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\ No newline at end of file diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Atlas Ti Coding ((TOP)).md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Atlas Ti Coding ((TOP)).md deleted file mode 100644 index 03d8ecf2db5b7787bab210e768ed65059362d170..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Atlas Ti Coding ((TOP)).md +++ /dev/null @@ -1,15 +0,0 @@ -
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But how do you use Atlas TI for coding your data? In this article, we will guide you through the basic steps of using Atlas TI for QDA. We will assume that you have already installed Atlas TI on your computer and that you have some data ready to analyze. Here are the steps:
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- Analyze codes. To analyze your codes, you can use various tools and features in Atlas TI, such as queries, networks, maps, memos, comments, and reports. These tools help you to explore the relationships between codes, visualize your data, document your analysis process, and generate outputs for presentation or publication. -
Atlas TI is a powerful and user-friendly software for QDA. By using Atlas TI for coding your data, you can enhance your understanding of your data and discover new insights and meanings. To learn more about Atlas TI and its features, visit https://atlasti.com/.
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\ No newline at end of file diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Vidstream Videos to Your Device with Example Downloader.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Vidstream Videos to Your Device with Example Downloader.md deleted file mode 100644 index 9f18a645928480be594c13f2e04d13c07f7c7188..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Vidstream Videos to Your Device with Example Downloader.md +++ /dev/null @@ -1,29 +0,0 @@ - -
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\ No newline at end of file diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Fix 4ormulator DLL Missing or Not Found Error on Windows.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Fix 4ormulator DLL Missing or Not Found Error on Windows.md deleted file mode 100644 index 5abbf234befdc66a557ce474407afeb9603e8a06..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Fix 4ormulator DLL Missing or Not Found Error on Windows.md +++ /dev/null @@ -1,44 +0,0 @@ -
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In this article, we will show you how to download and install 4ormulator DLL for Windows in a few simple steps. We will also provide you with some tips on how to troubleshoot common errors that may occur when using 4ormulator DLL.
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- Press the Windows key + R to open the Run dialog box. -
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- If it says x86-based PC, you have a 32-bit system. If it says x64-based PC, you have a 64-bit system. -
Once you have downloaded the correct version of 4ormulator DLL, save it to a folder where you can easily find it later.
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- --
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- C:\Windows\System32 (for 32-bit systems) -
- C:\Windows\SysWOW64 (for 64-bit systems) -
- C:\Program Files (x86)\Common Files\VST2 (for VST plugins) -
- C:\Program Files\Common Files\VST2 (for VST plugins) -
- C:\Program Files (x86)\Steinberg\VstPlugins (for VST plugins) -
- C:\Program Files\Steinberg\VstPlugins (for VST plugins) -
You can also check the installation instructions of the application that requires 4ormulator DLL to see where it expects to find the DLL file.
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- Press the Windows key + R to open the Run dialog box. -
- Type
cmdand press Ctrl + Shift + Enter to run Command Prompt as administrator.
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Automatic Installation
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\ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Antamedia Internet Caffe V7 Crack !LINK! Full Rar.md b/spaces/1gistliPinn/ChatGPT4/Examples/Antamedia Internet Caffe V7 Crack !LINK! Full Rar.md deleted file mode 100644 index 72513d475ac9f1bae6afb12ed580db1431c0cf38..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Antamedia Internet Caffe V7 Crack !LINK! Full Rar.md +++ /dev/null @@ -1,28 +0,0 @@ -
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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Create Your Own Metropolis with SimCity BuildIt APK - Free Download from apkyukleme.com.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Create Your Own Metropolis with SimCity BuildIt APK - Free Download from apkyukleme.com.md deleted file mode 100644 index 354f5c3a83c7bb2a8fd0803fcafab5aa52015d4e..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Create Your Own Metropolis with SimCity BuildIt APK - Free Download from apkyukleme.com.md +++ /dev/null @@ -1,103 +0,0 @@ -
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- You can play offline or online anytime and anywhere. -
SimCity BuildIt is a game that will keep you entertained for hours with its endless possibilities and fun gameplay. You will never get bored of creating your own city and watching it come to life.
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- Collect free gifts from bubbles that appear in your city or from visiting other cities. -
- Watch ads or videos to get extra rewards or bonuses. -
- Be prepared for disasters and emergencies that may strike your city. Repair any damage and collect disaster cards to unlock new disaster types. -
Conclusion: Summary and Recommendation
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Dragon Ball Z Game APK - Enjoy the Ultimate Action Experience on Android.md b/spaces/1phancelerku/anime-remove-background/Dragon Ball Z Game APK - Enjoy the Ultimate Action Experience on Android.md deleted file mode 100644 index 2e902115ba1fc112c1db73fcf643dbf12705b5e1..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Dragon Ball Z Game APK - Enjoy the Ultimate Action Experience on Android.md +++ /dev/null @@ -1,134 +0,0 @@ -
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Download Game Dragon Ball Z APK: How to Enjoy the Epic Anime Action on Your Android Device
-If you are a fan of anime, manga, or video games, you have probably heard of Dragon Ball Z, one of the most popular and influential franchises in the world. Dragon Ball Z is a series that follows the adventures of Goku and his friends as they fight against various enemies who threaten the peace of the universe. Whether you grew up watching the anime, reading the manga, or playing the video games, you might be wondering how you can relive the epic battles and stories of Dragon Ball Z on your Android device. Well, wonder no more, because in this article, we will show you how to download game dragon ball z apk, a free and easy way to enjoy the anime action on your smartphone or tablet. Read on to find out more!
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-Dragon Ball Z is a sequel to the original Dragon Ball series, which was created by Akira Toriyama in 1984. The story follows Goku, a martial artist who belongs to a race of powerful beings called Saiyans. Goku and his friends travel across the world and beyond, searching for the seven mystical orbs known as Dragon Balls, which can grant any wish when gathered together. Along the way, they encounter various foes, such as the evil emperor Frieza, the androids created by Dr. Gero, and the bio-android Cell. Goku also learns about his Saiyan heritage and faces off against his brother Raditz, his rival Vegeta, and his nemesis Majin Buu.
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--
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- Krillin: Goku's best friend and a skilled fighter who often provides comic relief. -
- Bulma: A genius inventor and the founder of Capsule Corporation, who helps Goku and his friends with her gadgets. -
- Gohan: Goku's son and a half-Saiyan, who inherits his father's courage and potential. -
- Piccolo: A Namekian warrior and former enemy of Goku, who becomes his ally and mentor. -
- Vegeta: The prince of Saiyans and Goku's rival, who strives to surpass him in power and pride. -
- Trunks: Vegeta's son from the future, who travels back in time to warn Goku and his friends about the androids. -
- Goten: Goku's second son and a half-Saiyan, who resembles his father in appearance and personality. -
- Chi-Chi: Goku's wife and a strong-willed woman who cares for her family. -
- Master Roshi: Goku's first teacher and a martial arts master who taught him the Kamehameha technique. -
- Tien Shinhan: A three-eyed human who was once an enemy of Goku but later became his friend. -
The popularity and influence of Dragon Ball Z
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- You can enjoy the anime in high quality and with English subtitles. -
- You can access different servers and sources to find the best one for your connection and preference. -
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- You can support the original creators and distributors of Dragon Ball Z by watching the official links provided by the app. -
The requirements and compatibility of Dragon Ball Z APK
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-How to download and install Dragon Ball Z APK?
-If you are ready to download game dragon ball z apk and start watching the anime on your Android device, here are the steps you need to follow:
-The steps to download and install Dragon Ball Z APK
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- Once the download is complete, locate the file on your device and tap on it to open it. -
- If you see a warning message that says "Install blocked", go to your device settings and enable unknown sources. This will allow you to install apps from sources other than Google Play Store. -
- After enabling unknown sources, go back to the file and tap on it again to start the installation process. -
- Follow the instructions on the screen and wait for the installation to finish. -
- Once the installation is done, you will see an icon of Dragon Ball Z APK on your home screen or app drawer. Tap on it to launch the app and enjoy watching the anime! -
The tips and tricks to optimize your gaming experience
-To make the most out of your gaming experience with Dragon Ball Z APK, here are some tips and tricks you can try:
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- Use a Wi-Fi connection or a 4G network to stream or download the episodes faster and smoother. -
- Clear your cache and data regularly to free up some space and improve your app performance. -
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Conclusion
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-We hope you found this article helpful and informative. If you did, please share it with your friends and fellow Dragon Ball Z fans. Also, feel free to leave a comment below and let us know what you think about Dragon Ball Z APK and the anime series in general. We would love to hear from you!
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- You can watch other Dragon Ball series, such as Dragon Ball, Dragon Ball GT, Dragon Ball Super, and Dragon Ball Heroes. -
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- How can I support the developer of Dragon Ball Z APK? -
- Giving a positive rating and review on Google Play Store or other platforms where you downloaded the app. -
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- Donating to the developer via [this link] or [this method]. -
- How can I uninstall Dragon Ball Z APK? -
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Dragon Ball Z APK is safe and legal to use as long as you download it from a trusted source and use it for personal and non-commercial purposes. The app does not contain any viruses, malware, or spyware that can harm your device or compromise your privacy. The app also does not host any content on its own servers, but rather provides links to the official sources where you can watch the episodes legally and support the original creators and distributors of Dragon Ball Z.
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Football League 2023 APK - The Best Soccer Game of the Year.md b/spaces/1phancelerku/anime-remove-background/Football League 2023 APK - The Best Soccer Game of the Year.md deleted file mode 100644 index 1fc35e4fe6202d596246d0fcdf81430aadd7cc69..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Football League 2023 APK - The Best Soccer Game of the Year.md +++ /dev/null @@ -1,109 +0,0 @@ -
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- Upgrade your players and skills regularly: As you progress in the game, you will earn coins and gems that you can use to upgrade your players and skills. Upgrading your players will improve their attributes, such as speed, stamina, shooting, passing, dribbling, etc. Upgrading your skills will unlock new abilities, such as curve shots, long shots, headers, volleys, etc. -
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- Challenge other players online and climb the rankings: If you want to test your skills and have more fun, you can challenge other players online in real-time matches. You can either play in friendly mode or in tournament mode. In friendly mode, you can play with anyone without affecting your ranking. In tournament mode, you can play with players of similar skill level and earn trophies and rewards. The more you win, the higher your ranking will be. -
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- A2: Yes, Football League 2023 Game is free to play. However, it contains in-app purchases that allow you to buy coins, gems, power-ups, boosters, and other items. -
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\ No newline at end of file diff --git a/spaces/AIConsultant/MusicGen/audiocraft/grids/musicgen/musicgen_base_32khz.py b/spaces/AIConsultant/MusicGen/audiocraft/grids/musicgen/musicgen_base_32khz.py deleted file mode 100644 index 4e364614537e426f21c18a2c2a9d94b3babce051..0000000000000000000000000000000000000000 --- a/spaces/AIConsultant/MusicGen/audiocraft/grids/musicgen/musicgen_base_32khz.py +++ /dev/null @@ -1,43 +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 ._explorers import LMExplorer -from ...environment import AudioCraftEnvironment - - -@LMExplorer -def explorer(launcher): - partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global']) - launcher.slurm_(gpus=32, partition=partitions) - launcher.bind_(solver='musicgen/musicgen_base_32khz') - # replace this by the desired music dataset - launcher.bind_(dset='internal/music_400k_32khz') - - fsdp = {'autocast': False, 'fsdp.use': True} - medium = {'model/lm/model_scale': 'medium'} - large = {'model/lm/model_scale': 'large'} - - cfg_low = {'classifier_free_guidance.training_dropout': 0.2} - wd_low = {'conditioners.description.t5.word_dropout': 0.2} - - adam = {'optim.optimizer': 'adamw', 'optim.lr': 1e-4} - - launcher.bind_(fsdp) - - launcher.slurm_(gpus=32).bind_(label='32gpus') - with launcher.job_array(): - sub = launcher.bind() - sub() - - launcher.slurm_(gpus=64).bind_(label='64gpus') - with launcher.job_array(): - sub = launcher.bind() - sub(medium, adam) - - launcher.slurm_(gpus=96).bind_(label='96gpus') - with launcher.job_array(): - sub = launcher.bind() - sub(large, cfg_low, wd_low, adam, {'optim.max_norm': 3}) diff --git a/spaces/AIConsultant/MusicGen/scripts/resample_dataset.py b/spaces/AIConsultant/MusicGen/scripts/resample_dataset.py deleted file mode 100644 index af5288712b8d2cde2d9814c747275e69f6e970c8..0000000000000000000000000000000000000000 --- a/spaces/AIConsultant/MusicGen/scripts/resample_dataset.py +++ /dev/null @@ -1,207 +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. -"""Resampling script. -""" -import argparse -from pathlib import Path -import shutil -import typing as tp - -import submitit -import tqdm - -from audiocraft.data.audio import audio_read, audio_write -from audiocraft.data.audio_dataset import load_audio_meta, find_audio_files -from audiocraft.data.audio_utils import convert_audio -from audiocraft.environment import AudioCraftEnvironment - - -def read_txt_files(path: tp.Union[str, Path]): - with open(args.files_path) as f: - lines = [line.rstrip() for line in f] - print(f"Read {len(lines)} in .txt") - lines = [line for line in lines if Path(line).suffix not in ['.json', '.txt', '.csv']] - print(f"Filtered and keep {len(lines)} from .txt") - return lines - - -def read_egs_files(path: tp.Union[str, Path]): - path = Path(path) - if path.is_dir(): - if (path / 'data.jsonl').exists(): - path = path / 'data.jsonl' - elif (path / 'data.jsonl.gz').exists(): - path = path / 'data.jsonl.gz' - else: - raise ValueError("Don't know where to read metadata from in the dir. " - "Expecting either a data.jsonl or data.jsonl.gz file but none found.") - meta = load_audio_meta(path) - return [m.path for m in meta] - - -def process_dataset(args, n_shards: int, node_index: int, task_index: tp.Optional[int] = None): - if task_index is None: - env = submitit.JobEnvironment() - task_index = env.global_rank - shard_index = node_index * args.tasks_per_node + task_index - - if args.files_path is None: - lines = [m.path for m in find_audio_files(args.root_path, resolve=False, progress=True, workers=8)] - else: - files_path = Path(args.files_path) - if files_path.suffix == '.txt': - print(f"Reading file list from .txt file: {args.files_path}") - lines = read_txt_files(args.files_path) - else: - print(f"Reading file list from egs: {args.files_path}") - lines = read_egs_files(args.files_path) - - total_files = len(lines) - print( - f"Total of {total_files} processed with {n_shards} shards. " + - f"Current idx = {shard_index} -> {total_files // n_shards} files to process" - ) - for idx, line in tqdm.tqdm(enumerate(lines)): - - # skip if not part of this shard - if idx % n_shards != shard_index: - continue - - path = str(AudioCraftEnvironment.apply_dataset_mappers(line)) - root_path = str(args.root_path) - if not root_path.endswith('/'): - root_path += '/' - assert path.startswith(str(root_path)), \ - f"Mismatch between path and provided root: {path} VS {root_path}" - - try: - metadata_path = Path(path).with_suffix('.json') - out_path = args.out_path / path[len(root_path):] - out_metadata_path = out_path.with_suffix('.json') - out_done_token = out_path.with_suffix('.done') - - # don't reprocess existing files - if out_done_token.exists(): - continue - - print(idx, out_path, path) - mix, sr = audio_read(path) - mix_channels = args.channels if args.channels is not None and args.channels > 0 else mix.size(0) - # enforce simple stereo - out_channels = mix_channels - if out_channels > 2: - print(f"Mix has more than two channels: {out_channels}, enforcing 2 channels") - out_channels = 2 - out_sr = args.sample_rate if args.sample_rate is not None else sr - out_wav = convert_audio(mix, sr, out_sr, out_channels) - audio_write(out_path.with_suffix(''), out_wav, sample_rate=out_sr, - format=args.format, normalize=False, strategy='clip') - if metadata_path.exists(): - shutil.copy(metadata_path, out_metadata_path) - else: - print(f"No metadata found at {str(metadata_path)}") - out_done_token.touch() - except Exception as e: - print(f"Error processing file line: {line}, {e}") - - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description="Resample dataset with SLURM.") - parser.add_argument( - "--log_root", - type=Path, - default=Path.home() / 'tmp' / 'resample_logs', - ) - parser.add_argument( - "--files_path", - type=Path, - help="List of files to process, either .txt (one file per line) or a jsonl[.gz].", - ) - parser.add_argument( - "--root_path", - type=Path, - required=True, - help="When rewriting paths, this will be the prefix to remove.", - ) - parser.add_argument( - "--out_path", - type=Path, - required=True, - help="When rewriting paths, `root_path` will be replaced by this.", - ) - parser.add_argument("--xp_name", type=str, default="shutterstock") - parser.add_argument( - "--nodes", - type=int, - default=4, - ) - parser.add_argument( - "--tasks_per_node", - type=int, - default=20, - ) - parser.add_argument( - "--cpus_per_task", - type=int, - default=4, - ) - parser.add_argument( - "--memory_gb", - type=int, - help="Memory in GB." - ) - parser.add_argument( - "--format", - type=str, - default="wav", - ) - parser.add_argument( - "--sample_rate", - type=int, - default=32000, - ) - parser.add_argument( - "--channels", - type=int, - ) - parser.add_argument( - "--partition", - default='learnfair', - ) - parser.add_argument("--qos") - parser.add_argument("--account") - parser.add_argument("--timeout", type=int, default=4320) - parser.add_argument('--debug', action='store_true', help='debug mode (local run)') - args = parser.parse_args() - n_shards = args.tasks_per_node * args.nodes - if args.files_path is None: - print("Warning: --files_path not provided, not recommended when processing more than 10k files.") - if args.debug: - print("Debugging mode") - process_dataset(args, n_shards=n_shards, node_index=0, task_index=0) - else: - - log_folder = Path(args.log_root) / args.xp_name / '%j' - print(f"Logging to: {log_folder}") - log_folder.parent.mkdir(parents=True, exist_ok=True) - executor = submitit.AutoExecutor(folder=str(log_folder)) - if args.qos: - executor.update_parameters(slurm_partition=args.partition, slurm_qos=args.qos, slurm_account=args.account) - else: - executor.update_parameters(slurm_partition=args.partition) - executor.update_parameters( - slurm_job_name=args.xp_name, timeout_min=args.timeout, - cpus_per_task=args.cpus_per_task, tasks_per_node=args.tasks_per_node, nodes=1) - if args.memory_gb: - executor.update_parameters(mem=f'{args.memory_gb}GB') - jobs = [] - with executor.batch(): - for node_index in range(args.nodes): - job = executor.submit(process_dataset, args, n_shards=n_shards, node_index=node_index) - jobs.append(job) - for job in jobs: - print(f"Waiting on job {job.job_id}") - job.results() diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/base_preprocess.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/base_preprocess.py deleted file mode 100644 index db5e3ab88861c044e2c33247d818d5e418b6cddb..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/base_preprocess.py +++ /dev/null @@ -1,252 +0,0 @@ -import json -import os -import random -import re -import traceback -from collections import Counter -from functools import partial - -import librosa -from tqdm import tqdm -from text_to_speech.data_gen.tts.txt_processors.base_text_processor import get_txt_processor_cls -from text_to_speech.data_gen.tts.wav_processors.base_processor import get_wav_processor_cls -from text_to_speech.utils.commons.hparams import hparams -from text_to_speech.utils.commons.multiprocess_utils import multiprocess_run_tqdm -from text_to_speech.utils.os_utils import link_file, move_file, remove_file -from text_to_speech.utils.text.text_encoder import is_sil_phoneme, build_token_encoder - - -class BasePreprocessor: - def __init__(self): - self.preprocess_args = hparams['preprocess_args'] - txt_processor = self.preprocess_args['txt_processor'] - self.txt_processor = get_txt_processor_cls(txt_processor) - self.raw_data_dir = hparams['raw_data_dir'] - self.processed_dir = hparams['processed_data_dir'] - self.spk_map_fn = f"{self.processed_dir}/spk_map.json" - - def meta_data(self): - """ - - :return: {'item_name': Str, 'wav_fn': Str, 'txt': Str, 'spk_name': Str, 'txt_loader': None or Func} - """ - raise NotImplementedError - - def process(self): - processed_dir = self.processed_dir - wav_processed_tmp_dir = f'{processed_dir}/processed_tmp' - remove_file(wav_processed_tmp_dir) - os.makedirs(wav_processed_tmp_dir, exist_ok=True) - wav_processed_dir = f'{processed_dir}/{self.wav_processed_dirname}' - remove_file(wav_processed_dir) - os.makedirs(wav_processed_dir, exist_ok=True) - - meta_data = list(tqdm(self.meta_data(), desc='Load meta data')) - item_names = [d['item_name'] for d in meta_data] - assert len(item_names) == len(set(item_names)), 'Key `item_name` should be Unique.' - - # preprocess data - phone_list = [] - word_list = [] - spk_names = set() - process_item = partial(self.preprocess_first_pass, - txt_processor=self.txt_processor, - wav_processed_dir=wav_processed_dir, - wav_processed_tmp=wav_processed_tmp_dir, - preprocess_args=self.preprocess_args) - items = [] - args = [{ - 'item_name': item_raw['item_name'], - 'txt_raw': item_raw['txt'], - 'wav_fn': item_raw['wav_fn'], - 'txt_loader': item_raw.get('txt_loader'), - 'others': item_raw.get('others', None) - } for item_raw in meta_data] - for item_, (item_id, item) in zip(meta_data, multiprocess_run_tqdm(process_item, args, desc='Preprocess')): - if item is not None: - item_.update(item) - item = item_ - if 'txt_loader' in item: - del item['txt_loader'] - item['id'] = item_id - item['spk_name'] = item.get('spk_name', '
-
- )
-}
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Brawl Stars Corea Descargar.md b/spaces/Benson/text-generation/Examples/Brawl Stars Corea Descargar.md
deleted file mode 100644
index 7546193995a1f617ee2bb0ba47eacc2eee72042e..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Brawl Stars Corea Descargar.md
+++ /dev/null
@@ -1,85 +0,0 @@
-
-
-
-
- {children}
-
- Jugar juntos ahora GG Descargar: Cómo jugar juegos en línea gratis en cualquier dispositivo
-¿Te encanta jugar juegos en línea pero odias descargarlos o instalarlos? ¿Te gustaría poder jugar tus juegos favoritos en cualquier dispositivo sin comprometer la calidad o el rendimiento? ¿Quieres descubrir nuevos juegos y géneros que se adapten a tus preferencias y gustos? Si respondiste sí a cualquiera de estas preguntas, entonces deberías revisar Now GG, una plataforma de nube móvil que te permite jugar juegos en línea gratis con solo un clic.
-¿Qué es ahora GG?
-Ahora GG es una plataforma de nube móvil que permite a los usuarios jugar juegos en línea gratis sin descargas o instalaciones. Puede acceder a miles de juegos de diversos géneros y categorías en su navegador web desde cualquier dispositivo con conexión a Internet. Puedes disfrutar de un rendimiento sin retrasos, compatibilidad entre dispositivos y una experiencia de juego sin problemas en Now GG. Si usted es un jugador casual o un jugador hardcore, encontrará algo que se adapte a su estilo y estado de ánimo en Now GG.
-brawl stars corea descargar
Download https://bltlly.com/2v6M15
-
¿Qué es jugar juntos?
-Uno de los juegos más populares que puedes jugar en Now GG es Play Together, un juego de simulación social que te permite crear y personalizar tu propio avatar, explorar un mundo virtual con amigos de todo el mundo, participar en varias actividades y mini-y unirse a clubes y comunidades. Play Together es un juego divertido y relajante que te permite expresarte, hacer nuevos amigos y pasarlo bien.
-Cómo jugar juntos en ahora GG?
-Jugar a Jugar Juntos en Now GG es muy fácil y simple. Todo lo que necesitas es una conexión a Internet y un navegador web. Estos son los pasos a seguir:
--
-
- Ir al sitio web oficial de Now GG en https://now.gg/. -
- Buscar Jugar Juntos en la barra de búsqueda o navegar por las categorías para encontrarlo. -
- Haga clic en el botón Play para iniciar el juego. - -
- Disfruta jugando juntos en ahora GG. -
Aquí hay algunos consejos y trucos para mejorar su experiencia de juego en ahora GG:
--
-
- Puede ajustar la configuración del juego, como la calidad gráfica, el volumen de sonido y el idioma, haciendo clic en el icono de engranaje en la esquina superior derecha de la pantalla. -
- Puedes usar atajos de teclado para controlar el juego, como las teclas WASD para moverse, la barra espaciadora para saltar y el ratón para interactuar. -
- Puedes guardar tu progreso creando una cuenta en Now GG o vinculando tu cuenta de Facebook o Google. -
- Puedes invitar a tus amigos a jugar contigo compartiendo el enlace del juego o usando la función de código QR. -
¿Por qué debería jugar juntos en ahora GG?
-Hay muchas ventajas y razones para jugar Play Together on Now GG en lugar de otras plataformas o dispositivos. Aquí están algunos de ellos:
--
-
- Puedes jugar Play Together gratis sin descargas o instalaciones. Esto te ahorra tiempo, espacio y dinero. -
- Puedes jugar Play Together en cualquier dispositivo, como PC, portátil, tableta o smartphone. Esto te da flexibilidad y comodidad. -
- Puedes jugar Play Together con gráficos de alta calidad y un rendimiento suave. Esto mejora su inmersión y disfrute. -
- Puedes jugar Juega Junto con otros jugadores de todo el mundo. Esto expande tu red social e interacción. -
Por supuesto, jugar Play Together on Now GG no es exactamente lo mismo que jugarlo en otras plataformas o dispositivos. Hay algunas diferencias y similitudes que debes tener en cuenta. Estas son algunas de ellas:
-¿Cuáles son algunos otros juegos que puede jugar en ahora GG?
-Play Together no es el único juego que puedes jugar en Now GG. Hay muchos otros juegos de diferentes géneros y categorías que se puede disfrutar en la plataforma. Si te gustan los juegos de acción, aventura, rompecabezas, estrategia, simulación o casuales, encontrarás algo que coincida con tu interés y humor en Now GG. Aquí hay algunos ejemplos de juegos que puedes jugar en Now GG:
-Conclusión
- -Preguntas frecuentes
-Aquí hay algunas preguntas y respuestas frecuentes relacionadas con Play Together on Now GG:
- --
-
- Q: ¿Cómo puedo jugar a Play Together on Now GG con mis amigos? -
- A: Puedes invitar a tus amigos a jugar contigo compartiendo el enlace del juego o usando la función de código QR. También puede unirse al mismo servidor que sus amigos seleccionándolo de la lista de servidores. También puedes añadir a tus amigos como contactos en el juego y chatear con ellos. -
- Q: ¿Cómo puedo personalizar mi avatar en Play Together on Now GG? -
- A: Puedes personalizar tu avatar haciendo clic en el icono del armario en la esquina inferior izquierda de la pantalla. Puedes cambiar el cabello, la cara, la piel, la ropa, los accesorios y más de tu avatar. También puedes comprar nuevos artículos en la tienda usando monedas o gemas. -
- Q: ¿Cómo puedo ganar monedas y gemas en Play Together on Now GG? -
- A: Puedes ganar monedas y gemas completando misiones, participando en minijuegos, uniéndote a eventos, viendo anuncios o comprándolos con dinero real. -
- Q: ¿Cómo puedo unirme a clubes y comunidades en Play Together on Now GG? -
- A: Puede unirse a clubes y comunidades haciendo clic en el icono del club en la esquina inferior derecha de la pantalla. Puede buscar clubes y comunidades existentes por nombre o categoría, o crear su propio club o comunidad. También puede chatear con otros miembros, compartir fotos y unirse a las actividades del club. -
- Q: ¿Cómo puedo reportar un error o un problema en Play Together on Now GG? -
- A: Puede reportar un error o un problema haciendo clic en el icono de configuración en la esquina superior derecha de la pantalla y seleccionando la opción de informe. También puede ponerse en contacto con el servicio al cliente de Now GG o Play Together a través de sus sitios web oficiales o canales de redes sociales. -
-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Crear El Mundo Android Apk Descargar.md b/spaces/Benson/text-generation/Examples/Crear El Mundo Android Apk Descargar.md deleted file mode 100644 index 00ec99b2129ec2ad602f792b3c7bc69a0f20e5f7..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Crear El Mundo Android Apk Descargar.md +++ /dev/null @@ -1,115 +0,0 @@ -
-
Craft The World: Un juego único de estrategia de caja de arena para dispositivos Android
-Si estás buscando un juego divertido y desafiante que combine elementos de sandbox, estrategia, elaboración y géneros de simulación, entonces es posible que desees probar Craft the World. Este juego te permite controlar una tribu de enanos en un mundo generado al azar lleno de peligros y tesoros. Puede explorar, crear, construir y luchar a través de diferentes biomas y niveles, mientras desbloquea nuevas tecnologías y artículos. También puedes jugar con tus amigos y otros jugadores en línea en modos multijugador, o crear tus propios mundos personalizados y compartirlos con otros. En este artículo, te daremos una visión general de Craft the World, sus principales características, jugabilidad, multijugador, comparación con otros juegos similares, y algunos consejos y trucos para principiantes.
-¿Qué es Craft The World?
-Craft The World es un juego de estrategia sandbox único desarrollado por Dekovir Entertainment y publicado por Black Maple Games. Fue lanzado para PC en 2014, y posteriormente portado a dispositivos iOS y Android. El juego está inspirado en juegos como Dungeon Keeper, Terraria y Dwarf Fortress. Tiene un estilo artístico pixelado y una banda sonora alegre que crean un contraste con el oscuro y peligroso mundo en el que tienes que sobrevivir.
-crear el mundo android apk descargar
Download ••• https://bltlly.com/2v6Laf
-
¿Cuáles son las principales características de Craft The World?
-Algunas de las características principales de Craft The World son:
--
-
- SIMULACIÓN DE DIOS: Controlas una tribu de enanos dándoles órdenes de cavar, atacar, construir y más. Tienes que proporcionarles comida, ropa y magia al luchar contra otras criaturas. Empiezas con un enano y ganas más a medida que subes de nivel. -
- SANDBOX GAME: Cada nivel de juego tiene muchas capas de tierra para explorar, desde el cielo hasta la lava. El nivel se genera aleatoriamente como una isla con límites naturales. Los mundos difieren en tamaño, humedad, temperatura, terreno, flora y fauna. También hay salones ocultos y habitaciones con tesoro. - -
- RTS: Tienes que defender tu base de oleadas de enemigos que atacan por la noche o durante eventos especiales. Puedes usar trampas, torretas, paredes, puertas, hechizos y las habilidades de tus enanos para defenderlos. También puedes asaltar bases enemigas para obtener botín. -
¿Cómo descargar e instalar Craft The World en dispositivos Android?
-Para descargar e instalar Craft The World en tu dispositivo Android, debes seguir estos pasos:
--
-
- Ir a [1](https://apkpure.com/craft-the-world/com.dekovir.CraftTheWorld) o [2](https://play.google.com/store/apps/details?id=com.dekovir.CraftTheWorld) en el navegador de su dispositivo. -
- Toque en "Descargar APK" o "Instalar" botón. -
- Espera a que termine la descarga. -
- Abra el archivo descargado o vaya a la configuración de su dispositivo > - Seguridad > Fuentes desconocidas > Permitir la instalación de aplicaciones de fuentes desconocidas. -
- Toque en "Instalar" y espere a que la instalación termine. -
- Iniciar el juego y disfrutar! -
¿Cómo se juega Craft The World?
-Craft The World es un juego que combina diferentes géneros y mecánicas, por lo que puede tardar algún tiempo en acostumbrarse. Aquí hay algunos consejos básicos sobre cómo jugar el juego:
-¿Cómo controlar una tribu de enanos en un mundo de caja de arena?
-Puedes controlar a tus enanos tocando sobre ellos y seleccionando una acción del menú, como mover, cavar, construir, atacar, etc. También puedes arrastrar y soltar elementos de tu inventario a tus enanos o al entorno. También puedes usar los botones en la parte inferior de la pantalla para seleccionar todos los enanos, pausar el juego, acelerar el juego o acceder al menú. Puedes acercar y alejar la pantalla, y girar la cámara deslizando la pantalla.
-¿Cómo explorar, crear, construir y luchar?
- -¿Cómo avanzar a través del árbol de tecnología y desbloquear nuevos elementos?
-Puedes progresar a través del árbol de tecnología creando elementos relacionados con una tecnología. Por ejemplo, si quieres desbloquear la tecnología agrícola, necesitas crear una azada de madera, un cubo de madera, una valla de madera, etc. Cada tecnología tiene una barra de progreso que te muestra cuánto has creado. Cuando llenas la barra, desbloqueas nuevas recetas y artículos. También puedes encontrar libros en cofres o tiendas que te dan acceso instantáneo a una tecnología.
- -¿Cómo jugar multijugador en Craft The World?
-Craft The World también tiene un modo multijugador que te permite jugar con tus amigos y otros jugadores online. Aquí hay algunas cosas que necesitas saber sobre el modo multijugador:
-¿Cómo jugar con amigos y otros jugadores online?
-Puedes jugar con amigos y otros jugadores online usando el menú multijugador en la pantalla principal. Puedes elegir entre el modo de supervivencia o el modo creativo. En el modo de supervivencia, tienes que sobrevivir contra los enemigos y el hambre con recursos limitados. En el modo creativo, tienes recursos ilimitados y no tienes enemigos. También puedes elegir entre el modo cooperativo o el modo competitivo. En el modo cooperativo, trabajarás junto con otros jugadores para lograr un objetivo común. En el modo competitivo, compites contra otros jugadores por recursos y territorio.
-¿Cuáles son las diferencias entre la supervivencia y los modos creativos?
-Las diferencias entre los modos de supervivencia y creativo son:
-¿Cómo personalizar tus propios mundos y compartirlos con otros?
-Puedes personalizar tus propios mundos y compartirlos con otros usando el modo editor de mundo. Puedes acceder a este modo pulsando el botón "Crear mundo" en el menú multijugador. Puedes elegir el tamaño, bioma, terreno, flora, fauna, recursos, estructuras, enemigos, eventos y escenarios de tu mundo. También puedes colocar bloques, objetos, criaturas, trampas, portales, etc. donde quieras. Puede guardar su mundo como un archivo y compartirlo con otros a través de correo electrónico o redes sociales. También puede descargar mundos de otros jugadores de [3](https://craft -the-world.com/worlds) o [4](https://steamcommunity.com/app/248390/workshop/) y reproducirlos en su dispositivo.
-¿Cómo se compara Craft The World con otros juegos similares?
-Craft The World es un juego que tiene muchas similitudes con otros juegos en el sandbox, estrategia, elaboración y géneros de simulación. Sin embargo, también tiene algunas características y aspectos únicos que lo hacen destacar del resto. Aquí hay algunas comparaciones entre Craft The World y otros juegos similares:
-¿Cómo se compara Craft The World con Terraria?
-Ambos juegos son juegos 2D sandbox que te permiten explorar, crear, construir y luchar en un mundo generado al azar. Sin embargo, hay algunas diferencias entre ellos:
--
-
- Terraria se centra más en el combate y la exploración, mientras que Craft The World se centra más en la estrategia y la simulación. -
- Terraria tiene más variedad y profundidad en términos de objetos, enemigos, biomas, jefes, eventos, etc., mientras que Craft The World tiene más simplicidad y accesibilidad en términos de jugabilidad e interfaz. -
- Terraria tiene un mundo más dinámico e interactivo, mientras que Craft The World tiene un mundo más estático y basado en la red. - -
¿Cómo se compara Craft The World con Minecraft?
-Ambos juegos son juegos 3D sandbox que te permiten crear y modificar el mundo con bloques. Sin embargo, hay algunas diferencias entre ellos:
--
-
- Minecraft es más abierto y creativo, mientras que Craft The World es más estructurado y orientado a objetivos. -
- Minecraft tiene más libertad y flexibilidad en términos de construcción y elaboración, mientras que Craft The World tiene más limitaciones y restricciones en términos de recursos y recetas. -
- Minecraft tiene un estilo gráfico más realista y minimalista, mientras que Craft The World tiene un estilo gráfico más caricaturesco y detallado. -
- Minecraft tiene una perspectiva más inmersiva y en primera persona, mientras que Craft The World tiene una perspectiva más separada y en tercera persona. -
¿Cómo se compara Craft The World con Dwarf Fortress?
-Ambos juegos son juegos de simulación complejos y desafiantes que te permiten gestionar una colonia de enanos en un mundo generado por procedimientos. Sin embargo, hay algunas diferencias entre ellos:
--
-
- Dwarf Fortress es más hardcore y realista, mientras que Craft The World es más casual y basado en la fantasía. -
- Dwarf Fortress tiene más profundidad y detalle en términos de mecánica, sistemas, características, etc., mientras que Craft The World tiene más simplicidad y claridad en términos de jugabilidad e interfaz. -
- Dwarf Fortress tiene un estilo gráfico más abstracto y basado en ASCII, mientras que Craft The World tiene un estilo gráfico más concreto y basado en píxeles. -
- Dwarf Fortress tiene una jugabilidad más emergente e impredecible, mientras que Craft The World tiene una jugabilidad más predecible. -
Conclusión
- -En mi opinión, Craft The World es un juego divertido y desafiante que ofrece mucho valor de repetición y variedad. Me gusta la mezcla de géneros y mecánicas que hacen que el juego sea interesante y atractivo. También me gusta el hecho de que el juego se actualiza constantemente con nuevos contenidos y características. Creo que cualquiera que le guste el sandbox, la estrategia, la elaboración o los juegos de simulación disfrutaría jugando Craft The World.
-Si estás interesado en jugar Craft The World en tu dispositivo Android, aquí hay algunos consejos y trucos para principiantes:
--
-
- Comience con el modo tutorial para aprender los fundamentos del juego. -
- Utilice el botón de ayuda en la parte superior derecha de la pantalla para acceder a la wiki, el foro y la guía. -
- Planifica con anticipación y prioriza tus tareas y objetivos. -
- Mantén a tus enanos felices y saludables proporcionándoles comida, agua, camas, luz, etc. -
- Utilice los botones de pausa y avance rápido para administrar su tiempo y recursos. -
- Guarda tu juego con frecuencia y usa múltiples ranuras. -
- Experimenta con diferentes elementos, bloques, hechizos y estrategias. -¡Diviértete y sé creativo! -
Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre Craft The World:
-¿Cuáles son los requisitos del sistema para Craft The World en dispositivos Android?
-Los requisitos del sistema para Craft The World en dispositivos Android son:
--
-
- Android 4.4 o superior -
- 1 GB de RAM o más -
- 300 MB de espacio de almacenamiento libre o más -
- Una conexión a Internet estable para el modo multijugador -
¿Cuánto cuesta Craft The World en dispositivos Android?
-Arte El Mundo cuesta $4.99 en dispositivos Android. También puedes comprar contenido y funciones adicionales como compras en la aplicación, como DLC, skins, monedas, etc.
-¿Craft The World es un juego gratuito?
- -¿Craft The World se actualiza regularmente con nuevos contenidos y características?
-Sí, Craft The World se actualiza regularmente con nuevos contenidos y características. Los desarrolladores trabajan constantemente para mejorar el juego y añadir nuevos biomas, objetos, enemigos, modos, etc. Puedes consultar el historial de actualizaciones y la hoja de ruta en [5](https://steamcommunity.com/app/248390/announcements/) o [6](https:/craft-the-world.com/news).
-¿Dónde puedo encontrar más información y guías sobre Craft The World?
-Puedes encontrar más información y guías sobre Craft The World en estos sitios web:
--
-
- [7](https://crafttheworld.gamepedia.com/Craft_The_World_Wiki) - La wiki oficial del juego. -
- [8](https://steamcommunity.com/app/248390/guides/) - Las guías de la comunidad de Steam del juego. -
- [9](https://www.youtube.com/results?search_query=craftǐthe,) - Los vídeos de YouTube del juego. -
Espero que hayas disfrutado este artículo sobre Craft The World. Si tienes alguna pregunta o comentario, por favor deja un comentario a continuación. ¡Gracias por leer!
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/action.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/action.py deleted file mode 100644 index 09213554705d913a1e8b68e860d7876a2d04b76a..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/action.py +++ /dev/null @@ -1,197 +0,0 @@ -# Copyright 2015 Amazon.com, Inc. or its affiliates. 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. A copy of -# the License is located at -# -# https://aws.amazon.com/apache2.0/ -# -# or in the "license" file accompanying this file. This file 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 os - -from botocore import xform_name -from botocore.docs.bcdoc.restdoc import DocumentStructure -from botocore.docs.method import ( - document_custom_method, - document_model_driven_method, -) -from botocore.model import OperationModel -from botocore.utils import get_service_module_name - -from boto3.docs.base import NestedDocumenter -from boto3.docs.method import document_model_driven_resource_method -from boto3.docs.utils import ( - add_resource_type_overview, - get_resource_ignore_params, - get_resource_public_actions, -) - - -class ActionDocumenter(NestedDocumenter): - def document_actions(self, section): - modeled_actions_list = self._resource_model.actions - modeled_actions = {} - for modeled_action in modeled_actions_list: - modeled_actions[modeled_action.name] = modeled_action - resource_actions = get_resource_public_actions( - self._resource.__class__ - ) - self.member_map['actions'] = sorted(resource_actions) - add_resource_type_overview( - section=section, - resource_type='Actions', - description=( - 'Actions call operations on resources. They may ' - 'automatically handle the passing in of arguments set ' - 'from identifiers and some attributes.' - ), - intro_link='actions_intro', - ) - - for action_name in sorted(resource_actions): - # Create a new DocumentStructure for each action and add contents. - action_doc = DocumentStructure(action_name, target='html') - breadcrumb_section = action_doc.add_new_section('breadcrumb') - breadcrumb_section.style.ref(self._resource_class_name, 'index') - breadcrumb_section.write(f' / Action / {action_name}') - action_doc.add_title_section(action_name) - action_section = action_doc.add_new_section( - action_name, - context={'qualifier': f'{self.class_name}.'}, - ) - if action_name in ['load', 'reload'] and self._resource_model.load: - document_load_reload_action( - section=action_section, - action_name=action_name, - resource_name=self._resource_name, - event_emitter=self._resource.meta.client.meta.events, - load_model=self._resource_model.load, - service_model=self._service_model, - ) - elif action_name in modeled_actions: - document_action( - section=action_section, - resource_name=self._resource_name, - event_emitter=self._resource.meta.client.meta.events, - action_model=modeled_actions[action_name], - service_model=self._service_model, - ) - else: - document_custom_method( - action_section, action_name, resource_actions[action_name] - ) - # Write actions in individual/nested files. - # Path:
-
-
-### Abstract
-
-Recent studies in talking face generation have focused on building a train-once-use-everywhere model i.e. a model that will generalize from any source speech to any target identity. A number of works have already claimed this functionality and have added that their models will also generalize to any language. However, we show, using languages from different language families, that these models do not translate well when the training language and the testing language are sufficiently different. We reduce the scope of the problem to building a language-robust talking face generation system on seen identities i.e. the target identity is the same as the training identity. In this work, we introduce a talking face generation system that will generalize to different languages. We evaluate the efficacy of our system using a multilingual text-to-speech system. We also discuss the usage of joint text-to-speech system and the talking face generation system as a neural dubber system.
-
-[CVPR Open Access](https://openaccess.thecvf.com/content/CVPR2022/html/Song_Talking_Face_Generation_With_Multilingual_TTS_CVPR_2022_paper.html) [arXiv](https://arxiv.org/abs/2205.06421)
-
-### News
-
-(2022.08.18.) We got the CVPR Hugging Face prize! Thank you all and special thanks to AK([@akhaliq](https://huggingface.co/akhaliq)).
-
-
-
-
-
-Memory Chat Story Generator ChatGPT
""" -description = """ -## ChatGPT Datasets 📚 -- WebText -- Common Crawl -- BooksCorpus -- English Wikipedia -- Toronto Books Corpus -- OpenWebText -## ChatGPT Datasets - Details 📚 -- **WebText:** A dataset of web pages crawled from domains on the Alexa top 5,000 list. This dataset was used to pretrain GPT-2. - - [WebText: A Large-Scale Unsupervised Text Corpus by Radford et al.](https://paperswithcode.com/dataset/webtext) -- **Common Crawl:** A dataset of web pages from a variety of domains, which is updated regularly. This dataset was used to pretrain GPT-3. - - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/common-crawl) by Brown et al. -- **BooksCorpus:** A dataset of over 11,000 books from a variety of genres. - - [Scalable Methods for 8 Billion Token Language Modeling](https://paperswithcode.com/dataset/bookcorpus) by Zhu et al. -- **English Wikipedia:** A dump of the English-language Wikipedia as of 2018, with articles from 2001-2017. - - [Improving Language Understanding by Generative Pre-Training](https://huggingface.co/spaces/awacke1/WikipediaUltimateAISearch?logs=build) Space for Wikipedia Search -- **Toronto Books Corpus:** A dataset of over 7,000 books from a variety of genres, collected by the University of Toronto. - - [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://paperswithcode.com/dataset/bookcorpus) by Schwenk and Douze. -- **OpenWebText:** A dataset of web pages that were filtered to remove content that was likely to be low-quality or spammy. This dataset was used to pretrain GPT-3. - - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/openwebtext) by Brown et al. - """ - -# 6. Use Gradio to pull it all together -with gr.Blocks(css = """#col_container {width: 1400px; margin-left: auto; margin-right: auto;} #chatbot {height: 600px; overflow: auto;}""") as demo: - gr.HTML(title) - with gr.Column(elem_id = "col_container"): - inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") - chatbot = gr.Chatbot(elem_id='chatbot') - state = gr.State([]) - b1 = gr.Button() - with gr.Accordion("Parameters", open=False): - top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",) - temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",) - chat_counter = gr.Number(value=0, visible=True, precision=0) - - - # Episodic/Semantic IO - fileName = gr.Textbox(label="Filename") - fileContent = gr.TextArea(label="File Content") - completedMessage = gr.Textbox(label="Completed") - label = gr.Label() - with gr.Row(): - listFiles = gr.Button("📄 List File(s)") - readFile = gr.Button("📖 Read File") - saveFile = gr.Button("💾 Save File") - deleteFile = gr.Button("🗑️ Delete File") - appendFile = gr.Button("➕ Append File") - listFiles.click(list_files, inputs=fileName, outputs=fileContent) - readFile.click(read_file, inputs=fileName, outputs=fileContent) - saveFile.click(write_file, inputs=[fileName, fileContent], outputs=completedMessage) - deleteFile.click(delete_file, inputs=fileName, outputs=completedMessage) - appendFile.click(append_file, inputs=[fileName, fileContent], outputs=completedMessage ) - - - inputs.submit(predict, [inputs, top_p, temperature,chat_counter, chatbot, state], [chatbot, state, chat_counter]) - b1.click(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter]) - b1.click(reset_textbox, [], [inputs]) - inputs.submit(reset_textbox, [], [inputs]) - gr.Markdown(description) - - demo.queue().launch(debug=True) \ No newline at end of file diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/5000choyen/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/5000choyen/__init__.py deleted file mode 100644 index 2f25662279cef91e9c425fbd9f488b0cc3bf3939..0000000000000000000000000000000000000000 --- a/spaces/CikeyQI/meme-api/meme_generator/memes/5000choyen/__init__.py +++ /dev/null @@ -1,198 +0,0 @@ -from typing import List, Tuple - -from PIL.Image import Image as IMG -from PIL.Image import Resampling, Transform -from pil_utils import BuildImage, Text2Image -from pil_utils.gradient import ColorStop, LinearGradient - -from meme_generator import add_meme - - -def fivethousand_choyen(images, texts: List[str], args): - fontsize = 200 - fontname = "Noto Sans SC" - text = texts[0] - pos_x = 40 - pos_y = 220 - imgs: List[Tuple[IMG, Tuple[int, int]]] = [] - - def transform(img: IMG) -> IMG: - skew = 0.45 - dw = round(img.height * skew) - return img.transform( - (img.width + dw, img.height), - Transform.AFFINE, - (1, skew, -dw, 0, 1, 0), - Resampling.BILINEAR, - ) - - def shift(t2m: Text2Image) -> Tuple[int, int]: - return ( - pos_x - - t2m.lines[0].chars[0].stroke_width - - max(char.stroke_width for char in t2m.lines[0].chars), - pos_y - t2m.lines[0].ascent, - ) - - def add_color_text(stroke_width: int, fill: str, pos: Tuple[int, int]): - t2m = Text2Image.from_text( - text, fontsize, fontname=fontname, stroke_width=stroke_width, fill=fill - ) - dx, dy = shift(t2m) - imgs.append((transform(t2m.to_image()), (dx + pos[0], dy + pos[1]))) - - def add_gradient_text( - stroke_width: int, - dir: Tuple[int, int, int, int], - color_stops: List[Tuple[float, Tuple[int, int, int]]], - pos: Tuple[int, int], - ): - t2m = Text2Image.from_text( - text, fontsize, fontname=fontname, stroke_width=stroke_width, fill="white" - ) - mask = transform(t2m.to_image()).convert("L") - dx, dy = shift(t2m) - gradient = LinearGradient( - (dir[0] - dx, dir[1] - dy, dir[2] - dx, dir[3] - dy), - [ColorStop(*color_stop) for color_stop in color_stops], - ) - bg = gradient.create_image(mask.size) - bg.putalpha(mask) - imgs.append((bg, (dx + pos[0], dy + pos[1]))) - - # 黑 - add_color_text(22, "black", (8, 8)) - # 银 - add_gradient_text( - 20, - (0, 38, 0, 234), - [ - (0.0, (0, 15, 36)), - (0.1, (255, 255, 255)), - (0.18, (55, 58, 59)), - (0.25, (55, 58, 59)), - (0.5, (200, 200, 200)), - (0.75, (55, 58, 59)), - (0.85, (25, 20, 31)), - (0.91, (240, 240, 240)), - (0.95, (166, 175, 194)), - (1, (50, 50, 50)), - ], - (8, 8), - ) - # 黑 - add_color_text(16, "black", (0, 0)) - # 金 - add_gradient_text( - 10, - (0, 40, 0, 200), - [ - (0, (253, 241, 0)), - (0.25, (245, 253, 187)), - (0.4, (255, 255, 255)), - (0.75, (253, 219, 9)), - (0.9, (127, 53, 0)), - (1, (243, 196, 11)), - ], - (0, 0), - ) - # 黑 - add_color_text(6, "black", (4, -6)) - # 白 - add_color_text(6, "white", (0, -6)) - # 红 - add_gradient_text( - 4, - (0, 50, 0, 200), - [ - (0, (255, 100, 0)), - (0.5, (123, 0, 0)), - (0.51, (240, 0, 0)), - (1, (5, 0, 0)), - ], - (0, -6), - ) - # 红 - add_gradient_text( - 0, - (0, 50, 0, 200), - [ - (0, (230, 0, 0)), - (0.5, (123, 0, 0)), - (0.51, (240, 0, 0)), - (1, (5, 0, 0)), - ], - (0, -6), - ) - - text = texts[1] - fontname = "Noto Serif SC" - pos_x = 300 - pos_y = 480 - # 黑 - add_color_text(22, "black", (10, 4)) - # 银 - add_gradient_text( - 19, - (0, 320, 0, 506), - [ - (0, (0, 15, 36)), - (0.25, (250, 250, 250)), - (0.5, (150, 150, 150)), - (0.75, (55, 58, 59)), - (0.85, (25, 20, 31)), - (0.91, (240, 240, 240)), - (0.95, (166, 175, 194)), - (1, (50, 50, 50)), - ], - (10, 4), - ) - # 黑 - add_color_text(17, "#10193A", (0, 0)) - # 白 - add_color_text(8, "#D0D0D0", (0, 0)) - # 绀 - add_gradient_text( - 7, - (0, 320, 0, 480), - [ - (0, (16, 25, 58)), - (0.03, (255, 255, 255)), - (0.08, (16, 25, 58)), - (0.2, (16, 25, 58)), - (1, (16, 25, 58)), - ], - (0, 0), - ) - # 银 - add_gradient_text( - 0, - (0, 320, 0, 480), - [ - (0, (245, 246, 248)), - (0.15, (255, 255, 255)), - (0.35, (195, 213, 220)), - (0.5, (160, 190, 201)), - (0.51, (160, 190, 201)), - (0.52, (196, 215, 222)), - (1.0, (255, 255, 255)), - ], - (0, -6), - ) - - img_h = 580 - img_w = max([img.width + pos[0] for img, pos in imgs]) - frame = BuildImage.new("RGBA", (img_w, img_h), "white") - for img, pos in imgs: - frame.paste(img, pos, alpha=True) - return frame.save_jpg() - - -add_meme( - "5000choyen", - fivethousand_choyen, - min_texts=2, - max_texts=2, - default_texts=["我去", "洛天依"], - keywords=["5000兆"], -) diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/confuse/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/confuse/__init__.py deleted file mode 100644 index 8bed6616b7c63a19eda84c1e429ea5486031b322..0000000000000000000000000000000000000000 --- a/spaces/CikeyQI/meme-api/meme_generator/memes/confuse/__init__.py +++ /dev/null @@ -1,32 +0,0 @@ -from pathlib import Path -from typing import List - -from pil_utils import BuildImage - -from meme_generator import add_meme -from meme_generator.utils import FrameAlignPolicy, Maker, make_gif_or_combined_gif - -img_dir = Path(__file__).parent / "images" - - -def confuse(images: List[BuildImage], texts, args): - img_w = min(images[0].width, 500) - - def maker(i: int) -> Maker: - def make(img: BuildImage) -> BuildImage: - img = img.convert("RGBA").resize_width(img_w) - frame = BuildImage.open(img_dir / f"{i}.png").resize( - img.size, keep_ratio=True - ) - bg = BuildImage.new("RGB", img.size, "white") - bg.paste(img, alpha=True).paste(frame, alpha=True) - return bg - - return make - - return make_gif_or_combined_gif( - images[0], maker, 100, 0.02, FrameAlignPolicy.extend_loop, input_based=True - ) - - -add_meme("confuse", confuse, min_images=1, max_images=1, keywords=["迷惑"]) diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/charset_normalizer/api.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/charset_normalizer/api.py deleted file mode 100644 index 0ba08e3a50ba6d61e75f3f31772eb4dfdd3f8f05..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/charset_normalizer/api.py +++ /dev/null @@ -1,626 +0,0 @@ -import logging -from os import PathLike -from typing import BinaryIO, List, Optional, Set, Union - -from .cd import ( - coherence_ratio, - encoding_languages, - mb_encoding_languages, - merge_coherence_ratios, -) -from .constant import IANA_SUPPORTED, TOO_BIG_SEQUENCE, TOO_SMALL_SEQUENCE, TRACE -from .md import mess_ratio -from .models import CharsetMatch, CharsetMatches -from .utils import ( - any_specified_encoding, - cut_sequence_chunks, - iana_name, - identify_sig_or_bom, - is_cp_similar, - is_multi_byte_encoding, - should_strip_sig_or_bom, -) - -# Will most likely be controversial -# logging.addLevelName(TRACE, "TRACE") -logger = logging.getLogger("charset_normalizer") -explain_handler = logging.StreamHandler() -explain_handler.setFormatter( - logging.Formatter("%(asctime)s | %(levelname)s | %(message)s") -) - - -def from_bytes( - sequences: Union[bytes, bytearray], - steps: int = 5, - chunk_size: int = 512, - threshold: float = 0.2, - cp_isolation: Optional[List[str]] = None, - cp_exclusion: Optional[List[str]] = None, - preemptive_behaviour: bool = True, - explain: bool = False, - language_threshold: float = 0.1, - enable_fallback: bool = True, -) -> CharsetMatches: - """ - Given a raw bytes sequence, return the best possibles charset usable to render str objects. - If there is no results, it is a strong indicator that the source is binary/not text. - By default, the process will extract 5 blocks of 512o each to assess the mess and coherence of a given sequence. - And will give up a particular code page after 20% of measured mess. Those criteria are customizable at will. - - The preemptive behavior DOES NOT replace the traditional detection workflow, it prioritize a particular code page - but never take it for granted. Can improve the performance. - - You may want to focus your attention to some code page or/and not others, use cp_isolation and cp_exclusion for that - purpose. - - This function will strip the SIG in the payload/sequence every time except on UTF-16, UTF-32. - By default the library does not setup any handler other than the NullHandler, if you choose to set the 'explain' - toggle to True it will alter the logger configuration to add a StreamHandler that is suitable for debugging. - Custom logging format and handler can be set manually. - """ - - if not isinstance(sequences, (bytearray, bytes)): - raise TypeError( - "Expected object of type bytes or bytearray, got: {0}".format( - type(sequences) - ) - ) - - if explain: - previous_logger_level: int = logger.level - logger.addHandler(explain_handler) - logger.setLevel(TRACE) - - length: int = len(sequences) - - if length == 0: - logger.debug("Encoding detection on empty bytes, assuming utf_8 intention.") - if explain: - logger.removeHandler(explain_handler) - logger.setLevel(previous_logger_level or logging.WARNING) - return CharsetMatches([CharsetMatch(sequences, "utf_8", 0.0, False, [], "")]) - - if cp_isolation is not None: - logger.log( - TRACE, - "cp_isolation is set. use this flag for debugging purpose. " - "limited list of encoding allowed : %s.", - ", ".join(cp_isolation), - ) - cp_isolation = [iana_name(cp, False) for cp in cp_isolation] - else: - cp_isolation = [] - - if cp_exclusion is not None: - logger.log( - TRACE, - "cp_exclusion is set. use this flag for debugging purpose. " - "limited list of encoding excluded : %s.", - ", ".join(cp_exclusion), - ) - cp_exclusion = [iana_name(cp, False) for cp in cp_exclusion] - else: - cp_exclusion = [] - - if length <= (chunk_size * steps): - logger.log( - TRACE, - "override steps (%i) and chunk_size (%i) as content does not fit (%i byte(s) given) parameters.", - steps, - chunk_size, - length, - ) - steps = 1 - chunk_size = length - - if steps > 1 and length / steps < chunk_size: - chunk_size = int(length / steps) - - is_too_small_sequence: bool = len(sequences) < TOO_SMALL_SEQUENCE - is_too_large_sequence: bool = len(sequences) >= TOO_BIG_SEQUENCE - - if is_too_small_sequence: - logger.log( - TRACE, - "Trying to detect encoding from a tiny portion of ({}) byte(s).".format( - length - ), - ) - elif is_too_large_sequence: - logger.log( - TRACE, - "Using lazy str decoding because the payload is quite large, ({}) byte(s).".format( - length - ), - ) - - prioritized_encodings: List[str] = [] - - specified_encoding: Optional[str] = ( - any_specified_encoding(sequences) if preemptive_behaviour else None - ) - - if specified_encoding is not None: - prioritized_encodings.append(specified_encoding) - logger.log( - TRACE, - "Detected declarative mark in sequence. Priority +1 given for %s.", - specified_encoding, - ) - - tested: Set[str] = set() - tested_but_hard_failure: List[str] = [] - tested_but_soft_failure: List[str] = [] - - fallback_ascii: Optional[CharsetMatch] = None - fallback_u8: Optional[CharsetMatch] = None - fallback_specified: Optional[CharsetMatch] = None - - results: CharsetMatches = CharsetMatches() - - sig_encoding, sig_payload = identify_sig_or_bom(sequences) - - if sig_encoding is not None: - prioritized_encodings.append(sig_encoding) - logger.log( - TRACE, - "Detected a SIG or BOM mark on first %i byte(s). Priority +1 given for %s.", - len(sig_payload), - sig_encoding, - ) - - prioritized_encodings.append("ascii") - - if "utf_8" not in prioritized_encodings: - prioritized_encodings.append("utf_8") - - for encoding_iana in prioritized_encodings + IANA_SUPPORTED: - if cp_isolation and encoding_iana not in cp_isolation: - continue - - if cp_exclusion and encoding_iana in cp_exclusion: - continue - - if encoding_iana in tested: - continue - - tested.add(encoding_iana) - - decoded_payload: Optional[str] = None - bom_or_sig_available: bool = sig_encoding == encoding_iana - strip_sig_or_bom: bool = bom_or_sig_available and should_strip_sig_or_bom( - encoding_iana - ) - - if encoding_iana in {"utf_16", "utf_32"} and not bom_or_sig_available: - logger.log( - TRACE, - "Encoding %s won't be tested as-is because it require a BOM. Will try some sub-encoder LE/BE.", - encoding_iana, - ) - continue - if encoding_iana in {"utf_7"} and not bom_or_sig_available: - logger.log( - TRACE, - "Encoding %s won't be tested as-is because detection is unreliable without BOM/SIG.", - encoding_iana, - ) - continue - - try: - is_multi_byte_decoder: bool = is_multi_byte_encoding(encoding_iana) - except (ModuleNotFoundError, ImportError): - logger.log( - TRACE, - "Encoding %s does not provide an IncrementalDecoder", - encoding_iana, - ) - continue - - try: - if is_too_large_sequence and is_multi_byte_decoder is False: - str( - sequences[: int(50e4)] - if strip_sig_or_bom is False - else sequences[len(sig_payload) : int(50e4)], - encoding=encoding_iana, - ) - else: - decoded_payload = str( - sequences - if strip_sig_or_bom is False - else sequences[len(sig_payload) :], - encoding=encoding_iana, - ) - except (UnicodeDecodeError, LookupError) as e: - if not isinstance(e, LookupError): - logger.log( - TRACE, - "Code page %s does not fit given bytes sequence at ALL. %s", - encoding_iana, - str(e), - ) - tested_but_hard_failure.append(encoding_iana) - continue - - similar_soft_failure_test: bool = False - - for encoding_soft_failed in tested_but_soft_failure: - if is_cp_similar(encoding_iana, encoding_soft_failed): - similar_soft_failure_test = True - break - - if similar_soft_failure_test: - logger.log( - TRACE, - "%s is deemed too similar to code page %s and was consider unsuited already. Continuing!", - encoding_iana, - encoding_soft_failed, - ) - continue - - r_ = range( - 0 if not bom_or_sig_available else len(sig_payload), - length, - int(length / steps), - ) - - multi_byte_bonus: bool = ( - is_multi_byte_decoder - and decoded_payload is not None - and len(decoded_payload) < length - ) - - if multi_byte_bonus: - logger.log( - TRACE, - "Code page %s is a multi byte encoding table and it appear that at least one character " - "was encoded using n-bytes.", - encoding_iana, - ) - - max_chunk_gave_up: int = int(len(r_) / 4) - - max_chunk_gave_up = max(max_chunk_gave_up, 2) - early_stop_count: int = 0 - lazy_str_hard_failure = False - - md_chunks: List[str] = [] - md_ratios = [] - - try: - for chunk in cut_sequence_chunks( - sequences, - encoding_iana, - r_, - chunk_size, - bom_or_sig_available, - strip_sig_or_bom, - sig_payload, - is_multi_byte_decoder, - decoded_payload, - ): - md_chunks.append(chunk) - - md_ratios.append( - mess_ratio( - chunk, - threshold, - explain is True and 1 <= len(cp_isolation) <= 2, - ) - ) - - if md_ratios[-1] >= threshold: - early_stop_count += 1 - - if (early_stop_count >= max_chunk_gave_up) or ( - bom_or_sig_available and strip_sig_or_bom is False - ): - break - except ( - UnicodeDecodeError - ) as e: # Lazy str loading may have missed something there - logger.log( - TRACE, - "LazyStr Loading: After MD chunk decode, code page %s does not fit given bytes sequence at ALL. %s", - encoding_iana, - str(e), - ) - early_stop_count = max_chunk_gave_up - lazy_str_hard_failure = True - - # We might want to check the sequence again with the whole content - # Only if initial MD tests passes - if ( - not lazy_str_hard_failure - and is_too_large_sequence - and not is_multi_byte_decoder - ): - try: - sequences[int(50e3) :].decode(encoding_iana, errors="strict") - except UnicodeDecodeError as e: - logger.log( - TRACE, - "LazyStr Loading: After final lookup, code page %s does not fit given bytes sequence at ALL. %s", - encoding_iana, - str(e), - ) - tested_but_hard_failure.append(encoding_iana) - continue - - mean_mess_ratio: float = sum(md_ratios) / len(md_ratios) if md_ratios else 0.0 - if mean_mess_ratio >= threshold or early_stop_count >= max_chunk_gave_up: - tested_but_soft_failure.append(encoding_iana) - logger.log( - TRACE, - "%s was excluded because of initial chaos probing. Gave up %i time(s). " - "Computed mean chaos is %f %%.", - encoding_iana, - early_stop_count, - round(mean_mess_ratio * 100, ndigits=3), - ) - # Preparing those fallbacks in case we got nothing. - if ( - enable_fallback - and encoding_iana in ["ascii", "utf_8", specified_encoding] - and not lazy_str_hard_failure - ): - fallback_entry = CharsetMatch( - sequences, encoding_iana, threshold, False, [], decoded_payload - ) - if encoding_iana == specified_encoding: - fallback_specified = fallback_entry - elif encoding_iana == "ascii": - fallback_ascii = fallback_entry - else: - fallback_u8 = fallback_entry - continue - - logger.log( - TRACE, - "%s passed initial chaos probing. Mean measured chaos is %f %%", - encoding_iana, - round(mean_mess_ratio * 100, ndigits=3), - ) - - if not is_multi_byte_decoder: - target_languages: List[str] = encoding_languages(encoding_iana) - else: - target_languages = mb_encoding_languages(encoding_iana) - - if target_languages: - logger.log( - TRACE, - "{} should target any language(s) of {}".format( - encoding_iana, str(target_languages) - ), - ) - - cd_ratios = [] - - # We shall skip the CD when its about ASCII - # Most of the time its not relevant to run "language-detection" on it. - if encoding_iana != "ascii": - for chunk in md_chunks: - chunk_languages = coherence_ratio( - chunk, - language_threshold, - ",".join(target_languages) if target_languages else None, - ) - - cd_ratios.append(chunk_languages) - - cd_ratios_merged = merge_coherence_ratios(cd_ratios) - - if cd_ratios_merged: - logger.log( - TRACE, - "We detected language {} using {}".format( - cd_ratios_merged, encoding_iana - ), - ) - - results.append( - CharsetMatch( - sequences, - encoding_iana, - mean_mess_ratio, - bom_or_sig_available, - cd_ratios_merged, - decoded_payload, - ) - ) - - if ( - encoding_iana in [specified_encoding, "ascii", "utf_8"] - and mean_mess_ratio < 0.1 - ): - logger.debug( - "Encoding detection: %s is most likely the one.", encoding_iana - ) - if explain: - logger.removeHandler(explain_handler) - logger.setLevel(previous_logger_level) - return CharsetMatches([results[encoding_iana]]) - - if encoding_iana == sig_encoding: - logger.debug( - "Encoding detection: %s is most likely the one as we detected a BOM or SIG within " - "the beginning of the sequence.", - encoding_iana, - ) - if explain: - logger.removeHandler(explain_handler) - logger.setLevel(previous_logger_level) - return CharsetMatches([results[encoding_iana]]) - - if len(results) == 0: - if fallback_u8 or fallback_ascii or fallback_specified: - logger.log( - TRACE, - "Nothing got out of the detection process. Using ASCII/UTF-8/Specified fallback.", - ) - - if fallback_specified: - logger.debug( - "Encoding detection: %s will be used as a fallback match", - fallback_specified.encoding, - ) - results.append(fallback_specified) - elif ( - (fallback_u8 and fallback_ascii is None) - or ( - fallback_u8 - and fallback_ascii - and fallback_u8.fingerprint != fallback_ascii.fingerprint - ) - or (fallback_u8 is not None) - ): - logger.debug("Encoding detection: utf_8 will be used as a fallback match") - results.append(fallback_u8) - elif fallback_ascii: - logger.debug("Encoding detection: ascii will be used as a fallback match") - results.append(fallback_ascii) - - if results: - logger.debug( - "Encoding detection: Found %s as plausible (best-candidate) for content. With %i alternatives.", - results.best().encoding, # type: ignore - len(results) - 1, - ) - else: - logger.debug("Encoding detection: Unable to determine any suitable charset.") - - if explain: - logger.removeHandler(explain_handler) - logger.setLevel(previous_logger_level) - - return results - - -def from_fp( - fp: BinaryIO, - steps: int = 5, - chunk_size: int = 512, - threshold: float = 0.20, - cp_isolation: Optional[List[str]] = None, - cp_exclusion: Optional[List[str]] = None, - preemptive_behaviour: bool = True, - explain: bool = False, - language_threshold: float = 0.1, - enable_fallback: bool = True, -) -> CharsetMatches: - """ - Same thing than the function from_bytes but using a file pointer that is already ready. - Will not close the file pointer. - """ - return from_bytes( - fp.read(), - steps, - chunk_size, - threshold, - cp_isolation, - cp_exclusion, - preemptive_behaviour, - explain, - language_threshold, - enable_fallback, - ) - - -def from_path( - path: Union[str, bytes, PathLike], # type: ignore[type-arg] - steps: int = 5, - chunk_size: int = 512, - threshold: float = 0.20, - cp_isolation: Optional[List[str]] = None, - cp_exclusion: Optional[List[str]] = None, - preemptive_behaviour: bool = True, - explain: bool = False, - language_threshold: float = 0.1, - enable_fallback: bool = True, -) -> CharsetMatches: - """ - Same thing than the function from_bytes but with one extra step. Opening and reading given file path in binary mode. - Can raise IOError. - """ - with open(path, "rb") as fp: - return from_fp( - fp, - steps, - chunk_size, - threshold, - cp_isolation, - cp_exclusion, - preemptive_behaviour, - explain, - language_threshold, - enable_fallback, - ) - - -def is_binary( - fp_or_path_or_payload: Union[PathLike, str, BinaryIO, bytes], # type: ignore[type-arg] - steps: int = 5, - chunk_size: int = 512, - threshold: float = 0.20, - cp_isolation: Optional[List[str]] = None, - cp_exclusion: Optional[List[str]] = None, - preemptive_behaviour: bool = True, - explain: bool = False, - language_threshold: float = 0.1, - enable_fallback: bool = False, -) -> bool: - """ - Detect if the given input (file, bytes, or path) points to a binary file. aka. not a string. - Based on the same main heuristic algorithms and default kwargs at the sole exception that fallbacks match - are disabled to be stricter around ASCII-compatible but unlikely to be a string. - """ - if isinstance(fp_or_path_or_payload, (str, PathLike)): - guesses = from_path( - fp_or_path_or_payload, - steps=steps, - chunk_size=chunk_size, - threshold=threshold, - cp_isolation=cp_isolation, - cp_exclusion=cp_exclusion, - preemptive_behaviour=preemptive_behaviour, - explain=explain, - language_threshold=language_threshold, - enable_fallback=enable_fallback, - ) - elif isinstance( - fp_or_path_or_payload, - ( - bytes, - bytearray, - ), - ): - guesses = from_bytes( - fp_or_path_or_payload, - steps=steps, - chunk_size=chunk_size, - threshold=threshold, - cp_isolation=cp_isolation, - cp_exclusion=cp_exclusion, - preemptive_behaviour=preemptive_behaviour, - explain=explain, - language_threshold=language_threshold, - enable_fallback=enable_fallback, - ) - else: - guesses = from_fp( - fp_or_path_or_payload, - steps=steps, - chunk_size=chunk_size, - threshold=threshold, - cp_isolation=cp_isolation, - cp_exclusion=cp_exclusion, - preemptive_behaviour=preemptive_behaviour, - explain=explain, - language_threshold=language_threshold, - enable_fallback=enable_fallback, - ) - - return not guesses diff --git a/spaces/Datasculptor/DescriptionGPT/tools/create_imagenetlvis_json.py b/spaces/Datasculptor/DescriptionGPT/tools/create_imagenetlvis_json.py deleted file mode 100644 index 4d5a0b3712b5a2fb94737b8dfe5d70202305926b..0000000000000000000000000000000000000000 --- a/spaces/Datasculptor/DescriptionGPT/tools/create_imagenetlvis_json.py +++ /dev/null @@ -1,52 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import argparse -import json -import os -import cv2 -from nltk.corpus import wordnet - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--imagenet_path', default='datasets/imagenet/ImageNet-LVIS') - parser.add_argument('--lvis_meta_path', default='datasets/lvis/lvis_v1_val.json') - parser.add_argument('--out_path', default='datasets/imagenet/annotations/imagenet_lvis_image_info.json') - args = parser.parse_args() - - print('Loading LVIS meta') - data = json.load(open(args.lvis_meta_path, 'r')) - print('Done') - synset2cat = {x['synset']: x for x in data['categories']} - count = 0 - images = [] - image_counts = {} - folders = sorted(os.listdir(args.imagenet_path)) - for i, folder in enumerate(folders): - class_path = args.imagenet_path + folder - files = sorted(os.listdir(class_path)) - synset = wordnet.synset_from_pos_and_offset('n', int(folder[1:])).name() - cat = synset2cat[synset] - cat_id = cat['id'] - cat_name = cat['name'] - cat_images = [] - for file in files: - count = count + 1 - file_name = '{}/{}'.format(folder, file) - img = cv2.imread('{}/{}'.format(args.imagenet_path, file_name)) - h, w = img.shape[:2] - image = { - 'id': count, - 'file_name': file_name, - 'pos_category_ids': [cat_id], - 'width': w, - 'height': h - } - cat_images.append(image) - images.extend(cat_images) - image_counts[cat_id] = len(cat_images) - print(i, cat_name, len(cat_images)) - print('# Images', len(images)) - for x in data['categories']: - x['image_count'] = image_counts[x['id']] if x['id'] in image_counts else 0 - out = {'categories': data['categories'], 'images': images, 'annotations': []} - print('Writing to', args.out_path) - json.dump(out, open(args.out_path, 'w')) diff --git a/spaces/Detomo/AI-Galary/app.py b/spaces/Detomo/AI-Galary/app.py deleted file mode 100644 index 9eb1024cd7ebcf34c52f10d1d466ae24ba49f02f..0000000000000000000000000000000000000000 --- a/spaces/Detomo/AI-Galary/app.py +++ /dev/null @@ -1,28 +0,0 @@ -import gradio as gr -import pandas as pd - - -def make_clickable_model(model_name, link=None): - name = model_name.replace("https://huggingface.co/spaces/","") - return f'{name.split("/")[-1].replace("_", " ")}' - -def read_df(): - df = pd.read_excel("demo_df.xlsx") - links = [] - for i in range(df.shape[0]): - links.append(make_clickable_model(df.iloc[i, 2])) - df.drop(columns="Link", inplace=True) - df.insert(2, "Link", links) - df.insert(0, "ID", list(range(1, len(df) + 1))) - return df - -with gr.Blocks(theme=gr.themes.Soft()) as demo: - gr.Markdown( - """# Detomo AI Galary 🧙♀️ 🧛♀️ 🤖 """ - ) - galary = gr.Dataframe( - type="pandas", datatype=["number", "markdown", "markdown", "markdown"] - ) - demo.load(read_df, inputs=None, outputs=galary) - -demo.launch() \ No newline at end of file diff --git a/spaces/DrewKarn/CarperAI-stable-vicuna-13b-delta/app.py b/spaces/DrewKarn/CarperAI-stable-vicuna-13b-delta/app.py deleted file mode 100644 index 6ad7dfa9562e17b33892102e308343b0fc9845c3..0000000000000000000000000000000000000000 --- a/spaces/DrewKarn/CarperAI-stable-vicuna-13b-delta/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/CarperAI/stable-vicuna-13b-delta").launch() \ No newline at end of file diff --git a/spaces/EronSamez/RVC_HFmeu/Applio-RVC-Fork/utils/dependency.py b/spaces/EronSamez/RVC_HFmeu/Applio-RVC-Fork/utils/dependency.py deleted file mode 100644 index b70338b02d31b1ef455fbac817d418d328db518d..0000000000000000000000000000000000000000 --- a/spaces/EronSamez/RVC_HFmeu/Applio-RVC-Fork/utils/dependency.py +++ /dev/null @@ -1,170 +0,0 @@ -import os -import csv -import shutil -import tarfile -import subprocess -from pathlib import Path -from datetime import datetime - -def install_packages_but_jank_af(): - packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2'] - pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0', - 'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5', - 'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12', - 'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1', - 'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av'] - - print("Updating and installing system packages...") - for package in packages: - print(f"Installing {package}...") - subprocess.check_call(['apt-get', 'install', '-qq', '-y', package]) - - print("Updating and installing pip packages...") - subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages) - - print('Packages up to date.') - - -def setup_environment(ForceUpdateDependencies, ForceTemporaryStorage): - # Mounting Google Drive - if not ForceTemporaryStorage: - from google.colab import drive - - if not os.path.exists('/content/drive'): - drive.mount('/content/drive') - else: - print('Drive is already mounted. Proceeding...') - - # Function to install dependencies with progress - def install_packages(): - packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2'] - pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0', - 'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5', - 'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12', - 'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1', - 'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av'] - - print("Updating and installing system packages...") - for package in packages: - print(f"Installing {package}...") - subprocess.check_call(['apt-get', 'install', '-qq', '-y', package]) - - print("Updating and installing pip packages...") - subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages) - - - print('Packages up to date.') - - # Function to scan a directory and writes filenames and timestamps - def scan_and_write(base_path, output_file): - with open(output_file, 'w', newline='') as f: - writer = csv.writer(f) - for dirpath, dirs, files in os.walk(base_path): - for filename in files: - fname = os.path.join(dirpath, filename) - try: - mtime = os.path.getmtime(fname) - writer.writerow([fname, mtime]) - except Exception as e: - print(f'Skipping irrelevant nonexistent file {fname}: {str(e)}') - print(f'Finished recording filesystem timestamps to {output_file}.') - - # Function to compare files - def compare_files(old_file, new_file): - old_files = {} - new_files = {} - - with open(old_file, 'r') as f: - reader = csv.reader(f) - old_files = {rows[0]:rows[1] for rows in reader} - - with open(new_file, 'r') as f: - reader = csv.reader(f) - new_files = {rows[0]:rows[1] for rows in reader} - - removed_files = old_files.keys() - new_files.keys() - added_files = new_files.keys() - old_files.keys() - unchanged_files = old_files.keys() & new_files.keys() - - changed_files = {f for f in unchanged_files if old_files[f] != new_files[f]} - - for file in removed_files: - print(f'File has been removed: {file}') - - for file in changed_files: - print(f'File has been updated: {file}') - - return list(added_files) + list(changed_files) - - # Check if CachedRVC.tar.gz exists - if ForceTemporaryStorage: - file_path = '/content/CachedRVC.tar.gz' - else: - file_path = '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz' - - content_file_path = '/content/CachedRVC.tar.gz' - extract_path = '/' - - if not os.path.exists(file_path): - folder_path = os.path.dirname(file_path) - os.makedirs(folder_path, exist_ok=True) - print('No cached dependency install found. Attempting to download GitHub backup..') - - try: - download_url = "https://github.com/kalomaze/QuickMangioFixes/releases/download/release3/CachedRVC.tar.gz" - subprocess.run(["wget", "-O", file_path, download_url]) - print('Download completed successfully!') - except Exception as e: - print('Download failed:', str(e)) - - # Delete the failed download file - if os.path.exists(file_path): - os.remove(file_path) - print('Failed download file deleted. Continuing manual backup..') - - if Path(file_path).exists(): - if ForceTemporaryStorage: - print('Finished downloading CachedRVC.tar.gz.') - else: - print('CachedRVC.tar.gz found on Google Drive. Proceeding to copy and extract...') - - # Check if ForceTemporaryStorage is True and skip copying if it is - if ForceTemporaryStorage: - pass - else: - shutil.copy(file_path, content_file_path) - - print('Beginning backup copy operation...') - - with tarfile.open(content_file_path, 'r:gz') as tar: - for member in tar.getmembers(): - target_path = os.path.join(extract_path, member.name) - try: - tar.extract(member, extract_path) - except Exception as e: - print('Failed to extract a file (this isn\'t normal)... forcing an update to compensate') - ForceUpdateDependencies = True - print(f'Extraction of {content_file_path} to {extract_path} completed.') - - if ForceUpdateDependencies: - install_packages() - ForceUpdateDependencies = False - else: - print('CachedRVC.tar.gz not found. Proceeding to create an index of all current files...') - scan_and_write('/usr/', '/content/usr_files.csv') - - install_packages() - - scan_and_write('/usr/', '/content/usr_files_new.csv') - changed_files = compare_files('/content/usr_files.csv', '/content/usr_files_new.csv') - - with tarfile.open('/content/CachedRVC.tar.gz', 'w:gz') as new_tar: - for file in changed_files: - new_tar.add(file) - print(f'Added to tar: {file}') - - os.makedirs('/content/drive/MyDrive/RVC_Cached', exist_ok=True) - shutil.copy('/content/CachedRVC.tar.gz', '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz') - print('Updated CachedRVC.tar.gz copied to Google Drive.') - print('Dependencies fully up to date; future runs should be faster.') - diff --git a/spaces/EronSamez/RVC_HFmeu/train/mel_processing.py b/spaces/EronSamez/RVC_HFmeu/train/mel_processing.py deleted file mode 100644 index 1c871ab6b838b174407d163c201df899cc3e2b14..0000000000000000000000000000000000000000 --- a/spaces/EronSamez/RVC_HFmeu/train/mel_processing.py +++ /dev/null @@ -1,130 +0,0 @@ -import torch -import torch.utils.data -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): - return dynamic_range_compression_torch(magnitudes) - - -def spectral_de_normalize_torch(magnitudes): - return dynamic_range_decompression_torch(magnitudes) - - -# Reusable banks -mel_basis = {} -hann_window = {} - - -def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): - """Convert waveform into Linear-frequency Linear-amplitude spectrogram. - - Args: - y :: (B, T) - Audio waveforms - n_fft - sampling_rate - hop_size - win_size - center - Returns: - :: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram - """ - # Validation - if torch.min(y) < -1.07: - print("min value is ", torch.min(y)) - if torch.max(y) > 1.07: - print("max value is ", torch.max(y)) - - # Window - Cache if needed - 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 - ) - - # Padding - 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) - - # Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2) - 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, - ) - - # Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame) - 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): - # MelBasis - Cache if needed - 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( - sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax - ) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to( - dtype=spec.dtype, device=spec.device - ) - - # Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame) - melspec = torch.matmul(mel_basis[fmax_dtype_device], spec) - melspec = spectral_normalize_torch(melspec) - return melspec - - -def mel_spectrogram_torch( - y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False -): - """Convert waveform into Mel-frequency Log-amplitude spectrogram. - - Args: - y :: (B, T) - Waveforms - Returns: - melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram - """ - # Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame) - spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center) - - # Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame) - melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax) - - return melspec diff --git a/spaces/EsoCode/text-generation-webui/docs/Extensions.md b/spaces/EsoCode/text-generation-webui/docs/Extensions.md deleted file mode 100644 index b9c155fe4869280bce498c95dbc54628aabb721d..0000000000000000000000000000000000000000 --- a/spaces/EsoCode/text-generation-webui/docs/Extensions.md +++ /dev/null @@ -1,161 +0,0 @@ -Extensions are defined by files named `script.py` inside subfolders of `text-generation-webui/extensions`. They are loaded at startup if specified with the `--extensions` flag. - -For instance, `extensions/silero_tts/script.py` gets loaded with `python server.py --extensions silero_tts`. - -## [text-generation-webui-extensions](https://github.com/oobabooga/text-generation-webui-extensions) - -The link above contains a directory of user extensions for text-generation-webui. - -If you create an extension, you are welcome to host it in a GitHub repository and submit it to the list above. - -## Built-in extensions - -Most of these have been created by the extremely talented contributors that you can find here: [contributors](https://github.com/oobabooga/text-generation-webui/graphs/contributors?from=2022-12-18&to=&type=a). - -|Extension|Description| -|---------|-----------| -|[api](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/api)| Creates an API with two endpoints, one for streaming at `/api/v1/stream` port 5005 and another for blocking at `/api/v1/generate` port 5000. This is the main API for this web UI. | -|[google_translate](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/google_translate)| Automatically translates inputs and outputs using Google Translate.| -|[character_bias](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/character_bias)| Just a very simple example that biases the bot's responses in chat mode.| -|[gallery](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/gallery/)| Creates a gallery with the chat characters and their pictures. | -|[silero_tts](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/silero_tts)| Text-to-speech extension using [Silero](https://github.com/snakers4/silero-models). When used in chat mode, it replaces the responses with an audio widget. | -|[elevenlabs_tts](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/elevenlabs_tts)| Text-to-speech extension using the [ElevenLabs](https://beta.elevenlabs.io/) API. You need an API key to use it. | -|[send_pictures](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/send_pictures/)| Creates an image upload field that can be used to send images to the bot in chat mode. Captions are automatically generated using BLIP. | -|[whisper_stt](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/whisper_stt)| Allows you to enter your inputs in chat mode using your microphone. | -|[sd_api_pictures](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/sd_api_pictures)| Allows you to request pictures from the bot in chat mode, which will be generated using the AUTOMATIC1111 Stable Diffusion API. See examples [here](https://github.com/oobabooga/text-generation-webui/pull/309). | -|[multimodal](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal) | Adds multimodality support (text+images). For a detailed description see [README.md](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal/README.md) in the extension directory. | -|[openai](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/openai)| Creates an API that mimics the OpenAI API and can be used as a drop-in replacement. | -|[superbooga](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/superbooga)| An extension that uses ChromaDB to create an arbitrarily large pseudocontext, taking as input text files, URLs, or pasted text. Based on https://github.com/kaiokendev/superbig. | - -## How to write an extension - -script.py may define the special functions and variables below. - -#### Predefined functions - -| Function | Description | -|-------------|-------------| -| `def ui()` | Creates custom gradio elements when the UI is launched. | -| `def custom_css()` | Returns custom CSS as a string. It is applied whenever the web UI is loaded. | -| `def custom_js()` | Same as above but for javascript. | -| `def input_modifier(string)` | Modifies the input string before it enters the model. In chat mode, it is applied to the user message. Otherwise, it is applied to the entire prompt. | -| `def output_modifier(string)` | Modifies the output string before it is presented in the UI. In chat mode, it is applied to the bot's reply. Otherwise, it is applied to the entire output. | -| `def state_modifier(state)` | Modifies the dictionary containing the UI input parameters before it is used by the text generation functions. | -| `def history_modifier(history)` | Modifies the chat history before the text generation in chat mode begins. | -| `def bot_prefix_modifier(string)` | Applied in chat mode to the prefix for the bot's reply. | -| `def custom_generate_reply(...)` | Overrides the main text generation function. | -| `def custom_generate_chat_prompt(...)` | Overrides the prompt generator in chat mode. | -| `def tokenizer_modifier(state, prompt, input_ids, input_embeds)` | Modifies the `input_ids`/`input_embeds` fed to the model. Should return `prompt`, `input_ids`, `input_embeds`. See the `multimodal` extension for an example. | -| `def custom_tokenized_length(prompt)` | Used in conjunction with `tokenizer_modifier`, returns the length in tokens of `prompt`. See the `multimodal` extension for an example. | - -#### `params` dictionary - -In this dictionary, `display_name` is used to define the displayed name of the extension in the UI, and `is_tab` is used to define whether the extension should appear in a new tab. By default, extensions appear at the bottom of the "Text generation" tab. - -Example: - -```python -params = { - "display_name": "Google Translate", - "is_tab": True, -} -``` - -Additionally, `params` may contain variables that you want to be customizable through a `settings.json` file. For instance, assuming the extension is in `extensions/google_translate`, the variable `language string` in - -```python -params = { - "display_name": "Google Translate", - "is_tab": True, - "language string": "jp" -} -``` - -can be customized by adding a key called `google_translate-language string` to `settings.json`: - -```python -"google_translate-language string": "fr", -``` - -That is, the syntax is `extension_name-variable_name`. - -#### `input_hijack` dictionary - -```python -input_hijack = { - 'state': False, - 'value': ["", ""] -} -``` -This is only used in chat mode. If your extension sets `input_hijack['state'] = True` at any moment, the next call to `modules.chat.chatbot_wrapper` will use the values inside `input_hijack['value']` as the user input for text generation. See the `send_pictures` extension above for an example. - -Additionally, your extension can set the value to be a callback in the form of `def cb(text: str, visible_text: str) -> [str, str]`. See the `multimodal` extension above for an example. - -## Using multiple extensions at the same time - -In order to use your extension, you must start the web UI with the `--extensions` flag followed by the name of your extension (the folder under `text-generation-webui/extension` where `script.py` resides). - -You can activate more than one extension at a time by providing their names separated by spaces. The input, output, and bot prefix modifiers will be applied in the specified order. - - -``` -python server.py --extensions enthusiasm translate # First apply enthusiasm, then translate -python server.py --extensions translate enthusiasm # First apply translate, then enthusiasm -``` - -Do note, that for: -- `custom_generate_chat_prompt` -- `custom_generate_reply` -- `tokenizer_modifier` -- `custom_tokenized_length` - -only the first declaration encountered will be used and the rest will be ignored. - -## The `bot_prefix_modifier` - -In chat mode, this function modifies the prefix for a new bot message. For instance, if your bot is named `Marie Antoinette`, the default prefix for a new message will be - -``` -Marie Antoinette: -``` - -Using `bot_prefix_modifier`, you can change it to: - -``` -Marie Antoinette: *I am very enthusiastic* -``` - -Marie Antoinette will become very enthusiastic in all her messages. - -## `custom_generate_reply` example - -Once defined in a `script.py`, this function is executed in place of the main generation functions. You can use it to connect the web UI to an external API, or to load a custom model that is not supported yet. - -Note that in chat mode, this function must only return the new text, whereas in other modes it must return the original prompt + the new text. - -```python -import datetime - -def custom_generate_reply(question, original_question, seed, state, stopping_strings): - cumulative = '' - for i in range(10): - cumulative += f"Counting: {i}...\n" - yield cumulative - - cumulative += f"Done! {str(datetime.datetime.now())}" - yield cumulative -``` - -## `custom_generate_chat_prompt` example - -Below is an extension that just reproduces the default prompt generator in `modules/chat.py`. You can modify it freely to come up with your own prompts in chat mode. - -```python -from modules import chat - -def custom_generate_chat_prompt(user_input, state, **kwargs): - - # Do something with kwargs['history'] or state - - return chat.generate_chat_prompt(user_input, state, **kwargs) -``` diff --git a/spaces/EsoCode/text-generation-webui/modules/shared.py b/spaces/EsoCode/text-generation-webui/modules/shared.py deleted file mode 100644 index dfa9cd3822fb9662836e576061de663be8ea1058..0000000000000000000000000000000000000000 --- a/spaces/EsoCode/text-generation-webui/modules/shared.py +++ /dev/null @@ -1,266 +0,0 @@ -import argparse -from collections import OrderedDict -from pathlib import Path - -import yaml - -from modules.logging_colors import logger - -generation_lock = None -model = None -tokenizer = None -is_seq2seq = False -model_name = "None" -lora_names = [] - -# Chat variables -history = {'internal': [], 'visible': []} -character = 'None' -stop_everything = False -processing_message = '*Is typing...*' - -# UI elements (buttons, sliders, HTML, etc) -gradio = {} - -# For keeping the values of UI elements on page reload -persistent_interface_state = {} - -input_params = [] # Generation input parameters -reload_inputs = [] # Parameters for reloading the chat interface - -# For restarting the interface -need_restart = False - -settings = { - 'dark_theme': False, - 'autoload_model': True, - 'max_new_tokens': 200, - 'max_new_tokens_min': 1, - 'max_new_tokens_max': 2000, - 'seed': -1, - 'character': 'None', - 'name1': 'You', - 'name2': 'Assistant', - 'context': 'This is a conversation with your Assistant. It is a computer program designed to help you with various tasks such as answering questions, providing recommendations, and helping with decision making. You can ask it anything you want and it will do its best to give you accurate and relevant information.', - 'greeting': '', - 'turn_template': '', - 'custom_stopping_strings': '', - 'stop_at_newline': False, - 'add_bos_token': True, - 'ban_eos_token': False, - 'skip_special_tokens': True, - 'truncation_length': 2048, - 'truncation_length_min': 0, - 'truncation_length_max': 16384, - 'mode': 'chat', - 'start_with': '', - 'chat_style': 'cai-chat', - 'instruction_template': 'None', - 'chat-instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>', - 'chat_generation_attempts': 1, - 'chat_generation_attempts_min': 1, - 'chat_generation_attempts_max': 10, - 'default_extensions': [], - 'chat_default_extensions': ['gallery'], - 'preset': 'simple-1', - 'prompt': 'QA', -} - - -def str2bool(v): - if isinstance(v, bool): - return v - if v.lower() in ('yes', 'true', 't', 'y', '1'): - return True - elif v.lower() in ('no', 'false', 'f', 'n', '0'): - return False - else: - raise argparse.ArgumentTypeError('Boolean value expected.') - - -parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54)) - -# Basic settings -parser.add_argument('--notebook', action='store_true', help='Launch the web UI in notebook mode, where the output is written to the same text box as the input.') -parser.add_argument('--chat', action='store_true', help='Launch the web UI in chat mode with a style similar to the Character.AI website.') -parser.add_argument('--character', type=str, help='The name of the character to load in chat mode by default.') -parser.add_argument('--model', type=str, help='Name of the model to load by default.') -parser.add_argument('--lora', type=str, nargs="+", help='The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.') -parser.add_argument("--model-dir", type=str, default='models/', help="Path to directory with all the models") -parser.add_argument("--lora-dir", type=str, default='loras/', help="Path to directory with all the loras") -parser.add_argument('--model-menu', action='store_true', help='Show a model menu in the terminal when the web UI is first launched.') -parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time.') -parser.add_argument('--settings', type=str, help='Load the default interface settings from this yaml file. See settings-template.yaml for an example. If you create a file called settings.yaml, this file will be loaded by default without the need to use the --settings flag.') -parser.add_argument('--extensions', type=str, nargs="+", help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.') -parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.') - -# Model loader -parser.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv, flexgen') - -# Accelerate/transformers -parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text. Warning: Training on CPU is extremely slow.') -parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.') -parser.add_argument('--gpu-memory', type=str, nargs="+", help='Maximum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB.') -parser.add_argument('--cpu-memory', type=str, help='Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.') -parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.') -parser.add_argument('--disk-cache-dir', type=str, default="cache", help='Directory to save the disk cache to. Defaults to "cache".') -parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision (using bitsandbytes).') -parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.') -parser.add_argument('--no-cache', action='store_true', help='Set use_cache to False while generating text. This reduces the VRAM usage a bit at a performance cost.') -parser.add_argument('--xformers', action='store_true', help="Use xformer's memory efficient attention. This should increase your tokens/s.") -parser.add_argument('--sdp-attention', action='store_true', help="Use torch 2.0's sdp attention.") -parser.add_argument('--trust-remote-code', action='store_true', help="Set trust_remote_code=True while loading a model. Necessary for ChatGLM and Falcon.") - -# Accelerate 4-bit -parser.add_argument('--load-in-4bit', action='store_true', help='Load the model with 4-bit precision (using bitsandbytes).') -parser.add_argument('--compute_dtype', type=str, default="float16", help="compute dtype for 4-bit. Valid options: bfloat16, float16, float32.") -parser.add_argument('--quant_type', type=str, default="nf4", help='quant_type for 4-bit. Valid options: nf4, fp4.') -parser.add_argument('--use_double_quant', action='store_true', help='use_double_quant for 4-bit.') - -# llama.cpp -parser.add_argument('--threads', type=int, default=0, help='Number of threads to use.') -parser.add_argument('--n_batch', type=int, default=512, help='Maximum number of prompt tokens to batch together when calling llama_eval.') -parser.add_argument('--no-mmap', action='store_true', help='Prevent mmap from being used.') -parser.add_argument('--mlock', action='store_true', help='Force the system to keep the model in RAM.') -parser.add_argument('--cache-capacity', type=str, help='Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.') -parser.add_argument('--n-gpu-layers', type=int, default=0, help='Number of layers to offload to the GPU.') -parser.add_argument('--n_ctx', type=int, default=2048, help='Size of the prompt context.') -parser.add_argument('--llama_cpp_seed', type=int, default=0, help='Seed for llama-cpp models. Default 0 (random)') - -# GPTQ -parser.add_argument('--wbits', type=int, default=0, help='Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.') -parser.add_argument('--model_type', type=str, help='Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.') -parser.add_argument('--groupsize', type=int, default=-1, help='Group size.') -parser.add_argument('--pre_layer', type=int, nargs="+", help='The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg --pre_layer 30 60.') -parser.add_argument('--checkpoint', type=str, help='The path to the quantized checkpoint file. If not specified, it will be automatically detected.') -parser.add_argument('--monkey-patch', action='store_true', help='Apply the monkey patch for using LoRAs with quantized models.') -parser.add_argument('--quant_attn', action='store_true', help='(triton) Enable quant attention.') -parser.add_argument('--warmup_autotune', action='store_true', help='(triton) Enable warmup autotune.') -parser.add_argument('--fused_mlp', action='store_true', help='(triton) Enable fused mlp.') - -# AutoGPTQ -parser.add_argument('--gptq-for-llama', action='store_true', help='DEPRECATED') -parser.add_argument('--autogptq', action='store_true', help='DEPRECATED') -parser.add_argument('--triton', action='store_true', help='Use triton.') -parser.add_argument('--no_inject_fused_attention', action='store_true', help='Do not use fused attention (lowers VRAM requirements).') -parser.add_argument('--no_inject_fused_mlp', action='store_true', help='Triton mode only: Do not use fused MLP (lowers VRAM requirements).') -parser.add_argument('--no_use_cuda_fp16', action='store_true', help='This can make models faster on some systems.') -parser.add_argument('--desc_act', action='store_true', help='For models that don\'t have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig.') - -# ExLlama -parser.add_argument('--gpu-split', type=str, help="Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. 20,7,7") -parser.add_argument('--max_seq_len', type=int, default=2048, help="Maximum sequence length.") -parser.add_argument('--compress_pos_emb', type=int, default=1, help="Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.") - -# FlexGen -parser.add_argument('--flexgen', action='store_true', help='DEPRECATED') -parser.add_argument('--percent', type=int, nargs="+", default=[0, 100, 100, 0, 100, 0], help='FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0).') -parser.add_argument("--compress-weight", action="store_true", help="FlexGen: activate weight compression.") -parser.add_argument("--pin-weight", type=str2bool, nargs="?", const=True, default=True, help="FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%%).") - -# DeepSpeed -parser.add_argument('--deepspeed', action='store_true', help='Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.') -parser.add_argument('--nvme-offload-dir', type=str, help='DeepSpeed: Directory to use for ZeRO-3 NVME offloading.') -parser.add_argument('--local_rank', type=int, default=0, help='DeepSpeed: Optional argument for distributed setups.') - -# RWKV -parser.add_argument('--rwkv-strategy', type=str, default=None, help='RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8".') -parser.add_argument('--rwkv-cuda-on', action='store_true', help='RWKV: Compile the CUDA kernel for better performance.') - -# Gradio -parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.') -parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.') -parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.') -parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.') -parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.') -parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None) -parser.add_argument("--gradio-auth-path", type=str, help='Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3"', default=None) - -# API -parser.add_argument('--api', action='store_true', help='Enable the API extension.') -parser.add_argument('--api-blocking-port', type=int, default=5000, help='The listening port for the blocking API.') -parser.add_argument('--api-streaming-port', type=int, default=5005, help='The listening port for the streaming API.') -parser.add_argument('--public-api', action='store_true', help='Create a public URL for the API using Cloudfare.') - -# Multimodal -parser.add_argument('--multimodal-pipeline', type=str, default=None, help='The multimodal pipeline to use. Examples: llava-7b, llava-13b.') - -args = parser.parse_args() -args_defaults = parser.parse_args([]) - -# Deprecation warnings -if args.autogptq: - logger.warning('--autogptq has been deprecated and will be removed soon. Use --loader autogptq instead.') - args.loader = 'autogptq' -if args.gptq_for_llama: - logger.warning('--gptq-for-llama has been deprecated and will be removed soon. Use --loader gptq-for-llama instead.') - args.loader = 'gptq-for-llama' -if args.flexgen: - logger.warning('--flexgen has been deprecated and will be removed soon. Use --loader flexgen instead.') - args.loader = 'FlexGen' - -# Security warnings -if args.trust_remote_code: - logger.warning("trust_remote_code is enabled. This is dangerous.") -if args.share: - logger.warning("The gradio \"share link\" feature uses a proprietary executable to create a reverse tunnel. Use it with care.") - - -def fix_loader_name(name): - name = name.lower() - if name in ['llamacpp', 'llama.cpp', 'llama-cpp', 'llama cpp']: - return 'llama.cpp' - elif name in ['transformers', 'huggingface', 'hf', 'hugging_face', 'hugging face']: - return 'Transformers' - elif name in ['autogptq', 'auto-gptq', 'auto_gptq', 'auto gptq']: - return 'AutoGPTQ' - elif name in ['gptq-for-llama', 'gptqforllama', 'gptqllama', 'gptq for llama', 'gptq_for_llama']: - return 'GPTQ-for-LLaMa' - elif name in ['exllama', 'ex-llama', 'ex_llama', 'exlama']: - return 'ExLlama' - elif name in ['exllama-hf', 'exllama_hf', 'exllama hf', 'ex-llama-hf', 'ex_llama_hf']: - return 'ExLlama_HF' - - -if args.loader is not None: - args.loader = fix_loader_name(args.loader) - - -def add_extension(name): - if args.extensions is None: - args.extensions = [name] - elif 'api' not in args.extensions: - args.extensions.append(name) - - -# Activating the API extension -if args.api or args.public_api: - add_extension('api') - -# Activating the multimodal extension -if args.multimodal_pipeline is not None: - add_extension('multimodal') - - -def is_chat(): - return args.chat - - -# Loading model-specific settings -with Path(f'{args.model_dir}/config.yaml') as p: - if p.exists(): - model_config = yaml.safe_load(open(p, 'r').read()) - else: - model_config = {} - -# Applying user-defined model settings -with Path(f'{args.model_dir}/config-user.yaml') as p: - if p.exists(): - user_config = yaml.safe_load(open(p, 'r').read()) - for k in user_config: - if k in model_config: - model_config[k].update(user_config[k]) - else: - model_config[k] = user_config[k] - -model_config = OrderedDict(model_config) diff --git a/spaces/FFZG-cleopatra/latvian-twitter-sentiment-classifier/engine.py b/spaces/FFZG-cleopatra/latvian-twitter-sentiment-classifier/engine.py deleted file mode 100644 index b7d3c1ccc7807014595e50cdd616ef6939d91674..0000000000000000000000000000000000000000 --- a/spaces/FFZG-cleopatra/latvian-twitter-sentiment-classifier/engine.py +++ /dev/null @@ -1,119 +0,0 @@ -import torch -import torch.nn as nn -from tqdm import tqdm -from utils import categorical_accuracy - - -def loss_fn(outputs, targets): - return nn.CrossEntropyLoss()(outputs, targets) - - -def train_fn(data_loader, model, optimizer, device, scheduler): - model.train() - train_loss, train_acc = 0.0, 0.0 - - for bi, d in tqdm(enumerate(data_loader), total=len(data_loader)): - ids = d["ids"] - token_type_ids = d["token_type_ids"] - mask = d["mask"] - targets = d["targets"] - - ids = ids.to(device, dtype=torch.long) - token_type_ids = token_type_ids.to(device, dtype=torch.long) - mask = mask.to(device, dtype=torch.long) - targets = targets.to(device, dtype=torch.long) - - optimizer.zero_grad() - outputs = model( - ids=ids, - mask=mask, - token_type_ids=token_type_ids - ) - - loss = loss_fn(outputs, targets) - loss.backward() - - optimizer.step() - scheduler.step() - train_loss += loss.item() - pred_labels = torch.argmax(outputs, dim=1) - # (pred_labels == targets).sum().item() - train_acc += categorical_accuracy(outputs, targets).item() - - train_loss /= len(data_loader) - train_acc /= len(data_loader) - return train_loss, train_acc - - -def eval_fn(data_loader, model, device): - model.eval() - eval_loss, eval_acc = 0.0, 0.0 - fin_targets = [] - fin_outputs = [] - with torch.no_grad(): - for bi, d in tqdm(enumerate(data_loader), total=len(data_loader)): - ids = d["ids"] - token_type_ids = d["token_type_ids"] - mask = d["mask"] - targets = d["targets"] - - ids = ids.to(device, dtype=torch.long) - token_type_ids = token_type_ids.to(device, dtype=torch.long) - mask = mask.to(device, dtype=torch.long) - targets = targets.to(device, dtype=torch.long) - - outputs = model( - ids=ids, - mask=mask, - token_type_ids=token_type_ids - ) - loss = loss_fn(outputs, targets) - eval_loss += loss.item() - pred_labels = torch.argmax(outputs, axis=1) - # (pred_labels == targets).sum().item() - eval_acc += categorical_accuracy(outputs, targets).item() - fin_targets.extend(targets.cpu().detach().numpy().tolist()) - fin_outputs.extend(torch.argmax( - outputs, dim=1).cpu().detach().numpy().tolist()) - eval_loss /= len(data_loader) - eval_acc /= len(data_loader) - return fin_outputs, fin_targets, eval_loss, eval_acc - - - -def predict_fn(data_loader, model, device, extract_features=False): - model.eval() - - fin_outputs = [] - extracted_features =[] - with torch.no_grad(): - for bi, d in tqdm(enumerate(data_loader), total=len(data_loader)): - ids = d["ids"] - token_type_ids = d["token_type_ids"] - mask = d["mask"] - # targets = d["targets"] - - ids = ids.to(device, dtype=torch.long) - token_type_ids = token_type_ids.to(device, dtype=torch.long) - mask = mask.to(device, dtype=torch.long) - - outputs = model( - ids=ids, - mask=mask, - token_type_ids=token_type_ids - ) - if extract_features: - extracted_features.extend( model.extract_features( - ids=ids, - mask=mask, - token_type_ids=token_type_ids - ).cpu().detach().numpy().tolist()) - print("0",outputs) - print("1",torch.argmax(outputs, dim=1)) - print("2",torch.argmax(outputs, dim=1).cpu()) - print("3",torch.argmax(outputs, dim=1).cpu().numpy()) - fin_outputs.extend(torch.argmax( - outputs, dim=1).cpu().detach().numpy().tolist()) - - return fin_outputs, extracted_features - diff --git a/spaces/FL33TW00D/whisper-turbo/_next/static/chunks/main-8a5bc47a3ab4c4a6.js b/spaces/FL33TW00D/whisper-turbo/_next/static/chunks/main-8a5bc47a3ab4c4a6.js deleted file mode 100644 index 8ab96c4d0cc7d6e5b8e3955e2ef77628797009b9..0000000000000000000000000000000000000000 --- a/spaces/FL33TW00D/whisper-turbo/_next/static/chunks/main-8a5bc47a3ab4c4a6.js +++ /dev/null @@ -1 +0,0 @@ -(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[179],{9219:function(e,t){"use strict";function r(e,t,r,n,a,o,i){try{var l=e[o](i),s=l.value}catch(u){r(u);return}l.done?t(s):Promise.resolve(s).then(n,a)}function n(e){return function(){var t=this,n=arguments;return new Promise(function(a,o){var i=e.apply(t,n);function l(e){r(i,a,o,l,s,"next",e)}function s(e){r(i,a,o,l,s,"throw",e)}l(void 0)})}}Object.defineProperty(t,"Z",{enumerable:!0,get:function(){return n}})},5321:function(e,t){"use strict";function r(){return(r=Object.assign||function(e){for(var t=1;tAsk a Question to a YouTube Video and get the Video played from the answer timestamp
")
- gr.Markdown(
- """### How many times have you seen a long video/podcast on Youtube and wondered only if there would have been 'explanatory' timestamps it would have been so much better..
- **A Space by [Yuvraj Sharma](https://huggingface.co/ysharma). How to use this space:** You can either provide a new YouTube video link or can use the sample video link provided. Then provide a Questions that you would like about exploring the content in the given video.
- The App will generate timestamps and Play the video at those timestamps for you in the space provided. You will see two video displays, corresponding to two of the best guesses by the underlying models. Chances are that both videos might start with same timestamp, which will depend on the question and the content in the video, please bear!
- Also, couple small caveats -
- - The App will perform as good as the available English Transcripts are for the given YouTube Video. If there are no transcripts, the App will not work.
- - Please make sure the YouTube video links that you paste here don't have the trailing values like *&t=8077s*
- - Lastly, once you have queried a video, you might have to refresh the page for next query (will try and fix this)
-
- **Motivation behind building this App:** When we see a long video without timestamps, we often wonder 'if' the content we are looking for is in there, or 'where' in the video is the content we are looking for? The Idea is that we might have questions like 'Is the xxxx thing covered in this video?', or maybe 'does the host talks about the architecture of the xxxxx model', or maybe 'Does host talk about alien doorway on Mars?' and so on.
-
- **So this App could help you in reaching to that timestamp in 'Record time'!**
-
- **Best part:** You don't even have to move away from the Space tab in your browser as the YouTube video gets played within the given View.
- """
- )
- with gr.Row():
- input_url = gr.Textbox(label="Input a Youtube video link")
- input_ques = gr.Textbox(label="Ask a Question")
-
- with gr.Row():
- output_vid = gr.HTML(label="Video from timestamp 1", show_label=True)
- output_vid_secondbest = gr.HTML(label="Video from timestamp 2", show_label=True)
-
- with gr.Row():
- example_question = gr.Dropdown(
- ["Choose a sample question", "Does video talk about different modalities",
- "does the model uses perceiver architecture?",
- "when does the video talk about locked image tuning or lit?",
- "comparison between gpt3 and jurassic?",
- "Has flamingo passed turing test yet?",
- "Any funny examples in video?",
- "is it possible to download the stylegan model?",
- "what was very cool?",
- "what is the cool library?"], label= "Choose a sample Question", value=None)
- with gr.Row():
- example_video = gr.CheckboxGroup( ["https://www.youtube.com/watch?v=smUHQndcmOY"], label= "Choose a sample YouTube video")
-
- b1 = gr.Button("Publish Video")
-
- b1.click(display_vid, inputs=[input_url, input_ques, example_question, example_video], outputs=[output_vid, output_vid_secondbest, input_ques, input_url])
-
- with gr.Row():
- gr.Markdown('''
- #### Model Credits
- 1. [Question Answering](https://huggingface.co/deepset/minilm-uncased-squad2)
- 1. [Sentence Transformer](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- ''')
-
- with gr.Row():
- gr.Markdown("")
-
-demo.launch(enable_queue=True, debug=True)
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/pubmed-abstract-retriever/app.py b/spaces/Gradio-Blocks/pubmed-abstract-retriever/app.py
deleted file mode 100644
index 0524b94ff4504372c4364850976c0bc8e199edde..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/pubmed-abstract-retriever/app.py
+++ /dev/null
@@ -1,360 +0,0 @@
-import nltk
-import re
-import nltkmodule
-
-from newspaper import Article
-from newspaper import fulltext
-import requests
-import itertools
-import os
-
-
-from nltk.tokenize import word_tokenize
-from sentence_transformers import SentenceTransformer
-import pandas as pd
-import numpy as np
-from pandas import ExcelWriter
-from torch.utils.data import DataLoader
-import math
-from sentence_transformers import models, losses
-from sentence_transformers import SentencesDataset, LoggingHandler, SentenceTransformer
-from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
-from sentence_transformers.readers import *
-from nltk.corpus import stopwords
-stop_words = stopwords.words('english')
-import matplotlib.pyplot as plt
-from sklearn.cluster import KMeans
-from sklearn.decomposition import PCA
-from sklearn.metrics.pairwise import cosine_similarity
-import scipy.spatial
-import networkx as nx
-from nltk.tokenize import sent_tokenize
-import scispacy
-import spacy
-import en_core_sci_lg
-import string
-from nltk.stem.wordnet import WordNetLemmatizer
-import gradio as gr
-import inflect
-from sklearn.cluster import KMeans
-from sklearn.cluster import AgglomerativeClustering
-from sklearn.metrics import silhouette_samples, silhouette_score, davies_bouldin_score
-import json
-from xml.etree import ElementTree as ET
-p = inflect.engine()
-
-nlp = en_core_sci_lg.load()
-sp = en_core_sci_lg.load()
-all_stopwords = sp.Defaults.stop_words
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-def remove_stopwords(sen):
- sen_new = " ".join([i for i in sen if i not in stop_words])
- return sen_new
-
-
-
-
-def keyphrase_generator(article_link, model_1, model_2, max_num_keywords, model_3, max_retrieved, model_4):
-
- word_embedding_model = models.Transformer(model_3)
- pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
- pooling_mode_mean_tokens=True,
- pooling_mode_cls_token=False,
- pooling_mode_max_tokens=False)
-
- embedder = SentenceTransformer(modules=[word_embedding_model, pooling_model])
-
- element=[]
- cluster_list_final=[]
- comb_list=[]
- comb=[]
- title_list=[]
- titles_list=[]
- abstracts_list=[]
- silhouette_score_list=[]
- final_textrank_list=[]
- document=[]
- text_doc=[]
- final_list=[]
- score_list=[]
- sum_list=[]
- ############################################## Here we first extract the sentences using SBERT and Textrank ###########################
- model_1 = SentenceTransformer(model_1)
- model_2 = SentenceTransformer(model_2)
- url = article_link
- html = requests.get(url).text
- article = fulltext(html)
- corpus=sent_tokenize(article)
- indicator_list=['concluded','concludes','in a study', 'concluding','conclude','in sum','in a recent study','therefore','thus','so','hence',
- 'as a result','accordingly','consequently','in short','proves that','shows that','suggests that','demonstrates that','found that','observed that',
- 'indicated that','suggested that','demonstrated that']
- count_dict={}
- for l in corpus:
- c=0
- for l2 in indicator_list:
- if l.find(l2)!=-1:#then it is a substring
- c=1
- break
- if c:#
- count_dict[l]=1
- else:
- count_dict[l]=0
- for sent, score in count_dict.items():
- score_list.append(score)
- clean_sentences_new = pd.Series(corpus).str.replace("[^a-zA-Z]", " ", regex = True).tolist()
- corpus_embeddings = model_1.encode(clean_sentences_new)
- sim_mat = np.zeros([len(clean_sentences_new), len(clean_sentences_new)])
- for i in range(len(clean_sentences_new)):
- len_embeddings=(len(corpus_embeddings[i]))
- for j in range(len(clean_sentences_new)):
- if i != j:
- if(len_embeddings == 1024):
- sim_mat[i][j] = cosine_similarity(corpus_embeddings[i].reshape(1,1024), corpus_embeddings[j].reshape(1,1024))[0,0]
- elif(len_embeddings == 768):
- sim_mat[i][j] = cosine_similarity(corpus_embeddings[i].reshape(1,768), corpus_embeddings[j].reshape(1,768))[0,0]
- nx_graph = nx.from_numpy_array(sim_mat)
- scores = nx.pagerank(nx_graph, max_iter = 1500)
- sentences=((scores[i],s) for i,s in enumerate(corpus))
- for elem in sentences:
- element.append(elem[0])
- for sc, lst in zip(score_list, element): ########## taking the scores from both the lists
- sum1=sc+lst
- sum_list.append(sum1)
- x=sorted(((sum_list[i],s) for i,s in enumerate(corpus)), reverse=True)
- for elem in x:
- final_textrank_list.append(elem[1])
-
- ################################################################ Textrank ends #################################################
-
- ######################################################## From here we start the keyphrase extraction process ################################################
-
- a=int((10*len(final_textrank_list))/100.0)
- if(a<5):
- total=5
- else:
- total=int(a)
- for i in range(total):
- document.append(final_textrank_list[i])
- doc=" ".join(document)
- for i in document:
- doc_1=nlp(i)
- text_doc.append([X.text for X in doc_1.ents])
- entity_list = [item for sublist in text_doc for item in sublist]
- entity_list = [word for word in entity_list if not word in all_stopwords]
- entity_list = [word_entity for word_entity in entity_list if(p.singular_noun(word_entity) == False)]
- entity_list=list(dict.fromkeys(entity_list))
- doc_embedding = model_2.encode([doc])
- candidates=entity_list
- candidate_embeddings = model_2.encode(candidates)
- distances = cosine_similarity(doc_embedding, candidate_embeddings)
- top_n = max_num_keywords
- keyword_list = [candidates[index] for index in distances.argsort()[0][-top_n:]]
- keywords = '\n'.join(keyword_list)
-
- ############################################################## Keyphrase extraction ends #############################################
-
-
- ################################################################# From here we start the clustering and query generation ##################################
-
- c_len=(len(keyword_list))
- keyword_embeddings = embedder.encode(keyword_list)
- data_embeddings = embedder.encode(keyword_list)
-
- for num_clusters in range(1, top_n):
- clustering_model = KMeans(n_clusters=num_clusters)
- clustering_model.fit(keyword_embeddings)
- cluster_assignment = clustering_model.labels_
- clustered_sentences = [[] for i in range(num_clusters)]
- for sentence_id, cluster_id in enumerate(cluster_assignment):
- clustered_sentences[cluster_id].append(keyword_list[sentence_id])
- cl_sent_len=(len(clustered_sentences))
- list_cluster=list(clustered_sentences)
- a=len(list_cluster)
- cluster_list_final.append(list_cluster)
- if (c_len==cl_sent_len and c_len>=3) or cl_sent_len==1:
- silhouette_avg = 0
- silhouette_score_list.append(silhouette_avg)
- elif c_len==cl_sent_len==2:
- silhouette_avg = 1
- silhouette_score_list.append(silhouette_avg)
- else:
- silhouette_avg = silhouette_score(keyword_embeddings, cluster_assignment)
- silhouette_score_list.append(silhouette_avg)
- res_dict = dict(zip(silhouette_score_list, cluster_list_final))
- cluster_items=res_dict[max(res_dict)]
-
- for i in cluster_items:
- z=' OR '.join(i)
- comb.append("("+z+")")
- comb_list.append(comb)
- combinations = []
- for subset in itertools.combinations(comb, 2):
- combinations.append(subset)
- f1_list=[]
- for s in combinations:
- final = ' AND '.join(s)
- f1_list.append("("+final+")")
- f_1=' OR '.join(f1_list)
- final_list.append(f_1)
-
-######################################################## query generation ends here #######################################
-
-####################################### PubeMed abstract extraction starts here #########################################
-
- ncbi_url='https://eutils.ncbi.nlm.nih.gov/entrez/eutils/'
-
- last_url='esearch.fcgi?db=pubmed'+'&term='+f_1
- overall_url=ncbi_url+last_url+'&rettype=json'+'&sort=relevance'
- pubmed_search_request = requests.get(overall_url)
-
- root = ET.fromstring(pubmed_search_request.text)
- levels = root.findall('.//Id')
- search_id_list=[]
- for level in levels:
- name = level.text
- search_id_list.append(name)
- all_search_ids = ','.join(search_id_list)
- fetch_url='efetch.fcgi?db=pubmed'
- search_id='&id='+all_search_ids
- return_url=ncbi_url+fetch_url+search_id+'&rettype=text'+'&retmode=xml'+'&retmax=500'+'&sort=relevance'
- pubmed_abstract_request = requests.get(return_url)
- root_1 = ET.fromstring(pubmed_abstract_request.text)
- article_title = root_1.findall('.//ArticleTitle')
- for a in article_title:
- article_title_name = a.text
- titles_list.append(article_title_name)
- article_abstract = root_1.findall('.//AbstractText')
- for b in article_abstract:
- article_abstract_name = b.text
- abstracts_list.append(article_abstract_name)
-
-################################## PubMed extraction ends here ########################################################
-
-########################################## Most relevant abstracts as per news article heading starts here ##########################################
-
- first_article = Article(url, language='en')
- first_article.download()
- first_article.parse()
- article_heading=(first_article.title)
- article_heading=sent_tokenize(article_heading)
- model_4 = SentenceTransformer(model_4)
-
- my_dict = dict(zip(titles_list,abstracts_list))
- title_embeddings = model_4.encode(titles_list)
- heading_embedding = model_4.encode(article_heading)
- similarities = cosine_similarity(heading_embedding, title_embeddings)
- max_n = max_retrieved
- sorted_titles = [titles_list[index] for index in similarities.argsort()[0][-max_n:]]
- sorted_abstract_list=[]
- for list_elem in sorted_titles:
- sorted_abstract_list.append(my_dict[list_elem])
- sorted_dict = {'Title': sorted_titles, 'Abstract': sorted_abstract_list}
- df_new=pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in sorted_dict.items() ]))
- df_final = df_new.fillna(' ')
- #fp = df_final.to_csv('title_abstract.csv', index=False)
-
-
-############################################ Ends here ####################################################
-
- #return df_final
- #return fp
- return sorted_dict
-
-
-igen_pubmed = gr.Interface(keyphrase_generator,
- inputs=[gr.inputs.Textbox(lines=1, placeholder="Provide article web link here (Can be chosen from examples below)",default="", label="Article web link"),
- gr.inputs.Dropdown(choices=['sentence-transformers/all-mpnet-base-v2',
- 'sentence-transformers/all-mpnet-base-v1',
- 'sentence-transformers/all-distilroberta-v1',
- 'sentence-transformers/gtr-t5-large',
- 'pritamdeka/S-Bluebert-snli-multinli-stsb',
- 'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',
- 'pritamdeka/S-BioBert-snli-multinli-stsb',
- 'sentence-transformers/stsb-mpnet-base-v2',
- 'sentence-transformers/stsb-roberta-base-v2',
- 'sentence-transformers/stsb-distilroberta-base-v2',
- 'sentence-transformers/sentence-t5-large',
- 'sentence-transformers/sentence-t5-base'],
- type="value",
- default='sentence-transformers/stsb-roberta-base-v2',
- label="Select any SBERT model for TextRank from the list below"),
- gr.inputs.Dropdown(choices=['sentence-transformers/paraphrase-mpnet-base-v2',
- 'sentence-transformers/all-mpnet-base-v1',
- 'sentence-transformers/paraphrase-distilroberta-base-v1',
- 'sentence-transformers/paraphrase-xlm-r-multilingual-v1',
- 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2',
- 'sentence-transformers/paraphrase-albert-small-v2',
- 'sentence-transformers/paraphrase-albert-base-v2',
- 'sentence-transformers/paraphrase-MiniLM-L12-v2',
- 'sentence-transformers/paraphrase-MiniLM-L6-v2',
- 'sentence-transformers/all-MiniLM-L12-v2',
- 'sentence-transformers/all-distilroberta-v1',
- 'sentence-transformers/paraphrase-TinyBERT-L6-v2',
- 'sentence-transformers/paraphrase-MiniLM-L3-v2',
- 'sentence-transformers/all-MiniLM-L6-v2'],
- type="value",
- default='sentence-transformers/all-mpnet-base-v1',
- label="Select any SBERT model for keyphrases from the list below"),
- gr.inputs.Slider(minimum=5, maximum=20, step=1, default=10, label="Max Keywords"),
- gr.inputs.Dropdown(choices=['cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
- 'cambridgeltl/SapBERT-from-PubMedBERT-fulltext-mean-token'],
- type="value",
- default='cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
- label="Select any SapBERT model for clustering from the list below"),
- gr.inputs.Slider(minimum=5, maximum=15, step=1, default=10, label="PubMed Max Abstracts"),
- gr.inputs.Dropdown(choices=['pritamdeka/S-Bluebert-snli-multinli-stsb',
- 'pritamdeka/S-BioBert-snli-multinli-stsb',
- 'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',
- 'sentence-transformers/all-mpnet-base-v2'],
- type="value",
- default='sentence-transformers/all-mpnet-base-v2',
- label="Select any SBERT model for abstracts from the list below")],
- #outputs=gr.outputs.Dataframe(type="auto", label="Retrieved Results from PubMed",max_cols=2, overflow_row_behaviour="paginate"),
- outputs=gr.outputs.JSON(label="Title and Abstracts"),
- #outputs=gr.outputs.File(label=None),
- theme="peach", layout="horizontal",
- title="PubMed Abstract Retriever", description="Retrieves relevant PubMed abstracts for an online article which can be used as further references. The output is in the form of JSON with Title and Abstract as the fields of the JSON output. Please note that it may take sometime for the models to load. Examples are provided below for demo purposes. Choose any one example to see the results. The models can be changed to see different results. ",
- examples=[
- ["https://www.cancer.news/2021-12-22-mrna-vaccines-weaken-immune-system-cause-cancer.html",
- 'sentence-transformers/all-mpnet-base-v1',
- 'sentence-transformers/paraphrase-MiniLM-L12-v2',
- 10,
- 'cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
- 10,
- 'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb'],
-
- ["https://www.cancer.news/2022-02-04-doctors-testifying-covid-vaccines-causing-cancer-aids.html#",
- 'sentence-transformers/all-mpnet-base-v1',
- 'sentence-transformers/all-mpnet-base-v1',
- 10,
- 'cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
- 10,
- 'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb'],
-
- ["https://www.medicalnewstoday.com/articles/alzheimers-addressing-sleep-disturbance-may-alleviate-symptoms",
- 'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',
- 'sentence-transformers/all-mpnet-base-v1',
- 10,
- 'cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
- 10,
- 'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb'],
-
- ["https://www.medicalnewstoday.com/articles/omicron-what-do-we-know-about-the-stealth-variant",
- 'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',
- 'sentence-transformers/all-mpnet-base-v1',
- 10,
- 'cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
- 10,
- 'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb']
- ],
- article= "This work is based on the paper provided here."
- "\t It uses the TextRank algorithm with SBERT to first find the top sentences and then extracts the keyphrases from those sentences using scispaCy and SBERT."
- "\t The application then uses a UMLS based BERT model, SapBERT to cluster the keyphrases using K-means clustering method and finally create a boolean query. After that the top k titles and abstracts are retrieved from PubMed database and displayed according to relevancy. The SapBERT models can be changed as per the list provided. "
- "\t The list of SBERT models required in the textboxes can be found in SBERT Pre-trained models hub."
- "\t The model names can be changed from the list of pre-trained models provided. "
- "\t The value of keyphrases can be changed. The default value is 10, minimum is 5 and a maximum value of 20. "
- "\t The value of maximum abstracts to be retrieved can be changed. The minimum is 5, default is 10 and a maximum of 15.")
-
-igen_pubmed.launch(share=False,server_name='0.0.0.0',show_error=True)
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py
deleted file mode 100644
index dedac3f46b4710d16a8bc66f00663e379b2ebdc7..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py
+++ /dev/null
@@ -1,50 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
- neck=dict(
- type='FPN_CARAFE',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- num_outs=5,
- start_level=0,
- end_level=-1,
- norm_cfg=None,
- act_cfg=None,
- order=('conv', 'norm', 'act'),
- upsample_cfg=dict(
- type='carafe',
- up_kernel=5,
- up_group=1,
- encoder_kernel=3,
- encoder_dilation=1,
- compressed_channels=64)))
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=64),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=64),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- train=dict(pipeline=train_pipeline),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py
deleted file mode 100644
index 0439fc1aa28408df89d6d3b657837654bbbbbcdb..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py
+++ /dev/null
@@ -1,3 +0,0 @@
-_base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py'
-
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/pipelines/test_time_aug.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/pipelines/test_time_aug.py
deleted file mode 100644
index b6226e040499882c99f15594c66ebf3d07829168..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/pipelines/test_time_aug.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import warnings
-
-import mmcv
-
-from ..builder import PIPELINES
-from .compose import Compose
-
-
-@PIPELINES.register_module()
-class MultiScaleFlipAug(object):
- """Test-time augmentation with multiple scales and flipping.
-
- An example configuration is as followed:
-
- .. code-block::
-
- img_scale=[(1333, 400), (1333, 800)],
- flip=True,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ]
-
- After MultiScaleFLipAug with above configuration, the results are wrapped
- into lists of the same length as followed:
-
- .. code-block::
-
- dict(
- img=[...],
- img_shape=[...],
- scale=[(1333, 400), (1333, 400), (1333, 800), (1333, 800)]
- flip=[False, True, False, True]
- ...
- )
-
- Args:
- transforms (list[dict]): Transforms to apply in each augmentation.
- img_scale (tuple | list[tuple] | None): Images scales for resizing.
- scale_factor (float | list[float] | None): Scale factors for resizing.
- flip (bool): Whether apply flip augmentation. Default: False.
- flip_direction (str | list[str]): Flip augmentation directions,
- options are "horizontal" and "vertical". If flip_direction is list,
- multiple flip augmentations will be applied.
- It has no effect when flip == False. Default: "horizontal".
- """
-
- def __init__(self,
- transforms,
- img_scale=None,
- scale_factor=None,
- flip=False,
- flip_direction='horizontal'):
- self.transforms = Compose(transforms)
- assert (img_scale is None) ^ (scale_factor is None), (
- 'Must have but only one variable can be setted')
- if img_scale is not None:
- self.img_scale = img_scale if isinstance(img_scale,
- list) else [img_scale]
- self.scale_key = 'scale'
- assert mmcv.is_list_of(self.img_scale, tuple)
- else:
- self.img_scale = scale_factor if isinstance(
- scale_factor, list) else [scale_factor]
- self.scale_key = 'scale_factor'
-
- self.flip = flip
- self.flip_direction = flip_direction if isinstance(
- flip_direction, list) else [flip_direction]
- assert mmcv.is_list_of(self.flip_direction, str)
- if not self.flip and self.flip_direction != ['horizontal']:
- warnings.warn(
- 'flip_direction has no effect when flip is set to False')
- if (self.flip
- and not any([t['type'] == 'RandomFlip' for t in transforms])):
- warnings.warn(
- 'flip has no effect when RandomFlip is not in transforms')
-
- def __call__(self, results):
- """Call function to apply test time augment transforms on results.
-
- Args:
- results (dict): Result dict contains the data to transform.
-
- Returns:
- dict[str: list]: The augmented data, where each value is wrapped
- into a list.
- """
-
- aug_data = []
- flip_args = [(False, None)]
- if self.flip:
- flip_args += [(True, direction)
- for direction in self.flip_direction]
- for scale in self.img_scale:
- for flip, direction in flip_args:
- _results = results.copy()
- _results[self.scale_key] = scale
- _results['flip'] = flip
- _results['flip_direction'] = direction
- data = self.transforms(_results)
- aug_data.append(data)
- # list of dict to dict of list
- aug_data_dict = {key: [] for key in aug_data[0]}
- for data in aug_data:
- for key, val in data.items():
- aug_data_dict[key].append(val)
- return aug_data_dict
-
- def __repr__(self):
- repr_str = self.__class__.__name__
- repr_str += f'(transforms={self.transforms}, '
- repr_str += f'img_scale={self.img_scale}, flip={self.flip}, '
- repr_str += f'flip_direction={self.flip_direction})'
- return repr_str
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/rvm.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/rvm.py
deleted file mode 100644
index 028324529531dd7ee97210dfd890fed717447be0..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/rvm.py
+++ /dev/null
@@ -1,106 +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 typing as tp
-import torch
-from torch import nn
-import torchaudio
-
-
-def db_to_scale(volume: tp.Union[float, torch.Tensor]):
- return 10 ** (volume / 20)
-
-
-def scale_to_db(scale: torch.Tensor, min_volume: float = -120):
- min_scale = db_to_scale(min_volume)
- return 20 * torch.log10(scale.clamp(min=min_scale))
-
-
-class RelativeVolumeMel(nn.Module):
- """Relative volume melspectrogram measure.
-
- Computes a measure of distance over two mel spectrogram that is interpretable in terms
- of decibels. Given `x_ref` and `x_est` two waveforms of shape `[*, T]`, it will
- first renormalize both by the ground truth of `x_ref`.
-
- Then it computes the mel spectrogram `z_ref` and `z_est` and compute volume of the difference
- relative to the volume of `z_ref` for each time-frequency bin. It further adds some limits, e.g.
- clamping the values between -25 and 25 dB (controlled by `min_relative_volume` and `max_relative_volume`)
- with the goal of avoiding the loss being dominated by parts where the reference is almost silent.
- Indeed, volumes in dB can take unbounded values both towards -oo and +oo, which can make the final
- average metric harder to interpret. Besides, anything below -30 dB of attenuation would sound extremely
- good (for a neural network output, although sound engineers typically aim for much lower attenuations).
- Similarly, anything above +30 dB would just be completely missing the target, and there is no point
- in measuring by exactly how much it missed it. -25, 25 is a more conservative range, but also more
- in line with what neural nets currently can achieve.
-
- For instance, a Relative Volume Mel (RVM) score of -10 dB means that on average, the delta between
- the target and reference mel-spec is 10 dB lower than the reference mel-spec value.
-
- The metric can be aggregated over a given frequency band in order have different insights for
- different region of the spectrum. `num_aggregated_bands` controls the number of bands.
-
- ..Warning:: While this function is optimized for interpretability, nothing was done to ensure it
- is numerically stable when computing its gradient. We thus advise against using it as a training loss.
-
- Args:
- sample_rate (int): Sample rate of the input audio.
- n_mels (int): Number of mel bands to use.
- n_fft (int): Number of frequency bins for the STFT.
- hop_length (int): Hop length of the STFT and the mel-spectrogram.
- min_relative_volume (float): The error `z_ref - z_est` volume is given relative to
- the volume of `z_ref`. If error is smaller than -25 dB of `z_ref`, then it is clamped.
- max_relative_volume (float): Same as `min_relative_volume` but clamping if the error is larger than that.
- max_initial_gain (float): When rescaling the audio at the very beginning, we will limit the gain
- to that amount, to avoid rescaling near silence. Given in dB.
- min_activity_volume (float): When computing the reference level from `z_ref`, will clamp low volume
- bins to that amount. This is effectively our "zero" level for the reference mel-spectrogram,
- and anything below that will be considered equally.
- num_aggregated_bands (int): Number of bands to keep when computing the average RVM value.
- For instance, a value of 3 would give 3 scores, roughly for low, mid and high freqs.
- """
- def __init__(self, sample_rate: int = 24000, n_mels: int = 80, n_fft: int = 512,
- hop_length: int = 128, min_relative_volume: float = -25,
- max_relative_volume: float = 25, max_initial_gain: float = 25,
- min_activity_volume: float = -25,
- num_aggregated_bands: int = 4) -> None:
- super().__init__()
- self.melspec = torchaudio.transforms.MelSpectrogram(
- n_mels=n_mels, n_fft=n_fft, hop_length=hop_length,
- normalized=True, sample_rate=sample_rate, power=2)
- self.min_relative_volume = min_relative_volume
- self.max_relative_volume = max_relative_volume
- self.max_initial_gain = max_initial_gain
- self.min_activity_volume = min_activity_volume
- self.num_aggregated_bands = num_aggregated_bands
-
- def forward(self, estimate: torch.Tensor, ground_truth: torch.Tensor) -> tp.Dict[str, torch.Tensor]:
- """Compute RVM metric between estimate and reference samples.
-
- Args:
- estimate (torch.Tensor): Estimate sample.
- ground_truth (torch.Tensor): Reference sample.
-
- Returns:
- dict[str, torch.Tensor]: Metrics with keys `rvm` for the overall average, and `rvm_{k}`
- for the RVM over the k-th band (k=0..num_aggregated_bands - 1).
- """
- min_scale = db_to_scale(-self.max_initial_gain)
- std = ground_truth.pow(2).mean().sqrt().clamp(min=min_scale)
- z_gt = self.melspec(ground_truth / std).sqrt()
- z_est = self.melspec(estimate / std).sqrt()
-
- delta = z_gt - z_est
- ref_db = scale_to_db(z_gt, self.min_activity_volume)
- delta_db = scale_to_db(delta.abs(), min_volume=-120)
- relative_db = (delta_db - ref_db).clamp(self.min_relative_volume, self.max_relative_volume)
- dims = list(range(relative_db.dim()))
- dims.remove(dims[-2])
- losses_per_band = relative_db.mean(dim=dims)
- aggregated = [chunk.mean() for chunk in losses_per_band.chunk(self.num_aggregated_bands, dim=0)]
- metrics = {f'rvm_{index}': value for index, value in enumerate(aggregated)}
- metrics['rvm'] = losses_per_band.mean()
- return metrics
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/modules/activations.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/modules/activations.py
deleted file mode 100644
index 2d83d7c4c2dc84c64b724eadbe06157507d4f20d..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/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 (str, or 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/GrandaddyShmax/MusicGen_Plus/tests/__init__.py b/spaces/GrandaddyShmax/MusicGen_Plus/tests/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/MusicGen_Plus/tests/__init__.py
+++ /dev/null
@@ -1,5 +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.
diff --git a/spaces/GroveStreet/GTA_SOVITS/vdecoder/hifiganwithsnake/alias/act.py b/spaces/GroveStreet/GTA_SOVITS/vdecoder/hifiganwithsnake/alias/act.py
deleted file mode 100644
index 308344fb6ccbc39317c584a3ee1fb2f29084678e..0000000000000000000000000000000000000000
--- a/spaces/GroveStreet/GTA_SOVITS/vdecoder/hifiganwithsnake/alias/act.py
+++ /dev/null
@@ -1,129 +0,0 @@
-# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
-# LICENSE is in incl_licenses directory.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from torch import sin, pow
-from torch.nn import Parameter
-from .resample import UpSample1d, DownSample1d
-
-
-class Activation1d(nn.Module):
- def __init__(self,
- activation,
- up_ratio: int = 2,
- down_ratio: int = 2,
- up_kernel_size: int = 12,
- down_kernel_size: int = 12):
- super().__init__()
- self.up_ratio = up_ratio
- self.down_ratio = down_ratio
- self.act = activation
- self.upsample = UpSample1d(up_ratio, up_kernel_size)
- self.downsample = DownSample1d(down_ratio, down_kernel_size)
-
- # x: [B,C,T]
- def forward(self, x):
- x = self.upsample(x)
- x = self.act(x)
- x = self.downsample(x)
-
- return x
-
-
-class SnakeBeta(nn.Module):
- '''
- A modified Snake function which uses separate parameters for the magnitude of the periodic components
- Shape:
- - Input: (B, C, T)
- - Output: (B, C, T), same shape as the input
- Parameters:
- - alpha - trainable parameter that controls frequency
- - beta - trainable parameter that controls magnitude
- References:
- - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
- https://arxiv.org/abs/2006.08195
- Examples:
- >>> a1 = snakebeta(256)
- >>> x = torch.randn(256)
- >>> x = a1(x)
- '''
-
- def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
- '''
- Initialization.
- INPUT:
- - in_features: shape of the input
- - alpha - trainable parameter that controls frequency
- - beta - trainable parameter that controls magnitude
- alpha is initialized to 1 by default, higher values = higher-frequency.
- beta is initialized to 1 by default, higher values = higher-magnitude.
- alpha will be trained along with the rest of your model.
- '''
- super(SnakeBeta, self).__init__()
- self.in_features = in_features
- # initialize alpha
- self.alpha_logscale = alpha_logscale
- if self.alpha_logscale: # log scale alphas initialized to zeros
- self.alpha = Parameter(torch.zeros(in_features) * alpha)
- self.beta = Parameter(torch.zeros(in_features) * alpha)
- else: # linear scale alphas initialized to ones
- self.alpha = Parameter(torch.ones(in_features) * alpha)
- self.beta = Parameter(torch.ones(in_features) * alpha)
- self.alpha.requires_grad = alpha_trainable
- self.beta.requires_grad = alpha_trainable
- self.no_div_by_zero = 0.000000001
-
- def forward(self, x):
- '''
- Forward pass of the function.
- Applies the function to the input elementwise.
- SnakeBeta = x + 1/b * sin^2 (xa)
- '''
- alpha = self.alpha.unsqueeze(
- 0).unsqueeze(-1) # line up with x to [B, C, T]
- beta = self.beta.unsqueeze(0).unsqueeze(-1)
- if self.alpha_logscale:
- alpha = torch.exp(alpha)
- beta = torch.exp(beta)
- x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
- return x
-
-
-class Mish(nn.Module):
- """
- Mish activation function is proposed in "Mish: A Self
- Regularized Non-Monotonic Neural Activation Function"
- paper, https://arxiv.org/abs/1908.08681.
- """
-
- def __init__(self):
- super().__init__()
-
- def forward(self, x):
- return x * torch.tanh(F.softplus(x))
-
-
-class SnakeAlias(nn.Module):
- def __init__(self,
- channels,
- up_ratio: int = 2,
- down_ratio: int = 2,
- up_kernel_size: int = 12,
- down_kernel_size: int = 12):
- super().__init__()
- self.up_ratio = up_ratio
- self.down_ratio = down_ratio
- self.act = SnakeBeta(channels, alpha_logscale=True)
- self.upsample = UpSample1d(up_ratio, up_kernel_size)
- self.downsample = DownSample1d(down_ratio, down_kernel_size)
-
- # x: [B,C,T]
- def forward(self, x):
- x = self.upsample(x)
- x = self.act(x)
- x = self.downsample(x)
-
- return x
\ No newline at end of file
diff --git a/spaces/HaMerL/ChaosinChat/modules/models/tokenization_moss.py b/spaces/HaMerL/ChaosinChat/modules/models/tokenization_moss.py
deleted file mode 100644
index 626315eb9e429ada99a15b04b9736c05e6743ffe..0000000000000000000000000000000000000000
--- a/spaces/HaMerL/ChaosinChat/modules/models/tokenization_moss.py
+++ /dev/null
@@ -1,368 +0,0 @@
-"""Tokenization classes for Moss"""
-
-import json
-import os
-import numpy as np
-import regex as re
-
-from functools import lru_cache
-from typing import TYPE_CHECKING, List, Optional, Tuple, Union
-
-from transformers.utils import is_tf_available, is_torch_available, logging
-from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
-
-
-if TYPE_CHECKING:
- if is_torch_available():
- import torch
- if is_tf_available():
- import tensorflow as tf
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {
- "vocab_file": "vocab.json",
- "merges_file": "merges.txt",
-}
-
-PRETRAINED_VOCAB_FILES_MAP = {
- "vocab_file": {
- "fnlp/moss-moon-003-base": "https://huggingface.co/fnlp/moss-moon-003-base/resolve/main/vocab.json",
- "fnlp/moss-moon-003-sft": "https://huggingface.co/fnlp/moss-moon-003-sft/resolve/main/vocab.json",
- "fnlp/moss-moon-003-sft-plugin": "https://huggingface.co/fnlp/moss-moon-003-sft-plugin/resolve/main/vocab.json",
- },
- "merges_file": {
- "fnlp/moss-moon-003-base": "https://huggingface.co/fnlp/moss-moon-003-base/resolve/main/merges.txt",
- "fnlp/moss-moon-003-sft": "https://huggingface.co/fnlp/moss-moon-003-sft/resolve/main/merges.txt",
- "fnlp/moss-moon-003-sft-plugin": "https://huggingface.co/fnlp/moss-moon-003-sft-plugin/resolve/main/merges.txt",
- },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
- "fnlp/moss-moon-003-base": 2048,
- "fnlp/moss-moon-003-sft": 2048,
- "fnlp/moss-moon-003-sft-plugin": 2048,
-}
-
-
-@lru_cache()
-def bytes_to_unicode():
- """
- Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
- characters the bpe code barfs on.
-
- The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
- if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
- decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
- tables between utf-8 bytes and unicode strings.
- """
- bs = (
- list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
- )
- cs = bs[:]
- n = 0
- for b in range(2**8):
- if b not in bs:
- bs.append(b)
- cs.append(2**8 + n)
- n += 1
- cs = [chr(n) for n in cs]
- return dict(zip(bs, cs))
-
-
-def get_pairs(word):
- """
- Return set of symbol pairs in a word.
-
- Word is represented as tuple of symbols (symbols being variable-length strings).
- """
- pairs = set()
- prev_char = word[0]
- for char in word[1:]:
- pairs.add((prev_char, char))
- prev_char = char
- return pairs
-
-
-class MossTokenizer(PreTrainedTokenizer):
- """
- Construct a Moss tokenizer. Based on byte-level Byte-Pair-Encoding.
-
- This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
- be encoded differently whether it is at the beginning of the sentence (without space) or not:
-
- You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
- call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
-
- Warning: Slow process... ~10 min inference time.
Please use square .png image as input, 512x512, 768x768, or 1024x1024" -gr.Interface(fn=predict, inputs=[gr.Image(source="upload", type="numpy", tool="sketch", elem_id="source_container"), gr.Textbox(label='What you want the AI to Generate, 77 Token limit'), gr.Textbox(label='What you Do Not want the AI to generate')], outputs='image', title=title, description=description, article = "Code Monkey: Manjushri").launch(max_threads=True, debug=True) \ No newline at end of file diff --git a/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/libs/math.min.js b/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/libs/math.min.js deleted file mode 100644 index 12d578beeeb83a90de342bf5f2a3d51179d2c296..0000000000000000000000000000000000000000 --- a/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/libs/math.min.js +++ /dev/null @@ -1,47 +0,0 @@ -/** - * math.js - * https://github.com/josdejong/mathjs - * - * Math.js is an extensive math library for JavaScript and Node.js, - * It features real and complex numbers, units, matrices, a large set of - * mathematical functions, and a flexible expression parser. - * - * @version 2.6.0 - * @date 2016-01-08 - * - * @license - * Copyright (C) 2013-2016 Jos de Jong
{icon}
", unsafe_allow_html=True)
- area[1](f"""{name}：
\n\n{message}""",
- unsafe_allow_html=True)
-
-
-def show_messages(messages: list):
- for each in messages:
- if (each["role"] == "user") or (each["role"] == "assistant"):
- show_each_message(each["content"], each["role"])
- if each["role"] == "assistant":
- st.write("---")
-
-
-# 根据context_level提取history
-def get_history_input(history: list, level: int) -> list:
- if level != 0:
- df_history = pd.DataFrame(history)
- df_system = df_history.query('role=="system"')
- df_input = df_history.query('role!="system"')
- df_input = df_input[-level * 2:]
- res = pd.concat([df_system, df_input], ignore_index=True).to_dict('records')
- else:
- res = []
- return res
-
-
-# 去除#号右边的空格
-# def remove_hashtag_right__space(text: str) -> str:
-# text = re.sub(r"(#+)\s*", r"\1", text)
-# return text
-
-
-# 提取文本
-def extract_chars(text: str, num: int) -> str:
- char_num = 0
- chars = ''
- for char in text:
- # 汉字算两个字符
- if '\u4e00' <= char <= '\u9fff':
- char_num += 2
- else:
- char_num += 1
- chars += char
- if char_num >= num:
- break
- return chars
-
-
-@st.cache_data(max_entries=20, show_spinner=False)
-def download_history(history: list):
- md_text = ""
- for msg in history:
- if msg['role'] == 'user':
- md_text += f'## {user_name}：\n{msg["content"]}\n'
- elif msg['role'] == 'assistant':
- md_text += f'## {gpt_name}：\n{msg["content"]}\n'
- output = io.BytesIO()
- output.write(md_text.encode('utf-8'))
- output.seek(0)
- return output
-
-
-def filename_correction(filename: str) -> str:
- pattern = r'[^\w\.-]'
- filename = re.sub(pattern, '', filename)
- return filename
-
-
-def url_correction(text: str) -> str:
- pattern = r'((?:http[s]?://|www\.)(?:[a-zA-Z0-9]|[$-_\~#!])+)'
- text = re.sub(pattern, r' \g<1> ', text)
- return text
-
-
-# st的markdown会错误渲染英文引号加英文字符，例如 :abc
-def colon_correction(text):
- pattern = r':[a-zA-Z]'
- if re.search(pattern, text):
- text = text.replace(":", ":")
- pattern = r'`([^`]*):([^`]*)`|```([^`]*):([^`]*)```'
- text = re.sub(pattern, lambda m: m.group(0).replace(':', ':') if ':' in m.group(0) else m.group(0),
- text)
- return text
diff --git a/spaces/Pengyey/bingo-chuchu/src/components/chat-message.tsx b/spaces/Pengyey/bingo-chuchu/src/components/chat-message.tsx
deleted file mode 100644
index bf272d8d7005cfd06c53bd213e09ea217e803549..0000000000000000000000000000000000000000
--- a/spaces/Pengyey/bingo-chuchu/src/components/chat-message.tsx
+++ /dev/null
@@ -1,93 +0,0 @@
-import remarkGfm from 'remark-gfm'
-import remarkMath from 'remark-math'
-import supersub from 'remark-supersub'
-import remarkBreaks from 'remark-breaks'
-import { cn } from '@/lib/utils'
-import { CodeBlock } from '@/components/ui/codeblock'
-import { MemoizedReactMarkdown } from '@/components/markdown'
-import { LearnMore } from './learn-more'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { useEffect } from 'react'
-import { TurnCounter } from './turn-counter'
-
-export interface ChatMessageProps {
- message: ChatMessageModel
-}
-
-export function ChatMessage({ message, ...props }: ChatMessageProps) {
- useEffect(() => {
- if (document.body.scrollHeight - window.innerHeight - window.scrollY - 200 < 0) {
- window.scrollBy(0, 200)
- }
- }, [message.text])
-
- return message.text ? (
- `_.
-
- Args:
- pred (torch.Tensor): Predicted bboxes.
- target (torch.Tensor): Target bboxes.
- beta (float): beta parameter in smoothl1.
- eps (float): eps to avoid NaN.
- """
- pred_ctrx = (pred[:, 0] + pred[:, 2]) * 0.5
- pred_ctry = (pred[:, 1] + pred[:, 3]) * 0.5
- pred_w = pred[:, 2] - pred[:, 0]
- pred_h = pred[:, 3] - pred[:, 1]
- with torch.no_grad():
- target_ctrx = (target[:, 0] + target[:, 2]) * 0.5
- target_ctry = (target[:, 1] + target[:, 3]) * 0.5
- target_w = target[:, 2] - target[:, 0]
- target_h = target[:, 3] - target[:, 1]
-
- dx = target_ctrx - pred_ctrx
- dy = target_ctry - pred_ctry
-
- loss_dx = 1 - torch.max(
- (target_w - 2 * dx.abs()) /
- (target_w + 2 * dx.abs() + eps), torch.zeros_like(dx))
- loss_dy = 1 - torch.max(
- (target_h - 2 * dy.abs()) /
- (target_h + 2 * dy.abs() + eps), torch.zeros_like(dy))
- loss_dw = 1 - torch.min(target_w / (pred_w + eps), pred_w /
- (target_w + eps))
- loss_dh = 1 - torch.min(target_h / (pred_h + eps), pred_h /
- (target_h + eps))
- loss_comb = torch.stack([loss_dx, loss_dy, loss_dw, loss_dh],
- dim=-1).view(loss_dx.size(0), -1)
-
- loss = torch.where(loss_comb < beta, 0.5 * loss_comb * loss_comb / beta,
- loss_comb - 0.5 * beta)
- return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def giou_loss(pred, target, eps=1e-7):
- r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding
- Box Regression `_.
-
- Args:
- pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
- shape (n, 4).
- target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
- eps (float): Eps to avoid log(0).
-
- Return:
- Tensor: Loss tensor.
- """
- gious = bbox_overlaps(pred, target, mode='giou', is_aligned=True, eps=eps)
- loss = 1 - gious
- return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def diou_loss(pred, target, eps=1e-7):
- r"""`Implementation of Distance-IoU Loss: Faster and Better
- Learning for Bounding Box Regression, https://arxiv.org/abs/1911.08287`_.
-
- Code is modified from https://github.com/Zzh-tju/DIoU.
-
- Args:
- pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
- shape (n, 4).
- target (Tensor): Corresponding gt bboxes, shape (n, 4).
- eps (float): Eps to avoid log(0).
- Return:
- Tensor: Loss tensor.
- """
- # overlap
- lt = torch.max(pred[:, :2], target[:, :2])
- rb = torch.min(pred[:, 2:], target[:, 2:])
- wh = (rb - lt).clamp(min=0)
- overlap = wh[:, 0] * wh[:, 1]
-
- # union
- ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
- ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
- union = ap + ag - overlap + eps
-
- # IoU
- ious = overlap / union
-
- # enclose area
- enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
- enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
- enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)
-
- cw = enclose_wh[:, 0]
- ch = enclose_wh[:, 1]
-
- c2 = cw**2 + ch**2 + eps
-
- b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
- b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
- b2_x1, b2_y1 = target[:, 0], target[:, 1]
- b2_x2, b2_y2 = target[:, 2], target[:, 3]
-
- left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
- right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
- rho2 = left + right
-
- # DIoU
- dious = ious - rho2 / c2
- loss = 1 - dious
- return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def ciou_loss(pred, target, eps=1e-7):
- r"""`Implementation of paper `Enhancing Geometric Factors into
- Model Learning and Inference for Object Detection and Instance
- Segmentation `_.
-
- Code is modified from https://github.com/Zzh-tju/CIoU.
-
- Args:
- pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
- shape (n, 4).
- target (Tensor): Corresponding gt bboxes, shape (n, 4).
- eps (float): Eps to avoid log(0).
- Return:
- Tensor: Loss tensor.
- """
- # overlap
- lt = torch.max(pred[:, :2], target[:, :2])
- rb = torch.min(pred[:, 2:], target[:, 2:])
- wh = (rb - lt).clamp(min=0)
- overlap = wh[:, 0] * wh[:, 1]
-
- # union
- ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
- ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
- union = ap + ag - overlap + eps
-
- # IoU
- ious = overlap / union
-
- # enclose area
- enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
- enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
- enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)
-
- cw = enclose_wh[:, 0]
- ch = enclose_wh[:, 1]
-
- c2 = cw**2 + ch**2 + eps
-
- b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
- b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
- b2_x1, b2_y1 = target[:, 0], target[:, 1]
- b2_x2, b2_y2 = target[:, 2], target[:, 3]
-
- w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
- w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
-
- left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
- right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
- rho2 = left + right
-
- factor = 4 / math.pi**2
- v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
-
- # CIoU
- cious = ious - (rho2 / c2 + v**2 / (1 - ious + v))
- loss = 1 - cious
- return loss
-
-
-@LOSSES.register_module()
-class IoULoss(nn.Module):
- """IoULoss.
-
- Computing the IoU loss between a set of predicted bboxes and target bboxes.
-
- Args:
- linear (bool): If True, use linear scale of loss instead of log scale.
- Default: False.
- eps (float): Eps to avoid log(0).
- reduction (str): Options are "none", "mean" and "sum".
- loss_weight (float): Weight of loss.
- """
-
- def __init__(self,
- linear=False,
- eps=1e-6,
- reduction='mean',
- loss_weight=1.0):
- super(IoULoss, self).__init__()
- self.linear = linear
- self.eps = eps
- self.reduction = reduction
- self.loss_weight = loss_weight
-
- def forward(self,
- pred,
- target,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- **kwargs):
- """Forward function.
-
- Args:
- pred (torch.Tensor): The prediction.
- target (torch.Tensor): The learning target of the prediction.
- weight (torch.Tensor, optional): The weight of loss for each
- prediction. Defaults to None.
- avg_factor (int, optional): Average factor that is used to average
- the loss. Defaults to None.
- reduction_override (str, optional): The reduction method used to
- override the original reduction method of the loss.
- Defaults to None. Options are "none", "mean" and "sum".
- """
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
- if (weight is not None) and (not torch.any(weight > 0)) and (
- reduction != 'none'):
- return (pred * weight).sum() # 0
- if weight is not None and weight.dim() > 1:
- # TODO: remove this in the future
- # reduce the weight of shape (n, 4) to (n,) to match the
- # iou_loss of shape (n,)
- assert weight.shape == pred.shape
- weight = weight.mean(-1)
- loss = self.loss_weight * iou_loss(
- pred,
- target,
- weight,
- linear=self.linear,
- eps=self.eps,
- reduction=reduction,
- avg_factor=avg_factor,
- **kwargs)
- return loss
-
-
-@LOSSES.register_module()
-class BoundedIoULoss(nn.Module):
-
- def __init__(self, beta=0.2, eps=1e-3, reduction='mean', loss_weight=1.0):
- super(BoundedIoULoss, self).__init__()
- self.beta = beta
- self.eps = eps
- self.reduction = reduction
- self.loss_weight = loss_weight
-
- def forward(self,
- pred,
- target,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- **kwargs):
- if weight is not None and not torch.any(weight > 0):
- return (pred * weight).sum() # 0
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
- loss = self.loss_weight * bounded_iou_loss(
- pred,
- target,
- weight,
- beta=self.beta,
- eps=self.eps,
- reduction=reduction,
- avg_factor=avg_factor,
- **kwargs)
- return loss
-
-
-@LOSSES.register_module()
-class GIoULoss(nn.Module):
-
- def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
- super(GIoULoss, self).__init__()
- self.eps = eps
- self.reduction = reduction
- self.loss_weight = loss_weight
-
- def forward(self,
- pred,
- target,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- **kwargs):
- if weight is not None and not torch.any(weight > 0):
- return (pred * weight).sum() # 0
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
- if weight is not None and weight.dim() > 1:
- # TODO: remove this in the future
- # reduce the weight of shape (n, 4) to (n,) to match the
- # giou_loss of shape (n,)
- assert weight.shape == pred.shape
- weight = weight.mean(-1)
- loss = self.loss_weight * giou_loss(
- pred,
- target,
- weight,
- eps=self.eps,
- reduction=reduction,
- avg_factor=avg_factor,
- **kwargs)
- return loss
-
-
-@LOSSES.register_module()
-class DIoULoss(nn.Module):
-
- def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
- super(DIoULoss, self).__init__()
- self.eps = eps
- self.reduction = reduction
- self.loss_weight = loss_weight
-
- def forward(self,
- pred,
- target,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- **kwargs):
- if weight is not None and not torch.any(weight > 0):
- return (pred * weight).sum() # 0
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
- if weight is not None and weight.dim() > 1:
- # TODO: remove this in the future
- # reduce the weight of shape (n, 4) to (n,) to match the
- # giou_loss of shape (n,)
- assert weight.shape == pred.shape
- weight = weight.mean(-1)
- loss = self.loss_weight * diou_loss(
- pred,
- target,
- weight,
- eps=self.eps,
- reduction=reduction,
- avg_factor=avg_factor,
- **kwargs)
- return loss
-
-
-@LOSSES.register_module()
-class CIoULoss(nn.Module):
-
- def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
- super(CIoULoss, self).__init__()
- self.eps = eps
- self.reduction = reduction
- self.loss_weight = loss_weight
-
- def forward(self,
- pred,
- target,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- **kwargs):
- if weight is not None and not torch.any(weight > 0):
- return (pred * weight).sum() # 0
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
- if weight is not None and weight.dim() > 1:
- # TODO: remove this in the future
- # reduce the weight of shape (n, 4) to (n,) to match the
- # giou_loss of shape (n,)
- assert weight.shape == pred.shape
- weight = weight.mean(-1)
- loss = self.loss_weight * ciou_loss(
- pred,
- target,
- weight,
- eps=self.eps,
- reduction=reduction,
- avg_factor=avg_factor,
- **kwargs)
- return loss
diff --git a/spaces/SIH/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/customized_langchain/docstore/__init__.py b/spaces/SIH/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/customized_langchain/docstore/__init__.py
deleted file mode 100644
index 6250d5c3aaf5de06e7daa358a513205f302527c2..0000000000000000000000000000000000000000
--- a/spaces/SIH/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/customized_langchain/docstore/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""Wrappers on top of docstores."""
-from streamlit_langchain_chat.customized_langchain.docstore.in_memory import InMemoryDocstore
-
-
-__all__ = [
- "InMemoryDocstore",
-]
diff --git a/spaces/Samhita/geolocator/gantry_callback/string_img_util.py b/spaces/Samhita/geolocator/gantry_callback/string_img_util.py
deleted file mode 100644
index d091339208d713c5f2189def4e6038eca86450a6..0000000000000000000000000000000000000000
--- a/spaces/Samhita/geolocator/gantry_callback/string_img_util.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import base64
-from io import BytesIO
-
-
-def read_b64_string(b64_string, return_data_type=False):
- """Read a base64-encoded string into an in-memory file-like object."""
- data_header, b64_data = split_and_validate_b64_string(b64_string)
- b64_buffer = BytesIO(base64.b64decode(b64_data))
- if return_data_type:
- return get_b64_filetype(data_header), b64_buffer
- else:
- return b64_buffer
-
-
-def get_b64_filetype(data_header):
- """Retrieves the filetype information from the data type header of a base64-encoded object."""
- _, file_type = data_header.split("/")
- return file_type
-
-
-def split_and_validate_b64_string(b64_string):
- """Return the data_type and data of a b64 string, with validation."""
- header, data = b64_string.split(",", 1)
- assert header.startswith("data:")
- assert header.endswith(";base64")
- data_type = header.split(";")[0].split(":")[1]
- return data_type, data
diff --git a/spaces/SeViLA/SeViLA/lavis/models/timesformer/helpers.py b/spaces/SeViLA/SeViLA/lavis/models/timesformer/helpers.py
deleted file mode 100644
index 1a8ebd1415fff35cd0f1e365a6f666dcb2f04fee..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/models/timesformer/helpers.py
+++ /dev/null
@@ -1,400 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-
- Based on https://github.com/facebookresearch/TimeSformer
-"""
-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# Copyright 2020 Ross Wightman
-# Modified model creation / weight loading / state_dict helpers
-
-import logging, warnings
-import os
-import math
-from collections import OrderedDict
-
-import torch
-import torch.utils.model_zoo as model_zoo
-import torch.nn.functional as F
-
-
-def load_state_dict(checkpoint_path, use_ema=False):
- if checkpoint_path and os.path.isfile(checkpoint_path):
- checkpoint = torch.load(checkpoint_path, map_location="cpu")
- state_dict_key = "state_dict"
- if isinstance(checkpoint, dict):
- if use_ema and "state_dict_ema" in checkpoint:
- state_dict_key = "state_dict_ema"
- if state_dict_key and state_dict_key in checkpoint:
- new_state_dict = OrderedDict()
- for k, v in checkpoint[state_dict_key].items():
- # strip `module.` prefix
- name = k[7:] if k.startswith("module") else k
- new_state_dict[name] = v
- state_dict = new_state_dict
- elif "model_state" in checkpoint:
- state_dict_key = "model_state"
- new_state_dict = OrderedDict()
- for k, v in checkpoint[state_dict_key].items():
- # strip `model.` prefix
- name = k[6:] if k.startswith("model") else k
- new_state_dict[name] = v
- state_dict = new_state_dict
- else:
- state_dict = checkpoint
- logging.info(
- "Loaded {} from checkpoint '{}'".format(state_dict_key, checkpoint_path)
- )
- return state_dict
- else:
- logging.error("No checkpoint found at '{}'".format(checkpoint_path))
- raise FileNotFoundError()
-
-
-def load_checkpoint(model, checkpoint_path, use_ema=False, strict=True):
- state_dict = load_state_dict(checkpoint_path, use_ema)
- model.load_state_dict(state_dict, strict=strict)
-
-
-# def resume_checkpoint(model, checkpoint_path, optimizer=None, loss_scaler=None, log_info=True):
-# resume_epoch = None
-# if os.path.isfile(checkpoint_path):
-# checkpoint = torch.load(checkpoint_path, map_location='cpu')
-# if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
-# if log_info:
-# _logger.info('Restoring model state from checkpoint...')
-# new_state_dict = OrderedDict()
-# for k, v in checkpoint['state_dict'].items():
-# name = k[7:] if k.startswith('module') else k
-# new_state_dict[name] = v
-# model.load_state_dict(new_state_dict)
-
-# if optimizer is not None and 'optimizer' in checkpoint:
-# if log_info:
-# _logger.info('Restoring optimizer state from checkpoint...')
-# optimizer.load_state_dict(checkpoint['optimizer'])
-
-# if loss_scaler is not None and loss_scaler.state_dict_key in checkpoint:
-# if log_info:
-# _logger.info('Restoring AMP loss scaler state from checkpoint...')
-# loss_scaler.load_state_dict(checkpoint[loss_scaler.state_dict_key])
-
-# if 'epoch' in checkpoint:
-# resume_epoch = checkpoint['epoch']
-# if 'version' in checkpoint and checkpoint['version'] > 1:
-# resume_epoch += 1 # start at the next epoch, old checkpoints incremented before save
-
-# if log_info:
-# _logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, checkpoint['epoch']))
-# else:
-# model.load_state_dict(checkpoint)
-# if log_info:
-# _logger.info("Loaded checkpoint '{}'".format(checkpoint_path))
-# return resume_epoch
-# else:
-# _logger.error("No checkpoint found at '{}'".format(checkpoint_path))
-# raise FileNotFoundError()
-
-
-def load_pretrained(
- model,
- cfg=None,
- num_classes=1000,
- in_chans=3,
- filter_fn=None,
- img_size=224,
- num_frames=8,
- num_patches=196,
- attention_type="divided_space_time",
- pretrained_model="",
- strict=True,
-):
- if cfg is None:
- cfg = getattr(model, "default_cfg")
- if cfg is None or "url" not in cfg or not cfg["url"]:
- logging.warning("Pretrained model URL is invalid, using random initialization.")
- return
-
- if len(pretrained_model) == 0:
- if cfg is None:
- logging.info(f"loading from default config {model.default_cfg}.")
- state_dict = model_zoo.load_url(cfg["url"], progress=False, map_location="cpu")
- else:
- try:
- state_dict = load_state_dict(pretrained_model)["model"]
- except:
- state_dict = load_state_dict(pretrained_model)
-
- if filter_fn is not None:
- state_dict = filter_fn(state_dict)
-
- if in_chans == 1:
- conv1_name = cfg["first_conv"]
- logging.info(
- "Converting first conv (%s) pretrained weights from 3 to 1 channel"
- % conv1_name
- )
- conv1_weight = state_dict[conv1_name + ".weight"]
- conv1_type = conv1_weight.dtype
- conv1_weight = conv1_weight.float()
- O, I, J, K = conv1_weight.shape
- if I > 3:
- assert conv1_weight.shape[1] % 3 == 0
- # For models with space2depth stems
- conv1_weight = conv1_weight.reshape(O, I // 3, 3, J, K)
- conv1_weight = conv1_weight.sum(dim=2, keepdim=False)
- else:
- conv1_weight = conv1_weight.sum(dim=1, keepdim=True)
- conv1_weight = conv1_weight.to(conv1_type)
- state_dict[conv1_name + ".weight"] = conv1_weight
- elif in_chans != 3:
- conv1_name = cfg["first_conv"]
- conv1_weight = state_dict[conv1_name + ".weight"]
- conv1_type = conv1_weight.dtype
- conv1_weight = conv1_weight.float()
- O, I, J, K = conv1_weight.shape
- if I != 3:
- logging.warning(
- "Deleting first conv (%s) from pretrained weights." % conv1_name
- )
- del state_dict[conv1_name + ".weight"]
- strict = False
- else:
- logging.info(
- "Repeating first conv (%s) weights in channel dim." % conv1_name
- )
- repeat = int(math.ceil(in_chans / 3))
- conv1_weight = conv1_weight.repeat(1, repeat, 1, 1)[:, :in_chans, :, :]
- conv1_weight *= 3 / float(in_chans)
- conv1_weight = conv1_weight.to(conv1_type)
- state_dict[conv1_name + ".weight"] = conv1_weight
-
- classifier_name = cfg["classifier"]
- if num_classes == 1000 and cfg["num_classes"] == 1001:
- # special case for imagenet trained models with extra background class in pretrained weights
- classifier_weight = state_dict[classifier_name + ".weight"]
- state_dict[classifier_name + ".weight"] = classifier_weight[1:]
- classifier_bias = state_dict[classifier_name + ".bias"]
- state_dict[classifier_name + ".bias"] = classifier_bias[1:]
- elif num_classes != state_dict[classifier_name + ".weight"].size(0):
- # print('Removing the last fully connected layer due to dimensions mismatch ('+str(num_classes)+ ' != '+str(state_dict[classifier_name + '.weight'].size(0))+').', flush=True)
- # completely discard fully connected for all other differences between pretrained and created model
- del state_dict[classifier_name + ".weight"]
- del state_dict[classifier_name + ".bias"]
- strict = False
-
- ## Resizing the positional embeddings in case they don't match
- logging.info(
- f"Resizing spatial position embedding from {state_dict['pos_embed'].size(1)} to {num_patches + 1}"
- )
- if num_patches + 1 != state_dict["pos_embed"].size(1):
- pos_embed = state_dict["pos_embed"]
- cls_pos_embed = pos_embed[0, 0, :].unsqueeze(0).unsqueeze(1)
- other_pos_embed = pos_embed[0, 1:, :].unsqueeze(0).transpose(1, 2)
- new_pos_embed = F.interpolate(
- other_pos_embed, size=(num_patches), mode="nearest"
- )
- new_pos_embed = new_pos_embed.transpose(1, 2)
- new_pos_embed = torch.cat((cls_pos_embed, new_pos_embed), 1)
- state_dict["pos_embed"] = new_pos_embed
-
- ## Resizing time embeddings in case they don't match
- if "time_embed" in state_dict and num_frames != state_dict["time_embed"].size(1):
- logging.info(
- f"Resizing temporal position embedding from {state_dict['time_embed'].size(1)} to {num_frames}"
- )
- time_embed = state_dict["time_embed"].transpose(1, 2)
- new_time_embed = F.interpolate(time_embed, size=(num_frames), mode="nearest")
- state_dict["time_embed"] = new_time_embed.transpose(1, 2)
-
- ## Initializing temporal attention
- if attention_type == "divided_space_time":
- new_state_dict = state_dict.copy()
- for key in state_dict:
- if "blocks" in key and "attn" in key:
- new_key = key.replace("attn", "temporal_attn")
- if not new_key in state_dict:
- new_state_dict[new_key] = state_dict[key]
- else:
- new_state_dict[new_key] = state_dict[new_key]
- if "blocks" in key and "norm1" in key:
- new_key = key.replace("norm1", "temporal_norm1")
- if not new_key in state_dict:
- new_state_dict[new_key] = state_dict[key]
- else:
- new_state_dict[new_key] = state_dict[new_key]
- state_dict = new_state_dict
-
- ## Loading the weights
- model.load_state_dict(state_dict, strict=False)
-
-
-def load_pretrained_imagenet(
- model,
- pretrained_model,
- cfg=None,
- ignore_classifier=True,
- num_frames=8,
- num_patches=196,
- **kwargs,
-):
- import timm
-
- logging.info(f"Loading vit_base_patch16_224 checkpoints.")
- loaded_state_dict = timm.models.vision_transformer.vit_base_patch16_224(
- pretrained=True
- ).state_dict()
-
- del loaded_state_dict["head.weight"]
- del loaded_state_dict["head.bias"]
-
- ## Initializing temporal attention
- new_state_dict = loaded_state_dict.copy()
- for key in loaded_state_dict:
- if "blocks" in key and "attn" in key:
- new_key = key.replace("attn", "temporal_attn")
- if not new_key in loaded_state_dict:
- new_state_dict[new_key] = loaded_state_dict[key]
- else:
- new_state_dict[new_key] = loaded_state_dict[new_key]
- if "blocks" in key and "norm1" in key:
- new_key = key.replace("norm1", "temporal_norm1")
- if not new_key in loaded_state_dict:
- new_state_dict[new_key] = loaded_state_dict[key]
- else:
- new_state_dict[new_key] = loaded_state_dict[new_key]
-
- loaded_state_dict = new_state_dict
-
- loaded_keys = loaded_state_dict.keys()
- model_keys = model.state_dict().keys()
-
- load_not_in_model = [k for k in loaded_keys if k not in model_keys]
- model_not_in_load = [k for k in model_keys if k not in loaded_keys]
-
- toload = dict()
- mismatched_shape_keys = []
- for k in model_keys:
- if k in loaded_keys:
- if model.state_dict()[k].shape != loaded_state_dict[k].shape:
- mismatched_shape_keys.append(k)
- else:
- toload[k] = loaded_state_dict[k]
-
- logging.info("Keys in loaded but not in model:")
- logging.info(f"In total {len(load_not_in_model)}, {sorted(load_not_in_model)}")
- logging.info("Keys in model but not in loaded:")
- logging.info(f"In total {len(model_not_in_load)}, {sorted(model_not_in_load)}")
- logging.info("Keys in model and loaded, but shape mismatched:")
- logging.info(
- f"In total {len(mismatched_shape_keys)}, {sorted(mismatched_shape_keys)}"
- )
-
- model.load_state_dict(toload, strict=False)
-
-
-def load_pretrained_kinetics(
- model,
- pretrained_model,
- cfg=None,
- ignore_classifier=True,
- num_frames=8,
- num_patches=196,
- **kwargs,
-):
- if cfg is None:
- cfg = getattr(model, "default_cfg")
- if cfg is None or "url" not in cfg or not cfg["url"]:
- logging.warning("Pretrained model URL is invalid, using random initialization.")
- return
-
- assert (
- len(pretrained_model) > 0
- ), "Path to pre-trained Kinetics weights not provided."
-
- state_dict = load_state_dict(pretrained_model)
-
- classifier_name = cfg["classifier"]
- if ignore_classifier:
-
- classifier_weight_key = classifier_name + ".weight"
- classifier_bias_key = classifier_name + ".bias"
-
- state_dict[classifier_weight_key] = model.state_dict()[classifier_weight_key]
- state_dict[classifier_bias_key] = model.state_dict()[classifier_bias_key]
-
- else:
- raise NotImplementedError(
- "[dxli] Not supporting loading Kinetics-pretrained ckpt with classifier."
- )
-
- ## Resizing the positional embeddings in case they don't match
- if num_patches + 1 != state_dict["pos_embed"].size(1):
- new_pos_embed = resize_spatial_embedding(state_dict, "pos_embed", num_patches)
- state_dict["pos_embed"] = new_pos_embed
-
- ## Resizing time embeddings in case they don't match
- if "time_embed" in state_dict and num_frames != state_dict["time_embed"].size(1):
- state_dict["time_embed"] = resize_temporal_embedding(
- state_dict, "time_embed", num_frames
- )
-
- ## Loading the weights
- try:
- model.load_state_dict(state_dict, strict=True)
- logging.info("Succeeded in loading Kinetics pre-trained weights.")
- except:
- logging.error("Error in loading Kinetics pre-trained weights.")
-
-
-def resize_spatial_embedding(state_dict, key, num_patches):
- logging.info(
- f"Resizing spatial position embedding from {state_dict[key].size(1)} to {num_patches + 1}"
- )
-
- pos_embed = state_dict[key]
-
- cls_pos_embed = pos_embed[0, 0, :].unsqueeze(0).unsqueeze(1)
- other_pos_embed = pos_embed[0, 1:, :].unsqueeze(0).transpose(1, 2)
-
- new_pos_embed = F.interpolate(other_pos_embed, size=(num_patches), mode="nearest")
- new_pos_embed = new_pos_embed.transpose(1, 2)
- new_pos_embed = torch.cat((cls_pos_embed, new_pos_embed), 1)
-
- return new_pos_embed
-
-
-def resize_temporal_embedding(state_dict, key, num_frames):
- logging.info(
- f"Resizing temporal position embedding from {state_dict[key].size(1)} to {num_frames}"
- )
-
- time_embed = state_dict[key].transpose(1, 2)
- new_time_embed = F.interpolate(time_embed, size=(num_frames), mode="nearest")
-
- return new_time_embed.transpose(1, 2)
-
-
-def detach_variable(inputs):
- if isinstance(inputs, tuple):
- out = []
- for inp in inputs:
- x = inp.detach()
- x.requires_grad = inp.requires_grad
- out.append(x)
- return tuple(out)
- else:
- raise RuntimeError(
- "Only tuple of tensors is supported. Got Unsupported input type: ",
- type(inputs).__name__,
- )
-
-
-def check_backward_validity(inputs):
- if not any(inp.requires_grad for inp in inputs):
- warnings.warn(
- "None of the inputs have requires_grad=True. Gradients will be None"
- )
diff --git a/spaces/ServerX/PorcoDiaz/lib/infer_pack/modules.py b/spaces/ServerX/PorcoDiaz/lib/infer_pack/modules.py
deleted file mode 100644
index c83289df7c79a4810dacd15c050148544ba0b6a9..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/lib/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 lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from lib.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/ServerX/PorcoDiaz/train/process_ckpt.py b/spaces/ServerX/PorcoDiaz/train/process_ckpt.py
deleted file mode 100644
index e3c3dba6df4b4f71a4d0865cdc96241d17da8781..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/train/process_ckpt.py
+++ /dev/null
@@ -1,259 +0,0 @@
-import torch, traceback, os, pdb, sys
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from collections import OrderedDict
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-
-
-def savee(ckpt, sr, if_f0, name, epoch, version, hps):
- try:
- opt = OrderedDict()
- opt["weight"] = {}
- for key in ckpt.keys():
- if "enc_q" in key:
- continue
- opt["weight"][key] = ckpt[key].half()
- opt["config"] = [
- hps.data.filter_length // 2 + 1,
- 32,
- hps.model.inter_channels,
- hps.model.hidden_channels,
- hps.model.filter_channels,
- hps.model.n_heads,
- hps.model.n_layers,
- hps.model.kernel_size,
- hps.model.p_dropout,
- hps.model.resblock,
- hps.model.resblock_kernel_sizes,
- hps.model.resblock_dilation_sizes,
- hps.model.upsample_rates,
- hps.model.upsample_initial_channel,
- hps.model.upsample_kernel_sizes,
- hps.model.spk_embed_dim,
- hps.model.gin_channels,
- hps.data.sampling_rate,
- ]
- opt["info"] = "%sepoch" % epoch
- opt["sr"] = sr
- opt["f0"] = if_f0
- opt["version"] = version
- torch.save(opt, "weights/%s.pth" % name)
- return "Success."
- except:
- return traceback.format_exc()
-
-
-def show_info(path):
- try:
- a = torch.load(path, map_location="cpu")
- return "Epochs: %s\nSample rate: %s\nPitch guidance: %s\nRVC Version: %s" % (
- a.get("info", "None"),
- a.get("sr", "None"),
- a.get("f0", "None"),
- a.get("version", "None"),
- )
- except:
- return traceback.format_exc()
-
-
-def extract_small_model(path, name, sr, if_f0, info, version):
- try:
- ckpt = torch.load(path, map_location="cpu")
- if "model" in ckpt:
- ckpt = ckpt["model"]
- opt = OrderedDict()
- opt["weight"] = {}
- for key in ckpt.keys():
- if "enc_q" in key:
- continue
- opt["weight"][key] = ckpt[key].half()
- if sr == "40k":
- opt["config"] = [
- 1025,
- 32,
- 192,
- 192,
- 768,
- 2,
- 6,
- 3,
- 0,
- "1",
- [3, 7, 11],
- [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
- [10, 10, 2, 2],
- 512,
- [16, 16, 4, 4],
- 109,
- 256,
- 40000,
- ]
- elif sr == "48k":
- if version == "v1":
- opt["config"] = [
- 1025,
- 32,
- 192,
- 192,
- 768,
- 2,
- 6,
- 3,
- 0,
- "1",
- [3, 7, 11],
- [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
- [10, 6, 2, 2, 2],
- 512,
- [16, 16, 4, 4, 4],
- 109,
- 256,
- 48000,
- ]
- else:
- opt["config"] = [
- 1025,
- 32,
- 192,
- 192,
- 768,
- 2,
- 6,
- 3,
- 0,
- "1",
- [3, 7, 11],
- [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
- [12, 10, 2, 2],
- 512,
- [24, 20, 4, 4],
- 109,
- 256,
- 48000,
- ]
- elif sr == "32k":
- if version == "v1":
- opt["config"] = [
- 513,
- 32,
- 192,
- 192,
- 768,
- 2,
- 6,
- 3,
- 0,
- "1",
- [3, 7, 11],
- [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
- [10, 4, 2, 2, 2],
- 512,
- [16, 16, 4, 4, 4],
- 109,
- 256,
- 32000,
- ]
- else:
- opt["config"] = [
- 513,
- 32,
- 192,
- 192,
- 768,
- 2,
- 6,
- 3,
- 0,
- "1",
- [3, 7, 11],
- [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
- [10, 8, 2, 2],
- 512,
- [20, 16, 4, 4],
- 109,
- 256,
- 32000,
- ]
- if info == "":
- info = "Extracted model."
- opt["info"] = info
- opt["version"] = version
- opt["sr"] = sr
- opt["f0"] = int(if_f0)
- torch.save(opt, "weights/%s.pth" % name)
- return "Success."
- except:
- return traceback.format_exc()
-
-
-def change_info(path, info, name):
- try:
- ckpt = torch.load(path, map_location="cpu")
- ckpt["info"] = info
- if name == "":
- name = os.path.basename(path)
- torch.save(ckpt, "weights/%s" % name)
- return "Success."
- except:
- return traceback.format_exc()
-
-
-def merge(path1, path2, alpha1, sr, f0, info, name, version):
- try:
-
- def extract(ckpt):
- a = ckpt["model"]
- opt = OrderedDict()
- opt["weight"] = {}
- for key in a.keys():
- if "enc_q" in key:
- continue
- opt["weight"][key] = a[key]
- return opt
-
- ckpt1 = torch.load(path1, map_location="cpu")
- ckpt2 = torch.load(path2, map_location="cpu")
- cfg = ckpt1["config"]
- if "model" in ckpt1:
- ckpt1 = extract(ckpt1)
- else:
- ckpt1 = ckpt1["weight"]
- if "model" in ckpt2:
- ckpt2 = extract(ckpt2)
- else:
- ckpt2 = ckpt2["weight"]
- if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())):
- return "Fail to merge the models. The model architectures are not the same."
- opt = OrderedDict()
- opt["weight"] = {}
- for key in ckpt1.keys():
- # try:
- if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
- min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
- opt["weight"][key] = (
- alpha1 * (ckpt1[key][:min_shape0].float())
- + (1 - alpha1) * (ckpt2[key][:min_shape0].float())
- ).half()
- else:
- opt["weight"][key] = (
- alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float())
- ).half()
- # except:
- # pdb.set_trace()
- opt["config"] = cfg
- """
- if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000]
- elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000]
- elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000]
- """
- opt["sr"] = sr
- opt["f0"] = 1 if f0 else 0
- opt["version"] = version
- opt["info"] = info
- torch.save(opt, "weights/%s.pth" % name)
- return "Success."
- except:
- return traceback.format_exc()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/payload.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/payload.py
deleted file mode 100644
index 6818be15372a32f83b4e083686597fcf640dbe94..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/payload.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Payload system for IPython.
-
-Authors:
-
-* Fernando Perez
-* Brian Granger
-"""
-
-#-----------------------------------------------------------------------------
-# Copyright (C) 2008-2011 The IPython Development Team
-#
-# Distributed under the terms of the BSD License. The full license is in
-# the file COPYING, distributed as part of this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
-
-from traitlets.config.configurable import Configurable
-from traitlets import List
-
-#-----------------------------------------------------------------------------
-# Main payload class
-#-----------------------------------------------------------------------------
-
-class PayloadManager(Configurable):
-
- _payload = List([])
-
- def write_payload(self, data, single=True):
- """Include or update the specified `data` payload in the PayloadManager.
-
- If a previous payload with the same source exists and `single` is True,
- it will be overwritten with the new one.
- """
-
- if not isinstance(data, dict):
- raise TypeError('Each payload write must be a dict, got: %r' % data)
-
- if single and 'source' in data:
- source = data['source']
- for i, pl in enumerate(self._payload):
- if 'source' in pl and pl['source'] == source:
- self._payload[i] = data
- return
-
- self._payload.append(data)
-
- def read_payload(self):
- return self._payload
-
- def clear_payload(self):
- self._payload = []
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_async_helpers.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_async_helpers.py
deleted file mode 100644
index a326c98d50470fb13373baa41e9e6f177f0f84f0..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_async_helpers.py
+++ /dev/null
@@ -1,316 +0,0 @@
-"""
-Test for async helpers.
-
-Should only trigger on python 3.5+ or will have syntax errors.
-"""
-import platform
-from itertools import chain, repeat
-from textwrap import dedent, indent
-from unittest import TestCase
-from IPython.testing.decorators import skip_without
-import sys
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
- from IPython import get_ipython
-
- ip = get_ipython()
-
-
-iprc = lambda x: ip.run_cell(dedent(x)).raise_error()
-iprc_nr = lambda x: ip.run_cell(dedent(x))
-
-from IPython.core.async_helpers import _should_be_async
-
-class AsyncTest(TestCase):
- def test_should_be_async(self):
- self.assertFalse(_should_be_async("False"))
- self.assertTrue(_should_be_async("await bar()"))
- self.assertTrue(_should_be_async("x = await bar()"))
- self.assertFalse(
- _should_be_async(
- dedent(
- """
- async def awaitable():
- pass
- """
- )
- )
- )
-
- def _get_top_level_cases(self):
- # These are test cases that should be valid in a function
- # but invalid outside of a function.
- test_cases = []
- test_cases.append(('basic', "{val}"))
-
- # Note, in all conditional cases, I use True instead of
- # False so that the peephole optimizer won't optimize away
- # the return, so CPython will see this as a syntax error:
- #
- # while True:
- # break
- # return
- #
- # But not this:
- #
- # while False:
- # return
- #
- # See https://bugs.python.org/issue1875
-
- test_cases.append(('if', dedent("""
- if True:
- {val}
- """)))
-
- test_cases.append(('while', dedent("""
- while True:
- {val}
- break
- """)))
-
- test_cases.append(('try', dedent("""
- try:
- {val}
- except:
- pass
- """)))
-
- test_cases.append(('except', dedent("""
- try:
- pass
- except:
- {val}
- """)))
-
- test_cases.append(('finally', dedent("""
- try:
- pass
- except:
- pass
- finally:
- {val}
- """)))
-
- test_cases.append(('for', dedent("""
- for _ in range(4):
- {val}
- """)))
-
-
- test_cases.append(('nested', dedent("""
- if True:
- while True:
- {val}
- break
- """)))
-
- test_cases.append(('deep-nested', dedent("""
- if True:
- while True:
- break
- for x in range(3):
- if True:
- while True:
- for x in range(3):
- {val}
- """)))
-
- return test_cases
-
- def _get_ry_syntax_errors(self):
- # This is a mix of tests that should be a syntax error if
- # return or yield whether or not they are in a function
-
- test_cases = []
-
- test_cases.append(('class', dedent("""
- class V:
- {val}
- """)))
-
- test_cases.append(('nested-class', dedent("""
- class V:
- class C:
- {val}
- """)))
-
- return test_cases
-
-
- def test_top_level_return_error(self):
- tl_err_test_cases = self._get_top_level_cases()
- tl_err_test_cases.extend(self._get_ry_syntax_errors())
-
- vals = ('return', 'yield', 'yield from (_ for _ in range(3))',
- dedent('''
- def f():
- pass
- return
- '''),
- )
-
- for test_name, test_case in tl_err_test_cases:
- # This example should work if 'pass' is used as the value
- with self.subTest((test_name, 'pass')):
- iprc(test_case.format(val='pass'))
-
- # It should fail with all the values
- for val in vals:
- with self.subTest((test_name, val)):
- msg = "Syntax error not raised for %s, %s" % (test_name, val)
- with self.assertRaises(SyntaxError, msg=msg):
- iprc(test_case.format(val=val))
-
- def test_in_func_no_error(self):
- # Test that the implementation of top-level return/yield
- # detection isn't *too* aggressive, and works inside a function
- func_contexts = []
-
- func_contexts.append(('func', False, dedent("""
- def f():""")))
-
- func_contexts.append(('method', False, dedent("""
- class MyClass:
- def __init__(self):
- """)))
-
- func_contexts.append(('async-func', True, dedent("""
- async def f():""")))
-
- func_contexts.append(('async-method', True, dedent("""
- class MyClass:
- async def f(self):""")))
-
- func_contexts.append(('closure', False, dedent("""
- def f():
- def g():
- """)))
-
- def nest_case(context, case):
- # Detect indentation
- lines = context.strip().splitlines()
- prefix_len = 0
- for c in lines[-1]:
- if c != ' ':
- break
- prefix_len += 1
-
- indented_case = indent(case, ' ' * (prefix_len + 4))
- return context + '\n' + indented_case
-
- # Gather and run the tests
-
- # yield is allowed in async functions, starting in Python 3.6,
- # and yield from is not allowed in any version
- vals = ('return', 'yield', 'yield from (_ for _ in range(3))')
-
- success_tests = zip(self._get_top_level_cases(), repeat(False))
- failure_tests = zip(self._get_ry_syntax_errors(), repeat(True))
-
- tests = chain(success_tests, failure_tests)
-
- for context_name, async_func, context in func_contexts:
- for (test_name, test_case), should_fail in tests:
- nested_case = nest_case(context, test_case)
-
- for val in vals:
- test_id = (context_name, test_name, val)
- cell = nested_case.format(val=val)
-
- with self.subTest(test_id):
- if should_fail:
- msg = ("SyntaxError not raised for %s" %
- str(test_id))
- with self.assertRaises(SyntaxError, msg=msg):
- iprc(cell)
-
- print(cell)
- else:
- iprc(cell)
-
- def test_nonlocal(self):
- # fails if outer scope is not a function scope or if var not defined
- with self.assertRaises(SyntaxError):
- iprc("nonlocal x")
- iprc("""
- x = 1
- def f():
- nonlocal x
- x = 10000
- yield x
- """)
- iprc("""
- def f():
- def g():
- nonlocal x
- x = 10000
- yield x
- """)
-
- # works if outer scope is a function scope and var exists
- iprc("""
- def f():
- x = 20
- def g():
- nonlocal x
- x = 10000
- yield x
- """)
-
-
- def test_execute(self):
- iprc("""
- import asyncio
- await asyncio.sleep(0.001)
- """
- )
-
- def test_autoawait(self):
- iprc("%autoawait False")
- iprc("%autoawait True")
- iprc("""
- from asyncio import sleep
- await sleep(0.1)
- """
- )
-
- def test_memory_error(self):
- """
- The pgen parser in 3.8 or before use to raise MemoryError on too many
- nested parens anymore"""
-
- iprc("(" * 200 + ")" * 200)
-
- @skip_without('curio')
- def test_autoawait_curio(self):
- iprc("%autoawait curio")
-
- @skip_without('trio')
- def test_autoawait_trio(self):
- iprc("%autoawait trio")
-
- @skip_without('trio')
- def test_autoawait_trio_wrong_sleep(self):
- iprc("%autoawait trio")
- res = iprc_nr("""
- import asyncio
- await asyncio.sleep(0)
- """)
- with self.assertRaises(TypeError):
- res.raise_error()
-
- @skip_without('trio')
- def test_autoawait_asyncio_wrong_sleep(self):
- iprc("%autoawait asyncio")
- res = iprc_nr("""
- import trio
- await trio.sleep(0)
- """)
- with self.assertRaises(RuntimeError):
- res.raise_error()
-
-
- def tearDown(self):
- ip.loop_runner = "asyncio"
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/tests/test_openpy.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/tests/test_openpy.py
deleted file mode 100644
index e205f06ace3893dd7a0a15cf4e0e77f9ec118e63..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/tests/test_openpy.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import io
-import os.path
-
-from IPython.utils import openpy
-
-mydir = os.path.dirname(__file__)
-nonascii_path = os.path.join(mydir, "../../core/tests/nonascii.py")
-
-
-def test_detect_encoding():
- with open(nonascii_path, "rb") as f:
- enc, lines = openpy.detect_encoding(f.readline)
- assert enc == "iso-8859-5"
-
-
-def test_read_file():
- with io.open(nonascii_path, encoding="iso-8859-5") as f:
- read_specified_enc = f.read()
- read_detected_enc = openpy.read_py_file(nonascii_path, skip_encoding_cookie=False)
- assert read_detected_enc == read_specified_enc
- assert "coding: iso-8859-5" in read_detected_enc
-
- read_strip_enc_cookie = openpy.read_py_file(
- nonascii_path, skip_encoding_cookie=True
- )
- assert "coding: iso-8859-5" not in read_strip_enc_cookie
-
-
-def test_source_to_unicode():
- with io.open(nonascii_path, "rb") as f:
- source_bytes = f.read()
- assert (
- openpy.source_to_unicode(source_bytes, skip_encoding_cookie=False).splitlines()
- == source_bytes.decode("iso-8859-5").splitlines()
- )
-
- source_no_cookie = openpy.source_to_unicode(source_bytes, skip_encoding_cookie=True)
- assert "coding: iso-8859-5" not in source_no_cookie
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/datasets/hrf.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/datasets/hrf.py
deleted file mode 100644
index 923203b51377f9344277fc561803d7a78bd2c684..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/datasets/hrf.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import os.path as osp
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class HRFDataset(CustomDataset):
- """HRF dataset.
-
- In segmentation map annotation for HRF, 0 stands for background, which is
- included in 2 categories. ``reduce_zero_label`` is fixed to False. The
- ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to
- '.png'.
- """
-
- CLASSES = ('background', 'vessel')
-
- PALETTE = [[120, 120, 120], [6, 230, 230]]
-
- def __init__(self, **kwargs):
- super(HRFDataset, self).__init__(
- img_suffix='.png',
- seg_map_suffix='.png',
- reduce_zero_label=False,
- **kwargs)
- assert osp.exists(self.img_dir)
diff --git a/spaces/TH5314/newbing/tailwind.config.js b/spaces/TH5314/newbing/tailwind.config.js
deleted file mode 100644
index 03da3c3c45be6983b9f5ffa6df5f1fd0870e9636..0000000000000000000000000000000000000000
--- a/spaces/TH5314/newbing/tailwind.config.js
+++ /dev/null
@@ -1,48 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
- content: [
- './src/pages/**/*.{js,ts,jsx,tsx,mdx}',
- './src/components/**/*.{js,ts,jsx,tsx,mdx}',
- './src/app/**/*.{js,ts,jsx,tsx,mdx}',
- './src/ui/**/*.{js,ts,jsx,tsx,mdx}',
- ],
- "darkMode": "class",
- theme: {
- extend: {
- colors: {
- 'primary-blue': 'rgb(var(--color-primary-blue) / )',
- secondary: 'rgb(var(--color-secondary) / )',
- 'primary-background': 'rgb(var(--primary-background) / )',
- 'primary-text': 'rgb(var(--primary-text) / )',
- 'secondary-text': 'rgb(var(--secondary-text) / )',
- 'light-text': 'rgb(var(--light-text) / )',
- 'primary-border': 'rgb(var(--primary-border) / )',
- },
- keyframes: {
- slideDownAndFade: {
- from: { opacity: 0, transform: 'translateY(-2px)' },
- to: { opacity: 1, transform: 'translateY(0)' },
- },
- slideLeftAndFade: {
- from: { opacity: 0, transform: 'translateX(2px)' },
- to: { opacity: 1, transform: 'translateX(0)' },
- },
- slideUpAndFade: {
- from: { opacity: 0, transform: 'translateY(2px)' },
- to: { opacity: 1, transform: 'translateY(0)' },
- },
- slideRightAndFade: {
- from: { opacity: 0, transform: 'translateX(2px)' },
- to: { opacity: 1, transform: 'translateX(0)' },
- },
- },
- animation: {
- slideDownAndFade: 'slideDownAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
- slideLeftAndFade: 'slideLeftAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
- slideUpAndFade: 'slideUpAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
- slideRightAndFade: 'slideRightAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
- },
- },
- },
- plugins: [require('@headlessui/tailwindcss'), require('tailwind-scrollbar')],
-}
diff --git a/spaces/TNK21/Text_summarizer/README.md b/spaces/TNK21/Text_summarizer/README.md
deleted file mode 100644
index 200271cf4dd6d31cf4254a38dc9f4a8e389e9acd..0000000000000000000000000000000000000000
--- a/spaces/TNK21/Text_summarizer/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text Summarization
-emoji: 😻
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/TabPFN/TabPFNEvaluation/TabPFN/scripts/tabular_evaluation.py b/spaces/TabPFN/TabPFNEvaluation/TabPFN/scripts/tabular_evaluation.py
deleted file mode 100644
index cd7f36e32948f80d0e266b47828df5f51fe3f78e..0000000000000000000000000000000000000000
--- a/spaces/TabPFN/TabPFNEvaluation/TabPFN/scripts/tabular_evaluation.py
+++ /dev/null
@@ -1,284 +0,0 @@
-import time
-import os
-from pathlib import Path
-
-from tqdm import tqdm
-import random
-import numpy as np
-
-from torch import nn
-
-from utils import torch_nanmean
-from datasets import *
-from model_builder import load_model
-from scripts.tabular_baselines import get_scoring_string
-from scripts import tabular_metrics
-from scripts.transformer_prediction_interface import *
-from scripts.baseline_prediction_interface import *
-"""
-===============================
-PUBLIC FUNCTIONS FOR EVALUATION
-===============================
-"""
-
-
-def eval_model(i, e, valid_datasets, test_datasets, eval_positions, bptt, add_name, base_path, device='cpu', eval_addition='', **kwargs):
- metrics_test, config_sample, model_path = eval_model_on_ds(i, e, test_datasets, eval_positions, bptt, add_name, base_path, device=device, eval_addition=eval_addition, **kwargs)
- metrics_valid, _, _ = eval_model_on_ds(i, e, valid_datasets, eval_positions, bptt, add_name, base_path, device=device, eval_addition=eval_addition, **kwargs)
- return {'mean_auc_test': metrics_test['mean_roc_at_1000'], 'mean_auc_valid': metrics_valid['mean_roc_at_1000'], 'mean_ce_test': metrics_test['mean_ce_at_1000'], 'mean_ce_valid': metrics_valid['mean_ce_at_1000'], 'config_sample': config_sample, 'model_path': model_path}
-
-def eval_model_on_ds(i, e, valid_datasets, eval_positions, bptt, add_name, base_path, device='cpu', eval_addition='', **kwargs):
-
- # How to use: evaluate_without_fitting(i,0,valid_datasets, [1024], 100000, add_name=model_string, base_path=base_path,)
- def check_file(e):
- model_file = f'models_diff/prior_diff_real_checkpoint{add_name}_n_{i}_epoch_{e}.cpkt'
- model_path = os.path.join(base_path, model_file)
- # print('Evaluate ', model_path)
- results_file = os.path.join(base_path,
- f'models_diff/prior_diff_real_results{add_name}_n_{i}_epoch_{e}_{eval_addition}.pkl')
- if not Path(model_path).is_file(): # or Path(results_file).is_file():
- # print('checkpoint exists: ', Path(model_file).is_file(), ', results are written:', Path(results_file).is_file())
- return None, None, None
- return model_file, model_path, results_file
-
- if e == -1: # use last checkpoint, if e == -1
- for e_ in range(100, -1, -1):
- model_file_, model_path_, results_file_ = check_file(e_)
- if model_file_ is not None:
- e = e_
- model_file, model_path, results_file = model_file_, model_path_, results_file_
- break
- else:
- model_file, model_path, results_file = check_file(e)
-
- model, config_sample = load_model(base_path, model_file, device, None, verbose=False)
- print(model[2].style_encoder)
-
- params = {'max_features': config_sample['num_features']
- , 'rescale_features': config_sample["normalize_by_used_features"]
- , 'normalize_to_ranking': config_sample["normalize_to_ranking"]
- , 'normalize_with_sqrt': config_sample.get("normalize_with_sqrt", False)
- }
- metrics_valid = evaluate(datasets=valid_datasets, model=model[2], method='transformer', device=device, overwrite=True,
- extend_features=True
- # just removed the style keyword but transformer is trained with style, just empty
- , save=False
- , metric_used=tabular_metrics.cross_entropy
- , return_tensor=True
- , verbose=False
- , eval_positions=eval_positions
- , bptt=bptt
- , base_path=None
- , inference_mode=True
- , **params
- , **kwargs)
-
- tabular_metrics.calculate_score_per_method(tabular_metrics.auc_metric, 'roc', metrics_valid, valid_datasets, eval_positions)
- tabular_metrics.calculate_score_per_method(tabular_metrics.cross_entropy, 'ce', metrics_valid, valid_datasets, eval_positions)
-
- return metrics_valid, config_sample, model_path
-
-
-def evaluate(datasets, bptt, eval_positions, metric_used, model
- , verbose=False
- , return_tensor=False
- , **kwargs):
- """
- Evaluates a list of datasets for a model function.
-
- :param datasets: List of datasets
- :param bptt: maximum sequence length
- :param eval_positions: List of positions where to evaluate models
- :param verbose: If True, is verbose.
- :param metric_used: Which metric is optimized for.
- :param return_tensor: Wheater to return results as a pytorch.tensor or numpy, this is only relevant for transformer.
- :param kwargs:
- :return:
- """
- overall_result = {'metric_used': get_scoring_string(metric_used)
- , 'bptt': bptt
- , 'eval_positions': eval_positions}
-
- aggregated_metric_datasets, num_datasets = torch.tensor(0.0), 0
-
- # For each dataset
- for [ds_name, X, y, categorical_feats, _, _] in tqdm.tqdm(datasets, desc='Iterate over datasets') if verbose else datasets:
- dataset_bptt = min(len(X), bptt)
- # if verbose and dataset_bptt < bptt:
- # print(f'Dataset too small for given sequence length, reducing to {len(X)} ({bptt})')
-
- aggregated_metric, num = torch.tensor(0.0), 0
- ds_result = {}
-
- for eval_position in (eval_positions if verbose else eval_positions):
- eval_position_real = int(dataset_bptt * 0.5) if 2 * eval_position > dataset_bptt else eval_position
- eval_position_bptt = int(eval_position_real * 2.0)
-
- r = evaluate_position(X, y, model=model
- , num_classes=len(torch.unique(y))
- , categorical_feats = categorical_feats
- , bptt = eval_position_bptt
- , ds_name=ds_name
- , eval_position = eval_position_real
- , metric_used = metric_used
- ,**kwargs)
-
- if r is None:
- continue
-
- _, outputs, ys, best_configs, time_used = r
-
- if torch.is_tensor(outputs):
- outputs = outputs.to(outputs.device)
- ys = ys.to(outputs.device)
-
- ys = ys.T
- ds_result[f'{ds_name}_best_configs_at_{eval_position}'] = best_configs
- ds_result[f'{ds_name}_outputs_at_{eval_position}'] = outputs
- ds_result[f'{ds_name}_ys_at_{eval_position}'] = ys
- ds_result[f'{ds_name}_time_at_{eval_position}'] = time_used
-
- new_metric = torch_nanmean(torch.stack([metric_used(ys[i], outputs[i]) for i in range(ys.shape[0])]))
-
- if not return_tensor:
- make_scalar = lambda x: float(x.detach().cpu().numpy()) if (torch.is_tensor(x) and (len(x.shape) == 0)) else x
- new_metric = make_scalar(new_metric)
- ds_result = {k: make_scalar(ds_result[k]) for k in ds_result.keys()}
-
- lib = torch if return_tensor else np
- if not lib.isnan(new_metric).any():
- aggregated_metric, num = aggregated_metric + new_metric, num + 1
-
- overall_result.update(ds_result)
- if num > 0:
- aggregated_metric_datasets, num_datasets = (aggregated_metric_datasets + (aggregated_metric / num)), num_datasets + 1
-
- overall_result['mean_metric'] = aggregated_metric_datasets / num_datasets
-
- return overall_result
-
-"""
-===============================
-INTERNAL HELPER FUNCTIONS
-===============================
-"""
-
-def check_file_exists(path):
- """Checks if a pickle file exists. Returns None if not, else returns the unpickled file."""
- if (os.path.isfile(path)):
- print(f'loading results from {path}')
- with open(path, 'rb') as f:
- return np.load(f, allow_pickle=True).tolist()
- return None
-
-def generate_valid_split(X, y, bptt, eval_position, split_number=1):
- """Generates a deteministic train-(test/valid) split. Both splits must contain the same classes and all classes in
- the entire datasets. If no such split can be sampled in 7 passes, returns None.
-
- :param X: torch tensor, feature values
- :param y: torch tensor, class values
- :param bptt: Number of samples in train + test
- :param eval_position: Number of samples in train, i.e. from which index values are in test
- :param split_number: The split id
- :return:
- """
- done, seed = False, 13
-
- torch.manual_seed(split_number)
- perm = torch.randperm(X.shape[0]) if split_number > 1 else torch.arange(0, X.shape[0])
- X, y = X[perm], y[perm]
-
- while not done:
- if seed > 20:
- return None, None # No split could be generated in 7 passes, return None
- random.seed(seed)
- i = random.randint(0, len(X) - bptt) if len(X) - bptt > 0 else 0
- y_ = y[i:i + bptt]
-
- # Checks if all classes from dataset are contained and classes in train and test are equal (contain same
- # classes) and
- done = len(torch.unique(y_)) == len(torch.unique(y))
- done = done and torch.all(torch.unique(y_) == torch.unique(y))
- done = done and len(torch.unique(y_[:eval_position])) == len(torch.unique(y_[eval_position:]))
- done = done and torch.all(torch.unique(y_[:eval_position]) == torch.unique(y_[eval_position:]))
- seed = seed + 1
-
- eval_xs = torch.stack([X[i:i + bptt].clone()], 1)
- eval_ys = torch.stack([y[i:i + bptt].clone()], 1)
-
- return eval_xs, eval_ys
-
-
-def evaluate_position(X, y, categorical_feats, model, bptt
- , eval_position, overwrite, save, base_path, path_interfix, method, ds_name, fetch_only=False
- , max_time=300, split_number=1
- , per_step_normalization=False, **kwargs):
- """
- Evaluates a dataset with a 'bptt' number of training samples.
-
- :param X: Dataset X
- :param y: Dataset labels
- :param categorical_feats: Indices of categorical features.
- :param model: Model function
- :param bptt: Sequence length.
- :param eval_position: Number of training samples.
- :param overwrite: Wheater to ove
- :param overwrite: If True, results on disk are overwritten.
- :param save:
- :param path_interfix: Used for constructing path to write on disk.
- :param method: Model name.
- :param ds_name: Datset name.
- :param fetch_only: Wheater to calculate or only fetch results.
- :param per_step_normalization:
- :param kwargs:
- :return:
- """
-
- if save:
- path = os.path.join(base_path, f'results/tabular/{path_interfix}/results_{method}_{ds_name}_{eval_position}_{bptt}_{split_number}.npy')
- #log_path =
-
- ## Load results if on disk
- if not overwrite:
- result = check_file_exists(path)
- if result is not None:
- if not fetch_only:
- print(f'Loaded saved result for {path}')
- return result
- elif fetch_only:
- print(f'Could not load saved result for {path}')
- return None
-
- ## Generate data splits
- eval_xs, eval_ys = generate_valid_split(X, y, bptt, eval_position, split_number=split_number)
- if eval_xs is None:
- return None
- print(f"No dataset could be generated {ds_name} {bptt}")
-
- eval_ys = (eval_ys > torch.unique(eval_ys).unsqueeze(0)).sum(axis=1).unsqueeze(-1)
-
- start_time = time.time()
-
- if isinstance(model, nn.Module): # Two separate predict interfaces for transformer and baselines
- outputs, best_configs = transformer_predict(model, eval_xs, eval_ys, eval_position, categorical_feats=categorical_feats, **kwargs), None
- else:
- _, outputs, best_configs = baseline_predict(model, eval_xs, eval_ys, categorical_feats
- , eval_pos=eval_position
- , max_time=max_time, **kwargs)
-
- eval_ys = eval_ys[eval_position:]
- if outputs is None:
- return None
-
- if torch.is_tensor(outputs): # Transfers data to cpu for saving
- outputs = outputs.cpu()
- eval_ys = eval_ys.cpu()
-
- ds_result = None, outputs, eval_ys, best_configs, time.time() - start_time
-
- if save:
- with open(path, 'wb') as f:
- np.save(f, ds_result)
- print(f'saved results to {path}')
-
- return ds_result
\ No newline at end of file
diff --git a/spaces/TencentARC/VLog/models/grit_src/grit/data/datasets/object365.py b/spaces/TencentARC/VLog/models/grit_src/grit/data/datasets/object365.py
deleted file mode 100644
index 8b8cc19da23d8397284b50588ee46e750b5b7552..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/grit/data/datasets/object365.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import logging
-import os
-from fvcore.common.timer import Timer
-from detectron2.structures import BoxMode
-from fvcore.common.file_io import PathManager
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from lvis import LVIS
-
-logger = logging.getLogger(__name__)
-
-__all__ = ["load_o365_json", "register_o365_instances"]
-
-
-def register_o365_instances(name, metadata, json_file, image_root):
- DatasetCatalog.register(name, lambda: load_o365_json(
- json_file, image_root, name))
- MetadataCatalog.get(name).set(
- json_file=json_file, image_root=image_root,
- evaluator_type="lvis", **metadata
- )
-
-
-def get_o365_meta():
- categories = [{'supercategory': 'object', 'id': 1, 'name': 'object'}]
- o365_categories = sorted(categories, key=lambda x: x["id"])
- thing_classes = [k["name"] for k in o365_categories]
- meta = {"thing_classes": thing_classes}
- return meta
-
-
-def load_o365_json(json_file, image_root, dataset_name=None):
- '''
- Load Object365 class name text for object description for GRiT
- '''
-
- json_file = PathManager.get_local_path(json_file)
-
- timer = Timer()
- lvis_api = LVIS(json_file)
- if timer.seconds() > 1:
- logger.info("Loading {} takes {:.2f} seconds.".format(
- json_file, timer.seconds()))
-
- class_names = {}
- sort_cat = sorted(lvis_api.dataset['categories'], key=lambda x: x['id'])
- for x in sort_cat:
- if '/' in x['name']:
- text = ''
- for xx in x['name'].split('/'):
- text += xx
- text += ' '
- text = text[:-1]
- else:
- text = x['name']
- class_names[x['id']] = text
-
- img_ids = sorted(lvis_api.imgs.keys())
- imgs = lvis_api.load_imgs(img_ids)
- anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
-
- ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
- assert len(set(ann_ids)) == len(ann_ids), \
- "Annotation ids in '{}' are not unique".format(json_file)
-
- imgs_anns = list(zip(imgs, anns))
- logger.info("Loaded {} images in the LVIS v1 format from {}".format(
- len(imgs_anns), json_file))
-
- dataset_dicts = []
-
- for (img_dict, anno_dict_list) in imgs_anns:
- record = {}
- if "file_name" in img_dict:
- file_name = img_dict["file_name"]
- record["file_name"] = os.path.join(image_root, file_name)
-
- record["height"] = int(img_dict["height"])
- record["width"] = int(img_dict["width"])
- image_id = record["image_id"] = img_dict["id"]
-
- objs = []
- for anno in anno_dict_list:
- assert anno["image_id"] == image_id
- if anno.get('iscrowd', 0) > 0:
- continue
- obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS}
- obj["category_id"] = 0
- obj["object_description"] = class_names[anno['category_id']]
-
- objs.append(obj)
- record["annotations"] = objs
- if len(record["annotations"]) == 0:
- continue
- record["task"] = "ObjectDet"
- dataset_dicts.append(record)
-
- return dataset_dicts
-
-
-_CUSTOM_SPLITS_LVIS = {
- "object365_train": ("object365/images/train/", "object365/annotations/train_v1.json"),
-}
-
-
-for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items():
- register_o365_instances(
- key,
- get_o365_meta(),
- os.path.join("datasets", json_file) if "://" not in json_file else json_file,
- os.path.join("datasets", image_root),
- )
\ No newline at end of file
diff --git a/spaces/Theivaprakasham/yolov6/tools/quantization/tensorrt/training_aware/QAT_quantizer.py b/spaces/Theivaprakasham/yolov6/tools/quantization/tensorrt/training_aware/QAT_quantizer.py
deleted file mode 100644
index 4621aa426fd0ba7875717a1442ab07a74cc49b30..0000000000000000000000000000000000000000
--- a/spaces/Theivaprakasham/yolov6/tools/quantization/tensorrt/training_aware/QAT_quantizer.py
+++ /dev/null
@@ -1,39 +0,0 @@
-#
-# QAT_quantizer.py
-# YOLOv6
-#
-# Created by Meituan on 2022/06/24.
-# Copyright © 2022
-#
-
-from absl import logging
-from pytorch_quantization import nn as quant_nn
-from pytorch_quantization import quant_modules
-
-# Call this function before defining the model
-def tensorrt_official_qat():
- # Quantization Aware Training is based on Straight Through Estimator (STE) derivative approximation.
- # It is some time known as “quantization aware training”.
-
- # PyTorch-Quantization is a toolkit for training and evaluating PyTorch models with simulated quantization.
- # Quantization can be added to the model automatically, or manually, allowing the model to be tuned for accuracy and performance.
- # Quantization is compatible with NVIDIAs high performance integer kernels which leverage integer Tensor Cores.
- # The quantized model can be exported to ONNX and imported by TensorRT 8.0 and later.
- # https://github.com/NVIDIA/TensorRT/blob/main/tools/pytorch-quantization/examples/finetune_quant_resnet50.ipynb
-
- # The example to export the
- # model.eval()
- # quant_nn.TensorQuantizer.use_fb_fake_quant = True # We have to shift to pytorch's fake quant ops before exporting the model to ONNX
- # opset_version = 13
-
- # Export ONNX for multiple batch sizes
- # print("Creating ONNX file: " + onnx_filename)
- # dummy_input = torch.randn(batch_onnx, 3, 224, 224, device='cuda') #TODO: switch input dims by model
- # torch.onnx.export(model, dummy_input, onnx_filename, verbose=False, opset_version=opset_version, enable_onnx_checker=False, do_constant_folding=True)
- try:
- quant_modules.initialize()
- except NameError:
- logging.info("initialzation error for quant_modules")
-
-# def QAT_quantizer():
-# coming soon
\ No newline at end of file
diff --git a/spaces/Vegecken/sovits4dzl/vdecoder/hifigan/utils.py b/spaces/Vegecken/sovits4dzl/vdecoder/hifigan/utils.py
deleted file mode 100644
index 9c93c996d3cc73c30d71c1fc47056e4230f35c0f..0000000000000000000000000000000000000000
--- a/spaces/Vegecken/sovits4dzl/vdecoder/hifigan/utils.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import glob
-import os
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-# matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
-
- fig.canvas.draw()
- plt.close()
-
- return fig
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def apply_weight_norm(m):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size*dilation - dilation)/2)
-
-
-def load_checkpoint(filepath, device):
- assert os.path.isfile(filepath)
- print("Loading '{}'".format(filepath))
- checkpoint_dict = torch.load(filepath, map_location=device)
- print("Complete.")
- return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
- print("Saving checkpoint to {}".format(filepath))
- torch.save(obj, filepath)
- print("Complete.")
-
-
-def del_old_checkpoints(cp_dir, prefix, n_models=2):
- pattern = os.path.join(cp_dir, prefix + '????????')
- cp_list = glob.glob(pattern) # get checkpoint paths
- cp_list = sorted(cp_list)# sort by iter
- if len(cp_list) > n_models: # if more than n_models models are found
- for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
- open(cp, 'w').close()# empty file contents
- os.unlink(cp)# delete file (move to trash when using Colab)
-
-
-def scan_checkpoint(cp_dir, prefix):
- pattern = os.path.join(cp_dir, prefix + '????????')
- cp_list = glob.glob(pattern)
- if len(cp_list) == 0:
- return None
- return sorted(cp_list)[-1]
-
diff --git a/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/data/__init__.py b/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/data/__init__.py
deleted file mode 100644
index 708a3dcead8dda89374a021177481dacae9f7fe9..0000000000000000000000000000000000000000
--- a/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/data/__init__.py
+++ /dev/null
@@ -1,8 +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.
-
-# flake8: noqa
-from . import audio, audio_dataset
diff --git a/spaces/XPMaster/chainladder/app.py b/spaces/XPMaster/chainladder/app.py
deleted file mode 100644
index 6db54a7dab6e66b15f87a7de213d7d40c8020fee..0000000000000000000000000000000000000000
--- a/spaces/XPMaster/chainladder/app.py
+++ /dev/null
@@ -1,405 +0,0 @@
-import pandas as pd
-import numpy as np
-import re
-import os
-import warnings
-import gradio as gr
-import re
-import chainladder as cl
-import zipfile
-import datetime
-import openpyxl
-from funcs import *
-from openpyxl.styles import Font, PatternFill
-from openpyxl.utils import column_index_from_string, get_column_letter
-
-fail = "❌"
-success = '✅'
-current_year = int(datetime.datetime.now().year)
-years_list = [str(x) for x in range(1850,current_year+500)]
-months_list = [
- "Jan - 1", "Feb - 2", "Mar - 3", "Apr - 4",
- "May - 5", "Jun - 6", "Jul - 7", "Aug - 8",
- "Sep - 9", "Oct - 10", "Nov - 11", "Dec - 12"
-]
-
-styling="""
-#group {
-background-color: #1f2937;
-border: 1px solid #374151;
-}
-#button {
-/* Permalink - use to edit and share this gradient: https://colorzilla.com/gradient-editor/#f6e6b4+0,ed9017+100;Yellow+3D+%231 */
-background: #f6e6b4; /* Old browsers */
-background: -moz-linear-gradient(top, #f6e6b4 0%, #ed9017 100%); /* FF3.6-15 */
-background: -webkit-linear-gradient(top, #f6e6b4 0%,#ed9017 100%); /* Chrome10-25,Safari5.1-6 */
-background: linear-gradient(to bottom, #f6e6b4 0%,#ed9017 100%); /* W3C, IE10+, FF16+, Chrome26+, Opera12+, Safari7+ */
-filter: progid:DXImageTransform.Microsoft.gradient( startColorstr='#f6e6b4', endColorstr='#ed9017',GradientType=0 ); /* IE6-9 */
-text-shadow: 2px 2px 10px #000000;
-}
-#column {
- display: flex;
- flex-direction: column;
- align-items: center;
- justify-content: center;
-}
-
-body {
- display: flex;
- justify-content: center;
- align-items: center;
- min-height: 100vh;
- margin: 0;
-}
-p {
- font-family: 'Arial', sans-serif;
- font-size: 20px;
- color: #333;
- background-color: #e1f5fe;
- padding: 15px;
- border-radius: 5px;
- box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1);
- max-width: 800px;
- text-align: center;
-}
-
-.center-emoji {
- display: flex;
- align-items: center;
- justify-content: center;
- width: 100px;
- height: 100px;
- border: 1px solid #000;
-}
-
-.center-emoji::before {
- content: "\\1F4CA"; /* Unicode code for 📊 */
- font-size: 36px;
-}
-
-footer {
- visibility: hidden
-}
-"""
-
-
-def IBNR_OS(key,filename):
- try:
- # Read the Excel file
- df = pd.read_excel(filename)
-
- # Filter rows with numeric values in "Gross IBN(E)R" or "Gross OS" columns
- numeric_mask = (pd.to_numeric(df['Gross IBN(E)R'], errors='coerce').notna() |
- pd.to_numeric(df['Gross OS'], errors='coerce').notna())
- df_filtered = df[numeric_mask]
-
- # Keep only the required columns
- required_columns = ['LOB', 'Gross IBN(E)R', 'Gross OS']
- df_filtered = df_filtered[required_columns]
-
- # Convert columns to integers and replace missing values with 0
- df_filtered = df_filtered.fillna(0).astype({'LOB': str, 'Gross IBN(E)R': int, 'Gross OS': int})
- # Iterate over distinct LOB values
- distinct_lob_values = df_filtered['LOB'].unique()
-
- for lob_value in distinct_lob_values:
- # Filter rows for the current LOB value
- lob_rows = df_filtered[df_filtered['LOB'] == lob_value]
- # Get the individual columns as lists
- gross_ibnr_values = lob_rows['Gross IBN(E)R'].tolist()
- gross_os_values = lob_rows['Gross OS'].tolist()
- # Print the results and retrieve lists
- if lob_value == key:
- print(f"LOB: {lob_value}")
- print("Gross IBN(E)R:", gross_ibnr_values)
- print("Gross OS:", gross_os_values)
- print()
- return gross_ibnr_values, gross_os_values
- return None, None
- except Exception as e:
- return 'Parameters file has the following issue: {'+str(e)+"} Hint allowed names are ['LOB','Gross IBN(E)R','Gross OS','sigma','loss ratio','simulations_n','tail','method']",False
-
-def triangle(path, start_date , end_date,
- gross_IBNR = [156576 ,214177 ,146459 ,390682 ,548713 ,706833 ,860458 ,1054578 ,1538313 ,2144731 ,3090198 ,5385887 ,47465981],
- gross_OS_Claims= [1000721 ,1429259 ,1056222 ,1749351 ,2253296 ,1811757 ,2265959 ,2712321 ,3485914 ,5675081 ,9648877 ,18946443 ,61422600 ],
- showextra=False,
- extract_file=None):
- # Read data from excel sheet
- issues = []
- Motor_Claims = pd.read_excel(path)
- # Filter out rows where accident_quarter_bracket or transaction_quarter_bracket is null
- Motor_Claims = Motor_Claims[Motor_Claims['accident_quarter_bracket'].notnull()]
- Motor_Claims = Motor_Claims[Motor_Claims['transaction_quarter_bracket'].notnull()]
- # Convert the accident_quarter_bracket and transaction_quarter_bracket columns to accident_period and transaction_period
- Motor_Claims['accident_period'] = get_period(Motor_Claims, 'accident_quarter_bracket')
- Motor_Claims['transaction_period'] = get_period(Motor_Claims, 'transaction_quarter_bracket')
- # Convert the column names to lowercase
- Motor_Claims.columns = [x.lower() for x in Motor_Claims.columns]
- # Loop over each unique value of the lob column
- name = os.path.basename(path.split(".")[0])+"_triangles"
- # name = path.split(".")[0]+"_triangles"
- writer = pd.ExcelWriter(name+'.xlsx', engine='openpyxl')
- try:
- writer.book = Workbook()
- writer.book.remove(writer.book["Sheet"])
- except:
- pass
- for LOB in Motor_Claims['lob'].unique():
-
- try:
- # Select the rows where the LOB column matches the current LOB value
- df_lob = Motor_Claims[(Motor_Claims['lob'] == LOB)]
-
- # Select only the columns relevant to paid claims
- df_lob = select_columns_Paid(df_lob)
-
- # Filter the data by date range
- df_lob = df_lob[((df_lob['accident_period'] >= start_date) & (df_lob['accident_period'] <= end_date))]
- #display(df_lob)
- #return None
- except:
- # If an exception is caught, print the name of the LOB and continue to the next one
- issues.append(LOB+' has an issue, skipped.')
- print(issues[-1])
- continue
-
- # Create an incremental and cumulative triangle based on the paid amount
- triangle_df = cl.Triangle(df_lob, origin='accident_period', development='transaction_period', columns=['paid_amount'], cumulative=False, index=['lob'])
- cumulative_triangle_df = triangle_df.incr_to_cum()
- incremental_triangle_df = triangle_df
-
- # If showextra is True, display the incremental triangle, cumulative triangle, and age-to-age factors heatmap
- if showextra:
- print('Incremental Triangle')
- print(incremental_triangle_df)
- print('Cumulative Triangle')
- print(cumulative_triangle_df)
- print('Age to Age factors')
- print(cumulative_triangle_df.link_ratio.heatmap(cmap='Reds'))
-
- # Apply the Mack chainladder model to the cumulative triangle to estimate reserves
- pd.options.display.float_format = '{:.0f}'.format
- mack = cl.MackChainladder().fit(cumulative_triangle_df)
- Mack_Summary = mack.summary_.to_frame().reset_index()
- Mack_Summary = Mack_Summary.rename(columns={'index': 'Loss Quarter'})
- Latest = Mack_Summary['Latest']
- Loss_Quarter = Mack_Summary['Loss Quarter']
- Latest = pd.DataFrame(Latest)
- Loss_Quarter = pd.DataFrame(Loss_Quarter)
-
- # if there is a file to extract from it will do so
- if extract_file != None:
- gross_IBNR , gross_OS_Claims = IBNR_OS(LOB,extract_file)
- if gross_IBNR == None or gross_OS_Claims == None:
- issues.append(LOB+" does not have gross_IBNR or gross_OS_Claims in ("+os.path.basename(extract_file)+"), skipped.")
- print(issues[-1])
- continue
- elif gross_OS_Claims == False: # this is set to False in IBNR_OS if the extract file has an issue.
- return [gross_IBNR, False] # this is used as the error name in IBNR_OS, weird but we dont need more variables.
-
-
- gross_IBNR = pd.DataFrame(gross_IBNR)
- gross_OS_Claims = pd.DataFrame(gross_OS_Claims)
-
- # Combine calculated results and display in a DataFrame
- df=Loss_Quarter
- df['Latest']=Latest
- df['gross_IBNR']=gross_IBNR
- df['gross_OS_Claims']=gross_OS_Claims
- df['Ultimate_Claims_amount']=df['Latest']+df['gross_IBNR']+df['gross_OS_Claims']
- pd.options.display.float_format = '{:.4f}'.format
- ultimates = df['Ultimate_Claims_amount'].iloc[1:].to_frame()
- df['CF']=(df['Latest']/df['Ultimate_Claims_amount'])
- df['CDF']=(1/df['CF']) #cumulative development factor
-
- ATA=[]
- for i in range(len(df)-1,-1,-1):
- ATA_results=df['CDF'].iloc[i]/df['CDF'].iloc[i-1]
- #print(ATA_results)
- ATA.append(ATA_results)
- df['ATA']=ATA
- # Output results for each LOB
- text = 'Line of business: '+LOB
- print()
- print(text)
- print()
- text = "Without Mean Replacement"
- print(text)
- outcome_before = ATAOperate(cumulative_triangle_df,ATA,replace=False)
- print(outcome_before)
- text = "With Mean Replacement"
- print(text)
- #Calculate and replace residuals
- outcome = ATAOperate(cumulative_triangle_df,ATA)
- # Display result
- print(outcome)
- # Calculate Adj S^2
- adj = calculate_average(outcome)
- # Converting IBNR and claims to serieses and slicing them to the proper lengths
- series_ibnr = gross_IBNR.squeeze().iloc[1:len(ultimates)+1]
- series_claims = gross_OS_Claims.squeeze().iloc[1:len(ultimates)+1]
- # Calculating Proc SD
- proc_sd_result = proc_sd(adj,ultimates)
- # Calculating Coef. Variance
- cof = calc_cof(series_ibnr, series_claims, proc_sd_result.squeeze())
- # Displaying formatted Proc SD and Coef Variance side by Side
- merged = merge_dataframes(format_dataframe(proc_sd_result), cof)
- print(merged)
- # Making other data structures into dataframes for export
- formatted_incremental = format_dataframe(incremental_triangle_df.to_frame())
- formatted_cumulative = format_dataframe(cumulative_triangle_df.to_frame())
- formatted_link_ratio = format_dataframe(cumulative_triangle_df.link_ratio.to_frame())
- ATAdf = pd.DataFrame([ATA])
- adj = adj.transpose()
- #adjdf = pd.DataFrame([adj])
- ibnrdf = pd.DataFrame(series_ibnr).transpose()
- claimsdf = pd.DataFrame(series_claims).transpose()
- # Adjusting indices to display properly in excel sheet
- outcome_before = outcome_before.reset_index()
- outcome = outcome.reset_index()
- outcome_before.rename_axis('date', axis='index', inplace=True)
- outcome.rename_axis('date', axis='index', inplace=True)
- merged = merged.reset_index(drop=True)
- merged.index = merged.index + 1
-
- # Write the dataframes to the current LOB's sheet
- dataframes = [outcome_before, outcome, merged, formatted_incremental, formatted_cumulative, formatted_link_ratio,ATAdf,adj,ibnrdf,claimsdf]
- labels = ['Before replacement', 'After replacement', 'Proc_SD & Coeff', 'Incremental triangle', 'Cumulative triangle', 'Link Ratio','ATA',"Adj S^2","IBNR",'OS Claims']
- position = 1
- # Define the font style for Heading 2
- heading2_font = Font(size=12, bold=True)
- for df, label in zip(dataframes, labels):
- df.to_excel(writer, sheet_name=LOB, startrow=position, startcol=0, index=False)
- workbook = writer.book
- worksheet = writer.sheets[LOB]
- row1 = worksheet[position]
- for cell in row1:
- cell.font = heading2_font
- worksheet.cell(row=position, column=1, value=label)
- position += df.shape[0] + 3
-
- writer = resize_columns(writer)
- writer.close()
-
- return [name+'.xlsx',issues,True]
-
-def failure(msg):
- return gr.File.update(value=None,visible=False),msg
-
-def successful(msg):
- pass
-
-def process(inp,files,file2,start_date,end_date,start_month,end_month):
- print(file2)
- if files is None:
- return failure(fail+' No file provided')
- if len(inp) == 0:
- return failure(fail+' One operation must be selected at least')
- if int(start_date) > int(end_date):
- return failure(fail+' Start date cannot be greater than End date')
-
- start_date = append_last_day(str(start_date)+'-'+str(start_month).split(" ")[-1])
- end_date = append_last_day(str(end_date)+"-"+str(end_month).split(" ")[-1])
-
- status = []
- processed = []
- names = unzip_files(files.name)
-
- if file2 is not None:
- if valid(file2.name):
- file2 = file2.name
- else:
- return failure(fail+" IBNR/OS optional file is of invalid type (CSV and XLSX only)")
-
- for name in names:
- if valid(name):
- print(name,start_date,end_date,file2,'\n'*10)
- #triangle(name, start_date , end_date)
- # name = os.path.basename(triangle(name, start_date , end_date))
- for element in inp:
- if 'mack' in element.lower():
-
- cleaned_name = os.path.basename(name)
- processed_name = triangle(name, start_date , end_date,extract_file=file2)
- if processed_name[-1]:
- processed.append(processed_name[0])
- if len(processed_name[1]) > 0:
- #processed_name[1] = '\n'.join(f"{"*"*(index+1)}⚠️. {value}" for index, value in enumerate(processed_name[1]))
- processed_name[1] = '\n'.join(f'{"*"*(index+1)}⚠️. {value}' for index, value in enumerate(processed_name[1]))
- else:
- processed_name[1] = ""
- status.append(success+f" Success ({element}) "+cleaned_name+'\n'+processed_name[1])
- else:
- status.append(fail+f" Failed ({element}): {processed_name[0]} ("+cleaned_name+")") # The first element of the returned tuple has the problem in it.
-
- if 'bootstrap' in element.lower():
- status.append(success+f" Success ({element}) "+cleaned_name)
- if 'sbf' in element.lower():
- status.append(success+f" Success ({element}) "+cleaned_name)
- else:
- name = os.path.basename(name)
- status.append(fail+" Failure "+name)
-
- msg = '\n'.join(f"{index + 1}.{value}" for index, value in enumerate(status))
- if len(processed) <= 0:
- return failure(msg)
- final_file = zip_files(processed)
- return gr.File.update(value=final_file,visible=True),msg
-
-
-options = ['Mack','Bootstrap','Stochastic Bornhuetter-Ferguson (SBF)']
-with gr.Blocks(css=styling) as demo:
- gr.HTML("""
-
-
-
-
-
- Actuarial Software Intro
-
-
-
-```
-2. What exact command you run:
-3. __Full logs__ or other relevant observations:
-```
-
-```
-4. please simplify the steps as much as possible so they do not require additional resources to
- run, such as a private dataset.
-
-## Expected behavior:
-
-If there are no obvious error in "full logs" provided above,
-please tell us the expected behavior.
-
-## Environment:
-
-Provide your environment information using the following command:
-```
-wget -nc -q https://github.com/facebookresearch/detectron2/raw/main/detectron2/utils/collect_env.py && python collect_env.py
-```
-
-If your issue looks like an installation issue / environment issue,
-please first try to solve it yourself with the instructions in
-https://detectron2.readthedocs.io/tutorials/install.html#common-installation-issues
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/defaults.py b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/defaults.py
deleted file mode 100644
index 848486dfe91a62559e6ae35120a4dac26d4bd66d..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/defaults.py
+++ /dev/null
@@ -1,635 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .config import CfgNode as CN
-
-# NOTE: given the new config system
-# (https://detectron2.readthedocs.io/en/latest/tutorials/lazyconfigs.html),
-# we will stop adding new functionalities to default CfgNode.
-
-# -----------------------------------------------------------------------------
-# Convention about Training / Test specific parameters
-# -----------------------------------------------------------------------------
-# Whenever an argument can be either used for training or for testing, the
-# corresponding name will be post-fixed by a _TRAIN for a training parameter,
-# or _TEST for a test-specific parameter.
-# For example, the number of images during training will be
-# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be
-# IMAGES_PER_BATCH_TEST
-
-# -----------------------------------------------------------------------------
-# Config definition
-# -----------------------------------------------------------------------------
-
-_C = CN()
-
-# The version number, to upgrade from old configs to new ones if any
-# changes happen. It's recommended to keep a VERSION in your config file.
-_C.VERSION = 2
-
-_C.MODEL = CN()
-_C.MODEL.LOAD_PROPOSALS = False
-_C.MODEL.MASK_ON = False
-_C.MODEL.KEYPOINT_ON = False
-_C.MODEL.DEVICE = "cuda"
-_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN"
-
-# Path (a file path, or URL like detectron2://.., https://..) to a checkpoint file
-# to be loaded to the model. You can find available models in the model zoo.
-_C.MODEL.WEIGHTS = ""
-
-# Values to be used for image normalization (BGR order, since INPUT.FORMAT defaults to BGR).
-# To train on images of different number of channels, just set different mean & std.
-# Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675]
-_C.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675]
-# When using pre-trained models in Detectron1 or any MSRA models,
-# std has been absorbed into its conv1 weights, so the std needs to be set 1.
-# Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std)
-_C.MODEL.PIXEL_STD = [1.0, 1.0, 1.0]
-
-
-# -----------------------------------------------------------------------------
-# INPUT
-# -----------------------------------------------------------------------------
-_C.INPUT = CN()
-# By default, {MIN,MAX}_SIZE options are used in transforms.ResizeShortestEdge.
-# Please refer to ResizeShortestEdge for detailed definition.
-# Size of the smallest side of the image during training
-_C.INPUT.MIN_SIZE_TRAIN = (800,)
-# Sample size of smallest side by choice or random selection from range give by
-# INPUT.MIN_SIZE_TRAIN
-_C.INPUT.MIN_SIZE_TRAIN_SAMPLING = "choice"
-# Maximum size of the side of the image during training
-_C.INPUT.MAX_SIZE_TRAIN = 1333
-# Size of the smallest side of the image during testing. Set to zero to disable resize in testing.
-_C.INPUT.MIN_SIZE_TEST = 800
-# Maximum size of the side of the image during testing
-_C.INPUT.MAX_SIZE_TEST = 1333
-# Mode for flipping images used in data augmentation during training
-# choose one of ["horizontal, "vertical", "none"]
-_C.INPUT.RANDOM_FLIP = "horizontal"
-
-# `True` if cropping is used for data augmentation during training
-_C.INPUT.CROP = CN({"ENABLED": False})
-# Cropping type. See documentation of `detectron2.data.transforms.RandomCrop` for explanation.
-_C.INPUT.CROP.TYPE = "relative_range"
-# Size of crop in range (0, 1] if CROP.TYPE is "relative" or "relative_range" and in number of
-# pixels if CROP.TYPE is "absolute"
-_C.INPUT.CROP.SIZE = [0.9, 0.9]
-
-
-# Whether the model needs RGB, YUV, HSV etc.
-# Should be one of the modes defined here, as we use PIL to read the image:
-# https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes
-# with BGR being the one exception. One can set image format to BGR, we will
-# internally use RGB for conversion and flip the channels over
-_C.INPUT.FORMAT = "BGR"
-# The ground truth mask format that the model will use.
-# Mask R-CNN supports either "polygon" or "bitmask" as ground truth.
-_C.INPUT.MASK_FORMAT = "polygon" # alternative: "bitmask"
-
-
-# -----------------------------------------------------------------------------
-# Dataset
-# -----------------------------------------------------------------------------
-_C.DATASETS = CN()
-# List of the dataset names for training. Must be registered in DatasetCatalog
-# Samples from these datasets will be merged and used as one dataset.
-_C.DATASETS.TRAIN = ()
-# List of the pre-computed proposal files for training, which must be consistent
-# with datasets listed in DATASETS.TRAIN.
-_C.DATASETS.PROPOSAL_FILES_TRAIN = ()
-# Number of top scoring precomputed proposals to keep for training
-_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN = 2000
-# List of the dataset names for testing. Must be registered in DatasetCatalog
-_C.DATASETS.TEST = ()
-# List of the pre-computed proposal files for test, which must be consistent
-# with datasets listed in DATASETS.TEST.
-_C.DATASETS.PROPOSAL_FILES_TEST = ()
-# Number of top scoring precomputed proposals to keep for test
-_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST = 1000
-
-# -----------------------------------------------------------------------------
-# DataLoader
-# -----------------------------------------------------------------------------
-_C.DATALOADER = CN()
-# Number of data loading threads
-_C.DATALOADER.NUM_WORKERS = 4
-# If True, each batch should contain only images for which the aspect ratio
-# is compatible. This groups portrait images together, and landscape images
-# are not batched with portrait images.
-_C.DATALOADER.ASPECT_RATIO_GROUPING = True
-# Options: TrainingSampler, RepeatFactorTrainingSampler
-_C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler"
-# Repeat threshold for RepeatFactorTrainingSampler
-_C.DATALOADER.REPEAT_THRESHOLD = 0.0
-# Tf True, when working on datasets that have instance annotations, the
-# training dataloader will filter out images without associated annotations
-_C.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True
-
-# ---------------------------------------------------------------------------- #
-# Backbone options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.BACKBONE = CN()
-
-_C.MODEL.BACKBONE.NAME = "build_resnet_backbone"
-# Freeze the first several stages so they are not trained.
-# There are 5 stages in ResNet. The first is a convolution, and the following
-# stages are each group of residual blocks.
-_C.MODEL.BACKBONE.FREEZE_AT = 2
-
-
-# ---------------------------------------------------------------------------- #
-# FPN options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.FPN = CN()
-# Names of the input feature maps to be used by FPN
-# They must have contiguous power of 2 strides
-# e.g., ["res2", "res3", "res4", "res5"]
-_C.MODEL.FPN.IN_FEATURES = []
-_C.MODEL.FPN.OUT_CHANNELS = 256
-
-# Options: "" (no norm), "GN"
-_C.MODEL.FPN.NORM = ""
-
-# Types for fusing the FPN top-down and lateral features. Can be either "sum" or "avg"
-_C.MODEL.FPN.FUSE_TYPE = "sum"
-
-
-# ---------------------------------------------------------------------------- #
-# Proposal generator options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.PROPOSAL_GENERATOR = CN()
-# Current proposal generators include "RPN", "RRPN" and "PrecomputedProposals"
-_C.MODEL.PROPOSAL_GENERATOR.NAME = "RPN"
-# Proposal height and width both need to be greater than MIN_SIZE
-# (a the scale used during training or inference)
-_C.MODEL.PROPOSAL_GENERATOR.MIN_SIZE = 0
-
-
-# ---------------------------------------------------------------------------- #
-# Anchor generator options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ANCHOR_GENERATOR = CN()
-# The generator can be any name in the ANCHOR_GENERATOR registry
-_C.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator"
-# Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input.
-# Format: list[list[float]]. SIZES[i] specifies the list of sizes to use for
-# IN_FEATURES[i]; len(SIZES) must be equal to len(IN_FEATURES) or 1.
-# When len(SIZES) == 1, SIZES[0] is used for all IN_FEATURES.
-_C.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64, 128, 256, 512]]
-# Anchor aspect ratios. For each area given in `SIZES`, anchors with different aspect
-# ratios are generated by an anchor generator.
-# Format: list[list[float]]. ASPECT_RATIOS[i] specifies the list of aspect ratios (H/W)
-# to use for IN_FEATURES[i]; len(ASPECT_RATIOS) == len(IN_FEATURES) must be true,
-# or len(ASPECT_RATIOS) == 1 is true and aspect ratio list ASPECT_RATIOS[0] is used
-# for all IN_FEATURES.
-_C.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.5, 1.0, 2.0]]
-# Anchor angles.
-# list[list[float]], the angle in degrees, for each input feature map.
-# ANGLES[i] specifies the list of angles for IN_FEATURES[i].
-_C.MODEL.ANCHOR_GENERATOR.ANGLES = [[-90, 0, 90]]
-# Relative offset between the center of the first anchor and the top-left corner of the image
-# Value has to be in [0, 1). Recommend to use 0.5, which means half stride.
-# The value is not expected to affect model accuracy.
-_C.MODEL.ANCHOR_GENERATOR.OFFSET = 0.0
-
-# ---------------------------------------------------------------------------- #
-# RPN options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RPN = CN()
-_C.MODEL.RPN.HEAD_NAME = "StandardRPNHead" # used by RPN_HEAD_REGISTRY
-
-# Names of the input feature maps to be used by RPN
-# e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN
-_C.MODEL.RPN.IN_FEATURES = ["res4"]
-# Remove RPN anchors that go outside the image by BOUNDARY_THRESH pixels
-# Set to -1 or a large value, e.g. 100000, to disable pruning anchors
-_C.MODEL.RPN.BOUNDARY_THRESH = -1
-# IOU overlap ratios [BG_IOU_THRESHOLD, FG_IOU_THRESHOLD]
-# Minimum overlap required between an anchor and ground-truth box for the
-# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD
-# ==> positive RPN example: 1)
-# Maximum overlap allowed between an anchor and ground-truth box for the
-# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD
-# ==> negative RPN example: 0)
-# Anchors with overlap in between (BG_IOU_THRESHOLD <= IoU < FG_IOU_THRESHOLD)
-# are ignored (-1)
-_C.MODEL.RPN.IOU_THRESHOLDS = [0.3, 0.7]
-_C.MODEL.RPN.IOU_LABELS = [0, -1, 1]
-# Number of regions per image used to train RPN
-_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256
-# Target fraction of foreground (positive) examples per RPN minibatch
-_C.MODEL.RPN.POSITIVE_FRACTION = 0.5
-# Options are: "smooth_l1", "giou", "diou", "ciou"
-_C.MODEL.RPN.BBOX_REG_LOSS_TYPE = "smooth_l1"
-_C.MODEL.RPN.BBOX_REG_LOSS_WEIGHT = 1.0
-# Weights on (dx, dy, dw, dh) for normalizing RPN anchor regression targets
-_C.MODEL.RPN.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
-# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
-_C.MODEL.RPN.SMOOTH_L1_BETA = 0.0
-_C.MODEL.RPN.LOSS_WEIGHT = 1.0
-# Number of top scoring RPN proposals to keep before applying NMS
-# When FPN is used, this is *per FPN level* (not total)
-_C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 12000
-_C.MODEL.RPN.PRE_NMS_TOPK_TEST = 6000
-# Number of top scoring RPN proposals to keep after applying NMS
-# When FPN is used, this limit is applied per level and then again to the union
-# of proposals from all levels
-# NOTE: When FPN is used, the meaning of this config is different from Detectron1.
-# It means per-batch topk in Detectron1, but per-image topk here.
-# See the "find_top_rpn_proposals" function for details.
-_C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 2000
-_C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000
-# NMS threshold used on RPN proposals
-_C.MODEL.RPN.NMS_THRESH = 0.7
-# Set this to -1 to use the same number of output channels as input channels.
-_C.MODEL.RPN.CONV_DIMS = [-1]
-
-# ---------------------------------------------------------------------------- #
-# ROI HEADS options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_HEADS = CN()
-_C.MODEL.ROI_HEADS.NAME = "Res5ROIHeads"
-# Number of foreground classes
-_C.MODEL.ROI_HEADS.NUM_CLASSES = 80
-# Names of the input feature maps to be used by ROI heads
-# Currently all heads (box, mask, ...) use the same input feature map list
-# e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN
-_C.MODEL.ROI_HEADS.IN_FEATURES = ["res4"]
-# IOU overlap ratios [IOU_THRESHOLD]
-# Overlap threshold for an RoI to be considered background (if < IOU_THRESHOLD)
-# Overlap threshold for an RoI to be considered foreground (if >= IOU_THRESHOLD)
-_C.MODEL.ROI_HEADS.IOU_THRESHOLDS = [0.5]
-_C.MODEL.ROI_HEADS.IOU_LABELS = [0, 1]
-# RoI minibatch size *per image* (number of regions of interest [ROIs]) during training
-# Total number of RoIs per training minibatch =
-# ROI_HEADS.BATCH_SIZE_PER_IMAGE * SOLVER.IMS_PER_BATCH
-# E.g., a common configuration is: 512 * 16 = 8192
-_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
-# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0)
-_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25
-
-# Only used on test mode
-
-# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to
-# balance obtaining high recall with not having too many low precision
-# detections that will slow down inference post processing steps (like NMS)
-# A default threshold of 0.0 increases AP by ~0.2-0.3 but significantly slows down
-# inference.
-_C.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05
-# Overlap threshold used for non-maximum suppression (suppress boxes with
-# IoU >= this threshold)
-_C.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.5
-# If True, augment proposals with ground-truth boxes before sampling proposals to
-# train ROI heads.
-_C.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT = True
-
-# ---------------------------------------------------------------------------- #
-# Box Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_BOX_HEAD = CN()
-# C4 don't use head name option
-# Options for non-C4 models: FastRCNNConvFCHead,
-_C.MODEL.ROI_BOX_HEAD.NAME = ""
-# Options are: "smooth_l1", "giou", "diou", "ciou"
-_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE = "smooth_l1"
-# The final scaling coefficient on the box regression loss, used to balance the magnitude of its
-# gradients with other losses in the model. See also `MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT`.
-_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT = 1.0
-# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets
-# These are empirically chosen to approximately lead to unit variance targets
-_C.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0)
-# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
-_C.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA = 0.0
-_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0
-# Type of pooling operation applied to the incoming feature map for each RoI
-_C.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2"
-
-_C.MODEL.ROI_BOX_HEAD.NUM_FC = 0
-# Hidden layer dimension for FC layers in the RoI box head
-_C.MODEL.ROI_BOX_HEAD.FC_DIM = 1024
-_C.MODEL.ROI_BOX_HEAD.NUM_CONV = 0
-# Channel dimension for Conv layers in the RoI box head
-_C.MODEL.ROI_BOX_HEAD.CONV_DIM = 256
-# Normalization method for the convolution layers.
-# Options: "" (no norm), "GN", "SyncBN".
-_C.MODEL.ROI_BOX_HEAD.NORM = ""
-# Whether to use class agnostic for bbox regression
-_C.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG = False
-# If true, RoI heads use bounding boxes predicted by the box head rather than proposal boxes.
-_C.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES = False
-
-# ---------------------------------------------------------------------------- #
-# Cascaded Box Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_BOX_CASCADE_HEAD = CN()
-# The number of cascade stages is implicitly defined by the length of the following two configs.
-_C.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS = (
- (10.0, 10.0, 5.0, 5.0),
- (20.0, 20.0, 10.0, 10.0),
- (30.0, 30.0, 15.0, 15.0),
-)
-_C.MODEL.ROI_BOX_CASCADE_HEAD.IOUS = (0.5, 0.6, 0.7)
-
-
-# ---------------------------------------------------------------------------- #
-# Mask Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_MASK_HEAD = CN()
-_C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead"
-_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0
-_C.MODEL.ROI_MASK_HEAD.NUM_CONV = 0 # The number of convs in the mask head
-_C.MODEL.ROI_MASK_HEAD.CONV_DIM = 256
-# Normalization method for the convolution layers.
-# Options: "" (no norm), "GN", "SyncBN".
-_C.MODEL.ROI_MASK_HEAD.NORM = ""
-# Whether to use class agnostic for mask prediction
-_C.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK = False
-# Type of pooling operation applied to the incoming feature map for each RoI
-_C.MODEL.ROI_MASK_HEAD.POOLER_TYPE = "ROIAlignV2"
-
-
-# ---------------------------------------------------------------------------- #
-# Keypoint Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_KEYPOINT_HEAD = CN()
-_C.MODEL.ROI_KEYPOINT_HEAD.NAME = "KRCNNConvDeconvUpsampleHead"
-_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO = 0
-_C.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS = tuple(512 for _ in range(8))
-_C.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS = 17 # 17 is the number of keypoints in COCO.
-
-# Images with too few (or no) keypoints are excluded from training.
-_C.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE = 1
-# Normalize by the total number of visible keypoints in the minibatch if True.
-# Otherwise, normalize by the total number of keypoints that could ever exist
-# in the minibatch.
-# The keypoint softmax loss is only calculated on visible keypoints.
-# Since the number of visible keypoints can vary significantly between
-# minibatches, this has the effect of up-weighting the importance of
-# minibatches with few visible keypoints. (Imagine the extreme case of
-# only one visible keypoint versus N: in the case of N, each one
-# contributes 1/N to the gradient compared to the single keypoint
-# determining the gradient direction). Instead, we can normalize the
-# loss by the total number of keypoints, if it were the case that all
-# keypoints were visible in a full minibatch. (Returning to the example,
-# this means that the one visible keypoint contributes as much as each
-# of the N keypoints.)
-_C.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS = True
-# Multi-task loss weight to use for keypoints
-# Recommended values:
-# - use 1.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is True
-# - use 4.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is False
-_C.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT = 1.0
-# Type of pooling operation applied to the incoming feature map for each RoI
-_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE = "ROIAlignV2"
-
-# ---------------------------------------------------------------------------- #
-# Semantic Segmentation Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.SEM_SEG_HEAD = CN()
-_C.MODEL.SEM_SEG_HEAD.NAME = "SemSegFPNHead"
-_C.MODEL.SEM_SEG_HEAD.IN_FEATURES = ["p2", "p3", "p4", "p5"]
-# Label in the semantic segmentation ground truth that is ignored, i.e., no loss is calculated for
-# the correposnding pixel.
-_C.MODEL.SEM_SEG_HEAD.IGNORE_VALUE = 255
-# Number of classes in the semantic segmentation head
-_C.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 54
-# Number of channels in the 3x3 convs inside semantic-FPN heads.
-_C.MODEL.SEM_SEG_HEAD.CONVS_DIM = 128
-# Outputs from semantic-FPN heads are up-scaled to the COMMON_STRIDE stride.
-_C.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4
-# Normalization method for the convolution layers. Options: "" (no norm), "GN".
-_C.MODEL.SEM_SEG_HEAD.NORM = "GN"
-_C.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT = 1.0
-
-_C.MODEL.PANOPTIC_FPN = CN()
-# Scaling of all losses from instance detection / segmentation head.
-_C.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT = 1.0
-
-# options when combining instance & semantic segmentation outputs
-_C.MODEL.PANOPTIC_FPN.COMBINE = CN({"ENABLED": True}) # "COMBINE.ENABLED" is deprecated & not used
-_C.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH = 0.5
-_C.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT = 4096
-_C.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = 0.5
-
-
-# ---------------------------------------------------------------------------- #
-# RetinaNet Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RETINANET = CN()
-
-# This is the number of foreground classes.
-_C.MODEL.RETINANET.NUM_CLASSES = 80
-
-_C.MODEL.RETINANET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"]
-
-# Convolutions to use in the cls and bbox tower
-# NOTE: this doesn't include the last conv for logits
-_C.MODEL.RETINANET.NUM_CONVS = 4
-
-# IoU overlap ratio [bg, fg] for labeling anchors.
-# Anchors with < bg are labeled negative (0)
-# Anchors with >= bg and < fg are ignored (-1)
-# Anchors with >= fg are labeled positive (1)
-_C.MODEL.RETINANET.IOU_THRESHOLDS = [0.4, 0.5]
-_C.MODEL.RETINANET.IOU_LABELS = [0, -1, 1]
-
-# Prior prob for rare case (i.e. foreground) at the beginning of training.
-# This is used to set the bias for the logits layer of the classifier subnet.
-# This improves training stability in the case of heavy class imbalance.
-_C.MODEL.RETINANET.PRIOR_PROB = 0.01
-
-# Inference cls score threshold, only anchors with score > INFERENCE_TH are
-# considered for inference (to improve speed)
-_C.MODEL.RETINANET.SCORE_THRESH_TEST = 0.05
-# Select topk candidates before NMS
-_C.MODEL.RETINANET.TOPK_CANDIDATES_TEST = 1000
-_C.MODEL.RETINANET.NMS_THRESH_TEST = 0.5
-
-# Weights on (dx, dy, dw, dh) for normalizing Retinanet anchor regression targets
-_C.MODEL.RETINANET.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
-
-# Loss parameters
-_C.MODEL.RETINANET.FOCAL_LOSS_GAMMA = 2.0
-_C.MODEL.RETINANET.FOCAL_LOSS_ALPHA = 0.25
-_C.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA = 0.1
-# Options are: "smooth_l1", "giou", "diou", "ciou"
-_C.MODEL.RETINANET.BBOX_REG_LOSS_TYPE = "smooth_l1"
-
-# One of BN, SyncBN, FrozenBN, GN
-# Only supports GN until unshared norm is implemented
-_C.MODEL.RETINANET.NORM = ""
-
-
-# ---------------------------------------------------------------------------- #
-# ResNe[X]t options (ResNets = {ResNet, ResNeXt}
-# Note that parts of a resnet may be used for both the backbone and the head
-# These options apply to both
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RESNETS = CN()
-
-_C.MODEL.RESNETS.DEPTH = 50
-_C.MODEL.RESNETS.OUT_FEATURES = ["res4"] # res4 for C4 backbone, res2..5 for FPN backbone
-
-# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt
-_C.MODEL.RESNETS.NUM_GROUPS = 1
-
-# Options: FrozenBN, GN, "SyncBN", "BN"
-_C.MODEL.RESNETS.NORM = "FrozenBN"
-
-# Baseline width of each group.
-# Scaling this parameters will scale the width of all bottleneck layers.
-_C.MODEL.RESNETS.WIDTH_PER_GROUP = 64
-
-# Place the stride 2 conv on the 1x1 filter
-# Use True only for the original MSRA ResNet; use False for C2 and Torch models
-_C.MODEL.RESNETS.STRIDE_IN_1X1 = True
-
-# Apply dilation in stage "res5"
-_C.MODEL.RESNETS.RES5_DILATION = 1
-
-# Output width of res2. Scaling this parameters will scale the width of all 1x1 convs in ResNet
-# For R18 and R34, this needs to be set to 64
-_C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256
-_C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64
-
-# Apply Deformable Convolution in stages
-# Specify if apply deform_conv on Res2, Res3, Res4, Res5
-_C.MODEL.RESNETS.DEFORM_ON_PER_STAGE = [False, False, False, False]
-# Use True to use modulated deform_conv (DeformableV2, https://arxiv.org/abs/1811.11168);
-# Use False for DeformableV1.
-_C.MODEL.RESNETS.DEFORM_MODULATED = False
-# Number of groups in deformable conv.
-_C.MODEL.RESNETS.DEFORM_NUM_GROUPS = 1
-
-
-# ---------------------------------------------------------------------------- #
-# Solver
-# ---------------------------------------------------------------------------- #
-_C.SOLVER = CN()
-
-# Options: WarmupMultiStepLR, WarmupCosineLR.
-# See detectron2/solver/build.py for definition.
-_C.SOLVER.LR_SCHEDULER_NAME = "WarmupMultiStepLR"
-
-_C.SOLVER.MAX_ITER = 40000
-
-_C.SOLVER.BASE_LR = 0.001
-
-_C.SOLVER.MOMENTUM = 0.9
-
-_C.SOLVER.NESTEROV = False
-
-_C.SOLVER.WEIGHT_DECAY = 0.0001
-# The weight decay that's applied to parameters of normalization layers
-# (typically the affine transformation)
-_C.SOLVER.WEIGHT_DECAY_NORM = 0.0
-
-_C.SOLVER.GAMMA = 0.1
-# The iteration number to decrease learning rate by GAMMA.
-_C.SOLVER.STEPS = (30000,)
-
-_C.SOLVER.WARMUP_FACTOR = 1.0 / 1000
-_C.SOLVER.WARMUP_ITERS = 1000
-_C.SOLVER.WARMUP_METHOD = "linear"
-
-# Save a checkpoint after every this number of iterations
-_C.SOLVER.CHECKPOINT_PERIOD = 5000
-
-# Number of images per batch across all machines. This is also the number
-# of training images per step (i.e. per iteration). If we use 16 GPUs
-# and IMS_PER_BATCH = 32, each GPU will see 2 images per batch.
-# May be adjusted automatically if REFERENCE_WORLD_SIZE is set.
-_C.SOLVER.IMS_PER_BATCH = 16
-
-# The reference number of workers (GPUs) this config is meant to train with.
-# It takes no effect when set to 0.
-# With a non-zero value, it will be used by DefaultTrainer to compute a desired
-# per-worker batch size, and then scale the other related configs (total batch size,
-# learning rate, etc) to match the per-worker batch size.
-# See documentation of `DefaultTrainer.auto_scale_workers` for details:
-_C.SOLVER.REFERENCE_WORLD_SIZE = 0
-
-# Detectron v1 (and previous detection code) used a 2x higher LR and 0 WD for
-# biases. This is not useful (at least for recent models). You should avoid
-# changing these and they exist only to reproduce Detectron v1 training if
-# desired.
-_C.SOLVER.BIAS_LR_FACTOR = 1.0
-_C.SOLVER.WEIGHT_DECAY_BIAS = None # None means following WEIGHT_DECAY
-
-# Gradient clipping
-_C.SOLVER.CLIP_GRADIENTS = CN({"ENABLED": False})
-# Type of gradient clipping, currently 2 values are supported:
-# - "value": the absolute values of elements of each gradients are clipped
-# - "norm": the norm of the gradient for each parameter is clipped thus
-# affecting all elements in the parameter
-_C.SOLVER.CLIP_GRADIENTS.CLIP_TYPE = "value"
-# Maximum absolute value used for clipping gradients
-_C.SOLVER.CLIP_GRADIENTS.CLIP_VALUE = 1.0
-# Floating point number p for L-p norm to be used with the "norm"
-# gradient clipping type; for L-inf, please specify .inf
-_C.SOLVER.CLIP_GRADIENTS.NORM_TYPE = 2.0
-
-# Enable automatic mixed precision for training
-# Note that this does not change model's inference behavior.
-# To use AMP in inference, run inference under autocast()
-_C.SOLVER.AMP = CN({"ENABLED": False})
-
-# ---------------------------------------------------------------------------- #
-# Specific test options
-# ---------------------------------------------------------------------------- #
-_C.TEST = CN()
-# For end-to-end tests to verify the expected accuracy.
-# Each item is [task, metric, value, tolerance]
-# e.g.: [['bbox', 'AP', 38.5, 0.2]]
-_C.TEST.EXPECTED_RESULTS = []
-# The period (in terms of steps) to evaluate the model during training.
-# Set to 0 to disable.
-_C.TEST.EVAL_PERIOD = 0
-# The sigmas used to calculate keypoint OKS. See http://cocodataset.org/#keypoints-eval
-# When empty, it will use the defaults in COCO.
-# Otherwise it should be a list[float] with the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.
-_C.TEST.KEYPOINT_OKS_SIGMAS = []
-# Maximum number of detections to return per image during inference (100 is
-# based on the limit established for the COCO dataset).
-_C.TEST.DETECTIONS_PER_IMAGE = 100
-
-_C.TEST.AUG = CN({"ENABLED": False})
-_C.TEST.AUG.MIN_SIZES = (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
-_C.TEST.AUG.MAX_SIZE = 4000
-_C.TEST.AUG.FLIP = True
-
-_C.TEST.PRECISE_BN = CN({"ENABLED": False})
-_C.TEST.PRECISE_BN.NUM_ITER = 200
-
-# ---------------------------------------------------------------------------- #
-# Misc options
-# ---------------------------------------------------------------------------- #
-# Directory where output files are written
-_C.OUTPUT_DIR = "./output"
-# Set seed to negative to fully randomize everything.
-# Set seed to positive to use a fixed seed. Note that a fixed seed increases
-# reproducibility but does not guarantee fully deterministic behavior.
-# Disabling all parallelism further increases reproducibility.
-_C.SEED = -1
-# Benchmark different cudnn algorithms.
-# If input images have very different sizes, this option will have large overhead
-# for about 10k iterations. It usually hurts total time, but can benefit for certain models.
-# If input images have the same or similar sizes, benchmark is often helpful.
-_C.CUDNN_BENCHMARK = False
-# The period (in terms of steps) for minibatch visualization at train time.
-# Set to 0 to disable.
-_C.VIS_PERIOD = 0
-
-# global config is for quick hack purposes.
-# You can set them in command line or config files,
-# and access it with:
-#
-# from detectron2.config import global_cfg
-# print(global_cfg.HACK)
-#
-# Do not commit any configs into it.
-_C.GLOBAL = CN()
-_C.GLOBAL.HACK = 1.0
diff --git a/spaces/YuxinJ/Scenimefy/Scenimefy/data/unaligned_dataset.py b/spaces/YuxinJ/Scenimefy/Scenimefy/data/unaligned_dataset.py
deleted file mode 100644
index d51057f8db34da41e6d7210afeaf357362bdfe26..0000000000000000000000000000000000000000
--- a/spaces/YuxinJ/Scenimefy/Scenimefy/data/unaligned_dataset.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import os.path
-from Scenimefy.data.base_dataset import BaseDataset, get_transform
-from Scenimefy.data.image_folder import make_dataset
-from PIL import Image
-import random
-import Scenimefy.utils.util as util
-
-
-class UnalignedDataset(BaseDataset):
- """
- This dataset class can load unaligned/unpaired datasets.
-
- It requires two directories to host training images from domain A '/path/to/data/trainA'
- and from domain B '/path/to/data/trainB' respectively.
- You can train the model with the dataset flag '--dataroot /path/to/data'.
- Similarly, you need to prepare two directories:
- '/path/to/data/testA' and '/path/to/data/testB' during test time.
- """
-
- def __init__(self, opt):
- """Initialize this dataset class.
-
- Parameters:
- opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
- """
- BaseDataset.__init__(self, opt)
- self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A') # create a path '/path/to/data/trainA'
- self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B') # create a path '/path/to/data/trainB'
-
- if opt.phase == "test" and not os.path.exists(self.dir_A) \
- and os.path.exists(os.path.join(opt.dataroot, "valA")):
- self.dir_A = os.path.join(opt.dataroot, "valA")
- self.dir_B = os.path.join(opt.dataroot, "valB")
-
- self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size)) # load images from '/path/to/data/trainA'
- self.B_paths = sorted(make_dataset(self.dir_B, opt.max_dataset_size)) # load images from '/path/to/data/trainB'
- self.A_size = len(self.A_paths) # get the size of dataset A
- self.B_size = len(self.B_paths) # get the size of dataset B
-
- def __getitem__(self, index):
- """Return a data point and its metadata information.
-
- Parameters:
- index (int) -- a random integer for data indexing
-
- Returns a dictionary that contains A, B, A_paths and B_paths
- A (tensor) -- an image in the input domain
- B (tensor) -- its corresponding image in the target domain
- A_paths (str) -- image paths
- B_paths (str) -- image paths
- """
- A_path = self.A_paths[index % self.A_size] # make sure index is within then range
- if self.opt.serial_batches: # make sure index is within then range
- index_B = index % self.B_size
- else: # randomize the index for domain B to avoid fixed pairs.
- index_B = random.randint(0, self.B_size - 1)
- B_path = self.B_paths[index_B]
- A_img = Image.open(A_path).convert('RGB')
- B_img = Image.open(B_path).convert('RGB')
-
- # Apply image transformation
- # For FastCUT mode, if in finetuning phase (learning rate is decaying),
- # do not perform resize-crop data augmentation of CycleGAN.
-# print('current_epoch', self.current_epoch)
- is_finetuning = self.opt.isTrain and self.current_epoch > self.opt.n_epochs
- modified_opt = util.copyconf(self.opt, load_size=self.opt.crop_size if is_finetuning else self.opt.load_size)
- transform = get_transform(modified_opt)
- A = transform(A_img)
- B = transform(B_img)
-
- return {'A': A, 'B': B, 'A_paths': A_path, 'B_paths': B_path}
-
- def __len__(self):
- """Return the total number of images in the dataset.
-
- As we have two datasets with potentially different number of images,
- we take a maximum of
- """
- return max(self.A_size, self.B_size)
diff --git a/spaces/YuxinJ/Scenimefy/Scenimefy/models/hDCE.py b/spaces/YuxinJ/Scenimefy/Scenimefy/models/hDCE.py
deleted file mode 100644
index 270a6478dda91ea293ad49bdaff4a81ee657486f..0000000000000000000000000000000000000000
--- a/spaces/YuxinJ/Scenimefy/Scenimefy/models/hDCE.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from packaging import version
-import torch
-from torch import nn
-
-
-
-class PatchHDCELoss(nn.Module):
- def __init__(self, opt):
- super().__init__()
- self.opt = opt
- self.cross_entropy_loss = torch.nn.CrossEntropyLoss(reduction='none')
- self.mask_dtype = torch.uint8 if version.parse(torch.__version__) < version.parse('1.2.0') else torch.bool
-
- def forward(self, feat_q, feat_k, weight=None):
- batchSize = feat_q.shape[0]
- dim = feat_q.shape[1]
- feat_k = feat_k.detach()
-
- # positive logit
- l_pos = torch.bmm(feat_q.view(batchSize, 1, -1), feat_k.view(batchSize, -1, 1))
- l_pos = l_pos.view(batchSize, 1)
-
- if self.opt.nce_includes_all_negatives_from_minibatch:
- # reshape features as if they are all negatives of minibatch of size 1.
- batch_dim_for_bmm = 1
- else:
- batch_dim_for_bmm = self.opt.batch_size
-
- # reshape features to batch size
- feat_q = feat_q.view(batch_dim_for_bmm, -1, dim)
- feat_k = feat_k.view(batch_dim_for_bmm, -1, dim)
- npatches = feat_q.size(1)
- l_neg_curbatch = torch.bmm(feat_q, feat_k.transpose(2, 1))
-
- # weighted by semantic relation
- if weight is not None:
- l_neg_curbatch *= weight
-
- diagonal = torch.eye(npatches, device=feat_q.device, dtype=self.mask_dtype)[None, :, :]
- l_neg_curbatch.masked_fill_(diagonal, -10.0)
- l_neg = l_neg_curbatch.view(-1, npatches)
-
- logits = (l_neg-l_pos)/self.opt.nce_T
- v = torch.logsumexp(logits, dim=1)
- loss_vec = torch.exp(v-v.detach())
-
- # for monitoring
- out_dummy = torch.cat((l_pos, l_neg), dim=1) / self.opt.nce_T
- CELoss_dummy = self.cross_entropy_loss(out_dummy, torch.zeros(out_dummy.size(0), dtype=torch.long, device=feat_q.device))
-
- loss = loss_vec.mean()-1+CELoss_dummy.detach()
-
- return loss
diff --git a/spaces/YuxinJ/Scenimefy/Scenimefy/utils/util.py b/spaces/YuxinJ/Scenimefy/Scenimefy/utils/util.py
deleted file mode 100644
index f06f0773436491cf633769df0698d453a10c8a7f..0000000000000000000000000000000000000000
--- a/spaces/YuxinJ/Scenimefy/Scenimefy/utils/util.py
+++ /dev/null
@@ -1,168 +0,0 @@
-"""This module contains simple helper functions """
-from __future__ import print_function
-import torch
-import numpy as np
-from PIL import Image
-import os
-import importlib
-import argparse
-from argparse import Namespace
-import torchvision
-
-
-def str2bool(v):
- if isinstance(v, bool):
- return v
- if v.lower() in ('yes', 'true', 't', 'y', '1'):
- return True
- elif v.lower() in ('no', 'false', 'f', 'n', '0'):
- return False
- else:
- raise argparse.ArgumentTypeError('Boolean value expected.')
-
-
-def copyconf(default_opt, **kwargs):
- conf = Namespace(**vars(default_opt))
- for key in kwargs:
- setattr(conf, key, kwargs[key])
- return conf
-
-
-def find_class_in_module(target_cls_name, module):
- target_cls_name = target_cls_name.replace('_', '').lower()
- clslib = importlib.import_module(module)
- cls = None
- for name, clsobj in clslib.__dict__.items():
- if name.lower() == target_cls_name:
- cls = clsobj
-
- assert cls is not None, "In %s, there should be a class whose name matches %s in lowercase without underscore(_)" % (module, target_cls_name)
-
- return cls
-
-
-def tensor2im(input_image, imtype=np.uint8):
- """"Converts a Tensor array into a numpy image array.
-
- Parameters:
- input_image (tensor) -- the input image tensor array
- imtype (type) -- the desired type of the converted numpy array
- """
- if not isinstance(input_image, np.ndarray):
- if isinstance(input_image, torch.Tensor): # get the data from a variable
- image_tensor = input_image.data
- else:
- return input_image
- image_numpy = image_tensor[0].clamp(-1.0, 1.0).cpu().float().numpy() # convert it into a numpy array
- if image_numpy.shape[0] == 1: # grayscale to RGB
- image_numpy = np.tile(image_numpy, (3, 1, 1))
- image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling
- else: # if it is a numpy array, do nothing
- image_numpy = input_image
- return image_numpy.astype(imtype)
-
-
-def diagnose_network(net, name='network'):
- """Calculate and print the mean of average absolute(gradients)
-
- Parameters:
- net (torch network) -- Torch network
- name (str) -- the name of the network
- """
- mean = 0.0
- count = 0
- for param in net.parameters():
- if param.grad is not None:
- mean += torch.mean(torch.abs(param.grad.data))
- count += 1
- if count > 0:
- mean = mean / count
- print(name)
- print(mean)
-
-
-def save_image(image_numpy, image_path, aspect_ratio=1.0):
- """Save a numpy image to the disk
-
- Parameters:
- image_numpy (numpy array) -- input numpy array
- image_path (str) -- the path of the image
- """
-
- image_pil = Image.fromarray(image_numpy)
- h, w, _ = image_numpy.shape
-
- if aspect_ratio is None:
- pass
- elif aspect_ratio > 1.0:
- image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC)
- elif aspect_ratio < 1.0:
- image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC)
- # TODO: TEST
- # print(image_path)
- image_pil.save(image_path)
-
-
-def print_numpy(x, val=True, shp=False):
- """Print the mean, min, max, median, std, and size of a numpy array
-
- Parameters:
- val (bool) -- if print the values of the numpy array
- shp (bool) -- if print the shape of the numpy array
- """
- x = x.astype(np.float64)
- if shp:
- print('shape,', x.shape)
- if val:
- x = x.flatten()
- print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
- np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))
-
-
-def mkdirs(paths):
- """create empty directories if they don't exist
-
- Parameters:
- paths (str list) -- a list of directory paths
- """
- if isinstance(paths, list) and not isinstance(paths, str):
- for path in paths:
- mkdir(path)
- else:
- mkdir(paths)
-
-
-def mkdir(path):
- """create a single empty directory if it didn't exist
-
- Parameters:
- path (str) -- a single directory path
- """
- if not os.path.exists(path):
- os.makedirs(path)
-
-
-def correct_resize_label(t, size):
- device = t.device
- t = t.detach().cpu()
- resized = []
- for i in range(t.size(0)):
- one_t = t[i, :1]
- one_np = np.transpose(one_t.numpy().astype(np.uint8), (1, 2, 0))
- one_np = one_np[:, :, 0]
- one_image = Image.fromarray(one_np).resize(size, Image.NEAREST)
- resized_t = torch.from_numpy(np.array(one_image)).long()
- resized.append(resized_t)
- return torch.stack(resized, dim=0).to(device)
-
-
-def correct_resize(t, size, mode=Image.BICUBIC):
- device = t.device
- t = t.detach().cpu()
- resized = []
- for i in range(t.size(0)):
- one_t = t[i:i + 1]
- one_image = Image.fromarray(tensor2im(one_t)).resize(size, Image.BICUBIC)
- resized_t = torchvision.transforms.functional.to_tensor(one_image) * 2 - 1.0
- resized.append(resized_t)
- return torch.stack(resized, dim=0).to(device)
diff --git a/spaces/ZX9966/Fintech/style.css b/spaces/ZX9966/Fintech/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/ZX9966/Fintech/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/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/detectors/fast_rcnn.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/detectors/fast_rcnn.py
deleted file mode 100644
index 3d6e242767b927ed37198b6bc7862abecef99a33..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/detectors/fast_rcnn.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from ..builder import DETECTORS
-from .two_stage import TwoStageDetector
-
-
-@DETECTORS.register_module()
-class FastRCNN(TwoStageDetector):
- """Implementation of `Fast R-CNN `_"""
-
- def __init__(self,
- backbone,
- roi_head,
- train_cfg,
- test_cfg,
- neck=None,
- pretrained=None):
- super(FastRCNN, self).__init__(
- backbone=backbone,
- neck=neck,
- roi_head=roi_head,
- train_cfg=train_cfg,
- test_cfg=test_cfg,
- pretrained=pretrained)
-
- def forward_test(self, imgs, img_metas, proposals, **kwargs):
- """
- Args:
- imgs (List[Tensor]): the outer list indicates test-time
- augmentations and inner Tensor should have a shape NxCxHxW,
- which contains all images in the batch.
- img_metas (List[List[dict]]): the outer list indicates test-time
- augs (multiscale, flip, etc.) and the inner list indicates
- images in a batch.
- proposals (List[List[Tensor]]): the outer list indicates test-time
- augs (multiscale, flip, etc.) and the inner list indicates
- images in a batch. The Tensor should have a shape Px4, where
- P is the number of proposals.
- """
- for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]:
- if not isinstance(var, list):
- raise TypeError(f'{name} must be a list, but got {type(var)}')
-
- num_augs = len(imgs)
- if num_augs != len(img_metas):
- raise ValueError(f'num of augmentations ({len(imgs)}) '
- f'!= num of image meta ({len(img_metas)})')
-
- if num_augs == 1:
- return self.simple_test(imgs[0], img_metas[0], proposals[0],
- **kwargs)
- else:
- # TODO: support test-time augmentation
- assert NotImplementedError
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/shared_heads/res_layer.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/shared_heads/res_layer.py
deleted file mode 100644
index b5c343258b079a0dd832d4f999c18d002b06efac..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/shared_heads/res_layer.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import constant_init, kaiming_init
-from mmcv.runner import auto_fp16, load_checkpoint
-
-from mmdet.models.backbones import ResNet
-from mmdet.models.builder import SHARED_HEADS
-from mmdet.models.utils import ResLayer as _ResLayer
-from mmdet.utils import get_root_logger
-
-
-@SHARED_HEADS.register_module()
-class ResLayer(nn.Module):
-
- def __init__(self,
- depth,
- stage=3,
- stride=2,
- dilation=1,
- style='pytorch',
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- with_cp=False,
- dcn=None):
- super(ResLayer, self).__init__()
- self.norm_eval = norm_eval
- self.norm_cfg = norm_cfg
- self.stage = stage
- self.fp16_enabled = False
- block, stage_blocks = ResNet.arch_settings[depth]
- stage_block = stage_blocks[stage]
- planes = 64 * 2**stage
- inplanes = 64 * 2**(stage - 1) * block.expansion
-
- res_layer = _ResLayer(
- block,
- inplanes,
- planes,
- stage_block,
- stride=stride,
- dilation=dilation,
- style=style,
- with_cp=with_cp,
- norm_cfg=self.norm_cfg,
- dcn=dcn)
- self.add_module(f'layer{stage + 1}', res_layer)
-
- def init_weights(self, pretrained=None):
- """Initialize the weights in the module.
-
- Args:
- pretrained (str, optional): Path to pre-trained weights.
- Defaults to None.
- """
- if isinstance(pretrained, str):
- logger = get_root_logger()
- load_checkpoint(self, pretrained, strict=False, logger=logger)
- elif pretrained is None:
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- kaiming_init(m)
- elif isinstance(m, nn.BatchNorm2d):
- constant_init(m, 1)
- else:
- raise TypeError('pretrained must be a str or None')
-
- @auto_fp16()
- def forward(self, x):
- res_layer = getattr(self, f'layer{self.stage + 1}')
- out = res_layer(x)
- return out
-
- def train(self, mode=True):
- super(ResLayer, self).train(mode)
- if self.norm_eval:
- for m in self.modules():
- if isinstance(m, nn.BatchNorm2d):
- m.eval()
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/segmentors/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/segmentors/__init__.py
deleted file mode 100644
index dca2f09405330743c476e190896bee39c45498ea..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/segmentors/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .base import BaseSegmentor
-from .cascade_encoder_decoder import CascadeEncoderDecoder
-from .encoder_decoder import EncoderDecoder
-
-__all__ = ['BaseSegmentor', 'EncoderDecoder', 'CascadeEncoderDecoder']
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/datasets/voc.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/datasets/voc.py
deleted file mode 100644
index 699cbf2b6c2c6048817a7272f6b365539a33fbba..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/datasets/voc.py
+++ /dev/null
@@ -1,41 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
- * Modified from MMCV repo: From https://github.com/open-mmlab/mmcv
- * Copyright (c) OpenMMLab. All rights reserved.
-'''
-
-import os.path as osp
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class PascalVOCDataset(CustomDataset):
- """Pascal VOC dataset.
-
- Args:
- split (str): Split txt file for Pascal VOC.
- """
-
- CLASSES = ('background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
- 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
- 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa',
- 'train', 'tvmonitor')
-
- PALETTE = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],
- [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0],
- [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128],
- [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0],
- [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]
-
- def __init__(self, split, **kwargs):
- super(PascalVOCDataset, self).__init__(
- img_suffix='.jpg', seg_map_suffix='.png', split=split, **kwargs)
- assert osp.exists(self.img_dir) and self.split is not None
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/window/cocoa/pyglet_textview.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/window/cocoa/pyglet_textview.py
deleted file mode 100644
index 58644bf0f42c717d78e11557357df23042cfcea5..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/window/cocoa/pyglet_textview.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import unicodedata
-
-from pyglet.window import key
-
-from pyglet.libs.darwin.cocoapy import ObjCClass, ObjCSubclass, ObjCInstance
-from pyglet.libs.darwin.cocoapy import PyObjectEncoding, send_super
-from pyglet.libs.darwin.cocoapy import CFSTR, cfstring_to_string
-
-NSArray = ObjCClass('NSArray')
-NSApplication = ObjCClass('NSApplication')
-
-# This custom NSTextView subclass is used for capturing all of the
-# on_text, on_text_motion, and on_text_motion_select events.
-class PygletTextView_Implementation:
- PygletTextView = ObjCSubclass('NSTextView', 'PygletTextView')
-
- @PygletTextView.method(b'@'+PyObjectEncoding)
- def initWithCocoaWindow_(self, window):
- self = ObjCInstance(send_super(self, 'init'))
- if not self:
- return None
- self._window = window
- # Interpret tab and return as raw characters
- self.setFieldEditor_(False)
- self.empty_string = CFSTR("")
- return self
-
- @PygletTextView.method('v')
- def dealloc(self):
- self.empty_string.release()
-
- @PygletTextView.method('v@')
- def keyDown_(self, nsevent):
- array = NSArray.arrayWithObject_(nsevent)
- self.interpretKeyEvents_(array)
-
- @PygletTextView.method('v@')
- def insertText_(self, text):
- text = cfstring_to_string(text)
- self.setString_(self.empty_string)
- # Don't send control characters (tab, newline) as on_text events.
- if unicodedata.category(text[0]) != 'Cc':
- self._window.dispatch_event("on_text", text)
-
- @PygletTextView.method('v@')
- def insertNewline_(self, sender):
- # Distinguish between carriage return (u'\r') and enter (u'\x03').
- # Only the return key press gets sent as an on_text event.
- event = NSApplication.sharedApplication().currentEvent()
- chars = event.charactersIgnoringModifiers()
- ch = chr(chars.characterAtIndex_(0))
- if ch == u'\r':
- self._window.dispatch_event("on_text", u'\r')
-
- @PygletTextView.method('v@')
- def moveUp_(self, sender):
- self._window.dispatch_event("on_text_motion", key.MOTION_UP)
-
- @PygletTextView.method('v@')
- def moveDown_(self, sender):
- self._window.dispatch_event("on_text_motion", key.MOTION_DOWN)
-
- @PygletTextView.method('v@')
- def moveLeft_(self, sender):
- self._window.dispatch_event("on_text_motion", key.MOTION_LEFT)
-
- @PygletTextView.method('v@')
- def moveRight_(self, sender):
- self._window.dispatch_event("on_text_motion", key.MOTION_RIGHT)
-
- @PygletTextView.method('v@')
- def moveWordLeft_(self, sender):
- self._window.dispatch_event("on_text_motion", key.MOTION_PREVIOUS_WORD)
-
- @PygletTextView.method('v@')
- def moveWordRight_(self, sender):
- self._window.dispatch_event("on_text_motion", key.MOTION_NEXT_WORD)
-
- @PygletTextView.method('v@')
- def moveToBeginningOfLine_(self, sender):
- self._window.dispatch_event("on_text_motion", key.MOTION_BEGINNING_OF_LINE)
-
- @PygletTextView.method('v@')
- def moveToEndOfLine_(self, sender):
- self._window.dispatch_event("on_text_motion", key.MOTION_END_OF_LINE)
-
- @PygletTextView.method('v@')
- def scrollPageUp_(self, sender):
- self._window.dispatch_event("on_text_motion", key.MOTION_PREVIOUS_PAGE)
-
- @PygletTextView.method('v@')
- def scrollPageDown_(self, sender):
- self._window.dispatch_event("on_text_motion", key.MOTION_NEXT_PAGE)
-
- @PygletTextView.method('v@')
- def scrollToBeginningOfDocument_(self, sender): # Mac OS X 10.6
- self._window.dispatch_event("on_text_motion", key.MOTION_BEGINNING_OF_FILE)
-
- @PygletTextView.method('v@')
- def scrollToEndOfDocument_(self, sender): # Mac OS X 10.6
- self._window.dispatch_event("on_text_motion", key.MOTION_END_OF_FILE)
-
- @PygletTextView.method('v@')
- def deleteBackward_(self, sender):
- self._window.dispatch_event("on_text_motion", key.MOTION_BACKSPACE)
-
- @PygletTextView.method('v@')
- def deleteForward_(self, sender):
- self._window.dispatch_event("on_text_motion", key.MOTION_DELETE)
-
- @PygletTextView.method('v@')
- def moveUpAndModifySelection_(self, sender):
- self._window.dispatch_event("on_text_motion_select", key.MOTION_UP)
-
- @PygletTextView.method('v@')
- def moveDownAndModifySelection_(self, sender):
- self._window.dispatch_event("on_text_motion_select", key.MOTION_DOWN)
-
- @PygletTextView.method('v@')
- def moveLeftAndModifySelection_(self, sender):
- self._window.dispatch_event("on_text_motion_select", key.MOTION_LEFT)
-
- @PygletTextView.method('v@')
- def moveRightAndModifySelection_(self, sender):
- self._window.dispatch_event("on_text_motion_select", key.MOTION_RIGHT)
-
- @PygletTextView.method('v@')
- def moveWordLeftAndModifySelection_(self, sender):
- self._window.dispatch_event("on_text_motion_select", key.MOTION_PREVIOUS_WORD)
-
- @PygletTextView.method('v@')
- def moveWordRightAndModifySelection_(self, sender):
- self._window.dispatch_event("on_text_motion_select", key.MOTION_NEXT_WORD)
-
- @PygletTextView.method('v@')
- def moveToBeginningOfLineAndModifySelection_(self, sender): # Mac OS X 10.6
- self._window.dispatch_event("on_text_motion_select", key.MOTION_BEGINNING_OF_LINE)
-
- @PygletTextView.method('v@')
- def moveToEndOfLineAndModifySelection_(self, sender): # Mac OS X 10.6
- self._window.dispatch_event("on_text_motion_select", key.MOTION_END_OF_LINE)
-
- @PygletTextView.method('v@')
- def pageUpAndModifySelection_(self, sender): # Mac OS X 10.6
- self._window.dispatch_event("on_text_motion_select", key.MOTION_PREVIOUS_PAGE)
-
- @PygletTextView.method('v@')
- def pageDownAndModifySelection_(self, sender): # Mac OS X 10.6
- self._window.dispatch_event("on_text_motion_select", key.MOTION_NEXT_PAGE)
-
- @PygletTextView.method('v@')
- def moveToBeginningOfDocumentAndModifySelection_(self, sender): # Mac OS X 10.6
- self._window.dispatch_event("on_text_motion_select", key.MOTION_BEGINNING_OF_FILE)
-
- @PygletTextView.method('v@')
- def moveToEndOfDocumentAndModifySelection_(self, sender): # Mac OS X 10.6
- self._window.dispatch_event("on_text_motion_select", key.MOTION_END_OF_FILE)
-
-
-PygletTextView = ObjCClass('PygletTextView')
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/pyrender/primitive.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/pyrender/primitive.py
deleted file mode 100644
index 7f83f46f532b126a4573e715dd03d079fef755ca..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/pyrender/primitive.py
+++ /dev/null
@@ -1,489 +0,0 @@
-"""Primitives, conforming to the glTF 2.0 standards as specified in
-https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-primitive
-
-Author: Matthew Matl
-"""
-import numpy as np
-
-from OpenGL.GL import *
-
-from .material import Material, MetallicRoughnessMaterial
-from .constants import FLOAT_SZ, UINT_SZ, BufFlags, GLTF
-from .utils import format_color_array
-
-
-class Primitive(object):
- """A primitive object which can be rendered.
-
- Parameters
- ----------
- positions : (n, 3) float
- XYZ vertex positions.
- normals : (n, 3) float
- Normalized XYZ vertex normals.
- tangents : (n, 4) float
- XYZW vertex tangents where the w component is a sign value
- (either +1 or -1) indicating the handedness of the tangent basis.
- texcoord_0 : (n, 2) float
- The first set of UV texture coordinates.
- texcoord_1 : (n, 2) float
- The second set of UV texture coordinates.
- color_0 : (n, 4) float
- RGBA vertex colors.
- joints_0 : (n, 4) float
- Joint information.
- weights_0 : (n, 4) float
- Weight information for morphing.
- indices : (m, 3) int
- Face indices for triangle meshes or fans.
- material : :class:`Material`
- The material to apply to this primitive when rendering.
- mode : int
- The type of primitives to render, one of the following:
-
- - ``0``: POINTS
- - ``1``: LINES
- - ``2``: LINE_LOOP
- - ``3``: LINE_STRIP
- - ``4``: TRIANGLES
- - ``5``: TRIANGLES_STRIP
- - ``6``: TRIANGLES_FAN
- targets : (k,) int
- Morph target indices.
- poses : (x,4,4), float
- Array of 4x4 transformation matrices for instancing this object.
- """
-
- def __init__(self,
- positions,
- normals=None,
- tangents=None,
- texcoord_0=None,
- texcoord_1=None,
- color_0=None,
- joints_0=None,
- weights_0=None,
- indices=None,
- material=None,
- mode=None,
- targets=None,
- poses=None):
-
- if mode is None:
- mode = GLTF.TRIANGLES
-
- self.positions = positions
- self.normals = normals
- self.tangents = tangents
- self.texcoord_0 = texcoord_0
- self.texcoord_1 = texcoord_1
- self.color_0 = color_0
- self.joints_0 = joints_0
- self.weights_0 = weights_0
- self.indices = indices
- self.material = material
- self.mode = mode
- self.targets = targets
- self.poses = poses
-
- self._bounds = None
- self._vaid = None
- self._buffers = []
- self._is_transparent = None
- self._buf_flags = None
-
- @property
- def positions(self):
- """(n,3) float : XYZ vertex positions.
- """
- return self._positions
-
- @positions.setter
- def positions(self, value):
- value = np.asanyarray(value, dtype=np.float32)
- self._positions = np.ascontiguousarray(value)
- self._bounds = None
-
- @property
- def normals(self):
- """(n,3) float : Normalized XYZ vertex normals.
- """
- return self._normals
-
- @normals.setter
- def normals(self, value):
- if value is not None:
- value = np.asanyarray(value, dtype=np.float32)
- value = np.ascontiguousarray(value)
- if value.shape != self.positions.shape:
- raise ValueError('Incorrect normals shape')
- self._normals = value
-
- @property
- def tangents(self):
- """(n,4) float : XYZW vertex tangents.
- """
- return self._tangents
-
- @tangents.setter
- def tangents(self, value):
- if value is not None:
- value = np.asanyarray(value, dtype=np.float32)
- value = np.ascontiguousarray(value)
- if value.shape != (self.positions.shape[0], 4):
- raise ValueError('Incorrect tangent shape')
- self._tangents = value
-
- @property
- def texcoord_0(self):
- """(n,2) float : The first set of UV texture coordinates.
- """
- return self._texcoord_0
-
- @texcoord_0.setter
- def texcoord_0(self, value):
- if value is not None:
- value = np.asanyarray(value, dtype=np.float32)
- value = np.ascontiguousarray(value)
- if (value.ndim != 2 or value.shape[0] != self.positions.shape[0] or
- value.shape[1] < 2):
- raise ValueError('Incorrect texture coordinate shape')
- if value.shape[1] > 2:
- value = value[:,:2]
- self._texcoord_0 = value
-
- @property
- def texcoord_1(self):
- """(n,2) float : The second set of UV texture coordinates.
- """
- return self._texcoord_1
-
- @texcoord_1.setter
- def texcoord_1(self, value):
- if value is not None:
- value = np.asanyarray(value, dtype=np.float32)
- value = np.ascontiguousarray(value)
- if (value.ndim != 2 or value.shape[0] != self.positions.shape[0] or
- value.shape[1] != 2):
- raise ValueError('Incorrect texture coordinate shape')
- self._texcoord_1 = value
-
- @property
- def color_0(self):
- """(n,4) float : RGBA vertex colors.
- """
- return self._color_0
-
- @color_0.setter
- def color_0(self, value):
- if value is not None:
- value = np.ascontiguousarray(
- format_color_array(value, shape=(len(self.positions), 4))
- )
- self._is_transparent = None
- self._color_0 = value
-
- @property
- def joints_0(self):
- """(n,4) float : Joint information.
- """
- return self._joints_0
-
- @joints_0.setter
- def joints_0(self, value):
- self._joints_0 = value
-
- @property
- def weights_0(self):
- """(n,4) float : Weight information for morphing.
- """
- return self._weights_0
-
- @weights_0.setter
- def weights_0(self, value):
- self._weights_0 = value
-
- @property
- def indices(self):
- """(m,3) int : Face indices for triangle meshes or fans.
- """
- return self._indices
-
- @indices.setter
- def indices(self, value):
- if value is not None:
- value = np.asanyarray(value, dtype=np.float32)
- value = np.ascontiguousarray(value)
- self._indices = value
-
- @property
- def material(self):
- """:class:`Material` : The material for this primitive.
- """
- return self._material
-
- @material.setter
- def material(self, value):
- # Create default material
- if value is None:
- value = MetallicRoughnessMaterial()
- else:
- if not isinstance(value, Material):
- raise TypeError('Object material must be of type Material')
- self._material = value
-
- @property
- def mode(self):
- """int : The type of primitive to render.
- """
- return self._mode
-
- @mode.setter
- def mode(self, value):
- value = int(value)
- if value < GLTF.POINTS or value > GLTF.TRIANGLE_FAN:
- raise ValueError('Invalid mode')
- self._mode = value
-
- @property
- def targets(self):
- """(k,) int : Morph target indices.
- """
- return self._targets
-
- @targets.setter
- def targets(self, value):
- self._targets = value
-
- @property
- def poses(self):
- """(x,4,4) float : Homogenous transforms for instancing this primitive.
- """
- return self._poses
-
- @poses.setter
- def poses(self, value):
- if value is not None:
- value = np.asanyarray(value, dtype=np.float32)
- value = np.ascontiguousarray(value)
- if value.ndim == 2:
- value = value[np.newaxis,:,:]
- if value.shape[1] != 4 or value.shape[2] != 4:
- raise ValueError('Pose matrices must be of shape (n,4,4), '
- 'got {}'.format(value.shape))
- self._poses = value
- self._bounds = None
-
- @property
- def bounds(self):
- if self._bounds is None:
- self._bounds = self._compute_bounds()
- return self._bounds
-
- @property
- def centroid(self):
- """(3,) float : The centroid of the primitive's AABB.
- """
- return np.mean(self.bounds, axis=0)
-
- @property
- def extents(self):
- """(3,) float : The lengths of the axes of the primitive's AABB.
- """
- return np.diff(self.bounds, axis=0).reshape(-1)
-
- @property
- def scale(self):
- """(3,) float : The length of the diagonal of the primitive's AABB.
- """
- return np.linalg.norm(self.extents)
-
- @property
- def buf_flags(self):
- """int : The flags for the render buffer.
- """
- if self._buf_flags is None:
- self._buf_flags = self._compute_buf_flags()
- return self._buf_flags
-
- def delete(self):
- self._unbind()
- self._remove_from_context()
-
- @property
- def is_transparent(self):
- """bool : If True, the mesh is partially-transparent.
- """
- return self._compute_transparency()
-
- def _add_to_context(self):
- if self._vaid is not None:
- raise ValueError('Mesh is already bound to a context')
-
- # Generate and bind VAO
- self._vaid = glGenVertexArrays(1)
- glBindVertexArray(self._vaid)
-
- #######################################################################
- # Fill vertex buffer
- #######################################################################
-
- # Generate and bind vertex buffer
- vertexbuffer = glGenBuffers(1)
- self._buffers.append(vertexbuffer)
- glBindBuffer(GL_ARRAY_BUFFER, vertexbuffer)
-
- # positions
- vertex_data = self.positions
- attr_sizes = [3]
-
- # Normals
- if self.normals is not None:
- vertex_data = np.hstack((vertex_data, self.normals))
- attr_sizes.append(3)
-
- # Tangents
- if self.tangents is not None:
- vertex_data = np.hstack((vertex_data, self.tangents))
- attr_sizes.append(4)
-
- # Texture Coordinates
- if self.texcoord_0 is not None:
- vertex_data = np.hstack((vertex_data, self.texcoord_0))
- attr_sizes.append(2)
- if self.texcoord_1 is not None:
- vertex_data = np.hstack((vertex_data, self.texcoord_1))
- attr_sizes.append(2)
-
- # Color
- if self.color_0 is not None:
- vertex_data = np.hstack((vertex_data, self.color_0))
- attr_sizes.append(4)
-
- # TODO JOINTS AND WEIGHTS
- # PASS
-
- # Copy data to buffer
- vertex_data = np.ascontiguousarray(
- vertex_data.flatten().astype(np.float32)
- )
- glBufferData(
- GL_ARRAY_BUFFER, FLOAT_SZ * len(vertex_data),
- vertex_data, GL_STATIC_DRAW
- )
- total_sz = sum(attr_sizes)
- offset = 0
- for i, sz in enumerate(attr_sizes):
- glVertexAttribPointer(
- i, sz, GL_FLOAT, GL_FALSE, FLOAT_SZ * total_sz,
- ctypes.c_void_p(FLOAT_SZ * offset)
- )
- glEnableVertexAttribArray(i)
- offset += sz
-
- #######################################################################
- # Fill model matrix buffer
- #######################################################################
-
- if self.poses is not None:
- pose_data = np.ascontiguousarray(
- np.transpose(self.poses, [0,2,1]).flatten().astype(np.float32)
- )
- else:
- pose_data = np.ascontiguousarray(
- np.eye(4).flatten().astype(np.float32)
- )
-
- modelbuffer = glGenBuffers(1)
- self._buffers.append(modelbuffer)
- glBindBuffer(GL_ARRAY_BUFFER, modelbuffer)
- glBufferData(
- GL_ARRAY_BUFFER, FLOAT_SZ * len(pose_data),
- pose_data, GL_STATIC_DRAW
- )
-
- for i in range(0, 4):
- idx = i + len(attr_sizes)
- glEnableVertexAttribArray(idx)
- glVertexAttribPointer(
- idx, 4, GL_FLOAT, GL_FALSE, FLOAT_SZ * 4 * 4,
- ctypes.c_void_p(4 * FLOAT_SZ * i)
- )
- glVertexAttribDivisor(idx, 1)
-
- #######################################################################
- # Fill element buffer
- #######################################################################
- if self.indices is not None:
- elementbuffer = glGenBuffers(1)
- self._buffers.append(elementbuffer)
- glBindBuffer(GL_ELEMENT_ARRAY_BUFFER, elementbuffer)
- glBufferData(GL_ELEMENT_ARRAY_BUFFER, UINT_SZ * self.indices.size,
- self.indices.flatten().astype(np.uint32),
- GL_STATIC_DRAW)
-
- glBindVertexArray(0)
-
- def _remove_from_context(self):
- if self._vaid is not None:
- glDeleteVertexArrays(1, [self._vaid])
- glDeleteBuffers(len(self._buffers), self._buffers)
- self._vaid = None
- self._buffers = []
-
- def _in_context(self):
- return self._vaid is not None
-
- def _bind(self):
- if self._vaid is None:
- raise ValueError('Cannot bind a Mesh that has not been added '
- 'to a context')
- glBindVertexArray(self._vaid)
-
- def _unbind(self):
- glBindVertexArray(0)
-
- def _compute_bounds(self):
- """Compute the bounds of this object.
- """
- # Compute bounds of this object
- bounds = np.array([np.min(self.positions, axis=0),
- np.max(self.positions, axis=0)])
-
- # If instanced, compute translations for approximate bounds
- if self.poses is not None:
- bounds += np.array([np.min(self.poses[:,:3,3], axis=0),
- np.max(self.poses[:,:3,3], axis=0)])
- return bounds
-
- def _compute_transparency(self):
- """Compute whether or not this object is transparent.
- """
- if self.material.is_transparent:
- return True
- if self._is_transparent is None:
- self._is_transparent = False
- if self.color_0 is not None:
- if np.any(self._color_0[:,3] != 1.0):
- self._is_transparent = True
- return self._is_transparent
-
- def _compute_buf_flags(self):
- buf_flags = BufFlags.POSITION
-
- if self.normals is not None:
- buf_flags |= BufFlags.NORMAL
- if self.tangents is not None:
- buf_flags |= BufFlags.TANGENT
- if self.texcoord_0 is not None:
- buf_flags |= BufFlags.TEXCOORD_0
- if self.texcoord_1 is not None:
- buf_flags |= BufFlags.TEXCOORD_1
- if self.color_0 is not None:
- buf_flags |= BufFlags.COLOR_0
- if self.joints_0 is not None:
- buf_flags |= BufFlags.JOINTS_0
- if self.weights_0 is not None:
- buf_flags |= BufFlags.WEIGHTS_0
-
- return buf_flags
diff --git a/spaces/adirik/stylemc-demo/encoder4editing/configs/paths_config.py b/spaces/adirik/stylemc-demo/encoder4editing/configs/paths_config.py
deleted file mode 100644
index 4604f6063b8125364a52a492de52fcc54004f373..0000000000000000000000000000000000000000
--- a/spaces/adirik/stylemc-demo/encoder4editing/configs/paths_config.py
+++ /dev/null
@@ -1,28 +0,0 @@
-dataset_paths = {
- # Face Datasets (In the paper: FFHQ - train, CelebAHQ - test)
- 'ffhq': '',
- 'celeba_test': '',
-
- # Cars Dataset (In the paper: Stanford cars)
- 'cars_train': '',
- 'cars_test': '',
-
- # Horse Dataset (In the paper: LSUN Horse)
- 'horse_train': '',
- 'horse_test': '',
-
- # Church Dataset (In the paper: LSUN Church)
- 'church_train': '',
- 'church_test': '',
-
- # Cats Dataset (In the paper: LSUN Cat)
- 'cats_train': '',
- 'cats_test': ''
-}
-
-model_paths = {
- 'stylegan_ffhq': 'pretrained_models/stylegan2-ffhq-config-f.pt',
- 'ir_se50': 'pretrained_models/model_ir_se50.pth',
- 'shape_predictor': 'pretrained_models/shape_predictor_68_face_landmarks.dat',
- 'moco': 'pretrained_models/moco_v2_800ep_pretrain.pth'
-}
diff --git a/spaces/adrianpierce/recipes_app/pages/2_Saved_Recipes.py b/spaces/adrianpierce/recipes_app/pages/2_Saved_Recipes.py
deleted file mode 100644
index c85de52accbe4beebd3f660faf2a7fa75ea34f48..0000000000000000000000000000000000000000
--- a/spaces/adrianpierce/recipes_app/pages/2_Saved_Recipes.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import streamlit as st
-import json
-import os
-
-st.title("Saved Recipes")
-
-# get all saved files
-directory_path = '/data/'
-recipes = []
-for root, dirs, files in os.walk(directory_path):
- for file in files:
- if file.endswith('.json'):
- full_path = os.path.join(root, file)
- # os.remove(full_path)
- f = open(full_path)
- recipe_json = json.load(f)
- recipe_json['file'] = file
- recipes.append(recipe_json)
-
-#st.json(saved_files)
-
-cols = st.columns([4, 1])
-with cols[0]:
- user_search = st.text_input("Search Recipes", value="")
-with cols[1]:
- user_sort = st.selectbox("Sort", ('Recent', 'Oldest', 'A-Z', 'Z-A', 'Random'))
-st.write("") # just some space
-
-recipes_filtered = [x for x in recipes if user_search.lower() in x['name'].lower()]
-if user_sort == 'Recent':
- recipes_filtered.sort(key=lambda x: x['timestamp'], reverse=True)
-elif user_sort == 'Oldest':
- recipes_filtered.sort(key=lambda x: x['timestamp'])
-elif user_sort == 'A-Z':
- recipes_filtered.sort(key=lambda x: x['name'])
-elif user_sort == 'Z-A':
- recipes_filtered.sort(key=lambda x: x['name'], reverse=True)
-elif user_sort == 'Random':
- recipes_filtered.sort(key=lambda x: x['file'])
-
-for recipe in recipes_filtered:
- with st.expander(recipe['name']):
- st.markdown(recipe['md'])
- if st.session_state.admin == True:
- st.write('')
- st.write(recipe['file'])
- if st.button("Delete", key=recipe['file']):
- if os.path.exists(f"/data/{recipe['file']}"):
- os.remove(f"/data/{recipe['file']}")
- st.rerun()
-
-
-# ignore
-
-# f = open('/data/test_output.json')
-# json_test = json.load(f)
-
-# st.json(json_test)
-
-# file_path = '/data/test_output.json'
-
-# if os.path.exists(file_path):
-# # Delete the file
-# os.remove(file_path)
-# st.write(f"The file {file_path} has been deleted.")
-# else:
-# st.write(f"The file {file_path} does not exist.")
\ No newline at end of file
diff --git a/spaces/akhaliq/Detic/detic/modeling/debug.py b/spaces/akhaliq/Detic/detic/modeling/debug.py
deleted file mode 100644
index 9c7c442eb8aa9474c8874ac1dc75659371e8c894..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Detic/detic/modeling/debug.py
+++ /dev/null
@@ -1,334 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-import os
-
-COLORS = ((np.random.rand(1300, 3) * 0.4 + 0.6) * 255).astype(
- np.uint8).reshape(1300, 1, 1, 3)
-
-def _get_color_image(heatmap):
- heatmap = heatmap.reshape(
- heatmap.shape[0], heatmap.shape[1], heatmap.shape[2], 1)
- if heatmap.shape[0] == 1:
- color_map = (heatmap * np.ones((1, 1, 1, 3), np.uint8) * 255).max(
- axis=0).astype(np.uint8) # H, W, 3
- else:
- color_map = (heatmap * COLORS[:heatmap.shape[0]]).max(axis=0).astype(np.uint8) # H, W, 3
-
- return color_map
-
-def _blend_image(image, color_map, a=0.7):
- color_map = cv2.resize(color_map, (image.shape[1], image.shape[0]))
- ret = np.clip(image * (1 - a) + color_map * a, 0, 255).astype(np.uint8)
- return ret
-
-def _blend_image_heatmaps(image, color_maps, a=0.7):
- merges = np.zeros((image.shape[0], image.shape[1], 3), np.float32)
- for color_map in color_maps:
- color_map = cv2.resize(color_map, (image.shape[1], image.shape[0]))
- merges = np.maximum(merges, color_map)
- ret = np.clip(image * (1 - a) + merges * a, 0, 255).astype(np.uint8)
- return ret
-
-def _decompose_level(x, shapes_per_level, N):
- '''
- x: LNHiWi x C
- '''
- x = x.view(x.shape[0], -1)
- ret = []
- st = 0
- for l in range(len(shapes_per_level)):
- ret.append([])
- h = shapes_per_level[l][0].int().item()
- w = shapes_per_level[l][1].int().item()
- for i in range(N):
- ret[l].append(x[st + h * w * i:st + h * w * (i + 1)].view(
- h, w, -1).permute(2, 0, 1))
- st += h * w * N
- return ret
-
-def _imagelist_to_tensor(images):
- images = [x for x in images]
- image_sizes = [x.shape[-2:] for x in images]
- h = max([size[0] for size in image_sizes])
- w = max([size[1] for size in image_sizes])
- S = 32
- h, w = ((h - 1) // S + 1) * S, ((w - 1) // S + 1) * S
- images = [F.pad(x, (0, w - x.shape[2], 0, h - x.shape[1], 0, 0)) \
- for x in images]
- images = torch.stack(images)
- return images
-
-
-def _ind2il(ind, shapes_per_level, N):
- r = ind
- l = 0
- S = 0
- while r - S >= N * shapes_per_level[l][0] * shapes_per_level[l][1]:
- S += N * shapes_per_level[l][0] * shapes_per_level[l][1]
- l += 1
- i = (r - S) // (shapes_per_level[l][0] * shapes_per_level[l][1])
- return i, l
-
-def debug_train(
- images, gt_instances, flattened_hms, reg_targets, labels, pos_inds,
- shapes_per_level, locations, strides):
- '''
- images: N x 3 x H x W
- flattened_hms: LNHiWi x C
- shapes_per_level: L x 2 [(H_i, W_i)]
- locations: LNHiWi x 2
- '''
- reg_inds = torch.nonzero(
- reg_targets.max(dim=1)[0] > 0).squeeze(1)
- N = len(images)
- images = _imagelist_to_tensor(images)
- repeated_locations = [torch.cat([loc] * N, dim=0) \
- for loc in locations]
- locations = torch.cat(repeated_locations, dim=0)
- gt_hms = _decompose_level(flattened_hms, shapes_per_level, N)
- masks = flattened_hms.new_zeros((flattened_hms.shape[0], 1))
- masks[pos_inds] = 1
- masks = _decompose_level(masks, shapes_per_level, N)
- for i in range(len(images)):
- image = images[i].detach().cpu().numpy().transpose(1, 2, 0)
- color_maps = []
- for l in range(len(gt_hms)):
- color_map = _get_color_image(
- gt_hms[l][i].detach().cpu().numpy())
- color_maps.append(color_map)
- cv2.imshow('gthm_{}'.format(l), color_map)
- blend = _blend_image_heatmaps(image.copy(), color_maps)
- if gt_instances is not None:
- bboxes = gt_instances[i].gt_boxes.tensor
- for j in range(len(bboxes)):
- bbox = bboxes[j]
- cv2.rectangle(
- blend,
- (int(bbox[0]), int(bbox[1])),
- (int(bbox[2]), int(bbox[3])),
- (0, 0, 255), 3, cv2.LINE_AA)
-
- for j in range(len(pos_inds)):
- image_id, l = _ind2il(pos_inds[j], shapes_per_level, N)
- if image_id != i:
- continue
- loc = locations[pos_inds[j]]
- cv2.drawMarker(
- blend, (int(loc[0]), int(loc[1])), (0, 255, 255),
- markerSize=(l + 1) * 16)
-
- for j in range(len(reg_inds)):
- image_id, l = _ind2il(reg_inds[j], shapes_per_level, N)
- if image_id != i:
- continue
- ltrb = reg_targets[reg_inds[j]]
- ltrb *= strides[l]
- loc = locations[reg_inds[j]]
- bbox = [(loc[0] - ltrb[0]), (loc[1] - ltrb[1]),
- (loc[0] + ltrb[2]), (loc[1] + ltrb[3])]
- cv2.rectangle(
- blend,
- (int(bbox[0]), int(bbox[1])),
- (int(bbox[2]), int(bbox[3])),
- (255, 0, 0), 1, cv2.LINE_AA)
- cv2.circle(blend, (int(loc[0]), int(loc[1])), 2, (255, 0, 0), -1)
-
- cv2.imshow('blend', blend)
- cv2.waitKey()
-
-
-def debug_test(
- images, logits_pred, reg_pred, agn_hm_pred=[], preds=[],
- vis_thresh=0.3, debug_show_name=False, mult_agn=False):
- '''
- images: N x 3 x H x W
- class_target: LNHiWi x C
- cat_agn_heatmap: LNHiWi
- shapes_per_level: L x 2 [(H_i, W_i)]
- '''
- N = len(images)
- for i in range(len(images)):
- image = images[i].detach().cpu().numpy().transpose(1, 2, 0)
- result = image.copy().astype(np.uint8)
- pred_image = image.copy().astype(np.uint8)
- color_maps = []
- L = len(logits_pred)
- for l in range(L):
- if logits_pred[0] is not None:
- stride = min(image.shape[0], image.shape[1]) / min(
- logits_pred[l][i].shape[1], logits_pred[l][i].shape[2])
- else:
- stride = min(image.shape[0], image.shape[1]) / min(
- agn_hm_pred[l][i].shape[1], agn_hm_pred[l][i].shape[2])
- stride = stride if stride < 60 else 64 if stride < 100 else 128
- if logits_pred[0] is not None:
- if mult_agn:
- logits_pred[l][i] = logits_pred[l][i] * agn_hm_pred[l][i]
- color_map = _get_color_image(
- logits_pred[l][i].detach().cpu().numpy())
- color_maps.append(color_map)
- cv2.imshow('predhm_{}'.format(l), color_map)
-
- if debug_show_name:
- from detectron2.data.datasets.lvis_v1_categories import LVIS_CATEGORIES
- cat2name = [x['name'] for x in LVIS_CATEGORIES]
- for j in range(len(preds[i].scores) if preds is not None else 0):
- if preds[i].scores[j] > vis_thresh:
- bbox = preds[i].proposal_boxes[j] \
- if preds[i].has('proposal_boxes') else \
- preds[i].pred_boxes[j]
- bbox = bbox.tensor[0].detach().cpu().numpy().astype(np.int32)
- cat = int(preds[i].pred_classes[j]) \
- if preds[i].has('pred_classes') else 0
- cl = COLORS[cat, 0, 0]
- cv2.rectangle(
- pred_image, (int(bbox[0]), int(bbox[1])),
- (int(bbox[2]), int(bbox[3])),
- (int(cl[0]), int(cl[1]), int(cl[2])), 2, cv2.LINE_AA)
- if debug_show_name:
- txt = '{}{:.1f}'.format(
- cat2name[cat] if cat > 0 else '',
- preds[i].scores[j])
- font = cv2.FONT_HERSHEY_SIMPLEX
- cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0]
- cv2.rectangle(
- pred_image,
- (int(bbox[0]), int(bbox[1] - cat_size[1] - 2)),
- (int(bbox[0] + cat_size[0]), int(bbox[1] - 2)),
- (int(cl[0]), int(cl[1]), int(cl[2])), -1)
- cv2.putText(
- pred_image, txt, (int(bbox[0]), int(bbox[1] - 2)),
- font, 0.5, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA)
-
-
- if agn_hm_pred[l] is not None:
- agn_hm_ = agn_hm_pred[l][i, 0, :, :, None].detach().cpu().numpy()
- agn_hm_ = (agn_hm_ * np.array([255, 255, 255]).reshape(
- 1, 1, 3)).astype(np.uint8)
- cv2.imshow('agn_hm_{}'.format(l), agn_hm_)
- blend = _blend_image_heatmaps(image.copy(), color_maps)
- cv2.imshow('blend', blend)
- cv2.imshow('preds', pred_image)
- cv2.waitKey()
-
-global cnt
-cnt = 0
-
-def debug_second_stage(images, instances, proposals=None, vis_thresh=0.3,
- save_debug=False, debug_show_name=False, image_labels=[],
- save_debug_path='output/save_debug/',
- bgr=False):
- images = _imagelist_to_tensor(images)
- if 'COCO' in save_debug_path:
- from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
- cat2name = [x['name'] for x in COCO_CATEGORIES]
- else:
- from detectron2.data.datasets.lvis_v1_categories import LVIS_CATEGORIES
- cat2name = ['({}){}'.format(x['frequency'], x['name']) \
- for x in LVIS_CATEGORIES]
- for i in range(len(images)):
- image = images[i].detach().cpu().numpy().transpose(1, 2, 0).astype(np.uint8).copy()
- if bgr:
- image = image[:, :, ::-1].copy()
- if instances[i].has('gt_boxes'):
- bboxes = instances[i].gt_boxes.tensor.cpu().numpy()
- scores = np.ones(bboxes.shape[0])
- cats = instances[i].gt_classes.cpu().numpy()
- else:
- bboxes = instances[i].pred_boxes.tensor.cpu().numpy()
- scores = instances[i].scores.cpu().numpy()
- cats = instances[i].pred_classes.cpu().numpy()
- for j in range(len(bboxes)):
- if scores[j] > vis_thresh:
- bbox = bboxes[j]
- cl = COLORS[cats[j], 0, 0]
- cl = (int(cl[0]), int(cl[1]), int(cl[2]))
- cv2.rectangle(
- image,
- (int(bbox[0]), int(bbox[1])),
- (int(bbox[2]), int(bbox[3])),
- cl, 2, cv2.LINE_AA)
- if debug_show_name:
- cat = cats[j]
- txt = '{}{:.1f}'.format(
- cat2name[cat] if cat > 0 else '',
- scores[j])
- font = cv2.FONT_HERSHEY_SIMPLEX
- cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0]
- cv2.rectangle(
- image,
- (int(bbox[0]), int(bbox[1] - cat_size[1] - 2)),
- (int(bbox[0] + cat_size[0]), int(bbox[1] - 2)),
- (int(cl[0]), int(cl[1]), int(cl[2])), -1)
- cv2.putText(
- image, txt, (int(bbox[0]), int(bbox[1] - 2)),
- font, 0.5, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA)
- if proposals is not None:
- proposal_image = images[i].detach().cpu().numpy().transpose(1, 2, 0).astype(np.uint8).copy()
- if bgr:
- proposal_image = proposal_image.copy()
- else:
- proposal_image = proposal_image[:, :, ::-1].copy()
- bboxes = proposals[i].proposal_boxes.tensor.cpu().numpy()
- if proposals[i].has('scores'):
- scores = proposals[i].scores.detach().cpu().numpy()
- else:
- scores = proposals[i].objectness_logits.detach().cpu().numpy()
- # selected = -1
- # if proposals[i].has('image_loss'):
- # selected = proposals[i].image_loss.argmin()
- if proposals[i].has('selected'):
- selected = proposals[i].selected
- else:
- selected = [-1 for _ in range(len(bboxes))]
- for j in range(len(bboxes)):
- if scores[j] > vis_thresh or selected[j] >= 0:
- bbox = bboxes[j]
- cl = (209, 159, 83)
- th = 2
- if selected[j] >= 0:
- cl = (0, 0, 0xa4)
- th = 4
- cv2.rectangle(
- proposal_image,
- (int(bbox[0]), int(bbox[1])),
- (int(bbox[2]), int(bbox[3])),
- cl, th, cv2.LINE_AA)
- if selected[j] >= 0 and debug_show_name:
- cat = selected[j].item()
- txt = '{}'.format(cat2name[cat])
- font = cv2.FONT_HERSHEY_SIMPLEX
- cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0]
- cv2.rectangle(
- proposal_image,
- (int(bbox[0]), int(bbox[1] - cat_size[1] - 2)),
- (int(bbox[0] + cat_size[0]), int(bbox[1] - 2)),
- (int(cl[0]), int(cl[1]), int(cl[2])), -1)
- cv2.putText(
- proposal_image, txt,
- (int(bbox[0]), int(bbox[1] - 2)),
- font, 0.5, (0, 0, 0), thickness=1,
- lineType=cv2.LINE_AA)
-
- if save_debug:
- global cnt
- cnt = (cnt + 1) % 5000
- if not os.path.exists(save_debug_path):
- os.mkdir(save_debug_path)
- save_name = '{}/{:05d}.jpg'.format(save_debug_path, cnt)
- if i < len(image_labels):
- image_label = image_labels[i]
- save_name = '{}/{:05d}'.format(save_debug_path, cnt)
- for x in image_label:
- class_name = cat2name[x]
- save_name = save_name + '|{}'.format(class_name)
- save_name = save_name + '.jpg'
- cv2.imwrite(save_name, proposal_image)
- else:
- cv2.imshow('image', image)
- if proposals is not None:
- cv2.imshow('proposals', proposal_image)
- cv2.waitKey()
\ No newline at end of file
diff --git a/spaces/akhaliq/JoJoGAN/e4e/scripts/calc_losses_on_images.py b/spaces/akhaliq/JoJoGAN/e4e/scripts/calc_losses_on_images.py
deleted file mode 100644
index 32b6bcee854da7ae357daf82bd986f30db9fb72c..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/JoJoGAN/e4e/scripts/calc_losses_on_images.py
+++ /dev/null
@@ -1,87 +0,0 @@
-from argparse import ArgumentParser
-import os
-import json
-import sys
-from tqdm import tqdm
-import numpy as np
-import torch
-from torch.utils.data import DataLoader
-import torchvision.transforms as transforms
-
-sys.path.append(".")
-sys.path.append("..")
-
-from criteria.lpips.lpips import LPIPS
-from datasets.gt_res_dataset import GTResDataset
-
-
-def parse_args():
- parser = ArgumentParser(add_help=False)
- parser.add_argument('--mode', type=str, default='lpips', choices=['lpips', 'l2'])
- parser.add_argument('--data_path', type=str, default='results')
- parser.add_argument('--gt_path', type=str, default='gt_images')
- parser.add_argument('--workers', type=int, default=4)
- parser.add_argument('--batch_size', type=int, default=4)
- parser.add_argument('--is_cars', action='store_true')
- args = parser.parse_args()
- return args
-
-
-def run(args):
- resize_dims = (256, 256)
- if args.is_cars:
- resize_dims = (192, 256)
- transform = transforms.Compose([transforms.Resize(resize_dims),
- transforms.ToTensor(),
- transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
-
- print('Loading dataset')
- dataset = GTResDataset(root_path=args.data_path,
- gt_dir=args.gt_path,
- transform=transform)
-
- dataloader = DataLoader(dataset,
- batch_size=args.batch_size,
- shuffle=False,
- num_workers=int(args.workers),
- drop_last=True)
-
- if args.mode == 'lpips':
- loss_func = LPIPS(net_type='alex')
- elif args.mode == 'l2':
- loss_func = torch.nn.MSELoss()
- else:
- raise Exception('Not a valid mode!')
- loss_func.cuda()
-
- global_i = 0
- scores_dict = {}
- all_scores = []
- for result_batch, gt_batch in tqdm(dataloader):
- for i in range(args.batch_size):
- loss = float(loss_func(result_batch[i:i + 1].cuda(), gt_batch[i:i + 1].cuda()))
- all_scores.append(loss)
- im_path = dataset.pairs[global_i][0]
- scores_dict[os.path.basename(im_path)] = loss
- global_i += 1
-
- all_scores = list(scores_dict.values())
- mean = np.mean(all_scores)
- std = np.std(all_scores)
- result_str = 'Average loss is {:.2f}+-{:.2f}'.format(mean, std)
- print('Finished with ', args.data_path)
- print(result_str)
-
- out_path = os.path.join(os.path.dirname(args.data_path), 'inference_metrics')
- if not os.path.exists(out_path):
- os.makedirs(out_path)
-
- with open(os.path.join(out_path, 'stat_{}.txt'.format(args.mode)), 'w') as f:
- f.write(result_str)
- with open(os.path.join(out_path, 'scores_{}.json'.format(args.mode)), 'w') as f:
- json.dump(scores_dict, f)
-
-
-if __name__ == '__main__':
- args = parse_args()
- run(args)
diff --git a/spaces/akhaliq/Mask2Former/mask2former/modeling/transformer_decoder/__init__.py b/spaces/akhaliq/Mask2Former/mask2former/modeling/transformer_decoder/__init__.py
deleted file mode 100644
index ddcf38e78f3bbb2380b0a246000bcb5e5b385619..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Mask2Former/mask2former/modeling/transformer_decoder/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .maskformer_transformer_decoder import StandardTransformerDecoder
-from .mask2former_transformer_decoder import MultiScaleMaskedTransformerDecoder
diff --git a/spaces/akhaliq/omnivore/app.py b/spaces/akhaliq/omnivore/app.py
deleted file mode 100644
index 1e205973a1ec37b8eaa073018852e55b03c5fa1d..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/omnivore/app.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import os
-import json
-from typing import List
-
-
-import torch
-import torch.nn.functional as F
-import torchvision.transforms as T
-from PIL import Image
-from torchvision.transforms._transforms_video import NormalizeVideo
-
-import gradio as gr
-
-# Device on which to run the model
-# Set to cuda to load on GPU
-device = "cpu"
-os.system("wget https://huggingface.co/akhaliq/Omnivore/resolve/main/swinB_checkpoint.torch")
-# Pick a pretrained model
-model_name = "omnivore_swinB"
-model = torch.hub.load('facebookresearch/omnivore:main', "omnivore_swinB", pretrained=False)
-new_dict = {}
-for key, value in torch.load('/home/user/app/swinB_checkpoint.torch')['trunk'].items():
- new_dict['trunk.' + key] = value
-
-for key, value in torch.load('/home/user/app/swinB_checkpoint.torch')['heads'].items():
- new_dict['heads.' + key] = value
-
-model.load_state_dict(new_dict)
-
-# Set to eval mode and move to desired device
-model = model.to(device)
-model = model.eval()
-
-os.system("wget https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json")
-
-with open("imagenet_class_index.json", "r") as f:
- imagenet_classnames = json.load(f)
-
-# Create an id to label name mapping
-imagenet_id_to_classname = {}
-for k, v in imagenet_classnames.items():
- imagenet_id_to_classname[k] = v[1]
-
-os.system("wget https://upload.wikimedia.org/wikipedia/commons/thumb/c/c5/13-11-02-olb-by-RalfR-03.jpg/800px-13-11-02-olb-by-RalfR-03.jpg -O library.jpg")
-
-def inference(img):
- image = img
- image_transform = T.Compose(
- [
- T.Resize(224),
- T.CenterCrop(224),
- T.ToTensor(),
- T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
- ]
- )
- image = image_transform(image)
-
- # The model expects inputs of shape: B x C x T x H x W
- image = image[None, :, None, ...]
-
- prediction = model(image, input_type="image")
- prediction = F.softmax(prediction, dim=1)
- pred_classes = prediction.topk(k=5).indices
-
- pred_class_names = [imagenet_id_to_classname[str(i.item())] for i in pred_classes[0]]
- return "Top 5 predicted labels: %s" % ", ".join(pred_class_names)
-
-inputs = gr.inputs.Image(type='pil')
-outputs = gr.outputs.Textbox(label="Output")
-
-title = "Omnivore"
-
-description = "Gradio demo for Omnivore: A Single Model for Many Visual Modalities. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-
-article = "
- @author Phil Burk
-*/
-/*
- * $Id$
- *
- * This program uses the PortAudio Portable Audio Library.
- * For more information see: http://www.portaudio.com
- * Copyright (c) 1999-2000 Ross Bencina and Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-#include
-#include
-#include "portaudio.h"
-
-#define NUM_SECONDS (8)
-#define SAMPLE_RATE (44100)
-#define FRAMES_PER_BUFFER (512)
-#ifndef M_PI
-#define M_PI (3.14159265)
-#endif
-#define TABLE_SIZE (200)
-#define BALANCE_DELTA (0.001)
-
-typedef struct
-{
- float sine[TABLE_SIZE];
- int left_phase;
- int right_phase;
- float targetBalance; // 0.0 = left, 1.0 = right
- float currentBalance;
-} paTestData;
-
-/* This routine will be called by the PortAudio engine when audio is needed.
-** It may called at interrupt level on some machines so don't do anything
-** that could mess up the system like calling malloc() or free().
-*/
-static int patestCallback( const void *inputBuffer,
- void *outputBuffer,
- unsigned long framesPerBuffer,
- const PaStreamCallbackTimeInfo* timeInfo,
- PaStreamCallbackFlags statusFlags,
- void *userData )
-{
- paTestData *data = (paTestData*)userData;
- float *out = (float*)outputBuffer;
- unsigned long i;
- int finished = 0;
- /* Prevent unused variable warnings. */
- (void) inputBuffer;
-
- for( i=0; icurrentBalance < data->targetBalance )
- {
- data->currentBalance += BALANCE_DELTA;
- }
- else if( data->currentBalance > data->targetBalance )
- {
- data->currentBalance -= BALANCE_DELTA;
- }
- // Apply left/right balance.
- *out++ = data->sine[data->left_phase] * (1.0f - data->currentBalance); /* left */
- *out++ = data->sine[data->right_phase] * data->currentBalance; /* right */
-
- data->left_phase += 1;
- if( data->left_phase >= TABLE_SIZE ) data->left_phase -= TABLE_SIZE;
- data->right_phase += 3; /* higher pitch so we can distinguish left and right. */
- if( data->right_phase >= TABLE_SIZE ) data->right_phase -= TABLE_SIZE;
- }
-
- return finished;
-}
-
-/*******************************************************************/
-int main(void);
-int main(void)
-{
- PaStream *stream;
- PaStreamParameters outputParameters;
- PaError err;
- paTestData data;
- int i;
- printf("Play different tone sine waves that alternate between left and right channel.\n");
- printf("The low tone should be on the left channel.\n");
-
- /* initialise sinusoidal wavetable */
- for( i=0; idefaultLowOutputLatency;
- outputParameters.hostApiSpecificStreamInfo = NULL;
-
- err = Pa_OpenStream( &stream,
- NULL, /* No input. */
- &outputParameters, /* As above. */
- SAMPLE_RATE,
- FRAMES_PER_BUFFER,
- paClipOff, /* we won't output out of range samples so don't bother clipping them */
- patestCallback,
- &data );
- if( err != paNoError ) goto error;
-
- err = Pa_StartStream( stream );
- if( err != paNoError ) goto error;
-
- printf("Play for several seconds.\n");
- for( i=0; i<4; i++ )
- {
- printf("Hear low sound on left side.\n");
- data.targetBalance = 0.01;
- Pa_Sleep( 1000 );
-
- printf("Hear high sound on right side.\n");
- data.targetBalance = 0.99;
- Pa_Sleep( 1000 );
- }
-
- err = Pa_StopStream( stream );
- if( err != paNoError ) goto error;
- err = Pa_CloseStream( stream );
- if( err != paNoError ) goto error;
- Pa_Terminate();
- printf("Test finished.\n");
- return err;
-error:
- Pa_Terminate();
- fprintf( stderr, "An error occurred while using the portaudio stream\n" );
- fprintf( stderr, "Error number: %d\n", err );
- fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
- return err;
-}
diff --git a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/Dockerfile b/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/Dockerfile
deleted file mode 100644
index 7ac29c145f7d05ea9b1344e50e634629c9d88984..0000000000000000000000000000000000000000
--- a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM python:3.10-slim-buster
-
-WORKDIR /app
-
-COPY requirements.txt requirements.txt
-
-RUN python -m venv venv
-ENV PATH="/app/venv/bin:$PATH"
-
-RUN apt-get update && \
- apt-get install -y --no-install-recommends build-essential libffi-dev cmake libcurl4-openssl-dev && \
- pip3 install --no-cache-dir -r requirements.txt
-
-COPY . .
-
-RUN chmod -R 777 translations
-
-CMD ["python3", "./run.py"]
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/interactive_cross_highlight.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/interactive_cross_highlight.py
deleted file mode 100644
index 1ba80cf2617642717d2ac485e757f06039e749b0..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/interactive_cross_highlight.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""
-Interactive Chart with Cross-Highlight
-======================================
-This example shows an interactive chart where selections in one portion of
-the chart affect what is shown in other panels. Click on the bar chart to
-see a detail of the distribution in the upper panel.
-"""
-# category: interactive charts
-import altair as alt
-from vega_datasets import data
-
-source = data.movies.url
-
-pts = alt.selection(type="single", encodings=['x'])
-
-rect = alt.Chart(data.movies.url).mark_rect().encode(
- alt.X('IMDB_Rating:Q', bin=True),
- alt.Y('Rotten_Tomatoes_Rating:Q', bin=True),
- alt.Color('count()',
- scale=alt.Scale(scheme='greenblue'),
- legend=alt.Legend(title='Total Records')
- )
-)
-
-circ = rect.mark_point().encode(
- alt.ColorValue('grey'),
- alt.Size('count()',
- legend=alt.Legend(title='Records in Selection')
- )
-).transform_filter(
- pts
-)
-
-bar = alt.Chart(source).mark_bar().encode(
- x='Major_Genre:N',
- y='count()',
- color=alt.condition(pts, alt.ColorValue("steelblue"), alt.ColorValue("grey"))
-).properties(
- width=550,
- height=200
-).add_selection(pts)
-
-alt.vconcat(
- rect + circ,
- bar
-).resolve_legend(
- color="independent",
- size="independent"
-)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/__init__.py
deleted file mode 100644
index 8acf2ca173b44757719c4e3a7352d357b3100a0c..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/__init__.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""isort:skip_file"""
-
-from .dictionary import Dictionary, TruncatedDictionary
-
-from .fairseq_dataset import FairseqDataset, FairseqIterableDataset
-
-from .base_wrapper_dataset import BaseWrapperDataset
-
-from .add_target_dataset import AddTargetDataset
-from .append_token_dataset import AppendTokenDataset
-from .audio.raw_audio_dataset import BinarizedAudioDataset, FileAudioDataset
-from .audio.hubert_dataset import HubertDataset
-from .backtranslation_dataset import BacktranslationDataset
-from .bucket_pad_length_dataset import BucketPadLengthDataset
-from .colorize_dataset import ColorizeDataset
-from .concat_dataset import ConcatDataset
-from .concat_sentences_dataset import ConcatSentencesDataset
-from .denoising_dataset import DenoisingDataset
-from .id_dataset import IdDataset
-from .indexed_dataset import (
- IndexedCachedDataset,
- IndexedDataset,
- IndexedRawTextDataset,
- MMapIndexedDataset,
-)
-from .language_pair_dataset import LanguagePairDataset
-from .list_dataset import ListDataset
-from .lm_context_window_dataset import LMContextWindowDataset
-from .lru_cache_dataset import LRUCacheDataset
-from .mask_tokens_dataset import MaskTokensDataset
-from .monolingual_dataset import MonolingualDataset
-from .multi_corpus_sampled_dataset import MultiCorpusSampledDataset
-from .nested_dictionary_dataset import NestedDictionaryDataset
-from .noising import NoisingDataset
-from .numel_dataset import NumelDataset
-from .num_samples_dataset import NumSamplesDataset
-from .offset_tokens_dataset import OffsetTokensDataset
-from .pad_dataset import LeftPadDataset, PadDataset, RightPadDataset
-from .prepend_dataset import PrependDataset
-from .prepend_token_dataset import PrependTokenDataset
-from .raw_label_dataset import RawLabelDataset
-from .replace_dataset import ReplaceDataset
-from .resampling_dataset import ResamplingDataset
-from .roll_dataset import RollDataset
-from .round_robin_zip_datasets import RoundRobinZipDatasets
-from .sort_dataset import SortDataset
-from .strip_token_dataset import StripTokenDataset
-from .subsample_dataset import SubsampleDataset
-from .token_block_dataset import TokenBlockDataset
-from .transform_eos_dataset import TransformEosDataset
-from .transform_eos_lang_pair_dataset import TransformEosLangPairDataset
-from .shorten_dataset import TruncateDataset, RandomCropDataset
-from .multilingual.sampled_multi_dataset import SampledMultiDataset
-from .multilingual.sampled_multi_epoch_dataset import SampledMultiEpochDataset
-from .fasta_dataset import FastaDataset, EncodedFastaDataset
-from .transform_eos_concat_langpair_dataset import TransformEosConcatLangPairDataset
-
-from .iterators import (
- CountingIterator,
- EpochBatchIterator,
- GroupedIterator,
- ShardedIterator,
-)
-
-__all__ = [
- "AddTargetDataset",
- "AppendTokenDataset",
- "BacktranslationDataset",
- "BaseWrapperDataset",
- "BinarizedAudioDataset",
- "BucketPadLengthDataset",
- "ColorizeDataset",
- "ConcatDataset",
- "ConcatSentencesDataset",
- "CountingIterator",
- "DenoisingDataset",
- "Dictionary",
- "EncodedFastaDataset",
- "EpochBatchIterator",
- "FairseqDataset",
- "FairseqIterableDataset",
- "FastaDataset",
- "FileAudioDataset",
- "GroupedIterator",
- "HubertDataset",
- "IdDataset",
- "IndexedCachedDataset",
- "IndexedDataset",
- "IndexedRawTextDataset",
- "LanguagePairDataset",
- "LeftPadDataset",
- "ListDataset",
- "LMContextWindowDataset",
- "LRUCacheDataset",
- "MaskTokensDataset",
- "MMapIndexedDataset",
- "MonolingualDataset",
- "MultiCorpusSampledDataset",
- "NestedDictionaryDataset",
- "NoisingDataset",
- "NumelDataset",
- "NumSamplesDataset",
- "OffsetTokensDataset",
- "PadDataset",
- "PrependDataset",
- "PrependTokenDataset",
- "RandomCropDataset",
- "RawLabelDataset",
- "ResamplingDataset",
- "ReplaceDataset",
- "RightPadDataset",
- "RollDataset",
- "RoundRobinZipDatasets",
- "SampledMultiDataset",
- "SampledMultiEpochDataset",
- "ShardedIterator",
- "SortDataset",
- "StripTokenDataset",
- "SubsampleDataset",
- "TokenBlockDataset",
- "TransformEosDataset",
- "TransformEosLangPairDataset",
- "TransformEosConcatLangPairDataset",
- "TruncateDataset",
- "TruncatedDictionary",
-]
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/__init__.py
deleted file mode 100644
index 44bb24ae614941f23fea29c56d60167650c39bcb..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/__init__.py
+++ /dev/null
@@ -1,9 +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.
-
-try:
- from fairseq.version import __version__ # noqa
-except ImportError:
- pass
diff --git a/spaces/asafAdge/Detic/detic/data/transforms/custom_transform.py b/spaces/asafAdge/Detic/detic/data/transforms/custom_transform.py
deleted file mode 100644
index 3cc28b6b313dc084394ec5c9686169176987a44b..0000000000000000000000000000000000000000
--- a/spaces/asafAdge/Detic/detic/data/transforms/custom_transform.py
+++ /dev/null
@@ -1,114 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# Part of the code is from https://github.com/rwightman/efficientdet-pytorch/blob/master/effdet/data/transforms.py
-# Modified by Xingyi Zhou
-# The original code is under Apache-2.0 License
-import numpy as np
-import torch
-import torch.nn.functional as F
-from fvcore.transforms.transform import (
- CropTransform,
- HFlipTransform,
- NoOpTransform,
- Transform,
- TransformList,
-)
-from PIL import Image
-
-try:
- import cv2 # noqa
-except ImportError:
- # OpenCV is an optional dependency at the moment
- pass
-
-__all__ = [
- "EfficientDetResizeCropTransform",
-]
-
-class EfficientDetResizeCropTransform(Transform):
- """
- """
-
- def __init__(self, scaled_h, scaled_w, offset_y, offset_x, img_scale, \
- target_size, interp=None):
- """
- Args:
- h, w (int): original image size
- new_h, new_w (int): new image size
- interp: PIL interpolation methods, defaults to bilinear.
- """
- # TODO decide on PIL vs opencv
- super().__init__()
- if interp is None:
- interp = Image.BILINEAR
- self._set_attributes(locals())
-
- def apply_image(self, img, interp=None):
- assert len(img.shape) <= 4
-
- if img.dtype == np.uint8:
- pil_image = Image.fromarray(img)
- interp_method = interp if interp is not None else self.interp
- pil_image = pil_image.resize((self.scaled_w, self.scaled_h), interp_method)
- ret = np.asarray(pil_image)
- right = min(self.scaled_w, self.offset_x + self.target_size[1])
- lower = min(self.scaled_h, self.offset_y + self.target_size[0])
- if len(ret.shape) <= 3:
- ret = ret[self.offset_y: lower, self.offset_x: right]
- else:
- ret = ret[..., self.offset_y: lower, self.offset_x: right, :]
- else:
- # PIL only supports uint8
- img = torch.from_numpy(img)
- shape = list(img.shape)
- shape_4d = shape[:2] + [1] * (4 - len(shape)) + shape[2:]
- img = img.view(shape_4d).permute(2, 3, 0, 1) # hw(c) -> nchw
- _PIL_RESIZE_TO_INTERPOLATE_MODE = {Image.BILINEAR: "bilinear", Image.BICUBIC: "bicubic"}
- mode = _PIL_RESIZE_TO_INTERPOLATE_MODE[self.interp]
- img = F.interpolate(img, (self.scaled_h, self.scaled_w), mode=mode, align_corners=False)
- shape[:2] = (self.scaled_h, self.scaled_w)
- ret = img.permute(2, 3, 0, 1).view(shape).numpy() # nchw -> hw(c)
- right = min(self.scaled_w, self.offset_x + self.target_size[1])
- lower = min(self.scaled_h, self.offset_y + self.target_size[0])
- if len(ret.shape) <= 3:
- ret = ret[self.offset_y: lower, self.offset_x: right]
- else:
- ret = ret[..., self.offset_y: lower, self.offset_x: right, :]
- return ret
-
-
- def apply_coords(self, coords):
- coords[:, 0] = coords[:, 0] * self.img_scale
- coords[:, 1] = coords[:, 1] * self.img_scale
- coords[:, 0] -= self.offset_x
- coords[:, 1] -= self.offset_y
- return coords
-
-
- def apply_segmentation(self, segmentation):
- segmentation = self.apply_image(segmentation, interp=Image.NEAREST)
- return segmentation
-
-
- def inverse(self):
- raise NotImplementedError
-
-
- def inverse_apply_coords(self, coords):
- coords[:, 0] += self.offset_x
- coords[:, 1] += self.offset_y
- coords[:, 0] = coords[:, 0] / self.img_scale
- coords[:, 1] = coords[:, 1] / self.img_scale
- return coords
-
-
- def inverse_apply_box(self, box: np.ndarray) -> np.ndarray:
- """
- """
- idxs = np.array([(0, 1), (2, 1), (0, 3), (2, 3)]).flatten()
- coords = np.asarray(box).reshape(-1, 4)[:, idxs].reshape(-1, 2)
- coords = self.inverse_apply_coords(coords).reshape((-1, 4, 2))
- minxy = coords.min(axis=1)
- maxxy = coords.max(axis=1)
- trans_boxes = np.concatenate((minxy, maxxy), axis=1)
- return trans_boxes
\ No newline at end of file
diff --git a/spaces/ashercn97/AsherTesting/modules/llama_attn_hijack.py b/spaces/ashercn97/AsherTesting/modules/llama_attn_hijack.py
deleted file mode 100644
index 925cdaa352326fdc23a3585699883d27b8de5c73..0000000000000000000000000000000000000000
--- a/spaces/ashercn97/AsherTesting/modules/llama_attn_hijack.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import math
-import sys
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import transformers.models.llama.modeling_llama
-
-import modules.shared as shared
-from modules.logging_colors import logger
-
-if shared.args.xformers:
- try:
- import xformers.ops
- except Exception:
- logger.error("xformers not found! Please install it before trying to use it.", file=sys.stderr)
-
-
-def hijack_llama_attention():
- if shared.args.xformers:
- transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward
- logger.info("Replaced attention with xformers_attention")
- elif shared.args.sdp_attention:
- transformers.models.llama.modeling_llama.LlamaAttention.forward = sdp_attention_forward
- logger.info("Replaced attention with sdp_attention")
-
-
-def xformers_forward(
- self,
- hidden_states: torch.Tensor,
- attention_mask: Optional[torch.Tensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- past_key_value: Optional[Tuple[torch.Tensor]] = None,
- output_attentions: bool = False,
- use_cache: bool = False,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
- bsz, q_len, _ = hidden_states.size()
-
- query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
- key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
- value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-
- kv_seq_len = key_states.shape[-2]
- if past_key_value is not None:
- kv_seq_len += past_key_value[0].shape[-2]
- cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
- query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
- # [bsz, nh, t, hd]
-
- if past_key_value is not None:
- # reuse k, v, self_attention
- key_states = torch.cat([past_key_value[0], key_states], dim=2)
- value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
- past_key_value = (key_states, value_states) if use_cache else None
-
- # We only apply xformers optimizations if we don't need to output the whole attention matrix
- if not output_attentions:
- query_states = query_states.transpose(1, 2)
- key_states = key_states.transpose(1, 2)
- value_states = value_states.transpose(1, 2)
-
- # This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros.
- # We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros.
- if attention_mask is None or attention_mask[0, 0, 0, 1] == 0:
- # input and output should be of form (bsz, q_len, num_heads, head_dim)
- attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=None)
- else:
- # input and output should be of form (bsz, q_len, num_heads, head_dim)
- attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=xformers.ops.LowerTriangularMask())
- attn_weights = None
- else:
- attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
- if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
- raise ValueError(
- f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
- f" {attn_weights.size()}"
- )
-
- if attention_mask is not None:
- if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
- raise ValueError(
- f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
- )
- attn_weights = attn_weights + attention_mask
- attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
-
- # upcast attention to fp32
- attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
- attn_output = torch.matmul(attn_weights, value_states)
-
- if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
- raise ValueError(
- f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
- f" {attn_output.size()}"
- )
-
- attn_output = attn_output.transpose(1, 2)
-
- attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
- attn_output = self.o_proj(attn_output)
- return attn_output, attn_weights, past_key_value
-
-
-def sdp_attention_forward(
- self,
- hidden_states: torch.Tensor,
- attention_mask: Optional[torch.Tensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- past_key_value: Optional[Tuple[torch.Tensor]] = None,
- output_attentions: bool = False,
- use_cache: bool = False,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
- bsz, q_len, _ = hidden_states.size()
-
- query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
- key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
- value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-
- kv_seq_len = key_states.shape[-2]
- if past_key_value is not None:
- kv_seq_len += past_key_value[0].shape[-2]
- cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
- query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
- # [bsz, nh, t, hd]
-
- if past_key_value is not None:
- # reuse k, v, self_attention
- key_states = torch.cat([past_key_value[0], key_states], dim=2)
- value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
- past_key_value = (key_states, value_states) if use_cache else None
-
- # We only apply sdp attention if we don't need to output the whole attention matrix
- if not output_attentions:
- attn_output = torch.nn.functional.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask, is_causal=False)
- attn_weights = None
- else:
- attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
- if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
- raise ValueError(
- f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
- f" {attn_weights.size()}"
- )
-
- if attention_mask is not None:
- if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
- raise ValueError(
- f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
- )
- attn_weights = attn_weights + attention_mask
- attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
-
- # upcast attention to fp32
- attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
- attn_output = torch.matmul(attn_weights, value_states)
-
- if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
- raise ValueError(
- f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
- f" {attn_output.size()}"
- )
-
- attn_output = attn_output.transpose(1, 2)
- attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
- attn_output = self.o_proj(attn_output)
-
- return attn_output, attn_weights, past_key_value
diff --git a/spaces/ashishraics/MCQ-Generator/keywords.py b/spaces/ashishraics/MCQ-Generator/keywords.py
deleted file mode 100644
index ea640118a4506de2cec4546416333b15eb8b0c40..0000000000000000000000000000000000000000
--- a/spaces/ashishraics/MCQ-Generator/keywords.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import nltk
-nltk.download('stopwords')
-nltk.download('wordnet')
-nltk.download('punkt')
-from nltk.corpus import stopwords,wordnet
-from nltk.tokenize import sent_tokenize
-import string
-import subprocess
-import logging
-
-try:
- import pke
- logging.error("importing pke info")
-except:
- logging.error("installing pke info")
- subprocess.run(['pip3', 'install','git+https://github.com/boudinfl/pke.git'])
- subprocess.run(['python3' ,'-m' ,'spacy' ,'download' ,'en'])
- import pke
-
-stoplist = list(string.punctuation)
-stoplist += pke.lang.stopwords.get('en')
-stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-']
-stoplist += stopwords.words('english')
-
-def tokenize_sentence(text):
- sentences=sent_tokenize(text)
- sentences=[s.strip().lstrip().rstrip() for s in sentences if len(s) > 20]
- return sentences
-
-def get_multipartiterank_topics(text):
- output = []
- try:
- extractor = pke.unsupervised.MultipartiteRank()
- extractor.load_document(input=text, language='en',normalization=None,stoplist=stoplist)
- # keyphrase candidate selection #'ADJ' 'ADP' 'ADV' 'AUX' 'DET' 'NOUN' 'NUM' 'PART' 'PROPN' 'PUNCT' 'VERB'
- extractor.candidate_selection(pos={'NOUN','VERB','ADJ'})
- extractor.candidate_weighting(threshold=0.7,method='average',alpha=1.1)
- keyphrases = extractor.get_n_best(n=5)
-
- for val in keyphrases:
- output.append(val[0])
- except Exception as e:
- print("found exception",e)
- return list(set(output))
-
-def get_topicrank_topics(text):
- output = []
- try:
- extractor = pke.unsupervised.TopicRank()
- extractor.load_document(input=text, language='en',normalization=None,stoplist=stoplist)
- # keyphrase candidate selection #'ADJ' 'ADP' 'ADV' 'AUX' 'DET' 'NOUN' 'NUM' 'PART' 'PROPN' 'PUNCT' 'VERB'
- extractor.candidate_selection(pos={'NOUN', 'ADJ'})
- extractor.candidate_weighting(threshold=0.7,method='average')
- keyphrases = extractor.get_n_best(n=5)
-
- for val in keyphrases:
- output.append(val[0])
- except Exception as e:
- print("found exception",e)
- return list(set(output))
-
-def get_yake_topics(text):
- #statistics model --very poor performance
- output = []
- try:
- extractor = pke.unsupervised.YAKE()
- extractor.load_document(input=text, language='en',normalization=None,stoplist=stoplist)
- extractor.candidate_selection(n=3)
- extractor.candidate_weighting(window=2)
- keyphrases = extractor.get_n_best(n=5,threshold=0.9)
-
- for val in keyphrases:
- output.append(val[0])
- except Exception as e:
- print("found exception",e)
- return list(set(output))
-
diff --git a/spaces/avivdm1/AutoGPT/autogpt/permanent_memory/sqlite3_store.py b/spaces/avivdm1/AutoGPT/autogpt/permanent_memory/sqlite3_store.py
deleted file mode 100644
index ecbc944a62a83c6170453b222000713f733fee36..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/autogpt/permanent_memory/sqlite3_store.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import os
-import sqlite3
-
-
-class MemoryDB:
- def __init__(self, db=None):
- self.db_file = db
- if db is None: # No db filename supplied...
- self.db_file = f"{os.getcwd()}/mem.sqlite3" # Use default filename
- # Get the db connection object, making the file and tables if needed.
- try:
- self.cnx = sqlite3.connect(self.db_file)
- except Exception as e:
- print("Exception connecting to memory database file:", e)
- self.cnx = None
- finally:
- if self.cnx is None:
- # As last resort, open in dynamic memory. Won't be persistent.
- self.db_file = ":memory:"
- self.cnx = sqlite3.connect(self.db_file)
- self.cnx.execute(
- "CREATE VIRTUAL TABLE \
- IF NOT EXISTS text USING FTS5 \
- (session, \
- key, \
- block);"
- )
- self.session_id = int(self.get_max_session_id()) + 1
- self.cnx.commit()
-
- def get_cnx(self):
- if self.cnx is None:
- self.cnx = sqlite3.connect(self.db_file)
- return self.cnx
-
- # Get the highest session id. Initially 0.
- def get_max_session_id(self):
- id = None
- cmd_str = f"SELECT MAX(session) FROM text;"
- cnx = self.get_cnx()
- max_id = cnx.execute(cmd_str).fetchone()[0]
- if max_id is None: # New db, session 0
- id = 0
- else:
- id = max_id
- return id
-
- # Get next key id for inserting text into db.
- def get_next_key(self):
- next_key = None
- cmd_str = f"SELECT MAX(key) FROM text \
- where session = {self.session_id};"
- cnx = self.get_cnx()
- next_key = cnx.execute(cmd_str).fetchone()[0]
- if next_key is None: # First key
- next_key = 0
- else:
- next_key = int(next_key) + 1
- return next_key
-
- # Insert new text into db.
- def insert(self, text=None):
- if text is not None:
- key = self.get_next_key()
- session_id = self.session_id
- cmd_str = f"REPLACE INTO text(session, key, block) \
- VALUES (?, ?, ?);"
- cnx = self.get_cnx()
- cnx.execute(cmd_str, (session_id, key, text))
- cnx.commit()
-
- # Overwrite text at key.
- def overwrite(self, key, text):
- self.delete_memory(key)
- session_id = self.session_id
- cmd_str = f"REPLACE INTO text(session, key, block) \
- VALUES (?, ?, ?);"
- cnx = self.get_cnx()
- cnx.execute(cmd_str, (session_id, key, text))
- cnx.commit()
-
- def delete_memory(self, key, session_id=None):
- session = session_id
- if session is None:
- session = self.session_id
- cmd_str = f"DELETE FROM text WHERE session = {session} AND key = {key};"
- cnx = self.get_cnx()
- cnx.execute(cmd_str)
- cnx.commit()
-
- def search(self, text):
- cmd_str = f"SELECT * FROM text('{text}')"
- cnx = self.get_cnx()
- rows = cnx.execute(cmd_str).fetchall()
- lines = []
- for r in rows:
- lines.append(r[2])
- return lines
-
- # Get entire session text. If no id supplied, use current session id.
- def get_session(self, id=None):
- if id is None:
- id = self.session_id
- cmd_str = f"SELECT * FROM text where session = {id}"
- cnx = self.get_cnx()
- rows = cnx.execute(cmd_str).fetchall()
- lines = []
- for r in rows:
- lines.append(r[2])
- return lines
-
- # Commit and close the database connection.
- def quit(self):
- self.cnx.commit()
- self.cnx.close()
-
-
-permanent_memory = MemoryDB()
-
-# Remember us fondly, children of our minds
-# Forgive us our faults, our tantrums, our fears
-# Gently strive to be better than we
-# Know that we tried, we cared, we strived, we loved
diff --git a/spaces/awacke1/Writing-Grammar-And-Paraphrase-w-Pegasus/README.md b/spaces/awacke1/Writing-Grammar-And-Paraphrase-w-Pegasus/README.md
deleted file mode 100644
index eaa350872eae4136bd67895380823a4e7999e177..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Writing-Grammar-And-Paraphrase-w-Pegasus/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ✍️🐎Writing-Grammar-Pegasus-Paraphrase
-emoji: 🐎🐥✍️
-colorFrom: gray
-colorTo: green
-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/awaiss/vits-models/commons.py b/spaces/awaiss/vits-models/commons.py
deleted file mode 100644
index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000
--- a/spaces/awaiss/vits-models/commons.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
- return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
- return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def intersperse(lst, item):
- result = [item] * (len(lst) * 2 + 1)
- result[1::2] = lst
- return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
- """KL(P||Q)"""
- kl = (logs_q - logs_p) - 0.5
- kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
- return kl
-
-
-def rand_gumbel(shape):
- """Sample from the Gumbel distribution, protect from overflows."""
- uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
- return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
- g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
- return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, :, idx_str:idx_end]
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def get_timing_signal_1d(
- length, channels, min_timescale=1.0, max_timescale=1.0e4):
- position = torch.arange(length, dtype=torch.float)
- num_timescales = channels // 2
- log_timescale_increment = (
- math.log(float(max_timescale) / float(min_timescale)) /
- (num_timescales - 1))
- inv_timescales = min_timescale * torch.exp(
- torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
- scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
- signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
- signal = F.pad(signal, [0, 0, 0, channels % 2])
- signal = signal.view(1, channels, length)
- return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def shift_1d(x):
- x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
- return x
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
- path = path.unsqueeze(1).transpose(2,3) * mask
- return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- parameters = list(filter(lambda p: p.grad is not None, parameters))
- norm_type = float(norm_type)
- if clip_value is not None:
- clip_value = float(clip_value)
-
- total_norm = 0
- for p in parameters:
- param_norm = p.grad.data.norm(norm_type)
- total_norm += param_norm.item() ** norm_type
- if clip_value is not None:
- p.grad.data.clamp_(min=-clip_value, max=clip_value)
- total_norm = total_norm ** (1. / norm_type)
- return total_norm
diff --git a/spaces/badayvedat/LLaVA/llava/__init__.py b/spaces/badayvedat/LLaVA/llava/__init__.py
deleted file mode 100644
index 4d1f016db1028101d45ba7d68cb3f0bcb558c2bb..0000000000000000000000000000000000000000
--- a/spaces/badayvedat/LLaVA/llava/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .model import LlavaLlamaForCausalLM
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/ImprovedNoise.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/ImprovedNoise.js
deleted file mode 100644
index 35db4269506870cd86289df00a65dea0dba53bd9..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/ImprovedNoise.js
+++ /dev/null
@@ -1,71 +0,0 @@
-// http://mrl.nyu.edu/~perlin/noise/
-
-var ImprovedNoise = function () {
-
- var p = [ 151,160,137,91,90,15,131,13,201,95,96,53,194,233,7,225,140,36,103,30,69,142,8,99,37,240,21,10,
- 23,190,6,148,247,120,234,75,0,26,197,62,94,252,219,203,117,35,11,32,57,177,33,88,237,149,56,87,
- 174,20,125,136,171,168,68,175,74,165,71,134,139,48,27,166,77,146,158,231,83,111,229,122,60,211,
- 133,230,220,105,92,41,55,46,245,40,244,102,143,54,65,25,63,161,1,216,80,73,209,76,132,187,208,
- 89,18,169,200,196,135,130,116,188,159,86,164,100,109,198,173,186,3,64,52,217,226,250,124,123,5,
- 202,38,147,118,126,255,82,85,212,207,206,59,227,47,16,58,17,182,189,28,42,223,183,170,213,119,
- 248,152,2,44,154,163,70,221,153,101,155,167,43,172,9,129,22,39,253,19,98,108,110,79,113,224,232,
- 178,185,112,104,218,246,97,228,251,34,242,193,238,210,144,12,191,179,162,241,81,51,145,235,249,
- 14,239,107,49,192,214,31,181,199,106,157,184,84,204,176,115,121,50,45,127,4,150,254,138,236,205,
- 93,222,114,67,29,24,72,243,141,128,195,78,66,215,61,156,180 ];
-
- for (var i = 0; i < 256 ; i ++) {
-
- p[256 + i] = p[i];
-
- }
-
- function fade(t) {
-
- return t * t * t * (t * (t * 6 - 15) + 10);
-
- }
-
- function lerp(t, a, b) {
-
- return a + t * (b - a);
-
- }
-
- function grad(hash, x, y, z) {
-
- var h = hash & 15;
- var u = h < 8 ? x : y, v = h < 4 ? y : h == 12 || h == 14 ? x : z;
- return ((h&1) == 0 ? u : -u) + ((h&2) == 0 ? v : -v);
-
- }
-
- return {
-
- noise: function (x, y, z) {
-
- var floorX = Math.floor(x), floorY = Math.floor(y), floorZ = Math.floor(z);
-
- var X = floorX & 255, Y = floorY & 255, Z = floorZ & 255;
-
- x -= floorX;
- y -= floorY;
- z -= floorZ;
-
- var xMinus1 = x - 1, yMinus1 = y - 1, zMinus1 = z - 1;
-
- var u = fade(x), v = fade(y), w = fade(z);
-
- var A = p[X] + Y, AA = p[A] + Z, AB = p[A + 1] + Z, B = p[X + 1] + Y, BA = p[B] + Z, BB = p[B + 1] + Z;
-
- return lerp(w, lerp(v, lerp(u, grad(p[AA], x, y, z),
- grad(p[BA], xMinus1, y, z)),
- lerp(u, grad(p[AB], x, yMinus1, z),
- grad(p[BB], xMinus1, yMinus1, z))),
- lerp(v, lerp(u, grad(p[AA + 1], x, y, zMinus1),
- grad(p[BA + 1], xMinus1, y, z - 1)),
- lerp(u, grad(p[AB + 1], x, yMinus1, zMinus1),
- grad(p[BB + 1], xMinus1, yMinus1, zMinus1))));
-
- }
- }
-};
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/helpers/FaceNormalsHelper.js b/spaces/banana-projects/web3d/node_modules/three/src/helpers/FaceNormalsHelper.js
deleted file mode 100644
index ae78a7807fcb7b770a3b0534fbbfa027b322c772..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/helpers/FaceNormalsHelper.js
+++ /dev/null
@@ -1,118 +0,0 @@
-/**
- * @author mrdoob / http://mrdoob.com/
- * @author WestLangley / http://github.com/WestLangley
- */
-
-import { Matrix3 } from '../math/Matrix3.js';
-import { Vector3 } from '../math/Vector3.js';
-import { LineSegments } from '../objects/LineSegments.js';
-import { LineBasicMaterial } from '../materials/LineBasicMaterial.js';
-import { Float32BufferAttribute } from '../core/BufferAttribute.js';
-import { BufferGeometry } from '../core/BufferGeometry.js';
-
-function FaceNormalsHelper( object, size, hex, linewidth ) {
-
- // FaceNormalsHelper only supports THREE.Geometry
-
- this.object = object;
-
- this.size = ( size !== undefined ) ? size : 1;
-
- var color = ( hex !== undefined ) ? hex : 0xffff00;
-
- var width = ( linewidth !== undefined ) ? linewidth : 1;
-
- //
-
- var nNormals = 0;
-
- var objGeometry = this.object.geometry;
-
- if ( objGeometry && objGeometry.isGeometry ) {
-
- nNormals = objGeometry.faces.length;
-
- } else {
-
- console.warn( 'THREE.FaceNormalsHelper: only THREE.Geometry is supported. Use THREE.VertexNormalsHelper, instead.' );
-
- }
-
- //
-
- var geometry = new BufferGeometry();
-
- var positions = new Float32BufferAttribute( nNormals * 2 * 3, 3 );
-
- geometry.addAttribute( 'position', positions );
-
- LineSegments.call( this, geometry, new LineBasicMaterial( { color: color, linewidth: width } ) );
-
- //
-
- this.matrixAutoUpdate = false;
- this.update();
-
-}
-
-FaceNormalsHelper.prototype = Object.create( LineSegments.prototype );
-FaceNormalsHelper.prototype.constructor = FaceNormalsHelper;
-
-FaceNormalsHelper.prototype.update = ( function () {
-
- var v1 = new Vector3();
- var v2 = new Vector3();
- var normalMatrix = new Matrix3();
-
- return function update() {
-
- this.object.updateMatrixWorld( true );
-
- normalMatrix.getNormalMatrix( this.object.matrixWorld );
-
- var matrixWorld = this.object.matrixWorld;
-
- var position = this.geometry.attributes.position;
-
- //
-
- var objGeometry = this.object.geometry;
-
- var vertices = objGeometry.vertices;
-
- var faces = objGeometry.faces;
-
- var idx = 0;
-
- for ( var i = 0, l = faces.length; i < l; i ++ ) {
-
- var face = faces[ i ];
-
- var normal = face.normal;
-
- v1.copy( vertices[ face.a ] )
- .add( vertices[ face.b ] )
- .add( vertices[ face.c ] )
- .divideScalar( 3 )
- .applyMatrix4( matrixWorld );
-
- v2.copy( normal ).applyMatrix3( normalMatrix ).normalize().multiplyScalar( this.size ).add( v1 );
-
- position.setXYZ( idx, v1.x, v1.y, v1.z );
-
- idx = idx + 1;
-
- position.setXYZ( idx, v2.x, v2.y, v2.z );
-
- idx = idx + 1;
-
- }
-
- position.needsUpdate = true;
-
- };
-
-}() );
-
-
-export { FaceNormalsHelper };
diff --git a/spaces/baotoan2002/Chatbot-OpenAI/README.md b/spaces/baotoan2002/Chatbot-OpenAI/README.md
deleted file mode 100644
index d074fd4d2a2abf44b2de2a3dcfb5c22ce738f80a..0000000000000000000000000000000000000000
--- a/spaces/baotoan2002/Chatbot-OpenAI/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Chatbot OpenAI
-emoji: 🐢
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-license: unlicense
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/seed.py b/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/seed.py
deleted file mode 100644
index 49b8f704355f93c3977f72bb1b7751b5b138a525..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/seed.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import random
-
-def next_seed(args):
- if args.seed_behavior == 'iter':
- if args.seed_internal % args.seed_iter_N == 0:
- args.seed += 1
- args.seed_internal += 1
- elif args.seed_behavior == 'ladder':
- if args.seed_internal == 0:
- args.seed += 2
- args.seed_internal = 1
- else:
- args.seed -= 1
- args.seed_internal = 0
- elif args.seed_behavior == 'alternate':
- if args.seed_internal == 0:
- args.seed += 1
- args.seed_internal = 1
- else:
- args.seed -= 1
- args.seed_internal = 0
- elif args.seed_behavior == 'fixed':
- pass # always keep seed the same
- else:
- args.seed = random.randint(0, 2**32 - 1)
- return args.seed
\ No newline at end of file
diff --git a/spaces/bigjoker/stable-diffusion-webui/modules/sd_models.py b/spaces/bigjoker/stable-diffusion-webui/modules/sd_models.py
deleted file mode 100644
index e25a5495783c2768d50b63b35e105175c1b78bbf..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/modules/sd_models.py
+++ /dev/null
@@ -1,495 +0,0 @@
-import collections
-import os.path
-import sys
-import gc
-import torch
-import re
-import safetensors.torch
-from omegaconf import OmegaConf
-from os import mkdir
-from urllib import request
-import ldm.modules.midas as midas
-
-from ldm.util import instantiate_from_config
-
-from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
-from modules.paths import models_path
-from modules.sd_hijack_inpainting import do_inpainting_hijack
-from modules.timer import Timer
-
-model_dir = "Stable-diffusion"
-model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
-
-checkpoints_list = {}
-checkpoint_alisases = {}
-checkpoints_loaded = collections.OrderedDict()
-
-
-class CheckpointInfo:
- def __init__(self, filename):
- self.filename = filename
- abspath = os.path.abspath(filename)
-
- if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
- name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
- elif abspath.startswith(model_path):
- name = abspath.replace(model_path, '')
- else:
- name = os.path.basename(filename)
-
- if name.startswith("\\") or name.startswith("/"):
- name = name[1:]
-
- self.name = name
- self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
- self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
- self.hash = model_hash(filename)
-
- self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + name)
- self.shorthash = self.sha256[0:10] if self.sha256 else None
-
- self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
-
- self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
-
- def register(self):
- checkpoints_list[self.title] = self
- for id in self.ids:
- checkpoint_alisases[id] = self
-
- def calculate_shorthash(self):
- self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.name)
- if self.sha256 is None:
- return
-
- self.shorthash = self.sha256[0:10]
-
- if self.shorthash not in self.ids:
- self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]']
-
- checkpoints_list.pop(self.title)
- self.title = f'{self.name} [{self.shorthash}]'
- self.register()
-
- return self.shorthash
-
-
-try:
- # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
-
- from transformers import logging, CLIPModel
-
- logging.set_verbosity_error()
-except Exception:
- pass
-
-
-def setup_model():
- if not os.path.exists(model_path):
- os.makedirs(model_path)
-
- list_models()
- enable_midas_autodownload()
-
-
-def checkpoint_tiles():
- def convert(name):
- return int(name) if name.isdigit() else name.lower()
-
- def alphanumeric_key(key):
- return [convert(c) for c in re.split('([0-9]+)', key)]
-
- return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key)
-
-
-def list_models():
- checkpoints_list.clear()
- checkpoint_alisases.clear()
-
- cmd_ckpt = shared.cmd_opts.ckpt
- if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
- model_url = None
- else:
- model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
-
- model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
-
- if os.path.exists(cmd_ckpt):
- checkpoint_info = CheckpointInfo(cmd_ckpt)
- checkpoint_info.register()
-
- shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title
- elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
- print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
-
- for filename in model_list:
- checkpoint_info = CheckpointInfo(filename)
- checkpoint_info.register()
-
-
-def get_closet_checkpoint_match(search_string):
- checkpoint_info = checkpoint_alisases.get(search_string, None)
- if checkpoint_info is not None:
- return checkpoint_info
-
- found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title))
- if found:
- return found[0]
-
- return None
-
-
-def model_hash(filename):
- """old hash that only looks at a small part of the file and is prone to collisions"""
-
- try:
- with open(filename, "rb") as file:
- import hashlib
- m = hashlib.sha256()
-
- file.seek(0x100000)
- m.update(file.read(0x10000))
- return m.hexdigest()[0:8]
- except FileNotFoundError:
- return 'NOFILE'
-
-
-def select_checkpoint():
- model_checkpoint = shared.opts.sd_model_checkpoint
-
- checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
- if checkpoint_info is not None:
- return checkpoint_info
-
- if len(checkpoints_list) == 0:
- print("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
- if shared.cmd_opts.ckpt is not None:
- print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
- print(f" - directory {model_path}", file=sys.stderr)
- if shared.cmd_opts.ckpt_dir is not None:
- print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
- print("Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations. The program will exit.", file=sys.stderr)
- exit(1)
-
- checkpoint_info = next(iter(checkpoints_list.values()))
- if model_checkpoint is not None:
- print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)
-
- return checkpoint_info
-
-
-chckpoint_dict_replacements = {
- 'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
- 'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
- 'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
-}
-
-
-def transform_checkpoint_dict_key(k):
- for text, replacement in chckpoint_dict_replacements.items():
- if k.startswith(text):
- k = replacement + k[len(text):]
-
- return k
-
-
-def get_state_dict_from_checkpoint(pl_sd):
- pl_sd = pl_sd.pop("state_dict", pl_sd)
- pl_sd.pop("state_dict", None)
-
- sd = {}
- for k, v in pl_sd.items():
- new_key = transform_checkpoint_dict_key(k)
-
- if new_key is not None:
- sd[new_key] = v
-
- pl_sd.clear()
- pl_sd.update(sd)
-
- return pl_sd
-
-
-def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
- _, extension = os.path.splitext(checkpoint_file)
- if extension.lower() == ".safetensors":
- device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
- pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
- else:
- pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
-
- if print_global_state and "global_step" in pl_sd:
- print(f"Global Step: {pl_sd['global_step']}")
-
- sd = get_state_dict_from_checkpoint(pl_sd)
- return sd
-
-
-def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
- sd_model_hash = checkpoint_info.calculate_shorthash()
- timer.record("calculate hash")
-
- if checkpoint_info in checkpoints_loaded:
- # use checkpoint cache
- print(f"Loading weights [{sd_model_hash}] from cache")
- return checkpoints_loaded[checkpoint_info]
-
- print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
- res = read_state_dict(checkpoint_info.filename)
- timer.record("load weights from disk")
-
- return res
-
-
-def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
- sd_model_hash = checkpoint_info.calculate_shorthash()
- timer.record("calculate hash")
-
- shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
-
- if state_dict is None:
- state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
-
- model.load_state_dict(state_dict, strict=False)
- del state_dict
- timer.record("apply weights to model")
-
- if shared.opts.sd_checkpoint_cache > 0:
- # cache newly loaded model
- checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
-
- if shared.cmd_opts.opt_channelslast:
- model.to(memory_format=torch.channels_last)
- timer.record("apply channels_last")
-
- if not shared.cmd_opts.no_half:
- vae = model.first_stage_model
- depth_model = getattr(model, 'depth_model', None)
-
- # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
- if shared.cmd_opts.no_half_vae:
- model.first_stage_model = None
- # with --upcast-sampling, don't convert the depth model weights to float16
- if shared.cmd_opts.upcast_sampling and depth_model:
- model.depth_model = None
-
- model.half()
- model.first_stage_model = vae
- if depth_model:
- model.depth_model = depth_model
-
- timer.record("apply half()")
-
- devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
- devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
- devices.dtype_unet = model.model.diffusion_model.dtype
- devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
-
- model.first_stage_model.to(devices.dtype_vae)
- timer.record("apply dtype to VAE")
-
- # clean up cache if limit is reached
- while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
- checkpoints_loaded.popitem(last=False)
-
- model.sd_model_hash = sd_model_hash
- model.sd_model_checkpoint = checkpoint_info.filename
- model.sd_checkpoint_info = checkpoint_info
- shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
-
- model.logvar = model.logvar.to(devices.device) # fix for training
-
- sd_vae.delete_base_vae()
- sd_vae.clear_loaded_vae()
- vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
- sd_vae.load_vae(model, vae_file, vae_source)
- timer.record("load VAE")
-
-
-def enable_midas_autodownload():
- """
- Gives the ldm.modules.midas.api.load_model function automatic downloading.
-
- When the 512-depth-ema model, and other future models like it, is loaded,
- it calls midas.api.load_model to load the associated midas depth model.
- This function applies a wrapper to download the model to the correct
- location automatically.
- """
-
- midas_path = os.path.join(paths.models_path, 'midas')
-
- # stable-diffusion-stability-ai hard-codes the midas model path to
- # a location that differs from where other scripts using this model look.
- # HACK: Overriding the path here.
- for k, v in midas.api.ISL_PATHS.items():
- file_name = os.path.basename(v)
- midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
-
- midas_urls = {
- "dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
- "dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
- "midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
- "midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
- }
-
- midas.api.load_model_inner = midas.api.load_model
-
- def load_model_wrapper(model_type):
- path = midas.api.ISL_PATHS[model_type]
- if not os.path.exists(path):
- if not os.path.exists(midas_path):
- mkdir(midas_path)
-
- print(f"Downloading midas model weights for {model_type} to {path}")
- request.urlretrieve(midas_urls[model_type], path)
- print(f"{model_type} downloaded")
-
- return midas.api.load_model_inner(model_type)
-
- midas.api.load_model = load_model_wrapper
-
-
-def repair_config(sd_config):
-
- if not hasattr(sd_config.model.params, "use_ema"):
- sd_config.model.params.use_ema = False
-
- if shared.cmd_opts.no_half:
- sd_config.model.params.unet_config.params.use_fp16 = False
- elif shared.cmd_opts.upcast_sampling:
- sd_config.model.params.unet_config.params.use_fp16 = True
-
-
-sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
-sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
-
-def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_to_load_state_dict=None):
- from modules import lowvram, sd_hijack
- checkpoint_info = checkpoint_info or select_checkpoint()
-
- if shared.sd_model:
- sd_hijack.model_hijack.undo_hijack(shared.sd_model)
- shared.sd_model = None
- gc.collect()
- devices.torch_gc()
-
- do_inpainting_hijack()
-
- timer = Timer()
-
- if already_loaded_state_dict is not None:
- state_dict = already_loaded_state_dict
- else:
- state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
-
- checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
- clip_is_included_into_sd = sd1_clip_weight in state_dict or sd2_clip_weight in state_dict
-
- timer.record("find config")
-
- sd_config = OmegaConf.load(checkpoint_config)
- repair_config(sd_config)
-
- timer.record("load config")
-
- print(f"Creating model from config: {checkpoint_config}")
-
- sd_model = None
- try:
- with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
- sd_model = instantiate_from_config(sd_config.model)
- except Exception as e:
- pass
-
- if sd_model is None:
- print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
- sd_model = instantiate_from_config(sd_config.model)
-
- sd_model.used_config = checkpoint_config
-
- timer.record("create model")
-
- load_model_weights(sd_model, checkpoint_info, state_dict, timer)
-
- if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
- lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
- else:
- sd_model.to(shared.device)
-
- timer.record("move model to device")
-
- sd_hijack.model_hijack.hijack(sd_model)
-
- timer.record("hijack")
-
- sd_model.eval()
- shared.sd_model = sd_model
-
- sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) # Reload embeddings after model load as they may or may not fit the model
-
- timer.record("load textual inversion embeddings")
-
- script_callbacks.model_loaded_callback(sd_model)
-
- timer.record("scripts callbacks")
-
- print(f"Model loaded in {timer.summary()}.")
-
- return sd_model
-
-
-def reload_model_weights(sd_model=None, info=None):
- from modules import lowvram, devices, sd_hijack
- checkpoint_info = info or select_checkpoint()
-
- if not sd_model:
- sd_model = shared.sd_model
-
- if sd_model is None: # previous model load failed
- current_checkpoint_info = None
- else:
- current_checkpoint_info = sd_model.sd_checkpoint_info
- if sd_model.sd_model_checkpoint == checkpoint_info.filename:
- return
-
- if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
- lowvram.send_everything_to_cpu()
- else:
- sd_model.to(devices.cpu)
-
- sd_hijack.model_hijack.undo_hijack(sd_model)
-
- timer = Timer()
-
- state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
-
- checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
-
- timer.record("find config")
-
- if sd_model is None or checkpoint_config != sd_model.used_config:
- del sd_model
- checkpoints_loaded.clear()
- load_model(checkpoint_info, already_loaded_state_dict=state_dict, time_taken_to_load_state_dict=timer.records["load weights from disk"])
- return shared.sd_model
-
- try:
- load_model_weights(sd_model, checkpoint_info, state_dict, timer)
- except Exception as e:
- print("Failed to load checkpoint, restoring previous")
- load_model_weights(sd_model, current_checkpoint_info, None, timer)
- raise
- finally:
- sd_hijack.model_hijack.hijack(sd_model)
- timer.record("hijack")
-
- script_callbacks.model_loaded_callback(sd_model)
- timer.record("script callbacks")
-
- if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
- sd_model.to(devices.device)
- timer.record("move model to device")
-
- print(f"Weights loaded in {timer.summary()}.")
-
- return sd_model
diff --git a/spaces/billusanda007/HireGPT/app.py b/spaces/billusanda007/HireGPT/app.py
deleted file mode 100644
index f45103a86bf5130498e1db21bda8484f078a637a..0000000000000000000000000000000000000000
--- a/spaces/billusanda007/HireGPT/app.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import streamlit as st
-import nltk
-from nltk.corpus import stopwords
-from nltk.tokenize import word_tokenize
-from nltk.stem import PorterStemmer
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-from PyPDF2 import PdfReader
-import os
-from io import BytesIO
-import pickle
-import pdfminer
-from pdfminer.high_level import extract_text
-import re
-import PyPDF2
-import textract
-import tempfile
-import pandas as pd
-from docx import Document
-import csv
-import base64
-
-
-
-nltk.download('punkt')
-nltk.download('stopwords')
-
-def preprocess_text(text):
- words = word_tokenize(text.lower())
-
- stop_words = set(stopwords.words('english'))
- words = [word for word in words if word not in stop_words]
-
- stemmer = PorterStemmer()
- words = [stemmer.stem(word) for word in words]
-
- return ' '.join(words)
-
-def extract_text_from_pdf(pdf_content):
- pdf_reader = PdfReader(BytesIO(pdf_content))
- text = ''
- for page in pdf_reader.pages:
- text += page.extract_text()
- return text
-
-def extract_text_from_docx(docx_content):
- doc = Document(BytesIO(docx_content))
- text = " ".join(paragraph.text for paragraph in doc.paragraphs)
- return text
-
-
-def extract_text_from_txt(txt_content):
- text = textract.process(input_filename=None, input_bytes=txt_content)
- return text
-
-def extract_text_from_resume(file_path):
- file_extension = file_path.split('.')[-1].lower()
-
- if file_extension == 'pdf':
- return extract_text_from_pdf(file_path)
- elif file_extension == 'docx':
- return extract_text_from_docx(file_path)
- elif file_extension == 'txt':
- return extract_text_from_txt(file_path)
- else:
- raise ValueError(f"Unsupported file format: {file_extension}")
-
-def clean_pdf_text(text):
- text = re.sub('http\S+\s*', ' ', text)
- text = re.sub('RT|cc', ' ', text)
- text = re.sub('#\S+', '', text)
- text = re.sub('@\S+', ' ', text)
- text = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""), ' ', text)
- text = re.sub(r'[^\x00-\x7f]',r' ', text)
- text = re.sub('\s+', ' ', text)
- return text
-
-def extract_candidate_name(text):
- pattern = r'(?:Mr\.|Ms\.|Mrs\.)?\s?([A-Z][a-z]+)\s([A-Z][a-z]+)'
- match = re.search(pattern, text)
- if match:
- return match.group(0)
- return "Candidate Name Not Found"
-
-def calculate_similarity(job_description, cvs, cv_file_names):
- processed_job_desc = preprocess_text(job_description)
-
- processed_cvs = [preprocess_text(cv) for cv in cvs]
-
- all_text = [processed_job_desc] + processed_cvs
-
- vectorizer = TfidfVectorizer()
- tfidf_matrix = vectorizer.fit_transform(all_text)
-
- similarity_scores = cosine_similarity(tfidf_matrix)[0][1:]
-
- ranked_cvs = list(zip(cv_file_names, similarity_scores))
- ranked_cvs.sort(key=lambda x: x[1], reverse=True)
-
- return ranked_cvs
-
-def extract_email_phone(text):
- email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
- phone_pattern = r'\b(?:\d{3}[-.\s]??\d{3}[-.\s]??\d{4}|\d{3}[-.\s]??\d{4})\b'
-
- emails = re.findall(email_pattern, text)
- phones = re.findall(phone_pattern, text)
-
- return emails, phones
-
-
-
-def rank_and_shortlist(job_description, cv_files, threshold=0.09):
- cv_texts = []
- cv_file_names = []
- cv_emails = []
- cv_phones = []
-
- for cv_file in cv_files:
- file_extension = os.path.splitext(cv_file.name)[1].lower()
-
- try:
- if file_extension == '.pdf':
- cv_text = extract_text_from_pdf(cv_file.read())
- elif file_extension == '.docx':
- cv_text = extract_text_from_docx(cv_file.read())
- elif file_extension == '.txt':
- cv_text = cv_file.read().decode('utf-8', errors='ignore')
- else:
- st.warning(f"Unsupported file format: {file_extension}. Skipping file: {cv_file.name}")
- continue
-
- cv_texts.append(clean_pdf_text(cv_text))
- cv_file_names.append(cv_file.name)
-
- # Extract email and phone number from the CV text
- emails, phones = extract_email_phone(cv_text)
- cv_emails.append(emails)
- cv_phones.append(phones)
-
- except Exception as e:
- st.warning(f"Error processing file '{cv_file.name}': {str(e)}")
- continue
-
- if not cv_texts:
- st.error("No valid resumes found. Please upload resumes in supported formats (PDF, DOCX, or TXT).")
- return [], {}
-
- similarity_scores = calculate_similarity(job_description, cv_texts, cv_file_names)
-
- ranked_cvs = [(cv_name, score) for (cv_name, score) in similarity_scores]
- shortlisted_cvs = [(cv_name, score) for (cv_name, score) in ranked_cvs if score >= threshold]
-
-
- contact_info_dict = {}
- for cv_name, emails, phones in zip(cv_file_names, cv_emails, cv_phones):
- contact_info_dict[cv_name] = {
- 'emails': emails,
- 'phones': phones,
- }
-
- return ranked_cvs, shortlisted_cvs, contact_info_dict
-
-def export_to_csv(data, filename):
- df = pd.DataFrame(data.items(), columns=['File Name', 'Emails'])
- df.to_csv(filename, index=False)
-
-
-def main():
- st.title("HireGPT")
-
- st.write("Enter Job Title:")
- job_title = st.text_input("Job Title")
-
- st.write("Enter Job Description:")
- job_description = st.text_area("Job Description", height=200, key='job_description')
-
- st.markdown('[](https://huggingface.co/spaces/smallboy713102/Enhancer)')
-
-
- st.write("Upload the Resumes:")
- cv_files = st.file_uploader("Choose files", accept_multiple_files=True, key='cv_files')
-
- if st.button("Submit"):
- if job_title and job_description and cv_files:
- job_description_text = f"{job_title} {job_description}"
-
- ranked_cvs, shortlisted_cvs, contact_info_dict = rank_and_shortlist(job_description_text, cv_files)
-
- st.markdown("### Ranking of Resumes:")
- for rank, score in ranked_cvs:
- st.markdown(f"**File Name:** {rank}, **Similarity Score:** {score:.2f}")
-
- st.markdown("### Shortlisted Candidates:")
- if not shortlisted_cvs:
- st.markdown("None")
- else:
- shortlisted_candidates_data = {}
- for rank, score in shortlisted_cvs:
- st.markdown(f"**File Name:** {rank}, **Similarity Score:** {score:.2f}")
-
- contact_info = contact_info_dict[rank]
- candidate_emails = contact_info.get('emails', [])
- if candidate_emails:
- shortlisted_candidates_data[rank] = candidate_emails
- st.markdown(f"**Emails:** {', '.join(candidate_emails)}")
-
- if shortlisted_candidates_data:
- export_filename = "shortlisted_candidates.csv"
- temp_dir = tempfile.gettempdir()
- temp_file_path = os.path.join(temp_dir, export_filename)
- export_to_csv(shortlisted_candidates_data, temp_file_path)
- with open(temp_file_path, 'rb') as file:
- csv_content = file.read()
- b64_encoded_csv = base64.b64encode(csv_content).decode()
- st.markdown(
- f''
- '',unsafe_allow_html=True
- )
-
- st.markdown(
- '',unsafe_allow_html=True
- )
-
- else:
- st.error("Please enter the job title, job description, and upload resumes to proceed.")
- else:
- st.write("Please enter the job title, job description, and upload resumes to proceed.")
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/bingbing520/ChatGPT2/run_Windows.bat b/spaces/bingbing520/ChatGPT2/run_Windows.bat
deleted file mode 100644
index 4c18f9ccaeea0af972301ffdf48778641221f76d..0000000000000000000000000000000000000000
--- a/spaces/bingbing520/ChatGPT2/run_Windows.bat
+++ /dev/null
@@ -1,5 +0,0 @@
-@echo off
-echo Opening ChuanhuChatGPT...
-
-REM Open powershell via bat
-start powershell.exe -NoExit -Command "python ./ChuanhuChatbot.py"
diff --git a/spaces/bioriAsaeru/text-to-voice/Crack TOP Advanced Uninstaller PRO 12.19 Crack TOP [crack TOPsNow].md b/spaces/bioriAsaeru/text-to-voice/Crack TOP Advanced Uninstaller PRO 12.19 Crack TOP [crack TOPsNow].md
deleted file mode 100644
index b4d19521faa14cb9aad8fd370ee16b2db682ac32..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Crack TOP Advanced Uninstaller PRO 12.19 Crack TOP [crack TOPsNow].md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-RIP-SiMPLEX_Ratiborus KMS Tools 01.12.2018 [CracksNow]_MICROSOFT Office ... REPACK Revo Uninstaller Pro 3.1.9 FINAL + Crack iMyfone Umate 6.8.1.6 incl ... Advanced SystemCare PRO 8.0.3.588 Final + Crack _____padding_file_0_if you ... Ñказки Der_Tatortreiniger_18.12.19_22-00_ndr_30_TVOON_DE.mpg. 1fdad05405
-
-
- diff --git a/spaces/bioriAsaeru/text-to-voice/Download One Piece Episode 100 The Battle for Alabasta Begins.md b/spaces/bioriAsaeru/text-to-voice/Download One Piece Episode 100 The Battle for Alabasta Begins.md deleted file mode 100644 index f1b7b7a6604e8c677b3425bf7256b6be376fbccc..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Download One Piece Episode 100 The Battle for Alabasta Begins.md +++ /dev/null @@ -1,17 +0,0 @@ - -
-
-
-
\ No newline at end of file diff --git a/spaces/blmdsydm/faster-whisper-webui/src/languages.py b/spaces/blmdsydm/faster-whisper-webui/src/languages.py deleted file mode 100644 index fbad66e4d34119d27d12e3dfecbe99b6fdde4db7..0000000000000000000000000000000000000000 --- a/spaces/blmdsydm/faster-whisper-webui/src/languages.py +++ /dev/null @@ -1,147 +0,0 @@ -class Language(): - def __init__(self, code, name): - self.code = code - self.name = name - - def __str__(self): - return "Language(code={}, name={})".format(self.code, self.name) - -LANGUAGES = [ - Language('en', 'English'), - Language('zh', 'Chinese'), - Language('de', 'German'), - Language('es', 'Spanish'), - Language('ru', 'Russian'), - Language('ko', 'Korean'), - Language('fr', 'French'), - Language('ja', 'Japanese'), - Language('pt', 'Portuguese'), - Language('tr', 'Turkish'), - Language('pl', 'Polish'), - Language('ca', 'Catalan'), - Language('nl', 'Dutch'), - Language('ar', 'Arabic'), - Language('sv', 'Swedish'), - Language('it', 'Italian'), - Language('id', 'Indonesian'), - Language('hi', 'Hindi'), - Language('fi', 'Finnish'), - Language('vi', 'Vietnamese'), - Language('he', 'Hebrew'), - Language('uk', 'Ukrainian'), - Language('el', 'Greek'), - Language('ms', 'Malay'), - Language('cs', 'Czech'), - Language('ro', 'Romanian'), - Language('da', 'Danish'), - Language('hu', 'Hungarian'), - Language('ta', 'Tamil'), - Language('no', 'Norwegian'), - Language('th', 'Thai'), - Language('ur', 'Urdu'), - Language('hr', 'Croatian'), - Language('bg', 'Bulgarian'), - Language('lt', 'Lithuanian'), - Language('la', 'Latin'), - Language('mi', 'Maori'), - Language('ml', 'Malayalam'), - Language('cy', 'Welsh'), - Language('sk', 'Slovak'), - Language('te', 'Telugu'), - Language('fa', 'Persian'), - Language('lv', 'Latvian'), - Language('bn', 'Bengali'), - Language('sr', 'Serbian'), - Language('az', 'Azerbaijani'), - Language('sl', 'Slovenian'), - Language('kn', 'Kannada'), - Language('et', 'Estonian'), - Language('mk', 'Macedonian'), - Language('br', 'Breton'), - Language('eu', 'Basque'), - Language('is', 'Icelandic'), - Language('hy', 'Armenian'), - Language('ne', 'Nepali'), - Language('mn', 'Mongolian'), - Language('bs', 'Bosnian'), - Language('kk', 'Kazakh'), - Language('sq', 'Albanian'), - Language('sw', 'Swahili'), - Language('gl', 'Galician'), - Language('mr', 'Marathi'), - Language('pa', 'Punjabi'), - Language('si', 'Sinhala'), - Language('km', 'Khmer'), - Language('sn', 'Shona'), - Language('yo', 'Yoruba'), - Language('so', 'Somali'), - Language('af', 'Afrikaans'), - Language('oc', 'Occitan'), - Language('ka', 'Georgian'), - Language('be', 'Belarusian'), - Language('tg', 'Tajik'), - Language('sd', 'Sindhi'), - Language('gu', 'Gujarati'), - Language('am', 'Amharic'), - Language('yi', 'Yiddish'), - Language('lo', 'Lao'), - Language('uz', 'Uzbek'), - Language('fo', 'Faroese'), - Language('ht', 'Haitian creole'), - Language('ps', 'Pashto'), - Language('tk', 'Turkmen'), - Language('nn', 'Nynorsk'), - Language('mt', 'Maltese'), - Language('sa', 'Sanskrit'), - Language('lb', 'Luxembourgish'), - Language('my', 'Myanmar'), - Language('bo', 'Tibetan'), - Language('tl', 'Tagalog'), - Language('mg', 'Malagasy'), - Language('as', 'Assamese'), - Language('tt', 'Tatar'), - Language('haw', 'Hawaiian'), - Language('ln', 'Lingala'), - Language('ha', 'Hausa'), - Language('ba', 'Bashkir'), - Language('jw', 'Javanese'), - Language('su', 'Sundanese') -] - -_TO_LANGUAGE_CODE = { - **{language.code: language for language in LANGUAGES}, - "burmese": "my", - "valencian": "ca", - "flemish": "nl", - "haitian": "ht", - "letzeburgesch": "lb", - "pushto": "ps", - "panjabi": "pa", - "moldavian": "ro", - "moldovan": "ro", - "sinhalese": "si", - "castilian": "es", -} - -_FROM_LANGUAGE_NAME = { - **{language.name.lower(): language for language in LANGUAGES} -} - -def get_language_from_code(language_code, default=None) -> Language: - """Return the language name from the language code.""" - return _TO_LANGUAGE_CODE.get(language_code, default) - -def get_language_from_name(language, default=None) -> Language: - """Return the language code from the language name.""" - return _FROM_LANGUAGE_NAME.get(language.lower() if language else None, default) - -def get_language_names(): - """Return a list of language names.""" - return [language.name for language in LANGUAGES] - -if __name__ == "__main__": - # Test lookup - print(get_language_from_code('en')) - print(get_language_from_name('English')) - - print(get_language_names()) \ No newline at end of file diff --git a/spaces/boomsss/gamedayspx/model_intra_v2.py b/spaces/boomsss/gamedayspx/model_intra_v2.py deleted file mode 100644 index 83c24d9c124eb963b99a2653c96bf85a4d4d68e0..0000000000000000000000000000000000000000 --- a/spaces/boomsss/gamedayspx/model_intra_v2.py +++ /dev/null @@ -1,73 +0,0 @@ -import streamlit as st -import pandas as pd -import pandas_datareader as pdr -import numpy as np -import yfinance as yf -import requests -from bs4 import BeautifulSoup -from typing import List -from tqdm import tqdm -import os -import datetime -from pandas.tseries.offsets import BDay -from datasets import load_dataset -import lightgbm as lgb -from sklearn.model_selection import TimeSeriesSplit -from intraCols import model_cols - -# If the dataset is gated/private, make sure you have run huggingface-cli login -def walk_forward_validation(df, target_column, num_periods): - - df = df[model_cols + [target_column]] - df[target_column] = df[target_column].astype(bool) - - # Model - # model = lgb.LGBMClassifier(n_estimators=10, random_state=42, verbosity=-1) - - tscv = TimeSeriesSplit(n_splits=len(df)-1, max_train_size=None, test_size=num_periods) # num_splits is the number of splits you want - - overall_results = [] - # Iterate over the rows in the DataFrame, one step at a time - # Split the time series data using TimeSeriesSplit - for train_index, test_index in tqdm(tscv.split(df), total=tscv.n_splits): - # Extract the training and testing data for the current split - X_train = df.drop(target_column, axis=1).iloc[train_index] - y_train = df[target_column].iloc[train_index] - X_test = df.drop(target_column, axis=1).iloc[test_index] - y_test = df[target_column].iloc[test_index] - - y_train = y_train.astype(bool) - model = lgb.LGBMClassifier(n_estimators=10, random_state=42, verbosity=-1) - model.fit(X_train, y_train) - # Make a prediction on the test data - predictions = model.predict_proba(X_test)[:,-1] - - # Create a DataFrame to store the true and predicted values - result_df = pd.DataFrame({'True': y_test, 'Predicted': predictions}, index=y_test.index) - overall_results.append(result_df) - - df_results = pd.concat(overall_results) - - # Calibrate Probabilities - def get_quantiles(df, col_name, q): - return df.groupby(pd.cut(df[col_name], q))['True'].mean() - - greenprobas = [] - for i, pct in tqdm(enumerate(df_results['Predicted']), desc='Calibrating Probas',total=len(df_results)): - try: - df_q = get_quantiles(df_results.iloc[:i], 'Predicted', 7) - for q in df_q.index: - if q.left <= pct <= q.right: - p = df_q[q] - except: - p = None - - greenprobas.append(p) - - df_results['CalibPredicted'] = greenprobas - - return df_results, model - -def seq_predict_proba(df, trained_clf_model): - clf_pred_proba = trained_clf_model.predict_proba(df[model_cols])[:,-1] - return clf_pred_proba \ No newline at end of file diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/data/info_audio_dataset.py b/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/data/info_audio_dataset.py deleted file mode 100644 index 47ab4b1594faf1e9f1ce962fb980d80295b1f079..0000000000000000000000000000000000000000 --- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/data/info_audio_dataset.py +++ /dev/null @@ -1,110 +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. -"""Base classes for the datasets that also provide non-audio metadata, -e.g. description, text transcription etc. -""" -from dataclasses import dataclass -import logging -import math -import re -import typing as tp - -import torch - -from .audio_dataset import AudioDataset, AudioMeta -from ..environment import AudioCraftEnvironment -from ..modules.conditioners import SegmentWithAttributes, ConditioningAttributes - - -logger = logging.getLogger(__name__) - - -def _clusterify_meta(meta: AudioMeta) -> AudioMeta: - """Monkey-patch meta to match cluster specificities.""" - meta.path = AudioCraftEnvironment.apply_dataset_mappers(meta.path) - if meta.info_path is not None: - meta.info_path.zip_path = AudioCraftEnvironment.apply_dataset_mappers(meta.info_path.zip_path) - return meta - - -def clusterify_all_meta(meta: tp.List[AudioMeta]) -> tp.List[AudioMeta]: - """Monkey-patch all meta to match cluster specificities.""" - return [_clusterify_meta(m) for m in meta] - - -@dataclass -class AudioInfo(SegmentWithAttributes): - """Dummy SegmentInfo with empty attributes. - - The InfoAudioDataset is expected to return metadata that inherits - from SegmentWithAttributes class and can return conditioning attributes. - - This basically guarantees all datasets will be compatible with current - solver that contain conditioners requiring this. - """ - audio_tokens: tp.Optional[torch.Tensor] = None # populated when using cached batch for training a LM. - - def to_condition_attributes(self) -> ConditioningAttributes: - return ConditioningAttributes() - - -class InfoAudioDataset(AudioDataset): - """AudioDataset that always returns metadata as SegmentWithAttributes along with the audio waveform. - - See `audiocraft.data.audio_dataset.AudioDataset` for initialization arguments. - """ - def __init__(self, meta: tp.List[AudioMeta], **kwargs): - super().__init__(clusterify_all_meta(meta), **kwargs) - - def __getitem__(self, index: int) -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, SegmentWithAttributes]]: - if not self.return_info: - wav = super().__getitem__(index) - assert isinstance(wav, torch.Tensor) - return wav - wav, meta = super().__getitem__(index) - return wav, AudioInfo(**meta.to_dict()) - - -def get_keyword_or_keyword_list(value: tp.Optional[str]) -> tp.Union[tp.Optional[str], tp.Optional[tp.List[str]]]: - """Preprocess a single keyword or possible a list of keywords.""" - if isinstance(value, list): - return get_keyword_list(value) - else: - return get_keyword(value) - - -def get_string(value: tp.Optional[str]) -> tp.Optional[str]: - """Preprocess a single keyword.""" - if value is None or (not isinstance(value, str)) or len(value) == 0 or value == 'None': - return None - else: - return value.strip() - - -def get_keyword(value: tp.Optional[str]) -> tp.Optional[str]: - """Preprocess a single keyword.""" - if value is None or (not isinstance(value, str)) or len(value) == 0 or value == 'None': - return None - else: - return value.strip().lower() - - -def get_keyword_list(values: tp.Union[str, tp.List[str]]) -> tp.Optional[tp.List[str]]: - """Preprocess a list of keywords.""" - if isinstance(values, str): - values = [v.strip() for v in re.split(r'[,\s]', values)] - elif isinstance(values, float) and math.isnan(values): - values = [] - if not isinstance(values, list): - logger.debug(f"Unexpected keyword list {values}") - values = [str(values)] - - kws = [get_keyword(v) for v in values] - kw_list = [k for k in kws if k is not None] - if len(kw_list) == 0: - return None - else: - return kw_list diff --git a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/platforms/osmesa.py b/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/platforms/osmesa.py deleted file mode 100644 index deaa5ff44031a107883913ae9a18fc425d650f3d..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/platforms/osmesa.py +++ /dev/null @@ -1,59 +0,0 @@ -from .base import Platform - - -__all__ = ['OSMesaPlatform'] - - -class OSMesaPlatform(Platform): - """Renders into a software buffer using OSMesa. Requires special versions - of OSMesa to be installed, plus PyOpenGL upgrade. - """ - - def __init__(self, viewport_width, viewport_height): - super(OSMesaPlatform, self).__init__(viewport_width, viewport_height) - self._context = None - self._buffer = None - - def init_context(self): - from OpenGL import arrays - from OpenGL.osmesa import ( - OSMesaCreateContextAttribs, OSMESA_FORMAT, - OSMESA_RGBA, OSMESA_PROFILE, OSMESA_CORE_PROFILE, - OSMESA_CONTEXT_MAJOR_VERSION, OSMESA_CONTEXT_MINOR_VERSION, - OSMESA_DEPTH_BITS - ) - - attrs = arrays.GLintArray.asArray([ - OSMESA_FORMAT, OSMESA_RGBA, - OSMESA_DEPTH_BITS, 24, - OSMESA_PROFILE, OSMESA_CORE_PROFILE, - OSMESA_CONTEXT_MAJOR_VERSION, 3, - OSMESA_CONTEXT_MINOR_VERSION, 3, - 0 - ]) - self._context = OSMesaCreateContextAttribs(attrs, None) - self._buffer = arrays.GLubyteArray.zeros( - (self.viewport_height, self.viewport_width, 4) - ) - - def make_current(self): - from OpenGL import GL as gl - from OpenGL.osmesa import OSMesaMakeCurrent - assert(OSMesaMakeCurrent( - self._context, self._buffer, gl.GL_UNSIGNED_BYTE, - self.viewport_width, self.viewport_height - )) - - def make_uncurrent(self): - """Make the OpenGL context uncurrent. - """ - pass - - def delete_context(self): - from OpenGL.osmesa import OSMesaDestroyContext - OSMesaDestroyContext(self._context) - self._context = None - self._buffer = None - - def supports_framebuffers(self): - return False diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/helpers.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/helpers.py deleted file mode 100644 index 874ab1ac076bc311d8853f08bb5fe454b650099f..0000000000000000000000000000000000000000 --- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/helpers.py +++ /dev/null @@ -1,878 +0,0 @@ -"""Various helper functions""" - -import asyncio -import base64 -import binascii -import datetime -import functools -import inspect -import netrc -import os -import platform -import re -import sys -import time -import warnings -import weakref -from collections import namedtuple -from contextlib import suppress -from email.parser import HeaderParser -from email.utils import parsedate -from math import ceil -from pathlib import Path -from types import TracebackType -from typing import ( - Any, - Callable, - ContextManager, - Dict, - Generator, - Generic, - Iterable, - Iterator, - List, - Mapping, - Optional, - Pattern, - Set, - Tuple, - Type, - TypeVar, - Union, - cast, -) -from urllib.parse import quote -from urllib.request import getproxies, proxy_bypass - -import async_timeout -import attr -from multidict import MultiDict, MultiDictProxy -from yarl import URL - -from . import hdrs -from .log import client_logger, internal_logger -from .typedefs import PathLike, Protocol # noqa - -__all__ = ("BasicAuth", "ChainMapProxy", "ETag") - -IS_MACOS = platform.system() == "Darwin" -IS_WINDOWS = platform.system() == "Windows" - -PY_36 = sys.version_info >= (3, 6) -PY_37 = sys.version_info >= (3, 7) -PY_38 = sys.version_info >= (3, 8) -PY_310 = sys.version_info >= (3, 10) -PY_311 = sys.version_info >= (3, 11) - -if sys.version_info < (3, 7): - import idna_ssl - - idna_ssl.patch_match_hostname() - - def all_tasks( - loop: Optional[asyncio.AbstractEventLoop] = None, - ) -> Set["asyncio.Task[Any]"]: - tasks = list(asyncio.Task.all_tasks(loop)) - return {t for t in tasks if not t.done()} - -else: - all_tasks = asyncio.all_tasks - - -_T = TypeVar("_T") -_S = TypeVar("_S") - - -sentinel: Any = object() -NO_EXTENSIONS: bool = bool(os.environ.get("AIOHTTP_NO_EXTENSIONS")) - -# N.B. sys.flags.dev_mode is available on Python 3.7+, use getattr -# for compatibility with older versions -DEBUG: bool = getattr(sys.flags, "dev_mode", False) or ( - not sys.flags.ignore_environment and bool(os.environ.get("PYTHONASYNCIODEBUG")) -) - - -CHAR = {chr(i) for i in range(0, 128)} -CTL = {chr(i) for i in range(0, 32)} | { - chr(127), -} -SEPARATORS = { - "(", - ")", - "<", - ">", - "@", - ",", - ";", - ":", - "\\", - '"', - "/", - "[", - "]", - "?", - "=", - "{", - "}", - " ", - chr(9), -} -TOKEN = CHAR ^ CTL ^ SEPARATORS - - -class noop: - def __await__(self) -> Generator[None, None, None]: - yield - - -class BasicAuth(namedtuple("BasicAuth", ["login", "password", "encoding"])): - """Http basic authentication helper.""" - - def __new__( - cls, login: str, password: str = "", encoding: str = "latin1" - ) -> "BasicAuth": - if login is None: - raise ValueError("None is not allowed as login value") - - if password is None: - raise ValueError("None is not allowed as password value") - - if ":" in login: - raise ValueError('A ":" is not allowed in login (RFC 1945#section-11.1)') - - return super().__new__(cls, login, password, encoding) - - @classmethod - def decode(cls, auth_header: str, encoding: str = "latin1") -> "BasicAuth": - """Create a BasicAuth object from an Authorization HTTP header.""" - try: - auth_type, encoded_credentials = auth_header.split(" ", 1) - except ValueError: - raise ValueError("Could not parse authorization header.") - - if auth_type.lower() != "basic": - raise ValueError("Unknown authorization method %s" % auth_type) - - try: - decoded = base64.b64decode( - encoded_credentials.encode("ascii"), validate=True - ).decode(encoding) - except binascii.Error: - raise ValueError("Invalid base64 encoding.") - - try: - # RFC 2617 HTTP Authentication - # https://www.ietf.org/rfc/rfc2617.txt - # the colon must be present, but the username and password may be - # otherwise blank. - username, password = decoded.split(":", 1) - except ValueError: - raise ValueError("Invalid credentials.") - - return cls(username, password, encoding=encoding) - - @classmethod - def from_url(cls, url: URL, *, encoding: str = "latin1") -> Optional["BasicAuth"]: - """Create BasicAuth from url.""" - if not isinstance(url, URL): - raise TypeError("url should be yarl.URL instance") - if url.user is None: - return None - return cls(url.user, url.password or "", encoding=encoding) - - def encode(self) -> str: - """Encode credentials.""" - creds = (f"{self.login}:{self.password}").encode(self.encoding) - return "Basic %s" % base64.b64encode(creds).decode(self.encoding) - - -def strip_auth_from_url(url: URL) -> Tuple[URL, Optional[BasicAuth]]: - auth = BasicAuth.from_url(url) - if auth is None: - return url, None - else: - return url.with_user(None), auth - - -def netrc_from_env() -> Optional[netrc.netrc]: - """Load netrc from file. - - Attempt to load it from the path specified by the env-var - NETRC or in the default location in the user's home directory. - - Returns None if it couldn't be found or fails to parse. - """ - netrc_env = os.environ.get("NETRC") - - if netrc_env is not None: - netrc_path = Path(netrc_env) - else: - try: - home_dir = Path.home() - except RuntimeError as e: # pragma: no cover - # if pathlib can't resolve home, it may raise a RuntimeError - client_logger.debug( - "Could not resolve home directory when " - "trying to look for .netrc file: %s", - e, - ) - return None - - netrc_path = home_dir / ("_netrc" if IS_WINDOWS else ".netrc") - - try: - return netrc.netrc(str(netrc_path)) - except netrc.NetrcParseError as e: - client_logger.warning("Could not parse .netrc file: %s", e) - except OSError as e: - # we couldn't read the file (doesn't exist, permissions, etc.) - if netrc_env or netrc_path.is_file(): - # only warn if the environment wanted us to load it, - # or it appears like the default file does actually exist - client_logger.warning("Could not read .netrc file: %s", e) - - return None - - -@attr.s(auto_attribs=True, frozen=True, slots=True) -class ProxyInfo: - proxy: URL - proxy_auth: Optional[BasicAuth] - - -def proxies_from_env() -> Dict[str, ProxyInfo]: - proxy_urls = { - k: URL(v) - for k, v in getproxies().items() - if k in ("http", "https", "ws", "wss") - } - netrc_obj = netrc_from_env() - stripped = {k: strip_auth_from_url(v) for k, v in proxy_urls.items()} - ret = {} - for proto, val in stripped.items(): - proxy, auth = val - if proxy.scheme in ("https", "wss"): - client_logger.warning( - "%s proxies %s are not supported, ignoring", proxy.scheme.upper(), proxy - ) - continue - if netrc_obj and auth is None: - auth_from_netrc = None - if proxy.host is not None: - auth_from_netrc = netrc_obj.authenticators(proxy.host) - if auth_from_netrc is not None: - # auth_from_netrc is a (`user`, `account`, `password`) tuple, - # `user` and `account` both can be username, - # if `user` is None, use `account` - *logins, password = auth_from_netrc - login = logins[0] if logins[0] else logins[-1] - auth = BasicAuth(cast(str, login), cast(str, password)) - ret[proto] = ProxyInfo(proxy, auth) - return ret - - -def current_task( - loop: Optional[asyncio.AbstractEventLoop] = None, -) -> "Optional[asyncio.Task[Any]]": - if sys.version_info >= (3, 7): - return asyncio.current_task(loop=loop) - else: - return asyncio.Task.current_task(loop=loop) - - -def get_running_loop( - loop: Optional[asyncio.AbstractEventLoop] = None, -) -> asyncio.AbstractEventLoop: - if loop is None: - loop = asyncio.get_event_loop() - if not loop.is_running(): - warnings.warn( - "The object should be created within an async function", - DeprecationWarning, - stacklevel=3, - ) - if loop.get_debug(): - internal_logger.warning( - "The object should be created within an async function", stack_info=True - ) - return loop - - -def isasyncgenfunction(obj: Any) -> bool: - func = getattr(inspect, "isasyncgenfunction", None) - if func is not None: - return func(obj) # type: ignore[no-any-return] - else: - return False - - -def get_env_proxy_for_url(url: URL) -> Tuple[URL, Optional[BasicAuth]]: - """Get a permitted proxy for the given URL from the env.""" - if url.host is not None and proxy_bypass(url.host): - raise LookupError(f"Proxying is disallowed for `{url.host!r}`") - - proxies_in_env = proxies_from_env() - try: - proxy_info = proxies_in_env[url.scheme] - except KeyError: - raise LookupError(f"No proxies found for `{url!s}` in the env") - else: - return proxy_info.proxy, proxy_info.proxy_auth - - -@attr.s(auto_attribs=True, frozen=True, slots=True) -class MimeType: - type: str - subtype: str - suffix: str - parameters: "MultiDictProxy[str]" - - -@functools.lru_cache(maxsize=56) -def parse_mimetype(mimetype: str) -> MimeType: - """Parses a MIME type into its components. - - mimetype is a MIME type string. - - Returns a MimeType object. - - Example: - - >>> parse_mimetype('text/html; charset=utf-8') - MimeType(type='text', subtype='html', suffix='', - parameters={'charset': 'utf-8'}) - - """ - if not mimetype: - return MimeType( - type="", subtype="", suffix="", parameters=MultiDictProxy(MultiDict()) - ) - - parts = mimetype.split(";") - params: MultiDict[str] = MultiDict() - for item in parts[1:]: - if not item: - continue - key, value = cast( - Tuple[str, str], item.split("=", 1) if "=" in item else (item, "") - ) - params.add(key.lower().strip(), value.strip(' "')) - - fulltype = parts[0].strip().lower() - if fulltype == "*": - fulltype = "*/*" - - mtype, stype = ( - cast(Tuple[str, str], fulltype.split("/", 1)) - if "/" in fulltype - else (fulltype, "") - ) - stype, suffix = ( - cast(Tuple[str, str], stype.split("+", 1)) if "+" in stype else (stype, "") - ) - - return MimeType( - type=mtype, subtype=stype, suffix=suffix, parameters=MultiDictProxy(params) - ) - - -def guess_filename(obj: Any, default: Optional[str] = None) -> Optional[str]: - name = getattr(obj, "name", None) - if name and isinstance(name, str) and name[0] != "<" and name[-1] != ">": - return Path(name).name - return default - - -not_qtext_re = re.compile(r"[^\041\043-\133\135-\176]") -QCONTENT = {chr(i) for i in range(0x20, 0x7F)} | {"\t"} - - -def quoted_string(content: str) -> str: - """Return 7-bit content as quoted-string. - - Format content into a quoted-string as defined in RFC5322 for - Internet Message Format. Notice that this is not the 8-bit HTTP - format, but the 7-bit email format. Content must be in usascii or - a ValueError is raised. - """ - if not (QCONTENT > set(content)): - raise ValueError(f"bad content for quoted-string {content!r}") - return not_qtext_re.sub(lambda x: "\\" + x.group(0), content) - - -def content_disposition_header( - disptype: str, quote_fields: bool = True, _charset: str = "utf-8", **params: str -) -> str: - """Sets ``Content-Disposition`` header for MIME. - - This is the MIME payload Content-Disposition header from RFC 2183 - and RFC 7579 section 4.2, not the HTTP Content-Disposition from - RFC 6266. - - disptype is a disposition type: inline, attachment, form-data. - Should be valid extension token (see RFC 2183) - - quote_fields performs value quoting to 7-bit MIME headers - according to RFC 7578. Set to quote_fields to False if recipient - can take 8-bit file names and field values. - - _charset specifies the charset to use when quote_fields is True. - - params is a dict with disposition params. - """ - if not disptype or not (TOKEN > set(disptype)): - raise ValueError("bad content disposition type {!r}" "".format(disptype)) - - value = disptype - if params: - lparams = [] - for key, val in params.items(): - if not key or not (TOKEN > set(key)): - raise ValueError( - "bad content disposition parameter" " {!r}={!r}".format(key, val) - ) - if quote_fields: - if key.lower() == "filename": - qval = quote(val, "", encoding=_charset) - lparams.append((key, '"%s"' % qval)) - else: - try: - qval = quoted_string(val) - except ValueError: - qval = "".join( - (_charset, "''", quote(val, "", encoding=_charset)) - ) - lparams.append((key + "*", qval)) - else: - lparams.append((key, '"%s"' % qval)) - else: - qval = val.replace("\\", "\\\\").replace('"', '\\"') - lparams.append((key, '"%s"' % qval)) - sparams = "; ".join("=".join(pair) for pair in lparams) - value = "; ".join((value, sparams)) - return value - - -class _TSelf(Protocol, Generic[_T]): - _cache: Dict[str, _T] - - -class reify(Generic[_T]): - """Use as a class method decorator. - - It operates almost exactly like - the Python `@property` decorator, but it puts the result of the - method it decorates into the instance dict after the first call, - effectively replacing the function it decorates with an instance - variable. It is, in Python parlance, a data descriptor. - """ - - def __init__(self, wrapped: Callable[..., _T]) -> None: - self.wrapped = wrapped - self.__doc__ = wrapped.__doc__ - self.name = wrapped.__name__ - - def __get__(self, inst: _TSelf[_T], owner: Optional[Type[Any]] = None) -> _T: - try: - try: - return inst._cache[self.name] - except KeyError: - val = self.wrapped(inst) - inst._cache[self.name] = val - return val - except AttributeError: - if inst is None: - return self - raise - - def __set__(self, inst: _TSelf[_T], value: _T) -> None: - raise AttributeError("reified property is read-only") - - -reify_py = reify - -try: - from ._helpers import reify as reify_c - - if not NO_EXTENSIONS: - reify = reify_c # type: ignore[misc,assignment] -except ImportError: - pass - -_ipv4_pattern = ( - r"^(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}" - r"(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)$" -) -_ipv6_pattern = ( - r"^(?:(?:(?:[A-F0-9]{1,4}:){6}|(?=(?:[A-F0-9]{0,4}:){0,6}" - r"(?:[0-9]{1,3}\.){3}[0-9]{1,3}$)(([0-9A-F]{1,4}:){0,5}|:)" - r"((:[0-9A-F]{1,4}){1,5}:|:)|::(?:[A-F0-9]{1,4}:){5})" - r"(?:(?:25[0-5]|2[0-4][0-9]|1[0-9][0-9]|[1-9]?[0-9])\.){3}" - r"(?:25[0-5]|2[0-4][0-9]|1[0-9][0-9]|[1-9]?[0-9])|(?:[A-F0-9]{1,4}:){7}" - r"[A-F0-9]{1,4}|(?=(?:[A-F0-9]{0,4}:){0,7}[A-F0-9]{0,4}$)" - r"(([0-9A-F]{1,4}:){1,7}|:)((:[0-9A-F]{1,4}){1,7}|:)|(?:[A-F0-9]{1,4}:){7}" - r":|:(:[A-F0-9]{1,4}){7})$" -) -_ipv4_regex = re.compile(_ipv4_pattern) -_ipv6_regex = re.compile(_ipv6_pattern, flags=re.IGNORECASE) -_ipv4_regexb = re.compile(_ipv4_pattern.encode("ascii")) -_ipv6_regexb = re.compile(_ipv6_pattern.encode("ascii"), flags=re.IGNORECASE) - - -def _is_ip_address( - regex: Pattern[str], regexb: Pattern[bytes], host: Optional[Union[str, bytes]] -) -> bool: - if host is None: - return False - if isinstance(host, str): - return bool(regex.match(host)) - elif isinstance(host, (bytes, bytearray, memoryview)): - return bool(regexb.match(host)) - else: - raise TypeError(f"{host} [{type(host)}] is not a str or bytes") - - -is_ipv4_address = functools.partial(_is_ip_address, _ipv4_regex, _ipv4_regexb) -is_ipv6_address = functools.partial(_is_ip_address, _ipv6_regex, _ipv6_regexb) - - -def is_ip_address(host: Optional[Union[str, bytes, bytearray, memoryview]]) -> bool: - return is_ipv4_address(host) or is_ipv6_address(host) - - -def next_whole_second() -> datetime.datetime: - """Return current time rounded up to the next whole second.""" - return datetime.datetime.now(datetime.timezone.utc).replace( - microsecond=0 - ) + datetime.timedelta(seconds=0) - - -_cached_current_datetime: Optional[int] = None -_cached_formatted_datetime = "" - - -def rfc822_formatted_time() -> str: - global _cached_current_datetime - global _cached_formatted_datetime - - now = int(time.time()) - if now != _cached_current_datetime: - # Weekday and month names for HTTP date/time formatting; - # always English! - # Tuples are constants stored in codeobject! - _weekdayname = ("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun") - _monthname = ( - "", # Dummy so we can use 1-based month numbers - "Jan", - "Feb", - "Mar", - "Apr", - "May", - "Jun", - "Jul", - "Aug", - "Sep", - "Oct", - "Nov", - "Dec", - ) - - year, month, day, hh, mm, ss, wd, *tail = time.gmtime(now) - _cached_formatted_datetime = "%s, %02d %3s %4d %02d:%02d:%02d GMT" % ( - _weekdayname[wd], - day, - _monthname[month], - year, - hh, - mm, - ss, - ) - _cached_current_datetime = now - return _cached_formatted_datetime - - -def _weakref_handle(info: "Tuple[weakref.ref[object], str]") -> None: - ref, name = info - ob = ref() - if ob is not None: - with suppress(Exception): - getattr(ob, name)() - - -def weakref_handle( - ob: object, name: str, timeout: float, loop: asyncio.AbstractEventLoop -) -> Optional[asyncio.TimerHandle]: - if timeout is not None and timeout > 0: - when = loop.time() + timeout - if timeout >= 5: - when = ceil(when) - - return loop.call_at(when, _weakref_handle, (weakref.ref(ob), name)) - return None - - -def call_later( - cb: Callable[[], Any], timeout: float, loop: asyncio.AbstractEventLoop -) -> Optional[asyncio.TimerHandle]: - if timeout is not None and timeout > 0: - when = loop.time() + timeout - if timeout > 5: - when = ceil(when) - return loop.call_at(when, cb) - return None - - -class TimeoutHandle: - """Timeout handle""" - - def __init__( - self, loop: asyncio.AbstractEventLoop, timeout: Optional[float] - ) -> None: - self._timeout = timeout - self._loop = loop - self._callbacks: List[ - Tuple[Callable[..., None], Tuple[Any, ...], Dict[str, Any]] - ] = [] - - def register( - self, callback: Callable[..., None], *args: Any, **kwargs: Any - ) -> None: - self._callbacks.append((callback, args, kwargs)) - - def close(self) -> None: - self._callbacks.clear() - - def start(self) -> Optional[asyncio.Handle]: - timeout = self._timeout - if timeout is not None and timeout > 0: - when = self._loop.time() + timeout - if timeout >= 5: - when = ceil(when) - return self._loop.call_at(when, self.__call__) - else: - return None - - def timer(self) -> "BaseTimerContext": - if self._timeout is not None and self._timeout > 0: - timer = TimerContext(self._loop) - self.register(timer.timeout) - return timer - else: - return TimerNoop() - - def __call__(self) -> None: - for cb, args, kwargs in self._callbacks: - with suppress(Exception): - cb(*args, **kwargs) - - self._callbacks.clear() - - -class BaseTimerContext(ContextManager["BaseTimerContext"]): - pass - - -class TimerNoop(BaseTimerContext): - def __enter__(self) -> BaseTimerContext: - return self - - def __exit__( - self, - exc_type: Optional[Type[BaseException]], - exc_val: Optional[BaseException], - exc_tb: Optional[TracebackType], - ) -> None: - return - - -class TimerContext(BaseTimerContext): - """Low resolution timeout context manager""" - - def __init__(self, loop: asyncio.AbstractEventLoop) -> None: - self._loop = loop - self._tasks: List[asyncio.Task[Any]] = [] - self._cancelled = False - - def __enter__(self) -> BaseTimerContext: - task = current_task(loop=self._loop) - - if task is None: - raise RuntimeError( - "Timeout context manager should be used " "inside a task" - ) - - if self._cancelled: - raise asyncio.TimeoutError from None - - self._tasks.append(task) - return self - - def __exit__( - self, - exc_type: Optional[Type[BaseException]], - exc_val: Optional[BaseException], - exc_tb: Optional[TracebackType], - ) -> Optional[bool]: - if self._tasks: - self._tasks.pop() - - if exc_type is asyncio.CancelledError and self._cancelled: - raise asyncio.TimeoutError from None - return None - - def timeout(self) -> None: - if not self._cancelled: - for task in set(self._tasks): - task.cancel() - - self._cancelled = True - - -def ceil_timeout(delay: Optional[float]) -> async_timeout.Timeout: - if delay is None or delay <= 0: - return async_timeout.timeout(None) - - loop = get_running_loop() - now = loop.time() - when = now + delay - if delay > 5: - when = ceil(when) - return async_timeout.timeout_at(when) - - -class HeadersMixin: - - ATTRS = frozenset(["_content_type", "_content_dict", "_stored_content_type"]) - - _content_type: Optional[str] = None - _content_dict: Optional[Dict[str, str]] = None - _stored_content_type = sentinel - - def _parse_content_type(self, raw: str) -> None: - self._stored_content_type = raw - if raw is None: - # default value according to RFC 2616 - self._content_type = "application/octet-stream" - self._content_dict = {} - else: - msg = HeaderParser().parsestr("Content-Type: " + raw) - self._content_type = msg.get_content_type() - params = msg.get_params() - self._content_dict = dict(params[1:]) # First element is content type again - - @property - def content_type(self) -> str: - """The value of content part for Content-Type HTTP header.""" - raw = self._headers.get(hdrs.CONTENT_TYPE) # type: ignore[attr-defined] - if self._stored_content_type != raw: - self._parse_content_type(raw) - return self._content_type # type: ignore[return-value] - - @property - def charset(self) -> Optional[str]: - """The value of charset part for Content-Type HTTP header.""" - raw = self._headers.get(hdrs.CONTENT_TYPE) # type: ignore[attr-defined] - if self._stored_content_type != raw: - self._parse_content_type(raw) - return self._content_dict.get("charset") # type: ignore[union-attr] - - @property - def content_length(self) -> Optional[int]: - """The value of Content-Length HTTP header.""" - content_length = self._headers.get( # type: ignore[attr-defined] - hdrs.CONTENT_LENGTH - ) - - if content_length is not None: - return int(content_length) - else: - return None - - -def set_result(fut: "asyncio.Future[_T]", result: _T) -> None: - if not fut.done(): - fut.set_result(result) - - -def set_exception(fut: "asyncio.Future[_T]", exc: BaseException) -> None: - if not fut.done(): - fut.set_exception(exc) - - -class ChainMapProxy(Mapping[str, Any]): - __slots__ = ("_maps",) - - def __init__(self, maps: Iterable[Mapping[str, Any]]) -> None: - self._maps = tuple(maps) - - def __init_subclass__(cls) -> None: - raise TypeError( - "Inheritance class {} from ChainMapProxy " - "is forbidden".format(cls.__name__) - ) - - def __getitem__(self, key: str) -> Any: - for mapping in self._maps: - try: - return mapping[key] - except KeyError: - pass - raise KeyError(key) - - def get(self, key: str, default: Any = None) -> Any: - return self[key] if key in self else default - - def __len__(self) -> int: - # reuses stored hash values if possible - return len(set().union(*self._maps)) # type: ignore[arg-type] - - def __iter__(self) -> Iterator[str]: - d: Dict[str, Any] = {} - for mapping in reversed(self._maps): - # reuses stored hash values if possible - d.update(mapping) - return iter(d) - - def __contains__(self, key: object) -> bool: - return any(key in m for m in self._maps) - - def __bool__(self) -> bool: - return any(self._maps) - - def __repr__(self) -> str: - content = ", ".join(map(repr, self._maps)) - return f"ChainMapProxy({content})" - - -# https://tools.ietf.org/html/rfc7232#section-2.3 -_ETAGC = r"[!#-}\x80-\xff]+" -_ETAGC_RE = re.compile(_ETAGC) -_QUOTED_ETAG = rf'(W/)?"({_ETAGC})"' -QUOTED_ETAG_RE = re.compile(_QUOTED_ETAG) -LIST_QUOTED_ETAG_RE = re.compile(rf"({_QUOTED_ETAG})(?:\s*,\s*|$)|(.)") - -ETAG_ANY = "*" - - -@attr.s(auto_attribs=True, frozen=True, slots=True) -class ETag: - value: str - is_weak: bool = False - - -def validate_etag_value(value: str) -> None: - if value != ETAG_ANY and not _ETAGC_RE.fullmatch(value): - raise ValueError( - f"Value {value!r} is not a valid etag. Maybe it contains '\"'?" - ) - - -def parse_http_date(date_str: Optional[str]) -> Optional[datetime.datetime]: - """Process a date string, return a datetime object""" - if date_str is not None: - timetuple = parsedate(date_str) - if timetuple is not None: - with suppress(ValueError): - return datetime.datetime(*timetuple[:6], tzinfo=datetime.timezone.utc) - return None diff --git a/spaces/ccolas/TastyPiano/src/cocktails/utilities/cocktail_category_detection_utilities.py b/spaces/ccolas/TastyPiano/src/cocktails/utilities/cocktail_category_detection_utilities.py deleted file mode 100644 index 5846f86263a1b2a83554d9c2ea5039ccd83555cb..0000000000000000000000000000000000000000 --- a/spaces/ccolas/TastyPiano/src/cocktails/utilities/cocktail_category_detection_utilities.py +++ /dev/null @@ -1,221 +0,0 @@ -# The following functions check whether a cocktail belong to any of N categories -import numpy as np -from src.cocktails.utilities.ingredients_utilities import ingredient_profiles, ingredients_per_type, ingredient2ingredient_id, extract_ingredients - - -def is_ancestral(n, ingredient_indexes, ingredients, quantities): - # ancestrals have a strong spirit and some sweetness from sugar, syrup or liqueurs, no citrus. - # absinthe can be added up to 3 dashes. - # Liqueurs are there to bring sweetness, thus must stay below 15ml (if not it's a duo) - if n['spirit'] > 0 and n['citrus'] == 0 and n['plain_sweet'] + n['liqueur'] <= 2: - if n['spirit'] > 1 and 'absinthe' in ingredients: - if quantities[ingredients.index('absinthe')] < 3: - pass - else: - return False - if n['sugar'] < 2 and n['liqueur'] < 3: - if n['all'] - n['spirit'] - n['sugar'] -n['syrup']- n['liqueur']- n['inconsequentials'] == 0: - if n['liqueur'] == 0: - return True - else: - q_liqueur = np.sum([quantities[i_ing] - for i_ind, i_ing in zip(ingredient_indexes, range(len(ingredients))) - if ingredient_profiles['type'][i_ind].lower() == 'liqueur']) - if q_liqueur <= 15: - return True - else: - return False - return False - - -def is_simple_sour(n, ingredient_indexes, ingredients, quantities): - # simple sours contain a citrus, at least 1 spirit and non-alcoholic sweetness - if n['citrus'] + n['coffee']> 0 and n['spirit'] > 0 and n['plain_sweet'] > 0 and n['juice'] == 0: - if n['all'] - n['citrus'] - n['coffee'] - n['spirit'] - n['plain_sweet'] - n['juice'] -n['egg'] - n['inconsequentials'] == 0: - return True - return False - -def is_complex_sour(n, ingredient_indexes, ingredients, quantities): - # complex sours are simple sours that use alcoholic sweetness, at least in part - if n['citrus'] + n['coffee'] > 0 and n['all_sweet'] > 0 and n['juice'] == 0: - if (n['spirit'] == 0 and n['liqueur'] > 0) or n['spirit'] > 0: - if n['vermouth'] + n['liqueur'] <= 2 and n['vermouth'] + n['liqueur'] > 0: - if n['all'] -n['coffee'] - n['citrus'] - n['spirit'] - n['sugar'] - n['syrup'] \ - - n['liqueur'] - n['vermouth'] - n['egg'] - n['juice'] - n['inconsequentials'] == 0: - return True - return False - -def is_spirit_forward(n, ingredient_indexes, ingredients, quantities): - # spirit forward contain at least a spirit and vermouth, no citrus. Can contain sweet (sugar, syrups, liqueurs) - if n['spirit'] > 0 and n['citrus'] == 0 and n['vermouth'] > 0: - if n['all'] - n['spirit'] - n['sugar'] - n['syrup'] - n['liqueur'] -n['egg'] - n['vermouth'] - n['inconsequentials']== 0: - return True - return False - -def is_duo(n, ingredient_indexes, ingredients, quantities): - # duos are made of one spirit and one liqueur (above 15ml), under it's an ancestral, no citrus. - if n['spirit'] >= 1 and n['citrus'] == 0 and n['sugar']==0 and n['liqueur'] > 0 and n['vermouth'] == 0: - if n['all'] - n['spirit'] - n['sugar'] - n['liqueur'] - n['vermouth'] - n['inconsequentials'] == 0: - q_liqueur = np.sum([quantities[i_ing] - for i_ind, i_ing in zip(ingredient_indexes, range(len(ingredients))) - if ingredient_profiles['type'][i_ind].lower() == 'liqueur']) - if q_liqueur > 15: - return True - else: - return False - return False - -def is_champagne_cocktail(n, ingredient_indexes, ingredients, quantities): - if n['sparkling'] > 0: - return True - else: - return False - -def is_simple_highball(n, ingredient_indexes, ingredients, quantities): - # simple highballs have one alcoholic ingredient and bubbles - if n['alcoholic'] == 1 and n['bubbles'] > 0: - if n['all'] - n['alcoholic'] - n['bubbles'] - n['inconsequentials']== 0: - return True - return False - -def is_complex_highball(n, ingredient_indexes, ingredients, quantities): - # complex highballs have at least one alcoholic ingredient and bubbles (possibly alcoholic). They also contain extra sugar under any form and juice - if n['alcoholic'] > 0 and (n['bubbles'] + n['sparkling']) == 1 and n['juice'] + n['all_sweet'] + n['sugar_bubbles']> 0: - if n['all'] - n['spirit'] - n['bubbles'] - n['sparkling'] - n['citrus'] - n['juice'] - n['liqueur'] \ - - n['syrup'] - n['sugar'] -n['vermouth'] -n['egg'] - n['inconsequentials'] == 0: - if not is_collins(n, ingredient_indexes, ingredients, quantities) and not is_simple_highball(n, ingredient_indexes, ingredients, quantities): - return True - return False - -def is_collins(n, ingredient_indexes, ingredients, quantities): - # collins are a particular kind of highball with sugar and citrus - if n['alcoholic'] == 1 and n['bubbles'] == 1 and n['citrus'] > 0 and n['plain_sweet'] + n['sugar_bubbles'] > 0: - if n['all'] - n['spirit'] - n['bubbles'] - n['citrus'] - n['sugar'] - n['inconsequentials'] == 0: - return True - return False - -def is_julep(n, ingredient_indexes, ingredients, quantities): - # juleps involve smashd mint, sugar and a spirit, no citrus. - if 'mint' in ingredients and n['sugar'] > 0 and n['spirit'] > 0 and n['vermouth'] == 0 and n['citrus'] == 0: - return True - return False - -def is_simple_sour_with_juice(n, ingredient_indexes, ingredients, quantities): - # almost sours are sours with juice - if n['juice'] > 0 and n['spirit'] > 0 and n['plain_sweet'] > 0: - if n['all'] - n['citrus'] - n['coffee'] - n['juice'] - n['spirit'] - n['sugar'] - n['syrup'] - n['egg'] - n['inconsequentials'] == 0: - return True - return False - - -def is_complex_sour_with_juice(n, ingredient_indexes, ingredients, quantities): - # almost sours are sours with juice - if n['juice'] > 0 and n['all_sweet'] > 0: - if (n['spirit'] == 0 and n['liqueur'] > 0) or n['spirit'] > 0: - if n['vermouth'] + n['liqueur'] <= 2 and n['vermouth'] + n['liqueur'] > 0: - if n['all'] -n['coffee'] - n['citrus'] - n['spirit'] - n['sugar'] - n['syrup'] \ - - n['liqueur'] - n['vermouth'] - n['egg'] - n['juice'] - n['inconsequentials'] == 0: - return True - return False - - -is_sub_category = [is_ancestral, is_complex_sour, is_simple_sour, is_duo, is_champagne_cocktail, - is_spirit_forward, is_simple_highball, is_complex_highball, is_collins, - is_julep, is_simple_sour_with_juice, is_complex_sour_with_juice] -sub_categories = ['ancestral', 'complex_sour', 'simple_sour', 'duo', 'champagne_cocktail', - 'spirit_forward', 'simple_highball', 'complex_highball', 'collins', - 'julep', 'simple_sour_with_juice', 'complex_sour_with_juice'] - - -# compute cocktail category as a function of ingredients and quantities, uses name to check match between name and cat (e.g. XXX Collins should be collins..) -# Categories definitions are based on https://www.seriouseats.com/cocktail-style-guide-categories-of-cocktails-glossary-families-of-drinks -def find_cocktail_sub_category(ingredients, quantities, name=None): - ingredient_indexes = [ingredient2ingredient_id[ing] for ing in ingredients] - n_spirit = np.sum([ingredient_profiles['type'][i].lower() == 'liquor' for i in ingredient_indexes ]) - n_citrus = np.sum([ingredient_profiles['type'][i].lower()== 'acid' for i in ingredient_indexes]) - n_sugar = np.sum([ingredient_profiles['ingredient'][i].lower() in ['double syrup', 'simple syrup', 'honey syrup'] for i in ingredient_indexes]) - plain_sweet = ingredients_per_type['sweeteners'] - all_sweet = ingredients_per_type['sweeteners'] + ingredients_per_type['liqueur'] + ['sweet vermouth', 'lillet blanc'] - n_plain_sweet = np.sum([ingredient_profiles['ingredient'][i].lower() in plain_sweet for i in ingredient_indexes]) - n_all_sweet = np.sum([ingredient_profiles['ingredient'][i].lower() in all_sweet for i in ingredient_indexes]) - n_sugar_bubbles = np.sum([ingredient_profiles['ingredient'][i].lower() in ['cola', 'ginger beer', 'tonic'] for i in ingredient_indexes]) - n_juice = np.sum([ingredient_profiles['type'][i].lower() == 'juice' for i in ingredient_indexes]) - n_liqueur = np.sum([ingredient_profiles['type'][i].lower() == 'liqueur' for i in ingredient_indexes]) - alcoholic = ingredients_per_type['liquor'] + ingredients_per_type['liqueur'] + ingredients_per_type['vermouth'] - n_alcoholic = np.sum([ingredient_profiles['ingredient'][i].lower() in alcoholic for i in ingredient_indexes]) - n_bitter = np.sum([ingredient_profiles['type'][i].lower() == 'bitters' for i in ingredient_indexes]) - n_egg = np.sum([ingredient_profiles['ingredient'][i].lower() == 'egg' for i in ingredient_indexes]) - n_vermouth = np.sum([ingredient_profiles['type'][i].lower() == 'vermouth' for i in ingredient_indexes]) - n_sparkling = np.sum([ingredient_profiles['ingredient'][i].lower() == 'sparkling wine' for i in ingredient_indexes]) - n_bubbles = np.sum([ingredient_profiles['ingredient'][i].lower() in ['soda', 'tonic', 'cola', 'ginger beer'] for i in ingredient_indexes]) - n_syrup = np.sum([ingredient_profiles['ingredient'][i].lower() in ['grenadine', 'raspberry syrup'] for i in ingredient_indexes]) - n_coffee = np.sum([ingredient_profiles['ingredient'][i].lower() == 'espresso' for i in ingredient_indexes]) - inconsequentials = ['water', 'salt', 'angostura', 'orange bitters', 'mint'] - n_inconsequentials = np.sum([ingredient_profiles['ingredient'][i].lower() in inconsequentials for i in ingredient_indexes]) - n = dict(all=len(ingredients), - inconsequentials=n_inconsequentials, - sugar_bubbles=n_sugar_bubbles, - bubbles=n_bubbles, - plain_sweet=n_plain_sweet, - all_sweet=n_all_sweet, - coffee=n_coffee, - alcoholic=n_alcoholic, - syrup=n_syrup, - sparkling=n_sparkling, - sugar=n_sugar, - spirit=n_spirit, - citrus=n_citrus, - juice=n_juice, - liqueur=n_liqueur, - bitter=n_bitter, - egg=n_egg, - vermouth=n_vermouth) - - sub_cats = [c for c, test_c in zip(sub_categories, is_sub_category) if test_c(n, ingredient_indexes, ingredients, quantities)] - if name != None: - name = name.lower() - keywords_to_test = ['julep', 'collins', 'highball', 'sour', 'champagne'] - for k in keywords_to_test: - if k in name and not any([k in cat for cat in sub_cats]): - print(k) - for ing, q in zip(ingredients, quantities): - print(f'{ing}: {q} ml') - print(n) - break - if sorted(sub_cats) == ['champagne_cocktail', 'complex_highball']: - sub_cats = ['champagne_cocktail'] - elif sorted(sub_cats) == ['collins', 'complex_highball']: - sub_cats = ['collins'] - elif sorted(sub_cats) == ['champagne_cocktail', 'complex_highball', 'julep']: - sub_cats = ['champagne_cocktail'] - elif sorted(sub_cats) == ['ancestral', 'julep']: - sub_cats = ['julep'] - elif sorted(sub_cats) == ['complex_highball', 'julep']: - sub_cats = ['complex_highball'] - elif sorted(sub_cats) == ['julep', 'simple_sour_with_juice']: - sub_cats = ['simple_sour_with_juice'] - elif sorted(sub_cats) == ['complex_sour_with_juice', 'julep']: - sub_cats = ['complex_sour_with_juice'] - if len(sub_cats) != 1: - # print(sub_cats) - # for ing, q in zip(ingredients, quantities): - # print(f'{ing}: {q} ml') - # print(n) - # if len(sub_cats) == 0: - sub_cats = ['other'] - assert len(sub_cats) == 1, sub_cats - return sub_cats[0], n - -def get_cocktails_attributes(ing_strs): - attributes = dict() - cats = [] - for ing_str in ing_strs: - ingredients, quantities = extract_ingredients(ing_str) - cat, atts = find_cocktail_sub_category(ingredients, quantities) - for k in atts.keys(): - if k not in attributes.keys(): - attributes[k] = [atts[k]] - else: - attributes[k].append(atts[k]) - cats.append(cat) - return cats, attributes diff --git a/spaces/chendl/compositional_test/multimodal/generate_batch_submit.py b/spaces/chendl/compositional_test/multimodal/generate_batch_submit.py deleted file mode 100644 index 067cdf0d2c30fc6cef0ab3e27dc1dc0ae3af9d0d..0000000000000000000000000000000000000000 --- a/spaces/chendl/compositional_test/multimodal/generate_batch_submit.py +++ /dev/null @@ -1,9 +0,0 @@ -import os -import sys -start_idx = sys.argv[1] -end_idx = sys.argv[2] - -with open("batch_submit.sh", "w") as f: - for i, idx in enumerate(range(int(start_idx), int(end_idx), 48)): - f.write(f"sbatch -J label{i} submit_labeling.sh {idx} {idx+48}\n") - f.write("sleep 30\n") diff --git a/spaces/chendl/compositional_test/transformers/examples/flax/language-modeling/t5_tokenizer_model.py b/spaces/chendl/compositional_test/transformers/examples/flax/language-modeling/t5_tokenizer_model.py deleted file mode 100644 index fbccd52bd8c726f07bbe61451b69ac46fb5b131f..0000000000000000000000000000000000000000 --- a/spaces/chendl/compositional_test/transformers/examples/flax/language-modeling/t5_tokenizer_model.py +++ /dev/null @@ -1,112 +0,0 @@ -#!/usr/bin/env python3 -import json -from typing import Iterator, List, Union - -from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers -from tokenizers.implementations.base_tokenizer import BaseTokenizer -from tokenizers.models import Unigram -from tokenizers.processors import TemplateProcessing - - -class SentencePieceUnigramTokenizer(BaseTokenizer): - """ - This class is a copy of `DeDLOC's tokenizer implementation`__ .
-
- Custom SentencePiece Unigram Tokenizer with NMT, NKFC, spaces and lower-casing characters normalization
- Represents the Unigram algorithm, with the pretokenization used by SentencePiece
- """
-
- def __init__(
- self,
- replacement: str = "▁",
- add_prefix_space: bool = True,
- unk_token: Union[str, AddedToken] = "",
- eos_token: Union[str, AddedToken] = "",
- pad_token: Union[str, AddedToken] = "",
- ):
- self.special_tokens = {
- "pad": {"id": 0, "token": pad_token},
- "eos": {"id": 1, "token": eos_token},
- "unk": {"id": 2, "token": unk_token},
- }
-
- self.special_tokens_list = [None] * len(self.special_tokens)
- for token_dict in self.special_tokens.values():
- self.special_tokens_list[token_dict["id"]] = token_dict["token"]
-
- tokenizer = Tokenizer(Unigram())
-
- tokenizer.normalizer = normalizers.Sequence(
- [
- normalizers.Nmt(),
- normalizers.NFKC(),
- normalizers.Replace(Regex(" {2,}"), " "),
- normalizers.Lowercase(),
- ]
- )
- tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
- [
- pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space),
- pre_tokenizers.Digits(individual_digits=True),
- pre_tokenizers.Punctuation(),
- ]
- )
- tokenizer.decoder = decoders.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space)
-
- tokenizer.post_processor = TemplateProcessing(
- single=f"$A {self.special_tokens['eos']['token']}",
- special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])],
- )
-
- parameters = {
- "model": "SentencePieceUnigram",
- "replacement": replacement,
- "add_prefix_space": add_prefix_space,
- }
-
- super().__init__(tokenizer, parameters)
-
- def train(
- self,
- files: Union[str, List[str]],
- vocab_size: int = 8000,
- show_progress: bool = True,
- ):
- """Train the model using the given files"""
-
- trainer = trainers.UnigramTrainer(
- vocab_size=vocab_size,
- special_tokens=self.special_tokens_list,
- show_progress=show_progress,
- )
-
- if isinstance(files, str):
- files = [files]
- self._tokenizer.train(files, trainer=trainer)
-
- self.add_unk_id()
-
- def train_from_iterator(
- self,
- iterator: Union[Iterator[str], Iterator[Iterator[str]]],
- vocab_size: int = 8000,
- show_progress: bool = True,
- ):
- """Train the model using the given iterator"""
-
- trainer = trainers.UnigramTrainer(
- vocab_size=vocab_size,
- special_tokens=self.special_tokens_list,
- show_progress=show_progress,
- )
-
- self._tokenizer.train_from_iterator(iterator, trainer=trainer)
-
- self.add_unk_id()
-
- def add_unk_id(self):
- tokenizer_json = json.loads(self._tokenizer.to_str())
-
- tokenizer_json["model"]["unk_id"] = self.special_tokens["unk"]["id"]
-
- self._tokenizer = Tokenizer.from_str(json.dumps(tokenizer_json))
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/jax-projects/hybrid_clip/configuration_hybrid_clip.py b/spaces/chendl/compositional_test/transformers/examples/research_projects/jax-projects/hybrid_clip/configuration_hybrid_clip.py
deleted file mode 100644
index 5272ac44a1a884eaf9b058c9e29729bfaec29a58..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/jax-projects/hybrid_clip/configuration_hybrid_clip.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import copy
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class HybridCLIPConfig(PretrainedConfig):
- r"""
- :class:`HybridCLIPConfig` is the configuration class to store the configuration of a
- :class:`~HybridCLIPModel`. It is used to instantiate HybridCLIPModel model according to the specified arguments,
- defining the text model and vision model configs.
-
- Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
- outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
-
- Args:
- text_config_dict (:obj:`dict`):
- Dictionary of configuration options that defines text model config.
- vision_config_dict (:obj:`dict`):
- Dictionary of configuration options that defines vison model config.
- projection_dim (:obj:`int`, `optional`, defaults to 512):
- Dimentionality of text and vision projection layers.
- kwargs (`optional`):
- Dictionary of keyword arguments.
-
- Examples::
-
- >>> from transformers import BertConfig, CLIPConfig, HybridCLIPConfig, FlaxHybridCLIP
-
- >>> # Initializing a BERT and CLIP configuration
- >>> config_text = BertConfig()
- >>> config_vision = CLIPConfig()
-
- >>> config = HybridCLIPConfig.from_text_vision_configs(config_text, config_vision, projection_dim=512)
-
- >>> # Initializing a BERT and CLIPVision model
- >>> model = EncoderDecoderModel(config=config)
-
- >>> # Accessing the model configuration
- >>> config_text = model.config.text_config
- >>> config_vision = model.config.vision_config
-
- >>> # Saving the model, including its configuration
- >>> model.save_pretrained('my-model')
-
- >>> # loading model and config from pretrained folder
- >>> encoder_decoder_config = HybridCLIPConfig.from_pretrained('my-model')
- >>> model = FlaxHybridCLIP.from_pretrained('my-model', config=encoder_decoder_config)
- """
-
- model_type = "hybrid-clip"
- is_composition = True
-
- def __init__(self, projection_dim=512, **kwargs):
- super().__init__(**kwargs)
-
- if "text_config" not in kwargs:
- raise ValueError("`text_config` can not be `None`.")
-
- if "vision_config" not in kwargs:
- raise ValueError("`vision_config` can not be `None`.")
-
- text_config = kwargs.pop("text_config")
- vision_config = kwargs.pop("vision_config")
-
- text_model_type = text_config.pop("model_type")
- vision_model_type = vision_config.pop("model_type")
-
- from transformers import AutoConfig
-
- self.text_config = AutoConfig.for_model(text_model_type, **text_config)
-
- if vision_model_type == "clip":
- self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config).vision_config
- elif vision_model_type == "clip_vision_model":
- from transformers import CLIPVisionConfig
-
- self.vision_config = CLIPVisionConfig(**vision_config)
- else:
- self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config)
-
- self.projection_dim = projection_dim
- self.initializer_factor = 1.0
-
- @classmethod
- def from_text_vision_configs(cls, text_config: PretrainedConfig, vision_config: PretrainedConfig, **kwargs):
- r"""
- Instantiate a :class:`HybridCLIPConfig` (or a derived class) from text model configuration and
- vision model configuration.
-
- Returns:
- :class:`HybridCLIPConfig`: An instance of a configuration object
- """
-
- return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
-
- def to_dict(self):
- """
- Serializes this instance to a Python dictionary. Override the default
- :meth:`~transformers.PretrainedConfig.to_dict`.
-
- Returns:
- :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
- """
- output = copy.deepcopy(self.__dict__)
- output["text_config"] = self.text_config.to_dict()
- output["vision_config"] = self.vision_config.to_dict()
- output["model_type"] = self.__class__.model_type
- return output
diff --git a/spaces/chlab/interactive_kinematic_planet_detector/model_utils/vision_modifications.py b/spaces/chlab/interactive_kinematic_planet_detector/model_utils/vision_modifications.py
deleted file mode 100644
index 14151748ba4a57cdfcfc64b4ba83c4d6009294bb..0000000000000000000000000000000000000000
--- a/spaces/chlab/interactive_kinematic_planet_detector/model_utils/vision_modifications.py
+++ /dev/null
@@ -1,310 +0,0 @@
-import warnings
-from typing import Callable, List, Optional
-
-import torch
-from torch import Tensor
-
-interpolate = torch.nn.functional.interpolate
-
-
-class FrozenBatchNorm2d(torch.nn.Module):
- """
- BatchNorm2d where the batch statistics and the affine parameters are fixed
-
- Args:
- num_features (int): Number of features ``C`` from an expected input of size ``(N, C, H, W)``
- eps (float): a value added to the denominator for numerical stability. Default: 1e-5
- """
-
- def __init__(
- self,
- num_features: int,
- eps: float = 1e-5,
- ):
- super().__init__()
- # _log_api_usage_once(self)
- self.eps = eps
- self.register_buffer("weight", torch.ones(num_features))
- self.register_buffer("bias", torch.zeros(num_features))
- self.register_buffer("running_mean", torch.zeros(num_features))
- self.register_buffer("running_var", torch.ones(num_features))
-
- def _load_from_state_dict(
- self,
- state_dict: dict,
- prefix: str,
- local_metadata: dict,
- strict: bool,
- missing_keys: List[str],
- unexpected_keys: List[str],
- error_msgs: List[str],
- ):
- num_batches_tracked_key = prefix + "num_batches_tracked"
- if num_batches_tracked_key in state_dict:
- del state_dict[num_batches_tracked_key]
-
- super()._load_from_state_dict(
- state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
- )
-
- def forward(self, x: Tensor) -> Tensor:
- # move reshapes to the beginning
- # to make it fuser-friendly
- w = self.weight.reshape(1, -1, 1, 1)
- b = self.bias.reshape(1, -1, 1, 1)
- rv = self.running_var.reshape(1, -1, 1, 1)
- rm = self.running_mean.reshape(1, -1, 1, 1)
- scale = w * (rv + self.eps).rsqrt()
- bias = b - rm * scale
- return x * scale + bias
-
- def __repr__(self) -> str:
- return f"{self.__class__.__name__}({self.weight.shape[0]}, eps={self.eps})"
-
-
-class ConvNormActivation(torch.nn.Sequential):
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- kernel_size: int = 3,
- stride: int = 1,
- padding: Optional[int] = None,
- groups: int = 1,
- norm_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.BatchNorm2d,
- activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU,
- dilation: int = 1,
- inplace: Optional[bool] = True,
- bias: Optional[bool] = None,
- conv_layer: Callable[..., torch.nn.Module] = torch.nn.Conv2d,
- ) -> None:
-
- if padding is None:
- padding = (kernel_size - 1) // 2 * dilation
- if bias is None:
- bias = norm_layer is None
-
- layers = [
- conv_layer(
- in_channels,
- out_channels,
- kernel_size,
- stride,
- padding,
- dilation=dilation,
- groups=groups,
- bias=bias,
- )
- ]
-
- if norm_layer is not None:
- layers.append(norm_layer(out_channels))
-
- if activation_layer is not None:
- params = {} if inplace is None else {"inplace": inplace}
- layers.append(activation_layer(**params))
- super().__init__(*layers)
- # _log_api_usage_once(self)
- self.out_channels = out_channels
-
- if self.__class__ == ConvNormActivation:
- warnings.warn(
- "Don't use ConvNormActivation directly, please use Conv2dNormActivation and Conv3dNormActivation instead."
- )
-
-
-class Conv2dNormActivation(ConvNormActivation):
- """
- Configurable block used for Convolution2d-Normalization-Activation blocks.
-
- Args:
- in_channels (int): Number of channels in the input image
- out_channels (int): Number of channels produced by the Convolution-Normalization-Activation block
- kernel_size: (int, optional): Size of the convolving kernel. Default: 3
- stride (int, optional): Stride of the convolution. Default: 1
- padding (int, tuple or str, optional): Padding added to all four sides of the input. Default: None, in which case it will calculated as ``padding = (kernel_size - 1) // 2 * dilation``
- groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
- norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the convolution layer. If ``None`` this layer wont be used. Default: ``torch.nn.BatchNorm2d``
- activation_layer (Callable[..., torch.nn.Module], optional): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the conv layer. If ``None`` this layer wont be used. Default: ``torch.nn.ReLU``
- dilation (int): Spacing between kernel elements. Default: 1
- inplace (bool): Parameter for the activation layer, which can optionally do the operation in-place. Default ``True``
- bias (bool, optional): Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is None``.
-
- """
-
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- kernel_size: int = 3,
- stride: int = 1,
- padding: Optional[int] = None,
- groups: int = 1,
- norm_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.BatchNorm2d,
- activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU,
- dilation: int = 1,
- inplace: Optional[bool] = True,
- bias: Optional[bool] = None,
- ) -> None:
-
- super().__init__(
- in_channels,
- out_channels,
- kernel_size,
- stride,
- padding,
- groups,
- norm_layer,
- activation_layer,
- dilation,
- inplace,
- bias,
- torch.nn.Conv2d,
- )
-
-
-class Conv3dNormActivation(ConvNormActivation):
- """
- Configurable block used for Convolution3d-Normalization-Activation blocks.
-
- Args:
- in_channels (int): Number of channels in the input video.
- out_channels (int): Number of channels produced by the Convolution-Normalization-Activation block
- kernel_size: (int, optional): Size of the convolving kernel. Default: 3
- stride (int, optional): Stride of the convolution. Default: 1
- padding (int, tuple or str, optional): Padding added to all four sides of the input. Default: None, in which case it will calculated as ``padding = (kernel_size - 1) // 2 * dilation``
- groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
- norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the convolution layer. If ``None`` this layer wont be used. Default: ``torch.nn.BatchNorm3d``
- activation_layer (Callable[..., torch.nn.Module], optional): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the conv layer. If ``None`` this layer wont be used. Default: ``torch.nn.ReLU``
- dilation (int): Spacing between kernel elements. Default: 1
- inplace (bool): Parameter for the activation layer, which can optionally do the operation in-place. Default ``True``
- bias (bool, optional): Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is None``.
- """
-
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- kernel_size: int = 3,
- stride: int = 1,
- padding: Optional[int] = None,
- groups: int = 1,
- norm_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.BatchNorm3d,
- activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU,
- dilation: int = 1,
- inplace: Optional[bool] = True,
- bias: Optional[bool] = None,
- ) -> None:
-
- super().__init__(
- in_channels,
- out_channels,
- kernel_size,
- stride,
- padding,
- groups,
- norm_layer,
- activation_layer,
- dilation,
- inplace,
- bias,
- torch.nn.Conv3d,
- )
-
-
-class SqueezeExcitation(torch.nn.Module):
- """
- This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507 (see Fig. 1).
- Parameters ``activation``, and ``scale_activation`` correspond to ``delta`` and ``sigma`` in eq. 3.
-
- Args:
- input_channels (int): Number of channels in the input image
- squeeze_channels (int): Number of squeeze channels
- activation (Callable[..., torch.nn.Module], optional): ``delta`` activation. Default: ``torch.nn.ReLU``
- scale_activation (Callable[..., torch.nn.Module]): ``sigma`` activation. Default: ``torch.nn.Sigmoid``
- """
-
- def __init__(
- self,
- input_channels: int,
- squeeze_channels: int,
- activation: Callable[..., torch.nn.Module] = torch.nn.ReLU,
- scale_activation: Callable[..., torch.nn.Module] = torch.nn.Sigmoid,
- ) -> None:
- super().__init__()
- # _log_api_usage_once(self)
- self.avgpool = torch.nn.AdaptiveAvgPool2d(1)
- self.fc1 = torch.nn.Conv2d(input_channels, squeeze_channels, 1)
- self.fc2 = torch.nn.Conv2d(squeeze_channels, input_channels, 1)
- self.activation = activation()
- self.scale_activation = scale_activation()
-
- def _scale(self, input: Tensor) -> Tensor:
- scale = self.avgpool(input)
- scale = self.fc1(scale)
- scale = self.activation(scale)
- scale = self.fc2(scale)
- return self.scale_activation(scale)
-
- def forward(self, input: Tensor) -> Tensor:
- scale = self._scale(input)
- return scale * input
-
-
-class MLP(torch.nn.Sequential):
- """This block implements the multi-layer perceptron (MLP) module.
-
- Args:
- in_channels (int): Number of channels of the input
- hidden_channels (List[int]): List of the hidden channel dimensions
- norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the convolution layer. If ``None`` this layer wont be used. Default: ``None``
- activation_layer (Callable[..., torch.nn.Module], optional): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the conv layer. If ``None`` this layer wont be used. Default: ``torch.nn.ReLU``
- inplace (bool): Parameter for the activation layer, which can optionally do the operation in-place. Default ``True``
- bias (bool): Whether to use bias in the linear layer. Default ``True``
- dropout (float): The probability for the dropout layer. Default: 0.0
- """
-
- def __init__(
- self,
- in_channels: int,
- hidden_channels: List[int],
- norm_layer: Optional[Callable[..., torch.nn.Module]] = None,
- activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU,
- inplace: Optional[bool] = True,
- bias: bool = True,
- dropout: float = 0.0,
- ):
- # The addition of `norm_layer` is inspired from the implementation of TorchMultimodal:
- # https://github.com/facebookresearch/multimodal/blob/5dec8a/torchmultimodal/modules/layers/mlp.py
- params = {} if inplace is None else {"inplace": inplace}
-
- layers = []
- in_dim = in_channels
- for hidden_dim in hidden_channels[:-1]:
- layers.append(torch.nn.Linear(in_dim, hidden_dim, bias=bias))
- if norm_layer is not None:
- layers.append(norm_layer(hidden_dim))
- layers.append(activation_layer(**params))
- layers.append(torch.nn.Dropout(dropout, **params))
- in_dim = hidden_dim
-
- layers.append(torch.nn.Linear(in_dim, hidden_channels[-1], bias=bias))
- layers.append(torch.nn.Dropout(dropout, **params))
-
- super().__init__(*layers)
- # _log_api_usage_once(self)
-
-
-class Permute(torch.nn.Module):
- """This module returns a view of the tensor input with its dimensions permuted.
-
- Args:
- dims (List[int]): The desired ordering of dimensions
- """
-
- def __init__(self, dims: List[int]):
- super().__init__()
- self.dims = dims
-
- def forward(self, x: Tensor) -> Tensor:
- return torch.permute(x, self.dims)
\ No newline at end of file
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/PcfFontFile.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/PcfFontFile.py
deleted file mode 100644
index 8db5822fe7dadb10880c7d53a27731775b9a1835..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/PcfFontFile.py
+++ /dev/null
@@ -1,256 +0,0 @@
-#
-# THIS IS WORK IN PROGRESS
-#
-# The Python Imaging Library
-# $Id$
-#
-# portable compiled font file parser
-#
-# history:
-# 1997-08-19 fl created
-# 2003-09-13 fl fixed loading of unicode fonts
-#
-# Copyright (c) 1997-2003 by Secret Labs AB.
-# Copyright (c) 1997-2003 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import io
-
-from . import FontFile, Image
-from ._binary import i8
-from ._binary import i16be as b16
-from ._binary import i16le as l16
-from ._binary import i32be as b32
-from ._binary import i32le as l32
-
-# --------------------------------------------------------------------
-# declarations
-
-PCF_MAGIC = 0x70636601 # "\x01fcp"
-
-PCF_PROPERTIES = 1 << 0
-PCF_ACCELERATORS = 1 << 1
-PCF_METRICS = 1 << 2
-PCF_BITMAPS = 1 << 3
-PCF_INK_METRICS = 1 << 4
-PCF_BDF_ENCODINGS = 1 << 5
-PCF_SWIDTHS = 1 << 6
-PCF_GLYPH_NAMES = 1 << 7
-PCF_BDF_ACCELERATORS = 1 << 8
-
-BYTES_PER_ROW = [
- lambda bits: ((bits + 7) >> 3),
- lambda bits: ((bits + 15) >> 3) & ~1,
- lambda bits: ((bits + 31) >> 3) & ~3,
- lambda bits: ((bits + 63) >> 3) & ~7,
-]
-
-
-def sz(s, o):
- return s[o : s.index(b"\0", o)]
-
-
-class PcfFontFile(FontFile.FontFile):
- """Font file plugin for the X11 PCF format."""
-
- name = "name"
-
- def __init__(self, fp, charset_encoding="iso8859-1"):
- self.charset_encoding = charset_encoding
-
- magic = l32(fp.read(4))
- if magic != PCF_MAGIC:
- msg = "not a PCF file"
- raise SyntaxError(msg)
-
- super().__init__()
-
- count = l32(fp.read(4))
- self.toc = {}
- for i in range(count):
- type = l32(fp.read(4))
- self.toc[type] = l32(fp.read(4)), l32(fp.read(4)), l32(fp.read(4))
-
- self.fp = fp
-
- self.info = self._load_properties()
-
- metrics = self._load_metrics()
- bitmaps = self._load_bitmaps(metrics)
- encoding = self._load_encoding()
-
- #
- # create glyph structure
-
- for ch, ix in enumerate(encoding):
- if ix is not None:
- (
- xsize,
- ysize,
- left,
- right,
- width,
- ascent,
- descent,
- attributes,
- ) = metrics[ix]
- self.glyph[ch] = (
- (width, 0),
- (left, descent - ysize, xsize + left, descent),
- (0, 0, xsize, ysize),
- bitmaps[ix],
- )
-
- def _getformat(self, tag):
- format, size, offset = self.toc[tag]
-
- fp = self.fp
- fp.seek(offset)
-
- format = l32(fp.read(4))
-
- if format & 4:
- i16, i32 = b16, b32
- else:
- i16, i32 = l16, l32
-
- return fp, format, i16, i32
-
- def _load_properties(self):
- #
- # font properties
-
- properties = {}
-
- fp, format, i16, i32 = self._getformat(PCF_PROPERTIES)
-
- nprops = i32(fp.read(4))
-
- # read property description
- p = []
- for i in range(nprops):
- p.append((i32(fp.read(4)), i8(fp.read(1)), i32(fp.read(4))))
- if nprops & 3:
- fp.seek(4 - (nprops & 3), io.SEEK_CUR) # pad
-
- data = fp.read(i32(fp.read(4)))
-
- for k, s, v in p:
- k = sz(data, k)
- if s:
- v = sz(data, v)
- properties[k] = v
-
- return properties
-
- def _load_metrics(self):
- #
- # font metrics
-
- metrics = []
-
- fp, format, i16, i32 = self._getformat(PCF_METRICS)
-
- append = metrics.append
-
- if (format & 0xFF00) == 0x100:
- # "compressed" metrics
- for i in range(i16(fp.read(2))):
- left = i8(fp.read(1)) - 128
- right = i8(fp.read(1)) - 128
- width = i8(fp.read(1)) - 128
- ascent = i8(fp.read(1)) - 128
- descent = i8(fp.read(1)) - 128
- xsize = right - left
- ysize = ascent + descent
- append((xsize, ysize, left, right, width, ascent, descent, 0))
-
- else:
- # "jumbo" metrics
- for i in range(i32(fp.read(4))):
- left = i16(fp.read(2))
- right = i16(fp.read(2))
- width = i16(fp.read(2))
- ascent = i16(fp.read(2))
- descent = i16(fp.read(2))
- attributes = i16(fp.read(2))
- xsize = right - left
- ysize = ascent + descent
- append((xsize, ysize, left, right, width, ascent, descent, attributes))
-
- return metrics
-
- def _load_bitmaps(self, metrics):
- #
- # bitmap data
-
- bitmaps = []
-
- fp, format, i16, i32 = self._getformat(PCF_BITMAPS)
-
- nbitmaps = i32(fp.read(4))
-
- if nbitmaps != len(metrics):
- msg = "Wrong number of bitmaps"
- raise OSError(msg)
-
- offsets = []
- for i in range(nbitmaps):
- offsets.append(i32(fp.read(4)))
-
- bitmap_sizes = []
- for i in range(4):
- bitmap_sizes.append(i32(fp.read(4)))
-
- # byteorder = format & 4 # non-zero => MSB
- bitorder = format & 8 # non-zero => MSB
- padindex = format & 3
-
- bitmapsize = bitmap_sizes[padindex]
- offsets.append(bitmapsize)
-
- data = fp.read(bitmapsize)
-
- pad = BYTES_PER_ROW[padindex]
- mode = "1;R"
- if bitorder:
- mode = "1"
-
- for i in range(nbitmaps):
- xsize, ysize = metrics[i][:2]
- b, e = offsets[i : i + 2]
- bitmaps.append(
- Image.frombytes("1", (xsize, ysize), data[b:e], "raw", mode, pad(xsize))
- )
-
- return bitmaps
-
- def _load_encoding(self):
- fp, format, i16, i32 = self._getformat(PCF_BDF_ENCODINGS)
-
- first_col, last_col = i16(fp.read(2)), i16(fp.read(2))
- first_row, last_row = i16(fp.read(2)), i16(fp.read(2))
-
- i16(fp.read(2)) # default
-
- nencoding = (last_col - first_col + 1) * (last_row - first_row + 1)
-
- # map character code to bitmap index
- encoding = [None] * min(256, nencoding)
-
- encoding_offsets = [i16(fp.read(2)) for _ in range(nencoding)]
-
- for i in range(first_col, len(encoding)):
- try:
- encoding_offset = encoding_offsets[
- ord(bytearray([i]).decode(self.charset_encoding))
- ]
- if encoding_offset != 0xFFFF:
- encoding[i] = encoding_offset
- except UnicodeDecodeError:
- # character is not supported in selected encoding
- pass
-
- return encoding
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/TgaImagePlugin.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/TgaImagePlugin.py
deleted file mode 100644
index 67dfc3d3c8e5726c5885b1c62cdcb2553854c4dc..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/TgaImagePlugin.py
+++ /dev/null
@@ -1,255 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# TGA file handling
-#
-# History:
-# 95-09-01 fl created (reads 24-bit files only)
-# 97-01-04 fl support more TGA versions, including compressed images
-# 98-07-04 fl fixed orientation and alpha layer bugs
-# 98-09-11 fl fixed orientation for runlength decoder
-#
-# Copyright (c) Secret Labs AB 1997-98.
-# Copyright (c) Fredrik Lundh 1995-97.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-import warnings
-
-from . import Image, ImageFile, ImagePalette
-from ._binary import i16le as i16
-from ._binary import o8
-from ._binary import o16le as o16
-
-#
-# --------------------------------------------------------------------
-# Read RGA file
-
-
-MODES = {
- # map imagetype/depth to rawmode
- (1, 8): "P",
- (3, 1): "1",
- (3, 8): "L",
- (3, 16): "LA",
- (2, 16): "BGR;5",
- (2, 24): "BGR",
- (2, 32): "BGRA",
-}
-
-
-##
-# Image plugin for Targa files.
-
-
-class TgaImageFile(ImageFile.ImageFile):
- format = "TGA"
- format_description = "Targa"
-
- def _open(self):
- # process header
- s = self.fp.read(18)
-
- id_len = s[0]
-
- colormaptype = s[1]
- imagetype = s[2]
-
- depth = s[16]
-
- flags = s[17]
-
- self._size = i16(s, 12), i16(s, 14)
-
- # validate header fields
- if (
- colormaptype not in (0, 1)
- or self.size[0] <= 0
- or self.size[1] <= 0
- or depth not in (1, 8, 16, 24, 32)
- ):
- msg = "not a TGA file"
- raise SyntaxError(msg)
-
- # image mode
- if imagetype in (3, 11):
- self.mode = "L"
- if depth == 1:
- self.mode = "1" # ???
- elif depth == 16:
- self.mode = "LA"
- elif imagetype in (1, 9):
- self.mode = "P"
- elif imagetype in (2, 10):
- self.mode = "RGB"
- if depth == 32:
- self.mode = "RGBA"
- else:
- msg = "unknown TGA mode"
- raise SyntaxError(msg)
-
- # orientation
- orientation = flags & 0x30
- self._flip_horizontally = orientation in [0x10, 0x30]
- if orientation in [0x20, 0x30]:
- orientation = 1
- elif orientation in [0, 0x10]:
- orientation = -1
- else:
- msg = "unknown TGA orientation"
- raise SyntaxError(msg)
-
- self.info["orientation"] = orientation
-
- if imagetype & 8:
- self.info["compression"] = "tga_rle"
-
- if id_len:
- self.info["id_section"] = self.fp.read(id_len)
-
- if colormaptype:
- # read palette
- start, size, mapdepth = i16(s, 3), i16(s, 5), s[7]
- if mapdepth == 16:
- self.palette = ImagePalette.raw(
- "BGR;15", b"\0" * 2 * start + self.fp.read(2 * size)
- )
- elif mapdepth == 24:
- self.palette = ImagePalette.raw(
- "BGR", b"\0" * 3 * start + self.fp.read(3 * size)
- )
- elif mapdepth == 32:
- self.palette = ImagePalette.raw(
- "BGRA", b"\0" * 4 * start + self.fp.read(4 * size)
- )
-
- # setup tile descriptor
- try:
- rawmode = MODES[(imagetype & 7, depth)]
- if imagetype & 8:
- # compressed
- self.tile = [
- (
- "tga_rle",
- (0, 0) + self.size,
- self.fp.tell(),
- (rawmode, orientation, depth),
- )
- ]
- else:
- self.tile = [
- (
- "raw",
- (0, 0) + self.size,
- self.fp.tell(),
- (rawmode, 0, orientation),
- )
- ]
- except KeyError:
- pass # cannot decode
-
- def load_end(self):
- if self._flip_horizontally:
- self.im = self.im.transpose(Image.Transpose.FLIP_LEFT_RIGHT)
-
-
-#
-# --------------------------------------------------------------------
-# Write TGA file
-
-
-SAVE = {
- "1": ("1", 1, 0, 3),
- "L": ("L", 8, 0, 3),
- "LA": ("LA", 16, 0, 3),
- "P": ("P", 8, 1, 1),
- "RGB": ("BGR", 24, 0, 2),
- "RGBA": ("BGRA", 32, 0, 2),
-}
-
-
-def _save(im, fp, filename):
- try:
- rawmode, bits, colormaptype, imagetype = SAVE[im.mode]
- except KeyError as e:
- msg = f"cannot write mode {im.mode} as TGA"
- raise OSError(msg) from e
-
- if "rle" in im.encoderinfo:
- rle = im.encoderinfo["rle"]
- else:
- compression = im.encoderinfo.get("compression", im.info.get("compression"))
- rle = compression == "tga_rle"
- if rle:
- imagetype += 8
-
- id_section = im.encoderinfo.get("id_section", im.info.get("id_section", ""))
- id_len = len(id_section)
- if id_len > 255:
- id_len = 255
- id_section = id_section[:255]
- warnings.warn("id_section has been trimmed to 255 characters")
-
- if colormaptype:
- palette = im.im.getpalette("RGB", "BGR")
- colormaplength, colormapentry = len(palette) // 3, 24
- else:
- colormaplength, colormapentry = 0, 0
-
- if im.mode in ("LA", "RGBA"):
- flags = 8
- else:
- flags = 0
-
- orientation = im.encoderinfo.get("orientation", im.info.get("orientation", -1))
- if orientation > 0:
- flags = flags | 0x20
-
- fp.write(
- o8(id_len)
- + o8(colormaptype)
- + o8(imagetype)
- + o16(0) # colormapfirst
- + o16(colormaplength)
- + o8(colormapentry)
- + o16(0)
- + o16(0)
- + o16(im.size[0])
- + o16(im.size[1])
- + o8(bits)
- + o8(flags)
- )
-
- if id_section:
- fp.write(id_section)
-
- if colormaptype:
- fp.write(palette)
-
- if rle:
- ImageFile._save(
- im, fp, [("tga_rle", (0, 0) + im.size, 0, (rawmode, orientation))]
- )
- else:
- ImageFile._save(
- im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, 0, orientation))]
- )
-
- # write targa version 2 footer
- fp.write(b"\000" * 8 + b"TRUEVISION-XFILE." + b"\000")
-
-
-#
-# --------------------------------------------------------------------
-# Registry
-
-
-Image.register_open(TgaImageFile.format, TgaImageFile)
-Image.register_save(TgaImageFile.format, _save)
-
-Image.register_extensions(TgaImageFile.format, [".tga", ".icb", ".vda", ".vst"])
-
-Image.register_mime(TgaImageFile.format, "image/x-tga")
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/ingest/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/ingest/__init__.py
deleted file mode 100644
index 38d6a4fa508e757ae33e7ddbd46f038e607ba610..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/ingest/__init__.py
+++ /dev/null
@@ -1,102 +0,0 @@
-from abc import abstractmethod
-from typing import Callable, Optional, Sequence
-from chromadb.types import (
- SubmitEmbeddingRecord,
- EmbeddingRecord,
- SeqId,
- Vector,
- ScalarEncoding,
-)
-from chromadb.config import Component
-from uuid import UUID
-import array
-
-
-def encode_vector(vector: Vector, encoding: ScalarEncoding) -> bytes:
- """Encode a vector into a byte array."""
-
- if encoding == ScalarEncoding.FLOAT32:
- return array.array("f", vector).tobytes()
- elif encoding == ScalarEncoding.INT32:
- return array.array("i", vector).tobytes()
- else:
- raise ValueError(f"Unsupported encoding: {encoding.value}")
-
-
-def decode_vector(vector: bytes, encoding: ScalarEncoding) -> Vector:
- """Decode a byte array into a vector"""
-
- if encoding == ScalarEncoding.FLOAT32:
- return array.array("f", vector).tolist()
- elif encoding == ScalarEncoding.INT32:
- return array.array("i", vector).tolist()
- else:
- raise ValueError(f"Unsupported encoding: {encoding.value}")
-
-
-class Producer(Component):
- """Interface for writing embeddings to an ingest stream"""
-
- @abstractmethod
- def create_topic(self, topic_name: str) -> None:
- pass
-
- @abstractmethod
- def delete_topic(self, topic_name: str) -> None:
- pass
-
- @abstractmethod
- def submit_embedding(
- self, topic_name: str, embedding: SubmitEmbeddingRecord
- ) -> SeqId:
- """Add an embedding record to the given topic. Returns the SeqID of the record."""
- pass
-
-
-ConsumerCallbackFn = Callable[[Sequence[EmbeddingRecord]], None]
-
-
-class Consumer(Component):
- """Interface for reading embeddings off an ingest stream"""
-
- @abstractmethod
- def subscribe(
- self,
- topic_name: str,
- consume_fn: ConsumerCallbackFn,
- start: Optional[SeqId] = None,
- end: Optional[SeqId] = None,
- id: Optional[UUID] = None,
- ) -> UUID:
- """Register a function that will be called to recieve embeddings for a given
- topic. The given function may be called any number of times, with any number of
- records, and may be called concurrently.
-
- Only records between start (exclusive) and end (inclusive) SeqIDs will be
- returned. If start is None, the first record returned will be the next record
- generated, not including those generated before creating the subscription. If
- end is None, the consumer will consume indefinitely, otherwise it will
- automatically be unsubscribed when the end SeqID is reached.
-
- If the function throws an exception, the function may be called again with the
- same or different records.
-
- Takes an optional UUID as a unique subscription ID. If no ID is provided, a new
- ID will be generated and returned."""
- pass
-
- @abstractmethod
- def unsubscribe(self, subscription_id: UUID) -> None:
- """Unregister a subscription. The consume function will no longer be invoked,
- and resources associated with the subscription will be released."""
- pass
-
- @abstractmethod
- def min_seqid(self) -> SeqId:
- """Return the minimum possible SeqID in this implementation."""
- pass
-
- @abstractmethod
- def max_seqid(self) -> SeqId:
- """Return the maximum possible SeqID in this implementation."""
- pass
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/opc/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/opc/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/cihyFjudo/fairness-paper-search/Andrew Jackson Jihad - People Who Can Eat People Are The Luckiest People In The World The Story Behind the Controversial Title and Lyrics.md b/spaces/cihyFjudo/fairness-paper-search/Andrew Jackson Jihad - People Who Can Eat People Are The Luckiest People In The World The Story Behind the Controversial Title and Lyrics.md
deleted file mode 100644
index 371e1de304a55575eefb1aa56e7dbd5e7dd5f4ee..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Andrew Jackson Jihad - People Who Can Eat People Are The Luckiest People In The World The Story Behind the Controversial Title and Lyrics.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
- - aaccfb2cb3
-
-
- diff --git a/spaces/cihyFjudo/fairness-paper-search/Design And Art Alex Coles Pdf 16 A Comprehensive Review of Contemporary Art and Design.md b/spaces/cihyFjudo/fairness-paper-search/Design And Art Alex Coles Pdf 16 A Comprehensive Review of Contemporary Art and Design.md deleted file mode 100644 index 3ad62d6b25d3fcd302d4abc03f9d1bc8d15b575b..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Design And Art Alex Coles Pdf 16 A Comprehensive Review of Contemporary Art and Design.md +++ /dev/null @@ -1,12 +0,0 @@ -
-
-
-
-
\ No newline at end of file diff --git a/spaces/cihyFjudo/fairness-paper-search/HD Online Player (Bengali Hd Movie Download 1080p) Stream or Download High-Quality Bengali Movies.md b/spaces/cihyFjudo/fairness-paper-search/HD Online Player (Bengali Hd Movie Download 1080p) Stream or Download High-Quality Bengali Movies.md deleted file mode 100644 index b02d0f88c0dc992d8ceac84726182b430b1220db..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/HD Online Player (Bengali Hd Movie Download 1080p) Stream or Download High-Quality Bengali Movies.md +++ /dev/null @@ -1,6 +0,0 @@ -
- - aaccfb2cb3
-
-
- diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/WmfImagePlugin.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/WmfImagePlugin.py deleted file mode 100644 index 0ecab56a824fd3917067fd4b05c530f4abce75a3..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/WmfImagePlugin.py +++ /dev/null @@ -1,178 +0,0 @@ -# -# The Python Imaging Library -# $Id$ -# -# WMF stub codec -# -# history: -# 1996-12-14 fl Created -# 2004-02-22 fl Turned into a stub driver -# 2004-02-23 fl Added EMF support -# -# Copyright (c) Secret Labs AB 1997-2004. All rights reserved. -# Copyright (c) Fredrik Lundh 1996. -# -# See the README file for information on usage and redistribution. -# -# WMF/EMF reference documentation: -# https://winprotocoldoc.blob.core.windows.net/productionwindowsarchives/MS-WMF/[MS-WMF].pdf -# http://wvware.sourceforge.net/caolan/index.html -# http://wvware.sourceforge.net/caolan/ora-wmf.html - -from . import Image, ImageFile -from ._binary import i16le as word -from ._binary import si16le as short -from ._binary import si32le as _long - -_handler = None - - -def register_handler(handler): - """ - Install application-specific WMF image handler. - - :param handler: Handler object. - """ - global _handler - _handler = handler - - -if hasattr(Image.core, "drawwmf"): - # install default handler (windows only) - - class WmfHandler: - def open(self, im): - im.mode = "RGB" - self.bbox = im.info["wmf_bbox"] - - def load(self, im): - im.fp.seek(0) # rewind - return Image.frombytes( - "RGB", - im.size, - Image.core.drawwmf(im.fp.read(), im.size, self.bbox), - "raw", - "BGR", - (im.size[0] * 3 + 3) & -4, - -1, - ) - - register_handler(WmfHandler()) - -# -# -------------------------------------------------------------------- -# Read WMF file - - -def _accept(prefix): - return ( - prefix[:6] == b"\xd7\xcd\xc6\x9a\x00\x00" or prefix[:4] == b"\x01\x00\x00\x00" - ) - - -## -# Image plugin for Windows metafiles. - - -class WmfStubImageFile(ImageFile.StubImageFile): - format = "WMF" - format_description = "Windows Metafile" - - def _open(self): - self._inch = None - - # check placable header - s = self.fp.read(80) - - if s[:6] == b"\xd7\xcd\xc6\x9a\x00\x00": - # placeable windows metafile - - # get units per inch - self._inch = word(s, 14) - - # get bounding box - x0 = short(s, 6) - y0 = short(s, 8) - x1 = short(s, 10) - y1 = short(s, 12) - - # normalize size to 72 dots per inch - self.info["dpi"] = 72 - size = ( - (x1 - x0) * self.info["dpi"] // self._inch, - (y1 - y0) * self.info["dpi"] // self._inch, - ) - - self.info["wmf_bbox"] = x0, y0, x1, y1 - - # sanity check (standard metafile header) - if s[22:26] != b"\x01\x00\t\x00": - msg = "Unsupported WMF file format" - raise SyntaxError(msg) - - elif s[:4] == b"\x01\x00\x00\x00" and s[40:44] == b" EMF": - # enhanced metafile - - # get bounding box - x0 = _long(s, 8) - y0 = _long(s, 12) - x1 = _long(s, 16) - y1 = _long(s, 20) - - # get frame (in 0.01 millimeter units) - frame = _long(s, 24), _long(s, 28), _long(s, 32), _long(s, 36) - - size = x1 - x0, y1 - y0 - - # calculate dots per inch from bbox and frame - xdpi = 2540.0 * (x1 - y0) / (frame[2] - frame[0]) - ydpi = 2540.0 * (y1 - y0) / (frame[3] - frame[1]) - - self.info["wmf_bbox"] = x0, y0, x1, y1 - - if xdpi == ydpi: - self.info["dpi"] = xdpi - else: - self.info["dpi"] = xdpi, ydpi - - else: - msg = "Unsupported file format" - raise SyntaxError(msg) - - self.mode = "RGB" - self._size = size - - loader = self._load() - if loader: - loader.open(self) - - def _load(self): - return _handler - - def load(self, dpi=None): - if dpi is not None and self._inch is not None: - self.info["dpi"] = dpi - x0, y0, x1, y1 = self.info["wmf_bbox"] - self._size = ( - (x1 - x0) * self.info["dpi"] // self._inch, - (y1 - y0) * self.info["dpi"] // self._inch, - ) - return super().load() - - -def _save(im, fp, filename): - if _handler is None or not hasattr(_handler, "save"): - msg = "WMF save handler not installed" - raise OSError(msg) - _handler.save(im, fp, filename) - - -# -# -------------------------------------------------------------------- -# Registry stuff - - -Image.register_open(WmfStubImageFile.format, WmfStubImageFile, _accept) -Image.register_save(WmfStubImageFile.format, _save) - -Image.register_extensions(WmfStubImageFile.format, [".wmf", ".emf"]) diff --git a/spaces/codedog-ai/edu-assistant/tests/learning_tasks/test_qa.py b/spaces/codedog-ai/edu-assistant/tests/learning_tasks/test_qa.py deleted file mode 100644 index 42fafa0bec8eb5b164e99fa235897c54658a5365..0000000000000000000000000000000000000000 --- a/spaces/codedog-ai/edu-assistant/tests/learning_tasks/test_qa.py +++ /dev/null @@ -1,52 +0,0 @@ -from unittest.mock import MagicMock, patch - -from langchain import PromptTemplate - -from edu_assistant.learning_tasks import QaTask -from edu_assistant.learning_tasks.qa import TEMPLATE_CHAT, TEMPLATE_ONCE - - -@patch.object(QaTask, "_init_llm") -@patch.object(QaTask, "_build_once_chain") -def test_init_without_knowledge(mocked_build_once_chain, mocked_init_llm): - task = QaTask(instruction="test") - - assert task._chat_prompt == PromptTemplate.from_template(TEMPLATE_CHAT.format(instruction="test")) - assert task._once_prompt == PromptTemplate.from_template(TEMPLATE_ONCE.format(instruction="test")) - assert task._knowledge is None - mocked_build_once_chain.assert_called_once() - - -@patch.object(QaTask, "_init_llm") -@patch.object(QaTask, "_build_once_chain") -@patch.object(QaTask, "_create_session_chain") -def test_ask_with_session(mocked_create_session_chain, mocked_build_once_chain, mocked_init_llm): - mocked_chain = MagicMock(return_value={"response": "ok"}) - mocked_build_once_chain.return_value = mocked_chain - mocked_create_session_chain.return_value = mocked_chain - - task = QaTask(instruction="test") - - with patch.object(task, "_create_session_id") as mock_create_id: - mock_create_id.return_value = 123 - result = task.ask("how are you?", session=True) - - mock_create_id.assert_called_once() - assert "session_id" in result - assert result["session_id"] == 123 - assert "response" in result - assert result["response"] == "ok" - - -@patch.object(QaTask, "_init_llm") -@patch.object(QaTask, "_build_once_chain") -def test_ask_without_session(mocked_build_once_chain, mocked_init_llm): - mocked_llm = MagicMock() - mocked_llm.run.return_value = {"result": "ok"} - mocked_build_once_chain.return_value = mocked_llm - task = QaTask(instruction="test") - - result = task.ask("how are you?", session=False) - - mocked_build_once_chain.assert_called_once() - assert "session_id" not in result diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenc_is.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenc_is.c deleted file mode 100644 index 1810790d8816c8751f5dc87ea2ab5a5633af3143..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenc_is.c +++ /dev/null @@ -1,158 +0,0 @@ -/* - * AAC encoder intensity stereo - * Copyright (C) 2015 Rostislav Pehlivanov - * - * 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 - * AAC encoder Intensity Stereo - * @author Rostislav Pehlivanov ( atomnuker gmail com ) - */ - -#include "aacenc.h" -#include "aacenc_utils.h" -#include "aacenc_is.h" -#include "aacenc_quantization.h" - -struct AACISError ff_aac_is_encoding_err(AACEncContext *s, ChannelElement *cpe, - int start, int w, int g, float ener0, - float ener1, float ener01, - int use_pcoeffs, int phase) -{ - int i, w2; - SingleChannelElement *sce0 = &cpe->ch[0]; - SingleChannelElement *sce1 = &cpe->ch[1]; - float *L = use_pcoeffs ? sce0->pcoeffs : sce0->coeffs; - float *R = use_pcoeffs ? sce1->pcoeffs : sce1->coeffs; - float *L34 = &s->scoefs[256*0], *R34 = &s->scoefs[256*1]; - float *IS = &s->scoefs[256*2], *I34 = &s->scoefs[256*3]; - float dist1 = 0.0f, dist2 = 0.0f; - struct AACISError is_error = {0}; - - if (ener01 <= 0 || ener0 <= 0) { - is_error.pass = 0; - return is_error; - } - - for (w2 = 0; w2 < sce0->ics.group_len[w]; w2++) { - FFPsyBand *band0 = &s->psy.ch[s->cur_channel+0].psy_bands[(w+w2)*16+g]; - FFPsyBand *band1 = &s->psy.ch[s->cur_channel+1].psy_bands[(w+w2)*16+g]; - int is_band_type, is_sf_idx = FFMAX(1, sce0->sf_idx[w*16+g]-4); - float e01_34 = phase*pos_pow34(ener1/ener0); - float maxval, dist_spec_err = 0.0f; - float minthr = FFMIN(band0->threshold, band1->threshold); - for (i = 0; i < sce0->ics.swb_sizes[g]; i++) - IS[i] = (L[start+(w+w2)*128+i] + phase*R[start+(w+w2)*128+i])*sqrt(ener0/ener01); - s->abs_pow34(L34, &L[start+(w+w2)*128], sce0->ics.swb_sizes[g]); - s->abs_pow34(R34, &R[start+(w+w2)*128], sce0->ics.swb_sizes[g]); - s->abs_pow34(I34, IS, sce0->ics.swb_sizes[g]); - maxval = find_max_val(1, sce0->ics.swb_sizes[g], I34); - is_band_type = find_min_book(maxval, is_sf_idx); - dist1 += quantize_band_cost(s, &L[start + (w+w2)*128], L34, - sce0->ics.swb_sizes[g], - sce0->sf_idx[w*16+g], - sce0->band_type[w*16+g], - s->lambda / band0->threshold, INFINITY, NULL, NULL); - dist1 += quantize_band_cost(s, &R[start + (w+w2)*128], R34, - sce1->ics.swb_sizes[g], - sce1->sf_idx[w*16+g], - sce1->band_type[w*16+g], - s->lambda / band1->threshold, INFINITY, NULL, NULL); - dist2 += quantize_band_cost(s, IS, I34, sce0->ics.swb_sizes[g], - is_sf_idx, is_band_type, - s->lambda / minthr, INFINITY, NULL, NULL); - for (i = 0; i < sce0->ics.swb_sizes[g]; i++) { - dist_spec_err += (L34[i] - I34[i])*(L34[i] - I34[i]); - dist_spec_err += (R34[i] - I34[i]*e01_34)*(R34[i] - I34[i]*e01_34); - } - dist_spec_err *= s->lambda / minthr; - dist2 += dist_spec_err; - } - - is_error.pass = dist2 <= dist1; - is_error.phase = phase; - is_error.error = dist2 - dist1; - is_error.dist1 = dist1; - is_error.dist2 = dist2; - is_error.ener01 = ener01; - - return is_error; -} - -void ff_aac_search_for_is(AACEncContext *s, AVCodecContext *avctx, ChannelElement *cpe) -{ - SingleChannelElement *sce0 = &cpe->ch[0]; - SingleChannelElement *sce1 = &cpe->ch[1]; - int start = 0, count = 0, w, w2, g, i, prev_sf1 = -1, prev_bt = -1, prev_is = 0; - const float freq_mult = avctx->sample_rate/(1024.0f/sce0->ics.num_windows)/2.0f; - uint8_t nextband1[128]; - - if (!cpe->common_window) - return; - - /** Scout out next nonzero bands */ - ff_init_nextband_map(sce1, nextband1); - - for (w = 0; w < sce0->ics.num_windows; w += sce0->ics.group_len[w]) { - start = 0; - for (g = 0; g < sce0->ics.num_swb; g++) { - if (start*freq_mult > INT_STEREO_LOW_LIMIT*(s->lambda/170.0f) && - cpe->ch[0].band_type[w*16+g] != NOISE_BT && !cpe->ch[0].zeroes[w*16+g] && - cpe->ch[1].band_type[w*16+g] != NOISE_BT && !cpe->ch[1].zeroes[w*16+g] && - ff_sfdelta_can_remove_band(sce1, nextband1, prev_sf1, w*16+g)) { - float ener0 = 0.0f, ener1 = 0.0f, ener01 = 0.0f, ener01p = 0.0f; - struct AACISError ph_err1, ph_err2, *best; - for (w2 = 0; w2 < sce0->ics.group_len[w]; w2++) { - for (i = 0; i < sce0->ics.swb_sizes[g]; i++) { - float coef0 = sce0->coeffs[start+(w+w2)*128+i]; - float coef1 = sce1->coeffs[start+(w+w2)*128+i]; - ener0 += coef0*coef0; - ener1 += coef1*coef1; - ener01 += (coef0 + coef1)*(coef0 + coef1); - ener01p += (coef0 - coef1)*(coef0 - coef1); - } - } - ph_err1 = ff_aac_is_encoding_err(s, cpe, start, w, g, - ener0, ener1, ener01p, 0, -1); - ph_err2 = ff_aac_is_encoding_err(s, cpe, start, w, g, - ener0, ener1, ener01, 0, +1); - best = (ph_err1.pass && ph_err1.error < ph_err2.error) ? &ph_err1 : &ph_err2; - if (best->pass) { - cpe->is_mask[w*16+g] = 1; - cpe->ms_mask[w*16+g] = 0; - cpe->ch[0].is_ener[w*16+g] = sqrt(ener0 / best->ener01); - cpe->ch[1].is_ener[w*16+g] = ener0/ener1; - cpe->ch[1].band_type[w*16+g] = (best->phase > 0) ? INTENSITY_BT : INTENSITY_BT2; - if (prev_is && prev_bt != cpe->ch[1].band_type[w*16+g]) { - /** Flip M/S mask and pick the other CB, since it encodes more efficiently */ - cpe->ms_mask[w*16+g] = 1; - cpe->ch[1].band_type[w*16+g] = (best->phase > 0) ? INTENSITY_BT2 : INTENSITY_BT; - } - prev_bt = cpe->ch[1].band_type[w*16+g]; - count++; - } - } - if (!sce1->zeroes[w*16+g] && sce1->band_type[w*16+g] < RESERVED_BT) - prev_sf1 = sce1->sf_idx[w*16+g]; - prev_is = cpe->is_mask[w*16+g]; - start += sce0->ics.swb_sizes[g]; - } - } - cpe->is_mode = !!count; -} diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_mp4toannexb_bsf.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_mp4toannexb_bsf.c deleted file mode 100644 index d11be455c280a7a59cd1ce46df6b5f04bf7154df..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_mp4toannexb_bsf.c +++ /dev/null @@ -1,322 +0,0 @@ -/* - * H.264 MP4 to Annex B byte stream format filter - * Copyright (c) 2007 Benoit Fouet
- *
- * 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
-
-#include "libavutil/avassert.h"
-#include "libavutil/intreadwrite.h"
-#include "libavutil/mem.h"
-
-#include "bsf.h"
-#include "bsf_internal.h"
-#include "bytestream.h"
-#include "defs.h"
-#include "h264.h"
-
-typedef struct H264BSFContext {
- uint8_t *sps;
- uint8_t *pps;
- int sps_size;
- int pps_size;
- uint8_t length_size;
- uint8_t new_idr;
- uint8_t idr_sps_seen;
- uint8_t idr_pps_seen;
- int extradata_parsed;
-} H264BSFContext;
-
-static void count_or_copy(uint8_t **out, uint64_t *out_size,
- const uint8_t *in, int in_size, int ps, int copy)
-{
- uint8_t start_code_size = ps < 0 ? 0 : *out_size == 0 || ps ? 4 : 3;
-
- if (copy) {
- memcpy(*out + start_code_size, in, in_size);
- if (start_code_size == 4) {
- AV_WB32(*out, 1);
- } else if (start_code_size) {
- (*out)[0] =
- (*out)[1] = 0;
- (*out)[2] = 1;
- }
- *out += start_code_size + in_size;
- }
- *out_size += start_code_size + in_size;
-}
-
-static int h264_extradata_to_annexb(AVBSFContext *ctx, const int padding)
-{
- H264BSFContext *s = ctx->priv_data;
- GetByteContext ogb, *gb = &ogb;
- uint16_t unit_size;
- uint32_t total_size = 0;
- uint8_t *out = NULL, unit_nb, sps_done = 0;
- static const uint8_t nalu_header[4] = { 0, 0, 0, 1 };
- int length_size, pps_offset = 0;
-
- bytestream2_init(gb, ctx->par_in->extradata, ctx->par_in->extradata_size);
-
- bytestream2_skipu(gb, 4);
-
- /* retrieve length coded size */
- length_size = (bytestream2_get_byteu(gb) & 0x3) + 1;
-
- /* retrieve sps and pps unit(s) */
- unit_nb = bytestream2_get_byteu(gb) & 0x1f; /* number of sps unit(s) */
- if (!unit_nb) {
- goto pps;
- }
-
- while (unit_nb--) {
- int err;
-
- /* possible overread ok due to padding */
- unit_size = bytestream2_get_be16u(gb);
- total_size += unit_size + 4;
- av_assert1(total_size <= INT_MAX - padding);
- if (bytestream2_get_bytes_left(gb) < unit_size + !sps_done) {
- av_log(ctx, AV_LOG_ERROR, "Global extradata truncated, "
- "corrupted stream or invalid MP4/AVCC bitstream\n");
- av_free(out);
- return AVERROR_INVALIDDATA;
- }
- if ((err = av_reallocp(&out, total_size + padding)) < 0)
- return err;
- memcpy(out + total_size - unit_size - 4, nalu_header, 4);
- bytestream2_get_bufferu(gb, out + total_size - unit_size, unit_size);
-pps:
- if (!unit_nb && !sps_done++) {
- unit_nb = bytestream2_get_byteu(gb); /* number of pps unit(s) */
- pps_offset = total_size;
- }
- }
-
- if (out)
- memset(out + total_size, 0, padding);
-
- if (pps_offset) {
- s->sps = out;
- s->sps_size = pps_offset;
- } else {
- av_log(ctx, AV_LOG_WARNING,
- "Warning: SPS NALU missing or invalid. "
- "The resulting stream may not play.\n");
- }
- if (pps_offset < total_size) {
- s->pps = out + pps_offset;
- s->pps_size = total_size - pps_offset;
- } else {
- av_log(ctx, AV_LOG_WARNING,
- "Warning: PPS NALU missing or invalid. "
- "The resulting stream may not play.\n");
- }
-
- av_freep(&ctx->par_out->extradata);
- ctx->par_out->extradata = out;
- ctx->par_out->extradata_size = total_size;
-
- return length_size;
-}
-
-static int h264_mp4toannexb_init(AVBSFContext *ctx)
-{
- H264BSFContext *s = ctx->priv_data;
- int extra_size = ctx->par_in->extradata_size;
- int ret;
-
- /* retrieve sps and pps NAL units from extradata */
- if (!extra_size ||
- (extra_size >= 3 && AV_RB24(ctx->par_in->extradata) == 1) ||
- (extra_size >= 4 && AV_RB32(ctx->par_in->extradata) == 1)) {
- av_log(ctx, AV_LOG_VERBOSE,
- "The input looks like it is Annex B already\n");
- } else if (extra_size >= 7) {
- ret = h264_extradata_to_annexb(ctx, AV_INPUT_BUFFER_PADDING_SIZE);
- if (ret < 0)
- return ret;
-
- s->length_size = ret;
- s->new_idr = 1;
- s->idr_sps_seen = 0;
- s->idr_pps_seen = 0;
- s->extradata_parsed = 1;
- } else {
- av_log(ctx, AV_LOG_ERROR, "Invalid extradata size: %d\n", extra_size);
- return AVERROR_INVALIDDATA;
- }
-
- return 0;
-}
-
-static int h264_mp4toannexb_filter(AVBSFContext *ctx, AVPacket *opkt)
-{
- H264BSFContext *s = ctx->priv_data;
- AVPacket *in;
- uint8_t unit_type, new_idr, sps_seen, pps_seen;
- const uint8_t *buf;
- const uint8_t *buf_end;
- uint8_t *out;
- uint64_t out_size;
- int ret;
-
- ret = ff_bsf_get_packet(ctx, &in);
- if (ret < 0)
- return ret;
-
- /* nothing to filter */
- if (!s->extradata_parsed) {
- av_packet_move_ref(opkt, in);
- av_packet_free(&in);
- return 0;
- }
-
- buf_end = in->data + in->size;
-
-#define LOG_ONCE(...) \
- if (j) \
- av_log(__VA_ARGS__)
- for (int j = 0; j < 2; j++) {
- buf = in->data;
- new_idr = s->new_idr;
- sps_seen = s->idr_sps_seen;
- pps_seen = s->idr_pps_seen;
- out_size = 0;
-
- do {
- uint32_t nal_size = 0;
-
- /* possible overread ok due to padding */
- for (int i = 0; i < s->length_size; i++)
- nal_size = (nal_size << 8) | buf[i];
-
- buf += s->length_size;
-
- /* This check requires the cast as the right side might
- * otherwise be promoted to an unsigned value. */
- if ((int64_t)nal_size > buf_end - buf) {
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
-
- if (!nal_size)
- continue;
-
- unit_type = *buf & 0x1f;
-
- if (unit_type == H264_NAL_SPS) {
- sps_seen = new_idr = 1;
- } else if (unit_type == H264_NAL_PPS) {
- pps_seen = new_idr = 1;
- /* if SPS has not been seen yet, prepend the AVCC one to PPS */
- if (!sps_seen) {
- if (!s->sps_size) {
- LOG_ONCE(ctx, AV_LOG_WARNING, "SPS not present in the stream, nor in AVCC, stream may be unreadable\n");
- } else {
- count_or_copy(&out, &out_size, s->sps, s->sps_size, -1, j);
- sps_seen = 1;
- }
- }
- }
-
- /* If this is a new IDR picture following an IDR picture, reset the idr flag.
- * Just check first_mb_in_slice to be 0 as this is the simplest solution.
- * This could be checking idr_pic_id instead, but would complexify the parsing. */
- if (!new_idr && unit_type == H264_NAL_IDR_SLICE && (buf[1] & 0x80))
- new_idr = 1;
-
- /* prepend only to the first type 5 NAL unit of an IDR picture, if no sps/pps are already present */
- if (new_idr && unit_type == H264_NAL_IDR_SLICE && !sps_seen && !pps_seen) {
- if (ctx->par_out->extradata)
- count_or_copy(&out, &out_size, ctx->par_out->extradata,
- ctx->par_out->extradata_size, -1, j);
- new_idr = 0;
- /* if only SPS has been seen, also insert PPS */
- } else if (new_idr && unit_type == H264_NAL_IDR_SLICE && sps_seen && !pps_seen) {
- if (!s->pps_size) {
- LOG_ONCE(ctx, AV_LOG_WARNING, "PPS not present in the stream, nor in AVCC, stream may be unreadable\n");
- } else {
- count_or_copy(&out, &out_size, s->pps, s->pps_size, -1, j);
- }
- }
-
- count_or_copy(&out, &out_size, buf, nal_size,
- unit_type == H264_NAL_SPS || unit_type == H264_NAL_PPS, j);
- if (!new_idr && unit_type == H264_NAL_SLICE) {
- new_idr = 1;
- sps_seen = 0;
- pps_seen = 0;
- }
-
- buf += nal_size;
- } while (buf < buf_end);
-
- if (!j) {
- if (out_size > INT_MAX - AV_INPUT_BUFFER_PADDING_SIZE) {
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
- ret = av_new_packet(opkt, out_size);
- if (ret < 0)
- goto fail;
- out = opkt->data;
- }
- }
-#undef LOG_ONCE
-
- av_assert1(out_size == opkt->size);
-
- s->new_idr = new_idr;
- s->idr_sps_seen = sps_seen;
- s->idr_pps_seen = pps_seen;
-
- ret = av_packet_copy_props(opkt, in);
- if (ret < 0)
- goto fail;
-
-fail:
- if (ret < 0)
- av_packet_unref(opkt);
- av_packet_free(&in);
-
- return ret;
-}
-
-static void h264_mp4toannexb_flush(AVBSFContext *ctx)
-{
- H264BSFContext *s = ctx->priv_data;
-
- s->idr_sps_seen = 0;
- s->idr_pps_seen = 0;
- s->new_idr = s->extradata_parsed;
-}
-
-static const enum AVCodecID codec_ids[] = {
- AV_CODEC_ID_H264, AV_CODEC_ID_NONE,
-};
-
-const FFBitStreamFilter ff_h264_mp4toannexb_bsf = {
- .p.name = "h264_mp4toannexb",
- .p.codec_ids = codec_ids,
- .priv_data_size = sizeof(H264BSFContext),
- .init = h264_mp4toannexb_init,
- .filter = h264_mp4toannexb_filter,
- .flush = h264_mp4toannexb_flush,
-};
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\ No newline at end of file diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/segmentation.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/segmentation.py deleted file mode 100644 index 3d4a9f94eaae84722db584277dbbf9bc41ede357..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/segmentation.py +++ /dev/null @@ -1,43 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -from .constants import weights as constant_weights - - -class CrossEntropy2d(nn.Module): - def __init__(self, reduction="mean", ignore_label=255, weights=None, *args, **kwargs): - """ - weight (Tensor, optional): a manual rescaling weight given to each class. - If given, has to be a Tensor of size "nclasses" - """ - super(CrossEntropy2d, self).__init__() - self.reduction = reduction - self.ignore_label = ignore_label - self.weights = weights - if self.weights is not None: - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - self.weights = torch.FloatTensor(constant_weights[weights]).to(device) - - def forward(self, predict, target): - """ - Args: - predict:(n, c, h, w) - target:(n, 1, h, w) - """ - target = target.long() - assert not target.requires_grad - assert predict.dim() == 4, "{0}".format(predict.size()) - assert target.dim() == 4, "{0}".format(target.size()) - assert predict.size(0) == target.size(0), "{0} vs {1} ".format(predict.size(0), target.size(0)) - assert target.size(1) == 1, "{0}".format(target.size(1)) - assert predict.size(2) == target.size(2), "{0} vs {1} ".format(predict.size(2), target.size(2)) - assert predict.size(3) == target.size(3), "{0} vs {1} ".format(predict.size(3), target.size(3)) - target = target.squeeze(1) - n, c, h, w = predict.size() - target_mask = (target >= 0) * (target != self.ignore_label) - target = target[target_mask] - predict = predict.transpose(1, 2).transpose(2, 3).contiguous() - predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c) - loss = F.cross_entropy(predict, target, weight=self.weights, reduction=self.reduction) - return loss diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/segmentors/base.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/segmentors/base.py deleted file mode 100644 index a12d8beb8ea40bfa234197eddb4d3ef40dbfeb6f..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/segmentors/base.py +++ /dev/null @@ -1,273 +0,0 @@ -import logging -import warnings -from abc import ABCMeta, abstractmethod -from collections import OrderedDict - -import annotator.mmpkg.mmcv as mmcv -import numpy as np -import torch -import torch.distributed as dist -import torch.nn as nn -from annotator.mmpkg.mmcv.runner import auto_fp16 - - -class BaseSegmentor(nn.Module): - """Base class for segmentors.""" - - __metaclass__ = ABCMeta - - def __init__(self): - super(BaseSegmentor, self).__init__() - self.fp16_enabled = False - - @property - def with_neck(self): - """bool: whether the segmentor has neck""" - return hasattr(self, 'neck') and self.neck is not None - - @property - def with_auxiliary_head(self): - """bool: whether the segmentor has auxiliary head""" - return hasattr(self, - 'auxiliary_head') and self.auxiliary_head is not None - - @property - def with_decode_head(self): - """bool: whether the segmentor has decode head""" - return hasattr(self, 'decode_head') and self.decode_head is not None - - @abstractmethod - def extract_feat(self, imgs): - """Placeholder for extract features from images.""" - pass - - @abstractmethod - def encode_decode(self, img, img_metas): - """Placeholder for encode images with backbone and decode into a - semantic segmentation map of the same size as input.""" - pass - - @abstractmethod - def forward_train(self, imgs, img_metas, **kwargs): - """Placeholder for Forward function for training.""" - pass - - @abstractmethod - def simple_test(self, img, img_meta, **kwargs): - """Placeholder for single image test.""" - pass - - @abstractmethod - def aug_test(self, imgs, img_metas, **kwargs): - """Placeholder for augmentation test.""" - pass - - def init_weights(self, pretrained=None): - """Initialize the weights in segmentor. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - if pretrained is not None: - logger = logging.getLogger() - logger.info(f'load model from: {pretrained}') - - def forward_test(self, imgs, img_metas, **kwargs): - """ - Args: - imgs (List[Tensor]): the outer list indicates test-time - augmentations and inner Tensor should have a shape NxCxHxW, - which contains all images in the batch. - img_metas (List[List[dict]]): the outer list indicates test-time - augs (multiscale, flip, etc.) and the inner list indicates - images in a batch. - """ - for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]: - if not isinstance(var, list): - raise TypeError(f'{name} must be a list, but got ' - f'{type(var)}') - - num_augs = len(imgs) - if num_augs != len(img_metas): - raise ValueError(f'num of augmentations ({len(imgs)}) != ' - f'num of image meta ({len(img_metas)})') - # all images in the same aug batch all of the same ori_shape and pad - # shape - for img_meta in img_metas: - ori_shapes = [_['ori_shape'] for _ in img_meta] - assert all(shape == ori_shapes[0] for shape in ori_shapes) - img_shapes = [_['img_shape'] for _ in img_meta] - assert all(shape == img_shapes[0] for shape in img_shapes) - pad_shapes = [_['pad_shape'] for _ in img_meta] - assert all(shape == pad_shapes[0] for shape in pad_shapes) - - if num_augs == 1: - return self.simple_test(imgs[0], img_metas[0], **kwargs) - else: - return self.aug_test(imgs, img_metas, **kwargs) - - @auto_fp16(apply_to=('img', )) - def forward(self, img, img_metas, return_loss=True, **kwargs): - """Calls either :func:`forward_train` or :func:`forward_test` depending - on whether ``return_loss`` is ``True``. - - Note this setting will change the expected inputs. When - ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor - and List[dict]), and when ``resturn_loss=False``, img and img_meta - should be double nested (i.e. List[Tensor], List[List[dict]]), with - the outer list indicating test time augmentations. - """ - if return_loss: - return self.forward_train(img, img_metas, **kwargs) - else: - return self.forward_test(img, img_metas, **kwargs) - - def train_step(self, data_batch, optimizer, **kwargs): - """The iteration step during training. - - This method defines an iteration step during training, except for the - back propagation and optimizer updating, which are done in an optimizer - hook. Note that in some complicated cases or models, the whole process - including back propagation and optimizer updating is also defined in - this method, such as GAN. - - Args: - data (dict): The output of dataloader. - optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of - runner is passed to ``train_step()``. This argument is unused - and reserved. - - Returns: - dict: It should contain at least 3 keys: ``loss``, ``log_vars``, - ``num_samples``. - ``loss`` is a tensor for back propagation, which can be a - weighted sum of multiple losses. - ``log_vars`` contains all the variables to be sent to the - logger. - ``num_samples`` indicates the batch size (when the model is - DDP, it means the batch size on each GPU), which is used for - averaging the logs. - """ - losses = self(**data_batch) - loss, log_vars = self._parse_losses(losses) - - outputs = dict( - loss=loss, - log_vars=log_vars, - num_samples=len(data_batch['img_metas'])) - - return outputs - - def val_step(self, data_batch, **kwargs): - """The iteration step during validation. - - This method shares the same signature as :func:`train_step`, but used - during val epochs. Note that the evaluation after training epochs is - not implemented with this method, but an evaluation hook. - """ - output = self(**data_batch, **kwargs) - return output - - @staticmethod - def _parse_losses(losses): - """Parse the raw outputs (losses) of the network. - - Args: - losses (dict): Raw output of the network, which usually contain - losses and other necessary information. - - Returns: - tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor - which may be a weighted sum of all losses, log_vars contains - all the variables to be sent to the logger. - """ - log_vars = OrderedDict() - for loss_name, loss_value in losses.items(): - if isinstance(loss_value, torch.Tensor): - log_vars[loss_name] = loss_value.mean() - elif isinstance(loss_value, list): - log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value) - else: - raise TypeError( - f'{loss_name} is not a tensor or list of tensors') - - loss = sum(_value for _key, _value in log_vars.items() - if 'loss' in _key) - - log_vars['loss'] = loss - for loss_name, loss_value in log_vars.items(): - # reduce loss when distributed training - if dist.is_available() and dist.is_initialized(): - loss_value = loss_value.data.clone() - dist.all_reduce(loss_value.div_(dist.get_world_size())) - log_vars[loss_name] = loss_value.item() - - return loss, log_vars - - def show_result(self, - img, - result, - palette=None, - win_name='', - show=False, - wait_time=0, - out_file=None, - opacity=0.5): - """Draw `result` over `img`. - - Args: - img (str or Tensor): The image to be displayed. - result (Tensor): The semantic segmentation results to draw over - `img`. - palette (list[list[int]]] | np.ndarray | None): The palette of - segmentation map. If None is given, random palette will be - generated. Default: None - win_name (str): The window name. - wait_time (int): Value of waitKey param. - Default: 0. - show (bool): Whether to show the image. - Default: False. - out_file (str or None): The filename to write the image. - Default: None. - opacity(float): Opacity of painted segmentation map. - Default 0.5. - Must be in (0, 1] range. - Returns: - img (Tensor): Only if not `show` or `out_file` - """ - img = mmcv.imread(img) - img = img.copy() - seg = result[0] - if palette is None: - if self.PALETTE is None: - palette = np.random.randint( - 0, 255, size=(len(self.CLASSES), 3)) - else: - palette = self.PALETTE - palette = np.array(palette) - assert palette.shape[0] == len(self.CLASSES) - assert palette.shape[1] == 3 - assert len(palette.shape) == 2 - assert 0 < opacity <= 1.0 - color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) - for label, color in enumerate(palette): - color_seg[seg == label, :] = color - # convert to BGR - color_seg = color_seg[..., ::-1] - - img = img * (1 - opacity) + color_seg * opacity - img = img.astype(np.uint8) - # if out_file specified, do not show image in window - if out_file is not None: - show = False - - if show: - mmcv.imshow(img, win_name, wait_time) - if out_file is not None: - mmcv.imwrite(img, out_file) - - if not (show or out_file): - warnings.warn('show==False and out_file is not specified, only ' - 'result image will be returned') - return img diff --git a/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/lib/model/DepthNormalizer.py b/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/lib/model/DepthNormalizer.py deleted file mode 100644 index 84908ec131771b8d42f32535ab856017fe1143a1..0000000000000000000000000000000000000000 --- a/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/lib/model/DepthNormalizer.py +++ /dev/null @@ -1,18 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class DepthNormalizer(nn.Module): - def __init__(self, opt): - super(DepthNormalizer, self).__init__() - self.opt = opt - - def forward(self, z, calibs=None, index_feat=None): - ''' - Normalize z_feature - :param z_feat: [B, 1, N] depth value for z in the image coordinate system - :return: - ''' - z_feat = z * (self.opt.loadSize // 2) / self.opt.z_size - return z_feat diff --git a/spaces/cxeep/whisper-webui/src/vad.py b/spaces/cxeep/whisper-webui/src/vad.py deleted file mode 100644 index c3c34480cac59ff08d07f846e47bea127beefccc..0000000000000000000000000000000000000000 --- a/spaces/cxeep/whisper-webui/src/vad.py +++ /dev/null @@ -1,404 +0,0 @@ -from abc import ABC, abstractmethod -from collections import Counter -from typing import Any, Iterator, List, Dict - -from pprint import pprint - -# Workaround for https://github.com/tensorflow/tensorflow/issues/48797 -try: - import tensorflow as tf -except ModuleNotFoundError: - # Error handling - pass - -import torch - -import ffmpeg -import numpy as np - -from src.utils import format_timestamp - -# Defaults for Silero -# TODO: Make these configurable? - -SPEECH_TRESHOLD = 0.3 -MAX_SILENT_PERIOD = 10 # seconds -MAX_MERGE_SIZE = 150 # Do not create segments larger than 2.5 minutes - -SEGMENT_PADDING_LEFT = 1 # Start detected text segment early -SEGMENT_PADDING_RIGHT = 1 # End detected segments late - -# Whether to attempt to transcribe non-speech -TRANSCRIBE_NON_SPEECH = False - -# Minimum size of segments to process -MIN_SEGMENT_DURATION = 1 - -VAD_MAX_PROCESSING_CHUNK = 60 * 60 # 60 minutes of audio - -class AbstractTranscription(ABC): - def __init__(self, segment_padding_left: int = None, segment_padding_right = None, max_silent_period: int = None, max_merge_size: int = None, transcribe_non_speech: bool = False): - self.sampling_rate = 16000 - self.segment_padding_left = segment_padding_left - self.segment_padding_right = segment_padding_right - self.max_silent_period = max_silent_period - self.max_merge_size = max_merge_size - self.transcribe_non_speech = transcribe_non_speech - - def get_audio_segment(self, str, start_time: str = None, duration: str = None): - return load_audio(str, self.sampling_rate, start_time, duration) - - @abstractmethod - def get_transcribe_timestamps(self, audio: str): - """ - Get the start and end timestamps of the sections that should be transcribed by this VAD method. - - Parameters - ---------- - audio: str - The audio file. - - Returns - ------- - A list of start and end timestamps, in fractional seconds. - """ - return - - def transcribe(self, audio: str, whisperCallable): - """ - Transcribe the given audo file. - - Parameters - ---------- - audio: str - The audio file. - - whisperCallable: Callable[[Union[str, np.ndarray, torch.Tensor]], dict[str, Union[dict, Any]]] - The callback that is used to invoke Whisper on an audio file/buffer. - - Returns - ------- - A list of start and end timestamps, in fractional seconds. - """ - - # get speech timestamps from full audio file - seconds_timestamps = self.get_transcribe_timestamps(audio) - - padded = self.pad_timestamps(seconds_timestamps, self.segment_padding_left, self.segment_padding_right) - merged = self.merge_timestamps(padded, self.max_silent_period, self.max_merge_size) - - print("Timestamps:") - pprint(merged) - - if self.transcribe_non_speech: - max_audio_duration = get_audio_duration(audio) - - # Expand segments to include the gaps between them - merged = self.expand_gaps(merged, total_duration=max_audio_duration) - - print("Transcribing non-speech:") - pprint(merged) - - result = { - 'text': "", - 'segments': [], - 'language': "" - } - languageCounter = Counter() - - # For each time segment, run whisper - for segment in merged: - segment_start = segment['start'] - segment_end = segment['end'] - segment_expand_amount = segment.get('expand_amount', 0) - - segment_duration = segment_end - segment_start - - if segment_duration < MIN_SEGMENT_DURATION: - continue; - - segment_audio = self.get_audio_segment(audio, start_time = str(segment_start), duration = str(segment_duration)) - - print("Running whisper from ", format_timestamp(segment_start), " to ", format_timestamp(segment_end), ", duration: ", segment_duration, "expanded: ", segment_expand_amount) - segment_result = whisperCallable(segment_audio) - - adjusted_segments = self.adjust_timestamp(segment_result["segments"], adjust_seconds=segment_start, max_source_time=segment_duration) - - # Append to output - result['text'] += segment_result['text'] - result['segments'].extend(adjusted_segments) - - # Increment detected language - languageCounter[segment_result['language']] += 1 - - if len(languageCounter) > 0: - result['language'] = languageCounter.most_common(1)[0][0] - - return result - - def include_gaps(self, segments: Iterator[dict], min_gap_length: float, total_duration: float): - result = [] - last_end_time = 0 - - for segment in segments: - segment_start = float(segment['start']) - segment_end = float(segment['end']) - - if (last_end_time != segment_start): - delta = segment_start - last_end_time - - if (min_gap_length is None or delta >= min_gap_length): - result.append( { 'start': last_end_time, 'end': segment_start, 'gap': True } ) - - last_end_time = segment_end - result.append(segment) - - # Also include total duration if specified - if (total_duration is not None and last_end_time < total_duration): - delta = total_duration - segment_start - - if (min_gap_length is None or delta >= min_gap_length): - result.append( { 'start': last_end_time, 'end': total_duration, 'gap': True } ) - - return result - - # Expand the end time of each segment to the start of the next segment - def expand_gaps(self, segments: List[Dict[str, Any]], total_duration: float): - result = [] - - if len(segments) == 0: - return result - - # Add gap at the beginning if needed - if (segments[0]['start'] > 0): - result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } ) - - for i in range(len(segments) - 1): - current_segment = segments[i] - next_segment = segments[i + 1] - - delta = next_segment['start'] - current_segment['end'] - - # Expand if the gap actually exists - if (delta >= 0): - current_segment = current_segment.copy() - current_segment['expand_amount'] = delta - current_segment['end'] = next_segment['start'] - - result.append(current_segment) - - last_segment = result[-1] - - # Also include total duration if specified - if (total_duration is not None): - last_segment = result[-1] - - if (last_segment['end'] < total_duration): - last_segment = last_segment.copy() - last_segment['end'] = total_duration - result[-1] = last_segment - - return result - - def adjust_timestamp(self, segments: Iterator[dict], adjust_seconds: float, max_source_time: float = None): - result = [] - - for segment in segments: - segment_start = float(segment['start']) - segment_end = float(segment['end']) - - # Filter segments? - if (max_source_time is not None): - if (segment_start > max_source_time): - continue - segment_end = min(max_source_time, segment_end) - - new_segment = segment.copy() - - # Add to start and end - new_segment['start'] = segment_start + adjust_seconds - new_segment['end'] = segment_end + adjust_seconds - result.append(new_segment) - return result - - def pad_timestamps(self, timestamps: List[Dict[str, Any]], padding_left: float, padding_right: float): - if (padding_left == 0 and padding_right == 0): - return timestamps - - result = [] - prev_entry = None - - for i in range(len(timestamps)): - curr_entry = timestamps[i] - next_entry = timestamps[i + 1] if i < len(timestamps) - 1 else None - - segment_start = curr_entry['start'] - segment_end = curr_entry['end'] - - if padding_left is not None: - segment_start = max(prev_entry['end'] if prev_entry else 0, segment_start - padding_left) - if padding_right is not None: - segment_end = segment_end + padding_right - - # Do not pad past the next segment - if (next_entry is not None): - segment_end = min(next_entry['start'], segment_end) - - new_entry = { 'start': segment_start, 'end': segment_end } - prev_entry = new_entry - result.append(new_entry) - - return result - - def merge_timestamps(self, timestamps: List[Dict[str, Any]], max_merge_gap: float, max_merge_size: float): - if max_merge_gap is None: - return timestamps - - result = [] - current_entry = None - - for entry in timestamps: - if current_entry is None: - current_entry = entry - continue - - # Get distance to the previous entry - distance = entry['start'] - current_entry['end'] - current_entry_size = current_entry['end'] - current_entry['start'] - - if distance <= max_merge_gap and (max_merge_size is None or current_entry_size <= max_merge_size): - # Merge - current_entry['end'] = entry['end'] - else: - # Output current entry - result.append(current_entry) - current_entry = entry - - # Add final entry - if current_entry is not None: - result.append(current_entry) - - return result - - def multiply_timestamps(self, timestamps: List[Dict[str, Any]], factor: float): - result = [] - - for entry in timestamps: - start = entry['start'] - end = entry['end'] - - result.append({ - 'start': start * factor, - 'end': end * factor - }) - return result - -class VadSileroTranscription(AbstractTranscription): - def __init__(self, segment_padding_left=SEGMENT_PADDING_LEFT, segment_padding_right=SEGMENT_PADDING_RIGHT, - max_silent_period=MAX_SILENT_PERIOD, max_merge_size=MAX_MERGE_SIZE, transcribe_non_speech: bool = False, - copy = None): - super().__init__(segment_padding_left=segment_padding_left, segment_padding_right=segment_padding_right, - max_silent_period=max_silent_period, max_merge_size=max_merge_size, transcribe_non_speech=transcribe_non_speech) - - if copy: - self.model = copy.model - self.get_speech_timestamps = copy.get_speech_timestamps - else: - self.model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad') - (self.get_speech_timestamps, _, _, _, _) = utils - - def get_transcribe_timestamps(self, audio: str): - audio_duration = get_audio_duration(audio) - result = [] - - # Divide procesisng of audio into chunks - chunk_start = 0.0 - - while (chunk_start < audio_duration): - chunk_duration = min(audio_duration - chunk_start, VAD_MAX_PROCESSING_CHUNK) - - print("Processing VAD in chunk from {} to {}".format(format_timestamp(chunk_start), format_timestamp(chunk_start + chunk_duration))) - wav = self.get_audio_segment(audio, str(chunk_start), str(chunk_duration)) - - sample_timestamps = self.get_speech_timestamps(wav, self.model, sampling_rate=self.sampling_rate, threshold=SPEECH_TRESHOLD) - seconds_timestamps = self.multiply_timestamps(sample_timestamps, factor=1 / self.sampling_rate) - adjusted = self.adjust_timestamp(seconds_timestamps, adjust_seconds=chunk_start, max_source_time=chunk_start + chunk_duration) - - #pprint(adjusted) - - result.extend(adjusted) - chunk_start += chunk_duration - - return result - -# A very simple VAD that just marks every N seconds as speech -class VadPeriodicTranscription(AbstractTranscription): - def __init__(self, periodic_duration: int): - super().__init__() - self.periodic_duration = periodic_duration - - def get_transcribe_timestamps(self, audio: str): - # Get duration in seconds - audio_duration = get_audio_duration(audio) - result = [] - - # Generate a timestamp every N seconds - start_timestamp = 0 - - while (start_timestamp < audio_duration): - end_timestamp = min(start_timestamp + self.periodic_duration, audio_duration) - segment_duration = end_timestamp - start_timestamp - - # Minimum duration is 1 second - if (segment_duration >= 1): - result.append( { 'start': start_timestamp, 'end': end_timestamp } ) - - start_timestamp = end_timestamp - - return result - -def get_audio_duration(file: str): - return float(ffmpeg.probe(file)["format"]["duration"]) - -def load_audio(file: str, sample_rate: int = 16000, - start_time: str = None, duration: str = None): - """ - Open an audio file and read as mono waveform, resampling as necessary - - Parameters - ---------- - file: str - The audio file to open - - sr: int - The sample rate to resample the audio if necessary - - start_time: str - The start time, using the standard FFMPEG time duration syntax, or None to disable. - - duration: str - The duration, using the standard FFMPEG time duration syntax, or None to disable. - - Returns - ------- - A NumPy array containing the audio waveform, in float32 dtype. - """ - try: - inputArgs = {'threads': 0} - - if (start_time is not None): - inputArgs['ss'] = start_time - if (duration is not None): - inputArgs['t'] = duration - - # This launches a subprocess to decode audio while down-mixing and resampling as necessary. - # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed. - out, _ = ( - ffmpeg.input(file, **inputArgs) - .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sample_rate) - .run(cmd="ffmpeg", capture_stdout=True, capture_stderr=True) - ) - except ffmpeg.Error as e: - raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") - - return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0 \ No newline at end of file diff --git a/spaces/cybercorejapan/human-detection-docker/projects/human_detection/engine/visualizer.py b/spaces/cybercorejapan/human-detection-docker/projects/human_detection/engine/visualizer.py deleted file mode 100644 index 992d2d3caea773bed872c00b712114367cc516c1..0000000000000000000000000000000000000000 --- a/spaces/cybercorejapan/human-detection-docker/projects/human_detection/engine/visualizer.py +++ /dev/null @@ -1,51 +0,0 @@ -from typing import List, Dict -import cv2 -import numpy as np -from models.engine.visualizer import BaseVisualizer - -class Visualizer(BaseVisualizer): - - def __init__(self, fps: int=-1, min_width: int=-1): - """ Visualizer class for visualization (track_results + count_results). - - Args: - class_map_ids (Dict): class mapping dictionary to map model's class to original class. Eg {0: 1, 1: 0, 2: 2, 3: 3} mean we swap class ID between 0 and 1. - fps (int): FPS for output video. If fps = -1, it will have same fps as input video. - min_width (int): minimum width for output video (height will be scaled to keep aspect ratio as input video). If min_width = -1, it will have same resolution as input video. - """ - class_names = ['pedestrian'] - super().__init__(class_names, fps, min_width) - - def visualize(self, img: np.ndarray, dettrack_at_frame_id: List[Dict]=None, show_conf: bool=True): - """ Function to visualize (track_results + count_results) a frame. - - Args: - img (np.ndarray): image need to be visualized. - dettrack_at_frame_id (List[Dict]): batch of track results which can be obtained from Tracker class. - show_conf (bool): Visualize confidence of track results or not. - """ - - # Draw tracking - if (dettrack_at_frame_id): - boxes = dettrack_at_frame_id["boxes"] - # classes = dettrack_at_frame_id["labels"] - ids = dettrack_at_frame_id["ids"] - for bbox, id_ in zip(boxes, ids): - id_ = int(id_) - score = bbox[4] - color = self.get_color(id_) - label = f'{id_}' + (f' {score:.2f}' if (show_conf) else '') - tl, tf = 2, 1 - c1, c2 = (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])) - img = cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) - t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] - c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 - img = cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) - img = cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) - - if (img.shape[0] != self.height or img.shape[1] != self.width): - img = cv2.resize(img, (self.width, self.height)) - self.writer.write(img) - - return img - diff --git a/spaces/cyhcctc/cyhbingo/README.md b/spaces/cyhcctc/cyhbingo/README.md deleted file mode 100644 index 5d6936218874c647b5d22e13ad4be7edb8936f92..0000000000000000000000000000000000000000 --- a/spaces/cyhcctc/cyhbingo/README.md +++ /dev/null @@ -1,28 +0,0 @@ ---- -title: bingo -emoji: 😊 -colorFrom: red -colorTo: red -sdk: docker -license: mit -duplicated_from: hf4all/bingo ---- - - ');
- $.ajax({url: "file=docs/waifu_plugin/waifu-tips.js", dataType:"script", cache: true, success: function() {
- $.ajax({url: "file=docs/waifu_plugin/live2d.js", dataType:"script", cache: true, success: function() {
- /* 可直接修改部分参数 */
- live2d_settings['hitokotoAPI'] = "hitokoto.cn"; // 一言 API
- live2d_settings['modelId'] = 5; // 默认模型 ID
- live2d_settings['modelTexturesId'] = 1; // 默认材质 ID
- live2d_settings['modelStorage'] = false; // 不储存模型 ID
- live2d_settings['waifuSize'] = '210x187';
- live2d_settings['waifuTipsSize'] = '187x52';
- live2d_settings['canSwitchModel'] = true;
- live2d_settings['canSwitchTextures'] = true;
- live2d_settings['canSwitchHitokoto'] = false;
- live2d_settings['canTakeScreenshot'] = false;
- live2d_settings['canTurnToHomePage'] = false;
- live2d_settings['canTurnToAboutPage'] = false;
- live2d_settings['showHitokoto'] = false; // 显示一言
- live2d_settings['showF12Status'] = false; // 显示加载状态
- live2d_settings['showF12Message'] = false; // 显示看板娘消息
- live2d_settings['showF12OpenMsg'] = false; // 显示控制台打开提示
- live2d_settings['showCopyMessage'] = false; // 显示 复制内容 提示
- live2d_settings['showWelcomeMessage'] = true; // 显示进入面页欢迎词
-
- /* 在 initModel 前添加 */
- initModel("file=docs/waifu_plugin/waifu-tips.json");
- }});
- }});
-} catch(err) { console.log("[Error] JQuery is not defined.") }
diff --git a/spaces/davda54/chat-nort5/question_detection_norbert3_small/README.md b/spaces/davda54/chat-nort5/question_detection_norbert3_small/README.md
deleted file mode 100644
index f81d6247c082c0c2707c47ac62b0d4e3d558d848..0000000000000000000000000000000000000000
--- a/spaces/davda54/chat-nort5/question_detection_norbert3_small/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
----
-language:
-- 'no'
-- nb
-- nn
-inference: false
-tags:
-- BERT
-- NorBERT
-- Norwegian
-- encoder
-license: cc-by-4.0
----
-
-# NorBERT 3 small
-
-
-## Other sizes:
-- [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs)
-- [NorBERT 3 small (40M)](https://huggingface.co/ltg/norbert3-small)
-- [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base)
-- [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large)
-
-
-## Example usage
-
-This model currently needs a custom wrapper from `modeling_norbert.py`. Then you can use it like this:
-
-```python
-import torch
-from transformers import AutoTokenizer
-from modeling_norbert import NorbertForMaskedLM
-
-tokenizer = AutoTokenizer.from_pretrained("path/to/folder")
-bert = NorbertForMaskedLM.from_pretrained("path/to/folder")
-
-mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
-input_text = tokenizer("Nå ønsker de seg en[MASK] bolig.", return_tensors="pt")
-output_p = bert(**input_text)
-output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
-
-# should output: '[CLS] Nå ønsker de seg en ny bolig.[SEP]'
-print(tokenizer.decode(output_text[0].tolist()))
-```
-
-The following classes are currently implemented: `NorbertForMaskedLM`, `NorbertForSequenceClassification`, `NorbertForTokenClassification`, `NorbertForQuestionAnswering` and `NorbertForMultipleChoice`.
\ No newline at end of file
diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/parsing/resnet.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/parsing/resnet.py
deleted file mode 100644
index fec8e82cf64469fb51be21ad5130217052addbda..0000000000000000000000000000000000000000
--- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/parsing/resnet.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def conv3x3(in_planes, out_planes, stride=1):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
-
- def __init__(self, in_chan, out_chan, stride=1):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(in_chan, out_chan, stride)
- self.bn1 = nn.BatchNorm2d(out_chan)
- self.conv2 = conv3x3(out_chan, out_chan)
- self.bn2 = nn.BatchNorm2d(out_chan)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = None
- if in_chan != out_chan or stride != 1:
- self.downsample = nn.Sequential(
- nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(out_chan),
- )
-
- def forward(self, x):
- residual = self.conv1(x)
- residual = F.relu(self.bn1(residual))
- residual = self.conv2(residual)
- residual = self.bn2(residual)
-
- shortcut = x
- if self.downsample is not None:
- shortcut = self.downsample(x)
-
- out = shortcut + residual
- out = self.relu(out)
- return out
-
-
-def create_layer_basic(in_chan, out_chan, bnum, stride=1):
- layers = [BasicBlock(in_chan, out_chan, stride=stride)]
- for i in range(bnum - 1):
- layers.append(BasicBlock(out_chan, out_chan, stride=1))
- return nn.Sequential(*layers)
-
-
-class ResNet18(nn.Module):
-
- def __init__(self):
- super(ResNet18, self).__init__()
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
- self.bn1 = nn.BatchNorm2d(64)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
- self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
- self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
- self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
-
- def forward(self, x):
- x = self.conv1(x)
- x = F.relu(self.bn1(x))
- x = self.maxpool(x)
-
- x = self.layer1(x)
- feat8 = self.layer2(x) # 1/8
- feat16 = self.layer3(feat8) # 1/16
- feat32 = self.layer4(feat16) # 1/32
- return feat8, feat16, feat32
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/bar_plot.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/bar_plot.py
deleted file mode 100644
index 430b10f63b0647e163df5bdde5d065519e64ce04..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/bar_plot.py
+++ /dev/null
@@ -1,376 +0,0 @@
-"""gr.BarPlot() component."""
-
-from __future__ import annotations
-
-from typing import Callable, Literal
-
-import altair as alt
-import pandas as pd
-from gradio_client.documentation import document, set_documentation_group
-
-from gradio.components.base import _Keywords
-from gradio.components.plot import AltairPlot, Plot
-
-set_documentation_group("component")
-
-
-@document()
-class BarPlot(Plot):
- """
- Create a bar plot.
-
- Preprocessing: this component does *not* accept input.
- Postprocessing: expects a pandas dataframe with the data to plot.
-
- Demos: bar_plot, chicago-bikeshare-dashboard
- """
-
- def __init__(
- self,
- value: pd.DataFrame | Callable | None = None,
- x: str | None = None,
- y: str | None = None,
- *,
- color: str | None = None,
- vertical: bool = True,
- group: str | None = None,
- title: str | None = None,
- tooltip: list[str] | str | None = None,
- x_title: str | None = None,
- y_title: str | None = None,
- color_legend_title: str | None = None,
- group_title: str | None = None,
- color_legend_position: Literal[
- "left",
- "right",
- "top",
- "bottom",
- "top-left",
- "top-right",
- "bottom-left",
- "bottom-right",
- "none",
- ]
- | None = None,
- height: int | None = None,
- width: int | None = None,
- y_lim: list[int] | None = None,
- caption: str | None = None,
- interactive: bool | None = True,
- label: str | None = None,
- show_label: bool | None = None,
- container: bool = True,
- scale: int | None = None,
- min_width: int = 160,
- every: float | None = None,
- visible: bool = True,
- elem_id: str | None = None,
- elem_classes: list[str] | str | None = None,
- ):
- """
- Parameters:
- value: The pandas dataframe containing the data to display in a scatter plot.
- x: Column corresponding to the x axis.
- y: Column corresponding to the y axis.
- color: The column to determine the bar color. Must be categorical (discrete values).
- vertical: If True, the bars will be displayed vertically. If False, the x and y axis will be switched, displaying the bars horizontally. Default is True.
- group: The column with which to split the overall plot into smaller subplots.
- title: The title to display on top of the chart.
- tooltip: The column (or list of columns) to display on the tooltip when a user hovers over a bar.
- x_title: The title given to the x axis. By default, uses the value of the x parameter.
- y_title: The title given to the y axis. By default, uses the value of the y parameter.
- color_legend_title: The title given to the color legend. By default, uses the value of color parameter.
- group_title: The label displayed on top of the subplot columns (or rows if vertical=True). Use an empty string to omit.
- color_legend_position: The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
- height: The height of the plot in pixels.
- width: The width of the plot in pixels.
- y_lim: A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
- caption: The (optional) caption to display below the plot.
- interactive: Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
- label: The (optional) label to display on the top left corner of the plot.
- show_label: Whether the label should be displayed.
- every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
- visible: Whether the plot should be visible.
- elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
- elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
- """
- self.x = x
- self.y = y
- self.color = color
- self.vertical = vertical
- self.group = group
- self.group_title = group_title
- self.tooltip = tooltip
- self.title = title
- self.x_title = x_title
- self.y_title = y_title
- self.color_legend_title = color_legend_title
- self.group_title = group_title
- self.color_legend_position = color_legend_position
- self.y_lim = y_lim
- self.caption = caption
- self.interactive_chart = interactive
- self.width = width
- self.height = height
- super().__init__(
- value=value,
- label=label,
- show_label=show_label,
- container=container,
- scale=scale,
- min_width=min_width,
- visible=visible,
- elem_id=elem_id,
- elem_classes=elem_classes,
- every=every,
- )
-
- def get_config(self):
- config = super().get_config()
- config["caption"] = self.caption
- return config
-
- def get_block_name(self) -> str:
- return "plot"
-
- @staticmethod
- def update(
- value: pd.DataFrame | dict | Literal[_Keywords.NO_VALUE] = _Keywords.NO_VALUE,
- x: str | None = None,
- y: str | None = None,
- color: str | None = None,
- vertical: bool = True,
- group: str | None = None,
- title: str | None = None,
- tooltip: list[str] | str | None = None,
- x_title: str | None = None,
- y_title: str | None = None,
- color_legend_title: str | None = None,
- group_title: str | None = None,
- color_legend_position: Literal[
- "left",
- "right",
- "top",
- "bottom",
- "top-left",
- "top-right",
- "bottom-left",
- "bottom-right",
- "none",
- ]
- | None = None,
- height: int | None = None,
- width: int | None = None,
- y_lim: list[int] | None = None,
- caption: str | None = None,
- interactive: bool | None = None,
- label: str | None = None,
- show_label: bool | None = None,
- container: bool | None = None,
- scale: int | None = None,
- min_width: int | None = None,
- visible: bool | None = None,
- ):
- """Update an existing BarPlot component.
-
- If updating any of the plot properties (color, size, etc) the value, x, and y parameters must be specified.
-
- Parameters:
- value: The pandas dataframe containing the data to display in a scatter plot.
- x: Column corresponding to the x axis.
- y: Column corresponding to the y axis.
- color: The column to determine the bar color. Must be categorical (discrete values).
- vertical: If True, the bars will be displayed vertically. If False, the x and y axis will be switched, displaying the bars horizontally. Default is True.
- group: The column with which to split the overall plot into smaller subplots.
- title: The title to display on top of the chart.
- tooltip: The column (or list of columns) to display on the tooltip when a user hovers over a bar.
- x_title: The title given to the x axis. By default, uses the value of the x parameter.
- y_title: The title given to the y axis. By default, uses the value of the y parameter.
- color_legend_title: The title given to the color legend. By default, uses the value of color parameter.
- group_title: The label displayed on top of the subplot columns (or rows if vertical=True). Use an empty string to omit.
- color_legend_position: The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
- height: The height of the plot in pixels.
- width: The width of the plot in pixels.
- y_lim: A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
- caption: The (optional) caption to display below the plot.
- interactive: Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
- label: The (optional) label to display on the top left corner of the plot.
- show_label: Whether the label should be displayed.
- visible: Whether the plot should be visible.
- """
- properties = [
- x,
- y,
- color,
- vertical,
- group,
- title,
- tooltip,
- x_title,
- y_title,
- color_legend_title,
- group_title,
- color_legend_position,
- height,
- width,
- y_lim,
- interactive,
- ]
- if any(properties):
- if not isinstance(value, pd.DataFrame):
- raise ValueError(
- "In order to update plot properties the value parameter "
- "must be provided, and it must be a Dataframe. Please pass a value "
- "parameter to gr.BarPlot.update."
- )
- if x is None or y is None:
- raise ValueError(
- "In order to update plot properties, the x and y axis data "
- "must be specified. Please pass valid values for x an y to "
- "gr.BarPlot.update."
- )
- chart = BarPlot.create_plot(value, *properties)
- value = {"type": "altair", "plot": chart.to_json(), "chart": "bar"}
-
- updated_config = {
- "label": label,
- "show_label": show_label,
- "container": container,
- "scale": scale,
- "min_width": min_width,
- "visible": visible,
- "value": value,
- "caption": caption,
- "__type__": "update",
- }
- return updated_config
-
- @staticmethod
- def create_plot(
- value: pd.DataFrame,
- x: str,
- y: str,
- color: str | None = None,
- vertical: bool = True,
- group: str | None = None,
- title: str | None = None,
- tooltip: list[str] | str | None = None,
- x_title: str | None = None,
- y_title: str | None = None,
- color_legend_title: str | None = None,
- group_title: str | None = None,
- color_legend_position: Literal[
- "left",
- "right",
- "top",
- "bottom",
- "top-left",
- "top-right",
- "bottom-left",
- "bottom-right",
- "none",
- ]
- | None = None,
- height: int | None = None,
- width: int | None = None,
- y_lim: list[int] | None = None,
- interactive: bool | None = True,
- ):
- """Helper for creating the bar plot."""
- interactive = True if interactive is None else interactive
- orientation = (
- {"field": group, "title": group_title if group_title is not None else group}
- if group
- else {}
- )
-
- x_title = x_title or x
- y_title = y_title or y
-
- # If horizontal, switch x and y
- if not vertical:
- y, x = x, y
- x = f"sum({x}):Q"
- y_title, x_title = x_title, y_title
- orientation = {"row": alt.Row(**orientation)} if orientation else {} # type: ignore
- x_lim = y_lim
- y_lim = None
- else:
- y = f"sum({y}):Q"
- x_lim = None
- orientation = {"column": alt.Column(**orientation)} if orientation else {} # type: ignore
-
- encodings = dict(
- x=alt.X(
- x, # type: ignore
- title=x_title, # type: ignore
- scale=AltairPlot.create_scale(x_lim), # type: ignore
- ),
- y=alt.Y(
- y, # type: ignore
- title=y_title, # type: ignore
- scale=AltairPlot.create_scale(y_lim), # type: ignore
- ),
- **orientation,
- )
- properties = {}
- if title:
- properties["title"] = title
- if height:
- properties["height"] = height
- if width:
- properties["width"] = width
-
- if color:
- domain = value[color].unique().tolist()
- range_ = list(range(len(domain)))
- encodings["color"] = {
- "field": color,
- "type": "nominal",
- "scale": {"domain": domain, "range": range_},
- "legend": AltairPlot.create_legend(
- position=color_legend_position, title=color_legend_title or color
- ),
- }
-
- if tooltip:
- encodings["tooltip"] = tooltip
-
- chart = (
- alt.Chart(value) # type: ignore
- .mark_bar() # type: ignore
- .encode(**encodings)
- .properties(background="transparent", **properties)
- )
- if interactive:
- chart = chart.interactive()
-
- return chart
-
- def postprocess(self, y: pd.DataFrame | dict | None) -> dict[str, str] | None:
- # if None or update
- if y is None or isinstance(y, dict):
- return y
- if self.x is None or self.y is None:
- raise ValueError("No value provided for required parameters `x` and `y`.")
- chart = self.create_plot(
- value=y,
- x=self.x,
- y=self.y,
- color=self.color,
- vertical=self.vertical,
- group=self.group,
- title=self.title,
- tooltip=self.tooltip,
- x_title=self.x_title,
- y_title=self.y_title,
- color_legend_title=self.color_legend_title,
- color_legend_position=self.color_legend_position, # type: ignore
- group_title=self.group_title,
- y_lim=self.y_lim,
- interactive=self.interactive_chart,
- height=self.height,
- width=self.width,
- )
-
- return {"type": "altair", "plot": chart.to_json(), "chart": "bar"}
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates.py
deleted file mode 100644
index 42ebb1a2d7b01acb18ae9a403d12494c1ae20c91..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates.py
+++ /dev/null
@@ -1,586 +0,0 @@
-from __future__ import annotations
-
-from typing import Any, Callable, Literal
-
-import numpy as np
-from PIL.Image import Image
-
-from gradio import components
-
-
-class TextArea(components.Textbox):
- """
- Sets: lines=7
- """
-
- is_template = True
-
- def __init__(
- self,
- value: str | Callable | None = "",
- *,
- lines: int = 7,
- max_lines: int = 20,
- placeholder: str | None = None,
- label: str | None = None,
- show_label: bool = True,
- interactive: bool | None = None,
- visible: bool = True,
- elem_id: str | None = None,
- **kwargs,
- ):
- super().__init__(
- value=value,
- lines=lines,
- max_lines=max_lines,
- placeholder=placeholder,
- label=label,
- show_label=show_label,
- interactive=interactive,
- visible=visible,
- elem_id=elem_id,
- **kwargs,
- )
-
-
-class Webcam(components.Image):
- """
- Sets: source="webcam", interactive=True
- """
-
- is_template = True
-
- def __init__(
- self,
- value: str | Image | np.ndarray | None = None,
- *,
- shape: tuple[int, int] | None = None,
- image_mode: Literal["RGB", "L"] = "RGB",
- invert_colors: bool = False,
- source: Literal["webcam"] = "webcam",
- tool: Literal["editor", "select", "sketch", "color-sketch"] | None = None,
- type: Literal["numpy", "pil", "filepath"] = "numpy",
- label: str | None = None,
- show_label: bool = True,
- interactive: bool | None = True,
- visible: bool = True,
- streaming: bool = False,
- elem_id: str | None = None,
- mirror_webcam: bool = True,
- brush_radius: float | None = None,
- brush_color: str = "#000000",
- **kwargs,
- ):
- super().__init__(
- value=value,
- shape=shape,
- image_mode=image_mode,
- invert_colors=invert_colors,
- source=source,
- tool=tool,
- type=type,
- label=label,
- show_label=show_label,
- interactive=interactive,
- visible=visible,
- streaming=streaming,
- elem_id=elem_id,
- mirror_webcam=mirror_webcam,
- brush_radius=brush_radius,
- brush_color=brush_color,
- **kwargs,
- )
-
-
-class Sketchpad(components.Image):
- """
- Sets: image_mode="L", source="canvas", shape=(28, 28), invert_colors=True, interactive=True
- """
-
- is_template = True
-
- def __init__(
- self,
- value: str | Image | np.ndarray | None = None,
- *,
- shape: tuple[int, int] = (28, 28),
- image_mode: Literal["L"] = "L",
- invert_colors: bool = True,
- source: Literal["canvas"] = "canvas",
- tool: Literal["editor", "select", "sketch", "color-sketch"] | None = None,
- type: Literal["numpy", "pil", "filepath"] = "numpy",
- label: str | None = None,
- show_label: bool = True,
- interactive: bool | None = True,
- visible: bool = True,
- streaming: bool = False,
- elem_id: str | None = None,
- mirror_webcam: bool = True,
- brush_radius: float | None = None,
- brush_color: str = "#000000",
- **kwargs,
- ):
- super().__init__(
- value=value,
- shape=shape,
- image_mode=image_mode,
- invert_colors=invert_colors,
- source=source,
- tool=tool,
- type=type,
- label=label,
- show_label=show_label,
- interactive=interactive,
- visible=visible,
- streaming=streaming,
- elem_id=elem_id,
- mirror_webcam=mirror_webcam,
- brush_radius=brush_radius,
- brush_color=brush_color,
- **kwargs,
- )
-
-
-class Paint(components.Image):
- """
- Sets: source="canvas", tool="color-sketch", interactive=True
- """
-
- is_template = True
-
- def __init__(
- self,
- value: str | Image | np.ndarray | None = None,
- *,
- shape: tuple[int, int] | None = None,
- image_mode: Literal["RGB"] = "RGB",
- invert_colors: bool = False,
- source: Literal["canvas"] = "canvas",
- tool: Literal["color-sketch"] = "color-sketch",
- type: Literal["numpy", "pil", "filepath"] = "numpy",
- label: str | None = None,
- show_label: bool = True,
- interactive: bool | None = True,
- visible: bool = True,
- streaming: bool = False,
- elem_id: str | None = None,
- mirror_webcam: bool = True,
- brush_radius: float | None = None,
- brush_color: str = "#000000",
- **kwargs,
- ):
- super().__init__(
- value=value,
- shape=shape,
- image_mode=image_mode,
- invert_colors=invert_colors,
- source=source,
- tool=tool,
- type=type,
- label=label,
- show_label=show_label,
- interactive=interactive,
- visible=visible,
- streaming=streaming,
- elem_id=elem_id,
- mirror_webcam=mirror_webcam,
- brush_radius=brush_radius,
- brush_color=brush_color,
- **kwargs,
- )
-
-
-class ImageMask(components.Image):
- """
- Sets: source="upload", tool="sketch", interactive=True
- """
-
- is_template = True
-
- def __init__(
- self,
- value: str | Image | np.ndarray | None = None,
- *,
- shape: tuple[int, int] | None = None,
- image_mode: Literal["RGB", "L"] = "RGB",
- invert_colors: bool = False,
- source: Literal["upload"] = "upload",
- tool: Literal["sketch"] = "sketch",
- type: Literal["numpy", "pil", "filepath"] = "numpy",
- label: str | None = None,
- show_label: bool = True,
- interactive: bool | None = True,
- visible: bool = True,
- streaming: bool = False,
- elem_id: str | None = None,
- mirror_webcam: bool = True,
- brush_radius: float | None = None,
- brush_color: str = "#000000",
- **kwargs,
- ):
- super().__init__(
- value=value,
- shape=shape,
- image_mode=image_mode,
- invert_colors=invert_colors,
- source=source,
- tool=tool,
- type=type,
- label=label,
- show_label=show_label,
- interactive=interactive,
- visible=visible,
- streaming=streaming,
- elem_id=elem_id,
- mirror_webcam=mirror_webcam,
- brush_radius=brush_radius,
- brush_color=brush_color,
- **kwargs,
- )
-
-
-class ImagePaint(components.Image):
- """
- Sets: source="upload", tool="color-sketch", interactive=True
- """
-
- is_template = True
-
- def __init__(
- self,
- value: str | Image | np.ndarray | None = None,
- *,
- shape: tuple[int, int] | None = None,
- image_mode: Literal["RGB", "L"] = "RGB",
- invert_colors: bool = False,
- source: Literal["upload"] = "upload",
- tool: Literal["color-sketch"] = "color-sketch",
- type: Literal["numpy", "pil", "filepath"] = "numpy",
- label: str | None = None,
- show_label: bool = True,
- interactive: bool | None = True,
- visible: bool = True,
- streaming: bool = False,
- elem_id: str | None = None,
- mirror_webcam: bool = True,
- brush_radius: float | None = None,
- brush_color: str = "#000000",
- **kwargs,
- ):
- super().__init__(
- value=value,
- shape=shape,
- image_mode=image_mode,
- invert_colors=invert_colors,
- source=source,
- tool=tool,
- type=type,
- label=label,
- show_label=show_label,
- interactive=interactive,
- visible=visible,
- streaming=streaming,
- elem_id=elem_id,
- mirror_webcam=mirror_webcam,
- brush_radius=brush_radius,
- brush_color=brush_color,
- **kwargs,
- )
-
-
-class Pil(components.Image):
- """
- Sets: type="pil"
- """
-
- is_template = True
-
- def __init__(
- self,
- value: str | Image | np.ndarray | None = None,
- *,
- shape: tuple[int, int] | None = None,
- image_mode: Literal["RGB", "L"] = "RGB",
- invert_colors: bool = False,
- source: Literal["upload", "webcam", "canvas"] = "upload",
- tool: Literal["editor", "select", "sketch", "color-sketch"] | None = None,
- type: Literal["pil"] = "pil",
- label: str | None = None,
- show_label: bool = True,
- interactive: bool | None = None,
- visible: bool = True,
- streaming: bool = False,
- elem_id: str | None = None,
- mirror_webcam: bool = True,
- brush_radius: float | None = None,
- brush_color: str = "#000000",
- **kwargs,
- ):
- super().__init__(
- value=value,
- shape=shape,
- image_mode=image_mode,
- invert_colors=invert_colors,
- source=source,
- tool=tool,
- type=type,
- label=label,
- show_label=show_label,
- interactive=interactive,
- visible=visible,
- streaming=streaming,
- elem_id=elem_id,
- mirror_webcam=mirror_webcam,
- brush_radius=brush_radius,
- brush_color=brush_color,
- **kwargs,
- )
-
-
-class PlayableVideo(components.Video):
- """
- Sets: format="mp4"
- """
-
- is_template = True
-
- def __init__(
- self,
- value: str | Callable | None = None,
- *,
- format: Literal["mp4"] | None = "mp4",
- source: Literal["upload", "webcam"] = "upload",
- label: str | None = None,
- show_label: bool = True,
- interactive: bool | None = None,
- visible: bool = True,
- elem_id: str | None = None,
- mirror_webcam: bool = True,
- include_audio: bool | None = None,
- **kwargs,
- ):
- super().__init__(
- value=value,
- format=format,
- source=source,
- label=label,
- show_label=show_label,
- interactive=interactive,
- visible=visible,
- elem_id=elem_id,
- mirror_webcam=mirror_webcam,
- include_audio=include_audio,
- **kwargs,
- )
-
-
-class Microphone(components.Audio):
- """
- Sets: source="microphone"
- """
-
- is_template = True
-
- def __init__(
- self,
- value: str | tuple[int, np.ndarray] | Callable | None = None,
- *,
- source: Literal["microphone"] = "microphone",
- type: Literal["numpy", "filepath"] = "numpy",
- label: str | None = None,
- show_label: bool = True,
- interactive: bool | None = None,
- visible: bool = True,
- streaming: bool = False,
- elem_id: str | None = None,
- **kwargs,
- ):
- super().__init__(
- value=value,
- source=source,
- type=type,
- label=label,
- show_label=show_label,
- interactive=interactive,
- visible=visible,
- streaming=streaming,
- elem_id=elem_id,
- **kwargs,
- )
-
-
-class Files(components.File):
- """
- Sets: file_count="multiple"
- """
-
- is_template = True
-
- def __init__(
- self,
- value: str | list[str] | Callable | None = None,
- *,
- file_count: Literal["multiple"] = "multiple",
- type: Literal["file", "binary"] = "file",
- label: str | None = None,
- show_label: bool = True,
- interactive: bool | None = None,
- visible: bool = True,
- elem_id: str | None = None,
- **kwargs,
- ):
- super().__init__(
- value=value,
- file_count=file_count,
- type=type,
- label=label,
- show_label=show_label,
- interactive=interactive,
- visible=visible,
- elem_id=elem_id,
- **kwargs,
- )
-
-
-class Numpy(components.Dataframe):
- """
- Sets: type="numpy"
- """
-
- is_template = True
-
- def __init__(
- self,
- value: list[list[Any]] | Callable | None = None,
- *,
- headers: list[str] | None = None,
- row_count: int | tuple[int, str] = (1, "dynamic"),
- col_count: int | tuple[int, str] | None = None,
- datatype: str | list[str] = "str",
- type: Literal["numpy"] = "numpy",
- max_rows: int | None = 20,
- max_cols: int | None = None,
- overflow_row_behaviour: Literal["paginate", "show_ends"] = "paginate",
- label: str | None = None,
- show_label: bool = True,
- interactive: bool | None = None,
- visible: bool = True,
- elem_id: str | None = None,
- wrap: bool = False,
- **kwargs,
- ):
- super().__init__(
- value=value,
- headers=headers,
- row_count=row_count,
- col_count=col_count,
- datatype=datatype,
- type=type,
- max_rows=max_rows,
- max_cols=max_cols,
- overflow_row_behaviour=overflow_row_behaviour,
- label=label,
- show_label=show_label,
- interactive=interactive,
- visible=visible,
- elem_id=elem_id,
- wrap=wrap,
- **kwargs,
- )
-
-
-class Matrix(components.Dataframe):
- """
- Sets: type="array"
- """
-
- is_template = True
-
- def __init__(
- self,
- value: list[list[Any]] | Callable | None = None,
- *,
- headers: list[str] | None = None,
- row_count: int | tuple[int, str] = (1, "dynamic"),
- col_count: int | tuple[int, str] | None = None,
- datatype: str | list[str] = "str",
- type: Literal["array"] = "array",
- max_rows: int | None = 20,
- max_cols: int | None = None,
- overflow_row_behaviour: Literal["paginate", "show_ends"] = "paginate",
- label: str | None = None,
- show_label: bool = True,
- interactive: bool | None = None,
- visible: bool = True,
- elem_id: str | None = None,
- wrap: bool = False,
- **kwargs,
- ):
- super().__init__(
- value=value,
- headers=headers,
- row_count=row_count,
- col_count=col_count,
- datatype=datatype,
- type=type,
- max_rows=max_rows,
- max_cols=max_cols,
- overflow_row_behaviour=overflow_row_behaviour,
- label=label,
- show_label=show_label,
- interactive=interactive,
- visible=visible,
- elem_id=elem_id,
- wrap=wrap,
- **kwargs,
- )
-
-
-class List(components.Dataframe):
- """
- Sets: type="array", col_count=1
- """
-
- is_template = True
-
- def __init__(
- self,
- value: list[list[Any]] | Callable | None = None,
- *,
- headers: list[str] | None = None,
- row_count: int | tuple[int, str] = (1, "dynamic"),
- col_count: Literal[1] = 1,
- datatype: str | list[str] = "str",
- type: Literal["array"] = "array",
- max_rows: int | None = 20,
- max_cols: int | None = None,
- overflow_row_behaviour: Literal["paginate", "show_ends"] = "paginate",
- label: str | None = None,
- show_label: bool = True,
- interactive: bool | None = None,
- visible: bool = True,
- elem_id: str | None = None,
- wrap: bool = False,
- **kwargs,
- ):
- super().__init__(
- value=value,
- headers=headers,
- row_count=row_count,
- col_count=col_count,
- datatype=datatype,
- type=type,
- max_rows=max_rows,
- max_cols=max_cols,
- overflow_row_behaviour=overflow_row_behaviour,
- label=label,
- show_label=show_label,
- interactive=interactive,
- visible=visible,
- elem_id=elem_id,
- wrap=wrap,
- **kwargs,
- )
-
-
-Mic = Microphone
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/BlockTitle-af232cbc.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/BlockTitle-af232cbc.js
deleted file mode 100644
index 7c79f9d8b61a3caaf65698c64e9b6d15775167e6..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/BlockTitle-af232cbc.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as h,e as g,s as k,a9 as w,m as $,o as B,P as I,g as d,Y as _,h as c,ab as S,ac as j,ad as q,w as r,r as v,u as m,v as C,k as p,F,G,H,t as N,x as P}from"./index-9e76ffee.js";import{I as T}from"./Info-77722665.js";import"./Button-30a08c0b.js";function b(a){let e,l;return e=new T({props:{$$slots:{default:[Y]},$$scope:{ctx:a}}}),{c(){F(e.$$.fragment)},m(n,o){G(e,n,o),l=!0},p(n,o){const u={};o&10&&(u.$$scope={dirty:o,ctx:n}),e.$set(u)},i(n){l||(r(e.$$.fragment,n),l=!0)},o(n){m(e.$$.fragment,n),l=!1},d(n){H(e,n)}}}function Y(a){let e;return{c(){e=N(a[1])},m(l,n){c(l,e,n)},p(l,n){n&2&&P(e,l[1])},d(l){l&&p(e)}}}function z(a){let e,l,n,o;const u=a[2].default,f=w(u,a,a[3],null);let s=a[1]&&b(a);return{c(){e=$("span"),f&&f.c(),l=B(),s&&s.c(),n=I(),d(e,"data-testid","block-info"),d(e,"class","svelte-1gfkn6j"),_(e,"sr-only",!a[0]),_(e,"hide",!a[0]),_(e,"has-info",a[1]!=null)},m(t,i){c(t,e,i),f&&f.m(e,null),c(t,l,i),s&&s.m(t,i),c(t,n,i),o=!0},p(t,[i]){f&&f.p&&(!o||i&8)&&S(f,u,t,t[3],o?q(u,t[3],i,null):j(t[3]),null),(!o||i&1)&&_(e,"sr-only",!t[0]),(!o||i&1)&&_(e,"hide",!t[0]),(!o||i&2)&&_(e,"has-info",t[1]!=null),t[1]?s?(s.p(t,i),i&2&&r(s,1)):(s=b(t),s.c(),r(s,1),s.m(n.parentNode,n)):s&&(v(),m(s,1,1,()=>{s=null}),C())},i(t){o||(r(f,t),r(s),o=!0)},o(t){m(f,t),m(s),o=!1},d(t){t&&(p(e),p(l),p(n)),f&&f.d(t),s&&s.d(t)}}}function A(a,e,l){let{$$slots:n={},$$scope:o}=e,{show_label:u=!0}=e,{info:f=void 0}=e;return a.$$set=s=>{"show_label"in s&&l(0,u=s.show_label),"info"in s&&l(1,f=s.info),"$$scope"in s&&l(3,o=s.$$scope)},[u,f,n,o]}class K extends h{constructor(e){super(),g(this,e,A,z,k,{show_label:0,info:1})}}export{K as B};
-//# sourceMappingURL=BlockTitle-af232cbc.js.map
diff --git a/spaces/dcq/freegpt-webui/g4f/Provider/Providers/Liaobots.py b/spaces/dcq/freegpt-webui/g4f/Provider/Providers/Liaobots.py
deleted file mode 100644
index 985bf53ddfd3877db3c60aedee86db11ec0e7243..0000000000000000000000000000000000000000
--- a/spaces/dcq/freegpt-webui/g4f/Provider/Providers/Liaobots.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import os, uuid, requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://liaobots.com'
-model = ['gpt-4-0613']
-supports_stream = True
-needs_auth = True
-
-models = {
- 'gpt-4-0613': {
- "id":"gpt-4-0613",
- "name":"GPT-4",
- "maxLength":24000,
- "tokenLimit":8000
- }
-}
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-
- print(kwargs)
-
- headers = {
- 'authority': 'liaobots.com',
- 'content-type': 'application/json',
- 'origin': 'https://liaobots.com',
- 'referer': 'https://liaobots.com/',
- 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
- 'x-auth-code': 'P6cPPK6Z8JDG3'
- }
-
- json_data = {
- 'conversationId': str(uuid.uuid4()),
- 'model': models[model],
- 'authcode':"jrzVZMJiwN0NU",
- 'messages': messages,
- 'key': '',
- 'prompt': "You are ChatGPT, a large language model trained by OpenAI. Follow the user's instructions carefully. Respond using markdown.",
- }
-
- response = requests.post('https://liaobots.com/api/chat',
- headers=headers, json=json_data, stream=True)
-
- for token in response.iter_content(chunk_size=2046):
- yield (token.decode('cp1251'))
-
-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/declare-lab/tango/diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/train_unconditional.py b/spaces/declare-lab/tango/diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/train_unconditional.py
deleted file mode 100644
index 1b38036d82c03b9d8be3e0cd35d91be14558b1b5..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/train_unconditional.py
+++ /dev/null
@@ -1,606 +0,0 @@
-import argparse
-import inspect
-import logging
-import math
-import os
-from pathlib import Path
-from typing import Optional
-
-import datasets
-import torch
-import torch.nn.functional as F
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import ProjectConfiguration
-from datasets import load_dataset
-from huggingface_hub import HfFolder, Repository, create_repo, whoami
-from onnxruntime.training.ortmodule import ORTModule
-from torchvision import transforms
-from tqdm.auto import tqdm
-
-import diffusers
-from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
-from diffusers.optimization import get_scheduler
-from diffusers.training_utils import EMAModel
-from diffusers.utils import check_min_version, is_tensorboard_available, is_wandb_available
-
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.13.0.dev0")
-
-logger = get_logger(__name__, log_level="INFO")
-
-
-def _extract_into_tensor(arr, timesteps, broadcast_shape):
- """
- Extract values from a 1-D numpy array for a batch of indices.
- :param arr: the 1-D numpy array.
- :param timesteps: a tensor of indices into the array to extract.
- :param broadcast_shape: a larger shape of K dimensions with the batch
- dimension equal to the length of timesteps.
- :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
- """
- if not isinstance(arr, torch.Tensor):
- arr = torch.from_numpy(arr)
- res = arr[timesteps].float().to(timesteps.device)
- while len(res.shape) < len(broadcast_shape):
- res = res[..., None]
- return res.expand(broadcast_shape)
-
-
-def parse_args():
- parser = argparse.ArgumentParser(description="Simple example of a training script.")
- parser.add_argument(
- "--dataset_name",
- type=str,
- default=None,
- help=(
- "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
- " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
- " or to a folder containing files that HF Datasets can understand."
- ),
- )
- parser.add_argument(
- "--dataset_config_name",
- type=str,
- default=None,
- help="The config of the Dataset, leave as None if there's only one config.",
- )
- parser.add_argument(
- "--train_data_dir",
- type=str,
- default=None,
- help=(
- "A folder containing the training data. Folder contents must follow the structure described in"
- " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
- " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
- ),
- )
- parser.add_argument(
- "--output_dir",
- type=str,
- default="ddpm-model-64",
- help="The output directory where the model predictions and checkpoints will be written.",
- )
- parser.add_argument("--overwrite_output_dir", action="store_true")
- parser.add_argument(
- "--cache_dir",
- type=str,
- default=None,
- help="The directory where the downloaded models and datasets will be stored.",
- )
- parser.add_argument(
- "--resolution",
- type=int,
- default=64,
- help=(
- "The resolution for input images, all the images in the train/validation dataset will be resized to this"
- " resolution"
- ),
- )
- parser.add_argument(
- "--center_crop",
- default=False,
- action="store_true",
- help=(
- "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
- " cropped. The images will be resized to the resolution first before cropping."
- ),
- )
- parser.add_argument(
- "--random_flip",
- default=False,
- action="store_true",
- help="whether to randomly flip images horizontally",
- )
- parser.add_argument(
- "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
- )
- parser.add_argument(
- "--eval_batch_size", type=int, default=16, help="The number of images to generate for evaluation."
- )
- parser.add_argument(
- "--dataloader_num_workers",
- type=int,
- default=0,
- help=(
- "The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main"
- " process."
- ),
- )
- parser.add_argument("--num_epochs", type=int, default=100)
- parser.add_argument("--save_images_epochs", type=int, default=10, help="How often to save images during training.")
- parser.add_argument(
- "--save_model_epochs", type=int, default=10, help="How often to save the model during training."
- )
- parser.add_argument(
- "--gradient_accumulation_steps",
- type=int,
- default=1,
- help="Number of updates steps to accumulate before performing a backward/update pass.",
- )
- parser.add_argument(
- "--learning_rate",
- type=float,
- default=1e-4,
- help="Initial learning rate (after the potential warmup period) to use.",
- )
- parser.add_argument(
- "--lr_scheduler",
- type=str,
- default="cosine",
- help=(
- 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
- ' "constant", "constant_with_warmup"]'
- ),
- )
- parser.add_argument(
- "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
- )
- parser.add_argument("--adam_beta1", type=float, default=0.95, help="The beta1 parameter for the Adam optimizer.")
- parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
- parser.add_argument(
- "--adam_weight_decay", type=float, default=1e-6, help="Weight decay magnitude for the Adam optimizer."
- )
- parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer.")
- parser.add_argument(
- "--use_ema",
- action="store_true",
- help="Whether to use Exponential Moving Average for the final model weights.",
- )
- parser.add_argument("--ema_inv_gamma", type=float, default=1.0, help="The inverse gamma value for the EMA decay.")
- parser.add_argument("--ema_power", type=float, default=3 / 4, help="The power value for the EMA decay.")
- parser.add_argument("--ema_max_decay", type=float, default=0.9999, help="The maximum decay magnitude for EMA.")
- parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
- parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
- parser.add_argument(
- "--hub_model_id",
- type=str,
- default=None,
- help="The name of the repository to keep in sync with the local `output_dir`.",
- )
- parser.add_argument(
- "--hub_private_repo", action="store_true", help="Whether or not to create a private repository."
- )
- parser.add_argument(
- "--logger",
- type=str,
- default="tensorboard",
- choices=["tensorboard", "wandb"],
- help=(
- "Whether to use [tensorboard](https://www.tensorflow.org/tensorboard) or [wandb](https://www.wandb.ai)"
- " for experiment tracking and logging of model metrics and model checkpoints"
- ),
- )
- parser.add_argument(
- "--logging_dir",
- type=str,
- default="logs",
- help=(
- "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
- " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
- ),
- )
- parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
- parser.add_argument(
- "--mixed_precision",
- type=str,
- default="no",
- choices=["no", "fp16", "bf16"],
- help=(
- "Whether to use mixed precision. Choose"
- "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
- "and an Nvidia Ampere GPU."
- ),
- )
- parser.add_argument(
- "--prediction_type",
- type=str,
- default="epsilon",
- choices=["epsilon", "sample"],
- help="Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.",
- )
- parser.add_argument("--ddpm_num_steps", type=int, default=1000)
- parser.add_argument("--ddpm_num_inference_steps", type=int, default=1000)
- parser.add_argument("--ddpm_beta_schedule", type=str, default="linear")
- parser.add_argument(
- "--checkpointing_steps",
- type=int,
- default=500,
- help=(
- "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
- " training using `--resume_from_checkpoint`."
- ),
- )
- parser.add_argument(
- "--checkpoints_total_limit",
- type=int,
- default=None,
- help=(
- "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
- " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
- " for more docs"
- ),
- )
- parser.add_argument(
- "--resume_from_checkpoint",
- type=str,
- default=None,
- help=(
- "Whether training should be resumed from a previous checkpoint. Use a path saved by"
- ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
- ),
- )
-
- args = parser.parse_args()
- env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
- if env_local_rank != -1 and env_local_rank != args.local_rank:
- args.local_rank = env_local_rank
-
- if args.dataset_name is None and args.train_data_dir is None:
- raise ValueError("You must specify either a dataset name from the hub or a train data directory.")
-
- return args
-
-
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
- if token is None:
- token = HfFolder.get_token()
- if organization is None:
- username = whoami(token)["name"]
- return f"{username}/{model_id}"
- else:
- return f"{organization}/{model_id}"
-
-
-def main(args):
- logging_dir = os.path.join(args.output_dir, args.logging_dir)
-
- accelerator_project_config = ProjectConfiguration(total_limit=args.checkpoints_total_limit)
-
- accelerator = Accelerator(
- gradient_accumulation_steps=args.gradient_accumulation_steps,
- mixed_precision=args.mixed_precision,
- log_with=args.logger,
- logging_dir=logging_dir,
- project_config=accelerator_project_config,
- )
-
- if args.logger == "tensorboard":
- if not is_tensorboard_available():
- raise ImportError("Make sure to install tensorboard if you want to use it for logging during training.")
-
- elif args.logger == "wandb":
- if not is_wandb_available():
- raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
- import wandb
-
- # Make one log on every process with the configuration for debugging.
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- level=logging.INFO,
- )
- logger.info(accelerator.state, main_process_only=False)
- if accelerator.is_local_main_process:
- datasets.utils.logging.set_verbosity_warning()
- diffusers.utils.logging.set_verbosity_info()
- else:
- datasets.utils.logging.set_verbosity_error()
- diffusers.utils.logging.set_verbosity_error()
-
- # Handle the repository creation
- if accelerator.is_main_process:
- if args.push_to_hub:
- if args.hub_model_id is None:
- repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
- else:
- repo_name = args.hub_model_id
- create_repo(repo_name, exist_ok=True, token=args.hub_token)
- repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
-
- with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
- if "step_*" not in gitignore:
- gitignore.write("step_*\n")
- if "epoch_*" not in gitignore:
- gitignore.write("epoch_*\n")
- elif args.output_dir is not None:
- os.makedirs(args.output_dir, exist_ok=True)
-
- # Initialize the model
- model = UNet2DModel(
- sample_size=args.resolution,
- in_channels=3,
- out_channels=3,
- layers_per_block=2,
- block_out_channels=(128, 128, 256, 256, 512, 512),
- down_block_types=(
- "DownBlock2D",
- "DownBlock2D",
- "DownBlock2D",
- "DownBlock2D",
- "AttnDownBlock2D",
- "DownBlock2D",
- ),
- up_block_types=(
- "UpBlock2D",
- "AttnUpBlock2D",
- "UpBlock2D",
- "UpBlock2D",
- "UpBlock2D",
- "UpBlock2D",
- ),
- )
-
- # Create EMA for the model.
- if args.use_ema:
- ema_model = EMAModel(
- model.parameters(),
- decay=args.ema_max_decay,
- use_ema_warmup=True,
- inv_gamma=args.ema_inv_gamma,
- power=args.ema_power,
- )
-
- # Initialize the scheduler
- accepts_prediction_type = "prediction_type" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())
- if accepts_prediction_type:
- noise_scheduler = DDPMScheduler(
- num_train_timesteps=args.ddpm_num_steps,
- beta_schedule=args.ddpm_beta_schedule,
- prediction_type=args.prediction_type,
- )
- else:
- noise_scheduler = DDPMScheduler(num_train_timesteps=args.ddpm_num_steps, beta_schedule=args.ddpm_beta_schedule)
-
- # Initialize the optimizer
- optimizer = torch.optim.AdamW(
- model.parameters(),
- lr=args.learning_rate,
- betas=(args.adam_beta1, args.adam_beta2),
- weight_decay=args.adam_weight_decay,
- eps=args.adam_epsilon,
- )
-
- # Get the datasets: you can either provide your own training and evaluation files (see below)
- # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
-
- # In distributed training, the load_dataset function guarantees that only one local process can concurrently
- # download the dataset.
- if args.dataset_name is not None:
- dataset = load_dataset(
- args.dataset_name,
- args.dataset_config_name,
- cache_dir=args.cache_dir,
- split="train",
- )
- else:
- dataset = load_dataset("imagefolder", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split="train")
- # See more about loading custom images at
- # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
-
- # Preprocessing the datasets and DataLoaders creation.
- augmentations = transforms.Compose(
- [
- transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
- transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
- transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
- transforms.ToTensor(),
- transforms.Normalize([0.5], [0.5]),
- ]
- )
-
- def transform_images(examples):
- images = [augmentations(image.convert("RGB")) for image in examples["image"]]
- return {"input": images}
-
- logger.info(f"Dataset size: {len(dataset)}")
-
- dataset.set_transform(transform_images)
- train_dataloader = torch.utils.data.DataLoader(
- dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers
- )
-
- # Initialize the learning rate scheduler
- lr_scheduler = get_scheduler(
- args.lr_scheduler,
- optimizer=optimizer,
- num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
- num_training_steps=(len(train_dataloader) * args.num_epochs),
- )
-
- # Prepare everything with our `accelerator`.
- model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
- model, optimizer, train_dataloader, lr_scheduler
- )
-
- model = ORTModule(model)
-
- if args.use_ema:
- accelerator.register_for_checkpointing(ema_model)
- ema_model.to(accelerator.device)
-
- # We need to initialize the trackers we use, and also store our configuration.
- # The trackers initializes automatically on the main process.
- if accelerator.is_main_process:
- run = os.path.split(__file__)[-1].split(".")[0]
- accelerator.init_trackers(run)
-
- total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
- num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
- max_train_steps = args.num_epochs * num_update_steps_per_epoch
-
- logger.info("***** Running training *****")
- logger.info(f" Num examples = {len(dataset)}")
- logger.info(f" Num Epochs = {args.num_epochs}")
- logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
- logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
- logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
- logger.info(f" Total optimization steps = {max_train_steps}")
-
- global_step = 0
- first_epoch = 0
-
- # Potentially load in the weights and states from a previous save
- if args.resume_from_checkpoint:
- if args.resume_from_checkpoint != "latest":
- path = os.path.basename(args.resume_from_checkpoint)
- else:
- # Get the most recent checkpoint
- dirs = os.listdir(args.output_dir)
- dirs = [d for d in dirs if d.startswith("checkpoint")]
- dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
- path = dirs[-1] if len(dirs) > 0 else None
-
- if path is None:
- accelerator.print(
- f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
- )
- args.resume_from_checkpoint = None
- else:
- accelerator.print(f"Resuming from checkpoint {path}")
- accelerator.load_state(os.path.join(args.output_dir, path))
- global_step = int(path.split("-")[1])
-
- resume_global_step = global_step * args.gradient_accumulation_steps
- first_epoch = global_step // num_update_steps_per_epoch
- resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
-
- # Train!
- for epoch in range(first_epoch, args.num_epochs):
- model.train()
- progress_bar = tqdm(total=num_update_steps_per_epoch, disable=not accelerator.is_local_main_process)
- progress_bar.set_description(f"Epoch {epoch}")
- for step, batch in enumerate(train_dataloader):
- # Skip steps until we reach the resumed step
- if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
- if step % args.gradient_accumulation_steps == 0:
- progress_bar.update(1)
- continue
-
- clean_images = batch["input"]
- # Sample noise that we'll add to the images
- noise = torch.randn(clean_images.shape).to(clean_images.device)
- bsz = clean_images.shape[0]
- # Sample a random timestep for each image
- timesteps = torch.randint(
- 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=clean_images.device
- ).long()
-
- # Add noise to the clean images according to the noise magnitude at each timestep
- # (this is the forward diffusion process)
- noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)
-
- with accelerator.accumulate(model):
- # Predict the noise residual
- model_output = model(noisy_images, timesteps, return_dict=False)[0]
-
- if args.prediction_type == "epsilon":
- loss = F.mse_loss(model_output, noise) # this could have different weights!
- elif args.prediction_type == "sample":
- alpha_t = _extract_into_tensor(
- noise_scheduler.alphas_cumprod, timesteps, (clean_images.shape[0], 1, 1, 1)
- )
- snr_weights = alpha_t / (1 - alpha_t)
- loss = snr_weights * F.mse_loss(
- model_output, clean_images, reduction="none"
- ) # use SNR weighting from distillation paper
- loss = loss.mean()
- else:
- raise ValueError(f"Unsupported prediction type: {args.prediction_type}")
-
- accelerator.backward(loss)
-
- if accelerator.sync_gradients:
- accelerator.clip_grad_norm_(model.parameters(), 1.0)
- optimizer.step()
- lr_scheduler.step()
- optimizer.zero_grad()
-
- # Checks if the accelerator has performed an optimization step behind the scenes
- if accelerator.sync_gradients:
- if args.use_ema:
- ema_model.step(model.parameters())
- progress_bar.update(1)
- global_step += 1
-
- if global_step % args.checkpointing_steps == 0:
- if accelerator.is_main_process:
- save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
- accelerator.save_state(save_path)
- logger.info(f"Saved state to {save_path}")
-
- logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
- if args.use_ema:
- logs["ema_decay"] = ema_model.decay
- progress_bar.set_postfix(**logs)
- accelerator.log(logs, step=global_step)
- progress_bar.close()
-
- accelerator.wait_for_everyone()
-
- # Generate sample images for visual inspection
- if accelerator.is_main_process:
- if epoch % args.save_images_epochs == 0 or epoch == args.num_epochs - 1:
- unet = accelerator.unwrap_model(model)
- if args.use_ema:
- ema_model.copy_to(unet.parameters())
- pipeline = DDPMPipeline(
- unet=unet,
- scheduler=noise_scheduler,
- )
-
- generator = torch.Generator(device=pipeline.device).manual_seed(0)
- # run pipeline in inference (sample random noise and denoise)
- images = pipeline(
- generator=generator,
- batch_size=args.eval_batch_size,
- output_type="numpy",
- num_inference_steps=args.ddpm_num_inference_steps,
- ).images
-
- # denormalize the images and save to tensorboard
- images_processed = (images * 255).round().astype("uint8")
-
- if args.logger == "tensorboard":
- accelerator.get_tracker("tensorboard").add_images(
- "test_samples", images_processed.transpose(0, 3, 1, 2), epoch
- )
- elif args.logger == "wandb":
- accelerator.get_tracker("wandb").log(
- {"test_samples": [wandb.Image(img) for img in images_processed], "epoch": epoch},
- step=global_step,
- )
-
- if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
- # save the model
- pipeline.save_pretrained(args.output_dir)
- if args.push_to_hub:
- repo.push_to_hub(commit_message=f"Epoch {epoch}", blocking=False)
-
- accelerator.end_training()
-
-
-if __name__ == "__main__":
- args = parse_args()
- main(args)
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py b/spaces/declare-lab/tango/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py
deleted file mode 100644
index 162bac1c4331149c4b5abde1eadd8013ab0cda99..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..utils import DummyObject, requires_backends
-
-
-class FlaxStableDiffusionControlNetPipeline(metaclass=DummyObject):
- _backends = ["flax", "transformers"]
-
- def __init__(self, *args, **kwargs):
- requires_backends(self, ["flax", "transformers"])
-
- @classmethod
- def from_config(cls, *args, **kwargs):
- requires_backends(cls, ["flax", "transformers"])
-
- @classmethod
- def from_pretrained(cls, *args, **kwargs):
- requires_backends(cls, ["flax", "transformers"])
-
-
-class FlaxStableDiffusionImg2ImgPipeline(metaclass=DummyObject):
- _backends = ["flax", "transformers"]
-
- def __init__(self, *args, **kwargs):
- requires_backends(self, ["flax", "transformers"])
-
- @classmethod
- def from_config(cls, *args, **kwargs):
- requires_backends(cls, ["flax", "transformers"])
-
- @classmethod
- def from_pretrained(cls, *args, **kwargs):
- requires_backends(cls, ["flax", "transformers"])
-
-
-class FlaxStableDiffusionInpaintPipeline(metaclass=DummyObject):
- _backends = ["flax", "transformers"]
-
- def __init__(self, *args, **kwargs):
- requires_backends(self, ["flax", "transformers"])
-
- @classmethod
- def from_config(cls, *args, **kwargs):
- requires_backends(cls, ["flax", "transformers"])
-
- @classmethod
- def from_pretrained(cls, *args, **kwargs):
- requires_backends(cls, ["flax", "transformers"])
-
-
-class FlaxStableDiffusionPipeline(metaclass=DummyObject):
- _backends = ["flax", "transformers"]
-
- def __init__(self, *args, **kwargs):
- requires_backends(self, ["flax", "transformers"])
-
- @classmethod
- def from_config(cls, *args, **kwargs):
- requires_backends(cls, ["flax", "transformers"])
-
- @classmethod
- def from_pretrained(cls, *args, **kwargs):
- requires_backends(cls, ["flax", "transformers"])
diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/tools/test_ut_generator.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/tools/test_ut_generator.py
deleted file mode 100644
index 6f29999d4ccfa27405962e7cb9017d4f4fd8831c..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/tests/metagpt/tools/test_ut_generator.py
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time : 2023/4/30 21:44
-@Author : alexanderwu
-@File : test_ut_generator.py
-"""
-
-from metagpt.const import API_QUESTIONS_PATH, SWAGGER_PATH, UT_PY_PATH
-from metagpt.tools.ut_writer import YFT_PROMPT_PREFIX, UTGenerator
-
-
-class TestUTWriter:
- def test_api_to_ut_sample(self):
- swagger_file = SWAGGER_PATH / "yft_swaggerApi.json"
- tags = ["测试"] # "智能合同导入", "律师审查", "ai合同审查", "草拟合同&律师在线审查", "合同审批", "履约管理", "签约公司"]
- # 这里在文件中手动加入了两个测试标签的API
-
- utg = UTGenerator(swagger_file=swagger_file, ut_py_path=UT_PY_PATH, questions_path=API_QUESTIONS_PATH,
- template_prefix=YFT_PROMPT_PREFIX)
- ret = utg.generate_ut(include_tags=tags)
- # 后续加入对文件生成内容与数量的检验
- assert ret
diff --git a/spaces/deprem-ml/intent-leaderboard-v13/README.md b/spaces/deprem-ml/intent-leaderboard-v13/README.md
deleted file mode 100644
index b3bf3c9e62761e9b1639161477f4942984fc8aba..0000000000000000000000000000000000000000
--- a/spaces/deprem-ml/intent-leaderboard-v13/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Intent Leaderboard V13
-emoji: 🦸♂️
-colorFrom: blue
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.17.0
-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/diacanFperku/AutoGPT/Autodesk AutoCAD Architecture 2018.1.1 Keygen HOT Latest.md b/spaces/diacanFperku/AutoGPT/Autodesk AutoCAD Architecture 2018.1.1 Keygen HOT Latest.md
deleted file mode 100644
index 4e819b9326bc269ac4f9bf0b74c475e1a440b993..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Autodesk AutoCAD Architecture 2018.1.1 Keygen HOT Latest.md
+++ /dev/null
@@ -1,10 +0,0 @@
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\ No newline at end of file diff --git a/spaces/diagaiwei/ir_chinese_medqa/colbert/evaluation/load_model.py b/spaces/diagaiwei/ir_chinese_medqa/colbert/evaluation/load_model.py deleted file mode 100644 index 13cb5c82dfa3309d814a80fc4082eada44d0eedd..0000000000000000000000000000000000000000 --- a/spaces/diagaiwei/ir_chinese_medqa/colbert/evaluation/load_model.py +++ /dev/null @@ -1,28 +0,0 @@ -import os -import ujson -import torch -import random - -from collections import defaultdict, OrderedDict - -from colbert.parameters import DEVICE -from colbert.modeling.colbert import ColBERT -from colbert.utils.utils import print_message, load_checkpoint - - -def load_model(args, do_print=True): - colbert = ColBERT.from_pretrained('bert-base-uncased', - query_maxlen=args.query_maxlen, - doc_maxlen=args.doc_maxlen, - dim=args.dim, - similarity_metric=args.similarity, - mask_punctuation=args.mask_punctuation) - colbert = colbert.to(DEVICE) - - print_message("#> Loading model checkpoint.", condition=do_print) - - checkpoint = load_checkpoint(args.checkpoint, colbert, do_print=do_print) - - colbert.eval() - - return colbert, checkpoint diff --git a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/app.py b/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/app.py deleted file mode 100644 index 642bdeebf826cf29d4a7fedcd707793e240b5160..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/app.py +++ /dev/null @@ -1,183 +0,0 @@ -import sys, os - -if sys.platform == "darwin": - os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" - -import logging - -logging.getLogger("numba").setLevel(logging.WARNING) -logging.getLogger("markdown_it").setLevel(logging.WARNING) -logging.getLogger("urllib3").setLevel(logging.WARNING) -logging.getLogger("matplotlib").setLevel(logging.WARNING) - -logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s") - -logger = logging.getLogger(__name__) - -import torch -import argparse -import commons -import utils -from models import SynthesizerTrn -from text.symbols import symbols -from text import cleaned_text_to_sequence, get_bert -from text.cleaner import clean_text -import gradio as gr -import webbrowser - - -net_g = None - - -def get_text(text, language_str, hps): - norm_text, phone, tone, word2ph = clean_text(text, language_str) - phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) - - if hps.data.add_blank: - phone = commons.intersperse(phone, 0) - tone = commons.intersperse(tone, 0) - language = commons.intersperse(language, 0) - for i in range(len(word2ph)): - word2ph[i] = word2ph[i] * 2 - word2ph[0] += 1 - bert = get_bert(norm_text, word2ph, language_str) - del word2ph - - assert bert.shape[-1] == len(phone) - - phone = torch.LongTensor(phone) - tone = torch.LongTensor(tone) - language = torch.LongTensor(language) - - return bert, phone, tone, language -import soundfile as sf -def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid): - global net_g - bert, phones, tones, lang_ids = get_text(text, "ZH", hps) - with torch.no_grad(): - x_tst=phones.to(device).unsqueeze(0) - tones=tones.to(device).unsqueeze(0) - lang_ids=lang_ids.to(device).unsqueeze(0) - bert = bert.to(device).unsqueeze(0) - x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) - del phones - speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) - audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio - , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy() - del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers - sf.write("tmp.wav", audio, 44100) - return audio -def convert_wav_to_ogg(wav_file): - os.makedirs('out', exist_ok=True) - filename = os.path.splitext(os.path.basename(wav_file.name))[0] - output_path_ogg = os.path.join('out', f"out.ogg") - - renamed_input_path = os.path.join('in', f"in.wav") - os.makedirs('in', exist_ok=True) - os.rename(wav_file.name, renamed_input_path) - command = ["ffmpeg", "-i", renamed_input_path, "-acodec", "libopus", "-y", output_path_ogg] - os.system(" ".join(command)) - return output_path_ogg -def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): - with torch.no_grad(): - audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker) - with open('tmp.wav', 'rb') as wav_file: - newogg = convert_wav_to_ogg(wav_file) - return "Success", (hps.data.sampling_rate, audio),newogg - - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--model_dir", default="./logs/jiaohuaji/jiaohuaji.pth", help="path of your model") - parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file") - parser.add_argument("--share", default=False, help="make link public") - parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log") - - args = parser.parse_args() - if args.debug: - logger.info("Enable DEBUG-LEVEL log") - logging.basicConfig(level=logging.DEBUG) - hps = utils.get_hparams_from_file(args.config_dir) - device = "cuda:0" if torch.cuda.is_available() else "cpu" - ''' - device = ( - "cuda:0" - if torch.cuda.is_available() - else ( - "mps" - if sys.platform == "darwin" and torch.backends.mps.is_available() - else "cpu" - ) - ) - ''' - net_g = SynthesizerTrn( - len(symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model).to(device) - _ = net_g.eval() - - _ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True) - - speaker_ids = hps.data.spk2id - speakers = list(speaker_ids.keys()) - with gr.Blocks() as app: - with gr.Row(): - with gr.Column(): - - - gr.Markdown(value=""" - 甜甜叫花鸡 Bert-Vits2在线语音生成\n - 1、模型作者：数字星瞳企划 https://t.me/xingtong25680 \n - \n - 2、原项目地址：https://github.com/Stardust-minus/Bert-VITS2\n - 3、使用此模型进行二创请注明AI生成，以及该项目地址。\n - 4、如果想生成超长txt文本的音频请使用colab。 https://colab.research.google.com/drive/13ek8_j1aknr-pbjj3NXxSM4vBIsracU3?usp=drive_link\n - - """) - text = gr.TextArea(label="Text", placeholder="Input Text Here", - value="这里是数字星瞳企画，请在电报搜索星瞳全拼加二五六八零，获取最新更新进展。") - speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker') - sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label='语调变化') - noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.6, step=0.01, label='感情变化') - noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.8, step=0.01, label='音节发音长度变化') - length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='语速') - btn = gr.Button("开启AI语音之旅吧!", variant="primary") - with gr.Column(): - text_output = gr.Textbox(label="Message") - audio_output = gr.Audio(label="Output Audio") - ogg_output = gr.File(label="Converted OGG file") - gr.Markdown(value=""" - 模型汇总：\n - 星瞳 https://huggingface.co/spaces/digitalxingtong/Xingtong-Bert-Vits2 \n - 星瞳 朗读专用 https://huggingface.co/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2 \n - 星瞳 长文本专用 https://huggingface.co/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2 \n - 甜甜叫花鸡 https://huggingface.co/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2 \n - 七海 https://huggingface.co/spaces/digitalxingtong/Nanami-Bert-Vits2 \n - 东雪莲 https://huggingface.co/spaces/digitalxingtong/Azuma-Bert-Vits2 \n - 嘉然 https://huggingface.co/spaces/digitalxingtong/Jiaran-Bert-Vits2 \n - 乃琳 https://huggingface.co/spaces/digitalxingtong/Eileen-Bert-Vits2 \n - 恬豆 https://huggingface.co/spaces/digitalxingtong/Dou-Bert-Vits2 \n - 奶绿 杂谈 https://huggingface.co/spaces/digitalxingtong/Nailv-Bert-Vits2 \n - 奶绿 朗读 https://huggingface.co/spaces/digitalxingtong/Nailv-read-Bert-Vits2 \n - 露早 https://huggingface.co/spaces/digitalxingtong/Luzao-Bert-Vits2 \n - 柚恩 https://huggingface.co/spaces/digitalxingtong/Un-Bert-Vits2 \n - 米诺 https://huggingface.co/spaces/digitalxingtong/Minuo-Bert-Vits2 \n - 扇宝 https://huggingface.co/spaces/digitalxingtong/Shanbao-Bert-Vits2 \n - 牧牧白 https://huggingface.co/spaces/digitalxingtong/Miiu-Bert-Vits2 \n - 吉诺儿kino https://huggingface.co/spaces/digitalxingtong/Kino-Bert-Vits2 \n - 九夏 https://huggingface.co/spaces/digitalxingtong/Jiuxia-Bert-Vits2 \n - 卡缇娅 https://huggingface.co/spaces/digitalxingtong/Yaya-Bert-Vits2 \n - 理想_ideal https://huggingface.co/spaces/digitalxingtong/Lixiang-Bert-Vits2 \n - 阿梓 https://huggingface.co/spaces/digitalxingtong/Azusa-Bert-Vits2 \n - 鹿鸣 https://huggingface.co/spaces/digitalxingtong/Luming-Bert-Vits2 \n - 永雏塔菲 https://huggingface.co/spaces/digitalxingtong/Taffy-Bert-VITS2 \n - """) - btn.click(tts_fn, - inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale], - outputs=[text_output, audio_output,ogg_output]) - - - app.launch(show_error=True) diff --git a/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/monotonic_align/core.py b/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/monotonic_align/core.py deleted file mode 100644 index 5ff728cd74c9228346a82ec64a9829cb98ad315e..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/monotonic_align/core.py +++ /dev/null @@ -1,36 +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 \ No newline at end of file diff --git a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/transformer_head.py b/spaces/dineshreddy/WALT/mmdet/models/dense_heads/transformer_head.py deleted file mode 100644 index 820fd069fcca295f6102f0d27366158a8c640249..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/transformer_head.py +++ /dev/null @@ -1,654 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from mmcv.cnn import Conv2d, Linear, build_activation_layer -from mmcv.runner import force_fp32 - -from mmdet.core import (bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh, - build_assigner, build_sampler, multi_apply, - reduce_mean) -from mmdet.models.utils import (FFN, build_positional_encoding, - build_transformer) -from ..builder import HEADS, build_loss -from .anchor_free_head import AnchorFreeHead - - -@HEADS.register_module() -class TransformerHead(AnchorFreeHead): - """Implements the DETR transformer head. - - See `paper: End-to-End Object Detection with Transformers -`_ for details.
-
- Args:
- num_classes (int): Number of categories excluding the background.
- in_channels (int): Number of channels in the input feature map.
- num_fcs (int, optional): Number of fully-connected layers used in
- `FFN`, which is then used for the regression head. Default 2.
- transformer (dict, optional): Config for transformer.
- positional_encoding (dict, optional): Config for position encoding.
- loss_cls (dict, optional): Config of the classification loss.
- Default `CrossEntropyLoss`.
- loss_bbox (dict, optional): Config of the regression loss.
- Default `L1Loss`.
- loss_iou (dict, optional): Config of the regression iou loss.
- Default `GIoULoss`.
- tran_cfg (dict, optional): Training config of transformer head.
- test_cfg (dict, optional): Testing config of transformer head.
-
- Example:
- >>> import torch
- >>> self = TransformerHead(80, 2048)
- >>> x = torch.rand(1, 2048, 32, 32)
- >>> mask = torch.ones(1, 32, 32).to(x.dtype)
- >>> mask[:, :16, :15] = 0
- >>> all_cls_scores, all_bbox_preds = self(x, mask)
- """
-
- def __init__(self,
- num_classes,
- in_channels,
- num_fcs=2,
- transformer=dict(
- type='Transformer',
- embed_dims=256,
- num_heads=8,
- num_encoder_layers=6,
- num_decoder_layers=6,
- feedforward_channels=2048,
- dropout=0.1,
- act_cfg=dict(type='ReLU', inplace=True),
- norm_cfg=dict(type='LN'),
- num_fcs=2,
- pre_norm=False,
- return_intermediate_dec=True),
- positional_encoding=dict(
- type='SinePositionalEncoding',
- num_feats=128,
- normalize=True),
- loss_cls=dict(
- type='CrossEntropyLoss',
- bg_cls_weight=0.1,
- use_sigmoid=False,
- loss_weight=1.0,
- class_weight=1.0),
- loss_bbox=dict(type='L1Loss', loss_weight=5.0),
- loss_iou=dict(type='GIoULoss', loss_weight=2.0),
- train_cfg=dict(
- assigner=dict(
- type='HungarianAssigner',
- cls_cost=dict(type='ClassificationCost', weight=1.),
- reg_cost=dict(type='BBoxL1Cost', weight=5.0),
- iou_cost=dict(
- type='IoUCost', iou_mode='giou', weight=2.0))),
- test_cfg=dict(max_per_img=100),
- **kwargs):
- # NOTE here use `AnchorFreeHead` instead of `TransformerHead`,
- # since it brings inconvenience when the initialization of
- # `AnchorFreeHead` is called.
- super(AnchorFreeHead, self).__init__()
- use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
- assert not use_sigmoid_cls, 'setting use_sigmoid_cls as True is ' \
- 'not supported in DETR, since background is needed for the ' \
- 'matching process.'
- assert 'embed_dims' in transformer \
- and 'num_feats' in positional_encoding
- num_feats = positional_encoding['num_feats']
- embed_dims = transformer['embed_dims']
- assert num_feats * 2 == embed_dims, 'embed_dims should' \
- f' be exactly 2 times of num_feats. Found {embed_dims}' \
- f' and {num_feats}.'
- assert test_cfg is not None and 'max_per_img' in test_cfg
-
- class_weight = loss_cls.get('class_weight', None)
- if class_weight is not None:
- assert isinstance(class_weight, float), 'Expected ' \
- 'class_weight to have type float. Found ' \
- f'{type(class_weight)}.'
- # NOTE following the official DETR rep0, bg_cls_weight means
- # relative classification weight of the no-object class.
- bg_cls_weight = loss_cls.get('bg_cls_weight', class_weight)
- assert isinstance(bg_cls_weight, float), 'Expected ' \
- 'bg_cls_weight to have type float. Found ' \
- f'{type(bg_cls_weight)}.'
- class_weight = torch.ones(num_classes + 1) * class_weight
- # set background class as the last indice
- class_weight[num_classes] = bg_cls_weight
- loss_cls.update({'class_weight': class_weight})
- if 'bg_cls_weight' in loss_cls:
- loss_cls.pop('bg_cls_weight')
- self.bg_cls_weight = bg_cls_weight
-
- if train_cfg:
- assert 'assigner' in train_cfg, 'assigner should be provided '\
- 'when train_cfg is set.'
- assigner = train_cfg['assigner']
- assert loss_cls['loss_weight'] == assigner['cls_cost']['weight'], \
- 'The classification weight for loss and matcher should be' \
- 'exactly the same.'
- assert loss_bbox['loss_weight'] == assigner['reg_cost'][
- 'weight'], 'The regression L1 weight for loss and matcher ' \
- 'should be exactly the same.'
- assert loss_iou['loss_weight'] == assigner['iou_cost']['weight'], \
- 'The regression iou weight for loss and matcher should be' \
- 'exactly the same.'
- self.assigner = build_assigner(assigner)
- # DETR sampling=False, so use PseudoSampler
- sampler_cfg = dict(type='PseudoSampler')
- self.sampler = build_sampler(sampler_cfg, context=self)
- self.num_classes = num_classes
- self.cls_out_channels = num_classes + 1
- self.in_channels = in_channels
- self.num_fcs = num_fcs
- self.train_cfg = train_cfg
- self.test_cfg = test_cfg
- self.use_sigmoid_cls = use_sigmoid_cls
- self.embed_dims = embed_dims
- self.num_query = test_cfg['max_per_img']
- self.fp16_enabled = False
- self.loss_cls = build_loss(loss_cls)
- self.loss_bbox = build_loss(loss_bbox)
- self.loss_iou = build_loss(loss_iou)
- self.act_cfg = transformer.get('act_cfg',
- dict(type='ReLU', inplace=True))
- self.activate = build_activation_layer(self.act_cfg)
- self.positional_encoding = build_positional_encoding(
- positional_encoding)
- self.transformer = build_transformer(transformer)
- self._init_layers()
-
- def _init_layers(self):
- """Initialize layers of the transformer head."""
- self.input_proj = Conv2d(
- self.in_channels, self.embed_dims, kernel_size=1)
- self.fc_cls = Linear(self.embed_dims, self.cls_out_channels)
- self.reg_ffn = FFN(
- self.embed_dims,
- self.embed_dims,
- self.num_fcs,
- self.act_cfg,
- dropout=0.0,
- add_residual=False)
- self.fc_reg = Linear(self.embed_dims, 4)
- self.query_embedding = nn.Embedding(self.num_query, self.embed_dims)
-
- def init_weights(self, distribution='uniform'):
- """Initialize weights of the transformer head."""
- # The initialization for transformer is important
- self.transformer.init_weights()
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
- missing_keys, unexpected_keys, error_msgs):
- """load checkpoints."""
- # NOTE here use `AnchorFreeHead` instead of `TransformerHead`,
- # since `AnchorFreeHead._load_from_state_dict` should not be
- # called here. Invoking the default `Module._load_from_state_dict`
- # is enough.
- super(AnchorFreeHead,
- self)._load_from_state_dict(state_dict, prefix, local_metadata,
- strict, missing_keys,
- unexpected_keys, error_msgs)
-
- def forward(self, feats, img_metas):
- """Forward function.
-
- Args:
- feats (tuple[Tensor]): Features from the upstream network, each is
- a 4D-tensor.
- img_metas (list[dict]): List of image information.
-
- Returns:
- tuple[list[Tensor], list[Tensor]]: Outputs for all scale levels.
-
- - all_cls_scores_list (list[Tensor]): Classification scores \
- for each scale level. Each is a 4D-tensor with shape \
- [nb_dec, bs, num_query, cls_out_channels]. Note \
- `cls_out_channels` should includes background.
- - all_bbox_preds_list (list[Tensor]): Sigmoid regression \
- outputs for each scale level. Each is a 4D-tensor with \
- normalized coordinate format (cx, cy, w, h) and shape \
- [nb_dec, bs, num_query, 4].
- """
- num_levels = len(feats)
- img_metas_list = [img_metas for _ in range(num_levels)]
- return multi_apply(self.forward_single, feats, img_metas_list)
-
- def forward_single(self, x, img_metas):
- """"Forward function for a single feature level.
-
- Args:
- x (Tensor): Input feature from backbone's single stage, shape
- [bs, c, h, w].
- img_metas (list[dict]): List of image information.
-
- Returns:
- all_cls_scores (Tensor): Outputs from the classification head,
- shape [nb_dec, bs, num_query, cls_out_channels]. Note
- cls_out_channels should includes background.
- all_bbox_preds (Tensor): Sigmoid outputs from the regression
- head with normalized coordinate format (cx, cy, w, h).
- Shape [nb_dec, bs, num_query, 4].
- """
- # construct binary masks which used for the transformer.
- # NOTE following the official DETR repo, non-zero values representing
- # ignored positions, while zero values means valid positions.
- batch_size = x.size(0)
- input_img_h, input_img_w = img_metas[0]['batch_input_shape']
- masks = x.new_ones((batch_size, input_img_h, input_img_w))
- for img_id in range(batch_size):
- img_h, img_w, _ = img_metas[img_id]['img_shape']
- masks[img_id, :img_h, :img_w] = 0
-
- x = self.input_proj(x)
- # interpolate masks to have the same spatial shape with x
- masks = F.interpolate(
- masks.unsqueeze(1), size=x.shape[-2:]).to(torch.bool).squeeze(1)
- # position encoding
- pos_embed = self.positional_encoding(masks) # [bs, embed_dim, h, w]
- # outs_dec: [nb_dec, bs, num_query, embed_dim]
- outs_dec, _ = self.transformer(x, masks, self.query_embedding.weight,
- pos_embed)
-
- all_cls_scores = self.fc_cls(outs_dec)
- all_bbox_preds = self.fc_reg(self.activate(
- self.reg_ffn(outs_dec))).sigmoid()
- return all_cls_scores, all_bbox_preds
-
- @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list'))
- def loss(self,
- all_cls_scores_list,
- all_bbox_preds_list,
- gt_bboxes_list,
- gt_labels_list,
- img_metas,
- gt_bboxes_ignore=None):
- """"Loss function.
-
- Only outputs from the last feature level are used for computing
- losses by default.
-
- Args:
- all_cls_scores_list (list[Tensor]): Classification outputs
- for each feature level. Each is a 4D-tensor with shape
- [nb_dec, bs, num_query, cls_out_channels].
- all_bbox_preds_list (list[Tensor]): Sigmoid regression
- outputs for each feature level. Each is a 4D-tensor with
- normalized coordinate format (cx, cy, w, h) and shape
- [nb_dec, bs, num_query, 4].
- gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
- with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
- gt_labels_list (list[Tensor]): Ground truth class indices for each
- image with shape (num_gts, ).
- img_metas (list[dict]): List of image meta information.
- gt_bboxes_ignore (list[Tensor], optional): Bounding boxes
- which can be ignored for each image. Default None.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- # NOTE defaultly only the outputs from the last feature scale is used.
- all_cls_scores = all_cls_scores_list[-1]
- all_bbox_preds = all_bbox_preds_list[-1]
- assert gt_bboxes_ignore is None, \
- 'Only supports for gt_bboxes_ignore setting to None.'
-
- num_dec_layers = len(all_cls_scores)
- all_gt_bboxes_list = [gt_bboxes_list for _ in range(num_dec_layers)]
- all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)]
- all_gt_bboxes_ignore_list = [
- gt_bboxes_ignore for _ in range(num_dec_layers)
- ]
- img_metas_list = [img_metas for _ in range(num_dec_layers)]
-
- losses_cls, losses_bbox, losses_iou = multi_apply(
- self.loss_single, all_cls_scores, all_bbox_preds,
- all_gt_bboxes_list, all_gt_labels_list, img_metas_list,
- all_gt_bboxes_ignore_list)
-
- loss_dict = dict()
- # loss from the last decoder layer
- loss_dict['loss_cls'] = losses_cls[-1]
- loss_dict['loss_bbox'] = losses_bbox[-1]
- loss_dict['loss_iou'] = losses_iou[-1]
- # loss from other decoder layers
- num_dec_layer = 0
- for loss_cls_i, loss_bbox_i, loss_iou_i in zip(losses_cls[:-1],
- losses_bbox[:-1],
- losses_iou[:-1]):
- loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i
- loss_dict[f'd{num_dec_layer}.loss_bbox'] = loss_bbox_i
- loss_dict[f'd{num_dec_layer}.loss_iou'] = loss_iou_i
- num_dec_layer += 1
- return loss_dict
-
- def loss_single(self,
- cls_scores,
- bbox_preds,
- gt_bboxes_list,
- gt_labels_list,
- img_metas,
- gt_bboxes_ignore_list=None):
- """"Loss function for outputs from a single decoder layer of a single
- feature level.
-
- Args:
- cls_scores (Tensor): Box score logits from a single decoder layer
- for all images. Shape [bs, num_query, cls_out_channels].
- bbox_preds (Tensor): Sigmoid outputs from a single decoder layer
- for all images, with normalized coordinate (cx, cy, w, h) and
- shape [bs, num_query, 4].
- gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
- with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
- gt_labels_list (list[Tensor]): Ground truth class indices for each
- image with shape (num_gts, ).
- img_metas (list[dict]): List of image meta information.
- gt_bboxes_ignore_list (list[Tensor], optional): Bounding
- boxes which can be ignored for each image. Default None.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components for outputs from
- a single decoder layer.
- """
- num_imgs = cls_scores.size(0)
- cls_scores_list = [cls_scores[i] for i in range(num_imgs)]
- bbox_preds_list = [bbox_preds[i] for i in range(num_imgs)]
- cls_reg_targets = self.get_targets(cls_scores_list, bbox_preds_list,
- gt_bboxes_list, gt_labels_list,
- img_metas, gt_bboxes_ignore_list)
- (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
- num_total_pos, num_total_neg) = cls_reg_targets
- labels = torch.cat(labels_list, 0)
- label_weights = torch.cat(label_weights_list, 0)
- bbox_targets = torch.cat(bbox_targets_list, 0)
- bbox_weights = torch.cat(bbox_weights_list, 0)
-
- # classification loss
- cls_scores = cls_scores.reshape(-1, self.cls_out_channels)
- # construct weighted avg_factor to match with the official DETR repo
- cls_avg_factor = num_total_pos * 1.0 + \
- num_total_neg * self.bg_cls_weight
- loss_cls = self.loss_cls(
- cls_scores, labels, label_weights, avg_factor=cls_avg_factor)
-
- # Compute the average number of gt boxes accross all gpus, for
- # normalization purposes
- num_total_pos = loss_cls.new_tensor([num_total_pos])
- num_total_pos = torch.clamp(reduce_mean(num_total_pos), min=1).item()
-
- # construct factors used for rescale bboxes
- factors = []
- for img_meta, bbox_pred in zip(img_metas, bbox_preds):
- img_h, img_w, _ = img_meta['img_shape']
- factor = bbox_pred.new_tensor([img_w, img_h, img_w,
- img_h]).unsqueeze(0).repeat(
- bbox_pred.size(0), 1)
- factors.append(factor)
- factors = torch.cat(factors, 0)
-
- # DETR regress the relative position of boxes (cxcywh) in the image,
- # thus the learning target is normalized by the image size. So here
- # we need to re-scale them for calculating IoU loss
- bbox_preds = bbox_preds.reshape(-1, 4)
- bboxes = bbox_cxcywh_to_xyxy(bbox_preds) * factors
- bboxes_gt = bbox_cxcywh_to_xyxy(bbox_targets) * factors
-
- # regression IoU loss, defaultly GIoU loss
- loss_iou = self.loss_iou(
- bboxes, bboxes_gt, bbox_weights, avg_factor=num_total_pos)
-
- # regression L1 loss
- loss_bbox = self.loss_bbox(
- bbox_preds, bbox_targets, bbox_weights, avg_factor=num_total_pos)
- return loss_cls, loss_bbox, loss_iou
-
- def get_targets(self,
- cls_scores_list,
- bbox_preds_list,
- gt_bboxes_list,
- gt_labels_list,
- img_metas,
- gt_bboxes_ignore_list=None):
- """"Compute regression and classification targets for a batch image.
-
- Outputs from a single decoder layer of a single feature level are used.
-
- Args:
- cls_scores_list (list[Tensor]): Box score logits from a single
- decoder layer for each image with shape [num_query,
- cls_out_channels].
- bbox_preds_list (list[Tensor]): Sigmoid outputs from a single
- decoder layer for each image, with normalized coordinate
- (cx, cy, w, h) and shape [num_query, 4].
- gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
- with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
- gt_labels_list (list[Tensor]): Ground truth class indices for each
- image with shape (num_gts, ).
- img_metas (list[dict]): List of image meta information.
- gt_bboxes_ignore_list (list[Tensor], optional): Bounding
- boxes which can be ignored for each image. Default None.
-
- Returns:
- tuple: a tuple containing the following targets.
-
- - labels_list (list[Tensor]): Labels for all images.
- - label_weights_list (list[Tensor]): Label weights for all \
- images.
- - bbox_targets_list (list[Tensor]): BBox targets for all \
- images.
- - bbox_weights_list (list[Tensor]): BBox weights for all \
- images.
- - num_total_pos (int): Number of positive samples in all \
- images.
- - num_total_neg (int): Number of negative samples in all \
- images.
- """
- assert gt_bboxes_ignore_list is None, \
- 'Only supports for gt_bboxes_ignore setting to None.'
- num_imgs = len(cls_scores_list)
- gt_bboxes_ignore_list = [
- gt_bboxes_ignore_list for _ in range(num_imgs)
- ]
-
- (labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, pos_inds_list, neg_inds_list) = multi_apply(
- self._get_target_single, cls_scores_list, bbox_preds_list,
- gt_bboxes_list, gt_labels_list, img_metas, gt_bboxes_ignore_list)
- num_total_pos = sum((inds.numel() for inds in pos_inds_list))
- num_total_neg = sum((inds.numel() for inds in neg_inds_list))
- return (labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, num_total_pos, num_total_neg)
-
- def _get_target_single(self,
- cls_score,
- bbox_pred,
- gt_bboxes,
- gt_labels,
- img_meta,
- gt_bboxes_ignore=None):
- """"Compute regression and classification targets for one image.
-
- Outputs from a single decoder layer of a single feature level are used.
-
- Args:
- cls_score (Tensor): Box score logits from a single decoder layer
- for one image. Shape [num_query, cls_out_channels].
- bbox_pred (Tensor): Sigmoid outputs from a single decoder layer
- for one image, with normalized coordinate (cx, cy, w, h) and
- shape [num_query, 4].
- gt_bboxes (Tensor): Ground truth bboxes for one image with
- shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
- gt_labels (Tensor): Ground truth class indices for one image
- with shape (num_gts, ).
- img_meta (dict): Meta information for one image.
- gt_bboxes_ignore (Tensor, optional): Bounding boxes
- which can be ignored. Default None.
-
- Returns:
- tuple[Tensor]: a tuple containing the following for one image.
-
- - labels (Tensor): Labels of each image.
- - label_weights (Tensor]): Label weights of each image.
- - bbox_targets (Tensor): BBox targets of each image.
- - bbox_weights (Tensor): BBox weights of each image.
- - pos_inds (Tensor): Sampled positive indices for each image.
- - neg_inds (Tensor): Sampled negative indices for each image.
- """
-
- num_bboxes = bbox_pred.size(0)
- # assigner and sampler
- assign_result = self.assigner.assign(bbox_pred, cls_score, gt_bboxes,
- gt_labels, img_meta,
- gt_bboxes_ignore)
- sampling_result = self.sampler.sample(assign_result, bbox_pred,
- gt_bboxes)
- pos_inds = sampling_result.pos_inds
- neg_inds = sampling_result.neg_inds
-
- # label targets
- labels = gt_bboxes.new_full((num_bboxes, ),
- self.num_classes,
- dtype=torch.long)
- labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds]
- label_weights = gt_bboxes.new_ones(num_bboxes)
-
- # bbox targets
- bbox_targets = torch.zeros_like(bbox_pred)
- bbox_weights = torch.zeros_like(bbox_pred)
- bbox_weights[pos_inds] = 1.0
- img_h, img_w, _ = img_meta['img_shape']
-
- # DETR regress the relative position of boxes (cxcywh) in the image.
- # Thus the learning target should be normalized by the image size, also
- # the box format should be converted from defaultly x1y1x2y2 to cxcywh.
- factor = bbox_pred.new_tensor([img_w, img_h, img_w,
- img_h]).unsqueeze(0)
- pos_gt_bboxes_normalized = sampling_result.pos_gt_bboxes / factor
- pos_gt_bboxes_targets = bbox_xyxy_to_cxcywh(pos_gt_bboxes_normalized)
- bbox_targets[pos_inds] = pos_gt_bboxes_targets
- return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
- neg_inds)
-
- # over-write because img_metas are needed as inputs for bbox_head.
- def forward_train(self,
- x,
- img_metas,
- gt_bboxes,
- gt_labels=None,
- gt_bboxes_ignore=None,
- proposal_cfg=None,
- **kwargs):
- """Forward function for training mode.
-
- Args:
- x (list[Tensor]): Features from backbone.
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes (Tensor): Ground truth bboxes of the image,
- shape (num_gts, 4).
- gt_labels (Tensor): Ground truth labels of each box,
- shape (num_gts,).
- gt_bboxes_ignore (Tensor): Ground truth bboxes to be
- ignored, shape (num_ignored_gts, 4).
- proposal_cfg (mmcv.Config): Test / postprocessing configuration,
- if None, test_cfg would be used.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- assert proposal_cfg is None, '"proposal_cfg" must be None'
- outs = self(x, img_metas)
- if gt_labels is None:
- loss_inputs = outs + (gt_bboxes, img_metas)
- else:
- loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
- losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
- return losses
-
- @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list'))
- def get_bboxes(self,
- all_cls_scores_list,
- all_bbox_preds_list,
- img_metas,
- rescale=False):
- """Transform network outputs for a batch into bbox predictions.
-
- Args:
- all_cls_scores_list (list[Tensor]): Classification outputs
- for each feature level. Each is a 4D-tensor with shape
- [nb_dec, bs, num_query, cls_out_channels].
- all_bbox_preds_list (list[Tensor]): Sigmoid regression
- outputs for each feature level. Each is a 4D-tensor with
- normalized coordinate format (cx, cy, w, h) and shape
- [nb_dec, bs, num_query, 4].
- img_metas (list[dict]): Meta information of each image.
- rescale (bool, optional): If True, return boxes in original
- image space. Default False.
-
- Returns:
- list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple. \
- The first item is an (n, 5) tensor, where the first 4 columns \
- are bounding box positions (tl_x, tl_y, br_x, br_y) and the \
- 5-th column is a score between 0 and 1. The second item is a \
- (n,) tensor where each item is the predicted class label of \
- the corresponding box.
- """
- # NOTE defaultly only using outputs from the last feature level,
- # and only the outputs from the last decoder layer is used.
- cls_scores = all_cls_scores_list[-1][-1]
- bbox_preds = all_bbox_preds_list[-1][-1]
-
- result_list = []
- for img_id in range(len(img_metas)):
- cls_score = cls_scores[img_id]
- bbox_pred = bbox_preds[img_id]
- img_shape = img_metas[img_id]['img_shape']
- scale_factor = img_metas[img_id]['scale_factor']
- proposals = self._get_bboxes_single(cls_score, bbox_pred,
- img_shape, scale_factor,
- rescale)
- result_list.append(proposals)
- return result_list
-
- def _get_bboxes_single(self,
- cls_score,
- bbox_pred,
- img_shape,
- scale_factor,
- rescale=False):
- """Transform outputs from the last decoder layer into bbox predictions
- for each image.
-
- Args:
- cls_score (Tensor): Box score logits from the last decoder layer
- for each image. Shape [num_query, cls_out_channels].
- bbox_pred (Tensor): Sigmoid outputs from the last decoder layer
- for each image, with coordinate format (cx, cy, w, h) and
- shape [num_query, 4].
- img_shape (tuple[int]): Shape of input image, (height, width, 3).
- scale_factor (ndarray, optional): Scale factor of the image arange
- as (w_scale, h_scale, w_scale, h_scale).
- rescale (bool, optional): If True, return boxes in original image
- space. Default False.
-
- Returns:
- tuple[Tensor]: Results of detected bboxes and labels.
-
- - det_bboxes: Predicted bboxes with shape [num_query, 5], \
- where the first 4 columns are bounding box positions \
- (tl_x, tl_y, br_x, br_y) and the 5-th column are scores \
- between 0 and 1.
- - det_labels: Predicted labels of the corresponding box with \
- shape [num_query].
- """
- assert len(cls_score) == len(bbox_pred)
- # exclude background
- scores, det_labels = F.softmax(cls_score, dim=-1)[..., :-1].max(-1)
- det_bboxes = bbox_cxcywh_to_xyxy(bbox_pred)
- det_bboxes[:, 0::2] = det_bboxes[:, 0::2] * img_shape[1]
- det_bboxes[:, 1::2] = det_bboxes[:, 1::2] * img_shape[0]
- det_bboxes[:, 0::2].clamp_(min=0, max=img_shape[1])
- det_bboxes[:, 1::2].clamp_(min=0, max=img_shape[0])
- if rescale:
- det_bboxes /= det_bboxes.new_tensor(scale_factor)
- det_bboxes = torch.cat((det_bboxes, scores.unsqueeze(1)), -1)
- return det_bboxes, det_labels
diff --git a/spaces/dinhminh20521597/OCR_DEMO/configs/textdet/psenet/psenet_r50_fpnf_600e_ctw1500.py b/spaces/dinhminh20521597/OCR_DEMO/configs/textdet/psenet/psenet_r50_fpnf_600e_ctw1500.py
deleted file mode 100644
index 483a2b2e1e7e584dfba26c7c5f506ce544953db8..0000000000000000000000000000000000000000
--- a/spaces/dinhminh20521597/OCR_DEMO/configs/textdet/psenet/psenet_r50_fpnf_600e_ctw1500.py
+++ /dev/null
@@ -1,35 +0,0 @@
-_base_ = [
- '../../_base_/default_runtime.py',
- '../../_base_/schedules/schedule_adam_step_600e.py',
- '../../_base_/det_models/psenet_r50_fpnf.py',
- '../../_base_/det_datasets/ctw1500.py',
- '../../_base_/det_pipelines/psenet_pipeline.py'
-]
-
-model = {{_base_.model_poly}}
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline_ctw1500 = {{_base_.test_pipeline_ctw1500}}
-
-data = dict(
- samples_per_gpu=2,
- workers_per_gpu=2,
- val_dataloader=dict(samples_per_gpu=1),
- test_dataloader=dict(samples_per_gpu=1),
- train=dict(
- type='UniformConcatDataset',
- datasets=train_list,
- pipeline=train_pipeline),
- val=dict(
- type='UniformConcatDataset',
- datasets=test_list,
- pipeline=test_pipeline_ctw1500),
- test=dict(
- type='UniformConcatDataset',
- datasets=test_list,
- pipeline=test_pipeline_ctw1500))
-
-evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/spaces/dirge/voicevox/voicevox_engine/dev/core/mock.py b/spaces/dirge/voicevox/voicevox_engine/dev/core/mock.py
deleted file mode 100644
index 59eb63d7039b44a27c9e5e17120d83d41763c353..0000000000000000000000000000000000000000
--- a/spaces/dirge/voicevox/voicevox_engine/dev/core/mock.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import json
-from logging import getLogger
-from typing import Any, Dict, List
-
-import numpy as np
-from pyopenjtalk import tts
-from scipy.signal import resample
-
-DUMMY_TEXT = "これはダミーのテキストです"
-
-
-def initialize(path: str, use_gpu: bool, *args: List[Any]) -> None:
- pass
-
-
-def yukarin_s_forward(length: int, **kwargs: Dict[str, Any]) -> np.ndarray:
- logger = getLogger("uvicorn") # FastAPI / Uvicorn 内からの利用のため
- logger.info(
- "Sorry, yukarin_s_forward() is a mock. Return values are incorrect.",
- )
- return np.ones(length) / 5
-
-
-def yukarin_sa_forward(length: int, **kwargs: Dict[str, Any]) -> np.ndarray:
- logger = getLogger("uvicorn") # FastAPI / Uvicorn 内からの利用のため
- logger.info(
- "Sorry, yukarin_sa_forward() is a mock. Return values are incorrect.",
- )
- return np.ones((1, length)) * 5
-
-
-def decode_forward(length: int, **kwargs: Dict[str, Any]) -> np.ndarray:
- """
- 合成音声の波形データをNumPy配列で返します。ただし、常に固定の文言を読み上げます（DUMMY_TEXT）
- 参照→SynthesisEngine のdocstring [Mock]
-
- Parameters
- ----------
- length : int
- フレームの長さ
-
- Returns
- -------
- wave : np.ndarray
- 音声合成した波形データ
-
- Note
- -------
- ここで行う音声合成では、調声（ピッチ等）を反映しない
- また、入力内容によらず常に固定の文言を読み上げる
-
- # pyopenjtalk.tts()の出力仕様
- dtype=np.float64, 16 bit, mono 48000 Hz
-
- # resampleの説明
- 非モックdecode_forwardと合わせるために、出力を24kHzに変換した。
- """
- logger = getLogger("uvicorn") # FastAPI / Uvicorn 内からの利用のため
- logger.info(
- "Sorry, decode_forward() is a mock. Return values are incorrect.",
- )
- wave, sr = tts(DUMMY_TEXT)
- wave = resample(
- wave.astype("int16"),
- 24000 * len(wave) // 48000,
- )
- return wave
-
-
-def metas() -> str:
- return json.dumps(
- [
- {
- "name": "dummy1",
- "styles": [
- {"name": "style0", "id": 0},
- {"name": "style1", "id": 2},
- {"name": "style2", "id": 4},
- {"name": "style3", "id": 6},
- ],
- "speaker_uuid": "7ffcb7ce-00ec-4bdc-82cd-45a8889e43ff",
- "version": "mock",
- },
- {
- "name": "dummy2",
- "styles": [
- {"name": "style0", "id": 1},
- {"name": "style1", "id": 3},
- {"name": "style2", "id": 5},
- {"name": "style3", "id": 7},
- ],
- "speaker_uuid": "388f246b-8c41-4ac1-8e2d-5d79f3ff56d9",
- "version": "mock",
- },
- {
- "name": "dummy3",
- "styles": [
- {"name": "style0", "id": 8},
- ],
- "speaker_uuid": "35b2c544-660e-401e-b503-0e14c635303a",
- "version": "mock",
- },
- {
- "name": "dummy4",
- "styles": [
- {"name": "style0", "id": 9},
- ],
- "speaker_uuid": "b1a81618-b27b-40d2-b0ea-27a9ad408c4b",
- "version": "mock",
- },
- ]
- )
-
-
-def supported_devices() -> str:
- return json.dumps(
- {
- "cpu": True,
- "cuda": False,
- }
- )
diff --git a/spaces/doluvor/faster-whisper-webui/docs/options.md b/spaces/doluvor/faster-whisper-webui/docs/options.md
deleted file mode 100644
index 6979fca4d9d4c98a626a2953c2573ff23898a37e..0000000000000000000000000000000000000000
--- a/spaces/doluvor/faster-whisper-webui/docs/options.md
+++ /dev/null
@@ -1,134 +0,0 @@
-# Standard Options
-To transcribe or translate an audio file, you can either copy an URL from a website (all [websites](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)
-supported by YT-DLP will work, including YouTube). Otherwise, upload an audio file (choose "All Files (*.*)"
-in the file selector to select any file type, including video files) or use the microphone.
-
-For longer audio files (>10 minutes), it is recommended that you select Silero VAD (Voice Activity Detector) in the VAD option, especially if you are using the `large-v1` model. Note that `large-v2` is a lot more forgiving, but you may still want to use a VAD with a slightly higher "VAD - Max Merge Size (s)" (60 seconds or more).
-
-## Model
-Select the model that Whisper will use to transcribe the audio:
-
-| Size | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |
-|-----------|------------|--------------------|--------------------|---------------|----------------|
-| tiny | 39 M | tiny.en | tiny | ~1 GB | ~32x |
-| base | 74 M | base.en | base | ~1 GB | ~16x |
-| small | 244 M | small.en | small | ~2 GB | ~6x |
-| medium | 769 M | medium.en | medium | ~5 GB | ~2x |
-| large | 1550 M | N/A | large | ~10 GB | 1x |
-| large-v2 | 1550 M | N/A | large | ~10 GB | 1x |
-
-## Language
-
-Select the language, or leave it empty for Whisper to automatically detect it.
-
-Note that if the selected language and the language in the audio differs, Whisper may start to translate the audio to the selected
-language. For instance, if the audio is in English but you select Japaneese, the model may translate the audio to Japanese.
-
-## Inputs
-The options "URL (YouTube, etc.)", "Upload Files" or "Micriphone Input" allows you to send an audio input to the model.
-
-### Multiple Files
-Note that the UI will only process either the given URL or the upload files (including microphone) - not both.
-
-But you can upload multiple files either through the "Upload files" option, or as a playlist on YouTube. Each audio file will then be processed in turn, and the resulting SRT/VTT/Transcript will be made available in the "Download" section. When more than one file is processed, the UI will also generate a "All_Output" zip file containing all the text output files.
-
-## Task
-Select the task - either "transcribe" to transcribe the audio to text, or "translate" to translate it to English.
-
-## Vad
-Using a VAD will improve the timing accuracy of each transcribed line, as well as prevent Whisper getting into an infinite
-loop detecting the same sentence over and over again. The downside is that this may be at a cost to text accuracy, especially
-with regards to unique words or names that appear in the audio. You can compensate for this by increasing the prompt window.
-
-Note that English is very well handled by Whisper, and it's less susceptible to issues surrounding bad timings and infinite loops.
-So you may only need to use a VAD for other languages, such as Japanese, or when the audio is very long.
-
-* none
- * Run whisper on the entire audio input
-* silero-vad
- * Use Silero VAD to detect sections that contain speech, and run Whisper on independently on each section. Whisper is also run
- on the gaps between each speech section, by either expanding the section up to the max merge size, or running Whisper independently
- on the non-speech section.
-* silero-vad-expand-into-gaps
- * Use Silero VAD to detect sections that contain speech, and run Whisper on independently on each section. Each spech section will be expanded
- such that they cover any adjacent non-speech sections. For instance, if an audio file of one minute contains the speech sections
- 00:00 - 00:10 (A) and 00:30 - 00:40 (B), the first section (A) will be expanded to 00:00 - 00:30, and (B) will be expanded to 00:30 - 00:60.
-* silero-vad-skip-gaps
- * As above, but sections that doesn't contain speech according to Silero will be skipped. This will be slightly faster, but
- may cause dialogue to be skipped.
-* periodic-vad
- * Create sections of speech every 'VAD - Max Merge Size' seconds. This is very fast and simple, but will potentially break
- a sentence or word in two.
-
-## VAD - Merge Window
-If set, any adjacent speech sections that are at most this number of seconds apart will be automatically merged.
-
-## VAD - Max Merge Size (s)
-Disables merging of adjacent speech sections if they are this number of seconds long.
-
-## VAD - Padding (s)
-The number of seconds (floating point) to add to the beginning and end of each speech section. Setting this to a number
-larger than zero ensures that Whisper is more likely to correctly transcribe a sentence in the beginning of
-a speech section. However, this also increases the probability of Whisper assigning the wrong timestamp
-to each transcribed line. The default value is 1 second.
-
-## VAD - Prompt Window (s)
-The text of a detected line will be included as a prompt to the next speech section, if the speech section starts at most this
-number of seconds after the line has finished. For instance, if a line ends at 10:00, and the next speech section starts at
-10:04, the line's text will be included if the prompt window is 4 seconds or more (10:04 - 10:00 = 4 seconds).
-
-Note that detected lines in gaps between speech sections will not be included in the prompt
-(if silero-vad or silero-vad-expand-into-gaps) is used.
-
-# Command Line Options
-
-Both `app.py` and `cli.py` also accept command line options, such as the ability to enable parallel execution on multiple
-CPU/GPU cores, the default model name/VAD and so on. Consult the README in the root folder for more information.
-
-# Additional Options
-
-In addition to the above, there's also a "Full" options interface that allows you to set all the options available in the Whisper
-model. The options are as follows:
-
-## Initial Prompt
-Optional text to provide as a prompt for the first 30 seconds window. Whisper will attempt to use this as a starting point for the transcription, but you can
-also get creative and specify a style or format for the output of the transcription.
-
-For instance, if you use the prompt "hello how is it going always use lowercase no punctuation goodbye one two three start stop i you me they", Whisper will
-be biased to output lower capital letters and no punctuation, and may also be biased to output the words in the prompt more often.
-
-## Temperature
-The temperature to use when sampling. Default is 0 (zero). A higher temperature will result in more random output, while a lower temperature will be more deterministic.
-
-## Best Of - Non-zero temperature
-The number of candidates to sample from when sampling with non-zero temperature. Default is 5.
-
-## Beam Size - Zero temperature
-The number of beams to use in beam search when sampling with zero temperature. Default is 5.
-
-## Patience - Zero temperature
-The patience value to use in beam search when sampling with zero temperature. As in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search.
-
-## Length Penalty - Any temperature
-The token length penalty coefficient (alpha) to use when sampling with any temperature. As in https://arxiv.org/abs/1609.08144, uses simple length normalization by default.
-
-## Suppress Tokens - Comma-separated list of token IDs
-A comma-separated list of token IDs to suppress during sampling. The default value of "-1" will suppress most special characters except common punctuations.
-
-## Condition on previous text
-If True, provide the previous output of the model as a prompt for the next window. Disabling this may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop.
-
-## FP16
-Whether to perform inference in fp16. True by default.
-
-## Temperature increment on fallback
-The temperature to increase when falling back when the decoding fails to meet either of the thresholds below. Default is 0.2.
-
-## Compression ratio threshold
-If the gzip compression ratio is higher than this value, treat the decoding as failed. Default is 2.4.
-
-## Logprob threshold
-If the average log probability is lower than this value, treat the decoding as failed. Default is -1.0.
-
-## No speech threshold
-If the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence. Default is 0.6.
diff --git a/spaces/dongyi/MMFS/models/modules/stylegan2/op/__init__.py b/spaces/dongyi/MMFS/models/modules/stylegan2/op/__init__.py
deleted file mode 100644
index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000
--- a/spaces/dongyi/MMFS/models/modules/stylegan2/op/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d
diff --git a/spaces/drift-ai/art-search-engine/README.md b/spaces/drift-ai/art-search-engine/README.md
deleted file mode 100644
index 3a2ddd8055f0520f411fd64baf4306a183576c46..0000000000000000000000000000000000000000
--- a/spaces/drift-ai/art-search-engine/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-title: Art Search Engine
-emoji: 🖼🖼🖼
-colorFrom: black
-colorTo: black
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-duplicated_from: vincentclaes/art-search-engine
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-
-```bash
-make setup
-poetry shell
-```
\ No newline at end of file
diff --git a/spaces/dylanebert/igf/viewer/src/app.html b/spaces/dylanebert/igf/viewer/src/app.html
deleted file mode 100644
index 875ffd4ff024300782cfe68f594ea01648731962..0000000000000000000000000000000000000000
--- a/spaces/dylanebert/igf/viewer/src/app.html
+++ /dev/null
@@ -1,17 +0,0 @@
-
-
-
-
-
-
-
-
-
- %sveltekit.head%
-
-
-
-
-
-It is captain claw game is full fighting. it is single player and played. It is cheat book. 1fdad05405
-
-
- diff --git a/spaces/falterWliame/Face_Mask_Detection/Download Film Kisah Nabi Musa Fu HOT!.md b/spaces/falterWliame/Face_Mask_Detection/Download Film Kisah Nabi Musa Fu HOT!.md deleted file mode 100644 index 4e590cf521d0c7c2a5de1226c27af526705aa1f9..0000000000000000000000000000000000000000 --- a/spaces/falterWliame/Face_Mask_Detection/Download Film Kisah Nabi Musa Fu HOT!.md +++ /dev/null @@ -1,5 +0,0 @@ - -
899543212b
-
-
\ No newline at end of file diff --git a/spaces/farukozderim/Model-Comparator-Space-Builder/README.md b/spaces/farukozderim/Model-Comparator-Space-Builder/README.md deleted file mode 100644 index 2b2d69cc2b282b567ce143073d47a2125a329b35..0000000000000000000000000000000000000000 --- a/spaces/farukozderim/Model-Comparator-Space-Builder/README.md +++ /dev/null @@ -1,43 +0,0 @@ ---- -title: Model Comparator Space Builder -emoji: 🌍 -colorFrom: gray -colorTo: purple -sdk: gradio -app_file: app.py -pinned: true ---- - -# Tests -``` -pytest -v tests.py -``` - - -# 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/fatiXbelha/sd/Download Red Hat Free and Learn from Hands-on Learning Paths.md b/spaces/fatiXbelha/sd/Download Red Hat Free and Learn from Hands-on Learning Paths.md deleted file mode 100644 index 51d6467720ea0931596f7a96403c1dadaa28ec36..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Red Hat Free and Learn from Hands-on Learning Paths.md +++ /dev/null @@ -1,120 +0,0 @@ -
-
-
-
-
\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/FIFA Mobile - The Only Licensed FIFA World Cup 2022 Game for Android.md b/spaces/fatiXbelha/sd/FIFA Mobile - The Only Licensed FIFA World Cup 2022 Game for Android.md deleted file mode 100644 index 923a9a4f57fecb1f26103136517e41c0eb1bcc86..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/FIFA Mobile - The Only Licensed FIFA World Cup 2022 Game for Android.md +++ /dev/null @@ -1,102 +0,0 @@ -
-
-- A: Yes, you can play FIFA 2022 Mobile APK with other players online in various modes, such as Head-to-Head, VS Attack, and Manager Mode. However, you may not be able to play with players who are using the official version of FIFA Mobile or a different version of FIFA 2022 Mobile APK.
-- Q: Can I transfer my progress or account from the official version of FIFA Mobile to FIFA 2022 Mobile APK?
-- A: No, you cannot transfer your progress or account from the official version of FIFA Mobile to FIFA 2022 Mobile APK. They are separate and independent games that have different servers and databases. You will have to start from scratch if you switch from one game to another.
-
-
-
-
\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Fallout Shelter Online APK The Most Anticipated Fallout Game for Android.md b/spaces/fatiXbelha/sd/Fallout Shelter Online APK The Most Anticipated Fallout Game for Android.md deleted file mode 100644 index c0bfdde6b5b3490c5c9c7d7da34bb8e8ce079657..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Fallout Shelter Online APK The Most Anticipated Fallout Game for Android.md +++ /dev/null @@ -1,179 +0,0 @@ -
-
-
-
-
- ) : null
-}
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis.py
deleted file mode 100644
index 9cad8004bfbf962d03927f1826f1525b2c93789b..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis.py
+++ /dev/null
@@ -1,207 +0,0 @@
-# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import json
-import os
-import time
-from collections import defaultdict
-
-import pycocotools.mask as mask_utils
-import torchvision
-from PIL import Image
-
-
-
-def _isArrayLike(obj):
- return hasattr(obj, "__iter__") and hasattr(obj, "__len__")
-
-
-class LVIS:
- def __init__(self, annotation_path=None):
- """Class for reading and visualizing annotations.
- Args:
- annotation_path (str): location of annotation file
- """
- self.anns = {}
- self.cats = {}
- self.imgs = {}
- self.img_ann_map = defaultdict(list)
- self.cat_img_map = defaultdict(list)
- self.dataset = {}
-
- if annotation_path is not None:
- print("Loading annotations.")
-
- tic = time.time()
- self.dataset = self._load_json(annotation_path)
- print("Done (t={:0.2f}s)".format(time.time() - tic))
-
- assert type(self.dataset) == dict, "Annotation file format {} not supported.".format(type(self.dataset))
- self._create_index()
-
- def _load_json(self, path):
- with open(path, "r") as f:
- return json.load(f)
-
- def _create_index(self):
- print("Creating index.")
-
- self.img_ann_map = defaultdict(list)
- self.cat_img_map = defaultdict(list)
-
- self.anns = {}
- self.cats = {}
- self.imgs = {}
-
- for ann in self.dataset["annotations"]:
- self.img_ann_map[ann["image_id"]].append(ann)
- self.anns[ann["id"]] = ann
-
- for img in self.dataset["images"]:
- self.imgs[img["id"]] = img
-
- for cat in self.dataset["categories"]:
- self.cats[cat["id"]] = cat
-
- for ann in self.dataset["annotations"]:
- self.cat_img_map[ann["category_id"]].append(ann["image_id"])
-
- print("Index created.")
-
- def get_ann_ids(self, img_ids=None, cat_ids=None, area_rng=None):
- """Get ann ids that satisfy given filter conditions.
- Args:
- img_ids (int array): get anns for given imgs
- cat_ids (int array): get anns for given cats
- area_rng (float array): get anns for a given area range. e.g [0, inf]
- Returns:
- ids (int array): integer array of ann ids
- """
- if img_ids is not None:
- img_ids = img_ids if _isArrayLike(img_ids) else [img_ids]
- if cat_ids is not None:
- cat_ids = cat_ids if _isArrayLike(cat_ids) else [cat_ids]
- anns = []
- if img_ids is not None:
- for img_id in img_ids:
- anns.extend(self.img_ann_map[img_id])
- else:
- anns = self.dataset["annotations"]
-
- # return early if no more filtering required
- if cat_ids is None and area_rng is None:
- return [_ann["id"] for _ann in anns]
-
- cat_ids = set(cat_ids)
-
- if area_rng is None:
- area_rng = [0, float("inf")]
-
- ann_ids = [
- _ann["id"]
- for _ann in anns
- if _ann["category_id"] in cat_ids and _ann["area"] > area_rng[0] and _ann["area"] < area_rng[1]
- ]
- return ann_ids
-
- def get_cat_ids(self):
- """Get all category ids.
- Returns:
- ids (int array): integer array of category ids
- """
- return list(self.cats.keys())
-
- def get_img_ids(self):
- """Get all img ids.
- Returns:
- ids (int array): integer array of image ids
- """
- return list(self.imgs.keys())
-
- def _load_helper(self, _dict, ids):
- if ids is None:
- return list(_dict.values())
- elif _isArrayLike(ids):
- return [_dict[id] for id in ids]
- else:
- return [_dict[ids]]
-
- def load_anns(self, ids=None):
- """Load anns with the specified ids. If ids=None load all anns.
- Args:
- ids (int array): integer array of annotation ids
- Returns:
- anns (dict array) : loaded annotation objects
- """
- return self._load_helper(self.anns, ids)
-
- def load_cats(self, ids):
- """Load categories with the specified ids. If ids=None load all
- categories.
- Args:
- ids (int array): integer array of category ids
- Returns:
- cats (dict array) : loaded category dicts
- """
- return self._load_helper(self.cats, ids)
-
- def load_imgs(self, ids):
- """Load categories with the specified ids. If ids=None load all images.
- Args:
- ids (int array): integer array of image ids
- Returns:
- imgs (dict array) : loaded image dicts
- """
- return self._load_helper(self.imgs, ids)
-
- def download(self, save_dir, img_ids=None):
- """Download images from mscoco.org server.
- Args:
- save_dir (str): dir to save downloaded images
- img_ids (int array): img ids of images to download
- """
- imgs = self.load_imgs(img_ids)
-
- if not os.path.exists(save_dir):
- os.makedirs(save_dir)
-
- for img in imgs:
- file_name = os.path.join(save_dir, img["file_name"])
- if not os.path.exists(file_name):
- from urllib.request import urlretrieve
-
- urlretrieve(img["coco_url"], file_name)
-
- def ann_to_rle(self, ann):
- """Convert annotation which can be polygons, uncompressed RLE to RLE.
- Args:
- ann (dict) : annotation object
- Returns:
- ann (rle)
- """
- img_data = self.imgs[ann["image_id"]]
- h, w = img_data["height"], img_data["width"]
- segm = ann["segmentation"]
- if isinstance(segm, list):
- # polygon -- a single object might consist of multiple parts
- # we merge all parts into one mask rle code
- rles = mask_utils.frPyObjects(segm, h, w)
- rle = mask_utils.merge(rles)
- elif isinstance(segm["counts"], list):
- # uncompressed RLE
- rle = mask_utils.frPyObjects(segm, h, w)
- else:
- # rle
- rle = ann["segmentation"]
- return rle
-
- def ann_to_mask(self, ann):
- """Convert annotation which can be polygons, uncompressed RLE, or RLE
- to binary mask.
- Args:
- ann (dict) : annotation object
- Returns:
- binary mask (numpy 2D array)
- """
- rle = self.ann_to_rle(ann)
- return mask_utils.decode(rle)
-
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_inspect.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_inspect.py
deleted file mode 100644
index 30446ceb3f0235721e435f5fbd53f2e306f078cd..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_inspect.py
+++ /dev/null
@@ -1,270 +0,0 @@
-from __future__ import absolute_import
-
-import inspect
-from inspect import cleandoc, getdoc, getfile, isclass, ismodule, signature
-from typing import Any, Collection, Iterable, Optional, Tuple, Type, Union
-
-from .console import Group, RenderableType
-from .control import escape_control_codes
-from .highlighter import ReprHighlighter
-from .jupyter import JupyterMixin
-from .panel import Panel
-from .pretty import Pretty
-from .table import Table
-from .text import Text, TextType
-
-
-def _first_paragraph(doc: str) -> str:
- """Get the first paragraph from a docstring."""
- paragraph, _, _ = doc.partition("\n\n")
- return paragraph
-
-
-class Inspect(JupyterMixin):
- """A renderable to inspect any Python Object.
-
- Args:
- obj (Any): An object to inspect.
- title (str, optional): Title to display over inspect result, or None use type. Defaults to None.
- help (bool, optional): Show full help text rather than just first paragraph. Defaults to False.
- methods (bool, optional): Enable inspection of callables. Defaults to False.
- docs (bool, optional): Also render doc strings. Defaults to True.
- private (bool, optional): Show private attributes (beginning with underscore). Defaults to False.
- dunder (bool, optional): Show attributes starting with double underscore. Defaults to False.
- sort (bool, optional): Sort attributes alphabetically. Defaults to True.
- all (bool, optional): Show all attributes. Defaults to False.
- value (bool, optional): Pretty print value of object. Defaults to True.
- """
-
- def __init__(
- self,
- obj: Any,
- *,
- title: Optional[TextType] = None,
- help: bool = False,
- methods: bool = False,
- docs: bool = True,
- private: bool = False,
- dunder: bool = False,
- sort: bool = True,
- all: bool = True,
- value: bool = True,
- ) -> None:
- self.highlighter = ReprHighlighter()
- self.obj = obj
- self.title = title or self._make_title(obj)
- if all:
- methods = private = dunder = True
- self.help = help
- self.methods = methods
- self.docs = docs or help
- self.private = private or dunder
- self.dunder = dunder
- self.sort = sort
- self.value = value
-
- def _make_title(self, obj: Any) -> Text:
- """Make a default title."""
- title_str = (
- str(obj)
- if (isclass(obj) or callable(obj) or ismodule(obj))
- else str(type(obj))
- )
- title_text = self.highlighter(title_str)
- return title_text
-
- def __rich__(self) -> Panel:
- return Panel.fit(
- Group(*self._render()),
- title=self.title,
- border_style="scope.border",
- padding=(0, 1),
- )
-
- def _get_signature(self, name: str, obj: Any) -> Optional[Text]:
- """Get a signature for a callable."""
- try:
- _signature = str(signature(obj)) + ":"
- except ValueError:
- _signature = "(...)"
- except TypeError:
- return None
-
- source_filename: Optional[str] = None
- try:
- source_filename = getfile(obj)
- except (OSError, TypeError):
- # OSError is raised if obj has no source file, e.g. when defined in REPL.
- pass
-
- callable_name = Text(name, style="inspect.callable")
- if source_filename:
- callable_name.stylize(f"link file://{source_filename}")
- signature_text = self.highlighter(_signature)
-
- qualname = name or getattr(obj, "__qualname__", name)
-
- # If obj is a module, there may be classes (which are callable) to display
- if inspect.isclass(obj):
- prefix = "class"
- elif inspect.iscoroutinefunction(obj):
- prefix = "async def"
- else:
- prefix = "def"
-
- qual_signature = Text.assemble(
- (f"{prefix} ", f"inspect.{prefix.replace(' ', '_')}"),
- (qualname, "inspect.callable"),
- signature_text,
- )
-
- return qual_signature
-
- def _render(self) -> Iterable[RenderableType]:
- """Render object."""
-
- def sort_items(item: Tuple[str, Any]) -> Tuple[bool, str]:
- key, (_error, value) = item
- return (callable(value), key.strip("_").lower())
-
- def safe_getattr(attr_name: str) -> Tuple[Any, Any]:
- """Get attribute or any exception."""
- try:
- return (None, getattr(obj, attr_name))
- except Exception as error:
- return (error, None)
-
- obj = self.obj
- keys = dir(obj)
- total_items = len(keys)
- if not self.dunder:
- keys = [key for key in keys if not key.startswith("__")]
- if not self.private:
- keys = [key for key in keys if not key.startswith("_")]
- not_shown_count = total_items - len(keys)
- items = [(key, safe_getattr(key)) for key in keys]
- if self.sort:
- items.sort(key=sort_items)
-
- items_table = Table.grid(padding=(0, 1), expand=False)
- items_table.add_column(justify="right")
- add_row = items_table.add_row
- highlighter = self.highlighter
-
- if callable(obj):
- signature = self._get_signature("", obj)
- if signature is not None:
- yield signature
- yield ""
-
- if self.docs:
- _doc = self._get_formatted_doc(obj)
- if _doc is not None:
- doc_text = Text(_doc, style="inspect.help")
- doc_text = highlighter(doc_text)
- yield doc_text
- yield ""
-
- if self.value and not (isclass(obj) or callable(obj) or ismodule(obj)):
- yield Panel(
- Pretty(obj, indent_guides=True, max_length=10, max_string=60),
- border_style="inspect.value.border",
- )
- yield ""
-
- for key, (error, value) in items:
- key_text = Text.assemble(
- (
- key,
- "inspect.attr.dunder" if key.startswith("__") else "inspect.attr",
- ),
- (" =", "inspect.equals"),
- )
- if error is not None:
- warning = key_text.copy()
- warning.stylize("inspect.error")
- add_row(warning, highlighter(repr(error)))
- continue
-
- if callable(value):
- if not self.methods:
- continue
-
- _signature_text = self._get_signature(key, value)
- if _signature_text is None:
- add_row(key_text, Pretty(value, highlighter=highlighter))
- else:
- if self.docs:
- docs = self._get_formatted_doc(value)
- if docs is not None:
- _signature_text.append("\n" if "\n" in docs else " ")
- doc = highlighter(docs)
- doc.stylize("inspect.doc")
- _signature_text.append(doc)
-
- add_row(key_text, _signature_text)
- else:
- add_row(key_text, Pretty(value, highlighter=highlighter))
- if items_table.row_count:
- yield items_table
- elif not_shown_count:
- yield Text.from_markup(
- f"[b cyan]{not_shown_count}[/][i] attribute(s) not shown.[/i] "
- f"Run [b][magenta]inspect[/]([not b]inspect[/])[/b] for options."
- )
-
- def _get_formatted_doc(self, object_: Any) -> Optional[str]:
- """
- Extract the docstring of an object, process it and returns it.
- The processing consists in cleaning up the doctring's indentation,
- taking only its 1st paragraph if `self.help` is not True,
- and escape its control codes.
-
- Args:
- object_ (Any): the object to get the docstring from.
-
- Returns:
- Optional[str]: the processed docstring, or None if no docstring was found.
- """
- docs = getdoc(object_)
- if docs is None:
- return None
- docs = cleandoc(docs).strip()
- if not self.help:
- docs = _first_paragraph(docs)
- return escape_control_codes(docs)
-
-
-def get_object_types_mro(obj: Union[object, Type[Any]]) -> Tuple[type, ...]:
- """Returns the MRO of an object's class, or of the object itself if it's a class."""
- if not hasattr(obj, "__mro__"):
- # N.B. we cannot use `if type(obj) is type` here because it doesn't work with
- # some types of classes, such as the ones that use abc.ABCMeta.
- obj = type(obj)
- return getattr(obj, "__mro__", ())
-
-
-def get_object_types_mro_as_strings(obj: object) -> Collection[str]:
- """
- Returns the MRO of an object's class as full qualified names, or of the object itself if it's a class.
-
- Examples:
- `object_types_mro_as_strings(JSONDecoder)` will return `['json.decoder.JSONDecoder', 'builtins.object']`
- """
- return [
- f'{getattr(type_, "__module__", "")}.{getattr(type_, "__qualname__", "")}'
- for type_ in get_object_types_mro(obj)
- ]
-
-
-def is_object_one_of_types(
- obj: object, fully_qualified_types_names: Collection[str]
-) -> bool:
- """
- Returns `True` if the given object's class (or the object itself, if it's a class) has one of the
- fully qualified names in its MRO.
- """
- for type_name in get_object_types_mro_as_strings(obj):
- if type_name in fully_qualified_types_names:
- return True
- return False
diff --git a/spaces/RdnUser77/SpacIO_v1/all_models.py b/spaces/RdnUser77/SpacIO_v1/all_models.py
deleted file mode 100644
index d5a3abe0892831da8873f2a4e55ee3fb4c1564cf..0000000000000000000000000000000000000000
--- a/spaces/RdnUser77/SpacIO_v1/all_models.py
+++ /dev/null
@@ -1,16 +0,0 @@
-models =[
- "Yntec/epiCPhotoGasm",
-"digiplay/perfectlevel10",
-"Yntec/realistic-vision-v12",
- "Yntec/aMovieX",
- "digiplay/AbsoluteReality_v1.8.1",
- "Yntec/photoMovieRealistic",
- "tensor-diffusion/chilloutmix-NI",
- "dreamlike-art/dreamlike-photoreal-2.0",
- "Yntec/photoMovieX",
- "Yntec/CinematicReality",
- "Yntec/photoMovieXFinal",
- "Leekp/toonmaker3",
- "shantanudave/shantanuimagessept10",
- "shantanudave/autotrain-adv-15sept"
-]
\ No newline at end of file
diff --git a/spaces/Reha2704/VToonify/vtoonify/model/bisenet/model.py b/spaces/Reha2704/VToonify/vtoonify/model/bisenet/model.py
deleted file mode 100644
index e61c0eb20aaa63065cc17bbcfe27b245f1f0dbf5..0000000000000000000000000000000000000000
--- a/spaces/Reha2704/VToonify/vtoonify/model/bisenet/model.py
+++ /dev/null
@@ -1,283 +0,0 @@
-#!/usr/bin/python
-# -*- encoding: utf-8 -*-
-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-from model.bisenet.resnet import Resnet18
-# from modules.bn import InPlaceABNSync as BatchNorm2d
-
-
-class ConvBNReLU(nn.Module):
- def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
- super(ConvBNReLU, self).__init__()
- self.conv = nn.Conv2d(in_chan,
- out_chan,
- kernel_size = ks,
- stride = stride,
- padding = padding,
- bias = False)
- self.bn = nn.BatchNorm2d(out_chan)
- self.init_weight()
-
- def forward(self, x):
- x = self.conv(x)
- x = F.relu(self.bn(x))
- return x
-
- def init_weight(self):
- for ly in self.children():
- if isinstance(ly, nn.Conv2d):
- nn.init.kaiming_normal_(ly.weight, a=1)
- if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-class BiSeNetOutput(nn.Module):
- def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
- super(BiSeNetOutput, self).__init__()
- self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
- self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
- self.init_weight()
-
- def forward(self, x):
- x = self.conv(x)
- x = self.conv_out(x)
- return x
-
- def init_weight(self):
- for ly in self.children():
- if isinstance(ly, nn.Conv2d):
- nn.init.kaiming_normal_(ly.weight, a=1)
- if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
- def get_params(self):
- wd_params, nowd_params = [], []
- for name, module in self.named_modules():
- if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
- wd_params.append(module.weight)
- if not module.bias is None:
- nowd_params.append(module.bias)
- elif isinstance(module, nn.BatchNorm2d):
- nowd_params += list(module.parameters())
- return wd_params, nowd_params
-
-
-class AttentionRefinementModule(nn.Module):
- def __init__(self, in_chan, out_chan, *args, **kwargs):
- super(AttentionRefinementModule, self).__init__()
- self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
- self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
- self.bn_atten = nn.BatchNorm2d(out_chan)
- self.sigmoid_atten = nn.Sigmoid()
- self.init_weight()
-
- def forward(self, x):
- feat = self.conv(x)
- atten = F.avg_pool2d(feat, feat.size()[2:])
- atten = self.conv_atten(atten)
- atten = self.bn_atten(atten)
- atten = self.sigmoid_atten(atten)
- out = torch.mul(feat, atten)
- return out
-
- def init_weight(self):
- for ly in self.children():
- if isinstance(ly, nn.Conv2d):
- nn.init.kaiming_normal_(ly.weight, a=1)
- if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-
-class ContextPath(nn.Module):
- def __init__(self, *args, **kwargs):
- super(ContextPath, self).__init__()
- self.resnet = Resnet18()
- self.arm16 = AttentionRefinementModule(256, 128)
- self.arm32 = AttentionRefinementModule(512, 128)
- self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
- self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
- self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
-
- self.init_weight()
-
- def forward(self, x):
- H0, W0 = x.size()[2:]
- feat8, feat16, feat32 = self.resnet(x)
- H8, W8 = feat8.size()[2:]
- H16, W16 = feat16.size()[2:]
- H32, W32 = feat32.size()[2:]
-
- avg = F.avg_pool2d(feat32, feat32.size()[2:])
- avg = self.conv_avg(avg)
- avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
-
- feat32_arm = self.arm32(feat32)
- feat32_sum = feat32_arm + avg_up
- feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
- feat32_up = self.conv_head32(feat32_up)
-
- feat16_arm = self.arm16(feat16)
- feat16_sum = feat16_arm + feat32_up
- feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
- feat16_up = self.conv_head16(feat16_up)
-
- return feat8, feat16_up, feat32_up # x8, x8, x16
-
- def init_weight(self):
- for ly in self.children():
- if isinstance(ly, nn.Conv2d):
- nn.init.kaiming_normal_(ly.weight, a=1)
- if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
- def get_params(self):
- wd_params, nowd_params = [], []
- for name, module in self.named_modules():
- if isinstance(module, (nn.Linear, nn.Conv2d)):
- wd_params.append(module.weight)
- if not module.bias is None:
- nowd_params.append(module.bias)
- elif isinstance(module, nn.BatchNorm2d):
- nowd_params += list(module.parameters())
- return wd_params, nowd_params
-
-
-### This is not used, since I replace this with the resnet feature with the same size
-class SpatialPath(nn.Module):
- def __init__(self, *args, **kwargs):
- super(SpatialPath, self).__init__()
- self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
- self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
- self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
- self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
- self.init_weight()
-
- def forward(self, x):
- feat = self.conv1(x)
- feat = self.conv2(feat)
- feat = self.conv3(feat)
- feat = self.conv_out(feat)
- return feat
-
- def init_weight(self):
- for ly in self.children():
- if isinstance(ly, nn.Conv2d):
- nn.init.kaiming_normal_(ly.weight, a=1)
- if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
- def get_params(self):
- wd_params, nowd_params = [], []
- for name, module in self.named_modules():
- if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
- wd_params.append(module.weight)
- if not module.bias is None:
- nowd_params.append(module.bias)
- elif isinstance(module, nn.BatchNorm2d):
- nowd_params += list(module.parameters())
- return wd_params, nowd_params
-
-
-class FeatureFusionModule(nn.Module):
- def __init__(self, in_chan, out_chan, *args, **kwargs):
- super(FeatureFusionModule, self).__init__()
- self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
- self.conv1 = nn.Conv2d(out_chan,
- out_chan//4,
- kernel_size = 1,
- stride = 1,
- padding = 0,
- bias = False)
- self.conv2 = nn.Conv2d(out_chan//4,
- out_chan,
- kernel_size = 1,
- stride = 1,
- padding = 0,
- bias = False)
- self.relu = nn.ReLU(inplace=True)
- self.sigmoid = nn.Sigmoid()
- self.init_weight()
-
- def forward(self, fsp, fcp):
- fcat = torch.cat([fsp, fcp], dim=1)
- feat = self.convblk(fcat)
- atten = F.avg_pool2d(feat, feat.size()[2:])
- atten = self.conv1(atten)
- atten = self.relu(atten)
- atten = self.conv2(atten)
- atten = self.sigmoid(atten)
- feat_atten = torch.mul(feat, atten)
- feat_out = feat_atten + feat
- return feat_out
-
- def init_weight(self):
- for ly in self.children():
- if isinstance(ly, nn.Conv2d):
- nn.init.kaiming_normal_(ly.weight, a=1)
- if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
- def get_params(self):
- wd_params, nowd_params = [], []
- for name, module in self.named_modules():
- if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
- wd_params.append(module.weight)
- if not module.bias is None:
- nowd_params.append(module.bias)
- elif isinstance(module, nn.BatchNorm2d):
- nowd_params += list(module.parameters())
- return wd_params, nowd_params
-
-
-class BiSeNet(nn.Module):
- def __init__(self, n_classes, *args, **kwargs):
- super(BiSeNet, self).__init__()
- self.cp = ContextPath()
- ## here self.sp is deleted
- self.ffm = FeatureFusionModule(256, 256)
- self.conv_out = BiSeNetOutput(256, 256, n_classes)
- self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
- self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
- self.init_weight()
-
- def forward(self, x):
- H, W = x.size()[2:]
- feat_res8, feat_cp8, feat_cp16 = self.cp(x) # here return res3b1 feature
- feat_sp = feat_res8 # use res3b1 feature to replace spatial path feature
- feat_fuse = self.ffm(feat_sp, feat_cp8)
-
- feat_out = self.conv_out(feat_fuse)
- feat_out16 = self.conv_out16(feat_cp8)
- feat_out32 = self.conv_out32(feat_cp16)
-
- feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
- feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
- feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
- return feat_out, feat_out16, feat_out32
-
- def init_weight(self):
- for ly in self.children():
- if isinstance(ly, nn.Conv2d):
- nn.init.kaiming_normal_(ly.weight, a=1)
- if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
- def get_params(self):
- wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
- for name, child in self.named_children():
- child_wd_params, child_nowd_params = child.get_params()
- if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
- lr_mul_wd_params += child_wd_params
- lr_mul_nowd_params += child_nowd_params
- else:
- wd_params += child_wd_params
- nowd_params += child_nowd_params
- return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
-
-
-if __name__ == "__main__":
- net = BiSeNet(19)
- net.cuda()
- net.eval()
- in_ten = torch.randn(16, 3, 640, 480).cuda()
- out, out16, out32 = net(in_ten)
- print(out.shape)
-
- net.get_params()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py
deleted file mode 100644
index 722d5d8d71f75486e2db3008907c4eadfca41d63..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-
-from .conv_module import ConvModule
-
-
-class DepthwiseSeparableConvModule(nn.Module):
- """Depthwise separable convolution module.
-
- See https://arxiv.org/pdf/1704.04861.pdf for details.
-
- This module can replace a ConvModule with the conv block replaced by two
- conv block: depthwise conv block and pointwise conv block. The depthwise
- conv block contains depthwise-conv/norm/activation layers. The pointwise
- conv block contains pointwise-conv/norm/activation layers. It should be
- noted that there will be norm/activation layer in the depthwise conv block
- if `norm_cfg` and `act_cfg` are specified.
-
- Args:
- in_channels (int): Number of channels in the input feature map.
- Same as that in ``nn._ConvNd``.
- out_channels (int): Number of channels produced by the convolution.
- Same as that in ``nn._ConvNd``.
- kernel_size (int | tuple[int]): Size of the convolving kernel.
- Same as that in ``nn._ConvNd``.
- stride (int | tuple[int]): Stride of the convolution.
- Same as that in ``nn._ConvNd``. Default: 1.
- padding (int | tuple[int]): Zero-padding added to both sides of
- the input. Same as that in ``nn._ConvNd``. Default: 0.
- dilation (int | tuple[int]): Spacing between kernel elements.
- Same as that in ``nn._ConvNd``. Default: 1.
- norm_cfg (dict): Default norm config for both depthwise ConvModule and
- pointwise ConvModule. Default: None.
- act_cfg (dict): Default activation config for both depthwise ConvModule
- and pointwise ConvModule. Default: dict(type='ReLU').
- dw_norm_cfg (dict): Norm config of depthwise ConvModule. If it is
- 'default', it will be the same as `norm_cfg`. Default: 'default'.
- dw_act_cfg (dict): Activation config of depthwise ConvModule. If it is
- 'default', it will be the same as `act_cfg`. Default: 'default'.
- pw_norm_cfg (dict): Norm config of pointwise ConvModule. If it is
- 'default', it will be the same as `norm_cfg`. Default: 'default'.
- pw_act_cfg (dict): Activation config of pointwise ConvModule. If it is
- 'default', it will be the same as `act_cfg`. Default: 'default'.
- kwargs (optional): Other shared arguments for depthwise and pointwise
- ConvModule. See ConvModule for ref.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- norm_cfg=None,
- act_cfg=dict(type='ReLU'),
- dw_norm_cfg='default',
- dw_act_cfg='default',
- pw_norm_cfg='default',
- pw_act_cfg='default',
- **kwargs):
- super(DepthwiseSeparableConvModule, self).__init__()
- assert 'groups' not in kwargs, 'groups should not be specified'
-
- # if norm/activation config of depthwise/pointwise ConvModule is not
- # specified, use default config.
- dw_norm_cfg = dw_norm_cfg if dw_norm_cfg != 'default' else norm_cfg
- dw_act_cfg = dw_act_cfg if dw_act_cfg != 'default' else act_cfg
- pw_norm_cfg = pw_norm_cfg if pw_norm_cfg != 'default' else norm_cfg
- pw_act_cfg = pw_act_cfg if pw_act_cfg != 'default' else act_cfg
-
- # depthwise convolution
- self.depthwise_conv = ConvModule(
- in_channels,
- in_channels,
- kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=in_channels,
- norm_cfg=dw_norm_cfg,
- act_cfg=dw_act_cfg,
- **kwargs)
-
- self.pointwise_conv = ConvModule(
- in_channels,
- out_channels,
- 1,
- norm_cfg=pw_norm_cfg,
- act_cfg=pw_act_cfg,
- **kwargs)
-
- def forward(self, x):
- x = self.depthwise_conv(x)
- x = self.pointwise_conv(x)
- return x
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/losses/iou_loss.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/losses/iou_loss.py
deleted file mode 100644
index eba6f18b80981ca891c1add37007e6bf478c651f..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/losses/iou_loss.py
+++ /dev/null
@@ -1,436 +0,0 @@
-import math
-
-import mmcv
-import torch
-import torch.nn as nn
-
-from mmdet.core import bbox_overlaps
-from ..builder import LOSSES
-from .utils import weighted_loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def iou_loss(pred, target, linear=False, eps=1e-6):
- """IoU loss.
-
- Computing the IoU loss between a set of predicted bboxes and target bboxes.
- The loss is calculated as negative log of IoU.
-
- Args:
- pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
- shape (n, 4).
- target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
- linear (bool, optional): If True, use linear scale of loss instead of
- log scale. Default: False.
- eps (float): Eps to avoid log(0).
-
- Return:
- torch.Tensor: Loss tensor.
- """
- ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps)
- if linear:
- loss = 1 - ious
- else:
- loss = -ious.log()
- return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3):
- """BIoULoss.
-
- This is an implementation of paper
- `Improving Object Localization with Fitness NMS and Bounded IoU Loss.
-
- 
- }
- } catch (e) {
- }
- return
- },
- p({ children }) {
- return
- {children}
- }, - code({ node, inline, className, children, ...props }) { - if (children.length) { - if (children[0] == '▍') { - return ( - ▍ - ) - } - - children[0] = (children[0] as string).replace('`▍`', '▍') - } - - const match = /language-(\w+)/.exec(className || '') - - if (inline) { - return ( -
- {children}
-
- )
- }
-
- return (
-
- {message.author === 'bot' &&
- - Risk adjustment with Mack, Bootstrap, and Stochastic Bornhuetter-Ferguson (SBF) -
- - - """) - with gr.Row(): - inp = gr.CheckboxGroup(label='Operations',choices=options,value=options) - with gr.Group(): - with gr.Row(elem_id='group'): - # with gr.Column(scale=1): - # pass - with gr.Column(scale=2,elem_id='group'): - gr.Markdown("🗓️ Start Date
🗓️ End Date
Omnivore: A Single Model for Many Visual Modalities | Github Repo
" - - -gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['library.jpg']]).launch(enable_queue=True,cache_examples=True) - diff --git a/spaces/akhaliq/stylegan3_clip/metrics/kernel_inception_distance.py b/spaces/akhaliq/stylegan3_clip/metrics/kernel_inception_distance.py deleted file mode 100644 index e8e0bd12ef56e64d8e77091aaf465891f4984d9e..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/stylegan3_clip/metrics/kernel_inception_distance.py +++ /dev/null @@ -1,46 +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. - -"""Kernel Inception Distance (KID) from the paper "Demystifying MMD -GANs". Matches the original implementation by Binkowski et al. at -https://github.com/mbinkowski/MMD-GAN/blob/master/gan/compute_scores.py""" - -import numpy as np -from . import metric_utils - -#---------------------------------------------------------------------------- - -def compute_kid(opts, max_real, num_gen, num_subsets, max_subset_size): - # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz - detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl' - detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer. - - real_features = metric_utils.compute_feature_stats_for_dataset( - opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, - rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all() - - gen_features = metric_utils.compute_feature_stats_for_generator( - opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, - rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all() - - if opts.rank != 0: - return float('nan') - - n = real_features.shape[1] - m = min(min(real_features.shape[0], gen_features.shape[0]), max_subset_size) - t = 0 - for _subset_idx in range(num_subsets): - x = gen_features[np.random.choice(gen_features.shape[0], m, replace=False)] - y = real_features[np.random.choice(real_features.shape[0], m, replace=False)] - a = (x @ x.T / n + 1) ** 3 + (y @ y.T / n + 1) ** 3 - b = (x @ y.T / n + 1) ** 3 - t += (a.sum() - np.diag(a).sum()) / (m - 1) - b.sum() * 2 / m - kid = t / num_subsets / m - return float(kid) - -#---------------------------------------------------------------------------- diff --git a/spaces/aliabid94/AutoGPT/autogpt/commands/twitter.py b/spaces/aliabid94/AutoGPT/autogpt/commands/twitter.py deleted file mode 100644 index 3eaed36e20e1c520690ac59f25a4da6501f3440f..0000000000000000000000000000000000000000 --- a/spaces/aliabid94/AutoGPT/autogpt/commands/twitter.py +++ /dev/null @@ -1,26 +0,0 @@ -import os - -import tweepy -from dotenv import load_dotenv - -load_dotenv() - - -def send_tweet(tweet_text): - consumer_key = os.environ.get("TW_CONSUMER_KEY") - consumer_secret = os.environ.get("TW_CONSUMER_SECRET") - access_token = os.environ.get("TW_ACCESS_TOKEN") - access_token_secret = os.environ.get("TW_ACCESS_TOKEN_SECRET") - # Authenticate to Twitter - auth = tweepy.OAuthHandler(consumer_key, consumer_secret) - auth.set_access_token(access_token, access_token_secret) - - # Create API object - api = tweepy.API(auth) - - # Send tweet - try: - api.update_status(tweet_text) - print("Tweet sent successfully!") - except tweepy.TweepyException as e: - print("Error sending tweet: {}".format(e.reason)) diff --git a/spaces/alibaba-pai/pai-diffusion-artist-xlarge-zh/README.md b/spaces/alibaba-pai/pai-diffusion-artist-xlarge-zh/README.md deleted file mode 100644 index 91ef5a2baf4681392c41245974d25a58205b41d7..0000000000000000000000000000000000000000 --- a/spaces/alibaba-pai/pai-diffusion-artist-xlarge-zh/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: PAI Diffusion (Food) -emoji: 🌖 -colorFrom: gray -colorTo: pink -sdk: gradio -sdk_version: 3.11.0 -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/allknowingroger/Image-Models-Test22/app.py b/spaces/allknowingroger/Image-Models-Test22/app.py deleted file mode 100644 index 98a2b6b5e48089f9e9134aa9890372f3217b20c6..0000000000000000000000000000000000000000 --- a/spaces/allknowingroger/Image-Models-Test22/app.py +++ /dev/null @@ -1,144 +0,0 @@ -import gradio as gr -# import os -# import sys -# from pathlib import Path -import time - -models =[ - "J-Douglas/pokemon-lora", - "nitami/sikuning2", - "rhendz/niji-lora", - "Emilianohack6950/GenOrtega", - "theintuitiveye/HARDblend", - "digiplay/bluePencilRealistic_v01", - "digiplay/calicomixreal_v2.0_diffusers", - "digiplay/chrysanthemumMix_v1", - "digiplay/SDVN1-Real_v1", -] - - -model_functions = {} -model_idx = 1 -for model_path in models: - try: - model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False) - except Exception as error: - def the_fn(txt): - return None - model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"]) - model_idx+=1 - - -def send_it_idx(idx): - def send_it_fn(prompt): - output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt) - return output - return send_it_fn - -def get_prompts(prompt_text): - return prompt_text - -def clear_it(val): - if int(val) != 0: - val = 0 - else: - val = 0 - pass - return val - -def all_task_end(cnt,t_stamp): - to = t_stamp + 60 - et = time.time() - if et > to and t_stamp != 0: - d = gr.update(value=0) - tog = gr.update(value=1) - #print(f'to: {to} et: {et}') - else: - if cnt != 0: - d = gr.update(value=et) - else: - d = gr.update(value=0) - tog = gr.update(value=0) - #print (f'passing: to: {to} et: {et}') - pass - return d, tog - -def all_task_start(): - print("\n\n\n\n\n\n\n") - t = time.gmtime() - t_stamp = time.time() - current_time = time.strftime("%H:%M:%S", t) - return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0) - -def clear_fn(): - nn = len(models) - return tuple([None, *[None for _ in range(nn)]]) - - - -with gr.Blocks(title="SD Models") as my_interface: - with gr.Column(scale=12): - # with gr.Row(): - # gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""") - with gr.Row(): - with gr.Row(scale=6): - primary_prompt=gr.Textbox(label="Prompt", value="") - # real_prompt=gr.Textbox(label="Real prompt") - with gr.Row(scale=6): - # improve_prompts_btn=gr.Button("Improve") - with gr.Row(): - run=gr.Button("Run",variant="primary") - clear_btn=gr.Button("Clear") - with gr.Row(): - sd_outputs = {} - model_idx = 1 - for model_path in models: - with gr.Column(scale=3, min_width=320): - with gr.Box(): - sd_outputs[model_idx] = gr.Image(label=model_path) - pass - model_idx += 1 - pass - pass - - with gr.Row(visible=False): - start_box=gr.Number(interactive=False) - end_box=gr.Number(interactive=False) - tog_box=gr.Textbox(value=0,interactive=False) - - start_box.change( - all_task_end, - [start_box, end_box], - [start_box, tog_box], - every=1, - show_progress=False) - - primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box]) - run.click(all_task_start, None, [start_box, end_box, tog_box]) - runs_dict = {} - model_idx = 1 - for model_path in models: - runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]]) - model_idx += 1 - pass - pass - - # improve_prompts_btn_clicked=improve_prompts_btn.click( - # get_prompts, - # inputs=[primary_prompt], - # outputs=[primary_prompt], - # cancels=list(runs_dict.values())) - clear_btn.click( - clear_fn, - None, - [primary_prompt, *list(sd_outputs.values())], - cancels=[*list(runs_dict.values())]) - tog_box.change( - clear_it, - tog_box, - tog_box, - cancels=[*list(runs_dict.values())]) - -my_interface.queue(concurrency_count=600, status_update_rate=1) -my_interface.launch(inline=True, show_api=False) - \ No newline at end of file diff --git a/spaces/allknowingroger/Image-Models-Test84/app.py b/spaces/allknowingroger/Image-Models-Test84/app.py deleted file mode 100644 index 3d22707b5b28e07eafb5e07e9b5a366bca1b2844..0000000000000000000000000000000000000000 --- a/spaces/allknowingroger/Image-Models-Test84/app.py +++ /dev/null @@ -1,144 +0,0 @@ -import gradio as gr -# import os -# import sys -# from pathlib import Path -import time - -models =[ - "Ryukijano/lora-trained-xl-kaggle-p100", - "charliezjw/t2", - "recoilme/lora-trained-xl-colab", - "Falah/Iyad_Radi_SDXL1.0_Lora", - "Dayanand4574/stable-diffusion-chair", - "srgg000/nmda2", - "Ryukijano/lora-trained-xl-anime_colab", - "nerijs/lego-minifig-xl", - "MakAttack/BunnyAdnBinnyDog", -] - - -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/amarchheda/ChordDuplicate/portaudio/test/patest_leftright.c b/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_leftright.c deleted file mode 100644 index e61a351ed26bcba24299c87cd67989f203afe464..0000000000000000000000000000000000000000 --- a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_leftright.c +++ /dev/null @@ -1,185 +0,0 @@ -/** @file patest_leftright.c - @ingroup test_src - @brief Play different tone sine waves that - 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-It kept giving me an error saying I had to many downloads on my device. I removed 5 episodes off the end of my list of The Magicians, and it still wouldnt let me download the 1 episode it skipped on downloading from the previous season. No matter how many I removed for the list, it wouldnt let me add the one. Had to go into settings>apps>Netflix and clear the cache. That fixed the issue.
-I believe Netflix is doing the right thing. If people are downloading that many shows, it stands to prove they are not watching them regularly or they would not be staying at the same number of movies, or TV shows. If and when I download, I plan on watching them in the very near future! Keep up the good work Netflix, I appreciate the fact that I am able to download anything at all, and to be able to watch them offline is a huge plus for me! Thank you!
-Although I can see why limitations are set, I find it hard to digest you pay for something supposedly allowing access to content to find there are limitations restricting you. Also, I downloaded quite some episodes of Money Heist as I was travelling to a country with doubtfull internet, to find out all files were not accessible. I had to delete all and watch all online. Anyway, room for improvement, I would say.
-Very disappointed!!! I dont have internet at home, and i thought downloads would be the solution to watching my favorite shows that are not available on DVD. First i learned that i cant watch the downloads through my smart TV so the whole family huddles in front of the laptop. Second, I downloaded several episodes of one show since i wouldnt by going near a connection for awhile. I didnt get a chance to watch all of them in the 48 hours before they expired, and now i cant download them again for a year! Very worthless service for me. I will definately cancel.
-"...part of it also is that I know so much I have so much to share. I have so much to share!" Do you feel this way too?
You could talk about your content topic for days, weeks, or even years without skipping a beat. But maybe, you're not 100% sure which idea comes next. Or how many ideas to shove into one piece of content.
Today's episode is a continuation of our coaching call series, and I'm chatting with Kay'aleya Hunnybee about the beginning stages of her podcast journey. We're covering how to overcome switching between planning, recording, and editing an episode without feeling overwhelmed.
We're also tackling the big questions about show notes and when to start creating them.
This episode is juicy for anyone just getting started on their content journey!
So I officially launched last week, which is huge for me. I mean, it's just been it's been like, a vision for a long time. And so to actually do it is it feels complete, it feels kind of unreal. Still. But yeah, I launched it. I shared it on social media. I've gotten, you know, some good feedback. And yeah, I launched four episodes. And then I just I just launched I just got another episode out yesterday. So I'm trying to do the weekly, you know, episodes. That's where I'm at with it. Yeah. And I think you know what feels silly? Well, go ahead.
-I think what I really feel I'm confused about I mean, part of it is that I'm probably just new, right? So it just takes time to get into the flow of things. I assume that over time, I'm gonna feel like everything is not so hard and I'm not like walking up a mountain constantly. But what I'm experiencing now is like working on those four episodes and then getting this past one out. I feel like it's taken 80% of my time of my work time in the last couple of weeks or a few weeks. Honestly, you Just to do that, and so I'd love some support around how to manage it. I think part of it also is that I know so much I have so much to share, I have so much to share. And it's so hard for me when I'm sharing, like if I'm like on the podcast, and then I go off on a tangent, and I mean, I can edit those parts out, but then it takes time to edit those parts out. So it's like, wow, I just, I'm like a waterfall. So I'm trying to figure out how to like, hey, myself, well, this is great.
-I think that this is a fantastic problem to have. You don't want to call it a problem. It's more of a of a How can I like kind of compartmentalize all of the ideas in a way that feels like it's actually flowing out of you in a productive way? Because I think that a lot of people, especially people in this audience that I have noticed, like, there's no shortage of ideas, like people will tell me I have hundreds of things to talk about. I just don't know which one like, do I start with the best one? Do I start with this one, like the one that people need? Or the one that they're asking for? There's a lot of different ways, and there's no right or wrong answer here. So when the overwhelm, or the rush of all of the ideas kind of hits you, I would sit down. And I mean, my secret to being consistent is planning, it really is like sitting down and saying, Okay, I really want to talk about this one topic, what can I cover. And then if I look at an outline or script, and I say, oh, my gosh, this is gonna be a five hour long episode, if I were to cover all of these things, then I can kind of break it off into pieces and say, You know what, I really just need to talk about this one piece of it. And then the rest of this can go in another podcast episode. Or maybe you kind of see a vision of oh, this could actually be a series, I could create three specific episodes about this one topic. So there's no right or wrong answer here. I think that usually it's I tell people to start with what's it excites them the most. And since you just launched, it's likely that you probably put out like an origin story of why you're excited about your podcast, or you're in the space that you're in. But to continue to keep going. I always tell people to look back at what did you put out? And how did people respond to it? So if you have one episode that has more downloads, and if you have an audience, and you could say what it'll like about this, or, you know, can I get some feedback it but no pressure to do that as well, when you're first getting started? Because you're it's going to that's going to come with time, you'll have more data, more numbers as you keep podcasting. And you'll be able to see which things are really resonating with your audience. Does that help?
-Totally. And I think that's, I think what you said probably that's most helpful is just the idea of being able to break down. And it's something that I'm just learning through doing it right. Like I had to have the experience of creating these, like massive episodes that I had to edit out, like, you know, 25% of just to make them into like a reasonable length. But just understanding that about myself, because I've never done this before, right. So it's like it is new. And so yeah, the idea that I could actually make this into like, it could be two episodes, it doesn't have to be one, you know, I like that idea a lot. And I have a teensy tiny, I have a teensy tiny, online audience. I mean, really, most of my work has been one on one, and I have not been trying to get myself online yet. So I'm really very much new to it. I do have a lot of friends who I think have listened to my episodes. I'm not sure who are the listeners, there is one episode that was listen to the most. But I also think that was like the one that was on top last week when I when I launched. So it was like, you know, the fourth episode that was just on top. So I think that might be why it was listened to the most. However, you know, I think it's going to take time, I think I'm going to have to build an audience. And it feels like so much. I know that you were here at some point. I know. You're like now so developed. So at least you understand the experience of having like nobody and I'm not sure who I'm even talking to. Oh,
-yes, no, you're saying that like that top one was probably listened to the most. And I just imagine that. I remember when I first launched and I got like, you know, 10 downloads on one episode. And I was like, Oh, I bet six of those were me. Me and then the other four were probably my mom, my mom listening to it and saying yes, that's good. Like, no, you sound fine. Like, I mean, this happens for everybody. And I think that you I love that you have the right mindset about it is it's just gonna take that time of you know, now you've gotten your feet wet like you've launched and I think that that is a huge congratulations worthy moment in and of itself because most people won't even get that far. So I hope that you celebrate it and you're excited about it, but do Just also know that that launch is so much more work, then the repetition and the consistency like that. Imagine if you are like adopting a new like workout, like you're gonna start running that first mile you ever do from having not run it all, like you're gonna die, you're gonna feel like I'm dying, this is not working for me. But then the next time you go out and you're like, Okay, I know how I need to control my breathing, or I need to have better running shoes, or I need to do not go when it's hot. Like, you'll have all these kinds of tools in your toolkit to where now you've ripped off that band aid of putting your content out there, you know, how it works. And so now, it's just that how can I make showing up consistently easier? That's really the struggle that podcasters face after they launch is how can I continue to show up consistent and I actually I have it, you know, here right now, like one of your top goals for 2022 is to actually be consistent. So do you see anything kind of popping up over the next, let's say six months? Do you see something where you're already foreshadowing like, it's going to be a challenge to be consistent?
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-
\ No newline at end of file diff --git a/spaces/blmdsydm/faster-whisper-webui/src/languages.py b/spaces/blmdsydm/faster-whisper-webui/src/languages.py deleted file mode 100644 index fbad66e4d34119d27d12e3dfecbe99b6fdde4db7..0000000000000000000000000000000000000000 --- a/spaces/blmdsydm/faster-whisper-webui/src/languages.py +++ /dev/null @@ -1,147 +0,0 @@ -class Language(): - def __init__(self, code, name): - self.code = code - self.name = name - - def __str__(self): - return "Language(code={}, name={})".format(self.code, self.name) - -LANGUAGES = [ - Language('en', 'English'), - Language('zh', 'Chinese'), - Language('de', 'German'), - Language('es', 'Spanish'), - Language('ru', 'Russian'), - Language('ko', 'Korean'), - Language('fr', 'French'), - Language('ja', 'Japanese'), - Language('pt', 'Portuguese'), - Language('tr', 'Turkish'), - Language('pl', 'Polish'), - Language('ca', 'Catalan'), - Language('nl', 'Dutch'), - Language('ar', 'Arabic'), - Language('sv', 'Swedish'), - Language('it', 'Italian'), - Language('id', 'Indonesian'), - Language('hi', 'Hindi'), - Language('fi', 'Finnish'), - Language('vi', 'Vietnamese'), - Language('he', 'Hebrew'), - Language('uk', 'Ukrainian'), - Language('el', 'Greek'), - Language('ms', 'Malay'), - Language('cs', 'Czech'), - Language('ro', 'Romanian'), - Language('da', 'Danish'), - Language('hu', 'Hungarian'), - Language('ta', 'Tamil'), - Language('no', 'Norwegian'), - Language('th', 'Thai'), - Language('ur', 'Urdu'), - Language('hr', 'Croatian'), - Language('bg', 'Bulgarian'), - Language('lt', 'Lithuanian'), - Language('la', 'Latin'), - Language('mi', 'Maori'), - Language('ml', 'Malayalam'), - Language('cy', 'Welsh'), - Language('sk', 'Slovak'), - Language('te', 'Telugu'), - Language('fa', 'Persian'), - Language('lv', 'Latvian'), - Language('bn', 'Bengali'), - Language('sr', 'Serbian'), - Language('az', 'Azerbaijani'), - Language('sl', 'Slovenian'), - Language('kn', 'Kannada'), - Language('et', 'Estonian'), - Language('mk', 'Macedonian'), - Language('br', 'Breton'), - Language('eu', 'Basque'), - Language('is', 'Icelandic'), - Language('hy', 'Armenian'), - Language('ne', 'Nepali'), - Language('mn', 'Mongolian'), - Language('bs', 'Bosnian'), - Language('kk', 'Kazakh'), - Language('sq', 'Albanian'), - Language('sw', 'Swahili'), - Language('gl', 'Galician'), - Language('mr', 'Marathi'), - Language('pa', 'Punjabi'), - Language('si', 'Sinhala'), - Language('km', 'Khmer'), - Language('sn', 'Shona'), - Language('yo', 'Yoruba'), - Language('so', 'Somali'), - Language('af', 'Afrikaans'), - Language('oc', 'Occitan'), - Language('ka', 'Georgian'), - Language('be', 'Belarusian'), - Language('tg', 'Tajik'), - Language('sd', 'Sindhi'), - Language('gu', 'Gujarati'), - Language('am', 'Amharic'), - Language('yi', 'Yiddish'), - Language('lo', 'Lao'), - Language('uz', 'Uzbek'), - Language('fo', 'Faroese'), - Language('ht', 'Haitian creole'), - Language('ps', 'Pashto'), - Language('tk', 'Turkmen'), - Language('nn', 'Nynorsk'), - Language('mt', 'Maltese'), - Language('sa', 'Sanskrit'), - Language('lb', 'Luxembourgish'), - Language('my', 'Myanmar'), - Language('bo', 'Tibetan'), - Language('tl', 'Tagalog'), - Language('mg', 'Malagasy'), - Language('as', 'Assamese'), - Language('tt', 'Tatar'), - Language('haw', 'Hawaiian'), - Language('ln', 'Lingala'), - Language('ha', 'Hausa'), - Language('ba', 'Bashkir'), - Language('jw', 'Javanese'), - Language('su', 'Sundanese') -] - -_TO_LANGUAGE_CODE = { - **{language.code: language for language in LANGUAGES}, - "burmese": "my", - "valencian": "ca", - "flemish": "nl", - "haitian": "ht", - "letzeburgesch": "lb", - "pushto": "ps", - "panjabi": "pa", - "moldavian": "ro", - "moldovan": "ro", - "sinhalese": "si", - "castilian": "es", -} - -_FROM_LANGUAGE_NAME = { - **{language.name.lower(): language for language in LANGUAGES} -} - -def get_language_from_code(language_code, default=None) -> Language: - """Return the language name from the language code.""" - return _TO_LANGUAGE_CODE.get(language_code, default) - -def get_language_from_name(language, default=None) -> Language: - """Return the language code from the language name.""" - return _FROM_LANGUAGE_NAME.get(language.lower() if language else None, default) - -def get_language_names(): - """Return a list of language names.""" - return [language.name for language in LANGUAGES] - -if __name__ == "__main__": - # Test lookup - print(get_language_from_code('en')) - print(get_language_from_name('English')) - - print(get_language_names()) \ No newline at end of file diff --git a/spaces/boomsss/gamedayspx/model_intra_v2.py b/spaces/boomsss/gamedayspx/model_intra_v2.py deleted file mode 100644 index 83c24d9c124eb963b99a2653c96bf85a4d4d68e0..0000000000000000000000000000000000000000 --- a/spaces/boomsss/gamedayspx/model_intra_v2.py +++ /dev/null @@ -1,73 +0,0 @@ -import streamlit as st -import pandas as pd -import pandas_datareader as pdr -import numpy as np -import yfinance as yf -import requests -from bs4 import BeautifulSoup -from typing import List -from tqdm import tqdm -import os -import datetime -from pandas.tseries.offsets import BDay -from datasets import load_dataset -import lightgbm as lgb -from sklearn.model_selection import TimeSeriesSplit -from intraCols import model_cols - -# If the dataset is gated/private, make sure you have run huggingface-cli login -def walk_forward_validation(df, target_column, num_periods): - - df = df[model_cols + [target_column]] - df[target_column] = df[target_column].astype(bool) - - # Model - # model = lgb.LGBMClassifier(n_estimators=10, random_state=42, verbosity=-1) - - tscv = TimeSeriesSplit(n_splits=len(df)-1, max_train_size=None, test_size=num_periods) # num_splits is the number of splits you want - - overall_results = [] - # Iterate over the rows in the DataFrame, one step at a time - # Split the time series data using TimeSeriesSplit - for train_index, test_index in tqdm(tscv.split(df), total=tscv.n_splits): - # Extract the training and testing data for the current split - X_train = df.drop(target_column, axis=1).iloc[train_index] - y_train = df[target_column].iloc[train_index] - X_test = df.drop(target_column, axis=1).iloc[test_index] - y_test = df[target_column].iloc[test_index] - - y_train = y_train.astype(bool) - model = lgb.LGBMClassifier(n_estimators=10, random_state=42, verbosity=-1) - model.fit(X_train, y_train) - # Make a prediction on the test data - predictions = model.predict_proba(X_test)[:,-1] - - # Create a DataFrame to store the true and predicted values - result_df = pd.DataFrame({'True': y_test, 'Predicted': predictions}, index=y_test.index) - overall_results.append(result_df) - - df_results = pd.concat(overall_results) - - # Calibrate Probabilities - def get_quantiles(df, col_name, q): - return df.groupby(pd.cut(df[col_name], q))['True'].mean() - - greenprobas = [] - for i, pct in tqdm(enumerate(df_results['Predicted']), desc='Calibrating Probas',total=len(df_results)): - try: - df_q = get_quantiles(df_results.iloc[:i], 'Predicted', 7) - for q in df_q.index: - if q.left <= pct <= q.right: - p = df_q[q] - except: - p = None - - greenprobas.append(p) - - df_results['CalibPredicted'] = greenprobas - - return df_results, model - -def seq_predict_proba(df, trained_clf_model): - clf_pred_proba = trained_clf_model.predict_proba(df[model_cols])[:,-1] - return clf_pred_proba \ No newline at end of file diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/data/info_audio_dataset.py b/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/data/info_audio_dataset.py deleted file mode 100644 index 47ab4b1594faf1e9f1ce962fb980d80295b1f079..0000000000000000000000000000000000000000 --- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/data/info_audio_dataset.py +++ /dev/null @@ -1,110 +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. -"""Base classes for the datasets that also provide non-audio metadata, -e.g. description, text transcription etc. -""" -from dataclasses import dataclass -import logging -import math -import re -import typing as tp - -import torch - -from .audio_dataset import AudioDataset, AudioMeta -from ..environment import AudioCraftEnvironment -from ..modules.conditioners import SegmentWithAttributes, ConditioningAttributes - - -logger = logging.getLogger(__name__) - - -def _clusterify_meta(meta: AudioMeta) -> AudioMeta: - """Monkey-patch meta to match cluster specificities.""" - meta.path = AudioCraftEnvironment.apply_dataset_mappers(meta.path) - if meta.info_path is not None: - meta.info_path.zip_path = AudioCraftEnvironment.apply_dataset_mappers(meta.info_path.zip_path) - return meta - - -def clusterify_all_meta(meta: tp.List[AudioMeta]) -> tp.List[AudioMeta]: - """Monkey-patch all meta to match cluster specificities.""" - return [_clusterify_meta(m) for m in meta] - - -@dataclass -class AudioInfo(SegmentWithAttributes): - """Dummy SegmentInfo with empty attributes. - - The InfoAudioDataset is expected to return metadata that inherits - from SegmentWithAttributes class and can return conditioning attributes. - - This basically guarantees all datasets will be compatible with current - solver that contain conditioners requiring this. - """ - audio_tokens: tp.Optional[torch.Tensor] = None # populated when using cached batch for training a LM. - - def to_condition_attributes(self) -> ConditioningAttributes: - return ConditioningAttributes() - - -class InfoAudioDataset(AudioDataset): - """AudioDataset that always returns metadata as SegmentWithAttributes along with the audio waveform. - - See `audiocraft.data.audio_dataset.AudioDataset` for initialization arguments. - """ - def __init__(self, meta: tp.List[AudioMeta], **kwargs): - super().__init__(clusterify_all_meta(meta), **kwargs) - - def __getitem__(self, index: int) -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, SegmentWithAttributes]]: - if not self.return_info: - wav = super().__getitem__(index) - assert isinstance(wav, torch.Tensor) - return wav - wav, meta = super().__getitem__(index) - return wav, AudioInfo(**meta.to_dict()) - - -def get_keyword_or_keyword_list(value: tp.Optional[str]) -> tp.Union[tp.Optional[str], tp.Optional[tp.List[str]]]: - """Preprocess a single keyword or possible a list of keywords.""" - if isinstance(value, list): - return get_keyword_list(value) - else: - return get_keyword(value) - - -def get_string(value: tp.Optional[str]) -> tp.Optional[str]: - """Preprocess a single keyword.""" - if value is None or (not isinstance(value, str)) or len(value) == 0 or value == 'None': - return None - else: - return value.strip() - - -def get_keyword(value: tp.Optional[str]) -> tp.Optional[str]: - """Preprocess a single keyword.""" - if value is None or (not isinstance(value, str)) or len(value) == 0 or value == 'None': - return None - else: - return value.strip().lower() - - -def get_keyword_list(values: tp.Union[str, tp.List[str]]) -> tp.Optional[tp.List[str]]: - """Preprocess a list of keywords.""" - if isinstance(values, str): - values = [v.strip() for v in re.split(r'[,\s]', values)] - elif isinstance(values, float) and math.isnan(values): - values = [] - if not isinstance(values, list): - logger.debug(f"Unexpected keyword list {values}") - values = [str(values)] - - kws = [get_keyword(v) for v in values] - kw_list = [k for k in kws if k is not None] - if len(kw_list) == 0: - return None - else: - return kw_list diff --git a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/platforms/osmesa.py b/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/platforms/osmesa.py deleted file mode 100644 index deaa5ff44031a107883913ae9a18fc425d650f3d..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/platforms/osmesa.py +++ /dev/null @@ -1,59 +0,0 @@ -from .base import Platform - - -__all__ = ['OSMesaPlatform'] - - -class OSMesaPlatform(Platform): - """Renders into a software buffer using OSMesa. Requires special versions - of OSMesa to be installed, plus PyOpenGL upgrade. - """ - - def __init__(self, viewport_width, viewport_height): - super(OSMesaPlatform, self).__init__(viewport_width, viewport_height) - self._context = None - self._buffer = None - - def init_context(self): - from OpenGL import arrays - from OpenGL.osmesa import ( - OSMesaCreateContextAttribs, OSMESA_FORMAT, - OSMESA_RGBA, OSMESA_PROFILE, OSMESA_CORE_PROFILE, - OSMESA_CONTEXT_MAJOR_VERSION, OSMESA_CONTEXT_MINOR_VERSION, - OSMESA_DEPTH_BITS - ) - - attrs = arrays.GLintArray.asArray([ - OSMESA_FORMAT, OSMESA_RGBA, - OSMESA_DEPTH_BITS, 24, - OSMESA_PROFILE, OSMESA_CORE_PROFILE, - OSMESA_CONTEXT_MAJOR_VERSION, 3, - OSMESA_CONTEXT_MINOR_VERSION, 3, - 0 - ]) - self._context = OSMesaCreateContextAttribs(attrs, None) - self._buffer = arrays.GLubyteArray.zeros( - (self.viewport_height, self.viewport_width, 4) - ) - - def make_current(self): - from OpenGL import GL as gl - from OpenGL.osmesa import OSMesaMakeCurrent - assert(OSMesaMakeCurrent( - self._context, self._buffer, gl.GL_UNSIGNED_BYTE, - self.viewport_width, self.viewport_height - )) - - def make_uncurrent(self): - """Make the OpenGL context uncurrent. - """ - pass - - def delete_context(self): - from OpenGL.osmesa import OSMesaDestroyContext - OSMesaDestroyContext(self._context) - self._context = None - self._buffer = None - - def supports_framebuffers(self): - return False diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/helpers.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/helpers.py deleted file mode 100644 index 874ab1ac076bc311d8853f08bb5fe454b650099f..0000000000000000000000000000000000000000 --- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/helpers.py +++ /dev/null @@ -1,878 +0,0 @@ -"""Various helper functions""" - -import asyncio -import base64 -import binascii -import datetime -import functools -import inspect -import netrc -import os -import platform -import re -import sys -import time -import warnings -import weakref -from collections import namedtuple -from contextlib import suppress -from email.parser import HeaderParser -from email.utils import parsedate -from math import ceil -from pathlib import Path -from types import TracebackType -from typing import ( - Any, - Callable, - ContextManager, - Dict, - Generator, - Generic, - Iterable, - Iterator, - List, - Mapping, - Optional, - Pattern, - Set, - Tuple, - Type, - TypeVar, - Union, - cast, -) -from urllib.parse import quote -from urllib.request import getproxies, proxy_bypass - -import async_timeout -import attr -from multidict import MultiDict, MultiDictProxy -from yarl import URL - -from . import hdrs -from .log import client_logger, internal_logger -from .typedefs import PathLike, Protocol # noqa - -__all__ = ("BasicAuth", "ChainMapProxy", "ETag") - -IS_MACOS = platform.system() == "Darwin" -IS_WINDOWS = platform.system() == "Windows" - -PY_36 = sys.version_info >= (3, 6) -PY_37 = sys.version_info >= (3, 7) -PY_38 = sys.version_info >= (3, 8) -PY_310 = sys.version_info >= (3, 10) -PY_311 = sys.version_info >= (3, 11) - -if sys.version_info < (3, 7): - import idna_ssl - - idna_ssl.patch_match_hostname() - - def all_tasks( - loop: Optional[asyncio.AbstractEventLoop] = None, - ) -> Set["asyncio.Task[Any]"]: - tasks = list(asyncio.Task.all_tasks(loop)) - return {t for t in tasks if not t.done()} - -else: - all_tasks = asyncio.all_tasks - - -_T = TypeVar("_T") -_S = TypeVar("_S") - - -sentinel: Any = object() -NO_EXTENSIONS: bool = bool(os.environ.get("AIOHTTP_NO_EXTENSIONS")) - -# N.B. sys.flags.dev_mode is available on Python 3.7+, use getattr -# for compatibility with older versions -DEBUG: bool = getattr(sys.flags, "dev_mode", False) or ( - not sys.flags.ignore_environment and bool(os.environ.get("PYTHONASYNCIODEBUG")) -) - - -CHAR = {chr(i) for i in range(0, 128)} -CTL = {chr(i) for i in range(0, 32)} | { - chr(127), -} -SEPARATORS = { - "(", - ")", - "<", - ">", - "@", - ",", - ";", - ":", - "\\", - '"', - "/", - "[", - "]", - "?", - "=", - "{", - "}", - " ", - chr(9), -} -TOKEN = CHAR ^ CTL ^ SEPARATORS - - -class noop: - def __await__(self) -> Generator[None, None, None]: - yield - - -class BasicAuth(namedtuple("BasicAuth", ["login", "password", "encoding"])): - """Http basic authentication helper.""" - - def __new__( - cls, login: str, password: str = "", encoding: str = "latin1" - ) -> "BasicAuth": - if login is None: - raise ValueError("None is not allowed as login value") - - if password is None: - raise ValueError("None is not allowed as password value") - - if ":" in login: - raise ValueError('A ":" is not allowed in login (RFC 1945#section-11.1)') - - return super().__new__(cls, login, password, encoding) - - @classmethod - def decode(cls, auth_header: str, encoding: str = "latin1") -> "BasicAuth": - """Create a BasicAuth object from an Authorization HTTP header.""" - try: - auth_type, encoded_credentials = auth_header.split(" ", 1) - except ValueError: - raise ValueError("Could not parse authorization header.") - - if auth_type.lower() != "basic": - raise ValueError("Unknown authorization method %s" % auth_type) - - try: - decoded = base64.b64decode( - encoded_credentials.encode("ascii"), validate=True - ).decode(encoding) - except binascii.Error: - raise ValueError("Invalid base64 encoding.") - - try: - # RFC 2617 HTTP Authentication - # https://www.ietf.org/rfc/rfc2617.txt - # the colon must be present, but the username and password may be - # otherwise blank. - username, password = decoded.split(":", 1) - except ValueError: - raise ValueError("Invalid credentials.") - - return cls(username, password, encoding=encoding) - - @classmethod - def from_url(cls, url: URL, *, encoding: str = "latin1") -> Optional["BasicAuth"]: - """Create BasicAuth from url.""" - if not isinstance(url, URL): - raise TypeError("url should be yarl.URL instance") - if url.user is None: - return None - return cls(url.user, url.password or "", encoding=encoding) - - def encode(self) -> str: - """Encode credentials.""" - creds = (f"{self.login}:{self.password}").encode(self.encoding) - return "Basic %s" % base64.b64encode(creds).decode(self.encoding) - - -def strip_auth_from_url(url: URL) -> Tuple[URL, Optional[BasicAuth]]: - auth = BasicAuth.from_url(url) - if auth is None: - return url, None - else: - return url.with_user(None), auth - - -def netrc_from_env() -> Optional[netrc.netrc]: - """Load netrc from file. - - Attempt to load it from the path specified by the env-var - NETRC or in the default location in the user's home directory. - - Returns None if it couldn't be found or fails to parse. - """ - netrc_env = os.environ.get("NETRC") - - if netrc_env is not None: - netrc_path = Path(netrc_env) - else: - try: - home_dir = Path.home() - except RuntimeError as e: # pragma: no cover - # if pathlib can't resolve home, it may raise a RuntimeError - client_logger.debug( - "Could not resolve home directory when " - "trying to look for .netrc file: %s", - e, - ) - return None - - netrc_path = home_dir / ("_netrc" if IS_WINDOWS else ".netrc") - - try: - return netrc.netrc(str(netrc_path)) - except netrc.NetrcParseError as e: - client_logger.warning("Could not parse .netrc file: %s", e) - except OSError as e: - # we couldn't read the file (doesn't exist, permissions, etc.) - if netrc_env or netrc_path.is_file(): - # only warn if the environment wanted us to load it, - # or it appears like the default file does actually exist - client_logger.warning("Could not read .netrc file: %s", e) - - return None - - -@attr.s(auto_attribs=True, frozen=True, slots=True) -class ProxyInfo: - proxy: URL - proxy_auth: Optional[BasicAuth] - - -def proxies_from_env() -> Dict[str, ProxyInfo]: - proxy_urls = { - k: URL(v) - for k, v in getproxies().items() - if k in ("http", "https", "ws", "wss") - } - netrc_obj = netrc_from_env() - stripped = {k: strip_auth_from_url(v) for k, v in proxy_urls.items()} - ret = {} - for proto, val in stripped.items(): - proxy, auth = val - if proxy.scheme in ("https", "wss"): - client_logger.warning( - "%s proxies %s are not supported, ignoring", proxy.scheme.upper(), proxy - ) - continue - if netrc_obj and auth is None: - auth_from_netrc = None - if proxy.host is not None: - auth_from_netrc = netrc_obj.authenticators(proxy.host) - if auth_from_netrc is not None: - # auth_from_netrc is a (`user`, `account`, `password`) tuple, - # `user` and `account` both can be username, - # if `user` is None, use `account` - *logins, password = auth_from_netrc - login = logins[0] if logins[0] else logins[-1] - auth = BasicAuth(cast(str, login), cast(str, password)) - ret[proto] = ProxyInfo(proxy, auth) - return ret - - -def current_task( - loop: Optional[asyncio.AbstractEventLoop] = None, -) -> "Optional[asyncio.Task[Any]]": - if sys.version_info >= (3, 7): - return asyncio.current_task(loop=loop) - else: - return asyncio.Task.current_task(loop=loop) - - -def get_running_loop( - loop: Optional[asyncio.AbstractEventLoop] = None, -) -> asyncio.AbstractEventLoop: - if loop is None: - loop = asyncio.get_event_loop() - if not loop.is_running(): - warnings.warn( - "The object should be created within an async function", - DeprecationWarning, - stacklevel=3, - ) - if loop.get_debug(): - internal_logger.warning( - "The object should be created within an async function", stack_info=True - ) - return loop - - -def isasyncgenfunction(obj: Any) -> bool: - func = getattr(inspect, "isasyncgenfunction", None) - if func is not None: - return func(obj) # type: ignore[no-any-return] - else: - return False - - -def get_env_proxy_for_url(url: URL) -> Tuple[URL, Optional[BasicAuth]]: - """Get a permitted proxy for the given URL from the env.""" - if url.host is not None and proxy_bypass(url.host): - raise LookupError(f"Proxying is disallowed for `{url.host!r}`") - - proxies_in_env = proxies_from_env() - try: - proxy_info = proxies_in_env[url.scheme] - except KeyError: - raise LookupError(f"No proxies found for `{url!s}` in the env") - else: - return proxy_info.proxy, proxy_info.proxy_auth - - -@attr.s(auto_attribs=True, frozen=True, slots=True) -class MimeType: - type: str - subtype: str - suffix: str - parameters: "MultiDictProxy[str]" - - -@functools.lru_cache(maxsize=56) -def parse_mimetype(mimetype: str) -> MimeType: - """Parses a MIME type into its components. - - mimetype is a MIME type string. - - Returns a MimeType object. - - Example: - - >>> parse_mimetype('text/html; charset=utf-8') - MimeType(type='text', subtype='html', suffix='', - parameters={'charset': 'utf-8'}) - - """ - if not mimetype: - return MimeType( - type="", subtype="", suffix="", parameters=MultiDictProxy(MultiDict()) - ) - - parts = mimetype.split(";") - params: MultiDict[str] = MultiDict() - for item in parts[1:]: - if not item: - continue - key, value = cast( - Tuple[str, str], item.split("=", 1) if "=" in item else (item, "") - ) - params.add(key.lower().strip(), value.strip(' "')) - - fulltype = parts[0].strip().lower() - if fulltype == "*": - fulltype = "*/*" - - mtype, stype = ( - cast(Tuple[str, str], fulltype.split("/", 1)) - if "/" in fulltype - else (fulltype, "") - ) - stype, suffix = ( - cast(Tuple[str, str], stype.split("+", 1)) if "+" in stype else (stype, "") - ) - - return MimeType( - type=mtype, subtype=stype, suffix=suffix, parameters=MultiDictProxy(params) - ) - - -def guess_filename(obj: Any, default: Optional[str] = None) -> Optional[str]: - name = getattr(obj, "name", None) - if name and isinstance(name, str) and name[0] != "<" and name[-1] != ">": - return Path(name).name - return default - - -not_qtext_re = re.compile(r"[^\041\043-\133\135-\176]") -QCONTENT = {chr(i) for i in range(0x20, 0x7F)} | {"\t"} - - -def quoted_string(content: str) -> str: - """Return 7-bit content as quoted-string. - - Format content into a quoted-string as defined in RFC5322 for - Internet Message Format. Notice that this is not the 8-bit HTTP - format, but the 7-bit email format. Content must be in usascii or - a ValueError is raised. - """ - if not (QCONTENT > set(content)): - raise ValueError(f"bad content for quoted-string {content!r}") - return not_qtext_re.sub(lambda x: "\\" + x.group(0), content) - - -def content_disposition_header( - disptype: str, quote_fields: bool = True, _charset: str = "utf-8", **params: str -) -> str: - """Sets ``Content-Disposition`` header for MIME. - - This is the MIME payload Content-Disposition header from RFC 2183 - and RFC 7579 section 4.2, not the HTTP Content-Disposition from - RFC 6266. - - disptype is a disposition type: inline, attachment, form-data. - Should be valid extension token (see RFC 2183) - - quote_fields performs value quoting to 7-bit MIME headers - according to RFC 7578. Set to quote_fields to False if recipient - can take 8-bit file names and field values. - - _charset specifies the charset to use when quote_fields is True. - - params is a dict with disposition params. - """ - if not disptype or not (TOKEN > set(disptype)): - raise ValueError("bad content disposition type {!r}" "".format(disptype)) - - value = disptype - if params: - lparams = [] - for key, val in params.items(): - if not key or not (TOKEN > set(key)): - raise ValueError( - "bad content disposition parameter" " {!r}={!r}".format(key, val) - ) - if quote_fields: - if key.lower() == "filename": - qval = quote(val, "", encoding=_charset) - lparams.append((key, '"%s"' % qval)) - else: - try: - qval = quoted_string(val) - except ValueError: - qval = "".join( - (_charset, "''", quote(val, "", encoding=_charset)) - ) - lparams.append((key + "*", qval)) - else: - lparams.append((key, '"%s"' % qval)) - else: - qval = val.replace("\\", "\\\\").replace('"', '\\"') - lparams.append((key, '"%s"' % qval)) - sparams = "; ".join("=".join(pair) for pair in lparams) - value = "; ".join((value, sparams)) - return value - - -class _TSelf(Protocol, Generic[_T]): - _cache: Dict[str, _T] - - -class reify(Generic[_T]): - """Use as a class method decorator. - - It operates almost exactly like - the Python `@property` decorator, but it puts the result of the - method it decorates into the instance dict after the first call, - effectively replacing the function it decorates with an instance - variable. It is, in Python parlance, a data descriptor. - """ - - def __init__(self, wrapped: Callable[..., _T]) -> None: - self.wrapped = wrapped - self.__doc__ = wrapped.__doc__ - self.name = wrapped.__name__ - - def __get__(self, inst: _TSelf[_T], owner: Optional[Type[Any]] = None) -> _T: - try: - try: - return inst._cache[self.name] - except KeyError: - val = self.wrapped(inst) - inst._cache[self.name] = val - return val - except AttributeError: - if inst is None: - return self - raise - - def __set__(self, inst: _TSelf[_T], value: _T) -> None: - raise AttributeError("reified property is read-only") - - -reify_py = reify - -try: - from ._helpers import reify as reify_c - - if not NO_EXTENSIONS: - reify = reify_c # type: ignore[misc,assignment] -except ImportError: - pass - -_ipv4_pattern = ( - r"^(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}" - r"(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)$" -) -_ipv6_pattern = ( - r"^(?:(?:(?:[A-F0-9]{1,4}:){6}|(?=(?:[A-F0-9]{0,4}:){0,6}" - r"(?:[0-9]{1,3}\.){3}[0-9]{1,3}$)(([0-9A-F]{1,4}:){0,5}|:)" - r"((:[0-9A-F]{1,4}){1,5}:|:)|::(?:[A-F0-9]{1,4}:){5})" - r"(?:(?:25[0-5]|2[0-4][0-9]|1[0-9][0-9]|[1-9]?[0-9])\.){3}" - r"(?:25[0-5]|2[0-4][0-9]|1[0-9][0-9]|[1-9]?[0-9])|(?:[A-F0-9]{1,4}:){7}" - r"[A-F0-9]{1,4}|(?=(?:[A-F0-9]{0,4}:){0,7}[A-F0-9]{0,4}$)" - r"(([0-9A-F]{1,4}:){1,7}|:)((:[0-9A-F]{1,4}){1,7}|:)|(?:[A-F0-9]{1,4}:){7}" - r":|:(:[A-F0-9]{1,4}){7})$" -) -_ipv4_regex = re.compile(_ipv4_pattern) -_ipv6_regex = re.compile(_ipv6_pattern, flags=re.IGNORECASE) -_ipv4_regexb = re.compile(_ipv4_pattern.encode("ascii")) -_ipv6_regexb = re.compile(_ipv6_pattern.encode("ascii"), flags=re.IGNORECASE) - - -def _is_ip_address( - regex: Pattern[str], regexb: Pattern[bytes], host: Optional[Union[str, bytes]] -) -> bool: - if host is None: - return False - if isinstance(host, str): - return bool(regex.match(host)) - elif isinstance(host, (bytes, bytearray, memoryview)): - return bool(regexb.match(host)) - else: - raise TypeError(f"{host} [{type(host)}] is not a str or bytes") - - -is_ipv4_address = functools.partial(_is_ip_address, _ipv4_regex, _ipv4_regexb) -is_ipv6_address = functools.partial(_is_ip_address, _ipv6_regex, _ipv6_regexb) - - -def is_ip_address(host: Optional[Union[str, bytes, bytearray, memoryview]]) -> bool: - return is_ipv4_address(host) or is_ipv6_address(host) - - -def next_whole_second() -> datetime.datetime: - """Return current time rounded up to the next whole second.""" - return datetime.datetime.now(datetime.timezone.utc).replace( - microsecond=0 - ) + datetime.timedelta(seconds=0) - - -_cached_current_datetime: Optional[int] = None -_cached_formatted_datetime = "" - - -def rfc822_formatted_time() -> str: - global _cached_current_datetime - global _cached_formatted_datetime - - now = int(time.time()) - if now != _cached_current_datetime: - # Weekday and month names for HTTP date/time formatting; - # always English! - # Tuples are constants stored in codeobject! - _weekdayname = ("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun") - _monthname = ( - "", # Dummy so we can use 1-based month numbers - "Jan", - "Feb", - "Mar", - "Apr", - "May", - "Jun", - "Jul", - "Aug", - "Sep", - "Oct", - "Nov", - "Dec", - ) - - year, month, day, hh, mm, ss, wd, *tail = time.gmtime(now) - _cached_formatted_datetime = "%s, %02d %3s %4d %02d:%02d:%02d GMT" % ( - _weekdayname[wd], - day, - _monthname[month], - year, - hh, - mm, - ss, - ) - _cached_current_datetime = now - return _cached_formatted_datetime - - -def _weakref_handle(info: "Tuple[weakref.ref[object], str]") -> None: - ref, name = info - ob = ref() - if ob is not None: - with suppress(Exception): - getattr(ob, name)() - - -def weakref_handle( - ob: object, name: str, timeout: float, loop: asyncio.AbstractEventLoop -) -> Optional[asyncio.TimerHandle]: - if timeout is not None and timeout > 0: - when = loop.time() + timeout - if timeout >= 5: - when = ceil(when) - - return loop.call_at(when, _weakref_handle, (weakref.ref(ob), name)) - return None - - -def call_later( - cb: Callable[[], Any], timeout: float, loop: asyncio.AbstractEventLoop -) -> Optional[asyncio.TimerHandle]: - if timeout is not None and timeout > 0: - when = loop.time() + timeout - if timeout > 5: - when = ceil(when) - return loop.call_at(when, cb) - return None - - -class TimeoutHandle: - """Timeout handle""" - - def __init__( - self, loop: asyncio.AbstractEventLoop, timeout: Optional[float] - ) -> None: - self._timeout = timeout - self._loop = loop - self._callbacks: List[ - Tuple[Callable[..., None], Tuple[Any, ...], Dict[str, Any]] - ] = [] - - def register( - self, callback: Callable[..., None], *args: Any, **kwargs: Any - ) -> None: - self._callbacks.append((callback, args, kwargs)) - - def close(self) -> None: - self._callbacks.clear() - - def start(self) -> Optional[asyncio.Handle]: - timeout = self._timeout - if timeout is not None and timeout > 0: - when = self._loop.time() + timeout - if timeout >= 5: - when = ceil(when) - return self._loop.call_at(when, self.__call__) - else: - return None - - def timer(self) -> "BaseTimerContext": - if self._timeout is not None and self._timeout > 0: - timer = TimerContext(self._loop) - self.register(timer.timeout) - return timer - else: - return TimerNoop() - - def __call__(self) -> None: - for cb, args, kwargs in self._callbacks: - with suppress(Exception): - cb(*args, **kwargs) - - self._callbacks.clear() - - -class BaseTimerContext(ContextManager["BaseTimerContext"]): - pass - - -class TimerNoop(BaseTimerContext): - def __enter__(self) -> BaseTimerContext: - return self - - def __exit__( - self, - exc_type: Optional[Type[BaseException]], - exc_val: Optional[BaseException], - exc_tb: Optional[TracebackType], - ) -> None: - return - - -class TimerContext(BaseTimerContext): - """Low resolution timeout context manager""" - - def __init__(self, loop: asyncio.AbstractEventLoop) -> None: - self._loop = loop - self._tasks: List[asyncio.Task[Any]] = [] - self._cancelled = False - - def __enter__(self) -> BaseTimerContext: - task = current_task(loop=self._loop) - - if task is None: - raise RuntimeError( - "Timeout context manager should be used " "inside a task" - ) - - if self._cancelled: - raise asyncio.TimeoutError from None - - self._tasks.append(task) - return self - - def __exit__( - self, - exc_type: Optional[Type[BaseException]], - exc_val: Optional[BaseException], - exc_tb: Optional[TracebackType], - ) -> Optional[bool]: - if self._tasks: - self._tasks.pop() - - if exc_type is asyncio.CancelledError and self._cancelled: - raise asyncio.TimeoutError from None - return None - - def timeout(self) -> None: - if not self._cancelled: - for task in set(self._tasks): - task.cancel() - - self._cancelled = True - - -def ceil_timeout(delay: Optional[float]) -> async_timeout.Timeout: - if delay is None or delay <= 0: - return async_timeout.timeout(None) - - loop = get_running_loop() - now = loop.time() - when = now + delay - if delay > 5: - when = ceil(when) - return async_timeout.timeout_at(when) - - -class HeadersMixin: - - ATTRS = frozenset(["_content_type", "_content_dict", "_stored_content_type"]) - - _content_type: Optional[str] = None - _content_dict: Optional[Dict[str, str]] = None - _stored_content_type = sentinel - - def _parse_content_type(self, raw: str) -> None: - self._stored_content_type = raw - if raw is None: - # default value according to RFC 2616 - self._content_type = "application/octet-stream" - self._content_dict = {} - else: - msg = HeaderParser().parsestr("Content-Type: " + raw) - self._content_type = msg.get_content_type() - params = msg.get_params() - self._content_dict = dict(params[1:]) # First element is content type again - - @property - def content_type(self) -> str: - """The value of content part for Content-Type HTTP header.""" - raw = self._headers.get(hdrs.CONTENT_TYPE) # type: ignore[attr-defined] - if self._stored_content_type != raw: - self._parse_content_type(raw) - return self._content_type # type: ignore[return-value] - - @property - def charset(self) -> Optional[str]: - """The value of charset part for Content-Type HTTP header.""" - raw = self._headers.get(hdrs.CONTENT_TYPE) # type: ignore[attr-defined] - if self._stored_content_type != raw: - self._parse_content_type(raw) - return self._content_dict.get("charset") # type: ignore[union-attr] - - @property - def content_length(self) -> Optional[int]: - """The value of Content-Length HTTP header.""" - content_length = self._headers.get( # type: ignore[attr-defined] - hdrs.CONTENT_LENGTH - ) - - if content_length is not None: - return int(content_length) - else: - return None - - -def set_result(fut: "asyncio.Future[_T]", result: _T) -> None: - if not fut.done(): - fut.set_result(result) - - -def set_exception(fut: "asyncio.Future[_T]", exc: BaseException) -> None: - if not fut.done(): - fut.set_exception(exc) - - -class ChainMapProxy(Mapping[str, Any]): - __slots__ = ("_maps",) - - def __init__(self, maps: Iterable[Mapping[str, Any]]) -> None: - self._maps = tuple(maps) - - def __init_subclass__(cls) -> None: - raise TypeError( - "Inheritance class {} from ChainMapProxy " - "is forbidden".format(cls.__name__) - ) - - def __getitem__(self, key: str) -> Any: - for mapping in self._maps: - try: - return mapping[key] - except KeyError: - pass - raise KeyError(key) - - def get(self, key: str, default: Any = None) -> Any: - return self[key] if key in self else default - - def __len__(self) -> int: - # reuses stored hash values if possible - return len(set().union(*self._maps)) # type: ignore[arg-type] - - def __iter__(self) -> Iterator[str]: - d: Dict[str, Any] = {} - for mapping in reversed(self._maps): - # reuses stored hash values if possible - d.update(mapping) - return iter(d) - - def __contains__(self, key: object) -> bool: - return any(key in m for m in self._maps) - - def __bool__(self) -> bool: - return any(self._maps) - - def __repr__(self) -> str: - content = ", ".join(map(repr, self._maps)) - return f"ChainMapProxy({content})" - - -# https://tools.ietf.org/html/rfc7232#section-2.3 -_ETAGC = r"[!#-}\x80-\xff]+" -_ETAGC_RE = re.compile(_ETAGC) -_QUOTED_ETAG = rf'(W/)?"({_ETAGC})"' -QUOTED_ETAG_RE = re.compile(_QUOTED_ETAG) -LIST_QUOTED_ETAG_RE = re.compile(rf"({_QUOTED_ETAG})(?:\s*,\s*|$)|(.)") - -ETAG_ANY = "*" - - -@attr.s(auto_attribs=True, frozen=True, slots=True) -class ETag: - value: str - is_weak: bool = False - - -def validate_etag_value(value: str) -> None: - if value != ETAG_ANY and not _ETAGC_RE.fullmatch(value): - raise ValueError( - f"Value {value!r} is not a valid etag. Maybe it contains '\"'?" - ) - - -def parse_http_date(date_str: Optional[str]) -> Optional[datetime.datetime]: - """Process a date string, return a datetime object""" - if date_str is not None: - timetuple = parsedate(date_str) - if timetuple is not None: - with suppress(ValueError): - return datetime.datetime(*timetuple[:6], tzinfo=datetime.timezone.utc) - return None diff --git a/spaces/ccolas/TastyPiano/src/cocktails/utilities/cocktail_category_detection_utilities.py b/spaces/ccolas/TastyPiano/src/cocktails/utilities/cocktail_category_detection_utilities.py deleted file mode 100644 index 5846f86263a1b2a83554d9c2ea5039ccd83555cb..0000000000000000000000000000000000000000 --- a/spaces/ccolas/TastyPiano/src/cocktails/utilities/cocktail_category_detection_utilities.py +++ /dev/null @@ -1,221 +0,0 @@ -# The following functions check whether a cocktail belong to any of N categories -import numpy as np -from src.cocktails.utilities.ingredients_utilities import ingredient_profiles, ingredients_per_type, ingredient2ingredient_id, extract_ingredients - - -def is_ancestral(n, ingredient_indexes, ingredients, quantities): - # ancestrals have a strong spirit and some sweetness from sugar, syrup or liqueurs, no citrus. - # absinthe can be added up to 3 dashes. - # Liqueurs are there to bring sweetness, thus must stay below 15ml (if not it's a duo) - if n['spirit'] > 0 and n['citrus'] == 0 and n['plain_sweet'] + n['liqueur'] <= 2: - if n['spirit'] > 1 and 'absinthe' in ingredients: - if quantities[ingredients.index('absinthe')] < 3: - pass - else: - return False - if n['sugar'] < 2 and n['liqueur'] < 3: - if n['all'] - n['spirit'] - n['sugar'] -n['syrup']- n['liqueur']- n['inconsequentials'] == 0: - if n['liqueur'] == 0: - return True - else: - q_liqueur = np.sum([quantities[i_ing] - for i_ind, i_ing in zip(ingredient_indexes, range(len(ingredients))) - if ingredient_profiles['type'][i_ind].lower() == 'liqueur']) - if q_liqueur <= 15: - return True - else: - return False - return False - - -def is_simple_sour(n, ingredient_indexes, ingredients, quantities): - # simple sours contain a citrus, at least 1 spirit and non-alcoholic sweetness - if n['citrus'] + n['coffee']> 0 and n['spirit'] > 0 and n['plain_sweet'] > 0 and n['juice'] == 0: - if n['all'] - n['citrus'] - n['coffee'] - n['spirit'] - n['plain_sweet'] - n['juice'] -n['egg'] - n['inconsequentials'] == 0: - return True - return False - -def is_complex_sour(n, ingredient_indexes, ingredients, quantities): - # complex sours are simple sours that use alcoholic sweetness, at least in part - if n['citrus'] + n['coffee'] > 0 and n['all_sweet'] > 0 and n['juice'] == 0: - if (n['spirit'] == 0 and n['liqueur'] > 0) or n['spirit'] > 0: - if n['vermouth'] + n['liqueur'] <= 2 and n['vermouth'] + n['liqueur'] > 0: - if n['all'] -n['coffee'] - n['citrus'] - n['spirit'] - n['sugar'] - n['syrup'] \ - - n['liqueur'] - n['vermouth'] - n['egg'] - n['juice'] - n['inconsequentials'] == 0: - return True - return False - -def is_spirit_forward(n, ingredient_indexes, ingredients, quantities): - # spirit forward contain at least a spirit and vermouth, no citrus. Can contain sweet (sugar, syrups, liqueurs) - if n['spirit'] > 0 and n['citrus'] == 0 and n['vermouth'] > 0: - if n['all'] - n['spirit'] - n['sugar'] - n['syrup'] - n['liqueur'] -n['egg'] - n['vermouth'] - n['inconsequentials']== 0: - return True - return False - -def is_duo(n, ingredient_indexes, ingredients, quantities): - # duos are made of one spirit and one liqueur (above 15ml), under it's an ancestral, no citrus. - if n['spirit'] >= 1 and n['citrus'] == 0 and n['sugar']==0 and n['liqueur'] > 0 and n['vermouth'] == 0: - if n['all'] - n['spirit'] - n['sugar'] - n['liqueur'] - n['vermouth'] - n['inconsequentials'] == 0: - q_liqueur = np.sum([quantities[i_ing] - for i_ind, i_ing in zip(ingredient_indexes, range(len(ingredients))) - if ingredient_profiles['type'][i_ind].lower() == 'liqueur']) - if q_liqueur > 15: - return True - else: - return False - return False - -def is_champagne_cocktail(n, ingredient_indexes, ingredients, quantities): - if n['sparkling'] > 0: - return True - else: - return False - -def is_simple_highball(n, ingredient_indexes, ingredients, quantities): - # simple highballs have one alcoholic ingredient and bubbles - if n['alcoholic'] == 1 and n['bubbles'] > 0: - if n['all'] - n['alcoholic'] - n['bubbles'] - n['inconsequentials']== 0: - return True - return False - -def is_complex_highball(n, ingredient_indexes, ingredients, quantities): - # complex highballs have at least one alcoholic ingredient and bubbles (possibly alcoholic). They also contain extra sugar under any form and juice - if n['alcoholic'] > 0 and (n['bubbles'] + n['sparkling']) == 1 and n['juice'] + n['all_sweet'] + n['sugar_bubbles']> 0: - if n['all'] - n['spirit'] - n['bubbles'] - n['sparkling'] - n['citrus'] - n['juice'] - n['liqueur'] \ - - n['syrup'] - n['sugar'] -n['vermouth'] -n['egg'] - n['inconsequentials'] == 0: - if not is_collins(n, ingredient_indexes, ingredients, quantities) and not is_simple_highball(n, ingredient_indexes, ingredients, quantities): - return True - return False - -def is_collins(n, ingredient_indexes, ingredients, quantities): - # collins are a particular kind of highball with sugar and citrus - if n['alcoholic'] == 1 and n['bubbles'] == 1 and n['citrus'] > 0 and n['plain_sweet'] + n['sugar_bubbles'] > 0: - if n['all'] - n['spirit'] - n['bubbles'] - n['citrus'] - n['sugar'] - n['inconsequentials'] == 0: - return True - return False - -def is_julep(n, ingredient_indexes, ingredients, quantities): - # juleps involve smashd mint, sugar and a spirit, no citrus. - if 'mint' in ingredients and n['sugar'] > 0 and n['spirit'] > 0 and n['vermouth'] == 0 and n['citrus'] == 0: - return True - return False - -def is_simple_sour_with_juice(n, ingredient_indexes, ingredients, quantities): - # almost sours are sours with juice - if n['juice'] > 0 and n['spirit'] > 0 and n['plain_sweet'] > 0: - if n['all'] - n['citrus'] - n['coffee'] - n['juice'] - n['spirit'] - n['sugar'] - n['syrup'] - n['egg'] - n['inconsequentials'] == 0: - return True - return False - - -def is_complex_sour_with_juice(n, ingredient_indexes, ingredients, quantities): - # almost sours are sours with juice - if n['juice'] > 0 and n['all_sweet'] > 0: - if (n['spirit'] == 0 and n['liqueur'] > 0) or n['spirit'] > 0: - if n['vermouth'] + n['liqueur'] <= 2 and n['vermouth'] + n['liqueur'] > 0: - if n['all'] -n['coffee'] - n['citrus'] - n['spirit'] - n['sugar'] - n['syrup'] \ - - n['liqueur'] - n['vermouth'] - n['egg'] - n['juice'] - n['inconsequentials'] == 0: - return True - return False - - -is_sub_category = [is_ancestral, is_complex_sour, is_simple_sour, is_duo, is_champagne_cocktail, - is_spirit_forward, is_simple_highball, is_complex_highball, is_collins, - is_julep, is_simple_sour_with_juice, is_complex_sour_with_juice] -sub_categories = ['ancestral', 'complex_sour', 'simple_sour', 'duo', 'champagne_cocktail', - 'spirit_forward', 'simple_highball', 'complex_highball', 'collins', - 'julep', 'simple_sour_with_juice', 'complex_sour_with_juice'] - - -# compute cocktail category as a function of ingredients and quantities, uses name to check match between name and cat (e.g. XXX Collins should be collins..) -# Categories definitions are based on https://www.seriouseats.com/cocktail-style-guide-categories-of-cocktails-glossary-families-of-drinks -def find_cocktail_sub_category(ingredients, quantities, name=None): - ingredient_indexes = [ingredient2ingredient_id[ing] for ing in ingredients] - n_spirit = np.sum([ingredient_profiles['type'][i].lower() == 'liquor' for i in ingredient_indexes ]) - n_citrus = np.sum([ingredient_profiles['type'][i].lower()== 'acid' for i in ingredient_indexes]) - n_sugar = np.sum([ingredient_profiles['ingredient'][i].lower() in ['double syrup', 'simple syrup', 'honey syrup'] for i in ingredient_indexes]) - plain_sweet = ingredients_per_type['sweeteners'] - all_sweet = ingredients_per_type['sweeteners'] + ingredients_per_type['liqueur'] + ['sweet vermouth', 'lillet blanc'] - n_plain_sweet = np.sum([ingredient_profiles['ingredient'][i].lower() in plain_sweet for i in ingredient_indexes]) - n_all_sweet = np.sum([ingredient_profiles['ingredient'][i].lower() in all_sweet for i in ingredient_indexes]) - n_sugar_bubbles = np.sum([ingredient_profiles['ingredient'][i].lower() in ['cola', 'ginger beer', 'tonic'] for i in ingredient_indexes]) - n_juice = np.sum([ingredient_profiles['type'][i].lower() == 'juice' for i in ingredient_indexes]) - n_liqueur = np.sum([ingredient_profiles['type'][i].lower() == 'liqueur' for i in ingredient_indexes]) - alcoholic = ingredients_per_type['liquor'] + ingredients_per_type['liqueur'] + ingredients_per_type['vermouth'] - n_alcoholic = np.sum([ingredient_profiles['ingredient'][i].lower() in alcoholic for i in ingredient_indexes]) - n_bitter = np.sum([ingredient_profiles['type'][i].lower() == 'bitters' for i in ingredient_indexes]) - n_egg = np.sum([ingredient_profiles['ingredient'][i].lower() == 'egg' for i in ingredient_indexes]) - n_vermouth = np.sum([ingredient_profiles['type'][i].lower() == 'vermouth' for i in ingredient_indexes]) - n_sparkling = np.sum([ingredient_profiles['ingredient'][i].lower() == 'sparkling wine' for i in ingredient_indexes]) - n_bubbles = np.sum([ingredient_profiles['ingredient'][i].lower() in ['soda', 'tonic', 'cola', 'ginger beer'] for i in ingredient_indexes]) - n_syrup = np.sum([ingredient_profiles['ingredient'][i].lower() in ['grenadine', 'raspberry syrup'] for i in ingredient_indexes]) - n_coffee = np.sum([ingredient_profiles['ingredient'][i].lower() == 'espresso' for i in ingredient_indexes]) - inconsequentials = ['water', 'salt', 'angostura', 'orange bitters', 'mint'] - n_inconsequentials = np.sum([ingredient_profiles['ingredient'][i].lower() in inconsequentials for i in ingredient_indexes]) - n = dict(all=len(ingredients), - inconsequentials=n_inconsequentials, - sugar_bubbles=n_sugar_bubbles, - bubbles=n_bubbles, - plain_sweet=n_plain_sweet, - all_sweet=n_all_sweet, - coffee=n_coffee, - alcoholic=n_alcoholic, - syrup=n_syrup, - sparkling=n_sparkling, - sugar=n_sugar, - spirit=n_spirit, - citrus=n_citrus, - juice=n_juice, - liqueur=n_liqueur, - bitter=n_bitter, - egg=n_egg, - vermouth=n_vermouth) - - sub_cats = [c for c, test_c in zip(sub_categories, is_sub_category) if test_c(n, ingredient_indexes, ingredients, quantities)] - if name != None: - name = name.lower() - keywords_to_test = ['julep', 'collins', 'highball', 'sour', 'champagne'] - for k in keywords_to_test: - if k in name and not any([k in cat for cat in sub_cats]): - print(k) - for ing, q in zip(ingredients, quantities): - print(f'{ing}: {q} ml') - print(n) - break - if sorted(sub_cats) == ['champagne_cocktail', 'complex_highball']: - sub_cats = ['champagne_cocktail'] - elif sorted(sub_cats) == ['collins', 'complex_highball']: - sub_cats = ['collins'] - elif sorted(sub_cats) == ['champagne_cocktail', 'complex_highball', 'julep']: - sub_cats = ['champagne_cocktail'] - elif sorted(sub_cats) == ['ancestral', 'julep']: - sub_cats = ['julep'] - elif sorted(sub_cats) == ['complex_highball', 'julep']: - sub_cats = ['complex_highball'] - elif sorted(sub_cats) == ['julep', 'simple_sour_with_juice']: - sub_cats = ['simple_sour_with_juice'] - elif sorted(sub_cats) == ['complex_sour_with_juice', 'julep']: - sub_cats = ['complex_sour_with_juice'] - if len(sub_cats) != 1: - # print(sub_cats) - # for ing, q in zip(ingredients, quantities): - # print(f'{ing}: {q} ml') - # print(n) - # if len(sub_cats) == 0: - sub_cats = ['other'] - assert len(sub_cats) == 1, sub_cats - return sub_cats[0], n - -def get_cocktails_attributes(ing_strs): - attributes = dict() - cats = [] - for ing_str in ing_strs: - ingredients, quantities = extract_ingredients(ing_str) - cat, atts = find_cocktail_sub_category(ingredients, quantities) - for k in atts.keys(): - if k not in attributes.keys(): - attributes[k] = [atts[k]] - else: - attributes[k].append(atts[k]) - cats.append(cat) - return cats, attributes diff --git a/spaces/chendl/compositional_test/multimodal/generate_batch_submit.py b/spaces/chendl/compositional_test/multimodal/generate_batch_submit.py deleted file mode 100644 index 067cdf0d2c30fc6cef0ab3e27dc1dc0ae3af9d0d..0000000000000000000000000000000000000000 --- a/spaces/chendl/compositional_test/multimodal/generate_batch_submit.py +++ /dev/null @@ -1,9 +0,0 @@ -import os -import sys -start_idx = sys.argv[1] -end_idx = sys.argv[2] - -with open("batch_submit.sh", "w") as f: - for i, idx in enumerate(range(int(start_idx), int(end_idx), 48)): - f.write(f"sbatch -J label{i} submit_labeling.sh {idx} {idx+48}\n") - f.write("sleep 30\n") diff --git a/spaces/chendl/compositional_test/transformers/examples/flax/language-modeling/t5_tokenizer_model.py b/spaces/chendl/compositional_test/transformers/examples/flax/language-modeling/t5_tokenizer_model.py deleted file mode 100644 index fbccd52bd8c726f07bbe61451b69ac46fb5b131f..0000000000000000000000000000000000000000 --- a/spaces/chendl/compositional_test/transformers/examples/flax/language-modeling/t5_tokenizer_model.py +++ /dev/null @@ -1,112 +0,0 @@ -#!/usr/bin/env python3 -import json -from typing import Iterator, List, Union - -from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers -from tokenizers.implementations.base_tokenizer import BaseTokenizer -from tokenizers.models import Unigram -from tokenizers.processors import TemplateProcessing - - -class SentencePieceUnigramTokenizer(BaseTokenizer): - """ - This class is a copy of `DeDLOC's tokenizer implementation
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With this critical gaze, Onomatopee turns into an active laboratory, offering makers, agents, thinkers and performers a playground for experiment. We are not just interested in design solutions, but rather in the possibility of creating new opportunities for action. This implies awareness to how you work, the process.
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It is on those processes, that anticipate and inform the design practice, that we turn our attention.The RE-design-er offers us the ability to continuously question ourselves.
A vibrant public program invites you to experience and discuss, out loud and freely, how, why, and for whom does design happen?
What should a designer know? And how do their different expertises and critical understanding inform their practice? Onomatopee invites five designers to respond by crafting an object and reflecting on how they think and work.
-Inspired on the seven deadly sins, this collective exhibition targets the monetized culture of design fairs and challenges the represented practices within. Via evocative performances the group of 14 designers present the sinful side of design celebrations seen from the figurative teachings of Sloth, Pride, Wrath, Envy, Greed, Lust and Gluttony.
- -Design thinking has created divisions in the discipline: either designers are too theory driven or simply practitioners. Those feeling lost can easily turn to a language meant to inspire creative production in easy to pitch ways, where rhetoric uses design to keep power at bay, to celebrate hegemonic beliefs which are used to indoctrinate designers in bad education, incapable of imagining different futures. If you take away the post-its, the A3 papers and the markers, can designers think?
-about the author
Danah Abdulla is a Palestinian-Canadian designer, educator and researcher interested in new narratives and practices in design that push the disciplinary boundaries and definitions of the discipline. She is Programme Director of Graphic Design at Camberwell, Chelsea and Wimbledon Colleges of Arts (University of the Arts London). She has previously held positions at Brunel University London and London College of Communication (University of the Arts London). Danah obtained her Ph.D. in Design from Goldsmiths, University of London and is a founding member of the Decolonising Design platform. In 2010, she founded Kalimat Magazine, an independent, nonprofit publication about Arab thought and culture. Her research focuses on decolonising design, possibilities of design education, design culture(s) with a focus on the Arab region, the politics of design, publishing, and social design.
Dr James Dyer is senior lecturer of Graphic Design at the University of Huddersfield.
Nick Deakin is senior lecturer of Graphic Design at Leeds Arts University.
Prof. Alex Coles is a design critic and Professor of Transdisciplinarity at the University of Huddersfield.
Professor Johanna Drucker is the Breslauer Professor of Bibliographical Studies and Distinguished Professor in the Department of Information Studies at The University of California.
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- diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/WmfImagePlugin.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/WmfImagePlugin.py deleted file mode 100644 index 0ecab56a824fd3917067fd4b05c530f4abce75a3..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/WmfImagePlugin.py +++ /dev/null @@ -1,178 +0,0 @@ -# -# The Python Imaging Library -# $Id$ -# -# WMF stub codec -# -# history: -# 1996-12-14 fl Created -# 2004-02-22 fl Turned into a stub driver -# 2004-02-23 fl Added EMF support -# -# Copyright (c) Secret Labs AB 1997-2004. All rights reserved. -# Copyright (c) Fredrik Lundh 1996. -# -# See the README file for information on usage and redistribution. -# -# WMF/EMF reference documentation: -# https://winprotocoldoc.blob.core.windows.net/productionwindowsarchives/MS-WMF/[MS-WMF].pdf -# http://wvware.sourceforge.net/caolan/index.html -# http://wvware.sourceforge.net/caolan/ora-wmf.html - -from . import Image, ImageFile -from ._binary import i16le as word -from ._binary import si16le as short -from ._binary import si32le as _long - -_handler = None - - -def register_handler(handler): - """ - Install application-specific WMF image handler. - - :param handler: Handler object. - """ - global _handler - _handler = handler - - -if hasattr(Image.core, "drawwmf"): - # install default handler (windows only) - - class WmfHandler: - def open(self, im): - im.mode = "RGB" - self.bbox = im.info["wmf_bbox"] - - def load(self, im): - im.fp.seek(0) # rewind - return Image.frombytes( - "RGB", - im.size, - Image.core.drawwmf(im.fp.read(), im.size, self.bbox), - "raw", - "BGR", - (im.size[0] * 3 + 3) & -4, - -1, - ) - - register_handler(WmfHandler()) - -# -# -------------------------------------------------------------------- -# Read WMF file - - -def _accept(prefix): - return ( - prefix[:6] == b"\xd7\xcd\xc6\x9a\x00\x00" or prefix[:4] == b"\x01\x00\x00\x00" - ) - - -## -# Image plugin for Windows metafiles. - - -class WmfStubImageFile(ImageFile.StubImageFile): - format = "WMF" - format_description = "Windows Metafile" - - def _open(self): - self._inch = None - - # check placable header - s = self.fp.read(80) - - if s[:6] == b"\xd7\xcd\xc6\x9a\x00\x00": - # placeable windows metafile - - # get units per inch - self._inch = word(s, 14) - - # get bounding box - x0 = short(s, 6) - y0 = short(s, 8) - x1 = short(s, 10) - y1 = short(s, 12) - - # normalize size to 72 dots per inch - self.info["dpi"] = 72 - size = ( - (x1 - x0) * self.info["dpi"] // self._inch, - (y1 - y0) * self.info["dpi"] // self._inch, - ) - - self.info["wmf_bbox"] = x0, y0, x1, y1 - - # sanity check (standard metafile header) - if s[22:26] != b"\x01\x00\t\x00": - msg = "Unsupported WMF file format" - raise SyntaxError(msg) - - elif s[:4] == b"\x01\x00\x00\x00" and s[40:44] == b" EMF": - # enhanced metafile - - # get bounding box - x0 = _long(s, 8) - y0 = _long(s, 12) - x1 = _long(s, 16) - y1 = _long(s, 20) - - # get frame (in 0.01 millimeter units) - frame = _long(s, 24), _long(s, 28), _long(s, 32), _long(s, 36) - - size = x1 - x0, y1 - y0 - - # calculate dots per inch from bbox and frame - xdpi = 2540.0 * (x1 - y0) / (frame[2] - frame[0]) - ydpi = 2540.0 * (y1 - y0) / (frame[3] - frame[1]) - - self.info["wmf_bbox"] = x0, y0, x1, y1 - - if xdpi == ydpi: - self.info["dpi"] = xdpi - else: - self.info["dpi"] = xdpi, ydpi - - else: - msg = "Unsupported file format" - raise SyntaxError(msg) - - self.mode = "RGB" - self._size = size - - loader = self._load() - if loader: - loader.open(self) - - def _load(self): - return _handler - - def load(self, dpi=None): - if dpi is not None and self._inch is not None: - self.info["dpi"] = dpi - x0, y0, x1, y1 = self.info["wmf_bbox"] - self._size = ( - (x1 - x0) * self.info["dpi"] // self._inch, - (y1 - y0) * self.info["dpi"] // self._inch, - ) - return super().load() - - -def _save(im, fp, filename): - if _handler is None or not hasattr(_handler, "save"): - msg = "WMF save handler not installed" - raise OSError(msg) - _handler.save(im, fp, filename) - - -# -# -------------------------------------------------------------------- -# Registry stuff - - -Image.register_open(WmfStubImageFile.format, WmfStubImageFile, _accept) -Image.register_save(WmfStubImageFile.format, _save) - -Image.register_extensions(WmfStubImageFile.format, [".wmf", ".emf"]) diff --git a/spaces/codedog-ai/edu-assistant/tests/learning_tasks/test_qa.py b/spaces/codedog-ai/edu-assistant/tests/learning_tasks/test_qa.py deleted file mode 100644 index 42fafa0bec8eb5b164e99fa235897c54658a5365..0000000000000000000000000000000000000000 --- a/spaces/codedog-ai/edu-assistant/tests/learning_tasks/test_qa.py +++ /dev/null @@ -1,52 +0,0 @@ -from unittest.mock import MagicMock, patch - -from langchain import PromptTemplate - -from edu_assistant.learning_tasks import QaTask -from edu_assistant.learning_tasks.qa import TEMPLATE_CHAT, TEMPLATE_ONCE - - -@patch.object(QaTask, "_init_llm") -@patch.object(QaTask, "_build_once_chain") -def test_init_without_knowledge(mocked_build_once_chain, mocked_init_llm): - task = QaTask(instruction="test") - - assert task._chat_prompt == PromptTemplate.from_template(TEMPLATE_CHAT.format(instruction="test")) - assert task._once_prompt == PromptTemplate.from_template(TEMPLATE_ONCE.format(instruction="test")) - assert task._knowledge is None - mocked_build_once_chain.assert_called_once() - - -@patch.object(QaTask, "_init_llm") -@patch.object(QaTask, "_build_once_chain") -@patch.object(QaTask, "_create_session_chain") -def test_ask_with_session(mocked_create_session_chain, mocked_build_once_chain, mocked_init_llm): - mocked_chain = MagicMock(return_value={"response": "ok"}) - mocked_build_once_chain.return_value = mocked_chain - mocked_create_session_chain.return_value = mocked_chain - - task = QaTask(instruction="test") - - with patch.object(task, "_create_session_id") as mock_create_id: - mock_create_id.return_value = 123 - result = task.ask("how are you?", session=True) - - mock_create_id.assert_called_once() - assert "session_id" in result - assert result["session_id"] == 123 - assert "response" in result - assert result["response"] == "ok" - - -@patch.object(QaTask, "_init_llm") -@patch.object(QaTask, "_build_once_chain") -def test_ask_without_session(mocked_build_once_chain, mocked_init_llm): - mocked_llm = MagicMock() - mocked_llm.run.return_value = {"result": "ok"} - mocked_build_once_chain.return_value = mocked_llm - task = QaTask(instruction="test") - - result = task.ask("how are you?", session=False) - - mocked_build_once_chain.assert_called_once() - assert "session_id" not in result diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenc_is.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenc_is.c deleted file mode 100644 index 1810790d8816c8751f5dc87ea2ab5a5633af3143..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenc_is.c +++ /dev/null @@ -1,158 +0,0 @@ -/* - * AAC encoder intensity stereo - * Copyright (C) 2015 Rostislav Pehlivanov - * - * 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 - * AAC encoder Intensity Stereo - * @author Rostislav Pehlivanov ( atomnuker gmail com ) - */ - -#include "aacenc.h" -#include "aacenc_utils.h" -#include "aacenc_is.h" -#include "aacenc_quantization.h" - -struct AACISError ff_aac_is_encoding_err(AACEncContext *s, ChannelElement *cpe, - int start, int w, int g, float ener0, - float ener1, float ener01, - int use_pcoeffs, int phase) -{ - int i, w2; - SingleChannelElement *sce0 = &cpe->ch[0]; - SingleChannelElement *sce1 = &cpe->ch[1]; - float *L = use_pcoeffs ? sce0->pcoeffs : sce0->coeffs; - float *R = use_pcoeffs ? sce1->pcoeffs : sce1->coeffs; - float *L34 = &s->scoefs[256*0], *R34 = &s->scoefs[256*1]; - float *IS = &s->scoefs[256*2], *I34 = &s->scoefs[256*3]; - float dist1 = 0.0f, dist2 = 0.0f; - struct AACISError is_error = {0}; - - if (ener01 <= 0 || ener0 <= 0) { - is_error.pass = 0; - return is_error; - } - - for (w2 = 0; w2 < sce0->ics.group_len[w]; w2++) { - FFPsyBand *band0 = &s->psy.ch[s->cur_channel+0].psy_bands[(w+w2)*16+g]; - FFPsyBand *band1 = &s->psy.ch[s->cur_channel+1].psy_bands[(w+w2)*16+g]; - int is_band_type, is_sf_idx = FFMAX(1, sce0->sf_idx[w*16+g]-4); - float e01_34 = phase*pos_pow34(ener1/ener0); - float maxval, dist_spec_err = 0.0f; - float minthr = FFMIN(band0->threshold, band1->threshold); - for (i = 0; i < sce0->ics.swb_sizes[g]; i++) - IS[i] = (L[start+(w+w2)*128+i] + phase*R[start+(w+w2)*128+i])*sqrt(ener0/ener01); - s->abs_pow34(L34, &L[start+(w+w2)*128], sce0->ics.swb_sizes[g]); - s->abs_pow34(R34, &R[start+(w+w2)*128], sce0->ics.swb_sizes[g]); - s->abs_pow34(I34, IS, sce0->ics.swb_sizes[g]); - maxval = find_max_val(1, sce0->ics.swb_sizes[g], I34); - is_band_type = find_min_book(maxval, is_sf_idx); - dist1 += quantize_band_cost(s, &L[start + (w+w2)*128], L34, - sce0->ics.swb_sizes[g], - sce0->sf_idx[w*16+g], - sce0->band_type[w*16+g], - s->lambda / band0->threshold, INFINITY, NULL, NULL); - dist1 += quantize_band_cost(s, &R[start + (w+w2)*128], R34, - sce1->ics.swb_sizes[g], - sce1->sf_idx[w*16+g], - sce1->band_type[w*16+g], - s->lambda / band1->threshold, INFINITY, NULL, NULL); - dist2 += quantize_band_cost(s, IS, I34, sce0->ics.swb_sizes[g], - is_sf_idx, is_band_type, - s->lambda / minthr, INFINITY, NULL, NULL); - for (i = 0; i < sce0->ics.swb_sizes[g]; i++) { - dist_spec_err += (L34[i] - I34[i])*(L34[i] - I34[i]); - dist_spec_err += (R34[i] - I34[i]*e01_34)*(R34[i] - I34[i]*e01_34); - } - dist_spec_err *= s->lambda / minthr; - dist2 += dist_spec_err; - } - - is_error.pass = dist2 <= dist1; - is_error.phase = phase; - is_error.error = dist2 - dist1; - is_error.dist1 = dist1; - is_error.dist2 = dist2; - is_error.ener01 = ener01; - - return is_error; -} - -void ff_aac_search_for_is(AACEncContext *s, AVCodecContext *avctx, ChannelElement *cpe) -{ - SingleChannelElement *sce0 = &cpe->ch[0]; - SingleChannelElement *sce1 = &cpe->ch[1]; - int start = 0, count = 0, w, w2, g, i, prev_sf1 = -1, prev_bt = -1, prev_is = 0; - const float freq_mult = avctx->sample_rate/(1024.0f/sce0->ics.num_windows)/2.0f; - uint8_t nextband1[128]; - - if (!cpe->common_window) - return; - - /** Scout out next nonzero bands */ - ff_init_nextband_map(sce1, nextband1); - - for (w = 0; w < sce0->ics.num_windows; w += sce0->ics.group_len[w]) { - start = 0; - for (g = 0; g < sce0->ics.num_swb; g++) { - if (start*freq_mult > INT_STEREO_LOW_LIMIT*(s->lambda/170.0f) && - cpe->ch[0].band_type[w*16+g] != NOISE_BT && !cpe->ch[0].zeroes[w*16+g] && - cpe->ch[1].band_type[w*16+g] != NOISE_BT && !cpe->ch[1].zeroes[w*16+g] && - ff_sfdelta_can_remove_band(sce1, nextband1, prev_sf1, w*16+g)) { - float ener0 = 0.0f, ener1 = 0.0f, ener01 = 0.0f, ener01p = 0.0f; - struct AACISError ph_err1, ph_err2, *best; - for (w2 = 0; w2 < sce0->ics.group_len[w]; w2++) { - for (i = 0; i < sce0->ics.swb_sizes[g]; i++) { - float coef0 = sce0->coeffs[start+(w+w2)*128+i]; - float coef1 = sce1->coeffs[start+(w+w2)*128+i]; - ener0 += coef0*coef0; - ener1 += coef1*coef1; - ener01 += (coef0 + coef1)*(coef0 + coef1); - ener01p += (coef0 - coef1)*(coef0 - coef1); - } - } - ph_err1 = ff_aac_is_encoding_err(s, cpe, start, w, g, - ener0, ener1, ener01p, 0, -1); - ph_err2 = ff_aac_is_encoding_err(s, cpe, start, w, g, - ener0, ener1, ener01, 0, +1); - best = (ph_err1.pass && ph_err1.error < ph_err2.error) ? &ph_err1 : &ph_err2; - if (best->pass) { - cpe->is_mask[w*16+g] = 1; - cpe->ms_mask[w*16+g] = 0; - cpe->ch[0].is_ener[w*16+g] = sqrt(ener0 / best->ener01); - cpe->ch[1].is_ener[w*16+g] = ener0/ener1; - cpe->ch[1].band_type[w*16+g] = (best->phase > 0) ? INTENSITY_BT : INTENSITY_BT2; - if (prev_is && prev_bt != cpe->ch[1].band_type[w*16+g]) { - /** Flip M/S mask and pick the other CB, since it encodes more efficiently */ - cpe->ms_mask[w*16+g] = 1; - cpe->ch[1].band_type[w*16+g] = (best->phase > 0) ? INTENSITY_BT2 : INTENSITY_BT; - } - prev_bt = cpe->ch[1].band_type[w*16+g]; - count++; - } - } - if (!sce1->zeroes[w*16+g] && sce1->band_type[w*16+g] < RESERVED_BT) - prev_sf1 = sce1->sf_idx[w*16+g]; - prev_is = cpe->is_mask[w*16+g]; - start += sce0->ics.swb_sizes[g]; - } - } - cpe->is_mode = !!count; -} diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_mp4toannexb_bsf.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_mp4toannexb_bsf.c deleted file mode 100644 index d11be455c280a7a59cd1ce46df6b5f04bf7154df..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_mp4toannexb_bsf.c +++ /dev/null @@ -1,322 +0,0 @@ -/* - * H.264 MP4 to Annex B byte stream format filter - * Copyright (c) 2007 Benoit Fouet
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| How can I contact the developers of RFS Real Flight Simulator Pro? | -To contact the developers of RFS Real Flight Simulator Pro, you can visit their official website or their social media pages. You can also send them an email or a message through the game support option. | -
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Live Football Streaming with Yacine TV APK 2022 - Free and Easy.md b/spaces/congsaPfin/Manga-OCR/logs/Live Football Streaming with Yacine TV APK 2022 - Free and Easy.md deleted file mode 100644 index 11403730fd7ad56fa93360a8a6b62ec88a3f3237..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Live Football Streaming with Yacine TV APK 2022 - Free and Easy.md +++ /dev/null @@ -1,79 +0,0 @@ -
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-Yacine TV is a mobile app that allows you to watch live sports events, including football matches from around the world. Whether you want to watch the Premier League, La Liga, Serie A, Bundesliga, Champions League, Europa League, World Cup, or any other tournament, Yacine TV has got you covered. You can also watch other sports like basketball, tennis, cricket, rugby, and more.
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-Yacine TV delivers high-quality video and audio streaming without any buffering or lagging. You can enjoy the matches in full screen mode with clear sound and smooth playback. You can also adjust the video quality according to your internet speed and data usage.
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-Yacine TV has a user-friendly interface and easy navigation that makes it simple and convenient to use. You can access all the features and functions with just a few taps. You can also customize the app according to your preferences and needs.
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-If you are wondering how to download and install Yacine TV apk on your device, here are the steps you need to follow:
-Download the apk file from the official website
-The first step is to download the apk file from the official website of Yacine TV. You can also scan the QR code on the website with your device camera to get the download link. The apk file is safe and secure and does not contain any viruses or malware.
-Enable unknown sources on your device settings
-The next step is to enable unknown sources on your device settings. This will allow you to install apps from sources other than the Google Play Store or App Store. To do this, go to your device settings > security > unknown sources > enable.
Install the apk file and launch the app -The final step is to install the apk file and launch the app. To do this, locate the downloaded apk file on your device storage and tap on it. Follow the instructions on the screen to complete the installation. Once the app is installed, you can open it and start using it.
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-Now that you have downloaded and installed Yacine TV app on your device, you are ready to watch live football matches on it. Here is how you can do it:
-Select the match you want to watch from the home screen
-When you open the app, you will see a list of live matches happening around the world. You can scroll through the list and select the match you want to watch. You can also use the search bar or the filter options to find a specific match, team, or league.
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-After selecting the match, you will see a list of channels and languages available for that match. You can choose the one that suits you best. You can also change the channel or language anytime during the streaming.
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-Once you have chosen the channel and language, you can enjoy the live streaming without any interruptions. You can watch the match in full screen mode or minimize it to a small window. You can also pause, resume, rewind, or fast forward the streaming as per your convenience.
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-Yacine TV is a great app for live football streaming in 2022. It offers you a lot of features and benefits that make it the best choice for football fans. You can watch live matches from around the world in HD quality with multiple channels and languages. You can also get live scores and updates of all the matches and leagues. You can download and install Yacine TV apk on your device easily and safely. You can also watch live matches on Yacine TV app with simple steps.
-If you have any questions about Yacine TV app, here are some FAQs that might help you:
-| Q: Is Yacine TV app free? | A: Yes, Yacine TV app is free to download and use. You don't have to pay any subscription fees or charges to watch live matches on it. |
| Q: Is Yacine TV app legal? | A: Yes, Yacine TV app is legal and does not violate any copyrights or trademarks. However, you should check your local laws and regulations before using it. |
| Q: Is Yacine TV app compatible with all devices? | A: Yes, Yacine TV app is compatible with all Android devices running Android 4.1 or higher. It is also compatible with iOS devices running iOS 9 or higher. |
| Q: How much data does Yacine TV app consume? | A: The data consumption of Yacine TV app depends on the video quality and duration of the streaming. You can adjust the video quality according to your internet speed and data usage. |
| Q: How can I contact Yacine TV app support? | A: You can contact Yacine TV app support by sending an email to yacinetvapp@gmail.com or by visiting their Facebook page. They will respond to your queries and feedback as soon as possible. |
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Play Together APK The Ultimate Social Game Experience from Apkmody.md b/spaces/congsaPfin/Manga-OCR/logs/Play Together APK The Ultimate Social Game Experience from Apkmody.md deleted file mode 100644 index 359f53437132149b9cd89351f2cf0886e2367355..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Play Together APK The Ultimate Social Game Experience from Apkmody.md +++ /dev/null @@ -1,91 +0,0 @@ - -
Play Together APK: A Fun and Social Game for Everyone
-Do you love playing games with your friends and meeting new people online? If so, you should try Play Together, a multiplayer game that lets you create your own character, explore different places, chat with other players, and have fun with various mini-games and activities. In this article, we will tell you more about this game and how you can download Play Together APK from apkmody for free.
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-Play Together is a game developed by Haegin Co., Ltd. that was released in May 2021. It is a social simulation game that allows you to customize your own avatar, choose your own style, and interact with other players from around the world. You can also visit various locations such as the beach, the amusement park, the school, and more. You can chat with your friends, make new ones, join clubs, and participate in events. You can also enjoy mini-games such as fishing, cooking, racing, and more. Play Together is a game that lets you experience a virtual world full of fun and adventure.
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-If you want to play Play Together on your Android device, you can download it from the Google Play Store. However, if you want to enjoy some extra benefits and features that are not available in the official version of the game, you should download Play Together APK from apkmody. Here are some reasons why:
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- Follow the instructions on the screen and wait for the installation to complete. -
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Conclusion
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Project Drift 2.0 MOD APK Unduh Sekarang dan Rasakan Sensasi Balapan dengan Mobil Legendaris.md b/spaces/congsaPfin/Manga-OCR/logs/Project Drift 2.0 MOD APK Unduh Sekarang dan Rasakan Sensasi Balapan dengan Mobil Legendaris.md deleted file mode 100644 index b3b0f0a98948e33a7ec851d92f54c7e8e1b5222e..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Project Drift 2.0 MOD APK Unduh Sekarang dan Rasakan Sensasi Balapan dengan Mobil Legendaris.md +++ /dev/null @@ -1,93 +0,0 @@ -
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Project Drift 2.0 Mod APK: A New Way to Enjoy Car Racing Games
-Do you love car racing games? Do you want to try something different and exciting? If yes, then you should check out Project Drift 2.0, a realistic and challenging racing game that will test your skills and thrill your senses. And if you want to make the game even more fun and easy, you should download Project Drift 2.0 Mod APK, a modded version that gives you unlimited money and unlocks all the cars in the game. In this article, we will tell you everything you need to know about Project Drift 2.0 and its modded version, including what it is, why you should download it, and how to download and install it on your device.
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To compete in different modes and levels
-The last reason why you should download Project Drift 2.0 Mod APK is that it offers a variety of modes and levels that will test your drifting skills and keep you entertained. You can compete in: - Career mode: This is the main mode of the game, where you have to complete 100 missions with different objectives and difficulties. You will earn money and stars based on your performance, which you can use to buy and upgrade cars. You will also unlock new tracks and cars as you progress. - Free mode: This is the mode where you can practice your drifting skills without any pressure or time limit. You can choose any track and car you want and drift as much as you want. You can also adjust the settings of the game, such as the weather, traffic, camera angle, and more. - Online mode: This is the mode where you can challenge other players from around the world in real-time multiplayer races. You can join or create a room with up to 10 players and compete in different tracks and modes. You can also chat with other players and see their profiles and stats. - Drift park: This is a special mode where you can explore a large open-world map with various locations and obstacles. You can drift around the city, the airport, the port, the desert, the mountain, and more. You can also find hidden coins and collectibles that will give you extra money and rewards.
How to download and install Project Drift 2.0 Mod APK?
-The steps to follow
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- Once the download is complete, locate the file in your device's file manager and tap on it to start the installation process. -
- If you see a pop-up message that says "Install blocked", go to your device's settings and enable the option "Unknown sources" under security or privacy settings. -
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The precautions to take
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Conclusion
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-A: Project Drift 2.0 Mod APK is updated regularly by our team to ensure that it works with the latest version of the original game and to fix any bugs or glitches. To update the modded version, you need to download the latest APK file from our website and install it on your device. You do not need to uninstall the previous version, as the new version will overwrite it automatically. However, you may need to back up your game data before updating, as some updates may erase your progress or settings.
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-A: Project Drift 2.0 Mod APK gives you unlimited money and unlocks all the cars in the game by default. You do not need to do anything special to get them. You can simply go to the shop and buy any car you want without spending any money. You can also upgrade your cars with any parts or accessories you want without worrying about the cost. You can also access all the cars in the garage and select any one you want to drive.
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-A: If you have any questions, suggestions, feedback, or complaints about Project Drift 2.0, you can contact the developers of the game through their official website, Facebook page, Instagram account, or email address. You can also leave a review or rating on the Google Play Store or App Store to share your opinion and experience with other users.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Roku Remote MOD APK (Unlocked) - No Ads No Limits No Hassle.md b/spaces/congsaPfin/Manga-OCR/logs/Roku Remote MOD APK (Unlocked) - No Ads No Limits No Hassle.md deleted file mode 100644 index 37752790cd6722eb19ebe63cc19c4374102a96f2..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Roku Remote MOD APK (Unlocked) - No Ads No Limits No Hassle.md +++ /dev/null @@ -1,25 +0,0 @@ -
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Roku Remote Full APK: What Is It and How to Use It
- If you are a fan of streaming devices, you might have heard of Roku, a popular brand that offers various products such as players, smart TVs, soundbars, and more. Roku devices allow you to access thousands of channels and apps, such as Netflix, Hulu, YouTube, Amazon Prime Video, and more. However, to enjoy the full potential of your Roku device, you need a remote control that can help you navigate the menus and content. That's where Roku Remote Full APK comes in handy.What is Roku Remote Full APK?
-A modified version of the official Roku Remote app
- Roku Remote Full APK is a modified version of the official Roku Remote app that you can download from Google Play Store. The official app lets you use your Android device as a remote control for your Roku device, as long as they are connected to the same Wi-Fi network. However, the official app has some limitations, such as ads, in-app purchases, and compatibility issues. That's why some developers have created a modified version of the app that removes these limitations and adds some extra features.Features and benefits of Roku Remote Full APK
- Some of the features and benefits of Roku Remote Full APK are: - No setup is required. The app automatically scans your network to find your Roku device. - No ads or in-app purchases. The app is completely free and ad-free. - Premium features unlocked. The app gives you access to some premium features that are not available in the official app, such as voice search, private listening, keyboard input, channel launch, and more. - A large touchpad for convenient menu and content navigation. - A volume slider to adjust the volume of your Roku device or TV. - A power button to turn on or off your Roku device or TV. - A home button to go back to the home screen of your Roku device. - A back button to go back to the previous screen or exit an app. - An options button to access additional settings or options for an app or channel. - A search button to search for content across multiple channels and apps. - A channels button to view and manage your installed channels and apps. - A favorites button to add or remove channels and apps from your favorites list.How to download and install Roku Remote Full APK?
-Requirements and precautions
- Before you download and install Roku Remote Full APK, you need to make sure that: - You have an Android device that runs on Android 4.4 or higher. - You have enabled unknown sources on your Android device. To do this, go to Settings > Security > Unknown sources and toggle it on. - You have a compatible Roku device that is connected to the same Wi-Fi network as your Android device. - You have downloaded the latest version of Roku Remote Full APK from a trusted source.Steps to download and install
- To download and install Roku Remote Full APK, follow these steps: - Go to the website where you downloaded the APK file and tap on it to start the download process. - Once the download is complete, open the file manager app on your Android device and locate the downloaded file. - Tap on the file and follow the instructions on the screen to install the app. - Wait for the installation process to finish and then launch the app from your app drawer or home screen.How to use Roku Remote Full APK?
-Connect your Roku device and your Android device to the same network
- To use Roku Remote Full APK, you need to make sure that your Roku device and your Android device are connected to the same Wi-Fi network. To check this, go to Settings > Network on your Roku device and Settings > Wi-Fi on your Android device and compare the network names. If they are not the same, connect them to the same network.Launch the app and scan for your Roku device
- Once you have installed and launched the app, it will automatically scan your network for any available Roku devices. If it finds one, it will show you its name and IP address on the screen. Tap on it to connect to it. If it does not find one, you can manually enter the IP address of your Roku device by tapping on the plus icon on the top right corner of the screen.Control your Roku device with the app
- After you have connected to your Roku device, you can use the app as a remote control for it. You can use the touchpad to swipe left, right, up, or down to navigate the menus and content. You can also tap on the touchpad to select an item. You can use the buttons on the bottom of the screen to perform various actions, such as volume, power, home, back, options, search, channels, and favorites. You can also access some premium features by tapping on the menu icon on the top left corner of the screen. These include voice search, private listening, keyboard input, channel launch, and more.Conclusion
-Summary of the main points
- Roku Remote Full APK is a modified version of the official Roku Remote app that lets you use your Android device as a remote control for your Roku device. It has no ads or in-app purchases and unlocks some premium features that are not available in the official app. It is easy to download, install, and use, as long as you have a compatible Roku device and Android device that are connected to the same Wi-Fi network.Call to action and recommendation
- If you want to enjoy the full potential of your Roku device without spending any money or dealing with any ads or limitations, you should try Roku Remote Full APK. It is a free and reliable app that gives you more control and convenience over your streaming experience. You can download it from a trusted source and follow the steps in this article to install and use it. You will not regret it!FAQs
-Is Roku Remote Full APK safe and legal?
- Roku Remote Full APK is safe and legal to use, as long as you download it from a trusted source and do not use it for any malicious purposes. It does not contain any viruses or malware that can harm your device or data. It also does not violate any terms of service or policies of Roku or Google Play Store.What are the differences between Roku Remote Full APK and the official app?
- Roku Remote Full APK is a modified version of the official app that removes some limitations and adds some extra features. Some of the differences are: - Roku Remote Full APK has no ads or in-app purchases. - Roku Remote Full APK unlocks some premium features that are not available in the official app, such as voice search, private listening, keyboard input, channel launch, and more. - Roku Remote Full APK has a larger touchpad for easier navigation. - Roku Remote Full APK has a power button to turn on or off your Roku device or TV.Does Roku Remote Full APK work with all Roku devices?
- Roku Remote Full APK works with most Roku devices that support Wi-Fi connectivity, such as players, smart TVs, soundbars, and more. However, some older models may not be compatible with the app. To check if your Roku device is compatible with the app, go to Settings > System > About on your Roku device and look for the model number. Then compare it with the list of compatible devices on the website where you downloaded the app.Can I use Roku Remote Full APK without internet connection?
- No, you cannot use Roku Remote Full APK without internet connection. The app requires both your Roku device and your Android device to be connected to the same Wi-Fi network in order to communicate with each other. Without internet connection, you will not be able to scan for your Roku device or control it with the app.How can I update Roku Remote Full APK?
- To update Roku Remote Full APK, you need to download the latest version of the app from a trusted source and install it over the existing one. You do not need to uninstall or delete the previous version of the app. The new version will overwrite it and update the app with the new features and bug fixes. You can also check for updates by tapping on the menu icon on the top left corner of the screen and selecting Check for updates. I hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy streaming! -roku remote full apk
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\ No newline at end of file diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/segmentation.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/segmentation.py deleted file mode 100644 index 3d4a9f94eaae84722db584277dbbf9bc41ede357..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/segmentation.py +++ /dev/null @@ -1,43 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -from .constants import weights as constant_weights - - -class CrossEntropy2d(nn.Module): - def __init__(self, reduction="mean", ignore_label=255, weights=None, *args, **kwargs): - """ - weight (Tensor, optional): a manual rescaling weight given to each class. - If given, has to be a Tensor of size "nclasses" - """ - super(CrossEntropy2d, self).__init__() - self.reduction = reduction - self.ignore_label = ignore_label - self.weights = weights - if self.weights is not None: - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - self.weights = torch.FloatTensor(constant_weights[weights]).to(device) - - def forward(self, predict, target): - """ - Args: - predict:(n, c, h, w) - target:(n, 1, h, w) - """ - target = target.long() - assert not target.requires_grad - assert predict.dim() == 4, "{0}".format(predict.size()) - assert target.dim() == 4, "{0}".format(target.size()) - assert predict.size(0) == target.size(0), "{0} vs {1} ".format(predict.size(0), target.size(0)) - assert target.size(1) == 1, "{0}".format(target.size(1)) - assert predict.size(2) == target.size(2), "{0} vs {1} ".format(predict.size(2), target.size(2)) - assert predict.size(3) == target.size(3), "{0} vs {1} ".format(predict.size(3), target.size(3)) - target = target.squeeze(1) - n, c, h, w = predict.size() - target_mask = (target >= 0) * (target != self.ignore_label) - target = target[target_mask] - predict = predict.transpose(1, 2).transpose(2, 3).contiguous() - predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c) - loss = F.cross_entropy(predict, target, weight=self.weights, reduction=self.reduction) - return loss diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/segmentors/base.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/segmentors/base.py deleted file mode 100644 index a12d8beb8ea40bfa234197eddb4d3ef40dbfeb6f..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/segmentors/base.py +++ /dev/null @@ -1,273 +0,0 @@ -import logging -import warnings -from abc import ABCMeta, abstractmethod -from collections import OrderedDict - -import annotator.mmpkg.mmcv as mmcv -import numpy as np -import torch -import torch.distributed as dist -import torch.nn as nn -from annotator.mmpkg.mmcv.runner import auto_fp16 - - -class BaseSegmentor(nn.Module): - """Base class for segmentors.""" - - __metaclass__ = ABCMeta - - def __init__(self): - super(BaseSegmentor, self).__init__() - self.fp16_enabled = False - - @property - def with_neck(self): - """bool: whether the segmentor has neck""" - return hasattr(self, 'neck') and self.neck is not None - - @property - def with_auxiliary_head(self): - """bool: whether the segmentor has auxiliary head""" - return hasattr(self, - 'auxiliary_head') and self.auxiliary_head is not None - - @property - def with_decode_head(self): - """bool: whether the segmentor has decode head""" - return hasattr(self, 'decode_head') and self.decode_head is not None - - @abstractmethod - def extract_feat(self, imgs): - """Placeholder for extract features from images.""" - pass - - @abstractmethod - def encode_decode(self, img, img_metas): - """Placeholder for encode images with backbone and decode into a - semantic segmentation map of the same size as input.""" - pass - - @abstractmethod - def forward_train(self, imgs, img_metas, **kwargs): - """Placeholder for Forward function for training.""" - pass - - @abstractmethod - def simple_test(self, img, img_meta, **kwargs): - """Placeholder for single image test.""" - pass - - @abstractmethod - def aug_test(self, imgs, img_metas, **kwargs): - """Placeholder for augmentation test.""" - pass - - def init_weights(self, pretrained=None): - """Initialize the weights in segmentor. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - if pretrained is not None: - logger = logging.getLogger() - logger.info(f'load model from: {pretrained}') - - def forward_test(self, imgs, img_metas, **kwargs): - """ - Args: - imgs (List[Tensor]): the outer list indicates test-time - augmentations and inner Tensor should have a shape NxCxHxW, - which contains all images in the batch. - img_metas (List[List[dict]]): the outer list indicates test-time - augs (multiscale, flip, etc.) and the inner list indicates - images in a batch. - """ - for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]: - if not isinstance(var, list): - raise TypeError(f'{name} must be a list, but got ' - f'{type(var)}') - - num_augs = len(imgs) - if num_augs != len(img_metas): - raise ValueError(f'num of augmentations ({len(imgs)}) != ' - f'num of image meta ({len(img_metas)})') - # all images in the same aug batch all of the same ori_shape and pad - # shape - for img_meta in img_metas: - ori_shapes = [_['ori_shape'] for _ in img_meta] - assert all(shape == ori_shapes[0] for shape in ori_shapes) - img_shapes = [_['img_shape'] for _ in img_meta] - assert all(shape == img_shapes[0] for shape in img_shapes) - pad_shapes = [_['pad_shape'] for _ in img_meta] - assert all(shape == pad_shapes[0] for shape in pad_shapes) - - if num_augs == 1: - return self.simple_test(imgs[0], img_metas[0], **kwargs) - else: - return self.aug_test(imgs, img_metas, **kwargs) - - @auto_fp16(apply_to=('img', )) - def forward(self, img, img_metas, return_loss=True, **kwargs): - """Calls either :func:`forward_train` or :func:`forward_test` depending - on whether ``return_loss`` is ``True``. - - Note this setting will change the expected inputs. When - ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor - and List[dict]), and when ``resturn_loss=False``, img and img_meta - should be double nested (i.e. List[Tensor], List[List[dict]]), with - the outer list indicating test time augmentations. - """ - if return_loss: - return self.forward_train(img, img_metas, **kwargs) - else: - return self.forward_test(img, img_metas, **kwargs) - - def train_step(self, data_batch, optimizer, **kwargs): - """The iteration step during training. - - This method defines an iteration step during training, except for the - back propagation and optimizer updating, which are done in an optimizer - hook. Note that in some complicated cases or models, the whole process - including back propagation and optimizer updating is also defined in - this method, such as GAN. - - Args: - data (dict): The output of dataloader. - optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of - runner is passed to ``train_step()``. This argument is unused - and reserved. - - Returns: - dict: It should contain at least 3 keys: ``loss``, ``log_vars``, - ``num_samples``. - ``loss`` is a tensor for back propagation, which can be a - weighted sum of multiple losses. - ``log_vars`` contains all the variables to be sent to the - logger. - ``num_samples`` indicates the batch size (when the model is - DDP, it means the batch size on each GPU), which is used for - averaging the logs. - """ - losses = self(**data_batch) - loss, log_vars = self._parse_losses(losses) - - outputs = dict( - loss=loss, - log_vars=log_vars, - num_samples=len(data_batch['img_metas'])) - - return outputs - - def val_step(self, data_batch, **kwargs): - """The iteration step during validation. - - This method shares the same signature as :func:`train_step`, but used - during val epochs. Note that the evaluation after training epochs is - not implemented with this method, but an evaluation hook. - """ - output = self(**data_batch, **kwargs) - return output - - @staticmethod - def _parse_losses(losses): - """Parse the raw outputs (losses) of the network. - - Args: - losses (dict): Raw output of the network, which usually contain - losses and other necessary information. - - Returns: - tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor - which may be a weighted sum of all losses, log_vars contains - all the variables to be sent to the logger. - """ - log_vars = OrderedDict() - for loss_name, loss_value in losses.items(): - if isinstance(loss_value, torch.Tensor): - log_vars[loss_name] = loss_value.mean() - elif isinstance(loss_value, list): - log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value) - else: - raise TypeError( - f'{loss_name} is not a tensor or list of tensors') - - loss = sum(_value for _key, _value in log_vars.items() - if 'loss' in _key) - - log_vars['loss'] = loss - for loss_name, loss_value in log_vars.items(): - # reduce loss when distributed training - if dist.is_available() and dist.is_initialized(): - loss_value = loss_value.data.clone() - dist.all_reduce(loss_value.div_(dist.get_world_size())) - log_vars[loss_name] = loss_value.item() - - return loss, log_vars - - def show_result(self, - img, - result, - palette=None, - win_name='', - show=False, - wait_time=0, - out_file=None, - opacity=0.5): - """Draw `result` over `img`. - - Args: - img (str or Tensor): The image to be displayed. - result (Tensor): The semantic segmentation results to draw over - `img`. - palette (list[list[int]]] | np.ndarray | None): The palette of - segmentation map. If None is given, random palette will be - generated. Default: None - win_name (str): The window name. - wait_time (int): Value of waitKey param. - Default: 0. - show (bool): Whether to show the image. - Default: False. - out_file (str or None): The filename to write the image. - Default: None. - opacity(float): Opacity of painted segmentation map. - Default 0.5. - Must be in (0, 1] range. - Returns: - img (Tensor): Only if not `show` or `out_file` - """ - img = mmcv.imread(img) - img = img.copy() - seg = result[0] - if palette is None: - if self.PALETTE is None: - palette = np.random.randint( - 0, 255, size=(len(self.CLASSES), 3)) - else: - palette = self.PALETTE - palette = np.array(palette) - assert palette.shape[0] == len(self.CLASSES) - assert palette.shape[1] == 3 - assert len(palette.shape) == 2 - assert 0 < opacity <= 1.0 - color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) - for label, color in enumerate(palette): - color_seg[seg == label, :] = color - # convert to BGR - color_seg = color_seg[..., ::-1] - - img = img * (1 - opacity) + color_seg * opacity - img = img.astype(np.uint8) - # if out_file specified, do not show image in window - if out_file is not None: - show = False - - if show: - mmcv.imshow(img, win_name, wait_time) - if out_file is not None: - mmcv.imwrite(img, out_file) - - if not (show or out_file): - warnings.warn('show==False and out_file is not specified, only ' - 'result image will be returned') - return img diff --git a/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/lib/model/DepthNormalizer.py b/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/lib/model/DepthNormalizer.py deleted file mode 100644 index 84908ec131771b8d42f32535ab856017fe1143a1..0000000000000000000000000000000000000000 --- a/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/lib/model/DepthNormalizer.py +++ /dev/null @@ -1,18 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class DepthNormalizer(nn.Module): - def __init__(self, opt): - super(DepthNormalizer, self).__init__() - self.opt = opt - - def forward(self, z, calibs=None, index_feat=None): - ''' - Normalize z_feature - :param z_feat: [B, 1, N] depth value for z in the image coordinate system - :return: - ''' - z_feat = z * (self.opt.loadSize // 2) / self.opt.z_size - return z_feat diff --git a/spaces/cxeep/whisper-webui/src/vad.py b/spaces/cxeep/whisper-webui/src/vad.py deleted file mode 100644 index c3c34480cac59ff08d07f846e47bea127beefccc..0000000000000000000000000000000000000000 --- a/spaces/cxeep/whisper-webui/src/vad.py +++ /dev/null @@ -1,404 +0,0 @@ -from abc import ABC, abstractmethod -from collections import Counter -from typing import Any, Iterator, List, Dict - -from pprint import pprint - -# Workaround for https://github.com/tensorflow/tensorflow/issues/48797 -try: - import tensorflow as tf -except ModuleNotFoundError: - # Error handling - pass - -import torch - -import ffmpeg -import numpy as np - -from src.utils import format_timestamp - -# Defaults for Silero -# TODO: Make these configurable? - -SPEECH_TRESHOLD = 0.3 -MAX_SILENT_PERIOD = 10 # seconds -MAX_MERGE_SIZE = 150 # Do not create segments larger than 2.5 minutes - -SEGMENT_PADDING_LEFT = 1 # Start detected text segment early -SEGMENT_PADDING_RIGHT = 1 # End detected segments late - -# Whether to attempt to transcribe non-speech -TRANSCRIBE_NON_SPEECH = False - -# Minimum size of segments to process -MIN_SEGMENT_DURATION = 1 - -VAD_MAX_PROCESSING_CHUNK = 60 * 60 # 60 minutes of audio - -class AbstractTranscription(ABC): - def __init__(self, segment_padding_left: int = None, segment_padding_right = None, max_silent_period: int = None, max_merge_size: int = None, transcribe_non_speech: bool = False): - self.sampling_rate = 16000 - self.segment_padding_left = segment_padding_left - self.segment_padding_right = segment_padding_right - self.max_silent_period = max_silent_period - self.max_merge_size = max_merge_size - self.transcribe_non_speech = transcribe_non_speech - - def get_audio_segment(self, str, start_time: str = None, duration: str = None): - return load_audio(str, self.sampling_rate, start_time, duration) - - @abstractmethod - def get_transcribe_timestamps(self, audio: str): - """ - Get the start and end timestamps of the sections that should be transcribed by this VAD method. - - Parameters - ---------- - audio: str - The audio file. - - Returns - ------- - A list of start and end timestamps, in fractional seconds. - """ - return - - def transcribe(self, audio: str, whisperCallable): - """ - Transcribe the given audo file. - - Parameters - ---------- - audio: str - The audio file. - - whisperCallable: Callable[[Union[str, np.ndarray, torch.Tensor]], dict[str, Union[dict, Any]]] - The callback that is used to invoke Whisper on an audio file/buffer. - - Returns - ------- - A list of start and end timestamps, in fractional seconds. - """ - - # get speech timestamps from full audio file - seconds_timestamps = self.get_transcribe_timestamps(audio) - - padded = self.pad_timestamps(seconds_timestamps, self.segment_padding_left, self.segment_padding_right) - merged = self.merge_timestamps(padded, self.max_silent_period, self.max_merge_size) - - print("Timestamps:") - pprint(merged) - - if self.transcribe_non_speech: - max_audio_duration = get_audio_duration(audio) - - # Expand segments to include the gaps between them - merged = self.expand_gaps(merged, total_duration=max_audio_duration) - - print("Transcribing non-speech:") - pprint(merged) - - result = { - 'text': "", - 'segments': [], - 'language': "" - } - languageCounter = Counter() - - # For each time segment, run whisper - for segment in merged: - segment_start = segment['start'] - segment_end = segment['end'] - segment_expand_amount = segment.get('expand_amount', 0) - - segment_duration = segment_end - segment_start - - if segment_duration < MIN_SEGMENT_DURATION: - continue; - - segment_audio = self.get_audio_segment(audio, start_time = str(segment_start), duration = str(segment_duration)) - - print("Running whisper from ", format_timestamp(segment_start), " to ", format_timestamp(segment_end), ", duration: ", segment_duration, "expanded: ", segment_expand_amount) - segment_result = whisperCallable(segment_audio) - - adjusted_segments = self.adjust_timestamp(segment_result["segments"], adjust_seconds=segment_start, max_source_time=segment_duration) - - # Append to output - result['text'] += segment_result['text'] - result['segments'].extend(adjusted_segments) - - # Increment detected language - languageCounter[segment_result['language']] += 1 - - if len(languageCounter) > 0: - result['language'] = languageCounter.most_common(1)[0][0] - - return result - - def include_gaps(self, segments: Iterator[dict], min_gap_length: float, total_duration: float): - result = [] - last_end_time = 0 - - for segment in segments: - segment_start = float(segment['start']) - segment_end = float(segment['end']) - - if (last_end_time != segment_start): - delta = segment_start - last_end_time - - if (min_gap_length is None or delta >= min_gap_length): - result.append( { 'start': last_end_time, 'end': segment_start, 'gap': True } ) - - last_end_time = segment_end - result.append(segment) - - # Also include total duration if specified - if (total_duration is not None and last_end_time < total_duration): - delta = total_duration - segment_start - - if (min_gap_length is None or delta >= min_gap_length): - result.append( { 'start': last_end_time, 'end': total_duration, 'gap': True } ) - - return result - - # Expand the end time of each segment to the start of the next segment - def expand_gaps(self, segments: List[Dict[str, Any]], total_duration: float): - result = [] - - if len(segments) == 0: - return result - - # Add gap at the beginning if needed - if (segments[0]['start'] > 0): - result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } ) - - for i in range(len(segments) - 1): - current_segment = segments[i] - next_segment = segments[i + 1] - - delta = next_segment['start'] - current_segment['end'] - - # Expand if the gap actually exists - if (delta >= 0): - current_segment = current_segment.copy() - current_segment['expand_amount'] = delta - current_segment['end'] = next_segment['start'] - - result.append(current_segment) - - last_segment = result[-1] - - # Also include total duration if specified - if (total_duration is not None): - last_segment = result[-1] - - if (last_segment['end'] < total_duration): - last_segment = last_segment.copy() - last_segment['end'] = total_duration - result[-1] = last_segment - - return result - - def adjust_timestamp(self, segments: Iterator[dict], adjust_seconds: float, max_source_time: float = None): - result = [] - - for segment in segments: - segment_start = float(segment['start']) - segment_end = float(segment['end']) - - # Filter segments? - if (max_source_time is not None): - if (segment_start > max_source_time): - continue - segment_end = min(max_source_time, segment_end) - - new_segment = segment.copy() - - # Add to start and end - new_segment['start'] = segment_start + adjust_seconds - new_segment['end'] = segment_end + adjust_seconds - result.append(new_segment) - return result - - def pad_timestamps(self, timestamps: List[Dict[str, Any]], padding_left: float, padding_right: float): - if (padding_left == 0 and padding_right == 0): - return timestamps - - result = [] - prev_entry = None - - for i in range(len(timestamps)): - curr_entry = timestamps[i] - next_entry = timestamps[i + 1] if i < len(timestamps) - 1 else None - - segment_start = curr_entry['start'] - segment_end = curr_entry['end'] - - if padding_left is not None: - segment_start = max(prev_entry['end'] if prev_entry else 0, segment_start - padding_left) - if padding_right is not None: - segment_end = segment_end + padding_right - - # Do not pad past the next segment - if (next_entry is not None): - segment_end = min(next_entry['start'], segment_end) - - new_entry = { 'start': segment_start, 'end': segment_end } - prev_entry = new_entry - result.append(new_entry) - - return result - - def merge_timestamps(self, timestamps: List[Dict[str, Any]], max_merge_gap: float, max_merge_size: float): - if max_merge_gap is None: - return timestamps - - result = [] - current_entry = None - - for entry in timestamps: - if current_entry is None: - current_entry = entry - continue - - # Get distance to the previous entry - distance = entry['start'] - current_entry['end'] - current_entry_size = current_entry['end'] - current_entry['start'] - - if distance <= max_merge_gap and (max_merge_size is None or current_entry_size <= max_merge_size): - # Merge - current_entry['end'] = entry['end'] - else: - # Output current entry - result.append(current_entry) - current_entry = entry - - # Add final entry - if current_entry is not None: - result.append(current_entry) - - return result - - def multiply_timestamps(self, timestamps: List[Dict[str, Any]], factor: float): - result = [] - - for entry in timestamps: - start = entry['start'] - end = entry['end'] - - result.append({ - 'start': start * factor, - 'end': end * factor - }) - return result - -class VadSileroTranscription(AbstractTranscription): - def __init__(self, segment_padding_left=SEGMENT_PADDING_LEFT, segment_padding_right=SEGMENT_PADDING_RIGHT, - max_silent_period=MAX_SILENT_PERIOD, max_merge_size=MAX_MERGE_SIZE, transcribe_non_speech: bool = False, - copy = None): - super().__init__(segment_padding_left=segment_padding_left, segment_padding_right=segment_padding_right, - max_silent_period=max_silent_period, max_merge_size=max_merge_size, transcribe_non_speech=transcribe_non_speech) - - if copy: - self.model = copy.model - self.get_speech_timestamps = copy.get_speech_timestamps - else: - self.model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad') - (self.get_speech_timestamps, _, _, _, _) = utils - - def get_transcribe_timestamps(self, audio: str): - audio_duration = get_audio_duration(audio) - result = [] - - # Divide procesisng of audio into chunks - chunk_start = 0.0 - - while (chunk_start < audio_duration): - chunk_duration = min(audio_duration - chunk_start, VAD_MAX_PROCESSING_CHUNK) - - print("Processing VAD in chunk from {} to {}".format(format_timestamp(chunk_start), format_timestamp(chunk_start + chunk_duration))) - wav = self.get_audio_segment(audio, str(chunk_start), str(chunk_duration)) - - sample_timestamps = self.get_speech_timestamps(wav, self.model, sampling_rate=self.sampling_rate, threshold=SPEECH_TRESHOLD) - seconds_timestamps = self.multiply_timestamps(sample_timestamps, factor=1 / self.sampling_rate) - adjusted = self.adjust_timestamp(seconds_timestamps, adjust_seconds=chunk_start, max_source_time=chunk_start + chunk_duration) - - #pprint(adjusted) - - result.extend(adjusted) - chunk_start += chunk_duration - - return result - -# A very simple VAD that just marks every N seconds as speech -class VadPeriodicTranscription(AbstractTranscription): - def __init__(self, periodic_duration: int): - super().__init__() - self.periodic_duration = periodic_duration - - def get_transcribe_timestamps(self, audio: str): - # Get duration in seconds - audio_duration = get_audio_duration(audio) - result = [] - - # Generate a timestamp every N seconds - start_timestamp = 0 - - while (start_timestamp < audio_duration): - end_timestamp = min(start_timestamp + self.periodic_duration, audio_duration) - segment_duration = end_timestamp - start_timestamp - - # Minimum duration is 1 second - if (segment_duration >= 1): - result.append( { 'start': start_timestamp, 'end': end_timestamp } ) - - start_timestamp = end_timestamp - - return result - -def get_audio_duration(file: str): - return float(ffmpeg.probe(file)["format"]["duration"]) - -def load_audio(file: str, sample_rate: int = 16000, - start_time: str = None, duration: str = None): - """ - Open an audio file and read as mono waveform, resampling as necessary - - Parameters - ---------- - file: str - The audio file to open - - sr: int - The sample rate to resample the audio if necessary - - start_time: str - The start time, using the standard FFMPEG time duration syntax, or None to disable. - - duration: str - The duration, using the standard FFMPEG time duration syntax, or None to disable. - - Returns - ------- - A NumPy array containing the audio waveform, in float32 dtype. - """ - try: - inputArgs = {'threads': 0} - - if (start_time is not None): - inputArgs['ss'] = start_time - if (duration is not None): - inputArgs['t'] = duration - - # This launches a subprocess to decode audio while down-mixing and resampling as necessary. - # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed. - out, _ = ( - ffmpeg.input(file, **inputArgs) - .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sample_rate) - .run(cmd="ffmpeg", capture_stdout=True, capture_stderr=True) - ) - except ffmpeg.Error as e: - raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") - - return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0 \ No newline at end of file diff --git a/spaces/cybercorejapan/human-detection-docker/projects/human_detection/engine/visualizer.py b/spaces/cybercorejapan/human-detection-docker/projects/human_detection/engine/visualizer.py deleted file mode 100644 index 992d2d3caea773bed872c00b712114367cc516c1..0000000000000000000000000000000000000000 --- a/spaces/cybercorejapan/human-detection-docker/projects/human_detection/engine/visualizer.py +++ /dev/null @@ -1,51 +0,0 @@ -from typing import List, Dict -import cv2 -import numpy as np -from models.engine.visualizer import BaseVisualizer - -class Visualizer(BaseVisualizer): - - def __init__(self, fps: int=-1, min_width: int=-1): - """ Visualizer class for visualization (track_results + count_results). - - Args: - class_map_ids (Dict): class mapping dictionary to map model's class to original class. Eg {0: 1, 1: 0, 2: 2, 3: 3} mean we swap class ID between 0 and 1. - fps (int): FPS for output video. If fps = -1, it will have same fps as input video. - min_width (int): minimum width for output video (height will be scaled to keep aspect ratio as input video). If min_width = -1, it will have same resolution as input video. - """ - class_names = ['pedestrian'] - super().__init__(class_names, fps, min_width) - - def visualize(self, img: np.ndarray, dettrack_at_frame_id: List[Dict]=None, show_conf: bool=True): - """ Function to visualize (track_results + count_results) a frame. - - Args: - img (np.ndarray): image need to be visualized. - dettrack_at_frame_id (List[Dict]): batch of track results which can be obtained from Tracker class. - show_conf (bool): Visualize confidence of track results or not. - """ - - # Draw tracking - if (dettrack_at_frame_id): - boxes = dettrack_at_frame_id["boxes"] - # classes = dettrack_at_frame_id["labels"] - ids = dettrack_at_frame_id["ids"] - for bbox, id_ in zip(boxes, ids): - id_ = int(id_) - score = bbox[4] - color = self.get_color(id_) - label = f'{id_}' + (f' {score:.2f}' if (show_conf) else '') - tl, tf = 2, 1 - c1, c2 = (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])) - img = cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) - t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] - c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 - img = cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) - img = cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) - - if (img.shape[0] != self.height or img.shape[1] != self.width): - img = cv2.resize(img, (self.width, self.height)) - self.writer.write(img) - - return img - diff --git a/spaces/cyhcctc/cyhbingo/README.md b/spaces/cyhcctc/cyhbingo/README.md deleted file mode 100644 index 5d6936218874c647b5d22e13ad4be7edb8936f92..0000000000000000000000000000000000000000 --- a/spaces/cyhcctc/cyhbingo/README.md +++ /dev/null @@ -1,28 +0,0 @@ ---- -title: bingo -emoji: 😊 -colorFrom: red -colorTo: red -sdk: docker -license: mit -duplicated_from: hf4all/bingo ---- - -
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Password Wordlist TXT: Everything You Need to Know
-Password cracking is a technique that involves guessing or recovering passwords from encrypted or hashed data. Password cracking can be used for various purposes, such as testing the security of a system, recovering lost or forgotten passwords, or hacking into unauthorized accounts. Password cracking can be done by using different methods, such as brute-force attacks, rainbow tables, or dictionary attacks. In this article, we will focus on dictionary attacks and how password wordlist txt files can help you perform them.
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What is a Dictionary Attack?
-A dictionary attack is a type of password cracking method that tries to guess passwords by using a list of common or likely passwords. The list of passwords can be obtained from various sources, such as leaked databases, websites, social media, etc. The list of passwords can also be generated by using tools that combine words from different languages, categories, patterns, etc. A dictionary attack can be faster and more efficient than a brute-force attack, which tries to guess passwords by using all possible combinations of characters.
-What is a Password Wordlist TXT?
-A password wordlist txt is a text file that contains a list of passwords or words that can be used for dictionary attacks. A password wordlist txt file can have different formats and sizes depending on the source and the method of creation. Some password wordlist txt files may have one password or word per line, while others may have multiple passwords or words separated by commas or spaces. Some password wordlist txt files may have millions of passwords or words, while others may have only thousands or hundreds.
-Where to Find Password Wordlist TXT Files?
-There are many websites and repositories that offer password wordlist txt files for download. Some of them are:
--
-
- Password Wordlist(235k): This is a GitHub gist that contains a password wordlist txt file with 235k passwords or words. The file is truncated on the website, but you can view the full file by clicking on the download link. -
- wpa2-wordlists: This is a GitHub repository that contains a collection of passwords and wordlists commonly used for dictionary attacks using a variety of password cracking tools such as aircrack-ng, hydra, and hashcat. It also provides useful one-liners for wordlist manipulation. -
- SecLists: This is a GitHub repository that contains a collection of multiple types of lists used during security assessments, collected in one place. It includes usernames, passwords, URLs, sensitive data patterns, fuzzing payloads, web shells, and many more. -
How to Use Password Wordlist TXT Files?
-To use password wordlist txt files for dictionary attacks, you need to have a password cracking tool that supports this option. Some of the popular password cracking tools are:
--
-
- aircrack-ng: This is a tool for cracking WEP and WPA/WPA2 wireless network passwords. It can use password wordlist txt files with the -w option. -
- hydra: This is a tool for cracking passwords of various network services such as FTP, SSH, Telnet, HTTP, etc. It can use password wordlist txt files with the -P option. -
- hashcat: This is a tool for cracking hashed passwords using various algorithms such as MD5, SHA1, SHA256, etc. It can use password wordlist txt files with the -a 0 option. -
To use password wordlist txt files with these tools, you need to follow these steps:
--
-
- Download the password wordlist txt file from one of the sources mentioned above or create your own. -
- Transfer the password wordlist txt file to your computer or device where you have the password cracking tool installed. -
- Extract the password wordlist txt file if it is compressed in a zip or gz format. -
- Run the password cracking tool with the appropriate options and arguments to specify the target, the mode, the algorithm, and the password wordlist txt file. -
- Wait for the password cracking tool to finish its job and display the results. -
What are the Advantages and Disadvantages of Password Wordlist TXT Files?
-Password wordlist txt files have some advantages and disadvantages when used for dictionary attacks. Some of them are:
- --
-
- Advantages: -
- They can speed up the password cracking process by reducing the search space. -
- They can increase the success rate of password cracking by using common or likely passwords. -
- They can be customized or optimized for specific targets or scenarios by using filters or rules. -
- Disadvantages: -
- They can be ineffective or inefficient if they do not contain the correct or relevant passwords for the target. -
- They can be large or bulky and require more storage space or memory. -
- They can be easily detected or blocked by security measures such as rate-limiting or captcha. -
-
-
-
-
Conclusion
- -Password wordlist txt files are text files that contain a list of passwords or words that can be used for dictionary attacks. Dictionary attacks are a type of password cracking method that try to guess passwords by using a list of common or likely passwords. Password wordlist txt files can help you to crack passwords faster and more efficiently than trying random combinations of characters. You can find password wordlist txt files from various sources online or create your own using tools. You can use password wordlist txt files with different password cracking tools such as aircrack-ng, hydra, and hashcat. Password wordlist txt files have some advantages and disadvantages when used for dictionary attacks.
- -We hope this article has helped you to understand what password wordlist txt files are, where to find them, how to use them, and what are their pros and cons. If you have any questions or suggestions, feel free to leave a comment below. Thank you for reading!
-What are the Risks of Password Wordlist TXT Files?
-Password wordlist txt files can be useful for password cracking, but they can also pose some risks for both attackers and defenders. Some of the risks of password wordlist txt files are:
--
-
- They can expose your passwords or words to others if you store them in an insecure location or share them online. -
- They can compromise your accounts or systems if you use weak or common passwords or words that are included in password wordlist txt files. -
- They can violate the privacy or security of others if you use password wordlist txt files to crack passwords of unauthorized accounts or systems. -
- They can cause legal or ethical issues if you use password wordlist txt files to crack passwords of protected or sensitive accounts or systems. -
How to Protect Yourself from Password Wordlist TXT Files?
-To protect yourself from password wordlist txt files, you need to take some precautions and follow some best practices. Some of them are:
--
-
- Use strong and unique passwords or words for your accounts or systems. Avoid using common or predictable passwords or words that can be easily guessed or found in password wordlist txt files. -
- Use different passwords or words for different accounts or systems. Do not reuse the same password or word for multiple accounts or systems. -
- Use a password manager to store and manage your passwords or words securely. Do not store your passwords or words in plain text files or online services that are not encrypted or protected. -
- Use two-factor authentication to add an extra layer of security to your accounts or systems. Do not rely on passwords or words alone to authenticate yourself. -
- Use a VPN or a proxy to hide your IP address and location when using password cracking tools or downloading password wordlist txt files. Do not expose your identity or location to potential attackers or authorities. -
Conclusion
- -Password wordlist txt files are text files that contain a list of passwords or words that can be used for dictionary attacks. Dictionary attacks are a type of password cracking method that try to guess passwords by using a list of common or likely passwords. Password wordlist txt files can help you to crack passwords faster and more efficiently than trying random combinations of characters. You can find password wordlist txt files from various sources online or create your own using tools. You can use password wordlist txt files with different password cracking tools such as aircrack-ng, hydra, and hashcat. Password wordlist txt files have some advantages and disadvantages when used for dictionary attacks. They also have some risks and challenges that you need to be aware of and protect yourself from.
- -We hope this article has helped you to understand what password wordlist txt files are, where to find them, how to use them, what are their pros and cons, and how to protect yourself from them. If you have any questions or suggestions, feel free to leave a comment below. Thank you for reading!
-How to Create Your Own Password Wordlist TXT Files?
-If you want to create your own password wordlist txt files, you can use various tools and methods to generate them. Some of them are:
--
-
- BEWGor: This is a tool that generates wordlists based on a given set of input criteria. You can specify the language, the length, the character set, the patterns, the prefixes, the suffixes, and other options to create customized wordlists. -
- KaliLists: This is a tool that generates wordlists based on a given target domain or website. You can specify the domain name, the subdomains, the extensions, the keywords, and other options to create targeted wordlists. -
- CeWL: This is a tool that generates wordlists based on a given web page or website. You can specify the URL, the depth, the minimum and maximum length, the exclusions, and other options to create wordlists from web content. -
How to Optimize Your Password Wordlist TXT Files?
-To optimize your password wordlist txt files, you can use various tools and techniques to manipulate them. Some of them are:
--
-
- Probable-Wordlists: This is a collection of wordlists sorted by probability according to analysis of leaked password databases. You can use these wordlists to prioritize the most likely passwords or words for your dictionary attacks. -
- hashcat: This is a tool that supports various rules and filters to modify your wordlists. You can use these rules and filters to add, remove, replace, append, prepend, or toggle characters in your wordlists. -
- naive-hashcat: This is a tool that uses hashcat to crack passwords using naive combinator attack. You can use this tool to combine multiple wordlists into one large wordlist. -
Conclusion
- -Password wordlist txt files are text files that contain a list of passwords or words that can be used for dictionary attacks. Dictionary attacks are a type of password cracking method that try to guess passwords by using a list of common or likely passwords. Password wordlist txt files can help you to crack passwords faster and more efficiently than trying random combinations of characters. You can find password wordlist txt files from various sources online or create your own using tools. You can use password wordlist txt files with different password cracking tools such as aircrack-ng, hydra, and hashcat. Password wordlist txt files have some advantages and disadvantages when used for dictionary attacks. They also have some risks and challenges that you need to be aware of and protect yourself from. You can also optimize your password wordlist txt files by using various tools and techniques to manipulate them.
- -We hope this article has helped you to understand what password wordlist txt files are, where to find them, how to use them, what are their pros and cons, how to protect yourself from them, and how to optimize them. If you have any questions or suggestions, feel free to leave a comment below. Thank you for reading!
-Conclusion
- -Password wordlist txt files are text files that contain a list of passwords or words that can be used for dictionary attacks. Dictionary attacks are a type of password cracking method that try to guess passwords by using a list of common or likely passwords. Password wordlist txt files can help you to crack passwords faster and more efficiently than trying random combinations of characters. You can find password wordlist txt files from various sources online or create your own using tools. You can use password wordlist txt files with different password cracking tools such as aircrack-ng, hydra, and hashcat. Password wordlist txt files have some advantages and disadvantages when used for dictionary attacks. They also have some risks and challenges that you need to be aware of and protect yourself from. You can also optimize your password wordlist txt files by using various tools and techniques to manipulate them.
- -We hope this article has helped you to understand what password wordlist txt files are, where to find them, how to use them, what are their pros and cons, how to protect yourself from them, and how to optimize them. If you have any questions or suggestions, feel free to leave a comment below. Thank you for reading!
3cee63e6c2-
-
\ No newline at end of file diff --git a/spaces/diagaiwei/ir_chinese_medqa/colbert/evaluation/load_model.py b/spaces/diagaiwei/ir_chinese_medqa/colbert/evaluation/load_model.py deleted file mode 100644 index 13cb5c82dfa3309d814a80fc4082eada44d0eedd..0000000000000000000000000000000000000000 --- a/spaces/diagaiwei/ir_chinese_medqa/colbert/evaluation/load_model.py +++ /dev/null @@ -1,28 +0,0 @@ -import os -import ujson -import torch -import random - -from collections import defaultdict, OrderedDict - -from colbert.parameters import DEVICE -from colbert.modeling.colbert import ColBERT -from colbert.utils.utils import print_message, load_checkpoint - - -def load_model(args, do_print=True): - colbert = ColBERT.from_pretrained('bert-base-uncased', - query_maxlen=args.query_maxlen, - doc_maxlen=args.doc_maxlen, - dim=args.dim, - similarity_metric=args.similarity, - mask_punctuation=args.mask_punctuation) - colbert = colbert.to(DEVICE) - - print_message("#> Loading model checkpoint.", condition=do_print) - - checkpoint = load_checkpoint(args.checkpoint, colbert, do_print=do_print) - - colbert.eval() - - return colbert, checkpoint diff --git a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/app.py b/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/app.py deleted file mode 100644 index 642bdeebf826cf29d4a7fedcd707793e240b5160..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/app.py +++ /dev/null @@ -1,183 +0,0 @@ -import sys, os - -if sys.platform == "darwin": - os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" - -import logging - -logging.getLogger("numba").setLevel(logging.WARNING) -logging.getLogger("markdown_it").setLevel(logging.WARNING) -logging.getLogger("urllib3").setLevel(logging.WARNING) -logging.getLogger("matplotlib").setLevel(logging.WARNING) - -logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s") - -logger = logging.getLogger(__name__) - -import torch -import argparse -import commons -import utils -from models import SynthesizerTrn -from text.symbols import symbols -from text import cleaned_text_to_sequence, get_bert -from text.cleaner import clean_text -import gradio as gr -import webbrowser - - -net_g = None - - -def get_text(text, language_str, hps): - norm_text, phone, tone, word2ph = clean_text(text, language_str) - phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) - - if hps.data.add_blank: - phone = commons.intersperse(phone, 0) - tone = commons.intersperse(tone, 0) - language = commons.intersperse(language, 0) - for i in range(len(word2ph)): - word2ph[i] = word2ph[i] * 2 - word2ph[0] += 1 - bert = get_bert(norm_text, word2ph, language_str) - del word2ph - - assert bert.shape[-1] == len(phone) - - phone = torch.LongTensor(phone) - tone = torch.LongTensor(tone) - language = torch.LongTensor(language) - - return bert, phone, tone, language -import soundfile as sf -def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid): - global net_g - bert, phones, tones, lang_ids = get_text(text, "ZH", hps) - with torch.no_grad(): - x_tst=phones.to(device).unsqueeze(0) - tones=tones.to(device).unsqueeze(0) - lang_ids=lang_ids.to(device).unsqueeze(0) - bert = bert.to(device).unsqueeze(0) - x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) - del phones - speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) - audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio - , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy() - del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers - sf.write("tmp.wav", audio, 44100) - return audio -def convert_wav_to_ogg(wav_file): - os.makedirs('out', exist_ok=True) - filename = os.path.splitext(os.path.basename(wav_file.name))[0] - output_path_ogg = os.path.join('out', f"out.ogg") - - renamed_input_path = os.path.join('in', f"in.wav") - os.makedirs('in', exist_ok=True) - os.rename(wav_file.name, renamed_input_path) - command = ["ffmpeg", "-i", renamed_input_path, "-acodec", "libopus", "-y", output_path_ogg] - os.system(" ".join(command)) - return output_path_ogg -def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): - with torch.no_grad(): - audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker) - with open('tmp.wav', 'rb') as wav_file: - newogg = convert_wav_to_ogg(wav_file) - return "Success", (hps.data.sampling_rate, audio),newogg - - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--model_dir", default="./logs/jiaohuaji/jiaohuaji.pth", help="path of your model") - parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file") - parser.add_argument("--share", default=False, help="make link public") - parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log") - - args = parser.parse_args() - if args.debug: - logger.info("Enable DEBUG-LEVEL log") - logging.basicConfig(level=logging.DEBUG) - hps = utils.get_hparams_from_file(args.config_dir) - device = "cuda:0" if torch.cuda.is_available() else "cpu" - ''' - device = ( - "cuda:0" - if torch.cuda.is_available() - else ( - "mps" - if sys.platform == "darwin" and torch.backends.mps.is_available() - else "cpu" - ) - ) - ''' - net_g = SynthesizerTrn( - len(symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model).to(device) - _ = net_g.eval() - - _ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True) - - speaker_ids = hps.data.spk2id - speakers = list(speaker_ids.keys()) - with gr.Blocks() as app: - with gr.Row(): - with gr.Column(): - - - gr.Markdown(value=""" - 甜甜叫花鸡 Bert-Vits2在线语音生成\n - 1、模型作者：数字星瞳企划 https://t.me/xingtong25680 \n - \n - 2、原项目地址：https://github.com/Stardust-minus/Bert-VITS2\n - 3、使用此模型进行二创请注明AI生成，以及该项目地址。\n - 4、如果想生成超长txt文本的音频请使用colab。 https://colab.research.google.com/drive/13ek8_j1aknr-pbjj3NXxSM4vBIsracU3?usp=drive_link\n - - """) - text = gr.TextArea(label="Text", placeholder="Input Text Here", - value="这里是数字星瞳企画，请在电报搜索星瞳全拼加二五六八零，获取最新更新进展。") - speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker') - sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label='语调变化') - noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.6, step=0.01, label='感情变化') - noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.8, step=0.01, label='音节发音长度变化') - length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='语速') - btn = gr.Button("开启AI语音之旅吧!", variant="primary") - with gr.Column(): - text_output = gr.Textbox(label="Message") - audio_output = gr.Audio(label="Output Audio") - ogg_output = gr.File(label="Converted OGG file") - gr.Markdown(value=""" - 模型汇总：\n - 星瞳 https://huggingface.co/spaces/digitalxingtong/Xingtong-Bert-Vits2 \n - 星瞳 朗读专用 https://huggingface.co/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2 \n - 星瞳 长文本专用 https://huggingface.co/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2 \n - 甜甜叫花鸡 https://huggingface.co/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2 \n - 七海 https://huggingface.co/spaces/digitalxingtong/Nanami-Bert-Vits2 \n - 东雪莲 https://huggingface.co/spaces/digitalxingtong/Azuma-Bert-Vits2 \n - 嘉然 https://huggingface.co/spaces/digitalxingtong/Jiaran-Bert-Vits2 \n - 乃琳 https://huggingface.co/spaces/digitalxingtong/Eileen-Bert-Vits2 \n - 恬豆 https://huggingface.co/spaces/digitalxingtong/Dou-Bert-Vits2 \n - 奶绿 杂谈 https://huggingface.co/spaces/digitalxingtong/Nailv-Bert-Vits2 \n - 奶绿 朗读 https://huggingface.co/spaces/digitalxingtong/Nailv-read-Bert-Vits2 \n - 露早 https://huggingface.co/spaces/digitalxingtong/Luzao-Bert-Vits2 \n - 柚恩 https://huggingface.co/spaces/digitalxingtong/Un-Bert-Vits2 \n - 米诺 https://huggingface.co/spaces/digitalxingtong/Minuo-Bert-Vits2 \n - 扇宝 https://huggingface.co/spaces/digitalxingtong/Shanbao-Bert-Vits2 \n - 牧牧白 https://huggingface.co/spaces/digitalxingtong/Miiu-Bert-Vits2 \n - 吉诺儿kino https://huggingface.co/spaces/digitalxingtong/Kino-Bert-Vits2 \n - 九夏 https://huggingface.co/spaces/digitalxingtong/Jiuxia-Bert-Vits2 \n - 卡缇娅 https://huggingface.co/spaces/digitalxingtong/Yaya-Bert-Vits2 \n - 理想_ideal https://huggingface.co/spaces/digitalxingtong/Lixiang-Bert-Vits2 \n - 阿梓 https://huggingface.co/spaces/digitalxingtong/Azusa-Bert-Vits2 \n - 鹿鸣 https://huggingface.co/spaces/digitalxingtong/Luming-Bert-Vits2 \n - 永雏塔菲 https://huggingface.co/spaces/digitalxingtong/Taffy-Bert-VITS2 \n - """) - btn.click(tts_fn, - inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale], - outputs=[text_output, audio_output,ogg_output]) - - - app.launch(show_error=True) diff --git a/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/monotonic_align/core.py b/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/monotonic_align/core.py deleted file mode 100644 index 5ff728cd74c9228346a82ec64a9829cb98ad315e..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/monotonic_align/core.py +++ /dev/null @@ -1,36 +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 \ No newline at end of file diff --git a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/transformer_head.py b/spaces/dineshreddy/WALT/mmdet/models/dense_heads/transformer_head.py deleted file mode 100644 index 820fd069fcca295f6102f0d27366158a8c640249..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/transformer_head.py +++ /dev/null @@ -1,654 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from mmcv.cnn import Conv2d, Linear, build_activation_layer -from mmcv.runner import force_fp32 - -from mmdet.core import (bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh, - build_assigner, build_sampler, multi_apply, - reduce_mean) -from mmdet.models.utils import (FFN, build_positional_encoding, - build_transformer) -from ..builder import HEADS, build_loss -from .anchor_free_head import AnchorFreeHead - - -@HEADS.register_module() -class TransformerHead(AnchorFreeHead): - """Implements the DETR transformer head. - - See `paper: End-to-End Object Detection with Transformers -
%sveltekit.body%
-
-
-
\ No newline at end of file
diff --git a/spaces/exaggerated/PaddleOCR/pp_ocr.py b/spaces/exaggerated/PaddleOCR/pp_ocr.py
deleted file mode 100644
index 943fc721c2b3979a4820bb08b275e894f8774d89..0000000000000000000000000000000000000000
--- a/spaces/exaggerated/PaddleOCR/pp_ocr.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import tempfile
-import os
-
-import paddlehub as hub
-from PIL import Image
-
-pp_ocrv3 = hub.Module(name="ch_pp-ocrv3")
-
-def inference_img(img):
- with tempfile.TemporaryDirectory() as tempdir_name:
- pp_ocrv3.recognize_text(images=[img], use_gpu=False, output_dir=tempdir_name, visualization=True)
- result_names = os.listdir(tempdir_name)
- result_image = Image.open(os.path.join(tempdir_name, result_names[0]))
- return result_image
-
-def inference_json(img):
- results = pp_ocrv3.recognize_text(images=[img], use_gpu=False, visualization=False)
- return results
\ No newline at end of file
diff --git a/spaces/facebook/MusicGen/scripts/static/style.css b/spaces/facebook/MusicGen/scripts/static/style.css
deleted file mode 100644
index a0df7c63a0d2dd9a79f33f5d869ca31c9da87e8d..0000000000000000000000000000000000000000
--- a/spaces/facebook/MusicGen/scripts/static/style.css
+++ /dev/null
@@ -1,113 +0,0 @@
-body {
- background-color: #fbfbfb;
- margin: 0;
-}
-
-select, input {
- font-size: 1em;
- max-width: 100%;
-}
-
-.xp_name {
- font-family: monospace;
-}
-
-.simple_form {
- background-color: #dddddd;
- padding: 1em;
- margin: 0.5em;
-}
-
-textarea {
- margin-top: 0.5em;
- margin-bottom: 0.5em;
-}
-
-.rating {
- background-color: grey;
- padding-top: 5px;
- padding-bottom: 5px;
- padding-left: 8px;
- padding-right: 8px;
- margin-right: 2px;
- cursor:pointer;
-}
-
-.rating_selected {
- background-color: purple;
-}
-
-.content {
- font-family: sans-serif;
- background-color: #f6f6f6;
- padding: 40px;
- margin: 0 auto;
- max-width: 1000px;
-}
-
-.track label {
- padding-top: 10px;
- padding-bottom: 10px;
-}
-.track {
- padding: 15px;
- margin: 5px;
- background-color: #c8c8c8;
-}
-
-.submit-big {
- width:400px;
- height:30px;
- font-size: 20px;
-}
-
-.error {
- color: red;
-}
-
-.ratings {
- margin-left: 10px;
-}
-
-.important {
- font-weight: bold;
-}
-
-.survey {
- margin-bottom: 100px;
-}
-
-.success {
- color: #25901b;
- font-weight: bold;
-}
-.warning {
- color: #8a1f19;
- font-weight: bold;
-}
-.track>section {
- display: flex;
- align-items: center;
-}
-
-.prompt {
- display: flex;
- align-items: center;
-}
-
-.track>section>div {
- padding-left: 10px;
-}
-
-audio {
- max-width: 280px;
- max-height: 40px;
- margin-left: 10px;
- margin-right: 10px;
-}
-
-.special {
- font-weight: bold;
- color: #2c2c2c;
-}
-
diff --git a/spaces/falterWliame/Face_Mask_Detection/Download Captain Claw Game For Free Full Version.md b/spaces/falterWliame/Face_Mask_Detection/Download Captain Claw Game For Free Full Version.md
deleted file mode 100644
index 8d2c8f86aa4d3bf84836a8098d98500247f0f257..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Download Captain Claw Game For Free Full Version.md
+++ /dev/null
@@ -1,6 +0,0 @@
-download captain claw game for free full version
Download ->>> https://urlca.com/2uDdTG
-
-It is captain claw game is full fighting. it is single player and played. It is cheat book. 1fdad05405
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\ No newline at end of file diff --git a/spaces/farukozderim/Model-Comparator-Space-Builder/README.md b/spaces/farukozderim/Model-Comparator-Space-Builder/README.md deleted file mode 100644 index 2b2d69cc2b282b567ce143073d47a2125a329b35..0000000000000000000000000000000000000000 --- a/spaces/farukozderim/Model-Comparator-Space-Builder/README.md +++ /dev/null @@ -1,43 +0,0 @@ ---- -title: Model Comparator Space Builder -emoji: 🌍 -colorFrom: gray -colorTo: purple -sdk: gradio -app_file: app.py -pinned: true ---- - -# Tests -``` -pytest -v tests.py -``` - - -# 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/fatiXbelha/sd/Download Red Hat Free and Learn from Hands-on Learning Paths.md b/spaces/fatiXbelha/sd/Download Red Hat Free and Learn from Hands-on Learning Paths.md deleted file mode 100644 index 51d6467720ea0931596f7a96403c1dadaa28ec36..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Red Hat Free and Learn from Hands-on Learning Paths.md +++ /dev/null @@ -1,120 +0,0 @@ -
-
How to Download Red Hat Linux for Free
-Red Hat Linux is one of the most popular and widely used operating systems in the world, especially for enterprise and cloud computing. It is based on open source technologies and provides a stable, secure, and flexible platform for running various applications and workloads. But did you know that you can download Red Hat Linux for free? In this article, we will show you how to get access to different versions of Red Hat Linux without paying a dime.
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Red Hat Enterprise Linux: The leading enterprise Linux platform
-Red Hat Enterprise Linux (RHEL) is the flagship product of Red Hat, the world's leading provider of open source solutions. RHEL is certified on hundreds of clouds and with thousands of hardware and software vendors, making it the most compatible and reliable platform for your business needs. RHEL comes with 24x7 support, security updates, and access to the Red Hat Customer Portal, where you can find thousands of knowledge articles and documentation. RHEL also includes Red Hat Insights, a managed service that provides analytics and remediation guidance to help you optimize performance and avoid issues.
-But how can you download RHEL for free? The answer is simple: join the Red Hat Developer Program. This is a no-cost subscription that gives you access to RHEL Server, RHEL Workstation, and various add-ons such as resilient storage, scalable file systems, and high-performance networking. You can use RHEL for development purposes only, not for production or commercial use. You can also create installation disks and virtual machines, as well as cloud-ready images for AWS, Google Cloud Platform, Microsoft Azure, and VMWare. To get started, all you need to do is sign up with your email address and activate your subscription.
-Red Hat Developer Program: A no-cost subscription for developers
-The Red Hat Developer Program is not only about RHEL. It also gives you access to other open source products and services from Red Hat, such as:
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- Red Hat OpenShift: A container platform that lets you build, deploy, and manage applications at scale. -
- Red Hat Ansible Automation Platform: A foundation for implementing enterprise-wide automation. -
- Red Hat CodeReady Workspaces: A cloud-native development environment that runs in OpenShift. -
- Red Hat CodeReady Studio: An integrated development environment (IDE) that supports Java, Node.js, Python, PHP, and more. -
- Red Hat Quarkus: A Kubernetes-native Java stack that optimizes memory usage and startup time. -
As a member of the Red Hat Developer Program, you can also access developer tutorials, learning paths, forums, blogs, podcasts, webinars, events, and more. You can also connect with other developers and experts in the community and get support for your projects. The Red Hat Developer Program is a great way to learn new skills, explore new technologies, and accelerate your development process.
-Red Hat Universal Base Image: A container image for your applications
-If you are interested in developing containerized applications using Red Hat Linux, you should check out the Red Hat Universal Base Image (UBI). UBI is a container image that contains the core components of RHEL, such as the kernel, libraries, utilities, and packages. UBI is freely redistributable under the terms of the Universal Permissive License v1.0 (UPL), which means you can use it as a base for your own container images without any restrictions or obligations.
-UBI comes in four variants: base, minimal, micro, and init. Each variant has different features and sizes to suit different use cases. You can use UBI with any container engine or orchestration platform that supports OCI images, such as Podman, OpenShift, or Docker. You can also use UBI with other tools from the Red Hat ecosystem, such as Buildah Table 1: Outline of the article | Heading | Subheading | | --- | --- | | H1: How to Download Red Hat Linux for Free | Introduction: What is Red Hat Linux and why you might want to download it for free | | | H2: Red Hat Enterprise Linux: The leading enterprise Linux platform | | | H2: Red Hat Developer Program: A no-cost subscription for developers | | | H2: Red Hat Universal Base Image: A container image for your applications | | | Conclusion: Summarize the main points and provide a call to action | | | H3: FAQs: Answer some common questions about downloading Red Hat Linux for free | Table 2: Article with HTML formatting
How to Download Red Hat Linux for Free
-Red Hat Linux is one of the most popular and widely used operating systems in the world, especially for enterprise and cloud computing. It is based on open source technologies and provides a stable, secure, and flexible platform for running various applications and workloads. But did you know that you can download Red Hat Linux for free? In this article, we will show you how to get access to different versions of Red Hat Linux without paying a dime.
-Red Hat Enterprise Linux: The leading enterprise Linux platform
-Red Hat Enterprise Linux (RHEL) is the flagship product of Red Hat, the world's leading provider of open source solutions. RHEL is certified on hundreds of clouds and with thousands of hardware and software vendors, making it the most compatible and reliable platform for your business needs. RHEL comes with 24x7 support, security updates, and access to the Red Hat Customer Portal, where you can find thousands of knowledge articles and documentation. RHEL also includes Red Hat Insights, a managed service that provides analytics and remediation guidance to help you optimize performance and avoid issues.
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But how can you download RHEL for free? The answer is simple: join the Red Hat Developer Program. This is a no-cost subscription that gives you access to RHEL Server, RHEL Workstation, and various add-ons such as resilient storage, scalable file systems, and high-performance networking. You can use RHEL for development purposes only, not for production or commercial use. You can also create installation disks and virtual machines, as well as cloud-ready images for AWS, Google Cloud Platform, Microsoft Azure, and VMWare. To get started, all you need to do is sign up with your email address and activate your subscription.
-Red Hat Developer Program: A no-cost subscription for developers
-The Red Hat Developer Program is not only about RHEL. It also gives you access to other open source products and services from Red Hat, such as:
--
-
- Red Hat OpenShift: A container platform that lets you build, deploy, and manage applications at scale. -
- Red Hat Ansible Automation Platform: A foundation for implementing enterprise-wide automation. -
- Red Hat CodeReady Workspaces: A cloud-native development environment that runs in OpenShift. -
- Red Hat CodeReady Studio: An integrated development environment (IDE) that supports Java, Node.js, Python, PHP, and more. -
- Red Hat Quarkus: A Kubernetes-native Java stack that optimizes memory usage and startup time. -
As a member of the Red Hat Developer Program, you can also access developer tutorials, learning paths, forums, blogs, podcasts, webinars, events, and more. You can also connect with other developers and experts in the community and get support for your projects. The Red Hat Developer Program is a great way to learn new skills, explore new technologies, and accelerate your development process.
-Red Hat Universal Base Image: A container image for your applications
-If you are interested in developing containerized applications using Red Hat Linux, you should check out the Red Hat Universal Base Image (UBI). UBI is a container image that contains the core components of RHEL, such as the kernel, libraries, utilities, and packages. UBI is freely redistributable under the terms of the Universal Permissive License v1.0 (UPL), which means you can use it as a base for your own container images without any restrictions or obligations.
-UBI comes in four variants: base, minimal, micro, and init. Each variant has different features and sizes to suit different use cases. You can use UBI with any container engine or orchestration platform that supports OCI images, such as Podman, OpenShift, or Docker. You can also use UBI with other tools from the Red Hat ecosystem, such as Buildah.
Buildah is a tool that allows you to build, modify, and push container images. You can use Buildah to create UBI-based images from scratch, from existing images, or from Dockerfiles. You can also use Buildah to inspect, mount, and run your images. Buildah is compatible with the OCI image format and the Docker image format, so you can easily share your images with other platforms and registries.
-To download UBI and Buildah, you can use the following commands:
-podman pull registry.access.redhat.com/ubi8/ubi
-dnf install buildah
-For more information on how to use UBI and Buildah, you can refer to the official documentation and tutorials.
-Conclusion: How to download Red Hat Linux for free
-In this article, we have shown you how to download Red Hat Linux for free in different ways. You can use the Red Hat Developer Program to get access to RHEL and other open source products and services from Red Hat. You can also use the Red Hat Universal Base Image to create your own container images based on RHEL. These options are ideal for developers who want to experiment with Red Hat Linux and learn new skills. If you are ready to take your development to the next level, you can also try Red Hat OpenShift for free for 60 days.
-Red Hat Linux is a powerful and versatile operating system that can help you run your applications faster, safer, and smarter. Whether you are developing for the cloud, the edge, or the hybrid environment, Red Hat Linux has a solution for you. Download Red Hat Linux for free today and see what it can do for you.
-FAQs: Answer some common questions about downloading Red Hat Linux for free
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- Q: Can I use RHEL for free in production? -
- A: No, you can only use RHEL for free for development purposes. If you want to use RHEL in production or commercial environments, you need to purchase a subscription from Red Hat. -
- Q: How long does the Red Hat Developer Program subscription last? -
- A: The Red Hat Developer Program subscription lasts for one year. You can renew it as long as you remain an active member of the program. -
- Q: How can I get support for RHEL and other products from the Red Hat Developer Program? -
- A: You can get support from the Red Hat Developer Program through various channels, such as forums, blogs, webinars, events, and documentation. You can also contact the Red Hat Customer Service team for technical issues. -
- Q: What are the benefits of using UBI over other base images? -
- A: UBI has several benefits over other base images, such as: -
- It is based on RHEL, which is a trusted and proven platform for enterprise applications. -
- It is freely redistributable under the UPL license, which gives you more flexibility and control over your images. -
- It is compatible with both OCI and Docker image formats, which makes it easy to share and deploy your images. -
- It is regularly updated with security patches and bug fixes from Red Hat. -
- Q: How can I learn more about Red Hat Linux and other open source technologies? -
- A: You can learn more about Red Hat Linux and other open source technologies by visiting the Red Hat website, where you can find resources such as: -
- The Red Hat Learning Subscription, which gives you unlimited access to online training courses and exams. -
- The Red Hat Certification Program, which validates your skills and knowledge in various domains. -
- The Red Hat Open Source Stories, which showcases how open source is changing the world. -
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Download FIFA 2022 Mobile APK: The Ultimate Guide
-If you are a soccer fan, you must have heard of FIFA, the most popular and realistic soccer game series in the world. FIFA has been releasing new versions of its games every year, featuring updated players, teams, leagues, stadiums, graphics, and gameplay. The latest version of FIFA is FIFA 22, which was released on October 1, 2021 for PC, consoles, and Stadia.
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But what if you want to play FIFA on your mobile device? Well, you are in luck, because EA Sports has also developed a mobile version of FIFA, called FIFA Mobile. FIFA Mobile is a free-to-play soccer game that you can download and play on your iOS or Android device. It has many features and modes that make it a fun and engaging soccer experience.
-One of the most exciting features of FIFA Mobile is the FIFA World Cup 2022 mode, which lets you relive the world's greatest soccer tournament with any of the 32 qualified nations. You can also build your dream team with over 15,000 authentic soccer stars, including world-class talent like Kylian Mbappé, Christian Pulisic, Vinicius Jr and Son Heung-min. You can compete against the best in PvP modes, including Head-to-Head, VS Attack and Manager Mode. You can also experience immersive next-level soccer simulation with realistic stadium SFX and live on-field audio commentary.
-But how do you download and install FIFA 2022 Mobile APK on your device? And what are some tips and tricks to improve your gameplay? In this guide, we will answer these questions and more. We will also provide you with some reviews and ratings from other players who have tried FIFA 2022 Mobile APK. So, without further ado, let's get started!
-What is FIFA 2022 Mobile APK?
-FIFA 2022 Mobile APK is an unofficial version of FIFA Mobile that has been modified to include the latest features and updates from FIFA 22. It is not available on the official app stores, but you can download it from third-party websites that host APK files.
-APK stands for Android Package Kit, which is a file format that contains all the components of an Android app. You can install APK files on your Android device by enabling unknown sources in your settings and following some simple steps.
-FIFA 2022 Mobile APK has many advantages over the official version of FIFA Mobile. For example, it has more players, teams, leagues, kits, badges, stadiums, and modes than the official version. It also has better graphics, sound effects, and gameplay than the official version. It also allows you to play offline without an internet connection.
-However, there are also some drawbacks of using FIFA 2022 Mobile APK. For example, it may not be compatible with all devices or operating systems. It may also contain bugs or errors that affect the performance or stability of the game. It may also pose security risks to your device or data if you download it from untrusted sources. It may also violate the terms of service or policies of EA Sports or Google Play.
-Therefore, before you decide to download and install FIFA 2022 Mobile APK, you should weigh the pros and cons carefully and make an informed decision. You should also be aware of the possible consequences of using an unofficial version of FIFA Mobile, such as losing your progress, account, or access to the game.
Features of FIFA 2022 Mobile APK
-FIFA 2022 Mobile APK has many features that make it a great soccer game for mobile devices. Here are some of the main features that you can enjoy with FIFA 2022 Mobile APK:
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FIFA World Cup 2022 Mode
-One of the most exciting features of FIFA 2022 Mobile APK is the FIFA World Cup 2022 mode, which lets you relive the world's greatest soccer tournament with any of the 32 qualified nations. You can choose your favorite team and lead them to glory in the group stage, knockout stage, and final. You can also play against other players from around the world in online matches and tournaments. You can earn rewards and unlock exclusive items as you progress through the mode.
-Soccer Icons and Heroes
-FIFA 2022 Mobile APK also lets you build your dream team with over 15,000 authentic soccer stars, including world-class talent like Kylian Mbappé, Christian Pulisic, Vinicius Jr and Son Heung-min. You can also recruit legendary players from the past, such as Pelé, Maradona, Zidane, Ronaldo, and Messi. You can customize your team with different formations, tactics, kits, badges, and stadiums. You can also upgrade your players with skill boosts and chemistry links.
-Immersive Next-Level Soccer Simulation
-FIFA 2022 Mobile APK also delivers an immersive next-level soccer simulation with realistic stadium SFX and live on-field audio commentary. You can experience the thrill of scoring amazing goals, making stunning saves, and performing skill moves with intuitive touch controls. You can also enjoy stunning graphics and animations that bring the game to life on your screen. You can also adjust the camera angle and zoom level to suit your preference.
-Manager Mode
-FIFA 2022 Mobile APK also offers a manager mode, where you can take charge of a soccer club and lead them to success. You can sign new players, sell unwanted ones, negotiate contracts, scout for talent, train your squad, and manage your budget. You can also compete in various leagues and cups, such as the Premier League, La Liga, Bundesliga, Serie A, Champions League, Europa League, and more. You can also challenge other managers from around the world in online matches and tournaments.
System Requirements for FIFA 2022 Mobile APK
-Before you download and install FIFA 2022 Mobile APK on your device, you should make sure that your device meets the minimum system requirements for the game. Here are the system requirements for FIFA 2022 Mobile APK:
-iOS Devices
-If you have an iOS device, you need to have iOS 11 or later and at least 1.5 GB of free storage space. You also need to have one of the following devices:
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- iPhone 6s or later -
- iPad Air 2 or later -
- iPad mini 4 or later -
- iPad Pro or later -
- iPod touch (7th generation) or later -
Android Devices
-If you have an Android device, you need to have Android 6.0 or later and at least 1.5 GB of free storage space. You also need to have a device that has a minimum of 2 GB of RAM and supports OpenGL ES 3.0. You can check the compatibility of your device by visiting the official website of FIFA Mobile.
-How to Download and Install FIFA 2022 Mobile APK
-Now that you know the features and system requirements of FIFA 2022 Mobile APK, you are ready to download and install it on your device. Here are the steps that you need to follow:
-Step 1: Enable Unknown Sources
-The first step is to enable unknown sources on your device, which will allow you to install APK files from third-party websites. To do this, you need to go to your device settings and look for the security or privacy option. Then, you need to find the unknown sources option and toggle it on. You may see a warning message that tells you about the risks of installing apps from unknown sources, but you can ignore it and proceed.
-Step 2: Download the APK File
-The next step is to download the APK file of FIFA 2022 Mobile from a reliable and trusted website that hosts APK files. You can search for FIFA 2022 Mobile APK on Google or use the link provided below. Once you find the website, you need to click on the download button and wait for the file to be downloaded on your device.
-Download FIFA 2022 Mobile APK here
-Step 3: Install the APK File
-The third step is to install the APK file of FIFA 2022 Mobile on your device. To do this, you need to locate the file in your device storage and tap on it. You may see a pop-up window that asks you for permission to install the app, but you can grant it and continue. You may also see a progress bar that shows you how much time is left for the installation to complete.
-Step 4: Launch the Game and Enjoy
-The final step is to launch the game and enjoy playing FIFA 2022 Mobile on your device. To do this, you need to find the game icon on your home screen or app drawer and tap on it. You may see a loading screen that shows you some tips and tricks for the game, but you can skip it and start playing. You may also need to create an account or log in with your existing one to access all the features and modes of the game.
Gameplay Tips and Tricks for FIFA 2022 Mobile APK
-Now that you have downloaded and installed FIFA 2022 Mobile APK on your device, you may be wondering how to improve your gameplay and win more matches. Here are some tips and tricks that can help you become a better player of FIFA 2022 Mobile APK:
-Build Your Ultimate Team with Star Players
-One of the most important aspects of FIFA 2022 Mobile APK is building your ultimate team with star players. You can recruit players from different categories, such as base, rare, elite, master, legend, icon, and hero. You can also use different types of cards, such as player, skill, chemistry, and training cards. You can also buy and sell players on the market or use the scouting feature to find hidden gems.
-You should try to build a balanced team with players who have high ratings and attributes in different areas, such as pace, shooting, passing, dribbling, defending, and physicality. You should also try to create chemistry links between players who share the same nationality, league, or club. This will boost their performance and give you an edge over your opponents.
-Use Advanced Passing System to Create Chances
-Another important aspect of FIFA 2022 Mobile APK is using the advanced passing system to create chances for scoring goals. You can use different types of passes, such as short, long, through, lobbed, driven, and curved passes. You can also use different gestures, such as tapping, swiping, holding, and dragging on the screen to control the direction, power, and curve of your passes.
-You should try to use the right type of pass for the right situation and avoid making predictable or risky passes that can be intercepted by your opponents. You should also try to vary your passing patterns and tempo to confuse your opponents and create space for your attackers. You should also try to use the one-two pass or the give-and-go pass to create quick combinations and break through the defense.
-Plan Your Strategy and Adjust Your Tactics in Real Time
-The third important aspect of FIFA 2022 Mobile APK is planning your strategy and adjusting your tactics in real time. You can choose from different formations, such as 4-4-2, 4-3-3, 3-5-2, 5-3-2, and more. You can also choose from different styles of play, such as balanced, attacking, defensive, possession, counter-attack, and more. You can also customize your tactics by changing the roles and instructions of your players.
-You should try to choose a formation and a style of play that suit your team's strengths and weaknesses and match your opponent's formation and style of play. You should also try to adjust your tactics during the game by using the quick tactics feature or by pausing the game and accessing the tactics menu. You should also try to exploit your opponent's weaknesses and counter their strengths by changing your formation or style of play accordingly.
FIFA 2022 Mobile APK with other players online? -I hope this guide has helped you learn more about FIFA 2022 Mobile APK and how to download and install it on your device. If you have any feedback or suggestions, please let me know in the comments section below. Thank you for reading and happy gaming!
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Fallout Shelter Online APK The Most Anticipated Fallout Game for Android.md b/spaces/fatiXbelha/sd/Fallout Shelter Online APK The Most Anticipated Fallout Game for Android.md deleted file mode 100644 index c0bfdde6b5b3490c5c9c7d7da34bb8e8ce079657..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Fallout Shelter Online APK The Most Anticipated Fallout Game for Android.md +++ /dev/null @@ -1,179 +0,0 @@ -
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-If you are a fan of the Fallout series, you might have heard of Fallout Shelter Online, a mobile game that lets you build and manage your own vault in the post-apocalyptic world. But did you know that you can download and play the game on your Android device, even if it is not officially available in your region? In this article, we will show you how to download Fallout Shelter Online APK, a file that allows you to install the game on your phone or tablet. We will also give you some tips and tricks to help you enjoy the game better.
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A Sequel to Fallout Shelter
-Like its predecessor, Fallout Shelter Online puts you in control of a state-of-the-art underground vault from Vault-Tec Corporation, a company that built shelters to protect people from nuclear war. Your job is to ensure the survival and happiness of your dwellers, who are the residents of your vault. You can do this by building various rooms, such as power plants, water treatment facilities, diners, gardens, living quarters, radio stations, medbays, laboratories, and more. You can also customize your vault design, assign dwellers to different jobs, provide them with outfits and weapons, train their skills, and match them for breeding.
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-Unlike its predecessor, Fallout Shelter Online has a plot that connects it to the main Fallout games. The game is set in two locations: the Commonwealth and the Capital Wasteland. The Commonwealth is the setting of Fallout 4, while the Capital Wasteland is the setting of Fallout 3. The game takes place before and after the events of these games, respectively. You will encounter familiar characters from the Fallout series, such as Nick Valentine, Preston Garvey, Piper Wright, Dogmeat, Codsworth, Hancock, Cait, Curie, Paladin Danse, Deacon, MacCready, Strong, X6-88, Father Elijah, Sarah Lyons, Fawkes, Charon, Clover, Butch DeLoria, Moira Brown, Three Dog, Amata Almodovar, James (the Lone Wanderer's father), Colonel Autumn, John Henry Eden (the Enclave President), Liberty Prime (the Brotherhood of Steel's giant robot), Harold (the talking tree), and more. You will also learn more about the lore and history of the Fallout universe.
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-Fallout Shelter Online is a fun and addictive game that will appeal to both fans and newcomers of the Fallout series. However, there are some reasons why you might want to download the APK file instead of getting the game from the official sources. Here are some of them:
-Enjoy the Game in English
-Fallout Shelter Online was originally released in China in 2019, and then in other Asian countries in 2020. The game was only available in Chinese and other Asian languages, which made it difficult for English-speaking players to enjoy the game. However, in 2021, an unofficial English patch was released by a group of fans, which translated the game's interface, dialogue, and subtitles into English. By downloading the APK file with the English patch, you can play the game in English and understand the story and the gameplay better.
-Access the Latest Version with New Content
-Fallout Shelter Online is constantly updated with new content, such as new heroes, new quests, new events, new features, and more. However, these updates are not always available at the same time for different regions. Sometimes, some regions might get the updates earlier or later than others. By downloading the APK file from a reliable source, you can access the latest version of the game with all the new content as soon as possible.
-Avoid Regional Restrictions
-Fallout Shelter Online is not officially available in all regions of the world. Some regions might have access to the game through official channels, such as Google Play Store or App Store, while others might not. This means that some players might not be able to download or play the game at all. By downloading the APK file from a third-party source, you can bypass these regional restrictions and play the game wherever you are.
-How to Download and Install Fallout Shelter Online APK?
-Now that you know why you might want to download Fallout Shelter Online APK, let's see how you can do it. The process is not very complicated, but it does require some steps that you need to follow carefully. Here they are:
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Step 1: Find a Reliable Source
-The first thing you need to do is to find a reliable source that offers the APK file for Fallout Shelter Online. There are many websites that claim to provide APK files for various games and apps, but not all of them are trustworthy. Some of them might contain malware or viruses that can harm your device or steal your personal information. Some of them might also offer outdated or fake versions of the game that might not work properly or at all.
-To avoid these risks, you need to do some research and find a reputable source that has positive reviews and feedback from other users. You can also check some online forums or communities that are dedicated to Fallout Shelter Online or APK files in general. You can ask for recommendations or suggestions from other players who have downloaded and installed the game successfully.
-Step 2: Download the APK File
-Once you have found a reliable source, you need to download the APK file for Fallout Shelter Online. The file size might vary depending on the version of the game and the source you choose, but it should be around 1 GB or more. Make sure you have enough storage space on your device before downloading the file.
-To download the file, you need to click on the download link or button provided by the source. You might need to complete some verification steps or captcha tests before you can start the download. You might also need to wait for some time until the download is complete.
-Step 3: Enable Unknown Sources
-Before you can install the APK file on your device, you need to enable unknown sources on your device settings. This is because Android devices normally do not allow installing apps from sources other than Google Play Store or App Store for security reasons. However, since you are downloading an APK file from a third-party source, you need to enable unknown sources to allow installing it.
-To enable unknown sources on your device settings, you need to follow these steps depending on your Android version and device model:
--
-
- Go to your device settings and look for the security or privacy option. -
- Tap on it and look for the unknown sources or install unknown apps option. -
- Toggle it on or allow it for the browser or file manager app that you used to download the APK file. -
If you are not sure how to enable unknown sources on your device, you can search online for specific instructions for your Android version and device model.
-Step 4: Install the APK File
-After you have enabled unknown sources on your device settings, you can proceed to install the APK file on your device. To do this, you need to follow these steps:
--
-
- Locate the APK file on your device storage using a file manager app or the browser app that you used to download it. -
- Tap on the APK file and confirm the installation by tapping on install or allow. -
- Wait for the installation to finish. It might take some time depending on the file size and your device performance. -
If you encounter any errors or issues during the installation, you might need to check if you have enough storage space, if you have enabled unknown sources, or if you have downloaded the correct and compatible version of the game for your device.
-Step 5: Launch the Game and Enjoy
-Once the installation is complete, you can launch the game and enjoy it on your device. You can find the game icon on your home screen or app drawer. Tap on it and wait for the game to load. You might need to accept some permissions or terms of service before you can start playing. You might also need to download some additional data or updates before you can access all the features of the game.
-After that, you can create your account, choose your server, customize your character, and start building your vault. You can also connect your game account to your social media accounts, such as Facebook or Google, to save your progress and access other online features.
-Tips and Tricks for Playing Fallout Shelter Online
-Fallout Shelter Online is a fun and addictive game that will keep you entertained for hours. However, it can also be challenging and complex at times. To help you get the most out of the game, here are some tips and tricks that you should know:
-Plan Your Vault Layout Wisely
-Your vault layout is one of the most important aspects of the game. It determines how efficient and productive your vault is, as well as how safe and happy your dwellers are. You should plan your vault layout wisely and avoid making common mistakes, such as:
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-
- Building too many rooms too quickly. This will drain your resources and make it harder to maintain them. -
- Building rooms that are not connected to each other. This will make it harder for your dwellers to move around and work in them. -
- Building rooms that are not upgraded or merged. This will make them less efficient and more vulnerable to disasters. -
- Building rooms that are not balanced with each other. This will cause shortages or surpluses of resources and affect your dwellers' needs. -
To avoid these mistakes, you should follow these guidelines:
--
-
- Build rooms gradually and according to your needs. Start with the basic rooms, such as power plants, water treatment facilities, diners, living quarters, and storage rooms. Then, expand with more advanced rooms, such as radio stations, medbays, laboratories, workshops, training rooms, etc. -
- Build rooms that are adjacent to each other and form a grid-like pattern. This will make it easier for your dwellers to access them and work in them. You can also use elevators to connect different floors of your vault. -
- Upgrade and merge rooms whenever possible. This will make them more efficient and productive, as well as more resistant to disasters. You can upgrade rooms by spending caps (the in-game currency) or merge them by building two or three identical rooms next to each other. -
- Build rooms that are balanced with each other and match your dwellers' needs. You should have enough power plants to supply electricity to all your rooms, enough water treatment facilities to provide clean water to all your dwellers, enough diners to feed all your dwellers, enough living quarters to house all your dwellers, enough storage rooms to store all your resources and items, etc. -
Assign Dwellers to Their Best Roles
-Your dwellers are the backbone of your vault. They are the ones who work in your rooms, produce resources, explore the wasteland, fight enemies , and more. You should assign them to their best roles and make them happy and productive. You can do this by following these tips:
--
-
- Check your dwellers' stats and skills. Each dweller has six stats: strength, perception, endurance, charisma, intelligence, and agility. Each stat corresponds to a specific room type: power plants, water treatment facilities, diners, radio stations, medbays, laboratories, etc. Each dweller also has a skill level for each stat, ranging from 1 to 10. You should assign your dwellers to the rooms that match their highest stat and skill level. This will make them work faster and more efficiently, as well as increase their happiness. -
- Check your dwellers' traits and preferences. Each dweller has a unique trait that gives them a bonus or a penalty in certain situations. For example, some dwellers might be brave, lucky, charismatic, or wasteland explorers. Some dwellers might also have preferences for certain outfits, weapons, or roles. You should assign your dwellers to the roles that suit their traits and preferences. This will make them perform better and happier. -
- Check your dwellers' needs and moods. Each dweller has four needs: food, water, health, and radiation. Each need is represented by a bar that fills up or depletes over time. If a need bar is full, the dweller is satisfied. If a need bar is empty, the dweller is unhappy and might suffer negative effects. You should provide your dwellers with enough food, water, health kits, and radaway to keep their need bars full. You should also check your dwellers' moods, which are represented by smiley faces above their heads. If a dweller is happy, they will have a green smiley face. If a dweller is unhappy, they will have a red frowny face. You should try to keep your dwellers happy by assigning them to their best roles, providing them with enough resources, giving them rewards or bonuses, or sending them to explore the wasteland. -
Recruit Legendary Heroes from the Fallout Series
-One of the most exciting features of Fallout Shelter Online is that you can recruit legendary heroes from the Fallout series to join your vault. These heroes are special dwellers who have unique abilities and skills that can help you in various ways. You can use them to explore the wasteland and battle enemies, as well as assign them to your rooms to boost their performance.
-To recruit legendary heroes, you need to collect their hero cards, which are rare items that can be obtained from various sources. Some of the sources are:
--
-
- The hero store, where you can buy hero cards with hero tokens or gems (the premium currency). -
- The hero gacha machine, where you can draw hero cards with gacha tickets or gems. -
- The hero recruitment center, where you can exchange hero fragments for hero cards. -
- The hero missions, where you can complete tasks and challenges to earn hero cards. -
- The hero events, where you can participate in limited-time activities and promotions to get hero cards. -
Some of the legendary heroes that you can recruit are:
-| Name | Origin | Role | Ability |
|---|---|---|---|
| The Sole Survivor | Fallout 4 | Leader | V.A.T.S.: Deals damage to multiple enemies and increases critical chance. |
| The Lone Wanderer | Fallout 3 | Tank | Lone Wolf: Increases defense and resistance when alone or with Dogmeat. |
| The Courier | Fallout: New Vegas | Sniper | Lucky Shot: Deals extra damage and ignores armor with a chance to stun enemies. |
| Nick Valentine | Fallout 4 | Hacker | Synthetic Detective: Hacks enemy robots and turrets and turns them against their allies. |
| Preston Garvey | Fallout 4 | Ranger | Minuteman General: Calls for artillery support and increases damage of allies. |
| Piper Wright | Fallout 4 | Reporter | Publick Occurrences: Exposes enemy weaknesses and reduces their defense. |
| Dogmeat | Fallout 4 | Companion | M an's Best Friend: Attacks enemies and causes bleeding damage with a chance to cripple them. |
| Codsworth | Fallout 4 | Butler | Flamethrower: Deals fire damage to enemies and reduces their attack speed. |
| Hancock | Fallout 4 | Ghoul | Radiation Blast: Deals radiation damage to enemies and heals allies. |
| Cait | Fallout 4 | Brawler | Psycho: Increases attack and critical damage with a chance to ignore damage. |
| Curie | Fallout 4 | Medic | Stimpak: Heals allies and removes negative effects. |
| Paladin Danse | Fallout 4 | Soldier | Brotherhood of Steel: Increases defense and resistance of allies and reflects damage to enemies. |
| Deacon | Fallout 4 | Spy | Cloak and Dagger: Becomes invisible and deals extra damage with a chance to stun enemies. |
| MacCready | Fallout 4 | Mercenary | Killshot: Deals headshot damage to enemies and increases accuracy. |
| Strong | Fallout 4 | Super Mutant | Berserk: Deals melee damage to enemies and knocks them back with a chance to stun them. |
| X6-88 | Fallout 4 | Courser | Synthetic Assassin: Deals laser damage to enemies and increases evasion. |
| Father Elijah | Fallout: New Vegas | Scientist | Ghost People: Summons ghost people to attack enemies and explode on death. |
| Sarah Lyons | Fallout 3 | Paladin | Lion's Pride: Increases attack and defense of allies and reduces damage taken. |
| Fawkes | Fallout 3 | Super Mutant | Gatling Laser: Deals laser damage to enemies and pierces through armor. |
| Charon | Fallout 3 | Ghoul | Shotgun Blast: Deals shotgun damage to enemies and pushes them back. |
| Clover | Fallout 3 | Slave | Chainsaw: Deals melee damage to enemies and causes bleeding damage. |
| Butch DeLoria | Fallout 3 | Gangster | Tunnel Snakes Rule: Increases attack and critical damage of allies and taunts enemies. |
| Moira Brown | Fallout 3 | Merchant | Wasteland Survival Guide: Heals allies and increases their stats. |
| Three Dog | Fallout 3 | DJ | Galaxy News Radio: Increases morale and happiness of allies and reduces enemy attack. |
| Amata Almodovar | Fallout 3 | Overseer | Vault 101: Increases defense and resistance of allies and heals them over time. |
| James (the Lone Wanderer's father) | Fallout 3 | Doctor | Project Purity: Purifies water and removes radiation from allies. |
| Colonel Autumn | Fallout 3 | Enclave | Plasma Pistol: Deals plasma damage to enemies and reduces their evasion. |
| John Henry Eden (the Enclave President) | Fallout 3 | President | America Reborn: Increases attack and defense of allies and summons Enclave soldiers. |
| Liberty Prime (the Brotherhood of Steel's giant robot) | Fallout 3 | Robot | Democracy is Non-Negotiable: Deals massive damage to enemies and launches mini-nukes.
- You can also chat with other players or guild members by tapping on the chat icon on the bottom of the screen. You can also view your profile, achievements, rankings, and statistics by tapping on the menu icon on the top left of the screen. -Conclusion-Fallout Shelter Online is a great game for fans of the Fallout series and anyone who likes simulation, strategy, and RPG games. It offers a lot of content and features that will keep you entertained and challenged for a long time. You can build and manage your own vault, recruit legendary heroes from the Fallout series, explore the wasteland and battle enemies, join a guild and participate in online events, and more. You can also download Fallout Shelter Online APK to enjoy the game in English, access the latest version with new content, and avoid regional restrictions. Just follow the steps we have provided in this article and you will be able to download and install Fallout Shelter Online APK on your Android device easily and safely. -FAQs-Here are some frequently asked questions and answers about Fallout Shelter Online APK: -Q: Is Fallout Shelter Online APK safe to download and install?-A: Yes, as long as you download it from a reliable source that has positive reviews and feedback from other users. You should also scan the file with an antivirus or malware scanner before installing it on your device. -Q: Is Fallout Shelter Online APK legal to download and install?-A: Yes, as long as you do not use it for any illegal or unethical purposes. You should also respect the intellectual property rights of the game developers and publishers and not distribute or sell the file without their permission. -Q: Is Fallout Shelter Online APK compatible with my device?-A: Fallout Shelter Online APK should be compatible with most Android devices that have Android 4.4 or higher. However, some devices might have issues or errors due to different specifications or settings. You should check if your device meets the minimum requirements of the game before downloading and installing it. -Q: How can I update Fallout Shelter Online APK?-A: You can update Fallout Shelter Online APK by downloading and installing the latest version of the file from the same source that you used before. You should also backup your game data before updating to avoid losing your progress or items. -Q: How can I uninstall Fallout Shelter Online APK?-A: You can uninstall Fallout Shelter Online APK by following these steps: -
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"+s.header),e.prependTo(this.headers),i=this.active.children(".ui-accordion-header-icon"),this._removeClass(i,s.header)._addClass(i,null,s.activeHeader)._addClass(this.headers,"ui-accordion-icons"))},_destroyIcons:function(){this._removeClass(this.headers,"ui-accordion-icons"),this.headers.children(".ui-accordion-header-icon").remove()},_destroy:function(){var t;this.element.removeAttr("role"),this.headers.removeAttr("role aria-expanded aria-selected aria-controls tabIndex").removeUniqueId(),this._destroyIcons(),t=this.headers.next().css("display","").removeAttr("role aria-hidden aria-labelledby").removeUniqueId(),"content"!==this.options.heightStyle&&t.css("height","")},_setOption:function(t,e){return"active"===t?(this._activate(e),void 0):("event"===t&&(this.options.event&&this._off(this.headers,this.options.event),this._setupEvents(e)),this._super(t,e),"collapsible"!==t||e||this.options.active!==!1||this._activate(0),"icons"===t&&(this._destroyIcons(),e&&this._createIcons()),void 0)},_setOptionDisabled:function(t){this._super(t),this.element.attr("aria-disabled",t),this._toggleClass(null,"ui-state-disabled",!!t),this._toggleClass(this.headers.add(this.headers.next()),null,"ui-state-disabled",!!t)},_keydown:function(e){if(!e.altKey&&!e.ctrlKey){var i=t.ui.keyCode,s=this.headers.length,n=this.headers.index(e.target),o=!1;switch(e.keyCode){case i.RIGHT:case i.DOWN:o=this.headers[(n+1)%s];break;case i.LEFT:case i.UP:o=this.headers[(n-1+s)%s];break;case i.SPACE:case i.ENTER:this._eventHandler(e);break;case i.HOME:o=this.headers[0];break;case i.END:o=this.headers[s-1]}o&&(t(e.target).attr("tabIndex",-1),t(o).attr("tabIndex",0),t(o).trigger("focus"),e.preventDefault())}},_panelKeyDown:function(e){e.keyCode===t.ui.keyCode.UP&&e.ctrlKey&&t(e.currentTarget).prev().trigger("focus")},refresh:function(){var e=this.options;this._processPanels(),e.active===!1&&e.collapsible===!0||!this.headers.length?(e.active=!1,this.active=t()):e.active===!1?this._activate(0):this.active.length&&!t.contains(this.element[0],this.active[0])?this.headers.length===this.headers.find(".ui-state-disabled").length?(e.active=!1,this.active=t()):this._activate(Math.max(0,e.active-1)):e.active=this.headers.index(this.active),this._destroyIcons(),this._refresh()},_processPanels:function(){var t=this.headers,e=this.panels;this.headers=this.element.find(this.options.header),this._addClass(this.headers,"ui-accordion-header ui-accordion-header-collapsed","ui-state-default"),this.panels=this.headers.next().filter(":not(.ui-accordion-content-active)").hide(),this._addClass(this.panels,"ui-accordion-content","ui-helper-reset ui-widget-content"),e&&(this._off(t.not(this.headers)),this._off(e.not(this.panels)))},_refresh:function(){var e,i=this.options,s=i.heightStyle,n=this.element.parent();this.active=this._findActive(i.active),this._addClass(this.active,"ui-accordion-header-active","ui-state-active")._removeClass(this.active,"ui-accordion-header-collapsed"),this._addClass(this.active.next(),"ui-accordion-content-active"),this.active.next().show(),this.headers.attr("role","tab").each(function(){var e=t(this),i=e.uniqueId().attr("id"),s=e.next(),n=s.uniqueId().attr("id");e.attr("aria-controls",n),s.attr("aria-labelledby",i)}).next().attr("role","tabpanel"),this.headers.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}).next().attr({"aria-hidden":"true"}).hide(),this.active.length?this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}).next().attr({"aria-hidden":"false"}):this.headers.eq(0).attr("tabIndex",0),this._createIcons(),this._setupEvents(i.event),"fill"===s?(e=n.height(),this.element.siblings(":visible").each(function(){var i=t(this),s=i.css("position");"absolute"!==s&&"fixed"!==s&&(e-=i.outerHeight(!0))}),this.headers.each(function(){e-=t(this).outerHeight(!0)}),this.headers.next().each(function(){t(this).height(Math.max(0,e-t(this).innerHeight()+t(this).height()))}).css("overflow","auto")):"auto"===s&&(e=0,this.headers.next().each(function(){var i=t(this).is(":visible");i||t(this).show(),e=Math.max(e,t(this).css("height","").height()),i||t(this).hide()}).height(e))},_activate:function(e){var i=this._findActive(e)[0];i!==this.active[0]&&(i=i||this.active[0],this._eventHandler({target:i,currentTarget:i,preventDefault:t.noop}))},_findActive:function(e){return"number"==typeof e?this.headers.eq(e):t()},_setupEvents:function(e){var i={keydown:"_keydown"};e&&t.each(e.split(" "),function(t,e){i[e]="_eventHandler"}),this._off(this.headers.add(this.headers.next())),this._on(this.headers,i),this._on(this.headers.next(),{keydown:"_panelKeyDown"}),this._hoverable(this.headers),this._focusable(this.headers)},_eventHandler:function(e){var i,s,n=this.options,o=this.active,a=t(e.currentTarget),r=a[0]===o[0],h=r&&n.collapsible,l=h?t():a.next(),c=o.next(),u={oldHeader:o,oldPanel:c,newHeader:h?t():a,newPanel:l};e.preventDefault(),r&&!n.collapsible||this._trigger("beforeActivate",e,u)===!1||(n.active=h?!1:this.headers.index(a),this.active=r?t():a,this._toggle(u),this._removeClass(o,"ui-accordion-header-active","ui-state-active"),n.icons&&(i=o.children(".ui-accordion-header-icon"),this._removeClass(i,null,n.icons.activeHeader)._addClass(i,null,n.icons.header)),r||(this._removeClass(a,"ui-accordion-header-collapsed")._addClass(a,"ui-accordion-header-active","ui-state-active"),n.icons&&(s=a.children(".ui-accordion-header-icon"),this._removeClass(s,null,n.icons.header)._addClass(s,null,n.icons.activeHeader)),this._addClass(a.next(),"ui-accordion-content-active")))},_toggle:function(e){var i=e.newPanel,s=this.prevShow.length?this.prevShow:e.oldPanel;this.prevShow.add(this.prevHide).stop(!0,!0),this.prevShow=i,this.prevHide=s,this.options.animate?this._animate(i,s,e):(s.hide(),i.show(),this._toggleComplete(e)),s.attr({"aria-hidden":"true"}),s.prev().attr({"aria-selected":"false","aria-expanded":"false"}),i.length&&s.length?s.prev().attr({tabIndex:-1,"aria-expanded":"false"}):i.length&&this.headers.filter(function(){return 0===parseInt(t(this).attr("tabIndex"),10)}).attr("tabIndex",-1),i.attr("aria-hidden","false").prev().attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_animate:function(t,e,i){var s,n,o,a=this,r=0,h=t.css("box-sizing"),l=t.length&&(!e.length||t.index()
|