dev/null
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    How to Download English In Mind 3 Teacher's Book for Free
    
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    If you are looking for a comprehensive and engaging course for teaching English as a second language, you might want to check out English In Mind 3, a book that has proven to be a perfect fit for classes around the world. This book contains extra photocopiable grammar and communication activities, teaching tips and ideas, and a testmaker audio CD/CD-ROM that allows you to create and edit your own tests. But how can you get this book for free?
    
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    English In Mind 3 Teachers Book Free 803

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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Install and Use Aimbot Gunbound Thor Hammer in Minutes.md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Install and Use Aimbot Gunbound Thor Hammer in Minutes.md
deleted file mode 100644
index eed49604d7ae582701fad05aa71cd682fcc14685..0000000000000000000000000000000000000000
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    Gunbound is a popular online game that involves shooting projectiles at your opponents using various vehicles and weapons. The game is based on the concept of "angle shooting", which means that you have to adjust the angle and power of your shots according to the wind and gravity. The game has a large fan base and a competitive scene, with many players striving to improve their skills and rank.
    
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    In this article, we will show you how to download and install Aimbot Gunbound Thor Hammer on your computer and how to use it to cheat in Gunbound. We will also share some tips and warnings on how to avoid getting banned or detected by the game's anti-cheat system.
    
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    How to Download and Install Aimbot Gunbound Thor Hammer
    
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    To download and install Aimbot Gunbound Thor Hammer on your computer, you will need to follow these steps:
    
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    1. Find a reliable torrent site that has Aimbot Gunbound Thor Hammer torrent file. You can use sites like MPGH, GitHub or YouTube.
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    3. Download a VPN service such as ExpressVPN, NordVPN or Surfshark on your computer.
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    5. Download the torrent file of Aimbot Gunbound Thor Hammer from the torrent site and open it with your torrent client.
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    6. Wait for the download to complete and extract the files to a folder on your computer.
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    7. Run the setup file and follow the instructions to install the software on your computer.
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    8. Run the software and enter your username and password when prompted.
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    9. Enjoy cheating in Gunbound with Aimbot Gunbound Thor Hammer.
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    How to Use Aimbot Gunbound Thor Hammer
    
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    To use Aimbot Gunbound Thor Hammer to cheat in Gunbound, you will need to follow these steps:
    
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    1. Launch your host application and load Aimbot Gunbound Thor Hammer as a VST plugin.
      2. 
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    2. Select one of the modes from the top panel of the plugin interface: Normal, Sniper or Crazy.
      3. 
-
    3. Adjust the settings of the selected mode using the knobs below the panel: Accuracy, Delay, Speed and Angle.
      4. 
-
    4. Launch Gunbound and join a game room.
      5. 
-
    5. Select your vehicle and weapon from the game menu.
      6. 
-
    6. Press F12 to activate the aimbot. You will see a red line indicating where your shot will land.
      7. 
-
    7. Press F11 to shoot automatically or press spacebar to shoot manually.
      8. 
-
    8. Repeat steps 6 and 7 for each shot until you win or lose the game.
      9. 
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-
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    Tips and Warnings for Using Aimbot Gunbound Thor Hammer
    
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    Here are some tips and warnings for using Aimbot Gunbound Thor Hammer:
    
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    • You can use the randomize function to generate random settings for each shot, making it harder for others to detect your cheat.
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    • You can use the hide function to hide the red line from your screen, making it more discreet for yourself.
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    • You can use the hotkeys function to assign different keys for activating, shooting and hiding the aimbot.
      • 
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    • You can use the VPN service to protect your privacy and security while torrenting. A VPN service will encrypt your traffic and hide your IP address from your ISP and other third parties. This will prevent them from tracking your online activity and sending you notices or fines for downloading copyrighted content.
      • 
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    • You can use the anti-ban function to avoid getting banned or detected by the game's anti-cheat system. The anti-ban function will change your IP address every time you launch the game, making it harder for the game server to identify you. However, this function is not 100% foolproof, so use it at your own risk.
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    Conclusion
    
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    Aimbot Gunbound Thor Hammer is a powerful and easy-to-use software that allows you to cheat in Gunbound by automatically aiming and shooting for you. It can calculate the perfect angle and power for every shot, regardless of the wind and gravity. It can also detect the position and movement of your enemies, making it easier to hit them.
    
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    If you want to download and install Aimbot Gunbound Thor Hammer on your computer, you can use a torrent site, a torrent client and a VPN service to do so safely and quickly. You can also use this guide to learn how to use Aimbot Gunbound Thor Hammer to cheat in Gunbound with different modes and settings.
    
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    We hope this article has helped you learn how to download and use Aimbot Gunbound Thor Hammer on your computer. If you have any questions or feedback, please let us know in the comments below.
    
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    What is Gunbound and Why is it Popular?
    
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    Gunbound is a free-to-play online game that was released in 2002 by Softnyx, a South Korean game developer. The game is inspired by the classic Worms series, where players control small characters called "mobiles" and shoot projectiles at each other on various maps. The game has a turn-based system, where each player has a limited time to aim and shoot their weapon. The game also features a weather system, where the wind and gravity can affect the trajectory and damage of the projectiles.
    
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    Gunbound has a large fan base and a competitive scene, with many players striving to improve their skills and rank. The game has several modes, such as solo, team, score and jewel. The game also has a variety of mobiles and weapons to choose from, each with their own strengths and weaknesses. The game also has a social aspect, where players can chat, make friends and join guilds.
    
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    Gunbound is popular because it is fun, addictive and challenging. The game requires strategy, skill and luck to win. The game also has a colorful and cute graphics style, a catchy soundtrack and a humorous tone. The game is suitable for players of all ages and backgrounds.
    
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    What are the Benefits and Risks of Using Aimbot Gunbound Thor Hammer?
    
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    Aimbot Gunbound Thor Hammer is a cheat tool that can give you an unfair advantage over your opponents in Gunbound. It can automatically aim and shoot for you, regardless of the wind and gravity. It can also detect the position and movement of your enemies, making it easier to hit them.
    
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    Using Aimbot Gunbound Thor Hammer can have some benefits and risks for you as a player. Here are some of them:
    
-
-
      
-
    • Benefits:
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      • You can win more games and rank up faster.
        • 
-    
      • You can impress your friends and enemies with your skills.
        • 
-    
      • You can have more fun and satisfaction in playing the game.
        • 
-  
      
-
      • 
-
    • Risks:
-  
        
-    
      • You can get banned or detected by the game's anti-cheat system.
        • 
-    
      • You can lose your account and progress in the game.
        • 
-    
      • You can ruin the game experience for other players.
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      • You can lose your respect and reputation in the game community.
        • 
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-
    Using Aimbot Gunbound Thor Hammer is a personal choice that depends on your preferences and ethics as a player. However, you should be aware of the consequences of using it and respect the rules and rights of other players.
    
-
    How to Avoid Getting Banned or Detected by Using Aimbot Gunbound Thor Hammer
    
-
-
    If you decide to use Aimbot Gunbound Thor Hammer to cheat in Gunbound, you should be careful and smart to avoid getting banned or detected by the game's anti-cheat system. The game's anti-cheat system can monitor your game activity and detect any suspicious behavior or software. If you get caught, you can face serious consequences such as account suspension, deletion or permanent ban.
    
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    Here are some tips on how to avoid getting banned or detected by using Aimbot Gunbound Thor Hammer:
    
-
-
      
-
    • Do not use the aimbot in every game or shot. Use it sparingly and randomly to make it look natural and human.
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-
    • Do not use the aimbot in public or ranked games. Use it only in private or unranked games with your friends or bots.
      • 
-
    • Do not use the aimbot with high accuracy, speed or angle settings. Use moderate or low settings to make it less obvious and more realistic.
      • 
-
    • Do not use the aimbot with the same mobile or weapon every time. Change your mobile or weapon frequently to avoid suspicion and detection.
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-
    • Do not use the aimbot with the same mode every time. Change your mode occasionally to avoid repetition and pattern recognition.
      • 
-
    • Do not brag or boast about your skills or rank in the game. Be humble and respectful to other players and do not draw attention to yourself.
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-
    • Do not share or distribute the aimbot software to anyone. Keep it for your personal use only and do not expose it to others.
      • 
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-
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    By following these tips, you can reduce the risk of getting banned or detected by using Aimbot Gunbound Thor Hammer. However, you should always remember that there is no guarantee that you will be safe from the game's anti-cheat system. Therefore, you should use the aimbot at your own risk and responsibility.
    
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    How to Play Fair and Have Fun in Gunbound Without Using Aimbot Gunbound Thor Hammer
    
-
-
    If you want to play fair and have fun in Gunbound without using Aimbot Gunbound Thor Hammer, you should follow these steps:
    
-
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-
    1. Learn the basics and mechanics of the game. Read the game manual, watch tutorials and guides, and practice with different mobiles and weapons.
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-
    2. Improve your skills and strategies in the game. Watch replays and streams of other players, join a guild or a team, and participate in tournaments and events.
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    3. Enjoy the game and its features. Explore the different maps and modes, customize your avatar and mobile, collect items and gold, and chat with other players.
      4. 
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    4. Respect the game and its community. Follow the game rules and etiquette, report any cheaters or hackers, and support the game developers and moderators.
      5. 
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-
-
    By following these steps, you can play fair and have fun in Gunbound without using Aimbot Gunbound Thor Hammer. You can also earn the respect and admiration of other players and the game community. You can also feel proud and satisfied with your achievements and progress in the game.
    
-
-
    Conclusion
    
-
-
    Aimbot Gunbound Thor Hammer is a cheat tool that can help you cheat in Gunbound by automatically aiming and shooting for you. It can give you an unfair advantage over your opponents, but it can also get you banned or detected by the game's anti-cheat system. Therefore, you should be careful and smart when using it, or avoid using it altogether.
    
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    If you want to play fair and have fun in Gunbound without using Aimbot Gunbound Thor Hammer, you should learn the basics and mechanics of the game, improve your skills and strategies in the game, enjoy the game and its features, and respect the game and its community. By doing so, you can have a better and more rewarding gaming experience.
    
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    We hope this article has helped you learn more about Aimbot Gunbound Thor Hammer and how to use it or avoid it. If you have any questions or feedback, please let us know in the comments below.
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Angry Birds Rio Mod APK The Ultimate Guide to Unlock All Levels and Characters.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Angry Birds Rio Mod APK The Ultimate Guide to Unlock All Levels and Characters.md
deleted file mode 100644
index f1b32e6f1eb6e89e5b87227d2ad753e4b503993b..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Angry Birds Rio Mod APK The Ultimate Guide to Unlock All Levels and Characters.md	
+++ /dev/null
@@ -1,184 +0,0 @@
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    The original Angry Birds 2 game
    
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    Angry Birds Rio is a spin-off game that was released in 2011 to promote the animated movie Rio. In this game, you have to help the original Angry Birds escape from their cages and save their friends Blu and Jewel from Nigel, a cockatoo who wants to capture all the exotic birds in the world. The game has many levels that are based on scenes from the movie, such as the warehouse 
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    Angry Birds 2 Rio Mod Apk is a modified version of Angry Birds 2 Rio, which gives you some advantages and disadvantages over the original game. Some of the advantages are:
    
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    However, some of the disadvantages are:
    
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    • You may not be able to download the mod apk from the official sources, such as the Google Play Store or the App Store. You may have to rely on third-party websites or sources that may not be safe or reliable.
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    • You may not be able to update the mod apk via the official channels, such as the Google Play Store or the App Store. You may have to manually download and install the latest version of the mod apk from third-party websites or sources.
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    • You may disadvantage the original developers of the game, who have worked hard to create and maintain the game. You may also violate their terms of service or intellectual property rights by using the mod apk.
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    • You may not get any warranty or support from the original developers of the game, who may not be responsible for any problems or issues that you may encounter while using the mod apk.
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    Therefore, before you decide to download and install Angry Birds 2 Rio Mod Apk, you should weigh the pros and cons carefully and make sure that you are aware of the risks and benefits involved.
    
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    How to Download and Install Angry Birds 2 Rio Mod Apk?
    
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    If you have decided to download and install Angry Birds 2 Rio Mod Apk, you should follow these steps:
    
- 
    The download link and the file size
    
- 
    The first step is to find a reliable and trustworthy website or source that provides the download link for Angry Birds 2 Rio Mod Apk. You can search for it on Google or other search engines, or you can ask for recommendations from your friends or other users who have used it before. You should also check the reviews and ratings of the website or source to make sure that it is safe and legit.
    
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    Once you have found a suitable website or source, you should click on the download link and wait for the file to be downloaded to your device. The file size of Angry Birds 2 Rio Mod Apk may vary depending on the version and the features that it offers. However, it should not be too large or too small, as it may indicate that it is corrupted or incomplete. A reasonable file size for Angry Birds 2 Rio Mod Apk is around 100 MB.
    
- 
    The installation steps and the permissions required
    
- 
    The next step is to install Angry Birds 2 Rio Mod Apk on your device. Before you do that, you should make sure that you have enabled the option to install apps from unknown sources on your device. This option allows you to install apps that are not from the official sources, such as the Google Play Store or the App Store. To enable this option, you should go to your device settings, then security, then unknown sources, and then toggle it on.
    
- 
    After you have enabled this option, you should locate the downloaded file of Angry Birds 2 Rio Mod Apk on your device storage, then tap on it to start the installation process. You may be asked to grant some permissions to the app, such as access to your device storage, network connection, location, camera, microphone, etc. You should review these permissions carefully and decide whether you want to allow them or not. If you are not sure about a permission, you can deny it or cancel the installation.
    
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    The installation process may take a few minutes depending on your device speed and performance. Once it is done, you should see a confirmation message that says that Angry Birds 2 Rio Mod Apk has been successfully installed on your device.
    
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    The compatibility and the updates
    
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    The last step is to check whether Angry Birds 2 Rio Mod Apk is compatible with your device and whether it needs any updates. To check the compatibility, you should open the app and see if it runs smoothly and without any errors or glitches. If it does not work properly or crashes frequently, it may mean that your device is not compatible with it or that it has some bugs or issues that need to be fixed.
    
- 
    To check for updates, you should go to the app settings and see if there is an option to update it via the official channels, such as the Google Play Store or the App Store. If there is, you should tap on it and follow the instructions to update it. However, if there is no option to update it via the official channels, you may have to manually download and install the latest version of Angry Birds 2 Rio Mod Apk from third-party websites or sources. You should be careful when doing this, as some websites or sources may provide fake or malicious files that may harm your device or compromise your privacy.
    
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    By following these steps, you should be able to download and install Angry Birds 2 Rio Mod Apk on your device and enjoy playing it with more features and benefits.
    
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    What are the Features of Angry Birds 2 Rio Mod Apk?
    
- 
    Angry Birds 2 Rio Mod Apk has many features that make it different from the original game. Some of these features are:
    
- 
    The gameplay and the levels
    
- 
    The gameplay of Angry Birds 2 Rio Mod Apk is similar to the original game, where you have to use a slingshot to launch different types of birds at structures made of various materials, such as wood, stone, glass, metal, etc. Your goal is to destroy these structures and eliminate all the pigs that are hiding inside them. However, there are some differences and additions that make the gameplay more fun and exciting.
    
- 
    For example, Angry Birds 2 Rio Mod Apk has multi-stage levels, where you have to complete several objectives before moving on to the next stage. These objectives may include destroying a certain number of structures, rescuing a certain number of birds, collecting a certain number of stars, etc. The levels also have different themes and settings that are based on scenes from the Rio movies, such as the warehouse, the jungle, the beach, the carnival, etc.
    
- 
    Another example is that Angry Birds 2 Rio Mod Apk has boss battles, where you have to face powerful pigs that have special skills or weapons. These bosses may include Nigel, the cockatoo who wants to capture all the exotic birds in the world; Mauro, the marmoset who leads a gang of monkeys; Gabi, the poisonous frog who is in love with Nigel; etc. You have to use your birds' abilities and power-ups to defeat these bosses and save your friends.
    
- 
    The characters and the power-ups
    
- 
    Angry Birds 2 Rio Mod Apk has many characters that you can use to play the game. These characters include the original Angry Birds, such as Red, Chuck, Bomb, Matilda, The Blues, Terence, etc.; as well as some new ones that are based on the Rio movies, such as Blu, Jewel, Rafael, Nico, Pedro, Luiz, etc. Each character has its own unique ability and personality that can help you in different situations.
    
- 
    For example, Red can knock down structures with his strong impact; Chuck can speed up and cut through materials with his sharp beak; Bomb can explode and cause massive damage; Matilda can drop eggs that explode on impact; The Blues can split into three smaller birds; Terence can smash through anything with his huge size; Blu can use his tongue to grab objects and swing them around; Jewel can fly fast and break glass with her voice; Rafael can use his tail feathers as a boomerang; Nico can use his bottle cap hat as a frisbee; Pedro can use his headphones as a lasso; Luiz can use his drool as a glue; etc.
    
- 
    Angry Birds 2 Rio Mod Apk also has many power-ups that you can use to enhance your birds' abilities or get some help from other birds. These power-ups include:
    
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    Power-upDescription
    BlizzardsFreezes all the structures and pigs in ice for a short time.
    DuckiesFills up the structures and pigs with water for a short time.
    ChilisSets all the structures and pigs on fire for a short time.
    PiggiesTurns all the pigs into harmless balloons for a short time.
    EagleCalls Mighty Eagle to swoop down and destroy everything in his path.
    SambaCalls Samba Burst to dance and shake everything in his path.
    BubblesCalls Bubbles to inflate and explode everything in his path.
    Stella StellaCalls Stella to create a bubble that lifts everything in its radius.
    HalCalls Hal to use his boomerang ability to hit everything in his path.
    
- 
    You can get these power-ups by collecting feathers, stars, or gems in the game. You can also buy them with real money or use Angry Birds 2 Rio Mod Apk to get them for free.
    
- 
    The graphics and the sound effects
    
- 
    Angry Birds 2 Rio Mod Apk has amazing graphics and sound effects that make the game more enjoyable and immersive. The graphics are colorful and detailed, with realistic physics and animations. The sound effects are lively and fun, with catchy music and funny voices. The game also has some references and Easter eggs from the Rio movies, such as the characters' dialogues, the background scenes, the items, etc.
    
- 
    Angry Birds 2 Rio Mod Apk is a game that will make you feel like you are in Rio de Janeiro, with its vibrant and exotic atmosphere. You will love the game's visuals and sounds, as they will enhance your gaming experience and mood.
    
- 
    What are the Tips and Tricks for Playing Angry Birds 2 Rio Mod Apk?
    
- 
    Angry Birds 2 Rio Mod Apk is a game that requires some skills and strategies to play well. Here are some tips and tricks that can help you improve your performance and score in the game:
    
- 
    How to choose the right bird for each level
    
- 
    One of the features of Angry Birds 2 Rio Mod Apk is that you can choose which bird you want to use for each level, instead of following a fixed order. This gives you more flexibility and options to play the game. However, it also means that you have to be smart and careful when choosing your birds, as each bird has its own strengths and weaknesses.
    
- 
    You should choose your birds based on the level's layout, structure, material, pig placement, etc. You should also consider the bird's ability, size, weight, speed, etc. For example, you should use Red for levels that have strong structures or large pigs; Chuck for levels that have weak structures or small pigs; Bomb for levels that have clustered structures or explosive materials; Matilda for levels that have high structures or multiple layers; The Blues for levels that have wide structures or multiple targets; Terence for levels that have hard structures or heavy materials; Blu for levels that have movable structures or objects; Jewel for levels that have glass structures or windows; Rafael for levels that have curved structures or corners; Nico for levels that have flat structures or surfaces; Pedro for levels that have long structures or ropes; Luiz for levels that have sticky structures or glue; etc.
    
- 
    You should also try to use different birds for different stages of the level, as this will give you more chances to destroy the structures and pigs. You should also try to save some birds for the last stage of the level, as this will give you more points and stars.
    
- 
    How to use the power-ups wisely
    
- 
    Another feature of Angry Birds 2 Rio Mod Apk is that you can use power-ups to enhance your birds' abilities or get some help from other birds. These power-ups can be very useful and helpful in some situations, but they can also be wasteful and unnecessary in others. Therefore, you should use them wisely and sparingly.
    
- 
    You should use power-ups only when you really need them or when they can make a big difference in your score. You should not use them when you can easily complete the level without them or when they can ruin your strategy or plan. For example, you should use Blizzards when there are many wooden structures or pigs in the level; Duckies when there are many stone structures or pigs in the level; Chilis when there are many metal structures or pigs in the level; Piggies when there are many hard-to-reach pigs in the level; Eagle when there are many structures or pigs in the level; Samba when there are many glass structures or windows in the level; Bubbles when there are many small structures or objects in the level; Stella when there are many high structures or layers in the level; Hal when there are many curved structures or corners in the level; etc.
    
- 
    You should also try to use power-ups at the right time and place, as this will maximize their effect and impact. You should not use them too early or too late, as this will reduce their value and benefit. For example, you should use Blizzards before launching your bird, as this will freeze everything in ice for a short time; Duckies after launching your bird, as this will fill up everything with water for a short time; Chil is after launching your bird, as this will set everything on fire for a short time; Piggies before launching your bird, as this will turn all the pigs into balloons for a short time; Eagle after launching your bird, as this will call Mighty Eagle to swoop down and destroy everything in his path; Samba before launching your bird, as this will call Samba Burst to dance and shake everything in his path; Bubbles after launching your bird, as this will call Bubbles to inflate and explode everything in his path; Stella before launching your bird, as this will call Stella to create a bubble that lifts everything in its radius; Hal after launching your bird, as this will call Hal to use his boomerang ability to hit everything in his path; etc.
    
- 
    You should also try to use power-ups in combination with each other or with your birds' abilities, as this will create more damage and destruction. You should not use power-ups that cancel each other out or that have no effect on the level. For example, you should use Blizzards and Chilis together, as this will create a thermal shock that will crack the ice and the fire; Duckies and Piggies together, as this will create a flood that will drown the pigs and the water; Eagle and Samba together, as this will create a powerful attack that will smash everything in their way; Bubbles and Stella together, as this will create a huge bubble that will lift and burst everything in its radius; Hal and Nico together, as this will create a boomerang-frisbee combo that will hit everything in their path; etc.
    
- 
    How to defeat the bosses and the enemies
    
- 
    The final feature of Angry Birds 2 Rio Mod Apk is that it has boss battles, where you have to face powerful pigs that have special skills or weapons. These bosses include Nigel, Mauro, Gabi, etc. You also have to deal with some enemies that are not pigs, such as monkeys, frogs, snakes, etc. These enemies can be annoying and dangerous, as they can attack you or interfere with your shots.
    
- 
    To defeat the bosses and the enemies, you have to use some strategies and tactics. Here are some tips and tricks that can help you:
    
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    • You should aim for the weak spots of the bosses and the enemies, such as their heads, eyes, mouths, etc. This will cause more damage and pain to them.
      • 
-
    • You should use the right birds and power-ups for each boss and enemy, depending on their characteristics and behavior. For example, you should use Bomb or Chilis for Nigel, who is immune to ice and water; Chuck or Duckies for Mauro, who is afraid of water and can be knocked off his perch; Matilda or Piggies for Gabi, who is poisonous and can be neutralized by balloons; etc.
      • 
-
    • You should avoid the attacks and traps of the bosses and the enemies, such as Nigel's feathers, Mauro's bananas, Gabi's kisses, etc. These attacks and traps can hurt you or hinder you from completing the level.
      • 
-
    • You should try to hit the bosses and the enemies multiple times or with multiple birds or power-ups, as this will reduce their health and stamina faster.
      • 
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    • You should try to finish the boss battles and the enemy encounters quickly, as this will give you more points and stars.
      • 
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- 
    By following these tips and tricks, you should be able to defeat the bosses and the enemies in Angry Birds 2 Rio Mod Apk and save your friends from their clutches.
    
- 
    Conclusion
    
- 
    Angry Birds 2 Rio Mod Apk is a fun and exciting crossover game that combines the elements of Angry Birds 2 and Angry Birds Rio. It is based on the animated movies Rio and Rio 2, which feature the adventures of Blu and Jewel, two rare macaws who live in Brazil. In this game, you will join the original Angry Birds as they travel to Rio de Janeiro to rescue their friends from Nigel, a cockatoo who wants to capture all the exotic birds in the world.
    
- 
    Angry Birds 2 Rio Mod Apk has many features that make it different from the original game. It gives you unlimited gems, coins, lives , power-ups, and other resources that you can use to play the game without any limitations. It also lets you unlock all the levels, characters, and items in the game, including some exclusive ones that are not available in the original game. You can also enjoy the game with more features and benefits, such as faster loading, smoother performance, and no ads.
    
- 
    Angry Birds 2 Rio Mod Apk is a game that requires some skills and strategies to play well. We have given you some tips and tricks that can help you improve your performance and score in the game. You should choose the right bird for each level, use the power-ups wisely, and defeat the bosses and the enemies.
    
- 
    Angry Birds 2 Rio Mod Apk is a game that will make you feel like you are in Rio de Janeiro, with its vibrant and exotic atmosphere. You will love the game's graphics and sound effects, as they will enhance your gaming experience and mood.
    
- 
    If you are a fan of the Angry Birds franchise and the Rio movies, you should definitely try Angry Birds 2 Rio Mod Apk. It is a fun and exciting crossover game that will keep you entertained and engaged for hours. Download and install it now and enjoy playing it with more features and benefits.
    
- 
    FAQs
    
- 
    Here are some frequently asked questions about Angry Birds 2 Rio Mod Apk:
    
- 
    Q: Is Angry Birds 2 Rio Mod Apk safe to use?
    
-
    A: Angry Birds 2 Rio Mod Apk is generally safe to use, as long as you download it from a reliable and trustworthy website or source. However, you should always be careful when downloading and installing any mod apk, as some of them may contain viruses, malware, spyware, or other harmful files that may damage your device or compromise your privacy. You should also scan the file with an antivirus or anti-malware program before installing it.
    
- 
    Q: Is Angry Birds 2 Rio Mod Apk legal to use?
    
-
    A: Angry Birds 2 Rio Mod Apk is not legal to use, as it violates the terms of service and intellectual property rights of the original developers of the game. By using the mod apk, you are disadvantaging the original developers who have worked hard to create and maintain the game. You are also depriving them of their rightful revenue and profit from the game. Therefore, you should respect their work and support them by playing the original game instead of the mod apk.
    
- 
    Q: How can I uninstall Angry Birds 2 Rio Mod Apk?
    
-
    A: If you want to uninstall Angry Birds 2 Rio Mod Apk from your device, you can follow these steps:
    
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-
    1. Go to your device settings, then apps, then Angry Birds 2 Rio Mod Apk.
      2. 
-
    2. Tap on uninstall and confirm your action.
      3. 
-
    3. Wait for the app to be uninstalled from your device.
      4. 
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- 
    You can also delete the downloaded file of Angry Birds 2 Rio Mod Apk from your device storage if you want to free up some space.
    
- 
    Q: Can I play Angry Birds 2 Rio Mod Apk online or offline?
    
-
    A: You can play Angry Birds 2 Rio Mod Apk both online and offline. However, some features of the game may require an internet connection to work properly, such as connecting to your Facebook account, syncing your progress across devices, accessing some online content or events, etc. Therefore, it is recommended that you play the game online whenever possible to enjoy all its features and benefits.
    
- 
    Q: Can I play Angry Birds 2 Rio Mod Apk with my friends or other players?
    
-
    A: Yes, you can play Angry Birds 2 Rio Mod Apk with your friends or other players. The game has a multiplayer mode where you can compete with other players in different challenges and tournaments. You can also join clans or teams with other players and cooperate with them in various tasks and missions. You can also chat with other players and share your tips and tricks with them.
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Download BEST Lanschool Crack 7.7 Chrisfolk.md b/spaces/tioseFevbu/cartoon-converter/scripts/Download BEST Lanschool Crack 7.7 Chrisfolk.md
deleted file mode 100644
index f18bc391409fcfd9eed20bd5225a829a59b8230f..0000000000000000000000000000000000000000
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+++ /dev/null
@@ -1,37 +0,0 @@
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    How to Use LanSchool 7.7 in Your Classroom
    
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    LanSchool is a classroom management software that helps teachers connect with students, monitor their activities, and guide their learning. LanSchool 7.7 is the latest version of the software that supports Android devices, Windows 8, and Mac OS X Mountain Lion. In this article, we will show you how to use LanSchool 7.7 in your classroom and how it can benefit your teaching and learning.
    
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    LanSchool 7.7 is a software that allows teachers to control and interact with students' devices in a networked classroom. With LanSchool 7.7, teachers can:
    
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    To use LanSchool 7.7 in your classroom, you need to install the LanSchool Teacher console on your own device and the LanSchool Student client on each student device. You can download the software from the official website[^1^] or contact your IT administrator for assistance.
    
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    Once you have installed the software, you need to configure the settings and preferences according to your needs. You can choose between different modes of communication (broadcasting, multicast, peer-to-peer), security options (passwords, encryption), and customization options (themes, icons, sounds).
    
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Jmp Student Authorization Code.md b/spaces/tioseFevbu/cartoon-converter/scripts/Jmp Student Authorization Code.md
deleted file mode 100644
index 871568350cc31aae09353094fd2d5c9ae5202a84..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Jmp Student Authorization Code.md	
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    If you are a student who wants to learn statistics and data analysis in a fun and easy way, you might have heard of JMP, a software that helps you explore, analyze, and visualize data. But what is JMP Student Authorization Code and how can you get it? In this article, we will answer these questions and show you how to use JMP Student Subscription, a version of JMP software designed for students.
    
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    Introduction
    
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    JMP is a software that was created by SAS, a leader in analytics and data science. JMP stands for John's Macintosh Project, named after John Sall, one of the founders of SAS and the chief architect of JMP. JMP is a software that combines powerful statistical analysis with interactive data visualization and dynamic linking. This means that you can explore your data in different ways, see how they are related, and discover hidden patterns and insights.
    
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    How to Get JMP Student Authorization Code
    
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    Option 1: Get it from a textbook that includes JMP Student Subscription
    
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    Another way to get a JMP Student Authorization Code is to purchase it online for $29.95. You can do this by going to www.onthehub.com/jmp, which is a website that offers academic software discounts for students. To purchase the authorization code, you need to follow these steps:
    
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    Basic steps to start using JMP Student Subscription
    
-
    To start using JMP Student Subscription, you need to launch the software and activate it with your authorization code. You can do this by following these steps:
    
-
      
-
    1. Double-click the JMP icon on your desktop or in your applications folder.
      2. 
-
    2. The first time you launch JMP, you will see a window asking you to enter your authorization code. Enter the code in the box and click OK.
      3. 
-
    3. You will see a welcome screen with some options to learn more about JMP. You can watch a video tutorial, read a quick guide, or browse some sample data sets. You can also skip this screen and go directly to the main window of JMP.
      4. 
-
    
-
    The main window of JMP is where you can open a data set and explore it with JMP tools. To open a data set, you can do one of the following:
    
-
      
-
    • Click File > Open and browse your computer for a data file. JMP supports various formats, such as Excel, CSV, TXT, SAS, SPSS, and more.
      • 
-
    • Click File > New > Data Table and enter your data manually or copy and paste it from another source.
      • 
-
    • Select one of the sample data sets from the welcome screen or from File > Open Sample Data.
      • 
-
     
    Once you have opened a data set, you will see a data table with rows and columns. Each row represents an observation or a case, and each column represents a variable or a measurement. You can edit, sort, filter, or transform your data in the data table. You can also add new columns or rows, or delete existing ones.
    
-
    To perform basic statistical analysis and create graphs with JMP, you can use the Analyze and Graph menus. These menus provide various options to analyze your data, such as summary statistics, hypothesis testing, regression, ANOVA, and more. You can also create different types of graphs, such as histograms, scatterplots, box plots, pie charts, and more. To use these menus, you need to select the variables that you want to analyze or graph, and then choose the appropriate option from the menu. JMP will then display the results in a new window, where you can customize the output or save it as a report.
    
- 
    Advanced features of JMP Student Subscription
    
-
    JMP Student Subscription also offers some advanced features that you can use to enhance your data analysis and visualization skills. Some of these features are:
    
- 
    Interactive data visualization and dynamic linking
    
-
    JMP Student Subscription allows you to create interactive data visualizations that let you explore your data in different ways. You can use tools such as sliders, filters, brushes, lassos, or markers to manipulate your graphs and see how they change. You can also use dynamic linking to link multiple graphs together and see how they are related. For example, you can link a histogram and a scatterplot and see how the distribution of one variable affects the relationship between two variables.
    
- 
    Data mining and predictive modeling techniques
    
-
    JMP Student Subscription also provides some data mining and predictive modeling techniques that let you discover hidden patterns and insights from your data. You can use tools such as clustering, classification, association rules, neural networks, decision trees, or bootstrap forest to group your data into meaningful segments, classify your data into categories, find associations between variables, or build models that predict outcomes based on inputs.
    
- 
    Design of experiments and quality control tools
    
-
    JMP Student Subscription also offers some design of experiments and quality control tools that let you design and optimize your experiments or processes. You can use tools such as factorial design, response surface design, mixture design, or optimal design to plan your experiments efficiently and effectively. You can also use tools such as control charts, capability analysis, Pareto charts, or cause-and-effect diagrams to monitor and improve your processes or products.
    
- 
    Conclusion
    
-
    In this article, we have explained what JMP Student Authorization Code is and how to get it. We have also shown you how to use JMP Student Subscription to perform basic and advanced statistical analysis and create graphs with your data. JMP Student Subscription is a powerful and easy-to-use software that helps you learn statistics and data analysis in a fun and interactive way. If you are a student who wants to improve your data skills and enjoy your statistics courses, we highly recommend you to try JMP Student Subscription today.
    
- 
    FAQs
    
-
    What is the difference between JMP Student Subscription and JMP Pro?
    
-
    JMP Student Subscription is a version of JMP software that provides all the statistical analysis and graphical tools covered in introductory and many intermediate statistics courses. JMP Pro is a version of JMP software that provides additional advanced features for more complex data analysis and modeling. JMP Pro is mainly used by professionals in research and development fields.
    
- 
    How long does the JMP Student Authorization Code last?
    
-
    The JMP Student Authorization Code lasts for 12 months from the date of activation. You can check the expiration date of your code by clicking Help > About JMP in the software.
    
- 
    Can I use JMP Student Subscription on multiple devices?
    
-
    You can use JMP Student Subscription on up to two devices with the same authorization code. However, you cannot use the software on both devices at the same time.
    
- 
    What are the system requirements for JMP Student Subscription?
    
-
    The system requirements for JMP Student Subscription are:
    
-
      
-
    • Windows: Windows 10 (64-bit), 8 GB RAM minimum (16 GB recommended), 1 GB disk space minimum (4 GB recommended)
      • 
-
    • Mac: macOS 10.15 (Catalina) or later (64-bit), 8 GB RAM minimum (16 GB recommended), 1 GB disk space minimum (4 GB recommended)
      • 
-
    
- 
    Where can I find more resources and support for JMP Student Subscription?
    
-
    You can find more resources and support for JMP Student Subscription on the following websites:
    
-
      
-
    • JMP Learning Library: A collection of tutorials, videos, examples, and exercises to help you learn JMP.
      • 
-
    • JMP User Community: A forum where you can ask questions, share ideas, and connect with other JMP users.
      • 
-
    • JMP Technical Support: A service where you can contact JMP experts for technical assistance and troubleshooting.
      • 
-
    
- 
    I hope you enjoyed this article and learned something new. If you have any feedback or questions, please feel free to leave a comment below. Thank you for reading and happy data analysis!
     b2dd77e56b
    
-
    
-
    
\ No newline at end of file
diff --git a/spaces/tkurtulus/sea-animals-classification/app.py b/spaces/tkurtulus/sea-animals-classification/app.py
deleted file mode 100644
index 9d90607c461158cfa49570cae92ce0ff41ca4c7d..0000000000000000000000000000000000000000
--- a/spaces/tkurtulus/sea-animals-classification/app.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from fastai.vision.all import *
-import gradio as gr
-
-title = 'Sea Animals Classification'
-description = '''
-With this Spaces, you can classify 19 different sea animals with uploading their pictures or using examples given below. 
-
-Here are the list of animals into this model. 'corals', 'crabs', 'dolphin', 'eel', 'jelly fish', 'lobster', 'nudibranchs', 'octopus', 'penguin', 'puffers', 'sea rays', 'sea urchins', 'seahorse', 'seal', 'sharks', 'squid', 'starfish', 'turtle_tortoise', 'whale'
-
-
    Source of training dataset : https://www.kaggle.com/datasets/vencerlanz09/sea-animals-image-dataste
-
    You can gather information about how this model is trained : https://www.kaggle.com/code/tolgakurtulus/sea-animals-classification-with-fastai
-
-Enjoy it! 🐟
-'''
-
-article = "
    visitor badge
    "
-
-learn = load_learner('model.pkl')
-
-image = gr.inputs.Image(shape=(128, 128))
-label = gr.outputs.Label()
-examples = ['coral.jpg', 'crabs.jpg', 'sea_rays.jpg', 'turtle_tortoise.jpg']
-
-categories = ('corals','crabs','dolphin','eel','jelly fish','lobster','nudibranchs','octopus','penguin','puffers','sea rays','sea urchins','seahorse','seal','sharks','squid','starfish','turtle_tortoise','whale')
-
-def classify_img(img):
-    pred,idx,probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-interface = gr.Interface(fn=classify_img,
-                inputs=image,
-                title=title,
-                article = article,
-                description=description,
-                outputs=label,
-                examples=examples)
-
-interface.launch(inline=False)
\ No newline at end of file
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py
deleted file mode 100644
index 6a6c92460f1d58b8e8d361fb56ee123f2668ad9f..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,5 +0,0 @@
-_base_ = [
-    '../_base_/models/mask_rcnn_r50_fpn.py',
-    '../_base_/datasets/coco_instance.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
diff --git a/spaces/tomzhang1019/ChatGPT/run_Linux.sh b/spaces/tomzhang1019/ChatGPT/run_Linux.sh
deleted file mode 100644
index 2d26597ae47519f42336ccffc16646713a192ae1..0000000000000000000000000000000000000000
--- a/spaces/tomzhang1019/ChatGPT/run_Linux.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$(readlink -f "$0")")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir" || exit
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
-	# 如果有更新，关闭当前运行的服务器
-	pkill -f ChuanhuChatbot.py
-
-	# 拉取最新更改
-	git pull
-
-	# 安装依赖
-	pip3 install -r requirements.txt
-
-	# 重新启动服务器
-	nohup python3 ChuanhuChatbot.py &
-fi
-
-# 检查ChuanhuChatbot.py是否在运行
-if ! pgrep -f ChuanhuChatbot.py > /dev/null; then
-	# 如果没有运行，启动服务器
-	nohup python3 ChuanhuChatbot.py &
-fi
diff --git a/spaces/trttung1610/musicgen/Makefile b/spaces/trttung1610/musicgen/Makefile
deleted file mode 100644
index be8a8b03aa984ac5ed95c98e05887fe108dce073..0000000000000000000000000000000000000000
--- a/spaces/trttung1610/musicgen/Makefile
+++ /dev/null
@@ -1,40 +0,0 @@
-INTEG=AUDIOCRAFT_DORA_DIR="/tmp/magma_$(USER)" python3 -m dora -v run --clear device=cpu dataset.num_workers=0 optim.epochs=1 \
-	dataset.train.num_samples=10 dataset.valid.num_samples=10 \
-	dataset.evaluate.num_samples=10 dataset.generate.num_samples=2 sample_rate=16000 \
-	logging.level=DEBUG
-INTEG_COMPRESSION = $(INTEG) solver=compression/debug rvq.n_q=2 rvq.bins=48 checkpoint.save_last=true   # SIG is 616d7b3c
-INTEG_MUSICGEN = $(INTEG) solver=musicgen/debug dset=audio/example compression_model_checkpoint=//sig/5091833e \
-	transformer_lm.n_q=2 transformer_lm.card=48 transformer_lm.dim=16 checkpoint.save_last=false  # Using compression model from 616d7b3c
-INTEG_AUDIOGEN = $(INTEG) solver=audiogen/debug dset=audio/example compression_model_checkpoint=//sig/5091833e \
-	transformer_lm.n_q=2 transformer_lm.card=48 transformer_lm.dim=16 checkpoint.save_last=false  # Using compression model from 616d7b3c
-INTEG_MBD = $(INTEG) solver=diffusion/debug dset=audio/example  \
-	checkpoint.save_last=false  # Using compression model from 616d7b3c
-
-default: linter tests
-
-install:
-	pip install -U pip
-	pip install -U -e '.[dev]'
-
-linter:
-	flake8 audiocraft && mypy audiocraft
-	flake8 tests && mypy tests
-
-tests:
-	coverage run -m pytest tests
-	coverage report
-
-tests_integ:
-	$(INTEG_COMPRESSION)
-	$(INTEG_MBD)
-	$(INTEG_MUSICGEN)
-	$(INTEG_AUDIOGEN)
-
-
-api_docs:
-	pdoc3 --html -o api_docs -f audiocraft
-
-dist:
-	python setup.py sdist
-
-.PHONY: linter tests api_docs dist
diff --git a/spaces/ty00369/IDEA-CCNL-Taiyi-BLIP-750M-Chinese/app.py b/spaces/ty00369/IDEA-CCNL-Taiyi-BLIP-750M-Chinese/app.py
deleted file mode 100644
index 71219aae9b34ec0aa34cbb6f7e6452719da59aac..0000000000000000000000000000000000000000
--- a/spaces/ty00369/IDEA-CCNL-Taiyi-BLIP-750M-Chinese/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/IDEA-CCNL/Taiyi-BLIP-750M-Chinese").launch()
\ No newline at end of file
diff --git a/spaces/umichVision/virtex-redcaps/virtex/utils/beam_search.py b/spaces/umichVision/virtex-redcaps/virtex/utils/beam_search.py
deleted file mode 100644
index 5aa4226d072819ab4ccd8c8fc35e2410b6493cdd..0000000000000000000000000000000000000000
--- a/spaces/umichVision/virtex-redcaps/virtex/utils/beam_search.py
+++ /dev/null
@@ -1,266 +0,0 @@
-r"""
-This Beam Search implementation is adapted with minor modifications from
-`AllenNLP `_.
-
-Thanks to the developers of AllenNLP!
-"""
-from typing import Callable, List, Tuple
-import warnings
-
-import torch
-from torch.nn import functional as F
-
-class AutoRegressiveBeamSearch(object):
-    r"""
-    Implements the beam search algorithm for decoding the most likely captions.
-    This only works for auto-regressive models (Transformer-like) and not
-    recurrent models (LSTM-like).
-
-    Parameters
-    ----------
-    eos_index: int
-        The index of the end token (``[EOS]``) in vocabulary.
-    max_steps: int, optional (default = 50)
-        The maximum number of decoding steps.
-    beam_size: int, optional (default = 5)
-        The width of the beam used.
-    per_node_beam_size: int, optional (default = 2)
-        The maximum number of candidates to consider per node, at each step in
-        the search. Setting this parameter to a number smaller than `beam_size`
-        may give better results, as it can introduce more diversity into the
-        search. See `Beam Search Strategies for Neural Machine Translation.
-        Freitag and Al-Onaizan, 2017 `_.
-    """
-
-    def __init__(
-        self,
-        eos_index: int,
-        max_steps: int = 50,
-        beam_size: int = 5,
-        per_node_beam_size: int = 2,
-    ):
-        self._eos_index = eos_index
-        self.max_steps = max_steps
-        self.beam_size = beam_size
-        self.per_node_beam_size = per_node_beam_size or beam_size
-
-    def search(
-        self, start_predictions: torch.Tensor, step: Callable[..., torch.Tensor]
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        r"""
-        Given a starting state and a step function, apply beam search to find
-        the most likely target captions.
-
-        Parameters
-        ----------
-        start_predictions : torch.Tensor
-            Tensor containing the initial predictions, shape ``(batch_size, )``.
-            Usually the initial predictions are just the index of the start
-            token (``[SOS]``) in the vocabulary.
-        step : Callable[..., torch.Tensor]
-            A function that is responsible for computing the next most likely
-            tokens, given the past predictions. Predictions from all previous
-            timesteps are required, not just the last timestep, because our
-            model is auto-regressive instead of recurrent.  The function should
-            The function is expected to return a tensor of shape
-            ``(group_size, target_vocab_size)`` containing
-            the logits of the tokens for the next step.
-
-        Returns
-        -------
-        Tuple[torch.Tensor, torch.Tensor]
-            Tuple of ``(predictions, logprobs)``, where ``predictions``
-            has shape ``(batch_size, beam_size, max_steps)`` and ``logprobs``
-            has shape ``(batch_size, beam_size)``.
-        """
-        batch_size = start_predictions.size()[0]
-
-        # List of `(batch_size, beam_size)` tensors. One for each time step.
-        # Does not include the start symbols, which are implicit.
-        predictions: List[torch.Tensor] = []
-
-        # List of (batch_size, beam_size) tensors. One for each time step. None
-        # for the first.  Stores the index n for the parent prediction, i.e.
-        # predictions[t-1][i][n], that it came from.
-        backpointers: List[torch.Tensor] = []
-
-        # Calculate the first timestep. This is done outside the main loop
-        # because we are going from a single decoder input (the output from the
-        # encoder) to the top `beam_size` decoder outputs. On the other hand,
-        # within the main loop we are going from the `beam_size` elements of the
-        # beam to `beam_size`^2 candidates from which we will select the top
-        # `beam_size` elements for the next iteration.
-        # shape: (batch_size, num_classes)
-        start_class_logits = step(start_predictions)
-
-        # Convert logits to logprobs.
-        # shape: (batch_size * beam_size, vocab_size)
-        start_class_logprobs = F.log_softmax(start_class_logits, dim=1)
-
-        num_classes = start_class_logprobs.size()[1]
-
-        # Make sure `per_node_beam_size` is not larger than `num_classes`.
-        if self.per_node_beam_size > num_classes:
-            raise ValueError(
-                f"Target vocab size ({num_classes:d}) too small "
-                f"relative to per_node_beam_size ({self.per_node_beam_size:d}).\n"
-                f"Please decrease beam_size or per_node_beam_size."
-            )
-
-        # shape: (batch_size, beam_size), (batch_size, beam_size)
-        start_top_logprobs, start_predicted_classes = start_class_logprobs.topk(
-            self.beam_size
-        )
-        if (
-            self.beam_size == 1
-            and (start_predicted_classes == self._eos_index).all()
-        ):
-            warnings.warn(
-                "Empty captions predicted. You may want to increase beam "
-                "size or ensure your step function is working properly.",
-                RuntimeWarning,
-            )
-            return start_predicted_classes.unsqueeze(-1), start_top_logprobs
-
-        # The log probs for the last time step.
-        # shape: (batch_size, beam_size)
-        last_logprobs = start_top_logprobs
-
-        # shape: [(batch_size, beam_size)]
-        predictions.append(start_predicted_classes)
-
-        # Log probability tensor that mandates that the end token is selected.
-        # shape: (batch_size * beam_size, num_classes)
-        logprobs_after_end = start_class_logprobs.new_full(
-            (batch_size * self.beam_size, num_classes), float("-inf")
-        )
-        logprobs_after_end[:, self._eos_index] = 0.0
-
-        for timestep in range(self.max_steps - 1):
-            # shape: (batch_size * beam_size,)
-            last_predictions = predictions[-1].reshape(batch_size * self.beam_size)
-
-            # If every predicted token from the last step is `self._eos_index`,
-            # then we can stop early.
-            if (last_predictions == self._eos_index).all():
-                break
-
-            # Take a step. This get the predicted log probs of the next classes.
-            predictions_so_far = torch.stack(predictions).permute(1, 2, 0).view(
-                batch_size * self.beam_size, -1
-            )
-            # shape: (batch_size * beam_size, num_classes)
-            class_logits = step(predictions_so_far)
-
-            # Convert logits to logprobs.
-            # shape: (batch_size * beam_size, vocab_size)
-            class_logprobs = F.log_softmax(class_logits, dim=1)
-
-            # Set logprobs of last predicted tokens as high negative value to avoid
-            # repetition in caption.
-            for index in range(batch_size * self.beam_size):
-                class_logprobs[index, predictions_so_far[index, -1]] = -10000
-
-            # shape: (batch_size * beam_size, num_classes)
-            last_predictions_expanded = last_predictions.unsqueeze(-1).expand(
-                batch_size * self.beam_size, num_classes
-            )
-            # Here we are finding any beams where we predicted the end token in
-            # the previous timestep and replacing the distribution with a
-            # one-hot distribution, forcing the beam to predict the end token
-            # this timestep as well.
-            # shape: (batch_size * beam_size, num_classes)
-            cleaned_logprobs = torch.where(
-                last_predictions_expanded == self._eos_index,
-                logprobs_after_end,
-                class_logprobs,
-            )
-            # shape (both): (batch_size * beam_size, per_node_beam_size)
-            top_logprobs, predicted_classes = cleaned_logprobs.topk(
-                self.per_node_beam_size
-            )
-            # Here we expand the last log probs to `(batch_size * beam_size,
-            # per_node_beam_size)` so that we can add them to the current log
-            # probs for this timestep. This lets us maintain the log
-            # probability of each element on the beam.
-            # shape: (batch_size * beam_size, per_node_beam_size)
-            expanded_last_logprobs = (
-                last_logprobs.unsqueeze(2)
-                .expand(batch_size, self.beam_size, self.per_node_beam_size)
-                .reshape(batch_size * self.beam_size, self.per_node_beam_size)
-            )
-            # shape: (batch_size * beam_size, per_node_beam_size)
-            summed_top_logprobs = top_logprobs + expanded_last_logprobs
-
-            # shape: (batch_size, beam_size * per_node_beam_size)
-            reshaped_summed = summed_top_logprobs.reshape(
-                batch_size, self.beam_size * self.per_node_beam_size
-            )
-            # shape: (batch_size, beam_size * per_node_beam_size)
-            reshaped_predicted_classes = predicted_classes.reshape(
-                batch_size, self.beam_size * self.per_node_beam_size
-            )
-            # Keep only the top `beam_size` beam indices.
-            # shape: (batch_size, beam_size), (batch_size, beam_size)
-            restricted_beam_logprobs, restricted_beam_indices = reshaped_summed.topk(
-                self.beam_size
-            )
-            # Use the beam indices to extract the corresponding classes.
-            # shape: (batch_size, beam_size)
-            restricted_predicted_classes = reshaped_predicted_classes.gather(
-                1, restricted_beam_indices
-            )
-            predictions.append(restricted_predicted_classes)
-
-            # shape: (batch_size, beam_size)
-            last_logprobs = restricted_beam_logprobs
-
-            # The beam indices come from a `beam_size * per_node_beam_size`
-            # dimension where the indices with a common ancestor are grouped
-            # together. Hence dividing by `per_node_beam_size` gives the
-            # ancestor. (Note that this is integer division as the tensor is a
-            # LongTensor.)
-            # shape: (batch_size, beam_size)
-            backpointer = restricted_beam_indices // self.per_node_beam_size
-
-            backpointers.append(backpointer)
-
-        if not torch.isfinite(last_logprobs).all():
-            warnings.warn(
-                "Infinite log probs encountered. Some final captions may not "
-                "make sense. This can happen when the beam size is larger than"
-                " the number of valid (non-zero probability) transitions that "
-                "the step function produces.",
-                RuntimeWarning,
-            )
-
-        # Reconstruct the captions.
-        # shape: [(batch_size, beam_size, 1)]
-        reconstructed_predictions = [predictions[-1].unsqueeze(2)]
-
-        # shape: (batch_size, beam_size)
-        cur_backpointers = backpointers[-1]
-
-        for timestep in range(len(predictions) - 2, 0, -1):
-            # shape: (batch_size, beam_size, 1)
-            cur_preds = (
-                predictions[timestep].gather(1, cur_backpointers).unsqueeze(2)
-            )
-            reconstructed_predictions.append(cur_preds)
-
-            # shape: (batch_size, beam_size)
-            cur_backpointers = backpointers[timestep - 1].gather(1, cur_backpointers)
-
-        # shape: (batch_size, beam_size, 1)
-        final_preds = predictions[0].gather(1, cur_backpointers).unsqueeze(2)
-
-        reconstructed_predictions.append(final_preds)
-
-        # shape: (batch_size, beam_size, max_steps)
-        all_predictions = torch.cat(list(reversed(reconstructed_predictions)), 2)
-
-        # Select the top-beam and its logprobs.
-        all_predictions = all_predictions[:, 0, :]
-        last_logprobs = last_logprobs[:, 0]
-
-        return all_predictions, last_logprobs
diff --git a/spaces/uwx/waveformer/README.md b/spaces/uwx/waveformer/README.md
deleted file mode 100644
index 6d4c636e5fdb44c089b20644e5cb5723d06619d3..0000000000000000000000000000000000000000
--- a/spaces/uwx/waveformer/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Waveformer
-emoji: 📉
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.8.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vaibhavarduino/ChatGPT/README.md b/spaces/vaibhavarduino/ChatGPT/README.md
deleted file mode 100644
index 6c533bb69655458a4bf64c9b3e567d9aadaf77b5..0000000000000000000000000000000000000000
--- a/spaces/vaibhavarduino/ChatGPT/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Inta
-emoji: 🐠
-colorFrom: red
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: cc
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vaibhavarduino/anime-plus/op/conv2d_gradfix.py b/spaces/vaibhavarduino/anime-plus/op/conv2d_gradfix.py
deleted file mode 100644
index bb2f94bbcb8132299fd4d538972d32bd7ff6e7d6..0000000000000000000000000000000000000000
--- a/spaces/vaibhavarduino/anime-plus/op/conv2d_gradfix.py
+++ /dev/null
@@ -1,227 +0,0 @@
-import contextlib
-import warnings
-
-import torch
-from torch import autograd
-from torch.nn import functional as F
-
-enabled = True
-weight_gradients_disabled = False
-
-
-@contextlib.contextmanager
-def no_weight_gradients():
-    global weight_gradients_disabled
-
-    old = weight_gradients_disabled
-    weight_gradients_disabled = True
-    yield
-    weight_gradients_disabled = old
-
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
-    if could_use_op(input):
-        return conv2d_gradfix(
-            transpose=False,
-            weight_shape=weight.shape,
-            stride=stride,
-            padding=padding,
-            output_padding=0,
-            dilation=dilation,
-            groups=groups,
-        ).apply(input, weight, bias)
-
-    return F.conv2d(
-        input=input,
-        weight=weight,
-        bias=bias,
-        stride=stride,
-        padding=padding,
-        dilation=dilation,
-        groups=groups,
-    )
-
-
-def conv_transpose2d(
-    input,
-    weight,
-    bias=None,
-    stride=1,
-    padding=0,
-    output_padding=0,
-    groups=1,
-    dilation=1,
-):
-    if could_use_op(input):
-        return conv2d_gradfix(
-            transpose=True,
-            weight_shape=weight.shape,
-            stride=stride,
-            padding=padding,
-            output_padding=output_padding,
-            groups=groups,
-            dilation=dilation,
-        ).apply(input, weight, bias)
-
-    return F.conv_transpose2d(
-        input=input,
-        weight=weight,
-        bias=bias,
-        stride=stride,
-        padding=padding,
-        output_padding=output_padding,
-        dilation=dilation,
-        groups=groups,
-    )
-
-
-def could_use_op(input):
-    if (not enabled) or (not torch.backends.cudnn.enabled):
-        return False
-
-    if input.device.type != "cuda":
-        return False
-
-    if any(torch.__version__.startswith(x) for x in ["1.7.", "1.8."]):
-        return True
-
-    warnings.warn(
-        f"conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d()."
-    )
-
-    return False
-
-
-def ensure_tuple(xs, ndim):
-    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
-
-    return xs
-
-
-conv2d_gradfix_cache = dict()
-
-
-def conv2d_gradfix(
-    transpose, weight_shape, stride, padding, output_padding, dilation, groups
-):
-    ndim = 2
-    weight_shape = tuple(weight_shape)
-    stride = ensure_tuple(stride, ndim)
-    padding = ensure_tuple(padding, ndim)
-    output_padding = ensure_tuple(output_padding, ndim)
-    dilation = ensure_tuple(dilation, ndim)
-
-    key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
-    if key in conv2d_gradfix_cache:
-        return conv2d_gradfix_cache[key]
-
-    common_kwargs = dict(
-        stride=stride, padding=padding, dilation=dilation, groups=groups
-    )
-
-    def calc_output_padding(input_shape, output_shape):
-        if transpose:
-            return [0, 0]
-
-        return [
-            input_shape[i + 2]
-            - (output_shape[i + 2] - 1) * stride[i]
-            - (1 - 2 * padding[i])
-            - dilation[i] * (weight_shape[i + 2] - 1)
-            for i in range(ndim)
-        ]
-
-    class Conv2d(autograd.Function):
-        @staticmethod
-        def forward(ctx, input, weight, bias):
-            if not transpose:
-                out = F.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
-
-            else:
-                out = F.conv_transpose2d(
-                    input=input,
-                    weight=weight,
-                    bias=bias,
-                    output_padding=output_padding,
-                    **common_kwargs,
-                )
-
-            ctx.save_for_backward(input, weight)
-
-            return out
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            input, weight = ctx.saved_tensors
-            grad_input, grad_weight, grad_bias = None, None, None
-
-            if ctx.needs_input_grad[0]:
-                p = calc_output_padding(
-                    input_shape=input.shape, output_shape=grad_output.shape
-                )
-                grad_input = conv2d_gradfix(
-                    transpose=(not transpose),
-                    weight_shape=weight_shape,
-                    output_padding=p,
-                    **common_kwargs,
-                ).apply(grad_output, weight, None)
-
-            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
-                grad_weight = Conv2dGradWeight.apply(grad_output, input)
-
-            if ctx.needs_input_grad[2]:
-                grad_bias = grad_output.sum((0, 2, 3))
-
-            return grad_input, grad_weight, grad_bias
-
-    class Conv2dGradWeight(autograd.Function):
-        @staticmethod
-        def forward(ctx, grad_output, input):
-            op = torch._C._jit_get_operation(
-                "aten::cudnn_convolution_backward_weight"
-                if not transpose
-                else "aten::cudnn_convolution_transpose_backward_weight"
-            )
-            flags = [
-                torch.backends.cudnn.benchmark,
-                torch.backends.cudnn.deterministic,
-                torch.backends.cudnn.allow_tf32,
-            ]
-            grad_weight = op(
-                weight_shape,
-                grad_output,
-                input,
-                padding,
-                stride,
-                dilation,
-                groups,
-                *flags,
-            )
-            ctx.save_for_backward(grad_output, input)
-
-            return grad_weight
-
-        @staticmethod
-        def backward(ctx, grad_grad_weight):
-            grad_output, input = ctx.saved_tensors
-            grad_grad_output, grad_grad_input = None, None
-
-            if ctx.needs_input_grad[0]:
-                grad_grad_output = Conv2d.apply(input, grad_grad_weight, None)
-
-            if ctx.needs_input_grad[1]:
-                p = calc_output_padding(
-                    input_shape=input.shape, output_shape=grad_output.shape
-                )
-                grad_grad_input = conv2d_gradfix(
-                    transpose=(not transpose),
-                    weight_shape=weight_shape,
-                    output_padding=p,
-                    **common_kwargs,
-                ).apply(grad_output, grad_grad_weight, None)
-
-            return grad_grad_output, grad_grad_input
-
-    conv2d_gradfix_cache[key] = Conv2d
-
-    return Conv2d
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/vit/sam/modules/encoders.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/vit/sam/modules/encoders.md
deleted file mode 100644
index 8c338bc6de4a03419d435d29fcc23026b6338e78..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/vit/sam/modules/encoders.md
+++ /dev/null
@@ -1,54 +0,0 @@
----
-description: Learn about Ultralytics ViT encoder, position embeddings, attention, window partition, and more in our comprehensive documentation.
-keywords: Ultralytics YOLO, ViT Encoder, Position Embeddings, Attention, Window Partition, Rel Pos Encoding
----
-
-## ImageEncoderViT
----
-### ::: ultralytics.vit.sam.modules.encoders.ImageEncoderViT
-

    
-
-## PromptEncoder
----
-### ::: ultralytics.vit.sam.modules.encoders.PromptEncoder
-

    
-
-## PositionEmbeddingRandom
----
-### ::: ultralytics.vit.sam.modules.encoders.PositionEmbeddingRandom
-

    
-
-## Block
----
-### ::: ultralytics.vit.sam.modules.encoders.Block
-

    
-
-## Attention
----
-### ::: ultralytics.vit.sam.modules.encoders.Attention
-

    
-
-## PatchEmbed
----
-### ::: ultralytics.vit.sam.modules.encoders.PatchEmbed
-

    
-
-## window_partition
----
-### ::: ultralytics.vit.sam.modules.encoders.window_partition
-

    
-
-## window_unpartition
----
-### ::: ultralytics.vit.sam.modules.encoders.window_unpartition
-

    
-
-## get_rel_pos
----
-### ::: ultralytics.vit.sam.modules.encoders.get_rel_pos
-

    
-
-## add_decomposed_rel_pos
----
-### ::: ultralytics.vit.sam.modules.encoders.add_decomposed_rel_pos
-

    
\ No newline at end of file
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/usage/cfg.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/usage/cfg.md
deleted file mode 100644
index b027da310e66bf9680bde4c6a45d41a836fb5b73..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/usage/cfg.md
+++ /dev/null
@@ -1,254 +0,0 @@
----
-comments: true
-description: Learn about YOLO settings and modes for different tasks like detection, segmentation etc. Train and predict with custom argparse commands.
-keywords: YOLO settings, hyperparameters, YOLOv8, Ultralytics, YOLO guide, YOLO commands, YOLO tasks, YOLO modes, YOLO training, YOLO detect, YOLO segment, YOLO classify, YOLO pose, YOLO train, YOLO val, YOLO predict, YOLO export, YOLO track, YOLO benchmark
----
-
-YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings
-and hyperparameters can affect the model's behavior at various stages of the model development process, including
-training, validation, and prediction.
-
-YOLOv8 'yolo' CLI commands use the following syntax:
-
-!!! example ""
-
-    === "CLI"
-    
-        ```bash
-        yolo TASK MODE ARGS
-        ```
-
-    === "Python"
-    
-        ```python
-        from ultralytics import YOLO
-        
-        # Load a YOLOv8 model from a pre-trained weights file
-        model = YOLO('yolov8n.pt')
-         
-        # Run MODE mode using the custom arguments ARGS (guess TASK)
-        model.MODE(ARGS)
-        ```
-
-Where:
-
-- `TASK` (optional) is one of `[detect, segment, classify, pose]`. If it is not passed explicitly YOLOv8 will try to
-  guess
-  the `TASK` from the model type.
-- `MODE` (required) is one of `[train, val, predict, export, track, benchmark]`
-- `ARGS` (optional) are any number of custom `arg=value` pairs like `imgsz=320` that override defaults.
-  For a full list of available `ARGS` see the [Configuration](cfg.md) page and `defaults.yaml`
-  GitHub [source](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/cfg/default.yaml).
-
-#### Tasks
-
-YOLO models can be used for a variety of tasks, including detection, segmentation, classification and pose. These tasks
-differ in the type of output they produce and the specific problem they are designed to solve.
-
-**Detect**: For identifying and localizing objects or regions of interest in an image or video.  
-**Segment**: For dividing an image or video into regions or pixels that correspond to different objects or classes.  
-**Classify**: For predicting the class label of an input image.  
-**Pose**: For identifying objects and estimating their keypoints in an image or video.
-
-| Key    | Value      | Description                                     |
-|--------|------------|-------------------------------------------------|
-| `task` | `'detect'` | YOLO task, i.e. detect, segment, classify, pose |
-
-[Tasks Guide](../tasks/index.md){ .md-button .md-button--primary}
-
-#### Modes
-
-YOLO models can be used in different modes depending on the specific problem you are trying to solve. These modes
-include:
-
-**Train**: For training a YOLOv8 model on a custom dataset.  
-**Val**: For validating a YOLOv8 model after it has been trained.  
-**Predict**: For making predictions using a trained YOLOv8 model on new images or videos.  
-**Export**: For exporting a YOLOv8 model to a format that can be used for deployment.  
-**Track**: For tracking objects in real-time using a YOLOv8 model.  
-**Benchmark**: For benchmarking YOLOv8 exports (ONNX, TensorRT, etc.) speed and accuracy.
-
-| Key    | Value     | Description                                                   |
-|--------|-----------|---------------------------------------------------------------|
-| `mode` | `'train'` | YOLO mode, i.e. train, val, predict, export, track, benchmark |
-
-[Modes Guide](../modes/index.md){ .md-button .md-button--primary}
-
-## Train
-
-The training settings for YOLO models encompass various hyperparameters and configurations used during the training process. These settings influence the model's performance, speed, and accuracy. Key training settings include batch size, learning rate, momentum, and weight decay. Additionally, the choice of optimizer, loss function, and training dataset composition can impact the training process. Careful tuning and experimentation with these settings are crucial for optimizing performance.
-
-| Key               | Value    | Description                                                                       |
-|-------------------|----------|-----------------------------------------------------------------------------------|
-| `model`           | `None`   | path to model file, i.e. yolov8n.pt, yolov8n.yaml                                 |
-| `data`            | `None`   | path to data file, i.e. coco128.yaml                                              |
-| `epochs`          | `100`    | number of epochs to train for                                                     |
-| `patience`        | `50`     | epochs to wait for no observable improvement for early stopping of training       |
-| `batch`           | `16`     | number of images per batch (-1 for AutoBatch)                                     |
-| `imgsz`           | `640`    | size of input images as integer or w,h                                            |
-| `save`            | `True`   | save train checkpoints and predict results                                        |
-| `save_period`     | `-1`     | Save checkpoint every x epochs (disabled if < 1)                                  |
-| `cache`           | `False`  | True/ram, disk or False. Use cache for data loading                               |
-| `device`          | `None`   | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu              |
-| `workers`         | `8`      | number of worker threads for data loading (per RANK if DDP)                       |
-| `project`         | `None`   | project name                                                                      |
-| `name`            | `None`   | experiment name                                                                   |
-| `exist_ok`        | `False`  | whether to overwrite existing experiment                                          |
-| `pretrained`      | `False`  | whether to use a pretrained model                                                 |
-| `optimizer`       | `'auto'` | optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto] |
-| `verbose`         | `False`  | whether to print verbose output                                                   |
-| `seed`            | `0`      | random seed for reproducibility                                                   |
-| `deterministic`   | `True`   | whether to enable deterministic mode                                              |
-| `single_cls`      | `False`  | train multi-class data as single-class                                            |
-| `rect`            | `False`  | rectangular training with each batch collated for minimum padding                 |
-| `cos_lr`          | `False`  | use cosine learning rate scheduler                                                |
-| `close_mosaic`    | `0`      | (int) disable mosaic augmentation for final epochs                                |
-| `resume`          | `False`  | resume training from last checkpoint                                              |
-| `amp`             | `True`   | Automatic Mixed Precision (AMP) training, choices=[True, False]                   |
-| `fraction`        | `1.0`    | dataset fraction to train on (default is 1.0, all images in train set)            |
-| `profile`         | `False`  | profile ONNX and TensorRT speeds during training for loggers                      |
-| `lr0`             | `0.01`   | initial learning rate (i.e. SGD=1E-2, Adam=1E-3)                                  |
-| `lrf`             | `0.01`   | final learning rate (lr0 * lrf)                                                   |
-| `momentum`        | `0.937`  | SGD momentum/Adam beta1                                                           |
-| `weight_decay`    | `0.0005` | optimizer weight decay 5e-4                                                       |
-| `warmup_epochs`   | `3.0`    | warmup epochs (fractions ok)                                                      |
-| `warmup_momentum` | `0.8`    | warmup initial momentum                                                           |
-| `warmup_bias_lr`  | `0.1`    | warmup initial bias lr                                                            |
-| `box`             | `7.5`    | box loss gain                                                                     |
-| `cls`             | `0.5`    | cls loss gain (scale with pixels)                                                 |
-| `dfl`             | `1.5`    | dfl loss gain                                                                     |
-| `pose`            | `12.0`   | pose loss gain (pose-only)                                                        |
-| `kobj`            | `2.0`    | keypoint obj loss gain (pose-only)                                                |
-| `label_smoothing` | `0.0`    | label smoothing (fraction)                                                        |
-| `nbs`             | `64`     | nominal batch size                                                                |
-| `overlap_mask`    | `True`   | masks should overlap during training (segment train only)                         |
-| `mask_ratio`      | `4`      | mask downsample ratio (segment train only)                                        |
-| `dropout`         | `0.0`    | use dropout regularization (classify train only)                                  |
-| `val`             | `True`   | validate/test during training                                                     |
-
-[Train Guide](../modes/train.md){ .md-button .md-button--primary}
-
-## Predict
-
-The prediction settings for YOLO models encompass a range of hyperparameters and configurations that influence the model's performance, speed, and accuracy during inference on new data. Careful tuning and experimentation with these settings are essential to achieve optimal performance for a specific task. Key settings include the confidence threshold, Non-Maximum Suppression (NMS) threshold, and the number of classes considered. Additional factors affecting the prediction process are input data size and format, the presence of supplementary features such as masks or multiple labels per box, and the particular task the model is employed for.
-
-| Key            | Value                  | Description                                                                    |
-|----------------|------------------------|--------------------------------------------------------------------------------|
-| `source`       | `'ultralytics/assets'` | source directory for images or videos                                          |
-| `conf`         | `0.25`                 | object confidence threshold for detection                                      |
-| `iou`          | `0.7`                  | intersection over union (IoU) threshold for NMS                                |
-| `half`         | `False`                | use half precision (FP16)                                                      |
-| `device`       | `None`                 | device to run on, i.e. cuda device=0/1/2/3 or device=cpu                       |
-| `show`         | `False`                | show results if possible                                                       |
-| `save`         | `False`                | save images with results                                                       |
-| `save_txt`     | `False`                | save results as .txt file                                                      |
-| `save_conf`    | `False`                | save results with confidence scores                                            |
-| `save_crop`    | `False`                | save cropped images with results                                               |
-| `show_labels`  | `True`                 | show object labels in plots                                                    |
-| `show_conf`    | `True`                 | show object confidence scores in plots                                         |
-| `max_det`      | `300`                  | maximum number of detections per image                                         |
-| `vid_stride`   | `False`                | video frame-rate stride                                                        |
-| `line_width`   | `None`                 | The line width of the bounding boxes. If None, it is scaled to the image size. |
-| `visualize`    | `False`                | visualize model features                                                       |
-| `augment`      | `False`                | apply image augmentation to prediction sources                                 |
-| `agnostic_nms` | `False`                | class-agnostic NMS                                                             |
-| `retina_masks` | `False`                | use high-resolution segmentation masks                                         |
-| `classes`      | `None`                 | filter results by class, i.e. class=0, or class=[0,2,3]                        |
-| `boxes`        | `True`                 | Show boxes in segmentation predictions                                         |
-
-[Predict Guide](../modes/predict.md){ .md-button .md-button--primary}
-
-## Val
-
-The val (validation) settings for YOLO models involve various hyperparameters and configurations used to evaluate the model's performance on a validation dataset. These settings influence the model's performance, speed, and accuracy. Common YOLO validation settings include batch size, validation frequency during training, and performance evaluation metrics. Other factors affecting the validation process include the validation dataset's size and composition, as well as the specific task the model is employed for. Careful tuning and experimentation with these settings are crucial to ensure optimal performance on the validation dataset and detect and prevent overfitting.
-
-| Key           | Value   | Description                                                        |
-|---------------|---------|--------------------------------------------------------------------|
-| `save_json`   | `False` | save results to JSON file                                          |
-| `save_hybrid` | `False` | save hybrid version of labels (labels + additional predictions)    |
-| `conf`        | `0.001` | object confidence threshold for detection                          |
-| `iou`         | `0.6`   | intersection over union (IoU) threshold for NMS                    |
-| `max_det`     | `300`   | maximum number of detections per image                             |
-| `half`        | `True`  | use half precision (FP16)                                          |
-| `device`      | `None`  | device to run on, i.e. cuda device=0/1/2/3 or device=cpu           |
-| `dnn`         | `False` | use OpenCV DNN for ONNX inference                                  |
-| `plots`       | `False` | show plots during training                                         |
-| `rect`        | `False` | rectangular val with each batch collated for minimum padding       |
-| `split`       | `val`   | dataset split to use for validation, i.e. 'val', 'test' or 'train' |
-
-[Val Guide](../modes/val.md){ .md-button .md-button--primary}
-
-## Export
-
-Export settings for YOLO models encompass configurations and options related to saving or exporting the model for use in different environments or platforms. These settings can impact the model's performance, size, and compatibility with various systems. Key export settings include the exported model file format (e.g., ONNX, TensorFlow SavedModel), the target device (e.g., CPU, GPU), and additional features such as masks or multiple labels per box. The export process may also be affected by the model's specific task and the requirements or constraints of the destination environment or platform. It is crucial to thoughtfully configure these settings to ensure the exported model is optimized for the intended use case and functions effectively in the target environment.
-
-| Key         | Value           | Description                                          |
-|-------------|-----------------|------------------------------------------------------|
-| `format`    | `'torchscript'` | format to export to                                  |
-| `imgsz`     | `640`           | image size as scalar or (h, w) list, i.e. (640, 480) |
-| `keras`     | `False`         | use Keras for TF SavedModel export                   |
-| `optimize`  | `False`         | TorchScript: optimize for mobile                     |
-| `half`      | `False`         | FP16 quantization                                    |
-| `int8`      | `False`         | INT8 quantization                                    |
-| `dynamic`   | `False`         | ONNX/TF/TensorRT: dynamic axes                       |
-| `simplify`  | `False`         | ONNX: simplify model                                 |
-| `opset`     | `None`          | ONNX: opset version (optional, defaults to latest)   |
-| `workspace` | `4`             | TensorRT: workspace size (GB)                        |
-| `nms`       | `False`         | CoreML: add NMS                                      |
-
-[Export Guide](../modes/export.md){ .md-button .md-button--primary}
-
-## Augmentation
-
-Augmentation settings for YOLO models refer to the various transformations and modifications
-applied to the training data to increase the diversity and size of the dataset. These settings can affect the model's
-performance, speed, and accuracy. Some common YOLO augmentation settings include the type and intensity of the
-transformations applied (e.g. random flips, rotations, cropping, color changes), the probability with which each
-transformation is applied, and the presence of additional features such as masks or multiple labels per box. Other
-factors that may affect the augmentation process include the size and composition of the original dataset and the
-specific task the model is being used for. It is important to carefully tune and experiment with these settings to
-ensure that the augmented dataset is diverse and representative enough to train a high-performing model.
-
-| Key           | Value | Description                                     |
-|---------------|-------|-------------------------------------------------|
-| `hsv_h`       | 0.015 | image HSV-Hue augmentation (fraction)           |
-| `hsv_s`       | 0.7   | image HSV-Saturation augmentation (fraction)    |
-| `hsv_v`       | 0.4   | image HSV-Value augmentation (fraction)         |
-| `degrees`     | 0.0   | image rotation (+/- deg)                        |
-| `translate`   | 0.1   | image translation (+/- fraction)                |
-| `scale`       | 0.5   | image scale (+/- gain)                          |
-| `shear`       | 0.0   | image shear (+/- deg)                           |
-| `perspective` | 0.0   | image perspective (+/- fraction), range 0-0.001 |
-| `flipud`      | 0.0   | image flip up-down (probability)                |
-| `fliplr`      | 0.5   | image flip left-right (probability)             |
-| `mosaic`      | 1.0   | image mosaic (probability)                      |
-| `mixup`       | 0.0   | image mixup (probability)                       |
-| `copy_paste`  | 0.0   | segment copy-paste (probability)                |
-
-## Logging, checkpoints, plotting and file management
-
-Logging, checkpoints, plotting, and file management are important considerations when training a YOLO model.
-
-- Logging: It is often helpful to log various metrics and statistics during training to track the model's progress and
-  diagnose any issues that may arise. This can be done using a logging library such as TensorBoard or by writing log
-  messages to a file.
-- Checkpoints: It is a good practice to save checkpoints of the model at regular intervals during training. This allows
-  you to resume training from a previous point if the training process is interrupted or if you want to experiment with
-  different training configurations.
-- Plotting: Visualizing the model's performance and training progress can be helpful for understanding how the model is
-  behaving and identifying potential issues. This can be done using a plotting library such as matplotlib or by
-  generating plots using a logging library such as TensorBoard.
-- File management: Managing the various files generated during the training process, such as model checkpoints, log
-  files, and plots, can be challenging. It is important to have a clear and organized file structure to keep track of
-  these files and make it easy to access and analyze them as needed.
-
-Effective logging, checkpointing, plotting, and file management can help you keep track of the model's progress and make
-it easier to debug and optimize the training process.
-
-| Key        | Value    | Description                                                                                    |
-|------------|----------|------------------------------------------------------------------------------------------------|
-| `project`  | `'runs'` | project name                                                                                   |
-| `name`     | `'exp'`  | experiment name. `exp` gets automatically incremented if not specified, i.e, `exp`, `exp2` ... |
-| `exist_ok` | `False`  | whether to overwrite existing experiment                                                       |
-| `plots`    | `False`  | save plots during train/val                                                                    |
-| `save`     | `False`  | save train checkpoints and predict results                                                     |
\ No newline at end of file
diff --git a/spaces/victor/ChatUI/style.css b/spaces/victor/ChatUI/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/victor/ChatUI/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/vivien/trompeloeil/src/World/components/lights.js b/spaces/vivien/trompeloeil/src/World/components/lights.js
deleted file mode 100644
index 84776742e3baab8710ed181ee0d179ee0559e402..0000000000000000000000000000000000000000
--- a/spaces/vivien/trompeloeil/src/World/components/lights.js
+++ /dev/null
@@ -1,16 +0,0 @@
-import { DirectionalLight, HemisphereLight } from 'https://unpkg.com/three@0.117.0/build/three.module.js';
-
-function createLights() {
-  const ambientLight = new HemisphereLight(
-    'white',
-    'darkslategrey',
-    5,
-  );
-
-  const mainLight = new DirectionalLight('white', 4);
-  mainLight.position.set(10, 10, 10);
-
-  return { ambientLight, mainLight };
-}
-
-export { createLights };
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/hooks/closure.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/hooks/closure.py
deleted file mode 100644
index b955f81f425be4ac3e6bb3f4aac653887989e872..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/hooks/closure.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class ClosureHook(Hook):
-
-    def __init__(self, fn_name, fn):
-        assert hasattr(self, fn_name)
-        assert callable(fn)
-        setattr(self, fn_name, fn)
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/backbones/fast_scnn.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/backbones/fast_scnn.py
deleted file mode 100644
index 38c2350177cbc2066f45add568d30eb6041f74f3..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/backbones/fast_scnn.py
+++ /dev/null
@@ -1,375 +0,0 @@
-import torch
-import torch.nn as nn
-from annotator.uniformer.mmcv.cnn import (ConvModule, DepthwiseSeparableConvModule, constant_init,
-                      kaiming_init)
-from torch.nn.modules.batchnorm import _BatchNorm
-
-from annotator.uniformer.mmseg.models.decode_heads.psp_head import PPM
-from annotator.uniformer.mmseg.ops import resize
-from ..builder import BACKBONES
-from ..utils.inverted_residual import InvertedResidual
-
-
-class LearningToDownsample(nn.Module):
-    """Learning to downsample module.
-
-    Args:
-        in_channels (int): Number of input channels.
-        dw_channels (tuple[int]): Number of output channels of the first and
-            the second depthwise conv (dwconv) layers.
-        out_channels (int): Number of output channels of the whole
-            'learning to downsample' module.
-        conv_cfg (dict | None): Config of conv layers. Default: None
-        norm_cfg (dict | None): Config of norm layers. Default:
-            dict(type='BN')
-        act_cfg (dict): Config of activation layers. Default:
-            dict(type='ReLU')
-    """
-
-    def __init__(self,
-                 in_channels,
-                 dw_channels,
-                 out_channels,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU')):
-        super(LearningToDownsample, self).__init__()
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-        dw_channels1 = dw_channels[0]
-        dw_channels2 = dw_channels[1]
-
-        self.conv = ConvModule(
-            in_channels,
-            dw_channels1,
-            3,
-            stride=2,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        self.dsconv1 = DepthwiseSeparableConvModule(
-            dw_channels1,
-            dw_channels2,
-            kernel_size=3,
-            stride=2,
-            padding=1,
-            norm_cfg=self.norm_cfg)
-        self.dsconv2 = DepthwiseSeparableConvModule(
-            dw_channels2,
-            out_channels,
-            kernel_size=3,
-            stride=2,
-            padding=1,
-            norm_cfg=self.norm_cfg)
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.dsconv1(x)
-        x = self.dsconv2(x)
-        return x
-
-
-class GlobalFeatureExtractor(nn.Module):
-    """Global feature extractor module.
-
-    Args:
-        in_channels (int): Number of input channels of the GFE module.
-            Default: 64
-        block_channels (tuple[int]): Tuple of ints. Each int specifies the
-            number of output channels of each Inverted Residual module.
-            Default: (64, 96, 128)
-        out_channels(int): Number of output channels of the GFE module.
-            Default: 128
-        expand_ratio (int): Adjusts number of channels of the hidden layer
-            in InvertedResidual by this amount.
-            Default: 6
-        num_blocks (tuple[int]): Tuple of ints. Each int specifies the
-            number of times each Inverted Residual module is repeated.
-            The repeated Inverted Residual modules are called a 'group'.
-            Default: (3, 3, 3)
-        strides (tuple[int]): Tuple of ints. Each int specifies
-            the downsampling factor of each 'group'.
-            Default: (2, 2, 1)
-        pool_scales (tuple[int]): Tuple of ints. Each int specifies
-            the parameter required in 'global average pooling' within PPM.
-            Default: (1, 2, 3, 6)
-        conv_cfg (dict | None): Config of conv layers. Default: None
-        norm_cfg (dict | None): Config of norm layers. Default:
-            dict(type='BN')
-        act_cfg (dict): Config of activation layers. Default:
-            dict(type='ReLU')
-        align_corners (bool): align_corners argument of F.interpolate.
-            Default: False
-    """
-
-    def __init__(self,
-                 in_channels=64,
-                 block_channels=(64, 96, 128),
-                 out_channels=128,
-                 expand_ratio=6,
-                 num_blocks=(3, 3, 3),
-                 strides=(2, 2, 1),
-                 pool_scales=(1, 2, 3, 6),
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 align_corners=False):
-        super(GlobalFeatureExtractor, self).__init__()
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-        assert len(block_channels) == len(num_blocks) == 3
-        self.bottleneck1 = self._make_layer(in_channels, block_channels[0],
-                                            num_blocks[0], strides[0],
-                                            expand_ratio)
-        self.bottleneck2 = self._make_layer(block_channels[0],
-                                            block_channels[1], num_blocks[1],
-                                            strides[1], expand_ratio)
-        self.bottleneck3 = self._make_layer(block_channels[1],
-                                            block_channels[2], num_blocks[2],
-                                            strides[2], expand_ratio)
-        self.ppm = PPM(
-            pool_scales,
-            block_channels[2],
-            block_channels[2] // 4,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg,
-            align_corners=align_corners)
-        self.out = ConvModule(
-            block_channels[2] * 2,
-            out_channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-    def _make_layer(self,
-                    in_channels,
-                    out_channels,
-                    blocks,
-                    stride=1,
-                    expand_ratio=6):
-        layers = [
-            InvertedResidual(
-                in_channels,
-                out_channels,
-                stride,
-                expand_ratio,
-                norm_cfg=self.norm_cfg)
-        ]
-        for i in range(1, blocks):
-            layers.append(
-                InvertedResidual(
-                    out_channels,
-                    out_channels,
-                    1,
-                    expand_ratio,
-                    norm_cfg=self.norm_cfg))
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.bottleneck1(x)
-        x = self.bottleneck2(x)
-        x = self.bottleneck3(x)
-        x = torch.cat([x, *self.ppm(x)], dim=1)
-        x = self.out(x)
-        return x
-
-
-class FeatureFusionModule(nn.Module):
-    """Feature fusion module.
-
-    Args:
-        higher_in_channels (int): Number of input channels of the
-            higher-resolution branch.
-        lower_in_channels (int): Number of input channels of the
-            lower-resolution branch.
-        out_channels (int): Number of output channels.
-        conv_cfg (dict | None): Config of conv layers. Default: None
-        norm_cfg (dict | None): Config of norm layers. Default:
-            dict(type='BN')
-        act_cfg (dict): Config of activation layers. Default:
-            dict(type='ReLU')
-        align_corners (bool): align_corners argument of F.interpolate.
-            Default: False
-    """
-
-    def __init__(self,
-                 higher_in_channels,
-                 lower_in_channels,
-                 out_channels,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 align_corners=False):
-        super(FeatureFusionModule, self).__init__()
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-        self.align_corners = align_corners
-        self.dwconv = ConvModule(
-            lower_in_channels,
-            out_channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        self.conv_lower_res = ConvModule(
-            out_channels,
-            out_channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=None)
-        self.conv_higher_res = ConvModule(
-            higher_in_channels,
-            out_channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=None)
-        self.relu = nn.ReLU(True)
-
-    def forward(self, higher_res_feature, lower_res_feature):
-        lower_res_feature = resize(
-            lower_res_feature,
-            size=higher_res_feature.size()[2:],
-            mode='bilinear',
-            align_corners=self.align_corners)
-        lower_res_feature = self.dwconv(lower_res_feature)
-        lower_res_feature = self.conv_lower_res(lower_res_feature)
-
-        higher_res_feature = self.conv_higher_res(higher_res_feature)
-        out = higher_res_feature + lower_res_feature
-        return self.relu(out)
-
-
-@BACKBONES.register_module()
-class FastSCNN(nn.Module):
-    """Fast-SCNN Backbone.
-
-    Args:
-        in_channels (int): Number of input image channels. Default: 3.
-        downsample_dw_channels (tuple[int]): Number of output channels after
-            the first conv layer & the second conv layer in
-            Learning-To-Downsample (LTD) module.
-            Default: (32, 48).
-        global_in_channels (int): Number of input channels of
-            Global Feature Extractor(GFE).
-            Equal to number of output channels of LTD.
-            Default: 64.
-        global_block_channels (tuple[int]): Tuple of integers that describe
-            the output channels for each of the MobileNet-v2 bottleneck
-            residual blocks in GFE.
-            Default: (64, 96, 128).
-        global_block_strides (tuple[int]): Tuple of integers
-            that describe the strides (downsampling factors) for each of the
-            MobileNet-v2 bottleneck residual blocks in GFE.
-            Default: (2, 2, 1).
-        global_out_channels (int): Number of output channels of GFE.
-            Default: 128.
-        higher_in_channels (int): Number of input channels of the higher
-            resolution branch in FFM.
-            Equal to global_in_channels.
-            Default: 64.
-        lower_in_channels (int): Number of input channels of  the lower
-            resolution branch in FFM.
-            Equal to global_out_channels.
-            Default: 128.
-        fusion_out_channels (int): Number of output channels of FFM.
-            Default: 128.
-        out_indices (tuple): Tuple of indices of list
-            [higher_res_features, lower_res_features, fusion_output].
-            Often set to (0,1,2) to enable aux. heads.
-            Default: (0, 1, 2).
-        conv_cfg (dict | None): Config of conv layers. Default: None
-        norm_cfg (dict | None): Config of norm layers. Default:
-            dict(type='BN')
-        act_cfg (dict): Config of activation layers. Default:
-            dict(type='ReLU')
-        align_corners (bool): align_corners argument of F.interpolate.
-            Default: False
-    """
-
-    def __init__(self,
-                 in_channels=3,
-                 downsample_dw_channels=(32, 48),
-                 global_in_channels=64,
-                 global_block_channels=(64, 96, 128),
-                 global_block_strides=(2, 2, 1),
-                 global_out_channels=128,
-                 higher_in_channels=64,
-                 lower_in_channels=128,
-                 fusion_out_channels=128,
-                 out_indices=(0, 1, 2),
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 align_corners=False):
-
-        super(FastSCNN, self).__init__()
-        if global_in_channels != higher_in_channels:
-            raise AssertionError('Global Input Channels must be the same \
-                                 with Higher Input Channels!')
-        elif global_out_channels != lower_in_channels:
-            raise AssertionError('Global Output Channels must be the same \
-                                with Lower Input Channels!')
-
-        self.in_channels = in_channels
-        self.downsample_dw_channels1 = downsample_dw_channels[0]
-        self.downsample_dw_channels2 = downsample_dw_channels[1]
-        self.global_in_channels = global_in_channels
-        self.global_block_channels = global_block_channels
-        self.global_block_strides = global_block_strides
-        self.global_out_channels = global_out_channels
-        self.higher_in_channels = higher_in_channels
-        self.lower_in_channels = lower_in_channels
-        self.fusion_out_channels = fusion_out_channels
-        self.out_indices = out_indices
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-        self.align_corners = align_corners
-        self.learning_to_downsample = LearningToDownsample(
-            in_channels,
-            downsample_dw_channels,
-            global_in_channels,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        self.global_feature_extractor = GlobalFeatureExtractor(
-            global_in_channels,
-            global_block_channels,
-            global_out_channels,
-            strides=self.global_block_strides,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg,
-            align_corners=self.align_corners)
-        self.feature_fusion = FeatureFusionModule(
-            higher_in_channels,
-            lower_in_channels,
-            fusion_out_channels,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg,
-            align_corners=self.align_corners)
-
-    def init_weights(self, pretrained=None):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                kaiming_init(m)
-            elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
-                constant_init(m, 1)
-
-    def forward(self, x):
-        higher_res_features = self.learning_to_downsample(x)
-        lower_res_features = self.global_feature_extractor(higher_res_features)
-        fusion_output = self.feature_fusion(higher_res_features,
-                                            lower_res_features)
-
-        outs = [higher_res_features, lower_res_features, fusion_output]
-        outs = [outs[i] for i in self.out_indices]
-        return tuple(outs)
diff --git a/spaces/whgwd2023/bingo/src/components/tone-selector.tsx b/spaces/whgwd2023/bingo/src/components/tone-selector.tsx
deleted file mode 100644
index 5c6e464c91f564b895acd121f0a4a79ed9c5c356..0000000000000000000000000000000000000000
--- a/spaces/whgwd2023/bingo/src/components/tone-selector.tsx
+++ /dev/null
@@ -1,43 +0,0 @@
-import React from 'react'
-import { BingConversationStyle } from '@/lib/bots/bing/types'
-import { cn } from '@/lib/utils'
-
-type ToneItem = {
-  type: BingConversationStyle,
-  name: string
-}
-
-const ToneList: ToneItem[] = [
-  { name: '有创造力', type: BingConversationStyle.Creative },
-  { name: '更平衡', type: BingConversationStyle.Balanced },
-  { name: '更精确', type: BingConversationStyle.Precise }
-]
-
-interface ToneSelectorProps {
-  type: BingConversationStyle | ''
-  onChange?: (type: BingConversationStyle) => void
-}
-
-export function ToneSelector({ type, onChange }: ToneSelectorProps) {
-  return (
-    
    
-      
    
-        选择对话样式
-      
    
-      
    
-        
      
-          {
-            ToneList.map(tone => (
-              
    •  onChange?.(tone.type)}>
-                
-              
      • 
-            ))
-          }
-        
    
-      
    
-    
    
-  )
-}
diff --git a/spaces/whitphx/gradio-static-test/dist/assets/index-f1d49236.js b/spaces/whitphx/gradio-static-test/dist/assets/index-f1d49236.js
deleted file mode 100644
index cbf73b2f02d1c4976e9e13b4c4d67f5045128b9d..0000000000000000000000000000000000000000
--- a/spaces/whitphx/gradio-static-test/dist/assets/index-f1d49236.js
+++ /dev/null
@@ -1,344 +0,0 @@
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-]`).replace("lheading",ue.lheading).replace("blockquote"," {0,3}>").replace("|fences","").replace("|list","").replace("|html","").getRegex()};const ae={escape:/^\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/,autolink:/^<(scheme:[^\s\x00-\x1f<>]*|email)>/,url:Dr,tag:"^comment|^|^<[a-zA-Z][\\w-]*(?:attribute)*?\\s*/?>|^<\\?[\\s\\S]*?\\?>|^|^",link:/^!?\[(label)\]\(\s*(href)(?:\s+(title))?\s*\)/,reflink:/^!?\[(label)\]\[(ref)\]/,nolink:/^!?\[(ref)\](?:\[\])?/,reflinkSearch:"reflink|nolink(?!\\()",emStrong:{lDelim:/^(?:\*+(?:([punct_])|[^\s*]))|^_+(?:([punct*])|([^\s_]))/,rDelimAst:/^(?:[^_*\\]|\\.)*?\_\_(?:[^_*\\]|\\.)*?\*(?:[^_*\\]|\\.)*?(?=\_\_)|(?:[^*\\]|\\.)+(?=[^*])|[punct_](\*+)(?=[\s]|$)|(?:[^punct*_\s\\]|\\.)(\*+)(?=[punct_\s]|$)|[punct_\s](\*+)(?=[^punct*_\s])|[\s](\*+)(?=[punct_])|[punct_](\*+)(?=[punct_])|(?:[^punct*_\s\\]|\\.)(\*+)(?=[^punct*_\s])/,rDelimUnd:/^(?:[^_*\\]|\\.)*?\*\*(?:[^_*\\]|\\.)*?\_(?:[^_*\\]|\\.)*?(?=\*\*)|(?:[^_\\]|\\.)+(?=[^_])|[punct*](\_+)(?=[\s]|$)|(?:[^punct*_\s\\]|\\.)(\_+)(?=[punct*\s]|$)|[punct*\s](\_+)(?=[^punct*_\s])|[\s](\_+)(?=[punct*])|[punct*](\_+)(?=[punct*])/},code:/^(`+)([^`]|[^`][\s\S]*?[^`])\1(?!`)/,br:/^( {2,}|\\)\n(?!\s*$)/,del:Dr,text:/^(`+|[^`])(?:(?= {2,}\n)|[\s\S]*?(?:(?=[\\?@\\[\\]`^{|}~";ae.punctuation=Ce(ae.punctuation).replace(/punctuation/g,ae._punctuation).getRegex();ae.blockSkip=/\[[^\]]*?\]\([^\)]*?\)|`[^`]*?`|<[^>]*?>/g;ae.escapedEmSt=/(?:^|[^\\])(?:\\\\)*\\[*_]/g;ae._comment=Ce(ue._comment).replace("(?:-->|$)","-->").getRegex();ae.emStrong.lDelim=Ce(ae.emStrong.lDelim).replace(/punct/g,ae._punctuation).getRegex();ae.emStrong.rDelimAst=Ce(ae.emStrong.rDelimAst,"g").replace(/punct/g,ae._punctuation).getRegex();ae.emStrong.rDelimUnd=Ce(ae.emStrong.rDelimUnd,"g").replace(/punct/g,ae._punctuation).getRegex();ae._escapes=/\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/g;ae._scheme=/[a-zA-Z][a-zA-Z0-9+.-]{1,31}/;ae._email=/[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+(@)[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)+(?![-_])/;ae.autolink=Ce(ae.autolink).replace("scheme",ae._scheme).replace("email",ae._email).getRegex();ae._attribute=/\s+[a-zA-Z:_][\w.:-]*(?:\s*=\s*"[^"]*"|\s*=\s*'[^']*'|\s*=\s*[^\s"'=<>`]+)?/;ae.tag=Ce(ae.tag).replace("comment",ae._comment).replace("attribute",ae._attribute).getRegex();ae._label=/(?:\[(?:\\.|[^\[\]\\])*\]|\\.|`[^`]*`|[^\[\]\\`])*?/;ae._href=/<(?:\\.|[^\n<>\\])+>|[^\s\x00-\x1f]*/;ae._title=/"(?:\\"?|[^"\\])*"|'(?:\\'?|[^'\\])*'|\((?:\\\)?|[^)\\])*\)/;ae.link=Ce(ae.link).replace("label",ae._label).replace("href",ae._href).replace("title",ae._title).getRegex();ae.reflink=Ce(ae.reflink).replace("label",ae._label).replace("ref",ue._label).getRegex();ae.nolink=Ce(ae.nolink).replace("ref",ue._label).getRegex();ae.reflinkSearch=Ce(ae.reflinkSearch,"g").replace("reflink",ae.reflink).replace("nolink",ae.nolink).getRegex();ae.normal={...ae};ae.pedantic={...ae.normal,strong:{start:/^__|\*\*/,middle:/^__(?=\S)([\s\S]*?\S)__(?!_)|^\*\*(?=\S)([\s\S]*?\S)\*\*(?!\*)/,endAst:/\*\*(?!\*)/g,endUnd:/__(?!_)/g},em:{start:/^_|\*/,middle:/^()\*(?=\S)([\s\S]*?\S)\*(?!\*)|^_(?=\S)([\s\S]*?\S)_(?!_)/,endAst:/\*(?!\*)/g,endUnd:/_(?!_)/g},link:Ce(/^!?\[(label)\]\((.*?)\)/).replace("label",ae._label).getRegex(),reflink:Ce(/^!?\[(label)\]\s*\[([^\]]*)\]/).replace("label",ae._label).getRegex()};ae.gfm={...ae.normal,escape:Ce(ae.escape).replace("])","~|])").getRegex(),_extended_email:/[A-Za-z0-9._+-]+(@)[a-zA-Z0-9-_]+(?:\.[a-zA-Z0-9-_]*[a-zA-Z0-9])+(?![-_])/,url:/^((?:ftp|https?):\/\/|www\.)(?:[a-zA-Z0-9\-]+\.?)+[^\s<]*|^email/,_backpedal:/(?:[^?!.,:;*_'"~()&]+|\([^)]*\)|&(?![a-zA-Z0-9]+;$)|[?!.,:;*_'"~)]+(?!$))+/,del:/^(~~?)(?=[^\s~])([\s\S]*?[^\s~])\1(?=[^~]|$)/,text:/^([`~]+|[^`~])(?:(?= {2,}\n)|(?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)|[\s\S]*?(?:(?=[\\.5&&(l="x"+l.toString(16)),s+="&#"+l+";";return s}class r0{constructor(s){this.tokens=[],this.tokens.links=Object.create(null),this.options=s||g0,this.options.tokenizer=this.options.tokenizer||new Pn,this.tokenizer=this.options.tokenizer,this.tokenizer.options=this.options,this.tokenizer.lexer=this,this.inlineQueue=[],this.state={inLink:!1,inRawBlock:!1,top:!0};const o={block:ue.normal,inline:ae.normal};this.options.pedantic?(o.block=ue.pedantic,o.inline=ae.pedantic):this.options.gfm&&(o.block=ue.gfm,this.options.breaks?o.inline=ae.breaks:o.inline=ae.gfm),this.tokenizer.rules=o}static get rules(){return{block:ue,inline:ae}}static lex(s,o){return new r0(o).lex(s)}static lexInline(s,o){return new r0(o).inlineTokens(s)}lex(s){s=s.replace(/\r\n|\r/g,`
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-`:o.push(l);continue}if(l=this.tokenizer.code(s)){s=s.substring(l.raw.length),m=o[o.length-1],m&&(m.type==="paragraph"||m.type==="text")?(m.raw+=`
-`+l.raw,m.text+=`
-`+l.text,this.inlineQueue[this.inlineQueue.length-1].src=m.text):o.push(l);continue}if(l=this.tokenizer.fences(s)){s=s.substring(l.raw.length),o.push(l);continue}if(l=this.tokenizer.heading(s)){s=s.substring(l.raw.length),o.push(l);continue}if(l=this.tokenizer.hr(s)){s=s.substring(l.raw.length),o.push(l);continue}if(l=this.tokenizer.blockquote(s)){s=s.substring(l.raw.length),o.push(l);continue}if(l=this.tokenizer.list(s)){s=s.substring(l.raw.length),o.push(l);continue}if(l=this.tokenizer.html(s)){s=s.substring(l.raw.length),o.push(l);continue}if(l=this.tokenizer.def(s)){s=s.substring(l.raw.length),m=o[o.length-1],m&&(m.type==="paragraph"||m.type==="text")?(m.raw+=`
-`+l.raw,m.text+=`
-`+l.raw,this.inlineQueue[this.inlineQueue.length-1].src=m.text):this.tokens.links[l.tag]||(this.tokens.links[l.tag]={href:l.href,title:l.title});continue}if(l=this.tokenizer.table(s)){s=s.substring(l.raw.length),o.push(l);continue}if(l=this.tokenizer.lheading(s)){s=s.substring(l.raw.length),o.push(l);continue}if(p=s,this.options.extensions&&this.options.extensions.startBlock){let S=1/0;const F=s.slice(1);let X;this.options.extensions.startBlock.forEach(function(K){X=K.call({lexer:this},F),typeof X=="number"&&X>=0&&(S=Math.min(S,X))}),S<1/0&&S>=0&&(p=s.substring(0,S+1))}if(this.state.top&&(l=this.tokenizer.paragraph(p))){m=o[o.length-1],w&&m.type==="paragraph"?(m.raw+=`
-`+l.raw,m.text+=`
-`+l.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=m.text):o.push(l),w=p.length!==s.length,s=s.substring(l.raw.length);continue}if(l=this.tokenizer.text(s)){s=s.substring(l.raw.length),m=o[o.length-1],m&&m.type==="text"?(m.raw+=`
-`+l.raw,m.text+=`
-`+l.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=m.text):o.push(l);continue}if(s){const S="Infinite loop on byte: "+s.charCodeAt(0);if(this.options.silent){console.error(S);break}else throw new Error(S)}}return this.state.top=!0,o}inline(s,o=[]){return this.inlineQueue.push({src:s,tokens:o}),o}inlineTokens(s,o=[]){let l,m,p,w=s,S,F,X;if(this.tokens.links){const K=Object.keys(this.tokens.links);if(K.length>0)for(;(S=this.tokenizer.rules.inline.reflinkSearch.exec(w))!=null;)K.includes(S[0].slice(S[0].lastIndexOf("[")+1,-1))&&(w=w.slice(0,S.index)+"["+mi("a",S[0].length-2)+"]"+w.slice(this.tokenizer.rules.inline.reflinkSearch.lastIndex))}for(;(S=this.tokenizer.rules.inline.blockSkip.exec(w))!=null;)w=w.slice(0,S.index)+"["+mi("a",S[0].length-2)+"]"+w.slice(this.tokenizer.rules.inline.blockSkip.lastIndex);for(;(S=this.tokenizer.rules.inline.escapedEmSt.exec(w))!=null;)w=w.slice(0,S.index+S[0].length-2)+"++"+w.slice(this.tokenizer.rules.inline.escapedEmSt.lastIndex),this.tokenizer.rules.inline.escapedEmSt.lastIndex--;for(;s;)if(F||(X=""),F=!1,!(this.options.extensions&&this.options.extensions.inline&&this.options.extensions.inline.some(K=>(l=K.call({lexer:this},s,o))?(s=s.substring(l.raw.length),o.push(l),!0):!1))){if(l=this.tokenizer.escape(s)){s=s.substring(l.raw.length),o.push(l);continue}if(l=this.tokenizer.tag(s)){s=s.substring(l.raw.length),m=o[o.length-1],m&&l.type==="text"&&m.type==="text"?(m.raw+=l.raw,m.text+=l.text):o.push(l);continue}if(l=this.tokenizer.link(s)){s=s.substring(l.raw.length),o.push(l);continue}if(l=this.tokenizer.reflink(s,this.tokens.links)){s=s.substring(l.raw.length),m=o[o.length-1],m&&l.type==="text"&&m.type==="text"?(m.raw+=l.raw,m.text+=l.text):o.push(l);continue}if(l=this.tokenizer.emStrong(s,w,X)){s=s.substring(l.raw.length),o.push(l);continue}if(l=this.tokenizer.codespan(s)){s=s.substring(l.raw.length),o.push(l);continue}if(l=this.tokenizer.br(s)){s=s.substring(l.raw.length),o.push(l);continue}if(l=this.tokenizer.del(s)){s=s.substring(l.raw.length),o.push(l);continue}if(l=this.tokenizer.autolink(s,fi)){s=s.substring(l.raw.length),o.push(l);continue}if(!this.state.inLink&&(l=this.tokenizer.url(s,fi))){s=s.substring(l.raw.length),o.push(l);continue}if(p=s,this.options.extensions&&this.options.extensions.startInline){let K=1/0;const Q=s.slice(1);let te;this.options.extensions.startInline.forEach(function(G){te=G.call({lexer:this},Q),typeof te=="number"&&te>=0&&(K=Math.min(K,te))}),K<1/0&&K>=0&&(p=s.substring(0,K+1))}if(l=this.tokenizer.inlineText(p,ro)){s=s.substring(l.raw.length),l.raw.slice(-1)!=="_"&&(X=l.raw.slice(-1)),F=!0,m=o[o.length-1],m&&m.type==="text"?(m.raw+=l.raw,m.text+=l.text):o.push(l);continue}if(s){const K="Infinite loop on byte: "+s.charCodeAt(0);if(this.options.silent){console.error(K);break}else throw new Error(K)}}return o}}class Hn{constructor(s){this.options=s||g0}code(s,o,l){const m=(o||"").match(/\S*/)[0];if(this.options.highlight){const p=this.options.highlight(s,m);p!=null&&p!==s&&(l=!0,s=p)}return s=s.replace(/\n$/,"")+`
-`,m?'
    '+(l?s:Ze(s,!0))+`
    
-`:"
    "+(l?s:Ze(s,!0))+`
    
-`}blockquote(s){return`
    
-${s}
    
-`}html(s,o){return s}heading(s,o,l,m){if(this.options.headerIds){const p=this.options.headerPrefix+m.slug(l);return`${s}
-`}return`${s}
-`}hr(){return this.options.xhtml?`
    
-`:`
    
-`}list(s,o,l){const m=o?"ol":"ul",p=o&&l!==1?' start="'+l+'"':"";return"<"+m+p+`>
-`+s+"
-`}listitem(s){return`
  4. ${s}
    5. 
-`}checkbox(s){return" "}paragraph(s){return`
    ${s}
    
-`}table(s,o){return o&&(o=`${o}`),`
-
-`+s+`
-`+o+`
    
-`}tablerow(s){return`
-${s}
-`}tablecell(s,o){const l=o.header?"th":"td";return(o.align?`<${l} align="${o.align}">`:`<${l}>`)+s+`
-`}strong(s){return`${s}`}em(s){return`${s}`}codespan(s){return`${s}`}br(){return this.options.xhtml?"
    ":"
    "}del(s){return`${s}`}link(s,o,l){if(s=ci(this.options.sanitize,this.options.baseUrl,s),s===null)return l;let m='",m}image(s,o,l){if(s=ci(this.options.sanitize,this.options.baseUrl,s),s===null)return l;let m=`${l}":">",m}text(s){return s}}class Vi{strong(s){return s}em(s){return s}codespan(s){return s}del(s){return s}html(s){return s}text(s){return s}link(s,o,l){return""+l}image(s,o,l){return""+l}br(){return""}}class Wi{constructor(){this.seen={}}serialize(s){return s.toLowerCase().trim().replace(/<[!\/a-z].*?>/ig,"").replace(/[\u2000-\u206F\u2E00-\u2E7F\\'!"#$%&()*+,./:;<=>?@[\]^`{|}~]/g,"").replace(/\s/g,"-")}getNextSafeSlug(s,o){let l=s,m=0;if(this.seen.hasOwnProperty(l)){m=this.seen[s];do m++,l=s+"-"+m;while(this.seen.hasOwnProperty(l))}return o||(this.seen[s]=m,this.seen[l]=0),l}slug(s,o={}){const l=this.serialize(s);return this.getNextSafeSlug(l,o.dryrun)}}class n0{constructor(s){this.options=s||g0,this.options.renderer=this.options.renderer||new Hn,this.renderer=this.options.renderer,this.renderer.options=this.options,this.textRenderer=new Vi,this.slugger=new Wi}static parse(s,o){return new n0(o).parse(s)}static parseInline(s,o){return new n0(o).parseInline(s)}parse(s,o=!0){let l="",m,p,w,S,F,X,K,Q,te,G,Ae,de,he,H,C,z,_,D,I;const O=s.length;for(m=0;m0&&C.tokens[0].type==="paragraph"?(C.tokens[0].text=D+" "+C.tokens[0].text,C.tokens[0].tokens&&C.tokens[0].tokens.length>0&&C.tokens[0].tokens[0].type==="text"&&(C.tokens[0].tokens[0].text=D+" "+C.tokens[0].tokens[0].text)):C.tokens.unshift({type:"text",text:D}):H+=D),H+=this.parse(C.tokens,he),te+=this.renderer.listitem(H,_,z);l+=this.renderer.list(te,Ae,de);continue}case"html":{l+=this.renderer.html(G.text,G.block);continue}case"paragraph":{l+=this.renderer.paragraph(this.parseInline(G.tokens));continue}case"text":{for(te=G.tokens?this.parseInline(G.tokens):G.text;m+1{if(l.message+=`
-Please report this to https://github.com/markedjs/marked.`,y){const m="
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diff --git a/spaces/willgibs/ControlNet-v1-1/app_segmentation.py b/spaces/willgibs/ControlNet-v1-1/app_segmentation.py
deleted file mode 100644
index f54873436be0b9c7eb5c041801554b2059d3580f..0000000000000000000000000000000000000000
--- a/spaces/willgibs/ControlNet-v1-1/app_segmentation.py
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button('Run')
-                with gr.Accordion('Advanced options', open=False):
-                    preprocessor_name = gr.Radio(label='Preprocessor',
-                                                 choices=['UPerNet', 'None'],
-                                                 type='value',
-                                                 value='UPerNet')
-                    num_samples = gr.Slider(label='Number of images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    preprocess_resolution = gr.Slider(
-                        label='Preprocess resolution',
-                        minimum=128,
-                        maximum=512,
-                        value=512,
-                        step=1)
-                    num_steps = gr.Slider(label='Number of steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=1000000,
-                                     step=1,
-                                     value=0,
-                                     randomize=True)
-                    randomize_seed = gr.Checkbox(label='Randomize seed',
-                                                 value=True)
-                    a_prompt = gr.Textbox(
-                        label='Additional prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output', show_label=False).style(
-                    columns=2, object_fit='scale-down')
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            preprocess_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            preprocessor_name,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name='segmentation',
-        )
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='segmentation')
-    demo = create_demo(model.process_segmentation)
-    demo.queue().launch()
diff --git a/spaces/williamcfrancis/Deep-Blind-Motion-Deblurring/sidekick/callbs/trainmonitor.py b/spaces/williamcfrancis/Deep-Blind-Motion-Deblurring/sidekick/callbs/trainmonitor.py
deleted file mode 100644
index 9132ebf4f93495aebbd10e3da4a8662ce1dbca5d..0000000000000000000000000000000000000000
--- a/spaces/williamcfrancis/Deep-Blind-Motion-Deblurring/sidekick/callbs/trainmonitor.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from tensorflow.keras import callbacks
-import matplotlib
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-import numpy as np
-import json
-import os
-
-class TrainMonitor(callbacks.BaseLogger):
-    def __init__(self, figPath, jsonPath=None, startAt=0):
-        super(TrainMonitor, self).__init__()
-
-        self.figPath= figPath
-        self.jsonPath= jsonPath
-        self.startAt= startAt
-
-    def on_train_begin(self, logs={}):
-        self.H={}
-
-        if self.jsonPath is not None:
-            if os.path.exists(self.jsonPath):
-                self.H = json.loads(open(self.jsonPath).read())
-
-                if self.startAt > 0:
-                    for k in self.H.keys():
-                        self.H[k] = self.H[k][:self.startAt]
-
-    def on_epoch_end(self, epoch, logs={}):
-        for keys, values in logs.items():
-            l= self.H.get(keys, [])
-            l.append(float(values))
-            self.H[keys] = l
-
-        if self.jsonPath is not None:
-            with open(self.jsonPath, 'w') as f:
-                f.write(json.dumps(self.H))
-                f.close()
-
-        if len(self.H["loss"]) > 1:
-            N = np.arange(0, len(self.H["loss"]), 1)
-            plt.style.use("ggplot")
-            plt.figure()
-            plt.plot(N, self.H["loss"], label="train_loss")
-            plt.plot(N, self.H["val_loss"], label="val_loss")
-            plt.plot(N, self.H["accuracy"], label="train_acc")
-            plt.plot(N, self.H["val_accuracy"], label="val_acc")
-            plt.title("Training Loss and Accuracy [Epoch {}]".format(len(self.H["loss"])))
-            plt.xlabel("Epoch #")
-            plt.ylabel("Loss/Accuracy")
-            plt.legend()
-            plt.savefig(self.figPath)
-            plt.close()
\ No newline at end of file
diff --git a/spaces/xdecoder/Demo/tasks/open_sem.py b/spaces/xdecoder/Demo/tasks/open_sem.py
deleted file mode 100644
index 04b95fc9fff82951cf6683a5a2f0632bf30837e4..0000000000000000000000000000000000000000
--- a/spaces/xdecoder/Demo/tasks/open_sem.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# --------------------------------------------------------
-# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
-# Copyright (c) 2022 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Xueyan Zou (xueyan@cs.wisc.edu)
-# --------------------------------------------------------
-
-import os
-import cv2
-import torch
-import numpy as np
-from PIL import Image
-from torchvision import transforms
-from utils.visualizer import Visualizer
-from detectron2.utils.colormap import random_color
-from detectron2.data import MetadataCatalog
-
-
-t = []
-t.append(transforms.Resize(512, interpolation=Image.BICUBIC))
-transform = transforms.Compose(t)
-metadata = MetadataCatalog.get('ade20k_panoptic_train')
-
-def open_semseg(model, image, texts, inpainting_text, *args, **kwargs):    
-    stuff_classes = [x.strip() for x in texts.split(',')]
-    stuff_colors = [random_color(rgb=True, maximum=255).astype(np.int32).tolist() for _ in range(len(stuff_classes))]
-    stuff_dataset_id_to_contiguous_id = {x:x for x in range(len(stuff_classes))}
-
-    MetadataCatalog.get("demo").set(
-        stuff_colors=stuff_colors,
-        stuff_classes=stuff_classes,
-        stuff_dataset_id_to_contiguous_id=stuff_dataset_id_to_contiguous_id,
-    )
-    model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(stuff_classes + ["background"], is_eval=True)
-    metadata = MetadataCatalog.get('demo')
-    model.model.metadata = metadata
-    model.model.sem_seg_head.num_classes = len(stuff_classes)
-
-    with torch.no_grad():
-        image_ori = transform(image)
-        width = image_ori.size[0]
-        height = image_ori.size[1]
-        image = transform(image_ori)
-        image = np.asarray(image)
-        images = torch.from_numpy(image.copy()).permute(2,0,1).cuda()
-
-        batch_inputs = [{'image': images, 'height': height, 'width': width}]
-        outputs = model.forward(batch_inputs)
-        visual = Visualizer(image_ori, metadata=metadata)
-
-        sem_seg = outputs[-1]['sem_seg'].max(0)[1]
-        demo = visual.draw_sem_seg(sem_seg.cpu(), alpha=0.5) # rgb Image
-        res = demo.get_image()
-        
-    MetadataCatalog.remove('demo')
-    torch.cuda.empty_cache()
-    return Image.fromarray(res), '', None
\ No newline at end of file
diff --git a/spaces/xfys/yolov5_tracking/yolov5/utils/loggers/wandb/wandb_utils.py b/spaces/xfys/yolov5_tracking/yolov5/utils/loggers/wandb/wandb_utils.py
deleted file mode 100644
index 4ea32b1d4c6ec62920a9e90af085346d0f7a5f2c..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/yolov5/utils/loggers/wandb/wandb_utils.py
+++ /dev/null
@@ -1,193 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-
-# WARNING ⚠️ wandb is deprecated and will be removed in future release.
-# See supported integrations at https://github.com/ultralytics/yolov5#integrations
-
-import logging
-import os
-import sys
-from contextlib import contextmanager
-from pathlib import Path
-
-from utils.general import LOGGER, colorstr
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[3]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-RANK = int(os.getenv('RANK', -1))
-DEPRECATION_WARNING = f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. " \
-                      f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.'
-
-try:
-    import wandb
-
-    assert hasattr(wandb, '__version__')  # verify package import not local dir
-    LOGGER.warning(DEPRECATION_WARNING)
-except (ImportError, AssertionError):
-    wandb = None
-
-
-class WandbLogger():
-    """Log training runs, datasets, models, and predictions to Weights & Biases.
-
-    This logger sends information to W&B at wandb.ai. By default, this information
-    includes hyperparameters, system configuration and metrics, model metrics,
-    and basic data metrics and analyses.
-
-    By providing additional command line arguments to train.py, datasets,
-    models and predictions can also be logged.
-
-    For more on how this logger is used, see the Weights & Biases documentation:
-    https://docs.wandb.com/guides/integrations/yolov5
-    """
-
-    def __init__(self, opt, run_id=None, job_type='Training'):
-        """
-        - Initialize WandbLogger instance
-        - Upload dataset if opt.upload_dataset is True
-        - Setup training processes if job_type is 'Training'
-
-        arguments:
-        opt (namespace) -- Commandline arguments for this run
-        run_id (str) -- Run ID of W&B run to be resumed
-        job_type (str) -- To set the job_type for this run
-
-       """
-        # Pre-training routine --
-        self.job_type = job_type
-        self.wandb, self.wandb_run = wandb, wandb.run if wandb else None
-        self.val_artifact, self.train_artifact = None, None
-        self.train_artifact_path, self.val_artifact_path = None, None
-        self.result_artifact = None
-        self.val_table, self.result_table = None, None
-        self.max_imgs_to_log = 16
-        self.data_dict = None
-        if self.wandb:
-            self.wandb_run = wandb.init(config=opt,
-                                        resume='allow',
-                                        project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
-                                        entity=opt.entity,
-                                        name=opt.name if opt.name != 'exp' else None,
-                                        job_type=job_type,
-                                        id=run_id,
-                                        allow_val_change=True) if not wandb.run else wandb.run
-
-        if self.wandb_run:
-            if self.job_type == 'Training':
-                if isinstance(opt.data, dict):
-                    # This means another dataset manager has already processed the dataset info (e.g. ClearML)
-                    # and they will have stored the already processed dict in opt.data
-                    self.data_dict = opt.data
-                self.setup_training(opt)
-
-    def setup_training(self, opt):
-        """
-        Setup the necessary processes for training YOLO models:
-          - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
-          - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
-          - Setup log_dict, initialize bbox_interval
-
-        arguments:
-        opt (namespace) -- commandline arguments for this run
-
-        """
-        self.log_dict, self.current_epoch = {}, 0
-        self.bbox_interval = opt.bbox_interval
-        if isinstance(opt.resume, str):
-            model_dir, _ = self.download_model_artifact(opt)
-            if model_dir:
-                self.weights = Path(model_dir) / 'last.pt'
-                config = self.wandb_run.config
-                opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str(
-                    self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \
-                    config.hyp, config.imgsz
-
-        if opt.bbox_interval == -1:
-            self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
-            if opt.evolve or opt.noplots:
-                self.bbox_interval = opt.bbox_interval = opt.epochs + 1  # disable bbox_interval
-
-    def log_model(self, path, opt, epoch, fitness_score, best_model=False):
-        """
-        Log the model checkpoint as W&B artifact
-
-        arguments:
-        path (Path)   -- Path of directory containing the checkpoints
-        opt (namespace) -- Command line arguments for this run
-        epoch (int)  -- Current epoch number
-        fitness_score (float) -- fitness score for current epoch
-        best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
-        """
-        model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model',
-                                        type='model',
-                                        metadata={
-                                            'original_url': str(path),
-                                            'epochs_trained': epoch + 1,
-                                            'save period': opt.save_period,
-                                            'project': opt.project,
-                                            'total_epochs': opt.epochs,
-                                            'fitness_score': fitness_score})
-        model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
-        wandb.log_artifact(model_artifact,
-                           aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
-        LOGGER.info(f'Saving model artifact on epoch {epoch + 1}')
-
-    def val_one_image(self, pred, predn, path, names, im):
-        pass
-
-    def log(self, log_dict):
-        """
-        save the metrics to the logging dictionary
-
-        arguments:
-        log_dict (Dict) -- metrics/media to be logged in current step
-        """
-        if self.wandb_run:
-            for key, value in log_dict.items():
-                self.log_dict[key] = value
-
-    def end_epoch(self):
-        """
-        commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
-
-        arguments:
-        best_result (boolean): Boolean representing if the result of this evaluation is best or not
-        """
-        if self.wandb_run:
-            with all_logging_disabled():
-                try:
-                    wandb.log(self.log_dict)
-                except BaseException as e:
-                    LOGGER.info(
-                        f'An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}'
-                    )
-                    self.wandb_run.finish()
-                    self.wandb_run = None
-                self.log_dict = {}
-
-    def finish_run(self):
-        """
-        Log metrics if any and finish the current W&B run
-        """
-        if self.wandb_run:
-            if self.log_dict:
-                with all_logging_disabled():
-                    wandb.log(self.log_dict)
-            wandb.run.finish()
-            LOGGER.warning(DEPRECATION_WARNING)
-
-
-@contextmanager
-def all_logging_disabled(highest_level=logging.CRITICAL):
-    """ source - https://gist.github.com/simon-weber/7853144
-    A context manager that will prevent any logging messages triggered during the body from being processed.
-    :param highest_level: the maximum logging level in use.
-      This would only need to be changed if a custom level greater than CRITICAL is defined.
-    """
-    previous_level = logging.root.manager.disable
-    logging.disable(highest_level)
-    try:
-        yield
-    finally:
-        logging.disable(previous_level)
diff --git a/spaces/xinyu1205/recognize-anything/GroundingDINO/groundingdino/util/misc.py b/spaces/xinyu1205/recognize-anything/GroundingDINO/groundingdino/util/misc.py
deleted file mode 100644
index d64b84ef24bea0c98e76824feb1903f6bfebe7a5..0000000000000000000000000000000000000000
--- a/spaces/xinyu1205/recognize-anything/GroundingDINO/groundingdino/util/misc.py
+++ /dev/null
@@ -1,717 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Misc functions, including distributed helpers.
-
-Mostly copy-paste from torchvision references.
-"""
-import colorsys
-import datetime
-import functools
-import io
-import json
-import os
-import pickle
-import subprocess
-import time
-from collections import OrderedDict, defaultdict, deque
-from typing import List, Optional
-
-import numpy as np
-import torch
-import torch.distributed as dist
-
-# needed due to empty tensor bug in pytorch and torchvision 0.5
-import torchvision
-from torch import Tensor
-
-__torchvision_need_compat_flag = float(torchvision.__version__.split(".")[1]) < 7
-if __torchvision_need_compat_flag:
-    from torchvision.ops import _new_empty_tensor
-    from torchvision.ops.misc import _output_size
-
-
-class SmoothedValue(object):
-    """Track a series of values and provide access to smoothed values over a
-    window or the global series average.
-    """
-
-    def __init__(self, window_size=20, fmt=None):
-        if fmt is None:
-            fmt = "{median:.4f} ({global_avg:.4f})"
-        self.deque = deque(maxlen=window_size)
-        self.total = 0.0
-        self.count = 0
-        self.fmt = fmt
-
-    def update(self, value, n=1):
-        self.deque.append(value)
-        self.count += n
-        self.total += value * n
-
-    def synchronize_between_processes(self):
-        """
-        Warning: does not synchronize the deque!
-        """
-        if not is_dist_avail_and_initialized():
-            return
-        t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
-        dist.barrier()
-        dist.all_reduce(t)
-        t = t.tolist()
-        self.count = int(t[0])
-        self.total = t[1]
-
-    @property
-    def median(self):
-        d = torch.tensor(list(self.deque))
-        if d.shape[0] == 0:
-            return 0
-        return d.median().item()
-
-    @property
-    def avg(self):
-        d = torch.tensor(list(self.deque), dtype=torch.float32)
-        return d.mean().item()
-
-    @property
-    def global_avg(self):
-        if os.environ.get("SHILONG_AMP", None) == "1":
-            eps = 1e-4
-        else:
-            eps = 1e-6
-        return self.total / (self.count + eps)
-
-    @property
-    def max(self):
-        return max(self.deque)
-
-    @property
-    def value(self):
-        return self.deque[-1]
-
-    def __str__(self):
-        return self.fmt.format(
-            median=self.median,
-            avg=self.avg,
-            global_avg=self.global_avg,
-            max=self.max,
-            value=self.value,
-        )
-
-
-@functools.lru_cache()
-def _get_global_gloo_group():
-    """
-    Return a process group based on gloo backend, containing all the ranks
-    The result is cached.
-    """
-
-    if dist.get_backend() == "nccl":
-        return dist.new_group(backend="gloo")
-
-    return dist.group.WORLD
-
-
-def all_gather_cpu(data):
-    """
-    Run all_gather on arbitrary picklable data (not necessarily tensors)
-    Args:
-        data: any picklable object
-    Returns:
-        list[data]: list of data gathered from each rank
-    """
-
-    world_size = get_world_size()
-    if world_size == 1:
-        return [data]
-
-    cpu_group = _get_global_gloo_group()
-
-    buffer = io.BytesIO()
-    torch.save(data, buffer)
-    data_view = buffer.getbuffer()
-    device = "cuda" if cpu_group is None else "cpu"
-    tensor = torch.ByteTensor(data_view).to(device)
-
-    # obtain Tensor size of each rank
-    local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)
-    size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]
-    if cpu_group is None:
-        dist.all_gather(size_list, local_size)
-    else:
-        print("gathering on cpu")
-        dist.all_gather(size_list, local_size, group=cpu_group)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-    assert isinstance(local_size.item(), int)
-    local_size = int(local_size.item())
-
-    # receiving Tensor from all ranks
-    # we pad the tensor because torch all_gather does not support
-    # gathering tensors of different shapes
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))
-    if local_size != max_size:
-        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)
-        tensor = torch.cat((tensor, padding), dim=0)
-    if cpu_group is None:
-        dist.all_gather(tensor_list, tensor)
-    else:
-        dist.all_gather(tensor_list, tensor, group=cpu_group)
-
-    data_list = []
-    for size, tensor in zip(size_list, tensor_list):
-        tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]
-        buffer = io.BytesIO(tensor.cpu().numpy())
-        obj = torch.load(buffer)
-        data_list.append(obj)
-
-    return data_list
-
-
-def all_gather(data):
-    """
-    Run all_gather on arbitrary picklable data (not necessarily tensors)
-    Args:
-        data: any picklable object
-    Returns:
-        list[data]: list of data gathered from each rank
-    """
-
-    if os.getenv("CPU_REDUCE") == "1":
-        return all_gather_cpu(data)
-
-    world_size = get_world_size()
-    if world_size == 1:
-        return [data]
-
-    # serialized to a Tensor
-    buffer = pickle.dumps(data)
-    storage = torch.ByteStorage.from_buffer(buffer)
-    tensor = torch.ByteTensor(storage).to("cuda")
-
-    # obtain Tensor size of each rank
-    local_size = torch.tensor([tensor.numel()], device="cuda")
-    size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
-    dist.all_gather(size_list, local_size)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-
-    # receiving Tensor from all ranks
-    # we pad the tensor because torch all_gather does not support
-    # gathering tensors of different shapes
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
-    if local_size != max_size:
-        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
-        tensor = torch.cat((tensor, padding), dim=0)
-    dist.all_gather(tensor_list, tensor)
-
-    data_list = []
-    for size, tensor in zip(size_list, tensor_list):
-        buffer = tensor.cpu().numpy().tobytes()[:size]
-        data_list.append(pickle.loads(buffer))
-
-    return data_list
-
-
-def reduce_dict(input_dict, average=True):
-    """
-    Args:
-        input_dict (dict): all the values will be reduced
-        average (bool): whether to do average or sum
-    Reduce the values in the dictionary from all processes so that all processes
-    have the averaged results. Returns a dict with the same fields as
-    input_dict, after reduction.
-    """
-    world_size = get_world_size()
-    if world_size < 2:
-        return input_dict
-    with torch.no_grad():
-        names = []
-        values = []
-        # sort the keys so that they are consistent across processes
-        for k in sorted(input_dict.keys()):
-            names.append(k)
-            values.append(input_dict[k])
-        values = torch.stack(values, dim=0)
-        dist.all_reduce(values)
-        if average:
-            values /= world_size
-        reduced_dict = {k: v for k, v in zip(names, values)}
-    return reduced_dict
-
-
-class MetricLogger(object):
-    def __init__(self, delimiter="\t"):
-        self.meters = defaultdict(SmoothedValue)
-        self.delimiter = delimiter
-
-    def update(self, **kwargs):
-        for k, v in kwargs.items():
-            if isinstance(v, torch.Tensor):
-                v = v.item()
-            assert isinstance(v, (float, int))
-            self.meters[k].update(v)
-
-    def __getattr__(self, attr):
-        if attr in self.meters:
-            return self.meters[attr]
-        if attr in self.__dict__:
-            return self.__dict__[attr]
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
-
-    def __str__(self):
-        loss_str = []
-        for name, meter in self.meters.items():
-            # print(name, str(meter))
-            # import ipdb;ipdb.set_trace()
-            if meter.count > 0:
-                loss_str.append("{}: {}".format(name, str(meter)))
-        return self.delimiter.join(loss_str)
-
-    def synchronize_between_processes(self):
-        for meter in self.meters.values():
-            meter.synchronize_between_processes()
-
-    def add_meter(self, name, meter):
-        self.meters[name] = meter
-
-    def log_every(self, iterable, print_freq, header=None, logger=None):
-        if logger is None:
-            print_func = print
-        else:
-            print_func = logger.info
-
-        i = 0
-        if not header:
-            header = ""
-        start_time = time.time()
-        end = time.time()
-        iter_time = SmoothedValue(fmt="{avg:.4f}")
-        data_time = SmoothedValue(fmt="{avg:.4f}")
-        space_fmt = ":" + str(len(str(len(iterable)))) + "d"
-        if torch.cuda.is_available():
-            log_msg = self.delimiter.join(
-                [
-                    header,
-                    "[{0" + space_fmt + "}/{1}]",
-                    "eta: {eta}",
-                    "{meters}",
-                    "time: {time}",
-                    "data: {data}",
-                    "max mem: {memory:.0f}",
-                ]
-            )
-        else:
-            log_msg = self.delimiter.join(
-                [
-                    header,
-                    "[{0" + space_fmt + "}/{1}]",
-                    "eta: {eta}",
-                    "{meters}",
-                    "time: {time}",
-                    "data: {data}",
-                ]
-            )
-        MB = 1024.0 * 1024.0
-        for obj in iterable:
-            data_time.update(time.time() - end)
-            yield obj
-            # import ipdb; ipdb.set_trace()
-            iter_time.update(time.time() - end)
-            if i % print_freq == 0 or i == len(iterable) - 1:
-                eta_seconds = iter_time.global_avg * (len(iterable) - i)
-                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
-                if torch.cuda.is_available():
-                    print_func(
-                        log_msg.format(
-                            i,
-                            len(iterable),
-                            eta=eta_string,
-                            meters=str(self),
-                            time=str(iter_time),
-                            data=str(data_time),
-                            memory=torch.cuda.max_memory_allocated() / MB,
-                        )
-                    )
-                else:
-                    print_func(
-                        log_msg.format(
-                            i,
-                            len(iterable),
-                            eta=eta_string,
-                            meters=str(self),
-                            time=str(iter_time),
-                            data=str(data_time),
-                        )
-                    )
-            i += 1
-            end = time.time()
-        total_time = time.time() - start_time
-        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-        print_func(
-            "{} Total time: {} ({:.4f} s / it)".format(
-                header, total_time_str, total_time / len(iterable)
-            )
-        )
-
-
-def get_sha():
-    cwd = os.path.dirname(os.path.abspath(__file__))
-
-    def _run(command):
-        return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
-
-    sha = "N/A"
-    diff = "clean"
-    branch = "N/A"
-    try:
-        sha = _run(["git", "rev-parse", "HEAD"])
-        subprocess.check_output(["git", "diff"], cwd=cwd)
-        diff = _run(["git", "diff-index", "HEAD"])
-        diff = "has uncommited changes" if diff else "clean"
-        branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
-    except Exception:
-        pass
-    message = f"sha: {sha}, status: {diff}, branch: {branch}"
-    return message
-
-
-def collate_fn(batch):
-    # import ipdb; ipdb.set_trace()
-    batch = list(zip(*batch))
-    batch[0] = nested_tensor_from_tensor_list(batch[0])
-    return tuple(batch)
-
-
-def _max_by_axis(the_list):
-    # type: (List[List[int]]) -> List[int]
-    maxes = the_list[0]
-    for sublist in the_list[1:]:
-        for index, item in enumerate(sublist):
-            maxes[index] = max(maxes[index], item)
-    return maxes
-
-
-class NestedTensor(object):
-    def __init__(self, tensors, mask: Optional[Tensor]):
-        self.tensors = tensors
-        self.mask = mask
-        if mask == "auto":
-            self.mask = torch.zeros_like(tensors).to(tensors.device)
-            if self.mask.dim() == 3:
-                self.mask = self.mask.sum(0).to(bool)
-            elif self.mask.dim() == 4:
-                self.mask = self.mask.sum(1).to(bool)
-            else:
-                raise ValueError(
-                    "tensors dim must be 3 or 4 but {}({})".format(
-                        self.tensors.dim(), self.tensors.shape
-                    )
-                )
-
-    def imgsize(self):
-        res = []
-        for i in range(self.tensors.shape[0]):
-            mask = self.mask[i]
-            maxH = (~mask).sum(0).max()
-            maxW = (~mask).sum(1).max()
-            res.append(torch.Tensor([maxH, maxW]))
-        return res
-
-    def to(self, device):
-        # type: (Device) -> NestedTensor # noqa
-        cast_tensor = self.tensors.to(device)
-        mask = self.mask
-        if mask is not None:
-            assert mask is not None
-            cast_mask = mask.to(device)
-        else:
-            cast_mask = None
-        return NestedTensor(cast_tensor, cast_mask)
-
-    def to_img_list_single(self, tensor, mask):
-        assert tensor.dim() == 3, "dim of tensor should be 3 but {}".format(tensor.dim())
-        maxH = (~mask).sum(0).max()
-        maxW = (~mask).sum(1).max()
-        img = tensor[:, :maxH, :maxW]
-        return img
-
-    def to_img_list(self):
-        """remove the padding and convert to img list
-
-        Returns:
-            [type]: [description]
-        """
-        if self.tensors.dim() == 3:
-            return self.to_img_list_single(self.tensors, self.mask)
-        else:
-            res = []
-            for i in range(self.tensors.shape[0]):
-                tensor_i = self.tensors[i]
-                mask_i = self.mask[i]
-                res.append(self.to_img_list_single(tensor_i, mask_i))
-            return res
-
-    @property
-    def device(self):
-        return self.tensors.device
-
-    def decompose(self):
-        return self.tensors, self.mask
-
-    def __repr__(self):
-        return str(self.tensors)
-
-    @property
-    def shape(self):
-        return {"tensors.shape": self.tensors.shape, "mask.shape": self.mask.shape}
-
-
-def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
-    # TODO make this more general
-    if tensor_list[0].ndim == 3:
-        if torchvision._is_tracing():
-            # nested_tensor_from_tensor_list() does not export well to ONNX
-            # call _onnx_nested_tensor_from_tensor_list() instead
-            return _onnx_nested_tensor_from_tensor_list(tensor_list)
-
-        # TODO make it support different-sized images
-        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
-        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
-        batch_shape = [len(tensor_list)] + max_size
-        b, c, h, w = batch_shape
-        dtype = tensor_list[0].dtype
-        device = tensor_list[0].device
-        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
-        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
-        for img, pad_img, m in zip(tensor_list, tensor, mask):
-            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-            m[: img.shape[1], : img.shape[2]] = False
-    else:
-        raise ValueError("not supported")
-    return NestedTensor(tensor, mask)
-
-
-# _onnx_nested_tensor_from_tensor_list() is an implementation of
-# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
-@torch.jit.unused
-def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
-    max_size = []
-    for i in range(tensor_list[0].dim()):
-        max_size_i = torch.max(
-            torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
-        ).to(torch.int64)
-        max_size.append(max_size_i)
-    max_size = tuple(max_size)
-
-    # work around for
-    # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-    # m[: img.shape[1], :img.shape[2]] = False
-    # which is not yet supported in onnx
-    padded_imgs = []
-    padded_masks = []
-    for img in tensor_list:
-        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
-        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
-        padded_imgs.append(padded_img)
-
-        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
-        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
-        padded_masks.append(padded_mask.to(torch.bool))
-
-    tensor = torch.stack(padded_imgs)
-    mask = torch.stack(padded_masks)
-
-    return NestedTensor(tensor, mask=mask)
-
-
-def setup_for_distributed(is_master):
-    """
-    This function disables printing when not in master process
-    """
-    import builtins as __builtin__
-
-    builtin_print = __builtin__.print
-
-    def print(*args, **kwargs):
-        force = kwargs.pop("force", False)
-        if is_master or force:
-            builtin_print(*args, **kwargs)
-
-    __builtin__.print = print
-
-
-def is_dist_avail_and_initialized():
-    if not dist.is_available():
-        return False
-    if not dist.is_initialized():
-        return False
-    return True
-
-
-def get_world_size():
-    if not is_dist_avail_and_initialized():
-        return 1
-    return dist.get_world_size()
-
-
-def get_rank():
-    if not is_dist_avail_and_initialized():
-        return 0
-    return dist.get_rank()
-
-
-def is_main_process():
-    return get_rank() == 0
-
-
-def save_on_master(*args, **kwargs):
-    if is_main_process():
-        torch.save(*args, **kwargs)
-
-
-def init_distributed_mode(args):
-    if "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "":  # 'RANK' in os.environ and
-        args.rank = int(os.environ["RANK"])
-        args.world_size = int(os.environ["WORLD_SIZE"])
-        args.gpu = args.local_rank = int(os.environ["LOCAL_RANK"])
-
-        # launch by torch.distributed.launch
-        # Single node
-        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 1 --rank 0 ...
-        # Multi nodes
-        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 0 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
-        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 1 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
-        # args.rank = int(os.environ.get('OMPI_COMM_WORLD_RANK'))
-        # local_world_size = int(os.environ['GPU_PER_NODE_COUNT'])
-        # args.world_size = args.world_size * local_world_size
-        # args.gpu = args.local_rank = int(os.environ['LOCAL_RANK'])
-        # args.rank = args.rank * local_world_size + args.local_rank
-        print(
-            "world size: {}, rank: {}, local rank: {}".format(
-                args.world_size, args.rank, args.local_rank
-            )
-        )
-        print(json.dumps(dict(os.environ), indent=2))
-    elif "SLURM_PROCID" in os.environ:
-        args.rank = int(os.environ["SLURM_PROCID"])
-        args.gpu = args.local_rank = int(os.environ["SLURM_LOCALID"])
-        args.world_size = int(os.environ["SLURM_NPROCS"])
-
-        print(
-            "world size: {}, world rank: {}, local rank: {}, device_count: {}".format(
-                args.world_size, args.rank, args.local_rank, torch.cuda.device_count()
-            )
-        )
-    else:
-        print("Not using distributed mode")
-        args.distributed = False
-        args.world_size = 1
-        args.rank = 0
-        args.local_rank = 0
-        return
-
-    print("world_size:{} rank:{} local_rank:{}".format(args.world_size, args.rank, args.local_rank))
-    args.distributed = True
-    torch.cuda.set_device(args.local_rank)
-    args.dist_backend = "nccl"
-    print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True)
-
-    torch.distributed.init_process_group(
-        backend=args.dist_backend,
-        world_size=args.world_size,
-        rank=args.rank,
-        init_method=args.dist_url,
-    )
-
-    print("Before torch.distributed.barrier()")
-    torch.distributed.barrier()
-    print("End torch.distributed.barrier()")
-    setup_for_distributed(args.rank == 0)
-
-
-@torch.no_grad()
-def accuracy(output, target, topk=(1,)):
-    """Computes the precision@k for the specified values of k"""
-    if target.numel() == 0:
-        return [torch.zeros([], device=output.device)]
-    maxk = max(topk)
-    batch_size = target.size(0)
-
-    _, pred = output.topk(maxk, 1, True, True)
-    pred = pred.t()
-    correct = pred.eq(target.view(1, -1).expand_as(pred))
-
-    res = []
-    for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0)
-        res.append(correct_k.mul_(100.0 / batch_size))
-    return res
-
-
-@torch.no_grad()
-def accuracy_onehot(pred, gt):
-    """_summary_
-
-    Args:
-        pred (_type_): n, c
-        gt (_type_): n, c
-    """
-    tp = ((pred - gt).abs().sum(-1) < 1e-4).float().sum()
-    acc = tp / gt.shape[0] * 100
-    return acc
-
-
-def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
-    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
-    """
-    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
-    This will eventually be supported natively by PyTorch, and this
-    class can go away.
-    """
-    if __torchvision_need_compat_flag < 0.7:
-        if input.numel() > 0:
-            return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
-
-        output_shape = _output_size(2, input, size, scale_factor)
-        output_shape = list(input.shape[:-2]) + list(output_shape)
-        return _new_empty_tensor(input, output_shape)
-    else:
-        return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
-
-
-class color_sys:
-    def __init__(self, num_colors) -> None:
-        self.num_colors = num_colors
-        colors = []
-        for i in np.arange(0.0, 360.0, 360.0 / num_colors):
-            hue = i / 360.0
-            lightness = (50 + np.random.rand() * 10) / 100.0
-            saturation = (90 + np.random.rand() * 10) / 100.0
-            colors.append(
-                tuple([int(j * 255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)])
-            )
-        self.colors = colors
-
-    def __call__(self, idx):
-        return self.colors[idx]
-
-
-def inverse_sigmoid(x, eps=1e-3):
-    x = x.clamp(min=0, max=1)
-    x1 = x.clamp(min=eps)
-    x2 = (1 - x).clamp(min=eps)
-    return torch.log(x1 / x2)
-
-
-def clean_state_dict(state_dict):
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        if k[:7] == "module.":
-            k = k[7:]  # remove `module.`
-        new_state_dict[k] = v
-    return new_state_dict
diff --git a/spaces/xly66624/Brayton-cycle/system.py b/spaces/xly66624/Brayton-cycle/system.py
deleted file mode 100644
index 3a54be777dce38f4ab9556580246347248c775ce..0000000000000000000000000000000000000000
--- a/spaces/xly66624/Brayton-cycle/system.py
+++ /dev/null
@@ -1,566 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import CoolProp.CoolProp as cp
-import gradio as gr
-import pandas as pd
-from scipy.integrate import quad
-import matplotlib
-import sympy as sp
-import functools
-import time
-from joblib import Parallel, delayed
-import logging
-import RCP
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-matplotlib.use('Agg')
-
-
-def timer_decorator(func):
-    @functools.wraps(func)
-    def wrapper(*args, **kwargs):
-        start_time = time.time()
-        result = func(*args, **kwargs)
-        end_time = time.time()
-        execution_time = end_time - start_time
-        logging.info(f"Function {func.__name__} executed in {execution_time} seconds")
-        return result
-    return wrapper
-
-epsilon1, epsilon2, x = sp.symbols('epsilon1 epsilon2 x')
-matrix1 = sp.Matrix([[1,0,0,0,-epsilon1],
-                     [-1,0,-1,0,1],
-                     [1-x*epsilon2,-1,0,0,0],
-                     [1,-1,0,-x,0],
-                     [0,1-x,0,x,-1]])
-
-matrix2 = sp.Matrix([
-                    [1,0,0,0,-epsilon1],
-                    [-1,0,-1,0,1],
-                    [epsilon2-1,1,0,0,0],
-                    [1,-1,0,-x,0],
-                    [0,1-x,0,x,-1]])
-
-matrix3 = sp.Matrix([[1,0,0,0,-epsilon1],
-                     [-1,0,-1,0,1],
-                     [1-x*epsilon2,-1,0,0,0],
-                     [1,-1,0,-x,0],
-                     [0,1-x,0,x,-1]])
-inv1 = sp.lambdify([epsilon1, epsilon2, x], matrix1.inv(), modules='numpy')
-inv2 = sp.lambdify([epsilon1, epsilon2, x], matrix2.inv(), modules='numpy')
-inv3 = sp.lambdify([epsilon1, epsilon2, x], matrix3.inv(), modules='numpy')
-
-
-# adiabatic process
-def ad(p1:float,h:float,p2:float, fluid:str):
-    s = cp.PropsSI('S', 'P', p1, 'H', h, fluid)
-    h2 = cp.PropsSI('H', 'P', p2, 'S', s, fluid)
-    return h2
-# Define function to plot T-s diagram
-def plot_TS_diagram(fluidname:str, Tmin:float, ax):
-    t = np.linspace(Tmin, cp.PropsSI('T_CRITICAL', fluidname), 200)
-    sL = cp.PropsSI('S', 'T', t, 'Q', 0, fluidname)
-    sV = cp.PropsSI('S', 'T', t, 'Q', 1, fluidname)
-    ax.plot(sL, t, 'b-', label='Saturated Liquid')
-    ax.plot(sV, t, 'r-', label='Saturated Vapor')
-
-# Define function to plot isobaric process on T-s diagram
-def plot_isobar_TS_diagram(fluidname:str, p:float, Tmin:float, Tmax:float, ax,label=''):
-    T = np.linspace(Tmin, Tmax, 101)
-    s = cp.PropsSI('S', 'T', T, 'P', p, fluidname)
-    ax.plot(s, T, 'g-',label=label)
-
-# Plot the isothermal process on the T-s diagram
-def plot_isothermal_TS_diagram(s_min:float, s_max:float, Tmin:float, Tmax:float,ax,label=''):
-    ax.plot([s_min, s_max], [Tmin, Tmax], 'k-',label=label)
-
-
-class syst:
-    def __init__(self, fluid:str, p_max:float=None, t_max:float=None, x:float=None, e1=0.9, e2=0.9, e3=0.9, epsilon1=0.86, epsilon2=0.86):
-        self.T = np.zeros(11)
-        self.s = np.zeros(11)
-        self.p = np.zeros(11)
-        self.h = np.zeros(11)
-        self. fluid = fluid
-        self.e1 = e1 # Turbine
-        self.e2 = e2 # MC
-        self.e3 = e3 # RC
-        self.epsilon1 = epsilon1 # HTR
-        self.epsilon2 = epsilon2 # LTR
-        self.x = x
-        self.p_max = p_max
-        self.t_max = t_max
-        self.p_min = cp.PropsSI('P_CRITICAL', fluid) + 2000
-        self.t_min = cp.PropsSI('T_CRITICAL', fluid) + 5
-
-        if self.p_min > self.p_max:
-            raise ValueError('最大压力低于临界压力')
-        if self.t_min > self.t_max:
-            raise ValueError('最大温度低于临界温度')
-        
-        self.T[1] = t_max
-        self.p[1] = self.p[6] = self.p[7] = self.p[8] = self.p[9] = self.p[10] = p_max
-        self.p[5] = self.p[2] = self.p[3] = self.p[4] = self.p_min
-
-        self.h[1] = cp.PropsSI('H', 'P', self.p[1], 'T', self.T[1], self.fluid)
-        self.h[2] = ad(p_max, self.h[1], self.p[2], fluid)
-        self.h[2] =self.h[1] - self.e1 * (self.h[1] - self.h[2]) # Turbine
-
-        self.h[5] = cp.PropsSI('H', 'P', self.p_min, 'T', self.t_min, self.fluid)
-        self.h[6] = ad(self.p[5], self.h[5], self.p[6], self.fluid)
-        self.h[6] =self.h[5] +  (self.h[6] - self.h[5])/self.e2 # MC
-        self.T[6] = cp.PropsSI('T', 'P', self.p[6], 'H', self.h[6], self.fluid)
-        self.T[2] = cp.PropsSI('T', 'P', self.p[2], 'H', self.h[2], self.fluid)
-
-
-    def hsolve1(self, error = 1e-6):
-        # liearization
-        a = 0
-        b = 0
-        c = 0
-        iter = 0
-        # A = np.array([[1,0,0,0,-self.epsilon1],[-1,0,-1,0,1],[1-self.x*self.epsilon2,-1,0,0,0],[1,-1,0,-self.x,0],[0,1-self.x,0,self.x,-1]])
-        while True:
-            t = np.array([(1-self.epsilon1)*self.h[2]+self.epsilon1*c ,-self.h[2],self.x*self.epsilon2*(a-self.h[6]),-self.x*self.h[6],b*(self.x-1)])
-            sol = np.dot(inv1(self.epsilon1, self.epsilon2, self.x), t)
-            h4 = sol[1]
-            h3 = sol[0]
-            h10 = sol[4]
-            b1 = (ad(self.p_min, h4, self.p_max, self.fluid)-h4)/self.e3
-            c = cp.PropsSI('H', 'P', self.p_min, 'T', cp.PropsSI('T', 'P', self.p_max, 'H', h10, self.fluid), self.fluid) -h10
-            a = cp.PropsSI('H', 'P', self.p_max, 'T', cp.PropsSI('T', 'P', self.p_min, 'H', h3, self.fluid), self.fluid) -h3
-            if abs(b1-b) < error:
-                self.h[3] = sol[0]
-                self.h[4] = sol[1]
-                self.h[7] = self.h[4] + b1
-                self.h[8] = sol[2]
-                self.h[9] = sol[3]
-                self.h[10] = sol[4]
-                break
-            b = b1
-            iter += 1
-            if iter > 200:
-                gr.Error(f'Iteration exceeds 200, x = {self.x}')
-                break
-        return  (self.h[1]-self.h[2]-self.x*(self.h[6]-self.h[5])-(1-self.x)*(self.h[7]-self.h[4]))/(self.h[1]-self.h[8]), 1-self.x*(self.h[4]-self.h[5])/(self.h[1]-self.h[8])
-    
-    def hsolve2(self, error = 1e-6):
-        # liearization
-        b = 0
-        c = 0
-        iter = 0
-        # A = np.array([[1,0,0,0,-self.epsilon1],[-1,0,-1,0,1],[self.epsilon2-1,1,0,0,0],[1,-1,0,-self.x,0],[0,1-self.x,0,self.x,-1]])
-        while True:
-            h6 = cp.PropsSI('H', 'P', self.p_min, 'T', self.T[6], self.fluid)
-            t = np.array([(1-self.epsilon1)*self.h[2]+self.epsilon1*c ,-self.h[2],self.epsilon2*h6,-self.x*self.h[6],b*(self.x-1)])
-            sol = np.dot(inv2(self.epsilon1, self.epsilon2, self.x), t)
-            h4 = sol[1]
-            h10 = sol[4]
-            b1 = (ad(self.p_min, h4, self.p_max, self.fluid)-h4)/self.e3
-            c = cp.PropsSI('H', 'P', self.p_min, 'T', cp.PropsSI('T', 'P', self.p_max, 'H', h10, self.fluid), self.fluid) -h10
-
-            if abs(b1-b) < error:
-                self.h[3] = sol[0]
-                self.h[4] = sol[1]
-                self.h[7] = self.h[4] + b1
-                self.h[8] = sol[2]
-                self.h[9] = sol[3]
-                self.h[10] = sol[4]
-                break
-            b = b1
-            iter += 1
-            if iter > 200:
-                self.h[3] = sol[0]
-                self.h[4] = sol[1]
-                self.h[7] = self.h[4] + b1
-                self.h[8] = sol[2]
-                self.h[9] = sol[3]
-                self.h[10] = sol[4]
-                gr.Error(f'Iteration exceeds 200, x = {self.x}')
-                break
-        return  (self.h[1]-self.h[2]-self.x*(self.h[6]-self.h[5])-(1-self.x)*(self.h[7]-self.h[4]))/(self.h[1]-self.h[8]), 1-self.x*(self.h[4]-self.h[5])/(self.h[1]-self.h[8])
-    
-    def hsolve3(self, error = 1e-6):
-        # liearization
-        a = 0
-        b = 0
-        iter = 0
-        # A = np.array([[1,0,0,0,-self.epsilon1],[-1,0,-1,0,1],[1-self.x*self.epsilon2,-1,0,0,0],[1,-1,0,-self.x,0],[0,1-self.x,0,self.x,-1]])
-        while True:
-            t = np.array([self.h[2]-self.epsilon1*cp.PropsSI('H', 'T', self.T[2], 'P', self.p_max, self.fluid) ,-self.h[2],self.x*self.epsilon2*(a-self.h[6]),-self.x*self.h[6],b*(self.x-1)])
-            sol = np.dot(inv3(self.epsilon1, self.epsilon2, self.x), t)
-            h4 = sol[1]
-            h3 = sol[0]
-            b1 = (ad(self.p_min, h4, self.p_max, self.fluid)-h4)/self.e3
-            a = cp.PropsSI('H', 'P', self.p_max, 'T', cp.PropsSI('T', 'P', self.p_min, 'H', h3, self.fluid), self.fluid) -h3
-            if abs(b1-b) < error:
-                self.h[3] = sol[0]
-                self.h[4] = sol[1]
-                self.h[7] = self.h[4] + b1
-                self.h[8] = sol[2]
-                self.h[9] = sol[3]
-                self.h[10] = sol[4]
-                break
-            b = b1
-            iter += 1
-            if iter > 800:
-                self.h[3] = sol[0]
-                self.h[4] = sol[1]
-                self.h[7] = self.h[4] + b1
-                self.h[8] = sol[2]
-                self.h[9] = sol[3]
-                self.h[10] = sol[4]
-                gr.Error(f'Iteration exceeds 800, x = {self.x}')
-                break
-        return  (self.h[1]-self.h[2]-self.x*(self.h[6]-self.h[5])-(1-self.x)*(self.h[7]-self.h[4]))/(self.h[1]-self.h[8]), 1-self.x*(self.h[4]-self.h[5])/(self.h[1]-self.h[8])
-
-#--------------------------------------------------------------------------------------------------------------#
-
-    def hsolve_x2(self, error = 1e-6):
-        '''
-        This funtion is used to find the value of split ratio which makes the heat capacity of the two inlets in the LTR  equal.
-        Coincidentally, the system has maximum efficiency when the split ratio is equal to this value.
-        '''
-        #initialization
-        iter = 0
-        t1 = self.x
-        self.x = 0.5
-        while True:
-            self.hsolve1()
-            x1 = (self.h[3]-cp.PropsSI('H', 'P', self.p_min, 'T', self.T[6], self.fluid))/(cp.PropsSI('H', 'P', self.p_max, 'T', cp.PropsSI('T', 'P', self.p_min, 'H', self.h[3], self.fluid), self.fluid)-self.h[6])
-            if abs(x1-self.x) < error:
-                break
-            if iter > 800:
-                gr.Error(f'Iteration exceeds 800, x = {self.x}')
-                break
-            self.x = x1
-            iter += 1
-        t2 = self.x
-        self.x = t1
-        return t2
-
-#---------------------------------------------------------------------------------------------------#
-    def hsolve_x1(self, error = 1e-8):
-        '''
-        Find the value of split ratio which makes the heat capacity of the two inlets in the HTR equal. 
-        Since the mass flow of the two inlets in the HTR are equal, this happens when their temperature are equal.
-        '''
-        t1 = self.x
-        self.x = 0.5
-        min = 0
-        max = 1
-        iter = 0
-        while True:
-            self.hsolve1()
-            if (self.h[2] - cp.PropsSI('H', 'P', self.p_min, 'T', cp.PropsSI('T', 'H', self.h[10], 'P', self.p_max, self.fluid), self.fluid)) > (cp.PropsSI('H', 'P', self.p_max, 'T', self.T[2], self.fluid) - self.h[10]):
-                min = self.x
-                self.x = (self.x + max)/2
-            else:
-                max = self.x
-                self.x = (self.x+min)/2
-            if abs(max-min) < error:
-                break
-
-            if iter > 800:
-                gr.Error(f'Iteration exceeds 800, x = {self.x}')
-                break
-        t2 = self.x
-        self.x = t1
-        return t2
-#---------------------------------------------------------------------------------------------------#
-
-    def hsolve(self, error = 1e-6):
-        x1 = self.hsolve_x1()
-        x2 = self.hsolve_x2()
-
-        if self.x < x1:
-            eta1, eta2 = self.hsolve3(error = error)
-
-        elif self.x < x2:
-            eta1, eta2 = self.hsolve1(error = error)
-
-        else:
-            eta1, eta2 = self.hsolve2(error = error)
-
-
-        return eta1, eta2
-
-#---------------------------------------------------------------------------------------------------#
-@timer_decorator
-def plot_eta(fluid:str, t_max:float = 700, p_max:float = 20):
-    fig = plt.figure()
-    ax = fig.add_subplot(111)
-    p_max = p_max*1e6
-    sys = syst(fluid, t_max = t_max, p_max = p_max)
-    x1 = sys.hsolve_x1()
-    x2 = sys.hsolve_x2()
-    x = np.linspace(max(x1,0.3), 1, 100)
-
-    print(f"x1 = {x1}, x2 = {x2}")
-
-    def calculate_eta(i):
-        sys.x = x[i]
-        if x[i] < x1:
-            return sys.hsolve3()[0]
-        elif x[i] < x2:
-            return sys.hsolve1()[0]
-        else:
-            return sys.hsolve2()[0]
-
-    eta = Parallel(n_jobs=-1)(delayed(calculate_eta)(i) for i in range(100))
-    eta_max = np.max(eta)
-    ax.plot(x, eta)
-    ax.set_xlabel('Split Ratio')
-    ax.set_ylabel('Efficiency')
-    ax.legend(title = f'$\mathrm{{p}}_{{\mathrm{{max}}}}$ : {p_max/1e6} MPa \n$\mathrm{{T}}_{{\mathrm{{max}}}}$ : {t_max} K')
-    plt.close(fig)
-    # plt.show()
-    return fig, x2, eta_max
-
-@timer_decorator
-def find_max(fluid:str, t_max:float=900, p1:float=15, p2:float=30):
-    s = 0
-    p = np.linspace(p1,p2,100)
-
-    def calculate_eta(i):
-        sys = syst(fluid=fluid, t_max=t_max, p_max=i*1e6)
-        x = sys.hsolve_x2()
-        eta_i = 1 - x * (sys.h[4] - sys.h[5]) / (sys.h[1] - sys.h[8])
-        return np.array([eta_i, x])
-
-    eta = np.array(Parallel(n_jobs=-1)(delayed(calculate_eta)(i) for i in p))
-
-    index = np.argmax(eta[:,0])
-    max_eta = eta[index,0]
-    s = eta[index,1]
-    p_max = p[index]
-
-    fig = plt.figure()
-    ax = fig.add_subplot(111)
-    ax.plot(p, eta[:,0])
-    ax.set_xlabel('Maximum Pressure (MPa)')
-    ax.set_ylabel('Efficiency')
-    ax.set_title('Maximum Efficiency')
-    ax.legend(title = f'Maximum Temperature: {t_max:.2f} K\nMaximum Efficiency: {max_eta:.4f}\nOptimal Pressure: {p_max:.2f} MPa\nsplit ratio: {s:.2f}')
-    plt.close(fig)
-    # plt.show()
-    return fig, max_eta, p_max, s
-
-
-@timer_decorator
-def plot_system(x:float, p_max:float, t_max:float, fluid:str):
-    p_max = p_max*1e6
-    fig1 = plt.figure(dpi=150)
-    ax1 = fig1.add_subplot(111)
-    fig2 = plt.figure(dpi=150)
-    ax2 = fig2.add_subplot(111)
-
-    sys = syst(fluid=fluid, t_max=t_max, p_max=p_max, x=x)
-    eta1, eta2 = sys.hsolve()
-
-
-    for i in range(1,11):
-        sys.s[i] = cp.PropsSI('S', 'P', sys.p[i], 'H', sys.h[i], fluid)
-        sys.T[i] = cp.PropsSI('T', 'P', sys.p[i], 'H', sys.h[i], fluid)
-
-    if sys.T[2] < sys.T[3]:
-        gr.Error('The temperature of the hot stream is lower than that of the cold stream, reset the split ratio.', UserWarning)
-
-    for i in range(1,11):
-        print(f'T{i} = {sys.T[i]} K', f's{i} = {sys.s[i]} J/kgK, h{i} = {sys.h[i]} J/kg, p{i} = {sys.p[i]} Pa')
-        ax1.plot(sys.s[i],sys.T[i],'o',color = 'red')
-        ax1.annotate(i, xy=(sys.s[i], sys.T[i]), 
-                     xytext=(sys.s[i] -30, sys.T[i]+10),
-                     fontsize=6,
-                     bbox=dict(boxstyle='circle', fc='white', ec='black'))
-
-    ax1.set_xlabel('Entropy (J/kgK)')
-    ax1.set_ylabel('Temperature (K)')
-
-    # Plot the table of the system
-    df = pd.DataFrame({'Temperature [K]':np.round(sys.T[1:],2), 'Entropy [J/(Kg K)]':np.round(sys.s[1:],2), 'Enthalpy [J/Kg]':np.round(sys.h[1:],2), 'Pressure [MPa]':np.round(sys.p[1:]/1e6,2)})
-    group_number = np.arange(1,11)
-    df.insert(loc=0, column='State', value=np.round(group_number,0))
-
-    ax2.axis('off')
-    ax2.table(cellText=df.values, colLabels=df.columns, loc='center')
-
-    # Plot the temperature-entropy curve of carbon dioxide under saturated state
-    plot_TS_diagram(fluid, 216.492,ax1)
-    plot_isobar_TS_diagram(fluid,sys.p[2],sys.T[5],sys.T[2],ax1, label='isobaric process')
-    plot_isobar_TS_diagram(fluid,sys.p[1],sys.T[6],sys.T[1],ax1)
-    plot_isothermal_TS_diagram(sys.s[2], sys.s[1],sys.T[2],sys.T[1],ax1,label='adiabatic process')
-    plot_isothermal_TS_diagram(sys.s[5],sys.s[6],sys.T[5],sys.T[6],ax1)
-    plot_isothermal_TS_diagram(sys.s[4], sys.s[7],sys.T[4],sys.T[7],ax1)
-    ax1.set_title(f'T-s diagram of the Brayton cycle')
-    ax1.set_xlabel('Entropy (J/kgK)')
-    ax1.set_ylabel('Temperature (K)')
-    ax1.legend(title=f'$\eta$ = {eta1:.5f}\n$p_{{max}}$ = {p_max/1e6:.2f} MPa\n$p_{{min}}$ = {sys.p_min/1e6:.2f} MPa\n$T_{{max}}$ = {t_max:.2f} K\n$T_{{min}}$ = {sys.t_min:.2f} K\n$x$ = {x:.2f}\n',
-               loc='best')
-    
-    print(f'eta1 = {eta1:.8f}, eta2 = {eta2:.8f}')
-    plt.close(fig1)
-    plt.close(fig2)
-    # plt.show()
-    return fig1, fig2
-
-@timer_decorator
-def heat_exchanger(fluid:str, x:float, p_max:float, t_max:float, Q:float, plot=False):
-    Q = Q*1e3
-    p_max = p_max*1e6
-    sys = syst(fluid=fluid, t_max=t_max, p_max=p_max, x=x)
-    sys.hsolve()
-    m = Q/(sys.h[1] - sys.h[8])
-    v = m*sys.h[4] - x*m*sys.h[6]
-    u = m*(sys.h[2] - sys.h[8])
-    for i in range(1,11):
-        sys.T[i] = cp.PropsSI('T', 'P', sys.p[i], 'H', sys.h[i], sys.fluid)
-    #calculate the mass flow of the system
-
-    def t1(h):
-        return cp.PropsSI('T', 'H', h, 'P', sys.p_min, sys.fluid)
-
-    def t2(h):
-        return cp.PropsSI('T', 'H', h, 'P', p_max, sys.fluid)
-
-    def f(h):
-        return m/(t2((m*h-v)/(m*sys.x)) - t1(h))
-    
-    def g(h):
-        return m/(t2((m*h-u)/m) - t1(h))
-    
-    HA1 = -quad(g, sys.h[3], sys.h[2])[0]
-    HA2 = -quad(f, sys.h[4], sys.h[3])[0]
-    R1 = RCP.RCP(sys.T[2], sys.T[3], sys.T[10], sys.T[8], m, m, sys.p_min, p_max, fluid, HA1)
-    R2 = RCP.RCP(sys.T[3], sys.T[4], sys.T[6], sys.T[9], m, m*sys.x, sys.p_min, p_max, fluid, HA2)
-    fig1, ax1 = plt.subplots(dpi=150)
-    fig2, ax2 = plt.subplots(dpi=150)
-    if not plot:
-        return m, R1.N, R1.A, R1.l, R1.uh, R1.uc, R2.N, R2.A, R2.l, R2.uh, R2.uc, fig1, fig2
-    else:
-        S = np.array([])
-        m1 = np.array([])
-        m2 = np.array([])
-        for i in np.linspace(sys.h[4], sys.h[3], 100):
-            s = -quad(f, i, sys.h[3])[0]/R2.H
-            S = np.append(S, s)
-            m1 = np.append(m1, t1(i))
-            m2 = np.append(m2, t2((i-v/m)/x))
-
-        ax1.plot(S, m2)
-        ax1.plot(S, m1)
-        ax1.set_xlabel('A ($\mathrm{m}^2$)')
-        ax1.set_ylabel('Temperature (K)')
-        ax1.set_title('LTR')
-        ax1.legend(title='k = {:.2f} J/($\mathrm{{m}}^{{2}}$K)\nA = {:.2f} $\mathrm{{m}}^2$'.format(R2.H, S[0]))
-        ax1.annotate('({:.2f}, {:.2f})'.format(S[-1], m2[-1]), xy=(S[-1], m2[-1]), xytext = (-40, -10), textcoords='offset points', fontsize=8)
-        ax1.annotate('({:.2f}, {:.2f})'.format(S[0], m1[0]), xy=(S[0], m1[0]), xytext = (-40, -10), textcoords='offset points', fontsize=8)
-
-        T = np.array([])
-        l1 = np.array([])
-        l2 = np.array([])
-        for i in np.linspace(sys.h[3],sys.h[2], 100):
-            t = -quad(g, i, sys.h[2])[0]/R1.H
-            T = np.append(T, t)
-            l1 = np.append(l1, t1(i))
-            l2 = np.append(l2, t2((i-u/m)))
-
-        ax2.plot(T, l2)
-        ax2.plot(T, l1)
-        ax2.set_xlabel('A ($\mathrm{m}^2$)')
-        ax2.set_ylabel('Temperature (K)')
-        ax2.set_title('HTR')
-        ax2.legend(title='k = {:.2f} J/($\mathrm{{m}}^{{2}}$K)\nA = {:.2f} $\mathrm{{m}}^2$'.format(R1.H, T[0]))
-        ax2.annotate('({:.2f}, {:.2f})'.format(T[-1], l2[-1]), xy=(T[-1], l2[-1]), xytext = (-40, -10), textcoords='offset points', fontsize=8)
-        ax2.annotate('({:.2f}, {:.2f})'.format(T[0], l1[0]), xy=(T[0], l1[0]), xytext = (-40, -10), textcoords='offset points', fontsize=8)
-        plt.close(fig1)
-        plt.close(fig2)
-        return m, R1.N, R1.A, R1.l, R1.uh, R1.uc, R2.N, R2.A, R2.l, R2.uh, R2.uc, fig1, fig2
-
-
-
-
-# if __name__ == '__main__':
-    # plot_system(0.7658757246781286, 25.151515151515152, 900, "CO2")
-    # plot_eta("CO2",900,26.06)
-    # find_max("CO2", 900)
-    # find_max_scatter("CO2", 900, 15, 30)
-    # plot_eta_T("CO2")
-
-
-    # fig, ax = plt.subplots(dpi=150)
-    # p = np.linspace(7.5e6, 20e6, 5)
-    # t = np.linspace(300, 1000, 100)
-    # h = np.zeros(100)
-    # for i in range(5):
-    #     for j in range(100):
-    #         h[j] = cp.PropsSI('H', 'T', t[j], 'P', p[i], 'CO2')/1e3
-    #     ax.plot(t, h, label=f'{p[i]/1e6} MPa')
-    # ax.set_xlabel("Temperature [K]")
-    # ax.set_ylabel("Enthalpy [KJ/Kg]")
-    # ax.legend()
-    # plt.show()
-
-    # fig, ax = plt.subplots(dpi=150)
-    # p_min = 7.4e6
-    # p_max = 20e6
-    # t = np.linspace(300, 1000, 100)
-    # s = np.zeros(100)
-    # for i in range(100):
-    #     s[i] = cp.PropsSI('Cpmass', 'P', p_min, 'T', t[i], 'CO2')/cp.PropsSI('S', 'P', p_max, 'T', t[i], 'CO2')
-    # ax.plot(t,s)
-    # plt.show()
-    # dh1 = np.zeros(100)
-    # dh2 = np.zeros(100)
-    # dh3 = np.zeros(100)
-    # dh4 = np.zeros(100)
-    # sys1 = syst("CO2", 20e6, 900)
-    # sys2 = syst("CO2", 20e6, 900)
-    # x = np.linspace(0.2,1,100)
-    # for i in range(100):
-    #     sys1.x = x[i]
-    #     sys2.x = x[i]
-    #     sys1.hsolve1()
-    #     sys2.hsolve2()
-    #     dh1[i] = sys1.h[3] - cp.PropsSI('H', 'P', sys1.p_min, 'T', cp.PropsSI('T', 'P', sys1.p_max, 'H', sys1.h[6], "CO2"), "CO2")
-    #     dh2[i] = x[i]*(cp.PropsSI('H', 'P', sys1.p_max, 'T', cp.PropsSI('T', 'P', sys1.p_min, 'H', sys1.h[3], "CO2"),"CO2")-sys1.h[6])
-    #     dh3[i] = sys2.h[3] - cp.PropsSI('H', 'P', sys2.p_min, 'T', cp.PropsSI('T', 'P', sys2.p_max, 'H', sys2.h[6], "CO2"), "CO2")
-    #     dh4[i] = x[i]*(cp.PropsSI('H', 'P', sys2.p_max, 'T', cp.PropsSI('T', 'P', sys2.p_min, 'H', sys2.h[3], "CO2"),"CO2")-sys2.h[6])
-
-    # fig, ax = plt.subplots(dpi=150)
-    # ax.plot(x,dh1,label='sys11 ')
-    # ax.plot(x,dh2,label='sys12')
-    # ax.plot(x, dh3, label='sys21')
-    # ax.plot(x, dh4, label='sys22')
-    # ax.legend()
-    # plt.show()
-
-
-def heat_exchanger1(fluid:str, x:float, p_max:float, t_max:float, Q:float, plot=False):
-    Q = Q*1e3
-    p_max = p_max*1e6
-    sys = syst(fluid=fluid, t_max=t_max, p_max=p_max, x=x)
-    sys.hsolve()
-    m = Q/(sys.h[1] - sys.h[8])
-    v = m*sys.h[4] - x*m*sys.h[6]
-    u = m*(sys.h[2] - sys.h[8])
-    for i in range(1,11):
-        sys.T[i] = cp.PropsSI('T', 'P', sys.p[i], 'H', sys.h[i], sys.fluid)
-    #calculate the mass flow of the system
-
-    def t1(h):
-        return cp.PropsSI('T', 'H', h, 'P', sys.p_min, sys.fluid)
-
-    def t2(h):
-        return cp.PropsSI('T', 'H', h, 'P', p_max, sys.fluid)
-
-    def f(h):
-        return m/(t2((m*h-v)/(m*sys.x)) - t1(h))
-    
-    def g(h):
-        return m/(t2((m*h-u)/m) - t1(h))
-    
-    HA1 = -quad(g, sys.h[3], sys.h[2])[0]
-    HA2 = -quad(f, sys.h[4], sys.h[3])[0]
-    print(HA1, HA2)
-if __name__ == '__main__':
-    heat_exchanger1("CO2", 0.7, 20, 900, 277)
\ No newline at end of file
diff --git a/spaces/xuetao/bingo3/Dockerfile b/spaces/xuetao/bingo3/Dockerfile
deleted file mode 100644
index 3aa2b29b5fc4fa8b8238955acd7f1fde13ce5e1a..0000000000000000000000000000000000000000
--- a/spaces/xuetao/bingo3/Dockerfile
+++ /dev/null
@@ -1,36 +0,0 @@
-FROM node:18
-
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -o -u 1000 user && mkdir -p $HOME/app && chown -R user $HOME
-
-# Switch to the "user" user
-USER user
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Install app dependencies
-# A wildcard is used to ensure both package.json AND package-lock.json are copied
-# where available (npm@5+)
-COPY --chown=user package*.json $HOME/app/
-
-RUN npm install
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app/
-
-RUN npm run build
-
-ENV PORT 7860
-EXPOSE 7860
-
-CMD npm start
diff --git a/spaces/yahma/rwkv-instruct/config.py b/spaces/yahma/rwkv-instruct/config.py
deleted file mode 100644
index 5f2f8752eabb2e9fe4222fc8ae5ae9e8b108b3e2..0000000000000000000000000000000000000000
--- a/spaces/yahma/rwkv-instruct/config.py
+++ /dev/null
@@ -1,76 +0,0 @@
-from rwkvstic.agnostic.backends import TORCH, TORCH_QUANT
-import torch
-
-quantized = {
-    "mode": TORCH_QUANT,
-    "runtimedtype": torch.bfloat16,
-    "useGPU": torch.cuda.is_available(),
-    "chunksize": 32,  # larger = more accurate, but more memory (and slower)
-    "target": 24  # your gpu max size, excess vram offloaded to cpu
-}
-
-# UNCOMMENT TO SELECT OPTIONS
-# Not full list of options, see https://pypi.org/project/rwkvstic/ and https://huggingface.co/BlinkDL/ for more models/modes
-
-# RWKV 1B5 instruct test 2 model
-# Approximate
-# [Vram usage: 6.0GB]
-# [File size: 3.0GB]
-
-
-config = {
-    "path": "https://huggingface.co/BlinkDL/rwkv-4-pile-1b5/resolve/main/RWKV-4-Pile-1B5-Instruct-test2-20230209.pth",
-    "mode": TORCH,
-    "runtimedtype": torch.float32,
-    "useGPU": torch.cuda.is_available(),
-    "dtype": torch.float32
-}
-
-title = "RWKV-4 (1.5b Instruct Test 2)"
-
-# RWKV 1B5 instruct model quantized
-# Approximate
-# [Vram usage: 1.3GB]
-# [File size: 3.0GB]
-
-# config = {
-#     "path": "https://huggingface.co/BlinkDL/rwkv-4-pile-1b5/resolve/main/RWKV-4-Pile-1B5-Instruct-test1-20230124.pth",
-#     **quantized
-# }
-
-# title = "RWKV-4 (1.5b Instruct Quantized)"
-
-# RWKV 7B instruct pre-quantized (settings baked into model)
-# Approximate
-# [Vram usage: 7.0GB]
-# [File size: 8.0GB]
-
-# config = {
-#     "path": "https://huggingface.co/Hazzzardous/RWKV-8Bit/resolve/main/RWKV-4-Pile-7B-Instruct.pqth"
-# }
-
-# title = "RWKV-4 (7b Instruct Quantized)"
-
-# RWKV 14B quantized (latest as of feb 9)
-# Approximate
-# [Vram usage: 15.0GB]
-# [File size: 28.0GB]
-
-# config = {
-#     "path": "https://huggingface.co/BlinkDL/rwkv-4-pile-14b/resolve/main/RWKV-4-Pile-14B-20230204-7324.pth",
-#     **quantized
-# }
-
-# title = "RWKV-4 (14b Quantized)"
-
-
-# RWKV 14B pre-quantized (latest as of feb 9)
-# Approximate
-# [Vram usage: 15.0GB]
-# [File size: 14.4GB]
-
-# config = {
-#     "path": "https://huggingface.co/Hazzzardous/RWKV-8Bit/resolve/main/RWKV-4-Pile-14B-20230204-7324.pqth"
-# }
-
-# title = "RWKV-4 (14b Quantized)"
\ No newline at end of file
diff --git a/spaces/yderre-aubay/midi-player-demo/src/main/components/ControlPane/LineGraph/LineGraphItems.tsx b/spaces/yderre-aubay/midi-player-demo/src/main/components/ControlPane/LineGraph/LineGraphItems.tsx
deleted file mode 100644
index 1003d9faa82cc2dac357d5a92fe04bff0d240f82..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/main/components/ControlPane/LineGraph/LineGraphItems.tsx
+++ /dev/null
@@ -1,88 +0,0 @@
-import { BorderedCircles, Rectangles } from "@ryohey/webgl-react"
-import Color from "color"
-import { partition } from "lodash"
-import { FC } from "react"
-import { IPoint, IRect } from "../../../../common/geometry"
-import { joinObjects } from "../../../../common/helpers/array"
-import { colorToVec4 } from "../../../gl/color"
-import { useTheme } from "../../../hooks/useTheme"
-
-export interface LineGraphItemsProps {
-  width: number
-  scrollLeft: number
-  items: (IPoint & { id: number })[]
-  controlPoints: (IRect & { id: number })[]
-  selectedEventIds: number[]
-  lineWidth: number
-  zIndex: number
-}
-
-export const LineGraphItems: FC = ({
-  width,
-  scrollLeft,
-  items,
-  selectedEventIds,
-  controlPoints,
-  lineWidth,
-  zIndex,
-}) => {
-  const theme = useTheme()
-  const right = scrollLeft + width
-  const values = items.map((i) => ({ ...i, id: i.id }))
-  const rects = createLineRects(values, lineWidth, right)
-  const [highlightedItems, nonHighlightedItems] = partition(
-    controlPoints,
-    (i) => selectedEventIds.includes(i.id),
-  )
-
-  return (
-    <>
-      
-      
-      
-    
-  )
-}
-
-const createLineRects = (
-  values: IPoint[],
-  lineWidth: number,
-  right: number,
-): IRect[] => {
-  const horizontalLineRects = values.map(({ x, y }, i) => {
-    const next = values[i + 1]
-    const nextX = next ? next.x : right // 次がなければ右端まで描画する
-    return {
-      x,
-      y: y - lineWidth / 2,
-      width: nextX - x,
-      height: lineWidth,
-    }
-  })
-
-  // add vertical lines between horizontal lines
-  return joinObjects(horizontalLineRects, (prev, next) => {
-    const y = Math.min(prev.y, next.y)
-    const height = Math.abs(prev.y - next.y) + lineWidth
-    return {
-      x: next.x - lineWidth / 2,
-      y,
-      width: lineWidth,
-      height,
-    }
-  })
-}
diff --git a/spaces/yeqingmei123/face-test/e4e/models/stylegan2/__init__.py b/spaces/yeqingmei123/face-test/e4e/models/stylegan2/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ygangang/VToonify/vtoonify/model/stylegan/op_gpu/fused_bias_act.cpp b/spaces/ygangang/VToonify/vtoonify/model/stylegan/op_gpu/fused_bias_act.cpp
deleted file mode 100644
index 71f612cdbaaca03822eedc002a980d055d2f485c..0000000000000000000000000000000000000000
--- a/spaces/ygangang/VToonify/vtoonify/model/stylegan/op_gpu/fused_bias_act.cpp
+++ /dev/null
@@ -1,32 +0,0 @@
-
-#include 
-#include 
-
-torch::Tensor fused_bias_act_op(const torch::Tensor &input,
-                                const torch::Tensor &bias,
-                                const torch::Tensor &refer, int act, int grad,
-                                float alpha, float scale);
-
-#define CHECK_CUDA(x)                                                          \
-  TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x)                                                    \
-  TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x)                                                         \
-  CHECK_CUDA(x);                                                               \
-  CHECK_CONTIGUOUS(x)
-
-torch::Tensor fused_bias_act(const torch::Tensor &input,
-                             const torch::Tensor &bias,
-                             const torch::Tensor &refer, int act, int grad,
-                             float alpha, float scale) {
-  CHECK_INPUT(input);
-  CHECK_INPUT(bias);
-
-  at::DeviceGuard guard(input.device());
-
-  return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/convert_pytorch_checkpoint_to_tf2.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/convert_pytorch_checkpoint_to_tf2.py
deleted file mode 100644
index f1358408a5cb57ca03503ac56773cb4d9d77ce89..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/convert_pytorch_checkpoint_to_tf2.py
+++ /dev/null
@@ -1,492 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The HuggingFace Inc. team.
-#
-# 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.
-""" Convert pytorch checkpoints to TensorFlow"""
-
-
-import argparse
-import os
-
-from . import (
-    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
-    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
-    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
-    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
-    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
-    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    AlbertConfig,
-    BartConfig,
-    BertConfig,
-    CamembertConfig,
-    CTRLConfig,
-    DistilBertConfig,
-    DPRConfig,
-    ElectraConfig,
-    FlaubertConfig,
-    GPT2Config,
-    LayoutLMConfig,
-    LxmertConfig,
-    OpenAIGPTConfig,
-    RobertaConfig,
-    T5Config,
-    TFAlbertForPreTraining,
-    TFBartForConditionalGeneration,
-    TFBartForSequenceClassification,
-    TFBertForPreTraining,
-    TFBertForQuestionAnswering,
-    TFBertForSequenceClassification,
-    TFCamembertForMaskedLM,
-    TFCTRLLMHeadModel,
-    TFDistilBertForMaskedLM,
-    TFDistilBertForQuestionAnswering,
-    TFDPRContextEncoder,
-    TFDPRQuestionEncoder,
-    TFDPRReader,
-    TFElectraForPreTraining,
-    TFFlaubertWithLMHeadModel,
-    TFGPT2LMHeadModel,
-    TFLayoutLMForMaskedLM,
-    TFLxmertForPreTraining,
-    TFLxmertVisualFeatureEncoder,
-    TFOpenAIGPTLMHeadModel,
-    TFRobertaForCausalLM,
-    TFRobertaForMaskedLM,
-    TFRobertaForSequenceClassification,
-    TFT5ForConditionalGeneration,
-    TFTransfoXLLMHeadModel,
-    TFWav2Vec2Model,
-    TFXLMRobertaForMaskedLM,
-    TFXLMWithLMHeadModel,
-    TFXLNetLMHeadModel,
-    TransfoXLConfig,
-    Wav2Vec2Config,
-    Wav2Vec2Model,
-    XLMConfig,
-    XLMRobertaConfig,
-    XLNetConfig,
-    is_torch_available,
-    load_pytorch_checkpoint_in_tf2_model,
-)
-from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging
-
-
-if is_torch_available():
-    import numpy as np
-    import torch
-
-    from . import (
-        AlbertForPreTraining,
-        BartForConditionalGeneration,
-        BertForPreTraining,
-        BertForQuestionAnswering,
-        BertForSequenceClassification,
-        CamembertForMaskedLM,
-        CTRLLMHeadModel,
-        DistilBertForMaskedLM,
-        DistilBertForQuestionAnswering,
-        DPRContextEncoder,
-        DPRQuestionEncoder,
-        DPRReader,
-        ElectraForPreTraining,
-        FlaubertWithLMHeadModel,
-        GPT2LMHeadModel,
-        LayoutLMForMaskedLM,
-        LxmertForPreTraining,
-        LxmertVisualFeatureEncoder,
-        OpenAIGPTLMHeadModel,
-        RobertaForMaskedLM,
-        RobertaForSequenceClassification,
-        T5ForConditionalGeneration,
-        TransfoXLLMHeadModel,
-        XLMRobertaForMaskedLM,
-        XLMWithLMHeadModel,
-        XLNetLMHeadModel,
-    )
-
-
-logging.set_verbosity_info()
-
-MODEL_CLASSES = {
-    "bart": (
-        BartConfig,
-        TFBartForConditionalGeneration,
-        TFBartForSequenceClassification,
-        BartForConditionalGeneration,
-        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
-    ),
-    "bert": (
-        BertConfig,
-        TFBertForPreTraining,
-        BertForPreTraining,
-        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "bert-large-uncased-whole-word-masking-finetuned-squad": (
-        BertConfig,
-        TFBertForQuestionAnswering,
-        BertForQuestionAnswering,
-        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "bert-large-cased-whole-word-masking-finetuned-squad": (
-        BertConfig,
-        TFBertForQuestionAnswering,
-        BertForQuestionAnswering,
-        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "bert-base-cased-finetuned-mrpc": (
-        BertConfig,
-        TFBertForSequenceClassification,
-        BertForSequenceClassification,
-        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "dpr": (
-        DPRConfig,
-        TFDPRQuestionEncoder,
-        TFDPRContextEncoder,
-        TFDPRReader,
-        DPRQuestionEncoder,
-        DPRContextEncoder,
-        DPRReader,
-        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
-        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
-        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
-    ),
-    "gpt2": (
-        GPT2Config,
-        TFGPT2LMHeadModel,
-        GPT2LMHeadModel,
-        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "xlnet": (
-        XLNetConfig,
-        TFXLNetLMHeadModel,
-        XLNetLMHeadModel,
-        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "xlm": (
-        XLMConfig,
-        TFXLMWithLMHeadModel,
-        XLMWithLMHeadModel,
-        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "xlm-roberta": (
-        XLMRobertaConfig,
-        TFXLMRobertaForMaskedLM,
-        XLMRobertaForMaskedLM,
-        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "transfo-xl": (
-        TransfoXLConfig,
-        TFTransfoXLLMHeadModel,
-        TransfoXLLMHeadModel,
-        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "openai-gpt": (
-        OpenAIGPTConfig,
-        TFOpenAIGPTLMHeadModel,
-        OpenAIGPTLMHeadModel,
-        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "roberta": (
-        RobertaConfig,
-        TFRobertaForCausalLM,
-        TFRobertaForMaskedLM,
-        RobertaForMaskedLM,
-        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "layoutlm": (
-        LayoutLMConfig,
-        TFLayoutLMForMaskedLM,
-        LayoutLMForMaskedLM,
-        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
-    ),
-    "roberta-large-mnli": (
-        RobertaConfig,
-        TFRobertaForSequenceClassification,
-        RobertaForSequenceClassification,
-        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "camembert": (
-        CamembertConfig,
-        TFCamembertForMaskedLM,
-        CamembertForMaskedLM,
-        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "flaubert": (
-        FlaubertConfig,
-        TFFlaubertWithLMHeadModel,
-        FlaubertWithLMHeadModel,
-        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "distilbert": (
-        DistilBertConfig,
-        TFDistilBertForMaskedLM,
-        DistilBertForMaskedLM,
-        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "distilbert-base-distilled-squad": (
-        DistilBertConfig,
-        TFDistilBertForQuestionAnswering,
-        DistilBertForQuestionAnswering,
-        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "lxmert": (
-        LxmertConfig,
-        TFLxmertForPreTraining,
-        LxmertForPreTraining,
-        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "lxmert-visual-feature-encoder": (
-        LxmertConfig,
-        TFLxmertVisualFeatureEncoder,
-        LxmertVisualFeatureEncoder,
-        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "ctrl": (
-        CTRLConfig,
-        TFCTRLLMHeadModel,
-        CTRLLMHeadModel,
-        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "albert": (
-        AlbertConfig,
-        TFAlbertForPreTraining,
-        AlbertForPreTraining,
-        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "t5": (
-        T5Config,
-        TFT5ForConditionalGeneration,
-        T5ForConditionalGeneration,
-        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "electra": (
-        ElectraConfig,
-        TFElectraForPreTraining,
-        ElectraForPreTraining,
-        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-    "wav2vec2": (
-        Wav2Vec2Config,
-        TFWav2Vec2Model,
-        Wav2Vec2Model,
-        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
-}
-
-
-def convert_pt_checkpoint_to_tf(
-    model_type, pytorch_checkpoint_path, config_file, tf_dump_path, compare_with_pt_model=False, use_cached_models=True
-):
-    if model_type not in MODEL_CLASSES:
-        raise ValueError(f"Unrecognized model type, should be one of {list(MODEL_CLASSES.keys())}.")
-
-    config_class, model_class, pt_model_class, aws_config_map = MODEL_CLASSES[model_type]
-
-    # Initialise TF model
-    if config_file in aws_config_map:
-        config_file = cached_file(config_file, CONFIG_NAME, force_download=not use_cached_models)
-    config = config_class.from_json_file(config_file)
-    config.output_hidden_states = True
-    config.output_attentions = True
-    print(f"Building TensorFlow model from configuration: {config}")
-    tf_model = model_class(config)
-
-    # Load weights from tf checkpoint
-    if pytorch_checkpoint_path in aws_config_map.keys():
-        pytorch_checkpoint_path = cached_file(
-            pytorch_checkpoint_path, WEIGHTS_NAME, force_download=not use_cached_models
-        )
-    # Load PyTorch checkpoint in tf2 model:
-    tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path)
-
-    if compare_with_pt_model:
-        tfo = tf_model(tf_model.dummy_inputs, training=False)  # build the network
-
-        state_dict = torch.load(pytorch_checkpoint_path, map_location="cpu")
-        pt_model = pt_model_class.from_pretrained(
-            pretrained_model_name_or_path=None, config=config, state_dict=state_dict
-        )
-
-        with torch.no_grad():
-            pto = pt_model(**pt_model.dummy_inputs)
-
-        np_pt = pto[0].numpy()
-        np_tf = tfo[0].numpy()
-        diff = np.amax(np.abs(np_pt - np_tf))
-        print(f"Max absolute difference between models outputs {diff}")
-        assert diff <= 2e-2, f"Error, model absolute difference is >2e-2: {diff}"
-
-    # Save pytorch-model
-    print(f"Save TensorFlow model to {tf_dump_path}")
-    tf_model.save_weights(tf_dump_path, save_format="h5")
-
-
-def convert_all_pt_checkpoints_to_tf(
-    args_model_type,
-    tf_dump_path,
-    model_shortcut_names_or_path=None,
-    config_shortcut_names_or_path=None,
-    compare_with_pt_model=False,
-    use_cached_models=False,
-    remove_cached_files=False,
-    only_convert_finetuned_models=False,
-):
-    if args_model_type is None:
-        model_types = list(MODEL_CLASSES.keys())
-    else:
-        model_types = [args_model_type]
-
-    for j, model_type in enumerate(model_types, start=1):
-        print("=" * 100)
-        print(f" Converting model type {j}/{len(model_types)}: {model_type}")
-        print("=" * 100)
-        if model_type not in MODEL_CLASSES:
-            raise ValueError(f"Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys())}.")
-
-        config_class, model_class, pt_model_class, aws_model_maps, aws_config_map = MODEL_CLASSES[model_type]
-
-        if model_shortcut_names_or_path is None:
-            model_shortcut_names_or_path = list(aws_model_maps.keys())
-        if config_shortcut_names_or_path is None:
-            config_shortcut_names_or_path = model_shortcut_names_or_path
-
-        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
-            zip(model_shortcut_names_or_path, config_shortcut_names_or_path), start=1
-        ):
-            print("-" * 100)
-            if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
-                if not only_convert_finetuned_models:
-                    print(f"    Skipping finetuned checkpoint {model_shortcut_name}")
-                    continue
-                model_type = model_shortcut_name
-            elif only_convert_finetuned_models:
-                print(f"    Skipping not finetuned checkpoint {model_shortcut_name}")
-                continue
-            print(
-                f"    Converting checkpoint {i}/{len(aws_config_map)}: {model_shortcut_name} - model_type {model_type}"
-            )
-            print("-" * 100)
-
-            if config_shortcut_name in aws_config_map:
-                config_file = cached_file(config_shortcut_name, CONFIG_NAME, force_download=not use_cached_models)
-            else:
-                config_file = config_shortcut_name
-
-            if model_shortcut_name in aws_model_maps:
-                model_file = cached_file(model_shortcut_name, WEIGHTS_NAME, force_download=not use_cached_models)
-            else:
-                model_file = model_shortcut_name
-
-            if os.path.isfile(model_shortcut_name):
-                model_shortcut_name = "converted_model"
-
-            convert_pt_checkpoint_to_tf(
-                model_type=model_type,
-                pytorch_checkpoint_path=model_file,
-                config_file=config_file,
-                tf_dump_path=os.path.join(tf_dump_path, model_shortcut_name + "-tf_model.h5"),
-                compare_with_pt_model=compare_with_pt_model,
-            )
-            if remove_cached_files:
-                os.remove(config_file)
-                os.remove(model_file)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--tf_dump_path", default=None, type=str, required=True, help="Path to the output Tensorflow dump file."
-    )
-    parser.add_argument(
-        "--model_type",
-        default=None,
-        type=str,
-        help=(
-            f"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and "
-            "convert all the models from AWS."
-        ),
-    )
-    parser.add_argument(
-        "--pytorch_checkpoint_path",
-        default=None,
-        type=str,
-        help=(
-            "Path to the PyTorch checkpoint path or shortcut name to download from AWS. "
-            "If not given, will download and convert all the checkpoints from AWS."
-        ),
-    )
-    parser.add_argument(
-        "--config_file",
-        default=None,
-        type=str,
-        help=(
-            "The config json file corresponding to the pre-trained model. \n"
-            "This specifies the model architecture. If not given and "
-            "--pytorch_checkpoint_path is not given or is a shortcut name "
-            "use the configuration associated to the shortcut name on the AWS"
-        ),
-    )
-    parser.add_argument(
-        "--compare_with_pt_model", action="store_true", help="Compare Tensorflow and PyTorch model predictions."
-    )
-    parser.add_argument(
-        "--use_cached_models",
-        action="store_true",
-        help="Use cached models if possible instead of updating to latest checkpoint versions.",
-    )
-    parser.add_argument(
-        "--remove_cached_files",
-        action="store_true",
-        help="Remove pytorch models after conversion (save memory when converting in batches).",
-    )
-    parser.add_argument("--only_convert_finetuned_models", action="store_true", help="Only convert finetuned models.")
-    args = parser.parse_args()
-
-    # if args.pytorch_checkpoint_path is not None:
-    #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
-    #                                 args.pytorch_checkpoint_path,
-    #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
-    #                                 args.tf_dump_path,
-    #                                 compare_with_pt_model=args.compare_with_pt_model,
-    #                                 use_cached_models=args.use_cached_models)
-    # else:
-    convert_all_pt_checkpoints_to_tf(
-        args.model_type.lower() if args.model_type is not None else None,
-        args.tf_dump_path,
-        model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
-        if args.pytorch_checkpoint_path is not None
-        else None,
-        config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
-        compare_with_pt_model=args.compare_with_pt_model,
-        use_cached_models=args.use_cached_models,
-        remove_cached_files=args.remove_cached_files,
-        only_convert_finetuned_models=args.only_convert_finetuned_models,
-    )
diff --git a/spaces/yl12053/so-vits-4.1-Kitasan-Black/pretrain/meta.py b/spaces/yl12053/so-vits-4.1-Kitasan-Black/pretrain/meta.py
deleted file mode 100644
index cc35dd3c0dfe8436e7d635f2db507cedca75ed49..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Kitasan-Black/pretrain/meta.py
+++ /dev/null
@@ -1,31 +0,0 @@
-def download_dict():
-    return {
-        "vec768l12": {
-            "url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr",
-            "output": "./pretrain/checkpoint_best_legacy_500.pt"
-        },
-        "vec256l9": {
-            "url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr",
-            "output": "./pretrain/checkpoint_best_legacy_500.pt"
-        },
-        "hubertsoft": {
-            "url": "https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt",
-            "output": "./pretrain/hubert-soft-0d54a1f4.pt"
-        },
-        "whisper-ppg": {
-            "url": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
-            "output": "./pretrain/medium.pt"
-        }
-    }
-
-
-def get_speech_encoder(config_path="configs/config.json"):
-    import json
-
-    with open(config_path, "r") as f:
-        data = f.read()
-        config = json.loads(data)
-        speech_encoder = config["model"]["speech_encoder"]
-        dict = download_dict()
-
-        return dict[speech_encoder]["url"], dict[speech_encoder]["output"]
diff --git a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vencoder/whisper/decoding.py b/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vencoder/whisper/decoding.py
deleted file mode 100644
index 603546d4c9ff67514d2567576935b974fe373bef..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vencoder/whisper/decoding.py
+++ /dev/null
@@ -1,712 +0,0 @@
-from dataclasses import dataclass, field
-from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import Tensor
-from torch.distributions import Categorical
-
-from .audio import CHUNK_LENGTH
-from .tokenizer import Tokenizer, get_tokenizer
-from .utils import compression_ratio
-
-if TYPE_CHECKING:
-    from .model import Whisper
-
-
-@torch.no_grad()
-def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]:
-    """
-    Detect the spoken language in the audio, and return them as list of strings, along with the ids
-    of the most probable language tokens and the probability distribution over all language tokens.
-    This is performed outside the main decode loop in order to not interfere with kv-caching.
-
-    Returns
-    -------
-    language_tokens : Tensor, shape = (n_audio,)
-        ids of the most probable language tokens, which appears after the startoftranscript token.
-    language_probs : List[Dict[str, float]], length = n_audio
-        list of dictionaries containing the probability distribution over all languages.
-    """
-    if tokenizer is None:
-        tokenizer = get_tokenizer(model.is_multilingual)
-    if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence:
-        raise ValueError(f"This model doesn't have language tokens so it can't perform lang id")
-
-    single = mel.ndim == 2
-    if single:
-        mel = mel.unsqueeze(0)
-
-    # skip encoder forward pass if already-encoded audio features were given
-    if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state):
-        mel = model.encoder(mel)
-
-    # forward pass using a single token, startoftranscript
-    n_audio = mel.shape[0]
-    x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device)  # [n_audio, 1]
-    logits = model.logits(x, mel)[:, 0]
-
-    # collect detected languages; suppress all non-language tokens
-    mask = torch.ones(logits.shape[-1], dtype=torch.bool)
-    mask[list(tokenizer.all_language_tokens)] = False
-    logits[:, mask] = -np.inf
-    language_tokens = logits.argmax(dim=-1)
-    language_token_probs = logits.softmax(dim=-1).cpu()
-    language_probs = [
-        {
-            c: language_token_probs[i, j].item()
-            for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes)
-        }
-        for i in range(n_audio)
-    ]
-
-    if single:
-        language_tokens = language_tokens[0]
-        language_probs = language_probs[0]
-
-    return language_tokens, language_probs
-
-
-@dataclass(frozen=True)
-class DecodingOptions:
-    task: str = "transcribe"  # whether to perform X->X "transcribe" or X->English "translate"
-    language: Optional[str] = None  # language that the audio is in; uses detected language if None
-
-    # sampling-related options
-    temperature: float = 0.0
-    sample_len: Optional[int] = None  # maximum number of tokens to sample
-    best_of: Optional[int] = None     # number of independent samples to collect, when t > 0
-    beam_size: Optional[int] = None   # number of beams in beam search, when t == 0
-    patience: Optional[float] = None  # patience in beam search (https://arxiv.org/abs/2204.05424)
-
-    # options for ranking generations (either beams or best-of-N samples)
-    length_penalty: Optional[float] = None   # "alpha" in Google NMT, None defaults to length norm
-
-    # prompt, prefix, and token suppression
-    prompt: Optional[Union[str, List[int]]] = None   # text or tokens for the previous context
-    prefix: Optional[Union[str, List[int]]] = None   # text or tokens to prefix the current context
-    suppress_blank: bool = True                      # this will suppress blank outputs
-
-    # list of tokens ids (or comma-separated token ids) to suppress
-    # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()`
-    suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1"
-
-    # timestamp sampling options
-    without_timestamps: bool = False              # use <|notimestamps|> to sample text tokens only
-    max_initial_timestamp: Optional[float] = 1.0  # the initial timestamp cannot be later than this
-
-    # implementation details
-    fp16: bool = True  # use fp16 for most of the calculation
-
-
-@dataclass(frozen=True)
-class DecodingResult:
-    audio_features: Tensor
-    language: str
-    language_probs: Optional[Dict[str, float]] = None
-    tokens: List[int] = field(default_factory=list)
-    text: str = ""
-    avg_logprob: float = np.nan
-    no_speech_prob: float = np.nan
-    temperature: float = np.nan
-    compression_ratio: float = np.nan
-
-
-class Inference:
-    def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
-        """Perform a forward pass on the decoder and return per-token logits"""
-        raise NotImplementedError
-
-    def rearrange_kv_cache(self, source_indices) -> None:
-        """Update the key-value cache according to the updated beams"""
-        raise NotImplementedError
-
-    def cleanup_caching(self) -> None:
-        """Clean up any resources or hooks after decoding is finished"""
-        pass
-
-
-class PyTorchInference(Inference):
-    def __init__(self, model: "Whisper", initial_token_length: int):
-        self.model: "Whisper" = model
-        self.initial_token_length = initial_token_length
-        self.kv_cache = {}
-        self.hooks = []
-
-    def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
-        if not self.kv_cache:
-            self.kv_cache, self.hooks = self.model.install_kv_cache_hooks()
-
-        if tokens.shape[-1] > self.initial_token_length:
-            # only need to use the last token except in the first forward pass
-            tokens = tokens[:, -1:]
-
-        return self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache)
-
-    def cleanup_caching(self):
-        for hook in self.hooks:
-            hook.remove()
-
-        self.kv_cache = {}
-        self.hooks = []
-
-    def rearrange_kv_cache(self, source_indices):
-        for module, tensor in self.kv_cache.items():
-            # update the key/value cache to contain the selected sequences
-            self.kv_cache[module] = tensor[source_indices].detach()
-
-
-class SequenceRanker:
-    def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]) -> List[int]:
-        """
-        Given a list of groups of samples and their cumulative log probabilities,
-        return the indices of the samples in each group to select as the final result
-        """
-        raise NotImplementedError
-
-
-class MaximumLikelihoodRanker(SequenceRanker):
-    """
-    Select the sample with the highest log probabilities, penalized using either
-    a simple length normalization or Google NMT paper's length penalty
-    """
-
-    def __init__(self, length_penalty: Optional[float]):
-        self.length_penalty = length_penalty
-
-    def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]):
-        def scores(logprobs, lengths):
-            result = []
-            for logprob, length in zip(logprobs, lengths):
-                if self.length_penalty is None:
-                    penalty = length
-                else:
-                    # from the Google NMT paper
-                    penalty = ((5 + length) / 6) ** self.length_penalty
-                result.append(logprob / penalty)
-            return result
-
-        # get the sequence with the highest score
-        lengths = [[len(t) for t in s] for s in tokens]
-        return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)]
-
-
-class TokenDecoder:
-    def reset(self):
-        """Initialize any stateful variables for decoding a new sequence"""
-
-    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
-        """Specify how to select the next token, based on the current trace and logits
-
-        Parameters
-        ----------
-        tokens : Tensor, shape = (n_batch, current_sequence_length)
-            all tokens in the context so far, including the prefix and sot_sequence tokens
-
-        logits : Tensor, shape = (n_batch, vocab_size)
-            per-token logits of the probability distribution at the current step
-
-        sum_logprobs : Tensor, shape = (n_batch)
-            cumulative log probabilities for each sequence
-
-        Returns
-        -------
-        tokens : Tensor, shape = (n_batch, current_sequence_length + 1)
-            the tokens, appended with the selected next token
-
-        completed : bool
-            True if all sequences has reached the end of text
-
-        """
-        raise NotImplementedError
-
-    def finalize(
-        self, tokens: Tensor, sum_logprobs: Tensor
-    ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]:
-        """Finalize search and return the final candidate sequences
-
-        Parameters
-        ----------
-        tokens : Tensor, shape = (n_audio, n_group, current_sequence_length)
-            all tokens in the context so far, including the prefix and sot_sequence
-
-        sum_logprobs : Tensor, shape = (n_audio, n_group)
-            cumulative log probabilities for each sequence
-
-        Returns
-        -------
-        tokens : Sequence[Sequence[Tensor]], length = n_audio
-            sequence of Tensors containing candidate token sequences, for each audio input
-
-        sum_logprobs : List[List[float]], length = n_audio
-            sequence of cumulative log probabilities corresponding to the above
-
-        """
-        raise NotImplementedError
-
-
-class GreedyDecoder(TokenDecoder):
-    def __init__(self, temperature: float, eot: int):
-        self.temperature = temperature
-        self.eot = eot
-
-    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
-        temperature = self.temperature
-        if temperature == 0:
-            next_tokens = logits.argmax(dim=-1)
-        else:
-            next_tokens = Categorical(logits=logits / temperature).sample()
-
-        logprobs = F.log_softmax(logits.float(), dim=-1)
-        current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens]
-        sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot)
-
-        next_tokens[tokens[:, -1] == self.eot] = self.eot
-        tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1)
-
-        completed = (tokens[:, -1] == self.eot).all()
-        return tokens, completed
-
-    def finalize(self, tokens: Tensor, sum_logprobs: Tensor):
-        # make sure each sequence has at least one EOT token at the end
-        tokens = F.pad(tokens, (0, 1), value=self.eot)
-        return tokens, sum_logprobs.tolist()
-
-
-class BeamSearchDecoder(TokenDecoder):
-    def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None):
-        self.beam_size = beam_size
-        self.eot = eot
-        self.inference = inference
-        self.patience = patience or 1.0
-        self.max_candidates: int = round(beam_size * self.patience)
-        self.finished_sequences = None
-
-        assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})"
-
-    def reset(self):
-        self.finished_sequences = None
-
-    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
-        if tokens.shape[0] % self.beam_size != 0:
-            raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0")
-
-        n_audio = tokens.shape[0] // self.beam_size
-        if self.finished_sequences is None:  # for the first update
-            self.finished_sequences = [{} for _ in range(n_audio)]
-
-        logprobs = F.log_softmax(logits.float(), dim=-1)
-        next_tokens, source_indices, finished_sequences = [], [], []
-        for i in range(n_audio):
-            scores, sources, finished = {}, {}, {}
-
-            # STEP 1: calculate the cumulative log probabilities for possible candidates
-            for j in range(self.beam_size):
-                idx = i * self.beam_size + j
-                prefix = tokens[idx].tolist()
-                for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)):
-                    new_logprob = (sum_logprobs[idx] + logprob).item()
-                    sequence = tuple(prefix + [token.item()])
-                    scores[sequence] = new_logprob
-                    sources[sequence] = idx
-
-            # STEP 2: rank the candidates and keep the top beam_size sequences for each audio
-            saved = 0
-            for sequence in sorted(scores, key=scores.get, reverse=True):
-                if sequence[-1] == self.eot:
-                    finished[sequence] = scores[sequence]
-                else:
-                    sum_logprobs[len(next_tokens)] = scores[sequence]
-                    next_tokens.append(sequence)
-                    source_indices.append(sources[sequence])
-
-                    saved += 1
-                    if saved == self.beam_size:
-                        break
-
-            finished_sequences.append(finished)
-
-        tokens = torch.tensor(next_tokens, device=tokens.device)
-        self.inference.rearrange_kv_cache(source_indices)
-
-        # add newly finished sequences to self.finished_sequences
-        assert len(self.finished_sequences) == len(finished_sequences)
-        for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences):
-            for seq in sorted(newly_finished, key=newly_finished.get, reverse=True):
-                if len(previously_finished) >= self.max_candidates:
-                    break  # the candidate list is full
-                previously_finished[seq] = newly_finished[seq]
-
-        # mark as completed if all audio has enough number of samples
-        completed = all(
-            len(sequences) >= self.max_candidates for sequences in self.finished_sequences
-        )
-        return tokens, completed
-
-    def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor):
-        # collect all finished sequences, including patience, and add unfinished ones if not enough
-        sum_logprobs = sum_logprobs.cpu()
-        for i, sequences in enumerate(self.finished_sequences):
-            if len(sequences) < self.beam_size:  # when not enough sequences are finished
-                for j in list(np.argsort(sum_logprobs[i]))[::-1]:
-                    sequence = preceding_tokens[i, j].tolist() + [self.eot]
-                    sequences[tuple(sequence)] = sum_logprobs[i][j].item()
-                    if len(sequences) >= self.beam_size:
-                        break
-
-        tokens: List[List[Tensor]] = [
-            [torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences
-        ]
-        sum_logprobs: List[List[float]] = [
-            list(sequences.values()) for sequences in self.finished_sequences
-        ]
-        return tokens, sum_logprobs
-
-
-class LogitFilter:
-    def apply(self, logits: Tensor, tokens: Tensor) -> None:
-        """Apply any filtering or masking to logits in-place
-
-        Parameters
-        ----------
-        logits : Tensor, shape = (n_batch, vocab_size)
-            per-token logits of the probability distribution at the current step
-
-        tokens : Tensor, shape = (n_batch, current_sequence_length)
-            all tokens in the context so far, including the prefix and sot_sequence tokens
-
-        """
-        raise NotImplementedError
-
-
-class SuppressBlank(LogitFilter):
-    def __init__(self, tokenizer: Tokenizer, sample_begin: int):
-        self.tokenizer = tokenizer
-        self.sample_begin = sample_begin
-
-    def apply(self, logits: Tensor, tokens: Tensor):
-        if tokens.shape[1] == self.sample_begin:
-            logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf
-
-
-class SuppressTokens(LogitFilter):
-    def __init__(self, suppress_tokens: Sequence[int]):
-        self.suppress_tokens = list(suppress_tokens)
-
-    def apply(self, logits: Tensor, tokens: Tensor):
-        logits[:, self.suppress_tokens] = -np.inf
-
-
-class ApplyTimestampRules(LogitFilter):
-    def __init__(
-        self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int]
-    ):
-        self.tokenizer = tokenizer
-        self.sample_begin = sample_begin
-        self.max_initial_timestamp_index = max_initial_timestamp_index
-
-    def apply(self, logits: Tensor, tokens: Tensor):
-        # suppress <|notimestamps|> which is handled by without_timestamps
-        if self.tokenizer.no_timestamps is not None:
-            logits[:, self.tokenizer.no_timestamps] = -np.inf
-
-        # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
-        for k in range(tokens.shape[0]):
-            seq = [t for t in tokens[k, self.sample_begin :].tolist()]
-            last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin
-            penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin
-
-            if last_was_timestamp:
-                if penultimate_was_timestamp:  # has to be non-timestamp
-                    logits[k, self.tokenizer.timestamp_begin :] = -np.inf
-                else:  # cannot be normal text tokens
-                    logits[k, : self.tokenizer.eot] = -np.inf
-
-        if tokens.shape[1] == self.sample_begin:
-            # suppress generating non-timestamp tokens at the beginning
-            logits[:, : self.tokenizer.timestamp_begin] = -np.inf
-
-            # apply the `max_initial_timestamp` option
-            if self.max_initial_timestamp_index is not None:
-                last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index
-                logits[:, last_allowed + 1 :] = -np.inf
-
-        # if sum of probability over timestamps is above any other token, sample timestamp
-        logprobs = F.log_softmax(logits.float(), dim=-1)
-        for k in range(tokens.shape[0]):
-            timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1)
-            max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max()
-            if timestamp_logprob > max_text_token_logprob:
-                logits[k, : self.tokenizer.timestamp_begin] = -np.inf
-
-
-class DecodingTask:
-    inference: Inference
-    sequence_ranker: SequenceRanker
-    decoder: TokenDecoder
-    logit_filters: List[LogitFilter]
-
-    def __init__(self, model: "Whisper", options: DecodingOptions):
-        self.model = model
-
-        language = options.language or "en"
-        tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task)
-        self.tokenizer: Tokenizer = tokenizer
-        self.options: DecodingOptions = self._verify_options(options)
-
-        self.n_group: int = options.beam_size or options.best_of or 1
-        self.n_ctx: int = model.dims.n_text_ctx
-        self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2
-
-        self.sot_sequence: Tuple[int] = tokenizer.sot_sequence
-        if self.options.without_timestamps:
-            self.sot_sequence = tokenizer.sot_sequence_including_notimestamps
-
-        self.initial_tokens: Tuple[int] = self._get_initial_tokens()
-        self.sample_begin: int = len(self.initial_tokens)
-        self.sot_index: int = self.initial_tokens.index(tokenizer.sot)
-
-        # inference: implements the forward pass through the decoder, including kv caching
-        self.inference = PyTorchInference(model, len(self.initial_tokens))
-
-        # sequence ranker: implements how to rank a group of sampled sequences
-        self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty)
-
-        # decoder: implements how to select the next tokens, given the autoregressive distribution
-        if options.beam_size is not None:
-            self.decoder = BeamSearchDecoder(
-                options.beam_size, tokenizer.eot, self.inference, options.patience
-            )
-        else:
-            self.decoder = GreedyDecoder(options.temperature, tokenizer.eot)
-
-        # logit filters: applies various rules to suppress or penalize certain tokens
-        self.logit_filters = []
-        if self.options.suppress_blank:
-            self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin))
-        if self.options.suppress_tokens:
-            self.logit_filters.append(SuppressTokens(self._get_suppress_tokens()))
-        if not options.without_timestamps:
-            precision = CHUNK_LENGTH / model.dims.n_audio_ctx  # usually 0.02 seconds
-            max_initial_timestamp_index = None
-            if options.max_initial_timestamp:
-                max_initial_timestamp_index = round(self.options.max_initial_timestamp / precision)
-            self.logit_filters.append(
-                ApplyTimestampRules(tokenizer, self.sample_begin, max_initial_timestamp_index)
-            )
-
-    def _verify_options(self, options: DecodingOptions) -> DecodingOptions:
-        if options.beam_size is not None and options.best_of is not None:
-            raise ValueError("beam_size and best_of can't be given together")
-        if options.temperature == 0:
-            if options.best_of is not None:
-                raise ValueError("best_of with greedy sampling (T=0) is not compatible")
-        if options.patience is not None and options.beam_size is None:
-            raise ValueError("patience requires beam_size to be given")
-        if options.length_penalty is not None and not (0 <= options.length_penalty <= 1):
-            raise ValueError("length_penalty (alpha) should be a value between 0 and 1")
-
-        return options
-
-    def _get_initial_tokens(self) -> Tuple[int]:
-        tokens = list(self.sot_sequence)
-        prefix = self.options.prefix
-        prompt = self.options.prompt
-
-        if prefix:
-            prefix_tokens = (
-                self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix
-            )
-            if self.sample_len is not None:
-                max_prefix_len = self.n_ctx // 2 - self.sample_len
-                prefix_tokens = prefix_tokens[-max_prefix_len:]
-            tokens = tokens + prefix_tokens
-
-        if prompt:
-            prompt_tokens = (
-                self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt
-            )
-            tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens
-
-        return tuple(tokens)
-
-    def _get_suppress_tokens(self) -> Tuple[int]:
-        suppress_tokens = self.options.suppress_tokens
-
-        if isinstance(suppress_tokens, str):
-            suppress_tokens = [int(t) for t in suppress_tokens.split(",")]
-
-        if -1 in suppress_tokens:
-            suppress_tokens = [t for t in suppress_tokens if t >= 0]
-            suppress_tokens.extend(self.tokenizer.non_speech_tokens)
-        elif suppress_tokens is None or len(suppress_tokens) == 0:
-            suppress_tokens = []  # interpret empty string as an empty list
-        else:
-            assert isinstance(suppress_tokens, list), "suppress_tokens must be a list"
-
-        suppress_tokens.extend(
-            [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm]
-        )
-        if self.tokenizer.no_speech is not None:
-            # no-speech probability is collected separately
-            suppress_tokens.append(self.tokenizer.no_speech)
-
-        return tuple(sorted(set(suppress_tokens)))
-
-    def _get_audio_features(self, mel: Tensor):
-        if self.options.fp16:
-            mel = mel.half()
-
-        if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state):
-            # encoded audio features are given; skip audio encoding
-            print("encoded audio features are given; skip audio encoding")
-            audio_features = mel
-        else:
-            print(mel.shape)
-            print("===============================")
-            audio_features = self.model.encoder(mel)
-
-        if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32):
-            return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}")
-
-        return audio_features
-
-    def _detect_language(self, audio_features: Tensor, tokens: Tensor):
-        languages = [self.options.language] * audio_features.shape[0]
-        lang_probs = None
-
-        if self.options.language is None or self.options.task == "lang_id":
-            lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer)
-            languages = [max(probs, key=probs.get) for probs in lang_probs]
-            if self.options.language is None:
-                tokens[:, self.sot_index + 1] = lang_tokens  # write language tokens
-
-        return languages, lang_probs
-
-    def _main_loop(self, audio_features: Tensor, tokens: Tensor):
-        assert audio_features.shape[0] == tokens.shape[0]
-        n_batch = tokens.shape[0]
-        sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device)
-        no_speech_probs = [np.nan] * n_batch
-
-        try:
-            for i in range(self.sample_len):
-                logits = self.inference.logits(tokens, audio_features)
-
-                if i == 0 and self.tokenizer.no_speech is not None:  # save no_speech_probs
-                    probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1)
-                    no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist()
-
-                # now we need to consider the logits at the last token only
-                logits = logits[:, -1]
-
-                # apply the logit filters, e.g. for suppressing or applying penalty to
-                for logit_filter in self.logit_filters:
-                    logit_filter.apply(logits, tokens)
-
-                # expand the tokens tensor with the selected next tokens
-                tokens, completed = self.decoder.update(tokens, logits, sum_logprobs)
-
-                if completed or tokens.shape[-1] > self.n_ctx:
-                    break
-        finally:
-            self.inference.cleanup_caching()
-
-        return tokens, sum_logprobs, no_speech_probs
-
-    @torch.no_grad()
-    def run(self, mel: Tensor) -> List[DecodingResult]:
-        self.decoder.reset()
-        tokenizer: Tokenizer = self.tokenizer
-        n_audio: int = mel.shape[0]
-
-        audio_features: Tensor = self._get_audio_features(mel)  # encoder forward pass
-        tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1)
-
-        # detect language if requested, overwriting the language token
-        languages, language_probs = self._detect_language(audio_features, tokens)
-        if self.options.task == "lang_id":
-            return [
-                DecodingResult(audio_features=features, language=language, language_probs=probs)
-                for features, language, probs in zip(audio_features, languages, language_probs)
-            ]
-
-        # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling
-        audio_features = audio_features.repeat_interleave(self.n_group, dim=0)
-        tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device)
-
-        # call the main sampling loop
-        tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens)
-
-        # reshape the tensors to have (n_audio, n_group) as the first two dimensions
-        audio_features = audio_features[:: self.n_group]
-        no_speech_probs = no_speech_probs[:: self.n_group]
-        assert audio_features.shape[0] == len(no_speech_probs) == n_audio
-
-        tokens = tokens.reshape(n_audio, self.n_group, -1)
-        sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group)
-
-        # get the final candidates for each group, and slice between the first sampled token and EOT
-        tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs)
-        tokens: List[List[Tensor]] = [
-            [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens
-        ]
-
-        # select the top-ranked sample in each group
-        selected = self.sequence_ranker.rank(tokens, sum_logprobs)
-        tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)]
-        texts: List[str] = [tokenizer.decode(t).strip() for t in tokens]
-
-        sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)]
-        avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)]
-
-        fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs)
-        if len(set(map(len, fields))) != 1:
-            raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}")
-
-        return [
-            DecodingResult(
-                audio_features=features,
-                language=language,
-                tokens=tokens,
-                text=text,
-                avg_logprob=avg_logprob,
-                no_speech_prob=no_speech_prob,
-                temperature=self.options.temperature,
-                compression_ratio=compression_ratio(text),
-            )
-            for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields)
-        ]
-
-
-@torch.no_grad()
-def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]:
-    """
-    Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s).
-
-    Parameters
-    ----------
-    model: Whisper
-        the Whisper model instance
-
-    mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000)
-        A tensor containing the Mel spectrogram(s)
-
-    options: DecodingOptions
-        A dataclass that contains all necessary options for decoding 30-second segments
-
-    Returns
-    -------
-    result: Union[DecodingResult, List[DecodingResult]]
-        The result(s) of decoding contained in `DecodingResult` dataclass instance(s)
-    """
-    single = mel.ndim == 2
-    if single:
-        mel = mel.unsqueeze(0)
-    result = DecodingTask(model, options).run(mel)
-    
-    if single:
-        result = result[0]
-
-    return result
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ.py
deleted file mode 100644
index 2a7c376da5f9269197c44079f3e0f3b09cdc63fa..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from .mask_rcnn_R_50_FPN_100ep_LSJ import (
-    dataloader,
-    lr_multiplier,
-    model,
-    optimizer,
-    train,
-)
-
-train.max_iter *= 2  # 100ep -> 200ep
-
-lr_multiplier.scheduler.milestones = [
-    milestone * 2 for milestone in lr_multiplier.scheduler.milestones
-]
-lr_multiplier.scheduler.num_updates = train.max_iter
diff --git a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/cross-fade.js b/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/cross-fade.js
deleted file mode 100644
index caaa90d7eb33655bfe761b05ba9e52d6a479f05a..0000000000000000000000000000000000000000
--- a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/cross-fade.js
+++ /dev/null
@@ -1,35 +0,0 @@
-let list = require('postcss').list
-
-let Value = require('../value')
-
-class CrossFade extends Value {
-  replace(string, prefix) {
-    return list
-      .space(string)
-      .map(value => {
-        if (value.slice(0, +this.name.length + 1) !== this.name + '(') {
-          return value
-        }
-
-        let close = value.lastIndexOf(')')
-        let after = value.slice(close + 1)
-        let args = value.slice(this.name.length + 1, close)
-
-        if (prefix === '-webkit-') {
-          let match = args.match(/\d*.?\d+%?/)
-          if (match) {
-            args = args.slice(match[0].length).trim()
-            args += `, ${match[0]}`
-          } else {
-            args += ', 0.5'
-          }
-        }
-        return prefix + this.name + '(' + args + ')' + after
-      })
-      .join(' ')
-  }
-}
-
-CrossFade.names = ['cross-fade']
-
-module.exports = CrossFade
diff --git a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/text-decoration.js b/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/text-decoration.js
deleted file mode 100644
index 148d98a1906ba4975f81e32bd058b6d06db300bd..0000000000000000000000000000000000000000
--- a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/text-decoration.js
+++ /dev/null
@@ -1,25 +0,0 @@
-let Declaration = require('../declaration')
-
-const BASIC = [
-  'none',
-  'underline',
-  'overline',
-  'line-through',
-  'blink',
-  'inherit',
-  'initial',
-  'unset'
-]
-
-class TextDecoration extends Declaration {
-  /**
-   * Do not add prefixes for basic values.
-   */
-  check(decl) {
-    return decl.value.split(/\s+/).some(i => !BASIC.includes(i))
-  }
-}
-
-TextDecoration.names = ['text-decoration']
-
-module.exports = TextDecoration
diff --git a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/writing-mode.js b/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/writing-mode.js
deleted file mode 100644
index aa7c075f51b0010bd445fe27a7465676829b4e43..0000000000000000000000000000000000000000
--- a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/writing-mode.js
+++ /dev/null
@@ -1,42 +0,0 @@
-let Declaration = require('../declaration')
-
-class WritingMode extends Declaration {
-  insert(decl, prefix, prefixes) {
-    if (prefix === '-ms-') {
-      let cloned = this.set(this.clone(decl), prefix)
-
-      if (this.needCascade(decl)) {
-        cloned.raws.before = this.calcBefore(prefixes, decl, prefix)
-      }
-      let direction = 'ltr'
-
-      decl.parent.nodes.forEach(i => {
-        if (i.prop === 'direction') {
-          if (i.value === 'rtl' || i.value === 'ltr') direction = i.value
-        }
-      })
-
-      cloned.value = WritingMode.msValues[direction][decl.value] || decl.value
-      return decl.parent.insertBefore(decl, cloned)
-    }
-
-    return super.insert(decl, prefix, prefixes)
-  }
-}
-
-WritingMode.names = ['writing-mode']
-
-WritingMode.msValues = {
-  ltr: {
-    'horizontal-tb': 'lr-tb',
-    'vertical-rl': 'tb-rl',
-    'vertical-lr': 'tb-lr'
-  },
-  rtl: {
-    'horizontal-tb': 'rl-tb',
-    'vertical-rl': 'bt-rl',
-    'vertical-lr': 'bt-lr'
-  }
-}
-
-module.exports = WritingMode
diff --git a/spaces/youplala/StoreCopilot/assets/core.css b/spaces/youplala/StoreCopilot/assets/core.css
deleted file mode 100644
index 67ff1cc9f167d942bf6fc0f06bbab543543ba783..0000000000000000000000000000000000000000
--- a/spaces/youplala/StoreCopilot/assets/core.css
+++ /dev/null
@@ -1,12 +0,0 @@
-/*!
- * Bootstrap  v5.2.3 (https://getbootstrap.com/)
- * Copyright 2011-2022 The Bootstrap Authors
- * Copyright 2011-2022 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
- */:root{--bs-blue: #007bff;--bs-indigo: #6610f2;--bs-purple: #004857;--bs-pink: #e83e8c;--bs-red: #ea5455;--bs-orange: #fd7e14;--bs-yellow: #ff9f43;--bs-green: #28c76f;--bs-teal: #20c997;--bs-cyan: #00cfe8;--bs-black: #4b465c;--bs-white: #fff;--bs-gray: rgba(75, 70, 92, 0.6);--bs-gray-dark: rgba(75, 70, 92, 0.8);--bs-gray-25: rgba(75, 70, 92, 0.015);--bs-gray-50: rgba(75, 70, 92, 0.03);--bs-primary: #004857;--bs-secondary: #a8aaae;--bs-success: #28c76f;--bs-info: #00cfe8;--bs-warning: #ff9f43;--bs-danger: #ea5455;--bs-light: #dfdfe3;--bs-dark: #4b4b4b;--bs-gray: rgba(75, 70, 92, 0.05);--bs-primary-rgb: 115, 103, 240;--bs-secondary-rgb: 168, 170, 174;--bs-success-rgb: 40, 199, 111;--bs-info-rgb: 0, 207, 232;--bs-warning-rgb: 255, 159, 67;--bs-danger-rgb: 234, 84, 85;--bs-light-rgb: 223, 223, 227;--bs-dark-rgb: 75, 75, 75;--bs-gray-rgb: 75, 70, 92;--bs-white-rgb: 255, 255, 255;--bs-black-rgb: 75, 70, 92;--bs-body-color-rgb: 111, 107, 125;--bs-body-bg-rgb: 248, 247, 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.accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: transparent;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: #4b465c;--bs-breadcrumb-item-padding-x: 0.875rem;--bs-breadcrumb-item-active-color: #4b465c;display:flex;flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' class='icon icon-tabler icon-tabler-chevron-right' width='16' height='24' viewBox='0 0 24 24' stroke-width='1.75' stroke='%234b465c' fill='none' stroke-linecap='round' stroke-linejoin='round'%3E%3Cpath stroke='none' d='M0 0h24v24H0z' fill='none'%3E%3C/path%3E%3Cpolyline points='9 6 15 12 9 18'%3E%3C/polyline%3E%3C/svg%3E")) /* rtl: var(--bs-breadcrumb-divider, url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' class='icon icon-tabler icon-tabler-chevron-left' width='16' height='24' viewBox='0 0 24 24' stroke-width='1.75' stroke='%234b465c' fill='none' stroke-linecap='round' stroke-linejoin='round'%3E%3Cpath stroke='none' d='M0 0h24v24H0z' fill='none'%3E%3C/path%3E%3Cpolyline points='15 6 9 12 15 18'%3E%3C/polyline%3E%3C/svg%3E")) */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.35rem;--bs-pagination-padding-y: 0.594rem;--bs-pagination-font-size:0.9375rem;--bs-pagination-color: #5d596c;--bs-pagination-bg: rgba(75, 70, 92, 0.08);--bs-pagination-border-width: 0px;--bs-pagination-border-color: rgba(75, 70, 92, 0.08);--bs-pagination-border-radius: 0.375rem;--bs-pagination-hover-color: #5d596c;--bs-pagination-hover-bg: rgba(75, 70, 92, 0.16);--bs-pagination-hover-border-color: rgba(75, 70, 92, 0.16);--bs-pagination-focus-color: #5d596c;--bs-pagination-focus-bg: rgba(75, 70, 92, 0.16);--bs-pagination-focus-box-shadow: none;--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #004857;--bs-pagination-active-border-color: #004857;--bs-pagination-disabled-color: #a5a3ae;--bs-pagination-disabled-bg: rgba(75, 70, 92, 0.08);--bs-pagination-disabled-border-color: rgba(75, 70, 92, 0.08);display:flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:.25rem}.page-item .page-link{border-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 0.4rem;--bs-pagination-padding-y: 0.875rem;--bs-pagination-font-size:1rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.15rem;--bs-pagination-padding-y: 0.5rem;--bs-pagination-font-size:0.75rem;--bs-pagination-border-radius: 0.25rem}.badge{--bs-badge-padding-x: 1em;--bs-badge-padding-y: 0.49em;--bs-badge-font-size:0.81em;--bs-badge-font-weight: 600;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 0.875rem;--bs-alert-padding-y: 0.687rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.375rem;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700}.alert-dismissible{padding-right:2.625rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:.85875rem .875rem}@keyframes progress-bar-stripes{0%{background-position-x:.75rem}}.progress{--bs-progress-height: 0.75rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #f1f0f2;--bs-progress-border-radius: 3.125rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(75, 70, 92, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #004857;--bs-progress-bar-transition: width 0.6s ease;display:flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;flex-direction:column;justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #6f6b7d;--bs-list-group-bg: transparent;--bs-list-group-border-color: #dbdade;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.375rem;--bs-list-group-item-padding-x: 1.25rem;--bs-list-group-item-padding-y: 0.57rem;--bs-list-group-action-color: rgba(75, 70, 92, 0.7);--bs-list-group-action-hover-color: #004857;--bs-list-group-action-hover-bg: #f4f3fe;--bs-list-group-action-active-color: #004857;--bs-list-group-action-active-bg: #f4f3fe;--bs-list-group-disabled-color: #a5a3ae;--bs-list-group-disabled-bg: transparent;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #004857;--bs-list-group-active-border-color: #004857;display:flex;flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.btn-close{box-sizing:content-box;width:1.125rem;height:1.125rem;padding:.25em .25em;color:#4b465c;background:rgba(0,0,0,0) url("data:image/svg+xml,%3Csvg width='19' height='18' viewBox='0 0 19 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M14 4.5L5 13.5' stroke='%234b465c' stroke-width='1.75' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M14 4.5L5 13.5' stroke='white' stroke-opacity='0.2' stroke-width='1.75' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M5 4.5L14 13.5' stroke='%234b465c' stroke-width='1.75' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M5 4.5L14 13.5' stroke='white' stroke-opacity='0.2' stroke-width='1.75' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A") center/1.125rem auto no-repeat;border:0;border-radius:.375rem;opacity:.5}.btn-close:hover{color:#4b465c;text-decoration:none;opacity:.75}.btn-close:focus{outline:0;box-shadow:none;opacity:.75}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;opacity:.25}.btn-close-white{filter:invert(1) grayscale(100%) brightness(200%)}.toast{--bs-toast-zindex: 1095;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.406rem;--bs-toast-spacing: 1rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.9375rem;--bs-toast-color: #6f6b7d;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 0px;--bs-toast-border-color: var(--bs-border-color-translucent);--bs-toast-border-radius: 0.375rem;--bs-toast-box-shadow: 0 0.25rem 1rem rgba(165, 163, 174, 0.45);--bs-toast-header-color: #5d596c;--bs-toast-header-bg: #fff;--bs-toast-header-border-color: rgba(219, 218, 222, 0.3);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1095;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1090;--bs-modal-width: 35rem;--bs-modal-padding: 1.5rem;--bs-modal-margin: 1.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: 0px;--bs-modal-border-radius: 0.375rem;--bs-modal-box-shadow: 0 0.31rem 1.25rem 0 rgba(75, 70, 92, 0.4);--bs-modal-inner-border-radius: calc(0.375rem - 0px);--bs-modal-header-padding-x: 0.25rem;--bs-modal-header-padding-y: 1.25rem;--bs-modal-header-padding: 1.25rem 1.5rem 0.25rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: 0px;--bs-modal-title-line-height: 1.47;--bs-modal-footer-gap: 1rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: 0px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .15s ease-out;transform:translateY(-100px) scale(0.8)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:translateY(0) scale(1)}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1089;--bs-backdrop-bg: #4b465c;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;flex-shrink:0;align-items:center;justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;flex-shrink:0;flex-wrap:wrap;align-items:center;justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.31rem 1.25rem 0 rgba(75, 70, 92, 0.4)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 22.5rem}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 50rem}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down 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auto;width:34px;height:5px;padding:0;margin-right:5px;margin-left:5px;text-indent:-999px;cursor:pointer;background-color:#fff;background-clip:padding-box;border:0;border-top:0 solid rgba(0,0,0,0);border-bottom:0 solid rgba(0,0,0,0);opacity:.4;transition:opacity .6s ease}@media(prefers-reduced-motion: reduce){.carousel-indicators [data-bs-target]{transition:none}}.carousel-indicators .active{opacity:1}.carousel-caption{position:absolute;right:15%;bottom:1.437rem;left:15%;padding-top:1.25rem;padding-bottom:1.25rem;color:#fff;text-align:center}.carousel-dark .carousel-control-prev-icon,.carousel-dark .carousel-control-next-icon{filter:invert(1) grayscale(100)}.carousel-dark .carousel-indicators [data-bs-target]{background-color:#4b465c}.carousel-dark 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0.75s;--bs-spinner-animation-name: spinner-grow;background-color:currentcolor;opacity:0}.spinner-grow-sm{--bs-spinner-width: 1rem;--bs-spinner-height: 1rem}@media(prefers-reduced-motion: reduce){.spinner-border,.spinner-grow{--bs-spinner-animation-speed: 1.5s}}.offcanvas,.offcanvas-xxl,.offcanvas-xl,.offcanvas-lg,.offcanvas-md,.offcanvas-sm{--bs-offcanvas-zindex: 1090;--bs-offcanvas-width: 400px;--bs-offcanvas-height: 30vh;--bs-offcanvas-padding-x: 1.5rem;--bs-offcanvas-padding-y: 1.5rem;--bs-offcanvas-color: ;--bs-offcanvas-bg: #fff;--bs-offcanvas-border-width: 0px;--bs-offcanvas-border-color: var(--bs-border-color-translucent);--bs-offcanvas-box-shadow: 0 0.31rem 1.25rem 0 rgba(75, 70, 92, 0.4)}@media(max-width: 575.98px){.offcanvas-sm{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:transform .25s ease-in-out}}@media(max-width: 575.98px)and (prefers-reduced-motion: reduce){.offcanvas-sm{transition:none}}@media(max-width: 575.98px){.offcanvas-sm.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}}@media(max-width: 575.98px){.offcanvas-sm.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}}@media(max-width: 575.98px){.offcanvas-sm.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) 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diff --git a/spaces/yufiofficial/MusicGenQ/audiocraft/models/encodec.py b/spaces/yufiofficial/MusicGenQ/audiocraft/models/encodec.py
deleted file mode 100644
index 69621a695887b0b41614c51cae020f6fd0af221d..0000000000000000000000000000000000000000
--- a/spaces/yufiofficial/MusicGenQ/audiocraft/models/encodec.py
+++ /dev/null
@@ -1,302 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from abc import ABC, abstractmethod
-import typing as tp
-
-from einops import rearrange
-import torch
-from torch import nn
-
-from .. import quantization as qt
-
-
-class CompressionModel(ABC, nn.Module):
-
-    @abstractmethod
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        ...
-
-    @abstractmethod
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        """See `EncodecModel.encode`"""
-        ...
-
-    @abstractmethod
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        """See `EncodecModel.decode`"""
-        ...
-
-    @property
-    @abstractmethod
-    def channels(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def frame_rate(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def sample_rate(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def cardinality(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def num_codebooks(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def total_codebooks(self) -> int:
-        ...
-
-    @abstractmethod
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-        """
-        ...
-
-
-class EncodecModel(CompressionModel):
-    """Encodec model operating on the raw waveform.
-
-    Args:
-        encoder (nn.Module): Encoder network.
-        decoder (nn.Module): Decoder network.
-        quantizer (qt.BaseQuantizer): Quantizer network.
-        frame_rate (int): Frame rate for the latent representation.
-        sample_rate (int): Audio sample rate.
-        channels (int): Number of audio channels.
-        causal (bool): Whether to use a causal version of the model.
-        renormalize (bool): Whether to renormalize the audio before running the model.
-    """
-    # we need assignement to override the property in the abstract class,
-    # I couldn't find a better way...
-    frame_rate: int = 0
-    sample_rate: int = 0
-    channels: int = 0
-
-    def __init__(self,
-                 encoder: nn.Module,
-                 decoder: nn.Module,
-                 quantizer: qt.BaseQuantizer,
-                 frame_rate: int,
-                 sample_rate: int,
-                 channels: int,
-                 causal: bool = False,
-                 renormalize: bool = False):
-        super().__init__()
-        self.encoder = encoder
-        self.decoder = decoder
-        self.quantizer = quantizer
-        self.frame_rate = frame_rate
-        self.sample_rate = sample_rate
-        self.channels = channels
-        self.renormalize = renormalize
-        self.causal = causal
-        if self.causal:
-            # we force disabling here to avoid handling linear overlap of segments
-            # as supported in original EnCodec codebase.
-            assert not self.renormalize, 'Causal model does not support renormalize'
-
-    @property
-    def total_codebooks(self):
-        """Total number of quantizer codebooks available.
-        """
-        return self.quantizer.total_codebooks
-
-    @property
-    def num_codebooks(self):
-        """Active number of codebooks used by the quantizer.
-        """
-        return self.quantizer.num_codebooks
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-        """
-        self.quantizer.set_num_codebooks(n)
-
-    @property
-    def cardinality(self):
-        """Cardinality of each codebook.
-        """
-        return self.quantizer.bins
-
-    def preprocess(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        scale: tp.Optional[torch.Tensor]
-        if self.renormalize:
-            mono = x.mean(dim=1, keepdim=True)
-            volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt()
-            scale = 1e-8 + volume
-            x = x / scale
-            scale = scale.view(-1, 1)
-        else:
-            scale = None
-        return x, scale
-
-    def postprocess(self,
-                    x: torch.Tensor,
-                    scale: tp.Optional[torch.Tensor] = None) -> torch.Tensor:
-        if scale is not None:
-            assert self.renormalize
-            x = x * scale.view(-1, 1, 1)
-        return x
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        assert x.dim() == 3
-        length = x.shape[-1]
-        x, scale = self.preprocess(x)
-
-        emb = self.encoder(x)
-        q_res = self.quantizer(emb, self.frame_rate)
-        out = self.decoder(q_res.x)
-
-        # remove extra padding added by the encoder and decoder
-        assert out.shape[-1] >= length, (out.shape[-1], length)
-        out = out[..., :length]
-
-        q_res.x = self.postprocess(out, scale)
-
-        return q_res
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        """Encode the given input tensor to quantized representation along with scale parameter.
-
-        Args:
-            x (torch.Tensor): Float tensor of shape [B, C, T]
-
-        Returns:
-            codes, scale (tp.Tuple[torch.Tensor, torch.Tensor]): Tuple composed of:
-                codes a float tensor of shape [B, K, T] with K the number of codebooks used and T the timestep.
-                scale a float tensor containing the scale for audio renormalizealization.
-        """
-        assert x.dim() == 3
-        x, scale = self.preprocess(x)
-        emb = self.encoder(x)
-        codes = self.quantizer.encode(emb)
-        return codes, scale
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        """Decode the given codes to a reconstructed representation, using the scale to perform
-        audio denormalization if needed.
-
-        Args:
-            codes (torch.Tensor): Int tensor of shape [B, K, T]
-            scale (tp.Optional[torch.Tensor]): Float tensor containing the scale value.
-
-        Returns:
-            out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
-        """
-        emb = self.quantizer.decode(codes)
-        out = self.decoder(emb)
-        out = self.postprocess(out, scale)
-        # out contains extra padding added by the encoder and decoder
-        return out
-
-
-class FlattenedCompressionModel(CompressionModel):
-    """Wraps a CompressionModel and flatten its codebooks, e.g.
-    instead of returning [B, K, T], return [B, S, T * (K // S)] with
-    S the number of codebooks per step, and `K // S` the number of 'virtual steps'
-    for each real time step.
-
-    Args:
-        model (CompressionModel): compression model to wrap.
-        codebooks_per_step (int): number of codebooks to keep per step,
-            this must divide the number of codebooks provided by the wrapped model.
-        extend_cardinality (bool): if True, and for instance if codebooks_per_step = 1,
-            if each codebook has a cardinality N, then the first codebook will
-            use the range [0, N - 1], and the second [N, 2 N - 1] etc.
-            On decoding, this can lead to potentially invalid sequences.
-            Any invalid entry will be silently remapped to the proper range
-            with a modulo.
-    """
-    def __init__(self, model: CompressionModel, codebooks_per_step: int = 1,
-                 extend_cardinality: bool = True):
-        super().__init__()
-        self.model = model
-        self.codebooks_per_step = codebooks_per_step
-        self.extend_cardinality = extend_cardinality
-
-    @property
-    def total_codebooks(self):
-        return self.model.total_codebooks
-
-    @property
-    def num_codebooks(self):
-        """Active number of codebooks used by the quantizer.
-
-        ..Warning:: this reports the number of codebooks after the flattening
-        of the codebooks!
-        """
-        assert self.model.num_codebooks % self.codebooks_per_step == 0
-        return self.codebooks_per_step
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-
-        ..Warning:: this sets the number of codebooks **before** the flattening
-        of the codebooks.
-        """
-        assert n % self.codebooks_per_step == 0
-        self.model.set_num_codebooks(n)
-
-    @property
-    def num_virtual_steps(self) -> int:
-        """Return the number of virtual steps, e.g. one real step
-        will be split into that many steps.
-        """
-        return self.model.num_codebooks // self.codebooks_per_step
-
-    @property
-    def frame_rate(self) -> int:
-        return self.model.frame_rate * self.num_virtual_steps
-
-    @property
-    def sample_rate(self) -> int:
-        return self.model.sample_rate
-
-    @property
-    def channels(self) -> int:
-        return self.model.channels
-
-    @property
-    def cardinality(self):
-        """Cardinality of each codebook.
-        """
-        if self.extend_cardinality:
-            return self.model.cardinality * self.num_virtual_steps
-        else:
-            return self.model.cardinality
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        raise NotImplementedError("Not supported, use encode and decode.")
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        indices, scales = self.model.encode(x)
-        B, K, T = indices.shape
-        indices = rearrange(indices, 'b (k v) t -> b k t v', k=self.codebooks_per_step)
-        if self.extend_cardinality:
-            for virtual_step in range(1, self.num_virtual_steps):
-                indices[..., virtual_step] += self.model.cardinality * virtual_step
-        indices = rearrange(indices, 'b k t v -> b k (t v)')
-        return (indices, scales)
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        B, K, T = codes.shape
-        assert T % self.num_virtual_steps == 0
-        codes = rearrange(codes, 'b k (t v) -> b (k v) t', v=self.num_virtual_steps)
-        # We silently ignore potential errors from the LM when
-        # using extend_cardinality.
-        codes = codes % self.model.cardinality
-        return self.model.decode(codes, scale)
diff --git a/spaces/zama-fhe/encrypted_image_filtering/app.py b/spaces/zama-fhe/encrypted_image_filtering/app.py
deleted file mode 100644
index fe1c5680b56fb753e639826c72520b888d86a332..0000000000000000000000000000000000000000
--- a/spaces/zama-fhe/encrypted_image_filtering/app.py
+++ /dev/null
@@ -1,532 +0,0 @@
-"""A local gradio app that filters images using FHE."""
-
-import os
-import shutil
-import subprocess
-import time
-import gradio as gr
-import numpy
-import requests
-from itertools import chain
-
-from common import (
-    AVAILABLE_FILTERS,
-    CLIENT_TMP_PATH,
-    SERVER_TMP_PATH,
-    EXAMPLES,
-    FILTERS_PATH,
-    INPUT_SHAPE,
-    KEYS_PATH,
-    REPO_DIR,
-    SERVER_URL,
-)
-from client_server_interface import FHEClient
-
-# Uncomment here to have both the server and client in the same terminal
-subprocess.Popen(["uvicorn", "server:app"], cwd=REPO_DIR)
-time.sleep(3)
-
-
-def decrypt_output_with_wrong_key(encrypted_image, filter_name):
-    """Decrypt the encrypted output using a different private key.
-    """
-    # Retrieve the filter's deployment path
-    filter_path = FILTERS_PATH / f"{filter_name}/deployment"
-
-    # Instantiate the client interface and generate a new private key
-    wrong_client = FHEClient(filter_path, filter_name)
-    wrong_client.generate_private_and_evaluation_keys(force=True)
-
-    # Deserialize, decrypt and post-process the encrypted output using the new private key
-    output_image = wrong_client.deserialize_decrypt_post_process(encrypted_image)
-
-    # For filters that are expected to output black and white images, generate two other random 
-    # channels for better display 
-    if filter_name in ["black and white", "ridge detection"]:
-        # Green channel
-        wrong_client.generate_private_and_evaluation_keys(force=True)
-        output_image[:, :, 1] = wrong_client.deserialize_decrypt_post_process(encrypted_image)[:, :, 0]
-
-        # Blue channel
-        wrong_client.generate_private_and_evaluation_keys(force=True)
-        output_image[:, :, 2] = wrong_client.deserialize_decrypt_post_process(encrypted_image)[:, :, 0]
-
-    return output_image
-
-
-def shorten_bytes_object(bytes_object, limit=500):
-    """Shorten the input bytes object to a given length.
-
-    Encrypted data is too large for displaying it in the browser using Gradio. This function
-    provides a shorten representation of it.
-
-    Args:
-        bytes_object (bytes): The input to shorten
-        limit (int): The length to consider. Default to 500.
-
-    Returns:
-        str: Hexadecimal string shorten representation of the input byte object. 
-
-    """
-    # Define a shift for better display
-    shift = 100
-    return bytes_object[shift : limit + shift].hex()
-
-
-def get_client(user_id, filter_name):
-    """Get the client API.
-
-    Args:
-        user_id (int): The current user's ID.
-        filter_name (str): The filter chosen by the user
-
-    Returns:
-        FHEClient: The client API.
-    """
-    return FHEClient(
-        FILTERS_PATH / f"{filter_name}/deployment", 
-        filter_name,
-        key_dir=KEYS_PATH / f"{filter_name}_{user_id}", 
-    )
-
-
-def get_client_file_path(name, user_id, filter_name):
-    """Get the correct temporary file path for the client.
-
-    Args:
-        name (str): The desired file name.
-        user_id (int): The current user's ID.
-        filter_name (str): The filter chosen by the user
-
-    Returns:
-        pathlib.Path: The file path.
-    """
-    return CLIENT_TMP_PATH / f"{name}_{filter_name}_{user_id}"
-
-
-def clean_temporary_files(n_keys=20):
-    """Clean keys and encrypted images.
-
-    A maximum of n_keys keys and associated temporary files are allowed to be stored. Once this 
-    limit is reached, the oldest files are deleted.
-
-    Args:
-        n_keys (int): The maximum number of keys and associated files to be stored. Default to 20.
-
-    """
-    # Get the oldest key files in the key directory
-    key_dirs = sorted(KEYS_PATH.iterdir(), key=os.path.getmtime)
-
-    # If more than n_keys keys are found, remove the oldest
-    user_ids = []
-    if len(key_dirs) > n_keys:
-        n_keys_to_delete = len(key_dirs) - n_keys
-        for key_dir in key_dirs[:n_keys_to_delete]:
-            user_ids.append(key_dir.name)
-            shutil.rmtree(key_dir)
-
-    # Get all the encrypted objects in the temporary folder
-    client_files = CLIENT_TMP_PATH.iterdir()
-    server_files = SERVER_TMP_PATH.iterdir()
-
-    # Delete all files related to the ids whose keys were deleted
-    for file in chain(client_files, server_files):
-        for user_id in user_ids:
-            if user_id in file.name:
-                file.unlink()
-
-
-def keygen(filter_name):
-    """Generate the private key associated to a filter.
-
-    Args:
-        filter_name (str): The current filter to consider.
-
-    Returns:
-        (user_id, True) (Tuple[int, bool]): The current user's ID and a boolean used for visual display.
-
-    """
-    # Clean temporary files
-    clean_temporary_files()
-
-    # Create an ID for the current user
-    user_id = numpy.random.randint(0, 2**32)
-
-    # Retrieve the client API
-    client = get_client(user_id, filter_name)
-
-    # Generate a private key
-    client.generate_private_and_evaluation_keys(force=True)
-
-    # Retrieve the serialized evaluation key. In this case, as circuits are fully leveled, this
-    # evaluation key is empty. However, for software reasons, it is still needed for proper FHE
-    # execution
-    evaluation_key = client.get_serialized_evaluation_keys()
-
-    # Save evaluation_key as bytes in a file as it is too large to pass through regular Gradio
-    # buttons (see https://github.com/gradio-app/gradio/issues/1877)
-    evaluation_key_path = get_client_file_path("evaluation_key", user_id, filter_name)
-
-    with evaluation_key_path.open("wb") as evaluation_key_file:
-        evaluation_key_file.write(evaluation_key)
-
-    return (user_id, True)
-
-
-def encrypt(user_id, input_image, filter_name):
-    """Encrypt the given image for a specific user and filter.
-
-    Args:
-        user_id (int): The current user's ID.
-        input_image (numpy.ndarray): The image to encrypt.
-        filter_name (str): The current filter to consider.
-
-    Returns:
-        (input_image, encrypted_image_short) (Tuple[bytes]): The encrypted image and one of its
-        representation.
-
-    """
-    if user_id == "":
-        raise gr.Error("Please generate the private key first.")
-
-    if input_image is None:
-        raise gr.Error("Please choose an image first.")
-
-    # Retrieve the client API
-    client = get_client(user_id, filter_name)
-
-    # Pre-process, encrypt and serialize the image
-    encrypted_image = client.encrypt_serialize(input_image)
-
-    # Save encrypted_image to bytes in a file, since too large to pass through regular Gradio
-    # buttons, https://github.com/gradio-app/gradio/issues/1877
-    encrypted_image_path = get_client_file_path("encrypted_image", user_id, filter_name)
-
-    with encrypted_image_path.open("wb") as encrypted_image_file:
-        encrypted_image_file.write(encrypted_image)
-
-    # Create a truncated version of the encrypted image for display
-    encrypted_image_short = shorten_bytes_object(encrypted_image)
-
-    return (input_image, encrypted_image_short)
-
-
-def send_input(user_id, filter_name):
-    """Send the encrypted input image as well as the evaluation key to the server.
-
-    Args:
-        user_id (int): The current user's ID.
-        filter_name (str): The current filter to consider.
-    """
-    # Get the evaluation key path
-    evaluation_key_path = get_client_file_path("evaluation_key", user_id, filter_name)
-
-    if user_id == "" or not evaluation_key_path.is_file():
-        raise gr.Error("Please generate the private key first.")
-
-    encrypted_input_path = get_client_file_path("encrypted_image", user_id, filter_name)
-
-    if not encrypted_input_path.is_file():
-        raise gr.Error("Please generate the private key and then encrypt an image first.")
-
-    # Define the data and files to post
-    data = {
-        "user_id": user_id,
-        "filter": filter_name,
-    }
-
-    files = [
-        ("files", open(encrypted_input_path, "rb")),
-        ("files", open(evaluation_key_path, "rb")),
-    ]
-
-    # Send the encrypted input image and evaluation key to the server
-    url = SERVER_URL + "send_input"
-    with requests.post(
-        url=url,
-        data=data,
-        files=files,
-    ) as response:
-        return response.ok
-
-
-def run_fhe(user_id, filter_name):
-    """Apply the filter on the encrypted image previously sent using FHE.
-
-    Args:
-        user_id (int): The current user's ID.
-        filter_name (str): The current filter to consider.
-    """
-    data = {
-        "user_id": user_id,
-        "filter": filter_name,
-    }
-
-    # Trigger the FHE execution on the encrypted image previously sent
-    url = SERVER_URL + "run_fhe"
-    with requests.post(
-        url=url,
-        data=data,
-    ) as response:
-        if response.ok:
-            return response.json()
-        else:
-            raise gr.Error("Please wait for the input image to be sent to the server.")
-
-
-def get_output(user_id, filter_name):
-    """Retrieve the encrypted output image.
-
-    Args:
-        user_id (int): The current user's ID.
-        filter_name (str): The current filter to consider.
-
-    Returns:
-        encrypted_output_image_short (bytes): A representation of the encrypted result.
-
-    """
-    data = {
-        "user_id": user_id,
-        "filter": filter_name,
-    }
-
-    # Retrieve the encrypted output image
-    url = SERVER_URL + "get_output"
-    with requests.post(
-        url=url,
-        data=data,
-    ) as response:
-        if response.ok:
-            encrypted_output = response.content
-
-            # Save the encrypted output to bytes in a file as it is too large to pass through regular
-            # Gradio buttons (see https://github.com/gradio-app/gradio/issues/1877)
-            encrypted_output_path = get_client_file_path("encrypted_output", user_id, filter_name)
-
-            with encrypted_output_path.open("wb") as encrypted_output_file:
-                encrypted_output_file.write(encrypted_output)
-
-            # Decrypt the image using a different (wrong) key for display
-            output_image_representation = decrypt_output_with_wrong_key(encrypted_output, filter_name)
-
-            return output_image_representation
-        else:
-            raise gr.Error("Please wait for the FHE execution to be completed.")
-
-
-def decrypt_output(user_id, filter_name):
-    """Decrypt the result.
-
-    Args:
-        user_id (int): The current user's ID.
-        filter_name (str): The current filter to consider.
-
-    Returns:
-        (output_image, False, False) ((Tuple[numpy.ndarray, bool, bool]): The decrypted output, as
-            well as two booleans used for resetting Gradio checkboxes
-
-    """
-    if user_id == "":
-        raise gr.Error("Please generate the private key first.")
-
-    # Get the encrypted output path
-    encrypted_output_path = get_client_file_path("encrypted_output", user_id, filter_name)
-
-    if not encrypted_output_path.is_file():
-        raise gr.Error("Please run the FHE execution first.")
-
-    # Load the encrypted output as bytes
-    with encrypted_output_path.open("rb") as encrypted_output_file:
-        encrypted_output_image = encrypted_output_file.read()
-
-    # Retrieve the client API
-    client = get_client(user_id, filter_name)
-
-    # Deserialize, decrypt and post-process the encrypted output
-    output_image = client.deserialize_decrypt_post_process(encrypted_output_image)
-
-    return output_image, False, False
-
-
-demo = gr.Blocks()
-
-
-print("Starting the demo...")
-with demo:
-    gr.Markdown(
-        """
-        

-            
-        

-        
Image Filtering On Encrypted Data Using Fully Homomorphic Encryption

-        

-             Concrete-ML
-            —
-             Documentation
-            —
-             Community
-            —
-             @zama_fhe
-        

-        

-            
-        

-        

-            Test the app below, review
-             our tutorial
-            , and try the build for yourself!
-        

-        """
-    )
-
-    gr.Markdown("## Client side")
-    gr.Markdown("### Step 1: Upload an image. ")
-    gr.Markdown(
-        f"The image will automatically be resized to shape ({INPUT_SHAPE[0]}x{INPUT_SHAPE[1]}). "
-        "The image here, however, is displayed in its original resolution. The true image used "
-        "in this demo can be seen in Step 8."
-    )
-    with gr.Row():
-        input_image = gr.Image(
-            value=None, label="Upload an image here.", shape=INPUT_SHAPE, source="upload", interactive=True,
-        )
-
-        examples = gr.Examples(
-            examples=EXAMPLES, inputs=[input_image], examples_per_page=5, label="Examples to use."
-        )
-
-    gr.Markdown("### Step 2: Choose your filter.")
-    filter_name = gr.Dropdown(
-        choices=AVAILABLE_FILTERS, value="inverted", label="Choose your filter", interactive=True
-    )
-
-    gr.Markdown("#### Notes")
-    gr.Markdown(
-        """
-        - The private key is used to encrypt and decrypt the data and will never be shared.
-        - No public key is required for these filter operators.
-        """
-    )
-    
-    gr.Markdown("### Step 3: Generate the private key.")
-    keygen_button = gr.Button("Generate the private key.")
-
-    with gr.Row():
-        keygen_checkbox = gr.Checkbox(label="Private key generated:", interactive=False)
-
-    user_id = gr.Textbox(label="", max_lines=2, interactive=False, visible=False)
-    
-    gr.Markdown("### Step 4: Encrypt the image using FHE.")
-    encrypt_button = gr.Button("Encrypt the image using FHE.")
-
-    with gr.Row():
-        encrypted_input = gr.Textbox(
-            label="Encrypted input representation:", max_lines=2, interactive=False
-        )
-
-    gr.Markdown("## Server side")
-    gr.Markdown(
-        "The encrypted value is received by the server. The server can then compute the filter "
-        "directly over encrypted values. Once the computation is finished, the server returns "
-        "the encrypted results to the client."
-    )
-
-    gr.Markdown("### Step 5: Send the encrypted image to the server.")
-    send_input_button = gr.Button("Send the encrypted image to the server.")
-    send_input_checkbox = gr.Checkbox(label="Encrypted image sent.", interactive=False)
-
-    gr.Markdown("### Step 6: Run FHE execution.")
-    execute_fhe_button = gr.Button("Run FHE execution.")
-    fhe_execution_time = gr.Textbox(
-        label="Total FHE execution time (in seconds):", max_lines=1, interactive=False
-    )
-
-    gr.Markdown("### Step 7: Receive the encrypted output image from the server.")
-    gr.Markdown(
-        "The image displayed here is the encrypted result sent by the server, which has been "
-        "decrypted using a different private key. This is only used to visually represent an "
-        "encrypted image."
-    )
-    get_output_button = gr.Button("Receive the encrypted output image from the server.")
-
-    with gr.Row():
-        encrypted_output_representation = gr.Image(
-            label=f"Encrypted output representation ({INPUT_SHAPE[0]}x{INPUT_SHAPE[1]}):", 
-            interactive=False,
-            height=256,
-            width=256,
-        )
-
-    gr.Markdown("## Client side")
-    gr.Markdown(
-        "The encrypted output is sent back to the client, who can finally decrypt it with the "
-        "private key. Only the client is aware of the original image and its transformed version."
-    )
-
-    gr.Markdown("### Step 8: Decrypt the output.")
-    gr.Markdown(
-        "The image displayed on the left is the input image used during the demo. The output image "
-        "can be seen on the right."
-    )   
-    decrypt_button = gr.Button("Decrypt the output")
-
-    # Final input vs output display
-    with gr.Row():
-        original_image = gr.Image(
-            input_image.value,
-            label=f"Input image ({INPUT_SHAPE[0]}x{INPUT_SHAPE[1]}):",
-            interactive=False,
-            height=256,
-            width=256,   
-        )
-
-        output_image = gr.Image(
-            label=f"Output image ({INPUT_SHAPE[0]}x{INPUT_SHAPE[1]}):", 
-            interactive=False,
-            height=256,
-            width=256,
-        )
-
-    # Button to generate the private key
-    keygen_button.click(
-        keygen,
-        inputs=[filter_name],
-        outputs=[user_id, keygen_checkbox],
-    )
-
-    # Button to encrypt inputs on the client side
-    encrypt_button.click(
-        encrypt,
-        inputs=[user_id, input_image, filter_name],
-        outputs=[original_image, encrypted_input],
-    )
-
-    # Button to send the encodings to the server using post method
-    send_input_button.click(
-        send_input, inputs=[user_id, filter_name], outputs=[send_input_checkbox]
-    )
-
-    # Button to send the encodings to the server using post method
-    execute_fhe_button.click(run_fhe, inputs=[user_id, filter_name], outputs=[fhe_execution_time])
-
-    # Button to send the encodings to the server using post method
-    get_output_button.click(
-        get_output, 
-        inputs=[user_id, filter_name], 
-        outputs=[encrypted_output_representation]
-    )
-
-    # Button to decrypt the output on the client side
-    decrypt_button.click(
-        decrypt_output,
-        inputs=[user_id, filter_name],
-        outputs=[output_image, keygen_checkbox, send_input_checkbox],
-    )
-
-    gr.Markdown(
-        "The app was built with [Concrete-ML](https://github.com/zama-ai/concrete-ml), a "
-        "Privacy-Preserving Machine Learning (PPML) open-source set of tools by [Zama](https://zama.ai/). "
-        "Try it yourself and don't forget to star on Github ⭐."
-    )
-
-demo.launch(share=False)
diff --git a/spaces/zekewilliams/ControlNet/app_pose.py b/spaces/zekewilliams/ControlNet/app_pose.py
deleted file mode 100644
index ef409d4a89c17a6633be1ece9194afe0bb0a8d56..0000000000000000000000000000000000000000
--- a/spaces/zekewilliams/ControlNet/app_pose.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_pose2image.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Control Stable Diffusion with Human Pose')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='numpy')
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    is_pose_image = gr.Checkbox(label='Is pose image',
-                                                value=False)
-                    gr.Markdown(
-                        'You can use [PoseMaker2](https://huggingface.co/spaces/jonigata/PoseMaker2) to create pose images.'
-                    )
-                    num_samples = gr.Slider(label='Images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    detect_resolution = gr.Slider(label='OpenPose Resolution',
-                                                  minimum=128,
-                                                  maximum=512,
-                                                  value=512,
-                                                  step=1)
-                    num_steps = gr.Slider(label='Steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance Scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=-1,
-                                     maximum=2147483647,
-                                     step=1,
-                                     randomize=True)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            detect_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            is_pose_image,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=result,
-                         api_name='pose')
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model()
-    demo = create_demo(model.process_pose)
-    demo.queue().launch()
diff --git a/spaces/ziguo/Real-ESRGAN/inference_realesrgan_video.py b/spaces/ziguo/Real-ESRGAN/inference_realesrgan_video.py
deleted file mode 100644
index 639b848e6578a2480ee0784e664c7751e325c477..0000000000000000000000000000000000000000
--- a/spaces/ziguo/Real-ESRGAN/inference_realesrgan_video.py
+++ /dev/null
@@ -1,199 +0,0 @@
-import argparse
-import glob
-import mimetypes
-import os
-import queue
-import shutil
-import torch
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.utils.logger import AvgTimer
-from tqdm import tqdm
-
-from realesrgan import IOConsumer, PrefetchReader, RealESRGANer
-from realesrgan.archs.srvgg_arch import SRVGGNetCompact
-
-
-def main():
-    """Inference demo for Real-ESRGAN.
-    It mainly for restoring anime videos.
-
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder')
-    parser.add_argument(
-        '-n',
-        '--model_name',
-        type=str,
-        default='RealESRGAN_x4plus',
-        help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus'
-              'RealESRGANv2-anime-xsx2 | RealESRGANv2-animevideo-xsx2-nousm | RealESRGANv2-animevideo-xsx2'
-              'RealESRGANv2-anime-xsx4 | RealESRGANv2-animevideo-xsx4-nousm | RealESRGANv2-animevideo-xsx4'))
-    parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
-    parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
-    parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored video')
-    parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
-    parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
-    parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
-    parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
-    parser.add_argument('--half', action='store_true', help='Use half precision during inference')
-    parser.add_argument('-v', '--video', action='store_true', help='Output a video using ffmpeg')
-    parser.add_argument('-a', '--audio', action='store_true', help='Keep audio')
-    parser.add_argument('--fps', type=float, default=None, help='FPS of the output video')
-    parser.add_argument('--consumer', type=int, default=4, help='Number of IO consumers')
-
-    parser.add_argument(
-        '--alpha_upsampler',
-        type=str,
-        default='realesrgan',
-        help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
-    parser.add_argument(
-        '--ext',
-        type=str,
-        default='auto',
-        help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
-    args = parser.parse_args()
-
-    # ---------------------- determine models according to model names ---------------------- #
-    args.model_name = args.model_name.split('.')[0]
-    if args.model_name in ['RealESRGAN_x4plus', 'RealESRNet_x4plus']:  # x4 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-        netscale = 4
-    elif args.model_name in ['RealESRGAN_x4plus_anime_6B']:  # x4 RRDBNet model with 6 blocks
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
-        netscale = 4
-    elif args.model_name in ['RealESRGAN_x2plus']:  # x2 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-        netscale = 2
-    elif args.model_name in [
-            'RealESRGANv2-anime-xsx2', 'RealESRGANv2-animevideo-xsx2-nousm', 'RealESRGANv2-animevideo-xsx2'
-    ]:  # x2 VGG-style model (XS size)
-        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=2, act_type='prelu')
-        netscale = 2
-    elif args.model_name in [
-            'RealESRGANv2-anime-xsx4', 'RealESRGANv2-animevideo-xsx4-nousm', 'RealESRGANv2-animevideo-xsx4'
-    ]:  # x4 VGG-style model (XS size)
-        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
-        netscale = 4
-
-    # ---------------------- determine model paths ---------------------- #
-    model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth')
-    if not os.path.isfile(model_path):
-        model_path = os.path.join('realesrgan/weights', args.model_name + '.pth')
-    if not os.path.isfile(model_path):
-        raise ValueError(f'Model {args.model_name} does not exist.')
-
-    # restorer
-    upsampler = RealESRGANer(
-        scale=netscale,
-        model_path=model_path,
-        model=model,
-        tile=args.tile,
-        tile_pad=args.tile_pad,
-        pre_pad=args.pre_pad,
-        half=args.half)
-
-    if args.face_enhance:  # Use GFPGAN for face enhancement
-        from gfpgan import GFPGANer
-        face_enhancer = GFPGANer(
-            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth',
-            upscale=args.outscale,
-            arch='clean',
-            channel_multiplier=2,
-            bg_upsampler=upsampler)
-    os.makedirs(args.output, exist_ok=True)
-    # for saving restored frames
-    save_frame_folder = os.path.join(args.output, 'frames_tmpout')
-    os.makedirs(save_frame_folder, exist_ok=True)
-
-    if mimetypes.guess_type(args.input)[0].startswith('video'):  # is a video file
-        video_name = os.path.splitext(os.path.basename(args.input))[0]
-        frame_folder = os.path.join('tmp_frames', video_name)
-        os.makedirs(frame_folder, exist_ok=True)
-        # use ffmpeg to extract frames
-        os.system(f'ffmpeg -i {args.input} -qscale:v 1 -qmin 1 -qmax 1 -vsync 0  {frame_folder}/frame%08d.png')
-        # get image path list
-        paths = sorted(glob.glob(os.path.join(frame_folder, '*')))
-        if args.video:
-            if args.fps is None:
-                # get input video fps
-                import ffmpeg
-                probe = ffmpeg.probe(args.input)
-                video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video']
-                args.fps = eval(video_streams[0]['avg_frame_rate'])
-    elif mimetypes.guess_type(args.input)[0].startswith('image'):  # is an image file
-        paths = [args.input]
-        video_name = 'video'
-    else:
-        paths = sorted(glob.glob(os.path.join(args.input, '*')))
-        video_name = 'video'
-
-    timer = AvgTimer()
-    timer.start()
-    pbar = tqdm(total=len(paths), unit='frame', desc='inference')
-    # set up prefetch reader
-    reader = PrefetchReader(paths, num_prefetch_queue=4)
-    reader.start()
-
-    que = queue.Queue()
-    consumers = [IOConsumer(args, que, f'IO_{i}') for i in range(args.consumer)]
-    for consumer in consumers:
-        consumer.start()
-
-    for idx, (path, img) in enumerate(zip(paths, reader)):
-        imgname, extension = os.path.splitext(os.path.basename(path))
-        if len(img.shape) == 3 and img.shape[2] == 4:
-            img_mode = 'RGBA'
-        else:
-            img_mode = None
-
-        try:
-            if args.face_enhance:
-                _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
-            else:
-                output, _ = upsampler.enhance(img, outscale=args.outscale)
-        except RuntimeError as error:
-            print('Error', error)
-            print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
-
-        else:
-            if args.ext == 'auto':
-                extension = extension[1:]
-            else:
-                extension = args.ext
-            if img_mode == 'RGBA':  # RGBA images should be saved in png format
-                extension = 'png'
-            save_path = os.path.join(save_frame_folder, f'{imgname}_out.{extension}')
-
-            que.put({'output': output, 'save_path': save_path})
-
-        pbar.update(1)
-        torch.cuda.synchronize()
-        timer.record()
-        avg_fps = 1. / (timer.get_avg_time() + 1e-7)
-        pbar.set_description(f'idx {idx}, fps {avg_fps:.2f}')
-
-    for _ in range(args.consumer):
-        que.put('quit')
-    for consumer in consumers:
-        consumer.join()
-    pbar.close()
-
-    # merge frames to video
-    if args.video:
-        video_save_path = os.path.join(args.output, f'{video_name}_{args.suffix}.mp4')
-        if args.audio:
-            os.system(
-                f'ffmpeg -r {args.fps} -i {save_frame_folder}/frame%08d_out.{extension} -i {args.input}'
-                f' -map 0:v:0 -map 1:a:0 -c:a copy -c:v libx264 -r {args.fps} -pix_fmt yuv420p  {video_save_path}')
-        else:
-            os.system(f'ffmpeg -r {args.fps} -i {save_frame_folder}/frame%08d_out.{extension} '
-                      f'-c:v libx264 -r {args.fps} -pix_fmt yuv420p {video_save_path}')
-
-        # delete tmp file
-        shutil.rmtree(save_frame_folder)
-        if os.path.isdir(frame_folder):
-            shutil.rmtree(frame_folder)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/zomehwh/vits-models-pcr/attentions.py b/spaces/zomehwh/vits-models-pcr/attentions.py
deleted file mode 100644
index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/vits-models-pcr/attentions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x